) specifiers.\\n def noop(x, y, z):\\n return True\\n\\n if x1 == x2 and o1.startswith('>'):\\n lx = noop\\n if y1 == y2 and o2.startswith('>'):\\n ly = noop\\n\\n # Check for overlap.\\n if (gte(x1, y1, True) and ly(x1, y2, True) or\\n gy(x2, y1, True) and ly(x2, y2, True) or\\n gte(y1, x1, True) and lx(y1, x2, True) or\\n gx(y2, x1, True) and lx(y2, x2, True)\\n ):\\n return 0\\n if gte(y1, x2, True):\\n return 1\\n if gte(x1, y2, True):\\n return -1\\n raise AssertionError('Unexpected case comparing version ranges')\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/commands.py::_compare_ranges.noop\",\n \"text\": \"jupyterlab/commands.py::_compare_ranges.noop\\n def noop(x, y, z):\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/commands.py::_is_disabled\",\n \"text\": \"jupyterlab/commands.py::_is_disabled\\ndef _is_disabled(name, disabled=[]):\\n \\\"\\\"\\\"Test whether the package is disabled.\\n \\\"\\\"\\\"\\n for pattern in disabled:\\n if name == pattern:\\n return True\\n if re.compile(pattern).match(name) is not None:\\n return True\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/commands.py::_format_compatibility_errors\",\n \"text\": \"jupyterlab/commands.py::_format_compatibility_errors\\ndef _format_compatibility_errors(name, version, errors):\\n \\\"\\\"\\\"Format a message for compatibility errors.\\n \\\"\\\"\\\"\\n msgs = []\\n l0 = 10\\n l1 = 10\\n for error in errors:\\n pkg, jlab, ext = error\\n jlab = str(Range(jlab, True))\\n ext = str(Range(ext, True))\\n msgs.append((pkg, jlab, ext))\\n l0 = max(l0, len(pkg) + 1)\\n l1 = max(l1, len(jlab) + 1)\\n\\n msg = '\\\\n\\\"%s@%s\\\" is not compatible with the current JupyterLab'\\n msg = msg % (name, version)\\n msg += '\\\\nConflicting Dependencies:\\\\n'\\n msg += 'JupyterLab'.ljust(l0)\\n msg += 'Extension'.ljust(l1)\\n msg += 'Package\\\\n'\\n\\n for (pkg, jlab, ext) in msgs:\\n msg += jlab.ljust(l0) + ext.ljust(l1) + pkg + '\\\\n'\\n\\n return msg\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/commands.py::_log_multiple_compat_errors\",\n \"text\": \"jupyterlab/commands.py::_log_multiple_compat_errors\\ndef _log_multiple_compat_errors(logger, errors_map):\\n \\\"\\\"\\\"Log compatability errors for multiple extensions at once\\\"\\\"\\\"\\n\\n outdated = []\\n others = []\\n\\n for name, (version, errors) in errors_map.items():\\n age = _compat_error_age(errors)\\n if age > 0:\\n outdated.append(name)\\n else:\\n others.append(name)\\n\\n if outdated:\\n logger.warn('\\\\n '.join(\\n ['\\\\n The following extension are outdated:'] +\\n outdated +\\n ['\\\\n Consider running \\\"jupyter labextension update --all\\\" '\\n 'to check for updates.\\\\n']\\n ))\\n\\n for name in others:\\n version, errors = errors_map[name]\\n msg = _format_compatibility_errors(name, version, errors)\\n logger.warn(msg + '\\\\n')\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/commands.py::_log_single_compat_errors\",\n \"text\": \"jupyterlab/commands.py::_log_single_compat_errors\\ndef _log_single_compat_errors(logger, name, version, errors):\\n \\\"\\\"\\\"Log compatability errors for a single extension\\\"\\\"\\\"\\n\\n age = _compat_error_age(errors)\\n if age > 0:\\n logger.warn('The extension \\\"%s\\\" is outdated.\\\\n', name)\\n else:\\n msg = _format_compatibility_errors(name, version, errors)\\n logger.warn(msg + '\\\\n')\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/commands.py::_compat_error_age\",\n \"text\": \"jupyterlab/commands.py::_compat_error_age\\ndef _compat_error_age(errors):\\n \\\"\\\"\\\"Compare all incompatabilites for an extension.\\n\\n Returns a number > 0 if all extensions are older than that supported by lab.\\n Returns a number < 0 if all extensions are newer than that supported by lab.\\n Returns 0 otherwise (i.e. a mix).\\n \\\"\\\"\\\"\\n # Do any extensions depend on too old lab packages?\\n any_older = False\\n # Do any extensions depend on too new lab packages?\\n any_newer = False\\n\\n for _, jlab, ext in errors:\\n c = _compare_ranges(ext, jlab)\\n any_newer = any_newer or c < 0\\n any_older = any_older or c > 0\\n if any_older and not any_newer:\\n return 1\\n elif any_newer and not any_older:\\n return -1\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/commands.py::_get_core_extensions\",\n \"text\": \"jupyterlab/commands.py::_get_core_extensions\\ndef _get_core_extensions(core_data):\\n \\\"\\\"\\\"Get the core extensions.\\n \\\"\\\"\\\"\\n data = core_data['jupyterlab']\\n return list(data['extensions']) + list(data['mimeExtensions'])\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/commands.py::_semver_prerelease_key\",\n \"text\": \"jupyterlab/commands.py::_semver_prerelease_key\\ndef _semver_prerelease_key(prerelease):\\n \\\"\\\"\\\"Sort key for prereleases.\\n\\n Precedence for two pre-release versions with the same\\n major, minor, and patch version MUST be determined by\\n comparing each dot separated identifier from left to\\n right until a difference is found as follows:\\n identifiers consisting of only digits are compare\\n numerically and identifiers with letters or hyphens\\n are compared lexically in ASCII sort order. Numeric\\n identifiers always have lower precedence than non-\\n numeric identifiers. A larger set of pre-release\\n fields has a higher precedence than a smaller set,\\n if all of the preceding identifiers are equal.\\n \\\"\\\"\\\"\\n for entry in prerelease:\\n if isinstance(entry, int):\\n # Assure numerics always sort before string\\n yield ('', entry)\\n else:\\n # Use ASCII compare:\\n yield (entry,)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/commands.py::_semver_key\",\n \"text\": \"jupyterlab/commands.py::_semver_key\\ndef _semver_key(version, prerelease_first=False):\\n \\\"\\\"\\\"A sort key-function for sorting semver version string.\\n\\n The default sorting order is ascending (0.x -> 1.x -> 2.x).\\n\\n If `prerelease_first`, pre-releases will come before\\n ALL other semver keys (not just those with same version).\\n I.e (1.0-pre, 2.0-pre -> 0.x -> 1.x -> 2.x).\\n\\n Otherwise it will sort in the standard way that it simply\\n comes before any release with shared version string\\n (0.x -> 1.0-pre -> 1.x -> 2.0-pre -> 2.x).\\n \\\"\\\"\\\"\\n v = make_semver(version, True)\\n if prerelease_first:\\n key = (0,) if v.prerelease else (1,)\\n else:\\n key = ()\\n key = key + (v.major, v.minor, v.patch)\\n if not prerelease_first:\\n # NOT having a prerelease is > having one\\n key = key + (0,) if v.prerelease else (1,)\\n if v.prerelease:\\n key = key + tuple(_semver_prerelease_key(\\n v.prerelease))\\n\\n return key\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/commands.py::_fetch_package_metadata\",\n \"text\": \"jupyterlab/commands.py::_fetch_package_metadata\\ndef _fetch_package_metadata(registry, name, logger):\\n \\\"\\\"\\\"Fetch the metadata for a package from the npm registry\\\"\\\"\\\"\\n req = Request(\\n urljoin(registry, quote(name, safe='@')),\\n headers={\\n 'Accept': ('application/vnd.npm.install-v1+json;'\\n ' q=1.0, application/json; q=0.8, */*')\\n }\\n )\\n try:\\n logger.debug('Fetching URL: %s' % (req.full_url))\\n except AttributeError:\\n logger.debug('Fetching URL: %s' % (req.get_full_url()))\\n try:\\n with contextlib.closing(urlopen(req)) as response:\\n return json.loads(response.read().decode('utf-8'))\\n except URLError as exc:\\n logger.warning(\\n 'Failed to fetch package metadata for %r: %r',\\n name, exc)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/extension.py::load_config\",\n \"text\": \"jupyterlab/extension.py::load_config\\ndef load_config(nbapp):\\n \\\"\\\"\\\"Load the JupyterLab configuration and defaults for a given application.\\n \\\"\\\"\\\"\\n from jupyterlab_server import LabConfig\\n from .commands import (\\n get_app_dir,\\n get_app_info,\\n get_workspaces_dir,\\n get_user_settings_dir,\\n pjoin\\n )\\n\\n app_dir = getattr(nbapp, 'app_dir', get_app_dir())\\n info = get_app_info(app_dir)\\n static_url = info['staticUrl']\\n user_settings_dir = getattr(\\n nbapp, 'user_settings_dir', get_user_settings_dir()\\n )\\n workspaces_dir = getattr(nbapp, 'workspaces_dir', get_workspaces_dir())\\n\\n config = LabConfig()\\n config.app_dir = app_dir\\n config.app_name = 'JupyterLab'\\n config.app_namespace = 'jupyterlab'\\n config.app_settings_dir = pjoin(app_dir, 'settings')\\n config.app_version = info['version']\\n config.cache_files = True\\n config.schemas_dir = pjoin(app_dir, 'schemas')\\n config.templates_dir = pjoin(app_dir, 'static')\\n config.themes_dir = pjoin(app_dir, 'themes')\\n config.user_settings_dir = user_settings_dir\\n config.workspaces_dir = workspaces_dir\\n\\n if getattr(nbapp, 'override_static_url', ''):\\n static_url = nbapp.override_static_url\\n if getattr(nbapp, 'override_theme_url', ''):\\n config.themes_url = nbapp.override_theme_url\\n config.themes_dir = ''\\n\\n if static_url:\\n config.static_url = static_url\\n else:\\n config.static_dir = pjoin(app_dir, 'static')\\n\\n return config\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/extension.py::load_jupyter_server_extension\",\n \"text\": \"jupyterlab/extension.py::load_jupyter_server_extension\\ndef load_jupyter_server_extension(nbapp):\\n \\\"\\\"\\\"Load the JupyterLab server extension.\\n \\\"\\\"\\\"\\n # Delay imports to speed up jlpmapp\\n from json import dumps\\n from jupyterlab_server import add_handlers\\n from notebook.utils import url_path_join as ujoin, url_escape\\n from notebook._version import version_info\\n from tornado.ioloop import IOLoop\\n from markupsafe import Markup\\n from .handlers.build_handler import build_path, Builder, BuildHandler\\n from .handlers.extension_manager_handler import (\\n extensions_handler_path, ExtensionManager, ExtensionHandler\\n )\\n from .handlers.error_handler import ErrorHandler\\n from .commands import (\\n DEV_DIR, HERE, ensure_app, ensure_core, ensure_dev, watch,\\n watch_dev, get_app_dir\\n )\\n\\n web_app = nbapp.web_app\\n logger = nbapp.log\\n base_url = nbapp.base_url\\n\\n # Handle the app_dir\\n app_dir = getattr(nbapp, 'app_dir', get_app_dir())\\n\\n # Check for core mode.\\n core_mode = False\\n if getattr(nbapp, 'core_mode', False) or app_dir.startswith(HERE):\\n app_dir = HERE\\n core_mode = True\\n logger.info('Running JupyterLab in core mode')\\n\\n # Check for dev mode.\\n dev_mode = False\\n if getattr(nbapp, 'dev_mode', False) or app_dir.startswith(DEV_DIR):\\n app_dir = DEV_DIR\\n dev_mode = True\\n logger.info('Running JupyterLab in dev mode')\\n\\n # Set the value on nbapp so it will get picked up in load_config\\n nbapp.app_dir = app_dir\\n\\n config = load_config(nbapp)\\n config.app_name = 'JupyterLab'\\n config.app_namespace = 'jupyterlab'\\n\\n config.app_url = '/lab'\\n\\n config.cache_files = True\\n\\n # Check for watch.\\n watch_mode = getattr(nbapp, 'watch', False)\\n\\n if watch_mode and core_mode:\\n logger.warn('Cannot watch in core mode, did you mean --dev-mode?')\\n watch_mode = False\\n\\n if core_mode and dev_mode:\\n logger.warn('Conflicting modes, choosi\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/handlers/build_handler.py::Builder.__init__\",\n \"text\": \"jupyterlab/handlers/build_handler.py::Builder.__init__\\n def __init__(self, log, core_mode, app_dir, core_config=None):\\n self.log = log\\n self.core_mode = core_mode\\n self.app_dir = app_dir\\n self.core_config = core_config\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/handlers/build_handler.py::Builder.get_status\",\n \"text\": \"jupyterlab/handlers/build_handler.py::Builder.get_status\\n @gen.coroutine\\n def get_status(self):\\n if self.core_mode:\\n raise gen.Return(dict(status='stable', message=''))\\n if self.building:\\n raise gen.Return(dict(status='building', message=''))\\n\\n try:\\n messages = yield self._run_build_check(\\n self.app_dir, self.log, self.core_config)\\n status = 'needed' if messages else 'stable'\\n if messages:\\n self.log.warn('Build recommended')\\n [self.log.warn(m) for m in messages]\\n else:\\n self.log.info('Build is up to date')\\n except ValueError as e:\\n self.log.warn(\\n 'Could not determine jupyterlab build status without nodejs'\\n )\\n status = 'stable'\\n messages = []\\n\\n raise gen.Return(dict(status=status, message='\\\\n'.join(messages)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/build_handler.py::Builder.build\",\n \"text\": \"jupyterlab/handlers/build_handler.py::Builder.build\\n @gen.coroutine\\n def build(self):\\n if self._canceling:\\n raise ValueError('Cancel in progress')\\n if not self.building:\\n self.canceled = False\\n self._future = future = gen.Future()\\n self.building = True\\n self._kill_event = evt = Event()\\n try:\\n yield self._run_build(\\n self.app_dir, self.log, evt, self.core_config)\\n future.set_result(True)\\n except Exception as e:\\n if str(e) == 'Aborted':\\n future.set_result(False)\\n else:\\n future.set_exception(e)\\n finally:\\n self.building = False\\n try:\\n yield self._future\\n except Exception as e:\\n raise e\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/build_handler.py::Builder.cancel\",\n \"text\": \"jupyterlab/handlers/build_handler.py::Builder.cancel\\n @gen.coroutine\\n def cancel(self):\\n if not self.building:\\n raise ValueError('No current build')\\n self._canceling = True\\n yield self._future\\n self._canceling = False\\n self.canceled = True\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/build_handler.py::Builder._run_build_check\",\n \"text\": \"jupyterlab/handlers/build_handler.py::Builder._run_build_check\\n @run_on_executor\\n def _run_build_check(self, app_dir, logger, core_config):\\n return build_check(\\n app_dir=app_dir, logger=logger, core_config=core_config)\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/handlers/build_handler.py::Builder._run_build\",\n \"text\": \"jupyterlab/handlers/build_handler.py::Builder._run_build\\n @run_on_executor\\n def _run_build(self, app_dir, logger, kill_event, core_config):\\n kwargs = dict(\\n app_dir=app_dir, logger=logger, kill_event=kill_event,\\n core_config=core_config, command='build')\\n try:\\n return build(**kwargs)\\n except Exception as e:\\n if self._kill_event.is_set():\\n return\\n self.log.warn('Build failed, running a clean and rebuild')\\n clean(app_dir)\\n return build(**kwargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/handlers/build_handler.py::BuildHandler.initialize\",\n \"text\": \"jupyterlab/handlers/build_handler.py::BuildHandler.initialize\\n def initialize(self, builder):\\n self.builder = builder\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/build_handler.py::BuildHandler.get\",\n \"text\": \"jupyterlab/handlers/build_handler.py::BuildHandler.get\\n @web.authenticated\\n @gen.coroutine\\n def get(self):\\n data = yield self.builder.get_status()\\n self.finish(json.dumps(data))\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/build_handler.py::BuildHandler.delete\",\n \"text\": \"jupyterlab/handlers/build_handler.py::BuildHandler.delete\\n @web.authenticated\\n @gen.coroutine\\n def delete(self):\\n self.log.warning('Canceling build')\\n try:\\n yield self.builder.cancel()\\n except Exception as e:\\n raise web.HTTPError(500, str(e))\\n self.set_status(204)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/build_handler.py::BuildHandler.post\",\n \"text\": \"jupyterlab/handlers/build_handler.py::BuildHandler.post\\n @web.authenticated\\n @gen.coroutine\\n def post(self):\\n self.log.debug('Starting build')\\n try:\\n yield self.builder.build()\\n except Exception as e:\\n raise web.HTTPError(500, str(e))\\n\\n if self.builder.canceled:\\n raise web.HTTPError(400, 'Build canceled')\\n\\n self.log.debug('Build succeeded')\\n self.set_status(200)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::_make_extension_entry\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::_make_extension_entry\\ndef _make_extension_entry(name, description, url, enabled, core, latest_version,\\n installed_version, status, installed=None):\\n \\\"\\\"\\\"Create an extension entry that can be sent to the client\\\"\\\"\\\"\\n ret = dict(\\n name=name,\\n description=description,\\n url=url,\\n enabled=enabled,\\n core=core,\\n latest_version=latest_version,\\n installed_version=installed_version,\\n status=status,\\n )\\n if installed is not None:\\n ret['installed'] = installed\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::_ensure_compat_errors\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::_ensure_compat_errors\\ndef _ensure_compat_errors(info, app_dir, logger):\\n \\\"\\\"\\\"Ensure that the app info has compat_errors field\\\"\\\"\\\"\\n handler = _AppHandler(app_dir, logger)\\n info['compat_errors'] = handler._get_extension_compat()\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::_build_check_info\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::_build_check_info\\ndef _build_check_info(app_dir, logger):\\n \\\"\\\"\\\"Get info about packages scheduled for (un)install/update\\\"\\\"\\\"\\n handler = _AppHandler(app_dir, logger)\\n messages = handler.build_check(fast=True)\\n # Decode the messages into a dict:\\n status = {'install': [], 'uninstall': [], 'update': []}\\n for msg in messages:\\n for key, pattern in _message_map.items():\\n match = pattern.match(msg)\\n if match:\\n status[key].append(match.group('name'))\\n return status\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.__init__\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.__init__\\n def __init__(self, log, app_dir, core_config=None):\\n self.log = log\\n self.app_dir = app_dir\\n self.core_config = core_config\\n self._outdated = None\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.list_extensions\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.list_extensions\\n @gen.coroutine\\n def list_extensions(self):\\n \\\"\\\"\\\"Handle a request for all installed extensions\\\"\\\"\\\"\\n info = get_app_info(app_dir=self.app_dir, logger=self.log)\\n build_check_info = _build_check_info(self.app_dir, self.log)\\n _ensure_compat_errors(info, self.app_dir, self.log)\\n extensions = []\\n # TODO: Ensure loops can run in parallel\\n for name, data in info['extensions'].items():\\n status = 'ok'\\n pkg_info = yield self._get_pkg_info(name, data)\\n if info['compat_errors'].get(name, None):\\n status = 'error'\\n else:\\n for packages in build_check_info.values():\\n if name in packages:\\n status = 'warning'\\n extensions.append(_make_extension_entry(\\n name=name,\\n description=pkg_info['description'],\\n url=data['url'],\\n enabled=(name not in info['disabled']),\\n core=False,\\n # Use wanted version to ensure we limit ourselves\\n # within semver restrictions\\n latest_version=pkg_info['latest_version'],\\n installed_version=data['version'],\\n status=status,\\n ))\\n for name in build_check_info['uninstall']:\\n data = yield self._get_scheduled_uninstall_info(name)\\n if data is not None:\\n extensions.append(_make_extension_entry(\\n name=name,\\n description=data['description'],\\n url=data.get('homepage', ''),\\n installed=False,\\n enabled=False,\\n core=False,\\n latest_version=data['version'],\\n installed_version=data['version'],\\n status='warning',\\n ))\\n raise gen.Return\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.install\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.install\\n @gen.coroutine\\n def install(self, extension):\\n \\\"\\\"\\\"Handle an install/update request\\\"\\\"\\\"\\n try:\\n install_extension(\\n extension, app_dir=self.app_dir, logger=self.log,\\n core_config=self.core_config)\\n except ValueError as e:\\n raise gen.Return(dict(status='error', message=str(e)))\\n raise gen.Return(dict(status='ok',))\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.uninstall\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.uninstall\\n @gen.coroutine\\n def uninstall(self, extension):\\n \\\"\\\"\\\"Handle an uninstall request\\\"\\\"\\\"\\n did_uninstall = uninstall_extension(\\n extension, app_dir=self.app_dir, logger=self.log,\\n core_config=self.core_config)\\n raise gen.Return(dict(status='ok' if did_uninstall else 'error',))\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.enable\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.enable\\n @gen.coroutine\\n def enable(self, extension):\\n \\\"\\\"\\\"Handle an enable request\\\"\\\"\\\"\\n enable_extension(\\n extension, app_dir=self.app_dir, logger=self.log,\\n core_config=self.core_config)\\n raise gen.Return(dict(status='ok',))\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.disable\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.disable\\n @gen.coroutine\\n def disable(self, extension):\\n \\\"\\\"\\\"Handle a disable request\\\"\\\"\\\"\\n disable_extension(\\n extension, app_dir=self.app_dir, logger=self.log,\\n core_config=self.core_config)\\n raise gen.Return(dict(status='ok',))\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager._get_pkg_info\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager._get_pkg_info\\n @gen.coroutine\\n def _get_pkg_info(self, name, data):\\n \\\"\\\"\\\"Get information about a package\\\"\\\"\\\"\\n info = read_package(data['path'])\\n\\n # Get latest version that is compatible with current lab:\\n outdated = yield self._get_outdated()\\n if outdated and name in outdated:\\n info['latest_version'] = outdated[name]\\n else:\\n # Fallback to indicating that current is latest\\n info['latest_version'] = info['version']\\n\\n raise gen.Return(info)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager._get_outdated\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager._get_outdated\\n def _get_outdated(self):\\n \\\"\\\"\\\"Get a Future to information from `npm/yarn outdated`.\\n\\n This will cache the results. To refresh the cache, set\\n self._outdated to None before calling. To bypass the cache,\\n call self._load_outdated directly.\\n \\\"\\\"\\\"\\n # Ensure self._outdated is a Future for data on outdated extensions\\n if self._outdated is None:\\n self._outdated = self._load_outdated()\\n # Return the Future\\n return self._outdated\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.refresh_outdated\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager.refresh_outdated\\n def refresh_outdated(self):\\n self._outdated = self._load_outdated()\\n return self._outdated\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager._load_outdated\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager._load_outdated\\n @gen.coroutine\\n def _load_outdated(self):\\n \\\"\\\"\\\"Get the latest compatible version\\\"\\\"\\\"\\n info = get_app_info(app_dir=self.app_dir, logger=self.log)\\n names = tuple(info['extensions'].keys())\\n data = yield self.executor.submit(\\n get_latest_compatible_package_versions,\\n names,\\n app_dir=self.app_dir,\\n logger=self.log,\\n core_config=self.core_config,\\n )\\n raise gen.Return(data)\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager._get_scheduled_uninstall_info\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionManager._get_scheduled_uninstall_info\\n @gen.coroutine\\n def _get_scheduled_uninstall_info(self, name):\\n \\\"\\\"\\\"Get information about a package that is scheduled for uninstallation\\\"\\\"\\\"\\n target = os.path.join(\\n self.app_dir, 'staging', 'node_modules', name, 'package.json')\\n if os.path.exists(target):\\n with open(target) as fid:\\n raise gen.Return(json.load(fid))\\n else:\\n raise gen.Return(None)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionHandler.initialize\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionHandler.initialize\\n def initialize(self, manager):\\n self.manager = manager\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionHandler.get\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionHandler.get\\n @web.authenticated\\n @gen.coroutine\\n def get(self):\\n \\\"\\\"\\\"GET query returns info on all installed extensions\\\"\\\"\\\"\\n if self.get_argument('refresh', False) == '1':\\n yield self.manager.refresh_outdated()\\n extensions = yield self.manager.list_extensions()\\n self.finish(json.dumps(extensions))\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionHandler.post\",\n \"text\": \"jupyterlab/handlers/extension_manager_handler.py::ExtensionHandler.post\\n @web.authenticated\\n @gen.coroutine\\n def post(self):\\n \\\"\\\"\\\"POST query performs an action on a specific extension\\\"\\\"\\\"\\n data = self.get_json_body()\\n cmd = data['cmd']\\n name = data['extension_name']\\n if (cmd not in ('install', 'uninstall', 'enable', 'disable') or\\n not name):\\n raise web.HTTPError(\\n 422, 'Could not process instrution %r with extension name %r' % (\\n cmd, name))\\n\\n # TODO: Can we trust extension_name? Does it need sanitation?\\n # It comes from an authenticated session, but its name is\\n # ultimately from the NPM repository.\\n ret_value = None\\n try:\\n if cmd == 'install':\\n ret_value = yield self.manager.install(name)\\n elif cmd == 'uninstall':\\n ret_value = yield self.manager.uninstall(name)\\n elif cmd == 'enable':\\n ret_value = yield self.manager.enable(name)\\n elif cmd == 'disable':\\n ret_value = yield self.manager.disable(name)\\n except gen.Return as e:\\n ret_value = e.value\\n except Exception as e:\\n raise web.HTTPError(500, str(e))\\n\\n if ret_value is None:\\n self.set_status(200)\\n else:\\n self.finish(json.dumps(ret_value))\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labapp.py::LabBuildApp.start\",\n \"text\": \"jupyterlab/labapp.py::LabBuildApp.start\\n def start(self):\\n parts = ['build']\\n parts.append('none' if self.dev_build is None else\\n 'dev' if self.dev_build else\\n 'prod')\\n if self.minimize:\\n parts.append('minimize')\\n command = ':'.join(parts)\\n\\n app_dir = self.app_dir or get_app_dir()\\n self.log.info('JupyterLab %s', version)\\n with self.debug_logging():\\n if self.pre_clean:\\n self.log.info('Cleaning %s' % app_dir)\\n clean(self.app_dir)\\n self.log.info('Building in %s', app_dir)\\n build(app_dir=app_dir, name=self.name, version=self.version,\\n command=command, logger=self.log,\\n core_config=self.core_config)\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/labapp.py::LabCleanApp.start\",\n \"text\": \"jupyterlab/labapp.py::LabCleanApp.start\\n def start(self):\\n clean(self.app_dir, logger=self.log)\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/labapp.py::LabPathApp.start\",\n \"text\": \"jupyterlab/labapp.py::LabPathApp.start\\n def start(self):\\n print('Application directory: %s' % get_app_dir())\\n print('User Settings directory: %s' % get_user_settings_dir())\\n print('Workspaces directory: %s' % get_workspaces_dir())\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labapp.py::LabWorkspaceExportApp.start\",\n \"text\": \"jupyterlab/labapp.py::LabWorkspaceExportApp.start\\n def start(self):\\n app = LabApp(config=self.config)\\n base_url = app.base_url\\n config = load_config(app)\\n directory = config.workspaces_dir\\n app_url = config.app_url\\n\\n if len(self.extra_args) > 1:\\n print('Too many arguments were provided for workspace export.')\\n self.exit(1)\\n\\n raw = (app_url if not self.extra_args\\n else ujoin(config.workspaces_url, self.extra_args[0]))\\n slug = slugify(raw, base_url)\\n workspace_path = pjoin(directory, slug + WORKSPACE_EXTENSION)\\n\\n if osp.exists(workspace_path):\\n with open(workspace_path) as fid:\\n try: # to load the workspace file.\\n print(fid.read())\\n except Exception as e:\\n print(json.dumps(dict(data=dict(), metadata=dict(id=raw))))\\n else:\\n print(json.dumps(dict(data=dict(), metadata=dict(id=raw))))\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labapp.py::LabWorkspaceImportApp.start\",\n \"text\": \"jupyterlab/labapp.py::LabWorkspaceImportApp.start\\n def start(self):\\n app = LabApp(config=self.config)\\n base_url = app.base_url\\n config = load_config(app)\\n directory = config.workspaces_dir\\n app_url = config.app_url\\n workspaces_url = config.workspaces_url\\n\\n if len(self.extra_args) != 1:\\n print('One argument is required for workspace import.')\\n self.exit(1)\\n\\n workspace = dict()\\n with self._smart_open() as fid:\\n try: # to load, parse, and validate the workspace file.\\n workspace = self._validate(fid, base_url, app_url, workspaces_url)\\n except Exception as e:\\n print('%s is not a valid workspace:\\\\n%s' % (fid.name, e))\\n self.exit(1)\\n\\n if not osp.exists(directory):\\n try:\\n os.makedirs(directory)\\n except Exception as e:\\n print('Workspaces directory could not be created:\\\\n%s' % e)\\n self.exit(1)\\n\\n slug = slugify(workspace['metadata']['id'], base_url)\\n workspace_path = pjoin(directory, slug + WORKSPACE_EXTENSION)\\n\\n # Write the workspace data to a file.\\n with open(workspace_path, 'w') as fid:\\n fid.write(json.dumps(workspace))\\n\\n print('Saved workspace: %s' % workspace_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labapp.py::LabWorkspaceImportApp._smart_open\",\n \"text\": \"jupyterlab/labapp.py::LabWorkspaceImportApp._smart_open\\n def _smart_open(self):\\n file_name = self.extra_args[0]\\n\\n if file_name == '-':\\n return sys.stdin\\n else:\\n file_path = osp.abspath(file_name)\\n\\n if not osp.exists(file_path):\\n print('%s does not exist.' % file_name)\\n self.exit(1)\\n\\n return open(file_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labapp.py::LabWorkspaceImportApp._validate\",\n \"text\": \"jupyterlab/labapp.py::LabWorkspaceImportApp._validate\\n def _validate(self, data, base_url, app_url, workspaces_url):\\n workspace = json.load(data)\\n\\n if 'data' not in workspace:\\n raise Exception('The `data` field is missing.')\\n\\n # If workspace_name is set in config, inject the\\n # name into the workspace metadata.\\n if self.workspace_name is not None:\\n if self.workspace_name == \\\"\\\":\\n workspace_id = ujoin(base_url, app_url)\\n else:\\n workspace_id = ujoin(base_url, workspaces_url, self.workspace_name)\\n workspace['metadata'] = {'id': workspace_id}\\n # else check that the workspace_id is valid.\\n else:\\n if 'id' not in workspace['metadata']:\\n raise Exception('The `id` field is missing in `metadata`.')\\n else:\\n id = workspace['metadata']['id']\\n if id != ujoin(base_url, app_url) and not id.startswith(ujoin(base_url, workspaces_url)):\\n error = '%s does not match app_url or start with workspaces_url.'\\n raise Exception(error % id)\\n\\n return workspace\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labapp.py::LabWorkspaceApp.start\",\n \"text\": \"jupyterlab/labapp.py::LabWorkspaceApp.start\\n def start(self):\\n super().start()\\n print('Either `export` or `import` must be specified.')\\n self.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labapp.py::LabApp.init_webapp\",\n \"text\": \"jupyterlab/labapp.py::LabApp.init_webapp\\n def init_webapp(self, *args, **kwargs):\\n super().init_webapp(*args, **kwargs)\\n settings = self.web_app.settings\\n if 'page_config_data' not in settings:\\n settings['page_config_data'] = {}\\n\\n # Handle quit button with support for Notebook < 5.6\\n settings['page_config_data']['quitButton'] = getattr(self, 'quit_button', False)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labapp.py::LabApp.init_server_extensions\",\n \"text\": \"jupyterlab/labapp.py::LabApp.init_server_extensions\\n def init_server_extensions(self):\\n \\\"\\\"\\\"Load any extensions specified by config.\\n\\n Import the module, then call the load_jupyter_server_extension function,\\n if one exists.\\n\\n If the JupyterLab server extension is not enabled, it will\\n be manually loaded with a warning.\\n\\n The extension API is experimental, and may change in future releases.\\n \\\"\\\"\\\"\\n super(LabApp, self).init_server_extensions()\\n msg = 'JupyterLab server extension not enabled, manually loading...'\\n if not self.nbserver_extensions.get('jupyterlab', False):\\n self.log.warning(msg)\\n load_jupyter_server_extension(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::BaseExtensionApp.start\",\n \"text\": \"jupyterlab/labextensions.py::BaseExtensionApp.start\\n def start(self):\\n if self.app_dir and self.app_dir.startswith(HERE):\\n raise ValueError('Cannot run lab extension commands in core app')\\n with self.debug_logging():\\n ans = self.run_task()\\n if ans and self.should_build:\\n parts = ['build']\\n parts.append('none' if self.dev_build is None else\\n 'dev' if self.dev_build else\\n 'prod')\\n if self.minimize:\\n parts.append('minimize')\\n command = ':'.join(parts)\\n\\n build(app_dir=self.app_dir, clean_staging=self.should_clean,\\n logger=self.log, command=command,\\n core_config=self.core_config)\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::BaseExtensionApp.run_task\",\n \"text\": \"jupyterlab/labextensions.py::BaseExtensionApp.run_task\\n def run_task(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::BaseExtensionApp._log_format_default\",\n \"text\": \"jupyterlab/labextensions.py::BaseExtensionApp._log_format_default\\n def _log_format_default(self):\\n \\\"\\\"\\\"A default format for messages\\\"\\\"\\\"\\n return \\\"%(message)s\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::InstallLabExtensionApp.run_task\",\n \"text\": \"jupyterlab/labextensions.py::InstallLabExtensionApp.run_task\\n def run_task(self):\\n pinned_versions = self.pin.split(',')\\n self.extra_args = self.extra_args or [os.getcwd()]\\n return any([\\n install_extension(\\n arg,\\n self.app_dir,\\n logger=self.log,\\n core_config=self.core_config,\\n # Pass in pinned alias if we have it\\n pin=pinned_versions[i] if i < len(pinned_versions) else None\\n )\\n for i, arg in enumerate(self.extra_args)\\n ])\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::UpdateLabExtensionApp.run_task\",\n \"text\": \"jupyterlab/labextensions.py::UpdateLabExtensionApp.run_task\\n def run_task(self):\\n if not self.all and not self.extra_args:\\n self.log.warn('Specify an extension to update, or use --all to update all extensions')\\n return False\\n if self.all:\\n return update_extension(\\n all_=True, app_dir=self.app_dir, logger=self.log,\\n core_config=self.core_config)\\n return any([\\n update_extension(\\n name=arg, app_dir=self.app_dir, logger=self.log,\\n core_config=self.core_config)\\n for arg in self.extra_args\\n ])\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::LinkLabExtensionApp.run_task\",\n \"text\": \"jupyterlab/labextensions.py::LinkLabExtensionApp.run_task\\n def run_task(self):\\n self.extra_args = self.extra_args or [os.getcwd()]\\n return any([\\n link_package(\\n arg, self.app_dir, logger=self.log,\\n core_config=self.core_config)\\n for arg in self.extra_args\\n ])\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::UnlinkLabExtensionApp.run_task\",\n \"text\": \"jupyterlab/labextensions.py::UnlinkLabExtensionApp.run_task\\n def run_task(self):\\n self.extra_args = self.extra_args or [os.getcwd()]\\n return any([\\n unlink_package(\\n arg, self.app_dir, logger=self.log,\\n core_config=self.core_config)\\n for arg in self.extra_args\\n ])\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::UninstallLabExtensionApp.run_task\",\n \"text\": \"jupyterlab/labextensions.py::UninstallLabExtensionApp.run_task\\n def run_task(self):\\n self.extra_args = self.extra_args or [os.getcwd()]\\n return any([\\n uninstall_extension(\\n arg, all_=self.all, app_dir=self.app_dir, logger=self.log,\\n core_config=self.core_config)\\n for arg in self.extra_args\\n ])\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::ListLabExtensionsApp.run_task\",\n \"text\": \"jupyterlab/labextensions.py::ListLabExtensionsApp.run_task\\n def run_task(self):\\n list_extensions(\\n self.app_dir, logger=self.log, core_config=self.core_config)\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::EnableLabExtensionsApp.run_task\",\n \"text\": \"jupyterlab/labextensions.py::EnableLabExtensionsApp.run_task\\n def run_task(self):\\n [enable_extension(\\n arg, self.app_dir, logger=self.log, core_config=self.core_config)\\n for arg in self.extra_args]\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::DisableLabExtensionsApp.run_task\",\n \"text\": \"jupyterlab/labextensions.py::DisableLabExtensionsApp.run_task\\n def run_task(self):\\n [disable_extension(\\n arg, self.app_dir, logger=self.log, core_config=self.core_config)\\n for arg in self.extra_args]\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::CheckLabExtensionsApp.run_task\",\n \"text\": \"jupyterlab/labextensions.py::CheckLabExtensionsApp.run_task\\n def run_task(self):\\n all_enabled = all(\\n check_extension(\\n arg, self.app_dir,\\n self.should_check_installed_only,\\n logger=self.log,\\n core_config=self.core_config)\\n for arg in self.extra_args)\\n if not all_enabled:\\n self.exit(1)\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::LabExtensionApp.start\",\n \"text\": \"jupyterlab/labextensions.py::LabExtensionApp.start\\n def start(self):\\n \\\"\\\"\\\"Perform the App's functions as configured\\\"\\\"\\\"\\n super(LabExtensionApp, self).start()\\n\\n # The above should have called a subcommand and raised NoStart; if we\\n # get here, it didn't, so we should self.log.info a message.\\n subcmds = \\\", \\\".join(sorted(self.subcommands))\\n self.exit(\\\"Please supply at least one subcommand: %s\\\" % subcmds)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":249,"cells":{"instance_id":{"kind":"string","value":"conan-io__conan-5348"},"repo":{"kind":"string","value":"conan-io/conan"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"VS 2019 with older toolsets broken since 1.15.0\nThis is the same as https://github.com/conan-io/conan/issues/5318, but for Visual Studio 2019.\r\n\r\nWith Visual Studio 2019 (16.0), you can also install the Visual Studio 2017 (v141) and 2015 (v140) build tools. This broke with https://github.com/conan-io/conan/commit/6d3282fad34365eeee530e3b69e4eb91ec3e9338, as only the v142 toolset can be used now if you don't have older IDEs installed.\n"},"gold_files":{"kind":"list like","value":["conans/client/generators/cmake_common.py"],"string":"[\n \"conans/client/generators/cmake_common.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["conans/client/generators/cmake_common.py::<module>"],"string":"[\n \"conans/client/generators/cmake_common.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"conans/client/build/cmake_flags.py","text":"conans/client/build/cmake_flags.py\ndef get_toolset(settings)\ndef get_generator(settings)\ndef get_generator_platform(settings, generator)\ndef is_multi_configuration(generator)\ndef is_toolset_supported(generator)\ndef is_generator_platform_supported(generator)\ndef verbose_definition(value)\ndef in_local_cache_definition(value)\ndef runtime_definition(runtime)\ndef build_type_definition(build_type, generator)\nclass CMakeDefinitionsBuilder\n def __init__(self, conanfile, cmake_system_name=True, make_program=None, parallel=True, generator=None, set_cmake_flags=False, forced_build_type=None, output=None)\n def _ss(self, setname) # safe setting\n def _get_cpp_standard_vars(self)\n def _cmake_cross_build_defines(self)\n def _get_make_program_definition(self)\n def get_definitions(self)","label":0},{"cid":"conans/client/build/msbuild.py","text":"conans/client/build/msbuild.py\nclass MSBuild\n def __init__(self, conanfile)\n def build(self, project_file, targets=None, upgrade_project=True, build_type=None, arch=None, parallel=True, force_vcvars=False, toolset=None, platforms=None, use_env=True, vcvars_ver=None, winsdk_version=None, properties=None, output_binary_log=None, property_file_name=None, verbosity=None, definitions=None) # :param project_file: Path to the .sln file.\n def get_command(self, project_file, props_file_path=None, targets=None, upgrade_project=True, build_type=None, arch=None, parallel=True, toolset=None, platforms=None, use_env=False, properties=None, output_binary_log=None, verbosity=None)\n def _get_props_file_contents(self, definitions=None)\n def format_macro(name, value)\n def get_version(settings)","label":0},{"cid":"conans/client/generators/cmake_common.py","text":"conans/client/generators/cmake_common.py\ndef _cmake_string_representation(value) # Escapes the specified string for use in a CMake command surrounded with double q\ndef _build_type_str(build_type)\ndef cmake_user_info_vars(deps_user_info)\ndef cmake_dependency_vars(name, deps, build_type='')\ndef cmake_package_info(name, version)\ndef cmake_settings_info(settings)\ndef cmake_dependencies(dependencies, build_type='')\ndef cmake_global_vars(deps, build_type='')\ndef generate_targets_section(dependencies)\ndef _conan_basic_setup_common(addtional_macros, cmake_multi=False)","label":1},{"cid":"conans/client/generators/visualstudio_multi.py","text":"conans/client/generators/visualstudio_multi.py\nclass _VSSettings\n def __init__(self, settings)\n def filename(self)\n def condition(self)\nclass VisualStudioMultiGenerator\n def filename(self)\n def content(self)","label":0},{"cid":"conans/client/migrations_settings.py","text":"conans/client/migrations_settings.py","label":0},{"cid":"conans/client/tools/win.py","text":"conans/client/tools/win.py\ndef _visual_compiler_cygwin(output, version)\ndef _system_registry_key(key, subkey, query)\ndef _visual_compiler(output, version) # \"version have to be 8.0, or 9.0 or... anything .0\ndef latest_vs_version_installed(output)\ndef msvs_toolset(settings)\ndef latest_visual_studio_version_installed(output)\ndef msvc_build_command(settings, sln_path, targets=None, upgrade_project=True, build_type=None, arch=None, parallel=True, force_vcvars=False, toolset=None, platforms=None, output=None) # Do both: set the environment variables and call the .sln build\ndef build_sln_command(settings, sln_path, targets=None, upgrade_project=True, build_type=None, arch=None, parallel=True, toolset=None, platforms=None, output=None, verbosity=None, definitions=None) # Use example:\ndef vs_installation_path(version, preference=None)\ndef vswhere(all_=False, prerelease=False, products=None, requires=None, version='', latest=False, legacy=False, property_='', nologo=True)\ndef vs_comntools(compiler_version)\ndef find_windows_10_sdk() # finds valid Windows 10 SDK version which can be passed to vcvarsall.bat (vcvars_\ndef vcvars_command(settings, arch=None, compiler_version=None, force=False, vcvars_ver=None, winsdk_version=None, output=None)\ndef vcvars_dict(settings, arch=None, compiler_version=None, force=False, filter_known_paths=False, vcvars_ver=None, winsdk_version=None, only_diff=True, output=None)\ndef vcvars(*args, **kwargs)\ndef escape_windows_cmd(command) # To use in a regular windows cmd.exe\ndef get_cased_path(name)\ndef unix_path(path, path_flavor=None) # \"Used to translate windows paths to MSYS unix paths like\ndef run_in_windows_bash(conanfile, bashcmd, cwd=None, subsystem=None, msys_mingw=True, env=None, with_login=True) # Will run a unix command inside a bash terminal","label":0}],"string":"[\n {\n \"cid\": \"conans/client/build/cmake_flags.py\",\n \"text\": \"conans/client/build/cmake_flags.py\\ndef get_toolset(settings)\\ndef get_generator(settings)\\ndef get_generator_platform(settings, generator)\\ndef is_multi_configuration(generator)\\ndef is_toolset_supported(generator)\\ndef is_generator_platform_supported(generator)\\ndef verbose_definition(value)\\ndef in_local_cache_definition(value)\\ndef runtime_definition(runtime)\\ndef build_type_definition(build_type, generator)\\nclass CMakeDefinitionsBuilder\\n def __init__(self, conanfile, cmake_system_name=True, make_program=None, parallel=True, generator=None, set_cmake_flags=False, forced_build_type=None, output=None)\\n def _ss(self, setname) # safe setting\\n def _get_cpp_standard_vars(self)\\n def _cmake_cross_build_defines(self)\\n def _get_make_program_definition(self)\\n def get_definitions(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/msbuild.py\",\n \"text\": \"conans/client/build/msbuild.py\\nclass MSBuild\\n def __init__(self, conanfile)\\n def build(self, project_file, targets=None, upgrade_project=True, build_type=None, arch=None, parallel=True, force_vcvars=False, toolset=None, platforms=None, use_env=True, vcvars_ver=None, winsdk_version=None, properties=None, output_binary_log=None, property_file_name=None, verbosity=None, definitions=None) # :param project_file: Path to the .sln file.\\n def get_command(self, project_file, props_file_path=None, targets=None, upgrade_project=True, build_type=None, arch=None, parallel=True, toolset=None, platforms=None, use_env=False, properties=None, output_binary_log=None, verbosity=None)\\n def _get_props_file_contents(self, definitions=None)\\n def format_macro(name, value)\\n def get_version(settings)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/cmake_common.py\",\n \"text\": \"conans/client/generators/cmake_common.py\\ndef _cmake_string_representation(value) # Escapes the specified string for use in a CMake command surrounded with double q\\ndef _build_type_str(build_type)\\ndef cmake_user_info_vars(deps_user_info)\\ndef cmake_dependency_vars(name, deps, build_type='')\\ndef cmake_package_info(name, version)\\ndef cmake_settings_info(settings)\\ndef cmake_dependencies(dependencies, build_type='')\\ndef cmake_global_vars(deps, build_type='')\\ndef generate_targets_section(dependencies)\\ndef _conan_basic_setup_common(addtional_macros, cmake_multi=False)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/generators/visualstudio_multi.py\",\n \"text\": \"conans/client/generators/visualstudio_multi.py\\nclass _VSSettings\\n def __init__(self, settings)\\n def filename(self)\\n def condition(self)\\nclass VisualStudioMultiGenerator\\n def filename(self)\\n def content(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations_settings.py\",\n \"text\": \"conans/client/migrations_settings.py\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py\",\n \"text\": \"conans/client/tools/win.py\\ndef _visual_compiler_cygwin(output, version)\\ndef _system_registry_key(key, subkey, query)\\ndef _visual_compiler(output, version) # \\\"version have to be 8.0, or 9.0 or... anything .0\\ndef latest_vs_version_installed(output)\\ndef msvs_toolset(settings)\\ndef latest_visual_studio_version_installed(output)\\ndef msvc_build_command(settings, sln_path, targets=None, upgrade_project=True, build_type=None, arch=None, parallel=True, force_vcvars=False, toolset=None, platforms=None, output=None) # Do both: set the environment variables and call the .sln build\\ndef build_sln_command(settings, sln_path, targets=None, upgrade_project=True, build_type=None, arch=None, parallel=True, toolset=None, platforms=None, output=None, verbosity=None, definitions=None) # Use example:\\ndef vs_installation_path(version, preference=None)\\ndef vswhere(all_=False, prerelease=False, products=None, requires=None, version='', latest=False, legacy=False, property_='', nologo=True)\\ndef vs_comntools(compiler_version)\\ndef find_windows_10_sdk() # finds valid Windows 10 SDK version which can be passed to vcvarsall.bat (vcvars_\\ndef vcvars_command(settings, arch=None, compiler_version=None, force=False, vcvars_ver=None, winsdk_version=None, output=None)\\ndef vcvars_dict(settings, arch=None, compiler_version=None, force=False, filter_known_paths=False, vcvars_ver=None, winsdk_version=None, only_diff=True, output=None)\\ndef vcvars(*args, **kwargs)\\ndef escape_windows_cmd(command) # To use in a regular windows cmd.exe\\ndef get_cased_path(name)\\ndef unix_path(path, path_flavor=None) # \\\"Used to translate windows paths to MSYS unix paths like\\ndef run_in_windows_bash(conanfile, bashcmd, cwd=None, subsystem=None, msys_mingw=True, env=None, with_login=True) # Will run a unix command inside a bash terminal\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"conans/client/build/cmake_flags.py::get_toolset","text":"conans/client/build/cmake_flags.py::get_toolset\ndef get_toolset(settings):\n if settings.get_safe(\"compiler\") == \"Visual Studio\":\n subs_toolset = settings.get_safe(\"compiler.toolset\")\n if subs_toolset:\n return subs_toolset\n return None","label":0},{"cid":"conans/client/build/cmake_flags.py::get_generator","text":"conans/client/build/cmake_flags.py::get_generator\ndef get_generator(settings):\n if \"CONAN_CMAKE_GENERATOR\" in os.environ:\n return os.environ[\"CONAN_CMAKE_GENERATOR\"]\n\n compiler = settings.get_safe(\"compiler\")\n arch = settings.get_safe(\"arch\")\n compiler_version = settings.get_safe(\"compiler.version\")\n os_build, _, _, _ = get_cross_building_settings(settings)\n os_host = settings.get_safe(\"os\")\n\n if not compiler or not compiler_version or not arch:\n if os_build == \"Windows\":\n logger.warning(\"CMake generator could not be deduced from settings\")\n return None\n return \"Unix Makefiles\"\n\n if compiler == \"Visual Studio\":\n _visuals = {'8': '8 2005',\n '9': '9 2008',\n '10': '10 2010',\n '11': '11 2012',\n '12': '12 2013',\n '14': '14 2015',\n '15': '15 2017',\n '16': '16 2019'}\n base = \"Visual Studio %s\" % _visuals.get(compiler_version,\n \"UnknownVersion %s\" % compiler_version)\n if os_host != \"WindowsCE\" and Version(compiler_version) < \"16\":\n if arch == \"x86_64\":\n base += \" Win64\"\n elif \"arm\" in arch:\n base += \" ARM\"\n return base\n\n # The generator depends on the build machine, not the target\n if os_build == \"Windows\" and compiler != \"qcc\":\n return \"MinGW Makefiles\" # it is valid only under Windows\n\n return \"Unix Makefiles\"","label":0},{"cid":"conans/client/build/cmake_flags.py::get_generator_platform","text":"conans/client/build/cmake_flags.py::get_generator_platform\ndef get_generator_platform(settings, generator):\n if \"CONAN_CMAKE_GENERATOR_PLATFORM\" in os.environ:\n return os.environ[\"CONAN_CMAKE_GENERATOR_PLATFORM\"]\n\n compiler = settings.get_safe(\"compiler\")\n arch = settings.get_safe(\"arch\")\n compiler_version = settings.get_safe(\"compiler.version\")\n\n if settings.get_safe(\"os\") == \"WindowsCE\":\n return settings.get_safe(\"os.platform\")\n\n if compiler == \"Visual Studio\" and Version(compiler_version) >= \"16\" \\\n and \"Visual\" in generator:\n return {\"x86\": \"Win32\",\n \"x86_64\": \"x64\",\n \"armv7\": \"ARM\",\n \"armv8\": \"ARM64\"}.get(arch)\n return None","label":0},{"cid":"conans/client/build/cmake_flags.py::is_multi_configuration","text":"conans/client/build/cmake_flags.py::is_multi_configuration\ndef is_multi_configuration(generator):\n if not generator:\n return False\n return \"Visual\" in generator or \"Xcode\" in generator","label":0},{"cid":"conans/client/build/cmake_flags.py::is_toolset_supported","text":"conans/client/build/cmake_flags.py::is_toolset_supported\ndef is_toolset_supported(generator):\n # https://cmake.org/cmake/help/v3.14/variable/CMAKE_GENERATOR_TOOLSET.html\n if not generator:\n return False\n return \"Visual\" in generator or \"Xcode\" in generator or \"Green Hills MULTI\" in generator","label":0},{"cid":"conans/client/build/cmake_flags.py::is_generator_platform_supported","text":"conans/client/build/cmake_flags.py::is_generator_platform_supported\ndef is_generator_platform_supported(generator):\n # https://cmake.org/cmake/help/v3.14/variable/CMAKE_GENERATOR_PLATFORM.html\n if not generator:\n return False\n return \"Visual\" in generator or \"Green Hills MULTI\" in generator","label":0},{"cid":"conans/client/build/cmake_flags.py::verbose_definition","text":"conans/client/build/cmake_flags.py::verbose_definition\ndef verbose_definition(value):\n return {verbose_definition_name: \"ON\" if value else \"OFF\"}","label":0},{"cid":"conans/client/build/cmake_flags.py::in_local_cache_definition","text":"conans/client/build/cmake_flags.py::in_local_cache_definition\ndef in_local_cache_definition(value):\n return {cmake_in_local_cache_var_name: \"ON\" if value else \"OFF\"}","label":0},{"cid":"conans/client/build/cmake_flags.py::runtime_definition","text":"conans/client/build/cmake_flags.py::runtime_definition\ndef runtime_definition(runtime):\n return {runtime_definition_var_name: \"/%s\" % runtime} if runtime else {}","label":0},{"cid":"conans/client/build/cmake_flags.py::build_type_definition","text":"conans/client/build/cmake_flags.py::build_type_definition\ndef build_type_definition(build_type, generator):\n if build_type and not is_multi_configuration(generator):\n return {\"CMAKE_BUILD_TYPE\": build_type}\n return {}","label":0},{"cid":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.__init__","text":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.__init__\n def __init__(self, conanfile, cmake_system_name=True, make_program=None,\n parallel=True, generator=None, set_cmake_flags=False,\n forced_build_type=None, output=None):\n self._conanfile = conanfile\n self._forced_cmake_system_name = cmake_system_name\n self._make_program = make_program\n self._parallel = parallel\n self._generator = generator\n self._set_cmake_flags = set_cmake_flags\n self._forced_build_type = forced_build_type\n self._output = output","label":0},{"cid":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._ss","text":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._ss\n def _ss(self, setname):\n \"\"\"safe setting\"\"\"\n return self._conanfile.settings.get_safe(setname)","label":0},{"cid":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_cpp_standard_vars","text":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_cpp_standard_vars\n def _get_cpp_standard_vars(self):\n cppstd = cppstd_from_settings(self._conanfile.settings)\n compiler = self._ss(\"compiler\")\n compiler_version = self._ss(\"compiler.version\")\n\n if not cppstd:\n return {}\n\n ret = {}\n if cppstd.startswith(\"gnu\"):\n ret[\"CONAN_CMAKE_CXX_STANDARD\"] = cppstd[3:]\n ret[\"CONAN_CMAKE_CXX_EXTENSIONS\"] = \"ON\"\n else:\n ret[\"CONAN_CMAKE_CXX_STANDARD\"] = cppstd\n ret[\"CONAN_CMAKE_CXX_EXTENSIONS\"] = \"OFF\"\n\n ret[\"CONAN_STD_CXX_FLAG\"] = cppstd_flag(compiler, compiler_version, cppstd)\n return ret","label":0},{"cid":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._cmake_cross_build_defines","text":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._cmake_cross_build_defines\n def _cmake_cross_build_defines(self):\n\n os_ = self._ss(\"os\")\n arch = self._ss(\"arch\")\n os_ver_str = \"os.api_level\" if os_ == \"Android\" else \"os.version\"\n op_system_version = self._ss(os_ver_str)\n\n env_sn = get_env(\"CONAN_CMAKE_SYSTEM_NAME\", \"\")\n env_sn = {\"False\": False, \"True\": True, \"\": None}.get(env_sn, env_sn)\n cmake_system_name = env_sn or self._forced_cmake_system_name\n\n os_build, _, _, _ = get_cross_building_settings(self._conanfile.settings)\n compiler = self._ss(\"compiler\")\n libcxx = self._ss(\"compiler.libcxx\")\n\n ret = OrderedDict()\n os_ver = get_env(\"CONAN_CMAKE_SYSTEM_VERSION\", op_system_version)\n toolchain_file = get_env(\"CONAN_CMAKE_TOOLCHAIN_FILE\", \"\")\n\n if toolchain_file != \"\":\n logger.info(\"Setting Cross build toolchain file: %s\" % toolchain_file)\n ret[\"CMAKE_TOOLCHAIN_FILE\"] = toolchain_file\n return ret\n\n if cmake_system_name is False:\n return ret\n\n # System name and system version\n if cmake_system_name is not True: # String not empty\n ret[\"CMAKE_SYSTEM_NAME\"] = cmake_system_name\n else: # detect if we are cross building and the system name and version\n if cross_building(self._conanfile.settings): # We are cross building\n if os_ != os_build:\n if os_: # the_os is the host (regular setting)\n ret[\"CMAKE_SYSTEM_NAME\"] = {\"iOS\": \"Darwin\",\n \"tvOS\": \"Darwin\",\n \"watchOS\": \"Darwin\",\n \"Neutrino\": \"QNX\"}.get(os_, os_)\n else:\n ret[\"CMAKE_SYSTEM_NAME\"] = \"Generic\"\n if os_ver:\n ret[\"CMAKE_SYSTEM_VERSION\"] = os_ver\n ","label":0},{"cid":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_make_program_definition","text":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_make_program_definition\n def _get_make_program_definition(self):\n make_program = os.getenv(\"CONAN_MAKE_PROGRAM\") or self._make_program\n if make_program:\n if not tools.which(make_program):\n self._output.warn(\"The specified make program '%s' cannot be found and will be \"\n \"ignored\" % make_program)\n else:\n self._output.info(\"Using '%s' as CMAKE_MAKE_PROGRAM\" % make_program)\n return {\"CMAKE_MAKE_PROGRAM\": make_program}\n\n return {}","label":0},{"cid":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.get_definitions","text":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.get_definitions\n def get_definitions(self):\n\n compiler = self._ss(\"compiler\")\n compiler_version = self._ss(\"compiler.version\")\n arch = self._ss(\"arch\")\n os_ = self._ss(\"os\")\n libcxx = self._ss(\"compiler.libcxx\")\n runtime = self._ss(\"compiler.runtime\")\n build_type = self._ss(\"build_type\")\n\n ret = OrderedDict()\n ret.update(runtime_definition(runtime))\n\n if self._forced_build_type and self._forced_build_type != build_type:\n self._output.warn(\"Forced CMake build type ('%s') different from the settings build \"\n \"type ('%s')\" % (self._forced_build_type, build_type))\n build_type = self._forced_build_type\n\n ret.update(build_type_definition(build_type, self._generator))\n\n if str(os_) == \"Macos\":\n if arch == \"x86\":\n ret[\"CMAKE_OSX_ARCHITECTURES\"] = \"i386\"\n\n ret.update(self._cmake_cross_build_defines())\n ret.update(self._get_cpp_standard_vars())\n\n ret[\"CONAN_EXPORTED\"] = \"1\"\n ret.update(in_local_cache_definition(self._conanfile.in_local_cache))\n\n if compiler:\n ret[\"CONAN_COMPILER\"] = compiler\n if compiler_version:\n ret[\"CONAN_COMPILER_VERSION\"] = str(compiler_version)\n\n # C, CXX, LINK FLAGS\n if compiler == \"Visual Studio\":\n if self._parallel:\n flag = parallel_compiler_cl_flag(output=self._output)\n ret['CONAN_CXX_FLAGS'] = flag\n ret['CONAN_C_FLAGS'] = flag\n else: # arch_flag is only set for non Visual Studio\n arch_flag = architecture_flag(compiler=compiler, os=os_, arch=arch)\n if arch_flag:\n ret['CONAN_CXX_FLAGS'] = arch_flag\n ret['CONAN_SHARED_LINKER_FLAGS'] = arch_flag\n ret['CONAN_C_FLAGS'] = arch_flag\n if self._set_cmake_flags:\n ","label":0},{"cid":"conans/client/build/msbuild.py::MSBuild.__init__","text":"conans/client/build/msbuild.py::MSBuild.__init__\n def __init__(self, conanfile):\n if isinstance(conanfile, ConanFile):\n self._conanfile = conanfile\n self._settings = self._conanfile.settings\n self._output = self._conanfile.output\n self.build_env = VisualStudioBuildEnvironment(self._conanfile,\n with_build_type_flags=False)\n else: # backwards compatible with build_sln_command\n self._settings = conanfile\n self.build_env = None","label":0},{"cid":"conans/client/build/msbuild.py::MSBuild.build","text":"conans/client/build/msbuild.py::MSBuild.build\n def build(self, project_file, targets=None, upgrade_project=True, build_type=None, arch=None,\n parallel=True, force_vcvars=False, toolset=None, platforms=None, use_env=True,\n vcvars_ver=None, winsdk_version=None, properties=None, output_binary_log=None,\n property_file_name=None, verbosity=None, definitions=None):\n \"\"\"\n :param project_file: Path to the .sln file.\n :param targets: List of targets to build.\n :param upgrade_project: Will call devenv to upgrade the solution to your\n current Visual Studio.\n :param build_type: Use a custom build type instead of the default settings.build_type one.\n :param arch: Use a custom architecture name instead of the settings.arch one.\n It will be used to build the /p:Configuration= parameter of MSBuild.\n It can be used as the key of the platforms parameter.\n E.g. arch=\"x86\", platforms={\"x86\": \"i386\"}\n :param parallel: Will use the configured number of cores in the conan.conf file or\n tools.cpu_count():\n In the solution: Building the solution with the projects in parallel. (/m: parameter).\n CL compiler: Building the sources in parallel. (/MP: compiler flag)\n :param force_vcvars: Will ignore if the environment is already set for a different\n Visual Studio version.\n :param toolset: Specify a toolset. Will append a /p:PlatformToolset option.\n :param platforms: Dictionary with the mapping of archs/platforms from Conan naming to another\n one. It is useful for Visual Studio solutions that have a different naming in architectures.\n Example: platforms={\"x86\":\"Win32\"} (Visual solution uses \"Win32\" instead of \"x86\").\n This dictionary will update the default one:\n msvc_arch = {'x86': 'x86', 'x86_64': 'x64', 'armv7': 'ARM', 'armv8': 'ARM64'}\n :param use_env: Applies the argument /p:UseEnv=true to the","label":0},{"cid":"conans/client/build/msbuild.py::MSBuild.get_command","text":"conans/client/build/msbuild.py::MSBuild.get_command\n def get_command(self, project_file, props_file_path=None, targets=None, upgrade_project=True,\n build_type=None, arch=None, parallel=True, toolset=None, platforms=None,\n use_env=False, properties=None, output_binary_log=None, verbosity=None):\n\n targets = targets or []\n properties = properties or {}\n command = []\n\n if upgrade_project and not get_env(\"CONAN_SKIP_VS_PROJECTS_UPGRADE\", False):\n command.append('devenv \"%s\" /upgrade &&' % project_file)\n else:\n self._output.info(\"Skipped sln project upgrade\")\n\n build_type = build_type or self._settings.get_safe(\"build_type\")\n arch = arch or self._settings.get_safe(\"arch\")\n if toolset is None: # False value to skip adjusting\n toolset = tools.msvs_toolset(self._settings)\n verbosity = os.getenv(\"CONAN_MSBUILD_VERBOSITY\") or verbosity or \"minimal\"\n if not build_type:\n raise ConanException(\"Cannot build_sln_command, build_type not defined\")\n if not arch:\n raise ConanException(\"Cannot build_sln_command, arch not defined\")\n\n command.append('msbuild \"%s\" /p:Configuration=\"%s\"' % (project_file, build_type))\n msvc_arch = {'x86': 'x86',\n 'x86_64': 'x64',\n 'armv7': 'ARM',\n 'armv8': 'ARM64'}\n if platforms:\n msvc_arch.update(platforms)\n msvc_arch = msvc_arch.get(str(arch))\n if self._settings.get_safe(\"os\") == \"WindowsCE\":\n msvc_arch = self._settings.get_safe(\"os.platform\")\n try:\n sln = tools.load(project_file)\n pattern = re.compile(r\"GlobalSection\\(SolutionConfigurationPlatforms\\)\"\n r\"(.*?)EndGlobalSection\", re.DOTALL)\n solution_global = pattern.search(sln).group(1)\n lines = solution_global.splitlines()\n lin","label":0},{"cid":"conans/client/build/msbuild.py::MSBuild._get_props_file_contents","text":"conans/client/build/msbuild.py::MSBuild._get_props_file_contents\n def _get_props_file_contents(self, definitions=None):\n\n def format_macro(name, value):\n return \"%s=%s\" % (name, value) if value else name\n\n # how to specify runtime in command line:\n # https://stackoverflow.com/questions/38840332/msbuild-overrides-properties-while-building-vc-project\n runtime_library = {\"MT\": \"MultiThreaded\",\n \"MTd\": \"MultiThreadedDebug\",\n \"MD\": \"MultiThreadedDLL\",\n \"MDd\": \"MultiThreadedDebugDLL\"}.get(\n self._settings.get_safe(\"compiler.runtime\"), \"\")\n\n if self.build_env:\n # Take the flags from the build env, the user was able to alter them if needed\n flags = copy.copy(self.build_env.flags)\n flags.append(self.build_env.std)\n else: # To be removed when build_sln_command is deprecated\n flags = vs_build_type_flags(self._settings, with_flags=False)\n flags.append(vs_std_cpp(self._settings))\n\n if definitions:\n definitions = \";\".join([format_macro(name, definitions[name]) for name in definitions])\n\n flags_str = \" \".join(list(filter(None, flags))) # Removes empty and None elements\n additional_node = \"<AdditionalOptions>\" \\\n \"{} %(AdditionalOptions)\" \\\n \"</AdditionalOptions>\".format(flags_str) if flags_str else \"\"\n runtime_node = \"<RuntimeLibrary>\" \\\n \"{}\" \\\n \"</RuntimeLibrary>\".format(runtime_library) if runtime_library else \"\"\n definitions_node = \"<PreprocessorDefinitions>\" \\\n \"{};%(PreprocessorDefinitions)\" \\\n \"</PreprocessorDefinitions>\".format(definitions) if definitions else \"\"\n template = \"\"\"<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<Project xmlns=\"http://schemas.microsoft.com/","label":0},{"cid":"conans/client/build/msbuild.py::MSBuild._get_props_file_contents.format_macro","text":"conans/client/build/msbuild.py::MSBuild._get_props_file_contents.format_macro\n def format_macro(name, value):\n return \"%s=%s\" % (name, value) if value else name","label":0},{"cid":"conans/client/build/msbuild.py::MSBuild.get_version","text":"conans/client/build/msbuild.py::MSBuild.get_version\n @staticmethod\n def get_version(settings):\n msbuild_cmd = \"msbuild -version\"\n vcvars = tools_vcvars_command(settings)\n command = \"%s && %s\" % (vcvars, msbuild_cmd)\n try:\n out, _ = subprocess.Popen(command, stdout=subprocess.PIPE, shell=True).communicate()\n version_line = decode_text(out).split(\"\\n\")[-1]\n prog = re.compile(\"(\\d+\\.){2,3}\\d+\")\n result = prog.match(version_line).group()\n return Version(result)\n except Exception as e:\n raise ConanException(\"Error retrieving MSBuild version: '{}'\".format(e))","label":0},{"cid":"conans/client/generators/cmake_common.py::_cmake_string_representation","text":"conans/client/generators/cmake_common.py::_cmake_string_representation\ndef _cmake_string_representation(value):\n \"\"\"Escapes the specified string for use in a CMake command surrounded with double quotes\n :param value the string to escape\"\"\"\n return '\"{0}\"'.format(value.replace('\\\\', '\\\\\\\\')\n .replace('$', '\\\\$')\n .replace('\"', '\\\\\"'))","label":0},{"cid":"conans/client/generators/cmake_common.py::_build_type_str","text":"conans/client/generators/cmake_common.py::_build_type_str\ndef _build_type_str(build_type):\n if build_type:\n return \"_\" + str(build_type).upper()\n return \"\"","label":0},{"cid":"conans/client/generators/cmake_common.py::cmake_user_info_vars","text":"conans/client/generators/cmake_common.py::cmake_user_info_vars\ndef cmake_user_info_vars(deps_user_info):\n lines = []\n for dep, the_vars in deps_user_info.items():\n for name, value in the_vars.vars.items():\n lines.append('set(CONAN_USER_%s_%s %s)'\n % (dep.upper(), name, _cmake_string_representation(value)))\n return \"\\n\".join(lines)","label":0},{"cid":"conans/client/generators/cmake_common.py::cmake_dependency_vars","text":"conans/client/generators/cmake_common.py::cmake_dependency_vars\ndef cmake_dependency_vars(name, deps, build_type=\"\"):\n build_type = _build_type_str(build_type)\n return _cmake_single_dep_vars.format(dep=name.upper(), deps=deps, build_type=build_type)","label":0},{"cid":"conans/client/generators/cmake_common.py::cmake_package_info","text":"conans/client/generators/cmake_common.py::cmake_package_info\ndef cmake_package_info(name, version):\n return _cmake_package_info.format(name=name, version=version)","label":0},{"cid":"conans/client/generators/cmake_common.py::cmake_settings_info","text":"conans/client/generators/cmake_common.py::cmake_settings_info\ndef cmake_settings_info(settings):\n settings_info = \"\"\n for item in settings.items():\n key, value = item\n name = \"CONAN_SETTINGS_%s\" % key.upper().replace(\".\", \"_\")\n settings_info += \"set({key} {value})\\n\".format(key=name,\n value=_cmake_string_representation(value))\n return settings_info","label":0},{"cid":"conans/client/generators/cmake_common.py::cmake_dependencies","text":"conans/client/generators/cmake_common.py::cmake_dependencies\ndef cmake_dependencies(dependencies, build_type=\"\"):\n build_type = _build_type_str(build_type)\n dependencies = \" \".join(dependencies)\n return \"set(CONAN_DEPENDENCIES{build_type} {dependencies})\".format(dependencies=dependencies,\n build_type=build_type)","label":0},{"cid":"conans/client/generators/cmake_common.py::cmake_global_vars","text":"conans/client/generators/cmake_common.py::cmake_global_vars\ndef cmake_global_vars(deps, build_type=\"\"):\n if not build_type:\n cmd_line_args = \"\"\"# Storing original command line args (CMake helper) flags\nset(CONAN_CMD_CXX_FLAGS ${CONAN_CXX_FLAGS})\n\nset(CONAN_CMD_SHARED_LINKER_FLAGS ${CONAN_SHARED_LINKER_FLAGS})\nset(CONAN_CMD_C_FLAGS ${CONAN_C_FLAGS})\n# Defining accumulated conan variables for all deps\n\"\"\"\n else:\n cmd_line_args = \"\"\n return _cmake_multi_dep_vars.format(cmd_line_args=cmd_line_args,\n deps=deps, build_type=_build_type_str(build_type))","label":0},{"cid":"conans/client/generators/cmake_common.py::generate_targets_section","text":"conans/client/generators/cmake_common.py::generate_targets_section\ndef generate_targets_section(dependencies):\n section = []\n section.append(\"\\n### Definition of macros and functions ###\\n\")\n section.append('macro(conan_define_targets)\\n'\n ' if(${CMAKE_VERSION} VERSION_LESS \"3.1.2\")\\n'\n ' message(FATAL_ERROR \"TARGETS not supported by your CMake version!\")\\n'\n ' endif() # CMAKE > 3.x\\n'\n ' set(CMAKE_CXX_FLAGS \"${CMAKE_CXX_FLAGS} ${CONAN_CMD_CXX_FLAGS}\")\\n'\n ' set(CMAKE_C_FLAGS \"${CMAKE_C_FLAGS} ${CONAN_CMD_C_FLAGS}\")\\n'\n ' set(CMAKE_SHARED_LINKER_FLAGS \"${CMAKE_SHARED_LINKER_FLAGS} ${CONAN_CMD_SHARED_LINKER_FLAGS}\")\\n')\n\n for dep_name, dep_info in dependencies:\n use_deps = [\"CONAN_PKG::%s\" % d for d in dep_info.public_deps]\n deps = \"\" if not use_deps else \" \".join(use_deps)\n section.append(_target_template.format(name=\"CONAN_PKG::%s\" % dep_name, deps=deps,\n uname=dep_name.upper(), pkg_name=dep_name))\n\n all_targets = \" \".join([\"CONAN_PKG::%s\" % name for name, _ in dependencies])\n section.append(' set(CONAN_TARGETS %s)\\n' % all_targets)\n section.append('endmacro()\\n')\n return section","label":0},{"cid":"conans/client/generators/cmake_common.py::_conan_basic_setup_common","text":"conans/client/generators/cmake_common.py::_conan_basic_setup_common\ndef _conan_basic_setup_common(addtional_macros, cmake_multi=False):\n output_dirs_section = \"\"\"\n if(NOT ARGUMENTS_NO_OUTPUT_DIRS)\n conan_message(STATUS \"Conan: Adjusting output directories\")\n conan_output_dirs_setup()\n endif()\"\"\"\n\n output_dirs_multi_section = \"\"\"\n if(ARGUMENTS_NO_OUTPUT_DIRS)\n conan_message(WARNING \"Conan: NO_OUTPUT_DIRS has no effect with cmake_multi generator\")\n endif()\"\"\"\n\n main_section = \"\"\"\nmacro(conan_basic_setup)\n set(options TARGETS NO_OUTPUT_DIRS SKIP_RPATH KEEP_RPATHS SKIP_STD SKIP_FPIC)\n cmake_parse_arguments(ARGUMENTS \"${options}\" \"${oneValueArgs}\" \"${multiValueArgs}\" ${ARGN} )\n\n if(CONAN_EXPORTED)\n conan_message(STATUS \"Conan: called by CMake conan helper\")\n endif()\n\n if(CONAN_IN_LOCAL_CACHE)\n conan_message(STATUS \"Conan: called inside local cache\")\n endif()\n%%OUTPUT_DIRS_SECTION%%\n\n if(NOT ARGUMENTS_TARGETS)\n conan_message(STATUS \"Conan: Using cmake global configuration\")\n conan_global_flags()\n else()\n conan_message(STATUS \"Conan: Using cmake targets configuration\")\n conan_define_targets()\n endif()\n\n if(ARGUMENTS_SKIP_RPATH)\n # Change by \"DEPRECATION\" or \"SEND_ERROR\" when we are ready\n conan_message(WARNING \"Conan: SKIP_RPATH is deprecated, it has been renamed to KEEP_RPATHS\")\n endif()\n\n if(NOT ARGUMENTS_SKIP_RPATH AND NOT ARGUMENTS_KEEP_RPATHS)\n # Parameter has renamed, but we keep the compatibility with old SKIP_RPATH\n conan_message(STATUS \"Conan: Adjusting default RPATHs Conan policies\")\n conan_set_rpath()\n endif()\n\n if(NOT ARGUMENTS_SKIP_STD)\n conan_message(STATUS \"Conan: Adjusting language standard\")\n conan_set_std()\n endif()\n\n if(NOT ARGUMENTS_SKIP_FPIC)\n conan_set_fpic()\n endif()\n\n conan_check_compiler()\n conan_set_libcxx()\n conan_set_vs_runtime()\n conan_set_find_","label":0},{"cid":"conans/client/generators/visualstudio_multi.py::_VSSettings.__init__","text":"conans/client/generators/visualstudio_multi.py::_VSSettings.__init__\n def __init__(self, settings):\n toolset = msvs_toolset(settings)\n if toolset is None:\n raise ConanException(\"Undefined Visual Studio version %s\" %\n settings.get_safe(\"compiler.version\"))\n\n self._props = [(\"Configuration\", settings.get_safe(\"build_type\")),\n (\"Platform\", {'x86': 'Win32',\n 'x86_64': 'x64'}.get(settings.get_safe(\"arch\"))),\n (\"PlatformToolset\", toolset)]","label":0},{"cid":"conans/client/generators/visualstudio_multi.py::_VSSettings.filename","text":"conans/client/generators/visualstudio_multi.py::_VSSettings.filename\n @property\n def filename(self):\n name = \"conanbuildinfo%s.props\" % \"\".join(\"_%s\" % v for _, v in self._props if v)\n return name.lower()","label":0},{"cid":"conans/client/generators/visualstudio_multi.py::_VSSettings.condition","text":"conans/client/generators/visualstudio_multi.py::_VSSettings.condition\n @property\n def condition(self):\n return \" And \".join(\"'$(%s)' == '%s'\" % (k, v) for k, v in self._props if v)","label":0},{"cid":"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.filename","text":"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.filename\n @property\n def filename(self):\n pass","label":0},{"cid":"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.content","text":"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.content\n @property\n def content(self):\n vs_settings = _VSSettings(self.conanfile.settings)\n condition = vs_settings.condition\n name_current = vs_settings.filename\n name_multi = \"conanbuildinfo_multi.props\"\n\n # read the exising mult_filename or use the template if it doesn't exist\n multi_path = os.path.join(self.output_path, name_multi)\n if os.path.isfile(multi_path):\n content_multi = load(multi_path)\n else:\n content_multi = self.multi_content_template\n\n # parse the multi_file and add a new import statement if needed\n dom = minidom.parseString(content_multi)\n import_group = dom.getElementsByTagName('ImportGroup')[0]\n children = import_group.getElementsByTagName(\"Import\")\n for node in children:\n if name_current == node.getAttribute(\"Project\") and condition == node.getAttribute(\"Condition\"):\n # the import statement already exists\n break\n else:\n # create a new import statement\n import_node = dom.createElement('Import')\n import_node.setAttribute('Condition', condition)\n import_node.setAttribute('Project', name_current)\n # add it to the import group\n import_group.appendChild(import_node)\n content_multi = dom.toprettyxml()\n content_multi = \"\\n\".join(line for line in content_multi.splitlines() if line.strip())\n\n return {name_multi: content_multi,\n vs_settings.filename: VisualStudioGenerator(self.conanfile).content}","label":0},{"cid":"conans/client/tools/win.py::_visual_compiler_cygwin","text":"conans/client/tools/win.py::_visual_compiler_cygwin\ndef _visual_compiler_cygwin(output, version):\n if os.path.isfile(\"/proc/registry/HKEY_LOCAL_MACHINE/SOFTWARE/Microsoft/Windows/\"\n \"CurrentVersion/ProgramFilesDir (x86)\"):\n is_64bits = True\n else:\n is_64bits = False\n\n if is_64bits:\n key_name = r'HKEY_LOCAL_MACHINE\\SOFTWARE\\Wow6432Node\\Microsoft\\VisualStudio\\SxS\\VC7'\n else:\n key_name = r'HKEY_LOCAL_MACHINE\\SOFTWARE\\Microsoft\\VisualStudio\\SxS\\VC7'\n\n if not os.path.isfile(\"/proc/registry/\" + key_name.replace('\\\\', '/') + \"/\" + version):\n return None\n\n installed_version = Version(version).major(fill=False)\n compiler = \"Visual Studio\"\n output.success(\"CYGWIN: Found %s %s\" % (compiler, installed_version))\n return compiler, installed_version","label":0},{"cid":"conans/client/tools/win.py::_system_registry_key","text":"conans/client/tools/win.py::_system_registry_key\ndef _system_registry_key(key, subkey, query):\n from six.moves import winreg # @UnresolvedImport\n try:\n hkey = winreg.OpenKey(key, subkey)\n except (OSError, WindowsError): # Raised by OpenKey/Ex if the function fails (py3, py2)\n return None\n else:\n try:\n value, _ = winreg.QueryValueEx(hkey, query)\n return value\n except EnvironmentError:\n return None\n finally:\n winreg.CloseKey(hkey)","label":0},{"cid":"conans/client/tools/win.py::_visual_compiler","text":"conans/client/tools/win.py::_visual_compiler\ndef _visual_compiler(output, version):\n \"\"\"\"version have to be 8.0, or 9.0 or... anything .0\"\"\"\n if platform.system().startswith(\"CYGWIN\"):\n return _visual_compiler_cygwin(output, version)\n\n if Version(version) >= \"15\":\n vs_path = os.getenv('vs%s0comntools' % version)\n path = vs_path or vs_installation_path(version)\n if path:\n compiler = \"Visual Studio\"\n output.success(\"Found %s %s\" % (compiler, version))\n return compiler, version\n return None\n\n version = \"%s.0\" % version\n\n from six.moves import winreg # @UnresolvedImport\n is_64bits = _system_registry_key(winreg.HKEY_LOCAL_MACHINE,\n r\"SOFTWARE\\Microsoft\\Windows\\CurrentVersion\",\n \"ProgramFilesDir (x86)\") is not None\n\n if is_64bits:\n key_name = r'SOFTWARE\\Wow6432Node\\Microsoft\\VisualStudio\\SxS\\VC7'\n else:\n key_name = r'HKEY_LOCAL_MACHINE\\SOFTWARE\\Microsoft\\VisualStudio\\SxS\\VC7'\n\n if _system_registry_key(winreg.HKEY_LOCAL_MACHINE, key_name, version):\n installed_version = Version(version).major(fill=False)\n compiler = \"Visual Studio\"\n output.success(\"Found %s %s\" % (compiler, installed_version))\n return compiler, installed_version\n\n return None","label":0},{"cid":"conans/client/tools/win.py::latest_vs_version_installed","text":"conans/client/tools/win.py::latest_vs_version_installed\ndef latest_vs_version_installed(output):\n return latest_visual_studio_version_installed(output=output)","label":0},{"cid":"conans/client/tools/win.py::msvs_toolset","text":"conans/client/tools/win.py::msvs_toolset\ndef msvs_toolset(settings):\n toolset = settings.get_safe(\"compiler.toolset\")\n if not toolset:\n vs_version = settings.get_safe(\"compiler.version\")\n toolset = MSVS_DEFAULT_TOOLSETS.get(vs_version)\n return toolset","label":0},{"cid":"conans/client/tools/win.py::latest_visual_studio_version_installed","text":"conans/client/tools/win.py::latest_visual_studio_version_installed\ndef latest_visual_studio_version_installed(output):\n msvc_sersions = reversed(sorted(list(MSVS_DEFAULT_TOOLSETS.keys()), key=int))\n for version in msvc_sersions:\n vs = _visual_compiler(output, version)\n if vs:\n return vs[1]\n return None","label":0},{"cid":"conans/client/tools/win.py::msvc_build_command","text":"conans/client/tools/win.py::msvc_build_command\n@deprecation.deprecated(deprecated_in=\"1.2\", removed_in=\"2.0\",\n details=\"Use the MSBuild() build helper instead\")\ndef msvc_build_command(settings, sln_path, targets=None, upgrade_project=True, build_type=None,\n arch=None, parallel=True, force_vcvars=False, toolset=None, platforms=None,\n output=None):\n \"\"\" Do both: set the environment variables and call the .sln build\n \"\"\"\n vcvars = vcvars_command(settings, force=force_vcvars, output=output)\n build = build_sln_command(settings, sln_path, targets, upgrade_project, build_type, arch,\n parallel, toolset=toolset, platforms=platforms, output=output)\n command = \"%s && %s\" % (vcvars, build)\n return command","label":0},{"cid":"conans/client/tools/win.py::build_sln_command","text":"conans/client/tools/win.py::build_sln_command\n@deprecation.deprecated(deprecated_in=\"1.2\", removed_in=\"2.0\",\n details=\"Use the MSBuild() build helper instead\")\ndef build_sln_command(settings, sln_path, targets=None, upgrade_project=True, build_type=None,\n arch=None, parallel=True, toolset=None, platforms=None, output=None,\n verbosity=None, definitions=None):\n \"\"\"\n Use example:\n build_command = build_sln_command(self.settings, \"myfile.sln\", targets=[\"SDL2_image\"])\n command = \"%s && %s\" % (tools.vcvars_command(self.settings), build_command)\n self.run(command)\n \"\"\"\n from conans.client.build.msbuild import MSBuild\n tmp = MSBuild(settings)\n output = default_output(output, fn_name='conans.client.tools.win.build_sln_command')\n tmp._output = output\n\n # Generate the properties file\n props_file_contents = tmp._get_props_file_contents(definitions)\n tmp_path = os.path.join(mkdir_tmp(), \".conan_properties\")\n save(tmp_path, props_file_contents)\n\n # Build command\n command = tmp.get_command(sln_path, tmp_path,\n targets=targets, upgrade_project=upgrade_project,\n build_type=build_type, arch=arch, parallel=parallel,\n toolset=toolset, platforms=platforms, use_env=False,\n verbosity=verbosity)\n\n return command","label":0},{"cid":"conans/client/tools/win.py::vs_installation_path","text":"conans/client/tools/win.py::vs_installation_path\ndef vs_installation_path(version, preference=None):\n\n vs_installation_path = None\n\n if not preference:\n env_prefs = get_env(\"CONAN_VS_INSTALLATION_PREFERENCE\", list())\n\n if env_prefs:\n preference = env_prefs\n else: # default values\n preference = [\"Enterprise\", \"Professional\", \"Community\", \"BuildTools\"]\n\n # Try with vswhere()\n try:\n legacy_products = vswhere(legacy=True)\n all_products = vswhere(products=[\"*\"])\n products = legacy_products + all_products\n except ConanException:\n products = None\n\n vs_paths = []\n\n if products:\n # remove repeated products\n seen_products = []\n for product in products:\n if product not in seen_products:\n seen_products.append(product)\n\n # Append products with \"productId\" by order of preference\n for product_type in preference:\n for product in seen_products:\n product = dict(product)\n if (product[\"installationVersion\"].startswith((\"%d.\" % int(version)))\n and \"productId\" in product):\n if product_type in product[\"productId\"]:\n vs_paths.append(product[\"installationPath\"])\n\n # Append products without \"productId\" (Legacy installations)\n for product in seen_products:\n product = dict(product)\n if (product[\"installationVersion\"].startswith((\"%d.\" % int(version)))\n and \"productId\" not in product):\n vs_paths.append(product[\"installationPath\"])\n\n # If vswhere does not find anything or not available, try with vs_comntools()\n if not vs_paths:\n vs_path = vs_comntools(version)\n\n if vs_path:\n sub_path_to_remove = os.path.join(\"\", \"Common7\", \"Tools\", \"\")\n # Remove '\\\\Common7\\\\Tools\\\\' to get same output as vswhere\n if vs_path.endswith(sub","label":0},{"cid":"conans/client/tools/win.py::vswhere","text":"conans/client/tools/win.py::vswhere\ndef vswhere(all_=False, prerelease=False, products=None, requires=None, version=\"\", latest=False,\n legacy=False, property_=\"\", nologo=True):\n\n # 'version' option only works if Visual Studio 2017 is installed:\n # https://github.com/Microsoft/vswhere/issues/91\n\n products = list() if products is None else products\n requires = list() if requires is None else requires\n\n if legacy and (products or requires):\n raise ConanException(\"The 'legacy' parameter cannot be specified with either the \"\n \"'products' or 'requires' parameter\")\n\n installer_path = None\n program_files = get_env(\"ProgramFiles(x86)\") or get_env(\"ProgramFiles\")\n if program_files:\n expected_path = os.path.join(program_files, \"Microsoft Visual Studio\", \"Installer\",\n \"vswhere.exe\")\n if os.path.isfile(expected_path):\n installer_path = expected_path\n vswhere_path = installer_path or which(\"vswhere\")\n\n if not vswhere_path:\n raise ConanException(\"Cannot locate vswhere in 'Program Files'/'Program Files (x86)' \"\n \"directory nor in PATH\")\n\n arguments = list()\n arguments.append(vswhere_path)\n\n # Output json format\n arguments.append(\"-format\")\n arguments.append(\"json\")\n\n if all_:\n arguments.append(\"-all\")\n\n if prerelease:\n arguments.append(\"-prerelease\")\n\n if products:\n arguments.append(\"-products\")\n arguments.extend(products)\n\n if requires:\n arguments.append(\"-requires\")\n arguments.extend(requires)\n\n if len(version) is not 0:\n arguments.append(\"-version\")\n arguments.append(version)\n\n if latest:\n arguments.append(\"-latest\")\n\n if legacy:\n arguments.append(\"-legacy\")\n\n if len(property_) is not 0:\n arguments.append(\"-property\")\n arguments.append(property_)\n\n if nologo:\n arguments.append(\"-","label":0},{"cid":"conans/client/tools/win.py::vs_comntools","text":"conans/client/tools/win.py::vs_comntools\ndef vs_comntools(compiler_version):\n env_var = \"vs%s0comntools\" % compiler_version\n vs_path = os.getenv(env_var)\n return vs_path","label":0},{"cid":"conans/client/tools/win.py::find_windows_10_sdk","text":"conans/client/tools/win.py::find_windows_10_sdk\ndef find_windows_10_sdk():\n \"\"\"finds valid Windows 10 SDK version which can be passed to vcvarsall.bat (vcvars_command)\"\"\"\n # uses the same method as VCVarsQueryRegistry.bat\n from six.moves import winreg # @UnresolvedImport\n hives = [\n (winreg.HKEY_LOCAL_MACHINE, r'SOFTWARE\\Wow6432Node'),\n (winreg.HKEY_CURRENT_USER, r'SOFTWARE\\Wow6432Node'),\n (winreg.HKEY_LOCAL_MACHINE, r'SOFTWARE'),\n (winreg.HKEY_CURRENT_USER, r'SOFTWARE')\n ]\n for key, subkey in hives:\n subkey = r'%s\\Microsoft\\Microsoft SDKs\\Windows\\v10.0' % subkey\n installation_folder = _system_registry_key(key, subkey, 'InstallationFolder')\n if installation_folder:\n if os.path.isdir(installation_folder):\n include_dir = os.path.join(installation_folder, 'include')\n for sdk_version in os.listdir(include_dir):\n if (os.path.isdir(os.path.join(include_dir, sdk_version))\n and sdk_version.startswith('10.')):\n windows_h = os.path.join(include_dir, sdk_version, 'um', 'Windows.h')\n if os.path.isfile(windows_h):\n return sdk_version\n return None","label":0},{"cid":"conans/client/tools/win.py::vcvars_command","text":"conans/client/tools/win.py::vcvars_command\ndef vcvars_command(settings, arch=None, compiler_version=None, force=False, vcvars_ver=None,\n winsdk_version=None, output=None):\n output = default_output(output, 'conans.client.tools.win.vcvars_command')\n\n arch_setting = arch or settings.get_safe(\"arch\")\n\n compiler = settings.get_safe(\"compiler\")\n if compiler == 'Visual Studio':\n compiler_version = compiler_version or settings.get_safe(\"compiler.version\")\n else:\n # vcvars might be still needed for other compilers, e.g. clang-cl or Intel C++,\n # as they might be using Microsoft STL and other tools\n # (e.g. resource compiler, manifest tool, etc)\n # in this case, use the latest Visual Studio available on the machine\n last_version = latest_vs_version_installed(output=output)\n\n compiler_version = compiler_version or last_version\n os_setting = settings.get_safe(\"os\")\n if not compiler_version:\n raise ConanException(\"compiler.version setting required for vcvars not defined\")\n\n # https://msdn.microsoft.com/en-us/library/f2ccy3wt.aspx\n arch_setting = arch_setting or 'x86_64'\n arch_build = settings.get_safe(\"arch_build\") or detected_architecture()\n if os_setting == 'WindowsCE':\n vcvars_arch = \"x86\"\n elif arch_build == 'x86_64':\n # Only uses x64 tooling if arch_build explicitly defines it, otherwise\n # Keep the VS default, which is x86 toolset\n # This will probably be changed in conan 2.0\n if ((settings.get_safe(\"arch_build\") or\n os.getenv(\"PreferredToolArchitecture\") == \"x64\")\n and int(compiler_version) >= 12):\n x86_cross = \"amd64_x86\"\n else:\n x86_cross = \"x86\"\n vcvars_arch = {'x86': x86_cross,\n 'x86_64': 'amd64',\n 'armv7': 'amd64_arm',\n 'armv8': 'amd64_arm64'}.get(arch_setting)\n elif arch_build == 'x86':\n ","label":0},{"cid":"conans/client/tools/win.py::vcvars_dict","text":"conans/client/tools/win.py::vcvars_dict\ndef vcvars_dict(settings, arch=None, compiler_version=None, force=False, filter_known_paths=False,\n vcvars_ver=None, winsdk_version=None, only_diff=True, output=None):\n known_path_lists = (\"include\", \"lib\", \"libpath\", \"path\")\n cmd = vcvars_command(settings, arch=arch,\n compiler_version=compiler_version, force=force,\n vcvars_ver=vcvars_ver, winsdk_version=winsdk_version, output=output)\n cmd += \" && set\"\n ret = check_output(cmd)\n new_env = {}\n for line in ret.splitlines():\n line = line.strip()\n\n if line == \"\\n\" or not line:\n continue\n try:\n name_var, value = line.split(\"=\", 1)\n new_value = value.split(os.pathsep) if name_var.lower() in known_path_lists else value\n # Return only new vars & changed ones, but only with the changed elements if the var is\n # a list\n if only_diff:\n old_value = os.environ.get(name_var)\n if name_var.lower() == \"path\":\n old_values_lower = [v.lower() for v in old_value.split(os.pathsep)]\n # Clean all repeated entries, not append if the element was already there\n new_env[name_var] = [v for v in new_value if v.lower() not in old_values_lower]\n elif old_value and value.endswith(os.pathsep + old_value):\n # The new value ends with separator and the old value, is a list,\n # get only the new elements\n new_env[name_var] = value[:-(len(old_value) + 1)].split(os.pathsep)\n elif value != old_value:\n # Only if the vcvars changed something, we return the variable,\n # otherwise is not vcvars related\n new_env[name_var] = new_value\n else:\n new_env[name_var] = new_value\n\n except ValueError:\n ","label":0},{"cid":"conans/client/tools/win.py::vcvars","text":"conans/client/tools/win.py::vcvars\n@contextmanager\ndef vcvars(*args, **kwargs):\n if platform.system() == \"Windows\":\n new_env = vcvars_dict(*args, **kwargs)\n with environment_append(new_env):\n yield\n else:\n yield","label":0},{"cid":"conans/client/tools/win.py::escape_windows_cmd","text":"conans/client/tools/win.py::escape_windows_cmd\ndef escape_windows_cmd(command):\n \"\"\" To use in a regular windows cmd.exe\n 1. Adds escapes so the argument can be unpacked by CommandLineToArgvW()\n 2. Adds escapes for cmd.exe so the argument survives cmd.exe's substitutions.\n\n Useful to escape commands to be executed in a windows bash (msys2, cygwin etc)\n \"\"\"\n quoted_arg = subprocess.list2cmdline([command])\n return \"\".join([\"^%s\" % arg if arg in r'()%!^\"<>&|' else arg for arg in quoted_arg])","label":0},{"cid":"conans/client/tools/win.py::get_cased_path","text":"conans/client/tools/win.py::get_cased_path\ndef get_cased_path(name):\n if platform.system() != \"Windows\":\n return name\n if not os.path.isabs(name):\n name = os.path.abspath(name)\n\n result = []\n current = name\n while True:\n parent, child = os.path.split(current)\n if parent == current:\n break\n\n child_cased = child\n if os.path.exists(parent):\n children = os.listdir(parent)\n for c in children:\n if c.upper() == child.upper():\n child_cased = c\n break\n result.append(child_cased)\n current = parent\n drive, _ = os.path.splitdrive(current)\n result.append(drive)\n return os.sep.join(reversed(result))","label":0},{"cid":"conans/client/tools/win.py::unix_path","text":"conans/client/tools/win.py::unix_path\ndef unix_path(path, path_flavor=None):\n \"\"\"\"Used to translate windows paths to MSYS unix paths like\n c/users/path/to/file. Not working in a regular console or MinGW!\"\"\"\n if not path:\n return None\n\n if os.path.exists(path):\n path = get_cased_path(path) # if the path doesn't exist (and abs) we cannot guess the casing\n\n path_flavor = path_flavor or OSInfo.detect_windows_subsystem() or MSYS2\n path = path.replace(\":/\", \":\\\\\")\n pattern = re.compile(r'([a-z]):\\\\', re.IGNORECASE)\n path = pattern.sub('/\\\\1/', path).replace('\\\\', '/')\n if path_flavor in (MSYS, MSYS2):\n return path.lower()\n elif path_flavor == CYGWIN:\n return '/cygdrive' + path.lower()\n elif path_flavor == WSL:\n return '/mnt' + path[0:2].lower() + path[2:]\n elif path_flavor == SFU:\n path = path.lower()\n return '/dev/fs' + path[0] + path[1:].capitalize()\n return None","label":0},{"cid":"conans/client/tools/win.py::run_in_windows_bash","text":"conans/client/tools/win.py::run_in_windows_bash\ndef run_in_windows_bash(conanfile, bashcmd, cwd=None, subsystem=None, msys_mingw=True, env=None,\n with_login=True):\n \"\"\" Will run a unix command inside a bash terminal\n It requires to have MSYS2, CYGWIN, or WSL\n \"\"\"\n env = env or {}\n if platform.system() != \"Windows\":\n raise ConanException(\"Command only for Windows operating system\")\n subsystem = subsystem or OSInfo.detect_windows_subsystem()\n\n if not subsystem:\n raise ConanException(\"Cannot recognize the Windows subsystem, install MSYS2/cygwin \"\n \"or specify a build_require to apply it.\")\n\n if subsystem == MSYS2 and msys_mingw:\n # This needs to be set so that msys2 bash profile will set up the environment correctly.\n env_vars = {\"MSYSTEM\": (\"MINGW32\" if conanfile.settings.get_safe(\"arch\") == \"x86\" else\n \"MINGW64\"),\n \"MSYS2_PATH_TYPE\": \"inherit\"}\n else:\n env_vars = {}\n\n with environment_append(env_vars):\n\n hack_env = \"\"\n # In the bash.exe from WSL this trick do not work, always the /usr/bin etc at first place\n if subsystem != WSL:\n\n def get_path_value(container, subsystem_name):\n \"\"\"Gets the path from the container dict and returns a\n string with the path for the subsystem_name\"\"\"\n _path_key = next((name for name in container.keys() if \"path\" == name.lower()), None)\n if _path_key:\n _path_value = container.get(_path_key)\n if isinstance(_path_value, list):\n return \":\".join([unix_path(path, path_flavor=subsystem_name)\n for path in _path_value])\n else:\n return unix_path(_path_value, path_flavor=subsystem_name)\n\n # First get the PATH from the conanfile.env\n ","label":0}],"string":"[\n {\n \"cid\": \"conans/client/build/cmake_flags.py::get_toolset\",\n \"text\": \"conans/client/build/cmake_flags.py::get_toolset\\ndef get_toolset(settings):\\n if settings.get_safe(\\\"compiler\\\") == \\\"Visual Studio\\\":\\n subs_toolset = settings.get_safe(\\\"compiler.toolset\\\")\\n if subs_toolset:\\n return subs_toolset\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::get_generator\",\n \"text\": \"conans/client/build/cmake_flags.py::get_generator\\ndef get_generator(settings):\\n if \\\"CONAN_CMAKE_GENERATOR\\\" in os.environ:\\n return os.environ[\\\"CONAN_CMAKE_GENERATOR\\\"]\\n\\n compiler = settings.get_safe(\\\"compiler\\\")\\n arch = settings.get_safe(\\\"arch\\\")\\n compiler_version = settings.get_safe(\\\"compiler.version\\\")\\n os_build, _, _, _ = get_cross_building_settings(settings)\\n os_host = settings.get_safe(\\\"os\\\")\\n\\n if not compiler or not compiler_version or not arch:\\n if os_build == \\\"Windows\\\":\\n logger.warning(\\\"CMake generator could not be deduced from settings\\\")\\n return None\\n return \\\"Unix Makefiles\\\"\\n\\n if compiler == \\\"Visual Studio\\\":\\n _visuals = {'8': '8 2005',\\n '9': '9 2008',\\n '10': '10 2010',\\n '11': '11 2012',\\n '12': '12 2013',\\n '14': '14 2015',\\n '15': '15 2017',\\n '16': '16 2019'}\\n base = \\\"Visual Studio %s\\\" % _visuals.get(compiler_version,\\n \\\"UnknownVersion %s\\\" % compiler_version)\\n if os_host != \\\"WindowsCE\\\" and Version(compiler_version) < \\\"16\\\":\\n if arch == \\\"x86_64\\\":\\n base += \\\" Win64\\\"\\n elif \\\"arm\\\" in arch:\\n base += \\\" ARM\\\"\\n return base\\n\\n # The generator depends on the build machine, not the target\\n if os_build == \\\"Windows\\\" and compiler != \\\"qcc\\\":\\n return \\\"MinGW Makefiles\\\" # it is valid only under Windows\\n\\n return \\\"Unix Makefiles\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::get_generator_platform\",\n \"text\": \"conans/client/build/cmake_flags.py::get_generator_platform\\ndef get_generator_platform(settings, generator):\\n if \\\"CONAN_CMAKE_GENERATOR_PLATFORM\\\" in os.environ:\\n return os.environ[\\\"CONAN_CMAKE_GENERATOR_PLATFORM\\\"]\\n\\n compiler = settings.get_safe(\\\"compiler\\\")\\n arch = settings.get_safe(\\\"arch\\\")\\n compiler_version = settings.get_safe(\\\"compiler.version\\\")\\n\\n if settings.get_safe(\\\"os\\\") == \\\"WindowsCE\\\":\\n return settings.get_safe(\\\"os.platform\\\")\\n\\n if compiler == \\\"Visual Studio\\\" and Version(compiler_version) >= \\\"16\\\" \\\\\\n and \\\"Visual\\\" in generator:\\n return {\\\"x86\\\": \\\"Win32\\\",\\n \\\"x86_64\\\": \\\"x64\\\",\\n \\\"armv7\\\": \\\"ARM\\\",\\n \\\"armv8\\\": \\\"ARM64\\\"}.get(arch)\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::is_multi_configuration\",\n \"text\": \"conans/client/build/cmake_flags.py::is_multi_configuration\\ndef is_multi_configuration(generator):\\n if not generator:\\n return False\\n return \\\"Visual\\\" in generator or \\\"Xcode\\\" in generator\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::is_toolset_supported\",\n \"text\": \"conans/client/build/cmake_flags.py::is_toolset_supported\\ndef is_toolset_supported(generator):\\n # https://cmake.org/cmake/help/v3.14/variable/CMAKE_GENERATOR_TOOLSET.html\\n if not generator:\\n return False\\n return \\\"Visual\\\" in generator or \\\"Xcode\\\" in generator or \\\"Green Hills MULTI\\\" in generator\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::is_generator_platform_supported\",\n \"text\": \"conans/client/build/cmake_flags.py::is_generator_platform_supported\\ndef is_generator_platform_supported(generator):\\n # https://cmake.org/cmake/help/v3.14/variable/CMAKE_GENERATOR_PLATFORM.html\\n if not generator:\\n return False\\n return \\\"Visual\\\" in generator or \\\"Green Hills MULTI\\\" in generator\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::verbose_definition\",\n \"text\": \"conans/client/build/cmake_flags.py::verbose_definition\\ndef verbose_definition(value):\\n return {verbose_definition_name: \\\"ON\\\" if value else \\\"OFF\\\"}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::in_local_cache_definition\",\n \"text\": \"conans/client/build/cmake_flags.py::in_local_cache_definition\\ndef in_local_cache_definition(value):\\n return {cmake_in_local_cache_var_name: \\\"ON\\\" if value else \\\"OFF\\\"}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::runtime_definition\",\n \"text\": \"conans/client/build/cmake_flags.py::runtime_definition\\ndef runtime_definition(runtime):\\n return {runtime_definition_var_name: \\\"/%s\\\" % runtime} if runtime else {}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::build_type_definition\",\n \"text\": \"conans/client/build/cmake_flags.py::build_type_definition\\ndef build_type_definition(build_type, generator):\\n if build_type and not is_multi_configuration(generator):\\n return {\\\"CMAKE_BUILD_TYPE\\\": build_type}\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.__init__\",\n \"text\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.__init__\\n def __init__(self, conanfile, cmake_system_name=True, make_program=None,\\n parallel=True, generator=None, set_cmake_flags=False,\\n forced_build_type=None, output=None):\\n self._conanfile = conanfile\\n self._forced_cmake_system_name = cmake_system_name\\n self._make_program = make_program\\n self._parallel = parallel\\n self._generator = generator\\n self._set_cmake_flags = set_cmake_flags\\n self._forced_build_type = forced_build_type\\n self._output = output\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._ss\",\n \"text\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._ss\\n def _ss(self, setname):\\n \\\"\\\"\\\"safe setting\\\"\\\"\\\"\\n return self._conanfile.settings.get_safe(setname)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_cpp_standard_vars\",\n \"text\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_cpp_standard_vars\\n def _get_cpp_standard_vars(self):\\n cppstd = cppstd_from_settings(self._conanfile.settings)\\n compiler = self._ss(\\\"compiler\\\")\\n compiler_version = self._ss(\\\"compiler.version\\\")\\n\\n if not cppstd:\\n return {}\\n\\n ret = {}\\n if cppstd.startswith(\\\"gnu\\\"):\\n ret[\\\"CONAN_CMAKE_CXX_STANDARD\\\"] = cppstd[3:]\\n ret[\\\"CONAN_CMAKE_CXX_EXTENSIONS\\\"] = \\\"ON\\\"\\n else:\\n ret[\\\"CONAN_CMAKE_CXX_STANDARD\\\"] = cppstd\\n ret[\\\"CONAN_CMAKE_CXX_EXTENSIONS\\\"] = \\\"OFF\\\"\\n\\n ret[\\\"CONAN_STD_CXX_FLAG\\\"] = cppstd_flag(compiler, compiler_version, cppstd)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._cmake_cross_build_defines\",\n \"text\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._cmake_cross_build_defines\\n def _cmake_cross_build_defines(self):\\n\\n os_ = self._ss(\\\"os\\\")\\n arch = self._ss(\\\"arch\\\")\\n os_ver_str = \\\"os.api_level\\\" if os_ == \\\"Android\\\" else \\\"os.version\\\"\\n op_system_version = self._ss(os_ver_str)\\n\\n env_sn = get_env(\\\"CONAN_CMAKE_SYSTEM_NAME\\\", \\\"\\\")\\n env_sn = {\\\"False\\\": False, \\\"True\\\": True, \\\"\\\": None}.get(env_sn, env_sn)\\n cmake_system_name = env_sn or self._forced_cmake_system_name\\n\\n os_build, _, _, _ = get_cross_building_settings(self._conanfile.settings)\\n compiler = self._ss(\\\"compiler\\\")\\n libcxx = self._ss(\\\"compiler.libcxx\\\")\\n\\n ret = OrderedDict()\\n os_ver = get_env(\\\"CONAN_CMAKE_SYSTEM_VERSION\\\", op_system_version)\\n toolchain_file = get_env(\\\"CONAN_CMAKE_TOOLCHAIN_FILE\\\", \\\"\\\")\\n\\n if toolchain_file != \\\"\\\":\\n logger.info(\\\"Setting Cross build toolchain file: %s\\\" % toolchain_file)\\n ret[\\\"CMAKE_TOOLCHAIN_FILE\\\"] = toolchain_file\\n return ret\\n\\n if cmake_system_name is False:\\n return ret\\n\\n # System name and system version\\n if cmake_system_name is not True: # String not empty\\n ret[\\\"CMAKE_SYSTEM_NAME\\\"] = cmake_system_name\\n else: # detect if we are cross building and the system name and version\\n if cross_building(self._conanfile.settings): # We are cross building\\n if os_ != os_build:\\n if os_: # the_os is the host (regular setting)\\n ret[\\\"CMAKE_SYSTEM_NAME\\\"] = {\\\"iOS\\\": \\\"Darwin\\\",\\n \\\"tvOS\\\": \\\"Darwin\\\",\\n \\\"watchOS\\\": \\\"Darwin\\\",\\n \\\"Neutrino\\\": \\\"QNX\\\"}.get(os_, os_)\\n else:\\n ret[\\\"CMAKE_SYSTEM_NAME\\\"] = \\\"Generic\\\"\\n if os_ver:\\n ret[\\\"CMAKE_SYSTEM_VERSION\\\"] = os_ver\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_make_program_definition\",\n \"text\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_make_program_definition\\n def _get_make_program_definition(self):\\n make_program = os.getenv(\\\"CONAN_MAKE_PROGRAM\\\") or self._make_program\\n if make_program:\\n if not tools.which(make_program):\\n self._output.warn(\\\"The specified make program '%s' cannot be found and will be \\\"\\n \\\"ignored\\\" % make_program)\\n else:\\n self._output.info(\\\"Using '%s' as CMAKE_MAKE_PROGRAM\\\" % make_program)\\n return {\\\"CMAKE_MAKE_PROGRAM\\\": make_program}\\n\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.get_definitions\",\n \"text\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.get_definitions\\n def get_definitions(self):\\n\\n compiler = self._ss(\\\"compiler\\\")\\n compiler_version = self._ss(\\\"compiler.version\\\")\\n arch = self._ss(\\\"arch\\\")\\n os_ = self._ss(\\\"os\\\")\\n libcxx = self._ss(\\\"compiler.libcxx\\\")\\n runtime = self._ss(\\\"compiler.runtime\\\")\\n build_type = self._ss(\\\"build_type\\\")\\n\\n ret = OrderedDict()\\n ret.update(runtime_definition(runtime))\\n\\n if self._forced_build_type and self._forced_build_type != build_type:\\n self._output.warn(\\\"Forced CMake build type ('%s') different from the settings build \\\"\\n \\\"type ('%s')\\\" % (self._forced_build_type, build_type))\\n build_type = self._forced_build_type\\n\\n ret.update(build_type_definition(build_type, self._generator))\\n\\n if str(os_) == \\\"Macos\\\":\\n if arch == \\\"x86\\\":\\n ret[\\\"CMAKE_OSX_ARCHITECTURES\\\"] = \\\"i386\\\"\\n\\n ret.update(self._cmake_cross_build_defines())\\n ret.update(self._get_cpp_standard_vars())\\n\\n ret[\\\"CONAN_EXPORTED\\\"] = \\\"1\\\"\\n ret.update(in_local_cache_definition(self._conanfile.in_local_cache))\\n\\n if compiler:\\n ret[\\\"CONAN_COMPILER\\\"] = compiler\\n if compiler_version:\\n ret[\\\"CONAN_COMPILER_VERSION\\\"] = str(compiler_version)\\n\\n # C, CXX, LINK FLAGS\\n if compiler == \\\"Visual Studio\\\":\\n if self._parallel:\\n flag = parallel_compiler_cl_flag(output=self._output)\\n ret['CONAN_CXX_FLAGS'] = flag\\n ret['CONAN_C_FLAGS'] = flag\\n else: # arch_flag is only set for non Visual Studio\\n arch_flag = architecture_flag(compiler=compiler, os=os_, arch=arch)\\n if arch_flag:\\n ret['CONAN_CXX_FLAGS'] = arch_flag\\n ret['CONAN_SHARED_LINKER_FLAGS'] = arch_flag\\n ret['CONAN_C_FLAGS'] = arch_flag\\n if self._set_cmake_flags:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/msbuild.py::MSBuild.__init__\",\n \"text\": \"conans/client/build/msbuild.py::MSBuild.__init__\\n def __init__(self, conanfile):\\n if isinstance(conanfile, ConanFile):\\n self._conanfile = conanfile\\n self._settings = self._conanfile.settings\\n self._output = self._conanfile.output\\n self.build_env = VisualStudioBuildEnvironment(self._conanfile,\\n with_build_type_flags=False)\\n else: # backwards compatible with build_sln_command\\n self._settings = conanfile\\n self.build_env = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/msbuild.py::MSBuild.build\",\n \"text\": \"conans/client/build/msbuild.py::MSBuild.build\\n def build(self, project_file, targets=None, upgrade_project=True, build_type=None, arch=None,\\n parallel=True, force_vcvars=False, toolset=None, platforms=None, use_env=True,\\n vcvars_ver=None, winsdk_version=None, properties=None, output_binary_log=None,\\n property_file_name=None, verbosity=None, definitions=None):\\n \\\"\\\"\\\"\\n :param project_file: Path to the .sln file.\\n :param targets: List of targets to build.\\n :param upgrade_project: Will call devenv to upgrade the solution to your\\n current Visual Studio.\\n :param build_type: Use a custom build type instead of the default settings.build_type one.\\n :param arch: Use a custom architecture name instead of the settings.arch one.\\n It will be used to build the /p:Configuration= parameter of MSBuild.\\n It can be used as the key of the platforms parameter.\\n E.g. arch=\\\"x86\\\", platforms={\\\"x86\\\": \\\"i386\\\"}\\n :param parallel: Will use the configured number of cores in the conan.conf file or\\n tools.cpu_count():\\n In the solution: Building the solution with the projects in parallel. (/m: parameter).\\n CL compiler: Building the sources in parallel. (/MP: compiler flag)\\n :param force_vcvars: Will ignore if the environment is already set for a different\\n Visual Studio version.\\n :param toolset: Specify a toolset. Will append a /p:PlatformToolset option.\\n :param platforms: Dictionary with the mapping of archs/platforms from Conan naming to another\\n one. It is useful for Visual Studio solutions that have a different naming in architectures.\\n Example: platforms={\\\"x86\\\":\\\"Win32\\\"} (Visual solution uses \\\"Win32\\\" instead of \\\"x86\\\").\\n This dictionary will update the default one:\\n msvc_arch = {'x86': 'x86', 'x86_64': 'x64', 'armv7': 'ARM', 'armv8': 'ARM64'}\\n :param use_env: Applies the argument /p:UseEnv=true to the\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/msbuild.py::MSBuild.get_command\",\n \"text\": \"conans/client/build/msbuild.py::MSBuild.get_command\\n def get_command(self, project_file, props_file_path=None, targets=None, upgrade_project=True,\\n build_type=None, arch=None, parallel=True, toolset=None, platforms=None,\\n use_env=False, properties=None, output_binary_log=None, verbosity=None):\\n\\n targets = targets or []\\n properties = properties or {}\\n command = []\\n\\n if upgrade_project and not get_env(\\\"CONAN_SKIP_VS_PROJECTS_UPGRADE\\\", False):\\n command.append('devenv \\\"%s\\\" /upgrade &&' % project_file)\\n else:\\n self._output.info(\\\"Skipped sln project upgrade\\\")\\n\\n build_type = build_type or self._settings.get_safe(\\\"build_type\\\")\\n arch = arch or self._settings.get_safe(\\\"arch\\\")\\n if toolset is None: # False value to skip adjusting\\n toolset = tools.msvs_toolset(self._settings)\\n verbosity = os.getenv(\\\"CONAN_MSBUILD_VERBOSITY\\\") or verbosity or \\\"minimal\\\"\\n if not build_type:\\n raise ConanException(\\\"Cannot build_sln_command, build_type not defined\\\")\\n if not arch:\\n raise ConanException(\\\"Cannot build_sln_command, arch not defined\\\")\\n\\n command.append('msbuild \\\"%s\\\" /p:Configuration=\\\"%s\\\"' % (project_file, build_type))\\n msvc_arch = {'x86': 'x86',\\n 'x86_64': 'x64',\\n 'armv7': 'ARM',\\n 'armv8': 'ARM64'}\\n if platforms:\\n msvc_arch.update(platforms)\\n msvc_arch = msvc_arch.get(str(arch))\\n if self._settings.get_safe(\\\"os\\\") == \\\"WindowsCE\\\":\\n msvc_arch = self._settings.get_safe(\\\"os.platform\\\")\\n try:\\n sln = tools.load(project_file)\\n pattern = re.compile(r\\\"GlobalSection\\\\(SolutionConfigurationPlatforms\\\\)\\\"\\n r\\\"(.*?)EndGlobalSection\\\", re.DOTALL)\\n solution_global = pattern.search(sln).group(1)\\n lines = solution_global.splitlines()\\n lin\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/msbuild.py::MSBuild._get_props_file_contents\",\n \"text\": \"conans/client/build/msbuild.py::MSBuild._get_props_file_contents\\n def _get_props_file_contents(self, definitions=None):\\n\\n def format_macro(name, value):\\n return \\\"%s=%s\\\" % (name, value) if value else name\\n\\n # how to specify runtime in command line:\\n # https://stackoverflow.com/questions/38840332/msbuild-overrides-properties-while-building-vc-project\\n runtime_library = {\\\"MT\\\": \\\"MultiThreaded\\\",\\n \\\"MTd\\\": \\\"MultiThreadedDebug\\\",\\n \\\"MD\\\": \\\"MultiThreadedDLL\\\",\\n \\\"MDd\\\": \\\"MultiThreadedDebugDLL\\\"}.get(\\n self._settings.get_safe(\\\"compiler.runtime\\\"), \\\"\\\")\\n\\n if self.build_env:\\n # Take the flags from the build env, the user was able to alter them if needed\\n flags = copy.copy(self.build_env.flags)\\n flags.append(self.build_env.std)\\n else: # To be removed when build_sln_command is deprecated\\n flags = vs_build_type_flags(self._settings, with_flags=False)\\n flags.append(vs_std_cpp(self._settings))\\n\\n if definitions:\\n definitions = \\\";\\\".join([format_macro(name, definitions[name]) for name in definitions])\\n\\n flags_str = \\\" \\\".join(list(filter(None, flags))) # Removes empty and None elements\\n additional_node = \\\"<AdditionalOptions>\\\" \\\\\\n \\\"{} %(AdditionalOptions)\\\" \\\\\\n \\\"</AdditionalOptions>\\\".format(flags_str) if flags_str else \\\"\\\"\\n runtime_node = \\\"<RuntimeLibrary>\\\" \\\\\\n \\\"{}\\\" \\\\\\n \\\"</RuntimeLibrary>\\\".format(runtime_library) if runtime_library else \\\"\\\"\\n definitions_node = \\\"<PreprocessorDefinitions>\\\" \\\\\\n \\\"{};%(PreprocessorDefinitions)\\\" \\\\\\n \\\"</PreprocessorDefinitions>\\\".format(definitions) if definitions else \\\"\\\"\\n template = \\\"\\\"\\\"<?xml version=\\\"1.0\\\" encoding=\\\"utf-8\\\"?>\\n<Project xmlns=\\\"http://schemas.microsoft.com/\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/msbuild.py::MSBuild._get_props_file_contents.format_macro\",\n \"text\": \"conans/client/build/msbuild.py::MSBuild._get_props_file_contents.format_macro\\n def format_macro(name, value):\\n return \\\"%s=%s\\\" % (name, value) if value else name\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/msbuild.py::MSBuild.get_version\",\n \"text\": \"conans/client/build/msbuild.py::MSBuild.get_version\\n @staticmethod\\n def get_version(settings):\\n msbuild_cmd = \\\"msbuild -version\\\"\\n vcvars = tools_vcvars_command(settings)\\n command = \\\"%s && %s\\\" % (vcvars, msbuild_cmd)\\n try:\\n out, _ = subprocess.Popen(command, stdout=subprocess.PIPE, shell=True).communicate()\\n version_line = decode_text(out).split(\\\"\\\\n\\\")[-1]\\n prog = re.compile(\\\"(\\\\d+\\\\.){2,3}\\\\d+\\\")\\n result = prog.match(version_line).group()\\n return Version(result)\\n except Exception as e:\\n raise ConanException(\\\"Error retrieving MSBuild version: '{}'\\\".format(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/cmake_common.py::_cmake_string_representation\",\n \"text\": \"conans/client/generators/cmake_common.py::_cmake_string_representation\\ndef _cmake_string_representation(value):\\n \\\"\\\"\\\"Escapes the specified string for use in a CMake command surrounded with double quotes\\n :param value the string to escape\\\"\\\"\\\"\\n return '\\\"{0}\\\"'.format(value.replace('\\\\\\\\', '\\\\\\\\\\\\\\\\')\\n .replace('$', '\\\\\\\\$')\\n .replace('\\\"', '\\\\\\\\\\\"'))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/cmake_common.py::_build_type_str\",\n \"text\": \"conans/client/generators/cmake_common.py::_build_type_str\\ndef _build_type_str(build_type):\\n if build_type:\\n return \\\"_\\\" + str(build_type).upper()\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/cmake_common.py::cmake_user_info_vars\",\n \"text\": \"conans/client/generators/cmake_common.py::cmake_user_info_vars\\ndef cmake_user_info_vars(deps_user_info):\\n lines = []\\n for dep, the_vars in deps_user_info.items():\\n for name, value in the_vars.vars.items():\\n lines.append('set(CONAN_USER_%s_%s %s)'\\n % (dep.upper(), name, _cmake_string_representation(value)))\\n return \\\"\\\\n\\\".join(lines)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/cmake_common.py::cmake_dependency_vars\",\n \"text\": \"conans/client/generators/cmake_common.py::cmake_dependency_vars\\ndef cmake_dependency_vars(name, deps, build_type=\\\"\\\"):\\n build_type = _build_type_str(build_type)\\n return _cmake_single_dep_vars.format(dep=name.upper(), deps=deps, build_type=build_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/cmake_common.py::cmake_package_info\",\n \"text\": \"conans/client/generators/cmake_common.py::cmake_package_info\\ndef cmake_package_info(name, version):\\n return _cmake_package_info.format(name=name, version=version)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/cmake_common.py::cmake_settings_info\",\n \"text\": \"conans/client/generators/cmake_common.py::cmake_settings_info\\ndef cmake_settings_info(settings):\\n settings_info = \\\"\\\"\\n for item in settings.items():\\n key, value = item\\n name = \\\"CONAN_SETTINGS_%s\\\" % key.upper().replace(\\\".\\\", \\\"_\\\")\\n settings_info += \\\"set({key} {value})\\\\n\\\".format(key=name,\\n value=_cmake_string_representation(value))\\n return settings_info\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/cmake_common.py::cmake_dependencies\",\n \"text\": \"conans/client/generators/cmake_common.py::cmake_dependencies\\ndef cmake_dependencies(dependencies, build_type=\\\"\\\"):\\n build_type = _build_type_str(build_type)\\n dependencies = \\\" \\\".join(dependencies)\\n return \\\"set(CONAN_DEPENDENCIES{build_type} {dependencies})\\\".format(dependencies=dependencies,\\n build_type=build_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/cmake_common.py::cmake_global_vars\",\n \"text\": \"conans/client/generators/cmake_common.py::cmake_global_vars\\ndef cmake_global_vars(deps, build_type=\\\"\\\"):\\n if not build_type:\\n cmd_line_args = \\\"\\\"\\\"# Storing original command line args (CMake helper) flags\\nset(CONAN_CMD_CXX_FLAGS ${CONAN_CXX_FLAGS})\\n\\nset(CONAN_CMD_SHARED_LINKER_FLAGS ${CONAN_SHARED_LINKER_FLAGS})\\nset(CONAN_CMD_C_FLAGS ${CONAN_C_FLAGS})\\n# Defining accumulated conan variables for all deps\\n\\\"\\\"\\\"\\n else:\\n cmd_line_args = \\\"\\\"\\n return _cmake_multi_dep_vars.format(cmd_line_args=cmd_line_args,\\n deps=deps, build_type=_build_type_str(build_type))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/cmake_common.py::generate_targets_section\",\n \"text\": \"conans/client/generators/cmake_common.py::generate_targets_section\\ndef generate_targets_section(dependencies):\\n section = []\\n section.append(\\\"\\\\n### Definition of macros and functions ###\\\\n\\\")\\n section.append('macro(conan_define_targets)\\\\n'\\n ' if(${CMAKE_VERSION} VERSION_LESS \\\"3.1.2\\\")\\\\n'\\n ' message(FATAL_ERROR \\\"TARGETS not supported by your CMake version!\\\")\\\\n'\\n ' endif() # CMAKE > 3.x\\\\n'\\n ' set(CMAKE_CXX_FLAGS \\\"${CMAKE_CXX_FLAGS} ${CONAN_CMD_CXX_FLAGS}\\\")\\\\n'\\n ' set(CMAKE_C_FLAGS \\\"${CMAKE_C_FLAGS} ${CONAN_CMD_C_FLAGS}\\\")\\\\n'\\n ' set(CMAKE_SHARED_LINKER_FLAGS \\\"${CMAKE_SHARED_LINKER_FLAGS} ${CONAN_CMD_SHARED_LINKER_FLAGS}\\\")\\\\n')\\n\\n for dep_name, dep_info in dependencies:\\n use_deps = [\\\"CONAN_PKG::%s\\\" % d for d in dep_info.public_deps]\\n deps = \\\"\\\" if not use_deps else \\\" \\\".join(use_deps)\\n section.append(_target_template.format(name=\\\"CONAN_PKG::%s\\\" % dep_name, deps=deps,\\n uname=dep_name.upper(), pkg_name=dep_name))\\n\\n all_targets = \\\" \\\".join([\\\"CONAN_PKG::%s\\\" % name for name, _ in dependencies])\\n section.append(' set(CONAN_TARGETS %s)\\\\n' % all_targets)\\n section.append('endmacro()\\\\n')\\n return section\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/cmake_common.py::_conan_basic_setup_common\",\n \"text\": \"conans/client/generators/cmake_common.py::_conan_basic_setup_common\\ndef _conan_basic_setup_common(addtional_macros, cmake_multi=False):\\n output_dirs_section = \\\"\\\"\\\"\\n if(NOT ARGUMENTS_NO_OUTPUT_DIRS)\\n conan_message(STATUS \\\"Conan: Adjusting output directories\\\")\\n conan_output_dirs_setup()\\n endif()\\\"\\\"\\\"\\n\\n output_dirs_multi_section = \\\"\\\"\\\"\\n if(ARGUMENTS_NO_OUTPUT_DIRS)\\n conan_message(WARNING \\\"Conan: NO_OUTPUT_DIRS has no effect with cmake_multi generator\\\")\\n endif()\\\"\\\"\\\"\\n\\n main_section = \\\"\\\"\\\"\\nmacro(conan_basic_setup)\\n set(options TARGETS NO_OUTPUT_DIRS SKIP_RPATH KEEP_RPATHS SKIP_STD SKIP_FPIC)\\n cmake_parse_arguments(ARGUMENTS \\\"${options}\\\" \\\"${oneValueArgs}\\\" \\\"${multiValueArgs}\\\" ${ARGN} )\\n\\n if(CONAN_EXPORTED)\\n conan_message(STATUS \\\"Conan: called by CMake conan helper\\\")\\n endif()\\n\\n if(CONAN_IN_LOCAL_CACHE)\\n conan_message(STATUS \\\"Conan: called inside local cache\\\")\\n endif()\\n%%OUTPUT_DIRS_SECTION%%\\n\\n if(NOT ARGUMENTS_TARGETS)\\n conan_message(STATUS \\\"Conan: Using cmake global configuration\\\")\\n conan_global_flags()\\n else()\\n conan_message(STATUS \\\"Conan: Using cmake targets configuration\\\")\\n conan_define_targets()\\n endif()\\n\\n if(ARGUMENTS_SKIP_RPATH)\\n # Change by \\\"DEPRECATION\\\" or \\\"SEND_ERROR\\\" when we are ready\\n conan_message(WARNING \\\"Conan: SKIP_RPATH is deprecated, it has been renamed to KEEP_RPATHS\\\")\\n endif()\\n\\n if(NOT ARGUMENTS_SKIP_RPATH AND NOT ARGUMENTS_KEEP_RPATHS)\\n # Parameter has renamed, but we keep the compatibility with old SKIP_RPATH\\n conan_message(STATUS \\\"Conan: Adjusting default RPATHs Conan policies\\\")\\n conan_set_rpath()\\n endif()\\n\\n if(NOT ARGUMENTS_SKIP_STD)\\n conan_message(STATUS \\\"Conan: Adjusting language standard\\\")\\n conan_set_std()\\n endif()\\n\\n if(NOT ARGUMENTS_SKIP_FPIC)\\n conan_set_fpic()\\n endif()\\n\\n conan_check_compiler()\\n conan_set_libcxx()\\n conan_set_vs_runtime()\\n conan_set_find_\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/visualstudio_multi.py::_VSSettings.__init__\",\n \"text\": \"conans/client/generators/visualstudio_multi.py::_VSSettings.__init__\\n def __init__(self, settings):\\n toolset = msvs_toolset(settings)\\n if toolset is None:\\n raise ConanException(\\\"Undefined Visual Studio version %s\\\" %\\n settings.get_safe(\\\"compiler.version\\\"))\\n\\n self._props = [(\\\"Configuration\\\", settings.get_safe(\\\"build_type\\\")),\\n (\\\"Platform\\\", {'x86': 'Win32',\\n 'x86_64': 'x64'}.get(settings.get_safe(\\\"arch\\\"))),\\n (\\\"PlatformToolset\\\", toolset)]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/visualstudio_multi.py::_VSSettings.filename\",\n \"text\": \"conans/client/generators/visualstudio_multi.py::_VSSettings.filename\\n @property\\n def filename(self):\\n name = \\\"conanbuildinfo%s.props\\\" % \\\"\\\".join(\\\"_%s\\\" % v for _, v in self._props if v)\\n return name.lower()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/visualstudio_multi.py::_VSSettings.condition\",\n \"text\": \"conans/client/generators/visualstudio_multi.py::_VSSettings.condition\\n @property\\n def condition(self):\\n return \\\" And \\\".join(\\\"'$(%s)' == '%s'\\\" % (k, v) for k, v in self._props if v)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.filename\",\n \"text\": \"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.filename\\n @property\\n def filename(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.content\",\n \"text\": \"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.content\\n @property\\n def content(self):\\n vs_settings = _VSSettings(self.conanfile.settings)\\n condition = vs_settings.condition\\n name_current = vs_settings.filename\\n name_multi = \\\"conanbuildinfo_multi.props\\\"\\n\\n # read the exising mult_filename or use the template if it doesn't exist\\n multi_path = os.path.join(self.output_path, name_multi)\\n if os.path.isfile(multi_path):\\n content_multi = load(multi_path)\\n else:\\n content_multi = self.multi_content_template\\n\\n # parse the multi_file and add a new import statement if needed\\n dom = minidom.parseString(content_multi)\\n import_group = dom.getElementsByTagName('ImportGroup')[0]\\n children = import_group.getElementsByTagName(\\\"Import\\\")\\n for node in children:\\n if name_current == node.getAttribute(\\\"Project\\\") and condition == node.getAttribute(\\\"Condition\\\"):\\n # the import statement already exists\\n break\\n else:\\n # create a new import statement\\n import_node = dom.createElement('Import')\\n import_node.setAttribute('Condition', condition)\\n import_node.setAttribute('Project', name_current)\\n # add it to the import group\\n import_group.appendChild(import_node)\\n content_multi = dom.toprettyxml()\\n content_multi = \\\"\\\\n\\\".join(line for line in content_multi.splitlines() if line.strip())\\n\\n return {name_multi: content_multi,\\n vs_settings.filename: VisualStudioGenerator(self.conanfile).content}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::_visual_compiler_cygwin\",\n \"text\": \"conans/client/tools/win.py::_visual_compiler_cygwin\\ndef _visual_compiler_cygwin(output, version):\\n if os.path.isfile(\\\"/proc/registry/HKEY_LOCAL_MACHINE/SOFTWARE/Microsoft/Windows/\\\"\\n \\\"CurrentVersion/ProgramFilesDir (x86)\\\"):\\n is_64bits = True\\n else:\\n is_64bits = False\\n\\n if is_64bits:\\n key_name = r'HKEY_LOCAL_MACHINE\\\\SOFTWARE\\\\Wow6432Node\\\\Microsoft\\\\VisualStudio\\\\SxS\\\\VC7'\\n else:\\n key_name = r'HKEY_LOCAL_MACHINE\\\\SOFTWARE\\\\Microsoft\\\\VisualStudio\\\\SxS\\\\VC7'\\n\\n if not os.path.isfile(\\\"/proc/registry/\\\" + key_name.replace('\\\\\\\\', '/') + \\\"/\\\" + version):\\n return None\\n\\n installed_version = Version(version).major(fill=False)\\n compiler = \\\"Visual Studio\\\"\\n output.success(\\\"CYGWIN: Found %s %s\\\" % (compiler, installed_version))\\n return compiler, installed_version\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::_system_registry_key\",\n \"text\": \"conans/client/tools/win.py::_system_registry_key\\ndef _system_registry_key(key, subkey, query):\\n from six.moves import winreg # @UnresolvedImport\\n try:\\n hkey = winreg.OpenKey(key, subkey)\\n except (OSError, WindowsError): # Raised by OpenKey/Ex if the function fails (py3, py2)\\n return None\\n else:\\n try:\\n value, _ = winreg.QueryValueEx(hkey, query)\\n return value\\n except EnvironmentError:\\n return None\\n finally:\\n winreg.CloseKey(hkey)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::_visual_compiler\",\n \"text\": \"conans/client/tools/win.py::_visual_compiler\\ndef _visual_compiler(output, version):\\n \\\"\\\"\\\"\\\"version have to be 8.0, or 9.0 or... anything .0\\\"\\\"\\\"\\n if platform.system().startswith(\\\"CYGWIN\\\"):\\n return _visual_compiler_cygwin(output, version)\\n\\n if Version(version) >= \\\"15\\\":\\n vs_path = os.getenv('vs%s0comntools' % version)\\n path = vs_path or vs_installation_path(version)\\n if path:\\n compiler = \\\"Visual Studio\\\"\\n output.success(\\\"Found %s %s\\\" % (compiler, version))\\n return compiler, version\\n return None\\n\\n version = \\\"%s.0\\\" % version\\n\\n from six.moves import winreg # @UnresolvedImport\\n is_64bits = _system_registry_key(winreg.HKEY_LOCAL_MACHINE,\\n r\\\"SOFTWARE\\\\Microsoft\\\\Windows\\\\CurrentVersion\\\",\\n \\\"ProgramFilesDir (x86)\\\") is not None\\n\\n if is_64bits:\\n key_name = r'SOFTWARE\\\\Wow6432Node\\\\Microsoft\\\\VisualStudio\\\\SxS\\\\VC7'\\n else:\\n key_name = r'HKEY_LOCAL_MACHINE\\\\SOFTWARE\\\\Microsoft\\\\VisualStudio\\\\SxS\\\\VC7'\\n\\n if _system_registry_key(winreg.HKEY_LOCAL_MACHINE, key_name, version):\\n installed_version = Version(version).major(fill=False)\\n compiler = \\\"Visual Studio\\\"\\n output.success(\\\"Found %s %s\\\" % (compiler, installed_version))\\n return compiler, installed_version\\n\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::latest_vs_version_installed\",\n \"text\": \"conans/client/tools/win.py::latest_vs_version_installed\\ndef latest_vs_version_installed(output):\\n return latest_visual_studio_version_installed(output=output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::msvs_toolset\",\n \"text\": \"conans/client/tools/win.py::msvs_toolset\\ndef msvs_toolset(settings):\\n toolset = settings.get_safe(\\\"compiler.toolset\\\")\\n if not toolset:\\n vs_version = settings.get_safe(\\\"compiler.version\\\")\\n toolset = MSVS_DEFAULT_TOOLSETS.get(vs_version)\\n return toolset\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::latest_visual_studio_version_installed\",\n \"text\": \"conans/client/tools/win.py::latest_visual_studio_version_installed\\ndef latest_visual_studio_version_installed(output):\\n msvc_sersions = reversed(sorted(list(MSVS_DEFAULT_TOOLSETS.keys()), key=int))\\n for version in msvc_sersions:\\n vs = _visual_compiler(output, version)\\n if vs:\\n return vs[1]\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::msvc_build_command\",\n \"text\": \"conans/client/tools/win.py::msvc_build_command\\n@deprecation.deprecated(deprecated_in=\\\"1.2\\\", removed_in=\\\"2.0\\\",\\n details=\\\"Use the MSBuild() build helper instead\\\")\\ndef msvc_build_command(settings, sln_path, targets=None, upgrade_project=True, build_type=None,\\n arch=None, parallel=True, force_vcvars=False, toolset=None, platforms=None,\\n output=None):\\n \\\"\\\"\\\" Do both: set the environment variables and call the .sln build\\n \\\"\\\"\\\"\\n vcvars = vcvars_command(settings, force=force_vcvars, output=output)\\n build = build_sln_command(settings, sln_path, targets, upgrade_project, build_type, arch,\\n parallel, toolset=toolset, platforms=platforms, output=output)\\n command = \\\"%s && %s\\\" % (vcvars, build)\\n return command\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::build_sln_command\",\n \"text\": \"conans/client/tools/win.py::build_sln_command\\n@deprecation.deprecated(deprecated_in=\\\"1.2\\\", removed_in=\\\"2.0\\\",\\n details=\\\"Use the MSBuild() build helper instead\\\")\\ndef build_sln_command(settings, sln_path, targets=None, upgrade_project=True, build_type=None,\\n arch=None, parallel=True, toolset=None, platforms=None, output=None,\\n verbosity=None, definitions=None):\\n \\\"\\\"\\\"\\n Use example:\\n build_command = build_sln_command(self.settings, \\\"myfile.sln\\\", targets=[\\\"SDL2_image\\\"])\\n command = \\\"%s && %s\\\" % (tools.vcvars_command(self.settings), build_command)\\n self.run(command)\\n \\\"\\\"\\\"\\n from conans.client.build.msbuild import MSBuild\\n tmp = MSBuild(settings)\\n output = default_output(output, fn_name='conans.client.tools.win.build_sln_command')\\n tmp._output = output\\n\\n # Generate the properties file\\n props_file_contents = tmp._get_props_file_contents(definitions)\\n tmp_path = os.path.join(mkdir_tmp(), \\\".conan_properties\\\")\\n save(tmp_path, props_file_contents)\\n\\n # Build command\\n command = tmp.get_command(sln_path, tmp_path,\\n targets=targets, upgrade_project=upgrade_project,\\n build_type=build_type, arch=arch, parallel=parallel,\\n toolset=toolset, platforms=platforms, use_env=False,\\n verbosity=verbosity)\\n\\n return command\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::vs_installation_path\",\n \"text\": \"conans/client/tools/win.py::vs_installation_path\\ndef vs_installation_path(version, preference=None):\\n\\n vs_installation_path = None\\n\\n if not preference:\\n env_prefs = get_env(\\\"CONAN_VS_INSTALLATION_PREFERENCE\\\", list())\\n\\n if env_prefs:\\n preference = env_prefs\\n else: # default values\\n preference = [\\\"Enterprise\\\", \\\"Professional\\\", \\\"Community\\\", \\\"BuildTools\\\"]\\n\\n # Try with vswhere()\\n try:\\n legacy_products = vswhere(legacy=True)\\n all_products = vswhere(products=[\\\"*\\\"])\\n products = legacy_products + all_products\\n except ConanException:\\n products = None\\n\\n vs_paths = []\\n\\n if products:\\n # remove repeated products\\n seen_products = []\\n for product in products:\\n if product not in seen_products:\\n seen_products.append(product)\\n\\n # Append products with \\\"productId\\\" by order of preference\\n for product_type in preference:\\n for product in seen_products:\\n product = dict(product)\\n if (product[\\\"installationVersion\\\"].startswith((\\\"%d.\\\" % int(version)))\\n and \\\"productId\\\" in product):\\n if product_type in product[\\\"productId\\\"]:\\n vs_paths.append(product[\\\"installationPath\\\"])\\n\\n # Append products without \\\"productId\\\" (Legacy installations)\\n for product in seen_products:\\n product = dict(product)\\n if (product[\\\"installationVersion\\\"].startswith((\\\"%d.\\\" % int(version)))\\n and \\\"productId\\\" not in product):\\n vs_paths.append(product[\\\"installationPath\\\"])\\n\\n # If vswhere does not find anything or not available, try with vs_comntools()\\n if not vs_paths:\\n vs_path = vs_comntools(version)\\n\\n if vs_path:\\n sub_path_to_remove = os.path.join(\\\"\\\", \\\"Common7\\\", \\\"Tools\\\", \\\"\\\")\\n # Remove '\\\\\\\\Common7\\\\\\\\Tools\\\\\\\\' to get same output as vswhere\\n if vs_path.endswith(sub\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::vswhere\",\n \"text\": \"conans/client/tools/win.py::vswhere\\ndef vswhere(all_=False, prerelease=False, products=None, requires=None, version=\\\"\\\", latest=False,\\n legacy=False, property_=\\\"\\\", nologo=True):\\n\\n # 'version' option only works if Visual Studio 2017 is installed:\\n # https://github.com/Microsoft/vswhere/issues/91\\n\\n products = list() if products is None else products\\n requires = list() if requires is None else requires\\n\\n if legacy and (products or requires):\\n raise ConanException(\\\"The 'legacy' parameter cannot be specified with either the \\\"\\n \\\"'products' or 'requires' parameter\\\")\\n\\n installer_path = None\\n program_files = get_env(\\\"ProgramFiles(x86)\\\") or get_env(\\\"ProgramFiles\\\")\\n if program_files:\\n expected_path = os.path.join(program_files, \\\"Microsoft Visual Studio\\\", \\\"Installer\\\",\\n \\\"vswhere.exe\\\")\\n if os.path.isfile(expected_path):\\n installer_path = expected_path\\n vswhere_path = installer_path or which(\\\"vswhere\\\")\\n\\n if not vswhere_path:\\n raise ConanException(\\\"Cannot locate vswhere in 'Program Files'/'Program Files (x86)' \\\"\\n \\\"directory nor in PATH\\\")\\n\\n arguments = list()\\n arguments.append(vswhere_path)\\n\\n # Output json format\\n arguments.append(\\\"-format\\\")\\n arguments.append(\\\"json\\\")\\n\\n if all_:\\n arguments.append(\\\"-all\\\")\\n\\n if prerelease:\\n arguments.append(\\\"-prerelease\\\")\\n\\n if products:\\n arguments.append(\\\"-products\\\")\\n arguments.extend(products)\\n\\n if requires:\\n arguments.append(\\\"-requires\\\")\\n arguments.extend(requires)\\n\\n if len(version) is not 0:\\n arguments.append(\\\"-version\\\")\\n arguments.append(version)\\n\\n if latest:\\n arguments.append(\\\"-latest\\\")\\n\\n if legacy:\\n arguments.append(\\\"-legacy\\\")\\n\\n if len(property_) is not 0:\\n arguments.append(\\\"-property\\\")\\n arguments.append(property_)\\n\\n if nologo:\\n arguments.append(\\\"-\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::vs_comntools\",\n \"text\": \"conans/client/tools/win.py::vs_comntools\\ndef vs_comntools(compiler_version):\\n env_var = \\\"vs%s0comntools\\\" % compiler_version\\n vs_path = os.getenv(env_var)\\n return vs_path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::find_windows_10_sdk\",\n \"text\": \"conans/client/tools/win.py::find_windows_10_sdk\\ndef find_windows_10_sdk():\\n \\\"\\\"\\\"finds valid Windows 10 SDK version which can be passed to vcvarsall.bat (vcvars_command)\\\"\\\"\\\"\\n # uses the same method as VCVarsQueryRegistry.bat\\n from six.moves import winreg # @UnresolvedImport\\n hives = [\\n (winreg.HKEY_LOCAL_MACHINE, r'SOFTWARE\\\\Wow6432Node'),\\n (winreg.HKEY_CURRENT_USER, r'SOFTWARE\\\\Wow6432Node'),\\n (winreg.HKEY_LOCAL_MACHINE, r'SOFTWARE'),\\n (winreg.HKEY_CURRENT_USER, r'SOFTWARE')\\n ]\\n for key, subkey in hives:\\n subkey = r'%s\\\\Microsoft\\\\Microsoft SDKs\\\\Windows\\\\v10.0' % subkey\\n installation_folder = _system_registry_key(key, subkey, 'InstallationFolder')\\n if installation_folder:\\n if os.path.isdir(installation_folder):\\n include_dir = os.path.join(installation_folder, 'include')\\n for sdk_version in os.listdir(include_dir):\\n if (os.path.isdir(os.path.join(include_dir, sdk_version))\\n and sdk_version.startswith('10.')):\\n windows_h = os.path.join(include_dir, sdk_version, 'um', 'Windows.h')\\n if os.path.isfile(windows_h):\\n return sdk_version\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::vcvars_command\",\n \"text\": \"conans/client/tools/win.py::vcvars_command\\ndef vcvars_command(settings, arch=None, compiler_version=None, force=False, vcvars_ver=None,\\n winsdk_version=None, output=None):\\n output = default_output(output, 'conans.client.tools.win.vcvars_command')\\n\\n arch_setting = arch or settings.get_safe(\\\"arch\\\")\\n\\n compiler = settings.get_safe(\\\"compiler\\\")\\n if compiler == 'Visual Studio':\\n compiler_version = compiler_version or settings.get_safe(\\\"compiler.version\\\")\\n else:\\n # vcvars might be still needed for other compilers, e.g. clang-cl or Intel C++,\\n # as they might be using Microsoft STL and other tools\\n # (e.g. resource compiler, manifest tool, etc)\\n # in this case, use the latest Visual Studio available on the machine\\n last_version = latest_vs_version_installed(output=output)\\n\\n compiler_version = compiler_version or last_version\\n os_setting = settings.get_safe(\\\"os\\\")\\n if not compiler_version:\\n raise ConanException(\\\"compiler.version setting required for vcvars not defined\\\")\\n\\n # https://msdn.microsoft.com/en-us/library/f2ccy3wt.aspx\\n arch_setting = arch_setting or 'x86_64'\\n arch_build = settings.get_safe(\\\"arch_build\\\") or detected_architecture()\\n if os_setting == 'WindowsCE':\\n vcvars_arch = \\\"x86\\\"\\n elif arch_build == 'x86_64':\\n # Only uses x64 tooling if arch_build explicitly defines it, otherwise\\n # Keep the VS default, which is x86 toolset\\n # This will probably be changed in conan 2.0\\n if ((settings.get_safe(\\\"arch_build\\\") or\\n os.getenv(\\\"PreferredToolArchitecture\\\") == \\\"x64\\\")\\n and int(compiler_version) >= 12):\\n x86_cross = \\\"amd64_x86\\\"\\n else:\\n x86_cross = \\\"x86\\\"\\n vcvars_arch = {'x86': x86_cross,\\n 'x86_64': 'amd64',\\n 'armv7': 'amd64_arm',\\n 'armv8': 'amd64_arm64'}.get(arch_setting)\\n elif arch_build == 'x86':\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::vcvars_dict\",\n \"text\": \"conans/client/tools/win.py::vcvars_dict\\ndef vcvars_dict(settings, arch=None, compiler_version=None, force=False, filter_known_paths=False,\\n vcvars_ver=None, winsdk_version=None, only_diff=True, output=None):\\n known_path_lists = (\\\"include\\\", \\\"lib\\\", \\\"libpath\\\", \\\"path\\\")\\n cmd = vcvars_command(settings, arch=arch,\\n compiler_version=compiler_version, force=force,\\n vcvars_ver=vcvars_ver, winsdk_version=winsdk_version, output=output)\\n cmd += \\\" && set\\\"\\n ret = check_output(cmd)\\n new_env = {}\\n for line in ret.splitlines():\\n line = line.strip()\\n\\n if line == \\\"\\\\n\\\" or not line:\\n continue\\n try:\\n name_var, value = line.split(\\\"=\\\", 1)\\n new_value = value.split(os.pathsep) if name_var.lower() in known_path_lists else value\\n # Return only new vars & changed ones, but only with the changed elements if the var is\\n # a list\\n if only_diff:\\n old_value = os.environ.get(name_var)\\n if name_var.lower() == \\\"path\\\":\\n old_values_lower = [v.lower() for v in old_value.split(os.pathsep)]\\n # Clean all repeated entries, not append if the element was already there\\n new_env[name_var] = [v for v in new_value if v.lower() not in old_values_lower]\\n elif old_value and value.endswith(os.pathsep + old_value):\\n # The new value ends with separator and the old value, is a list,\\n # get only the new elements\\n new_env[name_var] = value[:-(len(old_value) + 1)].split(os.pathsep)\\n elif value != old_value:\\n # Only if the vcvars changed something, we return the variable,\\n # otherwise is not vcvars related\\n new_env[name_var] = new_value\\n else:\\n new_env[name_var] = new_value\\n\\n except ValueError:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::vcvars\",\n \"text\": \"conans/client/tools/win.py::vcvars\\n@contextmanager\\ndef vcvars(*args, **kwargs):\\n if platform.system() == \\\"Windows\\\":\\n new_env = vcvars_dict(*args, **kwargs)\\n with environment_append(new_env):\\n yield\\n else:\\n yield\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::escape_windows_cmd\",\n \"text\": \"conans/client/tools/win.py::escape_windows_cmd\\ndef escape_windows_cmd(command):\\n \\\"\\\"\\\" To use in a regular windows cmd.exe\\n 1. Adds escapes so the argument can be unpacked by CommandLineToArgvW()\\n 2. Adds escapes for cmd.exe so the argument survives cmd.exe's substitutions.\\n\\n Useful to escape commands to be executed in a windows bash (msys2, cygwin etc)\\n \\\"\\\"\\\"\\n quoted_arg = subprocess.list2cmdline([command])\\n return \\\"\\\".join([\\\"^%s\\\" % arg if arg in r'()%!^\\\"<>&|' else arg for arg in quoted_arg])\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::get_cased_path\",\n \"text\": \"conans/client/tools/win.py::get_cased_path\\ndef get_cased_path(name):\\n if platform.system() != \\\"Windows\\\":\\n return name\\n if not os.path.isabs(name):\\n name = os.path.abspath(name)\\n\\n result = []\\n current = name\\n while True:\\n parent, child = os.path.split(current)\\n if parent == current:\\n break\\n\\n child_cased = child\\n if os.path.exists(parent):\\n children = os.listdir(parent)\\n for c in children:\\n if c.upper() == child.upper():\\n child_cased = c\\n break\\n result.append(child_cased)\\n current = parent\\n drive, _ = os.path.splitdrive(current)\\n result.append(drive)\\n return os.sep.join(reversed(result))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::unix_path\",\n \"text\": \"conans/client/tools/win.py::unix_path\\ndef unix_path(path, path_flavor=None):\\n \\\"\\\"\\\"\\\"Used to translate windows paths to MSYS unix paths like\\n c/users/path/to/file. Not working in a regular console or MinGW!\\\"\\\"\\\"\\n if not path:\\n return None\\n\\n if os.path.exists(path):\\n path = get_cased_path(path) # if the path doesn't exist (and abs) we cannot guess the casing\\n\\n path_flavor = path_flavor or OSInfo.detect_windows_subsystem() or MSYS2\\n path = path.replace(\\\":/\\\", \\\":\\\\\\\\\\\")\\n pattern = re.compile(r'([a-z]):\\\\\\\\', re.IGNORECASE)\\n path = pattern.sub('/\\\\\\\\1/', path).replace('\\\\\\\\', '/')\\n if path_flavor in (MSYS, MSYS2):\\n return path.lower()\\n elif path_flavor == CYGWIN:\\n return '/cygdrive' + path.lower()\\n elif path_flavor == WSL:\\n return '/mnt' + path[0:2].lower() + path[2:]\\n elif path_flavor == SFU:\\n path = path.lower()\\n return '/dev/fs' + path[0] + path[1:].capitalize()\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/win.py::run_in_windows_bash\",\n \"text\": \"conans/client/tools/win.py::run_in_windows_bash\\ndef run_in_windows_bash(conanfile, bashcmd, cwd=None, subsystem=None, msys_mingw=True, env=None,\\n with_login=True):\\n \\\"\\\"\\\" Will run a unix command inside a bash terminal\\n It requires to have MSYS2, CYGWIN, or WSL\\n \\\"\\\"\\\"\\n env = env or {}\\n if platform.system() != \\\"Windows\\\":\\n raise ConanException(\\\"Command only for Windows operating system\\\")\\n subsystem = subsystem or OSInfo.detect_windows_subsystem()\\n\\n if not subsystem:\\n raise ConanException(\\\"Cannot recognize the Windows subsystem, install MSYS2/cygwin \\\"\\n \\\"or specify a build_require to apply it.\\\")\\n\\n if subsystem == MSYS2 and msys_mingw:\\n # This needs to be set so that msys2 bash profile will set up the environment correctly.\\n env_vars = {\\\"MSYSTEM\\\": (\\\"MINGW32\\\" if conanfile.settings.get_safe(\\\"arch\\\") == \\\"x86\\\" else\\n \\\"MINGW64\\\"),\\n \\\"MSYS2_PATH_TYPE\\\": \\\"inherit\\\"}\\n else:\\n env_vars = {}\\n\\n with environment_append(env_vars):\\n\\n hack_env = \\\"\\\"\\n # In the bash.exe from WSL this trick do not work, always the /usr/bin etc at first place\\n if subsystem != WSL:\\n\\n def get_path_value(container, subsystem_name):\\n \\\"\\\"\\\"Gets the path from the container dict and returns a\\n string with the path for the subsystem_name\\\"\\\"\\\"\\n _path_key = next((name for name in container.keys() if \\\"path\\\" == name.lower()), None)\\n if _path_key:\\n _path_value = container.get(_path_key)\\n if isinstance(_path_value, list):\\n return \\\":\\\".join([unix_path(path, path_flavor=subsystem_name)\\n for path in _path_value])\\n else:\\n return unix_path(_path_value, path_flavor=subsystem_name)\\n\\n # First get the PATH from the conanfile.env\\n \",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":250,"cells":{"instance_id":{"kind":"string","value":"wagtail__wagtail-1811"},"repo":{"kind":"string","value":"wagtail/wagtail"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"wagtailsearch.views.frontend needs updating to use non-deprecated search method\nCurrently using Page.search, which is deprecated in Wagtail 1.2 - should be updated to use the QuerySet-based mechanism instead.\n\nhttps://github.com/torchbox/wagtail/blob/master/wagtail/wagtailsearch/views/frontend.py#L41\n\n"},"gold_files":{"kind":"list like","value":["wagtail/wagtailsearch/views/frontend.py"],"string":"[\n \"wagtail/wagtailsearch/views/frontend.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["wagtail/wagtailsearch/views/frontend.py::search"],"string":"[\n \"wagtail/wagtailsearch/views/frontend.py::search\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/conf.py","text":"docs/conf.py","label":0},{"cid":"wagtail/wagtailadmin/taggable.py","text":"wagtail/wagtailadmin/taggable.py\nclass TagSearchable # Mixin to provide a 'search' method, searching on the 'title' field and tags,\n def get_tags(self)\n def get_indexed_objects(cls)\n def search(cls, q, results_per_page=None, page=1, prefetch_tags=False, filters={})\n def popular_tags(cls)","label":0},{"cid":"wagtail/wagtailcore/models.py","text":"wagtail/wagtailcore/models.py\nclass SiteManager\n def get_by_natural_key(self, hostname, port)\nclass Site\n class Meta\n def natural_key(self)\n def __str__(self)\n def find_for_request(request) # Find the site object responsible for responding to this HTTP\n def root_url(self)\n def clean_fields(self, exclude=None)\n def get_site_root_paths() # Return a list of (root_path, root_url) tuples, most specific path first -\ndef clear_site_root_paths_on_save(sender, instance, **kwargs)\ndef clear_site_root_paths_on_delete(sender, instance, **kwargs)\ndef get_page_types()\nclass PageManager\n def get_queryset(self)\n def live(self)\n def not_live(self)\n def in_menu(self)\n def not_in_menu(self)\n def page(self, other)\n def not_page(self, other)\n def descendant_of(self, other, inclusive=False)\n def not_descendant_of(self, other, inclusive=False)\n def child_of(self, other)\n def not_child_of(self, other)\n def ancestor_of(self, other, inclusive=False)\n def not_ancestor_of(self, other, inclusive=False)\n def parent_of(self, other)\n def not_parent_of(self, other)\n def sibling_of(self, other, inclusive=False)\n def not_sibling_of(self, other, inclusive=False)\n def type(self, model)\n def not_type(self, model)\n def public(self)\n def not_public(self)\n def search(self, query_string, fields=None, operator=None, order_by_relevance=True, backend='default')\n def specific(self)\nclass PageBase # Metaclass for Page\n def __init__(cls, name, bases, dct)\nclass Page\n def __init__(self, *args, **kwargs)\n def __str__(self)\n def set_url_path(self, parent) # Populate the url_path field based on this page's slug and the specified parent p\n def save(self, *args, **kwargs)\n def delete(self, *args, **kwargs)\n def check(cls, **kwargs)\n def _update_descendant_url_paths(self, old_url_path, new_url_path)\n def specific(self) # Return this page in its most specific subclassed form.\n def specific_class(self) # Return the class that this page would be if instantiated in its\n def route(self, request, path_components)\n def save_revision(self, user=None, submitted_for_moderation=False, approved_go_live_at=None, changed=True)\n def get_latest_revision(self)\n def get_latest_revision_as_page(self)\n def unpublish(self, set_expired=False, commit=True)\n def get_context(self, request, *args, **kwargs)\n def get_template(self, request, *args, **kwargs)\n def serve(self, request, *args, **kwargs)\n def is_navigable(self) # Return true if it's meaningful to browse subpages of this page -\n def full_url(self) # Return the full URL (including protocol / domain) to this page, or None if it is\n def url(self) # Return the 'most appropriate' URL for referring to this page from the pages we s\n def relative_url(self, current_site) # Return the 'most appropriate' URL for this page taking into account the site we'\n def get_indexed_objects(cls)\n def get_indexed_inst","label":0},{"cid":"wagtail/wagtailcore/utils.py","text":"wagtail/wagtailcore/utils.py\ndef camelcase_to_underscore(str)\ndef resolve_model_string(model_string, default_app=None) # Resolve an 'app_label.model_name' string into an actual model class.\ndef escape_script(text) # Escape `</script>` tags in 'text' so that it can be placed within a `<script>` b\ndef cautious_slugify(value) # Convert a string to ASCII exactly as Django's slugify does, with the exception","label":0},{"cid":"wagtail/wagtailimages/templatetags/wagtailimages_tags.py","text":"wagtail/wagtailimages/templatetags/wagtailimages_tags.py\ndef image(parser, token)\nclass ImageNode\n def __init__(self, image_expr, filter_spec, output_var_name=None, attrs={})\n def filter(self)\n def render(self, context)","label":0},{"cid":"wagtail/wagtailredirects/middleware.py","text":"wagtail/wagtailredirects/middleware.py\nclass RedirectMiddleware\n def process_response(self, request, response)","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py","text":"wagtail/wagtailsearch/backends/elasticsearch.py\nclass ElasticSearchMapping\n def __init__(self, model)\n def get_document_type(self)\n def get_field_mapping(self, field)\n def get_mapping(self)\n def get_document_id(self, obj)\n def get_document(self, obj)\n def __repr__(self)\nclass ElasticSearchQuery\n def _process_lookup(self, field, lookup, value)\n def _connect_filters(self, filters, connector, negated)\n def get_inner_query(self)\n def get_filters(self)\n def get_query(self)\n def get_sort(self)\n def __repr__(self)\n def to_es(self)\nclass ElasticSearchResults\n def _get_es_body(self, for_count=False)\n def _do_search(self)\n def _do_count(self)\nclass ElasticSearchIndexRebuilder\n def __init__(self, es, index_name)\n def reset_index(self)\n def start(self)\n def add_model(self, model)\n def add_items(self, model, obj_list)\n def finish(self)\nclass ElasticSearchAtomicIndexRebuilder\n def __init__(self, es, alias_name)\n def reset_index(self)\n def start(self)\n def finish(self)\nclass ElasticSearch\n def __init__(self, params)\n def get_rebuilder(self)\n def reset_index(self)\n def add_type(self, model)\n def refresh_index(self)\n def add(self, obj)\n def add_bulk(self, model, obj_list)\n def delete(self, obj)","label":0},{"cid":"wagtail/wagtailsearch/views/frontend.py","text":"wagtail/wagtailsearch/views/frontend.py\ndef search(request, template=None, template_ajax=None, results_per_page=10, use_json=False, json_attrs=['title', 'url'], show_unpublished=False, search_title_only=False, extra_filters={}, path=None)","label":1}],"string":"[\n {\n \"cid\": \"docs/conf.py\",\n \"text\": \"docs/conf.py\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/taggable.py\",\n \"text\": \"wagtail/wagtailadmin/taggable.py\\nclass TagSearchable # Mixin to provide a 'search' method, searching on the 'title' field and tags,\\n def get_tags(self)\\n def get_indexed_objects(cls)\\n def search(cls, q, results_per_page=None, page=1, prefetch_tags=False, filters={})\\n def popular_tags(cls)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py\",\n \"text\": \"wagtail/wagtailcore/models.py\\nclass SiteManager\\n def get_by_natural_key(self, hostname, port)\\nclass Site\\n class Meta\\n def natural_key(self)\\n def __str__(self)\\n def find_for_request(request) # Find the site object responsible for responding to this HTTP\\n def root_url(self)\\n def clean_fields(self, exclude=None)\\n def get_site_root_paths() # Return a list of (root_path, root_url) tuples, most specific path first -\\ndef clear_site_root_paths_on_save(sender, instance, **kwargs)\\ndef clear_site_root_paths_on_delete(sender, instance, **kwargs)\\ndef get_page_types()\\nclass PageManager\\n def get_queryset(self)\\n def live(self)\\n def not_live(self)\\n def in_menu(self)\\n def not_in_menu(self)\\n def page(self, other)\\n def not_page(self, other)\\n def descendant_of(self, other, inclusive=False)\\n def not_descendant_of(self, other, inclusive=False)\\n def child_of(self, other)\\n def not_child_of(self, other)\\n def ancestor_of(self, other, inclusive=False)\\n def not_ancestor_of(self, other, inclusive=False)\\n def parent_of(self, other)\\n def not_parent_of(self, other)\\n def sibling_of(self, other, inclusive=False)\\n def not_sibling_of(self, other, inclusive=False)\\n def type(self, model)\\n def not_type(self, model)\\n def public(self)\\n def not_public(self)\\n def search(self, query_string, fields=None, operator=None, order_by_relevance=True, backend='default')\\n def specific(self)\\nclass PageBase # Metaclass for Page\\n def __init__(cls, name, bases, dct)\\nclass Page\\n def __init__(self, *args, **kwargs)\\n def __str__(self)\\n def set_url_path(self, parent) # Populate the url_path field based on this page's slug and the specified parent p\\n def save(self, *args, **kwargs)\\n def delete(self, *args, **kwargs)\\n def check(cls, **kwargs)\\n def _update_descendant_url_paths(self, old_url_path, new_url_path)\\n def specific(self) # Return this page in its most specific subclassed form.\\n def specific_class(self) # Return the class that this page would be if instantiated in its\\n def route(self, request, path_components)\\n def save_revision(self, user=None, submitted_for_moderation=False, approved_go_live_at=None, changed=True)\\n def get_latest_revision(self)\\n def get_latest_revision_as_page(self)\\n def unpublish(self, set_expired=False, commit=True)\\n def get_context(self, request, *args, **kwargs)\\n def get_template(self, request, *args, **kwargs)\\n def serve(self, request, *args, **kwargs)\\n def is_navigable(self) # Return true if it's meaningful to browse subpages of this page -\\n def full_url(self) # Return the full URL (including protocol / domain) to this page, or None if it is\\n def url(self) # Return the 'most appropriate' URL for referring to this page from the pages we s\\n def relative_url(self, current_site) # Return the 'most appropriate' URL for this page taking into account the site we'\\n def get_indexed_objects(cls)\\n def get_indexed_inst\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/utils.py\",\n \"text\": \"wagtail/wagtailcore/utils.py\\ndef camelcase_to_underscore(str)\\ndef resolve_model_string(model_string, default_app=None) # Resolve an 'app_label.model_name' string into an actual model class.\\ndef escape_script(text) # Escape `</script>` tags in 'text' so that it can be placed within a `<script>` b\\ndef cautious_slugify(value) # Convert a string to ASCII exactly as Django's slugify does, with the exception\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailimages/templatetags/wagtailimages_tags.py\",\n \"text\": \"wagtail/wagtailimages/templatetags/wagtailimages_tags.py\\ndef image(parser, token)\\nclass ImageNode\\n def __init__(self, image_expr, filter_spec, output_var_name=None, attrs={})\\n def filter(self)\\n def render(self, context)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailredirects/middleware.py\",\n \"text\": \"wagtail/wagtailredirects/middleware.py\\nclass RedirectMiddleware\\n def process_response(self, request, response)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py\\nclass ElasticSearchMapping\\n def __init__(self, model)\\n def get_document_type(self)\\n def get_field_mapping(self, field)\\n def get_mapping(self)\\n def get_document_id(self, obj)\\n def get_document(self, obj)\\n def __repr__(self)\\nclass ElasticSearchQuery\\n def _process_lookup(self, field, lookup, value)\\n def _connect_filters(self, filters, connector, negated)\\n def get_inner_query(self)\\n def get_filters(self)\\n def get_query(self)\\n def get_sort(self)\\n def __repr__(self)\\n def to_es(self)\\nclass ElasticSearchResults\\n def _get_es_body(self, for_count=False)\\n def _do_search(self)\\n def _do_count(self)\\nclass ElasticSearchIndexRebuilder\\n def __init__(self, es, index_name)\\n def reset_index(self)\\n def start(self)\\n def add_model(self, model)\\n def add_items(self, model, obj_list)\\n def finish(self)\\nclass ElasticSearchAtomicIndexRebuilder\\n def __init__(self, es, alias_name)\\n def reset_index(self)\\n def start(self)\\n def finish(self)\\nclass ElasticSearch\\n def __init__(self, params)\\n def get_rebuilder(self)\\n def reset_index(self)\\n def add_type(self, model)\\n def refresh_index(self)\\n def add(self, obj)\\n def add_bulk(self, model, obj_list)\\n def delete(self, obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/views/frontend.py\",\n \"text\": \"wagtail/wagtailsearch/views/frontend.py\\ndef search(request, template=None, template_ajax=None, results_per_page=10, use_json=False, json_attrs=['title', 'url'], show_unpublished=False, search_title_only=False, extra_filters={}, path=None)\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"wagtail/wagtailadmin/taggable.py::TagSearchable.get_tags","text":"wagtail/wagtailadmin/taggable.py::TagSearchable.get_tags\n @property\n def get_tags(self):\n return ' '.join([tag.name for tag in self.tags.all()])","label":0},{"cid":"wagtail/wagtailadmin/taggable.py::TagSearchable.get_indexed_objects","text":"wagtail/wagtailadmin/taggable.py::TagSearchable.get_indexed_objects\n @classmethod\n def get_indexed_objects(cls):\n return super(TagSearchable, cls).get_indexed_objects().prefetch_related('tagged_items__tag')","label":0},{"cid":"wagtail/wagtailadmin/taggable.py::TagSearchable.search","text":"wagtail/wagtailadmin/taggable.py::TagSearchable.search\n @classmethod\n def search(cls, q, results_per_page=None, page=1, prefetch_tags=False, filters={}):\n warnings.warn(\n \"The {class_name}.search() method is deprecated. \"\n \"Please use the {class_name}.objects.search() method instead.\".format(class_name=cls.__name__),\n RemovedInWagtail14Warning, stacklevel=2)\n\n results = cls.objects.all()\n\n if prefetch_tags:\n results = results.prefetch_related('tagged_items__tag')\n\n if filters:\n results = results.filter(**filters)\n\n results = results.search(q)\n\n # If results_per_page is set, return a paginator\n if results_per_page is not None:\n paginator = Paginator(results, results_per_page)\n try:\n return paginator.page(page)\n except PageNotAnInteger:\n return paginator.page(1)\n except EmptyPage:\n return paginator.page(paginator.num_pages)\n else:\n return results","label":0},{"cid":"wagtail/wagtailadmin/taggable.py::TagSearchable.popular_tags","text":"wagtail/wagtailadmin/taggable.py::TagSearchable.popular_tags\n @classmethod\n def popular_tags(cls):\n content_type = ContentType.objects.get_for_model(cls)\n return Tag.objects.filter(\n taggit_taggeditem_items__content_type=content_type\n ).annotate(\n item_count=Count('taggit_taggeditem_items')\n ).order_by('-item_count')[:10]","label":0},{"cid":"wagtail/wagtailcore/models.py::SiteManager.get_by_natural_key","text":"wagtail/wagtailcore/models.py::SiteManager.get_by_natural_key\n def get_by_natural_key(self, hostname, port):\n return self.get(hostname=hostname, port=port)","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.natural_key","text":"wagtail/wagtailcore/models.py::Site.natural_key\n def natural_key(self):\n return (self.hostname, self.port)","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.__str__","text":"wagtail/wagtailcore/models.py::Site.__str__\n def __str__(self):\n return self.hostname + (\"\" if self.port == 80 else (\":%d\" % self.port)) + (\" [default]\" if self.is_default_site else \"\")","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.find_for_request","text":"wagtail/wagtailcore/models.py::Site.find_for_request\n @staticmethod\n def find_for_request(request):\n \"\"\"\n Find the site object responsible for responding to this HTTP\n request object. Try:\n\n * unique hostname first\n * then hostname and port\n * if there is no matching hostname at all, or no matching\n hostname:port combination, fall back to the unique default site,\n or raise an exception\n\n NB this means that high-numbered ports on an extant hostname may\n still be routed to a different hostname which is set as the default\n \"\"\"\n try:\n hostname = request.META['HTTP_HOST'].split(':')[0] # KeyError here goes to the final except clause\n try:\n # find a Site matching this specific hostname\n return Site.objects.get(hostname=hostname) # Site.DoesNotExist here goes to the final except clause\n except Site.MultipleObjectsReturned:\n # as there were more than one, try matching by port too\n port = request.META['SERVER_PORT'] # KeyError here goes to the final except clause\n return Site.objects.get(hostname=hostname, port=int(port)) # Site.DoesNotExist here goes to the final except clause\n except (Site.DoesNotExist, KeyError):\n # If no matching site exists, or request does not specify an HTTP_HOST (which\n # will often be the case for the Django test client), look for a catch-all Site.\n # If that fails, let the Site.DoesNotExist propagate back to the caller\n return Site.objects.get(is_default_site=True)","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.root_url","text":"wagtail/wagtailcore/models.py::Site.root_url\n @property\n def root_url(self):\n if self.port == 80:\n return 'http://%s' % self.hostname\n elif self.port == 443:\n return 'https://%s' % self.hostname\n else:\n return 'http://%s:%d' % (self.hostname, self.port)","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.clean_fields","text":"wagtail/wagtailcore/models.py::Site.clean_fields\n def clean_fields(self, exclude=None):\n super(Site, self).clean_fields(exclude)\n # Only one site can have the is_default_site flag set\n try:\n default = Site.objects.get(is_default_site=True)\n except Site.DoesNotExist:\n pass\n except Site.MultipleObjectsReturned:\n raise\n else:\n if self.is_default_site and self.pk != default.pk:\n raise ValidationError(\n {'is_default_site': [\n _(\"%(hostname)s is already configured as the default site. You must unset that before you can save this site as default.\")\n % {'hostname': default.hostname}\n ]}\n )","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.get_site_root_paths","text":"wagtail/wagtailcore/models.py::Site.get_site_root_paths\n @staticmethod\n def get_site_root_paths():\n \"\"\"\n Return a list of (root_path, root_url) tuples, most specific path first -\n used to translate url_paths into actual URLs with hostnames\n \"\"\"\n result = cache.get('wagtail_site_root_paths')\n\n if result is None:\n result = [\n (site.id, site.root_page.url_path, site.root_url)\n for site in Site.objects.select_related('root_page').order_by('-root_page__url_path')\n ]\n cache.set('wagtail_site_root_paths', result, 3600)\n\n return result","label":0},{"cid":"wagtail/wagtailcore/models.py::clear_site_root_paths_on_save","text":"wagtail/wagtailcore/models.py::clear_site_root_paths_on_save\n@receiver(post_save, sender=Site)\ndef clear_site_root_paths_on_save(sender, instance, **kwargs):\n cache.delete('wagtail_site_root_paths')","label":0},{"cid":"wagtail/wagtailcore/models.py::clear_site_root_paths_on_delete","text":"wagtail/wagtailcore/models.py::clear_site_root_paths_on_delete\n@receiver(post_delete, sender=Site)\ndef clear_site_root_paths_on_delete(sender, instance, **kwargs):\n cache.delete('wagtail_site_root_paths')","label":0},{"cid":"wagtail/wagtailcore/models.py::get_page_types","text":"wagtail/wagtailcore/models.py::get_page_types\ndef get_page_types():\n global _PAGE_CONTENT_TYPES\n if len(_PAGE_CONTENT_TYPES) != len(PAGE_MODEL_CLASSES):\n _PAGE_CONTENT_TYPES = [\n ContentType.objects.get_for_model(cls) for cls in PAGE_MODEL_CLASSES\n ]\n return _PAGE_CONTENT_TYPES","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.get_queryset","text":"wagtail/wagtailcore/models.py::PageManager.get_queryset\n def get_queryset(self):\n return PageQuerySet(self.model).order_by('path')","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.live","text":"wagtail/wagtailcore/models.py::PageManager.live\n def live(self):\n return self.get_queryset().live()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_live","text":"wagtail/wagtailcore/models.py::PageManager.not_live\n def not_live(self):\n return self.get_queryset().not_live()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.in_menu","text":"wagtail/wagtailcore/models.py::PageManager.in_menu\n def in_menu(self):\n return self.get_queryset().in_menu()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_in_menu","text":"wagtail/wagtailcore/models.py::PageManager.not_in_menu\n def not_in_menu(self):\n return self.get_queryset().not_in_menu()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.page","text":"wagtail/wagtailcore/models.py::PageManager.page\n def page(self, other):\n return self.get_queryset().page(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_page","text":"wagtail/wagtailcore/models.py::PageManager.not_page\n def not_page(self, other):\n return self.get_queryset().not_page(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.descendant_of","text":"wagtail/wagtailcore/models.py::PageManager.descendant_of\n def descendant_of(self, other, inclusive=False):\n return self.get_queryset().descendant_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_descendant_of","text":"wagtail/wagtailcore/models.py::PageManager.not_descendant_of\n def not_descendant_of(self, other, inclusive=False):\n return self.get_queryset().not_descendant_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.child_of","text":"wagtail/wagtailcore/models.py::PageManager.child_of\n def child_of(self, other):\n return self.get_queryset().child_of(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_child_of","text":"wagtail/wagtailcore/models.py::PageManager.not_child_of\n def not_child_of(self, other):\n return self.get_queryset().not_child_of(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.ancestor_of","text":"wagtail/wagtailcore/models.py::PageManager.ancestor_of\n def ancestor_of(self, other, inclusive=False):\n return self.get_queryset().ancestor_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_ancestor_of","text":"wagtail/wagtailcore/models.py::PageManager.not_ancestor_of\n def not_ancestor_of(self, other, inclusive=False):\n return self.get_queryset().not_ancestor_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.parent_of","text":"wagtail/wagtailcore/models.py::PageManager.parent_of\n def parent_of(self, other):\n return self.get_queryset().parent_of(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_parent_of","text":"wagtail/wagtailcore/models.py::PageManager.not_parent_of\n def not_parent_of(self, other):\n return self.get_queryset().not_parent_of(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.sibling_of","text":"wagtail/wagtailcore/models.py::PageManager.sibling_of\n def sibling_of(self, other, inclusive=False):\n return self.get_queryset().sibling_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_sibling_of","text":"wagtail/wagtailcore/models.py::PageManager.not_sibling_of\n def not_sibling_of(self, other, inclusive=False):\n return self.get_queryset().not_sibling_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.type","text":"wagtail/wagtailcore/models.py::PageManager.type\n def type(self, model):\n return self.get_queryset().type(model)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_type","text":"wagtail/wagtailcore/models.py::PageManager.not_type\n def not_type(self, model):\n return self.get_queryset().not_type(model)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.public","text":"wagtail/wagtailcore/models.py::PageManager.public\n def public(self):\n return self.get_queryset().public()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_public","text":"wagtail/wagtailcore/models.py::PageManager.not_public\n def not_public(self):\n return self.get_queryset().not_public()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.search","text":"wagtail/wagtailcore/models.py::PageManager.search\n def search(self, query_string, fields=None,\n operator=None, order_by_relevance=True, backend='default'):\n return self.get_queryset().search(query_string, fields=fields,\n operator=operator, order_by_relevance=order_by_relevance, backend=backend)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.specific","text":"wagtail/wagtailcore/models.py::PageManager.specific\n def specific(self):\n return self.get_queryset().specific()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageBase.__init__","text":"wagtail/wagtailcore/models.py::PageBase.__init__\n def __init__(cls, name, bases, dct):\n super(PageBase, cls).__init__(name, bases, dct)\n\n if cls._deferred:\n # this is an internal class built for Django's deferred-attribute mechanism;\n # don't proceed with all this page type registration stuff\n return\n\n # Add page manager\n PageManager().contribute_to_class(cls, 'objects')\n\n if 'template' not in dct:\n # Define a default template path derived from the app name and model name\n cls.template = \"%s/%s.html\" % (cls._meta.app_label, camelcase_to_underscore(name))\n\n if 'ajax_template' not in dct:\n cls.ajax_template = None\n\n cls._clean_subpage_types = None # to be filled in on first call to cls.clean_subpage_types\n cls._clean_parent_page_types = None # to be filled in on first call to cls.clean_parent_page_types\n\n # All pages should be creatable unless explicitly set otherwise.\n # This attribute is not inheritable.\n if 'is_creatable' not in dct:\n if 'is_abstract' in dct:\n warnings.warn(\n \"The is_abstract flag is deprecated - use is_creatable instead.\",\n RemovedInWagtail13Warning)\n cls.is_creatable = not dct['is_abstract']\n else:\n cls.is_creatable = not cls._meta.abstract\n\n if cls.is_creatable:\n # register this type in the list of page content types\n PAGE_MODEL_CLASSES.append(cls)","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.__init__","text":"wagtail/wagtailcore/models.py::Page.__init__\n def __init__(self, *args, **kwargs):\n super(Page, self).__init__(*args, **kwargs)\n if not self.id and not self.content_type_id:\n # this model is being newly created rather than retrieved from the db;\n # set content type to correctly represent the model class that this was\n # created as\n self.content_type = ContentType.objects.get_for_model(self)","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.__str__","text":"wagtail/wagtailcore/models.py::Page.__str__\n def __str__(self):\n return self.title","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.set_url_path","text":"wagtail/wagtailcore/models.py::Page.set_url_path\n def set_url_path(self, parent):\n \"\"\"\n Populate the url_path field based on this page's slug and the specified parent page.\n (We pass a parent in here, rather than retrieving it via get_parent, so that we can give\n new unsaved pages a meaningful URL when previewing them; at that point the page has not\n been assigned a position in the tree, as far as treebeard is concerned.\n \"\"\"\n if parent:\n self.url_path = parent.url_path + self.slug + '/'\n else:\n # a page without a parent is the tree root, which always has a url_path of '/'\n self.url_path = '/'\n\n return self.url_path","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.save","text":"wagtail/wagtailcore/models.py::Page.save\n @transaction.atomic # ensure that changes are only committed when we have updated all descendant URL paths, to preserve consistency\n def save(self, *args, **kwargs):\n update_descendant_url_paths = False\n is_new = self.id is None\n\n if is_new:\n # we are creating a record. If we're doing things properly, this should happen\n # through a treebeard method like add_child, in which case the 'path' field\n # has been set and so we can safely call get_parent\n self.set_url_path(self.get_parent())\n else:\n # Check that we are committing the slug to the database\n # Basically: If update_fields has been specified, and slug is not included, skip this step\n if not ('update_fields' in kwargs and 'slug' not in kwargs['update_fields']):\n # see if the slug has changed from the record in the db, in which case we need to\n # update url_path of self and all descendants\n old_record = Page.objects.get(id=self.id)\n if old_record.slug != self.slug:\n self.set_url_path(self.get_parent())\n update_descendant_url_paths = True\n old_url_path = old_record.url_path\n new_url_path = self.url_path\n\n result = super(Page, self).save(*args, **kwargs)\n\n if update_descendant_url_paths:\n self._update_descendant_url_paths(old_url_path, new_url_path)\n\n # Check if this is a root page of any sites and clear the 'wagtail_site_root_paths' key if so\n if Site.objects.filter(root_page=self).exists():\n cache.delete('wagtail_site_root_paths')\n\n # Log\n if is_new:\n cls = type(self)\n logger.info(\"Page created: \\\"%s\\\" id=%d content_type=%s.%s path=%s\", self.title, self.id, cls._meta.app_label, cls.__name__, self.url_path)\n\n return result","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.delete","text":"wagtail/wagtailcore/models.py::Page.delete\n def delete(self, *args, **kwargs):\n # Ensure that deletion always happens on an instance of Page, not a specific subclass. This\n # works around a bug in treebeard <= 3.0 where calling SpecificPage.delete() fails to delete\n # child pages that are not instances of SpecificPage\n if type(self) is Page:\n # this is a Page instance, so carry on as we were\n return super(Page, self).delete(*args, **kwargs)\n else:\n # retrieve an actual Page instance and delete that instead of self\n return Page.objects.get(id=self.id).delete(*args, **kwargs)","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.check","text":"wagtail/wagtailcore/models.py::Page.check\n @classmethod\n def check(cls, **kwargs):\n errors = super(Page, cls).check(**kwargs)\n\n # Check that foreign keys from pages are not configured to cascade\n # This is the default Django behaviour which must be explicitly overridden\n # to prevent pages disappearing unexpectedly and the tree being corrupted\n\n # get names of foreign keys pointing to parent classes (such as page_ptr)\n field_exceptions = [field.name\n for model in [cls] + list(cls._meta.get_parent_list())\n for field in model._meta.parents.values() if field]\n\n field_exceptions += ['content_type']\n\n\n for field in cls._meta.fields:\n if isinstance(field, models.ForeignKey) and field.name not in field_exceptions:\n if field.rel.on_delete == models.CASCADE:\n errors.append(\n checks.Warning(\n \"Field hasn't specified on_delete action\",\n hint=\"Set on_delete=models.SET_NULL and make sure the field is nullable.\",\n obj=field,\n id='wagtailcore.W001',\n )\n )\n\n return errors","label":0},{"cid":"wagtail/wagtailcore/utils.py::camelcase_to_underscore","text":"wagtail/wagtailcore/utils.py::camelcase_to_underscore\ndef camelcase_to_underscore(str):\n # http://djangosnippets.org/snippets/585/\n return re.sub('(((?<=[a-z])[A-Z])|([A-Z](?![A-Z]|$)))', '_\\\\1', str).lower().strip('_')","label":0},{"cid":"wagtail/wagtailcore/utils.py::resolve_model_string","text":"wagtail/wagtailcore/utils.py::resolve_model_string\ndef resolve_model_string(model_string, default_app=None):\n \"\"\"\n Resolve an 'app_label.model_name' string into an actual model class.\n If a model class is passed in, just return that.\n\n Raises a LookupError if a model can not be found, or ValueError if passed\n something that is neither a model or a string.\n \"\"\"\n if isinstance(model_string, string_types):\n try:\n app_label, model_name = model_string.split(\".\")\n except ValueError:\n if default_app is not None:\n # If we can't split, assume a model in current app\n app_label = default_app\n model_name = model_string\n else:\n raise ValueError(\"Can not resolve {0!r} into a model. Model names \"\n \"should be in the form app_label.model_name\".format(\n model_string), model_string)\n\n return apps.get_model(app_label, model_name)\n\n elif isinstance(model_string, type) and issubclass(model_string, Model):\n return model_string\n\n else:\n raise ValueError(\"Can not resolve {0!r} into a model\".format(model_string), model_string)","label":0},{"cid":"wagtail/wagtailcore/utils.py::escape_script","text":"wagtail/wagtailcore/utils.py::escape_script\ndef escape_script(text):\n \"\"\"\n Escape `</script>` tags in 'text' so that it can be placed within a `<script>` block without\n accidentally closing it. A '-' character will be inserted for each time it is escaped:\n `<-/script>`, `<--/script>` etc.\n \"\"\"\n return SCRIPT_RE.sub(r'<-\\1/script>', text)","label":0},{"cid":"wagtail/wagtailcore/utils.py::cautious_slugify","text":"wagtail/wagtailcore/utils.py::cautious_slugify\ndef cautious_slugify(value):\n \"\"\"\n Convert a string to ASCII exactly as Django's slugify does, with the exception\n that any non-ASCII alphanumeric characters (that cannot be ASCIIfied under Unicode\n normalisation) are escaped into codes like 'u0421' instead of being deleted entirely.\n\n This ensures that the result of slugifying e.g. Cyrillic text will not be an empty\n string, and can thus be safely used as an identifier (albeit not a human-readable one).\n \"\"\"\n value = force_text(value)\n\n # Normalize the string to decomposed unicode form. This causes accented Latin\n # characters to be split into 'base character' + 'accent modifier'; the latter will\n # be stripped out by the regexp, resulting in an ASCII-clean character that doesn't\n # need to be escaped\n value = unicodedata.normalize('NFKD', value)\n\n # Strip out characters that aren't letterlike, underscores or hyphens,\n # using the same regexp that slugify uses. This ensures that non-ASCII non-letters\n # (e.g. accent modifiers, fancy punctuation) get stripped rather than escaped\n value = SLUGIFY_RE.sub('', value)\n\n # Encode as ASCII, escaping non-ASCII characters with backslashreplace, then convert\n # back to a unicode string (which is what slugify expects)\n value = value.encode('ascii', 'backslashreplace').decode('ascii')\n\n # Pass to slugify to perform final conversion (whitespace stripping, applying\n # mark_safe); this will also strip out the backslashes from the 'backslashreplace'\n # conversion\n return slugify(value)","label":0},{"cid":"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::image","text":"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::image\n@register.tag(name=\"image\")\ndef image(parser, token):\n bits = token.split_contents()[1:]\n image_expr = parser.compile_filter(bits[0])\n filter_spec = bits[1]\n bits = bits[2:]\n\n if len(bits) == 2 and bits[0] == 'as':\n # token is of the form {% image self.photo max-320x200 as img %}\n return ImageNode(image_expr, filter_spec, output_var_name=bits[1])\n else:\n # token is of the form {% image self.photo max-320x200 %} - all additional tokens\n # should be kwargs, which become attributes\n attrs = {}\n for bit in bits:\n try:\n name, value = bit.split('=')\n except ValueError:\n raise template.TemplateSyntaxError(\"'image' tag should be of the form {% image self.photo max-320x200 [ custom-attr=\\\"value\\\" ... ] %} or {% image self.photo max-320x200 as img %}\")\n attrs[name] = parser.compile_filter(value) # setup to resolve context variables as value\n\n return ImageNode(image_expr, filter_spec, attrs=attrs)","label":0},{"cid":"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::ImageNode.__init__","text":"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::ImageNode.__init__\n def __init__(self, image_expr, filter_spec, output_var_name=None, attrs={}):\n self.image_expr = image_expr\n self.output_var_name = output_var_name\n self.attrs = attrs\n self.filter_spec = filter_spec","label":0},{"cid":"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::ImageNode.filter","text":"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::ImageNode.filter\n @cached_property\n def filter(self):\n _filter, _ = Filter.objects.get_or_create(spec=self.filter_spec)\n return _filter","label":0},{"cid":"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::ImageNode.render","text":"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::ImageNode.render\n def render(self, context):\n try:\n image = self.image_expr.resolve(context)\n except template.VariableDoesNotExist:\n return ''\n\n if not image:\n return ''\n\n try:\n rendition = image.get_rendition(self.filter)\n except SourceImageIOError:\n # It's fairly routine for people to pull down remote databases to their\n # local dev versions without retrieving the corresponding image files.\n # In such a case, we would get a SourceImageIOError at the point where we try to\n # create the resized version of a non-existent image. Since this is a\n # bit catastrophic for a missing image, we'll substitute a dummy\n # Rendition object so that we just output a broken link instead.\n Rendition = image.renditions.model # pick up any custom Image / Rendition classes that may be in use\n rendition = Rendition(image=image, width=0, height=0)\n rendition.file.name = 'not-found'\n\n if self.output_var_name:\n # return the rendition object in the given variable\n context[self.output_var_name] = rendition\n return ''\n else:\n # render the rendition's image tag now\n resolved_attrs = {}\n for key in self.attrs:\n resolved_attrs[key] = self.attrs[key].resolve(context)\n return rendition.img_tag(resolved_attrs)","label":0},{"cid":"wagtail/wagtailredirects/middleware.py::RedirectMiddleware.process_response","text":"wagtail/wagtailredirects/middleware.py::RedirectMiddleware.process_response\n def process_response(self, request, response):\n # No need to check for a redirect for non-404 responses.\n if response.status_code != 404:\n return response\n\n # Get the path\n path = models.Redirect.normalise_path(request.get_full_path())\n\n # Get the path without the query string or params\n path_without_query = urlparse(path)[2]\n\n # Find redirect\n try:\n redirect = models.Redirect.get_for_site(request.site).get(old_path=path)\n except models.Redirect.DoesNotExist:\n if path == path_without_query:\n # don't try again if we know we will get the same response\n return response\n\n try:\n redirect = models.Redirect.get_for_site(request.site).get(old_path=path_without_query)\n except models.Redirect.DoesNotExist:\n return response\n\n if redirect.is_permanent:\n return http.HttpResponsePermanentRedirect(redirect.link)\n else:\n return http.HttpResponseRedirect(redirect.link)","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.__init__","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.__init__\n def __init__(self, model):\n self.model = model","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_document_type","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_document_type\n def get_document_type(self):\n return self.model.indexed_get_content_type()","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_field_mapping","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_field_mapping\n def get_field_mapping(self, field):\n mapping = {'type': self.TYPE_MAP.get(field.get_type(self.model), 'string')}\n\n if isinstance(field, SearchField):\n if field.boost:\n mapping['boost'] = field.boost\n\n if field.partial_match:\n mapping['index_analyzer'] = 'edgengram_analyzer'\n\n mapping['include_in_all'] = True\n elif isinstance(field, FilterField):\n mapping['index'] = 'not_analyzed'\n mapping['include_in_all'] = False\n\n if 'es_extra' in field.kwargs:\n for key, value in field.kwargs['es_extra'].items():\n mapping[key] = value\n\n return field.get_index_name(self.model), mapping","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_mapping","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_mapping\n def get_mapping(self):\n # Make field list\n fields = {\n 'pk': dict(type='string', index='not_analyzed', store='yes', include_in_all=False),\n 'content_type': dict(type='string', index='not_analyzed', include_in_all=False),\n '_partials': dict(type='string', index_analyzer='edgengram_analyzer', include_in_all=False),\n }\n\n fields.update(dict(\n self.get_field_mapping(field) for field in self.model.get_search_fields()\n ))\n\n return {\n self.get_document_type(): {\n 'properties': fields,\n }\n }","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_document_id","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_document_id\n def get_document_id(self, obj):\n return obj.indexed_get_toplevel_content_type() + ':' + str(obj.pk)","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_document","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_document\n def get_document(self, obj):\n # Build document\n doc = dict(pk=str(obj.pk), content_type=self.model.indexed_get_content_type())\n partials = []\n for field in self.model.get_search_fields():\n value = field.get_value(obj)\n\n doc[field.get_index_name(self.model)] = value\n\n # Check if this field should be added into _partials\n if isinstance(field, SearchField) and field.partial_match:\n partials.append(value)\n\n # Add partials to document\n doc['_partials'] = partials\n\n return doc","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.__repr__","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.__repr__\n def __repr__(self):\n return '<ElasticSearchMapping: %s>' % (self.model.__name__, )","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery._process_lookup","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery._process_lookup\n def _process_lookup(self, field, lookup, value):\n # Get the name of the field in the index\n field_index_name = field.get_index_name(self.queryset.model)\n\n if lookup == 'exact':\n if value is None:\n return {\n 'missing': {\n 'field': field_index_name,\n }\n }\n else:\n return {\n 'term': {\n field_index_name: value,\n }\n }\n\n if lookup == 'isnull':\n if value:\n return {\n 'missing': {\n 'field': field_index_name,\n }\n }\n else:\n return {\n 'not': {\n 'missing': {\n 'field': field_index_name,\n }\n }\n }\n\n if lookup in ['startswith', 'prefix']:\n return {\n 'prefix': {\n field_index_name: value,\n }\n }\n\n if lookup in ['gt', 'gte', 'lt', 'lte']:\n return {\n 'range': {\n field_index_name: {\n lookup: value,\n }\n }\n }\n\n if lookup == 'range':\n lower, upper = value\n\n return {\n 'range': {\n field_index_name: {\n 'gte': lower,\n 'lte': upper,\n }\n }\n }\n\n if lookup == 'in':\n return {\n 'terms': {\n field_index_name: list(value),\n }\n }","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery._connect_filters","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery._connect_filters\n def _connect_filters(self, filters, connector, negated):\n if filters:\n if len(filters) == 1:\n filter_out = filters[0]\n else:\n filter_out = {\n connector.lower(): [\n fil for fil in filters if fil is not None\n ]\n }\n\n if negated:\n filter_out = {\n 'not': filter_out\n }\n\n return filter_out","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_inner_query","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_inner_query\n def get_inner_query(self):\n if self.query_string is not None:\n fields = self.fields or ['_all', '_partials']\n\n if len(fields) == 1:\n query = {\n 'match': {\n fields[0]: self.query_string,\n }\n }\n\n if self.operator != 'or':\n query['match']['operator'] = self.operator\n else:\n query = {\n 'multi_match': {\n 'query': self.query_string,\n 'fields': fields,\n }\n }\n\n if self.operator != 'or':\n query['multi_match']['operator'] = self.operator\n else:\n query = {\n 'match_all': {}\n }\n\n return query","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_filters","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_filters\n def get_filters(self):\n filters = []\n\n # Filter by content type\n filters.append({\n 'prefix': {\n 'content_type': self.queryset.model.indexed_get_content_type()\n }\n })\n\n # Apply filters from queryset\n queryset_filters = self._get_filters_from_queryset()\n if queryset_filters:\n filters.append(queryset_filters)\n\n return filters","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_query","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_query\n def get_query(self):\n inner_query = self.get_inner_query()\n filters = self.get_filters()\n\n if len(filters) == 1:\n return {\n 'filtered': {\n 'query': inner_query,\n 'filter': filters[0],\n }\n }\n elif len(filters) > 1:\n return {\n 'filtered': {\n 'query': inner_query,\n 'filter': {\n 'and': filters,\n }\n }\n }\n else:\n return inner_query","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_sort","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_sort\n def get_sort(self):\n # Ordering by relevance is the default in Elasticsearch\n if self.order_by_relevance:\n return\n\n # Get queryset and make sure its ordered\n if self.queryset.ordered:\n order_by_fields = self.queryset.query.order_by\n sort = []\n\n for order_by_field in order_by_fields:\n reverse = False\n field_name = order_by_field\n\n if order_by_field.startswith('-'):\n reverse = True\n field_name = order_by_field[1:]\n\n field = self._get_filterable_field(field_name)\n field_index_name = field.get_index_name(self.queryset.model)\n\n sort.append({\n field_index_name: 'desc' if reverse else 'asc'\n })\n\n return sort\n\n else:\n # Order by pk field\n return ['pk']","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.__repr__","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.__repr__\n def __repr__(self):\n return json.dumps(self.get_query())","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.to_es","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.to_es\n def to_es(self):\n warnings.warn(\n \"The ElasticSearchQuery.to_es() method is deprecated. \"\n \"Please use the ElasticSearchQuery.get_query() method instead.\",\n RemovedInWagtail14Warning, stacklevel=2)\n\n return self.get_query()","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchResults._get_es_body","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchResults._get_es_body\n def _get_es_body(self, for_count=False):\n # If to_es has been overridden, call it and raise a deprecation warning\n if isinstance(self.query, ElasticSearchQuery) and six.get_method_function(self.query.to_es) != ElasticSearchQuery.to_es:\n warnings.warn(\n \"The .to_es() method on Elasticsearch query classes is deprecated. \"\n \"Please rename {class_name}.to_es() to {class_name}.get_query()\".format(\n class_name=self.query.__class__.__name__\n ),\n RemovedInWagtail14Warning, stacklevel=2)\n\n body = {\n 'query': self.query.to_es(),\n }\n else:\n body = {\n 'query': self.query.get_query()\n }\n\n if not for_count:\n sort = self.query.get_sort()\n\n if sort is not None:\n body['sort'] = sort\n\n return body","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchResults._do_search","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchResults._do_search\n def _do_search(self):\n # Params for elasticsearch query\n params = dict(\n index=self.backend.es_index,\n body=self._get_es_body(),\n _source=False,\n fields='pk',\n from_=self.start,\n )\n\n # Add size if set\n if self.stop is not None:\n params['size'] = self.stop - self.start\n\n # Send to ElasticSearch\n hits = self.backend.es.search(**params)\n\n # Get pks from results\n pks = [hit['fields']['pk'][0] for hit in hits['hits']['hits']]\n\n # Initialise results dictionary\n results = dict((str(pk), None) for pk in pks)\n\n # Find objects in database and add them to dict\n queryset = self.query.queryset.filter(pk__in=pks)\n for obj in queryset:\n results[str(obj.pk)] = obj\n\n # Return results in order given by ElasticSearch\n return [results[str(pk)] for pk in pks if results[str(pk)]]","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchResults._do_count","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchResults._do_count\n def _do_count(self):\n # Get count\n hit_count = self.backend.es.count(\n index=self.backend.es_index,\n body=self._get_es_body(for_count=True),\n )['count']\n\n # Add limits\n hit_count -= self.start\n if self.stop is not None:\n hit_count = min(hit_count, self.stop - self.start)\n\n return max(hit_count, 0)","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.__init__","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.__init__\n def __init__(self, es, index_name):\n self.es = es\n self.index_name = index_name","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.reset_index","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.reset_index\n def reset_index(self):\n # Delete old index\n try:\n self.es.indices.delete(self.index_name)\n except NotFoundError:\n pass\n\n # Create new index\n self.es.indices.create(self.index_name, INDEX_SETTINGS)","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.start","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.start\n def start(self):\n # Reset the index\n self.reset_index()","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.add_model","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.add_model\n def add_model(self, model):\n # Get mapping\n mapping = ElasticSearchMapping(model)\n\n # Put mapping\n self.es.indices.put_mapping(index=self.index_name, doc_type=mapping.get_document_type(), body=mapping.get_mapping())","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.add_items","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.add_items\n def add_items(self, model, obj_list):\n if not class_is_indexed(model):\n return\n\n # Get mapping\n mapping = ElasticSearchMapping(model)\n doc_type = mapping.get_document_type()\n\n # Create list of actions\n actions = []\n for obj in obj_list:\n # Create the action\n action = {\n '_index': self.index_name,\n '_type': doc_type,\n '_id': mapping.get_document_id(obj),\n }\n action.update(mapping.get_document(obj))\n actions.append(action)\n\n # Run the actions\n bulk(self.es, actions)","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.finish","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.finish\n def finish(self):\n # Refresh index\n self.es.indices.refresh(self.index_name)","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.__init__","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.__init__\n def __init__(self, es, alias_name):\n self.es = es\n self.alias_name = alias_name\n self.index_name = alias_name + '_' + get_random_string(7).lower()","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.reset_index","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.reset_index\n def reset_index(self):\n # Delete old index using the alias\n # This should delete both the alias and the index\n try:\n self.es.indices.delete(self.alias_name)\n except NotFoundError:\n pass\n\n # Create new index\n self.es.indices.create(self.index_name, INDEX_SETTINGS)\n\n # Create a new alias\n self.es.indices.put_alias(name=self.alias_name, index=self.index_name)","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.start","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.start\n def start(self):\n # Create the new index\n self.es.indices.create(self.index_name, INDEX_SETTINGS)","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.finish","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.finish\n def finish(self):\n # Refresh the new index\n self.es.indices.refresh(self.index_name)\n\n # Create the alias if it doesnt exist yet\n if not self.es.indices.exists_alias(self.alias_name):\n # Make sure there isn't currently an index that clashes with alias_name\n # This can happen when the atomic rebuilder is first enabled\n try:\n self.es.indices.delete(self.alias_name)\n except NotFoundError:\n pass\n\n # Create the alias\n self.es.indices.put_alias(name=self.alias_name, index=self.index_name)\n\n else:\n # Alias already exists, update it and delete old index\n\n # Find index that alias currently points to, so we can delete it later\n old_index = set(self.es.indices.get_alias(name=self.alias_name).keys()) - {self.index_name}\n\n # Update alias to point to new index\n self.es.indices.put_alias(name=self.alias_name, index=self.index_name)\n\n # Delete old index\n # es.indices.get_alias can return multiple indices. Delete them all\n if old_index:\n try:\n self.es.indices.delete(','.join(old_index))\n except NotFoundError:\n pass","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.__init__","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.__init__\n def __init__(self, params):\n super(ElasticSearch, self).__init__(params)\n\n # Get settings\n self.es_hosts = params.pop('HOSTS', None)\n self.es_index = params.pop('INDEX', 'wagtail')\n self.es_timeout = params.pop('TIMEOUT', 10)\n\n if params.pop('ATOMIC_REBUILD', False):\n self.rebuilder_class = ElasticSearchAtomicIndexRebuilder\n else:\n self.rebuilder_class = ElasticSearchIndexRebuilder\n\n # If HOSTS is not set, convert URLS setting to HOSTS\n es_urls = params.pop('URLS', ['http://localhost:9200'])\n if self.es_hosts is None:\n self.es_hosts = []\n\n for url in es_urls:\n parsed_url = urlparse(url)\n\n use_ssl = parsed_url.scheme == 'https'\n port = parsed_url.port or (443 if use_ssl else 80)\n\n http_auth = None\n if parsed_url.username is not None and parsed_url.password is not None:\n http_auth = (parsed_url.username, parsed_url.password)\n\n self.es_hosts.append({\n 'host': parsed_url.hostname,\n 'port': port,\n 'url_prefix': parsed_url.path,\n 'use_ssl': use_ssl,\n 'http_auth': http_auth,\n })\n\n # Get ElasticSearch interface\n # Any remaining params are passed into the ElasticSearch constructor\n self.es = Elasticsearch(\n hosts=self.es_hosts,\n timeout=self.es_timeout,\n **params)","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.get_rebuilder","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.get_rebuilder\n def get_rebuilder(self):\n return self.rebuilder_class(self.es, self.es_index)","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.reset_index","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.reset_index\n def reset_index(self):\n # Use the rebuilder to reset the index\n self.get_rebuilder().reset_index()","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.add_type","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.add_type\n def add_type(self, model):\n # Get mapping\n mapping = ElasticSearchMapping(model)\n\n # Put mapping\n self.es.indices.put_mapping(index=self.es_index, doc_type=mapping.get_document_type(), body=mapping.get_mapping())","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.refresh_index","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.refresh_index\n def refresh_index(self):\n self.es.indices.refresh(self.es_index)","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.add","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.add\n def add(self, obj):\n # Make sure the object can be indexed\n if not class_is_indexed(obj.__class__):\n return\n\n # Get mapping\n mapping = ElasticSearchMapping(obj.__class__)\n\n # Add document to index\n self.es.index(self.es_index, mapping.get_document_type(), mapping.get_document(obj), id=mapping.get_document_id(obj))","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.add_bulk","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.add_bulk\n def add_bulk(self, model, obj_list):\n if not class_is_indexed(model):\n return\n\n # Get mapping\n mapping = ElasticSearchMapping(model)\n doc_type = mapping.get_document_type()\n\n # Create list of actions\n actions = []\n for obj in obj_list:\n # Create the action\n action = {\n '_index': self.es_index,\n '_type': doc_type,\n '_id': mapping.get_document_id(obj),\n }\n action.update(mapping.get_document(obj))\n actions.append(action)\n\n # Run the actions\n bulk(self.es, actions)","label":0},{"cid":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.delete","text":"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.delete\n def delete(self, obj):\n # Make sure the object can be indexed\n if not class_is_indexed(obj.__class__):\n return\n\n # Get mapping\n mapping = ElasticSearchMapping(obj.__class__)\n\n # Delete document\n try:\n self.es.delete(\n self.es_index,\n mapping.get_document_type(),\n mapping.get_document_id(obj),\n )\n except NotFoundError:\n pass # Document doesn't exist, ignore this exception","label":0},{"cid":"wagtail/wagtailsearch/views/frontend.py::search","text":"wagtail/wagtailsearch/views/frontend.py::search\ndef search(\n request,\n template=None,\n template_ajax=None,\n results_per_page=10,\n use_json=False,\n json_attrs=['title', 'url'],\n show_unpublished=False,\n search_title_only=False,\n extra_filters={},\n path=None):\n\n # Get default templates\n if template is None:\n if hasattr(settings, 'WAGTAILSEARCH_RESULTS_TEMPLATE'):\n template = settings.WAGTAILSEARCH_RESULTS_TEMPLATE\n else:\n template = 'wagtailsearch/search_results.html'\n\n if template_ajax is None:\n if hasattr(settings, 'WAGTAILSEARCH_RESULTS_TEMPLATE_AJAX'):\n template_ajax = settings.WAGTAILSEARCH_RESULTS_TEMPLATE_AJAX\n else:\n template_ajax = template\n\n # Get query string and page from GET paramters\n query_string = request.GET.get('q', '')\n page = request.GET.get('page', request.GET.get('p', 1))\n\n # Search\n if query_string != '':\n search_results = models.Page.search(\n query_string,\n show_unpublished=show_unpublished,\n search_title_only=search_title_only,\n extra_filters=extra_filters,\n path=path if path else request.site.root_page.path\n )\n\n # Get query object\n query = Query.get(query_string)\n\n # Add hit\n query.add_hit()\n\n # Pagination\n paginator = Paginator(search_results, results_per_page)\n try:\n search_results = paginator.page(page)\n except PageNotAnInteger:\n search_results = paginator.page(1)\n except EmptyPage:\n search_results = paginator.page(paginator.num_pages)\n else:\n query = None\n search_results = None\n\n if use_json:\n # Return a json response\n if search_results:\n search_results_json = []\n for result in search_results:\n result_specific = result.specific\n\n ","label":1}],"string":"[\n {\n \"cid\": \"wagtail/wagtailadmin/taggable.py::TagSearchable.get_tags\",\n \"text\": \"wagtail/wagtailadmin/taggable.py::TagSearchable.get_tags\\n @property\\n def get_tags(self):\\n return ' '.join([tag.name for tag in self.tags.all()])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/taggable.py::TagSearchable.get_indexed_objects\",\n \"text\": \"wagtail/wagtailadmin/taggable.py::TagSearchable.get_indexed_objects\\n @classmethod\\n def get_indexed_objects(cls):\\n return super(TagSearchable, cls).get_indexed_objects().prefetch_related('tagged_items__tag')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/taggable.py::TagSearchable.search\",\n \"text\": \"wagtail/wagtailadmin/taggable.py::TagSearchable.search\\n @classmethod\\n def search(cls, q, results_per_page=None, page=1, prefetch_tags=False, filters={}):\\n warnings.warn(\\n \\\"The {class_name}.search() method is deprecated. \\\"\\n \\\"Please use the {class_name}.objects.search() method instead.\\\".format(class_name=cls.__name__),\\n RemovedInWagtail14Warning, stacklevel=2)\\n\\n results = cls.objects.all()\\n\\n if prefetch_tags:\\n results = results.prefetch_related('tagged_items__tag')\\n\\n if filters:\\n results = results.filter(**filters)\\n\\n results = results.search(q)\\n\\n # If results_per_page is set, return a paginator\\n if results_per_page is not None:\\n paginator = Paginator(results, results_per_page)\\n try:\\n return paginator.page(page)\\n except PageNotAnInteger:\\n return paginator.page(1)\\n except EmptyPage:\\n return paginator.page(paginator.num_pages)\\n else:\\n return results\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/taggable.py::TagSearchable.popular_tags\",\n \"text\": \"wagtail/wagtailadmin/taggable.py::TagSearchable.popular_tags\\n @classmethod\\n def popular_tags(cls):\\n content_type = ContentType.objects.get_for_model(cls)\\n return Tag.objects.filter(\\n taggit_taggeditem_items__content_type=content_type\\n ).annotate(\\n item_count=Count('taggit_taggeditem_items')\\n ).order_by('-item_count')[:10]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::SiteManager.get_by_natural_key\",\n \"text\": \"wagtail/wagtailcore/models.py::SiteManager.get_by_natural_key\\n def get_by_natural_key(self, hostname, port):\\n return self.get(hostname=hostname, port=port)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.natural_key\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.natural_key\\n def natural_key(self):\\n return (self.hostname, self.port)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.__str__\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.__str__\\n def __str__(self):\\n return self.hostname + (\\\"\\\" if self.port == 80 else (\\\":%d\\\" % self.port)) + (\\\" [default]\\\" if self.is_default_site else \\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.find_for_request\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.find_for_request\\n @staticmethod\\n def find_for_request(request):\\n \\\"\\\"\\\"\\n Find the site object responsible for responding to this HTTP\\n request object. Try:\\n\\n * unique hostname first\\n * then hostname and port\\n * if there is no matching hostname at all, or no matching\\n hostname:port combination, fall back to the unique default site,\\n or raise an exception\\n\\n NB this means that high-numbered ports on an extant hostname may\\n still be routed to a different hostname which is set as the default\\n \\\"\\\"\\\"\\n try:\\n hostname = request.META['HTTP_HOST'].split(':')[0] # KeyError here goes to the final except clause\\n try:\\n # find a Site matching this specific hostname\\n return Site.objects.get(hostname=hostname) # Site.DoesNotExist here goes to the final except clause\\n except Site.MultipleObjectsReturned:\\n # as there were more than one, try matching by port too\\n port = request.META['SERVER_PORT'] # KeyError here goes to the final except clause\\n return Site.objects.get(hostname=hostname, port=int(port)) # Site.DoesNotExist here goes to the final except clause\\n except (Site.DoesNotExist, KeyError):\\n # If no matching site exists, or request does not specify an HTTP_HOST (which\\n # will often be the case for the Django test client), look for a catch-all Site.\\n # If that fails, let the Site.DoesNotExist propagate back to the caller\\n return Site.objects.get(is_default_site=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.root_url\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.root_url\\n @property\\n def root_url(self):\\n if self.port == 80:\\n return 'http://%s' % self.hostname\\n elif self.port == 443:\\n return 'https://%s' % self.hostname\\n else:\\n return 'http://%s:%d' % (self.hostname, self.port)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.clean_fields\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.clean_fields\\n def clean_fields(self, exclude=None):\\n super(Site, self).clean_fields(exclude)\\n # Only one site can have the is_default_site flag set\\n try:\\n default = Site.objects.get(is_default_site=True)\\n except Site.DoesNotExist:\\n pass\\n except Site.MultipleObjectsReturned:\\n raise\\n else:\\n if self.is_default_site and self.pk != default.pk:\\n raise ValidationError(\\n {'is_default_site': [\\n _(\\\"%(hostname)s is already configured as the default site. You must unset that before you can save this site as default.\\\")\\n % {'hostname': default.hostname}\\n ]}\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.get_site_root_paths\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.get_site_root_paths\\n @staticmethod\\n def get_site_root_paths():\\n \\\"\\\"\\\"\\n Return a list of (root_path, root_url) tuples, most specific path first -\\n used to translate url_paths into actual URLs with hostnames\\n \\\"\\\"\\\"\\n result = cache.get('wagtail_site_root_paths')\\n\\n if result is None:\\n result = [\\n (site.id, site.root_page.url_path, site.root_url)\\n for site in Site.objects.select_related('root_page').order_by('-root_page__url_path')\\n ]\\n cache.set('wagtail_site_root_paths', result, 3600)\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::clear_site_root_paths_on_save\",\n \"text\": \"wagtail/wagtailcore/models.py::clear_site_root_paths_on_save\\n@receiver(post_save, sender=Site)\\ndef clear_site_root_paths_on_save(sender, instance, **kwargs):\\n cache.delete('wagtail_site_root_paths')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::clear_site_root_paths_on_delete\",\n \"text\": \"wagtail/wagtailcore/models.py::clear_site_root_paths_on_delete\\n@receiver(post_delete, sender=Site)\\ndef clear_site_root_paths_on_delete(sender, instance, **kwargs):\\n cache.delete('wagtail_site_root_paths')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::get_page_types\",\n \"text\": \"wagtail/wagtailcore/models.py::get_page_types\\ndef get_page_types():\\n global _PAGE_CONTENT_TYPES\\n if len(_PAGE_CONTENT_TYPES) != len(PAGE_MODEL_CLASSES):\\n _PAGE_CONTENT_TYPES = [\\n ContentType.objects.get_for_model(cls) for cls in PAGE_MODEL_CLASSES\\n ]\\n return _PAGE_CONTENT_TYPES\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.get_queryset\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.get_queryset\\n def get_queryset(self):\\n return PageQuerySet(self.model).order_by('path')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.live\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.live\\n def live(self):\\n return self.get_queryset().live()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_live\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_live\\n def not_live(self):\\n return self.get_queryset().not_live()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.in_menu\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.in_menu\\n def in_menu(self):\\n return self.get_queryset().in_menu()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_in_menu\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_in_menu\\n def not_in_menu(self):\\n return self.get_queryset().not_in_menu()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.page\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.page\\n def page(self, other):\\n return self.get_queryset().page(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_page\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_page\\n def not_page(self, other):\\n return self.get_queryset().not_page(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.descendant_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.descendant_of\\n def descendant_of(self, other, inclusive=False):\\n return self.get_queryset().descendant_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_descendant_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_descendant_of\\n def not_descendant_of(self, other, inclusive=False):\\n return self.get_queryset().not_descendant_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.child_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.child_of\\n def child_of(self, other):\\n return self.get_queryset().child_of(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_child_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_child_of\\n def not_child_of(self, other):\\n return self.get_queryset().not_child_of(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.ancestor_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.ancestor_of\\n def ancestor_of(self, other, inclusive=False):\\n return self.get_queryset().ancestor_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_ancestor_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_ancestor_of\\n def not_ancestor_of(self, other, inclusive=False):\\n return self.get_queryset().not_ancestor_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.parent_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.parent_of\\n def parent_of(self, other):\\n return self.get_queryset().parent_of(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_parent_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_parent_of\\n def not_parent_of(self, other):\\n return self.get_queryset().not_parent_of(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.sibling_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.sibling_of\\n def sibling_of(self, other, inclusive=False):\\n return self.get_queryset().sibling_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_sibling_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_sibling_of\\n def not_sibling_of(self, other, inclusive=False):\\n return self.get_queryset().not_sibling_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.type\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.type\\n def type(self, model):\\n return self.get_queryset().type(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_type\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_type\\n def not_type(self, model):\\n return self.get_queryset().not_type(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.public\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.public\\n def public(self):\\n return self.get_queryset().public()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_public\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_public\\n def not_public(self):\\n return self.get_queryset().not_public()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.search\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.search\\n def search(self, query_string, fields=None,\\n operator=None, order_by_relevance=True, backend='default'):\\n return self.get_queryset().search(query_string, fields=fields,\\n operator=operator, order_by_relevance=order_by_relevance, backend=backend)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.specific\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.specific\\n def specific(self):\\n return self.get_queryset().specific()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageBase.__init__\",\n \"text\": \"wagtail/wagtailcore/models.py::PageBase.__init__\\n def __init__(cls, name, bases, dct):\\n super(PageBase, cls).__init__(name, bases, dct)\\n\\n if cls._deferred:\\n # this is an internal class built for Django's deferred-attribute mechanism;\\n # don't proceed with all this page type registration stuff\\n return\\n\\n # Add page manager\\n PageManager().contribute_to_class(cls, 'objects')\\n\\n if 'template' not in dct:\\n # Define a default template path derived from the app name and model name\\n cls.template = \\\"%s/%s.html\\\" % (cls._meta.app_label, camelcase_to_underscore(name))\\n\\n if 'ajax_template' not in dct:\\n cls.ajax_template = None\\n\\n cls._clean_subpage_types = None # to be filled in on first call to cls.clean_subpage_types\\n cls._clean_parent_page_types = None # to be filled in on first call to cls.clean_parent_page_types\\n\\n # All pages should be creatable unless explicitly set otherwise.\\n # This attribute is not inheritable.\\n if 'is_creatable' not in dct:\\n if 'is_abstract' in dct:\\n warnings.warn(\\n \\\"The is_abstract flag is deprecated - use is_creatable instead.\\\",\\n RemovedInWagtail13Warning)\\n cls.is_creatable = not dct['is_abstract']\\n else:\\n cls.is_creatable = not cls._meta.abstract\\n\\n if cls.is_creatable:\\n # register this type in the list of page content types\\n PAGE_MODEL_CLASSES.append(cls)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.__init__\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.__init__\\n def __init__(self, *args, **kwargs):\\n super(Page, self).__init__(*args, **kwargs)\\n if not self.id and not self.content_type_id:\\n # this model is being newly created rather than retrieved from the db;\\n # set content type to correctly represent the model class that this was\\n # created as\\n self.content_type = ContentType.objects.get_for_model(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.__str__\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.__str__\\n def __str__(self):\\n return self.title\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.set_url_path\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.set_url_path\\n def set_url_path(self, parent):\\n \\\"\\\"\\\"\\n Populate the url_path field based on this page's slug and the specified parent page.\\n (We pass a parent in here, rather than retrieving it via get_parent, so that we can give\\n new unsaved pages a meaningful URL when previewing them; at that point the page has not\\n been assigned a position in the tree, as far as treebeard is concerned.\\n \\\"\\\"\\\"\\n if parent:\\n self.url_path = parent.url_path + self.slug + '/'\\n else:\\n # a page without a parent is the tree root, which always has a url_path of '/'\\n self.url_path = '/'\\n\\n return self.url_path\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.save\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.save\\n @transaction.atomic # ensure that changes are only committed when we have updated all descendant URL paths, to preserve consistency\\n def save(self, *args, **kwargs):\\n update_descendant_url_paths = False\\n is_new = self.id is None\\n\\n if is_new:\\n # we are creating a record. If we're doing things properly, this should happen\\n # through a treebeard method like add_child, in which case the 'path' field\\n # has been set and so we can safely call get_parent\\n self.set_url_path(self.get_parent())\\n else:\\n # Check that we are committing the slug to the database\\n # Basically: If update_fields has been specified, and slug is not included, skip this step\\n if not ('update_fields' in kwargs and 'slug' not in kwargs['update_fields']):\\n # see if the slug has changed from the record in the db, in which case we need to\\n # update url_path of self and all descendants\\n old_record = Page.objects.get(id=self.id)\\n if old_record.slug != self.slug:\\n self.set_url_path(self.get_parent())\\n update_descendant_url_paths = True\\n old_url_path = old_record.url_path\\n new_url_path = self.url_path\\n\\n result = super(Page, self).save(*args, **kwargs)\\n\\n if update_descendant_url_paths:\\n self._update_descendant_url_paths(old_url_path, new_url_path)\\n\\n # Check if this is a root page of any sites and clear the 'wagtail_site_root_paths' key if so\\n if Site.objects.filter(root_page=self).exists():\\n cache.delete('wagtail_site_root_paths')\\n\\n # Log\\n if is_new:\\n cls = type(self)\\n logger.info(\\\"Page created: \\\\\\\"%s\\\\\\\" id=%d content_type=%s.%s path=%s\\\", self.title, self.id, cls._meta.app_label, cls.__name__, self.url_path)\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.delete\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.delete\\n def delete(self, *args, **kwargs):\\n # Ensure that deletion always happens on an instance of Page, not a specific subclass. This\\n # works around a bug in treebeard <= 3.0 where calling SpecificPage.delete() fails to delete\\n # child pages that are not instances of SpecificPage\\n if type(self) is Page:\\n # this is a Page instance, so carry on as we were\\n return super(Page, self).delete(*args, **kwargs)\\n else:\\n # retrieve an actual Page instance and delete that instead of self\\n return Page.objects.get(id=self.id).delete(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.check\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.check\\n @classmethod\\n def check(cls, **kwargs):\\n errors = super(Page, cls).check(**kwargs)\\n\\n # Check that foreign keys from pages are not configured to cascade\\n # This is the default Django behaviour which must be explicitly overridden\\n # to prevent pages disappearing unexpectedly and the tree being corrupted\\n\\n # get names of foreign keys pointing to parent classes (such as page_ptr)\\n field_exceptions = [field.name\\n for model in [cls] + list(cls._meta.get_parent_list())\\n for field in model._meta.parents.values() if field]\\n\\n field_exceptions += ['content_type']\\n\\n\\n for field in cls._meta.fields:\\n if isinstance(field, models.ForeignKey) and field.name not in field_exceptions:\\n if field.rel.on_delete == models.CASCADE:\\n errors.append(\\n checks.Warning(\\n \\\"Field hasn't specified on_delete action\\\",\\n hint=\\\"Set on_delete=models.SET_NULL and make sure the field is nullable.\\\",\\n obj=field,\\n id='wagtailcore.W001',\\n )\\n )\\n\\n return errors\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/utils.py::camelcase_to_underscore\",\n \"text\": \"wagtail/wagtailcore/utils.py::camelcase_to_underscore\\ndef camelcase_to_underscore(str):\\n # http://djangosnippets.org/snippets/585/\\n return re.sub('(((?<=[a-z])[A-Z])|([A-Z](?![A-Z]|$)))', '_\\\\\\\\1', str).lower().strip('_')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/utils.py::resolve_model_string\",\n \"text\": \"wagtail/wagtailcore/utils.py::resolve_model_string\\ndef resolve_model_string(model_string, default_app=None):\\n \\\"\\\"\\\"\\n Resolve an 'app_label.model_name' string into an actual model class.\\n If a model class is passed in, just return that.\\n\\n Raises a LookupError if a model can not be found, or ValueError if passed\\n something that is neither a model or a string.\\n \\\"\\\"\\\"\\n if isinstance(model_string, string_types):\\n try:\\n app_label, model_name = model_string.split(\\\".\\\")\\n except ValueError:\\n if default_app is not None:\\n # If we can't split, assume a model in current app\\n app_label = default_app\\n model_name = model_string\\n else:\\n raise ValueError(\\\"Can not resolve {0!r} into a model. Model names \\\"\\n \\\"should be in the form app_label.model_name\\\".format(\\n model_string), model_string)\\n\\n return apps.get_model(app_label, model_name)\\n\\n elif isinstance(model_string, type) and issubclass(model_string, Model):\\n return model_string\\n\\n else:\\n raise ValueError(\\\"Can not resolve {0!r} into a model\\\".format(model_string), model_string)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/utils.py::escape_script\",\n \"text\": \"wagtail/wagtailcore/utils.py::escape_script\\ndef escape_script(text):\\n \\\"\\\"\\\"\\n Escape `</script>` tags in 'text' so that it can be placed within a `<script>` block without\\n accidentally closing it. A '-' character will be inserted for each time it is escaped:\\n `<-/script>`, `<--/script>` etc.\\n \\\"\\\"\\\"\\n return SCRIPT_RE.sub(r'<-\\\\1/script>', text)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/utils.py::cautious_slugify\",\n \"text\": \"wagtail/wagtailcore/utils.py::cautious_slugify\\ndef cautious_slugify(value):\\n \\\"\\\"\\\"\\n Convert a string to ASCII exactly as Django's slugify does, with the exception\\n that any non-ASCII alphanumeric characters (that cannot be ASCIIfied under Unicode\\n normalisation) are escaped into codes like 'u0421' instead of being deleted entirely.\\n\\n This ensures that the result of slugifying e.g. Cyrillic text will not be an empty\\n string, and can thus be safely used as an identifier (albeit not a human-readable one).\\n \\\"\\\"\\\"\\n value = force_text(value)\\n\\n # Normalize the string to decomposed unicode form. This causes accented Latin\\n # characters to be split into 'base character' + 'accent modifier'; the latter will\\n # be stripped out by the regexp, resulting in an ASCII-clean character that doesn't\\n # need to be escaped\\n value = unicodedata.normalize('NFKD', value)\\n\\n # Strip out characters that aren't letterlike, underscores or hyphens,\\n # using the same regexp that slugify uses. This ensures that non-ASCII non-letters\\n # (e.g. accent modifiers, fancy punctuation) get stripped rather than escaped\\n value = SLUGIFY_RE.sub('', value)\\n\\n # Encode as ASCII, escaping non-ASCII characters with backslashreplace, then convert\\n # back to a unicode string (which is what slugify expects)\\n value = value.encode('ascii', 'backslashreplace').decode('ascii')\\n\\n # Pass to slugify to perform final conversion (whitespace stripping, applying\\n # mark_safe); this will also strip out the backslashes from the 'backslashreplace'\\n # conversion\\n return slugify(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::image\",\n \"text\": \"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::image\\n@register.tag(name=\\\"image\\\")\\ndef image(parser, token):\\n bits = token.split_contents()[1:]\\n image_expr = parser.compile_filter(bits[0])\\n filter_spec = bits[1]\\n bits = bits[2:]\\n\\n if len(bits) == 2 and bits[0] == 'as':\\n # token is of the form {% image self.photo max-320x200 as img %}\\n return ImageNode(image_expr, filter_spec, output_var_name=bits[1])\\n else:\\n # token is of the form {% image self.photo max-320x200 %} - all additional tokens\\n # should be kwargs, which become attributes\\n attrs = {}\\n for bit in bits:\\n try:\\n name, value = bit.split('=')\\n except ValueError:\\n raise template.TemplateSyntaxError(\\\"'image' tag should be of the form {% image self.photo max-320x200 [ custom-attr=\\\\\\\"value\\\\\\\" ... ] %} or {% image self.photo max-320x200 as img %}\\\")\\n attrs[name] = parser.compile_filter(value) # setup to resolve context variables as value\\n\\n return ImageNode(image_expr, filter_spec, attrs=attrs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::ImageNode.__init__\",\n \"text\": \"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::ImageNode.__init__\\n def __init__(self, image_expr, filter_spec, output_var_name=None, attrs={}):\\n self.image_expr = image_expr\\n self.output_var_name = output_var_name\\n self.attrs = attrs\\n self.filter_spec = filter_spec\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::ImageNode.filter\",\n \"text\": \"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::ImageNode.filter\\n @cached_property\\n def filter(self):\\n _filter, _ = Filter.objects.get_or_create(spec=self.filter_spec)\\n return _filter\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::ImageNode.render\",\n \"text\": \"wagtail/wagtailimages/templatetags/wagtailimages_tags.py::ImageNode.render\\n def render(self, context):\\n try:\\n image = self.image_expr.resolve(context)\\n except template.VariableDoesNotExist:\\n return ''\\n\\n if not image:\\n return ''\\n\\n try:\\n rendition = image.get_rendition(self.filter)\\n except SourceImageIOError:\\n # It's fairly routine for people to pull down remote databases to their\\n # local dev versions without retrieving the corresponding image files.\\n # In such a case, we would get a SourceImageIOError at the point where we try to\\n # create the resized version of a non-existent image. Since this is a\\n # bit catastrophic for a missing image, we'll substitute a dummy\\n # Rendition object so that we just output a broken link instead.\\n Rendition = image.renditions.model # pick up any custom Image / Rendition classes that may be in use\\n rendition = Rendition(image=image, width=0, height=0)\\n rendition.file.name = 'not-found'\\n\\n if self.output_var_name:\\n # return the rendition object in the given variable\\n context[self.output_var_name] = rendition\\n return ''\\n else:\\n # render the rendition's image tag now\\n resolved_attrs = {}\\n for key in self.attrs:\\n resolved_attrs[key] = self.attrs[key].resolve(context)\\n return rendition.img_tag(resolved_attrs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailredirects/middleware.py::RedirectMiddleware.process_response\",\n \"text\": \"wagtail/wagtailredirects/middleware.py::RedirectMiddleware.process_response\\n def process_response(self, request, response):\\n # No need to check for a redirect for non-404 responses.\\n if response.status_code != 404:\\n return response\\n\\n # Get the path\\n path = models.Redirect.normalise_path(request.get_full_path())\\n\\n # Get the path without the query string or params\\n path_without_query = urlparse(path)[2]\\n\\n # Find redirect\\n try:\\n redirect = models.Redirect.get_for_site(request.site).get(old_path=path)\\n except models.Redirect.DoesNotExist:\\n if path == path_without_query:\\n # don't try again if we know we will get the same response\\n return response\\n\\n try:\\n redirect = models.Redirect.get_for_site(request.site).get(old_path=path_without_query)\\n except models.Redirect.DoesNotExist:\\n return response\\n\\n if redirect.is_permanent:\\n return http.HttpResponsePermanentRedirect(redirect.link)\\n else:\\n return http.HttpResponseRedirect(redirect.link)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.__init__\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.__init__\\n def __init__(self, model):\\n self.model = model\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_document_type\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_document_type\\n def get_document_type(self):\\n return self.model.indexed_get_content_type()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_field_mapping\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_field_mapping\\n def get_field_mapping(self, field):\\n mapping = {'type': self.TYPE_MAP.get(field.get_type(self.model), 'string')}\\n\\n if isinstance(field, SearchField):\\n if field.boost:\\n mapping['boost'] = field.boost\\n\\n if field.partial_match:\\n mapping['index_analyzer'] = 'edgengram_analyzer'\\n\\n mapping['include_in_all'] = True\\n elif isinstance(field, FilterField):\\n mapping['index'] = 'not_analyzed'\\n mapping['include_in_all'] = False\\n\\n if 'es_extra' in field.kwargs:\\n for key, value in field.kwargs['es_extra'].items():\\n mapping[key] = value\\n\\n return field.get_index_name(self.model), mapping\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_mapping\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_mapping\\n def get_mapping(self):\\n # Make field list\\n fields = {\\n 'pk': dict(type='string', index='not_analyzed', store='yes', include_in_all=False),\\n 'content_type': dict(type='string', index='not_analyzed', include_in_all=False),\\n '_partials': dict(type='string', index_analyzer='edgengram_analyzer', include_in_all=False),\\n }\\n\\n fields.update(dict(\\n self.get_field_mapping(field) for field in self.model.get_search_fields()\\n ))\\n\\n return {\\n self.get_document_type(): {\\n 'properties': fields,\\n }\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_document_id\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_document_id\\n def get_document_id(self, obj):\\n return obj.indexed_get_toplevel_content_type() + ':' + str(obj.pk)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_document\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.get_document\\n def get_document(self, obj):\\n # Build document\\n doc = dict(pk=str(obj.pk), content_type=self.model.indexed_get_content_type())\\n partials = []\\n for field in self.model.get_search_fields():\\n value = field.get_value(obj)\\n\\n doc[field.get_index_name(self.model)] = value\\n\\n # Check if this field should be added into _partials\\n if isinstance(field, SearchField) and field.partial_match:\\n partials.append(value)\\n\\n # Add partials to document\\n doc['_partials'] = partials\\n\\n return doc\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.__repr__\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchMapping.__repr__\\n def __repr__(self):\\n return '<ElasticSearchMapping: %s>' % (self.model.__name__, )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery._process_lookup\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery._process_lookup\\n def _process_lookup(self, field, lookup, value):\\n # Get the name of the field in the index\\n field_index_name = field.get_index_name(self.queryset.model)\\n\\n if lookup == 'exact':\\n if value is None:\\n return {\\n 'missing': {\\n 'field': field_index_name,\\n }\\n }\\n else:\\n return {\\n 'term': {\\n field_index_name: value,\\n }\\n }\\n\\n if lookup == 'isnull':\\n if value:\\n return {\\n 'missing': {\\n 'field': field_index_name,\\n }\\n }\\n else:\\n return {\\n 'not': {\\n 'missing': {\\n 'field': field_index_name,\\n }\\n }\\n }\\n\\n if lookup in ['startswith', 'prefix']:\\n return {\\n 'prefix': {\\n field_index_name: value,\\n }\\n }\\n\\n if lookup in ['gt', 'gte', 'lt', 'lte']:\\n return {\\n 'range': {\\n field_index_name: {\\n lookup: value,\\n }\\n }\\n }\\n\\n if lookup == 'range':\\n lower, upper = value\\n\\n return {\\n 'range': {\\n field_index_name: {\\n 'gte': lower,\\n 'lte': upper,\\n }\\n }\\n }\\n\\n if lookup == 'in':\\n return {\\n 'terms': {\\n field_index_name: list(value),\\n }\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery._connect_filters\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery._connect_filters\\n def _connect_filters(self, filters, connector, negated):\\n if filters:\\n if len(filters) == 1:\\n filter_out = filters[0]\\n else:\\n filter_out = {\\n connector.lower(): [\\n fil for fil in filters if fil is not None\\n ]\\n }\\n\\n if negated:\\n filter_out = {\\n 'not': filter_out\\n }\\n\\n return filter_out\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_inner_query\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_inner_query\\n def get_inner_query(self):\\n if self.query_string is not None:\\n fields = self.fields or ['_all', '_partials']\\n\\n if len(fields) == 1:\\n query = {\\n 'match': {\\n fields[0]: self.query_string,\\n }\\n }\\n\\n if self.operator != 'or':\\n query['match']['operator'] = self.operator\\n else:\\n query = {\\n 'multi_match': {\\n 'query': self.query_string,\\n 'fields': fields,\\n }\\n }\\n\\n if self.operator != 'or':\\n query['multi_match']['operator'] = self.operator\\n else:\\n query = {\\n 'match_all': {}\\n }\\n\\n return query\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_filters\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_filters\\n def get_filters(self):\\n filters = []\\n\\n # Filter by content type\\n filters.append({\\n 'prefix': {\\n 'content_type': self.queryset.model.indexed_get_content_type()\\n }\\n })\\n\\n # Apply filters from queryset\\n queryset_filters = self._get_filters_from_queryset()\\n if queryset_filters:\\n filters.append(queryset_filters)\\n\\n return filters\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_query\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_query\\n def get_query(self):\\n inner_query = self.get_inner_query()\\n filters = self.get_filters()\\n\\n if len(filters) == 1:\\n return {\\n 'filtered': {\\n 'query': inner_query,\\n 'filter': filters[0],\\n }\\n }\\n elif len(filters) > 1:\\n return {\\n 'filtered': {\\n 'query': inner_query,\\n 'filter': {\\n 'and': filters,\\n }\\n }\\n }\\n else:\\n return inner_query\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_sort\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.get_sort\\n def get_sort(self):\\n # Ordering by relevance is the default in Elasticsearch\\n if self.order_by_relevance:\\n return\\n\\n # Get queryset and make sure its ordered\\n if self.queryset.ordered:\\n order_by_fields = self.queryset.query.order_by\\n sort = []\\n\\n for order_by_field in order_by_fields:\\n reverse = False\\n field_name = order_by_field\\n\\n if order_by_field.startswith('-'):\\n reverse = True\\n field_name = order_by_field[1:]\\n\\n field = self._get_filterable_field(field_name)\\n field_index_name = field.get_index_name(self.queryset.model)\\n\\n sort.append({\\n field_index_name: 'desc' if reverse else 'asc'\\n })\\n\\n return sort\\n\\n else:\\n # Order by pk field\\n return ['pk']\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.__repr__\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.__repr__\\n def __repr__(self):\\n return json.dumps(self.get_query())\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.to_es\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchQuery.to_es\\n def to_es(self):\\n warnings.warn(\\n \\\"The ElasticSearchQuery.to_es() method is deprecated. \\\"\\n \\\"Please use the ElasticSearchQuery.get_query() method instead.\\\",\\n RemovedInWagtail14Warning, stacklevel=2)\\n\\n return self.get_query()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchResults._get_es_body\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchResults._get_es_body\\n def _get_es_body(self, for_count=False):\\n # If to_es has been overridden, call it and raise a deprecation warning\\n if isinstance(self.query, ElasticSearchQuery) and six.get_method_function(self.query.to_es) != ElasticSearchQuery.to_es:\\n warnings.warn(\\n \\\"The .to_es() method on Elasticsearch query classes is deprecated. \\\"\\n \\\"Please rename {class_name}.to_es() to {class_name}.get_query()\\\".format(\\n class_name=self.query.__class__.__name__\\n ),\\n RemovedInWagtail14Warning, stacklevel=2)\\n\\n body = {\\n 'query': self.query.to_es(),\\n }\\n else:\\n body = {\\n 'query': self.query.get_query()\\n }\\n\\n if not for_count:\\n sort = self.query.get_sort()\\n\\n if sort is not None:\\n body['sort'] = sort\\n\\n return body\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchResults._do_search\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchResults._do_search\\n def _do_search(self):\\n # Params for elasticsearch query\\n params = dict(\\n index=self.backend.es_index,\\n body=self._get_es_body(),\\n _source=False,\\n fields='pk',\\n from_=self.start,\\n )\\n\\n # Add size if set\\n if self.stop is not None:\\n params['size'] = self.stop - self.start\\n\\n # Send to ElasticSearch\\n hits = self.backend.es.search(**params)\\n\\n # Get pks from results\\n pks = [hit['fields']['pk'][0] for hit in hits['hits']['hits']]\\n\\n # Initialise results dictionary\\n results = dict((str(pk), None) for pk in pks)\\n\\n # Find objects in database and add them to dict\\n queryset = self.query.queryset.filter(pk__in=pks)\\n for obj in queryset:\\n results[str(obj.pk)] = obj\\n\\n # Return results in order given by ElasticSearch\\n return [results[str(pk)] for pk in pks if results[str(pk)]]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchResults._do_count\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchResults._do_count\\n def _do_count(self):\\n # Get count\\n hit_count = self.backend.es.count(\\n index=self.backend.es_index,\\n body=self._get_es_body(for_count=True),\\n )['count']\\n\\n # Add limits\\n hit_count -= self.start\\n if self.stop is not None:\\n hit_count = min(hit_count, self.stop - self.start)\\n\\n return max(hit_count, 0)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.__init__\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.__init__\\n def __init__(self, es, index_name):\\n self.es = es\\n self.index_name = index_name\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.reset_index\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.reset_index\\n def reset_index(self):\\n # Delete old index\\n try:\\n self.es.indices.delete(self.index_name)\\n except NotFoundError:\\n pass\\n\\n # Create new index\\n self.es.indices.create(self.index_name, INDEX_SETTINGS)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.start\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.start\\n def start(self):\\n # Reset the index\\n self.reset_index()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.add_model\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.add_model\\n def add_model(self, model):\\n # Get mapping\\n mapping = ElasticSearchMapping(model)\\n\\n # Put mapping\\n self.es.indices.put_mapping(index=self.index_name, doc_type=mapping.get_document_type(), body=mapping.get_mapping())\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.add_items\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.add_items\\n def add_items(self, model, obj_list):\\n if not class_is_indexed(model):\\n return\\n\\n # Get mapping\\n mapping = ElasticSearchMapping(model)\\n doc_type = mapping.get_document_type()\\n\\n # Create list of actions\\n actions = []\\n for obj in obj_list:\\n # Create the action\\n action = {\\n '_index': self.index_name,\\n '_type': doc_type,\\n '_id': mapping.get_document_id(obj),\\n }\\n action.update(mapping.get_document(obj))\\n actions.append(action)\\n\\n # Run the actions\\n bulk(self.es, actions)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.finish\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchIndexRebuilder.finish\\n def finish(self):\\n # Refresh index\\n self.es.indices.refresh(self.index_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.__init__\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.__init__\\n def __init__(self, es, alias_name):\\n self.es = es\\n self.alias_name = alias_name\\n self.index_name = alias_name + '_' + get_random_string(7).lower()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.reset_index\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.reset_index\\n def reset_index(self):\\n # Delete old index using the alias\\n # This should delete both the alias and the index\\n try:\\n self.es.indices.delete(self.alias_name)\\n except NotFoundError:\\n pass\\n\\n # Create new index\\n self.es.indices.create(self.index_name, INDEX_SETTINGS)\\n\\n # Create a new alias\\n self.es.indices.put_alias(name=self.alias_name, index=self.index_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.start\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.start\\n def start(self):\\n # Create the new index\\n self.es.indices.create(self.index_name, INDEX_SETTINGS)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.finish\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearchAtomicIndexRebuilder.finish\\n def finish(self):\\n # Refresh the new index\\n self.es.indices.refresh(self.index_name)\\n\\n # Create the alias if it doesnt exist yet\\n if not self.es.indices.exists_alias(self.alias_name):\\n # Make sure there isn't currently an index that clashes with alias_name\\n # This can happen when the atomic rebuilder is first enabled\\n try:\\n self.es.indices.delete(self.alias_name)\\n except NotFoundError:\\n pass\\n\\n # Create the alias\\n self.es.indices.put_alias(name=self.alias_name, index=self.index_name)\\n\\n else:\\n # Alias already exists, update it and delete old index\\n\\n # Find index that alias currently points to, so we can delete it later\\n old_index = set(self.es.indices.get_alias(name=self.alias_name).keys()) - {self.index_name}\\n\\n # Update alias to point to new index\\n self.es.indices.put_alias(name=self.alias_name, index=self.index_name)\\n\\n # Delete old index\\n # es.indices.get_alias can return multiple indices. Delete them all\\n if old_index:\\n try:\\n self.es.indices.delete(','.join(old_index))\\n except NotFoundError:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.__init__\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.__init__\\n def __init__(self, params):\\n super(ElasticSearch, self).__init__(params)\\n\\n # Get settings\\n self.es_hosts = params.pop('HOSTS', None)\\n self.es_index = params.pop('INDEX', 'wagtail')\\n self.es_timeout = params.pop('TIMEOUT', 10)\\n\\n if params.pop('ATOMIC_REBUILD', False):\\n self.rebuilder_class = ElasticSearchAtomicIndexRebuilder\\n else:\\n self.rebuilder_class = ElasticSearchIndexRebuilder\\n\\n # If HOSTS is not set, convert URLS setting to HOSTS\\n es_urls = params.pop('URLS', ['http://localhost:9200'])\\n if self.es_hosts is None:\\n self.es_hosts = []\\n\\n for url in es_urls:\\n parsed_url = urlparse(url)\\n\\n use_ssl = parsed_url.scheme == 'https'\\n port = parsed_url.port or (443 if use_ssl else 80)\\n\\n http_auth = None\\n if parsed_url.username is not None and parsed_url.password is not None:\\n http_auth = (parsed_url.username, parsed_url.password)\\n\\n self.es_hosts.append({\\n 'host': parsed_url.hostname,\\n 'port': port,\\n 'url_prefix': parsed_url.path,\\n 'use_ssl': use_ssl,\\n 'http_auth': http_auth,\\n })\\n\\n # Get ElasticSearch interface\\n # Any remaining params are passed into the ElasticSearch constructor\\n self.es = Elasticsearch(\\n hosts=self.es_hosts,\\n timeout=self.es_timeout,\\n **params)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.get_rebuilder\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.get_rebuilder\\n def get_rebuilder(self):\\n return self.rebuilder_class(self.es, self.es_index)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.reset_index\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.reset_index\\n def reset_index(self):\\n # Use the rebuilder to reset the index\\n self.get_rebuilder().reset_index()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.add_type\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.add_type\\n def add_type(self, model):\\n # Get mapping\\n mapping = ElasticSearchMapping(model)\\n\\n # Put mapping\\n self.es.indices.put_mapping(index=self.es_index, doc_type=mapping.get_document_type(), body=mapping.get_mapping())\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.refresh_index\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.refresh_index\\n def refresh_index(self):\\n self.es.indices.refresh(self.es_index)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.add\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.add\\n def add(self, obj):\\n # Make sure the object can be indexed\\n if not class_is_indexed(obj.__class__):\\n return\\n\\n # Get mapping\\n mapping = ElasticSearchMapping(obj.__class__)\\n\\n # Add document to index\\n self.es.index(self.es_index, mapping.get_document_type(), mapping.get_document(obj), id=mapping.get_document_id(obj))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.add_bulk\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.add_bulk\\n def add_bulk(self, model, obj_list):\\n if not class_is_indexed(model):\\n return\\n\\n # Get mapping\\n mapping = ElasticSearchMapping(model)\\n doc_type = mapping.get_document_type()\\n\\n # Create list of actions\\n actions = []\\n for obj in obj_list:\\n # Create the action\\n action = {\\n '_index': self.es_index,\\n '_type': doc_type,\\n '_id': mapping.get_document_id(obj),\\n }\\n action.update(mapping.get_document(obj))\\n actions.append(action)\\n\\n # Run the actions\\n bulk(self.es, actions)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.delete\",\n \"text\": \"wagtail/wagtailsearch/backends/elasticsearch.py::ElasticSearch.delete\\n def delete(self, obj):\\n # Make sure the object can be indexed\\n if not class_is_indexed(obj.__class__):\\n return\\n\\n # Get mapping\\n mapping = ElasticSearchMapping(obj.__class__)\\n\\n # Delete document\\n try:\\n self.es.delete(\\n self.es_index,\\n mapping.get_document_type(),\\n mapping.get_document_id(obj),\\n )\\n except NotFoundError:\\n pass # Document doesn't exist, ignore this exception\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/views/frontend.py::search\",\n \"text\": \"wagtail/wagtailsearch/views/frontend.py::search\\ndef search(\\n request,\\n template=None,\\n template_ajax=None,\\n results_per_page=10,\\n use_json=False,\\n json_attrs=['title', 'url'],\\n show_unpublished=False,\\n search_title_only=False,\\n extra_filters={},\\n path=None):\\n\\n # Get default templates\\n if template is None:\\n if hasattr(settings, 'WAGTAILSEARCH_RESULTS_TEMPLATE'):\\n template = settings.WAGTAILSEARCH_RESULTS_TEMPLATE\\n else:\\n template = 'wagtailsearch/search_results.html'\\n\\n if template_ajax is None:\\n if hasattr(settings, 'WAGTAILSEARCH_RESULTS_TEMPLATE_AJAX'):\\n template_ajax = settings.WAGTAILSEARCH_RESULTS_TEMPLATE_AJAX\\n else:\\n template_ajax = template\\n\\n # Get query string and page from GET paramters\\n query_string = request.GET.get('q', '')\\n page = request.GET.get('page', request.GET.get('p', 1))\\n\\n # Search\\n if query_string != '':\\n search_results = models.Page.search(\\n query_string,\\n show_unpublished=show_unpublished,\\n search_title_only=search_title_only,\\n extra_filters=extra_filters,\\n path=path if path else request.site.root_page.path\\n )\\n\\n # Get query object\\n query = Query.get(query_string)\\n\\n # Add hit\\n query.add_hit()\\n\\n # Pagination\\n paginator = Paginator(search_results, results_per_page)\\n try:\\n search_results = paginator.page(page)\\n except PageNotAnInteger:\\n search_results = paginator.page(1)\\n except EmptyPage:\\n search_results = paginator.page(paginator.num_pages)\\n else:\\n query = None\\n search_results = None\\n\\n if use_json:\\n # Return a json response\\n if search_results:\\n search_results_json = []\\n for result in search_results:\\n result_specific = result.specific\\n\\n \",\n \"label\": 1\n }\n]","isConversational":false}}},{"rowIdx":251,"cells":{"instance_id":{"kind":"string","value":"docker__compose-246"},"repo":{"kind":"string","value":"docker/compose"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"fig up and fig run should start dependencies when you specify a specific service\n`web` links to `db`.\n\n```\n$ fig up web\n$ fig run web ./manage.py shell\n```\n\nThese things should start the `db` service.\n\n"},"gold_files":{"kind":"list like","value":["fig/cli/main.py","fig/project.py","fig/service.py"],"string":"[\n \"fig/cli/main.py\",\n \"fig/project.py\",\n \"fig/service.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["fig/cli/main.py::TopLevelCommand.run","fig/cli/main.py::TopLevelCommand.up","fig/project.py::<module>","fig/project.py::Project.from_dicts","fig/project.py::Project.get_services","fig/project.py::Project.up","fig/service.py::<module>"],"string":"[\n \"fig/cli/main.py::TopLevelCommand.run\",\n \"fig/cli/main.py::TopLevelCommand.up\",\n \"fig/project.py::<module>\",\n \"fig/project.py::Project.from_dicts\",\n \"fig/project.py::Project.get_services\",\n \"fig/project.py::Project.up\",\n \"fig/service.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"fig/__init__.py","text":"fig/__init__.py","label":0},{"cid":"fig/cli/__init__.py","text":"fig/cli/__init__.py","label":0},{"cid":"fig/cli/command.py","text":"fig/cli/command.py\nclass Command\n def __init__(self)\n def dispatch(self, *args, **kwargs)\n def perform_command(self, options, *args, **kwargs)\n def client(self)\n def project(self)\n def project_name(self)\n def formatter(self)\n def check_yaml_filename(self)","label":0},{"cid":"fig/cli/errors.py","text":"fig/cli/errors.py\nclass UserError\n def __init__(self, msg)\n def __unicode__(self)\nclass DockerNotFoundMac\n def __init__(self)\nclass DockerNotFoundUbuntu\n def __init__(self)\nclass DockerNotFoundGeneric\n def __init__(self)\nclass ConnectionErrorDockerOSX\n def __init__(self)\nclass ConnectionErrorGeneric\n def __init__(self, url)\nclass FigFileNotFound\n def __init__(self, filename)","label":0},{"cid":"fig/cli/log_printer.py","text":"fig/cli/log_printer.py\nclass LogPrinter\n def __init__(self, containers, attach_params=None)\n def run(self)\n def _calculate_prefix_width(self, containers) # Calculate the maximum width of container names so we can make the log\n def _make_log_generators(self)\n def _make_log_generator(self, container, color_fn)\n def _generate_prefix(self, container) # Generate the prefix for a log line without colour\n def _attach(self, container)","label":0},{"cid":"fig/cli/main.py","text":"fig/cli/main.py\ndef main()\ndef parse_doc_section(name, source)\nclass TopLevelCommand # Punctual, lightweight development environments using Docker.\n def docopt_options(self)\n def build(self, options) # Build or rebuild services.\n def help(self, options) # Get help on a command.\n def kill(self, options) # Force stop service containers.\n def logs(self, options) # View output from containers.\n def ps(self, options) # List containers.\n def rm(self, options) # Remove stopped service containers.\n def run(self, options) # Run a one-off command on a service.\n def scale(self, options) # Set number of containers to run for a service.\n def start(self, options) # Start existing containers.\n def stop(self, options) # Stop running containers without removing them.\n def up(self, options) # Build, (re)create, start and attach to containers for a service.\n def _attach_to_container(self, container_id, raw=False)\ndef list_containers(containers)","label":1},{"cid":"fig/container.py","text":"fig/container.py\nclass Container # Represents a Docker container, constructed from the output of\n def __init__(self, client, dictionary, has_been_inspected=False)\n def from_ps(cls, client, dictionary, **kwargs) # Construct a container object from the output of GET /containers/json.\n def from_id(cls, client, id)\n def create(cls, client, **options)\n def id(self)\n def image(self)\n def short_id(self)\n def name(self)\n def name_without_project(self)\n def number(self)\n def human_readable_ports(self)\n def human_readable_state(self)\n def human_readable_command(self)\n def environment(self)\n def is_running(self)\n def start(self, **options)\n def stop(self, **options)\n def kill(self)\n def remove(self, **options)\n def inspect_if_not_inspected(self)\n def wait(self)\n def logs(self, *args, **kwargs)\n def inspect(self)\n def links(self)\n def attach(self, *args, **kwargs)\n def attach_socket(self, **kwargs)\n def __repr__(self)\n def __eq__(self, other)","label":0},{"cid":"fig/packages/__init__.py","text":"fig/packages/__init__.py","label":0},{"cid":"fig/packages/docker/__init__.py","text":"fig/packages/docker/__init__.py","label":0},{"cid":"fig/packages/docker/auth/auth.py","text":"fig/packages/docker/auth/auth.py\ndef swap_protocol(url)\ndef expand_registry_url(hostname)\ndef resolve_repository_name(repo_name)\ndef resolve_authconfig(authconfig, registry=None) # Return the authentication data from the given auth configuration for a\ndef encode_auth(auth_info)\ndef decode_auth(auth)\ndef encode_header(auth)\ndef encode_full_header(auth) # Returns the given auth block encoded for the X-Registry-Config header.\ndef load_config(root=None) # Loads authentication data from a Docker configuration file in the given","label":0},{"cid":"fig/packages/docker/client.py","text":"fig/packages/docker/client.py\nclass Client\n def __init__(self, base_url=None, version=DEFAULT_DOCKER_API_VERSION, timeout=DEFAULT_TIMEOUT_SECONDS)\n def _set_request_timeout(self, kwargs) # Prepare the kwargs for an HTTP request by inserting the timeout\n def _post(self, url, **kwargs)\n def _get(self, url, **kwargs)\n def _delete(self, url, **kwargs)\n def _url(self, path)\n def _raise_for_status(self, response, explanation=None) # Raises stored :class:`APIError`, if one occurred.\n def _result(self, response, json=False, binary=False)\n def _container_config(self, image, command, hostname=None, user=None, detach=False, stdin_open=False, tty=False, mem_limit=0, ports=None, environment=None, dns=None, volumes=None, volumes_from=None, network_disabled=False, entrypoint=None, cpu_shares=None, working_dir=None, domainname=None)\n def _post_json(self, url, data, **kwargs)\n def _attach_params(self, override=None)\n def _attach_websocket(self, container, params=None)\n def _create_websocket_connection(self, url)\n def _get_raw_response_socket(self, response)\n def _stream_helper(self, response) # Generator for data coming from a chunked-encoded HTTP response.\n def _multiplexed_buffer_helper(self, response) # A generator of multiplexed data blocks read from a buffered\n def _multiplexed_socket_stream_helper(self, response) # A generator of multiplexed data blocks coming from a response\n def recvall(socket, size)\n def attach(self, container, stdout=True, stderr=True, stream=False, logs=False)\n def attach_socket(self, container, params=None, ws=False)\n def build(self, path=None, tag=None, quiet=False, fileobj=None, nocache=False, rm=False, stream=False, timeout=None)\n def commit(self, container, repository=None, tag=None, message=None, author=None, conf=None)\n def containers(self, quiet=False, all=False, trunc=True, latest=False, since=None, before=None, limit=-1)\n def copy(self, container, resource)\n def create_container(self, image, command=None, hostname=None, user=None, detach=False, stdin_open=False, tty=False, mem_limit=0, ports=None, environment=None, dns=None, volumes=None, volumes_from=None, network_disabled=False, name=None, entrypoint=None, cpu_shares=None, working_dir=None, domainname=None)\n def create_container_from_config(self, config, name=None)\n def diff(self, container)\n def events(self)\n def export(self, container)\n def history(self, image)\n def images(self, name=None, quiet=False, all=False, viz=False)\n def import_image(self, src=None, repository=None, tag=None, image=None)\n def info(self)\n def insert(self, image, url, path)\n def inspect_container(self, container)\n def inspect_image(self, image_id)\n def kill(self, container, signal=None)\n def login(self, username, password=None, email=None, registry=None, reauth=False)\n def logs(self, container, stdout=True, stderr=True, stream=False)\n def port(self, container, private_port","label":0},{"cid":"fig/packages/docker/errors.py","text":"fig/packages/docker/errors.py\nclass APIError\n def __init__(self, message, response, explanation=None)\n def __str__(self)\n def is_client_error(self)\n def is_server_error(self)\nclass DockerException\nclass InvalidRepository\nclass InvalidConfigFile","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py","text":"fig/packages/docker/unixconn/unixconn.py\nclass UnixHTTPConnection\n def __init__(self, base_url, unix_socket, timeout=60)\n def connect(self)\n def _extract_path(self, url)\n def request(self, method, url, **kwargs)\nclass UnixHTTPConnectionPool\n def __init__(self, base_url, socket_path, timeout=60)\n def _new_conn(self)\nclass UnixAdapter\n def __init__(self, base_url, timeout=60)\n def get_connection(self, socket_path, proxies=None)","label":0},{"cid":"fig/packages/docker/utils/utils.py","text":"fig/packages/docker/utils/utils.py\ndef mkbuildcontext(dockerfile)\ndef tar(path)\ndef compare_version(v1, v2) # Compare docker versions\ndef ping(url)\ndef _convert_port_binding(binding)\ndef convert_port_bindings(port_bindings)\ndef parse_repository_tag(repo)","label":0},{"cid":"fig/packages/six.py","text":"fig/packages/six.py\ndef _add_doc(func, doc) # Add documentation to a function.\ndef _import_module(name) # Import module, returning the module after the last dot.\nclass _LazyDescr\n def __init__(self, name)\n def __get__(self, obj, tp)\nclass MovedModule\n def __init__(self, name, old, new=None)\n def _resolve(self)\nclass MovedAttribute\n def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None)\n def _resolve(self)\nclass _MovedItems # Lazy loading of moved objects\ndef add_move(move) # Add an item to six.moves.\ndef remove_move(name) # Remove item from six.moves.\ndef iterkeys(d, **kw) # Return an iterator over the keys of a dictionary.\ndef itervalues(d, **kw) # Return an iterator over the values of a dictionary.\ndef iteritems(d, **kw) # Return an iterator over the (key, value) pairs of a dictionary.\ndef iterlists(d, **kw) # Return an iterator over the (key, [values]) pairs of a dictionary.\ndef with_metaclass(meta, base=object) # Create a base class with a metaclass.","label":0},{"cid":"fig/project.py","text":"fig/project.py\ndef sort_service_dicts(services)\n def visit(n)\nclass Project # A collection of services.\n def __init__(self, name, services, client)\n def from_dicts(cls, name, service_dicts, client) # Construct a ServiceCollection from a list of dicts representing services.\n def from_config(cls, name, config, client)\n def get_service(self, name) # Retrieve a service by name. Raises NoSuchService\n def get_services(self, service_names=None) # Returns a list of this project's services filtered\n def start(self, service_names=None, **options)\n def stop(self, service_names=None, **options)\n def kill(self, service_names=None, **options)\n def build(self, service_names=None, **options)\n def up(self, service_names=None)\n def remove_stopped(self, service_names=None, **options)\n def containers(self, service_names=None, *args, **kwargs)\nclass NoSuchService\n def __init__(self, name)\n def __str__(self)\nclass ConfigurationError\n def __init__(self, msg)\n def __str__(self)\nclass DependencyError","label":1},{"cid":"fig/service.py","text":"fig/service.py\nfrom __future__ import unicode_literals\nfrom __future__ import absolute_import\nfrom .packages.docker.errors import APIError\nimport logging\nimport re\nimport os\nimport sys\nimport json\nfrom .container import Container\n\nlog = logging.getLogger(__name__)\n\n\nDOCKER_CONFIG_KEYS = ['image', 'command', 'hostname', 'user', 'detach', 'stdin_open', 'tty', 'mem_limit', 'ports', 'environment', 'dns', 'volumes', 'volumes_from', 'entrypoint', 'privileged']\nDOCKER_CONFIG_HINTS = {\n 'link' : 'links',\n 'port' : 'ports',\n 'privilege' : 'privileged',\n 'priviliged': 'privileged',\n 'privilige' : 'privileged',\n 'volume' : 'volumes',\n}\n\n\nclass BuildError(Exception):\n def __init__(self, service, reason):\n self.service = service\n self.reason = reason\n\n\nclass CannotBeScaledError(Exception):\n pass\n\n\nclass ConfigError(ValueError):\n pass\n\n\nclass Service(object):\n def __init__(self, name, client=None, project='default', links=[], **options):\n if not re.match('^[a-zA-Z0-9]+$', name):\n raise ConfigError('Invalid name: %s' % name)\n if not re.match('^[a-zA-Z0-9]+$', project):\n raise ConfigError('Invalid project: %s' % project)\n if 'image' in options and 'build' in options:\n raise ConfigError('Service %s has both an image and build path specified. A service can either be built to image or use an existing image, not both.' % name)\n\n supported_options = DOCKER_CONFIG_KEYS + ['build', 'expose']\n\n for k in options:\n if k not in supported_options:\n msg = \"Unsupported config option for %s service: '%s'\" % (name, k)\n if k in DOCKER_CONFIG_HINTS:\n msg += \" (did you mean '%s'?)\" % DOCKER_CONFIG_HINTS[k]\n raise ConfigError(msg)\n\n self.name = name\n self.client = client\n self.project = project\n self.links = links or []\n self.options = options\n\n def containers(self, stopped=False, one_off=False):\n l = []\n for container in self.client.containers(all=stopped):\n name = get_container_name(container)\n if not name or not is_valid_name(name, one_off):\n continue\n project, name, number = parse_name(name)\n if project == self.project and name == self.name:\n l.append(Container.from_ps(self.client, container))\n return l\n\n def start(self, **options):\n for c in self.containers(stopped=True):\n if not c.is_running:\n log.info(\"Starting %s...\" % c.name)\n self.start_container(c, **options)\n\n def stop(self, **options):\n for c in self.containers():\n log.info(\"Stopping %s...\" % c.name)\n c.stop(**options)\n\n def kill(self, **options):\n for c in self.containers():\n log.info(\"Killing %s...\" % c.name)\n c.kill(**options)\n\n def scale(self, desired_num):\n \"\"\"\n ","label":1},{"cid":"setup.py","text":"setup.py\ndef read(*parts)\ndef find_version(*file_paths)","label":0}],"string":"[\n {\n \"cid\": \"fig/__init__.py\",\n \"text\": \"fig/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/__init__.py\",\n \"text\": \"fig/cli/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py\",\n \"text\": \"fig/cli/command.py\\nclass Command\\n def __init__(self)\\n def dispatch(self, *args, **kwargs)\\n def perform_command(self, options, *args, **kwargs)\\n def client(self)\\n def project(self)\\n def project_name(self)\\n def formatter(self)\\n def check_yaml_filename(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py\",\n \"text\": \"fig/cli/errors.py\\nclass UserError\\n def __init__(self, msg)\\n def __unicode__(self)\\nclass DockerNotFoundMac\\n def __init__(self)\\nclass DockerNotFoundUbuntu\\n def __init__(self)\\nclass DockerNotFoundGeneric\\n def __init__(self)\\nclass ConnectionErrorDockerOSX\\n def __init__(self)\\nclass ConnectionErrorGeneric\\n def __init__(self, url)\\nclass FigFileNotFound\\n def __init__(self, filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py\",\n \"text\": \"fig/cli/log_printer.py\\nclass LogPrinter\\n def __init__(self, containers, attach_params=None)\\n def run(self)\\n def _calculate_prefix_width(self, containers) # Calculate the maximum width of container names so we can make the log\\n def _make_log_generators(self)\\n def _make_log_generator(self, container, color_fn)\\n def _generate_prefix(self, container) # Generate the prefix for a log line without colour\\n def _attach(self, container)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py\",\n \"text\": \"fig/cli/main.py\\ndef main()\\ndef parse_doc_section(name, source)\\nclass TopLevelCommand # Punctual, lightweight development environments using Docker.\\n def docopt_options(self)\\n def build(self, options) # Build or rebuild services.\\n def help(self, options) # Get help on a command.\\n def kill(self, options) # Force stop service containers.\\n def logs(self, options) # View output from containers.\\n def ps(self, options) # List containers.\\n def rm(self, options) # Remove stopped service containers.\\n def run(self, options) # Run a one-off command on a service.\\n def scale(self, options) # Set number of containers to run for a service.\\n def start(self, options) # Start existing containers.\\n def stop(self, options) # Stop running containers without removing them.\\n def up(self, options) # Build, (re)create, start and attach to containers for a service.\\n def _attach_to_container(self, container_id, raw=False)\\ndef list_containers(containers)\",\n \"label\": 1\n },\n {\n \"cid\": \"fig/container.py\",\n \"text\": \"fig/container.py\\nclass Container # Represents a Docker container, constructed from the output of\\n def __init__(self, client, dictionary, has_been_inspected=False)\\n def from_ps(cls, client, dictionary, **kwargs) # Construct a container object from the output of GET /containers/json.\\n def from_id(cls, client, id)\\n def create(cls, client, **options)\\n def id(self)\\n def image(self)\\n def short_id(self)\\n def name(self)\\n def name_without_project(self)\\n def number(self)\\n def human_readable_ports(self)\\n def human_readable_state(self)\\n def human_readable_command(self)\\n def environment(self)\\n def is_running(self)\\n def start(self, **options)\\n def stop(self, **options)\\n def kill(self)\\n def remove(self, **options)\\n def inspect_if_not_inspected(self)\\n def wait(self)\\n def logs(self, *args, **kwargs)\\n def inspect(self)\\n def links(self)\\n def attach(self, *args, **kwargs)\\n def attach_socket(self, **kwargs)\\n def __repr__(self)\\n def __eq__(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/__init__.py\",\n \"text\": \"fig/packages/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/__init__.py\",\n \"text\": \"fig/packages/docker/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py\",\n \"text\": \"fig/packages/docker/auth/auth.py\\ndef swap_protocol(url)\\ndef expand_registry_url(hostname)\\ndef resolve_repository_name(repo_name)\\ndef resolve_authconfig(authconfig, registry=None) # Return the authentication data from the given auth configuration for a\\ndef encode_auth(auth_info)\\ndef decode_auth(auth)\\ndef encode_header(auth)\\ndef encode_full_header(auth) # Returns the given auth block encoded for the X-Registry-Config header.\\ndef load_config(root=None) # Loads authentication data from a Docker configuration file in the given\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py\",\n \"text\": \"fig/packages/docker/client.py\\nclass Client\\n def __init__(self, base_url=None, version=DEFAULT_DOCKER_API_VERSION, timeout=DEFAULT_TIMEOUT_SECONDS)\\n def _set_request_timeout(self, kwargs) # Prepare the kwargs for an HTTP request by inserting the timeout\\n def _post(self, url, **kwargs)\\n def _get(self, url, **kwargs)\\n def _delete(self, url, **kwargs)\\n def _url(self, path)\\n def _raise_for_status(self, response, explanation=None) # Raises stored :class:`APIError`, if one occurred.\\n def _result(self, response, json=False, binary=False)\\n def _container_config(self, image, command, hostname=None, user=None, detach=False, stdin_open=False, tty=False, mem_limit=0, ports=None, environment=None, dns=None, volumes=None, volumes_from=None, network_disabled=False, entrypoint=None, cpu_shares=None, working_dir=None, domainname=None)\\n def _post_json(self, url, data, **kwargs)\\n def _attach_params(self, override=None)\\n def _attach_websocket(self, container, params=None)\\n def _create_websocket_connection(self, url)\\n def _get_raw_response_socket(self, response)\\n def _stream_helper(self, response) # Generator for data coming from a chunked-encoded HTTP response.\\n def _multiplexed_buffer_helper(self, response) # A generator of multiplexed data blocks read from a buffered\\n def _multiplexed_socket_stream_helper(self, response) # A generator of multiplexed data blocks coming from a response\\n def recvall(socket, size)\\n def attach(self, container, stdout=True, stderr=True, stream=False, logs=False)\\n def attach_socket(self, container, params=None, ws=False)\\n def build(self, path=None, tag=None, quiet=False, fileobj=None, nocache=False, rm=False, stream=False, timeout=None)\\n def commit(self, container, repository=None, tag=None, message=None, author=None, conf=None)\\n def containers(self, quiet=False, all=False, trunc=True, latest=False, since=None, before=None, limit=-1)\\n def copy(self, container, resource)\\n def create_container(self, image, command=None, hostname=None, user=None, detach=False, stdin_open=False, tty=False, mem_limit=0, ports=None, environment=None, dns=None, volumes=None, volumes_from=None, network_disabled=False, name=None, entrypoint=None, cpu_shares=None, working_dir=None, domainname=None)\\n def create_container_from_config(self, config, name=None)\\n def diff(self, container)\\n def events(self)\\n def export(self, container)\\n def history(self, image)\\n def images(self, name=None, quiet=False, all=False, viz=False)\\n def import_image(self, src=None, repository=None, tag=None, image=None)\\n def info(self)\\n def insert(self, image, url, path)\\n def inspect_container(self, container)\\n def inspect_image(self, image_id)\\n def kill(self, container, signal=None)\\n def login(self, username, password=None, email=None, registry=None, reauth=False)\\n def logs(self, container, stdout=True, stderr=True, stream=False)\\n def port(self, container, private_port\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/errors.py\",\n \"text\": \"fig/packages/docker/errors.py\\nclass APIError\\n def __init__(self, message, response, explanation=None)\\n def __str__(self)\\n def is_client_error(self)\\n def is_server_error(self)\\nclass DockerException\\nclass InvalidRepository\\nclass InvalidConfigFile\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py\\nclass UnixHTTPConnection\\n def __init__(self, base_url, unix_socket, timeout=60)\\n def connect(self)\\n def _extract_path(self, url)\\n def request(self, method, url, **kwargs)\\nclass UnixHTTPConnectionPool\\n def __init__(self, base_url, socket_path, timeout=60)\\n def _new_conn(self)\\nclass UnixAdapter\\n def __init__(self, base_url, timeout=60)\\n def get_connection(self, socket_path, proxies=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py\",\n \"text\": \"fig/packages/docker/utils/utils.py\\ndef mkbuildcontext(dockerfile)\\ndef tar(path)\\ndef compare_version(v1, v2) # Compare docker versions\\ndef ping(url)\\ndef _convert_port_binding(binding)\\ndef convert_port_bindings(port_bindings)\\ndef parse_repository_tag(repo)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py\",\n \"text\": \"fig/packages/six.py\\ndef _add_doc(func, doc) # Add documentation to a function.\\ndef _import_module(name) # Import module, returning the module after the last dot.\\nclass _LazyDescr\\n def __init__(self, name)\\n def __get__(self, obj, tp)\\nclass MovedModule\\n def __init__(self, name, old, new=None)\\n def _resolve(self)\\nclass MovedAttribute\\n def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None)\\n def _resolve(self)\\nclass _MovedItems # Lazy loading of moved objects\\ndef add_move(move) # Add an item to six.moves.\\ndef remove_move(name) # Remove item from six.moves.\\ndef iterkeys(d, **kw) # Return an iterator over the keys of a dictionary.\\ndef itervalues(d, **kw) # Return an iterator over the values of a dictionary.\\ndef iteritems(d, **kw) # Return an iterator over the (key, value) pairs of a dictionary.\\ndef iterlists(d, **kw) # Return an iterator over the (key, [values]) pairs of a dictionary.\\ndef with_metaclass(meta, base=object) # Create a base class with a metaclass.\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py\",\n \"text\": \"fig/project.py\\ndef sort_service_dicts(services)\\n def visit(n)\\nclass Project # A collection of services.\\n def __init__(self, name, services, client)\\n def from_dicts(cls, name, service_dicts, client) # Construct a ServiceCollection from a list of dicts representing services.\\n def from_config(cls, name, config, client)\\n def get_service(self, name) # Retrieve a service by name. Raises NoSuchService\\n def get_services(self, service_names=None) # Returns a list of this project's services filtered\\n def start(self, service_names=None, **options)\\n def stop(self, service_names=None, **options)\\n def kill(self, service_names=None, **options)\\n def build(self, service_names=None, **options)\\n def up(self, service_names=None)\\n def remove_stopped(self, service_names=None, **options)\\n def containers(self, service_names=None, *args, **kwargs)\\nclass NoSuchService\\n def __init__(self, name)\\n def __str__(self)\\nclass ConfigurationError\\n def __init__(self, msg)\\n def __str__(self)\\nclass DependencyError\",\n \"label\": 1\n },\n {\n \"cid\": \"fig/service.py\",\n \"text\": \"fig/service.py\\nfrom __future__ import unicode_literals\\nfrom __future__ import absolute_import\\nfrom .packages.docker.errors import APIError\\nimport logging\\nimport re\\nimport os\\nimport sys\\nimport json\\nfrom .container import Container\\n\\nlog = logging.getLogger(__name__)\\n\\n\\nDOCKER_CONFIG_KEYS = ['image', 'command', 'hostname', 'user', 'detach', 'stdin_open', 'tty', 'mem_limit', 'ports', 'environment', 'dns', 'volumes', 'volumes_from', 'entrypoint', 'privileged']\\nDOCKER_CONFIG_HINTS = {\\n 'link' : 'links',\\n 'port' : 'ports',\\n 'privilege' : 'privileged',\\n 'priviliged': 'privileged',\\n 'privilige' : 'privileged',\\n 'volume' : 'volumes',\\n}\\n\\n\\nclass BuildError(Exception):\\n def __init__(self, service, reason):\\n self.service = service\\n self.reason = reason\\n\\n\\nclass CannotBeScaledError(Exception):\\n pass\\n\\n\\nclass ConfigError(ValueError):\\n pass\\n\\n\\nclass Service(object):\\n def __init__(self, name, client=None, project='default', links=[], **options):\\n if not re.match('^[a-zA-Z0-9]+$', name):\\n raise ConfigError('Invalid name: %s' % name)\\n if not re.match('^[a-zA-Z0-9]+$', project):\\n raise ConfigError('Invalid project: %s' % project)\\n if 'image' in options and 'build' in options:\\n raise ConfigError('Service %s has both an image and build path specified. A service can either be built to image or use an existing image, not both.' % name)\\n\\n supported_options = DOCKER_CONFIG_KEYS + ['build', 'expose']\\n\\n for k in options:\\n if k not in supported_options:\\n msg = \\\"Unsupported config option for %s service: '%s'\\\" % (name, k)\\n if k in DOCKER_CONFIG_HINTS:\\n msg += \\\" (did you mean '%s'?)\\\" % DOCKER_CONFIG_HINTS[k]\\n raise ConfigError(msg)\\n\\n self.name = name\\n self.client = client\\n self.project = project\\n self.links = links or []\\n self.options = options\\n\\n def containers(self, stopped=False, one_off=False):\\n l = []\\n for container in self.client.containers(all=stopped):\\n name = get_container_name(container)\\n if not name or not is_valid_name(name, one_off):\\n continue\\n project, name, number = parse_name(name)\\n if project == self.project and name == self.name:\\n l.append(Container.from_ps(self.client, container))\\n return l\\n\\n def start(self, **options):\\n for c in self.containers(stopped=True):\\n if not c.is_running:\\n log.info(\\\"Starting %s...\\\" % c.name)\\n self.start_container(c, **options)\\n\\n def stop(self, **options):\\n for c in self.containers():\\n log.info(\\\"Stopping %s...\\\" % c.name)\\n c.stop(**options)\\n\\n def kill(self, **options):\\n for c in self.containers():\\n log.info(\\\"Killing %s...\\\" % c.name)\\n c.kill(**options)\\n\\n def scale(self, desired_num):\\n \\\"\\\"\\\"\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\\ndef read(*parts)\\ndef find_version(*file_paths)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"fig/cli/command.py::Command.__init__","text":"fig/cli/command.py::Command.__init__\n def __init__(self):\n self.yaml_path = os.environ.get('FIG_FILE', None)\n self.explicit_project_name = None","label":0},{"cid":"fig/cli/command.py::Command.dispatch","text":"fig/cli/command.py::Command.dispatch\n def dispatch(self, *args, **kwargs):\n try:\n super(Command, self).dispatch(*args, **kwargs)\n except ConnectionError:\n if call_silently(['which', 'docker']) != 0:\n if is_mac():\n raise errors.DockerNotFoundMac()\n elif is_ubuntu():\n raise errors.DockerNotFoundUbuntu()\n else:\n raise errors.DockerNotFoundGeneric()\n elif call_silently(['which', 'docker-osx']) == 0:\n raise errors.ConnectionErrorDockerOSX()\n else:\n raise errors.ConnectionErrorGeneric(self.client.base_url)","label":0},{"cid":"fig/cli/command.py::Command.perform_command","text":"fig/cli/command.py::Command.perform_command\n def perform_command(self, options, *args, **kwargs):\n if options['--file'] is not None:\n self.yaml_path = os.path.join(self.base_dir, options['--file'])\n if options['--project-name'] is not None:\n self.explicit_project_name = options['--project-name']\n return super(Command, self).perform_command(options, *args, **kwargs)","label":0},{"cid":"fig/cli/command.py::Command.client","text":"fig/cli/command.py::Command.client\n @cached_property\n def client(self):\n return Client(docker_url())","label":0},{"cid":"fig/cli/command.py::Command.project","text":"fig/cli/command.py::Command.project\n @cached_property\n def project(self):\n try:\n yaml_path = self.yaml_path\n if yaml_path is None:\n yaml_path = self.check_yaml_filename()\n config = yaml.load(open(yaml_path))\n except IOError as e:\n if e.errno == errno.ENOENT:\n raise errors.FigFileNotFound(os.path.basename(e.filename))\n raise errors.UserError(six.text_type(e))\n\n try:\n return Project.from_config(self.project_name, config, self.client)\n except ConfigError as e:\n raise errors.UserError(six.text_type(e))","label":0},{"cid":"fig/cli/command.py::Command.project_name","text":"fig/cli/command.py::Command.project_name\n @cached_property\n def project_name(self):\n project = os.path.basename(os.getcwd())\n if self.explicit_project_name is not None:\n project = self.explicit_project_name\n project = re.sub(r'[^a-zA-Z0-9]', '', project)\n if not project:\n project = 'default'\n return project","label":0},{"cid":"fig/cli/command.py::Command.formatter","text":"fig/cli/command.py::Command.formatter\n @cached_property\n def formatter(self):\n return Formatter()","label":0},{"cid":"fig/cli/command.py::Command.check_yaml_filename","text":"fig/cli/command.py::Command.check_yaml_filename\n def check_yaml_filename(self):\n if os.path.exists(os.path.join(self.base_dir, 'fig.yaml')):\n\n log.warning(\"Fig just read the file 'fig.yaml' on startup, rather than 'fig.yml'\")\n log.warning(\"Please be aware that fig.yml the expected extension in most cases, and using .yaml can cause compatibility issues in future\")\n\n return os.path.join(self.base_dir, 'fig.yaml')\n else:\n return os.path.join(self.base_dir, 'fig.yml')","label":0},{"cid":"fig/cli/errors.py::UserError.__init__","text":"fig/cli/errors.py::UserError.__init__\n def __init__(self, msg):\n self.msg = dedent(msg).strip()","label":0},{"cid":"fig/cli/errors.py::UserError.__unicode__","text":"fig/cli/errors.py::UserError.__unicode__\n def __unicode__(self):\n return self.msg","label":0},{"cid":"fig/cli/errors.py::DockerNotFoundMac.__init__","text":"fig/cli/errors.py::DockerNotFoundMac.__init__\n def __init__(self):\n super(DockerNotFoundMac, self).__init__(\"\"\"\n Couldn't connect to Docker daemon. You might need to install docker-osx:\n\n https://github.com/noplay/docker-osx\n \"\"\")","label":0},{"cid":"fig/cli/errors.py::DockerNotFoundUbuntu.__init__","text":"fig/cli/errors.py::DockerNotFoundUbuntu.__init__\n def __init__(self):\n super(DockerNotFoundUbuntu, self).__init__(\"\"\"\n Couldn't connect to Docker daemon. You might need to install Docker:\n\n http://docs.docker.io/en/latest/installation/ubuntulinux/\n \"\"\")","label":0},{"cid":"fig/cli/errors.py::DockerNotFoundGeneric.__init__","text":"fig/cli/errors.py::DockerNotFoundGeneric.__init__\n def __init__(self):\n super(DockerNotFoundGeneric, self).__init__(\"\"\"\n Couldn't connect to Docker daemon. You might need to install Docker:\n\n http://docs.docker.io/en/latest/installation/\n \"\"\")","label":0},{"cid":"fig/cli/errors.py::ConnectionErrorDockerOSX.__init__","text":"fig/cli/errors.py::ConnectionErrorDockerOSX.__init__\n def __init__(self):\n super(ConnectionErrorDockerOSX, self).__init__(\"\"\"\n Couldn't connect to Docker daemon - you might need to run `docker-osx shell`.\n \"\"\")","label":0},{"cid":"fig/cli/errors.py::ConnectionErrorGeneric.__init__","text":"fig/cli/errors.py::ConnectionErrorGeneric.__init__\n def __init__(self, url):\n super(ConnectionErrorGeneric, self).__init__(\"\"\"\n Couldn't connect to Docker daemon at %s - is it running?\n\n If it's at a non-standard location, specify the URL with the DOCKER_HOST environment variable.\n \"\"\" % url)","label":0},{"cid":"fig/cli/errors.py::FigFileNotFound.__init__","text":"fig/cli/errors.py::FigFileNotFound.__init__\n def __init__(self, filename):\n super(FigFileNotFound, self).__init__(\"\"\"\n Can't find %s. Are you in the right directory?\n \"\"\" % filename)","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter.__init__","text":"fig/cli/log_printer.py::LogPrinter.__init__\n def __init__(self, containers, attach_params=None):\n self.containers = containers\n self.attach_params = attach_params or {}\n self.prefix_width = self._calculate_prefix_width(containers)\n self.generators = self._make_log_generators()","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter.run","text":"fig/cli/log_printer.py::LogPrinter.run\n def run(self):\n mux = Multiplexer(self.generators)\n for line in mux.loop():\n sys.stdout.write(line.encode(sys.__stdout__.encoding or 'utf-8'))","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter._calculate_prefix_width","text":"fig/cli/log_printer.py::LogPrinter._calculate_prefix_width\n def _calculate_prefix_width(self, containers):\n \"\"\"\n Calculate the maximum width of container names so we can make the log\n prefixes line up like so:\n\n db_1 | Listening\n web_1 | Listening\n \"\"\"\n prefix_width = 0\n for container in containers:\n prefix_width = max(prefix_width, len(container.name_without_project))\n return prefix_width","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter._make_log_generators","text":"fig/cli/log_printer.py::LogPrinter._make_log_generators\n def _make_log_generators(self):\n color_fns = cycle(colors.rainbow())\n generators = []\n\n for container in self.containers:\n color_fn = color_fns.next()\n generators.append(self._make_log_generator(container, color_fn))\n\n return generators","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter._make_log_generator","text":"fig/cli/log_printer.py::LogPrinter._make_log_generator\n def _make_log_generator(self, container, color_fn):\n prefix = color_fn(self._generate_prefix(container))\n # Attach to container before log printer starts running\n line_generator = split_buffer(self._attach(container), '\\n')\n\n for line in line_generator:\n yield prefix + line.decode('utf-8')\n\n exit_code = container.wait()\n yield color_fn(\"%s exited with code %s\\n\" % (container.name, exit_code))\n yield STOP","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter._generate_prefix","text":"fig/cli/log_printer.py::LogPrinter._generate_prefix\n def _generate_prefix(self, container):\n \"\"\"\n Generate the prefix for a log line without colour\n \"\"\"\n name = container.name_without_project\n padding = ' ' * (self.prefix_width - len(name))\n return ''.join([name, padding, ' | '])","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter._attach","text":"fig/cli/log_printer.py::LogPrinter._attach\n def _attach(self, container):\n params = {\n 'stdout': True,\n 'stderr': True,\n 'stream': True,\n }\n params.update(self.attach_params)\n params = dict((name, 1 if value else 0) for (name, value) in list(params.items()))\n return container.attach(**params)","label":0},{"cid":"fig/cli/main.py::main","text":"fig/cli/main.py::main\ndef main():\n console_handler = logging.StreamHandler(stream=sys.stderr)\n console_handler.setFormatter(logging.Formatter())\n console_handler.setLevel(logging.INFO)\n root_logger = logging.getLogger()\n root_logger.addHandler(console_handler)\n root_logger.setLevel(logging.DEBUG)\n\n # Disable requests logging\n logging.getLogger(\"requests\").propagate = False\n\n try:\n command = TopLevelCommand()\n command.sys_dispatch()\n except KeyboardInterrupt:\n log.error(\"\\nAborting.\")\n sys.exit(1)\n except (UserError, NoSuchService, ConfigurationError) as e:\n log.error(e.msg)\n sys.exit(1)\n except NoSuchCommand as e:\n log.error(\"No such command: %s\", e.command)\n log.error(\"\")\n log.error(\"\\n\".join(parse_doc_section(\"commands:\", getdoc(e.supercommand))))\n sys.exit(1)\n except APIError as e:\n log.error(e.explanation)\n sys.exit(1)\n except BuildError as e:\n log.error(\"Service '%s' failed to build: %s\" % (e.service.name, e.reason))\n sys.exit(1)","label":0},{"cid":"fig/cli/main.py::parse_doc_section","text":"fig/cli/main.py::parse_doc_section\ndef parse_doc_section(name, source):\n pattern = re.compile('^([^\\n]*' + name + '[^\\n]*\\n?(?:[ \\t].*?(?:\\n|$))*)',\n re.IGNORECASE | re.MULTILINE)\n return [s.strip() for s in pattern.findall(source)]","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.docopt_options","text":"fig/cli/main.py::TopLevelCommand.docopt_options\n def docopt_options(self):\n options = super(TopLevelCommand, self).docopt_options()\n options['version'] = \"fig %s\" % __version__\n return options","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.build","text":"fig/cli/main.py::TopLevelCommand.build\n def build(self, options):\n \"\"\"\n Build or rebuild services.\n\n Services are built once and then tagged as `project_service`,\n e.g. `figtest_db`. If you change a service's `Dockerfile` or the\n contents of its build directory, you can run `fig build` to rebuild it.\n\n Usage: build [SERVICE...]\n \"\"\"\n self.project.build(service_names=options['SERVICE'])","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.help","text":"fig/cli/main.py::TopLevelCommand.help\n def help(self, options):\n \"\"\"\n Get help on a command.\n\n Usage: help COMMAND\n \"\"\"\n command = options['COMMAND']\n if not hasattr(self, command):\n raise NoSuchCommand(command, self)\n raise SystemExit(getdoc(getattr(self, command)))","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.kill","text":"fig/cli/main.py::TopLevelCommand.kill\n def kill(self, options):\n \"\"\"\n Force stop service containers.\n\n Usage: kill [SERVICE...]\n \"\"\"\n self.project.kill(service_names=options['SERVICE'])","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.logs","text":"fig/cli/main.py::TopLevelCommand.logs\n def logs(self, options):\n \"\"\"\n View output from containers.\n\n Usage: logs [SERVICE...]\n \"\"\"\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\n print(\"Attaching to\", list_containers(containers))\n LogPrinter(containers, attach_params={'logs': True}).run()","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.ps","text":"fig/cli/main.py::TopLevelCommand.ps\n def ps(self, options):\n \"\"\"\n List containers.\n\n Usage: ps [options] [SERVICE...]\n\n Options:\n -q Only display IDs\n \"\"\"\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True) + self.project.containers(service_names=options['SERVICE'], one_off=True)\n\n if options['-q']:\n for container in containers:\n print(container.id)\n else:\n headers = [\n 'Name',\n 'Command',\n 'State',\n 'Ports',\n ]\n rows = []\n for container in containers:\n command = container.human_readable_command\n if len(command) > 30:\n command = '%s ...' % command[:26]\n rows.append([\n container.name,\n command,\n container.human_readable_state,\n container.human_readable_ports,\n ])\n print(Formatter().table(headers, rows))","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.rm","text":"fig/cli/main.py::TopLevelCommand.rm\n def rm(self, options):\n \"\"\"\n Remove stopped service containers.\n\n Usage: rm [options] [SERVICE...]\n\n Options:\n --force Don't ask to confirm removal\n -v Remove volumes associated with containers\n \"\"\"\n all_containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\n stopped_containers = [c for c in all_containers if not c.is_running]\n\n if len(stopped_containers) > 0:\n print(\"Going to remove\", list_containers(stopped_containers))\n if options.get('--force') \\\n or yesno(\"Are you sure? [yN] \", default=False):\n self.project.remove_stopped(\n service_names=options['SERVICE'],\n v=options.get('-v', False)\n )\n else:\n print(\"No stopped containers\")","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.run","text":"fig/cli/main.py::TopLevelCommand.run\n def run(self, options):\n \"\"\"\n Run a one-off command on a service.\n\n For example:\n\n $ fig run web python manage.py shell\n\n Note that this will not start any services that the command's service\n links to. So if, for example, your one-off command talks to your\n database, you will need to run `fig up -d db` first.\n\n Usage: run [options] SERVICE COMMAND [ARGS...]\n\n Options:\n -d Detached mode: Run container in the background, print new\n container name\n -T Disable pseudo-tty allocation. By default `fig run`\n allocates a TTY.\n --rm Remove container after run. Ignored in detached mode.\n \"\"\"\n service = self.project.get_service(options['SERVICE'])\n\n tty = True\n if options['-d'] or options['-T'] or not sys.stdin.isatty():\n tty = False\n\n container_options = {\n 'command': [options['COMMAND']] + options['ARGS'],\n 'tty': tty,\n 'stdin_open': not options['-d'],\n }\n container = service.create_container(one_off=True, **container_options)\n if options['-d']:\n service.start_container(container, ports=None, one_off=True)\n print(container.name)\n else:\n with self._attach_to_container(container.id, raw=tty) as c:\n service.start_container(container, ports=None, one_off=True)\n c.run()\n exit_code = container.wait()\n if options['--rm']:\n log.info(\"Removing %s...\" % container.name)\n self.client.remove_container(container.id)\n sys.exit(exit_code)","label":1},{"cid":"fig/cli/main.py::TopLevelCommand.scale","text":"fig/cli/main.py::TopLevelCommand.scale\n def scale(self, options):\n \"\"\"\n Set number of containers to run for a service.\n\n Numbers are specified in the form `service=num` as arguments.\n For example:\n\n $ fig scale web=2 worker=3\n\n Usage: scale [SERVICE=NUM...]\n \"\"\"\n for s in options['SERVICE=NUM']:\n if '=' not in s:\n raise UserError('Arguments to scale should be in the form service=num')\n service_name, num = s.split('=', 1)\n try:\n num = int(num)\n except ValueError:\n raise UserError('Number of containers for service \"%s\" is not a number' % service)\n try:\n self.project.get_service(service_name).scale(num)\n except CannotBeScaledError:\n raise UserError('Service \"%s\" cannot be scaled because it specifies a port on the host. If multiple containers for this service were created, the port would clash.\\n\\nRemove the \":\" from the port definition in fig.yml so Docker can choose a random port for each container.' % service_name)","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.start","text":"fig/cli/main.py::TopLevelCommand.start\n def start(self, options):\n \"\"\"\n Start existing containers.\n\n Usage: start [SERVICE...]\n \"\"\"\n self.project.start(service_names=options['SERVICE'])","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.stop","text":"fig/cli/main.py::TopLevelCommand.stop\n def stop(self, options):\n \"\"\"\n Stop running containers without removing them.\n\n They can be started again with `fig start`.\n\n Usage: stop [SERVICE...]\n \"\"\"\n self.project.stop(service_names=options['SERVICE'])","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.up","text":"fig/cli/main.py::TopLevelCommand.up\n def up(self, options):\n \"\"\"\n Build, (re)create, start and attach to containers for a service.\n\n By default, `fig up` will aggregate the output of each container, and\n when it exits, all containers will be stopped. If you run `fig up -d`,\n it'll start the containers in the background and leave them running.\n\n If there are existing containers for a service, `fig up` will stop\n and recreate them (preserving mounted volumes with volumes-from),\n so that changes in `fig.yml` are picked up.\n\n Usage: up [options] [SERVICE...]\n\n Options:\n -d Detached mode: Run containers in the background, print new\n container names\n \"\"\"\n detached = options['-d']\n\n to_attach = self.project.up(service_names=options['SERVICE'])\n\n if not detached:\n print(\"Attaching to\", list_containers(to_attach))\n log_printer = LogPrinter(to_attach, attach_params={\"logs\": True})\n\n try:\n log_printer.run()\n finally:\n def handler(signal, frame):\n self.project.kill(service_names=options['SERVICE'])\n sys.exit(0)\n signal.signal(signal.SIGINT, handler)\n\n print(\"Gracefully stopping... (press Ctrl+C again to force)\")\n self.project.stop(service_names=options['SERVICE'])","label":1},{"cid":"fig/cli/main.py::TopLevelCommand._attach_to_container","text":"fig/cli/main.py::TopLevelCommand._attach_to_container\n def _attach_to_container(self, container_id, raw=False):\n socket_in = self.client.attach_socket(container_id, params={'stdin': 1, 'stream': 1})\n socket_out = self.client.attach_socket(container_id, params={'stdout': 1, 'logs': 1, 'stream': 1})\n socket_err = self.client.attach_socket(container_id, params={'stderr': 1, 'logs': 1, 'stream': 1})\n\n return SocketClient(\n socket_in=socket_in,\n socket_out=socket_out,\n socket_err=socket_err,\n raw=raw,\n )","label":0},{"cid":"fig/cli/main.py::list_containers","text":"fig/cli/main.py::list_containers\ndef list_containers(containers):\n return \", \".join(c.name for c in containers)","label":0},{"cid":"fig/container.py::Container.__init__","text":"fig/container.py::Container.__init__\n def __init__(self, client, dictionary, has_been_inspected=False):\n self.client = client\n self.dictionary = dictionary\n self.has_been_inspected = has_been_inspected","label":0},{"cid":"fig/container.py::Container.from_ps","text":"fig/container.py::Container.from_ps\n @classmethod\n def from_ps(cls, client, dictionary, **kwargs):\n \"\"\"\n Construct a container object from the output of GET /containers/json.\n \"\"\"\n new_dictionary = {\n 'ID': dictionary['Id'],\n 'Image': dictionary['Image'],\n }\n for name in dictionary.get('Names', []):\n if len(name.split('/')) == 2:\n new_dictionary['Name'] = name\n return cls(client, new_dictionary, **kwargs)","label":0},{"cid":"fig/container.py::Container.from_id","text":"fig/container.py::Container.from_id\n @classmethod\n def from_id(cls, client, id):\n return cls(client, client.inspect_container(id))","label":0},{"cid":"fig/container.py::Container.create","text":"fig/container.py::Container.create\n @classmethod\n def create(cls, client, **options):\n response = client.create_container(**options)\n return cls.from_id(client, response['Id'])","label":0},{"cid":"fig/container.py::Container.id","text":"fig/container.py::Container.id\n @property\n def id(self):\n return self.dictionary['ID']","label":0},{"cid":"fig/container.py::Container.image","text":"fig/container.py::Container.image\n @property\n def image(self):\n return self.dictionary['Image']","label":0},{"cid":"fig/container.py::Container.short_id","text":"fig/container.py::Container.short_id\n @property\n def short_id(self):\n return self.id[:10]","label":0},{"cid":"fig/container.py::Container.name","text":"fig/container.py::Container.name\n @property\n def name(self):\n return self.dictionary['Name'][1:]","label":0},{"cid":"fig/container.py::Container.name_without_project","text":"fig/container.py::Container.name_without_project\n @property\n def name_without_project(self):\n return '_'.join(self.dictionary['Name'].split('_')[1:])","label":0},{"cid":"fig/container.py::Container.number","text":"fig/container.py::Container.number\n @property\n def number(self):\n try:\n return int(self.name.split('_')[-1])\n except ValueError:\n return None","label":0},{"cid":"fig/container.py::Container.human_readable_ports","text":"fig/container.py::Container.human_readable_ports\n @property\n def human_readable_ports(self):\n self.inspect_if_not_inspected()\n if not self.dictionary['NetworkSettings']['Ports']:\n return ''\n ports = []\n for private, public in list(self.dictionary['NetworkSettings']['Ports'].items()):\n if public:\n ports.append('%s->%s' % (public[0]['HostPort'], private))\n else:\n ports.append(private)\n return ', '.join(ports)","label":0},{"cid":"fig/container.py::Container.human_readable_state","text":"fig/container.py::Container.human_readable_state\n @property\n def human_readable_state(self):\n self.inspect_if_not_inspected()\n if self.dictionary['State']['Running']:\n if self.dictionary['State'].get('Ghost'):\n return 'Ghost'\n else:\n return 'Up'\n else:\n return 'Exit %s' % self.dictionary['State']['ExitCode']","label":0},{"cid":"fig/container.py::Container.human_readable_command","text":"fig/container.py::Container.human_readable_command\n @property\n def human_readable_command(self):\n self.inspect_if_not_inspected()\n if self.dictionary['Config']['Cmd']:\n return ' '.join(self.dictionary['Config']['Cmd'])\n else:\n return ''","label":0},{"cid":"fig/container.py::Container.environment","text":"fig/container.py::Container.environment\n @property\n def environment(self):\n self.inspect_if_not_inspected()\n out = {}\n for var in self.dictionary.get('Config', {}).get('Env', []):\n k, v = var.split('=', 1)\n out[k] = v\n return out","label":0},{"cid":"fig/container.py::Container.is_running","text":"fig/container.py::Container.is_running\n @property\n def is_running(self):\n self.inspect_if_not_inspected()\n return self.dictionary['State']['Running']","label":0},{"cid":"fig/container.py::Container.start","text":"fig/container.py::Container.start\n def start(self, **options):\n return self.client.start(self.id, **options)","label":0},{"cid":"fig/container.py::Container.stop","text":"fig/container.py::Container.stop\n def stop(self, **options):\n return self.client.stop(self.id, **options)","label":0},{"cid":"fig/container.py::Container.kill","text":"fig/container.py::Container.kill\n def kill(self):\n return self.client.kill(self.id)","label":0},{"cid":"fig/container.py::Container.remove","text":"fig/container.py::Container.remove\n def remove(self, **options):\n return self.client.remove_container(self.id, **options)","label":0},{"cid":"fig/container.py::Container.inspect_if_not_inspected","text":"fig/container.py::Container.inspect_if_not_inspected\n def inspect_if_not_inspected(self):\n if not self.has_been_inspected:\n self.inspect()","label":0},{"cid":"fig/container.py::Container.wait","text":"fig/container.py::Container.wait\n def wait(self):\n return self.client.wait(self.id)","label":0},{"cid":"fig/container.py::Container.logs","text":"fig/container.py::Container.logs\n def logs(self, *args, **kwargs):\n return self.client.logs(self.id, *args, **kwargs)","label":0},{"cid":"fig/container.py::Container.inspect","text":"fig/container.py::Container.inspect\n def inspect(self):\n self.dictionary = self.client.inspect_container(self.id)\n return self.dictionary","label":0},{"cid":"fig/container.py::Container.links","text":"fig/container.py::Container.links\n def links(self):\n links = []\n for container in self.client.containers():\n for name in container['Names']:\n bits = name.split('/')\n if len(bits) > 2 and bits[1] == self.name:\n links.append(bits[2])\n return links","label":0},{"cid":"fig/container.py::Container.attach","text":"fig/container.py::Container.attach\n def attach(self, *args, **kwargs):\n return self.client.attach(self.id, *args, **kwargs)","label":0},{"cid":"fig/container.py::Container.attach_socket","text":"fig/container.py::Container.attach_socket\n def attach_socket(self, **kwargs):\n return self.client.attach_socket(self.id, **kwargs)","label":0},{"cid":"fig/container.py::Container.__repr__","text":"fig/container.py::Container.__repr__\n def __repr__(self):\n return '<Container: %s>' % self.name","label":0},{"cid":"fig/container.py::Container.__eq__","text":"fig/container.py::Container.__eq__\n def __eq__(self, other):\n if type(self) != type(other):\n return False\n return self.id == other.id","label":0},{"cid":"fig/packages/docker/auth/auth.py::swap_protocol","text":"fig/packages/docker/auth/auth.py::swap_protocol\ndef swap_protocol(url):\n if url.startswith('http://'):\n return url.replace('http://', 'https://', 1)\n if url.startswith('https://'):\n return url.replace('https://', 'http://', 1)\n return url","label":0},{"cid":"fig/packages/docker/auth/auth.py::expand_registry_url","text":"fig/packages/docker/auth/auth.py::expand_registry_url\ndef expand_registry_url(hostname):\n if hostname.startswith('http:') or hostname.startswith('https:'):\n if '/' not in hostname[9:]:\n hostname = hostname + '/v1/'\n return hostname\n if utils.ping('https://' + hostname + '/v1/_ping'):\n return 'https://' + hostname + '/v1/'\n return 'http://' + hostname + '/v1/'","label":0},{"cid":"fig/packages/docker/auth/auth.py::resolve_repository_name","text":"fig/packages/docker/auth/auth.py::resolve_repository_name\ndef resolve_repository_name(repo_name):\n if '://' in repo_name:\n raise errors.InvalidRepository(\n 'Repository name cannot contain a scheme ({0})'.format(repo_name))\n parts = repo_name.split('/', 1)\n if '.' not in parts[0] and ':' not in parts[0] and parts[0] != 'localhost':\n # This is a docker index repo (ex: foo/bar or ubuntu)\n return INDEX_URL, repo_name\n if len(parts) < 2:\n raise errors.InvalidRepository(\n 'Invalid repository name ({0})'.format(repo_name))\n\n if 'index.docker.io' in parts[0]:\n raise errors.InvalidRepository(\n 'Invalid repository name, try \"{0}\" instead'.format(parts[1]))\n\n return expand_registry_url(parts[0]), parts[1]","label":0},{"cid":"fig/packages/docker/auth/auth.py::resolve_authconfig","text":"fig/packages/docker/auth/auth.py::resolve_authconfig\ndef resolve_authconfig(authconfig, registry=None):\n \"\"\"Return the authentication data from the given auth configuration for a\n specific registry. We'll do our best to infer the correct URL for the\n registry, trying both http and https schemes. Returns an empty dictionnary\n if no data exists.\"\"\"\n # Default to the public index server\n registry = registry or INDEX_URL\n\n # Ff its not the index server there are three cases:\n #\n # 1. this is a full config url -> it should be used as is\n # 2. it could be a full url, but with the wrong protocol\n # 3. it can be the hostname optionally with a port\n #\n # as there is only one auth entry which is fully qualified we need to start\n # parsing and matching\n if '/' not in registry:\n registry = registry + '/v1/'\n if not registry.startswith('http:') and not registry.startswith('https:'):\n registry = 'https://' + registry\n\n if registry in authconfig:\n return authconfig[registry]\n return authconfig.get(swap_protocol(registry), None)","label":0},{"cid":"fig/packages/docker/auth/auth.py::encode_auth","text":"fig/packages/docker/auth/auth.py::encode_auth\ndef encode_auth(auth_info):\n return base64.b64encode(auth_info.get('username', '') + b':' +\n auth_info.get('password', ''))","label":0},{"cid":"fig/packages/docker/auth/auth.py::decode_auth","text":"fig/packages/docker/auth/auth.py::decode_auth\ndef decode_auth(auth):\n if isinstance(auth, six.string_types):\n auth = auth.encode('ascii')\n s = base64.b64decode(auth)\n login, pwd = s.split(b':')\n return login.decode('ascii'), pwd.decode('ascii')","label":0},{"cid":"fig/packages/docker/auth/auth.py::encode_header","text":"fig/packages/docker/auth/auth.py::encode_header\ndef encode_header(auth):\n auth_json = json.dumps(auth).encode('ascii')\n return base64.b64encode(auth_json)","label":0},{"cid":"fig/packages/docker/auth/auth.py::encode_full_header","text":"fig/packages/docker/auth/auth.py::encode_full_header\ndef encode_full_header(auth):\n \"\"\" Returns the given auth block encoded for the X-Registry-Config header.\n \"\"\"\n return encode_header({'configs': auth})","label":0},{"cid":"fig/packages/docker/auth/auth.py::load_config","text":"fig/packages/docker/auth/auth.py::load_config\ndef load_config(root=None):\n \"\"\"Loads authentication data from a Docker configuration file in the given\n root directory.\"\"\"\n conf = {}\n data = None\n\n config_file = os.path.join(root or os.environ.get('HOME', '.'),\n DOCKER_CONFIG_FILENAME)\n\n # First try as JSON\n try:\n with open(config_file) as f:\n conf = {}\n for registry, entry in six.iteritems(json.load(f)):\n username, password = decode_auth(entry['auth'])\n conf[registry] = {\n 'username': username,\n 'password': password,\n 'email': entry['email'],\n 'serveraddress': registry,\n }\n return conf\n except:\n pass\n\n # If that fails, we assume the configuration file contains a single\n # authentication token for the public registry in the following format:\n #\n # auth = AUTH_TOKEN\n # email = email@domain.com\n try:\n data = []\n for line in fileinput.input(config_file):\n data.append(line.strip().split(' = ')[1])\n if len(data) < 2:\n # Not enough data\n raise errors.InvalidConfigFile(\n 'Invalid or empty configuration file!')\n\n username, password = decode_auth(data[0])\n conf[INDEX_URL] = {\n 'username': username,\n 'password': password,\n 'email': data[1],\n 'serveraddress': INDEX_URL,\n }\n return conf\n except:\n pass\n\n # If all fails, return an empty config\n return {}","label":0},{"cid":"fig/packages/docker/client.py::Client.__init__","text":"fig/packages/docker/client.py::Client.__init__\n def __init__(self, base_url=None, version=DEFAULT_DOCKER_API_VERSION,\n timeout=DEFAULT_TIMEOUT_SECONDS):\n super(Client, self).__init__()\n if base_url is None:\n base_url = \"http+unix://var/run/docker.sock\"\n if 'unix:///' in base_url:\n base_url = base_url.replace('unix:/', 'unix:')\n if base_url.startswith('unix:'):\n base_url = \"http+\" + base_url\n if base_url.startswith('tcp:'):\n base_url = base_url.replace('tcp:', 'http:')\n if base_url.endswith('/'):\n base_url = base_url[:-1]\n self.base_url = base_url\n self._version = version\n self._timeout = timeout\n self._auth_configs = auth.load_config()\n\n self.mount('http+unix://', unixconn.UnixAdapter(base_url, timeout))","label":0},{"cid":"fig/packages/docker/client.py::Client._set_request_timeout","text":"fig/packages/docker/client.py::Client._set_request_timeout\n def _set_request_timeout(self, kwargs):\n \"\"\"Prepare the kwargs for an HTTP request by inserting the timeout\n parameter, if not already present.\"\"\"\n kwargs.setdefault('timeout', self._timeout)\n return kwargs","label":0},{"cid":"fig/packages/docker/client.py::Client._post","text":"fig/packages/docker/client.py::Client._post\n def _post(self, url, **kwargs):\n return self.post(url, **self._set_request_timeout(kwargs))","label":0},{"cid":"fig/packages/docker/client.py::Client._get","text":"fig/packages/docker/client.py::Client._get\n def _get(self, url, **kwargs):\n return self.get(url, **self._set_request_timeout(kwargs))","label":0},{"cid":"fig/packages/docker/client.py::Client._delete","text":"fig/packages/docker/client.py::Client._delete\n def _delete(self, url, **kwargs):\n return self.delete(url, **self._set_request_timeout(kwargs))","label":0},{"cid":"fig/packages/docker/client.py::Client._url","text":"fig/packages/docker/client.py::Client._url\n def _url(self, path):\n return '{0}/v{1}{2}'.format(self.base_url, self._version, path)","label":0},{"cid":"fig/packages/docker/client.py::Client._raise_for_status","text":"fig/packages/docker/client.py::Client._raise_for_status\n def _raise_for_status(self, response, explanation=None):\n \"\"\"Raises stored :class:`APIError`, if one occurred.\"\"\"\n try:\n response.raise_for_status()\n except requests.exceptions.HTTPError as e:\n raise errors.APIError(e, response, explanation=explanation)","label":0},{"cid":"fig/packages/docker/client.py::Client._result","text":"fig/packages/docker/client.py::Client._result\n def _result(self, response, json=False, binary=False):\n assert not (json and binary)\n self._raise_for_status(response)\n\n if json:\n return response.json()\n if binary:\n return response.content\n return response.text","label":0},{"cid":"fig/packages/docker/client.py::Client._container_config","text":"fig/packages/docker/client.py::Client._container_config\n def _container_config(self, image, command, hostname=None, user=None,\n detach=False, stdin_open=False, tty=False,\n mem_limit=0, ports=None, environment=None, dns=None,\n volumes=None, volumes_from=None,\n network_disabled=False, entrypoint=None,\n cpu_shares=None, working_dir=None, domainname=None):\n if isinstance(command, six.string_types):\n command = shlex.split(str(command))\n if isinstance(environment, dict):\n environment = [\n '{0}={1}'.format(k, v) for k, v in environment.items()\n ]\n\n if isinstance(ports, list):\n exposed_ports = {}\n for port_definition in ports:\n port = port_definition\n proto = 'tcp'\n if isinstance(port_definition, tuple):\n if len(port_definition) == 2:\n proto = port_definition[1]\n port = port_definition[0]\n exposed_ports['{0}/{1}'.format(port, proto)] = {}\n ports = exposed_ports\n\n if isinstance(volumes, list):\n volumes_dict = {}\n for vol in volumes:\n volumes_dict[vol] = {}\n volumes = volumes_dict\n\n if volumes_from and not isinstance(volumes_from, six.string_types):\n volumes_from = ','.join(volumes_from)\n\n attach_stdin = False\n attach_stdout = False\n attach_stderr = False\n stdin_once = False\n\n if not detach:\n attach_stdout = True\n attach_stderr = True\n\n if stdin_open:\n attach_stdin = True\n stdin_once = True\n\n return {\n 'Hostname': hostname,\n 'Domainname': domainname,\n 'ExposedPorts': ports,\n 'User': user,\n 'Tty': tty,\n ","label":0},{"cid":"fig/packages/docker/client.py::Client._post_json","text":"fig/packages/docker/client.py::Client._post_json\n def _post_json(self, url, data, **kwargs):\n # Go <1.1 can't unserialize null to a string\n # so we do this disgusting thing here.\n data2 = {}\n if data is not None:\n for k, v in six.iteritems(data):\n if v is not None:\n data2[k] = v\n\n if 'headers' not in kwargs:\n kwargs['headers'] = {}\n kwargs['headers']['Content-Type'] = 'application/json'\n return self._post(url, data=json.dumps(data2), **kwargs)","label":0},{"cid":"fig/packages/docker/client.py::Client._attach_params","text":"fig/packages/docker/client.py::Client._attach_params\n def _attach_params(self, override=None):\n return override or {\n 'stdout': 1,\n 'stderr': 1,\n 'stream': 1\n }","label":0},{"cid":"fig/packages/docker/client.py::Client._attach_websocket","text":"fig/packages/docker/client.py::Client._attach_websocket\n def _attach_websocket(self, container, params=None):\n if six.PY3:\n raise NotImplementedError(\"This method is not currently supported \"\n \"under python 3\")\n url = self._url(\"/containers/{0}/attach/ws\".format(container))\n req = requests.Request(\"POST\", url, params=self._attach_params(params))\n full_url = req.prepare().url\n full_url = full_url.replace(\"http://\", \"ws://\", 1)\n full_url = full_url.replace(\"https://\", \"wss://\", 1)\n return self._create_websocket_connection(full_url)","label":0},{"cid":"fig/packages/docker/client.py::Client._create_websocket_connection","text":"fig/packages/docker/client.py::Client._create_websocket_connection\n def _create_websocket_connection(self, url):\n return websocket.create_connection(url)","label":0},{"cid":"fig/packages/docker/client.py::Client._get_raw_response_socket","text":"fig/packages/docker/client.py::Client._get_raw_response_socket\n def _get_raw_response_socket(self, response):\n self._raise_for_status(response)\n if six.PY3:\n return response.raw._fp.fp.raw._sock\n else:\n return response.raw._fp.fp._sock","label":0},{"cid":"fig/packages/docker/client.py::Client._stream_helper","text":"fig/packages/docker/client.py::Client._stream_helper\n def _stream_helper(self, response):\n \"\"\"Generator for data coming from a chunked-encoded HTTP response.\"\"\"\n socket_fp = self._get_raw_response_socket(response)\n socket_fp.setblocking(1)\n socket = socket_fp.makefile()\n while True:\n # Because Docker introduced newlines at the end of chunks in v0.9,\n # and only on some API endpoints, we have to cater for both cases.\n size_line = socket.readline()\n if size_line == '\\r\\n':\n size_line = socket.readline()\n\n size = int(size_line, 16)\n if size <= 0:\n break\n data = socket.readline()\n if not data:\n break\n yield data","label":0},{"cid":"fig/packages/docker/client.py::Client._multiplexed_buffer_helper","text":"fig/packages/docker/client.py::Client._multiplexed_buffer_helper\n def _multiplexed_buffer_helper(self, response):\n \"\"\"A generator of multiplexed data blocks read from a buffered\n response.\"\"\"\n buf = self._result(response, binary=True)\n walker = 0\n while True:\n if len(buf[walker:]) < 8:\n break\n _, length = struct.unpack_from('>BxxxL', buf[walker:])\n start = walker + STREAM_HEADER_SIZE_BYTES\n end = start + length\n walker = end\n yield str(buf[start:end])","label":0},{"cid":"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper","text":"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper\n def _multiplexed_socket_stream_helper(self, response):\n \"\"\"A generator of multiplexed data blocks coming from a response\n socket.\"\"\"\n socket = self._get_raw_response_socket(response)\n\n def recvall(socket, size):\n blocks = []\n while size > 0:\n block = socket.recv(size)\n if not block:\n return None\n\n blocks.append(block)\n size -= len(block)\n\n sep = bytes() if six.PY3 else str()\n data = sep.join(blocks)\n return data\n\n while True:\n socket.settimeout(None)\n header = recvall(socket, STREAM_HEADER_SIZE_BYTES)\n if not header:\n break\n _, length = struct.unpack('>BxxxL', header)\n if not length:\n break\n data = recvall(socket, length)\n if not data:\n break\n yield data","label":0},{"cid":"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper.recvall","text":"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper.recvall\n def recvall(socket, size):\n blocks = []\n while size > 0:\n block = socket.recv(size)\n if not block:\n return None\n\n blocks.append(block)\n size -= len(block)\n\n sep = bytes() if six.PY3 else str()\n data = sep.join(blocks)\n return data","label":0},{"cid":"fig/packages/docker/client.py::Client.attach","text":"fig/packages/docker/client.py::Client.attach\n def attach(self, container, stdout=True, stderr=True,\n stream=False, logs=False):\n if isinstance(container, dict):\n container = container.get('Id')\n params = {\n 'logs': logs and 1 or 0,\n 'stdout': stdout and 1 or 0,\n 'stderr': stderr and 1 or 0,\n 'stream': stream and 1 or 0,\n }\n u = self._url(\"/containers/{0}/attach\".format(container))\n response = self._post(u, params=params, stream=stream)\n\n # Stream multi-plexing was only introduced in API v1.6. Anything before\n # that needs old-style streaming.\n if utils.compare_version('1.6', self._version) < 0:\n def stream_result():\n self._raise_for_status(response)\n for line in response.iter_lines(chunk_size=1,\n decode_unicode=True):\n # filter out keep-alive new lines\n if line:\n yield line\n\n return stream_result() if stream else \\\n self._result(response, binary=True)\n\n return stream and self._multiplexed_socket_stream_helper(response) or \\\n ''.join([x for x in self._multiplexed_buffer_helper(response)])","label":0},{"cid":"fig/packages/docker/client.py::Client.attach_socket","text":"fig/packages/docker/client.py::Client.attach_socket\n def attach_socket(self, container, params=None, ws=False):\n if params is None:\n params = {\n 'stdout': 1,\n 'stderr': 1,\n 'stream': 1\n }\n\n if ws:\n return self._attach_websocket(container, params)\n\n if isinstance(container, dict):\n container = container.get('Id')\n\n u = self._url(\"/containers/{0}/attach\".format(container))\n return self._get_raw_response_socket(self.post(\n u, None, params=self._attach_params(params), stream=True))","label":0},{"cid":"fig/packages/docker/client.py::Client.build","text":"fig/packages/docker/client.py::Client.build\n def build(self, path=None, tag=None, quiet=False, fileobj=None,\n nocache=False, rm=False, stream=False, timeout=None):\n remote = context = headers = None\n if path is None and fileobj is None:\n raise TypeError(\"Either path or fileobj needs to be provided.\")\n\n if fileobj is not None:\n context = utils.mkbuildcontext(fileobj)\n elif path.startswith(('http://', 'https://', 'git://', 'github.com/')):\n remote = path\n else:\n context = utils.tar(path)\n\n if utils.compare_version('1.8', self._version) >= 0:\n stream = True\n\n u = self._url('/build')\n params = {\n 't': tag,\n 'remote': remote,\n 'q': quiet,\n 'nocache': nocache,\n 'rm': rm\n }\n if context is not None:\n headers = {'Content-Type': 'application/tar'}\n\n if utils.compare_version('1.9', self._version) >= 0:\n # If we don't have any auth data so far, try reloading the config\n # file one more time in case anything showed up in there.\n if not self._auth_configs:\n self._auth_configs = auth.load_config()\n\n # Send the full auth configuration (if any exists), since the build\n # could use any (or all) of the registries.\n if self._auth_configs:\n headers['X-Registry-Config'] = auth.encode_full_header(\n self._auth_configs\n )\n\n response = self._post(\n u,\n data=context,\n params=params,\n headers=headers,\n stream=stream,\n timeout=timeout,\n )\n\n if context is not None:\n context.close()\n\n if stream:\n return self._stream_helper(response)\n else:\n output = self._result(response)\n srch = r'Successfully built ([0-9a-f]+)'\n ","label":0},{"cid":"fig/packages/docker/client.py::Client.commit","text":"fig/packages/docker/client.py::Client.commit\n def commit(self, container, repository=None, tag=None, message=None,\n author=None, conf=None):\n params = {\n 'container': container,\n 'repo': repository,\n 'tag': tag,\n 'comment': message,\n 'author': author\n }\n u = self._url(\"/commit\")\n return self._result(self._post_json(u, data=conf, params=params),\n json=True)","label":0},{"cid":"fig/packages/docker/client.py::Client.containers","text":"fig/packages/docker/client.py::Client.containers\n def containers(self, quiet=False, all=False, trunc=True, latest=False,\n since=None, before=None, limit=-1):\n params = {\n 'limit': 1 if latest else limit,\n 'all': 1 if all else 0,\n 'trunc_cmd': 1 if trunc else 0,\n 'since': since,\n 'before': before\n }\n u = self._url(\"/containers/json\")\n res = self._result(self._get(u, params=params), True)\n\n if quiet:\n return [{'Id': x['Id']} for x in res]\n return res","label":0},{"cid":"fig/packages/docker/client.py::Client.copy","text":"fig/packages/docker/client.py::Client.copy\n def copy(self, container, resource):\n if isinstance(container, dict):\n container = container.get('Id')\n res = self._post_json(\n self._url(\"/containers/{0}/copy\".format(container)),\n data={\"Resource\": resource},\n stream=True\n )\n self._raise_for_status(res)\n return res.raw","label":0},{"cid":"fig/packages/docker/client.py::Client.create_container","text":"fig/packages/docker/client.py::Client.create_container\n def create_container(self, image, command=None, hostname=None, user=None,\n detach=False, stdin_open=False, tty=False,\n mem_limit=0, ports=None, environment=None, dns=None,\n volumes=None, volumes_from=None,\n network_disabled=False, name=None, entrypoint=None,\n cpu_shares=None, working_dir=None, domainname=None):\n\n config = self._container_config(\n image, command, hostname, user, detach, stdin_open, tty, mem_limit,\n ports, environment, dns, volumes, volumes_from, network_disabled,\n entrypoint, cpu_shares, working_dir, domainname\n )\n return self.create_container_from_config(config, name)","label":0},{"cid":"fig/packages/docker/client.py::Client.create_container_from_config","text":"fig/packages/docker/client.py::Client.create_container_from_config\n def create_container_from_config(self, config, name=None):\n u = self._url(\"/containers/create\")\n params = {\n 'name': name\n }\n res = self._post_json(u, data=config, params=params)\n return self._result(res, True)","label":0},{"cid":"fig/packages/docker/client.py::Client.diff","text":"fig/packages/docker/client.py::Client.diff\n def diff(self, container):\n if isinstance(container, dict):\n container = container.get('Id')\n return self._result(self._get(self._url(\"/containers/{0}/changes\".\n format(container))), True)","label":0},{"cid":"fig/packages/docker/client.py::Client.events","text":"fig/packages/docker/client.py::Client.events\n def events(self):\n return self._stream_helper(self.get(self._url('/events'), stream=True))","label":0},{"cid":"fig/packages/docker/client.py::Client.export","text":"fig/packages/docker/client.py::Client.export\n def export(self, container):\n if isinstance(container, dict):\n container = container.get('Id')\n res = self._get(self._url(\"/containers/{0}/export\".format(container)),\n stream=True)\n self._raise_for_status(res)\n return res.raw","label":0},{"cid":"fig/packages/docker/client.py::Client.history","text":"fig/packages/docker/client.py::Client.history\n def history(self, image):\n res = self._get(self._url(\"/images/{0}/history\".format(image)))\n self._raise_for_status(res)\n return self._result(res)","label":0},{"cid":"fig/packages/docker/client.py::Client.images","text":"fig/packages/docker/client.py::Client.images\n def images(self, name=None, quiet=False, all=False, viz=False):\n if viz:\n if utils.compare_version('1.7', self._version) >= 0:\n raise Exception('Viz output is not supported in API >= 1.7!')\n return self._result(self._get(self._url(\"images/viz\")))\n params = {\n 'filter': name,\n 'only_ids': 1 if quiet else 0,\n 'all': 1 if all else 0,\n }\n res = self._result(self._get(self._url(\"/images/json\"), params=params),\n True)\n if quiet:\n return [x['Id'] for x in res]\n return res","label":0},{"cid":"fig/packages/docker/client.py::Client.import_image","text":"fig/packages/docker/client.py::Client.import_image\n def import_image(self, src=None, repository=None, tag=None, image=None):\n u = self._url(\"/images/create\")\n params = {\n 'repo': repository,\n 'tag': tag\n }\n\n if src:\n try:\n # XXX: this is ways not optimal but the only way\n # for now to import tarballs through the API\n fic = open(src)\n data = fic.read()\n fic.close()\n src = \"-\"\n except IOError:\n # file does not exists or not a file (URL)\n data = None\n if isinstance(src, six.string_types):\n params['fromSrc'] = src\n return self._result(self._post(u, data=data, params=params))\n return self._result(self._post(u, data=src, params=params))\n\n if image:\n params['fromImage'] = image\n return self._result(self._post(u, data=None, params=params))\n\n raise Exception(\"Must specify a src or image\")","label":0},{"cid":"fig/packages/docker/client.py::Client.info","text":"fig/packages/docker/client.py::Client.info\n def info(self):\n return self._result(self._get(self._url(\"/info\")),\n True)","label":0},{"cid":"fig/packages/docker/client.py::Client.insert","text":"fig/packages/docker/client.py::Client.insert\n def insert(self, image, url, path):\n api_url = self._url(\"/images/\" + image + \"/insert\")\n params = {\n 'url': url,\n 'path': path\n }\n return self._result(self._post(api_url, params=params))","label":0},{"cid":"fig/packages/docker/client.py::Client.inspect_container","text":"fig/packages/docker/client.py::Client.inspect_container\n def inspect_container(self, container):\n if isinstance(container, dict):\n container = container.get('Id')\n return self._result(\n self._get(self._url(\"/containers/{0}/json\".format(container))),\n True)","label":0},{"cid":"fig/packages/docker/client.py::Client.inspect_image","text":"fig/packages/docker/client.py::Client.inspect_image\n def inspect_image(self, image_id):\n return self._result(\n self._get(self._url(\"/images/{0}/json\".format(image_id))),\n True\n )","label":0},{"cid":"fig/packages/docker/client.py::Client.kill","text":"fig/packages/docker/client.py::Client.kill\n def kill(self, container, signal=None):\n if isinstance(container, dict):\n container = container.get('Id')\n url = self._url(\"/containers/{0}/kill\".format(container))\n params = {}\n if signal is not None:\n params['signal'] = signal\n res = self._post(url, params=params)\n\n self._raise_for_status(res)","label":0},{"cid":"fig/packages/docker/client.py::Client.login","text":"fig/packages/docker/client.py::Client.login\n def login(self, username, password=None, email=None, registry=None,\n reauth=False):\n # If we don't have any auth data so far, try reloading the config file\n # one more time in case anything showed up in there.\n if not self._auth_configs:\n self._auth_configs = auth.load_config()\n\n registry = registry or auth.INDEX_URL\n\n authcfg = auth.resolve_authconfig(self._auth_configs, registry)\n # If we found an existing auth config for this registry and username\n # combination, we can return it immediately unless reauth is requested.\n if authcfg and authcfg.get('username', None) == username \\\n and not reauth:\n return authcfg\n\n req_data = {\n 'username': username,\n 'password': password,\n 'email': email,\n 'serveraddress': registry,\n }\n\n response = self._post_json(self._url('/auth'), data=req_data)\n if response.status_code == 200:\n self._auth_configs[registry] = req_data\n return self._result(response, json=True)","label":0},{"cid":"fig/packages/docker/client.py::Client.logs","text":"fig/packages/docker/client.py::Client.logs\n def logs(self, container, stdout=True, stderr=True, stream=False):\n return self.attach(\n container,\n stdout=stdout,\n stderr=stderr,\n stream=stream,\n logs=True\n )","label":0},{"cid":"fig/packages/docker/client.py::Client.port","text":"fig/packages/docker/client.py::Client.port\n def port(self, container, private_port):\n if isinstance(container, dict):\n container = container.get('Id')\n res = self._get(self._url(\"/containers/{0}/json\".format(container)))\n self._raise_for_status(res)\n json_ = res.json()\n s_port = str(private_port)\n h_ports = None\n\n h_ports = json_['NetworkSettings']['Ports'].get(s_port + '/udp')\n if h_ports is None:\n h_ports = json_['NetworkSettings']['Ports'].get(s_port + '/tcp')\n\n return h_ports","label":0},{"cid":"fig/packages/docker/errors.py::APIError.__init__","text":"fig/packages/docker/errors.py::APIError.__init__\n def __init__(self, message, response, explanation=None):\n # requests 1.2 supports response as a keyword argument, but\n # requests 1.1 doesn't\n super(APIError, self).__init__(message)\n self.response = response\n\n self.explanation = explanation\n\n if self.explanation is None and response.content:\n self.explanation = response.content.strip()","label":0},{"cid":"fig/packages/docker/errors.py::APIError.__str__","text":"fig/packages/docker/errors.py::APIError.__str__\n def __str__(self):\n message = super(APIError, self).__str__()\n\n if self.is_client_error():\n message = '%s Client Error: %s' % (\n self.response.status_code, self.response.reason)\n\n elif self.is_server_error():\n message = '%s Server Error: %s' % (\n self.response.status_code, self.response.reason)\n\n if self.explanation:\n message = '%s (\"%s\")' % (message, self.explanation)\n\n return message","label":0},{"cid":"fig/packages/docker/errors.py::APIError.is_client_error","text":"fig/packages/docker/errors.py::APIError.is_client_error\n def is_client_error(self):\n return 400 <= self.response.status_code < 500","label":0},{"cid":"fig/packages/docker/errors.py::APIError.is_server_error","text":"fig/packages/docker/errors.py::APIError.is_server_error\n def is_server_error(self):\n return 500 <= self.response.status_code < 600","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.__init__","text":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.__init__\n def __init__(self, base_url, unix_socket, timeout=60):\n httplib.HTTPConnection.__init__(self, 'localhost', timeout=timeout)\n self.base_url = base_url\n self.unix_socket = unix_socket\n self.timeout = timeout","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.connect","text":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.connect\n def connect(self):\n sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\n sock.settimeout(self.timeout)\n sock.connect(self.base_url.replace(\"http+unix:/\", \"\"))\n self.sock = sock","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection._extract_path","text":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection._extract_path\n def _extract_path(self, url):\n # remove the base_url entirely..\n return url.replace(self.base_url, \"\")","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.request","text":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.request\n def request(self, method, url, **kwargs):\n url = self._extract_path(self.unix_socket)\n super(UnixHTTPConnection, self).request(method, url, **kwargs)","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool.__init__","text":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool.__init__\n def __init__(self, base_url, socket_path, timeout=60):\n connectionpool.HTTPConnectionPool.__init__(self, 'localhost',\n timeout=timeout)\n self.base_url = base_url\n self.socket_path = socket_path\n self.timeout = timeout","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool._new_conn","text":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool._new_conn\n def _new_conn(self):\n return UnixHTTPConnection(self.base_url, self.socket_path,\n self.timeout)","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.__init__","text":"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.__init__\n def __init__(self, base_url, timeout=60):\n self.base_url = base_url\n self.timeout = timeout\n super(UnixAdapter, self).__init__()","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.get_connection","text":"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.get_connection\n def get_connection(self, socket_path, proxies=None):\n return UnixHTTPConnectionPool(self.base_url, socket_path, self.timeout)","label":0},{"cid":"fig/packages/docker/utils/utils.py::mkbuildcontext","text":"fig/packages/docker/utils/utils.py::mkbuildcontext\ndef mkbuildcontext(dockerfile):\n f = tempfile.NamedTemporaryFile()\n t = tarfile.open(mode='w', fileobj=f)\n if isinstance(dockerfile, io.StringIO):\n dfinfo = tarfile.TarInfo('Dockerfile')\n if six.PY3:\n raise TypeError('Please use io.BytesIO to create in-memory '\n 'Dockerfiles with Python 3')\n else:\n dfinfo.size = len(dockerfile.getvalue())\n elif isinstance(dockerfile, io.BytesIO):\n dfinfo = tarfile.TarInfo('Dockerfile')\n dfinfo.size = len(dockerfile.getvalue())\n else:\n dfinfo = t.gettarinfo(fileobj=dockerfile, arcname='Dockerfile')\n t.addfile(dfinfo, dockerfile)\n t.close()\n f.seek(0)\n return f","label":0},{"cid":"fig/packages/docker/utils/utils.py::tar","text":"fig/packages/docker/utils/utils.py::tar\ndef tar(path):\n f = tempfile.NamedTemporaryFile()\n t = tarfile.open(mode='w', fileobj=f)\n t.add(path, arcname='.')\n t.close()\n f.seek(0)\n return f","label":0},{"cid":"fig/packages/docker/utils/utils.py::compare_version","text":"fig/packages/docker/utils/utils.py::compare_version\ndef compare_version(v1, v2):\n \"\"\"Compare docker versions\n\n >>> v1 = '1.9'\n >>> v2 = '1.10'\n >>> compare_version(v1, v2)\n 1\n >>> compare_version(v2, v1)\n -1\n >>> compare_version(v2, v2)\n 0\n \"\"\"\n s1 = StrictVersion(v1)\n s2 = StrictVersion(v2)\n if s1 == s2:\n return 0\n elif s1 > s2:\n return -1\n else:\n return 1","label":0},{"cid":"fig/packages/docker/utils/utils.py::ping","text":"fig/packages/docker/utils/utils.py::ping\ndef ping(url):\n try:\n res = requests.get(url)\n except Exception:\n return False\n else:\n return res.status_code < 400","label":0},{"cid":"fig/packages/docker/utils/utils.py::_convert_port_binding","text":"fig/packages/docker/utils/utils.py::_convert_port_binding\ndef _convert_port_binding(binding):\n result = {'HostIp': '', 'HostPort': ''}\n if isinstance(binding, tuple):\n if len(binding) == 2:\n result['HostPort'] = binding[1]\n result['HostIp'] = binding[0]\n elif isinstance(binding[0], six.string_types):\n result['HostIp'] = binding[0]\n else:\n result['HostPort'] = binding[0]\n else:\n result['HostPort'] = binding\n\n if result['HostPort'] is None:\n result['HostPort'] = ''\n else:\n result['HostPort'] = str(result['HostPort'])\n\n return result","label":0},{"cid":"fig/packages/docker/utils/utils.py::convert_port_bindings","text":"fig/packages/docker/utils/utils.py::convert_port_bindings\ndef convert_port_bindings(port_bindings):\n result = {}\n for k, v in six.iteritems(port_bindings):\n key = str(k)\n if '/' not in key:\n key = key + '/tcp'\n if isinstance(v, list):\n result[key] = [_convert_port_binding(binding) for binding in v]\n else:\n result[key] = [_convert_port_binding(v)]\n return result","label":0},{"cid":"fig/packages/docker/utils/utils.py::parse_repository_tag","text":"fig/packages/docker/utils/utils.py::parse_repository_tag\ndef parse_repository_tag(repo):\n column_index = repo.rfind(':')\n if column_index < 0:\n return repo, \"\"\n tag = repo[column_index+1:]\n slash_index = tag.find('/')\n if slash_index < 0:\n return repo[:column_index], tag\n\n return repo, \"\"","label":0},{"cid":"fig/packages/six.py::_add_doc","text":"fig/packages/six.py::_add_doc\ndef _add_doc(func, doc):\n \"\"\"Add documentation to a function.\"\"\"\n func.__doc__ = doc","label":0},{"cid":"fig/packages/six.py::_import_module","text":"fig/packages/six.py::_import_module\ndef _import_module(name):\n \"\"\"Import module, returning the module after the last dot.\"\"\"\n __import__(name)\n return sys.modules[name]","label":0},{"cid":"fig/packages/six.py::_LazyDescr.__init__","text":"fig/packages/six.py::_LazyDescr.__init__\n def __init__(self, name):\n self.name = name","label":0},{"cid":"fig/packages/six.py::_LazyDescr.__get__","text":"fig/packages/six.py::_LazyDescr.__get__\n def __get__(self, obj, tp):\n result = self._resolve()\n setattr(obj, self.name, result)\n # This is a bit ugly, but it avoids running this again.\n delattr(tp, self.name)\n return result","label":0},{"cid":"fig/packages/six.py::MovedModule.__init__","text":"fig/packages/six.py::MovedModule.__init__\n def __init__(self, name, old, new=None):\n super(MovedModule, self).__init__(name)\n if PY3:\n if new is None:\n new = name\n self.mod = new\n else:\n self.mod = old","label":0},{"cid":"fig/packages/six.py::MovedModule._resolve","text":"fig/packages/six.py::MovedModule._resolve\n def _resolve(self):\n return _import_module(self.mod)","label":0},{"cid":"fig/packages/six.py::MovedAttribute.__init__","text":"fig/packages/six.py::MovedAttribute.__init__\n def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):\n super(MovedAttribute, self).__init__(name)\n if PY3:\n if new_mod is None:\n new_mod = name\n self.mod = new_mod\n if new_attr is None:\n if old_attr is None:\n new_attr = name\n else:\n new_attr = old_attr\n self.attr = new_attr\n else:\n self.mod = old_mod\n if old_attr is None:\n old_attr = name\n self.attr = old_attr","label":0},{"cid":"fig/packages/six.py::MovedAttribute._resolve","text":"fig/packages/six.py::MovedAttribute._resolve\n def _resolve(self):\n module = _import_module(self.mod)\n return getattr(module, self.attr)","label":0},{"cid":"fig/packages/six.py::add_move","text":"fig/packages/six.py::add_move\ndef add_move(move):\n \"\"\"Add an item to six.moves.\"\"\"\n setattr(_MovedItems, move.name, move)","label":0},{"cid":"fig/packages/six.py::remove_move","text":"fig/packages/six.py::remove_move\ndef remove_move(name):\n \"\"\"Remove item from six.moves.\"\"\"\n try:\n delattr(_MovedItems, name)\n except AttributeError:\n try:\n del moves.__dict__[name]\n except KeyError:\n raise AttributeError(\"no such move, %r\" % (name,))","label":0},{"cid":"fig/packages/six.py::iterkeys","text":"fig/packages/six.py::iterkeys\ndef iterkeys(d, **kw):\n \"\"\"Return an iterator over the keys of a dictionary.\"\"\"\n return iter(getattr(d, _iterkeys)(**kw))","label":0},{"cid":"fig/packages/six.py::itervalues","text":"fig/packages/six.py::itervalues\ndef itervalues(d, **kw):\n \"\"\"Return an iterator over the values of a dictionary.\"\"\"\n return iter(getattr(d, _itervalues)(**kw))","label":0},{"cid":"fig/packages/six.py::iteritems","text":"fig/packages/six.py::iteritems\ndef iteritems(d, **kw):\n \"\"\"Return an iterator over the (key, value) pairs of a dictionary.\"\"\"\n return iter(getattr(d, _iteritems)(**kw))","label":0},{"cid":"fig/packages/six.py::iterlists","text":"fig/packages/six.py::iterlists\ndef iterlists(d, **kw):\n \"\"\"Return an iterator over the (key, [values]) pairs of a dictionary.\"\"\"\n return iter(getattr(d, _iterlists)(**kw))","label":0},{"cid":"fig/packages/six.py::with_metaclass","text":"fig/packages/six.py::with_metaclass\ndef with_metaclass(meta, base=object):\n \"\"\"Create a base class with a metaclass.\"\"\"\n return meta(\"NewBase\", (base,), {})","label":0},{"cid":"fig/project.py::sort_service_dicts","text":"fig/project.py::sort_service_dicts\ndef sort_service_dicts(services):\n # Topological sort (Cormen/Tarjan algorithm).\n unmarked = services[:]\n temporary_marked = set()\n sorted_services = []\n\n get_service_names = lambda links: [link.split(':')[0] for link in links]\n\n def visit(n):\n if n['name'] in temporary_marked:\n if n['name'] in get_service_names(n.get('links', [])):\n raise DependencyError('A service can not link to itself: %s' % n['name'])\n else:\n raise DependencyError('Circular import between %s' % ' and '.join(temporary_marked))\n if n in unmarked:\n temporary_marked.add(n['name'])\n dependents = [m for m in services if n['name'] in get_service_names(m.get('links', []))]\n for m in dependents:\n visit(m)\n temporary_marked.remove(n['name'])\n unmarked.remove(n)\n sorted_services.insert(0, n)\n\n while unmarked:\n visit(unmarked[-1])\n\n return sorted_services","label":0},{"cid":"fig/project.py::sort_service_dicts.visit","text":"fig/project.py::sort_service_dicts.visit\n def visit(n):\n if n['name'] in temporary_marked:\n if n['name'] in get_service_names(n.get('links', [])):\n raise DependencyError('A service can not link to itself: %s' % n['name'])\n else:\n raise DependencyError('Circular import between %s' % ' and '.join(temporary_marked))\n if n in unmarked:\n temporary_marked.add(n['name'])\n dependents = [m for m in services if n['name'] in get_service_names(m.get('links', []))]\n for m in dependents:\n visit(m)\n temporary_marked.remove(n['name'])\n unmarked.remove(n)\n sorted_services.insert(0, n)","label":0},{"cid":"fig/project.py::Project.__init__","text":"fig/project.py::Project.__init__\n def __init__(self, name, services, client):\n self.name = name\n self.services = services\n self.client = client","label":0},{"cid":"fig/project.py::Project.from_dicts","text":"fig/project.py::Project.from_dicts\n @classmethod\n def from_dicts(cls, name, service_dicts, client):\n \"\"\"\n Construct a ServiceCollection from a list of dicts representing services.\n \"\"\"\n project = cls(name, [], client)\n for service_dict in sort_service_dicts(service_dicts):\n # Reference links by object\n links = []\n if 'links' in service_dict:\n for link in service_dict.get('links', []):\n if ':' in link:\n service_name, link_name = link.split(':', 1)\n else:\n service_name, link_name = link, None\n try:\n links.append((project.get_service(service_name), link_name))\n except NoSuchService:\n raise ConfigurationError('Service \"%s\" has a link to service \"%s\" which does not exist.' % (service_dict['name'], service_name))\n\n del service_dict['links']\n project.services.append(Service(client=client, project=name, links=links, **service_dict))\n return project","label":1},{"cid":"fig/project.py::Project.from_config","text":"fig/project.py::Project.from_config\n @classmethod\n def from_config(cls, name, config, client):\n dicts = []\n for service_name, service in list(config.items()):\n if not isinstance(service, dict):\n raise ConfigurationError('Service \"%s\" doesn\\'t have any configuration options. All top level keys in your fig.yml must map to a dictionary of configuration options.')\n service['name'] = service_name\n dicts.append(service)\n return cls.from_dicts(name, dicts, client)","label":0},{"cid":"fig/project.py::Project.get_service","text":"fig/project.py::Project.get_service\n def get_service(self, name):\n \"\"\"\n Retrieve a service by name. Raises NoSuchService\n if the named service does not exist.\n \"\"\"\n for service in self.services:\n if service.name == name:\n return service\n\n raise NoSuchService(name)","label":0},{"cid":"fig/project.py::Project.get_services","text":"fig/project.py::Project.get_services\n def get_services(self, service_names=None):\n \"\"\"\n Returns a list of this project's services filtered\n by the provided list of names, or all services if\n service_names is None or [].\n\n Preserves the original order of self.services.\n\n Raises NoSuchService if any of the named services\n do not exist.\n \"\"\"\n if service_names is None or len(service_names) == 0:\n return self.services\n else:\n unsorted = [self.get_service(name) for name in service_names]\n return [s for s in self.services if s in unsorted]","label":1},{"cid":"fig/project.py::Project.start","text":"fig/project.py::Project.start\n def start(self, service_names=None, **options):\n for service in self.get_services(service_names):\n service.start(**options)","label":0},{"cid":"fig/project.py::Project.stop","text":"fig/project.py::Project.stop\n def stop(self, service_names=None, **options):\n for service in reversed(self.get_services(service_names)):\n service.stop(**options)","label":0},{"cid":"fig/project.py::Project.kill","text":"fig/project.py::Project.kill\n def kill(self, service_names=None, **options):\n for service in reversed(self.get_services(service_names)):\n service.kill(**options)","label":0},{"cid":"fig/project.py::Project.build","text":"fig/project.py::Project.build\n def build(self, service_names=None, **options):\n for service in self.get_services(service_names):\n if service.can_be_built():\n service.build(**options)\n else:\n log.info('%s uses an image, skipping' % service.name)","label":0},{"cid":"fig/project.py::Project.up","text":"fig/project.py::Project.up\n def up(self, service_names=None):\n new_containers = []\n\n for service in self.get_services(service_names):\n for (_, new) in service.recreate_containers():\n new_containers.append(new)\n\n return new_containers","label":1},{"cid":"fig/project.py::Project.remove_stopped","text":"fig/project.py::Project.remove_stopped\n def remove_stopped(self, service_names=None, **options):\n for service in self.get_services(service_names):\n service.remove_stopped(**options)","label":0},{"cid":"fig/project.py::Project.containers","text":"fig/project.py::Project.containers\n def containers(self, service_names=None, *args, **kwargs):\n l = []\n for service in self.get_services(service_names):\n for container in service.containers(*args, **kwargs):\n l.append(container)\n return l","label":0},{"cid":"fig/project.py::NoSuchService.__init__","text":"fig/project.py::NoSuchService.__init__\n def __init__(self, name):\n self.name = name\n self.msg = \"No such service: %s\" % self.name","label":0},{"cid":"fig/project.py::NoSuchService.__str__","text":"fig/project.py::NoSuchService.__str__\n def __str__(self):\n return self.msg","label":0},{"cid":"fig/project.py::ConfigurationError.__init__","text":"fig/project.py::ConfigurationError.__init__\n def __init__(self, msg):\n self.msg = msg","label":0},{"cid":"fig/project.py::ConfigurationError.__str__","text":"fig/project.py::ConfigurationError.__str__\n def __str__(self):\n return self.msg","label":0},{"cid":"setup.py::read","text":"setup.py::read\ndef read(*parts):\n path = os.path.join(os.path.dirname(__file__), *parts)\n with codecs.open(path, encoding='utf-8') as fobj:\n return fobj.read()","label":0},{"cid":"setup.py::find_version","text":"setup.py::find_version\ndef find_version(*file_paths):\n version_file = read(*file_paths)\n version_match = re.search(r\"^__version__ = ['\\\"]([^'\\\"]*)['\\\"]\",\n version_file, re.M)\n if version_match:\n return version_match.group(1)\n raise RuntimeError(\"Unable to find version string.\")","label":0}],"string":"[\n {\n \"cid\": \"fig/cli/command.py::Command.__init__\",\n \"text\": \"fig/cli/command.py::Command.__init__\\n def __init__(self):\\n self.yaml_path = os.environ.get('FIG_FILE', None)\\n self.explicit_project_name = None\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.dispatch\",\n \"text\": \"fig/cli/command.py::Command.dispatch\\n def dispatch(self, *args, **kwargs):\\n try:\\n super(Command, self).dispatch(*args, **kwargs)\\n except ConnectionError:\\n if call_silently(['which', 'docker']) != 0:\\n if is_mac():\\n raise errors.DockerNotFoundMac()\\n elif is_ubuntu():\\n raise errors.DockerNotFoundUbuntu()\\n else:\\n raise errors.DockerNotFoundGeneric()\\n elif call_silently(['which', 'docker-osx']) == 0:\\n raise errors.ConnectionErrorDockerOSX()\\n else:\\n raise errors.ConnectionErrorGeneric(self.client.base_url)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.perform_command\",\n \"text\": \"fig/cli/command.py::Command.perform_command\\n def perform_command(self, options, *args, **kwargs):\\n if options['--file'] is not None:\\n self.yaml_path = os.path.join(self.base_dir, options['--file'])\\n if options['--project-name'] is not None:\\n self.explicit_project_name = options['--project-name']\\n return super(Command, self).perform_command(options, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.client\",\n \"text\": \"fig/cli/command.py::Command.client\\n @cached_property\\n def client(self):\\n return Client(docker_url())\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.project\",\n \"text\": \"fig/cli/command.py::Command.project\\n @cached_property\\n def project(self):\\n try:\\n yaml_path = self.yaml_path\\n if yaml_path is None:\\n yaml_path = self.check_yaml_filename()\\n config = yaml.load(open(yaml_path))\\n except IOError as e:\\n if e.errno == errno.ENOENT:\\n raise errors.FigFileNotFound(os.path.basename(e.filename))\\n raise errors.UserError(six.text_type(e))\\n\\n try:\\n return Project.from_config(self.project_name, config, self.client)\\n except ConfigError as e:\\n raise errors.UserError(six.text_type(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.project_name\",\n \"text\": \"fig/cli/command.py::Command.project_name\\n @cached_property\\n def project_name(self):\\n project = os.path.basename(os.getcwd())\\n if self.explicit_project_name is not None:\\n project = self.explicit_project_name\\n project = re.sub(r'[^a-zA-Z0-9]', '', project)\\n if not project:\\n project = 'default'\\n return project\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.formatter\",\n \"text\": \"fig/cli/command.py::Command.formatter\\n @cached_property\\n def formatter(self):\\n return Formatter()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.check_yaml_filename\",\n \"text\": \"fig/cli/command.py::Command.check_yaml_filename\\n def check_yaml_filename(self):\\n if os.path.exists(os.path.join(self.base_dir, 'fig.yaml')):\\n\\n log.warning(\\\"Fig just read the file 'fig.yaml' on startup, rather than 'fig.yml'\\\")\\n log.warning(\\\"Please be aware that fig.yml the expected extension in most cases, and using .yaml can cause compatibility issues in future\\\")\\n\\n return os.path.join(self.base_dir, 'fig.yaml')\\n else:\\n return os.path.join(self.base_dir, 'fig.yml')\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::UserError.__init__\",\n \"text\": \"fig/cli/errors.py::UserError.__init__\\n def __init__(self, msg):\\n self.msg = dedent(msg).strip()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::UserError.__unicode__\",\n \"text\": \"fig/cli/errors.py::UserError.__unicode__\\n def __unicode__(self):\\n return self.msg\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::DockerNotFoundMac.__init__\",\n \"text\": \"fig/cli/errors.py::DockerNotFoundMac.__init__\\n def __init__(self):\\n super(DockerNotFoundMac, self).__init__(\\\"\\\"\\\"\\n Couldn't connect to Docker daemon. You might need to install docker-osx:\\n\\n https://github.com/noplay/docker-osx\\n \\\"\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::DockerNotFoundUbuntu.__init__\",\n \"text\": \"fig/cli/errors.py::DockerNotFoundUbuntu.__init__\\n def __init__(self):\\n super(DockerNotFoundUbuntu, self).__init__(\\\"\\\"\\\"\\n Couldn't connect to Docker daemon. You might need to install Docker:\\n\\n http://docs.docker.io/en/latest/installation/ubuntulinux/\\n \\\"\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::DockerNotFoundGeneric.__init__\",\n \"text\": \"fig/cli/errors.py::DockerNotFoundGeneric.__init__\\n def __init__(self):\\n super(DockerNotFoundGeneric, self).__init__(\\\"\\\"\\\"\\n Couldn't connect to Docker daemon. You might need to install Docker:\\n\\n http://docs.docker.io/en/latest/installation/\\n \\\"\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::ConnectionErrorDockerOSX.__init__\",\n \"text\": \"fig/cli/errors.py::ConnectionErrorDockerOSX.__init__\\n def __init__(self):\\n super(ConnectionErrorDockerOSX, self).__init__(\\\"\\\"\\\"\\n Couldn't connect to Docker daemon - you might need to run `docker-osx shell`.\\n \\\"\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::ConnectionErrorGeneric.__init__\",\n \"text\": \"fig/cli/errors.py::ConnectionErrorGeneric.__init__\\n def __init__(self, url):\\n super(ConnectionErrorGeneric, self).__init__(\\\"\\\"\\\"\\n Couldn't connect to Docker daemon at %s - is it running?\\n\\n If it's at a non-standard location, specify the URL with the DOCKER_HOST environment variable.\\n \\\"\\\"\\\" % url)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::FigFileNotFound.__init__\",\n \"text\": \"fig/cli/errors.py::FigFileNotFound.__init__\\n def __init__(self, filename):\\n super(FigFileNotFound, self).__init__(\\\"\\\"\\\"\\n Can't find %s. Are you in the right directory?\\n \\\"\\\"\\\" % filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter.__init__\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter.__init__\\n def __init__(self, containers, attach_params=None):\\n self.containers = containers\\n self.attach_params = attach_params or {}\\n self.prefix_width = self._calculate_prefix_width(containers)\\n self.generators = self._make_log_generators()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter.run\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter.run\\n def run(self):\\n mux = Multiplexer(self.generators)\\n for line in mux.loop():\\n sys.stdout.write(line.encode(sys.__stdout__.encoding or 'utf-8'))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter._calculate_prefix_width\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter._calculate_prefix_width\\n def _calculate_prefix_width(self, containers):\\n \\\"\\\"\\\"\\n Calculate the maximum width of container names so we can make the log\\n prefixes line up like so:\\n\\n db_1 | Listening\\n web_1 | Listening\\n \\\"\\\"\\\"\\n prefix_width = 0\\n for container in containers:\\n prefix_width = max(prefix_width, len(container.name_without_project))\\n return prefix_width\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter._make_log_generators\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter._make_log_generators\\n def _make_log_generators(self):\\n color_fns = cycle(colors.rainbow())\\n generators = []\\n\\n for container in self.containers:\\n color_fn = color_fns.next()\\n generators.append(self._make_log_generator(container, color_fn))\\n\\n return generators\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter._make_log_generator\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter._make_log_generator\\n def _make_log_generator(self, container, color_fn):\\n prefix = color_fn(self._generate_prefix(container))\\n # Attach to container before log printer starts running\\n line_generator = split_buffer(self._attach(container), '\\\\n')\\n\\n for line in line_generator:\\n yield prefix + line.decode('utf-8')\\n\\n exit_code = container.wait()\\n yield color_fn(\\\"%s exited with code %s\\\\n\\\" % (container.name, exit_code))\\n yield STOP\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter._generate_prefix\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter._generate_prefix\\n def _generate_prefix(self, container):\\n \\\"\\\"\\\"\\n Generate the prefix for a log line without colour\\n \\\"\\\"\\\"\\n name = container.name_without_project\\n padding = ' ' * (self.prefix_width - len(name))\\n return ''.join([name, padding, ' | '])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter._attach\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter._attach\\n def _attach(self, container):\\n params = {\\n 'stdout': True,\\n 'stderr': True,\\n 'stream': True,\\n }\\n params.update(self.attach_params)\\n params = dict((name, 1 if value else 0) for (name, value) in list(params.items()))\\n return container.attach(**params)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::main\",\n \"text\": \"fig/cli/main.py::main\\ndef main():\\n console_handler = logging.StreamHandler(stream=sys.stderr)\\n console_handler.setFormatter(logging.Formatter())\\n console_handler.setLevel(logging.INFO)\\n root_logger = logging.getLogger()\\n root_logger.addHandler(console_handler)\\n root_logger.setLevel(logging.DEBUG)\\n\\n # Disable requests logging\\n logging.getLogger(\\\"requests\\\").propagate = False\\n\\n try:\\n command = TopLevelCommand()\\n command.sys_dispatch()\\n except KeyboardInterrupt:\\n log.error(\\\"\\\\nAborting.\\\")\\n sys.exit(1)\\n except (UserError, NoSuchService, ConfigurationError) as e:\\n log.error(e.msg)\\n sys.exit(1)\\n except NoSuchCommand as e:\\n log.error(\\\"No such command: %s\\\", e.command)\\n log.error(\\\"\\\")\\n log.error(\\\"\\\\n\\\".join(parse_doc_section(\\\"commands:\\\", getdoc(e.supercommand))))\\n sys.exit(1)\\n except APIError as e:\\n log.error(e.explanation)\\n sys.exit(1)\\n except BuildError as e:\\n log.error(\\\"Service '%s' failed to build: %s\\\" % (e.service.name, e.reason))\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::parse_doc_section\",\n \"text\": \"fig/cli/main.py::parse_doc_section\\ndef parse_doc_section(name, source):\\n pattern = re.compile('^([^\\\\n]*' + name + '[^\\\\n]*\\\\n?(?:[ \\\\t].*?(?:\\\\n|$))*)',\\n re.IGNORECASE | re.MULTILINE)\\n return [s.strip() for s in pattern.findall(source)]\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.docopt_options\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.docopt_options\\n def docopt_options(self):\\n options = super(TopLevelCommand, self).docopt_options()\\n options['version'] = \\\"fig %s\\\" % __version__\\n return options\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.build\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.build\\n def build(self, options):\\n \\\"\\\"\\\"\\n Build or rebuild services.\\n\\n Services are built once and then tagged as `project_service`,\\n e.g. `figtest_db`. If you change a service's `Dockerfile` or the\\n contents of its build directory, you can run `fig build` to rebuild it.\\n\\n Usage: build [SERVICE...]\\n \\\"\\\"\\\"\\n self.project.build(service_names=options['SERVICE'])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.help\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.help\\n def help(self, options):\\n \\\"\\\"\\\"\\n Get help on a command.\\n\\n Usage: help COMMAND\\n \\\"\\\"\\\"\\n command = options['COMMAND']\\n if not hasattr(self, command):\\n raise NoSuchCommand(command, self)\\n raise SystemExit(getdoc(getattr(self, command)))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.kill\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.kill\\n def kill(self, options):\\n \\\"\\\"\\\"\\n Force stop service containers.\\n\\n Usage: kill [SERVICE...]\\n \\\"\\\"\\\"\\n self.project.kill(service_names=options['SERVICE'])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.logs\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.logs\\n def logs(self, options):\\n \\\"\\\"\\\"\\n View output from containers.\\n\\n Usage: logs [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\\n print(\\\"Attaching to\\\", list_containers(containers))\\n LogPrinter(containers, attach_params={'logs': True}).run()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.ps\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.ps\\n def ps(self, options):\\n \\\"\\\"\\\"\\n List containers.\\n\\n Usage: ps [options] [SERVICE...]\\n\\n Options:\\n -q Only display IDs\\n \\\"\\\"\\\"\\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True) + self.project.containers(service_names=options['SERVICE'], one_off=True)\\n\\n if options['-q']:\\n for container in containers:\\n print(container.id)\\n else:\\n headers = [\\n 'Name',\\n 'Command',\\n 'State',\\n 'Ports',\\n ]\\n rows = []\\n for container in containers:\\n command = container.human_readable_command\\n if len(command) > 30:\\n command = '%s ...' % command[:26]\\n rows.append([\\n container.name,\\n command,\\n container.human_readable_state,\\n container.human_readable_ports,\\n ])\\n print(Formatter().table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.rm\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.rm\\n def rm(self, options):\\n \\\"\\\"\\\"\\n Remove stopped service containers.\\n\\n Usage: rm [options] [SERVICE...]\\n\\n Options:\\n --force Don't ask to confirm removal\\n -v Remove volumes associated with containers\\n \\\"\\\"\\\"\\n all_containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\\n stopped_containers = [c for c in all_containers if not c.is_running]\\n\\n if len(stopped_containers) > 0:\\n print(\\\"Going to remove\\\", list_containers(stopped_containers))\\n if options.get('--force') \\\\\\n or yesno(\\\"Are you sure? [yN] \\\", default=False):\\n self.project.remove_stopped(\\n service_names=options['SERVICE'],\\n v=options.get('-v', False)\\n )\\n else:\\n print(\\\"No stopped containers\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.run\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.run\\n def run(self, options):\\n \\\"\\\"\\\"\\n Run a one-off command on a service.\\n\\n For example:\\n\\n $ fig run web python manage.py shell\\n\\n Note that this will not start any services that the command's service\\n links to. So if, for example, your one-off command talks to your\\n database, you will need to run `fig up -d db` first.\\n\\n Usage: run [options] SERVICE COMMAND [ARGS...]\\n\\n Options:\\n -d Detached mode: Run container in the background, print new\\n container name\\n -T Disable pseudo-tty allocation. By default `fig run`\\n allocates a TTY.\\n --rm Remove container after run. Ignored in detached mode.\\n \\\"\\\"\\\"\\n service = self.project.get_service(options['SERVICE'])\\n\\n tty = True\\n if options['-d'] or options['-T'] or not sys.stdin.isatty():\\n tty = False\\n\\n container_options = {\\n 'command': [options['COMMAND']] + options['ARGS'],\\n 'tty': tty,\\n 'stdin_open': not options['-d'],\\n }\\n container = service.create_container(one_off=True, **container_options)\\n if options['-d']:\\n service.start_container(container, ports=None, one_off=True)\\n print(container.name)\\n else:\\n with self._attach_to_container(container.id, raw=tty) as c:\\n service.start_container(container, ports=None, one_off=True)\\n c.run()\\n exit_code = container.wait()\\n if options['--rm']:\\n log.info(\\\"Removing %s...\\\" % container.name)\\n self.client.remove_container(container.id)\\n sys.exit(exit_code)\",\n \"label\": 1\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.scale\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.scale\\n def scale(self, options):\\n \\\"\\\"\\\"\\n Set number of containers to run for a service.\\n\\n Numbers are specified in the form `service=num` as arguments.\\n For example:\\n\\n $ fig scale web=2 worker=3\\n\\n Usage: scale [SERVICE=NUM...]\\n \\\"\\\"\\\"\\n for s in options['SERVICE=NUM']:\\n if '=' not in s:\\n raise UserError('Arguments to scale should be in the form service=num')\\n service_name, num = s.split('=', 1)\\n try:\\n num = int(num)\\n except ValueError:\\n raise UserError('Number of containers for service \\\"%s\\\" is not a number' % service)\\n try:\\n self.project.get_service(service_name).scale(num)\\n except CannotBeScaledError:\\n raise UserError('Service \\\"%s\\\" cannot be scaled because it specifies a port on the host. If multiple containers for this service were created, the port would clash.\\\\n\\\\nRemove the \\\":\\\" from the port definition in fig.yml so Docker can choose a random port for each container.' % service_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.start\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.start\\n def start(self, options):\\n \\\"\\\"\\\"\\n Start existing containers.\\n\\n Usage: start [SERVICE...]\\n \\\"\\\"\\\"\\n self.project.start(service_names=options['SERVICE'])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.stop\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.stop\\n def stop(self, options):\\n \\\"\\\"\\\"\\n Stop running containers without removing them.\\n\\n They can be started again with `fig start`.\\n\\n Usage: stop [SERVICE...]\\n \\\"\\\"\\\"\\n self.project.stop(service_names=options['SERVICE'])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.up\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.up\\n def up(self, options):\\n \\\"\\\"\\\"\\n Build, (re)create, start and attach to containers for a service.\\n\\n By default, `fig up` will aggregate the output of each container, and\\n when it exits, all containers will be stopped. If you run `fig up -d`,\\n it'll start the containers in the background and leave them running.\\n\\n If there are existing containers for a service, `fig up` will stop\\n and recreate them (preserving mounted volumes with volumes-from),\\n so that changes in `fig.yml` are picked up.\\n\\n Usage: up [options] [SERVICE...]\\n\\n Options:\\n -d Detached mode: Run containers in the background, print new\\n container names\\n \\\"\\\"\\\"\\n detached = options['-d']\\n\\n to_attach = self.project.up(service_names=options['SERVICE'])\\n\\n if not detached:\\n print(\\\"Attaching to\\\", list_containers(to_attach))\\n log_printer = LogPrinter(to_attach, attach_params={\\\"logs\\\": True})\\n\\n try:\\n log_printer.run()\\n finally:\\n def handler(signal, frame):\\n self.project.kill(service_names=options['SERVICE'])\\n sys.exit(0)\\n signal.signal(signal.SIGINT, handler)\\n\\n print(\\\"Gracefully stopping... (press Ctrl+C again to force)\\\")\\n self.project.stop(service_names=options['SERVICE'])\",\n \"label\": 1\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand._attach_to_container\",\n \"text\": \"fig/cli/main.py::TopLevelCommand._attach_to_container\\n def _attach_to_container(self, container_id, raw=False):\\n socket_in = self.client.attach_socket(container_id, params={'stdin': 1, 'stream': 1})\\n socket_out = self.client.attach_socket(container_id, params={'stdout': 1, 'logs': 1, 'stream': 1})\\n socket_err = self.client.attach_socket(container_id, params={'stderr': 1, 'logs': 1, 'stream': 1})\\n\\n return SocketClient(\\n socket_in=socket_in,\\n socket_out=socket_out,\\n socket_err=socket_err,\\n raw=raw,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::list_containers\",\n \"text\": \"fig/cli/main.py::list_containers\\ndef list_containers(containers):\\n return \\\", \\\".join(c.name for c in containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.__init__\",\n \"text\": \"fig/container.py::Container.__init__\\n def __init__(self, client, dictionary, has_been_inspected=False):\\n self.client = client\\n self.dictionary = dictionary\\n self.has_been_inspected = has_been_inspected\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.from_ps\",\n \"text\": \"fig/container.py::Container.from_ps\\n @classmethod\\n def from_ps(cls, client, dictionary, **kwargs):\\n \\\"\\\"\\\"\\n Construct a container object from the output of GET /containers/json.\\n \\\"\\\"\\\"\\n new_dictionary = {\\n 'ID': dictionary['Id'],\\n 'Image': dictionary['Image'],\\n }\\n for name in dictionary.get('Names', []):\\n if len(name.split('/')) == 2:\\n new_dictionary['Name'] = name\\n return cls(client, new_dictionary, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.from_id\",\n \"text\": \"fig/container.py::Container.from_id\\n @classmethod\\n def from_id(cls, client, id):\\n return cls(client, client.inspect_container(id))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.create\",\n \"text\": \"fig/container.py::Container.create\\n @classmethod\\n def create(cls, client, **options):\\n response = client.create_container(**options)\\n return cls.from_id(client, response['Id'])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.id\",\n \"text\": \"fig/container.py::Container.id\\n @property\\n def id(self):\\n return self.dictionary['ID']\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.image\",\n \"text\": \"fig/container.py::Container.image\\n @property\\n def image(self):\\n return self.dictionary['Image']\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.short_id\",\n \"text\": \"fig/container.py::Container.short_id\\n @property\\n def short_id(self):\\n return self.id[:10]\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.name\",\n \"text\": \"fig/container.py::Container.name\\n @property\\n def name(self):\\n return self.dictionary['Name'][1:]\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.name_without_project\",\n \"text\": \"fig/container.py::Container.name_without_project\\n @property\\n def name_without_project(self):\\n return '_'.join(self.dictionary['Name'].split('_')[1:])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.number\",\n \"text\": \"fig/container.py::Container.number\\n @property\\n def number(self):\\n try:\\n return int(self.name.split('_')[-1])\\n except ValueError:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.human_readable_ports\",\n \"text\": \"fig/container.py::Container.human_readable_ports\\n @property\\n def human_readable_ports(self):\\n self.inspect_if_not_inspected()\\n if not self.dictionary['NetworkSettings']['Ports']:\\n return ''\\n ports = []\\n for private, public in list(self.dictionary['NetworkSettings']['Ports'].items()):\\n if public:\\n ports.append('%s->%s' % (public[0]['HostPort'], private))\\n else:\\n ports.append(private)\\n return ', '.join(ports)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.human_readable_state\",\n \"text\": \"fig/container.py::Container.human_readable_state\\n @property\\n def human_readable_state(self):\\n self.inspect_if_not_inspected()\\n if self.dictionary['State']['Running']:\\n if self.dictionary['State'].get('Ghost'):\\n return 'Ghost'\\n else:\\n return 'Up'\\n else:\\n return 'Exit %s' % self.dictionary['State']['ExitCode']\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.human_readable_command\",\n \"text\": \"fig/container.py::Container.human_readable_command\\n @property\\n def human_readable_command(self):\\n self.inspect_if_not_inspected()\\n if self.dictionary['Config']['Cmd']:\\n return ' '.join(self.dictionary['Config']['Cmd'])\\n else:\\n return ''\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.environment\",\n \"text\": \"fig/container.py::Container.environment\\n @property\\n def environment(self):\\n self.inspect_if_not_inspected()\\n out = {}\\n for var in self.dictionary.get('Config', {}).get('Env', []):\\n k, v = var.split('=', 1)\\n out[k] = v\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.is_running\",\n \"text\": \"fig/container.py::Container.is_running\\n @property\\n def is_running(self):\\n self.inspect_if_not_inspected()\\n return self.dictionary['State']['Running']\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.start\",\n \"text\": \"fig/container.py::Container.start\\n def start(self, **options):\\n return self.client.start(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.stop\",\n \"text\": \"fig/container.py::Container.stop\\n def stop(self, **options):\\n return self.client.stop(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.kill\",\n \"text\": \"fig/container.py::Container.kill\\n def kill(self):\\n return self.client.kill(self.id)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.remove\",\n \"text\": \"fig/container.py::Container.remove\\n def remove(self, **options):\\n return self.client.remove_container(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.inspect_if_not_inspected\",\n \"text\": \"fig/container.py::Container.inspect_if_not_inspected\\n def inspect_if_not_inspected(self):\\n if not self.has_been_inspected:\\n self.inspect()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.wait\",\n \"text\": \"fig/container.py::Container.wait\\n def wait(self):\\n return self.client.wait(self.id)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.logs\",\n \"text\": \"fig/container.py::Container.logs\\n def logs(self, *args, **kwargs):\\n return self.client.logs(self.id, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.inspect\",\n \"text\": \"fig/container.py::Container.inspect\\n def inspect(self):\\n self.dictionary = self.client.inspect_container(self.id)\\n return self.dictionary\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.links\",\n \"text\": \"fig/container.py::Container.links\\n def links(self):\\n links = []\\n for container in self.client.containers():\\n for name in container['Names']:\\n bits = name.split('/')\\n if len(bits) > 2 and bits[1] == self.name:\\n links.append(bits[2])\\n return links\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.attach\",\n \"text\": \"fig/container.py::Container.attach\\n def attach(self, *args, **kwargs):\\n return self.client.attach(self.id, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.attach_socket\",\n \"text\": \"fig/container.py::Container.attach_socket\\n def attach_socket(self, **kwargs):\\n return self.client.attach_socket(self.id, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.__repr__\",\n \"text\": \"fig/container.py::Container.__repr__\\n def __repr__(self):\\n return '<Container: %s>' % self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.__eq__\",\n \"text\": \"fig/container.py::Container.__eq__\\n def __eq__(self, other):\\n if type(self) != type(other):\\n return False\\n return self.id == other.id\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::swap_protocol\",\n \"text\": \"fig/packages/docker/auth/auth.py::swap_protocol\\ndef swap_protocol(url):\\n if url.startswith('http://'):\\n return url.replace('http://', 'https://', 1)\\n if url.startswith('https://'):\\n return url.replace('https://', 'http://', 1)\\n return url\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::expand_registry_url\",\n \"text\": \"fig/packages/docker/auth/auth.py::expand_registry_url\\ndef expand_registry_url(hostname):\\n if hostname.startswith('http:') or hostname.startswith('https:'):\\n if '/' not in hostname[9:]:\\n hostname = hostname + '/v1/'\\n return hostname\\n if utils.ping('https://' + hostname + '/v1/_ping'):\\n return 'https://' + hostname + '/v1/'\\n return 'http://' + hostname + '/v1/'\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::resolve_repository_name\",\n \"text\": \"fig/packages/docker/auth/auth.py::resolve_repository_name\\ndef resolve_repository_name(repo_name):\\n if '://' in repo_name:\\n raise errors.InvalidRepository(\\n 'Repository name cannot contain a scheme ({0})'.format(repo_name))\\n parts = repo_name.split('/', 1)\\n if '.' not in parts[0] and ':' not in parts[0] and parts[0] != 'localhost':\\n # This is a docker index repo (ex: foo/bar or ubuntu)\\n return INDEX_URL, repo_name\\n if len(parts) < 2:\\n raise errors.InvalidRepository(\\n 'Invalid repository name ({0})'.format(repo_name))\\n\\n if 'index.docker.io' in parts[0]:\\n raise errors.InvalidRepository(\\n 'Invalid repository name, try \\\"{0}\\\" instead'.format(parts[1]))\\n\\n return expand_registry_url(parts[0]), parts[1]\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::resolve_authconfig\",\n \"text\": \"fig/packages/docker/auth/auth.py::resolve_authconfig\\ndef resolve_authconfig(authconfig, registry=None):\\n \\\"\\\"\\\"Return the authentication data from the given auth configuration for a\\n specific registry. We'll do our best to infer the correct URL for the\\n registry, trying both http and https schemes. Returns an empty dictionnary\\n if no data exists.\\\"\\\"\\\"\\n # Default to the public index server\\n registry = registry or INDEX_URL\\n\\n # Ff its not the index server there are three cases:\\n #\\n # 1. this is a full config url -> it should be used as is\\n # 2. it could be a full url, but with the wrong protocol\\n # 3. it can be the hostname optionally with a port\\n #\\n # as there is only one auth entry which is fully qualified we need to start\\n # parsing and matching\\n if '/' not in registry:\\n registry = registry + '/v1/'\\n if not registry.startswith('http:') and not registry.startswith('https:'):\\n registry = 'https://' + registry\\n\\n if registry in authconfig:\\n return authconfig[registry]\\n return authconfig.get(swap_protocol(registry), None)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::encode_auth\",\n \"text\": \"fig/packages/docker/auth/auth.py::encode_auth\\ndef encode_auth(auth_info):\\n return base64.b64encode(auth_info.get('username', '') + b':' +\\n auth_info.get('password', ''))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::decode_auth\",\n \"text\": \"fig/packages/docker/auth/auth.py::decode_auth\\ndef decode_auth(auth):\\n if isinstance(auth, six.string_types):\\n auth = auth.encode('ascii')\\n s = base64.b64decode(auth)\\n login, pwd = s.split(b':')\\n return login.decode('ascii'), pwd.decode('ascii')\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::encode_header\",\n \"text\": \"fig/packages/docker/auth/auth.py::encode_header\\ndef encode_header(auth):\\n auth_json = json.dumps(auth).encode('ascii')\\n return base64.b64encode(auth_json)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::encode_full_header\",\n \"text\": \"fig/packages/docker/auth/auth.py::encode_full_header\\ndef encode_full_header(auth):\\n \\\"\\\"\\\" Returns the given auth block encoded for the X-Registry-Config header.\\n \\\"\\\"\\\"\\n return encode_header({'configs': auth})\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::load_config\",\n \"text\": \"fig/packages/docker/auth/auth.py::load_config\\ndef load_config(root=None):\\n \\\"\\\"\\\"Loads authentication data from a Docker configuration file in the given\\n root directory.\\\"\\\"\\\"\\n conf = {}\\n data = None\\n\\n config_file = os.path.join(root or os.environ.get('HOME', '.'),\\n DOCKER_CONFIG_FILENAME)\\n\\n # First try as JSON\\n try:\\n with open(config_file) as f:\\n conf = {}\\n for registry, entry in six.iteritems(json.load(f)):\\n username, password = decode_auth(entry['auth'])\\n conf[registry] = {\\n 'username': username,\\n 'password': password,\\n 'email': entry['email'],\\n 'serveraddress': registry,\\n }\\n return conf\\n except:\\n pass\\n\\n # If that fails, we assume the configuration file contains a single\\n # authentication token for the public registry in the following format:\\n #\\n # auth = AUTH_TOKEN\\n # email = email@domain.com\\n try:\\n data = []\\n for line in fileinput.input(config_file):\\n data.append(line.strip().split(' = ')[1])\\n if len(data) < 2:\\n # Not enough data\\n raise errors.InvalidConfigFile(\\n 'Invalid or empty configuration file!')\\n\\n username, password = decode_auth(data[0])\\n conf[INDEX_URL] = {\\n 'username': username,\\n 'password': password,\\n 'email': data[1],\\n 'serveraddress': INDEX_URL,\\n }\\n return conf\\n except:\\n pass\\n\\n # If all fails, return an empty config\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.__init__\",\n \"text\": \"fig/packages/docker/client.py::Client.__init__\\n def __init__(self, base_url=None, version=DEFAULT_DOCKER_API_VERSION,\\n timeout=DEFAULT_TIMEOUT_SECONDS):\\n super(Client, self).__init__()\\n if base_url is None:\\n base_url = \\\"http+unix://var/run/docker.sock\\\"\\n if 'unix:///' in base_url:\\n base_url = base_url.replace('unix:/', 'unix:')\\n if base_url.startswith('unix:'):\\n base_url = \\\"http+\\\" + base_url\\n if base_url.startswith('tcp:'):\\n base_url = base_url.replace('tcp:', 'http:')\\n if base_url.endswith('/'):\\n base_url = base_url[:-1]\\n self.base_url = base_url\\n self._version = version\\n self._timeout = timeout\\n self._auth_configs = auth.load_config()\\n\\n self.mount('http+unix://', unixconn.UnixAdapter(base_url, timeout))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._set_request_timeout\",\n \"text\": \"fig/packages/docker/client.py::Client._set_request_timeout\\n def _set_request_timeout(self, kwargs):\\n \\\"\\\"\\\"Prepare the kwargs for an HTTP request by inserting the timeout\\n parameter, if not already present.\\\"\\\"\\\"\\n kwargs.setdefault('timeout', self._timeout)\\n return kwargs\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._post\",\n \"text\": \"fig/packages/docker/client.py::Client._post\\n def _post(self, url, **kwargs):\\n return self.post(url, **self._set_request_timeout(kwargs))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._get\",\n \"text\": \"fig/packages/docker/client.py::Client._get\\n def _get(self, url, **kwargs):\\n return self.get(url, **self._set_request_timeout(kwargs))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._delete\",\n \"text\": \"fig/packages/docker/client.py::Client._delete\\n def _delete(self, url, **kwargs):\\n return self.delete(url, **self._set_request_timeout(kwargs))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._url\",\n \"text\": \"fig/packages/docker/client.py::Client._url\\n def _url(self, path):\\n return '{0}/v{1}{2}'.format(self.base_url, self._version, path)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._raise_for_status\",\n \"text\": \"fig/packages/docker/client.py::Client._raise_for_status\\n def _raise_for_status(self, response, explanation=None):\\n \\\"\\\"\\\"Raises stored :class:`APIError`, if one occurred.\\\"\\\"\\\"\\n try:\\n response.raise_for_status()\\n except requests.exceptions.HTTPError as e:\\n raise errors.APIError(e, response, explanation=explanation)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._result\",\n \"text\": \"fig/packages/docker/client.py::Client._result\\n def _result(self, response, json=False, binary=False):\\n assert not (json and binary)\\n self._raise_for_status(response)\\n\\n if json:\\n return response.json()\\n if binary:\\n return response.content\\n return response.text\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._container_config\",\n \"text\": \"fig/packages/docker/client.py::Client._container_config\\n def _container_config(self, image, command, hostname=None, user=None,\\n detach=False, stdin_open=False, tty=False,\\n mem_limit=0, ports=None, environment=None, dns=None,\\n volumes=None, volumes_from=None,\\n network_disabled=False, entrypoint=None,\\n cpu_shares=None, working_dir=None, domainname=None):\\n if isinstance(command, six.string_types):\\n command = shlex.split(str(command))\\n if isinstance(environment, dict):\\n environment = [\\n '{0}={1}'.format(k, v) for k, v in environment.items()\\n ]\\n\\n if isinstance(ports, list):\\n exposed_ports = {}\\n for port_definition in ports:\\n port = port_definition\\n proto = 'tcp'\\n if isinstance(port_definition, tuple):\\n if len(port_definition) == 2:\\n proto = port_definition[1]\\n port = port_definition[0]\\n exposed_ports['{0}/{1}'.format(port, proto)] = {}\\n ports = exposed_ports\\n\\n if isinstance(volumes, list):\\n volumes_dict = {}\\n for vol in volumes:\\n volumes_dict[vol] = {}\\n volumes = volumes_dict\\n\\n if volumes_from and not isinstance(volumes_from, six.string_types):\\n volumes_from = ','.join(volumes_from)\\n\\n attach_stdin = False\\n attach_stdout = False\\n attach_stderr = False\\n stdin_once = False\\n\\n if not detach:\\n attach_stdout = True\\n attach_stderr = True\\n\\n if stdin_open:\\n attach_stdin = True\\n stdin_once = True\\n\\n return {\\n 'Hostname': hostname,\\n 'Domainname': domainname,\\n 'ExposedPorts': ports,\\n 'User': user,\\n 'Tty': tty,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._post_json\",\n \"text\": \"fig/packages/docker/client.py::Client._post_json\\n def _post_json(self, url, data, **kwargs):\\n # Go <1.1 can't unserialize null to a string\\n # so we do this disgusting thing here.\\n data2 = {}\\n if data is not None:\\n for k, v in six.iteritems(data):\\n if v is not None:\\n data2[k] = v\\n\\n if 'headers' not in kwargs:\\n kwargs['headers'] = {}\\n kwargs['headers']['Content-Type'] = 'application/json'\\n return self._post(url, data=json.dumps(data2), **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._attach_params\",\n \"text\": \"fig/packages/docker/client.py::Client._attach_params\\n def _attach_params(self, override=None):\\n return override or {\\n 'stdout': 1,\\n 'stderr': 1,\\n 'stream': 1\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._attach_websocket\",\n \"text\": \"fig/packages/docker/client.py::Client._attach_websocket\\n def _attach_websocket(self, container, params=None):\\n if six.PY3:\\n raise NotImplementedError(\\\"This method is not currently supported \\\"\\n \\\"under python 3\\\")\\n url = self._url(\\\"/containers/{0}/attach/ws\\\".format(container))\\n req = requests.Request(\\\"POST\\\", url, params=self._attach_params(params))\\n full_url = req.prepare().url\\n full_url = full_url.replace(\\\"http://\\\", \\\"ws://\\\", 1)\\n full_url = full_url.replace(\\\"https://\\\", \\\"wss://\\\", 1)\\n return self._create_websocket_connection(full_url)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._create_websocket_connection\",\n \"text\": \"fig/packages/docker/client.py::Client._create_websocket_connection\\n def _create_websocket_connection(self, url):\\n return websocket.create_connection(url)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._get_raw_response_socket\",\n \"text\": \"fig/packages/docker/client.py::Client._get_raw_response_socket\\n def _get_raw_response_socket(self, response):\\n self._raise_for_status(response)\\n if six.PY3:\\n return response.raw._fp.fp.raw._sock\\n else:\\n return response.raw._fp.fp._sock\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._stream_helper\",\n \"text\": \"fig/packages/docker/client.py::Client._stream_helper\\n def _stream_helper(self, response):\\n \\\"\\\"\\\"Generator for data coming from a chunked-encoded HTTP response.\\\"\\\"\\\"\\n socket_fp = self._get_raw_response_socket(response)\\n socket_fp.setblocking(1)\\n socket = socket_fp.makefile()\\n while True:\\n # Because Docker introduced newlines at the end of chunks in v0.9,\\n # and only on some API endpoints, we have to cater for both cases.\\n size_line = socket.readline()\\n if size_line == '\\\\r\\\\n':\\n size_line = socket.readline()\\n\\n size = int(size_line, 16)\\n if size <= 0:\\n break\\n data = socket.readline()\\n if not data:\\n break\\n yield data\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._multiplexed_buffer_helper\",\n \"text\": \"fig/packages/docker/client.py::Client._multiplexed_buffer_helper\\n def _multiplexed_buffer_helper(self, response):\\n \\\"\\\"\\\"A generator of multiplexed data blocks read from a buffered\\n response.\\\"\\\"\\\"\\n buf = self._result(response, binary=True)\\n walker = 0\\n while True:\\n if len(buf[walker:]) < 8:\\n break\\n _, length = struct.unpack_from('>BxxxL', buf[walker:])\\n start = walker + STREAM_HEADER_SIZE_BYTES\\n end = start + length\\n walker = end\\n yield str(buf[start:end])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper\",\n \"text\": \"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper\\n def _multiplexed_socket_stream_helper(self, response):\\n \\\"\\\"\\\"A generator of multiplexed data blocks coming from a response\\n socket.\\\"\\\"\\\"\\n socket = self._get_raw_response_socket(response)\\n\\n def recvall(socket, size):\\n blocks = []\\n while size > 0:\\n block = socket.recv(size)\\n if not block:\\n return None\\n\\n blocks.append(block)\\n size -= len(block)\\n\\n sep = bytes() if six.PY3 else str()\\n data = sep.join(blocks)\\n return data\\n\\n while True:\\n socket.settimeout(None)\\n header = recvall(socket, STREAM_HEADER_SIZE_BYTES)\\n if not header:\\n break\\n _, length = struct.unpack('>BxxxL', header)\\n if not length:\\n break\\n data = recvall(socket, length)\\n if not data:\\n break\\n yield data\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper.recvall\",\n \"text\": \"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper.recvall\\n def recvall(socket, size):\\n blocks = []\\n while size > 0:\\n block = socket.recv(size)\\n if not block:\\n return None\\n\\n blocks.append(block)\\n size -= len(block)\\n\\n sep = bytes() if six.PY3 else str()\\n data = sep.join(blocks)\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.attach\",\n \"text\": \"fig/packages/docker/client.py::Client.attach\\n def attach(self, container, stdout=True, stderr=True,\\n stream=False, logs=False):\\n if isinstance(container, dict):\\n container = container.get('Id')\\n params = {\\n 'logs': logs and 1 or 0,\\n 'stdout': stdout and 1 or 0,\\n 'stderr': stderr and 1 or 0,\\n 'stream': stream and 1 or 0,\\n }\\n u = self._url(\\\"/containers/{0}/attach\\\".format(container))\\n response = self._post(u, params=params, stream=stream)\\n\\n # Stream multi-plexing was only introduced in API v1.6. Anything before\\n # that needs old-style streaming.\\n if utils.compare_version('1.6', self._version) < 0:\\n def stream_result():\\n self._raise_for_status(response)\\n for line in response.iter_lines(chunk_size=1,\\n decode_unicode=True):\\n # filter out keep-alive new lines\\n if line:\\n yield line\\n\\n return stream_result() if stream else \\\\\\n self._result(response, binary=True)\\n\\n return stream and self._multiplexed_socket_stream_helper(response) or \\\\\\n ''.join([x for x in self._multiplexed_buffer_helper(response)])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.attach_socket\",\n \"text\": \"fig/packages/docker/client.py::Client.attach_socket\\n def attach_socket(self, container, params=None, ws=False):\\n if params is None:\\n params = {\\n 'stdout': 1,\\n 'stderr': 1,\\n 'stream': 1\\n }\\n\\n if ws:\\n return self._attach_websocket(container, params)\\n\\n if isinstance(container, dict):\\n container = container.get('Id')\\n\\n u = self._url(\\\"/containers/{0}/attach\\\".format(container))\\n return self._get_raw_response_socket(self.post(\\n u, None, params=self._attach_params(params), stream=True))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.build\",\n \"text\": \"fig/packages/docker/client.py::Client.build\\n def build(self, path=None, tag=None, quiet=False, fileobj=None,\\n nocache=False, rm=False, stream=False, timeout=None):\\n remote = context = headers = None\\n if path is None and fileobj is None:\\n raise TypeError(\\\"Either path or fileobj needs to be provided.\\\")\\n\\n if fileobj is not None:\\n context = utils.mkbuildcontext(fileobj)\\n elif path.startswith(('http://', 'https://', 'git://', 'github.com/')):\\n remote = path\\n else:\\n context = utils.tar(path)\\n\\n if utils.compare_version('1.8', self._version) >= 0:\\n stream = True\\n\\n u = self._url('/build')\\n params = {\\n 't': tag,\\n 'remote': remote,\\n 'q': quiet,\\n 'nocache': nocache,\\n 'rm': rm\\n }\\n if context is not None:\\n headers = {'Content-Type': 'application/tar'}\\n\\n if utils.compare_version('1.9', self._version) >= 0:\\n # If we don't have any auth data so far, try reloading the config\\n # file one more time in case anything showed up in there.\\n if not self._auth_configs:\\n self._auth_configs = auth.load_config()\\n\\n # Send the full auth configuration (if any exists), since the build\\n # could use any (or all) of the registries.\\n if self._auth_configs:\\n headers['X-Registry-Config'] = auth.encode_full_header(\\n self._auth_configs\\n )\\n\\n response = self._post(\\n u,\\n data=context,\\n params=params,\\n headers=headers,\\n stream=stream,\\n timeout=timeout,\\n )\\n\\n if context is not None:\\n context.close()\\n\\n if stream:\\n return self._stream_helper(response)\\n else:\\n output = self._result(response)\\n srch = r'Successfully built ([0-9a-f]+)'\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.commit\",\n \"text\": \"fig/packages/docker/client.py::Client.commit\\n def commit(self, container, repository=None, tag=None, message=None,\\n author=None, conf=None):\\n params = {\\n 'container': container,\\n 'repo': repository,\\n 'tag': tag,\\n 'comment': message,\\n 'author': author\\n }\\n u = self._url(\\\"/commit\\\")\\n return self._result(self._post_json(u, data=conf, params=params),\\n json=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.containers\",\n \"text\": \"fig/packages/docker/client.py::Client.containers\\n def containers(self, quiet=False, all=False, trunc=True, latest=False,\\n since=None, before=None, limit=-1):\\n params = {\\n 'limit': 1 if latest else limit,\\n 'all': 1 if all else 0,\\n 'trunc_cmd': 1 if trunc else 0,\\n 'since': since,\\n 'before': before\\n }\\n u = self._url(\\\"/containers/json\\\")\\n res = self._result(self._get(u, params=params), True)\\n\\n if quiet:\\n return [{'Id': x['Id']} for x in res]\\n return res\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.copy\",\n \"text\": \"fig/packages/docker/client.py::Client.copy\\n def copy(self, container, resource):\\n if isinstance(container, dict):\\n container = container.get('Id')\\n res = self._post_json(\\n self._url(\\\"/containers/{0}/copy\\\".format(container)),\\n data={\\\"Resource\\\": resource},\\n stream=True\\n )\\n self._raise_for_status(res)\\n return res.raw\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.create_container\",\n \"text\": \"fig/packages/docker/client.py::Client.create_container\\n def create_container(self, image, command=None, hostname=None, user=None,\\n detach=False, stdin_open=False, tty=False,\\n mem_limit=0, ports=None, environment=None, dns=None,\\n volumes=None, volumes_from=None,\\n network_disabled=False, name=None, entrypoint=None,\\n cpu_shares=None, working_dir=None, domainname=None):\\n\\n config = self._container_config(\\n image, command, hostname, user, detach, stdin_open, tty, mem_limit,\\n ports, environment, dns, volumes, volumes_from, network_disabled,\\n entrypoint, cpu_shares, working_dir, domainname\\n )\\n return self.create_container_from_config(config, name)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.create_container_from_config\",\n \"text\": \"fig/packages/docker/client.py::Client.create_container_from_config\\n def create_container_from_config(self, config, name=None):\\n u = self._url(\\\"/containers/create\\\")\\n params = {\\n 'name': name\\n }\\n res = self._post_json(u, data=config, params=params)\\n return self._result(res, True)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.diff\",\n \"text\": \"fig/packages/docker/client.py::Client.diff\\n def diff(self, container):\\n if isinstance(container, dict):\\n container = container.get('Id')\\n return self._result(self._get(self._url(\\\"/containers/{0}/changes\\\".\\n format(container))), True)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.events\",\n \"text\": \"fig/packages/docker/client.py::Client.events\\n def events(self):\\n return self._stream_helper(self.get(self._url('/events'), stream=True))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.export\",\n \"text\": \"fig/packages/docker/client.py::Client.export\\n def export(self, container):\\n if isinstance(container, dict):\\n container = container.get('Id')\\n res = self._get(self._url(\\\"/containers/{0}/export\\\".format(container)),\\n stream=True)\\n self._raise_for_status(res)\\n return res.raw\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.history\",\n \"text\": \"fig/packages/docker/client.py::Client.history\\n def history(self, image):\\n res = self._get(self._url(\\\"/images/{0}/history\\\".format(image)))\\n self._raise_for_status(res)\\n return self._result(res)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.images\",\n \"text\": \"fig/packages/docker/client.py::Client.images\\n def images(self, name=None, quiet=False, all=False, viz=False):\\n if viz:\\n if utils.compare_version('1.7', self._version) >= 0:\\n raise Exception('Viz output is not supported in API >= 1.7!')\\n return self._result(self._get(self._url(\\\"images/viz\\\")))\\n params = {\\n 'filter': name,\\n 'only_ids': 1 if quiet else 0,\\n 'all': 1 if all else 0,\\n }\\n res = self._result(self._get(self._url(\\\"/images/json\\\"), params=params),\\n True)\\n if quiet:\\n return [x['Id'] for x in res]\\n return res\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.import_image\",\n \"text\": \"fig/packages/docker/client.py::Client.import_image\\n def import_image(self, src=None, repository=None, tag=None, image=None):\\n u = self._url(\\\"/images/create\\\")\\n params = {\\n 'repo': repository,\\n 'tag': tag\\n }\\n\\n if src:\\n try:\\n # XXX: this is ways not optimal but the only way\\n # for now to import tarballs through the API\\n fic = open(src)\\n data = fic.read()\\n fic.close()\\n src = \\\"-\\\"\\n except IOError:\\n # file does not exists or not a file (URL)\\n data = None\\n if isinstance(src, six.string_types):\\n params['fromSrc'] = src\\n return self._result(self._post(u, data=data, params=params))\\n return self._result(self._post(u, data=src, params=params))\\n\\n if image:\\n params['fromImage'] = image\\n return self._result(self._post(u, data=None, params=params))\\n\\n raise Exception(\\\"Must specify a src or image\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.info\",\n \"text\": \"fig/packages/docker/client.py::Client.info\\n def info(self):\\n return self._result(self._get(self._url(\\\"/info\\\")),\\n True)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.insert\",\n \"text\": \"fig/packages/docker/client.py::Client.insert\\n def insert(self, image, url, path):\\n api_url = self._url(\\\"/images/\\\" + image + \\\"/insert\\\")\\n params = {\\n 'url': url,\\n 'path': path\\n }\\n return self._result(self._post(api_url, params=params))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.inspect_container\",\n \"text\": \"fig/packages/docker/client.py::Client.inspect_container\\n def inspect_container(self, container):\\n if isinstance(container, dict):\\n container = container.get('Id')\\n return self._result(\\n self._get(self._url(\\\"/containers/{0}/json\\\".format(container))),\\n True)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.inspect_image\",\n \"text\": \"fig/packages/docker/client.py::Client.inspect_image\\n def inspect_image(self, image_id):\\n return self._result(\\n self._get(self._url(\\\"/images/{0}/json\\\".format(image_id))),\\n True\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.kill\",\n \"text\": \"fig/packages/docker/client.py::Client.kill\\n def kill(self, container, signal=None):\\n if isinstance(container, dict):\\n container = container.get('Id')\\n url = self._url(\\\"/containers/{0}/kill\\\".format(container))\\n params = {}\\n if signal is not None:\\n params['signal'] = signal\\n res = self._post(url, params=params)\\n\\n self._raise_for_status(res)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.login\",\n \"text\": \"fig/packages/docker/client.py::Client.login\\n def login(self, username, password=None, email=None, registry=None,\\n reauth=False):\\n # If we don't have any auth data so far, try reloading the config file\\n # one more time in case anything showed up in there.\\n if not self._auth_configs:\\n self._auth_configs = auth.load_config()\\n\\n registry = registry or auth.INDEX_URL\\n\\n authcfg = auth.resolve_authconfig(self._auth_configs, registry)\\n # If we found an existing auth config for this registry and username\\n # combination, we can return it immediately unless reauth is requested.\\n if authcfg and authcfg.get('username', None) == username \\\\\\n and not reauth:\\n return authcfg\\n\\n req_data = {\\n 'username': username,\\n 'password': password,\\n 'email': email,\\n 'serveraddress': registry,\\n }\\n\\n response = self._post_json(self._url('/auth'), data=req_data)\\n if response.status_code == 200:\\n self._auth_configs[registry] = req_data\\n return self._result(response, json=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.logs\",\n \"text\": \"fig/packages/docker/client.py::Client.logs\\n def logs(self, container, stdout=True, stderr=True, stream=False):\\n return self.attach(\\n container,\\n stdout=stdout,\\n stderr=stderr,\\n stream=stream,\\n logs=True\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.port\",\n \"text\": \"fig/packages/docker/client.py::Client.port\\n def port(self, container, private_port):\\n if isinstance(container, dict):\\n container = container.get('Id')\\n res = self._get(self._url(\\\"/containers/{0}/json\\\".format(container)))\\n self._raise_for_status(res)\\n json_ = res.json()\\n s_port = str(private_port)\\n h_ports = None\\n\\n h_ports = json_['NetworkSettings']['Ports'].get(s_port + '/udp')\\n if h_ports is None:\\n h_ports = json_['NetworkSettings']['Ports'].get(s_port + '/tcp')\\n\\n return h_ports\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/errors.py::APIError.__init__\",\n \"text\": \"fig/packages/docker/errors.py::APIError.__init__\\n def __init__(self, message, response, explanation=None):\\n # requests 1.2 supports response as a keyword argument, but\\n # requests 1.1 doesn't\\n super(APIError, self).__init__(message)\\n self.response = response\\n\\n self.explanation = explanation\\n\\n if self.explanation is None and response.content:\\n self.explanation = response.content.strip()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/errors.py::APIError.__str__\",\n \"text\": \"fig/packages/docker/errors.py::APIError.__str__\\n def __str__(self):\\n message = super(APIError, self).__str__()\\n\\n if self.is_client_error():\\n message = '%s Client Error: %s' % (\\n self.response.status_code, self.response.reason)\\n\\n elif self.is_server_error():\\n message = '%s Server Error: %s' % (\\n self.response.status_code, self.response.reason)\\n\\n if self.explanation:\\n message = '%s (\\\"%s\\\")' % (message, self.explanation)\\n\\n return message\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/errors.py::APIError.is_client_error\",\n \"text\": \"fig/packages/docker/errors.py::APIError.is_client_error\\n def is_client_error(self):\\n return 400 <= self.response.status_code < 500\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/errors.py::APIError.is_server_error\",\n \"text\": \"fig/packages/docker/errors.py::APIError.is_server_error\\n def is_server_error(self):\\n return 500 <= self.response.status_code < 600\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.__init__\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.__init__\\n def __init__(self, base_url, unix_socket, timeout=60):\\n httplib.HTTPConnection.__init__(self, 'localhost', timeout=timeout)\\n self.base_url = base_url\\n self.unix_socket = unix_socket\\n self.timeout = timeout\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.connect\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.connect\\n def connect(self):\\n sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\\n sock.settimeout(self.timeout)\\n sock.connect(self.base_url.replace(\\\"http+unix:/\\\", \\\"\\\"))\\n self.sock = sock\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection._extract_path\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection._extract_path\\n def _extract_path(self, url):\\n # remove the base_url entirely..\\n return url.replace(self.base_url, \\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.request\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.request\\n def request(self, method, url, **kwargs):\\n url = self._extract_path(self.unix_socket)\\n super(UnixHTTPConnection, self).request(method, url, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool.__init__\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool.__init__\\n def __init__(self, base_url, socket_path, timeout=60):\\n connectionpool.HTTPConnectionPool.__init__(self, 'localhost',\\n timeout=timeout)\\n self.base_url = base_url\\n self.socket_path = socket_path\\n self.timeout = timeout\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool._new_conn\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool._new_conn\\n def _new_conn(self):\\n return UnixHTTPConnection(self.base_url, self.socket_path,\\n self.timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.__init__\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.__init__\\n def __init__(self, base_url, timeout=60):\\n self.base_url = base_url\\n self.timeout = timeout\\n super(UnixAdapter, self).__init__()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.get_connection\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.get_connection\\n def get_connection(self, socket_path, proxies=None):\\n return UnixHTTPConnectionPool(self.base_url, socket_path, self.timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::mkbuildcontext\",\n \"text\": \"fig/packages/docker/utils/utils.py::mkbuildcontext\\ndef mkbuildcontext(dockerfile):\\n f = tempfile.NamedTemporaryFile()\\n t = tarfile.open(mode='w', fileobj=f)\\n if isinstance(dockerfile, io.StringIO):\\n dfinfo = tarfile.TarInfo('Dockerfile')\\n if six.PY3:\\n raise TypeError('Please use io.BytesIO to create in-memory '\\n 'Dockerfiles with Python 3')\\n else:\\n dfinfo.size = len(dockerfile.getvalue())\\n elif isinstance(dockerfile, io.BytesIO):\\n dfinfo = tarfile.TarInfo('Dockerfile')\\n dfinfo.size = len(dockerfile.getvalue())\\n else:\\n dfinfo = t.gettarinfo(fileobj=dockerfile, arcname='Dockerfile')\\n t.addfile(dfinfo, dockerfile)\\n t.close()\\n f.seek(0)\\n return f\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::tar\",\n \"text\": \"fig/packages/docker/utils/utils.py::tar\\ndef tar(path):\\n f = tempfile.NamedTemporaryFile()\\n t = tarfile.open(mode='w', fileobj=f)\\n t.add(path, arcname='.')\\n t.close()\\n f.seek(0)\\n return f\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::compare_version\",\n \"text\": \"fig/packages/docker/utils/utils.py::compare_version\\ndef compare_version(v1, v2):\\n \\\"\\\"\\\"Compare docker versions\\n\\n >>> v1 = '1.9'\\n >>> v2 = '1.10'\\n >>> compare_version(v1, v2)\\n 1\\n >>> compare_version(v2, v1)\\n -1\\n >>> compare_version(v2, v2)\\n 0\\n \\\"\\\"\\\"\\n s1 = StrictVersion(v1)\\n s2 = StrictVersion(v2)\\n if s1 == s2:\\n return 0\\n elif s1 > s2:\\n return -1\\n else:\\n return 1\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::ping\",\n \"text\": \"fig/packages/docker/utils/utils.py::ping\\ndef ping(url):\\n try:\\n res = requests.get(url)\\n except Exception:\\n return False\\n else:\\n return res.status_code < 400\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::_convert_port_binding\",\n \"text\": \"fig/packages/docker/utils/utils.py::_convert_port_binding\\ndef _convert_port_binding(binding):\\n result = {'HostIp': '', 'HostPort': ''}\\n if isinstance(binding, tuple):\\n if len(binding) == 2:\\n result['HostPort'] = binding[1]\\n result['HostIp'] = binding[0]\\n elif isinstance(binding[0], six.string_types):\\n result['HostIp'] = binding[0]\\n else:\\n result['HostPort'] = binding[0]\\n else:\\n result['HostPort'] = binding\\n\\n if result['HostPort'] is None:\\n result['HostPort'] = ''\\n else:\\n result['HostPort'] = str(result['HostPort'])\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::convert_port_bindings\",\n \"text\": \"fig/packages/docker/utils/utils.py::convert_port_bindings\\ndef convert_port_bindings(port_bindings):\\n result = {}\\n for k, v in six.iteritems(port_bindings):\\n key = str(k)\\n if '/' not in key:\\n key = key + '/tcp'\\n if isinstance(v, list):\\n result[key] = [_convert_port_binding(binding) for binding in v]\\n else:\\n result[key] = [_convert_port_binding(v)]\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::parse_repository_tag\",\n \"text\": \"fig/packages/docker/utils/utils.py::parse_repository_tag\\ndef parse_repository_tag(repo):\\n column_index = repo.rfind(':')\\n if column_index < 0:\\n return repo, \\\"\\\"\\n tag = repo[column_index+1:]\\n slash_index = tag.find('/')\\n if slash_index < 0:\\n return repo[:column_index], tag\\n\\n return repo, \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::_add_doc\",\n \"text\": \"fig/packages/six.py::_add_doc\\ndef _add_doc(func, doc):\\n \\\"\\\"\\\"Add documentation to a function.\\\"\\\"\\\"\\n func.__doc__ = doc\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::_import_module\",\n \"text\": \"fig/packages/six.py::_import_module\\ndef _import_module(name):\\n \\\"\\\"\\\"Import module, returning the module after the last dot.\\\"\\\"\\\"\\n __import__(name)\\n return sys.modules[name]\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::_LazyDescr.__init__\",\n \"text\": \"fig/packages/six.py::_LazyDescr.__init__\\n def __init__(self, name):\\n self.name = name\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::_LazyDescr.__get__\",\n \"text\": \"fig/packages/six.py::_LazyDescr.__get__\\n def __get__(self, obj, tp):\\n result = self._resolve()\\n setattr(obj, self.name, result)\\n # This is a bit ugly, but it avoids running this again.\\n delattr(tp, self.name)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::MovedModule.__init__\",\n \"text\": \"fig/packages/six.py::MovedModule.__init__\\n def __init__(self, name, old, new=None):\\n super(MovedModule, self).__init__(name)\\n if PY3:\\n if new is None:\\n new = name\\n self.mod = new\\n else:\\n self.mod = old\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::MovedModule._resolve\",\n \"text\": \"fig/packages/six.py::MovedModule._resolve\\n def _resolve(self):\\n return _import_module(self.mod)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::MovedAttribute.__init__\",\n \"text\": \"fig/packages/six.py::MovedAttribute.__init__\\n def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):\\n super(MovedAttribute, self).__init__(name)\\n if PY3:\\n if new_mod is None:\\n new_mod = name\\n self.mod = new_mod\\n if new_attr is None:\\n if old_attr is None:\\n new_attr = name\\n else:\\n new_attr = old_attr\\n self.attr = new_attr\\n else:\\n self.mod = old_mod\\n if old_attr is None:\\n old_attr = name\\n self.attr = old_attr\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::MovedAttribute._resolve\",\n \"text\": \"fig/packages/six.py::MovedAttribute._resolve\\n def _resolve(self):\\n module = _import_module(self.mod)\\n return getattr(module, self.attr)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::add_move\",\n \"text\": \"fig/packages/six.py::add_move\\ndef add_move(move):\\n \\\"\\\"\\\"Add an item to six.moves.\\\"\\\"\\\"\\n setattr(_MovedItems, move.name, move)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::remove_move\",\n \"text\": \"fig/packages/six.py::remove_move\\ndef remove_move(name):\\n \\\"\\\"\\\"Remove item from six.moves.\\\"\\\"\\\"\\n try:\\n delattr(_MovedItems, name)\\n except AttributeError:\\n try:\\n del moves.__dict__[name]\\n except KeyError:\\n raise AttributeError(\\\"no such move, %r\\\" % (name,))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::iterkeys\",\n \"text\": \"fig/packages/six.py::iterkeys\\ndef iterkeys(d, **kw):\\n \\\"\\\"\\\"Return an iterator over the keys of a dictionary.\\\"\\\"\\\"\\n return iter(getattr(d, _iterkeys)(**kw))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::itervalues\",\n \"text\": \"fig/packages/six.py::itervalues\\ndef itervalues(d, **kw):\\n \\\"\\\"\\\"Return an iterator over the values of a dictionary.\\\"\\\"\\\"\\n return iter(getattr(d, _itervalues)(**kw))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::iteritems\",\n \"text\": \"fig/packages/six.py::iteritems\\ndef iteritems(d, **kw):\\n \\\"\\\"\\\"Return an iterator over the (key, value) pairs of a dictionary.\\\"\\\"\\\"\\n return iter(getattr(d, _iteritems)(**kw))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::iterlists\",\n \"text\": \"fig/packages/six.py::iterlists\\ndef iterlists(d, **kw):\\n \\\"\\\"\\\"Return an iterator over the (key, [values]) pairs of a dictionary.\\\"\\\"\\\"\\n return iter(getattr(d, _iterlists)(**kw))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::with_metaclass\",\n \"text\": \"fig/packages/six.py::with_metaclass\\ndef with_metaclass(meta, base=object):\\n \\\"\\\"\\\"Create a base class with a metaclass.\\\"\\\"\\\"\\n return meta(\\\"NewBase\\\", (base,), {})\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::sort_service_dicts\",\n \"text\": \"fig/project.py::sort_service_dicts\\ndef sort_service_dicts(services):\\n # Topological sort (Cormen/Tarjan algorithm).\\n unmarked = services[:]\\n temporary_marked = set()\\n sorted_services = []\\n\\n get_service_names = lambda links: [link.split(':')[0] for link in links]\\n\\n def visit(n):\\n if n['name'] in temporary_marked:\\n if n['name'] in get_service_names(n.get('links', [])):\\n raise DependencyError('A service can not link to itself: %s' % n['name'])\\n else:\\n raise DependencyError('Circular import between %s' % ' and '.join(temporary_marked))\\n if n in unmarked:\\n temporary_marked.add(n['name'])\\n dependents = [m for m in services if n['name'] in get_service_names(m.get('links', []))]\\n for m in dependents:\\n visit(m)\\n temporary_marked.remove(n['name'])\\n unmarked.remove(n)\\n sorted_services.insert(0, n)\\n\\n while unmarked:\\n visit(unmarked[-1])\\n\\n return sorted_services\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::sort_service_dicts.visit\",\n \"text\": \"fig/project.py::sort_service_dicts.visit\\n def visit(n):\\n if n['name'] in temporary_marked:\\n if n['name'] in get_service_names(n.get('links', [])):\\n raise DependencyError('A service can not link to itself: %s' % n['name'])\\n else:\\n raise DependencyError('Circular import between %s' % ' and '.join(temporary_marked))\\n if n in unmarked:\\n temporary_marked.add(n['name'])\\n dependents = [m for m in services if n['name'] in get_service_names(m.get('links', []))]\\n for m in dependents:\\n visit(m)\\n temporary_marked.remove(n['name'])\\n unmarked.remove(n)\\n sorted_services.insert(0, n)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.__init__\",\n \"text\": \"fig/project.py::Project.__init__\\n def __init__(self, name, services, client):\\n self.name = name\\n self.services = services\\n self.client = client\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.from_dicts\",\n \"text\": \"fig/project.py::Project.from_dicts\\n @classmethod\\n def from_dicts(cls, name, service_dicts, client):\\n \\\"\\\"\\\"\\n Construct a ServiceCollection from a list of dicts representing services.\\n \\\"\\\"\\\"\\n project = cls(name, [], client)\\n for service_dict in sort_service_dicts(service_dicts):\\n # Reference links by object\\n links = []\\n if 'links' in service_dict:\\n for link in service_dict.get('links', []):\\n if ':' in link:\\n service_name, link_name = link.split(':', 1)\\n else:\\n service_name, link_name = link, None\\n try:\\n links.append((project.get_service(service_name), link_name))\\n except NoSuchService:\\n raise ConfigurationError('Service \\\"%s\\\" has a link to service \\\"%s\\\" which does not exist.' % (service_dict['name'], service_name))\\n\\n del service_dict['links']\\n project.services.append(Service(client=client, project=name, links=links, **service_dict))\\n return project\",\n \"label\": 1\n },\n {\n \"cid\": \"fig/project.py::Project.from_config\",\n \"text\": \"fig/project.py::Project.from_config\\n @classmethod\\n def from_config(cls, name, config, client):\\n dicts = []\\n for service_name, service in list(config.items()):\\n if not isinstance(service, dict):\\n raise ConfigurationError('Service \\\"%s\\\" doesn\\\\'t have any configuration options. All top level keys in your fig.yml must map to a dictionary of configuration options.')\\n service['name'] = service_name\\n dicts.append(service)\\n return cls.from_dicts(name, dicts, client)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.get_service\",\n \"text\": \"fig/project.py::Project.get_service\\n def get_service(self, name):\\n \\\"\\\"\\\"\\n Retrieve a service by name. Raises NoSuchService\\n if the named service does not exist.\\n \\\"\\\"\\\"\\n for service in self.services:\\n if service.name == name:\\n return service\\n\\n raise NoSuchService(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.get_services\",\n \"text\": \"fig/project.py::Project.get_services\\n def get_services(self, service_names=None):\\n \\\"\\\"\\\"\\n Returns a list of this project's services filtered\\n by the provided list of names, or all services if\\n service_names is None or [].\\n\\n Preserves the original order of self.services.\\n\\n Raises NoSuchService if any of the named services\\n do not exist.\\n \\\"\\\"\\\"\\n if service_names is None or len(service_names) == 0:\\n return self.services\\n else:\\n unsorted = [self.get_service(name) for name in service_names]\\n return [s for s in self.services if s in unsorted]\",\n \"label\": 1\n },\n {\n \"cid\": \"fig/project.py::Project.start\",\n \"text\": \"fig/project.py::Project.start\\n def start(self, service_names=None, **options):\\n for service in self.get_services(service_names):\\n service.start(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.stop\",\n \"text\": \"fig/project.py::Project.stop\\n def stop(self, service_names=None, **options):\\n for service in reversed(self.get_services(service_names)):\\n service.stop(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.kill\",\n \"text\": \"fig/project.py::Project.kill\\n def kill(self, service_names=None, **options):\\n for service in reversed(self.get_services(service_names)):\\n service.kill(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.build\",\n \"text\": \"fig/project.py::Project.build\\n def build(self, service_names=None, **options):\\n for service in self.get_services(service_names):\\n if service.can_be_built():\\n service.build(**options)\\n else:\\n log.info('%s uses an image, skipping' % service.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.up\",\n \"text\": \"fig/project.py::Project.up\\n def up(self, service_names=None):\\n new_containers = []\\n\\n for service in self.get_services(service_names):\\n for (_, new) in service.recreate_containers():\\n new_containers.append(new)\\n\\n return new_containers\",\n \"label\": 1\n },\n {\n \"cid\": \"fig/project.py::Project.remove_stopped\",\n \"text\": \"fig/project.py::Project.remove_stopped\\n def remove_stopped(self, service_names=None, **options):\\n for service in self.get_services(service_names):\\n service.remove_stopped(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.containers\",\n \"text\": \"fig/project.py::Project.containers\\n def containers(self, service_names=None, *args, **kwargs):\\n l = []\\n for service in self.get_services(service_names):\\n for container in service.containers(*args, **kwargs):\\n l.append(container)\\n return l\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::NoSuchService.__init__\",\n \"text\": \"fig/project.py::NoSuchService.__init__\\n def __init__(self, name):\\n self.name = name\\n self.msg = \\\"No such service: %s\\\" % self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::NoSuchService.__str__\",\n \"text\": \"fig/project.py::NoSuchService.__str__\\n def __str__(self):\\n return self.msg\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::ConfigurationError.__init__\",\n \"text\": \"fig/project.py::ConfigurationError.__init__\\n def __init__(self, msg):\\n self.msg = msg\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::ConfigurationError.__str__\",\n \"text\": \"fig/project.py::ConfigurationError.__str__\\n def __str__(self):\\n return self.msg\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::read\",\n \"text\": \"setup.py::read\\ndef read(*parts):\\n path = os.path.join(os.path.dirname(__file__), *parts)\\n with codecs.open(path, encoding='utf-8') as fobj:\\n return fobj.read()\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::find_version\",\n \"text\": \"setup.py::find_version\\ndef find_version(*file_paths):\\n version_file = read(*file_paths)\\n version_match = re.search(r\\\"^__version__ = ['\\\\\\\"]([^'\\\\\\\"]*)['\\\\\\\"]\\\",\\n version_file, re.M)\\n if version_match:\\n return version_match.group(1)\\n raise RuntimeError(\\\"Unable to find version string.\\\")\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":252,"cells":{"instance_id":{"kind":"string","value":"Lightning-AI__lightning-349"},"repo":{"kind":"string","value":"Lightning-AI/lightning"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Pass experiment tags to MLFlowLogger\n**Is your feature request related to a problem? Please describe.**\r\nWhen using MLFlowLogger, I'm unable to easily set experiment tags, like username or run name.\r\n\r\n**Describe the solution you'd like**\r\nAdd parameter `tags=None` which is passed to `MLFlowLogger`. Tags will be passed to `create_run` method\r\n\r\n**Describe alternatives you've considered**\r\nManually hack logger, get experiment from it and set tag there\r\n\r\nIf you don't see any drawbacks, I can make a PR\r\n\n"},"gold_files":{"kind":"list like","value":["pytorch_lightning/logging/mlflow_logger.py"],"string":"[\n \"pytorch_lightning/logging/mlflow_logger.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.__init__","pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.run_id"],"string":"[\n \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.__init__\",\n \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.run_id\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"examples/basic_examples/lightning_module_template.py","text":"examples/basic_examples/lightning_module_template.py\nclass LightningTemplateModel # Sample model to show how to define a template\n def __init__(self, hparams) # Pass in parsed HyperOptArgumentParser to the model\n def __build_model(self) # Layout model\n def forward(self, x) # No special modification required for lightning, define as you normally would\n def loss(self, labels, logits)\n def training_step(self, batch, batch_idx) # Lightning calls this inside the training loop\n def validation_step(self, batch, batch_idx) # Lightning calls this inside the validation loop\n def validation_end(self, outputs) # Called at the end of validation to aggregate outputs\n def configure_optimizers(self) # return whatever optimizers we want here\n def __dataloader(self, train)\n def train_dataloader(self)\n def val_dataloader(self)\n def test_dataloader(self)\n def add_model_specific_args(parent_parser, root_dir) # Parameters you define here will be available to your model through self.hparams","label":0},{"cid":"pytorch_lightning/logging/__init__.py","text":"pytorch_lightning/logging/__init__.py","label":0},{"cid":"pytorch_lightning/logging/base.py","text":"pytorch_lightning/logging/base.py\ndef rank_zero_only(fn) # Decorate a logger method to run it only on the process with rank 0\n def wrapped_fn(self, *args, **kwargs)\nclass LightningLoggerBase # Base class for experiment loggers\n def __init__(self)\n def log_metrics(self, metrics, step_num) # Record metrics\n def log_hyperparams(self, params) # Record hyperparameters\n def save(self) # Save log data\n def finalize(self, status) # Do any processing that is necessary to finalize an experiment\n def close(self) # Do any cleanup that is necessary to close an experiment\n def rank(self) # Process rank. In general, metrics should only be logged by the process\n def rank(self, value) # Set the process rank\n def version(self) # Return the experiment version","label":0},{"cid":"pytorch_lightning/logging/mlflow_logger.py","text":"pytorch_lightning/logging/mlflow_logger.py\nclass MLFlowLogger\n def __init__(self, experiment_name, tracking_uri=None)\n def run_id(self)\n def log_hyperparams(self, params)\n def log_metrics(self, metrics, step_num=None)\n def save(self)\n def finalize(self, status='FINISHED')","label":1},{"cid":"pytorch_lightning/root_module/grads.py","text":"pytorch_lightning/root_module/grads.py\nclass GradInformation\n def grad_norm(self, norm_type)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py","text":"pytorch_lightning/trainer/trainer.py\ndef reduce_distributed_output(output, nb_gpus)\nclass Trainer\n def __init__(self, logger=None, checkpoint_callback=None, early_stop_callback=None, default_save_path=None, gradient_clip_val=0, process_position=0, nb_gpu_nodes=1, gpus=None, log_gpu_memory=None, show_progress_bar=True, overfit_pct=0.0, track_grad_norm=-1, check_val_every_n_epoch=1, fast_dev_run=False, accumulate_grad_batches=1, max_nb_epochs=1000, min_nb_epochs=1, train_percent_check=1.0, val_percent_check=1.0, test_percent_check=1.0, val_check_interval=1.0, log_save_interval=100, row_log_interval=10, distributed_backend=None, use_amp=False, print_nan_grads=False, weights_summary='full', weights_save_path=None, amp_level='O1', nb_sanity_val_steps=5) # :param logger: Logger for experiment tracking\n def slurm_job_id(self)\n def __configure_weights_path(self, checkpoint_callback, weights_save_path) # Weight path set in this priority:\n def __init_amp(self, use_amp)\n def __configure_accumulated_gradients(self, accumulate_grad_batches)\n def __parse_gpu_ids(self, gpus) # :param gpus: Int, string or list of ids\n def __set_root_gpu(self, gpus)\n def num_gpus(self)\n def __set_distributed_mode(self, distributed_backend, nb_gpu_nodes)\n def __configure_slurm_ddp(self, nb_gpu_nodes)\n def __set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids)\n def data_parallel(self)\n def __determine_data_use_amount(self, train_percent_check, val_percent_check, test_percent_check, overfit_pct) # Use less data for debugging purposes\n def __get_model(self)\n def __is_function_implemented(self, f_name)\n def __is_overriden(self, f_name)\n def __training_tqdm_dict(self)\n def training_tqdm_dict(self) # Read-only for tqdm metrics\n def __layout_bookeeping(self)\n def __add_tqdm_metrics(self, metrics)\n def __evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False)\n def evaluate(self, model, dataloaders, max_batches, test=False) # Run evaluation code\n def get_dataloaders(self, model) # Dataloaders are provided by the model\n def fit(self, model)\n def init_optimizers(self, optimizers)\n def __single_gpu_train(self, model)\n def __dp_train(self, model)\n def ddp_train(self, gpu_nb, model) # Entry point into a DP thread\n def __init_tcp_connection(self) # Connect all procs in the world using the env:// init\n def resolve_root_node_address(self, root_node)\n def __run_pretrain_routine(self, model) # Sanity check a few things before starting actual training\n def __train(self)\n def run_training_epoch(self)\n def __log_metrics(self, metrics, grad_norm_dic) # Logs the metric dict passed in\n def test(self, model=None)\n def __metrics_to_scalars(self, metrics)\n def __log_vals_blacklist(self) # avoid logging some vals lightning uses to maintain state\n def transfer_batch_to_gpu(self, batch, gpu_id)\n def __training_forward(self, batch, batch_nb, o","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py","text":"pytorch_lightning/trainer/trainer_io.py\nclass TrainerIO\n def __get_model(self)\n def restore_weights(self, model) # To restore weights we have two cases.\n def restore_state_if_checkpoint_exists(self, model)\n def register_slurm_signal_handlers(self)\n def sig_handler(self, signum, frame)\n def term_handler(self, signum, frame)\n def save_checkpoint(self, filepath)\n def restore(self, checkpoint_path, on_gpu)\n def dump_checkpoint(self)\n def restore_hpc_weights_if_needed(self, model) # If there is a set of hpc weights, use as signal to restore model\n def restore_training_state(self, checkpoint) # Restore trainer state.\n def hpc_save(self, folderpath, logger)\n def hpc_load(self, folderpath, on_gpu)\n def max_ckpt_in_folder(self, path, name_key='ckpt_')\ndef load_hparams_from_tags_csv(tags_csv)\ndef convert(val)","label":0},{"cid":"pytorch_lightning/utilities/debugging.py","text":"pytorch_lightning/utilities/debugging.py\nclass MisconfigurationException","label":0},{"cid":"setup.py","text":"setup.py","label":0}],"string":"[\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py\",\n \"text\": \"examples/basic_examples/lightning_module_template.py\\nclass LightningTemplateModel # Sample model to show how to define a template\\n def __init__(self, hparams) # Pass in parsed HyperOptArgumentParser to the model\\n def __build_model(self) # Layout model\\n def forward(self, x) # No special modification required for lightning, define as you normally would\\n def loss(self, labels, logits)\\n def training_step(self, batch, batch_idx) # Lightning calls this inside the training loop\\n def validation_step(self, batch, batch_idx) # Lightning calls this inside the validation loop\\n def validation_end(self, outputs) # Called at the end of validation to aggregate outputs\\n def configure_optimizers(self) # return whatever optimizers we want here\\n def __dataloader(self, train)\\n def train_dataloader(self)\\n def val_dataloader(self)\\n def test_dataloader(self)\\n def add_model_specific_args(parent_parser, root_dir) # Parameters you define here will be available to your model through self.hparams\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/__init__.py\",\n \"text\": \"pytorch_lightning/logging/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/base.py\",\n \"text\": \"pytorch_lightning/logging/base.py\\ndef rank_zero_only(fn) # Decorate a logger method to run it only on the process with rank 0\\n def wrapped_fn(self, *args, **kwargs)\\nclass LightningLoggerBase # Base class for experiment loggers\\n def __init__(self)\\n def log_metrics(self, metrics, step_num) # Record metrics\\n def log_hyperparams(self, params) # Record hyperparameters\\n def save(self) # Save log data\\n def finalize(self, status) # Do any processing that is necessary to finalize an experiment\\n def close(self) # Do any cleanup that is necessary to close an experiment\\n def rank(self) # Process rank. In general, metrics should only be logged by the process\\n def rank(self, value) # Set the process rank\\n def version(self) # Return the experiment version\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/mlflow_logger.py\",\n \"text\": \"pytorch_lightning/logging/mlflow_logger.py\\nclass MLFlowLogger\\n def __init__(self, experiment_name, tracking_uri=None)\\n def run_id(self)\\n def log_hyperparams(self, params)\\n def log_metrics(self, metrics, step_num=None)\\n def save(self)\\n def finalize(self, status='FINISHED')\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/root_module/grads.py\",\n \"text\": \"pytorch_lightning/root_module/grads.py\\nclass GradInformation\\n def grad_norm(self, norm_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py\",\n \"text\": \"pytorch_lightning/trainer/trainer.py\\ndef reduce_distributed_output(output, nb_gpus)\\nclass Trainer\\n def __init__(self, logger=None, checkpoint_callback=None, early_stop_callback=None, default_save_path=None, gradient_clip_val=0, process_position=0, nb_gpu_nodes=1, gpus=None, log_gpu_memory=None, show_progress_bar=True, overfit_pct=0.0, track_grad_norm=-1, check_val_every_n_epoch=1, fast_dev_run=False, accumulate_grad_batches=1, max_nb_epochs=1000, min_nb_epochs=1, train_percent_check=1.0, val_percent_check=1.0, test_percent_check=1.0, val_check_interval=1.0, log_save_interval=100, row_log_interval=10, distributed_backend=None, use_amp=False, print_nan_grads=False, weights_summary='full', weights_save_path=None, amp_level='O1', nb_sanity_val_steps=5) # :param logger: Logger for experiment tracking\\n def slurm_job_id(self)\\n def __configure_weights_path(self, checkpoint_callback, weights_save_path) # Weight path set in this priority:\\n def __init_amp(self, use_amp)\\n def __configure_accumulated_gradients(self, accumulate_grad_batches)\\n def __parse_gpu_ids(self, gpus) # :param gpus: Int, string or list of ids\\n def __set_root_gpu(self, gpus)\\n def num_gpus(self)\\n def __set_distributed_mode(self, distributed_backend, nb_gpu_nodes)\\n def __configure_slurm_ddp(self, nb_gpu_nodes)\\n def __set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids)\\n def data_parallel(self)\\n def __determine_data_use_amount(self, train_percent_check, val_percent_check, test_percent_check, overfit_pct) # Use less data for debugging purposes\\n def __get_model(self)\\n def __is_function_implemented(self, f_name)\\n def __is_overriden(self, f_name)\\n def __training_tqdm_dict(self)\\n def training_tqdm_dict(self) # Read-only for tqdm metrics\\n def __layout_bookeeping(self)\\n def __add_tqdm_metrics(self, metrics)\\n def __evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False)\\n def evaluate(self, model, dataloaders, max_batches, test=False) # Run evaluation code\\n def get_dataloaders(self, model) # Dataloaders are provided by the model\\n def fit(self, model)\\n def init_optimizers(self, optimizers)\\n def __single_gpu_train(self, model)\\n def __dp_train(self, model)\\n def ddp_train(self, gpu_nb, model) # Entry point into a DP thread\\n def __init_tcp_connection(self) # Connect all procs in the world using the env:// init\\n def resolve_root_node_address(self, root_node)\\n def __run_pretrain_routine(self, model) # Sanity check a few things before starting actual training\\n def __train(self)\\n def run_training_epoch(self)\\n def __log_metrics(self, metrics, grad_norm_dic) # Logs the metric dict passed in\\n def test(self, model=None)\\n def __metrics_to_scalars(self, metrics)\\n def __log_vals_blacklist(self) # avoid logging some vals lightning uses to maintain state\\n def transfer_batch_to_gpu(self, batch, gpu_id)\\n def __training_forward(self, batch, batch_nb, o\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py\\nclass TrainerIO\\n def __get_model(self)\\n def restore_weights(self, model) # To restore weights we have two cases.\\n def restore_state_if_checkpoint_exists(self, model)\\n def register_slurm_signal_handlers(self)\\n def sig_handler(self, signum, frame)\\n def term_handler(self, signum, frame)\\n def save_checkpoint(self, filepath)\\n def restore(self, checkpoint_path, on_gpu)\\n def dump_checkpoint(self)\\n def restore_hpc_weights_if_needed(self, model) # If there is a set of hpc weights, use as signal to restore model\\n def restore_training_state(self, checkpoint) # Restore trainer state.\\n def hpc_save(self, folderpath, logger)\\n def hpc_load(self, folderpath, on_gpu)\\n def max_ckpt_in_folder(self, path, name_key='ckpt_')\\ndef load_hparams_from_tags_csv(tags_csv)\\ndef convert(val)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/utilities/debugging.py\",\n \"text\": \"pytorch_lightning/utilities/debugging.py\\nclass MisconfigurationException\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.__init__","text":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.__init__\n def __init__(self, hparams):\n \"\"\"\n Pass in parsed HyperOptArgumentParser to the model\n :param hparams:\n \"\"\"\n # init superclass\n super(LightningTemplateModel, self).__init__()\n self.hparams = hparams\n\n self.batch_size = hparams.batch_size\n\n # if you specify an example input, the summary will show input/output for each layer\n self.example_input_array = torch.rand(5, 28 * 28)\n\n # build model\n self.__build_model()","label":0},{"cid":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.__build_model","text":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.__build_model\n def __build_model(self):\n \"\"\"\n Layout model\n :return:\n \"\"\"\n self.c_d1 = nn.Linear(in_features=self.hparams.in_features,\n out_features=self.hparams.hidden_dim)\n self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)\n self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)\n\n self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim,\n out_features=self.hparams.out_features)","label":0},{"cid":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.forward","text":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.forward\n def forward(self, x):\n \"\"\"\n No special modification required for lightning, define as you normally would\n :param x:\n :return:\n \"\"\"\n\n x = self.c_d1(x)\n x = torch.tanh(x)\n x = self.c_d1_bn(x)\n x = self.c_d1_drop(x)\n\n x = self.c_d2(x)\n logits = F.log_softmax(x, dim=1)\n\n return logits","label":0},{"cid":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.loss","text":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.loss\n def loss(self, labels, logits):\n nll = F.nll_loss(logits, labels)\n return nll","label":0},{"cid":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.training_step","text":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.training_step\n def training_step(self, batch, batch_idx):\n \"\"\"\n Lightning calls this inside the training loop\n :param batch:\n :return:\n \"\"\"\n # forward pass\n x, y = batch\n x = x.view(x.size(0), -1)\n\n y_hat = self.forward(x)\n\n # calculate loss\n loss_val = self.loss(y, y_hat)\n\n # in DP mode (default) make sure if result is scalar, there's another dim in the beginning\n if self.trainer.use_dp or self.trainer.use_ddp2:\n loss_val = loss_val.unsqueeze(0)\n\n tqdm_dict = {'train_loss': loss_val}\n output = OrderedDict({\n 'loss': loss_val,\n 'progress_bar': tqdm_dict,\n 'log': tqdm_dict\n })\n\n # can also return just a scalar instead of a dict (return loss_val)\n return output","label":0},{"cid":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.validation_step","text":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.validation_step\n def validation_step(self, batch, batch_idx):\n \"\"\"\n Lightning calls this inside the validation loop\n :param batch:\n :return:\n \"\"\"\n x, y = batch\n x = x.view(x.size(0), -1)\n y_hat = self.forward(x)\n\n loss_val = self.loss(y, y_hat)\n\n # acc\n labels_hat = torch.argmax(y_hat, dim=1)\n val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)\n val_acc = torch.tensor(val_acc)\n\n if self.on_gpu:\n val_acc = val_acc.cuda(loss_val.device.index)\n\n # in DP mode (default) make sure if result is scalar, there's another dim in the beginning\n if self.trainer.use_dp or self.trainer.use_ddp2:\n loss_val = loss_val.unsqueeze(0)\n val_acc = val_acc.unsqueeze(0)\n\n output = OrderedDict({\n 'val_loss': loss_val,\n 'val_acc': val_acc,\n })\n\n # can also return just a scalar instead of a dict (return loss_val)\n return output","label":0},{"cid":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.validation_end","text":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.validation_end\n def validation_end(self, outputs):\n \"\"\"\n Called at the end of validation to aggregate outputs\n :param outputs: list of individual outputs of each validation step\n :return:\n \"\"\"\n # if returned a scalar from validation_step, outputs is a list of tensor scalars\n # we return just the average in this case (if we want)\n # return torch.stack(outputs).mean()\n\n val_loss_mean = 0\n val_acc_mean = 0\n for output in outputs:\n val_loss = output['val_loss']\n\n # reduce manually when using dp\n if self.trainer.use_dp:\n val_loss = torch.mean(val_loss)\n val_loss_mean += val_loss\n\n # reduce manually when using dp\n val_acc = output['val_acc']\n if self.trainer.use_dp or self.trainer.use_ddp2:\n val_acc = torch.mean(val_acc)\n\n val_acc_mean += val_acc\n\n val_loss_mean /= len(outputs)\n val_acc_mean /= len(outputs)\n tqdm_dict = {'val_loss': val_loss_mean, 'val_acc': val_acc_mean}\n result = {'progress_bar': tqdm_dict, 'log': tqdm_dict}\n return result","label":0},{"cid":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.configure_optimizers","text":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.configure_optimizers\n def configure_optimizers(self):\n \"\"\"\n return whatever optimizers we want here\n :return: list of optimizers\n \"\"\"\n optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)\n scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\n return [optimizer], [scheduler]","label":0},{"cid":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.__dataloader","text":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.__dataloader\n def __dataloader(self, train):\n # init data generators\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root=self.hparams.data_root, train=train,\n transform=transform, download=True)\n\n # when using multi-node (ddp) we need to add the datasampler\n train_sampler = None\n batch_size = self.hparams.batch_size\n\n if self.use_ddp:\n train_sampler = DistributedSampler(dataset)\n\n should_shuffle = train_sampler is None\n loader = DataLoader(\n dataset=dataset,\n batch_size=batch_size,\n shuffle=should_shuffle,\n sampler=train_sampler,\n num_workers=0\n )\n\n return loader","label":0},{"cid":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.train_dataloader","text":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.train_dataloader\n @pl.data_loader\n def train_dataloader(self):\n print('training data loader called')\n return self.__dataloader(train=True)","label":0},{"cid":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.val_dataloader","text":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.val_dataloader\n @pl.data_loader\n def val_dataloader(self):\n print('val data loader called')\n return self.__dataloader(train=False)","label":0},{"cid":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.test_dataloader","text":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.test_dataloader\n @pl.data_loader\n def test_dataloader(self):\n print('test data loader called')\n return self.__dataloader(train=False)","label":0},{"cid":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.add_model_specific_args","text":"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.add_model_specific_args\n @staticmethod\n def add_model_specific_args(parent_parser, root_dir): # pragma: no cover\n \"\"\"\n Parameters you define here will be available to your model through self.hparams\n :param parent_parser:\n :param root_dir:\n :return:\n \"\"\"\n parser = ArgumentParser(parents=[parent_parser])\n\n # param overwrites\n # parser.set_defaults(gradient_clip_val=5.0)\n\n # network params\n parser.add_argument('--in_features', default=28 * 28, type=int)\n parser.add_argument('--out_features', default=10, type=int)\n # use 500 for CPU, 50000 for GPU to see speed difference\n parser.add_argument('--hidden_dim', default=50000, type=int)\n parser.add_argument('--drop_prob', default=0.2, type=float)\n parser.add_argument('--learning_rate', default=0.001, type=float)\n\n # data\n parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)\n\n # training params (opt)\n parser.add_argument('--optimizer_name', default='adam', type=str)\n parser.add_argument('--batch_size', default=64, type=int)\n return parser","label":0},{"cid":"pytorch_lightning/logging/base.py::rank_zero_only","text":"pytorch_lightning/logging/base.py::rank_zero_only\ndef rank_zero_only(fn):\n \"\"\"Decorate a logger method to run it only on the process with rank 0\n\n :param fn: Function to decorate\n \"\"\"\n\n @wraps(fn)\n def wrapped_fn(self, *args, **kwargs):\n if self.rank == 0:\n fn(self, *args, **kwargs)\n\n return wrapped_fn","label":0},{"cid":"pytorch_lightning/logging/base.py::rank_zero_only.wrapped_fn","text":"pytorch_lightning/logging/base.py::rank_zero_only.wrapped_fn\n @wraps(fn)\n def wrapped_fn(self, *args, **kwargs):\n if self.rank == 0:\n fn(self, *args, **kwargs)","label":0},{"cid":"pytorch_lightning/logging/base.py::LightningLoggerBase.__init__","text":"pytorch_lightning/logging/base.py::LightningLoggerBase.__init__\n def __init__(self):\n self._rank = 0","label":0},{"cid":"pytorch_lightning/logging/base.py::LightningLoggerBase.log_metrics","text":"pytorch_lightning/logging/base.py::LightningLoggerBase.log_metrics\n def log_metrics(self, metrics, step_num):\n \"\"\"Record metrics\n\n :param metric: Dictionary with metric names as keys and measured\n quanties as values\n :param step_num: Step number at which the metrics should be recorded\n \"\"\"\n raise NotImplementedError()","label":0},{"cid":"pytorch_lightning/logging/base.py::LightningLoggerBase.log_hyperparams","text":"pytorch_lightning/logging/base.py::LightningLoggerBase.log_hyperparams\n def log_hyperparams(self, params):\n \"\"\"Record hyperparameters\n\n :param params: argparse.Namespace containing the hyperparameters\n \"\"\"\n raise NotImplementedError()","label":0},{"cid":"pytorch_lightning/logging/base.py::LightningLoggerBase.save","text":"pytorch_lightning/logging/base.py::LightningLoggerBase.save\n def save(self):\n \"\"\"Save log data\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/logging/base.py::LightningLoggerBase.finalize","text":"pytorch_lightning/logging/base.py::LightningLoggerBase.finalize\n def finalize(self, status):\n \"\"\"Do any processing that is necessary to finalize an experiment\n\n :param status: Status that the experiment finished with (e.g. success, failed, aborted)\n \"\"\"\n pass","label":0},{"cid":"pytorch_lightning/logging/base.py::LightningLoggerBase.close","text":"pytorch_lightning/logging/base.py::LightningLoggerBase.close\n def close(self):\n \"\"\"Do any cleanup that is necessary to close an experiment\"\"\"\n pass","label":0},{"cid":"pytorch_lightning/logging/base.py::LightningLoggerBase.rank","text":"pytorch_lightning/logging/base.py::LightningLoggerBase.rank\n @property\n def rank(self):\n \"\"\"\n Process rank. In general, metrics should only be logged by the process\n with rank 0\n \"\"\"\n return self._rank","label":0},{"cid":"pytorch_lightning/logging/base.py::LightningLoggerBase.rank","text":"pytorch_lightning/logging/base.py::LightningLoggerBase.rank\n @rank.setter\n def rank(self, value):\n \"\"\"Set the process rank\"\"\"\n self._rank = value","label":0},{"cid":"pytorch_lightning/logging/base.py::LightningLoggerBase.version","text":"pytorch_lightning/logging/base.py::LightningLoggerBase.version\n @property\n def version(self):\n \"\"\"Return the experiment version\"\"\"\n return None","label":0},{"cid":"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.__init__","text":"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.__init__\n def __init__(self, experiment_name, tracking_uri=None):\n super().__init__()\n self.client = mlflow.tracking.MlflowClient(tracking_uri)\n self.experiment_name = experiment_name\n self._run_id = None","label":1},{"cid":"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.run_id","text":"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.run_id\n @property\n def run_id(self):\n if self._run_id is not None:\n return self._run_id\n\n experiment = self.client.get_experiment_by_name(self.experiment_name)\n if experiment is None:\n logger.warning(\n f\"Experiment with name f{self.experiment_name} not found. Creating it.\"\n )\n self.client.create_experiment(self.experiment_name)\n experiment = self.client.get_experiment_by_name(self.experiment_name)\n\n run = self.client.create_run(experiment.experiment_id)\n self._run_id = run.info.run_id\n return self._run_id","label":1},{"cid":"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.log_hyperparams","text":"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.log_hyperparams\n @rank_zero_only\n def log_hyperparams(self, params):\n for k, v in vars(params).items():\n self.client.log_param(self.run_id, k, v)","label":0},{"cid":"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.log_metrics","text":"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.log_metrics\n @rank_zero_only\n def log_metrics(self, metrics, step_num=None):\n timestamp_ms = int(time() * 1000)\n for k, v in metrics.items():\n if isinstance(v, str):\n logger.warning(\n f\"Discarding metric with string value {k}={v}\"\n )\n continue\n self.client.log_metric(self.run_id, k, v, timestamp_ms, step_num)","label":0},{"cid":"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.save","text":"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.save\n def save(self):\n pass","label":0},{"cid":"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.finalize","text":"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.finalize\n @rank_zero_only\n def finalize(self, status=\"FINISHED\"):\n if status == 'success':\n status = 'FINISHED'\n self.client.set_terminated(self.run_id, status)","label":0},{"cid":"pytorch_lightning/root_module/grads.py::GradInformation.grad_norm","text":"pytorch_lightning/root_module/grads.py::GradInformation.grad_norm\n def grad_norm(self, norm_type):\n results = {}\n total_norm = 0\n for i, p in enumerate(self.parameters()):\n if p.requires_grad:\n try:\n param_norm = p.grad.data.norm(norm_type)\n total_norm += param_norm ** norm_type\n norm = param_norm ** (1 / norm_type)\n\n grad = round(norm.data.cpu().numpy().flatten()[0], 3)\n results['grad_{}_norm_{}'.format(norm_type, i)] = grad\n except Exception:\n # this param had no grad\n pass\n\n total_norm = total_norm ** (1. / norm_type)\n grad = round(total_norm.data.cpu().numpy().flatten()[0], 3)\n results['grad_{}_norm_total'.format(norm_type)] = grad\n return results","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::reduce_distributed_output","text":"pytorch_lightning/trainer/trainer.py::reduce_distributed_output\ndef reduce_distributed_output(output, nb_gpus):\n if nb_gpus <= 1:\n return output\n\n # when using DP, we get one output per gpu\n # average outputs and return\n if type(output) is torch.Tensor:\n return output.mean()\n\n for k, v in output.items():\n # recurse on nested dics\n if isinstance(output[k], dict):\n output[k] = reduce_distributed_output(output[k], nb_gpus)\n\n # reduce only metrics that have the same nb of gpus\n elif output[k].size(0) == nb_gpus:\n reduced = torch.mean(output[k])\n output[k] = reduced\n return output","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__init__","text":"pytorch_lightning/trainer/trainer.py::Trainer.__init__\n def __init__(self,\n logger=None,\n checkpoint_callback=None,\n early_stop_callback=None,\n default_save_path=None,\n gradient_clip_val=0,\n process_position=0,\n nb_gpu_nodes=1,\n gpus=None,\n log_gpu_memory=None,\n show_progress_bar=True,\n overfit_pct=0.0,\n track_grad_norm=-1,\n check_val_every_n_epoch=1,\n fast_dev_run=False,\n accumulate_grad_batches=1,\n max_nb_epochs=1000,\n min_nb_epochs=1,\n train_percent_check=1.0,\n val_percent_check=1.0,\n test_percent_check=1.0,\n val_check_interval=1.0,\n log_save_interval=100,\n row_log_interval=10,\n distributed_backend=None,\n use_amp=False,\n print_nan_grads=False,\n weights_summary='full',\n weights_save_path=None,\n amp_level='O1',\n nb_sanity_val_steps=5):\n \"\"\"\n\n :param logger: Logger for experiment tracking\n :param checkpoint_callback: Callback for checkpointing\n :param early_stop_callback: Callback for early stopping\n :param default_save_path: Default path for logs+weights if no logger/ckpt_callback passed\n :param gradient_clip_val: int. 0 means don't clip.\n :param process_position: shown in the tqdm bar\n :param nb_gpu_nodes: number of GPU nodes\n :param gpus: int. (ie: 2 gpus) OR list to specify which GPUs [0, 1] or '0,1'\n :param log_gpu_memory: str. None, 'min_max', 'all'\n :param show_progress_bar: Bool. If true shows tqdm bar\n :param overfit_pct: float. uses this much of all datasets\n :param track_grad_norm: int. -1 no","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id","text":"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id\n @property\n def slurm_job_id(self):\n try:\n job_id = os.environ['SLURM_JOB_ID']\n job_id = int(job_id)\n except Exception as e:\n job_id = None\n return job_id","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__configure_weights_path","text":"pytorch_lightning/trainer/trainer.py::Trainer.__configure_weights_path\n def __configure_weights_path(self, checkpoint_callback, weights_save_path):\n \"\"\"\n Weight path set in this priority:\n Checkpoint_callback's path (if passed in).\n User provided weights_saved_path\n Otherwise use os.getcwd()\n \"\"\"\n self.weights_save_path = weights_save_path\n\n if self.checkpoint_callback is not None:\n self.checkpoint_callback.save_function = self.save_checkpoint\n\n # if checkpoint callback used, then override the weights path\n self.weights_save_path = self.checkpoint_callback.filepath\n\n # if weights_save_path is still none here, set to current workingdir\n if self.weights_save_path is None:\n self.weights_save_path = self.default_save_path","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__init_amp","text":"pytorch_lightning/trainer/trainer.py::Trainer.__init_amp\n def __init_amp(self, use_amp):\n self.use_amp = use_amp and APEX_AVAILABLE\n if self.use_amp:\n print('using 16bit precision')\n\n if use_amp and not APEX_AVAILABLE: # pragma: no cover\n msg = \"\"\"\n You set use_amp=True but do not have apex installed.\n Install apex first using this guide and rerun with use_amp=True:\n https://github.com/NVIDIA/apex#linux\n\n this run will NOT use 16 bit precision\n \"\"\"\n raise ModuleNotFoundError(msg)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__configure_accumulated_gradients","text":"pytorch_lightning/trainer/trainer.py::Trainer.__configure_accumulated_gradients\n def __configure_accumulated_gradients(self, accumulate_grad_batches):\n self.accumulate_grad_batches = None\n\n if isinstance(accumulate_grad_batches, dict):\n self.accumulation_scheduler = GradientAccumulationScheduler(accumulate_grad_batches)\n elif isinstance(accumulate_grad_batches, int):\n schedule = {1: accumulate_grad_batches}\n self.accumulation_scheduler = GradientAccumulationScheduler(schedule)\n else:\n raise TypeError(\"Gradient accumulation supports only int and dict types\")","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__parse_gpu_ids","text":"pytorch_lightning/trainer/trainer.py::Trainer.__parse_gpu_ids\n def __parse_gpu_ids(self, gpus):\n \"\"\"\n :param gpus: Int, string or list of ids\n :return:\n \"\"\"\n # if gpus = -1 then use all available devices\n # otherwise, split the string using commas\n if gpus is not None:\n if type(gpus) is list:\n gpus = gpus\n elif type(gpus) is str:\n if gpus == '-1':\n gpus = list(range(0, torch.cuda.device_count()))\n else:\n gpus = [int(x.strip()) for x in gpus.split(',')]\n elif type(gpus) is int:\n gpus = gpus\n else:\n raise Exception('gpus has to be a string, int or list of ints')\n\n return gpus","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__set_root_gpu","text":"pytorch_lightning/trainer/trainer.py::Trainer.__set_root_gpu\n def __set_root_gpu(self, gpus):\n if gpus is None:\n return None\n\n # set root gpu\n root_gpu = 0\n if type(gpus) is list:\n root_gpu = gpus[0]\n\n return root_gpu","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus","text":"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus\n @property\n def num_gpus(self):\n gpus = self.data_parallel_device_ids\n if gpus is None:\n return 0\n\n if type(gpus) is list:\n return len(gpus)\n if type(gpus) is int:\n return gpus\n\n m = 'gpus must be int, none or list of ints'\n raise MisconfigurationException(m)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__set_distributed_mode","text":"pytorch_lightning/trainer/trainer.py::Trainer.__set_distributed_mode\n def __set_distributed_mode(self, distributed_backend, nb_gpu_nodes):\n # skip for CPU\n if self.num_gpus == 0:\n return\n\n # single GPU case\n # in single gpu case we allow ddp so we can train on multiple\n # nodes, 1 gpu per node\n if self.num_gpus == 1:\n self.single_gpu = True\n\n if distributed_backend is not None:\n self.use_dp = distributed_backend == 'dp'\n self.use_ddp = distributed_backend == 'ddp'\n self.use_ddp2 = distributed_backend == 'ddp2'\n\n # disable single gpu when using ddp2\n if self.use_ddp2:\n self.single_gpu = False\n\n # multiple GPU case\n elif self.num_gpus > 1:\n if distributed_backend is not None:\n # DP, DDP case\n self.use_dp = distributed_backend == 'dp'\n self.use_ddp = distributed_backend == 'ddp'\n self.use_ddp2 = distributed_backend == 'ddp2'\n\n elif distributed_backend is None:\n m = 'When using multiple GPUs set ' \\\n 'Trainer(distributed_backend=dp) (or ddp)'\n raise MisconfigurationException(m)\n\n # use ddp automatically if nb_gpu_nodes > 1\n if nb_gpu_nodes > 1 and self.use_dp: # pragma: no cover\n self.use_ddp = True\n self.use_dp = False\n w = 'DataParallel does not support nb_gpu_nodes > 1. ' \\\n 'Switching to DistributedDataParallel for you. ' \\\n 'To silence this warning set distributed_backend=ddp'\n warnings.warn(w)\n\n print('gpu available: {}, used: {}'.format(torch.cuda.is_available(), self.on_gpu))","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__configure_slurm_ddp","text":"pytorch_lightning/trainer/trainer.py::Trainer.__configure_slurm_ddp\n def __configure_slurm_ddp(self, nb_gpu_nodes):\n self.is_slurm_managing_tasks = False\n\n # extract SLURM flag vars\n # whenever we have the correct number of tasks, we let slurm manage processes\n # otherwise we launch the required number of processes\n if self.use_ddp:\n self.nb_requested_gpus = self.num_gpus * nb_gpu_nodes\n self.nb_slurm_tasks = 0\n try:\n self.nb_slurm_tasks = int(os.environ['SLURM_NTASKS'])\n self.is_slurm_managing_tasks = self.nb_slurm_tasks == self.nb_requested_gpus\n\n # in interactive mode we don't manage tasks\n job_name = os.environ['SLURM_JOB_NAME']\n if job_name == 'bash':\n self.is_slurm_managing_tasks = False\n\n except Exception:\n # likely not on slurm, so set the slurm managed flag to false\n self.is_slurm_managing_tasks = False\n\n # used for tests only, set this flag to simulate slurm managing a task\n try:\n should_fake = int(os.environ['FAKE_SLURM_MANAGING_TASKS'])\n if should_fake:\n self.is_slurm_managing_tasks = True\n except Exception as e:\n pass","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__set_nvidia_flags","text":"pytorch_lightning/trainer/trainer.py::Trainer.__set_nvidia_flags\n def __set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids):\n if data_parallel_device_ids is None:\n return\n\n # set the correct cuda visible devices (using pci order)\n os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n\n # when slurm is managing the task it sets the visible devices\n if not is_slurm_managing_tasks:\n if type(data_parallel_device_ids) is int:\n id_str = ','.join(str(x) for x in list(range(data_parallel_device_ids)))\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = id_str\n else:\n gpu_str = ','.join([str(x) for x in data_parallel_device_ids])\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_str\n\n print(f'VISIBLE GPUS: {os.environ[\"CUDA_VISIBLE_DEVICES\"]}')","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel","text":"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel\n @property\n def data_parallel(self):\n return self.use_dp or self.use_ddp or self.use_ddp2","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__determine_data_use_amount","text":"pytorch_lightning/trainer/trainer.py::Trainer.__determine_data_use_amount\n def __determine_data_use_amount(self, train_percent_check, val_percent_check,\n test_percent_check, overfit_pct):\n \"\"\"\n Use less data for debugging purposes\n \"\"\"\n self.train_percent_check = train_percent_check\n self.val_percent_check = val_percent_check\n self.test_percent_check = test_percent_check\n if overfit_pct > 0:\n self.train_percent_check = overfit_pct\n self.val_percent_check = overfit_pct\n self.test_percent_check = overfit_pct","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__get_model","text":"pytorch_lightning/trainer/trainer.py::Trainer.__get_model\n def __get_model(self):\n return self.model.module if self.data_parallel else self.model","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__is_function_implemented","text":"pytorch_lightning/trainer/trainer.py::Trainer.__is_function_implemented\n def __is_function_implemented(self, f_name):\n model = self.__get_model()\n f_op = getattr(model, f_name, None)\n return callable(f_op)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__is_overriden","text":"pytorch_lightning/trainer/trainer.py::Trainer.__is_overriden\n def __is_overriden(self, f_name):\n model = self.__get_model()\n super_object = LightningModule\n\n # when code pointers are different, it was overriden\n is_overriden = getattr(model, f_name).__code__ is not getattr(super_object, f_name).__code__\n return is_overriden","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__training_tqdm_dict","text":"pytorch_lightning/trainer/trainer.py::Trainer.__training_tqdm_dict\n @property\n def __training_tqdm_dict(self):\n tqdm_dict = {\n 'loss': '{0:.3f}'.format(self.avg_loss),\n 'epoch': '{}'.format(self.current_epoch),\n 'batch_nb': '{}'.format(self.batch_nb),\n }\n\n if self.logger is not None and self.logger.version is not None:\n tqdm_dict['v_nb'] = self.logger.version\n\n tqdm_dict.update(self.tqdm_metrics)\n\n if self.on_gpu:\n tqdm_dict['gpu'] = '{}'.format(torch.cuda.current_device())\n\n return tqdm_dict","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.training_tqdm_dict","text":"pytorch_lightning/trainer/trainer.py::Trainer.training_tqdm_dict\n @property\n def training_tqdm_dict(self):\n \"\"\"\n Read-only for tqdm metrics\n :return:\n \"\"\"\n return self.__training_tqdm_dict","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__layout_bookeeping","text":"pytorch_lightning/trainer/trainer.py::Trainer.__layout_bookeeping\n def __layout_bookeeping(self):\n\n # determine number of training batches\n self.nb_training_batches = len(self.get_train_dataloader())\n self.nb_training_batches = int(self.nb_training_batches * self.train_percent_check)\n\n # determine number of validation batches\n # val datasets could be none, 1 or 2+\n if self.get_val_dataloaders() is not None:\n self.nb_val_batches = sum(len(dataloader) for dataloader in self.get_val_dataloaders())\n self.nb_val_batches = int(self.nb_val_batches * self.val_percent_check)\n self.nb_val_batches = max(1, self.nb_val_batches)\n\n # determine number of test batches\n if self.get_test_dataloaders() is not None:\n self.nb_test_batches = sum(\n len(dataloader) for dataloader in self.get_test_dataloaders()\n )\n self.nb_test_batches = int(self.nb_test_batches * self.test_percent_check)\n self.nb_test_batches = max(1, self.nb_test_batches)\n\n # determine when to check validation\n self.val_check_batch = int(self.nb_training_batches * self.val_check_interval)\n self.val_check_batch = max(1, self.val_check_batch)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__add_tqdm_metrics","text":"pytorch_lightning/trainer/trainer.py::Trainer.__add_tqdm_metrics\n def __add_tqdm_metrics(self, metrics):\n for k, v in metrics.items():\n if type(v) is torch.Tensor:\n v = v.item()\n\n self.tqdm_metrics[k] = v","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__evaluation_forward","text":"pytorch_lightning/trainer/trainer.py::Trainer.__evaluation_forward\n def __evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False):\n # make dataloader_idx arg in validation_step optional\n args = [batch, batch_idx]\n\n if test and len(self.get_test_dataloaders()) > 1:\n args.append(dataloader_idx)\n\n elif not test and len(self.get_val_dataloaders()) > 1:\n args.append(dataloader_idx)\n\n # handle DP, DDP forward\n if self.use_ddp or self.use_dp or self.use_ddp2:\n output = model(*args)\n return output\n\n # single GPU\n if self.single_gpu:\n # for single GPU put inputs on gpu manually\n root_gpu = 0\n if type(self.data_parallel_device_ids) is list:\n root_gpu = self.data_parallel_device_ids[0]\n batch = self.transfer_batch_to_gpu(batch, root_gpu)\n args[0] = batch\n\n # CPU\n if test:\n output = model.test_step(*args)\n else:\n output = model.validation_step(*args)\n\n return output","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.evaluate","text":"pytorch_lightning/trainer/trainer.py::Trainer.evaluate\n def evaluate(self, model, dataloaders, max_batches, test=False):\n \"\"\"\n Run evaluation code\n :param model: PT model\n :param dataloaders: list of PT dataloaders\n :param max_batches: Scalar\n :param test: boolean\n :return:\n \"\"\"\n # enable eval mode\n model.zero_grad()\n model.eval()\n\n # disable gradients to save memory\n torch.set_grad_enabled(False)\n\n # bookkeeping\n outputs = []\n\n # run training\n for dataloader_idx, dataloader in enumerate(dataloaders):\n dl_outputs = []\n for batch_idx, batch in enumerate(dataloader):\n\n if batch is None: # pragma: no cover\n continue\n\n # stop short when on fast_dev_run (sets max_batch=1)\n if batch_idx >= max_batches:\n break\n\n # -----------------\n # RUN EVALUATION STEP\n # -----------------\n output = self.__evaluation_forward(model,\n batch,\n batch_idx,\n dataloader_idx,\n test)\n\n # track outputs for collation\n dl_outputs.append(output)\n\n # batch done\n if self.show_progress_bar:\n self.progress_bar.update(1)\n outputs.append(dl_outputs)\n\n eval_results = {}\n\n # with a single dataloader don't pass an array\n if len(dataloaders) == 1:\n outputs = outputs[0]\n\n # give model a chance to do something with the outputs (and method defined)\n model = self.__get_model()\n if test and self.__is_overriden('test_end'):\n eval_results = model.test_end(outputs)\n elif self.__is_","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.get_dataloaders","text":"pytorch_lightning/trainer/trainer.py::Trainer.get_dataloaders\n def get_dataloaders(self, model):\n \"\"\"\n Dataloaders are provided by the model\n :param model:\n :return:\n \"\"\"\n self.get_train_dataloader = model.train_dataloader\n self.get_test_dataloaders = model.test_dataloader\n self.get_val_dataloaders = model.val_dataloader\n\n # call warnings from proc zero only which triggers dataloaders\n # if those have to download data it will only happen on proc 0\n if self.proc_rank == 0:\n on_ddp = self.use_ddp or self.use_ddp2\n if on_ddp and not isinstance(self.get_train_dataloader().sampler, DistributedSampler):\n msg = \"\"\"\n You're using multiple gpus and multiple nodes without using a DistributedSampler\n to assign a subset of your data to each process. To silence this warning, pass a\n DistributedSampler to your DataLoader.\n\n ie: this:\n dataset = myDataset()\n dataloader = Dataloader(dataset)\n\n becomes:\n dataset = myDataset()\n dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)\n dataloader = Dataloader(dataset, sampler=dist_sampler)\n\n If you want each process to load the full dataset, ignore this warning.\n \"\"\"\n warnings.warn(msg)\n\n if on_ddp and self.get_val_dataloaders() is not None:\n for dataloader in self.get_val_dataloaders():\n if not isinstance(dataloader.sampler, DistributedSampler):\n msg = \"\"\"\n Your val_dataloader(s) don't use DistributedSampler.\n\n You're using multiple gpus and multiple nodes without using a\n DistributedSampler to assign a subset of your data to each process.\n To silence this","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.fit","text":"pytorch_lightning/trainer/trainer.py::Trainer.fit\n def fit(self, model):\n # when using multi-node or DDP within a node start each module in a separate process\n if self.use_ddp2:\n task = int(os.environ['SLURM_LOCALID'])\n self.ddp_train(task, model)\n\n elif self.use_ddp:\n if self.is_slurm_managing_tasks:\n task = int(os.environ['SLURM_LOCALID'])\n self.ddp_train(task, model)\n else:\n mp.spawn(self.ddp_train, nprocs=self.num_gpus, args=(model, ))\n\n # 1 gpu or dp option triggers training using DP module\n # easier to avoid NCCL issues\n elif self.use_dp:\n self.__dp_train(model)\n\n elif self.single_gpu:\n self.__single_gpu_train(model)\n\n # ON CPU\n else:\n # run through amp wrapper\n if self.use_amp:\n raise MisconfigurationException('amp + cpu is not supported.'\n ' Please use a GPU option')\n\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n self.__run_pretrain_routine(model)\n\n # return 1 when finished\n # used for testing or when we need to know that training succeeded\n return 1","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.init_optimizers","text":"pytorch_lightning/trainer/trainer.py::Trainer.init_optimizers\n def init_optimizers(self, optimizers):\n\n # single optimizer\n if isinstance(optimizers, Optimizer):\n return [optimizers], []\n\n # two lists\n elif len(optimizers) == 2 and isinstance(optimizers[0], list):\n optimizers, lr_schedulers = optimizers\n return optimizers, lr_schedulers\n\n # single list or tuple\n elif isinstance(optimizers, list) or isinstance(optimizers, tuple):\n return optimizers, []","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__single_gpu_train","text":"pytorch_lightning/trainer/trainer.py::Trainer.__single_gpu_train\n def __single_gpu_train(self, model):\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n model.cuda(self.root_gpu)\n\n if self.use_amp:\n # An example\n model, optimizers = amp.initialize(\n model, self.optimizers, opt_level=self.amp_level,\n )\n self.optimizers = optimizers\n\n self.__run_pretrain_routine(model)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__dp_train","text":"pytorch_lightning/trainer/trainer.py::Trainer.__dp_train\n def __dp_train(self, model):\n\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n model.cuda(self.root_gpu)\n\n # check for this bug (amp + dp + !01 doesn't work)\n # https://github.com/NVIDIA/apex/issues/227\n if self.use_dp and self.use_amp:\n m = f\"\"\"\n Amp level {self.amp_level} with DataParallel is not supported.\n See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227.\n We recommend you switch to ddp if you want to use amp\n \"\"\"\n raise MisconfigurationException(m)\n\n # create list of device ids\n device_ids = self.data_parallel_device_ids\n if type(device_ids) is int:\n device_ids = list(range(device_ids))\n\n model = LightningDataParallel(model, device_ids=device_ids)\n\n self.__run_pretrain_routine(model)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.ddp_train","text":"pytorch_lightning/trainer/trainer.py::Trainer.ddp_train\n def ddp_train(self, gpu_nb, model):\n \"\"\"\n Entry point into a DP thread\n :param gpu_nb:\n :param model:\n :param cluster_obj:\n :return:\n \"\"\"\n # node rank using relative slurm id\n # otherwise default to node rank 0\n try:\n node_id = os.environ['SLURM_NODEID']\n self.node_rank = int(node_id)\n except Exception:\n self.node_rank = 0\n\n # show progressbar only on progress_rank 0\n self.show_progress_bar = self.show_progress_bar and self.node_rank == 0 and gpu_nb == 0\n\n # determine which process we are and world size\n if self.use_ddp:\n self.proc_rank = self.node_rank * self.num_gpus + gpu_nb\n self.world_size = self.nb_gpu_nodes * self.num_gpus\n\n elif self.use_ddp2:\n self.proc_rank = self.node_rank\n self.world_size = self.nb_gpu_nodes\n\n # let the exp know the rank to avoid overwriting logs\n if self.logger is not None:\n self.logger.rank = self.proc_rank\n\n # set up server using proc 0's ip address\n # try to init for 20 times at max in case ports are taken\n # where to store ip_table\n self.__init_tcp_connection()\n\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n # MODEL\n # copy model to each gpu\n if self.distributed_backend == 'ddp':\n torch.cuda.set_device(gpu_nb)\n model.cuda(gpu_nb)\n\n # set model properties before going into wrapper\n model.trainer = self\n model.on_gpu = self.on_gpu\n model.use_dp = self.use_dp\n model.use_ddp2 = self.use_ddp2\n model.use_ddp = self.use_ddp\n model.use_amp = self.use_amp\n model.testing = self.testing\n\n # override root GPU\n self.root_gp","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__init_tcp_connection","text":"pytorch_lightning/trainer/trainer.py::Trainer.__init_tcp_connection\n def __init_tcp_connection(self):\n \"\"\"\n Connect all procs in the world using the env:// init\n Use the first node as the root address\n :param port:\n :param tries:\n :return:\n \"\"\"\n\n # use slurm job id for the port number\n # guarantees unique ports across jobs from same grid search\n try:\n # use the last 4 numbers in the job id as the id\n default_port = os.environ['SLURM_JOB_ID']\n default_port = default_port[-4:]\n\n # all ports should be in the 10k+ range\n default_port = int(default_port) + 15000\n\n except Exception as e:\n default_port = 12910\n\n # if user gave a port number, use that one instead\n try:\n default_port = os.environ['MASTER_PORT']\n except Exception:\n os.environ['MASTER_PORT'] = str(default_port)\n\n # figure out the root node addr\n try:\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\n except Exception:\n root_node = '127.0.0.2'\n\n root_node = self.resolve_root_node_address(root_node)\n os.environ['MASTER_ADDR'] = root_node\n dist.init_process_group(\"nccl\", rank=self.proc_rank, world_size=self.world_size)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.resolve_root_node_address","text":"pytorch_lightning/trainer/trainer.py::Trainer.resolve_root_node_address\n def resolve_root_node_address(self, root_node):\n if '[' in root_node:\n name = root_node.split('[')[0]\n number = root_node.split(',')[0]\n if '-' in number:\n number = number.split('-')[0]\n\n number = re.sub('[^0-9]', '', number)\n root_node = name + number\n\n return root_node","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__run_pretrain_routine","text":"pytorch_lightning/trainer/trainer.py::Trainer.__run_pretrain_routine\n def __run_pretrain_routine(self, model):\n \"\"\"\n Sanity check a few things before starting actual training\n :param model:\n :return:\n \"\"\"\n ref_model = model\n if self.data_parallel:\n ref_model = model.module\n\n # give model convenience properties\n ref_model.trainer = self\n\n # set local properties on the model\n ref_model.on_gpu = self.on_gpu\n ref_model.single_gpu = self.single_gpu\n ref_model.use_dp = self.use_dp\n ref_model.use_ddp = self.use_ddp\n ref_model.use_ddp2 = self.use_ddp2\n ref_model.use_amp = self.use_amp\n ref_model.testing = self.testing\n\n # register auto-resubmit when on SLURM\n self.register_slurm_signal_handlers()\n\n # transfer data loaders from model\n self.get_dataloaders(ref_model)\n\n # init training constants\n self.__layout_bookeeping()\n\n # print model summary\n if self.proc_rank == 0 and self.weights_summary is not None:\n if self.weights_summary in ['full', 'top']:\n ref_model.summarize(mode=self.weights_summary)\n else:\n m = \"weights_summary can be None, 'full' or 'top'\"\n raise MisconfigurationException(m)\n\n # link up experiment object\n if self.logger is not None:\n ref_model.logger = self.logger\n\n # save exp to get started\n if hasattr(ref_model, \"hparams\"):\n self.logger.log_hyperparams(ref_model.hparams)\n self.logger.save()\n\n # track model now.\n # if cluster resets state, the model will update with the saved weights\n self.model = model\n\n # restore training and model before hpc call\n self.restore_weights(model)\n\n # progress bar init\n if self.show_progress_bar:\n self.progress_bar = tqdm.tqdm(0, position=self.pro","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__train","text":"pytorch_lightning/trainer/trainer.py::Trainer.__train\n def __train(self):\n # run all epochs\n for epoch_nb in range(self.current_epoch, self.max_nb_epochs):\n # set seed for distributed sampler (enables shuffling for each epoch)\n if self.use_ddp and hasattr(self.get_train_dataloader().sampler, 'set_epoch'):\n self.get_train_dataloader().sampler.set_epoch(epoch_nb)\n\n # get model\n model = self.__get_model()\n\n # update training progress in trainer and model\n model.current_epoch = epoch_nb\n self.current_epoch = epoch_nb\n self.total_batches = self.nb_training_batches + self.nb_val_batches\n self.batch_loss_value = 0 # accumulated grads\n\n # limit the number of batches to 1 in fast_dev_run\n self.total_batches = 1\n\n # init progress_bar when requested\n if self.show_progress_bar:\n self.progress_bar.reset(self.total_batches)\n\n # changing gradient according accumulation_scheduler\n self.accumulation_scheduler.on_epoch_begin(epoch_nb, self)\n\n # -----------------\n # RUN TNG EPOCH\n # -----------------\n self.run_training_epoch()\n\n # update LR schedulers\n if self.lr_schedulers is not None:\n for lr_scheduler in self.lr_schedulers:\n lr_scheduler.step(self.current_epoch)\n\n # early stopping\n met_min_epochs = epoch_nb > self.min_nb_epochs\n if self.enable_early_stop and (met_min_epochs or self.fast_dev_run):\n should_stop = self.early_stop_callback.on_epoch_end(epoch=epoch_nb,\n logs=self.callback_metrics)\n # stop training\n stop = should_stop and met_min_epochs\n if stop:\n return\n\n if self.logger is not Non","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.run_training_epoch","text":"pytorch_lightning/trainer/trainer.py::Trainer.run_training_epoch\n def run_training_epoch(self):\n # before epoch hook\n if self.__is_function_implemented('on_epoch_start'):\n model = self.__get_model()\n model.on_epoch_start()\n\n # run epoch\n for batch_nb, batch in enumerate(self.get_train_dataloader()):\n self.batch_nb = batch_nb\n self.global_step += 1\n\n model = self.__get_model()\n model.global_step = self.global_step\n\n # stop when the flag is changed or we've gone past the amount\n # requested in the batches\n self.total_batch_nb += 1\n met_batch_limit = batch_nb > self.nb_training_batches\n if met_batch_limit:\n break\n\n # ---------------\n # RUN TRAIN STEP\n # ---------------\n output = self.__run_training_batch(batch, batch_nb)\n batch_result, grad_norm_dic, batch_step_metrics = output\n early_stop_epoch = batch_result == -1\n\n # ---------------\n # RUN VAL STEP\n # ---------------\n is_val_check_batch = (batch_nb + 1) % self.val_check_batch == 0\n can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0\n should_check_val = ((is_val_check_batch or early_stop_epoch) and can_check_epoch)\n\n # fast_dev_run always forces val checking after train batch\n if self.fast_dev_run or should_check_val:\n self.__run_evaluation(test=self.testing)\n\n # when logs should be saved\n should_save_log = (batch_nb + 1) % self.log_save_interval == 0 or early_stop_epoch\n if should_save_log or self.fast_dev_run:\n if self.proc_rank == 0 and self.logger is not None:\n self.logger.save()\n\n # when metrics should be logged\n should_log_metrics = batch_nb % self.row_log_interval =","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__log_metrics","text":"pytorch_lightning/trainer/trainer.py::Trainer.__log_metrics\n def __log_metrics(self, metrics, grad_norm_dic):\n \"\"\"\n Logs the metric dict passed in\n :param metrics:\n :param grad_norm_dic:\n :return:\n \"\"\"\n # added metrics by Lightning for convenience\n metrics['epoch'] = self.current_epoch\n\n # add gpu memory\n if self.on_gpu and self.log_gpu_memory:\n mem_map = memory.get_memory_profile(self.log_gpu_memory)\n metrics.update(mem_map)\n\n # add norms\n metrics.update(grad_norm_dic)\n\n # turn all tensors to scalars\n scalar_metrics = self.__metrics_to_scalars(metrics)\n\n # log actual metrics\n if self.proc_rank == 0 and self.logger is not None:\n self.logger.log_metrics(scalar_metrics, step_num=self.global_step)\n self.logger.save()","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.test","text":"pytorch_lightning/trainer/trainer.py::Trainer.test\n def test(self, model=None):\n if model is not None:\n self.testing = True\n self.fit(model)\n else:\n self.__run_evaluation(test=True)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__metrics_to_scalars","text":"pytorch_lightning/trainer/trainer.py::Trainer.__metrics_to_scalars\n def __metrics_to_scalars(self, metrics):\n new_metrics = {}\n for k, v in metrics.items():\n if isinstance(v, torch.Tensor):\n v = v.item()\n\n if type(v) is dict:\n v = self.__metrics_to_scalars(v)\n\n new_metrics[k] = v\n\n return new_metrics","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__log_vals_blacklist","text":"pytorch_lightning/trainer/trainer.py::Trainer.__log_vals_blacklist\n def __log_vals_blacklist(self):\n \"\"\"avoid logging some vals lightning uses to maintain state\"\"\"\n blacklist = {'batch_nb', 'v_nb', 'gpu'}\n return blacklist","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.transfer_batch_to_gpu","text":"pytorch_lightning/trainer/trainer.py::Trainer.transfer_batch_to_gpu\n def transfer_batch_to_gpu(self, batch, gpu_id):\n # base case: object can be directly moved using `cuda` or `to`\n if callable(getattr(batch, 'cuda', None)):\n return batch.cuda(gpu_id)\n\n elif callable(getattr(batch, 'to', None)):\n return batch.to(torch.device('cuda', gpu_id))\n\n # when list\n elif isinstance(batch, list):\n for i, x in enumerate(batch):\n batch[i] = self.transfer_batch_to_gpu(x, gpu_id)\n return batch\n\n # when tuple\n elif isinstance(batch, tuple):\n batch = list(batch)\n for i, x in enumerate(batch):\n batch[i] = self.transfer_batch_to_gpu(x, gpu_id)\n return tuple(batch)\n\n # when dict\n elif isinstance(batch, dict):\n for k, v in batch.items():\n batch[k] = self.transfer_batch_to_gpu(v, gpu_id)\n\n return batch\n\n # nothing matches, return the value as is without transform\n return batch","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__training_forward","text":"pytorch_lightning/trainer/trainer.py::Trainer.__training_forward\n def __training_forward(self, batch, batch_nb, opt_idx):\n \"\"\"\n Handle forward for each training case (distributed, single gpu, etc...)\n :param batch:\n :param batch_nb:\n :return:\n \"\"\"\n # ---------------\n # FORWARD\n # ---------------\n # enable not needing to add opt_idx to training_step\n args = [batch, batch_nb]\n if len(self.optimizers) > 1:\n args.append(opt_idx)\n\n if self.use_ddp or self.use_ddp2:\n output = self.model(*args)\n elif self.use_dp:\n output = self.model(*args)\n elif self.single_gpu:\n gpu_id = 0\n if type(self.data_parallel_device_ids) is list:\n gpu_id = self.data_parallel_device_ids[0]\n batch = self.transfer_batch_to_gpu(batch, gpu_id)\n args[0] = batch\n output = self.model.training_step(*args)\n\n else:\n output = self.model.training_step(*args)\n\n # format and reduce outputs accordingly\n output = self.__process_output(output, train=True)\n loss, progress_bar_metrics, log_metrics, callback_metrics = output\n return loss, progress_bar_metrics, log_metrics, callback_metrics","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.__get_model","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.__get_model\n def __get_model(self):\n is_dp_module = isinstance(self.model, (LightningDistributedDataParallel,\n LightningDataParallel))\n model = self.model.module if is_dp_module else self.model\n return model","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_weights","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_weights\n def restore_weights(self, model):\n \"\"\"\n To restore weights we have two cases.\n First, if we use the same experiment version, then restore the latest ckpt.\n AFTER that, if we find weights from hpc checkpoint, then restore that.\n :param model:\n :return:\n \"\"\"\n # restore weights if same exp version\n self.restore_state_if_checkpoint_exists(model)\n\n # if script called from hpc resubmit, load weights\n self.restore_hpc_weights_if_needed(model)\n\n # wait for all models to restore weights\n if self.use_ddp or self.use_ddp2:\n # wait for all processes to catch up\n dist.barrier()","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_state_if_checkpoint_exists","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_state_if_checkpoint_exists\n def restore_state_if_checkpoint_exists(self, model):\n # do nothing if there's not dir or callback\n no_ckpt_callback = self.checkpoint_callback is None\n if no_ckpt_callback or not os.path.exists(self.checkpoint_callback.filepath):\n return\n\n # restore trainer state and model if there is a weight for this experiment\n last_epoch = -1\n last_ckpt_name = None\n\n # find last epoch\n checkpoints = os.listdir(self.checkpoint_callback.filepath)\n for name in checkpoints:\n # ignore hpc ckpts\n if 'hpc_' in name:\n continue\n\n if '.ckpt' in name:\n epoch = name.split('epoch_')[1]\n epoch = int(re.sub('[^0-9]', '', epoch))\n\n if epoch > last_epoch:\n last_epoch = epoch\n last_ckpt_name = name\n\n # restore last checkpoint\n if last_ckpt_name is not None:\n last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name)\n self.restore(last_ckpt_path, self.on_gpu)\n print(f'model and trainer restored from checkpoint: {last_ckpt_path}')","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.register_slurm_signal_handlers","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.register_slurm_signal_handlers\n def register_slurm_signal_handlers(self):\n # see if we're using slurm (not interactive)\n on_slurm = False\n try:\n job_name = os.environ['SLURM_JOB_NAME']\n if job_name != 'bash':\n on_slurm = True\n except Exception as e:\n pass\n\n if on_slurm:\n print('set slurm handle signals')\n signal.signal(signal.SIGUSR1, self.sig_handler)\n signal.signal(signal.SIGTERM, self.term_handler)","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.sig_handler","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.sig_handler\n def sig_handler(self, signum, frame):\n if self.proc_rank == 0:\n # save weights\n print('handling SIGUSR1')\n self.hpc_save(self.weights_save_path, self.logger)\n\n # find job id\n job_id = os.environ['SLURM_JOB_ID']\n cmd = 'scontrol requeue {}'.format(job_id)\n\n # requeue job\n print('\\nrequeing job {}...'.format(job_id))\n result = call(cmd, shell=True)\n\n # print result text\n if result == 0:\n print('requeued exp ', job_id)\n else:\n print('requeue failed...')\n\n # close experiment to avoid issues\n self.logger.close()","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.term_handler","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.term_handler\n def term_handler(self, signum, frame):\n # save\n print(\"bypassing sigterm\")","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.save_checkpoint","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.save_checkpoint\n def save_checkpoint(self, filepath):\n checkpoint = self.dump_checkpoint()\n\n # do the actual save\n torch.save(checkpoint, filepath)","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore\n def restore(self, checkpoint_path, on_gpu):\n\n # if on_gpu:\n # checkpoint = torch.load(checkpoint_path)\n # else:\n # load on CPU first\n checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)\n\n # load model state\n model = self.__get_model()\n\n # load the state_dict on the model automatically\n model.load_state_dict(checkpoint['state_dict'])\n if on_gpu:\n model.cuda(self.root_gpu)\n\n # load training state (affects trainer only)\n self.restore_training_state(checkpoint)","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.dump_checkpoint","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.dump_checkpoint\n def dump_checkpoint(self):\n\n checkpoint = {\n 'epoch': self.current_epoch,\n 'global_step': self.global_step\n }\n\n if self.checkpoint_callback is not None:\n checkpoint['checkpoint_callback_best'] = self.checkpoint_callback.best\n\n if self.early_stop_callback is not None:\n checkpoint['early_stop_callback_wait'] = self.early_stop_callback.wait\n checkpoint['early_stop_callback_patience'] = self.early_stop_callback.patience\n\n # save optimizers\n optimizer_states = []\n for i, optimizer in enumerate(self.optimizers):\n optimizer_states.append(optimizer.state_dict())\n\n checkpoint['optimizer_states'] = optimizer_states\n\n # save lr schedulers\n lr_schedulers = []\n for i, scheduler in enumerate(self.lr_schedulers):\n lr_schedulers.append(scheduler.state_dict())\n\n checkpoint['lr_schedulers'] = lr_schedulers\n\n # add the state_dict from the model\n model = self.__get_model()\n checkpoint['state_dict'] = model.state_dict()\n\n # give the model a chance to add a few things\n model.on_save_checkpoint(checkpoint)\n\n return checkpoint","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_hpc_weights_if_needed","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_hpc_weights_if_needed\n def restore_hpc_weights_if_needed(self, model):\n \"\"\"\n If there is a set of hpc weights, use as signal to restore model\n :param model:\n :return:\n \"\"\"\n # look for hpc weights\n folderpath = self.weights_save_path\n if os.path.exists(folderpath):\n files = os.listdir(folderpath)\n hpc_weight_paths = [x for x in files if 'hpc_ckpt' in x]\n\n # if hpc weights exist restore model\n if len(hpc_weight_paths) > 0:\n self.hpc_load(folderpath, self.on_gpu)","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_training_state","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_training_state\n def restore_training_state(self, checkpoint):\n \"\"\"\n Restore trainer state.\n Model will get its change to update\n :param checkpoint:\n :return:\n \"\"\"\n if self.checkpoint_callback is not None:\n self.checkpoint_callback.best = checkpoint['checkpoint_callback_best']\n\n if self.early_stop_callback is not None:\n self.early_stop_callback.wait = checkpoint['early_stop_callback_wait']\n self.early_stop_callback.patience = checkpoint['early_stop_callback_patience']\n\n self.global_step = checkpoint['global_step']\n self.current_epoch = checkpoint['epoch']\n\n # restore the optimizers\n optimizer_states = checkpoint['optimizer_states']\n for optimizer, opt_state in zip(self.optimizers, optimizer_states):\n optimizer.load_state_dict(opt_state)\n\n # move optimizer to GPU 1 weight at a time\n # avoids OOM\n if self.root_gpu is not None:\n for state in optimizer.state.values():\n for k, v in state.items():\n if isinstance(v, torch.Tensor):\n state[k] = v.cuda(self.root_gpu)\n\n # restore the lr schedulers\n lr_schedulers = checkpoint['lr_schedulers']\n for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers):\n scheduler.load_state_dict(lrs_state)","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.hpc_save","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.hpc_save\n def hpc_save(self, folderpath, logger):\n # make sure the checkpoint folder exists\n os.makedirs(folderpath, exist_ok=True)\n\n # save logger to make sure we get all the metrics\n logger.save()\n\n ckpt_number = self.max_ckpt_in_folder(folderpath) + 1\n\n if not os.path.exists(folderpath):\n os.makedirs(folderpath, exist_ok=True)\n filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, ckpt_number)\n\n # give model a chance to do something on hpc_save\n model = self.__get_model()\n checkpoint = self.dump_checkpoint()\n\n model.on_hpc_save(checkpoint)\n\n # do the actual save\n torch.save(checkpoint, filepath)\n\n return filepath","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.hpc_load","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.hpc_load\n def hpc_load(self, folderpath, on_gpu):\n filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, self.max_ckpt_in_folder(folderpath))\n\n # load on CPU first\n checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)\n\n # load model state\n model = self.__get_model()\n\n # load the state_dict on the model automatically\n model.load_state_dict(checkpoint['state_dict'])\n\n if self.root_gpu is not None:\n model.cuda(self.root_gpu)\n\n # load training state (affects trainer only)\n self.restore_training_state(checkpoint)\n\n # call model hook\n model.on_hpc_load(checkpoint)\n\n print(f'restored hpc model from: {filepath}')","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.max_ckpt_in_folder","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIO.max_ckpt_in_folder\n def max_ckpt_in_folder(self, path, name_key='ckpt_'):\n files = os.listdir(path)\n files = [x for x in files if name_key in x]\n if len(files) == 0:\n return 0\n\n ckpt_vs = []\n for name in files:\n name = name.split(name_key)[-1]\n name = re.sub('[^0-9]', '', name)\n ckpt_vs.append(int(name))\n\n return max(ckpt_vs)","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::load_hparams_from_tags_csv","text":"pytorch_lightning/trainer/trainer_io.py::load_hparams_from_tags_csv\ndef load_hparams_from_tags_csv(tags_csv):\n from argparse import Namespace\n import pandas as pd\n\n tags_df = pd.read_csv(tags_csv)\n dic = tags_df.to_dict(orient='records')\n\n ns_dict = {row['key']: convert(row['value']) for row in dic}\n\n ns = Namespace(**ns_dict)\n return ns","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::convert","text":"pytorch_lightning/trainer/trainer_io.py::convert\ndef convert(val):\n constructors = [int, float, str]\n\n if type(val) is str:\n if val.lower() == 'true':\n return True\n if val.lower() == 'false':\n return False\n\n for c in constructors:\n try:\n return c(val)\n except ValueError:\n pass\n return val","label":0}],"string":"[\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.__init__\",\n \"text\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.__init__\\n def __init__(self, hparams):\\n \\\"\\\"\\\"\\n Pass in parsed HyperOptArgumentParser to the model\\n :param hparams:\\n \\\"\\\"\\\"\\n # init superclass\\n super(LightningTemplateModel, self).__init__()\\n self.hparams = hparams\\n\\n self.batch_size = hparams.batch_size\\n\\n # if you specify an example input, the summary will show input/output for each layer\\n self.example_input_array = torch.rand(5, 28 * 28)\\n\\n # build model\\n self.__build_model()\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.__build_model\",\n \"text\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.__build_model\\n def __build_model(self):\\n \\\"\\\"\\\"\\n Layout model\\n :return:\\n \\\"\\\"\\\"\\n self.c_d1 = nn.Linear(in_features=self.hparams.in_features,\\n out_features=self.hparams.hidden_dim)\\n self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)\\n self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)\\n\\n self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim,\\n out_features=self.hparams.out_features)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.forward\",\n \"text\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.forward\\n def forward(self, x):\\n \\\"\\\"\\\"\\n No special modification required for lightning, define as you normally would\\n :param x:\\n :return:\\n \\\"\\\"\\\"\\n\\n x = self.c_d1(x)\\n x = torch.tanh(x)\\n x = self.c_d1_bn(x)\\n x = self.c_d1_drop(x)\\n\\n x = self.c_d2(x)\\n logits = F.log_softmax(x, dim=1)\\n\\n return logits\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.loss\",\n \"text\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.loss\\n def loss(self, labels, logits):\\n nll = F.nll_loss(logits, labels)\\n return nll\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.training_step\",\n \"text\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.training_step\\n def training_step(self, batch, batch_idx):\\n \\\"\\\"\\\"\\n Lightning calls this inside the training loop\\n :param batch:\\n :return:\\n \\\"\\\"\\\"\\n # forward pass\\n x, y = batch\\n x = x.view(x.size(0), -1)\\n\\n y_hat = self.forward(x)\\n\\n # calculate loss\\n loss_val = self.loss(y, y_hat)\\n\\n # in DP mode (default) make sure if result is scalar, there's another dim in the beginning\\n if self.trainer.use_dp or self.trainer.use_ddp2:\\n loss_val = loss_val.unsqueeze(0)\\n\\n tqdm_dict = {'train_loss': loss_val}\\n output = OrderedDict({\\n 'loss': loss_val,\\n 'progress_bar': tqdm_dict,\\n 'log': tqdm_dict\\n })\\n\\n # can also return just a scalar instead of a dict (return loss_val)\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.validation_step\",\n \"text\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.validation_step\\n def validation_step(self, batch, batch_idx):\\n \\\"\\\"\\\"\\n Lightning calls this inside the validation loop\\n :param batch:\\n :return:\\n \\\"\\\"\\\"\\n x, y = batch\\n x = x.view(x.size(0), -1)\\n y_hat = self.forward(x)\\n\\n loss_val = self.loss(y, y_hat)\\n\\n # acc\\n labels_hat = torch.argmax(y_hat, dim=1)\\n val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)\\n val_acc = torch.tensor(val_acc)\\n\\n if self.on_gpu:\\n val_acc = val_acc.cuda(loss_val.device.index)\\n\\n # in DP mode (default) make sure if result is scalar, there's another dim in the beginning\\n if self.trainer.use_dp or self.trainer.use_ddp2:\\n loss_val = loss_val.unsqueeze(0)\\n val_acc = val_acc.unsqueeze(0)\\n\\n output = OrderedDict({\\n 'val_loss': loss_val,\\n 'val_acc': val_acc,\\n })\\n\\n # can also return just a scalar instead of a dict (return loss_val)\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.validation_end\",\n \"text\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.validation_end\\n def validation_end(self, outputs):\\n \\\"\\\"\\\"\\n Called at the end of validation to aggregate outputs\\n :param outputs: list of individual outputs of each validation step\\n :return:\\n \\\"\\\"\\\"\\n # if returned a scalar from validation_step, outputs is a list of tensor scalars\\n # we return just the average in this case (if we want)\\n # return torch.stack(outputs).mean()\\n\\n val_loss_mean = 0\\n val_acc_mean = 0\\n for output in outputs:\\n val_loss = output['val_loss']\\n\\n # reduce manually when using dp\\n if self.trainer.use_dp:\\n val_loss = torch.mean(val_loss)\\n val_loss_mean += val_loss\\n\\n # reduce manually when using dp\\n val_acc = output['val_acc']\\n if self.trainer.use_dp or self.trainer.use_ddp2:\\n val_acc = torch.mean(val_acc)\\n\\n val_acc_mean += val_acc\\n\\n val_loss_mean /= len(outputs)\\n val_acc_mean /= len(outputs)\\n tqdm_dict = {'val_loss': val_loss_mean, 'val_acc': val_acc_mean}\\n result = {'progress_bar': tqdm_dict, 'log': tqdm_dict}\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.configure_optimizers\",\n \"text\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.configure_optimizers\\n def configure_optimizers(self):\\n \\\"\\\"\\\"\\n return whatever optimizers we want here\\n :return: list of optimizers\\n \\\"\\\"\\\"\\n optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)\\n scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\\n return [optimizer], [scheduler]\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.__dataloader\",\n \"text\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.__dataloader\\n def __dataloader(self, train):\\n # init data generators\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root=self.hparams.data_root, train=train,\\n transform=transform, download=True)\\n\\n # when using multi-node (ddp) we need to add the datasampler\\n train_sampler = None\\n batch_size = self.hparams.batch_size\\n\\n if self.use_ddp:\\n train_sampler = DistributedSampler(dataset)\\n\\n should_shuffle = train_sampler is None\\n loader = DataLoader(\\n dataset=dataset,\\n batch_size=batch_size,\\n shuffle=should_shuffle,\\n sampler=train_sampler,\\n num_workers=0\\n )\\n\\n return loader\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.train_dataloader\",\n \"text\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.train_dataloader\\n @pl.data_loader\\n def train_dataloader(self):\\n print('training data loader called')\\n return self.__dataloader(train=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.val_dataloader\",\n \"text\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.val_dataloader\\n @pl.data_loader\\n def val_dataloader(self):\\n print('val data loader called')\\n return self.__dataloader(train=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.test_dataloader\",\n \"text\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.test_dataloader\\n @pl.data_loader\\n def test_dataloader(self):\\n print('test data loader called')\\n return self.__dataloader(train=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.add_model_specific_args\",\n \"text\": \"examples/basic_examples/lightning_module_template.py::LightningTemplateModel.add_model_specific_args\\n @staticmethod\\n def add_model_specific_args(parent_parser, root_dir): # pragma: no cover\\n \\\"\\\"\\\"\\n Parameters you define here will be available to your model through self.hparams\\n :param parent_parser:\\n :param root_dir:\\n :return:\\n \\\"\\\"\\\"\\n parser = ArgumentParser(parents=[parent_parser])\\n\\n # param overwrites\\n # parser.set_defaults(gradient_clip_val=5.0)\\n\\n # network params\\n parser.add_argument('--in_features', default=28 * 28, type=int)\\n parser.add_argument('--out_features', default=10, type=int)\\n # use 500 for CPU, 50000 for GPU to see speed difference\\n parser.add_argument('--hidden_dim', default=50000, type=int)\\n parser.add_argument('--drop_prob', default=0.2, type=float)\\n parser.add_argument('--learning_rate', default=0.001, type=float)\\n\\n # data\\n parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)\\n\\n # training params (opt)\\n parser.add_argument('--optimizer_name', default='adam', type=str)\\n parser.add_argument('--batch_size', default=64, type=int)\\n return parser\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/base.py::rank_zero_only\",\n \"text\": \"pytorch_lightning/logging/base.py::rank_zero_only\\ndef rank_zero_only(fn):\\n \\\"\\\"\\\"Decorate a logger method to run it only on the process with rank 0\\n\\n :param fn: Function to decorate\\n \\\"\\\"\\\"\\n\\n @wraps(fn)\\n def wrapped_fn(self, *args, **kwargs):\\n if self.rank == 0:\\n fn(self, *args, **kwargs)\\n\\n return wrapped_fn\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/base.py::rank_zero_only.wrapped_fn\",\n \"text\": \"pytorch_lightning/logging/base.py::rank_zero_only.wrapped_fn\\n @wraps(fn)\\n def wrapped_fn(self, *args, **kwargs):\\n if self.rank == 0:\\n fn(self, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.__init__\",\n \"text\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.__init__\\n def __init__(self):\\n self._rank = 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.log_metrics\",\n \"text\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.log_metrics\\n def log_metrics(self, metrics, step_num):\\n \\\"\\\"\\\"Record metrics\\n\\n :param metric: Dictionary with metric names as keys and measured\\n quanties as values\\n :param step_num: Step number at which the metrics should be recorded\\n \\\"\\\"\\\"\\n raise NotImplementedError()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.log_hyperparams\",\n \"text\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.log_hyperparams\\n def log_hyperparams(self, params):\\n \\\"\\\"\\\"Record hyperparameters\\n\\n :param params: argparse.Namespace containing the hyperparameters\\n \\\"\\\"\\\"\\n raise NotImplementedError()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.save\",\n \"text\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.save\\n def save(self):\\n \\\"\\\"\\\"Save log data\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.finalize\",\n \"text\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.finalize\\n def finalize(self, status):\\n \\\"\\\"\\\"Do any processing that is necessary to finalize an experiment\\n\\n :param status: Status that the experiment finished with (e.g. success, failed, aborted)\\n \\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.close\",\n \"text\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.close\\n def close(self):\\n \\\"\\\"\\\"Do any cleanup that is necessary to close an experiment\\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.rank\",\n \"text\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.rank\\n @property\\n def rank(self):\\n \\\"\\\"\\\"\\n Process rank. In general, metrics should only be logged by the process\\n with rank 0\\n \\\"\\\"\\\"\\n return self._rank\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.rank\",\n \"text\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.rank\\n @rank.setter\\n def rank(self, value):\\n \\\"\\\"\\\"Set the process rank\\\"\\\"\\\"\\n self._rank = value\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.version\",\n \"text\": \"pytorch_lightning/logging/base.py::LightningLoggerBase.version\\n @property\\n def version(self):\\n \\\"\\\"\\\"Return the experiment version\\\"\\\"\\\"\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.__init__\",\n \"text\": \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.__init__\\n def __init__(self, experiment_name, tracking_uri=None):\\n super().__init__()\\n self.client = mlflow.tracking.MlflowClient(tracking_uri)\\n self.experiment_name = experiment_name\\n self._run_id = None\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.run_id\",\n \"text\": \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.run_id\\n @property\\n def run_id(self):\\n if self._run_id is not None:\\n return self._run_id\\n\\n experiment = self.client.get_experiment_by_name(self.experiment_name)\\n if experiment is None:\\n logger.warning(\\n f\\\"Experiment with name f{self.experiment_name} not found. Creating it.\\\"\\n )\\n self.client.create_experiment(self.experiment_name)\\n experiment = self.client.get_experiment_by_name(self.experiment_name)\\n\\n run = self.client.create_run(experiment.experiment_id)\\n self._run_id = run.info.run_id\\n return self._run_id\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.log_hyperparams\",\n \"text\": \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.log_hyperparams\\n @rank_zero_only\\n def log_hyperparams(self, params):\\n for k, v in vars(params).items():\\n self.client.log_param(self.run_id, k, v)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.log_metrics\",\n \"text\": \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.log_metrics\\n @rank_zero_only\\n def log_metrics(self, metrics, step_num=None):\\n timestamp_ms = int(time() * 1000)\\n for k, v in metrics.items():\\n if isinstance(v, str):\\n logger.warning(\\n f\\\"Discarding metric with string value {k}={v}\\\"\\n )\\n continue\\n self.client.log_metric(self.run_id, k, v, timestamp_ms, step_num)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.save\",\n \"text\": \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.save\\n def save(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.finalize\",\n \"text\": \"pytorch_lightning/logging/mlflow_logger.py::MLFlowLogger.finalize\\n @rank_zero_only\\n def finalize(self, status=\\\"FINISHED\\\"):\\n if status == 'success':\\n status = 'FINISHED'\\n self.client.set_terminated(self.run_id, status)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/grads.py::GradInformation.grad_norm\",\n \"text\": \"pytorch_lightning/root_module/grads.py::GradInformation.grad_norm\\n def grad_norm(self, norm_type):\\n results = {}\\n total_norm = 0\\n for i, p in enumerate(self.parameters()):\\n if p.requires_grad:\\n try:\\n param_norm = p.grad.data.norm(norm_type)\\n total_norm += param_norm ** norm_type\\n norm = param_norm ** (1 / norm_type)\\n\\n grad = round(norm.data.cpu().numpy().flatten()[0], 3)\\n results['grad_{}_norm_{}'.format(norm_type, i)] = grad\\n except Exception:\\n # this param had no grad\\n pass\\n\\n total_norm = total_norm ** (1. / norm_type)\\n grad = round(total_norm.data.cpu().numpy().flatten()[0], 3)\\n results['grad_{}_norm_total'.format(norm_type)] = grad\\n return results\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::reduce_distributed_output\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::reduce_distributed_output\\ndef reduce_distributed_output(output, nb_gpus):\\n if nb_gpus <= 1:\\n return output\\n\\n # when using DP, we get one output per gpu\\n # average outputs and return\\n if type(output) is torch.Tensor:\\n return output.mean()\\n\\n for k, v in output.items():\\n # recurse on nested dics\\n if isinstance(output[k], dict):\\n output[k] = reduce_distributed_output(output[k], nb_gpus)\\n\\n # reduce only metrics that have the same nb of gpus\\n elif output[k].size(0) == nb_gpus:\\n reduced = torch.mean(output[k])\\n output[k] = reduced\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__init__\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__init__\\n def __init__(self,\\n logger=None,\\n checkpoint_callback=None,\\n early_stop_callback=None,\\n default_save_path=None,\\n gradient_clip_val=0,\\n process_position=0,\\n nb_gpu_nodes=1,\\n gpus=None,\\n log_gpu_memory=None,\\n show_progress_bar=True,\\n overfit_pct=0.0,\\n track_grad_norm=-1,\\n check_val_every_n_epoch=1,\\n fast_dev_run=False,\\n accumulate_grad_batches=1,\\n max_nb_epochs=1000,\\n min_nb_epochs=1,\\n train_percent_check=1.0,\\n val_percent_check=1.0,\\n test_percent_check=1.0,\\n val_check_interval=1.0,\\n log_save_interval=100,\\n row_log_interval=10,\\n distributed_backend=None,\\n use_amp=False,\\n print_nan_grads=False,\\n weights_summary='full',\\n weights_save_path=None,\\n amp_level='O1',\\n nb_sanity_val_steps=5):\\n \\\"\\\"\\\"\\n\\n :param logger: Logger for experiment tracking\\n :param checkpoint_callback: Callback for checkpointing\\n :param early_stop_callback: Callback for early stopping\\n :param default_save_path: Default path for logs+weights if no logger/ckpt_callback passed\\n :param gradient_clip_val: int. 0 means don't clip.\\n :param process_position: shown in the tqdm bar\\n :param nb_gpu_nodes: number of GPU nodes\\n :param gpus: int. (ie: 2 gpus) OR list to specify which GPUs [0, 1] or '0,1'\\n :param log_gpu_memory: str. None, 'min_max', 'all'\\n :param show_progress_bar: Bool. If true shows tqdm bar\\n :param overfit_pct: float. uses this much of all datasets\\n :param track_grad_norm: int. -1 no\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id\\n @property\\n def slurm_job_id(self):\\n try:\\n job_id = os.environ['SLURM_JOB_ID']\\n job_id = int(job_id)\\n except Exception as e:\\n job_id = None\\n return job_id\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__configure_weights_path\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__configure_weights_path\\n def __configure_weights_path(self, checkpoint_callback, weights_save_path):\\n \\\"\\\"\\\"\\n Weight path set in this priority:\\n Checkpoint_callback's path (if passed in).\\n User provided weights_saved_path\\n Otherwise use os.getcwd()\\n \\\"\\\"\\\"\\n self.weights_save_path = weights_save_path\\n\\n if self.checkpoint_callback is not None:\\n self.checkpoint_callback.save_function = self.save_checkpoint\\n\\n # if checkpoint callback used, then override the weights path\\n self.weights_save_path = self.checkpoint_callback.filepath\\n\\n # if weights_save_path is still none here, set to current workingdir\\n if self.weights_save_path is None:\\n self.weights_save_path = self.default_save_path\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__init_amp\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__init_amp\\n def __init_amp(self, use_amp):\\n self.use_amp = use_amp and APEX_AVAILABLE\\n if self.use_amp:\\n print('using 16bit precision')\\n\\n if use_amp and not APEX_AVAILABLE: # pragma: no cover\\n msg = \\\"\\\"\\\"\\n You set use_amp=True but do not have apex installed.\\n Install apex first using this guide and rerun with use_amp=True:\\n https://github.com/NVIDIA/apex#linux\\n\\n this run will NOT use 16 bit precision\\n \\\"\\\"\\\"\\n raise ModuleNotFoundError(msg)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__configure_accumulated_gradients\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__configure_accumulated_gradients\\n def __configure_accumulated_gradients(self, accumulate_grad_batches):\\n self.accumulate_grad_batches = None\\n\\n if isinstance(accumulate_grad_batches, dict):\\n self.accumulation_scheduler = GradientAccumulationScheduler(accumulate_grad_batches)\\n elif isinstance(accumulate_grad_batches, int):\\n schedule = {1: accumulate_grad_batches}\\n self.accumulation_scheduler = GradientAccumulationScheduler(schedule)\\n else:\\n raise TypeError(\\\"Gradient accumulation supports only int and dict types\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__parse_gpu_ids\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__parse_gpu_ids\\n def __parse_gpu_ids(self, gpus):\\n \\\"\\\"\\\"\\n :param gpus: Int, string or list of ids\\n :return:\\n \\\"\\\"\\\"\\n # if gpus = -1 then use all available devices\\n # otherwise, split the string using commas\\n if gpus is not None:\\n if type(gpus) is list:\\n gpus = gpus\\n elif type(gpus) is str:\\n if gpus == '-1':\\n gpus = list(range(0, torch.cuda.device_count()))\\n else:\\n gpus = [int(x.strip()) for x in gpus.split(',')]\\n elif type(gpus) is int:\\n gpus = gpus\\n else:\\n raise Exception('gpus has to be a string, int or list of ints')\\n\\n return gpus\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__set_root_gpu\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__set_root_gpu\\n def __set_root_gpu(self, gpus):\\n if gpus is None:\\n return None\\n\\n # set root gpu\\n root_gpu = 0\\n if type(gpus) is list:\\n root_gpu = gpus[0]\\n\\n return root_gpu\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus\\n @property\\n def num_gpus(self):\\n gpus = self.data_parallel_device_ids\\n if gpus is None:\\n return 0\\n\\n if type(gpus) is list:\\n return len(gpus)\\n if type(gpus) is int:\\n return gpus\\n\\n m = 'gpus must be int, none or list of ints'\\n raise MisconfigurationException(m)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__set_distributed_mode\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__set_distributed_mode\\n def __set_distributed_mode(self, distributed_backend, nb_gpu_nodes):\\n # skip for CPU\\n if self.num_gpus == 0:\\n return\\n\\n # single GPU case\\n # in single gpu case we allow ddp so we can train on multiple\\n # nodes, 1 gpu per node\\n if self.num_gpus == 1:\\n self.single_gpu = True\\n\\n if distributed_backend is not None:\\n self.use_dp = distributed_backend == 'dp'\\n self.use_ddp = distributed_backend == 'ddp'\\n self.use_ddp2 = distributed_backend == 'ddp2'\\n\\n # disable single gpu when using ddp2\\n if self.use_ddp2:\\n self.single_gpu = False\\n\\n # multiple GPU case\\n elif self.num_gpus > 1:\\n if distributed_backend is not None:\\n # DP, DDP case\\n self.use_dp = distributed_backend == 'dp'\\n self.use_ddp = distributed_backend == 'ddp'\\n self.use_ddp2 = distributed_backend == 'ddp2'\\n\\n elif distributed_backend is None:\\n m = 'When using multiple GPUs set ' \\\\\\n 'Trainer(distributed_backend=dp) (or ddp)'\\n raise MisconfigurationException(m)\\n\\n # use ddp automatically if nb_gpu_nodes > 1\\n if nb_gpu_nodes > 1 and self.use_dp: # pragma: no cover\\n self.use_ddp = True\\n self.use_dp = False\\n w = 'DataParallel does not support nb_gpu_nodes > 1. ' \\\\\\n 'Switching to DistributedDataParallel for you. ' \\\\\\n 'To silence this warning set distributed_backend=ddp'\\n warnings.warn(w)\\n\\n print('gpu available: {}, used: {}'.format(torch.cuda.is_available(), self.on_gpu))\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__configure_slurm_ddp\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__configure_slurm_ddp\\n def __configure_slurm_ddp(self, nb_gpu_nodes):\\n self.is_slurm_managing_tasks = False\\n\\n # extract SLURM flag vars\\n # whenever we have the correct number of tasks, we let slurm manage processes\\n # otherwise we launch the required number of processes\\n if self.use_ddp:\\n self.nb_requested_gpus = self.num_gpus * nb_gpu_nodes\\n self.nb_slurm_tasks = 0\\n try:\\n self.nb_slurm_tasks = int(os.environ['SLURM_NTASKS'])\\n self.is_slurm_managing_tasks = self.nb_slurm_tasks == self.nb_requested_gpus\\n\\n # in interactive mode we don't manage tasks\\n job_name = os.environ['SLURM_JOB_NAME']\\n if job_name == 'bash':\\n self.is_slurm_managing_tasks = False\\n\\n except Exception:\\n # likely not on slurm, so set the slurm managed flag to false\\n self.is_slurm_managing_tasks = False\\n\\n # used for tests only, set this flag to simulate slurm managing a task\\n try:\\n should_fake = int(os.environ['FAKE_SLURM_MANAGING_TASKS'])\\n if should_fake:\\n self.is_slurm_managing_tasks = True\\n except Exception as e:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__set_nvidia_flags\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__set_nvidia_flags\\n def __set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids):\\n if data_parallel_device_ids is None:\\n return\\n\\n # set the correct cuda visible devices (using pci order)\\n os.environ[\\\"CUDA_DEVICE_ORDER\\\"] = \\\"PCI_BUS_ID\\\"\\n\\n # when slurm is managing the task it sets the visible devices\\n if not is_slurm_managing_tasks:\\n if type(data_parallel_device_ids) is int:\\n id_str = ','.join(str(x) for x in list(range(data_parallel_device_ids)))\\n os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = id_str\\n else:\\n gpu_str = ','.join([str(x) for x in data_parallel_device_ids])\\n os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = gpu_str\\n\\n print(f'VISIBLE GPUS: {os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]}')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel\\n @property\\n def data_parallel(self):\\n return self.use_dp or self.use_ddp or self.use_ddp2\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__determine_data_use_amount\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__determine_data_use_amount\\n def __determine_data_use_amount(self, train_percent_check, val_percent_check,\\n test_percent_check, overfit_pct):\\n \\\"\\\"\\\"\\n Use less data for debugging purposes\\n \\\"\\\"\\\"\\n self.train_percent_check = train_percent_check\\n self.val_percent_check = val_percent_check\\n self.test_percent_check = test_percent_check\\n if overfit_pct > 0:\\n self.train_percent_check = overfit_pct\\n self.val_percent_check = overfit_pct\\n self.test_percent_check = overfit_pct\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__get_model\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__get_model\\n def __get_model(self):\\n return self.model.module if self.data_parallel else self.model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__is_function_implemented\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__is_function_implemented\\n def __is_function_implemented(self, f_name):\\n model = self.__get_model()\\n f_op = getattr(model, f_name, None)\\n return callable(f_op)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__is_overriden\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__is_overriden\\n def __is_overriden(self, f_name):\\n model = self.__get_model()\\n super_object = LightningModule\\n\\n # when code pointers are different, it was overriden\\n is_overriden = getattr(model, f_name).__code__ is not getattr(super_object, f_name).__code__\\n return is_overriden\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__training_tqdm_dict\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__training_tqdm_dict\\n @property\\n def __training_tqdm_dict(self):\\n tqdm_dict = {\\n 'loss': '{0:.3f}'.format(self.avg_loss),\\n 'epoch': '{}'.format(self.current_epoch),\\n 'batch_nb': '{}'.format(self.batch_nb),\\n }\\n\\n if self.logger is not None and self.logger.version is not None:\\n tqdm_dict['v_nb'] = self.logger.version\\n\\n tqdm_dict.update(self.tqdm_metrics)\\n\\n if self.on_gpu:\\n tqdm_dict['gpu'] = '{}'.format(torch.cuda.current_device())\\n\\n return tqdm_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.training_tqdm_dict\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.training_tqdm_dict\\n @property\\n def training_tqdm_dict(self):\\n \\\"\\\"\\\"\\n Read-only for tqdm metrics\\n :return:\\n \\\"\\\"\\\"\\n return self.__training_tqdm_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__layout_bookeeping\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__layout_bookeeping\\n def __layout_bookeeping(self):\\n\\n # determine number of training batches\\n self.nb_training_batches = len(self.get_train_dataloader())\\n self.nb_training_batches = int(self.nb_training_batches * self.train_percent_check)\\n\\n # determine number of validation batches\\n # val datasets could be none, 1 or 2+\\n if self.get_val_dataloaders() is not None:\\n self.nb_val_batches = sum(len(dataloader) for dataloader in self.get_val_dataloaders())\\n self.nb_val_batches = int(self.nb_val_batches * self.val_percent_check)\\n self.nb_val_batches = max(1, self.nb_val_batches)\\n\\n # determine number of test batches\\n if self.get_test_dataloaders() is not None:\\n self.nb_test_batches = sum(\\n len(dataloader) for dataloader in self.get_test_dataloaders()\\n )\\n self.nb_test_batches = int(self.nb_test_batches * self.test_percent_check)\\n self.nb_test_batches = max(1, self.nb_test_batches)\\n\\n # determine when to check validation\\n self.val_check_batch = int(self.nb_training_batches * self.val_check_interval)\\n self.val_check_batch = max(1, self.val_check_batch)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__add_tqdm_metrics\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__add_tqdm_metrics\\n def __add_tqdm_metrics(self, metrics):\\n for k, v in metrics.items():\\n if type(v) is torch.Tensor:\\n v = v.item()\\n\\n self.tqdm_metrics[k] = v\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__evaluation_forward\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__evaluation_forward\\n def __evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False):\\n # make dataloader_idx arg in validation_step optional\\n args = [batch, batch_idx]\\n\\n if test and len(self.get_test_dataloaders()) > 1:\\n args.append(dataloader_idx)\\n\\n elif not test and len(self.get_val_dataloaders()) > 1:\\n args.append(dataloader_idx)\\n\\n # handle DP, DDP forward\\n if self.use_ddp or self.use_dp or self.use_ddp2:\\n output = model(*args)\\n return output\\n\\n # single GPU\\n if self.single_gpu:\\n # for single GPU put inputs on gpu manually\\n root_gpu = 0\\n if type(self.data_parallel_device_ids) is list:\\n root_gpu = self.data_parallel_device_ids[0]\\n batch = self.transfer_batch_to_gpu(batch, root_gpu)\\n args[0] = batch\\n\\n # CPU\\n if test:\\n output = model.test_step(*args)\\n else:\\n output = model.validation_step(*args)\\n\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.evaluate\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.evaluate\\n def evaluate(self, model, dataloaders, max_batches, test=False):\\n \\\"\\\"\\\"\\n Run evaluation code\\n :param model: PT model\\n :param dataloaders: list of PT dataloaders\\n :param max_batches: Scalar\\n :param test: boolean\\n :return:\\n \\\"\\\"\\\"\\n # enable eval mode\\n model.zero_grad()\\n model.eval()\\n\\n # disable gradients to save memory\\n torch.set_grad_enabled(False)\\n\\n # bookkeeping\\n outputs = []\\n\\n # run training\\n for dataloader_idx, dataloader in enumerate(dataloaders):\\n dl_outputs = []\\n for batch_idx, batch in enumerate(dataloader):\\n\\n if batch is None: # pragma: no cover\\n continue\\n\\n # stop short when on fast_dev_run (sets max_batch=1)\\n if batch_idx >= max_batches:\\n break\\n\\n # -----------------\\n # RUN EVALUATION STEP\\n # -----------------\\n output = self.__evaluation_forward(model,\\n batch,\\n batch_idx,\\n dataloader_idx,\\n test)\\n\\n # track outputs for collation\\n dl_outputs.append(output)\\n\\n # batch done\\n if self.show_progress_bar:\\n self.progress_bar.update(1)\\n outputs.append(dl_outputs)\\n\\n eval_results = {}\\n\\n # with a single dataloader don't pass an array\\n if len(dataloaders) == 1:\\n outputs = outputs[0]\\n\\n # give model a chance to do something with the outputs (and method defined)\\n model = self.__get_model()\\n if test and self.__is_overriden('test_end'):\\n eval_results = model.test_end(outputs)\\n elif self.__is_\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.get_dataloaders\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.get_dataloaders\\n def get_dataloaders(self, model):\\n \\\"\\\"\\\"\\n Dataloaders are provided by the model\\n :param model:\\n :return:\\n \\\"\\\"\\\"\\n self.get_train_dataloader = model.train_dataloader\\n self.get_test_dataloaders = model.test_dataloader\\n self.get_val_dataloaders = model.val_dataloader\\n\\n # call warnings from proc zero only which triggers dataloaders\\n # if those have to download data it will only happen on proc 0\\n if self.proc_rank == 0:\\n on_ddp = self.use_ddp or self.use_ddp2\\n if on_ddp and not isinstance(self.get_train_dataloader().sampler, DistributedSampler):\\n msg = \\\"\\\"\\\"\\n You're using multiple gpus and multiple nodes without using a DistributedSampler\\n to assign a subset of your data to each process. To silence this warning, pass a\\n DistributedSampler to your DataLoader.\\n\\n ie: this:\\n dataset = myDataset()\\n dataloader = Dataloader(dataset)\\n\\n becomes:\\n dataset = myDataset()\\n dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)\\n dataloader = Dataloader(dataset, sampler=dist_sampler)\\n\\n If you want each process to load the full dataset, ignore this warning.\\n \\\"\\\"\\\"\\n warnings.warn(msg)\\n\\n if on_ddp and self.get_val_dataloaders() is not None:\\n for dataloader in self.get_val_dataloaders():\\n if not isinstance(dataloader.sampler, DistributedSampler):\\n msg = \\\"\\\"\\\"\\n Your val_dataloader(s) don't use DistributedSampler.\\n\\n You're using multiple gpus and multiple nodes without using a\\n DistributedSampler to assign a subset of your data to each process.\\n To silence this\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.fit\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.fit\\n def fit(self, model):\\n # when using multi-node or DDP within a node start each module in a separate process\\n if self.use_ddp2:\\n task = int(os.environ['SLURM_LOCALID'])\\n self.ddp_train(task, model)\\n\\n elif self.use_ddp:\\n if self.is_slurm_managing_tasks:\\n task = int(os.environ['SLURM_LOCALID'])\\n self.ddp_train(task, model)\\n else:\\n mp.spawn(self.ddp_train, nprocs=self.num_gpus, args=(model, ))\\n\\n # 1 gpu or dp option triggers training using DP module\\n # easier to avoid NCCL issues\\n elif self.use_dp:\\n self.__dp_train(model)\\n\\n elif self.single_gpu:\\n self.__single_gpu_train(model)\\n\\n # ON CPU\\n else:\\n # run through amp wrapper\\n if self.use_amp:\\n raise MisconfigurationException('amp + cpu is not supported.'\\n ' Please use a GPU option')\\n\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n self.__run_pretrain_routine(model)\\n\\n # return 1 when finished\\n # used for testing or when we need to know that training succeeded\\n return 1\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.init_optimizers\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.init_optimizers\\n def init_optimizers(self, optimizers):\\n\\n # single optimizer\\n if isinstance(optimizers, Optimizer):\\n return [optimizers], []\\n\\n # two lists\\n elif len(optimizers) == 2 and isinstance(optimizers[0], list):\\n optimizers, lr_schedulers = optimizers\\n return optimizers, lr_schedulers\\n\\n # single list or tuple\\n elif isinstance(optimizers, list) or isinstance(optimizers, tuple):\\n return optimizers, []\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__single_gpu_train\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__single_gpu_train\\n def __single_gpu_train(self, model):\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n model.cuda(self.root_gpu)\\n\\n if self.use_amp:\\n # An example\\n model, optimizers = amp.initialize(\\n model, self.optimizers, opt_level=self.amp_level,\\n )\\n self.optimizers = optimizers\\n\\n self.__run_pretrain_routine(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__dp_train\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__dp_train\\n def __dp_train(self, model):\\n\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n model.cuda(self.root_gpu)\\n\\n # check for this bug (amp + dp + !01 doesn't work)\\n # https://github.com/NVIDIA/apex/issues/227\\n if self.use_dp and self.use_amp:\\n m = f\\\"\\\"\\\"\\n Amp level {self.amp_level} with DataParallel is not supported.\\n See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227.\\n We recommend you switch to ddp if you want to use amp\\n \\\"\\\"\\\"\\n raise MisconfigurationException(m)\\n\\n # create list of device ids\\n device_ids = self.data_parallel_device_ids\\n if type(device_ids) is int:\\n device_ids = list(range(device_ids))\\n\\n model = LightningDataParallel(model, device_ids=device_ids)\\n\\n self.__run_pretrain_routine(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.ddp_train\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.ddp_train\\n def ddp_train(self, gpu_nb, model):\\n \\\"\\\"\\\"\\n Entry point into a DP thread\\n :param gpu_nb:\\n :param model:\\n :param cluster_obj:\\n :return:\\n \\\"\\\"\\\"\\n # node rank using relative slurm id\\n # otherwise default to node rank 0\\n try:\\n node_id = os.environ['SLURM_NODEID']\\n self.node_rank = int(node_id)\\n except Exception:\\n self.node_rank = 0\\n\\n # show progressbar only on progress_rank 0\\n self.show_progress_bar = self.show_progress_bar and self.node_rank == 0 and gpu_nb == 0\\n\\n # determine which process we are and world size\\n if self.use_ddp:\\n self.proc_rank = self.node_rank * self.num_gpus + gpu_nb\\n self.world_size = self.nb_gpu_nodes * self.num_gpus\\n\\n elif self.use_ddp2:\\n self.proc_rank = self.node_rank\\n self.world_size = self.nb_gpu_nodes\\n\\n # let the exp know the rank to avoid overwriting logs\\n if self.logger is not None:\\n self.logger.rank = self.proc_rank\\n\\n # set up server using proc 0's ip address\\n # try to init for 20 times at max in case ports are taken\\n # where to store ip_table\\n self.__init_tcp_connection()\\n\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n # MODEL\\n # copy model to each gpu\\n if self.distributed_backend == 'ddp':\\n torch.cuda.set_device(gpu_nb)\\n model.cuda(gpu_nb)\\n\\n # set model properties before going into wrapper\\n model.trainer = self\\n model.on_gpu = self.on_gpu\\n model.use_dp = self.use_dp\\n model.use_ddp2 = self.use_ddp2\\n model.use_ddp = self.use_ddp\\n model.use_amp = self.use_amp\\n model.testing = self.testing\\n\\n # override root GPU\\n self.root_gp\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__init_tcp_connection\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__init_tcp_connection\\n def __init_tcp_connection(self):\\n \\\"\\\"\\\"\\n Connect all procs in the world using the env:// init\\n Use the first node as the root address\\n :param port:\\n :param tries:\\n :return:\\n \\\"\\\"\\\"\\n\\n # use slurm job id for the port number\\n # guarantees unique ports across jobs from same grid search\\n try:\\n # use the last 4 numbers in the job id as the id\\n default_port = os.environ['SLURM_JOB_ID']\\n default_port = default_port[-4:]\\n\\n # all ports should be in the 10k+ range\\n default_port = int(default_port) + 15000\\n\\n except Exception as e:\\n default_port = 12910\\n\\n # if user gave a port number, use that one instead\\n try:\\n default_port = os.environ['MASTER_PORT']\\n except Exception:\\n os.environ['MASTER_PORT'] = str(default_port)\\n\\n # figure out the root node addr\\n try:\\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\\n except Exception:\\n root_node = '127.0.0.2'\\n\\n root_node = self.resolve_root_node_address(root_node)\\n os.environ['MASTER_ADDR'] = root_node\\n dist.init_process_group(\\\"nccl\\\", rank=self.proc_rank, world_size=self.world_size)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.resolve_root_node_address\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.resolve_root_node_address\\n def resolve_root_node_address(self, root_node):\\n if '[' in root_node:\\n name = root_node.split('[')[0]\\n number = root_node.split(',')[0]\\n if '-' in number:\\n number = number.split('-')[0]\\n\\n number = re.sub('[^0-9]', '', number)\\n root_node = name + number\\n\\n return root_node\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__run_pretrain_routine\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__run_pretrain_routine\\n def __run_pretrain_routine(self, model):\\n \\\"\\\"\\\"\\n Sanity check a few things before starting actual training\\n :param model:\\n :return:\\n \\\"\\\"\\\"\\n ref_model = model\\n if self.data_parallel:\\n ref_model = model.module\\n\\n # give model convenience properties\\n ref_model.trainer = self\\n\\n # set local properties on the model\\n ref_model.on_gpu = self.on_gpu\\n ref_model.single_gpu = self.single_gpu\\n ref_model.use_dp = self.use_dp\\n ref_model.use_ddp = self.use_ddp\\n ref_model.use_ddp2 = self.use_ddp2\\n ref_model.use_amp = self.use_amp\\n ref_model.testing = self.testing\\n\\n # register auto-resubmit when on SLURM\\n self.register_slurm_signal_handlers()\\n\\n # transfer data loaders from model\\n self.get_dataloaders(ref_model)\\n\\n # init training constants\\n self.__layout_bookeeping()\\n\\n # print model summary\\n if self.proc_rank == 0 and self.weights_summary is not None:\\n if self.weights_summary in ['full', 'top']:\\n ref_model.summarize(mode=self.weights_summary)\\n else:\\n m = \\\"weights_summary can be None, 'full' or 'top'\\\"\\n raise MisconfigurationException(m)\\n\\n # link up experiment object\\n if self.logger is not None:\\n ref_model.logger = self.logger\\n\\n # save exp to get started\\n if hasattr(ref_model, \\\"hparams\\\"):\\n self.logger.log_hyperparams(ref_model.hparams)\\n self.logger.save()\\n\\n # track model now.\\n # if cluster resets state, the model will update with the saved weights\\n self.model = model\\n\\n # restore training and model before hpc call\\n self.restore_weights(model)\\n\\n # progress bar init\\n if self.show_progress_bar:\\n self.progress_bar = tqdm.tqdm(0, position=self.pro\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__train\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__train\\n def __train(self):\\n # run all epochs\\n for epoch_nb in range(self.current_epoch, self.max_nb_epochs):\\n # set seed for distributed sampler (enables shuffling for each epoch)\\n if self.use_ddp and hasattr(self.get_train_dataloader().sampler, 'set_epoch'):\\n self.get_train_dataloader().sampler.set_epoch(epoch_nb)\\n\\n # get model\\n model = self.__get_model()\\n\\n # update training progress in trainer and model\\n model.current_epoch = epoch_nb\\n self.current_epoch = epoch_nb\\n self.total_batches = self.nb_training_batches + self.nb_val_batches\\n self.batch_loss_value = 0 # accumulated grads\\n\\n # limit the number of batches to 1 in fast_dev_run\\n self.total_batches = 1\\n\\n # init progress_bar when requested\\n if self.show_progress_bar:\\n self.progress_bar.reset(self.total_batches)\\n\\n # changing gradient according accumulation_scheduler\\n self.accumulation_scheduler.on_epoch_begin(epoch_nb, self)\\n\\n # -----------------\\n # RUN TNG EPOCH\\n # -----------------\\n self.run_training_epoch()\\n\\n # update LR schedulers\\n if self.lr_schedulers is not None:\\n for lr_scheduler in self.lr_schedulers:\\n lr_scheduler.step(self.current_epoch)\\n\\n # early stopping\\n met_min_epochs = epoch_nb > self.min_nb_epochs\\n if self.enable_early_stop and (met_min_epochs or self.fast_dev_run):\\n should_stop = self.early_stop_callback.on_epoch_end(epoch=epoch_nb,\\n logs=self.callback_metrics)\\n # stop training\\n stop = should_stop and met_min_epochs\\n if stop:\\n return\\n\\n if self.logger is not Non\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.run_training_epoch\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.run_training_epoch\\n def run_training_epoch(self):\\n # before epoch hook\\n if self.__is_function_implemented('on_epoch_start'):\\n model = self.__get_model()\\n model.on_epoch_start()\\n\\n # run epoch\\n for batch_nb, batch in enumerate(self.get_train_dataloader()):\\n self.batch_nb = batch_nb\\n self.global_step += 1\\n\\n model = self.__get_model()\\n model.global_step = self.global_step\\n\\n # stop when the flag is changed or we've gone past the amount\\n # requested in the batches\\n self.total_batch_nb += 1\\n met_batch_limit = batch_nb > self.nb_training_batches\\n if met_batch_limit:\\n break\\n\\n # ---------------\\n # RUN TRAIN STEP\\n # ---------------\\n output = self.__run_training_batch(batch, batch_nb)\\n batch_result, grad_norm_dic, batch_step_metrics = output\\n early_stop_epoch = batch_result == -1\\n\\n # ---------------\\n # RUN VAL STEP\\n # ---------------\\n is_val_check_batch = (batch_nb + 1) % self.val_check_batch == 0\\n can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0\\n should_check_val = ((is_val_check_batch or early_stop_epoch) and can_check_epoch)\\n\\n # fast_dev_run always forces val checking after train batch\\n if self.fast_dev_run or should_check_val:\\n self.__run_evaluation(test=self.testing)\\n\\n # when logs should be saved\\n should_save_log = (batch_nb + 1) % self.log_save_interval == 0 or early_stop_epoch\\n if should_save_log or self.fast_dev_run:\\n if self.proc_rank == 0 and self.logger is not None:\\n self.logger.save()\\n\\n # when metrics should be logged\\n should_log_metrics = batch_nb % self.row_log_interval =\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__log_metrics\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__log_metrics\\n def __log_metrics(self, metrics, grad_norm_dic):\\n \\\"\\\"\\\"\\n Logs the metric dict passed in\\n :param metrics:\\n :param grad_norm_dic:\\n :return:\\n \\\"\\\"\\\"\\n # added metrics by Lightning for convenience\\n metrics['epoch'] = self.current_epoch\\n\\n # add gpu memory\\n if self.on_gpu and self.log_gpu_memory:\\n mem_map = memory.get_memory_profile(self.log_gpu_memory)\\n metrics.update(mem_map)\\n\\n # add norms\\n metrics.update(grad_norm_dic)\\n\\n # turn all tensors to scalars\\n scalar_metrics = self.__metrics_to_scalars(metrics)\\n\\n # log actual metrics\\n if self.proc_rank == 0 and self.logger is not None:\\n self.logger.log_metrics(scalar_metrics, step_num=self.global_step)\\n self.logger.save()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.test\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.test\\n def test(self, model=None):\\n if model is not None:\\n self.testing = True\\n self.fit(model)\\n else:\\n self.__run_evaluation(test=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__metrics_to_scalars\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__metrics_to_scalars\\n def __metrics_to_scalars(self, metrics):\\n new_metrics = {}\\n for k, v in metrics.items():\\n if isinstance(v, torch.Tensor):\\n v = v.item()\\n\\n if type(v) is dict:\\n v = self.__metrics_to_scalars(v)\\n\\n new_metrics[k] = v\\n\\n return new_metrics\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__log_vals_blacklist\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__log_vals_blacklist\\n def __log_vals_blacklist(self):\\n \\\"\\\"\\\"avoid logging some vals lightning uses to maintain state\\\"\\\"\\\"\\n blacklist = {'batch_nb', 'v_nb', 'gpu'}\\n return blacklist\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.transfer_batch_to_gpu\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.transfer_batch_to_gpu\\n def transfer_batch_to_gpu(self, batch, gpu_id):\\n # base case: object can be directly moved using `cuda` or `to`\\n if callable(getattr(batch, 'cuda', None)):\\n return batch.cuda(gpu_id)\\n\\n elif callable(getattr(batch, 'to', None)):\\n return batch.to(torch.device('cuda', gpu_id))\\n\\n # when list\\n elif isinstance(batch, list):\\n for i, x in enumerate(batch):\\n batch[i] = self.transfer_batch_to_gpu(x, gpu_id)\\n return batch\\n\\n # when tuple\\n elif isinstance(batch, tuple):\\n batch = list(batch)\\n for i, x in enumerate(batch):\\n batch[i] = self.transfer_batch_to_gpu(x, gpu_id)\\n return tuple(batch)\\n\\n # when dict\\n elif isinstance(batch, dict):\\n for k, v in batch.items():\\n batch[k] = self.transfer_batch_to_gpu(v, gpu_id)\\n\\n return batch\\n\\n # nothing matches, return the value as is without transform\\n return batch\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__training_forward\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__training_forward\\n def __training_forward(self, batch, batch_nb, opt_idx):\\n \\\"\\\"\\\"\\n Handle forward for each training case (distributed, single gpu, etc...)\\n :param batch:\\n :param batch_nb:\\n :return:\\n \\\"\\\"\\\"\\n # ---------------\\n # FORWARD\\n # ---------------\\n # enable not needing to add opt_idx to training_step\\n args = [batch, batch_nb]\\n if len(self.optimizers) > 1:\\n args.append(opt_idx)\\n\\n if self.use_ddp or self.use_ddp2:\\n output = self.model(*args)\\n elif self.use_dp:\\n output = self.model(*args)\\n elif self.single_gpu:\\n gpu_id = 0\\n if type(self.data_parallel_device_ids) is list:\\n gpu_id = self.data_parallel_device_ids[0]\\n batch = self.transfer_batch_to_gpu(batch, gpu_id)\\n args[0] = batch\\n output = self.model.training_step(*args)\\n\\n else:\\n output = self.model.training_step(*args)\\n\\n # format and reduce outputs accordingly\\n output = self.__process_output(output, train=True)\\n loss, progress_bar_metrics, log_metrics, callback_metrics = output\\n return loss, progress_bar_metrics, log_metrics, callback_metrics\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.__get_model\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.__get_model\\n def __get_model(self):\\n is_dp_module = isinstance(self.model, (LightningDistributedDataParallel,\\n LightningDataParallel))\\n model = self.model.module if is_dp_module else self.model\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_weights\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_weights\\n def restore_weights(self, model):\\n \\\"\\\"\\\"\\n To restore weights we have two cases.\\n First, if we use the same experiment version, then restore the latest ckpt.\\n AFTER that, if we find weights from hpc checkpoint, then restore that.\\n :param model:\\n :return:\\n \\\"\\\"\\\"\\n # restore weights if same exp version\\n self.restore_state_if_checkpoint_exists(model)\\n\\n # if script called from hpc resubmit, load weights\\n self.restore_hpc_weights_if_needed(model)\\n\\n # wait for all models to restore weights\\n if self.use_ddp or self.use_ddp2:\\n # wait for all processes to catch up\\n dist.barrier()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_state_if_checkpoint_exists\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_state_if_checkpoint_exists\\n def restore_state_if_checkpoint_exists(self, model):\\n # do nothing if there's not dir or callback\\n no_ckpt_callback = self.checkpoint_callback is None\\n if no_ckpt_callback or not os.path.exists(self.checkpoint_callback.filepath):\\n return\\n\\n # restore trainer state and model if there is a weight for this experiment\\n last_epoch = -1\\n last_ckpt_name = None\\n\\n # find last epoch\\n checkpoints = os.listdir(self.checkpoint_callback.filepath)\\n for name in checkpoints:\\n # ignore hpc ckpts\\n if 'hpc_' in name:\\n continue\\n\\n if '.ckpt' in name:\\n epoch = name.split('epoch_')[1]\\n epoch = int(re.sub('[^0-9]', '', epoch))\\n\\n if epoch > last_epoch:\\n last_epoch = epoch\\n last_ckpt_name = name\\n\\n # restore last checkpoint\\n if last_ckpt_name is not None:\\n last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name)\\n self.restore(last_ckpt_path, self.on_gpu)\\n print(f'model and trainer restored from checkpoint: {last_ckpt_path}')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.register_slurm_signal_handlers\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.register_slurm_signal_handlers\\n def register_slurm_signal_handlers(self):\\n # see if we're using slurm (not interactive)\\n on_slurm = False\\n try:\\n job_name = os.environ['SLURM_JOB_NAME']\\n if job_name != 'bash':\\n on_slurm = True\\n except Exception as e:\\n pass\\n\\n if on_slurm:\\n print('set slurm handle signals')\\n signal.signal(signal.SIGUSR1, self.sig_handler)\\n signal.signal(signal.SIGTERM, self.term_handler)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.sig_handler\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.sig_handler\\n def sig_handler(self, signum, frame):\\n if self.proc_rank == 0:\\n # save weights\\n print('handling SIGUSR1')\\n self.hpc_save(self.weights_save_path, self.logger)\\n\\n # find job id\\n job_id = os.environ['SLURM_JOB_ID']\\n cmd = 'scontrol requeue {}'.format(job_id)\\n\\n # requeue job\\n print('\\\\nrequeing job {}...'.format(job_id))\\n result = call(cmd, shell=True)\\n\\n # print result text\\n if result == 0:\\n print('requeued exp ', job_id)\\n else:\\n print('requeue failed...')\\n\\n # close experiment to avoid issues\\n self.logger.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.term_handler\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.term_handler\\n def term_handler(self, signum, frame):\\n # save\\n print(\\\"bypassing sigterm\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.save_checkpoint\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.save_checkpoint\\n def save_checkpoint(self, filepath):\\n checkpoint = self.dump_checkpoint()\\n\\n # do the actual save\\n torch.save(checkpoint, filepath)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore\\n def restore(self, checkpoint_path, on_gpu):\\n\\n # if on_gpu:\\n # checkpoint = torch.load(checkpoint_path)\\n # else:\\n # load on CPU first\\n checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)\\n\\n # load model state\\n model = self.__get_model()\\n\\n # load the state_dict on the model automatically\\n model.load_state_dict(checkpoint['state_dict'])\\n if on_gpu:\\n model.cuda(self.root_gpu)\\n\\n # load training state (affects trainer only)\\n self.restore_training_state(checkpoint)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.dump_checkpoint\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.dump_checkpoint\\n def dump_checkpoint(self):\\n\\n checkpoint = {\\n 'epoch': self.current_epoch,\\n 'global_step': self.global_step\\n }\\n\\n if self.checkpoint_callback is not None:\\n checkpoint['checkpoint_callback_best'] = self.checkpoint_callback.best\\n\\n if self.early_stop_callback is not None:\\n checkpoint['early_stop_callback_wait'] = self.early_stop_callback.wait\\n checkpoint['early_stop_callback_patience'] = self.early_stop_callback.patience\\n\\n # save optimizers\\n optimizer_states = []\\n for i, optimizer in enumerate(self.optimizers):\\n optimizer_states.append(optimizer.state_dict())\\n\\n checkpoint['optimizer_states'] = optimizer_states\\n\\n # save lr schedulers\\n lr_schedulers = []\\n for i, scheduler in enumerate(self.lr_schedulers):\\n lr_schedulers.append(scheduler.state_dict())\\n\\n checkpoint['lr_schedulers'] = lr_schedulers\\n\\n # add the state_dict from the model\\n model = self.__get_model()\\n checkpoint['state_dict'] = model.state_dict()\\n\\n # give the model a chance to add a few things\\n model.on_save_checkpoint(checkpoint)\\n\\n return checkpoint\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_hpc_weights_if_needed\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_hpc_weights_if_needed\\n def restore_hpc_weights_if_needed(self, model):\\n \\\"\\\"\\\"\\n If there is a set of hpc weights, use as signal to restore model\\n :param model:\\n :return:\\n \\\"\\\"\\\"\\n # look for hpc weights\\n folderpath = self.weights_save_path\\n if os.path.exists(folderpath):\\n files = os.listdir(folderpath)\\n hpc_weight_paths = [x for x in files if 'hpc_ckpt' in x]\\n\\n # if hpc weights exist restore model\\n if len(hpc_weight_paths) > 0:\\n self.hpc_load(folderpath, self.on_gpu)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_training_state\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.restore_training_state\\n def restore_training_state(self, checkpoint):\\n \\\"\\\"\\\"\\n Restore trainer state.\\n Model will get its change to update\\n :param checkpoint:\\n :return:\\n \\\"\\\"\\\"\\n if self.checkpoint_callback is not None:\\n self.checkpoint_callback.best = checkpoint['checkpoint_callback_best']\\n\\n if self.early_stop_callback is not None:\\n self.early_stop_callback.wait = checkpoint['early_stop_callback_wait']\\n self.early_stop_callback.patience = checkpoint['early_stop_callback_patience']\\n\\n self.global_step = checkpoint['global_step']\\n self.current_epoch = checkpoint['epoch']\\n\\n # restore the optimizers\\n optimizer_states = checkpoint['optimizer_states']\\n for optimizer, opt_state in zip(self.optimizers, optimizer_states):\\n optimizer.load_state_dict(opt_state)\\n\\n # move optimizer to GPU 1 weight at a time\\n # avoids OOM\\n if self.root_gpu is not None:\\n for state in optimizer.state.values():\\n for k, v in state.items():\\n if isinstance(v, torch.Tensor):\\n state[k] = v.cuda(self.root_gpu)\\n\\n # restore the lr schedulers\\n lr_schedulers = checkpoint['lr_schedulers']\\n for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers):\\n scheduler.load_state_dict(lrs_state)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.hpc_save\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.hpc_save\\n def hpc_save(self, folderpath, logger):\\n # make sure the checkpoint folder exists\\n os.makedirs(folderpath, exist_ok=True)\\n\\n # save logger to make sure we get all the metrics\\n logger.save()\\n\\n ckpt_number = self.max_ckpt_in_folder(folderpath) + 1\\n\\n if not os.path.exists(folderpath):\\n os.makedirs(folderpath, exist_ok=True)\\n filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, ckpt_number)\\n\\n # give model a chance to do something on hpc_save\\n model = self.__get_model()\\n checkpoint = self.dump_checkpoint()\\n\\n model.on_hpc_save(checkpoint)\\n\\n # do the actual save\\n torch.save(checkpoint, filepath)\\n\\n return filepath\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.hpc_load\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.hpc_load\\n def hpc_load(self, folderpath, on_gpu):\\n filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, self.max_ckpt_in_folder(folderpath))\\n\\n # load on CPU first\\n checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)\\n\\n # load model state\\n model = self.__get_model()\\n\\n # load the state_dict on the model automatically\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n if self.root_gpu is not None:\\n model.cuda(self.root_gpu)\\n\\n # load training state (affects trainer only)\\n self.restore_training_state(checkpoint)\\n\\n # call model hook\\n model.on_hpc_load(checkpoint)\\n\\n print(f'restored hpc model from: {filepath}')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.max_ckpt_in_folder\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIO.max_ckpt_in_folder\\n def max_ckpt_in_folder(self, path, name_key='ckpt_'):\\n files = os.listdir(path)\\n files = [x for x in files if name_key in x]\\n if len(files) == 0:\\n return 0\\n\\n ckpt_vs = []\\n for name in files:\\n name = name.split(name_key)[-1]\\n name = re.sub('[^0-9]', '', name)\\n ckpt_vs.append(int(name))\\n\\n return max(ckpt_vs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::load_hparams_from_tags_csv\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::load_hparams_from_tags_csv\\ndef load_hparams_from_tags_csv(tags_csv):\\n from argparse import Namespace\\n import pandas as pd\\n\\n tags_df = pd.read_csv(tags_csv)\\n dic = tags_df.to_dict(orient='records')\\n\\n ns_dict = {row['key']: convert(row['value']) for row in dic}\\n\\n ns = Namespace(**ns_dict)\\n return ns\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::convert\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::convert\\ndef convert(val):\\n constructors = [int, float, str]\\n\\n if type(val) is str:\\n if val.lower() == 'true':\\n return True\\n if val.lower() == 'false':\\n return False\\n\\n for c in constructors:\\n try:\\n return c(val)\\n except ValueError:\\n pass\\n return val\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":253,"cells":{"instance_id":{"kind":"string","value":"wagtail__wagtail-6999"},"repo":{"kind":"string","value":"wagtail/wagtail"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Migration fails on MySQL when upgrading to Wagtail>=2.12rc1\n### Issue Summary\r\n\r\nThe [wagtailembeds.0008_allow_long_urls](https://github.com/wagtail/wagtail/blob/master/wagtail/embeds/migrations/0008_allow_long_urls.py) migration added in 2.12rc1 (https://github.com/wagtail/wagtail/commit/32ade8672fe32223b6ebdcb2255bf4de16908f97) results in the following error on MySQL 8.0 (and presumably also older versions):\r\n\r\n MySQLdb._exceptions.OperationalError:\r\n (1101, \"BLOB, TEXT, GEOMETRY or JSON column 'thumbnail_url' can't have a default value\")\r\n\r\nThis is expected behaviour. According to https://dev.mysql.com/doc/refman/8.0/en/blob.html:\r\n\r\n BLOB and TEXT columns cannot have DEFAULT values.\r\n\r\n### Steps to Reproduce\r\n_Update: Migration from 2.11 to 2.12 is not needed; can be reproduced more simply in steps 1–5 using 2.12rc1<=wagtail<=2.12.2_\r\n\r\nThis issue can be reproduced on a fresh Wagtail project as follows:\r\n\r\n1. Install Wagtail ~~< 2.12~~ >= 2.12rc1 with ~~`pip install 'wagtail<2.12'`~~ `pip install 'wagtail>=2.12rc1<=2.12.2'` \r\n2. Start a new project `wagtail start testproject`\r\n3. Configure the `DATABASES` setting to use a MySQL database\r\n```\r\nDATABASES = {\r\n 'default': {\r\n 'ENGINE': 'django.db.backends.mysql',\r\n 'NAME': 'testproject',\r\n 'USER': 'testuser',\r\n 'PASSWORD': 'testpass'\r\n }\r\n}\r\n```\r\n4. Prepare the database\r\n```\r\nmysql> create database testproject;\r\nmysql> create user 'testuser'@'localhost' identified by 'testpass';\r\nmysql> grant all privileges on testproject.* to 'testuser'@'localhost';\r\n```\r\n5. Run initial migrations `./manage.py migrate`\r\n~~6. Upgrade to Wagtail 2.12rc1 or greater with, e.g. `pip install --upgrade wagtail==2.12.2`~~\r\n~~7. Re-run migrations `./manage.py migrate`~~\r\n\r\n### Additional Notes\r\n\r\nPrior to MySQL 8.0.13, BLOB and TEXT could not be assigned DEFAULT values. As of 8.0.13, they can be assigned defaults, but only in the form of expressions as noted in https://dev.mysql.com/doc/refman/8.0/en/data-type-defaults.html#data-type-defaults-explicit\r\n\r\n### Technical details\r\n* Python 3.8.3\r\n* Django 3.1.7\r\n* Wagtail 2.11.5 -> 2.12rc1...2.12.2\r\n* MySQL 8.0.21 for osx10.15 on x86_64 (Homebrew)\r\n* mysqlclient 2.0.3\n"},"gold_files":{"kind":"list like","value":["wagtail/embeds/migrations/0008_allow_long_urls.py"],"string":"[\n \"wagtail/embeds/migrations/0008_allow_long_urls.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["wagtail/embeds/migrations/0008_allow_long_urls.py::<module>"],"string":"[\n \"wagtail/embeds/migrations/0008_allow_long_urls.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/conf.py","text":"docs/conf.py\ndef setup(app)","label":0},{"cid":"wagtail/contrib/forms/models.py","text":"wagtail/contrib/forms/models.py\nclass AbstractFormSubmission # Data for a form submission.\n def get_data(self) # Returns dict with form data.\n def __str__(self)\n class Meta\nclass FormSubmission # Data for a Form submission.\nclass AbstractFormField # Database Fields required for building a Django Form field.\n def save(self, *args, **kwargs) # When new fields are created, generate a template safe ascii name to use as the\n def _migrate_legacy_clean_name(cls) # Ensure that existing data stored will be accessible via the legacy clean_name.\n def check(cls, **kwargs)\n class Meta\nclass AbstractForm # A Form Page. Pages implementing a form should inherit from it\n def __init__(self, *args, **kwargs)\n class Meta\n def get_form_fields(self) # Form page expects `form_fields` to be declared.\n def get_data_fields(self) # Returns a list of tuples with (field_name, field_label).\n def get_form_class(self)\n def get_form_parameters(self)\n def get_form(self, *args, **kwargs)\n def get_landing_page_template(self, request, *args, **kwargs)\n def get_submission_class(self) # Returns submission class.\n def get_submissions_list_view_class(self)\n def process_form_submission(self, form) # Accepts form instance with submitted data, user and page.\n def render_landing_page(self, request, form_submission=None, *args, **kwargs) # Renders the landing page.\n def serve_submissions_list_view(self, request, *args, **kwargs) # Returns list submissions view for admin.\n def serve(self, request, *args, **kwargs)\n def serve_preview(self, request, mode_name)\nclass AbstractEmailForm # A Form Page that sends email. Pages implementing a form to be send to an email s\n def process_form_submission(self, form)\n def send_mail(self, form)\n def render_email(self, form)\n class Meta","label":0},{"cid":"wagtail/core/blocks/base.py","text":"wagtail/core/blocks/base.py\nclass BaseBlock\n def __new__(mcs, name, bases, attrs)\nclass Block\n class Meta\n def __new__(cls, *args, **kwargs)\n def __init__(self, **kwargs)\n def set_name(self, name)\n def set_meta_options(self, opts) # Update this block's meta options (out of the ones designated as mutable) from th\n def value_from_datadict(self, data, files, prefix)\n def value_omitted_from_data(self, data, files, name) # Used only for top-level blocks wrapped by BlockWidget (i.e.: typically only Stre\n def bind(self, value, prefix=None, errors=None) # Return a BoundBlock which represents the association of this block definition wi\n def get_default(self) # Return this block's default value (conventionally found in self.meta.default),\n def clean(self, value) # Validate value and return a cleaned version of it, or throw a ValidationError if\n def to_python(self, value) # Convert 'value' from a simple (JSON-serialisable) value to a (possibly complex) \n def bulk_to_python(self, values) # Apply the to_python conversion to a list of values. The default implementation s\n def get_prep_value(self, value) # The reverse of to_python; convert the python value into JSON-serialisable form.\n def get_form_state(self, value) # Convert a python value for this block into a JSON-serialisable representation co\n def get_context(self, value, parent_context=None) # Return a dict of context variables (derived from the block value and combined wi\n def get_template(self, context=None) # Return the template to use for rendering the block if specified on meta class.\n def render(self, value, context=None) # Return a text rendering of 'value', suitable for display on templates. By defaul\n def get_api_representation(self, value, context=None) # Can be used to customise the API response and defaults to the value returned by \n def render_basic(self, value, context=None) # Return a text rendering of 'value', suitable for display on templates. render() \n def get_searchable_content(self, value) # Returns a list of strings containing text content within this block to be used i\n def check(self, **kwargs) # Hook for the Django system checks framework -\n def _check_name(self, **kwargs) # Helper method called by container blocks as part of the system checks framework,\n def id_for_label(self, prefix) # Return the ID to be used as the 'for' attribute of <label> elements that refer t\n def required(self) # Flag used to determine whether labels for this block should display a 'required'\n def deconstruct(self)\n def __eq__(self, other) # Implement equality on block objects so that two blocks with matching definitions\nclass BoundBlock\n def __init__(self, block, value, prefix=None, errors=None)\n def render(self, context=None)\n def render_as_block(self, context=None) # Alias for render; the include_block tag will specifically check for the presence\n def id_for_label(self)\n def __str__(sel","label":0},{"cid":"wagtail/core/blocks/field_block.py","text":"wagtail/core/blocks/field_block.py\nclass FieldBlock # A block that wraps a Django form field\n def id_for_label(self, prefix)\n def value_from_form(self, value) # The value that we get back from the form field might not be the type\n def value_for_form(self, value) # Reverse of value_from_form; convert a value of this block's native value type\n def value_from_datadict(self, data, files, prefix)\n def value_omitted_from_data(self, data, files, prefix)\n def clean(self, value)\n def required(self)\n def get_form_state(self, value)\n class Meta\nclass FieldBlockAdapter\n def js_args(self, block)\n def media(self)\nclass CharBlock\n def __init__(self, required=True, help_text=None, max_length=None, min_length=None, validators=(), **kwargs)\n def get_searchable_content(self, value)\nclass TextBlock\n def __init__(self, required=True, help_text=None, rows=1, max_length=None, min_length=None, validators=(), **kwargs)\n def field(self)\n def get_searchable_content(self, value)\n class Meta\nclass BlockQuoteBlock\n def render_basic(self, value, context=None)\n class Meta\nclass FloatBlock\n def __init__(self, required=True, max_value=None, min_value=None, validators=(), *args, **kwargs)\n class Meta\nclass DecimalBlock\n def __init__(self, required=True, help_text=None, max_value=None, min_value=None, max_digits=None, decimal_places=None, validators=(), *args, **kwargs)\n class Meta\nclass RegexBlock\n def __init__(self, regex, required=True, help_text=None, max_length=None, min_length=None, error_messages=None, validators=(), *args, **kwargs)\n class Meta\nclass URLBlock\n def __init__(self, required=True, help_text=None, max_length=None, min_length=None, validators=(), **kwargs)\n class Meta\nclass BooleanBlock\n def __init__(self, required=True, help_text=None, **kwargs)\n class Meta\nclass DateBlock\n def __init__(self, required=True, help_text=None, format=None, validators=(), **kwargs)\n def field(self)\n def to_python(self, value)\n class Meta\nclass TimeBlock\n def __init__(self, required=True, help_text=None, format=None, validators=(), **kwargs)\n def field(self)\n def to_python(self, value)\n class Meta\nclass DateTimeBlock\n def __init__(self, required=True, help_text=None, format=None, validators=(), **kwargs)\n def field(self)\n def to_python(self, value)\n class Meta\nclass EmailBlock\n def __init__(self, required=True, help_text=None, validators=(), **kwargs)\n class Meta\nclass IntegerBlock\n def __init__(self, required=True, help_text=None, min_value=None, max_value=None, validators=(), **kwargs)\n class Meta\nclass BaseChoiceBlock\n def __init__(self, choices=None, default=None, required=True, help_text=None, widget=None, validators=(), **kwargs)\n def _get_callable_choices(self, choices, blank_choice=True) # Return a callable that we can pass into `forms.ChoiceField`, which will provide \n def choices_callable()\n class Meta\nclass ChoiceBlock\n ","label":0},{"cid":"wagtail/core/fields.py","text":"wagtail/core/fields.py\nclass RichTextField\n def __init__(self, *args, **kwargs)\n def formfield(self, **kwargs)\n def get_searchable_content(self, value)\nclass Creator # A placeholder class that provides a way to set the attribute on the model.\n def __init__(self, field)\n def __get__(self, obj, type=None)\n def __set__(self, obj, value)\nclass StreamField\n def __init__(self, block_types, **kwargs)\n def get_internal_type(self)\n def get_panel(self)\n def deconstruct(self)\n def to_python(self, value)\n def get_prep_value(self, value)\n def from_db_value(self, value, expression, connection)\n def formfield(self, **kwargs) # Override formfield to use a plain forms.Field so that we do no transformation on\n def value_to_string(self, obj)\n def get_searchable_content(self, value)\n def check(self, **kwargs)\n def contribute_to_class(self, cls, name, **kwargs)","label":0},{"cid":"wagtail/embeds/migrations/0008_allow_long_urls.py","text":"wagtail/embeds/migrations/0008_allow_long_urls.py\nclass Migration","label":1},{"cid":"wagtail/images/migrations/0016_deprecate_rendition_filter_relation.py","text":"wagtail/images/migrations/0016_deprecate_rendition_filter_relation.py\nclass Migration","label":0},{"cid":"wagtail/images/migrations/0017_reduce_focal_point_key_max_length.py","text":"wagtail/images/migrations/0017_reduce_focal_point_key_max_length.py\nclass Migration","label":0},{"cid":"wagtail/project_template/project_name/settings/base.py","text":"wagtail/project_template/project_name/settings/base.py","label":0},{"cid":"wagtail/search/utils.py","text":"wagtail/search/utils.py\ndef balanced_reduce(operator, seq, initializer=NOT_SET) # Has the same result as Python's reduce function, but performs the calculations i\ndef normalise_query_string(query_string)\ndef separate_filters_from_query(query_string)\ndef parse_query_string(query_string, operator=None, zero_terms=MATCH_NONE) # This takes a query string typed in by a user and extracts the following:","label":0}],"string":"[\n {\n \"cid\": \"docs/conf.py\",\n \"text\": \"docs/conf.py\\ndef setup(app)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py\",\n \"text\": \"wagtail/contrib/forms/models.py\\nclass AbstractFormSubmission # Data for a form submission.\\n def get_data(self) # Returns dict with form data.\\n def __str__(self)\\n class Meta\\nclass FormSubmission # Data for a Form submission.\\nclass AbstractFormField # Database Fields required for building a Django Form field.\\n def save(self, *args, **kwargs) # When new fields are created, generate a template safe ascii name to use as the\\n def _migrate_legacy_clean_name(cls) # Ensure that existing data stored will be accessible via the legacy clean_name.\\n def check(cls, **kwargs)\\n class Meta\\nclass AbstractForm # A Form Page. Pages implementing a form should inherit from it\\n def __init__(self, *args, **kwargs)\\n class Meta\\n def get_form_fields(self) # Form page expects `form_fields` to be declared.\\n def get_data_fields(self) # Returns a list of tuples with (field_name, field_label).\\n def get_form_class(self)\\n def get_form_parameters(self)\\n def get_form(self, *args, **kwargs)\\n def get_landing_page_template(self, request, *args, **kwargs)\\n def get_submission_class(self) # Returns submission class.\\n def get_submissions_list_view_class(self)\\n def process_form_submission(self, form) # Accepts form instance with submitted data, user and page.\\n def render_landing_page(self, request, form_submission=None, *args, **kwargs) # Renders the landing page.\\n def serve_submissions_list_view(self, request, *args, **kwargs) # Returns list submissions view for admin.\\n def serve(self, request, *args, **kwargs)\\n def serve_preview(self, request, mode_name)\\nclass AbstractEmailForm # A Form Page that sends email. Pages implementing a form to be send to an email s\\n def process_form_submission(self, form)\\n def send_mail(self, form)\\n def render_email(self, form)\\n class Meta\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py\",\n \"text\": \"wagtail/core/blocks/base.py\\nclass BaseBlock\\n def __new__(mcs, name, bases, attrs)\\nclass Block\\n class Meta\\n def __new__(cls, *args, **kwargs)\\n def __init__(self, **kwargs)\\n def set_name(self, name)\\n def set_meta_options(self, opts) # Update this block's meta options (out of the ones designated as mutable) from th\\n def value_from_datadict(self, data, files, prefix)\\n def value_omitted_from_data(self, data, files, name) # Used only for top-level blocks wrapped by BlockWidget (i.e.: typically only Stre\\n def bind(self, value, prefix=None, errors=None) # Return a BoundBlock which represents the association of this block definition wi\\n def get_default(self) # Return this block's default value (conventionally found in self.meta.default),\\n def clean(self, value) # Validate value and return a cleaned version of it, or throw a ValidationError if\\n def to_python(self, value) # Convert 'value' from a simple (JSON-serialisable) value to a (possibly complex) \\n def bulk_to_python(self, values) # Apply the to_python conversion to a list of values. The default implementation s\\n def get_prep_value(self, value) # The reverse of to_python; convert the python value into JSON-serialisable form.\\n def get_form_state(self, value) # Convert a python value for this block into a JSON-serialisable representation co\\n def get_context(self, value, parent_context=None) # Return a dict of context variables (derived from the block value and combined wi\\n def get_template(self, context=None) # Return the template to use for rendering the block if specified on meta class.\\n def render(self, value, context=None) # Return a text rendering of 'value', suitable for display on templates. By defaul\\n def get_api_representation(self, value, context=None) # Can be used to customise the API response and defaults to the value returned by \\n def render_basic(self, value, context=None) # Return a text rendering of 'value', suitable for display on templates. render() \\n def get_searchable_content(self, value) # Returns a list of strings containing text content within this block to be used i\\n def check(self, **kwargs) # Hook for the Django system checks framework -\\n def _check_name(self, **kwargs) # Helper method called by container blocks as part of the system checks framework,\\n def id_for_label(self, prefix) # Return the ID to be used as the 'for' attribute of <label> elements that refer t\\n def required(self) # Flag used to determine whether labels for this block should display a 'required'\\n def deconstruct(self)\\n def __eq__(self, other) # Implement equality on block objects so that two blocks with matching definitions\\nclass BoundBlock\\n def __init__(self, block, value, prefix=None, errors=None)\\n def render(self, context=None)\\n def render_as_block(self, context=None) # Alias for render; the include_block tag will specifically check for the presence\\n def id_for_label(self)\\n def __str__(sel\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py\",\n \"text\": \"wagtail/core/blocks/field_block.py\\nclass FieldBlock # A block that wraps a Django form field\\n def id_for_label(self, prefix)\\n def value_from_form(self, value) # The value that we get back from the form field might not be the type\\n def value_for_form(self, value) # Reverse of value_from_form; convert a value of this block's native value type\\n def value_from_datadict(self, data, files, prefix)\\n def value_omitted_from_data(self, data, files, prefix)\\n def clean(self, value)\\n def required(self)\\n def get_form_state(self, value)\\n class Meta\\nclass FieldBlockAdapter\\n def js_args(self, block)\\n def media(self)\\nclass CharBlock\\n def __init__(self, required=True, help_text=None, max_length=None, min_length=None, validators=(), **kwargs)\\n def get_searchable_content(self, value)\\nclass TextBlock\\n def __init__(self, required=True, help_text=None, rows=1, max_length=None, min_length=None, validators=(), **kwargs)\\n def field(self)\\n def get_searchable_content(self, value)\\n class Meta\\nclass BlockQuoteBlock\\n def render_basic(self, value, context=None)\\n class Meta\\nclass FloatBlock\\n def __init__(self, required=True, max_value=None, min_value=None, validators=(), *args, **kwargs)\\n class Meta\\nclass DecimalBlock\\n def __init__(self, required=True, help_text=None, max_value=None, min_value=None, max_digits=None, decimal_places=None, validators=(), *args, **kwargs)\\n class Meta\\nclass RegexBlock\\n def __init__(self, regex, required=True, help_text=None, max_length=None, min_length=None, error_messages=None, validators=(), *args, **kwargs)\\n class Meta\\nclass URLBlock\\n def __init__(self, required=True, help_text=None, max_length=None, min_length=None, validators=(), **kwargs)\\n class Meta\\nclass BooleanBlock\\n def __init__(self, required=True, help_text=None, **kwargs)\\n class Meta\\nclass DateBlock\\n def __init__(self, required=True, help_text=None, format=None, validators=(), **kwargs)\\n def field(self)\\n def to_python(self, value)\\n class Meta\\nclass TimeBlock\\n def __init__(self, required=True, help_text=None, format=None, validators=(), **kwargs)\\n def field(self)\\n def to_python(self, value)\\n class Meta\\nclass DateTimeBlock\\n def __init__(self, required=True, help_text=None, format=None, validators=(), **kwargs)\\n def field(self)\\n def to_python(self, value)\\n class Meta\\nclass EmailBlock\\n def __init__(self, required=True, help_text=None, validators=(), **kwargs)\\n class Meta\\nclass IntegerBlock\\n def __init__(self, required=True, help_text=None, min_value=None, max_value=None, validators=(), **kwargs)\\n class Meta\\nclass BaseChoiceBlock\\n def __init__(self, choices=None, default=None, required=True, help_text=None, widget=None, validators=(), **kwargs)\\n def _get_callable_choices(self, choices, blank_choice=True) # Return a callable that we can pass into `forms.ChoiceField`, which will provide \\n def choices_callable()\\n class Meta\\nclass ChoiceBlock\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py\",\n \"text\": \"wagtail/core/fields.py\\nclass RichTextField\\n def __init__(self, *args, **kwargs)\\n def formfield(self, **kwargs)\\n def get_searchable_content(self, value)\\nclass Creator # A placeholder class that provides a way to set the attribute on the model.\\n def __init__(self, field)\\n def __get__(self, obj, type=None)\\n def __set__(self, obj, value)\\nclass StreamField\\n def __init__(self, block_types, **kwargs)\\n def get_internal_type(self)\\n def get_panel(self)\\n def deconstruct(self)\\n def to_python(self, value)\\n def get_prep_value(self, value)\\n def from_db_value(self, value, expression, connection)\\n def formfield(self, **kwargs) # Override formfield to use a plain forms.Field so that we do no transformation on\\n def value_to_string(self, obj)\\n def get_searchable_content(self, value)\\n def check(self, **kwargs)\\n def contribute_to_class(self, cls, name, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/embeds/migrations/0008_allow_long_urls.py\",\n \"text\": \"wagtail/embeds/migrations/0008_allow_long_urls.py\\nclass Migration\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/images/migrations/0016_deprecate_rendition_filter_relation.py\",\n \"text\": \"wagtail/images/migrations/0016_deprecate_rendition_filter_relation.py\\nclass Migration\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/migrations/0017_reduce_focal_point_key_max_length.py\",\n \"text\": \"wagtail/images/migrations/0017_reduce_focal_point_key_max_length.py\\nclass Migration\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/project_template/project_name/settings/base.py\",\n \"text\": \"wagtail/project_template/project_name/settings/base.py\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/search/utils.py\",\n \"text\": \"wagtail/search/utils.py\\ndef balanced_reduce(operator, seq, initializer=NOT_SET) # Has the same result as Python's reduce function, but performs the calculations i\\ndef normalise_query_string(query_string)\\ndef separate_filters_from_query(query_string)\\ndef parse_query_string(query_string, operator=None, zero_terms=MATCH_NONE) # This takes a query string typed in by a user and extracts the following:\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"docs/conf.py::setup","text":"docs/conf.py::setup\ndef setup(app):\n app.add_css_file('css/custom.css')\n app.add_js_file('js/banner.js')\n\n github_doc_root = 'https://github.com/wagtail/wagtail/tree/main/docs/'\n\n app.add_config_value('recommonmark_config', {\n 'url_resolver': lambda url: github_doc_root + url,\n }, True)\n app.add_transform(AutoStructify)","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractFormSubmission.get_data","text":"wagtail/contrib/forms/models.py::AbstractFormSubmission.get_data\n def get_data(self):\n \"\"\"\n Returns dict with form data.\n\n You can override this method to add additional data.\n \"\"\"\n form_data = json.loads(self.form_data)\n form_data.update({\n 'submit_time': self.submit_time,\n })\n\n return form_data","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractFormSubmission.__str__","text":"wagtail/contrib/forms/models.py::AbstractFormSubmission.__str__\n def __str__(self):\n return self.form_data","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractFormField.save","text":"wagtail/contrib/forms/models.py::AbstractFormField.save\n def save(self, *args, **kwargs):\n \"\"\"\n When new fields are created, generate a template safe ascii name to use as the\n JSON storage reference for this field. Previously created fields will be updated\n to use the legacy unidecode method via checks & _migrate_legacy_clean_name.\n \"\"\"\n\n is_new = self.pk is None\n if is_new:\n clean_name = get_field_clean_name(self.label)\n self.clean_name = clean_name\n\n super().save(*args, **kwargs)","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractFormField._migrate_legacy_clean_name","text":"wagtail/contrib/forms/models.py::AbstractFormField._migrate_legacy_clean_name\n @classmethod\n def _migrate_legacy_clean_name(cls):\n \"\"\"\n Ensure that existing data stored will be accessible via the legacy clean_name.\n When checks run, replace any blank clean_name values with the unidecode conversion.\n \"\"\"\n\n try:\n objects = cls.objects.filter(clean_name__exact='')\n if objects.count() == 0:\n return None\n\n except (FieldError, DatabaseError):\n # attempting to query on clean_name before field has been added\n return None\n\n try:\n from unidecode import unidecode\n except ImportError as error:\n description = \"You have form submission data that was created on an older version of Wagtail and requires the unidecode library to retrieve it correctly. Please install the unidecode package.\"\n raise Exception(description) from error\n\n for obj in objects:\n legacy_clean_name = str(slugify(str(unidecode(obj.label))))\n obj.clean_name = legacy_clean_name\n obj.save()\n\n return Info(\n 'Added `clean_name` on %s form field(s)' % objects.count(),\n obj=cls\n )","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractFormField.check","text":"wagtail/contrib/forms/models.py::AbstractFormField.check\n @classmethod\n def check(cls, **kwargs):\n errors = super().check(**kwargs)\n\n messages = cls._migrate_legacy_clean_name()\n if messages:\n errors.append(messages)\n\n return errors","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.__init__","text":"wagtail/contrib/forms/models.py::AbstractForm.__init__\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n if not hasattr(self, 'landing_page_template'):\n name, ext = os.path.splitext(self.template)\n self.landing_page_template = name + '_landing' + ext","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_form_fields","text":"wagtail/contrib/forms/models.py::AbstractForm.get_form_fields\n def get_form_fields(self):\n \"\"\"\n Form page expects `form_fields` to be declared.\n If you want to change backwards relation name,\n you need to override this method.\n \"\"\"\n\n return self.form_fields.all()","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_data_fields","text":"wagtail/contrib/forms/models.py::AbstractForm.get_data_fields\n def get_data_fields(self):\n \"\"\"\n Returns a list of tuples with (field_name, field_label).\n \"\"\"\n\n data_fields = [\n ('submit_time', _('Submission date')),\n ]\n data_fields += [\n (field.clean_name, field.label)\n for field in self.get_form_fields()\n ]\n\n return data_fields","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_form_class","text":"wagtail/contrib/forms/models.py::AbstractForm.get_form_class\n def get_form_class(self):\n fb = self.form_builder(self.get_form_fields())\n return fb.get_form_class()","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_form_parameters","text":"wagtail/contrib/forms/models.py::AbstractForm.get_form_parameters\n def get_form_parameters(self):\n return {}","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_form","text":"wagtail/contrib/forms/models.py::AbstractForm.get_form\n def get_form(self, *args, **kwargs):\n form_class = self.get_form_class()\n form_params = self.get_form_parameters()\n form_params.update(kwargs)\n\n return form_class(*args, **form_params)","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_landing_page_template","text":"wagtail/contrib/forms/models.py::AbstractForm.get_landing_page_template\n def get_landing_page_template(self, request, *args, **kwargs):\n return self.landing_page_template","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_submission_class","text":"wagtail/contrib/forms/models.py::AbstractForm.get_submission_class\n def get_submission_class(self):\n \"\"\"\n Returns submission class.\n\n You can override this method to provide custom submission class.\n Your class must be inherited from AbstractFormSubmission.\n \"\"\"\n\n return FormSubmission","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_submissions_list_view_class","text":"wagtail/contrib/forms/models.py::AbstractForm.get_submissions_list_view_class\n def get_submissions_list_view_class(self):\n from .views import SubmissionsListView\n return self.submissions_list_view_class or SubmissionsListView","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.process_form_submission","text":"wagtail/contrib/forms/models.py::AbstractForm.process_form_submission\n def process_form_submission(self, form):\n \"\"\"\n Accepts form instance with submitted data, user and page.\n Creates submission instance.\n\n You can override this method if you want to have custom creation logic.\n For example, if you want to save reference to a user.\n \"\"\"\n\n return self.get_submission_class().objects.create(\n form_data=json.dumps(form.cleaned_data, cls=DjangoJSONEncoder),\n page=self,\n )","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.render_landing_page","text":"wagtail/contrib/forms/models.py::AbstractForm.render_landing_page\n def render_landing_page(self, request, form_submission=None, *args, **kwargs):\n \"\"\"\n Renders the landing page.\n\n You can override this method to return a different HttpResponse as\n landing page. E.g. you could return a redirect to a separate page.\n \"\"\"\n context = self.get_context(request)\n context['form_submission'] = form_submission\n return TemplateResponse(\n request,\n self.get_landing_page_template(request),\n context\n )","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.serve_submissions_list_view","text":"wagtail/contrib/forms/models.py::AbstractForm.serve_submissions_list_view\n def serve_submissions_list_view(self, request, *args, **kwargs):\n \"\"\"\n Returns list submissions view for admin.\n\n `list_submissions_view_class` can bse set to provide custom view class.\n Your class must be inherited from SubmissionsListView.\n \"\"\"\n view = self.get_submissions_list_view_class().as_view()\n return view(request, form_page=self, *args, **kwargs)","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.serve","text":"wagtail/contrib/forms/models.py::AbstractForm.serve\n def serve(self, request, *args, **kwargs):\n if request.method == 'POST':\n form = self.get_form(request.POST, request.FILES, page=self, user=request.user)\n\n if form.is_valid():\n form_submission = self.process_form_submission(form)\n return self.render_landing_page(request, form_submission, *args, **kwargs)\n else:\n form = self.get_form(page=self, user=request.user)\n\n context = self.get_context(request)\n context['form'] = form\n return TemplateResponse(\n request,\n self.get_template(request),\n context\n )","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.serve_preview","text":"wagtail/contrib/forms/models.py::AbstractForm.serve_preview\n def serve_preview(self, request, mode_name):\n if mode_name == 'landing':\n request.is_preview = True\n return self.render_landing_page(request)\n else:\n return super().serve_preview(request, mode_name)","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractEmailForm.process_form_submission","text":"wagtail/contrib/forms/models.py::AbstractEmailForm.process_form_submission\n def process_form_submission(self, form):\n submission = super().process_form_submission(form)\n if self.to_address:\n self.send_mail(form)\n return submission","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractEmailForm.send_mail","text":"wagtail/contrib/forms/models.py::AbstractEmailForm.send_mail\n def send_mail(self, form):\n addresses = [x.strip() for x in self.to_address.split(',')]\n send_mail(self.subject, self.render_email(form), addresses, self.from_address,)","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractEmailForm.render_email","text":"wagtail/contrib/forms/models.py::AbstractEmailForm.render_email\n def render_email(self, form):\n content = []\n\n cleaned_data = form.cleaned_data\n for field in form:\n if field.name not in cleaned_data:\n continue\n\n value = cleaned_data.get(field.name)\n\n if isinstance(value, list):\n value = ', '.join(value)\n\n # Format dates and datetimes with SHORT_DATE(TIME)_FORMAT\n if isinstance(value, datetime.datetime):\n value = date_format(value, settings.SHORT_DATETIME_FORMAT)\n elif isinstance(value, datetime.date):\n value = date_format(value, settings.SHORT_DATE_FORMAT)\n\n content.append('{}: {}'.format(field.label, value))\n\n return '\\n'.join(content)","label":0},{"cid":"wagtail/core/blocks/base.py::BaseBlock.__new__","text":"wagtail/core/blocks/base.py::BaseBlock.__new__\n def __new__(mcs, name, bases, attrs):\n meta_class = attrs.pop('Meta', None)\n\n cls = super(BaseBlock, mcs).__new__(mcs, name, bases, attrs)\n\n # Get all the Meta classes from all the bases\n meta_class_bases = [meta_class] + [getattr(base, '_meta_class', None)\n for base in bases]\n meta_class_bases = tuple(filter(bool, meta_class_bases))\n cls._meta_class = type(str(name + 'Meta'), meta_class_bases, {})\n\n return cls","label":0},{"cid":"wagtail/core/blocks/base.py::Block.__new__","text":"wagtail/core/blocks/base.py::Block.__new__\n def __new__(cls, *args, **kwargs):\n # adapted from django.utils.deconstruct.deconstructible; capture the arguments\n # so that we can return them in the 'deconstruct' method\n obj = super(Block, cls).__new__(cls)\n obj._constructor_args = (args, kwargs)\n return obj","label":0},{"cid":"wagtail/core/blocks/base.py::Block.__init__","text":"wagtail/core/blocks/base.py::Block.__init__\n def __init__(self, **kwargs):\n if 'classname' in self._constructor_args[1]:\n # Adding this so that migrations are not triggered\n # when form_classname is used instead of classname\n # in the initialisation of the FieldBlock\n classname = self._constructor_args[1].pop('classname')\n self._constructor_args[1].setdefault('form_classname', classname)\n\n self.meta = self._meta_class()\n\n for attr, value in kwargs.items():\n setattr(self.meta, attr, value)\n\n # Increase the creation counter, and save our local copy.\n self.creation_counter = Block.creation_counter\n Block.creation_counter += 1\n self.definition_prefix = 'blockdef-%d' % self.creation_counter\n\n self.label = self.meta.label or ''","label":0},{"cid":"wagtail/core/blocks/base.py::Block.set_name","text":"wagtail/core/blocks/base.py::Block.set_name\n def set_name(self, name):\n self.name = name\n if not self.meta.label:\n self.label = capfirst(force_str(name).replace('_', ' '))","label":0},{"cid":"wagtail/core/blocks/base.py::Block.set_meta_options","text":"wagtail/core/blocks/base.py::Block.set_meta_options\n def set_meta_options(self, opts):\n \"\"\"\n Update this block's meta options (out of the ones designated as mutable) from the given dict.\n Used by the StreamField constructor to pass on kwargs that are to be handled by the block,\n since the block object has already been created by that point, e.g.:\n body = StreamField(SomeStreamBlock(), max_num=5)\n \"\"\"\n for attr, value in opts.items():\n if attr in self.MUTABLE_META_ATTRIBUTES:\n setattr(self.meta, attr, value)\n else:\n raise TypeError(\"set_meta_options received unexpected option: %r\" % attr)","label":0},{"cid":"wagtail/core/blocks/base.py::Block.value_from_datadict","text":"wagtail/core/blocks/base.py::Block.value_from_datadict\n def value_from_datadict(self, data, files, prefix):\n raise NotImplementedError('%s.value_from_datadict' % self.__class__)","label":0},{"cid":"wagtail/core/blocks/base.py::Block.value_omitted_from_data","text":"wagtail/core/blocks/base.py::Block.value_omitted_from_data\n def value_omitted_from_data(self, data, files, name):\n \"\"\"\n Used only for top-level blocks wrapped by BlockWidget (i.e.: typically only StreamBlock)\n to inform ModelForm logic on Django >=1.10.2 whether the field is absent from the form\n submission (and should therefore revert to the field default).\n \"\"\"\n return name not in data","label":0},{"cid":"wagtail/core/blocks/base.py::Block.bind","text":"wagtail/core/blocks/base.py::Block.bind\n def bind(self, value, prefix=None, errors=None):\n \"\"\"\n Return a BoundBlock which represents the association of this block definition with a value\n and a prefix (and optionally, a ValidationError to be rendered).\n BoundBlock primarily exists as a convenience to allow rendering within templates:\n bound_block.render() rather than blockdef.render(value, prefix) which can't be called from\n within a template.\n \"\"\"\n return BoundBlock(self, value, prefix=prefix, errors=errors)","label":0},{"cid":"wagtail/core/blocks/base.py::Block.get_default","text":"wagtail/core/blocks/base.py::Block.get_default\n def get_default(self):\n \"\"\"\n Return this block's default value (conventionally found in self.meta.default),\n converted to the value type expected by this block. This caters for the case\n where that value type is not something that can be expressed statically at\n model definition type (e.g. something like StructValue which incorporates a\n pointer back to the block definion object).\n \"\"\"\n return self.meta.default","label":0},{"cid":"wagtail/core/blocks/base.py::Block.clean","text":"wagtail/core/blocks/base.py::Block.clean\n def clean(self, value):\n \"\"\"\n Validate value and return a cleaned version of it, or throw a ValidationError if validation fails.\n The thrown ValidationError instance will subsequently be passed to render() to display the\n error message; the ValidationError must therefore include all detail necessary to perform that\n rendering, such as identifying the specific child block(s) with errors, in the case of nested\n blocks. (It is suggested that you use the 'params' attribute for this; using error_list /\n error_dict is unreliable because Django tends to hack around with these when nested.)\n \"\"\"\n return value","label":0},{"cid":"wagtail/core/blocks/base.py::Block.to_python","text":"wagtail/core/blocks/base.py::Block.to_python\n def to_python(self, value):\n \"\"\"\n Convert 'value' from a simple (JSON-serialisable) value to a (possibly complex) Python value to be\n used in the rest of the block API and within front-end templates . In simple cases this might be\n the value itself; alternatively, it might be a 'smart' version of the value which behaves mostly\n like the original value but provides a native HTML rendering when inserted into a template; or it\n might be something totally different (e.g. an image chooser will use the image ID as the clean\n value, and turn this back into an actual image object here).\n \"\"\"\n return value","label":0},{"cid":"wagtail/core/blocks/base.py::Block.bulk_to_python","text":"wagtail/core/blocks/base.py::Block.bulk_to_python\n def bulk_to_python(self, values):\n \"\"\"\n Apply the to_python conversion to a list of values. The default implementation simply\n iterates over the list; subclasses may optimise this, e.g. by combining database lookups\n into a single query.\n \"\"\"\n return [self.to_python(value) for value in values]","label":0},{"cid":"wagtail/core/blocks/base.py::Block.get_prep_value","text":"wagtail/core/blocks/base.py::Block.get_prep_value\n def get_prep_value(self, value):\n \"\"\"\n The reverse of to_python; convert the python value into JSON-serialisable form.\n \"\"\"\n return value","label":0},{"cid":"wagtail/core/blocks/base.py::Block.get_form_state","text":"wagtail/core/blocks/base.py::Block.get_form_state\n def get_form_state(self, value):\n \"\"\"\n Convert a python value for this block into a JSON-serialisable representation containing\n all the data needed to present the value in a form field, to be received by the block's\n client-side component. Examples of where this conversion is not trivial include rich text\n (where it needs to be supplied in a format that the editor can process, e.g. ContentState\n for Draftail) and page / image / document choosers (where it needs to include all displayed\n data for the selected item, such as title or thumbnail).\n \"\"\"\n return value","label":0},{"cid":"wagtail/core/blocks/base.py::Block.get_context","text":"wagtail/core/blocks/base.py::Block.get_context\n def get_context(self, value, parent_context=None):\n \"\"\"\n Return a dict of context variables (derived from the block value and combined with the parent_context)\n to be used as the template context when rendering this value through a template.\n \"\"\"\n\n context = parent_context or {}\n context.update({\n 'self': value,\n self.TEMPLATE_VAR: value,\n })\n return context","label":0},{"cid":"wagtail/core/blocks/base.py::Block.get_template","text":"wagtail/core/blocks/base.py::Block.get_template\n def get_template(self, context=None):\n \"\"\"\n Return the template to use for rendering the block if specified on meta class.\n This extraction was added to make dynamic templates possible if you override this method\n \"\"\"\n return getattr(self.meta, 'template', None)","label":0},{"cid":"wagtail/core/blocks/base.py::Block.render","text":"wagtail/core/blocks/base.py::Block.render\n def render(self, value, context=None):\n \"\"\"\n Return a text rendering of 'value', suitable for display on templates. By default, this will\n use a template (with the passed context, supplemented by the result of get_context) if a\n 'template' property is specified on the block, and fall back on render_basic otherwise.\n \"\"\"\n template = self.get_template(context=context)\n if not template:\n return self.render_basic(value, context=context)\n\n if context is None:\n new_context = self.get_context(value)\n else:\n new_context = self.get_context(value, parent_context=dict(context))\n\n return mark_safe(render_to_string(template, new_context))","label":0},{"cid":"wagtail/core/blocks/base.py::Block.get_api_representation","text":"wagtail/core/blocks/base.py::Block.get_api_representation\n def get_api_representation(self, value, context=None):\n \"\"\"\n Can be used to customise the API response and defaults to the value returned by get_prep_value.\n \"\"\"\n return self.get_prep_value(value)","label":0},{"cid":"wagtail/core/blocks/base.py::Block.render_basic","text":"wagtail/core/blocks/base.py::Block.render_basic\n def render_basic(self, value, context=None):\n \"\"\"\n Return a text rendering of 'value', suitable for display on templates. render() will fall back on\n this if the block does not define a 'template' property.\n \"\"\"\n return force_str(value)","label":0},{"cid":"wagtail/core/blocks/base.py::Block.get_searchable_content","text":"wagtail/core/blocks/base.py::Block.get_searchable_content\n def get_searchable_content(self, value):\n \"\"\"\n Returns a list of strings containing text content within this block to be used in a search engine.\n \"\"\"\n return []","label":0},{"cid":"wagtail/core/blocks/base.py::Block.check","text":"wagtail/core/blocks/base.py::Block.check\n def check(self, **kwargs):\n \"\"\"\n Hook for the Django system checks framework -\n returns a list of django.core.checks.Error objects indicating validity errors in the block\n \"\"\"\n return []","label":0},{"cid":"wagtail/core/blocks/base.py::Block._check_name","text":"wagtail/core/blocks/base.py::Block._check_name\n def _check_name(self, **kwargs):\n \"\"\"\n Helper method called by container blocks as part of the system checks framework,\n to validate that this block's name is a valid identifier.\n (Not called universally, because not all blocks need names)\n \"\"\"\n errors = []\n if not self.name:\n errors.append(checks.Error(\n \"Block name %r is invalid\" % self.name,\n hint=\"Block name cannot be empty\",\n obj=kwargs.get('field', self),\n id='wagtailcore.E001',\n ))\n\n if ' ' in self.name:\n errors.append(checks.Error(\n \"Block name %r is invalid\" % self.name,\n hint=\"Block names cannot contain spaces\",\n obj=kwargs.get('field', self),\n id='wagtailcore.E001',\n ))\n\n if '-' in self.name:\n errors.append(checks.Error(\n \"Block name %r is invalid\" % self.name,\n \"Block names cannot contain dashes\",\n obj=kwargs.get('field', self),\n id='wagtailcore.E001',\n ))\n\n if self.name and self.name[0].isdigit():\n errors.append(checks.Error(\n \"Block name %r is invalid\" % self.name,\n \"Block names cannot begin with a digit\",\n obj=kwargs.get('field', self),\n id='wagtailcore.E001',\n ))\n\n if not errors and not re.match(r'^[_a-zA-Z][_a-zA-Z0-9]*$', self.name):\n errors.append(checks.Error(\n \"Block name %r is invalid\" % self.name,\n \"Block names should follow standard Python conventions for \"\n \"variable names: alpha-numeric and underscores, and cannot \"\n \"begin with a digit\",\n obj=kwargs.get('field', self),\n id='wagtailcore.E001',\n ))\n\n return errors","label":0},{"cid":"wagtail/core/blocks/base.py::Block.id_for_label","text":"wagtail/core/blocks/base.py::Block.id_for_label\n def id_for_label(self, prefix):\n \"\"\"\n Return the ID to be used as the 'for' attribute of <label> elements that refer to this block,\n when the given field prefix is in use. Return None if no 'for' attribute should be used.\n \"\"\"\n return None","label":0},{"cid":"wagtail/core/blocks/base.py::Block.required","text":"wagtail/core/blocks/base.py::Block.required\n @property\n def required(self):\n \"\"\"\n Flag used to determine whether labels for this block should display a 'required' asterisk.\n False by default, since Block does not provide any validation of its own - it's up to subclasses\n to define what required-ness means.\n \"\"\"\n return False","label":0},{"cid":"wagtail/core/blocks/base.py::Block.deconstruct","text":"wagtail/core/blocks/base.py::Block.deconstruct\n def deconstruct(self):\n # adapted from django.utils.deconstruct.deconstructible\n module_name = self.__module__\n name = self.__class__.__name__\n\n # Make sure it's actually there and not an inner class\n module = import_module(module_name)\n if not hasattr(module, name):\n raise ValueError(\n \"Could not find object %s in %s.\\n\"\n \"Please note that you cannot serialize things like inner \"\n \"classes. Please move the object into the main module \"\n \"body to use migrations.\\n\"\n % (name, module_name))\n\n # if the module defines a DECONSTRUCT_ALIASES dictionary, see if the class has an entry in there;\n # if so, use that instead of the real path\n try:\n path = module.DECONSTRUCT_ALIASES[self.__class__]\n except (AttributeError, KeyError):\n path = '%s.%s' % (module_name, name)\n\n return (\n path,\n self._constructor_args[0],\n self._constructor_args[1],\n )","label":0},{"cid":"wagtail/core/blocks/base.py::Block.__eq__","text":"wagtail/core/blocks/base.py::Block.__eq__\n def __eq__(self, other):\n \"\"\"\n Implement equality on block objects so that two blocks with matching definitions are considered\n equal. Block objects are intended to be immutable with the exception of set_name() and any meta\n attributes identified in MUTABLE_META_ATTRIBUTES, so checking these along with the result of\n deconstruct (which captures the constructor arguments) is sufficient to identify (valid) differences.\n\n This was originally necessary as a workaround for https://code.djangoproject.com/ticket/24340\n in Django <1.9; the deep_deconstruct function used to detect changes for migrations did not\n recurse into the block lists, and left them as Block instances. This __eq__ method therefore\n came into play when identifying changes within migrations.\n\n As of Django >=1.9, this *probably* isn't required any more. However, it may be useful in\n future as a way of identifying blocks that can be re-used within StreamField definitions\n (https://github.com/wagtail/wagtail/issues/4298#issuecomment-367656028).\n \"\"\"\n\n if not isinstance(other, Block):\n # if the other object isn't a block at all, it clearly isn't equal.\n return False\n\n # Note that we do not require the two blocks to be of the exact same class. This is because\n # we may wish the following blocks to be considered equal:\n #\n # class FooBlock(StructBlock):\n # first_name = CharBlock()\n # surname = CharBlock()\n #\n # class BarBlock(StructBlock):\n # first_name = CharBlock()\n # surname = CharBlock()\n #\n # FooBlock() == BarBlock() == StructBlock([('first_name', CharBlock()), ('surname': CharBlock())])\n #\n # For this to work, StructBlock will need to ensure that 'deconstruct' returns the s","label":0},{"cid":"wagtail/core/blocks/base.py::BoundBlock.__init__","text":"wagtail/core/blocks/base.py::BoundBlock.__init__\n def __init__(self, block, value, prefix=None, errors=None):\n self.block = block\n self.value = value\n self.prefix = prefix\n self.errors = errors","label":0},{"cid":"wagtail/core/blocks/base.py::BoundBlock.render","text":"wagtail/core/blocks/base.py::BoundBlock.render\n def render(self, context=None):\n return self.block.render(self.value, context=context)","label":0},{"cid":"wagtail/core/blocks/base.py::BoundBlock.render_as_block","text":"wagtail/core/blocks/base.py::BoundBlock.render_as_block\n def render_as_block(self, context=None):\n \"\"\"\n Alias for render; the include_block tag will specifically check for the presence of a method\n with this name. (This is because {% include_block %} is just as likely to be invoked on a bare\n value as a BoundBlock. If we looked for a `render` method instead, we'd run the risk of finding\n an unrelated method that just happened to have that name - for example, when called on a\n PageChooserBlock it could end up calling page.render.\n \"\"\"\n return self.block.render(self.value, context=context)","label":0},{"cid":"wagtail/core/blocks/base.py::BoundBlock.id_for_label","text":"wagtail/core/blocks/base.py::BoundBlock.id_for_label\n def id_for_label(self):\n return self.block.id_for_label(self.prefix)","label":0},{"cid":"wagtail/core/blocks/base.py::BoundBlock.__str__","text":"wagtail/core/blocks/base.py::BoundBlock.__str__\n def __str__(self):\n \"\"\"Render the value according to the block's native rendering\"\"\"\n return self.block.render(self.value)","label":0},{"cid":"wagtail/core/blocks/base.py::BoundBlock.__repr__","text":"wagtail/core/blocks/base.py::BoundBlock.__repr__\n def __repr__(self):\n return \"<block %s: %r>\" % (self.block.name or type(self.block).__name__, self.value)","label":0},{"cid":"wagtail/core/blocks/base.py::DeclarativeSubBlocksMetaclass.__new__","text":"wagtail/core/blocks/base.py::DeclarativeSubBlocksMetaclass.__new__\n def __new__(mcs, name, bases, attrs):\n # Collect sub-blocks declared on the current class.\n # These are available on the class as `declared_blocks`\n current_blocks = []\n for key, value in list(attrs.items()):\n if isinstance(value, Block):\n current_blocks.append((key, value))\n value.set_name(key)\n attrs.pop(key)\n current_blocks.sort(key=lambda x: x[1].creation_counter)\n attrs['declared_blocks'] = collections.OrderedDict(current_blocks)\n\n new_class = (super(DeclarativeSubBlocksMetaclass, mcs).__new__(\n mcs, name, bases, attrs))\n\n # Walk through the MRO, collecting all inherited sub-blocks, to make\n # the combined `base_blocks`.\n base_blocks = collections.OrderedDict()\n for base in reversed(new_class.__mro__):\n # Collect sub-blocks from base class.\n if hasattr(base, 'declared_blocks'):\n base_blocks.update(base.declared_blocks)\n\n # Field shadowing.\n for attr, value in base.__dict__.items():\n if value is None and attr in base_blocks:\n base_blocks.pop(attr)\n new_class.base_blocks = base_blocks\n\n return new_class","label":0},{"cid":"wagtail/core/blocks/base.py::BlockWidget.__init__","text":"wagtail/core/blocks/base.py::BlockWidget.__init__\n def __init__(self, block_def, attrs=None):\n super().__init__(attrs=attrs)\n self.block_def = block_def\n self.js_context = JSContext()\n self.block_json = json.dumps(self.js_context.pack(self.block_def))","label":0},{"cid":"wagtail/core/blocks/base.py::BlockWidget.render_with_errors","text":"wagtail/core/blocks/base.py::BlockWidget.render_with_errors\n def render_with_errors(self, name, value, attrs=None, errors=None, renderer=None):\n value_json = json.dumps(self.block_def.get_form_state(value))\n\n if errors:\n errors_json = json.dumps(self.js_context.pack(errors.as_data()))\n else:\n errors_json = '[]'\n\n return format_html(\n \"\"\"\n <div id=\"{id}\" data-block=\"{block_json}\" data-value=\"{value_json}\" data-errors=\"{errors_json}\"></div>\n <script>\n initBlockWidget('{id}');\n </script>\n \"\"\",\n id=name, block_json=self.block_json, value_json=value_json, errors_json=errors_json\n )","label":0},{"cid":"wagtail/core/blocks/base.py::BlockWidget.render","text":"wagtail/core/blocks/base.py::BlockWidget.render\n def render(self, name, value, attrs=None, renderer=None):\n return self.render_with_errors(name, value, attrs=attrs, errors=None, renderer=renderer)","label":0},{"cid":"wagtail/core/blocks/base.py::BlockWidget.media","text":"wagtail/core/blocks/base.py::BlockWidget.media\n @property\n def media(self):\n return self.js_context.media + forms.Media(\n js=[\n # needed for initBlockWidget, although these will almost certainly be\n # pulled in by the block adapters too\n versioned_static('wagtailadmin/js/telepath/telepath.js'),\n versioned_static('wagtailadmin/js/telepath/blocks.js'),\n ],\n css={'all': [\n versioned_static('wagtailadmin/css/panels/streamfield.css'),\n ]}\n )","label":0},{"cid":"wagtail/core/blocks/base.py::BlockWidget.value_from_datadict","text":"wagtail/core/blocks/base.py::BlockWidget.value_from_datadict\n def value_from_datadict(self, data, files, name):\n return self.block_def.value_from_datadict(data, files, name)","label":0},{"cid":"wagtail/core/blocks/base.py::BlockWidget.value_omitted_from_data","text":"wagtail/core/blocks/base.py::BlockWidget.value_omitted_from_data\n def value_omitted_from_data(self, data, files, name):\n return self.block_def.value_omitted_from_data(data, files, name)","label":0},{"cid":"wagtail/core/blocks/base.py::BlockField.__init__","text":"wagtail/core/blocks/base.py::BlockField.__init__\n def __init__(self, block=None, **kwargs):\n if block is None:\n raise ImproperlyConfigured(\"BlockField was not passed a 'block' object\")\n self.block = block\n\n if 'widget' not in kwargs:\n kwargs['widget'] = BlockWidget(block)\n\n super().__init__(**kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::FieldBlock.id_for_label","text":"wagtail/core/blocks/field_block.py::FieldBlock.id_for_label\n def id_for_label(self, prefix):\n return self.field.widget.id_for_label(prefix)","label":0},{"cid":"wagtail/core/blocks/field_block.py::FieldBlock.value_from_form","text":"wagtail/core/blocks/field_block.py::FieldBlock.value_from_form\n def value_from_form(self, value):\n \"\"\"\n The value that we get back from the form field might not be the type\n that this block works with natively; for example, the block may want to\n wrap a simple value such as a string in an object that provides a fancy\n HTML rendering (e.g. EmbedBlock).\n\n We therefore provide this method to perform any necessary conversion\n from the form field value to the block's native value. As standard,\n this returns the form field value unchanged.\n \"\"\"\n return value","label":0},{"cid":"wagtail/core/blocks/field_block.py::FieldBlock.value_for_form","text":"wagtail/core/blocks/field_block.py::FieldBlock.value_for_form\n def value_for_form(self, value):\n \"\"\"\n Reverse of value_from_form; convert a value of this block's native value type\n to one that can be rendered by the form field\n \"\"\"\n return value","label":0},{"cid":"wagtail/core/blocks/field_block.py::FieldBlock.value_from_datadict","text":"wagtail/core/blocks/field_block.py::FieldBlock.value_from_datadict\n def value_from_datadict(self, data, files, prefix):\n return self.value_from_form(self.field.widget.value_from_datadict(data, files, prefix))","label":0},{"cid":"wagtail/core/blocks/field_block.py::FieldBlock.value_omitted_from_data","text":"wagtail/core/blocks/field_block.py::FieldBlock.value_omitted_from_data\n def value_omitted_from_data(self, data, files, prefix):\n return self.field.widget.value_omitted_from_data(data, files, prefix)","label":0},{"cid":"wagtail/core/blocks/field_block.py::FieldBlock.clean","text":"wagtail/core/blocks/field_block.py::FieldBlock.clean\n def clean(self, value):\n # We need an annoying value_for_form -> value_from_form round trip here to account for\n # the possibility that the form field is set up to validate a different value type to\n # the one this block works with natively\n return self.value_from_form(self.field.clean(self.value_for_form(value)))","label":0},{"cid":"wagtail/core/blocks/field_block.py::FieldBlock.required","text":"wagtail/core/blocks/field_block.py::FieldBlock.required\n @property\n def required(self):\n # a FieldBlock is required if and only if its underlying form field is required\n return self.field.required","label":0},{"cid":"wagtail/core/blocks/field_block.py::FieldBlock.get_form_state","text":"wagtail/core/blocks/field_block.py::FieldBlock.get_form_state\n def get_form_state(self, value):\n return self.field.widget.format_value(self.field.prepare_value(self.value_for_form(value)))","label":0},{"cid":"wagtail/core/blocks/field_block.py::FieldBlockAdapter.js_args","text":"wagtail/core/blocks/field_block.py::FieldBlockAdapter.js_args\n def js_args(self, block):\n classname = [\n 'field',\n camelcase_to_underscore(block.field.__class__.__name__),\n 'widget-' + camelcase_to_underscore(block.field.widget.__class__.__name__),\n 'fieldname-' + block.name,\n ]\n\n form_classname = getattr(block.meta, 'form_classname', '')\n if form_classname:\n classname.append(form_classname)\n\n # Provided for backwards compatibility. Replaced with 'form_classname'\n legacy_classname = getattr(block.meta, 'classname', '')\n if legacy_classname:\n classname.append(legacy_classname)\n\n meta = {\n 'label': block.label,\n 'required': block.required,\n 'icon': block.meta.icon,\n 'classname': ' '.join(classname),\n }\n if block.field.help_text:\n meta['helpText'] = block.field.help_text\n\n return [\n block.name,\n block.field.widget,\n meta,\n ]","label":0},{"cid":"wagtail/core/blocks/field_block.py::FieldBlockAdapter.media","text":"wagtail/core/blocks/field_block.py::FieldBlockAdapter.media\n @cached_property\n def media(self):\n return forms.Media(js=[\n versioned_static('wagtailadmin/js/telepath/blocks.js'),\n ])","label":0},{"cid":"wagtail/core/blocks/field_block.py::CharBlock.__init__","text":"wagtail/core/blocks/field_block.py::CharBlock.__init__\n def __init__(self, required=True, help_text=None, max_length=None, min_length=None, validators=(), **kwargs):\n # CharField's 'label' and 'initial' parameters are not exposed, as Block handles that functionality natively\n # (via 'label' and 'default')\n self.field = forms.CharField(\n required=required,\n help_text=help_text,\n max_length=max_length,\n min_length=min_length,\n validators=validators,\n )\n super().__init__(**kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::CharBlock.get_searchable_content","text":"wagtail/core/blocks/field_block.py::CharBlock.get_searchable_content\n def get_searchable_content(self, value):\n return [force_str(value)]","label":0},{"cid":"wagtail/core/blocks/field_block.py::TextBlock.__init__","text":"wagtail/core/blocks/field_block.py::TextBlock.__init__\n def __init__(self, required=True, help_text=None, rows=1, max_length=None, min_length=None, validators=(), **kwargs):\n self.field_options = {\n 'required': required,\n 'help_text': help_text,\n 'max_length': max_length,\n 'min_length': min_length,\n 'validators': validators,\n }\n self.rows = rows\n super().__init__(**kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::TextBlock.field","text":"wagtail/core/blocks/field_block.py::TextBlock.field\n @cached_property\n def field(self):\n from wagtail.admin.widgets import AdminAutoHeightTextInput\n field_kwargs = {'widget': AdminAutoHeightTextInput(attrs={'rows': self.rows})}\n field_kwargs.update(self.field_options)\n return forms.CharField(**field_kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::TextBlock.get_searchable_content","text":"wagtail/core/blocks/field_block.py::TextBlock.get_searchable_content\n def get_searchable_content(self, value):\n return [force_str(value)]","label":0},{"cid":"wagtail/core/blocks/field_block.py::BlockQuoteBlock.render_basic","text":"wagtail/core/blocks/field_block.py::BlockQuoteBlock.render_basic\n def render_basic(self, value, context=None):\n if value:\n return format_html('<blockquote>{0}</blockquote>', value)\n else:\n return ''","label":0},{"cid":"wagtail/core/blocks/field_block.py::FloatBlock.__init__","text":"wagtail/core/blocks/field_block.py::FloatBlock.__init__\n def __init__(self, required=True, max_value=None, min_value=None, validators=(), *args,\n **kwargs):\n self.field = forms.FloatField(\n required=required,\n max_value=max_value,\n min_value=min_value,\n validators=validators,\n )\n super().__init__(*args, **kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::DecimalBlock.__init__","text":"wagtail/core/blocks/field_block.py::DecimalBlock.__init__\n def __init__(self, required=True, help_text=None, max_value=None, min_value=None,\n max_digits=None, decimal_places=None, validators=(), *args, **kwargs):\n self.field = forms.DecimalField(\n required=required,\n help_text=help_text,\n max_value=max_value,\n min_value=min_value,\n max_digits=max_digits,\n decimal_places=decimal_places,\n validators=validators,\n )\n super().__init__(*args, **kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::RegexBlock.__init__","text":"wagtail/core/blocks/field_block.py::RegexBlock.__init__\n def __init__(self, regex, required=True, help_text=None, max_length=None, min_length=None,\n error_messages=None, validators=(), *args, **kwargs):\n self.field = forms.RegexField(\n regex=regex,\n required=required,\n help_text=help_text,\n max_length=max_length,\n min_length=min_length,\n error_messages=error_messages,\n validators=validators,\n )\n super().__init__(*args, **kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::URLBlock.__init__","text":"wagtail/core/blocks/field_block.py::URLBlock.__init__\n def __init__(self, required=True, help_text=None, max_length=None, min_length=None, validators=(), **kwargs):\n self.field = forms.URLField(\n required=required,\n help_text=help_text,\n max_length=max_length,\n min_length=min_length,\n validators=validators,\n )\n super().__init__(**kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::BooleanBlock.__init__","text":"wagtail/core/blocks/field_block.py::BooleanBlock.__init__\n def __init__(self, required=True, help_text=None, **kwargs):\n # NOTE: As with forms.BooleanField, the default of required=True means that the checkbox\n # must be ticked to pass validation (i.e. it's equivalent to an \"I agree to the terms and\n # conditions\" box). To get the conventional yes/no behaviour, you must explicitly pass\n # required=False.\n self.field = forms.BooleanField(required=required, help_text=help_text)\n super().__init__(**kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::DateBlock.__init__","text":"wagtail/core/blocks/field_block.py::DateBlock.__init__\n def __init__(self, required=True, help_text=None, format=None, validators=(), **kwargs):\n self.field_options = {\n 'required': required,\n 'help_text': help_text,\n 'validators': validators,\n }\n try:\n self.field_options['input_formats'] = kwargs.pop('input_formats')\n except KeyError:\n pass\n self.format = format\n super().__init__(**kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::DateBlock.field","text":"wagtail/core/blocks/field_block.py::DateBlock.field\n @cached_property\n def field(self):\n from wagtail.admin.widgets import AdminDateInput\n field_kwargs = {\n 'widget': AdminDateInput(format=self.format),\n }\n field_kwargs.update(self.field_options)\n return forms.DateField(**field_kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::DateBlock.to_python","text":"wagtail/core/blocks/field_block.py::DateBlock.to_python\n def to_python(self, value):\n # Serialising to JSON uses DjangoJSONEncoder, which converts date/time objects to strings.\n # The reverse does not happen on decoding, because there's no way to know which strings\n # should be decoded; we have to convert strings back to dates here instead.\n if value is None or isinstance(value, datetime.date):\n return value\n else:\n return parse_date(value)","label":0},{"cid":"wagtail/core/blocks/field_block.py::TimeBlock.__init__","text":"wagtail/core/blocks/field_block.py::TimeBlock.__init__\n def __init__(self, required=True, help_text=None, format=None, validators=(), **kwargs):\n self.field_options = {\n 'required': required,\n 'help_text': help_text,\n 'validators': validators\n }\n self.format = format\n super().__init__(**kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::TimeBlock.field","text":"wagtail/core/blocks/field_block.py::TimeBlock.field\n @cached_property\n def field(self):\n from wagtail.admin.widgets import AdminTimeInput\n field_kwargs = {'widget': AdminTimeInput(format=self.format)}\n field_kwargs.update(self.field_options)\n return forms.TimeField(**field_kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::TimeBlock.to_python","text":"wagtail/core/blocks/field_block.py::TimeBlock.to_python\n def to_python(self, value):\n if value is None or isinstance(value, datetime.time):\n return value\n else:\n return parse_time(value)","label":0},{"cid":"wagtail/core/blocks/field_block.py::DateTimeBlock.__init__","text":"wagtail/core/blocks/field_block.py::DateTimeBlock.__init__\n def __init__(self, required=True, help_text=None, format=None, validators=(), **kwargs):\n self.field_options = {\n 'required': required,\n 'help_text': help_text,\n 'validators': validators,\n }\n self.format = format\n super().__init__(**kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::DateTimeBlock.field","text":"wagtail/core/blocks/field_block.py::DateTimeBlock.field\n @cached_property\n def field(self):\n from wagtail.admin.widgets import AdminDateTimeInput\n field_kwargs = {\n 'widget': AdminDateTimeInput(format=self.format),\n }\n field_kwargs.update(self.field_options)\n return forms.DateTimeField(**field_kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::DateTimeBlock.to_python","text":"wagtail/core/blocks/field_block.py::DateTimeBlock.to_python\n def to_python(self, value):\n if value is None or isinstance(value, datetime.datetime):\n return value\n else:\n return parse_datetime(value)","label":0},{"cid":"wagtail/core/blocks/field_block.py::EmailBlock.__init__","text":"wagtail/core/blocks/field_block.py::EmailBlock.__init__\n def __init__(self, required=True, help_text=None, validators=(), **kwargs):\n self.field = forms.EmailField(\n required=required,\n help_text=help_text,\n validators=validators,\n )\n super().__init__(**kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::IntegerBlock.__init__","text":"wagtail/core/blocks/field_block.py::IntegerBlock.__init__\n def __init__(self, required=True, help_text=None, min_value=None,\n max_value=None, validators=(), **kwargs):\n self.field = forms.IntegerField(\n required=required,\n help_text=help_text,\n min_value=min_value,\n max_value=max_value,\n validators=validators,\n )\n super().__init__(**kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::BaseChoiceBlock.__init__","text":"wagtail/core/blocks/field_block.py::BaseChoiceBlock.__init__\n def __init__(\n self, choices=None, default=None, required=True,\n help_text=None, widget=None, validators=(), **kwargs):\n\n self._required = required\n self._default = default\n\n if choices is None:\n # no choices specified, so pick up the choice defined at the class level\n choices = self.choices\n\n if callable(choices):\n # Support of callable choices. Wrap the callable in an iterator so that we can\n # handle this consistently with ordinary choice lists;\n # however, the `choices` constructor kwarg as reported by deconstruct() should\n # remain as the callable\n choices_for_constructor = choices\n choices = CallableChoiceIterator(choices)\n else:\n # Cast as a list\n choices_for_constructor = choices = list(choices)\n\n # keep a copy of all kwargs (including our normalised choices list) for deconstruct()\n # Note: we omit the `widget` kwarg, as widgets do not provide a serialization method\n # for migrations, and they are unlikely to be useful within the frozen ORM anyhow\n self._constructor_kwargs = kwargs.copy()\n self._constructor_kwargs['choices'] = choices_for_constructor\n if required is not True:\n self._constructor_kwargs['required'] = required\n if help_text is not None:\n self._constructor_kwargs['help_text'] = help_text\n\n # We will need to modify the choices list to insert a blank option, if there isn't\n # one already. We have to do this at render time in the case of callable choices - so rather\n # than having separate code paths for static vs dynamic lists, we'll _always_ pass a callable\n # to ChoiceField to perform this step at render time.\n\n callable_choices = self._get_callable_choices(choices)\n self.field = self.get_field(\n ","label":0},{"cid":"wagtail/core/blocks/field_block.py::BaseChoiceBlock._get_callable_choices","text":"wagtail/core/blocks/field_block.py::BaseChoiceBlock._get_callable_choices\n def _get_callable_choices(self, choices, blank_choice=True):\n \"\"\"\n Return a callable that we can pass into `forms.ChoiceField`, which will provide the\n choices list with the addition of a blank choice (if blank_choice=True and one does not\n already exist).\n \"\"\"\n def choices_callable():\n # Variable choices could be an instance of CallableChoiceIterator, which may be wrapping\n # something we don't want to evaluate multiple times (e.g. a database query). Cast as a\n # list now to prevent it getting evaluated twice (once while searching for a blank choice,\n # once while rendering the final ChoiceField).\n local_choices = list(choices)\n\n # If blank_choice=False has been specified, return the choices list as is\n if not blank_choice:\n return local_choices\n\n # Else: if choices does not already contain a blank option, insert one\n # (to match Django's own behaviour for modelfields:\n # https://github.com/django/django/blob/1.7.5/django/db/models/fields/__init__.py#L732-744)\n has_blank_choice = False\n for v1, v2 in local_choices:\n if isinstance(v2, (list, tuple)):\n # this is a named group, and v2 is the value list\n has_blank_choice = any([value in ('', None) for value, label in v2])\n if has_blank_choice:\n break\n else:\n # this is an individual choice; v1 is the value\n if v1 in ('', None):\n has_blank_choice = True\n break\n\n if not has_blank_choice:\n return BLANK_CHOICE_DASH + local_choices\n\n return local_choices\n return choices_callable","label":0},{"cid":"wagtail/core/blocks/field_block.py::BaseChoiceBlock._get_callable_choices.choices_callable","text":"wagtail/core/blocks/field_block.py::BaseChoiceBlock._get_callable_choices.choices_callable\n def choices_callable():\n # Variable choices could be an instance of CallableChoiceIterator, which may be wrapping\n # something we don't want to evaluate multiple times (e.g. a database query). Cast as a\n # list now to prevent it getting evaluated twice (once while searching for a blank choice,\n # once while rendering the final ChoiceField).\n local_choices = list(choices)\n\n # If blank_choice=False has been specified, return the choices list as is\n if not blank_choice:\n return local_choices\n\n # Else: if choices does not already contain a blank option, insert one\n # (to match Django's own behaviour for modelfields:\n # https://github.com/django/django/blob/1.7.5/django/db/models/fields/__init__.py#L732-744)\n has_blank_choice = False\n for v1, v2 in local_choices:\n if isinstance(v2, (list, tuple)):\n # this is a named group, and v2 is the value list\n has_blank_choice = any([value in ('', None) for value, label in v2])\n if has_blank_choice:\n break\n else:\n # this is an individual choice; v1 is the value\n if v1 in ('', None):\n has_blank_choice = True\n break\n\n if not has_blank_choice:\n return BLANK_CHOICE_DASH + local_choices\n\n return local_choices","label":0},{"cid":"wagtail/core/blocks/field_block.py::ChoiceBlock.get_field","text":"wagtail/core/blocks/field_block.py::ChoiceBlock.get_field\n def get_field(self, **kwargs):\n return forms.ChoiceField(**kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::ChoiceBlock._get_callable_choices","text":"wagtail/core/blocks/field_block.py::ChoiceBlock._get_callable_choices\n def _get_callable_choices(self, choices, blank_choice=None):\n # If we have a default choice and the field is required, we don't need to add a blank option.\n if blank_choice is None:\n blank_choice = not(self._default and self._required)\n return super()._get_callable_choices(choices, blank_choice=blank_choice)","label":0},{"cid":"wagtail/core/blocks/field_block.py::ChoiceBlock.deconstruct","text":"wagtail/core/blocks/field_block.py::ChoiceBlock.deconstruct\n def deconstruct(self):\n \"\"\"\n Always deconstruct ChoiceBlock instances as if they were plain ChoiceBlocks with their\n choice list passed in the constructor, even if they are actually subclasses. This allows\n users to define subclasses of ChoiceBlock in their models.py, with specific choice lists\n passed in, without references to those classes ending up frozen into migrations.\n \"\"\"\n return ('wagtail.core.blocks.ChoiceBlock', [], self._constructor_kwargs)","label":0},{"cid":"wagtail/core/blocks/field_block.py::ChoiceBlock.get_searchable_content","text":"wagtail/core/blocks/field_block.py::ChoiceBlock.get_searchable_content\n def get_searchable_content(self, value):\n # Return the display value as the searchable value\n text_value = force_str(value)\n for k, v in self.field.choices:\n if isinstance(v, (list, tuple)):\n # This is an optgroup, so look inside the group for options\n for k2, v2 in v:\n if value == k2 or text_value == force_str(k2):\n return [force_str(k), force_str(v2)]\n else:\n if value == k or text_value == force_str(k):\n return [force_str(v)]\n return [] # Value was not found in the list of choices","label":0},{"cid":"wagtail/core/blocks/field_block.py::MultipleChoiceBlock.get_field","text":"wagtail/core/blocks/field_block.py::MultipleChoiceBlock.get_field\n def get_field(self, **kwargs):\n return forms.MultipleChoiceField(**kwargs)","label":0},{"cid":"wagtail/core/fields.py::RichTextField.__init__","text":"wagtail/core/fields.py::RichTextField.__init__\n def __init__(self, *args, **kwargs):\n self.editor = kwargs.pop('editor', 'default')\n self.features = kwargs.pop('features', None)\n # TODO: preserve 'editor' and 'features' when deconstructing for migrations\n super().__init__(*args, **kwargs)","label":0},{"cid":"wagtail/core/fields.py::RichTextField.formfield","text":"wagtail/core/fields.py::RichTextField.formfield\n def formfield(self, **kwargs):\n from wagtail.admin.rich_text import get_rich_text_editor_widget\n defaults = {'widget': get_rich_text_editor_widget(self.editor, features=self.features)}\n defaults.update(kwargs)\n return super().formfield(**defaults)","label":0},{"cid":"wagtail/core/fields.py::RichTextField.get_searchable_content","text":"wagtail/core/fields.py::RichTextField.get_searchable_content\n def get_searchable_content(self, value):\n # Strip HTML tags to prevent search backend from indexing them\n source = force_str(value)\n return [get_text_for_indexing(source)]","label":0},{"cid":"wagtail/core/fields.py::Creator.__init__","text":"wagtail/core/fields.py::Creator.__init__\n def __init__(self, field):\n self.field = field","label":0},{"cid":"wagtail/core/fields.py::Creator.__get__","text":"wagtail/core/fields.py::Creator.__get__\n def __get__(self, obj, type=None):\n if obj is None:\n return self\n field_name = self.field.name\n\n if field_name not in obj.__dict__:\n # Field is deferred. Fetch it from db.\n obj.refresh_from_db(fields=[field_name])\n return obj.__dict__[field_name]","label":0},{"cid":"wagtail/core/fields.py::Creator.__set__","text":"wagtail/core/fields.py::Creator.__set__\n def __set__(self, obj, value):\n obj.__dict__[self.field.name] = self.field.to_python(value)","label":0},{"cid":"wagtail/core/fields.py::StreamField.__init__","text":"wagtail/core/fields.py::StreamField.__init__\n def __init__(self, block_types, **kwargs):\n\n # extract kwargs that are to be passed on to the block, not handled by super\n block_opts = {}\n for arg in ['min_num', 'max_num', 'block_counts']:\n if arg in kwargs:\n block_opts[arg] = kwargs.pop(arg)\n\n # for a top-level block, the 'blank' kwarg (defaulting to False) always overrides the\n # block's own 'required' meta attribute, even if not passed explicitly; this ensures\n # that the field and block have consistent definitions\n block_opts['required'] = not kwargs.get('blank', False)\n\n super().__init__(**kwargs)\n\n if isinstance(block_types, Block):\n # use the passed block as the top-level block\n self.stream_block = block_types\n elif isinstance(block_types, type):\n # block passed as a class - instantiate it\n self.stream_block = block_types()\n else:\n # construct a top-level StreamBlock from the list of block types\n self.stream_block = StreamBlock(block_types)\n\n self.stream_block.set_meta_options(block_opts)","label":0},{"cid":"wagtail/core/fields.py::StreamField.get_internal_type","text":"wagtail/core/fields.py::StreamField.get_internal_type\n def get_internal_type(self):\n return 'TextField'","label":0},{"cid":"wagtail/core/fields.py::StreamField.get_panel","text":"wagtail/core/fields.py::StreamField.get_panel\n def get_panel(self):\n from wagtail.admin.edit_handlers import StreamFieldPanel\n return StreamFieldPanel","label":0},{"cid":"wagtail/core/fields.py::StreamField.deconstruct","text":"wagtail/core/fields.py::StreamField.deconstruct\n def deconstruct(self):\n name, path, _, kwargs = super().deconstruct()\n block_types = list(self.stream_block.child_blocks.items())\n args = [block_types]\n return name, path, args, kwargs","label":0},{"cid":"wagtail/core/fields.py::StreamField.to_python","text":"wagtail/core/fields.py::StreamField.to_python\n def to_python(self, value):\n if value is None or value == '':\n return StreamValue(self.stream_block, [])\n elif isinstance(value, StreamValue):\n return value\n elif isinstance(value, str):\n try:\n unpacked_value = json.loads(value)\n except ValueError:\n # value is not valid JSON; most likely, this field was previously a\n # rich text field before being migrated to StreamField, and the data\n # was left intact in the migration. Return an empty stream instead\n # (but keep the raw text available as an attribute, so that it can be\n # used to migrate that data to StreamField)\n return StreamValue(self.stream_block, [], raw_text=value)\n\n if unpacked_value is None:\n # we get here if value is the literal string 'null'. This should probably\n # never happen if the rest of the (de)serialization code is working properly,\n # but better to handle it just in case...\n return StreamValue(self.stream_block, [])\n\n return self.stream_block.to_python(unpacked_value)\n else:\n # See if it looks like the standard non-smart representation of a\n # StreamField value: a list of (block_name, value) tuples\n try:\n [None for (x, y) in value]\n except (TypeError, ValueError):\n # Give up trying to make sense of the value\n raise TypeError(\"Cannot handle %r (type %r) as a value of StreamField\" % (value, type(value)))\n\n # Test succeeded, so return as a StreamValue-ified version of that value\n return StreamValue(self.stream_block, value)","label":0},{"cid":"wagtail/core/fields.py::StreamField.get_prep_value","text":"wagtail/core/fields.py::StreamField.get_prep_value\n def get_prep_value(self, value):\n if isinstance(value, StreamValue) and not(value) and value.raw_text is not None:\n # An empty StreamValue with a nonempty raw_text attribute should have that\n # raw_text attribute written back to the db. (This is probably only useful\n # for reverse migrations that convert StreamField data back into plain text\n # fields.)\n return value.raw_text\n else:\n return json.dumps(self.stream_block.get_prep_value(value), cls=DjangoJSONEncoder)","label":0},{"cid":"wagtail/core/fields.py::StreamField.from_db_value","text":"wagtail/core/fields.py::StreamField.from_db_value\n def from_db_value(self, value, expression, connection):\n return self.to_python(value)","label":0},{"cid":"wagtail/core/fields.py::StreamField.formfield","text":"wagtail/core/fields.py::StreamField.formfield\n def formfield(self, **kwargs):\n \"\"\"\n Override formfield to use a plain forms.Field so that we do no transformation on the value\n (as distinct from the usual fallback of forms.CharField, which transforms it into a string).\n \"\"\"\n defaults = {'form_class': BlockField, 'block': self.stream_block}\n defaults.update(kwargs)\n return super().formfield(**defaults)","label":0},{"cid":"wagtail/core/fields.py::StreamField.value_to_string","text":"wagtail/core/fields.py::StreamField.value_to_string\n def value_to_string(self, obj):\n value = self.value_from_object(obj)\n return self.get_prep_value(value)","label":0},{"cid":"wagtail/core/fields.py::StreamField.get_searchable_content","text":"wagtail/core/fields.py::StreamField.get_searchable_content\n def get_searchable_content(self, value):\n return self.stream_block.get_searchable_content(value)","label":0},{"cid":"wagtail/core/fields.py::StreamField.check","text":"wagtail/core/fields.py::StreamField.check\n def check(self, **kwargs):\n errors = super().check(**kwargs)\n errors.extend(self.stream_block.check(field=self, **kwargs))\n return errors","label":0},{"cid":"wagtail/core/fields.py::StreamField.contribute_to_class","text":"wagtail/core/fields.py::StreamField.contribute_to_class\n def contribute_to_class(self, cls, name, **kwargs):\n super().contribute_to_class(cls, name, **kwargs)\n\n # Add Creator descriptor to allow the field to be set from a list or a\n # JSON string.\n setattr(cls, self.name, Creator(self))","label":0},{"cid":"wagtail/search/utils.py::balanced_reduce","text":"wagtail/search/utils.py::balanced_reduce\ndef balanced_reduce(operator, seq, initializer=NOT_SET):\n \"\"\"\n Has the same result as Python's reduce function, but performs the calculations in a different order.\n\n This is important when the operator is constructing data structures such as search query clases.\n This method will make the resulting data structures flatter, so operations that need to traverse\n them don't end up crashing with recursion errors.\n\n For example:\n\n Python's builtin reduce() function will do the following calculation:\n\n reduce(add, [1, 2, 3, 4, 5, 6, 7, 8])\n (1 + (2 + (3 + (4 + (5 + (6 + (7 + 8)))))))\n\n When using this with query classes, it would create a large data structure with a depth of 7\n Whereas balanced_reduce will execute this like so:\n\n balanced_reduce(add, [1, 2, 3, 4, 5, 6, 7, 8])\n ((1 + 2) + (3 + 4)) + ((5 + 6) + (7 + 8))\n\n Which only has a depth of 2\n \"\"\"\n # Casting all iterables to list makes the implementation simpler\n if not isinstance(seq, list):\n seq = list(seq)\n\n # Note, it needs to be possible to use None as an initial value\n if initializer is not NOT_SET:\n if len(seq) == 0:\n return initializer\n else:\n return operator(initializer, balanced_reduce(operator, seq))\n\n if len(seq) == 0:\n raise TypeError(\"reduce() of empty sequence with no initial value\")\n elif len(seq) == 1:\n return seq[0]\n else:\n break_point = len(seq) // 2\n first_set = balanced_reduce(operator, seq[:break_point])\n second_set = balanced_reduce(operator, seq[break_point:])\n return operator(first_set, second_set)","label":0},{"cid":"wagtail/search/utils.py::normalise_query_string","text":"wagtail/search/utils.py::normalise_query_string\ndef normalise_query_string(query_string):\n # Truncate query string\n if len(query_string) > MAX_QUERY_STRING_LENGTH:\n query_string = query_string[:MAX_QUERY_STRING_LENGTH]\n # Convert query_string to lowercase\n query_string = query_string.lower()\n\n # Remove leading, trailing and multiple spaces\n query_string = re.sub(' +', ' ', query_string).strip()\n\n return query_string","label":0},{"cid":"wagtail/search/utils.py::separate_filters_from_query","text":"wagtail/search/utils.py::separate_filters_from_query\ndef separate_filters_from_query(query_string):\n filters_regexp = r'(\\w+):(\\w+|\".+\")'\n\n filters = {}\n for match_object in re.finditer(filters_regexp, query_string):\n key, value = match_object.groups()\n filters[key] = value.strip(\"\\\"\")\n\n query_string = re.sub(filters_regexp, '', query_string).strip()\n\n return filters, query_string","label":0},{"cid":"wagtail/search/utils.py::parse_query_string","text":"wagtail/search/utils.py::parse_query_string\ndef parse_query_string(query_string, operator=None, zero_terms=MATCH_NONE):\n \"\"\"\n This takes a query string typed in by a user and extracts the following:\n\n - Quoted terms (for phrase search)\n - Filters\n\n For example, the following query:\n\n `hello \"this is a phrase\" live:true` would be parsed into:\n\n filters: {'live': 'true'}\n tokens: And([PlainText('hello'), Phrase('this is a phrase')])\n \"\"\"\n filters, query_string = separate_filters_from_query(query_string)\n\n is_phrase = False\n tokens = []\n for part in query_string.split('\"'):\n part = part.strip()\n\n if part:\n if is_phrase:\n tokens.append(Phrase(part))\n else:\n tokens.append(PlainText(part, operator=operator or PlainText.DEFAULT_OPERATOR))\n\n is_phrase = not is_phrase\n\n if tokens:\n if operator == 'or':\n search_query = OR(tokens)\n else:\n search_query = AND(tokens)\n else:\n search_query = zero_terms\n\n return filters, search_query","label":0}],"string":"[\n {\n \"cid\": \"docs/conf.py::setup\",\n \"text\": \"docs/conf.py::setup\\ndef setup(app):\\n app.add_css_file('css/custom.css')\\n app.add_js_file('js/banner.js')\\n\\n github_doc_root = 'https://github.com/wagtail/wagtail/tree/main/docs/'\\n\\n app.add_config_value('recommonmark_config', {\\n 'url_resolver': lambda url: github_doc_root + url,\\n }, True)\\n app.add_transform(AutoStructify)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractFormSubmission.get_data\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractFormSubmission.get_data\\n def get_data(self):\\n \\\"\\\"\\\"\\n Returns dict with form data.\\n\\n You can override this method to add additional data.\\n \\\"\\\"\\\"\\n form_data = json.loads(self.form_data)\\n form_data.update({\\n 'submit_time': self.submit_time,\\n })\\n\\n return form_data\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractFormSubmission.__str__\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractFormSubmission.__str__\\n def __str__(self):\\n return self.form_data\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractFormField.save\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractFormField.save\\n def save(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n When new fields are created, generate a template safe ascii name to use as the\\n JSON storage reference for this field. Previously created fields will be updated\\n to use the legacy unidecode method via checks & _migrate_legacy_clean_name.\\n \\\"\\\"\\\"\\n\\n is_new = self.pk is None\\n if is_new:\\n clean_name = get_field_clean_name(self.label)\\n self.clean_name = clean_name\\n\\n super().save(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractFormField._migrate_legacy_clean_name\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractFormField._migrate_legacy_clean_name\\n @classmethod\\n def _migrate_legacy_clean_name(cls):\\n \\\"\\\"\\\"\\n Ensure that existing data stored will be accessible via the legacy clean_name.\\n When checks run, replace any blank clean_name values with the unidecode conversion.\\n \\\"\\\"\\\"\\n\\n try:\\n objects = cls.objects.filter(clean_name__exact='')\\n if objects.count() == 0:\\n return None\\n\\n except (FieldError, DatabaseError):\\n # attempting to query on clean_name before field has been added\\n return None\\n\\n try:\\n from unidecode import unidecode\\n except ImportError as error:\\n description = \\\"You have form submission data that was created on an older version of Wagtail and requires the unidecode library to retrieve it correctly. Please install the unidecode package.\\\"\\n raise Exception(description) from error\\n\\n for obj in objects:\\n legacy_clean_name = str(slugify(str(unidecode(obj.label))))\\n obj.clean_name = legacy_clean_name\\n obj.save()\\n\\n return Info(\\n 'Added `clean_name` on %s form field(s)' % objects.count(),\\n obj=cls\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractFormField.check\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractFormField.check\\n @classmethod\\n def check(cls, **kwargs):\\n errors = super().check(**kwargs)\\n\\n messages = cls._migrate_legacy_clean_name()\\n if messages:\\n errors.append(messages)\\n\\n return errors\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.__init__\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.__init__\\n def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n if not hasattr(self, 'landing_page_template'):\\n name, ext = os.path.splitext(self.template)\\n self.landing_page_template = name + '_landing' + ext\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form_fields\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form_fields\\n def get_form_fields(self):\\n \\\"\\\"\\\"\\n Form page expects `form_fields` to be declared.\\n If you want to change backwards relation name,\\n you need to override this method.\\n \\\"\\\"\\\"\\n\\n return self.form_fields.all()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_data_fields\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_data_fields\\n def get_data_fields(self):\\n \\\"\\\"\\\"\\n Returns a list of tuples with (field_name, field_label).\\n \\\"\\\"\\\"\\n\\n data_fields = [\\n ('submit_time', _('Submission date')),\\n ]\\n data_fields += [\\n (field.clean_name, field.label)\\n for field in self.get_form_fields()\\n ]\\n\\n return data_fields\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form_class\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form_class\\n def get_form_class(self):\\n fb = self.form_builder(self.get_form_fields())\\n return fb.get_form_class()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form_parameters\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form_parameters\\n def get_form_parameters(self):\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form\\n def get_form(self, *args, **kwargs):\\n form_class = self.get_form_class()\\n form_params = self.get_form_parameters()\\n form_params.update(kwargs)\\n\\n return form_class(*args, **form_params)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_landing_page_template\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_landing_page_template\\n def get_landing_page_template(self, request, *args, **kwargs):\\n return self.landing_page_template\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_submission_class\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_submission_class\\n def get_submission_class(self):\\n \\\"\\\"\\\"\\n Returns submission class.\\n\\n You can override this method to provide custom submission class.\\n Your class must be inherited from AbstractFormSubmission.\\n \\\"\\\"\\\"\\n\\n return FormSubmission\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_submissions_list_view_class\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_submissions_list_view_class\\n def get_submissions_list_view_class(self):\\n from .views import SubmissionsListView\\n return self.submissions_list_view_class or SubmissionsListView\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.process_form_submission\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.process_form_submission\\n def process_form_submission(self, form):\\n \\\"\\\"\\\"\\n Accepts form instance with submitted data, user and page.\\n Creates submission instance.\\n\\n You can override this method if you want to have custom creation logic.\\n For example, if you want to save reference to a user.\\n \\\"\\\"\\\"\\n\\n return self.get_submission_class().objects.create(\\n form_data=json.dumps(form.cleaned_data, cls=DjangoJSONEncoder),\\n page=self,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.render_landing_page\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.render_landing_page\\n def render_landing_page(self, request, form_submission=None, *args, **kwargs):\\n \\\"\\\"\\\"\\n Renders the landing page.\\n\\n You can override this method to return a different HttpResponse as\\n landing page. E.g. you could return a redirect to a separate page.\\n \\\"\\\"\\\"\\n context = self.get_context(request)\\n context['form_submission'] = form_submission\\n return TemplateResponse(\\n request,\\n self.get_landing_page_template(request),\\n context\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.serve_submissions_list_view\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.serve_submissions_list_view\\n def serve_submissions_list_view(self, request, *args, **kwargs):\\n \\\"\\\"\\\"\\n Returns list submissions view for admin.\\n\\n `list_submissions_view_class` can bse set to provide custom view class.\\n Your class must be inherited from SubmissionsListView.\\n \\\"\\\"\\\"\\n view = self.get_submissions_list_view_class().as_view()\\n return view(request, form_page=self, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.serve\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.serve\\n def serve(self, request, *args, **kwargs):\\n if request.method == 'POST':\\n form = self.get_form(request.POST, request.FILES, page=self, user=request.user)\\n\\n if form.is_valid():\\n form_submission = self.process_form_submission(form)\\n return self.render_landing_page(request, form_submission, *args, **kwargs)\\n else:\\n form = self.get_form(page=self, user=request.user)\\n\\n context = self.get_context(request)\\n context['form'] = form\\n return TemplateResponse(\\n request,\\n self.get_template(request),\\n context\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.serve_preview\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.serve_preview\\n def serve_preview(self, request, mode_name):\\n if mode_name == 'landing':\\n request.is_preview = True\\n return self.render_landing_page(request)\\n else:\\n return super().serve_preview(request, mode_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractEmailForm.process_form_submission\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractEmailForm.process_form_submission\\n def process_form_submission(self, form):\\n submission = super().process_form_submission(form)\\n if self.to_address:\\n self.send_mail(form)\\n return submission\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractEmailForm.send_mail\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractEmailForm.send_mail\\n def send_mail(self, form):\\n addresses = [x.strip() for x in self.to_address.split(',')]\\n send_mail(self.subject, self.render_email(form), addresses, self.from_address,)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractEmailForm.render_email\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractEmailForm.render_email\\n def render_email(self, form):\\n content = []\\n\\n cleaned_data = form.cleaned_data\\n for field in form:\\n if field.name not in cleaned_data:\\n continue\\n\\n value = cleaned_data.get(field.name)\\n\\n if isinstance(value, list):\\n value = ', '.join(value)\\n\\n # Format dates and datetimes with SHORT_DATE(TIME)_FORMAT\\n if isinstance(value, datetime.datetime):\\n value = date_format(value, settings.SHORT_DATETIME_FORMAT)\\n elif isinstance(value, datetime.date):\\n value = date_format(value, settings.SHORT_DATE_FORMAT)\\n\\n content.append('{}: {}'.format(field.label, value))\\n\\n return '\\\\n'.join(content)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BaseBlock.__new__\",\n \"text\": \"wagtail/core/blocks/base.py::BaseBlock.__new__\\n def __new__(mcs, name, bases, attrs):\\n meta_class = attrs.pop('Meta', None)\\n\\n cls = super(BaseBlock, mcs).__new__(mcs, name, bases, attrs)\\n\\n # Get all the Meta classes from all the bases\\n meta_class_bases = [meta_class] + [getattr(base, '_meta_class', None)\\n for base in bases]\\n meta_class_bases = tuple(filter(bool, meta_class_bases))\\n cls._meta_class = type(str(name + 'Meta'), meta_class_bases, {})\\n\\n return cls\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.__new__\",\n \"text\": \"wagtail/core/blocks/base.py::Block.__new__\\n def __new__(cls, *args, **kwargs):\\n # adapted from django.utils.deconstruct.deconstructible; capture the arguments\\n # so that we can return them in the 'deconstruct' method\\n obj = super(Block, cls).__new__(cls)\\n obj._constructor_args = (args, kwargs)\\n return obj\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.__init__\",\n \"text\": \"wagtail/core/blocks/base.py::Block.__init__\\n def __init__(self, **kwargs):\\n if 'classname' in self._constructor_args[1]:\\n # Adding this so that migrations are not triggered\\n # when form_classname is used instead of classname\\n # in the initialisation of the FieldBlock\\n classname = self._constructor_args[1].pop('classname')\\n self._constructor_args[1].setdefault('form_classname', classname)\\n\\n self.meta = self._meta_class()\\n\\n for attr, value in kwargs.items():\\n setattr(self.meta, attr, value)\\n\\n # Increase the creation counter, and save our local copy.\\n self.creation_counter = Block.creation_counter\\n Block.creation_counter += 1\\n self.definition_prefix = 'blockdef-%d' % self.creation_counter\\n\\n self.label = self.meta.label or ''\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.set_name\",\n \"text\": \"wagtail/core/blocks/base.py::Block.set_name\\n def set_name(self, name):\\n self.name = name\\n if not self.meta.label:\\n self.label = capfirst(force_str(name).replace('_', ' '))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.set_meta_options\",\n \"text\": \"wagtail/core/blocks/base.py::Block.set_meta_options\\n def set_meta_options(self, opts):\\n \\\"\\\"\\\"\\n Update this block's meta options (out of the ones designated as mutable) from the given dict.\\n Used by the StreamField constructor to pass on kwargs that are to be handled by the block,\\n since the block object has already been created by that point, e.g.:\\n body = StreamField(SomeStreamBlock(), max_num=5)\\n \\\"\\\"\\\"\\n for attr, value in opts.items():\\n if attr in self.MUTABLE_META_ATTRIBUTES:\\n setattr(self.meta, attr, value)\\n else:\\n raise TypeError(\\\"set_meta_options received unexpected option: %r\\\" % attr)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.value_from_datadict\",\n \"text\": \"wagtail/core/blocks/base.py::Block.value_from_datadict\\n def value_from_datadict(self, data, files, prefix):\\n raise NotImplementedError('%s.value_from_datadict' % self.__class__)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.value_omitted_from_data\",\n \"text\": \"wagtail/core/blocks/base.py::Block.value_omitted_from_data\\n def value_omitted_from_data(self, data, files, name):\\n \\\"\\\"\\\"\\n Used only for top-level blocks wrapped by BlockWidget (i.e.: typically only StreamBlock)\\n to inform ModelForm logic on Django >=1.10.2 whether the field is absent from the form\\n submission (and should therefore revert to the field default).\\n \\\"\\\"\\\"\\n return name not in data\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.bind\",\n \"text\": \"wagtail/core/blocks/base.py::Block.bind\\n def bind(self, value, prefix=None, errors=None):\\n \\\"\\\"\\\"\\n Return a BoundBlock which represents the association of this block definition with a value\\n and a prefix (and optionally, a ValidationError to be rendered).\\n BoundBlock primarily exists as a convenience to allow rendering within templates:\\n bound_block.render() rather than blockdef.render(value, prefix) which can't be called from\\n within a template.\\n \\\"\\\"\\\"\\n return BoundBlock(self, value, prefix=prefix, errors=errors)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.get_default\",\n \"text\": \"wagtail/core/blocks/base.py::Block.get_default\\n def get_default(self):\\n \\\"\\\"\\\"\\n Return this block's default value (conventionally found in self.meta.default),\\n converted to the value type expected by this block. This caters for the case\\n where that value type is not something that can be expressed statically at\\n model definition type (e.g. something like StructValue which incorporates a\\n pointer back to the block definion object).\\n \\\"\\\"\\\"\\n return self.meta.default\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.clean\",\n \"text\": \"wagtail/core/blocks/base.py::Block.clean\\n def clean(self, value):\\n \\\"\\\"\\\"\\n Validate value and return a cleaned version of it, or throw a ValidationError if validation fails.\\n The thrown ValidationError instance will subsequently be passed to render() to display the\\n error message; the ValidationError must therefore include all detail necessary to perform that\\n rendering, such as identifying the specific child block(s) with errors, in the case of nested\\n blocks. (It is suggested that you use the 'params' attribute for this; using error_list /\\n error_dict is unreliable because Django tends to hack around with these when nested.)\\n \\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.to_python\",\n \"text\": \"wagtail/core/blocks/base.py::Block.to_python\\n def to_python(self, value):\\n \\\"\\\"\\\"\\n Convert 'value' from a simple (JSON-serialisable) value to a (possibly complex) Python value to be\\n used in the rest of the block API and within front-end templates . In simple cases this might be\\n the value itself; alternatively, it might be a 'smart' version of the value which behaves mostly\\n like the original value but provides a native HTML rendering when inserted into a template; or it\\n might be something totally different (e.g. an image chooser will use the image ID as the clean\\n value, and turn this back into an actual image object here).\\n \\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.bulk_to_python\",\n \"text\": \"wagtail/core/blocks/base.py::Block.bulk_to_python\\n def bulk_to_python(self, values):\\n \\\"\\\"\\\"\\n Apply the to_python conversion to a list of values. The default implementation simply\\n iterates over the list; subclasses may optimise this, e.g. by combining database lookups\\n into a single query.\\n \\\"\\\"\\\"\\n return [self.to_python(value) for value in values]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.get_prep_value\",\n \"text\": \"wagtail/core/blocks/base.py::Block.get_prep_value\\n def get_prep_value(self, value):\\n \\\"\\\"\\\"\\n The reverse of to_python; convert the python value into JSON-serialisable form.\\n \\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.get_form_state\",\n \"text\": \"wagtail/core/blocks/base.py::Block.get_form_state\\n def get_form_state(self, value):\\n \\\"\\\"\\\"\\n Convert a python value for this block into a JSON-serialisable representation containing\\n all the data needed to present the value in a form field, to be received by the block's\\n client-side component. Examples of where this conversion is not trivial include rich text\\n (where it needs to be supplied in a format that the editor can process, e.g. ContentState\\n for Draftail) and page / image / document choosers (where it needs to include all displayed\\n data for the selected item, such as title or thumbnail).\\n \\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.get_context\",\n \"text\": \"wagtail/core/blocks/base.py::Block.get_context\\n def get_context(self, value, parent_context=None):\\n \\\"\\\"\\\"\\n Return a dict of context variables (derived from the block value and combined with the parent_context)\\n to be used as the template context when rendering this value through a template.\\n \\\"\\\"\\\"\\n\\n context = parent_context or {}\\n context.update({\\n 'self': value,\\n self.TEMPLATE_VAR: value,\\n })\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.get_template\",\n \"text\": \"wagtail/core/blocks/base.py::Block.get_template\\n def get_template(self, context=None):\\n \\\"\\\"\\\"\\n Return the template to use for rendering the block if specified on meta class.\\n This extraction was added to make dynamic templates possible if you override this method\\n \\\"\\\"\\\"\\n return getattr(self.meta, 'template', None)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.render\",\n \"text\": \"wagtail/core/blocks/base.py::Block.render\\n def render(self, value, context=None):\\n \\\"\\\"\\\"\\n Return a text rendering of 'value', suitable for display on templates. By default, this will\\n use a template (with the passed context, supplemented by the result of get_context) if a\\n 'template' property is specified on the block, and fall back on render_basic otherwise.\\n \\\"\\\"\\\"\\n template = self.get_template(context=context)\\n if not template:\\n return self.render_basic(value, context=context)\\n\\n if context is None:\\n new_context = self.get_context(value)\\n else:\\n new_context = self.get_context(value, parent_context=dict(context))\\n\\n return mark_safe(render_to_string(template, new_context))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.get_api_representation\",\n \"text\": \"wagtail/core/blocks/base.py::Block.get_api_representation\\n def get_api_representation(self, value, context=None):\\n \\\"\\\"\\\"\\n Can be used to customise the API response and defaults to the value returned by get_prep_value.\\n \\\"\\\"\\\"\\n return self.get_prep_value(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.render_basic\",\n \"text\": \"wagtail/core/blocks/base.py::Block.render_basic\\n def render_basic(self, value, context=None):\\n \\\"\\\"\\\"\\n Return a text rendering of 'value', suitable for display on templates. render() will fall back on\\n this if the block does not define a 'template' property.\\n \\\"\\\"\\\"\\n return force_str(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.get_searchable_content\",\n \"text\": \"wagtail/core/blocks/base.py::Block.get_searchable_content\\n def get_searchable_content(self, value):\\n \\\"\\\"\\\"\\n Returns a list of strings containing text content within this block to be used in a search engine.\\n \\\"\\\"\\\"\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.check\",\n \"text\": \"wagtail/core/blocks/base.py::Block.check\\n def check(self, **kwargs):\\n \\\"\\\"\\\"\\n Hook for the Django system checks framework -\\n returns a list of django.core.checks.Error objects indicating validity errors in the block\\n \\\"\\\"\\\"\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block._check_name\",\n \"text\": \"wagtail/core/blocks/base.py::Block._check_name\\n def _check_name(self, **kwargs):\\n \\\"\\\"\\\"\\n Helper method called by container blocks as part of the system checks framework,\\n to validate that this block's name is a valid identifier.\\n (Not called universally, because not all blocks need names)\\n \\\"\\\"\\\"\\n errors = []\\n if not self.name:\\n errors.append(checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n hint=\\\"Block name cannot be empty\\\",\\n obj=kwargs.get('field', self),\\n id='wagtailcore.E001',\\n ))\\n\\n if ' ' in self.name:\\n errors.append(checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n hint=\\\"Block names cannot contain spaces\\\",\\n obj=kwargs.get('field', self),\\n id='wagtailcore.E001',\\n ))\\n\\n if '-' in self.name:\\n errors.append(checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n \\\"Block names cannot contain dashes\\\",\\n obj=kwargs.get('field', self),\\n id='wagtailcore.E001',\\n ))\\n\\n if self.name and self.name[0].isdigit():\\n errors.append(checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n \\\"Block names cannot begin with a digit\\\",\\n obj=kwargs.get('field', self),\\n id='wagtailcore.E001',\\n ))\\n\\n if not errors and not re.match(r'^[_a-zA-Z][_a-zA-Z0-9]*$', self.name):\\n errors.append(checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n \\\"Block names should follow standard Python conventions for \\\"\\n \\\"variable names: alpha-numeric and underscores, and cannot \\\"\\n \\\"begin with a digit\\\",\\n obj=kwargs.get('field', self),\\n id='wagtailcore.E001',\\n ))\\n\\n return errors\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.id_for_label\",\n \"text\": \"wagtail/core/blocks/base.py::Block.id_for_label\\n def id_for_label(self, prefix):\\n \\\"\\\"\\\"\\n Return the ID to be used as the 'for' attribute of <label> elements that refer to this block,\\n when the given field prefix is in use. Return None if no 'for' attribute should be used.\\n \\\"\\\"\\\"\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.required\",\n \"text\": \"wagtail/core/blocks/base.py::Block.required\\n @property\\n def required(self):\\n \\\"\\\"\\\"\\n Flag used to determine whether labels for this block should display a 'required' asterisk.\\n False by default, since Block does not provide any validation of its own - it's up to subclasses\\n to define what required-ness means.\\n \\\"\\\"\\\"\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.deconstruct\",\n \"text\": \"wagtail/core/blocks/base.py::Block.deconstruct\\n def deconstruct(self):\\n # adapted from django.utils.deconstruct.deconstructible\\n module_name = self.__module__\\n name = self.__class__.__name__\\n\\n # Make sure it's actually there and not an inner class\\n module = import_module(module_name)\\n if not hasattr(module, name):\\n raise ValueError(\\n \\\"Could not find object %s in %s.\\\\n\\\"\\n \\\"Please note that you cannot serialize things like inner \\\"\\n \\\"classes. Please move the object into the main module \\\"\\n \\\"body to use migrations.\\\\n\\\"\\n % (name, module_name))\\n\\n # if the module defines a DECONSTRUCT_ALIASES dictionary, see if the class has an entry in there;\\n # if so, use that instead of the real path\\n try:\\n path = module.DECONSTRUCT_ALIASES[self.__class__]\\n except (AttributeError, KeyError):\\n path = '%s.%s' % (module_name, name)\\n\\n return (\\n path,\\n self._constructor_args[0],\\n self._constructor_args[1],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.__eq__\",\n \"text\": \"wagtail/core/blocks/base.py::Block.__eq__\\n def __eq__(self, other):\\n \\\"\\\"\\\"\\n Implement equality on block objects so that two blocks with matching definitions are considered\\n equal. Block objects are intended to be immutable with the exception of set_name() and any meta\\n attributes identified in MUTABLE_META_ATTRIBUTES, so checking these along with the result of\\n deconstruct (which captures the constructor arguments) is sufficient to identify (valid) differences.\\n\\n This was originally necessary as a workaround for https://code.djangoproject.com/ticket/24340\\n in Django <1.9; the deep_deconstruct function used to detect changes for migrations did not\\n recurse into the block lists, and left them as Block instances. This __eq__ method therefore\\n came into play when identifying changes within migrations.\\n\\n As of Django >=1.9, this *probably* isn't required any more. However, it may be useful in\\n future as a way of identifying blocks that can be re-used within StreamField definitions\\n (https://github.com/wagtail/wagtail/issues/4298#issuecomment-367656028).\\n \\\"\\\"\\\"\\n\\n if not isinstance(other, Block):\\n # if the other object isn't a block at all, it clearly isn't equal.\\n return False\\n\\n # Note that we do not require the two blocks to be of the exact same class. This is because\\n # we may wish the following blocks to be considered equal:\\n #\\n # class FooBlock(StructBlock):\\n # first_name = CharBlock()\\n # surname = CharBlock()\\n #\\n # class BarBlock(StructBlock):\\n # first_name = CharBlock()\\n # surname = CharBlock()\\n #\\n # FooBlock() == BarBlock() == StructBlock([('first_name', CharBlock()), ('surname': CharBlock())])\\n #\\n # For this to work, StructBlock will need to ensure that 'deconstruct' returns the s\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BoundBlock.__init__\",\n \"text\": \"wagtail/core/blocks/base.py::BoundBlock.__init__\\n def __init__(self, block, value, prefix=None, errors=None):\\n self.block = block\\n self.value = value\\n self.prefix = prefix\\n self.errors = errors\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BoundBlock.render\",\n \"text\": \"wagtail/core/blocks/base.py::BoundBlock.render\\n def render(self, context=None):\\n return self.block.render(self.value, context=context)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BoundBlock.render_as_block\",\n \"text\": \"wagtail/core/blocks/base.py::BoundBlock.render_as_block\\n def render_as_block(self, context=None):\\n \\\"\\\"\\\"\\n Alias for render; the include_block tag will specifically check for the presence of a method\\n with this name. (This is because {% include_block %} is just as likely to be invoked on a bare\\n value as a BoundBlock. If we looked for a `render` method instead, we'd run the risk of finding\\n an unrelated method that just happened to have that name - for example, when called on a\\n PageChooserBlock it could end up calling page.render.\\n \\\"\\\"\\\"\\n return self.block.render(self.value, context=context)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BoundBlock.id_for_label\",\n \"text\": \"wagtail/core/blocks/base.py::BoundBlock.id_for_label\\n def id_for_label(self):\\n return self.block.id_for_label(self.prefix)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BoundBlock.__str__\",\n \"text\": \"wagtail/core/blocks/base.py::BoundBlock.__str__\\n def __str__(self):\\n \\\"\\\"\\\"Render the value according to the block's native rendering\\\"\\\"\\\"\\n return self.block.render(self.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BoundBlock.__repr__\",\n \"text\": \"wagtail/core/blocks/base.py::BoundBlock.__repr__\\n def __repr__(self):\\n return \\\"<block %s: %r>\\\" % (self.block.name or type(self.block).__name__, self.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::DeclarativeSubBlocksMetaclass.__new__\",\n \"text\": \"wagtail/core/blocks/base.py::DeclarativeSubBlocksMetaclass.__new__\\n def __new__(mcs, name, bases, attrs):\\n # Collect sub-blocks declared on the current class.\\n # These are available on the class as `declared_blocks`\\n current_blocks = []\\n for key, value in list(attrs.items()):\\n if isinstance(value, Block):\\n current_blocks.append((key, value))\\n value.set_name(key)\\n attrs.pop(key)\\n current_blocks.sort(key=lambda x: x[1].creation_counter)\\n attrs['declared_blocks'] = collections.OrderedDict(current_blocks)\\n\\n new_class = (super(DeclarativeSubBlocksMetaclass, mcs).__new__(\\n mcs, name, bases, attrs))\\n\\n # Walk through the MRO, collecting all inherited sub-blocks, to make\\n # the combined `base_blocks`.\\n base_blocks = collections.OrderedDict()\\n for base in reversed(new_class.__mro__):\\n # Collect sub-blocks from base class.\\n if hasattr(base, 'declared_blocks'):\\n base_blocks.update(base.declared_blocks)\\n\\n # Field shadowing.\\n for attr, value in base.__dict__.items():\\n if value is None and attr in base_blocks:\\n base_blocks.pop(attr)\\n new_class.base_blocks = base_blocks\\n\\n return new_class\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BlockWidget.__init__\",\n \"text\": \"wagtail/core/blocks/base.py::BlockWidget.__init__\\n def __init__(self, block_def, attrs=None):\\n super().__init__(attrs=attrs)\\n self.block_def = block_def\\n self.js_context = JSContext()\\n self.block_json = json.dumps(self.js_context.pack(self.block_def))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BlockWidget.render_with_errors\",\n \"text\": \"wagtail/core/blocks/base.py::BlockWidget.render_with_errors\\n def render_with_errors(self, name, value, attrs=None, errors=None, renderer=None):\\n value_json = json.dumps(self.block_def.get_form_state(value))\\n\\n if errors:\\n errors_json = json.dumps(self.js_context.pack(errors.as_data()))\\n else:\\n errors_json = '[]'\\n\\n return format_html(\\n \\\"\\\"\\\"\\n <div id=\\\"{id}\\\" data-block=\\\"{block_json}\\\" data-value=\\\"{value_json}\\\" data-errors=\\\"{errors_json}\\\"></div>\\n <script>\\n initBlockWidget('{id}');\\n </script>\\n \\\"\\\"\\\",\\n id=name, block_json=self.block_json, value_json=value_json, errors_json=errors_json\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BlockWidget.render\",\n \"text\": \"wagtail/core/blocks/base.py::BlockWidget.render\\n def render(self, name, value, attrs=None, renderer=None):\\n return self.render_with_errors(name, value, attrs=attrs, errors=None, renderer=renderer)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BlockWidget.media\",\n \"text\": \"wagtail/core/blocks/base.py::BlockWidget.media\\n @property\\n def media(self):\\n return self.js_context.media + forms.Media(\\n js=[\\n # needed for initBlockWidget, although these will almost certainly be\\n # pulled in by the block adapters too\\n versioned_static('wagtailadmin/js/telepath/telepath.js'),\\n versioned_static('wagtailadmin/js/telepath/blocks.js'),\\n ],\\n css={'all': [\\n versioned_static('wagtailadmin/css/panels/streamfield.css'),\\n ]}\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BlockWidget.value_from_datadict\",\n \"text\": \"wagtail/core/blocks/base.py::BlockWidget.value_from_datadict\\n def value_from_datadict(self, data, files, name):\\n return self.block_def.value_from_datadict(data, files, name)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BlockWidget.value_omitted_from_data\",\n \"text\": \"wagtail/core/blocks/base.py::BlockWidget.value_omitted_from_data\\n def value_omitted_from_data(self, data, files, name):\\n return self.block_def.value_omitted_from_data(data, files, name)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BlockField.__init__\",\n \"text\": \"wagtail/core/blocks/base.py::BlockField.__init__\\n def __init__(self, block=None, **kwargs):\\n if block is None:\\n raise ImproperlyConfigured(\\\"BlockField was not passed a 'block' object\\\")\\n self.block = block\\n\\n if 'widget' not in kwargs:\\n kwargs['widget'] = BlockWidget(block)\\n\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::FieldBlock.id_for_label\",\n \"text\": \"wagtail/core/blocks/field_block.py::FieldBlock.id_for_label\\n def id_for_label(self, prefix):\\n return self.field.widget.id_for_label(prefix)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::FieldBlock.value_from_form\",\n \"text\": \"wagtail/core/blocks/field_block.py::FieldBlock.value_from_form\\n def value_from_form(self, value):\\n \\\"\\\"\\\"\\n The value that we get back from the form field might not be the type\\n that this block works with natively; for example, the block may want to\\n wrap a simple value such as a string in an object that provides a fancy\\n HTML rendering (e.g. EmbedBlock).\\n\\n We therefore provide this method to perform any necessary conversion\\n from the form field value to the block's native value. As standard,\\n this returns the form field value unchanged.\\n \\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::FieldBlock.value_for_form\",\n \"text\": \"wagtail/core/blocks/field_block.py::FieldBlock.value_for_form\\n def value_for_form(self, value):\\n \\\"\\\"\\\"\\n Reverse of value_from_form; convert a value of this block's native value type\\n to one that can be rendered by the form field\\n \\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::FieldBlock.value_from_datadict\",\n \"text\": \"wagtail/core/blocks/field_block.py::FieldBlock.value_from_datadict\\n def value_from_datadict(self, data, files, prefix):\\n return self.value_from_form(self.field.widget.value_from_datadict(data, files, prefix))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::FieldBlock.value_omitted_from_data\",\n \"text\": \"wagtail/core/blocks/field_block.py::FieldBlock.value_omitted_from_data\\n def value_omitted_from_data(self, data, files, prefix):\\n return self.field.widget.value_omitted_from_data(data, files, prefix)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::FieldBlock.clean\",\n \"text\": \"wagtail/core/blocks/field_block.py::FieldBlock.clean\\n def clean(self, value):\\n # We need an annoying value_for_form -> value_from_form round trip here to account for\\n # the possibility that the form field is set up to validate a different value type to\\n # the one this block works with natively\\n return self.value_from_form(self.field.clean(self.value_for_form(value)))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::FieldBlock.required\",\n \"text\": \"wagtail/core/blocks/field_block.py::FieldBlock.required\\n @property\\n def required(self):\\n # a FieldBlock is required if and only if its underlying form field is required\\n return self.field.required\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::FieldBlock.get_form_state\",\n \"text\": \"wagtail/core/blocks/field_block.py::FieldBlock.get_form_state\\n def get_form_state(self, value):\\n return self.field.widget.format_value(self.field.prepare_value(self.value_for_form(value)))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::FieldBlockAdapter.js_args\",\n \"text\": \"wagtail/core/blocks/field_block.py::FieldBlockAdapter.js_args\\n def js_args(self, block):\\n classname = [\\n 'field',\\n camelcase_to_underscore(block.field.__class__.__name__),\\n 'widget-' + camelcase_to_underscore(block.field.widget.__class__.__name__),\\n 'fieldname-' + block.name,\\n ]\\n\\n form_classname = getattr(block.meta, 'form_classname', '')\\n if form_classname:\\n classname.append(form_classname)\\n\\n # Provided for backwards compatibility. Replaced with 'form_classname'\\n legacy_classname = getattr(block.meta, 'classname', '')\\n if legacy_classname:\\n classname.append(legacy_classname)\\n\\n meta = {\\n 'label': block.label,\\n 'required': block.required,\\n 'icon': block.meta.icon,\\n 'classname': ' '.join(classname),\\n }\\n if block.field.help_text:\\n meta['helpText'] = block.field.help_text\\n\\n return [\\n block.name,\\n block.field.widget,\\n meta,\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::FieldBlockAdapter.media\",\n \"text\": \"wagtail/core/blocks/field_block.py::FieldBlockAdapter.media\\n @cached_property\\n def media(self):\\n return forms.Media(js=[\\n versioned_static('wagtailadmin/js/telepath/blocks.js'),\\n ])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::CharBlock.__init__\",\n \"text\": \"wagtail/core/blocks/field_block.py::CharBlock.__init__\\n def __init__(self, required=True, help_text=None, max_length=None, min_length=None, validators=(), **kwargs):\\n # CharField's 'label' and 'initial' parameters are not exposed, as Block handles that functionality natively\\n # (via 'label' and 'default')\\n self.field = forms.CharField(\\n required=required,\\n help_text=help_text,\\n max_length=max_length,\\n min_length=min_length,\\n validators=validators,\\n )\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::CharBlock.get_searchable_content\",\n \"text\": \"wagtail/core/blocks/field_block.py::CharBlock.get_searchable_content\\n def get_searchable_content(self, value):\\n return [force_str(value)]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::TextBlock.__init__\",\n \"text\": \"wagtail/core/blocks/field_block.py::TextBlock.__init__\\n def __init__(self, required=True, help_text=None, rows=1, max_length=None, min_length=None, validators=(), **kwargs):\\n self.field_options = {\\n 'required': required,\\n 'help_text': help_text,\\n 'max_length': max_length,\\n 'min_length': min_length,\\n 'validators': validators,\\n }\\n self.rows = rows\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::TextBlock.field\",\n \"text\": \"wagtail/core/blocks/field_block.py::TextBlock.field\\n @cached_property\\n def field(self):\\n from wagtail.admin.widgets import AdminAutoHeightTextInput\\n field_kwargs = {'widget': AdminAutoHeightTextInput(attrs={'rows': self.rows})}\\n field_kwargs.update(self.field_options)\\n return forms.CharField(**field_kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::TextBlock.get_searchable_content\",\n \"text\": \"wagtail/core/blocks/field_block.py::TextBlock.get_searchable_content\\n def get_searchable_content(self, value):\\n return [force_str(value)]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::BlockQuoteBlock.render_basic\",\n \"text\": \"wagtail/core/blocks/field_block.py::BlockQuoteBlock.render_basic\\n def render_basic(self, value, context=None):\\n if value:\\n return format_html('<blockquote>{0}</blockquote>', value)\\n else:\\n return ''\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::FloatBlock.__init__\",\n \"text\": \"wagtail/core/blocks/field_block.py::FloatBlock.__init__\\n def __init__(self, required=True, max_value=None, min_value=None, validators=(), *args,\\n **kwargs):\\n self.field = forms.FloatField(\\n required=required,\\n max_value=max_value,\\n min_value=min_value,\\n validators=validators,\\n )\\n super().__init__(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::DecimalBlock.__init__\",\n \"text\": \"wagtail/core/blocks/field_block.py::DecimalBlock.__init__\\n def __init__(self, required=True, help_text=None, max_value=None, min_value=None,\\n max_digits=None, decimal_places=None, validators=(), *args, **kwargs):\\n self.field = forms.DecimalField(\\n required=required,\\n help_text=help_text,\\n max_value=max_value,\\n min_value=min_value,\\n max_digits=max_digits,\\n decimal_places=decimal_places,\\n validators=validators,\\n )\\n super().__init__(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::RegexBlock.__init__\",\n \"text\": \"wagtail/core/blocks/field_block.py::RegexBlock.__init__\\n def __init__(self, regex, required=True, help_text=None, max_length=None, min_length=None,\\n error_messages=None, validators=(), *args, **kwargs):\\n self.field = forms.RegexField(\\n regex=regex,\\n required=required,\\n help_text=help_text,\\n max_length=max_length,\\n min_length=min_length,\\n error_messages=error_messages,\\n validators=validators,\\n )\\n super().__init__(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::URLBlock.__init__\",\n \"text\": \"wagtail/core/blocks/field_block.py::URLBlock.__init__\\n def __init__(self, required=True, help_text=None, max_length=None, min_length=None, validators=(), **kwargs):\\n self.field = forms.URLField(\\n required=required,\\n help_text=help_text,\\n max_length=max_length,\\n min_length=min_length,\\n validators=validators,\\n )\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::BooleanBlock.__init__\",\n \"text\": \"wagtail/core/blocks/field_block.py::BooleanBlock.__init__\\n def __init__(self, required=True, help_text=None, **kwargs):\\n # NOTE: As with forms.BooleanField, the default of required=True means that the checkbox\\n # must be ticked to pass validation (i.e. it's equivalent to an \\\"I agree to the terms and\\n # conditions\\\" box). To get the conventional yes/no behaviour, you must explicitly pass\\n # required=False.\\n self.field = forms.BooleanField(required=required, help_text=help_text)\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::DateBlock.__init__\",\n \"text\": \"wagtail/core/blocks/field_block.py::DateBlock.__init__\\n def __init__(self, required=True, help_text=None, format=None, validators=(), **kwargs):\\n self.field_options = {\\n 'required': required,\\n 'help_text': help_text,\\n 'validators': validators,\\n }\\n try:\\n self.field_options['input_formats'] = kwargs.pop('input_formats')\\n except KeyError:\\n pass\\n self.format = format\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::DateBlock.field\",\n \"text\": \"wagtail/core/blocks/field_block.py::DateBlock.field\\n @cached_property\\n def field(self):\\n from wagtail.admin.widgets import AdminDateInput\\n field_kwargs = {\\n 'widget': AdminDateInput(format=self.format),\\n }\\n field_kwargs.update(self.field_options)\\n return forms.DateField(**field_kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::DateBlock.to_python\",\n \"text\": \"wagtail/core/blocks/field_block.py::DateBlock.to_python\\n def to_python(self, value):\\n # Serialising to JSON uses DjangoJSONEncoder, which converts date/time objects to strings.\\n # The reverse does not happen on decoding, because there's no way to know which strings\\n # should be decoded; we have to convert strings back to dates here instead.\\n if value is None or isinstance(value, datetime.date):\\n return value\\n else:\\n return parse_date(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::TimeBlock.__init__\",\n \"text\": \"wagtail/core/blocks/field_block.py::TimeBlock.__init__\\n def __init__(self, required=True, help_text=None, format=None, validators=(), **kwargs):\\n self.field_options = {\\n 'required': required,\\n 'help_text': help_text,\\n 'validators': validators\\n }\\n self.format = format\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::TimeBlock.field\",\n \"text\": \"wagtail/core/blocks/field_block.py::TimeBlock.field\\n @cached_property\\n def field(self):\\n from wagtail.admin.widgets import AdminTimeInput\\n field_kwargs = {'widget': AdminTimeInput(format=self.format)}\\n field_kwargs.update(self.field_options)\\n return forms.TimeField(**field_kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::TimeBlock.to_python\",\n \"text\": \"wagtail/core/blocks/field_block.py::TimeBlock.to_python\\n def to_python(self, value):\\n if value is None or isinstance(value, datetime.time):\\n return value\\n else:\\n return parse_time(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::DateTimeBlock.__init__\",\n \"text\": \"wagtail/core/blocks/field_block.py::DateTimeBlock.__init__\\n def __init__(self, required=True, help_text=None, format=None, validators=(), **kwargs):\\n self.field_options = {\\n 'required': required,\\n 'help_text': help_text,\\n 'validators': validators,\\n }\\n self.format = format\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::DateTimeBlock.field\",\n \"text\": \"wagtail/core/blocks/field_block.py::DateTimeBlock.field\\n @cached_property\\n def field(self):\\n from wagtail.admin.widgets import AdminDateTimeInput\\n field_kwargs = {\\n 'widget': AdminDateTimeInput(format=self.format),\\n }\\n field_kwargs.update(self.field_options)\\n return forms.DateTimeField(**field_kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::DateTimeBlock.to_python\",\n \"text\": \"wagtail/core/blocks/field_block.py::DateTimeBlock.to_python\\n def to_python(self, value):\\n if value is None or isinstance(value, datetime.datetime):\\n return value\\n else:\\n return parse_datetime(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::EmailBlock.__init__\",\n \"text\": \"wagtail/core/blocks/field_block.py::EmailBlock.__init__\\n def __init__(self, required=True, help_text=None, validators=(), **kwargs):\\n self.field = forms.EmailField(\\n required=required,\\n help_text=help_text,\\n validators=validators,\\n )\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::IntegerBlock.__init__\",\n \"text\": \"wagtail/core/blocks/field_block.py::IntegerBlock.__init__\\n def __init__(self, required=True, help_text=None, min_value=None,\\n max_value=None, validators=(), **kwargs):\\n self.field = forms.IntegerField(\\n required=required,\\n help_text=help_text,\\n min_value=min_value,\\n max_value=max_value,\\n validators=validators,\\n )\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::BaseChoiceBlock.__init__\",\n \"text\": \"wagtail/core/blocks/field_block.py::BaseChoiceBlock.__init__\\n def __init__(\\n self, choices=None, default=None, required=True,\\n help_text=None, widget=None, validators=(), **kwargs):\\n\\n self._required = required\\n self._default = default\\n\\n if choices is None:\\n # no choices specified, so pick up the choice defined at the class level\\n choices = self.choices\\n\\n if callable(choices):\\n # Support of callable choices. Wrap the callable in an iterator so that we can\\n # handle this consistently with ordinary choice lists;\\n # however, the `choices` constructor kwarg as reported by deconstruct() should\\n # remain as the callable\\n choices_for_constructor = choices\\n choices = CallableChoiceIterator(choices)\\n else:\\n # Cast as a list\\n choices_for_constructor = choices = list(choices)\\n\\n # keep a copy of all kwargs (including our normalised choices list) for deconstruct()\\n # Note: we omit the `widget` kwarg, as widgets do not provide a serialization method\\n # for migrations, and they are unlikely to be useful within the frozen ORM anyhow\\n self._constructor_kwargs = kwargs.copy()\\n self._constructor_kwargs['choices'] = choices_for_constructor\\n if required is not True:\\n self._constructor_kwargs['required'] = required\\n if help_text is not None:\\n self._constructor_kwargs['help_text'] = help_text\\n\\n # We will need to modify the choices list to insert a blank option, if there isn't\\n # one already. We have to do this at render time in the case of callable choices - so rather\\n # than having separate code paths for static vs dynamic lists, we'll _always_ pass a callable\\n # to ChoiceField to perform this step at render time.\\n\\n callable_choices = self._get_callable_choices(choices)\\n self.field = self.get_field(\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::BaseChoiceBlock._get_callable_choices\",\n \"text\": \"wagtail/core/blocks/field_block.py::BaseChoiceBlock._get_callable_choices\\n def _get_callable_choices(self, choices, blank_choice=True):\\n \\\"\\\"\\\"\\n Return a callable that we can pass into `forms.ChoiceField`, which will provide the\\n choices list with the addition of a blank choice (if blank_choice=True and one does not\\n already exist).\\n \\\"\\\"\\\"\\n def choices_callable():\\n # Variable choices could be an instance of CallableChoiceIterator, which may be wrapping\\n # something we don't want to evaluate multiple times (e.g. a database query). Cast as a\\n # list now to prevent it getting evaluated twice (once while searching for a blank choice,\\n # once while rendering the final ChoiceField).\\n local_choices = list(choices)\\n\\n # If blank_choice=False has been specified, return the choices list as is\\n if not blank_choice:\\n return local_choices\\n\\n # Else: if choices does not already contain a blank option, insert one\\n # (to match Django's own behaviour for modelfields:\\n # https://github.com/django/django/blob/1.7.5/django/db/models/fields/__init__.py#L732-744)\\n has_blank_choice = False\\n for v1, v2 in local_choices:\\n if isinstance(v2, (list, tuple)):\\n # this is a named group, and v2 is the value list\\n has_blank_choice = any([value in ('', None) for value, label in v2])\\n if has_blank_choice:\\n break\\n else:\\n # this is an individual choice; v1 is the value\\n if v1 in ('', None):\\n has_blank_choice = True\\n break\\n\\n if not has_blank_choice:\\n return BLANK_CHOICE_DASH + local_choices\\n\\n return local_choices\\n return choices_callable\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::BaseChoiceBlock._get_callable_choices.choices_callable\",\n \"text\": \"wagtail/core/blocks/field_block.py::BaseChoiceBlock._get_callable_choices.choices_callable\\n def choices_callable():\\n # Variable choices could be an instance of CallableChoiceIterator, which may be wrapping\\n # something we don't want to evaluate multiple times (e.g. a database query). Cast as a\\n # list now to prevent it getting evaluated twice (once while searching for a blank choice,\\n # once while rendering the final ChoiceField).\\n local_choices = list(choices)\\n\\n # If blank_choice=False has been specified, return the choices list as is\\n if not blank_choice:\\n return local_choices\\n\\n # Else: if choices does not already contain a blank option, insert one\\n # (to match Django's own behaviour for modelfields:\\n # https://github.com/django/django/blob/1.7.5/django/db/models/fields/__init__.py#L732-744)\\n has_blank_choice = False\\n for v1, v2 in local_choices:\\n if isinstance(v2, (list, tuple)):\\n # this is a named group, and v2 is the value list\\n has_blank_choice = any([value in ('', None) for value, label in v2])\\n if has_blank_choice:\\n break\\n else:\\n # this is an individual choice; v1 is the value\\n if v1 in ('', None):\\n has_blank_choice = True\\n break\\n\\n if not has_blank_choice:\\n return BLANK_CHOICE_DASH + local_choices\\n\\n return local_choices\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::ChoiceBlock.get_field\",\n \"text\": \"wagtail/core/blocks/field_block.py::ChoiceBlock.get_field\\n def get_field(self, **kwargs):\\n return forms.ChoiceField(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::ChoiceBlock._get_callable_choices\",\n \"text\": \"wagtail/core/blocks/field_block.py::ChoiceBlock._get_callable_choices\\n def _get_callable_choices(self, choices, blank_choice=None):\\n # If we have a default choice and the field is required, we don't need to add a blank option.\\n if blank_choice is None:\\n blank_choice = not(self._default and self._required)\\n return super()._get_callable_choices(choices, blank_choice=blank_choice)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::ChoiceBlock.deconstruct\",\n \"text\": \"wagtail/core/blocks/field_block.py::ChoiceBlock.deconstruct\\n def deconstruct(self):\\n \\\"\\\"\\\"\\n Always deconstruct ChoiceBlock instances as if they were plain ChoiceBlocks with their\\n choice list passed in the constructor, even if they are actually subclasses. This allows\\n users to define subclasses of ChoiceBlock in their models.py, with specific choice lists\\n passed in, without references to those classes ending up frozen into migrations.\\n \\\"\\\"\\\"\\n return ('wagtail.core.blocks.ChoiceBlock', [], self._constructor_kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::ChoiceBlock.get_searchable_content\",\n \"text\": \"wagtail/core/blocks/field_block.py::ChoiceBlock.get_searchable_content\\n def get_searchable_content(self, value):\\n # Return the display value as the searchable value\\n text_value = force_str(value)\\n for k, v in self.field.choices:\\n if isinstance(v, (list, tuple)):\\n # This is an optgroup, so look inside the group for options\\n for k2, v2 in v:\\n if value == k2 or text_value == force_str(k2):\\n return [force_str(k), force_str(v2)]\\n else:\\n if value == k or text_value == force_str(k):\\n return [force_str(v)]\\n return [] # Value was not found in the list of choices\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/field_block.py::MultipleChoiceBlock.get_field\",\n \"text\": \"wagtail/core/blocks/field_block.py::MultipleChoiceBlock.get_field\\n def get_field(self, **kwargs):\\n return forms.MultipleChoiceField(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::RichTextField.__init__\",\n \"text\": \"wagtail/core/fields.py::RichTextField.__init__\\n def __init__(self, *args, **kwargs):\\n self.editor = kwargs.pop('editor', 'default')\\n self.features = kwargs.pop('features', None)\\n # TODO: preserve 'editor' and 'features' when deconstructing for migrations\\n super().__init__(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::RichTextField.formfield\",\n \"text\": \"wagtail/core/fields.py::RichTextField.formfield\\n def formfield(self, **kwargs):\\n from wagtail.admin.rich_text import get_rich_text_editor_widget\\n defaults = {'widget': get_rich_text_editor_widget(self.editor, features=self.features)}\\n defaults.update(kwargs)\\n return super().formfield(**defaults)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::RichTextField.get_searchable_content\",\n \"text\": \"wagtail/core/fields.py::RichTextField.get_searchable_content\\n def get_searchable_content(self, value):\\n # Strip HTML tags to prevent search backend from indexing them\\n source = force_str(value)\\n return [get_text_for_indexing(source)]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::Creator.__init__\",\n \"text\": \"wagtail/core/fields.py::Creator.__init__\\n def __init__(self, field):\\n self.field = field\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::Creator.__get__\",\n \"text\": \"wagtail/core/fields.py::Creator.__get__\\n def __get__(self, obj, type=None):\\n if obj is None:\\n return self\\n field_name = self.field.name\\n\\n if field_name not in obj.__dict__:\\n # Field is deferred. Fetch it from db.\\n obj.refresh_from_db(fields=[field_name])\\n return obj.__dict__[field_name]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::Creator.__set__\",\n \"text\": \"wagtail/core/fields.py::Creator.__set__\\n def __set__(self, obj, value):\\n obj.__dict__[self.field.name] = self.field.to_python(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::StreamField.__init__\",\n \"text\": \"wagtail/core/fields.py::StreamField.__init__\\n def __init__(self, block_types, **kwargs):\\n\\n # extract kwargs that are to be passed on to the block, not handled by super\\n block_opts = {}\\n for arg in ['min_num', 'max_num', 'block_counts']:\\n if arg in kwargs:\\n block_opts[arg] = kwargs.pop(arg)\\n\\n # for a top-level block, the 'blank' kwarg (defaulting to False) always overrides the\\n # block's own 'required' meta attribute, even if not passed explicitly; this ensures\\n # that the field and block have consistent definitions\\n block_opts['required'] = not kwargs.get('blank', False)\\n\\n super().__init__(**kwargs)\\n\\n if isinstance(block_types, Block):\\n # use the passed block as the top-level block\\n self.stream_block = block_types\\n elif isinstance(block_types, type):\\n # block passed as a class - instantiate it\\n self.stream_block = block_types()\\n else:\\n # construct a top-level StreamBlock from the list of block types\\n self.stream_block = StreamBlock(block_types)\\n\\n self.stream_block.set_meta_options(block_opts)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::StreamField.get_internal_type\",\n \"text\": \"wagtail/core/fields.py::StreamField.get_internal_type\\n def get_internal_type(self):\\n return 'TextField'\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::StreamField.get_panel\",\n \"text\": \"wagtail/core/fields.py::StreamField.get_panel\\n def get_panel(self):\\n from wagtail.admin.edit_handlers import StreamFieldPanel\\n return StreamFieldPanel\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::StreamField.deconstruct\",\n \"text\": \"wagtail/core/fields.py::StreamField.deconstruct\\n def deconstruct(self):\\n name, path, _, kwargs = super().deconstruct()\\n block_types = list(self.stream_block.child_blocks.items())\\n args = [block_types]\\n return name, path, args, kwargs\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::StreamField.to_python\",\n \"text\": \"wagtail/core/fields.py::StreamField.to_python\\n def to_python(self, value):\\n if value is None or value == '':\\n return StreamValue(self.stream_block, [])\\n elif isinstance(value, StreamValue):\\n return value\\n elif isinstance(value, str):\\n try:\\n unpacked_value = json.loads(value)\\n except ValueError:\\n # value is not valid JSON; most likely, this field was previously a\\n # rich text field before being migrated to StreamField, and the data\\n # was left intact in the migration. Return an empty stream instead\\n # (but keep the raw text available as an attribute, so that it can be\\n # used to migrate that data to StreamField)\\n return StreamValue(self.stream_block, [], raw_text=value)\\n\\n if unpacked_value is None:\\n # we get here if value is the literal string 'null'. This should probably\\n # never happen if the rest of the (de)serialization code is working properly,\\n # but better to handle it just in case...\\n return StreamValue(self.stream_block, [])\\n\\n return self.stream_block.to_python(unpacked_value)\\n else:\\n # See if it looks like the standard non-smart representation of a\\n # StreamField value: a list of (block_name, value) tuples\\n try:\\n [None for (x, y) in value]\\n except (TypeError, ValueError):\\n # Give up trying to make sense of the value\\n raise TypeError(\\\"Cannot handle %r (type %r) as a value of StreamField\\\" % (value, type(value)))\\n\\n # Test succeeded, so return as a StreamValue-ified version of that value\\n return StreamValue(self.stream_block, value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::StreamField.get_prep_value\",\n \"text\": \"wagtail/core/fields.py::StreamField.get_prep_value\\n def get_prep_value(self, value):\\n if isinstance(value, StreamValue) and not(value) and value.raw_text is not None:\\n # An empty StreamValue with a nonempty raw_text attribute should have that\\n # raw_text attribute written back to the db. (This is probably only useful\\n # for reverse migrations that convert StreamField data back into plain text\\n # fields.)\\n return value.raw_text\\n else:\\n return json.dumps(self.stream_block.get_prep_value(value), cls=DjangoJSONEncoder)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::StreamField.from_db_value\",\n \"text\": \"wagtail/core/fields.py::StreamField.from_db_value\\n def from_db_value(self, value, expression, connection):\\n return self.to_python(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::StreamField.formfield\",\n \"text\": \"wagtail/core/fields.py::StreamField.formfield\\n def formfield(self, **kwargs):\\n \\\"\\\"\\\"\\n Override formfield to use a plain forms.Field so that we do no transformation on the value\\n (as distinct from the usual fallback of forms.CharField, which transforms it into a string).\\n \\\"\\\"\\\"\\n defaults = {'form_class': BlockField, 'block': self.stream_block}\\n defaults.update(kwargs)\\n return super().formfield(**defaults)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::StreamField.value_to_string\",\n \"text\": \"wagtail/core/fields.py::StreamField.value_to_string\\n def value_to_string(self, obj):\\n value = self.value_from_object(obj)\\n return self.get_prep_value(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::StreamField.get_searchable_content\",\n \"text\": \"wagtail/core/fields.py::StreamField.get_searchable_content\\n def get_searchable_content(self, value):\\n return self.stream_block.get_searchable_content(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::StreamField.check\",\n \"text\": \"wagtail/core/fields.py::StreamField.check\\n def check(self, **kwargs):\\n errors = super().check(**kwargs)\\n errors.extend(self.stream_block.check(field=self, **kwargs))\\n return errors\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/fields.py::StreamField.contribute_to_class\",\n \"text\": \"wagtail/core/fields.py::StreamField.contribute_to_class\\n def contribute_to_class(self, cls, name, **kwargs):\\n super().contribute_to_class(cls, name, **kwargs)\\n\\n # Add Creator descriptor to allow the field to be set from a list or a\\n # JSON string.\\n setattr(cls, self.name, Creator(self))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/search/utils.py::balanced_reduce\",\n \"text\": \"wagtail/search/utils.py::balanced_reduce\\ndef balanced_reduce(operator, seq, initializer=NOT_SET):\\n \\\"\\\"\\\"\\n Has the same result as Python's reduce function, but performs the calculations in a different order.\\n\\n This is important when the operator is constructing data structures such as search query clases.\\n This method will make the resulting data structures flatter, so operations that need to traverse\\n them don't end up crashing with recursion errors.\\n\\n For example:\\n\\n Python's builtin reduce() function will do the following calculation:\\n\\n reduce(add, [1, 2, 3, 4, 5, 6, 7, 8])\\n (1 + (2 + (3 + (4 + (5 + (6 + (7 + 8)))))))\\n\\n When using this with query classes, it would create a large data structure with a depth of 7\\n Whereas balanced_reduce will execute this like so:\\n\\n balanced_reduce(add, [1, 2, 3, 4, 5, 6, 7, 8])\\n ((1 + 2) + (3 + 4)) + ((5 + 6) + (7 + 8))\\n\\n Which only has a depth of 2\\n \\\"\\\"\\\"\\n # Casting all iterables to list makes the implementation simpler\\n if not isinstance(seq, list):\\n seq = list(seq)\\n\\n # Note, it needs to be possible to use None as an initial value\\n if initializer is not NOT_SET:\\n if len(seq) == 0:\\n return initializer\\n else:\\n return operator(initializer, balanced_reduce(operator, seq))\\n\\n if len(seq) == 0:\\n raise TypeError(\\\"reduce() of empty sequence with no initial value\\\")\\n elif len(seq) == 1:\\n return seq[0]\\n else:\\n break_point = len(seq) // 2\\n first_set = balanced_reduce(operator, seq[:break_point])\\n second_set = balanced_reduce(operator, seq[break_point:])\\n return operator(first_set, second_set)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/search/utils.py::normalise_query_string\",\n \"text\": \"wagtail/search/utils.py::normalise_query_string\\ndef normalise_query_string(query_string):\\n # Truncate query string\\n if len(query_string) > MAX_QUERY_STRING_LENGTH:\\n query_string = query_string[:MAX_QUERY_STRING_LENGTH]\\n # Convert query_string to lowercase\\n query_string = query_string.lower()\\n\\n # Remove leading, trailing and multiple spaces\\n query_string = re.sub(' +', ' ', query_string).strip()\\n\\n return query_string\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/search/utils.py::separate_filters_from_query\",\n \"text\": \"wagtail/search/utils.py::separate_filters_from_query\\ndef separate_filters_from_query(query_string):\\n filters_regexp = r'(\\\\w+):(\\\\w+|\\\".+\\\")'\\n\\n filters = {}\\n for match_object in re.finditer(filters_regexp, query_string):\\n key, value = match_object.groups()\\n filters[key] = value.strip(\\\"\\\\\\\"\\\")\\n\\n query_string = re.sub(filters_regexp, '', query_string).strip()\\n\\n return filters, query_string\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/search/utils.py::parse_query_string\",\n \"text\": \"wagtail/search/utils.py::parse_query_string\\ndef parse_query_string(query_string, operator=None, zero_terms=MATCH_NONE):\\n \\\"\\\"\\\"\\n This takes a query string typed in by a user and extracts the following:\\n\\n - Quoted terms (for phrase search)\\n - Filters\\n\\n For example, the following query:\\n\\n `hello \\\"this is a phrase\\\" live:true` would be parsed into:\\n\\n filters: {'live': 'true'}\\n tokens: And([PlainText('hello'), Phrase('this is a phrase')])\\n \\\"\\\"\\\"\\n filters, query_string = separate_filters_from_query(query_string)\\n\\n is_phrase = False\\n tokens = []\\n for part in query_string.split('\\\"'):\\n part = part.strip()\\n\\n if part:\\n if is_phrase:\\n tokens.append(Phrase(part))\\n else:\\n tokens.append(PlainText(part, operator=operator or PlainText.DEFAULT_OPERATOR))\\n\\n is_phrase = not is_phrase\\n\\n if tokens:\\n if operator == 'or':\\n search_query = OR(tokens)\\n else:\\n search_query = AND(tokens)\\n else:\\n search_query = zero_terms\\n\\n return filters, search_query\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":254,"cells":{"instance_id":{"kind":"string","value":"ipython__ipython-6536"},"repo":{"kind":"string","value":"ipython/ipython"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"can't configure inline plot in qtconsole\nThis problem exists in ipython 3.0.dev (git master), but not in ipython 2.1.\nHas the configuration system changed in 3.0?\n\nSuppose I have the following lines in my ~/.ipython/profile_default/ipython_qtconsole_config.py\n\n``` python\nc.IPythonWidget.font_size = 20\nc.InlineBackend.rc = {'font.size': 18, 'figure.figsize': (9.0, 6.0)}\n```\n\nIf I start ipython by\n$ ipython qtconsole --pylab=inline\nthen IPythonWidget.font_size is correctly handled but InlineBackend.rc is ignored.\n\nipython 3.0 (git master)\nmatplotlib 1.5 (git master)\nMacOSX 10.8.5 / Python 2.7.2 (Apple)\n\n"},"gold_files":{"kind":"list like","value":["IPython/config/application.py","IPython/consoleapp.py","IPython/core/profileapp.py","IPython/kernel/zmq/kernelapp.py","docs/autogen_config.py"],"string":"[\n \"IPython/config/application.py\",\n \"IPython/consoleapp.py\",\n \"IPython/core/profileapp.py\",\n \"IPython/kernel/zmq/kernelapp.py\",\n \"docs/autogen_config.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["IPython/config/application.py::<module>","IPython/config/application.py::Application.flatten_flags","IPython/config/application.py::Application.generate_config_file","IPython/config/application.py::Application.print_alias_help","IPython/config/application.py::Application.print_help","IPython/consoleapp.py::<module>","IPython/core/profileapp.py::ProfileCreate.init_config_files","IPython/kernel/zmq/kernelapp.py::<module>","IPython/kernel/zmq/kernelapp.py::IPKernelApp._config_file_name_default","docs/autogen_config.py::<module>"],"string":"[\n \"IPython/config/application.py::<module>\",\n \"IPython/config/application.py::Application.flatten_flags\",\n \"IPython/config/application.py::Application.generate_config_file\",\n \"IPython/config/application.py::Application.print_alias_help\",\n \"IPython/config/application.py::Application.print_help\",\n \"IPython/consoleapp.py::<module>\",\n \"IPython/core/profileapp.py::ProfileCreate.init_config_files\",\n \"IPython/kernel/zmq/kernelapp.py::<module>\",\n \"IPython/kernel/zmq/kernelapp.py::IPKernelApp._config_file_name_default\",\n \"docs/autogen_config.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"IPython/config/application.py","text":"IPython/config/application.py\ndef catch_config_error(method, app, *args, **kwargs) # Method decorator for catching invalid config (Trait/ArgumentErrors) during init.\nclass ApplicationError\nclass LevelFormatter # Formatter with additional `highlevel` record\n def format(self, record)\nclass Application # A singleton application with full configuration support.\n def _log_level_changed(self, name, old, new) # Adjust the log level when log_level is set.\n def _log_datefmt_changed(self, name, old, new)\n def _log_format_changed(self, name, old, new) # Change the log formatter when log_format is set.\n def _log_default(self) # Start logging for this application.\n def _flags_changed(self, name, old, new) # ensure flags dict is valid\n def __init__(self, **kwargs)\n def _config_changed(self, name, old, new)\n def initialize(self, argv=None) # Do the basic steps to configure me.\n def start(self) # Start the app mainloop.\n def print_alias_help(self) # Print the alias part of the help.\n def print_flag_help(self) # Print the flag part of the help.\n def print_options(self)\n def print_subcommands(self) # Print the subcommand part of the help.\n def print_help(self, classes=False) # Print the help for each Configurable class in self.classes.\n def print_description(self) # Print the application description.\n def print_examples(self) # Print usage and examples.\n def print_version(self) # Print the version string.\n def update_config(self, config) # Fire the traits events when the config is updated.\n def initialize_subcommand(self, subc, argv=None) # Initialize a subcommand with argv.\n def flatten_flags(self) # flatten flags and aliases, so cl-args override as expected.\n def parse_command_line(self, argv=None) # Parse the command line arguments.\n def _load_config_files(cls, basefilename, path=None, log=None) # Load config files (py,json) by filename and path.\n def load_config_file(self, filename, path=None) # Load config files by filename and path.\n def generate_config_file(self) # generate default config file from Configurables\n def exit(self, exit_status=0)\n def launch_instance(cls, argv=None, **kwargs) # Launch a global instance of this Application\ndef boolean_flag(name, configurable, set_help='', unset_help='') # Helper for building basic --trait, --no-trait flags.","label":1},{"cid":"IPython/consoleapp.py","text":"IPython/consoleapp.py\nclass IPythonConsoleApp\n def _connection_file_default(self)\n def build_kernel_argv(self, argv=None) # build argv to be passed to kernel subprocess\n def init_connection_file(self) # find the connection file, and load the info if found.\n def init_ssh(self) # set up ssh tunnels, if needed.\n def _new_connection_file(self)\n def init_kernel_manager(self)\n def init_kernel_client(self)\n def initialize(self, argv=None) # Classes which mix this class in should call:","label":1},{"cid":"IPython/core/profileapp.py","text":"IPython/core/profileapp.py\ndef list_profiles_in(path) # list profiles in a given root directory\ndef list_bundled_profiles() # list profiles that are bundled with IPython.\nclass ProfileLocate\n def parse_command_line(self, argv=None)\n def start(self)\nclass ProfileList\n def _print_profiles(self, profiles) # print list of profiles, indented.\n def list_profile_dirs(self)\n def start(self)\nclass ProfileCreate\n def _log_format_default(self)\n def _copy_config_files_default(self)\n def _parallel_changed(self, name, old, new)\n def parse_command_line(self, argv)\n def _import_app(self, app_path) # import an app class\n def init_config_files(self)\n def stage_default_config_file(self)\nclass ProfileApp\n def start(self)","label":1},{"cid":"IPython/core/release.py","text":"IPython/core/release.py","label":0},{"cid":"IPython/core/usage.py","text":"IPython/core/usage.py\ndef page_guiref(arg_s=None) # Show a basic reference about the GUI Console.","label":0},{"cid":"IPython/html/notebookapp.py","text":"IPython/html/notebookapp.py\ndef random_ports(port, n) # Generate a list of n random ports near the given port.\ndef load_handlers(name) # Load the (URL pattern, handler) tuples for each component.\nclass NotebookWebApplication\n def __init__(self, ipython_app, kernel_manager, contents_manager, cluster_manager, session_manager, kernel_spec_manager, log, base_url, default_url, settings_overrides, jinja_env_options)\n def init_settings(self, ipython_app, kernel_manager, contents_manager, cluster_manager, session_manager, kernel_spec_manager, log, base_url, default_url, settings_overrides, jinja_env_options=None)\n def init_handlers(self, settings)\nclass NbserverListApp\n def start(self)\nclass NotebookApp\n def _log_level_default(self)\n def _log_datefmt_default(self) # Exclude date from default date format\n def _log_format_default(self) # override default log format to include time\n def _file_to_run_changed(self, name, old, new)\n def _ip_changed(self, name, old, new)\n def _cookie_secret_file_default(self)\n def _cookie_secret_default(self)\n def _write_cookie_secret_file(self, secret) # write my secret to my secret_file\n def _webapp_settings_changed(self, name, old, new)\n def _enable_mathjax_changed(self, name, old, new) # set mathjax url to empty if mathjax is disabled\n def _base_url_changed(self, name, old, new)\n def _base_project_url_changed(self, name, old, new)\n def _extra_static_paths_default(self)\n def static_file_path(self) # return extra paths + the default location\n def _nbextensions_path_default(self)\n def _mathjax_url_default(self)\n def _mathjax_url_changed(self, name, old, new)\n def _kernel_spec_manager_default(self)\n def _info_file_default(self)\n def _pylab_changed(self, name, old, new) # when --pylab is specified, display a warning and exit\n def _notebook_dir_changed(self, name, old, new) # Do a bit of validation of the notebook dir.\n def parse_command_line(self, argv=None)\n def init_kernel_argv(self) # construct the kernel arguments\n def init_configurables(self)\n def init_logging(self)\n def init_webapp(self) # initialize tornado webapp and httpserver\n def display_url(self)\n def connection_url(self)\n def _url(self, ip)\n def init_signal(self)\n def _handle_sigint(self, sig, frame) # SIGINT handler spawns confirmation dialog\n def _restore_sigint_handler(self) # callback for restoring original SIGINT handler\n def _confirm_exit(self) # confirm shutdown on ^C\n def _signal_stop(self, sig, frame)\n def _signal_info(self, sig, frame)\n def init_components(self) # Check the components submodule, and warn if it's unclean\n def initialize(self, argv=None)\n def cleanup_kernels(self) # Shutdown all kernels.\n def notebook_info(self) # Return the current working directory and the server url information\n def server_info(self) # Return a JSONable dict of information about this server.\n def write_server_info_file(se","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py","text":"IPython/kernel/zmq/kernelapp.py\nclass IPKernelApp\n def _config_file_name_default(self)\n def abs_connection_file(self)\n def init_crash_handler(self)\n def init_poller(self)\n def _bind_socket(self, s, port)\n def write_connection_file(self) # write connection info to JSON file\n def cleanup_connection_file(self)\n def init_connection_file(self)\n def init_sockets(self)\n def init_heartbeat(self) # start the heart beating\n def log_connection_info(self) # display connection info, and store ports\n def init_blackhole(self) # redirects stdout/stderr to devnull if necessary\n def init_io(self) # Redirect input streams and set a display hook.\n def init_signal(self)\n def init_kernel(self) # Create the Kernel object itself\n def init_gui_pylab(self) # Enable GUI event loop integration, taking pylab into account.\n def init_shell(self)\n def initialize(self, argv=None)\n def start(self)\ndef main() # Run an IPKernel as an application","label":1},{"cid":"IPython/kernel/zmq/pylab/config.py","text":"IPython/kernel/zmq/pylab/config.py\ndef pil_available() # Test if PIL/Pillow is available\nclass InlineBackendConfig\nclass InlineBackend # An object to store configuration of the inline backend.\n def _config_changed(self, name, old, new)\n def _update_figure_formatters(self)\n def _figure_formats_changed(self, name, old, new)\n def _figure_format_changed(self, name, old, new)","label":0},{"cid":"IPython/terminal/ipapp.py","text":"IPython/terminal/ipapp.py\nclass IPAppCrashHandler # sys.excepthook for IPython itself, leaves a detailed report on disk.\n def __init__(self, app)\n def make_report(self, traceback) # Return a string containing a crash report.\nclass LocateIPythonApp\n def start(self)\nclass TerminalIPythonApp\n def _classes_default(self) # This has to be in a method, for TerminalIPythonApp to be available.\n def _quick_changed(self, name, old, new)\n def _force_interact_changed(self, name, old, new)\n def _file_to_run_changed(self, name, old, new)\n def parse_command_line(self, argv=None) # override to allow old '-pylab' flag with deprecation warning\n def initialize(self, argv=None) # Do actions after construct, but before starting the app.\n def init_shell(self) # initialize the InteractiveShell instance\n def init_banner(self) # optionally display the banner\n def _pylab_changed(self, name, old, new) # Replace --pylab='inline' with --pylab='auto'\n def start(self)\ndef load_default_config(ipython_dir=None) # Load the default config file from the default ipython_dir.","label":0},{"cid":"docs/autogen_config.py","text":"docs/autogen_config.py\ndef document_config_options(classes)\ndef write_doc(name, title, classes, preamble=None)","label":1},{"cid":"tools/backport_pr.py","text":"tools/backport_pr.py\ndef find_rejects(root='.')\ndef get_current_branch()\ndef backport_pr(branch, num, project='ipython/ipython')\ndef already_backported(branch, since_tag=None) # return set of PRs that have been backported already\ndef should_backport(labels=None, milestone=None) # return set of PRs marked for backport","label":0},{"cid":"tools/github_stats.py","text":"tools/github_stats.py\ndef round_hour(dt)\ndef _parse_datetime(s) # Parse dates in the format returned by the Github API.\ndef issues2dict(issues) # Convert a list of issues to a dict, keyed by issue number.\ndef split_pulls(all_issues, project='ipython/ipython') # split a list of closed issues into non-PR Issues and Pull Requests\ndef issues_closed_since(period=timedelta(days=365), project='ipython/ipython', pulls=False) # Get all issues closed since a particular point in time. period\ndef sorted_by_field(issues, field='closed_at', reverse=False) # Return a list of issues sorted by closing date date.\ndef report(issues, show_urls=False) # Summary report about a list of issues, printing number and title.","label":0}],"string":"[\n {\n \"cid\": \"IPython/config/application.py\",\n \"text\": \"IPython/config/application.py\\ndef catch_config_error(method, app, *args, **kwargs) # Method decorator for catching invalid config (Trait/ArgumentErrors) during init.\\nclass ApplicationError\\nclass LevelFormatter # Formatter with additional `highlevel` record\\n def format(self, record)\\nclass Application # A singleton application with full configuration support.\\n def _log_level_changed(self, name, old, new) # Adjust the log level when log_level is set.\\n def _log_datefmt_changed(self, name, old, new)\\n def _log_format_changed(self, name, old, new) # Change the log formatter when log_format is set.\\n def _log_default(self) # Start logging for this application.\\n def _flags_changed(self, name, old, new) # ensure flags dict is valid\\n def __init__(self, **kwargs)\\n def _config_changed(self, name, old, new)\\n def initialize(self, argv=None) # Do the basic steps to configure me.\\n def start(self) # Start the app mainloop.\\n def print_alias_help(self) # Print the alias part of the help.\\n def print_flag_help(self) # Print the flag part of the help.\\n def print_options(self)\\n def print_subcommands(self) # Print the subcommand part of the help.\\n def print_help(self, classes=False) # Print the help for each Configurable class in self.classes.\\n def print_description(self) # Print the application description.\\n def print_examples(self) # Print usage and examples.\\n def print_version(self) # Print the version string.\\n def update_config(self, config) # Fire the traits events when the config is updated.\\n def initialize_subcommand(self, subc, argv=None) # Initialize a subcommand with argv.\\n def flatten_flags(self) # flatten flags and aliases, so cl-args override as expected.\\n def parse_command_line(self, argv=None) # Parse the command line arguments.\\n def _load_config_files(cls, basefilename, path=None, log=None) # Load config files (py,json) by filename and path.\\n def load_config_file(self, filename, path=None) # Load config files by filename and path.\\n def generate_config_file(self) # generate default config file from Configurables\\n def exit(self, exit_status=0)\\n def launch_instance(cls, argv=None, **kwargs) # Launch a global instance of this Application\\ndef boolean_flag(name, configurable, set_help='', unset_help='') # Helper for building basic --trait, --no-trait flags.\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/consoleapp.py\",\n \"text\": \"IPython/consoleapp.py\\nclass IPythonConsoleApp\\n def _connection_file_default(self)\\n def build_kernel_argv(self, argv=None) # build argv to be passed to kernel subprocess\\n def init_connection_file(self) # find the connection file, and load the info if found.\\n def init_ssh(self) # set up ssh tunnels, if needed.\\n def _new_connection_file(self)\\n def init_kernel_manager(self)\\n def init_kernel_client(self)\\n def initialize(self, argv=None) # Classes which mix this class in should call:\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/core/profileapp.py\",\n \"text\": \"IPython/core/profileapp.py\\ndef list_profiles_in(path) # list profiles in a given root directory\\ndef list_bundled_profiles() # list profiles that are bundled with IPython.\\nclass ProfileLocate\\n def parse_command_line(self, argv=None)\\n def start(self)\\nclass ProfileList\\n def _print_profiles(self, profiles) # print list of profiles, indented.\\n def list_profile_dirs(self)\\n def start(self)\\nclass ProfileCreate\\n def _log_format_default(self)\\n def _copy_config_files_default(self)\\n def _parallel_changed(self, name, old, new)\\n def parse_command_line(self, argv)\\n def _import_app(self, app_path) # import an app class\\n def init_config_files(self)\\n def stage_default_config_file(self)\\nclass ProfileApp\\n def start(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/core/release.py\",\n \"text\": \"IPython/core/release.py\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/usage.py\",\n \"text\": \"IPython/core/usage.py\\ndef page_guiref(arg_s=None) # Show a basic reference about the GUI Console.\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py\",\n \"text\": \"IPython/html/notebookapp.py\\ndef random_ports(port, n) # Generate a list of n random ports near the given port.\\ndef load_handlers(name) # Load the (URL pattern, handler) tuples for each component.\\nclass NotebookWebApplication\\n def __init__(self, ipython_app, kernel_manager, contents_manager, cluster_manager, session_manager, kernel_spec_manager, log, base_url, default_url, settings_overrides, jinja_env_options)\\n def init_settings(self, ipython_app, kernel_manager, contents_manager, cluster_manager, session_manager, kernel_spec_manager, log, base_url, default_url, settings_overrides, jinja_env_options=None)\\n def init_handlers(self, settings)\\nclass NbserverListApp\\n def start(self)\\nclass NotebookApp\\n def _log_level_default(self)\\n def _log_datefmt_default(self) # Exclude date from default date format\\n def _log_format_default(self) # override default log format to include time\\n def _file_to_run_changed(self, name, old, new)\\n def _ip_changed(self, name, old, new)\\n def _cookie_secret_file_default(self)\\n def _cookie_secret_default(self)\\n def _write_cookie_secret_file(self, secret) # write my secret to my secret_file\\n def _webapp_settings_changed(self, name, old, new)\\n def _enable_mathjax_changed(self, name, old, new) # set mathjax url to empty if mathjax is disabled\\n def _base_url_changed(self, name, old, new)\\n def _base_project_url_changed(self, name, old, new)\\n def _extra_static_paths_default(self)\\n def static_file_path(self) # return extra paths + the default location\\n def _nbextensions_path_default(self)\\n def _mathjax_url_default(self)\\n def _mathjax_url_changed(self, name, old, new)\\n def _kernel_spec_manager_default(self)\\n def _info_file_default(self)\\n def _pylab_changed(self, name, old, new) # when --pylab is specified, display a warning and exit\\n def _notebook_dir_changed(self, name, old, new) # Do a bit of validation of the notebook dir.\\n def parse_command_line(self, argv=None)\\n def init_kernel_argv(self) # construct the kernel arguments\\n def init_configurables(self)\\n def init_logging(self)\\n def init_webapp(self) # initialize tornado webapp and httpserver\\n def display_url(self)\\n def connection_url(self)\\n def _url(self, ip)\\n def init_signal(self)\\n def _handle_sigint(self, sig, frame) # SIGINT handler spawns confirmation dialog\\n def _restore_sigint_handler(self) # callback for restoring original SIGINT handler\\n def _confirm_exit(self) # confirm shutdown on ^C\\n def _signal_stop(self, sig, frame)\\n def _signal_info(self, sig, frame)\\n def init_components(self) # Check the components submodule, and warn if it's unclean\\n def initialize(self, argv=None)\\n def cleanup_kernels(self) # Shutdown all kernels.\\n def notebook_info(self) # Return the current working directory and the server url information\\n def server_info(self) # Return a JSONable dict of information about this server.\\n def write_server_info_file(se\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py\\nclass IPKernelApp\\n def _config_file_name_default(self)\\n def abs_connection_file(self)\\n def init_crash_handler(self)\\n def init_poller(self)\\n def _bind_socket(self, s, port)\\n def write_connection_file(self) # write connection info to JSON file\\n def cleanup_connection_file(self)\\n def init_connection_file(self)\\n def init_sockets(self)\\n def init_heartbeat(self) # start the heart beating\\n def log_connection_info(self) # display connection info, and store ports\\n def init_blackhole(self) # redirects stdout/stderr to devnull if necessary\\n def init_io(self) # Redirect input streams and set a display hook.\\n def init_signal(self)\\n def init_kernel(self) # Create the Kernel object itself\\n def init_gui_pylab(self) # Enable GUI event loop integration, taking pylab into account.\\n def init_shell(self)\\n def initialize(self, argv=None)\\n def start(self)\\ndef main() # Run an IPKernel as an application\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/kernel/zmq/pylab/config.py\",\n \"text\": \"IPython/kernel/zmq/pylab/config.py\\ndef pil_available() # Test if PIL/Pillow is available\\nclass InlineBackendConfig\\nclass InlineBackend # An object to store configuration of the inline backend.\\n def _config_changed(self, name, old, new)\\n def _update_figure_formatters(self)\\n def _figure_formats_changed(self, name, old, new)\\n def _figure_format_changed(self, name, old, new)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py\",\n \"text\": \"IPython/terminal/ipapp.py\\nclass IPAppCrashHandler # sys.excepthook for IPython itself, leaves a detailed report on disk.\\n def __init__(self, app)\\n def make_report(self, traceback) # Return a string containing a crash report.\\nclass LocateIPythonApp\\n def start(self)\\nclass TerminalIPythonApp\\n def _classes_default(self) # This has to be in a method, for TerminalIPythonApp to be available.\\n def _quick_changed(self, name, old, new)\\n def _force_interact_changed(self, name, old, new)\\n def _file_to_run_changed(self, name, old, new)\\n def parse_command_line(self, argv=None) # override to allow old '-pylab' flag with deprecation warning\\n def initialize(self, argv=None) # Do actions after construct, but before starting the app.\\n def init_shell(self) # initialize the InteractiveShell instance\\n def init_banner(self) # optionally display the banner\\n def _pylab_changed(self, name, old, new) # Replace --pylab='inline' with --pylab='auto'\\n def start(self)\\ndef load_default_config(ipython_dir=None) # Load the default config file from the default ipython_dir.\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/autogen_config.py\",\n \"text\": \"docs/autogen_config.py\\ndef document_config_options(classes)\\ndef write_doc(name, title, classes, preamble=None)\",\n \"label\": 1\n },\n {\n \"cid\": \"tools/backport_pr.py\",\n \"text\": \"tools/backport_pr.py\\ndef find_rejects(root='.')\\ndef get_current_branch()\\ndef backport_pr(branch, num, project='ipython/ipython')\\ndef already_backported(branch, since_tag=None) # return set of PRs that have been backported already\\ndef should_backport(labels=None, milestone=None) # return set of PRs marked for backport\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/github_stats.py\",\n \"text\": \"tools/github_stats.py\\ndef round_hour(dt)\\ndef _parse_datetime(s) # Parse dates in the format returned by the Github API.\\ndef issues2dict(issues) # Convert a list of issues to a dict, keyed by issue number.\\ndef split_pulls(all_issues, project='ipython/ipython') # split a list of closed issues into non-PR Issues and Pull Requests\\ndef issues_closed_since(period=timedelta(days=365), project='ipython/ipython', pulls=False) # Get all issues closed since a particular point in time. period\\ndef sorted_by_field(issues, field='closed_at', reverse=False) # Return a list of issues sorted by closing date date.\\ndef report(issues, show_urls=False) # Summary report about a list of issues, printing number and title.\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"IPython/config/application.py::catch_config_error","text":"IPython/config/application.py::catch_config_error\n@decorator\ndef catch_config_error(method, app, *args, **kwargs):\n \"\"\"Method decorator for catching invalid config (Trait/ArgumentErrors) during init.\n\n On a TraitError (generally caused by bad config), this will print the trait's\n message, and exit the app.\n \n For use on init methods, to prevent invoking excepthook on invalid input.\n \"\"\"\n try:\n return method(app, *args, **kwargs)\n except (TraitError, ArgumentError) as e:\n app.print_help()\n app.log.fatal(\"Bad config encountered during initialization:\")\n app.log.fatal(str(e))\n app.log.debug(\"Config at the time: %s\", app.config)\n app.exit(1)","label":0},{"cid":"IPython/config/application.py::LevelFormatter.format","text":"IPython/config/application.py::LevelFormatter.format\n def format(self, record):\n if record.levelno >= self.highlevel_limit:\n record.highlevel = self.highlevel_format % record.__dict__\n else:\n record.highlevel = \"\"\n return super(LevelFormatter, self).format(record)","label":0},{"cid":"IPython/config/application.py::Application._log_level_changed","text":"IPython/config/application.py::Application._log_level_changed\n def _log_level_changed(self, name, old, new):\n \"\"\"Adjust the log level when log_level is set.\"\"\"\n if isinstance(new, string_types):\n new = getattr(logging, new)\n self.log_level = new\n self.log.setLevel(new)","label":0},{"cid":"IPython/config/application.py::Application._log_datefmt_changed","text":"IPython/config/application.py::Application._log_datefmt_changed\n def _log_datefmt_changed(self, name, old, new):\n self._log_format_changed()","label":0},{"cid":"IPython/config/application.py::Application._log_format_changed","text":"IPython/config/application.py::Application._log_format_changed\n def _log_format_changed(self, name, old, new):\n \"\"\"Change the log formatter when log_format is set.\"\"\"\n _log_handler = self.log.handlers[0]\n _log_formatter = self._log_formatter_cls(fmt=new, datefmt=self.log_datefmt)\n _log_handler.setFormatter(_log_formatter)","label":0},{"cid":"IPython/config/application.py::Application._log_default","text":"IPython/config/application.py::Application._log_default\n def _log_default(self):\n \"\"\"Start logging for this application.\n\n The default is to log to stderr using a StreamHandler, if no default\n handler already exists. The log level starts at logging.WARN, but this\n can be adjusted by setting the ``log_level`` attribute.\n \"\"\"\n log = logging.getLogger(self.__class__.__name__)\n log.setLevel(self.log_level)\n log.propagate = False\n _log = log # copied from Logger.hasHandlers() (new in Python 3.2)\n while _log:\n if _log.handlers:\n return log\n if not _log.propagate:\n break\n else:\n _log = _log.parent\n if sys.executable.endswith('pythonw.exe'):\n # this should really go to a file, but file-logging is only\n # hooked up in parallel applications\n _log_handler = logging.StreamHandler(open(os.devnull, 'w'))\n else:\n _log_handler = logging.StreamHandler()\n _log_formatter = self._log_formatter_cls(fmt=self.log_format, datefmt=self.log_datefmt)\n _log_handler.setFormatter(_log_formatter)\n log.addHandler(_log_handler)\n return log","label":0},{"cid":"IPython/config/application.py::Application._flags_changed","text":"IPython/config/application.py::Application._flags_changed\n def _flags_changed(self, name, old, new):\n \"\"\"ensure flags dict is valid\"\"\"\n for key,value in iteritems(new):\n assert len(value) == 2, \"Bad flag: %r:%s\"%(key,value)\n assert isinstance(value[0], (dict, Config)), \"Bad flag: %r:%s\"%(key,value)\n assert isinstance(value[1], string_types), \"Bad flag: %r:%s\"%(key,value)","label":0},{"cid":"IPython/config/application.py::Application.__init__","text":"IPython/config/application.py::Application.__init__\n def __init__(self, **kwargs):\n SingletonConfigurable.__init__(self, **kwargs)\n # Ensure my class is in self.classes, so my attributes appear in command line\n # options and config files.\n if self.__class__ not in self.classes:\n self.classes.insert(0, self.__class__)","label":0},{"cid":"IPython/config/application.py::Application._config_changed","text":"IPython/config/application.py::Application._config_changed\n def _config_changed(self, name, old, new):\n SingletonConfigurable._config_changed(self, name, old, new)\n self.log.debug('Config changed:')\n self.log.debug(repr(new))","label":0},{"cid":"IPython/config/application.py::Application.initialize","text":"IPython/config/application.py::Application.initialize\n @catch_config_error\n def initialize(self, argv=None):\n \"\"\"Do the basic steps to configure me.\n\n Override in subclasses.\n \"\"\"\n self.parse_command_line(argv)","label":0},{"cid":"IPython/config/application.py::Application.start","text":"IPython/config/application.py::Application.start\n def start(self):\n \"\"\"Start the app mainloop.\n\n Override in subclasses.\n \"\"\"\n if self.subapp is not None:\n return self.subapp.start()","label":0},{"cid":"IPython/config/application.py::Application.print_alias_help","text":"IPython/config/application.py::Application.print_alias_help\n def print_alias_help(self):\n \"\"\"Print the alias part of the help.\"\"\"\n if not self.aliases:\n return\n\n lines = []\n classdict = {}\n for cls in self.classes:\n # include all parents (up to, but excluding Configurable) in available names\n for c in cls.mro()[:-3]:\n classdict[c.__name__] = c\n\n for alias, longname in iteritems(self.aliases):\n classname, traitname = longname.split('.',1)\n cls = classdict[classname]\n\n trait = cls.class_traits(config=True)[traitname]\n help = cls.class_get_trait_help(trait).splitlines()\n # reformat first line\n help[0] = help[0].replace(longname, alias) + ' (%s)'%longname\n if len(alias) == 1:\n help[0] = help[0].replace('--%s='%alias, '-%s '%alias)\n lines.extend(help)\n # lines.append('')\n print(os.linesep.join(lines))","label":1},{"cid":"IPython/config/application.py::Application.print_flag_help","text":"IPython/config/application.py::Application.print_flag_help\n def print_flag_help(self):\n \"\"\"Print the flag part of the help.\"\"\"\n if not self.flags:\n return\n\n lines = []\n for m, (cfg,help) in iteritems(self.flags):\n prefix = '--' if len(m) > 1 else '-'\n lines.append(prefix+m)\n lines.append(indent(dedent(help.strip())))\n # lines.append('')\n print(os.linesep.join(lines))","label":0},{"cid":"IPython/config/application.py::Application.print_options","text":"IPython/config/application.py::Application.print_options\n def print_options(self):\n if not self.flags and not self.aliases:\n return\n lines = ['Options']\n lines.append('-'*len(lines[0]))\n lines.append('')\n for p in wrap_paragraphs(self.option_description):\n lines.append(p)\n lines.append('')\n print(os.linesep.join(lines))\n self.print_flag_help()\n self.print_alias_help()\n print()","label":0},{"cid":"IPython/config/application.py::Application.print_subcommands","text":"IPython/config/application.py::Application.print_subcommands\n def print_subcommands(self):\n \"\"\"Print the subcommand part of the help.\"\"\"\n if not self.subcommands:\n return\n\n lines = [\"Subcommands\"]\n lines.append('-'*len(lines[0]))\n lines.append('')\n for p in wrap_paragraphs(self.subcommand_description.format(\n app=self.name)):\n lines.append(p)\n lines.append('')\n for subc, (cls, help) in iteritems(self.subcommands):\n lines.append(subc)\n if help:\n lines.append(indent(dedent(help.strip())))\n lines.append('')\n print(os.linesep.join(lines))","label":0},{"cid":"IPython/config/application.py::Application.print_help","text":"IPython/config/application.py::Application.print_help\n def print_help(self, classes=False):\n \"\"\"Print the help for each Configurable class in self.classes.\n\n If classes=False (the default), only flags and aliases are printed.\n \"\"\"\n self.print_description()\n self.print_subcommands()\n self.print_options()\n\n if classes:\n if self.classes:\n print(\"Class parameters\")\n print(\"----------------\")\n print()\n for p in wrap_paragraphs(self.keyvalue_description):\n print(p)\n print()\n\n for cls in self.classes:\n cls.class_print_help()\n print()\n else:\n print(\"To see all available configurables, use `--help-all`\")\n print()\n\n self.print_examples()","label":1},{"cid":"IPython/config/application.py::Application.print_description","text":"IPython/config/application.py::Application.print_description\n def print_description(self):\n \"\"\"Print the application description.\"\"\"\n for p in wrap_paragraphs(self.description):\n print(p)\n print()","label":0},{"cid":"IPython/config/application.py::Application.print_examples","text":"IPython/config/application.py::Application.print_examples\n def print_examples(self):\n \"\"\"Print usage and examples.\n\n This usage string goes at the end of the command line help string\n and should contain examples of the application's usage.\n \"\"\"\n if self.examples:\n print(\"Examples\")\n print(\"--------\")\n print()\n print(indent(dedent(self.examples.strip())))\n print()","label":0},{"cid":"IPython/config/application.py::Application.print_version","text":"IPython/config/application.py::Application.print_version\n def print_version(self):\n \"\"\"Print the version string.\"\"\"\n print(self.version)","label":0},{"cid":"IPython/config/application.py::Application.update_config","text":"IPython/config/application.py::Application.update_config\n def update_config(self, config):\n \"\"\"Fire the traits events when the config is updated.\"\"\"\n # Save a copy of the current config.\n newconfig = deepcopy(self.config)\n # Merge the new config into the current one.\n newconfig.merge(config)\n # Save the combined config as self.config, which triggers the traits\n # events.\n self.config = newconfig","label":0},{"cid":"IPython/config/application.py::Application.initialize_subcommand","text":"IPython/config/application.py::Application.initialize_subcommand\n @catch_config_error\n def initialize_subcommand(self, subc, argv=None):\n \"\"\"Initialize a subcommand with argv.\"\"\"\n subapp,help = self.subcommands.get(subc)\n\n if isinstance(subapp, string_types):\n subapp = import_item(subapp)\n\n # clear existing instances\n self.__class__.clear_instance()\n # instantiate\n self.subapp = subapp.instance(config=self.config)\n # and initialize subapp\n self.subapp.initialize(argv)","label":0},{"cid":"IPython/config/application.py::Application.flatten_flags","text":"IPython/config/application.py::Application.flatten_flags\n def flatten_flags(self):\n \"\"\"flatten flags and aliases, so cl-args override as expected.\n \n This prevents issues such as an alias pointing to InteractiveShell,\n but a config file setting the same trait in TerminalInteraciveShell\n getting inappropriate priority over the command-line arg.\n\n Only aliases with exactly one descendent in the class list\n will be promoted.\n \n \"\"\"\n # build a tree of classes in our list that inherit from a particular\n # it will be a dict by parent classname of classes in our list\n # that are descendents\n mro_tree = defaultdict(list)\n for cls in self.classes:\n clsname = cls.__name__\n for parent in cls.mro()[1:-3]:\n # exclude cls itself and Configurable,HasTraits,object\n mro_tree[parent.__name__].append(clsname)\n # flatten aliases, which have the form:\n # { 'alias' : 'Class.trait' }\n aliases = {}\n for alias, cls_trait in iteritems(self.aliases):\n cls,trait = cls_trait.split('.',1)\n children = mro_tree[cls]\n if len(children) == 1:\n # exactly one descendent, promote alias\n cls = children[0]\n aliases[alias] = '.'.join([cls,trait])\n \n # flatten flags, which are of the form:\n # { 'key' : ({'Cls' : {'trait' : value}}, 'help')}\n flags = {}\n for key, (flagdict, help) in iteritems(self.flags):\n newflag = {}\n for cls, subdict in iteritems(flagdict):\n children = mro_tree[cls]\n # exactly one descendent, promote flag section\n if len(children) == 1:\n cls = children[0]\n newflag[cls] = subdict\n flags[key] = (newflag, help)\n return flags, aliases","label":1},{"cid":"IPython/config/application.py::Application.parse_command_line","text":"IPython/config/application.py::Application.parse_command_line\n @catch_config_error\n def parse_command_line(self, argv=None):\n \"\"\"Parse the command line arguments.\"\"\"\n argv = sys.argv[1:] if argv is None else argv\n self.argv = [ py3compat.cast_unicode(arg) for arg in argv ]\n \n if argv and argv[0] == 'help':\n # turn `ipython help notebook` into `ipython notebook -h`\n argv = argv[1:] + ['-h']\n\n if self.subcommands and len(argv) > 0:\n # we have subcommands, and one may have been specified\n subc, subargv = argv[0], argv[1:]\n if re.match(r'^\\w(\\-?\\w)*$', subc) and subc in self.subcommands:\n # it's a subcommand, and *not* a flag or class parameter\n return self.initialize_subcommand(subc, subargv)\n\n # Arguments after a '--' argument are for the script IPython may be\n # about to run, not IPython iteslf. For arguments parsed here (help and\n # version), we want to only search the arguments up to the first\n # occurrence of '--', which we're calling interpreted_argv.\n try:\n interpreted_argv = argv[:argv.index('--')]\n except ValueError:\n interpreted_argv = argv\n\n if any(x in interpreted_argv for x in ('-h', '--help-all', '--help')):\n self.print_help('--help-all' in interpreted_argv)\n self.exit(0)\n\n if '--version' in interpreted_argv or '-V' in interpreted_argv:\n self.print_version()\n self.exit(0)\n \n # flatten flags&aliases, so cl-args get appropriate priority:\n flags,aliases = self.flatten_flags()\n loader = KVArgParseConfigLoader(argv=argv, aliases=aliases,\n flags=flags, log=self.log)\n config = loader.load_config()\n self.update_config(config)\n # store unparsed args in extra_args\n self.extra_args = loader.extra_args","label":0},{"cid":"IPython/config/application.py::Application._load_config_files","text":"IPython/config/application.py::Application._load_config_files\n @classmethod\n def _load_config_files(cls, basefilename, path=None, log=None):\n \"\"\"Load config files (py,json) by filename and path.\n\n yield each config object in turn.\n \"\"\"\n pyloader = PyFileConfigLoader(basefilename+'.py', path=path, log=log)\n jsonloader = JSONFileConfigLoader(basefilename+'.json', path=path, log=log)\n config = None\n for loader in [pyloader, jsonloader]:\n try:\n config = loader.load_config()\n except ConfigFileNotFound:\n pass\n except Exception:\n # try to get the full filename, but it will be empty in the\n # unlikely event that the error raised before filefind finished\n filename = loader.full_filename or basefilename\n # problem while running the file\n if log:\n log.error(\"Exception while loading config file %s\",\n filename, exc_info=True)\n else:\n if log:\n log.debug(\"Loaded config file: %s\", loader.full_filename)\n if config:\n yield config\n\n raise StopIteration","label":0},{"cid":"IPython/config/application.py::Application.load_config_file","text":"IPython/config/application.py::Application.load_config_file\n @catch_config_error\n def load_config_file(self, filename, path=None):\n \"\"\"Load config files by filename and path.\"\"\"\n filename, ext = os.path.splitext(filename)\n for config in self._load_config_files(filename, path=path, log=self.log):\n self.update_config(config)","label":0},{"cid":"IPython/config/application.py::Application.generate_config_file","text":"IPython/config/application.py::Application.generate_config_file\n def generate_config_file(self):\n \"\"\"generate default config file from Configurables\"\"\"\n lines = [\"# Configuration file for %s.\"%self.name]\n lines.append('')\n lines.append('c = get_config()')\n lines.append('')\n for cls in self.classes:\n lines.append(cls.class_config_section())\n return '\\n'.join(lines)","label":1},{"cid":"IPython/config/application.py::Application.exit","text":"IPython/config/application.py::Application.exit\n def exit(self, exit_status=0):\n self.log.debug(\"Exiting application: %s\" % self.name)\n sys.exit(exit_status)","label":0},{"cid":"IPython/config/application.py::Application.launch_instance","text":"IPython/config/application.py::Application.launch_instance\n @classmethod\n def launch_instance(cls, argv=None, **kwargs):\n \"\"\"Launch a global instance of this Application\n \n If a global instance already exists, this reinitializes and starts it\n \"\"\"\n app = cls.instance(**kwargs)\n app.initialize(argv)\n app.start()","label":0},{"cid":"IPython/config/application.py::boolean_flag","text":"IPython/config/application.py::boolean_flag\ndef boolean_flag(name, configurable, set_help='', unset_help=''):\n \"\"\"Helper for building basic --trait, --no-trait flags.\n\n Parameters\n ----------\n\n name : str\n The name of the flag.\n configurable : str\n The 'Class.trait' string of the trait to be set/unset with the flag\n set_help : unicode\n help string for --name flag\n unset_help : unicode\n help string for --no-name flag\n\n Returns\n -------\n\n cfg : dict\n A dict with two keys: 'name', and 'no-name', for setting and unsetting\n the trait, respectively.\n \"\"\"\n # default helpstrings\n set_help = set_help or \"set %s=True\"%configurable\n unset_help = unset_help or \"set %s=False\"%configurable\n\n cls,trait = configurable.split('.')\n\n setter = {cls : {trait : True}}\n unsetter = {cls : {trait : False}}\n return {name : (setter, set_help), 'no-'+name : (unsetter, unset_help)}","label":0},{"cid":"IPython/consoleapp.py::IPythonConsoleApp._connection_file_default","text":"IPython/consoleapp.py::IPythonConsoleApp._connection_file_default\n def _connection_file_default(self):\n return 'kernel-%i.json' % os.getpid()","label":0},{"cid":"IPython/consoleapp.py::IPythonConsoleApp.build_kernel_argv","text":"IPython/consoleapp.py::IPythonConsoleApp.build_kernel_argv\n def build_kernel_argv(self, argv=None):\n \"\"\"build argv to be passed to kernel subprocess\"\"\"\n if argv is None:\n argv = sys.argv[1:]\n self.kernel_argv = swallow_argv(argv, self.frontend_aliases, self.frontend_flags)","label":0},{"cid":"IPython/consoleapp.py::IPythonConsoleApp.init_connection_file","text":"IPython/consoleapp.py::IPythonConsoleApp.init_connection_file\n def init_connection_file(self):\n \"\"\"find the connection file, and load the info if found.\n \n The current working directory and the current profile's security\n directory will be searched for the file if it is not given by\n absolute path.\n \n When attempting to connect to an existing kernel and the `--existing`\n argument does not match an existing file, it will be interpreted as a\n fileglob, and the matching file in the current profile's security dir\n with the latest access time will be used.\n \n After this method is called, self.connection_file contains the *full path*\n to the connection file, never just its name.\n \"\"\"\n if self.existing:\n try:\n cf = find_connection_file(self.existing)\n except Exception:\n self.log.critical(\"Could not find existing kernel connection file %s\", self.existing)\n self.exit(1)\n self.log.debug(\"Connecting to existing kernel: %s\" % cf)\n self.connection_file = cf\n else:\n # not existing, check if we are going to write the file\n # and ensure that self.connection_file is a full path, not just the shortname\n try:\n cf = find_connection_file(self.connection_file)\n except Exception:\n # file might not exist\n if self.connection_file == os.path.basename(self.connection_file):\n # just shortname, put it in security dir\n cf = os.path.join(self.profile_dir.security_dir, self.connection_file)\n else:\n cf = self.connection_file\n self.connection_file = cf\n try:\n self.connection_file = filefind(self.connection_file, ['.', self.profile_dir.security_dir])\n except IOError:\n self.log.debug(\"Connecti","label":0},{"cid":"IPython/consoleapp.py::IPythonConsoleApp.init_ssh","text":"IPython/consoleapp.py::IPythonConsoleApp.init_ssh\n def init_ssh(self):\n \"\"\"set up ssh tunnels, if needed.\"\"\"\n if not self.existing or (not self.sshserver and not self.sshkey):\n return\n self.load_connection_file()\n \n transport = self.transport\n ip = self.ip\n \n if transport != 'tcp':\n self.log.error(\"Can only use ssh tunnels with TCP sockets, not %s\", transport)\n sys.exit(-1)\n \n if self.sshkey and not self.sshserver:\n # specifying just the key implies that we are connecting directly\n self.sshserver = ip\n ip = localhost()\n \n # build connection dict for tunnels:\n info = dict(ip=ip,\n shell_port=self.shell_port,\n iopub_port=self.iopub_port,\n stdin_port=self.stdin_port,\n hb_port=self.hb_port\n )\n \n self.log.info(\"Forwarding connections to %s via %s\"%(ip, self.sshserver))\n \n # tunnels return a new set of ports, which will be on localhost:\n self.ip = localhost()\n try:\n newports = tunnel_to_kernel(info, self.sshserver, self.sshkey)\n except:\n # even catch KeyboardInterrupt\n self.log.error(\"Could not setup tunnels\", exc_info=True)\n self.exit(1)\n \n self.shell_port, self.iopub_port, self.stdin_port, self.hb_port = newports\n \n cf = self.connection_file\n base,ext = os.path.splitext(cf)\n base = os.path.basename(base)\n self.connection_file = os.path.basename(base)+'-ssh'+ext\n self.log.info(\"To connect another client via this tunnel, use:\")\n self.log.info(\"--existing %s\" % self.connection_file)","label":0},{"cid":"IPython/consoleapp.py::IPythonConsoleApp._new_connection_file","text":"IPython/consoleapp.py::IPythonConsoleApp._new_connection_file\n def _new_connection_file(self):\n cf = ''\n while not cf:\n # we don't need a 128b id to distinguish kernels, use more readable\n # 48b node segment (12 hex chars). Users running more than 32k simultaneous\n # kernels can subclass.\n ident = str(uuid.uuid4()).split('-')[-1]\n cf = os.path.join(self.profile_dir.security_dir, 'kernel-%s.json' % ident)\n # only keep if it's actually new. Protect against unlikely collision\n # in 48b random search space\n cf = cf if not os.path.exists(cf) else ''\n return cf","label":0},{"cid":"IPython/consoleapp.py::IPythonConsoleApp.init_kernel_manager","text":"IPython/consoleapp.py::IPythonConsoleApp.init_kernel_manager\n def init_kernel_manager(self):\n # Don't let Qt or ZMQ swallow KeyboardInterupts.\n if self.existing:\n self.kernel_manager = None\n return\n signal.signal(signal.SIGINT, signal.SIG_DFL)\n\n # Create a KernelManager and start a kernel.\n try:\n self.kernel_manager = self.kernel_manager_class(\n ip=self.ip,\n session=self.session,\n transport=self.transport,\n shell_port=self.shell_port,\n iopub_port=self.iopub_port,\n stdin_port=self.stdin_port,\n hb_port=self.hb_port,\n connection_file=self.connection_file,\n kernel_name=self.kernel_name,\n parent=self,\n ipython_dir=self.ipython_dir,\n )\n except NoSuchKernel:\n self.log.critical(\"Could not find kernel %s\", self.kernel_name)\n self.exit(1)\n\n self.kernel_manager.client_factory = self.kernel_client_class\n self.kernel_manager.start_kernel(extra_arguments=self.kernel_argv)\n atexit.register(self.kernel_manager.cleanup_ipc_files)\n\n if self.sshserver:\n # ssh, write new connection file\n self.kernel_manager.write_connection_file()\n\n # in case KM defaults / ssh writing changes things:\n km = self.kernel_manager\n self.shell_port=km.shell_port\n self.iopub_port=km.iopub_port\n self.stdin_port=km.stdin_port\n self.hb_port=km.hb_port\n self.connection_file = km.connection_file\n\n atexit.register(self.kernel_manager.cleanup_connection_file)","label":0},{"cid":"IPython/consoleapp.py::IPythonConsoleApp.init_kernel_client","text":"IPython/consoleapp.py::IPythonConsoleApp.init_kernel_client\n def init_kernel_client(self):\n if self.kernel_manager is not None:\n self.kernel_client = self.kernel_manager.client()\n else:\n self.kernel_client = self.kernel_client_class(\n session=self.session,\n ip=self.ip,\n transport=self.transport,\n shell_port=self.shell_port,\n iopub_port=self.iopub_port,\n stdin_port=self.stdin_port,\n hb_port=self.hb_port,\n connection_file=self.connection_file,\n parent=self,\n )\n\n self.kernel_client.start_channels()","label":0},{"cid":"IPython/consoleapp.py::IPythonConsoleApp.initialize","text":"IPython/consoleapp.py::IPythonConsoleApp.initialize\n def initialize(self, argv=None):\n \"\"\"\n Classes which mix this class in should call:\n IPythonConsoleApp.initialize(self,argv)\n \"\"\"\n self.init_connection_file()\n default_secure(self.config)\n self.init_ssh()\n self.init_kernel_manager()\n self.init_kernel_client()","label":0},{"cid":"IPython/core/profileapp.py::list_profiles_in","text":"IPython/core/profileapp.py::list_profiles_in\ndef list_profiles_in(path):\n \"\"\"list profiles in a given root directory\"\"\"\n files = os.listdir(path)\n profiles = []\n for f in files:\n try:\n full_path = os.path.join(path, f)\n except UnicodeError:\n continue\n if os.path.isdir(full_path) and f.startswith('profile_'):\n profiles.append(f.split('_',1)[-1])\n return profiles","label":0},{"cid":"IPython/core/profileapp.py::list_bundled_profiles","text":"IPython/core/profileapp.py::list_bundled_profiles\ndef list_bundled_profiles():\n \"\"\"list profiles that are bundled with IPython.\"\"\"\n path = os.path.join(get_ipython_package_dir(), u'config', u'profile')\n files = os.listdir(path)\n profiles = []\n for profile in files:\n full_path = os.path.join(path, profile)\n if os.path.isdir(full_path) and profile != \"__pycache__\":\n profiles.append(profile)\n return profiles","label":0},{"cid":"IPython/core/profileapp.py::ProfileLocate.parse_command_line","text":"IPython/core/profileapp.py::ProfileLocate.parse_command_line\n def parse_command_line(self, argv=None):\n super(ProfileLocate, self).parse_command_line(argv)\n if self.extra_args:\n self.profile = self.extra_args[0]","label":0},{"cid":"IPython/core/profileapp.py::ProfileLocate.start","text":"IPython/core/profileapp.py::ProfileLocate.start\n def start(self):\n print(self.profile_dir.location)","label":0},{"cid":"IPython/core/profileapp.py::ProfileList._print_profiles","text":"IPython/core/profileapp.py::ProfileList._print_profiles\n def _print_profiles(self, profiles):\n \"\"\"print list of profiles, indented.\"\"\"\n for profile in profiles:\n print(' %s' % profile)","label":0},{"cid":"IPython/core/profileapp.py::ProfileList.list_profile_dirs","text":"IPython/core/profileapp.py::ProfileList.list_profile_dirs\n def list_profile_dirs(self):\n profiles = list_bundled_profiles()\n if profiles:\n print()\n print(\"Available profiles in IPython:\")\n self._print_profiles(profiles)\n print()\n print(\" The first request for a bundled profile will copy it\")\n print(\" into your IPython directory (%s),\" % self.ipython_dir)\n print(\" where you can customize it.\")\n \n profiles = list_profiles_in(self.ipython_dir)\n if profiles:\n print()\n print(\"Available profiles in %s:\" % self.ipython_dir)\n self._print_profiles(profiles)\n \n profiles = list_profiles_in(py3compat.getcwd())\n if profiles:\n print()\n print(\"Available profiles in current directory (%s):\" % py3compat.getcwd())\n self._print_profiles(profiles)\n \n print()\n print(\"To use any of the above profiles, start IPython with:\")\n print(\" ipython --profile=<name>\")\n print()","label":0},{"cid":"IPython/core/profileapp.py::ProfileList.start","text":"IPython/core/profileapp.py::ProfileList.start\n def start(self):\n self.list_profile_dirs()","label":0},{"cid":"IPython/core/profileapp.py::ProfileCreate._log_format_default","text":"IPython/core/profileapp.py::ProfileCreate._log_format_default\n def _log_format_default(self):\n return \"[%(name)s] %(message)s\"","label":0},{"cid":"IPython/core/profileapp.py::ProfileCreate._copy_config_files_default","text":"IPython/core/profileapp.py::ProfileCreate._copy_config_files_default\n def _copy_config_files_default(self):\n return True","label":0},{"cid":"IPython/core/profileapp.py::ProfileCreate._parallel_changed","text":"IPython/core/profileapp.py::ProfileCreate._parallel_changed\n def _parallel_changed(self, name, old, new):\n parallel_files = [ 'ipcontroller_config.py',\n 'ipengine_config.py',\n 'ipcluster_config.py'\n ]\n if new:\n for cf in parallel_files:\n self.config_files.append(cf)\n else:\n for cf in parallel_files:\n if cf in self.config_files:\n self.config_files.remove(cf)","label":0},{"cid":"IPython/core/profileapp.py::ProfileCreate.parse_command_line","text":"IPython/core/profileapp.py::ProfileCreate.parse_command_line\n def parse_command_line(self, argv):\n super(ProfileCreate, self).parse_command_line(argv)\n # accept positional arg as profile name\n if self.extra_args:\n self.profile = self.extra_args[0]","label":0},{"cid":"IPython/core/profileapp.py::ProfileCreate._import_app","text":"IPython/core/profileapp.py::ProfileCreate._import_app\n def _import_app(self, app_path):\n \"\"\"import an app class\"\"\"\n app = None\n name = app_path.rsplit('.', 1)[-1]\n try:\n app = import_item(app_path)\n except ImportError:\n self.log.info(\"Couldn't import %s, config file will be excluded\", name)\n except Exception:\n self.log.warn('Unexpected error importing %s', name, exc_info=True)\n return app","label":0},{"cid":"IPython/core/profileapp.py::ProfileCreate.init_config_files","text":"IPython/core/profileapp.py::ProfileCreate.init_config_files\n def init_config_files(self):\n super(ProfileCreate, self).init_config_files()\n # use local imports, since these classes may import from here\n from IPython.terminal.ipapp import TerminalIPythonApp\n apps = [TerminalIPythonApp]\n for app_path in (\n 'IPython.qt.console.qtconsoleapp.IPythonQtConsoleApp',\n 'IPython.html.notebookapp.NotebookApp',\n 'IPython.nbconvert.nbconvertapp.NbConvertApp',\n ):\n app = self._import_app(app_path)\n if app is not None:\n apps.append(app)\n if self.parallel:\n from IPython.parallel.apps.ipcontrollerapp import IPControllerApp\n from IPython.parallel.apps.ipengineapp import IPEngineApp\n from IPython.parallel.apps.ipclusterapp import IPClusterStart\n from IPython.parallel.apps.iploggerapp import IPLoggerApp\n apps.extend([\n IPControllerApp,\n IPEngineApp,\n IPClusterStart,\n IPLoggerApp,\n ])\n for App in apps:\n app = App()\n app.config.update(self.config)\n app.log = self.log\n app.overwrite = self.overwrite\n app.copy_config_files=True\n app.ipython_dir=self.ipython_dir\n app.profile_dir=self.profile_dir\n app.init_config_files()","label":1},{"cid":"IPython/core/profileapp.py::ProfileCreate.stage_default_config_file","text":"IPython/core/profileapp.py::ProfileCreate.stage_default_config_file\n def stage_default_config_file(self):\n pass","label":0},{"cid":"IPython/core/profileapp.py::ProfileApp.start","text":"IPython/core/profileapp.py::ProfileApp.start\n def start(self):\n if self.subapp is None:\n print(\"No subcommand specified. Must specify one of: %s\"%(self.subcommands.keys()))\n print()\n self.print_description()\n self.print_subcommands()\n self.exit(1)\n else:\n return self.subapp.start()","label":0},{"cid":"IPython/core/usage.py::page_guiref","text":"IPython/core/usage.py::page_guiref\ndef page_guiref(arg_s=None):\n \"\"\"Show a basic reference about the GUI Console.\"\"\"\n from IPython.core import page\n page.page(gui_reference)","label":0},{"cid":"IPython/html/notebookapp.py::random_ports","text":"IPython/html/notebookapp.py::random_ports\ndef random_ports(port, n):\n \"\"\"Generate a list of n random ports near the given port.\n\n The first 5 ports will be sequential, and the remaining n-5 will be\n randomly selected in the range [port-2*n, port+2*n].\n \"\"\"\n for i in range(min(5, n)):\n yield port + i\n for i in range(n-5):\n yield max(1, port + random.randint(-2*n, 2*n))","label":0},{"cid":"IPython/html/notebookapp.py::load_handlers","text":"IPython/html/notebookapp.py::load_handlers\ndef load_handlers(name):\n \"\"\"Load the (URL pattern, handler) tuples for each component.\"\"\"\n name = 'IPython.html.' + name\n mod = __import__(name, fromlist=['default_handlers'])\n return mod.default_handlers","label":0},{"cid":"IPython/html/notebookapp.py::NotebookWebApplication.__init__","text":"IPython/html/notebookapp.py::NotebookWebApplication.__init__\n def __init__(self, ipython_app, kernel_manager, contents_manager,\n cluster_manager, session_manager, kernel_spec_manager, log,\n base_url, default_url, settings_overrides, jinja_env_options):\n\n settings = self.init_settings(\n ipython_app, kernel_manager, contents_manager, cluster_manager,\n session_manager, kernel_spec_manager, log, base_url, default_url,\n settings_overrides, jinja_env_options)\n handlers = self.init_handlers(settings)\n\n super(NotebookWebApplication, self).__init__(handlers, **settings)","label":0},{"cid":"IPython/html/notebookapp.py::NotebookWebApplication.init_settings","text":"IPython/html/notebookapp.py::NotebookWebApplication.init_settings\n def init_settings(self, ipython_app, kernel_manager, contents_manager,\n cluster_manager, session_manager, kernel_spec_manager,\n log, base_url, default_url, settings_overrides,\n jinja_env_options=None):\n # Python < 2.6.5 doesn't accept unicode keys in f(**kwargs), and\n # base_url will always be unicode, which will in turn\n # make the patterns unicode, and ultimately result in unicode\n # keys in kwargs to handler._execute(**kwargs) in tornado.\n # This enforces that base_url be ascii in that situation.\n #\n # Note that the URLs these patterns check against are escaped,\n # and thus guaranteed to be ASCII: 'héllo' is really 'h%C3%A9llo'.\n base_url = py3compat.unicode_to_str(base_url, 'ascii')\n _template_path = settings_overrides.get(\"template_path\", os.path.join(os.path.dirname(__file__), \"templates\"))\n if isinstance(_template_path, str):\n _template_path = (_template_path,)\n template_path = [os.path.expanduser(path) for path in _template_path]\n\n jenv_opt = jinja_env_options if jinja_env_options else {}\n env = Environment(loader=FileSystemLoader(template_path), **jenv_opt)\n settings = dict(\n # basics\n log_function=log_request,\n base_url=base_url,\n default_url=default_url,\n template_path=template_path,\n static_path=ipython_app.static_file_path,\n static_handler_class = FileFindHandler,\n static_url_prefix = url_path_join(base_url,'/static/'),\n \n # authentication\n cookie_secret=ipython_app.cookie_secret,\n login_url=url_path_join(base_url,'/login'),\n password=ipython_app.password,\n \n # managers\n kernel_manager=kernel_manager,\n contents_manager=conten","label":0},{"cid":"IPython/html/notebookapp.py::NotebookWebApplication.init_handlers","text":"IPython/html/notebookapp.py::NotebookWebApplication.init_handlers\n def init_handlers(self, settings):\n # Load the (URL pattern, handler) tuples for each component.\n handlers = []\n handlers.extend(load_handlers('base.handlers'))\n handlers.extend(load_handlers('tree.handlers'))\n handlers.extend(load_handlers('auth.login'))\n handlers.extend(load_handlers('auth.logout'))\n handlers.extend(load_handlers('notebook.handlers'))\n handlers.extend(load_handlers('nbconvert.handlers'))\n handlers.extend(load_handlers('kernelspecs.handlers'))\n handlers.extend(load_handlers('services.kernels.handlers'))\n handlers.extend(load_handlers('services.contents.handlers'))\n handlers.extend(load_handlers('services.clusters.handlers'))\n handlers.extend(load_handlers('services.sessions.handlers'))\n handlers.extend(load_handlers('services.nbconvert.handlers'))\n handlers.extend(load_handlers('services.kernelspecs.handlers'))\n # FIXME: /files/ should be handled by the Contents service when it exists\n cm = settings['contents_manager']\n if hasattr(cm, 'root_dir'):\n handlers.append(\n (r\"/files/(.*)\", AuthenticatedFileHandler, {'path' : cm.root_dir}),\n )\n handlers.append(\n (r\"/nbextensions/(.*)\", FileFindHandler, {'path' : settings['nbextensions_path']}),\n )\n # set the URL that will be redirected from `/`\n handlers.append(\n (r'/?', web.RedirectHandler, {\n 'url' : url_path_join(settings['base_url'], settings['default_url']),\n 'permanent': False, # want 302, not 301\n })\n )\n # prepend base_url onto the patterns that we match\n new_handlers = []\n for handler in handlers:\n pattern = url_path_join(settings['base_url'], handler[0])\n new_handler = tuple([pattern] + list(handler[1:]))\n new_handlers.append(n","label":0},{"cid":"IPython/html/notebookapp.py::NbserverListApp.start","text":"IPython/html/notebookapp.py::NbserverListApp.start\n def start(self):\n if not self.json:\n print(\"Currently running servers:\")\n for serverinfo in list_running_servers(self.profile):\n if self.json:\n print(json.dumps(serverinfo))\n else:\n print(serverinfo['url'], \"::\", serverinfo['notebook_dir'])","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._log_level_default","text":"IPython/html/notebookapp.py::NotebookApp._log_level_default\n def _log_level_default(self):\n return logging.INFO","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._log_datefmt_default","text":"IPython/html/notebookapp.py::NotebookApp._log_datefmt_default\n def _log_datefmt_default(self):\n \"\"\"Exclude date from default date format\"\"\"\n return \"%H:%M:%S\"","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._log_format_default","text":"IPython/html/notebookapp.py::NotebookApp._log_format_default\n def _log_format_default(self):\n \"\"\"override default log format to include time\"\"\"\n return u\"%(color)s[%(levelname)1.1s %(asctime)s.%(msecs).03d %(name)s]%(end_color)s %(message)s\"","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._file_to_run_changed","text":"IPython/html/notebookapp.py::NotebookApp._file_to_run_changed\n def _file_to_run_changed(self, name, old, new):\n path, base = os.path.split(new)\n if path:\n self.file_to_run = base\n self.notebook_dir = path","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._ip_changed","text":"IPython/html/notebookapp.py::NotebookApp._ip_changed\n def _ip_changed(self, name, old, new):\n if new == u'*': self.ip = u''","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._cookie_secret_file_default","text":"IPython/html/notebookapp.py::NotebookApp._cookie_secret_file_default\n def _cookie_secret_file_default(self):\n if self.profile_dir is None:\n return ''\n return os.path.join(self.profile_dir.security_dir, 'notebook_cookie_secret')","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._cookie_secret_default","text":"IPython/html/notebookapp.py::NotebookApp._cookie_secret_default\n def _cookie_secret_default(self):\n if os.path.exists(self.cookie_secret_file):\n with io.open(self.cookie_secret_file, 'rb') as f:\n return f.read()\n else:\n secret = base64.encodestring(os.urandom(1024))\n self._write_cookie_secret_file(secret)\n return secret","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._write_cookie_secret_file","text":"IPython/html/notebookapp.py::NotebookApp._write_cookie_secret_file\n def _write_cookie_secret_file(self, secret):\n \"\"\"write my secret to my secret_file\"\"\"\n self.log.info(\"Writing notebook server cookie secret to %s\", self.cookie_secret_file)\n with io.open(self.cookie_secret_file, 'wb') as f:\n f.write(secret)\n try:\n os.chmod(self.cookie_secret_file, 0o600)\n except OSError:\n self.log.warn(\n \"Could not set permissions on %s\",\n self.cookie_secret_file\n )","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._webapp_settings_changed","text":"IPython/html/notebookapp.py::NotebookApp._webapp_settings_changed\n def _webapp_settings_changed(self, name, old, new):\n self.log.warn(\"\\n webapp_settings is deprecated, use tornado_settings.\\n\")\n self.tornado_settings = new","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._enable_mathjax_changed","text":"IPython/html/notebookapp.py::NotebookApp._enable_mathjax_changed\n def _enable_mathjax_changed(self, name, old, new):\n \"\"\"set mathjax url to empty if mathjax is disabled\"\"\"\n if not new:\n self.mathjax_url = u''","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._base_url_changed","text":"IPython/html/notebookapp.py::NotebookApp._base_url_changed\n def _base_url_changed(self, name, old, new):\n if not new.startswith('/'):\n self.base_url = '/'+new\n elif not new.endswith('/'):\n self.base_url = new+'/'","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._base_project_url_changed","text":"IPython/html/notebookapp.py::NotebookApp._base_project_url_changed\n def _base_project_url_changed(self, name, old, new):\n self.log.warn(\"base_project_url is deprecated, use base_url\")\n self.base_url = new","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._extra_static_paths_default","text":"IPython/html/notebookapp.py::NotebookApp._extra_static_paths_default\n def _extra_static_paths_default(self):\n return [os.path.join(self.profile_dir.location, 'static')]","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp.static_file_path","text":"IPython/html/notebookapp.py::NotebookApp.static_file_path\n @property\n def static_file_path(self):\n \"\"\"return extra paths + the default location\"\"\"\n return self.extra_static_paths + [DEFAULT_STATIC_FILES_PATH]","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._nbextensions_path_default","text":"IPython/html/notebookapp.py::NotebookApp._nbextensions_path_default\n def _nbextensions_path_default(self):\n return [os.path.join(get_ipython_dir(), 'nbextensions')]","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._mathjax_url_default","text":"IPython/html/notebookapp.py::NotebookApp._mathjax_url_default\n def _mathjax_url_default(self):\n if not self.enable_mathjax:\n return u''\n static_url_prefix = self.tornado_settings.get(\"static_url_prefix\",\n url_path_join(self.base_url, \"static\")\n )\n \n # try local mathjax, either in nbextensions/mathjax or static/mathjax\n for (url_prefix, search_path) in [\n (url_path_join(self.base_url, \"nbextensions\"), self.nbextensions_path),\n (static_url_prefix, self.static_file_path),\n ]:\n self.log.debug(\"searching for local mathjax in %s\", search_path)\n try:\n mathjax = filefind(os.path.join('mathjax', 'MathJax.js'), search_path)\n except IOError:\n continue\n else:\n url = url_path_join(url_prefix, u\"mathjax/MathJax.js\")\n self.log.info(\"Serving local MathJax from %s at %s\", mathjax, url)\n return url\n \n # no local mathjax, serve from CDN\n url = u\"https://cdn.mathjax.org/mathjax/latest/MathJax.js\"\n self.log.info(\"Using MathJax from CDN: %s\", url)\n return url","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._mathjax_url_changed","text":"IPython/html/notebookapp.py::NotebookApp._mathjax_url_changed\n def _mathjax_url_changed(self, name, old, new):\n if new and not self.enable_mathjax:\n # enable_mathjax=False overrides mathjax_url\n self.mathjax_url = u''\n else:\n self.log.info(\"Using MathJax: %s\", new)","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._kernel_spec_manager_default","text":"IPython/html/notebookapp.py::NotebookApp._kernel_spec_manager_default\n def _kernel_spec_manager_default(self):\n return KernelSpecManager(ipython_dir=self.ipython_dir)","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._info_file_default","text":"IPython/html/notebookapp.py::NotebookApp._info_file_default\n def _info_file_default(self):\n info_file = \"nbserver-%s.json\"%os.getpid()\n return os.path.join(self.profile_dir.security_dir, info_file)","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._pylab_changed","text":"IPython/html/notebookapp.py::NotebookApp._pylab_changed\n def _pylab_changed(self, name, old, new):\n \"\"\"when --pylab is specified, display a warning and exit\"\"\"\n if new != 'warn':\n backend = ' %s' % new\n else:\n backend = ''\n self.log.error(\"Support for specifying --pylab on the command line has been removed.\")\n self.log.error(\n \"Please use `%pylab{0}` or `%matplotlib{0}` in the notebook itself.\".format(backend)\n )\n self.exit(1)","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._notebook_dir_changed","text":"IPython/html/notebookapp.py::NotebookApp._notebook_dir_changed\n def _notebook_dir_changed(self, name, old, new):\n \"\"\"Do a bit of validation of the notebook dir.\"\"\"\n if not os.path.isabs(new):\n # If we receive a non-absolute path, make it absolute.\n self.notebook_dir = os.path.abspath(new)\n return\n if not os.path.isdir(new):\n raise TraitError(\"No such notebook dir: %r\" % new)\n \n # setting App.notebook_dir implies setting notebook and kernel dirs as well\n self.config.FileContentsManager.root_dir = new\n self.config.MappingKernelManager.root_dir = new","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp.parse_command_line","text":"IPython/html/notebookapp.py::NotebookApp.parse_command_line\n def parse_command_line(self, argv=None):\n super(NotebookApp, self).parse_command_line(argv)\n \n if self.extra_args:\n arg0 = self.extra_args[0]\n f = os.path.abspath(arg0)\n self.argv.remove(arg0)\n if not os.path.exists(f):\n self.log.critical(\"No such file or directory: %s\", f)\n self.exit(1)\n \n # Use config here, to ensure that it takes higher priority than\n # anything that comes from the profile.\n c = Config()\n if os.path.isdir(f):\n c.NotebookApp.notebook_dir = f\n elif os.path.isfile(f):\n c.NotebookApp.file_to_run = f\n self.update_config(c)","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp.init_kernel_argv","text":"IPython/html/notebookapp.py::NotebookApp.init_kernel_argv\n def init_kernel_argv(self):\n \"\"\"construct the kernel arguments\"\"\"\n # Kernel should get *absolute* path to profile directory\n self.kernel_argv = [\"--profile-dir\", self.profile_dir.location]","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp.init_configurables","text":"IPython/html/notebookapp.py::NotebookApp.init_configurables\n def init_configurables(self):\n # force Session default to be secure\n default_secure(self.config)\n kls = import_item(self.kernel_manager_class)\n self.kernel_manager = kls(\n parent=self, log=self.log, kernel_argv=self.kernel_argv,\n connection_dir = self.profile_dir.security_dir,\n )\n kls = import_item(self.contents_manager_class)\n self.contents_manager = kls(parent=self, log=self.log)\n kls = import_item(self.session_manager_class)\n self.session_manager = kls(parent=self, log=self.log,\n kernel_manager=self.kernel_manager,\n contents_manager=self.contents_manager)\n kls = import_item(self.cluster_manager_class)\n self.cluster_manager = kls(parent=self, log=self.log)\n self.cluster_manager.update_profiles()","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp.init_logging","text":"IPython/html/notebookapp.py::NotebookApp.init_logging\n def init_logging(self):\n # This prevents double log messages because tornado use a root logger that\n # self.log is a child of. The logging module dipatches log messages to a log\n # and all of its ancenstors until propagate is set to False.\n self.log.propagate = False\n \n # hook up tornado 3's loggers to our app handlers\n logger = logging.getLogger('tornado')\n logger.propagate = True\n logger.parent = self.log\n logger.setLevel(self.log.level)","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp.init_webapp","text":"IPython/html/notebookapp.py::NotebookApp.init_webapp\n def init_webapp(self):\n \"\"\"initialize tornado webapp and httpserver\"\"\"\n self.tornado_settings['allow_origin'] = self.allow_origin\n if self.allow_origin_pat:\n self.tornado_settings['allow_origin_pat'] = re.compile(self.allow_origin_pat)\n self.tornado_settings['allow_credentials'] = self.allow_credentials\n \n self.web_app = NotebookWebApplication(\n self, self.kernel_manager, self.contents_manager,\n self.cluster_manager, self.session_manager, self.kernel_spec_manager,\n self.log, self.base_url, self.default_url, self.tornado_settings,\n self.jinja_environment_options\n )\n if self.certfile:\n ssl_options = dict(certfile=self.certfile)\n if self.keyfile:\n ssl_options['keyfile'] = self.keyfile\n else:\n ssl_options = None\n self.web_app.password = self.password\n self.http_server = httpserver.HTTPServer(self.web_app, ssl_options=ssl_options,\n xheaders=self.trust_xheaders)\n if not self.ip:\n warning = \"WARNING: The notebook server is listening on all IP addresses\"\n if ssl_options is None:\n self.log.critical(warning + \" and not using encryption. This \"\n \"is not recommended.\")\n if not self.password:\n self.log.critical(warning + \" and not using authentication. \"\n \"This is highly insecure and not recommended.\")\n success = None\n for port in random_ports(self.port, self.port_retries+1):\n try:\n self.http_server.listen(port, self.ip)\n except socket.error as e:\n if e.errno == errno.EADDRINUSE:\n self.log.info('The port %i is already in use, trying another random port.' % port)\n continue\n eli","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp.display_url","text":"IPython/html/notebookapp.py::NotebookApp.display_url\n @property\n def display_url(self):\n ip = self.ip if self.ip else '[all ip addresses on your system]'\n return self._url(ip)","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp.connection_url","text":"IPython/html/notebookapp.py::NotebookApp.connection_url\n @property\n def connection_url(self):\n ip = self.ip if self.ip else 'localhost'\n return self._url(ip)","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._url","text":"IPython/html/notebookapp.py::NotebookApp._url\n def _url(self, ip):\n proto = 'https' if self.certfile else 'http'\n return \"%s://%s:%i%s\" % (proto, ip, self.port, self.base_url)","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp.init_signal","text":"IPython/html/notebookapp.py::NotebookApp.init_signal\n def init_signal(self):\n if not sys.platform.startswith('win'):\n signal.signal(signal.SIGINT, self._handle_sigint)\n signal.signal(signal.SIGTERM, self._signal_stop)\n if hasattr(signal, 'SIGUSR1'):\n # Windows doesn't support SIGUSR1\n signal.signal(signal.SIGUSR1, self._signal_info)\n if hasattr(signal, 'SIGINFO'):\n # only on BSD-based systems\n signal.signal(signal.SIGINFO, self._signal_info)","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._handle_sigint","text":"IPython/html/notebookapp.py::NotebookApp._handle_sigint\n def _handle_sigint(self, sig, frame):\n \"\"\"SIGINT handler spawns confirmation dialog\"\"\"\n # register more forceful signal handler for ^C^C case\n signal.signal(signal.SIGINT, self._signal_stop)\n # request confirmation dialog in bg thread, to avoid\n # blocking the App\n thread = threading.Thread(target=self._confirm_exit)\n thread.daemon = True\n thread.start()","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._restore_sigint_handler","text":"IPython/html/notebookapp.py::NotebookApp._restore_sigint_handler\n def _restore_sigint_handler(self):\n \"\"\"callback for restoring original SIGINT handler\"\"\"\n signal.signal(signal.SIGINT, self._handle_sigint)","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._confirm_exit","text":"IPython/html/notebookapp.py::NotebookApp._confirm_exit\n def _confirm_exit(self):\n \"\"\"confirm shutdown on ^C\n \n A second ^C, or answering 'y' within 5s will cause shutdown,\n otherwise original SIGINT handler will be restored.\n \n This doesn't work on Windows.\n \"\"\"\n info = self.log.info\n info('interrupted')\n print(self.notebook_info())\n sys.stdout.write(\"Shutdown this notebook server (y/[n])? \")\n sys.stdout.flush()\n r,w,x = select.select([sys.stdin], [], [], 5)\n if r:\n line = sys.stdin.readline()\n if line.lower().startswith('y') and 'n' not in line.lower():\n self.log.critical(\"Shutdown confirmed\")\n ioloop.IOLoop.instance().stop()\n return\n else:\n print(\"No answer for 5s:\", end=' ')\n print(\"resuming operation...\")\n # no answer, or answer is no:\n # set it back to original SIGINT handler\n # use IOLoop.add_callback because signal.signal must be called\n # from main thread\n ioloop.IOLoop.instance().add_callback(self._restore_sigint_handler)","label":0},{"cid":"IPython/html/notebookapp.py::NotebookApp._signal_stop","text":"IPython/html/notebookapp.py::NotebookApp._signal_stop\n def _signal_stop(self, sig, frame):\n self.log.critical(\"received signal %s, stopping\", sig)\n ioloop.IOLoop.instance().stop()","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp._config_file_name_default","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp._config_file_name_default\n def _config_file_name_default(self):\n return 'ipython_config.py'","label":1},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.abs_connection_file","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.abs_connection_file\n @property\n def abs_connection_file(self):\n if os.path.basename(self.connection_file) == self.connection_file:\n return os.path.join(self.profile_dir.security_dir, self.connection_file)\n else:\n return self.connection_file","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_crash_handler","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_crash_handler\n def init_crash_handler(self):\n # Install minimal exception handling\n sys.excepthook = FormattedTB(mode='Verbose', color_scheme='NoColor',\n ostream=sys.__stdout__)","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_poller","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_poller\n def init_poller(self):\n if sys.platform == 'win32':\n if self.interrupt or self.parent_handle:\n self.poller = ParentPollerWindows(self.interrupt, self.parent_handle)\n elif self.parent_handle:\n self.poller = ParentPollerUnix()","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp._bind_socket","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp._bind_socket\n def _bind_socket(self, s, port):\n iface = '%s://%s' % (self.transport, self.ip)\n if self.transport == 'tcp':\n if port <= 0:\n port = s.bind_to_random_port(iface)\n else:\n s.bind(\"tcp://%s:%i\" % (self.ip, port))\n elif self.transport == 'ipc':\n if port <= 0:\n port = 1\n path = \"%s-%i\" % (self.ip, port)\n while os.path.exists(path):\n port = port + 1\n path = \"%s-%i\" % (self.ip, port)\n else:\n path = \"%s-%i\" % (self.ip, port)\n s.bind(\"ipc://%s\" % path)\n return port","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.write_connection_file","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.write_connection_file\n def write_connection_file(self):\n \"\"\"write connection info to JSON file\"\"\"\n cf = self.abs_connection_file\n self.log.debug(\"Writing connection file: %s\", cf)\n write_connection_file(cf, ip=self.ip, key=self.session.key, transport=self.transport,\n shell_port=self.shell_port, stdin_port=self.stdin_port, hb_port=self.hb_port,\n iopub_port=self.iopub_port, control_port=self.control_port)","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.cleanup_connection_file","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.cleanup_connection_file\n def cleanup_connection_file(self):\n cf = self.abs_connection_file\n self.log.debug(\"Cleaning up connection file: %s\", cf)\n try:\n os.remove(cf)\n except (IOError, OSError):\n pass\n \n self.cleanup_ipc_files()","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_connection_file","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_connection_file\n def init_connection_file(self):\n if not self.connection_file:\n self.connection_file = \"kernel-%s.json\"%os.getpid()\n try:\n self.connection_file = filefind(self.connection_file, ['.', self.profile_dir.security_dir])\n except IOError:\n self.log.debug(\"Connection file not found: %s\", self.connection_file)\n # This means I own it, so I will clean it up:\n atexit.register(self.cleanup_connection_file)\n return\n try:\n self.load_connection_file()\n except Exception:\n self.log.error(\"Failed to load connection file: %r\", self.connection_file, exc_info=True)\n self.exit(1)","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_sockets","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_sockets\n def init_sockets(self):\n # Create a context, a session, and the kernel sockets.\n self.log.info(\"Starting the kernel at pid: %i\", os.getpid())\n context = zmq.Context.instance()\n # Uncomment this to try closing the context.\n # atexit.register(context.term)\n\n self.shell_socket = context.socket(zmq.ROUTER)\n self.shell_socket.linger = 1000\n self.shell_port = self._bind_socket(self.shell_socket, self.shell_port)\n self.log.debug(\"shell ROUTER Channel on port: %i\" % self.shell_port)\n\n self.iopub_socket = context.socket(zmq.PUB)\n self.iopub_socket.linger = 1000\n self.iopub_port = self._bind_socket(self.iopub_socket, self.iopub_port)\n self.log.debug(\"iopub PUB Channel on port: %i\" % self.iopub_port)\n\n self.stdin_socket = context.socket(zmq.ROUTER)\n self.stdin_socket.linger = 1000\n self.stdin_port = self._bind_socket(self.stdin_socket, self.stdin_port)\n self.log.debug(\"stdin ROUTER Channel on port: %i\" % self.stdin_port)\n\n self.control_socket = context.socket(zmq.ROUTER)\n self.control_socket.linger = 1000\n self.control_port = self._bind_socket(self.control_socket, self.control_port)\n self.log.debug(\"control ROUTER Channel on port: %i\" % self.control_port)","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_heartbeat","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_heartbeat\n def init_heartbeat(self):\n \"\"\"start the heart beating\"\"\"\n # heartbeat doesn't share context, because it mustn't be blocked\n # by the GIL, which is accessed by libzmq when freeing zero-copy messages\n hb_ctx = zmq.Context()\n self.heartbeat = Heartbeat(hb_ctx, (self.transport, self.ip, self.hb_port))\n self.hb_port = self.heartbeat.port\n self.log.debug(\"Heartbeat REP Channel on port: %i\" % self.hb_port)\n self.heartbeat.start()","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.log_connection_info","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.log_connection_info\n def log_connection_info(self):\n \"\"\"display connection info, and store ports\"\"\"\n basename = os.path.basename(self.connection_file)\n if basename == self.connection_file or \\\n os.path.dirname(self.connection_file) == self.profile_dir.security_dir:\n # use shortname\n tail = basename\n if self.profile != 'default':\n tail += \" --profile %s\" % self.profile\n else:\n tail = self.connection_file\n lines = [\n \"To connect another client to this kernel, use:\",\n \" --existing %s\" % tail,\n ]\n # log connection info\n # info-level, so often not shown.\n # frontends should use the %connect_info magic\n # to see the connection info\n for line in lines:\n self.log.info(line)\n # also raw print to the terminal if no parent_handle (`ipython kernel`)\n if not self.parent_handle:\n io.rprint(_ctrl_c_message)\n for line in lines:\n io.rprint(line)\n\n self.ports = dict(shell=self.shell_port, iopub=self.iopub_port,\n stdin=self.stdin_port, hb=self.hb_port,\n control=self.control_port)","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_blackhole","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_blackhole\n def init_blackhole(self):\n \"\"\"redirects stdout/stderr to devnull if necessary\"\"\"\n if self.no_stdout or self.no_stderr:\n blackhole = open(os.devnull, 'w')\n if self.no_stdout:\n sys.stdout = sys.__stdout__ = blackhole\n if self.no_stderr:\n sys.stderr = sys.__stderr__ = blackhole","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_io","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_io\n def init_io(self):\n \"\"\"Redirect input streams and set a display hook.\"\"\"\n if self.outstream_class:\n outstream_factory = import_item(str(self.outstream_class))\n sys.stdout = outstream_factory(self.session, self.iopub_socket, u'stdout')\n sys.stderr = outstream_factory(self.session, self.iopub_socket, u'stderr')\n if self.displayhook_class:\n displayhook_factory = import_item(str(self.displayhook_class))\n sys.displayhook = displayhook_factory(self.session, self.iopub_socket)","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_signal","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_signal\n def init_signal(self):\n signal.signal(signal.SIGINT, signal.SIG_IGN)","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_kernel","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_kernel\n def init_kernel(self):\n \"\"\"Create the Kernel object itself\"\"\"\n shell_stream = ZMQStream(self.shell_socket)\n control_stream = ZMQStream(self.control_socket)\n \n kernel_factory = self.kernel_class\n\n kernel = kernel_factory(parent=self, session=self.session,\n shell_streams=[shell_stream, control_stream],\n iopub_socket=self.iopub_socket,\n stdin_socket=self.stdin_socket,\n log=self.log,\n profile_dir=self.profile_dir,\n user_ns=self.user_ns,\n )\n kernel.record_ports(self.ports)\n self.kernel = kernel","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_gui_pylab","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_gui_pylab\n def init_gui_pylab(self):\n \"\"\"Enable GUI event loop integration, taking pylab into account.\"\"\"\n\n # Provide a wrapper for :meth:`InteractiveShellApp.init_gui_pylab`\n # to ensure that any exception is printed straight to stderr.\n # Normally _showtraceback associates the reply with an execution,\n # which means frontends will never draw it, as this exception\n # is not associated with any execute request.\n\n shell = self.shell\n _showtraceback = shell._showtraceback\n try:\n # replace error-sending traceback with stderr\n def print_tb(etype, evalue, stb):\n print (\"GUI event loop or pylab initialization failed\",\n file=io.stderr)\n print (shell.InteractiveTB.stb2text(stb), file=io.stderr)\n shell._showtraceback = print_tb\n InteractiveShellApp.init_gui_pylab(self)\n finally:\n shell._showtraceback = _showtraceback","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_shell","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_shell\n def init_shell(self):\n self.shell = getattr(self.kernel, 'shell', None)\n if self.shell:\n self.shell.configurables.append(self)","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.initialize","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.initialize\n @catch_config_error\n def initialize(self, argv=None):\n super(IPKernelApp, self).initialize(argv)\n default_secure(self.config)\n self.init_blackhole()\n self.init_connection_file()\n self.init_poller()\n self.init_sockets()\n self.init_heartbeat()\n # writing/displaying connection info must be *after* init_sockets/heartbeat\n self.log_connection_info()\n self.write_connection_file()\n self.init_io()\n self.init_signal()\n self.init_kernel()\n # shell init steps\n self.init_path()\n self.init_shell()\n if self.shell:\n self.init_gui_pylab()\n self.init_extensions()\n self.init_code()\n # flush stdout/stderr, so that anything written to these streams during\n # initialization do not get associated with the first execution request\n sys.stdout.flush()\n sys.stderr.flush()","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.start","text":"IPython/kernel/zmq/kernelapp.py::IPKernelApp.start\n def start(self):\n if self.poller is not None:\n self.poller.start()\n self.kernel.start()\n try:\n ioloop.IOLoop.instance().start()\n except KeyboardInterrupt:\n pass","label":0},{"cid":"IPython/kernel/zmq/kernelapp.py::main","text":"IPython/kernel/zmq/kernelapp.py::main\ndef main():\n \"\"\"Run an IPKernel as an application\"\"\"\n app = IPKernelApp.instance()\n app.initialize()\n app.start()","label":0},{"cid":"IPython/kernel/zmq/pylab/config.py::pil_available","text":"IPython/kernel/zmq/pylab/config.py::pil_available\ndef pil_available():\n \"\"\"Test if PIL/Pillow is available\"\"\"\n out = False\n try:\n from PIL import Image\n out = True\n except:\n pass\n return out","label":0},{"cid":"IPython/kernel/zmq/pylab/config.py::InlineBackend._config_changed","text":"IPython/kernel/zmq/pylab/config.py::InlineBackend._config_changed\n def _config_changed(self, name, old, new):\n # warn on change of renamed config section\n if new.InlineBackendConfig != old.InlineBackendConfig:\n warn(\"InlineBackendConfig has been renamed to InlineBackend\")\n super(InlineBackend, self)._config_changed(name, old, new)","label":0},{"cid":"IPython/kernel/zmq/pylab/config.py::InlineBackend._update_figure_formatters","text":"IPython/kernel/zmq/pylab/config.py::InlineBackend._update_figure_formatters\n def _update_figure_formatters(self):\n if self.shell is not None:\n from IPython.core.pylabtools import select_figure_formats\n select_figure_formats(self.shell, self.figure_formats, **self.print_figure_kwargs)","label":0},{"cid":"IPython/kernel/zmq/pylab/config.py::InlineBackend._figure_formats_changed","text":"IPython/kernel/zmq/pylab/config.py::InlineBackend._figure_formats_changed\n def _figure_formats_changed(self, name, old, new):\n if 'jpg' in new or 'jpeg' in new:\n if not pil_available():\n raise TraitError(\"Requires PIL/Pillow for JPG figures\")\n self._update_figure_formatters()","label":0},{"cid":"IPython/kernel/zmq/pylab/config.py::InlineBackend._figure_format_changed","text":"IPython/kernel/zmq/pylab/config.py::InlineBackend._figure_format_changed\n def _figure_format_changed(self, name, old, new):\n if new:\n self.figure_formats = {new}","label":0},{"cid":"IPython/terminal/ipapp.py::IPAppCrashHandler.__init__","text":"IPython/terminal/ipapp.py::IPAppCrashHandler.__init__\n def __init__(self, app):\n contact_name = release.author\n contact_email = release.author_email\n bug_tracker = 'https://github.com/ipython/ipython/issues'\n super(IPAppCrashHandler,self).__init__(\n app, contact_name, contact_email, bug_tracker\n )","label":0},{"cid":"IPython/terminal/ipapp.py::IPAppCrashHandler.make_report","text":"IPython/terminal/ipapp.py::IPAppCrashHandler.make_report\n def make_report(self,traceback):\n \"\"\"Return a string containing a crash report.\"\"\"\n\n sec_sep = self.section_sep\n # Start with parent report\n report = [super(IPAppCrashHandler, self).make_report(traceback)]\n # Add interactive-specific info we may have\n rpt_add = report.append\n try:\n rpt_add(sec_sep+\"History of session input:\")\n for line in self.app.shell.user_ns['_ih']:\n rpt_add(line)\n rpt_add('\\n*** Last line of input (may not be in above history):\\n')\n rpt_add(self.app.shell._last_input_line+'\\n')\n except:\n pass\n\n return ''.join(report)","label":0},{"cid":"IPython/terminal/ipapp.py::LocateIPythonApp.start","text":"IPython/terminal/ipapp.py::LocateIPythonApp.start\n def start(self):\n if self.subapp is not None:\n return self.subapp.start()\n else:\n print(self.ipython_dir)","label":0},{"cid":"IPython/terminal/ipapp.py::TerminalIPythonApp._classes_default","text":"IPython/terminal/ipapp.py::TerminalIPythonApp._classes_default\n def _classes_default(self):\n \"\"\"This has to be in a method, for TerminalIPythonApp to be available.\"\"\"\n return [\n InteractiveShellApp, # ShellApp comes before TerminalApp, because\n self.__class__, # it will also affect subclasses (e.g. QtConsole)\n TerminalInteractiveShell,\n PromptManager,\n HistoryManager,\n ProfileDir,\n PlainTextFormatter,\n IPCompleter,\n ScriptMagics,\n StoreMagics,\n ]","label":0},{"cid":"IPython/terminal/ipapp.py::TerminalIPythonApp._quick_changed","text":"IPython/terminal/ipapp.py::TerminalIPythonApp._quick_changed\n def _quick_changed(self, name, old, new):\n if new:\n self.load_config_file = lambda *a, **kw: None\n self.ignore_old_config=True","label":0},{"cid":"IPython/terminal/ipapp.py::TerminalIPythonApp._force_interact_changed","text":"IPython/terminal/ipapp.py::TerminalIPythonApp._force_interact_changed\n def _force_interact_changed(self, name, old, new):\n if new:\n self.interact = True","label":0},{"cid":"IPython/terminal/ipapp.py::TerminalIPythonApp._file_to_run_changed","text":"IPython/terminal/ipapp.py::TerminalIPythonApp._file_to_run_changed\n def _file_to_run_changed(self, name, old, new):\n if new:\n self.something_to_run = True\n if new and not self.force_interact:\n self.interact = False","label":0},{"cid":"IPython/terminal/ipapp.py::TerminalIPythonApp.parse_command_line","text":"IPython/terminal/ipapp.py::TerminalIPythonApp.parse_command_line\n def parse_command_line(self, argv=None):\n \"\"\"override to allow old '-pylab' flag with deprecation warning\"\"\"\n\n argv = sys.argv[1:] if argv is None else argv\n\n if '-pylab' in argv:\n # deprecated `-pylab` given,\n # warn and transform into current syntax\n argv = argv[:] # copy, don't clobber\n idx = argv.index('-pylab')\n warn.warn(\"`-pylab` flag has been deprecated.\\n\"\n \" Use `--matplotlib <backend>` and import pylab manually.\")\n argv[idx] = '--pylab'\n\n return super(TerminalIPythonApp, self).parse_command_line(argv)","label":0},{"cid":"IPython/terminal/ipapp.py::TerminalIPythonApp.initialize","text":"IPython/terminal/ipapp.py::TerminalIPythonApp.initialize\n @catch_config_error\n def initialize(self, argv=None):\n \"\"\"Do actions after construct, but before starting the app.\"\"\"\n super(TerminalIPythonApp, self).initialize(argv)\n if self.subapp is not None:\n # don't bother initializing further, starting subapp\n return\n if not self.ignore_old_config:\n check_for_old_config(self.ipython_dir)\n # print self.extra_args\n if self.extra_args and not self.something_to_run:\n self.file_to_run = self.extra_args[0]\n self.init_path()\n # create the shell\n self.init_shell()\n # and draw the banner\n self.init_banner()\n # Now a variety of things that happen after the banner is printed.\n self.init_gui_pylab()\n self.init_extensions()\n self.init_code()","label":0},{"cid":"IPython/terminal/ipapp.py::TerminalIPythonApp.init_shell","text":"IPython/terminal/ipapp.py::TerminalIPythonApp.init_shell\n def init_shell(self):\n \"\"\"initialize the InteractiveShell instance\"\"\"\n # Create an InteractiveShell instance.\n # shell.display_banner should always be False for the terminal\n # based app, because we call shell.show_banner() by hand below\n # so the banner shows *before* all extension loading stuff.\n self.shell = TerminalInteractiveShell.instance(parent=self,\n display_banner=False, profile_dir=self.profile_dir,\n ipython_dir=self.ipython_dir, user_ns=self.user_ns)\n self.shell.configurables.append(self)","label":0},{"cid":"IPython/terminal/ipapp.py::TerminalIPythonApp.init_banner","text":"IPython/terminal/ipapp.py::TerminalIPythonApp.init_banner\n def init_banner(self):\n \"\"\"optionally display the banner\"\"\"\n if self.display_banner and self.interact:\n self.shell.show_banner()\n # Make sure there is a space below the banner.\n if self.log_level <= logging.INFO: print()","label":0},{"cid":"IPython/terminal/ipapp.py::TerminalIPythonApp._pylab_changed","text":"IPython/terminal/ipapp.py::TerminalIPythonApp._pylab_changed\n def _pylab_changed(self, name, old, new):\n \"\"\"Replace --pylab='inline' with --pylab='auto'\"\"\"\n if new == 'inline':\n warn.warn(\"'inline' not available as pylab backend, \"\n \"using 'auto' instead.\")\n self.pylab = 'auto'","label":0},{"cid":"IPython/terminal/ipapp.py::TerminalIPythonApp.start","text":"IPython/terminal/ipapp.py::TerminalIPythonApp.start\n def start(self):\n if self.subapp is not None:\n return self.subapp.start()\n # perform any prexec steps:\n if self.interact:\n self.log.debug(\"Starting IPython's mainloop...\")\n self.shell.mainloop()\n else:\n self.log.debug(\"IPython not interactive...\")","label":0},{"cid":"IPython/terminal/ipapp.py::load_default_config","text":"IPython/terminal/ipapp.py::load_default_config\ndef load_default_config(ipython_dir=None):\n \"\"\"Load the default config file from the default ipython_dir.\n\n This is useful for embedded shells.\n \"\"\"\n if ipython_dir is None:\n ipython_dir = get_ipython_dir()\n\n profile_dir = os.path.join(ipython_dir, 'profile_default')\n\n config = Config()\n for cf in Application._load_config_files(\"ipython_config\", path=profile_dir):\n config.update(cf)\n\n return config","label":0},{"cid":"docs/autogen_config.py::document_config_options","text":"docs/autogen_config.py::document_config_options\ndef document_config_options(classes):\n lines = []\n for cls in classes:\n classname = cls.__name__\n for k, trait in sorted(cls.class_traits(config=True).items()):\n ttype = trait.__class__.__name__\n\n termline = classname + '.' + trait.name\n\n # Choices or type\n if 'Enum' in ttype:\n # include Enum choices\n termline += ' : ' + '|'.join(repr(x) for x in trait.values)\n else:\n termline += ' : ' + ttype\n lines.append(termline)\n\n # Default value\n try:\n dv = trait.get_default_value()\n dvr = repr(dv)\n except Exception:\n dvr = dv = None # ignore defaults we can't construct\n if (dv is not None) and (dvr is not None):\n if len(dvr) > 64:\n dvr = dvr[:61]+'...'\n # Double up backslashes, so they get to the rendered docs\n dvr = dvr.replace('\\\\n', '\\\\\\\\n')\n lines.append(' Default: ' + dvr)\n lines.append('')\n\n help = trait.get_metadata('help')\n if help is not None:\n help = '\\n\\n'.join(wrap_paragraphs(help, 76))\n lines.append(indent(help, 4))\n else:\n lines.append(' No description')\n\n lines.append('')\n return '\\n'.join(lines)","label":0},{"cid":"docs/autogen_config.py::write_doc","text":"docs/autogen_config.py::write_doc\ndef write_doc(name, title, classes, preamble=None):\n configdoc = document_config_options(classes)\n filename = '%s.rst' % name\n with open('source/config/options/%s' % filename, 'w') as f:\n f.write(title + '\\n')\n f.write(('=' * len(title)) + '\\n')\n f.write('\\n')\n if preamble is not None:\n f.write(preamble + '\\n\\n')\n f.write(configdoc)\n with open('source/config/options/generated', 'a') as f:\n f.write(filename + '\\n')","label":0},{"cid":"tools/backport_pr.py::find_rejects","text":"tools/backport_pr.py::find_rejects\ndef find_rejects(root='.'):\n for dirname, dirs, files in os.walk(root):\n for fname in files:\n if fname.endswith('.rej'):\n yield os.path.join(dirname, fname)","label":0},{"cid":"tools/backport_pr.py::get_current_branch","text":"tools/backport_pr.py::get_current_branch\ndef get_current_branch():\n branches = check_output(['git', 'branch'])\n for branch in branches.splitlines():\n if branch.startswith(b'*'):\n return branch[1:].strip().decode('utf-8')","label":0},{"cid":"tools/backport_pr.py::backport_pr","text":"tools/backport_pr.py::backport_pr\ndef backport_pr(branch, num, project='ipython/ipython'):\n current_branch = get_current_branch()\n if branch != current_branch:\n check_call(['git', 'checkout', branch])\n check_call(['git', 'pull'])\n pr = get_pull_request(project, num, auth=True)\n files = get_pull_request_files(project, num, auth=True)\n patch_url = pr['patch_url']\n title = pr['title']\n description = pr['body']\n fname = \"PR%i.patch\" % num\n if os.path.exists(fname):\n print(\"using patch from {fname}\".format(**locals()))\n with open(fname) as f:\n patch = f.read()\n else:\n req = urlopen(patch_url)\n patch = req.read()\n\n lines = description.splitlines()\n if len(lines) > 5:\n lines = lines[:5] + ['...']\n description = '\\n'.join(lines)\n\n msg = \"Backport PR #%i: %s\" % (num, title) + '\\n\\n' + description\n check = Popen(['git', 'apply', '--check', '--verbose'], stdin=PIPE)\n a,b = check.communicate(patch)\n\n if check.returncode:\n print(\"patch did not apply, saving to {fname}\".format(**locals()))\n print(\"edit {fname} until `cat {fname} | git apply --check` succeeds\".format(**locals()))\n print(\"then run tools/backport_pr.py {num} again\".format(**locals()))\n if not os.path.exists(fname):\n with open(fname, 'wb') as f:\n f.write(patch)\n return 1\n\n p = Popen(['git', 'apply'], stdin=PIPE)\n a,b = p.communicate(patch)\n\n filenames = [ f['filename'] for f in files ]\n\n check_call(['git', 'add'] + filenames)\n\n check_call(['git', 'commit', '-m', msg])\n\n print(\"PR #%i applied, with msg:\" % num)\n print()\n print(msg)\n print()\n\n if branch != current_branch:\n check_call(['git', 'checkout', current_branch])\n\n return 0","label":0},{"cid":"tools/backport_pr.py::already_backported","text":"tools/backport_pr.py::already_backported\ndef already_backported(branch, since_tag=None):\n \"\"\"return set of PRs that have been backported already\"\"\"\n if since_tag is None:\n since_tag = check_output(['git','describe', branch, '--abbrev=0']).decode('utf8').strip()\n cmd = ['git', 'log', '%s..%s' % (since_tag, branch), '--oneline']\n lines = check_output(cmd).decode('utf8')\n return set(int(num) for num in backport_re.findall(lines))","label":0},{"cid":"tools/backport_pr.py::should_backport","text":"tools/backport_pr.py::should_backport\ndef should_backport(labels=None, milestone=None):\n \"\"\"return set of PRs marked for backport\"\"\"\n if labels is None and milestone is None:\n raise ValueError(\"Specify one of labels or milestone.\")\n elif labels is not None and milestone is not None:\n raise ValueError(\"Specify only one of labels or milestone.\")\n if labels is not None:\n issues = get_issues_list(\"ipython/ipython\",\n labels=labels,\n state='closed',\n auth=True,\n )\n else:\n milestone_id = get_milestone_id(\"ipython/ipython\", milestone,\n auth=True)\n issues = get_issues_list(\"ipython/ipython\",\n milestone=milestone_id,\n state='closed',\n auth=True,\n )\n\n should_backport = set()\n for issue in issues:\n if not is_pull_request(issue):\n continue\n pr = get_pull_request(\"ipython/ipython\", issue['number'],\n auth=True)\n if not pr['merged']:\n print (\"Marked PR closed without merge: %i\" % pr['number'])\n continue\n if pr['base']['ref'] != 'master':\n continue\n should_backport.add(pr['number'])\n return should_backport","label":0},{"cid":"tools/github_stats.py::round_hour","text":"tools/github_stats.py::round_hour\ndef round_hour(dt):\n return dt.replace(minute=0,second=0,microsecond=0)","label":0},{"cid":"tools/github_stats.py::_parse_datetime","text":"tools/github_stats.py::_parse_datetime\ndef _parse_datetime(s):\n \"\"\"Parse dates in the format returned by the Github API.\"\"\"\n if s:\n return datetime.strptime(s, ISO8601)\n else:\n return datetime.fromtimestamp(0)","label":0},{"cid":"tools/github_stats.py::issues2dict","text":"tools/github_stats.py::issues2dict\ndef issues2dict(issues):\n \"\"\"Convert a list of issues to a dict, keyed by issue number.\"\"\"\n idict = {}\n for i in issues:\n idict[i['number']] = i\n return idict","label":0},{"cid":"tools/github_stats.py::split_pulls","text":"tools/github_stats.py::split_pulls\ndef split_pulls(all_issues, project=\"ipython/ipython\"):\n \"\"\"split a list of closed issues into non-PR Issues and Pull Requests\"\"\"\n pulls = []\n issues = []\n for i in all_issues:\n if is_pull_request(i):\n pull = get_pull_request(project, i['number'], auth=True)\n pulls.append(pull)\n else:\n issues.append(i)\n return issues, pulls","label":0},{"cid":"tools/github_stats.py::issues_closed_since","text":"tools/github_stats.py::issues_closed_since\ndef issues_closed_since(period=timedelta(days=365), project=\"ipython/ipython\", pulls=False):\n \"\"\"Get all issues closed since a particular point in time. period\n can either be a datetime object, or a timedelta object. In the\n latter case, it is used as a time before the present.\n \"\"\"\n\n which = 'pulls' if pulls else 'issues'\n\n if isinstance(period, timedelta):\n since = round_hour(datetime.utcnow() - period)\n else:\n since = period\n url = \"https://api.github.com/repos/%s/%s?state=closed&sort=updated&since=%s&per_page=%i\" % (project, which, since.strftime(ISO8601), PER_PAGE)\n allclosed = get_paged_request(url, headers=make_auth_header())\n \n filtered = [ i for i in allclosed if _parse_datetime(i['closed_at']) > since ]\n if pulls:\n filtered = [ i for i in filtered if _parse_datetime(i['merged_at']) > since ]\n # filter out PRs not against master (backports)\n filtered = [ i for i in filtered if i['base']['ref'] == 'master' ]\n else:\n filtered = [ i for i in filtered if not is_pull_request(i) ]\n \n return filtered","label":0},{"cid":"tools/github_stats.py::sorted_by_field","text":"tools/github_stats.py::sorted_by_field\ndef sorted_by_field(issues, field='closed_at', reverse=False):\n \"\"\"Return a list of issues sorted by closing date date.\"\"\"\n return sorted(issues, key = lambda i:i[field], reverse=reverse)","label":0},{"cid":"tools/github_stats.py::report","text":"tools/github_stats.py::report\ndef report(issues, show_urls=False):\n \"\"\"Summary report about a list of issues, printing number and title.\n \"\"\"\n # titles may have unicode in them, so we must encode everything below\n if show_urls:\n for i in issues:\n role = 'ghpull' if 'merged_at' in i else 'ghissue'\n print(u'* :%s:`%d`: %s' % (role, i['number'],\n i['title'].replace(u'`', u'``')))\n else:\n for i in issues:\n print(u'* %d: %s' % (i['number'], i['title'].replace(u'`', u'``')))","label":0}],"string":"[\n {\n \"cid\": \"IPython/config/application.py::catch_config_error\",\n \"text\": \"IPython/config/application.py::catch_config_error\\n@decorator\\ndef catch_config_error(method, app, *args, **kwargs):\\n \\\"\\\"\\\"Method decorator for catching invalid config (Trait/ArgumentErrors) during init.\\n\\n On a TraitError (generally caused by bad config), this will print the trait's\\n message, and exit the app.\\n \\n For use on init methods, to prevent invoking excepthook on invalid input.\\n \\\"\\\"\\\"\\n try:\\n return method(app, *args, **kwargs)\\n except (TraitError, ArgumentError) as e:\\n app.print_help()\\n app.log.fatal(\\\"Bad config encountered during initialization:\\\")\\n app.log.fatal(str(e))\\n app.log.debug(\\\"Config at the time: %s\\\", app.config)\\n app.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::LevelFormatter.format\",\n \"text\": \"IPython/config/application.py::LevelFormatter.format\\n def format(self, record):\\n if record.levelno >= self.highlevel_limit:\\n record.highlevel = self.highlevel_format % record.__dict__\\n else:\\n record.highlevel = \\\"\\\"\\n return super(LevelFormatter, self).format(record)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application._log_level_changed\",\n \"text\": \"IPython/config/application.py::Application._log_level_changed\\n def _log_level_changed(self, name, old, new):\\n \\\"\\\"\\\"Adjust the log level when log_level is set.\\\"\\\"\\\"\\n if isinstance(new, string_types):\\n new = getattr(logging, new)\\n self.log_level = new\\n self.log.setLevel(new)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application._log_datefmt_changed\",\n \"text\": \"IPython/config/application.py::Application._log_datefmt_changed\\n def _log_datefmt_changed(self, name, old, new):\\n self._log_format_changed()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application._log_format_changed\",\n \"text\": \"IPython/config/application.py::Application._log_format_changed\\n def _log_format_changed(self, name, old, new):\\n \\\"\\\"\\\"Change the log formatter when log_format is set.\\\"\\\"\\\"\\n _log_handler = self.log.handlers[0]\\n _log_formatter = self._log_formatter_cls(fmt=new, datefmt=self.log_datefmt)\\n _log_handler.setFormatter(_log_formatter)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application._log_default\",\n \"text\": \"IPython/config/application.py::Application._log_default\\n def _log_default(self):\\n \\\"\\\"\\\"Start logging for this application.\\n\\n The default is to log to stderr using a StreamHandler, if no default\\n handler already exists. The log level starts at logging.WARN, but this\\n can be adjusted by setting the ``log_level`` attribute.\\n \\\"\\\"\\\"\\n log = logging.getLogger(self.__class__.__name__)\\n log.setLevel(self.log_level)\\n log.propagate = False\\n _log = log # copied from Logger.hasHandlers() (new in Python 3.2)\\n while _log:\\n if _log.handlers:\\n return log\\n if not _log.propagate:\\n break\\n else:\\n _log = _log.parent\\n if sys.executable.endswith('pythonw.exe'):\\n # this should really go to a file, but file-logging is only\\n # hooked up in parallel applications\\n _log_handler = logging.StreamHandler(open(os.devnull, 'w'))\\n else:\\n _log_handler = logging.StreamHandler()\\n _log_formatter = self._log_formatter_cls(fmt=self.log_format, datefmt=self.log_datefmt)\\n _log_handler.setFormatter(_log_formatter)\\n log.addHandler(_log_handler)\\n return log\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application._flags_changed\",\n \"text\": \"IPython/config/application.py::Application._flags_changed\\n def _flags_changed(self, name, old, new):\\n \\\"\\\"\\\"ensure flags dict is valid\\\"\\\"\\\"\\n for key,value in iteritems(new):\\n assert len(value) == 2, \\\"Bad flag: %r:%s\\\"%(key,value)\\n assert isinstance(value[0], (dict, Config)), \\\"Bad flag: %r:%s\\\"%(key,value)\\n assert isinstance(value[1], string_types), \\\"Bad flag: %r:%s\\\"%(key,value)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.__init__\",\n \"text\": \"IPython/config/application.py::Application.__init__\\n def __init__(self, **kwargs):\\n SingletonConfigurable.__init__(self, **kwargs)\\n # Ensure my class is in self.classes, so my attributes appear in command line\\n # options and config files.\\n if self.__class__ not in self.classes:\\n self.classes.insert(0, self.__class__)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application._config_changed\",\n \"text\": \"IPython/config/application.py::Application._config_changed\\n def _config_changed(self, name, old, new):\\n SingletonConfigurable._config_changed(self, name, old, new)\\n self.log.debug('Config changed:')\\n self.log.debug(repr(new))\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.initialize\",\n \"text\": \"IPython/config/application.py::Application.initialize\\n @catch_config_error\\n def initialize(self, argv=None):\\n \\\"\\\"\\\"Do the basic steps to configure me.\\n\\n Override in subclasses.\\n \\\"\\\"\\\"\\n self.parse_command_line(argv)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.start\",\n \"text\": \"IPython/config/application.py::Application.start\\n def start(self):\\n \\\"\\\"\\\"Start the app mainloop.\\n\\n Override in subclasses.\\n \\\"\\\"\\\"\\n if self.subapp is not None:\\n return self.subapp.start()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.print_alias_help\",\n \"text\": \"IPython/config/application.py::Application.print_alias_help\\n def print_alias_help(self):\\n \\\"\\\"\\\"Print the alias part of the help.\\\"\\\"\\\"\\n if not self.aliases:\\n return\\n\\n lines = []\\n classdict = {}\\n for cls in self.classes:\\n # include all parents (up to, but excluding Configurable) in available names\\n for c in cls.mro()[:-3]:\\n classdict[c.__name__] = c\\n\\n for alias, longname in iteritems(self.aliases):\\n classname, traitname = longname.split('.',1)\\n cls = classdict[classname]\\n\\n trait = cls.class_traits(config=True)[traitname]\\n help = cls.class_get_trait_help(trait).splitlines()\\n # reformat first line\\n help[0] = help[0].replace(longname, alias) + ' (%s)'%longname\\n if len(alias) == 1:\\n help[0] = help[0].replace('--%s='%alias, '-%s '%alias)\\n lines.extend(help)\\n # lines.append('')\\n print(os.linesep.join(lines))\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/config/application.py::Application.print_flag_help\",\n \"text\": \"IPython/config/application.py::Application.print_flag_help\\n def print_flag_help(self):\\n \\\"\\\"\\\"Print the flag part of the help.\\\"\\\"\\\"\\n if not self.flags:\\n return\\n\\n lines = []\\n for m, (cfg,help) in iteritems(self.flags):\\n prefix = '--' if len(m) > 1 else '-'\\n lines.append(prefix+m)\\n lines.append(indent(dedent(help.strip())))\\n # lines.append('')\\n print(os.linesep.join(lines))\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.print_options\",\n \"text\": \"IPython/config/application.py::Application.print_options\\n def print_options(self):\\n if not self.flags and not self.aliases:\\n return\\n lines = ['Options']\\n lines.append('-'*len(lines[0]))\\n lines.append('')\\n for p in wrap_paragraphs(self.option_description):\\n lines.append(p)\\n lines.append('')\\n print(os.linesep.join(lines))\\n self.print_flag_help()\\n self.print_alias_help()\\n print()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.print_subcommands\",\n \"text\": \"IPython/config/application.py::Application.print_subcommands\\n def print_subcommands(self):\\n \\\"\\\"\\\"Print the subcommand part of the help.\\\"\\\"\\\"\\n if not self.subcommands:\\n return\\n\\n lines = [\\\"Subcommands\\\"]\\n lines.append('-'*len(lines[0]))\\n lines.append('')\\n for p in wrap_paragraphs(self.subcommand_description.format(\\n app=self.name)):\\n lines.append(p)\\n lines.append('')\\n for subc, (cls, help) in iteritems(self.subcommands):\\n lines.append(subc)\\n if help:\\n lines.append(indent(dedent(help.strip())))\\n lines.append('')\\n print(os.linesep.join(lines))\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.print_help\",\n \"text\": \"IPython/config/application.py::Application.print_help\\n def print_help(self, classes=False):\\n \\\"\\\"\\\"Print the help for each Configurable class in self.classes.\\n\\n If classes=False (the default), only flags and aliases are printed.\\n \\\"\\\"\\\"\\n self.print_description()\\n self.print_subcommands()\\n self.print_options()\\n\\n if classes:\\n if self.classes:\\n print(\\\"Class parameters\\\")\\n print(\\\"----------------\\\")\\n print()\\n for p in wrap_paragraphs(self.keyvalue_description):\\n print(p)\\n print()\\n\\n for cls in self.classes:\\n cls.class_print_help()\\n print()\\n else:\\n print(\\\"To see all available configurables, use `--help-all`\\\")\\n print()\\n\\n self.print_examples()\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/config/application.py::Application.print_description\",\n \"text\": \"IPython/config/application.py::Application.print_description\\n def print_description(self):\\n \\\"\\\"\\\"Print the application description.\\\"\\\"\\\"\\n for p in wrap_paragraphs(self.description):\\n print(p)\\n print()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.print_examples\",\n \"text\": \"IPython/config/application.py::Application.print_examples\\n def print_examples(self):\\n \\\"\\\"\\\"Print usage and examples.\\n\\n This usage string goes at the end of the command line help string\\n and should contain examples of the application's usage.\\n \\\"\\\"\\\"\\n if self.examples:\\n print(\\\"Examples\\\")\\n print(\\\"--------\\\")\\n print()\\n print(indent(dedent(self.examples.strip())))\\n print()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.print_version\",\n \"text\": \"IPython/config/application.py::Application.print_version\\n def print_version(self):\\n \\\"\\\"\\\"Print the version string.\\\"\\\"\\\"\\n print(self.version)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.update_config\",\n \"text\": \"IPython/config/application.py::Application.update_config\\n def update_config(self, config):\\n \\\"\\\"\\\"Fire the traits events when the config is updated.\\\"\\\"\\\"\\n # Save a copy of the current config.\\n newconfig = deepcopy(self.config)\\n # Merge the new config into the current one.\\n newconfig.merge(config)\\n # Save the combined config as self.config, which triggers the traits\\n # events.\\n self.config = newconfig\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.initialize_subcommand\",\n \"text\": \"IPython/config/application.py::Application.initialize_subcommand\\n @catch_config_error\\n def initialize_subcommand(self, subc, argv=None):\\n \\\"\\\"\\\"Initialize a subcommand with argv.\\\"\\\"\\\"\\n subapp,help = self.subcommands.get(subc)\\n\\n if isinstance(subapp, string_types):\\n subapp = import_item(subapp)\\n\\n # clear existing instances\\n self.__class__.clear_instance()\\n # instantiate\\n self.subapp = subapp.instance(config=self.config)\\n # and initialize subapp\\n self.subapp.initialize(argv)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.flatten_flags\",\n \"text\": \"IPython/config/application.py::Application.flatten_flags\\n def flatten_flags(self):\\n \\\"\\\"\\\"flatten flags and aliases, so cl-args override as expected.\\n \\n This prevents issues such as an alias pointing to InteractiveShell,\\n but a config file setting the same trait in TerminalInteraciveShell\\n getting inappropriate priority over the command-line arg.\\n\\n Only aliases with exactly one descendent in the class list\\n will be promoted.\\n \\n \\\"\\\"\\\"\\n # build a tree of classes in our list that inherit from a particular\\n # it will be a dict by parent classname of classes in our list\\n # that are descendents\\n mro_tree = defaultdict(list)\\n for cls in self.classes:\\n clsname = cls.__name__\\n for parent in cls.mro()[1:-3]:\\n # exclude cls itself and Configurable,HasTraits,object\\n mro_tree[parent.__name__].append(clsname)\\n # flatten aliases, which have the form:\\n # { 'alias' : 'Class.trait' }\\n aliases = {}\\n for alias, cls_trait in iteritems(self.aliases):\\n cls,trait = cls_trait.split('.',1)\\n children = mro_tree[cls]\\n if len(children) == 1:\\n # exactly one descendent, promote alias\\n cls = children[0]\\n aliases[alias] = '.'.join([cls,trait])\\n \\n # flatten flags, which are of the form:\\n # { 'key' : ({'Cls' : {'trait' : value}}, 'help')}\\n flags = {}\\n for key, (flagdict, help) in iteritems(self.flags):\\n newflag = {}\\n for cls, subdict in iteritems(flagdict):\\n children = mro_tree[cls]\\n # exactly one descendent, promote flag section\\n if len(children) == 1:\\n cls = children[0]\\n newflag[cls] = subdict\\n flags[key] = (newflag, help)\\n return flags, aliases\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/config/application.py::Application.parse_command_line\",\n \"text\": \"IPython/config/application.py::Application.parse_command_line\\n @catch_config_error\\n def parse_command_line(self, argv=None):\\n \\\"\\\"\\\"Parse the command line arguments.\\\"\\\"\\\"\\n argv = sys.argv[1:] if argv is None else argv\\n self.argv = [ py3compat.cast_unicode(arg) for arg in argv ]\\n \\n if argv and argv[0] == 'help':\\n # turn `ipython help notebook` into `ipython notebook -h`\\n argv = argv[1:] + ['-h']\\n\\n if self.subcommands and len(argv) > 0:\\n # we have subcommands, and one may have been specified\\n subc, subargv = argv[0], argv[1:]\\n if re.match(r'^\\\\w(\\\\-?\\\\w)*$', subc) and subc in self.subcommands:\\n # it's a subcommand, and *not* a flag or class parameter\\n return self.initialize_subcommand(subc, subargv)\\n\\n # Arguments after a '--' argument are for the script IPython may be\\n # about to run, not IPython iteslf. For arguments parsed here (help and\\n # version), we want to only search the arguments up to the first\\n # occurrence of '--', which we're calling interpreted_argv.\\n try:\\n interpreted_argv = argv[:argv.index('--')]\\n except ValueError:\\n interpreted_argv = argv\\n\\n if any(x in interpreted_argv for x in ('-h', '--help-all', '--help')):\\n self.print_help('--help-all' in interpreted_argv)\\n self.exit(0)\\n\\n if '--version' in interpreted_argv or '-V' in interpreted_argv:\\n self.print_version()\\n self.exit(0)\\n \\n # flatten flags&aliases, so cl-args get appropriate priority:\\n flags,aliases = self.flatten_flags()\\n loader = KVArgParseConfigLoader(argv=argv, aliases=aliases,\\n flags=flags, log=self.log)\\n config = loader.load_config()\\n self.update_config(config)\\n # store unparsed args in extra_args\\n self.extra_args = loader.extra_args\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application._load_config_files\",\n \"text\": \"IPython/config/application.py::Application._load_config_files\\n @classmethod\\n def _load_config_files(cls, basefilename, path=None, log=None):\\n \\\"\\\"\\\"Load config files (py,json) by filename and path.\\n\\n yield each config object in turn.\\n \\\"\\\"\\\"\\n pyloader = PyFileConfigLoader(basefilename+'.py', path=path, log=log)\\n jsonloader = JSONFileConfigLoader(basefilename+'.json', path=path, log=log)\\n config = None\\n for loader in [pyloader, jsonloader]:\\n try:\\n config = loader.load_config()\\n except ConfigFileNotFound:\\n pass\\n except Exception:\\n # try to get the full filename, but it will be empty in the\\n # unlikely event that the error raised before filefind finished\\n filename = loader.full_filename or basefilename\\n # problem while running the file\\n if log:\\n log.error(\\\"Exception while loading config file %s\\\",\\n filename, exc_info=True)\\n else:\\n if log:\\n log.debug(\\\"Loaded config file: %s\\\", loader.full_filename)\\n if config:\\n yield config\\n\\n raise StopIteration\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.load_config_file\",\n \"text\": \"IPython/config/application.py::Application.load_config_file\\n @catch_config_error\\n def load_config_file(self, filename, path=None):\\n \\\"\\\"\\\"Load config files by filename and path.\\\"\\\"\\\"\\n filename, ext = os.path.splitext(filename)\\n for config in self._load_config_files(filename, path=path, log=self.log):\\n self.update_config(config)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.generate_config_file\",\n \"text\": \"IPython/config/application.py::Application.generate_config_file\\n def generate_config_file(self):\\n \\\"\\\"\\\"generate default config file from Configurables\\\"\\\"\\\"\\n lines = [\\\"# Configuration file for %s.\\\"%self.name]\\n lines.append('')\\n lines.append('c = get_config()')\\n lines.append('')\\n for cls in self.classes:\\n lines.append(cls.class_config_section())\\n return '\\\\n'.join(lines)\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/config/application.py::Application.exit\",\n \"text\": \"IPython/config/application.py::Application.exit\\n def exit(self, exit_status=0):\\n self.log.debug(\\\"Exiting application: %s\\\" % self.name)\\n sys.exit(exit_status)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::Application.launch_instance\",\n \"text\": \"IPython/config/application.py::Application.launch_instance\\n @classmethod\\n def launch_instance(cls, argv=None, **kwargs):\\n \\\"\\\"\\\"Launch a global instance of this Application\\n \\n If a global instance already exists, this reinitializes and starts it\\n \\\"\\\"\\\"\\n app = cls.instance(**kwargs)\\n app.initialize(argv)\\n app.start()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/config/application.py::boolean_flag\",\n \"text\": \"IPython/config/application.py::boolean_flag\\ndef boolean_flag(name, configurable, set_help='', unset_help=''):\\n \\\"\\\"\\\"Helper for building basic --trait, --no-trait flags.\\n\\n Parameters\\n ----------\\n\\n name : str\\n The name of the flag.\\n configurable : str\\n The 'Class.trait' string of the trait to be set/unset with the flag\\n set_help : unicode\\n help string for --name flag\\n unset_help : unicode\\n help string for --no-name flag\\n\\n Returns\\n -------\\n\\n cfg : dict\\n A dict with two keys: 'name', and 'no-name', for setting and unsetting\\n the trait, respectively.\\n \\\"\\\"\\\"\\n # default helpstrings\\n set_help = set_help or \\\"set %s=True\\\"%configurable\\n unset_help = unset_help or \\\"set %s=False\\\"%configurable\\n\\n cls,trait = configurable.split('.')\\n\\n setter = {cls : {trait : True}}\\n unsetter = {cls : {trait : False}}\\n return {name : (setter, set_help), 'no-'+name : (unsetter, unset_help)}\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/consoleapp.py::IPythonConsoleApp._connection_file_default\",\n \"text\": \"IPython/consoleapp.py::IPythonConsoleApp._connection_file_default\\n def _connection_file_default(self):\\n return 'kernel-%i.json' % os.getpid()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/consoleapp.py::IPythonConsoleApp.build_kernel_argv\",\n \"text\": \"IPython/consoleapp.py::IPythonConsoleApp.build_kernel_argv\\n def build_kernel_argv(self, argv=None):\\n \\\"\\\"\\\"build argv to be passed to kernel subprocess\\\"\\\"\\\"\\n if argv is None:\\n argv = sys.argv[1:]\\n self.kernel_argv = swallow_argv(argv, self.frontend_aliases, self.frontend_flags)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/consoleapp.py::IPythonConsoleApp.init_connection_file\",\n \"text\": \"IPython/consoleapp.py::IPythonConsoleApp.init_connection_file\\n def init_connection_file(self):\\n \\\"\\\"\\\"find the connection file, and load the info if found.\\n \\n The current working directory and the current profile's security\\n directory will be searched for the file if it is not given by\\n absolute path.\\n \\n When attempting to connect to an existing kernel and the `--existing`\\n argument does not match an existing file, it will be interpreted as a\\n fileglob, and the matching file in the current profile's security dir\\n with the latest access time will be used.\\n \\n After this method is called, self.connection_file contains the *full path*\\n to the connection file, never just its name.\\n \\\"\\\"\\\"\\n if self.existing:\\n try:\\n cf = find_connection_file(self.existing)\\n except Exception:\\n self.log.critical(\\\"Could not find existing kernel connection file %s\\\", self.existing)\\n self.exit(1)\\n self.log.debug(\\\"Connecting to existing kernel: %s\\\" % cf)\\n self.connection_file = cf\\n else:\\n # not existing, check if we are going to write the file\\n # and ensure that self.connection_file is a full path, not just the shortname\\n try:\\n cf = find_connection_file(self.connection_file)\\n except Exception:\\n # file might not exist\\n if self.connection_file == os.path.basename(self.connection_file):\\n # just shortname, put it in security dir\\n cf = os.path.join(self.profile_dir.security_dir, self.connection_file)\\n else:\\n cf = self.connection_file\\n self.connection_file = cf\\n try:\\n self.connection_file = filefind(self.connection_file, ['.', self.profile_dir.security_dir])\\n except IOError:\\n self.log.debug(\\\"Connecti\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/consoleapp.py::IPythonConsoleApp.init_ssh\",\n \"text\": \"IPython/consoleapp.py::IPythonConsoleApp.init_ssh\\n def init_ssh(self):\\n \\\"\\\"\\\"set up ssh tunnels, if needed.\\\"\\\"\\\"\\n if not self.existing or (not self.sshserver and not self.sshkey):\\n return\\n self.load_connection_file()\\n \\n transport = self.transport\\n ip = self.ip\\n \\n if transport != 'tcp':\\n self.log.error(\\\"Can only use ssh tunnels with TCP sockets, not %s\\\", transport)\\n sys.exit(-1)\\n \\n if self.sshkey and not self.sshserver:\\n # specifying just the key implies that we are connecting directly\\n self.sshserver = ip\\n ip = localhost()\\n \\n # build connection dict for tunnels:\\n info = dict(ip=ip,\\n shell_port=self.shell_port,\\n iopub_port=self.iopub_port,\\n stdin_port=self.stdin_port,\\n hb_port=self.hb_port\\n )\\n \\n self.log.info(\\\"Forwarding connections to %s via %s\\\"%(ip, self.sshserver))\\n \\n # tunnels return a new set of ports, which will be on localhost:\\n self.ip = localhost()\\n try:\\n newports = tunnel_to_kernel(info, self.sshserver, self.sshkey)\\n except:\\n # even catch KeyboardInterrupt\\n self.log.error(\\\"Could not setup tunnels\\\", exc_info=True)\\n self.exit(1)\\n \\n self.shell_port, self.iopub_port, self.stdin_port, self.hb_port = newports\\n \\n cf = self.connection_file\\n base,ext = os.path.splitext(cf)\\n base = os.path.basename(base)\\n self.connection_file = os.path.basename(base)+'-ssh'+ext\\n self.log.info(\\\"To connect another client via this tunnel, use:\\\")\\n self.log.info(\\\"--existing %s\\\" % self.connection_file)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/consoleapp.py::IPythonConsoleApp._new_connection_file\",\n \"text\": \"IPython/consoleapp.py::IPythonConsoleApp._new_connection_file\\n def _new_connection_file(self):\\n cf = ''\\n while not cf:\\n # we don't need a 128b id to distinguish kernels, use more readable\\n # 48b node segment (12 hex chars). Users running more than 32k simultaneous\\n # kernels can subclass.\\n ident = str(uuid.uuid4()).split('-')[-1]\\n cf = os.path.join(self.profile_dir.security_dir, 'kernel-%s.json' % ident)\\n # only keep if it's actually new. Protect against unlikely collision\\n # in 48b random search space\\n cf = cf if not os.path.exists(cf) else ''\\n return cf\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/consoleapp.py::IPythonConsoleApp.init_kernel_manager\",\n \"text\": \"IPython/consoleapp.py::IPythonConsoleApp.init_kernel_manager\\n def init_kernel_manager(self):\\n # Don't let Qt or ZMQ swallow KeyboardInterupts.\\n if self.existing:\\n self.kernel_manager = None\\n return\\n signal.signal(signal.SIGINT, signal.SIG_DFL)\\n\\n # Create a KernelManager and start a kernel.\\n try:\\n self.kernel_manager = self.kernel_manager_class(\\n ip=self.ip,\\n session=self.session,\\n transport=self.transport,\\n shell_port=self.shell_port,\\n iopub_port=self.iopub_port,\\n stdin_port=self.stdin_port,\\n hb_port=self.hb_port,\\n connection_file=self.connection_file,\\n kernel_name=self.kernel_name,\\n parent=self,\\n ipython_dir=self.ipython_dir,\\n )\\n except NoSuchKernel:\\n self.log.critical(\\\"Could not find kernel %s\\\", self.kernel_name)\\n self.exit(1)\\n\\n self.kernel_manager.client_factory = self.kernel_client_class\\n self.kernel_manager.start_kernel(extra_arguments=self.kernel_argv)\\n atexit.register(self.kernel_manager.cleanup_ipc_files)\\n\\n if self.sshserver:\\n # ssh, write new connection file\\n self.kernel_manager.write_connection_file()\\n\\n # in case KM defaults / ssh writing changes things:\\n km = self.kernel_manager\\n self.shell_port=km.shell_port\\n self.iopub_port=km.iopub_port\\n self.stdin_port=km.stdin_port\\n self.hb_port=km.hb_port\\n self.connection_file = km.connection_file\\n\\n atexit.register(self.kernel_manager.cleanup_connection_file)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/consoleapp.py::IPythonConsoleApp.init_kernel_client\",\n \"text\": \"IPython/consoleapp.py::IPythonConsoleApp.init_kernel_client\\n def init_kernel_client(self):\\n if self.kernel_manager is not None:\\n self.kernel_client = self.kernel_manager.client()\\n else:\\n self.kernel_client = self.kernel_client_class(\\n session=self.session,\\n ip=self.ip,\\n transport=self.transport,\\n shell_port=self.shell_port,\\n iopub_port=self.iopub_port,\\n stdin_port=self.stdin_port,\\n hb_port=self.hb_port,\\n connection_file=self.connection_file,\\n parent=self,\\n )\\n\\n self.kernel_client.start_channels()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/consoleapp.py::IPythonConsoleApp.initialize\",\n \"text\": \"IPython/consoleapp.py::IPythonConsoleApp.initialize\\n def initialize(self, argv=None):\\n \\\"\\\"\\\"\\n Classes which mix this class in should call:\\n IPythonConsoleApp.initialize(self,argv)\\n \\\"\\\"\\\"\\n self.init_connection_file()\\n default_secure(self.config)\\n self.init_ssh()\\n self.init_kernel_manager()\\n self.init_kernel_client()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::list_profiles_in\",\n \"text\": \"IPython/core/profileapp.py::list_profiles_in\\ndef list_profiles_in(path):\\n \\\"\\\"\\\"list profiles in a given root directory\\\"\\\"\\\"\\n files = os.listdir(path)\\n profiles = []\\n for f in files:\\n try:\\n full_path = os.path.join(path, f)\\n except UnicodeError:\\n continue\\n if os.path.isdir(full_path) and f.startswith('profile_'):\\n profiles.append(f.split('_',1)[-1])\\n return profiles\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::list_bundled_profiles\",\n \"text\": \"IPython/core/profileapp.py::list_bundled_profiles\\ndef list_bundled_profiles():\\n \\\"\\\"\\\"list profiles that are bundled with IPython.\\\"\\\"\\\"\\n path = os.path.join(get_ipython_package_dir(), u'config', u'profile')\\n files = os.listdir(path)\\n profiles = []\\n for profile in files:\\n full_path = os.path.join(path, profile)\\n if os.path.isdir(full_path) and profile != \\\"__pycache__\\\":\\n profiles.append(profile)\\n return profiles\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::ProfileLocate.parse_command_line\",\n \"text\": \"IPython/core/profileapp.py::ProfileLocate.parse_command_line\\n def parse_command_line(self, argv=None):\\n super(ProfileLocate, self).parse_command_line(argv)\\n if self.extra_args:\\n self.profile = self.extra_args[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::ProfileLocate.start\",\n \"text\": \"IPython/core/profileapp.py::ProfileLocate.start\\n def start(self):\\n print(self.profile_dir.location)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::ProfileList._print_profiles\",\n \"text\": \"IPython/core/profileapp.py::ProfileList._print_profiles\\n def _print_profiles(self, profiles):\\n \\\"\\\"\\\"print list of profiles, indented.\\\"\\\"\\\"\\n for profile in profiles:\\n print(' %s' % profile)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::ProfileList.list_profile_dirs\",\n \"text\": \"IPython/core/profileapp.py::ProfileList.list_profile_dirs\\n def list_profile_dirs(self):\\n profiles = list_bundled_profiles()\\n if profiles:\\n print()\\n print(\\\"Available profiles in IPython:\\\")\\n self._print_profiles(profiles)\\n print()\\n print(\\\" The first request for a bundled profile will copy it\\\")\\n print(\\\" into your IPython directory (%s),\\\" % self.ipython_dir)\\n print(\\\" where you can customize it.\\\")\\n \\n profiles = list_profiles_in(self.ipython_dir)\\n if profiles:\\n print()\\n print(\\\"Available profiles in %s:\\\" % self.ipython_dir)\\n self._print_profiles(profiles)\\n \\n profiles = list_profiles_in(py3compat.getcwd())\\n if profiles:\\n print()\\n print(\\\"Available profiles in current directory (%s):\\\" % py3compat.getcwd())\\n self._print_profiles(profiles)\\n \\n print()\\n print(\\\"To use any of the above profiles, start IPython with:\\\")\\n print(\\\" ipython --profile=<name>\\\")\\n print()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::ProfileList.start\",\n \"text\": \"IPython/core/profileapp.py::ProfileList.start\\n def start(self):\\n self.list_profile_dirs()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::ProfileCreate._log_format_default\",\n \"text\": \"IPython/core/profileapp.py::ProfileCreate._log_format_default\\n def _log_format_default(self):\\n return \\\"[%(name)s] %(message)s\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::ProfileCreate._copy_config_files_default\",\n \"text\": \"IPython/core/profileapp.py::ProfileCreate._copy_config_files_default\\n def _copy_config_files_default(self):\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::ProfileCreate._parallel_changed\",\n \"text\": \"IPython/core/profileapp.py::ProfileCreate._parallel_changed\\n def _parallel_changed(self, name, old, new):\\n parallel_files = [ 'ipcontroller_config.py',\\n 'ipengine_config.py',\\n 'ipcluster_config.py'\\n ]\\n if new:\\n for cf in parallel_files:\\n self.config_files.append(cf)\\n else:\\n for cf in parallel_files:\\n if cf in self.config_files:\\n self.config_files.remove(cf)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::ProfileCreate.parse_command_line\",\n \"text\": \"IPython/core/profileapp.py::ProfileCreate.parse_command_line\\n def parse_command_line(self, argv):\\n super(ProfileCreate, self).parse_command_line(argv)\\n # accept positional arg as profile name\\n if self.extra_args:\\n self.profile = self.extra_args[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::ProfileCreate._import_app\",\n \"text\": \"IPython/core/profileapp.py::ProfileCreate._import_app\\n def _import_app(self, app_path):\\n \\\"\\\"\\\"import an app class\\\"\\\"\\\"\\n app = None\\n name = app_path.rsplit('.', 1)[-1]\\n try:\\n app = import_item(app_path)\\n except ImportError:\\n self.log.info(\\\"Couldn't import %s, config file will be excluded\\\", name)\\n except Exception:\\n self.log.warn('Unexpected error importing %s', name, exc_info=True)\\n return app\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::ProfileCreate.init_config_files\",\n \"text\": \"IPython/core/profileapp.py::ProfileCreate.init_config_files\\n def init_config_files(self):\\n super(ProfileCreate, self).init_config_files()\\n # use local imports, since these classes may import from here\\n from IPython.terminal.ipapp import TerminalIPythonApp\\n apps = [TerminalIPythonApp]\\n for app_path in (\\n 'IPython.qt.console.qtconsoleapp.IPythonQtConsoleApp',\\n 'IPython.html.notebookapp.NotebookApp',\\n 'IPython.nbconvert.nbconvertapp.NbConvertApp',\\n ):\\n app = self._import_app(app_path)\\n if app is not None:\\n apps.append(app)\\n if self.parallel:\\n from IPython.parallel.apps.ipcontrollerapp import IPControllerApp\\n from IPython.parallel.apps.ipengineapp import IPEngineApp\\n from IPython.parallel.apps.ipclusterapp import IPClusterStart\\n from IPython.parallel.apps.iploggerapp import IPLoggerApp\\n apps.extend([\\n IPControllerApp,\\n IPEngineApp,\\n IPClusterStart,\\n IPLoggerApp,\\n ])\\n for App in apps:\\n app = App()\\n app.config.update(self.config)\\n app.log = self.log\\n app.overwrite = self.overwrite\\n app.copy_config_files=True\\n app.ipython_dir=self.ipython_dir\\n app.profile_dir=self.profile_dir\\n app.init_config_files()\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/core/profileapp.py::ProfileCreate.stage_default_config_file\",\n \"text\": \"IPython/core/profileapp.py::ProfileCreate.stage_default_config_file\\n def stage_default_config_file(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/profileapp.py::ProfileApp.start\",\n \"text\": \"IPython/core/profileapp.py::ProfileApp.start\\n def start(self):\\n if self.subapp is None:\\n print(\\\"No subcommand specified. Must specify one of: %s\\\"%(self.subcommands.keys()))\\n print()\\n self.print_description()\\n self.print_subcommands()\\n self.exit(1)\\n else:\\n return self.subapp.start()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/usage.py::page_guiref\",\n \"text\": \"IPython/core/usage.py::page_guiref\\ndef page_guiref(arg_s=None):\\n \\\"\\\"\\\"Show a basic reference about the GUI Console.\\\"\\\"\\\"\\n from IPython.core import page\\n page.page(gui_reference)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::random_ports\",\n \"text\": \"IPython/html/notebookapp.py::random_ports\\ndef random_ports(port, n):\\n \\\"\\\"\\\"Generate a list of n random ports near the given port.\\n\\n The first 5 ports will be sequential, and the remaining n-5 will be\\n randomly selected in the range [port-2*n, port+2*n].\\n \\\"\\\"\\\"\\n for i in range(min(5, n)):\\n yield port + i\\n for i in range(n-5):\\n yield max(1, port + random.randint(-2*n, 2*n))\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::load_handlers\",\n \"text\": \"IPython/html/notebookapp.py::load_handlers\\ndef load_handlers(name):\\n \\\"\\\"\\\"Load the (URL pattern, handler) tuples for each component.\\\"\\\"\\\"\\n name = 'IPython.html.' + name\\n mod = __import__(name, fromlist=['default_handlers'])\\n return mod.default_handlers\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookWebApplication.__init__\",\n \"text\": \"IPython/html/notebookapp.py::NotebookWebApplication.__init__\\n def __init__(self, ipython_app, kernel_manager, contents_manager,\\n cluster_manager, session_manager, kernel_spec_manager, log,\\n base_url, default_url, settings_overrides, jinja_env_options):\\n\\n settings = self.init_settings(\\n ipython_app, kernel_manager, contents_manager, cluster_manager,\\n session_manager, kernel_spec_manager, log, base_url, default_url,\\n settings_overrides, jinja_env_options)\\n handlers = self.init_handlers(settings)\\n\\n super(NotebookWebApplication, self).__init__(handlers, **settings)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookWebApplication.init_settings\",\n \"text\": \"IPython/html/notebookapp.py::NotebookWebApplication.init_settings\\n def init_settings(self, ipython_app, kernel_manager, contents_manager,\\n cluster_manager, session_manager, kernel_spec_manager,\\n log, base_url, default_url, settings_overrides,\\n jinja_env_options=None):\\n # Python < 2.6.5 doesn't accept unicode keys in f(**kwargs), and\\n # base_url will always be unicode, which will in turn\\n # make the patterns unicode, and ultimately result in unicode\\n # keys in kwargs to handler._execute(**kwargs) in tornado.\\n # This enforces that base_url be ascii in that situation.\\n #\\n # Note that the URLs these patterns check against are escaped,\\n # and thus guaranteed to be ASCII: 'héllo' is really 'h%C3%A9llo'.\\n base_url = py3compat.unicode_to_str(base_url, 'ascii')\\n _template_path = settings_overrides.get(\\\"template_path\\\", os.path.join(os.path.dirname(__file__), \\\"templates\\\"))\\n if isinstance(_template_path, str):\\n _template_path = (_template_path,)\\n template_path = [os.path.expanduser(path) for path in _template_path]\\n\\n jenv_opt = jinja_env_options if jinja_env_options else {}\\n env = Environment(loader=FileSystemLoader(template_path), **jenv_opt)\\n settings = dict(\\n # basics\\n log_function=log_request,\\n base_url=base_url,\\n default_url=default_url,\\n template_path=template_path,\\n static_path=ipython_app.static_file_path,\\n static_handler_class = FileFindHandler,\\n static_url_prefix = url_path_join(base_url,'/static/'),\\n \\n # authentication\\n cookie_secret=ipython_app.cookie_secret,\\n login_url=url_path_join(base_url,'/login'),\\n password=ipython_app.password,\\n \\n # managers\\n kernel_manager=kernel_manager,\\n contents_manager=conten\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookWebApplication.init_handlers\",\n \"text\": \"IPython/html/notebookapp.py::NotebookWebApplication.init_handlers\\n def init_handlers(self, settings):\\n # Load the (URL pattern, handler) tuples for each component.\\n handlers = []\\n handlers.extend(load_handlers('base.handlers'))\\n handlers.extend(load_handlers('tree.handlers'))\\n handlers.extend(load_handlers('auth.login'))\\n handlers.extend(load_handlers('auth.logout'))\\n handlers.extend(load_handlers('notebook.handlers'))\\n handlers.extend(load_handlers('nbconvert.handlers'))\\n handlers.extend(load_handlers('kernelspecs.handlers'))\\n handlers.extend(load_handlers('services.kernels.handlers'))\\n handlers.extend(load_handlers('services.contents.handlers'))\\n handlers.extend(load_handlers('services.clusters.handlers'))\\n handlers.extend(load_handlers('services.sessions.handlers'))\\n handlers.extend(load_handlers('services.nbconvert.handlers'))\\n handlers.extend(load_handlers('services.kernelspecs.handlers'))\\n # FIXME: /files/ should be handled by the Contents service when it exists\\n cm = settings['contents_manager']\\n if hasattr(cm, 'root_dir'):\\n handlers.append(\\n (r\\\"/files/(.*)\\\", AuthenticatedFileHandler, {'path' : cm.root_dir}),\\n )\\n handlers.append(\\n (r\\\"/nbextensions/(.*)\\\", FileFindHandler, {'path' : settings['nbextensions_path']}),\\n )\\n # set the URL that will be redirected from `/`\\n handlers.append(\\n (r'/?', web.RedirectHandler, {\\n 'url' : url_path_join(settings['base_url'], settings['default_url']),\\n 'permanent': False, # want 302, not 301\\n })\\n )\\n # prepend base_url onto the patterns that we match\\n new_handlers = []\\n for handler in handlers:\\n pattern = url_path_join(settings['base_url'], handler[0])\\n new_handler = tuple([pattern] + list(handler[1:]))\\n new_handlers.append(n\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NbserverListApp.start\",\n \"text\": \"IPython/html/notebookapp.py::NbserverListApp.start\\n def start(self):\\n if not self.json:\\n print(\\\"Currently running servers:\\\")\\n for serverinfo in list_running_servers(self.profile):\\n if self.json:\\n print(json.dumps(serverinfo))\\n else:\\n print(serverinfo['url'], \\\"::\\\", serverinfo['notebook_dir'])\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._log_level_default\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._log_level_default\\n def _log_level_default(self):\\n return logging.INFO\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._log_datefmt_default\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._log_datefmt_default\\n def _log_datefmt_default(self):\\n \\\"\\\"\\\"Exclude date from default date format\\\"\\\"\\\"\\n return \\\"%H:%M:%S\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._log_format_default\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._log_format_default\\n def _log_format_default(self):\\n \\\"\\\"\\\"override default log format to include time\\\"\\\"\\\"\\n return u\\\"%(color)s[%(levelname)1.1s %(asctime)s.%(msecs).03d %(name)s]%(end_color)s %(message)s\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._file_to_run_changed\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._file_to_run_changed\\n def _file_to_run_changed(self, name, old, new):\\n path, base = os.path.split(new)\\n if path:\\n self.file_to_run = base\\n self.notebook_dir = path\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._ip_changed\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._ip_changed\\n def _ip_changed(self, name, old, new):\\n if new == u'*': self.ip = u''\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._cookie_secret_file_default\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._cookie_secret_file_default\\n def _cookie_secret_file_default(self):\\n if self.profile_dir is None:\\n return ''\\n return os.path.join(self.profile_dir.security_dir, 'notebook_cookie_secret')\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._cookie_secret_default\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._cookie_secret_default\\n def _cookie_secret_default(self):\\n if os.path.exists(self.cookie_secret_file):\\n with io.open(self.cookie_secret_file, 'rb') as f:\\n return f.read()\\n else:\\n secret = base64.encodestring(os.urandom(1024))\\n self._write_cookie_secret_file(secret)\\n return secret\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._write_cookie_secret_file\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._write_cookie_secret_file\\n def _write_cookie_secret_file(self, secret):\\n \\\"\\\"\\\"write my secret to my secret_file\\\"\\\"\\\"\\n self.log.info(\\\"Writing notebook server cookie secret to %s\\\", self.cookie_secret_file)\\n with io.open(self.cookie_secret_file, 'wb') as f:\\n f.write(secret)\\n try:\\n os.chmod(self.cookie_secret_file, 0o600)\\n except OSError:\\n self.log.warn(\\n \\\"Could not set permissions on %s\\\",\\n self.cookie_secret_file\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._webapp_settings_changed\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._webapp_settings_changed\\n def _webapp_settings_changed(self, name, old, new):\\n self.log.warn(\\\"\\\\n webapp_settings is deprecated, use tornado_settings.\\\\n\\\")\\n self.tornado_settings = new\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._enable_mathjax_changed\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._enable_mathjax_changed\\n def _enable_mathjax_changed(self, name, old, new):\\n \\\"\\\"\\\"set mathjax url to empty if mathjax is disabled\\\"\\\"\\\"\\n if not new:\\n self.mathjax_url = u''\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._base_url_changed\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._base_url_changed\\n def _base_url_changed(self, name, old, new):\\n if not new.startswith('/'):\\n self.base_url = '/'+new\\n elif not new.endswith('/'):\\n self.base_url = new+'/'\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._base_project_url_changed\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._base_project_url_changed\\n def _base_project_url_changed(self, name, old, new):\\n self.log.warn(\\\"base_project_url is deprecated, use base_url\\\")\\n self.base_url = new\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._extra_static_paths_default\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._extra_static_paths_default\\n def _extra_static_paths_default(self):\\n return [os.path.join(self.profile_dir.location, 'static')]\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp.static_file_path\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp.static_file_path\\n @property\\n def static_file_path(self):\\n \\\"\\\"\\\"return extra paths + the default location\\\"\\\"\\\"\\n return self.extra_static_paths + [DEFAULT_STATIC_FILES_PATH]\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._nbextensions_path_default\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._nbextensions_path_default\\n def _nbextensions_path_default(self):\\n return [os.path.join(get_ipython_dir(), 'nbextensions')]\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._mathjax_url_default\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._mathjax_url_default\\n def _mathjax_url_default(self):\\n if not self.enable_mathjax:\\n return u''\\n static_url_prefix = self.tornado_settings.get(\\\"static_url_prefix\\\",\\n url_path_join(self.base_url, \\\"static\\\")\\n )\\n \\n # try local mathjax, either in nbextensions/mathjax or static/mathjax\\n for (url_prefix, search_path) in [\\n (url_path_join(self.base_url, \\\"nbextensions\\\"), self.nbextensions_path),\\n (static_url_prefix, self.static_file_path),\\n ]:\\n self.log.debug(\\\"searching for local mathjax in %s\\\", search_path)\\n try:\\n mathjax = filefind(os.path.join('mathjax', 'MathJax.js'), search_path)\\n except IOError:\\n continue\\n else:\\n url = url_path_join(url_prefix, u\\\"mathjax/MathJax.js\\\")\\n self.log.info(\\\"Serving local MathJax from %s at %s\\\", mathjax, url)\\n return url\\n \\n # no local mathjax, serve from CDN\\n url = u\\\"https://cdn.mathjax.org/mathjax/latest/MathJax.js\\\"\\n self.log.info(\\\"Using MathJax from CDN: %s\\\", url)\\n return url\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._mathjax_url_changed\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._mathjax_url_changed\\n def _mathjax_url_changed(self, name, old, new):\\n if new and not self.enable_mathjax:\\n # enable_mathjax=False overrides mathjax_url\\n self.mathjax_url = u''\\n else:\\n self.log.info(\\\"Using MathJax: %s\\\", new)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._kernel_spec_manager_default\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._kernel_spec_manager_default\\n def _kernel_spec_manager_default(self):\\n return KernelSpecManager(ipython_dir=self.ipython_dir)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._info_file_default\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._info_file_default\\n def _info_file_default(self):\\n info_file = \\\"nbserver-%s.json\\\"%os.getpid()\\n return os.path.join(self.profile_dir.security_dir, info_file)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._pylab_changed\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._pylab_changed\\n def _pylab_changed(self, name, old, new):\\n \\\"\\\"\\\"when --pylab is specified, display a warning and exit\\\"\\\"\\\"\\n if new != 'warn':\\n backend = ' %s' % new\\n else:\\n backend = ''\\n self.log.error(\\\"Support for specifying --pylab on the command line has been removed.\\\")\\n self.log.error(\\n \\\"Please use `%pylab{0}` or `%matplotlib{0}` in the notebook itself.\\\".format(backend)\\n )\\n self.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._notebook_dir_changed\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._notebook_dir_changed\\n def _notebook_dir_changed(self, name, old, new):\\n \\\"\\\"\\\"Do a bit of validation of the notebook dir.\\\"\\\"\\\"\\n if not os.path.isabs(new):\\n # If we receive a non-absolute path, make it absolute.\\n self.notebook_dir = os.path.abspath(new)\\n return\\n if not os.path.isdir(new):\\n raise TraitError(\\\"No such notebook dir: %r\\\" % new)\\n \\n # setting App.notebook_dir implies setting notebook and kernel dirs as well\\n self.config.FileContentsManager.root_dir = new\\n self.config.MappingKernelManager.root_dir = new\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp.parse_command_line\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp.parse_command_line\\n def parse_command_line(self, argv=None):\\n super(NotebookApp, self).parse_command_line(argv)\\n \\n if self.extra_args:\\n arg0 = self.extra_args[0]\\n f = os.path.abspath(arg0)\\n self.argv.remove(arg0)\\n if not os.path.exists(f):\\n self.log.critical(\\\"No such file or directory: %s\\\", f)\\n self.exit(1)\\n \\n # Use config here, to ensure that it takes higher priority than\\n # anything that comes from the profile.\\n c = Config()\\n if os.path.isdir(f):\\n c.NotebookApp.notebook_dir = f\\n elif os.path.isfile(f):\\n c.NotebookApp.file_to_run = f\\n self.update_config(c)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp.init_kernel_argv\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp.init_kernel_argv\\n def init_kernel_argv(self):\\n \\\"\\\"\\\"construct the kernel arguments\\\"\\\"\\\"\\n # Kernel should get *absolute* path to profile directory\\n self.kernel_argv = [\\\"--profile-dir\\\", self.profile_dir.location]\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp.init_configurables\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp.init_configurables\\n def init_configurables(self):\\n # force Session default to be secure\\n default_secure(self.config)\\n kls = import_item(self.kernel_manager_class)\\n self.kernel_manager = kls(\\n parent=self, log=self.log, kernel_argv=self.kernel_argv,\\n connection_dir = self.profile_dir.security_dir,\\n )\\n kls = import_item(self.contents_manager_class)\\n self.contents_manager = kls(parent=self, log=self.log)\\n kls = import_item(self.session_manager_class)\\n self.session_manager = kls(parent=self, log=self.log,\\n kernel_manager=self.kernel_manager,\\n contents_manager=self.contents_manager)\\n kls = import_item(self.cluster_manager_class)\\n self.cluster_manager = kls(parent=self, log=self.log)\\n self.cluster_manager.update_profiles()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp.init_logging\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp.init_logging\\n def init_logging(self):\\n # This prevents double log messages because tornado use a root logger that\\n # self.log is a child of. The logging module dipatches log messages to a log\\n # and all of its ancenstors until propagate is set to False.\\n self.log.propagate = False\\n \\n # hook up tornado 3's loggers to our app handlers\\n logger = logging.getLogger('tornado')\\n logger.propagate = True\\n logger.parent = self.log\\n logger.setLevel(self.log.level)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp.init_webapp\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp.init_webapp\\n def init_webapp(self):\\n \\\"\\\"\\\"initialize tornado webapp and httpserver\\\"\\\"\\\"\\n self.tornado_settings['allow_origin'] = self.allow_origin\\n if self.allow_origin_pat:\\n self.tornado_settings['allow_origin_pat'] = re.compile(self.allow_origin_pat)\\n self.tornado_settings['allow_credentials'] = self.allow_credentials\\n \\n self.web_app = NotebookWebApplication(\\n self, self.kernel_manager, self.contents_manager,\\n self.cluster_manager, self.session_manager, self.kernel_spec_manager,\\n self.log, self.base_url, self.default_url, self.tornado_settings,\\n self.jinja_environment_options\\n )\\n if self.certfile:\\n ssl_options = dict(certfile=self.certfile)\\n if self.keyfile:\\n ssl_options['keyfile'] = self.keyfile\\n else:\\n ssl_options = None\\n self.web_app.password = self.password\\n self.http_server = httpserver.HTTPServer(self.web_app, ssl_options=ssl_options,\\n xheaders=self.trust_xheaders)\\n if not self.ip:\\n warning = \\\"WARNING: The notebook server is listening on all IP addresses\\\"\\n if ssl_options is None:\\n self.log.critical(warning + \\\" and not using encryption. This \\\"\\n \\\"is not recommended.\\\")\\n if not self.password:\\n self.log.critical(warning + \\\" and not using authentication. \\\"\\n \\\"This is highly insecure and not recommended.\\\")\\n success = None\\n for port in random_ports(self.port, self.port_retries+1):\\n try:\\n self.http_server.listen(port, self.ip)\\n except socket.error as e:\\n if e.errno == errno.EADDRINUSE:\\n self.log.info('The port %i is already in use, trying another random port.' % port)\\n continue\\n eli\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp.display_url\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp.display_url\\n @property\\n def display_url(self):\\n ip = self.ip if self.ip else '[all ip addresses on your system]'\\n return self._url(ip)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp.connection_url\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp.connection_url\\n @property\\n def connection_url(self):\\n ip = self.ip if self.ip else 'localhost'\\n return self._url(ip)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._url\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._url\\n def _url(self, ip):\\n proto = 'https' if self.certfile else 'http'\\n return \\\"%s://%s:%i%s\\\" % (proto, ip, self.port, self.base_url)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp.init_signal\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp.init_signal\\n def init_signal(self):\\n if not sys.platform.startswith('win'):\\n signal.signal(signal.SIGINT, self._handle_sigint)\\n signal.signal(signal.SIGTERM, self._signal_stop)\\n if hasattr(signal, 'SIGUSR1'):\\n # Windows doesn't support SIGUSR1\\n signal.signal(signal.SIGUSR1, self._signal_info)\\n if hasattr(signal, 'SIGINFO'):\\n # only on BSD-based systems\\n signal.signal(signal.SIGINFO, self._signal_info)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._handle_sigint\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._handle_sigint\\n def _handle_sigint(self, sig, frame):\\n \\\"\\\"\\\"SIGINT handler spawns confirmation dialog\\\"\\\"\\\"\\n # register more forceful signal handler for ^C^C case\\n signal.signal(signal.SIGINT, self._signal_stop)\\n # request confirmation dialog in bg thread, to avoid\\n # blocking the App\\n thread = threading.Thread(target=self._confirm_exit)\\n thread.daemon = True\\n thread.start()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._restore_sigint_handler\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._restore_sigint_handler\\n def _restore_sigint_handler(self):\\n \\\"\\\"\\\"callback for restoring original SIGINT handler\\\"\\\"\\\"\\n signal.signal(signal.SIGINT, self._handle_sigint)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._confirm_exit\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._confirm_exit\\n def _confirm_exit(self):\\n \\\"\\\"\\\"confirm shutdown on ^C\\n \\n A second ^C, or answering 'y' within 5s will cause shutdown,\\n otherwise original SIGINT handler will be restored.\\n \\n This doesn't work on Windows.\\n \\\"\\\"\\\"\\n info = self.log.info\\n info('interrupted')\\n print(self.notebook_info())\\n sys.stdout.write(\\\"Shutdown this notebook server (y/[n])? \\\")\\n sys.stdout.flush()\\n r,w,x = select.select([sys.stdin], [], [], 5)\\n if r:\\n line = sys.stdin.readline()\\n if line.lower().startswith('y') and 'n' not in line.lower():\\n self.log.critical(\\\"Shutdown confirmed\\\")\\n ioloop.IOLoop.instance().stop()\\n return\\n else:\\n print(\\\"No answer for 5s:\\\", end=' ')\\n print(\\\"resuming operation...\\\")\\n # no answer, or answer is no:\\n # set it back to original SIGINT handler\\n # use IOLoop.add_callback because signal.signal must be called\\n # from main thread\\n ioloop.IOLoop.instance().add_callback(self._restore_sigint_handler)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/notebookapp.py::NotebookApp._signal_stop\",\n \"text\": \"IPython/html/notebookapp.py::NotebookApp._signal_stop\\n def _signal_stop(self, sig, frame):\\n self.log.critical(\\\"received signal %s, stopping\\\", sig)\\n ioloop.IOLoop.instance().stop()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp._config_file_name_default\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp._config_file_name_default\\n def _config_file_name_default(self):\\n return 'ipython_config.py'\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.abs_connection_file\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.abs_connection_file\\n @property\\n def abs_connection_file(self):\\n if os.path.basename(self.connection_file) == self.connection_file:\\n return os.path.join(self.profile_dir.security_dir, self.connection_file)\\n else:\\n return self.connection_file\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_crash_handler\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_crash_handler\\n def init_crash_handler(self):\\n # Install minimal exception handling\\n sys.excepthook = FormattedTB(mode='Verbose', color_scheme='NoColor',\\n ostream=sys.__stdout__)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_poller\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_poller\\n def init_poller(self):\\n if sys.platform == 'win32':\\n if self.interrupt or self.parent_handle:\\n self.poller = ParentPollerWindows(self.interrupt, self.parent_handle)\\n elif self.parent_handle:\\n self.poller = ParentPollerUnix()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp._bind_socket\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp._bind_socket\\n def _bind_socket(self, s, port):\\n iface = '%s://%s' % (self.transport, self.ip)\\n if self.transport == 'tcp':\\n if port <= 0:\\n port = s.bind_to_random_port(iface)\\n else:\\n s.bind(\\\"tcp://%s:%i\\\" % (self.ip, port))\\n elif self.transport == 'ipc':\\n if port <= 0:\\n port = 1\\n path = \\\"%s-%i\\\" % (self.ip, port)\\n while os.path.exists(path):\\n port = port + 1\\n path = \\\"%s-%i\\\" % (self.ip, port)\\n else:\\n path = \\\"%s-%i\\\" % (self.ip, port)\\n s.bind(\\\"ipc://%s\\\" % path)\\n return port\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.write_connection_file\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.write_connection_file\\n def write_connection_file(self):\\n \\\"\\\"\\\"write connection info to JSON file\\\"\\\"\\\"\\n cf = self.abs_connection_file\\n self.log.debug(\\\"Writing connection file: %s\\\", cf)\\n write_connection_file(cf, ip=self.ip, key=self.session.key, transport=self.transport,\\n shell_port=self.shell_port, stdin_port=self.stdin_port, hb_port=self.hb_port,\\n iopub_port=self.iopub_port, control_port=self.control_port)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.cleanup_connection_file\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.cleanup_connection_file\\n def cleanup_connection_file(self):\\n cf = self.abs_connection_file\\n self.log.debug(\\\"Cleaning up connection file: %s\\\", cf)\\n try:\\n os.remove(cf)\\n except (IOError, OSError):\\n pass\\n \\n self.cleanup_ipc_files()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_connection_file\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_connection_file\\n def init_connection_file(self):\\n if not self.connection_file:\\n self.connection_file = \\\"kernel-%s.json\\\"%os.getpid()\\n try:\\n self.connection_file = filefind(self.connection_file, ['.', self.profile_dir.security_dir])\\n except IOError:\\n self.log.debug(\\\"Connection file not found: %s\\\", self.connection_file)\\n # This means I own it, so I will clean it up:\\n atexit.register(self.cleanup_connection_file)\\n return\\n try:\\n self.load_connection_file()\\n except Exception:\\n self.log.error(\\\"Failed to load connection file: %r\\\", self.connection_file, exc_info=True)\\n self.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_sockets\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_sockets\\n def init_sockets(self):\\n # Create a context, a session, and the kernel sockets.\\n self.log.info(\\\"Starting the kernel at pid: %i\\\", os.getpid())\\n context = zmq.Context.instance()\\n # Uncomment this to try closing the context.\\n # atexit.register(context.term)\\n\\n self.shell_socket = context.socket(zmq.ROUTER)\\n self.shell_socket.linger = 1000\\n self.shell_port = self._bind_socket(self.shell_socket, self.shell_port)\\n self.log.debug(\\\"shell ROUTER Channel on port: %i\\\" % self.shell_port)\\n\\n self.iopub_socket = context.socket(zmq.PUB)\\n self.iopub_socket.linger = 1000\\n self.iopub_port = self._bind_socket(self.iopub_socket, self.iopub_port)\\n self.log.debug(\\\"iopub PUB Channel on port: %i\\\" % self.iopub_port)\\n\\n self.stdin_socket = context.socket(zmq.ROUTER)\\n self.stdin_socket.linger = 1000\\n self.stdin_port = self._bind_socket(self.stdin_socket, self.stdin_port)\\n self.log.debug(\\\"stdin ROUTER Channel on port: %i\\\" % self.stdin_port)\\n\\n self.control_socket = context.socket(zmq.ROUTER)\\n self.control_socket.linger = 1000\\n self.control_port = self._bind_socket(self.control_socket, self.control_port)\\n self.log.debug(\\\"control ROUTER Channel on port: %i\\\" % self.control_port)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_heartbeat\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_heartbeat\\n def init_heartbeat(self):\\n \\\"\\\"\\\"start the heart beating\\\"\\\"\\\"\\n # heartbeat doesn't share context, because it mustn't be blocked\\n # by the GIL, which is accessed by libzmq when freeing zero-copy messages\\n hb_ctx = zmq.Context()\\n self.heartbeat = Heartbeat(hb_ctx, (self.transport, self.ip, self.hb_port))\\n self.hb_port = self.heartbeat.port\\n self.log.debug(\\\"Heartbeat REP Channel on port: %i\\\" % self.hb_port)\\n self.heartbeat.start()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.log_connection_info\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.log_connection_info\\n def log_connection_info(self):\\n \\\"\\\"\\\"display connection info, and store ports\\\"\\\"\\\"\\n basename = os.path.basename(self.connection_file)\\n if basename == self.connection_file or \\\\\\n os.path.dirname(self.connection_file) == self.profile_dir.security_dir:\\n # use shortname\\n tail = basename\\n if self.profile != 'default':\\n tail += \\\" --profile %s\\\" % self.profile\\n else:\\n tail = self.connection_file\\n lines = [\\n \\\"To connect another client to this kernel, use:\\\",\\n \\\" --existing %s\\\" % tail,\\n ]\\n # log connection info\\n # info-level, so often not shown.\\n # frontends should use the %connect_info magic\\n # to see the connection info\\n for line in lines:\\n self.log.info(line)\\n # also raw print to the terminal if no parent_handle (`ipython kernel`)\\n if not self.parent_handle:\\n io.rprint(_ctrl_c_message)\\n for line in lines:\\n io.rprint(line)\\n\\n self.ports = dict(shell=self.shell_port, iopub=self.iopub_port,\\n stdin=self.stdin_port, hb=self.hb_port,\\n control=self.control_port)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_blackhole\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_blackhole\\n def init_blackhole(self):\\n \\\"\\\"\\\"redirects stdout/stderr to devnull if necessary\\\"\\\"\\\"\\n if self.no_stdout or self.no_stderr:\\n blackhole = open(os.devnull, 'w')\\n if self.no_stdout:\\n sys.stdout = sys.__stdout__ = blackhole\\n if self.no_stderr:\\n sys.stderr = sys.__stderr__ = blackhole\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_io\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_io\\n def init_io(self):\\n \\\"\\\"\\\"Redirect input streams and set a display hook.\\\"\\\"\\\"\\n if self.outstream_class:\\n outstream_factory = import_item(str(self.outstream_class))\\n sys.stdout = outstream_factory(self.session, self.iopub_socket, u'stdout')\\n sys.stderr = outstream_factory(self.session, self.iopub_socket, u'stderr')\\n if self.displayhook_class:\\n displayhook_factory = import_item(str(self.displayhook_class))\\n sys.displayhook = displayhook_factory(self.session, self.iopub_socket)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_signal\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_signal\\n def init_signal(self):\\n signal.signal(signal.SIGINT, signal.SIG_IGN)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_kernel\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_kernel\\n def init_kernel(self):\\n \\\"\\\"\\\"Create the Kernel object itself\\\"\\\"\\\"\\n shell_stream = ZMQStream(self.shell_socket)\\n control_stream = ZMQStream(self.control_socket)\\n \\n kernel_factory = self.kernel_class\\n\\n kernel = kernel_factory(parent=self, session=self.session,\\n shell_streams=[shell_stream, control_stream],\\n iopub_socket=self.iopub_socket,\\n stdin_socket=self.stdin_socket,\\n log=self.log,\\n profile_dir=self.profile_dir,\\n user_ns=self.user_ns,\\n )\\n kernel.record_ports(self.ports)\\n self.kernel = kernel\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_gui_pylab\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_gui_pylab\\n def init_gui_pylab(self):\\n \\\"\\\"\\\"Enable GUI event loop integration, taking pylab into account.\\\"\\\"\\\"\\n\\n # Provide a wrapper for :meth:`InteractiveShellApp.init_gui_pylab`\\n # to ensure that any exception is printed straight to stderr.\\n # Normally _showtraceback associates the reply with an execution,\\n # which means frontends will never draw it, as this exception\\n # is not associated with any execute request.\\n\\n shell = self.shell\\n _showtraceback = shell._showtraceback\\n try:\\n # replace error-sending traceback with stderr\\n def print_tb(etype, evalue, stb):\\n print (\\\"GUI event loop or pylab initialization failed\\\",\\n file=io.stderr)\\n print (shell.InteractiveTB.stb2text(stb), file=io.stderr)\\n shell._showtraceback = print_tb\\n InteractiveShellApp.init_gui_pylab(self)\\n finally:\\n shell._showtraceback = _showtraceback\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_shell\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.init_shell\\n def init_shell(self):\\n self.shell = getattr(self.kernel, 'shell', None)\\n if self.shell:\\n self.shell.configurables.append(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.initialize\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.initialize\\n @catch_config_error\\n def initialize(self, argv=None):\\n super(IPKernelApp, self).initialize(argv)\\n default_secure(self.config)\\n self.init_blackhole()\\n self.init_connection_file()\\n self.init_poller()\\n self.init_sockets()\\n self.init_heartbeat()\\n # writing/displaying connection info must be *after* init_sockets/heartbeat\\n self.log_connection_info()\\n self.write_connection_file()\\n self.init_io()\\n self.init_signal()\\n self.init_kernel()\\n # shell init steps\\n self.init_path()\\n self.init_shell()\\n if self.shell:\\n self.init_gui_pylab()\\n self.init_extensions()\\n self.init_code()\\n # flush stdout/stderr, so that anything written to these streams during\\n # initialization do not get associated with the first execution request\\n sys.stdout.flush()\\n sys.stderr.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.start\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::IPKernelApp.start\\n def start(self):\\n if self.poller is not None:\\n self.poller.start()\\n self.kernel.start()\\n try:\\n ioloop.IOLoop.instance().start()\\n except KeyboardInterrupt:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/kernelapp.py::main\",\n \"text\": \"IPython/kernel/zmq/kernelapp.py::main\\ndef main():\\n \\\"\\\"\\\"Run an IPKernel as an application\\\"\\\"\\\"\\n app = IPKernelApp.instance()\\n app.initialize()\\n app.start()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/pylab/config.py::pil_available\",\n \"text\": \"IPython/kernel/zmq/pylab/config.py::pil_available\\ndef pil_available():\\n \\\"\\\"\\\"Test if PIL/Pillow is available\\\"\\\"\\\"\\n out = False\\n try:\\n from PIL import Image\\n out = True\\n except:\\n pass\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/pylab/config.py::InlineBackend._config_changed\",\n \"text\": \"IPython/kernel/zmq/pylab/config.py::InlineBackend._config_changed\\n def _config_changed(self, name, old, new):\\n # warn on change of renamed config section\\n if new.InlineBackendConfig != old.InlineBackendConfig:\\n warn(\\\"InlineBackendConfig has been renamed to InlineBackend\\\")\\n super(InlineBackend, self)._config_changed(name, old, new)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/pylab/config.py::InlineBackend._update_figure_formatters\",\n \"text\": \"IPython/kernel/zmq/pylab/config.py::InlineBackend._update_figure_formatters\\n def _update_figure_formatters(self):\\n if self.shell is not None:\\n from IPython.core.pylabtools import select_figure_formats\\n select_figure_formats(self.shell, self.figure_formats, **self.print_figure_kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/pylab/config.py::InlineBackend._figure_formats_changed\",\n \"text\": \"IPython/kernel/zmq/pylab/config.py::InlineBackend._figure_formats_changed\\n def _figure_formats_changed(self, name, old, new):\\n if 'jpg' in new or 'jpeg' in new:\\n if not pil_available():\\n raise TraitError(\\\"Requires PIL/Pillow for JPG figures\\\")\\n self._update_figure_formatters()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/kernel/zmq/pylab/config.py::InlineBackend._figure_format_changed\",\n \"text\": \"IPython/kernel/zmq/pylab/config.py::InlineBackend._figure_format_changed\\n def _figure_format_changed(self, name, old, new):\\n if new:\\n self.figure_formats = {new}\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::IPAppCrashHandler.__init__\",\n \"text\": \"IPython/terminal/ipapp.py::IPAppCrashHandler.__init__\\n def __init__(self, app):\\n contact_name = release.author\\n contact_email = release.author_email\\n bug_tracker = 'https://github.com/ipython/ipython/issues'\\n super(IPAppCrashHandler,self).__init__(\\n app, contact_name, contact_email, bug_tracker\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::IPAppCrashHandler.make_report\",\n \"text\": \"IPython/terminal/ipapp.py::IPAppCrashHandler.make_report\\n def make_report(self,traceback):\\n \\\"\\\"\\\"Return a string containing a crash report.\\\"\\\"\\\"\\n\\n sec_sep = self.section_sep\\n # Start with parent report\\n report = [super(IPAppCrashHandler, self).make_report(traceback)]\\n # Add interactive-specific info we may have\\n rpt_add = report.append\\n try:\\n rpt_add(sec_sep+\\\"History of session input:\\\")\\n for line in self.app.shell.user_ns['_ih']:\\n rpt_add(line)\\n rpt_add('\\\\n*** Last line of input (may not be in above history):\\\\n')\\n rpt_add(self.app.shell._last_input_line+'\\\\n')\\n except:\\n pass\\n\\n return ''.join(report)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::LocateIPythonApp.start\",\n \"text\": \"IPython/terminal/ipapp.py::LocateIPythonApp.start\\n def start(self):\\n if self.subapp is not None:\\n return self.subapp.start()\\n else:\\n print(self.ipython_dir)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::TerminalIPythonApp._classes_default\",\n \"text\": \"IPython/terminal/ipapp.py::TerminalIPythonApp._classes_default\\n def _classes_default(self):\\n \\\"\\\"\\\"This has to be in a method, for TerminalIPythonApp to be available.\\\"\\\"\\\"\\n return [\\n InteractiveShellApp, # ShellApp comes before TerminalApp, because\\n self.__class__, # it will also affect subclasses (e.g. QtConsole)\\n TerminalInteractiveShell,\\n PromptManager,\\n HistoryManager,\\n ProfileDir,\\n PlainTextFormatter,\\n IPCompleter,\\n ScriptMagics,\\n StoreMagics,\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::TerminalIPythonApp._quick_changed\",\n \"text\": \"IPython/terminal/ipapp.py::TerminalIPythonApp._quick_changed\\n def _quick_changed(self, name, old, new):\\n if new:\\n self.load_config_file = lambda *a, **kw: None\\n self.ignore_old_config=True\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::TerminalIPythonApp._force_interact_changed\",\n \"text\": \"IPython/terminal/ipapp.py::TerminalIPythonApp._force_interact_changed\\n def _force_interact_changed(self, name, old, new):\\n if new:\\n self.interact = True\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::TerminalIPythonApp._file_to_run_changed\",\n \"text\": \"IPython/terminal/ipapp.py::TerminalIPythonApp._file_to_run_changed\\n def _file_to_run_changed(self, name, old, new):\\n if new:\\n self.something_to_run = True\\n if new and not self.force_interact:\\n self.interact = False\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::TerminalIPythonApp.parse_command_line\",\n \"text\": \"IPython/terminal/ipapp.py::TerminalIPythonApp.parse_command_line\\n def parse_command_line(self, argv=None):\\n \\\"\\\"\\\"override to allow old '-pylab' flag with deprecation warning\\\"\\\"\\\"\\n\\n argv = sys.argv[1:] if argv is None else argv\\n\\n if '-pylab' in argv:\\n # deprecated `-pylab` given,\\n # warn and transform into current syntax\\n argv = argv[:] # copy, don't clobber\\n idx = argv.index('-pylab')\\n warn.warn(\\\"`-pylab` flag has been deprecated.\\\\n\\\"\\n \\\" Use `--matplotlib <backend>` and import pylab manually.\\\")\\n argv[idx] = '--pylab'\\n\\n return super(TerminalIPythonApp, self).parse_command_line(argv)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::TerminalIPythonApp.initialize\",\n \"text\": \"IPython/terminal/ipapp.py::TerminalIPythonApp.initialize\\n @catch_config_error\\n def initialize(self, argv=None):\\n \\\"\\\"\\\"Do actions after construct, but before starting the app.\\\"\\\"\\\"\\n super(TerminalIPythonApp, self).initialize(argv)\\n if self.subapp is not None:\\n # don't bother initializing further, starting subapp\\n return\\n if not self.ignore_old_config:\\n check_for_old_config(self.ipython_dir)\\n # print self.extra_args\\n if self.extra_args and not self.something_to_run:\\n self.file_to_run = self.extra_args[0]\\n self.init_path()\\n # create the shell\\n self.init_shell()\\n # and draw the banner\\n self.init_banner()\\n # Now a variety of things that happen after the banner is printed.\\n self.init_gui_pylab()\\n self.init_extensions()\\n self.init_code()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::TerminalIPythonApp.init_shell\",\n \"text\": \"IPython/terminal/ipapp.py::TerminalIPythonApp.init_shell\\n def init_shell(self):\\n \\\"\\\"\\\"initialize the InteractiveShell instance\\\"\\\"\\\"\\n # Create an InteractiveShell instance.\\n # shell.display_banner should always be False for the terminal\\n # based app, because we call shell.show_banner() by hand below\\n # so the banner shows *before* all extension loading stuff.\\n self.shell = TerminalInteractiveShell.instance(parent=self,\\n display_banner=False, profile_dir=self.profile_dir,\\n ipython_dir=self.ipython_dir, user_ns=self.user_ns)\\n self.shell.configurables.append(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::TerminalIPythonApp.init_banner\",\n \"text\": \"IPython/terminal/ipapp.py::TerminalIPythonApp.init_banner\\n def init_banner(self):\\n \\\"\\\"\\\"optionally display the banner\\\"\\\"\\\"\\n if self.display_banner and self.interact:\\n self.shell.show_banner()\\n # Make sure there is a space below the banner.\\n if self.log_level <= logging.INFO: print()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::TerminalIPythonApp._pylab_changed\",\n \"text\": \"IPython/terminal/ipapp.py::TerminalIPythonApp._pylab_changed\\n def _pylab_changed(self, name, old, new):\\n \\\"\\\"\\\"Replace --pylab='inline' with --pylab='auto'\\\"\\\"\\\"\\n if new == 'inline':\\n warn.warn(\\\"'inline' not available as pylab backend, \\\"\\n \\\"using 'auto' instead.\\\")\\n self.pylab = 'auto'\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::TerminalIPythonApp.start\",\n \"text\": \"IPython/terminal/ipapp.py::TerminalIPythonApp.start\\n def start(self):\\n if self.subapp is not None:\\n return self.subapp.start()\\n # perform any prexec steps:\\n if self.interact:\\n self.log.debug(\\\"Starting IPython's mainloop...\\\")\\n self.shell.mainloop()\\n else:\\n self.log.debug(\\\"IPython not interactive...\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/terminal/ipapp.py::load_default_config\",\n \"text\": \"IPython/terminal/ipapp.py::load_default_config\\ndef load_default_config(ipython_dir=None):\\n \\\"\\\"\\\"Load the default config file from the default ipython_dir.\\n\\n This is useful for embedded shells.\\n \\\"\\\"\\\"\\n if ipython_dir is None:\\n ipython_dir = get_ipython_dir()\\n\\n profile_dir = os.path.join(ipython_dir, 'profile_default')\\n\\n config = Config()\\n for cf in Application._load_config_files(\\\"ipython_config\\\", path=profile_dir):\\n config.update(cf)\\n\\n return config\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/autogen_config.py::document_config_options\",\n \"text\": \"docs/autogen_config.py::document_config_options\\ndef document_config_options(classes):\\n lines = []\\n for cls in classes:\\n classname = cls.__name__\\n for k, trait in sorted(cls.class_traits(config=True).items()):\\n ttype = trait.__class__.__name__\\n\\n termline = classname + '.' + trait.name\\n\\n # Choices or type\\n if 'Enum' in ttype:\\n # include Enum choices\\n termline += ' : ' + '|'.join(repr(x) for x in trait.values)\\n else:\\n termline += ' : ' + ttype\\n lines.append(termline)\\n\\n # Default value\\n try:\\n dv = trait.get_default_value()\\n dvr = repr(dv)\\n except Exception:\\n dvr = dv = None # ignore defaults we can't construct\\n if (dv is not None) and (dvr is not None):\\n if len(dvr) > 64:\\n dvr = dvr[:61]+'...'\\n # Double up backslashes, so they get to the rendered docs\\n dvr = dvr.replace('\\\\\\\\n', '\\\\\\\\\\\\\\\\n')\\n lines.append(' Default: ' + dvr)\\n lines.append('')\\n\\n help = trait.get_metadata('help')\\n if help is not None:\\n help = '\\\\n\\\\n'.join(wrap_paragraphs(help, 76))\\n lines.append(indent(help, 4))\\n else:\\n lines.append(' No description')\\n\\n lines.append('')\\n return '\\\\n'.join(lines)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/autogen_config.py::write_doc\",\n \"text\": \"docs/autogen_config.py::write_doc\\ndef write_doc(name, title, classes, preamble=None):\\n configdoc = document_config_options(classes)\\n filename = '%s.rst' % name\\n with open('source/config/options/%s' % filename, 'w') as f:\\n f.write(title + '\\\\n')\\n f.write(('=' * len(title)) + '\\\\n')\\n f.write('\\\\n')\\n if preamble is not None:\\n f.write(preamble + '\\\\n\\\\n')\\n f.write(configdoc)\\n with open('source/config/options/generated', 'a') as f:\\n f.write(filename + '\\\\n')\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/backport_pr.py::find_rejects\",\n \"text\": \"tools/backport_pr.py::find_rejects\\ndef find_rejects(root='.'):\\n for dirname, dirs, files in os.walk(root):\\n for fname in files:\\n if fname.endswith('.rej'):\\n yield os.path.join(dirname, fname)\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/backport_pr.py::get_current_branch\",\n \"text\": \"tools/backport_pr.py::get_current_branch\\ndef get_current_branch():\\n branches = check_output(['git', 'branch'])\\n for branch in branches.splitlines():\\n if branch.startswith(b'*'):\\n return branch[1:].strip().decode('utf-8')\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/backport_pr.py::backport_pr\",\n \"text\": \"tools/backport_pr.py::backport_pr\\ndef backport_pr(branch, num, project='ipython/ipython'):\\n current_branch = get_current_branch()\\n if branch != current_branch:\\n check_call(['git', 'checkout', branch])\\n check_call(['git', 'pull'])\\n pr = get_pull_request(project, num, auth=True)\\n files = get_pull_request_files(project, num, auth=True)\\n patch_url = pr['patch_url']\\n title = pr['title']\\n description = pr['body']\\n fname = \\\"PR%i.patch\\\" % num\\n if os.path.exists(fname):\\n print(\\\"using patch from {fname}\\\".format(**locals()))\\n with open(fname) as f:\\n patch = f.read()\\n else:\\n req = urlopen(patch_url)\\n patch = req.read()\\n\\n lines = description.splitlines()\\n if len(lines) > 5:\\n lines = lines[:5] + ['...']\\n description = '\\\\n'.join(lines)\\n\\n msg = \\\"Backport PR #%i: %s\\\" % (num, title) + '\\\\n\\\\n' + description\\n check = Popen(['git', 'apply', '--check', '--verbose'], stdin=PIPE)\\n a,b = check.communicate(patch)\\n\\n if check.returncode:\\n print(\\\"patch did not apply, saving to {fname}\\\".format(**locals()))\\n print(\\\"edit {fname} until `cat {fname} | git apply --check` succeeds\\\".format(**locals()))\\n print(\\\"then run tools/backport_pr.py {num} again\\\".format(**locals()))\\n if not os.path.exists(fname):\\n with open(fname, 'wb') as f:\\n f.write(patch)\\n return 1\\n\\n p = Popen(['git', 'apply'], stdin=PIPE)\\n a,b = p.communicate(patch)\\n\\n filenames = [ f['filename'] for f in files ]\\n\\n check_call(['git', 'add'] + filenames)\\n\\n check_call(['git', 'commit', '-m', msg])\\n\\n print(\\\"PR #%i applied, with msg:\\\" % num)\\n print()\\n print(msg)\\n print()\\n\\n if branch != current_branch:\\n check_call(['git', 'checkout', current_branch])\\n\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/backport_pr.py::already_backported\",\n \"text\": \"tools/backport_pr.py::already_backported\\ndef already_backported(branch, since_tag=None):\\n \\\"\\\"\\\"return set of PRs that have been backported already\\\"\\\"\\\"\\n if since_tag is None:\\n since_tag = check_output(['git','describe', branch, '--abbrev=0']).decode('utf8').strip()\\n cmd = ['git', 'log', '%s..%s' % (since_tag, branch), '--oneline']\\n lines = check_output(cmd).decode('utf8')\\n return set(int(num) for num in backport_re.findall(lines))\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/backport_pr.py::should_backport\",\n \"text\": \"tools/backport_pr.py::should_backport\\ndef should_backport(labels=None, milestone=None):\\n \\\"\\\"\\\"return set of PRs marked for backport\\\"\\\"\\\"\\n if labels is None and milestone is None:\\n raise ValueError(\\\"Specify one of labels or milestone.\\\")\\n elif labels is not None and milestone is not None:\\n raise ValueError(\\\"Specify only one of labels or milestone.\\\")\\n if labels is not None:\\n issues = get_issues_list(\\\"ipython/ipython\\\",\\n labels=labels,\\n state='closed',\\n auth=True,\\n )\\n else:\\n milestone_id = get_milestone_id(\\\"ipython/ipython\\\", milestone,\\n auth=True)\\n issues = get_issues_list(\\\"ipython/ipython\\\",\\n milestone=milestone_id,\\n state='closed',\\n auth=True,\\n )\\n\\n should_backport = set()\\n for issue in issues:\\n if not is_pull_request(issue):\\n continue\\n pr = get_pull_request(\\\"ipython/ipython\\\", issue['number'],\\n auth=True)\\n if not pr['merged']:\\n print (\\\"Marked PR closed without merge: %i\\\" % pr['number'])\\n continue\\n if pr['base']['ref'] != 'master':\\n continue\\n should_backport.add(pr['number'])\\n return should_backport\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/github_stats.py::round_hour\",\n \"text\": \"tools/github_stats.py::round_hour\\ndef round_hour(dt):\\n return dt.replace(minute=0,second=0,microsecond=0)\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/github_stats.py::_parse_datetime\",\n \"text\": \"tools/github_stats.py::_parse_datetime\\ndef _parse_datetime(s):\\n \\\"\\\"\\\"Parse dates in the format returned by the Github API.\\\"\\\"\\\"\\n if s:\\n return datetime.strptime(s, ISO8601)\\n else:\\n return datetime.fromtimestamp(0)\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/github_stats.py::issues2dict\",\n \"text\": \"tools/github_stats.py::issues2dict\\ndef issues2dict(issues):\\n \\\"\\\"\\\"Convert a list of issues to a dict, keyed by issue number.\\\"\\\"\\\"\\n idict = {}\\n for i in issues:\\n idict[i['number']] = i\\n return idict\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/github_stats.py::split_pulls\",\n \"text\": \"tools/github_stats.py::split_pulls\\ndef split_pulls(all_issues, project=\\\"ipython/ipython\\\"):\\n \\\"\\\"\\\"split a list of closed issues into non-PR Issues and Pull Requests\\\"\\\"\\\"\\n pulls = []\\n issues = []\\n for i in all_issues:\\n if is_pull_request(i):\\n pull = get_pull_request(project, i['number'], auth=True)\\n pulls.append(pull)\\n else:\\n issues.append(i)\\n return issues, pulls\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/github_stats.py::issues_closed_since\",\n \"text\": \"tools/github_stats.py::issues_closed_since\\ndef issues_closed_since(period=timedelta(days=365), project=\\\"ipython/ipython\\\", pulls=False):\\n \\\"\\\"\\\"Get all issues closed since a particular point in time. period\\n can either be a datetime object, or a timedelta object. In the\\n latter case, it is used as a time before the present.\\n \\\"\\\"\\\"\\n\\n which = 'pulls' if pulls else 'issues'\\n\\n if isinstance(period, timedelta):\\n since = round_hour(datetime.utcnow() - period)\\n else:\\n since = period\\n url = \\\"https://api.github.com/repos/%s/%s?state=closed&sort=updated&since=%s&per_page=%i\\\" % (project, which, since.strftime(ISO8601), PER_PAGE)\\n allclosed = get_paged_request(url, headers=make_auth_header())\\n \\n filtered = [ i for i in allclosed if _parse_datetime(i['closed_at']) > since ]\\n if pulls:\\n filtered = [ i for i in filtered if _parse_datetime(i['merged_at']) > since ]\\n # filter out PRs not against master (backports)\\n filtered = [ i for i in filtered if i['base']['ref'] == 'master' ]\\n else:\\n filtered = [ i for i in filtered if not is_pull_request(i) ]\\n \\n return filtered\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/github_stats.py::sorted_by_field\",\n \"text\": \"tools/github_stats.py::sorted_by_field\\ndef sorted_by_field(issues, field='closed_at', reverse=False):\\n \\\"\\\"\\\"Return a list of issues sorted by closing date date.\\\"\\\"\\\"\\n return sorted(issues, key = lambda i:i[field], reverse=reverse)\",\n \"label\": 0\n },\n {\n \"cid\": \"tools/github_stats.py::report\",\n \"text\": \"tools/github_stats.py::report\\ndef report(issues, show_urls=False):\\n \\\"\\\"\\\"Summary report about a list of issues, printing number and title.\\n \\\"\\\"\\\"\\n # titles may have unicode in them, so we must encode everything below\\n if show_urls:\\n for i in issues:\\n role = 'ghpull' if 'merged_at' in i else 'ghissue'\\n print(u'* :%s:`%d`: %s' % (role, i['number'],\\n i['title'].replace(u'`', u'``')))\\n else:\\n for i in issues:\\n print(u'* %d: %s' % (i['number'], i['title'].replace(u'`', u'``')))\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":255,"cells":{"instance_id":{"kind":"string","value":"ipython__ipython-7554"},"repo":{"kind":"string","value":"ipython/ipython"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Same widget does not update all interact. \nCf [Stackoverflow](https://stackoverflow.com/questions/25945543/can-values-from-ipython-widgets-propagate-to-other-cells/25992790#25992790) \n\nhaving the same widget (say a Checkbox) shared by 2 interact, the value of the checkbox is updated across cell, but the interactived' function not call. Ex:\n\n\n\nI clicked on first checkbox, second was highligted, but value did not changed from True to False.\n\n"},"gold_files":{"kind":"list like","value":["IPython/html/widgets/interaction.py","IPython/html/widgets/widget_output.py"],"string":"[\n \"IPython/html/widgets/interaction.py\",\n \"IPython/html/widgets/widget_output.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["IPython/html/widgets/interaction.py::<module>","IPython/html/widgets/interaction.py::interactive","IPython/html/widgets/interaction.py::interactive.call_f","IPython/html/widgets/widget_output.py::<module>","IPython/html/widgets/widget_output.py::Output.__enter__","IPython/html/widgets/widget_output.py::Output.__enter__.send_hook","IPython/html/widgets/widget_output.py::Output.__exit__"],"string":"[\n \"IPython/html/widgets/interaction.py::<module>\",\n \"IPython/html/widgets/interaction.py::interactive\",\n \"IPython/html/widgets/interaction.py::interactive.call_f\",\n \"IPython/html/widgets/widget_output.py::<module>\",\n \"IPython/html/widgets/widget_output.py::Output.__enter__\",\n \"IPython/html/widgets/widget_output.py::Output.__enter__.send_hook\",\n \"IPython/html/widgets/widget_output.py::Output.__exit__\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"IPython/core/magics/execution.py","text":"IPython/core/magics/execution.py\nclass TimeitResult # Object returned by the timeit magic with info about the run.\n def __init__(self, loops, repeat, best, all_runs, compile_time, precision)\n def _repr_pretty_(self, p, cycle)\nclass TimeitTemplateFiller # Fill in the AST template for timing execution.\n def __init__(self, ast_setup, ast_stmt)\n def visit_FunctionDef(self, node) # Fill in the setup statement\n def visit_For(self, node) # Fill in the statement to be timed\nclass Timer # Timer class that explicitly uses self.inner\n def timeit(self, number=timeit.default_number) # Time 'number' executions of the main statement.\nclass ExecutionMagics # Magics related to code execution, debugging, profiling, etc.\n def __init__(self, shell)\n def profile_missing_notice(self, *args, **kwargs)\n def prun(self, parameter_s='', cell=None) # Run a statement through the python code profiler.\n def _run_with_profiler(self, code, opts, namespace) # Run `code` with profiler. Used by ``%prun`` and ``%run -p``.\n def pdb(self, parameter_s='') # Control the automatic calling of the pdb interactive debugger.\n def debug(self, line='', cell=None) # Activate the interactive debugger.\n def _debug_post_mortem(self)\n def _debug_exec(self, code, breakpoint)\n def tb(self, s) # Print the last traceback with the currently active exception mode.\n def run(self, parameter_s='', runner=None, file_finder=get_py_filename) # Run the named file inside IPython as a program.\n def _run_with_debugger(self, code, code_ns, filename=None, bp_line=None, bp_file=None) # Run `code` in debugger with a break point.\n def _run_with_timing(run, nruns) # Run function `run` and print timing information.\n def timeit(self, line='', cell=None) # Time execution of a Python statement or expression\n def time(self, line='', cell=None, local_ns=None) # Time execution of a Python statement or expression.\n def macro(self, parameter_s='') # Define a macro for future re-execution. It accepts ranges of history,\n def capture(self, line, cell) # run the cell, capturing stdout, stderr, and IPython's rich display() calls.\ndef parse_breakpoint(text, current_file) # Returns (file, line) for file:line and (current_file, line) for line\ndef _format_time(timespan, precision=3) # Formats the timespan in a human readable form","label":0},{"cid":"IPython/html/widgets/interaction.py","text":"IPython/html/widgets/interaction.py\ndef _matches(o, pattern) # Match a pattern of types in a sequence.\ndef _get_min_max_value(min, max, value=None, step=None) # Return min, max, value given input values with possible None.\ndef _widget_abbrev_single_value(o) # Make widgets from single values, which can be used as parameter defaults.\ndef _widget_abbrev(o) # Make widgets from abbreviations: single values, lists or tuples.\ndef _widget_from_abbrev(abbrev, default=empty) # Build a Widget instance given an abbreviation or Widget.\ndef _yield_abbreviations_for_parameter(param, kwargs) # Get an abbreviation for a function parameter.\ndef _find_abbreviations(f, kwargs) # Find the abbreviations for a function and kwargs passed to interact.\ndef _widgets_from_abbreviations(seq) # Given a sequence of (name, abbrev) tuples, return a sequence of Widgets.\ndef interactive(__interact_f, **kwargs) # Builds a group of interactive widgets tied to a function and places the\n def call_f(name=None, old=None, new=None)\ndef interact(__interact_f=None, **kwargs) # Displays interactive widgets which are tied to a function.\ndef interact_manual(__interact_f=None, **kwargs) # interact_manual(f, **kwargs)\nclass fixed # A pseudo-widget whose value is fixed and never synced to the client.\n def __init__(self, value, **kwargs)","label":1},{"cid":"IPython/html/widgets/widget_bool.py","text":"IPython/html/widgets/widget_bool.py\nclass _Bool # A base class for creating widgets that represent booleans.\n def __init__(self, value=None, **kwargs)\nclass Checkbox # Displays a boolean `value` in the form of a checkbox.\nclass ToggleButton # Displays a boolean `value` in the form of a toggle button.","label":0},{"cid":"IPython/html/widgets/widget_output.py","text":"IPython/html/widgets/widget_output.py\nclass Output # Widget used as a context manager to display output.\n def clear_output(self, *pargs, **kwargs)\n def __enter__(self) # Called upon entering output widget context manager.\n def send_hook(stream, msg_or_type, content=None, parent=None, ident=None, buffers=None, track=False, header=None, metadata=None)\n def __exit__(self, exception_type, exception_value, traceback) # Called upon exiting output widget context manager.\n def _flush(self) # Flush stdout and stderr buffers.","label":1},{"cid":"IPython/qt/console/frontend_widget.py","text":"IPython/qt/console/frontend_widget.py\nclass FrontendHighlighter # A PygmentsHighlighter that understands and ignores prompts.\n def __init__(self, frontend, lexer=None)\n def highlightBlock(self, string) # Highlight a block of text. Reimplemented to highlight selectively.\n def rehighlightBlock(self, block) # Reimplemented to temporarily enable highlighting if disabled.\n def setFormat(self, start, count, format) # Reimplemented to highlight selectively.\nclass FrontendWidget # A Qt frontend for a generic Python kernel.\n def _lexer_class_changed(self, name, old, new)\n def _lexer_class_default(self)\n def _lexer_default(self)\n def __init__(self, *args, **kw)\n def copy(self) # Copy the currently selected text to the clipboard, removing prompts.\n def _is_complete(self, source, interactive) # Returns whether 'source' can be completely processed and a new\n def _execute(self, source, hidden) # Execute 'source'. If 'hidden', do not show any output.\n def _prompt_started_hook(self) # Called immediately after a new prompt is displayed.\n def _prompt_finished_hook(self) # Called immediately after a prompt is finished, i.e. when some input\n def _tab_pressed(self) # Called when the tab key is pressed. Returns whether to continue\n def _context_menu_make(self, pos) # Reimplemented to add an action for raw copy.\n def request_interrupt_kernel(self)\n def request_restart_kernel(self)\n def _event_filter_console_keypress(self, event) # Reimplemented for execution interruption and smart backspace.\n def _insert_continuation_prompt(self, cursor) # Reimplemented for auto-indentation.\n def _handle_clear_output(self, msg) # Handle clear output messages.\n def _silent_exec_callback(self, expr, callback) # Silently execute `expr` in the kernel and call `callback` with reply\n def _handle_exec_callback(self, msg) # Execute `callback` corresponding to `msg` reply, after ``_silent_exec_callback``\n def _handle_execute_reply(self, msg) # Handles replies for code execution.\n def _handle_input_request(self, msg) # Handle requests for raw_input.\n def callback(line)\n def _kernel_restarted_message(self, died=True)\n def _handle_kernel_died(self, since_last_heartbeat) # Handle the kernel's death (if we do not own the kernel).\n def _handle_kernel_restarted(self, died=True) # Notice that the autorestarter restarted the kernel.\n def _handle_inspect_reply(self, rep) # Handle replies for call tips.\n def _handle_execute_result(self, msg) # Handle display hook output.\n def _handle_stream(self, msg) # Handle stdout, stderr, and stdin.\n def _handle_shutdown_reply(self, msg) # Handle shutdown signal, only if from other console.\n def _handle_status(self, msg) # Handle status message\n def _started_channels(self) # Called when the KernelManager channels have started listening or\n def copy_raw(self) # Copy the currently selected text to the clipboard without attempting\n d","label":0}],"string":"[\n {\n \"cid\": \"IPython/core/magics/execution.py\",\n \"text\": \"IPython/core/magics/execution.py\\nclass TimeitResult # Object returned by the timeit magic with info about the run.\\n def __init__(self, loops, repeat, best, all_runs, compile_time, precision)\\n def _repr_pretty_(self, p, cycle)\\nclass TimeitTemplateFiller # Fill in the AST template for timing execution.\\n def __init__(self, ast_setup, ast_stmt)\\n def visit_FunctionDef(self, node) # Fill in the setup statement\\n def visit_For(self, node) # Fill in the statement to be timed\\nclass Timer # Timer class that explicitly uses self.inner\\n def timeit(self, number=timeit.default_number) # Time 'number' executions of the main statement.\\nclass ExecutionMagics # Magics related to code execution, debugging, profiling, etc.\\n def __init__(self, shell)\\n def profile_missing_notice(self, *args, **kwargs)\\n def prun(self, parameter_s='', cell=None) # Run a statement through the python code profiler.\\n def _run_with_profiler(self, code, opts, namespace) # Run `code` with profiler. Used by ``%prun`` and ``%run -p``.\\n def pdb(self, parameter_s='') # Control the automatic calling of the pdb interactive debugger.\\n def debug(self, line='', cell=None) # Activate the interactive debugger.\\n def _debug_post_mortem(self)\\n def _debug_exec(self, code, breakpoint)\\n def tb(self, s) # Print the last traceback with the currently active exception mode.\\n def run(self, parameter_s='', runner=None, file_finder=get_py_filename) # Run the named file inside IPython as a program.\\n def _run_with_debugger(self, code, code_ns, filename=None, bp_line=None, bp_file=None) # Run `code` in debugger with a break point.\\n def _run_with_timing(run, nruns) # Run function `run` and print timing information.\\n def timeit(self, line='', cell=None) # Time execution of a Python statement or expression\\n def time(self, line='', cell=None, local_ns=None) # Time execution of a Python statement or expression.\\n def macro(self, parameter_s='') # Define a macro for future re-execution. It accepts ranges of history,\\n def capture(self, line, cell) # run the cell, capturing stdout, stderr, and IPython's rich display() calls.\\ndef parse_breakpoint(text, current_file) # Returns (file, line) for file:line and (current_file, line) for line\\ndef _format_time(timespan, precision=3) # Formats the timespan in a human readable form\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py\",\n \"text\": \"IPython/html/widgets/interaction.py\\ndef _matches(o, pattern) # Match a pattern of types in a sequence.\\ndef _get_min_max_value(min, max, value=None, step=None) # Return min, max, value given input values with possible None.\\ndef _widget_abbrev_single_value(o) # Make widgets from single values, which can be used as parameter defaults.\\ndef _widget_abbrev(o) # Make widgets from abbreviations: single values, lists or tuples.\\ndef _widget_from_abbrev(abbrev, default=empty) # Build a Widget instance given an abbreviation or Widget.\\ndef _yield_abbreviations_for_parameter(param, kwargs) # Get an abbreviation for a function parameter.\\ndef _find_abbreviations(f, kwargs) # Find the abbreviations for a function and kwargs passed to interact.\\ndef _widgets_from_abbreviations(seq) # Given a sequence of (name, abbrev) tuples, return a sequence of Widgets.\\ndef interactive(__interact_f, **kwargs) # Builds a group of interactive widgets tied to a function and places the\\n def call_f(name=None, old=None, new=None)\\ndef interact(__interact_f=None, **kwargs) # Displays interactive widgets which are tied to a function.\\ndef interact_manual(__interact_f=None, **kwargs) # interact_manual(f, **kwargs)\\nclass fixed # A pseudo-widget whose value is fixed and never synced to the client.\\n def __init__(self, value, **kwargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/html/widgets/widget_bool.py\",\n \"text\": \"IPython/html/widgets/widget_bool.py\\nclass _Bool # A base class for creating widgets that represent booleans.\\n def __init__(self, value=None, **kwargs)\\nclass Checkbox # Displays a boolean `value` in the form of a checkbox.\\nclass ToggleButton # Displays a boolean `value` in the form of a toggle button.\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/widget_output.py\",\n \"text\": \"IPython/html/widgets/widget_output.py\\nclass Output # Widget used as a context manager to display output.\\n def clear_output(self, *pargs, **kwargs)\\n def __enter__(self) # Called upon entering output widget context manager.\\n def send_hook(stream, msg_or_type, content=None, parent=None, ident=None, buffers=None, track=False, header=None, metadata=None)\\n def __exit__(self, exception_type, exception_value, traceback) # Called upon exiting output widget context manager.\\n def _flush(self) # Flush stdout and stderr buffers.\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py\",\n \"text\": \"IPython/qt/console/frontend_widget.py\\nclass FrontendHighlighter # A PygmentsHighlighter that understands and ignores prompts.\\n def __init__(self, frontend, lexer=None)\\n def highlightBlock(self, string) # Highlight a block of text. Reimplemented to highlight selectively.\\n def rehighlightBlock(self, block) # Reimplemented to temporarily enable highlighting if disabled.\\n def setFormat(self, start, count, format) # Reimplemented to highlight selectively.\\nclass FrontendWidget # A Qt frontend for a generic Python kernel.\\n def _lexer_class_changed(self, name, old, new)\\n def _lexer_class_default(self)\\n def _lexer_default(self)\\n def __init__(self, *args, **kw)\\n def copy(self) # Copy the currently selected text to the clipboard, removing prompts.\\n def _is_complete(self, source, interactive) # Returns whether 'source' can be completely processed and a new\\n def _execute(self, source, hidden) # Execute 'source'. If 'hidden', do not show any output.\\n def _prompt_started_hook(self) # Called immediately after a new prompt is displayed.\\n def _prompt_finished_hook(self) # Called immediately after a prompt is finished, i.e. when some input\\n def _tab_pressed(self) # Called when the tab key is pressed. Returns whether to continue\\n def _context_menu_make(self, pos) # Reimplemented to add an action for raw copy.\\n def request_interrupt_kernel(self)\\n def request_restart_kernel(self)\\n def _event_filter_console_keypress(self, event) # Reimplemented for execution interruption and smart backspace.\\n def _insert_continuation_prompt(self, cursor) # Reimplemented for auto-indentation.\\n def _handle_clear_output(self, msg) # Handle clear output messages.\\n def _silent_exec_callback(self, expr, callback) # Silently execute `expr` in the kernel and call `callback` with reply\\n def _handle_exec_callback(self, msg) # Execute `callback` corresponding to `msg` reply, after ``_silent_exec_callback``\\n def _handle_execute_reply(self, msg) # Handles replies for code execution.\\n def _handle_input_request(self, msg) # Handle requests for raw_input.\\n def callback(line)\\n def _kernel_restarted_message(self, died=True)\\n def _handle_kernel_died(self, since_last_heartbeat) # Handle the kernel's death (if we do not own the kernel).\\n def _handle_kernel_restarted(self, died=True) # Notice that the autorestarter restarted the kernel.\\n def _handle_inspect_reply(self, rep) # Handle replies for call tips.\\n def _handle_execute_result(self, msg) # Handle display hook output.\\n def _handle_stream(self, msg) # Handle stdout, stderr, and stdin.\\n def _handle_shutdown_reply(self, msg) # Handle shutdown signal, only if from other console.\\n def _handle_status(self, msg) # Handle status message\\n def _started_channels(self) # Called when the KernelManager channels have started listening or\\n def copy_raw(self) # Copy the currently selected text to the clipboard without attempting\\n d\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"IPython/core/magics/execution.py::TimeitResult.__init__","text":"IPython/core/magics/execution.py::TimeitResult.__init__\n def __init__(self, loops, repeat, best, all_runs, compile_time, precision):\n self.loops = loops\n self.repeat = repeat\n self.best = best\n self.all_runs = all_runs\n self.compile_time = compile_time\n self._precision = precision","label":0},{"cid":"IPython/core/magics/execution.py::TimeitResult._repr_pretty_","text":"IPython/core/magics/execution.py::TimeitResult._repr_pretty_\n def _repr_pretty_(self, p , cycle):\n unic = u\"%d loops, best of %d: %s per loop\" % (self.loops, self.repeat,\n _format_time(self.best, self._precision))\n p.text(u'<TimeitResult : '+unic+u'>')","label":0},{"cid":"IPython/core/magics/execution.py::TimeitTemplateFiller.__init__","text":"IPython/core/magics/execution.py::TimeitTemplateFiller.__init__\n def __init__(self, ast_setup, ast_stmt):\n self.ast_setup = ast_setup\n self.ast_stmt = ast_stmt","label":0},{"cid":"IPython/core/magics/execution.py::TimeitTemplateFiller.visit_FunctionDef","text":"IPython/core/magics/execution.py::TimeitTemplateFiller.visit_FunctionDef\n def visit_FunctionDef(self, node):\n \"Fill in the setup statement\"\n self.generic_visit(node)\n if node.name == \"inner\":\n node.body[:1] = self.ast_setup.body\n\n return node","label":0},{"cid":"IPython/core/magics/execution.py::TimeitTemplateFiller.visit_For","text":"IPython/core/magics/execution.py::TimeitTemplateFiller.visit_For\n def visit_For(self, node):\n \"Fill in the statement to be timed\"\n if getattr(getattr(node.body[0], 'value', None), 'id', None) == 'stmt':\n node.body = self.ast_stmt.body\n return node","label":0},{"cid":"IPython/core/magics/execution.py::Timer.timeit","text":"IPython/core/magics/execution.py::Timer.timeit\n def timeit(self, number=timeit.default_number):\n \"\"\"Time 'number' executions of the main statement.\n\n To be precise, this executes the setup statement once, and\n then returns the time it takes to execute the main statement\n a number of times, as a float measured in seconds. The\n argument is the number of times through the loop, defaulting\n to one million. The main statement, the setup statement and\n the timer function to be used are passed to the constructor.\n \"\"\"\n it = itertools.repeat(None, number)\n gcold = gc.isenabled()\n gc.disable()\n try:\n timing = self.inner(it, self.timer)\n finally:\n if gcold:\n gc.enable()\n return timing","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics.__init__","text":"IPython/core/magics/execution.py::ExecutionMagics.__init__\n def __init__(self, shell):\n super(ExecutionMagics, self).__init__(shell)\n if profile is None:\n self.prun = self.profile_missing_notice\n # Default execution function used to actually run user code.\n self.default_runner = None","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics.profile_missing_notice","text":"IPython/core/magics/execution.py::ExecutionMagics.profile_missing_notice\n def profile_missing_notice(self, *args, **kwargs):\n error(\"\"\"\\\nThe profile module could not be found. It has been removed from the standard\npython packages because of its non-free license. To use profiling, install the\npython-profiler package from non-free.\"\"\")","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics.prun","text":"IPython/core/magics/execution.py::ExecutionMagics.prun\n @skip_doctest\n @line_cell_magic\n def prun(self, parameter_s='', cell=None):\n\n \"\"\"Run a statement through the python code profiler.\n\n Usage, in line mode:\n %prun [options] statement\n\n Usage, in cell mode:\n %%prun [options] [statement]\n code...\n code...\n\n In cell mode, the additional code lines are appended to the (possibly\n empty) statement in the first line. Cell mode allows you to easily\n profile multiline blocks without having to put them in a separate\n function.\n\n The given statement (which doesn't require quote marks) is run via the\n python profiler in a manner similar to the profile.run() function.\n Namespaces are internally managed to work correctly; profile.run\n cannot be used in IPython because it makes certain assumptions about\n namespaces which do not hold under IPython.\n\n Options:\n\n -l <limit>\n you can place restrictions on what or how much of the\n profile gets printed. The limit value can be:\n\n * A string: only information for function names containing this string\n is printed.\n\n * An integer: only these many lines are printed.\n\n * A float (between 0 and 1): this fraction of the report is printed\n (for example, use a limit of 0.4 to see the topmost 40% only).\n\n You can combine several limits with repeated use of the option. For\n example, ``-l __init__ -l 5`` will print only the topmost 5 lines of\n information about class constructors.\n\n -r\n return the pstats.Stats object generated by the profiling. This\n object has all the information about the profile in it, and you can\n later use it for further analysis or in other functions.\n\n -s <key>\n sort profile by given key. You can provide more than one key\n ","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics._run_with_profiler","text":"IPython/core/magics/execution.py::ExecutionMagics._run_with_profiler\n def _run_with_profiler(self, code, opts, namespace):\n \"\"\"\n Run `code` with profiler. Used by ``%prun`` and ``%run -p``.\n\n Parameters\n ----------\n code : str\n Code to be executed.\n opts : Struct\n Options parsed by `self.parse_options`.\n namespace : dict\n A dictionary for Python namespace (e.g., `self.shell.user_ns`).\n\n \"\"\"\n\n # Fill default values for unspecified options:\n opts.merge(Struct(D=[''], l=[], s=['time'], T=['']))\n\n prof = profile.Profile()\n try:\n prof = prof.runctx(code, namespace, namespace)\n sys_exit = ''\n except SystemExit:\n sys_exit = \"\"\"*** SystemExit exception caught in code being profiled.\"\"\"\n\n stats = pstats.Stats(prof).strip_dirs().sort_stats(*opts.s)\n\n lims = opts.l\n if lims:\n lims = [] # rebuild lims with ints/floats/strings\n for lim in opts.l:\n try:\n lims.append(int(lim))\n except ValueError:\n try:\n lims.append(float(lim))\n except ValueError:\n lims.append(lim)\n\n # Trap output.\n stdout_trap = StringIO()\n stats_stream = stats.stream\n try:\n stats.stream = stdout_trap\n stats.print_stats(*lims)\n finally:\n stats.stream = stats_stream\n\n output = stdout_trap.getvalue()\n output = output.rstrip()\n\n if 'q' not in opts:\n page.page(output)\n print(sys_exit, end=' ')\n\n dump_file = opts.D[0]\n text_file = opts.T[0]\n if dump_file:\n dump_file = unquote_filename(dump_file)\n prof.dump_stats(dump_file)\n print('\\n*** Profile stats marshalled to file',\\\n repr(dump_file)+'.',sys_exit)\n if","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics.pdb","text":"IPython/core/magics/execution.py::ExecutionMagics.pdb\n @line_magic\n def pdb(self, parameter_s=''):\n \"\"\"Control the automatic calling of the pdb interactive debugger.\n\n Call as '%pdb on', '%pdb 1', '%pdb off' or '%pdb 0'. If called without\n argument it works as a toggle.\n\n When an exception is triggered, IPython can optionally call the\n interactive pdb debugger after the traceback printout. %pdb toggles\n this feature on and off.\n\n The initial state of this feature is set in your configuration\n file (the option is ``InteractiveShell.pdb``).\n\n If you want to just activate the debugger AFTER an exception has fired,\n without having to type '%pdb on' and rerunning your code, you can use\n the %debug magic.\"\"\"\n\n par = parameter_s.strip().lower()\n\n if par:\n try:\n new_pdb = {'off':0,'0':0,'on':1,'1':1}[par]\n except KeyError:\n print ('Incorrect argument. Use on/1, off/0, '\n 'or nothing for a toggle.')\n return\n else:\n # toggle\n new_pdb = not self.shell.call_pdb\n\n # set on the shell\n self.shell.call_pdb = new_pdb\n print('Automatic pdb calling has been turned',on_off(new_pdb))","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics.debug","text":"IPython/core/magics/execution.py::ExecutionMagics.debug\n @skip_doctest\n @magic_arguments.magic_arguments()\n @magic_arguments.argument('--breakpoint', '-b', metavar='FILE:LINE',\n help=\"\"\"\n Set break point at LINE in FILE.\n \"\"\"\n )\n @magic_arguments.argument('statement', nargs='*',\n help=\"\"\"\n Code to run in debugger.\n You can omit this in cell magic mode.\n \"\"\"\n )\n @line_cell_magic\n def debug(self, line='', cell=None):\n \"\"\"Activate the interactive debugger.\n\n This magic command support two ways of activating debugger.\n One is to activate debugger before executing code. This way, you\n can set a break point, to step through the code from the point.\n You can use this mode by giving statements to execute and optionally\n a breakpoint.\n\n The other one is to activate debugger in post-mortem mode. You can\n activate this mode simply running %debug without any argument.\n If an exception has just occurred, this lets you inspect its stack\n frames interactively. Note that this will always work only on the last\n traceback that occurred, so you must call this quickly after an\n exception that you wish to inspect has fired, because if another one\n occurs, it clobbers the previous one.\n\n If you want IPython to automatically do this on every exception, see\n the %pdb magic for more details.\n \"\"\"\n args = magic_arguments.parse_argstring(self.debug, line)\n\n if not (args.breakpoint or args.statement or cell):\n self._debug_post_mortem()\n else:\n code = \"\\n\".join(args.statement)\n if cell:\n code += \"\\n\" + cell\n self._debug_exec(code, args.breakpoint)","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics._debug_post_mortem","text":"IPython/core/magics/execution.py::ExecutionMagics._debug_post_mortem\n def _debug_post_mortem(self):\n self.shell.debugger(force=True)","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics._debug_exec","text":"IPython/core/magics/execution.py::ExecutionMagics._debug_exec\n def _debug_exec(self, code, breakpoint):\n if breakpoint:\n (filename, bp_line) = breakpoint.split(':', 1)\n bp_line = int(bp_line)\n else:\n (filename, bp_line) = (None, None)\n self._run_with_debugger(code, self.shell.user_ns, filename, bp_line)","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics.tb","text":"IPython/core/magics/execution.py::ExecutionMagics.tb\n @line_magic\n def tb(self, s):\n \"\"\"Print the last traceback with the currently active exception mode.\n\n See %xmode for changing exception reporting modes.\"\"\"\n self.shell.showtraceback()","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics.run","text":"IPython/core/magics/execution.py::ExecutionMagics.run\n @skip_doctest\n @line_magic\n def run(self, parameter_s='', runner=None,\n file_finder=get_py_filename):\n \"\"\"Run the named file inside IPython as a program.\n\n Usage::\n \n %run [-n -i -e -G]\n [( -t [-N<N>] | -d [-b<N>] | -p [profile options] )]\n ( -m mod | file ) [args]\n\n Parameters after the filename are passed as command-line arguments to\n the program (put in sys.argv). Then, control returns to IPython's\n prompt.\n\n This is similar to running at a system prompt ``python file args``,\n but with the advantage of giving you IPython's tracebacks, and of\n loading all variables into your interactive namespace for further use\n (unless -p is used, see below).\n\n The file is executed in a namespace initially consisting only of\n ``__name__=='__main__'`` and sys.argv constructed as indicated. It thus\n sees its environment as if it were being run as a stand-alone program\n (except for sharing global objects such as previously imported\n modules). But after execution, the IPython interactive namespace gets\n updated with all variables defined in the program (except for __name__\n and sys.argv). This allows for very convenient loading of code for\n interactive work, while giving each program a 'clean sheet' to run in.\n\n Arguments are expanded using shell-like glob match. Patterns\n '*', '?', '[seq]' and '[!seq]' can be used. Additionally,\n tilde '~' will be expanded into user's home directory. Unlike\n real shells, quotation does not suppress expansions. Use\n *two* back slashes (e.g. ``\\\\\\\\*``) to suppress expansions.\n To completely disable these expansions, you can use -G flag.\n\n Options:\n\n -n\n __name__ is NOT set to '__main__', but to the running file's name\n without extensi","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics._run_with_debugger","text":"IPython/core/magics/execution.py::ExecutionMagics._run_with_debugger\n def _run_with_debugger(self, code, code_ns, filename=None,\n bp_line=None, bp_file=None):\n \"\"\"\n Run `code` in debugger with a break point.\n\n Parameters\n ----------\n code : str\n Code to execute.\n code_ns : dict\n A namespace in which `code` is executed.\n filename : str\n `code` is ran as if it is in `filename`.\n bp_line : int, optional\n Line number of the break point.\n bp_file : str, optional\n Path to the file in which break point is specified.\n `filename` is used if not given.\n\n Raises\n ------\n UsageError\n If the break point given by `bp_line` is not valid.\n\n \"\"\"\n deb = debugger.Pdb(self.shell.colors)\n # reset Breakpoint state, which is moronically kept\n # in a class\n bdb.Breakpoint.next = 1\n bdb.Breakpoint.bplist = {}\n bdb.Breakpoint.bpbynumber = [None]\n if bp_line is not None:\n # Set an initial breakpoint to stop execution\n maxtries = 10\n bp_file = bp_file or filename\n checkline = deb.checkline(bp_file, bp_line)\n if not checkline:\n for bp in range(bp_line + 1, bp_line + maxtries + 1):\n if deb.checkline(bp_file, bp):\n break\n else:\n msg = (\"\\nI failed to find a valid line to set \"\n \"a breakpoint\\n\"\n \"after trying up to line: %s.\\n\"\n \"Please set a valid breakpoint manually \"\n \"with the -b option.\" % bp)\n raise UsageError(msg)\n # if we find a good linenumber, set the breakpoint\n deb.do_break('%s:%s' % (bp_file, bp_line))\n\n if filename:\n # Mimic Pdb._runscrip","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics._run_with_timing","text":"IPython/core/magics/execution.py::ExecutionMagics._run_with_timing\n @staticmethod\n def _run_with_timing(run, nruns):\n \"\"\"\n Run function `run` and print timing information.\n\n Parameters\n ----------\n run : callable\n Any callable object which takes no argument.\n nruns : int\n Number of times to execute `run`.\n\n \"\"\"\n twall0 = time.time()\n if nruns == 1:\n t0 = clock2()\n run()\n t1 = clock2()\n t_usr = t1[0] - t0[0]\n t_sys = t1[1] - t0[1]\n print(\"\\nIPython CPU timings (estimated):\")\n print(\" User : %10.2f s.\" % t_usr)\n print(\" System : %10.2f s.\" % t_sys)\n else:\n runs = range(nruns)\n t0 = clock2()\n for nr in runs:\n run()\n t1 = clock2()\n t_usr = t1[0] - t0[0]\n t_sys = t1[1] - t0[1]\n print(\"\\nIPython CPU timings (estimated):\")\n print(\"Total runs performed:\", nruns)\n print(\" Times : %10s %10s\" % ('Total', 'Per run'))\n print(\" User : %10.2f s, %10.2f s.\" % (t_usr, t_usr / nruns))\n print(\" System : %10.2f s, %10.2f s.\" % (t_sys, t_sys / nruns))\n twall1 = time.time()\n print(\"Wall time: %10.2f s.\" % (twall1 - twall0))","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics.timeit","text":"IPython/core/magics/execution.py::ExecutionMagics.timeit\n @skip_doctest\n @line_cell_magic\n def timeit(self, line='', cell=None):\n \"\"\"Time execution of a Python statement or expression\n\n Usage, in line mode:\n %timeit [-n<N> -r<R> [-t|-c] -q -p<P> -o] statement\n or in cell mode:\n %%timeit [-n<N> -r<R> [-t|-c] -q -p<P> -o] setup_code\n code\n code...\n\n Time execution of a Python statement or expression using the timeit\n module. This function can be used both as a line and cell magic:\n\n - In line mode you can time a single-line statement (though multiple\n ones can be chained with using semicolons).\n\n - In cell mode, the statement in the first line is used as setup code\n (executed but not timed) and the body of the cell is timed. The cell\n body has access to any variables created in the setup code.\n\n Options:\n -n<N>: execute the given statement <N> times in a loop. If this value\n is not given, a fitting value is chosen.\n\n -r<R>: repeat the loop iteration <R> times and take the best result.\n Default: 3\n\n -t: use time.time to measure the time, which is the default on Unix.\n This function measures wall time.\n\n -c: use time.clock to measure the time, which is the default on\n Windows and measures wall time. On Unix, resource.getrusage is used\n instead and returns the CPU user time.\n\n -p<P>: use a precision of <P> digits to display the timing result.\n Default: 3\n\n -q: Quiet, do not print result.\n\n -o: return a TimeitResult that can be stored in a variable to inspect\n the result in more details.\n\n\n Examples\n --------\n ::\n\n In [1]: %timeit pass\n 10000000 loops, best of 3: 53.3 ns per loop\n\n In [2]: u = None\n\n In [3]: %timeit u is None\n 10000000 loops, best of 3: 184 ns per loop\n\n ","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics.time","text":"IPython/core/magics/execution.py::ExecutionMagics.time\n @skip_doctest\n @needs_local_scope\n @line_cell_magic\n def time(self,line='', cell=None, local_ns=None):\n \"\"\"Time execution of a Python statement or expression.\n\n The CPU and wall clock times are printed, and the value of the\n expression (if any) is returned. Note that under Win32, system time\n is always reported as 0, since it can not be measured.\n \n This function can be used both as a line and cell magic:\n\n - In line mode you can time a single-line statement (though multiple\n ones can be chained with using semicolons).\n\n - In cell mode, you can time the cell body (a directly \n following statement raises an error).\n\n This function provides very basic timing functionality. Use the timeit \n magic for more control over the measurement.\n\n Examples\n --------\n ::\n\n In [1]: %time 2**128\n CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s\n Wall time: 0.00\n Out[1]: 340282366920938463463374607431768211456L\n\n In [2]: n = 1000000\n\n In [3]: %time sum(range(n))\n CPU times: user 1.20 s, sys: 0.05 s, total: 1.25 s\n Wall time: 1.37\n Out[3]: 499999500000L\n\n In [4]: %time print 'hello world'\n hello world\n CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s\n Wall time: 0.00\n\n Note that the time needed by Python to compile the given expression\n will be reported if it is more than 0.1s. In this example, the\n actual exponentiation is done by Python at compilation time, so while\n the expression can take a noticeable amount of time to compute, that\n time is purely due to the compilation:\n\n In [5]: %time 3**9999;\n CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s\n Wall time: 0.00 s\n\n In [6]: %time 3**999999;\n CPU","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics.macro","text":"IPython/core/magics/execution.py::ExecutionMagics.macro\n @skip_doctest\n @line_magic\n def macro(self, parameter_s=''):\n \"\"\"Define a macro for future re-execution. It accepts ranges of history,\n filenames or string objects.\n\n Usage:\\\\\n %macro [options] name n1-n2 n3-n4 ... n5 .. n6 ...\n\n Options:\n\n -r: use 'raw' input. By default, the 'processed' history is used,\n so that magics are loaded in their transformed version to valid\n Python. If this option is given, the raw input as typed at the\n command line is used instead.\n \n -q: quiet macro definition. By default, a tag line is printed \n to indicate the macro has been created, and then the contents of \n the macro are printed. If this option is given, then no printout\n is produced once the macro is created.\n\n This will define a global variable called `name` which is a string\n made of joining the slices and lines you specify (n1,n2,... numbers\n above) from your input history into a single string. This variable\n acts like an automatic function which re-executes those lines as if\n you had typed them. You just type 'name' at the prompt and the code\n executes.\n\n The syntax for indicating input ranges is described in %history.\n\n Note: as a 'hidden' feature, you can also use traditional python slice\n notation, where N:M means numbers N through M-1.\n\n For example, if your history contains (print using %hist -n )::\n\n 44: x=1\n 45: y=3\n 46: z=x+y\n 47: print x\n 48: a=5\n 49: print 'x',x,'y',y\n\n you can create a macro with lines 44 through 47 (included) and line 49\n called my_macro with::\n\n In [55]: %macro my_macro 44-47 49\n\n Now, typing `my_macro` (without quotes) will re-execute all this code\n in one pass.\n\n You don't need to give the line","label":0},{"cid":"IPython/core/magics/execution.py::ExecutionMagics.capture","text":"IPython/core/magics/execution.py::ExecutionMagics.capture\n @magic_arguments.magic_arguments()\n @magic_arguments.argument('output', type=str, default='', nargs='?',\n help=\"\"\"The name of the variable in which to store output.\n This is a utils.io.CapturedIO object with stdout/err attributes\n for the text of the captured output.\n\n CapturedOutput also has a show() method for displaying the output,\n and __call__ as well, so you can use that to quickly display the\n output.\n\n If unspecified, captured output is discarded.\n \"\"\"\n )\n @magic_arguments.argument('--no-stderr', action=\"store_true\",\n help=\"\"\"Don't capture stderr.\"\"\"\n )\n @magic_arguments.argument('--no-stdout', action=\"store_true\",\n help=\"\"\"Don't capture stdout.\"\"\"\n )\n @magic_arguments.argument('--no-display', action=\"store_true\",\n help=\"\"\"Don't capture IPython's rich display.\"\"\"\n )\n @cell_magic\n def capture(self, line, cell):\n \"\"\"run the cell, capturing stdout, stderr, and IPython's rich display() calls.\"\"\"\n args = magic_arguments.parse_argstring(self.capture, line)\n out = not args.no_stdout\n err = not args.no_stderr\n disp = not args.no_display\n with capture_output(out, err, disp) as io:\n self.shell.run_cell(cell)\n if args.output:\n self.shell.user_ns[args.output] = io","label":0},{"cid":"IPython/core/magics/execution.py::parse_breakpoint","text":"IPython/core/magics/execution.py::parse_breakpoint\ndef parse_breakpoint(text, current_file):\n '''Returns (file, line) for file:line and (current_file, line) for line'''\n colon = text.find(':')\n if colon == -1:\n return current_file, int(text)\n else:\n return text[:colon], int(text[colon+1:])","label":0},{"cid":"IPython/core/magics/execution.py::_format_time","text":"IPython/core/magics/execution.py::_format_time\ndef _format_time(timespan, precision=3):\n \"\"\"Formats the timespan in a human readable form\"\"\"\n import math\n \n if timespan >= 60.0:\n # we have more than a minute, format that in a human readable form\n # Idea from http://snipplr.com/view/5713/\n parts = [(\"d\", 60*60*24),(\"h\", 60*60),(\"min\", 60), (\"s\", 1)]\n time = []\n leftover = timespan\n for suffix, length in parts:\n value = int(leftover / length)\n if value > 0:\n leftover = leftover % length\n time.append(u'%s%s' % (str(value), suffix))\n if leftover < 1:\n break\n return \" \".join(time)\n\n \n # Unfortunately the unicode 'micro' symbol can cause problems in\n # certain terminals. \n # See bug: https://bugs.launchpad.net/ipython/+bug/348466\n # Try to prevent crashes by being more secure than it needs to\n # E.g. eclipse is able to print a µ, but has no sys.stdout.encoding set.\n units = [u\"s\", u\"ms\",u'us',\"ns\"] # the save value \n if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:\n try:\n u'\\xb5'.encode(sys.stdout.encoding)\n units = [u\"s\", u\"ms\",u'\\xb5s',\"ns\"]\n except:\n pass\n scaling = [1, 1e3, 1e6, 1e9]\n \n if timespan > 0.0:\n order = min(-int(math.floor(math.log10(timespan)) // 3), 3)\n else:\n order = 3\n return u\"%.*g %s\" % (precision, timespan * scaling[order], units[order])","label":0},{"cid":"IPython/html/widgets/interaction.py::_matches","text":"IPython/html/widgets/interaction.py::_matches\ndef _matches(o, pattern):\n \"\"\"Match a pattern of types in a sequence.\"\"\"\n if not len(o) == len(pattern):\n return False\n comps = zip(o,pattern)\n return all(isinstance(obj,kind) for obj,kind in comps)","label":0},{"cid":"IPython/html/widgets/interaction.py::_get_min_max_value","text":"IPython/html/widgets/interaction.py::_get_min_max_value\ndef _get_min_max_value(min, max, value=None, step=None):\n \"\"\"Return min, max, value given input values with possible None.\"\"\"\n if value is None:\n if not max > min:\n raise ValueError('max must be greater than min: (min={0}, max={1})'.format(min, max))\n value = min + abs(min-max)/2\n value = type(min)(value)\n elif min is None and max is None:\n if value == 0.0:\n min, max, value = 0.0, 1.0, 0.5\n elif value == 0:\n min, max, value = 0, 1, 0\n elif isinstance(value, (int, float)):\n min, max = (-value, 3*value) if value > 0 else (3*value, -value)\n else:\n raise TypeError('expected a number, got: %r' % value)\n else:\n raise ValueError('unable to infer range, value from: ({0}, {1}, {2})'.format(min, max, value))\n if step is not None:\n # ensure value is on a step\n r = (value - min) % step\n value = value - r\n return min, max, value","label":0},{"cid":"IPython/html/widgets/interaction.py::_widget_abbrev_single_value","text":"IPython/html/widgets/interaction.py::_widget_abbrev_single_value\ndef _widget_abbrev_single_value(o):\n \"\"\"Make widgets from single values, which can be used as parameter defaults.\"\"\"\n if isinstance(o, string_types):\n return Text(value=unicode_type(o))\n elif isinstance(o, dict):\n return Dropdown(values=o)\n elif isinstance(o, bool):\n return Checkbox(value=o)\n elif isinstance(o, float):\n min, max, value = _get_min_max_value(None, None, o)\n return FloatSlider(value=o, min=min, max=max)\n elif isinstance(o, int):\n min, max, value = _get_min_max_value(None, None, o)\n return IntSlider(value=o, min=min, max=max)\n else:\n return None","label":0},{"cid":"IPython/html/widgets/interaction.py::_widget_abbrev","text":"IPython/html/widgets/interaction.py::_widget_abbrev\ndef _widget_abbrev(o):\n \"\"\"Make widgets from abbreviations: single values, lists or tuples.\"\"\"\n float_or_int = (float, int)\n if isinstance(o, (list, tuple)):\n if o and all(isinstance(x, string_types) for x in o):\n return Dropdown(values=[unicode_type(k) for k in o])\n elif _matches(o, (float_or_int, float_or_int)):\n min, max, value = _get_min_max_value(o[0], o[1])\n if all(isinstance(_, int) for _ in o):\n cls = IntSlider\n else:\n cls = FloatSlider\n return cls(value=value, min=min, max=max)\n elif _matches(o, (float_or_int, float_or_int, float_or_int)):\n step = o[2]\n if step <= 0:\n raise ValueError(\"step must be >= 0, not %r\" % step)\n min, max, value = _get_min_max_value(o[0], o[1], step=step)\n if all(isinstance(_, int) for _ in o):\n cls = IntSlider\n else:\n cls = FloatSlider\n return cls(value=value, min=min, max=max, step=step)\n else:\n return _widget_abbrev_single_value(o)","label":0},{"cid":"IPython/html/widgets/interaction.py::_widget_from_abbrev","text":"IPython/html/widgets/interaction.py::_widget_from_abbrev\ndef _widget_from_abbrev(abbrev, default=empty):\n \"\"\"Build a Widget instance given an abbreviation or Widget.\"\"\"\n if isinstance(abbrev, Widget) or isinstance(abbrev, fixed):\n return abbrev\n\n widget = _widget_abbrev(abbrev)\n if default is not empty and isinstance(abbrev, (list, tuple, dict)):\n # if it's not a single-value abbreviation,\n # set the initial value from the default\n try:\n widget.value = default\n except Exception:\n # ignore failure to set default\n pass\n if widget is None:\n raise ValueError(\"%r cannot be transformed to a Widget\" % (abbrev,))\n return widget","label":0},{"cid":"IPython/html/widgets/interaction.py::_yield_abbreviations_for_parameter","text":"IPython/html/widgets/interaction.py::_yield_abbreviations_for_parameter\ndef _yield_abbreviations_for_parameter(param, kwargs):\n \"\"\"Get an abbreviation for a function parameter.\"\"\"\n name = param.name\n kind = param.kind\n ann = param.annotation\n default = param.default\n not_found = (name, empty, empty)\n if kind in (Parameter.POSITIONAL_OR_KEYWORD, Parameter.KEYWORD_ONLY):\n if name in kwargs:\n value = kwargs.pop(name)\n elif ann is not empty:\n value = ann\n elif default is not empty:\n value = default\n else:\n yield not_found\n yield (name, value, default)\n elif kind == Parameter.VAR_KEYWORD:\n # In this case name=kwargs and we yield the items in kwargs with their keys.\n for k, v in kwargs.copy().items():\n kwargs.pop(k)\n yield k, v, empty","label":0},{"cid":"IPython/html/widgets/interaction.py::_find_abbreviations","text":"IPython/html/widgets/interaction.py::_find_abbreviations\ndef _find_abbreviations(f, kwargs):\n \"\"\"Find the abbreviations for a function and kwargs passed to interact.\"\"\"\n new_kwargs = []\n for param in signature(f).parameters.values():\n for name, value, default in _yield_abbreviations_for_parameter(param, kwargs):\n if value is empty:\n raise ValueError('cannot find widget or abbreviation for argument: {!r}'.format(name))\n new_kwargs.append((name, value, default))\n return new_kwargs","label":0},{"cid":"IPython/html/widgets/interaction.py::_widgets_from_abbreviations","text":"IPython/html/widgets/interaction.py::_widgets_from_abbreviations\ndef _widgets_from_abbreviations(seq):\n \"\"\"Given a sequence of (name, abbrev) tuples, return a sequence of Widgets.\"\"\"\n result = []\n for name, abbrev, default in seq:\n widget = _widget_from_abbrev(abbrev, default)\n if not widget.description:\n widget.description = name\n widget._kwarg = name\n result.append(widget)\n return result","label":0},{"cid":"IPython/html/widgets/interaction.py::interactive","text":"IPython/html/widgets/interaction.py::interactive\ndef interactive(__interact_f, **kwargs):\n \"\"\"\n Builds a group of interactive widgets tied to a function and places the\n group into a Box container.\n\n Returns\n -------\n container : a Box instance containing multiple widgets\n\n Parameters\n ----------\n __interact_f : function\n The function to which the interactive widgets are tied. The **kwargs\n should match the function signature.\n **kwargs : various, optional\n An interactive widget is created for each keyword argument that is a\n valid widget abbreviation.\n \"\"\"\n f = __interact_f\n co = kwargs.pop('clear_output', True)\n manual = kwargs.pop('__manual', False)\n kwargs_widgets = []\n container = Box()\n container.result = None\n container.args = []\n container.kwargs = dict()\n kwargs = kwargs.copy()\n\n new_kwargs = _find_abbreviations(f, kwargs)\n # Before we proceed, let's make sure that the user has passed a set of args+kwargs\n # that will lead to a valid call of the function. This protects against unspecified\n # and doubly-specified arguments.\n getcallargs(f, **{n:v for n,v,_ in new_kwargs})\n # Now build the widgets from the abbreviations.\n kwargs_widgets.extend(_widgets_from_abbreviations(new_kwargs))\n\n # This has to be done as an assignment, not using container.children.append,\n # so that traitlets notices the update. We skip any objects (such as fixed) that\n # are not DOMWidgets.\n c = [w for w in kwargs_widgets if isinstance(w, DOMWidget)]\n\n # If we are only to run the function on demand, add a button to request this\n if manual:\n manual_button = Button(description=\"Run %s\" % f.__name__)\n c.append(manual_button)\n container.children = c\n\n # Build the callback\n def call_f(name=None, old=None, new=None):\n container.kwargs = {}\n for widget in kwargs_widgets:\n value = widget.value\n container.kwargs[","label":1},{"cid":"IPython/html/widgets/interaction.py::interactive.call_f","text":"IPython/html/widgets/interaction.py::interactive.call_f\n def call_f(name=None, old=None, new=None):\n container.kwargs = {}\n for widget in kwargs_widgets:\n value = widget.value\n container.kwargs[widget._kwarg] = value\n if co:\n clear_output(wait=True)\n if manual:\n manual_button.disabled = True\n try:\n container.result = f(**container.kwargs)\n except Exception as e:\n ip = get_ipython()\n if ip is None:\n container.log.warn(\"Exception in interact callback: %s\", e, exc_info=True)\n else:\n ip.showtraceback()\n finally:\n if manual:\n manual_button.disabled = False","label":1},{"cid":"IPython/html/widgets/interaction.py::interact","text":"IPython/html/widgets/interaction.py::interact\ndef interact(__interact_f=None, **kwargs):\n \"\"\"\n Displays interactive widgets which are tied to a function.\n Expects the first argument to be a function. Parameters to this function are\n widget abbreviations passed in as keyword arguments (**kwargs). Can be used\n as a decorator (see examples).\n\n Returns\n -------\n f : __interact_f with interactive widget attached to it.\n\n Parameters\n ----------\n __interact_f : function\n The function to which the interactive widgets are tied. The **kwargs\n should match the function signature. Passed to :func:`interactive()`\n **kwargs : various, optional\n An interactive widget is created for each keyword argument that is a\n valid widget abbreviation. Passed to :func:`interactive()`\n\n Examples\n --------\n Renders an interactive text field that shows the greeting with the passed in\n text.\n\n 1. Invocation of interact as a function\n def greeting(text=\"World\"):\n print \"Hello {}\".format(text)\n interact(greeting, text=\"IPython Widgets\")\n \n 2. Invocation of interact as a decorator\n @interact\n def greeting(text=\"World\"):\n print \"Hello {}\".format(text)\n \n 3. Invocation of interact as a decorator with named parameters\n @interact(text=\"IPython Widgets\")\n def greeting(text=\"World\"):\n print \"Hello {}\".format(text)\n \n Renders an interactive slider widget and prints square of number.\n\n 1. Invocation of interact as a function\n def square(num=1):\n print \"{} squared is {}\".format(num, num*num)\n interact(square, num=5)\n\n 2. Invocation of interact as a decorator\n @interact\n def square(num=2):\n print \"{} squared is {}\".format(num, num*num)\n \n 3. Invocation of interact as a decorator with named parameters\n @interact(num=5)\n def square(num=2):\n print \"{} squared ","label":0},{"cid":"IPython/html/widgets/interaction.py::interact_manual","text":"IPython/html/widgets/interaction.py::interact_manual\ndef interact_manual(__interact_f=None, **kwargs):\n \"\"\"interact_manual(f, **kwargs)\n \n As `interact()`, generates widgets for each argument, but rather than running\n the function after each widget change, adds a \"Run\" button and waits for it\n to be clicked. Useful if the function is long-running and has several\n parameters to change.\n \"\"\"\n return interact(__interact_f, __manual=True, **kwargs)","label":0},{"cid":"IPython/html/widgets/interaction.py::fixed.__init__","text":"IPython/html/widgets/interaction.py::fixed.__init__\n def __init__(self, value, **kwargs):\n super(fixed, self).__init__(value=value, **kwargs)","label":0},{"cid":"IPython/html/widgets/widget_bool.py::_Bool.__init__","text":"IPython/html/widgets/widget_bool.py::_Bool.__init__\n def __init__(self, value=None, **kwargs):\n if value is not None:\n kwargs['value'] = value\n super(_Bool, self).__init__(**kwargs)","label":0},{"cid":"IPython/html/widgets/widget_output.py::Output.clear_output","text":"IPython/html/widgets/widget_output.py::Output.clear_output\n def clear_output(self, *pargs, **kwargs):\n with self:\n clear_output(*pargs, **kwargs)","label":0},{"cid":"IPython/html/widgets/widget_output.py::Output.__enter__","text":"IPython/html/widgets/widget_output.py::Output.__enter__\n def __enter__(self):\n \"\"\"Called upon entering output widget context manager.\"\"\"\n self._flush()\n kernel = get_ipython().kernel\n session = kernel.session\n send = session.send\n self._original_send = send\n self._session = session\n\n def send_hook(stream, msg_or_type, content=None, parent=None, ident=None,\n buffers=None, track=False, header=None, metadata=None): \n\n # Handle both prebuild messages and unbuilt messages.\n if isinstance(msg_or_type, (Message, dict)):\n msg_type = msg_or_type['msg_type']\n msg = dict(msg_or_type)\n else:\n msg_type = msg_or_type\n msg = session.msg(msg_type, content=content, parent=parent, \n header=header, metadata=metadata)\n\n # If this is a message type that we want to forward, forward it.\n if stream is kernel.iopub_socket and msg_type in ['clear_output', 'stream', 'display_data']:\n self.send(msg)\n else: \n send(stream, msg, ident=ident, buffers=buffers, track=track)\n\n session.send = send_hook","label":1},{"cid":"IPython/html/widgets/widget_output.py::Output.__enter__.send_hook","text":"IPython/html/widgets/widget_output.py::Output.__enter__.send_hook\n def send_hook(stream, msg_or_type, content=None, parent=None, ident=None,\n buffers=None, track=False, header=None, metadata=None): \n\n # Handle both prebuild messages and unbuilt messages.\n if isinstance(msg_or_type, (Message, dict)):\n msg_type = msg_or_type['msg_type']\n msg = dict(msg_or_type)\n else:\n msg_type = msg_or_type\n msg = session.msg(msg_type, content=content, parent=parent, \n header=header, metadata=metadata)\n\n # If this is a message type that we want to forward, forward it.\n if stream is kernel.iopub_socket and msg_type in ['clear_output', 'stream', 'display_data']:\n self.send(msg)\n else: \n send(stream, msg, ident=ident, buffers=buffers, track=track)","label":1},{"cid":"IPython/html/widgets/widget_output.py::Output.__exit__","text":"IPython/html/widgets/widget_output.py::Output.__exit__\n def __exit__(self, exception_type, exception_value, traceback):\n \"\"\"Called upon exiting output widget context manager.\"\"\"\n self._flush()\n self._session.send = self._original_send","label":1},{"cid":"IPython/html/widgets/widget_output.py::Output._flush","text":"IPython/html/widgets/widget_output.py::Output._flush\n def _flush(self):\n \"\"\"Flush stdout and stderr buffers.\"\"\"\n sys.stdout.flush()\n sys.stderr.flush()","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendHighlighter.__init__","text":"IPython/qt/console/frontend_widget.py::FrontendHighlighter.__init__\n def __init__(self, frontend, lexer=None):\n super(FrontendHighlighter, self).__init__(frontend._control.document(), lexer=lexer)\n self._current_offset = 0\n self._frontend = frontend\n self.highlighting_on = False","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendHighlighter.highlightBlock","text":"IPython/qt/console/frontend_widget.py::FrontendHighlighter.highlightBlock\n def highlightBlock(self, string):\n \"\"\" Highlight a block of text. Reimplemented to highlight selectively.\n \"\"\"\n if not self.highlighting_on:\n return\n\n # The input to this function is a unicode string that may contain\n # paragraph break characters, non-breaking spaces, etc. Here we acquire\n # the string as plain text so we can compare it.\n current_block = self.currentBlock()\n string = self._frontend._get_block_plain_text(current_block)\n\n # Decide whether to check for the regular or continuation prompt.\n if current_block.contains(self._frontend._prompt_pos):\n prompt = self._frontend._prompt\n else:\n prompt = self._frontend._continuation_prompt\n\n # Only highlight if we can identify a prompt, but make sure not to\n # highlight the prompt.\n if string.startswith(prompt):\n self._current_offset = len(prompt)\n string = string[len(prompt):]\n super(FrontendHighlighter, self).highlightBlock(string)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendHighlighter.rehighlightBlock","text":"IPython/qt/console/frontend_widget.py::FrontendHighlighter.rehighlightBlock\n def rehighlightBlock(self, block):\n \"\"\" Reimplemented to temporarily enable highlighting if disabled.\n \"\"\"\n old = self.highlighting_on\n self.highlighting_on = True\n super(FrontendHighlighter, self).rehighlightBlock(block)\n self.highlighting_on = old","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendHighlighter.setFormat","text":"IPython/qt/console/frontend_widget.py::FrontendHighlighter.setFormat\n def setFormat(self, start, count, format):\n \"\"\" Reimplemented to highlight selectively.\n \"\"\"\n start += self._current_offset\n super(FrontendHighlighter, self).setFormat(start, count, format)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._lexer_class_changed","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._lexer_class_changed\n def _lexer_class_changed(self, name, old, new):\n lexer_class = import_item(new)\n self.lexer = lexer_class()","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._lexer_class_default","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._lexer_class_default\n def _lexer_class_default(self):\n if py3compat.PY3:\n return 'pygments.lexers.Python3Lexer'\n else:\n return 'pygments.lexers.PythonLexer'","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._lexer_default","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._lexer_default\n def _lexer_default(self):\n lexer_class = import_item(self.lexer_class)\n return lexer_class()","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget.__init__","text":"IPython/qt/console/frontend_widget.py::FrontendWidget.__init__\n def __init__(self, *args, **kw):\n super(FrontendWidget, self).__init__(*args, **kw)\n # FIXME: remove this when PySide min version is updated past 1.0.7\n # forcefully disable calltips if PySide is < 1.0.7, because they crash\n if qt.QT_API == qt.QT_API_PYSIDE:\n import PySide\n if PySide.__version_info__ < (1,0,7):\n self.log.warn(\"PySide %s < 1.0.7 detected, disabling calltips\" % PySide.__version__)\n self.enable_calltips = False\n\n # FrontendWidget protected variables.\n self._bracket_matcher = BracketMatcher(self._control)\n self._call_tip_widget = CallTipWidget(self._control)\n self._copy_raw_action = QtGui.QAction('Copy (Raw Text)', None)\n self._hidden = False\n self._highlighter = FrontendHighlighter(self, lexer=self.lexer)\n self._input_splitter = self._input_splitter_class()\n self._kernel_manager = None\n self._kernel_client = None\n self._request_info = {}\n self._request_info['execute'] = {};\n self._callback_dict = {}\n self._display_banner = True\n\n # Configure the ConsoleWidget.\n self.tab_width = 4\n self._set_continuation_prompt('... ')\n\n # Configure the CallTipWidget.\n self._call_tip_widget.setFont(self.font)\n self.font_changed.connect(self._call_tip_widget.setFont)\n\n # Configure actions.\n action = self._copy_raw_action\n key = QtCore.Qt.CTRL | QtCore.Qt.SHIFT | QtCore.Qt.Key_C\n action.setEnabled(False)\n action.setShortcut(QtGui.QKeySequence(key))\n action.setShortcutContext(QtCore.Qt.WidgetWithChildrenShortcut)\n action.triggered.connect(self.copy_raw)\n self.copy_available.connect(action.setEnabled)\n self.addAction(action)\n\n # Connect signal handlers.\n document = self._control.document()\n document.contentsChange.co","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget.copy","text":"IPython/qt/console/frontend_widget.py::FrontendWidget.copy\n def copy(self):\n \"\"\" Copy the currently selected text to the clipboard, removing prompts.\n \"\"\"\n if self._page_control is not None and self._page_control.hasFocus():\n self._page_control.copy()\n elif self._control.hasFocus():\n text = self._control.textCursor().selection().toPlainText()\n if text:\n was_newline = text[-1] == '\\n'\n text = self._prompt_transformer.transform_cell(text)\n if not was_newline: # user doesn't need newline\n text = text[:-1]\n QtGui.QApplication.clipboard().setText(text)\n else:\n self.log.debug(\"frontend widget : unknown copy target\")","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._is_complete","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._is_complete\n def _is_complete(self, source, interactive):\n \"\"\" Returns whether 'source' can be completely processed and a new\n prompt created. When triggered by an Enter/Return key press,\n 'interactive' is True; otherwise, it is False.\n \"\"\"\n self._input_splitter.reset()\n try:\n complete = self._input_splitter.push(source)\n except SyntaxError:\n return True\n if interactive:\n complete = not self._input_splitter.push_accepts_more()\n return complete","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._execute","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._execute\n def _execute(self, source, hidden):\n \"\"\" Execute 'source'. If 'hidden', do not show any output.\n\n See parent class :meth:`execute` docstring for full details.\n \"\"\"\n msg_id = self.kernel_client.execute(source, hidden)\n self._request_info['execute'][msg_id] = self._ExecutionRequest(msg_id, 'user')\n self._hidden = hidden\n if not hidden:\n self.executing.emit(source)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._prompt_started_hook","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._prompt_started_hook\n def _prompt_started_hook(self):\n \"\"\" Called immediately after a new prompt is displayed.\n \"\"\"\n if not self._reading:\n self._highlighter.highlighting_on = True","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._prompt_finished_hook","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._prompt_finished_hook\n def _prompt_finished_hook(self):\n \"\"\" Called immediately after a prompt is finished, i.e. when some input\n will be processed and a new prompt displayed.\n \"\"\"\n # Flush all state from the input splitter so the next round of\n # reading input starts with a clean buffer.\n self._input_splitter.reset()\n\n if not self._reading:\n self._highlighter.highlighting_on = False","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._tab_pressed","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._tab_pressed\n def _tab_pressed(self):\n \"\"\" Called when the tab key is pressed. Returns whether to continue\n processing the event.\n \"\"\"\n # Perform tab completion if:\n # 1) The cursor is in the input buffer.\n # 2) There is a non-whitespace character before the cursor.\n text = self._get_input_buffer_cursor_line()\n if text is None:\n return False\n complete = bool(text[:self._get_input_buffer_cursor_column()].strip())\n if complete:\n self._complete()\n return not complete","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._context_menu_make","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._context_menu_make\n def _context_menu_make(self, pos):\n \"\"\" Reimplemented to add an action for raw copy.\n \"\"\"\n menu = super(FrontendWidget, self)._context_menu_make(pos)\n for before_action in menu.actions():\n if before_action.shortcut().matches(QtGui.QKeySequence.Paste) == \\\n QtGui.QKeySequence.ExactMatch:\n menu.insertAction(before_action, self._copy_raw_action)\n break\n return menu","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget.request_interrupt_kernel","text":"IPython/qt/console/frontend_widget.py::FrontendWidget.request_interrupt_kernel\n def request_interrupt_kernel(self):\n if self._executing:\n self.interrupt_kernel()","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget.request_restart_kernel","text":"IPython/qt/console/frontend_widget.py::FrontendWidget.request_restart_kernel\n def request_restart_kernel(self):\n message = 'Are you sure you want to restart the kernel?'\n self.restart_kernel(message, now=False)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._event_filter_console_keypress","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._event_filter_console_keypress\n def _event_filter_console_keypress(self, event):\n \"\"\" Reimplemented for execution interruption and smart backspace.\n \"\"\"\n key = event.key()\n if self._control_key_down(event.modifiers(), include_command=False):\n\n if key == QtCore.Qt.Key_C and self._executing:\n self.request_interrupt_kernel()\n return True\n\n elif key == QtCore.Qt.Key_Period:\n self.request_restart_kernel()\n return True\n\n elif not event.modifiers() & QtCore.Qt.AltModifier:\n\n # Smart backspace: remove four characters in one backspace if:\n # 1) everything left of the cursor is whitespace\n # 2) the four characters immediately left of the cursor are spaces\n if key == QtCore.Qt.Key_Backspace:\n col = self._get_input_buffer_cursor_column()\n cursor = self._control.textCursor()\n if col > 3 and not cursor.hasSelection():\n text = self._get_input_buffer_cursor_line()[:col]\n if text.endswith(' ') and not text.strip():\n cursor.movePosition(QtGui.QTextCursor.Left,\n QtGui.QTextCursor.KeepAnchor, 4)\n cursor.removeSelectedText()\n return True\n\n return super(FrontendWidget, self)._event_filter_console_keypress(event)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._insert_continuation_prompt","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._insert_continuation_prompt\n def _insert_continuation_prompt(self, cursor):\n \"\"\" Reimplemented for auto-indentation.\n \"\"\"\n super(FrontendWidget, self)._insert_continuation_prompt(cursor)\n cursor.insertText(' ' * self._input_splitter.indent_spaces)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_clear_output","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_clear_output\n def _handle_clear_output(self, msg):\n \"\"\"Handle clear output messages.\"\"\"\n if include_output(msg):\n wait = msg['content'].get('wait', True)\n if wait:\n self._pending_clearoutput = True\n else:\n self.clear_output()","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._silent_exec_callback","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._silent_exec_callback\n def _silent_exec_callback(self, expr, callback):\n \"\"\"Silently execute `expr` in the kernel and call `callback` with reply\n\n the `expr` is evaluated silently in the kernel (without) output in\n the frontend. Call `callback` with the\n `repr <http://docs.python.org/library/functions.html#repr> `_ as first argument\n\n Parameters\n ----------\n expr : string\n valid string to be executed by the kernel.\n callback : function\n function accepting one argument, as a string. The string will be\n the `repr` of the result of evaluating `expr`\n\n The `callback` is called with the `repr()` of the result of `expr` as\n first argument. To get the object, do `eval()` on the passed value.\n\n See Also\n --------\n _handle_exec_callback : private method, deal with calling callback with reply\n\n \"\"\"\n\n # generate uuid, which would be used as an indication of whether or\n # not the unique request originated from here (can use msg id ?)\n local_uuid = str(uuid.uuid1())\n msg_id = self.kernel_client.execute('',\n silent=True, user_expressions={ local_uuid:expr })\n self._callback_dict[local_uuid] = callback\n self._request_info['execute'][msg_id] = self._ExecutionRequest(msg_id, 'silent_exec_callback')","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_exec_callback","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_exec_callback\n def _handle_exec_callback(self, msg):\n \"\"\"Execute `callback` corresponding to `msg` reply, after ``_silent_exec_callback``\n\n Parameters\n ----------\n msg : raw message send by the kernel containing an `user_expressions`\n and having a 'silent_exec_callback' kind.\n\n Notes\n -----\n This function will look for a `callback` associated with the\n corresponding message id. Association has been made by\n `_silent_exec_callback`. `callback` is then called with the `repr()`\n of the value of corresponding `user_expressions` as argument.\n `callback` is then removed from the known list so that any message\n coming again with the same id won't trigger it.\n\n \"\"\"\n\n user_exp = msg['content'].get('user_expressions')\n if not user_exp:\n return\n for expression in user_exp:\n if expression in self._callback_dict:\n self._callback_dict.pop(expression)(user_exp[expression])","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_execute_reply","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_execute_reply\n def _handle_execute_reply(self, msg):\n \"\"\" Handles replies for code execution.\n \"\"\"\n self.log.debug(\"execute: %s\", msg.get('content', ''))\n msg_id = msg['parent_header']['msg_id']\n info = self._request_info['execute'].get(msg_id)\n # unset reading flag, because if execute finished, raw_input can't\n # still be pending.\n self._reading = False\n if info and info.kind == 'user' and not self._hidden:\n # Make sure that all output from the SUB channel has been processed\n # before writing a new prompt.\n self.kernel_client.iopub_channel.flush()\n\n # Reset the ANSI style information to prevent bad text in stdout\n # from messing up our colors. We're not a true terminal so we're\n # allowed to do this.\n if self.ansi_codes:\n self._ansi_processor.reset_sgr()\n\n content = msg['content']\n status = content['status']\n if status == 'ok':\n self._process_execute_ok(msg)\n elif status == 'error':\n self._process_execute_error(msg)\n elif status == 'aborted':\n self._process_execute_abort(msg)\n\n self._show_interpreter_prompt_for_reply(msg)\n self.executed.emit(msg)\n self._request_info['execute'].pop(msg_id)\n elif info and info.kind == 'silent_exec_callback' and not self._hidden:\n self._handle_exec_callback(msg)\n self._request_info['execute'].pop(msg_id)\n else:\n super(FrontendWidget, self)._handle_execute_reply(msg)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_input_request","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_input_request\n def _handle_input_request(self, msg):\n \"\"\" Handle requests for raw_input.\n \"\"\"\n self.log.debug(\"input: %s\", msg.get('content', ''))\n if self._hidden:\n raise RuntimeError('Request for raw input during hidden execution.')\n\n # Make sure that all output from the SUB channel has been processed\n # before entering readline mode.\n self.kernel_client.iopub_channel.flush()\n\n def callback(line):\n self.kernel_client.input(line)\n if self._reading:\n self.log.debug(\"Got second input request, assuming first was interrupted.\")\n self._reading = False\n self._readline(msg['content']['prompt'], callback=callback)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_input_request.callback","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_input_request.callback\n def callback(line):\n self.kernel_client.input(line)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._kernel_restarted_message","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._kernel_restarted_message\n def _kernel_restarted_message(self, died=True):\n msg = \"Kernel died, restarting\" if died else \"Kernel restarting\"\n self._append_html(\"<br>%s<hr><br>\" % msg,\n before_prompt=False\n )","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_kernel_died","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_kernel_died\n def _handle_kernel_died(self, since_last_heartbeat):\n \"\"\"Handle the kernel's death (if we do not own the kernel).\n \"\"\"\n self.log.warn(\"kernel died: %s\", since_last_heartbeat)\n if self.custom_restart:\n self.custom_restart_kernel_died.emit(since_last_heartbeat)\n else:\n self._kernel_restarted_message(died=True)\n self.reset()","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_kernel_restarted","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_kernel_restarted\n def _handle_kernel_restarted(self, died=True):\n \"\"\"Notice that the autorestarter restarted the kernel.\n\n There's nothing to do but show a message.\n \"\"\"\n self.log.warn(\"kernel restarted\")\n self._kernel_restarted_message(died=died)\n self.reset()","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_inspect_reply","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_inspect_reply\n def _handle_inspect_reply(self, rep):\n \"\"\"Handle replies for call tips.\"\"\"\n self.log.debug(\"oinfo: %s\", rep.get('content', ''))\n cursor = self._get_cursor()\n info = self._request_info.get('call_tip')\n if info and info.id == rep['parent_header']['msg_id'] and \\\n info.pos == cursor.position():\n content = rep['content']\n if content.get('status') == 'ok' and content.get('found', False):\n self._call_tip_widget.show_inspect_data(content)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_execute_result","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_execute_result\n def _handle_execute_result(self, msg):\n \"\"\" Handle display hook output.\n \"\"\"\n self.log.debug(\"execute_result: %s\", msg.get('content', ''))\n if self.include_output(msg):\n self.flush_clearoutput()\n text = msg['content']['data']\n self._append_plain_text(text + '\\n', before_prompt=True)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_stream","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_stream\n def _handle_stream(self, msg):\n \"\"\" Handle stdout, stderr, and stdin.\n \"\"\"\n self.log.debug(\"stream: %s\", msg.get('content', ''))\n if self.include_output(msg):\n self.flush_clearoutput()\n self.append_stream(msg['content']['text'])","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_shutdown_reply","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_shutdown_reply\n def _handle_shutdown_reply(self, msg):\n \"\"\" Handle shutdown signal, only if from other console.\n \"\"\"\n self.log.info(\"shutdown: %s\", msg.get('content', ''))\n restart = msg.get('content', {}).get('restart', False)\n if not self._hidden and not self.from_here(msg):\n # got shutdown reply, request came from session other than ours\n if restart:\n # someone restarted the kernel, handle it\n self._handle_kernel_restarted(died=False)\n else:\n # kernel was shutdown permanently\n # this triggers exit_requested if the kernel was local,\n # and a dialog if the kernel was remote,\n # so we don't suddenly clear the qtconsole without asking.\n if self._local_kernel:\n self.exit_requested.emit(self)\n else:\n title = self.window().windowTitle()\n reply = QtGui.QMessageBox.question(self, title,\n \"Kernel has been shutdown permanently. \"\n \"Close the Console?\",\n QtGui.QMessageBox.Yes,QtGui.QMessageBox.No)\n if reply == QtGui.QMessageBox.Yes:\n self.exit_requested.emit(self)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_status","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_status\n def _handle_status(self, msg):\n \"\"\"Handle status message\"\"\"\n # This is where a busy/idle indicator would be triggered,\n # when we make one.\n state = msg['content'].get('execution_state', '')\n if state == 'starting':\n # kernel started while we were running\n if self._executing:\n self._handle_kernel_restarted(died=True)\n elif state == 'idle':\n pass\n elif state == 'busy':\n pass","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget._started_channels","text":"IPython/qt/console/frontend_widget.py::FrontendWidget._started_channels\n def _started_channels(self):\n \"\"\" Called when the KernelManager channels have started listening or\n when the frontend is assigned an already listening KernelManager.\n \"\"\"\n self.reset(clear=True)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget.copy_raw","text":"IPython/qt/console/frontend_widget.py::FrontendWidget.copy_raw\n def copy_raw(self):\n \"\"\" Copy the currently selected text to the clipboard without attempting\n to remove prompts or otherwise alter the text.\n \"\"\"\n self._control.copy()","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget.execute_file","text":"IPython/qt/console/frontend_widget.py::FrontendWidget.execute_file\n def execute_file(self, path, hidden=False):\n \"\"\" Attempts to execute file with 'path'. If 'hidden', no output is\n shown.\n \"\"\"\n self.execute('execfile(%r)' % path, hidden=hidden)","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget.interrupt_kernel","text":"IPython/qt/console/frontend_widget.py::FrontendWidget.interrupt_kernel\n def interrupt_kernel(self):\n \"\"\" Attempts to interrupt the running kernel.\n \n Also unsets _reading flag, to avoid runtime errors\n if raw_input is called again.\n \"\"\"\n if self.custom_interrupt:\n self._reading = False\n self.custom_interrupt_requested.emit()\n elif self.kernel_manager:\n self._reading = False\n self.kernel_manager.interrupt_kernel()\n else:\n self._append_plain_text('Cannot interrupt a kernel I did not start.\\n')","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget.reset","text":"IPython/qt/console/frontend_widget.py::FrontendWidget.reset\n def reset(self, clear=False):\n \"\"\" Resets the widget to its initial state if ``clear`` parameter\n is True, otherwise\n prints a visual indication of the fact that the kernel restarted, but\n does not clear the traces from previous usage of the kernel before it\n was restarted. With ``clear=True``, it is similar to ``%clear``, but\n also re-writes the banner and aborts execution if necessary.\n \"\"\"\n if self._executing:\n self._executing = False\n self._request_info['execute'] = {}\n self._reading = False\n self._highlighter.highlighting_on = False\n\n if clear:\n self._control.clear()\n if self._display_banner:\n self._append_plain_text(self.banner)\n if self.kernel_banner:\n self._append_plain_text(self.kernel_banner)\n\n # update output marker for stdout/stderr, so that startup\n # messages appear after banner:\n self._append_before_prompt_pos = self._get_cursor().position()\n self._show_interpreter_prompt()","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget.restart_kernel","text":"IPython/qt/console/frontend_widget.py::FrontendWidget.restart_kernel\n def restart_kernel(self, message, now=False):\n \"\"\" Attempts to restart the running kernel.\n \"\"\"\n # FIXME: now should be configurable via a checkbox in the dialog. Right\n # now at least the heartbeat path sets it to True and the manual restart\n # to False. But those should just be the pre-selected states of a\n # checkbox that the user could override if so desired. But I don't know\n # enough Qt to go implementing the checkbox now.\n\n if self.custom_restart:\n self.custom_restart_requested.emit()\n return\n\n if self.kernel_manager:\n # Pause the heart beat channel to prevent further warnings.\n self.kernel_client.hb_channel.pause()\n\n # Prompt the user to restart the kernel. Un-pause the heartbeat if\n # they decline. (If they accept, the heartbeat will be un-paused\n # automatically when the kernel is restarted.)\n if self.confirm_restart:\n buttons = QtGui.QMessageBox.Yes | QtGui.QMessageBox.No\n result = QtGui.QMessageBox.question(self, 'Restart kernel?',\n message, buttons)\n do_restart = result == QtGui.QMessageBox.Yes\n else:\n # confirm_restart is False, so we don't need to ask user\n # anything, just do the restart\n do_restart = True\n if do_restart:\n try:\n self.kernel_manager.restart_kernel(now=now)\n except RuntimeError as e:\n self._append_plain_text(\n 'Error restarting kernel: %s\\n' % e,\n before_prompt=True\n )\n else:\n self._append_html(\"<br>Restarting kernel...\\n<hr><br>\",\n before_prompt=True,\n ","label":0},{"cid":"IPython/qt/console/frontend_widget.py::FrontendWidget.append_stream","text":"IPython/qt/console/frontend_widget.py::FrontendWidget.append_stream\n def append_stream(self, text):\n \"\"\"Appends text to the output stream.\"\"\"\n # Most consoles treat tabs as being 8 space characters. Convert tabs\n # to spaces so that output looks as expected regardless of this\n # widget's tab width.\n text = text.expandtabs(8)\n self._append_plain_text(text, before_prompt=True)\n self._control.moveCursor(QtGui.QTextCursor.End)","label":0}],"string":"[\n {\n \"cid\": \"IPython/core/magics/execution.py::TimeitResult.__init__\",\n \"text\": \"IPython/core/magics/execution.py::TimeitResult.__init__\\n def __init__(self, loops, repeat, best, all_runs, compile_time, precision):\\n self.loops = loops\\n self.repeat = repeat\\n self.best = best\\n self.all_runs = all_runs\\n self.compile_time = compile_time\\n self._precision = precision\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::TimeitResult._repr_pretty_\",\n \"text\": \"IPython/core/magics/execution.py::TimeitResult._repr_pretty_\\n def _repr_pretty_(self, p , cycle):\\n unic = u\\\"%d loops, best of %d: %s per loop\\\" % (self.loops, self.repeat,\\n _format_time(self.best, self._precision))\\n p.text(u'<TimeitResult : '+unic+u'>')\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::TimeitTemplateFiller.__init__\",\n \"text\": \"IPython/core/magics/execution.py::TimeitTemplateFiller.__init__\\n def __init__(self, ast_setup, ast_stmt):\\n self.ast_setup = ast_setup\\n self.ast_stmt = ast_stmt\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::TimeitTemplateFiller.visit_FunctionDef\",\n \"text\": \"IPython/core/magics/execution.py::TimeitTemplateFiller.visit_FunctionDef\\n def visit_FunctionDef(self, node):\\n \\\"Fill in the setup statement\\\"\\n self.generic_visit(node)\\n if node.name == \\\"inner\\\":\\n node.body[:1] = self.ast_setup.body\\n\\n return node\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::TimeitTemplateFiller.visit_For\",\n \"text\": \"IPython/core/magics/execution.py::TimeitTemplateFiller.visit_For\\n def visit_For(self, node):\\n \\\"Fill in the statement to be timed\\\"\\n if getattr(getattr(node.body[0], 'value', None), 'id', None) == 'stmt':\\n node.body = self.ast_stmt.body\\n return node\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::Timer.timeit\",\n \"text\": \"IPython/core/magics/execution.py::Timer.timeit\\n def timeit(self, number=timeit.default_number):\\n \\\"\\\"\\\"Time 'number' executions of the main statement.\\n\\n To be precise, this executes the setup statement once, and\\n then returns the time it takes to execute the main statement\\n a number of times, as a float measured in seconds. The\\n argument is the number of times through the loop, defaulting\\n to one million. The main statement, the setup statement and\\n the timer function to be used are passed to the constructor.\\n \\\"\\\"\\\"\\n it = itertools.repeat(None, number)\\n gcold = gc.isenabled()\\n gc.disable()\\n try:\\n timing = self.inner(it, self.timer)\\n finally:\\n if gcold:\\n gc.enable()\\n return timing\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics.__init__\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics.__init__\\n def __init__(self, shell):\\n super(ExecutionMagics, self).__init__(shell)\\n if profile is None:\\n self.prun = self.profile_missing_notice\\n # Default execution function used to actually run user code.\\n self.default_runner = None\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics.profile_missing_notice\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics.profile_missing_notice\\n def profile_missing_notice(self, *args, **kwargs):\\n error(\\\"\\\"\\\"\\\\\\nThe profile module could not be found. It has been removed from the standard\\npython packages because of its non-free license. To use profiling, install the\\npython-profiler package from non-free.\\\"\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics.prun\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics.prun\\n @skip_doctest\\n @line_cell_magic\\n def prun(self, parameter_s='', cell=None):\\n\\n \\\"\\\"\\\"Run a statement through the python code profiler.\\n\\n Usage, in line mode:\\n %prun [options] statement\\n\\n Usage, in cell mode:\\n %%prun [options] [statement]\\n code...\\n code...\\n\\n In cell mode, the additional code lines are appended to the (possibly\\n empty) statement in the first line. Cell mode allows you to easily\\n profile multiline blocks without having to put them in a separate\\n function.\\n\\n The given statement (which doesn't require quote marks) is run via the\\n python profiler in a manner similar to the profile.run() function.\\n Namespaces are internally managed to work correctly; profile.run\\n cannot be used in IPython because it makes certain assumptions about\\n namespaces which do not hold under IPython.\\n\\n Options:\\n\\n -l <limit>\\n you can place restrictions on what or how much of the\\n profile gets printed. The limit value can be:\\n\\n * A string: only information for function names containing this string\\n is printed.\\n\\n * An integer: only these many lines are printed.\\n\\n * A float (between 0 and 1): this fraction of the report is printed\\n (for example, use a limit of 0.4 to see the topmost 40% only).\\n\\n You can combine several limits with repeated use of the option. For\\n example, ``-l __init__ -l 5`` will print only the topmost 5 lines of\\n information about class constructors.\\n\\n -r\\n return the pstats.Stats object generated by the profiling. This\\n object has all the information about the profile in it, and you can\\n later use it for further analysis or in other functions.\\n\\n -s <key>\\n sort profile by given key. You can provide more than one key\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics._run_with_profiler\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics._run_with_profiler\\n def _run_with_profiler(self, code, opts, namespace):\\n \\\"\\\"\\\"\\n Run `code` with profiler. Used by ``%prun`` and ``%run -p``.\\n\\n Parameters\\n ----------\\n code : str\\n Code to be executed.\\n opts : Struct\\n Options parsed by `self.parse_options`.\\n namespace : dict\\n A dictionary for Python namespace (e.g., `self.shell.user_ns`).\\n\\n \\\"\\\"\\\"\\n\\n # Fill default values for unspecified options:\\n opts.merge(Struct(D=[''], l=[], s=['time'], T=['']))\\n\\n prof = profile.Profile()\\n try:\\n prof = prof.runctx(code, namespace, namespace)\\n sys_exit = ''\\n except SystemExit:\\n sys_exit = \\\"\\\"\\\"*** SystemExit exception caught in code being profiled.\\\"\\\"\\\"\\n\\n stats = pstats.Stats(prof).strip_dirs().sort_stats(*opts.s)\\n\\n lims = opts.l\\n if lims:\\n lims = [] # rebuild lims with ints/floats/strings\\n for lim in opts.l:\\n try:\\n lims.append(int(lim))\\n except ValueError:\\n try:\\n lims.append(float(lim))\\n except ValueError:\\n lims.append(lim)\\n\\n # Trap output.\\n stdout_trap = StringIO()\\n stats_stream = stats.stream\\n try:\\n stats.stream = stdout_trap\\n stats.print_stats(*lims)\\n finally:\\n stats.stream = stats_stream\\n\\n output = stdout_trap.getvalue()\\n output = output.rstrip()\\n\\n if 'q' not in opts:\\n page.page(output)\\n print(sys_exit, end=' ')\\n\\n dump_file = opts.D[0]\\n text_file = opts.T[0]\\n if dump_file:\\n dump_file = unquote_filename(dump_file)\\n prof.dump_stats(dump_file)\\n print('\\\\n*** Profile stats marshalled to file',\\\\\\n repr(dump_file)+'.',sys_exit)\\n if\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics.pdb\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics.pdb\\n @line_magic\\n def pdb(self, parameter_s=''):\\n \\\"\\\"\\\"Control the automatic calling of the pdb interactive debugger.\\n\\n Call as '%pdb on', '%pdb 1', '%pdb off' or '%pdb 0'. If called without\\n argument it works as a toggle.\\n\\n When an exception is triggered, IPython can optionally call the\\n interactive pdb debugger after the traceback printout. %pdb toggles\\n this feature on and off.\\n\\n The initial state of this feature is set in your configuration\\n file (the option is ``InteractiveShell.pdb``).\\n\\n If you want to just activate the debugger AFTER an exception has fired,\\n without having to type '%pdb on' and rerunning your code, you can use\\n the %debug magic.\\\"\\\"\\\"\\n\\n par = parameter_s.strip().lower()\\n\\n if par:\\n try:\\n new_pdb = {'off':0,'0':0,'on':1,'1':1}[par]\\n except KeyError:\\n print ('Incorrect argument. Use on/1, off/0, '\\n 'or nothing for a toggle.')\\n return\\n else:\\n # toggle\\n new_pdb = not self.shell.call_pdb\\n\\n # set on the shell\\n self.shell.call_pdb = new_pdb\\n print('Automatic pdb calling has been turned',on_off(new_pdb))\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics.debug\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics.debug\\n @skip_doctest\\n @magic_arguments.magic_arguments()\\n @magic_arguments.argument('--breakpoint', '-b', metavar='FILE:LINE',\\n help=\\\"\\\"\\\"\\n Set break point at LINE in FILE.\\n \\\"\\\"\\\"\\n )\\n @magic_arguments.argument('statement', nargs='*',\\n help=\\\"\\\"\\\"\\n Code to run in debugger.\\n You can omit this in cell magic mode.\\n \\\"\\\"\\\"\\n )\\n @line_cell_magic\\n def debug(self, line='', cell=None):\\n \\\"\\\"\\\"Activate the interactive debugger.\\n\\n This magic command support two ways of activating debugger.\\n One is to activate debugger before executing code. This way, you\\n can set a break point, to step through the code from the point.\\n You can use this mode by giving statements to execute and optionally\\n a breakpoint.\\n\\n The other one is to activate debugger in post-mortem mode. You can\\n activate this mode simply running %debug without any argument.\\n If an exception has just occurred, this lets you inspect its stack\\n frames interactively. Note that this will always work only on the last\\n traceback that occurred, so you must call this quickly after an\\n exception that you wish to inspect has fired, because if another one\\n occurs, it clobbers the previous one.\\n\\n If you want IPython to automatically do this on every exception, see\\n the %pdb magic for more details.\\n \\\"\\\"\\\"\\n args = magic_arguments.parse_argstring(self.debug, line)\\n\\n if not (args.breakpoint or args.statement or cell):\\n self._debug_post_mortem()\\n else:\\n code = \\\"\\\\n\\\".join(args.statement)\\n if cell:\\n code += \\\"\\\\n\\\" + cell\\n self._debug_exec(code, args.breakpoint)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics._debug_post_mortem\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics._debug_post_mortem\\n def _debug_post_mortem(self):\\n self.shell.debugger(force=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics._debug_exec\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics._debug_exec\\n def _debug_exec(self, code, breakpoint):\\n if breakpoint:\\n (filename, bp_line) = breakpoint.split(':', 1)\\n bp_line = int(bp_line)\\n else:\\n (filename, bp_line) = (None, None)\\n self._run_with_debugger(code, self.shell.user_ns, filename, bp_line)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics.tb\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics.tb\\n @line_magic\\n def tb(self, s):\\n \\\"\\\"\\\"Print the last traceback with the currently active exception mode.\\n\\n See %xmode for changing exception reporting modes.\\\"\\\"\\\"\\n self.shell.showtraceback()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics.run\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics.run\\n @skip_doctest\\n @line_magic\\n def run(self, parameter_s='', runner=None,\\n file_finder=get_py_filename):\\n \\\"\\\"\\\"Run the named file inside IPython as a program.\\n\\n Usage::\\n \\n %run [-n -i -e -G]\\n [( -t [-N<N>] | -d [-b<N>] | -p [profile options] )]\\n ( -m mod | file ) [args]\\n\\n Parameters after the filename are passed as command-line arguments to\\n the program (put in sys.argv). Then, control returns to IPython's\\n prompt.\\n\\n This is similar to running at a system prompt ``python file args``,\\n but with the advantage of giving you IPython's tracebacks, and of\\n loading all variables into your interactive namespace for further use\\n (unless -p is used, see below).\\n\\n The file is executed in a namespace initially consisting only of\\n ``__name__=='__main__'`` and sys.argv constructed as indicated. It thus\\n sees its environment as if it were being run as a stand-alone program\\n (except for sharing global objects such as previously imported\\n modules). But after execution, the IPython interactive namespace gets\\n updated with all variables defined in the program (except for __name__\\n and sys.argv). This allows for very convenient loading of code for\\n interactive work, while giving each program a 'clean sheet' to run in.\\n\\n Arguments are expanded using shell-like glob match. Patterns\\n '*', '?', '[seq]' and '[!seq]' can be used. Additionally,\\n tilde '~' will be expanded into user's home directory. Unlike\\n real shells, quotation does not suppress expansions. Use\\n *two* back slashes (e.g. ``\\\\\\\\\\\\\\\\*``) to suppress expansions.\\n To completely disable these expansions, you can use -G flag.\\n\\n Options:\\n\\n -n\\n __name__ is NOT set to '__main__', but to the running file's name\\n without extensi\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics._run_with_debugger\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics._run_with_debugger\\n def _run_with_debugger(self, code, code_ns, filename=None,\\n bp_line=None, bp_file=None):\\n \\\"\\\"\\\"\\n Run `code` in debugger with a break point.\\n\\n Parameters\\n ----------\\n code : str\\n Code to execute.\\n code_ns : dict\\n A namespace in which `code` is executed.\\n filename : str\\n `code` is ran as if it is in `filename`.\\n bp_line : int, optional\\n Line number of the break point.\\n bp_file : str, optional\\n Path to the file in which break point is specified.\\n `filename` is used if not given.\\n\\n Raises\\n ------\\n UsageError\\n If the break point given by `bp_line` is not valid.\\n\\n \\\"\\\"\\\"\\n deb = debugger.Pdb(self.shell.colors)\\n # reset Breakpoint state, which is moronically kept\\n # in a class\\n bdb.Breakpoint.next = 1\\n bdb.Breakpoint.bplist = {}\\n bdb.Breakpoint.bpbynumber = [None]\\n if bp_line is not None:\\n # Set an initial breakpoint to stop execution\\n maxtries = 10\\n bp_file = bp_file or filename\\n checkline = deb.checkline(bp_file, bp_line)\\n if not checkline:\\n for bp in range(bp_line + 1, bp_line + maxtries + 1):\\n if deb.checkline(bp_file, bp):\\n break\\n else:\\n msg = (\\\"\\\\nI failed to find a valid line to set \\\"\\n \\\"a breakpoint\\\\n\\\"\\n \\\"after trying up to line: %s.\\\\n\\\"\\n \\\"Please set a valid breakpoint manually \\\"\\n \\\"with the -b option.\\\" % bp)\\n raise UsageError(msg)\\n # if we find a good linenumber, set the breakpoint\\n deb.do_break('%s:%s' % (bp_file, bp_line))\\n\\n if filename:\\n # Mimic Pdb._runscrip\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics._run_with_timing\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics._run_with_timing\\n @staticmethod\\n def _run_with_timing(run, nruns):\\n \\\"\\\"\\\"\\n Run function `run` and print timing information.\\n\\n Parameters\\n ----------\\n run : callable\\n Any callable object which takes no argument.\\n nruns : int\\n Number of times to execute `run`.\\n\\n \\\"\\\"\\\"\\n twall0 = time.time()\\n if nruns == 1:\\n t0 = clock2()\\n run()\\n t1 = clock2()\\n t_usr = t1[0] - t0[0]\\n t_sys = t1[1] - t0[1]\\n print(\\\"\\\\nIPython CPU timings (estimated):\\\")\\n print(\\\" User : %10.2f s.\\\" % t_usr)\\n print(\\\" System : %10.2f s.\\\" % t_sys)\\n else:\\n runs = range(nruns)\\n t0 = clock2()\\n for nr in runs:\\n run()\\n t1 = clock2()\\n t_usr = t1[0] - t0[0]\\n t_sys = t1[1] - t0[1]\\n print(\\\"\\\\nIPython CPU timings (estimated):\\\")\\n print(\\\"Total runs performed:\\\", nruns)\\n print(\\\" Times : %10s %10s\\\" % ('Total', 'Per run'))\\n print(\\\" User : %10.2f s, %10.2f s.\\\" % (t_usr, t_usr / nruns))\\n print(\\\" System : %10.2f s, %10.2f s.\\\" % (t_sys, t_sys / nruns))\\n twall1 = time.time()\\n print(\\\"Wall time: %10.2f s.\\\" % (twall1 - twall0))\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics.timeit\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics.timeit\\n @skip_doctest\\n @line_cell_magic\\n def timeit(self, line='', cell=None):\\n \\\"\\\"\\\"Time execution of a Python statement or expression\\n\\n Usage, in line mode:\\n %timeit [-n<N> -r<R> [-t|-c] -q -p<P> -o] statement\\n or in cell mode:\\n %%timeit [-n<N> -r<R> [-t|-c] -q -p<P> -o] setup_code\\n code\\n code...\\n\\n Time execution of a Python statement or expression using the timeit\\n module. This function can be used both as a line and cell magic:\\n\\n - In line mode you can time a single-line statement (though multiple\\n ones can be chained with using semicolons).\\n\\n - In cell mode, the statement in the first line is used as setup code\\n (executed but not timed) and the body of the cell is timed. The cell\\n body has access to any variables created in the setup code.\\n\\n Options:\\n -n<N>: execute the given statement <N> times in a loop. If this value\\n is not given, a fitting value is chosen.\\n\\n -r<R>: repeat the loop iteration <R> times and take the best result.\\n Default: 3\\n\\n -t: use time.time to measure the time, which is the default on Unix.\\n This function measures wall time.\\n\\n -c: use time.clock to measure the time, which is the default on\\n Windows and measures wall time. On Unix, resource.getrusage is used\\n instead and returns the CPU user time.\\n\\n -p<P>: use a precision of <P> digits to display the timing result.\\n Default: 3\\n\\n -q: Quiet, do not print result.\\n\\n -o: return a TimeitResult that can be stored in a variable to inspect\\n the result in more details.\\n\\n\\n Examples\\n --------\\n ::\\n\\n In [1]: %timeit pass\\n 10000000 loops, best of 3: 53.3 ns per loop\\n\\n In [2]: u = None\\n\\n In [3]: %timeit u is None\\n 10000000 loops, best of 3: 184 ns per loop\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics.time\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics.time\\n @skip_doctest\\n @needs_local_scope\\n @line_cell_magic\\n def time(self,line='', cell=None, local_ns=None):\\n \\\"\\\"\\\"Time execution of a Python statement or expression.\\n\\n The CPU and wall clock times are printed, and the value of the\\n expression (if any) is returned. Note that under Win32, system time\\n is always reported as 0, since it can not be measured.\\n \\n This function can be used both as a line and cell magic:\\n\\n - In line mode you can time a single-line statement (though multiple\\n ones can be chained with using semicolons).\\n\\n - In cell mode, you can time the cell body (a directly \\n following statement raises an error).\\n\\n This function provides very basic timing functionality. Use the timeit \\n magic for more control over the measurement.\\n\\n Examples\\n --------\\n ::\\n\\n In [1]: %time 2**128\\n CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s\\n Wall time: 0.00\\n Out[1]: 340282366920938463463374607431768211456L\\n\\n In [2]: n = 1000000\\n\\n In [3]: %time sum(range(n))\\n CPU times: user 1.20 s, sys: 0.05 s, total: 1.25 s\\n Wall time: 1.37\\n Out[3]: 499999500000L\\n\\n In [4]: %time print 'hello world'\\n hello world\\n CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s\\n Wall time: 0.00\\n\\n Note that the time needed by Python to compile the given expression\\n will be reported if it is more than 0.1s. In this example, the\\n actual exponentiation is done by Python at compilation time, so while\\n the expression can take a noticeable amount of time to compute, that\\n time is purely due to the compilation:\\n\\n In [5]: %time 3**9999;\\n CPU times: user 0.00 s, sys: 0.00 s, total: 0.00 s\\n Wall time: 0.00 s\\n\\n In [6]: %time 3**999999;\\n CPU\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics.macro\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics.macro\\n @skip_doctest\\n @line_magic\\n def macro(self, parameter_s=''):\\n \\\"\\\"\\\"Define a macro for future re-execution. It accepts ranges of history,\\n filenames or string objects.\\n\\n Usage:\\\\\\\\\\n %macro [options] name n1-n2 n3-n4 ... n5 .. n6 ...\\n\\n Options:\\n\\n -r: use 'raw' input. By default, the 'processed' history is used,\\n so that magics are loaded in their transformed version to valid\\n Python. If this option is given, the raw input as typed at the\\n command line is used instead.\\n \\n -q: quiet macro definition. By default, a tag line is printed \\n to indicate the macro has been created, and then the contents of \\n the macro are printed. If this option is given, then no printout\\n is produced once the macro is created.\\n\\n This will define a global variable called `name` which is a string\\n made of joining the slices and lines you specify (n1,n2,... numbers\\n above) from your input history into a single string. This variable\\n acts like an automatic function which re-executes those lines as if\\n you had typed them. You just type 'name' at the prompt and the code\\n executes.\\n\\n The syntax for indicating input ranges is described in %history.\\n\\n Note: as a 'hidden' feature, you can also use traditional python slice\\n notation, where N:M means numbers N through M-1.\\n\\n For example, if your history contains (print using %hist -n )::\\n\\n 44: x=1\\n 45: y=3\\n 46: z=x+y\\n 47: print x\\n 48: a=5\\n 49: print 'x',x,'y',y\\n\\n you can create a macro with lines 44 through 47 (included) and line 49\\n called my_macro with::\\n\\n In [55]: %macro my_macro 44-47 49\\n\\n Now, typing `my_macro` (without quotes) will re-execute all this code\\n in one pass.\\n\\n You don't need to give the line\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::ExecutionMagics.capture\",\n \"text\": \"IPython/core/magics/execution.py::ExecutionMagics.capture\\n @magic_arguments.magic_arguments()\\n @magic_arguments.argument('output', type=str, default='', nargs='?',\\n help=\\\"\\\"\\\"The name of the variable in which to store output.\\n This is a utils.io.CapturedIO object with stdout/err attributes\\n for the text of the captured output.\\n\\n CapturedOutput also has a show() method for displaying the output,\\n and __call__ as well, so you can use that to quickly display the\\n output.\\n\\n If unspecified, captured output is discarded.\\n \\\"\\\"\\\"\\n )\\n @magic_arguments.argument('--no-stderr', action=\\\"store_true\\\",\\n help=\\\"\\\"\\\"Don't capture stderr.\\\"\\\"\\\"\\n )\\n @magic_arguments.argument('--no-stdout', action=\\\"store_true\\\",\\n help=\\\"\\\"\\\"Don't capture stdout.\\\"\\\"\\\"\\n )\\n @magic_arguments.argument('--no-display', action=\\\"store_true\\\",\\n help=\\\"\\\"\\\"Don't capture IPython's rich display.\\\"\\\"\\\"\\n )\\n @cell_magic\\n def capture(self, line, cell):\\n \\\"\\\"\\\"run the cell, capturing stdout, stderr, and IPython's rich display() calls.\\\"\\\"\\\"\\n args = magic_arguments.parse_argstring(self.capture, line)\\n out = not args.no_stdout\\n err = not args.no_stderr\\n disp = not args.no_display\\n with capture_output(out, err, disp) as io:\\n self.shell.run_cell(cell)\\n if args.output:\\n self.shell.user_ns[args.output] = io\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::parse_breakpoint\",\n \"text\": \"IPython/core/magics/execution.py::parse_breakpoint\\ndef parse_breakpoint(text, current_file):\\n '''Returns (file, line) for file:line and (current_file, line) for line'''\\n colon = text.find(':')\\n if colon == -1:\\n return current_file, int(text)\\n else:\\n return text[:colon], int(text[colon+1:])\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/core/magics/execution.py::_format_time\",\n \"text\": \"IPython/core/magics/execution.py::_format_time\\ndef _format_time(timespan, precision=3):\\n \\\"\\\"\\\"Formats the timespan in a human readable form\\\"\\\"\\\"\\n import math\\n \\n if timespan >= 60.0:\\n # we have more than a minute, format that in a human readable form\\n # Idea from http://snipplr.com/view/5713/\\n parts = [(\\\"d\\\", 60*60*24),(\\\"h\\\", 60*60),(\\\"min\\\", 60), (\\\"s\\\", 1)]\\n time = []\\n leftover = timespan\\n for suffix, length in parts:\\n value = int(leftover / length)\\n if value > 0:\\n leftover = leftover % length\\n time.append(u'%s%s' % (str(value), suffix))\\n if leftover < 1:\\n break\\n return \\\" \\\".join(time)\\n\\n \\n # Unfortunately the unicode 'micro' symbol can cause problems in\\n # certain terminals. \\n # See bug: https://bugs.launchpad.net/ipython/+bug/348466\\n # Try to prevent crashes by being more secure than it needs to\\n # E.g. eclipse is able to print a µ, but has no sys.stdout.encoding set.\\n units = [u\\\"s\\\", u\\\"ms\\\",u'us',\\\"ns\\\"] # the save value \\n if hasattr(sys.stdout, 'encoding') and sys.stdout.encoding:\\n try:\\n u'\\\\xb5'.encode(sys.stdout.encoding)\\n units = [u\\\"s\\\", u\\\"ms\\\",u'\\\\xb5s',\\\"ns\\\"]\\n except:\\n pass\\n scaling = [1, 1e3, 1e6, 1e9]\\n \\n if timespan > 0.0:\\n order = min(-int(math.floor(math.log10(timespan)) // 3), 3)\\n else:\\n order = 3\\n return u\\\"%.*g %s\\\" % (precision, timespan * scaling[order], units[order])\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py::_matches\",\n \"text\": \"IPython/html/widgets/interaction.py::_matches\\ndef _matches(o, pattern):\\n \\\"\\\"\\\"Match a pattern of types in a sequence.\\\"\\\"\\\"\\n if not len(o) == len(pattern):\\n return False\\n comps = zip(o,pattern)\\n return all(isinstance(obj,kind) for obj,kind in comps)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py::_get_min_max_value\",\n \"text\": \"IPython/html/widgets/interaction.py::_get_min_max_value\\ndef _get_min_max_value(min, max, value=None, step=None):\\n \\\"\\\"\\\"Return min, max, value given input values with possible None.\\\"\\\"\\\"\\n if value is None:\\n if not max > min:\\n raise ValueError('max must be greater than min: (min={0}, max={1})'.format(min, max))\\n value = min + abs(min-max)/2\\n value = type(min)(value)\\n elif min is None and max is None:\\n if value == 0.0:\\n min, max, value = 0.0, 1.0, 0.5\\n elif value == 0:\\n min, max, value = 0, 1, 0\\n elif isinstance(value, (int, float)):\\n min, max = (-value, 3*value) if value > 0 else (3*value, -value)\\n else:\\n raise TypeError('expected a number, got: %r' % value)\\n else:\\n raise ValueError('unable to infer range, value from: ({0}, {1}, {2})'.format(min, max, value))\\n if step is not None:\\n # ensure value is on a step\\n r = (value - min) % step\\n value = value - r\\n return min, max, value\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py::_widget_abbrev_single_value\",\n \"text\": \"IPython/html/widgets/interaction.py::_widget_abbrev_single_value\\ndef _widget_abbrev_single_value(o):\\n \\\"\\\"\\\"Make widgets from single values, which can be used as parameter defaults.\\\"\\\"\\\"\\n if isinstance(o, string_types):\\n return Text(value=unicode_type(o))\\n elif isinstance(o, dict):\\n return Dropdown(values=o)\\n elif isinstance(o, bool):\\n return Checkbox(value=o)\\n elif isinstance(o, float):\\n min, max, value = _get_min_max_value(None, None, o)\\n return FloatSlider(value=o, min=min, max=max)\\n elif isinstance(o, int):\\n min, max, value = _get_min_max_value(None, None, o)\\n return IntSlider(value=o, min=min, max=max)\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py::_widget_abbrev\",\n \"text\": \"IPython/html/widgets/interaction.py::_widget_abbrev\\ndef _widget_abbrev(o):\\n \\\"\\\"\\\"Make widgets from abbreviations: single values, lists or tuples.\\\"\\\"\\\"\\n float_or_int = (float, int)\\n if isinstance(o, (list, tuple)):\\n if o and all(isinstance(x, string_types) for x in o):\\n return Dropdown(values=[unicode_type(k) for k in o])\\n elif _matches(o, (float_or_int, float_or_int)):\\n min, max, value = _get_min_max_value(o[0], o[1])\\n if all(isinstance(_, int) for _ in o):\\n cls = IntSlider\\n else:\\n cls = FloatSlider\\n return cls(value=value, min=min, max=max)\\n elif _matches(o, (float_or_int, float_or_int, float_or_int)):\\n step = o[2]\\n if step <= 0:\\n raise ValueError(\\\"step must be >= 0, not %r\\\" % step)\\n min, max, value = _get_min_max_value(o[0], o[1], step=step)\\n if all(isinstance(_, int) for _ in o):\\n cls = IntSlider\\n else:\\n cls = FloatSlider\\n return cls(value=value, min=min, max=max, step=step)\\n else:\\n return _widget_abbrev_single_value(o)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py::_widget_from_abbrev\",\n \"text\": \"IPython/html/widgets/interaction.py::_widget_from_abbrev\\ndef _widget_from_abbrev(abbrev, default=empty):\\n \\\"\\\"\\\"Build a Widget instance given an abbreviation or Widget.\\\"\\\"\\\"\\n if isinstance(abbrev, Widget) or isinstance(abbrev, fixed):\\n return abbrev\\n\\n widget = _widget_abbrev(abbrev)\\n if default is not empty and isinstance(abbrev, (list, tuple, dict)):\\n # if it's not a single-value abbreviation,\\n # set the initial value from the default\\n try:\\n widget.value = default\\n except Exception:\\n # ignore failure to set default\\n pass\\n if widget is None:\\n raise ValueError(\\\"%r cannot be transformed to a Widget\\\" % (abbrev,))\\n return widget\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py::_yield_abbreviations_for_parameter\",\n \"text\": \"IPython/html/widgets/interaction.py::_yield_abbreviations_for_parameter\\ndef _yield_abbreviations_for_parameter(param, kwargs):\\n \\\"\\\"\\\"Get an abbreviation for a function parameter.\\\"\\\"\\\"\\n name = param.name\\n kind = param.kind\\n ann = param.annotation\\n default = param.default\\n not_found = (name, empty, empty)\\n if kind in (Parameter.POSITIONAL_OR_KEYWORD, Parameter.KEYWORD_ONLY):\\n if name in kwargs:\\n value = kwargs.pop(name)\\n elif ann is not empty:\\n value = ann\\n elif default is not empty:\\n value = default\\n else:\\n yield not_found\\n yield (name, value, default)\\n elif kind == Parameter.VAR_KEYWORD:\\n # In this case name=kwargs and we yield the items in kwargs with their keys.\\n for k, v in kwargs.copy().items():\\n kwargs.pop(k)\\n yield k, v, empty\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py::_find_abbreviations\",\n \"text\": \"IPython/html/widgets/interaction.py::_find_abbreviations\\ndef _find_abbreviations(f, kwargs):\\n \\\"\\\"\\\"Find the abbreviations for a function and kwargs passed to interact.\\\"\\\"\\\"\\n new_kwargs = []\\n for param in signature(f).parameters.values():\\n for name, value, default in _yield_abbreviations_for_parameter(param, kwargs):\\n if value is empty:\\n raise ValueError('cannot find widget or abbreviation for argument: {!r}'.format(name))\\n new_kwargs.append((name, value, default))\\n return new_kwargs\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py::_widgets_from_abbreviations\",\n \"text\": \"IPython/html/widgets/interaction.py::_widgets_from_abbreviations\\ndef _widgets_from_abbreviations(seq):\\n \\\"\\\"\\\"Given a sequence of (name, abbrev) tuples, return a sequence of Widgets.\\\"\\\"\\\"\\n result = []\\n for name, abbrev, default in seq:\\n widget = _widget_from_abbrev(abbrev, default)\\n if not widget.description:\\n widget.description = name\\n widget._kwarg = name\\n result.append(widget)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py::interactive\",\n \"text\": \"IPython/html/widgets/interaction.py::interactive\\ndef interactive(__interact_f, **kwargs):\\n \\\"\\\"\\\"\\n Builds a group of interactive widgets tied to a function and places the\\n group into a Box container.\\n\\n Returns\\n -------\\n container : a Box instance containing multiple widgets\\n\\n Parameters\\n ----------\\n __interact_f : function\\n The function to which the interactive widgets are tied. The **kwargs\\n should match the function signature.\\n **kwargs : various, optional\\n An interactive widget is created for each keyword argument that is a\\n valid widget abbreviation.\\n \\\"\\\"\\\"\\n f = __interact_f\\n co = kwargs.pop('clear_output', True)\\n manual = kwargs.pop('__manual', False)\\n kwargs_widgets = []\\n container = Box()\\n container.result = None\\n container.args = []\\n container.kwargs = dict()\\n kwargs = kwargs.copy()\\n\\n new_kwargs = _find_abbreviations(f, kwargs)\\n # Before we proceed, let's make sure that the user has passed a set of args+kwargs\\n # that will lead to a valid call of the function. This protects against unspecified\\n # and doubly-specified arguments.\\n getcallargs(f, **{n:v for n,v,_ in new_kwargs})\\n # Now build the widgets from the abbreviations.\\n kwargs_widgets.extend(_widgets_from_abbreviations(new_kwargs))\\n\\n # This has to be done as an assignment, not using container.children.append,\\n # so that traitlets notices the update. We skip any objects (such as fixed) that\\n # are not DOMWidgets.\\n c = [w for w in kwargs_widgets if isinstance(w, DOMWidget)]\\n\\n # If we are only to run the function on demand, add a button to request this\\n if manual:\\n manual_button = Button(description=\\\"Run %s\\\" % f.__name__)\\n c.append(manual_button)\\n container.children = c\\n\\n # Build the callback\\n def call_f(name=None, old=None, new=None):\\n container.kwargs = {}\\n for widget in kwargs_widgets:\\n value = widget.value\\n container.kwargs[\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py::interactive.call_f\",\n \"text\": \"IPython/html/widgets/interaction.py::interactive.call_f\\n def call_f(name=None, old=None, new=None):\\n container.kwargs = {}\\n for widget in kwargs_widgets:\\n value = widget.value\\n container.kwargs[widget._kwarg] = value\\n if co:\\n clear_output(wait=True)\\n if manual:\\n manual_button.disabled = True\\n try:\\n container.result = f(**container.kwargs)\\n except Exception as e:\\n ip = get_ipython()\\n if ip is None:\\n container.log.warn(\\\"Exception in interact callback: %s\\\", e, exc_info=True)\\n else:\\n ip.showtraceback()\\n finally:\\n if manual:\\n manual_button.disabled = False\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py::interact\",\n \"text\": \"IPython/html/widgets/interaction.py::interact\\ndef interact(__interact_f=None, **kwargs):\\n \\\"\\\"\\\"\\n Displays interactive widgets which are tied to a function.\\n Expects the first argument to be a function. Parameters to this function are\\n widget abbreviations passed in as keyword arguments (**kwargs). Can be used\\n as a decorator (see examples).\\n\\n Returns\\n -------\\n f : __interact_f with interactive widget attached to it.\\n\\n Parameters\\n ----------\\n __interact_f : function\\n The function to which the interactive widgets are tied. The **kwargs\\n should match the function signature. Passed to :func:`interactive()`\\n **kwargs : various, optional\\n An interactive widget is created for each keyword argument that is a\\n valid widget abbreviation. Passed to :func:`interactive()`\\n\\n Examples\\n --------\\n Renders an interactive text field that shows the greeting with the passed in\\n text.\\n\\n 1. Invocation of interact as a function\\n def greeting(text=\\\"World\\\"):\\n print \\\"Hello {}\\\".format(text)\\n interact(greeting, text=\\\"IPython Widgets\\\")\\n \\n 2. Invocation of interact as a decorator\\n @interact\\n def greeting(text=\\\"World\\\"):\\n print \\\"Hello {}\\\".format(text)\\n \\n 3. Invocation of interact as a decorator with named parameters\\n @interact(text=\\\"IPython Widgets\\\")\\n def greeting(text=\\\"World\\\"):\\n print \\\"Hello {}\\\".format(text)\\n \\n Renders an interactive slider widget and prints square of number.\\n\\n 1. Invocation of interact as a function\\n def square(num=1):\\n print \\\"{} squared is {}\\\".format(num, num*num)\\n interact(square, num=5)\\n\\n 2. Invocation of interact as a decorator\\n @interact\\n def square(num=2):\\n print \\\"{} squared is {}\\\".format(num, num*num)\\n \\n 3. Invocation of interact as a decorator with named parameters\\n @interact(num=5)\\n def square(num=2):\\n print \\\"{} squared \",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py::interact_manual\",\n \"text\": \"IPython/html/widgets/interaction.py::interact_manual\\ndef interact_manual(__interact_f=None, **kwargs):\\n \\\"\\\"\\\"interact_manual(f, **kwargs)\\n \\n As `interact()`, generates widgets for each argument, but rather than running\\n the function after each widget change, adds a \\\"Run\\\" button and waits for it\\n to be clicked. Useful if the function is long-running and has several\\n parameters to change.\\n \\\"\\\"\\\"\\n return interact(__interact_f, __manual=True, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/interaction.py::fixed.__init__\",\n \"text\": \"IPython/html/widgets/interaction.py::fixed.__init__\\n def __init__(self, value, **kwargs):\\n super(fixed, self).__init__(value=value, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/widget_bool.py::_Bool.__init__\",\n \"text\": \"IPython/html/widgets/widget_bool.py::_Bool.__init__\\n def __init__(self, value=None, **kwargs):\\n if value is not None:\\n kwargs['value'] = value\\n super(_Bool, self).__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/widget_output.py::Output.clear_output\",\n \"text\": \"IPython/html/widgets/widget_output.py::Output.clear_output\\n def clear_output(self, *pargs, **kwargs):\\n with self:\\n clear_output(*pargs, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/html/widgets/widget_output.py::Output.__enter__\",\n \"text\": \"IPython/html/widgets/widget_output.py::Output.__enter__\\n def __enter__(self):\\n \\\"\\\"\\\"Called upon entering output widget context manager.\\\"\\\"\\\"\\n self._flush()\\n kernel = get_ipython().kernel\\n session = kernel.session\\n send = session.send\\n self._original_send = send\\n self._session = session\\n\\n def send_hook(stream, msg_or_type, content=None, parent=None, ident=None,\\n buffers=None, track=False, header=None, metadata=None): \\n\\n # Handle both prebuild messages and unbuilt messages.\\n if isinstance(msg_or_type, (Message, dict)):\\n msg_type = msg_or_type['msg_type']\\n msg = dict(msg_or_type)\\n else:\\n msg_type = msg_or_type\\n msg = session.msg(msg_type, content=content, parent=parent, \\n header=header, metadata=metadata)\\n\\n # If this is a message type that we want to forward, forward it.\\n if stream is kernel.iopub_socket and msg_type in ['clear_output', 'stream', 'display_data']:\\n self.send(msg)\\n else: \\n send(stream, msg, ident=ident, buffers=buffers, track=track)\\n\\n session.send = send_hook\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/html/widgets/widget_output.py::Output.__enter__.send_hook\",\n \"text\": \"IPython/html/widgets/widget_output.py::Output.__enter__.send_hook\\n def send_hook(stream, msg_or_type, content=None, parent=None, ident=None,\\n buffers=None, track=False, header=None, metadata=None): \\n\\n # Handle both prebuild messages and unbuilt messages.\\n if isinstance(msg_or_type, (Message, dict)):\\n msg_type = msg_or_type['msg_type']\\n msg = dict(msg_or_type)\\n else:\\n msg_type = msg_or_type\\n msg = session.msg(msg_type, content=content, parent=parent, \\n header=header, metadata=metadata)\\n\\n # If this is a message type that we want to forward, forward it.\\n if stream is kernel.iopub_socket and msg_type in ['clear_output', 'stream', 'display_data']:\\n self.send(msg)\\n else: \\n send(stream, msg, ident=ident, buffers=buffers, track=track)\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/html/widgets/widget_output.py::Output.__exit__\",\n \"text\": \"IPython/html/widgets/widget_output.py::Output.__exit__\\n def __exit__(self, exception_type, exception_value, traceback):\\n \\\"\\\"\\\"Called upon exiting output widget context manager.\\\"\\\"\\\"\\n self._flush()\\n self._session.send = self._original_send\",\n \"label\": 1\n },\n {\n \"cid\": \"IPython/html/widgets/widget_output.py::Output._flush\",\n \"text\": \"IPython/html/widgets/widget_output.py::Output._flush\\n def _flush(self):\\n \\\"\\\"\\\"Flush stdout and stderr buffers.\\\"\\\"\\\"\\n sys.stdout.flush()\\n sys.stderr.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendHighlighter.__init__\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendHighlighter.__init__\\n def __init__(self, frontend, lexer=None):\\n super(FrontendHighlighter, self).__init__(frontend._control.document(), lexer=lexer)\\n self._current_offset = 0\\n self._frontend = frontend\\n self.highlighting_on = False\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendHighlighter.highlightBlock\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendHighlighter.highlightBlock\\n def highlightBlock(self, string):\\n \\\"\\\"\\\" Highlight a block of text. Reimplemented to highlight selectively.\\n \\\"\\\"\\\"\\n if not self.highlighting_on:\\n return\\n\\n # The input to this function is a unicode string that may contain\\n # paragraph break characters, non-breaking spaces, etc. Here we acquire\\n # the string as plain text so we can compare it.\\n current_block = self.currentBlock()\\n string = self._frontend._get_block_plain_text(current_block)\\n\\n # Decide whether to check for the regular or continuation prompt.\\n if current_block.contains(self._frontend._prompt_pos):\\n prompt = self._frontend._prompt\\n else:\\n prompt = self._frontend._continuation_prompt\\n\\n # Only highlight if we can identify a prompt, but make sure not to\\n # highlight the prompt.\\n if string.startswith(prompt):\\n self._current_offset = len(prompt)\\n string = string[len(prompt):]\\n super(FrontendHighlighter, self).highlightBlock(string)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendHighlighter.rehighlightBlock\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendHighlighter.rehighlightBlock\\n def rehighlightBlock(self, block):\\n \\\"\\\"\\\" Reimplemented to temporarily enable highlighting if disabled.\\n \\\"\\\"\\\"\\n old = self.highlighting_on\\n self.highlighting_on = True\\n super(FrontendHighlighter, self).rehighlightBlock(block)\\n self.highlighting_on = old\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendHighlighter.setFormat\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendHighlighter.setFormat\\n def setFormat(self, start, count, format):\\n \\\"\\\"\\\" Reimplemented to highlight selectively.\\n \\\"\\\"\\\"\\n start += self._current_offset\\n super(FrontendHighlighter, self).setFormat(start, count, format)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._lexer_class_changed\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._lexer_class_changed\\n def _lexer_class_changed(self, name, old, new):\\n lexer_class = import_item(new)\\n self.lexer = lexer_class()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._lexer_class_default\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._lexer_class_default\\n def _lexer_class_default(self):\\n if py3compat.PY3:\\n return 'pygments.lexers.Python3Lexer'\\n else:\\n return 'pygments.lexers.PythonLexer'\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._lexer_default\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._lexer_default\\n def _lexer_default(self):\\n lexer_class = import_item(self.lexer_class)\\n return lexer_class()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.__init__\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.__init__\\n def __init__(self, *args, **kw):\\n super(FrontendWidget, self).__init__(*args, **kw)\\n # FIXME: remove this when PySide min version is updated past 1.0.7\\n # forcefully disable calltips if PySide is < 1.0.7, because they crash\\n if qt.QT_API == qt.QT_API_PYSIDE:\\n import PySide\\n if PySide.__version_info__ < (1,0,7):\\n self.log.warn(\\\"PySide %s < 1.0.7 detected, disabling calltips\\\" % PySide.__version__)\\n self.enable_calltips = False\\n\\n # FrontendWidget protected variables.\\n self._bracket_matcher = BracketMatcher(self._control)\\n self._call_tip_widget = CallTipWidget(self._control)\\n self._copy_raw_action = QtGui.QAction('Copy (Raw Text)', None)\\n self._hidden = False\\n self._highlighter = FrontendHighlighter(self, lexer=self.lexer)\\n self._input_splitter = self._input_splitter_class()\\n self._kernel_manager = None\\n self._kernel_client = None\\n self._request_info = {}\\n self._request_info['execute'] = {};\\n self._callback_dict = {}\\n self._display_banner = True\\n\\n # Configure the ConsoleWidget.\\n self.tab_width = 4\\n self._set_continuation_prompt('... ')\\n\\n # Configure the CallTipWidget.\\n self._call_tip_widget.setFont(self.font)\\n self.font_changed.connect(self._call_tip_widget.setFont)\\n\\n # Configure actions.\\n action = self._copy_raw_action\\n key = QtCore.Qt.CTRL | QtCore.Qt.SHIFT | QtCore.Qt.Key_C\\n action.setEnabled(False)\\n action.setShortcut(QtGui.QKeySequence(key))\\n action.setShortcutContext(QtCore.Qt.WidgetWithChildrenShortcut)\\n action.triggered.connect(self.copy_raw)\\n self.copy_available.connect(action.setEnabled)\\n self.addAction(action)\\n\\n # Connect signal handlers.\\n document = self._control.document()\\n document.contentsChange.co\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.copy\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.copy\\n def copy(self):\\n \\\"\\\"\\\" Copy the currently selected text to the clipboard, removing prompts.\\n \\\"\\\"\\\"\\n if self._page_control is not None and self._page_control.hasFocus():\\n self._page_control.copy()\\n elif self._control.hasFocus():\\n text = self._control.textCursor().selection().toPlainText()\\n if text:\\n was_newline = text[-1] == '\\\\n'\\n text = self._prompt_transformer.transform_cell(text)\\n if not was_newline: # user doesn't need newline\\n text = text[:-1]\\n QtGui.QApplication.clipboard().setText(text)\\n else:\\n self.log.debug(\\\"frontend widget : unknown copy target\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._is_complete\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._is_complete\\n def _is_complete(self, source, interactive):\\n \\\"\\\"\\\" Returns whether 'source' can be completely processed and a new\\n prompt created. When triggered by an Enter/Return key press,\\n 'interactive' is True; otherwise, it is False.\\n \\\"\\\"\\\"\\n self._input_splitter.reset()\\n try:\\n complete = self._input_splitter.push(source)\\n except SyntaxError:\\n return True\\n if interactive:\\n complete = not self._input_splitter.push_accepts_more()\\n return complete\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._execute\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._execute\\n def _execute(self, source, hidden):\\n \\\"\\\"\\\" Execute 'source'. If 'hidden', do not show any output.\\n\\n See parent class :meth:`execute` docstring for full details.\\n \\\"\\\"\\\"\\n msg_id = self.kernel_client.execute(source, hidden)\\n self._request_info['execute'][msg_id] = self._ExecutionRequest(msg_id, 'user')\\n self._hidden = hidden\\n if not hidden:\\n self.executing.emit(source)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._prompt_started_hook\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._prompt_started_hook\\n def _prompt_started_hook(self):\\n \\\"\\\"\\\" Called immediately after a new prompt is displayed.\\n \\\"\\\"\\\"\\n if not self._reading:\\n self._highlighter.highlighting_on = True\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._prompt_finished_hook\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._prompt_finished_hook\\n def _prompt_finished_hook(self):\\n \\\"\\\"\\\" Called immediately after a prompt is finished, i.e. when some input\\n will be processed and a new prompt displayed.\\n \\\"\\\"\\\"\\n # Flush all state from the input splitter so the next round of\\n # reading input starts with a clean buffer.\\n self._input_splitter.reset()\\n\\n if not self._reading:\\n self._highlighter.highlighting_on = False\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._tab_pressed\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._tab_pressed\\n def _tab_pressed(self):\\n \\\"\\\"\\\" Called when the tab key is pressed. Returns whether to continue\\n processing the event.\\n \\\"\\\"\\\"\\n # Perform tab completion if:\\n # 1) The cursor is in the input buffer.\\n # 2) There is a non-whitespace character before the cursor.\\n text = self._get_input_buffer_cursor_line()\\n if text is None:\\n return False\\n complete = bool(text[:self._get_input_buffer_cursor_column()].strip())\\n if complete:\\n self._complete()\\n return not complete\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._context_menu_make\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._context_menu_make\\n def _context_menu_make(self, pos):\\n \\\"\\\"\\\" Reimplemented to add an action for raw copy.\\n \\\"\\\"\\\"\\n menu = super(FrontendWidget, self)._context_menu_make(pos)\\n for before_action in menu.actions():\\n if before_action.shortcut().matches(QtGui.QKeySequence.Paste) == \\\\\\n QtGui.QKeySequence.ExactMatch:\\n menu.insertAction(before_action, self._copy_raw_action)\\n break\\n return menu\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.request_interrupt_kernel\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.request_interrupt_kernel\\n def request_interrupt_kernel(self):\\n if self._executing:\\n self.interrupt_kernel()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.request_restart_kernel\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.request_restart_kernel\\n def request_restart_kernel(self):\\n message = 'Are you sure you want to restart the kernel?'\\n self.restart_kernel(message, now=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._event_filter_console_keypress\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._event_filter_console_keypress\\n def _event_filter_console_keypress(self, event):\\n \\\"\\\"\\\" Reimplemented for execution interruption and smart backspace.\\n \\\"\\\"\\\"\\n key = event.key()\\n if self._control_key_down(event.modifiers(), include_command=False):\\n\\n if key == QtCore.Qt.Key_C and self._executing:\\n self.request_interrupt_kernel()\\n return True\\n\\n elif key == QtCore.Qt.Key_Period:\\n self.request_restart_kernel()\\n return True\\n\\n elif not event.modifiers() & QtCore.Qt.AltModifier:\\n\\n # Smart backspace: remove four characters in one backspace if:\\n # 1) everything left of the cursor is whitespace\\n # 2) the four characters immediately left of the cursor are spaces\\n if key == QtCore.Qt.Key_Backspace:\\n col = self._get_input_buffer_cursor_column()\\n cursor = self._control.textCursor()\\n if col > 3 and not cursor.hasSelection():\\n text = self._get_input_buffer_cursor_line()[:col]\\n if text.endswith(' ') and not text.strip():\\n cursor.movePosition(QtGui.QTextCursor.Left,\\n QtGui.QTextCursor.KeepAnchor, 4)\\n cursor.removeSelectedText()\\n return True\\n\\n return super(FrontendWidget, self)._event_filter_console_keypress(event)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._insert_continuation_prompt\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._insert_continuation_prompt\\n def _insert_continuation_prompt(self, cursor):\\n \\\"\\\"\\\" Reimplemented for auto-indentation.\\n \\\"\\\"\\\"\\n super(FrontendWidget, self)._insert_continuation_prompt(cursor)\\n cursor.insertText(' ' * self._input_splitter.indent_spaces)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_clear_output\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_clear_output\\n def _handle_clear_output(self, msg):\\n \\\"\\\"\\\"Handle clear output messages.\\\"\\\"\\\"\\n if include_output(msg):\\n wait = msg['content'].get('wait', True)\\n if wait:\\n self._pending_clearoutput = True\\n else:\\n self.clear_output()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._silent_exec_callback\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._silent_exec_callback\\n def _silent_exec_callback(self, expr, callback):\\n \\\"\\\"\\\"Silently execute `expr` in the kernel and call `callback` with reply\\n\\n the `expr` is evaluated silently in the kernel (without) output in\\n the frontend. Call `callback` with the\\n `repr <http://docs.python.org/library/functions.html#repr> `_ as first argument\\n\\n Parameters\\n ----------\\n expr : string\\n valid string to be executed by the kernel.\\n callback : function\\n function accepting one argument, as a string. The string will be\\n the `repr` of the result of evaluating `expr`\\n\\n The `callback` is called with the `repr()` of the result of `expr` as\\n first argument. To get the object, do `eval()` on the passed value.\\n\\n See Also\\n --------\\n _handle_exec_callback : private method, deal with calling callback with reply\\n\\n \\\"\\\"\\\"\\n\\n # generate uuid, which would be used as an indication of whether or\\n # not the unique request originated from here (can use msg id ?)\\n local_uuid = str(uuid.uuid1())\\n msg_id = self.kernel_client.execute('',\\n silent=True, user_expressions={ local_uuid:expr })\\n self._callback_dict[local_uuid] = callback\\n self._request_info['execute'][msg_id] = self._ExecutionRequest(msg_id, 'silent_exec_callback')\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_exec_callback\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_exec_callback\\n def _handle_exec_callback(self, msg):\\n \\\"\\\"\\\"Execute `callback` corresponding to `msg` reply, after ``_silent_exec_callback``\\n\\n Parameters\\n ----------\\n msg : raw message send by the kernel containing an `user_expressions`\\n and having a 'silent_exec_callback' kind.\\n\\n Notes\\n -----\\n This function will look for a `callback` associated with the\\n corresponding message id. Association has been made by\\n `_silent_exec_callback`. `callback` is then called with the `repr()`\\n of the value of corresponding `user_expressions` as argument.\\n `callback` is then removed from the known list so that any message\\n coming again with the same id won't trigger it.\\n\\n \\\"\\\"\\\"\\n\\n user_exp = msg['content'].get('user_expressions')\\n if not user_exp:\\n return\\n for expression in user_exp:\\n if expression in self._callback_dict:\\n self._callback_dict.pop(expression)(user_exp[expression])\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_execute_reply\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_execute_reply\\n def _handle_execute_reply(self, msg):\\n \\\"\\\"\\\" Handles replies for code execution.\\n \\\"\\\"\\\"\\n self.log.debug(\\\"execute: %s\\\", msg.get('content', ''))\\n msg_id = msg['parent_header']['msg_id']\\n info = self._request_info['execute'].get(msg_id)\\n # unset reading flag, because if execute finished, raw_input can't\\n # still be pending.\\n self._reading = False\\n if info and info.kind == 'user' and not self._hidden:\\n # Make sure that all output from the SUB channel has been processed\\n # before writing a new prompt.\\n self.kernel_client.iopub_channel.flush()\\n\\n # Reset the ANSI style information to prevent bad text in stdout\\n # from messing up our colors. We're not a true terminal so we're\\n # allowed to do this.\\n if self.ansi_codes:\\n self._ansi_processor.reset_sgr()\\n\\n content = msg['content']\\n status = content['status']\\n if status == 'ok':\\n self._process_execute_ok(msg)\\n elif status == 'error':\\n self._process_execute_error(msg)\\n elif status == 'aborted':\\n self._process_execute_abort(msg)\\n\\n self._show_interpreter_prompt_for_reply(msg)\\n self.executed.emit(msg)\\n self._request_info['execute'].pop(msg_id)\\n elif info and info.kind == 'silent_exec_callback' and not self._hidden:\\n self._handle_exec_callback(msg)\\n self._request_info['execute'].pop(msg_id)\\n else:\\n super(FrontendWidget, self)._handle_execute_reply(msg)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_input_request\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_input_request\\n def _handle_input_request(self, msg):\\n \\\"\\\"\\\" Handle requests for raw_input.\\n \\\"\\\"\\\"\\n self.log.debug(\\\"input: %s\\\", msg.get('content', ''))\\n if self._hidden:\\n raise RuntimeError('Request for raw input during hidden execution.')\\n\\n # Make sure that all output from the SUB channel has been processed\\n # before entering readline mode.\\n self.kernel_client.iopub_channel.flush()\\n\\n def callback(line):\\n self.kernel_client.input(line)\\n if self._reading:\\n self.log.debug(\\\"Got second input request, assuming first was interrupted.\\\")\\n self._reading = False\\n self._readline(msg['content']['prompt'], callback=callback)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_input_request.callback\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_input_request.callback\\n def callback(line):\\n self.kernel_client.input(line)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._kernel_restarted_message\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._kernel_restarted_message\\n def _kernel_restarted_message(self, died=True):\\n msg = \\\"Kernel died, restarting\\\" if died else \\\"Kernel restarting\\\"\\n self._append_html(\\\"<br>%s<hr><br>\\\" % msg,\\n before_prompt=False\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_kernel_died\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_kernel_died\\n def _handle_kernel_died(self, since_last_heartbeat):\\n \\\"\\\"\\\"Handle the kernel's death (if we do not own the kernel).\\n \\\"\\\"\\\"\\n self.log.warn(\\\"kernel died: %s\\\", since_last_heartbeat)\\n if self.custom_restart:\\n self.custom_restart_kernel_died.emit(since_last_heartbeat)\\n else:\\n self._kernel_restarted_message(died=True)\\n self.reset()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_kernel_restarted\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_kernel_restarted\\n def _handle_kernel_restarted(self, died=True):\\n \\\"\\\"\\\"Notice that the autorestarter restarted the kernel.\\n\\n There's nothing to do but show a message.\\n \\\"\\\"\\\"\\n self.log.warn(\\\"kernel restarted\\\")\\n self._kernel_restarted_message(died=died)\\n self.reset()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_inspect_reply\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_inspect_reply\\n def _handle_inspect_reply(self, rep):\\n \\\"\\\"\\\"Handle replies for call tips.\\\"\\\"\\\"\\n self.log.debug(\\\"oinfo: %s\\\", rep.get('content', ''))\\n cursor = self._get_cursor()\\n info = self._request_info.get('call_tip')\\n if info and info.id == rep['parent_header']['msg_id'] and \\\\\\n info.pos == cursor.position():\\n content = rep['content']\\n if content.get('status') == 'ok' and content.get('found', False):\\n self._call_tip_widget.show_inspect_data(content)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_execute_result\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_execute_result\\n def _handle_execute_result(self, msg):\\n \\\"\\\"\\\" Handle display hook output.\\n \\\"\\\"\\\"\\n self.log.debug(\\\"execute_result: %s\\\", msg.get('content', ''))\\n if self.include_output(msg):\\n self.flush_clearoutput()\\n text = msg['content']['data']\\n self._append_plain_text(text + '\\\\n', before_prompt=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_stream\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_stream\\n def _handle_stream(self, msg):\\n \\\"\\\"\\\" Handle stdout, stderr, and stdin.\\n \\\"\\\"\\\"\\n self.log.debug(\\\"stream: %s\\\", msg.get('content', ''))\\n if self.include_output(msg):\\n self.flush_clearoutput()\\n self.append_stream(msg['content']['text'])\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_shutdown_reply\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_shutdown_reply\\n def _handle_shutdown_reply(self, msg):\\n \\\"\\\"\\\" Handle shutdown signal, only if from other console.\\n \\\"\\\"\\\"\\n self.log.info(\\\"shutdown: %s\\\", msg.get('content', ''))\\n restart = msg.get('content', {}).get('restart', False)\\n if not self._hidden and not self.from_here(msg):\\n # got shutdown reply, request came from session other than ours\\n if restart:\\n # someone restarted the kernel, handle it\\n self._handle_kernel_restarted(died=False)\\n else:\\n # kernel was shutdown permanently\\n # this triggers exit_requested if the kernel was local,\\n # and a dialog if the kernel was remote,\\n # so we don't suddenly clear the qtconsole without asking.\\n if self._local_kernel:\\n self.exit_requested.emit(self)\\n else:\\n title = self.window().windowTitle()\\n reply = QtGui.QMessageBox.question(self, title,\\n \\\"Kernel has been shutdown permanently. \\\"\\n \\\"Close the Console?\\\",\\n QtGui.QMessageBox.Yes,QtGui.QMessageBox.No)\\n if reply == QtGui.QMessageBox.Yes:\\n self.exit_requested.emit(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_status\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._handle_status\\n def _handle_status(self, msg):\\n \\\"\\\"\\\"Handle status message\\\"\\\"\\\"\\n # This is where a busy/idle indicator would be triggered,\\n # when we make one.\\n state = msg['content'].get('execution_state', '')\\n if state == 'starting':\\n # kernel started while we were running\\n if self._executing:\\n self._handle_kernel_restarted(died=True)\\n elif state == 'idle':\\n pass\\n elif state == 'busy':\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._started_channels\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget._started_channels\\n def _started_channels(self):\\n \\\"\\\"\\\" Called when the KernelManager channels have started listening or\\n when the frontend is assigned an already listening KernelManager.\\n \\\"\\\"\\\"\\n self.reset(clear=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.copy_raw\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.copy_raw\\n def copy_raw(self):\\n \\\"\\\"\\\" Copy the currently selected text to the clipboard without attempting\\n to remove prompts or otherwise alter the text.\\n \\\"\\\"\\\"\\n self._control.copy()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.execute_file\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.execute_file\\n def execute_file(self, path, hidden=False):\\n \\\"\\\"\\\" Attempts to execute file with 'path'. If 'hidden', no output is\\n shown.\\n \\\"\\\"\\\"\\n self.execute('execfile(%r)' % path, hidden=hidden)\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.interrupt_kernel\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.interrupt_kernel\\n def interrupt_kernel(self):\\n \\\"\\\"\\\" Attempts to interrupt the running kernel.\\n \\n Also unsets _reading flag, to avoid runtime errors\\n if raw_input is called again.\\n \\\"\\\"\\\"\\n if self.custom_interrupt:\\n self._reading = False\\n self.custom_interrupt_requested.emit()\\n elif self.kernel_manager:\\n self._reading = False\\n self.kernel_manager.interrupt_kernel()\\n else:\\n self._append_plain_text('Cannot interrupt a kernel I did not start.\\\\n')\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.reset\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.reset\\n def reset(self, clear=False):\\n \\\"\\\"\\\" Resets the widget to its initial state if ``clear`` parameter\\n is True, otherwise\\n prints a visual indication of the fact that the kernel restarted, but\\n does not clear the traces from previous usage of the kernel before it\\n was restarted. With ``clear=True``, it is similar to ``%clear``, but\\n also re-writes the banner and aborts execution if necessary.\\n \\\"\\\"\\\"\\n if self._executing:\\n self._executing = False\\n self._request_info['execute'] = {}\\n self._reading = False\\n self._highlighter.highlighting_on = False\\n\\n if clear:\\n self._control.clear()\\n if self._display_banner:\\n self._append_plain_text(self.banner)\\n if self.kernel_banner:\\n self._append_plain_text(self.kernel_banner)\\n\\n # update output marker for stdout/stderr, so that startup\\n # messages appear after banner:\\n self._append_before_prompt_pos = self._get_cursor().position()\\n self._show_interpreter_prompt()\",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.restart_kernel\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.restart_kernel\\n def restart_kernel(self, message, now=False):\\n \\\"\\\"\\\" Attempts to restart the running kernel.\\n \\\"\\\"\\\"\\n # FIXME: now should be configurable via a checkbox in the dialog. Right\\n # now at least the heartbeat path sets it to True and the manual restart\\n # to False. But those should just be the pre-selected states of a\\n # checkbox that the user could override if so desired. But I don't know\\n # enough Qt to go implementing the checkbox now.\\n\\n if self.custom_restart:\\n self.custom_restart_requested.emit()\\n return\\n\\n if self.kernel_manager:\\n # Pause the heart beat channel to prevent further warnings.\\n self.kernel_client.hb_channel.pause()\\n\\n # Prompt the user to restart the kernel. Un-pause the heartbeat if\\n # they decline. (If they accept, the heartbeat will be un-paused\\n # automatically when the kernel is restarted.)\\n if self.confirm_restart:\\n buttons = QtGui.QMessageBox.Yes | QtGui.QMessageBox.No\\n result = QtGui.QMessageBox.question(self, 'Restart kernel?',\\n message, buttons)\\n do_restart = result == QtGui.QMessageBox.Yes\\n else:\\n # confirm_restart is False, so we don't need to ask user\\n # anything, just do the restart\\n do_restart = True\\n if do_restart:\\n try:\\n self.kernel_manager.restart_kernel(now=now)\\n except RuntimeError as e:\\n self._append_plain_text(\\n 'Error restarting kernel: %s\\\\n' % e,\\n before_prompt=True\\n )\\n else:\\n self._append_html(\\\"<br>Restarting kernel...\\\\n<hr><br>\\\",\\n before_prompt=True,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.append_stream\",\n \"text\": \"IPython/qt/console/frontend_widget.py::FrontendWidget.append_stream\\n def append_stream(self, text):\\n \\\"\\\"\\\"Appends text to the output stream.\\\"\\\"\\\"\\n # Most consoles treat tabs as being 8 space characters. Convert tabs\\n # to spaces so that output looks as expected regardless of this\\n # widget's tab width.\\n text = text.expandtabs(8)\\n self._append_plain_text(text, before_prompt=True)\\n self._control.moveCursor(QtGui.QTextCursor.End)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":256,"cells":{"instance_id":{"kind":"string","value":"PrefectHQ__prefect-2599"},"repo":{"kind":"string","value":"PrefectHQ/prefect"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"All agents with default labels are not registering their labels\nCurrently, all agents that have default labels (e.g., `LocalAgent`) are not registering their labels when first registering with Prefect Cloud. This is because the cloud registration step occurs in the `__init__` of the base agent class, which is being called _before_ `self.labels` is fully realized. \r\n\r\nTo be clear, the only consequence of this is that labels are not properly being displayed in the UI - because the agent queries for work using its labels, all work is being properly directed.\r\n\n"},"gold_files":{"kind":"list like","value":["src/prefect/agent/agent.py"],"string":"[\n \"src/prefect/agent/agent.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["src/prefect/agent/agent.py::Agent.__init__","src/prefect/agent/agent.py::Agent.start"],"string":"[\n \"src/prefect/agent/agent.py::Agent.__init__\",\n \"src/prefect/agent/agent.py::Agent.start\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"src/prefect/agent/agent.py","text":"src/prefect/agent/agent.py\ndef exit_handler(agent: 'Agent')\n def _exit_handler(*args: Any, **kwargs: Any)\nclass HealthHandler # Respond to /api/health\n def get(self)\nclass Agent # Base class for Agents. Information on using the Prefect agents can be found at\n def __init__(self, name: str=None, labels: Iterable[str]=None, env_vars: dict=None, max_polls: int=None, agent_address: str=None, no_cloud_logs: bool=False)\n def _verify_token(self, token: str) # Checks whether a token with a `RUNNER` scope was provided\n def _register_agent(self) # Register this agent with Prefect Cloud and retrieve agent ID\n def start(self) # The main entrypoint to the agent. This function loops and constantly polls for\n def setup(self)\n def cleanup(self)\n def on_shutdown(self) # Invoked when the event loop is exiting and the agent is shutting down. Intended\n def agent_connect(self) # Verify agent connection to Prefect API by querying\n def deploy_and_update_flow_run(self, flow_run: 'GraphQLResult') # Deploy a flow run and update Cloud with the resulting deployment info.\n def on_flow_run_deploy_attempt(self, fut: 'Future', flow_run_id: str) # Indicates that a flow run deployment has been deployed (successfully or otherwis\n def agent_process(self, executor: 'ThreadPoolExecutor') # Full process for finding flow runs, updating states, and deploying.\n def query_flow_runs(self) # Query Prefect Cloud for flow runs which need to be deployed and executed\n def update_state(self, flow_run: GraphQLResult) # After a flow run is grabbed this function sets the state to Submitted so it\n def mark_failed(self, flow_run: GraphQLResult, exc: Exception) # Mark a flow run as `Failed`\n def deploy_flow(self, flow_run: GraphQLResult) # Meant to be overridden by a platform specific deployment option\n def heartbeat(self) # Meant to be overridden by a platform specific heartbeat option","label":1},{"cid":"src/prefect/agent/local/agent.py","text":"src/prefect/agent/local/agent.py\nclass LocalAgent # Agent which deploys flow runs locally as subprocesses. There are a range of kwar\n def __init__(self, name: str=None, labels: Iterable[str]=None, env_vars: dict=None, import_paths: List[str]=None, show_flow_logs: bool=False, hostname_label: bool=True, max_polls: int=None, agent_address: str=None, no_cloud_logs: bool=False)\n def heartbeat(self)\n def deploy_flow(self, flow_run: GraphQLResult) # Deploy flow runs on your local machine as Docker containers\n def populate_env_vars(self, flow_run: GraphQLResult) # Populate metadata and variables in the environment variables for a flow run\n def generate_supervisor_conf(token: str=None, labels: Iterable[str]=None, import_paths: List[str]=None, show_flow_logs: bool=False) # Generate and output an installable supervisorctl configuration file for the agen","label":0},{"cid":"src/prefect/client/client.py","text":"src/prefect/client/client.py\nclass Client # Client for communication with Prefect Cloud\n def __init__(self, api_server: str=None, api_token: str=None)\n def get(self, path: str, server: str=None, headers: dict=None, params: Dict[str, JSONLike]=None, token: str=None) # Convenience function for calling the Prefect API with token auth and GET request\n def post(self, path: str, server: str=None, headers: dict=None, params: Dict[str, JSONLike]=None, token: str=None) # Convenience function for calling the Prefect API with token auth and POST reques\n def graphql(self, query: Any, raise_on_error: bool=True, headers: Dict[str, str]=None, variables: Dict[str, JSONLike]=None, token: str=None) # Convenience function for running queries against the Prefect GraphQL API\n def _request(self, method: str, path: str, params: Dict[str, JSONLike]=None, server: str=None, headers: dict=None, token: str=None) # Runs any specified request (GET, POST, DELETE) against the server\n def attach_headers(self, headers: dict) # Set headers to be attached to this Client\n def _local_settings_path(self) # Returns the local settings directory corresponding to the current API servers\n def _save_local_settings(self, settings: dict) # Writes settings to local storage\n def _load_local_settings(self) # Loads settings from local storage\n def save_api_token(self) # Saves the API token in local storage.\n def get_auth_token(self) # Returns an auth token:\n def get_available_tenants(self) # Returns a list of available tenants.\n def login_to_tenant(self, tenant_slug: str=None, tenant_id: str=None) # Log in to a specific tenant\n def logout_from_tenant(self)\n def _refresh_access_token(self) # Refresh the client's JWT access token.\n def register(self, flow: 'Flow', project_name: str=None, build: bool=True, set_schedule_active: bool=True, version_group_id: str=None, compressed: bool=True, no_url: bool=False) # Push a new flow to Prefect Cloud\n def get_cloud_url(self, subdirectory: str, id: str, as_user: bool=True) # Convenience method for creating Prefect Cloud URLs for a given subdirectory.\n def get_default_tenant_slug(self, as_user: bool=True) # Get the default tenant slug for the currently authenticated user\n def create_project(self, project_name: str, project_description: str=None) # Create a new Project\n def create_flow_run(self, flow_id: str=None, context: dict=None, parameters: dict=None, scheduled_start_time: datetime.datetime=None, idempotency_key: str=None, run_name: str=None, version_group_id: str=None) # Create a new flow run for the given flow id. If `start_time` is not provided, t\n def get_flow_run_info(self, flow_run_id: str) # Retrieves version and current state information for the given flow run.\n def update_flow_run_heartbeat(self, flow_run_id: str) # Convenience method for heartbeating a flow run.\n def update_task_run_heartbeat(self, task_run_id: str) # Convenience method for heartbeating a ","label":0},{"cid":"src/prefect/environments/execution/fargate/fargate_task.py","text":"src/prefect/environments/execution/fargate/fargate_task.py\nclass FargateTaskEnvironment # FargateTaskEnvironment is an environment which deploys your flow (stored in a Do\n def __init__(self, launch_type: str='FARGATE', aws_access_key_id: str=None, aws_secret_access_key: str=None, aws_session_token: str=None, region_name: str=None, executor_kwargs: dict=None, labels: List[str]=None, on_start: Callable=None, on_exit: Callable=None, **kwargs)\n def _parse_kwargs(self, user_kwargs: dict) # Parse the kwargs passed in and separate them out for `register_task_definition`\n def dependencies(self)\n def setup(self, storage: 'Docker') # Register the task definition if it does not already exist.\n def execute(self, storage: 'Docker', flow_location: str, **kwargs: Any) # Run the Fargate task that was defined for this flow.\n def run_flow(self) # Run the flow from specified flow_file_path location using the default executor","label":0}],"string":"[\n {\n \"cid\": \"src/prefect/agent/agent.py\",\n \"text\": \"src/prefect/agent/agent.py\\ndef exit_handler(agent: 'Agent')\\n def _exit_handler(*args: Any, **kwargs: Any)\\nclass HealthHandler # Respond to /api/health\\n def get(self)\\nclass Agent # Base class for Agents. Information on using the Prefect agents can be found at\\n def __init__(self, name: str=None, labels: Iterable[str]=None, env_vars: dict=None, max_polls: int=None, agent_address: str=None, no_cloud_logs: bool=False)\\n def _verify_token(self, token: str) # Checks whether a token with a `RUNNER` scope was provided\\n def _register_agent(self) # Register this agent with Prefect Cloud and retrieve agent ID\\n def start(self) # The main entrypoint to the agent. This function loops and constantly polls for\\n def setup(self)\\n def cleanup(self)\\n def on_shutdown(self) # Invoked when the event loop is exiting and the agent is shutting down. Intended\\n def agent_connect(self) # Verify agent connection to Prefect API by querying\\n def deploy_and_update_flow_run(self, flow_run: 'GraphQLResult') # Deploy a flow run and update Cloud with the resulting deployment info.\\n def on_flow_run_deploy_attempt(self, fut: 'Future', flow_run_id: str) # Indicates that a flow run deployment has been deployed (successfully or otherwis\\n def agent_process(self, executor: 'ThreadPoolExecutor') # Full process for finding flow runs, updating states, and deploying.\\n def query_flow_runs(self) # Query Prefect Cloud for flow runs which need to be deployed and executed\\n def update_state(self, flow_run: GraphQLResult) # After a flow run is grabbed this function sets the state to Submitted so it\\n def mark_failed(self, flow_run: GraphQLResult, exc: Exception) # Mark a flow run as `Failed`\\n def deploy_flow(self, flow_run: GraphQLResult) # Meant to be overridden by a platform specific deployment option\\n def heartbeat(self) # Meant to be overridden by a platform specific heartbeat option\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/agent/local/agent.py\",\n \"text\": \"src/prefect/agent/local/agent.py\\nclass LocalAgent # Agent which deploys flow runs locally as subprocesses. There are a range of kwar\\n def __init__(self, name: str=None, labels: Iterable[str]=None, env_vars: dict=None, import_paths: List[str]=None, show_flow_logs: bool=False, hostname_label: bool=True, max_polls: int=None, agent_address: str=None, no_cloud_logs: bool=False)\\n def heartbeat(self)\\n def deploy_flow(self, flow_run: GraphQLResult) # Deploy flow runs on your local machine as Docker containers\\n def populate_env_vars(self, flow_run: GraphQLResult) # Populate metadata and variables in the environment variables for a flow run\\n def generate_supervisor_conf(token: str=None, labels: Iterable[str]=None, import_paths: List[str]=None, show_flow_logs: bool=False) # Generate and output an installable supervisorctl configuration file for the agen\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py\",\n \"text\": \"src/prefect/client/client.py\\nclass Client # Client for communication with Prefect Cloud\\n def __init__(self, api_server: str=None, api_token: str=None)\\n def get(self, path: str, server: str=None, headers: dict=None, params: Dict[str, JSONLike]=None, token: str=None) # Convenience function for calling the Prefect API with token auth and GET request\\n def post(self, path: str, server: str=None, headers: dict=None, params: Dict[str, JSONLike]=None, token: str=None) # Convenience function for calling the Prefect API with token auth and POST reques\\n def graphql(self, query: Any, raise_on_error: bool=True, headers: Dict[str, str]=None, variables: Dict[str, JSONLike]=None, token: str=None) # Convenience function for running queries against the Prefect GraphQL API\\n def _request(self, method: str, path: str, params: Dict[str, JSONLike]=None, server: str=None, headers: dict=None, token: str=None) # Runs any specified request (GET, POST, DELETE) against the server\\n def attach_headers(self, headers: dict) # Set headers to be attached to this Client\\n def _local_settings_path(self) # Returns the local settings directory corresponding to the current API servers\\n def _save_local_settings(self, settings: dict) # Writes settings to local storage\\n def _load_local_settings(self) # Loads settings from local storage\\n def save_api_token(self) # Saves the API token in local storage.\\n def get_auth_token(self) # Returns an auth token:\\n def get_available_tenants(self) # Returns a list of available tenants.\\n def login_to_tenant(self, tenant_slug: str=None, tenant_id: str=None) # Log in to a specific tenant\\n def logout_from_tenant(self)\\n def _refresh_access_token(self) # Refresh the client's JWT access token.\\n def register(self, flow: 'Flow', project_name: str=None, build: bool=True, set_schedule_active: bool=True, version_group_id: str=None, compressed: bool=True, no_url: bool=False) # Push a new flow to Prefect Cloud\\n def get_cloud_url(self, subdirectory: str, id: str, as_user: bool=True) # Convenience method for creating Prefect Cloud URLs for a given subdirectory.\\n def get_default_tenant_slug(self, as_user: bool=True) # Get the default tenant slug for the currently authenticated user\\n def create_project(self, project_name: str, project_description: str=None) # Create a new Project\\n def create_flow_run(self, flow_id: str=None, context: dict=None, parameters: dict=None, scheduled_start_time: datetime.datetime=None, idempotency_key: str=None, run_name: str=None, version_group_id: str=None) # Create a new flow run for the given flow id. If `start_time` is not provided, t\\n def get_flow_run_info(self, flow_run_id: str) # Retrieves version and current state information for the given flow run.\\n def update_flow_run_heartbeat(self, flow_run_id: str) # Convenience method for heartbeating a flow run.\\n def update_task_run_heartbeat(self, task_run_id: str) # Convenience method for heartbeating a \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/fargate/fargate_task.py\",\n \"text\": \"src/prefect/environments/execution/fargate/fargate_task.py\\nclass FargateTaskEnvironment # FargateTaskEnvironment is an environment which deploys your flow (stored in a Do\\n def __init__(self, launch_type: str='FARGATE', aws_access_key_id: str=None, aws_secret_access_key: str=None, aws_session_token: str=None, region_name: str=None, executor_kwargs: dict=None, labels: List[str]=None, on_start: Callable=None, on_exit: Callable=None, **kwargs)\\n def _parse_kwargs(self, user_kwargs: dict) # Parse the kwargs passed in and separate them out for `register_task_definition`\\n def dependencies(self)\\n def setup(self, storage: 'Docker') # Register the task definition if it does not already exist.\\n def execute(self, storage: 'Docker', flow_location: str, **kwargs: Any) # Run the Fargate task that was defined for this flow.\\n def run_flow(self) # Run the flow from specified flow_file_path location using the default executor\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"src/prefect/agent/agent.py::exit_handler","text":"src/prefect/agent/agent.py::exit_handler\n@contextmanager\ndef exit_handler(agent: \"Agent\") -> Generator:\n exit_event = threading.Event()\n\n def _exit_handler(*args: Any, **kwargs: Any) -> None:\n agent.logger.info(\"Keyboard Interrupt received: Agent is shutting down.\")\n exit_event.set()\n\n original = signal.getsignal(signal.SIGINT)\n try:\n signal.signal(signal.SIGINT, _exit_handler)\n yield exit_event\n except SystemExit:\n pass\n finally:\n signal.signal(signal.SIGINT, original)","label":0},{"cid":"src/prefect/agent/agent.py::exit_handler._exit_handler","text":"src/prefect/agent/agent.py::exit_handler._exit_handler\n def _exit_handler(*args: Any, **kwargs: Any) -> None:\n agent.logger.info(\"Keyboard Interrupt received: Agent is shutting down.\")\n exit_event.set()","label":0},{"cid":"src/prefect/agent/agent.py::HealthHandler.get","text":"src/prefect/agent/agent.py::HealthHandler.get\n def get(self) -> None:\n # Empty json blob, may add more info later\n self.write({})","label":0},{"cid":"src/prefect/agent/agent.py::Agent.__init__","text":"src/prefect/agent/agent.py::Agent.__init__\n def __init__(\n self,\n name: str = None,\n labels: Iterable[str] = None,\n env_vars: dict = None,\n max_polls: int = None,\n agent_address: str = None,\n no_cloud_logs: bool = False,\n ) -> None:\n self.name = name or config.cloud.agent.get(\"name\", \"agent\")\n\n self.labels = labels or list(config.cloud.agent.get(\"labels\", []))\n self.env_vars = env_vars or config.cloud.agent.get(\"env_vars\", dict())\n self.max_polls = max_polls\n self.log_to_cloud = False if no_cloud_logs else True\n\n self.agent_address = agent_address or config.cloud.agent.get(\n \"agent_address\", \"\"\n )\n self._api_server = None # type: ignore\n self._api_server_loop = None # type: Optional[IOLoop]\n self._api_server_thread = None # type: Optional[threading.Thread]\n\n logger = logging.getLogger(self.name)\n logger.setLevel(config.cloud.agent.get(\"level\"))\n if not any([isinstance(h, logging.StreamHandler) for h in logger.handlers]):\n ch = logging.StreamHandler(sys.stdout)\n formatter = logging.Formatter(context.config.logging.format)\n formatter.converter = time.gmtime # type: ignore\n ch.setFormatter(formatter)\n logger.addHandler(ch)\n\n self.logger = logger\n self.submitting_flow_runs = set() # type: Set[str]\n\n self.logger.debug(\"Verbose logs enabled\")\n self.logger.debug(f\"Environment variables: {[*self.env_vars]}\")\n self.logger.debug(f\"Max polls: {self.max_polls}\")\n self.logger.debug(f\"Agent address: {self.agent_address}\")\n self.logger.debug(f\"Log to Cloud: {self.log_to_cloud}\")\n\n token = config.cloud.agent.get(\"auth_token\")\n\n self.logger.debug(f\"Prefect backend: {config.backend}\")\n\n self.client = Client(api_token=token)\n if config.backend == \"cloud\":\n self._verify_token(token)\n ","label":1},{"cid":"src/prefect/agent/agent.py::Agent._verify_token","text":"src/prefect/agent/agent.py::Agent._verify_token\n def _verify_token(self, token: str) -> None:\n \"\"\"\n Checks whether a token with a `RUNNER` scope was provided\n Args:\n - token (str): The provided agent token to verify\n Raises:\n - AuthorizationError: if token is empty or does not have a RUNNER role\n \"\"\"\n if not token:\n raise AuthorizationError(\"No agent API token provided.\")\n\n # Check if RUNNER role\n result = self.client.graphql(query=\"query { auth_info { api_token_scope } }\")\n if (\n not result.data # type: ignore\n or result.data.auth_info.api_token_scope != \"RUNNER\" # type: ignore\n ):\n raise AuthorizationError(\"Provided token does not have a RUNNER scope.\")","label":0},{"cid":"src/prefect/agent/agent.py::Agent._register_agent","text":"src/prefect/agent/agent.py::Agent._register_agent\n def _register_agent(self) -> str:\n \"\"\"\n Register this agent with Prefect Cloud and retrieve agent ID\n\n Returns:\n - The agent ID as a string\n \"\"\"\n agent_id = self.client.register_agent(\n agent_type=type(self).__name__, name=self.name, labels=self.labels # type: ignore\n )\n\n self.logger.debug(f\"Agent ID: {agent_id}\")\n\n return agent_id","label":0},{"cid":"src/prefect/agent/agent.py::Agent.start","text":"src/prefect/agent/agent.py::Agent.start\n def start(self) -> None:\n \"\"\"\n The main entrypoint to the agent. This function loops and constantly polls for\n new flow runs to deploy\n \"\"\"\n try:\n self.setup()\n\n with exit_handler(self) as exit_event:\n\n # Loop intervals for query sleep backoff\n loop_intervals = {\n 0: 0.25,\n 1: 0.5,\n 2: 1.0,\n 3: 2.0,\n 4: 4.0,\n 5: 8.0,\n 6: 10.0,\n }\n\n index = 0\n remaining_polls = math.inf if self.max_polls is None else self.max_polls\n\n # the max workers default has changed in 3.8. For stable results the\n # default 3.8 behavior is elected here.\n max_workers = min(32, (os.cpu_count() or 1) + 4)\n\n with ThreadPoolExecutor(max_workers=max_workers) as executor:\n self.logger.debug(\"Max Workers: {}\".format(max_workers))\n while (\n not exit_event.wait(timeout=loop_intervals[index])\n and remaining_polls\n ):\n self.heartbeat()\n\n if self.agent_process(executor):\n index = 0\n elif index < max(loop_intervals.keys()):\n index += 1\n\n remaining_polls -= 1\n\n self.logger.debug(\n \"Next query for flow runs in {} seconds\".format(\n loop_intervals[index]\n )\n )\n finally:\n self.cleanup()","label":1},{"cid":"src/prefect/agent/agent.py::Agent.setup","text":"src/prefect/agent/agent.py::Agent.setup\n def setup(self) -> None:\n self.agent_connect()\n\n if self.agent_address:\n parsed = urlparse(self.agent_address)\n if not parsed.port:\n raise ValueError(\"Must specify port in agent address\")\n port = cast(int, parsed.port)\n hostname = parsed.hostname or \"\"\n app = web.Application([(\"/api/health\", HealthHandler)])\n\n def run() -> None:\n self.logger.debug(\n f\"Agent API server listening on port {self.agent_address}\"\n )\n self._api_server = app.listen(port, address=hostname) # type: ignore\n self._api_server_loop = IOLoop.current()\n self._api_server_loop.start() # type: ignore\n\n self._api_server_thread = threading.Thread(\n name=\"api-server\", target=run, daemon=True\n )\n self._api_server_thread.start()","label":0},{"cid":"src/prefect/agent/agent.py::Agent.cleanup","text":"src/prefect/agent/agent.py::Agent.cleanup\n def cleanup(self) -> None:\n self.on_shutdown()\n\n if self._api_server is not None:\n self.logger.debug(\"Stopping agent API server\")\n self._api_server.stop()\n\n if self._api_server_loop is not None:\n self.logger.debug(\"Stopping agent API server loop\")\n\n def stop_server() -> None:\n try:\n loop = cast(IOLoop, self._api_server_loop)\n loop.stop()\n except Exception:\n pass\n\n self._api_server_loop.add_callback(stop_server)\n\n if self._api_server_thread is not None:\n self.logger.debug(\"Joining agent API threads\")\n # Give the server a small period to shutdown nicely, otherwise it\n # will terminate on exit anyway since it's a daemon thread.\n self._api_server_thread.join(timeout=1)","label":0},{"cid":"src/prefect/agent/agent.py::Agent.on_shutdown","text":"src/prefect/agent/agent.py::Agent.on_shutdown\n def on_shutdown(self) -> None:\n \"\"\"\n Invoked when the event loop is exiting and the agent is shutting down. Intended\n as a hook for child classes to optionally implement.\n \"\"\"","label":0},{"cid":"src/prefect/agent/agent.py::Agent.agent_connect","text":"src/prefect/agent/agent.py::Agent.agent_connect\n def agent_connect(self) -> None:\n \"\"\"\n Verify agent connection to Prefect API by querying\n \"\"\"\n print(ascii_name)\n self.logger.info(\n \"Starting {} with labels {}\".format(type(self).__name__, self.labels)\n )\n self.logger.info(\n \"Agent documentation can be found at https://docs.prefect.io/orchestration/\"\n )\n\n self.logger.info(\n \"Agent connecting to the Prefect API at {}\".format(config.cloud.api)\n )\n try:\n self.client.graphql(query=\"query { hello }\")\n except Exception as exc:\n self.logger.error(\n \"There was an error connecting to {}\".format(config.cloud.api)\n )\n self.logger.error(exc)\n\n self.logger.info(\"Waiting for flow runs...\")","label":0},{"cid":"src/prefect/agent/agent.py::Agent.deploy_and_update_flow_run","text":"src/prefect/agent/agent.py::Agent.deploy_and_update_flow_run\n def deploy_and_update_flow_run(self, flow_run: \"GraphQLResult\") -> None:\n \"\"\"\n Deploy a flow run and update Cloud with the resulting deployment info.\n If any errors occur when submitting the flow run, capture the error and log to Cloud.\n\n Args:\n - flow_run (GraphQLResult): The specific flow run to deploy\n \"\"\"\n # Deploy flow run and mark failed if any deployment error\n try:\n self.update_state(flow_run)\n deployment_info = self.deploy_flow(flow_run)\n if getattr(flow_run, \"id\", None):\n self.client.write_run_logs(\n [\n dict(\n flow_run_id=getattr(flow_run, \"id\"), # type: ignore\n name=self.name,\n message=\"Submitted for execution: {}\".format(\n deployment_info\n ),\n level=\"INFO\",\n )\n ]\n )\n except Exception as exc:\n ## if the state update failed, we don't want to follow up with another state update\n if \"State update failed\" in str(exc):\n self.logger.debug(\"Updating Flow Run state failed: {}\".format(str(exc)))\n return\n self.logger.error(\n \"Logging platform error for flow run {}\".format(\n getattr(flow_run, \"id\", \"UNKNOWN\") # type: ignore\n )\n )\n if getattr(flow_run, \"id\", None):\n self.client.write_run_logs(\n [\n dict(\n flow_run_id=getattr(flow_run, \"id\"), # type: ignore\n name=self.name,\n message=str(exc),\n level=\"ERROR\",\n )\n ","label":0},{"cid":"src/prefect/agent/agent.py::Agent.on_flow_run_deploy_attempt","text":"src/prefect/agent/agent.py::Agent.on_flow_run_deploy_attempt\n def on_flow_run_deploy_attempt(self, fut: \"Future\", flow_run_id: str) -> None:\n \"\"\"\n Indicates that a flow run deployment has been deployed (successfully or otherwise).\n This is intended to be a future callback hook, called in the agent's main thread\n when the background thread has completed the deploy_and_update_flow_run() call, either\n successfully, in error, or cancelled. In all cases the agent should be open to\n attempting to deploy the flow run if the flow run id is still in the Cloud run queue.\n\n Args:\n - fut (Future): a callback requirement, the future which has completed or been cancelled.\n - flow_run_id (str): the id of the flow run that the future represents.\n \"\"\"\n self.submitting_flow_runs.remove(flow_run_id)\n self.logger.debug(\"Completed flow run submission (id: {})\".format(flow_run_id))","label":0},{"cid":"src/prefect/agent/agent.py::Agent.agent_process","text":"src/prefect/agent/agent.py::Agent.agent_process\n def agent_process(self, executor: \"ThreadPoolExecutor\") -> bool:\n \"\"\"\n Full process for finding flow runs, updating states, and deploying.\n\n Args:\n - executor (ThreadPoolExecutor): the interface to submit flow deployments in background threads\n\n Returns:\n - bool: whether or not flow runs were found\n \"\"\"\n flow_runs = None\n try:\n flow_runs = self.query_flow_runs()\n\n if flow_runs:\n self.logger.info(\n \"Found {} flow run(s) to submit for execution.\".format(\n len(flow_runs)\n )\n )\n\n for flow_run in flow_runs:\n fut = executor.submit(self.deploy_and_update_flow_run, flow_run)\n self.submitting_flow_runs.add(flow_run.id)\n fut.add_done_callback(\n functools.partial(\n self.on_flow_run_deploy_attempt, flow_run_id=flow_run.id\n )\n )\n\n except Exception as exc:\n self.logger.error(exc)\n\n return bool(flow_runs)","label":0},{"cid":"src/prefect/agent/agent.py::Agent.query_flow_runs","text":"src/prefect/agent/agent.py::Agent.query_flow_runs\n def query_flow_runs(self) -> list:\n \"\"\"\n Query Prefect Cloud for flow runs which need to be deployed and executed\n\n Returns:\n - list: A list of GraphQLResult flow run objects\n \"\"\"\n self.logger.debug(\"Querying for flow runs\")\n # keep a copy of what was curringly running before the query (future callbacks may be updating this set)\n currently_submitting_flow_runs = self.submitting_flow_runs.copy()\n\n # Get scheduled flow runs from queue\n mutation = {\n \"mutation($input: get_runs_in_queue_input!)\": {\n \"get_runs_in_queue(input: $input)\": {\"flow_run_ids\"}\n }\n }\n\n now = pendulum.now(\"UTC\")\n result = self.client.graphql(\n mutation,\n variables={\n \"input\": {\"before\": now.isoformat(), \"labels\": list(self.labels),}\n },\n )\n\n # we queried all of the available flow runs, however, some may have already been pulled\n # by this agent and are in the process of being submitted in the background. We do not\n # want to act on these \"duplicate\" flow runs until we've been assured that the background\n # thread has attempted to submit the work (successful or otherwise).\n flow_run_ids = set(result.data.get_runs_in_queue.flow_run_ids) # type: ignore\n\n if flow_run_ids:\n msg = \"Found flow runs {}\".format(\n result.data.get_runs_in_queue.flow_run_ids\n )\n else:\n msg = \"No flow runs found\"\n\n already_submitting = flow_run_ids & currently_submitting_flow_runs\n target_flow_run_ids = flow_run_ids - already_submitting\n\n if already_submitting:\n msg += \" ({} already submitting: {})\".format(\n len(already_submitting), list(already_submitting)\n )\n\n self.logger.debug(msg)\n\n # Query metadata fow flow runs found ","label":0},{"cid":"src/prefect/agent/agent.py::Agent.update_state","text":"src/prefect/agent/agent.py::Agent.update_state\n def update_state(self, flow_run: GraphQLResult) -> None:\n \"\"\"\n After a flow run is grabbed this function sets the state to Submitted so it\n won't be picked up by any other processes\n\n Args:\n - flow_run (GraphQLResult): A GraphQLResult flow run object\n \"\"\"\n self.logger.debug(\n \"Updating states for flow run {}\".format(flow_run.id) # type: ignore\n )\n\n # Set flow run state to `Submitted` if it is currently `Scheduled`\n if state.StateSchema().load(flow_run.serialized_state).is_scheduled():\n\n self.logger.debug(\n \"Flow run {} is in a Scheduled state, updating to Submitted\".format(\n flow_run.id # type: ignore\n )\n )\n self.client.set_flow_run_state(\n flow_run_id=flow_run.id,\n version=flow_run.version,\n state=Submitted(\n message=\"Submitted for execution\",\n state=state.StateSchema().load(flow_run.serialized_state),\n ),\n )\n\n # Set task run states to `Submitted` if they are currently `Scheduled`\n for task_run in flow_run.task_runs:\n if state.StateSchema().load(task_run.serialized_state).is_scheduled():\n\n self.logger.debug(\n \"Task run {} is in a Scheduled state, updating to Submitted\".format(\n task_run.id # type: ignore\n )\n )\n self.client.set_task_run_state(\n task_run_id=task_run.id,\n version=task_run.version,\n state=Submitted(\n message=\"Submitted for execution.\",\n state=state.StateSchema().load(task_run.serialized_state),\n ),\n )","label":0},{"cid":"src/prefect/agent/agent.py::Agent.mark_failed","text":"src/prefect/agent/agent.py::Agent.mark_failed\n def mark_failed(self, flow_run: GraphQLResult, exc: Exception) -> None:\n \"\"\"\n Mark a flow run as `Failed`\n\n Args:\n - flow_run (GraphQLResult): A GraphQLResult flow run object\n - exc (Exception): An exception that was raised to use as the `Failed`\n message\n \"\"\"\n self.client.set_flow_run_state(\n flow_run_id=flow_run.id,\n version=flow_run.version,\n state=Failed(message=str(exc)),\n )\n self.logger.error(\"Error while deploying flow: {}\".format(repr(exc)))","label":0},{"cid":"src/prefect/agent/agent.py::Agent.deploy_flow","text":"src/prefect/agent/agent.py::Agent.deploy_flow\n def deploy_flow(self, flow_run: GraphQLResult) -> str:\n \"\"\"\n Meant to be overridden by a platform specific deployment option\n\n Args:\n - flow_run (GraphQLResult): A GraphQLResult flow run object\n\n Returns:\n - str: Information about the deployment\n\n Raises:\n - ValueError: if deployment attempted on unsupported Storage type\n \"\"\"\n raise NotImplementedError()","label":0},{"cid":"src/prefect/agent/agent.py::Agent.heartbeat","text":"src/prefect/agent/agent.py::Agent.heartbeat\n def heartbeat(self) -> None:\n \"\"\"\n Meant to be overridden by a platform specific heartbeat option\n \"\"\"","label":0},{"cid":"src/prefect/agent/local/agent.py::LocalAgent.__init__","text":"src/prefect/agent/local/agent.py::LocalAgent.__init__\n def __init__(\n self,\n name: str = None,\n labels: Iterable[str] = None,\n env_vars: dict = None,\n import_paths: List[str] = None,\n show_flow_logs: bool = False,\n hostname_label: bool = True,\n max_polls: int = None,\n agent_address: str = None,\n no_cloud_logs: bool = False,\n ) -> None:\n self.processes = set()\n self.import_paths = import_paths or []\n self.show_flow_logs = show_flow_logs\n super().__init__(\n name=name,\n labels=labels,\n env_vars=env_vars,\n max_polls=max_polls,\n agent_address=agent_address,\n no_cloud_logs=no_cloud_logs,\n )\n hostname = socket.gethostname()\n if hostname_label and (hostname not in self.labels):\n assert isinstance(self.labels, list)\n self.labels.append(hostname)\n self.labels.extend(\n [\"azure-flow-storage\", \"gcs-flow-storage\", \"s3-flow-storage\"]\n )\n\n self.logger.debug(f\"Import paths: {self.import_paths}\")\n self.logger.debug(f\"Show flow logs: {self.show_flow_logs}\")","label":0},{"cid":"src/prefect/agent/local/agent.py::LocalAgent.heartbeat","text":"src/prefect/agent/local/agent.py::LocalAgent.heartbeat\n def heartbeat(self) -> None:\n for process in list(self.processes):\n if process.poll() is not None:\n self.processes.remove(process)\n if process.returncode:\n self.logger.info(\n \"Process PID {} returned non-zero exit code\".format(process.pid)\n )\n super().heartbeat()","label":0},{"cid":"src/prefect/agent/local/agent.py::LocalAgent.deploy_flow","text":"src/prefect/agent/local/agent.py::LocalAgent.deploy_flow\n def deploy_flow(self, flow_run: GraphQLResult) -> str:\n \"\"\"\n Deploy flow runs on your local machine as Docker containers\n\n Args:\n - flow_run (GraphQLResult): A GraphQLResult flow run object\n\n Returns:\n - str: Information about the deployment\n\n Raises:\n - ValueError: if deployment attempted on unsupported Storage type\n \"\"\"\n self.logger.info(\n \"Deploying flow run {}\".format(flow_run.id) # type: ignore\n )\n\n if not isinstance(\n StorageSchema().load(flow_run.flow.storage), (Local, Azure, GCS, S3)\n ):\n self.logger.error(\n \"Storage for flow run {} is not a supported type.\".format(flow_run.id)\n )\n raise ValueError(\"Unsupported Storage type\")\n\n env_vars = self.populate_env_vars(flow_run=flow_run)\n current_env = os.environ.copy()\n current_env.update(env_vars)\n\n python_path = []\n if current_env.get(\"PYTHONPATH\"):\n python_path.append(current_env.get(\"PYTHONPATH\"))\n\n python_path.append(os.getcwd())\n\n if self.import_paths:\n python_path += self.import_paths\n\n current_env[\"PYTHONPATH\"] = \":\".join(python_path)\n\n stdout = sys.stdout if self.show_flow_logs else DEVNULL\n\n # note: we will allow these processes to be orphaned if the agent were to exit\n # before the flow runs have completed. The lifecycle of the agent should not\n # dictate the lifecycle of the flow run. However, if the user has elected to\n # show flow logs, these log entries will continue to stream to the users terminal\n # until these child processes exit, even if the agent has already exited.\n p = Popen(\n [\"prefect\", \"execute\", \"cloud-flow\"],\n stdout=stdout,\n stderr=STDOUT,\n env=current_env,\n )\n\n self.process","label":0},{"cid":"src/prefect/agent/local/agent.py::LocalAgent.populate_env_vars","text":"src/prefect/agent/local/agent.py::LocalAgent.populate_env_vars\n def populate_env_vars(self, flow_run: GraphQLResult) -> dict:\n \"\"\"\n Populate metadata and variables in the environment variables for a flow run\n\n Args:\n - flow_run (GraphQLResult): A flow run object\n\n Returns:\n - dict: a dictionary representing the populated environment variables\n \"\"\"\n all_vars = {\n \"PREFECT__CLOUD__API\": config.cloud.api,\n \"PREFECT__CLOUD__AUTH_TOKEN\": self.client._api_token,\n \"PREFECT__CLOUD__AGENT__LABELS\": str(self.labels),\n \"PREFECT__CONTEXT__FLOW_RUN_ID\": flow_run.id, # type: ignore\n \"PREFECT__CONTEXT__FLOW_ID\": flow_run.flow.id, # type: ignore\n \"PREFECT__CLOUD__USE_LOCAL_SECRETS\": \"false\",\n \"PREFECT__LOGGING__LOG_TO_CLOUD\": str(self.log_to_cloud).lower(),\n \"PREFECT__LOGGING__LEVEL\": \"DEBUG\",\n \"PREFECT__ENGINE__FLOW_RUNNER__DEFAULT_CLASS\": \"prefect.engine.cloud.CloudFlowRunner\",\n \"PREFECT__ENGINE__TASK_RUNNER__DEFAULT_CLASS\": \"prefect.engine.cloud.CloudTaskRunner\",\n **self.env_vars,\n }\n return {k: v for k, v in all_vars.items() if v is not None}","label":0},{"cid":"src/prefect/agent/local/agent.py::LocalAgent.generate_supervisor_conf","text":"src/prefect/agent/local/agent.py::LocalAgent.generate_supervisor_conf\n @staticmethod\n def generate_supervisor_conf(\n token: str = None,\n labels: Iterable[str] = None,\n import_paths: List[str] = None,\n show_flow_logs: bool = False,\n ) -> str:\n \"\"\"\n Generate and output an installable supervisorctl configuration file for the agent.\n\n Args:\n - token (str, optional): A `RUNNER` token to give the agent\n - labels (List[str], optional): a list of labels, which are arbitrary string\n identifiers used by Prefect Agents when polling for work\n - import_paths (List[str], optional): system paths which will be provided to each Flow's runtime environment;\n useful for Flows which import from locally hosted scripts or packages\n - show_flow_logs (bool, optional): a boolean specifying whether the agent should re-route Flow run logs\n to stdout; defaults to `False`\n\n Returns:\n - str: A string representation of the generated configuration file\n \"\"\"\n\n # Use defaults if not provided\n token = token or \"\"\n labels = labels or []\n import_paths = import_paths or []\n\n with open(\n os.path.join(os.path.dirname(__file__), \"supervisord.conf\"), \"r\"\n ) as conf_file:\n conf = conf_file.read()\n\n add_opts = \"\"\n add_opts += \"-t {token} \".format(token=token) if token else \"\"\n add_opts += \"-f \" if show_flow_logs else \"\"\n add_opts += (\n \" \".join(\"-l {label} \".format(label=label) for label in labels)\n if labels\n else \"\"\n )\n add_opts += (\n \" \".join(\"-p {path}\".format(path=path) for path in import_paths)\n if import_paths\n else \"\"\n )\n conf = conf.replace(\"{{OPTS}}\", add_opts)\n return conf","label":0},{"cid":"src/prefect/client/client.py::Client.__init__","text":"src/prefect/client/client.py::Client.__init__\n def __init__(self, api_server: str = None, api_token: str = None):\n self._access_token = None\n self._refresh_token = None\n self._access_token_expires_at = pendulum.now()\n self._active_tenant_id = None\n self._attached_headers = {} # type: Dict[str, str]\n self.logger = create_diagnostic_logger(\"Diagnostics\")\n\n # store api server\n self.api_server = api_server or prefect.context.config.cloud.get(\"graphql\")\n\n # store api token\n self._api_token = api_token or prefect.context.config.cloud.get(\n \"auth_token\", None\n )\n\n # if no api token was passed, attempt to load state from local storage\n if not self._api_token and prefect.config.backend == \"cloud\":\n settings = self._load_local_settings()\n self._api_token = settings.get(\"api_token\")\n\n if self._api_token:\n self._active_tenant_id = settings.get(\"active_tenant_id\")\n if self._active_tenant_id:\n try:\n self.login_to_tenant(tenant_id=self._active_tenant_id)\n except AuthorizationError:\n # if an authorization error is raised, then the token is invalid and should\n # be cleared\n self.logout_from_tenant()","label":0},{"cid":"src/prefect/client/client.py::Client.get","text":"src/prefect/client/client.py::Client.get\n def get(\n self,\n path: str,\n server: str = None,\n headers: dict = None,\n params: Dict[str, JSONLike] = None,\n token: str = None,\n ) -> dict:\n \"\"\"\n Convenience function for calling the Prefect API with token auth and GET request\n\n Args:\n - path (str): the path of the API url. For example, to GET\n http://prefect-server/v1/auth/login, path would be 'auth/login'.\n - server (str, optional): the server to send the GET request to;\n defaults to `self.api_server`\n - headers (dict, optional): Headers to pass with the request\n - params (dict): GET parameters\n - token (str): an auth token. If not supplied, the `client.access_token` is used.\n\n Returns:\n - dict: Dictionary representation of the request made\n \"\"\"\n response = self._request(\n method=\"GET\",\n path=path,\n params=params,\n server=server,\n headers=headers,\n token=token,\n )\n if response.text:\n return response.json()\n else:\n return {}","label":0},{"cid":"src/prefect/client/client.py::Client.post","text":"src/prefect/client/client.py::Client.post\n def post(\n self,\n path: str,\n server: str = None,\n headers: dict = None,\n params: Dict[str, JSONLike] = None,\n token: str = None,\n ) -> dict:\n \"\"\"\n Convenience function for calling the Prefect API with token auth and POST request\n\n Args:\n - path (str): the path of the API url. For example, to POST\n http://prefect-server/v1/auth/login, path would be 'auth/login'.\n - server (str, optional): the server to send the POST request to;\n defaults to `self.api_server`\n - headers(dict): headers to pass with the request\n - params (dict): POST parameters\n - token (str): an auth token. If not supplied, the `client.access_token` is used.\n\n Returns:\n - dict: Dictionary representation of the request made\n \"\"\"\n response = self._request(\n method=\"POST\",\n path=path,\n params=params,\n server=server,\n headers=headers,\n token=token,\n )\n if response.text:\n return response.json()\n else:\n return {}","label":0},{"cid":"src/prefect/client/client.py::Client.graphql","text":"src/prefect/client/client.py::Client.graphql\n def graphql(\n self,\n query: Any,\n raise_on_error: bool = True,\n headers: Dict[str, str] = None,\n variables: Dict[str, JSONLike] = None,\n token: str = None,\n ) -> GraphQLResult:\n \"\"\"\n Convenience function for running queries against the Prefect GraphQL API\n\n Args:\n - query (Any): A representation of a graphql query to be executed. It will be\n parsed by prefect.utilities.graphql.parse_graphql().\n - raise_on_error (bool): if True, a `ClientError` will be raised if the GraphQL\n returns any `errors`.\n - headers (dict): any additional headers that should be passed as part of the\n request\n - variables (dict): Variables to be filled into a query with the key being\n equivalent to the variables that are accepted by the query\n - token (str): an auth token. If not supplied, the `client.access_token` is used.\n\n Returns:\n - dict: Data returned from the GraphQL query\n\n Raises:\n - ClientError if there are errors raised by the GraphQL mutation\n \"\"\"\n result = self.post(\n path=\"\",\n server=self.api_server,\n headers=headers,\n params=dict(query=parse_graphql(query), variables=json.dumps(variables)),\n token=token,\n )\n\n if raise_on_error and \"errors\" in result:\n if \"UNAUTHENTICATED\" in str(result[\"errors\"]):\n raise AuthorizationError(result[\"errors\"])\n elif \"Malformed Authorization header\" in str(result[\"errors\"]):\n raise AuthorizationError(result[\"errors\"])\n raise ClientError(result[\"errors\"])\n else:\n return GraphQLResult(result) # type: ignore","label":0},{"cid":"src/prefect/client/client.py::Client._request","text":"src/prefect/client/client.py::Client._request\n def _request(\n self,\n method: str,\n path: str,\n params: Dict[str, JSONLike] = None,\n server: str = None,\n headers: dict = None,\n token: str = None,\n ) -> \"requests.models.Response\":\n \"\"\"\n Runs any specified request (GET, POST, DELETE) against the server\n\n Args:\n - method (str): The type of request to be made (GET, POST, DELETE)\n - path (str): Path of the API URL\n - params (dict, optional): Parameters used for the request\n - server (str, optional): The server to make requests against, base API\n server is used if not specified\n - headers (dict, optional): Headers to pass with the request\n - token (str): an auth token. If not supplied, the `client.access_token` is used.\n\n Returns:\n - requests.models.Response: The response returned from the request\n\n Raises:\n - ClientError: if the client token is not in the context (due to not being logged in)\n - ValueError: if a method is specified outside of the accepted GET, POST, DELETE\n - requests.HTTPError: if a status code is returned that is not `200` or `401`\n \"\"\"\n if server is None:\n server = self.api_server\n assert isinstance(server, str) # mypy assert\n\n if token is None:\n token = self.get_auth_token()\n\n # 'import requests' is expensive time-wise, we should do this just-in-time to keep\n # the 'import prefect' time low\n import requests\n\n url = urljoin(server, path.lstrip(\"/\")).rstrip(\"/\")\n\n params = params or {}\n\n headers = headers or {}\n if token:\n headers[\"Authorization\"] = \"Bearer {}\".format(token)\n headers[\"X-PREFECT-CORE-VERSION\"] = str(prefect.__version__)\n\n if self._attached_headers:\n headers.update(self._attached_headers)\n\n ","label":0},{"cid":"src/prefect/client/client.py::Client.attach_headers","text":"src/prefect/client/client.py::Client.attach_headers\n def attach_headers(self, headers: dict) -> None:\n \"\"\"\n Set headers to be attached to this Client\n\n Args:\n - headers (dict): A dictionary of headers to attach to this client. These headers\n get added on to the existing dictionary of headers.\n \"\"\"\n self._attached_headers.update(headers)","label":0},{"cid":"src/prefect/client/client.py::Client._local_settings_path","text":"src/prefect/client/client.py::Client._local_settings_path\n @property\n def _local_settings_path(self) -> Path:\n \"\"\"\n Returns the local settings directory corresponding to the current API servers\n \"\"\"\n path = \"{home}/client/{server}\".format(\n home=prefect.context.config.home_dir,\n server=slugify(self.api_server, regex_pattern=r\"[^-\\.a-z0-9]+\"),\n )\n return Path(os.path.expanduser(path)) / \"settings.toml\"","label":0},{"cid":"src/prefect/client/client.py::Client._save_local_settings","text":"src/prefect/client/client.py::Client._save_local_settings\n def _save_local_settings(self, settings: dict) -> None:\n \"\"\"\n Writes settings to local storage\n \"\"\"\n self._local_settings_path.parent.mkdir(exist_ok=True, parents=True)\n with self._local_settings_path.open(\"w+\") as f:\n toml.dump(settings, f)","label":0},{"cid":"src/prefect/client/client.py::Client._load_local_settings","text":"src/prefect/client/client.py::Client._load_local_settings\n def _load_local_settings(self) -> dict:\n \"\"\"\n Loads settings from local storage\n \"\"\"\n if self._local_settings_path.exists():\n with self._local_settings_path.open(\"r\") as f:\n return toml.load(f) # type: ignore\n return {}","label":0},{"cid":"src/prefect/client/client.py::Client.save_api_token","text":"src/prefect/client/client.py::Client.save_api_token\n def save_api_token(self) -> None:\n \"\"\"\n Saves the API token in local storage.\n \"\"\"\n settings = self._load_local_settings()\n settings[\"api_token\"] = self._api_token\n self._save_local_settings(settings)","label":0},{"cid":"src/prefect/client/client.py::Client.get_auth_token","text":"src/prefect/client/client.py::Client.get_auth_token\n def get_auth_token(self) -> str:\n \"\"\"\n Returns an auth token:\n - if no explicit access token is stored, returns the api token\n - if there is an access token:\n - if there's a refresh token and the access token expires in the next 30 seconds,\n then we refresh the access token and store the result\n - return the access token\n\n Returns:\n - str: the access token\n \"\"\"\n if not self._access_token:\n return self._api_token\n\n expiration = self._access_token_expires_at or pendulum.now()\n if self._refresh_token and pendulum.now().add(seconds=30) > expiration:\n self._refresh_access_token()\n\n return self._access_token","label":0},{"cid":"src/prefect/client/client.py::Client.get_available_tenants","text":"src/prefect/client/client.py::Client.get_available_tenants\n def get_available_tenants(self) -> List[Dict]:\n \"\"\"\n Returns a list of available tenants.\n\n NOTE: this should only be called by users who have provided a USER-scoped API token.\n\n Returns:\n - List[Dict]: a list of dictionaries containing the id, slug, and name of\n available tenants\n \"\"\"\n result = self.graphql(\n {\"query\": {\"tenant(order_by: {slug: asc})\": {\"id\", \"slug\", \"name\"}}},\n # use the API token to see all available tenants\n token=self._api_token,\n ) # type: ignore\n return result.data.tenant # type: ignore","label":0},{"cid":"src/prefect/client/client.py::Client.login_to_tenant","text":"src/prefect/client/client.py::Client.login_to_tenant\n def login_to_tenant(self, tenant_slug: str = None, tenant_id: str = None) -> bool:\n \"\"\"\n Log in to a specific tenant\n\n NOTE: this should only be called by users who have provided a USER-scoped API token.\n\n Args:\n - tenant_slug (str): the tenant's slug\n - tenant_id (str): the tenant's id\n\n Returns:\n - bool: True if the login was successful\n\n Raises:\n - ValueError: if at least one of `tenant_slug` or `tenant_id` isn't provided\n - ValueError: if the `tenant_id` is not a valid UUID\n - ValueError: if no matching tenants are found\n\n \"\"\"\n\n if tenant_slug is None and tenant_id is None:\n raise ValueError(\n \"At least one of `tenant_slug` or `tenant_id` must be provided.\"\n )\n elif tenant_id:\n try:\n uuid.UUID(tenant_id)\n except ValueError:\n raise ValueError(\"The `tenant_id` must be a valid UUID.\")\n\n tenant = self.graphql(\n {\n \"query($slug: String, $id: uuid)\": {\n \"tenant(where: {slug: { _eq: $slug }, id: { _eq: $id } })\": {\"id\"}\n }\n },\n variables=dict(slug=tenant_slug, id=tenant_id),\n # use the API token to query the tenant\n token=self._api_token,\n ) # type: ignore\n if not tenant.data.tenant: # type: ignore\n raise ValueError(\"No matching tenants found.\")\n\n tenant_id = tenant.data.tenant[0].id # type: ignore\n\n payload = self.graphql(\n {\n \"mutation($input: switch_tenant_input!)\": {\n \"switch_tenant(input: $input)\": {\n \"access_token\",\n \"expires_at\",\n \"refresh_token\",\n }\n }\n },\n variables=dict(in","label":0},{"cid":"src/prefect/client/client.py::Client.logout_from_tenant","text":"src/prefect/client/client.py::Client.logout_from_tenant\n def logout_from_tenant(self) -> None:\n self._access_token = None\n self._refresh_token = None\n self._active_tenant_id = None\n\n # remove the tenant setting\n settings = self._load_local_settings()\n settings[\"active_tenant_id\"] = None\n self._save_local_settings(settings)","label":0},{"cid":"src/prefect/client/client.py::Client._refresh_access_token","text":"src/prefect/client/client.py::Client._refresh_access_token\n def _refresh_access_token(self) -> bool:\n \"\"\"\n Refresh the client's JWT access token.\n\n NOTE: this should only be called by users who have provided a USER-scoped API token.\n\n Returns:\n - bool: True if the refresh succeeds\n \"\"\"\n payload = self.graphql(\n {\n \"mutation($input: refresh_token_input!)\": {\n \"refresh_token(input: $input)\": {\n \"access_token\",\n \"expires_at\",\n \"refresh_token\",\n }\n }\n },\n variables=dict(input=dict(access_token=self._access_token)),\n # pass the refresh token as the auth header\n token=self._refresh_token,\n ) # type: ignore\n self._access_token = payload.data.refresh_token.access_token # type: ignore\n self._access_token_expires_at = pendulum.parse( # type: ignore\n payload.data.refresh_token.expires_at # type: ignore\n ) # type: ignore\n self._refresh_token = payload.data.refresh_token.refresh_token # type: ignore\n\n return True","label":0},{"cid":"src/prefect/client/client.py::Client.register","text":"src/prefect/client/client.py::Client.register\n def register(\n self,\n flow: \"Flow\",\n project_name: str = None,\n build: bool = True,\n set_schedule_active: bool = True,\n version_group_id: str = None,\n compressed: bool = True,\n no_url: bool = False,\n ) -> str:\n \"\"\"\n Push a new flow to Prefect Cloud\n\n Args:\n - flow (Flow): a flow to register\n - project_name (str, optional): the project that should contain this flow.\n - build (bool, optional): if `True`, the flow's environment is built\n prior to serialization; defaults to `True`\n - set_schedule_active (bool, optional): if `False`, will set the\n schedule to inactive in the database to prevent auto-scheduling runs (if the Flow has a schedule).\n Defaults to `True`. This can be changed later.\n - version_group_id (str, optional): the UUID version group ID to use for versioning this Flow\n in Cloud; if not provided, the version group ID associated with this Flow's project and name\n will be used.\n - compressed (bool, optional): if `True`, the serialized flow will be; defaults to `True`\n compressed\n - no_url (bool, optional): if `True`, the stdout from this function will not contain the\n URL link to the newly-registered flow in the Cloud UI\n\n Returns:\n - str: the ID of the newly-registered flow\n\n Raises:\n - ClientError: if the register failed\n \"\"\"\n required_parameters = {p for p in flow.parameters() if p.required}\n if flow.schedule is not None and required_parameters:\n required_names = {p.name for p in required_parameters}\n if not all(\n [\n required_names <= set(c.parameter_defaults.keys())\n for c in flow.schedule.clocks\n ]\n ","label":0},{"cid":"src/prefect/client/client.py::Client.get_cloud_url","text":"src/prefect/client/client.py::Client.get_cloud_url\n def get_cloud_url(self, subdirectory: str, id: str, as_user: bool = True) -> str:\n \"\"\"\n Convenience method for creating Prefect Cloud URLs for a given subdirectory.\n\n Args:\n - subdirectory (str): the subdirectory to use (e.g., `\"flow-run\"`)\n - id (str): the ID of the page\n - as_user (bool, optional): whether this query is being made from a USER scoped token;\n defaults to `True`. Only used internally for queries made from RUNNERs\n\n Returns:\n - str: the URL corresponding to the appropriate base URL, tenant slug, subdirectory and ID\n\n Example:\n\n ```python\n from prefect import Client\n\n client = Client()\n client.get_cloud_url(\"flow-run\", \"424242-ca-94611-111-55\")\n # returns \"https://cloud.prefect.io/my-tenant-slug/flow-run/424242-ca-94611-111-55\"\n ```\n \"\"\"\n # Generate direct link to UI\n if prefect.config.backend == \"cloud\":\n tenant_slug = self.get_default_tenant_slug(as_user=as_user)\n else:\n tenant_slug = \"\"\n\n base_url = (\n re.sub(\"api-\", \"\", prefect.config.cloud.api)\n if re.search(\"api-\", prefect.config.cloud.api)\n else re.sub(\"api\", \"cloud\", prefect.config.cloud.api)\n )\n\n full_url = prefect.config.cloud.api\n if tenant_slug:\n full_url = \"/\".join([base_url.rstrip(\"/\"), tenant_slug, subdirectory, id])\n elif prefect.config.backend == \"server\":\n full_url = \"/\".join([prefect.config.server.ui.endpoint, subdirectory, id,])\n\n return full_url","label":0},{"cid":"src/prefect/client/client.py::Client.get_default_tenant_slug","text":"src/prefect/client/client.py::Client.get_default_tenant_slug\n def get_default_tenant_slug(self, as_user: bool = True) -> str:\n \"\"\"\n Get the default tenant slug for the currently authenticated user\n\n Args:\n - as_user (bool, optional): whether this query is being made from a USER scoped token;\n defaults to `True`. Only used internally for queries made from RUNNERs\n\n Returns:\n - str: the slug of the current default tenant for this user\n \"\"\"\n if as_user:\n query = {\n \"query\": {\"user\": {\"default_membership\": {\"tenant\": \"slug\"}}}\n } # type: dict\n else:\n query = {\"query\": {\"tenant\": {\"slug\"}}}\n\n res = self.graphql(query)\n\n if as_user:\n user = res.get(\"data\").user[0]\n slug = user.default_membership.tenant.slug\n else:\n slug = res.get(\"data\").tenant[0].slug\n return slug","label":0},{"cid":"src/prefect/client/client.py::Client.create_project","text":"src/prefect/client/client.py::Client.create_project\n def create_project(self, project_name: str, project_description: str = None) -> str:\n \"\"\"\n Create a new Project\n\n Args:\n - project_name (str): the project that should contain this flow\n - project_description (str, optional): the project description\n\n Returns:\n - str: the ID of the newly-created project\n\n Raises:\n - ClientError: if the project creation failed\n \"\"\"\n project_mutation = {\n \"mutation($input: create_project_input!)\": {\n \"create_project(input: $input)\": {\"id\"}\n }\n }\n\n res = self.graphql(\n project_mutation,\n variables=dict(\n input=dict(name=project_name, description=project_description)\n ),\n ) # type: Any\n\n return res.data.create_project.id","label":0},{"cid":"src/prefect/client/client.py::Client.create_flow_run","text":"src/prefect/client/client.py::Client.create_flow_run\n def create_flow_run(\n self,\n flow_id: str = None,\n context: dict = None,\n parameters: dict = None,\n scheduled_start_time: datetime.datetime = None,\n idempotency_key: str = None,\n run_name: str = None,\n version_group_id: str = None,\n ) -> str:\n \"\"\"\n Create a new flow run for the given flow id. If `start_time` is not provided, the flow run will be scheduled to start immediately.\n If both `flow_id` and `version_group_id` are provided, only the `flow_id` will be used.\n\n Args:\n - flow_id (str, optional): the id of the Flow you wish to schedule\n - context (dict, optional): the run context\n - parameters (dict, optional): a dictionary of parameter values to pass to the flow run\n - scheduled_start_time (datetime, optional): the time to schedule the execution for; if not provided, defaults to now\n - idempotency_key (str, optional): an idempotency key; if provided, this run will be cached for 24\n hours. Any subsequent attempts to create a run with the same idempotency key\n will return the ID of the originally created run (no new run will be created after the first).\n An error will be raised if parameters or context are provided and don't match the original.\n Each subsequent request will reset the TTL for 24 hours.\n - run_name (str, optional): The name assigned to this flow run\n - version_group_id (str, optional): if provided, the unique unarchived flow within this version group will be scheduled\n to run. This input can be used as a stable API for running flows which are regularly updated.\n\n Returns:\n - str: the ID of the newly-created flow run\n\n Raises:\n - ClientError: if the GraphQL query is bad for any reason\n \"\"\"\n create_mutation = ","label":0},{"cid":"src/prefect/client/client.py::Client.get_flow_run_info","text":"src/prefect/client/client.py::Client.get_flow_run_info\n def get_flow_run_info(self, flow_run_id: str) -> FlowRunInfoResult:\n \"\"\"\n Retrieves version and current state information for the given flow run.\n\n Args:\n - flow_run_id (str): the id of the flow run to get information for\n\n Returns:\n - GraphQLResult: an object representing information about the flow run\n\n Raises:\n - ClientError: if the GraphQL mutation is bad for any reason\n \"\"\"\n query = {\n \"query\": {\n with_args(\"flow_run_by_pk\", {\"id\": flow_run_id}): {\n \"id\": True,\n \"name\": True,\n \"flow_id\": True,\n \"parameters\": True,\n \"context\": True,\n \"version\": True,\n \"scheduled_start_time\": True,\n \"serialized_state\": True,\n # load all task runs except dynamic task runs\n with_args(\"task_runs\", {\"where\": {\"map_index\": {\"_eq\": -1}}}): {\n \"id\": True,\n \"task\": {\"id\": True, \"slug\": True},\n \"version\": True,\n \"serialized_state\": True,\n },\n }\n }\n }\n result = self.graphql(query).data.flow_run_by_pk # type: ignore\n\n if result is None:\n raise ClientError('Flow run ID not found: \"{}\"'.format(flow_run_id))\n\n # convert scheduled_start_time from string to datetime\n result.scheduled_start_time = pendulum.parse(result.scheduled_start_time)\n\n # create \"state\" attribute from serialized_state\n result.state = prefect.engine.state.State.deserialize(\n result.pop(\"serialized_state\")\n )\n\n # reformat task_runs\n task_runs = []\n for tr in result.task_runs:\n tr.state = prefect.engine.state.State.deserialize(\n ","label":0},{"cid":"src/prefect/client/client.py::Client.update_flow_run_heartbeat","text":"src/prefect/client/client.py::Client.update_flow_run_heartbeat\n def update_flow_run_heartbeat(self, flow_run_id: str) -> None:\n \"\"\"\n Convenience method for heartbeating a flow run.\n\n Does NOT raise an error if the update fails.\n\n Args:\n - flow_run_id (str): the flow run ID to heartbeat\n\n \"\"\"\n mutation = {\n \"mutation\": {\n with_args(\n \"update_flow_run_heartbeat\", {\"input\": {\"flow_run_id\": flow_run_id}}\n ): {\"success\"}\n }\n }\n self.graphql(mutation, raise_on_error=True)","label":0},{"cid":"src/prefect/client/client.py::Client.update_task_run_heartbeat","text":"src/prefect/client/client.py::Client.update_task_run_heartbeat\n def update_task_run_heartbeat(self, task_run_id: str) -> None:\n \"\"\"\n Convenience method for heartbeating a task run.\n\n Does NOT raise an error if the update fails.\n\n Args:\n - task_run_id (str): the task run ID to heartbeat\n\n \"\"\"\n mutation = {\n \"mutation\": {\n with_args(\n \"update_task_run_heartbeat\", {\"input\": {\"task_run_id\": task_run_id}}\n ): {\"success\"}\n }\n }\n self.graphql(mutation, raise_on_error=True)","label":0},{"cid":"src/prefect/client/client.py::Client.set_flow_run_state","text":"src/prefect/client/client.py::Client.set_flow_run_state\n def set_flow_run_state(\n self, flow_run_id: str, version: int, state: \"prefect.engine.state.State\"\n ) -> None:\n \"\"\"\n Sets new state for a flow run in the database.\n\n Args:\n - flow_run_id (str): the id of the flow run to set state for\n - version (int): the current version of the flow run state\n - state (State): the new state for this flow run\n\n Raises:\n - ClientError: if the GraphQL mutation is bad for any reason\n \"\"\"\n mutation = {\n \"mutation($input: set_flow_run_states_input!)\": {\n \"set_flow_run_states(input: $input)\": {\"states\": {\"id\"}}\n }\n }\n\n serialized_state = state.serialize()\n\n result = self.graphql(\n mutation,\n variables=dict(\n input=dict(\n states=[\n dict(\n state=serialized_state,\n flow_run_id=flow_run_id,\n version=version,\n )\n ]\n )\n ),\n ) # type: Any","label":0},{"cid":"src/prefect/client/client.py::Client.get_latest_cached_states","text":"src/prefect/client/client.py::Client.get_latest_cached_states\n def get_latest_cached_states(\n self, task_id: str, cache_key: Optional[str], created_after: datetime.datetime\n ) -> List[\"prefect.engine.state.State\"]:\n \"\"\"\n Pulls all Cached states for the given task that were created after the provided date.\n\n Args:\n - task_id (str): the task id for this task run\n - cache_key (Optional[str]): the cache key for this Task's cache; if `None`, the task id alone will be used\n - created_after (datetime.datetime): the earliest date the state should have been created at\n\n Returns:\n - List[State]: a list of Cached states created after the given date\n \"\"\"\n where_clause = {\n \"where\": {\n \"state\": {\"_eq\": \"Cached\"},\n \"_or\": [\n {\n \"_and\": [\n {\"cache_key\": {\"_eq\": cache_key}},\n {\"cache_key\": {\"_is_null\": False}},\n ]\n },\n {\"task_id\": {\"_eq\": task_id}},\n ],\n \"state_timestamp\": {\"_gte\": created_after.isoformat()},\n },\n \"order_by\": {\"state_timestamp\": EnumValue(\"desc\")},\n }\n query = {\"query\": {with_args(\"task_run\", where_clause): \"serialized_state\"}}\n result = self.graphql(query) # type: Any\n deserializer = prefect.engine.state.State.deserialize\n valid_states = [\n deserializer(res.serialized_state) for res in result.data.task_run\n ]\n return valid_states","label":0},{"cid":"src/prefect/client/client.py::Client.get_task_run_info","text":"src/prefect/client/client.py::Client.get_task_run_info\n def get_task_run_info(\n self, flow_run_id: str, task_id: str, map_index: Optional[int] = None\n ) -> TaskRunInfoResult:\n \"\"\"\n Retrieves version and current state information for the given task run.\n\n Args:\n - flow_run_id (str): the id of the flow run that this task run lives in\n - task_id (str): the task id for this task run\n - map_index (int, optional): the mapping index for this task run; if\n `None`, it is assumed this task is _not_ mapped\n\n Returns:\n - NamedTuple: a tuple containing `id, task_id, version, state`\n\n Raises:\n - ClientError: if the GraphQL mutation is bad for any reason\n \"\"\"\n\n mutation = {\n \"mutation\": {\n with_args(\n \"get_or_create_task_run\",\n {\n \"input\": {\n \"flow_run_id\": flow_run_id,\n \"task_id\": task_id,\n \"map_index\": -1 if map_index is None else map_index,\n }\n },\n ): {\n \"id\": True,\n }\n }\n }\n result = self.graphql(mutation) # type: Any\n\n if result is None:\n raise ClientError(\"Failed to create task run.\")\n\n task_run_id = result.data.get_or_create_task_run.id\n\n query = {\n \"query\": {\n with_args(\"task_run_by_pk\", {\"id\": task_run_id}): {\n \"version\": True,\n \"serialized_state\": True,\n \"task\": {\"slug\": True},\n }\n }\n }\n task_run = self.graphql(query).data.task_run_by_pk # type: ignore\n\n if task_run is None:\n raise ClientError('Task run ID not found: \"{}\"'.format(task_run_id))\n\n state = prefect.engine.state.State.dese","label":0},{"cid":"src/prefect/client/client.py::Client.set_task_run_state","text":"src/prefect/client/client.py::Client.set_task_run_state\n def set_task_run_state(\n self,\n task_run_id: str,\n version: int,\n state: \"prefect.engine.state.State\",\n cache_for: datetime.timedelta = None,\n ) -> \"prefect.engine.state.State\":\n \"\"\"\n Sets new state for a task run.\n\n Args:\n - task_run_id (str): the id of the task run to set state for\n - version (int): the current version of the task run state\n - state (State): the new state for this task run\n - cache_for (timedelta, optional): how long to store the result of this task for, using the\n serializer set in config; if not provided, no caching occurs\n\n Raises:\n - ClientError: if the GraphQL mutation is bad for any reason\n\n Returns:\n - State: the state the current task run should be considered in\n \"\"\"\n mutation = {\n \"mutation($input: set_task_run_states_input!)\": {\n \"set_task_run_states(input: $input)\": {\n \"states\": {\"id\", \"status\", \"message\"}\n }\n }\n }\n\n serialized_state = state.serialize()\n\n result = self.graphql(\n mutation,\n variables=dict(\n input=dict(\n states=[\n dict(\n state=serialized_state,\n task_run_id=task_run_id,\n version=version,\n )\n ]\n )\n ),\n ) # type: Any\n state_payload = result.data.set_task_run_states.states[0]\n if state_payload.status == \"QUEUED\":\n # If appropriate, the state attribute of the Queued state can be\n # set by the caller of this method\n return prefect.engine.state.Queued(\n message=state_payload.get(\"message\"),\n start_time=pend","label":0},{"cid":"src/prefect/client/client.py::Client.set_secret","text":"src/prefect/client/client.py::Client.set_secret\n def set_secret(self, name: str, value: Any) -> None:\n \"\"\"\n Set a secret with the given name and value.\n\n Args:\n - name (str): the name of the secret; used for retrieving the secret\n during task runs\n - value (Any): the value of the secret\n\n Raises:\n - ClientError: if the GraphQL mutation is bad for any reason\n - ValueError: if the secret-setting was unsuccessful\n \"\"\"\n mutation = {\n \"mutation($input: set_secret_input!)\": {\n \"set_secret(input: $input)\": {\"success\"}\n }\n }\n\n result = self.graphql(\n mutation, variables=dict(input=dict(name=name, value=value))\n ) # type: Any\n\n if not result.data.set_secret.success:\n raise ValueError(\"Setting secret failed.\")","label":0},{"cid":"src/prefect/client/client.py::Client.get_task_tag_limit","text":"src/prefect/client/client.py::Client.get_task_tag_limit\n def get_task_tag_limit(self, tag: str) -> Optional[int]:\n \"\"\"\n Retrieve the current task tag concurrency limit for a given tag.\n\n Args:\n - tag (str): the tag to update\n\n Raises:\n - ClientError: if the GraphQL query fails\n \"\"\"\n query = {\n \"query\": {\n with_args(\"task_tag_limit\", {\"where\": {\"tag\": {\"_eq\": tag}}}): {\n \"limit\": True\n }\n }\n }\n\n result = self.graphql(query) # type: Any\n if result.data.task_tag_limit:\n return result.data.task_tag_limit[0].limit\n else:\n return None","label":0},{"cid":"src/prefect/client/client.py::Client.update_task_tag_limit","text":"src/prefect/client/client.py::Client.update_task_tag_limit\n def update_task_tag_limit(self, tag: str, limit: int) -> None:\n \"\"\"\n Update the task tag concurrency limit for a given tag; requires tenant admin permissions.\n\n Args:\n - tag (str): the tag to update\n - limit (int): the concurrency limit to enforce on the tag; should be a value >= 0\n\n Raises:\n - ClientError: if the GraphQL mutation is bad for any reason\n - ValueError: if the tag limit-setting was unsuccessful, or if a bad limit was provided\n \"\"\"\n if limit < 0:\n raise ValueError(\"Concurrency limits must be >= 0\")\n\n mutation = {\n \"mutation($input: update_task_tag_limit_input!)\": {\n \"update_task_tag_limit(input: $input)\": {\"id\"}\n }\n }\n\n result = self.graphql(\n mutation, variables=dict(input=dict(tag=tag, limit=limit))\n ) # type: Any\n\n if not result.data.update_task_tag_limit.id:\n raise ValueError(\"Updating the task tag concurrency limit failed.\")","label":0},{"cid":"src/prefect/client/client.py::Client.delete_task_tag_limit","text":"src/prefect/client/client.py::Client.delete_task_tag_limit\n def delete_task_tag_limit(self, limit_id: str) -> None:\n \"\"\"\n Deletes a given task tag concurrency limit; requires tenant admin permissions.\n\n Args:\n - limit_id (str): the ID of the tag to delete\n\n Raises:\n - ClientError: if the GraphQL mutation is bad for any reason\n - ValueError: if the tag deletion was unsuccessful, or if a bad tag ID was provided\n \"\"\"\n mutation = {\n \"mutation($input: delete_task_tag_limit_input!)\": {\n \"delete_task_tag_limit(input: $input)\": {\"success\"}\n }\n }\n\n result = self.graphql(\n mutation, variables=dict(input=dict(limit_id=limit_id))\n ) # type: Any\n\n if not result.data.delete_task_tag_limit.success:\n raise ValueError(\"Deleting the task tag concurrency limit failed.\")","label":0},{"cid":"src/prefect/client/client.py::Client.write_run_logs","text":"src/prefect/client/client.py::Client.write_run_logs\n def write_run_logs(self, logs: List[Dict]) -> None:\n \"\"\"\n Uploads a collection of logs to Cloud.\n\n Args:\n - logs (List[Dict]): a list of log entries to add\n\n Raises:\n - ValueError: if uploading the logs fail\n \"\"\"\n mutation = {\n \"mutation($input: write_run_logs_input!)\": {\n \"write_run_logs(input: $input)\": {\"success\"}\n }\n }\n\n result = self.graphql(\n mutation, variables=dict(input=dict(logs=logs))\n ) # type: Any\n\n if not result.data.write_run_logs.success:\n raise ValueError(\"Writing logs failed.\")","label":0},{"cid":"src/prefect/client/client.py::Client.register_agent","text":"src/prefect/client/client.py::Client.register_agent\n def register_agent(\n self, agent_type: str, name: str = None, labels: List[str] = None\n ) -> str:\n \"\"\"\n Register an agent with Cloud\n\n Args:\n - agent_type (str): The type of agent being registered\n - name: (str, optional): The name of the agent being registered\n - labels (List[str], optional): A list of any present labels on the agent\n being registered\n\n Returns:\n - The agent ID as a string\n \"\"\"\n mutation = {\n \"mutation($input: register_agent_input!)\": {\n \"register_agent(input: $input)\": {\"id\"}\n }\n }\n\n result = self.graphql(\n mutation,\n variables=dict(input=dict(type=agent_type, name=name, labels=labels)),\n )\n\n if not result.data.register_agent.id:\n raise ValueError(\"Error registering agent\")\n\n return result.data.register_agent.id","label":0},{"cid":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.__init__","text":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.__init__\n def __init__( # type: ignore\n self,\n launch_type: str = \"FARGATE\",\n aws_access_key_id: str = None,\n aws_secret_access_key: str = None,\n aws_session_token: str = None,\n region_name: str = None,\n executor_kwargs: dict = None,\n labels: List[str] = None,\n on_start: Callable = None,\n on_exit: Callable = None,\n **kwargs\n ) -> None:\n self.launch_type = launch_type\n # Not serialized, only stored on the object\n self.aws_access_key_id = aws_access_key_id or os.getenv(\"AWS_ACCESS_KEY_ID\")\n self.aws_secret_access_key = aws_secret_access_key or os.getenv(\n \"AWS_SECRET_ACCESS_KEY\"\n )\n self.aws_session_token = aws_session_token or os.getenv(\"AWS_SESSION_TOKEN\")\n self.region_name = region_name or os.getenv(\"REGION_NAME\")\n\n # Parse accepted kwargs for definition and run\n self.task_definition_kwargs, self.task_run_kwargs = self._parse_kwargs(kwargs)\n\n self.executor_kwargs = executor_kwargs or dict()\n super().__init__(labels=labels, on_start=on_start, on_exit=on_exit)","label":0},{"cid":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment._parse_kwargs","text":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment._parse_kwargs\n def _parse_kwargs(self, user_kwargs: dict) -> tuple:\n \"\"\"\n Parse the kwargs passed in and separate them out for `register_task_definition`\n and `run_task`. This is required because boto3 does not allow extra kwargs\n and if they are provided it will raise botocore.exceptions.ParamValidationError.\n\n Args:\n - user_kwargs (dict): The kwargs passed to the initialization of the environment\n\n Returns:\n tuple: a tuple of two dictionaries (task_definition_kwargs, task_run_kwargs)\n \"\"\"\n definition_kwarg_list = [\n \"family\",\n \"taskRoleArn\",\n \"executionRoleArn\",\n \"networkMode\",\n \"containerDefinitions\",\n \"volumes\",\n \"placementConstraints\",\n \"requiresCompatibilities\",\n \"cpu\",\n \"memory\",\n \"tags\",\n \"pidMode\",\n \"ipcMode\",\n \"proxyConfiguration\",\n \"inferenceAccelerators\",\n ]\n\n run_kwarg_list = [\n \"cluster\",\n \"taskDefinition\",\n \"count\",\n \"startedBy\",\n \"group\",\n \"placementConstraints\",\n \"placementStrategy\",\n \"platformVersion\",\n \"networkConfiguration\",\n \"tags\",\n \"enableECSManagedTags\",\n \"propagateTags\",\n ]\n\n task_definition_kwargs = {}\n for key, item in user_kwargs.items():\n if key in definition_kwarg_list:\n task_definition_kwargs.update({key: item})\n\n task_run_kwargs = {}\n for key, item in user_kwargs.items():\n if key in run_kwarg_list:\n task_run_kwargs.update({key: item})\n\n return task_definition_kwargs, task_run_kwargs","label":0},{"cid":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.dependencies","text":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.dependencies\n @property\n def dependencies(self) -> list:\n return [\"boto3\", \"botocore\"]","label":0},{"cid":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.setup","text":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.setup\n def setup(self, storage: \"Docker\") -> None: # type: ignore\n \"\"\"\n Register the task definition if it does not already exist.\n\n Args:\n - storage (Storage): the Storage object that contains the flow\n \"\"\"\n from boto3 import client as boto3_client\n from botocore.exceptions import ClientError\n\n boto3_c = boto3_client(\n \"ecs\",\n aws_access_key_id=self.aws_access_key_id,\n aws_secret_access_key=self.aws_secret_access_key,\n aws_session_token=self.aws_session_token,\n region_name=self.region_name,\n )\n\n definition_exists = True\n try:\n boto3_c.describe_task_definition(\n taskDefinition=self.task_definition_kwargs.get(\"family\")\n )\n except ClientError:\n definition_exists = False\n\n if not definition_exists:\n env_values = [\n {\"name\": \"PREFECT__CLOUD__GRAPHQL\", \"value\": config.cloud.graphql},\n {\"name\": \"PREFECT__CLOUD__USE_LOCAL_SECRETS\", \"value\": \"false\"},\n {\n \"name\": \"PREFECT__ENGINE__FLOW_RUNNER__DEFAULT_CLASS\",\n \"value\": \"prefect.engine.cloud.CloudFlowRunner\",\n },\n {\n \"name\": \"PREFECT__ENGINE__TASK_RUNNER__DEFAULT_CLASS\",\n \"value\": \"prefect.engine.cloud.CloudTaskRunner\",\n },\n {\"name\": \"PREFECT__LOGGING__LOG_TO_CLOUD\", \"value\": \"true\"},\n {\n \"name\": \"PREFECT__LOGGING__EXTRA_LOGGERS\",\n \"value\": str(config.logging.extra_loggers),\n },\n ]\n\n # create containerDefinitions if they do not exist\n if not self.task_definition_kwargs.get(\"containerDefinitions\"):\n self.task_definition_kwargs[\"containerDefinit","label":0},{"cid":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.execute","text":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.execute\n def execute( # type: ignore\n self, storage: \"Docker\", flow_location: str, **kwargs: Any\n ) -> None:\n \"\"\"\n Run the Fargate task that was defined for this flow.\n\n Args:\n - storage (Storage): the Storage object that contains the flow\n - flow_location (str): the location of the Flow to execute\n - **kwargs (Any): additional keyword arguments to pass to the runner\n \"\"\"\n from boto3 import client as boto3_client\n\n flow_run_id = prefect.context.get(\"flow_run_id\", \"unknown\")\n container_overrides = [\n {\n \"name\": \"flow-container\",\n \"environment\": [\n {\n \"name\": \"PREFECT__CLOUD__AUTH_TOKEN\",\n \"value\": config.cloud.agent.auth_token\n or config.cloud.auth_token,\n },\n {\"name\": \"PREFECT__CONTEXT__FLOW_RUN_ID\", \"value\": flow_run_id},\n {\"name\": \"PREFECT__CONTEXT__IMAGE\", \"value\": storage.name},\n {\n \"name\": \"PREFECT__CONTEXT__FLOW_FILE_PATH\",\n \"value\": flow_location,\n },\n ],\n }\n ]\n\n boto3_c = boto3_client(\n \"ecs\",\n aws_access_key_id=self.aws_access_key_id,\n aws_secret_access_key=self.aws_secret_access_key,\n aws_session_token=self.aws_session_token,\n region_name=self.region_name,\n )\n\n boto3_c.run_task(\n overrides={\"containerOverrides\": container_overrides},\n launchType=self.launch_type,\n **self.task_run_kwargs\n )","label":0},{"cid":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.run_flow","text":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.run_flow\n def run_flow(self) -> None:\n \"\"\"\n Run the flow from specified flow_file_path location using the default executor\n \"\"\"\n\n # Call on_start callback if specified\n if self.on_start:\n self.on_start()\n\n try:\n from prefect.engine import (\n get_default_flow_runner_class,\n get_default_executor_class,\n )\n\n # Load serialized flow from file and run it with the executor\n with open(\n prefect.context.get(\n \"flow_file_path\", \"/root/.prefect/flow_env.prefect\"\n ),\n \"rb\",\n ) as f:\n flow = cloudpickle.load(f)\n\n runner_cls = get_default_flow_runner_class()\n executor_cls = get_default_executor_class()(**self.executor_kwargs)\n runner_cls(flow=flow).run(executor=executor_cls)\n except Exception as exc:\n self.logger.exception(\n \"Unexpected error raised during flow run: {}\".format(exc)\n )\n raise exc\n finally:\n # Call on_exit callback if specified\n if self.on_exit:\n self.on_exit()","label":0}],"string":"[\n {\n \"cid\": \"src/prefect/agent/agent.py::exit_handler\",\n \"text\": \"src/prefect/agent/agent.py::exit_handler\\n@contextmanager\\ndef exit_handler(agent: \\\"Agent\\\") -> Generator:\\n exit_event = threading.Event()\\n\\n def _exit_handler(*args: Any, **kwargs: Any) -> None:\\n agent.logger.info(\\\"Keyboard Interrupt received: Agent is shutting down.\\\")\\n exit_event.set()\\n\\n original = signal.getsignal(signal.SIGINT)\\n try:\\n signal.signal(signal.SIGINT, _exit_handler)\\n yield exit_event\\n except SystemExit:\\n pass\\n finally:\\n signal.signal(signal.SIGINT, original)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::exit_handler._exit_handler\",\n \"text\": \"src/prefect/agent/agent.py::exit_handler._exit_handler\\n def _exit_handler(*args: Any, **kwargs: Any) -> None:\\n agent.logger.info(\\\"Keyboard Interrupt received: Agent is shutting down.\\\")\\n exit_event.set()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::HealthHandler.get\",\n \"text\": \"src/prefect/agent/agent.py::HealthHandler.get\\n def get(self) -> None:\\n # Empty json blob, may add more info later\\n self.write({})\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.__init__\",\n \"text\": \"src/prefect/agent/agent.py::Agent.__init__\\n def __init__(\\n self,\\n name: str = None,\\n labels: Iterable[str] = None,\\n env_vars: dict = None,\\n max_polls: int = None,\\n agent_address: str = None,\\n no_cloud_logs: bool = False,\\n ) -> None:\\n self.name = name or config.cloud.agent.get(\\\"name\\\", \\\"agent\\\")\\n\\n self.labels = labels or list(config.cloud.agent.get(\\\"labels\\\", []))\\n self.env_vars = env_vars or config.cloud.agent.get(\\\"env_vars\\\", dict())\\n self.max_polls = max_polls\\n self.log_to_cloud = False if no_cloud_logs else True\\n\\n self.agent_address = agent_address or config.cloud.agent.get(\\n \\\"agent_address\\\", \\\"\\\"\\n )\\n self._api_server = None # type: ignore\\n self._api_server_loop = None # type: Optional[IOLoop]\\n self._api_server_thread = None # type: Optional[threading.Thread]\\n\\n logger = logging.getLogger(self.name)\\n logger.setLevel(config.cloud.agent.get(\\\"level\\\"))\\n if not any([isinstance(h, logging.StreamHandler) for h in logger.handlers]):\\n ch = logging.StreamHandler(sys.stdout)\\n formatter = logging.Formatter(context.config.logging.format)\\n formatter.converter = time.gmtime # type: ignore\\n ch.setFormatter(formatter)\\n logger.addHandler(ch)\\n\\n self.logger = logger\\n self.submitting_flow_runs = set() # type: Set[str]\\n\\n self.logger.debug(\\\"Verbose logs enabled\\\")\\n self.logger.debug(f\\\"Environment variables: {[*self.env_vars]}\\\")\\n self.logger.debug(f\\\"Max polls: {self.max_polls}\\\")\\n self.logger.debug(f\\\"Agent address: {self.agent_address}\\\")\\n self.logger.debug(f\\\"Log to Cloud: {self.log_to_cloud}\\\")\\n\\n token = config.cloud.agent.get(\\\"auth_token\\\")\\n\\n self.logger.debug(f\\\"Prefect backend: {config.backend}\\\")\\n\\n self.client = Client(api_token=token)\\n if config.backend == \\\"cloud\\\":\\n self._verify_token(token)\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent._verify_token\",\n \"text\": \"src/prefect/agent/agent.py::Agent._verify_token\\n def _verify_token(self, token: str) -> None:\\n \\\"\\\"\\\"\\n Checks whether a token with a `RUNNER` scope was provided\\n Args:\\n - token (str): The provided agent token to verify\\n Raises:\\n - AuthorizationError: if token is empty or does not have a RUNNER role\\n \\\"\\\"\\\"\\n if not token:\\n raise AuthorizationError(\\\"No agent API token provided.\\\")\\n\\n # Check if RUNNER role\\n result = self.client.graphql(query=\\\"query { auth_info { api_token_scope } }\\\")\\n if (\\n not result.data # type: ignore\\n or result.data.auth_info.api_token_scope != \\\"RUNNER\\\" # type: ignore\\n ):\\n raise AuthorizationError(\\\"Provided token does not have a RUNNER scope.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent._register_agent\",\n \"text\": \"src/prefect/agent/agent.py::Agent._register_agent\\n def _register_agent(self) -> str:\\n \\\"\\\"\\\"\\n Register this agent with Prefect Cloud and retrieve agent ID\\n\\n Returns:\\n - The agent ID as a string\\n \\\"\\\"\\\"\\n agent_id = self.client.register_agent(\\n agent_type=type(self).__name__, name=self.name, labels=self.labels # type: ignore\\n )\\n\\n self.logger.debug(f\\\"Agent ID: {agent_id}\\\")\\n\\n return agent_id\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.start\",\n \"text\": \"src/prefect/agent/agent.py::Agent.start\\n def start(self) -> None:\\n \\\"\\\"\\\"\\n The main entrypoint to the agent. This function loops and constantly polls for\\n new flow runs to deploy\\n \\\"\\\"\\\"\\n try:\\n self.setup()\\n\\n with exit_handler(self) as exit_event:\\n\\n # Loop intervals for query sleep backoff\\n loop_intervals = {\\n 0: 0.25,\\n 1: 0.5,\\n 2: 1.0,\\n 3: 2.0,\\n 4: 4.0,\\n 5: 8.0,\\n 6: 10.0,\\n }\\n\\n index = 0\\n remaining_polls = math.inf if self.max_polls is None else self.max_polls\\n\\n # the max workers default has changed in 3.8. For stable results the\\n # default 3.8 behavior is elected here.\\n max_workers = min(32, (os.cpu_count() or 1) + 4)\\n\\n with ThreadPoolExecutor(max_workers=max_workers) as executor:\\n self.logger.debug(\\\"Max Workers: {}\\\".format(max_workers))\\n while (\\n not exit_event.wait(timeout=loop_intervals[index])\\n and remaining_polls\\n ):\\n self.heartbeat()\\n\\n if self.agent_process(executor):\\n index = 0\\n elif index < max(loop_intervals.keys()):\\n index += 1\\n\\n remaining_polls -= 1\\n\\n self.logger.debug(\\n \\\"Next query for flow runs in {} seconds\\\".format(\\n loop_intervals[index]\\n )\\n )\\n finally:\\n self.cleanup()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.setup\",\n \"text\": \"src/prefect/agent/agent.py::Agent.setup\\n def setup(self) -> None:\\n self.agent_connect()\\n\\n if self.agent_address:\\n parsed = urlparse(self.agent_address)\\n if not parsed.port:\\n raise ValueError(\\\"Must specify port in agent address\\\")\\n port = cast(int, parsed.port)\\n hostname = parsed.hostname or \\\"\\\"\\n app = web.Application([(\\\"/api/health\\\", HealthHandler)])\\n\\n def run() -> None:\\n self.logger.debug(\\n f\\\"Agent API server listening on port {self.agent_address}\\\"\\n )\\n self._api_server = app.listen(port, address=hostname) # type: ignore\\n self._api_server_loop = IOLoop.current()\\n self._api_server_loop.start() # type: ignore\\n\\n self._api_server_thread = threading.Thread(\\n name=\\\"api-server\\\", target=run, daemon=True\\n )\\n self._api_server_thread.start()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.cleanup\",\n \"text\": \"src/prefect/agent/agent.py::Agent.cleanup\\n def cleanup(self) -> None:\\n self.on_shutdown()\\n\\n if self._api_server is not None:\\n self.logger.debug(\\\"Stopping agent API server\\\")\\n self._api_server.stop()\\n\\n if self._api_server_loop is not None:\\n self.logger.debug(\\\"Stopping agent API server loop\\\")\\n\\n def stop_server() -> None:\\n try:\\n loop = cast(IOLoop, self._api_server_loop)\\n loop.stop()\\n except Exception:\\n pass\\n\\n self._api_server_loop.add_callback(stop_server)\\n\\n if self._api_server_thread is not None:\\n self.logger.debug(\\\"Joining agent API threads\\\")\\n # Give the server a small period to shutdown nicely, otherwise it\\n # will terminate on exit anyway since it's a daemon thread.\\n self._api_server_thread.join(timeout=1)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.on_shutdown\",\n \"text\": \"src/prefect/agent/agent.py::Agent.on_shutdown\\n def on_shutdown(self) -> None:\\n \\\"\\\"\\\"\\n Invoked when the event loop is exiting and the agent is shutting down. Intended\\n as a hook for child classes to optionally implement.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.agent_connect\",\n \"text\": \"src/prefect/agent/agent.py::Agent.agent_connect\\n def agent_connect(self) -> None:\\n \\\"\\\"\\\"\\n Verify agent connection to Prefect API by querying\\n \\\"\\\"\\\"\\n print(ascii_name)\\n self.logger.info(\\n \\\"Starting {} with labels {}\\\".format(type(self).__name__, self.labels)\\n )\\n self.logger.info(\\n \\\"Agent documentation can be found at https://docs.prefect.io/orchestration/\\\"\\n )\\n\\n self.logger.info(\\n \\\"Agent connecting to the Prefect API at {}\\\".format(config.cloud.api)\\n )\\n try:\\n self.client.graphql(query=\\\"query { hello }\\\")\\n except Exception as exc:\\n self.logger.error(\\n \\\"There was an error connecting to {}\\\".format(config.cloud.api)\\n )\\n self.logger.error(exc)\\n\\n self.logger.info(\\\"Waiting for flow runs...\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.deploy_and_update_flow_run\",\n \"text\": \"src/prefect/agent/agent.py::Agent.deploy_and_update_flow_run\\n def deploy_and_update_flow_run(self, flow_run: \\\"GraphQLResult\\\") -> None:\\n \\\"\\\"\\\"\\n Deploy a flow run and update Cloud with the resulting deployment info.\\n If any errors occur when submitting the flow run, capture the error and log to Cloud.\\n\\n Args:\\n - flow_run (GraphQLResult): The specific flow run to deploy\\n \\\"\\\"\\\"\\n # Deploy flow run and mark failed if any deployment error\\n try:\\n self.update_state(flow_run)\\n deployment_info = self.deploy_flow(flow_run)\\n if getattr(flow_run, \\\"id\\\", None):\\n self.client.write_run_logs(\\n [\\n dict(\\n flow_run_id=getattr(flow_run, \\\"id\\\"), # type: ignore\\n name=self.name,\\n message=\\\"Submitted for execution: {}\\\".format(\\n deployment_info\\n ),\\n level=\\\"INFO\\\",\\n )\\n ]\\n )\\n except Exception as exc:\\n ## if the state update failed, we don't want to follow up with another state update\\n if \\\"State update failed\\\" in str(exc):\\n self.logger.debug(\\\"Updating Flow Run state failed: {}\\\".format(str(exc)))\\n return\\n self.logger.error(\\n \\\"Logging platform error for flow run {}\\\".format(\\n getattr(flow_run, \\\"id\\\", \\\"UNKNOWN\\\") # type: ignore\\n )\\n )\\n if getattr(flow_run, \\\"id\\\", None):\\n self.client.write_run_logs(\\n [\\n dict(\\n flow_run_id=getattr(flow_run, \\\"id\\\"), # type: ignore\\n name=self.name,\\n message=str(exc),\\n level=\\\"ERROR\\\",\\n )\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.on_flow_run_deploy_attempt\",\n \"text\": \"src/prefect/agent/agent.py::Agent.on_flow_run_deploy_attempt\\n def on_flow_run_deploy_attempt(self, fut: \\\"Future\\\", flow_run_id: str) -> None:\\n \\\"\\\"\\\"\\n Indicates that a flow run deployment has been deployed (successfully or otherwise).\\n This is intended to be a future callback hook, called in the agent's main thread\\n when the background thread has completed the deploy_and_update_flow_run() call, either\\n successfully, in error, or cancelled. In all cases the agent should be open to\\n attempting to deploy the flow run if the flow run id is still in the Cloud run queue.\\n\\n Args:\\n - fut (Future): a callback requirement, the future which has completed or been cancelled.\\n - flow_run_id (str): the id of the flow run that the future represents.\\n \\\"\\\"\\\"\\n self.submitting_flow_runs.remove(flow_run_id)\\n self.logger.debug(\\\"Completed flow run submission (id: {})\\\".format(flow_run_id))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.agent_process\",\n \"text\": \"src/prefect/agent/agent.py::Agent.agent_process\\n def agent_process(self, executor: \\\"ThreadPoolExecutor\\\") -> bool:\\n \\\"\\\"\\\"\\n Full process for finding flow runs, updating states, and deploying.\\n\\n Args:\\n - executor (ThreadPoolExecutor): the interface to submit flow deployments in background threads\\n\\n Returns:\\n - bool: whether or not flow runs were found\\n \\\"\\\"\\\"\\n flow_runs = None\\n try:\\n flow_runs = self.query_flow_runs()\\n\\n if flow_runs:\\n self.logger.info(\\n \\\"Found {} flow run(s) to submit for execution.\\\".format(\\n len(flow_runs)\\n )\\n )\\n\\n for flow_run in flow_runs:\\n fut = executor.submit(self.deploy_and_update_flow_run, flow_run)\\n self.submitting_flow_runs.add(flow_run.id)\\n fut.add_done_callback(\\n functools.partial(\\n self.on_flow_run_deploy_attempt, flow_run_id=flow_run.id\\n )\\n )\\n\\n except Exception as exc:\\n self.logger.error(exc)\\n\\n return bool(flow_runs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.query_flow_runs\",\n \"text\": \"src/prefect/agent/agent.py::Agent.query_flow_runs\\n def query_flow_runs(self) -> list:\\n \\\"\\\"\\\"\\n Query Prefect Cloud for flow runs which need to be deployed and executed\\n\\n Returns:\\n - list: A list of GraphQLResult flow run objects\\n \\\"\\\"\\\"\\n self.logger.debug(\\\"Querying for flow runs\\\")\\n # keep a copy of what was curringly running before the query (future callbacks may be updating this set)\\n currently_submitting_flow_runs = self.submitting_flow_runs.copy()\\n\\n # Get scheduled flow runs from queue\\n mutation = {\\n \\\"mutation($input: get_runs_in_queue_input!)\\\": {\\n \\\"get_runs_in_queue(input: $input)\\\": {\\\"flow_run_ids\\\"}\\n }\\n }\\n\\n now = pendulum.now(\\\"UTC\\\")\\n result = self.client.graphql(\\n mutation,\\n variables={\\n \\\"input\\\": {\\\"before\\\": now.isoformat(), \\\"labels\\\": list(self.labels),}\\n },\\n )\\n\\n # we queried all of the available flow runs, however, some may have already been pulled\\n # by this agent and are in the process of being submitted in the background. We do not\\n # want to act on these \\\"duplicate\\\" flow runs until we've been assured that the background\\n # thread has attempted to submit the work (successful or otherwise).\\n flow_run_ids = set(result.data.get_runs_in_queue.flow_run_ids) # type: ignore\\n\\n if flow_run_ids:\\n msg = \\\"Found flow runs {}\\\".format(\\n result.data.get_runs_in_queue.flow_run_ids\\n )\\n else:\\n msg = \\\"No flow runs found\\\"\\n\\n already_submitting = flow_run_ids & currently_submitting_flow_runs\\n target_flow_run_ids = flow_run_ids - already_submitting\\n\\n if already_submitting:\\n msg += \\\" ({} already submitting: {})\\\".format(\\n len(already_submitting), list(already_submitting)\\n )\\n\\n self.logger.debug(msg)\\n\\n # Query metadata fow flow runs found \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.update_state\",\n \"text\": \"src/prefect/agent/agent.py::Agent.update_state\\n def update_state(self, flow_run: GraphQLResult) -> None:\\n \\\"\\\"\\\"\\n After a flow run is grabbed this function sets the state to Submitted so it\\n won't be picked up by any other processes\\n\\n Args:\\n - flow_run (GraphQLResult): A GraphQLResult flow run object\\n \\\"\\\"\\\"\\n self.logger.debug(\\n \\\"Updating states for flow run {}\\\".format(flow_run.id) # type: ignore\\n )\\n\\n # Set flow run state to `Submitted` if it is currently `Scheduled`\\n if state.StateSchema().load(flow_run.serialized_state).is_scheduled():\\n\\n self.logger.debug(\\n \\\"Flow run {} is in a Scheduled state, updating to Submitted\\\".format(\\n flow_run.id # type: ignore\\n )\\n )\\n self.client.set_flow_run_state(\\n flow_run_id=flow_run.id,\\n version=flow_run.version,\\n state=Submitted(\\n message=\\\"Submitted for execution\\\",\\n state=state.StateSchema().load(flow_run.serialized_state),\\n ),\\n )\\n\\n # Set task run states to `Submitted` if they are currently `Scheduled`\\n for task_run in flow_run.task_runs:\\n if state.StateSchema().load(task_run.serialized_state).is_scheduled():\\n\\n self.logger.debug(\\n \\\"Task run {} is in a Scheduled state, updating to Submitted\\\".format(\\n task_run.id # type: ignore\\n )\\n )\\n self.client.set_task_run_state(\\n task_run_id=task_run.id,\\n version=task_run.version,\\n state=Submitted(\\n message=\\\"Submitted for execution.\\\",\\n state=state.StateSchema().load(task_run.serialized_state),\\n ),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.mark_failed\",\n \"text\": \"src/prefect/agent/agent.py::Agent.mark_failed\\n def mark_failed(self, flow_run: GraphQLResult, exc: Exception) -> None:\\n \\\"\\\"\\\"\\n Mark a flow run as `Failed`\\n\\n Args:\\n - flow_run (GraphQLResult): A GraphQLResult flow run object\\n - exc (Exception): An exception that was raised to use as the `Failed`\\n message\\n \\\"\\\"\\\"\\n self.client.set_flow_run_state(\\n flow_run_id=flow_run.id,\\n version=flow_run.version,\\n state=Failed(message=str(exc)),\\n )\\n self.logger.error(\\\"Error while deploying flow: {}\\\".format(repr(exc)))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.deploy_flow\",\n \"text\": \"src/prefect/agent/agent.py::Agent.deploy_flow\\n def deploy_flow(self, flow_run: GraphQLResult) -> str:\\n \\\"\\\"\\\"\\n Meant to be overridden by a platform specific deployment option\\n\\n Args:\\n - flow_run (GraphQLResult): A GraphQLResult flow run object\\n\\n Returns:\\n - str: Information about the deployment\\n\\n Raises:\\n - ValueError: if deployment attempted on unsupported Storage type\\n \\\"\\\"\\\"\\n raise NotImplementedError()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/agent.py::Agent.heartbeat\",\n \"text\": \"src/prefect/agent/agent.py::Agent.heartbeat\\n def heartbeat(self) -> None:\\n \\\"\\\"\\\"\\n Meant to be overridden by a platform specific heartbeat option\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/local/agent.py::LocalAgent.__init__\",\n \"text\": \"src/prefect/agent/local/agent.py::LocalAgent.__init__\\n def __init__(\\n self,\\n name: str = None,\\n labels: Iterable[str] = None,\\n env_vars: dict = None,\\n import_paths: List[str] = None,\\n show_flow_logs: bool = False,\\n hostname_label: bool = True,\\n max_polls: int = None,\\n agent_address: str = None,\\n no_cloud_logs: bool = False,\\n ) -> None:\\n self.processes = set()\\n self.import_paths = import_paths or []\\n self.show_flow_logs = show_flow_logs\\n super().__init__(\\n name=name,\\n labels=labels,\\n env_vars=env_vars,\\n max_polls=max_polls,\\n agent_address=agent_address,\\n no_cloud_logs=no_cloud_logs,\\n )\\n hostname = socket.gethostname()\\n if hostname_label and (hostname not in self.labels):\\n assert isinstance(self.labels, list)\\n self.labels.append(hostname)\\n self.labels.extend(\\n [\\\"azure-flow-storage\\\", \\\"gcs-flow-storage\\\", \\\"s3-flow-storage\\\"]\\n )\\n\\n self.logger.debug(f\\\"Import paths: {self.import_paths}\\\")\\n self.logger.debug(f\\\"Show flow logs: {self.show_flow_logs}\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/local/agent.py::LocalAgent.heartbeat\",\n \"text\": \"src/prefect/agent/local/agent.py::LocalAgent.heartbeat\\n def heartbeat(self) -> None:\\n for process in list(self.processes):\\n if process.poll() is not None:\\n self.processes.remove(process)\\n if process.returncode:\\n self.logger.info(\\n \\\"Process PID {} returned non-zero exit code\\\".format(process.pid)\\n )\\n super().heartbeat()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/local/agent.py::LocalAgent.deploy_flow\",\n \"text\": \"src/prefect/agent/local/agent.py::LocalAgent.deploy_flow\\n def deploy_flow(self, flow_run: GraphQLResult) -> str:\\n \\\"\\\"\\\"\\n Deploy flow runs on your local machine as Docker containers\\n\\n Args:\\n - flow_run (GraphQLResult): A GraphQLResult flow run object\\n\\n Returns:\\n - str: Information about the deployment\\n\\n Raises:\\n - ValueError: if deployment attempted on unsupported Storage type\\n \\\"\\\"\\\"\\n self.logger.info(\\n \\\"Deploying flow run {}\\\".format(flow_run.id) # type: ignore\\n )\\n\\n if not isinstance(\\n StorageSchema().load(flow_run.flow.storage), (Local, Azure, GCS, S3)\\n ):\\n self.logger.error(\\n \\\"Storage for flow run {} is not a supported type.\\\".format(flow_run.id)\\n )\\n raise ValueError(\\\"Unsupported Storage type\\\")\\n\\n env_vars = self.populate_env_vars(flow_run=flow_run)\\n current_env = os.environ.copy()\\n current_env.update(env_vars)\\n\\n python_path = []\\n if current_env.get(\\\"PYTHONPATH\\\"):\\n python_path.append(current_env.get(\\\"PYTHONPATH\\\"))\\n\\n python_path.append(os.getcwd())\\n\\n if self.import_paths:\\n python_path += self.import_paths\\n\\n current_env[\\\"PYTHONPATH\\\"] = \\\":\\\".join(python_path)\\n\\n stdout = sys.stdout if self.show_flow_logs else DEVNULL\\n\\n # note: we will allow these processes to be orphaned if the agent were to exit\\n # before the flow runs have completed. The lifecycle of the agent should not\\n # dictate the lifecycle of the flow run. However, if the user has elected to\\n # show flow logs, these log entries will continue to stream to the users terminal\\n # until these child processes exit, even if the agent has already exited.\\n p = Popen(\\n [\\\"prefect\\\", \\\"execute\\\", \\\"cloud-flow\\\"],\\n stdout=stdout,\\n stderr=STDOUT,\\n env=current_env,\\n )\\n\\n self.process\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/local/agent.py::LocalAgent.populate_env_vars\",\n \"text\": \"src/prefect/agent/local/agent.py::LocalAgent.populate_env_vars\\n def populate_env_vars(self, flow_run: GraphQLResult) -> dict:\\n \\\"\\\"\\\"\\n Populate metadata and variables in the environment variables for a flow run\\n\\n Args:\\n - flow_run (GraphQLResult): A flow run object\\n\\n Returns:\\n - dict: a dictionary representing the populated environment variables\\n \\\"\\\"\\\"\\n all_vars = {\\n \\\"PREFECT__CLOUD__API\\\": config.cloud.api,\\n \\\"PREFECT__CLOUD__AUTH_TOKEN\\\": self.client._api_token,\\n \\\"PREFECT__CLOUD__AGENT__LABELS\\\": str(self.labels),\\n \\\"PREFECT__CONTEXT__FLOW_RUN_ID\\\": flow_run.id, # type: ignore\\n \\\"PREFECT__CONTEXT__FLOW_ID\\\": flow_run.flow.id, # type: ignore\\n \\\"PREFECT__CLOUD__USE_LOCAL_SECRETS\\\": \\\"false\\\",\\n \\\"PREFECT__LOGGING__LOG_TO_CLOUD\\\": str(self.log_to_cloud).lower(),\\n \\\"PREFECT__LOGGING__LEVEL\\\": \\\"DEBUG\\\",\\n \\\"PREFECT__ENGINE__FLOW_RUNNER__DEFAULT_CLASS\\\": \\\"prefect.engine.cloud.CloudFlowRunner\\\",\\n \\\"PREFECT__ENGINE__TASK_RUNNER__DEFAULT_CLASS\\\": \\\"prefect.engine.cloud.CloudTaskRunner\\\",\\n **self.env_vars,\\n }\\n return {k: v for k, v in all_vars.items() if v is not None}\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/local/agent.py::LocalAgent.generate_supervisor_conf\",\n \"text\": \"src/prefect/agent/local/agent.py::LocalAgent.generate_supervisor_conf\\n @staticmethod\\n def generate_supervisor_conf(\\n token: str = None,\\n labels: Iterable[str] = None,\\n import_paths: List[str] = None,\\n show_flow_logs: bool = False,\\n ) -> str:\\n \\\"\\\"\\\"\\n Generate and output an installable supervisorctl configuration file for the agent.\\n\\n Args:\\n - token (str, optional): A `RUNNER` token to give the agent\\n - labels (List[str], optional): a list of labels, which are arbitrary string\\n identifiers used by Prefect Agents when polling for work\\n - import_paths (List[str], optional): system paths which will be provided to each Flow's runtime environment;\\n useful for Flows which import from locally hosted scripts or packages\\n - show_flow_logs (bool, optional): a boolean specifying whether the agent should re-route Flow run logs\\n to stdout; defaults to `False`\\n\\n Returns:\\n - str: A string representation of the generated configuration file\\n \\\"\\\"\\\"\\n\\n # Use defaults if not provided\\n token = token or \\\"\\\"\\n labels = labels or []\\n import_paths = import_paths or []\\n\\n with open(\\n os.path.join(os.path.dirname(__file__), \\\"supervisord.conf\\\"), \\\"r\\\"\\n ) as conf_file:\\n conf = conf_file.read()\\n\\n add_opts = \\\"\\\"\\n add_opts += \\\"-t {token} \\\".format(token=token) if token else \\\"\\\"\\n add_opts += \\\"-f \\\" if show_flow_logs else \\\"\\\"\\n add_opts += (\\n \\\" \\\".join(\\\"-l {label} \\\".format(label=label) for label in labels)\\n if labels\\n else \\\"\\\"\\n )\\n add_opts += (\\n \\\" \\\".join(\\\"-p {path}\\\".format(path=path) for path in import_paths)\\n if import_paths\\n else \\\"\\\"\\n )\\n conf = conf.replace(\\\"{{OPTS}}\\\", add_opts)\\n return conf\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.__init__\",\n \"text\": \"src/prefect/client/client.py::Client.__init__\\n def __init__(self, api_server: str = None, api_token: str = None):\\n self._access_token = None\\n self._refresh_token = None\\n self._access_token_expires_at = pendulum.now()\\n self._active_tenant_id = None\\n self._attached_headers = {} # type: Dict[str, str]\\n self.logger = create_diagnostic_logger(\\\"Diagnostics\\\")\\n\\n # store api server\\n self.api_server = api_server or prefect.context.config.cloud.get(\\\"graphql\\\")\\n\\n # store api token\\n self._api_token = api_token or prefect.context.config.cloud.get(\\n \\\"auth_token\\\", None\\n )\\n\\n # if no api token was passed, attempt to load state from local storage\\n if not self._api_token and prefect.config.backend == \\\"cloud\\\":\\n settings = self._load_local_settings()\\n self._api_token = settings.get(\\\"api_token\\\")\\n\\n if self._api_token:\\n self._active_tenant_id = settings.get(\\\"active_tenant_id\\\")\\n if self._active_tenant_id:\\n try:\\n self.login_to_tenant(tenant_id=self._active_tenant_id)\\n except AuthorizationError:\\n # if an authorization error is raised, then the token is invalid and should\\n # be cleared\\n self.logout_from_tenant()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.get\",\n \"text\": \"src/prefect/client/client.py::Client.get\\n def get(\\n self,\\n path: str,\\n server: str = None,\\n headers: dict = None,\\n params: Dict[str, JSONLike] = None,\\n token: str = None,\\n ) -> dict:\\n \\\"\\\"\\\"\\n Convenience function for calling the Prefect API with token auth and GET request\\n\\n Args:\\n - path (str): the path of the API url. For example, to GET\\n http://prefect-server/v1/auth/login, path would be 'auth/login'.\\n - server (str, optional): the server to send the GET request to;\\n defaults to `self.api_server`\\n - headers (dict, optional): Headers to pass with the request\\n - params (dict): GET parameters\\n - token (str): an auth token. If not supplied, the `client.access_token` is used.\\n\\n Returns:\\n - dict: Dictionary representation of the request made\\n \\\"\\\"\\\"\\n response = self._request(\\n method=\\\"GET\\\",\\n path=path,\\n params=params,\\n server=server,\\n headers=headers,\\n token=token,\\n )\\n if response.text:\\n return response.json()\\n else:\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.post\",\n \"text\": \"src/prefect/client/client.py::Client.post\\n def post(\\n self,\\n path: str,\\n server: str = None,\\n headers: dict = None,\\n params: Dict[str, JSONLike] = None,\\n token: str = None,\\n ) -> dict:\\n \\\"\\\"\\\"\\n Convenience function for calling the Prefect API with token auth and POST request\\n\\n Args:\\n - path (str): the path of the API url. For example, to POST\\n http://prefect-server/v1/auth/login, path would be 'auth/login'.\\n - server (str, optional): the server to send the POST request to;\\n defaults to `self.api_server`\\n - headers(dict): headers to pass with the request\\n - params (dict): POST parameters\\n - token (str): an auth token. If not supplied, the `client.access_token` is used.\\n\\n Returns:\\n - dict: Dictionary representation of the request made\\n \\\"\\\"\\\"\\n response = self._request(\\n method=\\\"POST\\\",\\n path=path,\\n params=params,\\n server=server,\\n headers=headers,\\n token=token,\\n )\\n if response.text:\\n return response.json()\\n else:\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.graphql\",\n \"text\": \"src/prefect/client/client.py::Client.graphql\\n def graphql(\\n self,\\n query: Any,\\n raise_on_error: bool = True,\\n headers: Dict[str, str] = None,\\n variables: Dict[str, JSONLike] = None,\\n token: str = None,\\n ) -> GraphQLResult:\\n \\\"\\\"\\\"\\n Convenience function for running queries against the Prefect GraphQL API\\n\\n Args:\\n - query (Any): A representation of a graphql query to be executed. It will be\\n parsed by prefect.utilities.graphql.parse_graphql().\\n - raise_on_error (bool): if True, a `ClientError` will be raised if the GraphQL\\n returns any `errors`.\\n - headers (dict): any additional headers that should be passed as part of the\\n request\\n - variables (dict): Variables to be filled into a query with the key being\\n equivalent to the variables that are accepted by the query\\n - token (str): an auth token. If not supplied, the `client.access_token` is used.\\n\\n Returns:\\n - dict: Data returned from the GraphQL query\\n\\n Raises:\\n - ClientError if there are errors raised by the GraphQL mutation\\n \\\"\\\"\\\"\\n result = self.post(\\n path=\\\"\\\",\\n server=self.api_server,\\n headers=headers,\\n params=dict(query=parse_graphql(query), variables=json.dumps(variables)),\\n token=token,\\n )\\n\\n if raise_on_error and \\\"errors\\\" in result:\\n if \\\"UNAUTHENTICATED\\\" in str(result[\\\"errors\\\"]):\\n raise AuthorizationError(result[\\\"errors\\\"])\\n elif \\\"Malformed Authorization header\\\" in str(result[\\\"errors\\\"]):\\n raise AuthorizationError(result[\\\"errors\\\"])\\n raise ClientError(result[\\\"errors\\\"])\\n else:\\n return GraphQLResult(result) # type: ignore\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client._request\",\n \"text\": \"src/prefect/client/client.py::Client._request\\n def _request(\\n self,\\n method: str,\\n path: str,\\n params: Dict[str, JSONLike] = None,\\n server: str = None,\\n headers: dict = None,\\n token: str = None,\\n ) -> \\\"requests.models.Response\\\":\\n \\\"\\\"\\\"\\n Runs any specified request (GET, POST, DELETE) against the server\\n\\n Args:\\n - method (str): The type of request to be made (GET, POST, DELETE)\\n - path (str): Path of the API URL\\n - params (dict, optional): Parameters used for the request\\n - server (str, optional): The server to make requests against, base API\\n server is used if not specified\\n - headers (dict, optional): Headers to pass with the request\\n - token (str): an auth token. If not supplied, the `client.access_token` is used.\\n\\n Returns:\\n - requests.models.Response: The response returned from the request\\n\\n Raises:\\n - ClientError: if the client token is not in the context (due to not being logged in)\\n - ValueError: if a method is specified outside of the accepted GET, POST, DELETE\\n - requests.HTTPError: if a status code is returned that is not `200` or `401`\\n \\\"\\\"\\\"\\n if server is None:\\n server = self.api_server\\n assert isinstance(server, str) # mypy assert\\n\\n if token is None:\\n token = self.get_auth_token()\\n\\n # 'import requests' is expensive time-wise, we should do this just-in-time to keep\\n # the 'import prefect' time low\\n import requests\\n\\n url = urljoin(server, path.lstrip(\\\"/\\\")).rstrip(\\\"/\\\")\\n\\n params = params or {}\\n\\n headers = headers or {}\\n if token:\\n headers[\\\"Authorization\\\"] = \\\"Bearer {}\\\".format(token)\\n headers[\\\"X-PREFECT-CORE-VERSION\\\"] = str(prefect.__version__)\\n\\n if self._attached_headers:\\n headers.update(self._attached_headers)\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.attach_headers\",\n \"text\": \"src/prefect/client/client.py::Client.attach_headers\\n def attach_headers(self, headers: dict) -> None:\\n \\\"\\\"\\\"\\n Set headers to be attached to this Client\\n\\n Args:\\n - headers (dict): A dictionary of headers to attach to this client. These headers\\n get added on to the existing dictionary of headers.\\n \\\"\\\"\\\"\\n self._attached_headers.update(headers)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client._local_settings_path\",\n \"text\": \"src/prefect/client/client.py::Client._local_settings_path\\n @property\\n def _local_settings_path(self) -> Path:\\n \\\"\\\"\\\"\\n Returns the local settings directory corresponding to the current API servers\\n \\\"\\\"\\\"\\n path = \\\"{home}/client/{server}\\\".format(\\n home=prefect.context.config.home_dir,\\n server=slugify(self.api_server, regex_pattern=r\\\"[^-\\\\.a-z0-9]+\\\"),\\n )\\n return Path(os.path.expanduser(path)) / \\\"settings.toml\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client._save_local_settings\",\n \"text\": \"src/prefect/client/client.py::Client._save_local_settings\\n def _save_local_settings(self, settings: dict) -> None:\\n \\\"\\\"\\\"\\n Writes settings to local storage\\n \\\"\\\"\\\"\\n self._local_settings_path.parent.mkdir(exist_ok=True, parents=True)\\n with self._local_settings_path.open(\\\"w+\\\") as f:\\n toml.dump(settings, f)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client._load_local_settings\",\n \"text\": \"src/prefect/client/client.py::Client._load_local_settings\\n def _load_local_settings(self) -> dict:\\n \\\"\\\"\\\"\\n Loads settings from local storage\\n \\\"\\\"\\\"\\n if self._local_settings_path.exists():\\n with self._local_settings_path.open(\\\"r\\\") as f:\\n return toml.load(f) # type: ignore\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.save_api_token\",\n \"text\": \"src/prefect/client/client.py::Client.save_api_token\\n def save_api_token(self) -> None:\\n \\\"\\\"\\\"\\n Saves the API token in local storage.\\n \\\"\\\"\\\"\\n settings = self._load_local_settings()\\n settings[\\\"api_token\\\"] = self._api_token\\n self._save_local_settings(settings)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.get_auth_token\",\n \"text\": \"src/prefect/client/client.py::Client.get_auth_token\\n def get_auth_token(self) -> str:\\n \\\"\\\"\\\"\\n Returns an auth token:\\n - if no explicit access token is stored, returns the api token\\n - if there is an access token:\\n - if there's a refresh token and the access token expires in the next 30 seconds,\\n then we refresh the access token and store the result\\n - return the access token\\n\\n Returns:\\n - str: the access token\\n \\\"\\\"\\\"\\n if not self._access_token:\\n return self._api_token\\n\\n expiration = self._access_token_expires_at or pendulum.now()\\n if self._refresh_token and pendulum.now().add(seconds=30) > expiration:\\n self._refresh_access_token()\\n\\n return self._access_token\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.get_available_tenants\",\n \"text\": \"src/prefect/client/client.py::Client.get_available_tenants\\n def get_available_tenants(self) -> List[Dict]:\\n \\\"\\\"\\\"\\n Returns a list of available tenants.\\n\\n NOTE: this should only be called by users who have provided a USER-scoped API token.\\n\\n Returns:\\n - List[Dict]: a list of dictionaries containing the id, slug, and name of\\n available tenants\\n \\\"\\\"\\\"\\n result = self.graphql(\\n {\\\"query\\\": {\\\"tenant(order_by: {slug: asc})\\\": {\\\"id\\\", \\\"slug\\\", \\\"name\\\"}}},\\n # use the API token to see all available tenants\\n token=self._api_token,\\n ) # type: ignore\\n return result.data.tenant # type: ignore\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.login_to_tenant\",\n \"text\": \"src/prefect/client/client.py::Client.login_to_tenant\\n def login_to_tenant(self, tenant_slug: str = None, tenant_id: str = None) -> bool:\\n \\\"\\\"\\\"\\n Log in to a specific tenant\\n\\n NOTE: this should only be called by users who have provided a USER-scoped API token.\\n\\n Args:\\n - tenant_slug (str): the tenant's slug\\n - tenant_id (str): the tenant's id\\n\\n Returns:\\n - bool: True if the login was successful\\n\\n Raises:\\n - ValueError: if at least one of `tenant_slug` or `tenant_id` isn't provided\\n - ValueError: if the `tenant_id` is not a valid UUID\\n - ValueError: if no matching tenants are found\\n\\n \\\"\\\"\\\"\\n\\n if tenant_slug is None and tenant_id is None:\\n raise ValueError(\\n \\\"At least one of `tenant_slug` or `tenant_id` must be provided.\\\"\\n )\\n elif tenant_id:\\n try:\\n uuid.UUID(tenant_id)\\n except ValueError:\\n raise ValueError(\\\"The `tenant_id` must be a valid UUID.\\\")\\n\\n tenant = self.graphql(\\n {\\n \\\"query($slug: String, $id: uuid)\\\": {\\n \\\"tenant(where: {slug: { _eq: $slug }, id: { _eq: $id } })\\\": {\\\"id\\\"}\\n }\\n },\\n variables=dict(slug=tenant_slug, id=tenant_id),\\n # use the API token to query the tenant\\n token=self._api_token,\\n ) # type: ignore\\n if not tenant.data.tenant: # type: ignore\\n raise ValueError(\\\"No matching tenants found.\\\")\\n\\n tenant_id = tenant.data.tenant[0].id # type: ignore\\n\\n payload = self.graphql(\\n {\\n \\\"mutation($input: switch_tenant_input!)\\\": {\\n \\\"switch_tenant(input: $input)\\\": {\\n \\\"access_token\\\",\\n \\\"expires_at\\\",\\n \\\"refresh_token\\\",\\n }\\n }\\n },\\n variables=dict(in\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.logout_from_tenant\",\n \"text\": \"src/prefect/client/client.py::Client.logout_from_tenant\\n def logout_from_tenant(self) -> None:\\n self._access_token = None\\n self._refresh_token = None\\n self._active_tenant_id = None\\n\\n # remove the tenant setting\\n settings = self._load_local_settings()\\n settings[\\\"active_tenant_id\\\"] = None\\n self._save_local_settings(settings)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client._refresh_access_token\",\n \"text\": \"src/prefect/client/client.py::Client._refresh_access_token\\n def _refresh_access_token(self) -> bool:\\n \\\"\\\"\\\"\\n Refresh the client's JWT access token.\\n\\n NOTE: this should only be called by users who have provided a USER-scoped API token.\\n\\n Returns:\\n - bool: True if the refresh succeeds\\n \\\"\\\"\\\"\\n payload = self.graphql(\\n {\\n \\\"mutation($input: refresh_token_input!)\\\": {\\n \\\"refresh_token(input: $input)\\\": {\\n \\\"access_token\\\",\\n \\\"expires_at\\\",\\n \\\"refresh_token\\\",\\n }\\n }\\n },\\n variables=dict(input=dict(access_token=self._access_token)),\\n # pass the refresh token as the auth header\\n token=self._refresh_token,\\n ) # type: ignore\\n self._access_token = payload.data.refresh_token.access_token # type: ignore\\n self._access_token_expires_at = pendulum.parse( # type: ignore\\n payload.data.refresh_token.expires_at # type: ignore\\n ) # type: ignore\\n self._refresh_token = payload.data.refresh_token.refresh_token # type: ignore\\n\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.register\",\n \"text\": \"src/prefect/client/client.py::Client.register\\n def register(\\n self,\\n flow: \\\"Flow\\\",\\n project_name: str = None,\\n build: bool = True,\\n set_schedule_active: bool = True,\\n version_group_id: str = None,\\n compressed: bool = True,\\n no_url: bool = False,\\n ) -> str:\\n \\\"\\\"\\\"\\n Push a new flow to Prefect Cloud\\n\\n Args:\\n - flow (Flow): a flow to register\\n - project_name (str, optional): the project that should contain this flow.\\n - build (bool, optional): if `True`, the flow's environment is built\\n prior to serialization; defaults to `True`\\n - set_schedule_active (bool, optional): if `False`, will set the\\n schedule to inactive in the database to prevent auto-scheduling runs (if the Flow has a schedule).\\n Defaults to `True`. This can be changed later.\\n - version_group_id (str, optional): the UUID version group ID to use for versioning this Flow\\n in Cloud; if not provided, the version group ID associated with this Flow's project and name\\n will be used.\\n - compressed (bool, optional): if `True`, the serialized flow will be; defaults to `True`\\n compressed\\n - no_url (bool, optional): if `True`, the stdout from this function will not contain the\\n URL link to the newly-registered flow in the Cloud UI\\n\\n Returns:\\n - str: the ID of the newly-registered flow\\n\\n Raises:\\n - ClientError: if the register failed\\n \\\"\\\"\\\"\\n required_parameters = {p for p in flow.parameters() if p.required}\\n if flow.schedule is not None and required_parameters:\\n required_names = {p.name for p in required_parameters}\\n if not all(\\n [\\n required_names <= set(c.parameter_defaults.keys())\\n for c in flow.schedule.clocks\\n ]\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.get_cloud_url\",\n \"text\": \"src/prefect/client/client.py::Client.get_cloud_url\\n def get_cloud_url(self, subdirectory: str, id: str, as_user: bool = True) -> str:\\n \\\"\\\"\\\"\\n Convenience method for creating Prefect Cloud URLs for a given subdirectory.\\n\\n Args:\\n - subdirectory (str): the subdirectory to use (e.g., `\\\"flow-run\\\"`)\\n - id (str): the ID of the page\\n - as_user (bool, optional): whether this query is being made from a USER scoped token;\\n defaults to `True`. Only used internally for queries made from RUNNERs\\n\\n Returns:\\n - str: the URL corresponding to the appropriate base URL, tenant slug, subdirectory and ID\\n\\n Example:\\n\\n ```python\\n from prefect import Client\\n\\n client = Client()\\n client.get_cloud_url(\\\"flow-run\\\", \\\"424242-ca-94611-111-55\\\")\\n # returns \\\"https://cloud.prefect.io/my-tenant-slug/flow-run/424242-ca-94611-111-55\\\"\\n ```\\n \\\"\\\"\\\"\\n # Generate direct link to UI\\n if prefect.config.backend == \\\"cloud\\\":\\n tenant_slug = self.get_default_tenant_slug(as_user=as_user)\\n else:\\n tenant_slug = \\\"\\\"\\n\\n base_url = (\\n re.sub(\\\"api-\\\", \\\"\\\", prefect.config.cloud.api)\\n if re.search(\\\"api-\\\", prefect.config.cloud.api)\\n else re.sub(\\\"api\\\", \\\"cloud\\\", prefect.config.cloud.api)\\n )\\n\\n full_url = prefect.config.cloud.api\\n if tenant_slug:\\n full_url = \\\"/\\\".join([base_url.rstrip(\\\"/\\\"), tenant_slug, subdirectory, id])\\n elif prefect.config.backend == \\\"server\\\":\\n full_url = \\\"/\\\".join([prefect.config.server.ui.endpoint, subdirectory, id,])\\n\\n return full_url\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.get_default_tenant_slug\",\n \"text\": \"src/prefect/client/client.py::Client.get_default_tenant_slug\\n def get_default_tenant_slug(self, as_user: bool = True) -> str:\\n \\\"\\\"\\\"\\n Get the default tenant slug for the currently authenticated user\\n\\n Args:\\n - as_user (bool, optional): whether this query is being made from a USER scoped token;\\n defaults to `True`. Only used internally for queries made from RUNNERs\\n\\n Returns:\\n - str: the slug of the current default tenant for this user\\n \\\"\\\"\\\"\\n if as_user:\\n query = {\\n \\\"query\\\": {\\\"user\\\": {\\\"default_membership\\\": {\\\"tenant\\\": \\\"slug\\\"}}}\\n } # type: dict\\n else:\\n query = {\\\"query\\\": {\\\"tenant\\\": {\\\"slug\\\"}}}\\n\\n res = self.graphql(query)\\n\\n if as_user:\\n user = res.get(\\\"data\\\").user[0]\\n slug = user.default_membership.tenant.slug\\n else:\\n slug = res.get(\\\"data\\\").tenant[0].slug\\n return slug\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.create_project\",\n \"text\": \"src/prefect/client/client.py::Client.create_project\\n def create_project(self, project_name: str, project_description: str = None) -> str:\\n \\\"\\\"\\\"\\n Create a new Project\\n\\n Args:\\n - project_name (str): the project that should contain this flow\\n - project_description (str, optional): the project description\\n\\n Returns:\\n - str: the ID of the newly-created project\\n\\n Raises:\\n - ClientError: if the project creation failed\\n \\\"\\\"\\\"\\n project_mutation = {\\n \\\"mutation($input: create_project_input!)\\\": {\\n \\\"create_project(input: $input)\\\": {\\\"id\\\"}\\n }\\n }\\n\\n res = self.graphql(\\n project_mutation,\\n variables=dict(\\n input=dict(name=project_name, description=project_description)\\n ),\\n ) # type: Any\\n\\n return res.data.create_project.id\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.create_flow_run\",\n \"text\": \"src/prefect/client/client.py::Client.create_flow_run\\n def create_flow_run(\\n self,\\n flow_id: str = None,\\n context: dict = None,\\n parameters: dict = None,\\n scheduled_start_time: datetime.datetime = None,\\n idempotency_key: str = None,\\n run_name: str = None,\\n version_group_id: str = None,\\n ) -> str:\\n \\\"\\\"\\\"\\n Create a new flow run for the given flow id. If `start_time` is not provided, the flow run will be scheduled to start immediately.\\n If both `flow_id` and `version_group_id` are provided, only the `flow_id` will be used.\\n\\n Args:\\n - flow_id (str, optional): the id of the Flow you wish to schedule\\n - context (dict, optional): the run context\\n - parameters (dict, optional): a dictionary of parameter values to pass to the flow run\\n - scheduled_start_time (datetime, optional): the time to schedule the execution for; if not provided, defaults to now\\n - idempotency_key (str, optional): an idempotency key; if provided, this run will be cached for 24\\n hours. Any subsequent attempts to create a run with the same idempotency key\\n will return the ID of the originally created run (no new run will be created after the first).\\n An error will be raised if parameters or context are provided and don't match the original.\\n Each subsequent request will reset the TTL for 24 hours.\\n - run_name (str, optional): The name assigned to this flow run\\n - version_group_id (str, optional): if provided, the unique unarchived flow within this version group will be scheduled\\n to run. This input can be used as a stable API for running flows which are regularly updated.\\n\\n Returns:\\n - str: the ID of the newly-created flow run\\n\\n Raises:\\n - ClientError: if the GraphQL query is bad for any reason\\n \\\"\\\"\\\"\\n create_mutation = \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.get_flow_run_info\",\n \"text\": \"src/prefect/client/client.py::Client.get_flow_run_info\\n def get_flow_run_info(self, flow_run_id: str) -> FlowRunInfoResult:\\n \\\"\\\"\\\"\\n Retrieves version and current state information for the given flow run.\\n\\n Args:\\n - flow_run_id (str): the id of the flow run to get information for\\n\\n Returns:\\n - GraphQLResult: an object representing information about the flow run\\n\\n Raises:\\n - ClientError: if the GraphQL mutation is bad for any reason\\n \\\"\\\"\\\"\\n query = {\\n \\\"query\\\": {\\n with_args(\\\"flow_run_by_pk\\\", {\\\"id\\\": flow_run_id}): {\\n \\\"id\\\": True,\\n \\\"name\\\": True,\\n \\\"flow_id\\\": True,\\n \\\"parameters\\\": True,\\n \\\"context\\\": True,\\n \\\"version\\\": True,\\n \\\"scheduled_start_time\\\": True,\\n \\\"serialized_state\\\": True,\\n # load all task runs except dynamic task runs\\n with_args(\\\"task_runs\\\", {\\\"where\\\": {\\\"map_index\\\": {\\\"_eq\\\": -1}}}): {\\n \\\"id\\\": True,\\n \\\"task\\\": {\\\"id\\\": True, \\\"slug\\\": True},\\n \\\"version\\\": True,\\n \\\"serialized_state\\\": True,\\n },\\n }\\n }\\n }\\n result = self.graphql(query).data.flow_run_by_pk # type: ignore\\n\\n if result is None:\\n raise ClientError('Flow run ID not found: \\\"{}\\\"'.format(flow_run_id))\\n\\n # convert scheduled_start_time from string to datetime\\n result.scheduled_start_time = pendulum.parse(result.scheduled_start_time)\\n\\n # create \\\"state\\\" attribute from serialized_state\\n result.state = prefect.engine.state.State.deserialize(\\n result.pop(\\\"serialized_state\\\")\\n )\\n\\n # reformat task_runs\\n task_runs = []\\n for tr in result.task_runs:\\n tr.state = prefect.engine.state.State.deserialize(\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.update_flow_run_heartbeat\",\n \"text\": \"src/prefect/client/client.py::Client.update_flow_run_heartbeat\\n def update_flow_run_heartbeat(self, flow_run_id: str) -> None:\\n \\\"\\\"\\\"\\n Convenience method for heartbeating a flow run.\\n\\n Does NOT raise an error if the update fails.\\n\\n Args:\\n - flow_run_id (str): the flow run ID to heartbeat\\n\\n \\\"\\\"\\\"\\n mutation = {\\n \\\"mutation\\\": {\\n with_args(\\n \\\"update_flow_run_heartbeat\\\", {\\\"input\\\": {\\\"flow_run_id\\\": flow_run_id}}\\n ): {\\\"success\\\"}\\n }\\n }\\n self.graphql(mutation, raise_on_error=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.update_task_run_heartbeat\",\n \"text\": \"src/prefect/client/client.py::Client.update_task_run_heartbeat\\n def update_task_run_heartbeat(self, task_run_id: str) -> None:\\n \\\"\\\"\\\"\\n Convenience method for heartbeating a task run.\\n\\n Does NOT raise an error if the update fails.\\n\\n Args:\\n - task_run_id (str): the task run ID to heartbeat\\n\\n \\\"\\\"\\\"\\n mutation = {\\n \\\"mutation\\\": {\\n with_args(\\n \\\"update_task_run_heartbeat\\\", {\\\"input\\\": {\\\"task_run_id\\\": task_run_id}}\\n ): {\\\"success\\\"}\\n }\\n }\\n self.graphql(mutation, raise_on_error=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.set_flow_run_state\",\n \"text\": \"src/prefect/client/client.py::Client.set_flow_run_state\\n def set_flow_run_state(\\n self, flow_run_id: str, version: int, state: \\\"prefect.engine.state.State\\\"\\n ) -> None:\\n \\\"\\\"\\\"\\n Sets new state for a flow run in the database.\\n\\n Args:\\n - flow_run_id (str): the id of the flow run to set state for\\n - version (int): the current version of the flow run state\\n - state (State): the new state for this flow run\\n\\n Raises:\\n - ClientError: if the GraphQL mutation is bad for any reason\\n \\\"\\\"\\\"\\n mutation = {\\n \\\"mutation($input: set_flow_run_states_input!)\\\": {\\n \\\"set_flow_run_states(input: $input)\\\": {\\\"states\\\": {\\\"id\\\"}}\\n }\\n }\\n\\n serialized_state = state.serialize()\\n\\n result = self.graphql(\\n mutation,\\n variables=dict(\\n input=dict(\\n states=[\\n dict(\\n state=serialized_state,\\n flow_run_id=flow_run_id,\\n version=version,\\n )\\n ]\\n )\\n ),\\n ) # type: Any\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.get_latest_cached_states\",\n \"text\": \"src/prefect/client/client.py::Client.get_latest_cached_states\\n def get_latest_cached_states(\\n self, task_id: str, cache_key: Optional[str], created_after: datetime.datetime\\n ) -> List[\\\"prefect.engine.state.State\\\"]:\\n \\\"\\\"\\\"\\n Pulls all Cached states for the given task that were created after the provided date.\\n\\n Args:\\n - task_id (str): the task id for this task run\\n - cache_key (Optional[str]): the cache key for this Task's cache; if `None`, the task id alone will be used\\n - created_after (datetime.datetime): the earliest date the state should have been created at\\n\\n Returns:\\n - List[State]: a list of Cached states created after the given date\\n \\\"\\\"\\\"\\n where_clause = {\\n \\\"where\\\": {\\n \\\"state\\\": {\\\"_eq\\\": \\\"Cached\\\"},\\n \\\"_or\\\": [\\n {\\n \\\"_and\\\": [\\n {\\\"cache_key\\\": {\\\"_eq\\\": cache_key}},\\n {\\\"cache_key\\\": {\\\"_is_null\\\": False}},\\n ]\\n },\\n {\\\"task_id\\\": {\\\"_eq\\\": task_id}},\\n ],\\n \\\"state_timestamp\\\": {\\\"_gte\\\": created_after.isoformat()},\\n },\\n \\\"order_by\\\": {\\\"state_timestamp\\\": EnumValue(\\\"desc\\\")},\\n }\\n query = {\\\"query\\\": {with_args(\\\"task_run\\\", where_clause): \\\"serialized_state\\\"}}\\n result = self.graphql(query) # type: Any\\n deserializer = prefect.engine.state.State.deserialize\\n valid_states = [\\n deserializer(res.serialized_state) for res in result.data.task_run\\n ]\\n return valid_states\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.get_task_run_info\",\n \"text\": \"src/prefect/client/client.py::Client.get_task_run_info\\n def get_task_run_info(\\n self, flow_run_id: str, task_id: str, map_index: Optional[int] = None\\n ) -> TaskRunInfoResult:\\n \\\"\\\"\\\"\\n Retrieves version and current state information for the given task run.\\n\\n Args:\\n - flow_run_id (str): the id of the flow run that this task run lives in\\n - task_id (str): the task id for this task run\\n - map_index (int, optional): the mapping index for this task run; if\\n `None`, it is assumed this task is _not_ mapped\\n\\n Returns:\\n - NamedTuple: a tuple containing `id, task_id, version, state`\\n\\n Raises:\\n - ClientError: if the GraphQL mutation is bad for any reason\\n \\\"\\\"\\\"\\n\\n mutation = {\\n \\\"mutation\\\": {\\n with_args(\\n \\\"get_or_create_task_run\\\",\\n {\\n \\\"input\\\": {\\n \\\"flow_run_id\\\": flow_run_id,\\n \\\"task_id\\\": task_id,\\n \\\"map_index\\\": -1 if map_index is None else map_index,\\n }\\n },\\n ): {\\n \\\"id\\\": True,\\n }\\n }\\n }\\n result = self.graphql(mutation) # type: Any\\n\\n if result is None:\\n raise ClientError(\\\"Failed to create task run.\\\")\\n\\n task_run_id = result.data.get_or_create_task_run.id\\n\\n query = {\\n \\\"query\\\": {\\n with_args(\\\"task_run_by_pk\\\", {\\\"id\\\": task_run_id}): {\\n \\\"version\\\": True,\\n \\\"serialized_state\\\": True,\\n \\\"task\\\": {\\\"slug\\\": True},\\n }\\n }\\n }\\n task_run = self.graphql(query).data.task_run_by_pk # type: ignore\\n\\n if task_run is None:\\n raise ClientError('Task run ID not found: \\\"{}\\\"'.format(task_run_id))\\n\\n state = prefect.engine.state.State.dese\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.set_task_run_state\",\n \"text\": \"src/prefect/client/client.py::Client.set_task_run_state\\n def set_task_run_state(\\n self,\\n task_run_id: str,\\n version: int,\\n state: \\\"prefect.engine.state.State\\\",\\n cache_for: datetime.timedelta = None,\\n ) -> \\\"prefect.engine.state.State\\\":\\n \\\"\\\"\\\"\\n Sets new state for a task run.\\n\\n Args:\\n - task_run_id (str): the id of the task run to set state for\\n - version (int): the current version of the task run state\\n - state (State): the new state for this task run\\n - cache_for (timedelta, optional): how long to store the result of this task for, using the\\n serializer set in config; if not provided, no caching occurs\\n\\n Raises:\\n - ClientError: if the GraphQL mutation is bad for any reason\\n\\n Returns:\\n - State: the state the current task run should be considered in\\n \\\"\\\"\\\"\\n mutation = {\\n \\\"mutation($input: set_task_run_states_input!)\\\": {\\n \\\"set_task_run_states(input: $input)\\\": {\\n \\\"states\\\": {\\\"id\\\", \\\"status\\\", \\\"message\\\"}\\n }\\n }\\n }\\n\\n serialized_state = state.serialize()\\n\\n result = self.graphql(\\n mutation,\\n variables=dict(\\n input=dict(\\n states=[\\n dict(\\n state=serialized_state,\\n task_run_id=task_run_id,\\n version=version,\\n )\\n ]\\n )\\n ),\\n ) # type: Any\\n state_payload = result.data.set_task_run_states.states[0]\\n if state_payload.status == \\\"QUEUED\\\":\\n # If appropriate, the state attribute of the Queued state can be\\n # set by the caller of this method\\n return prefect.engine.state.Queued(\\n message=state_payload.get(\\\"message\\\"),\\n start_time=pend\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.set_secret\",\n \"text\": \"src/prefect/client/client.py::Client.set_secret\\n def set_secret(self, name: str, value: Any) -> None:\\n \\\"\\\"\\\"\\n Set a secret with the given name and value.\\n\\n Args:\\n - name (str): the name of the secret; used for retrieving the secret\\n during task runs\\n - value (Any): the value of the secret\\n\\n Raises:\\n - ClientError: if the GraphQL mutation is bad for any reason\\n - ValueError: if the secret-setting was unsuccessful\\n \\\"\\\"\\\"\\n mutation = {\\n \\\"mutation($input: set_secret_input!)\\\": {\\n \\\"set_secret(input: $input)\\\": {\\\"success\\\"}\\n }\\n }\\n\\n result = self.graphql(\\n mutation, variables=dict(input=dict(name=name, value=value))\\n ) # type: Any\\n\\n if not result.data.set_secret.success:\\n raise ValueError(\\\"Setting secret failed.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.get_task_tag_limit\",\n \"text\": \"src/prefect/client/client.py::Client.get_task_tag_limit\\n def get_task_tag_limit(self, tag: str) -> Optional[int]:\\n \\\"\\\"\\\"\\n Retrieve the current task tag concurrency limit for a given tag.\\n\\n Args:\\n - tag (str): the tag to update\\n\\n Raises:\\n - ClientError: if the GraphQL query fails\\n \\\"\\\"\\\"\\n query = {\\n \\\"query\\\": {\\n with_args(\\\"task_tag_limit\\\", {\\\"where\\\": {\\\"tag\\\": {\\\"_eq\\\": tag}}}): {\\n \\\"limit\\\": True\\n }\\n }\\n }\\n\\n result = self.graphql(query) # type: Any\\n if result.data.task_tag_limit:\\n return result.data.task_tag_limit[0].limit\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.update_task_tag_limit\",\n \"text\": \"src/prefect/client/client.py::Client.update_task_tag_limit\\n def update_task_tag_limit(self, tag: str, limit: int) -> None:\\n \\\"\\\"\\\"\\n Update the task tag concurrency limit for a given tag; requires tenant admin permissions.\\n\\n Args:\\n - tag (str): the tag to update\\n - limit (int): the concurrency limit to enforce on the tag; should be a value >= 0\\n\\n Raises:\\n - ClientError: if the GraphQL mutation is bad for any reason\\n - ValueError: if the tag limit-setting was unsuccessful, or if a bad limit was provided\\n \\\"\\\"\\\"\\n if limit < 0:\\n raise ValueError(\\\"Concurrency limits must be >= 0\\\")\\n\\n mutation = {\\n \\\"mutation($input: update_task_tag_limit_input!)\\\": {\\n \\\"update_task_tag_limit(input: $input)\\\": {\\\"id\\\"}\\n }\\n }\\n\\n result = self.graphql(\\n mutation, variables=dict(input=dict(tag=tag, limit=limit))\\n ) # type: Any\\n\\n if not result.data.update_task_tag_limit.id:\\n raise ValueError(\\\"Updating the task tag concurrency limit failed.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.delete_task_tag_limit\",\n \"text\": \"src/prefect/client/client.py::Client.delete_task_tag_limit\\n def delete_task_tag_limit(self, limit_id: str) -> None:\\n \\\"\\\"\\\"\\n Deletes a given task tag concurrency limit; requires tenant admin permissions.\\n\\n Args:\\n - limit_id (str): the ID of the tag to delete\\n\\n Raises:\\n - ClientError: if the GraphQL mutation is bad for any reason\\n - ValueError: if the tag deletion was unsuccessful, or if a bad tag ID was provided\\n \\\"\\\"\\\"\\n mutation = {\\n \\\"mutation($input: delete_task_tag_limit_input!)\\\": {\\n \\\"delete_task_tag_limit(input: $input)\\\": {\\\"success\\\"}\\n }\\n }\\n\\n result = self.graphql(\\n mutation, variables=dict(input=dict(limit_id=limit_id))\\n ) # type: Any\\n\\n if not result.data.delete_task_tag_limit.success:\\n raise ValueError(\\\"Deleting the task tag concurrency limit failed.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.write_run_logs\",\n \"text\": \"src/prefect/client/client.py::Client.write_run_logs\\n def write_run_logs(self, logs: List[Dict]) -> None:\\n \\\"\\\"\\\"\\n Uploads a collection of logs to Cloud.\\n\\n Args:\\n - logs (List[Dict]): a list of log entries to add\\n\\n Raises:\\n - ValueError: if uploading the logs fail\\n \\\"\\\"\\\"\\n mutation = {\\n \\\"mutation($input: write_run_logs_input!)\\\": {\\n \\\"write_run_logs(input: $input)\\\": {\\\"success\\\"}\\n }\\n }\\n\\n result = self.graphql(\\n mutation, variables=dict(input=dict(logs=logs))\\n ) # type: Any\\n\\n if not result.data.write_run_logs.success:\\n raise ValueError(\\\"Writing logs failed.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/client.py::Client.register_agent\",\n \"text\": \"src/prefect/client/client.py::Client.register_agent\\n def register_agent(\\n self, agent_type: str, name: str = None, labels: List[str] = None\\n ) -> str:\\n \\\"\\\"\\\"\\n Register an agent with Cloud\\n\\n Args:\\n - agent_type (str): The type of agent being registered\\n - name: (str, optional): The name of the agent being registered\\n - labels (List[str], optional): A list of any present labels on the agent\\n being registered\\n\\n Returns:\\n - The agent ID as a string\\n \\\"\\\"\\\"\\n mutation = {\\n \\\"mutation($input: register_agent_input!)\\\": {\\n \\\"register_agent(input: $input)\\\": {\\\"id\\\"}\\n }\\n }\\n\\n result = self.graphql(\\n mutation,\\n variables=dict(input=dict(type=agent_type, name=name, labels=labels)),\\n )\\n\\n if not result.data.register_agent.id:\\n raise ValueError(\\\"Error registering agent\\\")\\n\\n return result.data.register_agent.id\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.__init__\",\n \"text\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.__init__\\n def __init__( # type: ignore\\n self,\\n launch_type: str = \\\"FARGATE\\\",\\n aws_access_key_id: str = None,\\n aws_secret_access_key: str = None,\\n aws_session_token: str = None,\\n region_name: str = None,\\n executor_kwargs: dict = None,\\n labels: List[str] = None,\\n on_start: Callable = None,\\n on_exit: Callable = None,\\n **kwargs\\n ) -> None:\\n self.launch_type = launch_type\\n # Not serialized, only stored on the object\\n self.aws_access_key_id = aws_access_key_id or os.getenv(\\\"AWS_ACCESS_KEY_ID\\\")\\n self.aws_secret_access_key = aws_secret_access_key or os.getenv(\\n \\\"AWS_SECRET_ACCESS_KEY\\\"\\n )\\n self.aws_session_token = aws_session_token or os.getenv(\\\"AWS_SESSION_TOKEN\\\")\\n self.region_name = region_name or os.getenv(\\\"REGION_NAME\\\")\\n\\n # Parse accepted kwargs for definition and run\\n self.task_definition_kwargs, self.task_run_kwargs = self._parse_kwargs(kwargs)\\n\\n self.executor_kwargs = executor_kwargs or dict()\\n super().__init__(labels=labels, on_start=on_start, on_exit=on_exit)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment._parse_kwargs\",\n \"text\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment._parse_kwargs\\n def _parse_kwargs(self, user_kwargs: dict) -> tuple:\\n \\\"\\\"\\\"\\n Parse the kwargs passed in and separate them out for `register_task_definition`\\n and `run_task`. This is required because boto3 does not allow extra kwargs\\n and if they are provided it will raise botocore.exceptions.ParamValidationError.\\n\\n Args:\\n - user_kwargs (dict): The kwargs passed to the initialization of the environment\\n\\n Returns:\\n tuple: a tuple of two dictionaries (task_definition_kwargs, task_run_kwargs)\\n \\\"\\\"\\\"\\n definition_kwarg_list = [\\n \\\"family\\\",\\n \\\"taskRoleArn\\\",\\n \\\"executionRoleArn\\\",\\n \\\"networkMode\\\",\\n \\\"containerDefinitions\\\",\\n \\\"volumes\\\",\\n \\\"placementConstraints\\\",\\n \\\"requiresCompatibilities\\\",\\n \\\"cpu\\\",\\n \\\"memory\\\",\\n \\\"tags\\\",\\n \\\"pidMode\\\",\\n \\\"ipcMode\\\",\\n \\\"proxyConfiguration\\\",\\n \\\"inferenceAccelerators\\\",\\n ]\\n\\n run_kwarg_list = [\\n \\\"cluster\\\",\\n \\\"taskDefinition\\\",\\n \\\"count\\\",\\n \\\"startedBy\\\",\\n \\\"group\\\",\\n \\\"placementConstraints\\\",\\n \\\"placementStrategy\\\",\\n \\\"platformVersion\\\",\\n \\\"networkConfiguration\\\",\\n \\\"tags\\\",\\n \\\"enableECSManagedTags\\\",\\n \\\"propagateTags\\\",\\n ]\\n\\n task_definition_kwargs = {}\\n for key, item in user_kwargs.items():\\n if key in definition_kwarg_list:\\n task_definition_kwargs.update({key: item})\\n\\n task_run_kwargs = {}\\n for key, item in user_kwargs.items():\\n if key in run_kwarg_list:\\n task_run_kwargs.update({key: item})\\n\\n return task_definition_kwargs, task_run_kwargs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.dependencies\",\n \"text\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.dependencies\\n @property\\n def dependencies(self) -> list:\\n return [\\\"boto3\\\", \\\"botocore\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.setup\",\n \"text\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.setup\\n def setup(self, storage: \\\"Docker\\\") -> None: # type: ignore\\n \\\"\\\"\\\"\\n Register the task definition if it does not already exist.\\n\\n Args:\\n - storage (Storage): the Storage object that contains the flow\\n \\\"\\\"\\\"\\n from boto3 import client as boto3_client\\n from botocore.exceptions import ClientError\\n\\n boto3_c = boto3_client(\\n \\\"ecs\\\",\\n aws_access_key_id=self.aws_access_key_id,\\n aws_secret_access_key=self.aws_secret_access_key,\\n aws_session_token=self.aws_session_token,\\n region_name=self.region_name,\\n )\\n\\n definition_exists = True\\n try:\\n boto3_c.describe_task_definition(\\n taskDefinition=self.task_definition_kwargs.get(\\\"family\\\")\\n )\\n except ClientError:\\n definition_exists = False\\n\\n if not definition_exists:\\n env_values = [\\n {\\\"name\\\": \\\"PREFECT__CLOUD__GRAPHQL\\\", \\\"value\\\": config.cloud.graphql},\\n {\\\"name\\\": \\\"PREFECT__CLOUD__USE_LOCAL_SECRETS\\\", \\\"value\\\": \\\"false\\\"},\\n {\\n \\\"name\\\": \\\"PREFECT__ENGINE__FLOW_RUNNER__DEFAULT_CLASS\\\",\\n \\\"value\\\": \\\"prefect.engine.cloud.CloudFlowRunner\\\",\\n },\\n {\\n \\\"name\\\": \\\"PREFECT__ENGINE__TASK_RUNNER__DEFAULT_CLASS\\\",\\n \\\"value\\\": \\\"prefect.engine.cloud.CloudTaskRunner\\\",\\n },\\n {\\\"name\\\": \\\"PREFECT__LOGGING__LOG_TO_CLOUD\\\", \\\"value\\\": \\\"true\\\"},\\n {\\n \\\"name\\\": \\\"PREFECT__LOGGING__EXTRA_LOGGERS\\\",\\n \\\"value\\\": str(config.logging.extra_loggers),\\n },\\n ]\\n\\n # create containerDefinitions if they do not exist\\n if not self.task_definition_kwargs.get(\\\"containerDefinitions\\\"):\\n self.task_definition_kwargs[\\\"containerDefinit\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.execute\",\n \"text\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.execute\\n def execute( # type: ignore\\n self, storage: \\\"Docker\\\", flow_location: str, **kwargs: Any\\n ) -> None:\\n \\\"\\\"\\\"\\n Run the Fargate task that was defined for this flow.\\n\\n Args:\\n - storage (Storage): the Storage object that contains the flow\\n - flow_location (str): the location of the Flow to execute\\n - **kwargs (Any): additional keyword arguments to pass to the runner\\n \\\"\\\"\\\"\\n from boto3 import client as boto3_client\\n\\n flow_run_id = prefect.context.get(\\\"flow_run_id\\\", \\\"unknown\\\")\\n container_overrides = [\\n {\\n \\\"name\\\": \\\"flow-container\\\",\\n \\\"environment\\\": [\\n {\\n \\\"name\\\": \\\"PREFECT__CLOUD__AUTH_TOKEN\\\",\\n \\\"value\\\": config.cloud.agent.auth_token\\n or config.cloud.auth_token,\\n },\\n {\\\"name\\\": \\\"PREFECT__CONTEXT__FLOW_RUN_ID\\\", \\\"value\\\": flow_run_id},\\n {\\\"name\\\": \\\"PREFECT__CONTEXT__IMAGE\\\", \\\"value\\\": storage.name},\\n {\\n \\\"name\\\": \\\"PREFECT__CONTEXT__FLOW_FILE_PATH\\\",\\n \\\"value\\\": flow_location,\\n },\\n ],\\n }\\n ]\\n\\n boto3_c = boto3_client(\\n \\\"ecs\\\",\\n aws_access_key_id=self.aws_access_key_id,\\n aws_secret_access_key=self.aws_secret_access_key,\\n aws_session_token=self.aws_session_token,\\n region_name=self.region_name,\\n )\\n\\n boto3_c.run_task(\\n overrides={\\\"containerOverrides\\\": container_overrides},\\n launchType=self.launch_type,\\n **self.task_run_kwargs\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.run_flow\",\n \"text\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.run_flow\\n def run_flow(self) -> None:\\n \\\"\\\"\\\"\\n Run the flow from specified flow_file_path location using the default executor\\n \\\"\\\"\\\"\\n\\n # Call on_start callback if specified\\n if self.on_start:\\n self.on_start()\\n\\n try:\\n from prefect.engine import (\\n get_default_flow_runner_class,\\n get_default_executor_class,\\n )\\n\\n # Load serialized flow from file and run it with the executor\\n with open(\\n prefect.context.get(\\n \\\"flow_file_path\\\", \\\"/root/.prefect/flow_env.prefect\\\"\\n ),\\n \\\"rb\\\",\\n ) as f:\\n flow = cloudpickle.load(f)\\n\\n runner_cls = get_default_flow_runner_class()\\n executor_cls = get_default_executor_class()(**self.executor_kwargs)\\n runner_cls(flow=flow).run(executor=executor_cls)\\n except Exception as exc:\\n self.logger.exception(\\n \\\"Unexpected error raised during flow run: {}\\\".format(exc)\\n )\\n raise exc\\n finally:\\n # Call on_exit callback if specified\\n if self.on_exit:\\n self.on_exit()\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":257,"cells":{"instance_id":{"kind":"string","value":"googleapis__google-cloud-python-1925"},"repo":{"kind":"string","value":"googleapis/google-cloud-python"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Regenerate Bigtable V2 protos\n(after the version landed in #1903).\n\n@sduskis reports:\n\n> There were some relatively minor changes in the v2 protos. Mostly the changes were in the comments. There were minor changes to rename fields from \"name\" to \"parent\" to better reflect the meaning of some of the proto elements. The latest version of the protos can be found in the official\n> google api repository at https://github.com/googleapis/googleapis/tree/master/google/bigtable.\n\n"},"gold_files":{"kind":"list like","value":["gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py","gcloud/bigtable/_generated_v2/bigtable_pb2.py","gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py","gcloud/bigtable/_generated_v2/operations_grpc_pb2.py","gcloud/bigtable/_generated_v2/table_pb2.py","gcloud/bigtable/cluster.py","gcloud/bigtable/instance.py","gcloud/bigtable/table.py","scripts/make_datastore_grpc.py","scripts/make_operations_grpc.py","scripts/rewrite_imports.py"],"string":"[\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py\",\n \"gcloud/bigtable/_generated_v2/operations_grpc_pb2.py\",\n \"gcloud/bigtable/_generated_v2/table_pb2.py\",\n \"gcloud/bigtable/cluster.py\",\n \"gcloud/bigtable/instance.py\",\n \"gcloud/bigtable/table.py\",\n \"scripts/make_datastore_grpc.py\",\n \"scripts/make_operations_grpc.py\",\n \"scripts/rewrite_imports.py\"\n]","isConversational":false},"gold_units":{"kind":"list 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\"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::<module>\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.CreateCluster\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.CreateInstance\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.DeleteCluster\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.DeleteInstance\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.GetCluster\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.GetInstance\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.ListClusters\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.ListInstances\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.UpdateCluster\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.UpdateInstance\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.CreateCluster\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.CreateInstance\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.DeleteCluster\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.DeleteInstance\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.GetCluster\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.GetInstance\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.ListClusters\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.ListInstances\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.UpdateCluster\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.UpdateInstance\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::beta_create_BigtableInstanceAdmin_server\",\n \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::beta_create_BigtableInstanceAdmin_stub\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::<module>\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.CheckAndMutateRow\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.MutateRow\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.MutateRows\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.ReadModifyWriteRow\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.ReadRows\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.SampleRowKeys\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.CheckAndMutateRow\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.MutateRow\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.MutateRows\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.ReadModifyWriteRow\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.ReadRows\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.SampleRowKeys\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::beta_create_Bigtable_server\",\n \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::beta_create_Bigtable_stub\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::<module>\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.CreateTable\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.DeleteTable\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.DropRowRange\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.GetTable\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.ListTables\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.ModifyColumnFamilies\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.CreateTable\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.DeleteTable\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.DropRowRange\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.GetTable\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.ListTables\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.ModifyColumnFamilies\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::beta_create_BigtableTableAdmin_server\",\n \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::beta_create_BigtableTableAdmin_stub\",\n \"gcloud/bigtable/_generated_v2/operations_grpc_pb2.py::<module>\",\n \"gcloud/bigtable/_generated_v2/table_pb2.py::<module>\",\n \"gcloud/bigtable/cluster.py::_prepare_create_request\",\n \"gcloud/bigtable/instance.py::Instance.list_clusters\",\n \"gcloud/bigtable/instance.py::Instance.list_tables\",\n \"gcloud/bigtable/instance.py::_prepare_create_request\",\n \"gcloud/bigtable/table.py::Table.create\",\n \"scripts/make_datastore_grpc.py::<module>\",\n \"scripts/make_datastore_grpc.py::get_pb2_contents_with_grpc\",\n \"scripts/make_datastore_grpc.py::get_pb2_contents_without_grpc\",\n \"scripts/make_operations_grpc.py::<module>\",\n \"scripts/make_operations_grpc.py::get_pb2_contents_with_grpc\",\n \"scripts/make_operations_grpc.py::get_pb2_contents_without_grpc\",\n \"scripts/rewrite_imports.py::transform_line\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/conf.py","text":"docs/conf.py","label":0},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py\nclass BetaBigtableInstanceAdminServicer # <fill me in later!>\n def CreateInstance(self, request, context)\n def GetInstance(self, request, context)\n def ListInstances(self, request, context)\n def UpdateInstance(self, request, context)\n def DeleteInstance(self, request, context)\n def CreateCluster(self, request, context)\n def GetCluster(self, request, context)\n def ListClusters(self, request, context)\n def UpdateCluster(self, request, context)\n def DeleteCluster(self, request, context)\nclass BetaBigtableInstanceAdminStub # The interface to which stubs will conform.\n def CreateInstance(self, request, timeout)\n def GetInstance(self, request, timeout)\n def ListInstances(self, request, timeout)\n def UpdateInstance(self, request, timeout)\n def DeleteInstance(self, request, timeout)\n def CreateCluster(self, request, timeout)\n def GetCluster(self, request, timeout)\n def ListClusters(self, request, timeout)\n def UpdateCluster(self, request, timeout)\n def DeleteCluster(self, request, timeout)\ndef beta_create_BigtableInstanceAdmin_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None)\ndef beta_create_BigtableInstanceAdmin_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py\nclass BetaBigtableServicer # <fill me in later!>\n def ReadRows(self, request, context)\n def SampleRowKeys(self, request, context)\n def MutateRow(self, request, context)\n def MutateRows(self, request, context)\n def CheckAndMutateRow(self, request, context)\n def ReadModifyWriteRow(self, request, context)\nclass BetaBigtableStub # The interface to which stubs will conform.\n def ReadRows(self, request, timeout)\n def SampleRowKeys(self, request, timeout)\n def MutateRow(self, request, timeout)\n def MutateRows(self, request, timeout)\n def CheckAndMutateRow(self, request, timeout)\n def ReadModifyWriteRow(self, request, timeout)\ndef beta_create_Bigtable_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None)\ndef beta_create_Bigtable_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py\nclass BetaBigtableTableAdminServicer # <fill me in later!>\n def CreateTable(self, request, context)\n def ListTables(self, request, context)\n def GetTable(self, request, context)\n def DeleteTable(self, request, context)\n def ModifyColumnFamilies(self, request, context)\n def DropRowRange(self, request, context)\nclass BetaBigtableTableAdminStub # The interface to which stubs will conform.\n def CreateTable(self, request, timeout)\n def ListTables(self, request, timeout)\n def GetTable(self, request, timeout)\n def DeleteTable(self, request, timeout)\n def ModifyColumnFamilies(self, request, timeout)\n def DropRowRange(self, request, timeout)\ndef beta_create_BigtableTableAdmin_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None)\ndef beta_create_BigtableTableAdmin_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None)","label":1},{"cid":"gcloud/bigtable/_generated_v2/operations_grpc_pb2.py","text":"gcloud/bigtable/_generated_v2/operations_grpc_pb2.py","label":1},{"cid":"gcloud/bigtable/_generated_v2/table_pb2.py","text":"gcloud/bigtable/_generated_v2/table_pb2.py","label":1},{"cid":"gcloud/bigtable/cluster.py","text":"gcloud/bigtable/cluster.py\ndef _prepare_create_request(cluster) # Creates a protobuf request for a CreateCluster request.\ndef _parse_pb_any_to_native(any_val, expected_type=None) # Convert a serialized \"google.protobuf.Any\" value to actual type.\ndef _process_operation(operation_pb) # Processes a create protobuf response.\nclass Operation # Representation of a Google API Long-Running Operation.\n def __init__(self, op_type, op_id, cluster=None)\n def __eq__(self, other)\n def __ne__(self, other)\n def finished(self) # Check if the operation has finished.\nclass Cluster # Representation of a Google Cloud Bigtable Cluster.\n def __init__(self, cluster_id, instance, serve_nodes=DEFAULT_SERVE_NODES)\n def _update_from_pb(self, cluster_pb) # Refresh self from the server-provided protobuf.\n def from_pb(cls, cluster_pb, instance) # Creates a cluster instance from a protobuf.\n def copy(self) # Make a copy of this cluster.\n def name(self) # Cluster name used in requests.\n def __eq__(self, other)\n def __ne__(self, other)\n def reload(self) # Reload the metadata for this cluster.\n def create(self) # Create this cluster.\n def update(self) # Update this cluster.\n def delete(self) # Delete this cluster.","label":1},{"cid":"gcloud/bigtable/instance.py","text":"gcloud/bigtable/instance.py\ndef _prepare_create_request(instance) # Creates a protobuf request for a CreateInstance request.\ndef _parse_pb_any_to_native(any_val, expected_type=None) # Convert a serialized \"google.protobuf.Any\" value to actual type.\ndef _process_operation(operation_pb) # Processes a create protobuf response.\nclass Operation # Representation of a Google API Long-Running Operation.\n def __init__(self, op_type, op_id, begin, instance=None)\n def __eq__(self, other)\n def __ne__(self, other)\n def finished(self) # Check if the operation has finished.\nclass Instance # Representation of a Google Cloud Bigtable Instance.\n def __init__(self, instance_id, client, display_name=None)\n def _update_from_pb(self, instance_pb) # Refresh self from the server-provided protobuf.\n def from_pb(cls, instance_pb, client) # Creates a instance instance from a protobuf.\n def copy(self) # Make a copy of this instance.\n def name(self) # Instance name used in requests.\n def __eq__(self, other)\n def __ne__(self, other)\n def reload(self) # Reload the metadata for this instance.\n def create(self) # Create this instance.\n def update(self) # Update this instance.\n def delete(self) # Delete this instance.\n def cluster(self, cluster_id, serve_nodes=3) # Factory to create a cluster associated with this client.\n def list_clusters(self) # Lists clusters in this instance.\n def table(self, table_id) # Factory to create a table associated with this instance.\n def list_tables(self) # List the tables in this instance.","label":1},{"cid":"gcloud/bigtable/row_filters.py","text":"gcloud/bigtable/row_filters.py\nclass RowFilter # Basic filter to apply to cells in a row.\n def __ne__(self, other)\nclass _BoolFilter # Row filter that uses a boolean flag.\n def __init__(self, flag)\n def __eq__(self, other)\nclass SinkFilter # Advanced row filter to skip parent filters.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass PassAllFilter # Row filter equivalent to not filtering at all.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass BlockAllFilter # Row filter that doesn't match any cells.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass _RegexFilter # Row filter that uses a regular expression.\n def __init__(self, regex)\n def __eq__(self, other)\nclass RowKeyRegexFilter # Row filter for a row key regular expression.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass RowSampleFilter # Matches all cells from a row with probability p.\n def __init__(self, sample)\n def __eq__(self, other)\n def to_pb(self) # Converts the row filter to a protobuf.\nclass FamilyNameRegexFilter # Row filter for a family name regular expression.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass ColumnQualifierRegexFilter # Row filter for a column qualifier regular expression.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass TimestampRange # Range of time with inclusive lower and exclusive upper bounds.\n def __init__(self, start=None, end=None)\n def __eq__(self, other)\n def __ne__(self, other)\n def to_pb(self) # Converts the :class:`TimestampRange` to a protobuf.\nclass TimestampRangeFilter # Row filter that limits cells to a range of time.\n def __init__(self, range_)\n def __eq__(self, other)\n def to_pb(self) # Converts the row filter to a protobuf.\nclass ColumnRangeFilter # A row filter to restrict to a range of columns.\n def __init__(self, column_family_id, start_column=None, end_column=None, inclusive_start=None, inclusive_end=None)\n def __eq__(self, other)\n def to_pb(self) # Converts the row filter to a protobuf.\nclass ValueRegexFilter # Row filter for a value regular expression.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass ValueRangeFilter # A range of values to restrict to in a row filter.\n def __init__(self, start_value=None, end_value=None, inclusive_start=None, inclusive_end=None)\n def __eq__(self, other)\n def to_pb(self) # Converts the row filter to a protobuf.\nclass _CellCountFilter # Row filter that uses an integer count of cells.\n def __init__(self, num_cells)\n def __eq__(self, other)\nclass CellsRowOffsetFilter # Row filter to skip cells in a row.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass CellsRowLimitFilter # Row filter to limit cells in a row.\n def to_pb(self) # Converts the row filter to a protobuf.\nclass CellsColumnLimitFilter # Row filter to limit cells in a column.\n def to_pb(self) # Converts","label":0},{"cid":"gcloud/bigtable/table.py","text":"gcloud/bigtable/table.py\nclass Table # Representation of a Google Cloud Bigtable Table.\n def __init__(self, table_id, instance)\n def name(self) # Table name used in requests.\n def column_family(self, column_family_id, gc_rule=None) # Factory to create a column family associated with this table.\n def row(self, row_key, filter_=None, append=False) # Factory to create a row associated with this table.\n def __eq__(self, other)\n def __ne__(self, other)\n def create(self, initial_split_keys=None) # Creates this table.\n def delete(self) # Delete this table.\n def list_column_families(self) # List the column families owned by this table.\n def read_row(self, row_key, filter_=None) # Read a single row from this table.\n def read_rows(self, start_key=None, end_key=None, limit=None, filter_=None) # Read rows from this table.\n def sample_row_keys(self) # Read a sample of row keys in the table.\ndef _create_row_request(table_name, row_key=None, start_key=None, end_key=None, filter_=None, limit=None) # Creates a request to read rows in a table.","label":1},{"cid":"gcloud/datastore/entity.py","text":"gcloud/datastore/entity.py\nclass Entity # Entities are akin to rows in a relational database\n def __init__(self, key=None, exclude_from_indexes=())\n def __eq__(self, other) # Compare two entities for equality.\n def __ne__(self, other) # Compare two entities for inequality.\n def kind(self) # Get the kind of the current entity.\n def exclude_from_indexes(self) # Names of fields which are *not* to be indexed for this entity.\n def __repr__(self)","label":0},{"cid":"gcloud/datastore/helpers.py","text":"gcloud/datastore/helpers.py\ndef _get_meaning(value_pb, is_list=False) # Get the meaning from a protobuf value.\ndef _new_value_pb(entity_pb, name) # Add (by name) a new ``Value`` protobuf to an entity protobuf.\ndef _property_tuples(entity_pb) # Iterator of name, ``Value`` tuples from entity properties.\ndef entity_from_protobuf(pb) # Factory method for creating an entity based on a protobuf.\ndef _set_pb_meaning_from_entity(entity, name, value, value_pb, is_list=False) # Add meaning information (from an entity) to a protobuf.\ndef entity_to_protobuf(entity) # Converts an entity into a protobuf.\ndef key_from_protobuf(pb) # Factory method for creating a key based on a protobuf.\ndef _pb_attr_value(val) # Given a value, return the protobuf attribute name and proper value.\ndef _get_value_from_value_pb(value_pb) # Given a protobuf for a Value, get the correct value.\ndef _set_protobuf_value(value_pb, val) # Assign 'val' to the correct subfield of 'value_pb'.\nclass GeoPoint # Simple container for a geo point value.\n def __init__(self, latitude, longitude)\n def to_protobuf(self) # Convert the current object to protobuf.\n def __eq__(self, other) # Compare two geo points for equality.\n def __ne__(self, other) # Compare two geo points for inequality.","label":0},{"cid":"scripts/make_datastore_grpc.py","text":"scripts/make_datastore_grpc.py\ndef get_pb2_contents_with_grpc() # Get pb2 lines generated by protoc with gRPC plugin.\ndef get_pb2_contents_without_grpc() # Get pb2 lines generated by protoc without gRPC plugin.\ndef get_pb2_grpc_only() # Get pb2 lines that are only in gRPC.\ndef main() # Write gRPC-only lines to custom module.","label":1},{"cid":"scripts/make_operations_grpc.py","text":"scripts/make_operations_grpc.py\ndef get_pb2_contents_with_grpc() # Get pb2 lines generated by protoc with gRPC plugin.\ndef get_pb2_contents_without_grpc() # Get pb2 lines generated by protoc without gRPC plugin.\ndef get_pb2_grpc_only() # Get pb2 lines that are only in gRPC.\ndef main() # Write gRPC-only lines to custom module.","label":1},{"cid":"scripts/rewrite_imports.py","text":"scripts/rewrite_imports.py\ndef transform_old_to_new(line, old_module, new_module, ignore_import_from=False) # Transforms from an old module to a new one.\ndef transform_line(line) # Transforms an import line in a PB2 module.\ndef rewrite_file(filename) # Rewrites a given PB2 modules.\ndef main() # Rewrites all PB2 files.","label":1},{"cid":"scripts/run_pylint.py","text":"scripts/run_pylint.py\ndef read_config(filename) # Reads pylintrc config onto native ConfigParser object.\ndef make_test_rc(base_rc_filename, additions_dict, replacements_dict, target_filename) # Combines a base rc and test additions into single file.\ndef valid_filename(filename) # Checks if a file is a Python file and is not ignored.\ndef is_production_filename(filename) # Checks if the file contains production code.\ndef get_files_for_linting(allow_limited=True) # Gets a list of files in the repository.\ndef get_python_files(all_files=None) # Gets a list of all Python files in the repository that need linting.\ndef lint_fileset(filenames, rcfile, description) # Lints a group of files using a given rcfile.\ndef main() # Script entry point. Lints both sets of files.","label":0},{"cid":"setup.py","text":"setup.py","label":0}],"string":"[\n {\n \"cid\": \"docs/conf.py\",\n \"text\": \"docs/conf.py\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py\\nclass BetaBigtableInstanceAdminServicer # <fill me in later!>\\n def CreateInstance(self, request, context)\\n def GetInstance(self, request, context)\\n def ListInstances(self, request, context)\\n def UpdateInstance(self, request, context)\\n def DeleteInstance(self, request, context)\\n def CreateCluster(self, request, context)\\n def GetCluster(self, request, context)\\n def ListClusters(self, request, context)\\n def UpdateCluster(self, request, context)\\n def DeleteCluster(self, request, context)\\nclass BetaBigtableInstanceAdminStub # The interface to which stubs will conform.\\n def CreateInstance(self, request, timeout)\\n def GetInstance(self, request, timeout)\\n def ListInstances(self, request, timeout)\\n def UpdateInstance(self, request, timeout)\\n def DeleteInstance(self, request, timeout)\\n def CreateCluster(self, request, timeout)\\n def GetCluster(self, request, timeout)\\n def ListClusters(self, request, timeout)\\n def UpdateCluster(self, request, timeout)\\n def DeleteCluster(self, request, timeout)\\ndef beta_create_BigtableInstanceAdmin_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None)\\ndef beta_create_BigtableInstanceAdmin_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py\\nclass BetaBigtableServicer # <fill me in later!>\\n def ReadRows(self, request, context)\\n def SampleRowKeys(self, request, context)\\n def MutateRow(self, request, context)\\n def MutateRows(self, request, context)\\n def CheckAndMutateRow(self, request, context)\\n def ReadModifyWriteRow(self, request, context)\\nclass BetaBigtableStub # The interface to which stubs will conform.\\n def ReadRows(self, request, timeout)\\n def SampleRowKeys(self, request, timeout)\\n def MutateRow(self, request, timeout)\\n def MutateRows(self, request, timeout)\\n def CheckAndMutateRow(self, request, timeout)\\n def ReadModifyWriteRow(self, request, timeout)\\ndef beta_create_Bigtable_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None)\\ndef beta_create_Bigtable_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py\\nclass BetaBigtableTableAdminServicer # <fill me in later!>\\n def CreateTable(self, request, context)\\n def ListTables(self, request, context)\\n def GetTable(self, request, context)\\n def DeleteTable(self, request, context)\\n def ModifyColumnFamilies(self, request, context)\\n def DropRowRange(self, request, context)\\nclass BetaBigtableTableAdminStub # The interface to which stubs will conform.\\n def CreateTable(self, request, timeout)\\n def ListTables(self, request, timeout)\\n def GetTable(self, request, timeout)\\n def DeleteTable(self, request, timeout)\\n def ModifyColumnFamilies(self, request, timeout)\\n def DropRowRange(self, request, timeout)\\ndef beta_create_BigtableTableAdmin_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None)\\ndef beta_create_BigtableTableAdmin_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/operations_grpc_pb2.py\",\n \"text\": \"gcloud/bigtable/_generated_v2/operations_grpc_pb2.py\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/table_pb2.py\",\n \"text\": \"gcloud/bigtable/_generated_v2/table_pb2.py\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py\",\n \"text\": \"gcloud/bigtable/cluster.py\\ndef _prepare_create_request(cluster) # Creates a protobuf request for a CreateCluster request.\\ndef _parse_pb_any_to_native(any_val, expected_type=None) # Convert a serialized \\\"google.protobuf.Any\\\" value to actual type.\\ndef _process_operation(operation_pb) # Processes a create protobuf response.\\nclass Operation # Representation of a Google API Long-Running Operation.\\n def __init__(self, op_type, op_id, cluster=None)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def finished(self) # Check if the operation has finished.\\nclass Cluster # Representation of a Google Cloud Bigtable Cluster.\\n def __init__(self, cluster_id, instance, serve_nodes=DEFAULT_SERVE_NODES)\\n def _update_from_pb(self, cluster_pb) # Refresh self from the server-provided protobuf.\\n def from_pb(cls, cluster_pb, instance) # Creates a cluster instance from a protobuf.\\n def copy(self) # Make a copy of this cluster.\\n def name(self) # Cluster name used in requests.\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def reload(self) # Reload the metadata for this cluster.\\n def create(self) # Create this cluster.\\n def update(self) # Update this cluster.\\n def delete(self) # Delete this cluster.\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py\",\n \"text\": \"gcloud/bigtable/instance.py\\ndef _prepare_create_request(instance) # Creates a protobuf request for a CreateInstance request.\\ndef _parse_pb_any_to_native(any_val, expected_type=None) # Convert a serialized \\\"google.protobuf.Any\\\" value to actual type.\\ndef _process_operation(operation_pb) # Processes a create protobuf response.\\nclass Operation # Representation of a Google API Long-Running Operation.\\n def __init__(self, op_type, op_id, begin, instance=None)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def finished(self) # Check if the operation has finished.\\nclass Instance # Representation of a Google Cloud Bigtable Instance.\\n def __init__(self, instance_id, client, display_name=None)\\n def _update_from_pb(self, instance_pb) # Refresh self from the server-provided protobuf.\\n def from_pb(cls, instance_pb, client) # Creates a instance instance from a protobuf.\\n def copy(self) # Make a copy of this instance.\\n def name(self) # Instance name used in requests.\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def reload(self) # Reload the metadata for this instance.\\n def create(self) # Create this instance.\\n def update(self) # Update this instance.\\n def delete(self) # Delete this instance.\\n def cluster(self, cluster_id, serve_nodes=3) # Factory to create a cluster associated with this client.\\n def list_clusters(self) # Lists clusters in this instance.\\n def table(self, table_id) # Factory to create a table associated with this instance.\\n def list_tables(self) # List the tables in this instance.\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py\",\n \"text\": \"gcloud/bigtable/row_filters.py\\nclass RowFilter # Basic filter to apply to cells in a row.\\n def __ne__(self, other)\\nclass _BoolFilter # Row filter that uses a boolean flag.\\n def __init__(self, flag)\\n def __eq__(self, other)\\nclass SinkFilter # Advanced row filter to skip parent filters.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass PassAllFilter # Row filter equivalent to not filtering at all.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass BlockAllFilter # Row filter that doesn't match any cells.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass _RegexFilter # Row filter that uses a regular expression.\\n def __init__(self, regex)\\n def __eq__(self, other)\\nclass RowKeyRegexFilter # Row filter for a row key regular expression.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass RowSampleFilter # Matches all cells from a row with probability p.\\n def __init__(self, sample)\\n def __eq__(self, other)\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass FamilyNameRegexFilter # Row filter for a family name regular expression.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass ColumnQualifierRegexFilter # Row filter for a column qualifier regular expression.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass TimestampRange # Range of time with inclusive lower and exclusive upper bounds.\\n def __init__(self, start=None, end=None)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def to_pb(self) # Converts the :class:`TimestampRange` to a protobuf.\\nclass TimestampRangeFilter # Row filter that limits cells to a range of time.\\n def __init__(self, range_)\\n def __eq__(self, other)\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass ColumnRangeFilter # A row filter to restrict to a range of columns.\\n def __init__(self, column_family_id, start_column=None, end_column=None, inclusive_start=None, inclusive_end=None)\\n def __eq__(self, other)\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass ValueRegexFilter # Row filter for a value regular expression.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass ValueRangeFilter # A range of values to restrict to in a row filter.\\n def __init__(self, start_value=None, end_value=None, inclusive_start=None, inclusive_end=None)\\n def __eq__(self, other)\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass _CellCountFilter # Row filter that uses an integer count of cells.\\n def __init__(self, num_cells)\\n def __eq__(self, other)\\nclass CellsRowOffsetFilter # Row filter to skip cells in a row.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass CellsRowLimitFilter # Row filter to limit cells in a row.\\n def to_pb(self) # Converts the row filter to a protobuf.\\nclass CellsColumnLimitFilter # Row filter to limit cells in a column.\\n def to_pb(self) # Converts\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/table.py\",\n \"text\": \"gcloud/bigtable/table.py\\nclass Table # Representation of a Google Cloud Bigtable Table.\\n def __init__(self, table_id, instance)\\n def name(self) # Table name used in requests.\\n def column_family(self, column_family_id, gc_rule=None) # Factory to create a column family associated with this table.\\n def row(self, row_key, filter_=None, append=False) # Factory to create a row associated with this table.\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def create(self, initial_split_keys=None) # Creates this table.\\n def delete(self) # Delete this table.\\n def list_column_families(self) # List the column families owned by this table.\\n def read_row(self, row_key, filter_=None) # Read a single row from this table.\\n def read_rows(self, start_key=None, end_key=None, limit=None, filter_=None) # Read rows from this table.\\n def sample_row_keys(self) # Read a sample of row keys in the table.\\ndef _create_row_request(table_name, row_key=None, start_key=None, end_key=None, filter_=None, limit=None) # Creates a request to read rows in a table.\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/datastore/entity.py\",\n \"text\": \"gcloud/datastore/entity.py\\nclass Entity # Entities are akin to rows in a relational database\\n def __init__(self, key=None, exclude_from_indexes=())\\n def __eq__(self, other) # Compare two entities for equality.\\n def __ne__(self, other) # Compare two entities for inequality.\\n def kind(self) # Get the kind of the current entity.\\n def exclude_from_indexes(self) # Names of fields which are *not* to be indexed for this entity.\\n def __repr__(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py\",\n \"text\": \"gcloud/datastore/helpers.py\\ndef _get_meaning(value_pb, is_list=False) # Get the meaning from a protobuf value.\\ndef _new_value_pb(entity_pb, name) # Add (by name) a new ``Value`` protobuf to an entity protobuf.\\ndef _property_tuples(entity_pb) # Iterator of name, ``Value`` tuples from entity properties.\\ndef entity_from_protobuf(pb) # Factory method for creating an entity based on a protobuf.\\ndef _set_pb_meaning_from_entity(entity, name, value, value_pb, is_list=False) # Add meaning information (from an entity) to a protobuf.\\ndef entity_to_protobuf(entity) # Converts an entity into a protobuf.\\ndef key_from_protobuf(pb) # Factory method for creating a key based on a protobuf.\\ndef _pb_attr_value(val) # Given a value, return the protobuf attribute name and proper value.\\ndef _get_value_from_value_pb(value_pb) # Given a protobuf for a Value, get the correct value.\\ndef _set_protobuf_value(value_pb, val) # Assign 'val' to the correct subfield of 'value_pb'.\\nclass GeoPoint # Simple container for a geo point value.\\n def __init__(self, latitude, longitude)\\n def to_protobuf(self) # Convert the current object to protobuf.\\n def __eq__(self, other) # Compare two geo points for equality.\\n def __ne__(self, other) # Compare two geo points for inequality.\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_datastore_grpc.py\",\n \"text\": \"scripts/make_datastore_grpc.py\\ndef get_pb2_contents_with_grpc() # Get pb2 lines generated by protoc with gRPC plugin.\\ndef get_pb2_contents_without_grpc() # Get pb2 lines generated by protoc without gRPC plugin.\\ndef get_pb2_grpc_only() # Get pb2 lines that are only in gRPC.\\ndef main() # Write gRPC-only lines to custom module.\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/make_operations_grpc.py\",\n \"text\": \"scripts/make_operations_grpc.py\\ndef get_pb2_contents_with_grpc() # Get pb2 lines generated by protoc with gRPC plugin.\\ndef get_pb2_contents_without_grpc() # Get pb2 lines generated by protoc without gRPC plugin.\\ndef get_pb2_grpc_only() # Get pb2 lines that are only in gRPC.\\ndef main() # Write gRPC-only lines to custom module.\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/rewrite_imports.py\",\n \"text\": \"scripts/rewrite_imports.py\\ndef transform_old_to_new(line, old_module, new_module, ignore_import_from=False) # Transforms from an old module to a new one.\\ndef transform_line(line) # Transforms an import line in a PB2 module.\\ndef rewrite_file(filename) # Rewrites a given PB2 modules.\\ndef main() # Rewrites all PB2 files.\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/run_pylint.py\",\n \"text\": \"scripts/run_pylint.py\\ndef read_config(filename) # Reads pylintrc config onto native ConfigParser object.\\ndef make_test_rc(base_rc_filename, additions_dict, replacements_dict, target_filename) # Combines a base rc and test additions into single file.\\ndef valid_filename(filename) # Checks if a file is a Python file and is not ignored.\\ndef is_production_filename(filename) # Checks if the file contains production code.\\ndef get_files_for_linting(allow_limited=True) # Gets a list of files in the repository.\\ndef get_python_files(all_files=None) # Gets a list of all Python files in the repository that need linting.\\ndef lint_fileset(filenames, rcfile, description) # Lints a group of files using a given rcfile.\\ndef main() # Script entry point. Lints both sets of files.\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.CreateInstance","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.CreateInstance\n def CreateInstance(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.GetInstance","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.GetInstance\n def GetInstance(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.ListInstances","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.ListInstances\n def ListInstances(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.UpdateInstance","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.UpdateInstance\n def UpdateInstance(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.DeleteInstance","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.DeleteInstance\n def DeleteInstance(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.CreateCluster","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.CreateCluster\n def CreateCluster(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.GetCluster","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.GetCluster\n def GetCluster(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.ListClusters","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.ListClusters\n def ListClusters(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.UpdateCluster","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.UpdateCluster\n def UpdateCluster(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.DeleteCluster","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.DeleteCluster\n def DeleteCluster(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.CreateInstance","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.CreateInstance\n def CreateInstance(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.GetInstance","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.GetInstance\n def GetInstance(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.ListInstances","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.ListInstances\n def ListInstances(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.UpdateInstance","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.UpdateInstance\n def UpdateInstance(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.DeleteInstance","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.DeleteInstance\n def DeleteInstance(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.CreateCluster","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.CreateCluster\n def CreateCluster(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.GetCluster","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.GetCluster\n def GetCluster(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.ListClusters","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.ListClusters\n def ListClusters(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.UpdateCluster","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.UpdateCluster\n def UpdateCluster(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.DeleteCluster","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.DeleteCluster\n def DeleteCluster(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::beta_create_BigtableInstanceAdmin_server","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::beta_create_BigtableInstanceAdmin_server\ndef beta_create_BigtableInstanceAdmin_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None):\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.longrunning.operations_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.bigtable.admin.v2.instance_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.bigtable.admin.v2.instance_pb2\n import google.bigtable.admin.v2.instance_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.protobuf.empty_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.longrunning.operations_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.bigtable.admin.v2.instance_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.bigtable.admin.v2.instance_pb2\n import google.longrunning.operations_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.protobuf.empty_pb2\n request_deserializers = {\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'CreateCluster'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.CreateClusterRequest.FromString,\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'CreateInstance'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.CreateInstanceRequest.FromString,\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'DeleteCluster'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.DeleteClusterRequest.FromString,\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'DeleteInstance'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.DeleteInstanceRequest.FromString,\n ('google.bigtable.admin.v2.BigtableInstanceA","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::beta_create_BigtableInstanceAdmin_stub","text":"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::beta_create_BigtableInstanceAdmin_stub\ndef beta_create_BigtableInstanceAdmin_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None):\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.longrunning.operations_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.bigtable.admin.v2.instance_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.bigtable.admin.v2.instance_pb2\n import google.bigtable.admin.v2.instance_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.protobuf.empty_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.longrunning.operations_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.bigtable.admin.v2.instance_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.bigtable.admin.v2.instance_pb2\n import google.longrunning.operations_pb2\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\n import google.protobuf.empty_pb2\n request_serializers = {\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'CreateCluster'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.CreateClusterRequest.SerializeToString,\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'CreateInstance'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.CreateInstanceRequest.SerializeToString,\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'DeleteCluster'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.DeleteClusterRequest.SerializeToString,\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'DeleteInstance'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.DeleteInstanceRequest.SerializeToString,\n ('google.bigtable.admin.v2.Bi","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.ReadRows","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.ReadRows\n def ReadRows(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.SampleRowKeys","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.SampleRowKeys\n def SampleRowKeys(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.MutateRow","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.MutateRow\n def MutateRow(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.MutateRows","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.MutateRows\n def MutateRows(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.CheckAndMutateRow","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.CheckAndMutateRow\n def CheckAndMutateRow(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.ReadModifyWriteRow","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.ReadModifyWriteRow\n def ReadModifyWriteRow(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.ReadRows","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.ReadRows\n def ReadRows(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.SampleRowKeys","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.SampleRowKeys\n def SampleRowKeys(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.MutateRow","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.MutateRow\n def MutateRow(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.MutateRows","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.MutateRows\n def MutateRows(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.CheckAndMutateRow","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.CheckAndMutateRow\n def CheckAndMutateRow(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.ReadModifyWriteRow","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.ReadModifyWriteRow\n def ReadModifyWriteRow(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::beta_create_Bigtable_server","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::beta_create_Bigtable_server\ndef beta_create_Bigtable_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None):\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n request_deserializers = {\n ('google.bigtable.v2.Bigtable', 'CheckAndMutateRow'): google.bigtable.v2.bigtable_pb2.CheckAndMutateRowRequest.FromString,\n ('google.bigtable.v2.Bigtable', 'MutateRow'): google.bigtable.v2.bigtable_pb2.MutateRowRequest.FromString,\n ('google.bigtable.v2.Bigtable', 'MutateRows'): google.bigtable.v2.bigtable_pb2.MutateRowsRequest.FromString,\n ('google.bigtable.v2.Bigtable', 'ReadModifyWriteRow'): google.bigtable.v2.bigtable_pb2.ReadModifyWriteRowRequest.FromString,\n ('google.bigtable.v2.Bigtable', 'ReadRows'): google.bigtable.v2.bigtable_pb2.ReadRowsRequest.FromString,\n ('google.bigtable.v2.Bigtable', 'SampleRowKeys'): google.bigtable.v2.bigtable_pb2.SampleRowKeysRequest.FromString,\n }\n response_serializers = {\n ('google.bigtable.v2.Bigtable', 'CheckAndMutateRow'): google.bigtable.v2.bigtable_pb2.CheckAndMutateRowResponse.SerializeToString,\n ('google.bigtable.v2.Bigtable', 'MutateRow'): google.bigtable.v2.bigtable_pb2.MutateRowResponse.SerializeToString,\n ('google.bigtable.v2.Bigtable', 'MutateRows'): google.bigtable.v2.bigtable_pb2.MutateRowsResponse.SerializeToString,\n ('google.bigtable.v2.Bigtable', 'ReadModifyWriteRow'): google.bigtable.v2.bigtable_pb2.ReadModifyWriteRowResponse.SerializeToString,\n ('google.bigtable.v2.Bigtable', 'Rea","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::beta_create_Bigtable_stub","text":"gcloud/bigtable/_generated_v2/bigtable_pb2.py::beta_create_Bigtable_stub\ndef beta_create_Bigtable_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None):\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n import google.bigtable.v2.bigtable_pb2\n request_serializers = {\n ('google.bigtable.v2.Bigtable', 'CheckAndMutateRow'): google.bigtable.v2.bigtable_pb2.CheckAndMutateRowRequest.SerializeToString,\n ('google.bigtable.v2.Bigtable', 'MutateRow'): google.bigtable.v2.bigtable_pb2.MutateRowRequest.SerializeToString,\n ('google.bigtable.v2.Bigtable', 'MutateRows'): google.bigtable.v2.bigtable_pb2.MutateRowsRequest.SerializeToString,\n ('google.bigtable.v2.Bigtable', 'ReadModifyWriteRow'): google.bigtable.v2.bigtable_pb2.ReadModifyWriteRowRequest.SerializeToString,\n ('google.bigtable.v2.Bigtable', 'ReadRows'): google.bigtable.v2.bigtable_pb2.ReadRowsRequest.SerializeToString,\n ('google.bigtable.v2.Bigtable', 'SampleRowKeys'): google.bigtable.v2.bigtable_pb2.SampleRowKeysRequest.SerializeToString,\n }\n response_deserializers = {\n ('google.bigtable.v2.Bigtable', 'CheckAndMutateRow'): google.bigtable.v2.bigtable_pb2.CheckAndMutateRowResponse.FromString,\n ('google.bigtable.v2.Bigtable', 'MutateRow'): google.bigtable.v2.bigtable_pb2.MutateRowResponse.FromString,\n ('google.bigtable.v2.Bigtable', 'MutateRows'): google.bigtable.v2.bigtable_pb2.MutateRowsResponse.FromString,\n ('google.bigtable.v2.Bigtable', 'ReadModifyWriteRow'): google.bigtable.v2.bigtable_pb2.ReadModifyWriteRowResponse.FromString,\n ('google.bigtable.v2.Bigtable', '","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.CreateTable","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.CreateTable\n def CreateTable(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.ListTables","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.ListTables\n def ListTables(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.GetTable","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.GetTable\n def GetTable(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.DeleteTable","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.DeleteTable\n def DeleteTable(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.ModifyColumnFamilies","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.ModifyColumnFamilies\n def ModifyColumnFamilies(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.DropRowRange","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.DropRowRange\n def DropRowRange(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.CreateTable","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.CreateTable\n def CreateTable(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.ListTables","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.ListTables\n def ListTables(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.GetTable","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.GetTable\n def GetTable(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.DeleteTable","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.DeleteTable\n def DeleteTable(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.ModifyColumnFamilies","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.ModifyColumnFamilies\n def ModifyColumnFamilies(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.DropRowRange","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.DropRowRange\n def DropRowRange(self, request, timeout):\n raise NotImplementedError()","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::beta_create_BigtableTableAdmin_server","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::beta_create_BigtableTableAdmin_server\ndef beta_create_BigtableTableAdmin_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None):\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.bigtable.admin.v2.table_pb2\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.bigtable.admin.v2.table_pb2\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.protobuf.empty_pb2\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.bigtable.admin.v2.table_pb2\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.protobuf.empty_pb2\n request_deserializers = {\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'CreateTable'): google.bigtable.admin.v2.bigtable_table_admin_pb2.CreateTableRequest.FromString,\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'DeleteTable'): google.bigtable.admin.v2.bigtable_table_admin_pb2.DeleteTableRequest.FromString,\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'DropRowRange'): google.bigtable.admin.v2.bigtable_table_admin_pb2.DropRowRangeRequest.FromString,\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'GetTable'): google.bigtable.admin.v2.bigtable_table_admin_pb2.GetTableRequest.FromString,\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'ListTables'): google.bigtable.admin.v2.bigtable_table_admin_pb2.ListTablesRequest.FromString,\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'ModifyColumnFamilies'): google.bigtable.admin.v2.bigtable_table_admin_pb2.ModifyColumnFamiliesRequest.FromString,\n }\n response_serializers = {\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'CreateTable'): google.bigtable.admin.v2.table_pb2.Table.SerializeToString,\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'DeleteTable'): goo","label":1},{"cid":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::beta_create_BigtableTableAdmin_stub","text":"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::beta_create_BigtableTableAdmin_stub\ndef beta_create_BigtableTableAdmin_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None):\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.bigtable.admin.v2.table_pb2\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.bigtable.admin.v2.table_pb2\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.protobuf.empty_pb2\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.bigtable.admin.v2.table_pb2\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\n import google.protobuf.empty_pb2\n request_serializers = {\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'CreateTable'): google.bigtable.admin.v2.bigtable_table_admin_pb2.CreateTableRequest.SerializeToString,\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'DeleteTable'): google.bigtable.admin.v2.bigtable_table_admin_pb2.DeleteTableRequest.SerializeToString,\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'DropRowRange'): google.bigtable.admin.v2.bigtable_table_admin_pb2.DropRowRangeRequest.SerializeToString,\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'GetTable'): google.bigtable.admin.v2.bigtable_table_admin_pb2.GetTableRequest.SerializeToString,\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'ListTables'): google.bigtable.admin.v2.bigtable_table_admin_pb2.ListTablesRequest.SerializeToString,\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'ModifyColumnFamilies'): google.bigtable.admin.v2.bigtable_table_admin_pb2.ModifyColumnFamiliesRequest.SerializeToString,\n }\n response_deserializers = {\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'CreateTable'): google.bigtable.admin.v2.table_pb2.Table.FromString,\n ('google.bigtable.admin.v2.BigtableTableAdm","label":1},{"cid":"gcloud/bigtable/cluster.py::_prepare_create_request","text":"gcloud/bigtable/cluster.py::_prepare_create_request\ndef _prepare_create_request(cluster):\n \"\"\"Creates a protobuf request for a CreateCluster request.\n\n :type cluster: :class:`Cluster`\n :param cluster: The cluster to be created.\n\n :rtype: :class:`.messages_v2_pb2.CreateClusterRequest`\n :returns: The CreateCluster request object containing the cluster info.\n \"\"\"\n return messages_v2_pb2.CreateClusterRequest(\n name=cluster._instance.name,\n cluster_id=cluster.cluster_id,\n cluster=data_v2_pb2.Cluster(\n serve_nodes=cluster.serve_nodes,\n ),\n )","label":1},{"cid":"gcloud/bigtable/cluster.py::_parse_pb_any_to_native","text":"gcloud/bigtable/cluster.py::_parse_pb_any_to_native\ndef _parse_pb_any_to_native(any_val, expected_type=None):\n \"\"\"Convert a serialized \"google.protobuf.Any\" value to actual type.\n\n :type any_val: :class:`google.protobuf.any_pb2.Any`\n :param any_val: A serialized protobuf value container.\n\n :type expected_type: str\n :param expected_type: (Optional) The type URL we expect ``any_val``\n to have.\n\n :rtype: object\n :returns: The de-serialized object.\n :raises: :class:`ValueError <exceptions.ValueError>` if the\n ``expected_type`` does not match the ``type_url`` on the input.\n \"\"\"\n if expected_type is not None and expected_type != any_val.type_url:\n raise ValueError('Expected type: %s, Received: %s' % (\n expected_type, any_val.type_url))\n container_class = _TYPE_URL_MAP[any_val.type_url]\n return container_class.FromString(any_val.value)","label":0},{"cid":"gcloud/bigtable/cluster.py::_process_operation","text":"gcloud/bigtable/cluster.py::_process_operation\ndef _process_operation(operation_pb):\n \"\"\"Processes a create protobuf response.\n\n :type operation_pb: :class:`google.longrunning.operations_pb2.Operation`\n :param operation_pb: The long-running operation response from a\n Create/Update/Undelete cluster request.\n\n :rtype: tuple\n :returns: integer ID of the operation (``operation_id``).\n :raises: :class:`ValueError <exceptions.ValueError>` if the operation name\n doesn't match the :data:`_OPERATION_NAME_RE` regex.\n \"\"\"\n match = _OPERATION_NAME_RE.match(operation_pb.name)\n if match is None:\n raise ValueError('Operation name was not in the expected '\n 'format after a cluster modification.',\n operation_pb.name)\n operation_id = int(match.group('operation_id'))\n\n return operation_id","label":0},{"cid":"gcloud/bigtable/cluster.py::Operation.__init__","text":"gcloud/bigtable/cluster.py::Operation.__init__\n def __init__(self, op_type, op_id, cluster=None):\n self.op_type = op_type\n self.op_id = op_id\n self._cluster = cluster\n self._complete = False","label":0},{"cid":"gcloud/bigtable/cluster.py::Operation.__eq__","text":"gcloud/bigtable/cluster.py::Operation.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other.op_type == self.op_type and\n other.op_id == self.op_id and\n other._cluster == self._cluster and\n other._complete == self._complete)","label":0},{"cid":"gcloud/bigtable/cluster.py::Operation.__ne__","text":"gcloud/bigtable/cluster.py::Operation.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"gcloud/bigtable/cluster.py::Operation.finished","text":"gcloud/bigtable/cluster.py::Operation.finished\n def finished(self):\n \"\"\"Check if the operation has finished.\n\n :rtype: bool\n :returns: A boolean indicating if the current operation has completed.\n :raises: :class:`ValueError <exceptions.ValueError>` if the operation\n has already completed.\n \"\"\"\n if self._complete:\n raise ValueError('The operation has completed.')\n\n operation_name = ('operations/' + self._cluster.name +\n '/operations/%d' % (self.op_id,))\n request_pb = operations_pb2.GetOperationRequest(name=operation_name)\n # We expect a `google.longrunning.operations_pb2.Operation`.\n client = self._cluster._instance._client\n operation_pb = client._operations_stub.GetOperation(\n request_pb, client.timeout_seconds)\n\n if operation_pb.done:\n self._complete = True\n return True\n else:\n return False","label":0},{"cid":"gcloud/bigtable/cluster.py::Cluster.__init__","text":"gcloud/bigtable/cluster.py::Cluster.__init__\n def __init__(self, cluster_id, instance,\n serve_nodes=DEFAULT_SERVE_NODES):\n self.cluster_id = cluster_id\n self._instance = instance\n self.serve_nodes = serve_nodes\n self.location = None","label":0},{"cid":"gcloud/bigtable/cluster.py::Cluster._update_from_pb","text":"gcloud/bigtable/cluster.py::Cluster._update_from_pb\n def _update_from_pb(self, cluster_pb):\n \"\"\"Refresh self from the server-provided protobuf.\n\n Helper for :meth:`from_pb` and :meth:`reload`.\n \"\"\"\n if not cluster_pb.serve_nodes: # Simple field (int32)\n raise ValueError('Cluster protobuf does not contain serve_nodes')\n self.serve_nodes = cluster_pb.serve_nodes\n self.location = cluster_pb.location","label":0},{"cid":"gcloud/bigtable/cluster.py::Cluster.from_pb","text":"gcloud/bigtable/cluster.py::Cluster.from_pb\n @classmethod\n def from_pb(cls, cluster_pb, instance):\n \"\"\"Creates a cluster instance from a protobuf.\n\n :type cluster_pb: :class:`bigtable_cluster_data_pb2.Cluster`\n :param cluster_pb: A cluster protobuf object.\n\n :type instance: :class:`.instance.Instance>`\n :param instance: The instance that owns the cluster.\n\n :rtype: :class:`Cluster`\n :returns: The cluster parsed from the protobuf response.\n :raises:\n :class:`ValueError <exceptions.ValueError>` if the cluster\n name does not match\n ``projects/{project}/instances/{instance}/clusters/{cluster_id}``\n or if the parsed project ID does not match the project ID\n on the client.\n \"\"\"\n match = _CLUSTER_NAME_RE.match(cluster_pb.name)\n if match is None:\n raise ValueError('Cluster protobuf name was not in the '\n 'expected format.', cluster_pb.name)\n if match.group('project') != instance._client.project:\n raise ValueError('Project ID on cluster does not match the '\n 'project ID on the client')\n if match.group('instance') != instance.instance_id:\n raise ValueError('Instance ID on cluster does not match the '\n 'instance ID on the client')\n\n result = cls(match.group('cluster_id'), instance)\n result._update_from_pb(cluster_pb)\n return result","label":0},{"cid":"gcloud/bigtable/cluster.py::Cluster.copy","text":"gcloud/bigtable/cluster.py::Cluster.copy\n def copy(self):\n \"\"\"Make a copy of this cluster.\n\n Copies the local data stored as simple types and copies the client\n attached to this instance.\n\n :rtype: :class:`.Cluster`\n :returns: A copy of the current cluster.\n \"\"\"\n new_instance = self._instance.copy()\n return self.__class__(self.cluster_id, new_instance,\n serve_nodes=self.serve_nodes)","label":0},{"cid":"gcloud/bigtable/cluster.py::Cluster.name","text":"gcloud/bigtable/cluster.py::Cluster.name\n @property\n def name(self):\n \"\"\"Cluster name used in requests.\n\n .. note::\n This property will not change if ``_instance`` and ``cluster_id``\n do not, but the return value is not cached.\n\n The cluster name is of the form\n\n ``\"projects/{project}/instances/{instance}/clusters/{cluster_id}\"``\n\n :rtype: str\n :returns: The cluster name.\n \"\"\"\n return self._instance.name + '/clusters/' + self.cluster_id","label":0},{"cid":"gcloud/bigtable/cluster.py::Cluster.__eq__","text":"gcloud/bigtable/cluster.py::Cluster.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n # NOTE: This does not compare the configuration values, such as\n # the serve_nodes. Instead, it only compares\n # identifying values instance, cluster ID and client. This is\n # intentional, since the same cluster can be in different states\n # if not synchronized. Clusters with similar instance/cluster\n # settings but different clients can't be used in the same way.\n return (other.cluster_id == self.cluster_id and\n other._instance == self._instance)","label":0},{"cid":"gcloud/bigtable/cluster.py::Cluster.__ne__","text":"gcloud/bigtable/cluster.py::Cluster.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"gcloud/bigtable/cluster.py::Cluster.reload","text":"gcloud/bigtable/cluster.py::Cluster.reload\n def reload(self):\n \"\"\"Reload the metadata for this cluster.\"\"\"\n request_pb = messages_v2_pb2.GetClusterRequest(name=self.name)\n # We expect a `._generated.bigtable_cluster_data_pb2.Cluster`.\n cluster_pb = self._instance._client._cluster_stub.GetCluster(\n request_pb, self._instance._client.timeout_seconds)\n\n # NOTE: _update_from_pb does not check that the project, instance and\n # cluster ID on the response match the request.\n self._update_from_pb(cluster_pb)","label":0},{"cid":"gcloud/bigtable/cluster.py::Cluster.create","text":"gcloud/bigtable/cluster.py::Cluster.create\n def create(self):\n \"\"\"Create this cluster.\n\n .. note::\n\n Uses the ``project``, ``instance`` and ``cluster_id`` on the\n current :class:`Cluster` in addition to the ``serve_nodes``.\n To change them before creating, reset the values via\n\n .. code:: python\n\n cluster.serve_nodes = 8\n cluster.cluster_id = 'i-changed-my-mind'\n\n before calling :meth:`create`.\n\n :rtype: :class:`Operation`\n :returns: The long-running operation corresponding to the\n create operation.\n \"\"\"\n request_pb = _prepare_create_request(self)\n # We expect a `google.longrunning.operations_pb2.Operation`.\n operation_pb = self._instance._client._cluster_stub.CreateCluster(\n request_pb, self._instance._client.timeout_seconds)\n\n op_id = _process_operation(operation_pb)\n return Operation('create', op_id, cluster=self)","label":0},{"cid":"gcloud/bigtable/cluster.py::Cluster.update","text":"gcloud/bigtable/cluster.py::Cluster.update\n def update(self):\n \"\"\"Update this cluster.\n\n .. note::\n\n Updates the ``serve_nodes``. If you'd like to\n change them before updating, reset the values via\n\n .. code:: python\n\n cluster.serve_nodes = 8\n\n before calling :meth:`update`.\n\n :rtype: :class:`Operation`\n :returns: The long-running operation corresponding to the\n update operation.\n \"\"\"\n request_pb = data_v2_pb2.Cluster(\n name=self.name,\n serve_nodes=self.serve_nodes,\n )\n # Ignore expected `._generated.bigtable_cluster_data_pb2.Cluster`.\n operation_pb = self._instance._client._cluster_stub.UpdateCluster(\n request_pb, self._instance._client.timeout_seconds)\n\n op_id = _process_operation(operation_pb)\n return Operation('update', op_id, cluster=self)","label":0},{"cid":"gcloud/bigtable/cluster.py::Cluster.delete","text":"gcloud/bigtable/cluster.py::Cluster.delete\n def delete(self):\n \"\"\"Delete this cluster.\n\n Marks a cluster and all of its tables for permanent deletion in 7 days.\n\n Immediately upon completion of the request:\n\n * Billing will cease for all of the cluster's reserved resources.\n * The cluster's ``delete_time`` field will be set 7 days in the future.\n\n Soon afterward:\n\n * All tables within the cluster will become unavailable.\n\n Prior to the cluster's ``delete_time``:\n\n * The cluster can be recovered with a call to ``UndeleteCluster``.\n * All other attempts to modify or delete the cluster will be rejected.\n\n At the cluster's ``delete_time``:\n\n * The cluster and **all of its tables** will immediately and\n irrevocably disappear from the API, and their data will be\n permanently deleted.\n \"\"\"\n request_pb = messages_v2_pb2.DeleteClusterRequest(name=self.name)\n # We expect a `google.protobuf.empty_pb2.Empty`\n self._instance._client._cluster_stub.DeleteCluster(\n request_pb, self._instance._client.timeout_seconds)","label":0},{"cid":"gcloud/bigtable/instance.py::_prepare_create_request","text":"gcloud/bigtable/instance.py::_prepare_create_request\ndef _prepare_create_request(instance):\n \"\"\"Creates a protobuf request for a CreateInstance request.\n\n :type instance: :class:`Instance`\n :param instance: The instance to be created.\n\n :rtype: :class:`.messages_v2_pb2.CreateInstanceRequest`\n :returns: The CreateInstance request object containing the instance info.\n \"\"\"\n parent_name = ('projects/' + instance._client.project)\n return messages_v2_pb2.CreateInstanceRequest(\n name=parent_name,\n instance_id=instance.instance_id,\n instance=data_v2_pb2.Instance(\n display_name=instance.display_name,\n ),\n )","label":1},{"cid":"gcloud/bigtable/instance.py::_parse_pb_any_to_native","text":"gcloud/bigtable/instance.py::_parse_pb_any_to_native\ndef _parse_pb_any_to_native(any_val, expected_type=None):\n \"\"\"Convert a serialized \"google.protobuf.Any\" value to actual type.\n\n :type any_val: :class:`google.protobuf.any_pb2.Any`\n :param any_val: A serialized protobuf value container.\n\n :type expected_type: str\n :param expected_type: (Optional) The type URL we expect ``any_val``\n to have.\n\n :rtype: object\n :returns: The de-serialized object.\n :raises: :class:`ValueError <exceptions.ValueError>` if the\n ``expected_type`` does not match the ``type_url`` on the input.\n \"\"\"\n if expected_type is not None and expected_type != any_val.type_url:\n raise ValueError('Expected type: %s, Received: %s' % (\n expected_type, any_val.type_url))\n container_class = _TYPE_URL_MAP[any_val.type_url]\n return container_class.FromString(any_val.value)","label":0},{"cid":"gcloud/bigtable/instance.py::_process_operation","text":"gcloud/bigtable/instance.py::_process_operation\ndef _process_operation(operation_pb):\n \"\"\"Processes a create protobuf response.\n\n :type operation_pb: :class:`google.longrunning.operations_pb2.Operation`\n :param operation_pb: The long-running operation response from a\n Create/Update/Undelete instance request.\n\n :rtype: tuple\n :returns: A pair of an integer and datetime stamp. The integer is the ID\n of the operation (``operation_id``) and the timestamp when\n the create operation began (``operation_begin``).\n :raises: :class:`ValueError <exceptions.ValueError>` if the operation name\n doesn't match the :data:`_OPERATION_NAME_RE` regex.\n \"\"\"\n match = _OPERATION_NAME_RE.match(operation_pb.name)\n if match is None:\n raise ValueError('Operation name was not in the expected '\n 'format after a instance modification.',\n operation_pb.name)\n operation_id = int(match.group('operation_id'))\n\n request_metadata = _parse_pb_any_to_native(operation_pb.metadata)\n operation_begin = _pb_timestamp_to_datetime(\n request_metadata.request_time)\n\n return operation_id, operation_begin","label":0},{"cid":"gcloud/bigtable/instance.py::Operation.__init__","text":"gcloud/bigtable/instance.py::Operation.__init__\n def __init__(self, op_type, op_id, begin, instance=None):\n self.op_type = op_type\n self.op_id = op_id\n self.begin = begin\n self._instance = instance\n self._complete = False","label":0},{"cid":"gcloud/bigtable/instance.py::Operation.__eq__","text":"gcloud/bigtable/instance.py::Operation.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other.op_type == self.op_type and\n other.op_id == self.op_id and\n other.begin == self.begin and\n other._instance == self._instance and\n other._complete == self._complete)","label":0},{"cid":"gcloud/bigtable/instance.py::Operation.__ne__","text":"gcloud/bigtable/instance.py::Operation.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"gcloud/bigtable/instance.py::Operation.finished","text":"gcloud/bigtable/instance.py::Operation.finished\n def finished(self):\n \"\"\"Check if the operation has finished.\n\n :rtype: bool\n :returns: A boolean indicating if the current operation has completed.\n :raises: :class:`ValueError <exceptions.ValueError>` if the operation\n has already completed.\n \"\"\"\n if self._complete:\n raise ValueError('The operation has completed.')\n\n operation_name = ('operations/' + self._instance.name +\n '/operations/%d' % (self.op_id,))\n request_pb = operations_pb2.GetOperationRequest(name=operation_name)\n # We expect a `google.longrunning.operations_pb2.Operation`.\n operation_pb = self._instance._client._operations_stub.GetOperation(\n request_pb, self._instance._client.timeout_seconds)\n\n if operation_pb.done:\n self._complete = True\n return True\n else:\n return False","label":0},{"cid":"gcloud/bigtable/instance.py::Instance.__init__","text":"gcloud/bigtable/instance.py::Instance.__init__\n def __init__(self, instance_id, client,\n display_name=None):\n self.instance_id = instance_id\n self.display_name = display_name or instance_id\n self._client = client","label":0},{"cid":"gcloud/bigtable/instance.py::Instance._update_from_pb","text":"gcloud/bigtable/instance.py::Instance._update_from_pb\n def _update_from_pb(self, instance_pb):\n \"\"\"Refresh self from the server-provided protobuf.\n\n Helper for :meth:`from_pb` and :meth:`reload`.\n \"\"\"\n if not instance_pb.display_name: # Simple field (string)\n raise ValueError('Instance protobuf does not contain display_name')\n self.display_name = instance_pb.display_name","label":0},{"cid":"gcloud/bigtable/instance.py::Instance.from_pb","text":"gcloud/bigtable/instance.py::Instance.from_pb\n @classmethod\n def from_pb(cls, instance_pb, client):\n \"\"\"Creates a instance instance from a protobuf.\n\n :type instance_pb: :class:`instance_pb2.Instance`\n :param instance_pb: A instance protobuf object.\n\n :type client: :class:`Client <gcloud.bigtable.client.Client>`\n :param client: The client that owns the instance.\n\n :rtype: :class:`Instance`\n :returns: The instance parsed from the protobuf response.\n :raises: :class:`ValueError <exceptions.ValueError>` if the instance\n name does not match\n ``projects/{project}/instances/{instance_id}``\n or if the parsed project ID does not match the project ID\n on the client.\n \"\"\"\n match = _INSTANCE_NAME_RE.match(instance_pb.name)\n if match is None:\n raise ValueError('Instance protobuf name was not in the '\n 'expected format.', instance_pb.name)\n if match.group('project') != client.project:\n raise ValueError('Project ID on instance does not match the '\n 'project ID on the client')\n\n result = cls(match.group('instance_id'), client)\n result._update_from_pb(instance_pb)\n return result","label":0},{"cid":"gcloud/bigtable/instance.py::Instance.copy","text":"gcloud/bigtable/instance.py::Instance.copy\n def copy(self):\n \"\"\"Make a copy of this instance.\n\n Copies the local data stored as simple types and copies the client\n attached to this instance.\n\n :rtype: :class:`.Instance`\n :returns: A copy of the current instance.\n \"\"\"\n new_client = self._client.copy()\n return self.__class__(self.instance_id, new_client,\n display_name=self.display_name)","label":0},{"cid":"gcloud/bigtable/instance.py::Instance.name","text":"gcloud/bigtable/instance.py::Instance.name\n @property\n def name(self):\n \"\"\"Instance name used in requests.\n\n .. note::\n This property will not change if ``instance_id`` does not,\n but the return value is not cached.\n\n The instance name is of the form\n\n ``\"projects/{project}/instances/{instance_id}\"``\n\n :rtype: str\n :returns: The instance name.\n \"\"\"\n return self._client.project_name + '/instances/' + self.instance_id","label":0},{"cid":"gcloud/bigtable/instance.py::Instance.__eq__","text":"gcloud/bigtable/instance.py::Instance.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n # NOTE: This does not compare the configuration values, such as\n # the display_name. Instead, it only compares\n # identifying values instance ID and client. This is\n # intentional, since the same instance can be in different states\n # if not synchronized. Instances with similar instance\n # settings but different clients can't be used in the same way.\n return (other.instance_id == self.instance_id and\n other._client == self._client)","label":0},{"cid":"gcloud/bigtable/instance.py::Instance.__ne__","text":"gcloud/bigtable/instance.py::Instance.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"gcloud/bigtable/instance.py::Instance.reload","text":"gcloud/bigtable/instance.py::Instance.reload\n def reload(self):\n \"\"\"Reload the metadata for this instance.\"\"\"\n request_pb = messages_v2_pb2.GetInstanceRequest(name=self.name)\n # We expect `data_v2_pb2.Instance`.\n instance_pb = self._client._instance_stub.GetInstance(\n request_pb, self._client.timeout_seconds)\n\n # NOTE: _update_from_pb does not check that the project and\n # instance ID on the response match the request.\n self._update_from_pb(instance_pb)","label":0},{"cid":"gcloud/bigtable/instance.py::Instance.create","text":"gcloud/bigtable/instance.py::Instance.create\n def create(self):\n \"\"\"Create this instance.\n\n .. note::\n\n Uses the ``project`` and ``instance_id`` on the current\n :class:`Instance` in addition to the ``display_name``.\n To change them before creating, reset the values via\n\n .. code:: python\n\n instance.display_name = 'New display name'\n instance.instance_id = 'i-changed-my-mind'\n\n before calling :meth:`create`.\n\n :rtype: :class:`Operation`\n :returns: The long-running operation corresponding to the\n create operation.\n \"\"\"\n request_pb = _prepare_create_request(self)\n # We expect a `google.longrunning.operations_pb2.Operation`.\n operation_pb = self._client._instance_stub.CreateInstance(\n request_pb, self._client.timeout_seconds)\n\n op_id, op_begin = _process_operation(operation_pb)\n return Operation('create', op_id, op_begin, instance=self)","label":0},{"cid":"gcloud/bigtable/instance.py::Instance.update","text":"gcloud/bigtable/instance.py::Instance.update\n def update(self):\n \"\"\"Update this instance.\n\n .. note::\n\n Updates the ``display_name``. To change that value before\n updating, reset its values via\n\n .. code:: python\n\n instance.display_name = 'New display name'\n\n before calling :meth:`update`.\n \"\"\"\n request_pb = data_v2_pb2.Instance(\n name=self.name,\n display_name=self.display_name,\n )\n # Ignore the expected `data_v2_pb2.Instance`.\n self._client._instance_stub.UpdateInstance(\n request_pb, self._client.timeout_seconds)","label":0},{"cid":"gcloud/bigtable/instance.py::Instance.delete","text":"gcloud/bigtable/instance.py::Instance.delete\n def delete(self):\n \"\"\"Delete this instance.\n\n Marks a instance and all of its tables for permanent deletion\n in 7 days.\n\n Immediately upon completion of the request:\n\n * Billing will cease for all of the instance's reserved resources.\n * The instance's ``delete_time`` field will be set 7 days in\n the future.\n\n Soon afterward:\n\n * All tables within the instance will become unavailable.\n\n Prior to the instance's ``delete_time``:\n\n * The instance can be recovered with a call to ``UndeleteInstance``.\n * All other attempts to modify or delete the instance will be rejected.\n\n At the instance's ``delete_time``:\n\n * The instance and **all of its tables** will immediately and\n irrevocably disappear from the API, and their data will be\n permanently deleted.\n \"\"\"\n request_pb = messages_v2_pb2.DeleteInstanceRequest(name=self.name)\n # We expect a `google.protobuf.empty_pb2.Empty`\n self._client._instance_stub.DeleteInstance(\n request_pb, self._client.timeout_seconds)","label":0},{"cid":"gcloud/bigtable/instance.py::Instance.cluster","text":"gcloud/bigtable/instance.py::Instance.cluster\n def cluster(self, cluster_id, serve_nodes=3):\n \"\"\"Factory to create a cluster associated with this client.\n\n :type cluster_id: str\n :param cluster_id: The ID of the cluster.\n\n :type serve_nodes: int\n :param serve_nodes: (Optional) The number of nodes in the cluster.\n Defaults to 3.\n\n :rtype: :class:`.Cluster`\n :returns: The cluster owned by this client.\n \"\"\"\n return Cluster(cluster_id, self, serve_nodes=serve_nodes)","label":0},{"cid":"gcloud/bigtable/instance.py::Instance.list_clusters","text":"gcloud/bigtable/instance.py::Instance.list_clusters\n def list_clusters(self):\n \"\"\"Lists clusters in this instance.\n\n :rtype: tuple\n :returns: A pair of results, the first is a list of :class:`.Cluster` s\n returned and the second is a list of strings (the failed\n locations in the request).\n \"\"\"\n request_pb = messages_v2_pb2.ListClustersRequest(name=self.name)\n # We expect a `.cluster_messages_v1_pb2.ListClustersResponse`\n list_clusters_response = self._client._instance_stub.ListClusters(\n request_pb, self._client.timeout_seconds)\n\n failed_locations = [\n location for location in list_clusters_response.failed_locations]\n clusters = [Cluster.from_pb(cluster_pb, self)\n for cluster_pb in list_clusters_response.clusters]\n return clusters, failed_locations","label":1},{"cid":"gcloud/bigtable/instance.py::Instance.table","text":"gcloud/bigtable/instance.py::Instance.table\n def table(self, table_id):\n \"\"\"Factory to create a table associated with this instance.\n\n :type table_id: str\n :param table_id: The ID of the table.\n\n :rtype: :class:`Table <gcloud.bigtable.table.Table>`\n :returns: The table owned by this instance.\n \"\"\"\n return Table(table_id, self)","label":0},{"cid":"gcloud/bigtable/instance.py::Instance.list_tables","text":"gcloud/bigtable/instance.py::Instance.list_tables\n def list_tables(self):\n \"\"\"List the tables in this instance.\n\n :rtype: list of :class:`Table <gcloud.bigtable.table.Table>`\n :returns: The list of tables owned by the instance.\n :raises: :class:`ValueError <exceptions.ValueError>` if one of the\n returned tables has a name that is not of the expected format.\n \"\"\"\n request_pb = table_messages_v2_pb2.ListTablesRequest(name=self.name)\n # We expect a `table_messages_v2_pb2.ListTablesResponse`\n table_list_pb = self._client._table_stub.ListTables(\n request_pb, self._client.timeout_seconds)\n\n result = []\n for table_pb in table_list_pb.tables:\n table_prefix = self.name + '/tables/'\n if not table_pb.name.startswith(table_prefix):\n raise ValueError('Table name %s not of expected format' % (\n table_pb.name,))\n table_id = table_pb.name[len(table_prefix):]\n result.append(self.table(table_id))\n\n return result","label":1},{"cid":"gcloud/bigtable/row_filters.py::RowFilter.__ne__","text":"gcloud/bigtable/row_filters.py::RowFilter.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"gcloud/bigtable/row_filters.py::_BoolFilter.__init__","text":"gcloud/bigtable/row_filters.py::_BoolFilter.__init__\n def __init__(self, flag):\n self.flag = flag","label":0},{"cid":"gcloud/bigtable/row_filters.py::_BoolFilter.__eq__","text":"gcloud/bigtable/row_filters.py::_BoolFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.flag == self.flag","label":0},{"cid":"gcloud/bigtable/row_filters.py::SinkFilter.to_pb","text":"gcloud/bigtable/row_filters.py::SinkFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(sink=self.flag)","label":0},{"cid":"gcloud/bigtable/row_filters.py::PassAllFilter.to_pb","text":"gcloud/bigtable/row_filters.py::PassAllFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(pass_all_filter=self.flag)","label":0},{"cid":"gcloud/bigtable/row_filters.py::BlockAllFilter.to_pb","text":"gcloud/bigtable/row_filters.py::BlockAllFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(block_all_filter=self.flag)","label":0},{"cid":"gcloud/bigtable/row_filters.py::_RegexFilter.__init__","text":"gcloud/bigtable/row_filters.py::_RegexFilter.__init__\n def __init__(self, regex):\n self.regex = _to_bytes(regex)","label":0},{"cid":"gcloud/bigtable/row_filters.py::_RegexFilter.__eq__","text":"gcloud/bigtable/row_filters.py::_RegexFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.regex == self.regex","label":0},{"cid":"gcloud/bigtable/row_filters.py::RowKeyRegexFilter.to_pb","text":"gcloud/bigtable/row_filters.py::RowKeyRegexFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(row_key_regex_filter=self.regex)","label":0},{"cid":"gcloud/bigtable/row_filters.py::RowSampleFilter.__init__","text":"gcloud/bigtable/row_filters.py::RowSampleFilter.__init__\n def __init__(self, sample):\n self.sample = sample","label":0},{"cid":"gcloud/bigtable/row_filters.py::RowSampleFilter.__eq__","text":"gcloud/bigtable/row_filters.py::RowSampleFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.sample == self.sample","label":0},{"cid":"gcloud/bigtable/row_filters.py::RowSampleFilter.to_pb","text":"gcloud/bigtable/row_filters.py::RowSampleFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(row_sample_filter=self.sample)","label":0},{"cid":"gcloud/bigtable/row_filters.py::FamilyNameRegexFilter.to_pb","text":"gcloud/bigtable/row_filters.py::FamilyNameRegexFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(family_name_regex_filter=self.regex)","label":0},{"cid":"gcloud/bigtable/row_filters.py::ColumnQualifierRegexFilter.to_pb","text":"gcloud/bigtable/row_filters.py::ColumnQualifierRegexFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(column_qualifier_regex_filter=self.regex)","label":0},{"cid":"gcloud/bigtable/row_filters.py::TimestampRange.__init__","text":"gcloud/bigtable/row_filters.py::TimestampRange.__init__\n def __init__(self, start=None, end=None):\n self.start = start\n self.end = end","label":0},{"cid":"gcloud/bigtable/row_filters.py::TimestampRange.__eq__","text":"gcloud/bigtable/row_filters.py::TimestampRange.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other.start == self.start and\n other.end == self.end)","label":0},{"cid":"gcloud/bigtable/row_filters.py::TimestampRange.__ne__","text":"gcloud/bigtable/row_filters.py::TimestampRange.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"gcloud/bigtable/row_filters.py::TimestampRange.to_pb","text":"gcloud/bigtable/row_filters.py::TimestampRange.to_pb\n def to_pb(self):\n \"\"\"Converts the :class:`TimestampRange` to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.TimestampRange`\n :returns: The converted current object.\n \"\"\"\n timestamp_range_kwargs = {}\n if self.start is not None:\n timestamp_range_kwargs['start_timestamp_micros'] = (\n _microseconds_from_datetime(self.start))\n if self.end is not None:\n timestamp_range_kwargs['end_timestamp_micros'] = (\n _microseconds_from_datetime(self.end))\n return data_v2_pb2.TimestampRange(**timestamp_range_kwargs)","label":0},{"cid":"gcloud/bigtable/row_filters.py::TimestampRangeFilter.__init__","text":"gcloud/bigtable/row_filters.py::TimestampRangeFilter.__init__\n def __init__(self, range_):\n self.range_ = range_","label":0},{"cid":"gcloud/bigtable/row_filters.py::TimestampRangeFilter.__eq__","text":"gcloud/bigtable/row_filters.py::TimestampRangeFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.range_ == self.range_","label":0},{"cid":"gcloud/bigtable/row_filters.py::TimestampRangeFilter.to_pb","text":"gcloud/bigtable/row_filters.py::TimestampRangeFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n First converts the ``range_`` on the current object to a protobuf and\n then uses it in the ``timestamp_range_filter`` field.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(\n timestamp_range_filter=self.range_.to_pb())","label":0},{"cid":"gcloud/bigtable/row_filters.py::ColumnRangeFilter.__init__","text":"gcloud/bigtable/row_filters.py::ColumnRangeFilter.__init__\n def __init__(self, column_family_id, start_column=None, end_column=None,\n inclusive_start=None, inclusive_end=None):\n self.column_family_id = column_family_id\n\n if inclusive_start is None:\n inclusive_start = True\n elif start_column is None:\n raise ValueError('Inclusive start was specified but no '\n 'start column was given.')\n self.start_column = start_column\n self.inclusive_start = inclusive_start\n\n if inclusive_end is None:\n inclusive_end = True\n elif end_column is None:\n raise ValueError('Inclusive end was specified but no '\n 'end column was given.')\n self.end_column = end_column\n self.inclusive_end = inclusive_end","label":0},{"cid":"gcloud/bigtable/row_filters.py::ColumnRangeFilter.__eq__","text":"gcloud/bigtable/row_filters.py::ColumnRangeFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other.column_family_id == self.column_family_id and\n other.start_column == self.start_column and\n other.end_column == self.end_column and\n other.inclusive_start == self.inclusive_start and\n other.inclusive_end == self.inclusive_end)","label":0},{"cid":"gcloud/bigtable/row_filters.py::ColumnRangeFilter.to_pb","text":"gcloud/bigtable/row_filters.py::ColumnRangeFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n First converts to a :class:`.data_v2_pb2.ColumnRange` and then uses it\n in the ``column_range_filter`` field.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n column_range_kwargs = {'family_name': self.column_family_id}\n if self.start_column is not None:\n if self.inclusive_start:\n key = 'start_qualifier_closed'\n else:\n key = 'start_qualifier_open'\n column_range_kwargs[key] = _to_bytes(self.start_column)\n if self.end_column is not None:\n if self.inclusive_end:\n key = 'end_qualifier_closed'\n else:\n key = 'end_qualifier_open'\n column_range_kwargs[key] = _to_bytes(self.end_column)\n\n column_range = data_v2_pb2.ColumnRange(**column_range_kwargs)\n return data_v2_pb2.RowFilter(column_range_filter=column_range)","label":0},{"cid":"gcloud/bigtable/row_filters.py::ValueRegexFilter.to_pb","text":"gcloud/bigtable/row_filters.py::ValueRegexFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(value_regex_filter=self.regex)","label":0},{"cid":"gcloud/bigtable/row_filters.py::ValueRangeFilter.__init__","text":"gcloud/bigtable/row_filters.py::ValueRangeFilter.__init__\n def __init__(self, start_value=None, end_value=None,\n inclusive_start=None, inclusive_end=None):\n if inclusive_start is None:\n inclusive_start = True\n elif start_value is None:\n raise ValueError('Inclusive start was specified but no '\n 'start value was given.')\n self.start_value = start_value\n self.inclusive_start = inclusive_start\n\n if inclusive_end is None:\n inclusive_end = True\n elif end_value is None:\n raise ValueError('Inclusive end was specified but no '\n 'end value was given.')\n self.end_value = end_value\n self.inclusive_end = inclusive_end","label":0},{"cid":"gcloud/bigtable/row_filters.py::ValueRangeFilter.__eq__","text":"gcloud/bigtable/row_filters.py::ValueRangeFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other.start_value == self.start_value and\n other.end_value == self.end_value and\n other.inclusive_start == self.inclusive_start and\n other.inclusive_end == self.inclusive_end)","label":0},{"cid":"gcloud/bigtable/row_filters.py::ValueRangeFilter.to_pb","text":"gcloud/bigtable/row_filters.py::ValueRangeFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n First converts to a :class:`.data_v2_pb2.ValueRange` and then uses\n it to create a row filter protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n value_range_kwargs = {}\n if self.start_value is not None:\n if self.inclusive_start:\n key = 'start_value_closed'\n else:\n key = 'start_value_open'\n value_range_kwargs[key] = _to_bytes(self.start_value)\n if self.end_value is not None:\n if self.inclusive_end:\n key = 'end_value_closed'\n else:\n key = 'end_value_open'\n value_range_kwargs[key] = _to_bytes(self.end_value)\n\n value_range = data_v2_pb2.ValueRange(**value_range_kwargs)\n return data_v2_pb2.RowFilter(value_range_filter=value_range)","label":0},{"cid":"gcloud/bigtable/row_filters.py::_CellCountFilter.__init__","text":"gcloud/bigtable/row_filters.py::_CellCountFilter.__init__\n def __init__(self, num_cells):\n self.num_cells = num_cells","label":0},{"cid":"gcloud/bigtable/row_filters.py::_CellCountFilter.__eq__","text":"gcloud/bigtable/row_filters.py::_CellCountFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.num_cells == self.num_cells","label":0},{"cid":"gcloud/bigtable/row_filters.py::CellsRowOffsetFilter.to_pb","text":"gcloud/bigtable/row_filters.py::CellsRowOffsetFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(\n cells_per_row_offset_filter=self.num_cells)","label":0},{"cid":"gcloud/bigtable/row_filters.py::CellsRowLimitFilter.to_pb","text":"gcloud/bigtable/row_filters.py::CellsRowLimitFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(cells_per_row_limit_filter=self.num_cells)","label":0},{"cid":"gcloud/bigtable/row_filters.py::CellsColumnLimitFilter.to_pb","text":"gcloud/bigtable/row_filters.py::CellsColumnLimitFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(\n cells_per_column_limit_filter=self.num_cells)","label":0},{"cid":"gcloud/bigtable/row_filters.py::StripValueTransformerFilter.to_pb","text":"gcloud/bigtable/row_filters.py::StripValueTransformerFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(strip_value_transformer=self.flag)","label":0},{"cid":"gcloud/bigtable/row_filters.py::ApplyLabelFilter.__init__","text":"gcloud/bigtable/row_filters.py::ApplyLabelFilter.__init__\n def __init__(self, label):\n self.label = label","label":0},{"cid":"gcloud/bigtable/row_filters.py::ApplyLabelFilter.__eq__","text":"gcloud/bigtable/row_filters.py::ApplyLabelFilter.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.label == self.label","label":0},{"cid":"gcloud/bigtable/row_filters.py::ApplyLabelFilter.to_pb","text":"gcloud/bigtable/row_filters.py::ApplyLabelFilter.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n return data_v2_pb2.RowFilter(apply_label_transformer=self.label)","label":0},{"cid":"gcloud/bigtable/row_filters.py::_FilterCombination.__init__","text":"gcloud/bigtable/row_filters.py::_FilterCombination.__init__\n def __init__(self, filters=None):\n if filters is None:\n filters = []\n self.filters = filters","label":0},{"cid":"gcloud/bigtable/row_filters.py::_FilterCombination.__eq__","text":"gcloud/bigtable/row_filters.py::_FilterCombination.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return other.filters == self.filters","label":0},{"cid":"gcloud/bigtable/row_filters.py::RowFilterChain.to_pb","text":"gcloud/bigtable/row_filters.py::RowFilterChain.to_pb\n def to_pb(self):\n \"\"\"Converts the row filter to a protobuf.\n\n :rtype: :class:`.data_v2_pb2.RowFilter`\n :returns: The converted current object.\n \"\"\"\n chain = data_v2_pb2.RowFilter.Chain(\n filters=[row_filter.to_pb() for row_filter in self.filters])\n return data_v2_pb2.RowFilter(chain=chain)","label":0},{"cid":"gcloud/bigtable/table.py::Table.__init__","text":"gcloud/bigtable/table.py::Table.__init__\n def __init__(self, table_id, instance):\n self.table_id = table_id\n self._instance = instance","label":0},{"cid":"gcloud/bigtable/table.py::Table.name","text":"gcloud/bigtable/table.py::Table.name\n @property\n def name(self):\n \"\"\"Table name used in requests.\n\n .. note::\n\n This property will not change if ``table_id`` does not, but the\n return value is not cached.\n\n The table name is of the form\n\n ``\"projects/../zones/../clusters/../tables/{table_id}\"``\n\n :rtype: str\n :returns: The table name.\n \"\"\"\n return self._instance.name + '/tables/' + self.table_id","label":0},{"cid":"gcloud/bigtable/table.py::Table.column_family","text":"gcloud/bigtable/table.py::Table.column_family\n def column_family(self, column_family_id, gc_rule=None):\n \"\"\"Factory to create a column family associated with this table.\n\n :type column_family_id: str\n :param column_family_id: The ID of the column family. Must be of the\n form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\n\n :type gc_rule: :class:`.GarbageCollectionRule`\n :param gc_rule: (Optional) The garbage collection settings for this\n column family.\n\n :rtype: :class:`.ColumnFamily`\n :returns: A column family owned by this table.\n \"\"\"\n return ColumnFamily(column_family_id, self, gc_rule=gc_rule)","label":0},{"cid":"gcloud/bigtable/table.py::Table.row","text":"gcloud/bigtable/table.py::Table.row\n def row(self, row_key, filter_=None, append=False):\n \"\"\"Factory to create a row associated with this table.\n\n .. warning::\n\n At most one of ``filter_`` and ``append`` can be used in a\n :class:`Row`.\n\n :type row_key: bytes\n :param row_key: The key for the row being created.\n\n :type filter_: :class:`.RowFilter`\n :param filter_: (Optional) Filter to be used for conditional mutations.\n See :class:`.DirectRow` for more details.\n\n :type append: bool\n :param append: (Optional) Flag to determine if the row should be used\n for append mutations.\n\n :rtype: :class:`.DirectRow`\n :returns: A row owned by this table.\n :raises: :class:`ValueError <exceptions.ValueError>` if both\n ``filter_`` and ``append`` are used.\n \"\"\"\n if append and filter_ is not None:\n raise ValueError('At most one of filter_ and append can be set')\n if append:\n return AppendRow(row_key, self)\n elif filter_ is not None:\n return ConditionalRow(row_key, self, filter_=filter_)\n else:\n return DirectRow(row_key, self)","label":0},{"cid":"gcloud/bigtable/table.py::Table.__eq__","text":"gcloud/bigtable/table.py::Table.__eq__\n def __eq__(self, other):\n if not isinstance(other, self.__class__):\n return False\n return (other.table_id == self.table_id and\n other._instance == self._instance)","label":0},{"cid":"gcloud/bigtable/table.py::Table.__ne__","text":"gcloud/bigtable/table.py::Table.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"gcloud/bigtable/table.py::Table.create","text":"gcloud/bigtable/table.py::Table.create\n def create(self, initial_split_keys=None):\n \"\"\"Creates this table.\n\n .. note::\n\n Though a :class:`._generated.bigtable_table_data_pb2.Table` is also\n allowed (as the ``table`` property) in a create table request, we\n do not support it in this method. As mentioned in the\n :class:`Table` docstring, the name is the only useful property in\n the table proto.\n\n .. note::\n\n A create request returns a\n :class:`._generated.bigtable_table_data_pb2.Table` but we don't use\n this response. The proto definition allows for the inclusion of a\n ``current_operation`` in the response, but it does not appear that\n the Cloud Bigtable API returns any operation.\n\n :type initial_split_keys: list\n :param initial_split_keys: (Optional) List of row keys that will be\n used to initially split the table into\n several tablets (Tablets are similar to\n HBase regions). Given two split keys,\n ``\"s1\"`` and ``\"s2\"``, three tablets will be\n created, spanning the key ranges:\n ``[, s1)``, ``[s1, s2)``, ``[s2, )``.\n \"\"\"\n split_pb = table_admin_messages_v2_pb2.CreateTableRequest.Split\n if initial_split_keys is not None:\n initial_split_keys = [\n split_pb(key=key) for key in initial_split_keys]\n request_pb = table_admin_messages_v2_pb2.CreateTableRequest(\n initial_splits=initial_split_keys or [],\n name=self._instance.name,\n table_id=self.table_id,\n )\n client = self._instance._client\n # We expect a `._generated.bigtable_table_data_pb2.Table`\n client._table_stub.CreateTable(request_pb, client.timeout_seconds)","label":1},{"cid":"gcloud/bigtable/table.py::Table.delete","text":"gcloud/bigtable/table.py::Table.delete\n def delete(self):\n \"\"\"Delete this table.\"\"\"\n request_pb = table_admin_messages_v2_pb2.DeleteTableRequest(\n name=self.name)\n client = self._instance._client\n # We expect a `google.protobuf.empty_pb2.Empty`\n client._table_stub.DeleteTable(request_pb, client.timeout_seconds)","label":0},{"cid":"gcloud/bigtable/table.py::Table.list_column_families","text":"gcloud/bigtable/table.py::Table.list_column_families\n def list_column_families(self):\n \"\"\"List the column families owned by this table.\n\n :rtype: dict\n :returns: Dictionary of column families attached to this table. Keys\n are strings (column family names) and values are\n :class:`.ColumnFamily` instances.\n :raises: :class:`ValueError <exceptions.ValueError>` if the column\n family name from the response does not agree with the computed\n name from the column family ID.\n \"\"\"\n request_pb = table_admin_messages_v2_pb2.GetTableRequest(\n name=self.name)\n client = self._instance._client\n # We expect a `._generated.bigtable_table_data_pb2.Table`\n table_pb = client._table_stub.GetTable(request_pb,\n client.timeout_seconds)\n\n result = {}\n for column_family_id, value_pb in table_pb.column_families.items():\n gc_rule = _gc_rule_from_pb(value_pb.gc_rule)\n column_family = self.column_family(column_family_id,\n gc_rule=gc_rule)\n result[column_family_id] = column_family\n return result","label":0},{"cid":"gcloud/bigtable/table.py::Table.read_row","text":"gcloud/bigtable/table.py::Table.read_row\n def read_row(self, row_key, filter_=None):\n \"\"\"Read a single row from this table.\n\n :type row_key: bytes\n :param row_key: The key of the row to read from.\n\n :type filter_: :class:`.RowFilter`\n :param filter_: (Optional) The filter to apply to the contents of the\n row. If unset, returns the entire row.\n\n :rtype: :class:`.PartialRowData`, :data:`NoneType <types.NoneType>`\n :returns: The contents of the row if any chunks were returned in\n the response, otherwise :data:`None`.\n :raises: :class:`ValueError <exceptions.ValueError>` if a commit row\n chunk is never encountered.\n \"\"\"\n request_pb = _create_row_request(self.name, row_key=row_key,\n filter_=filter_)\n client = self._instance._client\n response_iterator = client._data_stub.ReadRows(request_pb,\n client.timeout_seconds)\n rows_data = PartialRowsData(response_iterator)\n rows_data.consume_all()\n if rows_data.state != rows_data.NEW_ROW:\n raise ValueError('The row remains partial / is not committed.')\n\n if len(rows_data.rows) == 0:\n return None\n\n return rows_data.rows[row_key]","label":0},{"cid":"gcloud/bigtable/table.py::Table.read_rows","text":"gcloud/bigtable/table.py::Table.read_rows\n def read_rows(self, start_key=None, end_key=None, limit=None,\n filter_=None):\n \"\"\"Read rows from this table.\n\n :type start_key: bytes\n :param start_key: (Optional) The beginning of a range of row keys to\n read from. The range will include ``start_key``. If\n left empty, will be interpreted as the empty string.\n\n :type end_key: bytes\n :param end_key: (Optional) The end of a range of row keys to read from.\n The range will not include ``end_key``. If left empty,\n will be interpreted as an infinite string.\n\n :type limit: int\n :param limit: (Optional) The read will terminate after committing to N\n rows' worth of results. The default (zero) is to return\n all results.\n\n :type filter_: :class:`.RowFilter`\n :param filter_: (Optional) The filter to apply to the contents of the\n specified row(s). If unset, reads every column in\n each row.\n\n :rtype: :class:`.PartialRowsData`\n :returns: A :class:`.PartialRowsData` convenience wrapper for consuming\n the streamed results.\n \"\"\"\n request_pb = _create_row_request(\n self.name, start_key=start_key, end_key=end_key, filter_=filter_,\n limit=limit)\n client = self._instance._client\n response_iterator = client._data_stub.ReadRows(request_pb,\n client.timeout_seconds)\n # We expect an iterator of `data_messages_v2_pb2.ReadRowsResponse`\n return PartialRowsData(response_iterator)","label":0},{"cid":"gcloud/bigtable/table.py::Table.sample_row_keys","text":"gcloud/bigtable/table.py::Table.sample_row_keys\n def sample_row_keys(self):\n \"\"\"Read a sample of row keys in the table.\n\n The returned row keys will delimit contiguous sections of the table of\n approximately equal size, which can be used to break up the data for\n distributed tasks like mapreduces.\n\n The elements in the iterator are a SampleRowKeys response and they have\n the properties ``offset_bytes`` and ``row_key``. They occur in sorted\n order. The table might have contents before the first row key in the\n list and after the last one, but a key containing the empty string\n indicates \"end of table\" and will be the last response given, if\n present.\n\n .. note::\n\n Row keys in this list may not have ever been written to or read\n from, and users should therefore not make any assumptions about the\n row key structure that are specific to their use case.\n\n The ``offset_bytes`` field on a response indicates the approximate\n total storage space used by all rows in the table which precede\n ``row_key``. Buffering the contents of all rows between two subsequent\n samples would require space roughly equal to the difference in their\n ``offset_bytes`` fields.\n\n :rtype: :class:`grpc.framework.alpha._reexport._CancellableIterator`\n :returns: A cancel-able iterator. Can be consumed by calling ``next()``\n or by casting to a :class:`list` and can be cancelled by\n calling ``cancel()``.\n \"\"\"\n request_pb = data_messages_v2_pb2.SampleRowKeysRequest(\n table_name=self.name)\n client = self._instance._client\n response_iterator = client._data_stub.SampleRowKeys(\n request_pb, client.timeout_seconds)\n return response_iterator","label":0},{"cid":"gcloud/bigtable/table.py::_create_row_request","text":"gcloud/bigtable/table.py::_create_row_request\ndef _create_row_request(table_name, row_key=None, start_key=None, end_key=None,\n filter_=None, limit=None):\n \"\"\"Creates a request to read rows in a table.\n\n :type table_name: str\n :param table_name: The name of the table to read from.\n\n :type row_key: bytes\n :param row_key: (Optional) The key of a specific row to read from.\n\n :type start_key: bytes\n :param start_key: (Optional) The beginning of a range of row keys to\n read from. The range will include ``start_key``. If\n left empty, will be interpreted as the empty string.\n\n :type end_key: bytes\n :param end_key: (Optional) The end of a range of row keys to read from.\n The range will not include ``end_key``. If left empty,\n will be interpreted as an infinite string.\n\n :type filter_: :class:`.RowFilter`\n :param filter_: (Optional) The filter to apply to the contents of the\n specified row(s). If unset, reads the entire table.\n\n :type limit: int\n :param limit: (Optional) The read will terminate after committing to N\n rows' worth of results. The default (zero) is to return\n all results.\n\n :rtype: :class:`data_messages_v2_pb2.ReadRowsRequest`\n :returns: The ``ReadRowsRequest`` protobuf corresponding to the inputs.\n :raises: :class:`ValueError <exceptions.ValueError>` if both\n ``row_key`` and one of ``start_key`` and ``end_key`` are set\n \"\"\"\n request_kwargs = {'table_name': table_name}\n if (row_key is not None and\n (start_key is not None or end_key is not None)):\n raise ValueError('Row key and row range cannot be '\n 'set simultaneously')\n range_kwargs = {}\n if start_key is not None or end_key is not None:\n if start_key is not None:\n range_kwargs['start_key_closed'] = _to_bytes(start_key)\n ","label":0},{"cid":"gcloud/datastore/entity.py::Entity.__init__","text":"gcloud/datastore/entity.py::Entity.__init__\n def __init__(self, key=None, exclude_from_indexes=()):\n super(Entity, self).__init__()\n self.key = key\n self._exclude_from_indexes = set(_ensure_tuple_or_list(\n 'exclude_from_indexes', exclude_from_indexes))\n # NOTE: This will be populated when parsing a protobuf in\n # gcloud.datastore.helpers.entity_from_protobuf.\n self._meanings = {}","label":0},{"cid":"gcloud/datastore/entity.py::Entity.__eq__","text":"gcloud/datastore/entity.py::Entity.__eq__\n def __eq__(self, other):\n \"\"\"Compare two entities for equality.\n\n Entities compare equal if their keys compare equal, and their\n properties compare equal.\n\n :rtype: boolean\n :returns: True if the entities compare equal, else False.\n \"\"\"\n if not isinstance(other, Entity):\n return False\n\n return (self.key == other.key and\n self._exclude_from_indexes == other._exclude_from_indexes and\n self._meanings == other._meanings and\n super(Entity, self).__eq__(other))","label":0},{"cid":"gcloud/datastore/entity.py::Entity.__ne__","text":"gcloud/datastore/entity.py::Entity.__ne__\n def __ne__(self, other):\n \"\"\"Compare two entities for inequality.\n\n Entities compare equal if their keys compare equal, and their\n properties compare equal.\n\n :rtype: boolean\n :returns: False if the entities compare equal, else True.\n \"\"\"\n return not self.__eq__(other)","label":0},{"cid":"gcloud/datastore/entity.py::Entity.kind","text":"gcloud/datastore/entity.py::Entity.kind\n @property\n def kind(self):\n \"\"\"Get the kind of the current entity.\n\n .. note::\n This relies entirely on the :class:`gcloud.datastore.key.Key`\n set on the entity. That means that we're not storing the kind\n of the entity at all, just the properties and a pointer to a\n Key which knows its Kind.\n \"\"\"\n if self.key:\n return self.key.kind","label":0},{"cid":"gcloud/datastore/entity.py::Entity.exclude_from_indexes","text":"gcloud/datastore/entity.py::Entity.exclude_from_indexes\n @property\n def exclude_from_indexes(self):\n \"\"\"Names of fields which are *not* to be indexed for this entity.\n\n :rtype: sequence of field names\n \"\"\"\n return frozenset(self._exclude_from_indexes)","label":0},{"cid":"gcloud/datastore/entity.py::Entity.__repr__","text":"gcloud/datastore/entity.py::Entity.__repr__\n def __repr__(self):\n if self.key:\n return '<Entity%s %s>' % (self.key.path,\n super(Entity, self).__repr__())\n else:\n return '<Entity %s>' % (super(Entity, self).__repr__())","label":0},{"cid":"gcloud/datastore/helpers.py::_get_meaning","text":"gcloud/datastore/helpers.py::_get_meaning\ndef _get_meaning(value_pb, is_list=False):\n \"\"\"Get the meaning from a protobuf value.\n\n :type value_pb: :class:`gcloud.datastore._generated.entity_pb2.Value`\n :param value_pb: The protobuf value to be checked for an\n associated meaning.\n\n :type is_list: bool\n :param is_list: Boolean indicating if the ``value_pb`` contains\n a list value.\n\n :rtype: int\n :returns: The meaning for the ``value_pb`` if one is set, else\n :data:`None`. For a list value, if there are disagreeing\n means it just returns a list of meanings. If all the\n list meanings agree, it just condenses them.\n \"\"\"\n meaning = None\n if is_list:\n # An empty list will have no values, hence no shared meaning\n # set among them.\n if len(value_pb.array_value.values) == 0:\n return None\n\n # We check among all the meanings, some of which may be None,\n # the rest which may be enum/int values.\n all_meanings = [_get_meaning(sub_value_pb)\n for sub_value_pb in value_pb.array_value.values]\n unique_meanings = set(all_meanings)\n if len(unique_meanings) == 1:\n # If there is a unique meaning, we preserve it.\n meaning = unique_meanings.pop()\n else: # We know len(value_pb.array_value.values) > 0.\n # If the meaning is not unique, just return all of them.\n meaning = all_meanings\n elif value_pb.meaning: # Simple field (int32)\n meaning = value_pb.meaning\n\n return meaning","label":0},{"cid":"gcloud/datastore/helpers.py::_new_value_pb","text":"gcloud/datastore/helpers.py::_new_value_pb\ndef _new_value_pb(entity_pb, name):\n \"\"\"Add (by name) a new ``Value`` protobuf to an entity protobuf.\n\n :type entity_pb: :class:`gcloud.datastore._generated.entity_pb2.Entity`\n :param entity_pb: An entity protobuf to add a new property to.\n\n :type name: string\n :param name: The name of the new property.\n\n :rtype: :class:`gcloud.datastore._generated.entity_pb2.Value`\n :returns: The new ``Value`` protobuf that was added to the entity.\n \"\"\"\n return entity_pb.properties.get_or_create(name)","label":0},{"cid":"gcloud/datastore/helpers.py::_property_tuples","text":"gcloud/datastore/helpers.py::_property_tuples\ndef _property_tuples(entity_pb):\n \"\"\"Iterator of name, ``Value`` tuples from entity properties.\n\n :type entity_pb: :class:`gcloud.datastore._generated.entity_pb2.Entity`\n :param entity_pb: An entity protobuf to add a new property to.\n\n :rtype: :class:`generator`\n :returns: An iterator that yields tuples of a name and ``Value``\n corresponding to properties on the entity.\n \"\"\"\n return six.iteritems(entity_pb.properties)","label":0},{"cid":"gcloud/datastore/helpers.py::entity_from_protobuf","text":"gcloud/datastore/helpers.py::entity_from_protobuf\ndef entity_from_protobuf(pb):\n \"\"\"Factory method for creating an entity based on a protobuf.\n\n The protobuf should be one returned from the Cloud Datastore\n Protobuf API.\n\n :type pb: :class:`gcloud.datastore._generated.entity_pb2.Entity`\n :param pb: The Protobuf representing the entity.\n\n :rtype: :class:`gcloud.datastore.entity.Entity`\n :returns: The entity derived from the protobuf.\n \"\"\"\n key = None\n if pb.HasField('key'): # Message field (Key)\n key = key_from_protobuf(pb.key)\n\n entity_props = {}\n entity_meanings = {}\n exclude_from_indexes = []\n\n for prop_name, value_pb in _property_tuples(pb):\n value = _get_value_from_value_pb(value_pb)\n entity_props[prop_name] = value\n\n # Check if the property has an associated meaning.\n is_list = isinstance(value, list)\n meaning = _get_meaning(value_pb, is_list=is_list)\n if meaning is not None:\n entity_meanings[prop_name] = (meaning, value)\n\n # Check if ``value_pb`` was excluded from index. Lists need to be\n # special-cased and we require all ``exclude_from_indexes`` values\n # in a list agree.\n if is_list:\n exclude_values = set(value_pb.exclude_from_indexes\n for value_pb in value_pb.array_value.values)\n if len(exclude_values) != 1:\n raise ValueError('For an array_value, subvalues must either '\n 'all be indexed or all excluded from '\n 'indexes.')\n\n if exclude_values.pop():\n exclude_from_indexes.append(prop_name)\n else:\n if value_pb.exclude_from_indexes:\n exclude_from_indexes.append(prop_name)\n\n entity = Entity(key=key, exclude_from_indexes=exclude_from_indexes)\n entity.update(entity_props)\n entity._meanings.update(entity_meanings)\n return entity","label":0},{"cid":"gcloud/datastore/helpers.py::_set_pb_meaning_from_entity","text":"gcloud/datastore/helpers.py::_set_pb_meaning_from_entity\ndef _set_pb_meaning_from_entity(entity, name, value, value_pb,\n is_list=False):\n \"\"\"Add meaning information (from an entity) to a protobuf.\n\n :type entity: :class:`gcloud.datastore.entity.Entity`\n :param entity: The entity to be turned into a protobuf.\n\n :type name: string\n :param name: The name of the property.\n\n :type value: object\n :param value: The current value stored as property ``name``.\n\n :type value_pb: :class:`gcloud.datastore._generated.entity_pb2.Value`\n :param value_pb: The protobuf value to add meaning / meanings to.\n\n :type is_list: bool\n :param is_list: (Optional) Boolean indicating if the ``value`` is\n a list value.\n \"\"\"\n if name not in entity._meanings:\n return\n\n meaning, orig_value = entity._meanings[name]\n # Only add the meaning back to the protobuf if the value is\n # unchanged from when it was originally read from the API.\n if orig_value is not value:\n return\n\n # For lists, we set meaning on each sub-element.\n if is_list:\n if not isinstance(meaning, list):\n meaning = itertools.repeat(meaning)\n val_iter = six.moves.zip(value_pb.array_value.values,\n meaning)\n for sub_value_pb, sub_meaning in val_iter:\n if sub_meaning is not None:\n sub_value_pb.meaning = sub_meaning\n else:\n value_pb.meaning = meaning","label":0},{"cid":"gcloud/datastore/helpers.py::entity_to_protobuf","text":"gcloud/datastore/helpers.py::entity_to_protobuf\ndef entity_to_protobuf(entity):\n \"\"\"Converts an entity into a protobuf.\n\n :type entity: :class:`gcloud.datastore.entity.Entity`\n :param entity: The entity to be turned into a protobuf.\n\n :rtype: :class:`gcloud.datastore._generated.entity_pb2.Entity`\n :returns: The protobuf representing the entity.\n \"\"\"\n entity_pb = _entity_pb2.Entity()\n if entity.key is not None:\n key_pb = entity.key.to_protobuf()\n entity_pb.key.CopyFrom(key_pb)\n\n for name, value in entity.items():\n value_is_list = isinstance(value, list)\n if value_is_list and len(value) == 0:\n continue\n\n value_pb = _new_value_pb(entity_pb, name)\n # Set the appropriate value.\n _set_protobuf_value(value_pb, value)\n\n # Add index information to protobuf.\n if name in entity.exclude_from_indexes:\n if not value_is_list:\n value_pb.exclude_from_indexes = True\n\n for sub_value in value_pb.array_value.values:\n sub_value.exclude_from_indexes = True\n\n # Add meaning information to protobuf.\n _set_pb_meaning_from_entity(entity, name, value, value_pb,\n is_list=value_is_list)\n\n return entity_pb","label":0},{"cid":"gcloud/datastore/helpers.py::key_from_protobuf","text":"gcloud/datastore/helpers.py::key_from_protobuf\ndef key_from_protobuf(pb):\n \"\"\"Factory method for creating a key based on a protobuf.\n\n The protobuf should be one returned from the Cloud Datastore\n Protobuf API.\n\n :type pb: :class:`gcloud.datastore._generated.entity_pb2.Key`\n :param pb: The Protobuf representing the key.\n\n :rtype: :class:`gcloud.datastore.key.Key`\n :returns: a new `Key` instance\n \"\"\"\n path_args = []\n for element in pb.path:\n path_args.append(element.kind)\n if element.id: # Simple field (int64)\n path_args.append(element.id)\n # This is safe: we expect proto objects returned will only have\n # one of `name` or `id` set.\n if element.name: # Simple field (string)\n path_args.append(element.name)\n\n project = None\n if pb.partition_id.project_id: # Simple field (string)\n project = pb.partition_id.project_id\n namespace = None\n if pb.partition_id.namespace_id: # Simple field (string)\n namespace = pb.partition_id.namespace_id\n\n return Key(*path_args, namespace=namespace, project=project)","label":0},{"cid":"gcloud/datastore/helpers.py::_pb_attr_value","text":"gcloud/datastore/helpers.py::_pb_attr_value\ndef _pb_attr_value(val):\n \"\"\"Given a value, return the protobuf attribute name and proper value.\n\n The Protobuf API uses different attribute names based on value types\n rather than inferring the type. This function simply determines the\n proper attribute name based on the type of the value provided and\n returns the attribute name as well as a properly formatted value.\n\n Certain value types need to be coerced into a different type (such\n as a `datetime.datetime` into an integer timestamp, or a\n `gcloud.datastore.key.Key` into a Protobuf representation. This\n function handles that for you.\n\n .. note::\n Values which are \"text\" ('unicode' in Python2, 'str' in Python3) map\n to 'string_value' in the datastore; values which are \"bytes\"\n ('str' in Python2, 'bytes' in Python3) map to 'blob_value'.\n\n For example:\n\n >>> _pb_attr_value(1234)\n ('integer_value', 1234)\n >>> _pb_attr_value('my_string')\n ('string_value', 'my_string')\n\n :type val: `datetime.datetime`, :class:`gcloud.datastore.key.Key`,\n bool, float, integer, string\n :param val: The value to be scrutinized.\n\n :returns: A tuple of the attribute name and proper value type.\n \"\"\"\n\n if isinstance(val, datetime.datetime):\n name = 'timestamp'\n value = _datetime_to_pb_timestamp(val)\n elif isinstance(val, Key):\n name, value = 'key', val.to_protobuf()\n elif isinstance(val, bool):\n name, value = 'boolean', val\n elif isinstance(val, float):\n name, value = 'double', val\n elif isinstance(val, six.integer_types):\n name, value = 'integer', val\n elif isinstance(val, six.text_type):\n name, value = 'string', val\n elif isinstance(val, (bytes, str)):\n name, value = 'blob', val\n elif isinstance(val, Entity):\n name, value = 'entity', val\n elif isinstance(val, list):\n name, value = 'array', val\n elif isinstanc","label":0},{"cid":"gcloud/datastore/helpers.py::_get_value_from_value_pb","text":"gcloud/datastore/helpers.py::_get_value_from_value_pb\ndef _get_value_from_value_pb(value_pb):\n \"\"\"Given a protobuf for a Value, get the correct value.\n\n The Cloud Datastore Protobuf API returns a Property Protobuf which\n has one value set and the rest blank. This function retrieves the\n the one value provided.\n\n Some work is done to coerce the return value into a more useful type\n (particularly in the case of a timestamp value, or a key value).\n\n :type value_pb: :class:`gcloud.datastore._generated.entity_pb2.Value`\n :param value_pb: The Value Protobuf.\n\n :returns: The value provided by the Protobuf.\n :raises: :class:`ValueError <exceptions.ValueError>` if no value type\n has been set.\n \"\"\"\n value_type = value_pb.WhichOneof('value_type')\n\n if value_type == 'timestamp_value':\n result = _pb_timestamp_to_datetime(value_pb.timestamp_value)\n\n elif value_type == 'key_value':\n result = key_from_protobuf(value_pb.key_value)\n\n elif value_type == 'boolean_value':\n result = value_pb.boolean_value\n\n elif value_type == 'double_value':\n result = value_pb.double_value\n\n elif value_type == 'integer_value':\n result = value_pb.integer_value\n\n elif value_type == 'string_value':\n result = value_pb.string_value\n\n elif value_type == 'blob_value':\n result = value_pb.blob_value\n\n elif value_type == 'entity_value':\n result = entity_from_protobuf(value_pb.entity_value)\n\n elif value_type == 'array_value':\n result = [_get_value_from_value_pb(value)\n for value in value_pb.array_value.values]\n\n elif value_type == 'geo_point_value':\n result = GeoPoint(value_pb.geo_point_value.latitude,\n value_pb.geo_point_value.longitude)\n\n elif value_type == 'null_value':\n result = None\n\n else:\n raise ValueError('Value protobuf did not have any value set')\n\n return result","label":0},{"cid":"gcloud/datastore/helpers.py::_set_protobuf_value","text":"gcloud/datastore/helpers.py::_set_protobuf_value\ndef _set_protobuf_value(value_pb, val):\n \"\"\"Assign 'val' to the correct subfield of 'value_pb'.\n\n The Protobuf API uses different attribute names based on value types\n rather than inferring the type.\n\n Some value types (entities, keys, lists) cannot be directly\n assigned; this function handles them correctly.\n\n :type value_pb: :class:`gcloud.datastore._generated.entity_pb2.Value`\n :param value_pb: The value protobuf to which the value is being assigned.\n\n :type val: :class:`datetime.datetime`, boolean, float, integer, string,\n :class:`gcloud.datastore.key.Key`,\n :class:`gcloud.datastore.entity.Entity`\n :param val: The value to be assigned.\n \"\"\"\n attr, val = _pb_attr_value(val)\n if attr == 'key_value':\n value_pb.key_value.CopyFrom(val)\n elif attr == 'timestamp_value':\n value_pb.timestamp_value.CopyFrom(val)\n elif attr == 'entity_value':\n entity_pb = entity_to_protobuf(val)\n value_pb.entity_value.CopyFrom(entity_pb)\n elif attr == 'array_value':\n l_pb = value_pb.array_value.values\n for item in val:\n i_pb = l_pb.add()\n _set_protobuf_value(i_pb, item)\n elif attr == 'geo_point_value':\n value_pb.geo_point_value.CopyFrom(val)\n else: # scalar, just assign\n setattr(value_pb, attr, val)","label":0},{"cid":"gcloud/datastore/helpers.py::GeoPoint.__init__","text":"gcloud/datastore/helpers.py::GeoPoint.__init__\n def __init__(self, latitude, longitude):\n self.latitude = latitude\n self.longitude = longitude","label":0},{"cid":"gcloud/datastore/helpers.py::GeoPoint.to_protobuf","text":"gcloud/datastore/helpers.py::GeoPoint.to_protobuf\n def to_protobuf(self):\n \"\"\"Convert the current object to protobuf.\n\n :rtype: :class:`google.type.latlng_pb2.LatLng`.\n :returns: The current point as a protobuf.\n \"\"\"\n return latlng_pb2.LatLng(latitude=self.latitude,\n longitude=self.longitude)","label":0},{"cid":"gcloud/datastore/helpers.py::GeoPoint.__eq__","text":"gcloud/datastore/helpers.py::GeoPoint.__eq__\n def __eq__(self, other):\n \"\"\"Compare two geo points for equality.\n\n :rtype: boolean\n :returns: True if the points compare equal, else False.\n \"\"\"\n if not isinstance(other, GeoPoint):\n return False\n\n return (self.latitude == other.latitude and\n self.longitude == other.longitude)","label":0},{"cid":"gcloud/datastore/helpers.py::GeoPoint.__ne__","text":"gcloud/datastore/helpers.py::GeoPoint.__ne__\n def __ne__(self, other):\n \"\"\"Compare two geo points for inequality.\n\n :rtype: boolean\n :returns: False if the points compare equal, else True.\n \"\"\"\n return not self.__eq__(other)","label":0},{"cid":"scripts/make_datastore_grpc.py::get_pb2_contents_with_grpc","text":"scripts/make_datastore_grpc.py::get_pb2_contents_with_grpc\ndef get_pb2_contents_with_grpc():\n \"\"\"Get pb2 lines generated by protoc with gRPC plugin.\n\n :rtype: list\n :returns: A list of lines in the generated file.\n \"\"\"\n temp_dir = tempfile.mkdtemp()\n generated_path = os.path.join(temp_dir, 'google', 'datastore',\n 'v1beta3', 'datastore_pb2.py')\n try:\n return_code = subprocess.call([\n PROTOC_CMD,\n '--proto_path',\n PROTOS_DIR,\n '--python_out',\n temp_dir,\n '--plugin',\n 'protoc-gen-grpc=' + GRPC_PLUGIN,\n '--grpc_out',\n temp_dir,\n PROTO_PATH,\n ])\n if return_code != 0:\n sys.exit(return_code)\n with open(generated_path, 'rb') as file_obj:\n return file_obj.readlines()\n finally:\n shutil.rmtree(temp_dir, ignore_errors=True)","label":1},{"cid":"scripts/make_datastore_grpc.py::get_pb2_contents_without_grpc","text":"scripts/make_datastore_grpc.py::get_pb2_contents_without_grpc\ndef get_pb2_contents_without_grpc():\n \"\"\"Get pb2 lines generated by protoc without gRPC plugin.\n\n :rtype: list\n :returns: A list of lines in the generated file.\n \"\"\"\n temp_dir = tempfile.mkdtemp()\n generated_path = os.path.join(temp_dir, 'google', 'datastore',\n 'v1beta3', 'datastore_pb2.py')\n try:\n return_code = subprocess.call([\n PROTOC_CMD,\n '--proto_path',\n PROTOS_DIR,\n '--python_out',\n temp_dir,\n PROTO_PATH,\n ])\n if return_code != 0:\n sys.exit(return_code)\n with open(generated_path, 'rb') as file_obj:\n return file_obj.readlines()\n finally:\n shutil.rmtree(temp_dir, ignore_errors=True)","label":1},{"cid":"scripts/make_datastore_grpc.py::get_pb2_grpc_only","text":"scripts/make_datastore_grpc.py::get_pb2_grpc_only\ndef get_pb2_grpc_only():\n \"\"\"Get pb2 lines that are only in gRPC.\n\n :rtype: list\n :returns: A list of lines that are only in the pb2 file\n generated with the gRPC plugin.\n \"\"\"\n grpc_contents = get_pb2_contents_with_grpc()\n non_grpc_contents = get_pb2_contents_without_grpc()\n\n grpc_only_lines = []\n curr_non_grpc_line = 0\n for line in grpc_contents:\n if line == non_grpc_contents[curr_non_grpc_line]:\n curr_non_grpc_line += 1\n else:\n grpc_only_lines.append(line)\n\n return grpc_only_lines","label":0},{"cid":"scripts/make_datastore_grpc.py::main","text":"scripts/make_datastore_grpc.py::main\ndef main():\n \"\"\"Write gRPC-only lines to custom module.\"\"\"\n grpc_only_lines = get_pb2_grpc_only()\n with open(GRPC_ONLY_FILE, 'wb') as file_obj:\n file_obj.write(''.join(grpc_only_lines))","label":0},{"cid":"scripts/make_operations_grpc.py::get_pb2_contents_with_grpc","text":"scripts/make_operations_grpc.py::get_pb2_contents_with_grpc\ndef get_pb2_contents_with_grpc():\n \"\"\"Get pb2 lines generated by protoc with gRPC plugin.\n\n :rtype: list\n :returns: A list of lines in the generated file.\n \"\"\"\n temp_dir = tempfile.mkdtemp()\n generated_path = os.path.join(temp_dir, 'google', 'longrunning',\n 'operations_pb2.py')\n try:\n return_code = subprocess.call([\n PROTOC_CMD,\n '--proto_path',\n PROTOS_DIR,\n '--python_out',\n temp_dir,\n '--plugin',\n 'protoc-gen-grpc=' + GRPC_PLUGIN,\n '--grpc_out',\n temp_dir,\n PROTO_PATH,\n ])\n if return_code != 0:\n sys.exit(return_code)\n with open(generated_path, 'rb') as file_obj:\n return file_obj.readlines()\n finally:\n shutil.rmtree(temp_dir, ignore_errors=True)","label":1},{"cid":"scripts/make_operations_grpc.py::get_pb2_contents_without_grpc","text":"scripts/make_operations_grpc.py::get_pb2_contents_without_grpc\ndef get_pb2_contents_without_grpc():\n \"\"\"Get pb2 lines generated by protoc without gRPC plugin.\n\n :rtype: list\n :returns: A list of lines in the generated file.\n \"\"\"\n temp_dir = tempfile.mkdtemp()\n generated_path = os.path.join(temp_dir, 'google', 'longrunning',\n 'operations_pb2.py')\n try:\n return_code = subprocess.call([\n PROTOC_CMD,\n '--proto_path',\n PROTOS_DIR,\n '--python_out',\n temp_dir,\n PROTO_PATH,\n ])\n if return_code != 0:\n sys.exit(return_code)\n with open(generated_path, 'rb') as file_obj:\n return file_obj.readlines()\n finally:\n shutil.rmtree(temp_dir, ignore_errors=True)","label":1},{"cid":"scripts/make_operations_grpc.py::get_pb2_grpc_only","text":"scripts/make_operations_grpc.py::get_pb2_grpc_only\ndef get_pb2_grpc_only():\n \"\"\"Get pb2 lines that are only in gRPC.\n\n :rtype: list\n :returns: A list of lines that are only in the pb2 file\n generated with the gRPC plugin.\n \"\"\"\n grpc_contents = get_pb2_contents_with_grpc()\n non_grpc_contents = get_pb2_contents_without_grpc()\n\n grpc_only_lines = []\n curr_non_grpc_line = 0\n for line in grpc_contents:\n if line == non_grpc_contents[curr_non_grpc_line]:\n curr_non_grpc_line += 1\n else:\n grpc_only_lines.append(line)\n\n return grpc_only_lines","label":0},{"cid":"scripts/make_operations_grpc.py::main","text":"scripts/make_operations_grpc.py::main\ndef main():\n \"\"\"Write gRPC-only lines to custom module.\"\"\"\n grpc_only_lines = get_pb2_grpc_only()\n with open(GRPC_ONLY_FILE, 'wb') as file_obj:\n file_obj.writelines(grpc_only_lines)","label":0},{"cid":"scripts/rewrite_imports.py::transform_old_to_new","text":"scripts/rewrite_imports.py::transform_old_to_new\ndef transform_old_to_new(line, old_module, new_module,\n ignore_import_from=False):\n \"\"\"Transforms from an old module to a new one.\n\n First checks if a line starts with\n \"from {old_module} import ...\"\n then checks if the line contains\n \"import {old_module} ...\"\n then checks if the line starts with (ignoring whitespace)\n \"{old_module} ...\"\n and finally checks if the line contians\n \"'some-dict-key': {old_module} ...\"\n\n In any of these cases, \"{old_module}\" is replaced with \"{new_module}\".\n If none match, nothing is returned.\n\n :type line: str\n :param line: The line to be transformed.\n\n :type old_module: str\n :param old_module: The import to be re-written.\n\n :type new_module: str\n :param new_module: The new location of the re-written import.\n\n :type ignore_import_from: bool\n :param ignore_import_from: Flag to determine if the \"from * import\"\n statements should be ignored.\n\n :rtype: :class:`str` or :data:`NoneType <types.NoneType>`\n :returns: The transformed line if the old module was found, otherwise\n does nothing.\n \"\"\"\n if not ignore_import_from:\n import_from_statement = IMPORT_FROM_TEMPLATE % (old_module,)\n if line.startswith(import_from_statement):\n new_import_from_statement = IMPORT_FROM_TEMPLATE % (new_module,)\n # Only replace the first instance of the import statement.\n return line.replace(import_from_statement,\n new_import_from_statement, 1)\n\n # If the line doesn't start with a \"from * import *\" statement, it\n # may still contain a \"import * ...\" statement.\n import_statement = IMPORT_TEMPLATE % (old_module,)\n if import_statement in line:\n new_import_statement = IMPORT_TEMPLATE % (new_module,)\n # Only replace the first instance of the import statement.\n retu","label":0},{"cid":"scripts/rewrite_imports.py::transform_line","text":"scripts/rewrite_imports.py::transform_line\ndef transform_line(line):\n \"\"\"Transforms an import line in a PB2 module.\n\n If the line is not an import of one of the packages in ``REPLACEMENTS``,\n does nothing and returns the original. Otherwise it replaces the package\n matched with our local package.\n\n :type line: str\n :param line: The line to be transformed.\n\n :rtype: str\n :returns: The transformed line.\n \"\"\"\n for old_module, new_module in REPLACEMENTS.iteritems():\n result = transform_old_to_new(line, old_module, new_module)\n if result is not None:\n return result\n\n # If no matches, there is nothing to transform.\n return line","label":1},{"cid":"scripts/rewrite_imports.py::rewrite_file","text":"scripts/rewrite_imports.py::rewrite_file\ndef rewrite_file(filename):\n \"\"\"Rewrites a given PB2 modules.\n\n :type filename: str\n :param filename: The name of the file to be rewritten.\n \"\"\"\n with open(filename, 'rU') as file_obj:\n content_lines = file_obj.read().split('\\n')\n\n new_content = []\n for line in content_lines:\n new_content.append(transform_line(line))\n\n with open(filename, 'w') as file_obj:\n file_obj.write('\\n'.join(new_content))","label":0},{"cid":"scripts/rewrite_imports.py::main","text":"scripts/rewrite_imports.py::main\ndef main():\n \"\"\"Rewrites all PB2 files.\"\"\"\n for filename in sys.argv[1:]:\n rewrite_file(filename)","label":0},{"cid":"scripts/run_pylint.py::read_config","text":"scripts/run_pylint.py::read_config\ndef read_config(filename):\n \"\"\"Reads pylintrc config onto native ConfigParser object.\"\"\"\n config = ConfigParser.ConfigParser()\n with open(filename, 'r') as file_obj:\n config.readfp(file_obj)\n return config","label":0},{"cid":"scripts/run_pylint.py::make_test_rc","text":"scripts/run_pylint.py::make_test_rc\ndef make_test_rc(base_rc_filename, additions_dict,\n replacements_dict, target_filename):\n \"\"\"Combines a base rc and test additions into single file.\"\"\"\n main_cfg = read_config(base_rc_filename)\n\n # Create fresh config for test, which must extend production.\n test_cfg = ConfigParser.ConfigParser()\n test_cfg._sections = copy.deepcopy(main_cfg._sections)\n\n for section, opts in additions_dict.items():\n curr_section = test_cfg._sections.setdefault(\n section, test_cfg._dict())\n for opt, opt_val in opts.items():\n curr_val = curr_section.get(opt)\n if curr_val is None:\n raise KeyError('Expected to be adding to existing option.')\n curr_val = curr_val.rstrip(',')\n curr_section[opt] = '%s, %s' % (curr_val, opt_val)\n\n for section, opts in replacements_dict.items():\n curr_section = test_cfg._sections.setdefault(\n section, test_cfg._dict())\n for opt, opt_val in opts.items():\n curr_val = curr_section.get(opt)\n if curr_val is None:\n raise KeyError('Expected to be replacing existing option.')\n curr_section[opt] = '%s' % (opt_val,)\n\n with open(target_filename, 'w') as file_obj:\n test_cfg.write(file_obj)","label":0},{"cid":"scripts/run_pylint.py::valid_filename","text":"scripts/run_pylint.py::valid_filename\ndef valid_filename(filename):\n \"\"\"Checks if a file is a Python file and is not ignored.\"\"\"\n for directory in IGNORED_DIRECTORIES:\n if filename.startswith(directory):\n return False\n return (filename.endswith('.py') and\n filename not in IGNORED_FILES)","label":0},{"cid":"scripts/run_pylint.py::is_production_filename","text":"scripts/run_pylint.py::is_production_filename\ndef is_production_filename(filename):\n \"\"\"Checks if the file contains production code.\n\n :rtype: bool\n :returns: Boolean indicating production status.\n \"\"\"\n return 'test' not in filename and 'docs' not in filename","label":0},{"cid":"scripts/run_pylint.py::get_files_for_linting","text":"scripts/run_pylint.py::get_files_for_linting\ndef get_files_for_linting(allow_limited=True):\n \"\"\"Gets a list of files in the repository.\n\n By default, returns all files via ``git ls-files``. However, in some cases\n uses a specific commit or branch (a so-called diff base) to compare\n against for changed files. (This requires ``allow_limited=True``.)\n\n To speed up linting on Travis pull requests against master, we manually\n set the diff base to origin/master. We don't do this on non-pull requests\n since origin/master will be equivalent to the currently checked out code.\n One could potentially use ${TRAVIS_COMMIT_RANGE} to find a diff base but\n this value is not dependable.\n\n To allow faster local ``tox`` runs, the environment variables\n ``GCLOUD_REMOTE_FOR_LINT`` and ``GCLOUD_BRANCH_FOR_LINT`` can be set to\n specify a remote branch to diff against.\n\n :type allow_limited: bool\n :param allow_limited: Boolean indicating if a reduced set of files can\n be used.\n\n :rtype: pair\n :returns: Tuple of the diff base using the the list of filenames to be\n linted.\n \"\"\"\n diff_base = None\n if (os.getenv('TRAVIS_BRANCH') == 'master' and\n os.getenv('TRAVIS_PULL_REQUEST') != 'false'):\n # In the case of a pull request into master, we want to\n # diff against HEAD in master.\n diff_base = 'origin/master'\n elif os.getenv('TRAVIS') is None:\n # Only allow specified remote and branch in local dev.\n remote = os.getenv('GCLOUD_REMOTE_FOR_LINT')\n branch = os.getenv('GCLOUD_BRANCH_FOR_LINT')\n if remote is not None and branch is not None:\n diff_base = '%s/%s' % (remote, branch)\n\n if diff_base is not None and allow_limited:\n result = subprocess.check_output(['git', 'diff', '--name-only',\n diff_base])\n print('Using files changed relative to %s:' % (diff_base,))\n print('-' ","label":0},{"cid":"scripts/run_pylint.py::get_python_files","text":"scripts/run_pylint.py::get_python_files\ndef get_python_files(all_files=None):\n \"\"\"Gets a list of all Python files in the repository that need linting.\n\n Relies on :func:`get_files_for_linting()` to determine which files should\n be considered.\n\n NOTE: This requires ``git`` to be installed and requires that this\n is run within the ``git`` repository.\n\n :type all_files: list or ``NoneType``\n :param all_files: Optional list of files to be linted.\n\n :rtype: tuple\n :returns: A tuple containing two lists and a boolean. The first list\n contains all production files, the next all test files and\n the boolean indicates if a restricted fileset was used.\n \"\"\"\n using_restricted = False\n if all_files is None:\n all_files, diff_base = get_files_for_linting()\n using_restricted = diff_base is not None\n\n library_files = []\n non_library_files = []\n for filename in all_files:\n if valid_filename(filename):\n if is_production_filename(filename):\n library_files.append(filename)\n else:\n non_library_files.append(filename)\n\n return library_files, non_library_files, using_restricted","label":0},{"cid":"scripts/run_pylint.py::lint_fileset","text":"scripts/run_pylint.py::lint_fileset\ndef lint_fileset(filenames, rcfile, description):\n \"\"\"Lints a group of files using a given rcfile.\"\"\"\n # Only lint filenames that exist. For example, 'git diff --name-only'\n # could spit out deleted / renamed files. Another alternative could\n # be to use 'git diff --name-status' and filter out files with a\n # status of 'D'.\n filenames = [filename for filename in filenames\n if os.path.exists(filename)]\n if filenames:\n rc_flag = '--rcfile=%s' % (rcfile,)\n pylint_shell_command = ['pylint', rc_flag] + filenames\n status_code = subprocess.call(pylint_shell_command)\n if status_code != 0:\n error_message = ('Pylint failed on %s with '\n 'status %d.' % (description, status_code))\n print(error_message, file=sys.stderr)\n sys.exit(status_code)\n else:\n print('Skipping %s, no files to lint.' % (description,))","label":0},{"cid":"scripts/run_pylint.py::main","text":"scripts/run_pylint.py::main\ndef main():\n \"\"\"Script entry point. Lints both sets of files.\"\"\"\n make_test_rc(PRODUCTION_RC, TEST_RC_ADDITIONS,\n TEST_RC_REPLACEMENTS, TEST_RC)\n library_files, non_library_files, using_restricted = get_python_files()\n try:\n lint_fileset(library_files, PRODUCTION_RC, 'library code')\n lint_fileset(non_library_files, TEST_RC, 'test code')\n except SystemExit:\n if not using_restricted:\n raise\n\n message = 'Restricted lint failed, expanding to full fileset.'\n print(message, file=sys.stderr)\n all_files, _ = get_files_for_linting(allow_limited=False)\n library_files, non_library_files, _ = get_python_files(\n all_files=all_files)\n lint_fileset(library_files, PRODUCTION_RC, 'library code')\n lint_fileset(non_library_files, TEST_RC, 'test code')","label":0}],"string":"[\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.CreateInstance\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.CreateInstance\\n def CreateInstance(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.GetInstance\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.GetInstance\\n def GetInstance(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.ListInstances\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.ListInstances\\n def ListInstances(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.UpdateInstance\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.UpdateInstance\\n def UpdateInstance(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.DeleteInstance\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.DeleteInstance\\n def DeleteInstance(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.CreateCluster\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.CreateCluster\\n def CreateCluster(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.GetCluster\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.GetCluster\\n def GetCluster(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.ListClusters\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.ListClusters\\n def ListClusters(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.UpdateCluster\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.UpdateCluster\\n def UpdateCluster(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.DeleteCluster\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminServicer.DeleteCluster\\n def DeleteCluster(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.CreateInstance\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.CreateInstance\\n def CreateInstance(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.GetInstance\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.GetInstance\\n def GetInstance(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.ListInstances\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.ListInstances\\n def ListInstances(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.UpdateInstance\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.UpdateInstance\\n def UpdateInstance(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.DeleteInstance\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.DeleteInstance\\n def DeleteInstance(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.CreateCluster\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.CreateCluster\\n def CreateCluster(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.GetCluster\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.GetCluster\\n def GetCluster(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.ListClusters\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.ListClusters\\n def ListClusters(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.UpdateCluster\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.UpdateCluster\\n def UpdateCluster(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.DeleteCluster\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::BetaBigtableInstanceAdminStub.DeleteCluster\\n def DeleteCluster(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::beta_create_BigtableInstanceAdmin_server\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::beta_create_BigtableInstanceAdmin_server\\ndef beta_create_BigtableInstanceAdmin_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None):\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.longrunning.operations_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.bigtable.admin.v2.instance_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.bigtable.admin.v2.instance_pb2\\n import google.bigtable.admin.v2.instance_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.protobuf.empty_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.longrunning.operations_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.bigtable.admin.v2.instance_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.bigtable.admin.v2.instance_pb2\\n import google.longrunning.operations_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.protobuf.empty_pb2\\n request_deserializers = {\\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'CreateCluster'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.CreateClusterRequest.FromString,\\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'CreateInstance'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.CreateInstanceRequest.FromString,\\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'DeleteCluster'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.DeleteClusterRequest.FromString,\\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'DeleteInstance'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.DeleteInstanceRequest.FromString,\\n ('google.bigtable.admin.v2.BigtableInstanceA\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::beta_create_BigtableInstanceAdmin_stub\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_instance_admin_pb2.py::beta_create_BigtableInstanceAdmin_stub\\ndef beta_create_BigtableInstanceAdmin_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None):\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.longrunning.operations_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.bigtable.admin.v2.instance_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.bigtable.admin.v2.instance_pb2\\n import google.bigtable.admin.v2.instance_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.protobuf.empty_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.longrunning.operations_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.bigtable.admin.v2.instance_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.bigtable.admin.v2.instance_pb2\\n import google.longrunning.operations_pb2\\n import google.bigtable.admin.v2.bigtable_instance_admin_pb2\\n import google.protobuf.empty_pb2\\n request_serializers = {\\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'CreateCluster'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.CreateClusterRequest.SerializeToString,\\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'CreateInstance'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.CreateInstanceRequest.SerializeToString,\\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'DeleteCluster'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.DeleteClusterRequest.SerializeToString,\\n ('google.bigtable.admin.v2.BigtableInstanceAdmin', 'DeleteInstance'): google.bigtable.admin.v2.bigtable_instance_admin_pb2.DeleteInstanceRequest.SerializeToString,\\n ('google.bigtable.admin.v2.Bi\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.ReadRows\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.ReadRows\\n def ReadRows(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.SampleRowKeys\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.SampleRowKeys\\n def SampleRowKeys(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.MutateRow\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.MutateRow\\n def MutateRow(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.MutateRows\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.MutateRows\\n def MutateRows(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.CheckAndMutateRow\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.CheckAndMutateRow\\n def CheckAndMutateRow(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.ReadModifyWriteRow\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableServicer.ReadModifyWriteRow\\n def ReadModifyWriteRow(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.ReadRows\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.ReadRows\\n def ReadRows(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.SampleRowKeys\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.SampleRowKeys\\n def SampleRowKeys(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.MutateRow\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.MutateRow\\n def MutateRow(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.MutateRows\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.MutateRows\\n def MutateRows(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.CheckAndMutateRow\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.CheckAndMutateRow\\n def CheckAndMutateRow(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.ReadModifyWriteRow\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::BetaBigtableStub.ReadModifyWriteRow\\n def ReadModifyWriteRow(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::beta_create_Bigtable_server\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::beta_create_Bigtable_server\\ndef beta_create_Bigtable_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None):\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n request_deserializers = {\\n ('google.bigtable.v2.Bigtable', 'CheckAndMutateRow'): google.bigtable.v2.bigtable_pb2.CheckAndMutateRowRequest.FromString,\\n ('google.bigtable.v2.Bigtable', 'MutateRow'): google.bigtable.v2.bigtable_pb2.MutateRowRequest.FromString,\\n ('google.bigtable.v2.Bigtable', 'MutateRows'): google.bigtable.v2.bigtable_pb2.MutateRowsRequest.FromString,\\n ('google.bigtable.v2.Bigtable', 'ReadModifyWriteRow'): google.bigtable.v2.bigtable_pb2.ReadModifyWriteRowRequest.FromString,\\n ('google.bigtable.v2.Bigtable', 'ReadRows'): google.bigtable.v2.bigtable_pb2.ReadRowsRequest.FromString,\\n ('google.bigtable.v2.Bigtable', 'SampleRowKeys'): google.bigtable.v2.bigtable_pb2.SampleRowKeysRequest.FromString,\\n }\\n response_serializers = {\\n ('google.bigtable.v2.Bigtable', 'CheckAndMutateRow'): google.bigtable.v2.bigtable_pb2.CheckAndMutateRowResponse.SerializeToString,\\n ('google.bigtable.v2.Bigtable', 'MutateRow'): google.bigtable.v2.bigtable_pb2.MutateRowResponse.SerializeToString,\\n ('google.bigtable.v2.Bigtable', 'MutateRows'): google.bigtable.v2.bigtable_pb2.MutateRowsResponse.SerializeToString,\\n ('google.bigtable.v2.Bigtable', 'ReadModifyWriteRow'): google.bigtable.v2.bigtable_pb2.ReadModifyWriteRowResponse.SerializeToString,\\n ('google.bigtable.v2.Bigtable', 'Rea\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::beta_create_Bigtable_stub\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_pb2.py::beta_create_Bigtable_stub\\ndef beta_create_Bigtable_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None):\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n import google.bigtable.v2.bigtable_pb2\\n request_serializers = {\\n ('google.bigtable.v2.Bigtable', 'CheckAndMutateRow'): google.bigtable.v2.bigtable_pb2.CheckAndMutateRowRequest.SerializeToString,\\n ('google.bigtable.v2.Bigtable', 'MutateRow'): google.bigtable.v2.bigtable_pb2.MutateRowRequest.SerializeToString,\\n ('google.bigtable.v2.Bigtable', 'MutateRows'): google.bigtable.v2.bigtable_pb2.MutateRowsRequest.SerializeToString,\\n ('google.bigtable.v2.Bigtable', 'ReadModifyWriteRow'): google.bigtable.v2.bigtable_pb2.ReadModifyWriteRowRequest.SerializeToString,\\n ('google.bigtable.v2.Bigtable', 'ReadRows'): google.bigtable.v2.bigtable_pb2.ReadRowsRequest.SerializeToString,\\n ('google.bigtable.v2.Bigtable', 'SampleRowKeys'): google.bigtable.v2.bigtable_pb2.SampleRowKeysRequest.SerializeToString,\\n }\\n response_deserializers = {\\n ('google.bigtable.v2.Bigtable', 'CheckAndMutateRow'): google.bigtable.v2.bigtable_pb2.CheckAndMutateRowResponse.FromString,\\n ('google.bigtable.v2.Bigtable', 'MutateRow'): google.bigtable.v2.bigtable_pb2.MutateRowResponse.FromString,\\n ('google.bigtable.v2.Bigtable', 'MutateRows'): google.bigtable.v2.bigtable_pb2.MutateRowsResponse.FromString,\\n ('google.bigtable.v2.Bigtable', 'ReadModifyWriteRow'): google.bigtable.v2.bigtable_pb2.ReadModifyWriteRowResponse.FromString,\\n ('google.bigtable.v2.Bigtable', '\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.CreateTable\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.CreateTable\\n def CreateTable(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.ListTables\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.ListTables\\n def ListTables(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.GetTable\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.GetTable\\n def GetTable(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.DeleteTable\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.DeleteTable\\n def DeleteTable(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.ModifyColumnFamilies\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.ModifyColumnFamilies\\n def ModifyColumnFamilies(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.DropRowRange\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminServicer.DropRowRange\\n def DropRowRange(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.CreateTable\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.CreateTable\\n def CreateTable(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.ListTables\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.ListTables\\n def ListTables(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.GetTable\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.GetTable\\n def GetTable(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.DeleteTable\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.DeleteTable\\n def DeleteTable(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.ModifyColumnFamilies\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.ModifyColumnFamilies\\n def ModifyColumnFamilies(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.DropRowRange\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::BetaBigtableTableAdminStub.DropRowRange\\n def DropRowRange(self, request, timeout):\\n raise NotImplementedError()\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::beta_create_BigtableTableAdmin_server\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::beta_create_BigtableTableAdmin_server\\ndef beta_create_BigtableTableAdmin_server(servicer, pool=None, pool_size=None, default_timeout=None, maximum_timeout=None):\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.bigtable.admin.v2.table_pb2\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.bigtable.admin.v2.table_pb2\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.protobuf.empty_pb2\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.bigtable.admin.v2.table_pb2\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.protobuf.empty_pb2\\n request_deserializers = {\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'CreateTable'): google.bigtable.admin.v2.bigtable_table_admin_pb2.CreateTableRequest.FromString,\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'DeleteTable'): google.bigtable.admin.v2.bigtable_table_admin_pb2.DeleteTableRequest.FromString,\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'DropRowRange'): google.bigtable.admin.v2.bigtable_table_admin_pb2.DropRowRangeRequest.FromString,\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'GetTable'): google.bigtable.admin.v2.bigtable_table_admin_pb2.GetTableRequest.FromString,\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'ListTables'): google.bigtable.admin.v2.bigtable_table_admin_pb2.ListTablesRequest.FromString,\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'ModifyColumnFamilies'): google.bigtable.admin.v2.bigtable_table_admin_pb2.ModifyColumnFamiliesRequest.FromString,\\n }\\n response_serializers = {\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'CreateTable'): google.bigtable.admin.v2.table_pb2.Table.SerializeToString,\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'DeleteTable'): goo\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::beta_create_BigtableTableAdmin_stub\",\n \"text\": \"gcloud/bigtable/_generated_v2/bigtable_table_admin_pb2.py::beta_create_BigtableTableAdmin_stub\\ndef beta_create_BigtableTableAdmin_stub(channel, host=None, metadata_transformer=None, pool=None, pool_size=None):\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.bigtable.admin.v2.table_pb2\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.bigtable.admin.v2.table_pb2\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.protobuf.empty_pb2\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.bigtable.admin.v2.table_pb2\\n import google.bigtable.admin.v2.bigtable_table_admin_pb2\\n import google.protobuf.empty_pb2\\n request_serializers = {\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'CreateTable'): google.bigtable.admin.v2.bigtable_table_admin_pb2.CreateTableRequest.SerializeToString,\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'DeleteTable'): google.bigtable.admin.v2.bigtable_table_admin_pb2.DeleteTableRequest.SerializeToString,\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'DropRowRange'): google.bigtable.admin.v2.bigtable_table_admin_pb2.DropRowRangeRequest.SerializeToString,\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'GetTable'): google.bigtable.admin.v2.bigtable_table_admin_pb2.GetTableRequest.SerializeToString,\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'ListTables'): google.bigtable.admin.v2.bigtable_table_admin_pb2.ListTablesRequest.SerializeToString,\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'ModifyColumnFamilies'): google.bigtable.admin.v2.bigtable_table_admin_pb2.ModifyColumnFamiliesRequest.SerializeToString,\\n }\\n response_deserializers = {\\n ('google.bigtable.admin.v2.BigtableTableAdmin', 'CreateTable'): google.bigtable.admin.v2.table_pb2.Table.FromString,\\n ('google.bigtable.admin.v2.BigtableTableAdm\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::_prepare_create_request\",\n \"text\": \"gcloud/bigtable/cluster.py::_prepare_create_request\\ndef _prepare_create_request(cluster):\\n \\\"\\\"\\\"Creates a protobuf request for a CreateCluster request.\\n\\n :type cluster: :class:`Cluster`\\n :param cluster: The cluster to be created.\\n\\n :rtype: :class:`.messages_v2_pb2.CreateClusterRequest`\\n :returns: The CreateCluster request object containing the cluster info.\\n \\\"\\\"\\\"\\n return messages_v2_pb2.CreateClusterRequest(\\n name=cluster._instance.name,\\n cluster_id=cluster.cluster_id,\\n cluster=data_v2_pb2.Cluster(\\n serve_nodes=cluster.serve_nodes,\\n ),\\n )\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::_parse_pb_any_to_native\",\n \"text\": \"gcloud/bigtable/cluster.py::_parse_pb_any_to_native\\ndef _parse_pb_any_to_native(any_val, expected_type=None):\\n \\\"\\\"\\\"Convert a serialized \\\"google.protobuf.Any\\\" value to actual type.\\n\\n :type any_val: :class:`google.protobuf.any_pb2.Any`\\n :param any_val: A serialized protobuf value container.\\n\\n :type expected_type: str\\n :param expected_type: (Optional) The type URL we expect ``any_val``\\n to have.\\n\\n :rtype: object\\n :returns: The de-serialized object.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the\\n ``expected_type`` does not match the ``type_url`` on the input.\\n \\\"\\\"\\\"\\n if expected_type is not None and expected_type != any_val.type_url:\\n raise ValueError('Expected type: %s, Received: %s' % (\\n expected_type, any_val.type_url))\\n container_class = _TYPE_URL_MAP[any_val.type_url]\\n return container_class.FromString(any_val.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::_process_operation\",\n \"text\": \"gcloud/bigtable/cluster.py::_process_operation\\ndef _process_operation(operation_pb):\\n \\\"\\\"\\\"Processes a create protobuf response.\\n\\n :type operation_pb: :class:`google.longrunning.operations_pb2.Operation`\\n :param operation_pb: The long-running operation response from a\\n Create/Update/Undelete cluster request.\\n\\n :rtype: tuple\\n :returns: integer ID of the operation (``operation_id``).\\n :raises: :class:`ValueError <exceptions.ValueError>` if the operation name\\n doesn't match the :data:`_OPERATION_NAME_RE` regex.\\n \\\"\\\"\\\"\\n match = _OPERATION_NAME_RE.match(operation_pb.name)\\n if match is None:\\n raise ValueError('Operation name was not in the expected '\\n 'format after a cluster modification.',\\n operation_pb.name)\\n operation_id = int(match.group('operation_id'))\\n\\n return operation_id\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Operation.__init__\",\n \"text\": \"gcloud/bigtable/cluster.py::Operation.__init__\\n def __init__(self, op_type, op_id, cluster=None):\\n self.op_type = op_type\\n self.op_id = op_id\\n self._cluster = cluster\\n self._complete = False\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Operation.__eq__\",\n \"text\": \"gcloud/bigtable/cluster.py::Operation.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other.op_type == self.op_type and\\n other.op_id == self.op_id and\\n other._cluster == self._cluster and\\n other._complete == self._complete)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Operation.__ne__\",\n \"text\": \"gcloud/bigtable/cluster.py::Operation.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Operation.finished\",\n \"text\": \"gcloud/bigtable/cluster.py::Operation.finished\\n def finished(self):\\n \\\"\\\"\\\"Check if the operation has finished.\\n\\n :rtype: bool\\n :returns: A boolean indicating if the current operation has completed.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the operation\\n has already completed.\\n \\\"\\\"\\\"\\n if self._complete:\\n raise ValueError('The operation has completed.')\\n\\n operation_name = ('operations/' + self._cluster.name +\\n '/operations/%d' % (self.op_id,))\\n request_pb = operations_pb2.GetOperationRequest(name=operation_name)\\n # We expect a `google.longrunning.operations_pb2.Operation`.\\n client = self._cluster._instance._client\\n operation_pb = client._operations_stub.GetOperation(\\n request_pb, client.timeout_seconds)\\n\\n if operation_pb.done:\\n self._complete = True\\n return True\\n else:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Cluster.__init__\",\n \"text\": \"gcloud/bigtable/cluster.py::Cluster.__init__\\n def __init__(self, cluster_id, instance,\\n serve_nodes=DEFAULT_SERVE_NODES):\\n self.cluster_id = cluster_id\\n self._instance = instance\\n self.serve_nodes = serve_nodes\\n self.location = None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Cluster._update_from_pb\",\n \"text\": \"gcloud/bigtable/cluster.py::Cluster._update_from_pb\\n def _update_from_pb(self, cluster_pb):\\n \\\"\\\"\\\"Refresh self from the server-provided protobuf.\\n\\n Helper for :meth:`from_pb` and :meth:`reload`.\\n \\\"\\\"\\\"\\n if not cluster_pb.serve_nodes: # Simple field (int32)\\n raise ValueError('Cluster protobuf does not contain serve_nodes')\\n self.serve_nodes = cluster_pb.serve_nodes\\n self.location = cluster_pb.location\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Cluster.from_pb\",\n \"text\": \"gcloud/bigtable/cluster.py::Cluster.from_pb\\n @classmethod\\n def from_pb(cls, cluster_pb, instance):\\n \\\"\\\"\\\"Creates a cluster instance from a protobuf.\\n\\n :type cluster_pb: :class:`bigtable_cluster_data_pb2.Cluster`\\n :param cluster_pb: A cluster protobuf object.\\n\\n :type instance: :class:`.instance.Instance>`\\n :param instance: The instance that owns the cluster.\\n\\n :rtype: :class:`Cluster`\\n :returns: The cluster parsed from the protobuf response.\\n :raises:\\n :class:`ValueError <exceptions.ValueError>` if the cluster\\n name does not match\\n ``projects/{project}/instances/{instance}/clusters/{cluster_id}``\\n or if the parsed project ID does not match the project ID\\n on the client.\\n \\\"\\\"\\\"\\n match = _CLUSTER_NAME_RE.match(cluster_pb.name)\\n if match is None:\\n raise ValueError('Cluster protobuf name was not in the '\\n 'expected format.', cluster_pb.name)\\n if match.group('project') != instance._client.project:\\n raise ValueError('Project ID on cluster does not match the '\\n 'project ID on the client')\\n if match.group('instance') != instance.instance_id:\\n raise ValueError('Instance ID on cluster does not match the '\\n 'instance ID on the client')\\n\\n result = cls(match.group('cluster_id'), instance)\\n result._update_from_pb(cluster_pb)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Cluster.copy\",\n \"text\": \"gcloud/bigtable/cluster.py::Cluster.copy\\n def copy(self):\\n \\\"\\\"\\\"Make a copy of this cluster.\\n\\n Copies the local data stored as simple types and copies the client\\n attached to this instance.\\n\\n :rtype: :class:`.Cluster`\\n :returns: A copy of the current cluster.\\n \\\"\\\"\\\"\\n new_instance = self._instance.copy()\\n return self.__class__(self.cluster_id, new_instance,\\n serve_nodes=self.serve_nodes)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Cluster.name\",\n \"text\": \"gcloud/bigtable/cluster.py::Cluster.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"Cluster name used in requests.\\n\\n .. note::\\n This property will not change if ``_instance`` and ``cluster_id``\\n do not, but the return value is not cached.\\n\\n The cluster name is of the form\\n\\n ``\\\"projects/{project}/instances/{instance}/clusters/{cluster_id}\\\"``\\n\\n :rtype: str\\n :returns: The cluster name.\\n \\\"\\\"\\\"\\n return self._instance.name + '/clusters/' + self.cluster_id\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Cluster.__eq__\",\n \"text\": \"gcloud/bigtable/cluster.py::Cluster.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n # NOTE: This does not compare the configuration values, such as\\n # the serve_nodes. Instead, it only compares\\n # identifying values instance, cluster ID and client. This is\\n # intentional, since the same cluster can be in different states\\n # if not synchronized. Clusters with similar instance/cluster\\n # settings but different clients can't be used in the same way.\\n return (other.cluster_id == self.cluster_id and\\n other._instance == self._instance)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Cluster.__ne__\",\n \"text\": \"gcloud/bigtable/cluster.py::Cluster.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Cluster.reload\",\n \"text\": \"gcloud/bigtable/cluster.py::Cluster.reload\\n def reload(self):\\n \\\"\\\"\\\"Reload the metadata for this cluster.\\\"\\\"\\\"\\n request_pb = messages_v2_pb2.GetClusterRequest(name=self.name)\\n # We expect a `._generated.bigtable_cluster_data_pb2.Cluster`.\\n cluster_pb = self._instance._client._cluster_stub.GetCluster(\\n request_pb, self._instance._client.timeout_seconds)\\n\\n # NOTE: _update_from_pb does not check that the project, instance and\\n # cluster ID on the response match the request.\\n self._update_from_pb(cluster_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Cluster.create\",\n \"text\": \"gcloud/bigtable/cluster.py::Cluster.create\\n def create(self):\\n \\\"\\\"\\\"Create this cluster.\\n\\n .. note::\\n\\n Uses the ``project``, ``instance`` and ``cluster_id`` on the\\n current :class:`Cluster` in addition to the ``serve_nodes``.\\n To change them before creating, reset the values via\\n\\n .. code:: python\\n\\n cluster.serve_nodes = 8\\n cluster.cluster_id = 'i-changed-my-mind'\\n\\n before calling :meth:`create`.\\n\\n :rtype: :class:`Operation`\\n :returns: The long-running operation corresponding to the\\n create operation.\\n \\\"\\\"\\\"\\n request_pb = _prepare_create_request(self)\\n # We expect a `google.longrunning.operations_pb2.Operation`.\\n operation_pb = self._instance._client._cluster_stub.CreateCluster(\\n request_pb, self._instance._client.timeout_seconds)\\n\\n op_id = _process_operation(operation_pb)\\n return Operation('create', op_id, cluster=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Cluster.update\",\n \"text\": \"gcloud/bigtable/cluster.py::Cluster.update\\n def update(self):\\n \\\"\\\"\\\"Update this cluster.\\n\\n .. note::\\n\\n Updates the ``serve_nodes``. If you'd like to\\n change them before updating, reset the values via\\n\\n .. code:: python\\n\\n cluster.serve_nodes = 8\\n\\n before calling :meth:`update`.\\n\\n :rtype: :class:`Operation`\\n :returns: The long-running operation corresponding to the\\n update operation.\\n \\\"\\\"\\\"\\n request_pb = data_v2_pb2.Cluster(\\n name=self.name,\\n serve_nodes=self.serve_nodes,\\n )\\n # Ignore expected `._generated.bigtable_cluster_data_pb2.Cluster`.\\n operation_pb = self._instance._client._cluster_stub.UpdateCluster(\\n request_pb, self._instance._client.timeout_seconds)\\n\\n op_id = _process_operation(operation_pb)\\n return Operation('update', op_id, cluster=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/cluster.py::Cluster.delete\",\n \"text\": \"gcloud/bigtable/cluster.py::Cluster.delete\\n def delete(self):\\n \\\"\\\"\\\"Delete this cluster.\\n\\n Marks a cluster and all of its tables for permanent deletion in 7 days.\\n\\n Immediately upon completion of the request:\\n\\n * Billing will cease for all of the cluster's reserved resources.\\n * The cluster's ``delete_time`` field will be set 7 days in the future.\\n\\n Soon afterward:\\n\\n * All tables within the cluster will become unavailable.\\n\\n Prior to the cluster's ``delete_time``:\\n\\n * The cluster can be recovered with a call to ``UndeleteCluster``.\\n * All other attempts to modify or delete the cluster will be rejected.\\n\\n At the cluster's ``delete_time``:\\n\\n * The cluster and **all of its tables** will immediately and\\n irrevocably disappear from the API, and their data will be\\n permanently deleted.\\n \\\"\\\"\\\"\\n request_pb = messages_v2_pb2.DeleteClusterRequest(name=self.name)\\n # We expect a `google.protobuf.empty_pb2.Empty`\\n self._instance._client._cluster_stub.DeleteCluster(\\n request_pb, self._instance._client.timeout_seconds)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::_prepare_create_request\",\n \"text\": \"gcloud/bigtable/instance.py::_prepare_create_request\\ndef _prepare_create_request(instance):\\n \\\"\\\"\\\"Creates a protobuf request for a CreateInstance request.\\n\\n :type instance: :class:`Instance`\\n :param instance: The instance to be created.\\n\\n :rtype: :class:`.messages_v2_pb2.CreateInstanceRequest`\\n :returns: The CreateInstance request object containing the instance info.\\n \\\"\\\"\\\"\\n parent_name = ('projects/' + instance._client.project)\\n return messages_v2_pb2.CreateInstanceRequest(\\n name=parent_name,\\n instance_id=instance.instance_id,\\n instance=data_v2_pb2.Instance(\\n display_name=instance.display_name,\\n ),\\n )\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::_parse_pb_any_to_native\",\n \"text\": \"gcloud/bigtable/instance.py::_parse_pb_any_to_native\\ndef _parse_pb_any_to_native(any_val, expected_type=None):\\n \\\"\\\"\\\"Convert a serialized \\\"google.protobuf.Any\\\" value to actual type.\\n\\n :type any_val: :class:`google.protobuf.any_pb2.Any`\\n :param any_val: A serialized protobuf value container.\\n\\n :type expected_type: str\\n :param expected_type: (Optional) The type URL we expect ``any_val``\\n to have.\\n\\n :rtype: object\\n :returns: The de-serialized object.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the\\n ``expected_type`` does not match the ``type_url`` on the input.\\n \\\"\\\"\\\"\\n if expected_type is not None and expected_type != any_val.type_url:\\n raise ValueError('Expected type: %s, Received: %s' % (\\n expected_type, any_val.type_url))\\n container_class = _TYPE_URL_MAP[any_val.type_url]\\n return container_class.FromString(any_val.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::_process_operation\",\n \"text\": \"gcloud/bigtable/instance.py::_process_operation\\ndef _process_operation(operation_pb):\\n \\\"\\\"\\\"Processes a create protobuf response.\\n\\n :type operation_pb: :class:`google.longrunning.operations_pb2.Operation`\\n :param operation_pb: The long-running operation response from a\\n Create/Update/Undelete instance request.\\n\\n :rtype: tuple\\n :returns: A pair of an integer and datetime stamp. The integer is the ID\\n of the operation (``operation_id``) and the timestamp when\\n the create operation began (``operation_begin``).\\n :raises: :class:`ValueError <exceptions.ValueError>` if the operation name\\n doesn't match the :data:`_OPERATION_NAME_RE` regex.\\n \\\"\\\"\\\"\\n match = _OPERATION_NAME_RE.match(operation_pb.name)\\n if match is None:\\n raise ValueError('Operation name was not in the expected '\\n 'format after a instance modification.',\\n operation_pb.name)\\n operation_id = int(match.group('operation_id'))\\n\\n request_metadata = _parse_pb_any_to_native(operation_pb.metadata)\\n operation_begin = _pb_timestamp_to_datetime(\\n request_metadata.request_time)\\n\\n return operation_id, operation_begin\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Operation.__init__\",\n \"text\": \"gcloud/bigtable/instance.py::Operation.__init__\\n def __init__(self, op_type, op_id, begin, instance=None):\\n self.op_type = op_type\\n self.op_id = op_id\\n self.begin = begin\\n self._instance = instance\\n self._complete = False\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Operation.__eq__\",\n \"text\": \"gcloud/bigtable/instance.py::Operation.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other.op_type == self.op_type and\\n other.op_id == self.op_id and\\n other.begin == self.begin and\\n other._instance == self._instance and\\n other._complete == self._complete)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Operation.__ne__\",\n \"text\": \"gcloud/bigtable/instance.py::Operation.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Operation.finished\",\n \"text\": \"gcloud/bigtable/instance.py::Operation.finished\\n def finished(self):\\n \\\"\\\"\\\"Check if the operation has finished.\\n\\n :rtype: bool\\n :returns: A boolean indicating if the current operation has completed.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the operation\\n has already completed.\\n \\\"\\\"\\\"\\n if self._complete:\\n raise ValueError('The operation has completed.')\\n\\n operation_name = ('operations/' + self._instance.name +\\n '/operations/%d' % (self.op_id,))\\n request_pb = operations_pb2.GetOperationRequest(name=operation_name)\\n # We expect a `google.longrunning.operations_pb2.Operation`.\\n operation_pb = self._instance._client._operations_stub.GetOperation(\\n request_pb, self._instance._client.timeout_seconds)\\n\\n if operation_pb.done:\\n self._complete = True\\n return True\\n else:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.__init__\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.__init__\\n def __init__(self, instance_id, client,\\n display_name=None):\\n self.instance_id = instance_id\\n self.display_name = display_name or instance_id\\n self._client = client\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance._update_from_pb\",\n \"text\": \"gcloud/bigtable/instance.py::Instance._update_from_pb\\n def _update_from_pb(self, instance_pb):\\n \\\"\\\"\\\"Refresh self from the server-provided protobuf.\\n\\n Helper for :meth:`from_pb` and :meth:`reload`.\\n \\\"\\\"\\\"\\n if not instance_pb.display_name: # Simple field (string)\\n raise ValueError('Instance protobuf does not contain display_name')\\n self.display_name = instance_pb.display_name\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.from_pb\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.from_pb\\n @classmethod\\n def from_pb(cls, instance_pb, client):\\n \\\"\\\"\\\"Creates a instance instance from a protobuf.\\n\\n :type instance_pb: :class:`instance_pb2.Instance`\\n :param instance_pb: A instance protobuf object.\\n\\n :type client: :class:`Client <gcloud.bigtable.client.Client>`\\n :param client: The client that owns the instance.\\n\\n :rtype: :class:`Instance`\\n :returns: The instance parsed from the protobuf response.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the instance\\n name does not match\\n ``projects/{project}/instances/{instance_id}``\\n or if the parsed project ID does not match the project ID\\n on the client.\\n \\\"\\\"\\\"\\n match = _INSTANCE_NAME_RE.match(instance_pb.name)\\n if match is None:\\n raise ValueError('Instance protobuf name was not in the '\\n 'expected format.', instance_pb.name)\\n if match.group('project') != client.project:\\n raise ValueError('Project ID on instance does not match the '\\n 'project ID on the client')\\n\\n result = cls(match.group('instance_id'), client)\\n result._update_from_pb(instance_pb)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.copy\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.copy\\n def copy(self):\\n \\\"\\\"\\\"Make a copy of this instance.\\n\\n Copies the local data stored as simple types and copies the client\\n attached to this instance.\\n\\n :rtype: :class:`.Instance`\\n :returns: A copy of the current instance.\\n \\\"\\\"\\\"\\n new_client = self._client.copy()\\n return self.__class__(self.instance_id, new_client,\\n display_name=self.display_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.name\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"Instance name used in requests.\\n\\n .. note::\\n This property will not change if ``instance_id`` does not,\\n but the return value is not cached.\\n\\n The instance name is of the form\\n\\n ``\\\"projects/{project}/instances/{instance_id}\\\"``\\n\\n :rtype: str\\n :returns: The instance name.\\n \\\"\\\"\\\"\\n return self._client.project_name + '/instances/' + self.instance_id\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.__eq__\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n # NOTE: This does not compare the configuration values, such as\\n # the display_name. Instead, it only compares\\n # identifying values instance ID and client. This is\\n # intentional, since the same instance can be in different states\\n # if not synchronized. Instances with similar instance\\n # settings but different clients can't be used in the same way.\\n return (other.instance_id == self.instance_id and\\n other._client == self._client)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.__ne__\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.reload\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.reload\\n def reload(self):\\n \\\"\\\"\\\"Reload the metadata for this instance.\\\"\\\"\\\"\\n request_pb = messages_v2_pb2.GetInstanceRequest(name=self.name)\\n # We expect `data_v2_pb2.Instance`.\\n instance_pb = self._client._instance_stub.GetInstance(\\n request_pb, self._client.timeout_seconds)\\n\\n # NOTE: _update_from_pb does not check that the project and\\n # instance ID on the response match the request.\\n self._update_from_pb(instance_pb)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.create\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.create\\n def create(self):\\n \\\"\\\"\\\"Create this instance.\\n\\n .. note::\\n\\n Uses the ``project`` and ``instance_id`` on the current\\n :class:`Instance` in addition to the ``display_name``.\\n To change them before creating, reset the values via\\n\\n .. code:: python\\n\\n instance.display_name = 'New display name'\\n instance.instance_id = 'i-changed-my-mind'\\n\\n before calling :meth:`create`.\\n\\n :rtype: :class:`Operation`\\n :returns: The long-running operation corresponding to the\\n create operation.\\n \\\"\\\"\\\"\\n request_pb = _prepare_create_request(self)\\n # We expect a `google.longrunning.operations_pb2.Operation`.\\n operation_pb = self._client._instance_stub.CreateInstance(\\n request_pb, self._client.timeout_seconds)\\n\\n op_id, op_begin = _process_operation(operation_pb)\\n return Operation('create', op_id, op_begin, instance=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.update\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.update\\n def update(self):\\n \\\"\\\"\\\"Update this instance.\\n\\n .. note::\\n\\n Updates the ``display_name``. To change that value before\\n updating, reset its values via\\n\\n .. code:: python\\n\\n instance.display_name = 'New display name'\\n\\n before calling :meth:`update`.\\n \\\"\\\"\\\"\\n request_pb = data_v2_pb2.Instance(\\n name=self.name,\\n display_name=self.display_name,\\n )\\n # Ignore the expected `data_v2_pb2.Instance`.\\n self._client._instance_stub.UpdateInstance(\\n request_pb, self._client.timeout_seconds)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.delete\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.delete\\n def delete(self):\\n \\\"\\\"\\\"Delete this instance.\\n\\n Marks a instance and all of its tables for permanent deletion\\n in 7 days.\\n\\n Immediately upon completion of the request:\\n\\n * Billing will cease for all of the instance's reserved resources.\\n * The instance's ``delete_time`` field will be set 7 days in\\n the future.\\n\\n Soon afterward:\\n\\n * All tables within the instance will become unavailable.\\n\\n Prior to the instance's ``delete_time``:\\n\\n * The instance can be recovered with a call to ``UndeleteInstance``.\\n * All other attempts to modify or delete the instance will be rejected.\\n\\n At the instance's ``delete_time``:\\n\\n * The instance and **all of its tables** will immediately and\\n irrevocably disappear from the API, and their data will be\\n permanently deleted.\\n \\\"\\\"\\\"\\n request_pb = messages_v2_pb2.DeleteInstanceRequest(name=self.name)\\n # We expect a `google.protobuf.empty_pb2.Empty`\\n self._client._instance_stub.DeleteInstance(\\n request_pb, self._client.timeout_seconds)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.cluster\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.cluster\\n def cluster(self, cluster_id, serve_nodes=3):\\n \\\"\\\"\\\"Factory to create a cluster associated with this client.\\n\\n :type cluster_id: str\\n :param cluster_id: The ID of the cluster.\\n\\n :type serve_nodes: int\\n :param serve_nodes: (Optional) The number of nodes in the cluster.\\n Defaults to 3.\\n\\n :rtype: :class:`.Cluster`\\n :returns: The cluster owned by this client.\\n \\\"\\\"\\\"\\n return Cluster(cluster_id, self, serve_nodes=serve_nodes)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.list_clusters\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.list_clusters\\n def list_clusters(self):\\n \\\"\\\"\\\"Lists clusters in this instance.\\n\\n :rtype: tuple\\n :returns: A pair of results, the first is a list of :class:`.Cluster` s\\n returned and the second is a list of strings (the failed\\n locations in the request).\\n \\\"\\\"\\\"\\n request_pb = messages_v2_pb2.ListClustersRequest(name=self.name)\\n # We expect a `.cluster_messages_v1_pb2.ListClustersResponse`\\n list_clusters_response = self._client._instance_stub.ListClusters(\\n request_pb, self._client.timeout_seconds)\\n\\n failed_locations = [\\n location for location in list_clusters_response.failed_locations]\\n clusters = [Cluster.from_pb(cluster_pb, self)\\n for cluster_pb in list_clusters_response.clusters]\\n return clusters, failed_locations\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.table\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.table\\n def table(self, table_id):\\n \\\"\\\"\\\"Factory to create a table associated with this instance.\\n\\n :type table_id: str\\n :param table_id: The ID of the table.\\n\\n :rtype: :class:`Table <gcloud.bigtable.table.Table>`\\n :returns: The table owned by this instance.\\n \\\"\\\"\\\"\\n return Table(table_id, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/instance.py::Instance.list_tables\",\n \"text\": \"gcloud/bigtable/instance.py::Instance.list_tables\\n def list_tables(self):\\n \\\"\\\"\\\"List the tables in this instance.\\n\\n :rtype: list of :class:`Table <gcloud.bigtable.table.Table>`\\n :returns: The list of tables owned by the instance.\\n :raises: :class:`ValueError <exceptions.ValueError>` if one of the\\n returned tables has a name that is not of the expected format.\\n \\\"\\\"\\\"\\n request_pb = table_messages_v2_pb2.ListTablesRequest(name=self.name)\\n # We expect a `table_messages_v2_pb2.ListTablesResponse`\\n table_list_pb = self._client._table_stub.ListTables(\\n request_pb, self._client.timeout_seconds)\\n\\n result = []\\n for table_pb in table_list_pb.tables:\\n table_prefix = self.name + '/tables/'\\n if not table_pb.name.startswith(table_prefix):\\n raise ValueError('Table name %s not of expected format' % (\\n table_pb.name,))\\n table_id = table_pb.name[len(table_prefix):]\\n result.append(self.table(table_id))\\n\\n return result\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::RowFilter.__ne__\",\n \"text\": \"gcloud/bigtable/row_filters.py::RowFilter.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::_BoolFilter.__init__\",\n \"text\": \"gcloud/bigtable/row_filters.py::_BoolFilter.__init__\\n def __init__(self, flag):\\n self.flag = flag\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::_BoolFilter.__eq__\",\n \"text\": \"gcloud/bigtable/row_filters.py::_BoolFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.flag == self.flag\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::SinkFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::SinkFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(sink=self.flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::PassAllFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::PassAllFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(pass_all_filter=self.flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::BlockAllFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::BlockAllFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(block_all_filter=self.flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::_RegexFilter.__init__\",\n \"text\": \"gcloud/bigtable/row_filters.py::_RegexFilter.__init__\\n def __init__(self, regex):\\n self.regex = _to_bytes(regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::_RegexFilter.__eq__\",\n \"text\": \"gcloud/bigtable/row_filters.py::_RegexFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.regex == self.regex\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::RowKeyRegexFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::RowKeyRegexFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(row_key_regex_filter=self.regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::RowSampleFilter.__init__\",\n \"text\": \"gcloud/bigtable/row_filters.py::RowSampleFilter.__init__\\n def __init__(self, sample):\\n self.sample = sample\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::RowSampleFilter.__eq__\",\n \"text\": \"gcloud/bigtable/row_filters.py::RowSampleFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.sample == self.sample\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::RowSampleFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::RowSampleFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(row_sample_filter=self.sample)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::FamilyNameRegexFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::FamilyNameRegexFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(family_name_regex_filter=self.regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::ColumnQualifierRegexFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::ColumnQualifierRegexFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(column_qualifier_regex_filter=self.regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::TimestampRange.__init__\",\n \"text\": \"gcloud/bigtable/row_filters.py::TimestampRange.__init__\\n def __init__(self, start=None, end=None):\\n self.start = start\\n self.end = end\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::TimestampRange.__eq__\",\n \"text\": \"gcloud/bigtable/row_filters.py::TimestampRange.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other.start == self.start and\\n other.end == self.end)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::TimestampRange.__ne__\",\n \"text\": \"gcloud/bigtable/row_filters.py::TimestampRange.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::TimestampRange.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::TimestampRange.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the :class:`TimestampRange` to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.TimestampRange`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n timestamp_range_kwargs = {}\\n if self.start is not None:\\n timestamp_range_kwargs['start_timestamp_micros'] = (\\n _microseconds_from_datetime(self.start))\\n if self.end is not None:\\n timestamp_range_kwargs['end_timestamp_micros'] = (\\n _microseconds_from_datetime(self.end))\\n return data_v2_pb2.TimestampRange(**timestamp_range_kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::TimestampRangeFilter.__init__\",\n \"text\": \"gcloud/bigtable/row_filters.py::TimestampRangeFilter.__init__\\n def __init__(self, range_):\\n self.range_ = range_\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::TimestampRangeFilter.__eq__\",\n \"text\": \"gcloud/bigtable/row_filters.py::TimestampRangeFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.range_ == self.range_\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::TimestampRangeFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::TimestampRangeFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n First converts the ``range_`` on the current object to a protobuf and\\n then uses it in the ``timestamp_range_filter`` field.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(\\n timestamp_range_filter=self.range_.to_pb())\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::ColumnRangeFilter.__init__\",\n \"text\": \"gcloud/bigtable/row_filters.py::ColumnRangeFilter.__init__\\n def __init__(self, column_family_id, start_column=None, end_column=None,\\n inclusive_start=None, inclusive_end=None):\\n self.column_family_id = column_family_id\\n\\n if inclusive_start is None:\\n inclusive_start = True\\n elif start_column is None:\\n raise ValueError('Inclusive start was specified but no '\\n 'start column was given.')\\n self.start_column = start_column\\n self.inclusive_start = inclusive_start\\n\\n if inclusive_end is None:\\n inclusive_end = True\\n elif end_column is None:\\n raise ValueError('Inclusive end was specified but no '\\n 'end column was given.')\\n self.end_column = end_column\\n self.inclusive_end = inclusive_end\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::ColumnRangeFilter.__eq__\",\n \"text\": \"gcloud/bigtable/row_filters.py::ColumnRangeFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other.column_family_id == self.column_family_id and\\n other.start_column == self.start_column and\\n other.end_column == self.end_column and\\n other.inclusive_start == self.inclusive_start and\\n other.inclusive_end == self.inclusive_end)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::ColumnRangeFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::ColumnRangeFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n First converts to a :class:`.data_v2_pb2.ColumnRange` and then uses it\\n in the ``column_range_filter`` field.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n column_range_kwargs = {'family_name': self.column_family_id}\\n if self.start_column is not None:\\n if self.inclusive_start:\\n key = 'start_qualifier_closed'\\n else:\\n key = 'start_qualifier_open'\\n column_range_kwargs[key] = _to_bytes(self.start_column)\\n if self.end_column is not None:\\n if self.inclusive_end:\\n key = 'end_qualifier_closed'\\n else:\\n key = 'end_qualifier_open'\\n column_range_kwargs[key] = _to_bytes(self.end_column)\\n\\n column_range = data_v2_pb2.ColumnRange(**column_range_kwargs)\\n return data_v2_pb2.RowFilter(column_range_filter=column_range)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::ValueRegexFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::ValueRegexFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(value_regex_filter=self.regex)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::ValueRangeFilter.__init__\",\n \"text\": \"gcloud/bigtable/row_filters.py::ValueRangeFilter.__init__\\n def __init__(self, start_value=None, end_value=None,\\n inclusive_start=None, inclusive_end=None):\\n if inclusive_start is None:\\n inclusive_start = True\\n elif start_value is None:\\n raise ValueError('Inclusive start was specified but no '\\n 'start value was given.')\\n self.start_value = start_value\\n self.inclusive_start = inclusive_start\\n\\n if inclusive_end is None:\\n inclusive_end = True\\n elif end_value is None:\\n raise ValueError('Inclusive end was specified but no '\\n 'end value was given.')\\n self.end_value = end_value\\n self.inclusive_end = inclusive_end\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::ValueRangeFilter.__eq__\",\n \"text\": \"gcloud/bigtable/row_filters.py::ValueRangeFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other.start_value == self.start_value and\\n other.end_value == self.end_value and\\n other.inclusive_start == self.inclusive_start and\\n other.inclusive_end == self.inclusive_end)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::ValueRangeFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::ValueRangeFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n First converts to a :class:`.data_v2_pb2.ValueRange` and then uses\\n it to create a row filter protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n value_range_kwargs = {}\\n if self.start_value is not None:\\n if self.inclusive_start:\\n key = 'start_value_closed'\\n else:\\n key = 'start_value_open'\\n value_range_kwargs[key] = _to_bytes(self.start_value)\\n if self.end_value is not None:\\n if self.inclusive_end:\\n key = 'end_value_closed'\\n else:\\n key = 'end_value_open'\\n value_range_kwargs[key] = _to_bytes(self.end_value)\\n\\n value_range = data_v2_pb2.ValueRange(**value_range_kwargs)\\n return data_v2_pb2.RowFilter(value_range_filter=value_range)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::_CellCountFilter.__init__\",\n \"text\": \"gcloud/bigtable/row_filters.py::_CellCountFilter.__init__\\n def __init__(self, num_cells):\\n self.num_cells = num_cells\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::_CellCountFilter.__eq__\",\n \"text\": \"gcloud/bigtable/row_filters.py::_CellCountFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.num_cells == self.num_cells\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::CellsRowOffsetFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::CellsRowOffsetFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(\\n cells_per_row_offset_filter=self.num_cells)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::CellsRowLimitFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::CellsRowLimitFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(cells_per_row_limit_filter=self.num_cells)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::CellsColumnLimitFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::CellsColumnLimitFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(\\n cells_per_column_limit_filter=self.num_cells)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::StripValueTransformerFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::StripValueTransformerFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(strip_value_transformer=self.flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::ApplyLabelFilter.__init__\",\n \"text\": \"gcloud/bigtable/row_filters.py::ApplyLabelFilter.__init__\\n def __init__(self, label):\\n self.label = label\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::ApplyLabelFilter.__eq__\",\n \"text\": \"gcloud/bigtable/row_filters.py::ApplyLabelFilter.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.label == self.label\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::ApplyLabelFilter.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::ApplyLabelFilter.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n return data_v2_pb2.RowFilter(apply_label_transformer=self.label)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::_FilterCombination.__init__\",\n \"text\": \"gcloud/bigtable/row_filters.py::_FilterCombination.__init__\\n def __init__(self, filters=None):\\n if filters is None:\\n filters = []\\n self.filters = filters\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::_FilterCombination.__eq__\",\n \"text\": \"gcloud/bigtable/row_filters.py::_FilterCombination.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return other.filters == self.filters\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/row_filters.py::RowFilterChain.to_pb\",\n \"text\": \"gcloud/bigtable/row_filters.py::RowFilterChain.to_pb\\n def to_pb(self):\\n \\\"\\\"\\\"Converts the row filter to a protobuf.\\n\\n :rtype: :class:`.data_v2_pb2.RowFilter`\\n :returns: The converted current object.\\n \\\"\\\"\\\"\\n chain = data_v2_pb2.RowFilter.Chain(\\n filters=[row_filter.to_pb() for row_filter in self.filters])\\n return data_v2_pb2.RowFilter(chain=chain)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/table.py::Table.__init__\",\n \"text\": \"gcloud/bigtable/table.py::Table.__init__\\n def __init__(self, table_id, instance):\\n self.table_id = table_id\\n self._instance = instance\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/table.py::Table.name\",\n \"text\": \"gcloud/bigtable/table.py::Table.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"Table name used in requests.\\n\\n .. note::\\n\\n This property will not change if ``table_id`` does not, but the\\n return value is not cached.\\n\\n The table name is of the form\\n\\n ``\\\"projects/../zones/../clusters/../tables/{table_id}\\\"``\\n\\n :rtype: str\\n :returns: The table name.\\n \\\"\\\"\\\"\\n return self._instance.name + '/tables/' + self.table_id\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/table.py::Table.column_family\",\n \"text\": \"gcloud/bigtable/table.py::Table.column_family\\n def column_family(self, column_family_id, gc_rule=None):\\n \\\"\\\"\\\"Factory to create a column family associated with this table.\\n\\n :type column_family_id: str\\n :param column_family_id: The ID of the column family. Must be of the\\n form ``[_a-zA-Z0-9][-_.a-zA-Z0-9]*``.\\n\\n :type gc_rule: :class:`.GarbageCollectionRule`\\n :param gc_rule: (Optional) The garbage collection settings for this\\n column family.\\n\\n :rtype: :class:`.ColumnFamily`\\n :returns: A column family owned by this table.\\n \\\"\\\"\\\"\\n return ColumnFamily(column_family_id, self, gc_rule=gc_rule)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/table.py::Table.row\",\n \"text\": \"gcloud/bigtable/table.py::Table.row\\n def row(self, row_key, filter_=None, append=False):\\n \\\"\\\"\\\"Factory to create a row associated with this table.\\n\\n .. warning::\\n\\n At most one of ``filter_`` and ``append`` can be used in a\\n :class:`Row`.\\n\\n :type row_key: bytes\\n :param row_key: The key for the row being created.\\n\\n :type filter_: :class:`.RowFilter`\\n :param filter_: (Optional) Filter to be used for conditional mutations.\\n See :class:`.DirectRow` for more details.\\n\\n :type append: bool\\n :param append: (Optional) Flag to determine if the row should be used\\n for append mutations.\\n\\n :rtype: :class:`.DirectRow`\\n :returns: A row owned by this table.\\n :raises: :class:`ValueError <exceptions.ValueError>` if both\\n ``filter_`` and ``append`` are used.\\n \\\"\\\"\\\"\\n if append and filter_ is not None:\\n raise ValueError('At most one of filter_ and append can be set')\\n if append:\\n return AppendRow(row_key, self)\\n elif filter_ is not None:\\n return ConditionalRow(row_key, self, filter_=filter_)\\n else:\\n return DirectRow(row_key, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/table.py::Table.__eq__\",\n \"text\": \"gcloud/bigtable/table.py::Table.__eq__\\n def __eq__(self, other):\\n if not isinstance(other, self.__class__):\\n return False\\n return (other.table_id == self.table_id and\\n other._instance == self._instance)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/table.py::Table.__ne__\",\n \"text\": \"gcloud/bigtable/table.py::Table.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/table.py::Table.create\",\n \"text\": \"gcloud/bigtable/table.py::Table.create\\n def create(self, initial_split_keys=None):\\n \\\"\\\"\\\"Creates this table.\\n\\n .. note::\\n\\n Though a :class:`._generated.bigtable_table_data_pb2.Table` is also\\n allowed (as the ``table`` property) in a create table request, we\\n do not support it in this method. As mentioned in the\\n :class:`Table` docstring, the name is the only useful property in\\n the table proto.\\n\\n .. note::\\n\\n A create request returns a\\n :class:`._generated.bigtable_table_data_pb2.Table` but we don't use\\n this response. The proto definition allows for the inclusion of a\\n ``current_operation`` in the response, but it does not appear that\\n the Cloud Bigtable API returns any operation.\\n\\n :type initial_split_keys: list\\n :param initial_split_keys: (Optional) List of row keys that will be\\n used to initially split the table into\\n several tablets (Tablets are similar to\\n HBase regions). Given two split keys,\\n ``\\\"s1\\\"`` and ``\\\"s2\\\"``, three tablets will be\\n created, spanning the key ranges:\\n ``[, s1)``, ``[s1, s2)``, ``[s2, )``.\\n \\\"\\\"\\\"\\n split_pb = table_admin_messages_v2_pb2.CreateTableRequest.Split\\n if initial_split_keys is not None:\\n initial_split_keys = [\\n split_pb(key=key) for key in initial_split_keys]\\n request_pb = table_admin_messages_v2_pb2.CreateTableRequest(\\n initial_splits=initial_split_keys or [],\\n name=self._instance.name,\\n table_id=self.table_id,\\n )\\n client = self._instance._client\\n # We expect a `._generated.bigtable_table_data_pb2.Table`\\n client._table_stub.CreateTable(request_pb, client.timeout_seconds)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/bigtable/table.py::Table.delete\",\n \"text\": \"gcloud/bigtable/table.py::Table.delete\\n def delete(self):\\n \\\"\\\"\\\"Delete this table.\\\"\\\"\\\"\\n request_pb = table_admin_messages_v2_pb2.DeleteTableRequest(\\n name=self.name)\\n client = self._instance._client\\n # We expect a `google.protobuf.empty_pb2.Empty`\\n client._table_stub.DeleteTable(request_pb, client.timeout_seconds)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/table.py::Table.list_column_families\",\n \"text\": \"gcloud/bigtable/table.py::Table.list_column_families\\n def list_column_families(self):\\n \\\"\\\"\\\"List the column families owned by this table.\\n\\n :rtype: dict\\n :returns: Dictionary of column families attached to this table. Keys\\n are strings (column family names) and values are\\n :class:`.ColumnFamily` instances.\\n :raises: :class:`ValueError <exceptions.ValueError>` if the column\\n family name from the response does not agree with the computed\\n name from the column family ID.\\n \\\"\\\"\\\"\\n request_pb = table_admin_messages_v2_pb2.GetTableRequest(\\n name=self.name)\\n client = self._instance._client\\n # We expect a `._generated.bigtable_table_data_pb2.Table`\\n table_pb = client._table_stub.GetTable(request_pb,\\n client.timeout_seconds)\\n\\n result = {}\\n for column_family_id, value_pb in table_pb.column_families.items():\\n gc_rule = _gc_rule_from_pb(value_pb.gc_rule)\\n column_family = self.column_family(column_family_id,\\n gc_rule=gc_rule)\\n result[column_family_id] = column_family\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/table.py::Table.read_row\",\n \"text\": \"gcloud/bigtable/table.py::Table.read_row\\n def read_row(self, row_key, filter_=None):\\n \\\"\\\"\\\"Read a single row from this table.\\n\\n :type row_key: bytes\\n :param row_key: The key of the row to read from.\\n\\n :type filter_: :class:`.RowFilter`\\n :param filter_: (Optional) The filter to apply to the contents of the\\n row. If unset, returns the entire row.\\n\\n :rtype: :class:`.PartialRowData`, :data:`NoneType <types.NoneType>`\\n :returns: The contents of the row if any chunks were returned in\\n the response, otherwise :data:`None`.\\n :raises: :class:`ValueError <exceptions.ValueError>` if a commit row\\n chunk is never encountered.\\n \\\"\\\"\\\"\\n request_pb = _create_row_request(self.name, row_key=row_key,\\n filter_=filter_)\\n client = self._instance._client\\n response_iterator = client._data_stub.ReadRows(request_pb,\\n client.timeout_seconds)\\n rows_data = PartialRowsData(response_iterator)\\n rows_data.consume_all()\\n if rows_data.state != rows_data.NEW_ROW:\\n raise ValueError('The row remains partial / is not committed.')\\n\\n if len(rows_data.rows) == 0:\\n return None\\n\\n return rows_data.rows[row_key]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/table.py::Table.read_rows\",\n \"text\": \"gcloud/bigtable/table.py::Table.read_rows\\n def read_rows(self, start_key=None, end_key=None, limit=None,\\n filter_=None):\\n \\\"\\\"\\\"Read rows from this table.\\n\\n :type start_key: bytes\\n :param start_key: (Optional) The beginning of a range of row keys to\\n read from. The range will include ``start_key``. If\\n left empty, will be interpreted as the empty string.\\n\\n :type end_key: bytes\\n :param end_key: (Optional) The end of a range of row keys to read from.\\n The range will not include ``end_key``. If left empty,\\n will be interpreted as an infinite string.\\n\\n :type limit: int\\n :param limit: (Optional) The read will terminate after committing to N\\n rows' worth of results. The default (zero) is to return\\n all results.\\n\\n :type filter_: :class:`.RowFilter`\\n :param filter_: (Optional) The filter to apply to the contents of the\\n specified row(s). If unset, reads every column in\\n each row.\\n\\n :rtype: :class:`.PartialRowsData`\\n :returns: A :class:`.PartialRowsData` convenience wrapper for consuming\\n the streamed results.\\n \\\"\\\"\\\"\\n request_pb = _create_row_request(\\n self.name, start_key=start_key, end_key=end_key, filter_=filter_,\\n limit=limit)\\n client = self._instance._client\\n response_iterator = client._data_stub.ReadRows(request_pb,\\n client.timeout_seconds)\\n # We expect an iterator of `data_messages_v2_pb2.ReadRowsResponse`\\n return PartialRowsData(response_iterator)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/table.py::Table.sample_row_keys\",\n \"text\": \"gcloud/bigtable/table.py::Table.sample_row_keys\\n def sample_row_keys(self):\\n \\\"\\\"\\\"Read a sample of row keys in the table.\\n\\n The returned row keys will delimit contiguous sections of the table of\\n approximately equal size, which can be used to break up the data for\\n distributed tasks like mapreduces.\\n\\n The elements in the iterator are a SampleRowKeys response and they have\\n the properties ``offset_bytes`` and ``row_key``. They occur in sorted\\n order. The table might have contents before the first row key in the\\n list and after the last one, but a key containing the empty string\\n indicates \\\"end of table\\\" and will be the last response given, if\\n present.\\n\\n .. note::\\n\\n Row keys in this list may not have ever been written to or read\\n from, and users should therefore not make any assumptions about the\\n row key structure that are specific to their use case.\\n\\n The ``offset_bytes`` field on a response indicates the approximate\\n total storage space used by all rows in the table which precede\\n ``row_key``. Buffering the contents of all rows between two subsequent\\n samples would require space roughly equal to the difference in their\\n ``offset_bytes`` fields.\\n\\n :rtype: :class:`grpc.framework.alpha._reexport._CancellableIterator`\\n :returns: A cancel-able iterator. Can be consumed by calling ``next()``\\n or by casting to a :class:`list` and can be cancelled by\\n calling ``cancel()``.\\n \\\"\\\"\\\"\\n request_pb = data_messages_v2_pb2.SampleRowKeysRequest(\\n table_name=self.name)\\n client = self._instance._client\\n response_iterator = client._data_stub.SampleRowKeys(\\n request_pb, client.timeout_seconds)\\n return response_iterator\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/bigtable/table.py::_create_row_request\",\n \"text\": \"gcloud/bigtable/table.py::_create_row_request\\ndef _create_row_request(table_name, row_key=None, start_key=None, end_key=None,\\n filter_=None, limit=None):\\n \\\"\\\"\\\"Creates a request to read rows in a table.\\n\\n :type table_name: str\\n :param table_name: The name of the table to read from.\\n\\n :type row_key: bytes\\n :param row_key: (Optional) The key of a specific row to read from.\\n\\n :type start_key: bytes\\n :param start_key: (Optional) The beginning of a range of row keys to\\n read from. The range will include ``start_key``. If\\n left empty, will be interpreted as the empty string.\\n\\n :type end_key: bytes\\n :param end_key: (Optional) The end of a range of row keys to read from.\\n The range will not include ``end_key``. If left empty,\\n will be interpreted as an infinite string.\\n\\n :type filter_: :class:`.RowFilter`\\n :param filter_: (Optional) The filter to apply to the contents of the\\n specified row(s). If unset, reads the entire table.\\n\\n :type limit: int\\n :param limit: (Optional) The read will terminate after committing to N\\n rows' worth of results. The default (zero) is to return\\n all results.\\n\\n :rtype: :class:`data_messages_v2_pb2.ReadRowsRequest`\\n :returns: The ``ReadRowsRequest`` protobuf corresponding to the inputs.\\n :raises: :class:`ValueError <exceptions.ValueError>` if both\\n ``row_key`` and one of ``start_key`` and ``end_key`` are set\\n \\\"\\\"\\\"\\n request_kwargs = {'table_name': table_name}\\n if (row_key is not None and\\n (start_key is not None or end_key is not None)):\\n raise ValueError('Row key and row range cannot be '\\n 'set simultaneously')\\n range_kwargs = {}\\n if start_key is not None or end_key is not None:\\n if start_key is not None:\\n range_kwargs['start_key_closed'] = _to_bytes(start_key)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.__init__\",\n \"text\": \"gcloud/datastore/entity.py::Entity.__init__\\n def __init__(self, key=None, exclude_from_indexes=()):\\n super(Entity, self).__init__()\\n self.key = key\\n self._exclude_from_indexes = set(_ensure_tuple_or_list(\\n 'exclude_from_indexes', exclude_from_indexes))\\n # NOTE: This will be populated when parsing a protobuf in\\n # gcloud.datastore.helpers.entity_from_protobuf.\\n self._meanings = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.__eq__\",\n \"text\": \"gcloud/datastore/entity.py::Entity.__eq__\\n def __eq__(self, other):\\n \\\"\\\"\\\"Compare two entities for equality.\\n\\n Entities compare equal if their keys compare equal, and their\\n properties compare equal.\\n\\n :rtype: boolean\\n :returns: True if the entities compare equal, else False.\\n \\\"\\\"\\\"\\n if not isinstance(other, Entity):\\n return False\\n\\n return (self.key == other.key and\\n self._exclude_from_indexes == other._exclude_from_indexes and\\n self._meanings == other._meanings and\\n super(Entity, self).__eq__(other))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.__ne__\",\n \"text\": \"gcloud/datastore/entity.py::Entity.__ne__\\n def __ne__(self, other):\\n \\\"\\\"\\\"Compare two entities for inequality.\\n\\n Entities compare equal if their keys compare equal, and their\\n properties compare equal.\\n\\n :rtype: boolean\\n :returns: False if the entities compare equal, else True.\\n \\\"\\\"\\\"\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.kind\",\n \"text\": \"gcloud/datastore/entity.py::Entity.kind\\n @property\\n def kind(self):\\n \\\"\\\"\\\"Get the kind of the current entity.\\n\\n .. note::\\n This relies entirely on the :class:`gcloud.datastore.key.Key`\\n set on the entity. That means that we're not storing the kind\\n of the entity at all, just the properties and a pointer to a\\n Key which knows its Kind.\\n \\\"\\\"\\\"\\n if self.key:\\n return self.key.kind\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.exclude_from_indexes\",\n \"text\": \"gcloud/datastore/entity.py::Entity.exclude_from_indexes\\n @property\\n def exclude_from_indexes(self):\\n \\\"\\\"\\\"Names of fields which are *not* to be indexed for this entity.\\n\\n :rtype: sequence of field names\\n \\\"\\\"\\\"\\n return frozenset(self._exclude_from_indexes)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/entity.py::Entity.__repr__\",\n \"text\": \"gcloud/datastore/entity.py::Entity.__repr__\\n def __repr__(self):\\n if self.key:\\n return '<Entity%s %s>' % (self.key.path,\\n super(Entity, self).__repr__())\\n else:\\n return '<Entity %s>' % (super(Entity, self).__repr__())\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_get_meaning\",\n \"text\": \"gcloud/datastore/helpers.py::_get_meaning\\ndef _get_meaning(value_pb, is_list=False):\\n \\\"\\\"\\\"Get the meaning from a protobuf value.\\n\\n :type value_pb: :class:`gcloud.datastore._generated.entity_pb2.Value`\\n :param value_pb: The protobuf value to be checked for an\\n associated meaning.\\n\\n :type is_list: bool\\n :param is_list: Boolean indicating if the ``value_pb`` contains\\n a list value.\\n\\n :rtype: int\\n :returns: The meaning for the ``value_pb`` if one is set, else\\n :data:`None`. For a list value, if there are disagreeing\\n means it just returns a list of meanings. If all the\\n list meanings agree, it just condenses them.\\n \\\"\\\"\\\"\\n meaning = None\\n if is_list:\\n # An empty list will have no values, hence no shared meaning\\n # set among them.\\n if len(value_pb.array_value.values) == 0:\\n return None\\n\\n # We check among all the meanings, some of which may be None,\\n # the rest which may be enum/int values.\\n all_meanings = [_get_meaning(sub_value_pb)\\n for sub_value_pb in value_pb.array_value.values]\\n unique_meanings = set(all_meanings)\\n if len(unique_meanings) == 1:\\n # If there is a unique meaning, we preserve it.\\n meaning = unique_meanings.pop()\\n else: # We know len(value_pb.array_value.values) > 0.\\n # If the meaning is not unique, just return all of them.\\n meaning = all_meanings\\n elif value_pb.meaning: # Simple field (int32)\\n meaning = value_pb.meaning\\n\\n return meaning\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_new_value_pb\",\n \"text\": \"gcloud/datastore/helpers.py::_new_value_pb\\ndef _new_value_pb(entity_pb, name):\\n \\\"\\\"\\\"Add (by name) a new ``Value`` protobuf to an entity protobuf.\\n\\n :type entity_pb: :class:`gcloud.datastore._generated.entity_pb2.Entity`\\n :param entity_pb: An entity protobuf to add a new property to.\\n\\n :type name: string\\n :param name: The name of the new property.\\n\\n :rtype: :class:`gcloud.datastore._generated.entity_pb2.Value`\\n :returns: The new ``Value`` protobuf that was added to the entity.\\n \\\"\\\"\\\"\\n return entity_pb.properties.get_or_create(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_property_tuples\",\n \"text\": \"gcloud/datastore/helpers.py::_property_tuples\\ndef _property_tuples(entity_pb):\\n \\\"\\\"\\\"Iterator of name, ``Value`` tuples from entity properties.\\n\\n :type entity_pb: :class:`gcloud.datastore._generated.entity_pb2.Entity`\\n :param entity_pb: An entity protobuf to add a new property to.\\n\\n :rtype: :class:`generator`\\n :returns: An iterator that yields tuples of a name and ``Value``\\n corresponding to properties on the entity.\\n \\\"\\\"\\\"\\n return six.iteritems(entity_pb.properties)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::entity_from_protobuf\",\n \"text\": \"gcloud/datastore/helpers.py::entity_from_protobuf\\ndef entity_from_protobuf(pb):\\n \\\"\\\"\\\"Factory method for creating an entity based on a protobuf.\\n\\n The protobuf should be one returned from the Cloud Datastore\\n Protobuf API.\\n\\n :type pb: :class:`gcloud.datastore._generated.entity_pb2.Entity`\\n :param pb: The Protobuf representing the entity.\\n\\n :rtype: :class:`gcloud.datastore.entity.Entity`\\n :returns: The entity derived from the protobuf.\\n \\\"\\\"\\\"\\n key = None\\n if pb.HasField('key'): # Message field (Key)\\n key = key_from_protobuf(pb.key)\\n\\n entity_props = {}\\n entity_meanings = {}\\n exclude_from_indexes = []\\n\\n for prop_name, value_pb in _property_tuples(pb):\\n value = _get_value_from_value_pb(value_pb)\\n entity_props[prop_name] = value\\n\\n # Check if the property has an associated meaning.\\n is_list = isinstance(value, list)\\n meaning = _get_meaning(value_pb, is_list=is_list)\\n if meaning is not None:\\n entity_meanings[prop_name] = (meaning, value)\\n\\n # Check if ``value_pb`` was excluded from index. Lists need to be\\n # special-cased and we require all ``exclude_from_indexes`` values\\n # in a list agree.\\n if is_list:\\n exclude_values = set(value_pb.exclude_from_indexes\\n for value_pb in value_pb.array_value.values)\\n if len(exclude_values) != 1:\\n raise ValueError('For an array_value, subvalues must either '\\n 'all be indexed or all excluded from '\\n 'indexes.')\\n\\n if exclude_values.pop():\\n exclude_from_indexes.append(prop_name)\\n else:\\n if value_pb.exclude_from_indexes:\\n exclude_from_indexes.append(prop_name)\\n\\n entity = Entity(key=key, exclude_from_indexes=exclude_from_indexes)\\n entity.update(entity_props)\\n entity._meanings.update(entity_meanings)\\n return entity\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_set_pb_meaning_from_entity\",\n \"text\": \"gcloud/datastore/helpers.py::_set_pb_meaning_from_entity\\ndef _set_pb_meaning_from_entity(entity, name, value, value_pb,\\n is_list=False):\\n \\\"\\\"\\\"Add meaning information (from an entity) to a protobuf.\\n\\n :type entity: :class:`gcloud.datastore.entity.Entity`\\n :param entity: The entity to be turned into a protobuf.\\n\\n :type name: string\\n :param name: The name of the property.\\n\\n :type value: object\\n :param value: The current value stored as property ``name``.\\n\\n :type value_pb: :class:`gcloud.datastore._generated.entity_pb2.Value`\\n :param value_pb: The protobuf value to add meaning / meanings to.\\n\\n :type is_list: bool\\n :param is_list: (Optional) Boolean indicating if the ``value`` is\\n a list value.\\n \\\"\\\"\\\"\\n if name not in entity._meanings:\\n return\\n\\n meaning, orig_value = entity._meanings[name]\\n # Only add the meaning back to the protobuf if the value is\\n # unchanged from when it was originally read from the API.\\n if orig_value is not value:\\n return\\n\\n # For lists, we set meaning on each sub-element.\\n if is_list:\\n if not isinstance(meaning, list):\\n meaning = itertools.repeat(meaning)\\n val_iter = six.moves.zip(value_pb.array_value.values,\\n meaning)\\n for sub_value_pb, sub_meaning in val_iter:\\n if sub_meaning is not None:\\n sub_value_pb.meaning = sub_meaning\\n else:\\n value_pb.meaning = meaning\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::entity_to_protobuf\",\n \"text\": \"gcloud/datastore/helpers.py::entity_to_protobuf\\ndef entity_to_protobuf(entity):\\n \\\"\\\"\\\"Converts an entity into a protobuf.\\n\\n :type entity: :class:`gcloud.datastore.entity.Entity`\\n :param entity: The entity to be turned into a protobuf.\\n\\n :rtype: :class:`gcloud.datastore._generated.entity_pb2.Entity`\\n :returns: The protobuf representing the entity.\\n \\\"\\\"\\\"\\n entity_pb = _entity_pb2.Entity()\\n if entity.key is not None:\\n key_pb = entity.key.to_protobuf()\\n entity_pb.key.CopyFrom(key_pb)\\n\\n for name, value in entity.items():\\n value_is_list = isinstance(value, list)\\n if value_is_list and len(value) == 0:\\n continue\\n\\n value_pb = _new_value_pb(entity_pb, name)\\n # Set the appropriate value.\\n _set_protobuf_value(value_pb, value)\\n\\n # Add index information to protobuf.\\n if name in entity.exclude_from_indexes:\\n if not value_is_list:\\n value_pb.exclude_from_indexes = True\\n\\n for sub_value in value_pb.array_value.values:\\n sub_value.exclude_from_indexes = True\\n\\n # Add meaning information to protobuf.\\n _set_pb_meaning_from_entity(entity, name, value, value_pb,\\n is_list=value_is_list)\\n\\n return entity_pb\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::key_from_protobuf\",\n \"text\": \"gcloud/datastore/helpers.py::key_from_protobuf\\ndef key_from_protobuf(pb):\\n \\\"\\\"\\\"Factory method for creating a key based on a protobuf.\\n\\n The protobuf should be one returned from the Cloud Datastore\\n Protobuf API.\\n\\n :type pb: :class:`gcloud.datastore._generated.entity_pb2.Key`\\n :param pb: The Protobuf representing the key.\\n\\n :rtype: :class:`gcloud.datastore.key.Key`\\n :returns: a new `Key` instance\\n \\\"\\\"\\\"\\n path_args = []\\n for element in pb.path:\\n path_args.append(element.kind)\\n if element.id: # Simple field (int64)\\n path_args.append(element.id)\\n # This is safe: we expect proto objects returned will only have\\n # one of `name` or `id` set.\\n if element.name: # Simple field (string)\\n path_args.append(element.name)\\n\\n project = None\\n if pb.partition_id.project_id: # Simple field (string)\\n project = pb.partition_id.project_id\\n namespace = None\\n if pb.partition_id.namespace_id: # Simple field (string)\\n namespace = pb.partition_id.namespace_id\\n\\n return Key(*path_args, namespace=namespace, project=project)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_pb_attr_value\",\n \"text\": \"gcloud/datastore/helpers.py::_pb_attr_value\\ndef _pb_attr_value(val):\\n \\\"\\\"\\\"Given a value, return the protobuf attribute name and proper value.\\n\\n The Protobuf API uses different attribute names based on value types\\n rather than inferring the type. This function simply determines the\\n proper attribute name based on the type of the value provided and\\n returns the attribute name as well as a properly formatted value.\\n\\n Certain value types need to be coerced into a different type (such\\n as a `datetime.datetime` into an integer timestamp, or a\\n `gcloud.datastore.key.Key` into a Protobuf representation. This\\n function handles that for you.\\n\\n .. note::\\n Values which are \\\"text\\\" ('unicode' in Python2, 'str' in Python3) map\\n to 'string_value' in the datastore; values which are \\\"bytes\\\"\\n ('str' in Python2, 'bytes' in Python3) map to 'blob_value'.\\n\\n For example:\\n\\n >>> _pb_attr_value(1234)\\n ('integer_value', 1234)\\n >>> _pb_attr_value('my_string')\\n ('string_value', 'my_string')\\n\\n :type val: `datetime.datetime`, :class:`gcloud.datastore.key.Key`,\\n bool, float, integer, string\\n :param val: The value to be scrutinized.\\n\\n :returns: A tuple of the attribute name and proper value type.\\n \\\"\\\"\\\"\\n\\n if isinstance(val, datetime.datetime):\\n name = 'timestamp'\\n value = _datetime_to_pb_timestamp(val)\\n elif isinstance(val, Key):\\n name, value = 'key', val.to_protobuf()\\n elif isinstance(val, bool):\\n name, value = 'boolean', val\\n elif isinstance(val, float):\\n name, value = 'double', val\\n elif isinstance(val, six.integer_types):\\n name, value = 'integer', val\\n elif isinstance(val, six.text_type):\\n name, value = 'string', val\\n elif isinstance(val, (bytes, str)):\\n name, value = 'blob', val\\n elif isinstance(val, Entity):\\n name, value = 'entity', val\\n elif isinstance(val, list):\\n name, value = 'array', val\\n elif isinstanc\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_get_value_from_value_pb\",\n \"text\": \"gcloud/datastore/helpers.py::_get_value_from_value_pb\\ndef _get_value_from_value_pb(value_pb):\\n \\\"\\\"\\\"Given a protobuf for a Value, get the correct value.\\n\\n The Cloud Datastore Protobuf API returns a Property Protobuf which\\n has one value set and the rest blank. This function retrieves the\\n the one value provided.\\n\\n Some work is done to coerce the return value into a more useful type\\n (particularly in the case of a timestamp value, or a key value).\\n\\n :type value_pb: :class:`gcloud.datastore._generated.entity_pb2.Value`\\n :param value_pb: The Value Protobuf.\\n\\n :returns: The value provided by the Protobuf.\\n :raises: :class:`ValueError <exceptions.ValueError>` if no value type\\n has been set.\\n \\\"\\\"\\\"\\n value_type = value_pb.WhichOneof('value_type')\\n\\n if value_type == 'timestamp_value':\\n result = _pb_timestamp_to_datetime(value_pb.timestamp_value)\\n\\n elif value_type == 'key_value':\\n result = key_from_protobuf(value_pb.key_value)\\n\\n elif value_type == 'boolean_value':\\n result = value_pb.boolean_value\\n\\n elif value_type == 'double_value':\\n result = value_pb.double_value\\n\\n elif value_type == 'integer_value':\\n result = value_pb.integer_value\\n\\n elif value_type == 'string_value':\\n result = value_pb.string_value\\n\\n elif value_type == 'blob_value':\\n result = value_pb.blob_value\\n\\n elif value_type == 'entity_value':\\n result = entity_from_protobuf(value_pb.entity_value)\\n\\n elif value_type == 'array_value':\\n result = [_get_value_from_value_pb(value)\\n for value in value_pb.array_value.values]\\n\\n elif value_type == 'geo_point_value':\\n result = GeoPoint(value_pb.geo_point_value.latitude,\\n value_pb.geo_point_value.longitude)\\n\\n elif value_type == 'null_value':\\n result = None\\n\\n else:\\n raise ValueError('Value protobuf did not have any value set')\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::_set_protobuf_value\",\n \"text\": \"gcloud/datastore/helpers.py::_set_protobuf_value\\ndef _set_protobuf_value(value_pb, val):\\n \\\"\\\"\\\"Assign 'val' to the correct subfield of 'value_pb'.\\n\\n The Protobuf API uses different attribute names based on value types\\n rather than inferring the type.\\n\\n Some value types (entities, keys, lists) cannot be directly\\n assigned; this function handles them correctly.\\n\\n :type value_pb: :class:`gcloud.datastore._generated.entity_pb2.Value`\\n :param value_pb: The value protobuf to which the value is being assigned.\\n\\n :type val: :class:`datetime.datetime`, boolean, float, integer, string,\\n :class:`gcloud.datastore.key.Key`,\\n :class:`gcloud.datastore.entity.Entity`\\n :param val: The value to be assigned.\\n \\\"\\\"\\\"\\n attr, val = _pb_attr_value(val)\\n if attr == 'key_value':\\n value_pb.key_value.CopyFrom(val)\\n elif attr == 'timestamp_value':\\n value_pb.timestamp_value.CopyFrom(val)\\n elif attr == 'entity_value':\\n entity_pb = entity_to_protobuf(val)\\n value_pb.entity_value.CopyFrom(entity_pb)\\n elif attr == 'array_value':\\n l_pb = value_pb.array_value.values\\n for item in val:\\n i_pb = l_pb.add()\\n _set_protobuf_value(i_pb, item)\\n elif attr == 'geo_point_value':\\n value_pb.geo_point_value.CopyFrom(val)\\n else: # scalar, just assign\\n setattr(value_pb, attr, val)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::GeoPoint.__init__\",\n \"text\": \"gcloud/datastore/helpers.py::GeoPoint.__init__\\n def __init__(self, latitude, longitude):\\n self.latitude = latitude\\n self.longitude = longitude\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::GeoPoint.to_protobuf\",\n \"text\": \"gcloud/datastore/helpers.py::GeoPoint.to_protobuf\\n def to_protobuf(self):\\n \\\"\\\"\\\"Convert the current object to protobuf.\\n\\n :rtype: :class:`google.type.latlng_pb2.LatLng`.\\n :returns: The current point as a protobuf.\\n \\\"\\\"\\\"\\n return latlng_pb2.LatLng(latitude=self.latitude,\\n longitude=self.longitude)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::GeoPoint.__eq__\",\n \"text\": \"gcloud/datastore/helpers.py::GeoPoint.__eq__\\n def __eq__(self, other):\\n \\\"\\\"\\\"Compare two geo points for equality.\\n\\n :rtype: boolean\\n :returns: True if the points compare equal, else False.\\n \\\"\\\"\\\"\\n if not isinstance(other, GeoPoint):\\n return False\\n\\n return (self.latitude == other.latitude and\\n self.longitude == other.longitude)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/helpers.py::GeoPoint.__ne__\",\n \"text\": \"gcloud/datastore/helpers.py::GeoPoint.__ne__\\n def __ne__(self, other):\\n \\\"\\\"\\\"Compare two geo points for inequality.\\n\\n :rtype: boolean\\n :returns: False if the points compare equal, else True.\\n \\\"\\\"\\\"\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_datastore_grpc.py::get_pb2_contents_with_grpc\",\n \"text\": \"scripts/make_datastore_grpc.py::get_pb2_contents_with_grpc\\ndef get_pb2_contents_with_grpc():\\n \\\"\\\"\\\"Get pb2 lines generated by protoc with gRPC plugin.\\n\\n :rtype: list\\n :returns: A list of lines in the generated file.\\n \\\"\\\"\\\"\\n temp_dir = tempfile.mkdtemp()\\n generated_path = os.path.join(temp_dir, 'google', 'datastore',\\n 'v1beta3', 'datastore_pb2.py')\\n try:\\n return_code = subprocess.call([\\n PROTOC_CMD,\\n '--proto_path',\\n PROTOS_DIR,\\n '--python_out',\\n temp_dir,\\n '--plugin',\\n 'protoc-gen-grpc=' + GRPC_PLUGIN,\\n '--grpc_out',\\n temp_dir,\\n PROTO_PATH,\\n ])\\n if return_code != 0:\\n sys.exit(return_code)\\n with open(generated_path, 'rb') as file_obj:\\n return file_obj.readlines()\\n finally:\\n shutil.rmtree(temp_dir, ignore_errors=True)\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/make_datastore_grpc.py::get_pb2_contents_without_grpc\",\n \"text\": \"scripts/make_datastore_grpc.py::get_pb2_contents_without_grpc\\ndef get_pb2_contents_without_grpc():\\n \\\"\\\"\\\"Get pb2 lines generated by protoc without gRPC plugin.\\n\\n :rtype: list\\n :returns: A list of lines in the generated file.\\n \\\"\\\"\\\"\\n temp_dir = tempfile.mkdtemp()\\n generated_path = os.path.join(temp_dir, 'google', 'datastore',\\n 'v1beta3', 'datastore_pb2.py')\\n try:\\n return_code = subprocess.call([\\n PROTOC_CMD,\\n '--proto_path',\\n PROTOS_DIR,\\n '--python_out',\\n temp_dir,\\n PROTO_PATH,\\n ])\\n if return_code != 0:\\n sys.exit(return_code)\\n with open(generated_path, 'rb') as file_obj:\\n return file_obj.readlines()\\n finally:\\n shutil.rmtree(temp_dir, ignore_errors=True)\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/make_datastore_grpc.py::get_pb2_grpc_only\",\n \"text\": \"scripts/make_datastore_grpc.py::get_pb2_grpc_only\\ndef get_pb2_grpc_only():\\n \\\"\\\"\\\"Get pb2 lines that are only in gRPC.\\n\\n :rtype: list\\n :returns: A list of lines that are only in the pb2 file\\n generated with the gRPC plugin.\\n \\\"\\\"\\\"\\n grpc_contents = get_pb2_contents_with_grpc()\\n non_grpc_contents = get_pb2_contents_without_grpc()\\n\\n grpc_only_lines = []\\n curr_non_grpc_line = 0\\n for line in grpc_contents:\\n if line == non_grpc_contents[curr_non_grpc_line]:\\n curr_non_grpc_line += 1\\n else:\\n grpc_only_lines.append(line)\\n\\n return grpc_only_lines\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_datastore_grpc.py::main\",\n \"text\": \"scripts/make_datastore_grpc.py::main\\ndef main():\\n \\\"\\\"\\\"Write gRPC-only lines to custom module.\\\"\\\"\\\"\\n grpc_only_lines = get_pb2_grpc_only()\\n with open(GRPC_ONLY_FILE, 'wb') as file_obj:\\n file_obj.write(''.join(grpc_only_lines))\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_operations_grpc.py::get_pb2_contents_with_grpc\",\n \"text\": \"scripts/make_operations_grpc.py::get_pb2_contents_with_grpc\\ndef get_pb2_contents_with_grpc():\\n \\\"\\\"\\\"Get pb2 lines generated by protoc with gRPC plugin.\\n\\n :rtype: list\\n :returns: A list of lines in the generated file.\\n \\\"\\\"\\\"\\n temp_dir = tempfile.mkdtemp()\\n generated_path = os.path.join(temp_dir, 'google', 'longrunning',\\n 'operations_pb2.py')\\n try:\\n return_code = subprocess.call([\\n PROTOC_CMD,\\n '--proto_path',\\n PROTOS_DIR,\\n '--python_out',\\n temp_dir,\\n '--plugin',\\n 'protoc-gen-grpc=' + GRPC_PLUGIN,\\n '--grpc_out',\\n temp_dir,\\n PROTO_PATH,\\n ])\\n if return_code != 0:\\n sys.exit(return_code)\\n with open(generated_path, 'rb') as file_obj:\\n return file_obj.readlines()\\n finally:\\n shutil.rmtree(temp_dir, ignore_errors=True)\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/make_operations_grpc.py::get_pb2_contents_without_grpc\",\n \"text\": \"scripts/make_operations_grpc.py::get_pb2_contents_without_grpc\\ndef get_pb2_contents_without_grpc():\\n \\\"\\\"\\\"Get pb2 lines generated by protoc without gRPC plugin.\\n\\n :rtype: list\\n :returns: A list of lines in the generated file.\\n \\\"\\\"\\\"\\n temp_dir = tempfile.mkdtemp()\\n generated_path = os.path.join(temp_dir, 'google', 'longrunning',\\n 'operations_pb2.py')\\n try:\\n return_code = subprocess.call([\\n PROTOC_CMD,\\n '--proto_path',\\n PROTOS_DIR,\\n '--python_out',\\n temp_dir,\\n PROTO_PATH,\\n ])\\n if return_code != 0:\\n sys.exit(return_code)\\n with open(generated_path, 'rb') as file_obj:\\n return file_obj.readlines()\\n finally:\\n shutil.rmtree(temp_dir, ignore_errors=True)\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/make_operations_grpc.py::get_pb2_grpc_only\",\n \"text\": \"scripts/make_operations_grpc.py::get_pb2_grpc_only\\ndef get_pb2_grpc_only():\\n \\\"\\\"\\\"Get pb2 lines that are only in gRPC.\\n\\n :rtype: list\\n :returns: A list of lines that are only in the pb2 file\\n generated with the gRPC plugin.\\n \\\"\\\"\\\"\\n grpc_contents = get_pb2_contents_with_grpc()\\n non_grpc_contents = get_pb2_contents_without_grpc()\\n\\n grpc_only_lines = []\\n curr_non_grpc_line = 0\\n for line in grpc_contents:\\n if line == non_grpc_contents[curr_non_grpc_line]:\\n curr_non_grpc_line += 1\\n else:\\n grpc_only_lines.append(line)\\n\\n return grpc_only_lines\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/make_operations_grpc.py::main\",\n \"text\": \"scripts/make_operations_grpc.py::main\\ndef main():\\n \\\"\\\"\\\"Write gRPC-only lines to custom module.\\\"\\\"\\\"\\n grpc_only_lines = get_pb2_grpc_only()\\n with open(GRPC_ONLY_FILE, 'wb') as file_obj:\\n file_obj.writelines(grpc_only_lines)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/rewrite_imports.py::transform_old_to_new\",\n \"text\": \"scripts/rewrite_imports.py::transform_old_to_new\\ndef transform_old_to_new(line, old_module, new_module,\\n ignore_import_from=False):\\n \\\"\\\"\\\"Transforms from an old module to a new one.\\n\\n First checks if a line starts with\\n \\\"from {old_module} import ...\\\"\\n then checks if the line contains\\n \\\"import {old_module} ...\\\"\\n then checks if the line starts with (ignoring whitespace)\\n \\\"{old_module} ...\\\"\\n and finally checks if the line contians\\n \\\"'some-dict-key': {old_module} ...\\\"\\n\\n In any of these cases, \\\"{old_module}\\\" is replaced with \\\"{new_module}\\\".\\n If none match, nothing is returned.\\n\\n :type line: str\\n :param line: The line to be transformed.\\n\\n :type old_module: str\\n :param old_module: The import to be re-written.\\n\\n :type new_module: str\\n :param new_module: The new location of the re-written import.\\n\\n :type ignore_import_from: bool\\n :param ignore_import_from: Flag to determine if the \\\"from * import\\\"\\n statements should be ignored.\\n\\n :rtype: :class:`str` or :data:`NoneType <types.NoneType>`\\n :returns: The transformed line if the old module was found, otherwise\\n does nothing.\\n \\\"\\\"\\\"\\n if not ignore_import_from:\\n import_from_statement = IMPORT_FROM_TEMPLATE % (old_module,)\\n if line.startswith(import_from_statement):\\n new_import_from_statement = IMPORT_FROM_TEMPLATE % (new_module,)\\n # Only replace the first instance of the import statement.\\n return line.replace(import_from_statement,\\n new_import_from_statement, 1)\\n\\n # If the line doesn't start with a \\\"from * import *\\\" statement, it\\n # may still contain a \\\"import * ...\\\" statement.\\n import_statement = IMPORT_TEMPLATE % (old_module,)\\n if import_statement in line:\\n new_import_statement = IMPORT_TEMPLATE % (new_module,)\\n # Only replace the first instance of the import statement.\\n retu\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/rewrite_imports.py::transform_line\",\n \"text\": \"scripts/rewrite_imports.py::transform_line\\ndef transform_line(line):\\n \\\"\\\"\\\"Transforms an import line in a PB2 module.\\n\\n If the line is not an import of one of the packages in ``REPLACEMENTS``,\\n does nothing and returns the original. Otherwise it replaces the package\\n matched with our local package.\\n\\n :type line: str\\n :param line: The line to be transformed.\\n\\n :rtype: str\\n :returns: The transformed line.\\n \\\"\\\"\\\"\\n for old_module, new_module in REPLACEMENTS.iteritems():\\n result = transform_old_to_new(line, old_module, new_module)\\n if result is not None:\\n return result\\n\\n # If no matches, there is nothing to transform.\\n return line\",\n \"label\": 1\n },\n {\n \"cid\": \"scripts/rewrite_imports.py::rewrite_file\",\n \"text\": \"scripts/rewrite_imports.py::rewrite_file\\ndef rewrite_file(filename):\\n \\\"\\\"\\\"Rewrites a given PB2 modules.\\n\\n :type filename: str\\n :param filename: The name of the file to be rewritten.\\n \\\"\\\"\\\"\\n with open(filename, 'rU') as file_obj:\\n content_lines = file_obj.read().split('\\\\n')\\n\\n new_content = []\\n for line in content_lines:\\n new_content.append(transform_line(line))\\n\\n with open(filename, 'w') as file_obj:\\n file_obj.write('\\\\n'.join(new_content))\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/rewrite_imports.py::main\",\n \"text\": \"scripts/rewrite_imports.py::main\\ndef main():\\n \\\"\\\"\\\"Rewrites all PB2 files.\\\"\\\"\\\"\\n for filename in sys.argv[1:]:\\n rewrite_file(filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::read_config\",\n \"text\": \"scripts/run_pylint.py::read_config\\ndef read_config(filename):\\n \\\"\\\"\\\"Reads pylintrc config onto native ConfigParser object.\\\"\\\"\\\"\\n config = ConfigParser.ConfigParser()\\n with open(filename, 'r') as file_obj:\\n config.readfp(file_obj)\\n return config\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::make_test_rc\",\n \"text\": \"scripts/run_pylint.py::make_test_rc\\ndef make_test_rc(base_rc_filename, additions_dict,\\n replacements_dict, target_filename):\\n \\\"\\\"\\\"Combines a base rc and test additions into single file.\\\"\\\"\\\"\\n main_cfg = read_config(base_rc_filename)\\n\\n # Create fresh config for test, which must extend production.\\n test_cfg = ConfigParser.ConfigParser()\\n test_cfg._sections = copy.deepcopy(main_cfg._sections)\\n\\n for section, opts in additions_dict.items():\\n curr_section = test_cfg._sections.setdefault(\\n section, test_cfg._dict())\\n for opt, opt_val in opts.items():\\n curr_val = curr_section.get(opt)\\n if curr_val is None:\\n raise KeyError('Expected to be adding to existing option.')\\n curr_val = curr_val.rstrip(',')\\n curr_section[opt] = '%s, %s' % (curr_val, opt_val)\\n\\n for section, opts in replacements_dict.items():\\n curr_section = test_cfg._sections.setdefault(\\n section, test_cfg._dict())\\n for opt, opt_val in opts.items():\\n curr_val = curr_section.get(opt)\\n if curr_val is None:\\n raise KeyError('Expected to be replacing existing option.')\\n curr_section[opt] = '%s' % (opt_val,)\\n\\n with open(target_filename, 'w') as file_obj:\\n test_cfg.write(file_obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::valid_filename\",\n \"text\": \"scripts/run_pylint.py::valid_filename\\ndef valid_filename(filename):\\n \\\"\\\"\\\"Checks if a file is a Python file and is not ignored.\\\"\\\"\\\"\\n for directory in IGNORED_DIRECTORIES:\\n if filename.startswith(directory):\\n return False\\n return (filename.endswith('.py') and\\n filename not in IGNORED_FILES)\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::is_production_filename\",\n \"text\": \"scripts/run_pylint.py::is_production_filename\\ndef is_production_filename(filename):\\n \\\"\\\"\\\"Checks if the file contains production code.\\n\\n :rtype: bool\\n :returns: Boolean indicating production status.\\n \\\"\\\"\\\"\\n return 'test' not in filename and 'docs' not in filename\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::get_files_for_linting\",\n \"text\": \"scripts/run_pylint.py::get_files_for_linting\\ndef get_files_for_linting(allow_limited=True):\\n \\\"\\\"\\\"Gets a list of files in the repository.\\n\\n By default, returns all files via ``git ls-files``. However, in some cases\\n uses a specific commit or branch (a so-called diff base) to compare\\n against for changed files. (This requires ``allow_limited=True``.)\\n\\n To speed up linting on Travis pull requests against master, we manually\\n set the diff base to origin/master. We don't do this on non-pull requests\\n since origin/master will be equivalent to the currently checked out code.\\n One could potentially use ${TRAVIS_COMMIT_RANGE} to find a diff base but\\n this value is not dependable.\\n\\n To allow faster local ``tox`` runs, the environment variables\\n ``GCLOUD_REMOTE_FOR_LINT`` and ``GCLOUD_BRANCH_FOR_LINT`` can be set to\\n specify a remote branch to diff against.\\n\\n :type allow_limited: bool\\n :param allow_limited: Boolean indicating if a reduced set of files can\\n be used.\\n\\n :rtype: pair\\n :returns: Tuple of the diff base using the the list of filenames to be\\n linted.\\n \\\"\\\"\\\"\\n diff_base = None\\n if (os.getenv('TRAVIS_BRANCH') == 'master' and\\n os.getenv('TRAVIS_PULL_REQUEST') != 'false'):\\n # In the case of a pull request into master, we want to\\n # diff against HEAD in master.\\n diff_base = 'origin/master'\\n elif os.getenv('TRAVIS') is None:\\n # Only allow specified remote and branch in local dev.\\n remote = os.getenv('GCLOUD_REMOTE_FOR_LINT')\\n branch = os.getenv('GCLOUD_BRANCH_FOR_LINT')\\n if remote is not None and branch is not None:\\n diff_base = '%s/%s' % (remote, branch)\\n\\n if diff_base is not None and allow_limited:\\n result = subprocess.check_output(['git', 'diff', '--name-only',\\n diff_base])\\n print('Using files changed relative to %s:' % (diff_base,))\\n print('-' \",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::get_python_files\",\n \"text\": \"scripts/run_pylint.py::get_python_files\\ndef get_python_files(all_files=None):\\n \\\"\\\"\\\"Gets a list of all Python files in the repository that need linting.\\n\\n Relies on :func:`get_files_for_linting()` to determine which files should\\n be considered.\\n\\n NOTE: This requires ``git`` to be installed and requires that this\\n is run within the ``git`` repository.\\n\\n :type all_files: list or ``NoneType``\\n :param all_files: Optional list of files to be linted.\\n\\n :rtype: tuple\\n :returns: A tuple containing two lists and a boolean. The first list\\n contains all production files, the next all test files and\\n the boolean indicates if a restricted fileset was used.\\n \\\"\\\"\\\"\\n using_restricted = False\\n if all_files is None:\\n all_files, diff_base = get_files_for_linting()\\n using_restricted = diff_base is not None\\n\\n library_files = []\\n non_library_files = []\\n for filename in all_files:\\n if valid_filename(filename):\\n if is_production_filename(filename):\\n library_files.append(filename)\\n else:\\n non_library_files.append(filename)\\n\\n return library_files, non_library_files, using_restricted\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::lint_fileset\",\n \"text\": \"scripts/run_pylint.py::lint_fileset\\ndef lint_fileset(filenames, rcfile, description):\\n \\\"\\\"\\\"Lints a group of files using a given rcfile.\\\"\\\"\\\"\\n # Only lint filenames that exist. For example, 'git diff --name-only'\\n # could spit out deleted / renamed files. Another alternative could\\n # be to use 'git diff --name-status' and filter out files with a\\n # status of 'D'.\\n filenames = [filename for filename in filenames\\n if os.path.exists(filename)]\\n if filenames:\\n rc_flag = '--rcfile=%s' % (rcfile,)\\n pylint_shell_command = ['pylint', rc_flag] + filenames\\n status_code = subprocess.call(pylint_shell_command)\\n if status_code != 0:\\n error_message = ('Pylint failed on %s with '\\n 'status %d.' % (description, status_code))\\n print(error_message, file=sys.stderr)\\n sys.exit(status_code)\\n else:\\n print('Skipping %s, no files to lint.' % (description,))\",\n \"label\": 0\n },\n {\n \"cid\": \"scripts/run_pylint.py::main\",\n \"text\": \"scripts/run_pylint.py::main\\ndef main():\\n \\\"\\\"\\\"Script entry point. Lints both sets of files.\\\"\\\"\\\"\\n make_test_rc(PRODUCTION_RC, TEST_RC_ADDITIONS,\\n TEST_RC_REPLACEMENTS, TEST_RC)\\n library_files, non_library_files, using_restricted = get_python_files()\\n try:\\n lint_fileset(library_files, PRODUCTION_RC, 'library code')\\n lint_fileset(non_library_files, TEST_RC, 'test code')\\n except SystemExit:\\n if not using_restricted:\\n raise\\n\\n message = 'Restricted lint failed, expanding to full fileset.'\\n print(message, file=sys.stderr)\\n all_files, _ = get_files_for_linting(allow_limited=False)\\n library_files, non_library_files, _ = get_python_files(\\n all_files=all_files)\\n lint_fileset(library_files, PRODUCTION_RC, 'library code')\\n lint_fileset(non_library_files, TEST_RC, 'test code')\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":258,"cells":{"instance_id":{"kind":"string","value":"google__jax-443"},"repo":{"kind":"string","value":"google/jax"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"`stax.BatchNorm` incorrect normalization\njax.experimental.stax on current master, line 140 ( `BatchNorm` function):\r\n```python\r\nz = (x - mean) / (var + epsilon)**2\r\n```\r\nThe square in the denominator should probably be a square root.\n"},"gold_files":{"kind":"list like","value":["jax/experimental/stax.py"],"string":"[\n \"jax/experimental/stax.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["jax/experimental/stax.py::BatchNorm.apply_fun"],"string":"[\n \"jax/experimental/stax.py::BatchNorm.apply_fun\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/conf.py","text":"docs/conf.py","label":0},{"cid":"jax/api.py","text":"jax/api.py\ndef jit(fun, static_argnums=()) # Sets up `fun` for just-in-time compilation with XLA.\n def f_jitted(*args, **kwargs)\ndef disable_jit() # Context manager that disables `jit`.\ndef xla_computation(fun, static_argnums=())\n def pv_like(x)\n def computation_maker(*args, **kwargs)\ndef grad(fun, argnums=0) # Creates a function which evaluates the gradient of `fun`.\n def grad_f(*args, **kwargs)\ndef value_and_grad(fun, argnums=0) # Creates a function which evaluates both `fun` and the gradient of `fun`.\n def value_and_grad_f(*args, **kwargs)\ndef jacfwd(fun, argnums=0) # Jacobian of `fun` evaluated column-by-column using forward-mode AD.\n def jacfun(*args, **kwargs)\ndef jacrev(fun, argnums=0) # Jacobian of `fun` evaluated row-by-row using reverse-mode AD.\n def jacfun(*args, **kwargs)\ndef hessian(fun, argnums=0) # Hessian of `fun`.\ndef _std_basis(pytree)\ndef _unravel_array_into_pytree(pytree, axis, arr)\ndef _split(x, indices, axis)\ndef _dtype(x)\ndef vmap(fun, in_axes=0, out_axes=0) # Vectorizing map. Creates a function which maps `fun` over additional axes.\n def batched_fun(*args, **kwargs)\ndef pjit(fun, axis_name) # Set up SPMD function for JIT compilation and parallel execution with XLA.\n def f_jitted(*args, **kwargs)\ndef pmap(fun, axis_name, in_axes=0, out_axes=0) # Vectorizing pseudo-map for single-program multiple-data (SPMD) functions.\n def pmap_fun(*args, **kwargs)\ndef axisvar_split(fun, name, new_names) # Split axis variable names into new names in an SPMD function.\n def split_fun(*args, **kwargs)\ndef papply(fun, in_axes=0) # Apply a function using parallel computation by sharding inputs.\n def papply_fun(*args, **kwargs)\ndef jvp(fun, primals, tangents) # Computes a (forward-mode) Jacobian-vector product of `fun`.\n def trim_arg(primal, tangent)\ndef linearize(traceable, *primals)\ndef lift_linearized(jaxpr, consts, io_tree, out_pval, *py_args)\n def fun(*args)\ndef vjp(fun, *primals) # Compute a (reverse-mode) vector-Jacobian product of `fun`.\n def out_vjp_packed(cotangent_in)\ndef trace_to_jaxpr(traceable, py_pvals, **kwargs)\ndef lift_jaxpr(jaxpr, consts, io_tree, pvals, py_args)\n def fun(*args)\ndef make_jaxpr(fun) # Adapts `fun` to return its `jaxpr` program representation.\n def pv_like(x)\n def jaxpr_maker(*args, **kwargs)\ndef _argnums_partial(f, dyn_argnums, args)\ndef _argnums_partial_(dyn_argnums, fixed_args, *dyn_args)\ndef _check_args(args)\ndef _check_scalar(x)\ndef _primitive(fun)\n def traceable(*args, **kwargs)","label":0},{"cid":"jax/experimental/stax.py","text":"jax/experimental/stax.py\ndef relu(x)\ndef softplus(x)\ndef logsoftmax(x, axis=-1) # Apply log softmax to an array of logits, log-normalizing along an axis.\ndef softmax(x, axis=-1) # Apply softmax to an array of logits, exponentiating and normalizing along an axi\ndef fastvar(x, axis, keepdims) # A fast but less numerically-stable variance calculation than np.var.\ndef randn(stddev=0.01, rng=npr) # An initializer function for random normal coefficients.\n def init(shape)\ndef glorot(out_dim=0, in_dim=1, scale=onp.sqrt(2), rng=npr) # An initializer function for random Glorot-scaled coefficients.\n def init(shape)\ndef Dense(out_dim, W_init=glorot(), b_init=randn()) # Layer constructor function for a dense (fully-connected) layer.\n def init_fun(input_shape)\n def apply_fun(params, inputs, rng=None)\ndef GeneralConv(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=randn(1e-06)) # Layer construction function for a general convolution layer.\n def init_fun(input_shape)\n def apply_fun(params, inputs, rng=None)\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-05, center=True, scale=True, beta_init=zeros, gamma_init=ones) # Layer construction function for a batch normalization layer.\n def init_fun(input_shape)\n def apply_fun(params, x, rng=None)\ndef _elemwise_no_params(fun, **kwargs)\ndef _pooling_layer(reducer, init_val, rescaler=None)\n def PoolingLayer(window_shape, strides=None, padding='VALID') # Layer construction function for a pooling layer.\n def init_fun(input_shape)\n def apply_fun(params, inputs, rng=None)\ndef _normalize_by_window_size(dims, strides, padding)\n def rescale(outputs, inputs)\ndef Flatten() # Layer construction function for flattening all but the leading dim.\n def init_fun(input_shape)\n def apply_fun(params, inputs, rng=None)\ndef Identity() # Layer construction function for an identity layer.\ndef FanOut(num) # Layer construction function for a fan-out layer.\ndef FanInSum() # Layer construction function for a fan-in sum layer.\ndef FanInConcat(axis=-1) # Layer construction function for a fan-in concatenation layer.\n def init_fun(input_shape)\n def apply_fun(params, inputs, rng=None)\ndef Dropout(rate, mode='train') # Layer construction function for a dropout layer with given rate.\n def init_fun(input_shape)\n def apply_fun(params, inputs, rng)\ndef serial(*layers) # Combinator for composing layers in serial.\n def init_fun(input_shape)\n def apply_fun(params, inputs, rng=None)\ndef parallel(*layers) # Combinator for composing layers in parallel.\n def init_fun(input_shape)\n def apply_fun(params, inputs, rng=None)\ndef shape_dependent(make_layer) # Combinator to delay layer constructor pair until input shapes are known.\n def init_fun(input_shape)\n def apply_fun(params, inputs, rng=None)","label":1},{"cid":"jax/random.py","text":"jax/random.py\ndef PRNGKey(seed) # Create a pseudo-random number generator (PRNG) key given an integer seed.\ndef _is_prng_key(key)\ndef _make_rotate_left(dtype)\n def _rotate_left(x, d)\ndef _bit_stats(bits) # This is a debugging function to compute the statistics of bit fields.\ndef threefry_2x32(keypair, count) # Apply the Threefry 2x32 hash.\n def apply_round(v, rot)\ndef split(key, num=2) # Splits a PRNG key into `num` new keys by adding a leading axis.\ndef fold_in(key, data) # Folds in data to a PRNG key to form a new PRNG key.\ndef _random_bits(key, bit_width, shape) # Sample uniform random bits of given width and shape using PRNG key.\ndef uniform(key, shape, dtype=onp.float32, minval=0.0, maxval=1.0) # Sample uniform random values in [minval, maxval) with given shape/dtype.\ndef randint(key, shape, minval, maxval, dtype=onp.int32) # Sample uniform random values in [minval, maxval) with given shape/dtype.\ndef shuffle(key, x, axis=0) # Shuffle the elements of an array uniformly at random along an axis.\ndef normal(key, shape, dtype=onp.float32) # Sample standard normal random values with given shape and float dtype.\ndef bernoulli(key, mean=onp.float32(0.5), shape=()) # Sample Bernoulli random values with given shape and mean.","label":0}],"string":"[\n {\n \"cid\": \"docs/conf.py\",\n \"text\": \"docs/conf.py\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py\",\n \"text\": \"jax/api.py\\ndef jit(fun, static_argnums=()) # Sets up `fun` for just-in-time compilation with XLA.\\n def f_jitted(*args, **kwargs)\\ndef disable_jit() # Context manager that disables `jit`.\\ndef xla_computation(fun, static_argnums=())\\n def pv_like(x)\\n def computation_maker(*args, **kwargs)\\ndef grad(fun, argnums=0) # Creates a function which evaluates the gradient of `fun`.\\n def grad_f(*args, **kwargs)\\ndef value_and_grad(fun, argnums=0) # Creates a function which evaluates both `fun` and the gradient of `fun`.\\n def value_and_grad_f(*args, **kwargs)\\ndef jacfwd(fun, argnums=0) # Jacobian of `fun` evaluated column-by-column using forward-mode AD.\\n def jacfun(*args, **kwargs)\\ndef jacrev(fun, argnums=0) # Jacobian of `fun` evaluated row-by-row using reverse-mode AD.\\n def jacfun(*args, **kwargs)\\ndef hessian(fun, argnums=0) # Hessian of `fun`.\\ndef _std_basis(pytree)\\ndef _unravel_array_into_pytree(pytree, axis, arr)\\ndef _split(x, indices, axis)\\ndef _dtype(x)\\ndef vmap(fun, in_axes=0, out_axes=0) # Vectorizing map. Creates a function which maps `fun` over additional axes.\\n def batched_fun(*args, **kwargs)\\ndef pjit(fun, axis_name) # Set up SPMD function for JIT compilation and parallel execution with XLA.\\n def f_jitted(*args, **kwargs)\\ndef pmap(fun, axis_name, in_axes=0, out_axes=0) # Vectorizing pseudo-map for single-program multiple-data (SPMD) functions.\\n def pmap_fun(*args, **kwargs)\\ndef axisvar_split(fun, name, new_names) # Split axis variable names into new names in an SPMD function.\\n def split_fun(*args, **kwargs)\\ndef papply(fun, in_axes=0) # Apply a function using parallel computation by sharding inputs.\\n def papply_fun(*args, **kwargs)\\ndef jvp(fun, primals, tangents) # Computes a (forward-mode) Jacobian-vector product of `fun`.\\n def trim_arg(primal, tangent)\\ndef linearize(traceable, *primals)\\ndef lift_linearized(jaxpr, consts, io_tree, out_pval, *py_args)\\n def fun(*args)\\ndef vjp(fun, *primals) # Compute a (reverse-mode) vector-Jacobian product of `fun`.\\n def out_vjp_packed(cotangent_in)\\ndef trace_to_jaxpr(traceable, py_pvals, **kwargs)\\ndef lift_jaxpr(jaxpr, consts, io_tree, pvals, py_args)\\n def fun(*args)\\ndef make_jaxpr(fun) # Adapts `fun` to return its `jaxpr` program representation.\\n def pv_like(x)\\n def jaxpr_maker(*args, **kwargs)\\ndef _argnums_partial(f, dyn_argnums, args)\\ndef _argnums_partial_(dyn_argnums, fixed_args, *dyn_args)\\ndef _check_args(args)\\ndef _check_scalar(x)\\ndef _primitive(fun)\\n def traceable(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py\",\n \"text\": \"jax/experimental/stax.py\\ndef relu(x)\\ndef softplus(x)\\ndef logsoftmax(x, axis=-1) # Apply log softmax to an array of logits, log-normalizing along an axis.\\ndef softmax(x, axis=-1) # Apply softmax to an array of logits, exponentiating and normalizing along an axi\\ndef fastvar(x, axis, keepdims) # A fast but less numerically-stable variance calculation than np.var.\\ndef randn(stddev=0.01, rng=npr) # An initializer function for random normal coefficients.\\n def init(shape)\\ndef glorot(out_dim=0, in_dim=1, scale=onp.sqrt(2), rng=npr) # An initializer function for random Glorot-scaled coefficients.\\n def init(shape)\\ndef Dense(out_dim, W_init=glorot(), b_init=randn()) # Layer constructor function for a dense (fully-connected) layer.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, rng=None)\\ndef GeneralConv(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=randn(1e-06)) # Layer construction function for a general convolution layer.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, rng=None)\\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-05, center=True, scale=True, beta_init=zeros, gamma_init=ones) # Layer construction function for a batch normalization layer.\\n def init_fun(input_shape)\\n def apply_fun(params, x, rng=None)\\ndef _elemwise_no_params(fun, **kwargs)\\ndef _pooling_layer(reducer, init_val, rescaler=None)\\n def PoolingLayer(window_shape, strides=None, padding='VALID') # Layer construction function for a pooling layer.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, rng=None)\\ndef _normalize_by_window_size(dims, strides, padding)\\n def rescale(outputs, inputs)\\ndef Flatten() # Layer construction function for flattening all but the leading dim.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, rng=None)\\ndef Identity() # Layer construction function for an identity layer.\\ndef FanOut(num) # Layer construction function for a fan-out layer.\\ndef FanInSum() # Layer construction function for a fan-in sum layer.\\ndef FanInConcat(axis=-1) # Layer construction function for a fan-in concatenation layer.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, rng=None)\\ndef Dropout(rate, mode='train') # Layer construction function for a dropout layer with given rate.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, rng)\\ndef serial(*layers) # Combinator for composing layers in serial.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, rng=None)\\ndef parallel(*layers) # Combinator for composing layers in parallel.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, rng=None)\\ndef shape_dependent(make_layer) # Combinator to delay layer constructor pair until input shapes are known.\\n def init_fun(input_shape)\\n def apply_fun(params, inputs, rng=None)\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/random.py\",\n \"text\": \"jax/random.py\\ndef PRNGKey(seed) # Create a pseudo-random number generator (PRNG) key given an integer seed.\\ndef _is_prng_key(key)\\ndef _make_rotate_left(dtype)\\n def _rotate_left(x, d)\\ndef _bit_stats(bits) # This is a debugging function to compute the statistics of bit fields.\\ndef threefry_2x32(keypair, count) # Apply the Threefry 2x32 hash.\\n def apply_round(v, rot)\\ndef split(key, num=2) # Splits a PRNG key into `num` new keys by adding a leading axis.\\ndef fold_in(key, data) # Folds in data to a PRNG key to form a new PRNG key.\\ndef _random_bits(key, bit_width, shape) # Sample uniform random bits of given width and shape using PRNG key.\\ndef uniform(key, shape, dtype=onp.float32, minval=0.0, maxval=1.0) # Sample uniform random values in [minval, maxval) with given shape/dtype.\\ndef randint(key, shape, minval, maxval, dtype=onp.int32) # Sample uniform random values in [minval, maxval) with given shape/dtype.\\ndef shuffle(key, x, axis=0) # Shuffle the elements of an array uniformly at random along an axis.\\ndef normal(key, shape, dtype=onp.float32) # Sample standard normal random values with given shape and float dtype.\\ndef bernoulli(key, mean=onp.float32(0.5), shape=()) # Sample Bernoulli random values with given shape and mean.\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"jax/api.py::jit","text":"jax/api.py::jit\ndef jit(fun, static_argnums=()):\n \"\"\"Sets up `fun` for just-in-time compilation with XLA.\n\n Args:\n fun: Function to be jitted. Should be a pure function, as side-effects may\n only be executed once. Its positional arguments and return value should be\n arrays, scalars, or standard Python containers (tuple/list/dict) thereof.\n Keyword arguments and positional arguments specified by `static_argnums`\n can be anything at all. These are treated as static (see below).\n static_argnums: A tuple of ints. Specifies which arguments to treat as\n static (compile-time constant). Operations that only depend on static\n arguments will be constant-folded. Calling the jitted function with\n different values for these constants will trigger recompilation.\n\n Returns:\n A wrapped version of `fun`, set up for just-in-time compilation.\n\n In the following example, `selu` can be compiled into a single fused kernel by\n XLA:\n\n >>> @jax.jit\n >>> def selu(x, alpha=1.67, lmbda=1.05):\n >>> return lmbda * jax.numpy.where(x > 0, x, alpha * jax.numpy.exp(x) - alpha)\n >>>\n >>> key = jax.random.PRNGKey(0)\n >>> x = jax.random.normal(key, (10,))\n >>> selu(x)\n array([-0.54485154, 0.27744263, -0.29255125, -0.91421586, -0.62452525,\n -0.2474813 , -0.8574326 , -0.7823267 , 0.7682731 , 0.59566754],\n dtype=float32)\n\n \"\"\"\n @wraps(fun)\n def f_jitted(*args, **kwargs):\n if _jit_is_disabled or config.read('jax_disable_jit'):\n return fun(*args, **kwargs)\n f = lu.wrap_init(fun, kwargs)\n dyn_argnums = [i for i in range(len(args)) if i not in static_argnums]\n f, dyn_args = _argnums_partial(f, dyn_argnums, args)\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, dyn_args))\n _check_args(jaxtupletree_args)\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n jaxtupletree_out = xla.xla_call(jaxtree_fun, *jaxtupletree_args)\n return build_tree(out_tree(), jaxtupletree_out)","label":0},{"cid":"jax/api.py::jit.f_jitted","text":"jax/api.py::jit.f_jitted\n @wraps(fun)\n def f_jitted(*args, **kwargs):\n if _jit_is_disabled or config.read('jax_disable_jit'):\n return fun(*args, **kwargs)\n f = lu.wrap_init(fun, kwargs)\n dyn_argnums = [i for i in range(len(args)) if i not in static_argnums]\n f, dyn_args = _argnums_partial(f, dyn_argnums, args)\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, dyn_args))\n _check_args(jaxtupletree_args)\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n jaxtupletree_out = xla.xla_call(jaxtree_fun, *jaxtupletree_args)\n return build_tree(out_tree(), jaxtupletree_out)","label":0},{"cid":"jax/api.py::disable_jit","text":"jax/api.py::disable_jit\n@contextmanager\ndef disable_jit():\n \"\"\"Context manager that disables `jit`.\n\n For debugging purposes, it is useful to have a mechanism that disables `jit`\n everywhere in a block of code, namely the `disable_jit` decorator.\n\n Inside a `jit`-ted function the values flowing through\n traced code can be abstract (i.e., shaped arrays with an unknown values),\n instead of concrete (i.e., specific arrays with known values).\n\n For example:\n\n >>> @jax.jit\n >>> def f(x):\n >>> y = x *2\n >>> print(\"Value of y is\", y)\n >>> return y + 3\n >>>\n >>> print(f(jax.numpy.array([1, 2, 3])))\n Value of y is Traced<ShapedArray(int32[3]):JaxprTrace(level=-1/1)>\n [5 7 9]\n\n Here `y` has been abstracted by `jit` to a `ShapedArray`, which represents an\n array with a fixed shape and type but an arbitrary value. If we want to see a\n concrete values while debugging, we can use the `disable_jit` decorator, at\n the cost of slower code:\n\n >>> with jax.disable_jit():\n >>> print(f(np.array([1, 2, 3])))\n >>>\n Value of y is [2 4 6]\n [5 7 9]\n \"\"\"\n global _jit_is_disabled\n _jit_is_disabled, prev_val = True, _jit_is_disabled\n yield\n _jit_is_disabled = prev_val","label":0},{"cid":"jax/api.py::xla_computation","text":"jax/api.py::xla_computation\ndef xla_computation(fun, static_argnums=()):\n def pv_like(x):\n aval = xla.abstractify(x)\n return pe.PartialVal((aval, core.unit))\n\n wrapped = lu.wrap_init(fun)\n\n @wraps(fun)\n def computation_maker(*args, **kwargs):\n jax_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(wrapped, in_trees)\n pvals = map(pv_like, jax_args)\n jaxpr, _, consts = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\n return xla.build_jaxpr(jaxpr, consts, *map(xla.abstractify, args))\n\n return computation_maker","label":0},{"cid":"jax/api.py::xla_computation.pv_like","text":"jax/api.py::xla_computation.pv_like\n def pv_like(x):\n aval = xla.abstractify(x)\n return pe.PartialVal((aval, core.unit))","label":0},{"cid":"jax/api.py::xla_computation.computation_maker","text":"jax/api.py::xla_computation.computation_maker\n @wraps(fun)\n def computation_maker(*args, **kwargs):\n jax_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(wrapped, in_trees)\n pvals = map(pv_like, jax_args)\n jaxpr, _, consts = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\n return xla.build_jaxpr(jaxpr, consts, *map(xla.abstractify, args))","label":0},{"cid":"jax/api.py::grad","text":"jax/api.py::grad\ndef grad(fun, argnums=0):\n \"\"\"Creates a function which evaluates the gradient of `fun`.\n\n Args:\n fun: Function to be differentiated. Its arguments at positions specified by\n `argnums` should be arrays, scalars, or standard Python containers. It\n should return a scalar (which includes arrays with shape `()` but not\n arrays with shape `(1,)` etc.)\n argnums: Optional, integer or tuple of integers. Specifies which positional\n argument(s) to differentiate with respect to (default 0).\n\n Returns:\n A function with the same arguments as `fun`, that evaluates the gradient of\n `fun`. If `argnums` is an integer then the gradient has the same shape and\n type as the positional argument indicated by that integer. If argnums is a\n tuple of integers, the gradient is a tuple of values with the same shapes\n and types as the corresponding arguments.\n\n For example:\n\n >>> grad_tanh = jax.grad(jax.numpy.tanh)\n >>> grad_tanh(0.2)\n array(0.961043, dtype=float32)\n\n \"\"\"\n value_and_grad_f = value_and_grad(fun, argnums)\n\n docstr = (\"Gradient of {fun} with respect to positional argument(s) \"\n \"{argnums}. Takes the same arguments as {fun} but returns the \"\n \"gradient, which has the same shape as the arguments at \"\n \"positions {argnums}.\")\n\n @wraps(fun, docstr=docstr, argnums=argnums)\n def grad_f(*args, **kwargs):\n ans, g = value_and_grad_f(*args, **kwargs)\n return g\n\n return grad_f","label":0},{"cid":"jax/api.py::grad.grad_f","text":"jax/api.py::grad.grad_f\n @wraps(fun, docstr=docstr, argnums=argnums)\n def grad_f(*args, **kwargs):\n ans, g = value_and_grad_f(*args, **kwargs)\n return g","label":0},{"cid":"jax/api.py::value_and_grad","text":"jax/api.py::value_and_grad\ndef value_and_grad(fun, argnums=0):\n \"\"\"Creates a function which evaluates both `fun` and the gradient of `fun`.\n\n Args:\n fun: Function to be differentiated. Its arguments at positions specified by\n `argnums` should be arrays, scalars, or standard Python containers. It\n should return a scalar (which includes arrays with shape `()` but not\n arrays with shape `(1,)` etc.)\n argnums: Optional, integer or tuple of integers. Specifies which positional\n argument(s) to differentiate with respect to (default 0).\n\n Returns:\n A function with the same arguments as `fun` that evaluates both `fun` and\n the gradient of `fun` and returns them as a pair (a two-element tuple). If\n `argnums` is an integer then the gradient has the same shape and type as the\n positional argument indicated by that integer. If argnums is a tuple of\n integers, the gradient is a tuple of values with the same shapes and types\n as the corresponding arguments.\n \"\"\"\n\n docstr = (\"Value and gradient of {fun} with respect to positional \"\n \"argument(s) {argnums}. Takes the same arguments as {fun} but \"\n \"returns a two-element tuple where the first element is the value \"\n \"of {fun} and the second element is the gradient, which has the \"\n \"same shape as the arguments at positions {argnums}.\")\n\n @wraps(fun, docstr=docstr, argnums=argnums)\n def value_and_grad_f(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\n ans, vjp_py = vjp(f_partial, *dyn_args)\n _check_scalar(ans)\n g = vjp_py(onp.ones((), onp.result_type(ans)))\n g = g[0] if isinstance(argnums, int) else g\n return (ans, g)\n\n return value_and_grad_f","label":0},{"cid":"jax/api.py::value_and_grad.value_and_grad_f","text":"jax/api.py::value_and_grad.value_and_grad_f\n @wraps(fun, docstr=docstr, argnums=argnums)\n def value_and_grad_f(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\n ans, vjp_py = vjp(f_partial, *dyn_args)\n _check_scalar(ans)\n g = vjp_py(onp.ones((), onp.result_type(ans)))\n g = g[0] if isinstance(argnums, int) else g\n return (ans, g)","label":0},{"cid":"jax/api.py::jacfwd","text":"jax/api.py::jacfwd\ndef jacfwd(fun, argnums=0):\n \"\"\"Jacobian of `fun` evaluated column-by-column using forward-mode AD.\n\n Args:\n fun: Function whose Jacobian is to be computed.\n argnums: Optional, integer or tuple of integers. Specifies which positional\n argument(s) to differentiate with respect to (default `0`).\n\n Returns:\n A function with the same arguments as `fun`, that evaluates the Jacobian of\n `fun` using forward-mode automatic differentiation.\n\n >>> def f(x):\n >>> return jax.numpy.asarray(\n >>> [x[0], 5*x[2], 4*x[1]**2 - 2*x[2], x[2] * jax.numpy.sin(x[0])])\n >>> jax.jacfwd(f)(np.array([1., 2., 3.]))\n array([[ 1. , 0. , 0. ],\n [ 0. , 0. , 5. ],\n [ 0. , 16. , -2. ],\n [ 1.6209068 , 0. , 0.84147096]], dtype=float32)\n \"\"\"\n\n def jacfun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\n pushfwd = partial(jvp, f_partial, dyn_args)\n y, jac = vmap(pushfwd, out_axes=(None, -1))(_std_basis(dyn_args))\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\n return tree_map(partial(_unravel_array_into_pytree, example_args, -1), jac)\n\n return jacfun","label":0},{"cid":"jax/api.py::jacfwd.jacfun","text":"jax/api.py::jacfwd.jacfun\n def jacfun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\n pushfwd = partial(jvp, f_partial, dyn_args)\n y, jac = vmap(pushfwd, out_axes=(None, -1))(_std_basis(dyn_args))\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\n return tree_map(partial(_unravel_array_into_pytree, example_args, -1), jac)","label":0},{"cid":"jax/api.py::jacrev","text":"jax/api.py::jacrev\ndef jacrev(fun, argnums=0):\n \"\"\"Jacobian of `fun` evaluated row-by-row using reverse-mode AD.\n\n Args:\n fun: Function whose Jacobian is to be computed.\n argnums: Optional, integer or tuple of integers. Specifies which positional\n argument(s) to differentiate with respect to (default `0`).\n\n Returns:\n A function with the same arguments as `fun`, that evaluates the Jacobian of\n `fun` using reverse-mode automatic differentiation.\n\n >>> def f(x):\n >>> return jax.numpy.asarray(\n >>> [x[0], 5*x[2], 4*x[1]**2 - 2*x[2], x[2] * jax.numpy.sin(x[0])])\n >>> jax.jacrev(f)(np.array([1., 2., 3.]))\n array([[ 1. , 0. , 0. ],\n [ 0. , 0. , 5. ],\n [ 0. , 16. , -2. ],\n [ 1.6209068 , 0. , 0.84147096]], dtype=float32)\n \"\"\"\n def jacfun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\n y, pullback = vjp(f_partial, *dyn_args)\n jac = vmap(pullback)(_std_basis(y))\n jac = jac[0] if isinstance(argnums, int) else jac\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\n jac = tree_map(partial(_unravel_array_into_pytree, y, 0), jac)\n return tree_transpose(tree_structure(example_args), tree_structure(y), jac)\n\n return jacfun","label":0},{"cid":"jax/api.py::jacrev.jacfun","text":"jax/api.py::jacrev.jacfun\n def jacfun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\n y, pullback = vjp(f_partial, *dyn_args)\n jac = vmap(pullback)(_std_basis(y))\n jac = jac[0] if isinstance(argnums, int) else jac\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\n jac = tree_map(partial(_unravel_array_into_pytree, y, 0), jac)\n return tree_transpose(tree_structure(example_args), tree_structure(y), jac)","label":0},{"cid":"jax/api.py::hessian","text":"jax/api.py::hessian\ndef hessian(fun, argnums=0):\n \"\"\"Hessian of `fun`.\n\n Args:\n fun: Function whose Hessian is to be computed.\n argnums: Optional, integer or tuple of integers. Specifies which positional\n argument(s) to differentiate with respect to (default `0`).\n\n Returns:\n A function with the same arguments as `fun`, that evaluates the Hessian of\n `fun`.\n\n >>> g = lambda(x): x[0]**3 - 2*x[0]*x[1] - x[1]**6\n >>> jax.hessian(g)(jax.numpy.array([1., 2.]))\n array([[ 6., -2.],\n [ -2., -480.]], dtype=float32)\n \"\"\"\n\n return jacfwd(jacrev(fun, argnums=argnums), argnums=argnums)","label":0},{"cid":"jax/api.py::_std_basis","text":"jax/api.py::_std_basis\ndef _std_basis(pytree):\n leaves, _ = tree_flatten(pytree)\n ndim = sum(map(onp.size, leaves))\n return _unravel_array_into_pytree(pytree, 1, onp.eye(ndim))","label":0},{"cid":"jax/api.py::_unravel_array_into_pytree","text":"jax/api.py::_unravel_array_into_pytree\ndef _unravel_array_into_pytree(pytree, axis, arr):\n leaves, treedef = tree_flatten(pytree)\n axis = axis % arr.ndim\n dtypes = map(_dtype, leaves)\n shapes = [arr.shape[:axis] + onp.shape(l) + arr.shape[axis+1:] for l in leaves]\n parts = _split(arr, onp.cumsum(map(onp.size, leaves[:-1])), axis)\n reshaped_parts = [onp.reshape(part.astype(dtype), shape)\n for part, dtype, shape in zip(parts, dtypes, shapes)]\n return tree_unflatten(treedef, reshaped_parts)","label":0},{"cid":"jax/api.py::_split","text":"jax/api.py::_split\ndef _split(x, indices, axis):\n if isinstance(x, onp.ndarray):\n return onp.split(x, indices, axis)\n else:\n return x.split(indices, axis)","label":0},{"cid":"jax/api.py::_dtype","text":"jax/api.py::_dtype\ndef _dtype(x):\n return canonicalize_dtype(onp.result_type(x))","label":0},{"cid":"jax/api.py::vmap","text":"jax/api.py::vmap\ndef vmap(fun, in_axes=0, out_axes=0):\n \"\"\"Vectorizing map. Creates a function which maps `fun` over additional axes.\n\n Args:\n fun: Function to be mapped over additional axes.\n in_axes: Specifies which input axes to map over. These may be integers,\n `None`, or (possibly nested) tuples of integers or `None`.\n out_axes: Specifies which output axes to map over. These may be integers,\n `None`, or (possibly nested) tuples of integers or `None`.\n\n Returns:\n Batched/vectorized version of `fun` with arguments that correspond to those\n of `fun`, but with extra array axes at positions indicated by `in_axes`, and\n a return value that corresponds to that of `fun`, but with extra array axes\n at positions indicated by `out_axes`.\n\n For example, we can implement a matrix-matrix product using a vector dot\n product:\n\n >>> vv = lambda x, y: np.vdot(x, y) # ([a], [a]) -> []\n >>> mv = vmap(vv, (0, None), 0) # ([a,b], [b]) -> [a]\n >>> mm = vmap(mv, (None, 1), 1) # ([a,b], [b,c]) -> [a,c]\n\n (`[a,b]` indicates an array with shape (a,b))\n \"\"\"\n\n docstr = (\"Vectorized version of {fun}. Takes similar arguments as {fun} \"\n \"but with additional array axes over which {fun} is mapped.\")\n\n if (not isinstance(in_axes, (list, tuple, type(None), int))\n or not isinstance(out_axes, (list, tuple, type(None), int))):\n msg = (\"vmap arguments in_axes and out_axes must each be an integer, None, \"\n \"or a (nested) tuple of those types, got {} and {} respectively.\")\n raise TypeError(msg.format(type(in_axes), type(out_axes)))\n\n @wraps(fun, docstr=docstr)\n def batched_fun(*args, **kwargs):\n if not isinstance(fun, lu.WrappedFun):\n f = lu.wrap_init(fun, kwargs)\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = ba","label":0},{"cid":"jax/api.py::vmap.batched_fun","text":"jax/api.py::vmap.batched_fun\n @wraps(fun, docstr=docstr)\n def batched_fun(*args, **kwargs):\n if not isinstance(fun, lu.WrappedFun):\n f = lu.wrap_init(fun, kwargs)\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = batching.batch(jaxtree_fun, in_flat, in_axes_, out_axes)\n return build_tree(out_tree(), out_flat)","label":0},{"cid":"jax/api.py::pjit","text":"jax/api.py::pjit\ndef pjit(fun, axis_name):\n \"\"\"Set up SPMD function for JIT compilation and parallel execution with XLA.\"\"\"\n @wraps(fun)\n def f_jitted(*args, **kwargs):\n leaves, _ = tree_flatten(args)\n axis_sizes = set(onp.shape(leaf)[0] for leaf in leaves)\n if len(axis_sizes) != 1:\n msg = \"pjit requires all leading axes to have equal length, got {}.\"\n raise TypeError(msg.format(axis_sizes))\n axis_size = axis_sizes.pop()\n\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n _check_args(jaxtupletree_args)\n f = lu.wrap_init(fun, kwargs)\n f, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n jaxtupletree_out = pxla.xla_pcall(f, *jaxtupletree_args,\n axis_name=axis_name, axis_size=axis_size)\n return build_tree(out_tree(), jaxtupletree_out)\n\n f_jitted.__name__ = \"pjit({})\".format(f_jitted.__name__)\n return f_jitted","label":0},{"cid":"jax/api.py::pjit.f_jitted","text":"jax/api.py::pjit.f_jitted\n @wraps(fun)\n def f_jitted(*args, **kwargs):\n leaves, _ = tree_flatten(args)\n axis_sizes = set(onp.shape(leaf)[0] for leaf in leaves)\n if len(axis_sizes) != 1:\n msg = \"pjit requires all leading axes to have equal length, got {}.\"\n raise TypeError(msg.format(axis_sizes))\n axis_size = axis_sizes.pop()\n\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n _check_args(jaxtupletree_args)\n f = lu.wrap_init(fun, kwargs)\n f, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n jaxtupletree_out = pxla.xla_pcall(f, *jaxtupletree_args,\n axis_name=axis_name, axis_size=axis_size)\n return build_tree(out_tree(), jaxtupletree_out)","label":0},{"cid":"jax/api.py::pmap","text":"jax/api.py::pmap\ndef pmap(fun, axis_name, in_axes=0, out_axes=0):\n \"\"\"Vectorizing pseudo-map for single-program multiple-data (SPMD) functions.\"\"\"\n def pmap_fun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = parallel.pmap(jaxtree_fun, axis_name, args, in_axes_, out_axes)\n return build_tree(out_tree(), out_flat)\n\n return pmap_fun","label":0},{"cid":"jax/api.py::pmap.pmap_fun","text":"jax/api.py::pmap.pmap_fun\n def pmap_fun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = parallel.pmap(jaxtree_fun, axis_name, args, in_axes_, out_axes)\n return build_tree(out_tree(), out_flat)","label":0},{"cid":"jax/api.py::axisvar_split","text":"jax/api.py::axisvar_split\ndef axisvar_split(fun, name, new_names):\n \"\"\"Split axis variable names into new names in an SPMD function.\"\"\"\n def split_fun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = parallel.axisvar_split(jaxtree_fun, name, new_names).call_wrapped(*args)\n return build_tree(out_tree(), out_flat)\n\n return split_fun","label":0},{"cid":"jax/api.py::axisvar_split.split_fun","text":"jax/api.py::axisvar_split.split_fun\n def split_fun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = parallel.axisvar_split(jaxtree_fun, name, new_names).call_wrapped(*args)\n return build_tree(out_tree(), out_flat)","label":0},{"cid":"jax/api.py::papply","text":"jax/api.py::papply\ndef papply(fun, in_axes=0):\n \"\"\"Apply a function using parallel computation by sharding inputs.\"\"\"\n axis_name = parallel.newvar()\n\n def papply_fun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\n args_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = parallel.papply(jaxtree_fun, axis_name, args_flat, in_axes_)\n return build_tree(out_tree(), out_flat)\n\n return papply_fun, axis_name","label":0},{"cid":"jax/api.py::papply.papply_fun","text":"jax/api.py::papply.papply_fun\n def papply_fun(*args, **kwargs):\n f = lu.wrap_init(fun, kwargs)\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\n args_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\n out_flat = parallel.papply(jaxtree_fun, axis_name, args_flat, in_axes_)\n return build_tree(out_tree(), out_flat)","label":0},{"cid":"jax/api.py::jvp","text":"jax/api.py::jvp\ndef jvp(fun, primals, tangents):\n \"\"\"Computes a (forward-mode) Jacobian-vector product of `fun`.\n\n Args:\n fun: Function to be differentiated. Its arguments should be arrays, scalars,\n or standard Python containers of arrays or scalars. It should return an\n array, scalar, or standard Python container of arrays or scalars.\n primals: The primal values at which the Jacobian of `fun` should be\n evaluated. Should be a tuple of arrays, scalar, or standard Python\n container thereof. The length of the tuple is equal to the number of\n positional parameters of `fun`.\n tangents: The tangent vector for which the Jacobian-vector product should be\n evaluated. Should be a tuple of arrays, scalar, or standard Python\n container thereof, with the same tree structure and array shapes as\n `primals`.\n\n Returns:\n A `(primals_out, tangents_out)` pair, where `primals_out` is\n `fun(*primals)`, and `tangents_out` is the Jacobian-vector product of\n `function` evaluated at `primals` with `tangents`. The `tangents_out` value\n has the same Python tree structure and shapes as `primals_out`.\n\n For example:\n\n >>> jax.jvp(jax.numpy.sin, (0.1,), (0.2,))\n (array(0.09983342, dtype=float32), array(0.19900084, dtype=float32))\n \"\"\"\n def trim_arg(primal, tangent):\n primal_jtuple, tree_def = pytree_to_jaxtupletree(primal)\n tangent_jtuple, tree_def_2 = pytree_to_jaxtupletree(tangent)\n assert tree_def == tree_def_2, (tree_def, tree_def_2)\n return primal_jtuple, tangent_jtuple, tree_def\n\n if not isinstance(fun, lu.WrappedFun):\n fun = lu.wrap_init(fun)\n ps_flat, ts_flat, in_trees = unzip3(map(trim_arg, primals, tangents))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\n out_primal, out_tangent = ad.jvp(jaxtree_fun).call_wrapped(ps_flat, ts_flat)\n return (build_tree(out_tree(), out_primal), build_tree(out_tree(), out_tangent))","label":0},{"cid":"jax/api.py::jvp.trim_arg","text":"jax/api.py::jvp.trim_arg\n def trim_arg(primal, tangent):\n primal_jtuple, tree_def = pytree_to_jaxtupletree(primal)\n tangent_jtuple, tree_def_2 = pytree_to_jaxtupletree(tangent)\n assert tree_def == tree_def_2, (tree_def, tree_def_2)\n return primal_jtuple, tangent_jtuple, tree_def","label":0},{"cid":"jax/api.py::linearize","text":"jax/api.py::linearize\ndef linearize(traceable, *primals):\n fun = lu.wrap_init(traceable)\n primals_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, primals))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\n out_primal, out_pval, jaxpr, consts = ad.linearize(jaxtree_fun, *primals_flat)\n out_tree = out_tree()\n out_primal_py = build_tree(out_tree, out_primal)\n lifted_jvp = partial(lift_linearized, jaxpr, consts, (in_trees, out_tree), out_pval)\n return out_primal_py, lifted_jvp","label":0},{"cid":"jax/api.py::lift_linearized","text":"jax/api.py::lift_linearized\ndef lift_linearized(jaxpr, consts, io_tree, out_pval, *py_args):\n def fun(*args):\n primals = pack(args) # doesn't matter what these are-they'll be ignored\n tangents = pack(args)\n _, ans = eval_jaxpr(jaxpr, consts, (), primals, tangents)\n return pe.merge_pvals(ans, out_pval)\n\n return apply_jaxtree_fun(fun, io_tree, *py_args)","label":0},{"cid":"jax/api.py::lift_linearized.fun","text":"jax/api.py::lift_linearized.fun\n def fun(*args):\n primals = pack(args) # doesn't matter what these are-they'll be ignored\n tangents = pack(args)\n _, ans = eval_jaxpr(jaxpr, consts, (), primals, tangents)\n return pe.merge_pvals(ans, out_pval)","label":0},{"cid":"jax/api.py::vjp","text":"jax/api.py::vjp\ndef vjp(fun, *primals):\n \"\"\"Compute a (reverse-mode) vector-Jacobian product of `fun`.\n\n `grad` is implemented as a special case of `vjp`.\n\n Args:\n fun: Function to be differentiated. Its arguments should be arrays, scalars,\n or standard Python containers of arrays or scalars. It should return an\n array, scalar, or standard Python container of arrays or scalars.\n primals: A sequence of primal values at which the Jacobian of `fun`\n should be evaluated. The length of `primals` should be equal to the number\n of positional parameters to `fun`. Each primal value should be a tuple of\n arrays, scalar, or standard Python containers thereof.\n\n Returns:\n A `(primals_out, vjpfun)` pair, where `primals_out` is `fun(*primals)`.\n `vjpfun` is a function from a cotangent vector with the same shape as\n `primals_out` to a tuple of cotangent vectors with the same shape as\n `primals`, representing the vector-Jacobian product of `fun` evaluated at\n `primals`.\n\n >>> def f(x, y):\n >>> return jax.numpy.sin(x), jax.numpy.cos(y)\n >>> primals, g = jax.vjp(f, 0.5, 1.0)\n >>> g((-0.7, 0.3))\n (array(-0.61430776, dtype=float32), array(-0.2524413, dtype=float32))\n \"\"\"\n if not isinstance(fun, lu.WrappedFun):\n fun = lu.wrap_init(fun)\n primals_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, primals))\n _check_args(primals_flat)\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\n out_primal, out_vjp = ad.vjp(jaxtree_fun, primals_flat)\n out_tree = out_tree()\n out_primal_py = build_tree(out_tree, out_primal)\n ct_in_trees = [out_tree]\n ct_out_tree = PyTreeDef(node_types[tuple], None, in_trees)\n def out_vjp_packed(cotangent_in):\n return out_vjp(cotangent_in)\n vjp_py = partial(apply_jaxtree_fun, out_vjp_packed, (ct_in_trees, ct_out_tree))\n return out_primal_py, vjp_py","label":0},{"cid":"jax/api.py::vjp.out_vjp_packed","text":"jax/api.py::vjp.out_vjp_packed\n def out_vjp_packed(cotangent_in):\n return out_vjp(cotangent_in)","label":0},{"cid":"jax/api.py::trace_to_jaxpr","text":"jax/api.py::trace_to_jaxpr\ndef trace_to_jaxpr(traceable, py_pvals, **kwargs):\n fun = lu.wrap_init(traceable)\n pvals, in_trees = unzip2(map(tree_to_pval_tuples, py_pvals))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\n jaxpr, out_pval, consts = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\n return jaxpr, consts, out_pval, (in_trees, out_tree())","label":0},{"cid":"jax/api.py::lift_jaxpr","text":"jax/api.py::lift_jaxpr\ndef lift_jaxpr(jaxpr, consts, io_tree, pvals, py_args):\n def fun(*args):\n ans = eval_jaxpr(jaxpr, consts, (), *args)\n return pe.merge_pvals(ans, pvals)\n return apply_jaxtree_fun(fun, io_tree, *py_args)","label":0},{"cid":"jax/api.py::lift_jaxpr.fun","text":"jax/api.py::lift_jaxpr.fun\n def fun(*args):\n ans = eval_jaxpr(jaxpr, consts, (), *args)\n return pe.merge_pvals(ans, pvals)","label":0},{"cid":"jax/api.py::make_jaxpr","text":"jax/api.py::make_jaxpr\ndef make_jaxpr(fun):\n \"\"\"Adapts `fun` to return its `jaxpr` program representation.\n\n Args:\n fun: The function whose `jaxpr` is to be computed. Its positional arguments\n and return value should be arrays, scalars, or standard Python containers\n (tuple/list/dict) thereof.\n\n Returns:\n A wrapped version of `fun`, set up to return a `jaxpr`.\n\n A `jaxpr` is JAX's intermediate representation for program traces. The `jaxpr`\n language is based on the simply-typed first-order lambda calculus with\n let-bindings. `make_jaxpr` adapts a function to return its `jaxpr`, which we\n can inspect to understand what JAX is doing internally.\n\n The `jaxpr` returned is a trace of `fun` abstracted to `ShapedArray` level.\n Other levels of abstraction exist internally.\n\n We do not describe the semantics of the `jaxpr` language in detail here, but\n instead give a few examples.\n\n >>> def f(x): return jax.numpy.sin(jax.numpy.cos(x))\n >>> f(3.0)\n array(-0.83602184, dtype=float32)\n >>> jax.make_jaxpr(f)(3.0)\n { lambda ; ; a.\n let b = cos a\n c = sin b\n in c }\n >>> jax.make_jaxpr(jax.grad(f))(3.0)\n { lambda b ; ; a.\n let c = pack a\n (d) = id c\n e = cos d\n f = cos e\n g = mul b f\n h = neg g\n i = sin d\n j = mul h i\n k = pack j\n (l) = id k\n in l }\n \"\"\"\n def pv_like(x):\n aval = xla.abstractify(x)\n return pe.PartialVal((aval, core.unit))\n\n wrapped = lu.wrap_init(fun)\n\n @wraps(fun)\n def jaxpr_maker(*args, **kwargs):\n jax_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(wrapped, in_trees)\n pvals = map(pv_like, jax_args)\n jaxpr, _, _ = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\n return jaxpr\n\n jaxpr_maker.__name__ = \"make_jaxpr({})\".format(jaxpr_maker.__name__)\n return jaxpr_maker","label":0},{"cid":"jax/experimental/stax.py::relu","text":"jax/experimental/stax.py::relu\ndef relu(x): return np.maximum(x, 0.)","label":0},{"cid":"jax/experimental/stax.py::softplus","text":"jax/experimental/stax.py::softplus\ndef softplus(x): return np.logaddexp(x, 0.)","label":0},{"cid":"jax/experimental/stax.py::logsoftmax","text":"jax/experimental/stax.py::logsoftmax\ndef logsoftmax(x, axis=-1):\n \"\"\"Apply log softmax to an array of logits, log-normalizing along an axis.\"\"\"\n return x - logsumexp(x, axis, keepdims=True)","label":0},{"cid":"jax/experimental/stax.py::softmax","text":"jax/experimental/stax.py::softmax\ndef softmax(x, axis=-1):\n \"\"\"Apply softmax to an array of logits, exponentiating and normalizing along an axis.\"\"\"\n unnormalized = np.exp(x - x.max(axis, keepdims=True))\n return unnormalized / unnormalized.sum(axis, keepdims=True)","label":0},{"cid":"jax/experimental/stax.py::fastvar","text":"jax/experimental/stax.py::fastvar\ndef fastvar(x, axis, keepdims):\n \"\"\"A fast but less numerically-stable variance calculation than np.var.\"\"\"\n return np.mean(x**2, axis, keepdims=keepdims) - np.mean(x, axis, keepdims=keepdims)**2","label":0},{"cid":"jax/experimental/stax.py::randn","text":"jax/experimental/stax.py::randn\ndef randn(stddev=1e-2, rng=npr):\n \"\"\"An initializer function for random normal coefficients.\"\"\"\n def init(shape):\n return rng.normal(size=shape, scale=stddev).astype('float32')\n return init","label":0},{"cid":"jax/experimental/stax.py::randn.init","text":"jax/experimental/stax.py::randn.init\n def init(shape):\n return rng.normal(size=shape, scale=stddev).astype('float32')","label":0},{"cid":"jax/experimental/stax.py::glorot","text":"jax/experimental/stax.py::glorot\ndef glorot(out_dim=0, in_dim=1, scale=onp.sqrt(2), rng=npr):\n \"\"\"An initializer function for random Glorot-scaled coefficients.\"\"\"\n def init(shape):\n fan_in, fan_out = shape[in_dim], shape[out_dim]\n size = onp.prod(onp.delete(shape, [in_dim, out_dim]))\n std = scale / np.sqrt((fan_in + fan_out) / 2. * size)\n return rng.normal(size=shape, scale=std).astype('float32')\n return init","label":0},{"cid":"jax/experimental/stax.py::glorot.init","text":"jax/experimental/stax.py::glorot.init\n def init(shape):\n fan_in, fan_out = shape[in_dim], shape[out_dim]\n size = onp.prod(onp.delete(shape, [in_dim, out_dim]))\n std = scale / np.sqrt((fan_in + fan_out) / 2. * size)\n return rng.normal(size=shape, scale=std).astype('float32')","label":0},{"cid":"jax/experimental/stax.py::Dense","text":"jax/experimental/stax.py::Dense\ndef Dense(out_dim, W_init=glorot(), b_init=randn()):\n \"\"\"Layer constructor function for a dense (fully-connected) layer.\"\"\"\n def init_fun(input_shape):\n output_shape = input_shape[:-1] + (out_dim,)\n W, b = W_init((input_shape[-1], out_dim)), b_init((out_dim,))\n return output_shape, (W, b)\n def apply_fun(params, inputs, rng=None):\n W, b = params\n return np.dot(inputs, W) + b\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Dense.init_fun","text":"jax/experimental/stax.py::Dense.init_fun\n def init_fun(input_shape):\n output_shape = input_shape[:-1] + (out_dim,)\n W, b = W_init((input_shape[-1], out_dim)), b_init((out_dim,))\n return output_shape, (W, b)","label":0},{"cid":"jax/experimental/stax.py::Dense.apply_fun","text":"jax/experimental/stax.py::Dense.apply_fun\n def apply_fun(params, inputs, rng=None):\n W, b = params\n return np.dot(inputs, W) + b","label":0},{"cid":"jax/experimental/stax.py::GeneralConv","text":"jax/experimental/stax.py::GeneralConv\ndef GeneralConv(dimension_numbers, out_chan, filter_shape,\n strides=None, padding='VALID', W_init=None, b_init=randn(1e-6)):\n \"\"\"Layer construction function for a general convolution layer.\"\"\"\n lhs_spec, rhs_spec, out_spec = dimension_numbers\n one = (1,) * len(filter_shape)\n strides = strides or one\n W_init = W_init or glorot(rhs_spec.index('O'), rhs_spec.index('I'))\n def init_fun(input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_general_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n W, b = W_init(kernel_shape), b_init(bias_shape)\n return output_shape, (W, b)\n def apply_fun(params, inputs, rng=None):\n W, b = params\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\n dimension_numbers) + b\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::GeneralConv.init_fun","text":"jax/experimental/stax.py::GeneralConv.init_fun\n def init_fun(input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_general_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n W, b = W_init(kernel_shape), b_init(bias_shape)\n return output_shape, (W, b)","label":0},{"cid":"jax/experimental/stax.py::GeneralConv.apply_fun","text":"jax/experimental/stax.py::GeneralConv.apply_fun\n def apply_fun(params, inputs, rng=None):\n W, b = params\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\n dimension_numbers) + b","label":0},{"cid":"jax/experimental/stax.py::BatchNorm","text":"jax/experimental/stax.py::BatchNorm\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-5, center=True, scale=True,\n beta_init=zeros, gamma_init=ones):\n \"\"\"Layer construction function for a batch normalization layer.\"\"\"\n _beta_init = lambda shape: beta_init(shape) if center else ()\n _gamma_init = lambda shape: gamma_init(shape) if scale else ()\n axis = (axis,) if np.isscalar(axis) else axis\n def init_fun(input_shape):\n shape = (1 if i in axis else d for i, d in enumerate(input_shape))\n shape = tuple(itertools.dropwhile(lambda x: x == 1, shape))\n beta, gamma = _beta_init(shape), _gamma_init(shape)\n return input_shape, (beta, gamma)\n def apply_fun(params, x, rng=None):\n beta, gamma = params\n mean, var = np.mean(x, axis, keepdims=True), fastvar(x, axis, keepdims=True)\n z = (x - mean) / (var + epsilon)**2\n if center and scale: return gamma * z + beta\n if center: return z + beta\n if scale: return gamma * z\n return z\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::BatchNorm.init_fun","text":"jax/experimental/stax.py::BatchNorm.init_fun\n def init_fun(input_shape):\n shape = (1 if i in axis else d for i, d in enumerate(input_shape))\n shape = tuple(itertools.dropwhile(lambda x: x == 1, shape))\n beta, gamma = _beta_init(shape), _gamma_init(shape)\n return input_shape, (beta, gamma)","label":0},{"cid":"jax/experimental/stax.py::BatchNorm.apply_fun","text":"jax/experimental/stax.py::BatchNorm.apply_fun\n def apply_fun(params, x, rng=None):\n beta, gamma = params\n mean, var = np.mean(x, axis, keepdims=True), fastvar(x, axis, keepdims=True)\n z = (x - mean) / (var + epsilon)**2\n if center and scale: return gamma * z + beta\n if center: return z + beta\n if scale: return gamma * z\n return z","label":1},{"cid":"jax/experimental/stax.py::_elemwise_no_params","text":"jax/experimental/stax.py::_elemwise_no_params\ndef _elemwise_no_params(fun, **kwargs):\n init_fun = lambda input_shape: (input_shape, ())\n apply_fun = lambda params, inputs, rng=None: fun(inputs, **kwargs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer","text":"jax/experimental/stax.py::_pooling_layer\ndef _pooling_layer(reducer, init_val, rescaler=None):\n def PoolingLayer(window_shape, strides=None, padding='VALID'):\n \"\"\"Layer construction function for a pooling layer.\"\"\"\n strides = strides or (1,) * len(window_shape)\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\n dims = (1,) + window_shape + (1,) # NHWC\n strides = (1,) + strides + (1,)\n def init_fun(input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\n return out_shape, ()\n def apply_fun(params, inputs, rng=None):\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\n return rescale(out, inputs) if rescale else out\n return init_fun, apply_fun\n return PoolingLayer","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer\n def PoolingLayer(window_shape, strides=None, padding='VALID'):\n \"\"\"Layer construction function for a pooling layer.\"\"\"\n strides = strides or (1,) * len(window_shape)\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\n dims = (1,) + window_shape + (1,) # NHWC\n strides = (1,) + strides + (1,)\n def init_fun(input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\n return out_shape, ()\n def apply_fun(params, inputs, rng=None):\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\n return rescale(out, inputs) if rescale else out\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\n def init_fun(input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\n return out_shape, ()","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\n def apply_fun(params, inputs, rng=None):\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\n return rescale(out, inputs) if rescale else out","label":0},{"cid":"jax/experimental/stax.py::_normalize_by_window_size","text":"jax/experimental/stax.py::_normalize_by_window_size\ndef _normalize_by_window_size(dims, strides, padding):\n def rescale(outputs, inputs):\n one = np.ones(inputs.shape[1:-1], dtype=inputs.dtype)\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\n return outputs / window_sizes[..., np.newaxis]\n return rescale","label":0},{"cid":"jax/experimental/stax.py::_normalize_by_window_size.rescale","text":"jax/experimental/stax.py::_normalize_by_window_size.rescale\n def rescale(outputs, inputs):\n one = np.ones(inputs.shape[1:-1], dtype=inputs.dtype)\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\n return outputs / window_sizes[..., np.newaxis]","label":0},{"cid":"jax/experimental/stax.py::Flatten","text":"jax/experimental/stax.py::Flatten\ndef Flatten():\n \"\"\"Layer construction function for flattening all but the leading dim.\"\"\"\n def init_fun(input_shape):\n output_shape = input_shape[0], reduce(op.mul, input_shape[1:], 1)\n return output_shape, ()\n def apply_fun(params, inputs, rng=None):\n return np.reshape(inputs, (inputs.shape[0], -1))\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Flatten.init_fun","text":"jax/experimental/stax.py::Flatten.init_fun\n def init_fun(input_shape):\n output_shape = input_shape[0], reduce(op.mul, input_shape[1:], 1)\n return output_shape, ()","label":0},{"cid":"jax/experimental/stax.py::Flatten.apply_fun","text":"jax/experimental/stax.py::Flatten.apply_fun\n def apply_fun(params, inputs, rng=None):\n return np.reshape(inputs, (inputs.shape[0], -1))","label":0},{"cid":"jax/experimental/stax.py::Identity","text":"jax/experimental/stax.py::Identity\ndef Identity():\n \"\"\"Layer construction function for an identity layer.\"\"\"\n init_fun = lambda input_shape: (input_shape, ())\n apply_fun = lambda params, inputs, rng=None: inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanOut","text":"jax/experimental/stax.py::FanOut\ndef FanOut(num):\n \"\"\"Layer construction function for a fan-out layer.\"\"\"\n init_fun = lambda input_shape: ([input_shape] * num, ())\n apply_fun = lambda params, inputs, rng=None: [inputs] * num\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInSum","text":"jax/experimental/stax.py::FanInSum\ndef FanInSum():\n \"\"\"Layer construction function for a fan-in sum layer.\"\"\"\n init_fun = lambda input_shape: (input_shape[0], ())\n apply_fun = lambda params, inputs, rng=None: sum(inputs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInConcat","text":"jax/experimental/stax.py::FanInConcat\ndef FanInConcat(axis=-1):\n \"\"\"Layer construction function for a fan-in concatenation layer.\"\"\"\n def init_fun(input_shape):\n ax = axis % len(input_shape[0])\n concat_size = sum(shape[ax] for shape in input_shape)\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\n return out_shape, ()\n def apply_fun(params, inputs, rng=None):\n return np.concatenate(inputs, axis)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInConcat.init_fun","text":"jax/experimental/stax.py::FanInConcat.init_fun\n def init_fun(input_shape):\n ax = axis % len(input_shape[0])\n concat_size = sum(shape[ax] for shape in input_shape)\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\n return out_shape, ()","label":0},{"cid":"jax/experimental/stax.py::FanInConcat.apply_fun","text":"jax/experimental/stax.py::FanInConcat.apply_fun\n def apply_fun(params, inputs, rng=None):\n return np.concatenate(inputs, axis)","label":0},{"cid":"jax/experimental/stax.py::Dropout","text":"jax/experimental/stax.py::Dropout\ndef Dropout(rate, mode='train'):\n \"\"\"Layer construction function for a dropout layer with given rate.\"\"\"\n def init_fun(input_shape):\n return input_shape, ()\n def apply_fun(params, inputs, rng):\n if rng is None:\n msg = (\"Dropout layer requires apply_fun to be called with a PRNG key \"\n \"argument. That is, instead of `apply_fun(params, inputs)`, call \"\n \"it like `apply_fun(params, inputs, key)` where `key` is a \"\n \"jax.random.PRNGKey value.\")\n raise ValueError(msg)\n if mode == 'train':\n keep = random.bernoulli(rng, rate, inputs.shape)\n return np.where(keep, inputs / rate, 0)\n else:\n return inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Dropout.init_fun","text":"jax/experimental/stax.py::Dropout.init_fun\n def init_fun(input_shape):\n return input_shape, ()","label":0},{"cid":"jax/experimental/stax.py::Dropout.apply_fun","text":"jax/experimental/stax.py::Dropout.apply_fun\n def apply_fun(params, inputs, rng):\n if rng is None:\n msg = (\"Dropout layer requires apply_fun to be called with a PRNG key \"\n \"argument. That is, instead of `apply_fun(params, inputs)`, call \"\n \"it like `apply_fun(params, inputs, key)` where `key` is a \"\n \"jax.random.PRNGKey value.\")\n raise ValueError(msg)\n if mode == 'train':\n keep = random.bernoulli(rng, rate, inputs.shape)\n return np.where(keep, inputs / rate, 0)\n else:\n return inputs","label":0},{"cid":"jax/experimental/stax.py::serial","text":"jax/experimental/stax.py::serial\ndef serial(*layers):\n \"\"\"Combinator for composing layers in serial.\n\n Args:\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the serial\n composition of the given sequence of layers.\n \"\"\"\n nlayers = len(layers)\n init_funs, apply_funs = zip(*layers)\n def init_fun(input_shape):\n params = []\n for init_fun in init_funs:\n input_shape, param = init_fun(input_shape)\n params.append(param)\n return input_shape, params\n def apply_fun(params, inputs, rng=None):\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n for fun, param, rng in zip(apply_funs, params, rngs):\n inputs = fun(param, inputs, rng)\n return inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::serial.init_fun","text":"jax/experimental/stax.py::serial.init_fun\n def init_fun(input_shape):\n params = []\n for init_fun in init_funs:\n input_shape, param = init_fun(input_shape)\n params.append(param)\n return input_shape, params","label":0},{"cid":"jax/experimental/stax.py::serial.apply_fun","text":"jax/experimental/stax.py::serial.apply_fun\n def apply_fun(params, inputs, rng=None):\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n for fun, param, rng in zip(apply_funs, params, rngs):\n inputs = fun(param, inputs, rng)\n return inputs","label":0},{"cid":"jax/experimental/stax.py::parallel","text":"jax/experimental/stax.py::parallel\ndef parallel(*layers):\n \"\"\"Combinator for composing layers in parallel.\n\n The layer resulting from this combinator is often used with the FanOut and\n FanInSum layers.\n\n Args:\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the\n parallel composition of the given sequence of layers. In particular, the\n returned layer takes a sequence of inputs and returns a sequence of outputs\n with the same length as the argument `layers`.\n \"\"\"\n nlayers = len(layers)\n init_funs, apply_funs = zip(*layers)\n def init_fun(input_shape):\n return zip(*[init(shape) for init, shape in zip(init_funs, input_shape)])\n def apply_fun(params, inputs, rng=None):\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n return [f(p, x, r) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::parallel.init_fun","text":"jax/experimental/stax.py::parallel.init_fun\n def init_fun(input_shape):\n return zip(*[init(shape) for init, shape in zip(init_funs, input_shape)])","label":0},{"cid":"jax/experimental/stax.py::parallel.apply_fun","text":"jax/experimental/stax.py::parallel.apply_fun\n def apply_fun(params, inputs, rng=None):\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n return [f(p, x, r) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]","label":0},{"cid":"jax/experimental/stax.py::shape_dependent","text":"jax/experimental/stax.py::shape_dependent\ndef shape_dependent(make_layer):\n \"\"\"Combinator to delay layer constructor pair until input shapes are known.\n\n Args:\n make_layer: a one-argument function that takes an input shape as an argument\n (a tuple of positive integers) and returns an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the same\n layer as returned by `make_layer` but with its construction delayed until\n input shapes are known.\n \"\"\"\n def init_fun(input_shape):\n return make_layer(input_shape)[0](input_shape)\n def apply_fun(params, inputs, rng=None):\n return make_layer(inputs.shape)[1](params, inputs, rng)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::shape_dependent.init_fun","text":"jax/experimental/stax.py::shape_dependent.init_fun\n def init_fun(input_shape):\n return make_layer(input_shape)[0](input_shape)","label":0},{"cid":"jax/experimental/stax.py::shape_dependent.apply_fun","text":"jax/experimental/stax.py::shape_dependent.apply_fun\n def apply_fun(params, inputs, rng=None):\n return make_layer(inputs.shape)[1](params, inputs, rng)","label":0},{"cid":"jax/random.py::PRNGKey","text":"jax/random.py::PRNGKey\ndef PRNGKey(seed):\n \"\"\"Create a pseudo-random number generator (PRNG) key given an integer seed.\n\n Args:\n seed: a 64- or 32-bit integer used as the value of the key.\n\n Returns:\n A PRNG key, which is modeled as an array of shape (2,) and dtype uint32. The\n key is constructed from a 64-bit seed by effectively bit-casting to a pair\n of uint32 values (or from a 32-bit seed by first padding out with zeros).\n \"\"\"\n if onp.shape(seed):\n raise TypeError(\"PRNGKey seed must be a scalar.\")\n convert = lambda k: lax.reshape(lax.convert_element_type(k, onp.uint32), [1])\n if isinstance(seed, (int, onp.ndarray)):\n # Special handling of raw integer values, which may have be 64bit even\n # when jax_enable_x64=False and we don't want to drop the top 32 bits\n k1 = convert(onp.bitwise_and(onp.right_shift(seed, 32), 0xFFFFFFFF))\n else:\n k1 = convert(lax.shift_right_logical(seed, 32))\n k2 = convert(lax.bitwise_and(seed, 0xFFFFFFFF))\n return lax.concatenate([k1, k2], 0)","label":0},{"cid":"jax/random.py::_is_prng_key","text":"jax/random.py::_is_prng_key\ndef _is_prng_key(key):\n try:\n return key.shape == (2,) and key.dtype == onp.uint32\n except AttributeError:\n return False","label":0},{"cid":"jax/random.py::_make_rotate_left","text":"jax/random.py::_make_rotate_left\ndef _make_rotate_left(dtype):\n if not onp.issubdtype(dtype, onp.integer):\n raise TypeError(\"_rotate_left only accepts integer dtypes.\")\n nbits = onp.array(onp.iinfo(dtype).bits, dtype)\n\n def _rotate_left(x, d):\n if lax._dtype(d) != lax._dtype(x):\n d = lax.convert_element_type(d, x.dtype)\n return (x << d) | lax.shift_right_logical(x, nbits - d)\n return _rotate_left","label":0},{"cid":"jax/random.py::_make_rotate_left._rotate_left","text":"jax/random.py::_make_rotate_left._rotate_left\n def _rotate_left(x, d):\n if lax._dtype(d) != lax._dtype(x):\n d = lax.convert_element_type(d, x.dtype)\n return (x << d) | lax.shift_right_logical(x, nbits - d)","label":0},{"cid":"jax/random.py::_bit_stats","text":"jax/random.py::_bit_stats\ndef _bit_stats(bits):\n \"\"\"This is a debugging function to compute the statistics of bit fields.\"\"\"\n return onp.array([list(map(int, onp.binary_repr(x, 64))) for x in bits]).mean(0)","label":0},{"cid":"jax/random.py::threefry_2x32","text":"jax/random.py::threefry_2x32\n@jit\ndef threefry_2x32(keypair, count):\n \"\"\"Apply the Threefry 2x32 hash.\n\n Args:\n keypair: a pair of 32bit unsigned integers used for the key.\n count: an array of dtype uint32 used for the counts.\n\n Returns:\n An array of dtype uint32 with the same shape as `count`.\n \"\"\"\n # Based on ThreeFry2x32 by phawkins@ in //.../xla/client/lib/prng.cc\n key1, key2 = keypair\n if not lax._dtype(key1) == lax._dtype(key2) == lax._dtype(count) == onp.uint32:\n msg = \"threefry_2x32 requires uint32 arguments, got {}\"\n raise TypeError(msg.format([lax._dtype(x) for x in [key1, key2, count]]))\n\n rotate_left = _make_rotate_left(lax._dtype(count))\n\n def apply_round(v, rot):\n v = v[:]\n v[0] = v[0] + v[1]\n v[1] = rotate_left(v[1], rot)\n v[1] = v[0] ^ v[1]\n return v\n\n odd_size = count.size % 2\n if odd_size:\n x = list(np.split(np.concatenate([count.ravel(), onp.uint32([0])]), 2))\n else:\n x = list(np.split(count.ravel(), 2))\n\n rotations = [13, 15, 26, 6, 17, 29, 16, 24]\n ks = [key1, key2, key1 ^ key2 ^ onp.uint32(0x1BD11BDA)]\n\n x[0] = x[0] + ks[0]\n x[1] = x[1] + ks[1]\n\n for r in rotations[:4]:\n x = apply_round(x, r)\n x[0] = x[0] + ks[1]\n x[1] = x[1] + ks[2] + onp.uint32(1)\n\n for r in rotations[4:]:\n x = apply_round(x, r)\n x[0] = x[0] + ks[2]\n x[1] = x[1] + ks[0] + onp.uint32(2)\n\n for r in rotations[:4]:\n x = apply_round(x, r)\n x[0] = x[0] + ks[0]\n x[1] = x[1] + ks[1] + onp.uint32(3)\n\n for r in rotations[4:]:\n x = apply_round(x, r)\n x[0] = x[0] + ks[1]\n x[1] = x[1] + ks[2] + onp.uint32(4)\n\n for r in rotations[:4]:\n x = apply_round(x, r)\n x[0] = x[0] + ks[2]\n x[1] = x[1] + ks[0] + onp.uint32(5)\n\n out = np.concatenate(x)\n assert out.dtype == onp.uint32\n return lax.reshape(out[:-1] if odd_size else out, count.shape)","label":0},{"cid":"jax/random.py::threefry_2x32.apply_round","text":"jax/random.py::threefry_2x32.apply_round\n def apply_round(v, rot):\n v = v[:]\n v[0] = v[0] + v[1]\n v[1] = rotate_left(v[1], rot)\n v[1] = v[0] ^ v[1]\n return v","label":0},{"cid":"jax/random.py::split","text":"jax/random.py::split\n@partial(jit, static_argnums=(1,))\ndef split(key, num=2):\n \"\"\"Splits a PRNG key into `num` new keys by adding a leading axis.\n\n Args:\n key: a PRNGKey (an array with shape (2,) and dtype uint32).\n num: optional, a positive integer indicating the number of keys to produce\n (default 2).\n\n Returns:\n An array with shape (num, 2) and dtype uint32 representing `num` new keys.\n \"\"\"\n counts = lax.tie_in(key, lax.iota(onp.uint32, num * 2))\n return lax.reshape(threefry_2x32(key, counts), (num, 2))","label":0},{"cid":"jax/random.py::fold_in","text":"jax/random.py::fold_in\n@partial(jit, static_argnums=(1,))\ndef fold_in(key, data):\n \"\"\"Folds in data to a PRNG key to form a new PRNG key.\n\n Args:\n key: a PRNGKey (an array with shape (2,) and dtype uint32).\n data: an integer representing data to be folded in to the key.\n\n Returns:\n A new PRNGKey that is a deterministic function of the inputs and is\n statistically safe for producing a stream of new pseudo-random values.\n \"\"\"\n key2 = lax.tie_in(key, PRNGKey(data))\n return threefry_2x32(key, key2)","label":0},{"cid":"jax/random.py::_random_bits","text":"jax/random.py::_random_bits\ndef _random_bits(key, bit_width, shape):\n \"\"\"Sample uniform random bits of given width and shape using PRNG key.\"\"\"\n if not _is_prng_key(key):\n raise TypeError(\"_random_bits got invalid prng key.\")\n if bit_width not in (32, 64):\n raise TypeError(\"requires 32- or 64-bit field width.\")\n max_count = (bit_width // 32) * onp.prod(shape)\n if max_count >= onp.iinfo(onp.uint32).max:\n # TODO(mattjj): just split the key here\n raise TypeError(\"requesting more random bits than a single call provides.\")\n\n counts = lax.tie_in(key, lax.iota(onp.uint32, max_count))\n bits = threefry_2x32(key, counts)\n if bit_width == 64:\n bits = [lax.convert_element_type(x, onp.uint64) for x in np.split(bits, 2)]\n bits = (bits[0] << onp.uint64(32)) | bits[1]\n return lax.reshape(bits, shape)","label":0},{"cid":"jax/random.py::uniform","text":"jax/random.py::uniform\n@partial(jit, static_argnums=(1, 2))\ndef uniform(key, shape, dtype=onp.float32, minval=0., maxval=1.):\n \"\"\"Sample uniform random values in [minval, maxval) with given shape/dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: a tuple of nonnegative integers representing the shape.\n dtype: optional, a float dtype for the returned values (default float32).\n minval: optional, a minimum (inclusive) value for the range (default 0).\n maxval: optional, a maximum (exclusive) value for the range (default 1).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n if not onp.issubdtype(dtype, onp.floating):\n raise TypeError(\"uniform only accepts floating point dtypes.\")\n\n dtype = xla_bridge.canonicalize_dtype(dtype)\n minval = lax.convert_element_type(minval, dtype)\n maxval = lax.convert_element_type(maxval, dtype)\n finfo = onp.finfo(dtype)\n nbits, nmant = finfo.bits, finfo.nmant\n\n if nbits not in (32, 64):\n raise TypeError(\"uniform only accepts 32- or 64-bit dtypes.\")\n\n bits = _random_bits(key, nbits, shape)\n\n # The strategy here is to randomize only the mantissa bits with an exponent of\n # 1 (after applying the bias), then shift and scale to the desired range. The\n # bit-level transformation we use relies on Numpy and XLA having bit-for-bit\n # equivalent float representations, which might not be true on all platforms.\n float_bits = lax.bitwise_or(\n lax.shift_right_logical(bits, onp.array(nbits - nmant, lax._dtype(bits))),\n onp.array(1., dtype).view(onp.uint32 if nbits == 32 else onp.uint64))\n floats = lax.bitcast_convert_type(float_bits, dtype) - onp.array(1., dtype)\n return lax.max(\n minval,\n lax.reshape(floats * (maxval - minval) + minval, shape))","label":0},{"cid":"jax/random.py::randint","text":"jax/random.py::randint\n@partial(jit, static_argnums=(1, 4))\ndef randint(key, shape, minval, maxval, dtype=onp.int32):\n \"\"\"Sample uniform random values in [minval, maxval) with given shape/dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: a tuple of nonnegative integers representing the shape.\n minval: optional, a minimum (inclusive) value for the range (default 0).\n maxval: optional, a maximum (exclusive) value for the range (default 1).\n dtype: optional, an int dtype for the returned values (default int32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n if not onp.issubdtype(dtype, onp.integer):\n raise TypeError(\"randint only accepts integer dtypes.\")\n\n dtype = xla_bridge.canonicalize_dtype(dtype)\n minval = lax.convert_element_type(minval, dtype)\n maxval = lax.convert_element_type(maxval, dtype)\n nbits = onp.iinfo(dtype).bits\n\n if nbits not in (32, 64):\n raise TypeError(\"randint only accepts 32- or 64-bit dtypes.\")\n\n # if we don't have minval < maxval, just always return minval\n # https://github.com/google/jax/issues/222\n maxval = lax.max(lax.add(minval, onp.array(1, dtype)), maxval)\n\n # This algorithm is biased whenever (maxval - minval) is not a power of 2.\n # We generate double the number of random bits required by the dtype so as to\n # reduce that bias.\n k1, k2 = split(key)\n rbits = lambda key: _random_bits(key, nbits, shape)\n higher_bits, lower_bits = rbits(k1), rbits(k2)\n\n unsigned_dtype = onp.uint32 if nbits == 32 else onp.uint64\n span = lax.convert_element_type(maxval - minval, unsigned_dtype)\n\n # To compute a remainder operation on an integer that might have twice as many\n # bits as we can represent in the native unsigned dtype, we compute a\n # multiplier equal to 2**nbits % span (using that nbits is 32 or 64).\n multiplier = lax.rem(onp.array(2**16, unsigned_dtype), span)\n multiplier = lax.rem(lax.mul(multiplier, multiplier), span)\n if nbits == 64:\n multiplier = lax.re","label":0},{"cid":"jax/random.py::shuffle","text":"jax/random.py::shuffle\n@partial(jit, static_argnums=(2,))\ndef shuffle(key, x, axis=0):\n \"\"\"Shuffle the elements of an array uniformly at random along an axis.\n\n Args:\n key: a PRNGKey used as the random key.\n x: the array to be shuffled.\n axis: optional, an int axis along which to shuffle (default 0).\n\n Returns:\n A shuffled version of x.\n \"\"\"\n # On parallel architectures, Fisher-Yates is more expensive than doing\n # multiple sorts. This algorithm is based on one developed and analyzed by\n # tjablin@. We sort according to randomly-generated 32bit keys, but those keys\n # may have collisions. If we repeat the process, using fresh 32bit keys for\n # each sort, then whenever all pairs of elements have been assigned distinct\n # keys at some iteration (or equivalently when the strings formed by\n # concatenating the successive keys for each element are all distinct) then we\n # are guaranteed to have a perfect sample (assuming that either the sort is\n # stable or that any bias is not value-dependent). Since checking uniqueness\n # at runtime may be expensive, we use a heuristic static stop criterion\n # developed by tjablin@. See tensorflow/compiler/tf2xla/random_ops.cc for more\n # info, and for the original implementation of this algorithm. See also\n # Section 2 of http://people.csail.mit.edu/costis/6896sp11/lec5s.pdf for\n # another analysis (where the keys are generated one bit at a time).\n exponent = 3 # see tjablin@'s analysis for explanation of this parameter\n uint32max = onp.iinfo(onp.uint32).max\n num_rounds = int(onp.ceil(exponent * onp.log(x.size) / onp.log(uint32max)))\n\n for _ in range(num_rounds):\n key, subkey = split(key)\n sort_keys = _random_bits(subkey, 32, x.shape)\n _, x = lax.sort_key_val(sort_keys, x, axis)\n\n return x","label":0},{"cid":"jax/random.py::normal","text":"jax/random.py::normal\n@partial(jit, static_argnums=(1, 2))\ndef normal(key, shape, dtype=onp.float32):\n \"\"\"Sample standard normal random values with given shape and float dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: a tuple of nonnegative integers representing the shape.\n dtype: optional, a float dtype for the returned values (default float32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n lo = onp.nextafter(onp.array(-1., dtype), 0., dtype=dtype)\n hi = onp.array(1., dtype)\n u = uniform(key, shape, dtype, lo, hi)\n return onp.array(onp.sqrt(2), dtype) * lax.erf_inv(u)","label":0},{"cid":"jax/random.py::bernoulli","text":"jax/random.py::bernoulli\n@partial(jit, static_argnums=(2,))\ndef bernoulli(key, mean=onp.float32(0.5), shape=()):\n \"\"\"Sample Bernoulli random values with given shape and mean.\n\n Args:\n key: a PRNGKey used as the random key.\n mean: optional, an array-like broadcastable to `shape` for the mean of the\n random variables (default 0.5).\n shape: optional, a tuple of nonnegative integers representing the shape\n (default scalar).\n\n Returns:\n A random array with the specified shape and boolean dtype.\n \"\"\"\n shape = shape or onp.shape(mean)\n if not onp.issubdtype(lax._dtype(mean), onp.float32):\n mean = lax.convert_element_type(mean, onp.float32)\n if onp.shape(mean) != shape:\n mean = lax.broadcast(mean, shape)\n return lax.lt(uniform(key, shape), mean)","label":0}],"string":"[\n {\n \"cid\": \"jax/api.py::jit\",\n \"text\": \"jax/api.py::jit\\ndef jit(fun, static_argnums=()):\\n \\\"\\\"\\\"Sets up `fun` for just-in-time compilation with XLA.\\n\\n Args:\\n fun: Function to be jitted. Should be a pure function, as side-effects may\\n only be executed once. Its positional arguments and return value should be\\n arrays, scalars, or standard Python containers (tuple/list/dict) thereof.\\n Keyword arguments and positional arguments specified by `static_argnums`\\n can be anything at all. These are treated as static (see below).\\n static_argnums: A tuple of ints. Specifies which arguments to treat as\\n static (compile-time constant). Operations that only depend on static\\n arguments will be constant-folded. Calling the jitted function with\\n different values for these constants will trigger recompilation.\\n\\n Returns:\\n A wrapped version of `fun`, set up for just-in-time compilation.\\n\\n In the following example, `selu` can be compiled into a single fused kernel by\\n XLA:\\n\\n >>> @jax.jit\\n >>> def selu(x, alpha=1.67, lmbda=1.05):\\n >>> return lmbda * jax.numpy.where(x > 0, x, alpha * jax.numpy.exp(x) - alpha)\\n >>>\\n >>> key = jax.random.PRNGKey(0)\\n >>> x = jax.random.normal(key, (10,))\\n >>> selu(x)\\n array([-0.54485154, 0.27744263, -0.29255125, -0.91421586, -0.62452525,\\n -0.2474813 , -0.8574326 , -0.7823267 , 0.7682731 , 0.59566754],\\n dtype=float32)\\n\\n \\\"\\\"\\\"\\n @wraps(fun)\\n def f_jitted(*args, **kwargs):\\n if _jit_is_disabled or config.read('jax_disable_jit'):\\n return fun(*args, **kwargs)\\n f = lu.wrap_init(fun, kwargs)\\n dyn_argnums = [i for i in range(len(args)) if i not in static_argnums]\\n f, dyn_args = _argnums_partial(f, dyn_argnums, args)\\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, dyn_args))\\n _check_args(jaxtupletree_args)\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n jaxtupletree_out = xla.xla_call(jaxtree_fun, *jaxtupletree_args)\\n return build_tree(out_tree(), jaxtupletree_out)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jit.f_jitted\",\n \"text\": \"jax/api.py::jit.f_jitted\\n @wraps(fun)\\n def f_jitted(*args, **kwargs):\\n if _jit_is_disabled or config.read('jax_disable_jit'):\\n return fun(*args, **kwargs)\\n f = lu.wrap_init(fun, kwargs)\\n dyn_argnums = [i for i in range(len(args)) if i not in static_argnums]\\n f, dyn_args = _argnums_partial(f, dyn_argnums, args)\\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, dyn_args))\\n _check_args(jaxtupletree_args)\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n jaxtupletree_out = xla.xla_call(jaxtree_fun, *jaxtupletree_args)\\n return build_tree(out_tree(), jaxtupletree_out)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::disable_jit\",\n \"text\": \"jax/api.py::disable_jit\\n@contextmanager\\ndef disable_jit():\\n \\\"\\\"\\\"Context manager that disables `jit`.\\n\\n For debugging purposes, it is useful to have a mechanism that disables `jit`\\n everywhere in a block of code, namely the `disable_jit` decorator.\\n\\n Inside a `jit`-ted function the values flowing through\\n traced code can be abstract (i.e., shaped arrays with an unknown values),\\n instead of concrete (i.e., specific arrays with known values).\\n\\n For example:\\n\\n >>> @jax.jit\\n >>> def f(x):\\n >>> y = x *2\\n >>> print(\\\"Value of y is\\\", y)\\n >>> return y + 3\\n >>>\\n >>> print(f(jax.numpy.array([1, 2, 3])))\\n Value of y is Traced<ShapedArray(int32[3]):JaxprTrace(level=-1/1)>\\n [5 7 9]\\n\\n Here `y` has been abstracted by `jit` to a `ShapedArray`, which represents an\\n array with a fixed shape and type but an arbitrary value. If we want to see a\\n concrete values while debugging, we can use the `disable_jit` decorator, at\\n the cost of slower code:\\n\\n >>> with jax.disable_jit():\\n >>> print(f(np.array([1, 2, 3])))\\n >>>\\n Value of y is [2 4 6]\\n [5 7 9]\\n \\\"\\\"\\\"\\n global _jit_is_disabled\\n _jit_is_disabled, prev_val = True, _jit_is_disabled\\n yield\\n _jit_is_disabled = prev_val\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::xla_computation\",\n \"text\": \"jax/api.py::xla_computation\\ndef xla_computation(fun, static_argnums=()):\\n def pv_like(x):\\n aval = xla.abstractify(x)\\n return pe.PartialVal((aval, core.unit))\\n\\n wrapped = lu.wrap_init(fun)\\n\\n @wraps(fun)\\n def computation_maker(*args, **kwargs):\\n jax_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(wrapped, in_trees)\\n pvals = map(pv_like, jax_args)\\n jaxpr, _, consts = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\\n return xla.build_jaxpr(jaxpr, consts, *map(xla.abstractify, args))\\n\\n return computation_maker\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::xla_computation.pv_like\",\n \"text\": \"jax/api.py::xla_computation.pv_like\\n def pv_like(x):\\n aval = xla.abstractify(x)\\n return pe.PartialVal((aval, core.unit))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::xla_computation.computation_maker\",\n \"text\": \"jax/api.py::xla_computation.computation_maker\\n @wraps(fun)\\n def computation_maker(*args, **kwargs):\\n jax_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(wrapped, in_trees)\\n pvals = map(pv_like, jax_args)\\n jaxpr, _, consts = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\\n return xla.build_jaxpr(jaxpr, consts, *map(xla.abstractify, args))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::grad\",\n \"text\": \"jax/api.py::grad\\ndef grad(fun, argnums=0):\\n \\\"\\\"\\\"Creates a function which evaluates the gradient of `fun`.\\n\\n Args:\\n fun: Function to be differentiated. Its arguments at positions specified by\\n `argnums` should be arrays, scalars, or standard Python containers. It\\n should return a scalar (which includes arrays with shape `()` but not\\n arrays with shape `(1,)` etc.)\\n argnums: Optional, integer or tuple of integers. Specifies which positional\\n argument(s) to differentiate with respect to (default 0).\\n\\n Returns:\\n A function with the same arguments as `fun`, that evaluates the gradient of\\n `fun`. If `argnums` is an integer then the gradient has the same shape and\\n type as the positional argument indicated by that integer. If argnums is a\\n tuple of integers, the gradient is a tuple of values with the same shapes\\n and types as the corresponding arguments.\\n\\n For example:\\n\\n >>> grad_tanh = jax.grad(jax.numpy.tanh)\\n >>> grad_tanh(0.2)\\n array(0.961043, dtype=float32)\\n\\n \\\"\\\"\\\"\\n value_and_grad_f = value_and_grad(fun, argnums)\\n\\n docstr = (\\\"Gradient of {fun} with respect to positional argument(s) \\\"\\n \\\"{argnums}. Takes the same arguments as {fun} but returns the \\\"\\n \\\"gradient, which has the same shape as the arguments at \\\"\\n \\\"positions {argnums}.\\\")\\n\\n @wraps(fun, docstr=docstr, argnums=argnums)\\n def grad_f(*args, **kwargs):\\n ans, g = value_and_grad_f(*args, **kwargs)\\n return g\\n\\n return grad_f\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::grad.grad_f\",\n \"text\": \"jax/api.py::grad.grad_f\\n @wraps(fun, docstr=docstr, argnums=argnums)\\n def grad_f(*args, **kwargs):\\n ans, g = value_and_grad_f(*args, **kwargs)\\n return g\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::value_and_grad\",\n \"text\": \"jax/api.py::value_and_grad\\ndef value_and_grad(fun, argnums=0):\\n \\\"\\\"\\\"Creates a function which evaluates both `fun` and the gradient of `fun`.\\n\\n Args:\\n fun: Function to be differentiated. Its arguments at positions specified by\\n `argnums` should be arrays, scalars, or standard Python containers. It\\n should return a scalar (which includes arrays with shape `()` but not\\n arrays with shape `(1,)` etc.)\\n argnums: Optional, integer or tuple of integers. Specifies which positional\\n argument(s) to differentiate with respect to (default 0).\\n\\n Returns:\\n A function with the same arguments as `fun` that evaluates both `fun` and\\n the gradient of `fun` and returns them as a pair (a two-element tuple). If\\n `argnums` is an integer then the gradient has the same shape and type as the\\n positional argument indicated by that integer. If argnums is a tuple of\\n integers, the gradient is a tuple of values with the same shapes and types\\n as the corresponding arguments.\\n \\\"\\\"\\\"\\n\\n docstr = (\\\"Value and gradient of {fun} with respect to positional \\\"\\n \\\"argument(s) {argnums}. Takes the same arguments as {fun} but \\\"\\n \\\"returns a two-element tuple where the first element is the value \\\"\\n \\\"of {fun} and the second element is the gradient, which has the \\\"\\n \\\"same shape as the arguments at positions {argnums}.\\\")\\n\\n @wraps(fun, docstr=docstr, argnums=argnums)\\n def value_and_grad_f(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\\n ans, vjp_py = vjp(f_partial, *dyn_args)\\n _check_scalar(ans)\\n g = vjp_py(onp.ones((), onp.result_type(ans)))\\n g = g[0] if isinstance(argnums, int) else g\\n return (ans, g)\\n\\n return value_and_grad_f\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::value_and_grad.value_and_grad_f\",\n \"text\": \"jax/api.py::value_and_grad.value_and_grad_f\\n @wraps(fun, docstr=docstr, argnums=argnums)\\n def value_and_grad_f(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\\n ans, vjp_py = vjp(f_partial, *dyn_args)\\n _check_scalar(ans)\\n g = vjp_py(onp.ones((), onp.result_type(ans)))\\n g = g[0] if isinstance(argnums, int) else g\\n return (ans, g)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jacfwd\",\n \"text\": \"jax/api.py::jacfwd\\ndef jacfwd(fun, argnums=0):\\n \\\"\\\"\\\"Jacobian of `fun` evaluated column-by-column using forward-mode AD.\\n\\n Args:\\n fun: Function whose Jacobian is to be computed.\\n argnums: Optional, integer or tuple of integers. Specifies which positional\\n argument(s) to differentiate with respect to (default `0`).\\n\\n Returns:\\n A function with the same arguments as `fun`, that evaluates the Jacobian of\\n `fun` using forward-mode automatic differentiation.\\n\\n >>> def f(x):\\n >>> return jax.numpy.asarray(\\n >>> [x[0], 5*x[2], 4*x[1]**2 - 2*x[2], x[2] * jax.numpy.sin(x[0])])\\n >>> jax.jacfwd(f)(np.array([1., 2., 3.]))\\n array([[ 1. , 0. , 0. ],\\n [ 0. , 0. , 5. ],\\n [ 0. , 16. , -2. ],\\n [ 1.6209068 , 0. , 0.84147096]], dtype=float32)\\n \\\"\\\"\\\"\\n\\n def jacfun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\\n pushfwd = partial(jvp, f_partial, dyn_args)\\n y, jac = vmap(pushfwd, out_axes=(None, -1))(_std_basis(dyn_args))\\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\\n return tree_map(partial(_unravel_array_into_pytree, example_args, -1), jac)\\n\\n return jacfun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jacfwd.jacfun\",\n \"text\": \"jax/api.py::jacfwd.jacfun\\n def jacfun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\\n pushfwd = partial(jvp, f_partial, dyn_args)\\n y, jac = vmap(pushfwd, out_axes=(None, -1))(_std_basis(dyn_args))\\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\\n return tree_map(partial(_unravel_array_into_pytree, example_args, -1), jac)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jacrev\",\n \"text\": \"jax/api.py::jacrev\\ndef jacrev(fun, argnums=0):\\n \\\"\\\"\\\"Jacobian of `fun` evaluated row-by-row using reverse-mode AD.\\n\\n Args:\\n fun: Function whose Jacobian is to be computed.\\n argnums: Optional, integer or tuple of integers. Specifies which positional\\n argument(s) to differentiate with respect to (default `0`).\\n\\n Returns:\\n A function with the same arguments as `fun`, that evaluates the Jacobian of\\n `fun` using reverse-mode automatic differentiation.\\n\\n >>> def f(x):\\n >>> return jax.numpy.asarray(\\n >>> [x[0], 5*x[2], 4*x[1]**2 - 2*x[2], x[2] * jax.numpy.sin(x[0])])\\n >>> jax.jacrev(f)(np.array([1., 2., 3.]))\\n array([[ 1. , 0. , 0. ],\\n [ 0. , 0. , 5. ],\\n [ 0. , 16. , -2. ],\\n [ 1.6209068 , 0. , 0.84147096]], dtype=float32)\\n \\\"\\\"\\\"\\n def jacfun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\\n y, pullback = vjp(f_partial, *dyn_args)\\n jac = vmap(pullback)(_std_basis(y))\\n jac = jac[0] if isinstance(argnums, int) else jac\\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\\n jac = tree_map(partial(_unravel_array_into_pytree, y, 0), jac)\\n return tree_transpose(tree_structure(example_args), tree_structure(y), jac)\\n\\n return jacfun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jacrev.jacfun\",\n \"text\": \"jax/api.py::jacrev.jacfun\\n def jacfun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n f_partial, dyn_args = _argnums_partial(f, argnums, args)\\n y, pullback = vjp(f_partial, *dyn_args)\\n jac = vmap(pullback)(_std_basis(y))\\n jac = jac[0] if isinstance(argnums, int) else jac\\n example_args = dyn_args[0] if isinstance(argnums, int) else dyn_args\\n jac = tree_map(partial(_unravel_array_into_pytree, y, 0), jac)\\n return tree_transpose(tree_structure(example_args), tree_structure(y), jac)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::hessian\",\n \"text\": \"jax/api.py::hessian\\ndef hessian(fun, argnums=0):\\n \\\"\\\"\\\"Hessian of `fun`.\\n\\n Args:\\n fun: Function whose Hessian is to be computed.\\n argnums: Optional, integer or tuple of integers. Specifies which positional\\n argument(s) to differentiate with respect to (default `0`).\\n\\n Returns:\\n A function with the same arguments as `fun`, that evaluates the Hessian of\\n `fun`.\\n\\n >>> g = lambda(x): x[0]**3 - 2*x[0]*x[1] - x[1]**6\\n >>> jax.hessian(g)(jax.numpy.array([1., 2.]))\\n array([[ 6., -2.],\\n [ -2., -480.]], dtype=float32)\\n \\\"\\\"\\\"\\n\\n return jacfwd(jacrev(fun, argnums=argnums), argnums=argnums)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::_std_basis\",\n \"text\": \"jax/api.py::_std_basis\\ndef _std_basis(pytree):\\n leaves, _ = tree_flatten(pytree)\\n ndim = sum(map(onp.size, leaves))\\n return _unravel_array_into_pytree(pytree, 1, onp.eye(ndim))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::_unravel_array_into_pytree\",\n \"text\": \"jax/api.py::_unravel_array_into_pytree\\ndef _unravel_array_into_pytree(pytree, axis, arr):\\n leaves, treedef = tree_flatten(pytree)\\n axis = axis % arr.ndim\\n dtypes = map(_dtype, leaves)\\n shapes = [arr.shape[:axis] + onp.shape(l) + arr.shape[axis+1:] for l in leaves]\\n parts = _split(arr, onp.cumsum(map(onp.size, leaves[:-1])), axis)\\n reshaped_parts = [onp.reshape(part.astype(dtype), shape)\\n for part, dtype, shape in zip(parts, dtypes, shapes)]\\n return tree_unflatten(treedef, reshaped_parts)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::_split\",\n \"text\": \"jax/api.py::_split\\ndef _split(x, indices, axis):\\n if isinstance(x, onp.ndarray):\\n return onp.split(x, indices, axis)\\n else:\\n return x.split(indices, axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::_dtype\",\n \"text\": \"jax/api.py::_dtype\\ndef _dtype(x):\\n return canonicalize_dtype(onp.result_type(x))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::vmap\",\n \"text\": \"jax/api.py::vmap\\ndef vmap(fun, in_axes=0, out_axes=0):\\n \\\"\\\"\\\"Vectorizing map. Creates a function which maps `fun` over additional axes.\\n\\n Args:\\n fun: Function to be mapped over additional axes.\\n in_axes: Specifies which input axes to map over. These may be integers,\\n `None`, or (possibly nested) tuples of integers or `None`.\\n out_axes: Specifies which output axes to map over. These may be integers,\\n `None`, or (possibly nested) tuples of integers or `None`.\\n\\n Returns:\\n Batched/vectorized version of `fun` with arguments that correspond to those\\n of `fun`, but with extra array axes at positions indicated by `in_axes`, and\\n a return value that corresponds to that of `fun`, but with extra array axes\\n at positions indicated by `out_axes`.\\n\\n For example, we can implement a matrix-matrix product using a vector dot\\n product:\\n\\n >>> vv = lambda x, y: np.vdot(x, y) # ([a], [a]) -> []\\n >>> mv = vmap(vv, (0, None), 0) # ([a,b], [b]) -> [a]\\n >>> mm = vmap(mv, (None, 1), 1) # ([a,b], [b,c]) -> [a,c]\\n\\n (`[a,b]` indicates an array with shape (a,b))\\n \\\"\\\"\\\"\\n\\n docstr = (\\\"Vectorized version of {fun}. Takes similar arguments as {fun} \\\"\\n \\\"but with additional array axes over which {fun} is mapped.\\\")\\n\\n if (not isinstance(in_axes, (list, tuple, type(None), int))\\n or not isinstance(out_axes, (list, tuple, type(None), int))):\\n msg = (\\\"vmap arguments in_axes and out_axes must each be an integer, None, \\\"\\n \\\"or a (nested) tuple of those types, got {} and {} respectively.\\\")\\n raise TypeError(msg.format(type(in_axes), type(out_axes)))\\n\\n @wraps(fun, docstr=docstr)\\n def batched_fun(*args, **kwargs):\\n if not isinstance(fun, lu.WrappedFun):\\n f = lu.wrap_init(fun, kwargs)\\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = ba\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::vmap.batched_fun\",\n \"text\": \"jax/api.py::vmap.batched_fun\\n @wraps(fun, docstr=docstr)\\n def batched_fun(*args, **kwargs):\\n if not isinstance(fun, lu.WrappedFun):\\n f = lu.wrap_init(fun, kwargs)\\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = batching.batch(jaxtree_fun, in_flat, in_axes_, out_axes)\\n return build_tree(out_tree(), out_flat)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::pjit\",\n \"text\": \"jax/api.py::pjit\\ndef pjit(fun, axis_name):\\n \\\"\\\"\\\"Set up SPMD function for JIT compilation and parallel execution with XLA.\\\"\\\"\\\"\\n @wraps(fun)\\n def f_jitted(*args, **kwargs):\\n leaves, _ = tree_flatten(args)\\n axis_sizes = set(onp.shape(leaf)[0] for leaf in leaves)\\n if len(axis_sizes) != 1:\\n msg = \\\"pjit requires all leading axes to have equal length, got {}.\\\"\\n raise TypeError(msg.format(axis_sizes))\\n axis_size = axis_sizes.pop()\\n\\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n _check_args(jaxtupletree_args)\\n f = lu.wrap_init(fun, kwargs)\\n f, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n jaxtupletree_out = pxla.xla_pcall(f, *jaxtupletree_args,\\n axis_name=axis_name, axis_size=axis_size)\\n return build_tree(out_tree(), jaxtupletree_out)\\n\\n f_jitted.__name__ = \\\"pjit({})\\\".format(f_jitted.__name__)\\n return f_jitted\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::pjit.f_jitted\",\n \"text\": \"jax/api.py::pjit.f_jitted\\n @wraps(fun)\\n def f_jitted(*args, **kwargs):\\n leaves, _ = tree_flatten(args)\\n axis_sizes = set(onp.shape(leaf)[0] for leaf in leaves)\\n if len(axis_sizes) != 1:\\n msg = \\\"pjit requires all leading axes to have equal length, got {}.\\\"\\n raise TypeError(msg.format(axis_sizes))\\n axis_size = axis_sizes.pop()\\n\\n jaxtupletree_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n _check_args(jaxtupletree_args)\\n f = lu.wrap_init(fun, kwargs)\\n f, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n jaxtupletree_out = pxla.xla_pcall(f, *jaxtupletree_args,\\n axis_name=axis_name, axis_size=axis_size)\\n return build_tree(out_tree(), jaxtupletree_out)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::pmap\",\n \"text\": \"jax/api.py::pmap\\ndef pmap(fun, axis_name, in_axes=0, out_axes=0):\\n \\\"\\\"\\\"Vectorizing pseudo-map for single-program multiple-data (SPMD) functions.\\\"\\\"\\\"\\n def pmap_fun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = parallel.pmap(jaxtree_fun, axis_name, args, in_axes_, out_axes)\\n return build_tree(out_tree(), out_flat)\\n\\n return pmap_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::pmap.pmap_fun\",\n \"text\": \"jax/api.py::pmap.pmap_fun\\n def pmap_fun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = parallel.pmap(jaxtree_fun, axis_name, args, in_axes_, out_axes)\\n return build_tree(out_tree(), out_flat)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::axisvar_split\",\n \"text\": \"jax/api.py::axisvar_split\\ndef axisvar_split(fun, name, new_names):\\n \\\"\\\"\\\"Split axis variable names into new names in an SPMD function.\\\"\\\"\\\"\\n def split_fun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = parallel.axisvar_split(jaxtree_fun, name, new_names).call_wrapped(*args)\\n return build_tree(out_tree(), out_flat)\\n\\n return split_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::axisvar_split.split_fun\",\n \"text\": \"jax/api.py::axisvar_split.split_fun\\n def split_fun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n in_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = parallel.axisvar_split(jaxtree_fun, name, new_names).call_wrapped(*args)\\n return build_tree(out_tree(), out_flat)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::papply\",\n \"text\": \"jax/api.py::papply\\ndef papply(fun, in_axes=0):\\n \\\"\\\"\\\"Apply a function using parallel computation by sharding inputs.\\\"\\\"\\\"\\n axis_name = parallel.newvar()\\n\\n def papply_fun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\\n args_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = parallel.papply(jaxtree_fun, axis_name, args_flat, in_axes_)\\n return build_tree(out_tree(), out_flat)\\n\\n return papply_fun, axis_name\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::papply.papply_fun\",\n \"text\": \"jax/api.py::papply.papply_fun\\n def papply_fun(*args, **kwargs):\\n f = lu.wrap_init(fun, kwargs)\\n in_axes_ = in_axes if isinstance(in_axes, (list, tuple)) else (in_axes,) * len(args)\\n args_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(f, in_trees)\\n out_flat = parallel.papply(jaxtree_fun, axis_name, args_flat, in_axes_)\\n return build_tree(out_tree(), out_flat)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jvp\",\n \"text\": \"jax/api.py::jvp\\ndef jvp(fun, primals, tangents):\\n \\\"\\\"\\\"Computes a (forward-mode) Jacobian-vector product of `fun`.\\n\\n Args:\\n fun: Function to be differentiated. Its arguments should be arrays, scalars,\\n or standard Python containers of arrays or scalars. It should return an\\n array, scalar, or standard Python container of arrays or scalars.\\n primals: The primal values at which the Jacobian of `fun` should be\\n evaluated. Should be a tuple of arrays, scalar, or standard Python\\n container thereof. The length of the tuple is equal to the number of\\n positional parameters of `fun`.\\n tangents: The tangent vector for which the Jacobian-vector product should be\\n evaluated. Should be a tuple of arrays, scalar, or standard Python\\n container thereof, with the same tree structure and array shapes as\\n `primals`.\\n\\n Returns:\\n A `(primals_out, tangents_out)` pair, where `primals_out` is\\n `fun(*primals)`, and `tangents_out` is the Jacobian-vector product of\\n `function` evaluated at `primals` with `tangents`. The `tangents_out` value\\n has the same Python tree structure and shapes as `primals_out`.\\n\\n For example:\\n\\n >>> jax.jvp(jax.numpy.sin, (0.1,), (0.2,))\\n (array(0.09983342, dtype=float32), array(0.19900084, dtype=float32))\\n \\\"\\\"\\\"\\n def trim_arg(primal, tangent):\\n primal_jtuple, tree_def = pytree_to_jaxtupletree(primal)\\n tangent_jtuple, tree_def_2 = pytree_to_jaxtupletree(tangent)\\n assert tree_def == tree_def_2, (tree_def, tree_def_2)\\n return primal_jtuple, tangent_jtuple, tree_def\\n\\n if not isinstance(fun, lu.WrappedFun):\\n fun = lu.wrap_init(fun)\\n ps_flat, ts_flat, in_trees = unzip3(map(trim_arg, primals, tangents))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\\n out_primal, out_tangent = ad.jvp(jaxtree_fun).call_wrapped(ps_flat, ts_flat)\\n return (build_tree(out_tree(), out_primal), build_tree(out_tree(), out_tangent))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::jvp.trim_arg\",\n \"text\": \"jax/api.py::jvp.trim_arg\\n def trim_arg(primal, tangent):\\n primal_jtuple, tree_def = pytree_to_jaxtupletree(primal)\\n tangent_jtuple, tree_def_2 = pytree_to_jaxtupletree(tangent)\\n assert tree_def == tree_def_2, (tree_def, tree_def_2)\\n return primal_jtuple, tangent_jtuple, tree_def\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::linearize\",\n \"text\": \"jax/api.py::linearize\\ndef linearize(traceable, *primals):\\n fun = lu.wrap_init(traceable)\\n primals_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, primals))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\\n out_primal, out_pval, jaxpr, consts = ad.linearize(jaxtree_fun, *primals_flat)\\n out_tree = out_tree()\\n out_primal_py = build_tree(out_tree, out_primal)\\n lifted_jvp = partial(lift_linearized, jaxpr, consts, (in_trees, out_tree), out_pval)\\n return out_primal_py, lifted_jvp\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::lift_linearized\",\n \"text\": \"jax/api.py::lift_linearized\\ndef lift_linearized(jaxpr, consts, io_tree, out_pval, *py_args):\\n def fun(*args):\\n primals = pack(args) # doesn't matter what these are-they'll be ignored\\n tangents = pack(args)\\n _, ans = eval_jaxpr(jaxpr, consts, (), primals, tangents)\\n return pe.merge_pvals(ans, out_pval)\\n\\n return apply_jaxtree_fun(fun, io_tree, *py_args)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::lift_linearized.fun\",\n \"text\": \"jax/api.py::lift_linearized.fun\\n def fun(*args):\\n primals = pack(args) # doesn't matter what these are-they'll be ignored\\n tangents = pack(args)\\n _, ans = eval_jaxpr(jaxpr, consts, (), primals, tangents)\\n return pe.merge_pvals(ans, out_pval)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::vjp\",\n \"text\": \"jax/api.py::vjp\\ndef vjp(fun, *primals):\\n \\\"\\\"\\\"Compute a (reverse-mode) vector-Jacobian product of `fun`.\\n\\n `grad` is implemented as a special case of `vjp`.\\n\\n Args:\\n fun: Function to be differentiated. Its arguments should be arrays, scalars,\\n or standard Python containers of arrays or scalars. It should return an\\n array, scalar, or standard Python container of arrays or scalars.\\n primals: A sequence of primal values at which the Jacobian of `fun`\\n should be evaluated. The length of `primals` should be equal to the number\\n of positional parameters to `fun`. Each primal value should be a tuple of\\n arrays, scalar, or standard Python containers thereof.\\n\\n Returns:\\n A `(primals_out, vjpfun)` pair, where `primals_out` is `fun(*primals)`.\\n `vjpfun` is a function from a cotangent vector with the same shape as\\n `primals_out` to a tuple of cotangent vectors with the same shape as\\n `primals`, representing the vector-Jacobian product of `fun` evaluated at\\n `primals`.\\n\\n >>> def f(x, y):\\n >>> return jax.numpy.sin(x), jax.numpy.cos(y)\\n >>> primals, g = jax.vjp(f, 0.5, 1.0)\\n >>> g((-0.7, 0.3))\\n (array(-0.61430776, dtype=float32), array(-0.2524413, dtype=float32))\\n \\\"\\\"\\\"\\n if not isinstance(fun, lu.WrappedFun):\\n fun = lu.wrap_init(fun)\\n primals_flat, in_trees = unzip2(map(pytree_to_jaxtupletree, primals))\\n _check_args(primals_flat)\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\\n out_primal, out_vjp = ad.vjp(jaxtree_fun, primals_flat)\\n out_tree = out_tree()\\n out_primal_py = build_tree(out_tree, out_primal)\\n ct_in_trees = [out_tree]\\n ct_out_tree = PyTreeDef(node_types[tuple], None, in_trees)\\n def out_vjp_packed(cotangent_in):\\n return out_vjp(cotangent_in)\\n vjp_py = partial(apply_jaxtree_fun, out_vjp_packed, (ct_in_trees, ct_out_tree))\\n return out_primal_py, vjp_py\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::vjp.out_vjp_packed\",\n \"text\": \"jax/api.py::vjp.out_vjp_packed\\n def out_vjp_packed(cotangent_in):\\n return out_vjp(cotangent_in)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::trace_to_jaxpr\",\n \"text\": \"jax/api.py::trace_to_jaxpr\\ndef trace_to_jaxpr(traceable, py_pvals, **kwargs):\\n fun = lu.wrap_init(traceable)\\n pvals, in_trees = unzip2(map(tree_to_pval_tuples, py_pvals))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(fun, in_trees)\\n jaxpr, out_pval, consts = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\\n return jaxpr, consts, out_pval, (in_trees, out_tree())\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::lift_jaxpr\",\n \"text\": \"jax/api.py::lift_jaxpr\\ndef lift_jaxpr(jaxpr, consts, io_tree, pvals, py_args):\\n def fun(*args):\\n ans = eval_jaxpr(jaxpr, consts, (), *args)\\n return pe.merge_pvals(ans, pvals)\\n return apply_jaxtree_fun(fun, io_tree, *py_args)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::lift_jaxpr.fun\",\n \"text\": \"jax/api.py::lift_jaxpr.fun\\n def fun(*args):\\n ans = eval_jaxpr(jaxpr, consts, (), *args)\\n return pe.merge_pvals(ans, pvals)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/api.py::make_jaxpr\",\n \"text\": \"jax/api.py::make_jaxpr\\ndef make_jaxpr(fun):\\n \\\"\\\"\\\"Adapts `fun` to return its `jaxpr` program representation.\\n\\n Args:\\n fun: The function whose `jaxpr` is to be computed. Its positional arguments\\n and return value should be arrays, scalars, or standard Python containers\\n (tuple/list/dict) thereof.\\n\\n Returns:\\n A wrapped version of `fun`, set up to return a `jaxpr`.\\n\\n A `jaxpr` is JAX's intermediate representation for program traces. The `jaxpr`\\n language is based on the simply-typed first-order lambda calculus with\\n let-bindings. `make_jaxpr` adapts a function to return its `jaxpr`, which we\\n can inspect to understand what JAX is doing internally.\\n\\n The `jaxpr` returned is a trace of `fun` abstracted to `ShapedArray` level.\\n Other levels of abstraction exist internally.\\n\\n We do not describe the semantics of the `jaxpr` language in detail here, but\\n instead give a few examples.\\n\\n >>> def f(x): return jax.numpy.sin(jax.numpy.cos(x))\\n >>> f(3.0)\\n array(-0.83602184, dtype=float32)\\n >>> jax.make_jaxpr(f)(3.0)\\n { lambda ; ; a.\\n let b = cos a\\n c = sin b\\n in c }\\n >>> jax.make_jaxpr(jax.grad(f))(3.0)\\n { lambda b ; ; a.\\n let c = pack a\\n (d) = id c\\n e = cos d\\n f = cos e\\n g = mul b f\\n h = neg g\\n i = sin d\\n j = mul h i\\n k = pack j\\n (l) = id k\\n in l }\\n \\\"\\\"\\\"\\n def pv_like(x):\\n aval = xla.abstractify(x)\\n return pe.PartialVal((aval, core.unit))\\n\\n wrapped = lu.wrap_init(fun)\\n\\n @wraps(fun)\\n def jaxpr_maker(*args, **kwargs):\\n jax_args, in_trees = unzip2(map(pytree_to_jaxtupletree, args))\\n jaxtree_fun, out_tree = pytree_fun_to_jaxtupletree_fun(wrapped, in_trees)\\n pvals = map(pv_like, jax_args)\\n jaxpr, _, _ = pe.trace_to_jaxpr(jaxtree_fun, pvals, **kwargs)\\n return jaxpr\\n\\n jaxpr_maker.__name__ = \\\"make_jaxpr({})\\\".format(jaxpr_maker.__name__)\\n return jaxpr_maker\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::relu\",\n \"text\": \"jax/experimental/stax.py::relu\\ndef relu(x): return np.maximum(x, 0.)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::softplus\",\n \"text\": \"jax/experimental/stax.py::softplus\\ndef softplus(x): return np.logaddexp(x, 0.)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::logsoftmax\",\n \"text\": \"jax/experimental/stax.py::logsoftmax\\ndef logsoftmax(x, axis=-1):\\n \\\"\\\"\\\"Apply log softmax to an array of logits, log-normalizing along an axis.\\\"\\\"\\\"\\n return x - logsumexp(x, axis, keepdims=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::softmax\",\n \"text\": \"jax/experimental/stax.py::softmax\\ndef softmax(x, axis=-1):\\n \\\"\\\"\\\"Apply softmax to an array of logits, exponentiating and normalizing along an axis.\\\"\\\"\\\"\\n unnormalized = np.exp(x - x.max(axis, keepdims=True))\\n return unnormalized / unnormalized.sum(axis, keepdims=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::fastvar\",\n \"text\": \"jax/experimental/stax.py::fastvar\\ndef fastvar(x, axis, keepdims):\\n \\\"\\\"\\\"A fast but less numerically-stable variance calculation than np.var.\\\"\\\"\\\"\\n return np.mean(x**2, axis, keepdims=keepdims) - np.mean(x, axis, keepdims=keepdims)**2\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::randn\",\n \"text\": \"jax/experimental/stax.py::randn\\ndef randn(stddev=1e-2, rng=npr):\\n \\\"\\\"\\\"An initializer function for random normal coefficients.\\\"\\\"\\\"\\n def init(shape):\\n return rng.normal(size=shape, scale=stddev).astype('float32')\\n return init\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::randn.init\",\n \"text\": \"jax/experimental/stax.py::randn.init\\n def init(shape):\\n return rng.normal(size=shape, scale=stddev).astype('float32')\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::glorot\",\n \"text\": \"jax/experimental/stax.py::glorot\\ndef glorot(out_dim=0, in_dim=1, scale=onp.sqrt(2), rng=npr):\\n \\\"\\\"\\\"An initializer function for random Glorot-scaled coefficients.\\\"\\\"\\\"\\n def init(shape):\\n fan_in, fan_out = shape[in_dim], shape[out_dim]\\n size = onp.prod(onp.delete(shape, [in_dim, out_dim]))\\n std = scale / np.sqrt((fan_in + fan_out) / 2. * size)\\n return rng.normal(size=shape, scale=std).astype('float32')\\n return init\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::glorot.init\",\n \"text\": \"jax/experimental/stax.py::glorot.init\\n def init(shape):\\n fan_in, fan_out = shape[in_dim], shape[out_dim]\\n size = onp.prod(onp.delete(shape, [in_dim, out_dim]))\\n std = scale / np.sqrt((fan_in + fan_out) / 2. * size)\\n return rng.normal(size=shape, scale=std).astype('float32')\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense\",\n \"text\": \"jax/experimental/stax.py::Dense\\ndef Dense(out_dim, W_init=glorot(), b_init=randn()):\\n \\\"\\\"\\\"Layer constructor function for a dense (fully-connected) layer.\\\"\\\"\\\"\\n def init_fun(input_shape):\\n output_shape = input_shape[:-1] + (out_dim,)\\n W, b = W_init((input_shape[-1], out_dim)), b_init((out_dim,))\\n return output_shape, (W, b)\\n def apply_fun(params, inputs, rng=None):\\n W, b = params\\n return np.dot(inputs, W) + b\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense.init_fun\",\n \"text\": \"jax/experimental/stax.py::Dense.init_fun\\n def init_fun(input_shape):\\n output_shape = input_shape[:-1] + (out_dim,)\\n W, b = W_init((input_shape[-1], out_dim)), b_init((out_dim,))\\n return output_shape, (W, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Dense.apply_fun\\n def apply_fun(params, inputs, rng=None):\\n W, b = params\\n return np.dot(inputs, W) + b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv\",\n \"text\": \"jax/experimental/stax.py::GeneralConv\\ndef GeneralConv(dimension_numbers, out_chan, filter_shape,\\n strides=None, padding='VALID', W_init=None, b_init=randn(1e-6)):\\n \\\"\\\"\\\"Layer construction function for a general convolution layer.\\\"\\\"\\\"\\n lhs_spec, rhs_spec, out_spec = dimension_numbers\\n one = (1,) * len(filter_shape)\\n strides = strides or one\\n W_init = W_init or glorot(rhs_spec.index('O'), rhs_spec.index('I'))\\n def init_fun(input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_general_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n W, b = W_init(kernel_shape), b_init(bias_shape)\\n return output_shape, (W, b)\\n def apply_fun(params, inputs, rng=None):\\n W, b = params\\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\\n dimension_numbers) + b\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv.init_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConv.init_fun\\n def init_fun(input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_general_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n W, b = W_init(kernel_shape), b_init(bias_shape)\\n return output_shape, (W, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv.apply_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConv.apply_fun\\n def apply_fun(params, inputs, rng=None):\\n W, b = params\\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\\n dimension_numbers) + b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm\",\n \"text\": \"jax/experimental/stax.py::BatchNorm\\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-5, center=True, scale=True,\\n beta_init=zeros, gamma_init=ones):\\n \\\"\\\"\\\"Layer construction function for a batch normalization layer.\\\"\\\"\\\"\\n _beta_init = lambda shape: beta_init(shape) if center else ()\\n _gamma_init = lambda shape: gamma_init(shape) if scale else ()\\n axis = (axis,) if np.isscalar(axis) else axis\\n def init_fun(input_shape):\\n shape = (1 if i in axis else d for i, d in enumerate(input_shape))\\n shape = tuple(itertools.dropwhile(lambda x: x == 1, shape))\\n beta, gamma = _beta_init(shape), _gamma_init(shape)\\n return input_shape, (beta, gamma)\\n def apply_fun(params, x, rng=None):\\n beta, gamma = params\\n mean, var = np.mean(x, axis, keepdims=True), fastvar(x, axis, keepdims=True)\\n z = (x - mean) / (var + epsilon)**2\\n if center and scale: return gamma * z + beta\\n if center: return z + beta\\n if scale: return gamma * z\\n return z\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm.init_fun\",\n \"text\": \"jax/experimental/stax.py::BatchNorm.init_fun\\n def init_fun(input_shape):\\n shape = (1 if i in axis else d for i, d in enumerate(input_shape))\\n shape = tuple(itertools.dropwhile(lambda x: x == 1, shape))\\n beta, gamma = _beta_init(shape), _gamma_init(shape)\\n return input_shape, (beta, gamma)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm.apply_fun\",\n \"text\": \"jax/experimental/stax.py::BatchNorm.apply_fun\\n def apply_fun(params, x, rng=None):\\n beta, gamma = params\\n mean, var = np.mean(x, axis, keepdims=True), fastvar(x, axis, keepdims=True)\\n z = (x - mean) / (var + epsilon)**2\\n if center and scale: return gamma * z + beta\\n if center: return z + beta\\n if scale: return gamma * z\\n return z\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::_elemwise_no_params\",\n \"text\": \"jax/experimental/stax.py::_elemwise_no_params\\ndef _elemwise_no_params(fun, **kwargs):\\n init_fun = lambda input_shape: (input_shape, ())\\n apply_fun = lambda params, inputs, rng=None: fun(inputs, **kwargs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer\\ndef _pooling_layer(reducer, init_val, rescaler=None):\\n def PoolingLayer(window_shape, strides=None, padding='VALID'):\\n \\\"\\\"\\\"Layer construction function for a pooling layer.\\\"\\\"\\\"\\n strides = strides or (1,) * len(window_shape)\\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\\n dims = (1,) + window_shape + (1,) # NHWC\\n strides = (1,) + strides + (1,)\\n def init_fun(input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\\n return out_shape, ()\\n def apply_fun(params, inputs, rng=None):\\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\\n return rescale(out, inputs) if rescale else out\\n return init_fun, apply_fun\\n return PoolingLayer\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer\\n def PoolingLayer(window_shape, strides=None, padding='VALID'):\\n \\\"\\\"\\\"Layer construction function for a pooling layer.\\\"\\\"\\\"\\n strides = strides or (1,) * len(window_shape)\\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\\n dims = (1,) + window_shape + (1,) # NHWC\\n strides = (1,) + strides + (1,)\\n def init_fun(input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\\n return out_shape, ()\\n def apply_fun(params, inputs, rng=None):\\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\\n return rescale(out, inputs) if rescale else out\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\\n def init_fun(input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\\n return out_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\\n def apply_fun(params, inputs, rng=None):\\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\\n return rescale(out, inputs) if rescale else out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_normalize_by_window_size\",\n \"text\": \"jax/experimental/stax.py::_normalize_by_window_size\\ndef _normalize_by_window_size(dims, strides, padding):\\n def rescale(outputs, inputs):\\n one = np.ones(inputs.shape[1:-1], dtype=inputs.dtype)\\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\\n return outputs / window_sizes[..., np.newaxis]\\n return rescale\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_normalize_by_window_size.rescale\",\n \"text\": \"jax/experimental/stax.py::_normalize_by_window_size.rescale\\n def rescale(outputs, inputs):\\n one = np.ones(inputs.shape[1:-1], dtype=inputs.dtype)\\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\\n return outputs / window_sizes[..., np.newaxis]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten\",\n \"text\": \"jax/experimental/stax.py::Flatten\\ndef Flatten():\\n \\\"\\\"\\\"Layer construction function for flattening all but the leading dim.\\\"\\\"\\\"\\n def init_fun(input_shape):\\n output_shape = input_shape[0], reduce(op.mul, input_shape[1:], 1)\\n return output_shape, ()\\n def apply_fun(params, inputs, rng=None):\\n return np.reshape(inputs, (inputs.shape[0], -1))\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten.init_fun\",\n \"text\": \"jax/experimental/stax.py::Flatten.init_fun\\n def init_fun(input_shape):\\n output_shape = input_shape[0], reduce(op.mul, input_shape[1:], 1)\\n return output_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Flatten.apply_fun\\n def apply_fun(params, inputs, rng=None):\\n return np.reshape(inputs, (inputs.shape[0], -1))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Identity\",\n \"text\": \"jax/experimental/stax.py::Identity\\ndef Identity():\\n \\\"\\\"\\\"Layer construction function for an identity layer.\\\"\\\"\\\"\\n init_fun = lambda input_shape: (input_shape, ())\\n apply_fun = lambda params, inputs, rng=None: inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanOut\",\n \"text\": \"jax/experimental/stax.py::FanOut\\ndef FanOut(num):\\n \\\"\\\"\\\"Layer construction function for a fan-out layer.\\\"\\\"\\\"\\n init_fun = lambda input_shape: ([input_shape] * num, ())\\n apply_fun = lambda params, inputs, rng=None: [inputs] * num\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInSum\",\n \"text\": \"jax/experimental/stax.py::FanInSum\\ndef FanInSum():\\n \\\"\\\"\\\"Layer construction function for a fan-in sum layer.\\\"\\\"\\\"\\n init_fun = lambda input_shape: (input_shape[0], ())\\n apply_fun = lambda params, inputs, rng=None: sum(inputs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat\",\n \"text\": \"jax/experimental/stax.py::FanInConcat\\ndef FanInConcat(axis=-1):\\n \\\"\\\"\\\"Layer construction function for a fan-in concatenation layer.\\\"\\\"\\\"\\n def init_fun(input_shape):\\n ax = axis % len(input_shape[0])\\n concat_size = sum(shape[ax] for shape in input_shape)\\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\\n return out_shape, ()\\n def apply_fun(params, inputs, rng=None):\\n return np.concatenate(inputs, axis)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat.init_fun\",\n \"text\": \"jax/experimental/stax.py::FanInConcat.init_fun\\n def init_fun(input_shape):\\n ax = axis % len(input_shape[0])\\n concat_size = sum(shape[ax] for shape in input_shape)\\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\\n return out_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat.apply_fun\",\n \"text\": \"jax/experimental/stax.py::FanInConcat.apply_fun\\n def apply_fun(params, inputs, rng=None):\\n return np.concatenate(inputs, axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout\",\n \"text\": \"jax/experimental/stax.py::Dropout\\ndef Dropout(rate, mode='train'):\\n \\\"\\\"\\\"Layer construction function for a dropout layer with given rate.\\\"\\\"\\\"\\n def init_fun(input_shape):\\n return input_shape, ()\\n def apply_fun(params, inputs, rng):\\n if rng is None:\\n msg = (\\\"Dropout layer requires apply_fun to be called with a PRNG key \\\"\\n \\\"argument. That is, instead of `apply_fun(params, inputs)`, call \\\"\\n \\\"it like `apply_fun(params, inputs, key)` where `key` is a \\\"\\n \\\"jax.random.PRNGKey value.\\\")\\n raise ValueError(msg)\\n if mode == 'train':\\n keep = random.bernoulli(rng, rate, inputs.shape)\\n return np.where(keep, inputs / rate, 0)\\n else:\\n return inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout.init_fun\",\n \"text\": \"jax/experimental/stax.py::Dropout.init_fun\\n def init_fun(input_shape):\\n return input_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Dropout.apply_fun\\n def apply_fun(params, inputs, rng):\\n if rng is None:\\n msg = (\\\"Dropout layer requires apply_fun to be called with a PRNG key \\\"\\n \\\"argument. That is, instead of `apply_fun(params, inputs)`, call \\\"\\n \\\"it like `apply_fun(params, inputs, key)` where `key` is a \\\"\\n \\\"jax.random.PRNGKey value.\\\")\\n raise ValueError(msg)\\n if mode == 'train':\\n keep = random.bernoulli(rng, rate, inputs.shape)\\n return np.where(keep, inputs / rate, 0)\\n else:\\n return inputs\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial\",\n \"text\": \"jax/experimental/stax.py::serial\\ndef serial(*layers):\\n \\\"\\\"\\\"Combinator for composing layers in serial.\\n\\n Args:\\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the serial\\n composition of the given sequence of layers.\\n \\\"\\\"\\\"\\n nlayers = len(layers)\\n init_funs, apply_funs = zip(*layers)\\n def init_fun(input_shape):\\n params = []\\n for init_fun in init_funs:\\n input_shape, param = init_fun(input_shape)\\n params.append(param)\\n return input_shape, params\\n def apply_fun(params, inputs, rng=None):\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n for fun, param, rng in zip(apply_funs, params, rngs):\\n inputs = fun(param, inputs, rng)\\n return inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial.init_fun\",\n \"text\": \"jax/experimental/stax.py::serial.init_fun\\n def init_fun(input_shape):\\n params = []\\n for init_fun in init_funs:\\n input_shape, param = init_fun(input_shape)\\n params.append(param)\\n return input_shape, params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial.apply_fun\",\n \"text\": \"jax/experimental/stax.py::serial.apply_fun\\n def apply_fun(params, inputs, rng=None):\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n for fun, param, rng in zip(apply_funs, params, rngs):\\n inputs = fun(param, inputs, rng)\\n return inputs\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel\",\n \"text\": \"jax/experimental/stax.py::parallel\\ndef parallel(*layers):\\n \\\"\\\"\\\"Combinator for composing layers in parallel.\\n\\n The layer resulting from this combinator is often used with the FanOut and\\n FanInSum layers.\\n\\n Args:\\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the\\n parallel composition of the given sequence of layers. In particular, the\\n returned layer takes a sequence of inputs and returns a sequence of outputs\\n with the same length as the argument `layers`.\\n \\\"\\\"\\\"\\n nlayers = len(layers)\\n init_funs, apply_funs = zip(*layers)\\n def init_fun(input_shape):\\n return zip(*[init(shape) for init, shape in zip(init_funs, input_shape)])\\n def apply_fun(params, inputs, rng=None):\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n return [f(p, x, r) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel.init_fun\",\n \"text\": \"jax/experimental/stax.py::parallel.init_fun\\n def init_fun(input_shape):\\n return zip(*[init(shape) for init, shape in zip(init_funs, input_shape)])\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel.apply_fun\",\n \"text\": \"jax/experimental/stax.py::parallel.apply_fun\\n def apply_fun(params, inputs, rng=None):\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n return [f(p, x, r) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent\",\n \"text\": \"jax/experimental/stax.py::shape_dependent\\ndef shape_dependent(make_layer):\\n \\\"\\\"\\\"Combinator to delay layer constructor pair until input shapes are known.\\n\\n Args:\\n make_layer: a one-argument function that takes an input shape as an argument\\n (a tuple of positive integers) and returns an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the same\\n layer as returned by `make_layer` but with its construction delayed until\\n input shapes are known.\\n \\\"\\\"\\\"\\n def init_fun(input_shape):\\n return make_layer(input_shape)[0](input_shape)\\n def apply_fun(params, inputs, rng=None):\\n return make_layer(inputs.shape)[1](params, inputs, rng)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent.init_fun\",\n \"text\": \"jax/experimental/stax.py::shape_dependent.init_fun\\n def init_fun(input_shape):\\n return make_layer(input_shape)[0](input_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent.apply_fun\",\n \"text\": \"jax/experimental/stax.py::shape_dependent.apply_fun\\n def apply_fun(params, inputs, rng=None):\\n return make_layer(inputs.shape)[1](params, inputs, rng)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::PRNGKey\",\n \"text\": \"jax/random.py::PRNGKey\\ndef PRNGKey(seed):\\n \\\"\\\"\\\"Create a pseudo-random number generator (PRNG) key given an integer seed.\\n\\n Args:\\n seed: a 64- or 32-bit integer used as the value of the key.\\n\\n Returns:\\n A PRNG key, which is modeled as an array of shape (2,) and dtype uint32. The\\n key is constructed from a 64-bit seed by effectively bit-casting to a pair\\n of uint32 values (or from a 32-bit seed by first padding out with zeros).\\n \\\"\\\"\\\"\\n if onp.shape(seed):\\n raise TypeError(\\\"PRNGKey seed must be a scalar.\\\")\\n convert = lambda k: lax.reshape(lax.convert_element_type(k, onp.uint32), [1])\\n if isinstance(seed, (int, onp.ndarray)):\\n # Special handling of raw integer values, which may have be 64bit even\\n # when jax_enable_x64=False and we don't want to drop the top 32 bits\\n k1 = convert(onp.bitwise_and(onp.right_shift(seed, 32), 0xFFFFFFFF))\\n else:\\n k1 = convert(lax.shift_right_logical(seed, 32))\\n k2 = convert(lax.bitwise_and(seed, 0xFFFFFFFF))\\n return lax.concatenate([k1, k2], 0)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_is_prng_key\",\n \"text\": \"jax/random.py::_is_prng_key\\ndef _is_prng_key(key):\\n try:\\n return key.shape == (2,) and key.dtype == onp.uint32\\n except AttributeError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_make_rotate_left\",\n \"text\": \"jax/random.py::_make_rotate_left\\ndef _make_rotate_left(dtype):\\n if not onp.issubdtype(dtype, onp.integer):\\n raise TypeError(\\\"_rotate_left only accepts integer dtypes.\\\")\\n nbits = onp.array(onp.iinfo(dtype).bits, dtype)\\n\\n def _rotate_left(x, d):\\n if lax._dtype(d) != lax._dtype(x):\\n d = lax.convert_element_type(d, x.dtype)\\n return (x << d) | lax.shift_right_logical(x, nbits - d)\\n return _rotate_left\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_make_rotate_left._rotate_left\",\n \"text\": \"jax/random.py::_make_rotate_left._rotate_left\\n def _rotate_left(x, d):\\n if lax._dtype(d) != lax._dtype(x):\\n d = lax.convert_element_type(d, x.dtype)\\n return (x << d) | lax.shift_right_logical(x, nbits - d)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_bit_stats\",\n \"text\": \"jax/random.py::_bit_stats\\ndef _bit_stats(bits):\\n \\\"\\\"\\\"This is a debugging function to compute the statistics of bit fields.\\\"\\\"\\\"\\n return onp.array([list(map(int, onp.binary_repr(x, 64))) for x in bits]).mean(0)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::threefry_2x32\",\n \"text\": \"jax/random.py::threefry_2x32\\n@jit\\ndef threefry_2x32(keypair, count):\\n \\\"\\\"\\\"Apply the Threefry 2x32 hash.\\n\\n Args:\\n keypair: a pair of 32bit unsigned integers used for the key.\\n count: an array of dtype uint32 used for the counts.\\n\\n Returns:\\n An array of dtype uint32 with the same shape as `count`.\\n \\\"\\\"\\\"\\n # Based on ThreeFry2x32 by phawkins@ in //.../xla/client/lib/prng.cc\\n key1, key2 = keypair\\n if not lax._dtype(key1) == lax._dtype(key2) == lax._dtype(count) == onp.uint32:\\n msg = \\\"threefry_2x32 requires uint32 arguments, got {}\\\"\\n raise TypeError(msg.format([lax._dtype(x) for x in [key1, key2, count]]))\\n\\n rotate_left = _make_rotate_left(lax._dtype(count))\\n\\n def apply_round(v, rot):\\n v = v[:]\\n v[0] = v[0] + v[1]\\n v[1] = rotate_left(v[1], rot)\\n v[1] = v[0] ^ v[1]\\n return v\\n\\n odd_size = count.size % 2\\n if odd_size:\\n x = list(np.split(np.concatenate([count.ravel(), onp.uint32([0])]), 2))\\n else:\\n x = list(np.split(count.ravel(), 2))\\n\\n rotations = [13, 15, 26, 6, 17, 29, 16, 24]\\n ks = [key1, key2, key1 ^ key2 ^ onp.uint32(0x1BD11BDA)]\\n\\n x[0] = x[0] + ks[0]\\n x[1] = x[1] + ks[1]\\n\\n for r in rotations[:4]:\\n x = apply_round(x, r)\\n x[0] = x[0] + ks[1]\\n x[1] = x[1] + ks[2] + onp.uint32(1)\\n\\n for r in rotations[4:]:\\n x = apply_round(x, r)\\n x[0] = x[0] + ks[2]\\n x[1] = x[1] + ks[0] + onp.uint32(2)\\n\\n for r in rotations[:4]:\\n x = apply_round(x, r)\\n x[0] = x[0] + ks[0]\\n x[1] = x[1] + ks[1] + onp.uint32(3)\\n\\n for r in rotations[4:]:\\n x = apply_round(x, r)\\n x[0] = x[0] + ks[1]\\n x[1] = x[1] + ks[2] + onp.uint32(4)\\n\\n for r in rotations[:4]:\\n x = apply_round(x, r)\\n x[0] = x[0] + ks[2]\\n x[1] = x[1] + ks[0] + onp.uint32(5)\\n\\n out = np.concatenate(x)\\n assert out.dtype == onp.uint32\\n return lax.reshape(out[:-1] if odd_size else out, count.shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::threefry_2x32.apply_round\",\n \"text\": \"jax/random.py::threefry_2x32.apply_round\\n def apply_round(v, rot):\\n v = v[:]\\n v[0] = v[0] + v[1]\\n v[1] = rotate_left(v[1], rot)\\n v[1] = v[0] ^ v[1]\\n return v\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::split\",\n \"text\": \"jax/random.py::split\\n@partial(jit, static_argnums=(1,))\\ndef split(key, num=2):\\n \\\"\\\"\\\"Splits a PRNG key into `num` new keys by adding a leading axis.\\n\\n Args:\\n key: a PRNGKey (an array with shape (2,) and dtype uint32).\\n num: optional, a positive integer indicating the number of keys to produce\\n (default 2).\\n\\n Returns:\\n An array with shape (num, 2) and dtype uint32 representing `num` new keys.\\n \\\"\\\"\\\"\\n counts = lax.tie_in(key, lax.iota(onp.uint32, num * 2))\\n return lax.reshape(threefry_2x32(key, counts), (num, 2))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::fold_in\",\n \"text\": \"jax/random.py::fold_in\\n@partial(jit, static_argnums=(1,))\\ndef fold_in(key, data):\\n \\\"\\\"\\\"Folds in data to a PRNG key to form a new PRNG key.\\n\\n Args:\\n key: a PRNGKey (an array with shape (2,) and dtype uint32).\\n data: an integer representing data to be folded in to the key.\\n\\n Returns:\\n A new PRNGKey that is a deterministic function of the inputs and is\\n statistically safe for producing a stream of new pseudo-random values.\\n \\\"\\\"\\\"\\n key2 = lax.tie_in(key, PRNGKey(data))\\n return threefry_2x32(key, key2)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_random_bits\",\n \"text\": \"jax/random.py::_random_bits\\ndef _random_bits(key, bit_width, shape):\\n \\\"\\\"\\\"Sample uniform random bits of given width and shape using PRNG key.\\\"\\\"\\\"\\n if not _is_prng_key(key):\\n raise TypeError(\\\"_random_bits got invalid prng key.\\\")\\n if bit_width not in (32, 64):\\n raise TypeError(\\\"requires 32- or 64-bit field width.\\\")\\n max_count = (bit_width // 32) * onp.prod(shape)\\n if max_count >= onp.iinfo(onp.uint32).max:\\n # TODO(mattjj): just split the key here\\n raise TypeError(\\\"requesting more random bits than a single call provides.\\\")\\n\\n counts = lax.tie_in(key, lax.iota(onp.uint32, max_count))\\n bits = threefry_2x32(key, counts)\\n if bit_width == 64:\\n bits = [lax.convert_element_type(x, onp.uint64) for x in np.split(bits, 2)]\\n bits = (bits[0] << onp.uint64(32)) | bits[1]\\n return lax.reshape(bits, shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::uniform\",\n \"text\": \"jax/random.py::uniform\\n@partial(jit, static_argnums=(1, 2))\\ndef uniform(key, shape, dtype=onp.float32, minval=0., maxval=1.):\\n \\\"\\\"\\\"Sample uniform random values in [minval, maxval) with given shape/dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: a tuple of nonnegative integers representing the shape.\\n dtype: optional, a float dtype for the returned values (default float32).\\n minval: optional, a minimum (inclusive) value for the range (default 0).\\n maxval: optional, a maximum (exclusive) value for the range (default 1).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n if not onp.issubdtype(dtype, onp.floating):\\n raise TypeError(\\\"uniform only accepts floating point dtypes.\\\")\\n\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n minval = lax.convert_element_type(minval, dtype)\\n maxval = lax.convert_element_type(maxval, dtype)\\n finfo = onp.finfo(dtype)\\n nbits, nmant = finfo.bits, finfo.nmant\\n\\n if nbits not in (32, 64):\\n raise TypeError(\\\"uniform only accepts 32- or 64-bit dtypes.\\\")\\n\\n bits = _random_bits(key, nbits, shape)\\n\\n # The strategy here is to randomize only the mantissa bits with an exponent of\\n # 1 (after applying the bias), then shift and scale to the desired range. The\\n # bit-level transformation we use relies on Numpy and XLA having bit-for-bit\\n # equivalent float representations, which might not be true on all platforms.\\n float_bits = lax.bitwise_or(\\n lax.shift_right_logical(bits, onp.array(nbits - nmant, lax._dtype(bits))),\\n onp.array(1., dtype).view(onp.uint32 if nbits == 32 else onp.uint64))\\n floats = lax.bitcast_convert_type(float_bits, dtype) - onp.array(1., dtype)\\n return lax.max(\\n minval,\\n lax.reshape(floats * (maxval - minval) + minval, shape))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::randint\",\n \"text\": \"jax/random.py::randint\\n@partial(jit, static_argnums=(1, 4))\\ndef randint(key, shape, minval, maxval, dtype=onp.int32):\\n \\\"\\\"\\\"Sample uniform random values in [minval, maxval) with given shape/dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: a tuple of nonnegative integers representing the shape.\\n minval: optional, a minimum (inclusive) value for the range (default 0).\\n maxval: optional, a maximum (exclusive) value for the range (default 1).\\n dtype: optional, an int dtype for the returned values (default int32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n if not onp.issubdtype(dtype, onp.integer):\\n raise TypeError(\\\"randint only accepts integer dtypes.\\\")\\n\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n minval = lax.convert_element_type(minval, dtype)\\n maxval = lax.convert_element_type(maxval, dtype)\\n nbits = onp.iinfo(dtype).bits\\n\\n if nbits not in (32, 64):\\n raise TypeError(\\\"randint only accepts 32- or 64-bit dtypes.\\\")\\n\\n # if we don't have minval < maxval, just always return minval\\n # https://github.com/google/jax/issues/222\\n maxval = lax.max(lax.add(minval, onp.array(1, dtype)), maxval)\\n\\n # This algorithm is biased whenever (maxval - minval) is not a power of 2.\\n # We generate double the number of random bits required by the dtype so as to\\n # reduce that bias.\\n k1, k2 = split(key)\\n rbits = lambda key: _random_bits(key, nbits, shape)\\n higher_bits, lower_bits = rbits(k1), rbits(k2)\\n\\n unsigned_dtype = onp.uint32 if nbits == 32 else onp.uint64\\n span = lax.convert_element_type(maxval - minval, unsigned_dtype)\\n\\n # To compute a remainder operation on an integer that might have twice as many\\n # bits as we can represent in the native unsigned dtype, we compute a\\n # multiplier equal to 2**nbits % span (using that nbits is 32 or 64).\\n multiplier = lax.rem(onp.array(2**16, unsigned_dtype), span)\\n multiplier = lax.rem(lax.mul(multiplier, multiplier), span)\\n if nbits == 64:\\n multiplier = lax.re\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::shuffle\",\n \"text\": \"jax/random.py::shuffle\\n@partial(jit, static_argnums=(2,))\\ndef shuffle(key, x, axis=0):\\n \\\"\\\"\\\"Shuffle the elements of an array uniformly at random along an axis.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n x: the array to be shuffled.\\n axis: optional, an int axis along which to shuffle (default 0).\\n\\n Returns:\\n A shuffled version of x.\\n \\\"\\\"\\\"\\n # On parallel architectures, Fisher-Yates is more expensive than doing\\n # multiple sorts. This algorithm is based on one developed and analyzed by\\n # tjablin@. We sort according to randomly-generated 32bit keys, but those keys\\n # may have collisions. If we repeat the process, using fresh 32bit keys for\\n # each sort, then whenever all pairs of elements have been assigned distinct\\n # keys at some iteration (or equivalently when the strings formed by\\n # concatenating the successive keys for each element are all distinct) then we\\n # are guaranteed to have a perfect sample (assuming that either the sort is\\n # stable or that any bias is not value-dependent). Since checking uniqueness\\n # at runtime may be expensive, we use a heuristic static stop criterion\\n # developed by tjablin@. See tensorflow/compiler/tf2xla/random_ops.cc for more\\n # info, and for the original implementation of this algorithm. See also\\n # Section 2 of http://people.csail.mit.edu/costis/6896sp11/lec5s.pdf for\\n # another analysis (where the keys are generated one bit at a time).\\n exponent = 3 # see tjablin@'s analysis for explanation of this parameter\\n uint32max = onp.iinfo(onp.uint32).max\\n num_rounds = int(onp.ceil(exponent * onp.log(x.size) / onp.log(uint32max)))\\n\\n for _ in range(num_rounds):\\n key, subkey = split(key)\\n sort_keys = _random_bits(subkey, 32, x.shape)\\n _, x = lax.sort_key_val(sort_keys, x, axis)\\n\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::normal\",\n \"text\": \"jax/random.py::normal\\n@partial(jit, static_argnums=(1, 2))\\ndef normal(key, shape, dtype=onp.float32):\\n \\\"\\\"\\\"Sample standard normal random values with given shape and float dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: a tuple of nonnegative integers representing the shape.\\n dtype: optional, a float dtype for the returned values (default float32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n lo = onp.nextafter(onp.array(-1., dtype), 0., dtype=dtype)\\n hi = onp.array(1., dtype)\\n u = uniform(key, shape, dtype, lo, hi)\\n return onp.array(onp.sqrt(2), dtype) * lax.erf_inv(u)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::bernoulli\",\n \"text\": \"jax/random.py::bernoulli\\n@partial(jit, static_argnums=(2,))\\ndef bernoulli(key, mean=onp.float32(0.5), shape=()):\\n \\\"\\\"\\\"Sample Bernoulli random values with given shape and mean.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n mean: optional, an array-like broadcastable to `shape` for the mean of the\\n random variables (default 0.5).\\n shape: optional, a tuple of nonnegative integers representing the shape\\n (default scalar).\\n\\n Returns:\\n A random array with the specified shape and boolean dtype.\\n \\\"\\\"\\\"\\n shape = shape or onp.shape(mean)\\n if not onp.issubdtype(lax._dtype(mean), onp.float32):\\n mean = lax.convert_element_type(mean, onp.float32)\\n if onp.shape(mean) != shape:\\n mean = lax.broadcast(mean, shape)\\n return lax.lt(uniform(key, shape), mean)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":259,"cells":{"instance_id":{"kind":"string","value":"Lightning-AI__lightning-2787"},"repo":{"kind":"string","value":"Lightning-AI/lightning"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Integer limit_val_batches does not work with infinite DataLoader.\n## 🐛 Bug\r\n\r\nDocumentation states that you can use limit_val_batches=100 (with integer value) to limit number of batches. However, when using IterableDataset I get the following error:\r\npytorch_lightning.utilities.exceptions.MisconfigurationException: When using an infinite DataLoader (e.g. with an IterableDataset or when DataLoader does not implement `__len__`) for `limit_val_batches`, `Trainer(limit_val_batches)` must be `0.0` or `1.0`.\r\n\r\n### To Reproduce\r\nJust use:\r\npl.Trainer(val_check_interval=10, limit_val_batches=100)\r\n\r\nwith infinite DataLoader.\r\n\r\n### Expected behavior\r\n\r\nDo only N (100 in this case) validation steps.\r\n\r\n### Environment\r\n\r\n* CUDA:\r\n - GPU:\r\n - GeForce GTX 1080 Ti\r\n - available: True\r\n - version: 10.0.130\r\n* Packages:\r\n - numpy: 1.17.4\r\n - pyTorch_debug: False\r\n - pyTorch_version: 1.3.1\r\n - pytorch-lightning: 0.8.5\r\n - tensorboard: 2.2.2\r\n - tqdm: 4.46.1\r\n* System:\r\n - OS: Linux\r\n - architecture:\r\n - 64bit\r\n - \r\n - processor: x86_64\r\n - python: 3.7.4\r\n - version: #1 SMP Wed Mar 7 19:03:37 UTC 2018\n"},"gold_files":{"kind":"list like","value":["pytorch_lightning/core/lightning.py","pytorch_lightning/trainer/data_loading.py","pytorch_lightning/trainer/training_tricks.py"],"string":"[\n \"pytorch_lightning/core/lightning.py\",\n \"pytorch_lightning/trainer/data_loading.py\",\n \"pytorch_lightning/trainer/training_tricks.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pytorch_lightning/core/lightning.py::LightningModule.to_onnx","pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._reset_eval_dataloader","pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader","pytorch_lightning/trainer/training_tricks.py::_adjust_batch_size"],"string":"[\n \"pytorch_lightning/core/lightning.py::LightningModule.to_onnx\",\n \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._reset_eval_dataloader\",\n \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader\",\n \"pytorch_lightning/trainer/training_tricks.py::_adjust_batch_size\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"pytorch_lightning/core/lightning.py","text":"pytorch_lightning/core/lightning.py\nclass LightningModule\n def __init__(self, *args, **kwargs)\n def example_input_array(self)\n def example_input_array(self, example: Any)\n def datamodule(self)\n def datamodule(self, datamodule: Any)\n def on_gpu(self) # True if your model is currently running on GPUs.\n def print(self, *args, **kwargs) # Prints only from process 0. Use this in any distributed mode to log only once.\n def forward(self, *args, **kwargs) # Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to\n def training_step(self, *args, **kwargs) # Here you compute and return the training loss and some additional metrics for e.\n def training_end(self, *args, **kwargs) # Warnings:\n def training_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the training epoch with the outputs of all training steps.\n def training_step_end(self, *args, **kwargs) # Use this when training with dp or ddp2 because :meth:`training_step`\n def validation_step(self, *args, **kwargs) # Operates on a single batch of data from the validation set.\n def validation_step_end(self, *args, **kwargs) # Use this when validating with dp or ddp2 because :meth:`validation_step`\n def validation_end(self, outputs) # Warnings:\n def validation_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the validation epoch with the outputs of all validation ste\n def test_step(self, *args, **kwargs) # Operates on a single batch of data from the test set.\n def test_step_end(self, *args, **kwargs) # Use this when testing with dp or ddp2 because :meth:`test_step` will operate\n def test_end(self, outputs) # Warnings:\n def test_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of a test epoch with the output of all test steps.\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) # Override to init DDP in your own way or with your own wrapper.\n def _init_slurm_connection(self) # Sets up environment variables necessary for pytorch distributed communications\n def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool=True) # Override to define your custom way of setting up a distributed environment.\n def configure_apex(self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str) # Override to init AMP your own way.\n def configure_optimizers(self) # Choose what optimizers and learning-rate schedulers to use in your optimization.\n def optimizer_step(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int, second_order_closure: Optional[Callable]=None, on_tpu: bool=False, using_native_amp: bool=False, using_lbfgs: bool=False) # Override this method to adjust the default way the\n def optimizer_zero_grad(self","label":1},{"cid":"pytorch_lightning/trainer/__init__.py","text":"pytorch_lightning/trainer/__init__.py","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py","text":"pytorch_lightning/trainer/data_loading.py\ndef _has_iterable_dataset(dataloader: DataLoader)\ndef _has_len(dataloader: DataLoader) # Checks if a given Dataloader has __len__ method implemented i.e. if\nclass TrainerDataLoadingMixin\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def _check_batch_limits(self, name: str)\n def _worker_check(self, dataloader: DataLoader, name: str)\n def auto_add_sampler(self, dataloader: DataLoader, train: bool)\n def replace_sampler(self, dataloader, sampler)\n def _get_distributed_sampler(self, dataloader, train)\n def reset_train_dataloader(self, model: LightningModule) # Resets the train dataloader and initialises required variables\n def _reset_eval_dataloader(self, model: LightningModule, mode: str) # Generic method to reset a dataloader for evaluation.\n def reset_val_dataloader(self, model: LightningModule) # Resets the validation dataloader and determines the number of batches.\n def reset_test_dataloader(self, model) # Resets the validation dataloader and determines the number of batches.\n def request_dataloader(self, dataloader_fx: Callable) # Handles downloading data in the GPU or TPU case.\n def determine_data_use_amount(self, overfit_batches: float) # Use less data for debugging purposes","label":1},{"cid":"pytorch_lightning/trainer/evaluation_loop.py","text":"pytorch_lightning/trainer/evaluation_loop.py\nclass TrainerEvaluationLoopMixin\n def copy_trainer_model_properties(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def add_progress_bar_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_test_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_val_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def __call_eval_loop_hook_start(self, test_mode) # on_validation/test_epoch_start\n def __call_eval_loop_hook_end(self, test_mode) # on_validation/test_epoch_end\n def __call_eval_loop_hook_evt(self, test_mode, epoch_event)\n def _evaluate(self, model: LightningModule, dataloaders: List[DataLoader], max_batches: Union[int, List[int]], test_mode: bool=False) # Run evaluation code.\n def __update_callback_metrics(self, eval_results, using_eval_result)\n def __run_eval_epoch_end(self, test_mode, outputs, dataloaders, using_eval_result)\n def __gather_epoch_end_eval_results(self, outputs)\n def __eval_add_step_metrics(self, output)\n def __auto_reduce_result_objs(self, outputs)\n def run_evaluation(self, test_mode: bool=False)\n def __log_evaluation_epoch_metrics(self, eval_results, test_mode)\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode: bool=False)","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py","text":"pytorch_lightning/trainer/training_tricks.py\nclass TrainerTrainingTricksMixin\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\n def save_checkpoint(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def restore(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def fit(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def clip_gradients(self)\n def print_nan_gradients(self)\n def detect_nan_tensors(self, loss: Tensor)\n def configure_accumulated_gradients(self, accumulate_grad_batches)\n def scale_batch_size(self, model: LightningModule, mode: str='power', steps_per_trial: int=3, init_val: int=2, max_trials: int=25, batch_arg_name: str='batch_size') # Will iteratively try to find the largest batch size for a given model\n def __scale_batch_dump_params(self)\n def __scale_batch_reset_params(self, model, steps_per_trial)\n def __scale_batch_restore_params(self)\ndef _adjust_batch_size(trainer, batch_arg_name: str='batch_size', factor: float=1.0, value: Optional[int]=None, desc: str=None) # Function for adjusting the batch size. It is expected that the user\ndef _run_power_scaling(trainer, model, new_size, batch_arg_name, max_trials) # Batch scaling mode where the size is doubled at each iteration until an\ndef _run_binsearch_scaling(trainer, model, new_size, batch_arg_name, max_trials) # Batch scaling mode where the size is initially is doubled at each iteration","label":1}],"string":"[\n {\n \"cid\": \"pytorch_lightning/core/lightning.py\",\n \"text\": \"pytorch_lightning/core/lightning.py\\nclass LightningModule\\n def __init__(self, *args, **kwargs)\\n def example_input_array(self)\\n def example_input_array(self, example: Any)\\n def datamodule(self)\\n def datamodule(self, datamodule: Any)\\n def on_gpu(self) # True if your model is currently running on GPUs.\\n def print(self, *args, **kwargs) # Prints only from process 0. Use this in any distributed mode to log only once.\\n def forward(self, *args, **kwargs) # Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to\\n def training_step(self, *args, **kwargs) # Here you compute and return the training loss and some additional metrics for e.\\n def training_end(self, *args, **kwargs) # Warnings:\\n def training_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the training epoch with the outputs of all training steps.\\n def training_step_end(self, *args, **kwargs) # Use this when training with dp or ddp2 because :meth:`training_step`\\n def validation_step(self, *args, **kwargs) # Operates on a single batch of data from the validation set.\\n def validation_step_end(self, *args, **kwargs) # Use this when validating with dp or ddp2 because :meth:`validation_step`\\n def validation_end(self, outputs) # Warnings:\\n def validation_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of the validation epoch with the outputs of all validation ste\\n def test_step(self, *args, **kwargs) # Operates on a single batch of data from the test set.\\n def test_step_end(self, *args, **kwargs) # Use this when testing with dp or ddp2 because :meth:`test_step` will operate\\n def test_end(self, outputs) # Warnings:\\n def test_epoch_end(self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]) # Called at the end of a test epoch with the output of all test steps.\\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) # Override to init DDP in your own way or with your own wrapper.\\n def _init_slurm_connection(self) # Sets up environment variables necessary for pytorch distributed communications\\n def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool=True) # Override to define your custom way of setting up a distributed environment.\\n def configure_apex(self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str) # Override to init AMP your own way.\\n def configure_optimizers(self) # Choose what optimizers and learning-rate schedulers to use in your optimization.\\n def optimizer_step(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int, second_order_closure: Optional[Callable]=None, on_tpu: bool=False, using_native_amp: bool=False, using_lbfgs: bool=False) # Override this method to adjust the default way the\\n def optimizer_zero_grad(self\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/__init__.py\",\n \"text\": \"pytorch_lightning/trainer/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py\\ndef _has_iterable_dataset(dataloader: DataLoader)\\ndef _has_len(dataloader: DataLoader) # Checks if a given Dataloader has __len__ method implemented i.e. if\\nclass TrainerDataLoadingMixin\\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def _check_batch_limits(self, name: str)\\n def _worker_check(self, dataloader: DataLoader, name: str)\\n def auto_add_sampler(self, dataloader: DataLoader, train: bool)\\n def replace_sampler(self, dataloader, sampler)\\n def _get_distributed_sampler(self, dataloader, train)\\n def reset_train_dataloader(self, model: LightningModule) # Resets the train dataloader and initialises required variables\\n def _reset_eval_dataloader(self, model: LightningModule, mode: str) # Generic method to reset a dataloader for evaluation.\\n def reset_val_dataloader(self, model: LightningModule) # Resets the validation dataloader and determines the number of batches.\\n def reset_test_dataloader(self, model) # Resets the validation dataloader and determines the number of batches.\\n def request_dataloader(self, dataloader_fx: Callable) # Handles downloading data in the GPU or TPU case.\\n def determine_data_use_amount(self, overfit_batches: float) # Use less data for debugging purposes\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py\\nclass TrainerEvaluationLoopMixin\\n def copy_trainer_model_properties(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def add_progress_bar_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_test_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_val_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def __call_eval_loop_hook_start(self, test_mode) # on_validation/test_epoch_start\\n def __call_eval_loop_hook_end(self, test_mode) # on_validation/test_epoch_end\\n def __call_eval_loop_hook_evt(self, test_mode, epoch_event)\\n def _evaluate(self, model: LightningModule, dataloaders: List[DataLoader], max_batches: Union[int, List[int]], test_mode: bool=False) # Run evaluation code.\\n def __update_callback_metrics(self, eval_results, using_eval_result)\\n def __run_eval_epoch_end(self, test_mode, outputs, dataloaders, using_eval_result)\\n def __gather_epoch_end_eval_results(self, outputs)\\n def __eval_add_step_metrics(self, output)\\n def __auto_reduce_result_objs(self, outputs)\\n def run_evaluation(self, test_mode: bool=False)\\n def __log_evaluation_epoch_metrics(self, eval_results, test_mode)\\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode: bool=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py\\nclass TrainerTrainingTricksMixin\\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\\n def save_checkpoint(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def restore(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def fit(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def clip_gradients(self)\\n def print_nan_gradients(self)\\n def detect_nan_tensors(self, loss: Tensor)\\n def configure_accumulated_gradients(self, accumulate_grad_batches)\\n def scale_batch_size(self, model: LightningModule, mode: str='power', steps_per_trial: int=3, init_val: int=2, max_trials: int=25, batch_arg_name: str='batch_size') # Will iteratively try to find the largest batch size for a given model\\n def __scale_batch_dump_params(self)\\n def __scale_batch_reset_params(self, model, steps_per_trial)\\n def __scale_batch_restore_params(self)\\ndef _adjust_batch_size(trainer, batch_arg_name: str='batch_size', factor: float=1.0, value: Optional[int]=None, desc: str=None) # Function for adjusting the batch size. It is expected that the user\\ndef _run_power_scaling(trainer, model, new_size, batch_arg_name, max_trials) # Batch scaling mode where the size is doubled at each iteration until an\\ndef _run_binsearch_scaling(trainer, model, new_size, batch_arg_name, max_trials) # Batch scaling mode where the size is initially is doubled at each iteration\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"pytorch_lightning/core/lightning.py::LightningModule.__init__","text":"pytorch_lightning/core/lightning.py::LightningModule.__init__\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n self.exp_save_path = None\n\n #: The current epoch\n self.current_epoch = 0\n\n #: Total training batches seen across all epochs\n self.global_step = 0\n\n self.loaded_optimizer_states_dict = {}\n\n #: Pointer to the trainer object\n self.trainer = None\n\n #: Pointer to the logger object\n self.logger = None\n\n #: True if using dp\n self.use_dp = False\n\n #: True if using ddp\n self.use_ddp = False\n\n #: True if using ddp2\n self.use_ddp2 = False\n\n # True if on tpu\n self.use_tpu = False\n\n #: True if using amp\n self.use_amp = False\n\n # optionally can be set by user\n self._example_input_array = None\n self._datamodule = None","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.example_input_array","text":"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\n @property\n def example_input_array(self) -> Any:\n return self._example_input_array","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.example_input_array","text":"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\n @example_input_array.setter\n def example_input_array(self, example: Any) -> None:\n self._example_input_array = example","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.datamodule","text":"pytorch_lightning/core/lightning.py::LightningModule.datamodule\n @property\n def datamodule(self) -> Any:\n return self._datamodule","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.datamodule","text":"pytorch_lightning/core/lightning.py::LightningModule.datamodule\n @datamodule.setter\n def datamodule(self, datamodule: Any) -> None:\n self._datamodule = datamodule","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_gpu","text":"pytorch_lightning/core/lightning.py::LightningModule.on_gpu\n @property\n def on_gpu(self):\n \"\"\"\n True if your model is currently running on GPUs.\n Useful to set flags around the LightningModule for different CPU vs GPU behavior.\n \"\"\"\n return self.device.type == 'cuda'","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.print","text":"pytorch_lightning/core/lightning.py::LightningModule.print\n def print(self, *args, **kwargs) -> None:\n r\"\"\"\n Prints only from process 0. Use this in any distributed mode to log only once.\n\n Args:\n *args: The thing to print. Will be passed to Python's built-in print function.\n **kwargs: Will be passed to Python's built-in print function.\n\n Example:\n\n .. code-block:: python\n\n def forward(self, x):\n self.print(x, 'in forward')\n\n \"\"\"\n if self.trainer.is_global_zero:\n print(*args, **kwargs)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.forward","text":"pytorch_lightning/core/lightning.py::LightningModule.forward\n @abstractmethod\n def forward(self, *args, **kwargs):\n r\"\"\"\n Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define\n the operations you want to use for prediction (i.e.: on a server or as a feature extractor).\n\n Normally you'd call ``self()`` from your :meth:`training_step` method.\n This makes it easy to write a complex system for training with the outputs\n you'd want in a prediction setting.\n\n You may also find the :func:`~pytorch_lightning.core.decorators.auto_move_data` decorator useful\n when using the module outside Lightning in a production setting.\n\n Args:\n *args: Whatever you decide to pass into the forward method.\n **kwargs: Keyword arguments are also possible.\n\n Return:\n Predicted output\n\n Examples:\n .. code-block:: python\n\n # example if we were using this model as a feature extractor\n def forward(self, x):\n feature_maps = self.convnet(x)\n return feature_maps\n\n def training_step(self, batch, batch_idx):\n x, y = batch\n feature_maps = self(x)\n logits = self.classifier(feature_maps)\n\n # ...\n return loss\n\n # splitting it this way allows model to be used a feature extractor\n model = MyModelAbove()\n\n inputs = server.get_request()\n results = model(inputs)\n server.write_results(results)\n\n # -------------\n # This is in stark contrast to torch.nn.Module where normally you would have this:\n def forward(self, batch):\n x, y = batch\n feature_maps = self.convnet(x)\n logits = self.classifier(feature_maps)\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_step","text":"pytorch_lightning/core/lightning.py::LightningModule.training_step\n def training_step(self, *args, **kwargs) -> Union[int, Dict[str, Union[Tensor, Dict[str, Tensor]]]]:\n r\"\"\"\n Here you compute and return the training loss and some additional metrics for e.g.\n the progress bar or logger.\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): Integer displaying index of this batch\n optimizer_idx (int): When using multiple optimizers, this argument will also be present.\n hiddens(:class:`~torch.Tensor`): Passed in if\n :paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0.\n\n Return:\n Dict with loss key and optional log or progress bar keys.\n When implementing :meth:`training_step`, return whatever you need in that step:\n\n - loss -> tensor scalar **REQUIRED**\n - progress_bar -> Dict for progress bar display. Must have only tensors\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\n\n In this step you'd normally do the forward pass and calculate the loss for a batch.\n You can also do fancier things like multiple forward passes or something model specific.\n\n Examples:\n .. code-block:: python\n\n def training_step(self, batch, batch_idx):\n x, y, z = batch\n\n # implement your own\n out = self(x)\n loss = self.loss(out, x)\n\n logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS)\n\n # if using TestTubeLogger or TensorBoardLogger you can nest scalars\n logger_logs = {'losses': logger_logs} # optional (MUST ALL BE TENSORS)\n\n","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_end\n def training_end(self, *args, **kwargs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`training_step_end` instead.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\n def training_epoch_end(\n self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\n ) -> Dict[str, Dict[str, Tensor]]:\n \"\"\"Called at the end of the training epoch with the outputs of all training steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n train_outs = []\n for train_batch in train_data:\n out = training_step(train_batch)\n train_outs.append(out)\n training_epoch_end(train_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`training_step`, or if there are\n multiple dataloaders, a list containing a list of outputs for each dataloader.\n\n Return:\n Dict or OrderedDict.\n May contain the following optional keys:\n\n - log (metrics to be added to the logger; only tensors)\n - progress_bar (dict for progress bar display)\n - any metric used in a callback (e.g. early stopping).\n\n Note:\n If this method is not overridden, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def training_epoch_end(self, outputs):\n train_acc_mean = 0\n for output in outputs:\n train_acc_mean += output['train_acc']\n\n train_acc_mean /= len(outputs)\n\n # log training accuracy at the end of an epoch\n results = {\n 'log': {'train_acc': train_acc_mean.item()},\n 'progress_bar': {'train_acc': train_acc_m","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\n def training_step_end(self, *args, **kwargs) -> Dict[str, Union[Tensor, Dict[str, Tensor]]]:\n \"\"\"\n Use this when training with dp or ddp2 because :meth:`training_step`\n will operate on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]\n training_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in `training_step` for each batch part.\n\n Return:\n Dict with loss key and optional log or progress bar keys.\n\n - loss -> tensor scalar **REQUIRED**\n - progress_bar -> Dict for progress bar display. Must have only tensors\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\n\n Examples:\n .. code-block:: python\n\n # WITHOUT training_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def training_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with training_step_end to do softmax over the full batch\n def training_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_step","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_step\n def validation_step(self, *args, **kwargs) -> Dict[str, Tensor]:\n r\"\"\"\n Operates on a single batch of data from the validation set.\n In this step you'd might generate examples or calculate anything of interest like accuracy.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n val_outs = []\n for val_batch in val_data:\n out = validation_step(train_batch)\n val_outs.append(out)\n validation_epoch_end(val_outs)\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): The index of this batch\n dataloader_idx (int): The index of the dataloader that produced this batch\n (only if multiple val datasets used)\n\n Return:\n Dict or OrderedDict - passed to :meth:`validation_epoch_end`.\n If you defined :meth:`validation_step_end` it will go to that first.\n\n .. code-block:: python\n\n # pseudocode of order\n out = validation_step()\n if defined('validation_step_end'):\n out = validation_step_end(out)\n out = validation_epoch_end(out)\n\n\n .. code-block:: python\n\n # if you have one val dataloader:\n def validation_step(self, batch, batch_idx)\n\n # if you have multiple val dataloaders:\n def validation_step(self, batch, batch_idx, dataloader_idx)\n\n Examples:\n .. code-block:: python\n\n # CASE 1: A single validation dataset\n def validation_step(self, batch, batch_idx):\n x, y = batch\n\n # implement your own\n out = self(x)\n loss = self.los","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\n def validation_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\n \"\"\"\n Use this when validating with dp or ddp2 because :meth:`validation_step`\n will operate on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code.\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]\n validation_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in :meth:`validation_step`\n for each batch part.\n\n Return:\n Dict or OrderedDict - passed to the :meth:`validation_epoch_end` method.\n\n Examples:\n .. code-block:: python\n\n # WITHOUT validation_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def validation_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with validation_step_end to do softmax over the full batch\n def validation_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n return {'out': out}\n\n def validation_epoch_end(self, outputs):\n # this out is now the full size of the batch\n out = outputs['out']\n\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_end\n def validation_end(self, outputs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`validation_epoch_end` instead.\n Will be removed in 1.0.0.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\n def validation_epoch_end(\n self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\n ) -> Dict[str, Dict[str, Tensor]]:\n \"\"\"\n Called at the end of the validation epoch with the outputs of all validation steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n val_outs = []\n for val_batch in val_data:\n out = validation_step(val_batch)\n val_outs.append(out)\n validation_epoch_end(val_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`validation_step`, or if there\n are multiple dataloaders, a list containing a list of outputs for each dataloader.\n\n Return:\n Dict or OrderedDict.\n May have the following optional keys:\n\n - progress_bar (dict for progress bar display; only tensors)\n - log (dict of metrics to add to logger; only tensors).\n\n Note:\n If you didn't define a :meth:`validation_step`, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def validation_epoch_end(self, outputs):\n val_acc_mean = 0\n for output in outputs:\n val_acc_mean += output['val_acc']\n\n val_acc_mean /= len(outputs)\n tqdm_dict = {'val_acc': val_acc_mean.item()}\n\n # show val_acc in progress bar but only log val_loss\n results = {\n 'progress_bar': tqdm_dict,\n 'log': {'val_acc': val_ac","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_step","text":"pytorch_lightning/core/lightning.py::LightningModule.test_step\n def test_step(self, *args, **kwargs) -> Dict[str, Tensor]:\n r\"\"\"\n Operates on a single batch of data from the test set.\n In this step you'd normally generate examples or calculate anything of interest\n such as accuracy.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n test_outs = []\n for test_batch in test_data:\n out = test_step(test_batch)\n test_outs.append(out)\n test_epoch_end(test_outs)\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): The index of this batch.\n dataloader_idx (int): The index of the dataloader that produced this batch\n (only if multiple test datasets used).\n\n Return:\n Dict or OrderedDict - passed to the :meth:`test_epoch_end` method.\n If you defined :meth:`test_step_end` it will go to that first.\n\n .. code-block:: python\n\n # if you have one test dataloader:\n def test_step(self, batch, batch_idx)\n\n # if you have multiple test dataloaders:\n def test_step(self, batch, batch_idx, dataloader_idx)\n\n Examples:\n .. code-block:: python\n\n # CASE 1: A single test dataset\n def test_step(self, batch, batch_idx):\n x, y = batch\n\n # implement your own\n out = self(x)\n loss = self.loss(out, y)\n\n # log 6 example images\n # or generated text... or whatever\n sample_imgs = x[:6]\n grid = torchvision.utils.make_grid(sample_imgs)\n self.logger.experiment.add_image('example","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\n def test_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\n \"\"\"\n Use this when testing with dp or ddp2 because :meth:`test_step` will operate\n on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code.\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]\n test_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in :meth:`test_step` for each batch part.\n\n Return:\n Dict or OrderedDict - passed to the :meth:`test_epoch_end`.\n\n Examples:\n .. code-block:: python\n\n # WITHOUT test_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def test_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return {'loss': loss}\n\n # --------------\n # with test_step_end to do softmax over the full batch\n def test_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n return {'out': out}\n\n def test_step_end(self, outputs):\n # this out is now the full size of the batch\n out = outputs['out']\n\n # this softmax now uses the full batch size\n loss = nce_loss(loss)\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_end\n def test_end(self, outputs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`test_epoch_end` instead.\n Will be removed in 1.0.0.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\n def test_epoch_end(\n self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\n ) -> Dict[str, Dict[str, Tensor]]:\n \"\"\"\n Called at the end of a test epoch with the output of all test steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n test_outs = []\n for test_batch in test_data:\n out = test_step(test_batch)\n test_outs.append(out)\n test_epoch_end(test_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`test_step_end`, or if there\n are multiple dataloaders, a list containing a list of outputs for each dataloader\n\n Return:\n Dict or OrderedDict: Dict has the following optional keys:\n\n - progress_bar -> Dict for progress bar display. Must have only tensors.\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc).\n\n Note:\n If you didn't define a :meth:`test_step`, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n - If you want to manually set current step, specify it with the 'step' key in the 'log' Dict\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def test_epoch_end(self, outputs):\n test_acc_mean = 0\n for output in outputs:\n test_acc_mean += output['test_acc']\n\n test_acc_mean /= len(outputs)\n tqdm_dict = {'test_acc': test_acc_mean.item()}\n\n # show test_loss and test_acc in progress bar but only log test_loss\n results = {\n 'progress_bar': tqdm_dict,\n 'log': {'test_acc': test","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) -> DistributedDataParallel:\n r\"\"\"\n Override to init DDP in your own way or with your own wrapper.\n The only requirements are that:\n\n 1. On a validation batch, the call goes to ``model.validation_step``.\n 2. On a training batch, the call goes to ``model.training_step``.\n 3. On a testing batch, the call goes to ``model.test_step``.\n\n Args:\n model: the :class:`LightningModule` currently being optimized.\n device_ids: the list of GPU ids.\n\n Return:\n DDP wrapped model\n\n Examples:\n .. code-block:: python\n\n # default implementation used in Trainer\n def configure_ddp(self, model, device_ids):\n # Lightning DDP simply routes to test_step, val_step, etc...\n model = LightningDistributedDataParallel(\n model,\n device_ids=device_ids,\n find_unused_parameters=True\n )\n return model\n\n \"\"\"\n model = LightningDistributedDataParallel(model, device_ids=device_ids, find_unused_parameters=True)\n return model","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection","text":"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection\n def _init_slurm_connection(self) -> None:\n \"\"\"\n Sets up environment variables necessary for pytorch distributed communications\n based on slurm environment.\n \"\"\"\n # use slurm job id for the port number\n # guarantees unique ports across jobs from same grid search\n try:\n # use the last 4 numbers in the job id as the id\n default_port = os.environ['SLURM_JOB_ID']\n default_port = default_port[-4:]\n\n # all ports should be in the 10k+ range\n default_port = int(default_port) + 15000\n\n except Exception:\n default_port = 12910\n\n # if user gave a port number, use that one instead\n try:\n default_port = os.environ['MASTER_PORT']\n except Exception:\n os.environ['MASTER_PORT'] = str(default_port)\n\n # figure out the root node addr\n try:\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\n except Exception:\n root_node = '127.0.0.1'\n\n root_node = self.trainer.resolve_root_node_address(root_node)\n os.environ['MASTER_ADDR'] = root_node","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection","text":"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\n def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool = True) -> None:\n \"\"\"\n Override to define your custom way of setting up a distributed environment.\n\n Lightning's implementation uses env:// init by default and sets the first node as root\n for SLURM managed cluster.\n\n Args:\n global_rank: The global process idx.\n world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus).\n is_slurm_managing_tasks: is cluster managed by SLURM.\n\n \"\"\"\n if is_slurm_managing_tasks:\n self._init_slurm_connection()\n\n if 'MASTER_ADDR' not in os.environ:\n rank_zero_warn(\"MASTER_ADDR environment variable is not defined. Set as localhost\")\n os.environ['MASTER_ADDR'] = '127.0.0.1'\n log.debug(f\"MASTER_ADDR: {os.environ['MASTER_ADDR']}\")\n\n if 'MASTER_PORT' not in os.environ:\n rank_zero_warn(\"MASTER_PORT environment variable is not defined. Set as 12910\")\n os.environ['MASTER_PORT'] = '12910'\n log.debug(f\"MASTER_PORT: {os.environ['MASTER_PORT']}\")\n\n if 'WORLD_SIZE' in os.environ and int(os.environ['WORLD_SIZE']) != world_size:\n rank_zero_warn(\n f\"WORLD_SIZE environment variable ({os.environ['WORLD_SIZE']}) \"\n f\"is not equal to the computed world size ({world_size}). Ignored.\"\n )\n\n torch_backend = \"nccl\" if self.trainer.on_gpu else \"gloo\"\n log.info(f\"initializing ddp: GLOBAL_RANK: {global_rank}, MEMBER: {global_rank+1}/{world_size}\")\n torch_distrib.init_process_group(torch_backend, rank=global_rank, world_size=world_size)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_apex","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\n def configure_apex(\n self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str\n ) -> Tuple['LightningModule', List[Optimizer]]:\n r\"\"\"\n Override to init AMP your own way.\n Must return a model and list of optimizers.\n\n Args:\n amp: pointer to amp library object.\n model: pointer to current :class:`LightningModule`.\n optimizers: list of optimizers passed in :meth:`configure_optimizers`.\n amp_level: AMP mode chosen ('O1', 'O2', etc...)\n\n Return:\n Apex wrapped model and optimizers\n\n Examples:\n .. code-block:: python\n\n # Default implementation used by Trainer.\n def configure_apex(self, amp, model, optimizers, amp_level):\n model, optimizers = amp.initialize(\n model, optimizers, opt_level=amp_level,\n )\n\n return model, optimizers\n \"\"\"\n model, optimizers = amp.initialize(model, optimizers, opt_level=amp_level)\n\n return model, optimizers","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\n def configure_optimizers(\n self,\n ) -> Optional[Union[Optimizer, Sequence[Optimizer], Dict, Sequence[Dict], Tuple[List, List]]]:\n r\"\"\"\n Choose what optimizers and learning-rate schedulers to use in your optimization.\n Normally you'd need one. But in the case of GANs or similar you might have multiple.\n\n Return:\n Any of these 6 options.\n\n - Single optimizer.\n - List or Tuple - List of optimizers.\n - Two lists - The first list has multiple optimizers, the second a list of LR schedulers (or lr_dict).\n - Dictionary, with an 'optimizer' key, and (optionally) a 'lr_scheduler' key which value is a single LR scheduler or lr_dict.\n - Tuple of dictionaries as described, with an optional 'frequency' key.\n - None - Fit will run without any optimizer.\n\n Note:\n The 'frequency' value is an int corresponding to the number of sequential batches\n optimized with the specific optimizer. It should be given to none or to all of the optimizers.\n There is a difference between passing multiple optimizers in a list,\n and passing multiple optimizers in dictionaries with a frequency of 1:\n In the former case, all optimizers will operate on the given batch in each optimization step.\n In the latter, only one optimizer will operate on the given batch at every step.\n\n The lr_dict is a dictionary which contains scheduler and its associated configuration.\n It has five keys. The default configuration is shown below.\n\n .. code-block:: python\n\n {\n 'scheduler': lr_scheduler, # The LR schduler\n 'interval': 'epoch', # The unit of the scheduler's step size\n 'frequency': 1, # The frequency of the scheduler\n 'reduce_on_platea","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step","text":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\n def optimizer_step(\n self,\n epoch: int,\n batch_idx: int,\n optimizer: Optimizer,\n optimizer_idx: int,\n second_order_closure: Optional[Callable] = None,\n on_tpu: bool = False,\n using_native_amp: bool = False,\n using_lbfgs: bool = False,\n ) -> None:\n r\"\"\"\n Override this method to adjust the default way the\n :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.\n By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example\n once per optimizer.\n\n Args:\n epoch: Current epoch\n batch_idx: Index of current batch\n optimizer: A PyTorch optimizer\n optimizer_idx: If you used multiple optimizers this indexes into that list.\n second_order_closure: closure for second order methods\n on_tpu: true if TPU backward is required\n using_native_amp: True if using native amp\n using_lbfgs: True if the matching optimizer is lbfgs\n\n Examples:\n .. code-block:: python\n\n # DEFAULT\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\n second_order_closure, on_tpu, using_native_amp, using_lbfgs):\n optimizer.step()\n\n # Alternating schedule for optimizer steps (i.e.: GANs)\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\n second_order_closure, on_tpu, using_native_amp, using_lbfgs):\n # update generator opt every 2 steps\n if optimizer_idx == 0:\n if batch_idx % 2 == 0 :\n optimizer.step()\n optimizer.zero_grad()\n\n # update discriminator opt every 4 steps\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_zero_grad","text":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_zero_grad\n def optimizer_zero_grad(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int):\n optimizer.zero_grad()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch","text":"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\n def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list:\n r\"\"\"\n When using truncated backpropagation through time, each batch must be split along the\n time dimension. Lightning handles this by default, but for custom behavior override\n this function.\n\n Args:\n batch: Current batch\n split_size: The size of the split\n\n Return:\n List of batch splits. Each split will be passed to :meth:`training_step` to enable truncated\n back propagation through time. The default implementation splits root level Tensors and\n Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.\n\n Examples:\n .. code-block:: python\n\n def tbptt_split_batch(self, batch, split_size):\n splits = []\n for t in range(0, time_dims[0], split_size):\n batch_split = []\n for i, x in enumerate(batch):\n if isinstance(x, torch.Tensor):\n split_x = x[:, t:t + split_size]\n elif isinstance(x, collections.Sequence):\n split_x = [None] * len(x)\n for batch_idx in range(len(x)):\n split_x[batch_idx] = x[batch_idx][t:t + split_size]\n\n batch_split.append(split_x)\n\n splits.append(batch_split)\n\n return splits\n\n Note:\n Called in the training loop after\n :meth:`~pytorch_lightning.callbacks.base.Callback.on_batch_start`\n if :paramref:`~pytorch_lightning.trainer.Trainer.truncated_bptt_steps` > 0.\n Each returned batch split is passed separately to :meth:`training_step`.\n\n \"\"\"\n time_dims = [len(x[0]) for x in batch if isinstance(x","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.prepare_data","text":"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\n def prepare_data(self) -> None:\n \"\"\"\n Use this to download and prepare data.\n\n .. warning:: DO NOT set state to the model (use `setup` instead)\n since this is NOT called on every GPU in DDP/TPU\n\n Example::\n\n def prepare_data(self):\n # good\n download_data()\n tokenize()\n etc()\n\n # bad\n self.split = data_split\n self.some_state = some_other_state()\n\n In DDP prepare_data can be called in two ways (using Trainer(prepare_data_per_node)):\n\n 1. Once per node. This is the default and is only called on LOCAL_RANK=0.\n 2. Once in total. Only called on GLOBAL_RANK=0.\n\n Example::\n\n # DEFAULT\n # called once per node on LOCAL_RANK=0 of that node\n Trainer(prepare_data_per_node=True)\n\n # call on GLOBAL_RANK=0 (great for shared file systems)\n Trainer(prepare_data_per_node=False)\n\n This is called before requesting the dataloaders:\n\n .. code-block:: python\n\n model.prepare_data()\n if ddp/tpu: init()\n model.setup(stage)\n model.train_dataloader()\n model.val_dataloader()\n model.test_dataloader()\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\n def train_dataloader(self) -> DataLoader:\n \"\"\"\n Implement a PyTorch DataLoader for training.\n\n Return:\n Single PyTorch :class:`~torch.utils.data.DataLoader`.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n For data processing use the following pattern:\n\n - download in :meth:`prepare_data`\n - process and split in :meth:`setup`\n\n However, the above are only necessary for distributed processing.\n\n .. warning:: do not assign state in prepare_data\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`setup`\n - :meth:`train_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware.\n There is no need to set it yourself.\n\n Example:\n .. code-block:: python\n\n def train_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,\n download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.batch_size,\n shuffle=True\n )\n return loader\n\n \"\"\"\n rank_zero_warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\n def tng_dataloader(self): # todo: remove in v1.0.0\n \"\"\"\n Warnings:\n Deprecated in v0.5.0. Use :meth:`train_dataloader` instead. Will be removed in 1.0.0.\n \"\"\"\n output = self.train_dataloader()\n rank_zero_warn(\n \"`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0.\"\n \" and this method will be removed in v1.0.0\",\n DeprecationWarning,\n )\n return output","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\n def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\n r\"\"\"\n Implement one or multiple PyTorch DataLoaders for testing.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n For data processing use the following pattern:\n\n - download in :meth:`prepare_data`\n - process and split in :meth:`setup`\n\n However, the above are only necessary for distributed processing.\n\n .. warning:: do not assign state in prepare_data\n\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`setup`\n - :meth:`train_dataloader`\n - :meth:`val_dataloader`\n - :meth:`test_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware.\n There is no need to set it yourself.\n\n Return:\n Single or multiple PyTorch DataLoaders.\n\n Example:\n .. code-block:: python\n\n def test_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,\n download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.batch_size,\n shuffle=False\n )\n\n return loader\n\n # can also return multiple dataloaders\n def test_dataloader(self):\n return [loader_a, loader_b, ..., loader_n]\n\n Note:\n If you don't need a te","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\n def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\n r\"\"\"\n Implement one or multiple PyTorch DataLoaders for validation.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`train_dataloader`\n - :meth:`val_dataloader`\n - :meth:`test_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware\n There is no need to set it yourself.\n\n Return:\n Single or multiple PyTorch DataLoaders.\n\n Examples:\n .. code-block:: python\n\n def val_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=False,\n transform=transform, download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.batch_size,\n shuffle=False\n )\n\n return loader\n\n # can also return multiple dataloaders\n def val_dataloader(self):\n return [loader_a, loader_b, ..., loader_n]\n\n Note:\n If you don't need a validation dataset and a :meth:`validation_step`, you don't need to\n implement this method.\n\n Note:\n In the case where you return multiple validation dataloaders, the :meth:`validation_st","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.summarize","text":"pytorch_lightning/core/lightning.py::LightningModule.summarize\n def summarize(self, mode: str = ModelSummary.MODE_DEFAULT) -> ModelSummary:\n model_summary = ModelSummary(self, mode=mode)\n log.info('\\n' + str(model_summary))\n return model_summary","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.freeze","text":"pytorch_lightning/core/lightning.py::LightningModule.freeze\n def freeze(self) -> None:\n r\"\"\"\n Freeze all params for inference.\n\n Example:\n .. code-block:: python\n\n model = MyLightningModule(...)\n model.freeze()\n\n \"\"\"\n for param in self.parameters():\n param.requires_grad = False\n\n self.eval()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.unfreeze","text":"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\n def unfreeze(self) -> None:\n \"\"\"\n Unfreeze all parameters for training.\n\n .. code-block:: python\n\n model = MyLightningModule(...)\n model.unfreeze()\n\n \"\"\"\n for param in self.parameters():\n param.requires_grad = True\n\n self.train()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\n def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\n r\"\"\"\n Called by Lightning to restore your model.\n If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.\n\n Args:\n checkpoint: Loaded checkpoint\n\n\n Example:\n .. code-block:: python\n\n def on_load_checkpoint(self, checkpoint):\n # 99% of the time you don't need to implement this method\n self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']\n\n Note:\n Lightning auto-restores global step, epoch, and train state including amp scaling.\n There is no need for you to restore anything regarding training.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\n def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\n r\"\"\"\n Called by Lightning when saving a checkpoint to give you a chance to store anything\n else you might want to save.\n\n Args:\n checkpoint: Checkpoint to be saved\n\n Example:\n .. code-block:: python\n\n def on_save_checkpoint(self, checkpoint):\n # 99% of use cases you don't need to implement this method\n checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object\n\n Note:\n Lightning saves all aspects of training (epoch, global step, etc...)\n including amp scaling.\n There is no need for you to store anything about training.\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict","text":"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict\n def get_progress_bar_dict(self) -> Dict[str, Union[int, str]]:\n r\"\"\"\n Implement this to override the default items displayed in the progress bar.\n By default it includes the average loss value, split index of BPTT (if used)\n and the version of the experiment when using a logger.\n\n .. code-block::\n\n Epoch 1: 4%|▎ | 40/1095 [00:03<01:37, 10.84it/s, loss=4.501, v_num=10]\n\n Here is an example how to override the defaults:\n\n .. code-block:: python\n\n def get_progress_bar_dict(self):\n # don't show the version number\n items = super().get_progress_bar_dict()\n items.pop(\"v_num\", None)\n return items\n\n Return:\n Dictionary with the items to be displayed in the progress bar.\n \"\"\"\n # call .item() only once but store elements without graphs\n running_train_loss = self.trainer.running_loss.mean()\n avg_training_loss = running_train_loss.cpu().item() if running_train_loss is not None else float('NaN')\n tqdm_dict = {'loss': '{:.3f}'.format(avg_training_loss)}\n\n if self.trainer.truncated_bptt_steps is not None:\n tqdm_dict['split_idx'] = self.trainer.split_idx\n\n if self.trainer.logger is not None and self.trainer.logger.version is not None:\n version = self.trainer.logger.version\n # show last 4 places of long version strings\n version = version[-4:] if isinstance(version, str) else version\n tqdm_dict['v_num'] = version\n\n return tqdm_dict","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict","text":"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict\n def get_tqdm_dict(self) -> Dict[str, Union[int, str]]:\n \"\"\"\n Additional items to be displayed in the progress bar.\n\n Return:\n Dictionary with the items to be displayed in the progress bar.\n\n Warning:\n Deprecated since v0.7.3.\n Use :meth:`get_progress_bar_dict` instead.\n \"\"\"\n rank_zero_warn(\n \"`get_tqdm_dict` was renamed to `get_progress_bar_dict` in v0.7.3\"\n \" and this method will be removed in v1.0.0\",\n DeprecationWarning,\n )\n return self.get_progress_bar_dict()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule._auto_collect_arguments","text":"pytorch_lightning/core/lightning.py::LightningModule._auto_collect_arguments\n @classmethod\n def _auto_collect_arguments(cls, frame=None) -> Tuple[Dict, Dict]:\n \"\"\"\n Collect all module arguments in the current constructor and all child constructors.\n The child constructors are all the ``__init__`` methods that reach the current class through\n (chained) ``super().__init__()`` calls.\n\n Args:\n frame: instance frame\n\n Returns:\n self_arguments: arguments dictionary of the first instance\n parents_arguments: arguments dictionary of the parent's instances\n \"\"\"\n if not frame:\n frame = inspect.currentframe()\n\n frame_args = collect_init_args(frame.f_back, [])\n self_arguments = frame_args[-1]\n\n # set module_arguments in child\n self_arguments = self_arguments\n parents_arguments = {}\n\n # add all arguments from parents\n for args in frame_args[:-1]:\n parents_arguments.update(args)\n return self_arguments, parents_arguments","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.save_hyperparameters","text":"pytorch_lightning/core/lightning.py::LightningModule.save_hyperparameters\n def save_hyperparameters(self, *args, frame=None) -> None:\n \"\"\"Save all model arguments.\n\n Args:\n args: single object of `dict`, `NameSpace` or `OmegaConf`\n or string names or argumenst from class `__init__`\n\n >>> from collections import OrderedDict\n >>> class ManuallyArgsModel(LightningModule):\n ... def __init__(self, arg1, arg2, arg3):\n ... super().__init__()\n ... # manually assine arguments\n ... self.save_hyperparameters('arg1', 'arg3')\n ... def forward(self, *args, **kwargs):\n ... ...\n >>> model = ManuallyArgsModel(1, 'abc', 3.14)\n >>> model.hparams\n \"arg1\": 1\n \"arg3\": 3.14\n\n >>> class AutomaticArgsModel(LightningModule):\n ... def __init__(self, arg1, arg2, arg3):\n ... super().__init__()\n ... # equivalent automatic\n ... self.save_hyperparameters()\n ... def forward(self, *args, **kwargs):\n ... ...\n >>> model = AutomaticArgsModel(1, 'abc', 3.14)\n >>> model.hparams\n \"arg1\": 1\n \"arg2\": abc\n \"arg3\": 3.14\n\n >>> class SingleArgModel(LightningModule):\n ... def __init__(self, params):\n ... super().__init__()\n ... # manually assign single argument\n ... self.save_hyperparameters(params)\n ... def forward(self, *args, **kwargs):\n ... ...\n >>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14))\n >>> model.hparams\n \"p1\": 1\n \"p2\": abc\n \"p3\": 3.14\n \"\"\"\n if not frame:\n frame = inspect.currentframe().f_back\n init_args = get_init_args(frame)\n assert init_args, 'failed to inspect the self init'\n if not args:\n hp = init_args\n self._","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule._set_hparams","text":"pytorch_lightning/core/lightning.py::LightningModule._set_hparams\n def _set_hparams(self, hp: Union[dict, Namespace, str]) -> None:\n if isinstance(hp, Namespace):\n hp = vars(hp)\n if isinstance(hp, dict):\n hp = AttributeDict(hp)\n elif isinstance(hp, PRIMITIVE_TYPES):\n raise ValueError(f'Primitives {PRIMITIVE_TYPES} are not allowed.')\n elif not isinstance(hp, ALLOWED_CONFIG_TYPES):\n raise ValueError(f'Unsupported config type of {type(hp)}.')\n\n if isinstance(hp, dict) and isinstance(self.hparams, dict):\n self.hparams.update(hp)\n else:\n self._hparams = hp","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.to_onnx","text":"pytorch_lightning/core/lightning.py::LightningModule.to_onnx\n def to_onnx(self, file_path: str, input_sample: Optional[Tensor] = None, **kwargs):\n \"\"\"Saves the model in ONNX format\n\n Args:\n file_path: The path of the file the model should be saved to.\n input_sample: A sample of an input tensor for tracing.\n **kwargs: Will be passed to torch.onnx.export function.\n\n Example:\n >>> class SimpleModel(LightningModule):\n ... def __init__(self):\n ... super().__init__()\n ... self.l1 = torch.nn.Linear(in_features=64, out_features=4)\n ...\n ... def forward(self, x):\n ... return torch.relu(self.l1(x.view(x.size(0), -1)))\n\n >>> with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmpfile:\n ... model = SimpleModel()\n ... input_sample = torch.randn((1, 64))\n ... model.to_onnx(tmpfile.name, input_sample, export_params=True)\n ... os.path.isfile(tmpfile.name)\n True\n \"\"\"\n\n if isinstance(input_sample, Tensor):\n input_data = input_sample\n elif self.example_input_array is not None:\n input_data = self.example_input_array\n else:\n raise ValueError(f'input_sample and example_input_array tensors are both missing.')\n\n if 'example_outputs' not in kwargs:\n self.eval()\n kwargs['example_outputs'] = self(input_data)\n\n torch.onnx.export(self, input_data, file_path, **kwargs)","label":1},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.hparams","text":"pytorch_lightning/core/lightning.py::LightningModule.hparams\n @property\n def hparams(self) -> Union[AttributeDict, str]:\n if not hasattr(self, '_hparams'):\n self._hparams = AttributeDict()\n return self._hparams","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.hparams","text":"pytorch_lightning/core/lightning.py::LightningModule.hparams\n @hparams.setter\n def hparams(self, hp: Union[dict, Namespace, Any]):\n hparams_assignment_name = self.__get_hparams_assignment_variable()\n self._hparams_name = hparams_assignment_name\n self._set_hparams(hp)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.__get_hparams_assignment_variable","text":"pytorch_lightning/core/lightning.py::LightningModule.__get_hparams_assignment_variable\n def __get_hparams_assignment_variable(self):\n \"\"\"\n looks at the code of the class to figure out what the user named self.hparams\n this only happens when the user explicitly sets self.hparams\n \"\"\"\n try:\n class_code = inspect.getsource(self.__class__)\n lines = class_code.split('\\n')\n for line in lines:\n line = re.sub(r\"\\s+\", \"\", line, flags=re.UNICODE)\n if '.hparams=' in line:\n return line.split('=')[1]\n except Exception as e:\n return 'hparams'\n\n return None","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::_has_iterable_dataset","text":"pytorch_lightning/trainer/data_loading.py::_has_iterable_dataset\ndef _has_iterable_dataset(dataloader: DataLoader):\n return ITERABLE_DATASET_EXISTS and hasattr(dataloader, 'dataset') \\\n and isinstance(dataloader.dataset, IterableDataset)","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::_has_len","text":"pytorch_lightning/trainer/data_loading.py::_has_len\ndef _has_len(dataloader: DataLoader) -> bool:\n \"\"\" Checks if a given Dataloader has __len__ method implemented i.e. if\n it is a finite dataloader or infinite dataloader. \"\"\"\n\n try:\n # try getting the length\n if len(dataloader) == 0:\n raise ValueError('`Dataloader` returned 0 length.'\n ' Please make sure that your Dataloader at least returns 1 batch')\n has_len = True\n except TypeError:\n has_len = False\n except NotImplementedError: # e.g. raised by torchtext if a batch_size_fn is used\n has_len = False\n\n if has_len and _has_iterable_dataset(dataloader) and LooseVersion(torch.__version__) >= LooseVersion(\"1.4.0\"):\n rank_zero_warn(\n 'Your `IterableDataset` has `__len__` defined.'\n ' In combination with multi-processing data loading (e.g. batch size > 1),'\n ' this can lead to unintended side effects since the samples will be duplicated.'\n )\n return has_len","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_overridden","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_overridden\n @abstractmethod\n def is_overridden(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._check_batch_limits","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._check_batch_limits\n def _check_batch_limits(self, name: str) -> None:\n # TODO: verify it is still needed and deprecate it..\n value = getattr(self, name)\n\n # ints are fine\n if isinstance(value, int):\n return\n\n msg = f'`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}. (or pass in an int)'\n if name == 'val_check_interval':\n msg += ' If you want to disable validation set `limit_val_batches` to 0.0 instead.'\n\n if not 0. <= value <= 1.:\n raise ValueError(msg)","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._worker_check","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._worker_check\n def _worker_check(self, dataloader: DataLoader, name: str) -> None:\n on_windows = platform.system() == 'Windows'\n\n # ddp_spawn + num_workers > 0 don't mix! tell the user\n is_dataloader = isinstance(dataloader, DataLoader)\n using_spawn = self.distributed_backend == 'ddp_spawn'\n if is_dataloader and not on_windows:\n if dataloader.num_workers > 0 and using_spawn:\n rank_zero_warn('Dataloader(num_workers>0) and ddp_spawn do not mix well!'\n ' Your performance might suffer dramatically.'\n ' Please consider setting distributed_backend=ddp to use num_workers > 0'\n ' (this is a bottleneck of Python .spawn() and PyTorch')\n\n elif dataloader.num_workers == 0 and using_spawn:\n rank_zero_warn('You are using `distributed_backend=ddp_spawn` with num_workers=0.'\n ' For much faster performance, switch to `distributed_backend=ddp`'\n ' and set `num_workers>0`')\n\n elif dataloader.num_workers <= 2 and multiprocessing.cpu_count() > 2 and not using_spawn:\n num_cpus = multiprocessing.cpu_count()\n rank_zero_warn(f'The dataloader, {name}, does not have many workers which may be a bottleneck.'\n ' Consider increasing the value of the `num_workers` argument`'\n f' (try {num_cpus} which is the number of cpus on this machine)'\n ' in the `DataLoader` init to improve performance.')","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler\n def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:\n\n # don't do anything if it's not a dataloader\n is_dataloader = isinstance(dataloader, DataLoader)\n # don't manipulate iterable datasets\n is_iterable_ds = _has_iterable_dataset(dataloader)\n\n if not is_dataloader or is_iterable_ds:\n return dataloader\n need_dist_sampler = (self.use_ddp or self.use_ddp2 or self.use_horovod or self.use_tpu)\n\n if self.replace_sampler_ddp and need_dist_sampler:\n if not isinstance(dataloader.sampler, (SequentialSampler, RandomSampler)):\n raise MisconfigurationException(\n 'You seem to have configured a sampler in your DataLoader. This will be replaced '\n ' by `DistributedSampler` since `replace_sampler_ddp` is True and you are using'\n ' distributed training. Either remove the sampler from your DataLoader or set'\n ' `replace_sampler_ddp`=False if you want to use your custom sampler.')\n\n # replace with distributed sampler\n sampler = self._get_distributed_sampler(dataloader, train)\n dataloader = self.replace_sampler(dataloader, sampler)\n\n return dataloader","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.replace_sampler","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.replace_sampler\n def replace_sampler(self, dataloader, sampler):\n skip_keys = ['sampler', 'batch_sampler', 'dataset_kind']\n\n dl_args = {\n k: v for k, v in dataloader.__dict__.items() if not k.startswith('_') and k not in skip_keys\n }\n\n dl_args['sampler'] = sampler\n dataloader = type(dataloader)(**dl_args)\n return dataloader","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._get_distributed_sampler","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._get_distributed_sampler\n def _get_distributed_sampler(self, dataloader, train):\n if self.use_tpu:\n kwargs = dict(num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())\n elif self.use_horovod:\n kwargs = dict(num_replicas=hvd.size(), rank=hvd.rank())\n else:\n world_size = {\n 'ddp': self.num_nodes * self.num_processes,\n 'ddp_spawn': self.num_nodes * self.num_processes,\n 'ddp2': self.num_nodes,\n 'ddp_cpu': self.num_processes * self.num_nodes\n }\n assert self.distributed_backend is not None\n kwargs = dict(num_replicas=world_size[self.distributed_backend], rank=self.global_rank)\n\n kwargs['shuffle'] = train\n sampler = DistributedSampler(dataloader.dataset, **kwargs)\n return sampler","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader\n def reset_train_dataloader(self, model: LightningModule) -> None:\n \"\"\"Resets the train dataloader and initialises required variables\n (number of batches, when to validate, etc.).\n\n Args:\n model: The current `LightningModule`\n \"\"\"\n self.train_dataloader = self.request_dataloader(model.train_dataloader)\n\n self.num_training_batches = 0\n\n # automatically add samplers\n self.train_dataloader = self.auto_add_sampler(self.train_dataloader, train=True)\n\n self._worker_check(self.train_dataloader, 'train dataloader')\n self._check_batch_limits('limit_train_batches')\n\n if not _has_len(self.train_dataloader):\n self.num_training_batches = float('inf')\n else:\n # try getting the length\n if isinstance(self.limit_train_batches, float):\n self.num_training_batches = len(self.train_dataloader)\n self.num_training_batches = int(self.num_training_batches * self.limit_train_batches)\n else:\n self.num_training_batches = min(len(self.train_dataloader), self.limit_train_batches)\n\n # determine when to check validation\n # if int passed in, val checks that often\n # otherwise, it checks in [0, 1.0] % range of a training epoch\n if isinstance(self.val_check_interval, int):\n self.val_check_batch = self.val_check_interval\n if self.val_check_batch > self.num_training_batches:\n raise ValueError(\n f'`val_check_interval` ({self.val_check_interval}) must be less than or equal '\n f'to the number of the training batches ({self.num_training_batches}). '\n 'If you want to disable validation set `limit_val_batches` to 0.0 instead.')\n else:\n if not _has_len(self.train_dataloader):\n if self.","label":1},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._reset_eval_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._reset_eval_dataloader\n def _reset_eval_dataloader(\n self,\n model: LightningModule,\n mode: str\n ) -> Tuple[List[Union[int, float]], List[DataLoader]]:\n \"\"\"Generic method to reset a dataloader for evaluation.\n\n Args:\n model: The current `LightningModule`\n mode: Either `'val'` or `'test'`\n\n Returns:\n Tuple (num_batches, dataloaders)\n \"\"\"\n # use the training loader as val and test when overfitting\n if self.overfit_batches > 0:\n dataloaders = self.request_dataloader(getattr(model, 'train_dataloader'))\n else:\n dataloaders = self.request_dataloader(getattr(model, f'{mode}_dataloader'))\n\n if not isinstance(dataloaders, list):\n dataloaders = [dataloaders]\n\n for loader_i in range(len(dataloaders)):\n loader = dataloaders[loader_i]\n\n # shuffling in val and test set is bad practice\n if mode in ('val', 'test') and hasattr(loader, 'sampler') and isinstance(loader.sampler, RandomSampler):\n\n # when overfitting, the dataloader should not have sampler\n if self.overfit_batches > 0:\n rank_zero_warn('You requested to overfit but enabled training dataloader shuffling.'\n ' We are turning it off for you.')\n dataloaders[loader_i] = self.replace_sampler(loader, SequentialSampler(loader.dataset))\n\n else:\n rank_zero_warn(f'Your {mode}_dataloader has `shuffle=True`, it is best practice to turn'\n ' this off for validation and test dataloaders.')\n\n if any([dl is None for dl in dataloaders]):\n rank_zero_warn(\"One of given dataloaders is None and it will be skipped.\")\n\n # add samplers\n dataloaders = [self.auto_add_sampler(dl, train=False","label":1},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader\n def reset_val_dataloader(self, model: LightningModule) -> None:\n \"\"\"Resets the validation dataloader and determines the number of batches.\n\n Args:\n model: The current `LightningModule`\n \"\"\"\n has_loader = self.is_overridden('val_dataloader', model)\n has_step = self.is_overridden('validation_step', model)\n if has_loader and has_step:\n self.num_val_batches, self.val_dataloaders = self._reset_eval_dataloader(model, 'val')","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader\n def reset_test_dataloader(self, model) -> None:\n \"\"\"Resets the validation dataloader and determines the number of batches.\n\n Args:\n model: The current `LightningModule`\n \"\"\"\n has_loader = self.is_overridden('test_dataloader', model)\n has_step = self.is_overridden('test_step', model)\n if has_loader and has_step:\n self.num_test_batches, self.test_dataloaders =\\\n self._reset_eval_dataloader(model, 'test')","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_dataloader","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_dataloader\n def request_dataloader(self, dataloader_fx: Callable) -> DataLoader:\n \"\"\"Handles downloading data in the GPU or TPU case.\n\n Args:\n dataloader_fx: The bound dataloader getter\n\n Returns:\n The dataloader\n \"\"\"\n dataloader = dataloader_fx()\n\n # get the function we'll use to get data\n if self.use_ddp or self.use_ddp2:\n # all processes wait until data download has happened\n torch_distrib.barrier()\n\n # data download/load on TPU\n elif self.use_tpu and XLA_AVAILABLE:\n # all processes wait until data download has happened\n torch_xla.core.xla_model.rendezvous('pl.TrainerDataLoadingMixin.get_dataloaders')\n\n elif self.use_horovod:\n # all processes wait until data download has happened\n hvd.join()\n\n return dataloader","label":0},{"cid":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount","text":"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount\n def determine_data_use_amount(self, overfit_batches: float) -> None:\n \"\"\"Use less data for debugging purposes\"\"\"\n if overfit_batches > 0:\n if isinstance(overfit_batches, float) and overfit_batches > 1:\n raise ValueError('`overfit_batches` when used as a percentage must'\n f' be in range 0.0 < x < 1.0 but got {overfit_batches:.3f}.')\n self.limit_train_batches = overfit_batches\n self.limit_val_batches = overfit_batches\n self.limit_test_batches = overfit_batches","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.copy_trainer_model_properties","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.copy_trainer_model_properties\n @abstractmethod\n def copy_trainer_model_properties(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.get_model","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.get_model\n @abstractmethod\n def get_model(self) -> LightningModule:\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.is_overridden","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.is_overridden\n @abstractmethod\n def is_overridden(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_tpu","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_tpu\n @abstractmethod\n def transfer_batch_to_tpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_gpu","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_gpu\n @abstractmethod\n def transfer_batch_to_gpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.add_progress_bar_metrics","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.add_progress_bar_metrics\n @abstractmethod\n def add_progress_bar_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics\n @abstractmethod\n def log_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_test_dataloader","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_test_dataloader\n @abstractmethod\n def reset_test_dataloader(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_val_dataloader","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_val_dataloader\n @abstractmethod\n def reset_val_dataloader(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_start","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_start\n def __call_eval_loop_hook_start(self, test_mode):\n \"\"\"on_validation/test_epoch_start\"\"\"\n self.__call_eval_loop_hook_evt(test_mode, 'start')","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_end","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_end\n def __call_eval_loop_hook_end(self, test_mode):\n \"\"\"on_validation/test_epoch_end\"\"\"\n self.__call_eval_loop_hook_evt(test_mode, 'end')","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_evt","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_evt\n def __call_eval_loop_hook_evt(self, test_mode, epoch_event):\n model = self.get_model()\n\n # on_[train/validation]_epoch_start hook\n hook_root_name = 'test' if test_mode else 'validation'\n hook_name = f'on_{hook_root_name}_epoch_{epoch_event}'\n with self.profiler.profile(hook_name):\n # call hook\n getattr(self, hook_name)()\n\n # model hooks\n if self.is_function_implemented(hook_name):\n getattr(model, hook_name)()","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin._evaluate","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin._evaluate\n def _evaluate(\n self,\n model: LightningModule,\n dataloaders: List[DataLoader],\n max_batches: Union[int, List[int]],\n test_mode: bool = False\n ):\n \"\"\"Run evaluation code.\n\n Args:\n model: The model to evaluate.\n dataloaders: A list of PyTorch dataloaders.\n max_batches: An integer or list of integers with length of the number of dataloaders. Each\n entry is the number of batches to process in the corresponding dataloader.\n test_mode:\n \"\"\"\n # enable eval mode\n model.zero_grad()\n model.eval()\n\n # copy properties for forward overrides\n self.copy_trainer_model_properties(model)\n\n # disable gradients to save memory\n torch.set_grad_enabled(False)\n\n # bookkeeping\n outputs = []\n\n # convert max_batches to list\n if isinstance(max_batches, int):\n max_batches = [max_batches] * len(dataloaders)\n\n # --------------------------\n # ON_EVAL_EPOCH_START hook\n # --------------------------\n self.__call_eval_loop_hook_start(test_mode)\n\n # run validation\n for dataloader_idx, dataloader in enumerate(dataloaders):\n dl_outputs = []\n\n # on TPU we have to wrap it under the ParallelLoader\n if self.use_tpu:\n device = xm.xla_device(self.tpu_id)\n dataloader = xla_pl.ParallelLoader(dataloader, [device])\n dataloader = dataloader.per_device_loader(device)\n\n # each dataloader has a max num batches\n dl_max_batches = max_batches[dataloader_idx]\n\n for batch_idx, batch in enumerate(dataloader):\n if batch is None:\n continue\n\n # stop short when running on limited batches\n if batch_idx >= dl_max_batches:\n ","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__update_callback_metrics","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__update_callback_metrics\n def __update_callback_metrics(self, eval_results, using_eval_result):\n if using_eval_result:\n if isinstance(eval_results, list):\n for eval_result in eval_results:\n self.callback_metrics = eval_result.callback_metrics\n else:\n self.callback_metrics = eval_results.callback_metrics\n else:\n if isinstance(eval_results, list):\n for eval_result in eval_results:\n # with a scalar return, auto set it to \"val_loss\" for callbacks\n if isinstance(eval_result, torch.Tensor):\n flat = {'val_loss': eval_result}\n else:\n flat = flatten_dict(eval_result)\n self.callback_metrics.update(flat)\n else:\n # with a scalar return, auto set it to \"val_loss\" for callbacks\n if isinstance(eval_results, torch.Tensor):\n flat = {'val_loss': eval_results}\n else:\n flat = flatten_dict(eval_results)\n self.callback_metrics.update(flat)","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__run_eval_epoch_end","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__run_eval_epoch_end\n def __run_eval_epoch_end(self, test_mode, outputs, dataloaders, using_eval_result):\n model = self.get_model()\n\n # with a single dataloader don't pass an array\n eval_results = outputs\n if len(dataloaders) == 1:\n eval_results = outputs[0]\n\n user_reduced = False\n\n if test_mode:\n if self.is_overridden('test_end', model=model):\n # TODO: remove in v1.0.0\n if using_eval_result:\n eval_results = self.__gather_epoch_end_eval_results(outputs)\n\n eval_results = model.test_end(eval_results)\n user_reduced = True\n rank_zero_warn('Method `test_end` was deprecated in v0.7 and will be removed in v1.0.'\n ' Use `test_epoch_end` instead.', DeprecationWarning)\n\n elif self.is_overridden('test_epoch_end', model=model):\n if using_eval_result:\n eval_results = self.__gather_epoch_end_eval_results(outputs)\n\n eval_results = model.test_epoch_end(eval_results)\n user_reduced = True\n\n else:\n if self.is_overridden('validation_end', model=model):\n # TODO: remove in v1.0.0\n if using_eval_result:\n eval_results = self.__gather_epoch_end_eval_results(outputs)\n\n eval_results = model.validation_end(eval_results)\n user_reduced = True\n rank_zero_warn('Method `validation_end` was deprecated in v0.7 and will be removed in v1.0.'\n ' Use `validation_epoch_end` instead.', DeprecationWarning)\n\n elif self.is_overridden('validation_epoch_end', model=model):\n if using_eval_result:\n eval_results = self.__gather_epoch_end_eval_results(outputs)\n\n eval_results = model.v","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__gather_epoch_end_eval_results","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__gather_epoch_end_eval_results\n def __gather_epoch_end_eval_results(self, outputs):\n eval_results = []\n for epoch_output in outputs:\n result = epoch_output[0].__class__.gather(epoch_output)\n if 'checkpoint_on' in result:\n result.checkpoint_on = result.checkpoint_on.mean()\n if 'early_stop_on' in result:\n result.early_stop_on = result.early_stop_on.mean()\n\n eval_results.append(result)\n\n # with 1 dataloader don't pass in a list\n if len(eval_results) == 1:\n eval_results = eval_results[0]\n return eval_results","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__eval_add_step_metrics","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__eval_add_step_metrics\n def __eval_add_step_metrics(self, output):\n # track step level metrics\n if isinstance(output, EvalResult) and not self.running_sanity_check:\n step_log_metrics = output.batch_log_metrics\n step_pbar_metrics = output.batch_pbar_metrics\n\n if len(step_log_metrics) > 0:\n self.log_metrics(step_log_metrics, {})\n\n if len(step_pbar_metrics) > 0:\n self.add_progress_bar_metrics(step_pbar_metrics)","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__auto_reduce_result_objs","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__auto_reduce_result_objs\n def __auto_reduce_result_objs(self, outputs):\n # outputs has a list of results per dataloader\n eval_results = []\n for dl_output in outputs:\n result = dl_output[0]\n result = result.__class__.reduce_on_epoch_end(dl_output)\n if 'checkpoint_on' in result:\n result.checkpoint_on = result.checkpoint_on.mean()\n if 'early_stop_on' in result:\n result.early_stop_on = result.early_stop_on.mean()\n eval_results.append(result)\n\n return eval_results","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation\n def run_evaluation(self, test_mode: bool = False):\n # hook\n model = self.get_model()\n model.on_pre_performance_check()\n\n # select dataloaders\n if test_mode:\n self.reset_test_dataloader(model)\n\n dataloaders = self.test_dataloaders\n max_batches = self.num_test_batches\n else:\n # val\n if self.val_dataloaders is None:\n self.reset_val_dataloader(model)\n\n dataloaders = self.val_dataloaders\n max_batches = self.num_val_batches\n\n if dataloaders is None:\n return [], []\n\n # Validation/Test begin callbacks\n if test_mode:\n self.on_test_start()\n else:\n self.on_validation_start()\n\n # enable disabling validation step with limit_val_batches = 0\n should_skip = sum(max_batches) == 0\n if should_skip:\n return [], []\n\n # run evaluation (val_step + val_step_end + val_epoch_end)\n eval_results = self._evaluate(self.model, dataloaders, max_batches, test_mode)\n\n # log the final eval loop metrics\n eval_loop_results = self.__log_evaluation_epoch_metrics(eval_results, test_mode)\n\n # hook\n model.on_post_performance_check()\n\n # eventual dataset reloading\n if test_mode:\n if self.reload_dataloaders_every_epoch:\n self.reset_test_dataloader(model)\n else:\n # val\n if self.reload_dataloaders_every_epoch:\n self.reset_val_dataloader(model)\n\n # Validation/Test end callbacks\n if test_mode:\n self.on_test_end()\n else:\n self.on_validation_end()\n\n return eval_loop_results, eval_results","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__log_evaluation_epoch_metrics","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__log_evaluation_epoch_metrics\n def __log_evaluation_epoch_metrics(self, eval_results, test_mode):\n eval_loop_results = []\n if eval_results is not None and len(eval_results) > 0:\n\n # in eval, the user may return something at every validation step without final reduction\n if not isinstance(eval_results, list):\n eval_results = [eval_results]\n\n for result_idx, result in enumerate(eval_results):\n if isinstance(result, EvalResult):\n prog_bar_metrics = result.epoch_pbar_metrics\n log_metrics = result.epoch_log_metrics\n callback_metrics = result.callback_metrics\n else:\n _, prog_bar_metrics, log_metrics, callback_metrics, _ = self.process_output(result)\n\n # eval loop returns all metrics\n dataloader_result_metrics = {**prog_bar_metrics, **log_metrics, **callback_metrics}\n\n # add metrics to prog bar\n self.add_progress_bar_metrics(prog_bar_metrics)\n\n # log metrics\n self.log_metrics(log_metrics, {})\n\n # track metrics for callbacks\n self.callback_metrics.update(callback_metrics)\n\n if len(dataloader_result_metrics) > 0:\n eval_loop_results.append(dataloader_result_metrics)\n\n # log results of test\n if test_mode and self.is_global_zero and self.verbose_test:\n print('-' * 80)\n for result_idx, results in enumerate(eval_loop_results):\n print(f'DATALOADER:{result_idx} TEST RESULTS')\n pprint(results)\n print('-' * 80)\n\n return eval_loop_results","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluation_forward","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluation_forward\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode: bool = False):\n # make dataloader_idx arg in validation_step optional\n args = [batch, batch_idx]\n\n if (test_mode and len(self.test_dataloaders) > 1) \\\n or (not test_mode and len(self.val_dataloaders) > 1):\n args.append(dataloader_idx)\n\n # handle DP, DDP forward\n if self.use_ddp or self.use_dp or self.use_ddp2:\n output = model(*args)\n return output\n\n # Horovod\n if self.use_horovod and self.on_gpu:\n batch = self.transfer_batch_to_gpu(batch, hvd.local_rank())\n args[0] = batch\n\n # single GPU data transfer\n if self.use_single_gpu:\n # for single GPU put inputs on gpu manually\n root_gpu = 0\n if isinstance(self.data_parallel_device_ids, list):\n root_gpu = self.data_parallel_device_ids[0]\n batch = self.transfer_batch_to_gpu(batch, root_gpu)\n args[0] = batch\n\n # TPU data transfer\n if self.use_tpu:\n batch = self.transfer_batch_to_tpu(batch, self.tpu_id)\n args[0] = batch\n\n # CPU, TPU or gpu step\n if test_mode:\n output = model.test_step(*args)\n else:\n output = model.validation_step(*args)\n\n return output","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.get_model","text":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.get_model\n @abstractmethod\n def get_model(self) -> LightningModule:\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.save_checkpoint","text":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.save_checkpoint\n @abstractmethod\n def save_checkpoint(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.restore","text":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.restore\n @abstractmethod\n def restore(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.fit","text":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.fit\n @abstractmethod\n def fit(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.clip_gradients","text":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.clip_gradients\n def clip_gradients(self):\n\n # this code is a modification of torch.nn.utils.clip_grad_norm_\n # with TPU support based on https://github.com/pytorch/xla/blob/master/TROUBLESHOOTING.md\n if self.gradient_clip_val <= 0:\n return\n model = self.get_model()\n parameters = model.parameters()\n max_norm = float(self.gradient_clip_val)\n norm_type = float(2.0)\n if isinstance(parameters, torch.Tensor):\n parameters = [parameters]\n parameters = list(filter(lambda p: p.grad is not None, parameters))\n if norm_type == math.inf:\n total_norm = max(p.grad.data.abs().max() for p in parameters)\n else:\n device = parameters[0].device\n out = torch.empty(len(parameters), device=device)\n for i, p in enumerate(parameters):\n torch.norm(p.grad.data.to(device), norm_type, out=out[i])\n total_norm = torch.norm(out, norm_type)\n\n eps = EPSILON_FP16 if self.precision == 16 else EPSILON\n clip_coef = torch.tensor(max_norm, device=device) / (total_norm + eps)\n clip_coef = torch.min(clip_coef, torch.ones_like(clip_coef))\n for p in parameters:\n p.grad.data.mul_(clip_coef.to(p.grad.data.device))","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.print_nan_gradients","text":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.print_nan_gradients\n def print_nan_gradients(self) -> None:\n model = self.get_model()\n for param in model.parameters():\n if (param.grad is not None) and torch.isnan(param.grad.float()).any():\n log.info(param, param.grad)","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.detect_nan_tensors","text":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.detect_nan_tensors\n def detect_nan_tensors(self, loss: Tensor) -> None:\n model = self.get_model()\n\n # check if loss is nan\n if not torch.isfinite(loss).all():\n raise ValueError(\n 'The loss returned in `training_step` is nan or inf.'\n )\n # check if a network weight is nan\n for name, param in model.named_parameters():\n if not torch.isfinite(param).all():\n self.print_nan_gradients()\n raise ValueError(\n f'Detected nan and/or inf values in `{name}`.'\n ' Check your forward pass for numerically unstable operations.'\n )","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.configure_accumulated_gradients","text":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.configure_accumulated_gradients\n def configure_accumulated_gradients(self, accumulate_grad_batches):\n if isinstance(accumulate_grad_batches, dict):\n self.accumulation_scheduler = GradientAccumulationScheduler(accumulate_grad_batches)\n elif isinstance(accumulate_grad_batches, int):\n schedule = {0: accumulate_grad_batches}\n self.accumulation_scheduler = GradientAccumulationScheduler(schedule)\n else:\n raise TypeError(\"Gradient accumulation supports only int and dict types\")","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.scale_batch_size","text":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.scale_batch_size\n def scale_batch_size(self,\n model: LightningModule,\n mode: str = 'power',\n steps_per_trial: int = 3,\n init_val: int = 2,\n max_trials: int = 25,\n batch_arg_name: str = 'batch_size'):\n r\"\"\"\n Will iteratively try to find the largest batch size for a given model\n that does not give an out of memory (OOM) error.\n\n Args:\n model: Model to fit.\n\n mode: string setting the search mode. Either `power` or `binsearch`.\n If mode is `power` we keep multiplying the batch size by 2, until\n we get an OOM error. If mode is 'binsearch', we will initially\n also keep multiplying by 2 and after encountering an OOM error\n do a binary search between the last successful batch size and the\n batch size that failed.\n\n steps_per_trial: number of steps to run with a given batch size.\n Idealy 1 should be enough to test if a OOM error occurs,\n however in practise a few are needed\n\n init_val: initial batch size to start the search with\n\n max_trials: max number of increase in batch size done before\n algorithm is terminated\n\n \"\"\"\n if not hasattr(model, batch_arg_name):\n if not hasattr(model.hparams, batch_arg_name):\n raise MisconfigurationException(\n 'Neither of `model.batch_size` and `model.hparams.batch_size` found.'\n )\n\n if hasattr(model.train_dataloader, 'patch_loader_code'):\n raise MisconfigurationException('The batch scaling feature cannot be used with dataloaders'\n ' passed directly to `.fit()`. Please disable the feature or'\n ","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.__scale_batch_dump_params","text":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.__scale_batch_dump_params\n def __scale_batch_dump_params(self):\n # Prevent going into infinite loop\n self.__dumped_params = {\n 'max_steps': self.max_steps,\n 'weights_summary': self.weights_summary,\n 'logger': self.logger,\n 'callbacks': self.callbacks,\n 'checkpoint_callback': self.checkpoint_callback,\n 'early_stop_callback': self.early_stop_callback,\n 'auto_scale_batch_size': self.auto_scale_batch_size,\n 'limit_train_batches': self.limit_train_batches,\n 'model': self.model,\n }","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.__scale_batch_reset_params","text":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.__scale_batch_reset_params\n def __scale_batch_reset_params(self, model, steps_per_trial):\n self.auto_scale_batch_size = None # prevent recursion\n self.max_steps = steps_per_trial # take few steps\n self.weights_summary = None # not needed before full run\n self.logger = DummyLogger()\n self.callbacks = [] # not needed before full run\n self.checkpoint_callback = False # required for saving\n self.early_stop_callback = None\n self.limit_train_batches = 1.0\n self.optimizers, self.schedulers = [], [] # required for saving\n self.model = model # required for saving","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.__scale_batch_restore_params","text":"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.__scale_batch_restore_params\n def __scale_batch_restore_params(self):\n self.max_steps = self.__dumped_params['max_steps']\n self.weights_summary = self.__dumped_params['weights_summary']\n self.logger = self.__dumped_params['logger']\n self.callbacks = self.__dumped_params['callbacks']\n self.checkpoint_callback = self.__dumped_params['checkpoint_callback']\n self.auto_scale_batch_size = self.__dumped_params['auto_scale_batch_size']\n self.early_stop_callback = self.__dumped_params['early_stop_callback']\n self.limit_train_batches = self.__dumped_params['limit_train_batches']\n self.model = self.__dumped_params['model']\n del self.__dumped_params","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::_adjust_batch_size","text":"pytorch_lightning/trainer/training_tricks.py::_adjust_batch_size\ndef _adjust_batch_size(trainer,\n batch_arg_name: str = 'batch_size',\n factor: float = 1.0,\n value: Optional[int] = None,\n desc: str = None):\n \"\"\" Function for adjusting the batch size. It is expected that the user\n has provided a model that has a hparam field called `batch_size` i.e.\n `model.hparams.batch_size` should exist.\n\n Args:\n trainer: instance of pytorch_lightning.Trainer\n\n batch_arg_name: field where batch_size is stored in `model.hparams`\n\n factor: value which the old batch size is multiplied by to get the\n new batch size\n\n value: if a value is given, will override the batch size with this value.\n Note that the value of `factor` will not have an effect in this case\n\n desc: either `succeeded` or `failed`. Used purely for logging\n\n \"\"\"\n model = trainer.get_model()\n if hasattr(model, batch_arg_name):\n batch_size = getattr(model, batch_arg_name)\n else:\n batch_size = getattr(model.hparams, batch_arg_name)\n if value:\n if hasattr(model, batch_arg_name):\n setattr(model, batch_arg_name, value)\n else:\n setattr(model.hparams, batch_arg_name, value) \n new_size = value\n if desc:\n log.info(f'Batch size {batch_size} {desc}, trying batch size {new_size}')\n else:\n new_size = int(batch_size * factor)\n if desc:\n log.info(f'Batch size {batch_size} {desc}, trying batch size {new_size}')\n setattr(model.hparams, batch_arg_name, new_size)\n return new_size","label":1},{"cid":"pytorch_lightning/trainer/training_tricks.py::_run_power_scaling","text":"pytorch_lightning/trainer/training_tricks.py::_run_power_scaling\ndef _run_power_scaling(trainer, model, new_size, batch_arg_name, max_trials):\n \"\"\" Batch scaling mode where the size is doubled at each iteration until an\n OOM error is encountered. \"\"\"\n for _ in range(max_trials):\n garbage_collection_cuda()\n trainer.global_step = 0 # reset after each try\n try:\n # Try fit\n trainer.fit(model)\n # Double in size\n new_size = _adjust_batch_size(trainer, batch_arg_name, factor=2.0, desc='succeeded')\n except RuntimeError as exception:\n # Only these errors should trigger an adjustment\n if is_oom_error(exception):\n # If we fail in power mode, half the size and return\n garbage_collection_cuda()\n new_size = _adjust_batch_size(trainer, batch_arg_name, factor=0.5, desc='failed')\n break\n else:\n raise # some other error not memory related\n return new_size","label":0},{"cid":"pytorch_lightning/trainer/training_tricks.py::_run_binsearch_scaling","text":"pytorch_lightning/trainer/training_tricks.py::_run_binsearch_scaling\ndef _run_binsearch_scaling(trainer, model, new_size, batch_arg_name, max_trials):\n \"\"\" Batch scaling mode where the size is initially is doubled at each iteration\n until an OOM error is encountered. Hereafter, the batch size is further\n refined using a binary search \"\"\"\n high = None\n count = 0\n while True:\n garbage_collection_cuda()\n trainer.global_step = 0 # reset after each try\n try:\n # Try fit\n trainer.fit(model)\n count += 1\n if count > max_trials:\n break\n # Double in size\n low = new_size\n if high:\n if high - low <= 1:\n break\n midval = (high + low) // 2\n new_size = _adjust_batch_size(trainer, batch_arg_name, value=midval, desc='succeeded')\n else:\n new_size = _adjust_batch_size(trainer, batch_arg_name, factor=2.0, desc='succeeded')\n except RuntimeError as exception:\n # Only these errors should trigger an adjustment\n if is_oom_error(exception):\n # If we fail in power mode, half the size and return\n garbage_collection_cuda()\n high = new_size\n midval = (high + low) // 2\n new_size = _adjust_batch_size(trainer, value=midval, desc='failed')\n if high - low <= 1:\n break\n else:\n raise # some other error not memory related\n return new_size","label":0}],"string":"[\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.__init__\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.__init__\\n def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n\\n self.exp_save_path = None\\n\\n #: The current epoch\\n self.current_epoch = 0\\n\\n #: Total training batches seen across all epochs\\n self.global_step = 0\\n\\n self.loaded_optimizer_states_dict = {}\\n\\n #: Pointer to the trainer object\\n self.trainer = None\\n\\n #: Pointer to the logger object\\n self.logger = None\\n\\n #: True if using dp\\n self.use_dp = False\\n\\n #: True if using ddp\\n self.use_ddp = False\\n\\n #: True if using ddp2\\n self.use_ddp2 = False\\n\\n # True if on tpu\\n self.use_tpu = False\\n\\n #: True if using amp\\n self.use_amp = False\\n\\n # optionally can be set by user\\n self._example_input_array = None\\n self._datamodule = None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\\n @property\\n def example_input_array(self) -> Any:\\n return self._example_input_array\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\\n @example_input_array.setter\\n def example_input_array(self, example: Any) -> None:\\n self._example_input_array = example\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.datamodule\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.datamodule\\n @property\\n def datamodule(self) -> Any:\\n return self._datamodule\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.datamodule\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.datamodule\\n @datamodule.setter\\n def datamodule(self, datamodule: Any) -> None:\\n self._datamodule = datamodule\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.on_gpu\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.on_gpu\\n @property\\n def on_gpu(self):\\n \\\"\\\"\\\"\\n True if your model is currently running on GPUs.\\n Useful to set flags around the LightningModule for different CPU vs GPU behavior.\\n \\\"\\\"\\\"\\n return self.device.type == 'cuda'\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.print\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.print\\n def print(self, *args, **kwargs) -> None:\\n r\\\"\\\"\\\"\\n Prints only from process 0. Use this in any distributed mode to log only once.\\n\\n Args:\\n *args: The thing to print. Will be passed to Python's built-in print function.\\n **kwargs: Will be passed to Python's built-in print function.\\n\\n Example:\\n\\n .. code-block:: python\\n\\n def forward(self, x):\\n self.print(x, 'in forward')\\n\\n \\\"\\\"\\\"\\n if self.trainer.is_global_zero:\\n print(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.forward\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.forward\\n @abstractmethod\\n def forward(self, *args, **kwargs):\\n r\\\"\\\"\\\"\\n Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define\\n the operations you want to use for prediction (i.e.: on a server or as a feature extractor).\\n\\n Normally you'd call ``self()`` from your :meth:`training_step` method.\\n This makes it easy to write a complex system for training with the outputs\\n you'd want in a prediction setting.\\n\\n You may also find the :func:`~pytorch_lightning.core.decorators.auto_move_data` decorator useful\\n when using the module outside Lightning in a production setting.\\n\\n Args:\\n *args: Whatever you decide to pass into the forward method.\\n **kwargs: Keyword arguments are also possible.\\n\\n Return:\\n Predicted output\\n\\n Examples:\\n .. code-block:: python\\n\\n # example if we were using this model as a feature extractor\\n def forward(self, x):\\n feature_maps = self.convnet(x)\\n return feature_maps\\n\\n def training_step(self, batch, batch_idx):\\n x, y = batch\\n feature_maps = self(x)\\n logits = self.classifier(feature_maps)\\n\\n # ...\\n return loss\\n\\n # splitting it this way allows model to be used a feature extractor\\n model = MyModelAbove()\\n\\n inputs = server.get_request()\\n results = model(inputs)\\n server.write_results(results)\\n\\n # -------------\\n # This is in stark contrast to torch.nn.Module where normally you would have this:\\n def forward(self, batch):\\n x, y = batch\\n feature_maps = self.convnet(x)\\n logits = self.classifier(feature_maps)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step\\n def training_step(self, *args, **kwargs) -> Union[int, Dict[str, Union[Tensor, Dict[str, Tensor]]]]:\\n r\\\"\\\"\\\"\\n Here you compute and return the training loss and some additional metrics for e.g.\\n the progress bar or logger.\\n\\n Args:\\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\\n batch_idx (int): Integer displaying index of this batch\\n optimizer_idx (int): When using multiple optimizers, this argument will also be present.\\n hiddens(:class:`~torch.Tensor`): Passed in if\\n :paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0.\\n\\n Return:\\n Dict with loss key and optional log or progress bar keys.\\n When implementing :meth:`training_step`, return whatever you need in that step:\\n\\n - loss -> tensor scalar **REQUIRED**\\n - progress_bar -> Dict for progress bar display. Must have only tensors\\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\\n\\n In this step you'd normally do the forward pass and calculate the loss for a batch.\\n You can also do fancier things like multiple forward passes or something model specific.\\n\\n Examples:\\n .. code-block:: python\\n\\n def training_step(self, batch, batch_idx):\\n x, y, z = batch\\n\\n # implement your own\\n out = self(x)\\n loss = self.loss(out, x)\\n\\n logger_logs = {'training_loss': loss} # optional (MUST ALL BE TENSORS)\\n\\n # if using TestTubeLogger or TensorBoardLogger you can nest scalars\\n logger_logs = {'losses': logger_logs} # optional (MUST ALL BE TENSORS)\\n\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_end\\n def training_end(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. Use :meth:`training_step_end` instead.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\\n def training_epoch_end(\\n self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\\n ) -> Dict[str, Dict[str, Tensor]]:\\n \\\"\\\"\\\"Called at the end of the training epoch with the outputs of all training steps.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n train_outs = []\\n for train_batch in train_data:\\n out = training_step(train_batch)\\n train_outs.append(out)\\n training_epoch_end(train_outs)\\n\\n Args:\\n outputs: List of outputs you defined in :meth:`training_step`, or if there are\\n multiple dataloaders, a list containing a list of outputs for each dataloader.\\n\\n Return:\\n Dict or OrderedDict.\\n May contain the following optional keys:\\n\\n - log (metrics to be added to the logger; only tensors)\\n - progress_bar (dict for progress bar display)\\n - any metric used in a callback (e.g. early stopping).\\n\\n Note:\\n If this method is not overridden, this won't be called.\\n\\n - The outputs here are strictly for logging or progress bar.\\n - If you don't need to display anything, don't return anything.\\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\\n\\n Examples:\\n With a single dataloader:\\n\\n .. code-block:: python\\n\\n def training_epoch_end(self, outputs):\\n train_acc_mean = 0\\n for output in outputs:\\n train_acc_mean += output['train_acc']\\n\\n train_acc_mean /= len(outputs)\\n\\n # log training accuracy at the end of an epoch\\n results = {\\n 'log': {'train_acc': train_acc_mean.item()},\\n 'progress_bar': {'train_acc': train_acc_m\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\\n def training_step_end(self, *args, **kwargs) -> Dict[str, Union[Tensor, Dict[str, Tensor]]]:\\n \\\"\\\"\\\"\\n Use this when training with dp or ddp2 because :meth:`training_step`\\n will operate on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n Note:\\n If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]\\n training_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in `training_step` for each batch part.\\n\\n Return:\\n Dict with loss key and optional log or progress bar keys.\\n\\n - loss -> tensor scalar **REQUIRED**\\n - progress_bar -> Dict for progress bar display. Must have only tensors\\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc)\\n\\n Examples:\\n .. code-block:: python\\n\\n # WITHOUT training_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def training_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n return {'loss': loss}\\n\\n # --------------\\n # with training_step_end to do softmax over the full batch\\n def training_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step\\n def validation_step(self, *args, **kwargs) -> Dict[str, Tensor]:\\n r\\\"\\\"\\\"\\n Operates on a single batch of data from the validation set.\\n In this step you'd might generate examples or calculate anything of interest like accuracy.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n val_outs = []\\n for val_batch in val_data:\\n out = validation_step(train_batch)\\n val_outs.append(out)\\n validation_epoch_end(val_outs)\\n\\n Args:\\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\\n batch_idx (int): The index of this batch\\n dataloader_idx (int): The index of the dataloader that produced this batch\\n (only if multiple val datasets used)\\n\\n Return:\\n Dict or OrderedDict - passed to :meth:`validation_epoch_end`.\\n If you defined :meth:`validation_step_end` it will go to that first.\\n\\n .. code-block:: python\\n\\n # pseudocode of order\\n out = validation_step()\\n if defined('validation_step_end'):\\n out = validation_step_end(out)\\n out = validation_epoch_end(out)\\n\\n\\n .. code-block:: python\\n\\n # if you have one val dataloader:\\n def validation_step(self, batch, batch_idx)\\n\\n # if you have multiple val dataloaders:\\n def validation_step(self, batch, batch_idx, dataloader_idx)\\n\\n Examples:\\n .. code-block:: python\\n\\n # CASE 1: A single validation dataset\\n def validation_step(self, batch, batch_idx):\\n x, y = batch\\n\\n # implement your own\\n out = self(x)\\n loss = self.los\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\\n def validation_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\\n \\\"\\\"\\\"\\n Use this when validating with dp or ddp2 because :meth:`validation_step`\\n will operate on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n Note:\\n If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code.\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]\\n validation_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in :meth:`validation_step`\\n for each batch part.\\n\\n Return:\\n Dict or OrderedDict - passed to the :meth:`validation_epoch_end` method.\\n\\n Examples:\\n .. code-block:: python\\n\\n # WITHOUT validation_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def validation_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n return {'loss': loss}\\n\\n # --------------\\n # with validation_step_end to do softmax over the full batch\\n def validation_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n return {'out': out}\\n\\n def validation_epoch_end(self, outputs):\\n # this out is now the full size of the batch\\n out = outputs['out']\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_end\\n def validation_end(self, outputs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. Use :meth:`validation_epoch_end` instead.\\n Will be removed in 1.0.0.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\\n def validation_epoch_end(\\n self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\\n ) -> Dict[str, Dict[str, Tensor]]:\\n \\\"\\\"\\\"\\n Called at the end of the validation epoch with the outputs of all validation steps.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n val_outs = []\\n for val_batch in val_data:\\n out = validation_step(val_batch)\\n val_outs.append(out)\\n validation_epoch_end(val_outs)\\n\\n Args:\\n outputs: List of outputs you defined in :meth:`validation_step`, or if there\\n are multiple dataloaders, a list containing a list of outputs for each dataloader.\\n\\n Return:\\n Dict or OrderedDict.\\n May have the following optional keys:\\n\\n - progress_bar (dict for progress bar display; only tensors)\\n - log (dict of metrics to add to logger; only tensors).\\n\\n Note:\\n If you didn't define a :meth:`validation_step`, this won't be called.\\n\\n - The outputs here are strictly for logging or progress bar.\\n - If you don't need to display anything, don't return anything.\\n - If you want to manually set current step, you can specify the 'step' key in the 'log' dict.\\n\\n Examples:\\n With a single dataloader:\\n\\n .. code-block:: python\\n\\n def validation_epoch_end(self, outputs):\\n val_acc_mean = 0\\n for output in outputs:\\n val_acc_mean += output['val_acc']\\n\\n val_acc_mean /= len(outputs)\\n tqdm_dict = {'val_acc': val_acc_mean.item()}\\n\\n # show val_acc in progress bar but only log val_loss\\n results = {\\n 'progress_bar': tqdm_dict,\\n 'log': {'val_acc': val_ac\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step\\n def test_step(self, *args, **kwargs) -> Dict[str, Tensor]:\\n r\\\"\\\"\\\"\\n Operates on a single batch of data from the test set.\\n In this step you'd normally generate examples or calculate anything of interest\\n such as accuracy.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n test_outs = []\\n for test_batch in test_data:\\n out = test_step(test_batch)\\n test_outs.append(out)\\n test_epoch_end(test_outs)\\n\\n Args:\\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\\n batch_idx (int): The index of this batch.\\n dataloader_idx (int): The index of the dataloader that produced this batch\\n (only if multiple test datasets used).\\n\\n Return:\\n Dict or OrderedDict - passed to the :meth:`test_epoch_end` method.\\n If you defined :meth:`test_step_end` it will go to that first.\\n\\n .. code-block:: python\\n\\n # if you have one test dataloader:\\n def test_step(self, batch, batch_idx)\\n\\n # if you have multiple test dataloaders:\\n def test_step(self, batch, batch_idx, dataloader_idx)\\n\\n Examples:\\n .. code-block:: python\\n\\n # CASE 1: A single test dataset\\n def test_step(self, batch, batch_idx):\\n x, y = batch\\n\\n # implement your own\\n out = self(x)\\n loss = self.loss(out, y)\\n\\n # log 6 example images\\n # or generated text... or whatever\\n sample_imgs = x[:6]\\n grid = torchvision.utils.make_grid(sample_imgs)\\n self.logger.experiment.add_image('example\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\\n def test_step_end(self, *args, **kwargs) -> Dict[str, Tensor]:\\n \\\"\\\"\\\"\\n Use this when testing with dp or ddp2 because :meth:`test_step` will operate\\n on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n Note:\\n If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code.\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]\\n test_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in :meth:`test_step` for each batch part.\\n\\n Return:\\n Dict or OrderedDict - passed to the :meth:`test_epoch_end`.\\n\\n Examples:\\n .. code-block:: python\\n\\n # WITHOUT test_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def test_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n return {'loss': loss}\\n\\n # --------------\\n # with test_step_end to do softmax over the full batch\\n def test_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n return {'out': out}\\n\\n def test_step_end(self, outputs):\\n # this out is now the full size of the batch\\n out = outputs['out']\\n\\n # this softmax now uses the full batch size\\n loss = nce_loss(loss)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_end\\n def test_end(self, outputs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. Use :meth:`test_epoch_end` instead.\\n Will be removed in 1.0.0.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\\n def test_epoch_end(\\n self, outputs: Union[List[Dict[str, Tensor]], List[List[Dict[str, Tensor]]]]\\n ) -> Dict[str, Dict[str, Tensor]]:\\n \\\"\\\"\\\"\\n Called at the end of a test epoch with the output of all test steps.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n test_outs = []\\n for test_batch in test_data:\\n out = test_step(test_batch)\\n test_outs.append(out)\\n test_epoch_end(test_outs)\\n\\n Args:\\n outputs: List of outputs you defined in :meth:`test_step_end`, or if there\\n are multiple dataloaders, a list containing a list of outputs for each dataloader\\n\\n Return:\\n Dict or OrderedDict: Dict has the following optional keys:\\n\\n - progress_bar -> Dict for progress bar display. Must have only tensors.\\n - log -> Dict of metrics to add to logger. Must have only tensors (no images, etc).\\n\\n Note:\\n If you didn't define a :meth:`test_step`, this won't be called.\\n\\n - The outputs here are strictly for logging or progress bar.\\n - If you don't need to display anything, don't return anything.\\n - If you want to manually set current step, specify it with the 'step' key in the 'log' Dict\\n\\n Examples:\\n With a single dataloader:\\n\\n .. code-block:: python\\n\\n def test_epoch_end(self, outputs):\\n test_acc_mean = 0\\n for output in outputs:\\n test_acc_mean += output['test_acc']\\n\\n test_acc_mean /= len(outputs)\\n tqdm_dict = {'test_acc': test_acc_mean.item()}\\n\\n # show test_loss and test_acc in progress bar but only log test_loss\\n results = {\\n 'progress_bar': tqdm_dict,\\n 'log': {'test_acc': test\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) -> DistributedDataParallel:\\n r\\\"\\\"\\\"\\n Override to init DDP in your own way or with your own wrapper.\\n The only requirements are that:\\n\\n 1. On a validation batch, the call goes to ``model.validation_step``.\\n 2. On a training batch, the call goes to ``model.training_step``.\\n 3. On a testing batch, the call goes to ``model.test_step``.\\n\\n Args:\\n model: the :class:`LightningModule` currently being optimized.\\n device_ids: the list of GPU ids.\\n\\n Return:\\n DDP wrapped model\\n\\n Examples:\\n .. code-block:: python\\n\\n # default implementation used in Trainer\\n def configure_ddp(self, model, device_ids):\\n # Lightning DDP simply routes to test_step, val_step, etc...\\n model = LightningDistributedDataParallel(\\n model,\\n device_ids=device_ids,\\n find_unused_parameters=True\\n )\\n return model\\n\\n \\\"\\\"\\\"\\n model = LightningDistributedDataParallel(model, device_ids=device_ids, find_unused_parameters=True)\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection\\n def _init_slurm_connection(self) -> None:\\n \\\"\\\"\\\"\\n Sets up environment variables necessary for pytorch distributed communications\\n based on slurm environment.\\n \\\"\\\"\\\"\\n # use slurm job id for the port number\\n # guarantees unique ports across jobs from same grid search\\n try:\\n # use the last 4 numbers in the job id as the id\\n default_port = os.environ['SLURM_JOB_ID']\\n default_port = default_port[-4:]\\n\\n # all ports should be in the 10k+ range\\n default_port = int(default_port) + 15000\\n\\n except Exception:\\n default_port = 12910\\n\\n # if user gave a port number, use that one instead\\n try:\\n default_port = os.environ['MASTER_PORT']\\n except Exception:\\n os.environ['MASTER_PORT'] = str(default_port)\\n\\n # figure out the root node addr\\n try:\\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\\n except Exception:\\n root_node = '127.0.0.1'\\n\\n root_node = self.trainer.resolve_root_node_address(root_node)\\n os.environ['MASTER_ADDR'] = root_node\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\\n def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool = True) -> None:\\n \\\"\\\"\\\"\\n Override to define your custom way of setting up a distributed environment.\\n\\n Lightning's implementation uses env:// init by default and sets the first node as root\\n for SLURM managed cluster.\\n\\n Args:\\n global_rank: The global process idx.\\n world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus).\\n is_slurm_managing_tasks: is cluster managed by SLURM.\\n\\n \\\"\\\"\\\"\\n if is_slurm_managing_tasks:\\n self._init_slurm_connection()\\n\\n if 'MASTER_ADDR' not in os.environ:\\n rank_zero_warn(\\\"MASTER_ADDR environment variable is not defined. Set as localhost\\\")\\n os.environ['MASTER_ADDR'] = '127.0.0.1'\\n log.debug(f\\\"MASTER_ADDR: {os.environ['MASTER_ADDR']}\\\")\\n\\n if 'MASTER_PORT' not in os.environ:\\n rank_zero_warn(\\\"MASTER_PORT environment variable is not defined. Set as 12910\\\")\\n os.environ['MASTER_PORT'] = '12910'\\n log.debug(f\\\"MASTER_PORT: {os.environ['MASTER_PORT']}\\\")\\n\\n if 'WORLD_SIZE' in os.environ and int(os.environ['WORLD_SIZE']) != world_size:\\n rank_zero_warn(\\n f\\\"WORLD_SIZE environment variable ({os.environ['WORLD_SIZE']}) \\\"\\n f\\\"is not equal to the computed world size ({world_size}). Ignored.\\\"\\n )\\n\\n torch_backend = \\\"nccl\\\" if self.trainer.on_gpu else \\\"gloo\\\"\\n log.info(f\\\"initializing ddp: GLOBAL_RANK: {global_rank}, MEMBER: {global_rank+1}/{world_size}\\\")\\n torch_distrib.init_process_group(torch_backend, rank=global_rank, world_size=world_size)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\\n def configure_apex(\\n self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str\\n ) -> Tuple['LightningModule', List[Optimizer]]:\\n r\\\"\\\"\\\"\\n Override to init AMP your own way.\\n Must return a model and list of optimizers.\\n\\n Args:\\n amp: pointer to amp library object.\\n model: pointer to current :class:`LightningModule`.\\n optimizers: list of optimizers passed in :meth:`configure_optimizers`.\\n amp_level: AMP mode chosen ('O1', 'O2', etc...)\\n\\n Return:\\n Apex wrapped model and optimizers\\n\\n Examples:\\n .. code-block:: python\\n\\n # Default implementation used by Trainer.\\n def configure_apex(self, amp, model, optimizers, amp_level):\\n model, optimizers = amp.initialize(\\n model, optimizers, opt_level=amp_level,\\n )\\n\\n return model, optimizers\\n \\\"\\\"\\\"\\n model, optimizers = amp.initialize(model, optimizers, opt_level=amp_level)\\n\\n return model, optimizers\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\\n def configure_optimizers(\\n self,\\n ) -> Optional[Union[Optimizer, Sequence[Optimizer], Dict, Sequence[Dict], Tuple[List, List]]]:\\n r\\\"\\\"\\\"\\n Choose what optimizers and learning-rate schedulers to use in your optimization.\\n Normally you'd need one. But in the case of GANs or similar you might have multiple.\\n\\n Return:\\n Any of these 6 options.\\n\\n - Single optimizer.\\n - List or Tuple - List of optimizers.\\n - Two lists - The first list has multiple optimizers, the second a list of LR schedulers (or lr_dict).\\n - Dictionary, with an 'optimizer' key, and (optionally) a 'lr_scheduler' key which value is a single LR scheduler or lr_dict.\\n - Tuple of dictionaries as described, with an optional 'frequency' key.\\n - None - Fit will run without any optimizer.\\n\\n Note:\\n The 'frequency' value is an int corresponding to the number of sequential batches\\n optimized with the specific optimizer. It should be given to none or to all of the optimizers.\\n There is a difference between passing multiple optimizers in a list,\\n and passing multiple optimizers in dictionaries with a frequency of 1:\\n In the former case, all optimizers will operate on the given batch in each optimization step.\\n In the latter, only one optimizer will operate on the given batch at every step.\\n\\n The lr_dict is a dictionary which contains scheduler and its associated configuration.\\n It has five keys. The default configuration is shown below.\\n\\n .. code-block:: python\\n\\n {\\n 'scheduler': lr_scheduler, # The LR schduler\\n 'interval': 'epoch', # The unit of the scheduler's step size\\n 'frequency': 1, # The frequency of the scheduler\\n 'reduce_on_platea\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\\n def optimizer_step(\\n self,\\n epoch: int,\\n batch_idx: int,\\n optimizer: Optimizer,\\n optimizer_idx: int,\\n second_order_closure: Optional[Callable] = None,\\n on_tpu: bool = False,\\n using_native_amp: bool = False,\\n using_lbfgs: bool = False,\\n ) -> None:\\n r\\\"\\\"\\\"\\n Override this method to adjust the default way the\\n :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.\\n By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example\\n once per optimizer.\\n\\n Args:\\n epoch: Current epoch\\n batch_idx: Index of current batch\\n optimizer: A PyTorch optimizer\\n optimizer_idx: If you used multiple optimizers this indexes into that list.\\n second_order_closure: closure for second order methods\\n on_tpu: true if TPU backward is required\\n using_native_amp: True if using native amp\\n using_lbfgs: True if the matching optimizer is lbfgs\\n\\n Examples:\\n .. code-block:: python\\n\\n # DEFAULT\\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\\n second_order_closure, on_tpu, using_native_amp, using_lbfgs):\\n optimizer.step()\\n\\n # Alternating schedule for optimizer steps (i.e.: GANs)\\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\\n second_order_closure, on_tpu, using_native_amp, using_lbfgs):\\n # update generator opt every 2 steps\\n if optimizer_idx == 0:\\n if batch_idx % 2 == 0 :\\n optimizer.step()\\n optimizer.zero_grad()\\n\\n # update discriminator opt every 4 steps\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_zero_grad\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_zero_grad\\n def optimizer_zero_grad(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int):\\n optimizer.zero_grad()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\\n def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list:\\n r\\\"\\\"\\\"\\n When using truncated backpropagation through time, each batch must be split along the\\n time dimension. Lightning handles this by default, but for custom behavior override\\n this function.\\n\\n Args:\\n batch: Current batch\\n split_size: The size of the split\\n\\n Return:\\n List of batch splits. Each split will be passed to :meth:`training_step` to enable truncated\\n back propagation through time. The default implementation splits root level Tensors and\\n Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.\\n\\n Examples:\\n .. code-block:: python\\n\\n def tbptt_split_batch(self, batch, split_size):\\n splits = []\\n for t in range(0, time_dims[0], split_size):\\n batch_split = []\\n for i, x in enumerate(batch):\\n if isinstance(x, torch.Tensor):\\n split_x = x[:, t:t + split_size]\\n elif isinstance(x, collections.Sequence):\\n split_x = [None] * len(x)\\n for batch_idx in range(len(x)):\\n split_x[batch_idx] = x[batch_idx][t:t + split_size]\\n\\n batch_split.append(split_x)\\n\\n splits.append(batch_split)\\n\\n return splits\\n\\n Note:\\n Called in the training loop after\\n :meth:`~pytorch_lightning.callbacks.base.Callback.on_batch_start`\\n if :paramref:`~pytorch_lightning.trainer.Trainer.truncated_bptt_steps` > 0.\\n Each returned batch split is passed separately to :meth:`training_step`.\\n\\n \\\"\\\"\\\"\\n time_dims = [len(x[0]) for x in batch if isinstance(x\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\\n def prepare_data(self) -> None:\\n \\\"\\\"\\\"\\n Use this to download and prepare data.\\n\\n .. warning:: DO NOT set state to the model (use `setup` instead)\\n since this is NOT called on every GPU in DDP/TPU\\n\\n Example::\\n\\n def prepare_data(self):\\n # good\\n download_data()\\n tokenize()\\n etc()\\n\\n # bad\\n self.split = data_split\\n self.some_state = some_other_state()\\n\\n In DDP prepare_data can be called in two ways (using Trainer(prepare_data_per_node)):\\n\\n 1. Once per node. This is the default and is only called on LOCAL_RANK=0.\\n 2. Once in total. Only called on GLOBAL_RANK=0.\\n\\n Example::\\n\\n # DEFAULT\\n # called once per node on LOCAL_RANK=0 of that node\\n Trainer(prepare_data_per_node=True)\\n\\n # call on GLOBAL_RANK=0 (great for shared file systems)\\n Trainer(prepare_data_per_node=False)\\n\\n This is called before requesting the dataloaders:\\n\\n .. code-block:: python\\n\\n model.prepare_data()\\n if ddp/tpu: init()\\n model.setup(stage)\\n model.train_dataloader()\\n model.val_dataloader()\\n model.test_dataloader()\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\\n def train_dataloader(self) -> DataLoader:\\n \\\"\\\"\\\"\\n Implement a PyTorch DataLoader for training.\\n\\n Return:\\n Single PyTorch :class:`~torch.utils.data.DataLoader`.\\n\\n The dataloader you return will not be called every epoch unless you set\\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\\n\\n For data processing use the following pattern:\\n\\n - download in :meth:`prepare_data`\\n - process and split in :meth:`setup`\\n\\n However, the above are only necessary for distributed processing.\\n\\n .. warning:: do not assign state in prepare_data\\n\\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\\n - ...\\n - :meth:`prepare_data`\\n - :meth:`setup`\\n - :meth:`train_dataloader`\\n\\n Note:\\n Lightning adds the correct sampler for distributed and arbitrary hardware.\\n There is no need to set it yourself.\\n\\n Example:\\n .. code-block:: python\\n\\n def train_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,\\n download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.batch_size,\\n shuffle=True\\n )\\n return loader\\n\\n \\\"\\\"\\\"\\n rank_zero_warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\\n def tng_dataloader(self): # todo: remove in v1.0.0\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.5.0. Use :meth:`train_dataloader` instead. Will be removed in 1.0.0.\\n \\\"\\\"\\\"\\n output = self.train_dataloader()\\n rank_zero_warn(\\n \\\"`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0.\\\"\\n \\\" and this method will be removed in v1.0.0\\\",\\n DeprecationWarning,\\n )\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\\n def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\\n r\\\"\\\"\\\"\\n Implement one or multiple PyTorch DataLoaders for testing.\\n\\n The dataloader you return will not be called every epoch unless you set\\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\\n\\n For data processing use the following pattern:\\n\\n - download in :meth:`prepare_data`\\n - process and split in :meth:`setup`\\n\\n However, the above are only necessary for distributed processing.\\n\\n .. warning:: do not assign state in prepare_data\\n\\n\\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\\n - ...\\n - :meth:`prepare_data`\\n - :meth:`setup`\\n - :meth:`train_dataloader`\\n - :meth:`val_dataloader`\\n - :meth:`test_dataloader`\\n\\n Note:\\n Lightning adds the correct sampler for distributed and arbitrary hardware.\\n There is no need to set it yourself.\\n\\n Return:\\n Single or multiple PyTorch DataLoaders.\\n\\n Example:\\n .. code-block:: python\\n\\n def test_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,\\n download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.batch_size,\\n shuffle=False\\n )\\n\\n return loader\\n\\n # can also return multiple dataloaders\\n def test_dataloader(self):\\n return [loader_a, loader_b, ..., loader_n]\\n\\n Note:\\n If you don't need a te\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\\n def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\\n r\\\"\\\"\\\"\\n Implement one or multiple PyTorch DataLoaders for validation.\\n\\n The dataloader you return will not be called every epoch unless you set\\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\\n\\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\\n\\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\\n - ...\\n - :meth:`prepare_data`\\n - :meth:`train_dataloader`\\n - :meth:`val_dataloader`\\n - :meth:`test_dataloader`\\n\\n Note:\\n Lightning adds the correct sampler for distributed and arbitrary hardware\\n There is no need to set it yourself.\\n\\n Return:\\n Single or multiple PyTorch DataLoaders.\\n\\n Examples:\\n .. code-block:: python\\n\\n def val_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=False,\\n transform=transform, download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.batch_size,\\n shuffle=False\\n )\\n\\n return loader\\n\\n # can also return multiple dataloaders\\n def val_dataloader(self):\\n return [loader_a, loader_b, ..., loader_n]\\n\\n Note:\\n If you don't need a validation dataset and a :meth:`validation_step`, you don't need to\\n implement this method.\\n\\n Note:\\n In the case where you return multiple validation dataloaders, the :meth:`validation_st\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.summarize\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.summarize\\n def summarize(self, mode: str = ModelSummary.MODE_DEFAULT) -> ModelSummary:\\n model_summary = ModelSummary(self, mode=mode)\\n log.info('\\\\n' + str(model_summary))\\n return model_summary\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.freeze\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.freeze\\n def freeze(self) -> None:\\n r\\\"\\\"\\\"\\n Freeze all params for inference.\\n\\n Example:\\n .. code-block:: python\\n\\n model = MyLightningModule(...)\\n model.freeze()\\n\\n \\\"\\\"\\\"\\n for param in self.parameters():\\n param.requires_grad = False\\n\\n self.eval()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\\n def unfreeze(self) -> None:\\n \\\"\\\"\\\"\\n Unfreeze all parameters for training.\\n\\n .. code-block:: python\\n\\n model = MyLightningModule(...)\\n model.unfreeze()\\n\\n \\\"\\\"\\\"\\n for param in self.parameters():\\n param.requires_grad = True\\n\\n self.train()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\\n def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\\n r\\\"\\\"\\\"\\n Called by Lightning to restore your model.\\n If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.\\n\\n Args:\\n checkpoint: Loaded checkpoint\\n\\n\\n Example:\\n .. code-block:: python\\n\\n def on_load_checkpoint(self, checkpoint):\\n # 99% of the time you don't need to implement this method\\n self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']\\n\\n Note:\\n Lightning auto-restores global step, epoch, and train state including amp scaling.\\n There is no need for you to restore anything regarding training.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\\n def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\\n r\\\"\\\"\\\"\\n Called by Lightning when saving a checkpoint to give you a chance to store anything\\n else you might want to save.\\n\\n Args:\\n checkpoint: Checkpoint to be saved\\n\\n Example:\\n .. code-block:: python\\n\\n def on_save_checkpoint(self, checkpoint):\\n # 99% of use cases you don't need to implement this method\\n checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object\\n\\n Note:\\n Lightning saves all aspects of training (epoch, global step, etc...)\\n including amp scaling.\\n There is no need for you to store anything about training.\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict\\n def get_progress_bar_dict(self) -> Dict[str, Union[int, str]]:\\n r\\\"\\\"\\\"\\n Implement this to override the default items displayed in the progress bar.\\n By default it includes the average loss value, split index of BPTT (if used)\\n and the version of the experiment when using a logger.\\n\\n .. code-block::\\n\\n Epoch 1: 4%|▎ | 40/1095 [00:03<01:37, 10.84it/s, loss=4.501, v_num=10]\\n\\n Here is an example how to override the defaults:\\n\\n .. code-block:: python\\n\\n def get_progress_bar_dict(self):\\n # don't show the version number\\n items = super().get_progress_bar_dict()\\n items.pop(\\\"v_num\\\", None)\\n return items\\n\\n Return:\\n Dictionary with the items to be displayed in the progress bar.\\n \\\"\\\"\\\"\\n # call .item() only once but store elements without graphs\\n running_train_loss = self.trainer.running_loss.mean()\\n avg_training_loss = running_train_loss.cpu().item() if running_train_loss is not None else float('NaN')\\n tqdm_dict = {'loss': '{:.3f}'.format(avg_training_loss)}\\n\\n if self.trainer.truncated_bptt_steps is not None:\\n tqdm_dict['split_idx'] = self.trainer.split_idx\\n\\n if self.trainer.logger is not None and self.trainer.logger.version is not None:\\n version = self.trainer.logger.version\\n # show last 4 places of long version strings\\n version = version[-4:] if isinstance(version, str) else version\\n tqdm_dict['v_num'] = version\\n\\n return tqdm_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.get_tqdm_dict\\n def get_tqdm_dict(self) -> Dict[str, Union[int, str]]:\\n \\\"\\\"\\\"\\n Additional items to be displayed in the progress bar.\\n\\n Return:\\n Dictionary with the items to be displayed in the progress bar.\\n\\n Warning:\\n Deprecated since v0.7.3.\\n Use :meth:`get_progress_bar_dict` instead.\\n \\\"\\\"\\\"\\n rank_zero_warn(\\n \\\"`get_tqdm_dict` was renamed to `get_progress_bar_dict` in v0.7.3\\\"\\n \\\" and this method will be removed in v1.0.0\\\",\\n DeprecationWarning,\\n )\\n return self.get_progress_bar_dict()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule._auto_collect_arguments\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule._auto_collect_arguments\\n @classmethod\\n def _auto_collect_arguments(cls, frame=None) -> Tuple[Dict, Dict]:\\n \\\"\\\"\\\"\\n Collect all module arguments in the current constructor and all child constructors.\\n The child constructors are all the ``__init__`` methods that reach the current class through\\n (chained) ``super().__init__()`` calls.\\n\\n Args:\\n frame: instance frame\\n\\n Returns:\\n self_arguments: arguments dictionary of the first instance\\n parents_arguments: arguments dictionary of the parent's instances\\n \\\"\\\"\\\"\\n if not frame:\\n frame = inspect.currentframe()\\n\\n frame_args = collect_init_args(frame.f_back, [])\\n self_arguments = frame_args[-1]\\n\\n # set module_arguments in child\\n self_arguments = self_arguments\\n parents_arguments = {}\\n\\n # add all arguments from parents\\n for args in frame_args[:-1]:\\n parents_arguments.update(args)\\n return self_arguments, parents_arguments\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.save_hyperparameters\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.save_hyperparameters\\n def save_hyperparameters(self, *args, frame=None) -> None:\\n \\\"\\\"\\\"Save all model arguments.\\n\\n Args:\\n args: single object of `dict`, `NameSpace` or `OmegaConf`\\n or string names or argumenst from class `__init__`\\n\\n >>> from collections import OrderedDict\\n >>> class ManuallyArgsModel(LightningModule):\\n ... def __init__(self, arg1, arg2, arg3):\\n ... super().__init__()\\n ... # manually assine arguments\\n ... self.save_hyperparameters('arg1', 'arg3')\\n ... def forward(self, *args, **kwargs):\\n ... ...\\n >>> model = ManuallyArgsModel(1, 'abc', 3.14)\\n >>> model.hparams\\n \\\"arg1\\\": 1\\n \\\"arg3\\\": 3.14\\n\\n >>> class AutomaticArgsModel(LightningModule):\\n ... def __init__(self, arg1, arg2, arg3):\\n ... super().__init__()\\n ... # equivalent automatic\\n ... self.save_hyperparameters()\\n ... def forward(self, *args, **kwargs):\\n ... ...\\n >>> model = AutomaticArgsModel(1, 'abc', 3.14)\\n >>> model.hparams\\n \\\"arg1\\\": 1\\n \\\"arg2\\\": abc\\n \\\"arg3\\\": 3.14\\n\\n >>> class SingleArgModel(LightningModule):\\n ... def __init__(self, params):\\n ... super().__init__()\\n ... # manually assign single argument\\n ... self.save_hyperparameters(params)\\n ... def forward(self, *args, **kwargs):\\n ... ...\\n >>> model = SingleArgModel(Namespace(p1=1, p2='abc', p3=3.14))\\n >>> model.hparams\\n \\\"p1\\\": 1\\n \\\"p2\\\": abc\\n \\\"p3\\\": 3.14\\n \\\"\\\"\\\"\\n if not frame:\\n frame = inspect.currentframe().f_back\\n init_args = get_init_args(frame)\\n assert init_args, 'failed to inspect the self init'\\n if not args:\\n hp = init_args\\n self._\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule._set_hparams\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule._set_hparams\\n def _set_hparams(self, hp: Union[dict, Namespace, str]) -> None:\\n if isinstance(hp, Namespace):\\n hp = vars(hp)\\n if isinstance(hp, dict):\\n hp = AttributeDict(hp)\\n elif isinstance(hp, PRIMITIVE_TYPES):\\n raise ValueError(f'Primitives {PRIMITIVE_TYPES} are not allowed.')\\n elif not isinstance(hp, ALLOWED_CONFIG_TYPES):\\n raise ValueError(f'Unsupported config type of {type(hp)}.')\\n\\n if isinstance(hp, dict) and isinstance(self.hparams, dict):\\n self.hparams.update(hp)\\n else:\\n self._hparams = hp\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.to_onnx\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.to_onnx\\n def to_onnx(self, file_path: str, input_sample: Optional[Tensor] = None, **kwargs):\\n \\\"\\\"\\\"Saves the model in ONNX format\\n\\n Args:\\n file_path: The path of the file the model should be saved to.\\n input_sample: A sample of an input tensor for tracing.\\n **kwargs: Will be passed to torch.onnx.export function.\\n\\n Example:\\n >>> class SimpleModel(LightningModule):\\n ... def __init__(self):\\n ... super().__init__()\\n ... self.l1 = torch.nn.Linear(in_features=64, out_features=4)\\n ...\\n ... def forward(self, x):\\n ... return torch.relu(self.l1(x.view(x.size(0), -1)))\\n\\n >>> with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmpfile:\\n ... model = SimpleModel()\\n ... input_sample = torch.randn((1, 64))\\n ... model.to_onnx(tmpfile.name, input_sample, export_params=True)\\n ... os.path.isfile(tmpfile.name)\\n True\\n \\\"\\\"\\\"\\n\\n if isinstance(input_sample, Tensor):\\n input_data = input_sample\\n elif self.example_input_array is not None:\\n input_data = self.example_input_array\\n else:\\n raise ValueError(f'input_sample and example_input_array tensors are both missing.')\\n\\n if 'example_outputs' not in kwargs:\\n self.eval()\\n kwargs['example_outputs'] = self(input_data)\\n\\n torch.onnx.export(self, input_data, file_path, **kwargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.hparams\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.hparams\\n @property\\n def hparams(self) -> Union[AttributeDict, str]:\\n if not hasattr(self, '_hparams'):\\n self._hparams = AttributeDict()\\n return self._hparams\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.hparams\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.hparams\\n @hparams.setter\\n def hparams(self, hp: Union[dict, Namespace, Any]):\\n hparams_assignment_name = self.__get_hparams_assignment_variable()\\n self._hparams_name = hparams_assignment_name\\n self._set_hparams(hp)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.__get_hparams_assignment_variable\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.__get_hparams_assignment_variable\\n def __get_hparams_assignment_variable(self):\\n \\\"\\\"\\\"\\n looks at the code of the class to figure out what the user named self.hparams\\n this only happens when the user explicitly sets self.hparams\\n \\\"\\\"\\\"\\n try:\\n class_code = inspect.getsource(self.__class__)\\n lines = class_code.split('\\\\n')\\n for line in lines:\\n line = re.sub(r\\\"\\\\s+\\\", \\\"\\\", line, flags=re.UNICODE)\\n if '.hparams=' in line:\\n return line.split('=')[1]\\n except Exception as e:\\n return 'hparams'\\n\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::_has_iterable_dataset\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::_has_iterable_dataset\\ndef _has_iterable_dataset(dataloader: DataLoader):\\n return ITERABLE_DATASET_EXISTS and hasattr(dataloader, 'dataset') \\\\\\n and isinstance(dataloader.dataset, IterableDataset)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::_has_len\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::_has_len\\ndef _has_len(dataloader: DataLoader) -> bool:\\n \\\"\\\"\\\" Checks if a given Dataloader has __len__ method implemented i.e. if\\n it is a finite dataloader or infinite dataloader. \\\"\\\"\\\"\\n\\n try:\\n # try getting the length\\n if len(dataloader) == 0:\\n raise ValueError('`Dataloader` returned 0 length.'\\n ' Please make sure that your Dataloader at least returns 1 batch')\\n has_len = True\\n except TypeError:\\n has_len = False\\n except NotImplementedError: # e.g. raised by torchtext if a batch_size_fn is used\\n has_len = False\\n\\n if has_len and _has_iterable_dataset(dataloader) and LooseVersion(torch.__version__) >= LooseVersion(\\\"1.4.0\\\"):\\n rank_zero_warn(\\n 'Your `IterableDataset` has `__len__` defined.'\\n ' In combination with multi-processing data loading (e.g. batch size > 1),'\\n ' this can lead to unintended side effects since the samples will be duplicated.'\\n )\\n return has_len\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_overridden\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.is_overridden\\n @abstractmethod\\n def is_overridden(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._check_batch_limits\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._check_batch_limits\\n def _check_batch_limits(self, name: str) -> None:\\n # TODO: verify it is still needed and deprecate it..\\n value = getattr(self, name)\\n\\n # ints are fine\\n if isinstance(value, int):\\n return\\n\\n msg = f'`{name}` must lie in the range [0.0, 1.0], but got {value:.3f}. (or pass in an int)'\\n if name == 'val_check_interval':\\n msg += ' If you want to disable validation set `limit_val_batches` to 0.0 instead.'\\n\\n if not 0. <= value <= 1.:\\n raise ValueError(msg)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._worker_check\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._worker_check\\n def _worker_check(self, dataloader: DataLoader, name: str) -> None:\\n on_windows = platform.system() == 'Windows'\\n\\n # ddp_spawn + num_workers > 0 don't mix! tell the user\\n is_dataloader = isinstance(dataloader, DataLoader)\\n using_spawn = self.distributed_backend == 'ddp_spawn'\\n if is_dataloader and not on_windows:\\n if dataloader.num_workers > 0 and using_spawn:\\n rank_zero_warn('Dataloader(num_workers>0) and ddp_spawn do not mix well!'\\n ' Your performance might suffer dramatically.'\\n ' Please consider setting distributed_backend=ddp to use num_workers > 0'\\n ' (this is a bottleneck of Python .spawn() and PyTorch')\\n\\n elif dataloader.num_workers == 0 and using_spawn:\\n rank_zero_warn('You are using `distributed_backend=ddp_spawn` with num_workers=0.'\\n ' For much faster performance, switch to `distributed_backend=ddp`'\\n ' and set `num_workers>0`')\\n\\n elif dataloader.num_workers <= 2 and multiprocessing.cpu_count() > 2 and not using_spawn:\\n num_cpus = multiprocessing.cpu_count()\\n rank_zero_warn(f'The dataloader, {name}, does not have many workers which may be a bottleneck.'\\n ' Consider increasing the value of the `num_workers` argument`'\\n f' (try {num_cpus} which is the number of cpus on this machine)'\\n ' in the `DataLoader` init to improve performance.')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.auto_add_sampler\\n def auto_add_sampler(self, dataloader: DataLoader, train: bool) -> DataLoader:\\n\\n # don't do anything if it's not a dataloader\\n is_dataloader = isinstance(dataloader, DataLoader)\\n # don't manipulate iterable datasets\\n is_iterable_ds = _has_iterable_dataset(dataloader)\\n\\n if not is_dataloader or is_iterable_ds:\\n return dataloader\\n need_dist_sampler = (self.use_ddp or self.use_ddp2 or self.use_horovod or self.use_tpu)\\n\\n if self.replace_sampler_ddp and need_dist_sampler:\\n if not isinstance(dataloader.sampler, (SequentialSampler, RandomSampler)):\\n raise MisconfigurationException(\\n 'You seem to have configured a sampler in your DataLoader. This will be replaced '\\n ' by `DistributedSampler` since `replace_sampler_ddp` is True and you are using'\\n ' distributed training. Either remove the sampler from your DataLoader or set'\\n ' `replace_sampler_ddp`=False if you want to use your custom sampler.')\\n\\n # replace with distributed sampler\\n sampler = self._get_distributed_sampler(dataloader, train)\\n dataloader = self.replace_sampler(dataloader, sampler)\\n\\n return dataloader\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.replace_sampler\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.replace_sampler\\n def replace_sampler(self, dataloader, sampler):\\n skip_keys = ['sampler', 'batch_sampler', 'dataset_kind']\\n\\n dl_args = {\\n k: v for k, v in dataloader.__dict__.items() if not k.startswith('_') and k not in skip_keys\\n }\\n\\n dl_args['sampler'] = sampler\\n dataloader = type(dataloader)(**dl_args)\\n return dataloader\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._get_distributed_sampler\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._get_distributed_sampler\\n def _get_distributed_sampler(self, dataloader, train):\\n if self.use_tpu:\\n kwargs = dict(num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal())\\n elif self.use_horovod:\\n kwargs = dict(num_replicas=hvd.size(), rank=hvd.rank())\\n else:\\n world_size = {\\n 'ddp': self.num_nodes * self.num_processes,\\n 'ddp_spawn': self.num_nodes * self.num_processes,\\n 'ddp2': self.num_nodes,\\n 'ddp_cpu': self.num_processes * self.num_nodes\\n }\\n assert self.distributed_backend is not None\\n kwargs = dict(num_replicas=world_size[self.distributed_backend], rank=self.global_rank)\\n\\n kwargs['shuffle'] = train\\n sampler = DistributedSampler(dataloader.dataset, **kwargs)\\n return sampler\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_train_dataloader\\n def reset_train_dataloader(self, model: LightningModule) -> None:\\n \\\"\\\"\\\"Resets the train dataloader and initialises required variables\\n (number of batches, when to validate, etc.).\\n\\n Args:\\n model: The current `LightningModule`\\n \\\"\\\"\\\"\\n self.train_dataloader = self.request_dataloader(model.train_dataloader)\\n\\n self.num_training_batches = 0\\n\\n # automatically add samplers\\n self.train_dataloader = self.auto_add_sampler(self.train_dataloader, train=True)\\n\\n self._worker_check(self.train_dataloader, 'train dataloader')\\n self._check_batch_limits('limit_train_batches')\\n\\n if not _has_len(self.train_dataloader):\\n self.num_training_batches = float('inf')\\n else:\\n # try getting the length\\n if isinstance(self.limit_train_batches, float):\\n self.num_training_batches = len(self.train_dataloader)\\n self.num_training_batches = int(self.num_training_batches * self.limit_train_batches)\\n else:\\n self.num_training_batches = min(len(self.train_dataloader), self.limit_train_batches)\\n\\n # determine when to check validation\\n # if int passed in, val checks that often\\n # otherwise, it checks in [0, 1.0] % range of a training epoch\\n if isinstance(self.val_check_interval, int):\\n self.val_check_batch = self.val_check_interval\\n if self.val_check_batch > self.num_training_batches:\\n raise ValueError(\\n f'`val_check_interval` ({self.val_check_interval}) must be less than or equal '\\n f'to the number of the training batches ({self.num_training_batches}). '\\n 'If you want to disable validation set `limit_val_batches` to 0.0 instead.')\\n else:\\n if not _has_len(self.train_dataloader):\\n if self.\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._reset_eval_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin._reset_eval_dataloader\\n def _reset_eval_dataloader(\\n self,\\n model: LightningModule,\\n mode: str\\n ) -> Tuple[List[Union[int, float]], List[DataLoader]]:\\n \\\"\\\"\\\"Generic method to reset a dataloader for evaluation.\\n\\n Args:\\n model: The current `LightningModule`\\n mode: Either `'val'` or `'test'`\\n\\n Returns:\\n Tuple (num_batches, dataloaders)\\n \\\"\\\"\\\"\\n # use the training loader as val and test when overfitting\\n if self.overfit_batches > 0:\\n dataloaders = self.request_dataloader(getattr(model, 'train_dataloader'))\\n else:\\n dataloaders = self.request_dataloader(getattr(model, f'{mode}_dataloader'))\\n\\n if not isinstance(dataloaders, list):\\n dataloaders = [dataloaders]\\n\\n for loader_i in range(len(dataloaders)):\\n loader = dataloaders[loader_i]\\n\\n # shuffling in val and test set is bad practice\\n if mode in ('val', 'test') and hasattr(loader, 'sampler') and isinstance(loader.sampler, RandomSampler):\\n\\n # when overfitting, the dataloader should not have sampler\\n if self.overfit_batches > 0:\\n rank_zero_warn('You requested to overfit but enabled training dataloader shuffling.'\\n ' We are turning it off for you.')\\n dataloaders[loader_i] = self.replace_sampler(loader, SequentialSampler(loader.dataset))\\n\\n else:\\n rank_zero_warn(f'Your {mode}_dataloader has `shuffle=True`, it is best practice to turn'\\n ' this off for validation and test dataloaders.')\\n\\n if any([dl is None for dl in dataloaders]):\\n rank_zero_warn(\\\"One of given dataloaders is None and it will be skipped.\\\")\\n\\n # add samplers\\n dataloaders = [self.auto_add_sampler(dl, train=False\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_val_dataloader\\n def reset_val_dataloader(self, model: LightningModule) -> None:\\n \\\"\\\"\\\"Resets the validation dataloader and determines the number of batches.\\n\\n Args:\\n model: The current `LightningModule`\\n \\\"\\\"\\\"\\n has_loader = self.is_overridden('val_dataloader', model)\\n has_step = self.is_overridden('validation_step', model)\\n if has_loader and has_step:\\n self.num_val_batches, self.val_dataloaders = self._reset_eval_dataloader(model, 'val')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.reset_test_dataloader\\n def reset_test_dataloader(self, model) -> None:\\n \\\"\\\"\\\"Resets the validation dataloader and determines the number of batches.\\n\\n Args:\\n model: The current `LightningModule`\\n \\\"\\\"\\\"\\n has_loader = self.is_overridden('test_dataloader', model)\\n has_step = self.is_overridden('test_step', model)\\n if has_loader and has_step:\\n self.num_test_batches, self.test_dataloaders =\\\\\\n self._reset_eval_dataloader(model, 'test')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_dataloader\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.request_dataloader\\n def request_dataloader(self, dataloader_fx: Callable) -> DataLoader:\\n \\\"\\\"\\\"Handles downloading data in the GPU or TPU case.\\n\\n Args:\\n dataloader_fx: The bound dataloader getter\\n\\n Returns:\\n The dataloader\\n \\\"\\\"\\\"\\n dataloader = dataloader_fx()\\n\\n # get the function we'll use to get data\\n if self.use_ddp or self.use_ddp2:\\n # all processes wait until data download has happened\\n torch_distrib.barrier()\\n\\n # data download/load on TPU\\n elif self.use_tpu and XLA_AVAILABLE:\\n # all processes wait until data download has happened\\n torch_xla.core.xla_model.rendezvous('pl.TrainerDataLoadingMixin.get_dataloaders')\\n\\n elif self.use_horovod:\\n # all processes wait until data download has happened\\n hvd.join()\\n\\n return dataloader\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount\",\n \"text\": \"pytorch_lightning/trainer/data_loading.py::TrainerDataLoadingMixin.determine_data_use_amount\\n def determine_data_use_amount(self, overfit_batches: float) -> None:\\n \\\"\\\"\\\"Use less data for debugging purposes\\\"\\\"\\\"\\n if overfit_batches > 0:\\n if isinstance(overfit_batches, float) and overfit_batches > 1:\\n raise ValueError('`overfit_batches` when used as a percentage must'\\n f' be in range 0.0 < x < 1.0 but got {overfit_batches:.3f}.')\\n self.limit_train_batches = overfit_batches\\n self.limit_val_batches = overfit_batches\\n self.limit_test_batches = overfit_batches\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.copy_trainer_model_properties\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.copy_trainer_model_properties\\n @abstractmethod\\n def copy_trainer_model_properties(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.get_model\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.get_model\\n @abstractmethod\\n def get_model(self) -> LightningModule:\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.is_overridden\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.is_overridden\\n @abstractmethod\\n def is_overridden(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_tpu\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_tpu\\n @abstractmethod\\n def transfer_batch_to_tpu(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_gpu\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_gpu\\n @abstractmethod\\n def transfer_batch_to_gpu(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.add_progress_bar_metrics\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.add_progress_bar_metrics\\n @abstractmethod\\n def add_progress_bar_metrics(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics\\n @abstractmethod\\n def log_metrics(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_test_dataloader\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_test_dataloader\\n @abstractmethod\\n def reset_test_dataloader(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_val_dataloader\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_val_dataloader\\n @abstractmethod\\n def reset_val_dataloader(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_start\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_start\\n def __call_eval_loop_hook_start(self, test_mode):\\n \\\"\\\"\\\"on_validation/test_epoch_start\\\"\\\"\\\"\\n self.__call_eval_loop_hook_evt(test_mode, 'start')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_end\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_end\\n def __call_eval_loop_hook_end(self, test_mode):\\n \\\"\\\"\\\"on_validation/test_epoch_end\\\"\\\"\\\"\\n self.__call_eval_loop_hook_evt(test_mode, 'end')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_evt\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_evt\\n def __call_eval_loop_hook_evt(self, test_mode, epoch_event):\\n model = self.get_model()\\n\\n # on_[train/validation]_epoch_start hook\\n hook_root_name = 'test' if test_mode else 'validation'\\n hook_name = f'on_{hook_root_name}_epoch_{epoch_event}'\\n with self.profiler.profile(hook_name):\\n # call hook\\n getattr(self, hook_name)()\\n\\n # model hooks\\n if self.is_function_implemented(hook_name):\\n getattr(model, hook_name)()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin._evaluate\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin._evaluate\\n def _evaluate(\\n self,\\n model: LightningModule,\\n dataloaders: List[DataLoader],\\n max_batches: Union[int, List[int]],\\n test_mode: bool = False\\n ):\\n \\\"\\\"\\\"Run evaluation code.\\n\\n Args:\\n model: The model to evaluate.\\n dataloaders: A list of PyTorch dataloaders.\\n max_batches: An integer or list of integers with length of the number of dataloaders. Each\\n entry is the number of batches to process in the corresponding dataloader.\\n test_mode:\\n \\\"\\\"\\\"\\n # enable eval mode\\n model.zero_grad()\\n model.eval()\\n\\n # copy properties for forward overrides\\n self.copy_trainer_model_properties(model)\\n\\n # disable gradients to save memory\\n torch.set_grad_enabled(False)\\n\\n # bookkeeping\\n outputs = []\\n\\n # convert max_batches to list\\n if isinstance(max_batches, int):\\n max_batches = [max_batches] * len(dataloaders)\\n\\n # --------------------------\\n # ON_EVAL_EPOCH_START hook\\n # --------------------------\\n self.__call_eval_loop_hook_start(test_mode)\\n\\n # run validation\\n for dataloader_idx, dataloader in enumerate(dataloaders):\\n dl_outputs = []\\n\\n # on TPU we have to wrap it under the ParallelLoader\\n if self.use_tpu:\\n device = xm.xla_device(self.tpu_id)\\n dataloader = xla_pl.ParallelLoader(dataloader, [device])\\n dataloader = dataloader.per_device_loader(device)\\n\\n # each dataloader has a max num batches\\n dl_max_batches = max_batches[dataloader_idx]\\n\\n for batch_idx, batch in enumerate(dataloader):\\n if batch is None:\\n continue\\n\\n # stop short when running on limited batches\\n if batch_idx >= dl_max_batches:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__update_callback_metrics\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__update_callback_metrics\\n def __update_callback_metrics(self, eval_results, using_eval_result):\\n if using_eval_result:\\n if isinstance(eval_results, list):\\n for eval_result in eval_results:\\n self.callback_metrics = eval_result.callback_metrics\\n else:\\n self.callback_metrics = eval_results.callback_metrics\\n else:\\n if isinstance(eval_results, list):\\n for eval_result in eval_results:\\n # with a scalar return, auto set it to \\\"val_loss\\\" for callbacks\\n if isinstance(eval_result, torch.Tensor):\\n flat = {'val_loss': eval_result}\\n else:\\n flat = flatten_dict(eval_result)\\n self.callback_metrics.update(flat)\\n else:\\n # with a scalar return, auto set it to \\\"val_loss\\\" for callbacks\\n if isinstance(eval_results, torch.Tensor):\\n flat = {'val_loss': eval_results}\\n else:\\n flat = flatten_dict(eval_results)\\n self.callback_metrics.update(flat)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__run_eval_epoch_end\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__run_eval_epoch_end\\n def __run_eval_epoch_end(self, test_mode, outputs, dataloaders, using_eval_result):\\n model = self.get_model()\\n\\n # with a single dataloader don't pass an array\\n eval_results = outputs\\n if len(dataloaders) == 1:\\n eval_results = outputs[0]\\n\\n user_reduced = False\\n\\n if test_mode:\\n if self.is_overridden('test_end', model=model):\\n # TODO: remove in v1.0.0\\n if using_eval_result:\\n eval_results = self.__gather_epoch_end_eval_results(outputs)\\n\\n eval_results = model.test_end(eval_results)\\n user_reduced = True\\n rank_zero_warn('Method `test_end` was deprecated in v0.7 and will be removed in v1.0.'\\n ' Use `test_epoch_end` instead.', DeprecationWarning)\\n\\n elif self.is_overridden('test_epoch_end', model=model):\\n if using_eval_result:\\n eval_results = self.__gather_epoch_end_eval_results(outputs)\\n\\n eval_results = model.test_epoch_end(eval_results)\\n user_reduced = True\\n\\n else:\\n if self.is_overridden('validation_end', model=model):\\n # TODO: remove in v1.0.0\\n if using_eval_result:\\n eval_results = self.__gather_epoch_end_eval_results(outputs)\\n\\n eval_results = model.validation_end(eval_results)\\n user_reduced = True\\n rank_zero_warn('Method `validation_end` was deprecated in v0.7 and will be removed in v1.0.'\\n ' Use `validation_epoch_end` instead.', DeprecationWarning)\\n\\n elif self.is_overridden('validation_epoch_end', model=model):\\n if using_eval_result:\\n eval_results = self.__gather_epoch_end_eval_results(outputs)\\n\\n eval_results = model.v\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__gather_epoch_end_eval_results\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__gather_epoch_end_eval_results\\n def __gather_epoch_end_eval_results(self, outputs):\\n eval_results = []\\n for epoch_output in outputs:\\n result = epoch_output[0].__class__.gather(epoch_output)\\n if 'checkpoint_on' in result:\\n result.checkpoint_on = result.checkpoint_on.mean()\\n if 'early_stop_on' in result:\\n result.early_stop_on = result.early_stop_on.mean()\\n\\n eval_results.append(result)\\n\\n # with 1 dataloader don't pass in a list\\n if len(eval_results) == 1:\\n eval_results = eval_results[0]\\n return eval_results\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__eval_add_step_metrics\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__eval_add_step_metrics\\n def __eval_add_step_metrics(self, output):\\n # track step level metrics\\n if isinstance(output, EvalResult) and not self.running_sanity_check:\\n step_log_metrics = output.batch_log_metrics\\n step_pbar_metrics = output.batch_pbar_metrics\\n\\n if len(step_log_metrics) > 0:\\n self.log_metrics(step_log_metrics, {})\\n\\n if len(step_pbar_metrics) > 0:\\n self.add_progress_bar_metrics(step_pbar_metrics)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__auto_reduce_result_objs\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__auto_reduce_result_objs\\n def __auto_reduce_result_objs(self, outputs):\\n # outputs has a list of results per dataloader\\n eval_results = []\\n for dl_output in outputs:\\n result = dl_output[0]\\n result = result.__class__.reduce_on_epoch_end(dl_output)\\n if 'checkpoint_on' in result:\\n result.checkpoint_on = result.checkpoint_on.mean()\\n if 'early_stop_on' in result:\\n result.early_stop_on = result.early_stop_on.mean()\\n eval_results.append(result)\\n\\n return eval_results\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation\\n def run_evaluation(self, test_mode: bool = False):\\n # hook\\n model = self.get_model()\\n model.on_pre_performance_check()\\n\\n # select dataloaders\\n if test_mode:\\n self.reset_test_dataloader(model)\\n\\n dataloaders = self.test_dataloaders\\n max_batches = self.num_test_batches\\n else:\\n # val\\n if self.val_dataloaders is None:\\n self.reset_val_dataloader(model)\\n\\n dataloaders = self.val_dataloaders\\n max_batches = self.num_val_batches\\n\\n if dataloaders is None:\\n return [], []\\n\\n # Validation/Test begin callbacks\\n if test_mode:\\n self.on_test_start()\\n else:\\n self.on_validation_start()\\n\\n # enable disabling validation step with limit_val_batches = 0\\n should_skip = sum(max_batches) == 0\\n if should_skip:\\n return [], []\\n\\n # run evaluation (val_step + val_step_end + val_epoch_end)\\n eval_results = self._evaluate(self.model, dataloaders, max_batches, test_mode)\\n\\n # log the final eval loop metrics\\n eval_loop_results = self.__log_evaluation_epoch_metrics(eval_results, test_mode)\\n\\n # hook\\n model.on_post_performance_check()\\n\\n # eventual dataset reloading\\n if test_mode:\\n if self.reload_dataloaders_every_epoch:\\n self.reset_test_dataloader(model)\\n else:\\n # val\\n if self.reload_dataloaders_every_epoch:\\n self.reset_val_dataloader(model)\\n\\n # Validation/Test end callbacks\\n if test_mode:\\n self.on_test_end()\\n else:\\n self.on_validation_end()\\n\\n return eval_loop_results, eval_results\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__log_evaluation_epoch_metrics\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__log_evaluation_epoch_metrics\\n def __log_evaluation_epoch_metrics(self, eval_results, test_mode):\\n eval_loop_results = []\\n if eval_results is not None and len(eval_results) > 0:\\n\\n # in eval, the user may return something at every validation step without final reduction\\n if not isinstance(eval_results, list):\\n eval_results = [eval_results]\\n\\n for result_idx, result in enumerate(eval_results):\\n if isinstance(result, EvalResult):\\n prog_bar_metrics = result.epoch_pbar_metrics\\n log_metrics = result.epoch_log_metrics\\n callback_metrics = result.callback_metrics\\n else:\\n _, prog_bar_metrics, log_metrics, callback_metrics, _ = self.process_output(result)\\n\\n # eval loop returns all metrics\\n dataloader_result_metrics = {**prog_bar_metrics, **log_metrics, **callback_metrics}\\n\\n # add metrics to prog bar\\n self.add_progress_bar_metrics(prog_bar_metrics)\\n\\n # log metrics\\n self.log_metrics(log_metrics, {})\\n\\n # track metrics for callbacks\\n self.callback_metrics.update(callback_metrics)\\n\\n if len(dataloader_result_metrics) > 0:\\n eval_loop_results.append(dataloader_result_metrics)\\n\\n # log results of test\\n if test_mode and self.is_global_zero and self.verbose_test:\\n print('-' * 80)\\n for result_idx, results in enumerate(eval_loop_results):\\n print(f'DATALOADER:{result_idx} TEST RESULTS')\\n pprint(results)\\n print('-' * 80)\\n\\n return eval_loop_results\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluation_forward\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluation_forward\\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode: bool = False):\\n # make dataloader_idx arg in validation_step optional\\n args = [batch, batch_idx]\\n\\n if (test_mode and len(self.test_dataloaders) > 1) \\\\\\n or (not test_mode and len(self.val_dataloaders) > 1):\\n args.append(dataloader_idx)\\n\\n # handle DP, DDP forward\\n if self.use_ddp or self.use_dp or self.use_ddp2:\\n output = model(*args)\\n return output\\n\\n # Horovod\\n if self.use_horovod and self.on_gpu:\\n batch = self.transfer_batch_to_gpu(batch, hvd.local_rank())\\n args[0] = batch\\n\\n # single GPU data transfer\\n if self.use_single_gpu:\\n # for single GPU put inputs on gpu manually\\n root_gpu = 0\\n if isinstance(self.data_parallel_device_ids, list):\\n root_gpu = self.data_parallel_device_ids[0]\\n batch = self.transfer_batch_to_gpu(batch, root_gpu)\\n args[0] = batch\\n\\n # TPU data transfer\\n if self.use_tpu:\\n batch = self.transfer_batch_to_tpu(batch, self.tpu_id)\\n args[0] = batch\\n\\n # CPU, TPU or gpu step\\n if test_mode:\\n output = model.test_step(*args)\\n else:\\n output = model.validation_step(*args)\\n\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.get_model\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.get_model\\n @abstractmethod\\n def get_model(self) -> LightningModule:\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.save_checkpoint\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.save_checkpoint\\n @abstractmethod\\n def save_checkpoint(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.restore\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.restore\\n @abstractmethod\\n def restore(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.fit\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.fit\\n @abstractmethod\\n def fit(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.clip_gradients\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.clip_gradients\\n def clip_gradients(self):\\n\\n # this code is a modification of torch.nn.utils.clip_grad_norm_\\n # with TPU support based on https://github.com/pytorch/xla/blob/master/TROUBLESHOOTING.md\\n if self.gradient_clip_val <= 0:\\n return\\n model = self.get_model()\\n parameters = model.parameters()\\n max_norm = float(self.gradient_clip_val)\\n norm_type = float(2.0)\\n if isinstance(parameters, torch.Tensor):\\n parameters = [parameters]\\n parameters = list(filter(lambda p: p.grad is not None, parameters))\\n if norm_type == math.inf:\\n total_norm = max(p.grad.data.abs().max() for p in parameters)\\n else:\\n device = parameters[0].device\\n out = torch.empty(len(parameters), device=device)\\n for i, p in enumerate(parameters):\\n torch.norm(p.grad.data.to(device), norm_type, out=out[i])\\n total_norm = torch.norm(out, norm_type)\\n\\n eps = EPSILON_FP16 if self.precision == 16 else EPSILON\\n clip_coef = torch.tensor(max_norm, device=device) / (total_norm + eps)\\n clip_coef = torch.min(clip_coef, torch.ones_like(clip_coef))\\n for p in parameters:\\n p.grad.data.mul_(clip_coef.to(p.grad.data.device))\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.print_nan_gradients\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.print_nan_gradients\\n def print_nan_gradients(self) -> None:\\n model = self.get_model()\\n for param in model.parameters():\\n if (param.grad is not None) and torch.isnan(param.grad.float()).any():\\n log.info(param, param.grad)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.detect_nan_tensors\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.detect_nan_tensors\\n def detect_nan_tensors(self, loss: Tensor) -> None:\\n model = self.get_model()\\n\\n # check if loss is nan\\n if not torch.isfinite(loss).all():\\n raise ValueError(\\n 'The loss returned in `training_step` is nan or inf.'\\n )\\n # check if a network weight is nan\\n for name, param in model.named_parameters():\\n if not torch.isfinite(param).all():\\n self.print_nan_gradients()\\n raise ValueError(\\n f'Detected nan and/or inf values in `{name}`.'\\n ' Check your forward pass for numerically unstable operations.'\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.configure_accumulated_gradients\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.configure_accumulated_gradients\\n def configure_accumulated_gradients(self, accumulate_grad_batches):\\n if isinstance(accumulate_grad_batches, dict):\\n self.accumulation_scheduler = GradientAccumulationScheduler(accumulate_grad_batches)\\n elif isinstance(accumulate_grad_batches, int):\\n schedule = {0: accumulate_grad_batches}\\n self.accumulation_scheduler = GradientAccumulationScheduler(schedule)\\n else:\\n raise TypeError(\\\"Gradient accumulation supports only int and dict types\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.scale_batch_size\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.scale_batch_size\\n def scale_batch_size(self,\\n model: LightningModule,\\n mode: str = 'power',\\n steps_per_trial: int = 3,\\n init_val: int = 2,\\n max_trials: int = 25,\\n batch_arg_name: str = 'batch_size'):\\n r\\\"\\\"\\\"\\n Will iteratively try to find the largest batch size for a given model\\n that does not give an out of memory (OOM) error.\\n\\n Args:\\n model: Model to fit.\\n\\n mode: string setting the search mode. Either `power` or `binsearch`.\\n If mode is `power` we keep multiplying the batch size by 2, until\\n we get an OOM error. If mode is 'binsearch', we will initially\\n also keep multiplying by 2 and after encountering an OOM error\\n do a binary search between the last successful batch size and the\\n batch size that failed.\\n\\n steps_per_trial: number of steps to run with a given batch size.\\n Idealy 1 should be enough to test if a OOM error occurs,\\n however in practise a few are needed\\n\\n init_val: initial batch size to start the search with\\n\\n max_trials: max number of increase in batch size done before\\n algorithm is terminated\\n\\n \\\"\\\"\\\"\\n if not hasattr(model, batch_arg_name):\\n if not hasattr(model.hparams, batch_arg_name):\\n raise MisconfigurationException(\\n 'Neither of `model.batch_size` and `model.hparams.batch_size` found.'\\n )\\n\\n if hasattr(model.train_dataloader, 'patch_loader_code'):\\n raise MisconfigurationException('The batch scaling feature cannot be used with dataloaders'\\n ' passed directly to `.fit()`. Please disable the feature or'\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.__scale_batch_dump_params\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.__scale_batch_dump_params\\n def __scale_batch_dump_params(self):\\n # Prevent going into infinite loop\\n self.__dumped_params = {\\n 'max_steps': self.max_steps,\\n 'weights_summary': self.weights_summary,\\n 'logger': self.logger,\\n 'callbacks': self.callbacks,\\n 'checkpoint_callback': self.checkpoint_callback,\\n 'early_stop_callback': self.early_stop_callback,\\n 'auto_scale_batch_size': self.auto_scale_batch_size,\\n 'limit_train_batches': self.limit_train_batches,\\n 'model': self.model,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.__scale_batch_reset_params\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.__scale_batch_reset_params\\n def __scale_batch_reset_params(self, model, steps_per_trial):\\n self.auto_scale_batch_size = None # prevent recursion\\n self.max_steps = steps_per_trial # take few steps\\n self.weights_summary = None # not needed before full run\\n self.logger = DummyLogger()\\n self.callbacks = [] # not needed before full run\\n self.checkpoint_callback = False # required for saving\\n self.early_stop_callback = None\\n self.limit_train_batches = 1.0\\n self.optimizers, self.schedulers = [], [] # required for saving\\n self.model = model # required for saving\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.__scale_batch_restore_params\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::TrainerTrainingTricksMixin.__scale_batch_restore_params\\n def __scale_batch_restore_params(self):\\n self.max_steps = self.__dumped_params['max_steps']\\n self.weights_summary = self.__dumped_params['weights_summary']\\n self.logger = self.__dumped_params['logger']\\n self.callbacks = self.__dumped_params['callbacks']\\n self.checkpoint_callback = self.__dumped_params['checkpoint_callback']\\n self.auto_scale_batch_size = self.__dumped_params['auto_scale_batch_size']\\n self.early_stop_callback = self.__dumped_params['early_stop_callback']\\n self.limit_train_batches = self.__dumped_params['limit_train_batches']\\n self.model = self.__dumped_params['model']\\n del self.__dumped_params\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::_adjust_batch_size\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::_adjust_batch_size\\ndef _adjust_batch_size(trainer,\\n batch_arg_name: str = 'batch_size',\\n factor: float = 1.0,\\n value: Optional[int] = None,\\n desc: str = None):\\n \\\"\\\"\\\" Function for adjusting the batch size. It is expected that the user\\n has provided a model that has a hparam field called `batch_size` i.e.\\n `model.hparams.batch_size` should exist.\\n\\n Args:\\n trainer: instance of pytorch_lightning.Trainer\\n\\n batch_arg_name: field where batch_size is stored in `model.hparams`\\n\\n factor: value which the old batch size is multiplied by to get the\\n new batch size\\n\\n value: if a value is given, will override the batch size with this value.\\n Note that the value of `factor` will not have an effect in this case\\n\\n desc: either `succeeded` or `failed`. Used purely for logging\\n\\n \\\"\\\"\\\"\\n model = trainer.get_model()\\n if hasattr(model, batch_arg_name):\\n batch_size = getattr(model, batch_arg_name)\\n else:\\n batch_size = getattr(model.hparams, batch_arg_name)\\n if value:\\n if hasattr(model, batch_arg_name):\\n setattr(model, batch_arg_name, value)\\n else:\\n setattr(model.hparams, batch_arg_name, value) \\n new_size = value\\n if desc:\\n log.info(f'Batch size {batch_size} {desc}, trying batch size {new_size}')\\n else:\\n new_size = int(batch_size * factor)\\n if desc:\\n log.info(f'Batch size {batch_size} {desc}, trying batch size {new_size}')\\n setattr(model.hparams, batch_arg_name, new_size)\\n return new_size\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::_run_power_scaling\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::_run_power_scaling\\ndef _run_power_scaling(trainer, model, new_size, batch_arg_name, max_trials):\\n \\\"\\\"\\\" Batch scaling mode where the size is doubled at each iteration until an\\n OOM error is encountered. \\\"\\\"\\\"\\n for _ in range(max_trials):\\n garbage_collection_cuda()\\n trainer.global_step = 0 # reset after each try\\n try:\\n # Try fit\\n trainer.fit(model)\\n # Double in size\\n new_size = _adjust_batch_size(trainer, batch_arg_name, factor=2.0, desc='succeeded')\\n except RuntimeError as exception:\\n # Only these errors should trigger an adjustment\\n if is_oom_error(exception):\\n # If we fail in power mode, half the size and return\\n garbage_collection_cuda()\\n new_size = _adjust_batch_size(trainer, batch_arg_name, factor=0.5, desc='failed')\\n break\\n else:\\n raise # some other error not memory related\\n return new_size\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/training_tricks.py::_run_binsearch_scaling\",\n \"text\": \"pytorch_lightning/trainer/training_tricks.py::_run_binsearch_scaling\\ndef _run_binsearch_scaling(trainer, model, new_size, batch_arg_name, max_trials):\\n \\\"\\\"\\\" Batch scaling mode where the size is initially is doubled at each iteration\\n until an OOM error is encountered. Hereafter, the batch size is further\\n refined using a binary search \\\"\\\"\\\"\\n high = None\\n count = 0\\n while True:\\n garbage_collection_cuda()\\n trainer.global_step = 0 # reset after each try\\n try:\\n # Try fit\\n trainer.fit(model)\\n count += 1\\n if count > max_trials:\\n break\\n # Double in size\\n low = new_size\\n if high:\\n if high - low <= 1:\\n break\\n midval = (high + low) // 2\\n new_size = _adjust_batch_size(trainer, batch_arg_name, value=midval, desc='succeeded')\\n else:\\n new_size = _adjust_batch_size(trainer, batch_arg_name, factor=2.0, desc='succeeded')\\n except RuntimeError as exception:\\n # Only these errors should trigger an adjustment\\n if is_oom_error(exception):\\n # If we fail in power mode, half the size and return\\n garbage_collection_cuda()\\n high = new_size\\n midval = (high + low) // 2\\n new_size = _adjust_batch_size(trainer, value=midval, desc='failed')\\n if high - low <= 1:\\n break\\n else:\\n raise # some other error not memory related\\n return new_size\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":260,"cells":{"instance_id":{"kind":"string","value":"wagtail__wagtail-9119"},"repo":{"kind":"string","value":"wagtail/wagtail"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Locked pages report renders all users in \"locked_by\" filter\n### Issue Summary\r\n\r\nCurrently, the select-field for the \"locked_by\" filter on the \"Locked pages\"-report renders all users unfiltered from the user table.\r\nThis breaks the view for one of our projects, which tries to list all 500k users...\r\nActually, only the users which have locked a page should be listed.\r\n\r\nI will try to make a PR for that, in the meantime this quickfix works for us (with wagtail 4.0, django 3.2):\r\n```python\r\n# in one of our apps.py\r\nfrom django.apps import AppConfig\r\n\r\nclass BaseConfig(AppConfig):\r\n name = \"base\"\r\n\r\n def ready(self):\r\n patch_locked_pages_filter()\r\n\r\ndef patch_locked_pages_filter():\r\n import django_filters\r\n from django.contrib.auth import get_user_model\r\n\r\n from wagtail.admin.views.reports.locked_pages import LockedPagesView, LockedPagesReportFilterSet\r\n\r\n def get_users_for_filter():\r\n User = get_user_model()\r\n return User.objects.filter(locked_pages__isnull=False).order_by(User.USERNAME_FIELD)\r\n\r\n class PatchedLockedPagesReportFilterSet(LockedPagesReportFilterSet):\r\n locked_by = django_filters.ModelChoiceFilter(\r\n field_name=\"locked_by\", queryset=lambda request: get_users_for_filter()\r\n )\r\n\r\n LockedPagesView.filterset_class = PatchedLockedPagesReportFilterSet\r\n\r\n```\r\n\r\n### Steps to Reproduce\r\n\r\n1. Start a new project with `wagtail start myproject`\r\n2. Create many users, no matter if they have access to the CMS or not\r\n3. Open `/admin/reports/locked/` and find all those users in the \"locked_by\" dropdown on the right\r\n\r\n- I have confirmed that this issue can be reproduced as described on a fresh Wagtail project: yes\r\n\r\n### Technical details\r\n\r\n- Python version: 3.10\r\n- Django version: 3.2\r\n- Wagtail version: 4.0, 2.15,... (according to the git history seems to be there from the beginning)\r\n\n"},"gold_files":{"kind":"list like","value":["wagtail/admin/views/reports/locked_pages.py"],"string":"[\n \"wagtail/admin/views/reports/locked_pages.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["wagtail/admin/views/reports/locked_pages.py::<module>"],"string":"[\n \"wagtail/admin/views/reports/locked_pages.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"wagtail/admin/auth.py","text":"wagtail/admin/auth.py\ndef users_with_page_permission(page, permission_type, include_superusers=True)\ndef permission_denied(request) # Return a standard 'permission denied' response\ndef user_passes_test(test) # Given a test function that takes a user object and returns a boolean,\n def decorator(view_func)\n def wrapped_view_func(request, *args, **kwargs)\ndef permission_required(permission_name) # Replacement for django.contrib.auth.decorators.permission_required which returns\n def test(user)\ndef any_permission_required(*perms) # Decorator that accepts a list of permission names, and allows the user\n def test(user)\nclass PermissionPolicyChecker # Provides a view decorator that enforces the given permission policy,\n def __init__(self, policy)\n def require(self, action)\n def test(user)\n def require_any(self, *actions)\n def test(user)\ndef user_has_any_page_permission(user) # Check if a user has any permission to add, edit, or otherwise manage any\ndef reject_request(request)\ndef require_admin_access(view_func)\n def decorated_view(request, *args, **kwargs)","label":0},{"cid":"wagtail/admin/views/home.py","text":"wagtail/admin/views/home.py\nclass UpgradeNotificationPanel\n def get_upgrade_check_setting(self)\n def upgrade_check_lts_only(self)\n def get_context_data(self, parent_context: Mapping[str, Any])\n def render_html(self, parent_context: Mapping[str, Any]=None)\nclass PagesForModerationPanel\n def get_context_data(self, parent_context)\nclass UserPagesInWorkflowModerationPanel\n def get_context_data(self, parent_context)\nclass WorkflowPagesToModeratePanel\n def get_context_data(self, parent_context)\nclass LockedPagesPanel\n def get_context_data(self, parent_context)\nclass RecentEditsPanel\n def get_context_data(self, parent_context)\nclass HomeView\n def get_context_data(self, **kwargs)\n def get_media(self, panels=[])\n def get_panels(self)\n def get_site_details(self)\ndef error_test(request)\ndef default(request) # Called whenever a request comes in with the correct prefix (eg /admin/) but\ndef icons()\ndef sprite(request)","label":0},{"cid":"wagtail/admin/views/reports/audit_logging.py","text":"wagtail/admin/views/reports/audit_logging.py\ndef get_users_for_filter()\ndef get_content_types_for_filter()\nclass SiteHistoryReportFilterSet\n def filter_hide_commenting_actions(self, queryset, name, value)\n def filter_object_type(self, queryset, name, value)\n class Meta\nclass LogEntriesView\n def __init__(self, **kwargs)\n def get_filename(self)\n def get_filtered_queryset(self) # Since this report combines records from multiple log models, the standard patter\n def decorate_paginated_queryset(self, queryset)\n def get_action_label(self, action)","label":0},{"cid":"wagtail/admin/views/reports/locked_pages.py","text":"wagtail/admin/views/reports/locked_pages.py\nclass LockedPagesReportFilterSet\n class Meta\nclass LockedPagesView\n def get_filename(self)\n def get_queryset(self)\n def dispatch(self, request, *args, **kwargs)","label":1},{"cid":"wagtail/admin/views/reports/workflows.py","text":"wagtail/admin/views/reports/workflows.py\ndef get_requested_by_queryset(request)\nclass WorkflowReportFilterSet\n def filter_reviewable(self, queryset, name, value)\n class Meta\nclass WorkflowTasksReportFilterSet\n def filter_reviewable(self, queryset, name, value)\n class Meta\nclass WorkflowView\n def get_filename(self)\n def get_queryset(self)\nclass WorkflowTasksView\n def get_filename(self)\n def get_queryset(self)","label":0},{"cid":"wagtail/api/v2/views.py","text":"wagtail/api/v2/views.py\nclass BaseAPIViewSet\n def __init__(self, *args, **kwargs)\n def get_queryset(self)\n def listing_view(self, request)\n def detail_view(self, request, pk)\n def find_view(self, request)\n def find_object(self, queryset, request) # Override this to implement more find methods.\n def handle_exception(self, exc)\n def _convert_api_fields(cls, fields)\n def get_body_fields(cls, model)\n def get_body_fields_names(cls, model)\n def get_meta_fields(cls, model)\n def get_meta_fields_names(cls, model)\n def get_field_serializer_overrides(cls, model)\n def get_available_fields(cls, model, db_fields_only=False) # Returns a list of all the fields that can be used in the API for the\n def get_detail_default_fields(cls, model)\n def get_listing_default_fields(cls, model)\n def get_nested_default_fields(cls, model)\n def check_query_parameters(self, queryset) # Ensure that only valid query parameters are included in the URL.\n def _get_serializer_class(cls, router, model, fields_config, show_details=False, nested=False)\n def get_serializer_class(self)\n def get_serializer_context(self) # The serialization context differs between listing and detail views.\n def get_renderer_context(self)\n def get_urlpatterns(cls) # This returns a list of URL patterns for the endpoint\n def get_model_listing_urlpath(cls, model, namespace='')\n def get_object_detail_urlpath(cls, model, pk, namespace='')\nclass PagesAPIViewSet\n def get_detail_default_fields(cls, model)\n def get_listing_default_fields(cls, model)\n def get_root_page(self) # Returns the page that is used when the `&child_of=root` filter is used.\n def get_base_queryset(self) # Returns a queryset containing all pages that can be seen by this user.\n def get_queryset(self)\n def get_object(self)\n def find_object(self, queryset, request)\n def get_serializer_context(self) # The serialization context differs between listing and detail views.","label":0},{"cid":"wagtail/locks.py","text":"wagtail/locks.py\nclass BaseLock # Holds information about a lock on an object.\n def for_user(self, user) # Returns True if the lock applies to the given user.\n def get_message(self, user) # Returns a message to display to the given user describing the lock.\nclass BasicLock # A lock that is enabled when the \"locked\" attribute of a page is True.\n def __init__(self, page)\n def for_user(self, user)\n def get_message(self, user)\nclass WorkflowLock # A lock that requires the user to pass the Task.page_locked_for_user test on the \n def __init__(self, task, page)\n def for_user(self, user)\n def get_message(self, user)\nclass ScheduledForPublishLock # A lock that occurs when something is scheduled to be published.\n def __init__(self, page)\n def for_user(self, user)\n def get_message(self, user)","label":0},{"cid":"wagtail/permission_policies/base.py","text":"wagtail/permission_policies/base.py\nclass BasePermissionPolicy # A 'permission policy' is an object that handles all decisions about the actions\n def __init__(self, model)\n def user_has_permission(self, user, action) # Return whether the given user has permission to perform the given action\n def user_has_any_permission(self, user, actions) # Return whether the given user has permission to perform any of the given actions\n def users_with_any_permission(self, actions) # Return a queryset of users who have permission to perform any of the given actio\n def users_with_permission(self, action) # Return a queryset of users who have permission to perform the given action on\n def user_has_permission_for_instance(self, user, action, instance) # Return whether the given user has permission to perform the given action on the\n def user_has_any_permission_for_instance(self, user, actions, instance) # Return whether the given user has permission to perform any of the given actions\n def instances_user_has_any_permission_for(self, user, actions) # Return a queryset of all instances of this model for which the given user has\n def instances_user_has_permission_for(self, user, action) # Return a queryset of all instances of this model for which the given user has\n def users_with_any_permission_for_instance(self, actions, instance) # Return a queryset of all users who have permission to perform any of the given\n def users_with_permission_for_instance(self, action, instance)\nclass BlanketPermissionPolicy # A permission policy that gives everyone (including anonymous users)\n def user_has_permission(self, user, action)\n def user_has_any_permission(self, user, actions)\n def users_with_any_permission(self, actions)\n def users_with_permission(self, action)\nclass AuthenticationOnlyPermissionPolicy # A permission policy that gives all active authenticated users\n def user_has_permission(self, user, action)\n def user_has_any_permission(self, user, actions)\n def users_with_any_permission(self, actions)\n def users_with_permission(self, action)\nclass BaseDjangoAuthPermissionPolicy # Extends BasePermissionPolicy with helper methods useful for policies that need t\n def __init__(self, model, auth_model=None)\n def _content_type(self)\n def _get_permission_name(self, action) # Get the full app-label-qualified permission name (as required by\n def _get_users_with_any_permission_codenames_filter(self, permission_codenames) # Given a list of permission codenames, return a filter expression which\n def _get_users_with_any_permission_codenames(self, permission_codenames) # Given a list of permission codenames, return a queryset of users which\nclass ModelPermissionPolicy # A permission policy that enforces permissions at the model level, by consulting\n def user_has_permission(self, user, action)\n def users_with_any_permission(self, actions)\nclass OwnershipPermissionPolicy # A permission policy for o","label":0},{"cid":"wagtail/permission_policies/collections.py","text":"wagtail/permission_policies/collections.py\nclass CollectionPermissionLookupMixin\n def _get_permission_objects_for_actions(self, actions) # Get a queryset of the Permission objects for the given actions\n def _check_perm(self, user, actions, collection=None) # Equivalent to user.has_perm(self._get_permission_name(action)) on all listed act\n def _collections_with_perm(self, user, actions) # Return a queryset of collections on which this user has a GroupCollectionPermiss\n def _users_with_perm_filter(self, actions, collection=None) # Return a filter expression that will filter a user queryset to those with any\n def _users_with_perm(self, actions, collection=None) # Return a queryset of users with any permissions corresponding to 'actions',\n def collections_user_has_any_permission_for(self, user, actions) # Return a queryset of all collections in which the given user has\n def collections_user_has_permission_for(self, user, action) # Return a queryset of all collections in which the given user has\nclass CollectionPermissionPolicy # A permission policy for objects that are assigned locations in the Collection tr\n def user_has_permission(self, user, action) # Return whether the given user has permission to perform the given action\n def user_has_any_permission(self, user, actions) # Return whether the given user has permission to perform any of the given actions\n def users_with_any_permission(self, actions) # Return a queryset of users who have permission to perform any of the given actio\n def user_has_permission_for_instance(self, user, action, instance) # Return whether the given user has permission to perform the given action on the\n def user_has_any_permission_for_instance(self, user, actions, instance) # Return whether the given user has permission to perform any of the given actions\n def instances_user_has_any_permission_for(self, user, actions) # Return a queryset of all instances of this model for which the given user has\n def users_with_any_permission_for_instance(self, actions, instance) # Return a queryset of all users who have permission to perform any of the given\nclass CollectionOwnershipPermissionPolicy # A permission policy for objects that are assigned locations in the Collection tr\n def __init__(self, model, auth_model=None, owner_field_name='owner')\n def user_has_permission(self, user, action)\n def users_with_any_permission(self, actions)\n def user_has_permission_for_instance(self, user, action, instance)\n def user_has_any_permission_for_instance(self, user, actions, instance)\n def instances_user_has_any_permission_for(self, user, actions)\n def users_with_any_permission_for_instance(self, actions, instance)\n def collections_user_has_any_permission_for(self, user, actions) # Return a queryset of all collections in which the given user has\nclass CollectionMangementPermissionPolicy\n def _descendants_with_perm(self, user, action) # Return a queryset of col","label":0}],"string":"[\n {\n \"cid\": \"wagtail/admin/auth.py\",\n \"text\": \"wagtail/admin/auth.py\\ndef users_with_page_permission(page, permission_type, include_superusers=True)\\ndef permission_denied(request) # Return a standard 'permission denied' response\\ndef user_passes_test(test) # Given a test function that takes a user object and returns a boolean,\\n def decorator(view_func)\\n def wrapped_view_func(request, *args, **kwargs)\\ndef permission_required(permission_name) # Replacement for django.contrib.auth.decorators.permission_required which returns\\n def test(user)\\ndef any_permission_required(*perms) # Decorator that accepts a list of permission names, and allows the user\\n def test(user)\\nclass PermissionPolicyChecker # Provides a view decorator that enforces the given permission policy,\\n def __init__(self, policy)\\n def require(self, action)\\n def test(user)\\n def require_any(self, *actions)\\n def test(user)\\ndef user_has_any_page_permission(user) # Check if a user has any permission to add, edit, or otherwise manage any\\ndef reject_request(request)\\ndef require_admin_access(view_func)\\n def decorated_view(request, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py\",\n \"text\": \"wagtail/admin/views/home.py\\nclass UpgradeNotificationPanel\\n def get_upgrade_check_setting(self)\\n def upgrade_check_lts_only(self)\\n def get_context_data(self, parent_context: Mapping[str, Any])\\n def render_html(self, parent_context: Mapping[str, Any]=None)\\nclass PagesForModerationPanel\\n def get_context_data(self, parent_context)\\nclass UserPagesInWorkflowModerationPanel\\n def get_context_data(self, parent_context)\\nclass WorkflowPagesToModeratePanel\\n def get_context_data(self, parent_context)\\nclass LockedPagesPanel\\n def get_context_data(self, parent_context)\\nclass RecentEditsPanel\\n def get_context_data(self, parent_context)\\nclass HomeView\\n def get_context_data(self, **kwargs)\\n def get_media(self, panels=[])\\n def get_panels(self)\\n def get_site_details(self)\\ndef error_test(request)\\ndef default(request) # Called whenever a request comes in with the correct prefix (eg /admin/) but\\ndef icons()\\ndef sprite(request)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/audit_logging.py\",\n \"text\": \"wagtail/admin/views/reports/audit_logging.py\\ndef get_users_for_filter()\\ndef get_content_types_for_filter()\\nclass SiteHistoryReportFilterSet\\n def filter_hide_commenting_actions(self, queryset, name, value)\\n def filter_object_type(self, queryset, name, value)\\n class Meta\\nclass LogEntriesView\\n def __init__(self, **kwargs)\\n def get_filename(self)\\n def get_filtered_queryset(self) # Since this report combines records from multiple log models, the standard patter\\n def decorate_paginated_queryset(self, queryset)\\n def get_action_label(self, action)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/locked_pages.py\",\n \"text\": \"wagtail/admin/views/reports/locked_pages.py\\nclass LockedPagesReportFilterSet\\n class Meta\\nclass LockedPagesView\\n def get_filename(self)\\n def get_queryset(self)\\n def dispatch(self, request, *args, **kwargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/admin/views/reports/workflows.py\",\n \"text\": \"wagtail/admin/views/reports/workflows.py\\ndef get_requested_by_queryset(request)\\nclass WorkflowReportFilterSet\\n def filter_reviewable(self, queryset, name, value)\\n class Meta\\nclass WorkflowTasksReportFilterSet\\n def filter_reviewable(self, queryset, name, value)\\n class Meta\\nclass WorkflowView\\n def get_filename(self)\\n def get_queryset(self)\\nclass WorkflowTasksView\\n def get_filename(self)\\n def get_queryset(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py\",\n \"text\": \"wagtail/api/v2/views.py\\nclass BaseAPIViewSet\\n def __init__(self, *args, **kwargs)\\n def get_queryset(self)\\n def listing_view(self, request)\\n def detail_view(self, request, pk)\\n def find_view(self, request)\\n def find_object(self, queryset, request) # Override this to implement more find methods.\\n def handle_exception(self, exc)\\n def _convert_api_fields(cls, fields)\\n def get_body_fields(cls, model)\\n def get_body_fields_names(cls, model)\\n def get_meta_fields(cls, model)\\n def get_meta_fields_names(cls, model)\\n def get_field_serializer_overrides(cls, model)\\n def get_available_fields(cls, model, db_fields_only=False) # Returns a list of all the fields that can be used in the API for the\\n def get_detail_default_fields(cls, model)\\n def get_listing_default_fields(cls, model)\\n def get_nested_default_fields(cls, model)\\n def check_query_parameters(self, queryset) # Ensure that only valid query parameters are included in the URL.\\n def _get_serializer_class(cls, router, model, fields_config, show_details=False, nested=False)\\n def get_serializer_class(self)\\n def get_serializer_context(self) # The serialization context differs between listing and detail views.\\n def get_renderer_context(self)\\n def get_urlpatterns(cls) # This returns a list of URL patterns for the endpoint\\n def get_model_listing_urlpath(cls, model, namespace='')\\n def get_object_detail_urlpath(cls, model, pk, namespace='')\\nclass PagesAPIViewSet\\n def get_detail_default_fields(cls, model)\\n def get_listing_default_fields(cls, model)\\n def get_root_page(self) # Returns the page that is used when the `&child_of=root` filter is used.\\n def get_base_queryset(self) # Returns a queryset containing all pages that can be seen by this user.\\n def get_queryset(self)\\n def get_object(self)\\n def find_object(self, queryset, request)\\n def get_serializer_context(self) # The serialization context differs between listing and detail views.\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/locks.py\",\n \"text\": \"wagtail/locks.py\\nclass BaseLock # Holds information about a lock on an object.\\n def for_user(self, user) # Returns True if the lock applies to the given user.\\n def get_message(self, user) # Returns a message to display to the given user describing the lock.\\nclass BasicLock # A lock that is enabled when the \\\"locked\\\" attribute of a page is True.\\n def __init__(self, page)\\n def for_user(self, user)\\n def get_message(self, user)\\nclass WorkflowLock # A lock that requires the user to pass the Task.page_locked_for_user test on the \\n def __init__(self, task, page)\\n def for_user(self, user)\\n def get_message(self, user)\\nclass ScheduledForPublishLock # A lock that occurs when something is scheduled to be published.\\n def __init__(self, page)\\n def for_user(self, user)\\n def get_message(self, user)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py\",\n \"text\": \"wagtail/permission_policies/base.py\\nclass BasePermissionPolicy # A 'permission policy' is an object that handles all decisions about the actions\\n def __init__(self, model)\\n def user_has_permission(self, user, action) # Return whether the given user has permission to perform the given action\\n def user_has_any_permission(self, user, actions) # Return whether the given user has permission to perform any of the given actions\\n def users_with_any_permission(self, actions) # Return a queryset of users who have permission to perform any of the given actio\\n def users_with_permission(self, action) # Return a queryset of users who have permission to perform the given action on\\n def user_has_permission_for_instance(self, user, action, instance) # Return whether the given user has permission to perform the given action on the\\n def user_has_any_permission_for_instance(self, user, actions, instance) # Return whether the given user has permission to perform any of the given actions\\n def instances_user_has_any_permission_for(self, user, actions) # Return a queryset of all instances of this model for which the given user has\\n def instances_user_has_permission_for(self, user, action) # Return a queryset of all instances of this model for which the given user has\\n def users_with_any_permission_for_instance(self, actions, instance) # Return a queryset of all users who have permission to perform any of the given\\n def users_with_permission_for_instance(self, action, instance)\\nclass BlanketPermissionPolicy # A permission policy that gives everyone (including anonymous users)\\n def user_has_permission(self, user, action)\\n def user_has_any_permission(self, user, actions)\\n def users_with_any_permission(self, actions)\\n def users_with_permission(self, action)\\nclass AuthenticationOnlyPermissionPolicy # A permission policy that gives all active authenticated users\\n def user_has_permission(self, user, action)\\n def user_has_any_permission(self, user, actions)\\n def users_with_any_permission(self, actions)\\n def users_with_permission(self, action)\\nclass BaseDjangoAuthPermissionPolicy # Extends BasePermissionPolicy with helper methods useful for policies that need t\\n def __init__(self, model, auth_model=None)\\n def _content_type(self)\\n def _get_permission_name(self, action) # Get the full app-label-qualified permission name (as required by\\n def _get_users_with_any_permission_codenames_filter(self, permission_codenames) # Given a list of permission codenames, return a filter expression which\\n def _get_users_with_any_permission_codenames(self, permission_codenames) # Given a list of permission codenames, return a queryset of users which\\nclass ModelPermissionPolicy # A permission policy that enforces permissions at the model level, by consulting\\n def user_has_permission(self, user, action)\\n def users_with_any_permission(self, actions)\\nclass OwnershipPermissionPolicy # A permission policy for o\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py\",\n \"text\": \"wagtail/permission_policies/collections.py\\nclass CollectionPermissionLookupMixin\\n def _get_permission_objects_for_actions(self, actions) # Get a queryset of the Permission objects for the given actions\\n def _check_perm(self, user, actions, collection=None) # Equivalent to user.has_perm(self._get_permission_name(action)) on all listed act\\n def _collections_with_perm(self, user, actions) # Return a queryset of collections on which this user has a GroupCollectionPermiss\\n def _users_with_perm_filter(self, actions, collection=None) # Return a filter expression that will filter a user queryset to those with any\\n def _users_with_perm(self, actions, collection=None) # Return a queryset of users with any permissions corresponding to 'actions',\\n def collections_user_has_any_permission_for(self, user, actions) # Return a queryset of all collections in which the given user has\\n def collections_user_has_permission_for(self, user, action) # Return a queryset of all collections in which the given user has\\nclass CollectionPermissionPolicy # A permission policy for objects that are assigned locations in the Collection tr\\n def user_has_permission(self, user, action) # Return whether the given user has permission to perform the given action\\n def user_has_any_permission(self, user, actions) # Return whether the given user has permission to perform any of the given actions\\n def users_with_any_permission(self, actions) # Return a queryset of users who have permission to perform any of the given actio\\n def user_has_permission_for_instance(self, user, action, instance) # Return whether the given user has permission to perform the given action on the\\n def user_has_any_permission_for_instance(self, user, actions, instance) # Return whether the given user has permission to perform any of the given actions\\n def instances_user_has_any_permission_for(self, user, actions) # Return a queryset of all instances of this model for which the given user has\\n def users_with_any_permission_for_instance(self, actions, instance) # Return a queryset of all users who have permission to perform any of the given\\nclass CollectionOwnershipPermissionPolicy # A permission policy for objects that are assigned locations in the Collection tr\\n def __init__(self, model, auth_model=None, owner_field_name='owner')\\n def user_has_permission(self, user, action)\\n def users_with_any_permission(self, actions)\\n def user_has_permission_for_instance(self, user, action, instance)\\n def user_has_any_permission_for_instance(self, user, actions, instance)\\n def instances_user_has_any_permission_for(self, user, actions)\\n def users_with_any_permission_for_instance(self, actions, instance)\\n def collections_user_has_any_permission_for(self, user, actions) # Return a queryset of all collections in which the given user has\\nclass CollectionMangementPermissionPolicy\\n def _descendants_with_perm(self, user, action) # Return a queryset of col\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"wagtail/admin/auth.py::users_with_page_permission","text":"wagtail/admin/auth.py::users_with_page_permission\ndef users_with_page_permission(page, permission_type, include_superusers=True):\n # Get user model\n User = get_user_model()\n\n # Find GroupPagePermission records of the given type that apply to this page or an ancestor\n ancestors_and_self = list(page.get_ancestors()) + [page]\n perm = GroupPagePermission.objects.filter(\n permission_type=permission_type, page__in=ancestors_and_self\n )\n q = Q(groups__page_permissions__in=perm)\n\n # Include superusers\n if include_superusers:\n q |= Q(is_superuser=True)\n\n return User.objects.filter(is_active=True).filter(q).distinct()","label":0},{"cid":"wagtail/admin/auth.py::permission_denied","text":"wagtail/admin/auth.py::permission_denied\ndef permission_denied(request):\n \"\"\"Return a standard 'permission denied' response\"\"\"\n if request.headers.get(\"x-requested-with\") == \"XMLHttpRequest\":\n raise PermissionDenied\n\n from wagtail.admin import messages\n\n messages.error(request, _(\"Sorry, you do not have permission to access this area.\"))\n return redirect(\"wagtailadmin_home\")","label":0},{"cid":"wagtail/admin/auth.py::user_passes_test","text":"wagtail/admin/auth.py::user_passes_test\ndef user_passes_test(test):\n \"\"\"\n Given a test function that takes a user object and returns a boolean,\n return a view decorator that denies access to the user if the test returns false.\n \"\"\"\n\n def decorator(view_func):\n # decorator takes the view function, and returns the view wrapped in\n # a permission check\n\n @wraps(view_func)\n def wrapped_view_func(request, *args, **kwargs):\n if test(request.user):\n # permission check succeeds; run the view function as normal\n return view_func(request, *args, **kwargs)\n else:\n # permission check failed\n return permission_denied(request)\n\n return wrapped_view_func\n\n return decorator","label":0},{"cid":"wagtail/admin/auth.py::user_passes_test.decorator","text":"wagtail/admin/auth.py::user_passes_test.decorator\n def decorator(view_func):\n # decorator takes the view function, and returns the view wrapped in\n # a permission check\n\n @wraps(view_func)\n def wrapped_view_func(request, *args, **kwargs):\n if test(request.user):\n # permission check succeeds; run the view function as normal\n return view_func(request, *args, **kwargs)\n else:\n # permission check failed\n return permission_denied(request)\n\n return wrapped_view_func","label":0},{"cid":"wagtail/admin/auth.py::user_passes_test.decorator.wrapped_view_func","text":"wagtail/admin/auth.py::user_passes_test.decorator.wrapped_view_func\n @wraps(view_func)\n def wrapped_view_func(request, *args, **kwargs):\n if test(request.user):\n # permission check succeeds; run the view function as normal\n return view_func(request, *args, **kwargs)\n else:\n # permission check failed\n return permission_denied(request)","label":0},{"cid":"wagtail/admin/auth.py::permission_required","text":"wagtail/admin/auth.py::permission_required\ndef permission_required(permission_name):\n \"\"\"\n Replacement for django.contrib.auth.decorators.permission_required which returns a\n more meaningful 'permission denied' response than just redirecting to the login page.\n (The latter doesn't work anyway because Wagtail doesn't define LOGIN_URL...)\n \"\"\"\n\n def test(user):\n return user.has_perm(permission_name)\n\n # user_passes_test constructs a decorator function specific to the above test function\n return user_passes_test(test)","label":0},{"cid":"wagtail/admin/auth.py::permission_required.test","text":"wagtail/admin/auth.py::permission_required.test\n def test(user):\n return user.has_perm(permission_name)","label":0},{"cid":"wagtail/admin/auth.py::any_permission_required","text":"wagtail/admin/auth.py::any_permission_required\ndef any_permission_required(*perms):\n \"\"\"\n Decorator that accepts a list of permission names, and allows the user\n to pass if they have *any* of the permissions in the list\n \"\"\"\n\n def test(user):\n for perm in perms:\n if user.has_perm(perm):\n return True\n\n return False\n\n return user_passes_test(test)","label":0},{"cid":"wagtail/admin/auth.py::any_permission_required.test","text":"wagtail/admin/auth.py::any_permission_required.test\n def test(user):\n for perm in perms:\n if user.has_perm(perm):\n return True\n\n return False","label":0},{"cid":"wagtail/admin/auth.py::PermissionPolicyChecker.__init__","text":"wagtail/admin/auth.py::PermissionPolicyChecker.__init__\n def __init__(self, policy):\n self.policy = policy","label":0},{"cid":"wagtail/admin/auth.py::PermissionPolicyChecker.require","text":"wagtail/admin/auth.py::PermissionPolicyChecker.require\n def require(self, action):\n def test(user):\n return self.policy.user_has_permission(user, action)\n\n return user_passes_test(test)","label":0},{"cid":"wagtail/admin/auth.py::PermissionPolicyChecker.require.test","text":"wagtail/admin/auth.py::PermissionPolicyChecker.require.test\n def test(user):\n return self.policy.user_has_permission(user, action)","label":0},{"cid":"wagtail/admin/auth.py::PermissionPolicyChecker.require_any","text":"wagtail/admin/auth.py::PermissionPolicyChecker.require_any\n def require_any(self, *actions):\n def test(user):\n return self.policy.user_has_any_permission(user, actions)\n\n return user_passes_test(test)","label":0},{"cid":"wagtail/admin/auth.py::PermissionPolicyChecker.require_any.test","text":"wagtail/admin/auth.py::PermissionPolicyChecker.require_any.test\n def test(user):\n return self.policy.user_has_any_permission(user, actions)","label":0},{"cid":"wagtail/admin/auth.py::user_has_any_page_permission","text":"wagtail/admin/auth.py::user_has_any_page_permission\ndef user_has_any_page_permission(user):\n \"\"\"\n Check if a user has any permission to add, edit, or otherwise manage any\n page.\n \"\"\"\n # Can't do nothin' if you're not active.\n if not user.is_active:\n return False\n\n # Superusers can do anything.\n if user.is_superuser:\n return True\n\n # At least one of the users groups has a GroupPagePermission.\n # The user can probably do something.\n if GroupPagePermission.objects.filter(group__in=user.groups.all()).exists():\n return True\n\n # Specific permissions for a page type do not mean anything.\n\n # No luck! This user can not do anything with pages.\n return False","label":0},{"cid":"wagtail/admin/auth.py::reject_request","text":"wagtail/admin/auth.py::reject_request\ndef reject_request(request):\n if request.headers.get(\"x-requested-with\") == \"XMLHttpRequest\":\n raise PermissionDenied\n\n # import redirect_to_login here to avoid circular imports on model files that import\n # wagtail.admin.auth, specifically where custom user models are involved\n from django.contrib.auth.views import redirect_to_login as auth_redirect_to_login\n\n return auth_redirect_to_login(\n request.get_full_path(), login_url=reverse(\"wagtailadmin_login\")\n )","label":0},{"cid":"wagtail/admin/auth.py::require_admin_access","text":"wagtail/admin/auth.py::require_admin_access\ndef require_admin_access(view_func):\n def decorated_view(request, *args, **kwargs):\n\n user = request.user\n\n if user.is_anonymous:\n return reject_request(request)\n\n if user.has_perms([\"wagtailadmin.access_admin\"]):\n try:\n preferred_language = None\n if hasattr(user, \"wagtail_userprofile\"):\n preferred_language = (\n user.wagtail_userprofile.get_preferred_language()\n )\n l18n.set_language(preferred_language)\n time_zone = user.wagtail_userprofile.get_current_time_zone()\n activate_tz(time_zone)\n with LogContext(user=user):\n if preferred_language:\n with override(preferred_language):\n response = view_func(request, *args, **kwargs)\n\n if hasattr(response, \"render\"):\n # If the response has a render() method, Django treats it\n # like a TemplateResponse, so we should do the same\n # In this case, we need to guarantee that when the TemplateResponse\n # is rendered, it is done within the override context manager\n # or the user preferred_language will not be used\n # (this could be replaced with simply rendering the TemplateResponse\n # for simplicity but this does remove some of its middleware modification\n # potential)\n render = response.render\n\n def overridden_render(response):\n with override(preferred_language):\n return render()\n\n response.render = types.MethodType(\n ","label":0},{"cid":"wagtail/admin/auth.py::require_admin_access.decorated_view","text":"wagtail/admin/auth.py::require_admin_access.decorated_view\n def decorated_view(request, *args, **kwargs):\n\n user = request.user\n\n if user.is_anonymous:\n return reject_request(request)\n\n if user.has_perms([\"wagtailadmin.access_admin\"]):\n try:\n preferred_language = None\n if hasattr(user, \"wagtail_userprofile\"):\n preferred_language = (\n user.wagtail_userprofile.get_preferred_language()\n )\n l18n.set_language(preferred_language)\n time_zone = user.wagtail_userprofile.get_current_time_zone()\n activate_tz(time_zone)\n with LogContext(user=user):\n if preferred_language:\n with override(preferred_language):\n response = view_func(request, *args, **kwargs)\n\n if hasattr(response, \"render\"):\n # If the response has a render() method, Django treats it\n # like a TemplateResponse, so we should do the same\n # In this case, we need to guarantee that when the TemplateResponse\n # is rendered, it is done within the override context manager\n # or the user preferred_language will not be used\n # (this could be replaced with simply rendering the TemplateResponse\n # for simplicity but this does remove some of its middleware modification\n # potential)\n render = response.render\n\n def overridden_render(response):\n with override(preferred_language):\n return render()\n\n response.render = types.MethodType(\n overridden_re","label":0},{"cid":"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_upgrade_check_setting","text":"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_upgrade_check_setting\n def get_upgrade_check_setting(self) -> Union[bool, str]:\n return getattr(settings, \"WAGTAIL_ENABLE_UPDATE_CHECK\", True)","label":0},{"cid":"wagtail/admin/views/home.py::UpgradeNotificationPanel.upgrade_check_lts_only","text":"wagtail/admin/views/home.py::UpgradeNotificationPanel.upgrade_check_lts_only\n def upgrade_check_lts_only(self) -> bool:\n upgrade_check = self.get_upgrade_check_setting()\n if isinstance(upgrade_check, str) and upgrade_check.lower() == \"lts\":\n return True\n return False","label":0},{"cid":"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_context_data","text":"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_context_data\n def get_context_data(self, parent_context: Mapping[str, Any]) -> Mapping[str, Any]:\n return {\"lts_only\": self.upgrade_check_lts_only()}","label":0},{"cid":"wagtail/admin/views/home.py::UpgradeNotificationPanel.render_html","text":"wagtail/admin/views/home.py::UpgradeNotificationPanel.render_html\n def render_html(self, parent_context: Mapping[str, Any] = None) -> str:\n if (\n parent_context[\"request\"].user.is_superuser\n and self.get_upgrade_check_setting()\n ):\n return super().render_html(parent_context)\n else:\n return \"\"","label":0},{"cid":"wagtail/admin/views/home.py::PagesForModerationPanel.get_context_data","text":"wagtail/admin/views/home.py::PagesForModerationPanel.get_context_data\n def get_context_data(self, parent_context):\n request = parent_context[\"request\"]\n context = super().get_context_data(parent_context)\n user_perms = UserPagePermissionsProxy(request.user)\n context[\"page_revisions_for_moderation\"] = (\n user_perms.revisions_for_moderation()\n .select_related(\"user\")\n .order_by(\"-created_at\")\n )\n context[\"request\"] = request\n context[\"csrf_token\"] = parent_context[\"csrf_token\"]\n return context","label":0},{"cid":"wagtail/admin/views/home.py::UserPagesInWorkflowModerationPanel.get_context_data","text":"wagtail/admin/views/home.py::UserPagesInWorkflowModerationPanel.get_context_data\n def get_context_data(self, parent_context):\n request = parent_context[\"request\"]\n context = super().get_context_data(parent_context)\n if getattr(settings, \"WAGTAIL_WORKFLOW_ENABLED\", True):\n # Find in progress workflow states which are either requested by the user or on pages owned by the user\n context[\"workflow_states\"] = (\n WorkflowState.objects.active()\n .filter(Q(page__owner=request.user) | Q(requested_by=request.user))\n .select_related(\n \"page\",\n \"current_task_state\",\n \"current_task_state__task\",\n \"current_task_state__page_revision\",\n )\n .order_by(\"-current_task_state__started_at\")\n )\n else:\n context[\"workflow_states\"] = WorkflowState.objects.none()\n context[\"request\"] = request\n return context","label":0},{"cid":"wagtail/admin/views/home.py::WorkflowPagesToModeratePanel.get_context_data","text":"wagtail/admin/views/home.py::WorkflowPagesToModeratePanel.get_context_data\n def get_context_data(self, parent_context):\n request = parent_context[\"request\"]\n context = super().get_context_data(parent_context)\n if getattr(settings, \"WAGTAIL_WORKFLOW_ENABLED\", True):\n states = (\n TaskState.objects.reviewable_by(request.user)\n .select_related(\n \"page_revision\",\n \"task\",\n \"page_revision__user\",\n )\n .order_by(\"-started_at\")\n )\n context[\"states\"] = [\n (\n state,\n state.task.specific.get_actions(\n page=state.page_revision.content_object, user=request.user\n ),\n state.workflow_state.all_tasks_with_status(),\n )\n for state in states\n ]\n else:\n context[\"states\"] = []\n context[\"request\"] = request\n context[\"csrf_token\"] = parent_context[\"csrf_token\"]\n return context","label":0},{"cid":"wagtail/admin/views/home.py::LockedPagesPanel.get_context_data","text":"wagtail/admin/views/home.py::LockedPagesPanel.get_context_data\n def get_context_data(self, parent_context):\n request = parent_context[\"request\"]\n context = super().get_context_data(parent_context)\n context.update(\n {\n \"locked_pages\": Page.objects.filter(\n locked=True,\n locked_by=request.user,\n ),\n \"can_remove_locks\": UserPagePermissionsProxy(\n request.user\n ).can_remove_locks(),\n \"request\": request,\n \"csrf_token\": parent_context[\"csrf_token\"],\n }\n )\n return context","label":0},{"cid":"wagtail/admin/views/home.py::RecentEditsPanel.get_context_data","text":"wagtail/admin/views/home.py::RecentEditsPanel.get_context_data\n def get_context_data(self, parent_context):\n request = parent_context[\"request\"]\n context = super().get_context_data(parent_context)\n\n # Last n edited pages\n edit_count = getattr(settings, \"WAGTAILADMIN_RECENT_EDITS_LIMIT\", 5)\n if connection.vendor == \"mysql\":\n # MySQL can't handle the subselect created by the ORM version -\n # it fails with \"This version of MySQL doesn't yet support 'LIMIT & IN/ALL/ANY/SOME subquery'\"\n last_edits = Revision.objects.raw(\n \"\"\"\n SELECT wr.* FROM\n wagtailcore_revision wr JOIN (\n SELECT max(created_at) AS max_created_at, object_id FROM\n wagtailcore_revision WHERE user_id = %s AND base_content_type_id = %s GROUP BY object_id ORDER BY max_created_at DESC LIMIT %s\n ) AS max_rev ON max_rev.max_created_at = wr.created_at ORDER BY wr.created_at DESC\n \"\"\",\n [\n User._meta.pk.get_db_prep_value(request.user.pk, connection),\n get_default_page_content_type().id,\n edit_count,\n ],\n )\n else:\n last_edits_dates = (\n Revision.page_revisions.filter(user=request.user)\n .values(\"object_id\")\n .annotate(latest_date=Max(\"created_at\"))\n .order_by(\"-latest_date\")\n .values(\"latest_date\")[:edit_count]\n )\n last_edits = Revision.page_revisions.filter(\n created_at__in=last_edits_dates\n ).order_by(\"-created_at\")\n\n # The revision's object_id is a string, so cast it to int first.\n page_keys = [int(pr.object_id) for pr in last_edits]\n pages = Page.objects.specific().in_bulk(page_keys)\n context[\"last_edits\"] = [\n [revision, pages.get(in","label":0},{"cid":"wagtail/admin/views/home.py::HomeView.get_context_data","text":"wagtail/admin/views/home.py::HomeView.get_context_data\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n panels = self.get_panels()\n site_details = self.get_site_details()\n\n context[\"media\"] = self.get_media(panels)\n context[\"panels\"] = sorted(panels, key=lambda p: p.order)\n context[\"user\"] = self.request.user\n\n return {**context, **site_details}","label":0},{"cid":"wagtail/admin/views/home.py::HomeView.get_media","text":"wagtail/admin/views/home.py::HomeView.get_media\n def get_media(self, panels=[]):\n media = Media()\n\n for panel in panels:\n media += panel.media\n\n return media","label":0},{"cid":"wagtail/admin/views/home.py::HomeView.get_panels","text":"wagtail/admin/views/home.py::HomeView.get_panels\n def get_panels(self):\n request = self.request\n panels = [\n SiteSummaryPanel(request),\n UpgradeNotificationPanel(),\n WorkflowPagesToModeratePanel(),\n PagesForModerationPanel(),\n UserPagesInWorkflowModerationPanel(),\n RecentEditsPanel(),\n LockedPagesPanel(),\n ]\n\n for fn in hooks.get_hooks(\"construct_homepage_panels\"):\n fn(request, panels)\n\n return panels","label":0},{"cid":"wagtail/admin/views/home.py::HomeView.get_site_details","text":"wagtail/admin/views/home.py::HomeView.get_site_details\n def get_site_details(self):\n request = self.request\n site = get_site_for_user(request.user)\n\n return {\n \"root_page\": site[\"root_page\"],\n \"root_site\": site[\"root_site\"],\n \"site_name\": site[\"site_name\"],\n }","label":0},{"cid":"wagtail/admin/views/home.py::error_test","text":"wagtail/admin/views/home.py::error_test\ndef error_test(request):\n raise Exception(\"This is a test of the emergency broadcast system.\")","label":0},{"cid":"wagtail/admin/views/home.py::default","text":"wagtail/admin/views/home.py::default\n@permission_required(\"wagtailadmin.access_admin\", login_url=\"wagtailadmin_login\")\ndef default(request):\n \"\"\"\n Called whenever a request comes in with the correct prefix (eg /admin/) but\n doesn't actually correspond to a Wagtail view.\n\n For authenticated users, it'll raise a 404 error. Anonymous users will be\n redirected to the login page.\n \"\"\"\n raise Http404","label":0},{"cid":"wagtail/admin/views/home.py::icons","text":"wagtail/admin/views/home.py::icons\ndef icons():\n global _icons_html\n if _icons_html is None:\n icon_hooks = hooks.get_hooks(\"register_icons\")\n all_icons = sorted(\n itertools.chain.from_iterable(hook([]) for hook in icon_hooks)\n )\n combined_icon_markup = \"\"\n for icon in all_icons:\n combined_icon_markup += render_to_string(icon).replace(\"svg\", \"symbol\")\n\n _full_sprite_html = render_to_string(\n \"wagtailadmin/shared/icons.html\", {\"icons\": combined_icon_markup}\n )\n return _full_sprite_html","label":0},{"cid":"wagtail/admin/views/home.py::sprite","text":"wagtail/admin/views/home.py::sprite\ndef sprite(request):\n return HttpResponse(icons())","label":0},{"cid":"wagtail/admin/views/reports/audit_logging.py::get_users_for_filter","text":"wagtail/admin/views/reports/audit_logging.py::get_users_for_filter\ndef get_users_for_filter():\n user_ids = set()\n for log_model in log_action_registry.get_log_entry_models():\n user_ids.update(log_model.objects.all().get_user_ids())\n\n User = get_user_model()\n return User.objects.filter(pk__in=user_ids).order_by(User.USERNAME_FIELD)","label":0},{"cid":"wagtail/admin/views/reports/audit_logging.py::get_content_types_for_filter","text":"wagtail/admin/views/reports/audit_logging.py::get_content_types_for_filter\ndef get_content_types_for_filter():\n content_type_ids = set()\n for log_model in log_action_registry.get_log_entry_models():\n content_type_ids.update(log_model.objects.all().get_content_type_ids())\n\n return ContentType.objects.filter(pk__in=content_type_ids).order_by(\"model\")","label":0},{"cid":"wagtail/admin/views/reports/audit_logging.py::SiteHistoryReportFilterSet.filter_hide_commenting_actions","text":"wagtail/admin/views/reports/audit_logging.py::SiteHistoryReportFilterSet.filter_hide_commenting_actions\n def filter_hide_commenting_actions(self, queryset, name, value):\n if value:\n queryset = queryset.exclude(action__startswith=\"wagtail.comments\")\n return queryset","label":0},{"cid":"wagtail/admin/views/reports/audit_logging.py::SiteHistoryReportFilterSet.filter_object_type","text":"wagtail/admin/views/reports/audit_logging.py::SiteHistoryReportFilterSet.filter_object_type\n def filter_object_type(self, queryset, name, value):\n return queryset.filter_on_content_type(value)","label":0},{"cid":"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.__init__","text":"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.__init__\n def __init__(self, **kwargs):\n super().__init__(**kwargs)\n\n self.custom_field_preprocess = self.custom_field_preprocess.copy()\n self.custom_field_preprocess[\"action\"] = {\n self.FORMAT_CSV: self.get_action_label,\n self.FORMAT_XLSX: self.get_action_label,\n }\n self.custom_field_preprocess[\"content_type\"] = {\n self.FORMAT_CSV: get_content_type_label,\n self.FORMAT_XLSX: get_content_type_label,\n }","label":0},{"cid":"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.get_filename","text":"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.get_filename\n def get_filename(self):\n return \"audit-log-{}\".format(datetime.datetime.today().strftime(\"%Y-%m-%d\"))","label":0},{"cid":"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.get_filtered_queryset","text":"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.get_filtered_queryset\n def get_filtered_queryset(self):\n \"\"\"\n Since this report combines records from multiple log models, the standard pattern of\n returning a queryset from get_queryset() to be filtered by filter_queryset() is not\n possible - the subquery for each log model must be filtered separately before joining\n with union().\n\n Additionally, a union() on standard model-based querysets will return a queryset based on\n the first model in the union, so instances of the other model(s) would be returned as the\n wrong type. To avoid this, we construct values() querysets as follows:\n\n 1. For each model, construct a values() queryset consisting of id, timestamp and an\n annotation to indicate which model it is, and filter this with filter_queryset\n 2. Form a union() queryset from these queries, and order it by -timestamp\n (this is the result returned from get_filtered_queryset)\n 3. Apply pagination (done in MultipleObjectMixin.get_context_data)\n 4. (In decorate_paginated_queryset:) For each model included in the result set, look up\n the set of model instances by ID. Use these to form a final list of model instances\n in the same order as the query.\n \"\"\"\n queryset = None\n filters = None\n\n # Retrieve the set of registered log models, and cast it to a list so that we assign\n # an index number to each one; this index number will be used to distinguish models\n # in the combined results\n self.log_models = list(log_action_registry.get_log_entry_models())\n\n for log_model_index, log_model in enumerate(self.log_models):\n sub_queryset = (\n log_model.objects.viewable_by_user(self.request.user)\n .values(\"pk\", \"timestamp\")\n .annotate(\n log_model_index=Value(log_model_ind","label":0},{"cid":"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.decorate_paginated_queryset","text":"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.decorate_paginated_queryset\n def decorate_paginated_queryset(self, queryset):\n # build lists of ids from queryset, grouped by log model index\n pks_by_log_model_index = defaultdict(list)\n for row in queryset:\n pks_by_log_model_index[row[\"log_model_index\"]].append(row[\"pk\"])\n\n url_finder = AdminURLFinder(self.request.user)\n\n # for each log model found in the queryset, look up the set of log entries by id\n # and build a lookup table\n object_lookup = {}\n for log_model_index, pks in pks_by_log_model_index.items():\n log_entries = (\n self.log_models[log_model_index]\n .objects.prefetch_related(\"user__wagtail_userprofile\", \"content_type\")\n .filter(pk__in=pks)\n .with_instances()\n )\n for log_entry, instance in log_entries:\n # annotate log entry with an 'edit_url' property\n log_entry.edit_url = url_finder.get_edit_url(instance)\n object_lookup[(log_model_index, log_entry.pk)] = log_entry\n\n # return items from our lookup table in the order of the original queryset\n return [object_lookup[(row[\"log_model_index\"], row[\"pk\"])] for row in queryset]","label":0},{"cid":"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.get_action_label","text":"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.get_action_label\n def get_action_label(self, action):\n return force_str(log_action_registry.get_action_label(action))","label":0},{"cid":"wagtail/admin/views/reports/locked_pages.py::LockedPagesView.get_filename","text":"wagtail/admin/views/reports/locked_pages.py::LockedPagesView.get_filename\n def get_filename(self):\n return \"locked-pages-report-{}\".format(\n datetime.datetime.today().strftime(\"%Y-%m-%d\")\n )","label":0},{"cid":"wagtail/admin/views/reports/locked_pages.py::LockedPagesView.get_queryset","text":"wagtail/admin/views/reports/locked_pages.py::LockedPagesView.get_queryset\n def get_queryset(self):\n pages = (\n (\n UserPagePermissionsProxy(self.request.user).editable_pages()\n | Page.objects.filter(locked_by=self.request.user)\n )\n .filter(locked=True)\n .specific(defer=True)\n )\n\n if getattr(settings, \"WAGTAIL_I18N_ENABLED\", False):\n pages = pages.select_related(\"locale\")\n\n self.queryset = pages\n return super().get_queryset()","label":0},{"cid":"wagtail/admin/views/reports/locked_pages.py::LockedPagesView.dispatch","text":"wagtail/admin/views/reports/locked_pages.py::LockedPagesView.dispatch\n def dispatch(self, request, *args, **kwargs):\n if not UserPagePermissionsProxy(request.user).can_remove_locks():\n raise PermissionDenied\n return super().dispatch(request, *args, **kwargs)","label":0},{"cid":"wagtail/admin/views/reports/workflows.py::get_requested_by_queryset","text":"wagtail/admin/views/reports/workflows.py::get_requested_by_queryset\ndef get_requested_by_queryset(request):\n User = get_user_model()\n return User.objects.filter(\n pk__in=set(WorkflowState.objects.values_list(\"requested_by__pk\", flat=True))\n ).order_by(User.USERNAME_FIELD)","label":0},{"cid":"wagtail/admin/views/reports/workflows.py::WorkflowReportFilterSet.filter_reviewable","text":"wagtail/admin/views/reports/workflows.py::WorkflowReportFilterSet.filter_reviewable\n def filter_reviewable(self, queryset, name, value):\n if value and self.request and self.request.user:\n queryset = queryset.filter(\n current_task_state__in=TaskState.objects.reviewable_by(\n self.request.user\n )\n )\n return queryset","label":0},{"cid":"wagtail/admin/views/reports/workflows.py::WorkflowTasksReportFilterSet.filter_reviewable","text":"wagtail/admin/views/reports/workflows.py::WorkflowTasksReportFilterSet.filter_reviewable\n def filter_reviewable(self, queryset, name, value):\n if value and self.request and self.request.user:\n queryset = queryset.filter(\n id__in=TaskState.objects.reviewable_by(self.request.user).values_list(\n \"id\", flat=True\n )\n )\n return queryset","label":0},{"cid":"wagtail/admin/views/reports/workflows.py::WorkflowView.get_filename","text":"wagtail/admin/views/reports/workflows.py::WorkflowView.get_filename\n def get_filename(self):\n return \"workflow-report-{}\".format(\n datetime.datetime.today().strftime(\"%Y-%m-%d\")\n )","label":0},{"cid":"wagtail/admin/views/reports/workflows.py::WorkflowView.get_queryset","text":"wagtail/admin/views/reports/workflows.py::WorkflowView.get_queryset\n def get_queryset(self):\n pages = UserPagePermissionsProxy(self.request.user).editable_pages()\n return WorkflowState.objects.filter(page__in=pages).order_by(\"-created_at\")","label":0},{"cid":"wagtail/admin/views/reports/workflows.py::WorkflowTasksView.get_filename","text":"wagtail/admin/views/reports/workflows.py::WorkflowTasksView.get_filename\n def get_filename(self):\n return \"workflow-tasks-{}\".format(\n datetime.datetime.today().strftime(\"%Y-%m-%d\")\n )","label":0},{"cid":"wagtail/admin/views/reports/workflows.py::WorkflowTasksView.get_queryset","text":"wagtail/admin/views/reports/workflows.py::WorkflowTasksView.get_queryset\n def get_queryset(self):\n pages = UserPagePermissionsProxy(self.request.user).editable_pages()\n return TaskState.objects.filter(workflow_state__page__in=pages).order_by(\n \"-started_at\"\n )","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.__init__","text":"wagtail/api/v2/views.py::BaseAPIViewSet.__init__\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # seen_types is a mapping of type name strings (format: \"app_label.ModelName\")\n # to model classes. When an object is serialised in the API, its model\n # is added to this mapping. This is used by the Admin API which appends a\n # summary of the used types to the response.\n self.seen_types = OrderedDict()","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_queryset","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_queryset\n def get_queryset(self):\n return self.model.objects.all().order_by(\"id\")","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.listing_view","text":"wagtail/api/v2/views.py::BaseAPIViewSet.listing_view\n def listing_view(self, request):\n queryset = self.get_queryset()\n self.check_query_parameters(queryset)\n queryset = self.filter_queryset(queryset)\n queryset = self.paginate_queryset(queryset)\n serializer = self.get_serializer(queryset, many=True)\n return self.get_paginated_response(serializer.data)","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.detail_view","text":"wagtail/api/v2/views.py::BaseAPIViewSet.detail_view\n def detail_view(self, request, pk):\n instance = self.get_object()\n serializer = self.get_serializer(instance)\n return Response(serializer.data)","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.find_view","text":"wagtail/api/v2/views.py::BaseAPIViewSet.find_view\n def find_view(self, request):\n queryset = self.get_queryset()\n\n try:\n obj = self.find_object(queryset, request)\n\n if obj is None:\n raise self.model.DoesNotExist\n\n except self.model.DoesNotExist:\n raise Http404(\"not found\")\n\n # Generate redirect\n url = get_object_detail_url(\n self.request.wagtailapi_router, request, self.model, obj.pk\n )\n\n if url is None:\n # Shouldn't happen unless this endpoint isn't actually installed in the router\n raise Exception(\n \"Cannot generate URL to detail view. Is '{}' installed in the API router?\".format(\n self.__class__.__name__\n )\n )\n\n return redirect(url)","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.find_object","text":"wagtail/api/v2/views.py::BaseAPIViewSet.find_object\n def find_object(self, queryset, request):\n \"\"\"\n Override this to implement more find methods.\n \"\"\"\n if \"id\" in request.GET:\n return queryset.get(id=request.GET[\"id\"])","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.handle_exception","text":"wagtail/api/v2/views.py::BaseAPIViewSet.handle_exception\n def handle_exception(self, exc):\n if isinstance(exc, Http404):\n data = {\"message\": str(exc)}\n return Response(data, status=status.HTTP_404_NOT_FOUND)\n elif isinstance(exc, BadRequestError):\n data = {\"message\": str(exc)}\n return Response(data, status=status.HTTP_400_BAD_REQUEST)\n return super().handle_exception(exc)","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet._convert_api_fields","text":"wagtail/api/v2/views.py::BaseAPIViewSet._convert_api_fields\n @classmethod\n def _convert_api_fields(cls, fields):\n return [\n field if isinstance(field, APIField) else APIField(field)\n for field in fields\n ]","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_body_fields","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_body_fields\n @classmethod\n def get_body_fields(cls, model):\n return cls._convert_api_fields(\n cls.body_fields + list(getattr(model, \"api_fields\", ()))\n )","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_body_fields_names","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_body_fields_names\n @classmethod\n def get_body_fields_names(cls, model):\n return [field.name for field in cls.get_body_fields(model)]","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_meta_fields","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_meta_fields\n @classmethod\n def get_meta_fields(cls, model):\n return cls._convert_api_fields(\n cls.meta_fields + list(getattr(model, \"api_meta_fields\", ()))\n )","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_meta_fields_names","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_meta_fields_names\n @classmethod\n def get_meta_fields_names(cls, model):\n return [field.name for field in cls.get_meta_fields(model)]","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_field_serializer_overrides","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_field_serializer_overrides\n @classmethod\n def get_field_serializer_overrides(cls, model):\n return {\n field.name: field.serializer\n for field in cls.get_body_fields(model) + cls.get_meta_fields(model)\n if field.serializer is not None\n }","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_available_fields","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_available_fields\n @classmethod\n def get_available_fields(cls, model, db_fields_only=False):\n \"\"\"\n Returns a list of all the fields that can be used in the API for the\n specified model class.\n\n Setting db_fields_only to True will remove all fields that do not have\n an underlying column in the database (eg, type/detail_url and any custom\n fields that are callables)\n \"\"\"\n fields = cls.get_body_fields_names(model) + cls.get_meta_fields_names(model)\n\n if db_fields_only:\n # Get list of available database fields then remove any fields in our\n # list that isn't a database field\n database_fields = set()\n for field in model._meta.get_fields():\n database_fields.add(field.name)\n\n if hasattr(field, \"attname\"):\n database_fields.add(field.attname)\n\n fields = [field for field in fields if field in database_fields]\n\n return fields","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_detail_default_fields","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_detail_default_fields\n @classmethod\n def get_detail_default_fields(cls, model):\n return cls.get_available_fields(model)","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_listing_default_fields","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_listing_default_fields\n @classmethod\n def get_listing_default_fields(cls, model):\n return cls.listing_default_fields[:]","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_nested_default_fields","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_nested_default_fields\n @classmethod\n def get_nested_default_fields(cls, model):\n return cls.nested_default_fields[:]","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.check_query_parameters","text":"wagtail/api/v2/views.py::BaseAPIViewSet.check_query_parameters\n def check_query_parameters(self, queryset):\n \"\"\"\n Ensure that only valid query parameters are included in the URL.\n \"\"\"\n query_parameters = set(self.request.GET.keys())\n\n # All query parameters must be either a database field or an operation\n allowed_query_parameters = set(\n self.get_available_fields(queryset.model, db_fields_only=True)\n ).union(self.known_query_parameters)\n unknown_parameters = query_parameters - allowed_query_parameters\n if unknown_parameters:\n raise BadRequestError(\n \"query parameter is not an operation or a recognised field: %s\"\n % \", \".join(sorted(unknown_parameters))\n )","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet._get_serializer_class","text":"wagtail/api/v2/views.py::BaseAPIViewSet._get_serializer_class\n @classmethod\n def _get_serializer_class(\n cls, router, model, fields_config, show_details=False, nested=False\n ):\n # Get all available fields\n body_fields = cls.get_body_fields_names(model)\n meta_fields = cls.get_meta_fields_names(model)\n all_fields = body_fields + meta_fields\n\n # Remove any duplicates\n all_fields = list(OrderedDict.fromkeys(all_fields))\n\n if not show_details:\n # Remove detail only fields\n for field in cls.detail_only_fields:\n try:\n all_fields.remove(field)\n except KeyError:\n pass\n\n # Get list of configured fields\n if show_details:\n fields = set(cls.get_detail_default_fields(model))\n elif nested:\n fields = set(cls.get_nested_default_fields(model))\n else:\n fields = set(cls.get_listing_default_fields(model))\n\n # If first field is '*' start with all fields\n # If first field is '_' start with no fields\n if fields_config and fields_config[0][0] == \"*\":\n fields = set(all_fields)\n fields_config = fields_config[1:]\n elif fields_config and fields_config[0][0] == \"_\":\n fields = set()\n fields_config = fields_config[1:]\n\n mentioned_fields = set()\n sub_fields = {}\n\n for field_name, negated, field_sub_fields in fields_config:\n if negated:\n try:\n fields.remove(field_name)\n except KeyError:\n pass\n else:\n fields.add(field_name)\n if field_sub_fields:\n sub_fields[field_name] = field_sub_fields\n\n mentioned_fields.add(field_name)\n\n unknown_fields = mentioned_fields - set(all_fields)\n\n if unknown_fields:\n raise BadRequestError(\n ","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_serializer_class","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_serializer_class\n def get_serializer_class(self):\n request = self.request\n\n # Get model\n if self.action == \"listing_view\":\n model = self.get_queryset().model\n else:\n model = type(self.get_object())\n\n # Fields\n if \"fields\" in request.GET:\n try:\n fields_config = parse_fields_parameter(request.GET[\"fields\"])\n except ValueError as e:\n raise BadRequestError(\"fields error: %s\" % str(e))\n else:\n # Use default fields\n fields_config = []\n\n # Allow \"detail_only\" (eg parent) fields on detail view\n if self.action == \"listing_view\":\n show_details = False\n else:\n show_details = True\n\n return self._get_serializer_class(\n self.request.wagtailapi_router,\n model,\n fields_config,\n show_details=show_details,\n )","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_serializer_context","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_serializer_context\n def get_serializer_context(self):\n \"\"\"\n The serialization context differs between listing and detail views.\n \"\"\"\n return {\n \"request\": self.request,\n \"view\": self,\n \"router\": self.request.wagtailapi_router,\n }","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_renderer_context","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_renderer_context\n def get_renderer_context(self):\n context = super().get_renderer_context()\n context[\"indent\"] = 4\n return context","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_urlpatterns","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_urlpatterns\n @classmethod\n def get_urlpatterns(cls):\n \"\"\"\n This returns a list of URL patterns for the endpoint\n \"\"\"\n return [\n path(\"\", cls.as_view({\"get\": \"listing_view\"}), name=\"listing\"),\n path(\"<int:pk>/\", cls.as_view({\"get\": \"detail_view\"}), name=\"detail\"),\n path(\"find/\", cls.as_view({\"get\": \"find_view\"}), name=\"find\"),\n ]","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_model_listing_urlpath","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_model_listing_urlpath\n @classmethod\n def get_model_listing_urlpath(cls, model, namespace=\"\"):\n if namespace:\n url_name = namespace + \":listing\"\n else:\n url_name = \"listing\"\n\n return reverse(url_name)","label":0},{"cid":"wagtail/api/v2/views.py::BaseAPIViewSet.get_object_detail_urlpath","text":"wagtail/api/v2/views.py::BaseAPIViewSet.get_object_detail_urlpath\n @classmethod\n def get_object_detail_urlpath(cls, model, pk, namespace=\"\"):\n if namespace:\n url_name = namespace + \":detail\"\n else:\n url_name = \"detail\"\n\n return reverse(url_name, args=(pk,))","label":0},{"cid":"wagtail/api/v2/views.py::PagesAPIViewSet.get_detail_default_fields","text":"wagtail/api/v2/views.py::PagesAPIViewSet.get_detail_default_fields\n @classmethod\n def get_detail_default_fields(cls, model):\n detail_default_fields = super().get_detail_default_fields(model)\n\n # When i18n is disabled, remove \"locale\" from default fields\n if not getattr(settings, \"WAGTAIL_I18N_ENABLED\", False):\n detail_default_fields.remove(\"locale\")\n\n return detail_default_fields","label":0},{"cid":"wagtail/api/v2/views.py::PagesAPIViewSet.get_listing_default_fields","text":"wagtail/api/v2/views.py::PagesAPIViewSet.get_listing_default_fields\n @classmethod\n def get_listing_default_fields(cls, model):\n listing_default_fields = super().get_listing_default_fields(model)\n\n # When i18n is enabled, add \"locale\" to default fields\n if getattr(settings, \"WAGTAIL_I18N_ENABLED\", False):\n listing_default_fields.append(\"locale\")\n\n return listing_default_fields","label":0},{"cid":"wagtail/api/v2/views.py::PagesAPIViewSet.get_root_page","text":"wagtail/api/v2/views.py::PagesAPIViewSet.get_root_page\n def get_root_page(self):\n \"\"\"\n Returns the page that is used when the `&child_of=root` filter is used.\n \"\"\"\n return Site.find_for_request(self.request).root_page","label":0},{"cid":"wagtail/api/v2/views.py::PagesAPIViewSet.get_base_queryset","text":"wagtail/api/v2/views.py::PagesAPIViewSet.get_base_queryset\n def get_base_queryset(self):\n \"\"\"\n Returns a queryset containing all pages that can be seen by this user.\n\n This is used as the base for get_queryset and is also used to find the\n parent pages when using the child_of and descendant_of filters as well.\n \"\"\"\n\n request = self.request\n\n # Get all live pages\n queryset = Page.objects.all().live()\n\n # Exclude pages that the user doesn't have access to\n restricted_pages = [\n restriction.page\n for restriction in PageViewRestriction.objects.all().select_related(\"page\")\n if not restriction.accept_request(self.request)\n ]\n\n # Exclude the restricted pages and their descendants from the queryset\n for restricted_page in restricted_pages:\n queryset = queryset.not_descendant_of(restricted_page, inclusive=True)\n\n # Check if we have a specific site to look for\n if \"site\" in request.GET:\n # Optionally allow querying by port\n if \":\" in request.GET[\"site\"]:\n (hostname, port) = request.GET[\"site\"].split(\":\", 1)\n query = {\n \"hostname\": hostname,\n \"port\": port,\n }\n else:\n query = {\n \"hostname\": request.GET[\"site\"],\n }\n try:\n site = Site.objects.get(**query)\n except Site.MultipleObjectsReturned:\n raise BadRequestError(\n \"Your query returned multiple sites. Try adding a port number to your site filter.\"\n )\n else:\n # Otherwise, find the site from the request\n site = Site.find_for_request(self.request)\n\n if site:\n base_queryset = queryset\n queryset = base_queryset.descendant_of(site.root_page, inclusive=True)\n\n # If intern","label":0},{"cid":"wagtail/api/v2/views.py::PagesAPIViewSet.get_queryset","text":"wagtail/api/v2/views.py::PagesAPIViewSet.get_queryset\n def get_queryset(self):\n request = self.request\n\n # Allow pages to be filtered to a specific type\n try:\n models = page_models_from_string(\n request.GET.get(\"type\", \"wagtailcore.Page\")\n )\n except (LookupError, ValueError):\n raise BadRequestError(\"type doesn't exist\")\n\n if not models:\n return self.get_base_queryset()\n\n elif len(models) == 1:\n # If a single page type has been specified, swap out the Page-based queryset for one based on\n # the specific page model so that we can filter on any custom APIFields defined on that model\n return models[0].objects.filter(\n id__in=self.get_base_queryset().values_list(\"id\", flat=True)\n )\n\n else: # len(models) > 1\n return self.get_base_queryset().type(*models)","label":0},{"cid":"wagtail/api/v2/views.py::PagesAPIViewSet.get_object","text":"wagtail/api/v2/views.py::PagesAPIViewSet.get_object\n def get_object(self):\n base = super().get_object()\n return base.specific","label":0},{"cid":"wagtail/api/v2/views.py::PagesAPIViewSet.find_object","text":"wagtail/api/v2/views.py::PagesAPIViewSet.find_object\n def find_object(self, queryset, request):\n site = Site.find_for_request(request)\n if \"html_path\" in request.GET and site is not None:\n path = request.GET[\"html_path\"]\n path_components = [component for component in path.split(\"/\") if component]\n\n try:\n page, _, _ = site.root_page.specific.route(request, path_components)\n except Http404:\n return\n\n if queryset.filter(id=page.id).exists():\n return page\n\n return super().find_object(queryset, request)","label":0},{"cid":"wagtail/api/v2/views.py::PagesAPIViewSet.get_serializer_context","text":"wagtail/api/v2/views.py::PagesAPIViewSet.get_serializer_context\n def get_serializer_context(self):\n \"\"\"\n The serialization context differs between listing and detail views.\n \"\"\"\n context = super().get_serializer_context()\n context[\"base_queryset\"] = self.get_base_queryset()\n return context","label":0},{"cid":"wagtail/locks.py::BaseLock.for_user","text":"wagtail/locks.py::BaseLock.for_user\n def for_user(self, user):\n \"\"\"\n Returns True if the lock applies to the given user.\n \"\"\"\n return NotImplemented","label":0},{"cid":"wagtail/locks.py::BaseLock.get_message","text":"wagtail/locks.py::BaseLock.get_message\n def get_message(self, user):\n \"\"\"\n Returns a message to display to the given user describing the lock.\n \"\"\"\n return None","label":0},{"cid":"wagtail/locks.py::BasicLock.__init__","text":"wagtail/locks.py::BasicLock.__init__\n def __init__(self, page):\n self.page = page","label":0},{"cid":"wagtail/locks.py::BasicLock.for_user","text":"wagtail/locks.py::BasicLock.for_user\n def for_user(self, user):\n if getattr(settings, \"WAGTAILADMIN_GLOBAL_PAGE_EDIT_LOCK\", False):\n return True\n else:\n return user.pk != self.page.locked_by_id","label":0},{"cid":"wagtail/locks.py::BasicLock.get_message","text":"wagtail/locks.py::BasicLock.get_message\n def get_message(self, user):\n if self.page.locked_by_id == user.pk:\n if self.page.locked_at:\n return format_html(\n _(\"<b>Page '{}' was locked</b> by <b>you</b> on <b>{}</b>.\"),\n self.page.get_admin_display_title(),\n self.page.locked_at.strftime(\"%d %b %Y %H:%M\"),\n )\n\n else:\n return format_html(\n _(\"<b>Page '{}' is locked</b> by <b>you</b>.\"),\n self.page.get_admin_display_title(),\n )\n else:\n if self.page.locked_by and self.page.locked_at:\n return format_html(\n _(\"<b>Page '{}' was locked</b> by <b>{}</b> on <b>{}</b>.\"),\n self.page.get_admin_display_title(),\n str(self.page.locked_by),\n self.page.locked_at.strftime(\"%d %b %Y %H:%M\"),\n )\n else:\n # Page was probably locked with an old version of Wagtail, or a script\n return format_html(\n _(\"<b>Page '{}' is locked</b>.\"),\n self.page.get_admin_display_title(),\n )","label":0},{"cid":"wagtail/locks.py::WorkflowLock.__init__","text":"wagtail/locks.py::WorkflowLock.__init__\n def __init__(self, task, page):\n self.task = task\n self.page = page","label":0},{"cid":"wagtail/locks.py::WorkflowLock.for_user","text":"wagtail/locks.py::WorkflowLock.for_user\n def for_user(self, user):\n return self.task.page_locked_for_user(self.page, user)","label":0},{"cid":"wagtail/locks.py::WorkflowLock.get_message","text":"wagtail/locks.py::WorkflowLock.get_message\n def get_message(self, user):\n if self.for_user(user):\n if len(self.page.current_workflow_state.all_tasks_with_status()) == 1:\n # If only one task in workflow, show simple message\n workflow_info = _(\"This page is currently awaiting moderation.\")\n else:\n workflow_info = format_html(\n _(\"This page is awaiting <b>'{}'</b> in the <b>'{}'</b> workflow.\"),\n self.task.name,\n self.page.current_workflow_state.workflow.name,\n )\n\n return mark_safe(\n workflow_info\n + \" \"\n + _(\"Only reviewers for this task can edit the page.\")\n )","label":0},{"cid":"wagtail/locks.py::ScheduledForPublishLock.__init__","text":"wagtail/locks.py::ScheduledForPublishLock.__init__\n def __init__(self, page):\n self.page = page","label":0},{"cid":"wagtail/locks.py::ScheduledForPublishLock.for_user","text":"wagtail/locks.py::ScheduledForPublishLock.for_user\n def for_user(self, user):\n return True","label":0},{"cid":"wagtail/locks.py::ScheduledForPublishLock.get_message","text":"wagtail/locks.py::ScheduledForPublishLock.get_message\n def get_message(self, user):\n scheduled_revision = self.page.revisions.filter(\n approved_go_live_at__isnull=False\n ).first()\n\n return format_html(\n _(\"Page '{}' is locked and has been scheduled to go live at {}\"),\n self.page.get_admin_display_title(),\n scheduled_revision.approved_go_live_at.strftime(\"%d %b %Y %H:%M\"),\n )","label":0},{"cid":"wagtail/permission_policies/base.py::BasePermissionPolicy.__init__","text":"wagtail/permission_policies/base.py::BasePermissionPolicy.__init__\n def __init__(self, model):\n self.model = model","label":0},{"cid":"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_permission","text":"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_permission\n def user_has_permission(self, user, action):\n \"\"\"\n Return whether the given user has permission to perform the given action\n on some or all instances of this model\n \"\"\"\n return user in self.users_with_permission(action)","label":0},{"cid":"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_any_permission","text":"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_any_permission\n def user_has_any_permission(self, user, actions):\n \"\"\"\n Return whether the given user has permission to perform any of the given actions\n on some or all instances of this model\n \"\"\"\n return any(self.user_has_permission(user, action) for action in actions)","label":0},{"cid":"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_any_permission","text":"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_any_permission\n def users_with_any_permission(self, actions):\n \"\"\"\n Return a queryset of users who have permission to perform any of the given actions\n on some or all instances of this model\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_permission","text":"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_permission\n def users_with_permission(self, action):\n \"\"\"\n Return a queryset of users who have permission to perform the given action on\n some or all instances of this model\n \"\"\"\n return self.users_with_any_permission([action])","label":0},{"cid":"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_permission_for_instance","text":"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_permission_for_instance\n def user_has_permission_for_instance(self, user, action, instance):\n \"\"\"\n Return whether the given user has permission to perform the given action on the\n given model instance\n \"\"\"\n return self.user_has_permission(user, action)","label":0},{"cid":"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_any_permission_for_instance","text":"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_any_permission_for_instance\n def user_has_any_permission_for_instance(self, user, actions, instance):\n \"\"\"\n Return whether the given user has permission to perform any of the given actions\n on the given model instance\n \"\"\"\n return any(\n self.user_has_permission_for_instance(user, action, instance)\n for action in actions\n )","label":0},{"cid":"wagtail/permission_policies/base.py::BasePermissionPolicy.instances_user_has_any_permission_for","text":"wagtail/permission_policies/base.py::BasePermissionPolicy.instances_user_has_any_permission_for\n def instances_user_has_any_permission_for(self, user, actions):\n \"\"\"\n Return a queryset of all instances of this model for which the given user has\n permission to perform any of the given actions\n \"\"\"\n if self.user_has_any_permission(user, actions):\n return self.model.objects.all()\n else:\n return self.model.objects.none()","label":0},{"cid":"wagtail/permission_policies/base.py::BasePermissionPolicy.instances_user_has_permission_for","text":"wagtail/permission_policies/base.py::BasePermissionPolicy.instances_user_has_permission_for\n def instances_user_has_permission_for(self, user, action):\n \"\"\"\n Return a queryset of all instances of this model for which the given user has\n permission to perform the given action\n \"\"\"\n return self.instances_user_has_any_permission_for(user, [action])","label":0},{"cid":"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_any_permission_for_instance","text":"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_any_permission_for_instance\n def users_with_any_permission_for_instance(self, actions, instance):\n \"\"\"\n Return a queryset of all users who have permission to perform any of the given\n actions on the given model instance\n \"\"\"\n return self.users_with_any_permission(actions)","label":0},{"cid":"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_permission_for_instance","text":"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_permission_for_instance\n def users_with_permission_for_instance(self, action, instance):\n return self.users_with_any_permission_for_instance([action], instance)","label":0},{"cid":"wagtail/permission_policies/base.py::BlanketPermissionPolicy.user_has_permission","text":"wagtail/permission_policies/base.py::BlanketPermissionPolicy.user_has_permission\n def user_has_permission(self, user, action):\n return True","label":0},{"cid":"wagtail/permission_policies/base.py::BlanketPermissionPolicy.user_has_any_permission","text":"wagtail/permission_policies/base.py::BlanketPermissionPolicy.user_has_any_permission\n def user_has_any_permission(self, user, actions):\n return True","label":0},{"cid":"wagtail/permission_policies/base.py::BlanketPermissionPolicy.users_with_any_permission","text":"wagtail/permission_policies/base.py::BlanketPermissionPolicy.users_with_any_permission\n def users_with_any_permission(self, actions):\n # Here we filter out inactive users from the results, even though inactive users\n # - and for that matter anonymous users - still have permission according to the\n # user_has_permission method. This is appropriate because, for most applications,\n # setting is_active=False is equivalent to deleting the user account; you would\n # not want these accounts to appear in, for example, a dropdown of users to\n # assign a task to. The result here could never be completely logically correct\n # (because it will not include anonymous users), so as the next best thing we\n # return the \"least surprise\" result.\n return get_user_model().objects.filter(is_active=True)","label":0},{"cid":"wagtail/permission_policies/base.py::BlanketPermissionPolicy.users_with_permission","text":"wagtail/permission_policies/base.py::BlanketPermissionPolicy.users_with_permission\n def users_with_permission(self, action):\n return get_user_model().objects.filter(is_active=True)","label":0},{"cid":"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.user_has_permission","text":"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.user_has_permission\n def user_has_permission(self, user, action):\n return user.is_authenticated and user.is_active","label":0},{"cid":"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.user_has_any_permission","text":"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.user_has_any_permission\n def user_has_any_permission(self, user, actions):\n return user.is_authenticated and user.is_active","label":0},{"cid":"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.users_with_any_permission","text":"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.users_with_any_permission\n def users_with_any_permission(self, actions):\n return get_user_model().objects.filter(is_active=True)","label":0},{"cid":"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.users_with_permission","text":"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.users_with_permission\n def users_with_permission(self, action):\n return get_user_model().objects.filter(is_active=True)","label":0},{"cid":"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy.__init__","text":"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy.__init__\n def __init__(self, model, auth_model=None):\n # `auth_model` specifies the model to be used for permission record lookups;\n # usually this will match `model` (which specifies the type of instances that\n # `instances_user_has_permission_for` will return), but this may differ when\n # swappable models are in use - for example, an interface for editing user\n # records might use a custom User model but will typically still refer to the\n # permission records for auth.user.\n super().__init__(model)\n self.auth_model = auth_model or self.model\n self.app_label = self.auth_model._meta.app_label\n self.model_name = self.auth_model._meta.model_name","label":0},{"cid":"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._content_type","text":"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._content_type\n @cached_property\n def _content_type(self):\n return ContentType.objects.get_for_model(self.auth_model)","label":0},{"cid":"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._get_permission_name","text":"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._get_permission_name\n def _get_permission_name(self, action):\n \"\"\"\n Get the full app-label-qualified permission name (as required by\n user.has_perm(...) ) for the given action on this model\n \"\"\"\n return \"%s.%s_%s\" % (self.app_label, action, self.model_name)","label":0},{"cid":"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._get_users_with_any_permission_codenames_filter","text":"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._get_users_with_any_permission_codenames_filter\n def _get_users_with_any_permission_codenames_filter(self, permission_codenames):\n \"\"\"\n Given a list of permission codenames, return a filter expression which\n will find all users which have any of those permissions - either\n through group permissions, user permissions, or implicitly through\n being a superuser.\n \"\"\"\n permissions = Permission.objects.filter(\n content_type=self._content_type, codename__in=permission_codenames\n )\n return (\n Q(is_superuser=True)\n | Q(user_permissions__in=permissions)\n | Q(groups__permissions__in=permissions)\n ) & Q(is_active=True)","label":0},{"cid":"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._get_users_with_any_permission_codenames","text":"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._get_users_with_any_permission_codenames\n def _get_users_with_any_permission_codenames(self, permission_codenames):\n \"\"\"\n Given a list of permission codenames, return a queryset of users which\n have any of those permissions - either through group permissions, user\n permissions, or implicitly through being a superuser.\n \"\"\"\n filter_expr = self._get_users_with_any_permission_codenames_filter(\n permission_codenames\n )\n return get_user_model().objects.filter(filter_expr).distinct()","label":0},{"cid":"wagtail/permission_policies/base.py::ModelPermissionPolicy.user_has_permission","text":"wagtail/permission_policies/base.py::ModelPermissionPolicy.user_has_permission\n def user_has_permission(self, user, action):\n return user.has_perm(self._get_permission_name(action))","label":0},{"cid":"wagtail/permission_policies/base.py::ModelPermissionPolicy.users_with_any_permission","text":"wagtail/permission_policies/base.py::ModelPermissionPolicy.users_with_any_permission\n def users_with_any_permission(self, actions):\n permission_codenames = [\n \"%s_%s\" % (action, self.model_name) for action in actions\n ]\n return self._get_users_with_any_permission_codenames(permission_codenames)","label":0},{"cid":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.__init__","text":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.__init__\n def __init__(self, model, auth_model=None, owner_field_name=\"owner\"):\n super().__init__(model, auth_model=auth_model)\n self.owner_field_name = owner_field_name\n\n # make sure owner_field_name is a field that exists on the model\n try:\n self.model._meta.get_field(self.owner_field_name)\n except FieldDoesNotExist:\n raise ImproperlyConfigured(\n \"%s has no field named '%s'. To use this model with OwnershipPermissionPolicy, \"\n \"you must specify a valid field name as owner_field_name.\"\n % (self.model, self.owner_field_name)\n )","label":0},{"cid":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.user_has_permission","text":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.user_has_permission\n def user_has_permission(self, user, action):\n if action == \"add\":\n return user.has_perm(self._get_permission_name(\"add\"))\n elif action == \"change\" or action == \"delete\":\n return (\n # having 'add' permission means that there are *potentially*\n # some instances they can edit (namely: ones they own),\n # which is sufficient for returning True here\n user.has_perm(self._get_permission_name(\"add\"))\n or user.has_perm(self._get_permission_name(\"change\"))\n )\n else:\n # unrecognised actions are only allowed for active superusers\n return user.is_active and user.is_superuser","label":0},{"cid":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.users_with_any_permission","text":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.users_with_any_permission\n def users_with_any_permission(self, actions):\n if \"change\" in actions or \"delete\" in actions:\n # either 'add' or 'change' permission means that there are *potentially*\n # some instances they can edit\n permission_codenames = [\n \"add_%s\" % self.model_name,\n \"change_%s\" % self.model_name,\n ]\n elif \"add\" in actions:\n permission_codenames = [\n \"add_%s\" % self.model_name,\n ]\n else:\n # none of the actions passed in here are ones that we recognise, so only\n # allow them for active superusers\n return get_user_model().objects.filter(is_active=True, is_superuser=True)\n\n return self._get_users_with_any_permission_codenames(permission_codenames)","label":0},{"cid":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.user_has_permission_for_instance","text":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.user_has_permission_for_instance\n def user_has_permission_for_instance(self, user, action, instance):\n return self.user_has_any_permission_for_instance(user, [action], instance)","label":0},{"cid":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.user_has_any_permission_for_instance","text":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.user_has_any_permission_for_instance\n def user_has_any_permission_for_instance(self, user, actions, instance):\n if \"change\" in actions or \"delete\" in actions:\n if user.has_perm(self._get_permission_name(\"change\")):\n return True\n elif (\n user.has_perm(self._get_permission_name(\"add\"))\n and getattr(instance, self.owner_field_name) == user\n ):\n return True\n else:\n return False\n else:\n # 'change' and 'delete' are the only actions that are well-defined\n # for specific instances. Other actions are only available to\n # active superusers.\n return user.is_active and user.is_superuser","label":0},{"cid":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.instances_user_has_any_permission_for","text":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.instances_user_has_any_permission_for\n def instances_user_has_any_permission_for(self, user, actions):\n if user.is_active and user.is_superuser:\n # active superusers can perform any action (including unrecognised ones)\n # on any instance\n return self.model.objects.all()\n elif \"change\" in actions or \"delete\" in actions:\n if user.has_perm(self._get_permission_name(\"change\")):\n # user can edit all instances\n return self.model.objects.all()\n elif user.has_perm(self._get_permission_name(\"add\")):\n # user can edit their own instances\n return self.model.objects.filter(**{self.owner_field_name: user})\n else:\n # user has no permissions at all on this model\n return self.model.objects.none()\n else:\n # action is either not recognised, or is the 'add' action which is\n # not meaningful for existing instances. As such, non-superusers\n # cannot perform it on any existing instances.\n return self.model.objects.none()","label":0},{"cid":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.users_with_any_permission_for_instance","text":"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.users_with_any_permission_for_instance\n def users_with_any_permission_for_instance(self, actions, instance):\n if \"change\" in actions or \"delete\" in actions:\n # get filter expression for users with 'change' permission\n filter_expr = self._get_users_with_any_permission_codenames_filter(\n [\"change_%s\" % self.model_name]\n )\n\n # add on the item's owner, if they still have 'add' permission\n # (and the owner field isn't blank)\n owner = getattr(instance, self.owner_field_name)\n if owner is not None and owner.has_perm(self._get_permission_name(\"add\")):\n filter_expr = filter_expr | Q(pk=owner.pk)\n\n # return the filtered queryset\n return get_user_model().objects.filter(filter_expr).distinct()\n\n else:\n # action is either not recognised, or is the 'add' action which is\n # not meaningful for existing instances. As such, the action is only\n # available to superusers\n return get_user_model().objects.filter(is_active=True, is_superuser=True)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._get_permission_objects_for_actions","text":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._get_permission_objects_for_actions\n def _get_permission_objects_for_actions(self, actions):\n \"\"\"\n Get a queryset of the Permission objects for the given actions\n \"\"\"\n permission_codenames = [\n \"%s_%s\" % (action, self.model_name) for action in actions\n ]\n return Permission.objects.filter(\n content_type=self._content_type, codename__in=permission_codenames\n )","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._check_perm","text":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._check_perm\n def _check_perm(self, user, actions, collection=None):\n \"\"\"\n Equivalent to user.has_perm(self._get_permission_name(action)) on all listed actions,\n but using GroupCollectionPermission rather than group.permissions.\n If collection is specified, only consider GroupCollectionPermission records\n that apply to that collection.\n \"\"\"\n if not (user.is_active and user.is_authenticated):\n return False\n\n if user.is_superuser:\n return True\n\n collection_permissions = GroupCollectionPermission.objects.filter(\n group__user=user,\n permission__in=self._get_permission_objects_for_actions(actions),\n )\n\n if collection:\n collection_permissions = collection_permissions.filter(\n collection__in=collection.get_ancestors(inclusive=True)\n )\n\n return collection_permissions.exists()","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._collections_with_perm","text":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._collections_with_perm\n def _collections_with_perm(self, user, actions):\n \"\"\"\n Return a queryset of collections on which this user has a GroupCollectionPermission\n record for any of the given actions, either on the collection itself or an ancestor\n \"\"\"\n # Get the permission objects corresponding to these actions\n permissions = self._get_permission_objects_for_actions(actions)\n\n # Get the collections that have a GroupCollectionPermission record\n # for any of these permissions and any of the user's groups;\n # create a list of their paths\n collection_root_paths = Collection.objects.filter(\n group_permissions__group__in=user.groups.all(),\n group_permissions__permission__in=permissions,\n ).values_list(\"path\", flat=True)\n\n if collection_root_paths:\n # build a filter expression that will filter our model to just those\n # instances in collections with a path that starts with one of the above\n collection_path_filter = Q(path__startswith=collection_root_paths[0])\n for path in collection_root_paths[1:]:\n collection_path_filter = collection_path_filter | Q(\n path__startswith=path\n )\n\n return Collection.objects.all().filter(collection_path_filter)\n else:\n # no matching collections\n return Collection.objects.none()","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._users_with_perm_filter","text":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._users_with_perm_filter\n def _users_with_perm_filter(self, actions, collection=None):\n \"\"\"\n Return a filter expression that will filter a user queryset to those with any\n permissions corresponding to 'actions', via either GroupCollectionPermission\n or superuser privileges.\n If collection is specified, only consider GroupCollectionPermission records\n that apply to that collection.\n \"\"\"\n permissions = self._get_permission_objects_for_actions(actions)\n # Find all groups with GroupCollectionPermission records for\n # any of these permissions\n groups = Group.objects.filter(\n collection_permissions__permission__in=permissions,\n )\n\n if collection is not None:\n collections = collection.get_ancestors(inclusive=True)\n groups = groups.filter(collection_permissions__collection__in=collections)\n\n # Find all users who are superusers or in any of these groups, and are active\n return (Q(is_superuser=True) | Q(groups__in=groups)) & Q(is_active=True)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._users_with_perm","text":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._users_with_perm\n def _users_with_perm(self, actions, collection=None):\n \"\"\"\n Return a queryset of users with any permissions corresponding to 'actions',\n via either GroupCollectionPermission or superuser privileges.\n If collection is specified, only consider GroupCollectionPermission records\n that apply to that collection.\n \"\"\"\n return (\n get_user_model()\n .objects.filter(\n self._users_with_perm_filter(actions, collection=collection)\n )\n .distinct()\n )","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin.collections_user_has_any_permission_for","text":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin.collections_user_has_any_permission_for\n def collections_user_has_any_permission_for(self, user, actions):\n \"\"\"\n Return a queryset of all collections in which the given user has\n permission to perform any of the given actions\n \"\"\"\n if user.is_active and user.is_superuser:\n # active superusers can perform any action (including unrecognised ones)\n # in any collection\n return Collection.objects.all()\n\n if not user.is_authenticated:\n return Collection.objects.none()\n\n return self._collections_with_perm(user, actions)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin.collections_user_has_permission_for","text":"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin.collections_user_has_permission_for\n def collections_user_has_permission_for(self, user, action):\n \"\"\"\n Return a queryset of all collections in which the given user has\n permission to perform the given action\n \"\"\"\n return self.collections_user_has_any_permission_for(user, [action])","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_permission","text":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_permission\n def user_has_permission(self, user, action):\n \"\"\"\n Return whether the given user has permission to perform the given action\n on some or all instances of this model\n \"\"\"\n return self._check_perm(user, [action])","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_any_permission","text":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_any_permission\n def user_has_any_permission(self, user, actions):\n \"\"\"\n Return whether the given user has permission to perform any of the given actions\n on some or all instances of this model\n \"\"\"\n return self._check_perm(user, actions)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.users_with_any_permission","text":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.users_with_any_permission\n def users_with_any_permission(self, actions):\n \"\"\"\n Return a queryset of users who have permission to perform any of the given actions\n on some or all instances of this model\n \"\"\"\n return self._users_with_perm(actions)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_permission_for_instance","text":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_permission_for_instance\n def user_has_permission_for_instance(self, user, action, instance):\n \"\"\"\n Return whether the given user has permission to perform the given action on the\n given model instance\n \"\"\"\n return self._check_perm(user, [action], collection=instance.collection)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_any_permission_for_instance","text":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_any_permission_for_instance\n def user_has_any_permission_for_instance(self, user, actions, instance):\n \"\"\"\n Return whether the given user has permission to perform any of the given actions\n on the given model instance\n \"\"\"\n return self._check_perm(user, actions, collection=instance.collection)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.instances_user_has_any_permission_for","text":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.instances_user_has_any_permission_for\n def instances_user_has_any_permission_for(self, user, actions):\n \"\"\"\n Return a queryset of all instances of this model for which the given user has\n permission to perform any of the given actions\n \"\"\"\n if not (user.is_active and user.is_authenticated):\n return self.model.objects.none()\n elif user.is_superuser:\n return self.model.objects.all()\n else:\n # filter to just the collections with this permission\n return self.model.objects.filter(\n collection__in=list(self._collections_with_perm(user, actions))\n )","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.users_with_any_permission_for_instance","text":"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.users_with_any_permission_for_instance\n def users_with_any_permission_for_instance(self, actions, instance):\n \"\"\"\n Return a queryset of all users who have permission to perform any of the given\n actions on the given model instance\n \"\"\"\n return self._users_with_perm(actions, collection=instance.collection)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.__init__","text":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.__init__\n def __init__(self, model, auth_model=None, owner_field_name=\"owner\"):\n super().__init__(model, auth_model=auth_model)\n self.owner_field_name = owner_field_name\n\n # make sure owner_field_name is a field that exists on the model\n try:\n self.model._meta.get_field(self.owner_field_name)\n except FieldDoesNotExist:\n raise ImproperlyConfigured(\n \"%s has no field named '%s'. To use this model with \"\n \"CollectionOwnershipPermissionPolicy, you must specify a valid field name as \"\n \"owner_field_name.\" % (self.model, self.owner_field_name)\n )","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.user_has_permission","text":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.user_has_permission\n def user_has_permission(self, user, action):\n if action == \"add\":\n return self._check_perm(user, [\"add\"])\n elif action == \"choose\":\n return self._check_perm(user, [\"choose\"])\n elif action == \"change\" or action == \"delete\":\n # having 'add' permission means that there are *potentially*\n # some instances they can edit (namely: ones they own),\n # which is sufficient for returning True here\n return self._check_perm(user, [\"add\", \"change\"])\n else:\n # unrecognised actions are only allowed for active superusers\n return user.is_active and user.is_superuser","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.users_with_any_permission","text":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.users_with_any_permission\n def users_with_any_permission(self, actions):\n if \"change\" in actions or \"delete\" in actions:\n # either 'add' or 'change' permission means that there are *potentially*\n # some instances they can edit\n real_actions = [\"add\", \"change\"]\n elif \"add\" in actions:\n real_actions = [\"add\"]\n elif \"choose\" in actions:\n real_actions = [\"choose\"]\n else:\n # none of the actions passed in here are ones that we recognise, so only\n # allow them for active superusers\n return get_user_model().objects.filter(is_active=True, is_superuser=True)\n\n return self._users_with_perm(real_actions)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.user_has_permission_for_instance","text":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.user_has_permission_for_instance\n def user_has_permission_for_instance(self, user, action, instance):\n return self.user_has_any_permission_for_instance(user, [action], instance)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.user_has_any_permission_for_instance","text":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.user_has_any_permission_for_instance\n def user_has_any_permission_for_instance(self, user, actions, instance):\n if \"change\" in actions or \"delete\" in actions:\n if self._check_perm(user, [\"change\"], collection=instance.collection):\n return True\n elif (\n self._check_perm(user, [\"add\"], collection=instance.collection)\n and getattr(instance, self.owner_field_name) == user\n ):\n return True\n else:\n return False\n elif \"choose\" in actions:\n return self._check_perm(user, [\"choose\"], collection=instance.collection)\n else:\n # 'change' and 'delete' are the only actions that are well-defined\n # for specific instances. Other actions are only available to\n # active superusers.\n return user.is_active and user.is_superuser","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.instances_user_has_any_permission_for","text":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.instances_user_has_any_permission_for\n def instances_user_has_any_permission_for(self, user, actions):\n if user.is_active and user.is_superuser:\n # active superusers can perform any action (including unrecognised ones)\n # on any instance\n return self.model.objects.all()\n elif not user.is_authenticated:\n return self.model.objects.none()\n elif \"change\" in actions or \"delete\" in actions:\n # return instances which are:\n # - in (a descendant of) a collection for which they have 'change' permission\n # - OR in (a descendant of) a collection for which they have 'add' permission,\n # and are owned by them\n\n change_perm_filter = Q(\n collection__in=list(self._collections_with_perm(user, [\"change\"]))\n )\n\n add_perm_filter = Q(\n collection__in=list(self._collections_with_perm(user, [\"add\"]))\n ) & Q(**{self.owner_field_name: user})\n\n return self.model.objects.filter(change_perm_filter | add_perm_filter)\n elif \"choose\" in actions:\n # Return instances which are in (a descendant of) a collection for which they\n # have 'choose' permission.\n choose_perm_filter = Q(\n collection__in=list(self._collections_with_perm(user, [\"choose\"]))\n )\n return self.model.objects.filter(choose_perm_filter)\n else:\n # action is either not recognised, or is the 'add' action which is\n # not meaningful for existing instances. As such, non-superusers\n # cannot perform it on any existing instances.\n return self.model.objects.none()","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.users_with_any_permission_for_instance","text":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.users_with_any_permission_for_instance\n def users_with_any_permission_for_instance(self, actions, instance):\n if \"change\" in actions or \"delete\" in actions:\n # form a filter expression of the users with 'change' permission\n # over this instance's collection\n filter_expr = self._users_with_perm_filter(\n [\"change\"], collection=instance.collection\n )\n\n # add on the item's owner, if they still have 'add' permission\n # (and the owner field isn't blank)\n owner = getattr(instance, self.owner_field_name)\n if owner is not None and self._check_perm(\n owner, [\"add\"], collection=instance.collection\n ):\n filter_expr = filter_expr | Q(pk=owner.pk)\n\n # return the filtered queryset\n return get_user_model().objects.filter(filter_expr).distinct()\n elif \"choose\" in actions:\n # Form a filter expression of the users with 'choose' permission\n # over this instance's collection.\n filter_expr = self._users_with_perm_filter(\n [\"choose\"], collection=instance.collection\n )\n return get_user_model().objects.filter(filter_expr).distinct()\n else:\n # action is either not recognised, or is the 'add' action which is\n # not meaningful for existing instances. As such, the action is only\n # available to superusers\n return get_user_model().objects.filter(is_active=True, is_superuser=True)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.collections_user_has_any_permission_for","text":"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.collections_user_has_any_permission_for\n def collections_user_has_any_permission_for(self, user, actions):\n \"\"\"\n Return a queryset of all collections in which the given user has\n permission to perform any of the given actions\n \"\"\"\n if user.is_active and user.is_superuser:\n # active superusers can perform any action (including unrecognised ones)\n # in any collection\n return Collection.objects.all()\n\n elif not user.is_authenticated:\n return Collection.objects.none()\n\n elif \"change\" in actions or \"delete\" in actions:\n # return collections which are covered by either 'add' or 'change' permissions\n # (since collections with 'add' permissions can *potentially* contain instances\n # they own and can therefore edit)\n return self._collections_with_perm(user, [\"add\", \"change\"])\n\n elif \"add\" in actions:\n return self._collections_with_perm(user, [\"add\"])\n\n elif \"choose\" in actions:\n return self._collections_with_perm(user, [\"choose\"])\n\n else:\n # action is not recognised, and so non-superusers\n # cannot perform it on any existing collections\n return Collection.objects.none()","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy._descendants_with_perm","text":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy._descendants_with_perm\n def _descendants_with_perm(self, user, action):\n \"\"\"\n Return a queryset of collections descended from a collection on which this user has\n a GroupCollectionPermission record for this action. Used for actions, like edit and\n delete where the user cannot modify the collection where they are granted permission.\n \"\"\"\n # Get the permission object corresponding to this action\n permission = self._get_permission_objects_for_actions([action]).first()\n\n # Get the collections that have a GroupCollectionPermission record\n # for this permission and any of the user's groups;\n # create a list of their paths\n collection_roots = Collection.objects.filter(\n group_permissions__group__in=user.groups.all(),\n group_permissions__permission=permission,\n ).values(\"path\", \"depth\")\n\n if collection_roots:\n # build a filter expression that will filter our model to just those\n # instances in collections with a path that starts with one of the above\n # but excluding the collection on which permission was granted\n collection_path_filter = Q(\n path__startswith=collection_roots[0][\"path\"]\n ) & Q(depth__gt=collection_roots[0][\"depth\"])\n for collection in collection_roots[1:]:\n collection_path_filter = collection_path_filter | (\n Q(path__startswith=collection[\"path\"])\n & Q(depth__gt=collection[\"depth\"])\n )\n return Collection.objects.all().filter(collection_path_filter)\n else:\n # no matching collections\n return Collection.objects.none()","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_permission","text":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_permission\n def user_has_permission(self, user, action):\n \"\"\"\n Return whether the given user has permission to perform the given action\n on some or all instances of this model\n \"\"\"\n return self.user_has_any_permission(user, [action])","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_any_permission","text":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_any_permission\n def user_has_any_permission(self, user, actions):\n \"\"\"\n Return whether the given user has permission to perform any of the given actions\n on some or all instances of this model.\n \"\"\"\n return self._check_perm(user, actions)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.users_with_any_permission","text":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.users_with_any_permission\n def users_with_any_permission(self, actions):\n \"\"\"\n Return a queryset of users who have permission to perform any of the given actions\n on some or all instances of this model\n \"\"\"\n return self._users_with_perm(actions)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_permission_for_instance","text":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_permission_for_instance\n def user_has_permission_for_instance(self, user, action, instance):\n \"\"\"\n Return whether the given user has permission to perform the given action on the\n given model instance\n \"\"\"\n return self._check_perm(user, [action], collection=instance)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_any_permission_for_instance","text":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_any_permission_for_instance\n def user_has_any_permission_for_instance(self, user, actions, instance):\n \"\"\"\n Return whether the given user has permission to perform any of the given actions\n on the given model instance\n \"\"\"\n return self._check_perm(user, actions, collection=instance)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.users_with_any_permission_for_instance","text":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.users_with_any_permission_for_instance\n def users_with_any_permission_for_instance(self, actions, instance):\n \"\"\"\n Return a queryset of all users who have permission to perform any of the given\n actions on the given model instance\n \"\"\"\n return self._users_with_perm(actions, collection=instance)","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.instances_user_has_permission_for","text":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.instances_user_has_permission_for\n def instances_user_has_permission_for(self, user, action):\n if user.is_active and user.is_superuser:\n # active superusers can perform any action (including unrecognised ones)\n # in any collection - except for deleting the root collection\n if action == \"delete\":\n return Collection.objects.exclude(depth=1).all()\n else:\n return Collection.objects.all()\n\n elif not user.is_authenticated:\n return Collection.objects.none()\n\n else:\n if action == \"delete\":\n return self._descendants_with_perm(user, action)\n else:\n return self._collections_with_perm(user, [action])","label":0},{"cid":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.instances_user_has_any_permission_for","text":"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.instances_user_has_any_permission_for\n def instances_user_has_any_permission_for(self, user, actions):\n return self.collections_user_has_any_permission_for(user, actions)","label":0}],"string":"[\n {\n \"cid\": \"wagtail/admin/auth.py::users_with_page_permission\",\n \"text\": \"wagtail/admin/auth.py::users_with_page_permission\\ndef users_with_page_permission(page, permission_type, include_superusers=True):\\n # Get user model\\n User = get_user_model()\\n\\n # Find GroupPagePermission records of the given type that apply to this page or an ancestor\\n ancestors_and_self = list(page.get_ancestors()) + [page]\\n perm = GroupPagePermission.objects.filter(\\n permission_type=permission_type, page__in=ancestors_and_self\\n )\\n q = Q(groups__page_permissions__in=perm)\\n\\n # Include superusers\\n if include_superusers:\\n q |= Q(is_superuser=True)\\n\\n return User.objects.filter(is_active=True).filter(q).distinct()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::permission_denied\",\n \"text\": \"wagtail/admin/auth.py::permission_denied\\ndef permission_denied(request):\\n \\\"\\\"\\\"Return a standard 'permission denied' response\\\"\\\"\\\"\\n if request.headers.get(\\\"x-requested-with\\\") == \\\"XMLHttpRequest\\\":\\n raise PermissionDenied\\n\\n from wagtail.admin import messages\\n\\n messages.error(request, _(\\\"Sorry, you do not have permission to access this area.\\\"))\\n return redirect(\\\"wagtailadmin_home\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::user_passes_test\",\n \"text\": \"wagtail/admin/auth.py::user_passes_test\\ndef user_passes_test(test):\\n \\\"\\\"\\\"\\n Given a test function that takes a user object and returns a boolean,\\n return a view decorator that denies access to the user if the test returns false.\\n \\\"\\\"\\\"\\n\\n def decorator(view_func):\\n # decorator takes the view function, and returns the view wrapped in\\n # a permission check\\n\\n @wraps(view_func)\\n def wrapped_view_func(request, *args, **kwargs):\\n if test(request.user):\\n # permission check succeeds; run the view function as normal\\n return view_func(request, *args, **kwargs)\\n else:\\n # permission check failed\\n return permission_denied(request)\\n\\n return wrapped_view_func\\n\\n return decorator\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::user_passes_test.decorator\",\n \"text\": \"wagtail/admin/auth.py::user_passes_test.decorator\\n def decorator(view_func):\\n # decorator takes the view function, and returns the view wrapped in\\n # a permission check\\n\\n @wraps(view_func)\\n def wrapped_view_func(request, *args, **kwargs):\\n if test(request.user):\\n # permission check succeeds; run the view function as normal\\n return view_func(request, *args, **kwargs)\\n else:\\n # permission check failed\\n return permission_denied(request)\\n\\n return wrapped_view_func\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::user_passes_test.decorator.wrapped_view_func\",\n \"text\": \"wagtail/admin/auth.py::user_passes_test.decorator.wrapped_view_func\\n @wraps(view_func)\\n def wrapped_view_func(request, *args, **kwargs):\\n if test(request.user):\\n # permission check succeeds; run the view function as normal\\n return view_func(request, *args, **kwargs)\\n else:\\n # permission check failed\\n return permission_denied(request)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::permission_required\",\n \"text\": \"wagtail/admin/auth.py::permission_required\\ndef permission_required(permission_name):\\n \\\"\\\"\\\"\\n Replacement for django.contrib.auth.decorators.permission_required which returns a\\n more meaningful 'permission denied' response than just redirecting to the login page.\\n (The latter doesn't work anyway because Wagtail doesn't define LOGIN_URL...)\\n \\\"\\\"\\\"\\n\\n def test(user):\\n return user.has_perm(permission_name)\\n\\n # user_passes_test constructs a decorator function specific to the above test function\\n return user_passes_test(test)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::permission_required.test\",\n \"text\": \"wagtail/admin/auth.py::permission_required.test\\n def test(user):\\n return user.has_perm(permission_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::any_permission_required\",\n \"text\": \"wagtail/admin/auth.py::any_permission_required\\ndef any_permission_required(*perms):\\n \\\"\\\"\\\"\\n Decorator that accepts a list of permission names, and allows the user\\n to pass if they have *any* of the permissions in the list\\n \\\"\\\"\\\"\\n\\n def test(user):\\n for perm in perms:\\n if user.has_perm(perm):\\n return True\\n\\n return False\\n\\n return user_passes_test(test)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::any_permission_required.test\",\n \"text\": \"wagtail/admin/auth.py::any_permission_required.test\\n def test(user):\\n for perm in perms:\\n if user.has_perm(perm):\\n return True\\n\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::PermissionPolicyChecker.__init__\",\n \"text\": \"wagtail/admin/auth.py::PermissionPolicyChecker.__init__\\n def __init__(self, policy):\\n self.policy = policy\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::PermissionPolicyChecker.require\",\n \"text\": \"wagtail/admin/auth.py::PermissionPolicyChecker.require\\n def require(self, action):\\n def test(user):\\n return self.policy.user_has_permission(user, action)\\n\\n return user_passes_test(test)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::PermissionPolicyChecker.require.test\",\n \"text\": \"wagtail/admin/auth.py::PermissionPolicyChecker.require.test\\n def test(user):\\n return self.policy.user_has_permission(user, action)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::PermissionPolicyChecker.require_any\",\n \"text\": \"wagtail/admin/auth.py::PermissionPolicyChecker.require_any\\n def require_any(self, *actions):\\n def test(user):\\n return self.policy.user_has_any_permission(user, actions)\\n\\n return user_passes_test(test)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::PermissionPolicyChecker.require_any.test\",\n \"text\": \"wagtail/admin/auth.py::PermissionPolicyChecker.require_any.test\\n def test(user):\\n return self.policy.user_has_any_permission(user, actions)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::user_has_any_page_permission\",\n \"text\": \"wagtail/admin/auth.py::user_has_any_page_permission\\ndef user_has_any_page_permission(user):\\n \\\"\\\"\\\"\\n Check if a user has any permission to add, edit, or otherwise manage any\\n page.\\n \\\"\\\"\\\"\\n # Can't do nothin' if you're not active.\\n if not user.is_active:\\n return False\\n\\n # Superusers can do anything.\\n if user.is_superuser:\\n return True\\n\\n # At least one of the users groups has a GroupPagePermission.\\n # The user can probably do something.\\n if GroupPagePermission.objects.filter(group__in=user.groups.all()).exists():\\n return True\\n\\n # Specific permissions for a page type do not mean anything.\\n\\n # No luck! This user can not do anything with pages.\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::reject_request\",\n \"text\": \"wagtail/admin/auth.py::reject_request\\ndef reject_request(request):\\n if request.headers.get(\\\"x-requested-with\\\") == \\\"XMLHttpRequest\\\":\\n raise PermissionDenied\\n\\n # import redirect_to_login here to avoid circular imports on model files that import\\n # wagtail.admin.auth, specifically where custom user models are involved\\n from django.contrib.auth.views import redirect_to_login as auth_redirect_to_login\\n\\n return auth_redirect_to_login(\\n request.get_full_path(), login_url=reverse(\\\"wagtailadmin_login\\\")\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::require_admin_access\",\n \"text\": \"wagtail/admin/auth.py::require_admin_access\\ndef require_admin_access(view_func):\\n def decorated_view(request, *args, **kwargs):\\n\\n user = request.user\\n\\n if user.is_anonymous:\\n return reject_request(request)\\n\\n if user.has_perms([\\\"wagtailadmin.access_admin\\\"]):\\n try:\\n preferred_language = None\\n if hasattr(user, \\\"wagtail_userprofile\\\"):\\n preferred_language = (\\n user.wagtail_userprofile.get_preferred_language()\\n )\\n l18n.set_language(preferred_language)\\n time_zone = user.wagtail_userprofile.get_current_time_zone()\\n activate_tz(time_zone)\\n with LogContext(user=user):\\n if preferred_language:\\n with override(preferred_language):\\n response = view_func(request, *args, **kwargs)\\n\\n if hasattr(response, \\\"render\\\"):\\n # If the response has a render() method, Django treats it\\n # like a TemplateResponse, so we should do the same\\n # In this case, we need to guarantee that when the TemplateResponse\\n # is rendered, it is done within the override context manager\\n # or the user preferred_language will not be used\\n # (this could be replaced with simply rendering the TemplateResponse\\n # for simplicity but this does remove some of its middleware modification\\n # potential)\\n render = response.render\\n\\n def overridden_render(response):\\n with override(preferred_language):\\n return render()\\n\\n response.render = types.MethodType(\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/auth.py::require_admin_access.decorated_view\",\n \"text\": \"wagtail/admin/auth.py::require_admin_access.decorated_view\\n def decorated_view(request, *args, **kwargs):\\n\\n user = request.user\\n\\n if user.is_anonymous:\\n return reject_request(request)\\n\\n if user.has_perms([\\\"wagtailadmin.access_admin\\\"]):\\n try:\\n preferred_language = None\\n if hasattr(user, \\\"wagtail_userprofile\\\"):\\n preferred_language = (\\n user.wagtail_userprofile.get_preferred_language()\\n )\\n l18n.set_language(preferred_language)\\n time_zone = user.wagtail_userprofile.get_current_time_zone()\\n activate_tz(time_zone)\\n with LogContext(user=user):\\n if preferred_language:\\n with override(preferred_language):\\n response = view_func(request, *args, **kwargs)\\n\\n if hasattr(response, \\\"render\\\"):\\n # If the response has a render() method, Django treats it\\n # like a TemplateResponse, so we should do the same\\n # In this case, we need to guarantee that when the TemplateResponse\\n # is rendered, it is done within the override context manager\\n # or the user preferred_language will not be used\\n # (this could be replaced with simply rendering the TemplateResponse\\n # for simplicity but this does remove some of its middleware modification\\n # potential)\\n render = response.render\\n\\n def overridden_render(response):\\n with override(preferred_language):\\n return render()\\n\\n response.render = types.MethodType(\\n overridden_re\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_upgrade_check_setting\",\n \"text\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_upgrade_check_setting\\n def get_upgrade_check_setting(self) -> Union[bool, str]:\\n return getattr(settings, \\\"WAGTAIL_ENABLE_UPDATE_CHECK\\\", True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.upgrade_check_lts_only\",\n \"text\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.upgrade_check_lts_only\\n def upgrade_check_lts_only(self) -> bool:\\n upgrade_check = self.get_upgrade_check_setting()\\n if isinstance(upgrade_check, str) and upgrade_check.lower() == \\\"lts\\\":\\n return True\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.get_context_data\\n def get_context_data(self, parent_context: Mapping[str, Any]) -> Mapping[str, Any]:\\n return {\\\"lts_only\\\": self.upgrade_check_lts_only()}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.render_html\",\n \"text\": \"wagtail/admin/views/home.py::UpgradeNotificationPanel.render_html\\n def render_html(self, parent_context: Mapping[str, Any] = None) -> str:\\n if (\\n parent_context[\\\"request\\\"].user.is_superuser\\n and self.get_upgrade_check_setting()\\n ):\\n return super().render_html(parent_context)\\n else:\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::PagesForModerationPanel.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::PagesForModerationPanel.get_context_data\\n def get_context_data(self, parent_context):\\n request = parent_context[\\\"request\\\"]\\n context = super().get_context_data(parent_context)\\n user_perms = UserPagePermissionsProxy(request.user)\\n context[\\\"page_revisions_for_moderation\\\"] = (\\n user_perms.revisions_for_moderation()\\n .select_related(\\\"user\\\")\\n .order_by(\\\"-created_at\\\")\\n )\\n context[\\\"request\\\"] = request\\n context[\\\"csrf_token\\\"] = parent_context[\\\"csrf_token\\\"]\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::UserPagesInWorkflowModerationPanel.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::UserPagesInWorkflowModerationPanel.get_context_data\\n def get_context_data(self, parent_context):\\n request = parent_context[\\\"request\\\"]\\n context = super().get_context_data(parent_context)\\n if getattr(settings, \\\"WAGTAIL_WORKFLOW_ENABLED\\\", True):\\n # Find in progress workflow states which are either requested by the user or on pages owned by the user\\n context[\\\"workflow_states\\\"] = (\\n WorkflowState.objects.active()\\n .filter(Q(page__owner=request.user) | Q(requested_by=request.user))\\n .select_related(\\n \\\"page\\\",\\n \\\"current_task_state\\\",\\n \\\"current_task_state__task\\\",\\n \\\"current_task_state__page_revision\\\",\\n )\\n .order_by(\\\"-current_task_state__started_at\\\")\\n )\\n else:\\n context[\\\"workflow_states\\\"] = WorkflowState.objects.none()\\n context[\\\"request\\\"] = request\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::WorkflowPagesToModeratePanel.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::WorkflowPagesToModeratePanel.get_context_data\\n def get_context_data(self, parent_context):\\n request = parent_context[\\\"request\\\"]\\n context = super().get_context_data(parent_context)\\n if getattr(settings, \\\"WAGTAIL_WORKFLOW_ENABLED\\\", True):\\n states = (\\n TaskState.objects.reviewable_by(request.user)\\n .select_related(\\n \\\"page_revision\\\",\\n \\\"task\\\",\\n \\\"page_revision__user\\\",\\n )\\n .order_by(\\\"-started_at\\\")\\n )\\n context[\\\"states\\\"] = [\\n (\\n state,\\n state.task.specific.get_actions(\\n page=state.page_revision.content_object, user=request.user\\n ),\\n state.workflow_state.all_tasks_with_status(),\\n )\\n for state in states\\n ]\\n else:\\n context[\\\"states\\\"] = []\\n context[\\\"request\\\"] = request\\n context[\\\"csrf_token\\\"] = parent_context[\\\"csrf_token\\\"]\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::LockedPagesPanel.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::LockedPagesPanel.get_context_data\\n def get_context_data(self, parent_context):\\n request = parent_context[\\\"request\\\"]\\n context = super().get_context_data(parent_context)\\n context.update(\\n {\\n \\\"locked_pages\\\": Page.objects.filter(\\n locked=True,\\n locked_by=request.user,\\n ),\\n \\\"can_remove_locks\\\": UserPagePermissionsProxy(\\n request.user\\n ).can_remove_locks(),\\n \\\"request\\\": request,\\n \\\"csrf_token\\\": parent_context[\\\"csrf_token\\\"],\\n }\\n )\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::RecentEditsPanel.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::RecentEditsPanel.get_context_data\\n def get_context_data(self, parent_context):\\n request = parent_context[\\\"request\\\"]\\n context = super().get_context_data(parent_context)\\n\\n # Last n edited pages\\n edit_count = getattr(settings, \\\"WAGTAILADMIN_RECENT_EDITS_LIMIT\\\", 5)\\n if connection.vendor == \\\"mysql\\\":\\n # MySQL can't handle the subselect created by the ORM version -\\n # it fails with \\\"This version of MySQL doesn't yet support 'LIMIT & IN/ALL/ANY/SOME subquery'\\\"\\n last_edits = Revision.objects.raw(\\n \\\"\\\"\\\"\\n SELECT wr.* FROM\\n wagtailcore_revision wr JOIN (\\n SELECT max(created_at) AS max_created_at, object_id FROM\\n wagtailcore_revision WHERE user_id = %s AND base_content_type_id = %s GROUP BY object_id ORDER BY max_created_at DESC LIMIT %s\\n ) AS max_rev ON max_rev.max_created_at = wr.created_at ORDER BY wr.created_at DESC\\n \\\"\\\"\\\",\\n [\\n User._meta.pk.get_db_prep_value(request.user.pk, connection),\\n get_default_page_content_type().id,\\n edit_count,\\n ],\\n )\\n else:\\n last_edits_dates = (\\n Revision.page_revisions.filter(user=request.user)\\n .values(\\\"object_id\\\")\\n .annotate(latest_date=Max(\\\"created_at\\\"))\\n .order_by(\\\"-latest_date\\\")\\n .values(\\\"latest_date\\\")[:edit_count]\\n )\\n last_edits = Revision.page_revisions.filter(\\n created_at__in=last_edits_dates\\n ).order_by(\\\"-created_at\\\")\\n\\n # The revision's object_id is a string, so cast it to int first.\\n page_keys = [int(pr.object_id) for pr in last_edits]\\n pages = Page.objects.specific().in_bulk(page_keys)\\n context[\\\"last_edits\\\"] = [\\n [revision, pages.get(in\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::HomeView.get_context_data\",\n \"text\": \"wagtail/admin/views/home.py::HomeView.get_context_data\\n def get_context_data(self, **kwargs):\\n context = super().get_context_data(**kwargs)\\n panels = self.get_panels()\\n site_details = self.get_site_details()\\n\\n context[\\\"media\\\"] = self.get_media(panels)\\n context[\\\"panels\\\"] = sorted(panels, key=lambda p: p.order)\\n context[\\\"user\\\"] = self.request.user\\n\\n return {**context, **site_details}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::HomeView.get_media\",\n \"text\": \"wagtail/admin/views/home.py::HomeView.get_media\\n def get_media(self, panels=[]):\\n media = Media()\\n\\n for panel in panels:\\n media += panel.media\\n\\n return media\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::HomeView.get_panels\",\n \"text\": \"wagtail/admin/views/home.py::HomeView.get_panels\\n def get_panels(self):\\n request = self.request\\n panels = [\\n SiteSummaryPanel(request),\\n UpgradeNotificationPanel(),\\n WorkflowPagesToModeratePanel(),\\n PagesForModerationPanel(),\\n UserPagesInWorkflowModerationPanel(),\\n RecentEditsPanel(),\\n LockedPagesPanel(),\\n ]\\n\\n for fn in hooks.get_hooks(\\\"construct_homepage_panels\\\"):\\n fn(request, panels)\\n\\n return panels\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::HomeView.get_site_details\",\n \"text\": \"wagtail/admin/views/home.py::HomeView.get_site_details\\n def get_site_details(self):\\n request = self.request\\n site = get_site_for_user(request.user)\\n\\n return {\\n \\\"root_page\\\": site[\\\"root_page\\\"],\\n \\\"root_site\\\": site[\\\"root_site\\\"],\\n \\\"site_name\\\": site[\\\"site_name\\\"],\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::error_test\",\n \"text\": \"wagtail/admin/views/home.py::error_test\\ndef error_test(request):\\n raise Exception(\\\"This is a test of the emergency broadcast system.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::default\",\n \"text\": \"wagtail/admin/views/home.py::default\\n@permission_required(\\\"wagtailadmin.access_admin\\\", login_url=\\\"wagtailadmin_login\\\")\\ndef default(request):\\n \\\"\\\"\\\"\\n Called whenever a request comes in with the correct prefix (eg /admin/) but\\n doesn't actually correspond to a Wagtail view.\\n\\n For authenticated users, it'll raise a 404 error. Anonymous users will be\\n redirected to the login page.\\n \\\"\\\"\\\"\\n raise Http404\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::icons\",\n \"text\": \"wagtail/admin/views/home.py::icons\\ndef icons():\\n global _icons_html\\n if _icons_html is None:\\n icon_hooks = hooks.get_hooks(\\\"register_icons\\\")\\n all_icons = sorted(\\n itertools.chain.from_iterable(hook([]) for hook in icon_hooks)\\n )\\n combined_icon_markup = \\\"\\\"\\n for icon in all_icons:\\n combined_icon_markup += render_to_string(icon).replace(\\\"svg\\\", \\\"symbol\\\")\\n\\n _full_sprite_html = render_to_string(\\n \\\"wagtailadmin/shared/icons.html\\\", {\\\"icons\\\": combined_icon_markup}\\n )\\n return _full_sprite_html\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/home.py::sprite\",\n \"text\": \"wagtail/admin/views/home.py::sprite\\ndef sprite(request):\\n return HttpResponse(icons())\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/audit_logging.py::get_users_for_filter\",\n \"text\": \"wagtail/admin/views/reports/audit_logging.py::get_users_for_filter\\ndef get_users_for_filter():\\n user_ids = set()\\n for log_model in log_action_registry.get_log_entry_models():\\n user_ids.update(log_model.objects.all().get_user_ids())\\n\\n User = get_user_model()\\n return User.objects.filter(pk__in=user_ids).order_by(User.USERNAME_FIELD)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/audit_logging.py::get_content_types_for_filter\",\n \"text\": \"wagtail/admin/views/reports/audit_logging.py::get_content_types_for_filter\\ndef get_content_types_for_filter():\\n content_type_ids = set()\\n for log_model in log_action_registry.get_log_entry_models():\\n content_type_ids.update(log_model.objects.all().get_content_type_ids())\\n\\n return ContentType.objects.filter(pk__in=content_type_ids).order_by(\\\"model\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/audit_logging.py::SiteHistoryReportFilterSet.filter_hide_commenting_actions\",\n \"text\": \"wagtail/admin/views/reports/audit_logging.py::SiteHistoryReportFilterSet.filter_hide_commenting_actions\\n def filter_hide_commenting_actions(self, queryset, name, value):\\n if value:\\n queryset = queryset.exclude(action__startswith=\\\"wagtail.comments\\\")\\n return queryset\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/audit_logging.py::SiteHistoryReportFilterSet.filter_object_type\",\n \"text\": \"wagtail/admin/views/reports/audit_logging.py::SiteHistoryReportFilterSet.filter_object_type\\n def filter_object_type(self, queryset, name, value):\\n return queryset.filter_on_content_type(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.__init__\",\n \"text\": \"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.__init__\\n def __init__(self, **kwargs):\\n super().__init__(**kwargs)\\n\\n self.custom_field_preprocess = self.custom_field_preprocess.copy()\\n self.custom_field_preprocess[\\\"action\\\"] = {\\n self.FORMAT_CSV: self.get_action_label,\\n self.FORMAT_XLSX: self.get_action_label,\\n }\\n self.custom_field_preprocess[\\\"content_type\\\"] = {\\n self.FORMAT_CSV: get_content_type_label,\\n self.FORMAT_XLSX: get_content_type_label,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.get_filename\",\n \"text\": \"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.get_filename\\n def get_filename(self):\\n return \\\"audit-log-{}\\\".format(datetime.datetime.today().strftime(\\\"%Y-%m-%d\\\"))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.get_filtered_queryset\",\n \"text\": \"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.get_filtered_queryset\\n def get_filtered_queryset(self):\\n \\\"\\\"\\\"\\n Since this report combines records from multiple log models, the standard pattern of\\n returning a queryset from get_queryset() to be filtered by filter_queryset() is not\\n possible - the subquery for each log model must be filtered separately before joining\\n with union().\\n\\n Additionally, a union() on standard model-based querysets will return a queryset based on\\n the first model in the union, so instances of the other model(s) would be returned as the\\n wrong type. To avoid this, we construct values() querysets as follows:\\n\\n 1. For each model, construct a values() queryset consisting of id, timestamp and an\\n annotation to indicate which model it is, and filter this with filter_queryset\\n 2. Form a union() queryset from these queries, and order it by -timestamp\\n (this is the result returned from get_filtered_queryset)\\n 3. Apply pagination (done in MultipleObjectMixin.get_context_data)\\n 4. (In decorate_paginated_queryset:) For each model included in the result set, look up\\n the set of model instances by ID. Use these to form a final list of model instances\\n in the same order as the query.\\n \\\"\\\"\\\"\\n queryset = None\\n filters = None\\n\\n # Retrieve the set of registered log models, and cast it to a list so that we assign\\n # an index number to each one; this index number will be used to distinguish models\\n # in the combined results\\n self.log_models = list(log_action_registry.get_log_entry_models())\\n\\n for log_model_index, log_model in enumerate(self.log_models):\\n sub_queryset = (\\n log_model.objects.viewable_by_user(self.request.user)\\n .values(\\\"pk\\\", \\\"timestamp\\\")\\n .annotate(\\n log_model_index=Value(log_model_ind\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.decorate_paginated_queryset\",\n \"text\": \"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.decorate_paginated_queryset\\n def decorate_paginated_queryset(self, queryset):\\n # build lists of ids from queryset, grouped by log model index\\n pks_by_log_model_index = defaultdict(list)\\n for row in queryset:\\n pks_by_log_model_index[row[\\\"log_model_index\\\"]].append(row[\\\"pk\\\"])\\n\\n url_finder = AdminURLFinder(self.request.user)\\n\\n # for each log model found in the queryset, look up the set of log entries by id\\n # and build a lookup table\\n object_lookup = {}\\n for log_model_index, pks in pks_by_log_model_index.items():\\n log_entries = (\\n self.log_models[log_model_index]\\n .objects.prefetch_related(\\\"user__wagtail_userprofile\\\", \\\"content_type\\\")\\n .filter(pk__in=pks)\\n .with_instances()\\n )\\n for log_entry, instance in log_entries:\\n # annotate log entry with an 'edit_url' property\\n log_entry.edit_url = url_finder.get_edit_url(instance)\\n object_lookup[(log_model_index, log_entry.pk)] = log_entry\\n\\n # return items from our lookup table in the order of the original queryset\\n return [object_lookup[(row[\\\"log_model_index\\\"], row[\\\"pk\\\"])] for row in queryset]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.get_action_label\",\n \"text\": \"wagtail/admin/views/reports/audit_logging.py::LogEntriesView.get_action_label\\n def get_action_label(self, action):\\n return force_str(log_action_registry.get_action_label(action))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/locked_pages.py::LockedPagesView.get_filename\",\n \"text\": \"wagtail/admin/views/reports/locked_pages.py::LockedPagesView.get_filename\\n def get_filename(self):\\n return \\\"locked-pages-report-{}\\\".format(\\n datetime.datetime.today().strftime(\\\"%Y-%m-%d\\\")\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/locked_pages.py::LockedPagesView.get_queryset\",\n \"text\": \"wagtail/admin/views/reports/locked_pages.py::LockedPagesView.get_queryset\\n def get_queryset(self):\\n pages = (\\n (\\n UserPagePermissionsProxy(self.request.user).editable_pages()\\n | Page.objects.filter(locked_by=self.request.user)\\n )\\n .filter(locked=True)\\n .specific(defer=True)\\n )\\n\\n if getattr(settings, \\\"WAGTAIL_I18N_ENABLED\\\", False):\\n pages = pages.select_related(\\\"locale\\\")\\n\\n self.queryset = pages\\n return super().get_queryset()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/locked_pages.py::LockedPagesView.dispatch\",\n \"text\": \"wagtail/admin/views/reports/locked_pages.py::LockedPagesView.dispatch\\n def dispatch(self, request, *args, **kwargs):\\n if not UserPagePermissionsProxy(request.user).can_remove_locks():\\n raise PermissionDenied\\n return super().dispatch(request, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/workflows.py::get_requested_by_queryset\",\n \"text\": \"wagtail/admin/views/reports/workflows.py::get_requested_by_queryset\\ndef get_requested_by_queryset(request):\\n User = get_user_model()\\n return User.objects.filter(\\n pk__in=set(WorkflowState.objects.values_list(\\\"requested_by__pk\\\", flat=True))\\n ).order_by(User.USERNAME_FIELD)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/workflows.py::WorkflowReportFilterSet.filter_reviewable\",\n \"text\": \"wagtail/admin/views/reports/workflows.py::WorkflowReportFilterSet.filter_reviewable\\n def filter_reviewable(self, queryset, name, value):\\n if value and self.request and self.request.user:\\n queryset = queryset.filter(\\n current_task_state__in=TaskState.objects.reviewable_by(\\n self.request.user\\n )\\n )\\n return queryset\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/workflows.py::WorkflowTasksReportFilterSet.filter_reviewable\",\n \"text\": \"wagtail/admin/views/reports/workflows.py::WorkflowTasksReportFilterSet.filter_reviewable\\n def filter_reviewable(self, queryset, name, value):\\n if value and self.request and self.request.user:\\n queryset = queryset.filter(\\n id__in=TaskState.objects.reviewable_by(self.request.user).values_list(\\n \\\"id\\\", flat=True\\n )\\n )\\n return queryset\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/workflows.py::WorkflowView.get_filename\",\n \"text\": \"wagtail/admin/views/reports/workflows.py::WorkflowView.get_filename\\n def get_filename(self):\\n return \\\"workflow-report-{}\\\".format(\\n datetime.datetime.today().strftime(\\\"%Y-%m-%d\\\")\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/workflows.py::WorkflowView.get_queryset\",\n \"text\": \"wagtail/admin/views/reports/workflows.py::WorkflowView.get_queryset\\n def get_queryset(self):\\n pages = UserPagePermissionsProxy(self.request.user).editable_pages()\\n return WorkflowState.objects.filter(page__in=pages).order_by(\\\"-created_at\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/workflows.py::WorkflowTasksView.get_filename\",\n \"text\": \"wagtail/admin/views/reports/workflows.py::WorkflowTasksView.get_filename\\n def get_filename(self):\\n return \\\"workflow-tasks-{}\\\".format(\\n datetime.datetime.today().strftime(\\\"%Y-%m-%d\\\")\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/views/reports/workflows.py::WorkflowTasksView.get_queryset\",\n \"text\": \"wagtail/admin/views/reports/workflows.py::WorkflowTasksView.get_queryset\\n def get_queryset(self):\\n pages = UserPagePermissionsProxy(self.request.user).editable_pages()\\n return TaskState.objects.filter(workflow_state__page__in=pages).order_by(\\n \\\"-started_at\\\"\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.__init__\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.__init__\\n def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n\\n # seen_types is a mapping of type name strings (format: \\\"app_label.ModelName\\\")\\n # to model classes. When an object is serialised in the API, its model\\n # is added to this mapping. This is used by the Admin API which appends a\\n # summary of the used types to the response.\\n self.seen_types = OrderedDict()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_queryset\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_queryset\\n def get_queryset(self):\\n return self.model.objects.all().order_by(\\\"id\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.listing_view\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.listing_view\\n def listing_view(self, request):\\n queryset = self.get_queryset()\\n self.check_query_parameters(queryset)\\n queryset = self.filter_queryset(queryset)\\n queryset = self.paginate_queryset(queryset)\\n serializer = self.get_serializer(queryset, many=True)\\n return self.get_paginated_response(serializer.data)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.detail_view\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.detail_view\\n def detail_view(self, request, pk):\\n instance = self.get_object()\\n serializer = self.get_serializer(instance)\\n return Response(serializer.data)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.find_view\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.find_view\\n def find_view(self, request):\\n queryset = self.get_queryset()\\n\\n try:\\n obj = self.find_object(queryset, request)\\n\\n if obj is None:\\n raise self.model.DoesNotExist\\n\\n except self.model.DoesNotExist:\\n raise Http404(\\\"not found\\\")\\n\\n # Generate redirect\\n url = get_object_detail_url(\\n self.request.wagtailapi_router, request, self.model, obj.pk\\n )\\n\\n if url is None:\\n # Shouldn't happen unless this endpoint isn't actually installed in the router\\n raise Exception(\\n \\\"Cannot generate URL to detail view. Is '{}' installed in the API router?\\\".format(\\n self.__class__.__name__\\n )\\n )\\n\\n return redirect(url)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.find_object\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.find_object\\n def find_object(self, queryset, request):\\n \\\"\\\"\\\"\\n Override this to implement more find methods.\\n \\\"\\\"\\\"\\n if \\\"id\\\" in request.GET:\\n return queryset.get(id=request.GET[\\\"id\\\"])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.handle_exception\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.handle_exception\\n def handle_exception(self, exc):\\n if isinstance(exc, Http404):\\n data = {\\\"message\\\": str(exc)}\\n return Response(data, status=status.HTTP_404_NOT_FOUND)\\n elif isinstance(exc, BadRequestError):\\n data = {\\\"message\\\": str(exc)}\\n return Response(data, status=status.HTTP_400_BAD_REQUEST)\\n return super().handle_exception(exc)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet._convert_api_fields\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet._convert_api_fields\\n @classmethod\\n def _convert_api_fields(cls, fields):\\n return [\\n field if isinstance(field, APIField) else APIField(field)\\n for field in fields\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_body_fields\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_body_fields\\n @classmethod\\n def get_body_fields(cls, model):\\n return cls._convert_api_fields(\\n cls.body_fields + list(getattr(model, \\\"api_fields\\\", ()))\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_body_fields_names\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_body_fields_names\\n @classmethod\\n def get_body_fields_names(cls, model):\\n return [field.name for field in cls.get_body_fields(model)]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_meta_fields\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_meta_fields\\n @classmethod\\n def get_meta_fields(cls, model):\\n return cls._convert_api_fields(\\n cls.meta_fields + list(getattr(model, \\\"api_meta_fields\\\", ()))\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_meta_fields_names\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_meta_fields_names\\n @classmethod\\n def get_meta_fields_names(cls, model):\\n return [field.name for field in cls.get_meta_fields(model)]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_field_serializer_overrides\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_field_serializer_overrides\\n @classmethod\\n def get_field_serializer_overrides(cls, model):\\n return {\\n field.name: field.serializer\\n for field in cls.get_body_fields(model) + cls.get_meta_fields(model)\\n if field.serializer is not None\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_available_fields\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_available_fields\\n @classmethod\\n def get_available_fields(cls, model, db_fields_only=False):\\n \\\"\\\"\\\"\\n Returns a list of all the fields that can be used in the API for the\\n specified model class.\\n\\n Setting db_fields_only to True will remove all fields that do not have\\n an underlying column in the database (eg, type/detail_url and any custom\\n fields that are callables)\\n \\\"\\\"\\\"\\n fields = cls.get_body_fields_names(model) + cls.get_meta_fields_names(model)\\n\\n if db_fields_only:\\n # Get list of available database fields then remove any fields in our\\n # list that isn't a database field\\n database_fields = set()\\n for field in model._meta.get_fields():\\n database_fields.add(field.name)\\n\\n if hasattr(field, \\\"attname\\\"):\\n database_fields.add(field.attname)\\n\\n fields = [field for field in fields if field in database_fields]\\n\\n return fields\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_detail_default_fields\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_detail_default_fields\\n @classmethod\\n def get_detail_default_fields(cls, model):\\n return cls.get_available_fields(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_listing_default_fields\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_listing_default_fields\\n @classmethod\\n def get_listing_default_fields(cls, model):\\n return cls.listing_default_fields[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_nested_default_fields\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_nested_default_fields\\n @classmethod\\n def get_nested_default_fields(cls, model):\\n return cls.nested_default_fields[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.check_query_parameters\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.check_query_parameters\\n def check_query_parameters(self, queryset):\\n \\\"\\\"\\\"\\n Ensure that only valid query parameters are included in the URL.\\n \\\"\\\"\\\"\\n query_parameters = set(self.request.GET.keys())\\n\\n # All query parameters must be either a database field or an operation\\n allowed_query_parameters = set(\\n self.get_available_fields(queryset.model, db_fields_only=True)\\n ).union(self.known_query_parameters)\\n unknown_parameters = query_parameters - allowed_query_parameters\\n if unknown_parameters:\\n raise BadRequestError(\\n \\\"query parameter is not an operation or a recognised field: %s\\\"\\n % \\\", \\\".join(sorted(unknown_parameters))\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet._get_serializer_class\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet._get_serializer_class\\n @classmethod\\n def _get_serializer_class(\\n cls, router, model, fields_config, show_details=False, nested=False\\n ):\\n # Get all available fields\\n body_fields = cls.get_body_fields_names(model)\\n meta_fields = cls.get_meta_fields_names(model)\\n all_fields = body_fields + meta_fields\\n\\n # Remove any duplicates\\n all_fields = list(OrderedDict.fromkeys(all_fields))\\n\\n if not show_details:\\n # Remove detail only fields\\n for field in cls.detail_only_fields:\\n try:\\n all_fields.remove(field)\\n except KeyError:\\n pass\\n\\n # Get list of configured fields\\n if show_details:\\n fields = set(cls.get_detail_default_fields(model))\\n elif nested:\\n fields = set(cls.get_nested_default_fields(model))\\n else:\\n fields = set(cls.get_listing_default_fields(model))\\n\\n # If first field is '*' start with all fields\\n # If first field is '_' start with no fields\\n if fields_config and fields_config[0][0] == \\\"*\\\":\\n fields = set(all_fields)\\n fields_config = fields_config[1:]\\n elif fields_config and fields_config[0][0] == \\\"_\\\":\\n fields = set()\\n fields_config = fields_config[1:]\\n\\n mentioned_fields = set()\\n sub_fields = {}\\n\\n for field_name, negated, field_sub_fields in fields_config:\\n if negated:\\n try:\\n fields.remove(field_name)\\n except KeyError:\\n pass\\n else:\\n fields.add(field_name)\\n if field_sub_fields:\\n sub_fields[field_name] = field_sub_fields\\n\\n mentioned_fields.add(field_name)\\n\\n unknown_fields = mentioned_fields - set(all_fields)\\n\\n if unknown_fields:\\n raise BadRequestError(\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_serializer_class\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_serializer_class\\n def get_serializer_class(self):\\n request = self.request\\n\\n # Get model\\n if self.action == \\\"listing_view\\\":\\n model = self.get_queryset().model\\n else:\\n model = type(self.get_object())\\n\\n # Fields\\n if \\\"fields\\\" in request.GET:\\n try:\\n fields_config = parse_fields_parameter(request.GET[\\\"fields\\\"])\\n except ValueError as e:\\n raise BadRequestError(\\\"fields error: %s\\\" % str(e))\\n else:\\n # Use default fields\\n fields_config = []\\n\\n # Allow \\\"detail_only\\\" (eg parent) fields on detail view\\n if self.action == \\\"listing_view\\\":\\n show_details = False\\n else:\\n show_details = True\\n\\n return self._get_serializer_class(\\n self.request.wagtailapi_router,\\n model,\\n fields_config,\\n show_details=show_details,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_serializer_context\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_serializer_context\\n def get_serializer_context(self):\\n \\\"\\\"\\\"\\n The serialization context differs between listing and detail views.\\n \\\"\\\"\\\"\\n return {\\n \\\"request\\\": self.request,\\n \\\"view\\\": self,\\n \\\"router\\\": self.request.wagtailapi_router,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_renderer_context\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_renderer_context\\n def get_renderer_context(self):\\n context = super().get_renderer_context()\\n context[\\\"indent\\\"] = 4\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_urlpatterns\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_urlpatterns\\n @classmethod\\n def get_urlpatterns(cls):\\n \\\"\\\"\\\"\\n This returns a list of URL patterns for the endpoint\\n \\\"\\\"\\\"\\n return [\\n path(\\\"\\\", cls.as_view({\\\"get\\\": \\\"listing_view\\\"}), name=\\\"listing\\\"),\\n path(\\\"<int:pk>/\\\", cls.as_view({\\\"get\\\": \\\"detail_view\\\"}), name=\\\"detail\\\"),\\n path(\\\"find/\\\", cls.as_view({\\\"get\\\": \\\"find_view\\\"}), name=\\\"find\\\"),\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_model_listing_urlpath\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_model_listing_urlpath\\n @classmethod\\n def get_model_listing_urlpath(cls, model, namespace=\\\"\\\"):\\n if namespace:\\n url_name = namespace + \\\":listing\\\"\\n else:\\n url_name = \\\"listing\\\"\\n\\n return reverse(url_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_object_detail_urlpath\",\n \"text\": \"wagtail/api/v2/views.py::BaseAPIViewSet.get_object_detail_urlpath\\n @classmethod\\n def get_object_detail_urlpath(cls, model, pk, namespace=\\\"\\\"):\\n if namespace:\\n url_name = namespace + \\\":detail\\\"\\n else:\\n url_name = \\\"detail\\\"\\n\\n return reverse(url_name, args=(pk,))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_detail_default_fields\",\n \"text\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_detail_default_fields\\n @classmethod\\n def get_detail_default_fields(cls, model):\\n detail_default_fields = super().get_detail_default_fields(model)\\n\\n # When i18n is disabled, remove \\\"locale\\\" from default fields\\n if not getattr(settings, \\\"WAGTAIL_I18N_ENABLED\\\", False):\\n detail_default_fields.remove(\\\"locale\\\")\\n\\n return detail_default_fields\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_listing_default_fields\",\n \"text\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_listing_default_fields\\n @classmethod\\n def get_listing_default_fields(cls, model):\\n listing_default_fields = super().get_listing_default_fields(model)\\n\\n # When i18n is enabled, add \\\"locale\\\" to default fields\\n if getattr(settings, \\\"WAGTAIL_I18N_ENABLED\\\", False):\\n listing_default_fields.append(\\\"locale\\\")\\n\\n return listing_default_fields\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_root_page\",\n \"text\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_root_page\\n def get_root_page(self):\\n \\\"\\\"\\\"\\n Returns the page that is used when the `&child_of=root` filter is used.\\n \\\"\\\"\\\"\\n return Site.find_for_request(self.request).root_page\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_base_queryset\",\n \"text\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_base_queryset\\n def get_base_queryset(self):\\n \\\"\\\"\\\"\\n Returns a queryset containing all pages that can be seen by this user.\\n\\n This is used as the base for get_queryset and is also used to find the\\n parent pages when using the child_of and descendant_of filters as well.\\n \\\"\\\"\\\"\\n\\n request = self.request\\n\\n # Get all live pages\\n queryset = Page.objects.all().live()\\n\\n # Exclude pages that the user doesn't have access to\\n restricted_pages = [\\n restriction.page\\n for restriction in PageViewRestriction.objects.all().select_related(\\\"page\\\")\\n if not restriction.accept_request(self.request)\\n ]\\n\\n # Exclude the restricted pages and their descendants from the queryset\\n for restricted_page in restricted_pages:\\n queryset = queryset.not_descendant_of(restricted_page, inclusive=True)\\n\\n # Check if we have a specific site to look for\\n if \\\"site\\\" in request.GET:\\n # Optionally allow querying by port\\n if \\\":\\\" in request.GET[\\\"site\\\"]:\\n (hostname, port) = request.GET[\\\"site\\\"].split(\\\":\\\", 1)\\n query = {\\n \\\"hostname\\\": hostname,\\n \\\"port\\\": port,\\n }\\n else:\\n query = {\\n \\\"hostname\\\": request.GET[\\\"site\\\"],\\n }\\n try:\\n site = Site.objects.get(**query)\\n except Site.MultipleObjectsReturned:\\n raise BadRequestError(\\n \\\"Your query returned multiple sites. Try adding a port number to your site filter.\\\"\\n )\\n else:\\n # Otherwise, find the site from the request\\n site = Site.find_for_request(self.request)\\n\\n if site:\\n base_queryset = queryset\\n queryset = base_queryset.descendant_of(site.root_page, inclusive=True)\\n\\n # If intern\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_queryset\",\n \"text\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_queryset\\n def get_queryset(self):\\n request = self.request\\n\\n # Allow pages to be filtered to a specific type\\n try:\\n models = page_models_from_string(\\n request.GET.get(\\\"type\\\", \\\"wagtailcore.Page\\\")\\n )\\n except (LookupError, ValueError):\\n raise BadRequestError(\\\"type doesn't exist\\\")\\n\\n if not models:\\n return self.get_base_queryset()\\n\\n elif len(models) == 1:\\n # If a single page type has been specified, swap out the Page-based queryset for one based on\\n # the specific page model so that we can filter on any custom APIFields defined on that model\\n return models[0].objects.filter(\\n id__in=self.get_base_queryset().values_list(\\\"id\\\", flat=True)\\n )\\n\\n else: # len(models) > 1\\n return self.get_base_queryset().type(*models)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_object\",\n \"text\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_object\\n def get_object(self):\\n base = super().get_object()\\n return base.specific\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::PagesAPIViewSet.find_object\",\n \"text\": \"wagtail/api/v2/views.py::PagesAPIViewSet.find_object\\n def find_object(self, queryset, request):\\n site = Site.find_for_request(request)\\n if \\\"html_path\\\" in request.GET and site is not None:\\n path = request.GET[\\\"html_path\\\"]\\n path_components = [component for component in path.split(\\\"/\\\") if component]\\n\\n try:\\n page, _, _ = site.root_page.specific.route(request, path_components)\\n except Http404:\\n return\\n\\n if queryset.filter(id=page.id).exists():\\n return page\\n\\n return super().find_object(queryset, request)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_serializer_context\",\n \"text\": \"wagtail/api/v2/views.py::PagesAPIViewSet.get_serializer_context\\n def get_serializer_context(self):\\n \\\"\\\"\\\"\\n The serialization context differs between listing and detail views.\\n \\\"\\\"\\\"\\n context = super().get_serializer_context()\\n context[\\\"base_queryset\\\"] = self.get_base_queryset()\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/locks.py::BaseLock.for_user\",\n \"text\": \"wagtail/locks.py::BaseLock.for_user\\n def for_user(self, user):\\n \\\"\\\"\\\"\\n Returns True if the lock applies to the given user.\\n \\\"\\\"\\\"\\n return NotImplemented\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/locks.py::BaseLock.get_message\",\n \"text\": \"wagtail/locks.py::BaseLock.get_message\\n def get_message(self, user):\\n \\\"\\\"\\\"\\n Returns a message to display to the given user describing the lock.\\n \\\"\\\"\\\"\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/locks.py::BasicLock.__init__\",\n \"text\": \"wagtail/locks.py::BasicLock.__init__\\n def __init__(self, page):\\n self.page = page\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/locks.py::BasicLock.for_user\",\n \"text\": \"wagtail/locks.py::BasicLock.for_user\\n def for_user(self, user):\\n if getattr(settings, \\\"WAGTAILADMIN_GLOBAL_PAGE_EDIT_LOCK\\\", False):\\n return True\\n else:\\n return user.pk != self.page.locked_by_id\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/locks.py::BasicLock.get_message\",\n \"text\": \"wagtail/locks.py::BasicLock.get_message\\n def get_message(self, user):\\n if self.page.locked_by_id == user.pk:\\n if self.page.locked_at:\\n return format_html(\\n _(\\\"<b>Page '{}' was locked</b> by <b>you</b> on <b>{}</b>.\\\"),\\n self.page.get_admin_display_title(),\\n self.page.locked_at.strftime(\\\"%d %b %Y %H:%M\\\"),\\n )\\n\\n else:\\n return format_html(\\n _(\\\"<b>Page '{}' is locked</b> by <b>you</b>.\\\"),\\n self.page.get_admin_display_title(),\\n )\\n else:\\n if self.page.locked_by and self.page.locked_at:\\n return format_html(\\n _(\\\"<b>Page '{}' was locked</b> by <b>{}</b> on <b>{}</b>.\\\"),\\n self.page.get_admin_display_title(),\\n str(self.page.locked_by),\\n self.page.locked_at.strftime(\\\"%d %b %Y %H:%M\\\"),\\n )\\n else:\\n # Page was probably locked with an old version of Wagtail, or a script\\n return format_html(\\n _(\\\"<b>Page '{}' is locked</b>.\\\"),\\n self.page.get_admin_display_title(),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/locks.py::WorkflowLock.__init__\",\n \"text\": \"wagtail/locks.py::WorkflowLock.__init__\\n def __init__(self, task, page):\\n self.task = task\\n self.page = page\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/locks.py::WorkflowLock.for_user\",\n \"text\": \"wagtail/locks.py::WorkflowLock.for_user\\n def for_user(self, user):\\n return self.task.page_locked_for_user(self.page, user)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/locks.py::WorkflowLock.get_message\",\n \"text\": \"wagtail/locks.py::WorkflowLock.get_message\\n def get_message(self, user):\\n if self.for_user(user):\\n if len(self.page.current_workflow_state.all_tasks_with_status()) == 1:\\n # If only one task in workflow, show simple message\\n workflow_info = _(\\\"This page is currently awaiting moderation.\\\")\\n else:\\n workflow_info = format_html(\\n _(\\\"This page is awaiting <b>'{}'</b> in the <b>'{}'</b> workflow.\\\"),\\n self.task.name,\\n self.page.current_workflow_state.workflow.name,\\n )\\n\\n return mark_safe(\\n workflow_info\\n + \\\" \\\"\\n + _(\\\"Only reviewers for this task can edit the page.\\\")\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/locks.py::ScheduledForPublishLock.__init__\",\n \"text\": \"wagtail/locks.py::ScheduledForPublishLock.__init__\\n def __init__(self, page):\\n self.page = page\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/locks.py::ScheduledForPublishLock.for_user\",\n \"text\": \"wagtail/locks.py::ScheduledForPublishLock.for_user\\n def for_user(self, user):\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/locks.py::ScheduledForPublishLock.get_message\",\n \"text\": \"wagtail/locks.py::ScheduledForPublishLock.get_message\\n def get_message(self, user):\\n scheduled_revision = self.page.revisions.filter(\\n approved_go_live_at__isnull=False\\n ).first()\\n\\n return format_html(\\n _(\\\"Page '{}' is locked and has been scheduled to go live at {}\\\"),\\n self.page.get_admin_display_title(),\\n scheduled_revision.approved_go_live_at.strftime(\\\"%d %b %Y %H:%M\\\"),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.__init__\",\n \"text\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.__init__\\n def __init__(self, model):\\n self.model = model\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_permission\",\n \"text\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_permission\\n def user_has_permission(self, user, action):\\n \\\"\\\"\\\"\\n Return whether the given user has permission to perform the given action\\n on some or all instances of this model\\n \\\"\\\"\\\"\\n return user in self.users_with_permission(action)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_any_permission\",\n \"text\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_any_permission\\n def user_has_any_permission(self, user, actions):\\n \\\"\\\"\\\"\\n Return whether the given user has permission to perform any of the given actions\\n on some or all instances of this model\\n \\\"\\\"\\\"\\n return any(self.user_has_permission(user, action) for action in actions)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_any_permission\",\n \"text\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_any_permission\\n def users_with_any_permission(self, actions):\\n \\\"\\\"\\\"\\n Return a queryset of users who have permission to perform any of the given actions\\n on some or all instances of this model\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_permission\",\n \"text\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_permission\\n def users_with_permission(self, action):\\n \\\"\\\"\\\"\\n Return a queryset of users who have permission to perform the given action on\\n some or all instances of this model\\n \\\"\\\"\\\"\\n return self.users_with_any_permission([action])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_permission_for_instance\\n def user_has_permission_for_instance(self, user, action, instance):\\n \\\"\\\"\\\"\\n Return whether the given user has permission to perform the given action on the\\n given model instance\\n \\\"\\\"\\\"\\n return self.user_has_permission(user, action)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_any_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.user_has_any_permission_for_instance\\n def user_has_any_permission_for_instance(self, user, actions, instance):\\n \\\"\\\"\\\"\\n Return whether the given user has permission to perform any of the given actions\\n on the given model instance\\n \\\"\\\"\\\"\\n return any(\\n self.user_has_permission_for_instance(user, action, instance)\\n for action in actions\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.instances_user_has_any_permission_for\",\n \"text\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.instances_user_has_any_permission_for\\n def instances_user_has_any_permission_for(self, user, actions):\\n \\\"\\\"\\\"\\n Return a queryset of all instances of this model for which the given user has\\n permission to perform any of the given actions\\n \\\"\\\"\\\"\\n if self.user_has_any_permission(user, actions):\\n return self.model.objects.all()\\n else:\\n return self.model.objects.none()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.instances_user_has_permission_for\",\n \"text\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.instances_user_has_permission_for\\n def instances_user_has_permission_for(self, user, action):\\n \\\"\\\"\\\"\\n Return a queryset of all instances of this model for which the given user has\\n permission to perform the given action\\n \\\"\\\"\\\"\\n return self.instances_user_has_any_permission_for(user, [action])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_any_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_any_permission_for_instance\\n def users_with_any_permission_for_instance(self, actions, instance):\\n \\\"\\\"\\\"\\n Return a queryset of all users who have permission to perform any of the given\\n actions on the given model instance\\n \\\"\\\"\\\"\\n return self.users_with_any_permission(actions)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/base.py::BasePermissionPolicy.users_with_permission_for_instance\\n def users_with_permission_for_instance(self, action, instance):\\n return self.users_with_any_permission_for_instance([action], instance)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BlanketPermissionPolicy.user_has_permission\",\n \"text\": \"wagtail/permission_policies/base.py::BlanketPermissionPolicy.user_has_permission\\n def user_has_permission(self, user, action):\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BlanketPermissionPolicy.user_has_any_permission\",\n \"text\": \"wagtail/permission_policies/base.py::BlanketPermissionPolicy.user_has_any_permission\\n def user_has_any_permission(self, user, actions):\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BlanketPermissionPolicy.users_with_any_permission\",\n \"text\": \"wagtail/permission_policies/base.py::BlanketPermissionPolicy.users_with_any_permission\\n def users_with_any_permission(self, actions):\\n # Here we filter out inactive users from the results, even though inactive users\\n # - and for that matter anonymous users - still have permission according to the\\n # user_has_permission method. This is appropriate because, for most applications,\\n # setting is_active=False is equivalent to deleting the user account; you would\\n # not want these accounts to appear in, for example, a dropdown of users to\\n # assign a task to. The result here could never be completely logically correct\\n # (because it will not include anonymous users), so as the next best thing we\\n # return the \\\"least surprise\\\" result.\\n return get_user_model().objects.filter(is_active=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BlanketPermissionPolicy.users_with_permission\",\n \"text\": \"wagtail/permission_policies/base.py::BlanketPermissionPolicy.users_with_permission\\n def users_with_permission(self, action):\\n return get_user_model().objects.filter(is_active=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.user_has_permission\",\n \"text\": \"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.user_has_permission\\n def user_has_permission(self, user, action):\\n return user.is_authenticated and user.is_active\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.user_has_any_permission\",\n \"text\": \"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.user_has_any_permission\\n def user_has_any_permission(self, user, actions):\\n return user.is_authenticated and user.is_active\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.users_with_any_permission\",\n \"text\": \"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.users_with_any_permission\\n def users_with_any_permission(self, actions):\\n return get_user_model().objects.filter(is_active=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.users_with_permission\",\n \"text\": \"wagtail/permission_policies/base.py::AuthenticationOnlyPermissionPolicy.users_with_permission\\n def users_with_permission(self, action):\\n return get_user_model().objects.filter(is_active=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy.__init__\",\n \"text\": \"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy.__init__\\n def __init__(self, model, auth_model=None):\\n # `auth_model` specifies the model to be used for permission record lookups;\\n # usually this will match `model` (which specifies the type of instances that\\n # `instances_user_has_permission_for` will return), but this may differ when\\n # swappable models are in use - for example, an interface for editing user\\n # records might use a custom User model but will typically still refer to the\\n # permission records for auth.user.\\n super().__init__(model)\\n self.auth_model = auth_model or self.model\\n self.app_label = self.auth_model._meta.app_label\\n self.model_name = self.auth_model._meta.model_name\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._content_type\",\n \"text\": \"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._content_type\\n @cached_property\\n def _content_type(self):\\n return ContentType.objects.get_for_model(self.auth_model)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._get_permission_name\",\n \"text\": \"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._get_permission_name\\n def _get_permission_name(self, action):\\n \\\"\\\"\\\"\\n Get the full app-label-qualified permission name (as required by\\n user.has_perm(...) ) for the given action on this model\\n \\\"\\\"\\\"\\n return \\\"%s.%s_%s\\\" % (self.app_label, action, self.model_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._get_users_with_any_permission_codenames_filter\",\n \"text\": \"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._get_users_with_any_permission_codenames_filter\\n def _get_users_with_any_permission_codenames_filter(self, permission_codenames):\\n \\\"\\\"\\\"\\n Given a list of permission codenames, return a filter expression which\\n will find all users which have any of those permissions - either\\n through group permissions, user permissions, or implicitly through\\n being a superuser.\\n \\\"\\\"\\\"\\n permissions = Permission.objects.filter(\\n content_type=self._content_type, codename__in=permission_codenames\\n )\\n return (\\n Q(is_superuser=True)\\n | Q(user_permissions__in=permissions)\\n | Q(groups__permissions__in=permissions)\\n ) & Q(is_active=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._get_users_with_any_permission_codenames\",\n \"text\": \"wagtail/permission_policies/base.py::BaseDjangoAuthPermissionPolicy._get_users_with_any_permission_codenames\\n def _get_users_with_any_permission_codenames(self, permission_codenames):\\n \\\"\\\"\\\"\\n Given a list of permission codenames, return a queryset of users which\\n have any of those permissions - either through group permissions, user\\n permissions, or implicitly through being a superuser.\\n \\\"\\\"\\\"\\n filter_expr = self._get_users_with_any_permission_codenames_filter(\\n permission_codenames\\n )\\n return get_user_model().objects.filter(filter_expr).distinct()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::ModelPermissionPolicy.user_has_permission\",\n \"text\": \"wagtail/permission_policies/base.py::ModelPermissionPolicy.user_has_permission\\n def user_has_permission(self, user, action):\\n return user.has_perm(self._get_permission_name(action))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::ModelPermissionPolicy.users_with_any_permission\",\n \"text\": \"wagtail/permission_policies/base.py::ModelPermissionPolicy.users_with_any_permission\\n def users_with_any_permission(self, actions):\\n permission_codenames = [\\n \\\"%s_%s\\\" % (action, self.model_name) for action in actions\\n ]\\n return self._get_users_with_any_permission_codenames(permission_codenames)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.__init__\",\n \"text\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.__init__\\n def __init__(self, model, auth_model=None, owner_field_name=\\\"owner\\\"):\\n super().__init__(model, auth_model=auth_model)\\n self.owner_field_name = owner_field_name\\n\\n # make sure owner_field_name is a field that exists on the model\\n try:\\n self.model._meta.get_field(self.owner_field_name)\\n except FieldDoesNotExist:\\n raise ImproperlyConfigured(\\n \\\"%s has no field named '%s'. To use this model with OwnershipPermissionPolicy, \\\"\\n \\\"you must specify a valid field name as owner_field_name.\\\"\\n % (self.model, self.owner_field_name)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.user_has_permission\",\n \"text\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.user_has_permission\\n def user_has_permission(self, user, action):\\n if action == \\\"add\\\":\\n return user.has_perm(self._get_permission_name(\\\"add\\\"))\\n elif action == \\\"change\\\" or action == \\\"delete\\\":\\n return (\\n # having 'add' permission means that there are *potentially*\\n # some instances they can edit (namely: ones they own),\\n # which is sufficient for returning True here\\n user.has_perm(self._get_permission_name(\\\"add\\\"))\\n or user.has_perm(self._get_permission_name(\\\"change\\\"))\\n )\\n else:\\n # unrecognised actions are only allowed for active superusers\\n return user.is_active and user.is_superuser\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.users_with_any_permission\",\n \"text\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.users_with_any_permission\\n def users_with_any_permission(self, actions):\\n if \\\"change\\\" in actions or \\\"delete\\\" in actions:\\n # either 'add' or 'change' permission means that there are *potentially*\\n # some instances they can edit\\n permission_codenames = [\\n \\\"add_%s\\\" % self.model_name,\\n \\\"change_%s\\\" % self.model_name,\\n ]\\n elif \\\"add\\\" in actions:\\n permission_codenames = [\\n \\\"add_%s\\\" % self.model_name,\\n ]\\n else:\\n # none of the actions passed in here are ones that we recognise, so only\\n # allow them for active superusers\\n return get_user_model().objects.filter(is_active=True, is_superuser=True)\\n\\n return self._get_users_with_any_permission_codenames(permission_codenames)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.user_has_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.user_has_permission_for_instance\\n def user_has_permission_for_instance(self, user, action, instance):\\n return self.user_has_any_permission_for_instance(user, [action], instance)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.user_has_any_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.user_has_any_permission_for_instance\\n def user_has_any_permission_for_instance(self, user, actions, instance):\\n if \\\"change\\\" in actions or \\\"delete\\\" in actions:\\n if user.has_perm(self._get_permission_name(\\\"change\\\")):\\n return True\\n elif (\\n user.has_perm(self._get_permission_name(\\\"add\\\"))\\n and getattr(instance, self.owner_field_name) == user\\n ):\\n return True\\n else:\\n return False\\n else:\\n # 'change' and 'delete' are the only actions that are well-defined\\n # for specific instances. Other actions are only available to\\n # active superusers.\\n return user.is_active and user.is_superuser\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.instances_user_has_any_permission_for\",\n \"text\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.instances_user_has_any_permission_for\\n def instances_user_has_any_permission_for(self, user, actions):\\n if user.is_active and user.is_superuser:\\n # active superusers can perform any action (including unrecognised ones)\\n # on any instance\\n return self.model.objects.all()\\n elif \\\"change\\\" in actions or \\\"delete\\\" in actions:\\n if user.has_perm(self._get_permission_name(\\\"change\\\")):\\n # user can edit all instances\\n return self.model.objects.all()\\n elif user.has_perm(self._get_permission_name(\\\"add\\\")):\\n # user can edit their own instances\\n return self.model.objects.filter(**{self.owner_field_name: user})\\n else:\\n # user has no permissions at all on this model\\n return self.model.objects.none()\\n else:\\n # action is either not recognised, or is the 'add' action which is\\n # not meaningful for existing instances. As such, non-superusers\\n # cannot perform it on any existing instances.\\n return self.model.objects.none()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.users_with_any_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/base.py::OwnershipPermissionPolicy.users_with_any_permission_for_instance\\n def users_with_any_permission_for_instance(self, actions, instance):\\n if \\\"change\\\" in actions or \\\"delete\\\" in actions:\\n # get filter expression for users with 'change' permission\\n filter_expr = self._get_users_with_any_permission_codenames_filter(\\n [\\\"change_%s\\\" % self.model_name]\\n )\\n\\n # add on the item's owner, if they still have 'add' permission\\n # (and the owner field isn't blank)\\n owner = getattr(instance, self.owner_field_name)\\n if owner is not None and owner.has_perm(self._get_permission_name(\\\"add\\\")):\\n filter_expr = filter_expr | Q(pk=owner.pk)\\n\\n # return the filtered queryset\\n return get_user_model().objects.filter(filter_expr).distinct()\\n\\n else:\\n # action is either not recognised, or is the 'add' action which is\\n # not meaningful for existing instances. As such, the action is only\\n # available to superusers\\n return get_user_model().objects.filter(is_active=True, is_superuser=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._get_permission_objects_for_actions\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._get_permission_objects_for_actions\\n def _get_permission_objects_for_actions(self, actions):\\n \\\"\\\"\\\"\\n Get a queryset of the Permission objects for the given actions\\n \\\"\\\"\\\"\\n permission_codenames = [\\n \\\"%s_%s\\\" % (action, self.model_name) for action in actions\\n ]\\n return Permission.objects.filter(\\n content_type=self._content_type, codename__in=permission_codenames\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._check_perm\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._check_perm\\n def _check_perm(self, user, actions, collection=None):\\n \\\"\\\"\\\"\\n Equivalent to user.has_perm(self._get_permission_name(action)) on all listed actions,\\n but using GroupCollectionPermission rather than group.permissions.\\n If collection is specified, only consider GroupCollectionPermission records\\n that apply to that collection.\\n \\\"\\\"\\\"\\n if not (user.is_active and user.is_authenticated):\\n return False\\n\\n if user.is_superuser:\\n return True\\n\\n collection_permissions = GroupCollectionPermission.objects.filter(\\n group__user=user,\\n permission__in=self._get_permission_objects_for_actions(actions),\\n )\\n\\n if collection:\\n collection_permissions = collection_permissions.filter(\\n collection__in=collection.get_ancestors(inclusive=True)\\n )\\n\\n return collection_permissions.exists()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._collections_with_perm\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._collections_with_perm\\n def _collections_with_perm(self, user, actions):\\n \\\"\\\"\\\"\\n Return a queryset of collections on which this user has a GroupCollectionPermission\\n record for any of the given actions, either on the collection itself or an ancestor\\n \\\"\\\"\\\"\\n # Get the permission objects corresponding to these actions\\n permissions = self._get_permission_objects_for_actions(actions)\\n\\n # Get the collections that have a GroupCollectionPermission record\\n # for any of these permissions and any of the user's groups;\\n # create a list of their paths\\n collection_root_paths = Collection.objects.filter(\\n group_permissions__group__in=user.groups.all(),\\n group_permissions__permission__in=permissions,\\n ).values_list(\\\"path\\\", flat=True)\\n\\n if collection_root_paths:\\n # build a filter expression that will filter our model to just those\\n # instances in collections with a path that starts with one of the above\\n collection_path_filter = Q(path__startswith=collection_root_paths[0])\\n for path in collection_root_paths[1:]:\\n collection_path_filter = collection_path_filter | Q(\\n path__startswith=path\\n )\\n\\n return Collection.objects.all().filter(collection_path_filter)\\n else:\\n # no matching collections\\n return Collection.objects.none()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._users_with_perm_filter\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._users_with_perm_filter\\n def _users_with_perm_filter(self, actions, collection=None):\\n \\\"\\\"\\\"\\n Return a filter expression that will filter a user queryset to those with any\\n permissions corresponding to 'actions', via either GroupCollectionPermission\\n or superuser privileges.\\n If collection is specified, only consider GroupCollectionPermission records\\n that apply to that collection.\\n \\\"\\\"\\\"\\n permissions = self._get_permission_objects_for_actions(actions)\\n # Find all groups with GroupCollectionPermission records for\\n # any of these permissions\\n groups = Group.objects.filter(\\n collection_permissions__permission__in=permissions,\\n )\\n\\n if collection is not None:\\n collections = collection.get_ancestors(inclusive=True)\\n groups = groups.filter(collection_permissions__collection__in=collections)\\n\\n # Find all users who are superusers or in any of these groups, and are active\\n return (Q(is_superuser=True) | Q(groups__in=groups)) & Q(is_active=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._users_with_perm\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin._users_with_perm\\n def _users_with_perm(self, actions, collection=None):\\n \\\"\\\"\\\"\\n Return a queryset of users with any permissions corresponding to 'actions',\\n via either GroupCollectionPermission or superuser privileges.\\n If collection is specified, only consider GroupCollectionPermission records\\n that apply to that collection.\\n \\\"\\\"\\\"\\n return (\\n get_user_model()\\n .objects.filter(\\n self._users_with_perm_filter(actions, collection=collection)\\n )\\n .distinct()\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin.collections_user_has_any_permission_for\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin.collections_user_has_any_permission_for\\n def collections_user_has_any_permission_for(self, user, actions):\\n \\\"\\\"\\\"\\n Return a queryset of all collections in which the given user has\\n permission to perform any of the given actions\\n \\\"\\\"\\\"\\n if user.is_active and user.is_superuser:\\n # active superusers can perform any action (including unrecognised ones)\\n # in any collection\\n return Collection.objects.all()\\n\\n if not user.is_authenticated:\\n return Collection.objects.none()\\n\\n return self._collections_with_perm(user, actions)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin.collections_user_has_permission_for\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionLookupMixin.collections_user_has_permission_for\\n def collections_user_has_permission_for(self, user, action):\\n \\\"\\\"\\\"\\n Return a queryset of all collections in which the given user has\\n permission to perform the given action\\n \\\"\\\"\\\"\\n return self.collections_user_has_any_permission_for(user, [action])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_permission\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_permission\\n def user_has_permission(self, user, action):\\n \\\"\\\"\\\"\\n Return whether the given user has permission to perform the given action\\n on some or all instances of this model\\n \\\"\\\"\\\"\\n return self._check_perm(user, [action])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_any_permission\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_any_permission\\n def user_has_any_permission(self, user, actions):\\n \\\"\\\"\\\"\\n Return whether the given user has permission to perform any of the given actions\\n on some or all instances of this model\\n \\\"\\\"\\\"\\n return self._check_perm(user, actions)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.users_with_any_permission\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.users_with_any_permission\\n def users_with_any_permission(self, actions):\\n \\\"\\\"\\\"\\n Return a queryset of users who have permission to perform any of the given actions\\n on some or all instances of this model\\n \\\"\\\"\\\"\\n return self._users_with_perm(actions)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_permission_for_instance\\n def user_has_permission_for_instance(self, user, action, instance):\\n \\\"\\\"\\\"\\n Return whether the given user has permission to perform the given action on the\\n given model instance\\n \\\"\\\"\\\"\\n return self._check_perm(user, [action], collection=instance.collection)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_any_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.user_has_any_permission_for_instance\\n def user_has_any_permission_for_instance(self, user, actions, instance):\\n \\\"\\\"\\\"\\n Return whether the given user has permission to perform any of the given actions\\n on the given model instance\\n \\\"\\\"\\\"\\n return self._check_perm(user, actions, collection=instance.collection)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.instances_user_has_any_permission_for\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.instances_user_has_any_permission_for\\n def instances_user_has_any_permission_for(self, user, actions):\\n \\\"\\\"\\\"\\n Return a queryset of all instances of this model for which the given user has\\n permission to perform any of the given actions\\n \\\"\\\"\\\"\\n if not (user.is_active and user.is_authenticated):\\n return self.model.objects.none()\\n elif user.is_superuser:\\n return self.model.objects.all()\\n else:\\n # filter to just the collections with this permission\\n return self.model.objects.filter(\\n collection__in=list(self._collections_with_perm(user, actions))\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.users_with_any_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionPermissionPolicy.users_with_any_permission_for_instance\\n def users_with_any_permission_for_instance(self, actions, instance):\\n \\\"\\\"\\\"\\n Return a queryset of all users who have permission to perform any of the given\\n actions on the given model instance\\n \\\"\\\"\\\"\\n return self._users_with_perm(actions, collection=instance.collection)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.__init__\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.__init__\\n def __init__(self, model, auth_model=None, owner_field_name=\\\"owner\\\"):\\n super().__init__(model, auth_model=auth_model)\\n self.owner_field_name = owner_field_name\\n\\n # make sure owner_field_name is a field that exists on the model\\n try:\\n self.model._meta.get_field(self.owner_field_name)\\n except FieldDoesNotExist:\\n raise ImproperlyConfigured(\\n \\\"%s has no field named '%s'. To use this model with \\\"\\n \\\"CollectionOwnershipPermissionPolicy, you must specify a valid field name as \\\"\\n \\\"owner_field_name.\\\" % (self.model, self.owner_field_name)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.user_has_permission\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.user_has_permission\\n def user_has_permission(self, user, action):\\n if action == \\\"add\\\":\\n return self._check_perm(user, [\\\"add\\\"])\\n elif action == \\\"choose\\\":\\n return self._check_perm(user, [\\\"choose\\\"])\\n elif action == \\\"change\\\" or action == \\\"delete\\\":\\n # having 'add' permission means that there are *potentially*\\n # some instances they can edit (namely: ones they own),\\n # which is sufficient for returning True here\\n return self._check_perm(user, [\\\"add\\\", \\\"change\\\"])\\n else:\\n # unrecognised actions are only allowed for active superusers\\n return user.is_active and user.is_superuser\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.users_with_any_permission\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.users_with_any_permission\\n def users_with_any_permission(self, actions):\\n if \\\"change\\\" in actions or \\\"delete\\\" in actions:\\n # either 'add' or 'change' permission means that there are *potentially*\\n # some instances they can edit\\n real_actions = [\\\"add\\\", \\\"change\\\"]\\n elif \\\"add\\\" in actions:\\n real_actions = [\\\"add\\\"]\\n elif \\\"choose\\\" in actions:\\n real_actions = [\\\"choose\\\"]\\n else:\\n # none of the actions passed in here are ones that we recognise, so only\\n # allow them for active superusers\\n return get_user_model().objects.filter(is_active=True, is_superuser=True)\\n\\n return self._users_with_perm(real_actions)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.user_has_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.user_has_permission_for_instance\\n def user_has_permission_for_instance(self, user, action, instance):\\n return self.user_has_any_permission_for_instance(user, [action], instance)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.user_has_any_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.user_has_any_permission_for_instance\\n def user_has_any_permission_for_instance(self, user, actions, instance):\\n if \\\"change\\\" in actions or \\\"delete\\\" in actions:\\n if self._check_perm(user, [\\\"change\\\"], collection=instance.collection):\\n return True\\n elif (\\n self._check_perm(user, [\\\"add\\\"], collection=instance.collection)\\n and getattr(instance, self.owner_field_name) == user\\n ):\\n return True\\n else:\\n return False\\n elif \\\"choose\\\" in actions:\\n return self._check_perm(user, [\\\"choose\\\"], collection=instance.collection)\\n else:\\n # 'change' and 'delete' are the only actions that are well-defined\\n # for specific instances. Other actions are only available to\\n # active superusers.\\n return user.is_active and user.is_superuser\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.instances_user_has_any_permission_for\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.instances_user_has_any_permission_for\\n def instances_user_has_any_permission_for(self, user, actions):\\n if user.is_active and user.is_superuser:\\n # active superusers can perform any action (including unrecognised ones)\\n # on any instance\\n return self.model.objects.all()\\n elif not user.is_authenticated:\\n return self.model.objects.none()\\n elif \\\"change\\\" in actions or \\\"delete\\\" in actions:\\n # return instances which are:\\n # - in (a descendant of) a collection for which they have 'change' permission\\n # - OR in (a descendant of) a collection for which they have 'add' permission,\\n # and are owned by them\\n\\n change_perm_filter = Q(\\n collection__in=list(self._collections_with_perm(user, [\\\"change\\\"]))\\n )\\n\\n add_perm_filter = Q(\\n collection__in=list(self._collections_with_perm(user, [\\\"add\\\"]))\\n ) & Q(**{self.owner_field_name: user})\\n\\n return self.model.objects.filter(change_perm_filter | add_perm_filter)\\n elif \\\"choose\\\" in actions:\\n # Return instances which are in (a descendant of) a collection for which they\\n # have 'choose' permission.\\n choose_perm_filter = Q(\\n collection__in=list(self._collections_with_perm(user, [\\\"choose\\\"]))\\n )\\n return self.model.objects.filter(choose_perm_filter)\\n else:\\n # action is either not recognised, or is the 'add' action which is\\n # not meaningful for existing instances. As such, non-superusers\\n # cannot perform it on any existing instances.\\n return self.model.objects.none()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.users_with_any_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.users_with_any_permission_for_instance\\n def users_with_any_permission_for_instance(self, actions, instance):\\n if \\\"change\\\" in actions or \\\"delete\\\" in actions:\\n # form a filter expression of the users with 'change' permission\\n # over this instance's collection\\n filter_expr = self._users_with_perm_filter(\\n [\\\"change\\\"], collection=instance.collection\\n )\\n\\n # add on the item's owner, if they still have 'add' permission\\n # (and the owner field isn't blank)\\n owner = getattr(instance, self.owner_field_name)\\n if owner is not None and self._check_perm(\\n owner, [\\\"add\\\"], collection=instance.collection\\n ):\\n filter_expr = filter_expr | Q(pk=owner.pk)\\n\\n # return the filtered queryset\\n return get_user_model().objects.filter(filter_expr).distinct()\\n elif \\\"choose\\\" in actions:\\n # Form a filter expression of the users with 'choose' permission\\n # over this instance's collection.\\n filter_expr = self._users_with_perm_filter(\\n [\\\"choose\\\"], collection=instance.collection\\n )\\n return get_user_model().objects.filter(filter_expr).distinct()\\n else:\\n # action is either not recognised, or is the 'add' action which is\\n # not meaningful for existing instances. As such, the action is only\\n # available to superusers\\n return get_user_model().objects.filter(is_active=True, is_superuser=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.collections_user_has_any_permission_for\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionOwnershipPermissionPolicy.collections_user_has_any_permission_for\\n def collections_user_has_any_permission_for(self, user, actions):\\n \\\"\\\"\\\"\\n Return a queryset of all collections in which the given user has\\n permission to perform any of the given actions\\n \\\"\\\"\\\"\\n if user.is_active and user.is_superuser:\\n # active superusers can perform any action (including unrecognised ones)\\n # in any collection\\n return Collection.objects.all()\\n\\n elif not user.is_authenticated:\\n return Collection.objects.none()\\n\\n elif \\\"change\\\" in actions or \\\"delete\\\" in actions:\\n # return collections which are covered by either 'add' or 'change' permissions\\n # (since collections with 'add' permissions can *potentially* contain instances\\n # they own and can therefore edit)\\n return self._collections_with_perm(user, [\\\"add\\\", \\\"change\\\"])\\n\\n elif \\\"add\\\" in actions:\\n return self._collections_with_perm(user, [\\\"add\\\"])\\n\\n elif \\\"choose\\\" in actions:\\n return self._collections_with_perm(user, [\\\"choose\\\"])\\n\\n else:\\n # action is not recognised, and so non-superusers\\n # cannot perform it on any existing collections\\n return Collection.objects.none()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy._descendants_with_perm\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy._descendants_with_perm\\n def _descendants_with_perm(self, user, action):\\n \\\"\\\"\\\"\\n Return a queryset of collections descended from a collection on which this user has\\n a GroupCollectionPermission record for this action. Used for actions, like edit and\\n delete where the user cannot modify the collection where they are granted permission.\\n \\\"\\\"\\\"\\n # Get the permission object corresponding to this action\\n permission = self._get_permission_objects_for_actions([action]).first()\\n\\n # Get the collections that have a GroupCollectionPermission record\\n # for this permission and any of the user's groups;\\n # create a list of their paths\\n collection_roots = Collection.objects.filter(\\n group_permissions__group__in=user.groups.all(),\\n group_permissions__permission=permission,\\n ).values(\\\"path\\\", \\\"depth\\\")\\n\\n if collection_roots:\\n # build a filter expression that will filter our model to just those\\n # instances in collections with a path that starts with one of the above\\n # but excluding the collection on which permission was granted\\n collection_path_filter = Q(\\n path__startswith=collection_roots[0][\\\"path\\\"]\\n ) & Q(depth__gt=collection_roots[0][\\\"depth\\\"])\\n for collection in collection_roots[1:]:\\n collection_path_filter = collection_path_filter | (\\n Q(path__startswith=collection[\\\"path\\\"])\\n & Q(depth__gt=collection[\\\"depth\\\"])\\n )\\n return Collection.objects.all().filter(collection_path_filter)\\n else:\\n # no matching collections\\n return Collection.objects.none()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_permission\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_permission\\n def user_has_permission(self, user, action):\\n \\\"\\\"\\\"\\n Return whether the given user has permission to perform the given action\\n on some or all instances of this model\\n \\\"\\\"\\\"\\n return self.user_has_any_permission(user, [action])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_any_permission\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_any_permission\\n def user_has_any_permission(self, user, actions):\\n \\\"\\\"\\\"\\n Return whether the given user has permission to perform any of the given actions\\n on some or all instances of this model.\\n \\\"\\\"\\\"\\n return self._check_perm(user, actions)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.users_with_any_permission\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.users_with_any_permission\\n def users_with_any_permission(self, actions):\\n \\\"\\\"\\\"\\n Return a queryset of users who have permission to perform any of the given actions\\n on some or all instances of this model\\n \\\"\\\"\\\"\\n return self._users_with_perm(actions)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_permission_for_instance\\n def user_has_permission_for_instance(self, user, action, instance):\\n \\\"\\\"\\\"\\n Return whether the given user has permission to perform the given action on the\\n given model instance\\n \\\"\\\"\\\"\\n return self._check_perm(user, [action], collection=instance)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_any_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.user_has_any_permission_for_instance\\n def user_has_any_permission_for_instance(self, user, actions, instance):\\n \\\"\\\"\\\"\\n Return whether the given user has permission to perform any of the given actions\\n on the given model instance\\n \\\"\\\"\\\"\\n return self._check_perm(user, actions, collection=instance)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.users_with_any_permission_for_instance\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.users_with_any_permission_for_instance\\n def users_with_any_permission_for_instance(self, actions, instance):\\n \\\"\\\"\\\"\\n Return a queryset of all users who have permission to perform any of the given\\n actions on the given model instance\\n \\\"\\\"\\\"\\n return self._users_with_perm(actions, collection=instance)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.instances_user_has_permission_for\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.instances_user_has_permission_for\\n def instances_user_has_permission_for(self, user, action):\\n if user.is_active and user.is_superuser:\\n # active superusers can perform any action (including unrecognised ones)\\n # in any collection - except for deleting the root collection\\n if action == \\\"delete\\\":\\n return Collection.objects.exclude(depth=1).all()\\n else:\\n return Collection.objects.all()\\n\\n elif not user.is_authenticated:\\n return Collection.objects.none()\\n\\n else:\\n if action == \\\"delete\\\":\\n return self._descendants_with_perm(user, action)\\n else:\\n return self._collections_with_perm(user, [action])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.instances_user_has_any_permission_for\",\n \"text\": \"wagtail/permission_policies/collections.py::CollectionMangementPermissionPolicy.instances_user_has_any_permission_for\\n def instances_user_has_any_permission_for(self, user, actions):\\n return self.collections_user_has_any_permission_for(user, actions)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":261,"cells":{"instance_id":{"kind":"string","value":"PrefectHQ__prefect-1937"},"repo":{"kind":"string","value":"PrefectHQ/prefect"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Aggregate Minor Version API Docs\nInstead of `0.9.0` and `0.9.1` the latest documentation will point to `0.9.x`. This is due to the low changes in API documentation between patch releases. Will remove the extra step of regenerating all API docs every couple weeks.\n"},"gold_files":{"kind":"list like","value":["docs/generate_docs.py"],"string":"[\n \"docs/generate_docs.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["docs/generate_docs.py::<module>"],"string":"[\n \"docs/generate_docs.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/generate_docs.py","text":"docs/generate_docs.py\ndef patch_imports()\ndef load_outline(outline=outline_config['pages'], ext=outline_config.get('extension', '.md'), prefix=None)\ndef preprocess(f, remove_partial=True)\n def wrapped(*args, **kwargs)\ndef clean_line(line)\ndef format_lists(doc) # Convenience function for converting markdown lists to HTML for within-table list\ndef format_doc(obj, in_table=False)\ndef create_methods_table(members, title)\ndef get_call_signature(obj)\ndef format_signature(obj)\ndef create_absolute_path(obj)\ndef get_source(obj)\ndef format_subheader(obj, level=1, in_table=False)\ndef get_class_methods(obj)\ndef create_tutorial_notebooks(tutorial) # Utility that automagically creates an .ipynb notebook file from a markdown file ","label":1},{"cid":"src/prefect/client/secrets.py","text":"src/prefect/client/secrets.py\nclass Secret # A Secret is a serializable object used to represent a secret key & value.\n def __init__(self, name: str)\n def client(self)\n def exists(self) # Determine if the secret exists.\n def get(self) # Retrieve the secret value. If not found, returns `None`.","label":0},{"cid":"versioneer.py","text":"versioneer.py\nclass VersioneerConfig # Container for Versioneer configuration parameters.\ndef get_root() # Get the project root directory.\ndef get_config_from_root(root) # Read the project setup.cfg file to determine Versioneer config.\n def get(parser, name)\nclass NotThisMethod # Exception raised if a method is not valid for the current scenario.\ndef register_vcs_handler(vcs, method) # Decorator to mark a method as the handler for a particular VCS.\n def decorate(f) # Store f in HANDLERS[vcs][method].\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None) # Call the given command(s).\ndef git_get_keywords(versionfile_abs) # Extract version information from the given file.\ndef git_versions_from_keywords(keywords, tag_prefix, verbose) # Get version information from git keywords.\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command) # Get version from 'git describe' in the root of the source tree.\ndef do_vcs_install(manifest_in, versionfile_source, ipy) # Git-specific installation logic for Versioneer.\ndef versions_from_parentdir(parentdir_prefix, root, verbose) # Try to determine the version from the parent directory name.\ndef versions_from_file(filename) # Try to determine the version from _version.py if present.\ndef write_to_version_file(filename, versions) # Write the given version number to the given _version.py file.\ndef plus_or_dot(pieces) # Return a + if we don't already have one, else return a .\ndef render_pep440(pieces) # Build up version string, with post-release \"local version identifier\".\ndef render_pep440_pre(pieces) # TAG[.post.devDISTANCE] -- No -dirty.\ndef render_pep440_post(pieces) # TAG[.postDISTANCE[.dev0]+gHEX] .\ndef render_pep440_old(pieces) # TAG[.postDISTANCE[.dev0]] .\ndef render_git_describe(pieces) # TAG[-DISTANCE-gHEX][-dirty].\ndef render_git_describe_long(pieces) # TAG-DISTANCE-gHEX[-dirty].\ndef render(pieces, style) # Render the given version pieces into the requested style.\nclass VersioneerBadRootError # The project root directory is unknown or missing key files.\ndef get_versions(verbose=False) # Get the project version from whatever source is available.\ndef get_version() # Get the short version string for this project.\ndef get_cmdclass() # Get the custom setuptools/distutils subclasses used by Versioneer.\n class cmd_version\n def initialize_options(self)\n def finalize_options(self)\n def run(self)\n class cmd_build_py\n def run(self)\n class cmd_sdist\n def run(self)\n def make_release_tree(self, base_dir, files)\ndef do_setup() # Main VCS-independent setup function for installing Versioneer.\ndef scan_setup_py() # Validate the contents of setup.py against Versioneer's expectations.","label":0}],"string":"[\n {\n \"cid\": \"docs/generate_docs.py\",\n \"text\": \"docs/generate_docs.py\\ndef patch_imports()\\ndef load_outline(outline=outline_config['pages'], ext=outline_config.get('extension', '.md'), prefix=None)\\ndef preprocess(f, remove_partial=True)\\n def wrapped(*args, **kwargs)\\ndef clean_line(line)\\ndef format_lists(doc) # Convenience function for converting markdown lists to HTML for within-table list\\ndef format_doc(obj, in_table=False)\\ndef create_methods_table(members, title)\\ndef get_call_signature(obj)\\ndef format_signature(obj)\\ndef create_absolute_path(obj)\\ndef get_source(obj)\\ndef format_subheader(obj, level=1, in_table=False)\\ndef get_class_methods(obj)\\ndef create_tutorial_notebooks(tutorial) # Utility that automagically creates an .ipynb notebook file from a markdown file \",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/client/secrets.py\",\n \"text\": \"src/prefect/client/secrets.py\\nclass Secret # A Secret is a serializable object used to represent a secret key & value.\\n def __init__(self, name: str)\\n def client(self)\\n def exists(self) # Determine if the secret exists.\\n def get(self) # Retrieve the secret value. If not found, returns `None`.\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py\",\n \"text\": \"versioneer.py\\nclass VersioneerConfig # Container for Versioneer configuration parameters.\\ndef get_root() # Get the project root directory.\\ndef get_config_from_root(root) # Read the project setup.cfg file to determine Versioneer config.\\n def get(parser, name)\\nclass NotThisMethod # Exception raised if a method is not valid for the current scenario.\\ndef register_vcs_handler(vcs, method) # Decorator to mark a method as the handler for a particular VCS.\\n def decorate(f) # Store f in HANDLERS[vcs][method].\\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None) # Call the given command(s).\\ndef git_get_keywords(versionfile_abs) # Extract version information from the given file.\\ndef git_versions_from_keywords(keywords, tag_prefix, verbose) # Get version information from git keywords.\\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command) # Get version from 'git describe' in the root of the source tree.\\ndef do_vcs_install(manifest_in, versionfile_source, ipy) # Git-specific installation logic for Versioneer.\\ndef versions_from_parentdir(parentdir_prefix, root, verbose) # Try to determine the version from the parent directory name.\\ndef versions_from_file(filename) # Try to determine the version from _version.py if present.\\ndef write_to_version_file(filename, versions) # Write the given version number to the given _version.py file.\\ndef plus_or_dot(pieces) # Return a + if we don't already have one, else return a .\\ndef render_pep440(pieces) # Build up version string, with post-release \\\"local version identifier\\\".\\ndef render_pep440_pre(pieces) # TAG[.post.devDISTANCE] -- No -dirty.\\ndef render_pep440_post(pieces) # TAG[.postDISTANCE[.dev0]+gHEX] .\\ndef render_pep440_old(pieces) # TAG[.postDISTANCE[.dev0]] .\\ndef render_git_describe(pieces) # TAG[-DISTANCE-gHEX][-dirty].\\ndef render_git_describe_long(pieces) # TAG-DISTANCE-gHEX[-dirty].\\ndef render(pieces, style) # Render the given version pieces into the requested style.\\nclass VersioneerBadRootError # The project root directory is unknown or missing key files.\\ndef get_versions(verbose=False) # Get the project version from whatever source is available.\\ndef get_version() # Get the short version string for this project.\\ndef get_cmdclass() # Get the custom setuptools/distutils subclasses used by Versioneer.\\n class cmd_version\\n def initialize_options(self)\\n def finalize_options(self)\\n def run(self)\\n class cmd_build_py\\n def run(self)\\n class cmd_sdist\\n def run(self)\\n def make_release_tree(self, base_dir, files)\\ndef do_setup() # Main VCS-independent setup function for installing Versioneer.\\ndef scan_setup_py() # Validate the contents of setup.py against Versioneer's expectations.\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"docs/generate_docs.py::patch_imports","text":"docs/generate_docs.py::patch_imports\n@contextmanager\ndef patch_imports():\n try:\n\n def patched_import(*args, **kwargs):\n try:\n return real_import(*args, **kwargs)\n except Exception:\n return MagicMock(name=args[0])\n\n # swap\n real_import, builtins.__import__ = builtins.__import__, patched_import\n yield\n finally:\n builtins.__import__ = real_import","label":0},{"cid":"docs/generate_docs.py::load_outline","text":"docs/generate_docs.py::load_outline\ndef load_outline(\n outline=outline_config[\"pages\"],\n ext=outline_config.get(\"extension\", \".md\"),\n prefix=None,\n):\n OUTLINE = []\n for name, data in outline.items():\n fname = os.path.join(prefix or \"\", name)\n if \"module\" in data:\n page = {}\n page.update(\n page=f\"{fname}{ext}\",\n title=data.get(\"title\", \"\"),\n classes=[],\n functions=[],\n )\n module = importlib.import_module(data[\"module\"])\n page[\"top-level-doc\"] = module\n for fun in data.get(\"functions\", []):\n page[\"functions\"].append(getattr(module, fun))\n for clss in data.get(\"classes\", []):\n page[\"classes\"].append(getattr(module, clss))\n OUTLINE.append(page)\n else:\n OUTLINE.extend(load_outline(data, prefix=fname))\n return OUTLINE","label":0},{"cid":"docs/generate_docs.py::preprocess","text":"docs/generate_docs.py::preprocess\n@toolz.curry\ndef preprocess(f, remove_partial=True):\n def wrapped(*args, **kwargs):\n new_obj = getattr(args[0], \"__wrapped__\", args[0])\n if not isinstance(new_obj, partial):\n new_obj = getattr(new_obj, \"func\", new_obj)\n elif isinstance(new_obj, partial) and remove_partial:\n # because partial sets kwargs in the signature, we dont always\n # want that stripped for call signature inspection but we _do_\n # for doc inspection\n new_obj = getattr(new_obj, \"func\", new_obj)\n new_args = list(args)\n new_args[0] = new_obj\n if getattr(new_obj, \"__wrapped__\", None):\n return wrapped(*new_args, **kwargs)\n return f(*new_args, **kwargs)\n\n return wrapped","label":0},{"cid":"docs/generate_docs.py::preprocess.wrapped","text":"docs/generate_docs.py::preprocess.wrapped\n def wrapped(*args, **kwargs):\n new_obj = getattr(args[0], \"__wrapped__\", args[0])\n if not isinstance(new_obj, partial):\n new_obj = getattr(new_obj, \"func\", new_obj)\n elif isinstance(new_obj, partial) and remove_partial:\n # because partial sets kwargs in the signature, we dont always\n # want that stripped for call signature inspection but we _do_\n # for doc inspection\n new_obj = getattr(new_obj, \"func\", new_obj)\n new_args = list(args)\n new_args[0] = new_obj\n if getattr(new_obj, \"__wrapped__\", None):\n return wrapped(*new_args, **kwargs)\n return f(*new_args, **kwargs)","label":0},{"cid":"docs/generate_docs.py::clean_line","text":"docs/generate_docs.py::clean_line\ndef clean_line(line):\n line = (\n line.replace(\"Args:\", \"**Args**:\")\n .replace(\"Returns:\", \"**Returns**:\")\n .replace(\"Raises:\", \"**Raises**:\")\n .replace(\"Example:\", \"**Example**:\")\n .replace(\".**\", \".\\n\\n**\")\n )\n return line.lstrip()","label":0},{"cid":"docs/generate_docs.py::format_lists","text":"docs/generate_docs.py::format_lists\ndef format_lists(doc):\n \"Convenience function for converting markdown lists to HTML for within-table lists\"\n lists = re.findall(\n r\"(Args\\:|Returns\\:|Raises\\:)(.*?)\\s+(-.*?)(\\n\\n|$)\", doc, re.DOTALL\n ) # find formatted lists\n ul_tag = '<ul class=\"args\">'\n li_tag = '<li class=\"args\">'\n for section, _, items, ending in lists:\n if section.startswith((\"Returns:\", \"Raises:\")) and \":\" not in items:\n doc = doc.replace(\n items, f\"{ul_tag}{li_tag}\" + items.lstrip(\"- \") + \"</li></ul>\", 1\n )\n continue\n args = re.split(r\"-\\s+(.*?)\\:(?![^{]*\\})\", items) # collect all list items\n if not args:\n continue\n block = \"\"\n list_items = zip(args[1::2], args[2::2])\n for item, descr in list_items:\n block += f\"{li_tag}`{item}`:{descr}</li>\"\n list_block = f\"{ul_tag}{block}</ul>\"\n doc = doc.replace(items + \"\\n\\n\", list_block, 1).replace(items, list_block, 1)\n return doc.replace(\"\\n\\nRaises:\", \"Raises:\")","label":0},{"cid":"docs/generate_docs.py::format_doc","text":"docs/generate_docs.py::format_doc\n@preprocess\ndef format_doc(obj, in_table=False):\n doc = inspect.getdoc(obj)\n body = doc or \"\"\n code_blocks = re.findall(r\"```(.*?)```\", body, re.DOTALL)\n for num, block in enumerate(code_blocks):\n body = body.replace(block, f\"$CODEBLOCK{num}\", 1)\n body = re.sub(\n \"(?<!\\n)\\n{1}(?!\\n)\", \" \", format_lists(body)\n ) # removes poorly placed newlines\n body = body.replace(\"```\", \"\\n```\")\n lines = body.split(\"\\n\")\n cleaned = \"\\n\".join([clean_line(line) for line in lines])\n if in_table:\n cleaned = cleaned.replace(\"\\n\", \"<br>\").replace(\"```\", \"\")\n for num, block in enumerate(code_blocks):\n if in_table:\n block = block[block.startswith(\"python\") and 6 :].lstrip(\"\\n\")\n block = (\n '<pre class=\"language-python\"><code class=\"language-python\">'\n + format_code(block).replace(\"\\n\", \"<br>\").replace(\"*\", r\"\\*\")\n + \"</code></pre>\"\n )\n cleaned = cleaned.replace(f\"$CODEBLOCK{num}\", block.rstrip(\" \"))\n if in_table:\n return f'<p class=\"methods\">{cleaned}</p>'\n else:\n return cleaned","label":0},{"cid":"docs/generate_docs.py::create_methods_table","text":"docs/generate_docs.py::create_methods_table\ndef create_methods_table(members, title):\n table = \"\"\n if members:\n table = f\"|{title} \" + \" \" * 150 + \"|\\n\"\n table += \"|:----|\\n\"\n for method in members:\n table += format_subheader(method, level=2, in_table=True).replace(\"\\n\\n\", \"\\n\")\n table += format_doc(method, in_table=True)\n table += \"|\\n\"\n return table","label":0},{"cid":"docs/generate_docs.py::get_call_signature","text":"docs/generate_docs.py::get_call_signature\n@preprocess(remove_partial=False)\ndef get_call_signature(obj):\n assert callable(obj), f\"{obj} is not callable, cannot format signature.\"\n # collect data\n try:\n sig = inspect.getfullargspec(obj)\n except TypeError: # if obj is exception\n sig = inspect.getfullargspec(obj.__init__)\n args, defaults = sig.args, sig.defaults or []\n kwonly, kwonlydefaults = sig.kwonlyargs or [], sig.kwonlydefaults or {}\n varargs, varkwargs = sig.varargs, sig.varkw\n\n if args == []:\n standalone, kwargs = [], []\n else:\n if args[0] in [\"cls\", \"self\"]:\n args = args[1:] # remove cls or self from displayed signature\n\n standalone = args[: -len(defaults)] if defaults else args # true args\n kwargs = list(zip(args[-len(defaults) :], defaults)) # true kwargs\n\n varargs = [f\"*{varargs}\"] if varargs else []\n varkwargs = [f\"**{varkwargs}\"] if varkwargs else []\n if kwonly:\n kwargs.extend([(kw, default) for kw, default in kwonlydefaults.items()])\n kwonly = [k for k in kwonly if k not in kwonlydefaults]\n\n return standalone, varargs, kwonly, kwargs, varkwargs","label":0},{"cid":"docs/generate_docs.py::format_signature","text":"docs/generate_docs.py::format_signature\n@preprocess(remove_partial=False)\ndef format_signature(obj):\n standalone, varargs, kwonly, kwargs, varkwargs = get_call_signature(obj)\n add_quotes = lambda s: f'\"{s}\"' if isinstance(s, str) else s\n psig = \", \".join(\n standalone\n + varargs\n + kwonly\n + [f\"{name}={add_quotes(val)}\" for name, val in kwargs]\n + varkwargs\n )\n return psig","label":0},{"cid":"docs/generate_docs.py::create_absolute_path","text":"docs/generate_docs.py::create_absolute_path\n@preprocess\ndef create_absolute_path(obj):\n dir_struct = inspect.getfile(obj).split(os.sep)\n if (\"prefect\" not in dir_struct) or (\"test_generate_docs.py\" in dir_struct):\n return obj.__qualname__\n first_dir, offset = (\"src\", 1) if \"src\" in dir_struct else (\"prefect\", 0)\n begins_at = dir_struct.index(first_dir) + offset\n filename = dir_struct.pop(-1)\n dir_struct.append(filename[:-3] if filename.endswith(\".py\") else filename)\n path = \".\".join([d for d in dir_struct[begins_at:]])\n return f\"{path}.{obj.__qualname__}\"","label":0},{"cid":"docs/generate_docs.py::get_source","text":"docs/generate_docs.py::get_source\n@preprocess\ndef get_source(obj):\n commit = os.getenv(\"GIT_SHA\", \"master\")\n base_url = \"https://github.com/PrefectHQ/prefect/blob/{}/src/prefect/\".format(\n commit\n )\n dir_struct = inspect.getfile(obj).split(os.sep)\n if \"src\" not in dir_struct:\n link = \"[source]\" # dead-link\n else:\n begins_at = dir_struct.index(\"src\") + 2\n line_no = inspect.getsourcelines(obj)[1]\n url_ending = \"/\".join(dir_struct[begins_at:]) + f\"#L{line_no}\"\n link = f'<a href=\"{base_url}{url_ending}\">[source]</a>'\n source_tag = f'<span class=\"source\">{link}</span>'\n return source_tag","label":0},{"cid":"docs/generate_docs.py::format_subheader","text":"docs/generate_docs.py::format_subheader\n@preprocess(remove_partial=False)\ndef format_subheader(obj, level=1, in_table=False):\n class_sig = format_signature(obj)\n if inspect.isclass(obj):\n header = \"## {}\\n\".format(obj.__name__)\n elif not in_table:\n header = \"##\" + \"#\" * level\n else:\n header = \"|\"\n is_class = '<p class=\"prefect-sig\">class </p>' if inspect.isclass(obj) else \"\"\n class_name = f'<p class=\"prefect-class\">{create_absolute_path(obj)}</p>'\n div_class = \"class-sig\" if is_class else \"method-sig\"\n div_tag = f\"<div class='{div_class}' id='{slugify(create_absolute_path(obj))}'>\"\n\n call_sig = f\" {header} {div_tag}{is_class}{class_name}({class_sig}){get_source(obj)}</div>\\n\\n\"\n return call_sig","label":0},{"cid":"docs/generate_docs.py::get_class_methods","text":"docs/generate_docs.py::get_class_methods\ndef get_class_methods(obj):\n members = inspect.getmembers(\n obj, predicate=lambda x: inspect.isroutine(x) and obj.__name__ in x.__qualname__\n )\n public_members = [method for (name, method) in members if not name.startswith(\"_\")]\n return public_members","label":0},{"cid":"docs/generate_docs.py::create_tutorial_notebooks","text":"docs/generate_docs.py::create_tutorial_notebooks\ndef create_tutorial_notebooks(tutorial):\n \"\"\"\n Utility that automagically creates an .ipynb notebook file from a markdown file consisting\n of all python code blocks contained within the markdown file.\n\n Args:\n - tutorial (str): path to tutorial markdown file\n\n Will save the resulting notebook in tutorials/notebooks under the same name as the .md file provided.\n \"\"\"\n assert (\n os.path.basename(os.getcwd()) == \"docs\"\n ), \"Only run this utility from inside the docs/ directory!\"\n\n import nbformat as nbf\n\n os.makedirs(\".vuepress/public/notebooks\", exist_ok=True)\n text = open(tutorial, \"r\").read()\n code_blocks = re.findall(r\"```(.*?)```\", text, re.DOTALL)\n nb = nbf.v4.new_notebook()\n nb[\"cells\"] = []\n for code in code_blocks:\n if not code.startswith(\"python\"):\n continue\n code = code[7:]\n nb[\"cells\"].append(nbf.v4.new_code_cell(code))\n fname = os.path.basename(tutorial).split(\".md\")[0] + \".ipynb\"\n nbf.write(nb, f\".vuepress/public/notebooks/{fname}\")","label":0},{"cid":"src/prefect/client/secrets.py::Secret.__init__","text":"src/prefect/client/secrets.py::Secret.__init__\n def __init__(self, name: str):\n self.name = name","label":0},{"cid":"src/prefect/client/secrets.py::Secret.client","text":"src/prefect/client/secrets.py::Secret.client\n @property\n def client(self) -> Client:\n if not hasattr(self, \"_client\"):\n self._client = Client()\n return self._client","label":0},{"cid":"src/prefect/client/secrets.py::Secret.exists","text":"src/prefect/client/secrets.py::Secret.exists\n def exists(self) -> bool:\n \"\"\"\n Determine if the secret exists.\n\n Returns:\n - bool: a boolean specifying whether the Secret is accessible or not\n \"\"\"\n secrets = prefect.context.get(\"secrets\", {})\n if self.name in secrets:\n return True\n elif prefect.context.config.cloud.use_local_secrets is False:\n cloud_secrets = self.client.graphql(\"query{secretNames}\").data.secretNames\n if self.name in cloud_secrets:\n return True\n return False","label":0},{"cid":"src/prefect/client/secrets.py::Secret.get","text":"src/prefect/client/secrets.py::Secret.get\n def get(self) -> Optional[Any]:\n \"\"\"\n Retrieve the secret value. If not found, returns `None`.\n\n If using local secrets, `Secret.get()` will attempt to call `json.loads` on the\n value pulled from context. For this reason it is recommended to store local secrets as\n JSON documents to avoid ambiguous behavior.\n\n Returns:\n - Any: the value of the secret; if not found, raises an error\n\n Raises:\n - ValueError: if `.get()` is called within a Flow building context, or if `use_local_secrets=True`\n and your Secret doesn't exist\n - ClientError: if `use_local_secrets=False` and the Client fails to retrieve your secret\n \"\"\"\n if isinstance(prefect.context.get(\"flow\"), prefect.core.flow.Flow):\n raise ValueError(\n \"Secrets should only be retrieved during a Flow run, not while building a Flow.\"\n )\n\n secrets = prefect.context.get(\"secrets\", {})\n try:\n value = secrets[self.name]\n except KeyError:\n if prefect.context.config.cloud.use_local_secrets is False:\n result = self.client.graphql(\n \"\"\"\n query($name: String!) {\n secretValue(name: $name)\n }\n \"\"\",\n variables=dict(name=self.name),\n )\n # the result object is a Box, so we recursively restore builtin\n # dict/list classes\n result_dict = result.to_dict()\n value = result_dict[\"data\"][\"secretValue\"]\n else:\n raise ValueError(\n 'Local Secret \"{}\" was not found.'.format(self.name)\n ) from None\n try:\n return json.loads(value)\n except (json.JSONDecodeError, TypeError):\n return value","label":0},{"cid":"versioneer.py::get_root","text":"versioneer.py::get_root\ndef get_root():\n \"\"\"Get the project root directory.\n\n We require that all commands are run from the project root, i.e. the\n directory that contains setup.py, setup.cfg, and versioneer.py .\n \"\"\"\n root = os.path.realpath(os.path.abspath(os.getcwd()))\n setup_py = os.path.join(root, \"setup.py\")\n versioneer_py = os.path.join(root, \"versioneer.py\")\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n # allow 'python path/to/setup.py COMMAND'\n root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))\n setup_py = os.path.join(root, \"setup.py\")\n versioneer_py = os.path.join(root, \"versioneer.py\")\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n err = (\n \"Versioneer was unable to run the project root directory. \"\n \"Versioneer requires setup.py to be executed from \"\n \"its immediate directory (like 'python setup.py COMMAND'), \"\n \"or in a way that lets it use sys.argv[0] to find the root \"\n \"(like 'python path/to/setup.py COMMAND').\"\n )\n raise VersioneerBadRootError(err)\n try:\n # Certain runtime workflows (setup.py install/develop in a setuptools\n # tree) execute all dependencies in a single python process, so\n # \"versioneer\" may be imported multiple times, and python's shared\n # module-import table will cache the first one. So we can't use\n # os.path.dirname(__file__), as that will find whichever\n # versioneer.py was first imported, even in later projects.\n me = os.path.realpath(os.path.abspath(__file__))\n me_dir = os.path.normcase(os.path.splitext(me)[0])\n vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])\n if me_dir != vsr_dir:\n print(\n \"Warning: build in %s is using versioneer.py from %s\"\n % (os.path.dirname(me), versioneer_py)\n )\n exc","label":0},{"cid":"versioneer.py::get_config_from_root","text":"versioneer.py::get_config_from_root\ndef get_config_from_root(root):\n \"\"\"Read the project setup.cfg file to determine Versioneer config.\"\"\"\n # This might raise EnvironmentError (if setup.cfg is missing), or\n # configparser.NoSectionError (if it lacks a [versioneer] section), or\n # configparser.NoOptionError (if it lacks \"VCS=\"). See the docstring at\n # the top of versioneer.py for instructions on writing your setup.cfg .\n setup_cfg = os.path.join(root, \"setup.cfg\")\n parser = configparser.SafeConfigParser()\n with open(setup_cfg, \"r\") as f:\n parser.readfp(f)\n VCS = parser.get(\"versioneer\", \"VCS\") # mandatory\n\n def get(parser, name):\n if parser.has_option(\"versioneer\", name):\n return parser.get(\"versioneer\", name)\n return None\n\n cfg = VersioneerConfig()\n cfg.VCS = VCS\n cfg.style = get(parser, \"style\") or \"\"\n cfg.versionfile_source = get(parser, \"versionfile_source\")\n cfg.versionfile_build = get(parser, \"versionfile_build\")\n cfg.tag_prefix = get(parser, \"tag_prefix\")\n if cfg.tag_prefix in (\"''\", '\"\"'):\n cfg.tag_prefix = \"\"\n cfg.parentdir_prefix = get(parser, \"parentdir_prefix\")\n cfg.verbose = get(parser, \"verbose\")\n return cfg","label":0},{"cid":"versioneer.py::get_config_from_root.get","text":"versioneer.py::get_config_from_root.get\n def get(parser, name):\n if parser.has_option(\"versioneer\", name):\n return parser.get(\"versioneer\", name)\n return None","label":0},{"cid":"versioneer.py::register_vcs_handler","text":"versioneer.py::register_vcs_handler\ndef register_vcs_handler(vcs, method): # decorator\n \"\"\"Decorator to mark a method as the handler for a particular VCS.\"\"\"\n\n def decorate(f):\n \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n if vcs not in HANDLERS:\n HANDLERS[vcs] = {}\n HANDLERS[vcs][method] = f\n return f\n\n return decorate","label":0},{"cid":"versioneer.py::register_vcs_handler.decorate","text":"versioneer.py::register_vcs_handler.decorate\n def decorate(f):\n \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n if vcs not in HANDLERS:\n HANDLERS[vcs] = {}\n HANDLERS[vcs][method] = f\n return f","label":0},{"cid":"versioneer.py::run_command","text":"versioneer.py::run_command\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):\n \"\"\"Call the given command(s).\"\"\"\n assert isinstance(commands, list)\n p = None\n for c in commands:\n try:\n dispcmd = str([c] + args)\n # remember shell=False, so use git.cmd on windows, not just git\n p = subprocess.Popen(\n [c] + args,\n cwd=cwd,\n env=env,\n stdout=subprocess.PIPE,\n stderr=(subprocess.PIPE if hide_stderr else None),\n )\n break\n except EnvironmentError:\n e = sys.exc_info()[1]\n if e.errno == errno.ENOENT:\n continue\n if verbose:\n print(\"unable to run %s\" % dispcmd)\n print(e)\n return None, None\n else:\n if verbose:\n print(\"unable to find command, tried %s\" % (commands,))\n return None, None\n stdout = p.communicate()[0].strip()\n if sys.version_info[0] >= 3:\n stdout = stdout.decode()\n if p.returncode != 0:\n if verbose:\n print(\"unable to run %s (error)\" % dispcmd)\n print(\"stdout was %s\" % stdout)\n return None, p.returncode\n return stdout, p.returncode","label":0},{"cid":"versioneer.py::git_get_keywords","text":"versioneer.py::git_get_keywords\n@register_vcs_handler(\"git\", \"get_keywords\")\ndef git_get_keywords(versionfile_abs):\n \"\"\"Extract version information from the given file.\"\"\"\n # the code embedded in _version.py can just fetch the value of these\n # keywords. When used from setup.py, we don't want to import _version.py,\n # so we do it with a regexp instead. This function is not used from\n # _version.py.\n keywords = {}\n try:\n f = open(versionfile_abs, \"r\")\n for line in f.readlines():\n if line.strip().startswith(\"git_refnames =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"refnames\"] = mo.group(1)\n if line.strip().startswith(\"git_full =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"full\"] = mo.group(1)\n if line.strip().startswith(\"git_date =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"date\"] = mo.group(1)\n f.close()\n except EnvironmentError:\n pass\n return keywords","label":0},{"cid":"versioneer.py::git_versions_from_keywords","text":"versioneer.py::git_versions_from_keywords\n@register_vcs_handler(\"git\", \"keywords\")\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\n \"\"\"Get version information from git keywords.\"\"\"\n if not keywords:\n raise NotThisMethod(\"no keywords at all, weird\")\n date = keywords.get(\"date\")\n if date is not None:\n # git-2.2.0 added \"%cI\", which expands to an ISO-8601 -compliant\n # datestamp. However we prefer \"%ci\" (which expands to an \"ISO-8601\n # -like\" string, which we must then edit to make compliant), because\n # it's been around since git-1.5.3, and it's too difficult to\n # discover which version we're using, or to work around using an\n # older one.\n date = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n refnames = keywords[\"refnames\"].strip()\n if refnames.startswith(\"$Format\"):\n if verbose:\n print(\"keywords are unexpanded, not using\")\n raise NotThisMethod(\"unexpanded keywords, not a git-archive tarball\")\n refs = set([r.strip() for r in refnames.strip(\"()\").split(\",\")])\n # starting in git-1.8.3, tags are listed as \"tag: foo-1.0\" instead of\n # just \"foo-1.0\". If we see a \"tag: \" prefix, prefer those.\n TAG = \"tag: \"\n tags = set([r[len(TAG) :] for r in refs if r.startswith(TAG)])\n if not tags:\n # Either we're using git < 1.8.3, or there really are no tags. We use\n # a heuristic: assume all version tags have a digit. The old git %d\n # expansion behaves like git log --decorate=short and strips out the\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\n # between branches and tags. By ignoring refnames without digits, we\n # filter out many common branch names like \"release\" and\n # \"stabilization\", as well as \"HEAD\" and \"master\".\n tags = set([r for r in refs if re.search(r\"\\d\", r)])\n if verbose:\n print(\"discarding '%s', no digits\" % \",\".join(refs - tags))\n","label":0},{"cid":"versioneer.py::git_pieces_from_vcs","text":"versioneer.py::git_pieces_from_vcs\n@register_vcs_handler(\"git\", \"pieces_from_vcs\")\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):\n \"\"\"Get version from 'git describe' in the root of the source tree.\n\n This only gets called if the git-archive 'subst' keywords were *not*\n expanded, and _version.py hasn't already been rewritten with a short\n version string, meaning we're inside a checked out source tree.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n\n out, rc = run_command(GITS, [\"rev-parse\", \"--git-dir\"], cwd=root, hide_stderr=True)\n if rc != 0:\n if verbose:\n print(\"Directory %s not under git control\" % root)\n raise NotThisMethod(\"'git rev-parse --git-dir' returned error\")\n\n # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\n # if there isn't one, this yields HEX[-dirty] (no NUM)\n describe_out, rc = run_command(\n GITS,\n [\n \"describe\",\n \"--tags\",\n \"--dirty\",\n \"--always\",\n \"--long\",\n \"--match\",\n \"%s*\" % tag_prefix,\n ],\n cwd=root,\n )\n # --long was added in git-1.5.5\n if describe_out is None:\n raise NotThisMethod(\"'git describe' failed\")\n describe_out = describe_out.strip()\n full_out, rc = run_command(GITS, [\"rev-parse\", \"HEAD\"], cwd=root)\n if full_out is None:\n raise NotThisMethod(\"'git rev-parse' failed\")\n full_out = full_out.strip()\n\n pieces = {}\n pieces[\"long\"] = full_out\n pieces[\"short\"] = full_out[:7] # maybe improved later\n pieces[\"error\"] = None\n\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\n # TAG might have hyphens.\n git_describe = describe_out\n\n # look for -dirty suffix\n dirty = git_describe.endswith(\"-dirty\")\n pieces[\"dirty\"] = dirty\n if dirty:\n git_describe = git_describe[: git_describe.rindex(\"-dirty","label":0},{"cid":"versioneer.py::do_vcs_install","text":"versioneer.py::do_vcs_install\ndef do_vcs_install(manifest_in, versionfile_source, ipy):\n \"\"\"Git-specific installation logic for Versioneer.\n\n For Git, this means creating/changing .gitattributes to mark _version.py\n for export-subst keyword substitution.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n files = [manifest_in, versionfile_source]\n if ipy:\n files.append(ipy)\n try:\n me = __file__\n if me.endswith(\".pyc\") or me.endswith(\".pyo\"):\n me = os.path.splitext(me)[0] + \".py\"\n versioneer_file = os.path.relpath(me)\n except NameError:\n versioneer_file = \"versioneer.py\"\n files.append(versioneer_file)\n present = False\n try:\n f = open(\".gitattributes\", \"r\")\n for line in f.readlines():\n if line.strip().startswith(versionfile_source):\n if \"export-subst\" in line.strip().split()[1:]:\n present = True\n f.close()\n except EnvironmentError:\n pass\n if not present:\n f = open(\".gitattributes\", \"a+\")\n f.write(\"%s export-subst\\n\" % versionfile_source)\n f.close()\n files.append(\".gitattributes\")\n run_command(GITS, [\"add\", \"--\"] + files)","label":0},{"cid":"versioneer.py::versions_from_parentdir","text":"versioneer.py::versions_from_parentdir\ndef versions_from_parentdir(parentdir_prefix, root, verbose):\n \"\"\"Try to determine the version from the parent directory name.\n\n Source tarballs conventionally unpack into a directory that includes both\n the project name and a version string. We will also support searching up\n two directory levels for an appropriately named parent directory\n \"\"\"\n rootdirs = []\n\n for i in range(3):\n dirname = os.path.basename(root)\n if dirname.startswith(parentdir_prefix):\n return {\n \"version\": dirname[len(parentdir_prefix) :],\n \"full-revisionid\": None,\n \"dirty\": False,\n \"error\": None,\n \"date\": None,\n }\n else:\n rootdirs.append(root)\n root = os.path.dirname(root) # up a level\n\n if verbose:\n print(\n \"Tried directories %s but none started with prefix %s\"\n % (str(rootdirs), parentdir_prefix)\n )\n raise NotThisMethod(\"rootdir doesn't start with parentdir_prefix\")","label":0},{"cid":"versioneer.py::versions_from_file","text":"versioneer.py::versions_from_file\ndef versions_from_file(filename):\n \"\"\"Try to determine the version from _version.py if present.\"\"\"\n try:\n with open(filename) as f:\n contents = f.read()\n except EnvironmentError:\n raise NotThisMethod(\"unable to read _version.py\")\n mo = re.search(\n r\"version_json = '''\\n(.*)''' # END VERSION_JSON\", contents, re.M | re.S\n )\n if not mo:\n mo = re.search(\n r\"version_json = '''\\r\\n(.*)''' # END VERSION_JSON\", contents, re.M | re.S\n )\n if not mo:\n raise NotThisMethod(\"no version_json in _version.py\")\n return json.loads(mo.group(1))","label":0},{"cid":"versioneer.py::write_to_version_file","text":"versioneer.py::write_to_version_file\ndef write_to_version_file(filename, versions):\n \"\"\"Write the given version number to the given _version.py file.\"\"\"\n os.unlink(filename)\n contents = json.dumps(versions, sort_keys=True, indent=1, separators=(\",\", \": \"))\n with open(filename, \"w\") as f:\n f.write(SHORT_VERSION_PY % contents)\n\n print(\"set %s to '%s'\" % (filename, versions[\"version\"]))","label":0},{"cid":"versioneer.py::plus_or_dot","text":"versioneer.py::plus_or_dot\ndef plus_or_dot(pieces):\n \"\"\"Return a + if we don't already have one, else return a .\"\"\"\n if \"+\" in pieces.get(\"closest-tag\", \"\"):\n return \".\"\n return \"+\"","label":0},{"cid":"versioneer.py::render_pep440","text":"versioneer.py::render_pep440\ndef render_pep440(pieces):\n \"\"\"Build up version string, with post-release \"local version identifier\".\n\n Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\n get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\n\n Exceptions:\n 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += plus_or_dot(pieces)\n rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0+untagged.%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered","label":0},{"cid":"versioneer.py::render_pep440_pre","text":"versioneer.py::render_pep440_pre\ndef render_pep440_pre(pieces):\n \"\"\"TAG[.post.devDISTANCE] -- No -dirty.\n\n Exceptions:\n 1: no tags. 0.post.devDISTANCE\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \".post.dev%d\" % pieces[\"distance\"]\n else:\n # exception #1\n rendered = \"0.post.dev%d\" % pieces[\"distance\"]\n return rendered","label":0},{"cid":"versioneer.py::render_pep440_post","text":"versioneer.py::render_pep440_post\ndef render_pep440_post(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]+gHEX] .\n\n The \".dev0\" means dirty. Note that .dev0 sorts backwards\n (a dirty tree will appear \"older\" than the corresponding clean one),\n but you shouldn't be releasing software with -dirty anyways.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%s\" % pieces[\"short\"]\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += \"+g%s\" % pieces[\"short\"]\n return rendered","label":0},{"cid":"versioneer.py::render_pep440_old","text":"versioneer.py::render_pep440_old\ndef render_pep440_old(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]] .\n\n The \".dev0\" means dirty.\n\n Eexceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n return rendered","label":0},{"cid":"versioneer.py::render_git_describe","text":"versioneer.py::render_git_describe\ndef render_git_describe(pieces):\n \"\"\"TAG[-DISTANCE-gHEX][-dirty].\n\n Like 'git describe --tags --dirty --always'.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered","label":0},{"cid":"versioneer.py::render_git_describe_long","text":"versioneer.py::render_git_describe_long\ndef render_git_describe_long(pieces):\n \"\"\"TAG-DISTANCE-gHEX[-dirty].\n\n Like 'git describe --tags --dirty --always -long'.\n The distance/hash is unconditional.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered","label":0},{"cid":"versioneer.py::render","text":"versioneer.py::render\ndef render(pieces, style):\n \"\"\"Render the given version pieces into the requested style.\"\"\"\n if pieces[\"error\"]:\n return {\n \"version\": \"unknown\",\n \"full-revisionid\": pieces.get(\"long\"),\n \"dirty\": None,\n \"error\": pieces[\"error\"],\n \"date\": None,\n }\n\n if not style or style == \"default\":\n style = \"pep440\" # the default\n\n if style == \"pep440\":\n rendered = render_pep440(pieces)\n elif style == \"pep440-pre\":\n rendered = render_pep440_pre(pieces)\n elif style == \"pep440-post\":\n rendered = render_pep440_post(pieces)\n elif style == \"pep440-old\":\n rendered = render_pep440_old(pieces)\n elif style == \"git-describe\":\n rendered = render_git_describe(pieces)\n elif style == \"git-describe-long\":\n rendered = render_git_describe_long(pieces)\n else:\n raise ValueError(\"unknown style '%s'\" % style)\n\n return {\n \"version\": rendered,\n \"full-revisionid\": pieces[\"long\"],\n \"dirty\": pieces[\"dirty\"],\n \"error\": None,\n \"date\": pieces.get(\"date\"),\n }","label":0},{"cid":"versioneer.py::get_versions","text":"versioneer.py::get_versions\ndef get_versions(verbose=False):\n \"\"\"Get the project version from whatever source is available.\n\n Returns dict with two keys: 'version' and 'full'.\n \"\"\"\n if \"versioneer\" in sys.modules:\n # see the discussion in cmdclass.py:get_cmdclass()\n del sys.modules[\"versioneer\"]\n\n root = get_root()\n cfg = get_config_from_root(root)\n\n assert cfg.VCS is not None, \"please set [versioneer]VCS= in setup.cfg\"\n handlers = HANDLERS.get(cfg.VCS)\n assert handlers, \"unrecognized VCS '%s'\" % cfg.VCS\n verbose = verbose or cfg.verbose\n assert (\n cfg.versionfile_source is not None\n ), \"please set versioneer.versionfile_source\"\n assert cfg.tag_prefix is not None, \"please set versioneer.tag_prefix\"\n\n versionfile_abs = os.path.join(root, cfg.versionfile_source)\n\n # extract version from first of: _version.py, VCS command (e.g. 'git\n # describe'), parentdir. This is meant to work for developers using a\n # source checkout, for users of a tarball created by 'setup.py sdist',\n # and for users of a tarball/zipball created by 'git archive' or github's\n # download-from-tag feature or the equivalent in other VCSes.\n\n get_keywords_f = handlers.get(\"get_keywords\")\n from_keywords_f = handlers.get(\"keywords\")\n if get_keywords_f and from_keywords_f:\n try:\n keywords = get_keywords_f(versionfile_abs)\n ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)\n if verbose:\n print(\"got version from expanded keyword %s\" % ver)\n return ver\n except NotThisMethod:\n pass\n\n try:\n ver = versions_from_file(versionfile_abs)\n if verbose:\n print(\"got version from file %s %s\" % (versionfile_abs, ver))\n return ver\n except NotThisMethod:\n pass\n\n from_vcs_f = handlers.get(\"pieces_from_vcs\")\n if from_vcs_f:\n try:\n pieces = from_vcs_f(cfg.tag_prefix, root, verbose)\n ","label":0},{"cid":"versioneer.py::get_version","text":"versioneer.py::get_version\ndef get_version():\n \"\"\"Get the short version string for this project.\"\"\"\n return get_versions()[\"version\"]","label":0},{"cid":"versioneer.py::get_cmdclass","text":"versioneer.py::get_cmdclass\ndef get_cmdclass():\n \"\"\"Get the custom setuptools/distutils subclasses used by Versioneer.\"\"\"\n if \"versioneer\" in sys.modules:\n del sys.modules[\"versioneer\"]\n # this fixes the \"python setup.py develop\" case (also 'install' and\n # 'easy_install .'), in which subdependencies of the main project are\n # built (using setup.py bdist_egg) in the same python process. Assume\n # a main project A and a dependency B, which use different versions\n # of Versioneer. A's setup.py imports A's Versioneer, leaving it in\n # sys.modules by the time B's setup.py is executed, causing B to run\n # with the wrong versioneer. Setuptools wraps the sub-dep builds in a\n # sandbox that restores sys.modules to it's pre-build state, so the\n # parent is protected against the child's \"import versioneer\". By\n # removing ourselves from sys.modules here, before the child build\n # happens, we protect the child from the parent's versioneer too.\n # Also see https://github.com/warner/python-versioneer/issues/52\n\n cmds = {}\n\n # we add \"version\" to both distutils and setuptools\n from distutils.core import Command\n\n class cmd_version(Command):\n description = \"report generated version string\"\n user_options = []\n boolean_options = []\n\n def initialize_options(self):\n pass\n\n def finalize_options(self):\n pass\n\n def run(self):\n vers = get_versions(verbose=True)\n print(\"Version: %s\" % vers[\"version\"])\n print(\" full-revisionid: %s\" % vers.get(\"full-revisionid\"))\n print(\" dirty: %s\" % vers.get(\"dirty\"))\n print(\" date: %s\" % vers.get(\"date\"))\n if vers[\"error\"]:\n print(\" error: %s\" % vers[\"error\"])\n\n cmds[\"version\"] = cmd_version\n\n # we override \"build_py\" in both distutils and setuptools\n #\n # most invocation pathways end up runnin","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_version.initialize_options","text":"versioneer.py::get_cmdclass.cmd_version.initialize_options\n def initialize_options(self):\n pass","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_version.finalize_options","text":"versioneer.py::get_cmdclass.cmd_version.finalize_options\n def finalize_options(self):\n pass","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_version.run","text":"versioneer.py::get_cmdclass.cmd_version.run\n def run(self):\n vers = get_versions(verbose=True)\n print(\"Version: %s\" % vers[\"version\"])\n print(\" full-revisionid: %s\" % vers.get(\"full-revisionid\"))\n print(\" dirty: %s\" % vers.get(\"dirty\"))\n print(\" date: %s\" % vers.get(\"date\"))\n if vers[\"error\"]:\n print(\" error: %s\" % vers[\"error\"])","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_build_py.run","text":"versioneer.py::get_cmdclass.cmd_build_py.run\n def run(self):\n root = get_root()\n cfg = get_config_from_root(root)\n versions = get_versions()\n _build_py.run(self)\n # now locate _version.py in the new build/ directory and replace\n # it with an updated value\n if cfg.versionfile_build:\n target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build)\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(target_versionfile, versions)","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_sdist.run","text":"versioneer.py::get_cmdclass.cmd_sdist.run\n def run(self):\n versions = get_versions()\n self._versioneer_generated_versions = versions\n # unless we update this, the command will keep using the old\n # version\n self.distribution.metadata.version = versions[\"version\"]\n return _sdist.run(self)","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_sdist.make_release_tree","text":"versioneer.py::get_cmdclass.cmd_sdist.make_release_tree\n def make_release_tree(self, base_dir, files):\n root = get_root()\n cfg = get_config_from_root(root)\n _sdist.make_release_tree(self, base_dir, files)\n # now locate _version.py in the new base_dir directory\n # (remembering that it may be a hardlink) and replace it with an\n # updated value\n target_versionfile = os.path.join(base_dir, cfg.versionfile_source)\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(\n target_versionfile, self._versioneer_generated_versions\n )","label":0},{"cid":"versioneer.py::do_setup","text":"versioneer.py::do_setup\ndef do_setup():\n \"\"\"Main VCS-independent setup function for installing Versioneer.\"\"\"\n root = get_root()\n try:\n cfg = get_config_from_root(root)\n except (\n EnvironmentError,\n configparser.NoSectionError,\n configparser.NoOptionError,\n ) as e:\n if isinstance(e, (EnvironmentError, configparser.NoSectionError)):\n print(\"Adding sample versioneer config to setup.cfg\", file=sys.stderr)\n with open(os.path.join(root, \"setup.cfg\"), \"a\") as f:\n f.write(SAMPLE_CONFIG)\n print(CONFIG_ERROR, file=sys.stderr)\n return 1\n\n print(\" creating %s\" % cfg.versionfile_source)\n with open(cfg.versionfile_source, \"w\") as f:\n LONG = LONG_VERSION_PY[cfg.VCS]\n f.write(\n LONG\n % {\n \"DOLLAR\": \"$\",\n \"STYLE\": cfg.style,\n \"TAG_PREFIX\": cfg.tag_prefix,\n \"PARENTDIR_PREFIX\": cfg.parentdir_prefix,\n \"VERSIONFILE_SOURCE\": cfg.versionfile_source,\n }\n )\n\n ipy = os.path.join(os.path.dirname(cfg.versionfile_source), \"__init__.py\")\n if os.path.exists(ipy):\n try:\n with open(ipy, \"r\") as f:\n old = f.read()\n except EnvironmentError:\n old = \"\"\n if INIT_PY_SNIPPET not in old:\n print(\" appending to %s\" % ipy)\n with open(ipy, \"a\") as f:\n f.write(INIT_PY_SNIPPET)\n else:\n print(\" %s unmodified\" % ipy)\n else:\n print(\" %s doesn't exist, ok\" % ipy)\n ipy = None\n\n # Make sure both the top-level \"versioneer.py\" and versionfile_source\n # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so\n # they'll be copied into source distributions. Pip won't be able to\n # install the package without this.\n manifest_in = os.path.join(root, \"MANIFEST.in\")\n simple_includes = set()\n try:\n with open(manifest_in, \"r\") a","label":0},{"cid":"versioneer.py::scan_setup_py","text":"versioneer.py::scan_setup_py\ndef scan_setup_py():\n \"\"\"Validate the contents of setup.py against Versioneer's expectations.\"\"\"\n found = set()\n setters = False\n errors = 0\n with open(\"setup.py\", \"r\") as f:\n for line in f.readlines():\n if \"import versioneer\" in line:\n found.add(\"import\")\n if \"versioneer.get_cmdclass()\" in line:\n found.add(\"cmdclass\")\n if \"versioneer.get_version()\" in line:\n found.add(\"get_version\")\n if \"versioneer.VCS\" in line:\n setters = True\n if \"versioneer.versionfile_source\" in line:\n setters = True\n if len(found) != 3:\n print(\"\")\n print(\"Your setup.py appears to be missing some important items\")\n print(\"(but I might be wrong). Please make sure it has something\")\n print(\"roughly like the following:\")\n print(\"\")\n print(\" import versioneer\")\n print(\" setup( version=versioneer.get_version(),\")\n print(\" cmdclass=versioneer.get_cmdclass(), ...)\")\n print(\"\")\n errors += 1\n if setters:\n print(\"You should remove lines like 'versioneer.VCS = ' and\")\n print(\"'versioneer.versionfile_source = ' . This configuration\")\n print(\"now lives in setup.cfg, and should be removed from setup.py\")\n print(\"\")\n errors += 1\n return errors","label":0}],"string":"[\n {\n \"cid\": \"docs/generate_docs.py::patch_imports\",\n \"text\": \"docs/generate_docs.py::patch_imports\\n@contextmanager\\ndef patch_imports():\\n try:\\n\\n def patched_import(*args, **kwargs):\\n try:\\n return real_import(*args, **kwargs)\\n except Exception:\\n return MagicMock(name=args[0])\\n\\n # swap\\n real_import, builtins.__import__ = builtins.__import__, patched_import\\n yield\\n finally:\\n builtins.__import__ = real_import\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::load_outline\",\n \"text\": \"docs/generate_docs.py::load_outline\\ndef load_outline(\\n outline=outline_config[\\\"pages\\\"],\\n ext=outline_config.get(\\\"extension\\\", \\\".md\\\"),\\n prefix=None,\\n):\\n OUTLINE = []\\n for name, data in outline.items():\\n fname = os.path.join(prefix or \\\"\\\", name)\\n if \\\"module\\\" in data:\\n page = {}\\n page.update(\\n page=f\\\"{fname}{ext}\\\",\\n title=data.get(\\\"title\\\", \\\"\\\"),\\n classes=[],\\n functions=[],\\n )\\n module = importlib.import_module(data[\\\"module\\\"])\\n page[\\\"top-level-doc\\\"] = module\\n for fun in data.get(\\\"functions\\\", []):\\n page[\\\"functions\\\"].append(getattr(module, fun))\\n for clss in data.get(\\\"classes\\\", []):\\n page[\\\"classes\\\"].append(getattr(module, clss))\\n OUTLINE.append(page)\\n else:\\n OUTLINE.extend(load_outline(data, prefix=fname))\\n return OUTLINE\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::preprocess\",\n \"text\": \"docs/generate_docs.py::preprocess\\n@toolz.curry\\ndef preprocess(f, remove_partial=True):\\n def wrapped(*args, **kwargs):\\n new_obj = getattr(args[0], \\\"__wrapped__\\\", args[0])\\n if not isinstance(new_obj, partial):\\n new_obj = getattr(new_obj, \\\"func\\\", new_obj)\\n elif isinstance(new_obj, partial) and remove_partial:\\n # because partial sets kwargs in the signature, we dont always\\n # want that stripped for call signature inspection but we _do_\\n # for doc inspection\\n new_obj = getattr(new_obj, \\\"func\\\", new_obj)\\n new_args = list(args)\\n new_args[0] = new_obj\\n if getattr(new_obj, \\\"__wrapped__\\\", None):\\n return wrapped(*new_args, **kwargs)\\n return f(*new_args, **kwargs)\\n\\n return wrapped\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::preprocess.wrapped\",\n \"text\": \"docs/generate_docs.py::preprocess.wrapped\\n def wrapped(*args, **kwargs):\\n new_obj = getattr(args[0], \\\"__wrapped__\\\", args[0])\\n if not isinstance(new_obj, partial):\\n new_obj = getattr(new_obj, \\\"func\\\", new_obj)\\n elif isinstance(new_obj, partial) and remove_partial:\\n # because partial sets kwargs in the signature, we dont always\\n # want that stripped for call signature inspection but we _do_\\n # for doc inspection\\n new_obj = getattr(new_obj, \\\"func\\\", new_obj)\\n new_args = list(args)\\n new_args[0] = new_obj\\n if getattr(new_obj, \\\"__wrapped__\\\", None):\\n return wrapped(*new_args, **kwargs)\\n return f(*new_args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::clean_line\",\n \"text\": \"docs/generate_docs.py::clean_line\\ndef clean_line(line):\\n line = (\\n line.replace(\\\"Args:\\\", \\\"**Args**:\\\")\\n .replace(\\\"Returns:\\\", \\\"**Returns**:\\\")\\n .replace(\\\"Raises:\\\", \\\"**Raises**:\\\")\\n .replace(\\\"Example:\\\", \\\"**Example**:\\\")\\n .replace(\\\".**\\\", \\\".\\\\n\\\\n**\\\")\\n )\\n return line.lstrip()\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::format_lists\",\n \"text\": \"docs/generate_docs.py::format_lists\\ndef format_lists(doc):\\n \\\"Convenience function for converting markdown lists to HTML for within-table lists\\\"\\n lists = re.findall(\\n r\\\"(Args\\\\:|Returns\\\\:|Raises\\\\:)(.*?)\\\\s+(-.*?)(\\\\n\\\\n|$)\\\", doc, re.DOTALL\\n ) # find formatted lists\\n ul_tag = '<ul class=\\\"args\\\">'\\n li_tag = '<li class=\\\"args\\\">'\\n for section, _, items, ending in lists:\\n if section.startswith((\\\"Returns:\\\", \\\"Raises:\\\")) and \\\":\\\" not in items:\\n doc = doc.replace(\\n items, f\\\"{ul_tag}{li_tag}\\\" + items.lstrip(\\\"- \\\") + \\\"</li></ul>\\\", 1\\n )\\n continue\\n args = re.split(r\\\"-\\\\s+(.*?)\\\\:(?![^{]*\\\\})\\\", items) # collect all list items\\n if not args:\\n continue\\n block = \\\"\\\"\\n list_items = zip(args[1::2], args[2::2])\\n for item, descr in list_items:\\n block += f\\\"{li_tag}`{item}`:{descr}</li>\\\"\\n list_block = f\\\"{ul_tag}{block}</ul>\\\"\\n doc = doc.replace(items + \\\"\\\\n\\\\n\\\", list_block, 1).replace(items, list_block, 1)\\n return doc.replace(\\\"\\\\n\\\\nRaises:\\\", \\\"Raises:\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::format_doc\",\n \"text\": \"docs/generate_docs.py::format_doc\\n@preprocess\\ndef format_doc(obj, in_table=False):\\n doc = inspect.getdoc(obj)\\n body = doc or \\\"\\\"\\n code_blocks = re.findall(r\\\"```(.*?)```\\\", body, re.DOTALL)\\n for num, block in enumerate(code_blocks):\\n body = body.replace(block, f\\\"$CODEBLOCK{num}\\\", 1)\\n body = re.sub(\\n \\\"(?<!\\\\n)\\\\n{1}(?!\\\\n)\\\", \\\" \\\", format_lists(body)\\n ) # removes poorly placed newlines\\n body = body.replace(\\\"```\\\", \\\"\\\\n```\\\")\\n lines = body.split(\\\"\\\\n\\\")\\n cleaned = \\\"\\\\n\\\".join([clean_line(line) for line in lines])\\n if in_table:\\n cleaned = cleaned.replace(\\\"\\\\n\\\", \\\"<br>\\\").replace(\\\"```\\\", \\\"\\\")\\n for num, block in enumerate(code_blocks):\\n if in_table:\\n block = block[block.startswith(\\\"python\\\") and 6 :].lstrip(\\\"\\\\n\\\")\\n block = (\\n '<pre class=\\\"language-python\\\"><code class=\\\"language-python\\\">'\\n + format_code(block).replace(\\\"\\\\n\\\", \\\"<br>\\\").replace(\\\"*\\\", r\\\"\\\\*\\\")\\n + \\\"</code></pre>\\\"\\n )\\n cleaned = cleaned.replace(f\\\"$CODEBLOCK{num}\\\", block.rstrip(\\\" \\\"))\\n if in_table:\\n return f'<p class=\\\"methods\\\">{cleaned}</p>'\\n else:\\n return cleaned\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::create_methods_table\",\n \"text\": \"docs/generate_docs.py::create_methods_table\\ndef create_methods_table(members, title):\\n table = \\\"\\\"\\n if members:\\n table = f\\\"|{title} \\\" + \\\" \\\" * 150 + \\\"|\\\\n\\\"\\n table += \\\"|:----|\\\\n\\\"\\n for method in members:\\n table += format_subheader(method, level=2, in_table=True).replace(\\\"\\\\n\\\\n\\\", \\\"\\\\n\\\")\\n table += format_doc(method, in_table=True)\\n table += \\\"|\\\\n\\\"\\n return table\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::get_call_signature\",\n \"text\": \"docs/generate_docs.py::get_call_signature\\n@preprocess(remove_partial=False)\\ndef get_call_signature(obj):\\n assert callable(obj), f\\\"{obj} is not callable, cannot format signature.\\\"\\n # collect data\\n try:\\n sig = inspect.getfullargspec(obj)\\n except TypeError: # if obj is exception\\n sig = inspect.getfullargspec(obj.__init__)\\n args, defaults = sig.args, sig.defaults or []\\n kwonly, kwonlydefaults = sig.kwonlyargs or [], sig.kwonlydefaults or {}\\n varargs, varkwargs = sig.varargs, sig.varkw\\n\\n if args == []:\\n standalone, kwargs = [], []\\n else:\\n if args[0] in [\\\"cls\\\", \\\"self\\\"]:\\n args = args[1:] # remove cls or self from displayed signature\\n\\n standalone = args[: -len(defaults)] if defaults else args # true args\\n kwargs = list(zip(args[-len(defaults) :], defaults)) # true kwargs\\n\\n varargs = [f\\\"*{varargs}\\\"] if varargs else []\\n varkwargs = [f\\\"**{varkwargs}\\\"] if varkwargs else []\\n if kwonly:\\n kwargs.extend([(kw, default) for kw, default in kwonlydefaults.items()])\\n kwonly = [k for k in kwonly if k not in kwonlydefaults]\\n\\n return standalone, varargs, kwonly, kwargs, varkwargs\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::format_signature\",\n \"text\": \"docs/generate_docs.py::format_signature\\n@preprocess(remove_partial=False)\\ndef format_signature(obj):\\n standalone, varargs, kwonly, kwargs, varkwargs = get_call_signature(obj)\\n add_quotes = lambda s: f'\\\"{s}\\\"' if isinstance(s, str) else s\\n psig = \\\", \\\".join(\\n standalone\\n + varargs\\n + kwonly\\n + [f\\\"{name}={add_quotes(val)}\\\" for name, val in kwargs]\\n + varkwargs\\n )\\n return psig\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::create_absolute_path\",\n \"text\": \"docs/generate_docs.py::create_absolute_path\\n@preprocess\\ndef create_absolute_path(obj):\\n dir_struct = inspect.getfile(obj).split(os.sep)\\n if (\\\"prefect\\\" not in dir_struct) or (\\\"test_generate_docs.py\\\" in dir_struct):\\n return obj.__qualname__\\n first_dir, offset = (\\\"src\\\", 1) if \\\"src\\\" in dir_struct else (\\\"prefect\\\", 0)\\n begins_at = dir_struct.index(first_dir) + offset\\n filename = dir_struct.pop(-1)\\n dir_struct.append(filename[:-3] if filename.endswith(\\\".py\\\") else filename)\\n path = \\\".\\\".join([d for d in dir_struct[begins_at:]])\\n return f\\\"{path}.{obj.__qualname__}\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::get_source\",\n \"text\": \"docs/generate_docs.py::get_source\\n@preprocess\\ndef get_source(obj):\\n commit = os.getenv(\\\"GIT_SHA\\\", \\\"master\\\")\\n base_url = \\\"https://github.com/PrefectHQ/prefect/blob/{}/src/prefect/\\\".format(\\n commit\\n )\\n dir_struct = inspect.getfile(obj).split(os.sep)\\n if \\\"src\\\" not in dir_struct:\\n link = \\\"[source]\\\" # dead-link\\n else:\\n begins_at = dir_struct.index(\\\"src\\\") + 2\\n line_no = inspect.getsourcelines(obj)[1]\\n url_ending = \\\"/\\\".join(dir_struct[begins_at:]) + f\\\"#L{line_no}\\\"\\n link = f'<a href=\\\"{base_url}{url_ending}\\\">[source]</a>'\\n source_tag = f'<span class=\\\"source\\\">{link}</span>'\\n return source_tag\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::format_subheader\",\n \"text\": \"docs/generate_docs.py::format_subheader\\n@preprocess(remove_partial=False)\\ndef format_subheader(obj, level=1, in_table=False):\\n class_sig = format_signature(obj)\\n if inspect.isclass(obj):\\n header = \\\"## {}\\\\n\\\".format(obj.__name__)\\n elif not in_table:\\n header = \\\"##\\\" + \\\"#\\\" * level\\n else:\\n header = \\\"|\\\"\\n is_class = '<p class=\\\"prefect-sig\\\">class </p>' if inspect.isclass(obj) else \\\"\\\"\\n class_name = f'<p class=\\\"prefect-class\\\">{create_absolute_path(obj)}</p>'\\n div_class = \\\"class-sig\\\" if is_class else \\\"method-sig\\\"\\n div_tag = f\\\"<div class='{div_class}' id='{slugify(create_absolute_path(obj))}'>\\\"\\n\\n call_sig = f\\\" {header} {div_tag}{is_class}{class_name}({class_sig}){get_source(obj)}</div>\\\\n\\\\n\\\"\\n return call_sig\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::get_class_methods\",\n \"text\": \"docs/generate_docs.py::get_class_methods\\ndef get_class_methods(obj):\\n members = inspect.getmembers(\\n obj, predicate=lambda x: inspect.isroutine(x) and obj.__name__ in x.__qualname__\\n )\\n public_members = [method for (name, method) in members if not name.startswith(\\\"_\\\")]\\n return public_members\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/generate_docs.py::create_tutorial_notebooks\",\n \"text\": \"docs/generate_docs.py::create_tutorial_notebooks\\ndef create_tutorial_notebooks(tutorial):\\n \\\"\\\"\\\"\\n Utility that automagically creates an .ipynb notebook file from a markdown file consisting\\n of all python code blocks contained within the markdown file.\\n\\n Args:\\n - tutorial (str): path to tutorial markdown file\\n\\n Will save the resulting notebook in tutorials/notebooks under the same name as the .md file provided.\\n \\\"\\\"\\\"\\n assert (\\n os.path.basename(os.getcwd()) == \\\"docs\\\"\\n ), \\\"Only run this utility from inside the docs/ directory!\\\"\\n\\n import nbformat as nbf\\n\\n os.makedirs(\\\".vuepress/public/notebooks\\\", exist_ok=True)\\n text = open(tutorial, \\\"r\\\").read()\\n code_blocks = re.findall(r\\\"```(.*?)```\\\", text, re.DOTALL)\\n nb = nbf.v4.new_notebook()\\n nb[\\\"cells\\\"] = []\\n for code in code_blocks:\\n if not code.startswith(\\\"python\\\"):\\n continue\\n code = code[7:]\\n nb[\\\"cells\\\"].append(nbf.v4.new_code_cell(code))\\n fname = os.path.basename(tutorial).split(\\\".md\\\")[0] + \\\".ipynb\\\"\\n nbf.write(nb, f\\\".vuepress/public/notebooks/{fname}\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/secrets.py::Secret.__init__\",\n \"text\": \"src/prefect/client/secrets.py::Secret.__init__\\n def __init__(self, name: str):\\n self.name = name\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/secrets.py::Secret.client\",\n \"text\": \"src/prefect/client/secrets.py::Secret.client\\n @property\\n def client(self) -> Client:\\n if not hasattr(self, \\\"_client\\\"):\\n self._client = Client()\\n return self._client\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/secrets.py::Secret.exists\",\n \"text\": \"src/prefect/client/secrets.py::Secret.exists\\n def exists(self) -> bool:\\n \\\"\\\"\\\"\\n Determine if the secret exists.\\n\\n Returns:\\n - bool: a boolean specifying whether the Secret is accessible or not\\n \\\"\\\"\\\"\\n secrets = prefect.context.get(\\\"secrets\\\", {})\\n if self.name in secrets:\\n return True\\n elif prefect.context.config.cloud.use_local_secrets is False:\\n cloud_secrets = self.client.graphql(\\\"query{secretNames}\\\").data.secretNames\\n if self.name in cloud_secrets:\\n return True\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/client/secrets.py::Secret.get\",\n \"text\": \"src/prefect/client/secrets.py::Secret.get\\n def get(self) -> Optional[Any]:\\n \\\"\\\"\\\"\\n Retrieve the secret value. If not found, returns `None`.\\n\\n If using local secrets, `Secret.get()` will attempt to call `json.loads` on the\\n value pulled from context. For this reason it is recommended to store local secrets as\\n JSON documents to avoid ambiguous behavior.\\n\\n Returns:\\n - Any: the value of the secret; if not found, raises an error\\n\\n Raises:\\n - ValueError: if `.get()` is called within a Flow building context, or if `use_local_secrets=True`\\n and your Secret doesn't exist\\n - ClientError: if `use_local_secrets=False` and the Client fails to retrieve your secret\\n \\\"\\\"\\\"\\n if isinstance(prefect.context.get(\\\"flow\\\"), prefect.core.flow.Flow):\\n raise ValueError(\\n \\\"Secrets should only be retrieved during a Flow run, not while building a Flow.\\\"\\n )\\n\\n secrets = prefect.context.get(\\\"secrets\\\", {})\\n try:\\n value = secrets[self.name]\\n except KeyError:\\n if prefect.context.config.cloud.use_local_secrets is False:\\n result = self.client.graphql(\\n \\\"\\\"\\\"\\n query($name: String!) {\\n secretValue(name: $name)\\n }\\n \\\"\\\"\\\",\\n variables=dict(name=self.name),\\n )\\n # the result object is a Box, so we recursively restore builtin\\n # dict/list classes\\n result_dict = result.to_dict()\\n value = result_dict[\\\"data\\\"][\\\"secretValue\\\"]\\n else:\\n raise ValueError(\\n 'Local Secret \\\"{}\\\" was not found.'.format(self.name)\\n ) from None\\n try:\\n return json.loads(value)\\n except (json.JSONDecodeError, TypeError):\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_root\",\n \"text\": \"versioneer.py::get_root\\ndef get_root():\\n \\\"\\\"\\\"Get the project root directory.\\n\\n We require that all commands are run from the project root, i.e. the\\n directory that contains setup.py, setup.cfg, and versioneer.py .\\n \\\"\\\"\\\"\\n root = os.path.realpath(os.path.abspath(os.getcwd()))\\n setup_py = os.path.join(root, \\\"setup.py\\\")\\n versioneer_py = os.path.join(root, \\\"versioneer.py\\\")\\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\\n # allow 'python path/to/setup.py COMMAND'\\n root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))\\n setup_py = os.path.join(root, \\\"setup.py\\\")\\n versioneer_py = os.path.join(root, \\\"versioneer.py\\\")\\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\\n err = (\\n \\\"Versioneer was unable to run the project root directory. \\\"\\n \\\"Versioneer requires setup.py to be executed from \\\"\\n \\\"its immediate directory (like 'python setup.py COMMAND'), \\\"\\n \\\"or in a way that lets it use sys.argv[0] to find the root \\\"\\n \\\"(like 'python path/to/setup.py COMMAND').\\\"\\n )\\n raise VersioneerBadRootError(err)\\n try:\\n # Certain runtime workflows (setup.py install/develop in a setuptools\\n # tree) execute all dependencies in a single python process, so\\n # \\\"versioneer\\\" may be imported multiple times, and python's shared\\n # module-import table will cache the first one. So we can't use\\n # os.path.dirname(__file__), as that will find whichever\\n # versioneer.py was first imported, even in later projects.\\n me = os.path.realpath(os.path.abspath(__file__))\\n me_dir = os.path.normcase(os.path.splitext(me)[0])\\n vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])\\n if me_dir != vsr_dir:\\n print(\\n \\\"Warning: build in %s is using versioneer.py from %s\\\"\\n % (os.path.dirname(me), versioneer_py)\\n )\\n exc\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_config_from_root\",\n \"text\": \"versioneer.py::get_config_from_root\\ndef get_config_from_root(root):\\n \\\"\\\"\\\"Read the project setup.cfg file to determine Versioneer config.\\\"\\\"\\\"\\n # This might raise EnvironmentError (if setup.cfg is missing), or\\n # configparser.NoSectionError (if it lacks a [versioneer] section), or\\n # configparser.NoOptionError (if it lacks \\\"VCS=\\\"). See the docstring at\\n # the top of versioneer.py for instructions on writing your setup.cfg .\\n setup_cfg = os.path.join(root, \\\"setup.cfg\\\")\\n parser = configparser.SafeConfigParser()\\n with open(setup_cfg, \\\"r\\\") as f:\\n parser.readfp(f)\\n VCS = parser.get(\\\"versioneer\\\", \\\"VCS\\\") # mandatory\\n\\n def get(parser, name):\\n if parser.has_option(\\\"versioneer\\\", name):\\n return parser.get(\\\"versioneer\\\", name)\\n return None\\n\\n cfg = VersioneerConfig()\\n cfg.VCS = VCS\\n cfg.style = get(parser, \\\"style\\\") or \\\"\\\"\\n cfg.versionfile_source = get(parser, \\\"versionfile_source\\\")\\n cfg.versionfile_build = get(parser, \\\"versionfile_build\\\")\\n cfg.tag_prefix = get(parser, \\\"tag_prefix\\\")\\n if cfg.tag_prefix in (\\\"''\\\", '\\\"\\\"'):\\n cfg.tag_prefix = \\\"\\\"\\n cfg.parentdir_prefix = get(parser, \\\"parentdir_prefix\\\")\\n cfg.verbose = get(parser, \\\"verbose\\\")\\n return cfg\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_config_from_root.get\",\n \"text\": \"versioneer.py::get_config_from_root.get\\n def get(parser, name):\\n if parser.has_option(\\\"versioneer\\\", name):\\n return parser.get(\\\"versioneer\\\", name)\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::register_vcs_handler\",\n \"text\": \"versioneer.py::register_vcs_handler\\ndef register_vcs_handler(vcs, method): # decorator\\n \\\"\\\"\\\"Decorator to mark a method as the handler for a particular VCS.\\\"\\\"\\\"\\n\\n def decorate(f):\\n \\\"\\\"\\\"Store f in HANDLERS[vcs][method].\\\"\\\"\\\"\\n if vcs not in HANDLERS:\\n HANDLERS[vcs] = {}\\n HANDLERS[vcs][method] = f\\n return f\\n\\n return decorate\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::register_vcs_handler.decorate\",\n \"text\": \"versioneer.py::register_vcs_handler.decorate\\n def decorate(f):\\n \\\"\\\"\\\"Store f in HANDLERS[vcs][method].\\\"\\\"\\\"\\n if vcs not in HANDLERS:\\n HANDLERS[vcs] = {}\\n HANDLERS[vcs][method] = f\\n return f\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::run_command\",\n \"text\": \"versioneer.py::run_command\\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):\\n \\\"\\\"\\\"Call the given command(s).\\\"\\\"\\\"\\n assert isinstance(commands, list)\\n p = None\\n for c in commands:\\n try:\\n dispcmd = str([c] + args)\\n # remember shell=False, so use git.cmd on windows, not just git\\n p = subprocess.Popen(\\n [c] + args,\\n cwd=cwd,\\n env=env,\\n stdout=subprocess.PIPE,\\n stderr=(subprocess.PIPE if hide_stderr else None),\\n )\\n break\\n except EnvironmentError:\\n e = sys.exc_info()[1]\\n if e.errno == errno.ENOENT:\\n continue\\n if verbose:\\n print(\\\"unable to run %s\\\" % dispcmd)\\n print(e)\\n return None, None\\n else:\\n if verbose:\\n print(\\\"unable to find command, tried %s\\\" % (commands,))\\n return None, None\\n stdout = p.communicate()[0].strip()\\n if sys.version_info[0] >= 3:\\n stdout = stdout.decode()\\n if p.returncode != 0:\\n if verbose:\\n print(\\\"unable to run %s (error)\\\" % dispcmd)\\n print(\\\"stdout was %s\\\" % stdout)\\n return None, p.returncode\\n return stdout, p.returncode\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::git_get_keywords\",\n \"text\": \"versioneer.py::git_get_keywords\\n@register_vcs_handler(\\\"git\\\", \\\"get_keywords\\\")\\ndef git_get_keywords(versionfile_abs):\\n \\\"\\\"\\\"Extract version information from the given file.\\\"\\\"\\\"\\n # the code embedded in _version.py can just fetch the value of these\\n # keywords. When used from setup.py, we don't want to import _version.py,\\n # so we do it with a regexp instead. This function is not used from\\n # _version.py.\\n keywords = {}\\n try:\\n f = open(versionfile_abs, \\\"r\\\")\\n for line in f.readlines():\\n if line.strip().startswith(\\\"git_refnames =\\\"):\\n mo = re.search(r'=\\\\s*\\\"(.*)\\\"', line)\\n if mo:\\n keywords[\\\"refnames\\\"] = mo.group(1)\\n if line.strip().startswith(\\\"git_full =\\\"):\\n mo = re.search(r'=\\\\s*\\\"(.*)\\\"', line)\\n if mo:\\n keywords[\\\"full\\\"] = mo.group(1)\\n if line.strip().startswith(\\\"git_date =\\\"):\\n mo = re.search(r'=\\\\s*\\\"(.*)\\\"', line)\\n if mo:\\n keywords[\\\"date\\\"] = mo.group(1)\\n f.close()\\n except EnvironmentError:\\n pass\\n return keywords\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::git_versions_from_keywords\",\n \"text\": \"versioneer.py::git_versions_from_keywords\\n@register_vcs_handler(\\\"git\\\", \\\"keywords\\\")\\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\\n \\\"\\\"\\\"Get version information from git keywords.\\\"\\\"\\\"\\n if not keywords:\\n raise NotThisMethod(\\\"no keywords at all, weird\\\")\\n date = keywords.get(\\\"date\\\")\\n if date is not None:\\n # git-2.2.0 added \\\"%cI\\\", which expands to an ISO-8601 -compliant\\n # datestamp. However we prefer \\\"%ci\\\" (which expands to an \\\"ISO-8601\\n # -like\\\" string, which we must then edit to make compliant), because\\n # it's been around since git-1.5.3, and it's too difficult to\\n # discover which version we're using, or to work around using an\\n # older one.\\n date = date.strip().replace(\\\" \\\", \\\"T\\\", 1).replace(\\\" \\\", \\\"\\\", 1)\\n refnames = keywords[\\\"refnames\\\"].strip()\\n if refnames.startswith(\\\"$Format\\\"):\\n if verbose:\\n print(\\\"keywords are unexpanded, not using\\\")\\n raise NotThisMethod(\\\"unexpanded keywords, not a git-archive tarball\\\")\\n refs = set([r.strip() for r in refnames.strip(\\\"()\\\").split(\\\",\\\")])\\n # starting in git-1.8.3, tags are listed as \\\"tag: foo-1.0\\\" instead of\\n # just \\\"foo-1.0\\\". If we see a \\\"tag: \\\" prefix, prefer those.\\n TAG = \\\"tag: \\\"\\n tags = set([r[len(TAG) :] for r in refs if r.startswith(TAG)])\\n if not tags:\\n # Either we're using git < 1.8.3, or there really are no tags. We use\\n # a heuristic: assume all version tags have a digit. The old git %d\\n # expansion behaves like git log --decorate=short and strips out the\\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\\n # between branches and tags. By ignoring refnames without digits, we\\n # filter out many common branch names like \\\"release\\\" and\\n # \\\"stabilization\\\", as well as \\\"HEAD\\\" and \\\"master\\\".\\n tags = set([r for r in refs if re.search(r\\\"\\\\d\\\", r)])\\n if verbose:\\n print(\\\"discarding '%s', no digits\\\" % \\\",\\\".join(refs - tags))\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::git_pieces_from_vcs\",\n \"text\": \"versioneer.py::git_pieces_from_vcs\\n@register_vcs_handler(\\\"git\\\", \\\"pieces_from_vcs\\\")\\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):\\n \\\"\\\"\\\"Get version from 'git describe' in the root of the source tree.\\n\\n This only gets called if the git-archive 'subst' keywords were *not*\\n expanded, and _version.py hasn't already been rewritten with a short\\n version string, meaning we're inside a checked out source tree.\\n \\\"\\\"\\\"\\n GITS = [\\\"git\\\"]\\n if sys.platform == \\\"win32\\\":\\n GITS = [\\\"git.cmd\\\", \\\"git.exe\\\"]\\n\\n out, rc = run_command(GITS, [\\\"rev-parse\\\", \\\"--git-dir\\\"], cwd=root, hide_stderr=True)\\n if rc != 0:\\n if verbose:\\n print(\\\"Directory %s not under git control\\\" % root)\\n raise NotThisMethod(\\\"'git rev-parse --git-dir' returned error\\\")\\n\\n # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\\n # if there isn't one, this yields HEX[-dirty] (no NUM)\\n describe_out, rc = run_command(\\n GITS,\\n [\\n \\\"describe\\\",\\n \\\"--tags\\\",\\n \\\"--dirty\\\",\\n \\\"--always\\\",\\n \\\"--long\\\",\\n \\\"--match\\\",\\n \\\"%s*\\\" % tag_prefix,\\n ],\\n cwd=root,\\n )\\n # --long was added in git-1.5.5\\n if describe_out is None:\\n raise NotThisMethod(\\\"'git describe' failed\\\")\\n describe_out = describe_out.strip()\\n full_out, rc = run_command(GITS, [\\\"rev-parse\\\", \\\"HEAD\\\"], cwd=root)\\n if full_out is None:\\n raise NotThisMethod(\\\"'git rev-parse' failed\\\")\\n full_out = full_out.strip()\\n\\n pieces = {}\\n pieces[\\\"long\\\"] = full_out\\n pieces[\\\"short\\\"] = full_out[:7] # maybe improved later\\n pieces[\\\"error\\\"] = None\\n\\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\\n # TAG might have hyphens.\\n git_describe = describe_out\\n\\n # look for -dirty suffix\\n dirty = git_describe.endswith(\\\"-dirty\\\")\\n pieces[\\\"dirty\\\"] = dirty\\n if dirty:\\n git_describe = git_describe[: git_describe.rindex(\\\"-dirty\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::do_vcs_install\",\n \"text\": \"versioneer.py::do_vcs_install\\ndef do_vcs_install(manifest_in, versionfile_source, ipy):\\n \\\"\\\"\\\"Git-specific installation logic for Versioneer.\\n\\n For Git, this means creating/changing .gitattributes to mark _version.py\\n for export-subst keyword substitution.\\n \\\"\\\"\\\"\\n GITS = [\\\"git\\\"]\\n if sys.platform == \\\"win32\\\":\\n GITS = [\\\"git.cmd\\\", \\\"git.exe\\\"]\\n files = [manifest_in, versionfile_source]\\n if ipy:\\n files.append(ipy)\\n try:\\n me = __file__\\n if me.endswith(\\\".pyc\\\") or me.endswith(\\\".pyo\\\"):\\n me = os.path.splitext(me)[0] + \\\".py\\\"\\n versioneer_file = os.path.relpath(me)\\n except NameError:\\n versioneer_file = \\\"versioneer.py\\\"\\n files.append(versioneer_file)\\n present = False\\n try:\\n f = open(\\\".gitattributes\\\", \\\"r\\\")\\n for line in f.readlines():\\n if line.strip().startswith(versionfile_source):\\n if \\\"export-subst\\\" in line.strip().split()[1:]:\\n present = True\\n f.close()\\n except EnvironmentError:\\n pass\\n if not present:\\n f = open(\\\".gitattributes\\\", \\\"a+\\\")\\n f.write(\\\"%s export-subst\\\\n\\\" % versionfile_source)\\n f.close()\\n files.append(\\\".gitattributes\\\")\\n run_command(GITS, [\\\"add\\\", \\\"--\\\"] + files)\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::versions_from_parentdir\",\n \"text\": \"versioneer.py::versions_from_parentdir\\ndef versions_from_parentdir(parentdir_prefix, root, verbose):\\n \\\"\\\"\\\"Try to determine the version from the parent directory name.\\n\\n Source tarballs conventionally unpack into a directory that includes both\\n the project name and a version string. We will also support searching up\\n two directory levels for an appropriately named parent directory\\n \\\"\\\"\\\"\\n rootdirs = []\\n\\n for i in range(3):\\n dirname = os.path.basename(root)\\n if dirname.startswith(parentdir_prefix):\\n return {\\n \\\"version\\\": dirname[len(parentdir_prefix) :],\\n \\\"full-revisionid\\\": None,\\n \\\"dirty\\\": False,\\n \\\"error\\\": None,\\n \\\"date\\\": None,\\n }\\n else:\\n rootdirs.append(root)\\n root = os.path.dirname(root) # up a level\\n\\n if verbose:\\n print(\\n \\\"Tried directories %s but none started with prefix %s\\\"\\n % (str(rootdirs), parentdir_prefix)\\n )\\n raise NotThisMethod(\\\"rootdir doesn't start with parentdir_prefix\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::versions_from_file\",\n \"text\": \"versioneer.py::versions_from_file\\ndef versions_from_file(filename):\\n \\\"\\\"\\\"Try to determine the version from _version.py if present.\\\"\\\"\\\"\\n try:\\n with open(filename) as f:\\n contents = f.read()\\n except EnvironmentError:\\n raise NotThisMethod(\\\"unable to read _version.py\\\")\\n mo = re.search(\\n r\\\"version_json = '''\\\\n(.*)''' # END VERSION_JSON\\\", contents, re.M | re.S\\n )\\n if not mo:\\n mo = re.search(\\n r\\\"version_json = '''\\\\r\\\\n(.*)''' # END VERSION_JSON\\\", contents, re.M | re.S\\n )\\n if not mo:\\n raise NotThisMethod(\\\"no version_json in _version.py\\\")\\n return json.loads(mo.group(1))\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::write_to_version_file\",\n \"text\": \"versioneer.py::write_to_version_file\\ndef write_to_version_file(filename, versions):\\n \\\"\\\"\\\"Write the given version number to the given _version.py file.\\\"\\\"\\\"\\n os.unlink(filename)\\n contents = json.dumps(versions, sort_keys=True, indent=1, separators=(\\\",\\\", \\\": \\\"))\\n with open(filename, \\\"w\\\") as f:\\n f.write(SHORT_VERSION_PY % contents)\\n\\n print(\\\"set %s to '%s'\\\" % (filename, versions[\\\"version\\\"]))\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::plus_or_dot\",\n \"text\": \"versioneer.py::plus_or_dot\\ndef plus_or_dot(pieces):\\n \\\"\\\"\\\"Return a + if we don't already have one, else return a .\\\"\\\"\\\"\\n if \\\"+\\\" in pieces.get(\\\"closest-tag\\\", \\\"\\\"):\\n return \\\".\\\"\\n return \\\"+\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_pep440\",\n \"text\": \"versioneer.py::render_pep440\\ndef render_pep440(pieces):\\n \\\"\\\"\\\"Build up version string, with post-release \\\"local version identifier\\\".\\n\\n Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\\n get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\\n\\n Exceptions:\\n 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"] or pieces[\\\"dirty\\\"]:\\n rendered += plus_or_dot(pieces)\\n rendered += \\\"%d.g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dirty\\\"\\n else:\\n # exception #1\\n rendered = \\\"0+untagged.%d.g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dirty\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_pep440_pre\",\n \"text\": \"versioneer.py::render_pep440_pre\\ndef render_pep440_pre(pieces):\\n \\\"\\\"\\\"TAG[.post.devDISTANCE] -- No -dirty.\\n\\n Exceptions:\\n 1: no tags. 0.post.devDISTANCE\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"]:\\n rendered += \\\".post.dev%d\\\" % pieces[\\\"distance\\\"]\\n else:\\n # exception #1\\n rendered = \\\"0.post.dev%d\\\" % pieces[\\\"distance\\\"]\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_pep440_post\",\n \"text\": \"versioneer.py::render_pep440_post\\ndef render_pep440_post(pieces):\\n \\\"\\\"\\\"TAG[.postDISTANCE[.dev0]+gHEX] .\\n\\n The \\\".dev0\\\" means dirty. Note that .dev0 sorts backwards\\n (a dirty tree will appear \\\"older\\\" than the corresponding clean one),\\n but you shouldn't be releasing software with -dirty anyways.\\n\\n Exceptions:\\n 1: no tags. 0.postDISTANCE[.dev0]\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"] or pieces[\\\"dirty\\\"]:\\n rendered += \\\".post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n rendered += plus_or_dot(pieces)\\n rendered += \\\"g%s\\\" % pieces[\\\"short\\\"]\\n else:\\n # exception #1\\n rendered = \\\"0.post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n rendered += \\\"+g%s\\\" % pieces[\\\"short\\\"]\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_pep440_old\",\n \"text\": \"versioneer.py::render_pep440_old\\ndef render_pep440_old(pieces):\\n \\\"\\\"\\\"TAG[.postDISTANCE[.dev0]] .\\n\\n The \\\".dev0\\\" means dirty.\\n\\n Eexceptions:\\n 1: no tags. 0.postDISTANCE[.dev0]\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"] or pieces[\\\"dirty\\\"]:\\n rendered += \\\".post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n else:\\n # exception #1\\n rendered = \\\"0.post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_git_describe\",\n \"text\": \"versioneer.py::render_git_describe\\ndef render_git_describe(pieces):\\n \\\"\\\"\\\"TAG[-DISTANCE-gHEX][-dirty].\\n\\n Like 'git describe --tags --dirty --always'.\\n\\n Exceptions:\\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"]:\\n rendered += \\\"-%d-g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n else:\\n # exception #1\\n rendered = pieces[\\\"short\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\"-dirty\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_git_describe_long\",\n \"text\": \"versioneer.py::render_git_describe_long\\ndef render_git_describe_long(pieces):\\n \\\"\\\"\\\"TAG-DISTANCE-gHEX[-dirty].\\n\\n Like 'git describe --tags --dirty --always -long'.\\n The distance/hash is unconditional.\\n\\n Exceptions:\\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n rendered += \\\"-%d-g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n else:\\n # exception #1\\n rendered = pieces[\\\"short\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\"-dirty\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render\",\n \"text\": \"versioneer.py::render\\ndef render(pieces, style):\\n \\\"\\\"\\\"Render the given version pieces into the requested style.\\\"\\\"\\\"\\n if pieces[\\\"error\\\"]:\\n return {\\n \\\"version\\\": \\\"unknown\\\",\\n \\\"full-revisionid\\\": pieces.get(\\\"long\\\"),\\n \\\"dirty\\\": None,\\n \\\"error\\\": pieces[\\\"error\\\"],\\n \\\"date\\\": None,\\n }\\n\\n if not style or style == \\\"default\\\":\\n style = \\\"pep440\\\" # the default\\n\\n if style == \\\"pep440\\\":\\n rendered = render_pep440(pieces)\\n elif style == \\\"pep440-pre\\\":\\n rendered = render_pep440_pre(pieces)\\n elif style == \\\"pep440-post\\\":\\n rendered = render_pep440_post(pieces)\\n elif style == \\\"pep440-old\\\":\\n rendered = render_pep440_old(pieces)\\n elif style == \\\"git-describe\\\":\\n rendered = render_git_describe(pieces)\\n elif style == \\\"git-describe-long\\\":\\n rendered = render_git_describe_long(pieces)\\n else:\\n raise ValueError(\\\"unknown style '%s'\\\" % style)\\n\\n return {\\n \\\"version\\\": rendered,\\n \\\"full-revisionid\\\": pieces[\\\"long\\\"],\\n \\\"dirty\\\": pieces[\\\"dirty\\\"],\\n \\\"error\\\": None,\\n \\\"date\\\": pieces.get(\\\"date\\\"),\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_versions\",\n \"text\": \"versioneer.py::get_versions\\ndef get_versions(verbose=False):\\n \\\"\\\"\\\"Get the project version from whatever source is available.\\n\\n Returns dict with two keys: 'version' and 'full'.\\n \\\"\\\"\\\"\\n if \\\"versioneer\\\" in sys.modules:\\n # see the discussion in cmdclass.py:get_cmdclass()\\n del sys.modules[\\\"versioneer\\\"]\\n\\n root = get_root()\\n cfg = get_config_from_root(root)\\n\\n assert cfg.VCS is not None, \\\"please set [versioneer]VCS= in setup.cfg\\\"\\n handlers = HANDLERS.get(cfg.VCS)\\n assert handlers, \\\"unrecognized VCS '%s'\\\" % cfg.VCS\\n verbose = verbose or cfg.verbose\\n assert (\\n cfg.versionfile_source is not None\\n ), \\\"please set versioneer.versionfile_source\\\"\\n assert cfg.tag_prefix is not None, \\\"please set versioneer.tag_prefix\\\"\\n\\n versionfile_abs = os.path.join(root, cfg.versionfile_source)\\n\\n # extract version from first of: _version.py, VCS command (e.g. 'git\\n # describe'), parentdir. This is meant to work for developers using a\\n # source checkout, for users of a tarball created by 'setup.py sdist',\\n # and for users of a tarball/zipball created by 'git archive' or github's\\n # download-from-tag feature or the equivalent in other VCSes.\\n\\n get_keywords_f = handlers.get(\\\"get_keywords\\\")\\n from_keywords_f = handlers.get(\\\"keywords\\\")\\n if get_keywords_f and from_keywords_f:\\n try:\\n keywords = get_keywords_f(versionfile_abs)\\n ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)\\n if verbose:\\n print(\\\"got version from expanded keyword %s\\\" % ver)\\n return ver\\n except NotThisMethod:\\n pass\\n\\n try:\\n ver = versions_from_file(versionfile_abs)\\n if verbose:\\n print(\\\"got version from file %s %s\\\" % (versionfile_abs, ver))\\n return ver\\n except NotThisMethod:\\n pass\\n\\n from_vcs_f = handlers.get(\\\"pieces_from_vcs\\\")\\n if from_vcs_f:\\n try:\\n pieces = from_vcs_f(cfg.tag_prefix, root, verbose)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_version\",\n \"text\": \"versioneer.py::get_version\\ndef get_version():\\n \\\"\\\"\\\"Get the short version string for this project.\\\"\\\"\\\"\\n return get_versions()[\\\"version\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass\",\n \"text\": \"versioneer.py::get_cmdclass\\ndef get_cmdclass():\\n \\\"\\\"\\\"Get the custom setuptools/distutils subclasses used by Versioneer.\\\"\\\"\\\"\\n if \\\"versioneer\\\" in sys.modules:\\n del sys.modules[\\\"versioneer\\\"]\\n # this fixes the \\\"python setup.py develop\\\" case (also 'install' and\\n # 'easy_install .'), in which subdependencies of the main project are\\n # built (using setup.py bdist_egg) in the same python process. Assume\\n # a main project A and a dependency B, which use different versions\\n # of Versioneer. A's setup.py imports A's Versioneer, leaving it in\\n # sys.modules by the time B's setup.py is executed, causing B to run\\n # with the wrong versioneer. Setuptools wraps the sub-dep builds in a\\n # sandbox that restores sys.modules to it's pre-build state, so the\\n # parent is protected against the child's \\\"import versioneer\\\". By\\n # removing ourselves from sys.modules here, before the child build\\n # happens, we protect the child from the parent's versioneer too.\\n # Also see https://github.com/warner/python-versioneer/issues/52\\n\\n cmds = {}\\n\\n # we add \\\"version\\\" to both distutils and setuptools\\n from distutils.core import Command\\n\\n class cmd_version(Command):\\n description = \\\"report generated version string\\\"\\n user_options = []\\n boolean_options = []\\n\\n def initialize_options(self):\\n pass\\n\\n def finalize_options(self):\\n pass\\n\\n def run(self):\\n vers = get_versions(verbose=True)\\n print(\\\"Version: %s\\\" % vers[\\\"version\\\"])\\n print(\\\" full-revisionid: %s\\\" % vers.get(\\\"full-revisionid\\\"))\\n print(\\\" dirty: %s\\\" % vers.get(\\\"dirty\\\"))\\n print(\\\" date: %s\\\" % vers.get(\\\"date\\\"))\\n if vers[\\\"error\\\"]:\\n print(\\\" error: %s\\\" % vers[\\\"error\\\"])\\n\\n cmds[\\\"version\\\"] = cmd_version\\n\\n # we override \\\"build_py\\\" in both distutils and setuptools\\n #\\n # most invocation pathways end up runnin\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_version.initialize_options\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_version.initialize_options\\n def initialize_options(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_version.finalize_options\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_version.finalize_options\\n def finalize_options(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_version.run\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_version.run\\n def run(self):\\n vers = get_versions(verbose=True)\\n print(\\\"Version: %s\\\" % vers[\\\"version\\\"])\\n print(\\\" full-revisionid: %s\\\" % vers.get(\\\"full-revisionid\\\"))\\n print(\\\" dirty: %s\\\" % vers.get(\\\"dirty\\\"))\\n print(\\\" date: %s\\\" % vers.get(\\\"date\\\"))\\n if vers[\\\"error\\\"]:\\n print(\\\" error: %s\\\" % vers[\\\"error\\\"])\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_build_py.run\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_build_py.run\\n def run(self):\\n root = get_root()\\n cfg = get_config_from_root(root)\\n versions = get_versions()\\n _build_py.run(self)\\n # now locate _version.py in the new build/ directory and replace\\n # it with an updated value\\n if cfg.versionfile_build:\\n target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build)\\n print(\\\"UPDATING %s\\\" % target_versionfile)\\n write_to_version_file(target_versionfile, versions)\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_sdist.run\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_sdist.run\\n def run(self):\\n versions = get_versions()\\n self._versioneer_generated_versions = versions\\n # unless we update this, the command will keep using the old\\n # version\\n self.distribution.metadata.version = versions[\\\"version\\\"]\\n return _sdist.run(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_sdist.make_release_tree\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_sdist.make_release_tree\\n def make_release_tree(self, base_dir, files):\\n root = get_root()\\n cfg = get_config_from_root(root)\\n _sdist.make_release_tree(self, base_dir, files)\\n # now locate _version.py in the new base_dir directory\\n # (remembering that it may be a hardlink) and replace it with an\\n # updated value\\n target_versionfile = os.path.join(base_dir, cfg.versionfile_source)\\n print(\\\"UPDATING %s\\\" % target_versionfile)\\n write_to_version_file(\\n target_versionfile, self._versioneer_generated_versions\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::do_setup\",\n \"text\": \"versioneer.py::do_setup\\ndef do_setup():\\n \\\"\\\"\\\"Main VCS-independent setup function for installing Versioneer.\\\"\\\"\\\"\\n root = get_root()\\n try:\\n cfg = get_config_from_root(root)\\n except (\\n EnvironmentError,\\n configparser.NoSectionError,\\n configparser.NoOptionError,\\n ) as e:\\n if isinstance(e, (EnvironmentError, configparser.NoSectionError)):\\n print(\\\"Adding sample versioneer config to setup.cfg\\\", file=sys.stderr)\\n with open(os.path.join(root, \\\"setup.cfg\\\"), \\\"a\\\") as f:\\n f.write(SAMPLE_CONFIG)\\n print(CONFIG_ERROR, file=sys.stderr)\\n return 1\\n\\n print(\\\" creating %s\\\" % cfg.versionfile_source)\\n with open(cfg.versionfile_source, \\\"w\\\") as f:\\n LONG = LONG_VERSION_PY[cfg.VCS]\\n f.write(\\n LONG\\n % {\\n \\\"DOLLAR\\\": \\\"$\\\",\\n \\\"STYLE\\\": cfg.style,\\n \\\"TAG_PREFIX\\\": cfg.tag_prefix,\\n \\\"PARENTDIR_PREFIX\\\": cfg.parentdir_prefix,\\n \\\"VERSIONFILE_SOURCE\\\": cfg.versionfile_source,\\n }\\n )\\n\\n ipy = os.path.join(os.path.dirname(cfg.versionfile_source), \\\"__init__.py\\\")\\n if os.path.exists(ipy):\\n try:\\n with open(ipy, \\\"r\\\") as f:\\n old = f.read()\\n except EnvironmentError:\\n old = \\\"\\\"\\n if INIT_PY_SNIPPET not in old:\\n print(\\\" appending to %s\\\" % ipy)\\n with open(ipy, \\\"a\\\") as f:\\n f.write(INIT_PY_SNIPPET)\\n else:\\n print(\\\" %s unmodified\\\" % ipy)\\n else:\\n print(\\\" %s doesn't exist, ok\\\" % ipy)\\n ipy = None\\n\\n # Make sure both the top-level \\\"versioneer.py\\\" and versionfile_source\\n # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so\\n # they'll be copied into source distributions. Pip won't be able to\\n # install the package without this.\\n manifest_in = os.path.join(root, \\\"MANIFEST.in\\\")\\n simple_includes = set()\\n try:\\n with open(manifest_in, \\\"r\\\") a\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::scan_setup_py\",\n \"text\": \"versioneer.py::scan_setup_py\\ndef scan_setup_py():\\n \\\"\\\"\\\"Validate the contents of setup.py against Versioneer's expectations.\\\"\\\"\\\"\\n found = set()\\n setters = False\\n errors = 0\\n with open(\\\"setup.py\\\", \\\"r\\\") as f:\\n for line in f.readlines():\\n if \\\"import versioneer\\\" in line:\\n found.add(\\\"import\\\")\\n if \\\"versioneer.get_cmdclass()\\\" in line:\\n found.add(\\\"cmdclass\\\")\\n if \\\"versioneer.get_version()\\\" in line:\\n found.add(\\\"get_version\\\")\\n if \\\"versioneer.VCS\\\" in line:\\n setters = True\\n if \\\"versioneer.versionfile_source\\\" in line:\\n setters = True\\n if len(found) != 3:\\n print(\\\"\\\")\\n print(\\\"Your setup.py appears to be missing some important items\\\")\\n print(\\\"(but I might be wrong). Please make sure it has something\\\")\\n print(\\\"roughly like the following:\\\")\\n print(\\\"\\\")\\n print(\\\" import versioneer\\\")\\n print(\\\" setup( version=versioneer.get_version(),\\\")\\n print(\\\" cmdclass=versioneer.get_cmdclass(), ...)\\\")\\n print(\\\"\\\")\\n errors += 1\\n if setters:\\n print(\\\"You should remove lines like 'versioneer.VCS = ' and\\\")\\n print(\\\"'versioneer.versionfile_source = ' . This configuration\\\")\\n print(\\\"now lives in setup.cfg, and should be removed from setup.py\\\")\\n print(\\\"\\\")\\n errors += 1\\n return errors\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":262,"cells":{"instance_id":{"kind":"string","value":"ytdl-org__youtube-dl-7069"},"repo":{"kind":"string","value":"ytdl-org/youtube-dl"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"[mixcloud] Can't download all titles of an uploader\nuser@sandbox:~/deejaycorehead$ youtube-dl --proxy 'user:pass@host:port' --add-metadata http://mixcloud.com/deejaycorehead/\n[debug] System config: []\n[debug] User config: ['-v']\n[debug] Command-line args: ['--proxy', 'user:pass@host:port', '--add-metadata', 'http://mixcloud.com/deejaycorehead/']\n[debug] Encodings: locale UTF-8, fs UTF-8, out UTF-8, pref UTF-8\n[debug] youtube-dl version 2014.09.12\n[debug] Python version 2.7.3 - Linux-2.6.32-openvz-042stab083.2-amd64-x86_64-with-debian-7.6\n[debug] Proxy map: {u'http': 'user:pass@host:port', u'https': 'user:pass@host:port'}\n[generic] deejaycorehead: Requesting header\n[redirect] Following redirect to http://www.mixcloud.com/deejaycorehead/\n[generic] deejaycorehead: Requesting header\nWARNING: Falling back on generic information extractor.\n[generic] deejaycorehead: Downloading webpage\n[generic] deejaycorehead: Extracting information\nERROR: Unsupported URL: http://www.mixcloud.com/deejaycorehead/; please report this issue on https://yt-dl.org/bug . Be sure to call youtube-dl with the --verbose flag and include its complete output. Make sure you are using the latest version; type youtube-dl -U to update.\nTraceback (most recent call last):\n File \"/usr/local/bin/youtube-dl/youtube_dl/extractor/generic.py\", line 545, in _real_extract\n doc = parse_xml(webpage)\n File \"/usr/local/bin/youtube-dl/youtube_dl/utils.py\", line 1472, in parse_xml\n tree = xml.etree.ElementTree.XML(s.encode('utf-8'), **kwargs)\n File \"/usr/lib/python2.7/xml/etree/ElementTree.py\", line 1301, in XML\n parser.feed(text)\n File \"/usr/lib/python2.7/xml/etree/ElementTree.py\", line 1643, in feed\n self._raiseerror(v)\n File \"/usr/lib/python2.7/xml/etree/ElementTree.py\", line 1507, in _raiseerror\n raise err\nParseError: not well-formed (invalid token): line 5, column 4555\nTraceback (most recent call last):\n File \"/usr/local/bin/youtube-dl/youtube_dl/YoutubeDL.py\", line 523, in extract_info\n ie_result = ie.extract(url)\n File \"/usr/local/bin/youtube-dl/youtube_dl/extractor/common.py\", line 178, in extract\n return self._real_extract(url)\n File \"/usr/local/bin/youtube-dl/youtube_dl/extractor/generic.py\", line 890, in _real_extract\n raise ExtractorError('Unsupported URL: %s' % url)\nExtractorError: Unsupported URL: http://www.mixcloud.com/deejaycorehead/; please report this issue on https://yt-dl.org/bug . Be sure to call youtube-dl with the --verbose flag and include its complete output. Make sure you are using the latest version; type youtube-dl -U to update.\n\n"},"gold_files":{"kind":"list like","value":["youtube_dl/extractor/extractors.py","youtube_dl/extractor/mixcloud.py"],"string":"[\n \"youtube_dl/extractor/extractors.py\",\n \"youtube_dl/extractor/mixcloud.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["youtube_dl/extractor/extractors.py::<module>","youtube_dl/extractor/mixcloud.py::<module>","youtube_dl/extractor/mixcloud.py::MixcloudIE._real_extract"],"string":"[\n \"youtube_dl/extractor/extractors.py::<module>\",\n \"youtube_dl/extractor/mixcloud.py::<module>\",\n \"youtube_dl/extractor/mixcloud.py::MixcloudIE._real_extract\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"devscripts/gh-pages/add-version.py","text":"devscripts/gh-pages/add-version.py","label":0},{"cid":"devscripts/gh-pages/update-feed.py","text":"devscripts/gh-pages/update-feed.py","label":0},{"cid":"setup.py","text":"setup.py\nclass build_lazy_extractors\n def initialize_options(self)\n def finalize_options(self)\n def run(self)","label":0},{"cid":"youtube_dl/__init__.py","text":"youtube_dl/__init__.py\ndef _real_main(argv=None)\n def parse_retries(retries)\ndef main(argv=None)","label":0},{"cid":"youtube_dl/extractor/extractors.py","text":"youtube_dl/extractor/extractors.py","label":1},{"cid":"youtube_dl/extractor/mixcloud.py","text":"youtube_dl/extractor/mixcloud.py\nclass MixcloudIE\n def _check_url(self, url, track_id, ext)\n def _real_extract(self, url)","label":1},{"cid":"youtube_dl/update.py","text":"youtube_dl/update.py\ndef rsa_verify(message, signature, key)\ndef update_self(to_screen, verbose, opener) # Update the program file with the latest version from the repository\n def version_tuple(version_str)\ndef get_notes(versions, fromVersion)\ndef print_notes(to_screen, versions, fromVersion=__version__)","label":0}],"string":"[\n {\n \"cid\": \"devscripts/gh-pages/add-version.py\",\n \"text\": \"devscripts/gh-pages/add-version.py\",\n \"label\": 0\n },\n {\n \"cid\": \"devscripts/gh-pages/update-feed.py\",\n \"text\": \"devscripts/gh-pages/update-feed.py\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\\nclass build_lazy_extractors\\n def initialize_options(self)\\n def finalize_options(self)\\n def run(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/__init__.py\",\n \"text\": \"youtube_dl/__init__.py\\ndef _real_main(argv=None)\\n def parse_retries(retries)\\ndef main(argv=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/extractor/extractors.py\",\n \"text\": \"youtube_dl/extractor/extractors.py\",\n \"label\": 1\n },\n {\n \"cid\": \"youtube_dl/extractor/mixcloud.py\",\n \"text\": \"youtube_dl/extractor/mixcloud.py\\nclass MixcloudIE\\n def _check_url(self, url, track_id, ext)\\n def _real_extract(self, url)\",\n \"label\": 1\n },\n {\n \"cid\": \"youtube_dl/update.py\",\n \"text\": \"youtube_dl/update.py\\ndef rsa_verify(message, signature, key)\\ndef update_self(to_screen, verbose, opener) # Update the program file with the latest version from the repository\\n def version_tuple(version_str)\\ndef get_notes(versions, fromVersion)\\ndef print_notes(to_screen, versions, fromVersion=__version__)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"setup.py::build_lazy_extractors.initialize_options","text":"setup.py::build_lazy_extractors.initialize_options\n def initialize_options(self):\n pass","label":0},{"cid":"setup.py::build_lazy_extractors.finalize_options","text":"setup.py::build_lazy_extractors.finalize_options\n def finalize_options(self):\n pass","label":0},{"cid":"setup.py::build_lazy_extractors.run","text":"setup.py::build_lazy_extractors.run\n def run(self):\n spawn(\n [sys.executable, 'devscripts/make_lazy_extractors.py', 'youtube_dl/extractor/lazy_extractors.py'],\n dry_run=self.dry_run,\n )","label":0},{"cid":"youtube_dl/__init__.py::_real_main","text":"youtube_dl/__init__.py::_real_main\ndef _real_main(argv=None):\n # Compatibility fixes for Windows\n if sys.platform == 'win32':\n # https://github.com/rg3/youtube-dl/issues/820\n codecs.register(lambda name: codecs.lookup('utf-8') if name == 'cp65001' else None)\n\n workaround_optparse_bug9161()\n\n setproctitle('youtube-dl')\n\n parser, opts, args = parseOpts(argv)\n\n # Set user agent\n if opts.user_agent is not None:\n std_headers['User-Agent'] = opts.user_agent\n\n # Set referer\n if opts.referer is not None:\n std_headers['Referer'] = opts.referer\n\n # Custom HTTP headers\n if opts.headers is not None:\n for h in opts.headers:\n if h.find(':', 1) < 0:\n parser.error('wrong header formatting, it should be key:value, not \"%s\"' % h)\n key, value = h.split(':', 2)\n if opts.verbose:\n write_string('[debug] Adding header from command line option %s:%s\\n' % (key, value))\n std_headers[key] = value\n\n # Dump user agent\n if opts.dump_user_agent:\n compat_print(std_headers['User-Agent'])\n sys.exit(0)\n\n # Batch file verification\n batch_urls = []\n if opts.batchfile is not None:\n try:\n if opts.batchfile == '-':\n batchfd = sys.stdin\n else:\n batchfd = io.open(opts.batchfile, 'r', encoding='utf-8', errors='ignore')\n batch_urls = read_batch_urls(batchfd)\n if opts.verbose:\n write_string('[debug] Batch file urls: ' + repr(batch_urls) + '\\n')\n except IOError:\n sys.exit('ERROR: batch file could not be read')\n all_urls = batch_urls + args\n all_urls = [url.strip() for url in all_urls]\n _enc = preferredencoding()\n all_urls = [url.decode(_enc, 'ignore') if isinstance(url, bytes) else url for url in all_urls]\n\n if opts.list_extractors:\n for ie in list_extractors(opts.age_limit):\n compat_print(ie.IE_NAM","label":0},{"cid":"youtube_dl/__init__.py::_real_main.parse_retries","text":"youtube_dl/__init__.py::_real_main.parse_retries\n def parse_retries(retries):\n if retries in ('inf', 'infinite'):\n parsed_retries = float('inf')\n else:\n try:\n parsed_retries = int(retries)\n except (TypeError, ValueError):\n parser.error('invalid retry count specified')\n return parsed_retries","label":0},{"cid":"youtube_dl/__init__.py::main","text":"youtube_dl/__init__.py::main\ndef main(argv=None):\n try:\n _real_main(argv)\n except DownloadError:\n sys.exit(1)\n except SameFileError:\n sys.exit('ERROR: fixed output name but more than one file to download')\n except KeyboardInterrupt:\n sys.exit('\\nERROR: Interrupted by user')","label":0},{"cid":"youtube_dl/extractor/mixcloud.py::MixcloudIE._check_url","text":"youtube_dl/extractor/mixcloud.py::MixcloudIE._check_url\n def _check_url(self, url, track_id, ext):\n try:\n # We only want to know if the request succeed\n # don't download the whole file\n self._request_webpage(\n HEADRequest(url), track_id,\n 'Trying %s URL' % ext)\n return True\n except ExtractorError:\n return False","label":0},{"cid":"youtube_dl/extractor/mixcloud.py::MixcloudIE._real_extract","text":"youtube_dl/extractor/mixcloud.py::MixcloudIE._real_extract\n def _real_extract(self, url):\n mobj = re.match(self._VALID_URL, url)\n uploader = mobj.group(1)\n cloudcast_name = mobj.group(2)\n track_id = compat_urllib_parse_unquote('-'.join((uploader, cloudcast_name)))\n\n webpage = self._download_webpage(url, track_id)\n\n message = self._html_search_regex(\n r'(?s)<div[^>]+class=\"global-message cloudcast-disabled-notice-light\"[^>]*>(.+?)<(?:a|/div)',\n webpage, 'error message', default=None)\n\n preview_url = self._search_regex(\n r'\\s(?:data-preview-url|m-preview)=\"([^\"]+)\"',\n webpage, 'preview url', default=None if message else NO_DEFAULT)\n\n if message:\n raise ExtractorError('%s said: %s' % (self.IE_NAME, message), expected=True)\n\n song_url = re.sub(r'audiocdn(\\d+)', r'stream\\1', preview_url)\n song_url = song_url.replace('/previews/', '/c/originals/')\n if not self._check_url(song_url, track_id, 'mp3'):\n song_url = song_url.replace('.mp3', '.m4a').replace('originals/', 'm4a/64/')\n if not self._check_url(song_url, track_id, 'm4a'):\n raise ExtractorError('Unable to extract track url')\n\n PREFIX = (\n r'm-play-on-spacebar[^>]+'\n r'(?:\\s+[a-zA-Z0-9-]+(?:=\"[^\"]+\")?)*?\\s+')\n title = self._html_search_regex(\n PREFIX + r'm-title=\"([^\"]+)\"', webpage, 'title')\n thumbnail = self._proto_relative_url(self._html_search_regex(\n PREFIX + r'm-thumbnail-url=\"([^\"]+)\"', webpage, 'thumbnail',\n fatal=False))\n uploader = self._html_search_regex(\n PREFIX + r'm-owner-name=\"([^\"]+)\"',\n webpage, 'uploader', fatal=False)\n uploader_id = self._search_regex(\n r'\\s+\"profile\": \"([^\"]+)\",', webpage, 'uploader id', fatal=False)\n description = self._og_search_description(webpage)\n like_count = parse_count(self","label":1},{"cid":"youtube_dl/update.py::rsa_verify","text":"youtube_dl/update.py::rsa_verify\ndef rsa_verify(message, signature, key):\n from hashlib import sha256\n assert isinstance(message, bytes)\n byte_size = (len(bin(key[0])) - 2 + 8 - 1) // 8\n signature = ('%x' % pow(int(signature, 16), key[1], key[0])).encode()\n signature = (byte_size * 2 - len(signature)) * b'0' + signature\n asn1 = b'3031300d060960864801650304020105000420'\n asn1 += sha256(message).hexdigest().encode()\n if byte_size < len(asn1) // 2 + 11:\n return False\n expected = b'0001' + (byte_size - len(asn1) // 2 - 3) * b'ff' + b'00' + asn1\n return expected == signature","label":0},{"cid":"youtube_dl/update.py::update_self","text":"youtube_dl/update.py::update_self\ndef update_self(to_screen, verbose, opener):\n \"\"\"Update the program file with the latest version from the repository\"\"\"\n\n UPDATE_URL = 'https://rg3.github.io/youtube-dl/update/'\n VERSION_URL = UPDATE_URL + 'LATEST_VERSION'\n JSON_URL = UPDATE_URL + 'versions.json'\n UPDATES_RSA_KEY = (0x9d60ee4d8f805312fdb15a62f87b95bd66177b91df176765d13514a0f1754bcd2057295c5b6f1d35daa6742c3ffc9a82d3e118861c207995a8031e151d863c9927e304576bc80692bc8e094896fcf11b66f3e29e04e3a71e9a11558558acea1840aec37fc396fb6b65dc81a1c4144e03bd1c011de62e3f1357b327d08426fe93, 65537)\n\n if not isinstance(globals().get('__loader__'), zipimporter) and not hasattr(sys, 'frozen'):\n to_screen('It looks like you installed youtube-dl with a package manager, pip, setup.py or a tarball. Please use that to update.')\n return\n\n # Check if there is a new version\n try:\n newversion = opener.open(VERSION_URL).read().decode('utf-8').strip()\n except Exception:\n if verbose:\n to_screen(encode_compat_str(traceback.format_exc()))\n to_screen('ERROR: can\\'t find the current version. Please try again later.')\n return\n if newversion == __version__:\n to_screen('youtube-dl is up-to-date (' + __version__ + ')')\n return\n\n # Download and check versions info\n try:\n versions_info = opener.open(JSON_URL).read().decode('utf-8')\n versions_info = json.loads(versions_info)\n except Exception:\n if verbose:\n to_screen(encode_compat_str(traceback.format_exc()))\n to_screen('ERROR: can\\'t obtain versions info. Please try again later.')\n return\n if 'signature' not in versions_info:\n to_screen('ERROR: the versions file is not signed or corrupted. Aborting.')\n return\n signature = versions_info['signature']\n del versions_info['signature']\n if not rsa_verify(json.dumps(versions_info, sort_keys=True).encode('utf-8'), signature, UPDATES_RSA_KEY):\n ","label":0},{"cid":"youtube_dl/update.py::update_self.version_tuple","text":"youtube_dl/update.py::update_self.version_tuple\n def version_tuple(version_str):\n return tuple(map(int, version_str.split('.')))","label":0},{"cid":"youtube_dl/update.py::get_notes","text":"youtube_dl/update.py::get_notes\ndef get_notes(versions, fromVersion):\n notes = []\n for v, vdata in sorted(versions.items()):\n if v > fromVersion:\n notes.extend(vdata.get('notes', []))\n return notes","label":0},{"cid":"youtube_dl/update.py::print_notes","text":"youtube_dl/update.py::print_notes\ndef print_notes(to_screen, versions, fromVersion=__version__):\n notes = get_notes(versions, fromVersion)\n if notes:\n to_screen('PLEASE NOTE:')\n for note in notes:\n to_screen(note)","label":0}],"string":"[\n {\n \"cid\": \"setup.py::build_lazy_extractors.initialize_options\",\n \"text\": \"setup.py::build_lazy_extractors.initialize_options\\n def initialize_options(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::build_lazy_extractors.finalize_options\",\n \"text\": \"setup.py::build_lazy_extractors.finalize_options\\n def finalize_options(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::build_lazy_extractors.run\",\n \"text\": \"setup.py::build_lazy_extractors.run\\n def run(self):\\n spawn(\\n [sys.executable, 'devscripts/make_lazy_extractors.py', 'youtube_dl/extractor/lazy_extractors.py'],\\n dry_run=self.dry_run,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/__init__.py::_real_main\",\n \"text\": \"youtube_dl/__init__.py::_real_main\\ndef _real_main(argv=None):\\n # Compatibility fixes for Windows\\n if sys.platform == 'win32':\\n # https://github.com/rg3/youtube-dl/issues/820\\n codecs.register(lambda name: codecs.lookup('utf-8') if name == 'cp65001' else None)\\n\\n workaround_optparse_bug9161()\\n\\n setproctitle('youtube-dl')\\n\\n parser, opts, args = parseOpts(argv)\\n\\n # Set user agent\\n if opts.user_agent is not None:\\n std_headers['User-Agent'] = opts.user_agent\\n\\n # Set referer\\n if opts.referer is not None:\\n std_headers['Referer'] = opts.referer\\n\\n # Custom HTTP headers\\n if opts.headers is not None:\\n for h in opts.headers:\\n if h.find(':', 1) < 0:\\n parser.error('wrong header formatting, it should be key:value, not \\\"%s\\\"' % h)\\n key, value = h.split(':', 2)\\n if opts.verbose:\\n write_string('[debug] Adding header from command line option %s:%s\\\\n' % (key, value))\\n std_headers[key] = value\\n\\n # Dump user agent\\n if opts.dump_user_agent:\\n compat_print(std_headers['User-Agent'])\\n sys.exit(0)\\n\\n # Batch file verification\\n batch_urls = []\\n if opts.batchfile is not None:\\n try:\\n if opts.batchfile == '-':\\n batchfd = sys.stdin\\n else:\\n batchfd = io.open(opts.batchfile, 'r', encoding='utf-8', errors='ignore')\\n batch_urls = read_batch_urls(batchfd)\\n if opts.verbose:\\n write_string('[debug] Batch file urls: ' + repr(batch_urls) + '\\\\n')\\n except IOError:\\n sys.exit('ERROR: batch file could not be read')\\n all_urls = batch_urls + args\\n all_urls = [url.strip() for url in all_urls]\\n _enc = preferredencoding()\\n all_urls = [url.decode(_enc, 'ignore') if isinstance(url, bytes) else url for url in all_urls]\\n\\n if opts.list_extractors:\\n for ie in list_extractors(opts.age_limit):\\n compat_print(ie.IE_NAM\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/__init__.py::_real_main.parse_retries\",\n \"text\": \"youtube_dl/__init__.py::_real_main.parse_retries\\n def parse_retries(retries):\\n if retries in ('inf', 'infinite'):\\n parsed_retries = float('inf')\\n else:\\n try:\\n parsed_retries = int(retries)\\n except (TypeError, ValueError):\\n parser.error('invalid retry count specified')\\n return parsed_retries\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/__init__.py::main\",\n \"text\": \"youtube_dl/__init__.py::main\\ndef main(argv=None):\\n try:\\n _real_main(argv)\\n except DownloadError:\\n sys.exit(1)\\n except SameFileError:\\n sys.exit('ERROR: fixed output name but more than one file to download')\\n except KeyboardInterrupt:\\n sys.exit('\\\\nERROR: Interrupted by user')\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/extractor/mixcloud.py::MixcloudIE._check_url\",\n \"text\": \"youtube_dl/extractor/mixcloud.py::MixcloudIE._check_url\\n def _check_url(self, url, track_id, ext):\\n try:\\n # We only want to know if the request succeed\\n # don't download the whole file\\n self._request_webpage(\\n HEADRequest(url), track_id,\\n 'Trying %s URL' % ext)\\n return True\\n except ExtractorError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/extractor/mixcloud.py::MixcloudIE._real_extract\",\n \"text\": \"youtube_dl/extractor/mixcloud.py::MixcloudIE._real_extract\\n def _real_extract(self, url):\\n mobj = re.match(self._VALID_URL, url)\\n uploader = mobj.group(1)\\n cloudcast_name = mobj.group(2)\\n track_id = compat_urllib_parse_unquote('-'.join((uploader, cloudcast_name)))\\n\\n webpage = self._download_webpage(url, track_id)\\n\\n message = self._html_search_regex(\\n r'(?s)<div[^>]+class=\\\"global-message cloudcast-disabled-notice-light\\\"[^>]*>(.+?)<(?:a|/div)',\\n webpage, 'error message', default=None)\\n\\n preview_url = self._search_regex(\\n r'\\\\s(?:data-preview-url|m-preview)=\\\"([^\\\"]+)\\\"',\\n webpage, 'preview url', default=None if message else NO_DEFAULT)\\n\\n if message:\\n raise ExtractorError('%s said: %s' % (self.IE_NAME, message), expected=True)\\n\\n song_url = re.sub(r'audiocdn(\\\\d+)', r'stream\\\\1', preview_url)\\n song_url = song_url.replace('/previews/', '/c/originals/')\\n if not self._check_url(song_url, track_id, 'mp3'):\\n song_url = song_url.replace('.mp3', '.m4a').replace('originals/', 'm4a/64/')\\n if not self._check_url(song_url, track_id, 'm4a'):\\n raise ExtractorError('Unable to extract track url')\\n\\n PREFIX = (\\n r'm-play-on-spacebar[^>]+'\\n r'(?:\\\\s+[a-zA-Z0-9-]+(?:=\\\"[^\\\"]+\\\")?)*?\\\\s+')\\n title = self._html_search_regex(\\n PREFIX + r'm-title=\\\"([^\\\"]+)\\\"', webpage, 'title')\\n thumbnail = self._proto_relative_url(self._html_search_regex(\\n PREFIX + r'm-thumbnail-url=\\\"([^\\\"]+)\\\"', webpage, 'thumbnail',\\n fatal=False))\\n uploader = self._html_search_regex(\\n PREFIX + r'm-owner-name=\\\"([^\\\"]+)\\\"',\\n webpage, 'uploader', fatal=False)\\n uploader_id = self._search_regex(\\n r'\\\\s+\\\"profile\\\": \\\"([^\\\"]+)\\\",', webpage, 'uploader id', fatal=False)\\n description = self._og_search_description(webpage)\\n like_count = parse_count(self\",\n \"label\": 1\n },\n {\n \"cid\": \"youtube_dl/update.py::rsa_verify\",\n \"text\": \"youtube_dl/update.py::rsa_verify\\ndef rsa_verify(message, signature, key):\\n from hashlib import sha256\\n assert isinstance(message, bytes)\\n byte_size = (len(bin(key[0])) - 2 + 8 - 1) // 8\\n signature = ('%x' % pow(int(signature, 16), key[1], key[0])).encode()\\n signature = (byte_size * 2 - len(signature)) * b'0' + signature\\n asn1 = b'3031300d060960864801650304020105000420'\\n asn1 += sha256(message).hexdigest().encode()\\n if byte_size < len(asn1) // 2 + 11:\\n return False\\n expected = b'0001' + (byte_size - len(asn1) // 2 - 3) * b'ff' + b'00' + asn1\\n return expected == signature\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/update.py::update_self\",\n \"text\": \"youtube_dl/update.py::update_self\\ndef update_self(to_screen, verbose, opener):\\n \\\"\\\"\\\"Update the program file with the latest version from the repository\\\"\\\"\\\"\\n\\n UPDATE_URL = 'https://rg3.github.io/youtube-dl/update/'\\n VERSION_URL = UPDATE_URL + 'LATEST_VERSION'\\n JSON_URL = UPDATE_URL + 'versions.json'\\n UPDATES_RSA_KEY = (0x9d60ee4d8f805312fdb15a62f87b95bd66177b91df176765d13514a0f1754bcd2057295c5b6f1d35daa6742c3ffc9a82d3e118861c207995a8031e151d863c9927e304576bc80692bc8e094896fcf11b66f3e29e04e3a71e9a11558558acea1840aec37fc396fb6b65dc81a1c4144e03bd1c011de62e3f1357b327d08426fe93, 65537)\\n\\n if not isinstance(globals().get('__loader__'), zipimporter) and not hasattr(sys, 'frozen'):\\n to_screen('It looks like you installed youtube-dl with a package manager, pip, setup.py or a tarball. Please use that to update.')\\n return\\n\\n # Check if there is a new version\\n try:\\n newversion = opener.open(VERSION_URL).read().decode('utf-8').strip()\\n except Exception:\\n if verbose:\\n to_screen(encode_compat_str(traceback.format_exc()))\\n to_screen('ERROR: can\\\\'t find the current version. Please try again later.')\\n return\\n if newversion == __version__:\\n to_screen('youtube-dl is up-to-date (' + __version__ + ')')\\n return\\n\\n # Download and check versions info\\n try:\\n versions_info = opener.open(JSON_URL).read().decode('utf-8')\\n versions_info = json.loads(versions_info)\\n except Exception:\\n if verbose:\\n to_screen(encode_compat_str(traceback.format_exc()))\\n to_screen('ERROR: can\\\\'t obtain versions info. Please try again later.')\\n return\\n if 'signature' not in versions_info:\\n to_screen('ERROR: the versions file is not signed or corrupted. Aborting.')\\n return\\n signature = versions_info['signature']\\n del versions_info['signature']\\n if not rsa_verify(json.dumps(versions_info, sort_keys=True).encode('utf-8'), signature, UPDATES_RSA_KEY):\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/update.py::update_self.version_tuple\",\n \"text\": \"youtube_dl/update.py::update_self.version_tuple\\n def version_tuple(version_str):\\n return tuple(map(int, version_str.split('.')))\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/update.py::get_notes\",\n \"text\": \"youtube_dl/update.py::get_notes\\ndef get_notes(versions, fromVersion):\\n notes = []\\n for v, vdata in sorted(versions.items()):\\n if v > fromVersion:\\n notes.extend(vdata.get('notes', []))\\n return notes\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/update.py::print_notes\",\n \"text\": \"youtube_dl/update.py::print_notes\\ndef print_notes(to_screen, versions, fromVersion=__version__):\\n notes = get_notes(versions, fromVersion)\\n if notes:\\n to_screen('PLEASE NOTE:')\\n for note in notes:\\n to_screen(note)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":263,"cells":{"instance_id":{"kind":"string","value":"numpy__numpy-5500"},"repo":{"kind":"string","value":"numpy/numpy"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"loadtxt(comments=None) considers the string 'None' as a comment symbol\nNumpy 1.8.2\n`numpy.loadtxt(comments=None)` considers the string `'None'` as a comment symbol.\n\nExpected behaviour:\n`comments=None` should indicate that there are no comments symbols. Certainly not cast `None` to a string, what is this, Javascript?\n\nMasked array view fails if structured dtype has datetime component\nA view as `numpy.ma.MaskedArray` fails if the array has a structured dtype, including at least one part that is `datetime64`, as follows:\n\n```\n$ python3.3\nPython 3.3.2+ (default, Feb 28 2014, 00:52:16) \n[GCC 4.8.1] on linux\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n>>> import numpy\n>>> import numpy, numpy.ma, numpy.version\n>>> numpy.version.version\n'1.8.0'\n>>> A = numpy.empty(shape=(5,), dtype=[(\"A\", \"<f4\"), (\"B\", \"datetime64[ms]\")])\n>>> A.view(numpy.ma.MaskedArray)\nTraceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\n File \"/home/gerrit/venv/python-3.3/lib/python3.3/site-packages/numpy/ma/core.py\", line 2800, in __array_finalize__\n self._fill_value = _check_fill_value(None, self.dtype)\n File \"/home/gerrit/venv/python-3.3/lib/python3.3/site-packages/numpy/ma/core.py\", line 402, in _check_fill_value\n dtype=ndtype,)\nValueError: Error parsing datetime string \"?\" at position 0\n```\n\n"},"gold_files":{"kind":"list like","value":["numpy/lib/npyio.py","numpy/ma/core.py"],"string":"[\n \"numpy/lib/npyio.py\",\n \"numpy/ma/core.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["numpy/lib/npyio.py::loadtxt","numpy/lib/npyio.py::loadtxt.split_line","numpy/ma/core.py::<module>","numpy/ma/core.py::default_fill_value"],"string":"[\n \"numpy/lib/npyio.py::loadtxt\",\n \"numpy/lib/npyio.py::loadtxt.split_line\",\n \"numpy/ma/core.py::<module>\",\n \"numpy/ma/core.py::default_fill_value\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"numpy/lib/npyio.py","text":"numpy/lib/npyio.py\ndef seek_gzip_factory(f) # Use this factory to produce the class so that we can do a lazy\n class GzipFile\n def seek(self, offset, whence=0)\n def tell(self)\nclass BagObj # BagObj(obj)\n def __init__(self, obj)\n def __getattribute__(self, key)\ndef zipfile_factory(*args, **kwargs)\nclass NpzFile # NpzFile(fid)\n def __init__(self, fid, own_fid=False)\n def __enter__(self)\n def __exit__(self, exc_type, exc_value, traceback)\n def close(self) # Close the file.\n def __del__(self)\n def __getitem__(self, key)\n def __iter__(self)\n def items(self) # Return a list of tuples, with each tuple (filename, array in file).\n def iteritems(self) # Generator that returns tuples (filename, array in file).\n def keys(self) # Return files in the archive with a ``.npy`` extension.\n def iterkeys(self) # Return an iterator over the files in the archive.\n def __contains__(self, key)\ndef load(file, mmap_mode=None) # Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.\ndef save(file, arr) # Save an array to a binary file in NumPy ``.npy`` format.\ndef savez(file, *args, **kwds) # Save several arrays into a single file in uncompressed ``.npz`` format.\ndef savez_compressed(file, *args, **kwds) # Save several arrays into a single file in compressed ``.npz`` format.\ndef _savez(file, args, kwds, compress)\ndef _getconv(dtype) # Find the correct dtype converter. Adapted from matplotlib\ndef loadtxt(fname, dtype=float, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0) # Load data from a text file.\n def flatten_dtype(dt) # Unpack a structured data-type, and produce re-packing info.\n def pack_items(items, packing) # Pack items into nested lists based on re-packing info.\n def split_line(line) # Chop off comments, strip, and split at delimiter.\ndef savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\\n', header='', footer='', comments='# ') # Save an array to a text file.\ndef fromregex(file, regexp, dtype) # Construct an array from a text file, using regular expression parsing.\ndef genfromtxt(fname, dtype=float, comments='#', delimiter=None, skiprows=0, skip_header=0, skip_footer=0, converters=None, missing='', missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True) # Load data from a text file, with missing values handled as specified.\ndef ndfromtxt(fname, **kwargs) # Load ASCII data stored in a file and return it as a single array.\ndef mafromtxt(fname, **kwargs) # Load ASCII data stored in a text file and return a masked array.\ndef recfromtxt(fname, **kwargs) # Load ASCII data from a file and return it in a record array.\ndef recfromcsv(fname, **kwargs) # Load ASCII data stored in a comma-separated file.","label":1},{"cid":"numpy/ma/core.py","text":"numpy/ma/core.py\ndef doc_note(initialdoc, note) # Adds a Notes section to an existing docstring.\ndef get_object_signature(obj) # Get the signature from obj\nclass MAError # Class for masked array related errors.\nclass MaskError # Class for mask related errors.\ndef default_fill_value(obj) # Return the default fill value for the argument object.\ndef _recursive_extremum_fill_value(ndtype, extremum)\ndef minimum_fill_value(obj) # Return the maximum value that can be represented by the dtype of an object.\ndef maximum_fill_value(obj) # Return the minimum value that can be represented by the dtype of an object.\ndef _recursive_set_default_fill_value(dtypedescr)\ndef _recursive_set_fill_value(fillvalue, dtypedescr)\ndef _check_fill_value(fill_value, ndtype) # Private function validating the given `fill_value` for the given dtype.\ndef set_fill_value(a, fill_value) # Set the filling value of a, if a is a masked array.\ndef get_fill_value(a) # Return the filling value of a, if any. Otherwise, returns the\ndef common_fill_value(a, b) # Return the common filling value of two masked arrays, if any.\ndef filled(a, fill_value=None) # Return input as an array with masked data replaced by a fill value.\ndef get_masked_subclass(*arrays) # Return the youngest subclass of MaskedArray from a list of (masked) arrays.\ndef getdata(a, subok=True) # Return the data of a masked array as an ndarray.\ndef fix_invalid(a, mask=nomask, copy=True, fill_value=None) # Return input with invalid data masked and replaced by a fill value.\nclass _DomainCheckInterval # Define a valid interval, so that :\n def __init__(self, a, b) # domain_check_interval(a,b)(x) = true where x < a or y > b\n def __call__(self, x) # Execute the call behavior.\nclass _DomainTan # Define a valid interval for the `tan` function, so that:\n def __init__(self, eps) # domain_tan(eps) = true where abs(cos(x)) < eps)\n def __call__(self, x) # Executes the call behavior.\nclass _DomainSafeDivide # Define a domain for safe division.\n def __init__(self, tolerance=None)\n def __call__(self, a, b)\nclass _DomainGreater # DomainGreater(v)(x) is True where x <= v.\n def __init__(self, critical_value) # DomainGreater(v)(x) = true where x <= v\n def __call__(self, x) # Executes the call behavior.\nclass _DomainGreaterEqual # DomainGreaterEqual(v)(x) is True where x < v.\n def __init__(self, critical_value) # DomainGreaterEqual(v)(x) = true where x < v\n def __call__(self, x) # Executes the call behavior.\nclass _MaskedUnaryOperation # Defines masked version of unary operations, where invalid values are\n def __init__(self, mufunc, fill=0, domain=None) # _MaskedUnaryOperation(aufunc, fill=0, domain=None)\n def __call__(self, a, *args, **kwargs) # Execute the call behavior.\n def __str__(self)\nclass _MaskedBinaryOperation # Define masked version of binary operations, where invalid\n def __init__(self, mbfunc, fillx=0, filly=0) # abfunc(fillx, filly) must be defined.\n def __c","label":1},{"cid":"pavement.py","text":"pavement.py\ndef superpack_name(pyver, numver) # Return the filename of the superpack installer.\ndef internal_wininst_name(arch) # Return the name of the wininst as it will be inside the superpack (i.e.\ndef wininst_name(pyver) # Return the name of the installer built by wininst command.\ndef prepare_nsis_script(pyver, numver)\ndef bdist_wininst_arch(pyver, arch) # Arch specific wininst build.\ndef bdist_superpack(options) # Build all arch specific wininst installers.\n def copy_bdist(arch)\ndef bdist_wininst_nosse(options) # Build the nosse wininst installer.\ndef bdist_wininst_sse2(options) # Build the sse2 wininst installer.\ndef bdist_wininst_sse3(options) # Build the sse3 wininst installer.\ndef bdist_wininst_simple() # Simple wininst-based installer.\ndef _bdist_wininst(pyver, cfg_env=None)\ndef bootstrap(options) # create virtualenv in ./bootstrap\ndef clean() # Remove build, dist, egg-info garbage.\ndef clean_bootstrap()\ndef nuke(options) # Remove everything: build dir, installers, bootstrap dirs, etc...\ndef html(options) # Build numpy documentation and put it into build/docs\ndef latex() # Build numpy documentation in latex format.\ndef pdf()\n def build_pdf()\ndef dmg_name(fullversion, pyver, osxver=None) # Return name for dmg installer.\ndef macosx_version()\ndef mpkg_name(pyver)\ndef _build_mpkg(pyver)\ndef simple_dmg()\ndef bdist_mpkg(options)\ndef _create_dmg(pyver, src_dir, volname=None)\ndef dmg(options)\ndef tarball_name(type='gztar')\ndef sdist(options)\ndef compute_md5(idirs)\ndef compute_sha256(idirs)\ndef write_release_task(options, filename='NOTES.txt')\ndef write_log_task(options, filename='Changelog')\ndef write_release(options)\ndef write_log(options)\ndef write_release_and_log(options)","label":0}],"string":"[\n {\n \"cid\": \"numpy/lib/npyio.py\",\n \"text\": \"numpy/lib/npyio.py\\ndef seek_gzip_factory(f) # Use this factory to produce the class so that we can do a lazy\\n class GzipFile\\n def seek(self, offset, whence=0)\\n def tell(self)\\nclass BagObj # BagObj(obj)\\n def __init__(self, obj)\\n def __getattribute__(self, key)\\ndef zipfile_factory(*args, **kwargs)\\nclass NpzFile # NpzFile(fid)\\n def __init__(self, fid, own_fid=False)\\n def __enter__(self)\\n def __exit__(self, exc_type, exc_value, traceback)\\n def close(self) # Close the file.\\n def __del__(self)\\n def __getitem__(self, key)\\n def __iter__(self)\\n def items(self) # Return a list of tuples, with each tuple (filename, array in file).\\n def iteritems(self) # Generator that returns tuples (filename, array in file).\\n def keys(self) # Return files in the archive with a ``.npy`` extension.\\n def iterkeys(self) # Return an iterator over the files in the archive.\\n def __contains__(self, key)\\ndef load(file, mmap_mode=None) # Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.\\ndef save(file, arr) # Save an array to a binary file in NumPy ``.npy`` format.\\ndef savez(file, *args, **kwds) # Save several arrays into a single file in uncompressed ``.npz`` format.\\ndef savez_compressed(file, *args, **kwds) # Save several arrays into a single file in compressed ``.npz`` format.\\ndef _savez(file, args, kwds, compress)\\ndef _getconv(dtype) # Find the correct dtype converter. Adapted from matplotlib\\ndef loadtxt(fname, dtype=float, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0) # Load data from a text file.\\n def flatten_dtype(dt) # Unpack a structured data-type, and produce re-packing info.\\n def pack_items(items, packing) # Pack items into nested lists based on re-packing info.\\n def split_line(line) # Chop off comments, strip, and split at delimiter.\\ndef savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\\\\n', header='', footer='', comments='# ') # Save an array to a text file.\\ndef fromregex(file, regexp, dtype) # Construct an array from a text file, using regular expression parsing.\\ndef genfromtxt(fname, dtype=float, comments='#', delimiter=None, skiprows=0, skip_header=0, skip_footer=0, converters=None, missing='', missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True) # Load data from a text file, with missing values handled as specified.\\ndef ndfromtxt(fname, **kwargs) # Load ASCII data stored in a file and return it as a single array.\\ndef mafromtxt(fname, **kwargs) # Load ASCII data stored in a text file and return a masked array.\\ndef recfromtxt(fname, **kwargs) # Load ASCII data from a file and return it in a record array.\\ndef recfromcsv(fname, **kwargs) # Load ASCII data stored in a comma-separated file.\",\n \"label\": 1\n },\n {\n \"cid\": \"numpy/ma/core.py\",\n \"text\": \"numpy/ma/core.py\\ndef doc_note(initialdoc, note) # Adds a Notes section to an existing docstring.\\ndef get_object_signature(obj) # Get the signature from obj\\nclass MAError # Class for masked array related errors.\\nclass MaskError # Class for mask related errors.\\ndef default_fill_value(obj) # Return the default fill value for the argument object.\\ndef _recursive_extremum_fill_value(ndtype, extremum)\\ndef minimum_fill_value(obj) # Return the maximum value that can be represented by the dtype of an object.\\ndef maximum_fill_value(obj) # Return the minimum value that can be represented by the dtype of an object.\\ndef _recursive_set_default_fill_value(dtypedescr)\\ndef _recursive_set_fill_value(fillvalue, dtypedescr)\\ndef _check_fill_value(fill_value, ndtype) # Private function validating the given `fill_value` for the given dtype.\\ndef set_fill_value(a, fill_value) # Set the filling value of a, if a is a masked array.\\ndef get_fill_value(a) # Return the filling value of a, if any. Otherwise, returns the\\ndef common_fill_value(a, b) # Return the common filling value of two masked arrays, if any.\\ndef filled(a, fill_value=None) # Return input as an array with masked data replaced by a fill value.\\ndef get_masked_subclass(*arrays) # Return the youngest subclass of MaskedArray from a list of (masked) arrays.\\ndef getdata(a, subok=True) # Return the data of a masked array as an ndarray.\\ndef fix_invalid(a, mask=nomask, copy=True, fill_value=None) # Return input with invalid data masked and replaced by a fill value.\\nclass _DomainCheckInterval # Define a valid interval, so that :\\n def __init__(self, a, b) # domain_check_interval(a,b)(x) = true where x < a or y > b\\n def __call__(self, x) # Execute the call behavior.\\nclass _DomainTan # Define a valid interval for the `tan` function, so that:\\n def __init__(self, eps) # domain_tan(eps) = true where abs(cos(x)) < eps)\\n def __call__(self, x) # Executes the call behavior.\\nclass _DomainSafeDivide # Define a domain for safe division.\\n def __init__(self, tolerance=None)\\n def __call__(self, a, b)\\nclass _DomainGreater # DomainGreater(v)(x) is True where x <= v.\\n def __init__(self, critical_value) # DomainGreater(v)(x) = true where x <= v\\n def __call__(self, x) # Executes the call behavior.\\nclass _DomainGreaterEqual # DomainGreaterEqual(v)(x) is True where x < v.\\n def __init__(self, critical_value) # DomainGreaterEqual(v)(x) = true where x < v\\n def __call__(self, x) # Executes the call behavior.\\nclass _MaskedUnaryOperation # Defines masked version of unary operations, where invalid values are\\n def __init__(self, mufunc, fill=0, domain=None) # _MaskedUnaryOperation(aufunc, fill=0, domain=None)\\n def __call__(self, a, *args, **kwargs) # Execute the call behavior.\\n def __str__(self)\\nclass _MaskedBinaryOperation # Define masked version of binary operations, where invalid\\n def __init__(self, mbfunc, fillx=0, filly=0) # abfunc(fillx, filly) must be defined.\\n def __c\",\n \"label\": 1\n },\n {\n \"cid\": \"pavement.py\",\n \"text\": \"pavement.py\\ndef superpack_name(pyver, numver) # Return the filename of the superpack installer.\\ndef internal_wininst_name(arch) # Return the name of the wininst as it will be inside the superpack (i.e.\\ndef wininst_name(pyver) # Return the name of the installer built by wininst command.\\ndef prepare_nsis_script(pyver, numver)\\ndef bdist_wininst_arch(pyver, arch) # Arch specific wininst build.\\ndef bdist_superpack(options) # Build all arch specific wininst installers.\\n def copy_bdist(arch)\\ndef bdist_wininst_nosse(options) # Build the nosse wininst installer.\\ndef bdist_wininst_sse2(options) # Build the sse2 wininst installer.\\ndef bdist_wininst_sse3(options) # Build the sse3 wininst installer.\\ndef bdist_wininst_simple() # Simple wininst-based installer.\\ndef _bdist_wininst(pyver, cfg_env=None)\\ndef bootstrap(options) # create virtualenv in ./bootstrap\\ndef clean() # Remove build, dist, egg-info garbage.\\ndef clean_bootstrap()\\ndef nuke(options) # Remove everything: build dir, installers, bootstrap dirs, etc...\\ndef html(options) # Build numpy documentation and put it into build/docs\\ndef latex() # Build numpy documentation in latex format.\\ndef pdf()\\n def build_pdf()\\ndef dmg_name(fullversion, pyver, osxver=None) # Return name for dmg installer.\\ndef macosx_version()\\ndef mpkg_name(pyver)\\ndef _build_mpkg(pyver)\\ndef simple_dmg()\\ndef bdist_mpkg(options)\\ndef _create_dmg(pyver, src_dir, volname=None)\\ndef dmg(options)\\ndef tarball_name(type='gztar')\\ndef sdist(options)\\ndef compute_md5(idirs)\\ndef compute_sha256(idirs)\\ndef write_release_task(options, filename='NOTES.txt')\\ndef write_log_task(options, filename='Changelog')\\ndef write_release(options)\\ndef write_log(options)\\ndef write_release_and_log(options)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"numpy/lib/npyio.py::seek_gzip_factory","text":"numpy/lib/npyio.py::seek_gzip_factory\ndef seek_gzip_factory(f):\n \"\"\"Use this factory to produce the class so that we can do a lazy\n import on gzip.\n\n \"\"\"\n import gzip\n\n class GzipFile(gzip.GzipFile):\n\n def seek(self, offset, whence=0):\n # figure out new position (we can only seek forwards)\n if whence == 1:\n offset = self.offset + offset\n\n if whence not in [0, 1]:\n raise IOError(\"Illegal argument\")\n\n if offset < self.offset:\n # for negative seek, rewind and do positive seek\n self.rewind()\n count = offset - self.offset\n for i in range(count // 1024):\n self.read(1024)\n self.read(count % 1024)\n\n def tell(self):\n return self.offset\n\n if isinstance(f, str):\n f = GzipFile(f)\n elif isinstance(f, gzip.GzipFile):\n # cast to our GzipFile if its already a gzip.GzipFile\n\n try:\n name = f.name\n except AttributeError:\n # Backward compatibility for <= 2.5\n name = f.filename\n mode = f.mode\n\n f = GzipFile(fileobj=f.fileobj, filename=name)\n f.mode = mode\n\n return f","label":0},{"cid":"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.seek","text":"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.seek\n def seek(self, offset, whence=0):\n # figure out new position (we can only seek forwards)\n if whence == 1:\n offset = self.offset + offset\n\n if whence not in [0, 1]:\n raise IOError(\"Illegal argument\")\n\n if offset < self.offset:\n # for negative seek, rewind and do positive seek\n self.rewind()\n count = offset - self.offset\n for i in range(count // 1024):\n self.read(1024)\n self.read(count % 1024)","label":0},{"cid":"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.tell","text":"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.tell\n def tell(self):\n return self.offset","label":0},{"cid":"numpy/lib/npyio.py::BagObj.__init__","text":"numpy/lib/npyio.py::BagObj.__init__\n def __init__(self, obj):\n # Use weakref to make NpzFile objects collectable by refcount\n self._obj = weakref.proxy(obj)","label":0},{"cid":"numpy/lib/npyio.py::BagObj.__getattribute__","text":"numpy/lib/npyio.py::BagObj.__getattribute__\n def __getattribute__(self, key):\n try:\n return object.__getattribute__(self, '_obj')[key]\n except KeyError:\n raise AttributeError(key)","label":0},{"cid":"numpy/lib/npyio.py::zipfile_factory","text":"numpy/lib/npyio.py::zipfile_factory\ndef zipfile_factory(*args, **kwargs):\n import zipfile\n kwargs['allowZip64'] = True\n return zipfile.ZipFile(*args, **kwargs)","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__init__","text":"numpy/lib/npyio.py::NpzFile.__init__\n def __init__(self, fid, own_fid=False):\n # Import is postponed to here since zipfile depends on gzip, an\n # optional component of the so-called standard library.\n _zip = zipfile_factory(fid)\n self._files = _zip.namelist()\n self.files = []\n for x in self._files:\n if x.endswith('.npy'):\n self.files.append(x[:-4])\n else:\n self.files.append(x)\n self.zip = _zip\n self.f = BagObj(self)\n if own_fid:\n self.fid = fid\n else:\n self.fid = None","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__enter__","text":"numpy/lib/npyio.py::NpzFile.__enter__\n def __enter__(self):\n return self","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__exit__","text":"numpy/lib/npyio.py::NpzFile.__exit__\n def __exit__(self, exc_type, exc_value, traceback):\n self.close()","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.close","text":"numpy/lib/npyio.py::NpzFile.close\n def close(self):\n \"\"\"\n Close the file.\n\n \"\"\"\n if self.zip is not None:\n self.zip.close()\n self.zip = None\n if self.fid is not None:\n self.fid.close()\n self.fid = None\n self.f = None # break reference cycle","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__del__","text":"numpy/lib/npyio.py::NpzFile.__del__\n def __del__(self):\n self.close()","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__getitem__","text":"numpy/lib/npyio.py::NpzFile.__getitem__\n def __getitem__(self, key):\n # FIXME: This seems like it will copy strings around\n # more than is strictly necessary. The zipfile\n # will read the string and then\n # the format.read_array will copy the string\n # to another place in memory.\n # It would be better if the zipfile could read\n # (or at least uncompress) the data\n # directly into the array memory.\n member = 0\n if key in self._files:\n member = 1\n elif key in self.files:\n member = 1\n key += '.npy'\n if member:\n bytes = self.zip.open(key)\n magic = bytes.read(len(format.MAGIC_PREFIX))\n bytes.close()\n if magic == format.MAGIC_PREFIX:\n bytes = self.zip.open(key)\n return format.read_array(bytes)\n else:\n return self.zip.read(key)\n else:\n raise KeyError(\"%s is not a file in the archive\" % key)","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__iter__","text":"numpy/lib/npyio.py::NpzFile.__iter__\n def __iter__(self):\n return iter(self.files)","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.items","text":"numpy/lib/npyio.py::NpzFile.items\n def items(self):\n \"\"\"\n Return a list of tuples, with each tuple (filename, array in file).\n\n \"\"\"\n return [(f, self[f]) for f in self.files]","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.iteritems","text":"numpy/lib/npyio.py::NpzFile.iteritems\n def iteritems(self):\n \"\"\"Generator that returns tuples (filename, array in file).\"\"\"\n for f in self.files:\n yield (f, self[f])","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.keys","text":"numpy/lib/npyio.py::NpzFile.keys\n def keys(self):\n \"\"\"Return files in the archive with a ``.npy`` extension.\"\"\"\n return self.files","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.iterkeys","text":"numpy/lib/npyio.py::NpzFile.iterkeys\n def iterkeys(self):\n \"\"\"Return an iterator over the files in the archive.\"\"\"\n return self.__iter__()","label":0},{"cid":"numpy/lib/npyio.py::NpzFile.__contains__","text":"numpy/lib/npyio.py::NpzFile.__contains__\n def __contains__(self, key):\n return self.files.__contains__(key)","label":0},{"cid":"numpy/lib/npyio.py::load","text":"numpy/lib/npyio.py::load\ndef load(file, mmap_mode=None):\n \"\"\"\n Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.\n\n Parameters\n ----------\n file : file-like object or string\n The file to read. File-like objects must support the\n ``seek()`` and ``read()`` methods. Pickled files require that the\n file-like object support the ``readline()`` method as well.\n mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional\n If not None, then memory-map the file, using the given mode (see\n `numpy.memmap` for a detailed description of the modes). A\n memory-mapped array is kept on disk. However, it can be accessed\n and sliced like any ndarray. Memory mapping is especially useful\n for accessing small fragments of large files without reading the\n entire file into memory.\n\n Returns\n -------\n result : array, tuple, dict, etc.\n Data stored in the file. For ``.npz`` files, the returned instance\n of NpzFile class must be closed to avoid leaking file descriptors.\n\n Raises\n ------\n IOError\n If the input file does not exist or cannot be read.\n\n See Also\n --------\n save, savez, savez_compressed, loadtxt\n memmap : Create a memory-map to an array stored in a file on disk.\n\n Notes\n -----\n - If the file contains pickle data, then whatever object is stored\n in the pickle is returned.\n - If the file is a ``.npy`` file, then a single array is returned.\n - If the file is a ``.npz`` file, then a dictionary-like object is\n returned, containing ``{filename: array}`` key-value pairs, one for\n each file in the archive.\n - If the file is a ``.npz`` file, the returned value supports the\n context manager protocol in a similar fashion to the open function::\n\n with load('foo.npz') as data:\n a = data['a']\n\n The underlying file descriptor is closed when exiting the 'with'\n block.\n\n Examples\n --","label":0},{"cid":"numpy/lib/npyio.py::save","text":"numpy/lib/npyio.py::save\ndef save(file, arr):\n \"\"\"\n Save an array to a binary file in NumPy ``.npy`` format.\n\n Parameters\n ----------\n file : file or str\n File or filename to which the data is saved. If file is a file-object,\n then the filename is unchanged. If file is a string, a ``.npy``\n extension will be appended to the file name if it does not already\n have one.\n arr : array_like\n Array data to be saved.\n\n See Also\n --------\n savez : Save several arrays into a ``.npz`` archive\n savetxt, load\n\n Notes\n -----\n For a description of the ``.npy`` format, see `format`.\n\n Examples\n --------\n >>> from tempfile import TemporaryFile\n >>> outfile = TemporaryFile()\n\n >>> x = np.arange(10)\n >>> np.save(outfile, x)\n\n >>> outfile.seek(0) # Only needed here to simulate closing & reopening file\n >>> np.load(outfile)\n array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n\n \"\"\"\n own_fid = False\n if isinstance(file, basestring):\n if not file.endswith('.npy'):\n file = file + '.npy'\n fid = open(file, \"wb\")\n own_fid = True\n else:\n fid = file\n\n try:\n arr = np.asanyarray(arr)\n format.write_array(fid, arr)\n finally:\n if own_fid:\n fid.close()","label":0},{"cid":"numpy/lib/npyio.py::savez","text":"numpy/lib/npyio.py::savez\ndef savez(file, *args, **kwds):\n \"\"\"\n Save several arrays into a single file in uncompressed ``.npz`` format.\n\n If arguments are passed in with no keywords, the corresponding variable\n names, in the ``.npz`` file, are 'arr_0', 'arr_1', etc. If keyword\n arguments are given, the corresponding variable names, in the ``.npz``\n file will match the keyword names.\n\n Parameters\n ----------\n file : str or file\n Either the file name (string) or an open file (file-like object)\n where the data will be saved. If file is a string, the ``.npz``\n extension will be appended to the file name if it is not already there.\n args : Arguments, optional\n Arrays to save to the file. Since it is not possible for Python to\n know the names of the arrays outside `savez`, the arrays will be saved\n with names \"arr_0\", \"arr_1\", and so on. These arguments can be any\n expression.\n kwds : Keyword arguments, optional\n Arrays to save to the file. Arrays will be saved in the file with the\n keyword names.\n\n Returns\n -------\n None\n\n See Also\n --------\n save : Save a single array to a binary file in NumPy format.\n savetxt : Save an array to a file as plain text.\n savez_compressed : Save several arrays into a compressed ``.npz`` archive\n\n Notes\n -----\n The ``.npz`` file format is a zipped archive of files named after the\n variables they contain. The archive is not compressed and each file\n in the archive contains one variable in ``.npy`` format. For a\n description of the ``.npy`` format, see `format`.\n\n When opening the saved ``.npz`` file with `load` a `NpzFile` object is\n returned. This is a dictionary-like object which can be queried for\n its list of arrays (with the ``.files`` attribute), and for the arrays\n themselves.\n\n Examples\n --------\n >>> from tempfile import TemporaryFile\n >>> outfile = TemporaryFile()\n >>> ","label":0},{"cid":"numpy/lib/npyio.py::savez_compressed","text":"numpy/lib/npyio.py::savez_compressed\ndef savez_compressed(file, *args, **kwds):\n \"\"\"\n Save several arrays into a single file in compressed ``.npz`` format.\n\n If keyword arguments are given, then filenames are taken from the keywords.\n If arguments are passed in with no keywords, then stored file names are\n arr_0, arr_1, etc.\n\n Parameters\n ----------\n file : str\n File name of ``.npz`` file.\n args : Arguments\n Function arguments.\n kwds : Keyword arguments\n Keywords.\n\n See Also\n --------\n numpy.savez : Save several arrays into an uncompressed ``.npz`` file format\n numpy.load : Load the files created by savez_compressed.\n\n \"\"\"\n _savez(file, args, kwds, True)","label":0},{"cid":"numpy/lib/npyio.py::_savez","text":"numpy/lib/npyio.py::_savez\ndef _savez(file, args, kwds, compress):\n # Import is postponed to here since zipfile depends on gzip, an optional\n # component of the so-called standard library.\n import zipfile\n # Import deferred for startup time improvement\n import tempfile\n\n if isinstance(file, basestring):\n if not file.endswith('.npz'):\n file = file + '.npz'\n\n namedict = kwds\n for i, val in enumerate(args):\n key = 'arr_%d' % i\n if key in namedict.keys():\n raise ValueError(\n \"Cannot use un-named variables and keyword %s\" % key)\n namedict[key] = val\n\n if compress:\n compression = zipfile.ZIP_DEFLATED\n else:\n compression = zipfile.ZIP_STORED\n\n zipf = zipfile_factory(file, mode=\"w\", compression=compression)\n\n # Stage arrays in a temporary file on disk, before writing to zip.\n fd, tmpfile = tempfile.mkstemp(suffix='-numpy.npy')\n os.close(fd)\n try:\n for key, val in namedict.items():\n fname = key + '.npy'\n fid = open(tmpfile, 'wb')\n try:\n format.write_array(fid, np.asanyarray(val))\n fid.close()\n fid = None\n zipf.write(tmpfile, arcname=fname)\n finally:\n if fid:\n fid.close()\n finally:\n os.remove(tmpfile)\n\n zipf.close()","label":0},{"cid":"numpy/lib/npyio.py::_getconv","text":"numpy/lib/npyio.py::_getconv\ndef _getconv(dtype):\n \"\"\" Find the correct dtype converter. Adapted from matplotlib \"\"\"\n typ = dtype.type\n if issubclass(typ, np.bool_):\n return lambda x: bool(int(x))\n if issubclass(typ, np.uint64):\n return np.uint64\n if issubclass(typ, np.int64):\n return np.int64\n if issubclass(typ, np.integer):\n return lambda x: int(float(x))\n elif issubclass(typ, np.floating):\n return float\n elif issubclass(typ, np.complex):\n return complex\n elif issubclass(typ, np.bytes_):\n return bytes\n else:\n return str","label":0},{"cid":"numpy/lib/npyio.py::loadtxt","text":"numpy/lib/npyio.py::loadtxt\ndef loadtxt(fname, dtype=float, comments='#', delimiter=None,\n converters=None, skiprows=0, usecols=None, unpack=False,\n ndmin=0):\n \"\"\"\n Load data from a text file.\n\n Each row in the text file must have the same number of values.\n\n Parameters\n ----------\n fname : file or str\n File, filename, or generator to read. If the filename extension is\n ``.gz`` or ``.bz2``, the file is first decompressed. Note that\n generators should return byte strings for Python 3k.\n dtype : data-type, optional\n Data-type of the resulting array; default: float. If this is a\n record data-type, the resulting array will be 1-dimensional, and\n each row will be interpreted as an element of the array. In this\n case, the number of columns used must match the number of fields in\n the data-type.\n comments : str, optional\n The character used to indicate the start of a comment;\n default: '#'.\n delimiter : str, optional\n The string used to separate values. By default, this is any\n whitespace.\n converters : dict, optional\n A dictionary mapping column number to a function that will convert\n that column to a float. E.g., if column 0 is a date string:\n ``converters = {0: datestr2num}``. Converters can also be used to\n provide a default value for missing data (but see also `genfromtxt`):\n ``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None.\n skiprows : int, optional\n Skip the first `skiprows` lines; default: 0.\n usecols : sequence, optional\n Which columns to read, with 0 being the first. For example,\n ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.\n The default, None, results in all columns being read.\n unpack : bool, optional\n If True, the returned array is transposed, so that arguments may be\n unpacked using ``x, y, z =","label":1},{"cid":"numpy/lib/npyio.py::loadtxt.flatten_dtype","text":"numpy/lib/npyio.py::loadtxt.flatten_dtype\n def flatten_dtype(dt):\n \"\"\"Unpack a structured data-type, and produce re-packing info.\"\"\"\n if dt.names is None:\n # If the dtype is flattened, return.\n # If the dtype has a shape, the dtype occurs\n # in the list more than once.\n shape = dt.shape\n if len(shape) == 0:\n return ([dt.base], None)\n else:\n packing = [(shape[-1], list)]\n if len(shape) > 1:\n for dim in dt.shape[-2::-1]:\n packing = [(dim*packing[0][0], packing*dim)]\n return ([dt.base] * int(np.prod(dt.shape)), packing)\n else:\n types = []\n packing = []\n for field in dt.names:\n tp, bytes = dt.fields[field]\n flat_dt, flat_packing = flatten_dtype(tp)\n types.extend(flat_dt)\n # Avoid extra nesting for subarrays\n if len(tp.shape) > 0:\n packing.extend(flat_packing)\n else:\n packing.append((len(flat_dt), flat_packing))\n return (types, packing)","label":0},{"cid":"numpy/lib/npyio.py::loadtxt.pack_items","text":"numpy/lib/npyio.py::loadtxt.pack_items\n def pack_items(items, packing):\n \"\"\"Pack items into nested lists based on re-packing info.\"\"\"\n if packing is None:\n return items[0]\n elif packing is tuple:\n return tuple(items)\n elif packing is list:\n return list(items)\n else:\n start = 0\n ret = []\n for length, subpacking in packing:\n ret.append(pack_items(items[start:start+length], subpacking))\n start += length\n return tuple(ret)","label":0},{"cid":"numpy/lib/npyio.py::loadtxt.split_line","text":"numpy/lib/npyio.py::loadtxt.split_line\n def split_line(line):\n \"\"\"Chop off comments, strip, and split at delimiter.\"\"\"\n line = asbytes(line).split(comments)[0].strip(asbytes('\\r\\n'))\n if line:\n return line.split(delimiter)\n else:\n return []","label":1},{"cid":"numpy/lib/npyio.py::savetxt","text":"numpy/lib/npyio.py::savetxt\ndef savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\\n', header='',\n footer='', comments='# '):\n \"\"\"\n Save an array to a text file.\n\n Parameters\n ----------\n fname : filename or file handle\n If the filename ends in ``.gz``, the file is automatically saved in\n compressed gzip format. `loadtxt` understands gzipped files\n transparently.\n X : array_like\n Data to be saved to a text file.\n fmt : str or sequence of strs, optional\n A single format (%10.5f), a sequence of formats, or a\n multi-format string, e.g. 'Iteration %d -- %10.5f', in which\n case `delimiter` is ignored. For complex `X`, the legal options\n for `fmt` are:\n a) a single specifier, `fmt='%.4e'`, resulting in numbers formatted\n like `' (%s+%sj)' % (fmt, fmt)`\n b) a full string specifying every real and imaginary part, e.g.\n `' %.4e %+.4j %.4e %+.4j %.4e %+.4j'` for 3 columns\n c) a list of specifiers, one per column - in this case, the real\n and imaginary part must have separate specifiers,\n e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns\n delimiter : str, optional\n String or character separating columns.\n newline : str, optional\n String or character separating lines.\n\n .. versionadded:: 1.5.0\n header : str, optional\n String that will be written at the beginning of the file.\n\n .. versionadded:: 1.7.0\n footer : str, optional\n String that will be written at the end of the file.\n\n .. versionadded:: 1.7.0\n comments : str, optional\n String that will be prepended to the ``header`` and ``footer`` strings,\n to mark them as comments. Default: '# ', as expected by e.g.\n ``numpy.loadtxt``.\n\n .. versionadded:: 1.7.0\n\n\n See Also\n --------\n save : Save an array to a binary file in NumPy ``.npy`` format","label":0},{"cid":"numpy/lib/npyio.py::fromregex","text":"numpy/lib/npyio.py::fromregex\ndef fromregex(file, regexp, dtype):\n \"\"\"\n Construct an array from a text file, using regular expression parsing.\n\n The returned array is always a structured array, and is constructed from\n all matches of the regular expression in the file. Groups in the regular\n expression are converted to fields of the structured array.\n\n Parameters\n ----------\n file : str or file\n File name or file object to read.\n regexp : str or regexp\n Regular expression used to parse the file.\n Groups in the regular expression correspond to fields in the dtype.\n dtype : dtype or list of dtypes\n Dtype for the structured array.\n\n Returns\n -------\n output : ndarray\n The output array, containing the part of the content of `file` that\n was matched by `regexp`. `output` is always a structured array.\n\n Raises\n ------\n TypeError\n When `dtype` is not a valid dtype for a structured array.\n\n See Also\n --------\n fromstring, loadtxt\n\n Notes\n -----\n Dtypes for structured arrays can be specified in several forms, but all\n forms specify at least the data type and field name. For details see\n `doc.structured_arrays`.\n\n Examples\n --------\n >>> f = open('test.dat', 'w')\n >>> f.write(\"1312 foo\\\\n1534 bar\\\\n444 qux\")\n >>> f.close()\n\n >>> regexp = r\"(\\\\d+)\\\\s+(...)\" # match [digits, whitespace, anything]\n >>> output = np.fromregex('test.dat', regexp,\n ... [('num', np.int64), ('key', 'S3')])\n >>> output\n array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')],\n dtype=[('num', '<i8'), ('key', '|S3')])\n >>> output['num']\n array([1312, 1534, 444], dtype=int64)\n\n \"\"\"\n own_fh = False\n if not hasattr(file, \"read\"):\n file = open(file, 'rb')\n own_fh = True\n\n try:\n if not hasattr(regexp, 'match'):\n regexp = re.compile(asbytes(regexp))\n if not isinstance(dtyp","label":0},{"cid":"numpy/lib/npyio.py::genfromtxt","text":"numpy/lib/npyio.py::genfromtxt\ndef genfromtxt(fname, dtype=float, comments='#', delimiter=None,\n skiprows=0, skip_header=0, skip_footer=0, converters=None,\n missing='', missing_values=None, filling_values=None,\n usecols=None, names=None,\n excludelist=None, deletechars=None, replace_space='_',\n autostrip=False, case_sensitive=True, defaultfmt=\"f%i\",\n unpack=None, usemask=False, loose=True, invalid_raise=True):\n \"\"\"\n Load data from a text file, with missing values handled as specified.\n\n Each line past the first `skip_header` lines is split at the `delimiter`\n character, and characters following the `comments` character are discarded.\n\n Parameters\n ----------\n fname : file or str\n File, filename, or generator to read. If the filename extension is\n `.gz` or `.bz2`, the file is first decompressed. Note that\n generators must return byte strings in Python 3k.\n dtype : dtype, optional\n Data type of the resulting array.\n If None, the dtypes will be determined by the contents of each\n column, individually.\n comments : str, optional\n The character used to indicate the start of a comment.\n All the characters occurring on a line after a comment are discarded\n delimiter : str, int, or sequence, optional\n The string used to separate values. By default, any consecutive\n whitespaces act as delimiter. An integer or sequence of integers\n can also be provided as width(s) of each field.\n skip_rows : int, optional\n `skip_rows` was deprecated in numpy 1.5, and will be removed in\n numpy 2.0. Please use `skip_header` instead.\n skip_header : int, optional\n The number of lines to skip at the beginning of the file.\n skip_footer : int, optional\n The number of lines to skip at the end of the file.\n converters : variable, optional\n The set of functions that c","label":0},{"cid":"numpy/lib/npyio.py::ndfromtxt","text":"numpy/lib/npyio.py::ndfromtxt\ndef ndfromtxt(fname, **kwargs):\n \"\"\"\n Load ASCII data stored in a file and return it as a single array.\n\n Parameters\n ----------\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\n\n See Also\n --------\n numpy.genfromtxt : generic function.\n\n \"\"\"\n kwargs['usemask'] = False\n return genfromtxt(fname, **kwargs)","label":0},{"cid":"numpy/lib/npyio.py::mafromtxt","text":"numpy/lib/npyio.py::mafromtxt\ndef mafromtxt(fname, **kwargs):\n \"\"\"\n Load ASCII data stored in a text file and return a masked array.\n\n Parameters\n ----------\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\n\n See Also\n --------\n numpy.genfromtxt : generic function to load ASCII data.\n\n \"\"\"\n kwargs['usemask'] = True\n return genfromtxt(fname, **kwargs)","label":0},{"cid":"numpy/lib/npyio.py::recfromtxt","text":"numpy/lib/npyio.py::recfromtxt\ndef recfromtxt(fname, **kwargs):\n \"\"\"\n Load ASCII data from a file and return it in a record array.\n\n If ``usemask=False`` a standard `recarray` is returned,\n if ``usemask=True`` a MaskedRecords array is returned.\n\n Parameters\n ----------\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\n\n See Also\n --------\n numpy.genfromtxt : generic function\n\n Notes\n -----\n By default, `dtype` is None, which means that the data-type of the output\n array will be determined from the data.\n\n \"\"\"\n kwargs.setdefault(\"dtype\", None)\n usemask = kwargs.get('usemask', False)\n output = genfromtxt(fname, **kwargs)\n if usemask:\n from numpy.ma.mrecords import MaskedRecords\n output = output.view(MaskedRecords)\n else:\n output = output.view(np.recarray)\n return output","label":0},{"cid":"numpy/lib/npyio.py::recfromcsv","text":"numpy/lib/npyio.py::recfromcsv\ndef recfromcsv(fname, **kwargs):\n \"\"\"\n Load ASCII data stored in a comma-separated file.\n\n The returned array is a record array (if ``usemask=False``, see\n `recarray`) or a masked record array (if ``usemask=True``,\n see `ma.mrecords.MaskedRecords`).\n\n Parameters\n ----------\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\n\n See Also\n --------\n numpy.genfromtxt : generic function to load ASCII data.\n\n Notes\n -----\n By default, `dtype` is None, which means that the data-type of the output\n array will be determined from the data.\n\n \"\"\"\n # Set default kwargs for genfromtxt as relevant to csv import.\n kwargs.setdefault(\"case_sensitive\", \"lower\")\n kwargs.setdefault(\"names\", True)\n kwargs.setdefault(\"delimiter\", \",\")\n kwargs.setdefault(\"dtype\", None)\n output = genfromtxt(fname, **kwargs)\n\n usemask = kwargs.get(\"usemask\", False)\n if usemask:\n from numpy.ma.mrecords import MaskedRecords\n output = output.view(MaskedRecords)\n else:\n output = output.view(np.recarray)\n return output","label":0},{"cid":"numpy/ma/core.py::doc_note","text":"numpy/ma/core.py::doc_note\ndef doc_note(initialdoc, note):\n \"\"\"\n Adds a Notes section to an existing docstring.\n \"\"\"\n if initialdoc is None:\n return\n if note is None:\n return initialdoc\n newdoc = \"\"\"\n %s\n\n Notes\n -----\n %s\n \"\"\"\n return newdoc % (initialdoc, note)","label":0},{"cid":"numpy/ma/core.py::get_object_signature","text":"numpy/ma/core.py::get_object_signature\ndef get_object_signature(obj):\n \"\"\"\n Get the signature from obj\n \"\"\"\n try:\n sig = formatargspec(*getargspec(obj))\n except TypeError as errmsg:\n sig = ''\n# msg = \"Unable to retrieve the signature of %s '%s'\\n\"\\\n# \"(Initial error message: %s)\"\n# warnings.warn(msg % (type(obj),\n# getattr(obj, '__name__', '???'),\n# errmsg))\n return sig","label":0},{"cid":"numpy/ma/core.py::default_fill_value","text":"numpy/ma/core.py::default_fill_value\ndef default_fill_value(obj):\n \"\"\"\n Return the default fill value for the argument object.\n\n The default filling value depends on the datatype of the input\n array or the type of the input scalar:\n\n ======== ========\n datatype default\n ======== ========\n bool True\n int 999999\n float 1.e20\n complex 1.e20+0j\n object '?'\n string 'N/A'\n ======== ========\n\n\n Parameters\n ----------\n obj : ndarray, dtype or scalar\n The array data-type or scalar for which the default fill value\n is returned.\n\n Returns\n -------\n fill_value : scalar\n The default fill value.\n\n Examples\n --------\n >>> np.ma.default_fill_value(1)\n 999999\n >>> np.ma.default_fill_value(np.array([1.1, 2., np.pi]))\n 1e+20\n >>> np.ma.default_fill_value(np.dtype(complex))\n (1e+20+0j)\n\n \"\"\"\n if hasattr(obj, 'dtype'):\n defval = _check_fill_value(None, obj.dtype)\n elif isinstance(obj, np.dtype):\n if obj.subdtype:\n defval = default_filler.get(obj.subdtype[0].kind, '?')\n elif obj.kind == 'M':\n defval = default_filler.get(obj.str[1:], '?')\n else:\n defval = default_filler.get(obj.kind, '?')\n elif isinstance(obj, float):\n defval = default_filler['f']\n elif isinstance(obj, int) or isinstance(obj, long):\n defval = default_filler['i']\n elif isinstance(obj, str):\n defval = default_filler['S']\n elif isinstance(obj, unicode):\n defval = default_filler['U']\n elif isinstance(obj, complex):\n defval = default_filler['c']\n else:\n defval = default_filler['O']\n return defval","label":1},{"cid":"numpy/ma/core.py::_recursive_extremum_fill_value","text":"numpy/ma/core.py::_recursive_extremum_fill_value\ndef _recursive_extremum_fill_value(ndtype, extremum):\n names = ndtype.names\n if names:\n deflist = []\n for name in names:\n fval = _recursive_extremum_fill_value(ndtype[name], extremum)\n deflist.append(fval)\n return tuple(deflist)\n return extremum[ndtype]","label":0},{"cid":"numpy/ma/core.py::minimum_fill_value","text":"numpy/ma/core.py::minimum_fill_value\ndef minimum_fill_value(obj):\n \"\"\"\n Return the maximum value that can be represented by the dtype of an object.\n\n This function is useful for calculating a fill value suitable for\n taking the minimum of an array with a given dtype.\n\n Parameters\n ----------\n obj : ndarray or dtype\n An object that can be queried for it's numeric type.\n\n Returns\n -------\n val : scalar\n The maximum representable value.\n\n Raises\n ------\n TypeError\n If `obj` isn't a suitable numeric type.\n\n See Also\n --------\n maximum_fill_value : The inverse function.\n set_fill_value : Set the filling value of a masked array.\n MaskedArray.fill_value : Return current fill value.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.int8()\n >>> ma.minimum_fill_value(a)\n 127\n >>> a = np.int32()\n >>> ma.minimum_fill_value(a)\n 2147483647\n\n An array of numeric data can also be passed.\n\n >>> a = np.array([1, 2, 3], dtype=np.int8)\n >>> ma.minimum_fill_value(a)\n 127\n >>> a = np.array([1, 2, 3], dtype=np.float32)\n >>> ma.minimum_fill_value(a)\n inf\n\n \"\"\"\n errmsg = \"Unsuitable type for calculating minimum.\"\n if hasattr(obj, 'dtype'):\n return _recursive_extremum_fill_value(obj.dtype, min_filler)\n elif isinstance(obj, float):\n return min_filler[ntypes.typeDict['float_']]\n elif isinstance(obj, int):\n return min_filler[ntypes.typeDict['int_']]\n elif isinstance(obj, long):\n return min_filler[ntypes.typeDict['uint']]\n elif isinstance(obj, np.dtype):\n return min_filler[obj]\n else:\n raise TypeError(errmsg)","label":0},{"cid":"numpy/ma/core.py::maximum_fill_value","text":"numpy/ma/core.py::maximum_fill_value\ndef maximum_fill_value(obj):\n \"\"\"\n Return the minimum value that can be represented by the dtype of an object.\n\n This function is useful for calculating a fill value suitable for\n taking the maximum of an array with a given dtype.\n\n Parameters\n ----------\n obj : {ndarray, dtype}\n An object that can be queried for it's numeric type.\n\n Returns\n -------\n val : scalar\n The minimum representable value.\n\n Raises\n ------\n TypeError\n If `obj` isn't a suitable numeric type.\n\n See Also\n --------\n minimum_fill_value : The inverse function.\n set_fill_value : Set the filling value of a masked array.\n MaskedArray.fill_value : Return current fill value.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.int8()\n >>> ma.maximum_fill_value(a)\n -128\n >>> a = np.int32()\n >>> ma.maximum_fill_value(a)\n -2147483648\n\n An array of numeric data can also be passed.\n\n >>> a = np.array([1, 2, 3], dtype=np.int8)\n >>> ma.maximum_fill_value(a)\n -128\n >>> a = np.array([1, 2, 3], dtype=np.float32)\n >>> ma.maximum_fill_value(a)\n -inf\n\n \"\"\"\n errmsg = \"Unsuitable type for calculating maximum.\"\n if hasattr(obj, 'dtype'):\n return _recursive_extremum_fill_value(obj.dtype, max_filler)\n elif isinstance(obj, float):\n return max_filler[ntypes.typeDict['float_']]\n elif isinstance(obj, int):\n return max_filler[ntypes.typeDict['int_']]\n elif isinstance(obj, long):\n return max_filler[ntypes.typeDict['uint']]\n elif isinstance(obj, np.dtype):\n return max_filler[obj]\n else:\n raise TypeError(errmsg)","label":0},{"cid":"numpy/ma/core.py::_recursive_set_default_fill_value","text":"numpy/ma/core.py::_recursive_set_default_fill_value\ndef _recursive_set_default_fill_value(dtypedescr):\n deflist = []\n for currentdescr in dtypedescr:\n currenttype = currentdescr[1]\n if isinstance(currenttype, list):\n deflist.append(tuple(_recursive_set_default_fill_value(currenttype)))\n else:\n deflist.append(default_fill_value(np.dtype(currenttype)))\n return tuple(deflist)","label":0},{"cid":"numpy/ma/core.py::_recursive_set_fill_value","text":"numpy/ma/core.py::_recursive_set_fill_value\ndef _recursive_set_fill_value(fillvalue, dtypedescr):\n fillvalue = np.resize(fillvalue, len(dtypedescr))\n output_value = []\n for (fval, descr) in zip(fillvalue, dtypedescr):\n cdtype = descr[1]\n if isinstance(cdtype, list):\n output_value.append(tuple(_recursive_set_fill_value(fval, cdtype)))\n else:\n output_value.append(np.array(fval, dtype=cdtype).item())\n return tuple(output_value)","label":0},{"cid":"numpy/ma/core.py::_check_fill_value","text":"numpy/ma/core.py::_check_fill_value\ndef _check_fill_value(fill_value, ndtype):\n \"\"\"\n Private function validating the given `fill_value` for the given dtype.\n\n If fill_value is None, it is set to the default corresponding to the dtype\n if this latter is standard (no fields). If the datatype is flexible (named\n fields), fill_value is set to a tuple whose elements are the default fill\n values corresponding to each field.\n\n If fill_value is not None, its value is forced to the given dtype.\n\n \"\"\"\n ndtype = np.dtype(ndtype)\n fields = ndtype.fields\n if fill_value is None:\n if fields:\n descr = ndtype.descr\n fill_value = np.array(_recursive_set_default_fill_value(descr),\n dtype=ndtype,)\n else:\n fill_value = default_fill_value(ndtype)\n elif fields:\n fdtype = [(_[0], _[1]) for _ in ndtype.descr]\n if isinstance(fill_value, (ndarray, np.void)):\n try:\n fill_value = np.array(fill_value, copy=False, dtype=fdtype)\n except ValueError:\n err_msg = \"Unable to transform %s to dtype %s\"\n raise ValueError(err_msg % (fill_value, fdtype))\n else:\n descr = ndtype.descr\n fill_value = np.asarray(fill_value, dtype=object)\n fill_value = np.array(_recursive_set_fill_value(fill_value, descr),\n dtype=ndtype)\n else:\n if isinstance(fill_value, basestring) and (ndtype.char not in 'OSVU'):\n err_msg = \"Cannot set fill value of string with array of dtype %s\"\n raise TypeError(err_msg % ndtype)\n else:\n # In case we want to convert 1e20 to int...\n try:\n fill_value = np.array(fill_value, copy=False, dtype=ndtype)\n except OverflowError:\n # Raise TypeError instead of OverflowError. OverflowError\n # is seldom used, and the real probl","label":0},{"cid":"numpy/ma/core.py::set_fill_value","text":"numpy/ma/core.py::set_fill_value\ndef set_fill_value(a, fill_value):\n \"\"\"\n Set the filling value of a, if a is a masked array.\n\n This function changes the fill value of the masked array `a` in place.\n If `a` is not a masked array, the function returns silently, without\n doing anything.\n\n Parameters\n ----------\n a : array_like\n Input array.\n fill_value : dtype\n Filling value. A consistency test is performed to make sure\n the value is compatible with the dtype of `a`.\n\n Returns\n -------\n None\n Nothing returned by this function.\n\n See Also\n --------\n maximum_fill_value : Return the default fill value for a dtype.\n MaskedArray.fill_value : Return current fill value.\n MaskedArray.set_fill_value : Equivalent method.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.arange(5)\n >>> a\n array([0, 1, 2, 3, 4])\n >>> a = ma.masked_where(a < 3, a)\n >>> a\n masked_array(data = [-- -- -- 3 4],\n mask = [ True True True False False],\n fill_value=999999)\n >>> ma.set_fill_value(a, -999)\n >>> a\n masked_array(data = [-- -- -- 3 4],\n mask = [ True True True False False],\n fill_value=-999)\n\n Nothing happens if `a` is not a masked array.\n\n >>> a = range(5)\n >>> a\n [0, 1, 2, 3, 4]\n >>> ma.set_fill_value(a, 100)\n >>> a\n [0, 1, 2, 3, 4]\n >>> a = np.arange(5)\n >>> a\n array([0, 1, 2, 3, 4])\n >>> ma.set_fill_value(a, 100)\n >>> a\n array([0, 1, 2, 3, 4])\n\n \"\"\"\n if isinstance(a, MaskedArray):\n a.set_fill_value(fill_value)\n return","label":0},{"cid":"numpy/ma/core.py::get_fill_value","text":"numpy/ma/core.py::get_fill_value\ndef get_fill_value(a):\n \"\"\"\n Return the filling value of a, if any. Otherwise, returns the\n default filling value for that type.\n\n \"\"\"\n if isinstance(a, MaskedArray):\n result = a.fill_value\n else:\n result = default_fill_value(a)\n return result","label":0},{"cid":"numpy/ma/core.py::common_fill_value","text":"numpy/ma/core.py::common_fill_value\ndef common_fill_value(a, b):\n \"\"\"\n Return the common filling value of two masked arrays, if any.\n\n If ``a.fill_value == b.fill_value``, return the fill value,\n otherwise return None.\n\n Parameters\n ----------\n a, b : MaskedArray\n The masked arrays for which to compare fill values.\n\n Returns\n -------\n fill_value : scalar or None\n The common fill value, or None.\n\n Examples\n --------\n >>> x = np.ma.array([0, 1.], fill_value=3)\n >>> y = np.ma.array([0, 1.], fill_value=3)\n >>> np.ma.common_fill_value(x, y)\n 3.0\n\n \"\"\"\n t1 = get_fill_value(a)\n t2 = get_fill_value(b)\n if t1 == t2:\n return t1\n return None","label":0},{"cid":"numpy/ma/core.py::filled","text":"numpy/ma/core.py::filled\ndef filled(a, fill_value=None):\n \"\"\"\n Return input as an array with masked data replaced by a fill value.\n\n If `a` is not a `MaskedArray`, `a` itself is returned.\n If `a` is a `MaskedArray` and `fill_value` is None, `fill_value` is set to\n ``a.fill_value``.\n\n Parameters\n ----------\n a : MaskedArray or array_like\n An input object.\n fill_value : scalar, optional\n Filling value. Default is None.\n\n Returns\n -------\n a : ndarray\n The filled array.\n\n See Also\n --------\n compressed\n\n Examples\n --------\n >>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],\n ... [1, 0, 0],\n ... [0, 0, 0]])\n >>> x.filled()\n array([[999999, 1, 2],\n [999999, 4, 5],\n [ 6, 7, 8]])\n\n \"\"\"\n if hasattr(a, 'filled'):\n return a.filled(fill_value)\n elif isinstance(a, ndarray):\n # Should we check for contiguity ? and a.flags['CONTIGUOUS']:\n return a\n elif isinstance(a, dict):\n return np.array(a, 'O')\n else:\n return np.array(a)","label":0},{"cid":"numpy/ma/core.py::get_masked_subclass","text":"numpy/ma/core.py::get_masked_subclass\ndef get_masked_subclass(*arrays):\n \"\"\"\n Return the youngest subclass of MaskedArray from a list of (masked) arrays.\n In case of siblings, the first listed takes over.\n\n \"\"\"\n if len(arrays) == 1:\n arr = arrays[0]\n if isinstance(arr, MaskedArray):\n rcls = type(arr)\n else:\n rcls = MaskedArray\n else:\n arrcls = [type(a) for a in arrays]\n rcls = arrcls[0]\n if not issubclass(rcls, MaskedArray):\n rcls = MaskedArray\n for cls in arrcls[1:]:\n if issubclass(cls, rcls):\n rcls = cls\n # Don't return MaskedConstant as result: revert to MaskedArray\n if rcls.__name__ == 'MaskedConstant':\n return MaskedArray\n return rcls","label":0},{"cid":"numpy/ma/core.py::getdata","text":"numpy/ma/core.py::getdata\ndef getdata(a, subok=True):\n \"\"\"\n Return the data of a masked array as an ndarray.\n\n Return the data of `a` (if any) as an ndarray if `a` is a ``MaskedArray``,\n else return `a` as a ndarray or subclass (depending on `subok`) if not.\n\n Parameters\n ----------\n a : array_like\n Input ``MaskedArray``, alternatively a ndarray or a subclass thereof.\n subok : bool\n Whether to force the output to be a `pure` ndarray (False) or to\n return a subclass of ndarray if appropriate (True, default).\n\n See Also\n --------\n getmask : Return the mask of a masked array, or nomask.\n getmaskarray : Return the mask of a masked array, or full array of False.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = ma.masked_equal([[1,2],[3,4]], 2)\n >>> a\n masked_array(data =\n [[1 --]\n [3 4]],\n mask =\n [[False True]\n [False False]],\n fill_value=999999)\n >>> ma.getdata(a)\n array([[1, 2],\n [3, 4]])\n\n Equivalently use the ``MaskedArray`` `data` attribute.\n\n >>> a.data\n array([[1, 2],\n [3, 4]])\n\n \"\"\"\n try:\n data = a._data\n except AttributeError:\n data = np.array(a, copy=False, subok=subok)\n if not subok:\n return data.view(ndarray)\n return data","label":0},{"cid":"numpy/ma/core.py::fix_invalid","text":"numpy/ma/core.py::fix_invalid\ndef fix_invalid(a, mask=nomask, copy=True, fill_value=None):\n \"\"\"\n Return input with invalid data masked and replaced by a fill value.\n\n Invalid data means values of `nan`, `inf`, etc.\n\n Parameters\n ----------\n a : array_like\n Input array, a (subclass of) ndarray.\n copy : bool, optional\n Whether to use a copy of `a` (True) or to fix `a` in place (False).\n Default is True.\n fill_value : scalar, optional\n Value used for fixing invalid data. Default is None, in which case\n the ``a.fill_value`` is used.\n\n Returns\n -------\n b : MaskedArray\n The input array with invalid entries fixed.\n\n Notes\n -----\n A copy is performed by default.\n\n Examples\n --------\n >>> x = np.ma.array([1., -1, np.nan, np.inf], mask=[1] + [0]*3)\n >>> x\n masked_array(data = [-- -1.0 nan inf],\n mask = [ True False False False],\n fill_value = 1e+20)\n >>> np.ma.fix_invalid(x)\n masked_array(data = [-- -1.0 -- --],\n mask = [ True False True True],\n fill_value = 1e+20)\n\n >>> fixed = np.ma.fix_invalid(x)\n >>> fixed.data\n array([ 1.00000000e+00, -1.00000000e+00, 1.00000000e+20,\n 1.00000000e+20])\n >>> x.data\n array([ 1., -1., NaN, Inf])\n\n \"\"\"\n a = masked_array(a, copy=copy, mask=mask, subok=True)\n #invalid = (numpy.isnan(a._data) | numpy.isinf(a._data))\n invalid = np.logical_not(np.isfinite(a._data))\n if not invalid.any():\n return a\n a._mask |= invalid\n if fill_value is None:\n fill_value = a.fill_value\n a._data[invalid] = fill_value\n return a","label":0},{"cid":"numpy/ma/core.py::_DomainCheckInterval.__init__","text":"numpy/ma/core.py::_DomainCheckInterval.__init__\n def __init__(self, a, b):\n \"domain_check_interval(a,b)(x) = true where x < a or y > b\"\n if (a > b):\n (a, b) = (b, a)\n self.a = a\n self.b = b","label":0},{"cid":"numpy/ma/core.py::_DomainCheckInterval.__call__","text":"numpy/ma/core.py::_DomainCheckInterval.__call__\n def __call__ (self, x):\n \"Execute the call behavior.\"\n return umath.logical_or(umath.greater (x, self.b),\n umath.less(x, self.a))","label":0},{"cid":"numpy/ma/core.py::_DomainTan.__init__","text":"numpy/ma/core.py::_DomainTan.__init__\n def __init__(self, eps):\n \"domain_tan(eps) = true where abs(cos(x)) < eps)\"\n self.eps = eps","label":0},{"cid":"numpy/ma/core.py::_DomainTan.__call__","text":"numpy/ma/core.py::_DomainTan.__call__\n def __call__ (self, x):\n \"Executes the call behavior.\"\n return umath.less(umath.absolute(umath.cos(x)), self.eps)","label":0},{"cid":"numpy/ma/core.py::_DomainSafeDivide.__init__","text":"numpy/ma/core.py::_DomainSafeDivide.__init__\n def __init__ (self, tolerance=None):\n self.tolerance = tolerance","label":0},{"cid":"numpy/ma/core.py::_DomainSafeDivide.__call__","text":"numpy/ma/core.py::_DomainSafeDivide.__call__\n def __call__ (self, a, b):\n # Delay the selection of the tolerance to here in order to reduce numpy\n # import times. The calculation of these parameters is a substantial\n # component of numpy's import time.\n if self.tolerance is None:\n self.tolerance = np.finfo(float).tiny\n # don't call ma ufuncs from __array_wrap__ which would fail for scalars\n a, b = np.asarray(a), np.asarray(b)\n return umath.absolute(a) * self.tolerance >= umath.absolute(b)","label":0},{"cid":"numpy/ma/core.py::_DomainGreater.__init__","text":"numpy/ma/core.py::_DomainGreater.__init__\n def __init__(self, critical_value):\n \"DomainGreater(v)(x) = true where x <= v\"\n self.critical_value = critical_value","label":0},{"cid":"numpy/ma/core.py::_DomainGreater.__call__","text":"numpy/ma/core.py::_DomainGreater.__call__\n def __call__ (self, x):\n \"Executes the call behavior.\"\n return umath.less_equal(x, self.critical_value)","label":0},{"cid":"numpy/ma/core.py::_DomainGreaterEqual.__init__","text":"numpy/ma/core.py::_DomainGreaterEqual.__init__\n def __init__(self, critical_value):\n \"DomainGreaterEqual(v)(x) = true where x < v\"\n self.critical_value = critical_value","label":0},{"cid":"numpy/ma/core.py::_DomainGreaterEqual.__call__","text":"numpy/ma/core.py::_DomainGreaterEqual.__call__\n def __call__ (self, x):\n \"Executes the call behavior.\"\n return umath.less(x, self.critical_value)","label":0},{"cid":"numpy/ma/core.py::_MaskedUnaryOperation.__init__","text":"numpy/ma/core.py::_MaskedUnaryOperation.__init__\n def __init__ (self, mufunc, fill=0, domain=None):\n \"\"\" _MaskedUnaryOperation(aufunc, fill=0, domain=None)\n aufunc(fill) must be defined\n self(x) returns aufunc(x)\n with masked values where domain(x) is true or getmask(x) is true.\n \"\"\"\n self.f = mufunc\n self.fill = fill\n self.domain = domain\n self.__doc__ = getattr(mufunc, \"__doc__\", str(mufunc))\n self.__name__ = getattr(mufunc, \"__name__\", str(mufunc))\n ufunc_domain[mufunc] = domain\n ufunc_fills[mufunc] = fill","label":0},{"cid":"numpy/ma/core.py::_MaskedUnaryOperation.__call__","text":"numpy/ma/core.py::_MaskedUnaryOperation.__call__\n def __call__ (self, a, *args, **kwargs):\n \"Execute the call behavior.\"\n d = getdata(a)\n # Case 1.1. : Domained function\n if self.domain is not None:\n with np.errstate(divide='ignore', invalid='ignore'):\n result = self.f(d, *args, **kwargs)\n # Make a mask\n m = ~umath.isfinite(result)\n m |= self.domain(d)\n m |= getmask(a)\n # Case 1.2. : Function without a domain\n else:\n # Get the result and the mask\n result = self.f(d, *args, **kwargs)\n m = getmask(a)\n # Case 2.1. : The result is scalarscalar\n if not result.ndim:\n if m:\n return masked\n return result\n # Case 2.2. The result is an array\n # We need to fill the invalid data back w/ the input\n # Now, that's plain silly: in C, we would just skip the element and keep\n # the original, but we do have to do it that way in Python\n if m is not nomask:\n # In case result has a lower dtype than the inputs (as in equal)\n try:\n np.copyto(result, d, where=m)\n except TypeError:\n pass\n # Transform to\n if isinstance(a, MaskedArray):\n subtype = type(a)\n else:\n subtype = MaskedArray\n result = result.view(subtype)\n result._mask = m\n result._update_from(a)\n return result","label":0},{"cid":"numpy/ma/core.py::_MaskedUnaryOperation.__str__","text":"numpy/ma/core.py::_MaskedUnaryOperation.__str__\n def __str__ (self):\n return \"Masked version of %s. [Invalid values are masked]\" % str(self.f)","label":0},{"cid":"numpy/ma/core.py::_MaskedBinaryOperation.__init__","text":"numpy/ma/core.py::_MaskedBinaryOperation.__init__\n def __init__ (self, mbfunc, fillx=0, filly=0):\n \"\"\"abfunc(fillx, filly) must be defined.\n abfunc(x, filly) = x for all x to enable reduce.\n \"\"\"\n self.f = mbfunc\n self.fillx = fillx\n self.filly = filly\n self.__doc__ = getattr(mbfunc, \"__doc__\", str(mbfunc))\n self.__name__ = getattr(mbfunc, \"__name__\", str(mbfunc))\n ufunc_domain[mbfunc] = None\n ufunc_fills[mbfunc] = (fillx, filly)","label":0},{"cid":"numpy/ma/core.py::_MaskedBinaryOperation.__call__","text":"numpy/ma/core.py::_MaskedBinaryOperation.__call__\n def __call__ (self, a, b, *args, **kwargs):\n \"Execute the call behavior.\"\n # Get the data, as ndarray\n (da, db) = (getdata(a, subok=False), getdata(b, subok=False))\n # Get the mask\n (ma, mb) = (getmask(a), getmask(b))\n if ma is nomask:\n if mb is nomask:\n m = nomask\n else:\n m = umath.logical_or(getmaskarray(a), mb)\n elif mb is nomask:\n m = umath.logical_or(ma, getmaskarray(b))\n else:\n m = umath.logical_or(ma, mb)\n # Get the result\n with np.errstate(divide='ignore', invalid='ignore'):\n result = self.f(da, db, *args, **kwargs)\n # check it worked\n if result is NotImplemented:\n return NotImplemented\n # Case 1. : scalar\n if not result.ndim:\n if m:\n return masked\n return result\n # Case 2. : array\n # Revert result to da where masked\n if m is not nomask:\n np.copyto(result, da, casting='unsafe', where=m)\n # Transforms to a (subclass of) MaskedArray\n result = result.view(get_masked_subclass(a, b))\n result._mask = m\n # Update the optional info from the inputs\n if isinstance(b, MaskedArray):\n if isinstance(a, MaskedArray):\n result._update_from(a)\n else:\n result._update_from(b)\n elif isinstance(a, MaskedArray):\n result._update_from(a)\n return result","label":0},{"cid":"numpy/ma/core.py::_MaskedBinaryOperation.reduce","text":"numpy/ma/core.py::_MaskedBinaryOperation.reduce\n def reduce(self, target, axis=0, dtype=None):\n \"\"\"Reduce `target` along the given `axis`.\"\"\"\n if isinstance(target, MaskedArray):\n tclass = type(target)\n else:\n tclass = MaskedArray\n m = getmask(target)\n t = filled(target, self.filly)\n if t.shape == ():\n t = t.reshape(1)\n if m is not nomask:\n m = make_mask(m, copy=1)\n m.shape = (1,)\n if m is nomask:\n return self.f.reduce(t, axis).view(tclass)\n t = t.view(tclass)\n t._mask = m\n tr = self.f.reduce(getdata(t), axis, dtype=dtype or t.dtype)\n mr = umath.logical_and.reduce(m, axis)\n tr = tr.view(tclass)\n if mr.ndim > 0:\n tr._mask = mr\n return tr\n elif mr:\n return masked\n return tr","label":0},{"cid":"numpy/ma/core.py::_MaskedBinaryOperation.outer","text":"numpy/ma/core.py::_MaskedBinaryOperation.outer\n def outer (self, a, b):\n \"\"\"Return the function applied to the outer product of a and b.\n\n \"\"\"\n ma = getmask(a)\n mb = getmask(b)\n if ma is nomask and mb is nomask:\n m = nomask\n else:\n ma = getmaskarray(a)\n mb = getmaskarray(b)\n m = umath.logical_or.outer(ma, mb)\n if (not m.ndim) and m:\n return masked\n (da, db) = (getdata(a), getdata(b))\n d = self.f.outer(da, db)\n # check it worked\n if d is NotImplemented:\n return NotImplemented\n if m is not nomask:\n np.copyto(d, da, where=m)\n if d.shape:\n d = d.view(get_masked_subclass(a, b))\n d._mask = m\n return d","label":0},{"cid":"numpy/ma/core.py::_MaskedBinaryOperation.accumulate","text":"numpy/ma/core.py::_MaskedBinaryOperation.accumulate\n def accumulate (self, target, axis=0):\n \"\"\"Accumulate `target` along `axis` after filling with y fill\n value.\n\n \"\"\"\n if isinstance(target, MaskedArray):\n tclass = type(target)\n else:\n tclass = MaskedArray\n t = filled(target, self.filly)\n return self.f.accumulate(t, axis).view(tclass)","label":0},{"cid":"numpy/ma/core.py::_MaskedBinaryOperation.__str__","text":"numpy/ma/core.py::_MaskedBinaryOperation.__str__\n def __str__ (self):\n return \"Masked version of \" + str(self.f)","label":0},{"cid":"numpy/ma/core.py::_DomainedBinaryOperation.__init__","text":"numpy/ma/core.py::_DomainedBinaryOperation.__init__\n def __init__ (self, dbfunc, domain, fillx=0, filly=0):\n \"\"\"abfunc(fillx, filly) must be defined.\n abfunc(x, filly) = x for all x to enable reduce.\n \"\"\"\n self.f = dbfunc\n self.domain = domain\n self.fillx = fillx\n self.filly = filly\n self.__doc__ = getattr(dbfunc, \"__doc__\", str(dbfunc))\n self.__name__ = getattr(dbfunc, \"__name__\", str(dbfunc))\n ufunc_domain[dbfunc] = domain\n ufunc_fills[dbfunc] = (fillx, filly)","label":0},{"cid":"numpy/ma/core.py::_DomainedBinaryOperation.__call__","text":"numpy/ma/core.py::_DomainedBinaryOperation.__call__\n def __call__(self, a, b, *args, **kwargs):\n \"Execute the call behavior.\"\n # Get the data and the mask\n (da, db) = (getdata(a, subok=False), getdata(b, subok=False))\n (ma, mb) = (getmask(a), getmask(b))\n # Get the result\n with np.errstate(divide='ignore', invalid='ignore'):\n result = self.f(da, db, *args, **kwargs)\n # check it worked\n if result is NotImplemented:\n return NotImplemented\n # Get the mask as a combination of ma, mb and invalid\n m = ~umath.isfinite(result)\n m |= ma\n m |= mb\n # Apply the domain\n domain = ufunc_domain.get(self.f, None)\n if domain is not None:\n m |= filled(domain(da, db), True)\n # Take care of the scalar case first\n if (not m.ndim):\n if m:\n return masked\n else:\n return result\n # When the mask is True, put back da\n np.copyto(result, da, casting='unsafe', where=m)\n result = result.view(get_masked_subclass(a, b))\n result._mask = m\n if isinstance(b, MaskedArray):\n if isinstance(a, MaskedArray):\n result._update_from(a)\n else:\n result._update_from(b)\n elif isinstance(a, MaskedArray):\n result._update_from(a)\n return result","label":0},{"cid":"numpy/ma/core.py::_DomainedBinaryOperation.__str__","text":"numpy/ma/core.py::_DomainedBinaryOperation.__str__\n def __str__ (self):\n return \"Masked version of \" + str(self.f)","label":0},{"cid":"numpy/ma/core.py::_recursive_make_descr","text":"numpy/ma/core.py::_recursive_make_descr\ndef _recursive_make_descr(datatype, newtype=bool_):\n \"Private function allowing recursion in make_descr.\"\n # Do we have some name fields ?\n if datatype.names:\n descr = []\n for name in datatype.names:\n field = datatype.fields[name]\n if len(field) == 3:\n # Prepend the title to the name\n name = (field[-1], name)\n descr.append((name, _recursive_make_descr(field[0], newtype)))\n return descr\n # Is this some kind of composite a la (np.float,2)\n elif datatype.subdtype:\n mdescr = list(datatype.subdtype)\n mdescr[0] = newtype\n return tuple(mdescr)\n else:\n return newtype","label":0},{"cid":"numpy/ma/core.py::make_mask_descr","text":"numpy/ma/core.py::make_mask_descr\ndef make_mask_descr(ndtype):\n \"\"\"\n Construct a dtype description list from a given dtype.\n\n Returns a new dtype object, with the type of all fields in `ndtype` to a\n boolean type. Field names are not altered.\n\n Parameters\n ----------\n ndtype : dtype\n The dtype to convert.\n\n Returns\n -------\n result : dtype\n A dtype that looks like `ndtype`, the type of all fields is boolean.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> dtype = np.dtype({'names':['foo', 'bar'],\n 'formats':[np.float32, np.int]})\n >>> dtype\n dtype([('foo', '<f4'), ('bar', '<i4')])\n >>> ma.make_mask_descr(dtype)\n dtype([('foo', '|b1'), ('bar', '|b1')])\n >>> ma.make_mask_descr(np.float32)\n <type 'numpy.bool_'>\n\n \"\"\"\n # Make sure we do have a dtype\n if not isinstance(ndtype, np.dtype):\n ndtype = np.dtype(ndtype)\n return np.dtype(_recursive_make_descr(ndtype, np.bool))","label":0},{"cid":"numpy/ma/core.py::getmask","text":"numpy/ma/core.py::getmask\ndef getmask(a):\n \"\"\"\n Return the mask of a masked array, or nomask.\n\n Return the mask of `a` as an ndarray if `a` is a `MaskedArray` and the\n mask is not `nomask`, else return `nomask`. To guarantee a full array\n of booleans of the same shape as a, use `getmaskarray`.\n\n Parameters\n ----------\n a : array_like\n Input `MaskedArray` for which the mask is required.\n\n See Also\n --------\n getdata : Return the data of a masked array as an ndarray.\n getmaskarray : Return the mask of a masked array, or full array of False.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = ma.masked_equal([[1,2],[3,4]], 2)\n >>> a\n masked_array(data =\n [[1 --]\n [3 4]],\n mask =\n [[False True]\n [False False]],\n fill_value=999999)\n >>> ma.getmask(a)\n array([[False, True],\n [False, False]], dtype=bool)\n\n Equivalently use the `MaskedArray` `mask` attribute.\n\n >>> a.mask\n array([[False, True],\n [False, False]], dtype=bool)\n\n Result when mask == `nomask`\n\n >>> b = ma.masked_array([[1,2],[3,4]])\n >>> b\n masked_array(data =\n [[1 2]\n [3 4]],\n mask =\n False,\n fill_value=999999)\n >>> ma.nomask\n False\n >>> ma.getmask(b) == ma.nomask\n True\n >>> b.mask == ma.nomask\n True\n\n \"\"\"\n return getattr(a, '_mask', nomask)","label":0},{"cid":"numpy/ma/core.py::getmaskarray","text":"numpy/ma/core.py::getmaskarray\ndef getmaskarray(arr):\n \"\"\"\n Return the mask of a masked array, or full boolean array of False.\n\n Return the mask of `arr` as an ndarray if `arr` is a `MaskedArray` and\n the mask is not `nomask`, else return a full boolean array of False of\n the same shape as `arr`.\n\n Parameters\n ----------\n arr : array_like\n Input `MaskedArray` for which the mask is required.\n\n See Also\n --------\n getmask : Return the mask of a masked array, or nomask.\n getdata : Return the data of a masked array as an ndarray.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = ma.masked_equal([[1,2],[3,4]], 2)\n >>> a\n masked_array(data =\n [[1 --]\n [3 4]],\n mask =\n [[False True]\n [False False]],\n fill_value=999999)\n >>> ma.getmaskarray(a)\n array([[False, True],\n [False, False]], dtype=bool)\n\n Result when mask == ``nomask``\n\n >>> b = ma.masked_array([[1,2],[3,4]])\n >>> b\n masked_array(data =\n [[1 2]\n [3 4]],\n mask =\n False,\n fill_value=999999)\n >>> >ma.getmaskarray(b)\n array([[False, False],\n [False, False]], dtype=bool)\n\n \"\"\"\n mask = getmask(arr)\n if mask is nomask:\n mask = make_mask_none(np.shape(arr), getdata(arr).dtype)\n return mask","label":0},{"cid":"numpy/ma/core.py::is_mask","text":"numpy/ma/core.py::is_mask\ndef is_mask(m):\n \"\"\"\n Return True if m is a valid, standard mask.\n\n This function does not check the contents of the input, only that the\n type is MaskType. In particular, this function returns False if the\n mask has a flexible dtype.\n\n Parameters\n ----------\n m : array_like\n Array to test.\n\n Returns\n -------\n result : bool\n True if `m.dtype.type` is MaskType, False otherwise.\n\n See Also\n --------\n isMaskedArray : Test whether input is an instance of MaskedArray.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> m = ma.masked_equal([0, 1, 0, 2, 3], 0)\n >>> m\n masked_array(data = [-- 1 -- 2 3],\n mask = [ True False True False False],\n fill_value=999999)\n >>> ma.is_mask(m)\n False\n >>> ma.is_mask(m.mask)\n True\n\n Input must be an ndarray (or have similar attributes)\n for it to be considered a valid mask.\n\n >>> m = [False, True, False]\n >>> ma.is_mask(m)\n False\n >>> m = np.array([False, True, False])\n >>> m\n array([False, True, False], dtype=bool)\n >>> ma.is_mask(m)\n True\n\n Arrays with complex dtypes don't return True.\n\n >>> dtype = np.dtype({'names':['monty', 'pithon'],\n 'formats':[np.bool, np.bool]})\n >>> dtype\n dtype([('monty', '|b1'), ('pithon', '|b1')])\n >>> m = np.array([(True, False), (False, True), (True, False)],\n dtype=dtype)\n >>> m\n array([(True, False), (False, True), (True, False)],\n dtype=[('monty', '|b1'), ('pithon', '|b1')])\n >>> ma.is_mask(m)\n False\n\n \"\"\"\n try:\n return m.dtype.type is MaskType\n except AttributeError:\n return False","label":0},{"cid":"numpy/ma/core.py::make_mask","text":"numpy/ma/core.py::make_mask\ndef make_mask(m, copy=False, shrink=True, dtype=MaskType):\n \"\"\"\n Create a boolean mask from an array.\n\n Return `m` as a boolean mask, creating a copy if necessary or requested.\n The function can accept any sequence that is convertible to integers,\n or ``nomask``. Does not require that contents must be 0s and 1s, values\n of 0 are interepreted as False, everything else as True.\n\n Parameters\n ----------\n m : array_like\n Potential mask.\n copy : bool, optional\n Whether to return a copy of `m` (True) or `m` itself (False).\n shrink : bool, optional\n Whether to shrink `m` to ``nomask`` if all its values are False.\n dtype : dtype, optional\n Data-type of the output mask. By default, the output mask has\n a dtype of MaskType (bool). If the dtype is flexible, each field\n has a boolean dtype.\n\n Returns\n -------\n result : ndarray\n A boolean mask derived from `m`.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> m = [True, False, True, True]\n >>> ma.make_mask(m)\n array([ True, False, True, True], dtype=bool)\n >>> m = [1, 0, 1, 1]\n >>> ma.make_mask(m)\n array([ True, False, True, True], dtype=bool)\n >>> m = [1, 0, 2, -3]\n >>> ma.make_mask(m)\n array([ True, False, True, True], dtype=bool)\n\n Effect of the `shrink` parameter.\n\n >>> m = np.zeros(4)\n >>> m\n array([ 0., 0., 0., 0.])\n >>> ma.make_mask(m)\n False\n >>> ma.make_mask(m, shrink=False)\n array([False, False, False, False], dtype=bool)\n\n Using a flexible `dtype`.\n\n >>> m = [1, 0, 1, 1]\n >>> n = [0, 1, 0, 0]\n >>> arr = []\n >>> for man, mouse in zip(m, n):\n ... arr.append((man, mouse))\n >>> arr\n [(1, 0), (0, 1), (1, 0), (1, 0)]\n >>> dtype = np.dtype({'names':['man', 'mouse'],\n 'formats':[np.int, np.int]})\n >>> arr = np.array(arr, dtype=dtype)\n >>> arr\n array([(1, 0), (0, 1), (1,","label":0},{"cid":"numpy/ma/core.py::make_mask_none","text":"numpy/ma/core.py::make_mask_none\ndef make_mask_none(newshape, dtype=None):\n \"\"\"\n Return a boolean mask of the given shape, filled with False.\n\n This function returns a boolean ndarray with all entries False, that can\n be used in common mask manipulations. If a complex dtype is specified, the\n type of each field is converted to a boolean type.\n\n Parameters\n ----------\n newshape : tuple\n A tuple indicating the shape of the mask.\n dtype : {None, dtype}, optional\n If None, use a MaskType instance. Otherwise, use a new datatype with\n the same fields as `dtype`, converted to boolean types.\n\n Returns\n -------\n result : ndarray\n An ndarray of appropriate shape and dtype, filled with False.\n\n See Also\n --------\n make_mask : Create a boolean mask from an array.\n make_mask_descr : Construct a dtype description list from a given dtype.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> ma.make_mask_none((3,))\n array([False, False, False], dtype=bool)\n\n Defining a more complex dtype.\n\n >>> dtype = np.dtype({'names':['foo', 'bar'],\n 'formats':[np.float32, np.int]})\n >>> dtype\n dtype([('foo', '<f4'), ('bar', '<i4')])\n >>> ma.make_mask_none((3,), dtype=dtype)\n array([(False, False), (False, False), (False, False)],\n dtype=[('foo', '|b1'), ('bar', '|b1')])\n\n \"\"\"\n if dtype is None:\n result = np.zeros(newshape, dtype=MaskType)\n else:\n result = np.zeros(newshape, dtype=make_mask_descr(dtype))\n return result","label":0},{"cid":"numpy/ma/core.py::mask_or","text":"numpy/ma/core.py::mask_or\ndef mask_or (m1, m2, copy=False, shrink=True):\n \"\"\"\n Combine two masks with the ``logical_or`` operator.\n\n The result may be a view on `m1` or `m2` if the other is `nomask`\n (i.e. False).\n\n Parameters\n ----------\n m1, m2 : array_like\n Input masks.\n copy : bool, optional\n If copy is False and one of the inputs is `nomask`, return a view\n of the other input mask. Defaults to False.\n shrink : bool, optional\n Whether to shrink the output to `nomask` if all its values are\n False. Defaults to True.\n\n Returns\n -------\n mask : output mask\n The result masks values that are masked in either `m1` or `m2`.\n\n Raises\n ------\n ValueError\n If `m1` and `m2` have different flexible dtypes.\n\n Examples\n --------\n >>> m1 = np.ma.make_mask([0, 1, 1, 0])\n >>> m2 = np.ma.make_mask([1, 0, 0, 0])\n >>> np.ma.mask_or(m1, m2)\n array([ True, True, True, False], dtype=bool)\n\n \"\"\"\n def _recursive_mask_or(m1, m2, newmask):\n names = m1.dtype.names\n for name in names:\n current1 = m1[name]\n if current1.dtype.names:\n _recursive_mask_or(current1, m2[name], newmask[name])\n else:\n umath.logical_or(current1, m2[name], newmask[name])\n return\n #\n if (m1 is nomask) or (m1 is False):\n dtype = getattr(m2, 'dtype', MaskType)\n return make_mask(m2, copy=copy, shrink=shrink, dtype=dtype)\n if (m2 is nomask) or (m2 is False):\n dtype = getattr(m1, 'dtype', MaskType)\n return make_mask(m1, copy=copy, shrink=shrink, dtype=dtype)\n if m1 is m2 and is_mask(m1):\n return m1\n (dtype1, dtype2) = (getattr(m1, 'dtype', None), getattr(m2, 'dtype', None))\n if (dtype1 != dtype2):\n raise ValueError(\"Incompatible dtypes '%s'<>'%s'\" % (dtype1, dtype2))\n if dtype1.names:\n newmask = np.empty_like(m1)\n _recursive_mask_or(m1, m2, newmask)","label":0},{"cid":"numpy/ma/core.py::mask_or._recursive_mask_or","text":"numpy/ma/core.py::mask_or._recursive_mask_or\n def _recursive_mask_or(m1, m2, newmask):\n names = m1.dtype.names\n for name in names:\n current1 = m1[name]\n if current1.dtype.names:\n _recursive_mask_or(current1, m2[name], newmask[name])\n else:\n umath.logical_or(current1, m2[name], newmask[name])\n return","label":0},{"cid":"numpy/ma/core.py::flatten_mask","text":"numpy/ma/core.py::flatten_mask\ndef flatten_mask(mask):\n \"\"\"\n Returns a completely flattened version of the mask, where nested fields\n are collapsed.\n\n Parameters\n ----------\n mask : array_like\n Input array, which will be interpreted as booleans.\n\n Returns\n -------\n flattened_mask : ndarray of bools\n The flattened input.\n\n Examples\n --------\n >>> mask = np.array([0, 0, 1], dtype=np.bool)\n >>> flatten_mask(mask)\n array([False, False, True], dtype=bool)\n\n >>> mask = np.array([(0, 0), (0, 1)], dtype=[('a', bool), ('b', bool)])\n >>> flatten_mask(mask)\n array([False, False, False, True], dtype=bool)\n\n >>> mdtype = [('a', bool), ('b', [('ba', bool), ('bb', bool)])]\n >>> mask = np.array([(0, (0, 0)), (0, (0, 1))], dtype=mdtype)\n >>> flatten_mask(mask)\n array([False, False, False, False, False, True], dtype=bool)\n\n \"\"\"\n #\n def _flatmask(mask):\n \"Flatten the mask and returns a (maybe nested) sequence of booleans.\"\n mnames = mask.dtype.names\n if mnames:\n return [flatten_mask(mask[name]) for name in mnames]\n else:\n return mask\n #\n def _flatsequence(sequence):\n \"Generates a flattened version of the sequence.\"\n try:\n for element in sequence:\n if hasattr(element, '__iter__'):\n for f in _flatsequence(element):\n yield f\n else:\n yield element\n except TypeError:\n yield sequence\n #\n mask = np.asarray(mask)\n flattened = _flatsequence(_flatmask(mask))\n return np.array([_ for _ in flattened], dtype=bool)","label":0},{"cid":"numpy/ma/core.py::flatten_mask._flatmask","text":"numpy/ma/core.py::flatten_mask._flatmask\n def _flatmask(mask):\n \"Flatten the mask and returns a (maybe nested) sequence of booleans.\"\n mnames = mask.dtype.names\n if mnames:\n return [flatten_mask(mask[name]) for name in mnames]\n else:\n return mask","label":0},{"cid":"numpy/ma/core.py::flatten_mask._flatsequence","text":"numpy/ma/core.py::flatten_mask._flatsequence\n def _flatsequence(sequence):\n \"Generates a flattened version of the sequence.\"\n try:\n for element in sequence:\n if hasattr(element, '__iter__'):\n for f in _flatsequence(element):\n yield f\n else:\n yield element\n except TypeError:\n yield sequence","label":0},{"cid":"numpy/ma/core.py::_check_mask_axis","text":"numpy/ma/core.py::_check_mask_axis\ndef _check_mask_axis(mask, axis):\n \"Check whether there are masked values along the given axis\"\n if mask is not nomask:\n return mask.all(axis=axis)\n return nomask","label":0},{"cid":"numpy/ma/core.py::masked_where","text":"numpy/ma/core.py::masked_where\ndef masked_where(condition, a, copy=True):\n \"\"\"\n Mask an array where a condition is met.\n\n Return `a` as an array masked where `condition` is True.\n Any masked values of `a` or `condition` are also masked in the output.\n\n Parameters\n ----------\n condition : array_like\n Masking condition. When `condition` tests floating point values for\n equality, consider using ``masked_values`` instead.\n a : array_like\n Array to mask.\n copy : bool\n If True (default) make a copy of `a` in the result. If False modify\n `a` in place and return a view.\n\n Returns\n -------\n result : MaskedArray\n The result of masking `a` where `condition` is True.\n\n See Also\n --------\n masked_values : Mask using floating point equality.\n masked_equal : Mask where equal to a given value.\n masked_not_equal : Mask where `not` equal to a given value.\n masked_less_equal : Mask where less than or equal to a given value.\n masked_greater_equal : Mask where greater than or equal to a given value.\n masked_less : Mask where less than a given value.\n masked_greater : Mask where greater than a given value.\n masked_inside : Mask inside a given interval.\n masked_outside : Mask outside a given interval.\n masked_invalid : Mask invalid values (NaNs or infs).\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.arange(4)\n >>> a\n array([0, 1, 2, 3])\n >>> ma.masked_where(a <= 2, a)\n masked_array(data = [-- -- -- 3],\n mask = [ True True True False],\n fill_value=999999)\n\n Mask array `b` conditional on `a`.\n\n >>> b = ['a', 'b', 'c', 'd']\n >>> ma.masked_where(a == 2, b)\n masked_array(data = [a b -- d],\n mask = [False False True False],\n fill_value=N/A)\n\n Effect of the `copy` argument.\n\n >>> c = ma.masked_where(a <= 2, a)\n >>> c\n masked_array(data = [-- -- -- 3],\n mask = [ True True ","label":0},{"cid":"numpy/ma/core.py::masked_greater","text":"numpy/ma/core.py::masked_greater\ndef masked_greater(x, value, copy=True):\n \"\"\"\n Mask an array where greater than a given value.\n\n This function is a shortcut to ``masked_where``, with\n `condition` = (x > value).\n\n See Also\n --------\n masked_where : Mask where a condition is met.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.arange(4)\n >>> a\n array([0, 1, 2, 3])\n >>> ma.masked_greater(a, 2)\n masked_array(data = [0 1 2 --],\n mask = [False False False True],\n fill_value=999999)\n\n \"\"\"\n return masked_where(greater(x, value), x, copy=copy)","label":0},{"cid":"numpy/ma/core.py::masked_greater_equal","text":"numpy/ma/core.py::masked_greater_equal\ndef masked_greater_equal(x, value, copy=True):\n \"\"\"\n Mask an array where greater than or equal to a given value.\n\n This function is a shortcut to ``masked_where``, with\n `condition` = (x >= value).\n\n See Also\n --------\n masked_where : Mask where a condition is met.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.arange(4)\n >>> a\n array([0, 1, 2, 3])\n >>> ma.masked_greater_equal(a, 2)\n masked_array(data = [0 1 -- --],\n mask = [False False True True],\n fill_value=999999)\n\n \"\"\"\n return masked_where(greater_equal(x, value), x, copy=copy)","label":0},{"cid":"numpy/ma/core.py::masked_less","text":"numpy/ma/core.py::masked_less\ndef masked_less(x, value, copy=True):\n \"\"\"\n Mask an array where less than a given value.\n\n This function is a shortcut to ``masked_where``, with\n `condition` = (x < value).\n\n See Also\n --------\n masked_where : Mask where a condition is met.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.arange(4)\n >>> a\n array([0, 1, 2, 3])\n >>> ma.masked_less(a, 2)\n masked_array(data = [-- -- 2 3],\n mask = [ True True False False],\n fill_value=999999)\n\n \"\"\"\n return masked_where(less(x, value), x, copy=copy)","label":0},{"cid":"numpy/ma/core.py::masked_less_equal","text":"numpy/ma/core.py::masked_less_equal\ndef masked_less_equal(x, value, copy=True):\n \"\"\"\n Mask an array where less than or equal to a given value.\n\n This function is a shortcut to ``masked_where``, with\n `condition` = (x <= value).\n\n See Also\n --------\n masked_where : Mask where a condition is met.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.arange(4)\n >>> a\n array([0, 1, 2, 3])\n >>> ma.masked_less_equal(a, 2)\n masked_array(data = [-- -- -- 3],\n mask = [ True True True False],\n fill_value=999999)\n\n \"\"\"\n return masked_where(less_equal(x, value), x, copy=copy)","label":0},{"cid":"numpy/ma/core.py::masked_not_equal","text":"numpy/ma/core.py::masked_not_equal\ndef masked_not_equal(x, value, copy=True):\n \"\"\"\n Mask an array where `not` equal to a given value.\n\n This function is a shortcut to ``masked_where``, with\n `condition` = (x != value).\n\n See Also\n --------\n masked_where : Mask where a condition is met.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.arange(4)\n >>> a\n array([0, 1, 2, 3])\n >>> ma.masked_not_equal(a, 2)\n masked_array(data = [-- -- 2 --],\n mask = [ True True False True],\n fill_value=999999)\n\n \"\"\"\n return masked_where(not_equal(x, value), x, copy=copy)","label":0},{"cid":"numpy/ma/core.py::masked_equal","text":"numpy/ma/core.py::masked_equal\ndef masked_equal(x, value, copy=True):\n \"\"\"\n Mask an array where equal to a given value.\n\n This function is a shortcut to ``masked_where``, with\n `condition` = (x == value). For floating point arrays,\n consider using ``masked_values(x, value)``.\n\n See Also\n --------\n masked_where : Mask where a condition is met.\n masked_values : Mask using floating point equality.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.arange(4)\n >>> a\n array([0, 1, 2, 3])\n >>> ma.masked_equal(a, 2)\n masked_array(data = [0 1 -- 3],\n mask = [False False True False],\n fill_value=999999)\n\n \"\"\"\n # An alternative implementation relies on filling first: probably not needed.\n # d = filled(x, 0)\n # c = umath.equal(d, value)\n # m = mask_or(c, getmask(x))\n # return array(d, mask=m, copy=copy)\n output = masked_where(equal(x, value), x, copy=copy)\n output.fill_value = value\n return output","label":0},{"cid":"numpy/ma/core.py::masked_inside","text":"numpy/ma/core.py::masked_inside\ndef masked_inside(x, v1, v2, copy=True):\n \"\"\"\n Mask an array inside a given interval.\n\n Shortcut to ``masked_where``, where `condition` is True for `x` inside\n the interval [v1,v2] (v1 <= x <= v2). The boundaries `v1` and `v2`\n can be given in either order.\n\n See Also\n --------\n masked_where : Mask where a condition is met.\n\n Notes\n -----\n The array `x` is prefilled with its filling value.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> x = [0.31, 1.2, 0.01, 0.2, -0.4, -1.1]\n >>> ma.masked_inside(x, -0.3, 0.3)\n masked_array(data = [0.31 1.2 -- -- -0.4 -1.1],\n mask = [False False True True False False],\n fill_value=1e+20)\n\n The order of `v1` and `v2` doesn't matter.\n\n >>> ma.masked_inside(x, 0.3, -0.3)\n masked_array(data = [0.31 1.2 -- -- -0.4 -1.1],\n mask = [False False True True False False],\n fill_value=1e+20)\n\n \"\"\"\n if v2 < v1:\n (v1, v2) = (v2, v1)\n xf = filled(x)\n condition = (xf >= v1) & (xf <= v2)\n return masked_where(condition, x, copy=copy)","label":0},{"cid":"numpy/ma/core.py::masked_outside","text":"numpy/ma/core.py::masked_outside\ndef masked_outside(x, v1, v2, copy=True):\n \"\"\"\n Mask an array outside a given interval.\n\n Shortcut to ``masked_where``, where `condition` is True for `x` outside\n the interval [v1,v2] (x < v1)|(x > v2).\n The boundaries `v1` and `v2` can be given in either order.\n\n See Also\n --------\n masked_where : Mask where a condition is met.\n\n Notes\n -----\n The array `x` is prefilled with its filling value.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> x = [0.31, 1.2, 0.01, 0.2, -0.4, -1.1]\n >>> ma.masked_outside(x, -0.3, 0.3)\n masked_array(data = [-- -- 0.01 0.2 -- --],\n mask = [ True True False False True True],\n fill_value=1e+20)\n\n The order of `v1` and `v2` doesn't matter.\n\n >>> ma.masked_outside(x, 0.3, -0.3)\n masked_array(data = [-- -- 0.01 0.2 -- --],\n mask = [ True True False False True True],\n fill_value=1e+20)\n\n \"\"\"\n if v2 < v1:\n (v1, v2) = (v2, v1)\n xf = filled(x)\n condition = (xf < v1) | (xf > v2)\n return masked_where(condition, x, copy=copy)","label":0},{"cid":"numpy/ma/core.py::masked_object","text":"numpy/ma/core.py::masked_object\ndef masked_object(x, value, copy=True, shrink=True):\n \"\"\"\n Mask the array `x` where the data are exactly equal to value.\n\n This function is similar to `masked_values`, but only suitable\n for object arrays: for floating point, use `masked_values` instead.\n\n Parameters\n ----------\n x : array_like\n Array to mask\n value : object\n Comparison value\n copy : {True, False}, optional\n Whether to return a copy of `x`.\n shrink : {True, False}, optional\n Whether to collapse a mask full of False to nomask\n\n Returns\n -------\n result : MaskedArray\n The result of masking `x` where equal to `value`.\n\n See Also\n --------\n masked_where : Mask where a condition is met.\n masked_equal : Mask where equal to a given value (integers).\n masked_values : Mask using floating point equality.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> food = np.array(['green_eggs', 'ham'], dtype=object)\n >>> # don't eat spoiled food\n >>> eat = ma.masked_object(food, 'green_eggs')\n >>> print eat\n [-- ham]\n >>> # plain ol` ham is boring\n >>> fresh_food = np.array(['cheese', 'ham', 'pineapple'], dtype=object)\n >>> eat = ma.masked_object(fresh_food, 'green_eggs')\n >>> print eat\n [cheese ham pineapple]\n\n Note that `mask` is set to ``nomask`` if possible.\n\n >>> eat\n masked_array(data = [cheese ham pineapple],\n mask = False,\n fill_value=?)\n\n \"\"\"\n if isMaskedArray(x):\n condition = umath.equal(x._data, value)\n mask = x._mask\n else:\n condition = umath.equal(np.asarray(x), value)\n mask = nomask\n mask = mask_or(mask, make_mask(condition, shrink=shrink))\n return masked_array(x, mask=mask, copy=copy, fill_value=value)","label":0},{"cid":"numpy/ma/core.py::masked_values","text":"numpy/ma/core.py::masked_values\ndef masked_values(x, value, rtol=1e-5, atol=1e-8, copy=True, shrink=True):\n \"\"\"\n Mask using floating point equality.\n\n Return a MaskedArray, masked where the data in array `x` are approximately\n equal to `value`, i.e. where the following condition is True\n\n (abs(x - value) <= atol+rtol*abs(value))\n\n The fill_value is set to `value` and the mask is set to ``nomask`` if\n possible. For integers, consider using ``masked_equal``.\n\n Parameters\n ----------\n x : array_like\n Array to mask.\n value : float\n Masking value.\n rtol : float, optional\n Tolerance parameter.\n atol : float, optional\n Tolerance parameter (1e-8).\n copy : bool, optional\n Whether to return a copy of `x`.\n shrink : bool, optional\n Whether to collapse a mask full of False to ``nomask``.\n\n Returns\n -------\n result : MaskedArray\n The result of masking `x` where approximately equal to `value`.\n\n See Also\n --------\n masked_where : Mask where a condition is met.\n masked_equal : Mask where equal to a given value (integers).\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> x = np.array([1, 1.1, 2, 1.1, 3])\n >>> ma.masked_values(x, 1.1)\n masked_array(data = [1.0 -- 2.0 -- 3.0],\n mask = [False True False True False],\n fill_value=1.1)\n\n Note that `mask` is set to ``nomask`` if possible.\n\n >>> ma.masked_values(x, 1.5)\n masked_array(data = [ 1. 1.1 2. 1.1 3. ],\n mask = False,\n fill_value=1.5)\n\n For integers, the fill value will be different in general to the\n result of ``masked_equal``.\n\n >>> x = np.arange(5)\n >>> x\n array([0, 1, 2, 3, 4])\n >>> ma.masked_values(x, 2)\n masked_array(data = [0 1 -- 3 4],\n mask = [False False True False False],\n fill_value=2)\n >>> ma.masked_equal(x, 2)\n masked_array(data = [0 1 -- 3 4],\n mask = [False False True Fals","label":0},{"cid":"numpy/ma/core.py::masked_invalid","text":"numpy/ma/core.py::masked_invalid\ndef masked_invalid(a, copy=True):\n \"\"\"\n Mask an array where invalid values occur (NaNs or infs).\n\n This function is a shortcut to ``masked_where``, with\n `condition` = ~(np.isfinite(a)). Any pre-existing mask is conserved.\n Only applies to arrays with a dtype where NaNs or infs make sense\n (i.e. floating point types), but accepts any array_like object.\n\n See Also\n --------\n masked_where : Mask where a condition is met.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.arange(5, dtype=np.float)\n >>> a[2] = np.NaN\n >>> a[3] = np.PINF\n >>> a\n array([ 0., 1., NaN, Inf, 4.])\n >>> ma.masked_invalid(a)\n masked_array(data = [0.0 1.0 -- -- 4.0],\n mask = [False False True True False],\n fill_value=1e+20)\n\n \"\"\"\n a = np.array(a, copy=copy, subok=True)\n mask = getattr(a, '_mask', None)\n if mask is not None:\n condition = ~(np.isfinite(getdata(a)))\n if mask is not nomask:\n condition |= mask\n cls = type(a)\n else:\n condition = ~(np.isfinite(a))\n cls = MaskedArray\n result = a.view(cls)\n result._mask = condition\n return result","label":0},{"cid":"numpy/ma/core.py::_MaskedPrintOption.__init__","text":"numpy/ma/core.py::_MaskedPrintOption.__init__\n def __init__ (self, display):\n \"Create the masked_print_option object.\"\n self._display = display\n self._enabled = True","label":0},{"cid":"numpy/ma/core.py::_MaskedPrintOption.display","text":"numpy/ma/core.py::_MaskedPrintOption.display\n def display(self):\n \"Display the string to print for masked values.\"\n return self._display","label":0},{"cid":"numpy/ma/core.py::_MaskedPrintOption.set_display","text":"numpy/ma/core.py::_MaskedPrintOption.set_display\n def set_display (self, s):\n \"Set the string to print for masked values.\"\n self._display = s","label":0},{"cid":"numpy/ma/core.py::_MaskedPrintOption.enabled","text":"numpy/ma/core.py::_MaskedPrintOption.enabled\n def enabled(self):\n \"Is the use of the display value enabled?\"\n return self._enabled","label":0},{"cid":"numpy/ma/core.py::_MaskedPrintOption.enable","text":"numpy/ma/core.py::_MaskedPrintOption.enable\n def enable(self, shrink=1):\n \"Set the enabling shrink to `shrink`.\"\n self._enabled = shrink","label":0},{"cid":"numpy/ma/core.py::_MaskedPrintOption.__str__","text":"numpy/ma/core.py::_MaskedPrintOption.__str__\n def __str__ (self):\n return str(self._display)","label":0},{"cid":"numpy/ma/core.py::_recursive_printoption","text":"numpy/ma/core.py::_recursive_printoption\ndef _recursive_printoption(result, mask, printopt):\n \"\"\"\n Puts printoptions in result where mask is True.\n Private function allowing for recursion\n \"\"\"\n names = result.dtype.names\n for name in names:\n (curdata, curmask) = (result[name], mask[name])\n if curdata.dtype.names:\n _recursive_printoption(curdata, curmask, printopt)\n else:\n np.copyto(curdata, printopt, where=curmask)\n return","label":0},{"cid":"numpy/ma/core.py::_recursive_filled","text":"numpy/ma/core.py::_recursive_filled\ndef _recursive_filled(a, mask, fill_value):\n \"\"\"\n Recursively fill `a` with `fill_value`.\n Private function\n \"\"\"\n names = a.dtype.names\n for name in names:\n current = a[name]\n if current.dtype.names:\n _recursive_filled(current, mask[name], fill_value[name])\n else:\n np.copyto(current, fill_value[name], where=mask[name])","label":0},{"cid":"numpy/ma/core.py::flatten_structured_array","text":"numpy/ma/core.py::flatten_structured_array\ndef flatten_structured_array(a):\n \"\"\"\n Flatten a structured array.\n\n The data type of the output is chosen such that it can represent all of the\n (nested) fields.\n\n Parameters\n ----------\n a : structured array\n\n Returns\n -------\n output : masked array or ndarray\n A flattened masked array if the input is a masked array, otherwise a\n standard ndarray.\n\n Examples\n --------\n >>> ndtype = [('a', int), ('b', float)]\n >>> a = np.array([(1, 1), (2, 2)], dtype=ndtype)\n >>> flatten_structured_array(a)\n array([[1., 1.],\n [2., 2.]])\n\n \"\"\"\n #\n def flatten_sequence(iterable):\n \"\"\"Flattens a compound of nested iterables.\"\"\"\n for elm in iter(iterable):\n if hasattr(elm, '__iter__'):\n for f in flatten_sequence(elm):\n yield f\n else:\n yield elm\n #\n a = np.asanyarray(a)\n inishape = a.shape\n a = a.ravel()\n if isinstance(a, MaskedArray):\n out = np.array([tuple(flatten_sequence(d.item())) for d in a._data])\n out = out.view(MaskedArray)\n out._mask = np.array([tuple(flatten_sequence(d.item()))\n for d in getmaskarray(a)])\n else:\n out = np.array([tuple(flatten_sequence(d.item())) for d in a])\n if len(inishape) > 1:\n newshape = list(out.shape)\n newshape[0] = inishape\n out.shape = tuple(flatten_sequence(newshape))\n return out","label":0},{"cid":"numpy/ma/core.py::flatten_structured_array.flatten_sequence","text":"numpy/ma/core.py::flatten_structured_array.flatten_sequence\n def flatten_sequence(iterable):\n \"\"\"Flattens a compound of nested iterables.\"\"\"\n for elm in iter(iterable):\n if hasattr(elm, '__iter__'):\n for f in flatten_sequence(elm):\n yield f\n else:\n yield elm","label":0},{"cid":"numpy/ma/core.py::_arraymethod.__init__","text":"numpy/ma/core.py::_arraymethod.__init__\n def __init__(self, funcname, onmask=True):\n self.__name__ = funcname\n self._onmask = onmask\n self.obj = None\n self.__doc__ = self.getdoc()","label":0},{"cid":"numpy/ma/core.py::_arraymethod.getdoc","text":"numpy/ma/core.py::_arraymethod.getdoc\n def getdoc(self):\n \"Return the doc of the function (from the doc of the method).\"\n methdoc = getattr(ndarray, self.__name__, None) or \\\n getattr(np, self.__name__, None)\n if methdoc is not None:\n return methdoc.__doc__","label":0},{"cid":"numpy/ma/core.py::_arraymethod.__get__","text":"numpy/ma/core.py::_arraymethod.__get__\n def __get__(self, obj, objtype=None):\n self.obj = obj\n return self","label":0},{"cid":"numpy/ma/core.py::_arraymethod.__call__","text":"numpy/ma/core.py::_arraymethod.__call__\n def __call__(self, *args, **params):\n methodname = self.__name__\n instance = self.obj\n # Fallback : if the instance has not been initialized, use the first arg\n if instance is None:\n args = list(args)\n instance = args.pop(0)\n data = instance._data\n mask = instance._mask\n cls = type(instance)\n result = getattr(data, methodname)(*args, **params).view(cls)\n result._update_from(instance)\n if result.ndim:\n if not self._onmask:\n result.__setmask__(mask)\n elif mask is not nomask:\n result.__setmask__(getattr(mask, methodname)(*args, **params))\n else:\n if mask.ndim and (not mask.dtype.names and mask.all()):\n return masked\n return result","label":0},{"cid":"numpy/ma/core.py::MaskedIterator.__init__","text":"numpy/ma/core.py::MaskedIterator.__init__\n def __init__(self, ma):\n self.ma = ma\n self.dataiter = ma._data.flat\n #\n if ma._mask is nomask:\n self.maskiter = None\n else:\n self.maskiter = ma._mask.flat","label":0},{"cid":"numpy/ma/core.py::MaskedIterator.__iter__","text":"numpy/ma/core.py::MaskedIterator.__iter__\n def __iter__(self):\n return self","label":0},{"cid":"numpy/ma/core.py::MaskedIterator.__getitem__","text":"numpy/ma/core.py::MaskedIterator.__getitem__\n def __getitem__(self, indx):\n result = self.dataiter.__getitem__(indx).view(type(self.ma))\n if self.maskiter is not None:\n _mask = self.maskiter.__getitem__(indx)\n if isinstance(_mask, ndarray):\n # set shape to match that of data; this is needed for matrices\n _mask.shape = result.shape\n result._mask = _mask\n elif isinstance(_mask, np.void):\n return mvoid(result, mask=_mask, hardmask=self.ma._hardmask)\n elif _mask: # Just a scalar, masked\n return masked\n return result","label":0},{"cid":"numpy/ma/core.py::MaskedIterator.__setitem__","text":"numpy/ma/core.py::MaskedIterator.__setitem__\n def __setitem__(self, index, value):\n self.dataiter[index] = getdata(value)\n if self.maskiter is not None:\n self.maskiter[index] = getmaskarray(value)","label":0},{"cid":"numpy/ma/core.py::MaskedIterator.__next__","text":"numpy/ma/core.py::MaskedIterator.__next__\n def __next__(self):\n \"\"\"\n Return the next value, or raise StopIteration.\n\n Examples\n --------\n >>> x = np.ma.array([3, 2], mask=[0, 1])\n >>> fl = x.flat\n >>> fl.next()\n 3\n >>> fl.next()\n masked_array(data = --,\n mask = True,\n fill_value = 1e+20)\n >>> fl.next()\n Traceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\n File \"/home/ralf/python/numpy/numpy/ma/core.py\", line 2243, in next\n d = self.dataiter.next()\n StopIteration\n\n \"\"\"\n d = next(self.dataiter)\n if self.maskiter is not None:\n m = next(self.maskiter)\n if isinstance(m, np.void):\n return mvoid(d, mask=m, hardmask=self.ma._hardmask)\n elif m: # Just a scalar, masked\n return masked\n return d","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__new__","text":"numpy/ma/core.py::MaskedArray.__new__\n def __new__(cls, data=None, mask=nomask, dtype=None, copy=False,\n subok=True, ndmin=0, fill_value=None,\n keep_mask=True, hard_mask=None, shrink=True,\n **options):\n \"\"\"\n Create a new masked array from scratch.\n\n Notes\n -----\n A masked array can also be created by taking a .view(MaskedArray).\n\n \"\"\"\n # Process data............\n _data = np.array(data, dtype=dtype, copy=copy, subok=True, ndmin=ndmin)\n _baseclass = getattr(data, '_baseclass', type(_data))\n # Check that we're not erasing the mask..........\n if isinstance(data, MaskedArray) and (data.shape != _data.shape):\n copy = True\n # Careful, cls might not always be MaskedArray...\n if not isinstance(data, cls) or not subok:\n _data = ndarray.view(_data, cls)\n else:\n _data = ndarray.view(_data, type(data))\n # Backwards compatibility w/ numpy.core.ma .......\n if hasattr(data, '_mask') and not isinstance(data, ndarray):\n _data._mask = data._mask\n _sharedmask = True\n # Process mask ...............................\n # Number of named fields (or zero if none)\n names_ = _data.dtype.names or ()\n # Type of the mask\n if names_:\n mdtype = make_mask_descr(_data.dtype)\n else:\n mdtype = MaskType\n # Case 1. : no mask in input ............\n if mask is nomask:\n # Erase the current mask ?\n if not keep_mask:\n # With a reduced version\n if shrink:\n _data._mask = nomask\n # With full version\n else:\n _data._mask = np.zeros(_data.shape, dtype=mdtype)\n # Check whether we missed something\n elif isinstance(data, (tuple, list)):\n try:\n # If data is a sequence of mas","label":0},{"cid":"numpy/ma/core.py::MaskedArray._update_from","text":"numpy/ma/core.py::MaskedArray._update_from\n def _update_from(self, obj):\n \"\"\"Copies some attributes of obj to self.\n \"\"\"\n if obj is not None and isinstance(obj, ndarray):\n _baseclass = type(obj)\n else:\n _baseclass = ndarray\n # We need to copy the _basedict to avoid backward propagation\n _optinfo = {}\n _optinfo.update(getattr(obj, '_optinfo', {}))\n _optinfo.update(getattr(obj, '_basedict', {}))\n if not isinstance(obj, MaskedArray):\n _optinfo.update(getattr(obj, '__dict__', {}))\n _dict = dict(_fill_value=getattr(obj, '_fill_value', None),\n _hardmask=getattr(obj, '_hardmask', False),\n _sharedmask=getattr(obj, '_sharedmask', False),\n _isfield=getattr(obj, '_isfield', False),\n _baseclass=getattr(obj, '_baseclass', _baseclass),\n _optinfo=_optinfo,\n _basedict=_optinfo)\n self.__dict__.update(_dict)\n self.__dict__.update(_optinfo)\n return","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__array_finalize__","text":"numpy/ma/core.py::MaskedArray.__array_finalize__\n def __array_finalize__(self, obj):\n \"\"\"Finalizes the masked array.\n \"\"\"\n # Get main attributes .........\n self._update_from(obj)\n # We have to decide how to initialize self.mask, based on\n # obj.mask. This is very difficult. There might be some\n # correspondence between the elements in the array we are being\n # created from (= obj) and us. Or... there might not. This method can\n # be called in all kinds of places for all kinds of reasons -- could\n # be empty_like, could be slicing, could be a ufunc, could be a view,\n # ... The numpy subclassing interface simply doesn't give us any way\n # to know, which means that at best this method will be based on\n # guesswork and heuristics. To make things worse, there isn't even any\n # clear consensus about what the desired behavior is. For instance,\n # most users think that np.empty_like(marr) -- which goes via this\n # method -- should return a masked array with an empty mask (see\n # gh-3404 and linked discussions), but others disagree, and they have\n # existing code which depends on empty_like returning an array that\n # matches the input mask.\n #\n # Historically our algorithm was: if the template object mask had the\n # same *number of elements* as us, then we used *it's mask object\n # itself* as our mask, so that writes to us would also write to the\n # original array. This is horribly broken in multiple ways.\n #\n # Now what we do instead is, if the template object mask has the same\n # number of elements as us, and we do not have the same base pointer\n # as the template object (b/c views like arr[...] should keep the same\n # mask), then we make a copy of the template object mask and use\n # that. This is also horribly broken but somewhat less so. Maybe.\n if isins","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__array_wrap__","text":"numpy/ma/core.py::MaskedArray.__array_wrap__\n def __array_wrap__(self, obj, context=None):\n \"\"\"\n Special hook for ufuncs.\n Wraps the numpy array and sets the mask according to context.\n \"\"\"\n result = obj.view(type(self))\n result._update_from(self)\n #..........\n if context is not None:\n result._mask = result._mask.copy()\n (func, args, _) = context\n m = reduce(mask_or, [getmaskarray(arg) for arg in args])\n # Get the domain mask................\n domain = ufunc_domain.get(func, None)\n if domain is not None:\n # Take the domain, and make sure it's a ndarray\n if len(args) > 2:\n d = filled(reduce(domain, args), True)\n else:\n d = filled(domain(*args), True)\n # Fill the result where the domain is wrong\n try:\n # Binary domain: take the last value\n fill_value = ufunc_fills[func][-1]\n except TypeError:\n # Unary domain: just use this one\n fill_value = ufunc_fills[func]\n except KeyError:\n # Domain not recognized, use fill_value instead\n fill_value = self.fill_value\n result = result.copy()\n np.copyto(result, fill_value, where=d)\n # Update the mask\n if m is nomask:\n if d is not nomask:\n m = d\n else:\n # Don't modify inplace, we risk back-propagation\n m = (m | d)\n # Make sure the mask has the proper size\n if result.shape == () and m:\n return masked\n else:\n result._mask = m\n result._sharedmask = False\n #....\n return result","label":0},{"cid":"numpy/ma/core.py::MaskedArray.view","text":"numpy/ma/core.py::MaskedArray.view\n def view(self, dtype=None, type=None, fill_value=None):\n \"\"\"\n Return a view of the MaskedArray data\n\n Parameters\n ----------\n dtype : data-type or ndarray sub-class, optional\n Data-type descriptor of the returned view, e.g., float32 or int16.\n The default, None, results in the view having the same data-type\n as `a`. As with ``ndarray.view``, dtype can also be specified as\n an ndarray sub-class, which then specifies the type of the\n returned object (this is equivalent to setting the ``type``\n parameter).\n type : Python type, optional\n Type of the returned view, e.g., ndarray or matrix. Again, the\n default None results in type preservation.\n\n Notes\n -----\n\n ``a.view()`` is used two different ways:\n\n ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view\n of the array's memory with a different data-type. This can cause a\n reinterpretation of the bytes of memory.\n\n ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just\n returns an instance of `ndarray_subclass` that looks at the same array\n (same shape, dtype, etc.) This does not cause a reinterpretation of the\n memory.\n\n If `fill_value` is not specified, but `dtype` is specified (and is not\n an ndarray sub-class), the `fill_value` of the MaskedArray will be\n reset. If neither `fill_value` nor `dtype` are specified (or if\n `dtype` is an ndarray sub-class), then the fill value is preserved.\n Finally, if `fill_value` is specified, but `dtype` is not, the fill\n value is set to the specified value.\n\n For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of\n bytes per entry than the previous dtype (for example, converting a\n regular array to a structured array), then the behavi","label":0},{"cid":"numpy/ma/core.py::MaskedArray.astype","text":"numpy/ma/core.py::MaskedArray.astype\n def astype(self, newtype):\n \"\"\"\n Returns a copy of the MaskedArray cast to given newtype.\n\n Returns\n -------\n output : MaskedArray\n A copy of self cast to input newtype.\n The returned record shape matches self.shape.\n\n Examples\n --------\n >>> x = np.ma.array([[1,2,3.1],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)\n >>> print x\n [[1.0 -- 3.1]\n [-- 5.0 --]\n [7.0 -- 9.0]]\n >>> print x.astype(int32)\n [[1 -- 3]\n [-- 5 --]\n [7 -- 9]]\n\n \"\"\"\n newtype = np.dtype(newtype)\n output = self._data.astype(newtype).view(type(self))\n output._update_from(self)\n names = output.dtype.names\n if names is None:\n output._mask = self._mask.astype(bool)\n else:\n if self._mask is nomask:\n output._mask = nomask\n else:\n output._mask = self._mask.astype([(n, bool) for n in names])\n # Don't check _fill_value if it's None, that'll speed things up\n if self._fill_value is not None:\n output._fill_value = _check_fill_value(self._fill_value, newtype)\n return output","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__getitem__","text":"numpy/ma/core.py::MaskedArray.__getitem__\n def __getitem__(self, indx):\n \"\"\"x.__getitem__(y) <==> x[y]\n\n Return the item described by i, as a masked array.\n\n \"\"\"\n # This test is useful, but we should keep things light...\n# if getmask(indx) is not nomask:\n# msg = \"Masked arrays must be filled before they can be used as indices!\"\n# raise IndexError(msg)\n _data = ndarray.view(self, ndarray)\n dout = ndarray.__getitem__(_data, indx)\n # We could directly use ndarray.__getitem__ on self...\n # But then we would have to modify __array_finalize__ to prevent the\n # mask of being reshaped if it hasn't been set up properly yet...\n # So it's easier to stick to the current version\n _mask = self._mask\n if not getattr(dout, 'ndim', False):\n # A record ................\n if isinstance(dout, np.void):\n mask = _mask[indx]\n # We should always re-cast to mvoid, otherwise users can\n # change masks on rows that already have masked values, but not\n # on rows that have no masked values, which is inconsistent.\n dout = mvoid(dout, mask=mask, hardmask=self._hardmask)\n # Just a scalar............\n elif _mask is not nomask and _mask[indx]:\n return masked\n else:\n # Force dout to MA ........\n dout = dout.view(type(self))\n # Inherit attributes from self\n dout._update_from(self)\n # Check the fill_value ....\n if isinstance(indx, basestring):\n if self._fill_value is not None:\n dout._fill_value = self._fill_value[indx]\n dout._isfield = True\n # Update the mask if needed\n if _mask is not nomask:\n dout._mask = _mask[indx]\n dout._sharedmask = True\n# Note: Don't try to check for ","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__setitem__","text":"numpy/ma/core.py::MaskedArray.__setitem__\n def __setitem__(self, indx, value):\n \"\"\"x.__setitem__(i, y) <==> x[i]=y\n\n Set item described by index. If value is masked, masks those\n locations.\n\n \"\"\"\n if self is masked:\n raise MaskError('Cannot alter the masked element.')\n # This test is useful, but we should keep things light...\n# if getmask(indx) is not nomask:\n# msg = \"Masked arrays must be filled before they can be used as indices!\"\n# raise IndexError(msg)\n _data = ndarray.view(self, ndarray.__getattribute__(self, '_baseclass'))\n _mask = ndarray.__getattribute__(self, '_mask')\n if isinstance(indx, basestring):\n ndarray.__setitem__(_data, indx, value)\n if _mask is nomask:\n self._mask = _mask = make_mask_none(self.shape, self.dtype)\n _mask[indx] = getmask(value)\n return\n #........................................\n _dtype = ndarray.__getattribute__(_data, 'dtype')\n nbfields = len(_dtype.names or ())\n #........................................\n if value is masked:\n # The mask wasn't set: create a full version...\n if _mask is nomask:\n _mask = self._mask = make_mask_none(self.shape, _dtype)\n # Now, set the mask to its value.\n if nbfields:\n _mask[indx] = tuple([True] * nbfields)\n else:\n _mask[indx] = True\n if not self._isfield:\n self._sharedmask = False\n return\n #........................................\n # Get the _data part of the new value\n dval = value\n # Get the _mask part of the new value\n mval = getattr(value, '_mask', nomask)\n if nbfields and mval is nomask:\n mval = tuple([False] * nbfields)\n if _mask is nomask:\n # Set the data, then the mask\n ndarray.__set","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__getslice__","text":"numpy/ma/core.py::MaskedArray.__getslice__\n def __getslice__(self, i, j):\n \"\"\"x.__getslice__(i, j) <==> x[i:j]\n\n Return the slice described by (i, j). The use of negative\n indices is not supported.\n\n \"\"\"\n return self.__getitem__(slice(i, j))","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__setslice__","text":"numpy/ma/core.py::MaskedArray.__setslice__\n def __setslice__(self, i, j, value):\n \"\"\"x.__setslice__(i, j, value) <==> x[i:j]=value\n\n Set the slice (i,j) of a to value. If value is masked, mask\n those locations.\n\n \"\"\"\n self.__setitem__(slice(i, j), value)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__setmask__","text":"numpy/ma/core.py::MaskedArray.__setmask__\n def __setmask__(self, mask, copy=False):\n \"\"\"Set the mask.\n\n \"\"\"\n idtype = ndarray.__getattribute__(self, 'dtype')\n current_mask = ndarray.__getattribute__(self, '_mask')\n if mask is masked:\n mask = True\n # Make sure the mask is set\n if (current_mask is nomask):\n # Just don't do anything is there's nothing to do...\n if mask is nomask:\n return\n current_mask = self._mask = make_mask_none(self.shape, idtype)\n # No named fields.........\n if idtype.names is None:\n # Hardmask: don't unmask the data\n if self._hardmask:\n current_mask |= mask\n # Softmask: set everything to False\n # If it's obviously a compatible scalar, use a quick update\n # method...\n elif isinstance(mask, (int, float, np.bool_, np.number)):\n current_mask[...] = mask\n # ...otherwise fall back to the slower, general purpose way.\n else:\n current_mask.flat = mask\n # Named fields w/ ............\n else:\n mdtype = current_mask.dtype\n mask = np.array(mask, copy=False)\n # Mask is a singleton\n if not mask.ndim:\n # It's a boolean : make a record\n if mask.dtype.kind == 'b':\n mask = np.array(tuple([mask.item()]*len(mdtype)),\n dtype=mdtype)\n # It's a record: make sure the dtype is correct\n else:\n mask = mask.astype(mdtype)\n # Mask is a sequence\n else:\n # Make sure the new mask is a ndarray with the proper dtype\n try:\n mask = np.array(mask, copy=copy, dtype=mdtype)\n # Or assume it's a sequence of bool/int\n except TypeError:\n ","label":0},{"cid":"numpy/ma/core.py::MaskedArray._get_mask","text":"numpy/ma/core.py::MaskedArray._get_mask\n def _get_mask(self):\n \"\"\"Return the current mask.\n\n \"\"\"\n # We could try to force a reshape, but that wouldn't work in some cases.\n# return self._mask.reshape(self.shape)\n return self._mask","label":0},{"cid":"numpy/ma/core.py::MaskedArray._get_recordmask","text":"numpy/ma/core.py::MaskedArray._get_recordmask\n def _get_recordmask(self):\n \"\"\"\n Return the mask of the records.\n A record is masked when all the fields are masked.\n\n \"\"\"\n _mask = ndarray.__getattribute__(self, '_mask').view(ndarray)\n if _mask.dtype.names is None:\n return _mask\n return np.all(flatten_structured_array(_mask), axis= -1)","label":0},{"cid":"numpy/ma/core.py::MaskedArray._set_recordmask","text":"numpy/ma/core.py::MaskedArray._set_recordmask\n def _set_recordmask(self):\n \"\"\"Return the mask of the records.\n A record is masked when all the fields are masked.\n\n \"\"\"\n raise NotImplementedError(\"Coming soon: setting the mask per records!\")","label":0},{"cid":"numpy/ma/core.py::MaskedArray.harden_mask","text":"numpy/ma/core.py::MaskedArray.harden_mask\n def harden_mask(self):\n \"\"\"\n Force the mask to hard.\n\n Whether the mask of a masked array is hard or soft is determined by\n its `hardmask` property. `harden_mask` sets `hardmask` to True.\n\n See Also\n --------\n hardmask\n\n \"\"\"\n self._hardmask = True\n return self","label":0},{"cid":"numpy/ma/core.py::MaskedArray.soften_mask","text":"numpy/ma/core.py::MaskedArray.soften_mask\n def soften_mask(self):\n \"\"\"\n Force the mask to soft.\n\n Whether the mask of a masked array is hard or soft is determined by\n its `hardmask` property. `soften_mask` sets `hardmask` to False.\n\n See Also\n --------\n hardmask\n\n \"\"\"\n self._hardmask = False\n return self","label":0},{"cid":"numpy/ma/core.py::MaskedArray.unshare_mask","text":"numpy/ma/core.py::MaskedArray.unshare_mask\n def unshare_mask(self):\n \"\"\"\n Copy the mask and set the sharedmask flag to False.\n\n Whether the mask is shared between masked arrays can be seen from\n the `sharedmask` property. `unshare_mask` ensures the mask is not shared.\n A copy of the mask is only made if it was shared.\n\n See Also\n --------\n sharedmask\n\n \"\"\"\n if self._sharedmask:\n self._mask = self._mask.copy()\n self._sharedmask = False\n return self","label":0},{"cid":"numpy/ma/core.py::MaskedArray.shrink_mask","text":"numpy/ma/core.py::MaskedArray.shrink_mask\n def shrink_mask(self):\n \"\"\"\n Reduce a mask to nomask when possible.\n\n Parameters\n ----------\n None\n\n Returns\n -------\n None\n\n Examples\n --------\n >>> x = np.ma.array([[1,2 ], [3, 4]], mask=[0]*4)\n >>> x.mask\n array([[False, False],\n [False, False]], dtype=bool)\n >>> x.shrink_mask()\n >>> x.mask\n False\n\n \"\"\"\n m = self._mask\n if m.ndim and not m.any():\n self._mask = nomask\n return self","label":0},{"cid":"numpy/ma/core.py::MaskedArray._get_data","text":"numpy/ma/core.py::MaskedArray._get_data\n def _get_data(self):\n \"\"\"Return the current data, as a view of the original\n underlying data.\n\n \"\"\"\n return ndarray.view(self, self._baseclass)","label":0},{"cid":"numpy/ma/core.py::MaskedArray._get_flat","text":"numpy/ma/core.py::MaskedArray._get_flat\n def _get_flat(self):\n \"Return a flat iterator.\"\n return MaskedIterator(self)","label":0},{"cid":"numpy/ma/core.py::MaskedArray._set_flat","text":"numpy/ma/core.py::MaskedArray._set_flat\n def _set_flat (self, value):\n \"Set a flattened version of self to value.\"\n y = self.ravel()\n y[:] = value","label":0},{"cid":"numpy/ma/core.py::MaskedArray.get_fill_value","text":"numpy/ma/core.py::MaskedArray.get_fill_value\n def get_fill_value(self):\n \"\"\"\n Return the filling value of the masked array.\n\n Returns\n -------\n fill_value : scalar\n The filling value.\n\n Examples\n --------\n >>> for dt in [np.int32, np.int64, np.float64, np.complex128]:\n ... np.ma.array([0, 1], dtype=dt).get_fill_value()\n ...\n 999999\n 999999\n 1e+20\n (1e+20+0j)\n\n >>> x = np.ma.array([0, 1.], fill_value=-np.inf)\n >>> x.get_fill_value()\n -inf\n\n \"\"\"\n if self._fill_value is None:\n self._fill_value = _check_fill_value(None, self.dtype)\n return self._fill_value[()]","label":0},{"cid":"numpy/ma/core.py::MaskedArray.set_fill_value","text":"numpy/ma/core.py::MaskedArray.set_fill_value\n def set_fill_value(self, value=None):\n \"\"\"\n Set the filling value of the masked array.\n\n Parameters\n ----------\n value : scalar, optional\n The new filling value. Default is None, in which case a default\n based on the data type is used.\n\n See Also\n --------\n ma.set_fill_value : Equivalent function.\n\n Examples\n --------\n >>> x = np.ma.array([0, 1.], fill_value=-np.inf)\n >>> x.fill_value\n -inf\n >>> x.set_fill_value(np.pi)\n >>> x.fill_value\n 3.1415926535897931\n\n Reset to default:\n\n >>> x.set_fill_value()\n >>> x.fill_value\n 1e+20\n\n \"\"\"\n target = _check_fill_value(value, self.dtype)\n _fill_value = self._fill_value\n if _fill_value is None:\n # Create the attribute if it was undefined\n self._fill_value = target\n else:\n # Don't overwrite the attribute, just fill it (for propagation)\n _fill_value[()] = target","label":0},{"cid":"numpy/ma/core.py::MaskedArray.filled","text":"numpy/ma/core.py::MaskedArray.filled\n def filled(self, fill_value=None):\n \"\"\"\n Return a copy of self, with masked values filled with a given value.\n\n Parameters\n ----------\n fill_value : scalar, optional\n The value to use for invalid entries (None by default).\n If None, the `fill_value` attribute of the array is used instead.\n\n Returns\n -------\n filled_array : ndarray\n A copy of ``self`` with invalid entries replaced by *fill_value*\n (be it the function argument or the attribute of ``self``.\n\n Notes\n -----\n The result is **not** a MaskedArray!\n\n Examples\n --------\n >>> x = np.ma.array([1,2,3,4,5], mask=[0,0,1,0,1], fill_value=-999)\n >>> x.filled()\n array([1, 2, -999, 4, -999])\n >>> type(x.filled())\n <type 'numpy.ndarray'>\n\n Subclassing is preserved. This means that if the data part of the masked\n array is a matrix, `filled` returns a matrix:\n\n >>> x = np.ma.array(np.matrix([[1, 2], [3, 4]]), mask=[[0, 1], [1, 0]])\n >>> x.filled()\n matrix([[ 1, 999999],\n [999999, 4]])\n\n \"\"\"\n m = self._mask\n if m is nomask:\n return self._data\n #\n if fill_value is None:\n fill_value = self.fill_value\n else:\n fill_value = _check_fill_value(fill_value, self.dtype)\n #\n if self is masked_singleton:\n return np.asanyarray(fill_value)\n #\n if m.dtype.names:\n result = self._data.copy('K')\n _recursive_filled(result, self._mask, fill_value)\n elif not m.any():\n return self._data\n else:\n result = self._data.copy('K')\n try:\n np.copyto(result, fill_value, where=m)\n except (TypeError, AttributeError):\n fill_value = narray(fill_value, dtype=object)\n ","label":0},{"cid":"numpy/ma/core.py::MaskedArray.compressed","text":"numpy/ma/core.py::MaskedArray.compressed\n def compressed(self):\n \"\"\"\n Return all the non-masked data as a 1-D array.\n\n Returns\n -------\n data : ndarray\n A new `ndarray` holding the non-masked data is returned.\n\n Notes\n -----\n The result is **not** a MaskedArray!\n\n Examples\n --------\n >>> x = np.ma.array(np.arange(5), mask=[0]*2 + [1]*3)\n >>> x.compressed()\n array([0, 1])\n >>> type(x.compressed())\n <type 'numpy.ndarray'>\n\n \"\"\"\n data = ndarray.ravel(self._data)\n if self._mask is not nomask:\n data = data.compress(np.logical_not(ndarray.ravel(self._mask)))\n return data","label":0},{"cid":"numpy/ma/core.py::MaskedArray.compress","text":"numpy/ma/core.py::MaskedArray.compress\n def compress(self, condition, axis=None, out=None):\n \"\"\"\n Return `a` where condition is ``True``.\n\n If condition is a `MaskedArray`, missing values are considered\n as ``False``.\n\n Parameters\n ----------\n condition : var\n Boolean 1-d array selecting which entries to return. If len(condition)\n is less than the size of a along the axis, then output is truncated\n to length of condition array.\n axis : {None, int}, optional\n Axis along which the operation must be performed.\n out : {None, ndarray}, optional\n Alternative output array in which to place the result. It must have\n the same shape as the expected output but the type will be cast if\n necessary.\n\n Returns\n -------\n result : MaskedArray\n A :class:`MaskedArray` object.\n\n Notes\n -----\n Please note the difference with :meth:`compressed` !\n The output of :meth:`compress` has a mask, the output of\n :meth:`compressed` does not.\n\n Examples\n --------\n >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)\n >>> print x\n [[1 -- 3]\n [-- 5 --]\n [7 -- 9]]\n >>> x.compress([1, 0, 1])\n masked_array(data = [1 3],\n mask = [False False],\n fill_value=999999)\n\n >>> x.compress([1, 0, 1], axis=1)\n masked_array(data =\n [[1 3]\n [-- --]\n [7 9]],\n mask =\n [[False False]\n [ True True]\n [False False]],\n fill_value=999999)\n\n \"\"\"\n # Get the basic components\n (_data, _mask) = (self._data, self._mask)\n # Force the condition to a regular ndarray (forget the missing values...)\n condition = np.array(condition, copy=False, subok=False)\n #\n _new = _data.compress(condition","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__str__","text":"numpy/ma/core.py::MaskedArray.__str__\n def __str__(self):\n \"\"\"String representation.\n\n \"\"\"\n if masked_print_option.enabled():\n f = masked_print_option\n if self is masked:\n return str(f)\n m = self._mask\n if m is nomask:\n res = self._data\n else:\n if m.shape == ():\n if m.dtype.names:\n m = m.view((bool, len(m.dtype)))\n if m.any():\n return str(tuple((f if _m else _d) for _d, _m in\n zip(self._data.tolist(), m)))\n else:\n return str(self._data)\n elif m:\n return str(f)\n else:\n return str(self._data)\n # convert to object array to make filled work\n names = self.dtype.names\n if names is None:\n res = self._data.astype(\"O\")\n res.view(ndarray)[m] = f\n else:\n rdtype = _recursive_make_descr(self.dtype, \"O\")\n res = self._data.astype(rdtype)\n _recursive_printoption(res, m, f)\n else:\n res = self.filled(self.fill_value)\n return str(res)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__repr__","text":"numpy/ma/core.py::MaskedArray.__repr__\n def __repr__(self):\n \"\"\"Literal string representation.\n\n \"\"\"\n n = len(self.shape)\n if self._baseclass is np.ndarray:\n name = 'array'\n else:\n name = self._baseclass.__name__\n\n parameters = dict(name=name, nlen=\" \" * len(name),\n data=str(self), mask=str(self._mask),\n fill=str(self.fill_value), dtype=str(self.dtype))\n if self.dtype.names:\n if n <= 1:\n return _print_templates['short_flx'] % parameters\n return _print_templates['long_flx'] % parameters\n elif n <= 1:\n return _print_templates['short_std'] % parameters\n return _print_templates['long_std'] % parameters","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__eq__","text":"numpy/ma/core.py::MaskedArray.__eq__\n def __eq__(self, other):\n \"Check whether other equals self elementwise\"\n if self is masked:\n return masked\n omask = getattr(other, '_mask', nomask)\n if omask is nomask:\n check = ndarray.__eq__(self.filled(0), other)\n try:\n check = check.view(type(self))\n check._mask = self._mask\n except AttributeError:\n # Dang, we have a bool instead of an array: return the bool\n return check\n else:\n odata = filled(other, 0)\n check = ndarray.__eq__(self.filled(0), odata).view(type(self))\n if self._mask is nomask:\n check._mask = omask\n else:\n mask = mask_or(self._mask, omask)\n if mask.dtype.names:\n if mask.size > 1:\n axis = 1\n else:\n axis = None\n try:\n mask = mask.view((bool_, len(self.dtype))).all(axis)\n except ValueError:\n mask = np.all([[f[n].all() for n in mask.dtype.names]\n for f in mask], axis=axis)\n check._mask = mask\n return check","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__ne__","text":"numpy/ma/core.py::MaskedArray.__ne__\n def __ne__(self, other):\n \"Check whether other doesn't equal self elementwise\"\n if self is masked:\n return masked\n omask = getattr(other, '_mask', nomask)\n if omask is nomask:\n check = ndarray.__ne__(self.filled(0), other)\n try:\n check = check.view(type(self))\n check._mask = self._mask\n except AttributeError:\n # In case check is a boolean (or a numpy.bool)\n return check\n else:\n odata = filled(other, 0)\n check = ndarray.__ne__(self.filled(0), odata).view(type(self))\n if self._mask is nomask:\n check._mask = omask\n else:\n mask = mask_or(self._mask, omask)\n if mask.dtype.names:\n if mask.size > 1:\n axis = 1\n else:\n axis = None\n try:\n mask = mask.view((bool_, len(self.dtype))).all(axis)\n except ValueError:\n mask = np.all([[f[n].all() for n in mask.dtype.names]\n for f in mask], axis=axis)\n check._mask = mask\n return check","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__add__","text":"numpy/ma/core.py::MaskedArray.__add__\n def __add__(self, other):\n \"Add other to self, and return a new masked array.\"\n return add(self, other)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__radd__","text":"numpy/ma/core.py::MaskedArray.__radd__\n def __radd__(self, other):\n \"Add other to self, and return a new masked array.\"\n return add(self, other)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__sub__","text":"numpy/ma/core.py::MaskedArray.__sub__\n def __sub__(self, other):\n \"Subtract other to self, and return a new masked array.\"\n return subtract(self, other)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__rsub__","text":"numpy/ma/core.py::MaskedArray.__rsub__\n def __rsub__(self, other):\n \"Subtract other to self, and return a new masked array.\"\n return subtract(other, self)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__mul__","text":"numpy/ma/core.py::MaskedArray.__mul__\n def __mul__(self, other):\n \"Multiply other by self, and return a new masked array.\"\n return multiply(self, other)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__rmul__","text":"numpy/ma/core.py::MaskedArray.__rmul__\n def __rmul__(self, other):\n \"Multiply other by self, and return a new masked array.\"\n return multiply(self, other)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__div__","text":"numpy/ma/core.py::MaskedArray.__div__\n def __div__(self, other):\n \"Divide other into self, and return a new masked array.\"\n return divide(self, other)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__truediv__","text":"numpy/ma/core.py::MaskedArray.__truediv__\n def __truediv__(self, other):\n \"Divide other into self, and return a new masked array.\"\n return true_divide(self, other)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__rtruediv__","text":"numpy/ma/core.py::MaskedArray.__rtruediv__\n def __rtruediv__(self, other):\n \"Divide other into self, and return a new masked array.\"\n return true_divide(other, self)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__floordiv__","text":"numpy/ma/core.py::MaskedArray.__floordiv__\n def __floordiv__(self, other):\n \"Divide other into self, and return a new masked array.\"\n return floor_divide(self, other)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__rfloordiv__","text":"numpy/ma/core.py::MaskedArray.__rfloordiv__\n def __rfloordiv__(self, other):\n \"Divide other into self, and return a new masked array.\"\n return floor_divide(other, self)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__pow__","text":"numpy/ma/core.py::MaskedArray.__pow__\n def __pow__(self, other):\n \"Raise self to the power other, masking the potential NaNs/Infs\"\n return power(self, other)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__rpow__","text":"numpy/ma/core.py::MaskedArray.__rpow__\n def __rpow__(self, other):\n \"Raise self to the power other, masking the potential NaNs/Infs\"\n return power(other, self)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__iadd__","text":"numpy/ma/core.py::MaskedArray.__iadd__\n def __iadd__(self, other):\n \"Add other to self in-place.\"\n m = getmask(other)\n if self._mask is nomask:\n if m is not nomask and m.any():\n self._mask = make_mask_none(self.shape, self.dtype)\n self._mask += m\n else:\n if m is not nomask:\n self._mask += m\n ndarray.__iadd__(self._data, np.where(self._mask, 0, getdata(other)))\n return self","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__isub__","text":"numpy/ma/core.py::MaskedArray.__isub__\n def __isub__(self, other):\n \"Subtract other from self in-place.\"\n m = getmask(other)\n if self._mask is nomask:\n if m is not nomask and m.any():\n self._mask = make_mask_none(self.shape, self.dtype)\n self._mask += m\n elif m is not nomask:\n self._mask += m\n ndarray.__isub__(self._data, np.where(self._mask, 0, getdata(other)))\n return self","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__imul__","text":"numpy/ma/core.py::MaskedArray.__imul__\n def __imul__(self, other):\n \"Multiply self by other in-place.\"\n m = getmask(other)\n if self._mask is nomask:\n if m is not nomask and m.any():\n self._mask = make_mask_none(self.shape, self.dtype)\n self._mask += m\n elif m is not nomask:\n self._mask += m\n ndarray.__imul__(self._data, np.where(self._mask, 1, getdata(other)))\n return self","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__idiv__","text":"numpy/ma/core.py::MaskedArray.__idiv__\n def __idiv__(self, other):\n \"Divide self by other in-place.\"\n other_data = getdata(other)\n dom_mask = _DomainSafeDivide().__call__(self._data, other_data)\n other_mask = getmask(other)\n new_mask = mask_or(other_mask, dom_mask)\n # The following 3 lines control the domain filling\n if dom_mask.any():\n (_, fval) = ufunc_fills[np.divide]\n other_data = np.where(dom_mask, fval, other_data)\n# self._mask = mask_or(self._mask, new_mask)\n self._mask |= new_mask\n ndarray.__idiv__(self._data, np.where(self._mask, 1, other_data))\n return self","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__ifloordiv__","text":"numpy/ma/core.py::MaskedArray.__ifloordiv__\n def __ifloordiv__(self, other):\n \"Floor divide self by other in-place.\"\n other_data = getdata(other)\n dom_mask = _DomainSafeDivide().__call__(self._data, other_data)\n other_mask = getmask(other)\n new_mask = mask_or(other_mask, dom_mask)\n # The following 3 lines control the domain filling\n if dom_mask.any():\n (_, fval) = ufunc_fills[np.floor_divide]\n other_data = np.where(dom_mask, fval, other_data)\n# self._mask = mask_or(self._mask, new_mask)\n self._mask |= new_mask\n ndarray.__ifloordiv__(self._data, np.where(self._mask, 1, other_data))\n return self","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__itruediv__","text":"numpy/ma/core.py::MaskedArray.__itruediv__\n def __itruediv__(self, other):\n \"True divide self by other in-place.\"\n other_data = getdata(other)\n dom_mask = _DomainSafeDivide().__call__(self._data, other_data)\n other_mask = getmask(other)\n new_mask = mask_or(other_mask, dom_mask)\n # The following 3 lines control the domain filling\n if dom_mask.any():\n (_, fval) = ufunc_fills[np.true_divide]\n other_data = np.where(dom_mask, fval, other_data)\n# self._mask = mask_or(self._mask, new_mask)\n self._mask |= new_mask\n ndarray.__itruediv__(self._data, np.where(self._mask, 1, other_data))\n return self","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__ipow__","text":"numpy/ma/core.py::MaskedArray.__ipow__\n def __ipow__(self, other):\n \"Raise self to the power other, in place.\"\n other_data = getdata(other)\n other_mask = getmask(other)\n with np.errstate(divide='ignore', invalid='ignore'):\n ndarray.__ipow__(self._data, np.where(self._mask, 1, other_data))\n invalid = np.logical_not(np.isfinite(self._data))\n if invalid.any():\n if self._mask is not nomask:\n self._mask |= invalid\n else:\n self._mask = invalid\n np.copyto(self._data, self.fill_value, where=invalid)\n new_mask = mask_or(other_mask, invalid)\n self._mask = mask_or(self._mask, new_mask)\n return self","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__float__","text":"numpy/ma/core.py::MaskedArray.__float__\n def __float__(self):\n \"Convert to float.\"\n if self.size > 1:\n raise TypeError(\"Only length-1 arrays can be converted \"\n \"to Python scalars\")\n elif self._mask:\n warnings.warn(\"Warning: converting a masked element to nan.\")\n return np.nan\n return float(self.item())","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__int__","text":"numpy/ma/core.py::MaskedArray.__int__\n def __int__(self):\n \"Convert to int.\"\n if self.size > 1:\n raise TypeError(\"Only length-1 arrays can be converted \"\n \"to Python scalars\")\n elif self._mask:\n raise MaskError('Cannot convert masked element to a Python int.')\n return int(self.item())","label":0},{"cid":"numpy/ma/core.py::MaskedArray.get_imag","text":"numpy/ma/core.py::MaskedArray.get_imag\n def get_imag(self):\n \"\"\"\n Return the imaginary part of the masked array.\n\n The returned array is a view on the imaginary part of the `MaskedArray`\n whose `get_imag` method is called.\n\n Parameters\n ----------\n None\n\n Returns\n -------\n result : MaskedArray\n The imaginary part of the masked array.\n\n See Also\n --------\n get_real, real, imag\n\n Examples\n --------\n >>> x = np.ma.array([1+1.j, -2j, 3.45+1.6j], mask=[False, True, False])\n >>> x.get_imag()\n masked_array(data = [1.0 -- 1.6],\n mask = [False True False],\n fill_value = 1e+20)\n\n \"\"\"\n result = self._data.imag.view(type(self))\n result.__setmask__(self._mask)\n return result","label":0},{"cid":"numpy/ma/core.py::MaskedArray.get_real","text":"numpy/ma/core.py::MaskedArray.get_real\n def get_real(self):\n \"\"\"\n Return the real part of the masked array.\n\n The returned array is a view on the real part of the `MaskedArray`\n whose `get_real` method is called.\n\n Parameters\n ----------\n None\n\n Returns\n -------\n result : MaskedArray\n The real part of the masked array.\n\n See Also\n --------\n get_imag, real, imag\n\n Examples\n --------\n >>> x = np.ma.array([1+1.j, -2j, 3.45+1.6j], mask=[False, True, False])\n >>> x.get_real()\n masked_array(data = [1.0 -- 3.45],\n mask = [False True False],\n fill_value = 1e+20)\n\n \"\"\"\n result = self._data.real.view(type(self))\n result.__setmask__(self._mask)\n return result","label":0},{"cid":"numpy/ma/core.py::MaskedArray.count","text":"numpy/ma/core.py::MaskedArray.count\n def count(self, axis=None):\n \"\"\"\n Count the non-masked elements of the array along the given axis.\n\n Parameters\n ----------\n axis : int, optional\n Axis along which to count the non-masked elements. If `axis` is\n `None`, all non-masked elements are counted.\n\n Returns\n -------\n result : int or ndarray\n If `axis` is `None`, an integer count is returned. When `axis` is\n not `None`, an array with shape determined by the lengths of the\n remaining axes, is returned.\n\n See Also\n --------\n count_masked : Count masked elements in array or along a given axis.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = ma.arange(6).reshape((2, 3))\n >>> a[1, :] = ma.masked\n >>> a\n masked_array(data =\n [[0 1 2]\n [-- -- --]],\n mask =\n [[False False False]\n [ True True True]],\n fill_value = 999999)\n >>> a.count()\n 3\n\n When the `axis` keyword is specified an array of appropriate size is\n returned.\n\n >>> a.count(axis=0)\n array([1, 1, 1])\n >>> a.count(axis=1)\n array([3, 0])\n\n \"\"\"\n m = self._mask\n s = self.shape\n if m is nomask:\n if axis is None:\n return self.size\n else:\n n = s[axis]\n t = list(s)\n del t[axis]\n return np.full(t, n, dtype=np.intp)\n n1 = np.size(m, axis)\n n2 = np.sum(m, axis=axis, dtype=np.intp)\n if axis is None:\n return (n1 - n2)\n else:\n return narray(n1 - n2)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.ravel","text":"numpy/ma/core.py::MaskedArray.ravel\n def ravel(self):\n \"\"\"\n Returns a 1D version of self, as a view.\n\n Returns\n -------\n MaskedArray\n Output view is of shape ``(self.size,)`` (or\n ``(np.ma.product(self.shape),)``).\n\n Examples\n --------\n >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)\n >>> print x\n [[1 -- 3]\n [-- 5 --]\n [7 -- 9]]\n >>> print x.ravel()\n [1 -- 3 -- 5 -- 7 -- 9]\n\n \"\"\"\n r = ndarray.ravel(self._data).view(type(self))\n r._update_from(self)\n if self._mask is not nomask:\n r._mask = ndarray.ravel(self._mask).reshape(r.shape)\n else:\n r._mask = nomask\n return r","label":0},{"cid":"numpy/ma/core.py::MaskedArray.reshape","text":"numpy/ma/core.py::MaskedArray.reshape\n def reshape (self, *s, **kwargs):\n \"\"\"\n Give a new shape to the array without changing its data.\n\n Returns a masked array containing the same data, but with a new shape.\n The result is a view on the original array; if this is not possible, a\n ValueError is raised.\n\n Parameters\n ----------\n shape : int or tuple of ints\n The new shape should be compatible with the original shape. If an\n integer is supplied, then the result will be a 1-D array of that\n length.\n order : {'C', 'F'}, optional\n Determines whether the array data should be viewed as in C\n (row-major) or FORTRAN (column-major) order.\n\n Returns\n -------\n reshaped_array : array\n A new view on the array.\n\n See Also\n --------\n reshape : Equivalent function in the masked array module.\n numpy.ndarray.reshape : Equivalent method on ndarray object.\n numpy.reshape : Equivalent function in the NumPy module.\n\n Notes\n -----\n The reshaping operation cannot guarantee that a copy will not be made,\n to modify the shape in place, use ``a.shape = s``\n\n Examples\n --------\n >>> x = np.ma.array([[1,2],[3,4]], mask=[1,0,0,1])\n >>> print x\n [[-- 2]\n [3 --]]\n >>> x = x.reshape((4,1))\n >>> print x\n [[--]\n [2]\n [3]\n [--]]\n\n \"\"\"\n kwargs.update(order=kwargs.get('order', 'C'))\n result = self._data.reshape(*s, **kwargs).view(type(self))\n result._update_from(self)\n mask = self._mask\n if mask is not nomask:\n result._mask = mask.reshape(*s, **kwargs)\n return result","label":0},{"cid":"numpy/ma/core.py::MaskedArray.resize","text":"numpy/ma/core.py::MaskedArray.resize\n def resize(self, newshape, refcheck=True, order=False):\n \"\"\"\n .. warning::\n\n This method does nothing, except raise a ValueError exception. A\n masked array does not own its data and therefore cannot safely be\n resized in place. Use the `numpy.ma.resize` function instead.\n\n This method is difficult to implement safely and may be deprecated in\n future releases of NumPy.\n\n \"\"\"\n # Note : the 'order' keyword looks broken, let's just drop it\n# try:\n# ndarray.resize(self, newshape, refcheck=refcheck)\n# if self.mask is not nomask:\n# self._mask.resize(newshape, refcheck=refcheck)\n# except ValueError:\n# raise ValueError(\"Cannot resize an array that has been referenced \"\n# \"or is referencing another array in this way.\\n\"\n# \"Use the numpy.ma.resize function.\")\n# return None\n errmsg = \"A masked array does not own its data \"\\\n \"and therefore cannot be resized.\\n\" \\\n \"Use the numpy.ma.resize function instead.\"\n raise ValueError(errmsg)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.put","text":"numpy/ma/core.py::MaskedArray.put\n def put(self, indices, values, mode='raise'):\n \"\"\"\n Set storage-indexed locations to corresponding values.\n\n Sets self._data.flat[n] = values[n] for each n in indices.\n If `values` is shorter than `indices` then it will repeat.\n If `values` has some masked values, the initial mask is updated\n in consequence, else the corresponding values are unmasked.\n\n Parameters\n ----------\n indices : 1-D array_like\n Target indices, interpreted as integers.\n values : array_like\n Values to place in self._data copy at target indices.\n mode : {'raise', 'wrap', 'clip'}, optional\n Specifies how out-of-bounds indices will behave.\n 'raise' : raise an error.\n 'wrap' : wrap around.\n 'clip' : clip to the range.\n\n Notes\n -----\n `values` can be a scalar or length 1 array.\n\n Examples\n --------\n >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)\n >>> print x\n [[1 -- 3]\n [-- 5 --]\n [7 -- 9]]\n >>> x.put([0,4,8],[10,20,30])\n >>> print x\n [[10 -- 3]\n [-- 20 --]\n [7 -- 30]]\n\n >>> x.put(4,999)\n >>> print x\n [[10 -- 3]\n [-- 999 --]\n [7 -- 30]]\n\n \"\"\"\n m = self._mask\n # Hard mask: Get rid of the values/indices that fall on masked data\n if self._hardmask and self._mask is not nomask:\n mask = self._mask[indices]\n indices = narray(indices, copy=False)\n values = narray(values, copy=False, subok=True)\n values.resize(indices.shape)\n indices = indices[~mask]\n values = values[~mask]\n #....\n self._data.put(indices, values, mode=mode)\n #....\n if m is nomask:\n m = getmask(values)\n else:\n m = m.copy()\n if getmask(values","label":0},{"cid":"numpy/ma/core.py::MaskedArray.ids","text":"numpy/ma/core.py::MaskedArray.ids\n def ids (self):\n \"\"\"\n Return the addresses of the data and mask areas.\n\n Parameters\n ----------\n None\n\n Examples\n --------\n >>> x = np.ma.array([1, 2, 3], mask=[0, 1, 1])\n >>> x.ids()\n (166670640, 166659832)\n\n If the array has no mask, the address of `nomask` is returned. This address\n is typically not close to the data in memory:\n\n >>> x = np.ma.array([1, 2, 3])\n >>> x.ids()\n (166691080, 3083169284L)\n\n \"\"\"\n if self._mask is nomask:\n return (self.ctypes.data, id(nomask))\n return (self.ctypes.data, self._mask.ctypes.data)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.iscontiguous","text":"numpy/ma/core.py::MaskedArray.iscontiguous\n def iscontiguous(self):\n \"\"\"\n Return a boolean indicating whether the data is contiguous.\n\n Parameters\n ----------\n None\n\n Examples\n --------\n >>> x = np.ma.array([1, 2, 3])\n >>> x.iscontiguous()\n True\n\n `iscontiguous` returns one of the flags of the masked array:\n\n >>> x.flags\n C_CONTIGUOUS : True\n F_CONTIGUOUS : True\n OWNDATA : False\n WRITEABLE : True\n ALIGNED : True\n UPDATEIFCOPY : False\n\n \"\"\"\n return self.flags['CONTIGUOUS']","label":0},{"cid":"numpy/ma/core.py::MaskedArray.all","text":"numpy/ma/core.py::MaskedArray.all\n def all(self, axis=None, out=None):\n \"\"\"\n Check if all of the elements of `a` are true.\n\n Performs a :func:`logical_and` over the given axis and returns the result.\n Masked values are considered as True during computation.\n For convenience, the output array is masked where ALL the values along the\n current axis are masked: if the output would have been a scalar and that\n all the values are masked, then the output is `masked`.\n\n Parameters\n ----------\n axis : {None, integer}\n Axis to perform the operation over.\n If None, perform over flattened array.\n out : {None, array}, optional\n Array into which the result can be placed. Its type is preserved\n and it must be of the right shape to hold the output.\n\n See Also\n --------\n all : equivalent function\n\n Examples\n --------\n >>> np.ma.array([1,2,3]).all()\n True\n >>> a = np.ma.array([1,2,3], mask=True)\n >>> (a.all() is np.ma.masked)\n True\n\n \"\"\"\n mask = _check_mask_axis(self._mask, axis)\n if out is None:\n d = self.filled(True).all(axis=axis).view(type(self))\n if d.ndim:\n d.__setmask__(mask)\n elif mask:\n return masked\n return d\n self.filled(True).all(axis=axis, out=out)\n if isinstance(out, MaskedArray):\n if out.ndim or mask:\n out.__setmask__(mask)\n return out","label":0},{"cid":"numpy/ma/core.py::MaskedArray.any","text":"numpy/ma/core.py::MaskedArray.any\n def any(self, axis=None, out=None):\n \"\"\"\n Check if any of the elements of `a` are true.\n\n Performs a logical_or over the given axis and returns the result.\n Masked values are considered as False during computation.\n\n Parameters\n ----------\n axis : {None, integer}\n Axis to perform the operation over.\n If None, perform over flattened array and return a scalar.\n out : {None, array}, optional\n Array into which the result can be placed. Its type is preserved\n and it must be of the right shape to hold the output.\n\n See Also\n --------\n any : equivalent function\n\n \"\"\"\n mask = _check_mask_axis(self._mask, axis)\n if out is None:\n d = self.filled(False).any(axis=axis).view(type(self))\n if d.ndim:\n d.__setmask__(mask)\n elif mask:\n d = masked\n return d\n self.filled(False).any(axis=axis, out=out)\n if isinstance(out, MaskedArray):\n if out.ndim or mask:\n out.__setmask__(mask)\n return out","label":0},{"cid":"numpy/ma/core.py::MaskedArray.nonzero","text":"numpy/ma/core.py::MaskedArray.nonzero\n def nonzero(self):\n \"\"\"\n Return the indices of unmasked elements that are not zero.\n\n Returns a tuple of arrays, one for each dimension, containing the\n indices of the non-zero elements in that dimension. The corresponding\n non-zero values can be obtained with::\n\n a[a.nonzero()]\n\n To group the indices by element, rather than dimension, use\n instead::\n\n np.transpose(a.nonzero())\n\n The result of this is always a 2d array, with a row for each non-zero\n element.\n\n Parameters\n ----------\n None\n\n Returns\n -------\n tuple_of_arrays : tuple\n Indices of elements that are non-zero.\n\n See Also\n --------\n numpy.nonzero :\n Function operating on ndarrays.\n flatnonzero :\n Return indices that are non-zero in the flattened version of the input\n array.\n ndarray.nonzero :\n Equivalent ndarray method.\n count_nonzero :\n Counts the number of non-zero elements in the input array.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> x = ma.array(np.eye(3))\n >>> x\n masked_array(data =\n [[ 1. 0. 0.]\n [ 0. 1. 0.]\n [ 0. 0. 1.]],\n mask =\n False,\n fill_value=1e+20)\n >>> x.nonzero()\n (array([0, 1, 2]), array([0, 1, 2]))\n\n Masked elements are ignored.\n\n >>> x[1, 1] = ma.masked\n >>> x\n masked_array(data =\n [[1.0 0.0 0.0]\n [0.0 -- 0.0]\n [0.0 0.0 1.0]],\n mask =\n [[False False False]\n [False True False]\n [False False False]],\n fill_value=1e+20)\n >>> x.nonzero()\n (array([0, 2]), array([0, 2]))\n\n Indices can also be grouped by element.\n\n >>> np.transpose(x.nonzero())\n array([[0, 0","label":0},{"cid":"numpy/ma/core.py::MaskedArray.trace","text":"numpy/ma/core.py::MaskedArray.trace\n def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None):\n \"\"\"\n (this docstring should be overwritten)\n \"\"\"\n #!!!: implement out + test!\n m = self._mask\n if m is nomask:\n result = super(MaskedArray, self).trace(offset=offset, axis1=axis1,\n axis2=axis2, out=out)\n return result.astype(dtype)\n else:\n D = self.diagonal(offset=offset, axis1=axis1, axis2=axis2)\n return D.astype(dtype).filled(0).sum(axis=None, out=out)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.sum","text":"numpy/ma/core.py::MaskedArray.sum\n def sum(self, axis=None, dtype=None, out=None):\n \"\"\"\n Return the sum of the array elements over the given axis.\n Masked elements are set to 0 internally.\n\n Parameters\n ----------\n axis : {None, -1, int}, optional\n Axis along which the sum is computed. The default\n (`axis` = None) is to compute over the flattened array.\n dtype : {None, dtype}, optional\n Determines the type of the returned array and of the accumulator\n where the elements are summed. If dtype has the value None and\n the type of a is an integer type of precision less than the default\n platform integer, then the default platform integer precision is\n used. Otherwise, the dtype is the same as that of a.\n out : {None, ndarray}, optional\n Alternative output array in which to place the result. It must\n have the same shape and buffer length as the expected output\n but the type will be cast if necessary.\n\n Returns\n -------\n sum_along_axis : MaskedArray or scalar\n An array with the same shape as self, with the specified\n axis removed. If self is a 0-d array, or if `axis` is None, a scalar\n is returned. If an output array is specified, a reference to\n `out` is returned.\n\n Examples\n --------\n >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)\n >>> print x\n [[1 -- 3]\n [-- 5 --]\n [7 -- 9]]\n >>> print x.sum()\n 25\n >>> print x.sum(axis=1)\n [4 5 16]\n >>> print x.sum(axis=0)\n [8 5 12]\n >>> print type(x.sum(axis=0, dtype=np.int64)[0])\n <type 'numpy.int64'>\n\n \"\"\"\n _mask = ndarray.__getattribute__(self, '_mask')\n newmask = _check_mask_axis(_mask, axis)\n # No explicit output\n if out is None:\n ","label":0},{"cid":"numpy/ma/core.py::MaskedArray.cumsum","text":"numpy/ma/core.py::MaskedArray.cumsum\n def cumsum(self, axis=None, dtype=None, out=None):\n \"\"\"\n Return the cumulative sum of the elements along the given axis.\n The cumulative sum is calculated over the flattened array by\n default, otherwise over the specified axis.\n\n Masked values are set to 0 internally during the computation.\n However, their position is saved, and the result will be masked at\n the same locations.\n\n Parameters\n ----------\n axis : {None, -1, int}, optional\n Axis along which the sum is computed. The default (`axis` = None) is to\n compute over the flattened array. `axis` may be negative, in which case\n it counts from the last to the first axis.\n dtype : {None, dtype}, optional\n Type of the returned array and of the accumulator in which the\n elements are summed. If `dtype` is not specified, it defaults\n to the dtype of `a`, unless `a` has an integer dtype with a\n precision less than that of the default platform integer. In\n that case, the default platform integer is used.\n out : ndarray, optional\n Alternative output array in which to place the result. It must\n have the same shape and buffer length as the expected output\n but the type will be cast if necessary.\n\n Returns\n -------\n cumsum : ndarray.\n A new array holding the result is returned unless ``out`` is\n specified, in which case a reference to ``out`` is returned.\n\n Notes\n -----\n The mask is lost if `out` is not a valid :class:`MaskedArray` !\n\n Arithmetic is modular when using integer types, and no error is\n raised on overflow.\n\n Examples\n --------\n >>> marr = np.ma.array(np.arange(10), mask=[0,0,0,1,1,1,0,0,0,0])\n >>> print marr.cumsum()\n [0 1 3 -- -- -- 9 16 24 33]\n\n \"\"","label":0},{"cid":"numpy/ma/core.py::MaskedArray.prod","text":"numpy/ma/core.py::MaskedArray.prod\n def prod(self, axis=None, dtype=None, out=None):\n \"\"\"\n Return the product of the array elements over the given axis.\n Masked elements are set to 1 internally for computation.\n\n Parameters\n ----------\n axis : {None, int}, optional\n Axis over which the product is taken. If None is used, then the\n product is over all the array elements.\n dtype : {None, dtype}, optional\n Determines the type of the returned array and of the accumulator\n where the elements are multiplied. If ``dtype`` has the value ``None``\n and the type of a is an integer type of precision less than the default\n platform integer, then the default platform integer precision is\n used. Otherwise, the dtype is the same as that of a.\n out : {None, array}, optional\n Alternative output array in which to place the result. It must have\n the same shape as the expected output but the type will be cast if\n necessary.\n\n Returns\n -------\n product_along_axis : {array, scalar}, see dtype parameter above.\n Returns an array whose shape is the same as a with the specified\n axis removed. Returns a 0d array when a is 1d or axis=None.\n Returns a reference to the specified output array if specified.\n\n See Also\n --------\n prod : equivalent function\n\n Notes\n -----\n Arithmetic is modular when using integer types, and no error is raised\n on overflow.\n\n Examples\n --------\n >>> np.prod([1.,2.])\n 2.0\n >>> np.prod([1.,2.], dtype=np.int32)\n 2\n >>> np.prod([[1.,2.],[3.,4.]])\n 24.0\n >>> np.prod([[1.,2.],[3.,4.]], axis=1)\n array([ 2., 12.])\n\n \"\"\"\n _mask = ndarray.__getattribute__(self, '_mask')\n newmask = _check_mask_axis(_mask, axis)\n #","label":0},{"cid":"numpy/ma/core.py::MaskedArray.cumprod","text":"numpy/ma/core.py::MaskedArray.cumprod\n def cumprod(self, axis=None, dtype=None, out=None):\n \"\"\"\n Return the cumulative product of the elements along the given axis.\n The cumulative product is taken over the flattened array by\n default, otherwise over the specified axis.\n\n Masked values are set to 1 internally during the computation.\n However, their position is saved, and the result will be masked at\n the same locations.\n\n Parameters\n ----------\n axis : {None, -1, int}, optional\n Axis along which the product is computed. The default\n (`axis` = None) is to compute over the flattened array.\n dtype : {None, dtype}, optional\n Determines the type of the returned array and of the accumulator\n where the elements are multiplied. If ``dtype`` has the value ``None``\n and the type of ``a`` is an integer type of precision less than the\n default platform integer, then the default platform integer precision\n is used. Otherwise, the dtype is the same as that of ``a``.\n out : ndarray, optional\n Alternative output array in which to place the result. It must\n have the same shape and buffer length as the expected output\n but the type will be cast if necessary.\n\n Returns\n -------\n cumprod : ndarray\n A new array holding the result is returned unless out is specified,\n in which case a reference to out is returned.\n\n Notes\n -----\n The mask is lost if `out` is not a valid MaskedArray !\n\n Arithmetic is modular when using integer types, and no error is\n raised on overflow.\n\n \"\"\"\n result = self.filled(1).cumprod(axis=axis, dtype=dtype, out=out)\n if out is not None:\n if isinstance(out, MaskedArray):\n out.__setmask__(self._mask)\n return out\n result = result.vie","label":0},{"cid":"numpy/ma/core.py::MaskedArray.mean","text":"numpy/ma/core.py::MaskedArray.mean\n def mean(self, axis=None, dtype=None, out=None):\n \"\"\"\n Returns the average of the array elements.\n\n Masked entries are ignored.\n The average is taken over the flattened array by default, otherwise over\n the specified axis. Refer to `numpy.mean` for the full documentation.\n\n Parameters\n ----------\n a : array_like\n Array containing numbers whose mean is desired. If `a` is not an\n array, a conversion is attempted.\n axis : int, optional\n Axis along which the means are computed. The default is to compute\n the mean of the flattened array.\n dtype : dtype, optional\n Type to use in computing the mean. For integer inputs, the default\n is float64; for floating point, inputs it is the same as the input\n dtype.\n out : ndarray, optional\n Alternative output array in which to place the result. It must have\n the same shape as the expected output but the type will be cast if\n necessary.\n\n Returns\n -------\n mean : ndarray, see dtype parameter above\n If `out=None`, returns a new array containing the mean values,\n otherwise a reference to the output array is returned.\n\n See Also\n --------\n numpy.ma.mean : Equivalent function.\n numpy.mean : Equivalent function on non-masked arrays.\n numpy.ma.average: Weighted average.\n\n Examples\n --------\n >>> a = np.ma.array([1,2,3], mask=[False, False, True])\n >>> a\n masked_array(data = [1 2 --],\n mask = [False False True],\n fill_value = 999999)\n >>> a.mean()\n 1.5\n\n \"\"\"\n if self._mask is nomask:\n result = super(MaskedArray, self).mean(axis=axis, dtype=dtype)\n else:\n dsum = self.sum(axis=axis, dtype=dtype)\n cnt = self.c","label":0},{"cid":"numpy/ma/core.py::MaskedArray.anom","text":"numpy/ma/core.py::MaskedArray.anom\n def anom(self, axis=None, dtype=None):\n \"\"\"\n Compute the anomalies (deviations from the arithmetic mean)\n along the given axis.\n\n Returns an array of anomalies, with the same shape as the input and\n where the arithmetic mean is computed along the given axis.\n\n Parameters\n ----------\n axis : int, optional\n Axis over which the anomalies are taken.\n The default is to use the mean of the flattened array as reference.\n dtype : dtype, optional\n Type to use in computing the variance. For arrays of integer type\n the default is float32; for arrays of float types it is the same as\n the array type.\n\n See Also\n --------\n mean : Compute the mean of the array.\n\n Examples\n --------\n >>> a = np.ma.array([1,2,3])\n >>> a.anom()\n masked_array(data = [-1. 0. 1.],\n mask = False,\n fill_value = 1e+20)\n\n \"\"\"\n m = self.mean(axis, dtype)\n if not axis:\n return (self - m)\n else:\n return (self - expand_dims(m, axis))","label":0},{"cid":"numpy/ma/core.py::MaskedArray.var","text":"numpy/ma/core.py::MaskedArray.var\n def var(self, axis=None, dtype=None, out=None, ddof=0):\n \"\"\n # Easy case: nomask, business as usual\n if self._mask is nomask:\n return self._data.var(axis=axis, dtype=dtype, out=out, ddof=ddof)\n # Some data are masked, yay!\n cnt = self.count(axis=axis) - ddof\n danom = self.anom(axis=axis, dtype=dtype)\n if iscomplexobj(self):\n danom = umath.absolute(danom) ** 2\n else:\n danom *= danom\n dvar = divide(danom.sum(axis), cnt).view(type(self))\n # Apply the mask if it's not a scalar\n if dvar.ndim:\n dvar._mask = mask_or(self._mask.all(axis), (cnt <= 0))\n dvar._update_from(self)\n elif getattr(dvar, '_mask', False):\n # Make sure that masked is returned when the scalar is masked.\n dvar = masked\n if out is not None:\n if isinstance(out, MaskedArray):\n out.flat = 0\n out.__setmask__(True)\n elif out.dtype.kind in 'biu':\n errmsg = \"Masked data information would be lost in one or \"\\\n \"more location.\"\n raise MaskError(errmsg)\n else:\n out.flat = np.nan\n return out\n # In case with have an explicit output\n if out is not None:\n # Set the data\n out.flat = dvar\n # Set the mask if needed\n if isinstance(out, MaskedArray):\n out.__setmask__(dvar.mask)\n return out\n return dvar","label":0},{"cid":"numpy/ma/core.py::MaskedArray.std","text":"numpy/ma/core.py::MaskedArray.std\n def std(self, axis=None, dtype=None, out=None, ddof=0):\n \"\"\n dvar = self.var(axis=axis, dtype=dtype, out=out, ddof=ddof)\n if dvar is not masked:\n if out is not None:\n np.power(out, 0.5, out=out, casting='unsafe')\n return out\n dvar = sqrt(dvar)\n return dvar","label":0},{"cid":"numpy/ma/core.py::MaskedArray.round","text":"numpy/ma/core.py::MaskedArray.round\n def round(self, decimals=0, out=None):\n \"\"\"\n Return an array rounded a to the given number of decimals.\n\n Refer to `numpy.around` for full documentation.\n\n See Also\n --------\n numpy.around : equivalent function\n\n \"\"\"\n result = self._data.round(decimals=decimals, out=out).view(type(self))\n result._mask = self._mask\n result._update_from(self)\n # No explicit output: we're done\n if out is None:\n return result\n if isinstance(out, MaskedArray):\n out.__setmask__(self._mask)\n return out","label":0},{"cid":"numpy/ma/core.py::MaskedArray.argsort","text":"numpy/ma/core.py::MaskedArray.argsort\n def argsort(self, axis=None, kind='quicksort', order=None, fill_value=None):\n \"\"\"\n Return an ndarray of indices that sort the array along the\n specified axis. Masked values are filled beforehand to\n `fill_value`.\n\n Parameters\n ----------\n axis : int, optional\n Axis along which to sort. The default is -1 (last axis).\n If None, the flattened array is used.\n fill_value : var, optional\n Value used to fill the array before sorting.\n The default is the `fill_value` attribute of the input array.\n kind : {'quicksort', 'mergesort', 'heapsort'}, optional\n Sorting algorithm.\n order : list, optional\n When `a` is an array with fields defined, this argument specifies\n which fields to compare first, second, etc. Not all fields need be\n specified.\n\n Returns\n -------\n index_array : ndarray, int\n Array of indices that sort `a` along the specified axis.\n In other words, ``a[index_array]`` yields a sorted `a`.\n\n See Also\n --------\n sort : Describes sorting algorithms used.\n lexsort : Indirect stable sort with multiple keys.\n ndarray.sort : Inplace sort.\n\n Notes\n -----\n See `sort` for notes on the different sorting algorithms.\n\n Examples\n --------\n >>> a = np.ma.array([3,2,1], mask=[False, False, True])\n >>> a\n masked_array(data = [3 2 --],\n mask = [False False True],\n fill_value = 999999)\n >>> a.argsort()\n array([1, 0, 2])\n\n \"\"\"\n if fill_value is None:\n fill_value = default_fill_value(self)\n d = self.filled(fill_value).view(ndarray)\n return d.argsort(axis=axis, kind=kind, order=order)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.argmin","text":"numpy/ma/core.py::MaskedArray.argmin\n def argmin(self, axis=None, fill_value=None, out=None):\n \"\"\"\n Return array of indices to the minimum values along the given axis.\n\n Parameters\n ----------\n axis : {None, integer}\n If None, the index is into the flattened array, otherwise along\n the specified axis\n fill_value : {var}, optional\n Value used to fill in the masked values. If None, the output of\n minimum_fill_value(self._data) is used instead.\n out : {None, array}, optional\n Array into which the result can be placed. Its type is preserved\n and it must be of the right shape to hold the output.\n\n Returns\n -------\n {ndarray, scalar}\n If multi-dimension input, returns a new ndarray of indices to the\n minimum values along the given axis. Otherwise, returns a scalar\n of index to the minimum values along the given axis.\n\n Examples\n --------\n >>> x = np.ma.array(arange(4), mask=[1,1,0,0])\n >>> x.shape = (2,2)\n >>> print x\n [[-- --]\n [2 3]]\n >>> print x.argmin(axis=0, fill_value=-1)\n [0 0]\n >>> print x.argmin(axis=0, fill_value=9)\n [1 1]\n\n \"\"\"\n if fill_value is None:\n fill_value = minimum_fill_value(self)\n d = self.filled(fill_value).view(ndarray)\n return d.argmin(axis, out=out)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.argmax","text":"numpy/ma/core.py::MaskedArray.argmax\n def argmax(self, axis=None, fill_value=None, out=None):\n \"\"\"\n Returns array of indices of the maximum values along the given axis.\n Masked values are treated as if they had the value fill_value.\n\n Parameters\n ----------\n axis : {None, integer}\n If None, the index is into the flattened array, otherwise along\n the specified axis\n fill_value : {var}, optional\n Value used to fill in the masked values. If None, the output of\n maximum_fill_value(self._data) is used instead.\n out : {None, array}, optional\n Array into which the result can be placed. Its type is preserved\n and it must be of the right shape to hold the output.\n\n Returns\n -------\n index_array : {integer_array}\n\n Examples\n --------\n >>> a = np.arange(6).reshape(2,3)\n >>> a.argmax()\n 5\n >>> a.argmax(0)\n array([1, 1, 1])\n >>> a.argmax(1)\n array([2, 2])\n\n \"\"\"\n if fill_value is None:\n fill_value = maximum_fill_value(self._data)\n d = self.filled(fill_value).view(ndarray)\n return d.argmax(axis, out=out)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.sort","text":"numpy/ma/core.py::MaskedArray.sort\n def sort(self, axis= -1, kind='quicksort', order=None,\n endwith=True, fill_value=None):\n \"\"\"\n Sort the array, in-place\n\n Parameters\n ----------\n a : array_like\n Array to be sorted.\n axis : int, optional\n Axis along which to sort. If None, the array is flattened before\n sorting. The default is -1, which sorts along the last axis.\n kind : {'quicksort', 'mergesort', 'heapsort'}, optional\n Sorting algorithm. Default is 'quicksort'.\n order : list, optional\n When `a` is a structured array, this argument specifies which fields\n to compare first, second, and so on. This list does not need to\n include all of the fields.\n endwith : {True, False}, optional\n Whether missing values (if any) should be forced in the upper indices\n (at the end of the array) (True) or lower indices (at the beginning).\n When the array contains unmasked values of the largest (or smallest if\n False) representable value of the datatype the ordering of these values\n and the masked values is undefined. To enforce the masked values are\n at the end (beginning) in this case one must sort the mask.\n fill_value : {var}, optional\n Value used internally for the masked values.\n If ``fill_value`` is not None, it supersedes ``endwith``.\n\n Returns\n -------\n sorted_array : ndarray\n Array of the same type and shape as `a`.\n\n See Also\n --------\n ndarray.sort : Method to sort an array in-place.\n argsort : Indirect sort.\n lexsort : Indirect stable sort on multiple keys.\n searchsorted : Find elements in a sorted array.\n\n Notes\n -----\n See ``sort`` for notes on the different sorting algorithms.\n\n Examples\n --------\n >>> a = ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])\n >>> # Default\n >>> a.sort()\n >>> print a\n [1 3 5 -- --]\n\n >>> a = ma.array([1, 2, 5, 4, 3],mask=[0,","label":0},{"cid":"numpy/ma/core.py::MaskedArray.min","text":"numpy/ma/core.py::MaskedArray.min\n def min(self, axis=None, out=None, fill_value=None):\n \"\"\"\n Return the minimum along a given axis.\n\n Parameters\n ----------\n axis : {None, int}, optional\n Axis along which to operate. By default, ``axis`` is None and the\n flattened input is used.\n out : array_like, optional\n Alternative output array in which to place the result. Must be of\n the same shape and buffer length as the expected output.\n fill_value : {var}, optional\n Value used to fill in the masked values.\n If None, use the output of `minimum_fill_value`.\n\n Returns\n -------\n amin : array_like\n New array holding the result.\n If ``out`` was specified, ``out`` is returned.\n\n See Also\n --------\n minimum_fill_value\n Returns the minimum filling value for a given datatype.\n\n \"\"\"\n _mask = ndarray.__getattribute__(self, '_mask')\n newmask = _check_mask_axis(_mask, axis)\n if fill_value is None:\n fill_value = minimum_fill_value(self)\n # No explicit output\n if out is None:\n result = self.filled(fill_value).min(axis=axis, out=out).view(type(self))\n if result.ndim:\n # Set the mask\n result.__setmask__(newmask)\n # Get rid of Infs\n if newmask.ndim:\n np.copyto(result, result.fill_value, where=newmask)\n elif newmask:\n result = masked\n return result\n # Explicit output\n result = self.filled(fill_value).min(axis=axis, out=out)\n if isinstance(out, MaskedArray):\n outmask = getattr(out, '_mask', nomask)\n if (outmask is nomask):\n outmask = out._mask = make_mask_none(out.shape)\n outmask.flat = newmask\n else:\n if out.dtype.kind in 'biu':\n errmsg = \"Masked data information would be lost in one or more\"\\\n ","label":0},{"cid":"numpy/ma/core.py::MaskedArray.mini","text":"numpy/ma/core.py::MaskedArray.mini\n def mini(self, axis=None):\n \"\"\"\n Return the array minimum along the specified axis.\n\n Parameters\n ----------\n axis : int, optional\n The axis along which to find the minima. Default is None, in which case\n the minimum value in the whole array is returned.\n\n Returns\n -------\n min : scalar or MaskedArray\n If `axis` is None, the result is a scalar. Otherwise, if `axis` is\n given and the array is at least 2-D, the result is a masked array with\n dimension one smaller than the array on which `mini` is called.\n\n Examples\n --------\n >>> x = np.ma.array(np.arange(6), mask=[0 ,1, 0, 0, 0 ,1]).reshape(3, 2)\n >>> print x\n [[0 --]\n [2 3]\n [4 --]]\n >>> x.mini()\n 0\n >>> x.mini(axis=0)\n masked_array(data = [0 3],\n mask = [False False],\n fill_value = 999999)\n >>> print x.mini(axis=1)\n [0 2 4]\n\n \"\"\"\n if axis is None:\n return minimum(self)\n else:\n return minimum.reduce(self, axis)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.max","text":"numpy/ma/core.py::MaskedArray.max\n def max(self, axis=None, out=None, fill_value=None):\n \"\"\"\n Return the maximum along a given axis.\n\n Parameters\n ----------\n axis : {None, int}, optional\n Axis along which to operate. By default, ``axis`` is None and the\n flattened input is used.\n out : array_like, optional\n Alternative output array in which to place the result. Must\n be of the same shape and buffer length as the expected output.\n fill_value : {var}, optional\n Value used to fill in the masked values.\n If None, use the output of maximum_fill_value().\n\n Returns\n -------\n amax : array_like\n New array holding the result.\n If ``out`` was specified, ``out`` is returned.\n\n See Also\n --------\n maximum_fill_value\n Returns the maximum filling value for a given datatype.\n\n \"\"\"\n _mask = ndarray.__getattribute__(self, '_mask')\n newmask = _check_mask_axis(_mask, axis)\n if fill_value is None:\n fill_value = maximum_fill_value(self)\n # No explicit output\n if out is None:\n result = self.filled(fill_value).max(axis=axis, out=out).view(type(self))\n if result.ndim:\n # Set the mask\n result.__setmask__(newmask)\n # Get rid of Infs\n if newmask.ndim:\n np.copyto(result, result.fill_value, where=newmask)\n elif newmask:\n result = masked\n return result\n # Explicit output\n result = self.filled(fill_value).max(axis=axis, out=out)\n if isinstance(out, MaskedArray):\n outmask = getattr(out, '_mask', nomask)\n if (outmask is nomask):\n outmask = out._mask = make_mask_none(out.shape)\n outmask.flat = newmask\n else:\n\n if out.dtype.kind in 'biu","label":0},{"cid":"numpy/ma/core.py::MaskedArray.ptp","text":"numpy/ma/core.py::MaskedArray.ptp\n def ptp(self, axis=None, out=None, fill_value=None):\n \"\"\"\n Return (maximum - minimum) along the the given dimension\n (i.e. peak-to-peak value).\n\n Parameters\n ----------\n axis : {None, int}, optional\n Axis along which to find the peaks. If None (default) the\n flattened array is used.\n out : {None, array_like}, optional\n Alternative output array in which to place the result. It must\n have the same shape and buffer length as the expected output\n but the type will be cast if necessary.\n fill_value : {var}, optional\n Value used to fill in the masked values.\n\n Returns\n -------\n ptp : ndarray.\n A new array holding the result, unless ``out`` was\n specified, in which case a reference to ``out`` is returned.\n\n \"\"\"\n if out is None:\n result = self.max(axis=axis, fill_value=fill_value)\n result -= self.min(axis=axis, fill_value=fill_value)\n return result\n out.flat = self.max(axis=axis, out=out, fill_value=fill_value)\n min_value = self.min(axis=axis, fill_value=fill_value)\n np.subtract(out, min_value, out=out, casting='unsafe')\n return out","label":0},{"cid":"numpy/ma/core.py::MaskedArray.take","text":"numpy/ma/core.py::MaskedArray.take\n def take(self, indices, axis=None, out=None, mode='raise'):\n \"\"\"\n \"\"\"\n (_data, _mask) = (self._data, self._mask)\n cls = type(self)\n # Make sure the indices are not masked\n maskindices = getattr(indices, '_mask', nomask)\n if maskindices is not nomask:\n indices = indices.filled(0)\n # Get the data\n if out is None:\n out = _data.take(indices, axis=axis, mode=mode).view(cls)\n else:\n np.take(_data, indices, axis=axis, mode=mode, out=out)\n # Get the mask\n if isinstance(out, MaskedArray):\n if _mask is nomask:\n outmask = maskindices\n else:\n outmask = _mask.take(indices, axis=axis, mode=mode)\n outmask |= maskindices\n out.__setmask__(outmask)\n return out","label":0},{"cid":"numpy/ma/core.py::MaskedArray.tolist","text":"numpy/ma/core.py::MaskedArray.tolist\n def tolist(self, fill_value=None):\n \"\"\"\n Return the data portion of the masked array as a hierarchical Python list.\n\n Data items are converted to the nearest compatible Python type.\n Masked values are converted to `fill_value`. If `fill_value` is None,\n the corresponding entries in the output list will be ``None``.\n\n Parameters\n ----------\n fill_value : scalar, optional\n The value to use for invalid entries. Default is None.\n\n Returns\n -------\n result : list\n The Python list representation of the masked array.\n\n Examples\n --------\n >>> x = np.ma.array([[1,2,3], [4,5,6], [7,8,9]], mask=[0] + [1,0]*4)\n >>> x.tolist()\n [[1, None, 3], [None, 5, None], [7, None, 9]]\n >>> x.tolist(-999)\n [[1, -999, 3], [-999, 5, -999], [7, -999, 9]]\n\n \"\"\"\n _mask = self._mask\n # No mask ? Just return .data.tolist ?\n if _mask is nomask:\n return self._data.tolist()\n # Explicit fill_value: fill the array and get the list\n if fill_value is not None:\n return self.filled(fill_value).tolist()\n # Structured array .............\n names = self.dtype.names\n if names:\n result = self._data.astype([(_, object) for _ in names])\n for n in names:\n result[n][_mask[n]] = None\n return result.tolist()\n # Standard arrays ...............\n if _mask is nomask:\n return [None]\n # Set temps to save time when dealing w/ marrays...\n inishape = self.shape\n result = np.array(self._data.ravel(), dtype=object)\n result[_mask.ravel()] = None\n result.shape = inishape\n return result.tolist()","label":0},{"cid":"numpy/ma/core.py::MaskedArray.tostring","text":"numpy/ma/core.py::MaskedArray.tostring\n def tostring(self, fill_value=None, order='C'):\n \"\"\"\n This function is a compatibility alias for tobytes. Despite its name it\n returns bytes not strings.\n \"\"\"\n\n return self.tobytes(fill_value, order='C')","label":0},{"cid":"numpy/ma/core.py::MaskedArray.tobytes","text":"numpy/ma/core.py::MaskedArray.tobytes\n def tobytes(self, fill_value=None, order='C'):\n \"\"\"\n Return the array data as a string containing the raw bytes in the array.\n\n The array is filled with a fill value before the string conversion.\n\n .. versionadded:: 1.9.0\n\n Parameters\n ----------\n fill_value : scalar, optional\n Value used to fill in the masked values. Deafult is None, in which\n case `MaskedArray.fill_value` is used.\n order : {'C','F','A'}, optional\n Order of the data item in the copy. Default is 'C'.\n\n - 'C' -- C order (row major).\n - 'F' -- Fortran order (column major).\n - 'A' -- Any, current order of array.\n - None -- Same as 'A'.\n\n See Also\n --------\n ndarray.tobytes\n tolist, tofile\n\n Notes\n -----\n As for `ndarray.tobytes`, information about the shape, dtype, etc.,\n but also about `fill_value`, will be lost.\n\n Examples\n --------\n >>> x = np.ma.array(np.array([[1, 2], [3, 4]]), mask=[[0, 1], [1, 0]])\n >>> x.tobytes()\n '\\\\x01\\\\x00\\\\x00\\\\x00?B\\\\x0f\\\\x00?B\\\\x0f\\\\x00\\\\x04\\\\x00\\\\x00\\\\x00'\n\n \"\"\"\n return self.filled(fill_value).tobytes(order=order)","label":0},{"cid":"numpy/ma/core.py::MaskedArray.tofile","text":"numpy/ma/core.py::MaskedArray.tofile\n def tofile(self, fid, sep=\"\", format=\"%s\"):\n \"\"\"\n Save a masked array to a file in binary format.\n\n .. warning::\n This function is not implemented yet.\n\n Raises\n ------\n NotImplementedError\n When `tofile` is called.\n\n \"\"\"\n raise NotImplementedError(\"Not implemented yet, sorry...\")","label":0},{"cid":"numpy/ma/core.py::MaskedArray.toflex","text":"numpy/ma/core.py::MaskedArray.toflex\n def toflex(self):\n \"\"\"\n Transforms a masked array into a flexible-type array.\n\n The flexible type array that is returned will have two fields:\n\n * the ``_data`` field stores the ``_data`` part of the array.\n * the ``_mask`` field stores the ``_mask`` part of the array.\n\n Parameters\n ----------\n None\n\n Returns\n -------\n record : ndarray\n A new flexible-type `ndarray` with two fields: the first element\n containing a value, the second element containing the corresponding\n mask boolean. The returned record shape matches self.shape.\n\n Notes\n -----\n A side-effect of transforming a masked array into a flexible `ndarray` is\n that meta information (``fill_value``, ...) will be lost.\n\n Examples\n --------\n >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)\n >>> print x\n [[1 -- 3]\n [-- 5 --]\n [7 -- 9]]\n >>> print x.toflex()\n [[(1, False) (2, True) (3, False)]\n [(4, True) (5, False) (6, True)]\n [(7, False) (8, True) (9, False)]]\n\n \"\"\"\n # Get the basic dtype ....\n ddtype = self.dtype\n # Make sure we have a mask\n _mask = self._mask\n if _mask is None:\n _mask = make_mask_none(self.shape, ddtype)\n # And get its dtype\n mdtype = self._mask.dtype\n #\n record = np.ndarray(shape=self.shape,\n dtype=[('_data', ddtype), ('_mask', mdtype)])\n record['_data'] = self._data\n record['_mask'] = self._mask\n return record","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__getstate__","text":"numpy/ma/core.py::MaskedArray.__getstate__\n def __getstate__(self):\n \"\"\"Return the internal state of the masked array, for pickling\n purposes.\n\n \"\"\"\n cf = 'CF'[self.flags.fnc]\n state = (1,\n self.shape,\n self.dtype,\n self.flags.fnc,\n self._data.tobytes(cf),\n #self._data.tolist(),\n getmaskarray(self).tobytes(cf),\n #getmaskarray(self).tolist(),\n self._fill_value,\n )\n return state","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__setstate__","text":"numpy/ma/core.py::MaskedArray.__setstate__\n def __setstate__(self, state):\n \"\"\"Restore the internal state of the masked array, for\n pickling purposes. ``state`` is typically the output of the\n ``__getstate__`` output, and is a 5-tuple:\n\n - class name\n - a tuple giving the shape of the data\n - a typecode for the data\n - a binary string for the data\n - a binary string for the mask.\n\n \"\"\"\n (_, shp, typ, isf, raw, msk, flv) = state\n ndarray.__setstate__(self, (shp, typ, isf, raw))\n self._mask.__setstate__((shp, make_mask_descr(typ), isf, msk))\n self.fill_value = flv","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__reduce__","text":"numpy/ma/core.py::MaskedArray.__reduce__\n def __reduce__(self):\n \"\"\"Return a 3-tuple for pickling a MaskedArray.\n\n \"\"\"\n return (_mareconstruct,\n (self.__class__, self._baseclass, (0,), 'b',),\n self.__getstate__())","label":0},{"cid":"numpy/ma/core.py::MaskedArray.__deepcopy__","text":"numpy/ma/core.py::MaskedArray.__deepcopy__\n def __deepcopy__(self, memo=None):\n from copy import deepcopy\n copied = MaskedArray.__new__(type(self), self, copy=True)\n if memo is None:\n memo = {}\n memo[id(self)] = copied\n for (k, v) in self.__dict__.items():\n copied.__dict__[k] = deepcopy(v, memo)\n return copied","label":0},{"cid":"numpy/ma/core.py::_mareconstruct","text":"numpy/ma/core.py::_mareconstruct\ndef _mareconstruct(subtype, baseclass, baseshape, basetype,):\n \"\"\"Internal function that builds a new MaskedArray from the\n information stored in a pickle.\n\n \"\"\"\n _data = ndarray.__new__(baseclass, baseshape, basetype)\n _mask = ndarray.__new__(ndarray, baseshape, make_mask_descr(basetype))\n return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype,)","label":0},{"cid":"numpy/ma/core.py::mvoid.__new__","text":"numpy/ma/core.py::mvoid.__new__\n def __new__(self, data, mask=nomask, dtype=None, fill_value=None,\n hardmask=False, copy=False, subok=True):\n _data = np.array(data, copy=copy, subok=subok, dtype=dtype)\n _data = _data.view(self)\n _data._hardmask = hardmask\n if mask is not nomask:\n if isinstance(mask, np.void):\n _data._mask = mask\n else:\n try:\n # Mask is already a 0D array\n _data._mask = np.void(mask)\n except TypeError:\n # Transform the mask to a void\n mdtype = make_mask_descr(dtype)\n _data._mask = np.array(mask, dtype=mdtype)[()]\n if fill_value is not None:\n _data.fill_value = fill_value\n return _data","label":0},{"cid":"numpy/ma/core.py::mvoid._get_data","text":"numpy/ma/core.py::mvoid._get_data\n def _get_data(self):\n # Make sure that the _data part is a np.void\n return self.view(ndarray)[()]","label":0},{"cid":"numpy/ma/core.py::mvoid.__getitem__","text":"numpy/ma/core.py::mvoid.__getitem__\n def __getitem__(self, indx):\n \"Get the index...\"\n m = self._mask\n if m is not nomask and m[indx]:\n return masked\n return self._data[indx]","label":0},{"cid":"numpy/ma/core.py::mvoid.__setitem__","text":"numpy/ma/core.py::mvoid.__setitem__\n def __setitem__(self, indx, value):\n self._data[indx] = value\n if self._hardmask:\n self._mask[indx] |= getattr(value, \"_mask\", False)\n else:\n self._mask[indx] = getattr(value, \"_mask\", False)","label":0},{"cid":"numpy/ma/core.py::mvoid.__str__","text":"numpy/ma/core.py::mvoid.__str__\n def __str__(self):\n m = self._mask\n if (m is nomask):\n return self._data.__str__()\n m = tuple(m)\n if (not any(m)):\n return self._data.__str__()\n r = self._data.tolist()\n p = masked_print_option\n if not p.enabled():\n p = 'N/A'\n else:\n p = str(p)\n r = [(str(_), p)[int(_m)] for (_, _m) in zip(r, m)]\n return \"(%s)\" % \", \".join(r)","label":0},{"cid":"numpy/ma/core.py::mvoid.__repr__","text":"numpy/ma/core.py::mvoid.__repr__\n def __repr__(self):\n m = self._mask\n if (m is nomask):\n return self._data.__repr__()\n m = tuple(m)\n if not any(m):\n return self._data.__repr__()\n p = masked_print_option\n if not p.enabled():\n return self.filled(self.fill_value).__repr__()\n p = str(p)\n r = [(str(_), p)[int(_m)] for (_, _m) in zip(self._data.tolist(), m)]\n return \"(%s)\" % \", \".join(r)","label":0},{"cid":"numpy/ma/core.py::mvoid.__iter__","text":"numpy/ma/core.py::mvoid.__iter__\n def __iter__(self):\n \"Defines an iterator for mvoid\"\n (_data, _mask) = (self._data, self._mask)\n if _mask is nomask:\n for d in _data:\n yield d\n else:\n for (d, m) in zip(_data, _mask):\n if m:\n yield masked\n else:\n yield d","label":0},{"cid":"numpy/ma/core.py::mvoid.__len__","text":"numpy/ma/core.py::mvoid.__len__\n def __len__(self):\n return self._data.__len__()","label":0},{"cid":"numpy/ma/core.py::mvoid.filled","text":"numpy/ma/core.py::mvoid.filled\n def filled(self, fill_value=None):\n \"\"\"\n Return a copy with masked fields filled with a given value.\n\n Parameters\n ----------\n fill_value : scalar, optional\n The value to use for invalid entries (None by default).\n If None, the `fill_value` attribute is used instead.\n\n Returns\n -------\n filled_void\n A `np.void` object\n\n See Also\n --------\n MaskedArray.filled\n\n \"\"\"\n return asarray(self).filled(fill_value)[()]","label":0},{"cid":"numpy/ma/core.py::mvoid.tolist","text":"numpy/ma/core.py::mvoid.tolist\n def tolist(self):\n \"\"\"\n Transforms the mvoid object into a tuple.\n\n Masked fields are replaced by None.\n\n Returns\n -------\n returned_tuple\n Tuple of fields\n \"\"\"\n _mask = self._mask\n if _mask is nomask:\n return self._data.tolist()\n result = []\n for (d, m) in zip(self._data, self._mask):\n if m:\n result.append(None)\n else:\n # .item() makes sure we return a standard Python object\n result.append(d.item())\n return tuple(result)","label":0},{"cid":"numpy/ma/core.py::isMaskedArray","text":"numpy/ma/core.py::isMaskedArray\ndef isMaskedArray(x):\n \"\"\"\n Test whether input is an instance of MaskedArray.\n\n This function returns True if `x` is an instance of MaskedArray\n and returns False otherwise. Any object is accepted as input.\n\n Parameters\n ----------\n x : object\n Object to test.\n\n Returns\n -------\n result : bool\n True if `x` is a MaskedArray.\n\n See Also\n --------\n isMA : Alias to isMaskedArray.\n isarray : Alias to isMaskedArray.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = np.eye(3, 3)\n >>> a\n array([[ 1., 0., 0.],\n [ 0., 1., 0.],\n [ 0., 0., 1.]])\n >>> m = ma.masked_values(a, 0)\n >>> m\n masked_array(data =\n [[1.0 -- --]\n [-- 1.0 --]\n [-- -- 1.0]],\n mask =\n [[False True True]\n [ True False True]\n [ True True False]],\n fill_value=0.0)\n >>> ma.isMaskedArray(a)\n False\n >>> ma.isMaskedArray(m)\n True\n >>> ma.isMaskedArray([0, 1, 2])\n False\n\n \"\"\"\n return isinstance(x, MaskedArray)","label":0},{"cid":"numpy/ma/core.py::MaskedConstant.__new__","text":"numpy/ma/core.py::MaskedConstant.__new__\n def __new__(self):\n return self._data.view(self)","label":0},{"cid":"numpy/ma/core.py::MaskedConstant.__array_finalize__","text":"numpy/ma/core.py::MaskedConstant.__array_finalize__\n def __array_finalize__(self, obj):\n return","label":0},{"cid":"numpy/ma/core.py::MaskedConstant.__array_wrap__","text":"numpy/ma/core.py::MaskedConstant.__array_wrap__\n def __array_wrap__(self, obj):\n return self","label":0},{"cid":"numpy/ma/core.py::MaskedConstant.__str__","text":"numpy/ma/core.py::MaskedConstant.__str__\n def __str__(self):\n return str(masked_print_option._display)","label":0},{"cid":"numpy/ma/core.py::MaskedConstant.__repr__","text":"numpy/ma/core.py::MaskedConstant.__repr__\n def __repr__(self):\n return 'masked'","label":0},{"cid":"numpy/ma/core.py::MaskedConstant.flatten","text":"numpy/ma/core.py::MaskedConstant.flatten\n def flatten(self):\n return masked_array([self._data], dtype=float, mask=[True])","label":0},{"cid":"numpy/ma/core.py::MaskedConstant.__reduce__","text":"numpy/ma/core.py::MaskedConstant.__reduce__\n def __reduce__(self):\n \"\"\"Override of MaskedArray's __reduce__.\n \"\"\"\n return (self.__class__, ())","label":0},{"cid":"numpy/ma/core.py::array","text":"numpy/ma/core.py::array\ndef array(data, dtype=None, copy=False, order=False,\n mask=nomask, fill_value=None,\n keep_mask=True, hard_mask=False, shrink=True, subok=True, ndmin=0,\n ):\n \"\"\"array(data, dtype=None, copy=False, order=False, mask=nomask,\n fill_value=None, keep_mask=True, hard_mask=False, shrink=True,\n subok=True, ndmin=0)\n\n Acts as shortcut to MaskedArray, with options in a different order\n for convenience. And backwards compatibility...\n\n \"\"\"\n #!!!: we should try to put 'order' somwehere\n return MaskedArray(data, mask=mask, dtype=dtype, copy=copy, subok=subok,\n keep_mask=keep_mask, hard_mask=hard_mask,\n fill_value=fill_value, ndmin=ndmin, shrink=shrink)","label":0},{"cid":"numpy/ma/core.py::is_masked","text":"numpy/ma/core.py::is_masked\ndef is_masked(x):\n \"\"\"\n Determine whether input has masked values.\n\n Accepts any object as input, but always returns False unless the\n input is a MaskedArray containing masked values.\n\n Parameters\n ----------\n x : array_like\n Array to check for masked values.\n\n Returns\n -------\n result : bool\n True if `x` is a MaskedArray with masked values, False otherwise.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> x = ma.masked_equal([0, 1, 0, 2, 3], 0)\n >>> x\n masked_array(data = [-- 1 -- 2 3],\n mask = [ True False True False False],\n fill_value=999999)\n >>> ma.is_masked(x)\n True\n >>> x = ma.masked_equal([0, 1, 0, 2, 3], 42)\n >>> x\n masked_array(data = [0 1 0 2 3],\n mask = False,\n fill_value=999999)\n >>> ma.is_masked(x)\n False\n\n Always returns False if `x` isn't a MaskedArray.\n\n >>> x = [False, True, False]\n >>> ma.is_masked(x)\n False\n >>> x = 'a string'\n >>> ma.is_masked(x)\n False\n\n \"\"\"\n m = getmask(x)\n if m is nomask:\n return False\n elif m.any():\n return True\n return False","label":0},{"cid":"numpy/ma/core.py::_extrema_operation.__call__","text":"numpy/ma/core.py::_extrema_operation.__call__\n def __call__(self, a, b=None):\n \"Executes the call behavior.\"\n if b is None:\n return self.reduce(a)\n return where(self.compare(a, b), a, b)","label":0},{"cid":"numpy/ma/core.py::_extrema_operation.reduce","text":"numpy/ma/core.py::_extrema_operation.reduce\n def reduce(self, target, axis=None):\n \"Reduce target along the given axis.\"\n target = narray(target, copy=False, subok=True)\n m = getmask(target)\n if axis is not None:\n kargs = { 'axis' : axis }\n else:\n kargs = {}\n target = target.ravel()\n if not (m is nomask):\n m = m.ravel()\n if m is nomask:\n t = self.ufunc.reduce(target, **kargs)\n else:\n target = target.filled(self.fill_value_func(target)).view(type(target))\n t = self.ufunc.reduce(target, **kargs)\n m = umath.logical_and.reduce(m, **kargs)\n if hasattr(t, '_mask'):\n t._mask = m\n elif m:\n t = masked\n return t","label":0},{"cid":"numpy/ma/core.py::_extrema_operation.outer","text":"numpy/ma/core.py::_extrema_operation.outer\n def outer (self, a, b):\n \"Return the function applied to the outer product of a and b.\"\n ma = getmask(a)\n mb = getmask(b)\n if ma is nomask and mb is nomask:\n m = nomask\n else:\n ma = getmaskarray(a)\n mb = getmaskarray(b)\n m = logical_or.outer(ma, mb)\n result = self.ufunc.outer(filled(a), filled(b))\n if not isinstance(result, MaskedArray):\n result = result.view(MaskedArray)\n result._mask = m\n return result","label":0},{"cid":"numpy/ma/core.py::_minimum_operation.__init__","text":"numpy/ma/core.py::_minimum_operation.__init__\n def __init__ (self):\n \"\"\"minimum(a, b) or minimum(a)\nIn one argument case, returns the scalar minimum.\n \"\"\"\n self.ufunc = umath.minimum\n self.afunc = amin\n self.compare = less\n self.fill_value_func = minimum_fill_value","label":0},{"cid":"numpy/ma/core.py::_maximum_operation.__init__","text":"numpy/ma/core.py::_maximum_operation.__init__\n def __init__ (self):\n \"\"\"maximum(a, b) or maximum(a)\n In one argument case returns the scalar maximum.\n \"\"\"\n self.ufunc = umath.maximum\n self.afunc = amax\n self.compare = greater\n self.fill_value_func = maximum_fill_value","label":0},{"cid":"numpy/ma/core.py::min","text":"numpy/ma/core.py::min\ndef min(obj, axis=None, out=None, fill_value=None):\n try:\n return obj.min(axis=axis, fill_value=fill_value, out=out)\n except (AttributeError, TypeError):\n # If obj doesn't have a min method,\n # ...or if the method doesn't accept a fill_value argument\n return asanyarray(obj).min(axis=axis, fill_value=fill_value, out=out)","label":0},{"cid":"numpy/ma/core.py::max","text":"numpy/ma/core.py::max\ndef max(obj, axis=None, out=None, fill_value=None):\n try:\n return obj.max(axis=axis, fill_value=fill_value, out=out)\n except (AttributeError, TypeError):\n # If obj doesn't have a max method,\n # ...or if the method doesn't accept a fill_value argument\n return asanyarray(obj).max(axis=axis, fill_value=fill_value, out=out)","label":0},{"cid":"numpy/ma/core.py::ptp","text":"numpy/ma/core.py::ptp\ndef ptp(obj, axis=None, out=None, fill_value=None):\n \"\"\"a.ptp(axis=None) = a.max(axis)-a.min(axis)\"\"\"\n try:\n return obj.ptp(axis, out=out, fill_value=fill_value)\n except (AttributeError, TypeError):\n # If obj doesn't have a ptp method,\n # ...or if the method doesn't accept a fill_value argument\n return asanyarray(obj).ptp(axis=axis, fill_value=fill_value, out=out)","label":0},{"cid":"numpy/ma/core.py::_frommethod.__init__","text":"numpy/ma/core.py::_frommethod.__init__\n def __init__(self, methodname, reversed=False):\n self.__name__ = methodname\n self.__doc__ = self.getdoc()\n self.reversed = reversed","label":0},{"cid":"numpy/ma/core.py::_frommethod.getdoc","text":"numpy/ma/core.py::_frommethod.getdoc\n def getdoc(self):\n \"Return the doc of the function (from the doc of the method).\"\n meth = getattr(MaskedArray, self.__name__, None) or\\\n getattr(np, self.__name__, None)\n signature = self.__name__ + get_object_signature(meth)\n if meth is not None:\n doc = \"\"\" %s\\n%s\"\"\" % (signature, getattr(meth, '__doc__', None))\n return doc","label":0},{"cid":"numpy/ma/core.py::_frommethod.__call__","text":"numpy/ma/core.py::_frommethod.__call__\n def __call__(self, a, *args, **params):\n if self.reversed:\n args = list(args)\n arr = args[0]\n args[0] = a\n a = arr\n # Get the method from the array (if possible)\n method_name = self.__name__\n method = getattr(a, method_name, None)\n if method is not None:\n return method(*args, **params)\n # Still here ? Then a is not a MaskedArray\n method = getattr(MaskedArray, method_name, None)\n if method is not None:\n return method(MaskedArray(a), *args, **params)\n # Still here ? OK, let's call the corresponding np function\n method = getattr(np, method_name)\n return method(a, *args, **params)","label":0},{"cid":"numpy/ma/core.py::take","text":"numpy/ma/core.py::take\ndef take(a, indices, axis=None, out=None, mode='raise'):\n \"\"\"\n \"\"\"\n a = masked_array(a)\n return a.take(indices, axis=axis, out=out, mode=mode)","label":0},{"cid":"numpy/ma/core.py::power","text":"numpy/ma/core.py::power\ndef power(a, b, third=None):\n \"\"\"\n Returns element-wise base array raised to power from second array.\n\n This is the masked array version of `numpy.power`. For details see\n `numpy.power`.\n\n See Also\n --------\n numpy.power\n\n Notes\n -----\n The *out* argument to `numpy.power` is not supported, `third` has to be\n None.\n\n \"\"\"\n if third is not None:\n raise MaskError(\"3-argument power not supported.\")\n # Get the masks\n ma = getmask(a)\n mb = getmask(b)\n m = mask_or(ma, mb)\n # Get the rawdata\n fa = getdata(a)\n fb = getdata(b)\n # Get the type of the result (so that we preserve subclasses)\n if isinstance(a, MaskedArray):\n basetype = type(a)\n else:\n basetype = MaskedArray\n # Get the result and view it as a (subclass of) MaskedArray\n with np.errstate(divide='ignore', invalid='ignore'):\n result = np.where(m, fa, umath.power(fa, fb)).view(basetype)\n result._update_from(a)\n # Find where we're in trouble w/ NaNs and Infs\n invalid = np.logical_not(np.isfinite(result.view(ndarray)))\n # Add the initial mask\n if m is not nomask:\n if not (result.ndim):\n return masked\n result._mask = np.logical_or(m, invalid)\n # Fix the invalid parts\n if invalid.any():\n if not result.ndim:\n return masked\n elif result._mask is nomask:\n result._mask = invalid\n result._data[invalid] = result.fill_value\n return result","label":0},{"cid":"numpy/ma/core.py::argsort","text":"numpy/ma/core.py::argsort\ndef argsort(a, axis=None, kind='quicksort', order=None, fill_value=None):\n \"Function version of the eponymous method.\"\n if fill_value is None:\n fill_value = default_fill_value(a)\n d = filled(a, fill_value)\n if axis is None:\n return d.argsort(kind=kind, order=order)\n return d.argsort(axis, kind=kind, order=order)","label":0},{"cid":"numpy/ma/core.py::argmin","text":"numpy/ma/core.py::argmin\ndef argmin(a, axis=None, fill_value=None):\n \"Function version of the eponymous method.\"\n if fill_value is None:\n fill_value = default_fill_value(a)\n d = filled(a, fill_value)\n return d.argmin(axis=axis)","label":0},{"cid":"numpy/ma/core.py::argmax","text":"numpy/ma/core.py::argmax\ndef argmax(a, axis=None, fill_value=None):\n \"Function version of the eponymous method.\"\n if fill_value is None:\n fill_value = default_fill_value(a)\n try:\n fill_value = -fill_value\n except:\n pass\n d = filled(a, fill_value)\n return d.argmax(axis=axis)","label":0},{"cid":"numpy/ma/core.py::sort","text":"numpy/ma/core.py::sort\ndef sort(a, axis= -1, kind='quicksort', order=None, endwith=True, fill_value=None):\n \"Function version of the eponymous method.\"\n a = narray(a, copy=True, subok=True)\n if axis is None:\n a = a.flatten()\n axis = 0\n if fill_value is None:\n if endwith:\n # nan > inf\n if np.issubdtype(a.dtype, np.floating):\n filler = np.nan\n else:\n filler = minimum_fill_value(a)\n else:\n filler = maximum_fill_value(a)\n else:\n filler = fill_value\n\n sindx = filled(a, filler).argsort(axis=axis, kind=kind, order=order)\n\n # save meshgrid memory for 1d arrays\n if a.ndim == 1:\n indx = sindx\n else:\n indx = np.meshgrid(*[np.arange(x) for x in a.shape], sparse=True,\n indexing='ij')\n indx[axis] = sindx\n return a[indx]","label":0},{"cid":"numpy/ma/core.py::compressed","text":"numpy/ma/core.py::compressed\ndef compressed(x):\n \"\"\"\n Return all the non-masked data as a 1-D array.\n\n This function is equivalent to calling the \"compressed\" method of a\n `MaskedArray`, see `MaskedArray.compressed` for details.\n\n See Also\n --------\n MaskedArray.compressed\n Equivalent method.\n\n \"\"\"\n if not isinstance(x, MaskedArray):\n x = asanyarray(x)\n return x.compressed()","label":0},{"cid":"numpy/ma/core.py::concatenate","text":"numpy/ma/core.py::concatenate\ndef concatenate(arrays, axis=0):\n \"\"\"\n Concatenate a sequence of arrays along the given axis.\n\n Parameters\n ----------\n arrays : sequence of array_like\n The arrays must have the same shape, except in the dimension\n corresponding to `axis` (the first, by default).\n axis : int, optional\n The axis along which the arrays will be joined. Default is 0.\n\n Returns\n -------\n result : MaskedArray\n The concatenated array with any masked entries preserved.\n\n See Also\n --------\n numpy.concatenate : Equivalent function in the top-level NumPy module.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = ma.arange(3)\n >>> a[1] = ma.masked\n >>> b = ma.arange(2, 5)\n >>> a\n masked_array(data = [0 -- 2],\n mask = [False True False],\n fill_value = 999999)\n >>> b\n masked_array(data = [2 3 4],\n mask = False,\n fill_value = 999999)\n >>> ma.concatenate([a, b])\n masked_array(data = [0 -- 2 2 3 4],\n mask = [False True False False False False],\n fill_value = 999999)\n\n \"\"\"\n d = np.concatenate([getdata(a) for a in arrays], axis)\n rcls = get_masked_subclass(*arrays)\n data = d.view(rcls)\n # Check whether one of the arrays has a non-empty mask...\n for x in arrays:\n if getmask(x) is not nomask:\n break\n else:\n return data\n # OK, so we have to concatenate the masks\n dm = np.concatenate([getmaskarray(a) for a in arrays], axis)\n # If we decide to keep a '_shrinkmask' option, we want to check that ...\n # ... all of them are True, and then check for dm.any()\n# shrink = numpy.logical_or.reduce([getattr(a,'_shrinkmask',True) for a in arrays])\n# if shrink and not dm.any():\n if not dm.dtype.fields and not dm.any():\n data._mask = nomask\n else:\n data._mask = dm.reshape(d.shape)\n return data","label":0},{"cid":"numpy/ma/core.py::count","text":"numpy/ma/core.py::count\ndef count(a, axis=None):\n if isinstance(a, MaskedArray):\n return a.count(axis)\n return masked_array(a, copy=False).count(axis)","label":0},{"cid":"numpy/ma/core.py::diag","text":"numpy/ma/core.py::diag\ndef diag(v, k=0):\n \"\"\"\n Extract a diagonal or construct a diagonal array.\n\n This function is the equivalent of `numpy.diag` that takes masked\n values into account, see `numpy.diag` for details.\n\n See Also\n --------\n numpy.diag : Equivalent function for ndarrays.\n\n \"\"\"\n output = np.diag(v, k).view(MaskedArray)\n if getmask(v) is not nomask:\n output._mask = np.diag(v._mask, k)\n return output","label":0},{"cid":"numpy/ma/core.py::expand_dims","text":"numpy/ma/core.py::expand_dims\ndef expand_dims(x, axis):\n \"\"\"\n Expand the shape of an array.\n\n Expands the shape of the array by including a new axis before the one\n specified by the `axis` parameter. This function behaves the same as\n `numpy.expand_dims` but preserves masked elements.\n\n See Also\n --------\n numpy.expand_dims : Equivalent function in top-level NumPy module.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> x = ma.array([1, 2, 4])\n >>> x[1] = ma.masked\n >>> x\n masked_array(data = [1 -- 4],\n mask = [False True False],\n fill_value = 999999)\n >>> np.expand_dims(x, axis=0)\n array([[1, 2, 4]])\n >>> ma.expand_dims(x, axis=0)\n masked_array(data =\n [[1 -- 4]],\n mask =\n [[False True False]],\n fill_value = 999999)\n\n The same result can be achieved using slicing syntax with `np.newaxis`.\n\n >>> x[np.newaxis, :]\n masked_array(data =\n [[1 -- 4]],\n mask =\n [[False True False]],\n fill_value = 999999)\n\n \"\"\"\n result = n_expand_dims(x, axis)\n if isinstance(x, MaskedArray):\n new_shape = result.shape\n result = x.view()\n result.shape = new_shape\n if result._mask is not nomask:\n result._mask.shape = new_shape\n return result","label":0},{"cid":"numpy/ma/core.py::left_shift","text":"numpy/ma/core.py::left_shift\ndef left_shift (a, n):\n \"\"\"\n Shift the bits of an integer to the left.\n\n This is the masked array version of `numpy.left_shift`, for details\n see that function.\n\n See Also\n --------\n numpy.left_shift\n\n \"\"\"\n m = getmask(a)\n if m is nomask:\n d = umath.left_shift(filled(a), n)\n return masked_array(d)\n else:\n d = umath.left_shift(filled(a, 0), n)\n return masked_array(d, mask=m)","label":0},{"cid":"numpy/ma/core.py::right_shift","text":"numpy/ma/core.py::right_shift\ndef right_shift (a, n):\n \"\"\"\n Shift the bits of an integer to the right.\n\n This is the masked array version of `numpy.right_shift`, for details\n see that function.\n\n See Also\n --------\n numpy.right_shift\n\n \"\"\"\n m = getmask(a)\n if m is nomask:\n d = umath.right_shift(filled(a), n)\n return masked_array(d)\n else:\n d = umath.right_shift(filled(a, 0), n)\n return masked_array(d, mask=m)","label":0},{"cid":"numpy/ma/core.py::put","text":"numpy/ma/core.py::put\ndef put(a, indices, values, mode='raise'):\n \"\"\"\n Set storage-indexed locations to corresponding values.\n\n This function is equivalent to `MaskedArray.put`, see that method\n for details.\n\n See Also\n --------\n MaskedArray.put\n\n \"\"\"\n # We can't use 'frommethod', the order of arguments is different\n try:\n return a.put(indices, values, mode=mode)\n except AttributeError:\n return narray(a, copy=False).put(indices, values, mode=mode)","label":0},{"cid":"numpy/ma/core.py::putmask","text":"numpy/ma/core.py::putmask\ndef putmask(a, mask, values): #, mode='raise'):\n \"\"\"\n Changes elements of an array based on conditional and input values.\n\n This is the masked array version of `numpy.putmask`, for details see\n `numpy.putmask`.\n\n See Also\n --------\n numpy.putmask\n\n Notes\n -----\n Using a masked array as `values` will **not** transform a `ndarray` into\n a `MaskedArray`.\n\n \"\"\"\n # We can't use 'frommethod', the order of arguments is different\n if not isinstance(a, MaskedArray):\n a = a.view(MaskedArray)\n (valdata, valmask) = (getdata(values), getmask(values))\n if getmask(a) is nomask:\n if valmask is not nomask:\n a._sharedmask = True\n a._mask = make_mask_none(a.shape, a.dtype)\n np.copyto(a._mask, valmask, where=mask)\n elif a._hardmask:\n if valmask is not nomask:\n m = a._mask.copy()\n np.copyto(m, valmask, where=mask)\n a.mask |= m\n else:\n if valmask is nomask:\n valmask = getmaskarray(values)\n np.copyto(a._mask, valmask, where=mask)\n np.copyto(a._data, valdata, where=mask)\n return","label":0},{"cid":"numpy/ma/core.py::transpose","text":"numpy/ma/core.py::transpose\ndef transpose(a, axes=None):\n \"\"\"\n Permute the dimensions of an array.\n\n This function is exactly equivalent to `numpy.transpose`.\n\n See Also\n --------\n numpy.transpose : Equivalent function in top-level NumPy module.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> x = ma.arange(4).reshape((2,2))\n >>> x[1, 1] = ma.masked\n >>>> x\n masked_array(data =\n [[0 1]\n [2 --]],\n mask =\n [[False False]\n [False True]],\n fill_value = 999999)\n >>> ma.transpose(x)\n masked_array(data =\n [[0 2]\n [1 --]],\n mask =\n [[False False]\n [False True]],\n fill_value = 999999)\n\n \"\"\"\n #We can't use 'frommethod', as 'transpose' doesn't take keywords\n try:\n return a.transpose(axes)\n except AttributeError:\n return narray(a, copy=False).transpose(axes).view(MaskedArray)","label":0},{"cid":"numpy/ma/core.py::reshape","text":"numpy/ma/core.py::reshape\ndef reshape(a, new_shape, order='C'):\n \"\"\"\n Returns an array containing the same data with a new shape.\n\n Refer to `MaskedArray.reshape` for full documentation.\n\n See Also\n --------\n MaskedArray.reshape : equivalent function\n\n \"\"\"\n #We can't use 'frommethod', it whine about some parameters. Dmmit.\n try:\n return a.reshape(new_shape, order=order)\n except AttributeError:\n _tmp = narray(a, copy=False).reshape(new_shape, order=order)\n return _tmp.view(MaskedArray)","label":0},{"cid":"numpy/ma/core.py::resize","text":"numpy/ma/core.py::resize\ndef resize(x, new_shape):\n \"\"\"\n Return a new masked array with the specified size and shape.\n\n This is the masked equivalent of the `numpy.resize` function. The new\n array is filled with repeated copies of `x` (in the order that the\n data are stored in memory). If `x` is masked, the new array will be\n masked, and the new mask will be a repetition of the old one.\n\n See Also\n --------\n numpy.resize : Equivalent function in the top level NumPy module.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = ma.array([[1, 2] ,[3, 4]])\n >>> a[0, 1] = ma.masked\n >>> a\n masked_array(data =\n [[1 --]\n [3 4]],\n mask =\n [[False True]\n [False False]],\n fill_value = 999999)\n >>> np.resize(a, (3, 3))\n array([[1, 2, 3],\n [4, 1, 2],\n [3, 4, 1]])\n >>> ma.resize(a, (3, 3))\n masked_array(data =\n [[1 -- 3]\n [4 1 --]\n [3 4 1]],\n mask =\n [[False True False]\n [False False True]\n [False False False]],\n fill_value = 999999)\n\n A MaskedArray is always returned, regardless of the input type.\n\n >>> a = np.array([[1, 2] ,[3, 4]])\n >>> ma.resize(a, (3, 3))\n masked_array(data =\n [[1 2 3]\n [4 1 2]\n [3 4 1]],\n mask =\n False,\n fill_value = 999999)\n\n \"\"\"\n # We can't use _frommethods here, as N.resize is notoriously whiny.\n m = getmask(x)\n if m is not nomask:\n m = np.resize(m, new_shape)\n result = np.resize(x, new_shape).view(get_masked_subclass(x))\n if result.ndim:\n result._mask = m\n return result","label":0},{"cid":"numpy/ma/core.py::rank","text":"numpy/ma/core.py::rank\ndef rank(obj):\n \"maskedarray version of the numpy function.\"\n return np.rank(getdata(obj))","label":0},{"cid":"numpy/ma/core.py::shape","text":"numpy/ma/core.py::shape\ndef shape(obj):\n \"maskedarray version of the numpy function.\"\n return np.shape(getdata(obj))","label":0},{"cid":"numpy/ma/core.py::size","text":"numpy/ma/core.py::size\ndef size(obj, axis=None):\n \"maskedarray version of the numpy function.\"\n return np.size(getdata(obj), axis)","label":0},{"cid":"numpy/ma/core.py::where","text":"numpy/ma/core.py::where\ndef where (condition, x=None, y=None):\n \"\"\"\n Return a masked array with elements from x or y, depending on condition.\n\n Returns a masked array, shaped like condition, where the elements\n are from `x` when `condition` is True, and from `y` otherwise.\n If neither `x` nor `y` are given, the function returns a tuple of\n indices where `condition` is True (the result of\n ``condition.nonzero()``).\n\n Parameters\n ----------\n condition : array_like, bool\n The condition to meet. For each True element, yield the corresponding\n element from `x`, otherwise from `y`.\n x, y : array_like, optional\n Values from which to choose. `x` and `y` need to have the same shape\n as condition, or be broadcast-able to that shape.\n\n Returns\n -------\n out : MaskedArray or tuple of ndarrays\n The resulting masked array if `x` and `y` were given, otherwise\n the result of ``condition.nonzero()``.\n\n See Also\n --------\n numpy.where : Equivalent function in the top-level NumPy module.\n\n Examples\n --------\n >>> x = np.ma.array(np.arange(9.).reshape(3, 3), mask=[[0, 1, 0],\n ... [1, 0, 1],\n ... [0, 1, 0]])\n >>> print x\n [[0.0 -- 2.0]\n [-- 4.0 --]\n [6.0 -- 8.0]]\n >>> np.ma.where(x > 5) # return the indices where x > 5\n (array([2, 2]), array([0, 2]))\n\n >>> print np.ma.where(x > 5, x, -3.1416)\n [[-3.1416 -- -3.1416]\n [-- -3.1416 --]\n [6.0 -- 8.0]]\n\n \"\"\"\n if x is None and y is None:\n return filled(condition, 0).nonzero()\n elif x is None or y is None:\n raise ValueError(\"Either both or neither x and y should be given.\")\n # Get the condition ...............\n fc = filled(condition, 0).astype(MaskType)\n notfc = np.logical_not(fc)\n # Get the data ......................................\n xv = getdata(x)\n yv = getd","label":0},{"cid":"numpy/ma/core.py::choose","text":"numpy/ma/core.py::choose\ndef choose (indices, choices, out=None, mode='raise'):\n \"\"\"\n Use an index array to construct a new array from a set of choices.\n\n Given an array of integers and a set of n choice arrays, this method\n will create a new array that merges each of the choice arrays. Where a\n value in `a` is i, the new array will have the value that choices[i]\n contains in the same place.\n\n Parameters\n ----------\n a : ndarray of ints\n This array must contain integers in ``[0, n-1]``, where n is the\n number of choices.\n choices : sequence of arrays\n Choice arrays. The index array and all of the choices should be\n broadcastable to the same shape.\n out : array, optional\n If provided, the result will be inserted into this array. It should\n be of the appropriate shape and `dtype`.\n mode : {'raise', 'wrap', 'clip'}, optional\n Specifies how out-of-bounds indices will behave.\n\n * 'raise' : raise an error\n * 'wrap' : wrap around\n * 'clip' : clip to the range\n\n Returns\n -------\n merged_array : array\n\n See Also\n --------\n choose : equivalent function\n\n Examples\n --------\n >>> choice = np.array([[1,1,1], [2,2,2], [3,3,3]])\n >>> a = np.array([2, 1, 0])\n >>> np.ma.choose(a, choice)\n masked_array(data = [3 2 1],\n mask = False,\n fill_value=999999)\n\n \"\"\"\n def fmask (x):\n \"Returns the filled array, or True if masked.\"\n if x is masked:\n return True\n return filled(x)\n def nmask (x):\n \"Returns the mask, True if ``masked``, False if ``nomask``.\"\n if x is masked:\n return True\n return getmask(x)\n # Get the indices......\n c = filled(indices, 0)\n # Get the masks........\n masks = [nmask(x) for x in choices]\n data = [fmask(x) for x in choices]\n # Construct the mask\n outputmask = np.choose(c, masks, mode=mode)\n outputmask = make_mask(mask","label":0},{"cid":"numpy/ma/core.py::choose.fmask","text":"numpy/ma/core.py::choose.fmask\n def fmask (x):\n \"Returns the filled array, or True if masked.\"\n if x is masked:\n return True\n return filled(x)","label":0},{"cid":"numpy/ma/core.py::choose.nmask","text":"numpy/ma/core.py::choose.nmask\n def nmask (x):\n \"Returns the mask, True if ``masked``, False if ``nomask``.\"\n if x is masked:\n return True\n return getmask(x)","label":0},{"cid":"numpy/ma/core.py::round_","text":"numpy/ma/core.py::round_\ndef round_(a, decimals=0, out=None):\n \"\"\"\n Return a copy of a, rounded to 'decimals' places.\n\n When 'decimals' is negative, it specifies the number of positions\n to the left of the decimal point. The real and imaginary parts of\n complex numbers are rounded separately. Nothing is done if the\n array is not of float type and 'decimals' is greater than or equal\n to 0.\n\n Parameters\n ----------\n decimals : int\n Number of decimals to round to. May be negative.\n out : array_like\n Existing array to use for output.\n If not given, returns a default copy of a.\n\n Notes\n -----\n If out is given and does not have a mask attribute, the mask of a\n is lost!\n\n \"\"\"\n if out is None:\n return np.round_(a, decimals, out)\n else:\n np.round_(getdata(a), decimals, out)\n if hasattr(out, '_mask'):\n out._mask = getmask(a)\n return out","label":0},{"cid":"numpy/ma/core.py::inner","text":"numpy/ma/core.py::inner\ndef inner(a, b):\n \"\"\"\n Returns the inner product of a and b for arrays of floating point types.\n\n Like the generic NumPy equivalent the product sum is over the last dimension\n of a and b.\n\n Notes\n -----\n The first argument is not conjugated.\n\n \"\"\"\n fa = filled(a, 0)\n fb = filled(b, 0)\n if len(fa.shape) == 0:\n fa.shape = (1,)\n if len(fb.shape) == 0:\n fb.shape = (1,)\n return np.inner(fa, fb).view(MaskedArray)","label":0},{"cid":"numpy/ma/core.py::outer","text":"numpy/ma/core.py::outer\ndef outer(a, b):\n \"maskedarray version of the numpy function.\"\n fa = filled(a, 0).ravel()\n fb = filled(b, 0).ravel()\n d = np.outer(fa, fb)\n ma = getmask(a)\n mb = getmask(b)\n if ma is nomask and mb is nomask:\n return masked_array(d)\n ma = getmaskarray(a)\n mb = getmaskarray(b)\n m = make_mask(1 - np.outer(1 - ma, 1 - mb), copy=0)\n return masked_array(d, mask=m)","label":0},{"cid":"numpy/ma/core.py::allequal","text":"numpy/ma/core.py::allequal\ndef allequal (a, b, fill_value=True):\n \"\"\"\n Return True if all entries of a and b are equal, using\n fill_value as a truth value where either or both are masked.\n\n Parameters\n ----------\n a, b : array_like\n Input arrays to compare.\n fill_value : bool, optional\n Whether masked values in a or b are considered equal (True) or not\n (False).\n\n Returns\n -------\n y : bool\n Returns True if the two arrays are equal within the given\n tolerance, False otherwise. If either array contains NaN,\n then False is returned.\n\n See Also\n --------\n all, any\n numpy.ma.allclose\n\n Examples\n --------\n >>> a = ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1])\n >>> a\n masked_array(data = [10000000000.0 1e-07 --],\n mask = [False False True],\n fill_value=1e+20)\n\n >>> b = array([1e10, 1e-7, -42.0])\n >>> b\n array([ 1.00000000e+10, 1.00000000e-07, -4.20000000e+01])\n >>> ma.allequal(a, b, fill_value=False)\n False\n >>> ma.allequal(a, b)\n True\n\n \"\"\"\n m = mask_or(getmask(a), getmask(b))\n if m is nomask:\n x = getdata(a)\n y = getdata(b)\n d = umath.equal(x, y)\n return d.all()\n elif fill_value:\n x = getdata(a)\n y = getdata(b)\n d = umath.equal(x, y)\n dm = array(d, mask=m, copy=False)\n return dm.filled(True).all(None)\n else:\n return False","label":0},{"cid":"numpy/ma/core.py::allclose","text":"numpy/ma/core.py::allclose\ndef allclose (a, b, masked_equal=True, rtol=1e-5, atol=1e-8):\n \"\"\"\n Returns True if two arrays are element-wise equal within a tolerance.\n\n This function is equivalent to `allclose` except that masked values\n are treated as equal (default) or unequal, depending on the `masked_equal`\n argument.\n\n Parameters\n ----------\n a, b : array_like\n Input arrays to compare.\n masked_equal : bool, optional\n Whether masked values in `a` and `b` are considered equal (True) or not\n (False). They are considered equal by default.\n rtol : float, optional\n Relative tolerance. The relative difference is equal to ``rtol * b``.\n Default is 1e-5.\n atol : float, optional\n Absolute tolerance. The absolute difference is equal to `atol`.\n Default is 1e-8.\n\n Returns\n -------\n y : bool\n Returns True if the two arrays are equal within the given\n tolerance, False otherwise. If either array contains NaN, then\n False is returned.\n\n See Also\n --------\n all, any\n numpy.allclose : the non-masked `allclose`.\n\n Notes\n -----\n If the following equation is element-wise True, then `allclose` returns\n True::\n\n absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))\n\n Return True if all elements of `a` and `b` are equal subject to\n given tolerances.\n\n Examples\n --------\n >>> a = ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1])\n >>> a\n masked_array(data = [10000000000.0 1e-07 --],\n mask = [False False True],\n fill_value = 1e+20)\n >>> b = ma.array([1e10, 1e-8, -42.0], mask=[0, 0, 1])\n >>> ma.allclose(a, b)\n False\n\n >>> a = ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1])\n >>> b = ma.array([1.00001e10, 1e-9, -42.0], mask=[0, 0, 1])\n >>> ma.allclose(a, b)\n True\n >>> ma.allclose(a, b, masked_equal=False)\n False\n\n Masked values are not compared directly.\n\n >>> a = ma.array([1e10,","label":0},{"cid":"numpy/ma/core.py::asarray","text":"numpy/ma/core.py::asarray\ndef asarray(a, dtype=None, order=None):\n \"\"\"\n Convert the input to a masked array of the given data-type.\n\n No copy is performed if the input is already an `ndarray`. If `a` is\n a subclass of `MaskedArray`, a base class `MaskedArray` is returned.\n\n Parameters\n ----------\n a : array_like\n Input data, in any form that can be converted to a masked array. This\n includes lists, lists of tuples, tuples, tuples of tuples, tuples\n of lists, ndarrays and masked arrays.\n dtype : dtype, optional\n By default, the data-type is inferred from the input data.\n order : {'C', 'F'}, optional\n Whether to use row-major ('C') or column-major ('FORTRAN') memory\n representation. Default is 'C'.\n\n Returns\n -------\n out : MaskedArray\n Masked array interpretation of `a`.\n\n See Also\n --------\n asanyarray : Similar to `asarray`, but conserves subclasses.\n\n Examples\n --------\n >>> x = np.arange(10.).reshape(2, 5)\n >>> x\n array([[ 0., 1., 2., 3., 4.],\n [ 5., 6., 7., 8., 9.]])\n >>> np.ma.asarray(x)\n masked_array(data =\n [[ 0. 1. 2. 3. 4.]\n [ 5. 6. 7. 8. 9.]],\n mask =\n False,\n fill_value = 1e+20)\n >>> type(np.ma.asarray(x))\n <class 'numpy.ma.core.MaskedArray'>\n\n \"\"\"\n return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=False)","label":0},{"cid":"numpy/ma/core.py::asanyarray","text":"numpy/ma/core.py::asanyarray\ndef asanyarray(a, dtype=None):\n \"\"\"\n Convert the input to a masked array, conserving subclasses.\n\n If `a` is a subclass of `MaskedArray`, its class is conserved.\n No copy is performed if the input is already an `ndarray`.\n\n Parameters\n ----------\n a : array_like\n Input data, in any form that can be converted to an array.\n dtype : dtype, optional\n By default, the data-type is inferred from the input data.\n order : {'C', 'F'}, optional\n Whether to use row-major ('C') or column-major ('FORTRAN') memory\n representation. Default is 'C'.\n\n Returns\n -------\n out : MaskedArray\n MaskedArray interpretation of `a`.\n\n See Also\n --------\n asarray : Similar to `asanyarray`, but does not conserve subclass.\n\n Examples\n --------\n >>> x = np.arange(10.).reshape(2, 5)\n >>> x\n array([[ 0., 1., 2., 3., 4.],\n [ 5., 6., 7., 8., 9.]])\n >>> np.ma.asanyarray(x)\n masked_array(data =\n [[ 0. 1. 2. 3. 4.]\n [ 5. 6. 7. 8. 9.]],\n mask =\n False,\n fill_value = 1e+20)\n >>> type(np.ma.asanyarray(x))\n <class 'numpy.ma.core.MaskedArray'>\n\n \"\"\"\n return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=True)","label":0},{"cid":"numpy/ma/core.py::dump","text":"numpy/ma/core.py::dump\ndef dump(a, F):\n \"\"\"\n Pickle a masked array to a file.\n\n This is a wrapper around ``cPickle.dump``.\n\n Parameters\n ----------\n a : MaskedArray\n The array to be pickled.\n F : str or file-like object\n The file to pickle `a` to. If a string, the full path to the file.\n\n \"\"\"\n if not hasattr(F, 'readline'):\n F = open(F, 'w')\n return pickle.dump(a, F)","label":0},{"cid":"numpy/ma/core.py::dumps","text":"numpy/ma/core.py::dumps\ndef dumps(a):\n \"\"\"\n Return a string corresponding to the pickling of a masked array.\n\n This is a wrapper around ``cPickle.dumps``.\n\n Parameters\n ----------\n a : MaskedArray\n The array for which the string representation of the pickle is\n returned.\n\n \"\"\"\n return pickle.dumps(a)","label":0},{"cid":"numpy/ma/core.py::load","text":"numpy/ma/core.py::load\ndef load(F):\n \"\"\"\n Wrapper around ``cPickle.load`` which accepts either a file-like object\n or a filename.\n\n Parameters\n ----------\n F : str or file\n The file or file name to load.\n\n See Also\n --------\n dump : Pickle an array\n\n Notes\n -----\n This is different from `numpy.load`, which does not use cPickle but loads\n the NumPy binary .npy format.\n\n \"\"\"\n if not hasattr(F, 'readline'):\n F = open(F, 'r')\n return pickle.load(F)","label":0},{"cid":"numpy/ma/core.py::loads","text":"numpy/ma/core.py::loads\ndef loads(strg):\n \"\"\"\n Load a pickle from the current string.\n\n The result of ``cPickle.loads(strg)`` is returned.\n\n Parameters\n ----------\n strg : str\n The string to load.\n\n See Also\n --------\n dumps : Return a string corresponding to the pickling of a masked array.\n\n \"\"\"\n return pickle.loads(strg)","label":0},{"cid":"numpy/ma/core.py::fromfile","text":"numpy/ma/core.py::fromfile\ndef fromfile(file, dtype=float, count= -1, sep=''):\n raise NotImplementedError(\"Not yet implemented. Sorry\")","label":0},{"cid":"numpy/ma/core.py::fromflex","text":"numpy/ma/core.py::fromflex\ndef fromflex(fxarray):\n \"\"\"\n Build a masked array from a suitable flexible-type array.\n\n The input array has to have a data-type with ``_data`` and ``_mask``\n fields. This type of array is output by `MaskedArray.toflex`.\n\n Parameters\n ----------\n fxarray : ndarray\n The structured input array, containing ``_data`` and ``_mask``\n fields. If present, other fields are discarded.\n\n Returns\n -------\n result : MaskedArray\n The constructed masked array.\n\n See Also\n --------\n MaskedArray.toflex : Build a flexible-type array from a masked array.\n\n Examples\n --------\n >>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[0] + [1, 0] * 4)\n >>> rec = x.toflex()\n >>> rec\n array([[(0, False), (1, True), (2, False)],\n [(3, True), (4, False), (5, True)],\n [(6, False), (7, True), (8, False)]],\n dtype=[('_data', '<i4'), ('_mask', '|b1')])\n >>> x2 = np.ma.fromflex(rec)\n >>> x2\n masked_array(data =\n [[0 -- 2]\n [-- 4 --]\n [6 -- 8]],\n mask =\n [[False True False]\n [ True False True]\n [False True False]],\n fill_value = 999999)\n\n Extra fields can be present in the structured array but are discarded:\n\n >>> dt = [('_data', '<i4'), ('_mask', '|b1'), ('field3', '<f4')]\n >>> rec2 = np.zeros((2, 2), dtype=dt)\n >>> rec2\n array([[(0, False, 0.0), (0, False, 0.0)],\n [(0, False, 0.0), (0, False, 0.0)]],\n dtype=[('_data', '<i4'), ('_mask', '|b1'), ('field3', '<f4')])\n >>> y = np.ma.fromflex(rec2)\n >>> y\n masked_array(data =\n [[0 0]\n [0 0]],\n mask =\n [[False False]\n [False False]],\n fill_value = 999999)\n\n \"\"\"\n return masked_array(fxarray['_data'], mask=fxarray['_mask'])","label":0},{"cid":"numpy/ma/core.py::_convert2ma.__init__","text":"numpy/ma/core.py::_convert2ma.__init__\n def __init__(self, funcname, params=None):\n self._func = getattr(np, funcname)\n self.__doc__ = self.getdoc()\n self._extras = params or {}","label":0},{"cid":"numpy/ma/core.py::_convert2ma.getdoc","text":"numpy/ma/core.py::_convert2ma.getdoc\n def getdoc(self):\n \"Return the doc of the function (from the doc of the method).\"\n doc = getattr(self._func, '__doc__', None)\n sig = get_object_signature(self._func)\n if doc:\n # Add the signature of the function at the beginning of the doc\n if sig:\n sig = \"%s%s\\n\" % (self._func.__name__, sig)\n doc = sig + doc\n return doc","label":0},{"cid":"numpy/ma/core.py::_convert2ma.__call__","text":"numpy/ma/core.py::_convert2ma.__call__\n def __call__(self, a, *args, **params):\n # Find the common parameters to the call and the definition\n _extras = self._extras\n common_params = set(params).intersection(_extras)\n # Drop the common parameters from the call\n for p in common_params:\n _extras[p] = params.pop(p)\n # Get the result\n result = self._func.__call__(a, *args, **params).view(MaskedArray)\n if \"fill_value\" in common_params:\n result.fill_value = _extras.get(\"fill_value\", None)\n if \"hardmask\" in common_params:\n result._hardmask = bool(_extras.get(\"hard_mask\", False))\n return result","label":0},{"cid":"numpy/ma/core.py::append","text":"numpy/ma/core.py::append\ndef append(a, b, axis=None):\n \"\"\"Append values to the end of an array.\n\n .. versionadded:: 1.9.0\n\n Parameters\n ----------\n arr : array_like\n Values are appended to a copy of this array.\n values : array_like\n These values are appended to a copy of `arr`. It must be of the\n correct shape (the same shape as `arr`, excluding `axis`). If `axis`\n is not specified, `values` can be any shape and will be flattened\n before use.\n axis : int, optional\n The axis along which `values` are appended. If `axis` is not given,\n both `arr` and `values` are flattened before use.\n\n Returns\n -------\n append : MaskedArray\n A copy of `arr` with `values` appended to `axis`. Note that `append`\n does not occur in-place: a new array is allocated and filled. If\n `axis` is None, the result is a flattened array.\n\n See Also\n --------\n numpy.append : Equivalent function in the top-level NumPy module.\n\n Examples\n --------\n >>> import numpy.ma as ma\n >>> a = ma.masked_values([1, 2, 3], 2)\n >>> b = ma.masked_values([[4, 5, 6], [7, 8, 9]], 7)\n >>> print(ma.append(a, b))\n [1 -- 3 4 5 6 -- 8 9]\n \"\"\"\n return concatenate([a, b], axis)","label":0},{"cid":"pavement.py::superpack_name","text":"pavement.py::superpack_name\ndef superpack_name(pyver, numver):\n \"\"\"Return the filename of the superpack installer.\"\"\"\n return 'numpy-%s-win32-superpack-python%s.exe' % (numver, pyver)","label":0},{"cid":"pavement.py::internal_wininst_name","text":"pavement.py::internal_wininst_name\ndef internal_wininst_name(arch):\n \"\"\"Return the name of the wininst as it will be inside the superpack (i.e.\n with the arch encoded.\"\"\"\n ext = '.exe'\n return \"numpy-%s-%s%s\" % (FULLVERSION, arch, ext)","label":0},{"cid":"pavement.py::wininst_name","text":"pavement.py::wininst_name\ndef wininst_name(pyver):\n \"\"\"Return the name of the installer built by wininst command.\"\"\"\n ext = '.exe'\n return \"numpy-%s.win32-py%s%s\" % (FULLVERSION, pyver, ext)","label":0},{"cid":"pavement.py::prepare_nsis_script","text":"pavement.py::prepare_nsis_script\ndef prepare_nsis_script(pyver, numver):\n if not os.path.exists(SUPERPACK_BUILD):\n os.makedirs(SUPERPACK_BUILD)\n\n tpl = os.path.join('tools/win32build/nsis_scripts', 'numpy-superinstaller.nsi.in')\n source = open(tpl, 'r')\n target = open(os.path.join(SUPERPACK_BUILD, 'numpy-superinstaller.nsi'), 'w')\n\n installer_name = superpack_name(pyver, numver)\n cnt = \"\".join(source.readlines())\n cnt = cnt.replace('@NUMPY_INSTALLER_NAME@', installer_name)\n for arch in ['nosse', 'sse2', 'sse3']:\n cnt = cnt.replace('@%s_BINARY@' % arch.upper(),\n internal_wininst_name(arch))\n\n target.write(cnt)","label":0},{"cid":"pavement.py::bdist_wininst_arch","text":"pavement.py::bdist_wininst_arch\ndef bdist_wininst_arch(pyver, arch):\n \"\"\"Arch specific wininst build.\"\"\"\n if os.path.exists(\"build\"):\n shutil.rmtree(\"build\")\n\n _bdist_wininst(pyver, SITECFG[arch])","label":0},{"cid":"pavement.py::bdist_superpack","text":"pavement.py::bdist_superpack\n@task\n@cmdopts([(\"python-version=\", \"p\", \"python version\")])\ndef bdist_superpack(options):\n \"\"\"Build all arch specific wininst installers.\"\"\"\n pyver = options.python_version\n def copy_bdist(arch):\n # Copy the wininst in dist into the release directory\n source = os.path.join('dist', wininst_name(pyver))\n target = os.path.join(SUPERPACK_BINDIR, internal_wininst_name(arch))\n if os.path.exists(target):\n os.remove(target)\n if not os.path.exists(os.path.dirname(target)):\n os.makedirs(os.path.dirname(target))\n try:\n os.rename(source, target)\n except OSError:\n # When git is installed on OS X but not under Wine, the name of the\n # .exe has \"-Unknown\" in it instead of the correct git revision.\n # Try to fix this here:\n revidx = source.index(\".dev-\") + 5\n gitrev = source[revidx:revidx+7]\n os.rename(source.replace(gitrev, \"Unknown\"), target)\n\n bdist_wininst_arch(pyver, 'nosse')\n copy_bdist(\"nosse\")\n bdist_wininst_arch(pyver, 'sse2')\n copy_bdist(\"sse2\")\n bdist_wininst_arch(pyver, 'sse3')\n copy_bdist(\"sse3\")\n\n idirs = options.installers.installersdir\n pyver = options.python_version\n prepare_nsis_script(pyver, FULLVERSION)\n subprocess.check_call(MAKENSIS + ['numpy-superinstaller.nsi'],\n cwd=SUPERPACK_BUILD)\n\n # Copy the superpack into installers dir\n if not os.path.exists(idirs):\n os.makedirs(idirs)\n\n source = os.path.join(SUPERPACK_BUILD, superpack_name(pyver, FULLVERSION))\n target = os.path.join(idirs, superpack_name(pyver, FULLVERSION))\n shutil.copy(source, target)","label":0},{"cid":"pavement.py::bdist_superpack.copy_bdist","text":"pavement.py::bdist_superpack.copy_bdist\n def copy_bdist(arch):\n # Copy the wininst in dist into the release directory\n source = os.path.join('dist', wininst_name(pyver))\n target = os.path.join(SUPERPACK_BINDIR, internal_wininst_name(arch))\n if os.path.exists(target):\n os.remove(target)\n if not os.path.exists(os.path.dirname(target)):\n os.makedirs(os.path.dirname(target))\n try:\n os.rename(source, target)\n except OSError:\n # When git is installed on OS X but not under Wine, the name of the\n # .exe has \"-Unknown\" in it instead of the correct git revision.\n # Try to fix this here:\n revidx = source.index(\".dev-\") + 5\n gitrev = source[revidx:revidx+7]\n os.rename(source.replace(gitrev, \"Unknown\"), target)","label":0},{"cid":"pavement.py::bdist_wininst_nosse","text":"pavement.py::bdist_wininst_nosse\n@task\n@cmdopts([(\"python-version=\", \"p\", \"python version\")])\ndef bdist_wininst_nosse(options):\n \"\"\"Build the nosse wininst installer.\"\"\"\n bdist_wininst_arch(options.python_version, 'nosse')","label":0},{"cid":"pavement.py::bdist_wininst_sse2","text":"pavement.py::bdist_wininst_sse2\n@task\n@cmdopts([(\"python-version=\", \"p\", \"python version\")])\ndef bdist_wininst_sse2(options):\n \"\"\"Build the sse2 wininst installer.\"\"\"\n bdist_wininst_arch(options.python_version, 'sse2')","label":0},{"cid":"pavement.py::bdist_wininst_sse3","text":"pavement.py::bdist_wininst_sse3\n@task\n@cmdopts([(\"python-version=\", \"p\", \"python version\")])\ndef bdist_wininst_sse3(options):\n \"\"\"Build the sse3 wininst installer.\"\"\"\n bdist_wininst_arch(options.python_version, 'sse3')","label":0},{"cid":"pavement.py::bdist_wininst_simple","text":"pavement.py::bdist_wininst_simple\n@task\n@cmdopts([(\"python-version=\", \"p\", \"python version\")])\ndef bdist_wininst_simple():\n \"\"\"Simple wininst-based installer.\"\"\"\n pyver = options.bdist_wininst_simple.python_version\n _bdist_wininst(pyver)","label":0},{"cid":"pavement.py::_bdist_wininst","text":"pavement.py::_bdist_wininst\ndef _bdist_wininst(pyver, cfg_env=None):\n cmd = WINDOWS_PYTHON[pyver] + ['setup.py', 'build', '-c', 'mingw32', 'bdist_wininst']\n if cfg_env:\n for k, v in WINDOWS_ENV.items():\n cfg_env[k] = v\n else:\n cfg_env = WINDOWS_ENV\n subprocess.check_call(cmd, env=cfg_env)","label":0},{"cid":"pavement.py::bootstrap","text":"pavement.py::bootstrap\n@task\ndef bootstrap(options):\n \"\"\"create virtualenv in ./bootstrap\"\"\"\n try:\n import virtualenv\n except ImportError as e:\n raise RuntimeError(\"virtualenv is needed for bootstrap\")\n\n bdir = options.bootstrap_dir\n if not os.path.exists(bdir):\n os.makedirs(bdir)\n bscript = \"boostrap.py\"\n\n options.virtualenv.script_name = os.path.join(options.bootstrap_dir,\n bscript)\n options.virtualenv.no_site_packages = False\n options.bootstrap.no_site_packages = False\n call_task('paver.virtual.bootstrap')\n sh('cd %s; %s %s' % (bdir, sys.executable, bscript))","label":0},{"cid":"pavement.py::clean","text":"pavement.py::clean\n@task\ndef clean():\n \"\"\"Remove build, dist, egg-info garbage.\"\"\"\n d = ['build', 'dist', 'numpy.egg-info']\n for i in d:\n if os.path.exists(i):\n shutil.rmtree(i)\n\n bdir = os.path.join('doc', options.sphinx.builddir)\n if os.path.exists(bdir):\n shutil.rmtree(bdir)","label":0},{"cid":"pavement.py::clean_bootstrap","text":"pavement.py::clean_bootstrap\n@task\ndef clean_bootstrap():\n bdir = os.path.join(options.bootstrap.bootstrap_dir)\n if os.path.exists(bdir):\n shutil.rmtree(bdir)","label":0},{"cid":"pavement.py::nuke","text":"pavement.py::nuke\n@task\n@needs('clean', 'clean_bootstrap')\ndef nuke(options):\n \"\"\"Remove everything: build dir, installers, bootstrap dirs, etc...\"\"\"\n for d in [options.superpack.builddir, options.installers.releasedir]:\n if os.path.exists(d):\n shutil.rmtree(d)","label":0},{"cid":"pavement.py::html","text":"pavement.py::html\n@task\ndef html(options):\n \"\"\"Build numpy documentation and put it into build/docs\"\"\"\n # Don't use paver html target because of numpy bootstrapping problems\n bdir = os.path.join(\"doc\", options.sphinx.builddir, \"html\")\n if os.path.exists(bdir):\n shutil.rmtree(bdir)\n subprocess.check_call([\"make\", \"html\"], cwd=\"doc\")\n html_destdir = options.html.builddir\n if os.path.exists(html_destdir):\n shutil.rmtree(html_destdir)\n shutil.copytree(bdir, html_destdir)","label":0},{"cid":"pavement.py::latex","text":"pavement.py::latex\n@task\ndef latex():\n \"\"\"Build numpy documentation in latex format.\"\"\"\n subprocess.check_call([\"make\", \"latex\"], cwd=\"doc\")","label":0},{"cid":"pavement.py::pdf","text":"pavement.py::pdf\n@task\n@needs('latex')\ndef pdf():\n sdir = options.doc.sdir\n bdir = options.doc.bdir\n bdir_latex = options.doc.bdir_latex\n destdir_pdf = options.doc.destdir_pdf\n\n def build_pdf():\n subprocess.check_call([\"make\", \"all-pdf\"], cwd=str(bdir_latex))\n dry(\"Build pdf doc\", build_pdf)\n\n if os.path.exists(destdir_pdf):\n shutil.rmtree(destdir_pdf)\n os.makedirs(destdir_pdf)\n\n user = os.path.join(bdir_latex, \"numpy-user.pdf\")\n shutil.copy(user, os.path.join(destdir_pdf, \"userguide.pdf\"))\n ref = os.path.join(bdir_latex, \"numpy-ref.pdf\")\n shutil.copy(ref, os.path.join(destdir_pdf, \"reference.pdf\"))","label":0},{"cid":"pavement.py::pdf.build_pdf","text":"pavement.py::pdf.build_pdf\n def build_pdf():\n subprocess.check_call([\"make\", \"all-pdf\"], cwd=str(bdir_latex))","label":0},{"cid":"pavement.py::dmg_name","text":"pavement.py::dmg_name\ndef dmg_name(fullversion, pyver, osxver=None):\n \"\"\"Return name for dmg installer.\n\n Notes\n -----\n Python 2.7 has two binaries, one for 10.3 (ppc, i386) and one for 10.6\n (i386, x86_64). All other Python versions at python.org at the moment\n have binaries for 10.3 only. The \"macosx%s\" part of the dmg name should\n correspond to the python.org naming scheme.\n \"\"\"\n # assume that for the py2.7/osx10.6 build the deployment target is set\n # (should be done in the release script).\n if not osxver:\n osxver = os.environ.get('MACOSX_DEPLOYMENT_TARGET', '10.3')\n return \"numpy-%s-py%s-python.org-macosx%s.dmg\" % (fullversion, pyver,\n osxver)","label":0},{"cid":"pavement.py::macosx_version","text":"pavement.py::macosx_version\ndef macosx_version():\n if not sys.platform == 'darwin':\n raise ValueError(\"Not darwin ??\")\n st = subprocess.Popen([\"sw_vers\"], stdout=subprocess.PIPE)\n out = st.stdout.readlines()\n ver = re.compile(\"ProductVersion:\\s+([0-9]+)\\.([0-9]+)\\.([0-9]+)\")\n for i in out:\n m = ver.match(i)\n if m:\n return m.groups()","label":0},{"cid":"pavement.py::mpkg_name","text":"pavement.py::mpkg_name\ndef mpkg_name(pyver):\n maj, min = macosx_version()[:2]\n # Note that bdist_mpkg breaks this if building a dev version with a git\n # commit string attached. make_fullplatcomponents() in\n # bdist_mpkg/cmd_bdist_mpkg.py replaces '-' with '_', comment this out if\n # needed.\n return \"numpy-%s-py%s-macosx%s.%s.mpkg\" % (FULLVERSION, pyver, maj, min)","label":0},{"cid":"pavement.py::_build_mpkg","text":"pavement.py::_build_mpkg\ndef _build_mpkg(pyver):\n # account for differences between Python 2.7.1 versions from python.org\n if os.environ.get('MACOSX_DEPLOYMENT_TARGET', None) == \"10.6\":\n ldflags = \"-undefined dynamic_lookup -bundle -arch i386 -arch x86_64 -Wl,-search_paths_first\"\n else:\n ldflags = \"-undefined dynamic_lookup -bundle -arch i386 -arch ppc -Wl,-search_paths_first\"\n\n ldflags += \" -L%s\" % os.path.join(os.path.dirname(__file__), \"build\")\n sh(\"LDFLAGS='%s' %s setupegg.py bdist_mpkg\" % (ldflags, \" \".join(MPKG_PYTHON[pyver])))","label":0},{"cid":"pavement.py::simple_dmg","text":"pavement.py::simple_dmg\n@task\ndef simple_dmg():\n pyver = \"2.6\"\n src_dir = \"dmg-source\"\n\n # Clean the source dir\n if os.path.exists(src_dir):\n shutil.rmtree(src_dir)\n os.makedirs(src_dir)\n\n # Build the mpkg\n clean()\n _build_mpkg(pyver)\n\n # Build the dmg\n shutil.copytree(os.path.join(\"dist\", mpkg_name(pyver)),\n os.path.join(src_dir, mpkg_name(pyver)))\n _create_dmg(pyver, src_dir, \"NumPy Universal %s\" % FULLVERSION)","label":0},{"cid":"pavement.py::bdist_mpkg","text":"pavement.py::bdist_mpkg\n@task\ndef bdist_mpkg(options):\n call_task(\"clean\")\n try:\n pyver = options.bdist_mpkg.python_version\n except AttributeError:\n pyver = options.python_version\n\n _build_mpkg(pyver)","label":0},{"cid":"pavement.py::_create_dmg","text":"pavement.py::_create_dmg\ndef _create_dmg(pyver, src_dir, volname=None):\n # Build the dmg\n image_name = dmg_name(FULLVERSION, pyver)\n if os.path.exists(image_name):\n os.remove(image_name)\n cmd = [\"hdiutil\", \"create\", image_name, \"-srcdir\", src_dir]\n if volname:\n cmd.extend([\"-volname\", \"'%s'\" % volname])\n sh(\" \".join(cmd))","label":0},{"cid":"pavement.py::dmg","text":"pavement.py::dmg\n@task\n@cmdopts([(\"python-version=\", \"p\", \"python version\")])\ndef dmg(options):\n try:\n pyver = options.dmg.python_version\n except:\n pyver = DEFAULT_PYTHON\n idirs = options.installers.installersdir\n\n # Check if docs exist. If not, say so and quit.\n ref = os.path.join(options.doc.destdir_pdf, \"reference.pdf\")\n user = os.path.join(options.doc.destdir_pdf, \"userguide.pdf\")\n if (not os.path.exists(ref)) or (not os.path.exists(user)):\n import warnings\n warnings.warn(\"Docs need to be built first! Can't find them.\")\n\n # Build the mpkg package\n call_task(\"clean\")\n _build_mpkg(pyver)\n\n macosx_installer_dir = \"tools/numpy-macosx-installer\"\n dmg = os.path.join(macosx_installer_dir, dmg_name(FULLVERSION, pyver))\n if os.path.exists(dmg):\n os.remove(dmg)\n\n # Clean the image source\n content = os.path.join(macosx_installer_dir, 'content')\n if os.path.exists(content):\n shutil.rmtree(content)\n os.makedirs(content)\n\n # Copy mpkg into image source\n mpkg_source = os.path.join(\"dist\", mpkg_name(pyver))\n mpkg_target = os.path.join(content, \"numpy-%s-py%s.mpkg\" % (FULLVERSION, pyver))\n shutil.copytree(mpkg_source, mpkg_target)\n\n # Copy docs into image source\n pdf_docs = os.path.join(content, \"Documentation\")\n if os.path.exists(pdf_docs):\n shutil.rmtree(pdf_docs)\n os.makedirs(pdf_docs)\n shutil.copy(user, os.path.join(pdf_docs, \"userguide.pdf\"))\n shutil.copy(ref, os.path.join(pdf_docs, \"reference.pdf\"))\n\n # Build the dmg\n cmd = [\"./new-create-dmg\", \"--pkgname\", os.path.basename(mpkg_target),\n \"--volname\", \"numpy\", os.path.basename(dmg), \"./content\"]\n st = subprocess.check_call(cmd, cwd=macosx_installer_dir)\n\n source = dmg\n target = os.path.join(idirs, os.path.basename(dmg))\n if not os.path.exists(os.path.dirname(target)):\n os.makedirs(os.path.dirname(target))\n shutil.copy(source, target)","label":0},{"cid":"pavement.py::tarball_name","text":"pavement.py::tarball_name\ndef tarball_name(type='gztar'):\n root = 'numpy-%s' % FULLVERSION\n if type == 'gztar':\n return root + '.tar.gz'\n elif type == 'zip':\n return root + '.zip'\n raise ValueError(\"Unknown type %s\" % type)","label":0},{"cid":"pavement.py::sdist","text":"pavement.py::sdist\n@task\ndef sdist(options):\n # To be sure to bypass paver when building sdist... paver + numpy.distutils\n # do not play well together.\n sh('python setup.py sdist --formats=gztar,zip')\n\n # Copy the superpack into installers dir\n idirs = options.installers.installersdir\n if not os.path.exists(idirs):\n os.makedirs(idirs)\n\n for t in ['gztar', 'zip']:\n source = os.path.join('dist', tarball_name(t))\n target = os.path.join(idirs, tarball_name(t))\n shutil.copy(source, target)","label":0},{"cid":"pavement.py::compute_md5","text":"pavement.py::compute_md5\ndef compute_md5(idirs):\n released = paver.path.path(idirs).listdir()\n checksums = []\n for f in sorted(released):\n m = md5(open(f, 'r').read())\n checksums.append('%s %s' % (m.hexdigest(), os.path.basename(f)))\n\n return checksums","label":0},{"cid":"pavement.py::compute_sha256","text":"pavement.py::compute_sha256\ndef compute_sha256(idirs):\n # better checksum so gpg signed README.txt containing the sums can be used\n # to verify the binaries instead of signing all binaries\n released = paver.path.path(idirs).listdir()\n checksums = []\n for f in sorted(released):\n m = sha256(open(f, 'r').read())\n checksums.append('%s %s' % (m.hexdigest(), os.path.basename(f)))\n\n return checksums","label":0},{"cid":"pavement.py::write_release_task","text":"pavement.py::write_release_task\ndef write_release_task(options, filename='NOTES.txt'):\n idirs = options.installers.installersdir\n source = paver.path.path(RELEASE_NOTES)\n target = paver.path.path(filename)\n if target.exists():\n target.remove()\n source.copy(target)\n ftarget = open(str(target), 'a')\n ftarget.writelines(\"\"\"\nChecksums\n=========\n\nMD5\n~~~\n\n\"\"\")\n ftarget.writelines(['%s\\n' % c for c in compute_md5(idirs)])\n ftarget.writelines(\"\"\"\nSHA256\n~~~~~~\n\n\"\"\")\n ftarget.writelines(['%s\\n' % c for c in compute_sha256(idirs)])","label":0},{"cid":"pavement.py::write_log_task","text":"pavement.py::write_log_task\ndef write_log_task(options, filename='Changelog'):\n st = subprocess.Popen(\n ['git', 'log', '%s..%s' % (LOG_START, LOG_END)],\n stdout=subprocess.PIPE)\n\n out = st.communicate()[0]\n a = open(filename, 'w')\n a.writelines(out)\n a.close()","label":0},{"cid":"pavement.py::write_release","text":"pavement.py::write_release\n@task\ndef write_release(options):\n write_release_task(options)","label":0},{"cid":"pavement.py::write_log","text":"pavement.py::write_log\n@task\ndef write_log(options):\n write_log_task(options)","label":0},{"cid":"pavement.py::write_release_and_log","text":"pavement.py::write_release_and_log\n@task\ndef write_release_and_log(options):\n rdir = options.installers.releasedir\n write_release_task(options, os.path.join(rdir, 'NOTES.txt'))\n write_log_task(options, os.path.join(rdir, 'Changelog'))","label":0}],"string":"[\n {\n \"cid\": \"numpy/lib/npyio.py::seek_gzip_factory\",\n \"text\": \"numpy/lib/npyio.py::seek_gzip_factory\\ndef seek_gzip_factory(f):\\n \\\"\\\"\\\"Use this factory to produce the class so that we can do a lazy\\n import on gzip.\\n\\n \\\"\\\"\\\"\\n import gzip\\n\\n class GzipFile(gzip.GzipFile):\\n\\n def seek(self, offset, whence=0):\\n # figure out new position (we can only seek forwards)\\n if whence == 1:\\n offset = self.offset + offset\\n\\n if whence not in [0, 1]:\\n raise IOError(\\\"Illegal argument\\\")\\n\\n if offset < self.offset:\\n # for negative seek, rewind and do positive seek\\n self.rewind()\\n count = offset - self.offset\\n for i in range(count // 1024):\\n self.read(1024)\\n self.read(count % 1024)\\n\\n def tell(self):\\n return self.offset\\n\\n if isinstance(f, str):\\n f = GzipFile(f)\\n elif isinstance(f, gzip.GzipFile):\\n # cast to our GzipFile if its already a gzip.GzipFile\\n\\n try:\\n name = f.name\\n except AttributeError:\\n # Backward compatibility for <= 2.5\\n name = f.filename\\n mode = f.mode\\n\\n f = GzipFile(fileobj=f.fileobj, filename=name)\\n f.mode = mode\\n\\n return f\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.seek\",\n \"text\": \"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.seek\\n def seek(self, offset, whence=0):\\n # figure out new position (we can only seek forwards)\\n if whence == 1:\\n offset = self.offset + offset\\n\\n if whence not in [0, 1]:\\n raise IOError(\\\"Illegal argument\\\")\\n\\n if offset < self.offset:\\n # for negative seek, rewind and do positive seek\\n self.rewind()\\n count = offset - self.offset\\n for i in range(count // 1024):\\n self.read(1024)\\n self.read(count % 1024)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.tell\",\n \"text\": \"numpy/lib/npyio.py::seek_gzip_factory.GzipFile.tell\\n def tell(self):\\n return self.offset\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::BagObj.__init__\",\n \"text\": \"numpy/lib/npyio.py::BagObj.__init__\\n def __init__(self, obj):\\n # Use weakref to make NpzFile objects collectable by refcount\\n self._obj = weakref.proxy(obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::BagObj.__getattribute__\",\n \"text\": \"numpy/lib/npyio.py::BagObj.__getattribute__\\n def __getattribute__(self, key):\\n try:\\n return object.__getattribute__(self, '_obj')[key]\\n except KeyError:\\n raise AttributeError(key)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::zipfile_factory\",\n \"text\": \"numpy/lib/npyio.py::zipfile_factory\\ndef zipfile_factory(*args, **kwargs):\\n import zipfile\\n kwargs['allowZip64'] = True\\n return zipfile.ZipFile(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__init__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__init__\\n def __init__(self, fid, own_fid=False):\\n # Import is postponed to here since zipfile depends on gzip, an\\n # optional component of the so-called standard library.\\n _zip = zipfile_factory(fid)\\n self._files = _zip.namelist()\\n self.files = []\\n for x in self._files:\\n if x.endswith('.npy'):\\n self.files.append(x[:-4])\\n else:\\n self.files.append(x)\\n self.zip = _zip\\n self.f = BagObj(self)\\n if own_fid:\\n self.fid = fid\\n else:\\n self.fid = None\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__enter__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__enter__\\n def __enter__(self):\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__exit__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__exit__\\n def __exit__(self, exc_type, exc_value, traceback):\\n self.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.close\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.close\\n def close(self):\\n \\\"\\\"\\\"\\n Close the file.\\n\\n \\\"\\\"\\\"\\n if self.zip is not None:\\n self.zip.close()\\n self.zip = None\\n if self.fid is not None:\\n self.fid.close()\\n self.fid = None\\n self.f = None # break reference cycle\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__del__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__del__\\n def __del__(self):\\n self.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__getitem__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__getitem__\\n def __getitem__(self, key):\\n # FIXME: This seems like it will copy strings around\\n # more than is strictly necessary. The zipfile\\n # will read the string and then\\n # the format.read_array will copy the string\\n # to another place in memory.\\n # It would be better if the zipfile could read\\n # (or at least uncompress) the data\\n # directly into the array memory.\\n member = 0\\n if key in self._files:\\n member = 1\\n elif key in self.files:\\n member = 1\\n key += '.npy'\\n if member:\\n bytes = self.zip.open(key)\\n magic = bytes.read(len(format.MAGIC_PREFIX))\\n bytes.close()\\n if magic == format.MAGIC_PREFIX:\\n bytes = self.zip.open(key)\\n return format.read_array(bytes)\\n else:\\n return self.zip.read(key)\\n else:\\n raise KeyError(\\\"%s is not a file in the archive\\\" % key)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__iter__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__iter__\\n def __iter__(self):\\n return iter(self.files)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.items\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.items\\n def items(self):\\n \\\"\\\"\\\"\\n Return a list of tuples, with each tuple (filename, array in file).\\n\\n \\\"\\\"\\\"\\n return [(f, self[f]) for f in self.files]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.iteritems\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.iteritems\\n def iteritems(self):\\n \\\"\\\"\\\"Generator that returns tuples (filename, array in file).\\\"\\\"\\\"\\n for f in self.files:\\n yield (f, self[f])\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.keys\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.keys\\n def keys(self):\\n \\\"\\\"\\\"Return files in the archive with a ``.npy`` extension.\\\"\\\"\\\"\\n return self.files\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.iterkeys\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.iterkeys\\n def iterkeys(self):\\n \\\"\\\"\\\"Return an iterator over the files in the archive.\\\"\\\"\\\"\\n return self.__iter__()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::NpzFile.__contains__\",\n \"text\": \"numpy/lib/npyio.py::NpzFile.__contains__\\n def __contains__(self, key):\\n return self.files.__contains__(key)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::load\",\n \"text\": \"numpy/lib/npyio.py::load\\ndef load(file, mmap_mode=None):\\n \\\"\\\"\\\"\\n Load arrays or pickled objects from ``.npy``, ``.npz`` or pickled files.\\n\\n Parameters\\n ----------\\n file : file-like object or string\\n The file to read. File-like objects must support the\\n ``seek()`` and ``read()`` methods. Pickled files require that the\\n file-like object support the ``readline()`` method as well.\\n mmap_mode : {None, 'r+', 'r', 'w+', 'c'}, optional\\n If not None, then memory-map the file, using the given mode (see\\n `numpy.memmap` for a detailed description of the modes). A\\n memory-mapped array is kept on disk. However, it can be accessed\\n and sliced like any ndarray. Memory mapping is especially useful\\n for accessing small fragments of large files without reading the\\n entire file into memory.\\n\\n Returns\\n -------\\n result : array, tuple, dict, etc.\\n Data stored in the file. For ``.npz`` files, the returned instance\\n of NpzFile class must be closed to avoid leaking file descriptors.\\n\\n Raises\\n ------\\n IOError\\n If the input file does not exist or cannot be read.\\n\\n See Also\\n --------\\n save, savez, savez_compressed, loadtxt\\n memmap : Create a memory-map to an array stored in a file on disk.\\n\\n Notes\\n -----\\n - If the file contains pickle data, then whatever object is stored\\n in the pickle is returned.\\n - If the file is a ``.npy`` file, then a single array is returned.\\n - If the file is a ``.npz`` file, then a dictionary-like object is\\n returned, containing ``{filename: array}`` key-value pairs, one for\\n each file in the archive.\\n - If the file is a ``.npz`` file, the returned value supports the\\n context manager protocol in a similar fashion to the open function::\\n\\n with load('foo.npz') as data:\\n a = data['a']\\n\\n The underlying file descriptor is closed when exiting the 'with'\\n block.\\n\\n Examples\\n --\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::save\",\n \"text\": \"numpy/lib/npyio.py::save\\ndef save(file, arr):\\n \\\"\\\"\\\"\\n Save an array to a binary file in NumPy ``.npy`` format.\\n\\n Parameters\\n ----------\\n file : file or str\\n File or filename to which the data is saved. If file is a file-object,\\n then the filename is unchanged. If file is a string, a ``.npy``\\n extension will be appended to the file name if it does not already\\n have one.\\n arr : array_like\\n Array data to be saved.\\n\\n See Also\\n --------\\n savez : Save several arrays into a ``.npz`` archive\\n savetxt, load\\n\\n Notes\\n -----\\n For a description of the ``.npy`` format, see `format`.\\n\\n Examples\\n --------\\n >>> from tempfile import TemporaryFile\\n >>> outfile = TemporaryFile()\\n\\n >>> x = np.arange(10)\\n >>> np.save(outfile, x)\\n\\n >>> outfile.seek(0) # Only needed here to simulate closing & reopening file\\n >>> np.load(outfile)\\n array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\\n\\n \\\"\\\"\\\"\\n own_fid = False\\n if isinstance(file, basestring):\\n if not file.endswith('.npy'):\\n file = file + '.npy'\\n fid = open(file, \\\"wb\\\")\\n own_fid = True\\n else:\\n fid = file\\n\\n try:\\n arr = np.asanyarray(arr)\\n format.write_array(fid, arr)\\n finally:\\n if own_fid:\\n fid.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::savez\",\n \"text\": \"numpy/lib/npyio.py::savez\\ndef savez(file, *args, **kwds):\\n \\\"\\\"\\\"\\n Save several arrays into a single file in uncompressed ``.npz`` format.\\n\\n If arguments are passed in with no keywords, the corresponding variable\\n names, in the ``.npz`` file, are 'arr_0', 'arr_1', etc. If keyword\\n arguments are given, the corresponding variable names, in the ``.npz``\\n file will match the keyword names.\\n\\n Parameters\\n ----------\\n file : str or file\\n Either the file name (string) or an open file (file-like object)\\n where the data will be saved. If file is a string, the ``.npz``\\n extension will be appended to the file name if it is not already there.\\n args : Arguments, optional\\n Arrays to save to the file. Since it is not possible for Python to\\n know the names of the arrays outside `savez`, the arrays will be saved\\n with names \\\"arr_0\\\", \\\"arr_1\\\", and so on. These arguments can be any\\n expression.\\n kwds : Keyword arguments, optional\\n Arrays to save to the file. Arrays will be saved in the file with the\\n keyword names.\\n\\n Returns\\n -------\\n None\\n\\n See Also\\n --------\\n save : Save a single array to a binary file in NumPy format.\\n savetxt : Save an array to a file as plain text.\\n savez_compressed : Save several arrays into a compressed ``.npz`` archive\\n\\n Notes\\n -----\\n The ``.npz`` file format is a zipped archive of files named after the\\n variables they contain. The archive is not compressed and each file\\n in the archive contains one variable in ``.npy`` format. For a\\n description of the ``.npy`` format, see `format`.\\n\\n When opening the saved ``.npz`` file with `load` a `NpzFile` object is\\n returned. This is a dictionary-like object which can be queried for\\n its list of arrays (with the ``.files`` attribute), and for the arrays\\n themselves.\\n\\n Examples\\n --------\\n >>> from tempfile import TemporaryFile\\n >>> outfile = TemporaryFile()\\n >>> \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::savez_compressed\",\n \"text\": \"numpy/lib/npyio.py::savez_compressed\\ndef savez_compressed(file, *args, **kwds):\\n \\\"\\\"\\\"\\n Save several arrays into a single file in compressed ``.npz`` format.\\n\\n If keyword arguments are given, then filenames are taken from the keywords.\\n If arguments are passed in with no keywords, then stored file names are\\n arr_0, arr_1, etc.\\n\\n Parameters\\n ----------\\n file : str\\n File name of ``.npz`` file.\\n args : Arguments\\n Function arguments.\\n kwds : Keyword arguments\\n Keywords.\\n\\n See Also\\n --------\\n numpy.savez : Save several arrays into an uncompressed ``.npz`` file format\\n numpy.load : Load the files created by savez_compressed.\\n\\n \\\"\\\"\\\"\\n _savez(file, args, kwds, True)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::_savez\",\n \"text\": \"numpy/lib/npyio.py::_savez\\ndef _savez(file, args, kwds, compress):\\n # Import is postponed to here since zipfile depends on gzip, an optional\\n # component of the so-called standard library.\\n import zipfile\\n # Import deferred for startup time improvement\\n import tempfile\\n\\n if isinstance(file, basestring):\\n if not file.endswith('.npz'):\\n file = file + '.npz'\\n\\n namedict = kwds\\n for i, val in enumerate(args):\\n key = 'arr_%d' % i\\n if key in namedict.keys():\\n raise ValueError(\\n \\\"Cannot use un-named variables and keyword %s\\\" % key)\\n namedict[key] = val\\n\\n if compress:\\n compression = zipfile.ZIP_DEFLATED\\n else:\\n compression = zipfile.ZIP_STORED\\n\\n zipf = zipfile_factory(file, mode=\\\"w\\\", compression=compression)\\n\\n # Stage arrays in a temporary file on disk, before writing to zip.\\n fd, tmpfile = tempfile.mkstemp(suffix='-numpy.npy')\\n os.close(fd)\\n try:\\n for key, val in namedict.items():\\n fname = key + '.npy'\\n fid = open(tmpfile, 'wb')\\n try:\\n format.write_array(fid, np.asanyarray(val))\\n fid.close()\\n fid = None\\n zipf.write(tmpfile, arcname=fname)\\n finally:\\n if fid:\\n fid.close()\\n finally:\\n os.remove(tmpfile)\\n\\n zipf.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::_getconv\",\n \"text\": \"numpy/lib/npyio.py::_getconv\\ndef _getconv(dtype):\\n \\\"\\\"\\\" Find the correct dtype converter. Adapted from matplotlib \\\"\\\"\\\"\\n typ = dtype.type\\n if issubclass(typ, np.bool_):\\n return lambda x: bool(int(x))\\n if issubclass(typ, np.uint64):\\n return np.uint64\\n if issubclass(typ, np.int64):\\n return np.int64\\n if issubclass(typ, np.integer):\\n return lambda x: int(float(x))\\n elif issubclass(typ, np.floating):\\n return float\\n elif issubclass(typ, np.complex):\\n return complex\\n elif issubclass(typ, np.bytes_):\\n return bytes\\n else:\\n return str\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::loadtxt\",\n \"text\": \"numpy/lib/npyio.py::loadtxt\\ndef loadtxt(fname, dtype=float, comments='#', delimiter=None,\\n converters=None, skiprows=0, usecols=None, unpack=False,\\n ndmin=0):\\n \\\"\\\"\\\"\\n Load data from a text file.\\n\\n Each row in the text file must have the same number of values.\\n\\n Parameters\\n ----------\\n fname : file or str\\n File, filename, or generator to read. If the filename extension is\\n ``.gz`` or ``.bz2``, the file is first decompressed. Note that\\n generators should return byte strings for Python 3k.\\n dtype : data-type, optional\\n Data-type of the resulting array; default: float. If this is a\\n record data-type, the resulting array will be 1-dimensional, and\\n each row will be interpreted as an element of the array. In this\\n case, the number of columns used must match the number of fields in\\n the data-type.\\n comments : str, optional\\n The character used to indicate the start of a comment;\\n default: '#'.\\n delimiter : str, optional\\n The string used to separate values. By default, this is any\\n whitespace.\\n converters : dict, optional\\n A dictionary mapping column number to a function that will convert\\n that column to a float. E.g., if column 0 is a date string:\\n ``converters = {0: datestr2num}``. Converters can also be used to\\n provide a default value for missing data (but see also `genfromtxt`):\\n ``converters = {3: lambda s: float(s.strip() or 0)}``. Default: None.\\n skiprows : int, optional\\n Skip the first `skiprows` lines; default: 0.\\n usecols : sequence, optional\\n Which columns to read, with 0 being the first. For example,\\n ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.\\n The default, None, results in all columns being read.\\n unpack : bool, optional\\n If True, the returned array is transposed, so that arguments may be\\n unpacked using ``x, y, z =\",\n \"label\": 1\n },\n {\n \"cid\": \"numpy/lib/npyio.py::loadtxt.flatten_dtype\",\n \"text\": \"numpy/lib/npyio.py::loadtxt.flatten_dtype\\n def flatten_dtype(dt):\\n \\\"\\\"\\\"Unpack a structured data-type, and produce re-packing info.\\\"\\\"\\\"\\n if dt.names is None:\\n # If the dtype is flattened, return.\\n # If the dtype has a shape, the dtype occurs\\n # in the list more than once.\\n shape = dt.shape\\n if len(shape) == 0:\\n return ([dt.base], None)\\n else:\\n packing = [(shape[-1], list)]\\n if len(shape) > 1:\\n for dim in dt.shape[-2::-1]:\\n packing = [(dim*packing[0][0], packing*dim)]\\n return ([dt.base] * int(np.prod(dt.shape)), packing)\\n else:\\n types = []\\n packing = []\\n for field in dt.names:\\n tp, bytes = dt.fields[field]\\n flat_dt, flat_packing = flatten_dtype(tp)\\n types.extend(flat_dt)\\n # Avoid extra nesting for subarrays\\n if len(tp.shape) > 0:\\n packing.extend(flat_packing)\\n else:\\n packing.append((len(flat_dt), flat_packing))\\n return (types, packing)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::loadtxt.pack_items\",\n \"text\": \"numpy/lib/npyio.py::loadtxt.pack_items\\n def pack_items(items, packing):\\n \\\"\\\"\\\"Pack items into nested lists based on re-packing info.\\\"\\\"\\\"\\n if packing is None:\\n return items[0]\\n elif packing is tuple:\\n return tuple(items)\\n elif packing is list:\\n return list(items)\\n else:\\n start = 0\\n ret = []\\n for length, subpacking in packing:\\n ret.append(pack_items(items[start:start+length], subpacking))\\n start += length\\n return tuple(ret)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::loadtxt.split_line\",\n \"text\": \"numpy/lib/npyio.py::loadtxt.split_line\\n def split_line(line):\\n \\\"\\\"\\\"Chop off comments, strip, and split at delimiter.\\\"\\\"\\\"\\n line = asbytes(line).split(comments)[0].strip(asbytes('\\\\r\\\\n'))\\n if line:\\n return line.split(delimiter)\\n else:\\n return []\",\n \"label\": 1\n },\n {\n \"cid\": \"numpy/lib/npyio.py::savetxt\",\n \"text\": \"numpy/lib/npyio.py::savetxt\\ndef savetxt(fname, X, fmt='%.18e', delimiter=' ', newline='\\\\n', header='',\\n footer='', comments='# '):\\n \\\"\\\"\\\"\\n Save an array to a text file.\\n\\n Parameters\\n ----------\\n fname : filename or file handle\\n If the filename ends in ``.gz``, the file is automatically saved in\\n compressed gzip format. `loadtxt` understands gzipped files\\n transparently.\\n X : array_like\\n Data to be saved to a text file.\\n fmt : str or sequence of strs, optional\\n A single format (%10.5f), a sequence of formats, or a\\n multi-format string, e.g. 'Iteration %d -- %10.5f', in which\\n case `delimiter` is ignored. For complex `X`, the legal options\\n for `fmt` are:\\n a) a single specifier, `fmt='%.4e'`, resulting in numbers formatted\\n like `' (%s+%sj)' % (fmt, fmt)`\\n b) a full string specifying every real and imaginary part, e.g.\\n `' %.4e %+.4j %.4e %+.4j %.4e %+.4j'` for 3 columns\\n c) a list of specifiers, one per column - in this case, the real\\n and imaginary part must have separate specifiers,\\n e.g. `['%.3e + %.3ej', '(%.15e%+.15ej)']` for 2 columns\\n delimiter : str, optional\\n String or character separating columns.\\n newline : str, optional\\n String or character separating lines.\\n\\n .. versionadded:: 1.5.0\\n header : str, optional\\n String that will be written at the beginning of the file.\\n\\n .. versionadded:: 1.7.0\\n footer : str, optional\\n String that will be written at the end of the file.\\n\\n .. versionadded:: 1.7.0\\n comments : str, optional\\n String that will be prepended to the ``header`` and ``footer`` strings,\\n to mark them as comments. Default: '# ', as expected by e.g.\\n ``numpy.loadtxt``.\\n\\n .. versionadded:: 1.7.0\\n\\n\\n See Also\\n --------\\n save : Save an array to a binary file in NumPy ``.npy`` format\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::fromregex\",\n \"text\": \"numpy/lib/npyio.py::fromregex\\ndef fromregex(file, regexp, dtype):\\n \\\"\\\"\\\"\\n Construct an array from a text file, using regular expression parsing.\\n\\n The returned array is always a structured array, and is constructed from\\n all matches of the regular expression in the file. Groups in the regular\\n expression are converted to fields of the structured array.\\n\\n Parameters\\n ----------\\n file : str or file\\n File name or file object to read.\\n regexp : str or regexp\\n Regular expression used to parse the file.\\n Groups in the regular expression correspond to fields in the dtype.\\n dtype : dtype or list of dtypes\\n Dtype for the structured array.\\n\\n Returns\\n -------\\n output : ndarray\\n The output array, containing the part of the content of `file` that\\n was matched by `regexp`. `output` is always a structured array.\\n\\n Raises\\n ------\\n TypeError\\n When `dtype` is not a valid dtype for a structured array.\\n\\n See Also\\n --------\\n fromstring, loadtxt\\n\\n Notes\\n -----\\n Dtypes for structured arrays can be specified in several forms, but all\\n forms specify at least the data type and field name. For details see\\n `doc.structured_arrays`.\\n\\n Examples\\n --------\\n >>> f = open('test.dat', 'w')\\n >>> f.write(\\\"1312 foo\\\\\\\\n1534 bar\\\\\\\\n444 qux\\\")\\n >>> f.close()\\n\\n >>> regexp = r\\\"(\\\\\\\\d+)\\\\\\\\s+(...)\\\" # match [digits, whitespace, anything]\\n >>> output = np.fromregex('test.dat', regexp,\\n ... [('num', np.int64), ('key', 'S3')])\\n >>> output\\n array([(1312L, 'foo'), (1534L, 'bar'), (444L, 'qux')],\\n dtype=[('num', '<i8'), ('key', '|S3')])\\n >>> output['num']\\n array([1312, 1534, 444], dtype=int64)\\n\\n \\\"\\\"\\\"\\n own_fh = False\\n if not hasattr(file, \\\"read\\\"):\\n file = open(file, 'rb')\\n own_fh = True\\n\\n try:\\n if not hasattr(regexp, 'match'):\\n regexp = re.compile(asbytes(regexp))\\n if not isinstance(dtyp\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::genfromtxt\",\n \"text\": \"numpy/lib/npyio.py::genfromtxt\\ndef genfromtxt(fname, dtype=float, comments='#', delimiter=None,\\n skiprows=0, skip_header=0, skip_footer=0, converters=None,\\n missing='', missing_values=None, filling_values=None,\\n usecols=None, names=None,\\n excludelist=None, deletechars=None, replace_space='_',\\n autostrip=False, case_sensitive=True, defaultfmt=\\\"f%i\\\",\\n unpack=None, usemask=False, loose=True, invalid_raise=True):\\n \\\"\\\"\\\"\\n Load data from a text file, with missing values handled as specified.\\n\\n Each line past the first `skip_header` lines is split at the `delimiter`\\n character, and characters following the `comments` character are discarded.\\n\\n Parameters\\n ----------\\n fname : file or str\\n File, filename, or generator to read. If the filename extension is\\n `.gz` or `.bz2`, the file is first decompressed. Note that\\n generators must return byte strings in Python 3k.\\n dtype : dtype, optional\\n Data type of the resulting array.\\n If None, the dtypes will be determined by the contents of each\\n column, individually.\\n comments : str, optional\\n The character used to indicate the start of a comment.\\n All the characters occurring on a line after a comment are discarded\\n delimiter : str, int, or sequence, optional\\n The string used to separate values. By default, any consecutive\\n whitespaces act as delimiter. An integer or sequence of integers\\n can also be provided as width(s) of each field.\\n skip_rows : int, optional\\n `skip_rows` was deprecated in numpy 1.5, and will be removed in\\n numpy 2.0. Please use `skip_header` instead.\\n skip_header : int, optional\\n The number of lines to skip at the beginning of the file.\\n skip_footer : int, optional\\n The number of lines to skip at the end of the file.\\n converters : variable, optional\\n The set of functions that c\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::ndfromtxt\",\n \"text\": \"numpy/lib/npyio.py::ndfromtxt\\ndef ndfromtxt(fname, **kwargs):\\n \\\"\\\"\\\"\\n Load ASCII data stored in a file and return it as a single array.\\n\\n Parameters\\n ----------\\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\\n\\n See Also\\n --------\\n numpy.genfromtxt : generic function.\\n\\n \\\"\\\"\\\"\\n kwargs['usemask'] = False\\n return genfromtxt(fname, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::mafromtxt\",\n \"text\": \"numpy/lib/npyio.py::mafromtxt\\ndef mafromtxt(fname, **kwargs):\\n \\\"\\\"\\\"\\n Load ASCII data stored in a text file and return a masked array.\\n\\n Parameters\\n ----------\\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\\n\\n See Also\\n --------\\n numpy.genfromtxt : generic function to load ASCII data.\\n\\n \\\"\\\"\\\"\\n kwargs['usemask'] = True\\n return genfromtxt(fname, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::recfromtxt\",\n \"text\": \"numpy/lib/npyio.py::recfromtxt\\ndef recfromtxt(fname, **kwargs):\\n \\\"\\\"\\\"\\n Load ASCII data from a file and return it in a record array.\\n\\n If ``usemask=False`` a standard `recarray` is returned,\\n if ``usemask=True`` a MaskedRecords array is returned.\\n\\n Parameters\\n ----------\\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\\n\\n See Also\\n --------\\n numpy.genfromtxt : generic function\\n\\n Notes\\n -----\\n By default, `dtype` is None, which means that the data-type of the output\\n array will be determined from the data.\\n\\n \\\"\\\"\\\"\\n kwargs.setdefault(\\\"dtype\\\", None)\\n usemask = kwargs.get('usemask', False)\\n output = genfromtxt(fname, **kwargs)\\n if usemask:\\n from numpy.ma.mrecords import MaskedRecords\\n output = output.view(MaskedRecords)\\n else:\\n output = output.view(np.recarray)\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/lib/npyio.py::recfromcsv\",\n \"text\": \"numpy/lib/npyio.py::recfromcsv\\ndef recfromcsv(fname, **kwargs):\\n \\\"\\\"\\\"\\n Load ASCII data stored in a comma-separated file.\\n\\n The returned array is a record array (if ``usemask=False``, see\\n `recarray`) or a masked record array (if ``usemask=True``,\\n see `ma.mrecords.MaskedRecords`).\\n\\n Parameters\\n ----------\\n fname, kwargs : For a description of input parameters, see `genfromtxt`.\\n\\n See Also\\n --------\\n numpy.genfromtxt : generic function to load ASCII data.\\n\\n Notes\\n -----\\n By default, `dtype` is None, which means that the data-type of the output\\n array will be determined from the data.\\n\\n \\\"\\\"\\\"\\n # Set default kwargs for genfromtxt as relevant to csv import.\\n kwargs.setdefault(\\\"case_sensitive\\\", \\\"lower\\\")\\n kwargs.setdefault(\\\"names\\\", True)\\n kwargs.setdefault(\\\"delimiter\\\", \\\",\\\")\\n kwargs.setdefault(\\\"dtype\\\", None)\\n output = genfromtxt(fname, **kwargs)\\n\\n usemask = kwargs.get(\\\"usemask\\\", False)\\n if usemask:\\n from numpy.ma.mrecords import MaskedRecords\\n output = output.view(MaskedRecords)\\n else:\\n output = output.view(np.recarray)\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::doc_note\",\n \"text\": \"numpy/ma/core.py::doc_note\\ndef doc_note(initialdoc, note):\\n \\\"\\\"\\\"\\n Adds a Notes section to an existing docstring.\\n \\\"\\\"\\\"\\n if initialdoc is None:\\n return\\n if note is None:\\n return initialdoc\\n newdoc = \\\"\\\"\\\"\\n %s\\n\\n Notes\\n -----\\n %s\\n \\\"\\\"\\\"\\n return newdoc % (initialdoc, note)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::get_object_signature\",\n \"text\": \"numpy/ma/core.py::get_object_signature\\ndef get_object_signature(obj):\\n \\\"\\\"\\\"\\n Get the signature from obj\\n \\\"\\\"\\\"\\n try:\\n sig = formatargspec(*getargspec(obj))\\n except TypeError as errmsg:\\n sig = ''\\n# msg = \\\"Unable to retrieve the signature of %s '%s'\\\\n\\\"\\\\\\n# \\\"(Initial error message: %s)\\\"\\n# warnings.warn(msg % (type(obj),\\n# getattr(obj, '__name__', '???'),\\n# errmsg))\\n return sig\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::default_fill_value\",\n \"text\": \"numpy/ma/core.py::default_fill_value\\ndef default_fill_value(obj):\\n \\\"\\\"\\\"\\n Return the default fill value for the argument object.\\n\\n The default filling value depends on the datatype of the input\\n array or the type of the input scalar:\\n\\n ======== ========\\n datatype default\\n ======== ========\\n bool True\\n int 999999\\n float 1.e20\\n complex 1.e20+0j\\n object '?'\\n string 'N/A'\\n ======== ========\\n\\n\\n Parameters\\n ----------\\n obj : ndarray, dtype or scalar\\n The array data-type or scalar for which the default fill value\\n is returned.\\n\\n Returns\\n -------\\n fill_value : scalar\\n The default fill value.\\n\\n Examples\\n --------\\n >>> np.ma.default_fill_value(1)\\n 999999\\n >>> np.ma.default_fill_value(np.array([1.1, 2., np.pi]))\\n 1e+20\\n >>> np.ma.default_fill_value(np.dtype(complex))\\n (1e+20+0j)\\n\\n \\\"\\\"\\\"\\n if hasattr(obj, 'dtype'):\\n defval = _check_fill_value(None, obj.dtype)\\n elif isinstance(obj, np.dtype):\\n if obj.subdtype:\\n defval = default_filler.get(obj.subdtype[0].kind, '?')\\n elif obj.kind == 'M':\\n defval = default_filler.get(obj.str[1:], '?')\\n else:\\n defval = default_filler.get(obj.kind, '?')\\n elif isinstance(obj, float):\\n defval = default_filler['f']\\n elif isinstance(obj, int) or isinstance(obj, long):\\n defval = default_filler['i']\\n elif isinstance(obj, str):\\n defval = default_filler['S']\\n elif isinstance(obj, unicode):\\n defval = default_filler['U']\\n elif isinstance(obj, complex):\\n defval = default_filler['c']\\n else:\\n defval = default_filler['O']\\n return defval\",\n \"label\": 1\n },\n {\n \"cid\": \"numpy/ma/core.py::_recursive_extremum_fill_value\",\n \"text\": \"numpy/ma/core.py::_recursive_extremum_fill_value\\ndef _recursive_extremum_fill_value(ndtype, extremum):\\n names = ndtype.names\\n if names:\\n deflist = []\\n for name in names:\\n fval = _recursive_extremum_fill_value(ndtype[name], extremum)\\n deflist.append(fval)\\n return tuple(deflist)\\n return extremum[ndtype]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::minimum_fill_value\",\n \"text\": \"numpy/ma/core.py::minimum_fill_value\\ndef minimum_fill_value(obj):\\n \\\"\\\"\\\"\\n Return the maximum value that can be represented by the dtype of an object.\\n\\n This function is useful for calculating a fill value suitable for\\n taking the minimum of an array with a given dtype.\\n\\n Parameters\\n ----------\\n obj : ndarray or dtype\\n An object that can be queried for it's numeric type.\\n\\n Returns\\n -------\\n val : scalar\\n The maximum representable value.\\n\\n Raises\\n ------\\n TypeError\\n If `obj` isn't a suitable numeric type.\\n\\n See Also\\n --------\\n maximum_fill_value : The inverse function.\\n set_fill_value : Set the filling value of a masked array.\\n MaskedArray.fill_value : Return current fill value.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.int8()\\n >>> ma.minimum_fill_value(a)\\n 127\\n >>> a = np.int32()\\n >>> ma.minimum_fill_value(a)\\n 2147483647\\n\\n An array of numeric data can also be passed.\\n\\n >>> a = np.array([1, 2, 3], dtype=np.int8)\\n >>> ma.minimum_fill_value(a)\\n 127\\n >>> a = np.array([1, 2, 3], dtype=np.float32)\\n >>> ma.minimum_fill_value(a)\\n inf\\n\\n \\\"\\\"\\\"\\n errmsg = \\\"Unsuitable type for calculating minimum.\\\"\\n if hasattr(obj, 'dtype'):\\n return _recursive_extremum_fill_value(obj.dtype, min_filler)\\n elif isinstance(obj, float):\\n return min_filler[ntypes.typeDict['float_']]\\n elif isinstance(obj, int):\\n return min_filler[ntypes.typeDict['int_']]\\n elif isinstance(obj, long):\\n return min_filler[ntypes.typeDict['uint']]\\n elif isinstance(obj, np.dtype):\\n return min_filler[obj]\\n else:\\n raise TypeError(errmsg)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::maximum_fill_value\",\n \"text\": \"numpy/ma/core.py::maximum_fill_value\\ndef maximum_fill_value(obj):\\n \\\"\\\"\\\"\\n Return the minimum value that can be represented by the dtype of an object.\\n\\n This function is useful for calculating a fill value suitable for\\n taking the maximum of an array with a given dtype.\\n\\n Parameters\\n ----------\\n obj : {ndarray, dtype}\\n An object that can be queried for it's numeric type.\\n\\n Returns\\n -------\\n val : scalar\\n The minimum representable value.\\n\\n Raises\\n ------\\n TypeError\\n If `obj` isn't a suitable numeric type.\\n\\n See Also\\n --------\\n minimum_fill_value : The inverse function.\\n set_fill_value : Set the filling value of a masked array.\\n MaskedArray.fill_value : Return current fill value.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.int8()\\n >>> ma.maximum_fill_value(a)\\n -128\\n >>> a = np.int32()\\n >>> ma.maximum_fill_value(a)\\n -2147483648\\n\\n An array of numeric data can also be passed.\\n\\n >>> a = np.array([1, 2, 3], dtype=np.int8)\\n >>> ma.maximum_fill_value(a)\\n -128\\n >>> a = np.array([1, 2, 3], dtype=np.float32)\\n >>> ma.maximum_fill_value(a)\\n -inf\\n\\n \\\"\\\"\\\"\\n errmsg = \\\"Unsuitable type for calculating maximum.\\\"\\n if hasattr(obj, 'dtype'):\\n return _recursive_extremum_fill_value(obj.dtype, max_filler)\\n elif isinstance(obj, float):\\n return max_filler[ntypes.typeDict['float_']]\\n elif isinstance(obj, int):\\n return max_filler[ntypes.typeDict['int_']]\\n elif isinstance(obj, long):\\n return max_filler[ntypes.typeDict['uint']]\\n elif isinstance(obj, np.dtype):\\n return max_filler[obj]\\n else:\\n raise TypeError(errmsg)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_recursive_set_default_fill_value\",\n \"text\": \"numpy/ma/core.py::_recursive_set_default_fill_value\\ndef _recursive_set_default_fill_value(dtypedescr):\\n deflist = []\\n for currentdescr in dtypedescr:\\n currenttype = currentdescr[1]\\n if isinstance(currenttype, list):\\n deflist.append(tuple(_recursive_set_default_fill_value(currenttype)))\\n else:\\n deflist.append(default_fill_value(np.dtype(currenttype)))\\n return tuple(deflist)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_recursive_set_fill_value\",\n \"text\": \"numpy/ma/core.py::_recursive_set_fill_value\\ndef _recursive_set_fill_value(fillvalue, dtypedescr):\\n fillvalue = np.resize(fillvalue, len(dtypedescr))\\n output_value = []\\n for (fval, descr) in zip(fillvalue, dtypedescr):\\n cdtype = descr[1]\\n if isinstance(cdtype, list):\\n output_value.append(tuple(_recursive_set_fill_value(fval, cdtype)))\\n else:\\n output_value.append(np.array(fval, dtype=cdtype).item())\\n return tuple(output_value)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_check_fill_value\",\n \"text\": \"numpy/ma/core.py::_check_fill_value\\ndef _check_fill_value(fill_value, ndtype):\\n \\\"\\\"\\\"\\n Private function validating the given `fill_value` for the given dtype.\\n\\n If fill_value is None, it is set to the default corresponding to the dtype\\n if this latter is standard (no fields). If the datatype is flexible (named\\n fields), fill_value is set to a tuple whose elements are the default fill\\n values corresponding to each field.\\n\\n If fill_value is not None, its value is forced to the given dtype.\\n\\n \\\"\\\"\\\"\\n ndtype = np.dtype(ndtype)\\n fields = ndtype.fields\\n if fill_value is None:\\n if fields:\\n descr = ndtype.descr\\n fill_value = np.array(_recursive_set_default_fill_value(descr),\\n dtype=ndtype,)\\n else:\\n fill_value = default_fill_value(ndtype)\\n elif fields:\\n fdtype = [(_[0], _[1]) for _ in ndtype.descr]\\n if isinstance(fill_value, (ndarray, np.void)):\\n try:\\n fill_value = np.array(fill_value, copy=False, dtype=fdtype)\\n except ValueError:\\n err_msg = \\\"Unable to transform %s to dtype %s\\\"\\n raise ValueError(err_msg % (fill_value, fdtype))\\n else:\\n descr = ndtype.descr\\n fill_value = np.asarray(fill_value, dtype=object)\\n fill_value = np.array(_recursive_set_fill_value(fill_value, descr),\\n dtype=ndtype)\\n else:\\n if isinstance(fill_value, basestring) and (ndtype.char not in 'OSVU'):\\n err_msg = \\\"Cannot set fill value of string with array of dtype %s\\\"\\n raise TypeError(err_msg % ndtype)\\n else:\\n # In case we want to convert 1e20 to int...\\n try:\\n fill_value = np.array(fill_value, copy=False, dtype=ndtype)\\n except OverflowError:\\n # Raise TypeError instead of OverflowError. OverflowError\\n # is seldom used, and the real probl\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::set_fill_value\",\n \"text\": \"numpy/ma/core.py::set_fill_value\\ndef set_fill_value(a, fill_value):\\n \\\"\\\"\\\"\\n Set the filling value of a, if a is a masked array.\\n\\n This function changes the fill value of the masked array `a` in place.\\n If `a` is not a masked array, the function returns silently, without\\n doing anything.\\n\\n Parameters\\n ----------\\n a : array_like\\n Input array.\\n fill_value : dtype\\n Filling value. A consistency test is performed to make sure\\n the value is compatible with the dtype of `a`.\\n\\n Returns\\n -------\\n None\\n Nothing returned by this function.\\n\\n See Also\\n --------\\n maximum_fill_value : Return the default fill value for a dtype.\\n MaskedArray.fill_value : Return current fill value.\\n MaskedArray.set_fill_value : Equivalent method.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.arange(5)\\n >>> a\\n array([0, 1, 2, 3, 4])\\n >>> a = ma.masked_where(a < 3, a)\\n >>> a\\n masked_array(data = [-- -- -- 3 4],\\n mask = [ True True True False False],\\n fill_value=999999)\\n >>> ma.set_fill_value(a, -999)\\n >>> a\\n masked_array(data = [-- -- -- 3 4],\\n mask = [ True True True False False],\\n fill_value=-999)\\n\\n Nothing happens if `a` is not a masked array.\\n\\n >>> a = range(5)\\n >>> a\\n [0, 1, 2, 3, 4]\\n >>> ma.set_fill_value(a, 100)\\n >>> a\\n [0, 1, 2, 3, 4]\\n >>> a = np.arange(5)\\n >>> a\\n array([0, 1, 2, 3, 4])\\n >>> ma.set_fill_value(a, 100)\\n >>> a\\n array([0, 1, 2, 3, 4])\\n\\n \\\"\\\"\\\"\\n if isinstance(a, MaskedArray):\\n a.set_fill_value(fill_value)\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::get_fill_value\",\n \"text\": \"numpy/ma/core.py::get_fill_value\\ndef get_fill_value(a):\\n \\\"\\\"\\\"\\n Return the filling value of a, if any. Otherwise, returns the\\n default filling value for that type.\\n\\n \\\"\\\"\\\"\\n if isinstance(a, MaskedArray):\\n result = a.fill_value\\n else:\\n result = default_fill_value(a)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::common_fill_value\",\n \"text\": \"numpy/ma/core.py::common_fill_value\\ndef common_fill_value(a, b):\\n \\\"\\\"\\\"\\n Return the common filling value of two masked arrays, if any.\\n\\n If ``a.fill_value == b.fill_value``, return the fill value,\\n otherwise return None.\\n\\n Parameters\\n ----------\\n a, b : MaskedArray\\n The masked arrays for which to compare fill values.\\n\\n Returns\\n -------\\n fill_value : scalar or None\\n The common fill value, or None.\\n\\n Examples\\n --------\\n >>> x = np.ma.array([0, 1.], fill_value=3)\\n >>> y = np.ma.array([0, 1.], fill_value=3)\\n >>> np.ma.common_fill_value(x, y)\\n 3.0\\n\\n \\\"\\\"\\\"\\n t1 = get_fill_value(a)\\n t2 = get_fill_value(b)\\n if t1 == t2:\\n return t1\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::filled\",\n \"text\": \"numpy/ma/core.py::filled\\ndef filled(a, fill_value=None):\\n \\\"\\\"\\\"\\n Return input as an array with masked data replaced by a fill value.\\n\\n If `a` is not a `MaskedArray`, `a` itself is returned.\\n If `a` is a `MaskedArray` and `fill_value` is None, `fill_value` is set to\\n ``a.fill_value``.\\n\\n Parameters\\n ----------\\n a : MaskedArray or array_like\\n An input object.\\n fill_value : scalar, optional\\n Filling value. Default is None.\\n\\n Returns\\n -------\\n a : ndarray\\n The filled array.\\n\\n See Also\\n --------\\n compressed\\n\\n Examples\\n --------\\n >>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[[1, 0, 0],\\n ... [1, 0, 0],\\n ... [0, 0, 0]])\\n >>> x.filled()\\n array([[999999, 1, 2],\\n [999999, 4, 5],\\n [ 6, 7, 8]])\\n\\n \\\"\\\"\\\"\\n if hasattr(a, 'filled'):\\n return a.filled(fill_value)\\n elif isinstance(a, ndarray):\\n # Should we check for contiguity ? and a.flags['CONTIGUOUS']:\\n return a\\n elif isinstance(a, dict):\\n return np.array(a, 'O')\\n else:\\n return np.array(a)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::get_masked_subclass\",\n \"text\": \"numpy/ma/core.py::get_masked_subclass\\ndef get_masked_subclass(*arrays):\\n \\\"\\\"\\\"\\n Return the youngest subclass of MaskedArray from a list of (masked) arrays.\\n In case of siblings, the first listed takes over.\\n\\n \\\"\\\"\\\"\\n if len(arrays) == 1:\\n arr = arrays[0]\\n if isinstance(arr, MaskedArray):\\n rcls = type(arr)\\n else:\\n rcls = MaskedArray\\n else:\\n arrcls = [type(a) for a in arrays]\\n rcls = arrcls[0]\\n if not issubclass(rcls, MaskedArray):\\n rcls = MaskedArray\\n for cls in arrcls[1:]:\\n if issubclass(cls, rcls):\\n rcls = cls\\n # Don't return MaskedConstant as result: revert to MaskedArray\\n if rcls.__name__ == 'MaskedConstant':\\n return MaskedArray\\n return rcls\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::getdata\",\n \"text\": \"numpy/ma/core.py::getdata\\ndef getdata(a, subok=True):\\n \\\"\\\"\\\"\\n Return the data of a masked array as an ndarray.\\n\\n Return the data of `a` (if any) as an ndarray if `a` is a ``MaskedArray``,\\n else return `a` as a ndarray or subclass (depending on `subok`) if not.\\n\\n Parameters\\n ----------\\n a : array_like\\n Input ``MaskedArray``, alternatively a ndarray or a subclass thereof.\\n subok : bool\\n Whether to force the output to be a `pure` ndarray (False) or to\\n return a subclass of ndarray if appropriate (True, default).\\n\\n See Also\\n --------\\n getmask : Return the mask of a masked array, or nomask.\\n getmaskarray : Return the mask of a masked array, or full array of False.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = ma.masked_equal([[1,2],[3,4]], 2)\\n >>> a\\n masked_array(data =\\n [[1 --]\\n [3 4]],\\n mask =\\n [[False True]\\n [False False]],\\n fill_value=999999)\\n >>> ma.getdata(a)\\n array([[1, 2],\\n [3, 4]])\\n\\n Equivalently use the ``MaskedArray`` `data` attribute.\\n\\n >>> a.data\\n array([[1, 2],\\n [3, 4]])\\n\\n \\\"\\\"\\\"\\n try:\\n data = a._data\\n except AttributeError:\\n data = np.array(a, copy=False, subok=subok)\\n if not subok:\\n return data.view(ndarray)\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::fix_invalid\",\n \"text\": \"numpy/ma/core.py::fix_invalid\\ndef fix_invalid(a, mask=nomask, copy=True, fill_value=None):\\n \\\"\\\"\\\"\\n Return input with invalid data masked and replaced by a fill value.\\n\\n Invalid data means values of `nan`, `inf`, etc.\\n\\n Parameters\\n ----------\\n a : array_like\\n Input array, a (subclass of) ndarray.\\n copy : bool, optional\\n Whether to use a copy of `a` (True) or to fix `a` in place (False).\\n Default is True.\\n fill_value : scalar, optional\\n Value used for fixing invalid data. Default is None, in which case\\n the ``a.fill_value`` is used.\\n\\n Returns\\n -------\\n b : MaskedArray\\n The input array with invalid entries fixed.\\n\\n Notes\\n -----\\n A copy is performed by default.\\n\\n Examples\\n --------\\n >>> x = np.ma.array([1., -1, np.nan, np.inf], mask=[1] + [0]*3)\\n >>> x\\n masked_array(data = [-- -1.0 nan inf],\\n mask = [ True False False False],\\n fill_value = 1e+20)\\n >>> np.ma.fix_invalid(x)\\n masked_array(data = [-- -1.0 -- --],\\n mask = [ True False True True],\\n fill_value = 1e+20)\\n\\n >>> fixed = np.ma.fix_invalid(x)\\n >>> fixed.data\\n array([ 1.00000000e+00, -1.00000000e+00, 1.00000000e+20,\\n 1.00000000e+20])\\n >>> x.data\\n array([ 1., -1., NaN, Inf])\\n\\n \\\"\\\"\\\"\\n a = masked_array(a, copy=copy, mask=mask, subok=True)\\n #invalid = (numpy.isnan(a._data) | numpy.isinf(a._data))\\n invalid = np.logical_not(np.isfinite(a._data))\\n if not invalid.any():\\n return a\\n a._mask |= invalid\\n if fill_value is None:\\n fill_value = a.fill_value\\n a._data[invalid] = fill_value\\n return a\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_DomainCheckInterval.__init__\",\n \"text\": \"numpy/ma/core.py::_DomainCheckInterval.__init__\\n def __init__(self, a, b):\\n \\\"domain_check_interval(a,b)(x) = true where x < a or y > b\\\"\\n if (a > b):\\n (a, b) = (b, a)\\n self.a = a\\n self.b = b\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_DomainCheckInterval.__call__\",\n \"text\": \"numpy/ma/core.py::_DomainCheckInterval.__call__\\n def __call__ (self, x):\\n \\\"Execute the call behavior.\\\"\\n return umath.logical_or(umath.greater (x, self.b),\\n umath.less(x, self.a))\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_DomainTan.__init__\",\n \"text\": \"numpy/ma/core.py::_DomainTan.__init__\\n def __init__(self, eps):\\n \\\"domain_tan(eps) = true where abs(cos(x)) < eps)\\\"\\n self.eps = eps\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_DomainTan.__call__\",\n \"text\": \"numpy/ma/core.py::_DomainTan.__call__\\n def __call__ (self, x):\\n \\\"Executes the call behavior.\\\"\\n return umath.less(umath.absolute(umath.cos(x)), self.eps)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_DomainSafeDivide.__init__\",\n \"text\": \"numpy/ma/core.py::_DomainSafeDivide.__init__\\n def __init__ (self, tolerance=None):\\n self.tolerance = tolerance\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_DomainSafeDivide.__call__\",\n \"text\": \"numpy/ma/core.py::_DomainSafeDivide.__call__\\n def __call__ (self, a, b):\\n # Delay the selection of the tolerance to here in order to reduce numpy\\n # import times. The calculation of these parameters is a substantial\\n # component of numpy's import time.\\n if self.tolerance is None:\\n self.tolerance = np.finfo(float).tiny\\n # don't call ma ufuncs from __array_wrap__ which would fail for scalars\\n a, b = np.asarray(a), np.asarray(b)\\n return umath.absolute(a) * self.tolerance >= umath.absolute(b)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_DomainGreater.__init__\",\n \"text\": \"numpy/ma/core.py::_DomainGreater.__init__\\n def __init__(self, critical_value):\\n \\\"DomainGreater(v)(x) = true where x <= v\\\"\\n self.critical_value = critical_value\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_DomainGreater.__call__\",\n \"text\": \"numpy/ma/core.py::_DomainGreater.__call__\\n def __call__ (self, x):\\n \\\"Executes the call behavior.\\\"\\n return umath.less_equal(x, self.critical_value)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_DomainGreaterEqual.__init__\",\n \"text\": \"numpy/ma/core.py::_DomainGreaterEqual.__init__\\n def __init__(self, critical_value):\\n \\\"DomainGreaterEqual(v)(x) = true where x < v\\\"\\n self.critical_value = critical_value\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_DomainGreaterEqual.__call__\",\n \"text\": \"numpy/ma/core.py::_DomainGreaterEqual.__call__\\n def __call__ (self, x):\\n \\\"Executes the call behavior.\\\"\\n return umath.less(x, self.critical_value)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedUnaryOperation.__init__\",\n \"text\": \"numpy/ma/core.py::_MaskedUnaryOperation.__init__\\n def __init__ (self, mufunc, fill=0, domain=None):\\n \\\"\\\"\\\" _MaskedUnaryOperation(aufunc, fill=0, domain=None)\\n aufunc(fill) must be defined\\n self(x) returns aufunc(x)\\n with masked values where domain(x) is true or getmask(x) is true.\\n \\\"\\\"\\\"\\n self.f = mufunc\\n self.fill = fill\\n self.domain = domain\\n self.__doc__ = getattr(mufunc, \\\"__doc__\\\", str(mufunc))\\n self.__name__ = getattr(mufunc, \\\"__name__\\\", str(mufunc))\\n ufunc_domain[mufunc] = domain\\n ufunc_fills[mufunc] = fill\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedUnaryOperation.__call__\",\n \"text\": \"numpy/ma/core.py::_MaskedUnaryOperation.__call__\\n def __call__ (self, a, *args, **kwargs):\\n \\\"Execute the call behavior.\\\"\\n d = getdata(a)\\n # Case 1.1. : Domained function\\n if self.domain is not None:\\n with np.errstate(divide='ignore', invalid='ignore'):\\n result = self.f(d, *args, **kwargs)\\n # Make a mask\\n m = ~umath.isfinite(result)\\n m |= self.domain(d)\\n m |= getmask(a)\\n # Case 1.2. : Function without a domain\\n else:\\n # Get the result and the mask\\n result = self.f(d, *args, **kwargs)\\n m = getmask(a)\\n # Case 2.1. : The result is scalarscalar\\n if not result.ndim:\\n if m:\\n return masked\\n return result\\n # Case 2.2. The result is an array\\n # We need to fill the invalid data back w/ the input\\n # Now, that's plain silly: in C, we would just skip the element and keep\\n # the original, but we do have to do it that way in Python\\n if m is not nomask:\\n # In case result has a lower dtype than the inputs (as in equal)\\n try:\\n np.copyto(result, d, where=m)\\n except TypeError:\\n pass\\n # Transform to\\n if isinstance(a, MaskedArray):\\n subtype = type(a)\\n else:\\n subtype = MaskedArray\\n result = result.view(subtype)\\n result._mask = m\\n result._update_from(a)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedUnaryOperation.__str__\",\n \"text\": \"numpy/ma/core.py::_MaskedUnaryOperation.__str__\\n def __str__ (self):\\n return \\\"Masked version of %s. [Invalid values are masked]\\\" % str(self.f)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedBinaryOperation.__init__\",\n \"text\": \"numpy/ma/core.py::_MaskedBinaryOperation.__init__\\n def __init__ (self, mbfunc, fillx=0, filly=0):\\n \\\"\\\"\\\"abfunc(fillx, filly) must be defined.\\n abfunc(x, filly) = x for all x to enable reduce.\\n \\\"\\\"\\\"\\n self.f = mbfunc\\n self.fillx = fillx\\n self.filly = filly\\n self.__doc__ = getattr(mbfunc, \\\"__doc__\\\", str(mbfunc))\\n self.__name__ = getattr(mbfunc, \\\"__name__\\\", str(mbfunc))\\n ufunc_domain[mbfunc] = None\\n ufunc_fills[mbfunc] = (fillx, filly)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedBinaryOperation.__call__\",\n \"text\": \"numpy/ma/core.py::_MaskedBinaryOperation.__call__\\n def __call__ (self, a, b, *args, **kwargs):\\n \\\"Execute the call behavior.\\\"\\n # Get the data, as ndarray\\n (da, db) = (getdata(a, subok=False), getdata(b, subok=False))\\n # Get the mask\\n (ma, mb) = (getmask(a), getmask(b))\\n if ma is nomask:\\n if mb is nomask:\\n m = nomask\\n else:\\n m = umath.logical_or(getmaskarray(a), mb)\\n elif mb is nomask:\\n m = umath.logical_or(ma, getmaskarray(b))\\n else:\\n m = umath.logical_or(ma, mb)\\n # Get the result\\n with np.errstate(divide='ignore', invalid='ignore'):\\n result = self.f(da, db, *args, **kwargs)\\n # check it worked\\n if result is NotImplemented:\\n return NotImplemented\\n # Case 1. : scalar\\n if not result.ndim:\\n if m:\\n return masked\\n return result\\n # Case 2. : array\\n # Revert result to da where masked\\n if m is not nomask:\\n np.copyto(result, da, casting='unsafe', where=m)\\n # Transforms to a (subclass of) MaskedArray\\n result = result.view(get_masked_subclass(a, b))\\n result._mask = m\\n # Update the optional info from the inputs\\n if isinstance(b, MaskedArray):\\n if isinstance(a, MaskedArray):\\n result._update_from(a)\\n else:\\n result._update_from(b)\\n elif isinstance(a, MaskedArray):\\n result._update_from(a)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedBinaryOperation.reduce\",\n \"text\": \"numpy/ma/core.py::_MaskedBinaryOperation.reduce\\n def reduce(self, target, axis=0, dtype=None):\\n \\\"\\\"\\\"Reduce `target` along the given `axis`.\\\"\\\"\\\"\\n if isinstance(target, MaskedArray):\\n tclass = type(target)\\n else:\\n tclass = MaskedArray\\n m = getmask(target)\\n t = filled(target, self.filly)\\n if t.shape == ():\\n t = t.reshape(1)\\n if m is not nomask:\\n m = make_mask(m, copy=1)\\n m.shape = (1,)\\n if m is nomask:\\n return self.f.reduce(t, axis).view(tclass)\\n t = t.view(tclass)\\n t._mask = m\\n tr = self.f.reduce(getdata(t), axis, dtype=dtype or t.dtype)\\n mr = umath.logical_and.reduce(m, axis)\\n tr = tr.view(tclass)\\n if mr.ndim > 0:\\n tr._mask = mr\\n return tr\\n elif mr:\\n return masked\\n return tr\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedBinaryOperation.outer\",\n \"text\": \"numpy/ma/core.py::_MaskedBinaryOperation.outer\\n def outer (self, a, b):\\n \\\"\\\"\\\"Return the function applied to the outer product of a and b.\\n\\n \\\"\\\"\\\"\\n ma = getmask(a)\\n mb = getmask(b)\\n if ma is nomask and mb is nomask:\\n m = nomask\\n else:\\n ma = getmaskarray(a)\\n mb = getmaskarray(b)\\n m = umath.logical_or.outer(ma, mb)\\n if (not m.ndim) and m:\\n return masked\\n (da, db) = (getdata(a), getdata(b))\\n d = self.f.outer(da, db)\\n # check it worked\\n if d is NotImplemented:\\n return NotImplemented\\n if m is not nomask:\\n np.copyto(d, da, where=m)\\n if d.shape:\\n d = d.view(get_masked_subclass(a, b))\\n d._mask = m\\n return d\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedBinaryOperation.accumulate\",\n \"text\": \"numpy/ma/core.py::_MaskedBinaryOperation.accumulate\\n def accumulate (self, target, axis=0):\\n \\\"\\\"\\\"Accumulate `target` along `axis` after filling with y fill\\n value.\\n\\n \\\"\\\"\\\"\\n if isinstance(target, MaskedArray):\\n tclass = type(target)\\n else:\\n tclass = MaskedArray\\n t = filled(target, self.filly)\\n return self.f.accumulate(t, axis).view(tclass)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedBinaryOperation.__str__\",\n \"text\": \"numpy/ma/core.py::_MaskedBinaryOperation.__str__\\n def __str__ (self):\\n return \\\"Masked version of \\\" + str(self.f)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_DomainedBinaryOperation.__init__\",\n \"text\": \"numpy/ma/core.py::_DomainedBinaryOperation.__init__\\n def __init__ (self, dbfunc, domain, fillx=0, filly=0):\\n \\\"\\\"\\\"abfunc(fillx, filly) must be defined.\\n abfunc(x, filly) = x for all x to enable reduce.\\n \\\"\\\"\\\"\\n self.f = dbfunc\\n self.domain = domain\\n self.fillx = fillx\\n self.filly = filly\\n self.__doc__ = getattr(dbfunc, \\\"__doc__\\\", str(dbfunc))\\n self.__name__ = getattr(dbfunc, \\\"__name__\\\", str(dbfunc))\\n ufunc_domain[dbfunc] = domain\\n ufunc_fills[dbfunc] = (fillx, filly)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_DomainedBinaryOperation.__call__\",\n \"text\": \"numpy/ma/core.py::_DomainedBinaryOperation.__call__\\n def __call__(self, a, b, *args, **kwargs):\\n \\\"Execute the call behavior.\\\"\\n # Get the data and the mask\\n (da, db) = (getdata(a, subok=False), getdata(b, subok=False))\\n (ma, mb) = (getmask(a), getmask(b))\\n # Get the result\\n with np.errstate(divide='ignore', invalid='ignore'):\\n result = self.f(da, db, *args, **kwargs)\\n # check it worked\\n if result is NotImplemented:\\n return NotImplemented\\n # Get the mask as a combination of ma, mb and invalid\\n m = ~umath.isfinite(result)\\n m |= ma\\n m |= mb\\n # Apply the domain\\n domain = ufunc_domain.get(self.f, None)\\n if domain is not None:\\n m |= filled(domain(da, db), True)\\n # Take care of the scalar case first\\n if (not m.ndim):\\n if m:\\n return masked\\n else:\\n return result\\n # When the mask is True, put back da\\n np.copyto(result, da, casting='unsafe', where=m)\\n result = result.view(get_masked_subclass(a, b))\\n result._mask = m\\n if isinstance(b, MaskedArray):\\n if isinstance(a, MaskedArray):\\n result._update_from(a)\\n else:\\n result._update_from(b)\\n elif isinstance(a, MaskedArray):\\n result._update_from(a)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_DomainedBinaryOperation.__str__\",\n \"text\": \"numpy/ma/core.py::_DomainedBinaryOperation.__str__\\n def __str__ (self):\\n return \\\"Masked version of \\\" + str(self.f)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_recursive_make_descr\",\n \"text\": \"numpy/ma/core.py::_recursive_make_descr\\ndef _recursive_make_descr(datatype, newtype=bool_):\\n \\\"Private function allowing recursion in make_descr.\\\"\\n # Do we have some name fields ?\\n if datatype.names:\\n descr = []\\n for name in datatype.names:\\n field = datatype.fields[name]\\n if len(field) == 3:\\n # Prepend the title to the name\\n name = (field[-1], name)\\n descr.append((name, _recursive_make_descr(field[0], newtype)))\\n return descr\\n # Is this some kind of composite a la (np.float,2)\\n elif datatype.subdtype:\\n mdescr = list(datatype.subdtype)\\n mdescr[0] = newtype\\n return tuple(mdescr)\\n else:\\n return newtype\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::make_mask_descr\",\n \"text\": \"numpy/ma/core.py::make_mask_descr\\ndef make_mask_descr(ndtype):\\n \\\"\\\"\\\"\\n Construct a dtype description list from a given dtype.\\n\\n Returns a new dtype object, with the type of all fields in `ndtype` to a\\n boolean type. Field names are not altered.\\n\\n Parameters\\n ----------\\n ndtype : dtype\\n The dtype to convert.\\n\\n Returns\\n -------\\n result : dtype\\n A dtype that looks like `ndtype`, the type of all fields is boolean.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> dtype = np.dtype({'names':['foo', 'bar'],\\n 'formats':[np.float32, np.int]})\\n >>> dtype\\n dtype([('foo', '<f4'), ('bar', '<i4')])\\n >>> ma.make_mask_descr(dtype)\\n dtype([('foo', '|b1'), ('bar', '|b1')])\\n >>> ma.make_mask_descr(np.float32)\\n <type 'numpy.bool_'>\\n\\n \\\"\\\"\\\"\\n # Make sure we do have a dtype\\n if not isinstance(ndtype, np.dtype):\\n ndtype = np.dtype(ndtype)\\n return np.dtype(_recursive_make_descr(ndtype, np.bool))\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::getmask\",\n \"text\": \"numpy/ma/core.py::getmask\\ndef getmask(a):\\n \\\"\\\"\\\"\\n Return the mask of a masked array, or nomask.\\n\\n Return the mask of `a` as an ndarray if `a` is a `MaskedArray` and the\\n mask is not `nomask`, else return `nomask`. To guarantee a full array\\n of booleans of the same shape as a, use `getmaskarray`.\\n\\n Parameters\\n ----------\\n a : array_like\\n Input `MaskedArray` for which the mask is required.\\n\\n See Also\\n --------\\n getdata : Return the data of a masked array as an ndarray.\\n getmaskarray : Return the mask of a masked array, or full array of False.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = ma.masked_equal([[1,2],[3,4]], 2)\\n >>> a\\n masked_array(data =\\n [[1 --]\\n [3 4]],\\n mask =\\n [[False True]\\n [False False]],\\n fill_value=999999)\\n >>> ma.getmask(a)\\n array([[False, True],\\n [False, False]], dtype=bool)\\n\\n Equivalently use the `MaskedArray` `mask` attribute.\\n\\n >>> a.mask\\n array([[False, True],\\n [False, False]], dtype=bool)\\n\\n Result when mask == `nomask`\\n\\n >>> b = ma.masked_array([[1,2],[3,4]])\\n >>> b\\n masked_array(data =\\n [[1 2]\\n [3 4]],\\n mask =\\n False,\\n fill_value=999999)\\n >>> ma.nomask\\n False\\n >>> ma.getmask(b) == ma.nomask\\n True\\n >>> b.mask == ma.nomask\\n True\\n\\n \\\"\\\"\\\"\\n return getattr(a, '_mask', nomask)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::getmaskarray\",\n \"text\": \"numpy/ma/core.py::getmaskarray\\ndef getmaskarray(arr):\\n \\\"\\\"\\\"\\n Return the mask of a masked array, or full boolean array of False.\\n\\n Return the mask of `arr` as an ndarray if `arr` is a `MaskedArray` and\\n the mask is not `nomask`, else return a full boolean array of False of\\n the same shape as `arr`.\\n\\n Parameters\\n ----------\\n arr : array_like\\n Input `MaskedArray` for which the mask is required.\\n\\n See Also\\n --------\\n getmask : Return the mask of a masked array, or nomask.\\n getdata : Return the data of a masked array as an ndarray.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = ma.masked_equal([[1,2],[3,4]], 2)\\n >>> a\\n masked_array(data =\\n [[1 --]\\n [3 4]],\\n mask =\\n [[False True]\\n [False False]],\\n fill_value=999999)\\n >>> ma.getmaskarray(a)\\n array([[False, True],\\n [False, False]], dtype=bool)\\n\\n Result when mask == ``nomask``\\n\\n >>> b = ma.masked_array([[1,2],[3,4]])\\n >>> b\\n masked_array(data =\\n [[1 2]\\n [3 4]],\\n mask =\\n False,\\n fill_value=999999)\\n >>> >ma.getmaskarray(b)\\n array([[False, False],\\n [False, False]], dtype=bool)\\n\\n \\\"\\\"\\\"\\n mask = getmask(arr)\\n if mask is nomask:\\n mask = make_mask_none(np.shape(arr), getdata(arr).dtype)\\n return mask\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::is_mask\",\n \"text\": \"numpy/ma/core.py::is_mask\\ndef is_mask(m):\\n \\\"\\\"\\\"\\n Return True if m is a valid, standard mask.\\n\\n This function does not check the contents of the input, only that the\\n type is MaskType. In particular, this function returns False if the\\n mask has a flexible dtype.\\n\\n Parameters\\n ----------\\n m : array_like\\n Array to test.\\n\\n Returns\\n -------\\n result : bool\\n True if `m.dtype.type` is MaskType, False otherwise.\\n\\n See Also\\n --------\\n isMaskedArray : Test whether input is an instance of MaskedArray.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> m = ma.masked_equal([0, 1, 0, 2, 3], 0)\\n >>> m\\n masked_array(data = [-- 1 -- 2 3],\\n mask = [ True False True False False],\\n fill_value=999999)\\n >>> ma.is_mask(m)\\n False\\n >>> ma.is_mask(m.mask)\\n True\\n\\n Input must be an ndarray (or have similar attributes)\\n for it to be considered a valid mask.\\n\\n >>> m = [False, True, False]\\n >>> ma.is_mask(m)\\n False\\n >>> m = np.array([False, True, False])\\n >>> m\\n array([False, True, False], dtype=bool)\\n >>> ma.is_mask(m)\\n True\\n\\n Arrays with complex dtypes don't return True.\\n\\n >>> dtype = np.dtype({'names':['monty', 'pithon'],\\n 'formats':[np.bool, np.bool]})\\n >>> dtype\\n dtype([('monty', '|b1'), ('pithon', '|b1')])\\n >>> m = np.array([(True, False), (False, True), (True, False)],\\n dtype=dtype)\\n >>> m\\n array([(True, False), (False, True), (True, False)],\\n dtype=[('monty', '|b1'), ('pithon', '|b1')])\\n >>> ma.is_mask(m)\\n False\\n\\n \\\"\\\"\\\"\\n try:\\n return m.dtype.type is MaskType\\n except AttributeError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::make_mask\",\n \"text\": \"numpy/ma/core.py::make_mask\\ndef make_mask(m, copy=False, shrink=True, dtype=MaskType):\\n \\\"\\\"\\\"\\n Create a boolean mask from an array.\\n\\n Return `m` as a boolean mask, creating a copy if necessary or requested.\\n The function can accept any sequence that is convertible to integers,\\n or ``nomask``. Does not require that contents must be 0s and 1s, values\\n of 0 are interepreted as False, everything else as True.\\n\\n Parameters\\n ----------\\n m : array_like\\n Potential mask.\\n copy : bool, optional\\n Whether to return a copy of `m` (True) or `m` itself (False).\\n shrink : bool, optional\\n Whether to shrink `m` to ``nomask`` if all its values are False.\\n dtype : dtype, optional\\n Data-type of the output mask. By default, the output mask has\\n a dtype of MaskType (bool). If the dtype is flexible, each field\\n has a boolean dtype.\\n\\n Returns\\n -------\\n result : ndarray\\n A boolean mask derived from `m`.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> m = [True, False, True, True]\\n >>> ma.make_mask(m)\\n array([ True, False, True, True], dtype=bool)\\n >>> m = [1, 0, 1, 1]\\n >>> ma.make_mask(m)\\n array([ True, False, True, True], dtype=bool)\\n >>> m = [1, 0, 2, -3]\\n >>> ma.make_mask(m)\\n array([ True, False, True, True], dtype=bool)\\n\\n Effect of the `shrink` parameter.\\n\\n >>> m = np.zeros(4)\\n >>> m\\n array([ 0., 0., 0., 0.])\\n >>> ma.make_mask(m)\\n False\\n >>> ma.make_mask(m, shrink=False)\\n array([False, False, False, False], dtype=bool)\\n\\n Using a flexible `dtype`.\\n\\n >>> m = [1, 0, 1, 1]\\n >>> n = [0, 1, 0, 0]\\n >>> arr = []\\n >>> for man, mouse in zip(m, n):\\n ... arr.append((man, mouse))\\n >>> arr\\n [(1, 0), (0, 1), (1, 0), (1, 0)]\\n >>> dtype = np.dtype({'names':['man', 'mouse'],\\n 'formats':[np.int, np.int]})\\n >>> arr = np.array(arr, dtype=dtype)\\n >>> arr\\n array([(1, 0), (0, 1), (1,\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::make_mask_none\",\n \"text\": \"numpy/ma/core.py::make_mask_none\\ndef make_mask_none(newshape, dtype=None):\\n \\\"\\\"\\\"\\n Return a boolean mask of the given shape, filled with False.\\n\\n This function returns a boolean ndarray with all entries False, that can\\n be used in common mask manipulations. If a complex dtype is specified, the\\n type of each field is converted to a boolean type.\\n\\n Parameters\\n ----------\\n newshape : tuple\\n A tuple indicating the shape of the mask.\\n dtype : {None, dtype}, optional\\n If None, use a MaskType instance. Otherwise, use a new datatype with\\n the same fields as `dtype`, converted to boolean types.\\n\\n Returns\\n -------\\n result : ndarray\\n An ndarray of appropriate shape and dtype, filled with False.\\n\\n See Also\\n --------\\n make_mask : Create a boolean mask from an array.\\n make_mask_descr : Construct a dtype description list from a given dtype.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> ma.make_mask_none((3,))\\n array([False, False, False], dtype=bool)\\n\\n Defining a more complex dtype.\\n\\n >>> dtype = np.dtype({'names':['foo', 'bar'],\\n 'formats':[np.float32, np.int]})\\n >>> dtype\\n dtype([('foo', '<f4'), ('bar', '<i4')])\\n >>> ma.make_mask_none((3,), dtype=dtype)\\n array([(False, False), (False, False), (False, False)],\\n dtype=[('foo', '|b1'), ('bar', '|b1')])\\n\\n \\\"\\\"\\\"\\n if dtype is None:\\n result = np.zeros(newshape, dtype=MaskType)\\n else:\\n result = np.zeros(newshape, dtype=make_mask_descr(dtype))\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::mask_or\",\n \"text\": \"numpy/ma/core.py::mask_or\\ndef mask_or (m1, m2, copy=False, shrink=True):\\n \\\"\\\"\\\"\\n Combine two masks with the ``logical_or`` operator.\\n\\n The result may be a view on `m1` or `m2` if the other is `nomask`\\n (i.e. False).\\n\\n Parameters\\n ----------\\n m1, m2 : array_like\\n Input masks.\\n copy : bool, optional\\n If copy is False and one of the inputs is `nomask`, return a view\\n of the other input mask. Defaults to False.\\n shrink : bool, optional\\n Whether to shrink the output to `nomask` if all its values are\\n False. Defaults to True.\\n\\n Returns\\n -------\\n mask : output mask\\n The result masks values that are masked in either `m1` or `m2`.\\n\\n Raises\\n ------\\n ValueError\\n If `m1` and `m2` have different flexible dtypes.\\n\\n Examples\\n --------\\n >>> m1 = np.ma.make_mask([0, 1, 1, 0])\\n >>> m2 = np.ma.make_mask([1, 0, 0, 0])\\n >>> np.ma.mask_or(m1, m2)\\n array([ True, True, True, False], dtype=bool)\\n\\n \\\"\\\"\\\"\\n def _recursive_mask_or(m1, m2, newmask):\\n names = m1.dtype.names\\n for name in names:\\n current1 = m1[name]\\n if current1.dtype.names:\\n _recursive_mask_or(current1, m2[name], newmask[name])\\n else:\\n umath.logical_or(current1, m2[name], newmask[name])\\n return\\n #\\n if (m1 is nomask) or (m1 is False):\\n dtype = getattr(m2, 'dtype', MaskType)\\n return make_mask(m2, copy=copy, shrink=shrink, dtype=dtype)\\n if (m2 is nomask) or (m2 is False):\\n dtype = getattr(m1, 'dtype', MaskType)\\n return make_mask(m1, copy=copy, shrink=shrink, dtype=dtype)\\n if m1 is m2 and is_mask(m1):\\n return m1\\n (dtype1, dtype2) = (getattr(m1, 'dtype', None), getattr(m2, 'dtype', None))\\n if (dtype1 != dtype2):\\n raise ValueError(\\\"Incompatible dtypes '%s'<>'%s'\\\" % (dtype1, dtype2))\\n if dtype1.names:\\n newmask = np.empty_like(m1)\\n _recursive_mask_or(m1, m2, newmask)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::mask_or._recursive_mask_or\",\n \"text\": \"numpy/ma/core.py::mask_or._recursive_mask_or\\n def _recursive_mask_or(m1, m2, newmask):\\n names = m1.dtype.names\\n for name in names:\\n current1 = m1[name]\\n if current1.dtype.names:\\n _recursive_mask_or(current1, m2[name], newmask[name])\\n else:\\n umath.logical_or(current1, m2[name], newmask[name])\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::flatten_mask\",\n \"text\": \"numpy/ma/core.py::flatten_mask\\ndef flatten_mask(mask):\\n \\\"\\\"\\\"\\n Returns a completely flattened version of the mask, where nested fields\\n are collapsed.\\n\\n Parameters\\n ----------\\n mask : array_like\\n Input array, which will be interpreted as booleans.\\n\\n Returns\\n -------\\n flattened_mask : ndarray of bools\\n The flattened input.\\n\\n Examples\\n --------\\n >>> mask = np.array([0, 0, 1], dtype=np.bool)\\n >>> flatten_mask(mask)\\n array([False, False, True], dtype=bool)\\n\\n >>> mask = np.array([(0, 0), (0, 1)], dtype=[('a', bool), ('b', bool)])\\n >>> flatten_mask(mask)\\n array([False, False, False, True], dtype=bool)\\n\\n >>> mdtype = [('a', bool), ('b', [('ba', bool), ('bb', bool)])]\\n >>> mask = np.array([(0, (0, 0)), (0, (0, 1))], dtype=mdtype)\\n >>> flatten_mask(mask)\\n array([False, False, False, False, False, True], dtype=bool)\\n\\n \\\"\\\"\\\"\\n #\\n def _flatmask(mask):\\n \\\"Flatten the mask and returns a (maybe nested) sequence of booleans.\\\"\\n mnames = mask.dtype.names\\n if mnames:\\n return [flatten_mask(mask[name]) for name in mnames]\\n else:\\n return mask\\n #\\n def _flatsequence(sequence):\\n \\\"Generates a flattened version of the sequence.\\\"\\n try:\\n for element in sequence:\\n if hasattr(element, '__iter__'):\\n for f in _flatsequence(element):\\n yield f\\n else:\\n yield element\\n except TypeError:\\n yield sequence\\n #\\n mask = np.asarray(mask)\\n flattened = _flatsequence(_flatmask(mask))\\n return np.array([_ for _ in flattened], dtype=bool)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::flatten_mask._flatmask\",\n \"text\": \"numpy/ma/core.py::flatten_mask._flatmask\\n def _flatmask(mask):\\n \\\"Flatten the mask and returns a (maybe nested) sequence of booleans.\\\"\\n mnames = mask.dtype.names\\n if mnames:\\n return [flatten_mask(mask[name]) for name in mnames]\\n else:\\n return mask\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::flatten_mask._flatsequence\",\n \"text\": \"numpy/ma/core.py::flatten_mask._flatsequence\\n def _flatsequence(sequence):\\n \\\"Generates a flattened version of the sequence.\\\"\\n try:\\n for element in sequence:\\n if hasattr(element, '__iter__'):\\n for f in _flatsequence(element):\\n yield f\\n else:\\n yield element\\n except TypeError:\\n yield sequence\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_check_mask_axis\",\n \"text\": \"numpy/ma/core.py::_check_mask_axis\\ndef _check_mask_axis(mask, axis):\\n \\\"Check whether there are masked values along the given axis\\\"\\n if mask is not nomask:\\n return mask.all(axis=axis)\\n return nomask\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::masked_where\",\n \"text\": \"numpy/ma/core.py::masked_where\\ndef masked_where(condition, a, copy=True):\\n \\\"\\\"\\\"\\n Mask an array where a condition is met.\\n\\n Return `a` as an array masked where `condition` is True.\\n Any masked values of `a` or `condition` are also masked in the output.\\n\\n Parameters\\n ----------\\n condition : array_like\\n Masking condition. When `condition` tests floating point values for\\n equality, consider using ``masked_values`` instead.\\n a : array_like\\n Array to mask.\\n copy : bool\\n If True (default) make a copy of `a` in the result. If False modify\\n `a` in place and return a view.\\n\\n Returns\\n -------\\n result : MaskedArray\\n The result of masking `a` where `condition` is True.\\n\\n See Also\\n --------\\n masked_values : Mask using floating point equality.\\n masked_equal : Mask where equal to a given value.\\n masked_not_equal : Mask where `not` equal to a given value.\\n masked_less_equal : Mask where less than or equal to a given value.\\n masked_greater_equal : Mask where greater than or equal to a given value.\\n masked_less : Mask where less than a given value.\\n masked_greater : Mask where greater than a given value.\\n masked_inside : Mask inside a given interval.\\n masked_outside : Mask outside a given interval.\\n masked_invalid : Mask invalid values (NaNs or infs).\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.arange(4)\\n >>> a\\n array([0, 1, 2, 3])\\n >>> ma.masked_where(a <= 2, a)\\n masked_array(data = [-- -- -- 3],\\n mask = [ True True True False],\\n fill_value=999999)\\n\\n Mask array `b` conditional on `a`.\\n\\n >>> b = ['a', 'b', 'c', 'd']\\n >>> ma.masked_where(a == 2, b)\\n masked_array(data = [a b -- d],\\n mask = [False False True False],\\n fill_value=N/A)\\n\\n Effect of the `copy` argument.\\n\\n >>> c = ma.masked_where(a <= 2, a)\\n >>> c\\n masked_array(data = [-- -- -- 3],\\n mask = [ True True \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::masked_greater\",\n \"text\": \"numpy/ma/core.py::masked_greater\\ndef masked_greater(x, value, copy=True):\\n \\\"\\\"\\\"\\n Mask an array where greater than a given value.\\n\\n This function is a shortcut to ``masked_where``, with\\n `condition` = (x > value).\\n\\n See Also\\n --------\\n masked_where : Mask where a condition is met.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.arange(4)\\n >>> a\\n array([0, 1, 2, 3])\\n >>> ma.masked_greater(a, 2)\\n masked_array(data = [0 1 2 --],\\n mask = [False False False True],\\n fill_value=999999)\\n\\n \\\"\\\"\\\"\\n return masked_where(greater(x, value), x, copy=copy)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::masked_greater_equal\",\n \"text\": \"numpy/ma/core.py::masked_greater_equal\\ndef masked_greater_equal(x, value, copy=True):\\n \\\"\\\"\\\"\\n Mask an array where greater than or equal to a given value.\\n\\n This function is a shortcut to ``masked_where``, with\\n `condition` = (x >= value).\\n\\n See Also\\n --------\\n masked_where : Mask where a condition is met.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.arange(4)\\n >>> a\\n array([0, 1, 2, 3])\\n >>> ma.masked_greater_equal(a, 2)\\n masked_array(data = [0 1 -- --],\\n mask = [False False True True],\\n fill_value=999999)\\n\\n \\\"\\\"\\\"\\n return masked_where(greater_equal(x, value), x, copy=copy)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::masked_less\",\n \"text\": \"numpy/ma/core.py::masked_less\\ndef masked_less(x, value, copy=True):\\n \\\"\\\"\\\"\\n Mask an array where less than a given value.\\n\\n This function is a shortcut to ``masked_where``, with\\n `condition` = (x < value).\\n\\n See Also\\n --------\\n masked_where : Mask where a condition is met.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.arange(4)\\n >>> a\\n array([0, 1, 2, 3])\\n >>> ma.masked_less(a, 2)\\n masked_array(data = [-- -- 2 3],\\n mask = [ True True False False],\\n fill_value=999999)\\n\\n \\\"\\\"\\\"\\n return masked_where(less(x, value), x, copy=copy)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::masked_less_equal\",\n \"text\": \"numpy/ma/core.py::masked_less_equal\\ndef masked_less_equal(x, value, copy=True):\\n \\\"\\\"\\\"\\n Mask an array where less than or equal to a given value.\\n\\n This function is a shortcut to ``masked_where``, with\\n `condition` = (x <= value).\\n\\n See Also\\n --------\\n masked_where : Mask where a condition is met.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.arange(4)\\n >>> a\\n array([0, 1, 2, 3])\\n >>> ma.masked_less_equal(a, 2)\\n masked_array(data = [-- -- -- 3],\\n mask = [ True True True False],\\n fill_value=999999)\\n\\n \\\"\\\"\\\"\\n return masked_where(less_equal(x, value), x, copy=copy)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::masked_not_equal\",\n \"text\": \"numpy/ma/core.py::masked_not_equal\\ndef masked_not_equal(x, value, copy=True):\\n \\\"\\\"\\\"\\n Mask an array where `not` equal to a given value.\\n\\n This function is a shortcut to ``masked_where``, with\\n `condition` = (x != value).\\n\\n See Also\\n --------\\n masked_where : Mask where a condition is met.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.arange(4)\\n >>> a\\n array([0, 1, 2, 3])\\n >>> ma.masked_not_equal(a, 2)\\n masked_array(data = [-- -- 2 --],\\n mask = [ True True False True],\\n fill_value=999999)\\n\\n \\\"\\\"\\\"\\n return masked_where(not_equal(x, value), x, copy=copy)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::masked_equal\",\n \"text\": \"numpy/ma/core.py::masked_equal\\ndef masked_equal(x, value, copy=True):\\n \\\"\\\"\\\"\\n Mask an array where equal to a given value.\\n\\n This function is a shortcut to ``masked_where``, with\\n `condition` = (x == value). For floating point arrays,\\n consider using ``masked_values(x, value)``.\\n\\n See Also\\n --------\\n masked_where : Mask where a condition is met.\\n masked_values : Mask using floating point equality.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.arange(4)\\n >>> a\\n array([0, 1, 2, 3])\\n >>> ma.masked_equal(a, 2)\\n masked_array(data = [0 1 -- 3],\\n mask = [False False True False],\\n fill_value=999999)\\n\\n \\\"\\\"\\\"\\n # An alternative implementation relies on filling first: probably not needed.\\n # d = filled(x, 0)\\n # c = umath.equal(d, value)\\n # m = mask_or(c, getmask(x))\\n # return array(d, mask=m, copy=copy)\\n output = masked_where(equal(x, value), x, copy=copy)\\n output.fill_value = value\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::masked_inside\",\n \"text\": \"numpy/ma/core.py::masked_inside\\ndef masked_inside(x, v1, v2, copy=True):\\n \\\"\\\"\\\"\\n Mask an array inside a given interval.\\n\\n Shortcut to ``masked_where``, where `condition` is True for `x` inside\\n the interval [v1,v2] (v1 <= x <= v2). The boundaries `v1` and `v2`\\n can be given in either order.\\n\\n See Also\\n --------\\n masked_where : Mask where a condition is met.\\n\\n Notes\\n -----\\n The array `x` is prefilled with its filling value.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> x = [0.31, 1.2, 0.01, 0.2, -0.4, -1.1]\\n >>> ma.masked_inside(x, -0.3, 0.3)\\n masked_array(data = [0.31 1.2 -- -- -0.4 -1.1],\\n mask = [False False True True False False],\\n fill_value=1e+20)\\n\\n The order of `v1` and `v2` doesn't matter.\\n\\n >>> ma.masked_inside(x, 0.3, -0.3)\\n masked_array(data = [0.31 1.2 -- -- -0.4 -1.1],\\n mask = [False False True True False False],\\n fill_value=1e+20)\\n\\n \\\"\\\"\\\"\\n if v2 < v1:\\n (v1, v2) = (v2, v1)\\n xf = filled(x)\\n condition = (xf >= v1) & (xf <= v2)\\n return masked_where(condition, x, copy=copy)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::masked_outside\",\n \"text\": \"numpy/ma/core.py::masked_outside\\ndef masked_outside(x, v1, v2, copy=True):\\n \\\"\\\"\\\"\\n Mask an array outside a given interval.\\n\\n Shortcut to ``masked_where``, where `condition` is True for `x` outside\\n the interval [v1,v2] (x < v1)|(x > v2).\\n The boundaries `v1` and `v2` can be given in either order.\\n\\n See Also\\n --------\\n masked_where : Mask where a condition is met.\\n\\n Notes\\n -----\\n The array `x` is prefilled with its filling value.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> x = [0.31, 1.2, 0.01, 0.2, -0.4, -1.1]\\n >>> ma.masked_outside(x, -0.3, 0.3)\\n masked_array(data = [-- -- 0.01 0.2 -- --],\\n mask = [ True True False False True True],\\n fill_value=1e+20)\\n\\n The order of `v1` and `v2` doesn't matter.\\n\\n >>> ma.masked_outside(x, 0.3, -0.3)\\n masked_array(data = [-- -- 0.01 0.2 -- --],\\n mask = [ True True False False True True],\\n fill_value=1e+20)\\n\\n \\\"\\\"\\\"\\n if v2 < v1:\\n (v1, v2) = (v2, v1)\\n xf = filled(x)\\n condition = (xf < v1) | (xf > v2)\\n return masked_where(condition, x, copy=copy)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::masked_object\",\n \"text\": \"numpy/ma/core.py::masked_object\\ndef masked_object(x, value, copy=True, shrink=True):\\n \\\"\\\"\\\"\\n Mask the array `x` where the data are exactly equal to value.\\n\\n This function is similar to `masked_values`, but only suitable\\n for object arrays: for floating point, use `masked_values` instead.\\n\\n Parameters\\n ----------\\n x : array_like\\n Array to mask\\n value : object\\n Comparison value\\n copy : {True, False}, optional\\n Whether to return a copy of `x`.\\n shrink : {True, False}, optional\\n Whether to collapse a mask full of False to nomask\\n\\n Returns\\n -------\\n result : MaskedArray\\n The result of masking `x` where equal to `value`.\\n\\n See Also\\n --------\\n masked_where : Mask where a condition is met.\\n masked_equal : Mask where equal to a given value (integers).\\n masked_values : Mask using floating point equality.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> food = np.array(['green_eggs', 'ham'], dtype=object)\\n >>> # don't eat spoiled food\\n >>> eat = ma.masked_object(food, 'green_eggs')\\n >>> print eat\\n [-- ham]\\n >>> # plain ol` ham is boring\\n >>> fresh_food = np.array(['cheese', 'ham', 'pineapple'], dtype=object)\\n >>> eat = ma.masked_object(fresh_food, 'green_eggs')\\n >>> print eat\\n [cheese ham pineapple]\\n\\n Note that `mask` is set to ``nomask`` if possible.\\n\\n >>> eat\\n masked_array(data = [cheese ham pineapple],\\n mask = False,\\n fill_value=?)\\n\\n \\\"\\\"\\\"\\n if isMaskedArray(x):\\n condition = umath.equal(x._data, value)\\n mask = x._mask\\n else:\\n condition = umath.equal(np.asarray(x), value)\\n mask = nomask\\n mask = mask_or(mask, make_mask(condition, shrink=shrink))\\n return masked_array(x, mask=mask, copy=copy, fill_value=value)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::masked_values\",\n \"text\": \"numpy/ma/core.py::masked_values\\ndef masked_values(x, value, rtol=1e-5, atol=1e-8, copy=True, shrink=True):\\n \\\"\\\"\\\"\\n Mask using floating point equality.\\n\\n Return a MaskedArray, masked where the data in array `x` are approximately\\n equal to `value`, i.e. where the following condition is True\\n\\n (abs(x - value) <= atol+rtol*abs(value))\\n\\n The fill_value is set to `value` and the mask is set to ``nomask`` if\\n possible. For integers, consider using ``masked_equal``.\\n\\n Parameters\\n ----------\\n x : array_like\\n Array to mask.\\n value : float\\n Masking value.\\n rtol : float, optional\\n Tolerance parameter.\\n atol : float, optional\\n Tolerance parameter (1e-8).\\n copy : bool, optional\\n Whether to return a copy of `x`.\\n shrink : bool, optional\\n Whether to collapse a mask full of False to ``nomask``.\\n\\n Returns\\n -------\\n result : MaskedArray\\n The result of masking `x` where approximately equal to `value`.\\n\\n See Also\\n --------\\n masked_where : Mask where a condition is met.\\n masked_equal : Mask where equal to a given value (integers).\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> x = np.array([1, 1.1, 2, 1.1, 3])\\n >>> ma.masked_values(x, 1.1)\\n masked_array(data = [1.0 -- 2.0 -- 3.0],\\n mask = [False True False True False],\\n fill_value=1.1)\\n\\n Note that `mask` is set to ``nomask`` if possible.\\n\\n >>> ma.masked_values(x, 1.5)\\n masked_array(data = [ 1. 1.1 2. 1.1 3. ],\\n mask = False,\\n fill_value=1.5)\\n\\n For integers, the fill value will be different in general to the\\n result of ``masked_equal``.\\n\\n >>> x = np.arange(5)\\n >>> x\\n array([0, 1, 2, 3, 4])\\n >>> ma.masked_values(x, 2)\\n masked_array(data = [0 1 -- 3 4],\\n mask = [False False True False False],\\n fill_value=2)\\n >>> ma.masked_equal(x, 2)\\n masked_array(data = [0 1 -- 3 4],\\n mask = [False False True Fals\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::masked_invalid\",\n \"text\": \"numpy/ma/core.py::masked_invalid\\ndef masked_invalid(a, copy=True):\\n \\\"\\\"\\\"\\n Mask an array where invalid values occur (NaNs or infs).\\n\\n This function is a shortcut to ``masked_where``, with\\n `condition` = ~(np.isfinite(a)). Any pre-existing mask is conserved.\\n Only applies to arrays with a dtype where NaNs or infs make sense\\n (i.e. floating point types), but accepts any array_like object.\\n\\n See Also\\n --------\\n masked_where : Mask where a condition is met.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.arange(5, dtype=np.float)\\n >>> a[2] = np.NaN\\n >>> a[3] = np.PINF\\n >>> a\\n array([ 0., 1., NaN, Inf, 4.])\\n >>> ma.masked_invalid(a)\\n masked_array(data = [0.0 1.0 -- -- 4.0],\\n mask = [False False True True False],\\n fill_value=1e+20)\\n\\n \\\"\\\"\\\"\\n a = np.array(a, copy=copy, subok=True)\\n mask = getattr(a, '_mask', None)\\n if mask is not None:\\n condition = ~(np.isfinite(getdata(a)))\\n if mask is not nomask:\\n condition |= mask\\n cls = type(a)\\n else:\\n condition = ~(np.isfinite(a))\\n cls = MaskedArray\\n result = a.view(cls)\\n result._mask = condition\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedPrintOption.__init__\",\n \"text\": \"numpy/ma/core.py::_MaskedPrintOption.__init__\\n def __init__ (self, display):\\n \\\"Create the masked_print_option object.\\\"\\n self._display = display\\n self._enabled = True\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedPrintOption.display\",\n \"text\": \"numpy/ma/core.py::_MaskedPrintOption.display\\n def display(self):\\n \\\"Display the string to print for masked values.\\\"\\n return self._display\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedPrintOption.set_display\",\n \"text\": \"numpy/ma/core.py::_MaskedPrintOption.set_display\\n def set_display (self, s):\\n \\\"Set the string to print for masked values.\\\"\\n self._display = s\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedPrintOption.enabled\",\n \"text\": \"numpy/ma/core.py::_MaskedPrintOption.enabled\\n def enabled(self):\\n \\\"Is the use of the display value enabled?\\\"\\n return self._enabled\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedPrintOption.enable\",\n \"text\": \"numpy/ma/core.py::_MaskedPrintOption.enable\\n def enable(self, shrink=1):\\n \\\"Set the enabling shrink to `shrink`.\\\"\\n self._enabled = shrink\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_MaskedPrintOption.__str__\",\n \"text\": \"numpy/ma/core.py::_MaskedPrintOption.__str__\\n def __str__ (self):\\n return str(self._display)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_recursive_printoption\",\n \"text\": \"numpy/ma/core.py::_recursive_printoption\\ndef _recursive_printoption(result, mask, printopt):\\n \\\"\\\"\\\"\\n Puts printoptions in result where mask is True.\\n Private function allowing for recursion\\n \\\"\\\"\\\"\\n names = result.dtype.names\\n for name in names:\\n (curdata, curmask) = (result[name], mask[name])\\n if curdata.dtype.names:\\n _recursive_printoption(curdata, curmask, printopt)\\n else:\\n np.copyto(curdata, printopt, where=curmask)\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_recursive_filled\",\n \"text\": \"numpy/ma/core.py::_recursive_filled\\ndef _recursive_filled(a, mask, fill_value):\\n \\\"\\\"\\\"\\n Recursively fill `a` with `fill_value`.\\n Private function\\n \\\"\\\"\\\"\\n names = a.dtype.names\\n for name in names:\\n current = a[name]\\n if current.dtype.names:\\n _recursive_filled(current, mask[name], fill_value[name])\\n else:\\n np.copyto(current, fill_value[name], where=mask[name])\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::flatten_structured_array\",\n \"text\": \"numpy/ma/core.py::flatten_structured_array\\ndef flatten_structured_array(a):\\n \\\"\\\"\\\"\\n Flatten a structured array.\\n\\n The data type of the output is chosen such that it can represent all of the\\n (nested) fields.\\n\\n Parameters\\n ----------\\n a : structured array\\n\\n Returns\\n -------\\n output : masked array or ndarray\\n A flattened masked array if the input is a masked array, otherwise a\\n standard ndarray.\\n\\n Examples\\n --------\\n >>> ndtype = [('a', int), ('b', float)]\\n >>> a = np.array([(1, 1), (2, 2)], dtype=ndtype)\\n >>> flatten_structured_array(a)\\n array([[1., 1.],\\n [2., 2.]])\\n\\n \\\"\\\"\\\"\\n #\\n def flatten_sequence(iterable):\\n \\\"\\\"\\\"Flattens a compound of nested iterables.\\\"\\\"\\\"\\n for elm in iter(iterable):\\n if hasattr(elm, '__iter__'):\\n for f in flatten_sequence(elm):\\n yield f\\n else:\\n yield elm\\n #\\n a = np.asanyarray(a)\\n inishape = a.shape\\n a = a.ravel()\\n if isinstance(a, MaskedArray):\\n out = np.array([tuple(flatten_sequence(d.item())) for d in a._data])\\n out = out.view(MaskedArray)\\n out._mask = np.array([tuple(flatten_sequence(d.item()))\\n for d in getmaskarray(a)])\\n else:\\n out = np.array([tuple(flatten_sequence(d.item())) for d in a])\\n if len(inishape) > 1:\\n newshape = list(out.shape)\\n newshape[0] = inishape\\n out.shape = tuple(flatten_sequence(newshape))\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::flatten_structured_array.flatten_sequence\",\n \"text\": \"numpy/ma/core.py::flatten_structured_array.flatten_sequence\\n def flatten_sequence(iterable):\\n \\\"\\\"\\\"Flattens a compound of nested iterables.\\\"\\\"\\\"\\n for elm in iter(iterable):\\n if hasattr(elm, '__iter__'):\\n for f in flatten_sequence(elm):\\n yield f\\n else:\\n yield elm\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_arraymethod.__init__\",\n \"text\": \"numpy/ma/core.py::_arraymethod.__init__\\n def __init__(self, funcname, onmask=True):\\n self.__name__ = funcname\\n self._onmask = onmask\\n self.obj = None\\n self.__doc__ = self.getdoc()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_arraymethod.getdoc\",\n \"text\": \"numpy/ma/core.py::_arraymethod.getdoc\\n def getdoc(self):\\n \\\"Return the doc of the function (from the doc of the method).\\\"\\n methdoc = getattr(ndarray, self.__name__, None) or \\\\\\n getattr(np, self.__name__, None)\\n if methdoc is not None:\\n return methdoc.__doc__\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_arraymethod.__get__\",\n \"text\": \"numpy/ma/core.py::_arraymethod.__get__\\n def __get__(self, obj, objtype=None):\\n self.obj = obj\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_arraymethod.__call__\",\n \"text\": \"numpy/ma/core.py::_arraymethod.__call__\\n def __call__(self, *args, **params):\\n methodname = self.__name__\\n instance = self.obj\\n # Fallback : if the instance has not been initialized, use the first arg\\n if instance is None:\\n args = list(args)\\n instance = args.pop(0)\\n data = instance._data\\n mask = instance._mask\\n cls = type(instance)\\n result = getattr(data, methodname)(*args, **params).view(cls)\\n result._update_from(instance)\\n if result.ndim:\\n if not self._onmask:\\n result.__setmask__(mask)\\n elif mask is not nomask:\\n result.__setmask__(getattr(mask, methodname)(*args, **params))\\n else:\\n if mask.ndim and (not mask.dtype.names and mask.all()):\\n return masked\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedIterator.__init__\",\n \"text\": \"numpy/ma/core.py::MaskedIterator.__init__\\n def __init__(self, ma):\\n self.ma = ma\\n self.dataiter = ma._data.flat\\n #\\n if ma._mask is nomask:\\n self.maskiter = None\\n else:\\n self.maskiter = ma._mask.flat\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedIterator.__iter__\",\n \"text\": \"numpy/ma/core.py::MaskedIterator.__iter__\\n def __iter__(self):\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedIterator.__getitem__\",\n \"text\": \"numpy/ma/core.py::MaskedIterator.__getitem__\\n def __getitem__(self, indx):\\n result = self.dataiter.__getitem__(indx).view(type(self.ma))\\n if self.maskiter is not None:\\n _mask = self.maskiter.__getitem__(indx)\\n if isinstance(_mask, ndarray):\\n # set shape to match that of data; this is needed for matrices\\n _mask.shape = result.shape\\n result._mask = _mask\\n elif isinstance(_mask, np.void):\\n return mvoid(result, mask=_mask, hardmask=self.ma._hardmask)\\n elif _mask: # Just a scalar, masked\\n return masked\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedIterator.__setitem__\",\n \"text\": \"numpy/ma/core.py::MaskedIterator.__setitem__\\n def __setitem__(self, index, value):\\n self.dataiter[index] = getdata(value)\\n if self.maskiter is not None:\\n self.maskiter[index] = getmaskarray(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedIterator.__next__\",\n \"text\": \"numpy/ma/core.py::MaskedIterator.__next__\\n def __next__(self):\\n \\\"\\\"\\\"\\n Return the next value, or raise StopIteration.\\n\\n Examples\\n --------\\n >>> x = np.ma.array([3, 2], mask=[0, 1])\\n >>> fl = x.flat\\n >>> fl.next()\\n 3\\n >>> fl.next()\\n masked_array(data = --,\\n mask = True,\\n fill_value = 1e+20)\\n >>> fl.next()\\n Traceback (most recent call last):\\n File \\\"<stdin>\\\", line 1, in <module>\\n File \\\"/home/ralf/python/numpy/numpy/ma/core.py\\\", line 2243, in next\\n d = self.dataiter.next()\\n StopIteration\\n\\n \\\"\\\"\\\"\\n d = next(self.dataiter)\\n if self.maskiter is not None:\\n m = next(self.maskiter)\\n if isinstance(m, np.void):\\n return mvoid(d, mask=m, hardmask=self.ma._hardmask)\\n elif m: # Just a scalar, masked\\n return masked\\n return d\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__new__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__new__\\n def __new__(cls, data=None, mask=nomask, dtype=None, copy=False,\\n subok=True, ndmin=0, fill_value=None,\\n keep_mask=True, hard_mask=None, shrink=True,\\n **options):\\n \\\"\\\"\\\"\\n Create a new masked array from scratch.\\n\\n Notes\\n -----\\n A masked array can also be created by taking a .view(MaskedArray).\\n\\n \\\"\\\"\\\"\\n # Process data............\\n _data = np.array(data, dtype=dtype, copy=copy, subok=True, ndmin=ndmin)\\n _baseclass = getattr(data, '_baseclass', type(_data))\\n # Check that we're not erasing the mask..........\\n if isinstance(data, MaskedArray) and (data.shape != _data.shape):\\n copy = True\\n # Careful, cls might not always be MaskedArray...\\n if not isinstance(data, cls) or not subok:\\n _data = ndarray.view(_data, cls)\\n else:\\n _data = ndarray.view(_data, type(data))\\n # Backwards compatibility w/ numpy.core.ma .......\\n if hasattr(data, '_mask') and not isinstance(data, ndarray):\\n _data._mask = data._mask\\n _sharedmask = True\\n # Process mask ...............................\\n # Number of named fields (or zero if none)\\n names_ = _data.dtype.names or ()\\n # Type of the mask\\n if names_:\\n mdtype = make_mask_descr(_data.dtype)\\n else:\\n mdtype = MaskType\\n # Case 1. : no mask in input ............\\n if mask is nomask:\\n # Erase the current mask ?\\n if not keep_mask:\\n # With a reduced version\\n if shrink:\\n _data._mask = nomask\\n # With full version\\n else:\\n _data._mask = np.zeros(_data.shape, dtype=mdtype)\\n # Check whether we missed something\\n elif isinstance(data, (tuple, list)):\\n try:\\n # If data is a sequence of mas\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray._update_from\",\n \"text\": \"numpy/ma/core.py::MaskedArray._update_from\\n def _update_from(self, obj):\\n \\\"\\\"\\\"Copies some attributes of obj to self.\\n \\\"\\\"\\\"\\n if obj is not None and isinstance(obj, ndarray):\\n _baseclass = type(obj)\\n else:\\n _baseclass = ndarray\\n # We need to copy the _basedict to avoid backward propagation\\n _optinfo = {}\\n _optinfo.update(getattr(obj, '_optinfo', {}))\\n _optinfo.update(getattr(obj, '_basedict', {}))\\n if not isinstance(obj, MaskedArray):\\n _optinfo.update(getattr(obj, '__dict__', {}))\\n _dict = dict(_fill_value=getattr(obj, '_fill_value', None),\\n _hardmask=getattr(obj, '_hardmask', False),\\n _sharedmask=getattr(obj, '_sharedmask', False),\\n _isfield=getattr(obj, '_isfield', False),\\n _baseclass=getattr(obj, '_baseclass', _baseclass),\\n _optinfo=_optinfo,\\n _basedict=_optinfo)\\n self.__dict__.update(_dict)\\n self.__dict__.update(_optinfo)\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__array_finalize__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__array_finalize__\\n def __array_finalize__(self, obj):\\n \\\"\\\"\\\"Finalizes the masked array.\\n \\\"\\\"\\\"\\n # Get main attributes .........\\n self._update_from(obj)\\n # We have to decide how to initialize self.mask, based on\\n # obj.mask. This is very difficult. There might be some\\n # correspondence between the elements in the array we are being\\n # created from (= obj) and us. Or... there might not. This method can\\n # be called in all kinds of places for all kinds of reasons -- could\\n # be empty_like, could be slicing, could be a ufunc, could be a view,\\n # ... The numpy subclassing interface simply doesn't give us any way\\n # to know, which means that at best this method will be based on\\n # guesswork and heuristics. To make things worse, there isn't even any\\n # clear consensus about what the desired behavior is. For instance,\\n # most users think that np.empty_like(marr) -- which goes via this\\n # method -- should return a masked array with an empty mask (see\\n # gh-3404 and linked discussions), but others disagree, and they have\\n # existing code which depends on empty_like returning an array that\\n # matches the input mask.\\n #\\n # Historically our algorithm was: if the template object mask had the\\n # same *number of elements* as us, then we used *it's mask object\\n # itself* as our mask, so that writes to us would also write to the\\n # original array. This is horribly broken in multiple ways.\\n #\\n # Now what we do instead is, if the template object mask has the same\\n # number of elements as us, and we do not have the same base pointer\\n # as the template object (b/c views like arr[...] should keep the same\\n # mask), then we make a copy of the template object mask and use\\n # that. This is also horribly broken but somewhat less so. Maybe.\\n if isins\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__array_wrap__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__array_wrap__\\n def __array_wrap__(self, obj, context=None):\\n \\\"\\\"\\\"\\n Special hook for ufuncs.\\n Wraps the numpy array and sets the mask according to context.\\n \\\"\\\"\\\"\\n result = obj.view(type(self))\\n result._update_from(self)\\n #..........\\n if context is not None:\\n result._mask = result._mask.copy()\\n (func, args, _) = context\\n m = reduce(mask_or, [getmaskarray(arg) for arg in args])\\n # Get the domain mask................\\n domain = ufunc_domain.get(func, None)\\n if domain is not None:\\n # Take the domain, and make sure it's a ndarray\\n if len(args) > 2:\\n d = filled(reduce(domain, args), True)\\n else:\\n d = filled(domain(*args), True)\\n # Fill the result where the domain is wrong\\n try:\\n # Binary domain: take the last value\\n fill_value = ufunc_fills[func][-1]\\n except TypeError:\\n # Unary domain: just use this one\\n fill_value = ufunc_fills[func]\\n except KeyError:\\n # Domain not recognized, use fill_value instead\\n fill_value = self.fill_value\\n result = result.copy()\\n np.copyto(result, fill_value, where=d)\\n # Update the mask\\n if m is nomask:\\n if d is not nomask:\\n m = d\\n else:\\n # Don't modify inplace, we risk back-propagation\\n m = (m | d)\\n # Make sure the mask has the proper size\\n if result.shape == () and m:\\n return masked\\n else:\\n result._mask = m\\n result._sharedmask = False\\n #....\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.view\",\n \"text\": \"numpy/ma/core.py::MaskedArray.view\\n def view(self, dtype=None, type=None, fill_value=None):\\n \\\"\\\"\\\"\\n Return a view of the MaskedArray data\\n\\n Parameters\\n ----------\\n dtype : data-type or ndarray sub-class, optional\\n Data-type descriptor of the returned view, e.g., float32 or int16.\\n The default, None, results in the view having the same data-type\\n as `a`. As with ``ndarray.view``, dtype can also be specified as\\n an ndarray sub-class, which then specifies the type of the\\n returned object (this is equivalent to setting the ``type``\\n parameter).\\n type : Python type, optional\\n Type of the returned view, e.g., ndarray or matrix. Again, the\\n default None results in type preservation.\\n\\n Notes\\n -----\\n\\n ``a.view()`` is used two different ways:\\n\\n ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view\\n of the array's memory with a different data-type. This can cause a\\n reinterpretation of the bytes of memory.\\n\\n ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just\\n returns an instance of `ndarray_subclass` that looks at the same array\\n (same shape, dtype, etc.) This does not cause a reinterpretation of the\\n memory.\\n\\n If `fill_value` is not specified, but `dtype` is specified (and is not\\n an ndarray sub-class), the `fill_value` of the MaskedArray will be\\n reset. If neither `fill_value` nor `dtype` are specified (or if\\n `dtype` is an ndarray sub-class), then the fill value is preserved.\\n Finally, if `fill_value` is specified, but `dtype` is not, the fill\\n value is set to the specified value.\\n\\n For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of\\n bytes per entry than the previous dtype (for example, converting a\\n regular array to a structured array), then the behavi\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.astype\",\n \"text\": \"numpy/ma/core.py::MaskedArray.astype\\n def astype(self, newtype):\\n \\\"\\\"\\\"\\n Returns a copy of the MaskedArray cast to given newtype.\\n\\n Returns\\n -------\\n output : MaskedArray\\n A copy of self cast to input newtype.\\n The returned record shape matches self.shape.\\n\\n Examples\\n --------\\n >>> x = np.ma.array([[1,2,3.1],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)\\n >>> print x\\n [[1.0 -- 3.1]\\n [-- 5.0 --]\\n [7.0 -- 9.0]]\\n >>> print x.astype(int32)\\n [[1 -- 3]\\n [-- 5 --]\\n [7 -- 9]]\\n\\n \\\"\\\"\\\"\\n newtype = np.dtype(newtype)\\n output = self._data.astype(newtype).view(type(self))\\n output._update_from(self)\\n names = output.dtype.names\\n if names is None:\\n output._mask = self._mask.astype(bool)\\n else:\\n if self._mask is nomask:\\n output._mask = nomask\\n else:\\n output._mask = self._mask.astype([(n, bool) for n in names])\\n # Don't check _fill_value if it's None, that'll speed things up\\n if self._fill_value is not None:\\n output._fill_value = _check_fill_value(self._fill_value, newtype)\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__getitem__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__getitem__\\n def __getitem__(self, indx):\\n \\\"\\\"\\\"x.__getitem__(y) <==> x[y]\\n\\n Return the item described by i, as a masked array.\\n\\n \\\"\\\"\\\"\\n # This test is useful, but we should keep things light...\\n# if getmask(indx) is not nomask:\\n# msg = \\\"Masked arrays must be filled before they can be used as indices!\\\"\\n# raise IndexError(msg)\\n _data = ndarray.view(self, ndarray)\\n dout = ndarray.__getitem__(_data, indx)\\n # We could directly use ndarray.__getitem__ on self...\\n # But then we would have to modify __array_finalize__ to prevent the\\n # mask of being reshaped if it hasn't been set up properly yet...\\n # So it's easier to stick to the current version\\n _mask = self._mask\\n if not getattr(dout, 'ndim', False):\\n # A record ................\\n if isinstance(dout, np.void):\\n mask = _mask[indx]\\n # We should always re-cast to mvoid, otherwise users can\\n # change masks on rows that already have masked values, but not\\n # on rows that have no masked values, which is inconsistent.\\n dout = mvoid(dout, mask=mask, hardmask=self._hardmask)\\n # Just a scalar............\\n elif _mask is not nomask and _mask[indx]:\\n return masked\\n else:\\n # Force dout to MA ........\\n dout = dout.view(type(self))\\n # Inherit attributes from self\\n dout._update_from(self)\\n # Check the fill_value ....\\n if isinstance(indx, basestring):\\n if self._fill_value is not None:\\n dout._fill_value = self._fill_value[indx]\\n dout._isfield = True\\n # Update the mask if needed\\n if _mask is not nomask:\\n dout._mask = _mask[indx]\\n dout._sharedmask = True\\n# Note: Don't try to check for \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__setitem__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__setitem__\\n def __setitem__(self, indx, value):\\n \\\"\\\"\\\"x.__setitem__(i, y) <==> x[i]=y\\n\\n Set item described by index. If value is masked, masks those\\n locations.\\n\\n \\\"\\\"\\\"\\n if self is masked:\\n raise MaskError('Cannot alter the masked element.')\\n # This test is useful, but we should keep things light...\\n# if getmask(indx) is not nomask:\\n# msg = \\\"Masked arrays must be filled before they can be used as indices!\\\"\\n# raise IndexError(msg)\\n _data = ndarray.view(self, ndarray.__getattribute__(self, '_baseclass'))\\n _mask = ndarray.__getattribute__(self, '_mask')\\n if isinstance(indx, basestring):\\n ndarray.__setitem__(_data, indx, value)\\n if _mask is nomask:\\n self._mask = _mask = make_mask_none(self.shape, self.dtype)\\n _mask[indx] = getmask(value)\\n return\\n #........................................\\n _dtype = ndarray.__getattribute__(_data, 'dtype')\\n nbfields = len(_dtype.names or ())\\n #........................................\\n if value is masked:\\n # The mask wasn't set: create a full version...\\n if _mask is nomask:\\n _mask = self._mask = make_mask_none(self.shape, _dtype)\\n # Now, set the mask to its value.\\n if nbfields:\\n _mask[indx] = tuple([True] * nbfields)\\n else:\\n _mask[indx] = True\\n if not self._isfield:\\n self._sharedmask = False\\n return\\n #........................................\\n # Get the _data part of the new value\\n dval = value\\n # Get the _mask part of the new value\\n mval = getattr(value, '_mask', nomask)\\n if nbfields and mval is nomask:\\n mval = tuple([False] * nbfields)\\n if _mask is nomask:\\n # Set the data, then the mask\\n ndarray.__set\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__getslice__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__getslice__\\n def __getslice__(self, i, j):\\n \\\"\\\"\\\"x.__getslice__(i, j) <==> x[i:j]\\n\\n Return the slice described by (i, j). The use of negative\\n indices is not supported.\\n\\n \\\"\\\"\\\"\\n return self.__getitem__(slice(i, j))\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__setslice__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__setslice__\\n def __setslice__(self, i, j, value):\\n \\\"\\\"\\\"x.__setslice__(i, j, value) <==> x[i:j]=value\\n\\n Set the slice (i,j) of a to value. If value is masked, mask\\n those locations.\\n\\n \\\"\\\"\\\"\\n self.__setitem__(slice(i, j), value)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__setmask__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__setmask__\\n def __setmask__(self, mask, copy=False):\\n \\\"\\\"\\\"Set the mask.\\n\\n \\\"\\\"\\\"\\n idtype = ndarray.__getattribute__(self, 'dtype')\\n current_mask = ndarray.__getattribute__(self, '_mask')\\n if mask is masked:\\n mask = True\\n # Make sure the mask is set\\n if (current_mask is nomask):\\n # Just don't do anything is there's nothing to do...\\n if mask is nomask:\\n return\\n current_mask = self._mask = make_mask_none(self.shape, idtype)\\n # No named fields.........\\n if idtype.names is None:\\n # Hardmask: don't unmask the data\\n if self._hardmask:\\n current_mask |= mask\\n # Softmask: set everything to False\\n # If it's obviously a compatible scalar, use a quick update\\n # method...\\n elif isinstance(mask, (int, float, np.bool_, np.number)):\\n current_mask[...] = mask\\n # ...otherwise fall back to the slower, general purpose way.\\n else:\\n current_mask.flat = mask\\n # Named fields w/ ............\\n else:\\n mdtype = current_mask.dtype\\n mask = np.array(mask, copy=False)\\n # Mask is a singleton\\n if not mask.ndim:\\n # It's a boolean : make a record\\n if mask.dtype.kind == 'b':\\n mask = np.array(tuple([mask.item()]*len(mdtype)),\\n dtype=mdtype)\\n # It's a record: make sure the dtype is correct\\n else:\\n mask = mask.astype(mdtype)\\n # Mask is a sequence\\n else:\\n # Make sure the new mask is a ndarray with the proper dtype\\n try:\\n mask = np.array(mask, copy=copy, dtype=mdtype)\\n # Or assume it's a sequence of bool/int\\n except TypeError:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray._get_mask\",\n \"text\": \"numpy/ma/core.py::MaskedArray._get_mask\\n def _get_mask(self):\\n \\\"\\\"\\\"Return the current mask.\\n\\n \\\"\\\"\\\"\\n # We could try to force a reshape, but that wouldn't work in some cases.\\n# return self._mask.reshape(self.shape)\\n return self._mask\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray._get_recordmask\",\n \"text\": \"numpy/ma/core.py::MaskedArray._get_recordmask\\n def _get_recordmask(self):\\n \\\"\\\"\\\"\\n Return the mask of the records.\\n A record is masked when all the fields are masked.\\n\\n \\\"\\\"\\\"\\n _mask = ndarray.__getattribute__(self, '_mask').view(ndarray)\\n if _mask.dtype.names is None:\\n return _mask\\n return np.all(flatten_structured_array(_mask), axis= -1)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray._set_recordmask\",\n \"text\": \"numpy/ma/core.py::MaskedArray._set_recordmask\\n def _set_recordmask(self):\\n \\\"\\\"\\\"Return the mask of the records.\\n A record is masked when all the fields are masked.\\n\\n \\\"\\\"\\\"\\n raise NotImplementedError(\\\"Coming soon: setting the mask per records!\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.harden_mask\",\n \"text\": \"numpy/ma/core.py::MaskedArray.harden_mask\\n def harden_mask(self):\\n \\\"\\\"\\\"\\n Force the mask to hard.\\n\\n Whether the mask of a masked array is hard or soft is determined by\\n its `hardmask` property. `harden_mask` sets `hardmask` to True.\\n\\n See Also\\n --------\\n hardmask\\n\\n \\\"\\\"\\\"\\n self._hardmask = True\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.soften_mask\",\n \"text\": \"numpy/ma/core.py::MaskedArray.soften_mask\\n def soften_mask(self):\\n \\\"\\\"\\\"\\n Force the mask to soft.\\n\\n Whether the mask of a masked array is hard or soft is determined by\\n its `hardmask` property. `soften_mask` sets `hardmask` to False.\\n\\n See Also\\n --------\\n hardmask\\n\\n \\\"\\\"\\\"\\n self._hardmask = False\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.unshare_mask\",\n \"text\": \"numpy/ma/core.py::MaskedArray.unshare_mask\\n def unshare_mask(self):\\n \\\"\\\"\\\"\\n Copy the mask and set the sharedmask flag to False.\\n\\n Whether the mask is shared between masked arrays can be seen from\\n the `sharedmask` property. `unshare_mask` ensures the mask is not shared.\\n A copy of the mask is only made if it was shared.\\n\\n See Also\\n --------\\n sharedmask\\n\\n \\\"\\\"\\\"\\n if self._sharedmask:\\n self._mask = self._mask.copy()\\n self._sharedmask = False\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.shrink_mask\",\n \"text\": \"numpy/ma/core.py::MaskedArray.shrink_mask\\n def shrink_mask(self):\\n \\\"\\\"\\\"\\n Reduce a mask to nomask when possible.\\n\\n Parameters\\n ----------\\n None\\n\\n Returns\\n -------\\n None\\n\\n Examples\\n --------\\n >>> x = np.ma.array([[1,2 ], [3, 4]], mask=[0]*4)\\n >>> x.mask\\n array([[False, False],\\n [False, False]], dtype=bool)\\n >>> x.shrink_mask()\\n >>> x.mask\\n False\\n\\n \\\"\\\"\\\"\\n m = self._mask\\n if m.ndim and not m.any():\\n self._mask = nomask\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray._get_data\",\n \"text\": \"numpy/ma/core.py::MaskedArray._get_data\\n def _get_data(self):\\n \\\"\\\"\\\"Return the current data, as a view of the original\\n underlying data.\\n\\n \\\"\\\"\\\"\\n return ndarray.view(self, self._baseclass)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray._get_flat\",\n \"text\": \"numpy/ma/core.py::MaskedArray._get_flat\\n def _get_flat(self):\\n \\\"Return a flat iterator.\\\"\\n return MaskedIterator(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray._set_flat\",\n \"text\": \"numpy/ma/core.py::MaskedArray._set_flat\\n def _set_flat (self, value):\\n \\\"Set a flattened version of self to value.\\\"\\n y = self.ravel()\\n y[:] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.get_fill_value\",\n \"text\": \"numpy/ma/core.py::MaskedArray.get_fill_value\\n def get_fill_value(self):\\n \\\"\\\"\\\"\\n Return the filling value of the masked array.\\n\\n Returns\\n -------\\n fill_value : scalar\\n The filling value.\\n\\n Examples\\n --------\\n >>> for dt in [np.int32, np.int64, np.float64, np.complex128]:\\n ... np.ma.array([0, 1], dtype=dt).get_fill_value()\\n ...\\n 999999\\n 999999\\n 1e+20\\n (1e+20+0j)\\n\\n >>> x = np.ma.array([0, 1.], fill_value=-np.inf)\\n >>> x.get_fill_value()\\n -inf\\n\\n \\\"\\\"\\\"\\n if self._fill_value is None:\\n self._fill_value = _check_fill_value(None, self.dtype)\\n return self._fill_value[()]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.set_fill_value\",\n \"text\": \"numpy/ma/core.py::MaskedArray.set_fill_value\\n def set_fill_value(self, value=None):\\n \\\"\\\"\\\"\\n Set the filling value of the masked array.\\n\\n Parameters\\n ----------\\n value : scalar, optional\\n The new filling value. Default is None, in which case a default\\n based on the data type is used.\\n\\n See Also\\n --------\\n ma.set_fill_value : Equivalent function.\\n\\n Examples\\n --------\\n >>> x = np.ma.array([0, 1.], fill_value=-np.inf)\\n >>> x.fill_value\\n -inf\\n >>> x.set_fill_value(np.pi)\\n >>> x.fill_value\\n 3.1415926535897931\\n\\n Reset to default:\\n\\n >>> x.set_fill_value()\\n >>> x.fill_value\\n 1e+20\\n\\n \\\"\\\"\\\"\\n target = _check_fill_value(value, self.dtype)\\n _fill_value = self._fill_value\\n if _fill_value is None:\\n # Create the attribute if it was undefined\\n self._fill_value = target\\n else:\\n # Don't overwrite the attribute, just fill it (for propagation)\\n _fill_value[()] = target\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.filled\",\n \"text\": \"numpy/ma/core.py::MaskedArray.filled\\n def filled(self, fill_value=None):\\n \\\"\\\"\\\"\\n Return a copy of self, with masked values filled with a given value.\\n\\n Parameters\\n ----------\\n fill_value : scalar, optional\\n The value to use for invalid entries (None by default).\\n If None, the `fill_value` attribute of the array is used instead.\\n\\n Returns\\n -------\\n filled_array : ndarray\\n A copy of ``self`` with invalid entries replaced by *fill_value*\\n (be it the function argument or the attribute of ``self``.\\n\\n Notes\\n -----\\n The result is **not** a MaskedArray!\\n\\n Examples\\n --------\\n >>> x = np.ma.array([1,2,3,4,5], mask=[0,0,1,0,1], fill_value=-999)\\n >>> x.filled()\\n array([1, 2, -999, 4, -999])\\n >>> type(x.filled())\\n <type 'numpy.ndarray'>\\n\\n Subclassing is preserved. This means that if the data part of the masked\\n array is a matrix, `filled` returns a matrix:\\n\\n >>> x = np.ma.array(np.matrix([[1, 2], [3, 4]]), mask=[[0, 1], [1, 0]])\\n >>> x.filled()\\n matrix([[ 1, 999999],\\n [999999, 4]])\\n\\n \\\"\\\"\\\"\\n m = self._mask\\n if m is nomask:\\n return self._data\\n #\\n if fill_value is None:\\n fill_value = self.fill_value\\n else:\\n fill_value = _check_fill_value(fill_value, self.dtype)\\n #\\n if self is masked_singleton:\\n return np.asanyarray(fill_value)\\n #\\n if m.dtype.names:\\n result = self._data.copy('K')\\n _recursive_filled(result, self._mask, fill_value)\\n elif not m.any():\\n return self._data\\n else:\\n result = self._data.copy('K')\\n try:\\n np.copyto(result, fill_value, where=m)\\n except (TypeError, AttributeError):\\n fill_value = narray(fill_value, dtype=object)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.compressed\",\n \"text\": \"numpy/ma/core.py::MaskedArray.compressed\\n def compressed(self):\\n \\\"\\\"\\\"\\n Return all the non-masked data as a 1-D array.\\n\\n Returns\\n -------\\n data : ndarray\\n A new `ndarray` holding the non-masked data is returned.\\n\\n Notes\\n -----\\n The result is **not** a MaskedArray!\\n\\n Examples\\n --------\\n >>> x = np.ma.array(np.arange(5), mask=[0]*2 + [1]*3)\\n >>> x.compressed()\\n array([0, 1])\\n >>> type(x.compressed())\\n <type 'numpy.ndarray'>\\n\\n \\\"\\\"\\\"\\n data = ndarray.ravel(self._data)\\n if self._mask is not nomask:\\n data = data.compress(np.logical_not(ndarray.ravel(self._mask)))\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.compress\",\n \"text\": \"numpy/ma/core.py::MaskedArray.compress\\n def compress(self, condition, axis=None, out=None):\\n \\\"\\\"\\\"\\n Return `a` where condition is ``True``.\\n\\n If condition is a `MaskedArray`, missing values are considered\\n as ``False``.\\n\\n Parameters\\n ----------\\n condition : var\\n Boolean 1-d array selecting which entries to return. If len(condition)\\n is less than the size of a along the axis, then output is truncated\\n to length of condition array.\\n axis : {None, int}, optional\\n Axis along which the operation must be performed.\\n out : {None, ndarray}, optional\\n Alternative output array in which to place the result. It must have\\n the same shape as the expected output but the type will be cast if\\n necessary.\\n\\n Returns\\n -------\\n result : MaskedArray\\n A :class:`MaskedArray` object.\\n\\n Notes\\n -----\\n Please note the difference with :meth:`compressed` !\\n The output of :meth:`compress` has a mask, the output of\\n :meth:`compressed` does not.\\n\\n Examples\\n --------\\n >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)\\n >>> print x\\n [[1 -- 3]\\n [-- 5 --]\\n [7 -- 9]]\\n >>> x.compress([1, 0, 1])\\n masked_array(data = [1 3],\\n mask = [False False],\\n fill_value=999999)\\n\\n >>> x.compress([1, 0, 1], axis=1)\\n masked_array(data =\\n [[1 3]\\n [-- --]\\n [7 9]],\\n mask =\\n [[False False]\\n [ True True]\\n [False False]],\\n fill_value=999999)\\n\\n \\\"\\\"\\\"\\n # Get the basic components\\n (_data, _mask) = (self._data, self._mask)\\n # Force the condition to a regular ndarray (forget the missing values...)\\n condition = np.array(condition, copy=False, subok=False)\\n #\\n _new = _data.compress(condition\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__str__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__str__\\n def __str__(self):\\n \\\"\\\"\\\"String representation.\\n\\n \\\"\\\"\\\"\\n if masked_print_option.enabled():\\n f = masked_print_option\\n if self is masked:\\n return str(f)\\n m = self._mask\\n if m is nomask:\\n res = self._data\\n else:\\n if m.shape == ():\\n if m.dtype.names:\\n m = m.view((bool, len(m.dtype)))\\n if m.any():\\n return str(tuple((f if _m else _d) for _d, _m in\\n zip(self._data.tolist(), m)))\\n else:\\n return str(self._data)\\n elif m:\\n return str(f)\\n else:\\n return str(self._data)\\n # convert to object array to make filled work\\n names = self.dtype.names\\n if names is None:\\n res = self._data.astype(\\\"O\\\")\\n res.view(ndarray)[m] = f\\n else:\\n rdtype = _recursive_make_descr(self.dtype, \\\"O\\\")\\n res = self._data.astype(rdtype)\\n _recursive_printoption(res, m, f)\\n else:\\n res = self.filled(self.fill_value)\\n return str(res)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__repr__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__repr__\\n def __repr__(self):\\n \\\"\\\"\\\"Literal string representation.\\n\\n \\\"\\\"\\\"\\n n = len(self.shape)\\n if self._baseclass is np.ndarray:\\n name = 'array'\\n else:\\n name = self._baseclass.__name__\\n\\n parameters = dict(name=name, nlen=\\\" \\\" * len(name),\\n data=str(self), mask=str(self._mask),\\n fill=str(self.fill_value), dtype=str(self.dtype))\\n if self.dtype.names:\\n if n <= 1:\\n return _print_templates['short_flx'] % parameters\\n return _print_templates['long_flx'] % parameters\\n elif n <= 1:\\n return _print_templates['short_std'] % parameters\\n return _print_templates['long_std'] % parameters\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__eq__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__eq__\\n def __eq__(self, other):\\n \\\"Check whether other equals self elementwise\\\"\\n if self is masked:\\n return masked\\n omask = getattr(other, '_mask', nomask)\\n if omask is nomask:\\n check = ndarray.__eq__(self.filled(0), other)\\n try:\\n check = check.view(type(self))\\n check._mask = self._mask\\n except AttributeError:\\n # Dang, we have a bool instead of an array: return the bool\\n return check\\n else:\\n odata = filled(other, 0)\\n check = ndarray.__eq__(self.filled(0), odata).view(type(self))\\n if self._mask is nomask:\\n check._mask = omask\\n else:\\n mask = mask_or(self._mask, omask)\\n if mask.dtype.names:\\n if mask.size > 1:\\n axis = 1\\n else:\\n axis = None\\n try:\\n mask = mask.view((bool_, len(self.dtype))).all(axis)\\n except ValueError:\\n mask = np.all([[f[n].all() for n in mask.dtype.names]\\n for f in mask], axis=axis)\\n check._mask = mask\\n return check\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__ne__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__ne__\\n def __ne__(self, other):\\n \\\"Check whether other doesn't equal self elementwise\\\"\\n if self is masked:\\n return masked\\n omask = getattr(other, '_mask', nomask)\\n if omask is nomask:\\n check = ndarray.__ne__(self.filled(0), other)\\n try:\\n check = check.view(type(self))\\n check._mask = self._mask\\n except AttributeError:\\n # In case check is a boolean (or a numpy.bool)\\n return check\\n else:\\n odata = filled(other, 0)\\n check = ndarray.__ne__(self.filled(0), odata).view(type(self))\\n if self._mask is nomask:\\n check._mask = omask\\n else:\\n mask = mask_or(self._mask, omask)\\n if mask.dtype.names:\\n if mask.size > 1:\\n axis = 1\\n else:\\n axis = None\\n try:\\n mask = mask.view((bool_, len(self.dtype))).all(axis)\\n except ValueError:\\n mask = np.all([[f[n].all() for n in mask.dtype.names]\\n for f in mask], axis=axis)\\n check._mask = mask\\n return check\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__add__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__add__\\n def __add__(self, other):\\n \\\"Add other to self, and return a new masked array.\\\"\\n return add(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__radd__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__radd__\\n def __radd__(self, other):\\n \\\"Add other to self, and return a new masked array.\\\"\\n return add(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__sub__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__sub__\\n def __sub__(self, other):\\n \\\"Subtract other to self, and return a new masked array.\\\"\\n return subtract(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__rsub__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__rsub__\\n def __rsub__(self, other):\\n \\\"Subtract other to self, and return a new masked array.\\\"\\n return subtract(other, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__mul__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__mul__\\n def __mul__(self, other):\\n \\\"Multiply other by self, and return a new masked array.\\\"\\n return multiply(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__rmul__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__rmul__\\n def __rmul__(self, other):\\n \\\"Multiply other by self, and return a new masked array.\\\"\\n return multiply(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__div__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__div__\\n def __div__(self, other):\\n \\\"Divide other into self, and return a new masked array.\\\"\\n return divide(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__truediv__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__truediv__\\n def __truediv__(self, other):\\n \\\"Divide other into self, and return a new masked array.\\\"\\n return true_divide(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__rtruediv__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__rtruediv__\\n def __rtruediv__(self, other):\\n \\\"Divide other into self, and return a new masked array.\\\"\\n return true_divide(other, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__floordiv__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__floordiv__\\n def __floordiv__(self, other):\\n \\\"Divide other into self, and return a new masked array.\\\"\\n return floor_divide(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__rfloordiv__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__rfloordiv__\\n def __rfloordiv__(self, other):\\n \\\"Divide other into self, and return a new masked array.\\\"\\n return floor_divide(other, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__pow__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__pow__\\n def __pow__(self, other):\\n \\\"Raise self to the power other, masking the potential NaNs/Infs\\\"\\n return power(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__rpow__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__rpow__\\n def __rpow__(self, other):\\n \\\"Raise self to the power other, masking the potential NaNs/Infs\\\"\\n return power(other, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__iadd__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__iadd__\\n def __iadd__(self, other):\\n \\\"Add other to self in-place.\\\"\\n m = getmask(other)\\n if self._mask is nomask:\\n if m is not nomask and m.any():\\n self._mask = make_mask_none(self.shape, self.dtype)\\n self._mask += m\\n else:\\n if m is not nomask:\\n self._mask += m\\n ndarray.__iadd__(self._data, np.where(self._mask, 0, getdata(other)))\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__isub__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__isub__\\n def __isub__(self, other):\\n \\\"Subtract other from self in-place.\\\"\\n m = getmask(other)\\n if self._mask is nomask:\\n if m is not nomask and m.any():\\n self._mask = make_mask_none(self.shape, self.dtype)\\n self._mask += m\\n elif m is not nomask:\\n self._mask += m\\n ndarray.__isub__(self._data, np.where(self._mask, 0, getdata(other)))\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__imul__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__imul__\\n def __imul__(self, other):\\n \\\"Multiply self by other in-place.\\\"\\n m = getmask(other)\\n if self._mask is nomask:\\n if m is not nomask and m.any():\\n self._mask = make_mask_none(self.shape, self.dtype)\\n self._mask += m\\n elif m is not nomask:\\n self._mask += m\\n ndarray.__imul__(self._data, np.where(self._mask, 1, getdata(other)))\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__idiv__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__idiv__\\n def __idiv__(self, other):\\n \\\"Divide self by other in-place.\\\"\\n other_data = getdata(other)\\n dom_mask = _DomainSafeDivide().__call__(self._data, other_data)\\n other_mask = getmask(other)\\n new_mask = mask_or(other_mask, dom_mask)\\n # The following 3 lines control the domain filling\\n if dom_mask.any():\\n (_, fval) = ufunc_fills[np.divide]\\n other_data = np.where(dom_mask, fval, other_data)\\n# self._mask = mask_or(self._mask, new_mask)\\n self._mask |= new_mask\\n ndarray.__idiv__(self._data, np.where(self._mask, 1, other_data))\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__ifloordiv__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__ifloordiv__\\n def __ifloordiv__(self, other):\\n \\\"Floor divide self by other in-place.\\\"\\n other_data = getdata(other)\\n dom_mask = _DomainSafeDivide().__call__(self._data, other_data)\\n other_mask = getmask(other)\\n new_mask = mask_or(other_mask, dom_mask)\\n # The following 3 lines control the domain filling\\n if dom_mask.any():\\n (_, fval) = ufunc_fills[np.floor_divide]\\n other_data = np.where(dom_mask, fval, other_data)\\n# self._mask = mask_or(self._mask, new_mask)\\n self._mask |= new_mask\\n ndarray.__ifloordiv__(self._data, np.where(self._mask, 1, other_data))\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__itruediv__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__itruediv__\\n def __itruediv__(self, other):\\n \\\"True divide self by other in-place.\\\"\\n other_data = getdata(other)\\n dom_mask = _DomainSafeDivide().__call__(self._data, other_data)\\n other_mask = getmask(other)\\n new_mask = mask_or(other_mask, dom_mask)\\n # The following 3 lines control the domain filling\\n if dom_mask.any():\\n (_, fval) = ufunc_fills[np.true_divide]\\n other_data = np.where(dom_mask, fval, other_data)\\n# self._mask = mask_or(self._mask, new_mask)\\n self._mask |= new_mask\\n ndarray.__itruediv__(self._data, np.where(self._mask, 1, other_data))\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__ipow__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__ipow__\\n def __ipow__(self, other):\\n \\\"Raise self to the power other, in place.\\\"\\n other_data = getdata(other)\\n other_mask = getmask(other)\\n with np.errstate(divide='ignore', invalid='ignore'):\\n ndarray.__ipow__(self._data, np.where(self._mask, 1, other_data))\\n invalid = np.logical_not(np.isfinite(self._data))\\n if invalid.any():\\n if self._mask is not nomask:\\n self._mask |= invalid\\n else:\\n self._mask = invalid\\n np.copyto(self._data, self.fill_value, where=invalid)\\n new_mask = mask_or(other_mask, invalid)\\n self._mask = mask_or(self._mask, new_mask)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__float__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__float__\\n def __float__(self):\\n \\\"Convert to float.\\\"\\n if self.size > 1:\\n raise TypeError(\\\"Only length-1 arrays can be converted \\\"\\n \\\"to Python scalars\\\")\\n elif self._mask:\\n warnings.warn(\\\"Warning: converting a masked element to nan.\\\")\\n return np.nan\\n return float(self.item())\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__int__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__int__\\n def __int__(self):\\n \\\"Convert to int.\\\"\\n if self.size > 1:\\n raise TypeError(\\\"Only length-1 arrays can be converted \\\"\\n \\\"to Python scalars\\\")\\n elif self._mask:\\n raise MaskError('Cannot convert masked element to a Python int.')\\n return int(self.item())\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.get_imag\",\n \"text\": \"numpy/ma/core.py::MaskedArray.get_imag\\n def get_imag(self):\\n \\\"\\\"\\\"\\n Return the imaginary part of the masked array.\\n\\n The returned array is a view on the imaginary part of the `MaskedArray`\\n whose `get_imag` method is called.\\n\\n Parameters\\n ----------\\n None\\n\\n Returns\\n -------\\n result : MaskedArray\\n The imaginary part of the masked array.\\n\\n See Also\\n --------\\n get_real, real, imag\\n\\n Examples\\n --------\\n >>> x = np.ma.array([1+1.j, -2j, 3.45+1.6j], mask=[False, True, False])\\n >>> x.get_imag()\\n masked_array(data = [1.0 -- 1.6],\\n mask = [False True False],\\n fill_value = 1e+20)\\n\\n \\\"\\\"\\\"\\n result = self._data.imag.view(type(self))\\n result.__setmask__(self._mask)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.get_real\",\n \"text\": \"numpy/ma/core.py::MaskedArray.get_real\\n def get_real(self):\\n \\\"\\\"\\\"\\n Return the real part of the masked array.\\n\\n The returned array is a view on the real part of the `MaskedArray`\\n whose `get_real` method is called.\\n\\n Parameters\\n ----------\\n None\\n\\n Returns\\n -------\\n result : MaskedArray\\n The real part of the masked array.\\n\\n See Also\\n --------\\n get_imag, real, imag\\n\\n Examples\\n --------\\n >>> x = np.ma.array([1+1.j, -2j, 3.45+1.6j], mask=[False, True, False])\\n >>> x.get_real()\\n masked_array(data = [1.0 -- 3.45],\\n mask = [False True False],\\n fill_value = 1e+20)\\n\\n \\\"\\\"\\\"\\n result = self._data.real.view(type(self))\\n result.__setmask__(self._mask)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.count\",\n \"text\": \"numpy/ma/core.py::MaskedArray.count\\n def count(self, axis=None):\\n \\\"\\\"\\\"\\n Count the non-masked elements of the array along the given axis.\\n\\n Parameters\\n ----------\\n axis : int, optional\\n Axis along which to count the non-masked elements. If `axis` is\\n `None`, all non-masked elements are counted.\\n\\n Returns\\n -------\\n result : int or ndarray\\n If `axis` is `None`, an integer count is returned. When `axis` is\\n not `None`, an array with shape determined by the lengths of the\\n remaining axes, is returned.\\n\\n See Also\\n --------\\n count_masked : Count masked elements in array or along a given axis.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = ma.arange(6).reshape((2, 3))\\n >>> a[1, :] = ma.masked\\n >>> a\\n masked_array(data =\\n [[0 1 2]\\n [-- -- --]],\\n mask =\\n [[False False False]\\n [ True True True]],\\n fill_value = 999999)\\n >>> a.count()\\n 3\\n\\n When the `axis` keyword is specified an array of appropriate size is\\n returned.\\n\\n >>> a.count(axis=0)\\n array([1, 1, 1])\\n >>> a.count(axis=1)\\n array([3, 0])\\n\\n \\\"\\\"\\\"\\n m = self._mask\\n s = self.shape\\n if m is nomask:\\n if axis is None:\\n return self.size\\n else:\\n n = s[axis]\\n t = list(s)\\n del t[axis]\\n return np.full(t, n, dtype=np.intp)\\n n1 = np.size(m, axis)\\n n2 = np.sum(m, axis=axis, dtype=np.intp)\\n if axis is None:\\n return (n1 - n2)\\n else:\\n return narray(n1 - n2)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.ravel\",\n \"text\": \"numpy/ma/core.py::MaskedArray.ravel\\n def ravel(self):\\n \\\"\\\"\\\"\\n Returns a 1D version of self, as a view.\\n\\n Returns\\n -------\\n MaskedArray\\n Output view is of shape ``(self.size,)`` (or\\n ``(np.ma.product(self.shape),)``).\\n\\n Examples\\n --------\\n >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)\\n >>> print x\\n [[1 -- 3]\\n [-- 5 --]\\n [7 -- 9]]\\n >>> print x.ravel()\\n [1 -- 3 -- 5 -- 7 -- 9]\\n\\n \\\"\\\"\\\"\\n r = ndarray.ravel(self._data).view(type(self))\\n r._update_from(self)\\n if self._mask is not nomask:\\n r._mask = ndarray.ravel(self._mask).reshape(r.shape)\\n else:\\n r._mask = nomask\\n return r\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.reshape\",\n \"text\": \"numpy/ma/core.py::MaskedArray.reshape\\n def reshape (self, *s, **kwargs):\\n \\\"\\\"\\\"\\n Give a new shape to the array without changing its data.\\n\\n Returns a masked array containing the same data, but with a new shape.\\n The result is a view on the original array; if this is not possible, a\\n ValueError is raised.\\n\\n Parameters\\n ----------\\n shape : int or tuple of ints\\n The new shape should be compatible with the original shape. If an\\n integer is supplied, then the result will be a 1-D array of that\\n length.\\n order : {'C', 'F'}, optional\\n Determines whether the array data should be viewed as in C\\n (row-major) or FORTRAN (column-major) order.\\n\\n Returns\\n -------\\n reshaped_array : array\\n A new view on the array.\\n\\n See Also\\n --------\\n reshape : Equivalent function in the masked array module.\\n numpy.ndarray.reshape : Equivalent method on ndarray object.\\n numpy.reshape : Equivalent function in the NumPy module.\\n\\n Notes\\n -----\\n The reshaping operation cannot guarantee that a copy will not be made,\\n to modify the shape in place, use ``a.shape = s``\\n\\n Examples\\n --------\\n >>> x = np.ma.array([[1,2],[3,4]], mask=[1,0,0,1])\\n >>> print x\\n [[-- 2]\\n [3 --]]\\n >>> x = x.reshape((4,1))\\n >>> print x\\n [[--]\\n [2]\\n [3]\\n [--]]\\n\\n \\\"\\\"\\\"\\n kwargs.update(order=kwargs.get('order', 'C'))\\n result = self._data.reshape(*s, **kwargs).view(type(self))\\n result._update_from(self)\\n mask = self._mask\\n if mask is not nomask:\\n result._mask = mask.reshape(*s, **kwargs)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.resize\",\n \"text\": \"numpy/ma/core.py::MaskedArray.resize\\n def resize(self, newshape, refcheck=True, order=False):\\n \\\"\\\"\\\"\\n .. warning::\\n\\n This method does nothing, except raise a ValueError exception. A\\n masked array does not own its data and therefore cannot safely be\\n resized in place. Use the `numpy.ma.resize` function instead.\\n\\n This method is difficult to implement safely and may be deprecated in\\n future releases of NumPy.\\n\\n \\\"\\\"\\\"\\n # Note : the 'order' keyword looks broken, let's just drop it\\n# try:\\n# ndarray.resize(self, newshape, refcheck=refcheck)\\n# if self.mask is not nomask:\\n# self._mask.resize(newshape, refcheck=refcheck)\\n# except ValueError:\\n# raise ValueError(\\\"Cannot resize an array that has been referenced \\\"\\n# \\\"or is referencing another array in this way.\\\\n\\\"\\n# \\\"Use the numpy.ma.resize function.\\\")\\n# return None\\n errmsg = \\\"A masked array does not own its data \\\"\\\\\\n \\\"and therefore cannot be resized.\\\\n\\\" \\\\\\n \\\"Use the numpy.ma.resize function instead.\\\"\\n raise ValueError(errmsg)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.put\",\n \"text\": \"numpy/ma/core.py::MaskedArray.put\\n def put(self, indices, values, mode='raise'):\\n \\\"\\\"\\\"\\n Set storage-indexed locations to corresponding values.\\n\\n Sets self._data.flat[n] = values[n] for each n in indices.\\n If `values` is shorter than `indices` then it will repeat.\\n If `values` has some masked values, the initial mask is updated\\n in consequence, else the corresponding values are unmasked.\\n\\n Parameters\\n ----------\\n indices : 1-D array_like\\n Target indices, interpreted as integers.\\n values : array_like\\n Values to place in self._data copy at target indices.\\n mode : {'raise', 'wrap', 'clip'}, optional\\n Specifies how out-of-bounds indices will behave.\\n 'raise' : raise an error.\\n 'wrap' : wrap around.\\n 'clip' : clip to the range.\\n\\n Notes\\n -----\\n `values` can be a scalar or length 1 array.\\n\\n Examples\\n --------\\n >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)\\n >>> print x\\n [[1 -- 3]\\n [-- 5 --]\\n [7 -- 9]]\\n >>> x.put([0,4,8],[10,20,30])\\n >>> print x\\n [[10 -- 3]\\n [-- 20 --]\\n [7 -- 30]]\\n\\n >>> x.put(4,999)\\n >>> print x\\n [[10 -- 3]\\n [-- 999 --]\\n [7 -- 30]]\\n\\n \\\"\\\"\\\"\\n m = self._mask\\n # Hard mask: Get rid of the values/indices that fall on masked data\\n if self._hardmask and self._mask is not nomask:\\n mask = self._mask[indices]\\n indices = narray(indices, copy=False)\\n values = narray(values, copy=False, subok=True)\\n values.resize(indices.shape)\\n indices = indices[~mask]\\n values = values[~mask]\\n #....\\n self._data.put(indices, values, mode=mode)\\n #....\\n if m is nomask:\\n m = getmask(values)\\n else:\\n m = m.copy()\\n if getmask(values\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.ids\",\n \"text\": \"numpy/ma/core.py::MaskedArray.ids\\n def ids (self):\\n \\\"\\\"\\\"\\n Return the addresses of the data and mask areas.\\n\\n Parameters\\n ----------\\n None\\n\\n Examples\\n --------\\n >>> x = np.ma.array([1, 2, 3], mask=[0, 1, 1])\\n >>> x.ids()\\n (166670640, 166659832)\\n\\n If the array has no mask, the address of `nomask` is returned. This address\\n is typically not close to the data in memory:\\n\\n >>> x = np.ma.array([1, 2, 3])\\n >>> x.ids()\\n (166691080, 3083169284L)\\n\\n \\\"\\\"\\\"\\n if self._mask is nomask:\\n return (self.ctypes.data, id(nomask))\\n return (self.ctypes.data, self._mask.ctypes.data)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.iscontiguous\",\n \"text\": \"numpy/ma/core.py::MaskedArray.iscontiguous\\n def iscontiguous(self):\\n \\\"\\\"\\\"\\n Return a boolean indicating whether the data is contiguous.\\n\\n Parameters\\n ----------\\n None\\n\\n Examples\\n --------\\n >>> x = np.ma.array([1, 2, 3])\\n >>> x.iscontiguous()\\n True\\n\\n `iscontiguous` returns one of the flags of the masked array:\\n\\n >>> x.flags\\n C_CONTIGUOUS : True\\n F_CONTIGUOUS : True\\n OWNDATA : False\\n WRITEABLE : True\\n ALIGNED : True\\n UPDATEIFCOPY : False\\n\\n \\\"\\\"\\\"\\n return self.flags['CONTIGUOUS']\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.all\",\n \"text\": \"numpy/ma/core.py::MaskedArray.all\\n def all(self, axis=None, out=None):\\n \\\"\\\"\\\"\\n Check if all of the elements of `a` are true.\\n\\n Performs a :func:`logical_and` over the given axis and returns the result.\\n Masked values are considered as True during computation.\\n For convenience, the output array is masked where ALL the values along the\\n current axis are masked: if the output would have been a scalar and that\\n all the values are masked, then the output is `masked`.\\n\\n Parameters\\n ----------\\n axis : {None, integer}\\n Axis to perform the operation over.\\n If None, perform over flattened array.\\n out : {None, array}, optional\\n Array into which the result can be placed. Its type is preserved\\n and it must be of the right shape to hold the output.\\n\\n See Also\\n --------\\n all : equivalent function\\n\\n Examples\\n --------\\n >>> np.ma.array([1,2,3]).all()\\n True\\n >>> a = np.ma.array([1,2,3], mask=True)\\n >>> (a.all() is np.ma.masked)\\n True\\n\\n \\\"\\\"\\\"\\n mask = _check_mask_axis(self._mask, axis)\\n if out is None:\\n d = self.filled(True).all(axis=axis).view(type(self))\\n if d.ndim:\\n d.__setmask__(mask)\\n elif mask:\\n return masked\\n return d\\n self.filled(True).all(axis=axis, out=out)\\n if isinstance(out, MaskedArray):\\n if out.ndim or mask:\\n out.__setmask__(mask)\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.any\",\n \"text\": \"numpy/ma/core.py::MaskedArray.any\\n def any(self, axis=None, out=None):\\n \\\"\\\"\\\"\\n Check if any of the elements of `a` are true.\\n\\n Performs a logical_or over the given axis and returns the result.\\n Masked values are considered as False during computation.\\n\\n Parameters\\n ----------\\n axis : {None, integer}\\n Axis to perform the operation over.\\n If None, perform over flattened array and return a scalar.\\n out : {None, array}, optional\\n Array into which the result can be placed. Its type is preserved\\n and it must be of the right shape to hold the output.\\n\\n See Also\\n --------\\n any : equivalent function\\n\\n \\\"\\\"\\\"\\n mask = _check_mask_axis(self._mask, axis)\\n if out is None:\\n d = self.filled(False).any(axis=axis).view(type(self))\\n if d.ndim:\\n d.__setmask__(mask)\\n elif mask:\\n d = masked\\n return d\\n self.filled(False).any(axis=axis, out=out)\\n if isinstance(out, MaskedArray):\\n if out.ndim or mask:\\n out.__setmask__(mask)\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.nonzero\",\n \"text\": \"numpy/ma/core.py::MaskedArray.nonzero\\n def nonzero(self):\\n \\\"\\\"\\\"\\n Return the indices of unmasked elements that are not zero.\\n\\n Returns a tuple of arrays, one for each dimension, containing the\\n indices of the non-zero elements in that dimension. The corresponding\\n non-zero values can be obtained with::\\n\\n a[a.nonzero()]\\n\\n To group the indices by element, rather than dimension, use\\n instead::\\n\\n np.transpose(a.nonzero())\\n\\n The result of this is always a 2d array, with a row for each non-zero\\n element.\\n\\n Parameters\\n ----------\\n None\\n\\n Returns\\n -------\\n tuple_of_arrays : tuple\\n Indices of elements that are non-zero.\\n\\n See Also\\n --------\\n numpy.nonzero :\\n Function operating on ndarrays.\\n flatnonzero :\\n Return indices that are non-zero in the flattened version of the input\\n array.\\n ndarray.nonzero :\\n Equivalent ndarray method.\\n count_nonzero :\\n Counts the number of non-zero elements in the input array.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> x = ma.array(np.eye(3))\\n >>> x\\n masked_array(data =\\n [[ 1. 0. 0.]\\n [ 0. 1. 0.]\\n [ 0. 0. 1.]],\\n mask =\\n False,\\n fill_value=1e+20)\\n >>> x.nonzero()\\n (array([0, 1, 2]), array([0, 1, 2]))\\n\\n Masked elements are ignored.\\n\\n >>> x[1, 1] = ma.masked\\n >>> x\\n masked_array(data =\\n [[1.0 0.0 0.0]\\n [0.0 -- 0.0]\\n [0.0 0.0 1.0]],\\n mask =\\n [[False False False]\\n [False True False]\\n [False False False]],\\n fill_value=1e+20)\\n >>> x.nonzero()\\n (array([0, 2]), array([0, 2]))\\n\\n Indices can also be grouped by element.\\n\\n >>> np.transpose(x.nonzero())\\n array([[0, 0\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.trace\",\n \"text\": \"numpy/ma/core.py::MaskedArray.trace\\n def trace(self, offset=0, axis1=0, axis2=1, dtype=None, out=None):\\n \\\"\\\"\\\"\\n (this docstring should be overwritten)\\n \\\"\\\"\\\"\\n #!!!: implement out + test!\\n m = self._mask\\n if m is nomask:\\n result = super(MaskedArray, self).trace(offset=offset, axis1=axis1,\\n axis2=axis2, out=out)\\n return result.astype(dtype)\\n else:\\n D = self.diagonal(offset=offset, axis1=axis1, axis2=axis2)\\n return D.astype(dtype).filled(0).sum(axis=None, out=out)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.sum\",\n \"text\": \"numpy/ma/core.py::MaskedArray.sum\\n def sum(self, axis=None, dtype=None, out=None):\\n \\\"\\\"\\\"\\n Return the sum of the array elements over the given axis.\\n Masked elements are set to 0 internally.\\n\\n Parameters\\n ----------\\n axis : {None, -1, int}, optional\\n Axis along which the sum is computed. The default\\n (`axis` = None) is to compute over the flattened array.\\n dtype : {None, dtype}, optional\\n Determines the type of the returned array and of the accumulator\\n where the elements are summed. If dtype has the value None and\\n the type of a is an integer type of precision less than the default\\n platform integer, then the default platform integer precision is\\n used. Otherwise, the dtype is the same as that of a.\\n out : {None, ndarray}, optional\\n Alternative output array in which to place the result. It must\\n have the same shape and buffer length as the expected output\\n but the type will be cast if necessary.\\n\\n Returns\\n -------\\n sum_along_axis : MaskedArray or scalar\\n An array with the same shape as self, with the specified\\n axis removed. If self is a 0-d array, or if `axis` is None, a scalar\\n is returned. If an output array is specified, a reference to\\n `out` is returned.\\n\\n Examples\\n --------\\n >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)\\n >>> print x\\n [[1 -- 3]\\n [-- 5 --]\\n [7 -- 9]]\\n >>> print x.sum()\\n 25\\n >>> print x.sum(axis=1)\\n [4 5 16]\\n >>> print x.sum(axis=0)\\n [8 5 12]\\n >>> print type(x.sum(axis=0, dtype=np.int64)[0])\\n <type 'numpy.int64'>\\n\\n \\\"\\\"\\\"\\n _mask = ndarray.__getattribute__(self, '_mask')\\n newmask = _check_mask_axis(_mask, axis)\\n # No explicit output\\n if out is None:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.cumsum\",\n \"text\": \"numpy/ma/core.py::MaskedArray.cumsum\\n def cumsum(self, axis=None, dtype=None, out=None):\\n \\\"\\\"\\\"\\n Return the cumulative sum of the elements along the given axis.\\n The cumulative sum is calculated over the flattened array by\\n default, otherwise over the specified axis.\\n\\n Masked values are set to 0 internally during the computation.\\n However, their position is saved, and the result will be masked at\\n the same locations.\\n\\n Parameters\\n ----------\\n axis : {None, -1, int}, optional\\n Axis along which the sum is computed. The default (`axis` = None) is to\\n compute over the flattened array. `axis` may be negative, in which case\\n it counts from the last to the first axis.\\n dtype : {None, dtype}, optional\\n Type of the returned array and of the accumulator in which the\\n elements are summed. If `dtype` is not specified, it defaults\\n to the dtype of `a`, unless `a` has an integer dtype with a\\n precision less than that of the default platform integer. In\\n that case, the default platform integer is used.\\n out : ndarray, optional\\n Alternative output array in which to place the result. It must\\n have the same shape and buffer length as the expected output\\n but the type will be cast if necessary.\\n\\n Returns\\n -------\\n cumsum : ndarray.\\n A new array holding the result is returned unless ``out`` is\\n specified, in which case a reference to ``out`` is returned.\\n\\n Notes\\n -----\\n The mask is lost if `out` is not a valid :class:`MaskedArray` !\\n\\n Arithmetic is modular when using integer types, and no error is\\n raised on overflow.\\n\\n Examples\\n --------\\n >>> marr = np.ma.array(np.arange(10), mask=[0,0,0,1,1,1,0,0,0,0])\\n >>> print marr.cumsum()\\n [0 1 3 -- -- -- 9 16 24 33]\\n\\n \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.prod\",\n \"text\": \"numpy/ma/core.py::MaskedArray.prod\\n def prod(self, axis=None, dtype=None, out=None):\\n \\\"\\\"\\\"\\n Return the product of the array elements over the given axis.\\n Masked elements are set to 1 internally for computation.\\n\\n Parameters\\n ----------\\n axis : {None, int}, optional\\n Axis over which the product is taken. If None is used, then the\\n product is over all the array elements.\\n dtype : {None, dtype}, optional\\n Determines the type of the returned array and of the accumulator\\n where the elements are multiplied. If ``dtype`` has the value ``None``\\n and the type of a is an integer type of precision less than the default\\n platform integer, then the default platform integer precision is\\n used. Otherwise, the dtype is the same as that of a.\\n out : {None, array}, optional\\n Alternative output array in which to place the result. It must have\\n the same shape as the expected output but the type will be cast if\\n necessary.\\n\\n Returns\\n -------\\n product_along_axis : {array, scalar}, see dtype parameter above.\\n Returns an array whose shape is the same as a with the specified\\n axis removed. Returns a 0d array when a is 1d or axis=None.\\n Returns a reference to the specified output array if specified.\\n\\n See Also\\n --------\\n prod : equivalent function\\n\\n Notes\\n -----\\n Arithmetic is modular when using integer types, and no error is raised\\n on overflow.\\n\\n Examples\\n --------\\n >>> np.prod([1.,2.])\\n 2.0\\n >>> np.prod([1.,2.], dtype=np.int32)\\n 2\\n >>> np.prod([[1.,2.],[3.,4.]])\\n 24.0\\n >>> np.prod([[1.,2.],[3.,4.]], axis=1)\\n array([ 2., 12.])\\n\\n \\\"\\\"\\\"\\n _mask = ndarray.__getattribute__(self, '_mask')\\n newmask = _check_mask_axis(_mask, axis)\\n #\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.cumprod\",\n \"text\": \"numpy/ma/core.py::MaskedArray.cumprod\\n def cumprod(self, axis=None, dtype=None, out=None):\\n \\\"\\\"\\\"\\n Return the cumulative product of the elements along the given axis.\\n The cumulative product is taken over the flattened array by\\n default, otherwise over the specified axis.\\n\\n Masked values are set to 1 internally during the computation.\\n However, their position is saved, and the result will be masked at\\n the same locations.\\n\\n Parameters\\n ----------\\n axis : {None, -1, int}, optional\\n Axis along which the product is computed. The default\\n (`axis` = None) is to compute over the flattened array.\\n dtype : {None, dtype}, optional\\n Determines the type of the returned array and of the accumulator\\n where the elements are multiplied. If ``dtype`` has the value ``None``\\n and the type of ``a`` is an integer type of precision less than the\\n default platform integer, then the default platform integer precision\\n is used. Otherwise, the dtype is the same as that of ``a``.\\n out : ndarray, optional\\n Alternative output array in which to place the result. It must\\n have the same shape and buffer length as the expected output\\n but the type will be cast if necessary.\\n\\n Returns\\n -------\\n cumprod : ndarray\\n A new array holding the result is returned unless out is specified,\\n in which case a reference to out is returned.\\n\\n Notes\\n -----\\n The mask is lost if `out` is not a valid MaskedArray !\\n\\n Arithmetic is modular when using integer types, and no error is\\n raised on overflow.\\n\\n \\\"\\\"\\\"\\n result = self.filled(1).cumprod(axis=axis, dtype=dtype, out=out)\\n if out is not None:\\n if isinstance(out, MaskedArray):\\n out.__setmask__(self._mask)\\n return out\\n result = result.vie\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.mean\",\n \"text\": \"numpy/ma/core.py::MaskedArray.mean\\n def mean(self, axis=None, dtype=None, out=None):\\n \\\"\\\"\\\"\\n Returns the average of the array elements.\\n\\n Masked entries are ignored.\\n The average is taken over the flattened array by default, otherwise over\\n the specified axis. Refer to `numpy.mean` for the full documentation.\\n\\n Parameters\\n ----------\\n a : array_like\\n Array containing numbers whose mean is desired. If `a` is not an\\n array, a conversion is attempted.\\n axis : int, optional\\n Axis along which the means are computed. The default is to compute\\n the mean of the flattened array.\\n dtype : dtype, optional\\n Type to use in computing the mean. For integer inputs, the default\\n is float64; for floating point, inputs it is the same as the input\\n dtype.\\n out : ndarray, optional\\n Alternative output array in which to place the result. It must have\\n the same shape as the expected output but the type will be cast if\\n necessary.\\n\\n Returns\\n -------\\n mean : ndarray, see dtype parameter above\\n If `out=None`, returns a new array containing the mean values,\\n otherwise a reference to the output array is returned.\\n\\n See Also\\n --------\\n numpy.ma.mean : Equivalent function.\\n numpy.mean : Equivalent function on non-masked arrays.\\n numpy.ma.average: Weighted average.\\n\\n Examples\\n --------\\n >>> a = np.ma.array([1,2,3], mask=[False, False, True])\\n >>> a\\n masked_array(data = [1 2 --],\\n mask = [False False True],\\n fill_value = 999999)\\n >>> a.mean()\\n 1.5\\n\\n \\\"\\\"\\\"\\n if self._mask is nomask:\\n result = super(MaskedArray, self).mean(axis=axis, dtype=dtype)\\n else:\\n dsum = self.sum(axis=axis, dtype=dtype)\\n cnt = self.c\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.anom\",\n \"text\": \"numpy/ma/core.py::MaskedArray.anom\\n def anom(self, axis=None, dtype=None):\\n \\\"\\\"\\\"\\n Compute the anomalies (deviations from the arithmetic mean)\\n along the given axis.\\n\\n Returns an array of anomalies, with the same shape as the input and\\n where the arithmetic mean is computed along the given axis.\\n\\n Parameters\\n ----------\\n axis : int, optional\\n Axis over which the anomalies are taken.\\n The default is to use the mean of the flattened array as reference.\\n dtype : dtype, optional\\n Type to use in computing the variance. For arrays of integer type\\n the default is float32; for arrays of float types it is the same as\\n the array type.\\n\\n See Also\\n --------\\n mean : Compute the mean of the array.\\n\\n Examples\\n --------\\n >>> a = np.ma.array([1,2,3])\\n >>> a.anom()\\n masked_array(data = [-1. 0. 1.],\\n mask = False,\\n fill_value = 1e+20)\\n\\n \\\"\\\"\\\"\\n m = self.mean(axis, dtype)\\n if not axis:\\n return (self - m)\\n else:\\n return (self - expand_dims(m, axis))\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.var\",\n \"text\": \"numpy/ma/core.py::MaskedArray.var\\n def var(self, axis=None, dtype=None, out=None, ddof=0):\\n \\\"\\\"\\n # Easy case: nomask, business as usual\\n if self._mask is nomask:\\n return self._data.var(axis=axis, dtype=dtype, out=out, ddof=ddof)\\n # Some data are masked, yay!\\n cnt = self.count(axis=axis) - ddof\\n danom = self.anom(axis=axis, dtype=dtype)\\n if iscomplexobj(self):\\n danom = umath.absolute(danom) ** 2\\n else:\\n danom *= danom\\n dvar = divide(danom.sum(axis), cnt).view(type(self))\\n # Apply the mask if it's not a scalar\\n if dvar.ndim:\\n dvar._mask = mask_or(self._mask.all(axis), (cnt <= 0))\\n dvar._update_from(self)\\n elif getattr(dvar, '_mask', False):\\n # Make sure that masked is returned when the scalar is masked.\\n dvar = masked\\n if out is not None:\\n if isinstance(out, MaskedArray):\\n out.flat = 0\\n out.__setmask__(True)\\n elif out.dtype.kind in 'biu':\\n errmsg = \\\"Masked data information would be lost in one or \\\"\\\\\\n \\\"more location.\\\"\\n raise MaskError(errmsg)\\n else:\\n out.flat = np.nan\\n return out\\n # In case with have an explicit output\\n if out is not None:\\n # Set the data\\n out.flat = dvar\\n # Set the mask if needed\\n if isinstance(out, MaskedArray):\\n out.__setmask__(dvar.mask)\\n return out\\n return dvar\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.std\",\n \"text\": \"numpy/ma/core.py::MaskedArray.std\\n def std(self, axis=None, dtype=None, out=None, ddof=0):\\n \\\"\\\"\\n dvar = self.var(axis=axis, dtype=dtype, out=out, ddof=ddof)\\n if dvar is not masked:\\n if out is not None:\\n np.power(out, 0.5, out=out, casting='unsafe')\\n return out\\n dvar = sqrt(dvar)\\n return dvar\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.round\",\n \"text\": \"numpy/ma/core.py::MaskedArray.round\\n def round(self, decimals=0, out=None):\\n \\\"\\\"\\\"\\n Return an array rounded a to the given number of decimals.\\n\\n Refer to `numpy.around` for full documentation.\\n\\n See Also\\n --------\\n numpy.around : equivalent function\\n\\n \\\"\\\"\\\"\\n result = self._data.round(decimals=decimals, out=out).view(type(self))\\n result._mask = self._mask\\n result._update_from(self)\\n # No explicit output: we're done\\n if out is None:\\n return result\\n if isinstance(out, MaskedArray):\\n out.__setmask__(self._mask)\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.argsort\",\n \"text\": \"numpy/ma/core.py::MaskedArray.argsort\\n def argsort(self, axis=None, kind='quicksort', order=None, fill_value=None):\\n \\\"\\\"\\\"\\n Return an ndarray of indices that sort the array along the\\n specified axis. Masked values are filled beforehand to\\n `fill_value`.\\n\\n Parameters\\n ----------\\n axis : int, optional\\n Axis along which to sort. The default is -1 (last axis).\\n If None, the flattened array is used.\\n fill_value : var, optional\\n Value used to fill the array before sorting.\\n The default is the `fill_value` attribute of the input array.\\n kind : {'quicksort', 'mergesort', 'heapsort'}, optional\\n Sorting algorithm.\\n order : list, optional\\n When `a` is an array with fields defined, this argument specifies\\n which fields to compare first, second, etc. Not all fields need be\\n specified.\\n\\n Returns\\n -------\\n index_array : ndarray, int\\n Array of indices that sort `a` along the specified axis.\\n In other words, ``a[index_array]`` yields a sorted `a`.\\n\\n See Also\\n --------\\n sort : Describes sorting algorithms used.\\n lexsort : Indirect stable sort with multiple keys.\\n ndarray.sort : Inplace sort.\\n\\n Notes\\n -----\\n See `sort` for notes on the different sorting algorithms.\\n\\n Examples\\n --------\\n >>> a = np.ma.array([3,2,1], mask=[False, False, True])\\n >>> a\\n masked_array(data = [3 2 --],\\n mask = [False False True],\\n fill_value = 999999)\\n >>> a.argsort()\\n array([1, 0, 2])\\n\\n \\\"\\\"\\\"\\n if fill_value is None:\\n fill_value = default_fill_value(self)\\n d = self.filled(fill_value).view(ndarray)\\n return d.argsort(axis=axis, kind=kind, order=order)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.argmin\",\n \"text\": \"numpy/ma/core.py::MaskedArray.argmin\\n def argmin(self, axis=None, fill_value=None, out=None):\\n \\\"\\\"\\\"\\n Return array of indices to the minimum values along the given axis.\\n\\n Parameters\\n ----------\\n axis : {None, integer}\\n If None, the index is into the flattened array, otherwise along\\n the specified axis\\n fill_value : {var}, optional\\n Value used to fill in the masked values. If None, the output of\\n minimum_fill_value(self._data) is used instead.\\n out : {None, array}, optional\\n Array into which the result can be placed. Its type is preserved\\n and it must be of the right shape to hold the output.\\n\\n Returns\\n -------\\n {ndarray, scalar}\\n If multi-dimension input, returns a new ndarray of indices to the\\n minimum values along the given axis. Otherwise, returns a scalar\\n of index to the minimum values along the given axis.\\n\\n Examples\\n --------\\n >>> x = np.ma.array(arange(4), mask=[1,1,0,0])\\n >>> x.shape = (2,2)\\n >>> print x\\n [[-- --]\\n [2 3]]\\n >>> print x.argmin(axis=0, fill_value=-1)\\n [0 0]\\n >>> print x.argmin(axis=0, fill_value=9)\\n [1 1]\\n\\n \\\"\\\"\\\"\\n if fill_value is None:\\n fill_value = minimum_fill_value(self)\\n d = self.filled(fill_value).view(ndarray)\\n return d.argmin(axis, out=out)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.argmax\",\n \"text\": \"numpy/ma/core.py::MaskedArray.argmax\\n def argmax(self, axis=None, fill_value=None, out=None):\\n \\\"\\\"\\\"\\n Returns array of indices of the maximum values along the given axis.\\n Masked values are treated as if they had the value fill_value.\\n\\n Parameters\\n ----------\\n axis : {None, integer}\\n If None, the index is into the flattened array, otherwise along\\n the specified axis\\n fill_value : {var}, optional\\n Value used to fill in the masked values. If None, the output of\\n maximum_fill_value(self._data) is used instead.\\n out : {None, array}, optional\\n Array into which the result can be placed. Its type is preserved\\n and it must be of the right shape to hold the output.\\n\\n Returns\\n -------\\n index_array : {integer_array}\\n\\n Examples\\n --------\\n >>> a = np.arange(6).reshape(2,3)\\n >>> a.argmax()\\n 5\\n >>> a.argmax(0)\\n array([1, 1, 1])\\n >>> a.argmax(1)\\n array([2, 2])\\n\\n \\\"\\\"\\\"\\n if fill_value is None:\\n fill_value = maximum_fill_value(self._data)\\n d = self.filled(fill_value).view(ndarray)\\n return d.argmax(axis, out=out)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.sort\",\n \"text\": \"numpy/ma/core.py::MaskedArray.sort\\n def sort(self, axis= -1, kind='quicksort', order=None,\\n endwith=True, fill_value=None):\\n \\\"\\\"\\\"\\n Sort the array, in-place\\n\\n Parameters\\n ----------\\n a : array_like\\n Array to be sorted.\\n axis : int, optional\\n Axis along which to sort. If None, the array is flattened before\\n sorting. The default is -1, which sorts along the last axis.\\n kind : {'quicksort', 'mergesort', 'heapsort'}, optional\\n Sorting algorithm. Default is 'quicksort'.\\n order : list, optional\\n When `a` is a structured array, this argument specifies which fields\\n to compare first, second, and so on. This list does not need to\\n include all of the fields.\\n endwith : {True, False}, optional\\n Whether missing values (if any) should be forced in the upper indices\\n (at the end of the array) (True) or lower indices (at the beginning).\\n When the array contains unmasked values of the largest (or smallest if\\n False) representable value of the datatype the ordering of these values\\n and the masked values is undefined. To enforce the masked values are\\n at the end (beginning) in this case one must sort the mask.\\n fill_value : {var}, optional\\n Value used internally for the masked values.\\n If ``fill_value`` is not None, it supersedes ``endwith``.\\n\\n Returns\\n -------\\n sorted_array : ndarray\\n Array of the same type and shape as `a`.\\n\\n See Also\\n --------\\n ndarray.sort : Method to sort an array in-place.\\n argsort : Indirect sort.\\n lexsort : Indirect stable sort on multiple keys.\\n searchsorted : Find elements in a sorted array.\\n\\n Notes\\n -----\\n See ``sort`` for notes on the different sorting algorithms.\\n\\n Examples\\n --------\\n >>> a = ma.array([1, 2, 5, 4, 3],mask=[0, 1, 0, 1, 0])\\n >>> # Default\\n >>> a.sort()\\n >>> print a\\n [1 3 5 -- --]\\n\\n >>> a = ma.array([1, 2, 5, 4, 3],mask=[0,\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.min\",\n \"text\": \"numpy/ma/core.py::MaskedArray.min\\n def min(self, axis=None, out=None, fill_value=None):\\n \\\"\\\"\\\"\\n Return the minimum along a given axis.\\n\\n Parameters\\n ----------\\n axis : {None, int}, optional\\n Axis along which to operate. By default, ``axis`` is None and the\\n flattened input is used.\\n out : array_like, optional\\n Alternative output array in which to place the result. Must be of\\n the same shape and buffer length as the expected output.\\n fill_value : {var}, optional\\n Value used to fill in the masked values.\\n If None, use the output of `minimum_fill_value`.\\n\\n Returns\\n -------\\n amin : array_like\\n New array holding the result.\\n If ``out`` was specified, ``out`` is returned.\\n\\n See Also\\n --------\\n minimum_fill_value\\n Returns the minimum filling value for a given datatype.\\n\\n \\\"\\\"\\\"\\n _mask = ndarray.__getattribute__(self, '_mask')\\n newmask = _check_mask_axis(_mask, axis)\\n if fill_value is None:\\n fill_value = minimum_fill_value(self)\\n # No explicit output\\n if out is None:\\n result = self.filled(fill_value).min(axis=axis, out=out).view(type(self))\\n if result.ndim:\\n # Set the mask\\n result.__setmask__(newmask)\\n # Get rid of Infs\\n if newmask.ndim:\\n np.copyto(result, result.fill_value, where=newmask)\\n elif newmask:\\n result = masked\\n return result\\n # Explicit output\\n result = self.filled(fill_value).min(axis=axis, out=out)\\n if isinstance(out, MaskedArray):\\n outmask = getattr(out, '_mask', nomask)\\n if (outmask is nomask):\\n outmask = out._mask = make_mask_none(out.shape)\\n outmask.flat = newmask\\n else:\\n if out.dtype.kind in 'biu':\\n errmsg = \\\"Masked data information would be lost in one or more\\\"\\\\\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.mini\",\n \"text\": \"numpy/ma/core.py::MaskedArray.mini\\n def mini(self, axis=None):\\n \\\"\\\"\\\"\\n Return the array minimum along the specified axis.\\n\\n Parameters\\n ----------\\n axis : int, optional\\n The axis along which to find the minima. Default is None, in which case\\n the minimum value in the whole array is returned.\\n\\n Returns\\n -------\\n min : scalar or MaskedArray\\n If `axis` is None, the result is a scalar. Otherwise, if `axis` is\\n given and the array is at least 2-D, the result is a masked array with\\n dimension one smaller than the array on which `mini` is called.\\n\\n Examples\\n --------\\n >>> x = np.ma.array(np.arange(6), mask=[0 ,1, 0, 0, 0 ,1]).reshape(3, 2)\\n >>> print x\\n [[0 --]\\n [2 3]\\n [4 --]]\\n >>> x.mini()\\n 0\\n >>> x.mini(axis=0)\\n masked_array(data = [0 3],\\n mask = [False False],\\n fill_value = 999999)\\n >>> print x.mini(axis=1)\\n [0 2 4]\\n\\n \\\"\\\"\\\"\\n if axis is None:\\n return minimum(self)\\n else:\\n return minimum.reduce(self, axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.max\",\n \"text\": \"numpy/ma/core.py::MaskedArray.max\\n def max(self, axis=None, out=None, fill_value=None):\\n \\\"\\\"\\\"\\n Return the maximum along a given axis.\\n\\n Parameters\\n ----------\\n axis : {None, int}, optional\\n Axis along which to operate. By default, ``axis`` is None and the\\n flattened input is used.\\n out : array_like, optional\\n Alternative output array in which to place the result. Must\\n be of the same shape and buffer length as the expected output.\\n fill_value : {var}, optional\\n Value used to fill in the masked values.\\n If None, use the output of maximum_fill_value().\\n\\n Returns\\n -------\\n amax : array_like\\n New array holding the result.\\n If ``out`` was specified, ``out`` is returned.\\n\\n See Also\\n --------\\n maximum_fill_value\\n Returns the maximum filling value for a given datatype.\\n\\n \\\"\\\"\\\"\\n _mask = ndarray.__getattribute__(self, '_mask')\\n newmask = _check_mask_axis(_mask, axis)\\n if fill_value is None:\\n fill_value = maximum_fill_value(self)\\n # No explicit output\\n if out is None:\\n result = self.filled(fill_value).max(axis=axis, out=out).view(type(self))\\n if result.ndim:\\n # Set the mask\\n result.__setmask__(newmask)\\n # Get rid of Infs\\n if newmask.ndim:\\n np.copyto(result, result.fill_value, where=newmask)\\n elif newmask:\\n result = masked\\n return result\\n # Explicit output\\n result = self.filled(fill_value).max(axis=axis, out=out)\\n if isinstance(out, MaskedArray):\\n outmask = getattr(out, '_mask', nomask)\\n if (outmask is nomask):\\n outmask = out._mask = make_mask_none(out.shape)\\n outmask.flat = newmask\\n else:\\n\\n if out.dtype.kind in 'biu\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.ptp\",\n \"text\": \"numpy/ma/core.py::MaskedArray.ptp\\n def ptp(self, axis=None, out=None, fill_value=None):\\n \\\"\\\"\\\"\\n Return (maximum - minimum) along the the given dimension\\n (i.e. peak-to-peak value).\\n\\n Parameters\\n ----------\\n axis : {None, int}, optional\\n Axis along which to find the peaks. If None (default) the\\n flattened array is used.\\n out : {None, array_like}, optional\\n Alternative output array in which to place the result. It must\\n have the same shape and buffer length as the expected output\\n but the type will be cast if necessary.\\n fill_value : {var}, optional\\n Value used to fill in the masked values.\\n\\n Returns\\n -------\\n ptp : ndarray.\\n A new array holding the result, unless ``out`` was\\n specified, in which case a reference to ``out`` is returned.\\n\\n \\\"\\\"\\\"\\n if out is None:\\n result = self.max(axis=axis, fill_value=fill_value)\\n result -= self.min(axis=axis, fill_value=fill_value)\\n return result\\n out.flat = self.max(axis=axis, out=out, fill_value=fill_value)\\n min_value = self.min(axis=axis, fill_value=fill_value)\\n np.subtract(out, min_value, out=out, casting='unsafe')\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.take\",\n \"text\": \"numpy/ma/core.py::MaskedArray.take\\n def take(self, indices, axis=None, out=None, mode='raise'):\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n (_data, _mask) = (self._data, self._mask)\\n cls = type(self)\\n # Make sure the indices are not masked\\n maskindices = getattr(indices, '_mask', nomask)\\n if maskindices is not nomask:\\n indices = indices.filled(0)\\n # Get the data\\n if out is None:\\n out = _data.take(indices, axis=axis, mode=mode).view(cls)\\n else:\\n np.take(_data, indices, axis=axis, mode=mode, out=out)\\n # Get the mask\\n if isinstance(out, MaskedArray):\\n if _mask is nomask:\\n outmask = maskindices\\n else:\\n outmask = _mask.take(indices, axis=axis, mode=mode)\\n outmask |= maskindices\\n out.__setmask__(outmask)\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.tolist\",\n \"text\": \"numpy/ma/core.py::MaskedArray.tolist\\n def tolist(self, fill_value=None):\\n \\\"\\\"\\\"\\n Return the data portion of the masked array as a hierarchical Python list.\\n\\n Data items are converted to the nearest compatible Python type.\\n Masked values are converted to `fill_value`. If `fill_value` is None,\\n the corresponding entries in the output list will be ``None``.\\n\\n Parameters\\n ----------\\n fill_value : scalar, optional\\n The value to use for invalid entries. Default is None.\\n\\n Returns\\n -------\\n result : list\\n The Python list representation of the masked array.\\n\\n Examples\\n --------\\n >>> x = np.ma.array([[1,2,3], [4,5,6], [7,8,9]], mask=[0] + [1,0]*4)\\n >>> x.tolist()\\n [[1, None, 3], [None, 5, None], [7, None, 9]]\\n >>> x.tolist(-999)\\n [[1, -999, 3], [-999, 5, -999], [7, -999, 9]]\\n\\n \\\"\\\"\\\"\\n _mask = self._mask\\n # No mask ? Just return .data.tolist ?\\n if _mask is nomask:\\n return self._data.tolist()\\n # Explicit fill_value: fill the array and get the list\\n if fill_value is not None:\\n return self.filled(fill_value).tolist()\\n # Structured array .............\\n names = self.dtype.names\\n if names:\\n result = self._data.astype([(_, object) for _ in names])\\n for n in names:\\n result[n][_mask[n]] = None\\n return result.tolist()\\n # Standard arrays ...............\\n if _mask is nomask:\\n return [None]\\n # Set temps to save time when dealing w/ marrays...\\n inishape = self.shape\\n result = np.array(self._data.ravel(), dtype=object)\\n result[_mask.ravel()] = None\\n result.shape = inishape\\n return result.tolist()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.tostring\",\n \"text\": \"numpy/ma/core.py::MaskedArray.tostring\\n def tostring(self, fill_value=None, order='C'):\\n \\\"\\\"\\\"\\n This function is a compatibility alias for tobytes. Despite its name it\\n returns bytes not strings.\\n \\\"\\\"\\\"\\n\\n return self.tobytes(fill_value, order='C')\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.tobytes\",\n \"text\": \"numpy/ma/core.py::MaskedArray.tobytes\\n def tobytes(self, fill_value=None, order='C'):\\n \\\"\\\"\\\"\\n Return the array data as a string containing the raw bytes in the array.\\n\\n The array is filled with a fill value before the string conversion.\\n\\n .. versionadded:: 1.9.0\\n\\n Parameters\\n ----------\\n fill_value : scalar, optional\\n Value used to fill in the masked values. Deafult is None, in which\\n case `MaskedArray.fill_value` is used.\\n order : {'C','F','A'}, optional\\n Order of the data item in the copy. Default is 'C'.\\n\\n - 'C' -- C order (row major).\\n - 'F' -- Fortran order (column major).\\n - 'A' -- Any, current order of array.\\n - None -- Same as 'A'.\\n\\n See Also\\n --------\\n ndarray.tobytes\\n tolist, tofile\\n\\n Notes\\n -----\\n As for `ndarray.tobytes`, information about the shape, dtype, etc.,\\n but also about `fill_value`, will be lost.\\n\\n Examples\\n --------\\n >>> x = np.ma.array(np.array([[1, 2], [3, 4]]), mask=[[0, 1], [1, 0]])\\n >>> x.tobytes()\\n '\\\\\\\\x01\\\\\\\\x00\\\\\\\\x00\\\\\\\\x00?B\\\\\\\\x0f\\\\\\\\x00?B\\\\\\\\x0f\\\\\\\\x00\\\\\\\\x04\\\\\\\\x00\\\\\\\\x00\\\\\\\\x00'\\n\\n \\\"\\\"\\\"\\n return self.filled(fill_value).tobytes(order=order)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.tofile\",\n \"text\": \"numpy/ma/core.py::MaskedArray.tofile\\n def tofile(self, fid, sep=\\\"\\\", format=\\\"%s\\\"):\\n \\\"\\\"\\\"\\n Save a masked array to a file in binary format.\\n\\n .. warning::\\n This function is not implemented yet.\\n\\n Raises\\n ------\\n NotImplementedError\\n When `tofile` is called.\\n\\n \\\"\\\"\\\"\\n raise NotImplementedError(\\\"Not implemented yet, sorry...\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.toflex\",\n \"text\": \"numpy/ma/core.py::MaskedArray.toflex\\n def toflex(self):\\n \\\"\\\"\\\"\\n Transforms a masked array into a flexible-type array.\\n\\n The flexible type array that is returned will have two fields:\\n\\n * the ``_data`` field stores the ``_data`` part of the array.\\n * the ``_mask`` field stores the ``_mask`` part of the array.\\n\\n Parameters\\n ----------\\n None\\n\\n Returns\\n -------\\n record : ndarray\\n A new flexible-type `ndarray` with two fields: the first element\\n containing a value, the second element containing the corresponding\\n mask boolean. The returned record shape matches self.shape.\\n\\n Notes\\n -----\\n A side-effect of transforming a masked array into a flexible `ndarray` is\\n that meta information (``fill_value``, ...) will be lost.\\n\\n Examples\\n --------\\n >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)\\n >>> print x\\n [[1 -- 3]\\n [-- 5 --]\\n [7 -- 9]]\\n >>> print x.toflex()\\n [[(1, False) (2, True) (3, False)]\\n [(4, True) (5, False) (6, True)]\\n [(7, False) (8, True) (9, False)]]\\n\\n \\\"\\\"\\\"\\n # Get the basic dtype ....\\n ddtype = self.dtype\\n # Make sure we have a mask\\n _mask = self._mask\\n if _mask is None:\\n _mask = make_mask_none(self.shape, ddtype)\\n # And get its dtype\\n mdtype = self._mask.dtype\\n #\\n record = np.ndarray(shape=self.shape,\\n dtype=[('_data', ddtype), ('_mask', mdtype)])\\n record['_data'] = self._data\\n record['_mask'] = self._mask\\n return record\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__getstate__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__getstate__\\n def __getstate__(self):\\n \\\"\\\"\\\"Return the internal state of the masked array, for pickling\\n purposes.\\n\\n \\\"\\\"\\\"\\n cf = 'CF'[self.flags.fnc]\\n state = (1,\\n self.shape,\\n self.dtype,\\n self.flags.fnc,\\n self._data.tobytes(cf),\\n #self._data.tolist(),\\n getmaskarray(self).tobytes(cf),\\n #getmaskarray(self).tolist(),\\n self._fill_value,\\n )\\n return state\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__setstate__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__setstate__\\n def __setstate__(self, state):\\n \\\"\\\"\\\"Restore the internal state of the masked array, for\\n pickling purposes. ``state`` is typically the output of the\\n ``__getstate__`` output, and is a 5-tuple:\\n\\n - class name\\n - a tuple giving the shape of the data\\n - a typecode for the data\\n - a binary string for the data\\n - a binary string for the mask.\\n\\n \\\"\\\"\\\"\\n (_, shp, typ, isf, raw, msk, flv) = state\\n ndarray.__setstate__(self, (shp, typ, isf, raw))\\n self._mask.__setstate__((shp, make_mask_descr(typ), isf, msk))\\n self.fill_value = flv\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__reduce__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__reduce__\\n def __reduce__(self):\\n \\\"\\\"\\\"Return a 3-tuple for pickling a MaskedArray.\\n\\n \\\"\\\"\\\"\\n return (_mareconstruct,\\n (self.__class__, self._baseclass, (0,), 'b',),\\n self.__getstate__())\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedArray.__deepcopy__\",\n \"text\": \"numpy/ma/core.py::MaskedArray.__deepcopy__\\n def __deepcopy__(self, memo=None):\\n from copy import deepcopy\\n copied = MaskedArray.__new__(type(self), self, copy=True)\\n if memo is None:\\n memo = {}\\n memo[id(self)] = copied\\n for (k, v) in self.__dict__.items():\\n copied.__dict__[k] = deepcopy(v, memo)\\n return copied\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_mareconstruct\",\n \"text\": \"numpy/ma/core.py::_mareconstruct\\ndef _mareconstruct(subtype, baseclass, baseshape, basetype,):\\n \\\"\\\"\\\"Internal function that builds a new MaskedArray from the\\n information stored in a pickle.\\n\\n \\\"\\\"\\\"\\n _data = ndarray.__new__(baseclass, baseshape, basetype)\\n _mask = ndarray.__new__(ndarray, baseshape, make_mask_descr(basetype))\\n return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype,)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::mvoid.__new__\",\n \"text\": \"numpy/ma/core.py::mvoid.__new__\\n def __new__(self, data, mask=nomask, dtype=None, fill_value=None,\\n hardmask=False, copy=False, subok=True):\\n _data = np.array(data, copy=copy, subok=subok, dtype=dtype)\\n _data = _data.view(self)\\n _data._hardmask = hardmask\\n if mask is not nomask:\\n if isinstance(mask, np.void):\\n _data._mask = mask\\n else:\\n try:\\n # Mask is already a 0D array\\n _data._mask = np.void(mask)\\n except TypeError:\\n # Transform the mask to a void\\n mdtype = make_mask_descr(dtype)\\n _data._mask = np.array(mask, dtype=mdtype)[()]\\n if fill_value is not None:\\n _data.fill_value = fill_value\\n return _data\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::mvoid._get_data\",\n \"text\": \"numpy/ma/core.py::mvoid._get_data\\n def _get_data(self):\\n # Make sure that the _data part is a np.void\\n return self.view(ndarray)[()]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::mvoid.__getitem__\",\n \"text\": \"numpy/ma/core.py::mvoid.__getitem__\\n def __getitem__(self, indx):\\n \\\"Get the index...\\\"\\n m = self._mask\\n if m is not nomask and m[indx]:\\n return masked\\n return self._data[indx]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::mvoid.__setitem__\",\n \"text\": \"numpy/ma/core.py::mvoid.__setitem__\\n def __setitem__(self, indx, value):\\n self._data[indx] = value\\n if self._hardmask:\\n self._mask[indx] |= getattr(value, \\\"_mask\\\", False)\\n else:\\n self._mask[indx] = getattr(value, \\\"_mask\\\", False)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::mvoid.__str__\",\n \"text\": \"numpy/ma/core.py::mvoid.__str__\\n def __str__(self):\\n m = self._mask\\n if (m is nomask):\\n return self._data.__str__()\\n m = tuple(m)\\n if (not any(m)):\\n return self._data.__str__()\\n r = self._data.tolist()\\n p = masked_print_option\\n if not p.enabled():\\n p = 'N/A'\\n else:\\n p = str(p)\\n r = [(str(_), p)[int(_m)] for (_, _m) in zip(r, m)]\\n return \\\"(%s)\\\" % \\\", \\\".join(r)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::mvoid.__repr__\",\n \"text\": \"numpy/ma/core.py::mvoid.__repr__\\n def __repr__(self):\\n m = self._mask\\n if (m is nomask):\\n return self._data.__repr__()\\n m = tuple(m)\\n if not any(m):\\n return self._data.__repr__()\\n p = masked_print_option\\n if not p.enabled():\\n return self.filled(self.fill_value).__repr__()\\n p = str(p)\\n r = [(str(_), p)[int(_m)] for (_, _m) in zip(self._data.tolist(), m)]\\n return \\\"(%s)\\\" % \\\", \\\".join(r)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::mvoid.__iter__\",\n \"text\": \"numpy/ma/core.py::mvoid.__iter__\\n def __iter__(self):\\n \\\"Defines an iterator for mvoid\\\"\\n (_data, _mask) = (self._data, self._mask)\\n if _mask is nomask:\\n for d in _data:\\n yield d\\n else:\\n for (d, m) in zip(_data, _mask):\\n if m:\\n yield masked\\n else:\\n yield d\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::mvoid.__len__\",\n \"text\": \"numpy/ma/core.py::mvoid.__len__\\n def __len__(self):\\n return self._data.__len__()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::mvoid.filled\",\n \"text\": \"numpy/ma/core.py::mvoid.filled\\n def filled(self, fill_value=None):\\n \\\"\\\"\\\"\\n Return a copy with masked fields filled with a given value.\\n\\n Parameters\\n ----------\\n fill_value : scalar, optional\\n The value to use for invalid entries (None by default).\\n If None, the `fill_value` attribute is used instead.\\n\\n Returns\\n -------\\n filled_void\\n A `np.void` object\\n\\n See Also\\n --------\\n MaskedArray.filled\\n\\n \\\"\\\"\\\"\\n return asarray(self).filled(fill_value)[()]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::mvoid.tolist\",\n \"text\": \"numpy/ma/core.py::mvoid.tolist\\n def tolist(self):\\n \\\"\\\"\\\"\\n Transforms the mvoid object into a tuple.\\n\\n Masked fields are replaced by None.\\n\\n Returns\\n -------\\n returned_tuple\\n Tuple of fields\\n \\\"\\\"\\\"\\n _mask = self._mask\\n if _mask is nomask:\\n return self._data.tolist()\\n result = []\\n for (d, m) in zip(self._data, self._mask):\\n if m:\\n result.append(None)\\n else:\\n # .item() makes sure we return a standard Python object\\n result.append(d.item())\\n return tuple(result)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::isMaskedArray\",\n \"text\": \"numpy/ma/core.py::isMaskedArray\\ndef isMaskedArray(x):\\n \\\"\\\"\\\"\\n Test whether input is an instance of MaskedArray.\\n\\n This function returns True if `x` is an instance of MaskedArray\\n and returns False otherwise. Any object is accepted as input.\\n\\n Parameters\\n ----------\\n x : object\\n Object to test.\\n\\n Returns\\n -------\\n result : bool\\n True if `x` is a MaskedArray.\\n\\n See Also\\n --------\\n isMA : Alias to isMaskedArray.\\n isarray : Alias to isMaskedArray.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = np.eye(3, 3)\\n >>> a\\n array([[ 1., 0., 0.],\\n [ 0., 1., 0.],\\n [ 0., 0., 1.]])\\n >>> m = ma.masked_values(a, 0)\\n >>> m\\n masked_array(data =\\n [[1.0 -- --]\\n [-- 1.0 --]\\n [-- -- 1.0]],\\n mask =\\n [[False True True]\\n [ True False True]\\n [ True True False]],\\n fill_value=0.0)\\n >>> ma.isMaskedArray(a)\\n False\\n >>> ma.isMaskedArray(m)\\n True\\n >>> ma.isMaskedArray([0, 1, 2])\\n False\\n\\n \\\"\\\"\\\"\\n return isinstance(x, MaskedArray)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedConstant.__new__\",\n \"text\": \"numpy/ma/core.py::MaskedConstant.__new__\\n def __new__(self):\\n return self._data.view(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedConstant.__array_finalize__\",\n \"text\": \"numpy/ma/core.py::MaskedConstant.__array_finalize__\\n def __array_finalize__(self, obj):\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedConstant.__array_wrap__\",\n \"text\": \"numpy/ma/core.py::MaskedConstant.__array_wrap__\\n def __array_wrap__(self, obj):\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedConstant.__str__\",\n \"text\": \"numpy/ma/core.py::MaskedConstant.__str__\\n def __str__(self):\\n return str(masked_print_option._display)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedConstant.__repr__\",\n \"text\": \"numpy/ma/core.py::MaskedConstant.__repr__\\n def __repr__(self):\\n return 'masked'\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedConstant.flatten\",\n \"text\": \"numpy/ma/core.py::MaskedConstant.flatten\\n def flatten(self):\\n return masked_array([self._data], dtype=float, mask=[True])\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::MaskedConstant.__reduce__\",\n \"text\": \"numpy/ma/core.py::MaskedConstant.__reduce__\\n def __reduce__(self):\\n \\\"\\\"\\\"Override of MaskedArray's __reduce__.\\n \\\"\\\"\\\"\\n return (self.__class__, ())\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::array\",\n \"text\": \"numpy/ma/core.py::array\\ndef array(data, dtype=None, copy=False, order=False,\\n mask=nomask, fill_value=None,\\n keep_mask=True, hard_mask=False, shrink=True, subok=True, ndmin=0,\\n ):\\n \\\"\\\"\\\"array(data, dtype=None, copy=False, order=False, mask=nomask,\\n fill_value=None, keep_mask=True, hard_mask=False, shrink=True,\\n subok=True, ndmin=0)\\n\\n Acts as shortcut to MaskedArray, with options in a different order\\n for convenience. And backwards compatibility...\\n\\n \\\"\\\"\\\"\\n #!!!: we should try to put 'order' somwehere\\n return MaskedArray(data, mask=mask, dtype=dtype, copy=copy, subok=subok,\\n keep_mask=keep_mask, hard_mask=hard_mask,\\n fill_value=fill_value, ndmin=ndmin, shrink=shrink)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::is_masked\",\n \"text\": \"numpy/ma/core.py::is_masked\\ndef is_masked(x):\\n \\\"\\\"\\\"\\n Determine whether input has masked values.\\n\\n Accepts any object as input, but always returns False unless the\\n input is a MaskedArray containing masked values.\\n\\n Parameters\\n ----------\\n x : array_like\\n Array to check for masked values.\\n\\n Returns\\n -------\\n result : bool\\n True if `x` is a MaskedArray with masked values, False otherwise.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> x = ma.masked_equal([0, 1, 0, 2, 3], 0)\\n >>> x\\n masked_array(data = [-- 1 -- 2 3],\\n mask = [ True False True False False],\\n fill_value=999999)\\n >>> ma.is_masked(x)\\n True\\n >>> x = ma.masked_equal([0, 1, 0, 2, 3], 42)\\n >>> x\\n masked_array(data = [0 1 0 2 3],\\n mask = False,\\n fill_value=999999)\\n >>> ma.is_masked(x)\\n False\\n\\n Always returns False if `x` isn't a MaskedArray.\\n\\n >>> x = [False, True, False]\\n >>> ma.is_masked(x)\\n False\\n >>> x = 'a string'\\n >>> ma.is_masked(x)\\n False\\n\\n \\\"\\\"\\\"\\n m = getmask(x)\\n if m is nomask:\\n return False\\n elif m.any():\\n return True\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_extrema_operation.__call__\",\n \"text\": \"numpy/ma/core.py::_extrema_operation.__call__\\n def __call__(self, a, b=None):\\n \\\"Executes the call behavior.\\\"\\n if b is None:\\n return self.reduce(a)\\n return where(self.compare(a, b), a, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_extrema_operation.reduce\",\n \"text\": \"numpy/ma/core.py::_extrema_operation.reduce\\n def reduce(self, target, axis=None):\\n \\\"Reduce target along the given axis.\\\"\\n target = narray(target, copy=False, subok=True)\\n m = getmask(target)\\n if axis is not None:\\n kargs = { 'axis' : axis }\\n else:\\n kargs = {}\\n target = target.ravel()\\n if not (m is nomask):\\n m = m.ravel()\\n if m is nomask:\\n t = self.ufunc.reduce(target, **kargs)\\n else:\\n target = target.filled(self.fill_value_func(target)).view(type(target))\\n t = self.ufunc.reduce(target, **kargs)\\n m = umath.logical_and.reduce(m, **kargs)\\n if hasattr(t, '_mask'):\\n t._mask = m\\n elif m:\\n t = masked\\n return t\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_extrema_operation.outer\",\n \"text\": \"numpy/ma/core.py::_extrema_operation.outer\\n def outer (self, a, b):\\n \\\"Return the function applied to the outer product of a and b.\\\"\\n ma = getmask(a)\\n mb = getmask(b)\\n if ma is nomask and mb is nomask:\\n m = nomask\\n else:\\n ma = getmaskarray(a)\\n mb = getmaskarray(b)\\n m = logical_or.outer(ma, mb)\\n result = self.ufunc.outer(filled(a), filled(b))\\n if not isinstance(result, MaskedArray):\\n result = result.view(MaskedArray)\\n result._mask = m\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_minimum_operation.__init__\",\n \"text\": \"numpy/ma/core.py::_minimum_operation.__init__\\n def __init__ (self):\\n \\\"\\\"\\\"minimum(a, b) or minimum(a)\\nIn one argument case, returns the scalar minimum.\\n \\\"\\\"\\\"\\n self.ufunc = umath.minimum\\n self.afunc = amin\\n self.compare = less\\n self.fill_value_func = minimum_fill_value\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_maximum_operation.__init__\",\n \"text\": \"numpy/ma/core.py::_maximum_operation.__init__\\n def __init__ (self):\\n \\\"\\\"\\\"maximum(a, b) or maximum(a)\\n In one argument case returns the scalar maximum.\\n \\\"\\\"\\\"\\n self.ufunc = umath.maximum\\n self.afunc = amax\\n self.compare = greater\\n self.fill_value_func = maximum_fill_value\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::min\",\n \"text\": \"numpy/ma/core.py::min\\ndef min(obj, axis=None, out=None, fill_value=None):\\n try:\\n return obj.min(axis=axis, fill_value=fill_value, out=out)\\n except (AttributeError, TypeError):\\n # If obj doesn't have a min method,\\n # ...or if the method doesn't accept a fill_value argument\\n return asanyarray(obj).min(axis=axis, fill_value=fill_value, out=out)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::max\",\n \"text\": \"numpy/ma/core.py::max\\ndef max(obj, axis=None, out=None, fill_value=None):\\n try:\\n return obj.max(axis=axis, fill_value=fill_value, out=out)\\n except (AttributeError, TypeError):\\n # If obj doesn't have a max method,\\n # ...or if the method doesn't accept a fill_value argument\\n return asanyarray(obj).max(axis=axis, fill_value=fill_value, out=out)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::ptp\",\n \"text\": \"numpy/ma/core.py::ptp\\ndef ptp(obj, axis=None, out=None, fill_value=None):\\n \\\"\\\"\\\"a.ptp(axis=None) = a.max(axis)-a.min(axis)\\\"\\\"\\\"\\n try:\\n return obj.ptp(axis, out=out, fill_value=fill_value)\\n except (AttributeError, TypeError):\\n # If obj doesn't have a ptp method,\\n # ...or if the method doesn't accept a fill_value argument\\n return asanyarray(obj).ptp(axis=axis, fill_value=fill_value, out=out)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_frommethod.__init__\",\n \"text\": \"numpy/ma/core.py::_frommethod.__init__\\n def __init__(self, methodname, reversed=False):\\n self.__name__ = methodname\\n self.__doc__ = self.getdoc()\\n self.reversed = reversed\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_frommethod.getdoc\",\n \"text\": \"numpy/ma/core.py::_frommethod.getdoc\\n def getdoc(self):\\n \\\"Return the doc of the function (from the doc of the method).\\\"\\n meth = getattr(MaskedArray, self.__name__, None) or\\\\\\n getattr(np, self.__name__, None)\\n signature = self.__name__ + get_object_signature(meth)\\n if meth is not None:\\n doc = \\\"\\\"\\\" %s\\\\n%s\\\"\\\"\\\" % (signature, getattr(meth, '__doc__', None))\\n return doc\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_frommethod.__call__\",\n \"text\": \"numpy/ma/core.py::_frommethod.__call__\\n def __call__(self, a, *args, **params):\\n if self.reversed:\\n args = list(args)\\n arr = args[0]\\n args[0] = a\\n a = arr\\n # Get the method from the array (if possible)\\n method_name = self.__name__\\n method = getattr(a, method_name, None)\\n if method is not None:\\n return method(*args, **params)\\n # Still here ? Then a is not a MaskedArray\\n method = getattr(MaskedArray, method_name, None)\\n if method is not None:\\n return method(MaskedArray(a), *args, **params)\\n # Still here ? OK, let's call the corresponding np function\\n method = getattr(np, method_name)\\n return method(a, *args, **params)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::take\",\n \"text\": \"numpy/ma/core.py::take\\ndef take(a, indices, axis=None, out=None, mode='raise'):\\n \\\"\\\"\\\"\\n \\\"\\\"\\\"\\n a = masked_array(a)\\n return a.take(indices, axis=axis, out=out, mode=mode)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::power\",\n \"text\": \"numpy/ma/core.py::power\\ndef power(a, b, third=None):\\n \\\"\\\"\\\"\\n Returns element-wise base array raised to power from second array.\\n\\n This is the masked array version of `numpy.power`. For details see\\n `numpy.power`.\\n\\n See Also\\n --------\\n numpy.power\\n\\n Notes\\n -----\\n The *out* argument to `numpy.power` is not supported, `third` has to be\\n None.\\n\\n \\\"\\\"\\\"\\n if third is not None:\\n raise MaskError(\\\"3-argument power not supported.\\\")\\n # Get the masks\\n ma = getmask(a)\\n mb = getmask(b)\\n m = mask_or(ma, mb)\\n # Get the rawdata\\n fa = getdata(a)\\n fb = getdata(b)\\n # Get the type of the result (so that we preserve subclasses)\\n if isinstance(a, MaskedArray):\\n basetype = type(a)\\n else:\\n basetype = MaskedArray\\n # Get the result and view it as a (subclass of) MaskedArray\\n with np.errstate(divide='ignore', invalid='ignore'):\\n result = np.where(m, fa, umath.power(fa, fb)).view(basetype)\\n result._update_from(a)\\n # Find where we're in trouble w/ NaNs and Infs\\n invalid = np.logical_not(np.isfinite(result.view(ndarray)))\\n # Add the initial mask\\n if m is not nomask:\\n if not (result.ndim):\\n return masked\\n result._mask = np.logical_or(m, invalid)\\n # Fix the invalid parts\\n if invalid.any():\\n if not result.ndim:\\n return masked\\n elif result._mask is nomask:\\n result._mask = invalid\\n result._data[invalid] = result.fill_value\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::argsort\",\n \"text\": \"numpy/ma/core.py::argsort\\ndef argsort(a, axis=None, kind='quicksort', order=None, fill_value=None):\\n \\\"Function version of the eponymous method.\\\"\\n if fill_value is None:\\n fill_value = default_fill_value(a)\\n d = filled(a, fill_value)\\n if axis is None:\\n return d.argsort(kind=kind, order=order)\\n return d.argsort(axis, kind=kind, order=order)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::argmin\",\n \"text\": \"numpy/ma/core.py::argmin\\ndef argmin(a, axis=None, fill_value=None):\\n \\\"Function version of the eponymous method.\\\"\\n if fill_value is None:\\n fill_value = default_fill_value(a)\\n d = filled(a, fill_value)\\n return d.argmin(axis=axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::argmax\",\n \"text\": \"numpy/ma/core.py::argmax\\ndef argmax(a, axis=None, fill_value=None):\\n \\\"Function version of the eponymous method.\\\"\\n if fill_value is None:\\n fill_value = default_fill_value(a)\\n try:\\n fill_value = -fill_value\\n except:\\n pass\\n d = filled(a, fill_value)\\n return d.argmax(axis=axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::sort\",\n \"text\": \"numpy/ma/core.py::sort\\ndef sort(a, axis= -1, kind='quicksort', order=None, endwith=True, fill_value=None):\\n \\\"Function version of the eponymous method.\\\"\\n a = narray(a, copy=True, subok=True)\\n if axis is None:\\n a = a.flatten()\\n axis = 0\\n if fill_value is None:\\n if endwith:\\n # nan > inf\\n if np.issubdtype(a.dtype, np.floating):\\n filler = np.nan\\n else:\\n filler = minimum_fill_value(a)\\n else:\\n filler = maximum_fill_value(a)\\n else:\\n filler = fill_value\\n\\n sindx = filled(a, filler).argsort(axis=axis, kind=kind, order=order)\\n\\n # save meshgrid memory for 1d arrays\\n if a.ndim == 1:\\n indx = sindx\\n else:\\n indx = np.meshgrid(*[np.arange(x) for x in a.shape], sparse=True,\\n indexing='ij')\\n indx[axis] = sindx\\n return a[indx]\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::compressed\",\n \"text\": \"numpy/ma/core.py::compressed\\ndef compressed(x):\\n \\\"\\\"\\\"\\n Return all the non-masked data as a 1-D array.\\n\\n This function is equivalent to calling the \\\"compressed\\\" method of a\\n `MaskedArray`, see `MaskedArray.compressed` for details.\\n\\n See Also\\n --------\\n MaskedArray.compressed\\n Equivalent method.\\n\\n \\\"\\\"\\\"\\n if not isinstance(x, MaskedArray):\\n x = asanyarray(x)\\n return x.compressed()\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::concatenate\",\n \"text\": \"numpy/ma/core.py::concatenate\\ndef concatenate(arrays, axis=0):\\n \\\"\\\"\\\"\\n Concatenate a sequence of arrays along the given axis.\\n\\n Parameters\\n ----------\\n arrays : sequence of array_like\\n The arrays must have the same shape, except in the dimension\\n corresponding to `axis` (the first, by default).\\n axis : int, optional\\n The axis along which the arrays will be joined. Default is 0.\\n\\n Returns\\n -------\\n result : MaskedArray\\n The concatenated array with any masked entries preserved.\\n\\n See Also\\n --------\\n numpy.concatenate : Equivalent function in the top-level NumPy module.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = ma.arange(3)\\n >>> a[1] = ma.masked\\n >>> b = ma.arange(2, 5)\\n >>> a\\n masked_array(data = [0 -- 2],\\n mask = [False True False],\\n fill_value = 999999)\\n >>> b\\n masked_array(data = [2 3 4],\\n mask = False,\\n fill_value = 999999)\\n >>> ma.concatenate([a, b])\\n masked_array(data = [0 -- 2 2 3 4],\\n mask = [False True False False False False],\\n fill_value = 999999)\\n\\n \\\"\\\"\\\"\\n d = np.concatenate([getdata(a) for a in arrays], axis)\\n rcls = get_masked_subclass(*arrays)\\n data = d.view(rcls)\\n # Check whether one of the arrays has a non-empty mask...\\n for x in arrays:\\n if getmask(x) is not nomask:\\n break\\n else:\\n return data\\n # OK, so we have to concatenate the masks\\n dm = np.concatenate([getmaskarray(a) for a in arrays], axis)\\n # If we decide to keep a '_shrinkmask' option, we want to check that ...\\n # ... all of them are True, and then check for dm.any()\\n# shrink = numpy.logical_or.reduce([getattr(a,'_shrinkmask',True) for a in arrays])\\n# if shrink and not dm.any():\\n if not dm.dtype.fields and not dm.any():\\n data._mask = nomask\\n else:\\n data._mask = dm.reshape(d.shape)\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::count\",\n \"text\": \"numpy/ma/core.py::count\\ndef count(a, axis=None):\\n if isinstance(a, MaskedArray):\\n return a.count(axis)\\n return masked_array(a, copy=False).count(axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::diag\",\n \"text\": \"numpy/ma/core.py::diag\\ndef diag(v, k=0):\\n \\\"\\\"\\\"\\n Extract a diagonal or construct a diagonal array.\\n\\n This function is the equivalent of `numpy.diag` that takes masked\\n values into account, see `numpy.diag` for details.\\n\\n See Also\\n --------\\n numpy.diag : Equivalent function for ndarrays.\\n\\n \\\"\\\"\\\"\\n output = np.diag(v, k).view(MaskedArray)\\n if getmask(v) is not nomask:\\n output._mask = np.diag(v._mask, k)\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::expand_dims\",\n \"text\": \"numpy/ma/core.py::expand_dims\\ndef expand_dims(x, axis):\\n \\\"\\\"\\\"\\n Expand the shape of an array.\\n\\n Expands the shape of the array by including a new axis before the one\\n specified by the `axis` parameter. This function behaves the same as\\n `numpy.expand_dims` but preserves masked elements.\\n\\n See Also\\n --------\\n numpy.expand_dims : Equivalent function in top-level NumPy module.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> x = ma.array([1, 2, 4])\\n >>> x[1] = ma.masked\\n >>> x\\n masked_array(data = [1 -- 4],\\n mask = [False True False],\\n fill_value = 999999)\\n >>> np.expand_dims(x, axis=0)\\n array([[1, 2, 4]])\\n >>> ma.expand_dims(x, axis=0)\\n masked_array(data =\\n [[1 -- 4]],\\n mask =\\n [[False True False]],\\n fill_value = 999999)\\n\\n The same result can be achieved using slicing syntax with `np.newaxis`.\\n\\n >>> x[np.newaxis, :]\\n masked_array(data =\\n [[1 -- 4]],\\n mask =\\n [[False True False]],\\n fill_value = 999999)\\n\\n \\\"\\\"\\\"\\n result = n_expand_dims(x, axis)\\n if isinstance(x, MaskedArray):\\n new_shape = result.shape\\n result = x.view()\\n result.shape = new_shape\\n if result._mask is not nomask:\\n result._mask.shape = new_shape\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::left_shift\",\n \"text\": \"numpy/ma/core.py::left_shift\\ndef left_shift (a, n):\\n \\\"\\\"\\\"\\n Shift the bits of an integer to the left.\\n\\n This is the masked array version of `numpy.left_shift`, for details\\n see that function.\\n\\n See Also\\n --------\\n numpy.left_shift\\n\\n \\\"\\\"\\\"\\n m = getmask(a)\\n if m is nomask:\\n d = umath.left_shift(filled(a), n)\\n return masked_array(d)\\n else:\\n d = umath.left_shift(filled(a, 0), n)\\n return masked_array(d, mask=m)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::right_shift\",\n \"text\": \"numpy/ma/core.py::right_shift\\ndef right_shift (a, n):\\n \\\"\\\"\\\"\\n Shift the bits of an integer to the right.\\n\\n This is the masked array version of `numpy.right_shift`, for details\\n see that function.\\n\\n See Also\\n --------\\n numpy.right_shift\\n\\n \\\"\\\"\\\"\\n m = getmask(a)\\n if m is nomask:\\n d = umath.right_shift(filled(a), n)\\n return masked_array(d)\\n else:\\n d = umath.right_shift(filled(a, 0), n)\\n return masked_array(d, mask=m)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::put\",\n \"text\": \"numpy/ma/core.py::put\\ndef put(a, indices, values, mode='raise'):\\n \\\"\\\"\\\"\\n Set storage-indexed locations to corresponding values.\\n\\n This function is equivalent to `MaskedArray.put`, see that method\\n for details.\\n\\n See Also\\n --------\\n MaskedArray.put\\n\\n \\\"\\\"\\\"\\n # We can't use 'frommethod', the order of arguments is different\\n try:\\n return a.put(indices, values, mode=mode)\\n except AttributeError:\\n return narray(a, copy=False).put(indices, values, mode=mode)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::putmask\",\n \"text\": \"numpy/ma/core.py::putmask\\ndef putmask(a, mask, values): #, mode='raise'):\\n \\\"\\\"\\\"\\n Changes elements of an array based on conditional and input values.\\n\\n This is the masked array version of `numpy.putmask`, for details see\\n `numpy.putmask`.\\n\\n See Also\\n --------\\n numpy.putmask\\n\\n Notes\\n -----\\n Using a masked array as `values` will **not** transform a `ndarray` into\\n a `MaskedArray`.\\n\\n \\\"\\\"\\\"\\n # We can't use 'frommethod', the order of arguments is different\\n if not isinstance(a, MaskedArray):\\n a = a.view(MaskedArray)\\n (valdata, valmask) = (getdata(values), getmask(values))\\n if getmask(a) is nomask:\\n if valmask is not nomask:\\n a._sharedmask = True\\n a._mask = make_mask_none(a.shape, a.dtype)\\n np.copyto(a._mask, valmask, where=mask)\\n elif a._hardmask:\\n if valmask is not nomask:\\n m = a._mask.copy()\\n np.copyto(m, valmask, where=mask)\\n a.mask |= m\\n else:\\n if valmask is nomask:\\n valmask = getmaskarray(values)\\n np.copyto(a._mask, valmask, where=mask)\\n np.copyto(a._data, valdata, where=mask)\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::transpose\",\n \"text\": \"numpy/ma/core.py::transpose\\ndef transpose(a, axes=None):\\n \\\"\\\"\\\"\\n Permute the dimensions of an array.\\n\\n This function is exactly equivalent to `numpy.transpose`.\\n\\n See Also\\n --------\\n numpy.transpose : Equivalent function in top-level NumPy module.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> x = ma.arange(4).reshape((2,2))\\n >>> x[1, 1] = ma.masked\\n >>>> x\\n masked_array(data =\\n [[0 1]\\n [2 --]],\\n mask =\\n [[False False]\\n [False True]],\\n fill_value = 999999)\\n >>> ma.transpose(x)\\n masked_array(data =\\n [[0 2]\\n [1 --]],\\n mask =\\n [[False False]\\n [False True]],\\n fill_value = 999999)\\n\\n \\\"\\\"\\\"\\n #We can't use 'frommethod', as 'transpose' doesn't take keywords\\n try:\\n return a.transpose(axes)\\n except AttributeError:\\n return narray(a, copy=False).transpose(axes).view(MaskedArray)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::reshape\",\n \"text\": \"numpy/ma/core.py::reshape\\ndef reshape(a, new_shape, order='C'):\\n \\\"\\\"\\\"\\n Returns an array containing the same data with a new shape.\\n\\n Refer to `MaskedArray.reshape` for full documentation.\\n\\n See Also\\n --------\\n MaskedArray.reshape : equivalent function\\n\\n \\\"\\\"\\\"\\n #We can't use 'frommethod', it whine about some parameters. Dmmit.\\n try:\\n return a.reshape(new_shape, order=order)\\n except AttributeError:\\n _tmp = narray(a, copy=False).reshape(new_shape, order=order)\\n return _tmp.view(MaskedArray)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::resize\",\n \"text\": \"numpy/ma/core.py::resize\\ndef resize(x, new_shape):\\n \\\"\\\"\\\"\\n Return a new masked array with the specified size and shape.\\n\\n This is the masked equivalent of the `numpy.resize` function. The new\\n array is filled with repeated copies of `x` (in the order that the\\n data are stored in memory). If `x` is masked, the new array will be\\n masked, and the new mask will be a repetition of the old one.\\n\\n See Also\\n --------\\n numpy.resize : Equivalent function in the top level NumPy module.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = ma.array([[1, 2] ,[3, 4]])\\n >>> a[0, 1] = ma.masked\\n >>> a\\n masked_array(data =\\n [[1 --]\\n [3 4]],\\n mask =\\n [[False True]\\n [False False]],\\n fill_value = 999999)\\n >>> np.resize(a, (3, 3))\\n array([[1, 2, 3],\\n [4, 1, 2],\\n [3, 4, 1]])\\n >>> ma.resize(a, (3, 3))\\n masked_array(data =\\n [[1 -- 3]\\n [4 1 --]\\n [3 4 1]],\\n mask =\\n [[False True False]\\n [False False True]\\n [False False False]],\\n fill_value = 999999)\\n\\n A MaskedArray is always returned, regardless of the input type.\\n\\n >>> a = np.array([[1, 2] ,[3, 4]])\\n >>> ma.resize(a, (3, 3))\\n masked_array(data =\\n [[1 2 3]\\n [4 1 2]\\n [3 4 1]],\\n mask =\\n False,\\n fill_value = 999999)\\n\\n \\\"\\\"\\\"\\n # We can't use _frommethods here, as N.resize is notoriously whiny.\\n m = getmask(x)\\n if m is not nomask:\\n m = np.resize(m, new_shape)\\n result = np.resize(x, new_shape).view(get_masked_subclass(x))\\n if result.ndim:\\n result._mask = m\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::rank\",\n \"text\": \"numpy/ma/core.py::rank\\ndef rank(obj):\\n \\\"maskedarray version of the numpy function.\\\"\\n return np.rank(getdata(obj))\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::shape\",\n \"text\": \"numpy/ma/core.py::shape\\ndef shape(obj):\\n \\\"maskedarray version of the numpy function.\\\"\\n return np.shape(getdata(obj))\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::size\",\n \"text\": \"numpy/ma/core.py::size\\ndef size(obj, axis=None):\\n \\\"maskedarray version of the numpy function.\\\"\\n return np.size(getdata(obj), axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::where\",\n \"text\": \"numpy/ma/core.py::where\\ndef where (condition, x=None, y=None):\\n \\\"\\\"\\\"\\n Return a masked array with elements from x or y, depending on condition.\\n\\n Returns a masked array, shaped like condition, where the elements\\n are from `x` when `condition` is True, and from `y` otherwise.\\n If neither `x` nor `y` are given, the function returns a tuple of\\n indices where `condition` is True (the result of\\n ``condition.nonzero()``).\\n\\n Parameters\\n ----------\\n condition : array_like, bool\\n The condition to meet. For each True element, yield the corresponding\\n element from `x`, otherwise from `y`.\\n x, y : array_like, optional\\n Values from which to choose. `x` and `y` need to have the same shape\\n as condition, or be broadcast-able to that shape.\\n\\n Returns\\n -------\\n out : MaskedArray or tuple of ndarrays\\n The resulting masked array if `x` and `y` were given, otherwise\\n the result of ``condition.nonzero()``.\\n\\n See Also\\n --------\\n numpy.where : Equivalent function in the top-level NumPy module.\\n\\n Examples\\n --------\\n >>> x = np.ma.array(np.arange(9.).reshape(3, 3), mask=[[0, 1, 0],\\n ... [1, 0, 1],\\n ... [0, 1, 0]])\\n >>> print x\\n [[0.0 -- 2.0]\\n [-- 4.0 --]\\n [6.0 -- 8.0]]\\n >>> np.ma.where(x > 5) # return the indices where x > 5\\n (array([2, 2]), array([0, 2]))\\n\\n >>> print np.ma.where(x > 5, x, -3.1416)\\n [[-3.1416 -- -3.1416]\\n [-- -3.1416 --]\\n [6.0 -- 8.0]]\\n\\n \\\"\\\"\\\"\\n if x is None and y is None:\\n return filled(condition, 0).nonzero()\\n elif x is None or y is None:\\n raise ValueError(\\\"Either both or neither x and y should be given.\\\")\\n # Get the condition ...............\\n fc = filled(condition, 0).astype(MaskType)\\n notfc = np.logical_not(fc)\\n # Get the data ......................................\\n xv = getdata(x)\\n yv = getd\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::choose\",\n \"text\": \"numpy/ma/core.py::choose\\ndef choose (indices, choices, out=None, mode='raise'):\\n \\\"\\\"\\\"\\n Use an index array to construct a new array from a set of choices.\\n\\n Given an array of integers and a set of n choice arrays, this method\\n will create a new array that merges each of the choice arrays. Where a\\n value in `a` is i, the new array will have the value that choices[i]\\n contains in the same place.\\n\\n Parameters\\n ----------\\n a : ndarray of ints\\n This array must contain integers in ``[0, n-1]``, where n is the\\n number of choices.\\n choices : sequence of arrays\\n Choice arrays. The index array and all of the choices should be\\n broadcastable to the same shape.\\n out : array, optional\\n If provided, the result will be inserted into this array. It should\\n be of the appropriate shape and `dtype`.\\n mode : {'raise', 'wrap', 'clip'}, optional\\n Specifies how out-of-bounds indices will behave.\\n\\n * 'raise' : raise an error\\n * 'wrap' : wrap around\\n * 'clip' : clip to the range\\n\\n Returns\\n -------\\n merged_array : array\\n\\n See Also\\n --------\\n choose : equivalent function\\n\\n Examples\\n --------\\n >>> choice = np.array([[1,1,1], [2,2,2], [3,3,3]])\\n >>> a = np.array([2, 1, 0])\\n >>> np.ma.choose(a, choice)\\n masked_array(data = [3 2 1],\\n mask = False,\\n fill_value=999999)\\n\\n \\\"\\\"\\\"\\n def fmask (x):\\n \\\"Returns the filled array, or True if masked.\\\"\\n if x is masked:\\n return True\\n return filled(x)\\n def nmask (x):\\n \\\"Returns the mask, True if ``masked``, False if ``nomask``.\\\"\\n if x is masked:\\n return True\\n return getmask(x)\\n # Get the indices......\\n c = filled(indices, 0)\\n # Get the masks........\\n masks = [nmask(x) for x in choices]\\n data = [fmask(x) for x in choices]\\n # Construct the mask\\n outputmask = np.choose(c, masks, mode=mode)\\n outputmask = make_mask(mask\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::choose.fmask\",\n \"text\": \"numpy/ma/core.py::choose.fmask\\n def fmask (x):\\n \\\"Returns the filled array, or True if masked.\\\"\\n if x is masked:\\n return True\\n return filled(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::choose.nmask\",\n \"text\": \"numpy/ma/core.py::choose.nmask\\n def nmask (x):\\n \\\"Returns the mask, True if ``masked``, False if ``nomask``.\\\"\\n if x is masked:\\n return True\\n return getmask(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::round_\",\n \"text\": \"numpy/ma/core.py::round_\\ndef round_(a, decimals=0, out=None):\\n \\\"\\\"\\\"\\n Return a copy of a, rounded to 'decimals' places.\\n\\n When 'decimals' is negative, it specifies the number of positions\\n to the left of the decimal point. The real and imaginary parts of\\n complex numbers are rounded separately. Nothing is done if the\\n array is not of float type and 'decimals' is greater than or equal\\n to 0.\\n\\n Parameters\\n ----------\\n decimals : int\\n Number of decimals to round to. May be negative.\\n out : array_like\\n Existing array to use for output.\\n If not given, returns a default copy of a.\\n\\n Notes\\n -----\\n If out is given and does not have a mask attribute, the mask of a\\n is lost!\\n\\n \\\"\\\"\\\"\\n if out is None:\\n return np.round_(a, decimals, out)\\n else:\\n np.round_(getdata(a), decimals, out)\\n if hasattr(out, '_mask'):\\n out._mask = getmask(a)\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::inner\",\n \"text\": \"numpy/ma/core.py::inner\\ndef inner(a, b):\\n \\\"\\\"\\\"\\n Returns the inner product of a and b for arrays of floating point types.\\n\\n Like the generic NumPy equivalent the product sum is over the last dimension\\n of a and b.\\n\\n Notes\\n -----\\n The first argument is not conjugated.\\n\\n \\\"\\\"\\\"\\n fa = filled(a, 0)\\n fb = filled(b, 0)\\n if len(fa.shape) == 0:\\n fa.shape = (1,)\\n if len(fb.shape) == 0:\\n fb.shape = (1,)\\n return np.inner(fa, fb).view(MaskedArray)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::outer\",\n \"text\": \"numpy/ma/core.py::outer\\ndef outer(a, b):\\n \\\"maskedarray version of the numpy function.\\\"\\n fa = filled(a, 0).ravel()\\n fb = filled(b, 0).ravel()\\n d = np.outer(fa, fb)\\n ma = getmask(a)\\n mb = getmask(b)\\n if ma is nomask and mb is nomask:\\n return masked_array(d)\\n ma = getmaskarray(a)\\n mb = getmaskarray(b)\\n m = make_mask(1 - np.outer(1 - ma, 1 - mb), copy=0)\\n return masked_array(d, mask=m)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::allequal\",\n \"text\": \"numpy/ma/core.py::allequal\\ndef allequal (a, b, fill_value=True):\\n \\\"\\\"\\\"\\n Return True if all entries of a and b are equal, using\\n fill_value as a truth value where either or both are masked.\\n\\n Parameters\\n ----------\\n a, b : array_like\\n Input arrays to compare.\\n fill_value : bool, optional\\n Whether masked values in a or b are considered equal (True) or not\\n (False).\\n\\n Returns\\n -------\\n y : bool\\n Returns True if the two arrays are equal within the given\\n tolerance, False otherwise. If either array contains NaN,\\n then False is returned.\\n\\n See Also\\n --------\\n all, any\\n numpy.ma.allclose\\n\\n Examples\\n --------\\n >>> a = ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1])\\n >>> a\\n masked_array(data = [10000000000.0 1e-07 --],\\n mask = [False False True],\\n fill_value=1e+20)\\n\\n >>> b = array([1e10, 1e-7, -42.0])\\n >>> b\\n array([ 1.00000000e+10, 1.00000000e-07, -4.20000000e+01])\\n >>> ma.allequal(a, b, fill_value=False)\\n False\\n >>> ma.allequal(a, b)\\n True\\n\\n \\\"\\\"\\\"\\n m = mask_or(getmask(a), getmask(b))\\n if m is nomask:\\n x = getdata(a)\\n y = getdata(b)\\n d = umath.equal(x, y)\\n return d.all()\\n elif fill_value:\\n x = getdata(a)\\n y = getdata(b)\\n d = umath.equal(x, y)\\n dm = array(d, mask=m, copy=False)\\n return dm.filled(True).all(None)\\n else:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::allclose\",\n \"text\": \"numpy/ma/core.py::allclose\\ndef allclose (a, b, masked_equal=True, rtol=1e-5, atol=1e-8):\\n \\\"\\\"\\\"\\n Returns True if two arrays are element-wise equal within a tolerance.\\n\\n This function is equivalent to `allclose` except that masked values\\n are treated as equal (default) or unequal, depending on the `masked_equal`\\n argument.\\n\\n Parameters\\n ----------\\n a, b : array_like\\n Input arrays to compare.\\n masked_equal : bool, optional\\n Whether masked values in `a` and `b` are considered equal (True) or not\\n (False). They are considered equal by default.\\n rtol : float, optional\\n Relative tolerance. The relative difference is equal to ``rtol * b``.\\n Default is 1e-5.\\n atol : float, optional\\n Absolute tolerance. The absolute difference is equal to `atol`.\\n Default is 1e-8.\\n\\n Returns\\n -------\\n y : bool\\n Returns True if the two arrays are equal within the given\\n tolerance, False otherwise. If either array contains NaN, then\\n False is returned.\\n\\n See Also\\n --------\\n all, any\\n numpy.allclose : the non-masked `allclose`.\\n\\n Notes\\n -----\\n If the following equation is element-wise True, then `allclose` returns\\n True::\\n\\n absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))\\n\\n Return True if all elements of `a` and `b` are equal subject to\\n given tolerances.\\n\\n Examples\\n --------\\n >>> a = ma.array([1e10, 1e-7, 42.0], mask=[0, 0, 1])\\n >>> a\\n masked_array(data = [10000000000.0 1e-07 --],\\n mask = [False False True],\\n fill_value = 1e+20)\\n >>> b = ma.array([1e10, 1e-8, -42.0], mask=[0, 0, 1])\\n >>> ma.allclose(a, b)\\n False\\n\\n >>> a = ma.array([1e10, 1e-8, 42.0], mask=[0, 0, 1])\\n >>> b = ma.array([1.00001e10, 1e-9, -42.0], mask=[0, 0, 1])\\n >>> ma.allclose(a, b)\\n True\\n >>> ma.allclose(a, b, masked_equal=False)\\n False\\n\\n Masked values are not compared directly.\\n\\n >>> a = ma.array([1e10,\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::asarray\",\n \"text\": \"numpy/ma/core.py::asarray\\ndef asarray(a, dtype=None, order=None):\\n \\\"\\\"\\\"\\n Convert the input to a masked array of the given data-type.\\n\\n No copy is performed if the input is already an `ndarray`. If `a` is\\n a subclass of `MaskedArray`, a base class `MaskedArray` is returned.\\n\\n Parameters\\n ----------\\n a : array_like\\n Input data, in any form that can be converted to a masked array. This\\n includes lists, lists of tuples, tuples, tuples of tuples, tuples\\n of lists, ndarrays and masked arrays.\\n dtype : dtype, optional\\n By default, the data-type is inferred from the input data.\\n order : {'C', 'F'}, optional\\n Whether to use row-major ('C') or column-major ('FORTRAN') memory\\n representation. Default is 'C'.\\n\\n Returns\\n -------\\n out : MaskedArray\\n Masked array interpretation of `a`.\\n\\n See Also\\n --------\\n asanyarray : Similar to `asarray`, but conserves subclasses.\\n\\n Examples\\n --------\\n >>> x = np.arange(10.).reshape(2, 5)\\n >>> x\\n array([[ 0., 1., 2., 3., 4.],\\n [ 5., 6., 7., 8., 9.]])\\n >>> np.ma.asarray(x)\\n masked_array(data =\\n [[ 0. 1. 2. 3. 4.]\\n [ 5. 6. 7. 8. 9.]],\\n mask =\\n False,\\n fill_value = 1e+20)\\n >>> type(np.ma.asarray(x))\\n <class 'numpy.ma.core.MaskedArray'>\\n\\n \\\"\\\"\\\"\\n return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::asanyarray\",\n \"text\": \"numpy/ma/core.py::asanyarray\\ndef asanyarray(a, dtype=None):\\n \\\"\\\"\\\"\\n Convert the input to a masked array, conserving subclasses.\\n\\n If `a` is a subclass of `MaskedArray`, its class is conserved.\\n No copy is performed if the input is already an `ndarray`.\\n\\n Parameters\\n ----------\\n a : array_like\\n Input data, in any form that can be converted to an array.\\n dtype : dtype, optional\\n By default, the data-type is inferred from the input data.\\n order : {'C', 'F'}, optional\\n Whether to use row-major ('C') or column-major ('FORTRAN') memory\\n representation. Default is 'C'.\\n\\n Returns\\n -------\\n out : MaskedArray\\n MaskedArray interpretation of `a`.\\n\\n See Also\\n --------\\n asarray : Similar to `asanyarray`, but does not conserve subclass.\\n\\n Examples\\n --------\\n >>> x = np.arange(10.).reshape(2, 5)\\n >>> x\\n array([[ 0., 1., 2., 3., 4.],\\n [ 5., 6., 7., 8., 9.]])\\n >>> np.ma.asanyarray(x)\\n masked_array(data =\\n [[ 0. 1. 2. 3. 4.]\\n [ 5. 6. 7. 8. 9.]],\\n mask =\\n False,\\n fill_value = 1e+20)\\n >>> type(np.ma.asanyarray(x))\\n <class 'numpy.ma.core.MaskedArray'>\\n\\n \\\"\\\"\\\"\\n return masked_array(a, dtype=dtype, copy=False, keep_mask=True, subok=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::dump\",\n \"text\": \"numpy/ma/core.py::dump\\ndef dump(a, F):\\n \\\"\\\"\\\"\\n Pickle a masked array to a file.\\n\\n This is a wrapper around ``cPickle.dump``.\\n\\n Parameters\\n ----------\\n a : MaskedArray\\n The array to be pickled.\\n F : str or file-like object\\n The file to pickle `a` to. If a string, the full path to the file.\\n\\n \\\"\\\"\\\"\\n if not hasattr(F, 'readline'):\\n F = open(F, 'w')\\n return pickle.dump(a, F)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::dumps\",\n \"text\": \"numpy/ma/core.py::dumps\\ndef dumps(a):\\n \\\"\\\"\\\"\\n Return a string corresponding to the pickling of a masked array.\\n\\n This is a wrapper around ``cPickle.dumps``.\\n\\n Parameters\\n ----------\\n a : MaskedArray\\n The array for which the string representation of the pickle is\\n returned.\\n\\n \\\"\\\"\\\"\\n return pickle.dumps(a)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::load\",\n \"text\": \"numpy/ma/core.py::load\\ndef load(F):\\n \\\"\\\"\\\"\\n Wrapper around ``cPickle.load`` which accepts either a file-like object\\n or a filename.\\n\\n Parameters\\n ----------\\n F : str or file\\n The file or file name to load.\\n\\n See Also\\n --------\\n dump : Pickle an array\\n\\n Notes\\n -----\\n This is different from `numpy.load`, which does not use cPickle but loads\\n the NumPy binary .npy format.\\n\\n \\\"\\\"\\\"\\n if not hasattr(F, 'readline'):\\n F = open(F, 'r')\\n return pickle.load(F)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::loads\",\n \"text\": \"numpy/ma/core.py::loads\\ndef loads(strg):\\n \\\"\\\"\\\"\\n Load a pickle from the current string.\\n\\n The result of ``cPickle.loads(strg)`` is returned.\\n\\n Parameters\\n ----------\\n strg : str\\n The string to load.\\n\\n See Also\\n --------\\n dumps : Return a string corresponding to the pickling of a masked array.\\n\\n \\\"\\\"\\\"\\n return pickle.loads(strg)\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::fromfile\",\n \"text\": \"numpy/ma/core.py::fromfile\\ndef fromfile(file, dtype=float, count= -1, sep=''):\\n raise NotImplementedError(\\\"Not yet implemented. Sorry\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::fromflex\",\n \"text\": \"numpy/ma/core.py::fromflex\\ndef fromflex(fxarray):\\n \\\"\\\"\\\"\\n Build a masked array from a suitable flexible-type array.\\n\\n The input array has to have a data-type with ``_data`` and ``_mask``\\n fields. This type of array is output by `MaskedArray.toflex`.\\n\\n Parameters\\n ----------\\n fxarray : ndarray\\n The structured input array, containing ``_data`` and ``_mask``\\n fields. If present, other fields are discarded.\\n\\n Returns\\n -------\\n result : MaskedArray\\n The constructed masked array.\\n\\n See Also\\n --------\\n MaskedArray.toflex : Build a flexible-type array from a masked array.\\n\\n Examples\\n --------\\n >>> x = np.ma.array(np.arange(9).reshape(3, 3), mask=[0] + [1, 0] * 4)\\n >>> rec = x.toflex()\\n >>> rec\\n array([[(0, False), (1, True), (2, False)],\\n [(3, True), (4, False), (5, True)],\\n [(6, False), (7, True), (8, False)]],\\n dtype=[('_data', '<i4'), ('_mask', '|b1')])\\n >>> x2 = np.ma.fromflex(rec)\\n >>> x2\\n masked_array(data =\\n [[0 -- 2]\\n [-- 4 --]\\n [6 -- 8]],\\n mask =\\n [[False True False]\\n [ True False True]\\n [False True False]],\\n fill_value = 999999)\\n\\n Extra fields can be present in the structured array but are discarded:\\n\\n >>> dt = [('_data', '<i4'), ('_mask', '|b1'), ('field3', '<f4')]\\n >>> rec2 = np.zeros((2, 2), dtype=dt)\\n >>> rec2\\n array([[(0, False, 0.0), (0, False, 0.0)],\\n [(0, False, 0.0), (0, False, 0.0)]],\\n dtype=[('_data', '<i4'), ('_mask', '|b1'), ('field3', '<f4')])\\n >>> y = np.ma.fromflex(rec2)\\n >>> y\\n masked_array(data =\\n [[0 0]\\n [0 0]],\\n mask =\\n [[False False]\\n [False False]],\\n fill_value = 999999)\\n\\n \\\"\\\"\\\"\\n return masked_array(fxarray['_data'], mask=fxarray['_mask'])\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_convert2ma.__init__\",\n \"text\": \"numpy/ma/core.py::_convert2ma.__init__\\n def __init__(self, funcname, params=None):\\n self._func = getattr(np, funcname)\\n self.__doc__ = self.getdoc()\\n self._extras = params or {}\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_convert2ma.getdoc\",\n \"text\": \"numpy/ma/core.py::_convert2ma.getdoc\\n def getdoc(self):\\n \\\"Return the doc of the function (from the doc of the method).\\\"\\n doc = getattr(self._func, '__doc__', None)\\n sig = get_object_signature(self._func)\\n if doc:\\n # Add the signature of the function at the beginning of the doc\\n if sig:\\n sig = \\\"%s%s\\\\n\\\" % (self._func.__name__, sig)\\n doc = sig + doc\\n return doc\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::_convert2ma.__call__\",\n \"text\": \"numpy/ma/core.py::_convert2ma.__call__\\n def __call__(self, a, *args, **params):\\n # Find the common parameters to the call and the definition\\n _extras = self._extras\\n common_params = set(params).intersection(_extras)\\n # Drop the common parameters from the call\\n for p in common_params:\\n _extras[p] = params.pop(p)\\n # Get the result\\n result = self._func.__call__(a, *args, **params).view(MaskedArray)\\n if \\\"fill_value\\\" in common_params:\\n result.fill_value = _extras.get(\\\"fill_value\\\", None)\\n if \\\"hardmask\\\" in common_params:\\n result._hardmask = bool(_extras.get(\\\"hard_mask\\\", False))\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"numpy/ma/core.py::append\",\n \"text\": \"numpy/ma/core.py::append\\ndef append(a, b, axis=None):\\n \\\"\\\"\\\"Append values to the end of an array.\\n\\n .. versionadded:: 1.9.0\\n\\n Parameters\\n ----------\\n arr : array_like\\n Values are appended to a copy of this array.\\n values : array_like\\n These values are appended to a copy of `arr`. It must be of the\\n correct shape (the same shape as `arr`, excluding `axis`). If `axis`\\n is not specified, `values` can be any shape and will be flattened\\n before use.\\n axis : int, optional\\n The axis along which `values` are appended. If `axis` is not given,\\n both `arr` and `values` are flattened before use.\\n\\n Returns\\n -------\\n append : MaskedArray\\n A copy of `arr` with `values` appended to `axis`. Note that `append`\\n does not occur in-place: a new array is allocated and filled. If\\n `axis` is None, the result is a flattened array.\\n\\n See Also\\n --------\\n numpy.append : Equivalent function in the top-level NumPy module.\\n\\n Examples\\n --------\\n >>> import numpy.ma as ma\\n >>> a = ma.masked_values([1, 2, 3], 2)\\n >>> b = ma.masked_values([[4, 5, 6], [7, 8, 9]], 7)\\n >>> print(ma.append(a, b))\\n [1 -- 3 4 5 6 -- 8 9]\\n \\\"\\\"\\\"\\n return concatenate([a, b], axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::superpack_name\",\n \"text\": \"pavement.py::superpack_name\\ndef superpack_name(pyver, numver):\\n \\\"\\\"\\\"Return the filename of the superpack installer.\\\"\\\"\\\"\\n return 'numpy-%s-win32-superpack-python%s.exe' % (numver, pyver)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::internal_wininst_name\",\n \"text\": \"pavement.py::internal_wininst_name\\ndef internal_wininst_name(arch):\\n \\\"\\\"\\\"Return the name of the wininst as it will be inside the superpack (i.e.\\n with the arch encoded.\\\"\\\"\\\"\\n ext = '.exe'\\n return \\\"numpy-%s-%s%s\\\" % (FULLVERSION, arch, ext)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::wininst_name\",\n \"text\": \"pavement.py::wininst_name\\ndef wininst_name(pyver):\\n \\\"\\\"\\\"Return the name of the installer built by wininst command.\\\"\\\"\\\"\\n ext = '.exe'\\n return \\\"numpy-%s.win32-py%s%s\\\" % (FULLVERSION, pyver, ext)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::prepare_nsis_script\",\n \"text\": \"pavement.py::prepare_nsis_script\\ndef prepare_nsis_script(pyver, numver):\\n if not os.path.exists(SUPERPACK_BUILD):\\n os.makedirs(SUPERPACK_BUILD)\\n\\n tpl = os.path.join('tools/win32build/nsis_scripts', 'numpy-superinstaller.nsi.in')\\n source = open(tpl, 'r')\\n target = open(os.path.join(SUPERPACK_BUILD, 'numpy-superinstaller.nsi'), 'w')\\n\\n installer_name = superpack_name(pyver, numver)\\n cnt = \\\"\\\".join(source.readlines())\\n cnt = cnt.replace('@NUMPY_INSTALLER_NAME@', installer_name)\\n for arch in ['nosse', 'sse2', 'sse3']:\\n cnt = cnt.replace('@%s_BINARY@' % arch.upper(),\\n internal_wininst_name(arch))\\n\\n target.write(cnt)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::bdist_wininst_arch\",\n \"text\": \"pavement.py::bdist_wininst_arch\\ndef bdist_wininst_arch(pyver, arch):\\n \\\"\\\"\\\"Arch specific wininst build.\\\"\\\"\\\"\\n if os.path.exists(\\\"build\\\"):\\n shutil.rmtree(\\\"build\\\")\\n\\n _bdist_wininst(pyver, SITECFG[arch])\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::bdist_superpack\",\n \"text\": \"pavement.py::bdist_superpack\\n@task\\n@cmdopts([(\\\"python-version=\\\", \\\"p\\\", \\\"python version\\\")])\\ndef bdist_superpack(options):\\n \\\"\\\"\\\"Build all arch specific wininst installers.\\\"\\\"\\\"\\n pyver = options.python_version\\n def copy_bdist(arch):\\n # Copy the wininst in dist into the release directory\\n source = os.path.join('dist', wininst_name(pyver))\\n target = os.path.join(SUPERPACK_BINDIR, internal_wininst_name(arch))\\n if os.path.exists(target):\\n os.remove(target)\\n if not os.path.exists(os.path.dirname(target)):\\n os.makedirs(os.path.dirname(target))\\n try:\\n os.rename(source, target)\\n except OSError:\\n # When git is installed on OS X but not under Wine, the name of the\\n # .exe has \\\"-Unknown\\\" in it instead of the correct git revision.\\n # Try to fix this here:\\n revidx = source.index(\\\".dev-\\\") + 5\\n gitrev = source[revidx:revidx+7]\\n os.rename(source.replace(gitrev, \\\"Unknown\\\"), target)\\n\\n bdist_wininst_arch(pyver, 'nosse')\\n copy_bdist(\\\"nosse\\\")\\n bdist_wininst_arch(pyver, 'sse2')\\n copy_bdist(\\\"sse2\\\")\\n bdist_wininst_arch(pyver, 'sse3')\\n copy_bdist(\\\"sse3\\\")\\n\\n idirs = options.installers.installersdir\\n pyver = options.python_version\\n prepare_nsis_script(pyver, FULLVERSION)\\n subprocess.check_call(MAKENSIS + ['numpy-superinstaller.nsi'],\\n cwd=SUPERPACK_BUILD)\\n\\n # Copy the superpack into installers dir\\n if not os.path.exists(idirs):\\n os.makedirs(idirs)\\n\\n source = os.path.join(SUPERPACK_BUILD, superpack_name(pyver, FULLVERSION))\\n target = os.path.join(idirs, superpack_name(pyver, FULLVERSION))\\n shutil.copy(source, target)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::bdist_superpack.copy_bdist\",\n \"text\": \"pavement.py::bdist_superpack.copy_bdist\\n def copy_bdist(arch):\\n # Copy the wininst in dist into the release directory\\n source = os.path.join('dist', wininst_name(pyver))\\n target = os.path.join(SUPERPACK_BINDIR, internal_wininst_name(arch))\\n if os.path.exists(target):\\n os.remove(target)\\n if not os.path.exists(os.path.dirname(target)):\\n os.makedirs(os.path.dirname(target))\\n try:\\n os.rename(source, target)\\n except OSError:\\n # When git is installed on OS X but not under Wine, the name of the\\n # .exe has \\\"-Unknown\\\" in it instead of the correct git revision.\\n # Try to fix this here:\\n revidx = source.index(\\\".dev-\\\") + 5\\n gitrev = source[revidx:revidx+7]\\n os.rename(source.replace(gitrev, \\\"Unknown\\\"), target)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::bdist_wininst_nosse\",\n \"text\": \"pavement.py::bdist_wininst_nosse\\n@task\\n@cmdopts([(\\\"python-version=\\\", \\\"p\\\", \\\"python version\\\")])\\ndef bdist_wininst_nosse(options):\\n \\\"\\\"\\\"Build the nosse wininst installer.\\\"\\\"\\\"\\n bdist_wininst_arch(options.python_version, 'nosse')\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::bdist_wininst_sse2\",\n \"text\": \"pavement.py::bdist_wininst_sse2\\n@task\\n@cmdopts([(\\\"python-version=\\\", \\\"p\\\", \\\"python version\\\")])\\ndef bdist_wininst_sse2(options):\\n \\\"\\\"\\\"Build the sse2 wininst installer.\\\"\\\"\\\"\\n bdist_wininst_arch(options.python_version, 'sse2')\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::bdist_wininst_sse3\",\n \"text\": \"pavement.py::bdist_wininst_sse3\\n@task\\n@cmdopts([(\\\"python-version=\\\", \\\"p\\\", \\\"python version\\\")])\\ndef bdist_wininst_sse3(options):\\n \\\"\\\"\\\"Build the sse3 wininst installer.\\\"\\\"\\\"\\n bdist_wininst_arch(options.python_version, 'sse3')\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::bdist_wininst_simple\",\n \"text\": \"pavement.py::bdist_wininst_simple\\n@task\\n@cmdopts([(\\\"python-version=\\\", \\\"p\\\", \\\"python version\\\")])\\ndef bdist_wininst_simple():\\n \\\"\\\"\\\"Simple wininst-based installer.\\\"\\\"\\\"\\n pyver = options.bdist_wininst_simple.python_version\\n _bdist_wininst(pyver)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::_bdist_wininst\",\n \"text\": \"pavement.py::_bdist_wininst\\ndef _bdist_wininst(pyver, cfg_env=None):\\n cmd = WINDOWS_PYTHON[pyver] + ['setup.py', 'build', '-c', 'mingw32', 'bdist_wininst']\\n if cfg_env:\\n for k, v in WINDOWS_ENV.items():\\n cfg_env[k] = v\\n else:\\n cfg_env = WINDOWS_ENV\\n subprocess.check_call(cmd, env=cfg_env)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::bootstrap\",\n \"text\": \"pavement.py::bootstrap\\n@task\\ndef bootstrap(options):\\n \\\"\\\"\\\"create virtualenv in ./bootstrap\\\"\\\"\\\"\\n try:\\n import virtualenv\\n except ImportError as e:\\n raise RuntimeError(\\\"virtualenv is needed for bootstrap\\\")\\n\\n bdir = options.bootstrap_dir\\n if not os.path.exists(bdir):\\n os.makedirs(bdir)\\n bscript = \\\"boostrap.py\\\"\\n\\n options.virtualenv.script_name = os.path.join(options.bootstrap_dir,\\n bscript)\\n options.virtualenv.no_site_packages = False\\n options.bootstrap.no_site_packages = False\\n call_task('paver.virtual.bootstrap')\\n sh('cd %s; %s %s' % (bdir, sys.executable, bscript))\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::clean\",\n \"text\": \"pavement.py::clean\\n@task\\ndef clean():\\n \\\"\\\"\\\"Remove build, dist, egg-info garbage.\\\"\\\"\\\"\\n d = ['build', 'dist', 'numpy.egg-info']\\n for i in d:\\n if os.path.exists(i):\\n shutil.rmtree(i)\\n\\n bdir = os.path.join('doc', options.sphinx.builddir)\\n if os.path.exists(bdir):\\n shutil.rmtree(bdir)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::clean_bootstrap\",\n \"text\": \"pavement.py::clean_bootstrap\\n@task\\ndef clean_bootstrap():\\n bdir = os.path.join(options.bootstrap.bootstrap_dir)\\n if os.path.exists(bdir):\\n shutil.rmtree(bdir)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::nuke\",\n \"text\": \"pavement.py::nuke\\n@task\\n@needs('clean', 'clean_bootstrap')\\ndef nuke(options):\\n \\\"\\\"\\\"Remove everything: build dir, installers, bootstrap dirs, etc...\\\"\\\"\\\"\\n for d in [options.superpack.builddir, options.installers.releasedir]:\\n if os.path.exists(d):\\n shutil.rmtree(d)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::html\",\n \"text\": \"pavement.py::html\\n@task\\ndef html(options):\\n \\\"\\\"\\\"Build numpy documentation and put it into build/docs\\\"\\\"\\\"\\n # Don't use paver html target because of numpy bootstrapping problems\\n bdir = os.path.join(\\\"doc\\\", options.sphinx.builddir, \\\"html\\\")\\n if os.path.exists(bdir):\\n shutil.rmtree(bdir)\\n subprocess.check_call([\\\"make\\\", \\\"html\\\"], cwd=\\\"doc\\\")\\n html_destdir = options.html.builddir\\n if os.path.exists(html_destdir):\\n shutil.rmtree(html_destdir)\\n shutil.copytree(bdir, html_destdir)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::latex\",\n \"text\": \"pavement.py::latex\\n@task\\ndef latex():\\n \\\"\\\"\\\"Build numpy documentation in latex format.\\\"\\\"\\\"\\n subprocess.check_call([\\\"make\\\", \\\"latex\\\"], cwd=\\\"doc\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::pdf\",\n \"text\": \"pavement.py::pdf\\n@task\\n@needs('latex')\\ndef pdf():\\n sdir = options.doc.sdir\\n bdir = options.doc.bdir\\n bdir_latex = options.doc.bdir_latex\\n destdir_pdf = options.doc.destdir_pdf\\n\\n def build_pdf():\\n subprocess.check_call([\\\"make\\\", \\\"all-pdf\\\"], cwd=str(bdir_latex))\\n dry(\\\"Build pdf doc\\\", build_pdf)\\n\\n if os.path.exists(destdir_pdf):\\n shutil.rmtree(destdir_pdf)\\n os.makedirs(destdir_pdf)\\n\\n user = os.path.join(bdir_latex, \\\"numpy-user.pdf\\\")\\n shutil.copy(user, os.path.join(destdir_pdf, \\\"userguide.pdf\\\"))\\n ref = os.path.join(bdir_latex, \\\"numpy-ref.pdf\\\")\\n shutil.copy(ref, os.path.join(destdir_pdf, \\\"reference.pdf\\\"))\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::pdf.build_pdf\",\n \"text\": \"pavement.py::pdf.build_pdf\\n def build_pdf():\\n subprocess.check_call([\\\"make\\\", \\\"all-pdf\\\"], cwd=str(bdir_latex))\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::dmg_name\",\n \"text\": \"pavement.py::dmg_name\\ndef dmg_name(fullversion, pyver, osxver=None):\\n \\\"\\\"\\\"Return name for dmg installer.\\n\\n Notes\\n -----\\n Python 2.7 has two binaries, one for 10.3 (ppc, i386) and one for 10.6\\n (i386, x86_64). All other Python versions at python.org at the moment\\n have binaries for 10.3 only. The \\\"macosx%s\\\" part of the dmg name should\\n correspond to the python.org naming scheme.\\n \\\"\\\"\\\"\\n # assume that for the py2.7/osx10.6 build the deployment target is set\\n # (should be done in the release script).\\n if not osxver:\\n osxver = os.environ.get('MACOSX_DEPLOYMENT_TARGET', '10.3')\\n return \\\"numpy-%s-py%s-python.org-macosx%s.dmg\\\" % (fullversion, pyver,\\n osxver)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::macosx_version\",\n \"text\": \"pavement.py::macosx_version\\ndef macosx_version():\\n if not sys.platform == 'darwin':\\n raise ValueError(\\\"Not darwin ??\\\")\\n st = subprocess.Popen([\\\"sw_vers\\\"], stdout=subprocess.PIPE)\\n out = st.stdout.readlines()\\n ver = re.compile(\\\"ProductVersion:\\\\s+([0-9]+)\\\\.([0-9]+)\\\\.([0-9]+)\\\")\\n for i in out:\\n m = ver.match(i)\\n if m:\\n return m.groups()\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::mpkg_name\",\n \"text\": \"pavement.py::mpkg_name\\ndef mpkg_name(pyver):\\n maj, min = macosx_version()[:2]\\n # Note that bdist_mpkg breaks this if building a dev version with a git\\n # commit string attached. make_fullplatcomponents() in\\n # bdist_mpkg/cmd_bdist_mpkg.py replaces '-' with '_', comment this out if\\n # needed.\\n return \\\"numpy-%s-py%s-macosx%s.%s.mpkg\\\" % (FULLVERSION, pyver, maj, min)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::_build_mpkg\",\n \"text\": \"pavement.py::_build_mpkg\\ndef _build_mpkg(pyver):\\n # account for differences between Python 2.7.1 versions from python.org\\n if os.environ.get('MACOSX_DEPLOYMENT_TARGET', None) == \\\"10.6\\\":\\n ldflags = \\\"-undefined dynamic_lookup -bundle -arch i386 -arch x86_64 -Wl,-search_paths_first\\\"\\n else:\\n ldflags = \\\"-undefined dynamic_lookup -bundle -arch i386 -arch ppc -Wl,-search_paths_first\\\"\\n\\n ldflags += \\\" -L%s\\\" % os.path.join(os.path.dirname(__file__), \\\"build\\\")\\n sh(\\\"LDFLAGS='%s' %s setupegg.py bdist_mpkg\\\" % (ldflags, \\\" \\\".join(MPKG_PYTHON[pyver])))\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::simple_dmg\",\n \"text\": \"pavement.py::simple_dmg\\n@task\\ndef simple_dmg():\\n pyver = \\\"2.6\\\"\\n src_dir = \\\"dmg-source\\\"\\n\\n # Clean the source dir\\n if os.path.exists(src_dir):\\n shutil.rmtree(src_dir)\\n os.makedirs(src_dir)\\n\\n # Build the mpkg\\n clean()\\n _build_mpkg(pyver)\\n\\n # Build the dmg\\n shutil.copytree(os.path.join(\\\"dist\\\", mpkg_name(pyver)),\\n os.path.join(src_dir, mpkg_name(pyver)))\\n _create_dmg(pyver, src_dir, \\\"NumPy Universal %s\\\" % FULLVERSION)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::bdist_mpkg\",\n \"text\": \"pavement.py::bdist_mpkg\\n@task\\ndef bdist_mpkg(options):\\n call_task(\\\"clean\\\")\\n try:\\n pyver = options.bdist_mpkg.python_version\\n except AttributeError:\\n pyver = options.python_version\\n\\n _build_mpkg(pyver)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::_create_dmg\",\n \"text\": \"pavement.py::_create_dmg\\ndef _create_dmg(pyver, src_dir, volname=None):\\n # Build the dmg\\n image_name = dmg_name(FULLVERSION, pyver)\\n if os.path.exists(image_name):\\n os.remove(image_name)\\n cmd = [\\\"hdiutil\\\", \\\"create\\\", image_name, \\\"-srcdir\\\", src_dir]\\n if volname:\\n cmd.extend([\\\"-volname\\\", \\\"'%s'\\\" % volname])\\n sh(\\\" \\\".join(cmd))\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::dmg\",\n \"text\": \"pavement.py::dmg\\n@task\\n@cmdopts([(\\\"python-version=\\\", \\\"p\\\", \\\"python version\\\")])\\ndef dmg(options):\\n try:\\n pyver = options.dmg.python_version\\n except:\\n pyver = DEFAULT_PYTHON\\n idirs = options.installers.installersdir\\n\\n # Check if docs exist. If not, say so and quit.\\n ref = os.path.join(options.doc.destdir_pdf, \\\"reference.pdf\\\")\\n user = os.path.join(options.doc.destdir_pdf, \\\"userguide.pdf\\\")\\n if (not os.path.exists(ref)) or (not os.path.exists(user)):\\n import warnings\\n warnings.warn(\\\"Docs need to be built first! Can't find them.\\\")\\n\\n # Build the mpkg package\\n call_task(\\\"clean\\\")\\n _build_mpkg(pyver)\\n\\n macosx_installer_dir = \\\"tools/numpy-macosx-installer\\\"\\n dmg = os.path.join(macosx_installer_dir, dmg_name(FULLVERSION, pyver))\\n if os.path.exists(dmg):\\n os.remove(dmg)\\n\\n # Clean the image source\\n content = os.path.join(macosx_installer_dir, 'content')\\n if os.path.exists(content):\\n shutil.rmtree(content)\\n os.makedirs(content)\\n\\n # Copy mpkg into image source\\n mpkg_source = os.path.join(\\\"dist\\\", mpkg_name(pyver))\\n mpkg_target = os.path.join(content, \\\"numpy-%s-py%s.mpkg\\\" % (FULLVERSION, pyver))\\n shutil.copytree(mpkg_source, mpkg_target)\\n\\n # Copy docs into image source\\n pdf_docs = os.path.join(content, \\\"Documentation\\\")\\n if os.path.exists(pdf_docs):\\n shutil.rmtree(pdf_docs)\\n os.makedirs(pdf_docs)\\n shutil.copy(user, os.path.join(pdf_docs, \\\"userguide.pdf\\\"))\\n shutil.copy(ref, os.path.join(pdf_docs, \\\"reference.pdf\\\"))\\n\\n # Build the dmg\\n cmd = [\\\"./new-create-dmg\\\", \\\"--pkgname\\\", os.path.basename(mpkg_target),\\n \\\"--volname\\\", \\\"numpy\\\", os.path.basename(dmg), \\\"./content\\\"]\\n st = subprocess.check_call(cmd, cwd=macosx_installer_dir)\\n\\n source = dmg\\n target = os.path.join(idirs, os.path.basename(dmg))\\n if not os.path.exists(os.path.dirname(target)):\\n os.makedirs(os.path.dirname(target))\\n shutil.copy(source, target)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::tarball_name\",\n \"text\": \"pavement.py::tarball_name\\ndef tarball_name(type='gztar'):\\n root = 'numpy-%s' % FULLVERSION\\n if type == 'gztar':\\n return root + '.tar.gz'\\n elif type == 'zip':\\n return root + '.zip'\\n raise ValueError(\\\"Unknown type %s\\\" % type)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::sdist\",\n \"text\": \"pavement.py::sdist\\n@task\\ndef sdist(options):\\n # To be sure to bypass paver when building sdist... paver + numpy.distutils\\n # do not play well together.\\n sh('python setup.py sdist --formats=gztar,zip')\\n\\n # Copy the superpack into installers dir\\n idirs = options.installers.installersdir\\n if not os.path.exists(idirs):\\n os.makedirs(idirs)\\n\\n for t in ['gztar', 'zip']:\\n source = os.path.join('dist', tarball_name(t))\\n target = os.path.join(idirs, tarball_name(t))\\n shutil.copy(source, target)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::compute_md5\",\n \"text\": \"pavement.py::compute_md5\\ndef compute_md5(idirs):\\n released = paver.path.path(idirs).listdir()\\n checksums = []\\n for f in sorted(released):\\n m = md5(open(f, 'r').read())\\n checksums.append('%s %s' % (m.hexdigest(), os.path.basename(f)))\\n\\n return checksums\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::compute_sha256\",\n \"text\": \"pavement.py::compute_sha256\\ndef compute_sha256(idirs):\\n # better checksum so gpg signed README.txt containing the sums can be used\\n # to verify the binaries instead of signing all binaries\\n released = paver.path.path(idirs).listdir()\\n checksums = []\\n for f in sorted(released):\\n m = sha256(open(f, 'r').read())\\n checksums.append('%s %s' % (m.hexdigest(), os.path.basename(f)))\\n\\n return checksums\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::write_release_task\",\n \"text\": \"pavement.py::write_release_task\\ndef write_release_task(options, filename='NOTES.txt'):\\n idirs = options.installers.installersdir\\n source = paver.path.path(RELEASE_NOTES)\\n target = paver.path.path(filename)\\n if target.exists():\\n target.remove()\\n source.copy(target)\\n ftarget = open(str(target), 'a')\\n ftarget.writelines(\\\"\\\"\\\"\\nChecksums\\n=========\\n\\nMD5\\n~~~\\n\\n\\\"\\\"\\\")\\n ftarget.writelines(['%s\\\\n' % c for c in compute_md5(idirs)])\\n ftarget.writelines(\\\"\\\"\\\"\\nSHA256\\n~~~~~~\\n\\n\\\"\\\"\\\")\\n ftarget.writelines(['%s\\\\n' % c for c in compute_sha256(idirs)])\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::write_log_task\",\n \"text\": \"pavement.py::write_log_task\\ndef write_log_task(options, filename='Changelog'):\\n st = subprocess.Popen(\\n ['git', 'log', '%s..%s' % (LOG_START, LOG_END)],\\n stdout=subprocess.PIPE)\\n\\n out = st.communicate()[0]\\n a = open(filename, 'w')\\n a.writelines(out)\\n a.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::write_release\",\n \"text\": \"pavement.py::write_release\\n@task\\ndef write_release(options):\\n write_release_task(options)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::write_log\",\n \"text\": \"pavement.py::write_log\\n@task\\ndef write_log(options):\\n write_log_task(options)\",\n \"label\": 0\n },\n {\n \"cid\": \"pavement.py::write_release_and_log\",\n \"text\": \"pavement.py::write_release_and_log\\n@task\\ndef write_release_and_log(options):\\n rdir = options.installers.releasedir\\n write_release_task(options, os.path.join(rdir, 'NOTES.txt'))\\n write_log_task(options, os.path.join(rdir, 'Changelog'))\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":264,"cells":{"instance_id":{"kind":"string","value":"Lightning-AI__lightning-155"},"repo":{"kind":"string","value":"Lightning-AI/lightning"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"transfer_to_batch_gpu returns null when input has primitives\n**Describe the bug**\r\nwhen passing a batch such as: \r\n```python \r\nbatch = list(tensor, tensor, [0,1,2]) \r\n``` \r\nthe list of ints won't be returned correctly\r\n\r\n**Additional context**\r\nFix should add a return of the item it no condition matches\r\n\n"},"gold_files":{"kind":"list like","value":["pytorch_lightning/models/trainer.py"],"string":"[\n \"pytorch_lightning/models/trainer.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pytorch_lightning/models/trainer.py::<module>","pytorch_lightning/models/trainer.py::Trainer.transfer_batch_to_gpu"],"string":"[\n \"pytorch_lightning/models/trainer.py::<module>\",\n \"pytorch_lightning/models/trainer.py::Trainer.transfer_batch_to_gpu\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"examples/new_project_templates/lightning_module_template.py","text":"examples/new_project_templates/lightning_module_template.py\nclass LightningTemplateModel # Sample model to show how to define a template\n def __init__(self, hparams) # Pass in parsed HyperOptArgumentParser to the model\n def __build_model(self) # Layout model\n def forward(self, x) # No special modification required for lightning, define as you normally would\n def loss(self, labels, logits)\n def training_step(self, data_batch, batch_i) # Lightning calls this inside the training loop\n def validation_step(self, data_batch, batch_i) # Lightning calls this inside the validation loop\n def validation_end(self, outputs) # Called at the end of validation to aggregate outputs\n def configure_optimizers(self) # return whatever optimizers we want here\n def __dataloader(self, train)\n def tng_dataloader(self)\n def val_dataloader(self)\n def test_dataloader(self)\n def add_model_specific_args(parent_parser, root_dir) # Parameters you define here will be available to your model through self.hparams","label":0},{"cid":"examples/templates/gan.py","text":"examples/templates/gan.py\nclass Generator\n def __init__(self, latent_dim, img_shape)\n def block(in_feat, out_feat, normalize=True)\n def forward(self, z)\nclass Discriminator\n def __init__(self, img_shape)\n def forward(self, img)\nclass GAN\n def __init__(self, hparams)\n def forward(self, z)\n def adversarial_loss(self, y_hat, y)\n def training_step(self, batch, batch_nb, optimizer_i)\n def configure_optimizers(self)\n def tng_dataloader(self)\ndef main(hparams)","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py","text":"pytorch_lightning/callbacks/pt_callbacks.py\nclass Callback # Abstract base class used to build new callbacks.\n def __init__(self)\n def set_params(self, params)\n def set_model(self, model)\n def on_epoch_begin(self, epoch, logs=None)\n def on_epoch_end(self, epoch, logs=None)\n def on_batch_begin(self, batch, logs=None)\n def on_batch_end(self, batch, logs=None)\n def on_train_begin(self, logs=None)\n def on_train_end(self, logs=None)\nclass EarlyStopping # Stop training when a monitored quantity has stopped improving.\n def __init__(self, monitor='val_loss', min_delta=0.0, patience=0, verbose=0, mode='auto')\n def on_train_begin(self, logs=None)\n def on_epoch_end(self, epoch, logs=None)\n def on_train_end(self, logs=None)\nclass ModelCheckpoint # Save the model after every epoch.\n def __init__(self, filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', period=1, prefix='')\n def save_model(self, filepath, overwrite)\n def on_epoch_end(self, epoch, logs=None)","label":0},{"cid":"pytorch_lightning/models/trainer.py","text":"pytorch_lightning/models/trainer.py\ndef reduce_distributed_output(output, nb_gpus)\nclass Trainer\n def __init__(self, experiment=None, early_stop_callback=None, checkpoint_callback=None, gradient_clip=0, cluster=None, process_position=0, current_gpu_name=0, nb_gpu_nodes=1, gpus=None, progress_bar=True, overfit_pct=0.0, track_grad_norm=-1, check_val_every_n_epoch=1, fast_dev_run=False, accumulate_grad_batches=1, max_nb_epochs=1000, min_nb_epochs=1, train_percent_check=1.0, val_percent_check=1.0, test_percent_check=1.0, val_check_interval=1.0, log_save_interval=100, add_log_row_interval=10, distributed_backend='dp', use_amp=False, print_nan_grads=False, print_weights_summary=True, amp_level='O2', nb_sanity_val_steps=5) # :param experiment: Test-tube experiment\n def restore_state_if_existing_checkpoint(self)\n def data_parallel(self)\n def __determine_data_use_amount(self, train_percent_check, val_percent_check, test_percent_check, overfit_pct) # Use less data for debugging purposes\n def __get_model(self)\n def __is_function_implemented(self, f_name)\n def __is_overriden(self, f_name)\n def __tng_tqdm_dic(self)\n def tng_tqdm_dic(self) # Read-only for tqdm metrics\n def __layout_bookeeping(self)\n def __add_tqdm_metrics(self, metrics)\n def __validation_forward(self, model, data_batch, batch_i, dataloader_i)\n def validate(self, model, dataloader, max_batches, dataloader_i) # Run validation code\n def get_dataloaders(self, model) # Dataloaders are provided by the model\n def fit(self, model)\n def init_optimizers(self, optimizers)\n def __single_gpu_train(self, model)\n def __dp_train(self, model)\n def ddp_train(self, gpu_nb, model) # Entry point into a DP thread\n def __init_tcp_connection(self) # Connect all procs in the world using the env:// init\n def resolve_root_node_address(self, root_node)\n def __run_pretrain_routine(self, model) # Sanity check a few things before starting actual training\n def __train(self)\n def run_tng_epoch(self)\n def __metrics_to_scalars(self, metrics, blacklist=set())\n def __log_vals_blacklist(self) # avoid logging some vals lightning uses to maintain state\n def transfer_batch_to_gpu(self, batch, gpu_id)\n def __tng_forward(self, data_batch, batch_nb, opt_idx) # Handle forward for each training case (distributed, single gpu, etc...)\n def __clip_gradients(self)\n def __print_nan_grads(self)\n def __run_tng_batch(self, data_batch, batch_nb)\n def __run_validation(self)","label":1},{"cid":"pytorch_lightning/pt_overrides/override_data_parallel.py","text":"pytorch_lightning/pt_overrides/override_data_parallel.py\ndef _find_tensors(obj) # Recursively find all tensors contained in the specified object.\ndef get_a_var(obj)\nclass LightningDataParallel # Override the forward call in lightning so it goes to training and validation ste\n def forward(self, *inputs, **kwargs)\n def parallel_apply(self, replicas, inputs, kwargs)\nclass LightningDistributedDataParallel # Override the forward call in lightning so it goes to training and validation ste\n def parallel_apply(self, replicas, inputs, kwargs)\n def forward(self, *inputs, **kwargs)\ndef parallel_apply(modules, inputs, kwargs_tup=None, devices=None) # Applies each `module` in :attr:`modules` in parallel on arguments\n def _worker(i, module, input, kwargs, device=None)","label":0},{"cid":"pytorch_lightning/root_module/decorators.py","text":"pytorch_lightning/root_module/decorators.py\ndef data_loader(fn) # Decorator to make any fx with this use the lazy property\n def _data_loader(self)","label":0},{"cid":"pytorch_lightning/root_module/root_module.py","text":"pytorch_lightning/root_module/root_module.py\nclass LightningModule\n def __init__(self, *args, **kwargs)\n def forward(self, *args, **kwargs) # Expand model in into whatever you need.\n def training_step(self, *args, **kwargs) # return loss, dict with metrics for tqdm\n def validation_step(self, *args, **kwargs) # return whatever outputs will need to be aggregated in validation_end\n def validation_end(self, outputs) # Outputs has the appended output after each validation step\n def configure_optimizers(self) # Return a list of optimizers and a list of schedulers (could be empty)\n def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i) # Do something instead of the standard optimizer behavior\n def tng_dataloader(self) # Implement a PyTorch DataLoader\n def test_dataloader(self) # Implement a PyTorch DataLoader\n def val_dataloader(self) # Implement a PyTorch DataLoader\n def load_from_metrics(cls, weights_path, tags_csv, on_gpu, map_location=None) # Primary way of loading model from csv weights path\n def summarize(self)\n def freeze(self)\n def unfreeze(self)","label":0},{"cid":"pytorch_lightning/utilities/arg_parse.py","text":"pytorch_lightning/utilities/arg_parse.py\ndef add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None)","label":0},{"cid":"pytorch_lightning/utilities/debugging.py","text":"pytorch_lightning/utilities/debugging.py\nclass MisconfigurationException","label":0},{"cid":"setup.py","text":"setup.py","label":0}],"string":"[\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py\\nclass LightningTemplateModel # Sample model to show how to define a template\\n def __init__(self, hparams) # Pass in parsed HyperOptArgumentParser to the model\\n def __build_model(self) # Layout model\\n def forward(self, x) # No special modification required for lightning, define as you normally would\\n def loss(self, labels, logits)\\n def training_step(self, data_batch, batch_i) # Lightning calls this inside the training loop\\n def validation_step(self, data_batch, batch_i) # Lightning calls this inside the validation loop\\n def validation_end(self, outputs) # Called at the end of validation to aggregate outputs\\n def configure_optimizers(self) # return whatever optimizers we want here\\n def __dataloader(self, train)\\n def tng_dataloader(self)\\n def val_dataloader(self)\\n def test_dataloader(self)\\n def add_model_specific_args(parent_parser, root_dir) # Parameters you define here will be available to your model through self.hparams\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/templates/gan.py\",\n \"text\": \"examples/templates/gan.py\\nclass Generator\\n def __init__(self, latent_dim, img_shape)\\n def block(in_feat, out_feat, normalize=True)\\n def forward(self, z)\\nclass Discriminator\\n def __init__(self, img_shape)\\n def forward(self, img)\\nclass GAN\\n def __init__(self, hparams)\\n def forward(self, z)\\n def adversarial_loss(self, y_hat, y)\\n def training_step(self, batch, batch_nb, optimizer_i)\\n def configure_optimizers(self)\\n def tng_dataloader(self)\\ndef main(hparams)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py\\nclass Callback # Abstract base class used to build new callbacks.\\n def __init__(self)\\n def set_params(self, params)\\n def set_model(self, model)\\n def on_epoch_begin(self, epoch, logs=None)\\n def on_epoch_end(self, epoch, logs=None)\\n def on_batch_begin(self, batch, logs=None)\\n def on_batch_end(self, batch, logs=None)\\n def on_train_begin(self, logs=None)\\n def on_train_end(self, logs=None)\\nclass EarlyStopping # Stop training when a monitored quantity has stopped improving.\\n def __init__(self, monitor='val_loss', min_delta=0.0, patience=0, verbose=0, mode='auto')\\n def on_train_begin(self, logs=None)\\n def on_epoch_end(self, epoch, logs=None)\\n def on_train_end(self, logs=None)\\nclass ModelCheckpoint # Save the model after every epoch.\\n def __init__(self, filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', period=1, prefix='')\\n def save_model(self, filepath, overwrite)\\n def on_epoch_end(self, epoch, logs=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py\",\n \"text\": \"pytorch_lightning/models/trainer.py\\ndef reduce_distributed_output(output, nb_gpus)\\nclass Trainer\\n def __init__(self, experiment=None, early_stop_callback=None, checkpoint_callback=None, gradient_clip=0, cluster=None, process_position=0, current_gpu_name=0, nb_gpu_nodes=1, gpus=None, progress_bar=True, overfit_pct=0.0, track_grad_norm=-1, check_val_every_n_epoch=1, fast_dev_run=False, accumulate_grad_batches=1, max_nb_epochs=1000, min_nb_epochs=1, train_percent_check=1.0, val_percent_check=1.0, test_percent_check=1.0, val_check_interval=1.0, log_save_interval=100, add_log_row_interval=10, distributed_backend='dp', use_amp=False, print_nan_grads=False, print_weights_summary=True, amp_level='O2', nb_sanity_val_steps=5) # :param experiment: Test-tube experiment\\n def restore_state_if_existing_checkpoint(self)\\n def data_parallel(self)\\n def __determine_data_use_amount(self, train_percent_check, val_percent_check, test_percent_check, overfit_pct) # Use less data for debugging purposes\\n def __get_model(self)\\n def __is_function_implemented(self, f_name)\\n def __is_overriden(self, f_name)\\n def __tng_tqdm_dic(self)\\n def tng_tqdm_dic(self) # Read-only for tqdm metrics\\n def __layout_bookeeping(self)\\n def __add_tqdm_metrics(self, metrics)\\n def __validation_forward(self, model, data_batch, batch_i, dataloader_i)\\n def validate(self, model, dataloader, max_batches, dataloader_i) # Run validation code\\n def get_dataloaders(self, model) # Dataloaders are provided by the model\\n def fit(self, model)\\n def init_optimizers(self, optimizers)\\n def __single_gpu_train(self, model)\\n def __dp_train(self, model)\\n def ddp_train(self, gpu_nb, model) # Entry point into a DP thread\\n def __init_tcp_connection(self) # Connect all procs in the world using the env:// init\\n def resolve_root_node_address(self, root_node)\\n def __run_pretrain_routine(self, model) # Sanity check a few things before starting actual training\\n def __train(self)\\n def run_tng_epoch(self)\\n def __metrics_to_scalars(self, metrics, blacklist=set())\\n def __log_vals_blacklist(self) # avoid logging some vals lightning uses to maintain state\\n def transfer_batch_to_gpu(self, batch, gpu_id)\\n def __tng_forward(self, data_batch, batch_nb, opt_idx) # Handle forward for each training case (distributed, single gpu, etc...)\\n def __clip_gradients(self)\\n def __print_nan_grads(self)\\n def __run_tng_batch(self, data_batch, batch_nb)\\n def __run_validation(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/pt_overrides/override_data_parallel.py\",\n \"text\": \"pytorch_lightning/pt_overrides/override_data_parallel.py\\ndef _find_tensors(obj) # Recursively find all tensors contained in the specified object.\\ndef get_a_var(obj)\\nclass LightningDataParallel # Override the forward call in lightning so it goes to training and validation ste\\n def forward(self, *inputs, **kwargs)\\n def parallel_apply(self, replicas, inputs, kwargs)\\nclass LightningDistributedDataParallel # Override the forward call in lightning so it goes to training and validation ste\\n def parallel_apply(self, replicas, inputs, kwargs)\\n def forward(self, *inputs, **kwargs)\\ndef parallel_apply(modules, inputs, kwargs_tup=None, devices=None) # Applies each `module` in :attr:`modules` in parallel on arguments\\n def _worker(i, module, input, kwargs, device=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/decorators.py\",\n \"text\": \"pytorch_lightning/root_module/decorators.py\\ndef data_loader(fn) # Decorator to make any fx with this use the lazy property\\n def _data_loader(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py\",\n \"text\": \"pytorch_lightning/root_module/root_module.py\\nclass LightningModule\\n def __init__(self, *args, **kwargs)\\n def forward(self, *args, **kwargs) # Expand model in into whatever you need.\\n def training_step(self, *args, **kwargs) # return loss, dict with metrics for tqdm\\n def validation_step(self, *args, **kwargs) # return whatever outputs will need to be aggregated in validation_end\\n def validation_end(self, outputs) # Outputs has the appended output after each validation step\\n def configure_optimizers(self) # Return a list of optimizers and a list of schedulers (could be empty)\\n def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i) # Do something instead of the standard optimizer behavior\\n def tng_dataloader(self) # Implement a PyTorch DataLoader\\n def test_dataloader(self) # Implement a PyTorch DataLoader\\n def val_dataloader(self) # Implement a PyTorch DataLoader\\n def load_from_metrics(cls, weights_path, tags_csv, on_gpu, map_location=None) # Primary way of loading model from csv weights path\\n def summarize(self)\\n def freeze(self)\\n def unfreeze(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/utilities/arg_parse.py\",\n \"text\": \"pytorch_lightning/utilities/arg_parse.py\\ndef add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/utilities/debugging.py\",\n \"text\": \"pytorch_lightning/utilities/debugging.py\\nclass MisconfigurationException\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.__init__","text":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.__init__\n def __init__(self, hparams):\n \"\"\"\n Pass in parsed HyperOptArgumentParser to the model\n :param hparams:\n \"\"\"\n # init superclass\n super(LightningTemplateModel, self).__init__()\n self.hparams = hparams\n\n self.batch_size = hparams.batch_size\n\n # if you specify an example input, the summary will show input/output for each layer\n self.example_input_array = torch.rand(5, 28 * 28)\n\n # build model\n self.__build_model()","label":0},{"cid":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.__build_model","text":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.__build_model\n def __build_model(self):\n \"\"\"\n Layout model\n :return:\n \"\"\"\n self.c_d1 = nn.Linear(in_features=self.hparams.in_features,\n out_features=self.hparams.hidden_dim)\n self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)\n self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)\n\n self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim,\n out_features=self.hparams.out_features)","label":0},{"cid":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.forward","text":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.forward\n def forward(self, x):\n \"\"\"\n No special modification required for lightning, define as you normally would\n :param x:\n :return:\n \"\"\"\n\n x = self.c_d1(x)\n x = torch.tanh(x)\n x = self.c_d1_bn(x)\n x = self.c_d1_drop(x)\n\n x = self.c_d2(x)\n logits = F.log_softmax(x, dim=1)\n\n return logits","label":0},{"cid":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.loss","text":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.loss\n def loss(self, labels, logits):\n nll = F.nll_loss(logits, labels)\n return nll","label":0},{"cid":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.training_step","text":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.training_step\n def training_step(self, data_batch, batch_i):\n \"\"\"\n Lightning calls this inside the training loop\n :param data_batch:\n :return:\n \"\"\"\n # forward pass\n x, y = data_batch\n x = x.view(x.size(0), -1)\n\n y_hat = self.forward(x)\n\n # calculate loss\n loss_val = self.loss(y, y_hat)\n\n # in DP mode (default) make sure if result is scalar, there's another dim in the beginning\n if self.trainer.use_dp:\n loss_val = loss_val.unsqueeze(0)\n\n output = OrderedDict({\n 'loss': loss_val\n })\n\n # can also return just a scalar instead of a dict (return loss_val)\n return output","label":0},{"cid":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.validation_step","text":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.validation_step\n def validation_step(self, data_batch, batch_i):\n \"\"\"\n Lightning calls this inside the validation loop\n :param data_batch:\n :return:\n \"\"\"\n x, y = data_batch\n x = x.view(x.size(0), -1)\n y_hat = self.forward(x)\n\n loss_val = self.loss(y, y_hat)\n\n # acc\n labels_hat = torch.argmax(y_hat, dim=1)\n val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)\n val_acc = torch.tensor(val_acc)\n\n if self.on_gpu:\n val_acc = val_acc.cuda(loss_val.device.index)\n\n # in DP mode (default) make sure if result is scalar, there's another dim in the beginning\n if self.trainer.use_dp:\n loss_val = loss_val.unsqueeze(0)\n val_acc = val_acc.unsqueeze(0)\n\n output = OrderedDict({\n 'val_loss': loss_val,\n 'val_acc': val_acc,\n })\n\n # can also return just a scalar instead of a dict (return loss_val)\n return output","label":0},{"cid":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.validation_end","text":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.validation_end\n def validation_end(self, outputs):\n \"\"\"\n Called at the end of validation to aggregate outputs\n :param outputs: list of individual outputs of each validation step\n :return:\n \"\"\"\n # if returned a scalar from validation_step, outputs is a list of tensor scalars\n # we return just the average in this case (if we want)\n # return torch.stack(outputs).mean()\n\n val_loss_mean = 0\n val_acc_mean = 0\n for output in outputs:\n val_loss = output['val_loss']\n\n # reduce manually when using dp\n if self.trainer.use_dp:\n val_loss = torch.mean(val_loss)\n val_loss_mean += val_loss\n\n # reduce manually when using dp\n val_acc = output['val_acc']\n if self.trainer.use_dp:\n val_acc = torch.mean(val_acc)\n\n val_acc_mean += val_acc\n\n val_loss_mean /= len(outputs)\n val_acc_mean /= len(outputs)\n tqdm_dic = {'val_loss': val_loss_mean, 'val_acc': val_acc_mean}\n return tqdm_dic","label":0},{"cid":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.configure_optimizers","text":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.configure_optimizers\n def configure_optimizers(self):\n \"\"\"\n return whatever optimizers we want here\n :return: list of optimizers\n \"\"\"\n optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)\n scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\n return [optimizer], [scheduler]","label":0},{"cid":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.__dataloader","text":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.__dataloader\n def __dataloader(self, train):\n # init data generators\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root=self.hparams.data_root, train=train,\n transform=transform, download=True)\n\n # when using multi-node we need to add the datasampler\n train_sampler = None\n batch_size = self.hparams.batch_size\n\n try:\n if self.on_gpu:\n train_sampler = DistributedSampler(dataset, rank=self.trainer.proc_rank)\n batch_size = batch_size // self.trainer.world_size # scale batch size\n except Exception:\n pass\n\n should_shuffle = train_sampler is None\n loader = DataLoader(\n dataset=dataset,\n batch_size=batch_size,\n shuffle=should_shuffle,\n sampler=train_sampler\n )\n\n return loader","label":0},{"cid":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.tng_dataloader","text":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.tng_dataloader\n @pl.data_loader\n def tng_dataloader(self):\n print('tng data loader called')\n return self.__dataloader(train=True)","label":0},{"cid":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.val_dataloader","text":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.val_dataloader\n @pl.data_loader\n def val_dataloader(self):\n print('val data loader called')\n return self.__dataloader(train=False)","label":0},{"cid":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.test_dataloader","text":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.test_dataloader\n @pl.data_loader\n def test_dataloader(self):\n print('test data loader called')\n return self.__dataloader(train=False)","label":0},{"cid":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.add_model_specific_args","text":"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.add_model_specific_args\n @staticmethod\n def add_model_specific_args(parent_parser, root_dir): # pragma: no cover\n \"\"\"\n Parameters you define here will be available to your model through self.hparams\n :param parent_parser:\n :param root_dir:\n :return:\n \"\"\"\n parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])\n\n # param overwrites\n # parser.set_defaults(gradient_clip=5.0)\n\n # network params\n parser.add_argument('--in_features', default=28 * 28, type=int)\n parser.add_argument('--out_features', default=10, type=int)\n # use 500 for CPU, 50000 for GPU to see speed difference\n parser.add_argument('--hidden_dim', default=50000, type=int)\n parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)\n\n # data\n parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)\n\n # training params (opt)\n parser.opt_list('--learning_rate', default=0.001 * 8, type=float,\n options=[0.0001, 0.0005, 0.001, 0.005],\n tunable=False)\n parser.opt_list('--optimizer_name', default='adam', type=str,\n options=['adam'], tunable=False)\n\n # if using 2 nodes with 4 gpus each the batch size here\n # (256) will be 256 / (2*8) = 16 per gpu\n parser.opt_list('--batch_size', default=256 * 8, type=int,\n options=[32, 64, 128, 256], tunable=False,\n help='batch size will be divided over all gpus being used across all nodes')\n return parser","label":0},{"cid":"examples/templates/gan.py::Generator.__init__","text":"examples/templates/gan.py::Generator.__init__\n def __init__(self, latent_dim, img_shape):\n super(Generator, self).__init__()\n self.img_shape = img_shape\n\n def block(in_feat, out_feat, normalize=True):\n layers = [nn.Linear(in_feat, out_feat)]\n if normalize:\n layers.append(nn.BatchNorm1d(out_feat, 0.8))\n layers.append(nn.LeakyReLU(0.2, inplace=True))\n return layers\n\n self.model = nn.Sequential(\n *block(latent_dim, 128, normalize=False),\n *block(128, 256),\n *block(256, 512),\n *block(512, 1024),\n nn.Linear(1024, int(np.prod(img_shape))),\n nn.Tanh()\n )","label":0},{"cid":"examples/templates/gan.py::Generator.__init__.block","text":"examples/templates/gan.py::Generator.__init__.block\n def block(in_feat, out_feat, normalize=True):\n layers = [nn.Linear(in_feat, out_feat)]\n if normalize:\n layers.append(nn.BatchNorm1d(out_feat, 0.8))\n layers.append(nn.LeakyReLU(0.2, inplace=True))\n return layers","label":0},{"cid":"examples/templates/gan.py::Generator.forward","text":"examples/templates/gan.py::Generator.forward\n def forward(self, z):\n img = self.model(z)\n img = img.view(img.size(0), *self.img_shape)\n return img","label":0},{"cid":"examples/templates/gan.py::Discriminator.__init__","text":"examples/templates/gan.py::Discriminator.__init__\n def __init__(self, img_shape):\n super(Discriminator, self).__init__()\n\n self.model = nn.Sequential(\n nn.Linear(int(np.prod(img_shape)), 512),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Linear(512, 256),\n nn.LeakyReLU(0.2, inplace=True),\n nn.Linear(256, 1),\n nn.Sigmoid(),\n )","label":0},{"cid":"examples/templates/gan.py::Discriminator.forward","text":"examples/templates/gan.py::Discriminator.forward\n def forward(self, img):\n img_flat = img.view(img.size(0), -1)\n validity = self.model(img_flat)\n\n return validity","label":0},{"cid":"examples/templates/gan.py::GAN.__init__","text":"examples/templates/gan.py::GAN.__init__\n def __init__(self, hparams):\n super(GAN, self).__init__()\n self.hparams = hparams\n\n # networks\n mnist_shape = (1, 28, 28)\n self.generator = Generator(latent_dim=hparams.latent_dim, img_shape=mnist_shape)\n self.discriminator = Discriminator(img_shape=mnist_shape)\n\n # cache for generated images\n self.generated_imgs = None","label":0},{"cid":"examples/templates/gan.py::GAN.forward","text":"examples/templates/gan.py::GAN.forward\n def forward(self, z):\n return self.generator(z)","label":0},{"cid":"examples/templates/gan.py::GAN.adversarial_loss","text":"examples/templates/gan.py::GAN.adversarial_loss\n def adversarial_loss(self, y_hat, y):\n return F.binary_cross_entropy(y_hat, y)","label":0},{"cid":"examples/templates/gan.py::GAN.training_step","text":"examples/templates/gan.py::GAN.training_step\n def training_step(self, batch, batch_nb, optimizer_i):\n imgs, _ = batch\n\n # train generator\n if optimizer_i == 0:\n # sample noise\n z = torch.randn(imgs.shape[0], self.hparams.latent_dim)\n\n # match gpu device (or keep as cpu)\n if self.on_gpu:\n z = z.cuda(imgs.device.index)\n\n # generate images\n self.generated_imgs = self.forward(z)\n\n # log sampled images\n sample_imgs = self.generated_imgs[:6]\n grid = torchvision.utils.make_grid(sample_imgs)\n self.experiment.add_image('generated_images', grid, 0)\n\n # ground truth result (ie: all fake)\n valid = torch.ones(imgs.size(0), 1)\n\n # adversarial loss is binary cross-entropy\n g_loss = self.adversarial_loss(self.discriminator(self.generated_imgs), valid)\n\n return g_loss\n\n # train discriminator\n if optimizer_i == 1:\n # Measure discriminator's ability to classify real from generated samples\n\n # how well can it label as real?\n valid = torch.ones(imgs.size(0), 1)\n real_loss = self.adversarial_loss(self.discriminator(imgs), valid)\n\n # how well can it label as fake?\n fake = torch.zeros(imgs.size(0), 1)\n fake_loss = self.adversarial_loss(self.discriminator(self.generated_imgs.detach()), fake)\n\n # discriminator loss is the average of these\n d_loss = (real_loss + fake_loss) / 2\n\n return d_loss","label":0},{"cid":"examples/templates/gan.py::GAN.configure_optimizers","text":"examples/templates/gan.py::GAN.configure_optimizers\n def configure_optimizers(self):\n lr = self.hparams.lr\n b1 = self.hparams.b1\n b2 = self.hparams.b2\n\n opt_g = torch.optim.Adam(self.generator.parameters(), lr=lr, betas=(b1, b2))\n opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=(b1, b2))\n return [opt_g, opt_d], []","label":0},{"cid":"examples/templates/gan.py::GAN.tng_dataloader","text":"examples/templates/gan.py::GAN.tng_dataloader\n @pl.data_loader\n def tng_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize([0.5], [0.5])])\n dataset = MNIST(os.getcwd(), train=True, download=True, transform=transform)\n return DataLoader(dataset, batch_size=self.hparams.batch_size)","label":0},{"cid":"examples/templates/gan.py::main","text":"examples/templates/gan.py::main\ndef main(hparams):\n # save tensorboard logs\n exp = Experiment(save_dir=os.getcwd())\n\n # init model\n model = GAN(hparams)\n\n # fit trainer on CPU\n trainer = pl.Trainer(experiment=exp, max_nb_epochs=200)\n trainer.fit(model)","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.__init__","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.__init__\n def __init__(self):\n self.validation_data = None\n self.model = None","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_params","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_params\n def set_params(self, params):\n self.params = params","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_model","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_model\n def set_model(self, model):\n if type(model) is LightningDistributedDataParallel:\n model = model.module\n self.model = model","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_begin","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_begin\n def on_epoch_begin(self, epoch, logs=None):\n pass","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_end","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_end\n def on_epoch_end(self, epoch, logs=None):\n pass","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_begin","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_begin\n def on_batch_begin(self, batch, logs=None):\n pass","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_end","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_end\n def on_batch_end(self, batch, logs=None):\n pass","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_begin","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_begin\n def on_train_begin(self, logs=None):\n pass","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_end","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_end\n def on_train_end(self, logs=None):\n pass","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.__init__","text":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.__init__\n def __init__(self, monitor='val_loss',\n min_delta=0.0, patience=0, verbose=0, mode='auto'):\n super(EarlyStopping, self).__init__()\n\n self.monitor = monitor\n self.patience = patience\n self.verbose = verbose\n self.min_delta = min_delta\n self.wait = 0\n self.stopped_epoch = 0\n\n if mode not in ['auto', 'min', 'max']:\n print('EarlyStopping mode %s is unknown, fallback to auto mode.' % mode)\n mode = 'auto'\n\n if mode == 'min':\n self.monitor_op = np.less\n elif mode == 'max':\n self.monitor_op = np.greater\n else:\n if 'acc' in self.monitor:\n self.monitor_op = np.greater\n else:\n self.monitor_op = np.less\n\n if self.monitor_op == np.greater:\n self.min_delta *= 1\n else:\n self.min_delta *= -1\n\n self.on_train_begin()","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_begin","text":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_begin\n def on_train_begin(self, logs=None):\n # Allow instances to be re-used\n self.wait = 0\n self.stopped_epoch = 0\n self.best = np.Inf if self.monitor_op == np.less else -np.Inf","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_epoch_end","text":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_epoch_end\n def on_epoch_end(self, epoch, logs=None):\n current = logs.get(self.monitor)\n stop_training = False\n if current is None:\n print('Early stopping conditioned on metric `%s` '\n 'which is not available. Available metrics are: %s' %\n (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning)\n exit(-1)\n\n if self.monitor_op(current - self.min_delta, self.best):\n self.best = current\n self.wait = 0\n else:\n self.wait += 1\n if self.wait >= self.patience:\n self.stopped_epoch = epoch\n stop_training = True\n self.on_train_end()\n\n return stop_training","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_end","text":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_end\n def on_train_end(self, logs=None):\n if self.stopped_epoch > 0 and self.verbose > 0:\n print('Epoch %05d: early stopping' % (self.stopped_epoch + 1))","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.__init__","text":"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.__init__\n def __init__(self, filepath, monitor='val_loss', verbose=0,\n save_best_only=False, save_weights_only=False,\n mode='auto', period=1, prefix=''):\n super(ModelCheckpoint, self).__init__()\n self.monitor = monitor\n self.verbose = verbose\n self.filepath = filepath\n self.save_best_only = save_best_only\n self.save_weights_only = save_weights_only\n self.period = period\n self.epochs_since_last_save = 0\n self.prefix = prefix\n\n if mode not in ['auto', 'min', 'max']:\n print('ModelCheckpoint mode %s is unknown, '\n 'fallback to auto mode.' % (mode), RuntimeWarning)\n mode = 'auto'\n\n if mode == 'min':\n self.monitor_op = np.less\n self.best = np.Inf\n elif mode == 'max':\n self.monitor_op = np.greater\n self.best = -np.Inf\n else:\n if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):\n self.monitor_op = np.greater\n self.best = -np.Inf\n else:\n self.monitor_op = np.less\n self.best = np.Inf","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.save_model","text":"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.save_model\n def save_model(self, filepath, overwrite):\n dirpath = '/'.join(filepath.split('/')[:-1])\n\n # make paths\n os.makedirs(os.path.dirname(filepath), exist_ok=True)\n\n if overwrite:\n for filename in os.listdir(dirpath):\n if self.prefix in filename:\n path_to_delete = os.path.join(dirpath, filename)\n try:\n shutil.rmtree(path_to_delete)\n except OSError:\n os.remove(path_to_delete)\n\n # delegate the saving to the model\n self.save_function(filepath)","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.on_epoch_end","text":"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.on_epoch_end\n def on_epoch_end(self, epoch, logs=None):\n logs = logs or {}\n self.epochs_since_last_save += 1\n if self.epochs_since_last_save >= self.period:\n self.epochs_since_last_save = 0\n filepath = '{}/{}_ckpt_epoch_{}.ckpt'.format(self.filepath, self.prefix, epoch + 1)\n if self.save_best_only:\n current = logs.get(self.monitor)\n if current is None:\n print('Can save best model only with %s available,'\n ' skipping.' % (self.monitor), RuntimeWarning)\n else:\n if self.monitor_op(current, self.best):\n if self.verbose > 0:\n print('\\nEpoch %05d: %s improved from %0.5f to %0.5f,'\n ' saving model to %s'\n % (epoch + 1, self.monitor, self.best,\n current, filepath))\n self.best = current\n self.save_model(filepath, overwrite=True)\n\n else:\n if self.verbose > 0:\n print('\\nEpoch %05d: %s did not improve' %\n (epoch + 1, self.monitor))\n else:\n if self.verbose > 0:\n print('\\nEpoch %05d: saving model to %s' % (epoch + 1, filepath))\n self.save_model(filepath, overwrite=False)","label":0},{"cid":"pytorch_lightning/models/trainer.py::reduce_distributed_output","text":"pytorch_lightning/models/trainer.py::reduce_distributed_output\ndef reduce_distributed_output(output, nb_gpus):\n if nb_gpus <= 1:\n return output\n\n # when using DP, we get one output per gpu\n # average outputs and return\n if type(output) is torch.Tensor:\n return output.mean()\n\n for k, v in output.items():\n # recurse on nested dics\n if isinstance(output[k], dict):\n output[k] = reduce_distributed_output(output[k], nb_gpus)\n\n # reduce only metrics that have the same nb of gpus\n elif output[k].size(0) == nb_gpus:\n reduced = torch.mean(output[k])\n output[k] = reduced\n return output","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__init__","text":"pytorch_lightning/models/trainer.py::Trainer.__init__\n def __init__(self,\n experiment=None,\n early_stop_callback=None,\n checkpoint_callback=None,\n gradient_clip=0,\n cluster=None,\n process_position=0,\n current_gpu_name=0,\n nb_gpu_nodes=1,\n gpus=None,\n progress_bar=True,\n overfit_pct=0.0,\n track_grad_norm=-1,\n check_val_every_n_epoch=1,\n fast_dev_run=False,\n accumulate_grad_batches=1,\n max_nb_epochs=1000,\n min_nb_epochs=1,\n train_percent_check=1.0,\n val_percent_check=1.0,\n test_percent_check=1.0,\n val_check_interval=1.0,\n log_save_interval=100,\n add_log_row_interval=10,\n distributed_backend='dp',\n use_amp=False,\n print_nan_grads=False,\n print_weights_summary=True,\n amp_level='O2',\n nb_sanity_val_steps=5):\n \"\"\"\n\n :param experiment: Test-tube experiment\n :param early_stop_callback: from pytorch_lightning import EarlyStopping\n :param checkpoint_callback: from pytorch_lightning import Checkpoint\n :param gradient_clip:\n :param cluster:\n :param process_position:\n :param current_gpu_name:\n :param nb_gpu_nodes:\n :param gpus:\n :param progress_bar:\n :param overfit_pct:\n :param track_grad_norm:\n :param check_val_every_n_epoch:\n :param fast_dev_run:\n :param accumulate_grad_batches:\n :param max_nb_epochs:\n :param min_nb_epochs:\n :param train_percent_check:\n :param val_percent_check:\n :param test_percent_check:\n :param val_check_interval:\n :param log_save_interval:\n","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.restore_state_if_existing_checkpoint","text":"pytorch_lightning/models/trainer.py::Trainer.restore_state_if_existing_checkpoint\n def restore_state_if_existing_checkpoint(self):\n # restore trainer state and model if there is a weight for this experiment\n last_epoch = -1\n last_ckpt_name = None\n\n # do nothing if there's not dir or callback\n no_ckpt_callback = self.checkpoint_callback is None\n if no_ckpt_callback or not os.path.exists(self.checkpoint_callback.filepath):\n return\n\n # find last epoch\n checkpoints = os.listdir(self.checkpoint_callback.filepath)\n for name in checkpoints:\n # ignore hpc ckpts\n if 'hpc_' in name:\n continue\n\n if '.ckpt' in name:\n epoch = name.split('epoch_')[1]\n epoch = int(re.sub('[^0-9]', '', epoch))\n\n if epoch > last_epoch:\n last_epoch = epoch\n last_ckpt_name = name\n\n # restore last checkpoint\n if last_ckpt_name is not None:\n last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name)\n self.restore(last_ckpt_path, self.on_gpu)\n print(f'model and trainer restored from checkpoint: {last_ckpt_path}')","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.data_parallel","text":"pytorch_lightning/models/trainer.py::Trainer.data_parallel\n @property\n def data_parallel(self):\n return self.use_dp or self.use_ddp","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__determine_data_use_amount","text":"pytorch_lightning/models/trainer.py::Trainer.__determine_data_use_amount\n def __determine_data_use_amount(self, train_percent_check, val_percent_check,\n test_percent_check, overfit_pct):\n \"\"\"\n Use less data for debugging purposes\n \"\"\"\n self.train_percent_check = train_percent_check\n self.val_percent_check = val_percent_check\n self.test_percent_check = test_percent_check\n if overfit_pct > 0:\n self.train_percent_check = overfit_pct\n self.val_percent_check = overfit_pct\n self.test_percent_check = overfit_pct","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__get_model","text":"pytorch_lightning/models/trainer.py::Trainer.__get_model\n def __get_model(self):\n return self.model.module if self.data_parallel else self.model","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__is_function_implemented","text":"pytorch_lightning/models/trainer.py::Trainer.__is_function_implemented\n def __is_function_implemented(self, f_name):\n model = self.__get_model()\n f_op = getattr(model, f_name, None)\n return callable(f_op)","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__is_overriden","text":"pytorch_lightning/models/trainer.py::Trainer.__is_overriden\n def __is_overriden(self, f_name):\n model = self.__get_model()\n super_object = super(model.__class__, model)\n\n # when code pointers are different, it was overriden\n is_overriden = getattr(model, f_name).__code__ is not getattr(super_object, f_name).__code__\n return is_overriden","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__tng_tqdm_dic","text":"pytorch_lightning/models/trainer.py::Trainer.__tng_tqdm_dic\n @property\n def __tng_tqdm_dic(self):\n tqdm_dic = {\n 'loss': '{0:.3f}'.format(self.avg_loss),\n 'epoch': '{}'.format(self.current_epoch),\n 'batch_nb': '{}'.format(self.batch_nb),\n }\n\n if self.experiment is not None:\n tqdm_dic['v_nb'] = self.experiment.version\n\n tqdm_dic.update(self.tqdm_metrics)\n\n if self.on_gpu:\n tqdm_dic['gpu'] = '{}'.format(self.current_gpu_name)\n\n return tqdm_dic","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.tng_tqdm_dic","text":"pytorch_lightning/models/trainer.py::Trainer.tng_tqdm_dic\n @property\n def tng_tqdm_dic(self):\n \"\"\"\n Read-only for tqdm metrics\n :return:\n \"\"\"\n return self.__tng_tqdm_dic","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__layout_bookeeping","text":"pytorch_lightning/models/trainer.py::Trainer.__layout_bookeeping\n def __layout_bookeeping(self):\n\n # determine number of training batches\n self.nb_tng_batches = len(self.tng_dataloader)\n self.nb_tng_batches = int(self.nb_tng_batches * self.train_percent_check)\n\n # determine number of validation batches\n # val datasets could be none, 1 or 2+\n self.nb_val_batches = 0\n if self.val_dataloader is not None:\n self.nb_val_batches = sum(len(dataloader) for dataloader in self.val_dataloader)\n\n self.nb_val_batches = int(self.nb_val_batches * self.val_percent_check)\n self.nb_val_batches = max(1, self.nb_val_batches)\n\n # determine number of test batches\n self.nb_test_batches = len(self.test_dataloader) if self.test_dataloader is not None else 0\n self.nb_test_batches = int(self.nb_test_batches * self.test_percent_check)\n\n # determine when to check validation\n self.val_check_batch = int(self.nb_tng_batches * self.val_check_interval)\n self.val_check_batch = max(1, self.val_check_batch)","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__add_tqdm_metrics","text":"pytorch_lightning/models/trainer.py::Trainer.__add_tqdm_metrics\n def __add_tqdm_metrics(self, metrics):\n for k, v in metrics.items():\n if type(v) is torch.Tensor:\n v = v.item()\n\n self.tqdm_metrics[k] = v","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__validation_forward","text":"pytorch_lightning/models/trainer.py::Trainer.__validation_forward\n def __validation_forward(self, model, data_batch, batch_i, dataloader_i):\n # make dataloader_i arg in validation_step optional\n args = [data_batch, batch_i]\n if len(self.val_dataloader) > 1:\n args.append(dataloader_i)\n\n if self.use_ddp:\n output = model(*args)\n elif self.use_dp:\n output = model(*args)\n elif self.single_gpu:\n # put inputs on gpu manually\n gpu_id = self.data_parallel_device_ids[0]\n data_batch = self.transfer_batch_to_gpu(data_batch, gpu_id)\n args[0] = data_batch\n\n # do non dp, ddp step\n output = model.validation_step(*args)\n\n else:\n # CPU\n output = model.validation_step(*args)\n\n return output","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.validate","text":"pytorch_lightning/models/trainer.py::Trainer.validate\n def validate(self, model, dataloader, max_batches, dataloader_i):\n \"\"\"\n Run validation code\n :param model: PT model\n :param dataloader: PT dataloader\n :param max_batches: Scalar\n :return:\n \"\"\"\n\n # enable eval mode\n model.zero_grad()\n model.eval()\n\n # disable gradients to save memory\n torch.set_grad_enabled(False)\n\n # bookkeeping\n outputs = []\n\n # run training\n for batch_i, data_batch in enumerate(dataloader):\n\n if data_batch is None: # pragma: no cover\n continue\n\n # stop short when on fast dev run\n if max_batches is not None and batch_i >= max_batches:\n break\n\n # -----------------\n # RUN VALIDATION STEP\n # -----------------\n output = self.__validation_forward(model, data_batch, batch_i, dataloader_i)\n\n # track outputs for collation\n outputs.append(output)\n\n # batch done\n if self.progress_bar and self.prog_bar is not None:\n self.prog_bar.update(1)\n\n # give model a chance to do something with the outputs (and method defined)\n val_results = {}\n if self.__is_overriden('validation_end'):\n if self.data_parallel:\n val_results = model.module.validation_end(outputs)\n else:\n val_results = model.validation_end(outputs)\n\n # enable train mode again\n model.train()\n\n # enable gradients to save memory\n torch.set_grad_enabled(True)\n\n return val_results","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.get_dataloaders","text":"pytorch_lightning/models/trainer.py::Trainer.get_dataloaders\n def get_dataloaders(self, model):\n \"\"\"\n Dataloaders are provided by the model\n :param model:\n :return:\n \"\"\"\n self.tng_dataloader = model.tng_dataloader\n\n self.test_dataloader = model.test_dataloader\n self.val_dataloader = model.val_dataloader\n\n # handle returning an actual dataloader instead of a list of loaders\n have_val_loaders = self.val_dataloader is not None\n if have_val_loaders and not isinstance(self.val_dataloader, list):\n self.val_dataloader = [self.val_dataloader]\n\n if self.use_ddp and not isinstance(self.tng_dataloader.sampler, DistributedSampler):\n msg = \"\"\"\nYou're using multiple gpus and multiple nodes without using a DistributedSampler\nto assign a subset of your data to each process. To silence this warning, pass a\nDistributedSampler to your DataLoader.\n\nie: this:\ndataset = myDataset()\ndataloader = Dataloader(dataset)\n\nbecomes:\ndataset = myDataset()\ndist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)\ndataloader = Dataloader(dataset, sampler=dist_sampler)\n\nIf you want each process to load the full dataset, ignore this warning.\n\"\"\"\n warnings.warn(msg)\n\n if self.use_ddp and self.val_dataloader is not None:\n for dataloader in self.val_dataloader:\n if not isinstance(dataloader, DistributedSampler):\n msg = \"\"\"\n Your val_dataloader(s) are not all DistributedSamplers.\n You're using multiple gpus and multiple nodes without using a DistributedSampler\n to assign a subset of your data to each process. To silence this warning, pass a\n DistributedSampler to your DataLoader.\n\n ie: this:\n dataset = myDataset()\n dataloader = Dataloader(dataset)\n\n becomes:\n ","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.fit","text":"pytorch_lightning/models/trainer.py::Trainer.fit\n def fit(self, model):\n\n # when using multi-node or DDP within a node start each module in a separate process\n if self.use_ddp:\n # must copy only the meta of the exp so it survives pickle/unpickle\n # when going to new process\n if self.experiment is not None:\n self.experiment = self.experiment.get_meta_copy()\n\n if self.is_slurm_managing_tasks:\n task = int(os.environ['SLURM_LOCALID'])\n self.ddp_train(task, model)\n else:\n nb_gpus = self.nb_requested_gpus\n nb_tasks = self.nb_slurm_tasks\n msg = f\"\"\"\n You requested {nb_gpus}s GPUs but launched {nb_tasks}s slurm tasks.\n We will launch {nb_gpus}s processes for you.\n We recommend you let slurm manage the processes by setting:\n --ntasks-per-node={nb_gpus}s\n If you're not using SLURM, ignore this message!\n \"\"\"\n warnings.warn(msg)\n mp.spawn(self.ddp_train, nprocs=len(self.data_parallel_device_ids), args=(model, ))\n\n # 1 gpu or dp option triggers training using DP module\n # easier to avoid NCCL issues\n elif self.use_dp:\n self.__dp_train(model)\n\n elif self.single_gpu:\n self.__single_gpu_train(model)\n\n # ON CPU\n else:\n # run through amp wrapper\n if self.use_amp:\n raise MisconfigurationException('amp + cpu is not supported.'\n ' Please use a GPU option')\n\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n self.__run_pretrain_routine(model)\n\n # return 1 when finished\n # used for testing or when we need to know t","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.init_optimizers","text":"pytorch_lightning/models/trainer.py::Trainer.init_optimizers\n def init_optimizers(self, optimizers):\n\n # single optimizer\n if isinstance(optimizers, Optimizer):\n return [optimizers], []\n\n # two lists\n elif len(optimizers) == 2 and isinstance(optimizers[0], list):\n optimizers, lr_schedulers = optimizers\n return optimizers, lr_schedulers\n\n # single list or tuple\n elif isinstance(optimizers, list) or isinstance(optimizers, tuple):\n return optimizers, []","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__single_gpu_train","text":"pytorch_lightning/models/trainer.py::Trainer.__single_gpu_train\n def __single_gpu_train(self, model):\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n model.cuda(self.data_parallel_device_ids[0])\n\n if self.use_amp:\n # An example\n model, optimizers = amp.initialize(\n model, self.optimizers, opt_level=self.amp_level,\n )\n self.optimizers = optimizers\n\n self.__run_pretrain_routine(model)","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__dp_train","text":"pytorch_lightning/models/trainer.py::Trainer.__dp_train\n def __dp_train(self, model):\n\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n model.cuda(self.data_parallel_device_ids[0])\n\n # check for this bug (amp + dp + !01 doesn't work)\n # https://github.com/NVIDIA/apex/issues/227\n if self.use_dp and self.use_amp:\n m = f\"\"\"\n Amp level {self.amp_level} with DataParallel is not supported.\n See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227.\n We recommend you switch to ddp if you want to use amp\n \"\"\"\n raise MisconfigurationException(m)\n\n model = LightningDataParallel(model, device_ids=self.data_parallel_device_ids)\n\n self.__run_pretrain_routine(model)","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.ddp_train","text":"pytorch_lightning/models/trainer.py::Trainer.ddp_train\n def ddp_train(self, gpu_nb, model):\n \"\"\"\n Entry point into a DP thread\n :param gpu_nb:\n :param model:\n :param cluster_obj:\n :return:\n \"\"\"\n # node rank using relative slurm id\n # otherwise default to node rank 0\n try:\n node_id = os.environ['SLURM_NODEID']\n self.node_rank = int(node_id)\n except Exception:\n self.node_rank = 0\n\n # recover original exp before went into process\n # init in write mode only on proc 0\n if self.experiment is not None:\n self.experiment.debug = self.proc_rank > 0\n self.experiment = self.experiment.get_non_ddp_exp()\n\n # show progbar only on prog_rank 0\n self.prog_bar = self.prog_bar and self.node_rank == 0 and gpu_nb == 0\n\n # determine which process we are and world size\n self.proc_rank = self.node_rank * len(self.data_parallel_device_ids) + gpu_nb\n self.world_size = self.nb_gpu_nodes * len(self.data_parallel_device_ids)\n\n # let the exp know the rank to avoid overwriting logs\n if self.experiment is not None:\n self.experiment.rank = self.proc_rank\n\n # set up server using proc 0's ip address\n # try to init for 20 times at max in case ports are taken\n # where to store ip_table\n self.__init_tcp_connection()\n\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n # MODEL\n # copy model to each gpu\n torch.cuda.set_device(gpu_nb)\n model.cuda(gpu_nb)\n\n # AMP\n # run through amp wrapper before going to distributed DP\n if self.use_amp:\n # An example\n model, optimizers = amp.initialize(\n model, self.optimizers, opt_level=self.amp_level,\n )\n self.op","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__init_tcp_connection","text":"pytorch_lightning/models/trainer.py::Trainer.__init_tcp_connection\n def __init_tcp_connection(self):\n \"\"\"\n Connect all procs in the world using the env:// init\n Use the first node as the root address\n :param port:\n :param tries:\n :return:\n \"\"\"\n # sets the appropriate port\n try:\n port = os.environ['MASTER_PORT']\n except Exception:\n port = 12910\n os.environ['MASTER_PORT'] = str(port)\n\n # figure out the root node addr\n try:\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\n except Exception:\n root_node = '127.0.0.2'\n\n root_node = self.resolve_root_node_address(root_node)\n os.environ['MASTER_ADDR'] = root_node\n\n dist.init_process_group(\"nccl\", rank=self.proc_rank, world_size=self.world_size)","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.resolve_root_node_address","text":"pytorch_lightning/models/trainer.py::Trainer.resolve_root_node_address\n def resolve_root_node_address(self, root_node):\n if '[' in root_node:\n name = root_node.split('[')[0]\n number = root_node.split(',')[0]\n if '-' in number:\n number = number.split('-')[0]\n\n number = re.sub('[^0-9]', '', number)\n root_node = name + number\n\n return root_node","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__run_pretrain_routine","text":"pytorch_lightning/models/trainer.py::Trainer.__run_pretrain_routine\n def __run_pretrain_routine(self, model):\n \"\"\"\n Sanity check a few things before starting actual training\n :param model:\n :return:\n \"\"\"\n ref_model = model\n if self.data_parallel:\n ref_model = model.module\n\n ref_model.trainer = self\n\n # set local properties on the model\n ref_model.on_gpu = self.on_gpu\n\n # transfer data loaders from model\n self.get_dataloaders(ref_model)\n\n # init training constants\n self.__layout_bookeeping()\n\n # print model summary\n if self.proc_rank == 0 and self.print_weights_summary:\n ref_model.summarize()\n\n # give model convenience properties\n ref_model.trainer = self\n\n if self.experiment is not None:\n ref_model.experiment = self.experiment\n\n # save exp to get started\n if self.proc_rank == 0 and self.experiment is not None:\n self.experiment.save()\n\n # track model now.\n # if cluster resets state, the model will update with the saved weights\n self.model = model\n\n # restore training and model before hpc call\n self.restore_state_if_existing_checkpoint()\n\n # enable cluster checkpointing\n # also restores training state\n # hpc checkpoint overrides any other checkpoints loaded before\n if self.cluster is not None: # pragma: no cover\n self.enable_auto_hpc_walltime_manager()\n\n # run tiny validation (if validation defined) to make sure program won't crash during val\n ref_model.on_sanity_check_start()\n if self.val_dataloader is not None:\n for ds_i, dataloader in enumerate(self.val_dataloader):\n self.validate(model, dataloader, self.nb_sanity_val_steps, ds_i)\n\n # ---------------------------\n # CORE TRAINING LOOP\n # ---------------------------\n self.__train","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__train","text":"pytorch_lightning/models/trainer.py::Trainer.__train\n def __train(self):\n # run all epochs\n for epoch_nb in range(self.current_epoch, self.max_nb_epochs):\n # get model\n model = self.__get_model()\n\n # update training progress in trainer and model\n model.current_epoch = epoch_nb\n self.current_epoch = epoch_nb\n self.total_batches = self.nb_tng_batches + self.nb_val_batches\n self.batch_loss_value = 0 # accumulated grads\n\n # init progbar when requested\n if self.progress_bar:\n self.prog_bar = tqdm.tqdm(range(self.total_batches),\n position=self.process_position)\n\n # -----------------\n # RUN TNG EPOCH\n # -----------------\n self.run_tng_epoch()\n\n # update LR schedulers\n if self.lr_schedulers is not None:\n for lr_scheduler in self.lr_schedulers:\n lr_scheduler.step()\n\n # early stopping\n met_min_epochs = epoch_nb > self.min_nb_epochs\n if self.enable_early_stop and met_min_epochs:\n should_stop = self.early_stop_callback.on_epoch_end(epoch=epoch_nb,\n logs=self.__tng_tqdm_dic)\n # stop training\n stop = should_stop and met_min_epochs\n if stop:\n return","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.run_tng_epoch","text":"pytorch_lightning/models/trainer.py::Trainer.run_tng_epoch\n def run_tng_epoch(self):\n # before epoch hook\n if self.__is_function_implemented('on_epoch_start'):\n model = self.__get_model()\n model.on_epoch_start()\n\n # run epoch\n for batch_nb, data_batch in enumerate(self.tng_dataloader):\n self.batch_nb = batch_nb\n self.global_step += 1\n\n model = self.__get_model()\n model.global_step = self.global_step\n\n # stop when the flag is changed or we've gone past the amount\n # requested in the batches\n self.total_batch_nb += 1\n met_batch_limit = batch_nb > self.nb_tng_batches\n if met_batch_limit:\n break\n\n # ---------------\n # RUN TRAIN STEP\n # ---------------\n batch_result = self.__run_tng_batch(data_batch, batch_nb)\n early_stop_epoch = batch_result == -1\n\n # ---------------\n # RUN VAL STEP\n # ---------------\n is_val_check_batch = (batch_nb + 1) % self.val_check_batch == 0\n if self.fast_dev_run or is_val_check_batch or early_stop_epoch:\n self.__run_validation()\n\n # when batch should be saved\n if (batch_nb + 1) % self.log_save_interval == 0 or early_stop_epoch:\n if self.proc_rank == 0 and self.experiment is not None:\n self.experiment.save()\n\n # when metrics should be logged\n if batch_nb % self.add_log_row_interval == 0 or early_stop_epoch:\n # count items in memory\n # nb_params, nb_tensors = count_mem_items()\n\n model = self.__get_model()\n metrics = self.__tng_tqdm_dic\n\n # add gpu memory\n if self.on_gpu:\n mem_map = get_gpu_memory_map()\n metrics.update(mem_map)\n\n # add norms\n ","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__metrics_to_scalars","text":"pytorch_lightning/models/trainer.py::Trainer.__metrics_to_scalars\n def __metrics_to_scalars(self, metrics, blacklist=set()):\n new_metrics = {}\n for k, v in metrics.items():\n if type(v) is torch.Tensor:\n v = v.item()\n\n if type(v) is dict:\n v = self.__metrics_to_scalars(v)\n\n if k not in blacklist:\n new_metrics[k] = float(v)\n\n return new_metrics","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__log_vals_blacklist","text":"pytorch_lightning/models/trainer.py::Trainer.__log_vals_blacklist\n def __log_vals_blacklist(self):\n \"\"\"avoid logging some vals lightning uses to maintain state\"\"\"\n blacklist = {'batch_nb', 'v_nb', 'gpu'}\n return blacklist","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.transfer_batch_to_gpu","text":"pytorch_lightning/models/trainer.py::Trainer.transfer_batch_to_gpu\n def transfer_batch_to_gpu(self, batch, gpu_id):\n # base case\n if isinstance(batch, torch.Tensor):\n return batch.cuda(gpu_id)\n\n # when list\n elif isinstance(batch, list):\n for i, x in enumerate(batch):\n batch[i] = self.transfer_batch_to_gpu(x, gpu_id)\n return batch\n\n # when dict\n elif isinstance(batch, dict):\n for k, v in batch.items():\n batch[k] = self.transfer_batch_to_gpu(v, gpu_id)\n\n return batch","label":1},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__tng_forward","text":"pytorch_lightning/models/trainer.py::Trainer.__tng_forward\n def __tng_forward(self, data_batch, batch_nb, opt_idx):\n \"\"\"\n Handle forward for each training case (distributed, single gpu, etc...)\n :param data_batch:\n :param batch_nb:\n :return:\n \"\"\"\n # ---------------\n # FORWARD\n # ---------------\n # enable not needing to add opt_idx to training_step\n args = [data_batch, batch_nb]\n if len(self.optimizers) > 1:\n args.append(opt_idx)\n\n if self.use_ddp:\n output = self.model(*args)\n elif self.use_dp:\n output = self.model(*args)\n elif self.single_gpu:\n gpu_id = self.data_parallel_device_ids[0]\n data_batch = self.transfer_batch_to_gpu(data_batch, gpu_id)\n args[0] = data_batch\n output = self.model.training_step(*args)\n\n else:\n output = self.model.training_step(*args)\n\n # ---------------\n # TQDM metrics\n # ---------------\n try:\n prog_output = output['prog']\n\n # reduce prog metrics for tqdm when using dp\n if self.use_dp:\n nb_gpus = len(self.data_parallel_device_ids)\n prog_output = reduce_distributed_output(prog_output, nb_gpus)\n\n model_specific_tqdm_metrics_dic = prog_output\n except Exception:\n model_specific_tqdm_metrics_dic = {}\n\n # ---------------\n # EXTRACT LOSS\n # ---------------\n # if output dict doesn't have the keyword loss\n # then assume the output=loss if scalar\n try:\n loss = output['loss']\n except Exception:\n if type(output) is torch.Tensor:\n loss = output\n\n # when using dp need to reduce the loss\n if self.use_dp:\n loss = reduce_distributed_output(loss, len(self.data_parallel_device_ids))\n\n return loss, model_specific_tqdm_metrics_dic","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__clip_gradients","text":"pytorch_lightning/models/trainer.py::Trainer.__clip_gradients\n def __clip_gradients(self):\n if self.gradient_clip > 0:\n model = self.__get_model()\n torch.nn.utils.clip_grad_norm_(model.parameters(), self.gradient_clip)","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__print_nan_grads","text":"pytorch_lightning/models/trainer.py::Trainer.__print_nan_grads\n def __print_nan_grads(self):\n if self.print_nan_grads:\n model = self.__get_model()\n for param in model.parameters():\n print(param.grad.float().sum())","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__run_tng_batch","text":"pytorch_lightning/models/trainer.py::Trainer.__run_tng_batch\n def __run_tng_batch(self, data_batch, batch_nb):\n if data_batch is None:\n return 0\n\n # hook\n if self.__is_function_implemented('on_batch_start'):\n model_ref = self.__get_model()\n response = model_ref.on_batch_start(data_batch)\n\n if response == -1:\n return -1\n\n if self.progress_bar:\n self.prog_bar.update(1)\n\n # call training_step once per optimizer\n for opt_idx, optimizer in enumerate(self.optimizers):\n\n # forward pass\n loss, model_specific_tqdm_metrics = self.__tng_forward(data_batch, batch_nb, opt_idx)\n\n # track metrics\n self.__add_tqdm_metrics(model_specific_tqdm_metrics)\n\n # accumulate loss\n # (if accumulate_grad_batches = 1 no effect)\n loss = loss / self.accumulate_grad_batches\n\n # backward pass\n if self.use_amp:\n with amp.scale_loss(loss, optimizer) as scaled_loss:\n scaled_loss.backward()\n else:\n loss.backward()\n\n # insert after step hook\n if self.__is_function_implemented('on_after_backward'):\n model_ref = self.__get_model()\n model_ref.on_after_backward()\n\n # nan grads\n self.__print_nan_grads()\n\n # track total loss for logging (avoid mem leaks)\n self.batch_loss_value += loss.item()\n\n # gradient update with accumulated gradients\n if (self.batch_nb + 1) % self.accumulate_grad_batches == 0:\n # clip gradients\n self.__clip_gradients()\n\n # calls .step(), .zero_grad()\n # override function to modify this behavior\n model = self.__get_model()\n model.optimizer_step(self.current_epoch, batch_nb, optimizer, opt_idx)\n\n # calc","label":0},{"cid":"pytorch_lightning/models/trainer.py::Trainer.__run_validation","text":"pytorch_lightning/models/trainer.py::Trainer.__run_validation\n def __run_validation(self):\n # decide if can check epochs\n can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0\n if self.fast_dev_run:\n print('skipping to check performance bc of --fast_dev_run')\n elif not can_check_epoch:\n return\n\n # validate only if model has validation_step defined\n if self.__is_overriden('validation_step'):\n\n # hook\n if self.__is_function_implemented('on_pre_performance_check'):\n model = self.__get_model()\n model.on_pre_performance_check()\n\n # use val_percent_check set on end of epoch\n # use a small portion otherwise\n max_batches = self.nb_val_batches if not self.fast_dev_run else 1\n for ds_i, dataloader in enumerate(self.val_dataloader):\n val_out_metrics = self.validate(self.model, dataloader, max_batches, ds_i)\n self.__add_tqdm_metrics(val_out_metrics)\n\n # hook\n if self.__is_function_implemented('on_post_performance_check'):\n model = self.__get_model()\n model.on_post_performance_check()\n\n if self.progress_bar:\n # add model specific metrics\n tqdm_metrics = self.__tng_tqdm_dic\n self.prog_bar.set_postfix(**tqdm_metrics)\n\n # model checkpointing\n if self.proc_rank == 0 and self.checkpoint_callback is not None:\n print('save callback...')\n self.checkpoint_callback.on_epoch_end(epoch=self.current_epoch,\n logs=self.__tng_tqdm_dic)","label":0},{"cid":"pytorch_lightning/pt_overrides/override_data_parallel.py::_find_tensors","text":"pytorch_lightning/pt_overrides/override_data_parallel.py::_find_tensors\ndef _find_tensors(obj): # pragma: no cover\n r\"\"\"\n Recursively find all tensors contained in the specified object.\n \"\"\"\n if isinstance(obj, torch.Tensor):\n return [obj]\n if isinstance(obj, (list, tuple)):\n return itertools.chain(*map(_find_tensors, obj))\n if isinstance(obj, dict):\n return itertools.chain(*map(_find_tensors, obj.values()))\n return []","label":0},{"cid":"pytorch_lightning/pt_overrides/override_data_parallel.py::get_a_var","text":"pytorch_lightning/pt_overrides/override_data_parallel.py::get_a_var\ndef get_a_var(obj): # pragma: no cover\n if isinstance(obj, torch.Tensor):\n return obj\n\n if isinstance(obj, list) or isinstance(obj, tuple):\n for result in map(get_a_var, obj):\n if isinstance(result, torch.Tensor):\n return result\n if isinstance(obj, dict):\n for result in map(get_a_var, obj.items()):\n if isinstance(result, torch.Tensor):\n return result\n return None","label":0},{"cid":"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDataParallel.forward","text":"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDataParallel.forward\n def forward(self, *inputs, **kwargs):\n if not self.device_ids:\n return self.module(*inputs, **kwargs)\n\n for t in chain(self.module.parameters(), self.module.buffers()):\n if t.device != self.src_device_obj:\n raise RuntimeError(\"module must have its parameters and buffers \"\n \"on device {} (device_ids[0]) but found one of \"\n \"them on device: {}\".format(self.src_device_obj, t.device))\n\n inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)\n if len(self.device_ids) == 1:\n # lightning\n if self.module.training:\n return self.module.training_step(*inputs[0], **kwargs[0])\n else:\n return self.module.validation_step(*inputs[0], **kwargs[0])\n\n replicas = self.replicate(self.module, self.device_ids[:len(inputs)])\n outputs = self.parallel_apply(replicas, inputs, kwargs)\n return self.gather(outputs, self.output_device)","label":0},{"cid":"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDataParallel.parallel_apply","text":"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDataParallel.parallel_apply\n def parallel_apply(self, replicas, inputs, kwargs):\n return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])","label":0},{"cid":"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDistributedDataParallel.parallel_apply","text":"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDistributedDataParallel.parallel_apply\n def parallel_apply(self, replicas, inputs, kwargs):\n return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])","label":0},{"cid":"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDistributedDataParallel.forward","text":"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDistributedDataParallel.forward\n def forward(self, *inputs, **kwargs): # pragma: no cover\n self._sync_params()\n if self.device_ids:\n inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)\n if len(self.device_ids) == 1:\n # --------------\n # LIGHTNING MOD\n # --------------\n # normal\n # output = self.module(*inputs[0], **kwargs[0])\n\n # lightning\n if self.module.training:\n output = self.module.training_step(*inputs[0], **kwargs[0])\n else:\n output = self.module.validation_step(*inputs[0], **kwargs[0])\n else:\n outputs = self.parallel_apply(self._module_copies[:len(inputs)], inputs, kwargs)\n output = self.gather(outputs, self.output_device)\n else:\n output = self.module(*inputs, **kwargs)\n\n if torch.is_grad_enabled():\n # We'll return the output object verbatim since it is a freeform\n # object. We need to find any tensors in this object, though,\n # because we need to figure out which parameters were used during\n # this forward pass, to ensure we short circuit reduction for any\n # unused parameters. Only if `find_unused_parameters` is set.\n if self.find_unused_parameters:\n self.reducer.prepare_for_backward(list(_find_tensors(output)))\n else:\n self.reducer.prepare_for_backward([])\n return output","label":0},{"cid":"pytorch_lightning/pt_overrides/override_data_parallel.py::parallel_apply","text":"pytorch_lightning/pt_overrides/override_data_parallel.py::parallel_apply\ndef parallel_apply(modules, inputs, kwargs_tup=None, devices=None): # pragma: no cover\n r\"\"\"Applies each `module` in :attr:`modules` in parallel on arguments\n contained in :attr:`inputs` (positional) and :attr:`kwargs_tup` (keyword)\n on each of :attr:`devices`.\n\n Args:\n modules (Module): modules to be parallelized\n inputs (tensor): inputs to the modules\n devices (list of int or torch.device): CUDA devices\n\n :attr:`modules`, :attr:`inputs`, :attr:`kwargs_tup` (if given), and\n :attr:`devices` (if given) should all have same length. Moreover, each\n element of :attr:`inputs` can either be a single object as the only argument\n to a module, or a collection of positional arguments.\n \"\"\"\n assert len(modules) == len(inputs)\n if kwargs_tup is not None:\n assert len(modules) == len(kwargs_tup)\n else:\n kwargs_tup = ({},) * len(modules)\n if devices is not None:\n assert len(modules) == len(devices)\n else:\n devices = [None] * len(modules)\n devices = list(map(lambda x: _get_device_index(x, True), devices))\n lock = threading.Lock()\n results = {}\n grad_enabled = torch.is_grad_enabled()\n\n def _worker(i, module, input, kwargs, device=None):\n torch.set_grad_enabled(grad_enabled)\n if device is None:\n device = get_a_var(input).get_device()\n try:\n with torch.cuda.device(device):\n # this also avoids accidental slicing of `input` if it is a Tensor\n if not isinstance(input, (list, tuple)):\n input = (input,)\n\n # ---------------\n # CHANGE\n if module.training:\n output = module.training_step(*input, **kwargs)\n else:\n output = module.validation_step(*input, **kwargs)\n # ---------------\n\n with lock:\n ","label":0},{"cid":"pytorch_lightning/pt_overrides/override_data_parallel.py::parallel_apply._worker","text":"pytorch_lightning/pt_overrides/override_data_parallel.py::parallel_apply._worker\n def _worker(i, module, input, kwargs, device=None):\n torch.set_grad_enabled(grad_enabled)\n if device is None:\n device = get_a_var(input).get_device()\n try:\n with torch.cuda.device(device):\n # this also avoids accidental slicing of `input` if it is a Tensor\n if not isinstance(input, (list, tuple)):\n input = (input,)\n\n # ---------------\n # CHANGE\n if module.training:\n output = module.training_step(*input, **kwargs)\n else:\n output = module.validation_step(*input, **kwargs)\n # ---------------\n\n with lock:\n results[i] = output\n except Exception as e:\n with lock:\n results[i] = e","label":0},{"cid":"pytorch_lightning/root_module/decorators.py::data_loader","text":"pytorch_lightning/root_module/decorators.py::data_loader\ndef data_loader(fn):\n \"\"\"\n Decorator to make any fx with this use the lazy property\n :param fn:\n :return:\n \"\"\"\n\n attr_name = '_lazy_' + fn.__name__\n\n @property\n def _data_loader(self):\n if not hasattr(self, attr_name):\n setattr(self, attr_name, fn(self))\n return getattr(self, attr_name)\n\n return _data_loader","label":0},{"cid":"pytorch_lightning/root_module/decorators.py::data_loader._data_loader","text":"pytorch_lightning/root_module/decorators.py::data_loader._data_loader\n @property\n def _data_loader(self):\n if not hasattr(self, attr_name):\n setattr(self, attr_name, fn(self))\n return getattr(self, attr_name)","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.__init__","text":"pytorch_lightning/root_module/root_module.py::LightningModule.__init__\n def __init__(self, *args, **kwargs):\n super(LightningModule, self).__init__(*args, **kwargs)\n\n self.dtype = torch.FloatTensor\n self.exp_save_path = None\n self.current_epoch = 0\n self.global_step = 0\n self.loaded_optimizer_states_dict = {}\n self.trainer = None\n self.experiment = None\n self.example_input_array = None\n\n # track if gpu was requested for checkpointing\n self.on_gpu = False","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.forward","text":"pytorch_lightning/root_module/root_module.py::LightningModule.forward\n def forward(self, *args, **kwargs):\n \"\"\"\n Expand model in into whatever you need.\n Also need to return the target\n :param x:\n :return:\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.training_step","text":"pytorch_lightning/root_module/root_module.py::LightningModule.training_step\n def training_step(self, *args, **kwargs):\n \"\"\"\n return loss, dict with metrics for tqdm\n :param called with batch, batch_nb\n additional: optimizer_i if multiple optimizers used\n :return:\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.validation_step","text":"pytorch_lightning/root_module/root_module.py::LightningModule.validation_step\n def validation_step(self, *args, **kwargs):\n \"\"\"\n return whatever outputs will need to be aggregated in validation_end\n OPTIONAL\n :param called with batch, batch_nb\n additional: dataset_i if multiple val datasets used\n :return:\n \"\"\"\n pass","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.validation_end","text":"pytorch_lightning/root_module/root_module.py::LightningModule.validation_end\n def validation_end(self, outputs):\n \"\"\"\n Outputs has the appended output after each validation step\n OPTIONAL\n :param outputs:\n :return: dic_with_metrics for tqdm\n \"\"\"\n pass","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.configure_optimizers","text":"pytorch_lightning/root_module/root_module.py::LightningModule.configure_optimizers\n def configure_optimizers(self):\n \"\"\"\n Return a list of optimizers and a list of schedulers (could be empty)\n :return:\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.optimizer_step","text":"pytorch_lightning/root_module/root_module.py::LightningModule.optimizer_step\n def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i):\n \"\"\"\n Do something instead of the standard optimizer behavior\n :param epoch_nb:\n :param batch_nb:\n :param optimizer:\n :param optimizer_i:\n :return:\n \"\"\"\n optimizer.step()\n\n # clear gradients\n optimizer.zero_grad()","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.tng_dataloader","text":"pytorch_lightning/root_module/root_module.py::LightningModule.tng_dataloader\n @data_loader\n def tng_dataloader(self):\n \"\"\"\n Implement a PyTorch DataLoader\n :return:\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.test_dataloader","text":"pytorch_lightning/root_module/root_module.py::LightningModule.test_dataloader\n @data_loader\n def test_dataloader(self):\n \"\"\"\n Implement a PyTorch DataLoader\n :return:\n \"\"\"\n return None","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.val_dataloader","text":"pytorch_lightning/root_module/root_module.py::LightningModule.val_dataloader\n @data_loader\n def val_dataloader(self):\n \"\"\"\n Implement a PyTorch DataLoader\n :return:\n \"\"\"\n return None","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.load_from_metrics","text":"pytorch_lightning/root_module/root_module.py::LightningModule.load_from_metrics\n @classmethod\n def load_from_metrics(cls, weights_path, tags_csv, on_gpu, map_location=None):\n \"\"\"\n Primary way of loading model from csv weights path\n :param weights_path:\n :param tags_csv:\n :param on_gpu:\n :param map_location: dic for mapping storage {'cuda:1':'cuda:0'}\n :return:\n \"\"\"\n hparams = load_hparams_from_tags_csv(tags_csv)\n hparams.__setattr__('on_gpu', on_gpu)\n\n if on_gpu:\n if map_location is not None:\n checkpoint = torch.load(weights_path, map_location=map_location)\n else:\n checkpoint = torch.load(weights_path)\n else:\n checkpoint = torch.load(weights_path, map_location=lambda storage, loc: storage)\n\n # load the state_dict on the model automatically\n model = cls(hparams)\n model.load_state_dict(checkpoint['state_dict'])\n\n # give model a chance to load something\n model.on_load_checkpoint(checkpoint)\n\n return model","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.summarize","text":"pytorch_lightning/root_module/root_module.py::LightningModule.summarize\n def summarize(self):\n model_summary = ModelSummary(self)\n print(model_summary)","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.freeze","text":"pytorch_lightning/root_module/root_module.py::LightningModule.freeze\n def freeze(self):\n for param in self.parameters():\n param.requires_grad = False","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.unfreeze","text":"pytorch_lightning/root_module/root_module.py::LightningModule.unfreeze\n def unfreeze(self):\n for param in self.parameters():\n param.requires_grad = True","label":0},{"cid":"pytorch_lightning/utilities/arg_parse.py::add_default_args","text":"pytorch_lightning/utilities/arg_parse.py::add_default_args\ndef add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None):\n\n # tng, test, val check intervals\n parser.add_argument('--eval_test_set', dest='eval_test_set', action='store_true',\n help='true = run test set also')\n parser.add_argument('--check_val_every_n_epoch', default=1, type=int,\n help='check val every n epochs')\n parser.opt_list('--accumulate_grad_batches', default=1, type=int, tunable=False,\n help='accumulates gradients k times before applying update.'\n ' Simulates huge batch size')\n parser.add_argument('--max_nb_epochs', default=200, type=int, help='cap epochs')\n parser.add_argument('--min_nb_epochs', default=2, type=int, help='min epochs')\n parser.add_argument('--train_percent_check', default=1.0, type=float,\n help='how much of tng set to check')\n parser.add_argument('--val_percent_check', default=1.0, type=float,\n help='how much of val set to check')\n parser.add_argument('--test_percent_check', default=1.0, type=float,\n help='how much of test set to check')\n\n parser.add_argument('--val_check_interval', default=0.95, type=float,\n help='how much within 1 epoch to check val')\n parser.add_argument('--log_save_interval', default=100, type=int,\n help='how many batches between log saves')\n parser.add_argument('--add_log_row_interval', default=100, type=int,\n help='add log every k batches')\n\n # early stopping\n parser.add_argument('--disable_early_stop', dest='enable_early_stop', action='store_false')\n parser.add_argument('--early_stop_metric', default='val_acc', type=str)\n parser.add_argument('--early_stop_mode', default='min', type=str)\n parser.add_argument('--early_stop_patience', default=3, type=int,\n ","label":0}],"string":"[\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.__init__\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.__init__\\n def __init__(self, hparams):\\n \\\"\\\"\\\"\\n Pass in parsed HyperOptArgumentParser to the model\\n :param hparams:\\n \\\"\\\"\\\"\\n # init superclass\\n super(LightningTemplateModel, self).__init__()\\n self.hparams = hparams\\n\\n self.batch_size = hparams.batch_size\\n\\n # if you specify an example input, the summary will show input/output for each layer\\n self.example_input_array = torch.rand(5, 28 * 28)\\n\\n # build model\\n self.__build_model()\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.__build_model\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.__build_model\\n def __build_model(self):\\n \\\"\\\"\\\"\\n Layout model\\n :return:\\n \\\"\\\"\\\"\\n self.c_d1 = nn.Linear(in_features=self.hparams.in_features,\\n out_features=self.hparams.hidden_dim)\\n self.c_d1_bn = nn.BatchNorm1d(self.hparams.hidden_dim)\\n self.c_d1_drop = nn.Dropout(self.hparams.drop_prob)\\n\\n self.c_d2 = nn.Linear(in_features=self.hparams.hidden_dim,\\n out_features=self.hparams.out_features)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.forward\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.forward\\n def forward(self, x):\\n \\\"\\\"\\\"\\n No special modification required for lightning, define as you normally would\\n :param x:\\n :return:\\n \\\"\\\"\\\"\\n\\n x = self.c_d1(x)\\n x = torch.tanh(x)\\n x = self.c_d1_bn(x)\\n x = self.c_d1_drop(x)\\n\\n x = self.c_d2(x)\\n logits = F.log_softmax(x, dim=1)\\n\\n return logits\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.loss\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.loss\\n def loss(self, labels, logits):\\n nll = F.nll_loss(logits, labels)\\n return nll\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.training_step\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.training_step\\n def training_step(self, data_batch, batch_i):\\n \\\"\\\"\\\"\\n Lightning calls this inside the training loop\\n :param data_batch:\\n :return:\\n \\\"\\\"\\\"\\n # forward pass\\n x, y = data_batch\\n x = x.view(x.size(0), -1)\\n\\n y_hat = self.forward(x)\\n\\n # calculate loss\\n loss_val = self.loss(y, y_hat)\\n\\n # in DP mode (default) make sure if result is scalar, there's another dim in the beginning\\n if self.trainer.use_dp:\\n loss_val = loss_val.unsqueeze(0)\\n\\n output = OrderedDict({\\n 'loss': loss_val\\n })\\n\\n # can also return just a scalar instead of a dict (return loss_val)\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.validation_step\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.validation_step\\n def validation_step(self, data_batch, batch_i):\\n \\\"\\\"\\\"\\n Lightning calls this inside the validation loop\\n :param data_batch:\\n :return:\\n \\\"\\\"\\\"\\n x, y = data_batch\\n x = x.view(x.size(0), -1)\\n y_hat = self.forward(x)\\n\\n loss_val = self.loss(y, y_hat)\\n\\n # acc\\n labels_hat = torch.argmax(y_hat, dim=1)\\n val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)\\n val_acc = torch.tensor(val_acc)\\n\\n if self.on_gpu:\\n val_acc = val_acc.cuda(loss_val.device.index)\\n\\n # in DP mode (default) make sure if result is scalar, there's another dim in the beginning\\n if self.trainer.use_dp:\\n loss_val = loss_val.unsqueeze(0)\\n val_acc = val_acc.unsqueeze(0)\\n\\n output = OrderedDict({\\n 'val_loss': loss_val,\\n 'val_acc': val_acc,\\n })\\n\\n # can also return just a scalar instead of a dict (return loss_val)\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.validation_end\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.validation_end\\n def validation_end(self, outputs):\\n \\\"\\\"\\\"\\n Called at the end of validation to aggregate outputs\\n :param outputs: list of individual outputs of each validation step\\n :return:\\n \\\"\\\"\\\"\\n # if returned a scalar from validation_step, outputs is a list of tensor scalars\\n # we return just the average in this case (if we want)\\n # return torch.stack(outputs).mean()\\n\\n val_loss_mean = 0\\n val_acc_mean = 0\\n for output in outputs:\\n val_loss = output['val_loss']\\n\\n # reduce manually when using dp\\n if self.trainer.use_dp:\\n val_loss = torch.mean(val_loss)\\n val_loss_mean += val_loss\\n\\n # reduce manually when using dp\\n val_acc = output['val_acc']\\n if self.trainer.use_dp:\\n val_acc = torch.mean(val_acc)\\n\\n val_acc_mean += val_acc\\n\\n val_loss_mean /= len(outputs)\\n val_acc_mean /= len(outputs)\\n tqdm_dic = {'val_loss': val_loss_mean, 'val_acc': val_acc_mean}\\n return tqdm_dic\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.configure_optimizers\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.configure_optimizers\\n def configure_optimizers(self):\\n \\\"\\\"\\\"\\n return whatever optimizers we want here\\n :return: list of optimizers\\n \\\"\\\"\\\"\\n optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)\\n scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10)\\n return [optimizer], [scheduler]\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.__dataloader\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.__dataloader\\n def __dataloader(self, train):\\n # init data generators\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root=self.hparams.data_root, train=train,\\n transform=transform, download=True)\\n\\n # when using multi-node we need to add the datasampler\\n train_sampler = None\\n batch_size = self.hparams.batch_size\\n\\n try:\\n if self.on_gpu:\\n train_sampler = DistributedSampler(dataset, rank=self.trainer.proc_rank)\\n batch_size = batch_size // self.trainer.world_size # scale batch size\\n except Exception:\\n pass\\n\\n should_shuffle = train_sampler is None\\n loader = DataLoader(\\n dataset=dataset,\\n batch_size=batch_size,\\n shuffle=should_shuffle,\\n sampler=train_sampler\\n )\\n\\n return loader\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.tng_dataloader\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.tng_dataloader\\n @pl.data_loader\\n def tng_dataloader(self):\\n print('tng data loader called')\\n return self.__dataloader(train=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.val_dataloader\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.val_dataloader\\n @pl.data_loader\\n def val_dataloader(self):\\n print('val data loader called')\\n return self.__dataloader(train=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.test_dataloader\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.test_dataloader\\n @pl.data_loader\\n def test_dataloader(self):\\n print('test data loader called')\\n return self.__dataloader(train=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.add_model_specific_args\",\n \"text\": \"examples/new_project_templates/lightning_module_template.py::LightningTemplateModel.add_model_specific_args\\n @staticmethod\\n def add_model_specific_args(parent_parser, root_dir): # pragma: no cover\\n \\\"\\\"\\\"\\n Parameters you define here will be available to your model through self.hparams\\n :param parent_parser:\\n :param root_dir:\\n :return:\\n \\\"\\\"\\\"\\n parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])\\n\\n # param overwrites\\n # parser.set_defaults(gradient_clip=5.0)\\n\\n # network params\\n parser.add_argument('--in_features', default=28 * 28, type=int)\\n parser.add_argument('--out_features', default=10, type=int)\\n # use 500 for CPU, 50000 for GPU to see speed difference\\n parser.add_argument('--hidden_dim', default=50000, type=int)\\n parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)\\n\\n # data\\n parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)\\n\\n # training params (opt)\\n parser.opt_list('--learning_rate', default=0.001 * 8, type=float,\\n options=[0.0001, 0.0005, 0.001, 0.005],\\n tunable=False)\\n parser.opt_list('--optimizer_name', default='adam', type=str,\\n options=['adam'], tunable=False)\\n\\n # if using 2 nodes with 4 gpus each the batch size here\\n # (256) will be 256 / (2*8) = 16 per gpu\\n parser.opt_list('--batch_size', default=256 * 8, type=int,\\n options=[32, 64, 128, 256], tunable=False,\\n help='batch size will be divided over all gpus being used across all nodes')\\n return parser\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/templates/gan.py::Generator.__init__\",\n \"text\": \"examples/templates/gan.py::Generator.__init__\\n def __init__(self, latent_dim, img_shape):\\n super(Generator, self).__init__()\\n self.img_shape = img_shape\\n\\n def block(in_feat, out_feat, normalize=True):\\n layers = [nn.Linear(in_feat, out_feat)]\\n if normalize:\\n layers.append(nn.BatchNorm1d(out_feat, 0.8))\\n layers.append(nn.LeakyReLU(0.2, inplace=True))\\n return layers\\n\\n self.model = nn.Sequential(\\n *block(latent_dim, 128, normalize=False),\\n *block(128, 256),\\n *block(256, 512),\\n *block(512, 1024),\\n nn.Linear(1024, int(np.prod(img_shape))),\\n nn.Tanh()\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/templates/gan.py::Generator.__init__.block\",\n \"text\": \"examples/templates/gan.py::Generator.__init__.block\\n def block(in_feat, out_feat, normalize=True):\\n layers = [nn.Linear(in_feat, out_feat)]\\n if normalize:\\n layers.append(nn.BatchNorm1d(out_feat, 0.8))\\n layers.append(nn.LeakyReLU(0.2, inplace=True))\\n return layers\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/templates/gan.py::Generator.forward\",\n \"text\": \"examples/templates/gan.py::Generator.forward\\n def forward(self, z):\\n img = self.model(z)\\n img = img.view(img.size(0), *self.img_shape)\\n return img\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/templates/gan.py::Discriminator.__init__\",\n \"text\": \"examples/templates/gan.py::Discriminator.__init__\\n def __init__(self, img_shape):\\n super(Discriminator, self).__init__()\\n\\n self.model = nn.Sequential(\\n nn.Linear(int(np.prod(img_shape)), 512),\\n nn.LeakyReLU(0.2, inplace=True),\\n nn.Linear(512, 256),\\n nn.LeakyReLU(0.2, inplace=True),\\n nn.Linear(256, 1),\\n nn.Sigmoid(),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/templates/gan.py::Discriminator.forward\",\n \"text\": \"examples/templates/gan.py::Discriminator.forward\\n def forward(self, img):\\n img_flat = img.view(img.size(0), -1)\\n validity = self.model(img_flat)\\n\\n return validity\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/templates/gan.py::GAN.__init__\",\n \"text\": \"examples/templates/gan.py::GAN.__init__\\n def __init__(self, hparams):\\n super(GAN, self).__init__()\\n self.hparams = hparams\\n\\n # networks\\n mnist_shape = (1, 28, 28)\\n self.generator = Generator(latent_dim=hparams.latent_dim, img_shape=mnist_shape)\\n self.discriminator = Discriminator(img_shape=mnist_shape)\\n\\n # cache for generated images\\n self.generated_imgs = None\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/templates/gan.py::GAN.forward\",\n \"text\": \"examples/templates/gan.py::GAN.forward\\n def forward(self, z):\\n return self.generator(z)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/templates/gan.py::GAN.adversarial_loss\",\n \"text\": \"examples/templates/gan.py::GAN.adversarial_loss\\n def adversarial_loss(self, y_hat, y):\\n return F.binary_cross_entropy(y_hat, y)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/templates/gan.py::GAN.training_step\",\n \"text\": \"examples/templates/gan.py::GAN.training_step\\n def training_step(self, batch, batch_nb, optimizer_i):\\n imgs, _ = batch\\n\\n # train generator\\n if optimizer_i == 0:\\n # sample noise\\n z = torch.randn(imgs.shape[0], self.hparams.latent_dim)\\n\\n # match gpu device (or keep as cpu)\\n if self.on_gpu:\\n z = z.cuda(imgs.device.index)\\n\\n # generate images\\n self.generated_imgs = self.forward(z)\\n\\n # log sampled images\\n sample_imgs = self.generated_imgs[:6]\\n grid = torchvision.utils.make_grid(sample_imgs)\\n self.experiment.add_image('generated_images', grid, 0)\\n\\n # ground truth result (ie: all fake)\\n valid = torch.ones(imgs.size(0), 1)\\n\\n # adversarial loss is binary cross-entropy\\n g_loss = self.adversarial_loss(self.discriminator(self.generated_imgs), valid)\\n\\n return g_loss\\n\\n # train discriminator\\n if optimizer_i == 1:\\n # Measure discriminator's ability to classify real from generated samples\\n\\n # how well can it label as real?\\n valid = torch.ones(imgs.size(0), 1)\\n real_loss = self.adversarial_loss(self.discriminator(imgs), valid)\\n\\n # how well can it label as fake?\\n fake = torch.zeros(imgs.size(0), 1)\\n fake_loss = self.adversarial_loss(self.discriminator(self.generated_imgs.detach()), fake)\\n\\n # discriminator loss is the average of these\\n d_loss = (real_loss + fake_loss) / 2\\n\\n return d_loss\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/templates/gan.py::GAN.configure_optimizers\",\n \"text\": \"examples/templates/gan.py::GAN.configure_optimizers\\n def configure_optimizers(self):\\n lr = self.hparams.lr\\n b1 = self.hparams.b1\\n b2 = self.hparams.b2\\n\\n opt_g = torch.optim.Adam(self.generator.parameters(), lr=lr, betas=(b1, b2))\\n opt_d = torch.optim.Adam(self.discriminator.parameters(), lr=lr, betas=(b1, b2))\\n return [opt_g, opt_d], []\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/templates/gan.py::GAN.tng_dataloader\",\n \"text\": \"examples/templates/gan.py::GAN.tng_dataloader\\n @pl.data_loader\\n def tng_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize([0.5], [0.5])])\\n dataset = MNIST(os.getcwd(), train=True, download=True, transform=transform)\\n return DataLoader(dataset, batch_size=self.hparams.batch_size)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/templates/gan.py::main\",\n \"text\": \"examples/templates/gan.py::main\\ndef main(hparams):\\n # save tensorboard logs\\n exp = Experiment(save_dir=os.getcwd())\\n\\n # init model\\n model = GAN(hparams)\\n\\n # fit trainer on CPU\\n trainer = pl.Trainer(experiment=exp, max_nb_epochs=200)\\n trainer.fit(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.__init__\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.__init__\\n def __init__(self):\\n self.validation_data = None\\n self.model = None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_params\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_params\\n def set_params(self, params):\\n self.params = params\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_model\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_model\\n def set_model(self, model):\\n if type(model) is LightningDistributedDataParallel:\\n model = model.module\\n self.model = model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_begin\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_begin\\n def on_epoch_begin(self, epoch, logs=None):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_end\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_end\\n def on_epoch_end(self, epoch, logs=None):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_begin\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_begin\\n def on_batch_begin(self, batch, logs=None):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_end\\n def on_batch_end(self, batch, logs=None):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_begin\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_begin\\n def on_train_begin(self, logs=None):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_end\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_end\\n def on_train_end(self, logs=None):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.__init__\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.__init__\\n def __init__(self, monitor='val_loss',\\n min_delta=0.0, patience=0, verbose=0, mode='auto'):\\n super(EarlyStopping, self).__init__()\\n\\n self.monitor = monitor\\n self.patience = patience\\n self.verbose = verbose\\n self.min_delta = min_delta\\n self.wait = 0\\n self.stopped_epoch = 0\\n\\n if mode not in ['auto', 'min', 'max']:\\n print('EarlyStopping mode %s is unknown, fallback to auto mode.' % mode)\\n mode = 'auto'\\n\\n if mode == 'min':\\n self.monitor_op = np.less\\n elif mode == 'max':\\n self.monitor_op = np.greater\\n else:\\n if 'acc' in self.monitor:\\n self.monitor_op = np.greater\\n else:\\n self.monitor_op = np.less\\n\\n if self.monitor_op == np.greater:\\n self.min_delta *= 1\\n else:\\n self.min_delta *= -1\\n\\n self.on_train_begin()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_begin\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_begin\\n def on_train_begin(self, logs=None):\\n # Allow instances to be re-used\\n self.wait = 0\\n self.stopped_epoch = 0\\n self.best = np.Inf if self.monitor_op == np.less else -np.Inf\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_epoch_end\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_epoch_end\\n def on_epoch_end(self, epoch, logs=None):\\n current = logs.get(self.monitor)\\n stop_training = False\\n if current is None:\\n print('Early stopping conditioned on metric `%s` '\\n 'which is not available. Available metrics are: %s' %\\n (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning)\\n exit(-1)\\n\\n if self.monitor_op(current - self.min_delta, self.best):\\n self.best = current\\n self.wait = 0\\n else:\\n self.wait += 1\\n if self.wait >= self.patience:\\n self.stopped_epoch = epoch\\n stop_training = True\\n self.on_train_end()\\n\\n return stop_training\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_end\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_end\\n def on_train_end(self, logs=None):\\n if self.stopped_epoch > 0 and self.verbose > 0:\\n print('Epoch %05d: early stopping' % (self.stopped_epoch + 1))\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.__init__\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.__init__\\n def __init__(self, filepath, monitor='val_loss', verbose=0,\\n save_best_only=False, save_weights_only=False,\\n mode='auto', period=1, prefix=''):\\n super(ModelCheckpoint, self).__init__()\\n self.monitor = monitor\\n self.verbose = verbose\\n self.filepath = filepath\\n self.save_best_only = save_best_only\\n self.save_weights_only = save_weights_only\\n self.period = period\\n self.epochs_since_last_save = 0\\n self.prefix = prefix\\n\\n if mode not in ['auto', 'min', 'max']:\\n print('ModelCheckpoint mode %s is unknown, '\\n 'fallback to auto mode.' % (mode), RuntimeWarning)\\n mode = 'auto'\\n\\n if mode == 'min':\\n self.monitor_op = np.less\\n self.best = np.Inf\\n elif mode == 'max':\\n self.monitor_op = np.greater\\n self.best = -np.Inf\\n else:\\n if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):\\n self.monitor_op = np.greater\\n self.best = -np.Inf\\n else:\\n self.monitor_op = np.less\\n self.best = np.Inf\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.save_model\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.save_model\\n def save_model(self, filepath, overwrite):\\n dirpath = '/'.join(filepath.split('/')[:-1])\\n\\n # make paths\\n os.makedirs(os.path.dirname(filepath), exist_ok=True)\\n\\n if overwrite:\\n for filename in os.listdir(dirpath):\\n if self.prefix in filename:\\n path_to_delete = os.path.join(dirpath, filename)\\n try:\\n shutil.rmtree(path_to_delete)\\n except OSError:\\n os.remove(path_to_delete)\\n\\n # delegate the saving to the model\\n self.save_function(filepath)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.on_epoch_end\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.on_epoch_end\\n def on_epoch_end(self, epoch, logs=None):\\n logs = logs or {}\\n self.epochs_since_last_save += 1\\n if self.epochs_since_last_save >= self.period:\\n self.epochs_since_last_save = 0\\n filepath = '{}/{}_ckpt_epoch_{}.ckpt'.format(self.filepath, self.prefix, epoch + 1)\\n if self.save_best_only:\\n current = logs.get(self.monitor)\\n if current is None:\\n print('Can save best model only with %s available,'\\n ' skipping.' % (self.monitor), RuntimeWarning)\\n else:\\n if self.monitor_op(current, self.best):\\n if self.verbose > 0:\\n print('\\\\nEpoch %05d: %s improved from %0.5f to %0.5f,'\\n ' saving model to %s'\\n % (epoch + 1, self.monitor, self.best,\\n current, filepath))\\n self.best = current\\n self.save_model(filepath, overwrite=True)\\n\\n else:\\n if self.verbose > 0:\\n print('\\\\nEpoch %05d: %s did not improve' %\\n (epoch + 1, self.monitor))\\n else:\\n if self.verbose > 0:\\n print('\\\\nEpoch %05d: saving model to %s' % (epoch + 1, filepath))\\n self.save_model(filepath, overwrite=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::reduce_distributed_output\",\n \"text\": \"pytorch_lightning/models/trainer.py::reduce_distributed_output\\ndef reduce_distributed_output(output, nb_gpus):\\n if nb_gpus <= 1:\\n return output\\n\\n # when using DP, we get one output per gpu\\n # average outputs and return\\n if type(output) is torch.Tensor:\\n return output.mean()\\n\\n for k, v in output.items():\\n # recurse on nested dics\\n if isinstance(output[k], dict):\\n output[k] = reduce_distributed_output(output[k], nb_gpus)\\n\\n # reduce only metrics that have the same nb of gpus\\n elif output[k].size(0) == nb_gpus:\\n reduced = torch.mean(output[k])\\n output[k] = reduced\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__init__\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__init__\\n def __init__(self,\\n experiment=None,\\n early_stop_callback=None,\\n checkpoint_callback=None,\\n gradient_clip=0,\\n cluster=None,\\n process_position=0,\\n current_gpu_name=0,\\n nb_gpu_nodes=1,\\n gpus=None,\\n progress_bar=True,\\n overfit_pct=0.0,\\n track_grad_norm=-1,\\n check_val_every_n_epoch=1,\\n fast_dev_run=False,\\n accumulate_grad_batches=1,\\n max_nb_epochs=1000,\\n min_nb_epochs=1,\\n train_percent_check=1.0,\\n val_percent_check=1.0,\\n test_percent_check=1.0,\\n val_check_interval=1.0,\\n log_save_interval=100,\\n add_log_row_interval=10,\\n distributed_backend='dp',\\n use_amp=False,\\n print_nan_grads=False,\\n print_weights_summary=True,\\n amp_level='O2',\\n nb_sanity_val_steps=5):\\n \\\"\\\"\\\"\\n\\n :param experiment: Test-tube experiment\\n :param early_stop_callback: from pytorch_lightning import EarlyStopping\\n :param checkpoint_callback: from pytorch_lightning import Checkpoint\\n :param gradient_clip:\\n :param cluster:\\n :param process_position:\\n :param current_gpu_name:\\n :param nb_gpu_nodes:\\n :param gpus:\\n :param progress_bar:\\n :param overfit_pct:\\n :param track_grad_norm:\\n :param check_val_every_n_epoch:\\n :param fast_dev_run:\\n :param accumulate_grad_batches:\\n :param max_nb_epochs:\\n :param min_nb_epochs:\\n :param train_percent_check:\\n :param val_percent_check:\\n :param test_percent_check:\\n :param val_check_interval:\\n :param log_save_interval:\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.restore_state_if_existing_checkpoint\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.restore_state_if_existing_checkpoint\\n def restore_state_if_existing_checkpoint(self):\\n # restore trainer state and model if there is a weight for this experiment\\n last_epoch = -1\\n last_ckpt_name = None\\n\\n # do nothing if there's not dir or callback\\n no_ckpt_callback = self.checkpoint_callback is None\\n if no_ckpt_callback or not os.path.exists(self.checkpoint_callback.filepath):\\n return\\n\\n # find last epoch\\n checkpoints = os.listdir(self.checkpoint_callback.filepath)\\n for name in checkpoints:\\n # ignore hpc ckpts\\n if 'hpc_' in name:\\n continue\\n\\n if '.ckpt' in name:\\n epoch = name.split('epoch_')[1]\\n epoch = int(re.sub('[^0-9]', '', epoch))\\n\\n if epoch > last_epoch:\\n last_epoch = epoch\\n last_ckpt_name = name\\n\\n # restore last checkpoint\\n if last_ckpt_name is not None:\\n last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name)\\n self.restore(last_ckpt_path, self.on_gpu)\\n print(f'model and trainer restored from checkpoint: {last_ckpt_path}')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.data_parallel\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.data_parallel\\n @property\\n def data_parallel(self):\\n return self.use_dp or self.use_ddp\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__determine_data_use_amount\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__determine_data_use_amount\\n def __determine_data_use_amount(self, train_percent_check, val_percent_check,\\n test_percent_check, overfit_pct):\\n \\\"\\\"\\\"\\n Use less data for debugging purposes\\n \\\"\\\"\\\"\\n self.train_percent_check = train_percent_check\\n self.val_percent_check = val_percent_check\\n self.test_percent_check = test_percent_check\\n if overfit_pct > 0:\\n self.train_percent_check = overfit_pct\\n self.val_percent_check = overfit_pct\\n self.test_percent_check = overfit_pct\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__get_model\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__get_model\\n def __get_model(self):\\n return self.model.module if self.data_parallel else self.model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__is_function_implemented\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__is_function_implemented\\n def __is_function_implemented(self, f_name):\\n model = self.__get_model()\\n f_op = getattr(model, f_name, None)\\n return callable(f_op)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__is_overriden\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__is_overriden\\n def __is_overriden(self, f_name):\\n model = self.__get_model()\\n super_object = super(model.__class__, model)\\n\\n # when code pointers are different, it was overriden\\n is_overriden = getattr(model, f_name).__code__ is not getattr(super_object, f_name).__code__\\n return is_overriden\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__tng_tqdm_dic\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__tng_tqdm_dic\\n @property\\n def __tng_tqdm_dic(self):\\n tqdm_dic = {\\n 'loss': '{0:.3f}'.format(self.avg_loss),\\n 'epoch': '{}'.format(self.current_epoch),\\n 'batch_nb': '{}'.format(self.batch_nb),\\n }\\n\\n if self.experiment is not None:\\n tqdm_dic['v_nb'] = self.experiment.version\\n\\n tqdm_dic.update(self.tqdm_metrics)\\n\\n if self.on_gpu:\\n tqdm_dic['gpu'] = '{}'.format(self.current_gpu_name)\\n\\n return tqdm_dic\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.tng_tqdm_dic\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.tng_tqdm_dic\\n @property\\n def tng_tqdm_dic(self):\\n \\\"\\\"\\\"\\n Read-only for tqdm metrics\\n :return:\\n \\\"\\\"\\\"\\n return self.__tng_tqdm_dic\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__layout_bookeeping\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__layout_bookeeping\\n def __layout_bookeeping(self):\\n\\n # determine number of training batches\\n self.nb_tng_batches = len(self.tng_dataloader)\\n self.nb_tng_batches = int(self.nb_tng_batches * self.train_percent_check)\\n\\n # determine number of validation batches\\n # val datasets could be none, 1 or 2+\\n self.nb_val_batches = 0\\n if self.val_dataloader is not None:\\n self.nb_val_batches = sum(len(dataloader) for dataloader in self.val_dataloader)\\n\\n self.nb_val_batches = int(self.nb_val_batches * self.val_percent_check)\\n self.nb_val_batches = max(1, self.nb_val_batches)\\n\\n # determine number of test batches\\n self.nb_test_batches = len(self.test_dataloader) if self.test_dataloader is not None else 0\\n self.nb_test_batches = int(self.nb_test_batches * self.test_percent_check)\\n\\n # determine when to check validation\\n self.val_check_batch = int(self.nb_tng_batches * self.val_check_interval)\\n self.val_check_batch = max(1, self.val_check_batch)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__add_tqdm_metrics\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__add_tqdm_metrics\\n def __add_tqdm_metrics(self, metrics):\\n for k, v in metrics.items():\\n if type(v) is torch.Tensor:\\n v = v.item()\\n\\n self.tqdm_metrics[k] = v\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__validation_forward\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__validation_forward\\n def __validation_forward(self, model, data_batch, batch_i, dataloader_i):\\n # make dataloader_i arg in validation_step optional\\n args = [data_batch, batch_i]\\n if len(self.val_dataloader) > 1:\\n args.append(dataloader_i)\\n\\n if self.use_ddp:\\n output = model(*args)\\n elif self.use_dp:\\n output = model(*args)\\n elif self.single_gpu:\\n # put inputs on gpu manually\\n gpu_id = self.data_parallel_device_ids[0]\\n data_batch = self.transfer_batch_to_gpu(data_batch, gpu_id)\\n args[0] = data_batch\\n\\n # do non dp, ddp step\\n output = model.validation_step(*args)\\n\\n else:\\n # CPU\\n output = model.validation_step(*args)\\n\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.validate\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.validate\\n def validate(self, model, dataloader, max_batches, dataloader_i):\\n \\\"\\\"\\\"\\n Run validation code\\n :param model: PT model\\n :param dataloader: PT dataloader\\n :param max_batches: Scalar\\n :return:\\n \\\"\\\"\\\"\\n\\n # enable eval mode\\n model.zero_grad()\\n model.eval()\\n\\n # disable gradients to save memory\\n torch.set_grad_enabled(False)\\n\\n # bookkeeping\\n outputs = []\\n\\n # run training\\n for batch_i, data_batch in enumerate(dataloader):\\n\\n if data_batch is None: # pragma: no cover\\n continue\\n\\n # stop short when on fast dev run\\n if max_batches is not None and batch_i >= max_batches:\\n break\\n\\n # -----------------\\n # RUN VALIDATION STEP\\n # -----------------\\n output = self.__validation_forward(model, data_batch, batch_i, dataloader_i)\\n\\n # track outputs for collation\\n outputs.append(output)\\n\\n # batch done\\n if self.progress_bar and self.prog_bar is not None:\\n self.prog_bar.update(1)\\n\\n # give model a chance to do something with the outputs (and method defined)\\n val_results = {}\\n if self.__is_overriden('validation_end'):\\n if self.data_parallel:\\n val_results = model.module.validation_end(outputs)\\n else:\\n val_results = model.validation_end(outputs)\\n\\n # enable train mode again\\n model.train()\\n\\n # enable gradients to save memory\\n torch.set_grad_enabled(True)\\n\\n return val_results\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.get_dataloaders\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.get_dataloaders\\n def get_dataloaders(self, model):\\n \\\"\\\"\\\"\\n Dataloaders are provided by the model\\n :param model:\\n :return:\\n \\\"\\\"\\\"\\n self.tng_dataloader = model.tng_dataloader\\n\\n self.test_dataloader = model.test_dataloader\\n self.val_dataloader = model.val_dataloader\\n\\n # handle returning an actual dataloader instead of a list of loaders\\n have_val_loaders = self.val_dataloader is not None\\n if have_val_loaders and not isinstance(self.val_dataloader, list):\\n self.val_dataloader = [self.val_dataloader]\\n\\n if self.use_ddp and not isinstance(self.tng_dataloader.sampler, DistributedSampler):\\n msg = \\\"\\\"\\\"\\nYou're using multiple gpus and multiple nodes without using a DistributedSampler\\nto assign a subset of your data to each process. To silence this warning, pass a\\nDistributedSampler to your DataLoader.\\n\\nie: this:\\ndataset = myDataset()\\ndataloader = Dataloader(dataset)\\n\\nbecomes:\\ndataset = myDataset()\\ndist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)\\ndataloader = Dataloader(dataset, sampler=dist_sampler)\\n\\nIf you want each process to load the full dataset, ignore this warning.\\n\\\"\\\"\\\"\\n warnings.warn(msg)\\n\\n if self.use_ddp and self.val_dataloader is not None:\\n for dataloader in self.val_dataloader:\\n if not isinstance(dataloader, DistributedSampler):\\n msg = \\\"\\\"\\\"\\n Your val_dataloader(s) are not all DistributedSamplers.\\n You're using multiple gpus and multiple nodes without using a DistributedSampler\\n to assign a subset of your data to each process. To silence this warning, pass a\\n DistributedSampler to your DataLoader.\\n\\n ie: this:\\n dataset = myDataset()\\n dataloader = Dataloader(dataset)\\n\\n becomes:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.fit\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.fit\\n def fit(self, model):\\n\\n # when using multi-node or DDP within a node start each module in a separate process\\n if self.use_ddp:\\n # must copy only the meta of the exp so it survives pickle/unpickle\\n # when going to new process\\n if self.experiment is not None:\\n self.experiment = self.experiment.get_meta_copy()\\n\\n if self.is_slurm_managing_tasks:\\n task = int(os.environ['SLURM_LOCALID'])\\n self.ddp_train(task, model)\\n else:\\n nb_gpus = self.nb_requested_gpus\\n nb_tasks = self.nb_slurm_tasks\\n msg = f\\\"\\\"\\\"\\n You requested {nb_gpus}s GPUs but launched {nb_tasks}s slurm tasks.\\n We will launch {nb_gpus}s processes for you.\\n We recommend you let slurm manage the processes by setting:\\n --ntasks-per-node={nb_gpus}s\\n If you're not using SLURM, ignore this message!\\n \\\"\\\"\\\"\\n warnings.warn(msg)\\n mp.spawn(self.ddp_train, nprocs=len(self.data_parallel_device_ids), args=(model, ))\\n\\n # 1 gpu or dp option triggers training using DP module\\n # easier to avoid NCCL issues\\n elif self.use_dp:\\n self.__dp_train(model)\\n\\n elif self.single_gpu:\\n self.__single_gpu_train(model)\\n\\n # ON CPU\\n else:\\n # run through amp wrapper\\n if self.use_amp:\\n raise MisconfigurationException('amp + cpu is not supported.'\\n ' Please use a GPU option')\\n\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n self.__run_pretrain_routine(model)\\n\\n # return 1 when finished\\n # used for testing or when we need to know t\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.init_optimizers\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.init_optimizers\\n def init_optimizers(self, optimizers):\\n\\n # single optimizer\\n if isinstance(optimizers, Optimizer):\\n return [optimizers], []\\n\\n # two lists\\n elif len(optimizers) == 2 and isinstance(optimizers[0], list):\\n optimizers, lr_schedulers = optimizers\\n return optimizers, lr_schedulers\\n\\n # single list or tuple\\n elif isinstance(optimizers, list) or isinstance(optimizers, tuple):\\n return optimizers, []\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__single_gpu_train\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__single_gpu_train\\n def __single_gpu_train(self, model):\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n model.cuda(self.data_parallel_device_ids[0])\\n\\n if self.use_amp:\\n # An example\\n model, optimizers = amp.initialize(\\n model, self.optimizers, opt_level=self.amp_level,\\n )\\n self.optimizers = optimizers\\n\\n self.__run_pretrain_routine(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__dp_train\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__dp_train\\n def __dp_train(self, model):\\n\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n model.cuda(self.data_parallel_device_ids[0])\\n\\n # check for this bug (amp + dp + !01 doesn't work)\\n # https://github.com/NVIDIA/apex/issues/227\\n if self.use_dp and self.use_amp:\\n m = f\\\"\\\"\\\"\\n Amp level {self.amp_level} with DataParallel is not supported.\\n See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227.\\n We recommend you switch to ddp if you want to use amp\\n \\\"\\\"\\\"\\n raise MisconfigurationException(m)\\n\\n model = LightningDataParallel(model, device_ids=self.data_parallel_device_ids)\\n\\n self.__run_pretrain_routine(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.ddp_train\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.ddp_train\\n def ddp_train(self, gpu_nb, model):\\n \\\"\\\"\\\"\\n Entry point into a DP thread\\n :param gpu_nb:\\n :param model:\\n :param cluster_obj:\\n :return:\\n \\\"\\\"\\\"\\n # node rank using relative slurm id\\n # otherwise default to node rank 0\\n try:\\n node_id = os.environ['SLURM_NODEID']\\n self.node_rank = int(node_id)\\n except Exception:\\n self.node_rank = 0\\n\\n # recover original exp before went into process\\n # init in write mode only on proc 0\\n if self.experiment is not None:\\n self.experiment.debug = self.proc_rank > 0\\n self.experiment = self.experiment.get_non_ddp_exp()\\n\\n # show progbar only on prog_rank 0\\n self.prog_bar = self.prog_bar and self.node_rank == 0 and gpu_nb == 0\\n\\n # determine which process we are and world size\\n self.proc_rank = self.node_rank * len(self.data_parallel_device_ids) + gpu_nb\\n self.world_size = self.nb_gpu_nodes * len(self.data_parallel_device_ids)\\n\\n # let the exp know the rank to avoid overwriting logs\\n if self.experiment is not None:\\n self.experiment.rank = self.proc_rank\\n\\n # set up server using proc 0's ip address\\n # try to init for 20 times at max in case ports are taken\\n # where to store ip_table\\n self.__init_tcp_connection()\\n\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n # MODEL\\n # copy model to each gpu\\n torch.cuda.set_device(gpu_nb)\\n model.cuda(gpu_nb)\\n\\n # AMP\\n # run through amp wrapper before going to distributed DP\\n if self.use_amp:\\n # An example\\n model, optimizers = amp.initialize(\\n model, self.optimizers, opt_level=self.amp_level,\\n )\\n self.op\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__init_tcp_connection\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__init_tcp_connection\\n def __init_tcp_connection(self):\\n \\\"\\\"\\\"\\n Connect all procs in the world using the env:// init\\n Use the first node as the root address\\n :param port:\\n :param tries:\\n :return:\\n \\\"\\\"\\\"\\n # sets the appropriate port\\n try:\\n port = os.environ['MASTER_PORT']\\n except Exception:\\n port = 12910\\n os.environ['MASTER_PORT'] = str(port)\\n\\n # figure out the root node addr\\n try:\\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\\n except Exception:\\n root_node = '127.0.0.2'\\n\\n root_node = self.resolve_root_node_address(root_node)\\n os.environ['MASTER_ADDR'] = root_node\\n\\n dist.init_process_group(\\\"nccl\\\", rank=self.proc_rank, world_size=self.world_size)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.resolve_root_node_address\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.resolve_root_node_address\\n def resolve_root_node_address(self, root_node):\\n if '[' in root_node:\\n name = root_node.split('[')[0]\\n number = root_node.split(',')[0]\\n if '-' in number:\\n number = number.split('-')[0]\\n\\n number = re.sub('[^0-9]', '', number)\\n root_node = name + number\\n\\n return root_node\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__run_pretrain_routine\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__run_pretrain_routine\\n def __run_pretrain_routine(self, model):\\n \\\"\\\"\\\"\\n Sanity check a few things before starting actual training\\n :param model:\\n :return:\\n \\\"\\\"\\\"\\n ref_model = model\\n if self.data_parallel:\\n ref_model = model.module\\n\\n ref_model.trainer = self\\n\\n # set local properties on the model\\n ref_model.on_gpu = self.on_gpu\\n\\n # transfer data loaders from model\\n self.get_dataloaders(ref_model)\\n\\n # init training constants\\n self.__layout_bookeeping()\\n\\n # print model summary\\n if self.proc_rank == 0 and self.print_weights_summary:\\n ref_model.summarize()\\n\\n # give model convenience properties\\n ref_model.trainer = self\\n\\n if self.experiment is not None:\\n ref_model.experiment = self.experiment\\n\\n # save exp to get started\\n if self.proc_rank == 0 and self.experiment is not None:\\n self.experiment.save()\\n\\n # track model now.\\n # if cluster resets state, the model will update with the saved weights\\n self.model = model\\n\\n # restore training and model before hpc call\\n self.restore_state_if_existing_checkpoint()\\n\\n # enable cluster checkpointing\\n # also restores training state\\n # hpc checkpoint overrides any other checkpoints loaded before\\n if self.cluster is not None: # pragma: no cover\\n self.enable_auto_hpc_walltime_manager()\\n\\n # run tiny validation (if validation defined) to make sure program won't crash during val\\n ref_model.on_sanity_check_start()\\n if self.val_dataloader is not None:\\n for ds_i, dataloader in enumerate(self.val_dataloader):\\n self.validate(model, dataloader, self.nb_sanity_val_steps, ds_i)\\n\\n # ---------------------------\\n # CORE TRAINING LOOP\\n # ---------------------------\\n self.__train\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__train\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__train\\n def __train(self):\\n # run all epochs\\n for epoch_nb in range(self.current_epoch, self.max_nb_epochs):\\n # get model\\n model = self.__get_model()\\n\\n # update training progress in trainer and model\\n model.current_epoch = epoch_nb\\n self.current_epoch = epoch_nb\\n self.total_batches = self.nb_tng_batches + self.nb_val_batches\\n self.batch_loss_value = 0 # accumulated grads\\n\\n # init progbar when requested\\n if self.progress_bar:\\n self.prog_bar = tqdm.tqdm(range(self.total_batches),\\n position=self.process_position)\\n\\n # -----------------\\n # RUN TNG EPOCH\\n # -----------------\\n self.run_tng_epoch()\\n\\n # update LR schedulers\\n if self.lr_schedulers is not None:\\n for lr_scheduler in self.lr_schedulers:\\n lr_scheduler.step()\\n\\n # early stopping\\n met_min_epochs = epoch_nb > self.min_nb_epochs\\n if self.enable_early_stop and met_min_epochs:\\n should_stop = self.early_stop_callback.on_epoch_end(epoch=epoch_nb,\\n logs=self.__tng_tqdm_dic)\\n # stop training\\n stop = should_stop and met_min_epochs\\n if stop:\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.run_tng_epoch\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.run_tng_epoch\\n def run_tng_epoch(self):\\n # before epoch hook\\n if self.__is_function_implemented('on_epoch_start'):\\n model = self.__get_model()\\n model.on_epoch_start()\\n\\n # run epoch\\n for batch_nb, data_batch in enumerate(self.tng_dataloader):\\n self.batch_nb = batch_nb\\n self.global_step += 1\\n\\n model = self.__get_model()\\n model.global_step = self.global_step\\n\\n # stop when the flag is changed or we've gone past the amount\\n # requested in the batches\\n self.total_batch_nb += 1\\n met_batch_limit = batch_nb > self.nb_tng_batches\\n if met_batch_limit:\\n break\\n\\n # ---------------\\n # RUN TRAIN STEP\\n # ---------------\\n batch_result = self.__run_tng_batch(data_batch, batch_nb)\\n early_stop_epoch = batch_result == -1\\n\\n # ---------------\\n # RUN VAL STEP\\n # ---------------\\n is_val_check_batch = (batch_nb + 1) % self.val_check_batch == 0\\n if self.fast_dev_run or is_val_check_batch or early_stop_epoch:\\n self.__run_validation()\\n\\n # when batch should be saved\\n if (batch_nb + 1) % self.log_save_interval == 0 or early_stop_epoch:\\n if self.proc_rank == 0 and self.experiment is not None:\\n self.experiment.save()\\n\\n # when metrics should be logged\\n if batch_nb % self.add_log_row_interval == 0 or early_stop_epoch:\\n # count items in memory\\n # nb_params, nb_tensors = count_mem_items()\\n\\n model = self.__get_model()\\n metrics = self.__tng_tqdm_dic\\n\\n # add gpu memory\\n if self.on_gpu:\\n mem_map = get_gpu_memory_map()\\n metrics.update(mem_map)\\n\\n # add norms\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__metrics_to_scalars\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__metrics_to_scalars\\n def __metrics_to_scalars(self, metrics, blacklist=set()):\\n new_metrics = {}\\n for k, v in metrics.items():\\n if type(v) is torch.Tensor:\\n v = v.item()\\n\\n if type(v) is dict:\\n v = self.__metrics_to_scalars(v)\\n\\n if k not in blacklist:\\n new_metrics[k] = float(v)\\n\\n return new_metrics\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__log_vals_blacklist\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__log_vals_blacklist\\n def __log_vals_blacklist(self):\\n \\\"\\\"\\\"avoid logging some vals lightning uses to maintain state\\\"\\\"\\\"\\n blacklist = {'batch_nb', 'v_nb', 'gpu'}\\n return blacklist\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.transfer_batch_to_gpu\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.transfer_batch_to_gpu\\n def transfer_batch_to_gpu(self, batch, gpu_id):\\n # base case\\n if isinstance(batch, torch.Tensor):\\n return batch.cuda(gpu_id)\\n\\n # when list\\n elif isinstance(batch, list):\\n for i, x in enumerate(batch):\\n batch[i] = self.transfer_batch_to_gpu(x, gpu_id)\\n return batch\\n\\n # when dict\\n elif isinstance(batch, dict):\\n for k, v in batch.items():\\n batch[k] = self.transfer_batch_to_gpu(v, gpu_id)\\n\\n return batch\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__tng_forward\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__tng_forward\\n def __tng_forward(self, data_batch, batch_nb, opt_idx):\\n \\\"\\\"\\\"\\n Handle forward for each training case (distributed, single gpu, etc...)\\n :param data_batch:\\n :param batch_nb:\\n :return:\\n \\\"\\\"\\\"\\n # ---------------\\n # FORWARD\\n # ---------------\\n # enable not needing to add opt_idx to training_step\\n args = [data_batch, batch_nb]\\n if len(self.optimizers) > 1:\\n args.append(opt_idx)\\n\\n if self.use_ddp:\\n output = self.model(*args)\\n elif self.use_dp:\\n output = self.model(*args)\\n elif self.single_gpu:\\n gpu_id = self.data_parallel_device_ids[0]\\n data_batch = self.transfer_batch_to_gpu(data_batch, gpu_id)\\n args[0] = data_batch\\n output = self.model.training_step(*args)\\n\\n else:\\n output = self.model.training_step(*args)\\n\\n # ---------------\\n # TQDM metrics\\n # ---------------\\n try:\\n prog_output = output['prog']\\n\\n # reduce prog metrics for tqdm when using dp\\n if self.use_dp:\\n nb_gpus = len(self.data_parallel_device_ids)\\n prog_output = reduce_distributed_output(prog_output, nb_gpus)\\n\\n model_specific_tqdm_metrics_dic = prog_output\\n except Exception:\\n model_specific_tqdm_metrics_dic = {}\\n\\n # ---------------\\n # EXTRACT LOSS\\n # ---------------\\n # if output dict doesn't have the keyword loss\\n # then assume the output=loss if scalar\\n try:\\n loss = output['loss']\\n except Exception:\\n if type(output) is torch.Tensor:\\n loss = output\\n\\n # when using dp need to reduce the loss\\n if self.use_dp:\\n loss = reduce_distributed_output(loss, len(self.data_parallel_device_ids))\\n\\n return loss, model_specific_tqdm_metrics_dic\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__clip_gradients\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__clip_gradients\\n def __clip_gradients(self):\\n if self.gradient_clip > 0:\\n model = self.__get_model()\\n torch.nn.utils.clip_grad_norm_(model.parameters(), self.gradient_clip)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__print_nan_grads\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__print_nan_grads\\n def __print_nan_grads(self):\\n if self.print_nan_grads:\\n model = self.__get_model()\\n for param in model.parameters():\\n print(param.grad.float().sum())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__run_tng_batch\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__run_tng_batch\\n def __run_tng_batch(self, data_batch, batch_nb):\\n if data_batch is None:\\n return 0\\n\\n # hook\\n if self.__is_function_implemented('on_batch_start'):\\n model_ref = self.__get_model()\\n response = model_ref.on_batch_start(data_batch)\\n\\n if response == -1:\\n return -1\\n\\n if self.progress_bar:\\n self.prog_bar.update(1)\\n\\n # call training_step once per optimizer\\n for opt_idx, optimizer in enumerate(self.optimizers):\\n\\n # forward pass\\n loss, model_specific_tqdm_metrics = self.__tng_forward(data_batch, batch_nb, opt_idx)\\n\\n # track metrics\\n self.__add_tqdm_metrics(model_specific_tqdm_metrics)\\n\\n # accumulate loss\\n # (if accumulate_grad_batches = 1 no effect)\\n loss = loss / self.accumulate_grad_batches\\n\\n # backward pass\\n if self.use_amp:\\n with amp.scale_loss(loss, optimizer) as scaled_loss:\\n scaled_loss.backward()\\n else:\\n loss.backward()\\n\\n # insert after step hook\\n if self.__is_function_implemented('on_after_backward'):\\n model_ref = self.__get_model()\\n model_ref.on_after_backward()\\n\\n # nan grads\\n self.__print_nan_grads()\\n\\n # track total loss for logging (avoid mem leaks)\\n self.batch_loss_value += loss.item()\\n\\n # gradient update with accumulated gradients\\n if (self.batch_nb + 1) % self.accumulate_grad_batches == 0:\\n # clip gradients\\n self.__clip_gradients()\\n\\n # calls .step(), .zero_grad()\\n # override function to modify this behavior\\n model = self.__get_model()\\n model.optimizer_step(self.current_epoch, batch_nb, optimizer, opt_idx)\\n\\n # calc\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/models/trainer.py::Trainer.__run_validation\",\n \"text\": \"pytorch_lightning/models/trainer.py::Trainer.__run_validation\\n def __run_validation(self):\\n # decide if can check epochs\\n can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0\\n if self.fast_dev_run:\\n print('skipping to check performance bc of --fast_dev_run')\\n elif not can_check_epoch:\\n return\\n\\n # validate only if model has validation_step defined\\n if self.__is_overriden('validation_step'):\\n\\n # hook\\n if self.__is_function_implemented('on_pre_performance_check'):\\n model = self.__get_model()\\n model.on_pre_performance_check()\\n\\n # use val_percent_check set on end of epoch\\n # use a small portion otherwise\\n max_batches = self.nb_val_batches if not self.fast_dev_run else 1\\n for ds_i, dataloader in enumerate(self.val_dataloader):\\n val_out_metrics = self.validate(self.model, dataloader, max_batches, ds_i)\\n self.__add_tqdm_metrics(val_out_metrics)\\n\\n # hook\\n if self.__is_function_implemented('on_post_performance_check'):\\n model = self.__get_model()\\n model.on_post_performance_check()\\n\\n if self.progress_bar:\\n # add model specific metrics\\n tqdm_metrics = self.__tng_tqdm_dic\\n self.prog_bar.set_postfix(**tqdm_metrics)\\n\\n # model checkpointing\\n if self.proc_rank == 0 and self.checkpoint_callback is not None:\\n print('save callback...')\\n self.checkpoint_callback.on_epoch_end(epoch=self.current_epoch,\\n logs=self.__tng_tqdm_dic)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::_find_tensors\",\n \"text\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::_find_tensors\\ndef _find_tensors(obj): # pragma: no cover\\n r\\\"\\\"\\\"\\n Recursively find all tensors contained in the specified object.\\n \\\"\\\"\\\"\\n if isinstance(obj, torch.Tensor):\\n return [obj]\\n if isinstance(obj, (list, tuple)):\\n return itertools.chain(*map(_find_tensors, obj))\\n if isinstance(obj, dict):\\n return itertools.chain(*map(_find_tensors, obj.values()))\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::get_a_var\",\n \"text\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::get_a_var\\ndef get_a_var(obj): # pragma: no cover\\n if isinstance(obj, torch.Tensor):\\n return obj\\n\\n if isinstance(obj, list) or isinstance(obj, tuple):\\n for result in map(get_a_var, obj):\\n if isinstance(result, torch.Tensor):\\n return result\\n if isinstance(obj, dict):\\n for result in map(get_a_var, obj.items()):\\n if isinstance(result, torch.Tensor):\\n return result\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDataParallel.forward\",\n \"text\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDataParallel.forward\\n def forward(self, *inputs, **kwargs):\\n if not self.device_ids:\\n return self.module(*inputs, **kwargs)\\n\\n for t in chain(self.module.parameters(), self.module.buffers()):\\n if t.device != self.src_device_obj:\\n raise RuntimeError(\\\"module must have its parameters and buffers \\\"\\n \\\"on device {} (device_ids[0]) but found one of \\\"\\n \\\"them on device: {}\\\".format(self.src_device_obj, t.device))\\n\\n inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)\\n if len(self.device_ids) == 1:\\n # lightning\\n if self.module.training:\\n return self.module.training_step(*inputs[0], **kwargs[0])\\n else:\\n return self.module.validation_step(*inputs[0], **kwargs[0])\\n\\n replicas = self.replicate(self.module, self.device_ids[:len(inputs)])\\n outputs = self.parallel_apply(replicas, inputs, kwargs)\\n return self.gather(outputs, self.output_device)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDataParallel.parallel_apply\",\n \"text\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDataParallel.parallel_apply\\n def parallel_apply(self, replicas, inputs, kwargs):\\n return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDistributedDataParallel.parallel_apply\",\n \"text\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDistributedDataParallel.parallel_apply\\n def parallel_apply(self, replicas, inputs, kwargs):\\n return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDistributedDataParallel.forward\",\n \"text\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::LightningDistributedDataParallel.forward\\n def forward(self, *inputs, **kwargs): # pragma: no cover\\n self._sync_params()\\n if self.device_ids:\\n inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids)\\n if len(self.device_ids) == 1:\\n # --------------\\n # LIGHTNING MOD\\n # --------------\\n # normal\\n # output = self.module(*inputs[0], **kwargs[0])\\n\\n # lightning\\n if self.module.training:\\n output = self.module.training_step(*inputs[0], **kwargs[0])\\n else:\\n output = self.module.validation_step(*inputs[0], **kwargs[0])\\n else:\\n outputs = self.parallel_apply(self._module_copies[:len(inputs)], inputs, kwargs)\\n output = self.gather(outputs, self.output_device)\\n else:\\n output = self.module(*inputs, **kwargs)\\n\\n if torch.is_grad_enabled():\\n # We'll return the output object verbatim since it is a freeform\\n # object. We need to find any tensors in this object, though,\\n # because we need to figure out which parameters were used during\\n # this forward pass, to ensure we short circuit reduction for any\\n # unused parameters. Only if `find_unused_parameters` is set.\\n if self.find_unused_parameters:\\n self.reducer.prepare_for_backward(list(_find_tensors(output)))\\n else:\\n self.reducer.prepare_for_backward([])\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::parallel_apply\",\n \"text\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::parallel_apply\\ndef parallel_apply(modules, inputs, kwargs_tup=None, devices=None): # pragma: no cover\\n r\\\"\\\"\\\"Applies each `module` in :attr:`modules` in parallel on arguments\\n contained in :attr:`inputs` (positional) and :attr:`kwargs_tup` (keyword)\\n on each of :attr:`devices`.\\n\\n Args:\\n modules (Module): modules to be parallelized\\n inputs (tensor): inputs to the modules\\n devices (list of int or torch.device): CUDA devices\\n\\n :attr:`modules`, :attr:`inputs`, :attr:`kwargs_tup` (if given), and\\n :attr:`devices` (if given) should all have same length. Moreover, each\\n element of :attr:`inputs` can either be a single object as the only argument\\n to a module, or a collection of positional arguments.\\n \\\"\\\"\\\"\\n assert len(modules) == len(inputs)\\n if kwargs_tup is not None:\\n assert len(modules) == len(kwargs_tup)\\n else:\\n kwargs_tup = ({},) * len(modules)\\n if devices is not None:\\n assert len(modules) == len(devices)\\n else:\\n devices = [None] * len(modules)\\n devices = list(map(lambda x: _get_device_index(x, True), devices))\\n lock = threading.Lock()\\n results = {}\\n grad_enabled = torch.is_grad_enabled()\\n\\n def _worker(i, module, input, kwargs, device=None):\\n torch.set_grad_enabled(grad_enabled)\\n if device is None:\\n device = get_a_var(input).get_device()\\n try:\\n with torch.cuda.device(device):\\n # this also avoids accidental slicing of `input` if it is a Tensor\\n if not isinstance(input, (list, tuple)):\\n input = (input,)\\n\\n # ---------------\\n # CHANGE\\n if module.training:\\n output = module.training_step(*input, **kwargs)\\n else:\\n output = module.validation_step(*input, **kwargs)\\n # ---------------\\n\\n with lock:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::parallel_apply._worker\",\n \"text\": \"pytorch_lightning/pt_overrides/override_data_parallel.py::parallel_apply._worker\\n def _worker(i, module, input, kwargs, device=None):\\n torch.set_grad_enabled(grad_enabled)\\n if device is None:\\n device = get_a_var(input).get_device()\\n try:\\n with torch.cuda.device(device):\\n # this also avoids accidental slicing of `input` if it is a Tensor\\n if not isinstance(input, (list, tuple)):\\n input = (input,)\\n\\n # ---------------\\n # CHANGE\\n if module.training:\\n output = module.training_step(*input, **kwargs)\\n else:\\n output = module.validation_step(*input, **kwargs)\\n # ---------------\\n\\n with lock:\\n results[i] = output\\n except Exception as e:\\n with lock:\\n results[i] = e\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/decorators.py::data_loader\",\n \"text\": \"pytorch_lightning/root_module/decorators.py::data_loader\\ndef data_loader(fn):\\n \\\"\\\"\\\"\\n Decorator to make any fx with this use the lazy property\\n :param fn:\\n :return:\\n \\\"\\\"\\\"\\n\\n attr_name = '_lazy_' + fn.__name__\\n\\n @property\\n def _data_loader(self):\\n if not hasattr(self, attr_name):\\n setattr(self, attr_name, fn(self))\\n return getattr(self, attr_name)\\n\\n return _data_loader\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/decorators.py::data_loader._data_loader\",\n \"text\": \"pytorch_lightning/root_module/decorators.py::data_loader._data_loader\\n @property\\n def _data_loader(self):\\n if not hasattr(self, attr_name):\\n setattr(self, attr_name, fn(self))\\n return getattr(self, attr_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.__init__\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.__init__\\n def __init__(self, *args, **kwargs):\\n super(LightningModule, self).__init__(*args, **kwargs)\\n\\n self.dtype = torch.FloatTensor\\n self.exp_save_path = None\\n self.current_epoch = 0\\n self.global_step = 0\\n self.loaded_optimizer_states_dict = {}\\n self.trainer = None\\n self.experiment = None\\n self.example_input_array = None\\n\\n # track if gpu was requested for checkpointing\\n self.on_gpu = False\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.forward\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.forward\\n def forward(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n Expand model in into whatever you need.\\n Also need to return the target\\n :param x:\\n :return:\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.training_step\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.training_step\\n def training_step(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n return loss, dict with metrics for tqdm\\n :param called with batch, batch_nb\\n additional: optimizer_i if multiple optimizers used\\n :return:\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.validation_step\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.validation_step\\n def validation_step(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n return whatever outputs will need to be aggregated in validation_end\\n OPTIONAL\\n :param called with batch, batch_nb\\n additional: dataset_i if multiple val datasets used\\n :return:\\n \\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.validation_end\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.validation_end\\n def validation_end(self, outputs):\\n \\\"\\\"\\\"\\n Outputs has the appended output after each validation step\\n OPTIONAL\\n :param outputs:\\n :return: dic_with_metrics for tqdm\\n \\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.configure_optimizers\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.configure_optimizers\\n def configure_optimizers(self):\\n \\\"\\\"\\\"\\n Return a list of optimizers and a list of schedulers (could be empty)\\n :return:\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.optimizer_step\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.optimizer_step\\n def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i):\\n \\\"\\\"\\\"\\n Do something instead of the standard optimizer behavior\\n :param epoch_nb:\\n :param batch_nb:\\n :param optimizer:\\n :param optimizer_i:\\n :return:\\n \\\"\\\"\\\"\\n optimizer.step()\\n\\n # clear gradients\\n optimizer.zero_grad()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.tng_dataloader\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.tng_dataloader\\n @data_loader\\n def tng_dataloader(self):\\n \\\"\\\"\\\"\\n Implement a PyTorch DataLoader\\n :return:\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.test_dataloader\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.test_dataloader\\n @data_loader\\n def test_dataloader(self):\\n \\\"\\\"\\\"\\n Implement a PyTorch DataLoader\\n :return:\\n \\\"\\\"\\\"\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.val_dataloader\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.val_dataloader\\n @data_loader\\n def val_dataloader(self):\\n \\\"\\\"\\\"\\n Implement a PyTorch DataLoader\\n :return:\\n \\\"\\\"\\\"\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.load_from_metrics\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.load_from_metrics\\n @classmethod\\n def load_from_metrics(cls, weights_path, tags_csv, on_gpu, map_location=None):\\n \\\"\\\"\\\"\\n Primary way of loading model from csv weights path\\n :param weights_path:\\n :param tags_csv:\\n :param on_gpu:\\n :param map_location: dic for mapping storage {'cuda:1':'cuda:0'}\\n :return:\\n \\\"\\\"\\\"\\n hparams = load_hparams_from_tags_csv(tags_csv)\\n hparams.__setattr__('on_gpu', on_gpu)\\n\\n if on_gpu:\\n if map_location is not None:\\n checkpoint = torch.load(weights_path, map_location=map_location)\\n else:\\n checkpoint = torch.load(weights_path)\\n else:\\n checkpoint = torch.load(weights_path, map_location=lambda storage, loc: storage)\\n\\n # load the state_dict on the model automatically\\n model = cls(hparams)\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n # give model a chance to load something\\n model.on_load_checkpoint(checkpoint)\\n\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.summarize\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.summarize\\n def summarize(self):\\n model_summary = ModelSummary(self)\\n print(model_summary)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.freeze\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.freeze\\n def freeze(self):\\n for param in self.parameters():\\n param.requires_grad = False\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.unfreeze\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.unfreeze\\n def unfreeze(self):\\n for param in self.parameters():\\n param.requires_grad = True\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/utilities/arg_parse.py::add_default_args\",\n \"text\": \"pytorch_lightning/utilities/arg_parse.py::add_default_args\\ndef add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None):\\n\\n # tng, test, val check intervals\\n parser.add_argument('--eval_test_set', dest='eval_test_set', action='store_true',\\n help='true = run test set also')\\n parser.add_argument('--check_val_every_n_epoch', default=1, type=int,\\n help='check val every n epochs')\\n parser.opt_list('--accumulate_grad_batches', default=1, type=int, tunable=False,\\n help='accumulates gradients k times before applying update.'\\n ' Simulates huge batch size')\\n parser.add_argument('--max_nb_epochs', default=200, type=int, help='cap epochs')\\n parser.add_argument('--min_nb_epochs', default=2, type=int, help='min epochs')\\n parser.add_argument('--train_percent_check', default=1.0, type=float,\\n help='how much of tng set to check')\\n parser.add_argument('--val_percent_check', default=1.0, type=float,\\n help='how much of val set to check')\\n parser.add_argument('--test_percent_check', default=1.0, type=float,\\n help='how much of test set to check')\\n\\n parser.add_argument('--val_check_interval', default=0.95, type=float,\\n help='how much within 1 epoch to check val')\\n parser.add_argument('--log_save_interval', default=100, type=int,\\n help='how many batches between log saves')\\n parser.add_argument('--add_log_row_interval', default=100, type=int,\\n help='add log every k batches')\\n\\n # early stopping\\n parser.add_argument('--disable_early_stop', dest='enable_early_stop', action='store_false')\\n parser.add_argument('--early_stop_metric', default='val_acc', type=str)\\n parser.add_argument('--early_stop_mode', default='min', type=str)\\n parser.add_argument('--early_stop_patience', default=3, type=int,\\n \",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":265,"cells":{"instance_id":{"kind":"string","value":"wagtail__wagtail-964"},"repo":{"kind":"string","value":"wagtail/wagtail"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"It would be nice if InlinePanel definitions required less model name repetition\nIn the following example model code\n\n```\nclass BlogPageRelatedLink(Whatever):\n page = ParentalKey('myapp.BlogPage', related_name='related_links')\n\nclass BlogPage(Page):\n pass \n\nBlogPage.content_panels = [\n InlinePanel(BlogPage, 'related_links', label=\"Related links\"),\n]\n```\n\n... it would be nice if the `'myapp.BlogPage'` param in `ParentalKey` and the `BlogPage` param in `InlinePanel` weren't necessary. They're easily forgotten if you're copy-pasting models and appear avoidably repetitive. \n\nI don't understand a situation in which either param would _not_ point to the page model you're creating, although I expect situations exist.\n\n"},"gold_files":{"kind":"list like","value":["runtests.py","wagtail/wagtailadmin/edit_handlers.py"],"string":"[\n \"runtests.py\",\n \"wagtail/wagtailadmin/edit_handlers.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["runtests.py::runtests","wagtail/wagtailadmin/edit_handlers.py::<module>","wagtail/wagtailadmin/edit_handlers.py::InlinePanel.__init__"],"string":"[\n \"runtests.py::runtests\",\n \"wagtail/wagtailadmin/edit_handlers.py::<module>\",\n \"wagtail/wagtailadmin/edit_handlers.py::InlinePanel.__init__\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"runtests.py","text":"runtests.py\ndef runtests()","label":1},{"cid":"wagtail/wagtailadmin/edit_handlers.py","text":"wagtail/wagtailadmin/edit_handlers.py\ndef formfield_for_dbfield(db_field, **kwargs)\ndef widget_with_script(widget, script)\nclass WagtailAdminModelFormMetaclass\n def __new__(cls, name, bases, attrs)\ndef get_form_for_model(model, fields=None, exclude=None, formsets=None, exclude_formsets=None, widgets=None)\ndef extract_panel_definitions_from_model_class(model, exclude=None)\nclass EditHandler # Abstract class providing sensible default behaviours for objects implementing\n def widget_overrides(cls)\n def required_fields(cls)\n def required_formsets(cls)\n def get_form_class(cls, model)\n def __init__(self, instance=None, form=None)\n def classes(self) # Additional CSS classnames to add to whatever kind of object this is at output.\n def field_type(self) # The kind of field it is e.g boolean_field. Useful for better semantic markup of \n def render_as_object(self) # Render this object as it should appear within an ObjectList. Should not\n def render_as_field(self) # Render this object as it should appear within a <ul class=\"fields\"> list item\n def render_missing_fields(self) # Helper function: render all of the fields that are defined on the form but not \"\n def render_form_content(self) # Render this as an 'object', ensuring that all fields necessary for a valid form\nclass BaseCompositeEditHandler # Abstract class for EditHandlers that manage a set of sub-EditHandlers.\n def widget_overrides(cls)\n def required_fields(cls)\n def required_formsets(cls)\n def __init__(self, instance=None, form=None)\n def render(self)\nclass BaseTabbedInterface\nclass TabbedInterface\n def __init__(self, children)\n def bind_to_model(self, model)\nclass BaseObjectList\nclass ObjectList\n def __init__(self, children, heading='', classname='')\n def bind_to_model(self, model)\nclass BaseFieldRowPanel\nclass FieldRowPanel\n def __init__(self, children, classname='')\n def bind_to_model(self, model)\nclass BaseMultiFieldPanel\n def classes(self)\nclass MultiFieldPanel\n def __init__(self, children, heading='', classname='')\n def bind_to_model(self, model)\nclass BaseFieldPanel\n def widget_overrides(cls) # check if a specific widget has been defined for this field\n def __init__(self, instance=None, form=None)\n def classes(self)\n def field_type(self)\n def render_as_object(self)\n def render_as_field(self, show_help_text=True, extra_context={})\n def required_fields(self)\nclass FieldPanel\n def __init__(self, field_name, classname='', widget=None)\n def bind_to_model(self, model)\nclass BaseRichTextFieldPanel\nclass RichTextFieldPanel\n def __init__(self, field_name)\n def bind_to_model(self, model)\nclass BaseChooserPanel # Abstract superclass for panels that provide a modal interface for choosing (or c\n def get_chosen_item(self)\n def render_as_field(self, show_help_text=True, extra_context={})\nclass BasePageChooserPanel\n def widget_overrides(cls)\n def target_content_type(cls)\n de","label":1},{"cid":"wagtail/wagtailadmin/forms.py","text":"wagtail/wagtailadmin/forms.py\nclass SearchForm\n def __init__(self, *args, **kwargs)\nclass ExternalLinkChooserForm\nclass ExternalLinkChooserWithLinkTextForm\nclass EmailLinkChooserForm\nclass EmailLinkChooserWithLinkTextForm\nclass LoginForm\n def __init__(self, request=None, *args, **kwargs)\nclass PasswordResetForm\n def clean(self)\nclass CopyForm\n def __init__(self, *args, **kwargs)\n def clean(self)\nclass PageViewRestrictionForm\n def clean(self)","label":0},{"cid":"wagtail/wagtailadmin/utils.py","text":"wagtail/wagtailadmin/utils.py\ndef get_object_usage(obj) # Returns a queryset of pages that link to a particular object","label":0},{"cid":"wagtail/wagtailcore/models.py","text":"wagtail/wagtailcore/models.py\nclass SiteManager\n def get_by_natural_key(self, hostname, port)\nclass Site\n class Meta\n def natural_key(self)\n def __str__(self)\n def find_for_request(request) # Find the site object responsible for responding to this HTTP\n def root_url(self)\n def clean_fields(self, exclude=None)\n def get_site_root_paths() # Return a list of (root_path, root_url) tuples, most specific path first -\ndef clear_site_root_paths_on_save(sender, instance, **kwargs)\ndef clear_site_root_paths_on_delete(sender, instance, **kwargs)\ndef get_page_types()\nclass PageManager\n def get_queryset(self)\n def live(self)\n def not_live(self)\n def in_menu(self)\n def not_in_menu(self)\n def page(self, other)\n def not_page(self, other)\n def descendant_of(self, other, inclusive=False)\n def not_descendant_of(self, other, inclusive=False)\n def child_of(self, other)\n def not_child_of(self, other)\n def ancestor_of(self, other, inclusive=False)\n def not_ancestor_of(self, other, inclusive=False)\n def parent_of(self, other)\n def not_parent_of(self, other)\n def sibling_of(self, other, inclusive=False)\n def not_sibling_of(self, other, inclusive=False)\n def type(self, model)\n def not_type(self, model)\n def public(self)\n def not_public(self)\n def search(self, query_string, fields=None, backend='default')\nclass PageBase # Metaclass for Page\n def __init__(cls, name, bases, dct)\nclass Page\n def __init__(self, *args, **kwargs)\n def __str__(self)\n def set_url_path(self, parent) # Populate the url_path field based on this page's slug and the specified parent p\n def save(self, *args, **kwargs)\n def check(cls, **kwargs)\n def _update_descendant_url_paths(self, old_url_path, new_url_path)\n def specific(self) # Return this page in its most specific subclassed form.\n def specific_class(self) # Return the class that this page would be if instantiated in its\n def route(self, request, path_components)\n def save_revision(self, user=None, submitted_for_moderation=False, approved_go_live_at=None)\n def get_latest_revision(self)\n def get_latest_revision_as_page(self)\n def unpublish(self, set_expired=False, commit=True)\n def get_context(self, request, *args, **kwargs)\n def get_template(self, request, *args, **kwargs)\n def serve(self, request, *args, **kwargs)\n def is_navigable(self) # Return true if it's meaningful to browse subpages of this page -\n def full_url(self) # Return the full URL (including protocol / domain) to this page, or None if it is\n def url(self) # Return the 'most appropriate' URL for referring to this page from the pages we s\n def relative_url(self, current_site) # Return the 'most appropriate' URL for this page taking into account the site we'\n def get_indexed_objects(cls)\n def get_indexed_instance(self)\n def search(cls, query_string, show_unpublished=False, search_title_only=False, extra_filters={}, pre","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py","text":"wagtail/wagtailsearch/backends/db.py\nclass DBSearchQuery\n def _process_lookup(self, field, lookup, value)\n def _connect_filters(self, filters, connector, negated)\n def get_q(self)\nclass DBSearchResults\n def get_queryset(self)\n def _do_search(self)\n def _do_count(self)\nclass DBSearch\n def __init__(self, params)\n def reset_index(self)\n def add_type(self, model)\n def refresh_index(self)\n def add(self, obj)\n def add_bulk(self, model, obj_list)\n def delete(self, obj)\n def _search(self, queryset, query_string, fields=None)","label":0},{"cid":"wagtail/wagtailsearch/forms.py","text":"wagtail/wagtailsearch/forms.py\nclass QueryForm\nclass EditorsPickForm\n def __init__(self, *args, **kwargs)\n class Meta\nclass EditorsPickFormSet\n def add_fields(self, form, *args, **kwargs)\n def clean(self)","label":0},{"cid":"wagtail/wagtailusers/forms.py","text":"wagtail/wagtailusers/forms.py\nclass UserCreationForm\n class Meta\n def clean_username(self)\n def save(self, commit=True)\nclass UserEditForm\n class Meta\n def clean_username(self)\n def clean_password2(self)\n def save(self, commit=True)\nclass GroupForm\n def __init__(self, *args, **kwargs)\n class Meta\n def clean_name(self)\n def save(self)\nclass GroupPagePermissionForm\n def __init__(self, *args, **kwargs)\n class Meta\nclass BaseGroupPagePermissionFormSet\n def __init__(self, *args, **kwargs)\n def empty_form(self)\nclass NotificationPreferencesForm\n def __init__(self, *args, **kwargs)\n class Meta","label":0}],"string":"[\n {\n \"cid\": \"runtests.py\",\n \"text\": \"runtests.py\\ndef runtests()\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py\\ndef formfield_for_dbfield(db_field, **kwargs)\\ndef widget_with_script(widget, script)\\nclass WagtailAdminModelFormMetaclass\\n def __new__(cls, name, bases, attrs)\\ndef get_form_for_model(model, fields=None, exclude=None, formsets=None, exclude_formsets=None, widgets=None)\\ndef extract_panel_definitions_from_model_class(model, exclude=None)\\nclass EditHandler # Abstract class providing sensible default behaviours for objects implementing\\n def widget_overrides(cls)\\n def required_fields(cls)\\n def required_formsets(cls)\\n def get_form_class(cls, model)\\n def __init__(self, instance=None, form=None)\\n def classes(self) # Additional CSS classnames to add to whatever kind of object this is at output.\\n def field_type(self) # The kind of field it is e.g boolean_field. Useful for better semantic markup of \\n def render_as_object(self) # Render this object as it should appear within an ObjectList. Should not\\n def render_as_field(self) # Render this object as it should appear within a <ul class=\\\"fields\\\"> list item\\n def render_missing_fields(self) # Helper function: render all of the fields that are defined on the form but not \\\"\\n def render_form_content(self) # Render this as an 'object', ensuring that all fields necessary for a valid form\\nclass BaseCompositeEditHandler # Abstract class for EditHandlers that manage a set of sub-EditHandlers.\\n def widget_overrides(cls)\\n def required_fields(cls)\\n def required_formsets(cls)\\n def __init__(self, instance=None, form=None)\\n def render(self)\\nclass BaseTabbedInterface\\nclass TabbedInterface\\n def __init__(self, children)\\n def bind_to_model(self, model)\\nclass BaseObjectList\\nclass ObjectList\\n def __init__(self, children, heading='', classname='')\\n def bind_to_model(self, model)\\nclass BaseFieldRowPanel\\nclass FieldRowPanel\\n def __init__(self, children, classname='')\\n def bind_to_model(self, model)\\nclass BaseMultiFieldPanel\\n def classes(self)\\nclass MultiFieldPanel\\n def __init__(self, children, heading='', classname='')\\n def bind_to_model(self, model)\\nclass BaseFieldPanel\\n def widget_overrides(cls) # check if a specific widget has been defined for this field\\n def __init__(self, instance=None, form=None)\\n def classes(self)\\n def field_type(self)\\n def render_as_object(self)\\n def render_as_field(self, show_help_text=True, extra_context={})\\n def required_fields(self)\\nclass FieldPanel\\n def __init__(self, field_name, classname='', widget=None)\\n def bind_to_model(self, model)\\nclass BaseRichTextFieldPanel\\nclass RichTextFieldPanel\\n def __init__(self, field_name)\\n def bind_to_model(self, model)\\nclass BaseChooserPanel # Abstract superclass for panels that provide a modal interface for choosing (or c\\n def get_chosen_item(self)\\n def render_as_field(self, show_help_text=True, extra_context={})\\nclass BasePageChooserPanel\\n def widget_overrides(cls)\\n def target_content_type(cls)\\n de\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/wagtailadmin/forms.py\",\n \"text\": \"wagtail/wagtailadmin/forms.py\\nclass SearchForm\\n def __init__(self, *args, **kwargs)\\nclass ExternalLinkChooserForm\\nclass ExternalLinkChooserWithLinkTextForm\\nclass EmailLinkChooserForm\\nclass EmailLinkChooserWithLinkTextForm\\nclass LoginForm\\n def __init__(self, request=None, *args, **kwargs)\\nclass PasswordResetForm\\n def clean(self)\\nclass CopyForm\\n def __init__(self, *args, **kwargs)\\n def clean(self)\\nclass PageViewRestrictionForm\\n def clean(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/utils.py\",\n \"text\": \"wagtail/wagtailadmin/utils.py\\ndef get_object_usage(obj) # Returns a queryset of pages that link to a particular object\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py\",\n \"text\": \"wagtail/wagtailcore/models.py\\nclass SiteManager\\n def get_by_natural_key(self, hostname, port)\\nclass Site\\n class Meta\\n def natural_key(self)\\n def __str__(self)\\n def find_for_request(request) # Find the site object responsible for responding to this HTTP\\n def root_url(self)\\n def clean_fields(self, exclude=None)\\n def get_site_root_paths() # Return a list of (root_path, root_url) tuples, most specific path first -\\ndef clear_site_root_paths_on_save(sender, instance, **kwargs)\\ndef clear_site_root_paths_on_delete(sender, instance, **kwargs)\\ndef get_page_types()\\nclass PageManager\\n def get_queryset(self)\\n def live(self)\\n def not_live(self)\\n def in_menu(self)\\n def not_in_menu(self)\\n def page(self, other)\\n def not_page(self, other)\\n def descendant_of(self, other, inclusive=False)\\n def not_descendant_of(self, other, inclusive=False)\\n def child_of(self, other)\\n def not_child_of(self, other)\\n def ancestor_of(self, other, inclusive=False)\\n def not_ancestor_of(self, other, inclusive=False)\\n def parent_of(self, other)\\n def not_parent_of(self, other)\\n def sibling_of(self, other, inclusive=False)\\n def not_sibling_of(self, other, inclusive=False)\\n def type(self, model)\\n def not_type(self, model)\\n def public(self)\\n def not_public(self)\\n def search(self, query_string, fields=None, backend='default')\\nclass PageBase # Metaclass for Page\\n def __init__(cls, name, bases, dct)\\nclass Page\\n def __init__(self, *args, **kwargs)\\n def __str__(self)\\n def set_url_path(self, parent) # Populate the url_path field based on this page's slug and the specified parent p\\n def save(self, *args, **kwargs)\\n def check(cls, **kwargs)\\n def _update_descendant_url_paths(self, old_url_path, new_url_path)\\n def specific(self) # Return this page in its most specific subclassed form.\\n def specific_class(self) # Return the class that this page would be if instantiated in its\\n def route(self, request, path_components)\\n def save_revision(self, user=None, submitted_for_moderation=False, approved_go_live_at=None)\\n def get_latest_revision(self)\\n def get_latest_revision_as_page(self)\\n def unpublish(self, set_expired=False, commit=True)\\n def get_context(self, request, *args, **kwargs)\\n def get_template(self, request, *args, **kwargs)\\n def serve(self, request, *args, **kwargs)\\n def is_navigable(self) # Return true if it's meaningful to browse subpages of this page -\\n def full_url(self) # Return the full URL (including protocol / domain) to this page, or None if it is\\n def url(self) # Return the 'most appropriate' URL for referring to this page from the pages we s\\n def relative_url(self, current_site) # Return the 'most appropriate' URL for this page taking into account the site we'\\n def get_indexed_objects(cls)\\n def get_indexed_instance(self)\\n def search(cls, query_string, show_unpublished=False, search_title_only=False, extra_filters={}, pre\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py\\nclass DBSearchQuery\\n def _process_lookup(self, field, lookup, value)\\n def _connect_filters(self, filters, connector, negated)\\n def get_q(self)\\nclass DBSearchResults\\n def get_queryset(self)\\n def _do_search(self)\\n def _do_count(self)\\nclass DBSearch\\n def __init__(self, params)\\n def reset_index(self)\\n def add_type(self, model)\\n def refresh_index(self)\\n def add(self, obj)\\n def add_bulk(self, model, obj_list)\\n def delete(self, obj)\\n def _search(self, queryset, query_string, fields=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/forms.py\",\n \"text\": \"wagtail/wagtailsearch/forms.py\\nclass QueryForm\\nclass EditorsPickForm\\n def __init__(self, *args, **kwargs)\\n class Meta\\nclass EditorsPickFormSet\\n def add_fields(self, form, *args, **kwargs)\\n def clean(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailusers/forms.py\",\n \"text\": \"wagtail/wagtailusers/forms.py\\nclass UserCreationForm\\n class Meta\\n def clean_username(self)\\n def save(self, commit=True)\\nclass UserEditForm\\n class Meta\\n def clean_username(self)\\n def clean_password2(self)\\n def save(self, commit=True)\\nclass GroupForm\\n def __init__(self, *args, **kwargs)\\n class Meta\\n def clean_name(self)\\n def save(self)\\nclass GroupPagePermissionForm\\n def __init__(self, *args, **kwargs)\\n class Meta\\nclass BaseGroupPagePermissionFormSet\\n def __init__(self, *args, **kwargs)\\n def empty_form(self)\\nclass NotificationPreferencesForm\\n def __init__(self, *args, **kwargs)\\n class Meta\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"runtests.py::runtests","text":"runtests.py::runtests\ndef runtests():\n # Don't ignore DeprecationWarnings\n warnings.simplefilter('default', DeprecationWarning)\n\n argv = sys.argv[:1] + ['test'] + sys.argv[1:]\n try:\n execute_from_command_line(argv)\n finally:\n shutil.rmtree(STATIC_ROOT, ignore_errors=True)\n shutil.rmtree(MEDIA_ROOT, ignore_errors=True)","label":1},{"cid":"wagtail/wagtailadmin/edit_handlers.py::formfield_for_dbfield","text":"wagtail/wagtailadmin/edit_handlers.py::formfield_for_dbfield\ndef formfield_for_dbfield(db_field, **kwargs):\n # snarfed from django/contrib/admin/options.py\n\n # If we've got overrides for the formfield defined, use 'em. **kwargs\n # passed to formfield_for_dbfield override the defaults.\n for klass in db_field.__class__.mro():\n if klass in FORM_FIELD_OVERRIDES:\n kwargs = dict(copy.deepcopy(FORM_FIELD_OVERRIDES[klass]), **kwargs)\n return db_field.formfield(**kwargs)\n\n # For any other type of field, just call its formfield() method.\n return db_field.formfield(**kwargs)","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::widget_with_script","text":"wagtail/wagtailadmin/edit_handlers.py::widget_with_script\ndef widget_with_script(widget, script):\n return mark_safe('{0}<script>{1}</script>'.format(widget, script))","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::WagtailAdminModelFormMetaclass.__new__","text":"wagtail/wagtailadmin/edit_handlers.py::WagtailAdminModelFormMetaclass.__new__\n def __new__(cls, name, bases, attrs):\n if 'formfield_callback' not in attrs or attrs['formfield_callback'] is None:\n attrs['formfield_callback'] = formfield_for_dbfield\n\n new_class = super(WagtailAdminModelFormMetaclass, cls).__new__(cls, name, bases, attrs)\n return new_class","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::get_form_for_model","text":"wagtail/wagtailadmin/edit_handlers.py::get_form_for_model\ndef get_form_for_model(\n model,\n fields=None, exclude=None, formsets=None, exclude_formsets=None, widgets=None\n):\n\n # django's modelform_factory with a bit of custom behaviour\n # (dealing with Treebeard's tree-related fields that really should have\n # been editable=False)\n attrs = {'model': model}\n\n if fields is not None:\n attrs['fields'] = fields\n\n if exclude is not None:\n attrs['exclude'] = exclude\n if issubclass(model, Page):\n attrs['exclude'] = attrs.get('exclude', []) + ['content_type', 'path', 'depth', 'numchild']\n\n if widgets is not None:\n attrs['widgets'] = widgets\n\n if formsets is not None:\n attrs['formsets'] = formsets\n\n if exclude_formsets is not None:\n attrs['exclude_formsets'] = exclude_formsets\n\n # Give this new form class a reasonable name.\n class_name = model.__name__ + str('Form')\n form_class_attrs = {\n 'Meta': type(str('Meta'), (object,), attrs)\n }\n\n return WagtailAdminModelFormMetaclass(class_name, (WagtailAdminModelForm,), form_class_attrs)","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::extract_panel_definitions_from_model_class","text":"wagtail/wagtailadmin/edit_handlers.py::extract_panel_definitions_from_model_class\ndef extract_panel_definitions_from_model_class(model, exclude=None):\n if hasattr(model, 'panels'):\n return model.panels\n\n panels = []\n\n _exclude = []\n if exclude:\n _exclude.extend(exclude)\n if issubclass(model, Page):\n _exclude = ['content_type', 'path', 'depth', 'numchild']\n\n fields = fields_for_model(model, exclude=_exclude, formfield_callback=formfield_for_dbfield)\n\n for field_name, field in fields.items():\n try:\n panel_class = field.widget.get_panel()\n except AttributeError:\n panel_class = FieldPanel\n\n panel = panel_class(field_name)\n panels.append(panel)\n\n return panels","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.widget_overrides","text":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.widget_overrides\n @classmethod\n def widget_overrides(cls):\n return {}","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.required_fields","text":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.required_fields\n @classmethod\n def required_fields(cls):\n return []","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.required_formsets","text":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.required_formsets\n @classmethod\n def required_formsets(cls):\n return {}","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.get_form_class","text":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.get_form_class\n @classmethod\n def get_form_class(cls, model):\n if cls._form_class is None:\n cls._form_class = get_form_for_model(\n model,\n fields=cls.required_fields(),\n formsets=cls.required_formsets(), widgets=cls.widget_overrides())\n return cls._form_class","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.__init__","text":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.__init__\n def __init__(self, instance=None, form=None):\n if not instance:\n raise ValueError(\"EditHandler did not receive an instance object\")\n self.instance = instance\n\n if not form:\n raise ValueError(\"EditHandler did not receive a form object\")\n self.form = form","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.classes","text":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.classes\n def classes(self):\n \"\"\"\n Additional CSS classnames to add to whatever kind of object this is at output.\n Subclasses of EditHandler should override this, invoking super(B, self).classes() to\n append more classes specific to the situation.\n \"\"\"\n\n classes = []\n\n try:\n classes.append(self.classname)\n except AttributeError:\n pass\n\n return classes","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.field_type","text":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.field_type\n def field_type(self):\n \"\"\"\n The kind of field it is e.g boolean_field. Useful for better semantic markup of field display based on type\n \"\"\"\n return \"\"","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_as_object","text":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_as_object\n def render_as_object(self):\n \"\"\"\n Render this object as it should appear within an ObjectList. Should not\n include the <h2> heading or help text - ObjectList will supply those\n \"\"\"\n # by default, assume that the subclass provides a catch-all render() method\n return self.render()","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_as_field","text":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_as_field\n def render_as_field(self):\n \"\"\"\n Render this object as it should appear within a <ul class=\"fields\"> list item\n \"\"\"\n # by default, assume that the subclass provides a catch-all render() method\n return self.render()","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_missing_fields","text":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_missing_fields\n def render_missing_fields(self):\n \"\"\"\n Helper function: render all of the fields that are defined on the form but not \"claimed\" by\n any panels via required_fields. These fields are most likely to be hidden fields introduced\n by the forms framework itself, such as ORDER / DELETE fields on formset members.\n\n (If they aren't actually hidden fields, then they will appear as ugly unstyled / label-less fields\n outside of the panel furniture. But there's not much we can do about that.)\n \"\"\"\n rendered_fields = self.required_fields()\n missing_fields_html = [\n text_type(self.form[field_name])\n for field_name in self.form.fields\n if field_name not in rendered_fields\n ]\n\n return mark_safe(''.join(missing_fields_html))","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_form_content","text":"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_form_content\n def render_form_content(self):\n \"\"\"\n Render this as an 'object', ensuring that all fields necessary for a valid form\n submission are included\n \"\"\"\n return mark_safe(self.render_as_object() + self.render_missing_fields())","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.widget_overrides","text":"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.widget_overrides\n @classmethod\n def widget_overrides(cls):\n if cls._widget_overrides is None:\n # build a collated version of all its children's widget lists\n widgets = {}\n for handler_class in cls.children:\n widgets.update(handler_class.widget_overrides())\n cls._widget_overrides = widgets\n\n return cls._widget_overrides","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.required_fields","text":"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.required_fields\n @classmethod\n def required_fields(cls):\n if cls._required_fields is None:\n fields = []\n for handler_class in cls.children:\n fields.extend(handler_class.required_fields())\n cls._required_fields = fields\n\n return cls._required_fields","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.required_formsets","text":"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.required_formsets\n @classmethod\n def required_formsets(cls):\n if cls._required_formsets is None:\n formsets = {}\n for handler_class in cls.children:\n formsets.update(handler_class.required_formsets())\n cls._required_formsets = formsets\n\n return cls._required_formsets","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.__init__","text":"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.__init__\n def __init__(self, instance=None, form=None):\n super(BaseCompositeEditHandler, self).__init__(instance=instance, form=form)\n\n self.children = [\n handler_class(instance=self.instance, form=self.form)\n for handler_class in self.__class__.children\n ]","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.render","text":"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.render\n def render(self):\n return mark_safe(render_to_string(self.template, {\n 'self': self\n }))","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::TabbedInterface.__init__","text":"wagtail/wagtailadmin/edit_handlers.py::TabbedInterface.__init__\n def __init__(self, children):\n self.children = children","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::TabbedInterface.bind_to_model","text":"wagtail/wagtailadmin/edit_handlers.py::TabbedInterface.bind_to_model\n def bind_to_model(self, model):\n return type(str('_TabbedInterface'), (BaseTabbedInterface,), {\n 'model': model,\n 'children': [child.bind_to_model(model) for child in self.children],\n })","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::ObjectList.__init__","text":"wagtail/wagtailadmin/edit_handlers.py::ObjectList.__init__\n def __init__(self, children, heading=\"\", classname=\"\"):\n self.children = children\n self.heading = heading\n self.classname = classname","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::ObjectList.bind_to_model","text":"wagtail/wagtailadmin/edit_handlers.py::ObjectList.bind_to_model\n def bind_to_model(self, model):\n return type(str('_ObjectList'), (BaseObjectList,), {\n 'model': model,\n 'children': [child.bind_to_model(model) for child in self.children],\n 'heading': self.heading,\n 'classname': self.classname,\n })","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::FieldRowPanel.__init__","text":"wagtail/wagtailadmin/edit_handlers.py::FieldRowPanel.__init__\n def __init__(self, children, classname=\"\"):\n self.children = children\n self.classname = classname","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::FieldRowPanel.bind_to_model","text":"wagtail/wagtailadmin/edit_handlers.py::FieldRowPanel.bind_to_model\n def bind_to_model(self, model):\n return type(str('_FieldRowPanel'), (BaseFieldRowPanel,), {\n 'model': model,\n 'children': [child.bind_to_model(model) for child in self.children],\n 'classname': self.classname,\n })","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseMultiFieldPanel.classes","text":"wagtail/wagtailadmin/edit_handlers.py::BaseMultiFieldPanel.classes\n def classes(self):\n classes = super(BaseMultiFieldPanel, self).classes()\n classes.append(\"multi-field\")\n\n return classes","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::MultiFieldPanel.__init__","text":"wagtail/wagtailadmin/edit_handlers.py::MultiFieldPanel.__init__\n def __init__(self, children, heading=\"\", classname=\"\"):\n self.children = children\n self.heading = heading\n self.classname = classname","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::MultiFieldPanel.bind_to_model","text":"wagtail/wagtailadmin/edit_handlers.py::MultiFieldPanel.bind_to_model\n def bind_to_model(self, model):\n return type(str('_MultiFieldPanel'), (BaseMultiFieldPanel,), {\n 'model': model,\n 'children': [child.bind_to_model(model) for child in self.children],\n 'heading': self.heading,\n 'classname': self.classname,\n })","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.widget_overrides","text":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.widget_overrides\n @classmethod\n def widget_overrides(cls):\n \"\"\"check if a specific widget has been defined for this field\"\"\"\n if hasattr(cls, 'widget'):\n return {cls.field_name: cls.widget}\n else:\n return {}","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.__init__","text":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.__init__\n def __init__(self, instance=None, form=None):\n super(BaseFieldPanel, self).__init__(instance=instance, form=form)\n self.bound_field = self.form[self.field_name]\n\n self.heading = self.bound_field.label\n self.help_text = self.bound_field.help_text","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.classes","text":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.classes\n def classes(self):\n classes = super(BaseFieldPanel, self).classes()\n\n if self.bound_field.field.required:\n classes.append(\"required\")\n if self.bound_field.errors:\n classes.append(\"error\")\n\n classes.append(self.field_type())\n classes.append(\"single-field\")\n\n return classes","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.field_type","text":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.field_type\n def field_type(self):\n return camelcase_to_underscore(self.bound_field.field.__class__.__name__)","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.render_as_object","text":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.render_as_object\n def render_as_object(self):\n return mark_safe(render_to_string(self.object_template, {\n 'self': self,\n 'field_content': self.render_as_field(show_help_text=False),\n }))","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.render_as_field","text":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.render_as_field\n def render_as_field(self, show_help_text=True, extra_context={}):\n context = {\n 'field': self.bound_field,\n 'field_type': self.field_type(),\n 'show_help_text': show_help_text,\n }\n context.update(extra_context)\n return mark_safe(render_to_string(self.field_template, context))","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.required_fields","text":"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.required_fields\n @classmethod\n def required_fields(self):\n return [self.field_name]","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::FieldPanel.__init__","text":"wagtail/wagtailadmin/edit_handlers.py::FieldPanel.__init__\n def __init__(self, field_name, classname=\"\", widget=None):\n self.field_name = field_name\n self.classname = classname\n self.widget = widget","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::FieldPanel.bind_to_model","text":"wagtail/wagtailadmin/edit_handlers.py::FieldPanel.bind_to_model\n def bind_to_model(self, model):\n base = {\n 'model': model,\n 'field_name': self.field_name,\n 'classname': self.classname,\n }\n\n if self.widget:\n base['widget'] = self.widget\n\n return type(str('_FieldPanel'), (BaseFieldPanel,), base)","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::RichTextFieldPanel.__init__","text":"wagtail/wagtailadmin/edit_handlers.py::RichTextFieldPanel.__init__\n def __init__(self, field_name):\n self.field_name = field_name","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::RichTextFieldPanel.bind_to_model","text":"wagtail/wagtailadmin/edit_handlers.py::RichTextFieldPanel.bind_to_model\n def bind_to_model(self, model):\n return type(str('_RichTextFieldPanel'), (BaseRichTextFieldPanel,), {\n 'model': model,\n 'field_name': self.field_name,\n })","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseChooserPanel.get_chosen_item","text":"wagtail/wagtailadmin/edit_handlers.py::BaseChooserPanel.get_chosen_item\n def get_chosen_item(self):\n try:\n return getattr(self.instance, self.field_name)\n except ObjectDoesNotExist:\n # if the ForeignKey is null=False, Django decides to raise\n # a DoesNotExist exception here, rather than returning None\n # like every other unpopulated field type. Yay consistency!\n return None","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseChooserPanel.render_as_field","text":"wagtail/wagtailadmin/edit_handlers.py::BaseChooserPanel.render_as_field\n def render_as_field(self, show_help_text=True, extra_context={}):\n instance_obj = self.get_chosen_item()\n context = {\n 'field': self.bound_field,\n self.object_type_name: instance_obj,\n 'is_chosen': bool(instance_obj),\n 'show_help_text': show_help_text,\n }\n context.update(extra_context)\n return mark_safe(render_to_string(self.field_template, context))","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BasePageChooserPanel.widget_overrides","text":"wagtail/wagtailadmin/edit_handlers.py::BasePageChooserPanel.widget_overrides\n @classmethod\n def widget_overrides(cls):\n return {cls.field_name: widgets.AdminPageChooser(\n content_type=cls.target_content_type())}","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BasePageChooserPanel.target_content_type","text":"wagtail/wagtailadmin/edit_handlers.py::BasePageChooserPanel.target_content_type\n @classmethod\n def target_content_type(cls):\n if cls._target_content_type is None:\n if cls.page_type:\n try:\n model = resolve_model_string(cls.page_type)\n except LookupError:\n raise ImproperlyConfigured(\"{0}.page_type must be of the form 'app_label.model_name', given {1!r}\".format(\n cls.__name__, cls.page_type))\n except ValueError:\n raise ImproperlyConfigured(\"{0}.page_type refers to model {1!r} that has not been installed\".format(\n cls.__name__, cls.page_type))\n\n cls._target_content_type = ContentType.objects.get_for_model(model)\n else:\n # TODO: infer the content type by introspection on the foreign key\n cls._target_content_type = ContentType.objects.get_by_natural_key('wagtailcore', 'page')\n\n return cls._target_content_type","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BasePageChooserPanel.render_as_field","text":"wagtail/wagtailadmin/edit_handlers.py::BasePageChooserPanel.render_as_field\n def render_as_field(self, show_help_text=True, extra_context={}):\n context = {\n 'choose_another_text_str': ugettext_lazy(\"Choose another page\"),\n 'choose_one_text_str': ugettext_lazy(\"Choose a page\"),\n }\n context.update(extra_context)\n return super(BasePageChooserPanel, self).render_as_field(show_help_text, context)","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::PageChooserPanel.__init__","text":"wagtail/wagtailadmin/edit_handlers.py::PageChooserPanel.__init__\n def __init__(self, field_name, page_type=None):\n self.field_name = field_name\n self.page_type = page_type","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::PageChooserPanel.bind_to_model","text":"wagtail/wagtailadmin/edit_handlers.py::PageChooserPanel.bind_to_model\n def bind_to_model(self, model):\n return type(str('_PageChooserPanel'), (BasePageChooserPanel,), {\n 'model': model,\n 'field_name': self.field_name,\n 'page_type': self.page_type,\n })","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.get_panel_definitions","text":"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.get_panel_definitions\n @classmethod\n def get_panel_definitions(cls):\n # Look for a panels definition in the InlinePanel declaration\n if cls.panels is not None:\n return cls.panels\n # Failing that, get it from the model\n else:\n return extract_panel_definitions_from_model_class(cls.related.model, exclude=[cls.related.field.name])","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.get_child_edit_handler_class","text":"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.get_child_edit_handler_class\n @classmethod\n def get_child_edit_handler_class(cls):\n if cls._child_edit_handler_class is None:\n panels = cls.get_panel_definitions()\n cls._child_edit_handler_class = MultiFieldPanel(panels, heading=cls.heading).bind_to_model(cls.related.model)\n\n return cls._child_edit_handler_class","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.required_formsets","text":"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.required_formsets\n @classmethod\n def required_formsets(cls):\n child_edit_handler_class = cls.get_child_edit_handler_class()\n return {\n cls.relation_name: {\n 'fields': child_edit_handler_class.required_fields(),\n 'widgets': child_edit_handler_class.widget_overrides(),\n }\n }","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.__init__","text":"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.__init__\n def __init__(self, instance=None, form=None):\n super(BaseInlinePanel, self).__init__(instance=instance, form=form)\n\n self.formset = form.formsets[self.__class__.relation_name]\n\n child_edit_handler_class = self.__class__.get_child_edit_handler_class()\n self.children = []\n for subform in self.formset.forms:\n # override the DELETE field to have a hidden input\n subform.fields['DELETE'].widget = forms.HiddenInput()\n\n # ditto for the ORDER field, if present\n if self.formset.can_order:\n subform.fields['ORDER'].widget = forms.HiddenInput()\n\n self.children.append(\n child_edit_handler_class(instance=subform.instance, form=subform)\n )\n\n # if this formset is valid, it may have been re-ordered; respect that\n # in case the parent form errored and we need to re-render\n if self.formset.can_order and self.formset.is_valid():\n self.children = sorted(self.children, key=lambda x: x.form.cleaned_data['ORDER'])\n\n empty_form = self.formset.empty_form\n empty_form.fields['DELETE'].widget = forms.HiddenInput()\n if self.formset.can_order:\n empty_form.fields['ORDER'].widget = forms.HiddenInput()\n\n self.empty_child = child_edit_handler_class(instance=empty_form.instance, form=empty_form)","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.render","text":"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.render\n def render(self):\n formset = render_to_string(self.template, {\n 'self': self,\n 'can_order': self.formset.can_order,\n })\n js = self.render_js_init()\n return widget_with_script(formset, js)","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.render_js_init","text":"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.render_js_init\n def render_js_init(self):\n return mark_safe(render_to_string(self.js_template, {\n 'self': self,\n 'can_order': self.formset.can_order,\n }))","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::InlinePanel.__init__","text":"wagtail/wagtailadmin/edit_handlers.py::InlinePanel.__init__\n def __init__(self, base_model, relation_name, panels=None, label='', help_text=''):\n # the base_model param is now redundant; we set up relations based on the model passed to\n # bind_to_model instead\n self.relation_name = relation_name\n self.panels = panels\n self.label = label\n self.help_text = help_text","label":1},{"cid":"wagtail/wagtailadmin/edit_handlers.py::InlinePanel.bind_to_model","text":"wagtail/wagtailadmin/edit_handlers.py::InlinePanel.bind_to_model\n def bind_to_model(self, model):\n return type(str('_InlinePanel'), (BaseInlinePanel,), {\n 'model': model,\n 'relation_name': self.relation_name,\n 'related': getattr(model, self.relation_name).related,\n 'panels': self.panels,\n 'heading': self.label,\n 'help_text': self.help_text, # TODO: can we pick this out of the foreign key definition as an alternative? (with a bit of help from the inlineformset object, as we do for label/heading)\n })","label":0},{"cid":"wagtail/wagtailadmin/edit_handlers.py::PublishingPanel","text":"wagtail/wagtailadmin/edit_handlers.py::PublishingPanel\ndef PublishingPanel():\n return MultiFieldPanel([\n FieldRowPanel([\n FieldPanel('go_live_at'),\n FieldPanel('expire_at'),\n ], classname=\"label-above\"),\n ], ugettext_lazy('Scheduled publishing'), classname=\"publishing\")","label":0},{"cid":"wagtail/wagtailadmin/forms.py::SearchForm.__init__","text":"wagtail/wagtailadmin/forms.py::SearchForm.__init__\n def __init__(self, *args, **kwargs):\n _placeholder = kwargs.pop('placeholder', None)\n placeholder_suffix = kwargs.pop('placeholder_suffix', \"\")\n super(SearchForm, self).__init__(*args, **kwargs)\n if _placeholder is not None:\n placeholder = _placeholder\n else:\n placeholder = 'Search {0}'.format(placeholder_suffix)\n self.fields['q'].widget.attrs = {'placeholder': placeholder}","label":0},{"cid":"wagtail/wagtailadmin/forms.py::LoginForm.__init__","text":"wagtail/wagtailadmin/forms.py::LoginForm.__init__\n def __init__(self, request=None, *args, **kwargs):\n super(LoginForm, self).__init__(request=request, *args, **kwargs)\n self.fields['username'].widget.attrs['placeholder'] = ugettext_lazy(\"Enter your %s\") % self.username_field.verbose_name","label":0},{"cid":"wagtail/wagtailadmin/forms.py::PasswordResetForm.clean","text":"wagtail/wagtailadmin/forms.py::PasswordResetForm.clean\n def clean(self):\n cleaned_data = super(PasswordResetForm, self).clean()\n\n # Find users of this email address\n UserModel = get_user_model()\n email = cleaned_data.get('email')\n if not email:\n raise forms.ValidationError(_(\"Please fill your email address.\"))\n active_users = UserModel._default_manager.filter(email__iexact=email, is_active=True)\n\n if active_users.exists():\n # Check if all users of the email address are LDAP users (and give an error if they are)\n found_non_ldap_user = False\n for user in active_users:\n if user.has_usable_password():\n found_non_ldap_user = True\n break\n\n if not found_non_ldap_user:\n # All found users are LDAP users, give error message\n raise forms.ValidationError(_(\"Sorry, you cannot reset your password here as your user account is managed by another server.\"))\n else:\n # No user accounts exist\n raise forms.ValidationError(_(\"This email address is not recognised.\"))\n\n return cleaned_data","label":0},{"cid":"wagtail/wagtailadmin/forms.py::CopyForm.__init__","text":"wagtail/wagtailadmin/forms.py::CopyForm.__init__\n def __init__(self, *args, **kwargs):\n # CopyPage must be passed a 'page' kwarg indicating the page to be copied\n self.page = kwargs.pop('page')\n can_publish = kwargs.pop('can_publish')\n super(CopyForm, self).__init__(*args, **kwargs)\n\n self.fields['new_title'] = forms.CharField(initial=self.page.title, label=_(\"New title\"))\n self.fields['new_slug'] = forms.SlugField(initial=self.page.slug, label=_(\"New slug\"))\n self.fields['new_parent_page'] = forms.ModelChoiceField(\n initial=self.page.get_parent(),\n queryset=Page.objects.all(),\n widget=AdminPageChooser(),\n label=_(\"New parent page\"),\n help_text=_(\"This copy will be a child of this given parent page.\")\n )\n\n pages_to_copy = self.page.get_descendants(inclusive=True)\n subpage_count = pages_to_copy.count() - 1\n if subpage_count > 0:\n self.fields['copy_subpages'] = forms.BooleanField(\n required=False, initial=True, label=_(\"Copy subpages\"),\n help_text=ungettext(\n \"This will copy %(count)s subpage.\",\n \"This will copy %(count)s subpages.\",\n subpage_count) % {'count': subpage_count})\n\n if can_publish:\n pages_to_publish_count = pages_to_copy.live().count()\n if pages_to_publish_count > 0:\n # In the specific case that there are no subpages, customise the field label and help text\n if subpage_count == 0:\n label = _(\"Publish copied page\")\n help_text = _(\"This page is live. Would you like to publish its copy as well?\")\n else:\n label = _(\"Publish copies\")\n help_text = ungettext(\n \"%(count)s of the pages being copied is live. Would you like to publish its copy?\",\n \"","label":0},{"cid":"wagtail/wagtailadmin/forms.py::CopyForm.clean","text":"wagtail/wagtailadmin/forms.py::CopyForm.clean\n def clean(self):\n cleaned_data = super(CopyForm, self).clean()\n\n # Make sure the slug isn't already in use\n slug = cleaned_data.get('new_slug')\n\n # New parent page given in form or parent of source, if parent_page is empty\n parent_page = cleaned_data.get('new_parent_page') or self.page.get_parent()\n\n # Count the pages with the same slug within the context of our copy's parent page\n if slug and parent_page.get_children().filter(slug=slug).count():\n self._errors['new_slug'] = self.error_class(\n [_(\"This slug is already in use within the context of its parent page \\\"%s\\\"\" % parent_page)]\n )\n # The slug is no longer valid, hence remove it from cleaned_data\n del cleaned_data['new_slug']\n\n return cleaned_data","label":0},{"cid":"wagtail/wagtailadmin/forms.py::PageViewRestrictionForm.clean","text":"wagtail/wagtailadmin/forms.py::PageViewRestrictionForm.clean\n def clean(self):\n cleaned_data = super(PageViewRestrictionForm, self).clean()\n\n if cleaned_data.get('restriction_type') == 'password' and not cleaned_data.get('password'):\n self._errors[\"password\"] = self.error_class([_('This field is required.')])\n del cleaned_data['password']\n\n return cleaned_data","label":0},{"cid":"wagtail/wagtailadmin/utils.py::get_object_usage","text":"wagtail/wagtailadmin/utils.py::get_object_usage\ndef get_object_usage(obj):\n \"Returns a queryset of pages that link to a particular object\"\n\n pages = Page.objects.none()\n\n # get all the relation objects for obj\n relations = type(obj)._meta.get_all_related_objects(\n include_hidden=True,\n include_proxy_eq=True\n )\n for relation in relations:\n # if the relation is between obj and a page, get the page\n if issubclass(relation.model, Page):\n pages |= Page.objects.filter(\n id__in=relation.model._base_manager.filter(**{\n relation.field.name: obj.id\n }).values_list('id', flat=True)\n )\n else:\n # if the relation is between obj and an object that has a page as a\n # property, return the page\n for f in relation.model._meta.fields:\n if isinstance(f, ParentalKey) and issubclass(f.rel.to, Page):\n pages |= Page.objects.filter(\n id__in=relation.model._base_manager.filter(\n **{\n relation.field.name: obj.id\n }).values_list(f.attname, flat=True)\n )\n\n return pages","label":0},{"cid":"wagtail/wagtailcore/models.py::SiteManager.get_by_natural_key","text":"wagtail/wagtailcore/models.py::SiteManager.get_by_natural_key\n def get_by_natural_key(self, hostname, port):\n return self.get(hostname=hostname, port=port)","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.natural_key","text":"wagtail/wagtailcore/models.py::Site.natural_key\n def natural_key(self):\n return (self.hostname, self.port)","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.__str__","text":"wagtail/wagtailcore/models.py::Site.__str__\n def __str__(self):\n return self.hostname + (\"\" if self.port == 80 else (\":%d\" % self.port)) + (\" [default]\" if self.is_default_site else \"\")","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.find_for_request","text":"wagtail/wagtailcore/models.py::Site.find_for_request\n @staticmethod\n def find_for_request(request):\n \"\"\"\n Find the site object responsible for responding to this HTTP\n request object. Try:\n - unique hostname first\n - then hostname and port\n - if there is no matching hostname at all, or no matching\n hostname:port combination, fall back to the unique default site,\n or raise an exception\n NB this means that high-numbered ports on an extant hostname may\n still be routed to a different hostname which is set as the default\n \"\"\"\n try:\n hostname = request.META['HTTP_HOST'].split(':')[0] # KeyError here goes to the final except clause\n try:\n # find a Site matching this specific hostname\n return Site.objects.get(hostname=hostname) # Site.DoesNotExist here goes to the final except clause\n except Site.MultipleObjectsReturned:\n # as there were more than one, try matching by port too\n port = request.META['SERVER_PORT'] # KeyError here goes to the final except clause\n return Site.objects.get(hostname=hostname, port=int(port)) # Site.DoesNotExist here goes to the final except clause\n except (Site.DoesNotExist, KeyError):\n # If no matching site exists, or request does not specify an HTTP_HOST (which\n # will often be the case for the Django test client), look for a catch-all Site.\n # If that fails, let the Site.DoesNotExist propagate back to the caller\n return Site.objects.get(is_default_site=True)","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.root_url","text":"wagtail/wagtailcore/models.py::Site.root_url\n @property\n def root_url(self):\n if self.port == 80:\n return 'http://%s' % self.hostname\n elif self.port == 443:\n return 'https://%s' % self.hostname\n else:\n return 'http://%s:%d' % (self.hostname, self.port)","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.clean_fields","text":"wagtail/wagtailcore/models.py::Site.clean_fields\n def clean_fields(self, exclude=None):\n super(Site, self).clean_fields(exclude)\n # Only one site can have the is_default_site flag set\n try:\n default = Site.objects.get(is_default_site=True)\n except Site.DoesNotExist:\n pass\n except Site.MultipleObjectsReturned:\n raise\n else:\n if self.is_default_site and self.pk != default.pk:\n raise ValidationError(\n {'is_default_site': [\n _(\"%(hostname)s is already configured as the default site. You must unset that before you can save this site as default.\")\n % {'hostname': default.hostname}\n ]}\n )","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.get_site_root_paths","text":"wagtail/wagtailcore/models.py::Site.get_site_root_paths\n @staticmethod\n def get_site_root_paths():\n \"\"\"\n Return a list of (root_path, root_url) tuples, most specific path first -\n used to translate url_paths into actual URLs with hostnames\n \"\"\"\n result = cache.get('wagtail_site_root_paths')\n\n if result is None:\n result = [\n (site.id, site.root_page.url_path, site.root_url)\n for site in Site.objects.select_related('root_page').order_by('-root_page__url_path')\n ]\n cache.set('wagtail_site_root_paths', result, 3600)\n\n return result","label":0},{"cid":"wagtail/wagtailcore/models.py::clear_site_root_paths_on_save","text":"wagtail/wagtailcore/models.py::clear_site_root_paths_on_save\n@receiver(post_save, sender=Site)\ndef clear_site_root_paths_on_save(sender, instance, **kwargs):\n cache.delete('wagtail_site_root_paths')","label":0},{"cid":"wagtail/wagtailcore/models.py::clear_site_root_paths_on_delete","text":"wagtail/wagtailcore/models.py::clear_site_root_paths_on_delete\n@receiver(post_delete, sender=Site)\ndef clear_site_root_paths_on_delete(sender, instance, **kwargs):\n cache.delete('wagtail_site_root_paths')","label":0},{"cid":"wagtail/wagtailcore/models.py::get_page_types","text":"wagtail/wagtailcore/models.py::get_page_types\ndef get_page_types():\n global _PAGE_CONTENT_TYPES\n if len(_PAGE_CONTENT_TYPES) != len(PAGE_MODEL_CLASSES):\n _PAGE_CONTENT_TYPES = [\n ContentType.objects.get_for_model(cls) for cls in PAGE_MODEL_CLASSES\n ]\n return _PAGE_CONTENT_TYPES","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.get_queryset","text":"wagtail/wagtailcore/models.py::PageManager.get_queryset\n def get_queryset(self):\n return PageQuerySet(self.model).order_by('path')","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.live","text":"wagtail/wagtailcore/models.py::PageManager.live\n def live(self):\n return self.get_queryset().live()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_live","text":"wagtail/wagtailcore/models.py::PageManager.not_live\n def not_live(self):\n return self.get_queryset().not_live()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.in_menu","text":"wagtail/wagtailcore/models.py::PageManager.in_menu\n def in_menu(self):\n return self.get_queryset().in_menu()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_in_menu","text":"wagtail/wagtailcore/models.py::PageManager.not_in_menu\n def not_in_menu(self):\n return self.get_queryset().not_in_menu()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.page","text":"wagtail/wagtailcore/models.py::PageManager.page\n def page(self, other):\n return self.get_queryset().page(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_page","text":"wagtail/wagtailcore/models.py::PageManager.not_page\n def not_page(self, other):\n return self.get_queryset().not_page(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.descendant_of","text":"wagtail/wagtailcore/models.py::PageManager.descendant_of\n def descendant_of(self, other, inclusive=False):\n return self.get_queryset().descendant_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_descendant_of","text":"wagtail/wagtailcore/models.py::PageManager.not_descendant_of\n def not_descendant_of(self, other, inclusive=False):\n return self.get_queryset().not_descendant_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.child_of","text":"wagtail/wagtailcore/models.py::PageManager.child_of\n def child_of(self, other):\n return self.get_queryset().child_of(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_child_of","text":"wagtail/wagtailcore/models.py::PageManager.not_child_of\n def not_child_of(self, other):\n return self.get_queryset().not_child_of(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.ancestor_of","text":"wagtail/wagtailcore/models.py::PageManager.ancestor_of\n def ancestor_of(self, other, inclusive=False):\n return self.get_queryset().ancestor_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_ancestor_of","text":"wagtail/wagtailcore/models.py::PageManager.not_ancestor_of\n def not_ancestor_of(self, other, inclusive=False):\n return self.get_queryset().not_ancestor_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.parent_of","text":"wagtail/wagtailcore/models.py::PageManager.parent_of\n def parent_of(self, other):\n return self.get_queryset().parent_of(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_parent_of","text":"wagtail/wagtailcore/models.py::PageManager.not_parent_of\n def not_parent_of(self, other):\n return self.get_queryset().not_parent_of(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.sibling_of","text":"wagtail/wagtailcore/models.py::PageManager.sibling_of\n def sibling_of(self, other, inclusive=False):\n return self.get_queryset().sibling_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_sibling_of","text":"wagtail/wagtailcore/models.py::PageManager.not_sibling_of\n def not_sibling_of(self, other, inclusive=False):\n return self.get_queryset().not_sibling_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.type","text":"wagtail/wagtailcore/models.py::PageManager.type\n def type(self, model):\n return self.get_queryset().type(model)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_type","text":"wagtail/wagtailcore/models.py::PageManager.not_type\n def not_type(self, model):\n return self.get_queryset().not_type(model)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.public","text":"wagtail/wagtailcore/models.py::PageManager.public\n def public(self):\n return self.get_queryset().public()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_public","text":"wagtail/wagtailcore/models.py::PageManager.not_public\n def not_public(self):\n return self.get_queryset().not_public()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.search","text":"wagtail/wagtailcore/models.py::PageManager.search\n def search(self, query_string, fields=None, backend='default'):\n return self.get_queryset().search(query_string, fields=fields, backend=backend)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageBase.__init__","text":"wagtail/wagtailcore/models.py::PageBase.__init__\n def __init__(cls, name, bases, dct):\n super(PageBase, cls).__init__(name, bases, dct)\n\n if cls._deferred:\n # this is an internal class built for Django's deferred-attribute mechanism;\n # don't proceed with all this page type registration stuff\n return\n\n # Add page manager\n PageManager().contribute_to_class(cls, 'objects')\n\n if 'template' not in dct:\n # Define a default template path derived from the app name and model name\n cls.template = \"%s/%s.html\" % (cls._meta.app_label, camelcase_to_underscore(name))\n\n if 'ajax_template' not in dct:\n cls.ajax_template = None\n\n cls._clean_subpage_types = None # to be filled in on first call to cls.clean_subpage_types\n cls._clean_parent_page_types = None # to be filled in on first call to cls.clean_parent_page_types\n\n if not dct.get('is_abstract'):\n # subclasses are only abstract if the subclass itself defines itself so\n cls.is_abstract = False\n\n if not cls.is_abstract:\n # register this type in the list of page content types\n PAGE_MODEL_CLASSES.append(cls)","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.__init__","text":"wagtail/wagtailcore/models.py::Page.__init__\n def __init__(self, *args, **kwargs):\n super(Page, self).__init__(*args, **kwargs)\n if not self.id and not self.content_type_id:\n # this model is being newly created rather than retrieved from the db;\n # set content type to correctly represent the model class that this was\n # created as\n self.content_type = ContentType.objects.get_for_model(self)","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.__str__","text":"wagtail/wagtailcore/models.py::Page.__str__\n def __str__(self):\n return self.title","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.set_url_path","text":"wagtail/wagtailcore/models.py::Page.set_url_path\n def set_url_path(self, parent):\n \"\"\"\n Populate the url_path field based on this page's slug and the specified parent page.\n (We pass a parent in here, rather than retrieving it via get_parent, so that we can give\n new unsaved pages a meaningful URL when previewing them; at that point the page has not\n been assigned a position in the tree, as far as treebeard is concerned.\n \"\"\"\n if parent:\n self.url_path = parent.url_path + self.slug + '/'\n else:\n # a page without a parent is the tree root, which always has a url_path of '/'\n self.url_path = '/'\n\n return self.url_path","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.save","text":"wagtail/wagtailcore/models.py::Page.save\n @transaction.atomic # ensure that changes are only committed when we have updated all descendant URL paths, to preserve consistency\n def save(self, *args, **kwargs):\n update_descendant_url_paths = False\n is_new = self.id is None\n\n if is_new:\n # we are creating a record. If we're doing things properly, this should happen\n # through a treebeard method like add_child, in which case the 'path' field\n # has been set and so we can safely call get_parent\n self.set_url_path(self.get_parent())\n else:\n # Check that we are committing the slug to the database\n # Basically: If update_fields has been specified, and slug is not included, skip this step\n if not ('update_fields' in kwargs and 'slug' not in kwargs['update_fields']):\n # see if the slug has changed from the record in the db, in which case we need to\n # update url_path of self and all descendants\n old_record = Page.objects.get(id=self.id)\n if old_record.slug != self.slug:\n self.set_url_path(self.get_parent())\n update_descendant_url_paths = True\n old_url_path = old_record.url_path\n new_url_path = self.url_path\n\n result = super(Page, self).save(*args, **kwargs)\n\n if update_descendant_url_paths:\n self._update_descendant_url_paths(old_url_path, new_url_path)\n\n # Check if this is a root page of any sites and clear the 'wagtail_site_root_paths' key if so\n if Site.objects.filter(root_page=self).exists():\n cache.delete('wagtail_site_root_paths')\n\n # Log\n if is_new:\n cls = type(self)\n logger.info(\"Page created: \\\"%s\\\" id=%d content_type=%s.%s path=%s\", self.title, self.id, cls._meta.app_label, cls.__name__, self.url_path)\n\n return result","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.check","text":"wagtail/wagtailcore/models.py::Page.check\n @classmethod\n def check(cls, **kwargs):\n errors = super(Page, cls).check(**kwargs)\n\n # Check that foreign keys from pages are not configured to cascade\n # This is the default Django behaviour which must be explicitly overridden\n # to prevent pages disappearing unexpectedly and the tree being corrupted\n\n # get names of foreign keys pointing to parent classes (such as page_ptr)\n field_exceptions = [field.name\n for model in [cls] + list(cls._meta.get_parent_list())\n for field in model._meta.parents.values()]\n\n field_exceptions += ['content_type']\n\n\n for field in cls._meta.fields:\n if isinstance(field, models.ForeignKey) and field.name not in field_exceptions:\n if field.rel.on_delete == models.CASCADE:\n errors.append(\n checks.Warning(\n \"Field hasn't specified on_delete action\",\n hint=\"Set on_delete=models.SET_NULL and make sure the field is nullable.\",\n obj=field,\n id='wagtailcore.W001',\n )\n )\n\n return errors","label":0},{"cid":"wagtail/wagtailcore/models.py::Page._update_descendant_url_paths","text":"wagtail/wagtailcore/models.py::Page._update_descendant_url_paths\n def _update_descendant_url_paths(self, old_url_path, new_url_path):\n cursor = connection.cursor()\n if connection.vendor == 'sqlite':\n update_statement = \"\"\"\n UPDATE wagtailcore_page\n SET url_path = %s || substr(url_path, %s)\n WHERE path LIKE %s AND id <> %s\n \"\"\"\n elif connection.vendor == 'mysql':\n update_statement = \"\"\"\n UPDATE wagtailcore_page\n SET url_path= CONCAT(%s, substring(url_path, %s))\n WHERE path LIKE %s AND id <> %s\n \"\"\"\n else:\n update_statement = \"\"\"\n UPDATE wagtailcore_page\n SET url_path = %s || substring(url_path from %s)\n WHERE path LIKE %s AND id <> %s\n \"\"\"\n cursor.execute(update_statement, [new_url_path, len(old_url_path) + 1, self.path + '%', self.id])","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.specific","text":"wagtail/wagtailcore/models.py::Page.specific\n @cached_property\n def specific(self):\n \"\"\"\n Return this page in its most specific subclassed form.\n \"\"\"\n # the ContentType.objects manager keeps a cache, so this should potentially\n # avoid a database lookup over doing self.content_type. I think.\n content_type = ContentType.objects.get_for_id(self.content_type_id)\n if isinstance(self, content_type.model_class()):\n # self is already the an instance of the most specific class\n return self\n else:\n return content_type.get_object_for_this_type(id=self.id)","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearchQuery._process_lookup","text":"wagtail/wagtailsearch/backends/db.py::DBSearchQuery._process_lookup\n def _process_lookup(self, field, lookup, value):\n return models.Q(**{field.get_attname(self.queryset.model) + '__' + lookup: value})","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearchQuery._connect_filters","text":"wagtail/wagtailsearch/backends/db.py::DBSearchQuery._connect_filters\n def _connect_filters(self, filters, connector, negated):\n if connector == 'AND':\n q = models.Q(*filters)\n elif connector == 'OR':\n q = models.Q(filters[0])\n for fil in filters[1:]:\n q |= fil\n else:\n return\n\n if negated:\n q = ~q\n\n return q","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearchQuery.get_q","text":"wagtail/wagtailsearch/backends/db.py::DBSearchQuery.get_q\n def get_q(self):\n # Get filters as a q object\n q = self._get_filters_from_queryset()\n\n model = self.queryset.model\n\n if self.query_string is not None:\n # Get fields\n fields = self.fields or [field.field_name for field in model.get_searchable_search_fields()]\n\n # Get terms\n terms = self.query_string.split()\n if not terms:\n return model.objects.none()\n\n # Filter by terms\n for term in terms:\n term_query = models.Q()\n for field_name in fields:\n # Check if the field exists (this will filter out indexed callables)\n try:\n model._meta.get_field_by_name(field_name)\n except:\n continue\n\n # Filter on this field\n term_query |= models.Q(**{'%s__icontains' % field_name: term})\n\n q &= term_query\n\n return q","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearchResults.get_queryset","text":"wagtail/wagtailsearch/backends/db.py::DBSearchResults.get_queryset\n def get_queryset(self):\n model = self.query.queryset.model\n q = self.query.get_q()\n\n return model.objects.filter(q).distinct()[self.start:self.stop]","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearchResults._do_search","text":"wagtail/wagtailsearch/backends/db.py::DBSearchResults._do_search\n def _do_search(self):\n return self.get_queryset()","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearchResults._do_count","text":"wagtail/wagtailsearch/backends/db.py::DBSearchResults._do_count\n def _do_count(self):\n return self.get_queryset().count()","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearch.__init__","text":"wagtail/wagtailsearch/backends/db.py::DBSearch.__init__\n def __init__(self, params):\n super(DBSearch, self).__init__(params)","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearch.reset_index","text":"wagtail/wagtailsearch/backends/db.py::DBSearch.reset_index\n def reset_index(self):\n pass # Not needed","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearch.add_type","text":"wagtail/wagtailsearch/backends/db.py::DBSearch.add_type\n def add_type(self, model):\n pass # Not needed","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearch.refresh_index","text":"wagtail/wagtailsearch/backends/db.py::DBSearch.refresh_index\n def refresh_index(self):\n pass # Not needed","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearch.add","text":"wagtail/wagtailsearch/backends/db.py::DBSearch.add\n def add(self, obj):\n pass # Not needed","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearch.add_bulk","text":"wagtail/wagtailsearch/backends/db.py::DBSearch.add_bulk\n def add_bulk(self, model, obj_list):\n return # Not needed","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearch.delete","text":"wagtail/wagtailsearch/backends/db.py::DBSearch.delete\n def delete(self, obj):\n pass # Not needed","label":0},{"cid":"wagtail/wagtailsearch/backends/db.py::DBSearch._search","text":"wagtail/wagtailsearch/backends/db.py::DBSearch._search\n def _search(self, queryset, query_string, fields=None):\n return DBSearchResults(self, DBSearchQuery(queryset, query_string, fields=fields))","label":0},{"cid":"wagtail/wagtailsearch/forms.py::EditorsPickForm.__init__","text":"wagtail/wagtailsearch/forms.py::EditorsPickForm.__init__\n def __init__(self, *args, **kwargs):\n super(EditorsPickForm, self).__init__(*args, **kwargs)\n self.fields['page'].widget = AdminPageChooser()","label":0},{"cid":"wagtail/wagtailsearch/forms.py::EditorsPickFormSet.add_fields","text":"wagtail/wagtailsearch/forms.py::EditorsPickFormSet.add_fields\n def add_fields(self, form, *args, **kwargs):\n super(EditorsPickFormSet, self).add_fields(form, *args, **kwargs)\n\n # Hide delete and order fields\n form.fields['DELETE'].widget = forms.HiddenInput()\n form.fields['ORDER'].widget = forms.HiddenInput()\n\n # Remove query field\n del form.fields['query']","label":0},{"cid":"wagtail/wagtailsearch/forms.py::EditorsPickFormSet.clean","text":"wagtail/wagtailsearch/forms.py::EditorsPickFormSet.clean\n def clean(self):\n # Editors pick must have at least one recommended page to be valid\n # Check there is at least one non-deleted form.\n non_deleted_forms = self.total_form_count()\n non_empty_forms = 0\n for i in range(0, self.total_form_count()):\n form = self.forms[i]\n if self.can_delete and self._should_delete_form(form):\n non_deleted_forms -= 1\n if not (form.instance.id is None and not form.has_changed()):\n non_empty_forms += 1\n if (\n non_deleted_forms < self.minimum_forms\n or non_empty_forms < self.minimum_forms\n ):\n raise forms.ValidationError(self.minimum_forms_message)","label":0},{"cid":"wagtail/wagtailusers/forms.py::UserCreationForm.clean_username","text":"wagtail/wagtailusers/forms.py::UserCreationForm.clean_username\n def clean_username(self):\n # Method copied from parent\n\n username = self.cleaned_data[\"username\"]\n try:\n # When called from BaseUserCreationForm, the method fails if using a AUTH_MODEL_MODEL,\n # This is because the following line tries to perform a lookup on\n # the default \"auth_user\" table.\n User._default_manager.get(username=username)\n except User.DoesNotExist:\n return username\n raise forms.ValidationError(\n self.error_messages['duplicate_username'],\n code='duplicate_username',\n )","label":0},{"cid":"wagtail/wagtailusers/forms.py::UserCreationForm.save","text":"wagtail/wagtailusers/forms.py::UserCreationForm.save\n def save(self, commit=True):\n user = super(UserCreationForm, self).save(commit=False)\n\n # users can access django-admin iff they are a superuser\n user.is_staff = user.is_superuser\n\n if commit:\n user.save()\n self.save_m2m()\n return user","label":0},{"cid":"wagtail/wagtailusers/forms.py::UserEditForm.clean_username","text":"wagtail/wagtailusers/forms.py::UserEditForm.clean_username\n def clean_username(self):\n # Since User.username is unique, this check is redundant,\n # but it sets a nicer error message than the ORM. See #13147.\n username = self.cleaned_data[\"username\"]\n try:\n User._default_manager.exclude(id=self.instance.id).get(username=username)\n except User.DoesNotExist:\n return username\n raise forms.ValidationError(self.error_messages['duplicate_username'])","label":0},{"cid":"wagtail/wagtailusers/forms.py::UserEditForm.clean_password2","text":"wagtail/wagtailusers/forms.py::UserEditForm.clean_password2\n def clean_password2(self):\n password1 = self.cleaned_data.get(\"password1\")\n password2 = self.cleaned_data.get(\"password2\")\n if password1 != password2:\n raise forms.ValidationError(\n self.error_messages['password_mismatch'])\n return password2","label":0},{"cid":"wagtail/wagtailusers/forms.py::UserEditForm.save","text":"wagtail/wagtailusers/forms.py::UserEditForm.save\n def save(self, commit=True):\n user = super(UserEditForm, self).save(commit=False)\n\n # users can access django-admin iff they are a superuser\n user.is_staff = user.is_superuser\n\n if self.cleaned_data[\"password1\"]:\n user.set_password(self.cleaned_data[\"password1\"])\n if commit:\n user.save()\n self.save_m2m()\n return user","label":0},{"cid":"wagtail/wagtailusers/forms.py::GroupForm.__init__","text":"wagtail/wagtailusers/forms.py::GroupForm.__init__\n def __init__(self, *args, **kwargs):\n super(GroupForm, self).__init__(*args, **kwargs)\n self.registered_permissions = Permission.objects.none()\n for fn in hooks.get_hooks('register_permissions'):\n self.registered_permissions = self.registered_permissions | fn()\n self.fields['permissions'].queryset = self.registered_permissions","label":0},{"cid":"wagtail/wagtailusers/forms.py::GroupForm.clean_name","text":"wagtail/wagtailusers/forms.py::GroupForm.clean_name\n def clean_name(self):\n # Since Group.name is unique, this check is redundant,\n # but it sets a nicer error message than the ORM. See #13147.\n name = self.cleaned_data[\"name\"]\n try:\n Group._default_manager.exclude(id=self.instance.id).get(name=name)\n except Group.DoesNotExist:\n return name\n raise forms.ValidationError(self.error_messages['duplicate_name'])","label":0},{"cid":"wagtail/wagtailusers/forms.py::GroupForm.save","text":"wagtail/wagtailusers/forms.py::GroupForm.save\n def save(self):\n # We go back to the object to read (in order to reapply) the\n # permissions which were set on this group, but which are not\n # accessible in the wagtail admin interface, as otherwise these would\n # be clobbered by this form.\n try:\n untouchable_permissions = self.instance.permissions.exclude(pk__in=self.registered_permissions)\n bool(untouchable_permissions) # force this to be evaluated, as it's about to change\n except ValueError:\n # this form is not bound; we're probably creating a new group\n untouchable_permissions = []\n group = super(GroupForm, self).save()\n group.permissions.add(*untouchable_permissions)\n return group","label":0},{"cid":"wagtail/wagtailusers/forms.py::GroupPagePermissionForm.__init__","text":"wagtail/wagtailusers/forms.py::GroupPagePermissionForm.__init__\n def __init__(self, *args, **kwargs):\n super(GroupPagePermissionForm, self).__init__(*args, **kwargs)\n self.fields['page'].widget = AdminPageChooser()","label":0},{"cid":"wagtail/wagtailusers/forms.py::BaseGroupPagePermissionFormSet.__init__","text":"wagtail/wagtailusers/forms.py::BaseGroupPagePermissionFormSet.__init__\n def __init__(self, *args, **kwargs):\n super(BaseGroupPagePermissionFormSet, self).__init__(*args, **kwargs)\n self.form = GroupPagePermissionForm\n for form in self.forms:\n form.fields['DELETE'].widget = forms.HiddenInput()","label":0},{"cid":"wagtail/wagtailusers/forms.py::BaseGroupPagePermissionFormSet.empty_form","text":"wagtail/wagtailusers/forms.py::BaseGroupPagePermissionFormSet.empty_form\n @property\n def empty_form(self):\n empty_form = super(BaseGroupPagePermissionFormSet, self).empty_form\n empty_form.fields['DELETE'].widget = forms.HiddenInput()\n return empty_form","label":0},{"cid":"wagtail/wagtailusers/forms.py::NotificationPreferencesForm.__init__","text":"wagtail/wagtailusers/forms.py::NotificationPreferencesForm.__init__\n def __init__(self, *args, **kwargs):\n super(NotificationPreferencesForm, self).__init__(*args, **kwargs)\n user_perms = UserPagePermissionsProxy(self.instance.user)\n if not user_perms.can_publish_pages():\n del self.fields['submitted_notifications']\n if not user_perms.can_edit_pages():\n del self.fields['approved_notifications']\n del self.fields['rejected_notifications']","label":0}],"string":"[\n {\n \"cid\": \"runtests.py::runtests\",\n \"text\": \"runtests.py::runtests\\ndef runtests():\\n # Don't ignore DeprecationWarnings\\n warnings.simplefilter('default', DeprecationWarning)\\n\\n argv = sys.argv[:1] + ['test'] + sys.argv[1:]\\n try:\\n execute_from_command_line(argv)\\n finally:\\n shutil.rmtree(STATIC_ROOT, ignore_errors=True)\\n shutil.rmtree(MEDIA_ROOT, ignore_errors=True)\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::formfield_for_dbfield\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::formfield_for_dbfield\\ndef formfield_for_dbfield(db_field, **kwargs):\\n # snarfed from django/contrib/admin/options.py\\n\\n # If we've got overrides for the formfield defined, use 'em. **kwargs\\n # passed to formfield_for_dbfield override the defaults.\\n for klass in db_field.__class__.mro():\\n if klass in FORM_FIELD_OVERRIDES:\\n kwargs = dict(copy.deepcopy(FORM_FIELD_OVERRIDES[klass]), **kwargs)\\n return db_field.formfield(**kwargs)\\n\\n # For any other type of field, just call its formfield() method.\\n return db_field.formfield(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::widget_with_script\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::widget_with_script\\ndef widget_with_script(widget, script):\\n return mark_safe('{0}<script>{1}</script>'.format(widget, script))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::WagtailAdminModelFormMetaclass.__new__\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::WagtailAdminModelFormMetaclass.__new__\\n def __new__(cls, name, bases, attrs):\\n if 'formfield_callback' not in attrs or attrs['formfield_callback'] is None:\\n attrs['formfield_callback'] = formfield_for_dbfield\\n\\n new_class = super(WagtailAdminModelFormMetaclass, cls).__new__(cls, name, bases, attrs)\\n return new_class\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::get_form_for_model\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::get_form_for_model\\ndef get_form_for_model(\\n model,\\n fields=None, exclude=None, formsets=None, exclude_formsets=None, widgets=None\\n):\\n\\n # django's modelform_factory with a bit of custom behaviour\\n # (dealing with Treebeard's tree-related fields that really should have\\n # been editable=False)\\n attrs = {'model': model}\\n\\n if fields is not None:\\n attrs['fields'] = fields\\n\\n if exclude is not None:\\n attrs['exclude'] = exclude\\n if issubclass(model, Page):\\n attrs['exclude'] = attrs.get('exclude', []) + ['content_type', 'path', 'depth', 'numchild']\\n\\n if widgets is not None:\\n attrs['widgets'] = widgets\\n\\n if formsets is not None:\\n attrs['formsets'] = formsets\\n\\n if exclude_formsets is not None:\\n attrs['exclude_formsets'] = exclude_formsets\\n\\n # Give this new form class a reasonable name.\\n class_name = model.__name__ + str('Form')\\n form_class_attrs = {\\n 'Meta': type(str('Meta'), (object,), attrs)\\n }\\n\\n return WagtailAdminModelFormMetaclass(class_name, (WagtailAdminModelForm,), form_class_attrs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::extract_panel_definitions_from_model_class\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::extract_panel_definitions_from_model_class\\ndef extract_panel_definitions_from_model_class(model, exclude=None):\\n if hasattr(model, 'panels'):\\n return model.panels\\n\\n panels = []\\n\\n _exclude = []\\n if exclude:\\n _exclude.extend(exclude)\\n if issubclass(model, Page):\\n _exclude = ['content_type', 'path', 'depth', 'numchild']\\n\\n fields = fields_for_model(model, exclude=_exclude, formfield_callback=formfield_for_dbfield)\\n\\n for field_name, field in fields.items():\\n try:\\n panel_class = field.widget.get_panel()\\n except AttributeError:\\n panel_class = FieldPanel\\n\\n panel = panel_class(field_name)\\n panels.append(panel)\\n\\n return panels\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.widget_overrides\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.widget_overrides\\n @classmethod\\n def widget_overrides(cls):\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.required_fields\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.required_fields\\n @classmethod\\n def required_fields(cls):\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.required_formsets\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.required_formsets\\n @classmethod\\n def required_formsets(cls):\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.get_form_class\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.get_form_class\\n @classmethod\\n def get_form_class(cls, model):\\n if cls._form_class is None:\\n cls._form_class = get_form_for_model(\\n model,\\n fields=cls.required_fields(),\\n formsets=cls.required_formsets(), widgets=cls.widget_overrides())\\n return cls._form_class\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.__init__\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.__init__\\n def __init__(self, instance=None, form=None):\\n if not instance:\\n raise ValueError(\\\"EditHandler did not receive an instance object\\\")\\n self.instance = instance\\n\\n if not form:\\n raise ValueError(\\\"EditHandler did not receive a form object\\\")\\n self.form = form\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.classes\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.classes\\n def classes(self):\\n \\\"\\\"\\\"\\n Additional CSS classnames to add to whatever kind of object this is at output.\\n Subclasses of EditHandler should override this, invoking super(B, self).classes() to\\n append more classes specific to the situation.\\n \\\"\\\"\\\"\\n\\n classes = []\\n\\n try:\\n classes.append(self.classname)\\n except AttributeError:\\n pass\\n\\n return classes\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.field_type\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.field_type\\n def field_type(self):\\n \\\"\\\"\\\"\\n The kind of field it is e.g boolean_field. Useful for better semantic markup of field display based on type\\n \\\"\\\"\\\"\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_as_object\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_as_object\\n def render_as_object(self):\\n \\\"\\\"\\\"\\n Render this object as it should appear within an ObjectList. Should not\\n include the <h2> heading or help text - ObjectList will supply those\\n \\\"\\\"\\\"\\n # by default, assume that the subclass provides a catch-all render() method\\n return self.render()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_as_field\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_as_field\\n def render_as_field(self):\\n \\\"\\\"\\\"\\n Render this object as it should appear within a <ul class=\\\"fields\\\"> list item\\n \\\"\\\"\\\"\\n # by default, assume that the subclass provides a catch-all render() method\\n return self.render()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_missing_fields\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_missing_fields\\n def render_missing_fields(self):\\n \\\"\\\"\\\"\\n Helper function: render all of the fields that are defined on the form but not \\\"claimed\\\" by\\n any panels via required_fields. These fields are most likely to be hidden fields introduced\\n by the forms framework itself, such as ORDER / DELETE fields on formset members.\\n\\n (If they aren't actually hidden fields, then they will appear as ugly unstyled / label-less fields\\n outside of the panel furniture. But there's not much we can do about that.)\\n \\\"\\\"\\\"\\n rendered_fields = self.required_fields()\\n missing_fields_html = [\\n text_type(self.form[field_name])\\n for field_name in self.form.fields\\n if field_name not in rendered_fields\\n ]\\n\\n return mark_safe(''.join(missing_fields_html))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_form_content\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::EditHandler.render_form_content\\n def render_form_content(self):\\n \\\"\\\"\\\"\\n Render this as an 'object', ensuring that all fields necessary for a valid form\\n submission are included\\n \\\"\\\"\\\"\\n return mark_safe(self.render_as_object() + self.render_missing_fields())\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.widget_overrides\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.widget_overrides\\n @classmethod\\n def widget_overrides(cls):\\n if cls._widget_overrides is None:\\n # build a collated version of all its children's widget lists\\n widgets = {}\\n for handler_class in cls.children:\\n widgets.update(handler_class.widget_overrides())\\n cls._widget_overrides = widgets\\n\\n return cls._widget_overrides\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.required_fields\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.required_fields\\n @classmethod\\n def required_fields(cls):\\n if cls._required_fields is None:\\n fields = []\\n for handler_class in cls.children:\\n fields.extend(handler_class.required_fields())\\n cls._required_fields = fields\\n\\n return cls._required_fields\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.required_formsets\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.required_formsets\\n @classmethod\\n def required_formsets(cls):\\n if cls._required_formsets is None:\\n formsets = {}\\n for handler_class in cls.children:\\n formsets.update(handler_class.required_formsets())\\n cls._required_formsets = formsets\\n\\n return cls._required_formsets\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.__init__\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.__init__\\n def __init__(self, instance=None, form=None):\\n super(BaseCompositeEditHandler, self).__init__(instance=instance, form=form)\\n\\n self.children = [\\n handler_class(instance=self.instance, form=self.form)\\n for handler_class in self.__class__.children\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.render\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseCompositeEditHandler.render\\n def render(self):\\n return mark_safe(render_to_string(self.template, {\\n 'self': self\\n }))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::TabbedInterface.__init__\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::TabbedInterface.__init__\\n def __init__(self, children):\\n self.children = children\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::TabbedInterface.bind_to_model\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::TabbedInterface.bind_to_model\\n def bind_to_model(self, model):\\n return type(str('_TabbedInterface'), (BaseTabbedInterface,), {\\n 'model': model,\\n 'children': [child.bind_to_model(model) for child in self.children],\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::ObjectList.__init__\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::ObjectList.__init__\\n def __init__(self, children, heading=\\\"\\\", classname=\\\"\\\"):\\n self.children = children\\n self.heading = heading\\n self.classname = classname\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::ObjectList.bind_to_model\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::ObjectList.bind_to_model\\n def bind_to_model(self, model):\\n return type(str('_ObjectList'), (BaseObjectList,), {\\n 'model': model,\\n 'children': [child.bind_to_model(model) for child in self.children],\\n 'heading': self.heading,\\n 'classname': self.classname,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::FieldRowPanel.__init__\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::FieldRowPanel.__init__\\n def __init__(self, children, classname=\\\"\\\"):\\n self.children = children\\n self.classname = classname\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::FieldRowPanel.bind_to_model\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::FieldRowPanel.bind_to_model\\n def bind_to_model(self, model):\\n return type(str('_FieldRowPanel'), (BaseFieldRowPanel,), {\\n 'model': model,\\n 'children': [child.bind_to_model(model) for child in self.children],\\n 'classname': self.classname,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseMultiFieldPanel.classes\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseMultiFieldPanel.classes\\n def classes(self):\\n classes = super(BaseMultiFieldPanel, self).classes()\\n classes.append(\\\"multi-field\\\")\\n\\n return classes\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::MultiFieldPanel.__init__\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::MultiFieldPanel.__init__\\n def __init__(self, children, heading=\\\"\\\", classname=\\\"\\\"):\\n self.children = children\\n self.heading = heading\\n self.classname = classname\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::MultiFieldPanel.bind_to_model\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::MultiFieldPanel.bind_to_model\\n def bind_to_model(self, model):\\n return type(str('_MultiFieldPanel'), (BaseMultiFieldPanel,), {\\n 'model': model,\\n 'children': [child.bind_to_model(model) for child in self.children],\\n 'heading': self.heading,\\n 'classname': self.classname,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.widget_overrides\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.widget_overrides\\n @classmethod\\n def widget_overrides(cls):\\n \\\"\\\"\\\"check if a specific widget has been defined for this field\\\"\\\"\\\"\\n if hasattr(cls, 'widget'):\\n return {cls.field_name: cls.widget}\\n else:\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.__init__\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.__init__\\n def __init__(self, instance=None, form=None):\\n super(BaseFieldPanel, self).__init__(instance=instance, form=form)\\n self.bound_field = self.form[self.field_name]\\n\\n self.heading = self.bound_field.label\\n self.help_text = self.bound_field.help_text\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.classes\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.classes\\n def classes(self):\\n classes = super(BaseFieldPanel, self).classes()\\n\\n if self.bound_field.field.required:\\n classes.append(\\\"required\\\")\\n if self.bound_field.errors:\\n classes.append(\\\"error\\\")\\n\\n classes.append(self.field_type())\\n classes.append(\\\"single-field\\\")\\n\\n return classes\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.field_type\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.field_type\\n def field_type(self):\\n return camelcase_to_underscore(self.bound_field.field.__class__.__name__)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.render_as_object\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.render_as_object\\n def render_as_object(self):\\n return mark_safe(render_to_string(self.object_template, {\\n 'self': self,\\n 'field_content': self.render_as_field(show_help_text=False),\\n }))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.render_as_field\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.render_as_field\\n def render_as_field(self, show_help_text=True, extra_context={}):\\n context = {\\n 'field': self.bound_field,\\n 'field_type': self.field_type(),\\n 'show_help_text': show_help_text,\\n }\\n context.update(extra_context)\\n return mark_safe(render_to_string(self.field_template, context))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.required_fields\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseFieldPanel.required_fields\\n @classmethod\\n def required_fields(self):\\n return [self.field_name]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::FieldPanel.__init__\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::FieldPanel.__init__\\n def __init__(self, field_name, classname=\\\"\\\", widget=None):\\n self.field_name = field_name\\n self.classname = classname\\n self.widget = widget\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::FieldPanel.bind_to_model\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::FieldPanel.bind_to_model\\n def bind_to_model(self, model):\\n base = {\\n 'model': model,\\n 'field_name': self.field_name,\\n 'classname': self.classname,\\n }\\n\\n if self.widget:\\n base['widget'] = self.widget\\n\\n return type(str('_FieldPanel'), (BaseFieldPanel,), base)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::RichTextFieldPanel.__init__\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::RichTextFieldPanel.__init__\\n def __init__(self, field_name):\\n self.field_name = field_name\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::RichTextFieldPanel.bind_to_model\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::RichTextFieldPanel.bind_to_model\\n def bind_to_model(self, model):\\n return type(str('_RichTextFieldPanel'), (BaseRichTextFieldPanel,), {\\n 'model': model,\\n 'field_name': self.field_name,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseChooserPanel.get_chosen_item\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseChooserPanel.get_chosen_item\\n def get_chosen_item(self):\\n try:\\n return getattr(self.instance, self.field_name)\\n except ObjectDoesNotExist:\\n # if the ForeignKey is null=False, Django decides to raise\\n # a DoesNotExist exception here, rather than returning None\\n # like every other unpopulated field type. Yay consistency!\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseChooserPanel.render_as_field\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseChooserPanel.render_as_field\\n def render_as_field(self, show_help_text=True, extra_context={}):\\n instance_obj = self.get_chosen_item()\\n context = {\\n 'field': self.bound_field,\\n self.object_type_name: instance_obj,\\n 'is_chosen': bool(instance_obj),\\n 'show_help_text': show_help_text,\\n }\\n context.update(extra_context)\\n return mark_safe(render_to_string(self.field_template, context))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BasePageChooserPanel.widget_overrides\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BasePageChooserPanel.widget_overrides\\n @classmethod\\n def widget_overrides(cls):\\n return {cls.field_name: widgets.AdminPageChooser(\\n content_type=cls.target_content_type())}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BasePageChooserPanel.target_content_type\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BasePageChooserPanel.target_content_type\\n @classmethod\\n def target_content_type(cls):\\n if cls._target_content_type is None:\\n if cls.page_type:\\n try:\\n model = resolve_model_string(cls.page_type)\\n except LookupError:\\n raise ImproperlyConfigured(\\\"{0}.page_type must be of the form 'app_label.model_name', given {1!r}\\\".format(\\n cls.__name__, cls.page_type))\\n except ValueError:\\n raise ImproperlyConfigured(\\\"{0}.page_type refers to model {1!r} that has not been installed\\\".format(\\n cls.__name__, cls.page_type))\\n\\n cls._target_content_type = ContentType.objects.get_for_model(model)\\n else:\\n # TODO: infer the content type by introspection on the foreign key\\n cls._target_content_type = ContentType.objects.get_by_natural_key('wagtailcore', 'page')\\n\\n return cls._target_content_type\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BasePageChooserPanel.render_as_field\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BasePageChooserPanel.render_as_field\\n def render_as_field(self, show_help_text=True, extra_context={}):\\n context = {\\n 'choose_another_text_str': ugettext_lazy(\\\"Choose another page\\\"),\\n 'choose_one_text_str': ugettext_lazy(\\\"Choose a page\\\"),\\n }\\n context.update(extra_context)\\n return super(BasePageChooserPanel, self).render_as_field(show_help_text, context)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::PageChooserPanel.__init__\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::PageChooserPanel.__init__\\n def __init__(self, field_name, page_type=None):\\n self.field_name = field_name\\n self.page_type = page_type\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::PageChooserPanel.bind_to_model\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::PageChooserPanel.bind_to_model\\n def bind_to_model(self, model):\\n return type(str('_PageChooserPanel'), (BasePageChooserPanel,), {\\n 'model': model,\\n 'field_name': self.field_name,\\n 'page_type': self.page_type,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.get_panel_definitions\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.get_panel_definitions\\n @classmethod\\n def get_panel_definitions(cls):\\n # Look for a panels definition in the InlinePanel declaration\\n if cls.panels is not None:\\n return cls.panels\\n # Failing that, get it from the model\\n else:\\n return extract_panel_definitions_from_model_class(cls.related.model, exclude=[cls.related.field.name])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.get_child_edit_handler_class\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.get_child_edit_handler_class\\n @classmethod\\n def get_child_edit_handler_class(cls):\\n if cls._child_edit_handler_class is None:\\n panels = cls.get_panel_definitions()\\n cls._child_edit_handler_class = MultiFieldPanel(panels, heading=cls.heading).bind_to_model(cls.related.model)\\n\\n return cls._child_edit_handler_class\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.required_formsets\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.required_formsets\\n @classmethod\\n def required_formsets(cls):\\n child_edit_handler_class = cls.get_child_edit_handler_class()\\n return {\\n cls.relation_name: {\\n 'fields': child_edit_handler_class.required_fields(),\\n 'widgets': child_edit_handler_class.widget_overrides(),\\n }\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.__init__\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.__init__\\n def __init__(self, instance=None, form=None):\\n super(BaseInlinePanel, self).__init__(instance=instance, form=form)\\n\\n self.formset = form.formsets[self.__class__.relation_name]\\n\\n child_edit_handler_class = self.__class__.get_child_edit_handler_class()\\n self.children = []\\n for subform in self.formset.forms:\\n # override the DELETE field to have a hidden input\\n subform.fields['DELETE'].widget = forms.HiddenInput()\\n\\n # ditto for the ORDER field, if present\\n if self.formset.can_order:\\n subform.fields['ORDER'].widget = forms.HiddenInput()\\n\\n self.children.append(\\n child_edit_handler_class(instance=subform.instance, form=subform)\\n )\\n\\n # if this formset is valid, it may have been re-ordered; respect that\\n # in case the parent form errored and we need to re-render\\n if self.formset.can_order and self.formset.is_valid():\\n self.children = sorted(self.children, key=lambda x: x.form.cleaned_data['ORDER'])\\n\\n empty_form = self.formset.empty_form\\n empty_form.fields['DELETE'].widget = forms.HiddenInput()\\n if self.formset.can_order:\\n empty_form.fields['ORDER'].widget = forms.HiddenInput()\\n\\n self.empty_child = child_edit_handler_class(instance=empty_form.instance, form=empty_form)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.render\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.render\\n def render(self):\\n formset = render_to_string(self.template, {\\n 'self': self,\\n 'can_order': self.formset.can_order,\\n })\\n js = self.render_js_init()\\n return widget_with_script(formset, js)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.render_js_init\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::BaseInlinePanel.render_js_init\\n def render_js_init(self):\\n return mark_safe(render_to_string(self.js_template, {\\n 'self': self,\\n 'can_order': self.formset.can_order,\\n }))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::InlinePanel.__init__\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::InlinePanel.__init__\\n def __init__(self, base_model, relation_name, panels=None, label='', help_text=''):\\n # the base_model param is now redundant; we set up relations based on the model passed to\\n # bind_to_model instead\\n self.relation_name = relation_name\\n self.panels = panels\\n self.label = label\\n self.help_text = help_text\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::InlinePanel.bind_to_model\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::InlinePanel.bind_to_model\\n def bind_to_model(self, model):\\n return type(str('_InlinePanel'), (BaseInlinePanel,), {\\n 'model': model,\\n 'relation_name': self.relation_name,\\n 'related': getattr(model, self.relation_name).related,\\n 'panels': self.panels,\\n 'heading': self.label,\\n 'help_text': self.help_text, # TODO: can we pick this out of the foreign key definition as an alternative? (with a bit of help from the inlineformset object, as we do for label/heading)\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/edit_handlers.py::PublishingPanel\",\n \"text\": \"wagtail/wagtailadmin/edit_handlers.py::PublishingPanel\\ndef PublishingPanel():\\n return MultiFieldPanel([\\n FieldRowPanel([\\n FieldPanel('go_live_at'),\\n FieldPanel('expire_at'),\\n ], classname=\\\"label-above\\\"),\\n ], ugettext_lazy('Scheduled publishing'), classname=\\\"publishing\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/forms.py::SearchForm.__init__\",\n \"text\": \"wagtail/wagtailadmin/forms.py::SearchForm.__init__\\n def __init__(self, *args, **kwargs):\\n _placeholder = kwargs.pop('placeholder', None)\\n placeholder_suffix = kwargs.pop('placeholder_suffix', \\\"\\\")\\n super(SearchForm, self).__init__(*args, **kwargs)\\n if _placeholder is not None:\\n placeholder = _placeholder\\n else:\\n placeholder = 'Search {0}'.format(placeholder_suffix)\\n self.fields['q'].widget.attrs = {'placeholder': placeholder}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/forms.py::LoginForm.__init__\",\n \"text\": \"wagtail/wagtailadmin/forms.py::LoginForm.__init__\\n def __init__(self, request=None, *args, **kwargs):\\n super(LoginForm, self).__init__(request=request, *args, **kwargs)\\n self.fields['username'].widget.attrs['placeholder'] = ugettext_lazy(\\\"Enter your %s\\\") % self.username_field.verbose_name\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/forms.py::PasswordResetForm.clean\",\n \"text\": \"wagtail/wagtailadmin/forms.py::PasswordResetForm.clean\\n def clean(self):\\n cleaned_data = super(PasswordResetForm, self).clean()\\n\\n # Find users of this email address\\n UserModel = get_user_model()\\n email = cleaned_data.get('email')\\n if not email:\\n raise forms.ValidationError(_(\\\"Please fill your email address.\\\"))\\n active_users = UserModel._default_manager.filter(email__iexact=email, is_active=True)\\n\\n if active_users.exists():\\n # Check if all users of the email address are LDAP users (and give an error if they are)\\n found_non_ldap_user = False\\n for user in active_users:\\n if user.has_usable_password():\\n found_non_ldap_user = True\\n break\\n\\n if not found_non_ldap_user:\\n # All found users are LDAP users, give error message\\n raise forms.ValidationError(_(\\\"Sorry, you cannot reset your password here as your user account is managed by another server.\\\"))\\n else:\\n # No user accounts exist\\n raise forms.ValidationError(_(\\\"This email address is not recognised.\\\"))\\n\\n return cleaned_data\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/forms.py::CopyForm.__init__\",\n \"text\": \"wagtail/wagtailadmin/forms.py::CopyForm.__init__\\n def __init__(self, *args, **kwargs):\\n # CopyPage must be passed a 'page' kwarg indicating the page to be copied\\n self.page = kwargs.pop('page')\\n can_publish = kwargs.pop('can_publish')\\n super(CopyForm, self).__init__(*args, **kwargs)\\n\\n self.fields['new_title'] = forms.CharField(initial=self.page.title, label=_(\\\"New title\\\"))\\n self.fields['new_slug'] = forms.SlugField(initial=self.page.slug, label=_(\\\"New slug\\\"))\\n self.fields['new_parent_page'] = forms.ModelChoiceField(\\n initial=self.page.get_parent(),\\n queryset=Page.objects.all(),\\n widget=AdminPageChooser(),\\n label=_(\\\"New parent page\\\"),\\n help_text=_(\\\"This copy will be a child of this given parent page.\\\")\\n )\\n\\n pages_to_copy = self.page.get_descendants(inclusive=True)\\n subpage_count = pages_to_copy.count() - 1\\n if subpage_count > 0:\\n self.fields['copy_subpages'] = forms.BooleanField(\\n required=False, initial=True, label=_(\\\"Copy subpages\\\"),\\n help_text=ungettext(\\n \\\"This will copy %(count)s subpage.\\\",\\n \\\"This will copy %(count)s subpages.\\\",\\n subpage_count) % {'count': subpage_count})\\n\\n if can_publish:\\n pages_to_publish_count = pages_to_copy.live().count()\\n if pages_to_publish_count > 0:\\n # In the specific case that there are no subpages, customise the field label and help text\\n if subpage_count == 0:\\n label = _(\\\"Publish copied page\\\")\\n help_text = _(\\\"This page is live. Would you like to publish its copy as well?\\\")\\n else:\\n label = _(\\\"Publish copies\\\")\\n help_text = ungettext(\\n \\\"%(count)s of the pages being copied is live. Would you like to publish its copy?\\\",\\n \\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/forms.py::CopyForm.clean\",\n \"text\": \"wagtail/wagtailadmin/forms.py::CopyForm.clean\\n def clean(self):\\n cleaned_data = super(CopyForm, self).clean()\\n\\n # Make sure the slug isn't already in use\\n slug = cleaned_data.get('new_slug')\\n\\n # New parent page given in form or parent of source, if parent_page is empty\\n parent_page = cleaned_data.get('new_parent_page') or self.page.get_parent()\\n\\n # Count the pages with the same slug within the context of our copy's parent page\\n if slug and parent_page.get_children().filter(slug=slug).count():\\n self._errors['new_slug'] = self.error_class(\\n [_(\\\"This slug is already in use within the context of its parent page \\\\\\\"%s\\\\\\\"\\\" % parent_page)]\\n )\\n # The slug is no longer valid, hence remove it from cleaned_data\\n del cleaned_data['new_slug']\\n\\n return cleaned_data\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/forms.py::PageViewRestrictionForm.clean\",\n \"text\": \"wagtail/wagtailadmin/forms.py::PageViewRestrictionForm.clean\\n def clean(self):\\n cleaned_data = super(PageViewRestrictionForm, self).clean()\\n\\n if cleaned_data.get('restriction_type') == 'password' and not cleaned_data.get('password'):\\n self._errors[\\\"password\\\"] = self.error_class([_('This field is required.')])\\n del cleaned_data['password']\\n\\n return cleaned_data\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/utils.py::get_object_usage\",\n \"text\": \"wagtail/wagtailadmin/utils.py::get_object_usage\\ndef get_object_usage(obj):\\n \\\"Returns a queryset of pages that link to a particular object\\\"\\n\\n pages = Page.objects.none()\\n\\n # get all the relation objects for obj\\n relations = type(obj)._meta.get_all_related_objects(\\n include_hidden=True,\\n include_proxy_eq=True\\n )\\n for relation in relations:\\n # if the relation is between obj and a page, get the page\\n if issubclass(relation.model, Page):\\n pages |= Page.objects.filter(\\n id__in=relation.model._base_manager.filter(**{\\n relation.field.name: obj.id\\n }).values_list('id', flat=True)\\n )\\n else:\\n # if the relation is between obj and an object that has a page as a\\n # property, return the page\\n for f in relation.model._meta.fields:\\n if isinstance(f, ParentalKey) and issubclass(f.rel.to, Page):\\n pages |= Page.objects.filter(\\n id__in=relation.model._base_manager.filter(\\n **{\\n relation.field.name: obj.id\\n }).values_list(f.attname, flat=True)\\n )\\n\\n return pages\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::SiteManager.get_by_natural_key\",\n \"text\": \"wagtail/wagtailcore/models.py::SiteManager.get_by_natural_key\\n def get_by_natural_key(self, hostname, port):\\n return self.get(hostname=hostname, port=port)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.natural_key\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.natural_key\\n def natural_key(self):\\n return (self.hostname, self.port)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.__str__\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.__str__\\n def __str__(self):\\n return self.hostname + (\\\"\\\" if self.port == 80 else (\\\":%d\\\" % self.port)) + (\\\" [default]\\\" if self.is_default_site else \\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.find_for_request\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.find_for_request\\n @staticmethod\\n def find_for_request(request):\\n \\\"\\\"\\\"\\n Find the site object responsible for responding to this HTTP\\n request object. Try:\\n - unique hostname first\\n - then hostname and port\\n - if there is no matching hostname at all, or no matching\\n hostname:port combination, fall back to the unique default site,\\n or raise an exception\\n NB this means that high-numbered ports on an extant hostname may\\n still be routed to a different hostname which is set as the default\\n \\\"\\\"\\\"\\n try:\\n hostname = request.META['HTTP_HOST'].split(':')[0] # KeyError here goes to the final except clause\\n try:\\n # find a Site matching this specific hostname\\n return Site.objects.get(hostname=hostname) # Site.DoesNotExist here goes to the final except clause\\n except Site.MultipleObjectsReturned:\\n # as there were more than one, try matching by port too\\n port = request.META['SERVER_PORT'] # KeyError here goes to the final except clause\\n return Site.objects.get(hostname=hostname, port=int(port)) # Site.DoesNotExist here goes to the final except clause\\n except (Site.DoesNotExist, KeyError):\\n # If no matching site exists, or request does not specify an HTTP_HOST (which\\n # will often be the case for the Django test client), look for a catch-all Site.\\n # If that fails, let the Site.DoesNotExist propagate back to the caller\\n return Site.objects.get(is_default_site=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.root_url\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.root_url\\n @property\\n def root_url(self):\\n if self.port == 80:\\n return 'http://%s' % self.hostname\\n elif self.port == 443:\\n return 'https://%s' % self.hostname\\n else:\\n return 'http://%s:%d' % (self.hostname, self.port)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.clean_fields\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.clean_fields\\n def clean_fields(self, exclude=None):\\n super(Site, self).clean_fields(exclude)\\n # Only one site can have the is_default_site flag set\\n try:\\n default = Site.objects.get(is_default_site=True)\\n except Site.DoesNotExist:\\n pass\\n except Site.MultipleObjectsReturned:\\n raise\\n else:\\n if self.is_default_site and self.pk != default.pk:\\n raise ValidationError(\\n {'is_default_site': [\\n _(\\\"%(hostname)s is already configured as the default site. You must unset that before you can save this site as default.\\\")\\n % {'hostname': default.hostname}\\n ]}\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.get_site_root_paths\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.get_site_root_paths\\n @staticmethod\\n def get_site_root_paths():\\n \\\"\\\"\\\"\\n Return a list of (root_path, root_url) tuples, most specific path first -\\n used to translate url_paths into actual URLs with hostnames\\n \\\"\\\"\\\"\\n result = cache.get('wagtail_site_root_paths')\\n\\n if result is None:\\n result = [\\n (site.id, site.root_page.url_path, site.root_url)\\n for site in Site.objects.select_related('root_page').order_by('-root_page__url_path')\\n ]\\n cache.set('wagtail_site_root_paths', result, 3600)\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::clear_site_root_paths_on_save\",\n \"text\": \"wagtail/wagtailcore/models.py::clear_site_root_paths_on_save\\n@receiver(post_save, sender=Site)\\ndef clear_site_root_paths_on_save(sender, instance, **kwargs):\\n cache.delete('wagtail_site_root_paths')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::clear_site_root_paths_on_delete\",\n \"text\": \"wagtail/wagtailcore/models.py::clear_site_root_paths_on_delete\\n@receiver(post_delete, sender=Site)\\ndef clear_site_root_paths_on_delete(sender, instance, **kwargs):\\n cache.delete('wagtail_site_root_paths')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::get_page_types\",\n \"text\": \"wagtail/wagtailcore/models.py::get_page_types\\ndef get_page_types():\\n global _PAGE_CONTENT_TYPES\\n if len(_PAGE_CONTENT_TYPES) != len(PAGE_MODEL_CLASSES):\\n _PAGE_CONTENT_TYPES = [\\n ContentType.objects.get_for_model(cls) for cls in PAGE_MODEL_CLASSES\\n ]\\n return _PAGE_CONTENT_TYPES\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.get_queryset\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.get_queryset\\n def get_queryset(self):\\n return PageQuerySet(self.model).order_by('path')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.live\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.live\\n def live(self):\\n return self.get_queryset().live()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_live\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_live\\n def not_live(self):\\n return self.get_queryset().not_live()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.in_menu\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.in_menu\\n def in_menu(self):\\n return self.get_queryset().in_menu()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_in_menu\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_in_menu\\n def not_in_menu(self):\\n return self.get_queryset().not_in_menu()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.page\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.page\\n def page(self, other):\\n return self.get_queryset().page(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_page\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_page\\n def not_page(self, other):\\n return self.get_queryset().not_page(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.descendant_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.descendant_of\\n def descendant_of(self, other, inclusive=False):\\n return self.get_queryset().descendant_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_descendant_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_descendant_of\\n def not_descendant_of(self, other, inclusive=False):\\n return self.get_queryset().not_descendant_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.child_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.child_of\\n def child_of(self, other):\\n return self.get_queryset().child_of(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_child_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_child_of\\n def not_child_of(self, other):\\n return self.get_queryset().not_child_of(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.ancestor_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.ancestor_of\\n def ancestor_of(self, other, inclusive=False):\\n return self.get_queryset().ancestor_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_ancestor_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_ancestor_of\\n def not_ancestor_of(self, other, inclusive=False):\\n return self.get_queryset().not_ancestor_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.parent_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.parent_of\\n def parent_of(self, other):\\n return self.get_queryset().parent_of(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_parent_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_parent_of\\n def not_parent_of(self, other):\\n return self.get_queryset().not_parent_of(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.sibling_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.sibling_of\\n def sibling_of(self, other, inclusive=False):\\n return self.get_queryset().sibling_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_sibling_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_sibling_of\\n def not_sibling_of(self, other, inclusive=False):\\n return self.get_queryset().not_sibling_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.type\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.type\\n def type(self, model):\\n return self.get_queryset().type(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_type\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_type\\n def not_type(self, model):\\n return self.get_queryset().not_type(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.public\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.public\\n def public(self):\\n return self.get_queryset().public()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_public\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_public\\n def not_public(self):\\n return self.get_queryset().not_public()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.search\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.search\\n def search(self, query_string, fields=None, backend='default'):\\n return self.get_queryset().search(query_string, fields=fields, backend=backend)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageBase.__init__\",\n \"text\": \"wagtail/wagtailcore/models.py::PageBase.__init__\\n def __init__(cls, name, bases, dct):\\n super(PageBase, cls).__init__(name, bases, dct)\\n\\n if cls._deferred:\\n # this is an internal class built for Django's deferred-attribute mechanism;\\n # don't proceed with all this page type registration stuff\\n return\\n\\n # Add page manager\\n PageManager().contribute_to_class(cls, 'objects')\\n\\n if 'template' not in dct:\\n # Define a default template path derived from the app name and model name\\n cls.template = \\\"%s/%s.html\\\" % (cls._meta.app_label, camelcase_to_underscore(name))\\n\\n if 'ajax_template' not in dct:\\n cls.ajax_template = None\\n\\n cls._clean_subpage_types = None # to be filled in on first call to cls.clean_subpage_types\\n cls._clean_parent_page_types = None # to be filled in on first call to cls.clean_parent_page_types\\n\\n if not dct.get('is_abstract'):\\n # subclasses are only abstract if the subclass itself defines itself so\\n cls.is_abstract = False\\n\\n if not cls.is_abstract:\\n # register this type in the list of page content types\\n PAGE_MODEL_CLASSES.append(cls)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.__init__\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.__init__\\n def __init__(self, *args, **kwargs):\\n super(Page, self).__init__(*args, **kwargs)\\n if not self.id and not self.content_type_id:\\n # this model is being newly created rather than retrieved from the db;\\n # set content type to correctly represent the model class that this was\\n # created as\\n self.content_type = ContentType.objects.get_for_model(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.__str__\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.__str__\\n def __str__(self):\\n return self.title\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.set_url_path\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.set_url_path\\n def set_url_path(self, parent):\\n \\\"\\\"\\\"\\n Populate the url_path field based on this page's slug and the specified parent page.\\n (We pass a parent in here, rather than retrieving it via get_parent, so that we can give\\n new unsaved pages a meaningful URL when previewing them; at that point the page has not\\n been assigned a position in the tree, as far as treebeard is concerned.\\n \\\"\\\"\\\"\\n if parent:\\n self.url_path = parent.url_path + self.slug + '/'\\n else:\\n # a page without a parent is the tree root, which always has a url_path of '/'\\n self.url_path = '/'\\n\\n return self.url_path\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.save\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.save\\n @transaction.atomic # ensure that changes are only committed when we have updated all descendant URL paths, to preserve consistency\\n def save(self, *args, **kwargs):\\n update_descendant_url_paths = False\\n is_new = self.id is None\\n\\n if is_new:\\n # we are creating a record. If we're doing things properly, this should happen\\n # through a treebeard method like add_child, in which case the 'path' field\\n # has been set and so we can safely call get_parent\\n self.set_url_path(self.get_parent())\\n else:\\n # Check that we are committing the slug to the database\\n # Basically: If update_fields has been specified, and slug is not included, skip this step\\n if not ('update_fields' in kwargs and 'slug' not in kwargs['update_fields']):\\n # see if the slug has changed from the record in the db, in which case we need to\\n # update url_path of self and all descendants\\n old_record = Page.objects.get(id=self.id)\\n if old_record.slug != self.slug:\\n self.set_url_path(self.get_parent())\\n update_descendant_url_paths = True\\n old_url_path = old_record.url_path\\n new_url_path = self.url_path\\n\\n result = super(Page, self).save(*args, **kwargs)\\n\\n if update_descendant_url_paths:\\n self._update_descendant_url_paths(old_url_path, new_url_path)\\n\\n # Check if this is a root page of any sites and clear the 'wagtail_site_root_paths' key if so\\n if Site.objects.filter(root_page=self).exists():\\n cache.delete('wagtail_site_root_paths')\\n\\n # Log\\n if is_new:\\n cls = type(self)\\n logger.info(\\\"Page created: \\\\\\\"%s\\\\\\\" id=%d content_type=%s.%s path=%s\\\", self.title, self.id, cls._meta.app_label, cls.__name__, self.url_path)\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.check\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.check\\n @classmethod\\n def check(cls, **kwargs):\\n errors = super(Page, cls).check(**kwargs)\\n\\n # Check that foreign keys from pages are not configured to cascade\\n # This is the default Django behaviour which must be explicitly overridden\\n # to prevent pages disappearing unexpectedly and the tree being corrupted\\n\\n # get names of foreign keys pointing to parent classes (such as page_ptr)\\n field_exceptions = [field.name\\n for model in [cls] + list(cls._meta.get_parent_list())\\n for field in model._meta.parents.values()]\\n\\n field_exceptions += ['content_type']\\n\\n\\n for field in cls._meta.fields:\\n if isinstance(field, models.ForeignKey) and field.name not in field_exceptions:\\n if field.rel.on_delete == models.CASCADE:\\n errors.append(\\n checks.Warning(\\n \\\"Field hasn't specified on_delete action\\\",\\n hint=\\\"Set on_delete=models.SET_NULL and make sure the field is nullable.\\\",\\n obj=field,\\n id='wagtailcore.W001',\\n )\\n )\\n\\n return errors\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page._update_descendant_url_paths\",\n \"text\": \"wagtail/wagtailcore/models.py::Page._update_descendant_url_paths\\n def _update_descendant_url_paths(self, old_url_path, new_url_path):\\n cursor = connection.cursor()\\n if connection.vendor == 'sqlite':\\n update_statement = \\\"\\\"\\\"\\n UPDATE wagtailcore_page\\n SET url_path = %s || substr(url_path, %s)\\n WHERE path LIKE %s AND id <> %s\\n \\\"\\\"\\\"\\n elif connection.vendor == 'mysql':\\n update_statement = \\\"\\\"\\\"\\n UPDATE wagtailcore_page\\n SET url_path= CONCAT(%s, substring(url_path, %s))\\n WHERE path LIKE %s AND id <> %s\\n \\\"\\\"\\\"\\n else:\\n update_statement = \\\"\\\"\\\"\\n UPDATE wagtailcore_page\\n SET url_path = %s || substring(url_path from %s)\\n WHERE path LIKE %s AND id <> %s\\n \\\"\\\"\\\"\\n cursor.execute(update_statement, [new_url_path, len(old_url_path) + 1, self.path + '%', self.id])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.specific\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.specific\\n @cached_property\\n def specific(self):\\n \\\"\\\"\\\"\\n Return this page in its most specific subclassed form.\\n \\\"\\\"\\\"\\n # the ContentType.objects manager keeps a cache, so this should potentially\\n # avoid a database lookup over doing self.content_type. I think.\\n content_type = ContentType.objects.get_for_id(self.content_type_id)\\n if isinstance(self, content_type.model_class()):\\n # self is already the an instance of the most specific class\\n return self\\n else:\\n return content_type.get_object_for_this_type(id=self.id)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearchQuery._process_lookup\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearchQuery._process_lookup\\n def _process_lookup(self, field, lookup, value):\\n return models.Q(**{field.get_attname(self.queryset.model) + '__' + lookup: value})\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearchQuery._connect_filters\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearchQuery._connect_filters\\n def _connect_filters(self, filters, connector, negated):\\n if connector == 'AND':\\n q = models.Q(*filters)\\n elif connector == 'OR':\\n q = models.Q(filters[0])\\n for fil in filters[1:]:\\n q |= fil\\n else:\\n return\\n\\n if negated:\\n q = ~q\\n\\n return q\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearchQuery.get_q\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearchQuery.get_q\\n def get_q(self):\\n # Get filters as a q object\\n q = self._get_filters_from_queryset()\\n\\n model = self.queryset.model\\n\\n if self.query_string is not None:\\n # Get fields\\n fields = self.fields or [field.field_name for field in model.get_searchable_search_fields()]\\n\\n # Get terms\\n terms = self.query_string.split()\\n if not terms:\\n return model.objects.none()\\n\\n # Filter by terms\\n for term in terms:\\n term_query = models.Q()\\n for field_name in fields:\\n # Check if the field exists (this will filter out indexed callables)\\n try:\\n model._meta.get_field_by_name(field_name)\\n except:\\n continue\\n\\n # Filter on this field\\n term_query |= models.Q(**{'%s__icontains' % field_name: term})\\n\\n q &= term_query\\n\\n return q\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearchResults.get_queryset\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearchResults.get_queryset\\n def get_queryset(self):\\n model = self.query.queryset.model\\n q = self.query.get_q()\\n\\n return model.objects.filter(q).distinct()[self.start:self.stop]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearchResults._do_search\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearchResults._do_search\\n def _do_search(self):\\n return self.get_queryset()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearchResults._do_count\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearchResults._do_count\\n def _do_count(self):\\n return self.get_queryset().count()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.__init__\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.__init__\\n def __init__(self, params):\\n super(DBSearch, self).__init__(params)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.reset_index\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.reset_index\\n def reset_index(self):\\n pass # Not needed\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.add_type\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.add_type\\n def add_type(self, model):\\n pass # Not needed\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.refresh_index\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.refresh_index\\n def refresh_index(self):\\n pass # Not needed\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.add\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.add\\n def add(self, obj):\\n pass # Not needed\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.add_bulk\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.add_bulk\\n def add_bulk(self, model, obj_list):\\n return # Not needed\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.delete\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearch.delete\\n def delete(self, obj):\\n pass # Not needed\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/backends/db.py::DBSearch._search\",\n \"text\": \"wagtail/wagtailsearch/backends/db.py::DBSearch._search\\n def _search(self, queryset, query_string, fields=None):\\n return DBSearchResults(self, DBSearchQuery(queryset, query_string, fields=fields))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/forms.py::EditorsPickForm.__init__\",\n \"text\": \"wagtail/wagtailsearch/forms.py::EditorsPickForm.__init__\\n def __init__(self, *args, **kwargs):\\n super(EditorsPickForm, self).__init__(*args, **kwargs)\\n self.fields['page'].widget = AdminPageChooser()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/forms.py::EditorsPickFormSet.add_fields\",\n \"text\": \"wagtail/wagtailsearch/forms.py::EditorsPickFormSet.add_fields\\n def add_fields(self, form, *args, **kwargs):\\n super(EditorsPickFormSet, self).add_fields(form, *args, **kwargs)\\n\\n # Hide delete and order fields\\n form.fields['DELETE'].widget = forms.HiddenInput()\\n form.fields['ORDER'].widget = forms.HiddenInput()\\n\\n # Remove query field\\n del form.fields['query']\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailsearch/forms.py::EditorsPickFormSet.clean\",\n \"text\": \"wagtail/wagtailsearch/forms.py::EditorsPickFormSet.clean\\n def clean(self):\\n # Editors pick must have at least one recommended page to be valid\\n # Check there is at least one non-deleted form.\\n non_deleted_forms = self.total_form_count()\\n non_empty_forms = 0\\n for i in range(0, self.total_form_count()):\\n form = self.forms[i]\\n if self.can_delete and self._should_delete_form(form):\\n non_deleted_forms -= 1\\n if not (form.instance.id is None and not form.has_changed()):\\n non_empty_forms += 1\\n if (\\n non_deleted_forms < self.minimum_forms\\n or non_empty_forms < self.minimum_forms\\n ):\\n raise forms.ValidationError(self.minimum_forms_message)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailusers/forms.py::UserCreationForm.clean_username\",\n \"text\": \"wagtail/wagtailusers/forms.py::UserCreationForm.clean_username\\n def clean_username(self):\\n # Method copied from parent\\n\\n username = self.cleaned_data[\\\"username\\\"]\\n try:\\n # When called from BaseUserCreationForm, the method fails if using a AUTH_MODEL_MODEL,\\n # This is because the following line tries to perform a lookup on\\n # the default \\\"auth_user\\\" table.\\n User._default_manager.get(username=username)\\n except User.DoesNotExist:\\n return username\\n raise forms.ValidationError(\\n self.error_messages['duplicate_username'],\\n code='duplicate_username',\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailusers/forms.py::UserCreationForm.save\",\n \"text\": \"wagtail/wagtailusers/forms.py::UserCreationForm.save\\n def save(self, commit=True):\\n user = super(UserCreationForm, self).save(commit=False)\\n\\n # users can access django-admin iff they are a superuser\\n user.is_staff = user.is_superuser\\n\\n if commit:\\n user.save()\\n self.save_m2m()\\n return user\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailusers/forms.py::UserEditForm.clean_username\",\n \"text\": \"wagtail/wagtailusers/forms.py::UserEditForm.clean_username\\n def clean_username(self):\\n # Since User.username is unique, this check is redundant,\\n # but it sets a nicer error message than the ORM. See #13147.\\n username = self.cleaned_data[\\\"username\\\"]\\n try:\\n User._default_manager.exclude(id=self.instance.id).get(username=username)\\n except User.DoesNotExist:\\n return username\\n raise forms.ValidationError(self.error_messages['duplicate_username'])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailusers/forms.py::UserEditForm.clean_password2\",\n \"text\": \"wagtail/wagtailusers/forms.py::UserEditForm.clean_password2\\n def clean_password2(self):\\n password1 = self.cleaned_data.get(\\\"password1\\\")\\n password2 = self.cleaned_data.get(\\\"password2\\\")\\n if password1 != password2:\\n raise forms.ValidationError(\\n self.error_messages['password_mismatch'])\\n return password2\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailusers/forms.py::UserEditForm.save\",\n \"text\": \"wagtail/wagtailusers/forms.py::UserEditForm.save\\n def save(self, commit=True):\\n user = super(UserEditForm, self).save(commit=False)\\n\\n # users can access django-admin iff they are a superuser\\n user.is_staff = user.is_superuser\\n\\n if self.cleaned_data[\\\"password1\\\"]:\\n user.set_password(self.cleaned_data[\\\"password1\\\"])\\n if commit:\\n user.save()\\n self.save_m2m()\\n return user\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailusers/forms.py::GroupForm.__init__\",\n \"text\": \"wagtail/wagtailusers/forms.py::GroupForm.__init__\\n def __init__(self, *args, **kwargs):\\n super(GroupForm, self).__init__(*args, **kwargs)\\n self.registered_permissions = Permission.objects.none()\\n for fn in hooks.get_hooks('register_permissions'):\\n self.registered_permissions = self.registered_permissions | fn()\\n self.fields['permissions'].queryset = self.registered_permissions\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailusers/forms.py::GroupForm.clean_name\",\n \"text\": \"wagtail/wagtailusers/forms.py::GroupForm.clean_name\\n def clean_name(self):\\n # Since Group.name is unique, this check is redundant,\\n # but it sets a nicer error message than the ORM. See #13147.\\n name = self.cleaned_data[\\\"name\\\"]\\n try:\\n Group._default_manager.exclude(id=self.instance.id).get(name=name)\\n except Group.DoesNotExist:\\n return name\\n raise forms.ValidationError(self.error_messages['duplicate_name'])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailusers/forms.py::GroupForm.save\",\n \"text\": \"wagtail/wagtailusers/forms.py::GroupForm.save\\n def save(self):\\n # We go back to the object to read (in order to reapply) the\\n # permissions which were set on this group, but which are not\\n # accessible in the wagtail admin interface, as otherwise these would\\n # be clobbered by this form.\\n try:\\n untouchable_permissions = self.instance.permissions.exclude(pk__in=self.registered_permissions)\\n bool(untouchable_permissions) # force this to be evaluated, as it's about to change\\n except ValueError:\\n # this form is not bound; we're probably creating a new group\\n untouchable_permissions = []\\n group = super(GroupForm, self).save()\\n group.permissions.add(*untouchable_permissions)\\n return group\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailusers/forms.py::GroupPagePermissionForm.__init__\",\n \"text\": \"wagtail/wagtailusers/forms.py::GroupPagePermissionForm.__init__\\n def __init__(self, *args, **kwargs):\\n super(GroupPagePermissionForm, self).__init__(*args, **kwargs)\\n self.fields['page'].widget = AdminPageChooser()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailusers/forms.py::BaseGroupPagePermissionFormSet.__init__\",\n \"text\": \"wagtail/wagtailusers/forms.py::BaseGroupPagePermissionFormSet.__init__\\n def __init__(self, *args, **kwargs):\\n super(BaseGroupPagePermissionFormSet, self).__init__(*args, **kwargs)\\n self.form = GroupPagePermissionForm\\n for form in self.forms:\\n form.fields['DELETE'].widget = forms.HiddenInput()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailusers/forms.py::BaseGroupPagePermissionFormSet.empty_form\",\n \"text\": \"wagtail/wagtailusers/forms.py::BaseGroupPagePermissionFormSet.empty_form\\n @property\\n def empty_form(self):\\n empty_form = super(BaseGroupPagePermissionFormSet, self).empty_form\\n empty_form.fields['DELETE'].widget = forms.HiddenInput()\\n return empty_form\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailusers/forms.py::NotificationPreferencesForm.__init__\",\n \"text\": \"wagtail/wagtailusers/forms.py::NotificationPreferencesForm.__init__\\n def __init__(self, *args, **kwargs):\\n super(NotificationPreferencesForm, self).__init__(*args, **kwargs)\\n user_perms = UserPagePermissionsProxy(self.instance.user)\\n if not user_perms.can_publish_pages():\\n del self.fields['submitted_notifications']\\n if not user_perms.can_edit_pages():\\n del self.fields['approved_notifications']\\n del self.fields['rejected_notifications']\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":266,"cells":{"instance_id":{"kind":"string","value":"pyca__cryptography-7697"},"repo":{"kind":"string","value":"pyca/cryptography"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"RFC 9266: Channel Bindings for TLS 1.3 support\nCan you add the support of RFC 9266: Channel Bindings for TLS 1.3?\r\n- https://datatracker.ietf.org/doc/html/rfc9266\r\n\r\nLittle details, to know easily:\r\n- tls-unique for TLS =< 1.2\r\n- tls-exporter for TLS = 1.3\r\n\r\nThanks in advance.\n"},"gold_files":{"kind":"list like","value":["src/_cffi_src/openssl/ssl.py","src/cryptography/hazmat/bindings/openssl/_conditional.py"],"string":"[\n \"src/_cffi_src/openssl/ssl.py\",\n \"src/cryptography/hazmat/bindings/openssl/_conditional.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["src/_cffi_src/openssl/ssl.py::<module>","src/cryptography/hazmat/bindings/openssl/_conditional.py::<module>"],"string":"[\n \"src/_cffi_src/openssl/ssl.py::<module>\",\n \"src/cryptography/hazmat/bindings/openssl/_conditional.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/_ext/cryptography-docs.py","text":"docs/_ext/cryptography-docs.py\nclass HazmatDirective\n def run(self)\nclass Hazmat\ndef html_visit_hazmat_node(self, node)\ndef latex_visit_hazmat_node(self, node)\ndef depart_hazmat_node(self, node)\ndef setup(app)","label":0},{"cid":"docs/conf.py","text":"docs/conf.py","label":0},{"cid":"setup.py","text":"setup.py","label":0},{"cid":"src/_cffi_src/build_openssl.py","text":"src/_cffi_src/build_openssl.py\ndef _get_openssl_libraries(platform)\ndef _extra_compile_args(platform) # We set -Wconversion args here so that we only do Wconversion checks on the","label":0},{"cid":"src/_cffi_src/openssl/engine.py","text":"src/_cffi_src/openssl/engine.py","label":0},{"cid":"src/_cffi_src/openssl/ssl.py","text":"src/_cffi_src/openssl/ssl.py","label":1},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py\ndef cryptography_has_ec2m()\ndef cryptography_has_ssl3_method()\ndef cryptography_has_set_cert_cb()\ndef cryptography_has_ssl_st()\ndef cryptography_has_tls_st()\ndef cryptography_has_scrypt()\ndef cryptography_has_evp_pkey_dhx()\ndef cryptography_has_mem_functions()\ndef cryptography_has_x509_store_ctx_get_issuer()\ndef cryptography_has_ed448()\ndef cryptography_has_ed25519()\ndef cryptography_has_poly1305()\ndef cryptography_has_evp_digestfinal_xof()\ndef cryptography_has_evp_pkey_get_set_tls_encodedpoint()\ndef cryptography_has_fips()\ndef cryptography_has_ssl_sigalgs()\ndef cryptography_has_psk()\ndef cryptography_has_psk_tlsv13()\ndef cryptography_has_custom_ext()\ndef cryptography_has_openssl_cleanup()\ndef cryptography_has_tlsv13_functions()\ndef cryptography_has_keylog()\ndef cryptography_has_raw_key()\ndef cryptography_has_engine()\ndef cryptography_has_verified_chain()\ndef cryptography_has_srtp()\ndef cryptography_has_providers()\ndef cryptography_has_op_no_renegotiation()\ndef cryptography_has_dtls_get_data_mtu()\ndef cryptography_has_300_fips()\ndef cryptography_has_ssl_cookie()\ndef cryptography_has_pkcs7_funcs()\ndef cryptography_has_bn_flags()\ndef cryptography_has_evp_pkey_dh()\ndef cryptography_has_300_evp_cipher()\ndef cryptography_has_unexpected_eof_while_reading()\ndef cryptography_has_pkcs12_set_mac()\ndef cryptography_has_ssl_op_ignore_unexpected_eof()","label":1},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py","text":"src/cryptography/hazmat/bindings/openssl/binding.py\nclass _OpenSSLError\n def __init__(self, code: int, lib: int, reason: int)\n def _lib_reason_match(self, lib: int, reason: int)\n def code(self)\n def lib(self)\n def reason(self)\ndef _consume_errors(lib)\ndef _errors_with_text(errors: typing.List[_OpenSSLError])\ndef _consume_errors_with_text(lib)\ndef _openssl_assert(lib, ok: bool, errors: typing.Optional[typing.List[_OpenSSLError]]=None)\ndef _legacy_provider_error(loaded: bool)\ndef build_conditional_library(lib: typing.Any, conditional_names: typing.Dict[str, typing.Callable[[], typing.List[str]]])\nclass Binding # OpenSSL API wrapper.\n def __init__(self)\n def _enable_fips(self)\n def _register_osrandom_engine(cls)\n def _ensure_ffi_initialized(cls)\n def init_static_locks(cls)\ndef _verify_package_version(version: str)","label":0},{"cid":"src/cryptography/x509/certificate_transparency.py","text":"src/cryptography/x509/certificate_transparency.py\nclass LogEntryType\nclass Version\nclass SignatureAlgorithm # Signature algorithms that are valid for SCTs.\nclass SignedCertificateTimestamp\n def version(self) # Returns the SCT version.\n def log_id(self) # Returns an identifier indicating which log this SCT is for.\n def timestamp(self) # Returns the timestamp for this SCT.\n def entry_type(self) # Returns whether this is an SCT for a certificate or pre-certificate.\n def signature_hash_algorithm(self) # Returns the hash algorithm used for the SCT's signature.\n def signature_algorithm(self) # Returns the signing algorithm used for the SCT's signature.\n def signature(self) # Returns the signature for this SCT.\n def extension_bytes(self) # Returns the raw bytes of any extensions for this SCT.","label":0},{"cid":"src/cryptography/x509/extensions.py","text":"src/cryptography/x509/extensions.py\ndef _key_identifier_from_public_key(public_key: CERTIFICATE_PUBLIC_KEY_TYPES)\ndef _make_sequence_methods(field_name: str)\n def len_method(self)\n def iter_method(self)\n def getitem_method(self, idx)\nclass DuplicateExtension\n def __init__(self, msg: str, oid: ObjectIdentifier)\nclass ExtensionNotFound\n def __init__(self, msg: str, oid: ObjectIdentifier)\nclass ExtensionType\n def public_bytes(self) # Serializes the extension type to DER.\nclass Extensions\n def __init__(self, extensions: typing.Iterable['Extension[ExtensionType]'])\n def get_extension_for_oid(self, oid: ObjectIdentifier)\n def get_extension_for_class(self, extclass: typing.Type[ExtensionTypeVar])\n def __repr__(self)\nclass CRLNumber\n def __init__(self, crl_number: int)\n def __eq__(self, other: object)\n def __hash__(self)\n def __repr__(self)\n def crl_number(self)\n def public_bytes(self)\nclass AuthorityKeyIdentifier\n def __init__(self, key_identifier: typing.Optional[bytes], authority_cert_issuer: typing.Optional[typing.Iterable[GeneralName]], authority_cert_serial_number: typing.Optional[int])\n def from_issuer_public_key(cls, public_key: CERTIFICATE_ISSUER_PUBLIC_KEY_TYPES)\n def from_issuer_subject_key_identifier(cls, ski: 'SubjectKeyIdentifier')\n def __repr__(self)\n def __eq__(self, other: object)\n def __hash__(self)\n def key_identifier(self)\n def authority_cert_issuer(self)\n def authority_cert_serial_number(self)\n def public_bytes(self)\nclass SubjectKeyIdentifier\n def __init__(self, digest: bytes)\n def from_public_key(cls, public_key: CERTIFICATE_PUBLIC_KEY_TYPES)\n def digest(self)\n def key_identifier(self)\n def __repr__(self)\n def __eq__(self, other: object)\n def __hash__(self)\n def public_bytes(self)\nclass AuthorityInformationAccess\n def __init__(self, descriptions: typing.Iterable['AccessDescription'])\n def __repr__(self)\n def __eq__(self, other: object)\n def __hash__(self)\n def public_bytes(self)\nclass SubjectInformationAccess\n def __init__(self, descriptions: typing.Iterable['AccessDescription'])\n def __repr__(self)\n def __eq__(self, other: object)\n def __hash__(self)\n def public_bytes(self)\nclass AccessDescription\n def __init__(self, access_method: ObjectIdentifier, access_location: GeneralName)\n def __repr__(self)\n def __eq__(self, other: object)\n def __hash__(self)\n def access_method(self)\n def access_location(self)\nclass BasicConstraints\n def __init__(self, ca: bool, path_length: typing.Optional[int])\n def ca(self)\n def path_length(self)\n def __repr__(self)\n def __eq__(self, other: object)\n def __hash__(self)\n def public_bytes(self)\nclass DeltaCRLIndicator\n def __init__(self, crl_number: int)\n def crl_number(self)\n def __eq__(self, other: object)\n def __hash__(self)\n def __repr__(self)\n def public_bytes(self)\nclass CRLDistributionPoints\n def __init__(self","label":0}],"string":"[\n {\n \"cid\": \"docs/_ext/cryptography-docs.py\",\n \"text\": \"docs/_ext/cryptography-docs.py\\nclass HazmatDirective\\n def run(self)\\nclass Hazmat\\ndef html_visit_hazmat_node(self, node)\\ndef latex_visit_hazmat_node(self, node)\\ndef depart_hazmat_node(self, node)\\ndef setup(app)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/conf.py\",\n \"text\": \"docs/conf.py\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\",\n \"label\": 0\n },\n {\n \"cid\": \"src/_cffi_src/build_openssl.py\",\n \"text\": \"src/_cffi_src/build_openssl.py\\ndef _get_openssl_libraries(platform)\\ndef _extra_compile_args(platform) # We set -Wconversion args here so that we only do Wconversion checks on the\",\n \"label\": 0\n },\n {\n \"cid\": \"src/_cffi_src/openssl/engine.py\",\n \"text\": \"src/_cffi_src/openssl/engine.py\",\n \"label\": 0\n },\n {\n \"cid\": \"src/_cffi_src/openssl/ssl.py\",\n \"text\": \"src/_cffi_src/openssl/ssl.py\",\n \"label\": 1\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py\\ndef cryptography_has_ec2m()\\ndef cryptography_has_ssl3_method()\\ndef cryptography_has_set_cert_cb()\\ndef cryptography_has_ssl_st()\\ndef cryptography_has_tls_st()\\ndef cryptography_has_scrypt()\\ndef cryptography_has_evp_pkey_dhx()\\ndef cryptography_has_mem_functions()\\ndef cryptography_has_x509_store_ctx_get_issuer()\\ndef cryptography_has_ed448()\\ndef cryptography_has_ed25519()\\ndef cryptography_has_poly1305()\\ndef cryptography_has_evp_digestfinal_xof()\\ndef cryptography_has_evp_pkey_get_set_tls_encodedpoint()\\ndef cryptography_has_fips()\\ndef cryptography_has_ssl_sigalgs()\\ndef cryptography_has_psk()\\ndef cryptography_has_psk_tlsv13()\\ndef cryptography_has_custom_ext()\\ndef cryptography_has_openssl_cleanup()\\ndef cryptography_has_tlsv13_functions()\\ndef cryptography_has_keylog()\\ndef cryptography_has_raw_key()\\ndef cryptography_has_engine()\\ndef cryptography_has_verified_chain()\\ndef cryptography_has_srtp()\\ndef cryptography_has_providers()\\ndef cryptography_has_op_no_renegotiation()\\ndef cryptography_has_dtls_get_data_mtu()\\ndef cryptography_has_300_fips()\\ndef cryptography_has_ssl_cookie()\\ndef cryptography_has_pkcs7_funcs()\\ndef cryptography_has_bn_flags()\\ndef cryptography_has_evp_pkey_dh()\\ndef cryptography_has_300_evp_cipher()\\ndef cryptography_has_unexpected_eof_while_reading()\\ndef cryptography_has_pkcs12_set_mac()\\ndef cryptography_has_ssl_op_ignore_unexpected_eof()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py\\nclass _OpenSSLError\\n def __init__(self, code: int, lib: int, reason: int)\\n def _lib_reason_match(self, lib: int, reason: int)\\n def code(self)\\n def lib(self)\\n def reason(self)\\ndef _consume_errors(lib)\\ndef _errors_with_text(errors: typing.List[_OpenSSLError])\\ndef _consume_errors_with_text(lib)\\ndef _openssl_assert(lib, ok: bool, errors: typing.Optional[typing.List[_OpenSSLError]]=None)\\ndef _legacy_provider_error(loaded: bool)\\ndef build_conditional_library(lib: typing.Any, conditional_names: typing.Dict[str, typing.Callable[[], typing.List[str]]])\\nclass Binding # OpenSSL API wrapper.\\n def __init__(self)\\n def _enable_fips(self)\\n def _register_osrandom_engine(cls)\\n def _ensure_ffi_initialized(cls)\\n def init_static_locks(cls)\\ndef _verify_package_version(version: str)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/certificate_transparency.py\",\n \"text\": \"src/cryptography/x509/certificate_transparency.py\\nclass LogEntryType\\nclass Version\\nclass SignatureAlgorithm # Signature algorithms that are valid for SCTs.\\nclass SignedCertificateTimestamp\\n def version(self) # Returns the SCT version.\\n def log_id(self) # Returns an identifier indicating which log this SCT is for.\\n def timestamp(self) # Returns the timestamp for this SCT.\\n def entry_type(self) # Returns whether this is an SCT for a certificate or pre-certificate.\\n def signature_hash_algorithm(self) # Returns the hash algorithm used for the SCT's signature.\\n def signature_algorithm(self) # Returns the signing algorithm used for the SCT's signature.\\n def signature(self) # Returns the signature for this SCT.\\n def extension_bytes(self) # Returns the raw bytes of any extensions for this SCT.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py\",\n \"text\": \"src/cryptography/x509/extensions.py\\ndef _key_identifier_from_public_key(public_key: CERTIFICATE_PUBLIC_KEY_TYPES)\\ndef _make_sequence_methods(field_name: str)\\n def len_method(self)\\n def iter_method(self)\\n def getitem_method(self, idx)\\nclass DuplicateExtension\\n def __init__(self, msg: str, oid: ObjectIdentifier)\\nclass ExtensionNotFound\\n def __init__(self, msg: str, oid: ObjectIdentifier)\\nclass ExtensionType\\n def public_bytes(self) # Serializes the extension type to DER.\\nclass Extensions\\n def __init__(self, extensions: typing.Iterable['Extension[ExtensionType]'])\\n def get_extension_for_oid(self, oid: ObjectIdentifier)\\n def get_extension_for_class(self, extclass: typing.Type[ExtensionTypeVar])\\n def __repr__(self)\\nclass CRLNumber\\n def __init__(self, crl_number: int)\\n def __eq__(self, other: object)\\n def __hash__(self)\\n def __repr__(self)\\n def crl_number(self)\\n def public_bytes(self)\\nclass AuthorityKeyIdentifier\\n def __init__(self, key_identifier: typing.Optional[bytes], authority_cert_issuer: typing.Optional[typing.Iterable[GeneralName]], authority_cert_serial_number: typing.Optional[int])\\n def from_issuer_public_key(cls, public_key: CERTIFICATE_ISSUER_PUBLIC_KEY_TYPES)\\n def from_issuer_subject_key_identifier(cls, ski: 'SubjectKeyIdentifier')\\n def __repr__(self)\\n def __eq__(self, other: object)\\n def __hash__(self)\\n def key_identifier(self)\\n def authority_cert_issuer(self)\\n def authority_cert_serial_number(self)\\n def public_bytes(self)\\nclass SubjectKeyIdentifier\\n def __init__(self, digest: bytes)\\n def from_public_key(cls, public_key: CERTIFICATE_PUBLIC_KEY_TYPES)\\n def digest(self)\\n def key_identifier(self)\\n def __repr__(self)\\n def __eq__(self, other: object)\\n def __hash__(self)\\n def public_bytes(self)\\nclass AuthorityInformationAccess\\n def __init__(self, descriptions: typing.Iterable['AccessDescription'])\\n def __repr__(self)\\n def __eq__(self, other: object)\\n def __hash__(self)\\n def public_bytes(self)\\nclass SubjectInformationAccess\\n def __init__(self, descriptions: typing.Iterable['AccessDescription'])\\n def __repr__(self)\\n def __eq__(self, other: object)\\n def __hash__(self)\\n def public_bytes(self)\\nclass AccessDescription\\n def __init__(self, access_method: ObjectIdentifier, access_location: GeneralName)\\n def __repr__(self)\\n def __eq__(self, other: object)\\n def __hash__(self)\\n def access_method(self)\\n def access_location(self)\\nclass BasicConstraints\\n def __init__(self, ca: bool, path_length: typing.Optional[int])\\n def ca(self)\\n def path_length(self)\\n def __repr__(self)\\n def __eq__(self, other: object)\\n def __hash__(self)\\n def public_bytes(self)\\nclass DeltaCRLIndicator\\n def __init__(self, crl_number: int)\\n def crl_number(self)\\n def __eq__(self, other: object)\\n def __hash__(self)\\n def __repr__(self)\\n def public_bytes(self)\\nclass CRLDistributionPoints\\n def __init__(self\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"docs/_ext/cryptography-docs.py::HazmatDirective.run","text":"docs/_ext/cryptography-docs.py::HazmatDirective.run\n def run(self):\n message = DANGER_MESSAGE\n if self.content:\n message += DANGER_ALTERNATE.format(alternate=self.content[0])\n\n content = nodes.paragraph(\"\", message)\n admonition_node = Hazmat(\"\\n\".join(content))\n self.state.nested_parse(content, self.content_offset, admonition_node)\n admonition_node.line = self.lineno\n return [admonition_node]","label":0},{"cid":"docs/_ext/cryptography-docs.py::html_visit_hazmat_node","text":"docs/_ext/cryptography-docs.py::html_visit_hazmat_node\ndef html_visit_hazmat_node(self, node):\n return self.visit_admonition(node, \"danger\")","label":0},{"cid":"docs/_ext/cryptography-docs.py::latex_visit_hazmat_node","text":"docs/_ext/cryptography-docs.py::latex_visit_hazmat_node\ndef latex_visit_hazmat_node(self, node):\n return self.visit_admonition(node)","label":0},{"cid":"docs/_ext/cryptography-docs.py::depart_hazmat_node","text":"docs/_ext/cryptography-docs.py::depart_hazmat_node\ndef depart_hazmat_node(self, node):\n return self.depart_admonition(node)","label":0},{"cid":"docs/_ext/cryptography-docs.py::setup","text":"docs/_ext/cryptography-docs.py::setup\ndef setup(app):\n app.add_node(\n Hazmat,\n html=(html_visit_hazmat_node, depart_hazmat_node),\n latex=(latex_visit_hazmat_node, depart_hazmat_node),\n )\n app.add_directive(\"hazmat\", HazmatDirective)\n\n return {\n \"parallel_read_safe\": True,\n }","label":0},{"cid":"src/_cffi_src/build_openssl.py::_get_openssl_libraries","text":"src/_cffi_src/build_openssl.py::_get_openssl_libraries\ndef _get_openssl_libraries(platform):\n if os.environ.get(\"CRYPTOGRAPHY_SUPPRESS_LINK_FLAGS\", None):\n return []\n # OpenSSL goes by a different library name on different operating systems.\n if platform == \"win32\" and compiler_type() == \"msvc\":\n return [\n \"libssl\",\n \"libcrypto\",\n \"advapi32\",\n \"crypt32\",\n \"gdi32\",\n \"user32\",\n \"ws2_32\",\n ]\n else:\n # darwin, linux, mingw all use this path\n # In some circumstances, the order in which these libs are\n # specified on the linker command-line is significant;\n # libssl must come before libcrypto\n # (https://marc.info/?l=openssl-users&m=135361825921871)\n # -lpthread required due to usage of pthread an potential\n # existance of a static part containing e.g. pthread_atfork\n # (https://github.com/pyca/cryptography/issues/5084)\n if sys.platform == \"zos\":\n return [\"ssl\", \"crypto\"]\n else:\n return [\"ssl\", \"crypto\", \"pthread\"]","label":0},{"cid":"src/_cffi_src/build_openssl.py::_extra_compile_args","text":"src/_cffi_src/build_openssl.py::_extra_compile_args\ndef _extra_compile_args(platform):\n \"\"\"\n We set -Wconversion args here so that we only do Wconversion checks on the\n code we're compiling and not on cffi itself (as passing -Wconversion in\n CFLAGS would do). We set no error on sign conversion because some\n function signatures in LibreSSL differ from OpenSSL have changed on long\n vs. unsigned long in the past. Since that isn't a precision issue we don't\n care.\n \"\"\"\n # make sure the compiler used supports the flags to be added\n is_gcc = False\n if get_default_compiler() == \"unix\":\n d = dist.Distribution()\n cmd = config(d)\n cmd._check_compiler()\n is_gcc = (\n \"gcc\" in cmd.compiler.compiler[0]\n or \"clang\" in cmd.compiler.compiler[0]\n )\n if is_gcc or not (\n platform in [\"win32\", \"hp-ux11\", \"sunos5\"]\n or platform.startswith(\"aix\")\n ):\n return [\"-Wconversion\", \"-Wno-error=sign-conversion\"]\n else:\n return []","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ec2m","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ec2m\ndef cryptography_has_ec2m() -> typing.List[str]:\n return [\n \"EC_POINT_get_affine_coordinates_GF2m\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl3_method","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl3_method\ndef cryptography_has_ssl3_method() -> typing.List[str]:\n return [\n \"SSLv3_method\",\n \"SSLv3_client_method\",\n \"SSLv3_server_method\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_set_cert_cb","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_set_cert_cb\ndef cryptography_has_set_cert_cb() -> typing.List[str]:\n return [\n \"SSL_CTX_set_cert_cb\",\n \"SSL_set_cert_cb\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_st","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_st\ndef cryptography_has_ssl_st() -> typing.List[str]:\n return [\n \"SSL_ST_BEFORE\",\n \"SSL_ST_OK\",\n \"SSL_ST_INIT\",\n \"SSL_ST_RENEGOTIATE\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_tls_st","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_tls_st\ndef cryptography_has_tls_st() -> typing.List[str]:\n return [\n \"TLS_ST_BEFORE\",\n \"TLS_ST_OK\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_scrypt","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_scrypt\ndef cryptography_has_scrypt() -> typing.List[str]:\n return [\n \"EVP_PBE_scrypt\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_pkey_dhx","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_pkey_dhx\ndef cryptography_has_evp_pkey_dhx() -> typing.List[str]:\n return [\n \"EVP_PKEY_DHX\",\n \"d2i_DHxparams_bio\",\n \"i2d_DHxparams_bio\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_mem_functions","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_mem_functions\ndef cryptography_has_mem_functions() -> typing.List[str]:\n return [\n \"Cryptography_CRYPTO_set_mem_functions\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_x509_store_ctx_get_issuer","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_x509_store_ctx_get_issuer\ndef cryptography_has_x509_store_ctx_get_issuer() -> typing.List[str]:\n return [\n \"X509_STORE_get_get_issuer\",\n \"X509_STORE_set_get_issuer\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ed448","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ed448\ndef cryptography_has_ed448() -> typing.List[str]:\n return [\n \"EVP_PKEY_ED448\",\n \"NID_ED448\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ed25519","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ed25519\ndef cryptography_has_ed25519() -> typing.List[str]:\n return [\n \"NID_ED25519\",\n \"EVP_PKEY_ED25519\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_poly1305","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_poly1305\ndef cryptography_has_poly1305() -> typing.List[str]:\n return [\n \"NID_poly1305\",\n \"EVP_PKEY_POLY1305\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_digestfinal_xof","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_digestfinal_xof\ndef cryptography_has_evp_digestfinal_xof() -> typing.List[str]:\n return [\n \"EVP_DigestFinalXOF\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_pkey_get_set_tls_encodedpoint","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_pkey_get_set_tls_encodedpoint\ndef cryptography_has_evp_pkey_get_set_tls_encodedpoint() -> typing.List[str]:\n return [\n \"EVP_PKEY_get1_tls_encodedpoint\",\n \"EVP_PKEY_set1_tls_encodedpoint\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_fips","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_fips\ndef cryptography_has_fips() -> typing.List[str]:\n return [\n \"FIPS_mode_set\",\n \"FIPS_mode\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_sigalgs","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_sigalgs\ndef cryptography_has_ssl_sigalgs() -> typing.List[str]:\n return [\n \"SSL_CTX_set1_sigalgs_list\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_psk","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_psk\ndef cryptography_has_psk() -> typing.List[str]:\n return [\n \"SSL_CTX_use_psk_identity_hint\",\n \"SSL_CTX_set_psk_server_callback\",\n \"SSL_CTX_set_psk_client_callback\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_psk_tlsv13","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_psk_tlsv13\ndef cryptography_has_psk_tlsv13() -> typing.List[str]:\n return [\n \"SSL_CTX_set_psk_find_session_callback\",\n \"SSL_CTX_set_psk_use_session_callback\",\n \"Cryptography_SSL_SESSION_new\",\n \"SSL_CIPHER_find\",\n \"SSL_SESSION_set1_master_key\",\n \"SSL_SESSION_set_cipher\",\n \"SSL_SESSION_set_protocol_version\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_custom_ext","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_custom_ext\ndef cryptography_has_custom_ext() -> typing.List[str]:\n return [\n \"SSL_CTX_add_client_custom_ext\",\n \"SSL_CTX_add_server_custom_ext\",\n \"SSL_extension_supported\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_openssl_cleanup","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_openssl_cleanup\ndef cryptography_has_openssl_cleanup() -> typing.List[str]:\n return [\n \"OPENSSL_cleanup\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_tlsv13_functions","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_tlsv13_functions\ndef cryptography_has_tlsv13_functions() -> typing.List[str]:\n return [\n \"SSL_VERIFY_POST_HANDSHAKE\",\n \"SSL_CTX_set_ciphersuites\",\n \"SSL_verify_client_post_handshake\",\n \"SSL_CTX_set_post_handshake_auth\",\n \"SSL_set_post_handshake_auth\",\n \"SSL_SESSION_get_max_early_data\",\n \"SSL_write_early_data\",\n \"SSL_read_early_data\",\n \"SSL_CTX_set_max_early_data\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_keylog","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_keylog\ndef cryptography_has_keylog() -> typing.List[str]:\n return [\n \"SSL_CTX_set_keylog_callback\",\n \"SSL_CTX_get_keylog_callback\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_raw_key","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_raw_key\ndef cryptography_has_raw_key() -> typing.List[str]:\n return [\n \"EVP_PKEY_new_raw_private_key\",\n \"EVP_PKEY_new_raw_public_key\",\n \"EVP_PKEY_get_raw_private_key\",\n \"EVP_PKEY_get_raw_public_key\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_engine","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_engine\ndef cryptography_has_engine() -> typing.List[str]:\n return [\n \"ENGINE_by_id\",\n \"ENGINE_init\",\n \"ENGINE_finish\",\n \"ENGINE_get_default_RAND\",\n \"ENGINE_set_default_RAND\",\n \"ENGINE_unregister_RAND\",\n \"ENGINE_ctrl_cmd\",\n \"ENGINE_free\",\n \"ENGINE_get_name\",\n \"Cryptography_add_osrandom_engine\",\n \"ENGINE_ctrl_cmd_string\",\n \"ENGINE_load_builtin_engines\",\n \"ENGINE_load_private_key\",\n \"ENGINE_load_public_key\",\n \"SSL_CTX_set_client_cert_engine\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_verified_chain","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_verified_chain\ndef cryptography_has_verified_chain() -> typing.List[str]:\n return [\n \"SSL_get0_verified_chain\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_srtp","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_srtp\ndef cryptography_has_srtp() -> typing.List[str]:\n return [\n \"SSL_CTX_set_tlsext_use_srtp\",\n \"SSL_set_tlsext_use_srtp\",\n \"SSL_get_selected_srtp_profile\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_providers","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_providers\ndef cryptography_has_providers() -> typing.List[str]:\n return [\n \"OSSL_PROVIDER_load\",\n \"OSSL_PROVIDER_unload\",\n \"ERR_LIB_PROV\",\n \"PROV_R_WRONG_FINAL_BLOCK_LENGTH\",\n \"PROV_R_BAD_DECRYPT\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_op_no_renegotiation","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_op_no_renegotiation\ndef cryptography_has_op_no_renegotiation() -> typing.List[str]:\n return [\n \"SSL_OP_NO_RENEGOTIATION\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_dtls_get_data_mtu","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_dtls_get_data_mtu\ndef cryptography_has_dtls_get_data_mtu() -> typing.List[str]:\n return [\n \"DTLS_get_data_mtu\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_300_fips","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_300_fips\ndef cryptography_has_300_fips() -> typing.List[str]:\n return [\n \"EVP_default_properties_is_fips_enabled\",\n \"EVP_default_properties_enable_fips\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_cookie","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_cookie\ndef cryptography_has_ssl_cookie() -> typing.List[str]:\n return [\n \"SSL_OP_COOKIE_EXCHANGE\",\n \"DTLSv1_listen\",\n \"SSL_CTX_set_cookie_generate_cb\",\n \"SSL_CTX_set_cookie_verify_cb\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_pkcs7_funcs","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_pkcs7_funcs\ndef cryptography_has_pkcs7_funcs() -> typing.List[str]:\n return [\n \"SMIME_write_PKCS7\",\n \"PEM_write_bio_PKCS7_stream\",\n \"PKCS7_sign_add_signer\",\n \"PKCS7_final\",\n \"PKCS7_verify\",\n \"SMIME_read_PKCS7\",\n \"PKCS7_get0_signers\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_bn_flags","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_bn_flags\ndef cryptography_has_bn_flags() -> typing.List[str]:\n return [\n \"BN_FLG_CONSTTIME\",\n \"BN_set_flags\",\n \"BN_prime_checks_for_size\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_pkey_dh","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_pkey_dh\ndef cryptography_has_evp_pkey_dh() -> typing.List[str]:\n return [\n \"EVP_PKEY_set1_DH\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_300_evp_cipher","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_300_evp_cipher\ndef cryptography_has_300_evp_cipher() -> typing.List[str]:\n return [\"EVP_CIPHER_fetch\", \"EVP_CIPHER_free\"]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_unexpected_eof_while_reading","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_unexpected_eof_while_reading\ndef cryptography_has_unexpected_eof_while_reading() -> typing.List[str]:\n return [\"SSL_R_UNEXPECTED_EOF_WHILE_READING\"]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_pkcs12_set_mac","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_pkcs12_set_mac\ndef cryptography_has_pkcs12_set_mac() -> typing.List[str]:\n return [\"PKCS12_set_mac\"]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_op_ignore_unexpected_eof","text":"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_op_ignore_unexpected_eof\ndef cryptography_has_ssl_op_ignore_unexpected_eof() -> typing.List[str]:\n return [\n \"SSL_OP_IGNORE_UNEXPECTED_EOF\",\n ]","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.__init__","text":"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.__init__\n def __init__(self, code: int, lib: int, reason: int):\n self._code = code\n self._lib = lib\n self._reason = reason","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError._lib_reason_match","text":"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError._lib_reason_match\n def _lib_reason_match(self, lib: int, reason: int) -> bool:\n return lib == self.lib and reason == self.reason","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.code","text":"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.code\n @property\n def code(self) -> int:\n return self._code","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.lib","text":"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.lib\n @property\n def lib(self) -> int:\n return self._lib","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.reason","text":"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.reason\n @property\n def reason(self) -> int:\n return self._reason","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::_consume_errors","text":"src/cryptography/hazmat/bindings/openssl/binding.py::_consume_errors\ndef _consume_errors(lib) -> typing.List[_OpenSSLError]:\n errors = []\n while True:\n code: int = lib.ERR_get_error()\n if code == 0:\n break\n\n err_lib: int = lib.ERR_GET_LIB(code)\n err_reason: int = lib.ERR_GET_REASON(code)\n\n errors.append(_OpenSSLError(code, err_lib, err_reason))\n\n return errors","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::_errors_with_text","text":"src/cryptography/hazmat/bindings/openssl/binding.py::_errors_with_text\ndef _errors_with_text(\n errors: typing.List[_OpenSSLError],\n) -> typing.List[_OpenSSLErrorWithText]:\n errors_with_text = []\n for err in errors:\n buf = ffi.new(\"char[]\", 256)\n lib.ERR_error_string_n(err.code, buf, len(buf))\n err_text_reason: bytes = ffi.string(buf)\n\n errors_with_text.append(\n _OpenSSLErrorWithText(\n err.code, err.lib, err.reason, err_text_reason\n )\n )\n\n return errors_with_text","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::_consume_errors_with_text","text":"src/cryptography/hazmat/bindings/openssl/binding.py::_consume_errors_with_text\ndef _consume_errors_with_text(lib):\n return _errors_with_text(_consume_errors(lib))","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::_openssl_assert","text":"src/cryptography/hazmat/bindings/openssl/binding.py::_openssl_assert\ndef _openssl_assert(\n lib, ok: bool, errors: typing.Optional[typing.List[_OpenSSLError]] = None\n) -> None:\n if not ok:\n if errors is None:\n errors = _consume_errors(lib)\n errors_with_text = _errors_with_text(errors)\n\n raise InternalError(\n \"Unknown OpenSSL error. This error is commonly encountered when \"\n \"another library is not cleaning up the OpenSSL error stack. If \"\n \"you are using cryptography with another library that uses \"\n \"OpenSSL try disabling it before reporting a bug. Otherwise \"\n \"please file an issue at https://github.com/pyca/cryptography/\"\n \"issues with information on how to reproduce \"\n \"this. ({0!r})\".format(errors_with_text),\n errors_with_text,\n )","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::_legacy_provider_error","text":"src/cryptography/hazmat/bindings/openssl/binding.py::_legacy_provider_error\ndef _legacy_provider_error(loaded: bool) -> None:\n if not loaded:\n raise RuntimeError(\n \"OpenSSL 3.0's legacy provider failed to load. This is a fatal \"\n \"error by default, but cryptography supports running without \"\n \"legacy algorithms by setting the environment variable \"\n \"CRYPTOGRAPHY_OPENSSL_NO_LEGACY. If you did not expect this error,\"\n \" you have likely made a mistake with your OpenSSL configuration.\"\n )","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::build_conditional_library","text":"src/cryptography/hazmat/bindings/openssl/binding.py::build_conditional_library\ndef build_conditional_library(\n lib: typing.Any,\n conditional_names: typing.Dict[str, typing.Callable[[], typing.List[str]]],\n) -> typing.Any:\n conditional_lib = types.ModuleType(\"lib\")\n conditional_lib._original_lib = lib # type: ignore[attr-defined]\n excluded_names = set()\n for condition, names_cb in conditional_names.items():\n if not getattr(lib, condition):\n excluded_names.update(names_cb())\n\n for attr in dir(lib):\n if attr not in excluded_names:\n setattr(conditional_lib, attr, getattr(lib, attr))\n\n return conditional_lib","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::Binding.__init__","text":"src/cryptography/hazmat/bindings/openssl/binding.py::Binding.__init__\n def __init__(self) -> None:\n self._ensure_ffi_initialized()","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::Binding._enable_fips","text":"src/cryptography/hazmat/bindings/openssl/binding.py::Binding._enable_fips\n def _enable_fips(self) -> None:\n # This function enables FIPS mode for OpenSSL 3.0.0 on installs that\n # have the FIPS provider installed properly.\n _openssl_assert(self.lib, self.lib.CRYPTOGRAPHY_OPENSSL_300_OR_GREATER)\n self._base_provider = self.lib.OSSL_PROVIDER_load(\n self.ffi.NULL, b\"base\"\n )\n _openssl_assert(self.lib, self._base_provider != self.ffi.NULL)\n self.lib._fips_provider = self.lib.OSSL_PROVIDER_load(\n self.ffi.NULL, b\"fips\"\n )\n _openssl_assert(self.lib, self.lib._fips_provider != self.ffi.NULL)\n\n res = self.lib.EVP_default_properties_enable_fips(self.ffi.NULL, 1)\n _openssl_assert(self.lib, res == 1)","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::Binding._register_osrandom_engine","text":"src/cryptography/hazmat/bindings/openssl/binding.py::Binding._register_osrandom_engine\n @classmethod\n def _register_osrandom_engine(cls) -> None:\n # Clear any errors extant in the queue before we start. In many\n # scenarios other things may be interacting with OpenSSL in the same\n # process space and it has proven untenable to assume that they will\n # reliably clear the error queue. Once we clear it here we will\n # error on any subsequent unexpected item in the stack.\n cls.lib.ERR_clear_error()\n if cls.lib.CRYPTOGRAPHY_NEEDS_OSRANDOM_ENGINE:\n result = cls.lib.Cryptography_add_osrandom_engine()\n _openssl_assert(cls.lib, result in (1, 2))","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::Binding._ensure_ffi_initialized","text":"src/cryptography/hazmat/bindings/openssl/binding.py::Binding._ensure_ffi_initialized\n @classmethod\n def _ensure_ffi_initialized(cls) -> None:\n with cls._init_lock:\n if not cls._lib_loaded:\n cls.lib = build_conditional_library(lib, CONDITIONAL_NAMES)\n cls._lib_loaded = True\n cls._register_osrandom_engine()\n # As of OpenSSL 3.0.0 we must register a legacy cipher provider\n # to get RC2 (needed for junk asymmetric private key\n # serialization), RC4, Blowfish, IDEA, SEED, etc. These things\n # are ugly legacy, but we aren't going to get rid of them\n # any time soon.\n if cls.lib.CRYPTOGRAPHY_OPENSSL_300_OR_GREATER:\n if not os.environ.get(\"CRYPTOGRAPHY_OPENSSL_NO_LEGACY\"):\n cls._legacy_provider = cls.lib.OSSL_PROVIDER_load(\n cls.ffi.NULL, b\"legacy\"\n )\n cls._legacy_provider_loaded = (\n cls._legacy_provider != cls.ffi.NULL\n )\n _legacy_provider_error(cls._legacy_provider_loaded)\n\n cls._default_provider = cls.lib.OSSL_PROVIDER_load(\n cls.ffi.NULL, b\"default\"\n )\n _openssl_assert(\n cls.lib, cls._default_provider != cls.ffi.NULL\n )","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::Binding.init_static_locks","text":"src/cryptography/hazmat/bindings/openssl/binding.py::Binding.init_static_locks\n @classmethod\n def init_static_locks(cls) -> None:\n cls._ensure_ffi_initialized()","label":0},{"cid":"src/cryptography/hazmat/bindings/openssl/binding.py::_verify_package_version","text":"src/cryptography/hazmat/bindings/openssl/binding.py::_verify_package_version\ndef _verify_package_version(version: str) -> None:\n # Occasionally we run into situations where the version of the Python\n # package does not match the version of the shared object that is loaded.\n # This may occur in environments where multiple versions of cryptography\n # are installed and available in the python path. To avoid errors cropping\n # up later this code checks that the currently imported package and the\n # shared object that were loaded have the same version and raise an\n # ImportError if they do not\n so_package_version = ffi.string(lib.CRYPTOGRAPHY_PACKAGE_VERSION)\n if version.encode(\"ascii\") != so_package_version:\n raise ImportError(\n \"The version of cryptography does not match the loaded \"\n \"shared object. This can happen if you have multiple copies of \"\n \"cryptography installed in your Python path. Please try creating \"\n \"a new virtual environment to resolve this issue. \"\n \"Loaded python version: {}, shared object version: {}\".format(\n version, so_package_version\n )\n )","label":0},{"cid":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.version","text":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.version\n @abc.abstractproperty\n def version(self) -> Version:\n \"\"\"\n Returns the SCT version.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.log_id","text":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.log_id\n @abc.abstractproperty\n def log_id(self) -> bytes:\n \"\"\"\n Returns an identifier indicating which log this SCT is for.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.timestamp","text":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.timestamp\n @abc.abstractproperty\n def timestamp(self) -> datetime.datetime:\n \"\"\"\n Returns the timestamp for this SCT.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.entry_type","text":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.entry_type\n @abc.abstractproperty\n def entry_type(self) -> LogEntryType:\n \"\"\"\n Returns whether this is an SCT for a certificate or pre-certificate.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.signature_hash_algorithm","text":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.signature_hash_algorithm\n @abc.abstractproperty\n def signature_hash_algorithm(self) -> HashAlgorithm:\n \"\"\"\n Returns the hash algorithm used for the SCT's signature.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.signature_algorithm","text":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.signature_algorithm\n @abc.abstractproperty\n def signature_algorithm(self) -> SignatureAlgorithm:\n \"\"\"\n Returns the signing algorithm used for the SCT's signature.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.signature","text":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.signature\n @abc.abstractproperty\n def signature(self) -> bytes:\n \"\"\"\n Returns the signature for this SCT.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.extension_bytes","text":"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.extension_bytes\n @abc.abstractproperty\n def extension_bytes(self) -> bytes:\n \"\"\"\n Returns the raw bytes of any extensions for this SCT.\n \"\"\"","label":0},{"cid":"src/cryptography/x509/extensions.py::_key_identifier_from_public_key","text":"src/cryptography/x509/extensions.py::_key_identifier_from_public_key\ndef _key_identifier_from_public_key(\n public_key: CERTIFICATE_PUBLIC_KEY_TYPES,\n) -> bytes:\n if isinstance(public_key, RSAPublicKey):\n data = public_key.public_bytes(\n serialization.Encoding.DER,\n serialization.PublicFormat.PKCS1,\n )\n elif isinstance(public_key, EllipticCurvePublicKey):\n data = public_key.public_bytes(\n serialization.Encoding.X962,\n serialization.PublicFormat.UncompressedPoint,\n )\n else:\n # This is a very slow way to do this.\n serialized = public_key.public_bytes(\n serialization.Encoding.DER,\n serialization.PublicFormat.SubjectPublicKeyInfo,\n )\n data = asn1.parse_spki_for_data(serialized)\n\n return hashlib.sha1(data).digest()","label":0},{"cid":"src/cryptography/x509/extensions.py::_make_sequence_methods","text":"src/cryptography/x509/extensions.py::_make_sequence_methods\ndef _make_sequence_methods(field_name: str):\n def len_method(self) -> int:\n return len(getattr(self, field_name))\n\n def iter_method(self):\n return iter(getattr(self, field_name))\n\n def getitem_method(self, idx):\n return getattr(self, field_name)[idx]\n\n return len_method, iter_method, getitem_method","label":0},{"cid":"src/cryptography/x509/extensions.py::_make_sequence_methods.len_method","text":"src/cryptography/x509/extensions.py::_make_sequence_methods.len_method\n def len_method(self) -> int:\n return len(getattr(self, field_name))","label":0},{"cid":"src/cryptography/x509/extensions.py::_make_sequence_methods.iter_method","text":"src/cryptography/x509/extensions.py::_make_sequence_methods.iter_method\n def iter_method(self):\n return iter(getattr(self, field_name))","label":0},{"cid":"src/cryptography/x509/extensions.py::_make_sequence_methods.getitem_method","text":"src/cryptography/x509/extensions.py::_make_sequence_methods.getitem_method\n def getitem_method(self, idx):\n return getattr(self, field_name)[idx]","label":0},{"cid":"src/cryptography/x509/extensions.py::DuplicateExtension.__init__","text":"src/cryptography/x509/extensions.py::DuplicateExtension.__init__\n def __init__(self, msg: str, oid: ObjectIdentifier) -> None:\n super(DuplicateExtension, self).__init__(msg)\n self.oid = oid","label":0},{"cid":"src/cryptography/x509/extensions.py::ExtensionNotFound.__init__","text":"src/cryptography/x509/extensions.py::ExtensionNotFound.__init__\n def __init__(self, msg: str, oid: ObjectIdentifier) -> None:\n super(ExtensionNotFound, self).__init__(msg)\n self.oid = oid","label":0},{"cid":"src/cryptography/x509/extensions.py::ExtensionType.public_bytes","text":"src/cryptography/x509/extensions.py::ExtensionType.public_bytes\n def public_bytes(self) -> bytes:\n \"\"\"\n Serializes the extension type to DER.\n \"\"\"\n raise NotImplementedError(\n \"public_bytes is not implemented for extension type {0!r}\".format(\n self\n )\n )","label":0},{"cid":"src/cryptography/x509/extensions.py::Extensions.__init__","text":"src/cryptography/x509/extensions.py::Extensions.__init__\n def __init__(\n self, extensions: typing.Iterable[\"Extension[ExtensionType]\"]\n ) -> None:\n self._extensions = list(extensions)","label":0},{"cid":"src/cryptography/x509/extensions.py::Extensions.get_extension_for_oid","text":"src/cryptography/x509/extensions.py::Extensions.get_extension_for_oid\n def get_extension_for_oid(\n self, oid: ObjectIdentifier\n ) -> \"Extension[ExtensionType]\":\n for ext in self:\n if ext.oid == oid:\n return ext\n\n raise ExtensionNotFound(\"No {} extension was found\".format(oid), oid)","label":0},{"cid":"src/cryptography/x509/extensions.py::Extensions.get_extension_for_class","text":"src/cryptography/x509/extensions.py::Extensions.get_extension_for_class\n def get_extension_for_class(\n self, extclass: typing.Type[ExtensionTypeVar]\n ) -> \"Extension[ExtensionTypeVar]\":\n if extclass is UnrecognizedExtension:\n raise TypeError(\n \"UnrecognizedExtension can't be used with \"\n \"get_extension_for_class because more than one instance of the\"\n \" class may be present.\"\n )\n\n for ext in self:\n if isinstance(ext.value, extclass):\n return ext\n\n raise ExtensionNotFound(\n \"No {} extension was found\".format(extclass), extclass.oid\n )","label":0},{"cid":"src/cryptography/x509/extensions.py::Extensions.__repr__","text":"src/cryptography/x509/extensions.py::Extensions.__repr__\n def __repr__(self) -> str:\n return \"<Extensions({})>\".format(self._extensions)","label":0},{"cid":"src/cryptography/x509/extensions.py::CRLNumber.__init__","text":"src/cryptography/x509/extensions.py::CRLNumber.__init__\n def __init__(self, crl_number: int) -> None:\n if not isinstance(crl_number, int):\n raise TypeError(\"crl_number must be an integer\")\n\n self._crl_number = crl_number","label":0},{"cid":"src/cryptography/x509/extensions.py::CRLNumber.__eq__","text":"src/cryptography/x509/extensions.py::CRLNumber.__eq__\n def __eq__(self, other: object) -> bool:\n if not isinstance(other, CRLNumber):\n return NotImplemented\n\n return self.crl_number == other.crl_number","label":0},{"cid":"src/cryptography/x509/extensions.py::CRLNumber.__hash__","text":"src/cryptography/x509/extensions.py::CRLNumber.__hash__\n def __hash__(self) -> int:\n return hash(self.crl_number)","label":0},{"cid":"src/cryptography/x509/extensions.py::CRLNumber.__repr__","text":"src/cryptography/x509/extensions.py::CRLNumber.__repr__\n def __repr__(self) -> str:\n return \"<CRLNumber({})>\".format(self.crl_number)","label":0},{"cid":"src/cryptography/x509/extensions.py::CRLNumber.crl_number","text":"src/cryptography/x509/extensions.py::CRLNumber.crl_number\n @property\n def crl_number(self) -> int:\n return self._crl_number","label":0},{"cid":"src/cryptography/x509/extensions.py::CRLNumber.public_bytes","text":"src/cryptography/x509/extensions.py::CRLNumber.public_bytes\n def public_bytes(self) -> bytes:\n return rust_x509.encode_extension_value(self)","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__init__","text":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__init__\n def __init__(\n self,\n key_identifier: typing.Optional[bytes],\n authority_cert_issuer: typing.Optional[typing.Iterable[GeneralName]],\n authority_cert_serial_number: typing.Optional[int],\n ) -> None:\n if (authority_cert_issuer is None) != (\n authority_cert_serial_number is None\n ):\n raise ValueError(\n \"authority_cert_issuer and authority_cert_serial_number \"\n \"must both be present or both None\"\n )\n\n if authority_cert_issuer is not None:\n authority_cert_issuer = list(authority_cert_issuer)\n if not all(\n isinstance(x, GeneralName) for x in authority_cert_issuer\n ):\n raise TypeError(\n \"authority_cert_issuer must be a list of GeneralName \"\n \"objects\"\n )\n\n if authority_cert_serial_number is not None and not isinstance(\n authority_cert_serial_number, int\n ):\n raise TypeError(\"authority_cert_serial_number must be an integer\")\n\n self._key_identifier = key_identifier\n self._authority_cert_issuer = authority_cert_issuer\n self._authority_cert_serial_number = authority_cert_serial_number","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.from_issuer_public_key","text":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.from_issuer_public_key\n @classmethod\n def from_issuer_public_key(\n cls, public_key: CERTIFICATE_ISSUER_PUBLIC_KEY_TYPES\n ) -> \"AuthorityKeyIdentifier\":\n digest = _key_identifier_from_public_key(public_key)\n return cls(\n key_identifier=digest,\n authority_cert_issuer=None,\n authority_cert_serial_number=None,\n )","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.from_issuer_subject_key_identifier","text":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.from_issuer_subject_key_identifier\n @classmethod\n def from_issuer_subject_key_identifier(\n cls, ski: \"SubjectKeyIdentifier\"\n ) -> \"AuthorityKeyIdentifier\":\n return cls(\n key_identifier=ski.digest,\n authority_cert_issuer=None,\n authority_cert_serial_number=None,\n )","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__repr__","text":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__repr__\n def __repr__(self) -> str:\n return (\n \"<AuthorityKeyIdentifier(key_identifier={0.key_identifier!r}, \"\n \"authority_cert_issuer={0.authority_cert_issuer}, \"\n \"authority_cert_serial_number={0.authority_cert_serial_number}\"\n \")>\".format(self)\n )","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__eq__","text":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__eq__\n def __eq__(self, other: object) -> bool:\n if not isinstance(other, AuthorityKeyIdentifier):\n return NotImplemented\n\n return (\n self.key_identifier == other.key_identifier\n and self.authority_cert_issuer == other.authority_cert_issuer\n and self.authority_cert_serial_number\n == other.authority_cert_serial_number\n )","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__hash__","text":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__hash__\n def __hash__(self) -> int:\n if self.authority_cert_issuer is None:\n aci = None\n else:\n aci = tuple(self.authority_cert_issuer)\n return hash(\n (self.key_identifier, aci, self.authority_cert_serial_number)\n )","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.key_identifier","text":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.key_identifier\n @property\n def key_identifier(self) -> typing.Optional[bytes]:\n return self._key_identifier","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.authority_cert_issuer","text":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.authority_cert_issuer\n @property\n def authority_cert_issuer(\n self,\n ) -> typing.Optional[typing.List[GeneralName]]:\n return self._authority_cert_issuer","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.authority_cert_serial_number","text":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.authority_cert_serial_number\n @property\n def authority_cert_serial_number(self) -> typing.Optional[int]:\n return self._authority_cert_serial_number","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.public_bytes","text":"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.public_bytes\n def public_bytes(self) -> bytes:\n return rust_x509.encode_extension_value(self)","label":0},{"cid":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__init__","text":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__init__\n def __init__(self, digest: bytes) -> None:\n self._digest = digest","label":0},{"cid":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.from_public_key","text":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.from_public_key\n @classmethod\n def from_public_key(\n cls, public_key: CERTIFICATE_PUBLIC_KEY_TYPES\n ) -> \"SubjectKeyIdentifier\":\n return cls(_key_identifier_from_public_key(public_key))","label":0},{"cid":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.digest","text":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.digest\n @property\n def digest(self) -> bytes:\n return self._digest","label":0},{"cid":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.key_identifier","text":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.key_identifier\n @property\n def key_identifier(self) -> bytes:\n return self._digest","label":0},{"cid":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__repr__","text":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__repr__\n def __repr__(self) -> str:\n return \"<SubjectKeyIdentifier(digest={0!r})>\".format(self.digest)","label":0},{"cid":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__eq__","text":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__eq__\n def __eq__(self, other: object) -> bool:\n if not isinstance(other, SubjectKeyIdentifier):\n return NotImplemented\n\n return constant_time.bytes_eq(self.digest, other.digest)","label":0},{"cid":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__hash__","text":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__hash__\n def __hash__(self) -> int:\n return hash(self.digest)","label":0},{"cid":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.public_bytes","text":"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.public_bytes\n def public_bytes(self) -> bytes:\n return rust_x509.encode_extension_value(self)","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__init__","text":"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__init__\n def __init__(\n self, descriptions: typing.Iterable[\"AccessDescription\"]\n ) -> None:\n descriptions = list(descriptions)\n if not all(isinstance(x, AccessDescription) for x in descriptions):\n raise TypeError(\n \"Every item in the descriptions list must be an \"\n \"AccessDescription\"\n )\n\n self._descriptions = descriptions","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__repr__","text":"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__repr__\n def __repr__(self) -> str:\n return \"<AuthorityInformationAccess({})>\".format(self._descriptions)","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__eq__","text":"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__eq__\n def __eq__(self, other: object) -> bool:\n if not isinstance(other, AuthorityInformationAccess):\n return NotImplemented\n\n return self._descriptions == other._descriptions","label":0},{"cid":"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__hash__","text":"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__hash__\n def __hash__(self) -> int:\n return hash(tuple(self._descriptions))","label":0}],"string":"[\n {\n \"cid\": \"docs/_ext/cryptography-docs.py::HazmatDirective.run\",\n \"text\": \"docs/_ext/cryptography-docs.py::HazmatDirective.run\\n def run(self):\\n message = DANGER_MESSAGE\\n if self.content:\\n message += DANGER_ALTERNATE.format(alternate=self.content[0])\\n\\n content = nodes.paragraph(\\\"\\\", message)\\n admonition_node = Hazmat(\\\"\\\\n\\\".join(content))\\n self.state.nested_parse(content, self.content_offset, admonition_node)\\n admonition_node.line = self.lineno\\n return [admonition_node]\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/_ext/cryptography-docs.py::html_visit_hazmat_node\",\n \"text\": \"docs/_ext/cryptography-docs.py::html_visit_hazmat_node\\ndef html_visit_hazmat_node(self, node):\\n return self.visit_admonition(node, \\\"danger\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/_ext/cryptography-docs.py::latex_visit_hazmat_node\",\n \"text\": \"docs/_ext/cryptography-docs.py::latex_visit_hazmat_node\\ndef latex_visit_hazmat_node(self, node):\\n return self.visit_admonition(node)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/_ext/cryptography-docs.py::depart_hazmat_node\",\n \"text\": \"docs/_ext/cryptography-docs.py::depart_hazmat_node\\ndef depart_hazmat_node(self, node):\\n return self.depart_admonition(node)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/_ext/cryptography-docs.py::setup\",\n \"text\": \"docs/_ext/cryptography-docs.py::setup\\ndef setup(app):\\n app.add_node(\\n Hazmat,\\n html=(html_visit_hazmat_node, depart_hazmat_node),\\n latex=(latex_visit_hazmat_node, depart_hazmat_node),\\n )\\n app.add_directive(\\\"hazmat\\\", HazmatDirective)\\n\\n return {\\n \\\"parallel_read_safe\\\": True,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"src/_cffi_src/build_openssl.py::_get_openssl_libraries\",\n \"text\": \"src/_cffi_src/build_openssl.py::_get_openssl_libraries\\ndef _get_openssl_libraries(platform):\\n if os.environ.get(\\\"CRYPTOGRAPHY_SUPPRESS_LINK_FLAGS\\\", None):\\n return []\\n # OpenSSL goes by a different library name on different operating systems.\\n if platform == \\\"win32\\\" and compiler_type() == \\\"msvc\\\":\\n return [\\n \\\"libssl\\\",\\n \\\"libcrypto\\\",\\n \\\"advapi32\\\",\\n \\\"crypt32\\\",\\n \\\"gdi32\\\",\\n \\\"user32\\\",\\n \\\"ws2_32\\\",\\n ]\\n else:\\n # darwin, linux, mingw all use this path\\n # In some circumstances, the order in which these libs are\\n # specified on the linker command-line is significant;\\n # libssl must come before libcrypto\\n # (https://marc.info/?l=openssl-users&m=135361825921871)\\n # -lpthread required due to usage of pthread an potential\\n # existance of a static part containing e.g. pthread_atfork\\n # (https://github.com/pyca/cryptography/issues/5084)\\n if sys.platform == \\\"zos\\\":\\n return [\\\"ssl\\\", \\\"crypto\\\"]\\n else:\\n return [\\\"ssl\\\", \\\"crypto\\\", \\\"pthread\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/_cffi_src/build_openssl.py::_extra_compile_args\",\n \"text\": \"src/_cffi_src/build_openssl.py::_extra_compile_args\\ndef _extra_compile_args(platform):\\n \\\"\\\"\\\"\\n We set -Wconversion args here so that we only do Wconversion checks on the\\n code we're compiling and not on cffi itself (as passing -Wconversion in\\n CFLAGS would do). We set no error on sign conversion because some\\n function signatures in LibreSSL differ from OpenSSL have changed on long\\n vs. unsigned long in the past. Since that isn't a precision issue we don't\\n care.\\n \\\"\\\"\\\"\\n # make sure the compiler used supports the flags to be added\\n is_gcc = False\\n if get_default_compiler() == \\\"unix\\\":\\n d = dist.Distribution()\\n cmd = config(d)\\n cmd._check_compiler()\\n is_gcc = (\\n \\\"gcc\\\" in cmd.compiler.compiler[0]\\n or \\\"clang\\\" in cmd.compiler.compiler[0]\\n )\\n if is_gcc or not (\\n platform in [\\\"win32\\\", \\\"hp-ux11\\\", \\\"sunos5\\\"]\\n or platform.startswith(\\\"aix\\\")\\n ):\\n return [\\\"-Wconversion\\\", \\\"-Wno-error=sign-conversion\\\"]\\n else:\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ec2m\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ec2m\\ndef cryptography_has_ec2m() -> typing.List[str]:\\n return [\\n \\\"EC_POINT_get_affine_coordinates_GF2m\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl3_method\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl3_method\\ndef cryptography_has_ssl3_method() -> typing.List[str]:\\n return [\\n \\\"SSLv3_method\\\",\\n \\\"SSLv3_client_method\\\",\\n \\\"SSLv3_server_method\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_set_cert_cb\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_set_cert_cb\\ndef cryptography_has_set_cert_cb() -> typing.List[str]:\\n return [\\n \\\"SSL_CTX_set_cert_cb\\\",\\n \\\"SSL_set_cert_cb\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_st\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_st\\ndef cryptography_has_ssl_st() -> typing.List[str]:\\n return [\\n \\\"SSL_ST_BEFORE\\\",\\n \\\"SSL_ST_OK\\\",\\n \\\"SSL_ST_INIT\\\",\\n \\\"SSL_ST_RENEGOTIATE\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_tls_st\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_tls_st\\ndef cryptography_has_tls_st() -> typing.List[str]:\\n return [\\n \\\"TLS_ST_BEFORE\\\",\\n \\\"TLS_ST_OK\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_scrypt\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_scrypt\\ndef cryptography_has_scrypt() -> typing.List[str]:\\n return [\\n \\\"EVP_PBE_scrypt\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_pkey_dhx\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_pkey_dhx\\ndef cryptography_has_evp_pkey_dhx() -> typing.List[str]:\\n return [\\n \\\"EVP_PKEY_DHX\\\",\\n \\\"d2i_DHxparams_bio\\\",\\n \\\"i2d_DHxparams_bio\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_mem_functions\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_mem_functions\\ndef cryptography_has_mem_functions() -> typing.List[str]:\\n return [\\n \\\"Cryptography_CRYPTO_set_mem_functions\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_x509_store_ctx_get_issuer\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_x509_store_ctx_get_issuer\\ndef cryptography_has_x509_store_ctx_get_issuer() -> typing.List[str]:\\n return [\\n \\\"X509_STORE_get_get_issuer\\\",\\n \\\"X509_STORE_set_get_issuer\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ed448\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ed448\\ndef cryptography_has_ed448() -> typing.List[str]:\\n return [\\n \\\"EVP_PKEY_ED448\\\",\\n \\\"NID_ED448\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ed25519\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ed25519\\ndef cryptography_has_ed25519() -> typing.List[str]:\\n return [\\n \\\"NID_ED25519\\\",\\n \\\"EVP_PKEY_ED25519\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_poly1305\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_poly1305\\ndef cryptography_has_poly1305() -> typing.List[str]:\\n return [\\n \\\"NID_poly1305\\\",\\n \\\"EVP_PKEY_POLY1305\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_digestfinal_xof\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_digestfinal_xof\\ndef cryptography_has_evp_digestfinal_xof() -> typing.List[str]:\\n return [\\n \\\"EVP_DigestFinalXOF\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_pkey_get_set_tls_encodedpoint\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_pkey_get_set_tls_encodedpoint\\ndef cryptography_has_evp_pkey_get_set_tls_encodedpoint() -> typing.List[str]:\\n return [\\n \\\"EVP_PKEY_get1_tls_encodedpoint\\\",\\n \\\"EVP_PKEY_set1_tls_encodedpoint\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_fips\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_fips\\ndef cryptography_has_fips() -> typing.List[str]:\\n return [\\n \\\"FIPS_mode_set\\\",\\n \\\"FIPS_mode\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_sigalgs\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_sigalgs\\ndef cryptography_has_ssl_sigalgs() -> typing.List[str]:\\n return [\\n \\\"SSL_CTX_set1_sigalgs_list\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_psk\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_psk\\ndef cryptography_has_psk() -> typing.List[str]:\\n return [\\n \\\"SSL_CTX_use_psk_identity_hint\\\",\\n \\\"SSL_CTX_set_psk_server_callback\\\",\\n \\\"SSL_CTX_set_psk_client_callback\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_psk_tlsv13\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_psk_tlsv13\\ndef cryptography_has_psk_tlsv13() -> typing.List[str]:\\n return [\\n \\\"SSL_CTX_set_psk_find_session_callback\\\",\\n \\\"SSL_CTX_set_psk_use_session_callback\\\",\\n \\\"Cryptography_SSL_SESSION_new\\\",\\n \\\"SSL_CIPHER_find\\\",\\n \\\"SSL_SESSION_set1_master_key\\\",\\n \\\"SSL_SESSION_set_cipher\\\",\\n \\\"SSL_SESSION_set_protocol_version\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_custom_ext\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_custom_ext\\ndef cryptography_has_custom_ext() -> typing.List[str]:\\n return [\\n \\\"SSL_CTX_add_client_custom_ext\\\",\\n \\\"SSL_CTX_add_server_custom_ext\\\",\\n \\\"SSL_extension_supported\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_openssl_cleanup\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_openssl_cleanup\\ndef cryptography_has_openssl_cleanup() -> typing.List[str]:\\n return [\\n \\\"OPENSSL_cleanup\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_tlsv13_functions\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_tlsv13_functions\\ndef cryptography_has_tlsv13_functions() -> typing.List[str]:\\n return [\\n \\\"SSL_VERIFY_POST_HANDSHAKE\\\",\\n \\\"SSL_CTX_set_ciphersuites\\\",\\n \\\"SSL_verify_client_post_handshake\\\",\\n \\\"SSL_CTX_set_post_handshake_auth\\\",\\n \\\"SSL_set_post_handshake_auth\\\",\\n \\\"SSL_SESSION_get_max_early_data\\\",\\n \\\"SSL_write_early_data\\\",\\n \\\"SSL_read_early_data\\\",\\n \\\"SSL_CTX_set_max_early_data\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_keylog\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_keylog\\ndef cryptography_has_keylog() -> typing.List[str]:\\n return [\\n \\\"SSL_CTX_set_keylog_callback\\\",\\n \\\"SSL_CTX_get_keylog_callback\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_raw_key\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_raw_key\\ndef cryptography_has_raw_key() -> typing.List[str]:\\n return [\\n \\\"EVP_PKEY_new_raw_private_key\\\",\\n \\\"EVP_PKEY_new_raw_public_key\\\",\\n \\\"EVP_PKEY_get_raw_private_key\\\",\\n \\\"EVP_PKEY_get_raw_public_key\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_engine\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_engine\\ndef cryptography_has_engine() -> typing.List[str]:\\n return [\\n \\\"ENGINE_by_id\\\",\\n \\\"ENGINE_init\\\",\\n \\\"ENGINE_finish\\\",\\n \\\"ENGINE_get_default_RAND\\\",\\n \\\"ENGINE_set_default_RAND\\\",\\n \\\"ENGINE_unregister_RAND\\\",\\n \\\"ENGINE_ctrl_cmd\\\",\\n \\\"ENGINE_free\\\",\\n \\\"ENGINE_get_name\\\",\\n \\\"Cryptography_add_osrandom_engine\\\",\\n \\\"ENGINE_ctrl_cmd_string\\\",\\n \\\"ENGINE_load_builtin_engines\\\",\\n \\\"ENGINE_load_private_key\\\",\\n \\\"ENGINE_load_public_key\\\",\\n \\\"SSL_CTX_set_client_cert_engine\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_verified_chain\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_verified_chain\\ndef cryptography_has_verified_chain() -> typing.List[str]:\\n return [\\n \\\"SSL_get0_verified_chain\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_srtp\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_srtp\\ndef cryptography_has_srtp() -> typing.List[str]:\\n return [\\n \\\"SSL_CTX_set_tlsext_use_srtp\\\",\\n \\\"SSL_set_tlsext_use_srtp\\\",\\n \\\"SSL_get_selected_srtp_profile\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_providers\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_providers\\ndef cryptography_has_providers() -> typing.List[str]:\\n return [\\n \\\"OSSL_PROVIDER_load\\\",\\n \\\"OSSL_PROVIDER_unload\\\",\\n \\\"ERR_LIB_PROV\\\",\\n \\\"PROV_R_WRONG_FINAL_BLOCK_LENGTH\\\",\\n \\\"PROV_R_BAD_DECRYPT\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_op_no_renegotiation\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_op_no_renegotiation\\ndef cryptography_has_op_no_renegotiation() -> typing.List[str]:\\n return [\\n \\\"SSL_OP_NO_RENEGOTIATION\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_dtls_get_data_mtu\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_dtls_get_data_mtu\\ndef cryptography_has_dtls_get_data_mtu() -> typing.List[str]:\\n return [\\n \\\"DTLS_get_data_mtu\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_300_fips\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_300_fips\\ndef cryptography_has_300_fips() -> typing.List[str]:\\n return [\\n \\\"EVP_default_properties_is_fips_enabled\\\",\\n \\\"EVP_default_properties_enable_fips\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_cookie\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_cookie\\ndef cryptography_has_ssl_cookie() -> typing.List[str]:\\n return [\\n \\\"SSL_OP_COOKIE_EXCHANGE\\\",\\n \\\"DTLSv1_listen\\\",\\n \\\"SSL_CTX_set_cookie_generate_cb\\\",\\n \\\"SSL_CTX_set_cookie_verify_cb\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_pkcs7_funcs\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_pkcs7_funcs\\ndef cryptography_has_pkcs7_funcs() -> typing.List[str]:\\n return [\\n \\\"SMIME_write_PKCS7\\\",\\n \\\"PEM_write_bio_PKCS7_stream\\\",\\n \\\"PKCS7_sign_add_signer\\\",\\n \\\"PKCS7_final\\\",\\n \\\"PKCS7_verify\\\",\\n \\\"SMIME_read_PKCS7\\\",\\n \\\"PKCS7_get0_signers\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_bn_flags\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_bn_flags\\ndef cryptography_has_bn_flags() -> typing.List[str]:\\n return [\\n \\\"BN_FLG_CONSTTIME\\\",\\n \\\"BN_set_flags\\\",\\n \\\"BN_prime_checks_for_size\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_pkey_dh\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_evp_pkey_dh\\ndef cryptography_has_evp_pkey_dh() -> typing.List[str]:\\n return [\\n \\\"EVP_PKEY_set1_DH\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_300_evp_cipher\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_300_evp_cipher\\ndef cryptography_has_300_evp_cipher() -> typing.List[str]:\\n return [\\\"EVP_CIPHER_fetch\\\", \\\"EVP_CIPHER_free\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_unexpected_eof_while_reading\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_unexpected_eof_while_reading\\ndef cryptography_has_unexpected_eof_while_reading() -> typing.List[str]:\\n return [\\\"SSL_R_UNEXPECTED_EOF_WHILE_READING\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_pkcs12_set_mac\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_pkcs12_set_mac\\ndef cryptography_has_pkcs12_set_mac() -> typing.List[str]:\\n return [\\\"PKCS12_set_mac\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_op_ignore_unexpected_eof\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/_conditional.py::cryptography_has_ssl_op_ignore_unexpected_eof\\ndef cryptography_has_ssl_op_ignore_unexpected_eof() -> typing.List[str]:\\n return [\\n \\\"SSL_OP_IGNORE_UNEXPECTED_EOF\\\",\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.__init__\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.__init__\\n def __init__(self, code: int, lib: int, reason: int):\\n self._code = code\\n self._lib = lib\\n self._reason = reason\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError._lib_reason_match\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError._lib_reason_match\\n def _lib_reason_match(self, lib: int, reason: int) -> bool:\\n return lib == self.lib and reason == self.reason\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.code\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.code\\n @property\\n def code(self) -> int:\\n return self._code\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.lib\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.lib\\n @property\\n def lib(self) -> int:\\n return self._lib\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.reason\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_OpenSSLError.reason\\n @property\\n def reason(self) -> int:\\n return self._reason\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_consume_errors\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_consume_errors\\ndef _consume_errors(lib) -> typing.List[_OpenSSLError]:\\n errors = []\\n while True:\\n code: int = lib.ERR_get_error()\\n if code == 0:\\n break\\n\\n err_lib: int = lib.ERR_GET_LIB(code)\\n err_reason: int = lib.ERR_GET_REASON(code)\\n\\n errors.append(_OpenSSLError(code, err_lib, err_reason))\\n\\n return errors\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_errors_with_text\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_errors_with_text\\ndef _errors_with_text(\\n errors: typing.List[_OpenSSLError],\\n) -> typing.List[_OpenSSLErrorWithText]:\\n errors_with_text = []\\n for err in errors:\\n buf = ffi.new(\\\"char[]\\\", 256)\\n lib.ERR_error_string_n(err.code, buf, len(buf))\\n err_text_reason: bytes = ffi.string(buf)\\n\\n errors_with_text.append(\\n _OpenSSLErrorWithText(\\n err.code, err.lib, err.reason, err_text_reason\\n )\\n )\\n\\n return errors_with_text\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_consume_errors_with_text\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_consume_errors_with_text\\ndef _consume_errors_with_text(lib):\\n return _errors_with_text(_consume_errors(lib))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_openssl_assert\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_openssl_assert\\ndef _openssl_assert(\\n lib, ok: bool, errors: typing.Optional[typing.List[_OpenSSLError]] = None\\n) -> None:\\n if not ok:\\n if errors is None:\\n errors = _consume_errors(lib)\\n errors_with_text = _errors_with_text(errors)\\n\\n raise InternalError(\\n \\\"Unknown OpenSSL error. This error is commonly encountered when \\\"\\n \\\"another library is not cleaning up the OpenSSL error stack. If \\\"\\n \\\"you are using cryptography with another library that uses \\\"\\n \\\"OpenSSL try disabling it before reporting a bug. Otherwise \\\"\\n \\\"please file an issue at https://github.com/pyca/cryptography/\\\"\\n \\\"issues with information on how to reproduce \\\"\\n \\\"this. ({0!r})\\\".format(errors_with_text),\\n errors_with_text,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_legacy_provider_error\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_legacy_provider_error\\ndef _legacy_provider_error(loaded: bool) -> None:\\n if not loaded:\\n raise RuntimeError(\\n \\\"OpenSSL 3.0's legacy provider failed to load. This is a fatal \\\"\\n \\\"error by default, but cryptography supports running without \\\"\\n \\\"legacy algorithms by setting the environment variable \\\"\\n \\\"CRYPTOGRAPHY_OPENSSL_NO_LEGACY. If you did not expect this error,\\\"\\n \\\" you have likely made a mistake with your OpenSSL configuration.\\\"\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::build_conditional_library\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::build_conditional_library\\ndef build_conditional_library(\\n lib: typing.Any,\\n conditional_names: typing.Dict[str, typing.Callable[[], typing.List[str]]],\\n) -> typing.Any:\\n conditional_lib = types.ModuleType(\\\"lib\\\")\\n conditional_lib._original_lib = lib # type: ignore[attr-defined]\\n excluded_names = set()\\n for condition, names_cb in conditional_names.items():\\n if not getattr(lib, condition):\\n excluded_names.update(names_cb())\\n\\n for attr in dir(lib):\\n if attr not in excluded_names:\\n setattr(conditional_lib, attr, getattr(lib, attr))\\n\\n return conditional_lib\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::Binding.__init__\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::Binding.__init__\\n def __init__(self) -> None:\\n self._ensure_ffi_initialized()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::Binding._enable_fips\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::Binding._enable_fips\\n def _enable_fips(self) -> None:\\n # This function enables FIPS mode for OpenSSL 3.0.0 on installs that\\n # have the FIPS provider installed properly.\\n _openssl_assert(self.lib, self.lib.CRYPTOGRAPHY_OPENSSL_300_OR_GREATER)\\n self._base_provider = self.lib.OSSL_PROVIDER_load(\\n self.ffi.NULL, b\\\"base\\\"\\n )\\n _openssl_assert(self.lib, self._base_provider != self.ffi.NULL)\\n self.lib._fips_provider = self.lib.OSSL_PROVIDER_load(\\n self.ffi.NULL, b\\\"fips\\\"\\n )\\n _openssl_assert(self.lib, self.lib._fips_provider != self.ffi.NULL)\\n\\n res = self.lib.EVP_default_properties_enable_fips(self.ffi.NULL, 1)\\n _openssl_assert(self.lib, res == 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::Binding._register_osrandom_engine\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::Binding._register_osrandom_engine\\n @classmethod\\n def _register_osrandom_engine(cls) -> None:\\n # Clear any errors extant in the queue before we start. In many\\n # scenarios other things may be interacting with OpenSSL in the same\\n # process space and it has proven untenable to assume that they will\\n # reliably clear the error queue. Once we clear it here we will\\n # error on any subsequent unexpected item in the stack.\\n cls.lib.ERR_clear_error()\\n if cls.lib.CRYPTOGRAPHY_NEEDS_OSRANDOM_ENGINE:\\n result = cls.lib.Cryptography_add_osrandom_engine()\\n _openssl_assert(cls.lib, result in (1, 2))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::Binding._ensure_ffi_initialized\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::Binding._ensure_ffi_initialized\\n @classmethod\\n def _ensure_ffi_initialized(cls) -> None:\\n with cls._init_lock:\\n if not cls._lib_loaded:\\n cls.lib = build_conditional_library(lib, CONDITIONAL_NAMES)\\n cls._lib_loaded = True\\n cls._register_osrandom_engine()\\n # As of OpenSSL 3.0.0 we must register a legacy cipher provider\\n # to get RC2 (needed for junk asymmetric private key\\n # serialization), RC4, Blowfish, IDEA, SEED, etc. These things\\n # are ugly legacy, but we aren't going to get rid of them\\n # any time soon.\\n if cls.lib.CRYPTOGRAPHY_OPENSSL_300_OR_GREATER:\\n if not os.environ.get(\\\"CRYPTOGRAPHY_OPENSSL_NO_LEGACY\\\"):\\n cls._legacy_provider = cls.lib.OSSL_PROVIDER_load(\\n cls.ffi.NULL, b\\\"legacy\\\"\\n )\\n cls._legacy_provider_loaded = (\\n cls._legacy_provider != cls.ffi.NULL\\n )\\n _legacy_provider_error(cls._legacy_provider_loaded)\\n\\n cls._default_provider = cls.lib.OSSL_PROVIDER_load(\\n cls.ffi.NULL, b\\\"default\\\"\\n )\\n _openssl_assert(\\n cls.lib, cls._default_provider != cls.ffi.NULL\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::Binding.init_static_locks\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::Binding.init_static_locks\\n @classmethod\\n def init_static_locks(cls) -> None:\\n cls._ensure_ffi_initialized()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_verify_package_version\",\n \"text\": \"src/cryptography/hazmat/bindings/openssl/binding.py::_verify_package_version\\ndef _verify_package_version(version: str) -> None:\\n # Occasionally we run into situations where the version of the Python\\n # package does not match the version of the shared object that is loaded.\\n # This may occur in environments where multiple versions of cryptography\\n # are installed and available in the python path. To avoid errors cropping\\n # up later this code checks that the currently imported package and the\\n # shared object that were loaded have the same version and raise an\\n # ImportError if they do not\\n so_package_version = ffi.string(lib.CRYPTOGRAPHY_PACKAGE_VERSION)\\n if version.encode(\\\"ascii\\\") != so_package_version:\\n raise ImportError(\\n \\\"The version of cryptography does not match the loaded \\\"\\n \\\"shared object. This can happen if you have multiple copies of \\\"\\n \\\"cryptography installed in your Python path. Please try creating \\\"\\n \\\"a new virtual environment to resolve this issue. \\\"\\n \\\"Loaded python version: {}, shared object version: {}\\\".format(\\n version, so_package_version\\n )\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.version\",\n \"text\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.version\\n @abc.abstractproperty\\n def version(self) -> Version:\\n \\\"\\\"\\\"\\n Returns the SCT version.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.log_id\",\n \"text\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.log_id\\n @abc.abstractproperty\\n def log_id(self) -> bytes:\\n \\\"\\\"\\\"\\n Returns an identifier indicating which log this SCT is for.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.timestamp\",\n \"text\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.timestamp\\n @abc.abstractproperty\\n def timestamp(self) -> datetime.datetime:\\n \\\"\\\"\\\"\\n Returns the timestamp for this SCT.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.entry_type\",\n \"text\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.entry_type\\n @abc.abstractproperty\\n def entry_type(self) -> LogEntryType:\\n \\\"\\\"\\\"\\n Returns whether this is an SCT for a certificate or pre-certificate.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.signature_hash_algorithm\",\n \"text\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.signature_hash_algorithm\\n @abc.abstractproperty\\n def signature_hash_algorithm(self) -> HashAlgorithm:\\n \\\"\\\"\\\"\\n Returns the hash algorithm used for the SCT's signature.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.signature_algorithm\",\n \"text\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.signature_algorithm\\n @abc.abstractproperty\\n def signature_algorithm(self) -> SignatureAlgorithm:\\n \\\"\\\"\\\"\\n Returns the signing algorithm used for the SCT's signature.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.signature\",\n \"text\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.signature\\n @abc.abstractproperty\\n def signature(self) -> bytes:\\n \\\"\\\"\\\"\\n Returns the signature for this SCT.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.extension_bytes\",\n \"text\": \"src/cryptography/x509/certificate_transparency.py::SignedCertificateTimestamp.extension_bytes\\n @abc.abstractproperty\\n def extension_bytes(self) -> bytes:\\n \\\"\\\"\\\"\\n Returns the raw bytes of any extensions for this SCT.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::_key_identifier_from_public_key\",\n \"text\": \"src/cryptography/x509/extensions.py::_key_identifier_from_public_key\\ndef _key_identifier_from_public_key(\\n public_key: CERTIFICATE_PUBLIC_KEY_TYPES,\\n) -> bytes:\\n if isinstance(public_key, RSAPublicKey):\\n data = public_key.public_bytes(\\n serialization.Encoding.DER,\\n serialization.PublicFormat.PKCS1,\\n )\\n elif isinstance(public_key, EllipticCurvePublicKey):\\n data = public_key.public_bytes(\\n serialization.Encoding.X962,\\n serialization.PublicFormat.UncompressedPoint,\\n )\\n else:\\n # This is a very slow way to do this.\\n serialized = public_key.public_bytes(\\n serialization.Encoding.DER,\\n serialization.PublicFormat.SubjectPublicKeyInfo,\\n )\\n data = asn1.parse_spki_for_data(serialized)\\n\\n return hashlib.sha1(data).digest()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::_make_sequence_methods\",\n \"text\": \"src/cryptography/x509/extensions.py::_make_sequence_methods\\ndef _make_sequence_methods(field_name: str):\\n def len_method(self) -> int:\\n return len(getattr(self, field_name))\\n\\n def iter_method(self):\\n return iter(getattr(self, field_name))\\n\\n def getitem_method(self, idx):\\n return getattr(self, field_name)[idx]\\n\\n return len_method, iter_method, getitem_method\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::_make_sequence_methods.len_method\",\n \"text\": \"src/cryptography/x509/extensions.py::_make_sequence_methods.len_method\\n def len_method(self) -> int:\\n return len(getattr(self, field_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::_make_sequence_methods.iter_method\",\n \"text\": \"src/cryptography/x509/extensions.py::_make_sequence_methods.iter_method\\n def iter_method(self):\\n return iter(getattr(self, field_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::_make_sequence_methods.getitem_method\",\n \"text\": \"src/cryptography/x509/extensions.py::_make_sequence_methods.getitem_method\\n def getitem_method(self, idx):\\n return getattr(self, field_name)[idx]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::DuplicateExtension.__init__\",\n \"text\": \"src/cryptography/x509/extensions.py::DuplicateExtension.__init__\\n def __init__(self, msg: str, oid: ObjectIdentifier) -> None:\\n super(DuplicateExtension, self).__init__(msg)\\n self.oid = oid\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::ExtensionNotFound.__init__\",\n \"text\": \"src/cryptography/x509/extensions.py::ExtensionNotFound.__init__\\n def __init__(self, msg: str, oid: ObjectIdentifier) -> None:\\n super(ExtensionNotFound, self).__init__(msg)\\n self.oid = oid\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::ExtensionType.public_bytes\",\n \"text\": \"src/cryptography/x509/extensions.py::ExtensionType.public_bytes\\n def public_bytes(self) -> bytes:\\n \\\"\\\"\\\"\\n Serializes the extension type to DER.\\n \\\"\\\"\\\"\\n raise NotImplementedError(\\n \\\"public_bytes is not implemented for extension type {0!r}\\\".format(\\n self\\n )\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::Extensions.__init__\",\n \"text\": \"src/cryptography/x509/extensions.py::Extensions.__init__\\n def __init__(\\n self, extensions: typing.Iterable[\\\"Extension[ExtensionType]\\\"]\\n ) -> None:\\n self._extensions = list(extensions)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::Extensions.get_extension_for_oid\",\n \"text\": \"src/cryptography/x509/extensions.py::Extensions.get_extension_for_oid\\n def get_extension_for_oid(\\n self, oid: ObjectIdentifier\\n ) -> \\\"Extension[ExtensionType]\\\":\\n for ext in self:\\n if ext.oid == oid:\\n return ext\\n\\n raise ExtensionNotFound(\\\"No {} extension was found\\\".format(oid), oid)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::Extensions.get_extension_for_class\",\n \"text\": \"src/cryptography/x509/extensions.py::Extensions.get_extension_for_class\\n def get_extension_for_class(\\n self, extclass: typing.Type[ExtensionTypeVar]\\n ) -> \\\"Extension[ExtensionTypeVar]\\\":\\n if extclass is UnrecognizedExtension:\\n raise TypeError(\\n \\\"UnrecognizedExtension can't be used with \\\"\\n \\\"get_extension_for_class because more than one instance of the\\\"\\n \\\" class may be present.\\\"\\n )\\n\\n for ext in self:\\n if isinstance(ext.value, extclass):\\n return ext\\n\\n raise ExtensionNotFound(\\n \\\"No {} extension was found\\\".format(extclass), extclass.oid\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::Extensions.__repr__\",\n \"text\": \"src/cryptography/x509/extensions.py::Extensions.__repr__\\n def __repr__(self) -> str:\\n return \\\"<Extensions({})>\\\".format(self._extensions)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::CRLNumber.__init__\",\n \"text\": \"src/cryptography/x509/extensions.py::CRLNumber.__init__\\n def __init__(self, crl_number: int) -> None:\\n if not isinstance(crl_number, int):\\n raise TypeError(\\\"crl_number must be an integer\\\")\\n\\n self._crl_number = crl_number\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::CRLNumber.__eq__\",\n \"text\": \"src/cryptography/x509/extensions.py::CRLNumber.__eq__\\n def __eq__(self, other: object) -> bool:\\n if not isinstance(other, CRLNumber):\\n return NotImplemented\\n\\n return self.crl_number == other.crl_number\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::CRLNumber.__hash__\",\n \"text\": \"src/cryptography/x509/extensions.py::CRLNumber.__hash__\\n def __hash__(self) -> int:\\n return hash(self.crl_number)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::CRLNumber.__repr__\",\n \"text\": \"src/cryptography/x509/extensions.py::CRLNumber.__repr__\\n def __repr__(self) -> str:\\n return \\\"<CRLNumber({})>\\\".format(self.crl_number)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::CRLNumber.crl_number\",\n \"text\": \"src/cryptography/x509/extensions.py::CRLNumber.crl_number\\n @property\\n def crl_number(self) -> int:\\n return self._crl_number\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::CRLNumber.public_bytes\",\n \"text\": \"src/cryptography/x509/extensions.py::CRLNumber.public_bytes\\n def public_bytes(self) -> bytes:\\n return rust_x509.encode_extension_value(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__init__\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__init__\\n def __init__(\\n self,\\n key_identifier: typing.Optional[bytes],\\n authority_cert_issuer: typing.Optional[typing.Iterable[GeneralName]],\\n authority_cert_serial_number: typing.Optional[int],\\n ) -> None:\\n if (authority_cert_issuer is None) != (\\n authority_cert_serial_number is None\\n ):\\n raise ValueError(\\n \\\"authority_cert_issuer and authority_cert_serial_number \\\"\\n \\\"must both be present or both None\\\"\\n )\\n\\n if authority_cert_issuer is not None:\\n authority_cert_issuer = list(authority_cert_issuer)\\n if not all(\\n isinstance(x, GeneralName) for x in authority_cert_issuer\\n ):\\n raise TypeError(\\n \\\"authority_cert_issuer must be a list of GeneralName \\\"\\n \\\"objects\\\"\\n )\\n\\n if authority_cert_serial_number is not None and not isinstance(\\n authority_cert_serial_number, int\\n ):\\n raise TypeError(\\\"authority_cert_serial_number must be an integer\\\")\\n\\n self._key_identifier = key_identifier\\n self._authority_cert_issuer = authority_cert_issuer\\n self._authority_cert_serial_number = authority_cert_serial_number\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.from_issuer_public_key\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.from_issuer_public_key\\n @classmethod\\n def from_issuer_public_key(\\n cls, public_key: CERTIFICATE_ISSUER_PUBLIC_KEY_TYPES\\n ) -> \\\"AuthorityKeyIdentifier\\\":\\n digest = _key_identifier_from_public_key(public_key)\\n return cls(\\n key_identifier=digest,\\n authority_cert_issuer=None,\\n authority_cert_serial_number=None,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.from_issuer_subject_key_identifier\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.from_issuer_subject_key_identifier\\n @classmethod\\n def from_issuer_subject_key_identifier(\\n cls, ski: \\\"SubjectKeyIdentifier\\\"\\n ) -> \\\"AuthorityKeyIdentifier\\\":\\n return cls(\\n key_identifier=ski.digest,\\n authority_cert_issuer=None,\\n authority_cert_serial_number=None,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__repr__\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__repr__\\n def __repr__(self) -> str:\\n return (\\n \\\"<AuthorityKeyIdentifier(key_identifier={0.key_identifier!r}, \\\"\\n \\\"authority_cert_issuer={0.authority_cert_issuer}, \\\"\\n \\\"authority_cert_serial_number={0.authority_cert_serial_number}\\\"\\n \\\")>\\\".format(self)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__eq__\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__eq__\\n def __eq__(self, other: object) -> bool:\\n if not isinstance(other, AuthorityKeyIdentifier):\\n return NotImplemented\\n\\n return (\\n self.key_identifier == other.key_identifier\\n and self.authority_cert_issuer == other.authority_cert_issuer\\n and self.authority_cert_serial_number\\n == other.authority_cert_serial_number\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__hash__\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.__hash__\\n def __hash__(self) -> int:\\n if self.authority_cert_issuer is None:\\n aci = None\\n else:\\n aci = tuple(self.authority_cert_issuer)\\n return hash(\\n (self.key_identifier, aci, self.authority_cert_serial_number)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.key_identifier\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.key_identifier\\n @property\\n def key_identifier(self) -> typing.Optional[bytes]:\\n return self._key_identifier\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.authority_cert_issuer\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.authority_cert_issuer\\n @property\\n def authority_cert_issuer(\\n self,\\n ) -> typing.Optional[typing.List[GeneralName]]:\\n return self._authority_cert_issuer\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.authority_cert_serial_number\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.authority_cert_serial_number\\n @property\\n def authority_cert_serial_number(self) -> typing.Optional[int]:\\n return self._authority_cert_serial_number\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.public_bytes\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityKeyIdentifier.public_bytes\\n def public_bytes(self) -> bytes:\\n return rust_x509.encode_extension_value(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__init__\",\n \"text\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__init__\\n def __init__(self, digest: bytes) -> None:\\n self._digest = digest\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.from_public_key\",\n \"text\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.from_public_key\\n @classmethod\\n def from_public_key(\\n cls, public_key: CERTIFICATE_PUBLIC_KEY_TYPES\\n ) -> \\\"SubjectKeyIdentifier\\\":\\n return cls(_key_identifier_from_public_key(public_key))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.digest\",\n \"text\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.digest\\n @property\\n def digest(self) -> bytes:\\n return self._digest\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.key_identifier\",\n \"text\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.key_identifier\\n @property\\n def key_identifier(self) -> bytes:\\n return self._digest\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__repr__\",\n \"text\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__repr__\\n def __repr__(self) -> str:\\n return \\\"<SubjectKeyIdentifier(digest={0!r})>\\\".format(self.digest)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__eq__\",\n \"text\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__eq__\\n def __eq__(self, other: object) -> bool:\\n if not isinstance(other, SubjectKeyIdentifier):\\n return NotImplemented\\n\\n return constant_time.bytes_eq(self.digest, other.digest)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__hash__\",\n \"text\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.__hash__\\n def __hash__(self) -> int:\\n return hash(self.digest)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.public_bytes\",\n \"text\": \"src/cryptography/x509/extensions.py::SubjectKeyIdentifier.public_bytes\\n def public_bytes(self) -> bytes:\\n return rust_x509.encode_extension_value(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__init__\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__init__\\n def __init__(\\n self, descriptions: typing.Iterable[\\\"AccessDescription\\\"]\\n ) -> None:\\n descriptions = list(descriptions)\\n if not all(isinstance(x, AccessDescription) for x in descriptions):\\n raise TypeError(\\n \\\"Every item in the descriptions list must be an \\\"\\n \\\"AccessDescription\\\"\\n )\\n\\n self._descriptions = descriptions\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__repr__\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__repr__\\n def __repr__(self) -> str:\\n return \\\"<AuthorityInformationAccess({})>\\\".format(self._descriptions)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__eq__\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__eq__\\n def __eq__(self, other: object) -> bool:\\n if not isinstance(other, AuthorityInformationAccess):\\n return NotImplemented\\n\\n return self._descriptions == other._descriptions\",\n \"label\": 0\n },\n {\n \"cid\": \"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__hash__\",\n \"text\": \"src/cryptography/x509/extensions.py::AuthorityInformationAccess.__hash__\\n def __hash__(self) -> int:\\n return hash(tuple(self._descriptions))\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":267,"cells":{"instance_id":{"kind":"string","value":"wagtail__wagtail-945"},"repo":{"kind":"string","value":"wagtail/wagtail"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Update to treebeard 3.0\nThis fixes many of the Django 1.7 warnings we get.\n\nIt also drops support for both Python 3.2 and 3.3 so we might need to tread carefully if we decide to adopt it. We already have a dependency that doesn't support 3.2 (`libsass`) which doesn't seem to be causing any problems for the time being (tests all pass, but I've not cheked it recently).\n\n"},"gold_files":{"kind":"list like","value":["setup.py"],"string":"[\n \"setup.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["setup.py::<module>"],"string":"[\n \"setup.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/conf.py","text":"docs/conf.py","label":0},{"cid":"setup.py","text":"setup.py","label":1},{"cid":"wagtail/wagtailadmin/views/pages.py","text":"wagtail/wagtailadmin/views/pages.py\ndef explorer_nav(request)\ndef index(request, parent_page_id=None)\ndef add_subpage(request, parent_page_id)\ndef content_type_use(request, content_type_app_name, content_type_model_name)\ndef create(request, content_type_app_name, content_type_model_name, parent_page_id)\ndef edit(request, page_id)\ndef delete(request, page_id)\ndef view_draft(request, page_id)\ndef preview_on_edit(request, page_id)\ndef preview_on_create(request, content_type_app_name, content_type_model_name, parent_page_id)\ndef preview(request) # The HTML of a previewed page is written to the destination browser window using \ndef preview_loading(request) # This page is blank, but must be real HTML so its DOM can be written to once the \ndef unpublish(request, page_id)\ndef move_choose_destination(request, page_to_move_id, viewed_page_id=None)\ndef move_confirm(request, page_to_move_id, destination_id)\ndef set_page_position(request, page_to_move_id)\ndef copy(request, page_id)\ndef get_page_edit_handler(page_class)\ndef search(request)\ndef approve_moderation(request, revision_id)\ndef reject_moderation(request, revision_id)\ndef preview_for_moderation(request, revision_id)\ndef lock(request, page_id)\ndef unlock(request, page_id)","label":0},{"cid":"wagtail/wagtailcore/models.py","text":"wagtail/wagtailcore/models.py\nclass SiteManager\n def get_by_natural_key(self, hostname, port)\nclass Site\n class Meta\n def natural_key(self)\n def __str__(self)\n def find_for_request(request) # Find the site object responsible for responding to this HTTP\n def root_url(self)\n def clean_fields(self, exclude=None)\n def get_site_root_paths() # Return a list of (root_path, root_url) tuples, most specific path first -\ndef clear_site_root_paths_on_save(sender, instance, **kwargs)\ndef clear_site_root_paths_on_delete(sender, instance, **kwargs)\ndef get_page_types()\nclass PageManager\n def get_queryset(self)\n def live(self)\n def not_live(self)\n def in_menu(self)\n def not_in_menu(self)\n def page(self, other)\n def not_page(self, other)\n def descendant_of(self, other, inclusive=False)\n def not_descendant_of(self, other, inclusive=False)\n def child_of(self, other)\n def not_child_of(self, other)\n def ancestor_of(self, other, inclusive=False)\n def not_ancestor_of(self, other, inclusive=False)\n def parent_of(self, other)\n def not_parent_of(self, other)\n def sibling_of(self, other, inclusive=False)\n def not_sibling_of(self, other, inclusive=False)\n def type(self, model)\n def not_type(self, model)\n def public(self)\n def not_public(self)\n def search(self, query_string, fields=None, backend='default')\nclass PageBase # Metaclass for Page\n def __init__(cls, name, bases, dct)\nclass Page\n def __init__(self, *args, **kwargs)\n def __str__(self)\n def set_url_path(self, parent) # Populate the url_path field based on this page's slug and the specified parent p\n def save(self, *args, **kwargs)\n def check(cls, **kwargs)\n def _update_descendant_url_paths(self, old_url_path, new_url_path)\n def specific(self) # Return this page in its most specific subclassed form.\n def specific_class(self) # Return the class that this page would be if instantiated in its\n def route(self, request, path_components)\n def save_revision(self, user=None, submitted_for_moderation=False, approved_go_live_at=None)\n def get_latest_revision(self)\n def get_latest_revision_as_page(self)\n def unpublish(self, set_expired=False, commit=True)\n def get_context(self, request, *args, **kwargs)\n def get_template(self, request, *args, **kwargs)\n def serve(self, request, *args, **kwargs)\n def is_navigable(self) # Return true if it's meaningful to browse subpages of this page -\n def full_url(self) # Return the full URL (including protocol / domain) to this page, or None if it is\n def url(self) # Return the 'most appropriate' URL for referring to this page from the pages we s\n def relative_url(self, current_site) # Return the 'most appropriate' URL for this page taking into account the site we'\n def get_indexed_objects(cls)\n def get_indexed_instance(self)\n def search(cls, query_string, show_unpublished=False, search_title_only=False, extra_filters={}, pre","label":0}],"string":"[\n {\n \"cid\": \"docs/conf.py\",\n \"text\": \"docs/conf.py\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py\\ndef explorer_nav(request)\\ndef index(request, parent_page_id=None)\\ndef add_subpage(request, parent_page_id)\\ndef content_type_use(request, content_type_app_name, content_type_model_name)\\ndef create(request, content_type_app_name, content_type_model_name, parent_page_id)\\ndef edit(request, page_id)\\ndef delete(request, page_id)\\ndef view_draft(request, page_id)\\ndef preview_on_edit(request, page_id)\\ndef preview_on_create(request, content_type_app_name, content_type_model_name, parent_page_id)\\ndef preview(request) # The HTML of a previewed page is written to the destination browser window using \\ndef preview_loading(request) # This page is blank, but must be real HTML so its DOM can be written to once the \\ndef unpublish(request, page_id)\\ndef move_choose_destination(request, page_to_move_id, viewed_page_id=None)\\ndef move_confirm(request, page_to_move_id, destination_id)\\ndef set_page_position(request, page_to_move_id)\\ndef copy(request, page_id)\\ndef get_page_edit_handler(page_class)\\ndef search(request)\\ndef approve_moderation(request, revision_id)\\ndef reject_moderation(request, revision_id)\\ndef preview_for_moderation(request, revision_id)\\ndef lock(request, page_id)\\ndef unlock(request, page_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py\",\n \"text\": \"wagtail/wagtailcore/models.py\\nclass SiteManager\\n def get_by_natural_key(self, hostname, port)\\nclass Site\\n class Meta\\n def natural_key(self)\\n def __str__(self)\\n def find_for_request(request) # Find the site object responsible for responding to this HTTP\\n def root_url(self)\\n def clean_fields(self, exclude=None)\\n def get_site_root_paths() # Return a list of (root_path, root_url) tuples, most specific path first -\\ndef clear_site_root_paths_on_save(sender, instance, **kwargs)\\ndef clear_site_root_paths_on_delete(sender, instance, **kwargs)\\ndef get_page_types()\\nclass PageManager\\n def get_queryset(self)\\n def live(self)\\n def not_live(self)\\n def in_menu(self)\\n def not_in_menu(self)\\n def page(self, other)\\n def not_page(self, other)\\n def descendant_of(self, other, inclusive=False)\\n def not_descendant_of(self, other, inclusive=False)\\n def child_of(self, other)\\n def not_child_of(self, other)\\n def ancestor_of(self, other, inclusive=False)\\n def not_ancestor_of(self, other, inclusive=False)\\n def parent_of(self, other)\\n def not_parent_of(self, other)\\n def sibling_of(self, other, inclusive=False)\\n def not_sibling_of(self, other, inclusive=False)\\n def type(self, model)\\n def not_type(self, model)\\n def public(self)\\n def not_public(self)\\n def search(self, query_string, fields=None, backend='default')\\nclass PageBase # Metaclass for Page\\n def __init__(cls, name, bases, dct)\\nclass Page\\n def __init__(self, *args, **kwargs)\\n def __str__(self)\\n def set_url_path(self, parent) # Populate the url_path field based on this page's slug and the specified parent p\\n def save(self, *args, **kwargs)\\n def check(cls, **kwargs)\\n def _update_descendant_url_paths(self, old_url_path, new_url_path)\\n def specific(self) # Return this page in its most specific subclassed form.\\n def specific_class(self) # Return the class that this page would be if instantiated in its\\n def route(self, request, path_components)\\n def save_revision(self, user=None, submitted_for_moderation=False, approved_go_live_at=None)\\n def get_latest_revision(self)\\n def get_latest_revision_as_page(self)\\n def unpublish(self, set_expired=False, commit=True)\\n def get_context(self, request, *args, **kwargs)\\n def get_template(self, request, *args, **kwargs)\\n def serve(self, request, *args, **kwargs)\\n def is_navigable(self) # Return true if it's meaningful to browse subpages of this page -\\n def full_url(self) # Return the full URL (including protocol / domain) to this page, or None if it is\\n def url(self) # Return the 'most appropriate' URL for referring to this page from the pages we s\\n def relative_url(self, current_site) # Return the 'most appropriate' URL for this page taking into account the site we'\\n def get_indexed_objects(cls)\\n def get_indexed_instance(self)\\n def search(cls, query_string, show_unpublished=False, search_title_only=False, extra_filters={}, pre\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"wagtail/wagtailadmin/views/pages.py::explorer_nav","text":"wagtail/wagtailadmin/views/pages.py::explorer_nav\ndef explorer_nav(request):\n return render(request, 'wagtailadmin/shared/explorer_nav.html', {\n 'nodes': get_navigation_menu_items(),\n })","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::index","text":"wagtail/wagtailadmin/views/pages.py::index\ndef index(request, parent_page_id=None):\n if parent_page_id:\n parent_page = get_object_or_404(Page, id=parent_page_id)\n else:\n parent_page = Page.get_first_root_node()\n\n pages = parent_page.get_children().prefetch_related('content_type')\n\n # Get page ordering\n ordering = request.GET.get('ordering', '-latest_revision_created_at')\n if ordering not in ['title', '-title', 'content_type', '-content_type', 'live', '-live', 'latest_revision_created_at', '-latest_revision_created_at', 'ord']:\n ordering = '-latest_revision_created_at'\n\n # Pagination\n if ordering != 'ord':\n ordering_no_minus = ordering\n if ordering_no_minus.startswith('-'):\n ordering_no_minus = ordering[1:]\n pages = pages.order_by(ordering).annotate(null_position=Count(ordering_no_minus)).order_by('-null_position', ordering)\n\n p = request.GET.get('p', 1)\n paginator = Paginator(pages, 50)\n try:\n pages = paginator.page(p)\n except PageNotAnInteger:\n pages = paginator.page(1)\n except EmptyPage:\n pages = paginator.page(paginator.num_pages)\n\n return render(request, 'wagtailadmin/pages/index.html', {\n 'parent_page': parent_page,\n 'ordering': ordering,\n 'pages': pages,\n })","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::add_subpage","text":"wagtail/wagtailadmin/views/pages.py::add_subpage\ndef add_subpage(request, parent_page_id):\n parent_page = get_object_or_404(Page, id=parent_page_id).specific\n if not parent_page.permissions_for_user(request.user).can_add_subpage():\n raise PermissionDenied\n\n page_types = sorted(parent_page.allowed_subpage_types(),\n key=lambda pagetype: pagetype.model_class().get_verbose_name().lower()\n )\n\n if len(page_types) == 1:\n # Only one page type is available - redirect straight to the create form rather than\n # making the user choose\n content_type = page_types[0]\n return redirect('wagtailadmin_pages_create', content_type.app_label, content_type.model, parent_page.id)\n\n return render(request, 'wagtailadmin/pages/add_subpage.html', {\n 'parent_page': parent_page,\n 'page_types': page_types,\n })","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::content_type_use","text":"wagtail/wagtailadmin/views/pages.py::content_type_use\ndef content_type_use(request, content_type_app_name, content_type_model_name):\n try:\n content_type = ContentType.objects.get_by_natural_key(content_type_app_name, content_type_model_name)\n except ContentType.DoesNotExist:\n raise Http404\n\n p = request.GET.get(\"p\", 1)\n\n page_class = content_type.model_class()\n\n # page_class must be a Page type and not some other random model\n if not issubclass(page_class, Page):\n raise Http404\n\n pages = page_class.objects.all()\n\n paginator = Paginator(pages, 10)\n\n try:\n pages = paginator.page(p)\n except PageNotAnInteger:\n pages = paginator.page(1)\n except EmptyPage:\n pages = paginator.page(paginator.num_pages)\n\n return render(request, 'wagtailadmin/pages/content_type_use.html', {\n 'pages': pages,\n 'app_name': content_type_app_name,\n 'content_type': content_type,\n 'page_class': page_class,\n })","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::create","text":"wagtail/wagtailadmin/views/pages.py::create\ndef create(request, content_type_app_name, content_type_model_name, parent_page_id):\n parent_page = get_object_or_404(Page, id=parent_page_id).specific\n parent_page_perms = parent_page.permissions_for_user(request.user)\n if not parent_page_perms.can_add_subpage():\n raise PermissionDenied\n\n try:\n content_type = ContentType.objects.get_by_natural_key(content_type_app_name, content_type_model_name)\n except ContentType.DoesNotExist:\n raise Http404\n\n # Get class\n page_class = content_type.model_class()\n\n # Make sure the class is a descendant of Page\n if not issubclass(page_class, Page):\n raise Http404\n\n # page must be in the list of allowed subpage types for this parent ID\n if content_type not in parent_page.allowed_subpage_types():\n raise PermissionDenied\n\n page = page_class(owner=request.user)\n edit_handler_class = get_page_edit_handler(page_class)\n form_class = edit_handler_class.get_form_class(page_class)\n\n if request.POST:\n form = form_class(request.POST, request.FILES, instance=page)\n\n # Stick an extra validator into the form to make sure that the slug is not already in use\n def clean_slug(slug):\n # Make sure the slug isn't already in use\n if parent_page.get_children().filter(slug=slug).count() > 0:\n raise ValidationError(_(\"This slug is already in use\"))\n return slug\n form.fields['slug'].clean = clean_slug\n\n # Stick another validator into the form to check that the scheduled publishing settings are set correctly\n def clean():\n cleaned_data = form_class.clean(form)\n\n # Go live must be before expire\n go_live_at = cleaned_data.get('go_live_at')\n expire_at = cleaned_data.get('expire_at')\n\n if go_live_at and expire_at:\n if go_live_at > expire_at:\n msg = _('Go live date/ti","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::edit","text":"wagtail/wagtailadmin/views/pages.py::edit\ndef edit(request, page_id):\n latest_revision = get_object_or_404(Page, id=page_id).get_latest_revision()\n page = get_object_or_404(Page, id=page_id).get_latest_revision_as_page()\n parent = page.get_parent()\n\n content_type = ContentType.objects.get_for_model(page)\n\n page_perms = page.permissions_for_user(request.user)\n if not page_perms.can_edit():\n raise PermissionDenied\n\n edit_handler_class = get_page_edit_handler(page.__class__)\n form_class = edit_handler_class.get_form_class(page.__class__)\n\n errors_debug = None\n\n if request.POST:\n form = form_class(request.POST, request.FILES, instance=page)\n\n # Stick an extra validator into the form to make sure that the slug is not already in use\n def clean_slug(slug):\n # Make sure the slug isn't already in use\n if parent.get_children().filter(slug=slug).exclude(id=page_id).count() > 0:\n raise ValidationError(_(\"This slug is already in use\"))\n return slug\n form.fields['slug'].clean = clean_slug\n\n # Stick another validator into the form to check that the scheduled publishing settings are set correctly\n def clean():\n cleaned_data = form_class.clean(form)\n\n # Go live must be before expire\n go_live_at = cleaned_data.get('go_live_at')\n expire_at = cleaned_data.get('expire_at')\n\n if go_live_at and expire_at:\n if go_live_at > expire_at:\n msg = _('Go live date/time must be before expiry date/time')\n form._errors['go_live_at'] = form.error_class([msg])\n form._errors['expire_at'] = form.error_class([msg])\n del cleaned_data['go_live_at']\n del cleaned_data['expire_at']\n\n # Expire must be in the future\n expire_at = cleaned_data.get('expire_at')\n\n if expire_at and expire_at < time","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::delete","text":"wagtail/wagtailadmin/views/pages.py::delete\ndef delete(request, page_id):\n page = get_object_or_404(Page, id=page_id).specific\n if not page.permissions_for_user(request.user).can_delete():\n raise PermissionDenied\n\n if request.POST:\n parent_id = page.get_parent().id\n page.delete()\n\n messages.success(request, _(\"Page '{0}' deleted.\").format(page.title))\n\n for fn in hooks.get_hooks('after_delete_page'):\n result = fn(request, page)\n if hasattr(result, 'status_code'):\n return result\n\n return redirect('wagtailadmin_explore', parent_id)\n\n return render(request, 'wagtailadmin/pages/confirm_delete.html', {\n 'page': page,\n 'descendant_count': page.get_descendant_count()\n })","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::view_draft","text":"wagtail/wagtailadmin/views/pages.py::view_draft\ndef view_draft(request, page_id):\n page = get_object_or_404(Page, id=page_id).get_latest_revision_as_page()\n return page.serve_preview(page.dummy_request(), page.default_preview_mode)","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::preview_on_edit","text":"wagtail/wagtailadmin/views/pages.py::preview_on_edit\ndef preview_on_edit(request, page_id):\n # Receive the form submission that would typically be posted to the 'edit' view. If submission is valid,\n # return the rendered page; if not, re-render the edit form\n page = get_object_or_404(Page, id=page_id).get_latest_revision_as_page()\n edit_handler_class = get_page_edit_handler(page.__class__)\n form_class = edit_handler_class.get_form_class(page.__class__)\n\n form = form_class(request.POST, request.FILES, instance=page)\n\n if form.is_valid():\n form.save(commit=False)\n\n preview_mode = request.GET.get('mode', page.default_preview_mode)\n response = page.serve_preview(page.dummy_request(), preview_mode)\n response['X-Wagtail-Preview'] = 'ok'\n return response\n\n else:\n edit_handler = edit_handler_class(instance=page, form=form)\n\n response = render(request, 'wagtailadmin/pages/edit.html', {\n 'page': page,\n 'edit_handler': edit_handler,\n 'preview_modes': page.preview_modes,\n })\n response['X-Wagtail-Preview'] = 'error'\n return response","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::preview_on_create","text":"wagtail/wagtailadmin/views/pages.py::preview_on_create\ndef preview_on_create(request, content_type_app_name, content_type_model_name, parent_page_id):\n # Receive the form submission that would typically be posted to the 'create' view. If submission is valid,\n # return the rendered page; if not, re-render the edit form\n try:\n content_type = ContentType.objects.get_by_natural_key(content_type_app_name, content_type_model_name)\n except ContentType.DoesNotExist:\n raise Http404\n\n page_class = content_type.model_class()\n page = page_class()\n edit_handler_class = get_page_edit_handler(page_class)\n form_class = edit_handler_class.get_form_class(page_class)\n\n form = form_class(request.POST, request.FILES, instance=page)\n\n if form.is_valid():\n form.save(commit=False)\n\n # ensure that our unsaved page instance has a suitable url set\n parent_page = get_object_or_404(Page, id=parent_page_id).specific\n page.set_url_path(parent_page)\n\n # Set treebeard attributes\n page.depth = parent_page.depth + 1\n page.path = Page._get_children_path_interval(parent_page.path)[1]\n\n preview_mode = request.GET.get('mode', page.default_preview_mode)\n response = page.serve_preview(page.dummy_request(), preview_mode)\n response['X-Wagtail-Preview'] = 'ok'\n return response\n\n else:\n edit_handler = edit_handler_class(instance=page, form=form)\n parent_page = get_object_or_404(Page, id=parent_page_id).specific\n\n response = render(request, 'wagtailadmin/pages/create.html', {\n 'content_type': content_type,\n 'page_class': page_class,\n 'parent_page': parent_page,\n 'edit_handler': edit_handler,\n 'preview_modes': page.preview_modes,\n })\n response['X-Wagtail-Preview'] = 'error'\n return response","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::preview","text":"wagtail/wagtailadmin/views/pages.py::preview\ndef preview(request):\n \"\"\"\n The HTML of a previewed page is written to the destination browser window using document.write.\n This overwrites any previous content in the window, while keeping its URL intact. This in turn\n means that any content we insert that happens to trigger an HTTP request, such as an image or\n stylesheet tag, will report that original URL as its referrer.\n\n In Webkit browsers, a new window opened with window.open('', 'window_name') will have a location\n of 'about:blank', causing it to omit the Referer header on those HTTP requests. This means that\n any third-party font services that use the Referer header for access control will refuse to\n serve us.\n\n So, instead, we need to open the window on some arbitrary URL on our domain. (Provided that's\n also the same domain as our editor JS code, the browser security model will happily allow us to\n document.write over the page in question.)\n\n This, my friends, is that arbitrary URL.\n\n Since we're going to this trouble, we'll also take the opportunity to display a spinner on the\n placeholder page, providing some much-needed visual feedback.\n \"\"\"\n return render(request, 'wagtailadmin/pages/preview.html')","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::preview_loading","text":"wagtail/wagtailadmin/views/pages.py::preview_loading\ndef preview_loading(request):\n \"\"\"\n This page is blank, but must be real HTML so its DOM can be written to once the preview of the page has rendered\n \"\"\"\n return HttpResponse(\"<html><head><title></title></head><body></body></html>\")","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::unpublish","text":"wagtail/wagtailadmin/views/pages.py::unpublish\ndef unpublish(request, page_id):\n page = get_object_or_404(Page, id=page_id).specific\n if not page.permissions_for_user(request.user).can_unpublish():\n raise PermissionDenied\n\n if request.method == 'POST':\n page.unpublish()\n\n messages.success(request, _(\"Page '{0}' unpublished.\").format(page.title))\n\n return redirect('wagtailadmin_explore', page.get_parent().id)\n\n return render(request, 'wagtailadmin/pages/confirm_unpublish.html', {\n 'page': page,\n })","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::move_choose_destination","text":"wagtail/wagtailadmin/views/pages.py::move_choose_destination\ndef move_choose_destination(request, page_to_move_id, viewed_page_id=None):\n page_to_move = get_object_or_404(Page, id=page_to_move_id)\n page_perms = page_to_move.permissions_for_user(request.user)\n if not page_perms.can_move():\n raise PermissionDenied\n\n if viewed_page_id:\n viewed_page = get_object_or_404(Page, id=viewed_page_id)\n else:\n viewed_page = Page.get_first_root_node()\n\n viewed_page.can_choose = page_perms.can_move_to(viewed_page)\n\n child_pages = []\n for target in viewed_page.get_children():\n # can't move the page into itself or its descendants\n target.can_choose = page_perms.can_move_to(target)\n\n target.can_descend = not(target == page_to_move or target.is_child_of(page_to_move)) and target.get_children_count()\n\n child_pages.append(target)\n\n return render(request, 'wagtailadmin/pages/move_choose_destination.html', {\n 'page_to_move': page_to_move,\n 'viewed_page': viewed_page,\n 'child_pages': child_pages,\n })","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::move_confirm","text":"wagtail/wagtailadmin/views/pages.py::move_confirm\ndef move_confirm(request, page_to_move_id, destination_id):\n page_to_move = get_object_or_404(Page, id=page_to_move_id).specific\n destination = get_object_or_404(Page, id=destination_id)\n if not page_to_move.permissions_for_user(request.user).can_move_to(destination):\n raise PermissionDenied\n\n if request.POST:\n # any invalid moves *should* be caught by the permission check above,\n # so don't bother to catch InvalidMoveToDescendant\n\n page_to_move.move(destination, pos='last-child')\n\n messages.success(request, _(\"Page '{0}' moved.\").format(page_to_move.title))\n return redirect('wagtailadmin_explore', destination.id)\n\n return render(request, 'wagtailadmin/pages/confirm_move.html', {\n 'page_to_move': page_to_move,\n 'destination': destination,\n })","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::set_page_position","text":"wagtail/wagtailadmin/views/pages.py::set_page_position\ndef set_page_position(request, page_to_move_id):\n page_to_move = get_object_or_404(Page, id=page_to_move_id)\n parent_page = page_to_move.get_parent()\n\n if not parent_page.permissions_for_user(request.user).can_reorder_children():\n raise PermissionDenied\n\n if request.POST:\n # Get position parameter\n position = request.GET.get('position', None)\n\n # Find page thats already in this position\n position_page = None\n if position is not None:\n try:\n position_page = parent_page.get_children()[int(position)]\n except IndexError:\n pass # No page in this position\n\n # Move page\n\n # any invalid moves *should* be caught by the permission check above,\n # so don't bother to catch InvalidMoveToDescendant\n\n if position_page:\n # If the page has been moved to the right, insert it to the\n # right. If left, then left.\n old_position = list(parent_page.get_children()).index(page_to_move)\n if int(position) < old_position:\n page_to_move.move(position_page, pos='left')\n elif int(position) > old_position:\n page_to_move.move(position_page, pos='right')\n else:\n # Move page to end\n page_to_move.move(parent_page, pos='last-child')\n\n return HttpResponse('')","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::copy","text":"wagtail/wagtailadmin/views/pages.py::copy\ndef copy(request, page_id):\n page = Page.objects.get(id=page_id)\n\n # Parent page defaults to parent of source page\n parent_page = page.get_parent()\n\n # Check if the user has permission to publish subpages on the parent\n can_publish = parent_page.permissions_for_user(request.user).can_publish_subpage()\n\n # Create the form\n form = CopyForm(request.POST or None, page=page, can_publish=can_publish)\n\n # Check if user is submitting\n if request.method == 'POST':\n # Prefill parent_page in case the form is invalid (as prepopulated value for the form field,\n # because ModelChoiceField seems to not fall back to the user given value)\n parent_page = Page.objects.get(id=request.POST['new_parent_page'])\n\n if form.is_valid():\n # Receive the parent page (this should never be empty)\n if form.cleaned_data['new_parent_page']:\n parent_page = form.cleaned_data['new_parent_page']\n\n # Make sure this user has permission to add subpages on the parent\n if not parent_page.permissions_for_user(request.user).can_add_subpage():\n raise PermissionDenied\n\n # Re-check if the user has permission to publish subpages on the new parent\n can_publish = parent_page.permissions_for_user(request.user).can_publish_subpage()\n\n # Copy the page\n new_page = page.copy(\n recursive=form.cleaned_data.get('copy_subpages'),\n to=parent_page,\n update_attrs={\n 'title': form.cleaned_data['new_title'],\n 'slug': form.cleaned_data['new_slug'],\n },\n keep_live=(can_publish and form.cleaned_data.get('publish_copies')),\n user=request.user,\n )\n\n # Give a success message back to the user\n if form.cleaned_data.get('copy_subpages'):\n messages.success(","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::get_page_edit_handler","text":"wagtail/wagtailadmin/views/pages.py::get_page_edit_handler\ndef get_page_edit_handler(page_class):\n if page_class not in PAGE_EDIT_HANDLERS:\n PAGE_EDIT_HANDLERS[page_class] = TabbedInterface([\n ObjectList(page_class.content_panels, heading='Content'),\n ObjectList(page_class.promote_panels, heading='Promote'),\n ObjectList(page_class.settings_panels, heading='Settings', classname=\"settings\")\n ])\n\n return PAGE_EDIT_HANDLERS[page_class]","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::search","text":"wagtail/wagtailadmin/views/pages.py::search\n@vary_on_headers('X-Requested-With')\ndef search(request):\n pages = []\n q = None\n is_searching = False\n if 'q' in request.GET:\n form = SearchForm(request.GET)\n if form.is_valid():\n q = form.cleaned_data['q']\n\n # page number\n p = request.GET.get(\"p\", 1)\n is_searching = True\n pages = Page.search(q, show_unpublished=True, search_title_only=True, prefetch_related=['content_type'])\n\n # Pagination\n paginator = Paginator(pages, 20)\n try:\n pages = paginator.page(p)\n except PageNotAnInteger:\n pages = paginator.page(1)\n except EmptyPage:\n pages = paginator.page(paginator.num_pages)\n else:\n form = SearchForm()\n\n if request.is_ajax():\n return render(request, \"wagtailadmin/pages/search_results.html\", {\n 'pages': pages,\n 'is_searching': is_searching,\n 'query_string': q,\n })\n else:\n return render(request, \"wagtailadmin/pages/search.html\", {\n 'search_form': form,\n 'pages': pages,\n 'is_searching': is_searching,\n 'query_string': q,\n })","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::approve_moderation","text":"wagtail/wagtailadmin/views/pages.py::approve_moderation\ndef approve_moderation(request, revision_id):\n revision = get_object_or_404(PageRevision, id=revision_id)\n if not revision.page.permissions_for_user(request.user).can_publish():\n raise PermissionDenied\n\n if not revision.submitted_for_moderation:\n messages.error(request, _(\"The page '{0}' is not currently awaiting moderation.\").format(revision.page.title))\n return redirect('wagtailadmin_home')\n\n if request.method == 'POST':\n revision.approve_moderation()\n messages.success(request, _(\"Page '{0}' published.\").format(revision.page.title))\n tasks.send_notification.delay(revision.id, 'approved', request.user.id)\n\n return redirect('wagtailadmin_home')","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::reject_moderation","text":"wagtail/wagtailadmin/views/pages.py::reject_moderation\ndef reject_moderation(request, revision_id):\n revision = get_object_or_404(PageRevision, id=revision_id)\n if not revision.page.permissions_for_user(request.user).can_publish():\n raise PermissionDenied\n\n if not revision.submitted_for_moderation:\n messages.error(request, _(\"The page '{0}' is not currently awaiting moderation.\").format( revision.page.title))\n return redirect('wagtailadmin_home')\n\n if request.method == 'POST':\n revision.reject_moderation()\n messages.success(request, _(\"Page '{0}' rejected for publication.\").format(revision.page.title))\n tasks.send_notification.delay(revision.id, 'rejected', request.user.id)\n\n return redirect('wagtailadmin_home')","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::preview_for_moderation","text":"wagtail/wagtailadmin/views/pages.py::preview_for_moderation\n@require_GET\ndef preview_for_moderation(request, revision_id):\n revision = get_object_or_404(PageRevision, id=revision_id)\n if not revision.page.permissions_for_user(request.user).can_publish():\n raise PermissionDenied\n\n if not revision.submitted_for_moderation:\n messages.error(request, _(\"The page '{0}' is not currently awaiting moderation.\").format(revision.page.title))\n return redirect('wagtailadmin_home')\n\n page = revision.as_page_object()\n\n request.revision_id = revision_id\n\n # pass in the real user request rather than page.dummy_request(), so that request.user\n # and request.revision_id will be picked up by the wagtail user bar\n return page.serve_preview(request, page.default_preview_mode)","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::lock","text":"wagtail/wagtailadmin/views/pages.py::lock\n@require_POST\ndef lock(request, page_id):\n # Get the page\n page = get_object_or_404(Page, id=page_id).specific\n\n # Check permissions\n if not page.permissions_for_user(request.user).can_lock():\n raise PermissionDenied\n\n # Lock the page\n if not page.locked:\n page.locked = True\n page.save()\n\n messages.success(request, _(\"Page '{0}' is now locked.\").format(page.title))\n\n # Redirect\n redirect_to = request.POST.get('next', None)\n if redirect_to and is_safe_url(url=redirect_to, host=request.get_host()):\n return redirect(redirect_to)\n else:\n return redirect('wagtailadmin_explore', page.get_parent().id)","label":0},{"cid":"wagtail/wagtailadmin/views/pages.py::unlock","text":"wagtail/wagtailadmin/views/pages.py::unlock\n@require_POST\ndef unlock(request, page_id):\n # Get the page\n page = get_object_or_404(Page, id=page_id).specific\n\n # Check permissions\n if not page.permissions_for_user(request.user).can_lock():\n raise PermissionDenied\n\n # Unlock the page\n if page.locked:\n page.locked = False\n page.save()\n\n messages.success(request, _(\"Page '{0}' is now unlocked.\").format(page.title))\n\n # Redirect\n redirect_to = request.POST.get('next', None)\n if redirect_to and is_safe_url(url=redirect_to, host=request.get_host()):\n return redirect(redirect_to)\n else:\n return redirect('wagtailadmin_explore', page.get_parent().id)","label":0},{"cid":"wagtail/wagtailcore/models.py::SiteManager.get_by_natural_key","text":"wagtail/wagtailcore/models.py::SiteManager.get_by_natural_key\n def get_by_natural_key(self, hostname, port):\n return self.get(hostname=hostname, port=port)","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.natural_key","text":"wagtail/wagtailcore/models.py::Site.natural_key\n def natural_key(self):\n return (self.hostname, self.port)","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.__str__","text":"wagtail/wagtailcore/models.py::Site.__str__\n def __str__(self):\n return self.hostname + (\"\" if self.port == 80 else (\":%d\" % self.port)) + (\" [default]\" if self.is_default_site else \"\")","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.find_for_request","text":"wagtail/wagtailcore/models.py::Site.find_for_request\n @staticmethod\n def find_for_request(request):\n \"\"\"\n Find the site object responsible for responding to this HTTP\n request object. Try:\n - unique hostname first\n - then hostname and port\n - if there is no matching hostname at all, or no matching\n hostname:port combination, fall back to the unique default site,\n or raise an exception\n NB this means that high-numbered ports on an extant hostname may\n still be routed to a different hostname which is set as the default\n \"\"\"\n try:\n hostname = request.META['HTTP_HOST'].split(':')[0] # KeyError here goes to the final except clause\n try:\n # find a Site matching this specific hostname\n return Site.objects.get(hostname=hostname) # Site.DoesNotExist here goes to the final except clause\n except Site.MultipleObjectsReturned:\n # as there were more than one, try matching by port too\n port = request.META['SERVER_PORT'] # KeyError here goes to the final except clause\n return Site.objects.get(hostname=hostname, port=int(port)) # Site.DoesNotExist here goes to the final except clause\n except (Site.DoesNotExist, KeyError):\n # If no matching site exists, or request does not specify an HTTP_HOST (which\n # will often be the case for the Django test client), look for a catch-all Site.\n # If that fails, let the Site.DoesNotExist propagate back to the caller\n return Site.objects.get(is_default_site=True)","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.root_url","text":"wagtail/wagtailcore/models.py::Site.root_url\n @property\n def root_url(self):\n if self.port == 80:\n return 'http://%s' % self.hostname\n elif self.port == 443:\n return 'https://%s' % self.hostname\n else:\n return 'http://%s:%d' % (self.hostname, self.port)","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.clean_fields","text":"wagtail/wagtailcore/models.py::Site.clean_fields\n def clean_fields(self, exclude=None):\n super(Site, self).clean_fields(exclude)\n # Only one site can have the is_default_site flag set\n try:\n default = Site.objects.get(is_default_site=True)\n except Site.DoesNotExist:\n pass\n except Site.MultipleObjectsReturned:\n raise\n else:\n if self.is_default_site and self.pk != default.pk:\n raise ValidationError(\n {'is_default_site': [\n _(\"%(hostname)s is already configured as the default site. You must unset that before you can save this site as default.\")\n % {'hostname': default.hostname}\n ]}\n )","label":0},{"cid":"wagtail/wagtailcore/models.py::Site.get_site_root_paths","text":"wagtail/wagtailcore/models.py::Site.get_site_root_paths\n @staticmethod\n def get_site_root_paths():\n \"\"\"\n Return a list of (root_path, root_url) tuples, most specific path first -\n used to translate url_paths into actual URLs with hostnames\n \"\"\"\n result = cache.get('wagtail_site_root_paths')\n\n if result is None:\n result = [\n (site.id, site.root_page.url_path, site.root_url)\n for site in Site.objects.select_related('root_page').order_by('-root_page__url_path')\n ]\n cache.set('wagtail_site_root_paths', result, 3600)\n\n return result","label":0},{"cid":"wagtail/wagtailcore/models.py::clear_site_root_paths_on_save","text":"wagtail/wagtailcore/models.py::clear_site_root_paths_on_save\n@receiver(post_save, sender=Site)\ndef clear_site_root_paths_on_save(sender, instance, **kwargs):\n cache.delete('wagtail_site_root_paths')","label":0},{"cid":"wagtail/wagtailcore/models.py::clear_site_root_paths_on_delete","text":"wagtail/wagtailcore/models.py::clear_site_root_paths_on_delete\n@receiver(post_delete, sender=Site)\ndef clear_site_root_paths_on_delete(sender, instance, **kwargs):\n cache.delete('wagtail_site_root_paths')","label":0},{"cid":"wagtail/wagtailcore/models.py::get_page_types","text":"wagtail/wagtailcore/models.py::get_page_types\ndef get_page_types():\n global _PAGE_CONTENT_TYPES\n if len(_PAGE_CONTENT_TYPES) != len(PAGE_MODEL_CLASSES):\n _PAGE_CONTENT_TYPES = [\n ContentType.objects.get_for_model(cls) for cls in PAGE_MODEL_CLASSES\n ]\n return _PAGE_CONTENT_TYPES","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.get_queryset","text":"wagtail/wagtailcore/models.py::PageManager.get_queryset\n def get_queryset(self):\n return PageQuerySet(self.model).order_by('path')","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.live","text":"wagtail/wagtailcore/models.py::PageManager.live\n def live(self):\n return self.get_queryset().live()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_live","text":"wagtail/wagtailcore/models.py::PageManager.not_live\n def not_live(self):\n return self.get_queryset().not_live()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.in_menu","text":"wagtail/wagtailcore/models.py::PageManager.in_menu\n def in_menu(self):\n return self.get_queryset().in_menu()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_in_menu","text":"wagtail/wagtailcore/models.py::PageManager.not_in_menu\n def not_in_menu(self):\n return self.get_queryset().not_in_menu()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.page","text":"wagtail/wagtailcore/models.py::PageManager.page\n def page(self, other):\n return self.get_queryset().page(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_page","text":"wagtail/wagtailcore/models.py::PageManager.not_page\n def not_page(self, other):\n return self.get_queryset().not_page(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.descendant_of","text":"wagtail/wagtailcore/models.py::PageManager.descendant_of\n def descendant_of(self, other, inclusive=False):\n return self.get_queryset().descendant_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_descendant_of","text":"wagtail/wagtailcore/models.py::PageManager.not_descendant_of\n def not_descendant_of(self, other, inclusive=False):\n return self.get_queryset().not_descendant_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.child_of","text":"wagtail/wagtailcore/models.py::PageManager.child_of\n def child_of(self, other):\n return self.get_queryset().child_of(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_child_of","text":"wagtail/wagtailcore/models.py::PageManager.not_child_of\n def not_child_of(self, other):\n return self.get_queryset().not_child_of(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.ancestor_of","text":"wagtail/wagtailcore/models.py::PageManager.ancestor_of\n def ancestor_of(self, other, inclusive=False):\n return self.get_queryset().ancestor_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_ancestor_of","text":"wagtail/wagtailcore/models.py::PageManager.not_ancestor_of\n def not_ancestor_of(self, other, inclusive=False):\n return self.get_queryset().not_ancestor_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.parent_of","text":"wagtail/wagtailcore/models.py::PageManager.parent_of\n def parent_of(self, other):\n return self.get_queryset().parent_of(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_parent_of","text":"wagtail/wagtailcore/models.py::PageManager.not_parent_of\n def not_parent_of(self, other):\n return self.get_queryset().not_parent_of(other)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.sibling_of","text":"wagtail/wagtailcore/models.py::PageManager.sibling_of\n def sibling_of(self, other, inclusive=False):\n return self.get_queryset().sibling_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_sibling_of","text":"wagtail/wagtailcore/models.py::PageManager.not_sibling_of\n def not_sibling_of(self, other, inclusive=False):\n return self.get_queryset().not_sibling_of(other, inclusive)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.type","text":"wagtail/wagtailcore/models.py::PageManager.type\n def type(self, model):\n return self.get_queryset().type(model)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_type","text":"wagtail/wagtailcore/models.py::PageManager.not_type\n def not_type(self, model):\n return self.get_queryset().not_type(model)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.public","text":"wagtail/wagtailcore/models.py::PageManager.public\n def public(self):\n return self.get_queryset().public()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.not_public","text":"wagtail/wagtailcore/models.py::PageManager.not_public\n def not_public(self):\n return self.get_queryset().not_public()","label":0},{"cid":"wagtail/wagtailcore/models.py::PageManager.search","text":"wagtail/wagtailcore/models.py::PageManager.search\n def search(self, query_string, fields=None, backend='default'):\n return self.get_queryset().search(query_string, fields=fields, backend=backend)","label":0},{"cid":"wagtail/wagtailcore/models.py::PageBase.__init__","text":"wagtail/wagtailcore/models.py::PageBase.__init__\n def __init__(cls, name, bases, dct):\n super(PageBase, cls).__init__(name, bases, dct)\n\n if cls._deferred:\n # this is an internal class built for Django's deferred-attribute mechanism;\n # don't proceed with all this page type registration stuff\n return\n\n # Add page manager\n PageManager().contribute_to_class(cls, 'objects')\n\n if 'template' not in dct:\n # Define a default template path derived from the app name and model name\n cls.template = \"%s/%s.html\" % (cls._meta.app_label, camelcase_to_underscore(name))\n\n if 'ajax_template' not in dct:\n cls.ajax_template = None\n\n cls._clean_subpage_types = None # to be filled in on first call to cls.clean_subpage_types\n cls._clean_parent_page_types = None # to be filled in on first call to cls.clean_parent_page_types\n\n if not dct.get('is_abstract'):\n # subclasses are only abstract if the subclass itself defines itself so\n cls.is_abstract = False\n\n if not cls.is_abstract:\n # register this type in the list of page content types\n PAGE_MODEL_CLASSES.append(cls)","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.__init__","text":"wagtail/wagtailcore/models.py::Page.__init__\n def __init__(self, *args, **kwargs):\n super(Page, self).__init__(*args, **kwargs)\n if not self.id and not self.content_type_id:\n # this model is being newly created rather than retrieved from the db;\n # set content type to correctly represent the model class that this was\n # created as\n self.content_type = ContentType.objects.get_for_model(self)","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.__str__","text":"wagtail/wagtailcore/models.py::Page.__str__\n def __str__(self):\n return self.title","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.set_url_path","text":"wagtail/wagtailcore/models.py::Page.set_url_path\n def set_url_path(self, parent):\n \"\"\"\n Populate the url_path field based on this page's slug and the specified parent page.\n (We pass a parent in here, rather than retrieving it via get_parent, so that we can give\n new unsaved pages a meaningful URL when previewing them; at that point the page has not\n been assigned a position in the tree, as far as treebeard is concerned.\n \"\"\"\n if parent:\n self.url_path = parent.url_path + self.slug + '/'\n else:\n # a page without a parent is the tree root, which always has a url_path of '/'\n self.url_path = '/'\n\n return self.url_path","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.save","text":"wagtail/wagtailcore/models.py::Page.save\n @transaction.atomic # ensure that changes are only committed when we have updated all descendant URL paths, to preserve consistency\n def save(self, *args, **kwargs):\n update_descendant_url_paths = False\n is_new = self.id is None\n\n if is_new:\n # we are creating a record. If we're doing things properly, this should happen\n # through a treebeard method like add_child, in which case the 'path' field\n # has been set and so we can safely call get_parent\n self.set_url_path(self.get_parent())\n else:\n # Check that we are committing the slug to the database\n # Basically: If update_fields has been specified, and slug is not included, skip this step\n if not ('update_fields' in kwargs and 'slug' not in kwargs['update_fields']):\n # see if the slug has changed from the record in the db, in which case we need to\n # update url_path of self and all descendants\n old_record = Page.objects.get(id=self.id)\n if old_record.slug != self.slug:\n self.set_url_path(self.get_parent())\n update_descendant_url_paths = True\n old_url_path = old_record.url_path\n new_url_path = self.url_path\n\n result = super(Page, self).save(*args, **kwargs)\n\n if update_descendant_url_paths:\n self._update_descendant_url_paths(old_url_path, new_url_path)\n\n # Check if this is a root page of any sites and clear the 'wagtail_site_root_paths' key if so\n if Site.objects.filter(root_page=self).exists():\n cache.delete('wagtail_site_root_paths')\n\n # Log\n if is_new:\n cls = type(self)\n logger.info(\"Page created: \\\"%s\\\" id=%d content_type=%s.%s path=%s\", self.title, self.id, cls._meta.app_label, cls.__name__, self.url_path)\n\n return result","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.check","text":"wagtail/wagtailcore/models.py::Page.check\n @classmethod\n def check(cls, **kwargs):\n errors = super(Page, cls).check(**kwargs)\n\n # Check that foreign keys from pages are not configured to cascade\n # This is the default Django behaviour which must be explicitly overridden\n # to prevent pages disappearing unexpectedly and the tree being corrupted\n\n # get names of foreign keys pointing to parent classes (such as page_ptr)\n field_exceptions = [field.name\n for model in [cls] + list(cls._meta.get_parent_list())\n for field in model._meta.parents.values()]\n\n field_exceptions += ['content_type']\n\n\n for field in cls._meta.fields:\n if isinstance(field, models.ForeignKey) and field.name not in field_exceptions:\n if field.rel.on_delete == models.CASCADE:\n errors.append(\n checks.Warning(\n \"Field hasn't specified on_delete action\",\n hint=\"Set on_delete=models.SET_NULL and make sure the field is nullable.\",\n obj=field,\n id='wagtailcore.W001',\n )\n )\n\n return errors","label":0},{"cid":"wagtail/wagtailcore/models.py::Page._update_descendant_url_paths","text":"wagtail/wagtailcore/models.py::Page._update_descendant_url_paths\n def _update_descendant_url_paths(self, old_url_path, new_url_path):\n cursor = connection.cursor()\n if connection.vendor == 'sqlite':\n update_statement = \"\"\"\n UPDATE wagtailcore_page\n SET url_path = %s || substr(url_path, %s)\n WHERE path LIKE %s AND id <> %s\n \"\"\"\n elif connection.vendor == 'mysql':\n update_statement = \"\"\"\n UPDATE wagtailcore_page\n SET url_path= CONCAT(%s, substring(url_path, %s))\n WHERE path LIKE %s AND id <> %s\n \"\"\"\n else:\n update_statement = \"\"\"\n UPDATE wagtailcore_page\n SET url_path = %s || substring(url_path from %s)\n WHERE path LIKE %s AND id <> %s\n \"\"\"\n cursor.execute(update_statement, [new_url_path, len(old_url_path) + 1, self.path + '%', self.id])","label":0},{"cid":"wagtail/wagtailcore/models.py::Page.specific","text":"wagtail/wagtailcore/models.py::Page.specific\n @cached_property\n def specific(self):\n \"\"\"\n Return this page in its most specific subclassed form.\n \"\"\"\n # the ContentType.objects manager keeps a cache, so this should potentially\n # avoid a database lookup over doing self.content_type. I think.\n content_type = ContentType.objects.get_for_id(self.content_type_id)\n if isinstance(self, content_type.model_class()):\n # self is already the an instance of the most specific class\n return self\n else:\n return content_type.get_object_for_this_type(id=self.id)","label":0}],"string":"[\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::explorer_nav\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::explorer_nav\\ndef explorer_nav(request):\\n return render(request, 'wagtailadmin/shared/explorer_nav.html', {\\n 'nodes': get_navigation_menu_items(),\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::index\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::index\\ndef index(request, parent_page_id=None):\\n if parent_page_id:\\n parent_page = get_object_or_404(Page, id=parent_page_id)\\n else:\\n parent_page = Page.get_first_root_node()\\n\\n pages = parent_page.get_children().prefetch_related('content_type')\\n\\n # Get page ordering\\n ordering = request.GET.get('ordering', '-latest_revision_created_at')\\n if ordering not in ['title', '-title', 'content_type', '-content_type', 'live', '-live', 'latest_revision_created_at', '-latest_revision_created_at', 'ord']:\\n ordering = '-latest_revision_created_at'\\n\\n # Pagination\\n if ordering != 'ord':\\n ordering_no_minus = ordering\\n if ordering_no_minus.startswith('-'):\\n ordering_no_minus = ordering[1:]\\n pages = pages.order_by(ordering).annotate(null_position=Count(ordering_no_minus)).order_by('-null_position', ordering)\\n\\n p = request.GET.get('p', 1)\\n paginator = Paginator(pages, 50)\\n try:\\n pages = paginator.page(p)\\n except PageNotAnInteger:\\n pages = paginator.page(1)\\n except EmptyPage:\\n pages = paginator.page(paginator.num_pages)\\n\\n return render(request, 'wagtailadmin/pages/index.html', {\\n 'parent_page': parent_page,\\n 'ordering': ordering,\\n 'pages': pages,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::add_subpage\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::add_subpage\\ndef add_subpage(request, parent_page_id):\\n parent_page = get_object_or_404(Page, id=parent_page_id).specific\\n if not parent_page.permissions_for_user(request.user).can_add_subpage():\\n raise PermissionDenied\\n\\n page_types = sorted(parent_page.allowed_subpage_types(),\\n key=lambda pagetype: pagetype.model_class().get_verbose_name().lower()\\n )\\n\\n if len(page_types) == 1:\\n # Only one page type is available - redirect straight to the create form rather than\\n # making the user choose\\n content_type = page_types[0]\\n return redirect('wagtailadmin_pages_create', content_type.app_label, content_type.model, parent_page.id)\\n\\n return render(request, 'wagtailadmin/pages/add_subpage.html', {\\n 'parent_page': parent_page,\\n 'page_types': page_types,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::content_type_use\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::content_type_use\\ndef content_type_use(request, content_type_app_name, content_type_model_name):\\n try:\\n content_type = ContentType.objects.get_by_natural_key(content_type_app_name, content_type_model_name)\\n except ContentType.DoesNotExist:\\n raise Http404\\n\\n p = request.GET.get(\\\"p\\\", 1)\\n\\n page_class = content_type.model_class()\\n\\n # page_class must be a Page type and not some other random model\\n if not issubclass(page_class, Page):\\n raise Http404\\n\\n pages = page_class.objects.all()\\n\\n paginator = Paginator(pages, 10)\\n\\n try:\\n pages = paginator.page(p)\\n except PageNotAnInteger:\\n pages = paginator.page(1)\\n except EmptyPage:\\n pages = paginator.page(paginator.num_pages)\\n\\n return render(request, 'wagtailadmin/pages/content_type_use.html', {\\n 'pages': pages,\\n 'app_name': content_type_app_name,\\n 'content_type': content_type,\\n 'page_class': page_class,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::create\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::create\\ndef create(request, content_type_app_name, content_type_model_name, parent_page_id):\\n parent_page = get_object_or_404(Page, id=parent_page_id).specific\\n parent_page_perms = parent_page.permissions_for_user(request.user)\\n if not parent_page_perms.can_add_subpage():\\n raise PermissionDenied\\n\\n try:\\n content_type = ContentType.objects.get_by_natural_key(content_type_app_name, content_type_model_name)\\n except ContentType.DoesNotExist:\\n raise Http404\\n\\n # Get class\\n page_class = content_type.model_class()\\n\\n # Make sure the class is a descendant of Page\\n if not issubclass(page_class, Page):\\n raise Http404\\n\\n # page must be in the list of allowed subpage types for this parent ID\\n if content_type not in parent_page.allowed_subpage_types():\\n raise PermissionDenied\\n\\n page = page_class(owner=request.user)\\n edit_handler_class = get_page_edit_handler(page_class)\\n form_class = edit_handler_class.get_form_class(page_class)\\n\\n if request.POST:\\n form = form_class(request.POST, request.FILES, instance=page)\\n\\n # Stick an extra validator into the form to make sure that the slug is not already in use\\n def clean_slug(slug):\\n # Make sure the slug isn't already in use\\n if parent_page.get_children().filter(slug=slug).count() > 0:\\n raise ValidationError(_(\\\"This slug is already in use\\\"))\\n return slug\\n form.fields['slug'].clean = clean_slug\\n\\n # Stick another validator into the form to check that the scheduled publishing settings are set correctly\\n def clean():\\n cleaned_data = form_class.clean(form)\\n\\n # Go live must be before expire\\n go_live_at = cleaned_data.get('go_live_at')\\n expire_at = cleaned_data.get('expire_at')\\n\\n if go_live_at and expire_at:\\n if go_live_at > expire_at:\\n msg = _('Go live date/ti\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::edit\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::edit\\ndef edit(request, page_id):\\n latest_revision = get_object_or_404(Page, id=page_id).get_latest_revision()\\n page = get_object_or_404(Page, id=page_id).get_latest_revision_as_page()\\n parent = page.get_parent()\\n\\n content_type = ContentType.objects.get_for_model(page)\\n\\n page_perms = page.permissions_for_user(request.user)\\n if not page_perms.can_edit():\\n raise PermissionDenied\\n\\n edit_handler_class = get_page_edit_handler(page.__class__)\\n form_class = edit_handler_class.get_form_class(page.__class__)\\n\\n errors_debug = None\\n\\n if request.POST:\\n form = form_class(request.POST, request.FILES, instance=page)\\n\\n # Stick an extra validator into the form to make sure that the slug is not already in use\\n def clean_slug(slug):\\n # Make sure the slug isn't already in use\\n if parent.get_children().filter(slug=slug).exclude(id=page_id).count() > 0:\\n raise ValidationError(_(\\\"This slug is already in use\\\"))\\n return slug\\n form.fields['slug'].clean = clean_slug\\n\\n # Stick another validator into the form to check that the scheduled publishing settings are set correctly\\n def clean():\\n cleaned_data = form_class.clean(form)\\n\\n # Go live must be before expire\\n go_live_at = cleaned_data.get('go_live_at')\\n expire_at = cleaned_data.get('expire_at')\\n\\n if go_live_at and expire_at:\\n if go_live_at > expire_at:\\n msg = _('Go live date/time must be before expiry date/time')\\n form._errors['go_live_at'] = form.error_class([msg])\\n form._errors['expire_at'] = form.error_class([msg])\\n del cleaned_data['go_live_at']\\n del cleaned_data['expire_at']\\n\\n # Expire must be in the future\\n expire_at = cleaned_data.get('expire_at')\\n\\n if expire_at and expire_at < time\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::delete\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::delete\\ndef delete(request, page_id):\\n page = get_object_or_404(Page, id=page_id).specific\\n if not page.permissions_for_user(request.user).can_delete():\\n raise PermissionDenied\\n\\n if request.POST:\\n parent_id = page.get_parent().id\\n page.delete()\\n\\n messages.success(request, _(\\\"Page '{0}' deleted.\\\").format(page.title))\\n\\n for fn in hooks.get_hooks('after_delete_page'):\\n result = fn(request, page)\\n if hasattr(result, 'status_code'):\\n return result\\n\\n return redirect('wagtailadmin_explore', parent_id)\\n\\n return render(request, 'wagtailadmin/pages/confirm_delete.html', {\\n 'page': page,\\n 'descendant_count': page.get_descendant_count()\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::view_draft\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::view_draft\\ndef view_draft(request, page_id):\\n page = get_object_or_404(Page, id=page_id).get_latest_revision_as_page()\\n return page.serve_preview(page.dummy_request(), page.default_preview_mode)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::preview_on_edit\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::preview_on_edit\\ndef preview_on_edit(request, page_id):\\n # Receive the form submission that would typically be posted to the 'edit' view. If submission is valid,\\n # return the rendered page; if not, re-render the edit form\\n page = get_object_or_404(Page, id=page_id).get_latest_revision_as_page()\\n edit_handler_class = get_page_edit_handler(page.__class__)\\n form_class = edit_handler_class.get_form_class(page.__class__)\\n\\n form = form_class(request.POST, request.FILES, instance=page)\\n\\n if form.is_valid():\\n form.save(commit=False)\\n\\n preview_mode = request.GET.get('mode', page.default_preview_mode)\\n response = page.serve_preview(page.dummy_request(), preview_mode)\\n response['X-Wagtail-Preview'] = 'ok'\\n return response\\n\\n else:\\n edit_handler = edit_handler_class(instance=page, form=form)\\n\\n response = render(request, 'wagtailadmin/pages/edit.html', {\\n 'page': page,\\n 'edit_handler': edit_handler,\\n 'preview_modes': page.preview_modes,\\n })\\n response['X-Wagtail-Preview'] = 'error'\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::preview_on_create\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::preview_on_create\\ndef preview_on_create(request, content_type_app_name, content_type_model_name, parent_page_id):\\n # Receive the form submission that would typically be posted to the 'create' view. If submission is valid,\\n # return the rendered page; if not, re-render the edit form\\n try:\\n content_type = ContentType.objects.get_by_natural_key(content_type_app_name, content_type_model_name)\\n except ContentType.DoesNotExist:\\n raise Http404\\n\\n page_class = content_type.model_class()\\n page = page_class()\\n edit_handler_class = get_page_edit_handler(page_class)\\n form_class = edit_handler_class.get_form_class(page_class)\\n\\n form = form_class(request.POST, request.FILES, instance=page)\\n\\n if form.is_valid():\\n form.save(commit=False)\\n\\n # ensure that our unsaved page instance has a suitable url set\\n parent_page = get_object_or_404(Page, id=parent_page_id).specific\\n page.set_url_path(parent_page)\\n\\n # Set treebeard attributes\\n page.depth = parent_page.depth + 1\\n page.path = Page._get_children_path_interval(parent_page.path)[1]\\n\\n preview_mode = request.GET.get('mode', page.default_preview_mode)\\n response = page.serve_preview(page.dummy_request(), preview_mode)\\n response['X-Wagtail-Preview'] = 'ok'\\n return response\\n\\n else:\\n edit_handler = edit_handler_class(instance=page, form=form)\\n parent_page = get_object_or_404(Page, id=parent_page_id).specific\\n\\n response = render(request, 'wagtailadmin/pages/create.html', {\\n 'content_type': content_type,\\n 'page_class': page_class,\\n 'parent_page': parent_page,\\n 'edit_handler': edit_handler,\\n 'preview_modes': page.preview_modes,\\n })\\n response['X-Wagtail-Preview'] = 'error'\\n return response\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::preview\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::preview\\ndef preview(request):\\n \\\"\\\"\\\"\\n The HTML of a previewed page is written to the destination browser window using document.write.\\n This overwrites any previous content in the window, while keeping its URL intact. This in turn\\n means that any content we insert that happens to trigger an HTTP request, such as an image or\\n stylesheet tag, will report that original URL as its referrer.\\n\\n In Webkit browsers, a new window opened with window.open('', 'window_name') will have a location\\n of 'about:blank', causing it to omit the Referer header on those HTTP requests. This means that\\n any third-party font services that use the Referer header for access control will refuse to\\n serve us.\\n\\n So, instead, we need to open the window on some arbitrary URL on our domain. (Provided that's\\n also the same domain as our editor JS code, the browser security model will happily allow us to\\n document.write over the page in question.)\\n\\n This, my friends, is that arbitrary URL.\\n\\n Since we're going to this trouble, we'll also take the opportunity to display a spinner on the\\n placeholder page, providing some much-needed visual feedback.\\n \\\"\\\"\\\"\\n return render(request, 'wagtailadmin/pages/preview.html')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::preview_loading\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::preview_loading\\ndef preview_loading(request):\\n \\\"\\\"\\\"\\n This page is blank, but must be real HTML so its DOM can be written to once the preview of the page has rendered\\n \\\"\\\"\\\"\\n return HttpResponse(\\\"<html><head><title></title></head><body></body></html>\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::unpublish\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::unpublish\\ndef unpublish(request, page_id):\\n page = get_object_or_404(Page, id=page_id).specific\\n if not page.permissions_for_user(request.user).can_unpublish():\\n raise PermissionDenied\\n\\n if request.method == 'POST':\\n page.unpublish()\\n\\n messages.success(request, _(\\\"Page '{0}' unpublished.\\\").format(page.title))\\n\\n return redirect('wagtailadmin_explore', page.get_parent().id)\\n\\n return render(request, 'wagtailadmin/pages/confirm_unpublish.html', {\\n 'page': page,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::move_choose_destination\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::move_choose_destination\\ndef move_choose_destination(request, page_to_move_id, viewed_page_id=None):\\n page_to_move = get_object_or_404(Page, id=page_to_move_id)\\n page_perms = page_to_move.permissions_for_user(request.user)\\n if not page_perms.can_move():\\n raise PermissionDenied\\n\\n if viewed_page_id:\\n viewed_page = get_object_or_404(Page, id=viewed_page_id)\\n else:\\n viewed_page = Page.get_first_root_node()\\n\\n viewed_page.can_choose = page_perms.can_move_to(viewed_page)\\n\\n child_pages = []\\n for target in viewed_page.get_children():\\n # can't move the page into itself or its descendants\\n target.can_choose = page_perms.can_move_to(target)\\n\\n target.can_descend = not(target == page_to_move or target.is_child_of(page_to_move)) and target.get_children_count()\\n\\n child_pages.append(target)\\n\\n return render(request, 'wagtailadmin/pages/move_choose_destination.html', {\\n 'page_to_move': page_to_move,\\n 'viewed_page': viewed_page,\\n 'child_pages': child_pages,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::move_confirm\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::move_confirm\\ndef move_confirm(request, page_to_move_id, destination_id):\\n page_to_move = get_object_or_404(Page, id=page_to_move_id).specific\\n destination = get_object_or_404(Page, id=destination_id)\\n if not page_to_move.permissions_for_user(request.user).can_move_to(destination):\\n raise PermissionDenied\\n\\n if request.POST:\\n # any invalid moves *should* be caught by the permission check above,\\n # so don't bother to catch InvalidMoveToDescendant\\n\\n page_to_move.move(destination, pos='last-child')\\n\\n messages.success(request, _(\\\"Page '{0}' moved.\\\").format(page_to_move.title))\\n return redirect('wagtailadmin_explore', destination.id)\\n\\n return render(request, 'wagtailadmin/pages/confirm_move.html', {\\n 'page_to_move': page_to_move,\\n 'destination': destination,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::set_page_position\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::set_page_position\\ndef set_page_position(request, page_to_move_id):\\n page_to_move = get_object_or_404(Page, id=page_to_move_id)\\n parent_page = page_to_move.get_parent()\\n\\n if not parent_page.permissions_for_user(request.user).can_reorder_children():\\n raise PermissionDenied\\n\\n if request.POST:\\n # Get position parameter\\n position = request.GET.get('position', None)\\n\\n # Find page thats already in this position\\n position_page = None\\n if position is not None:\\n try:\\n position_page = parent_page.get_children()[int(position)]\\n except IndexError:\\n pass # No page in this position\\n\\n # Move page\\n\\n # any invalid moves *should* be caught by the permission check above,\\n # so don't bother to catch InvalidMoveToDescendant\\n\\n if position_page:\\n # If the page has been moved to the right, insert it to the\\n # right. If left, then left.\\n old_position = list(parent_page.get_children()).index(page_to_move)\\n if int(position) < old_position:\\n page_to_move.move(position_page, pos='left')\\n elif int(position) > old_position:\\n page_to_move.move(position_page, pos='right')\\n else:\\n # Move page to end\\n page_to_move.move(parent_page, pos='last-child')\\n\\n return HttpResponse('')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::copy\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::copy\\ndef copy(request, page_id):\\n page = Page.objects.get(id=page_id)\\n\\n # Parent page defaults to parent of source page\\n parent_page = page.get_parent()\\n\\n # Check if the user has permission to publish subpages on the parent\\n can_publish = parent_page.permissions_for_user(request.user).can_publish_subpage()\\n\\n # Create the form\\n form = CopyForm(request.POST or None, page=page, can_publish=can_publish)\\n\\n # Check if user is submitting\\n if request.method == 'POST':\\n # Prefill parent_page in case the form is invalid (as prepopulated value for the form field,\\n # because ModelChoiceField seems to not fall back to the user given value)\\n parent_page = Page.objects.get(id=request.POST['new_parent_page'])\\n\\n if form.is_valid():\\n # Receive the parent page (this should never be empty)\\n if form.cleaned_data['new_parent_page']:\\n parent_page = form.cleaned_data['new_parent_page']\\n\\n # Make sure this user has permission to add subpages on the parent\\n if not parent_page.permissions_for_user(request.user).can_add_subpage():\\n raise PermissionDenied\\n\\n # Re-check if the user has permission to publish subpages on the new parent\\n can_publish = parent_page.permissions_for_user(request.user).can_publish_subpage()\\n\\n # Copy the page\\n new_page = page.copy(\\n recursive=form.cleaned_data.get('copy_subpages'),\\n to=parent_page,\\n update_attrs={\\n 'title': form.cleaned_data['new_title'],\\n 'slug': form.cleaned_data['new_slug'],\\n },\\n keep_live=(can_publish and form.cleaned_data.get('publish_copies')),\\n user=request.user,\\n )\\n\\n # Give a success message back to the user\\n if form.cleaned_data.get('copy_subpages'):\\n messages.success(\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::get_page_edit_handler\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::get_page_edit_handler\\ndef get_page_edit_handler(page_class):\\n if page_class not in PAGE_EDIT_HANDLERS:\\n PAGE_EDIT_HANDLERS[page_class] = TabbedInterface([\\n ObjectList(page_class.content_panels, heading='Content'),\\n ObjectList(page_class.promote_panels, heading='Promote'),\\n ObjectList(page_class.settings_panels, heading='Settings', classname=\\\"settings\\\")\\n ])\\n\\n return PAGE_EDIT_HANDLERS[page_class]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::search\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::search\\n@vary_on_headers('X-Requested-With')\\ndef search(request):\\n pages = []\\n q = None\\n is_searching = False\\n if 'q' in request.GET:\\n form = SearchForm(request.GET)\\n if form.is_valid():\\n q = form.cleaned_data['q']\\n\\n # page number\\n p = request.GET.get(\\\"p\\\", 1)\\n is_searching = True\\n pages = Page.search(q, show_unpublished=True, search_title_only=True, prefetch_related=['content_type'])\\n\\n # Pagination\\n paginator = Paginator(pages, 20)\\n try:\\n pages = paginator.page(p)\\n except PageNotAnInteger:\\n pages = paginator.page(1)\\n except EmptyPage:\\n pages = paginator.page(paginator.num_pages)\\n else:\\n form = SearchForm()\\n\\n if request.is_ajax():\\n return render(request, \\\"wagtailadmin/pages/search_results.html\\\", {\\n 'pages': pages,\\n 'is_searching': is_searching,\\n 'query_string': q,\\n })\\n else:\\n return render(request, \\\"wagtailadmin/pages/search.html\\\", {\\n 'search_form': form,\\n 'pages': pages,\\n 'is_searching': is_searching,\\n 'query_string': q,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::approve_moderation\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::approve_moderation\\ndef approve_moderation(request, revision_id):\\n revision = get_object_or_404(PageRevision, id=revision_id)\\n if not revision.page.permissions_for_user(request.user).can_publish():\\n raise PermissionDenied\\n\\n if not revision.submitted_for_moderation:\\n messages.error(request, _(\\\"The page '{0}' is not currently awaiting moderation.\\\").format(revision.page.title))\\n return redirect('wagtailadmin_home')\\n\\n if request.method == 'POST':\\n revision.approve_moderation()\\n messages.success(request, _(\\\"Page '{0}' published.\\\").format(revision.page.title))\\n tasks.send_notification.delay(revision.id, 'approved', request.user.id)\\n\\n return redirect('wagtailadmin_home')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::reject_moderation\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::reject_moderation\\ndef reject_moderation(request, revision_id):\\n revision = get_object_or_404(PageRevision, id=revision_id)\\n if not revision.page.permissions_for_user(request.user).can_publish():\\n raise PermissionDenied\\n\\n if not revision.submitted_for_moderation:\\n messages.error(request, _(\\\"The page '{0}' is not currently awaiting moderation.\\\").format( revision.page.title))\\n return redirect('wagtailadmin_home')\\n\\n if request.method == 'POST':\\n revision.reject_moderation()\\n messages.success(request, _(\\\"Page '{0}' rejected for publication.\\\").format(revision.page.title))\\n tasks.send_notification.delay(revision.id, 'rejected', request.user.id)\\n\\n return redirect('wagtailadmin_home')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::preview_for_moderation\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::preview_for_moderation\\n@require_GET\\ndef preview_for_moderation(request, revision_id):\\n revision = get_object_or_404(PageRevision, id=revision_id)\\n if not revision.page.permissions_for_user(request.user).can_publish():\\n raise PermissionDenied\\n\\n if not revision.submitted_for_moderation:\\n messages.error(request, _(\\\"The page '{0}' is not currently awaiting moderation.\\\").format(revision.page.title))\\n return redirect('wagtailadmin_home')\\n\\n page = revision.as_page_object()\\n\\n request.revision_id = revision_id\\n\\n # pass in the real user request rather than page.dummy_request(), so that request.user\\n # and request.revision_id will be picked up by the wagtail user bar\\n return page.serve_preview(request, page.default_preview_mode)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::lock\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::lock\\n@require_POST\\ndef lock(request, page_id):\\n # Get the page\\n page = get_object_or_404(Page, id=page_id).specific\\n\\n # Check permissions\\n if not page.permissions_for_user(request.user).can_lock():\\n raise PermissionDenied\\n\\n # Lock the page\\n if not page.locked:\\n page.locked = True\\n page.save()\\n\\n messages.success(request, _(\\\"Page '{0}' is now locked.\\\").format(page.title))\\n\\n # Redirect\\n redirect_to = request.POST.get('next', None)\\n if redirect_to and is_safe_url(url=redirect_to, host=request.get_host()):\\n return redirect(redirect_to)\\n else:\\n return redirect('wagtailadmin_explore', page.get_parent().id)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailadmin/views/pages.py::unlock\",\n \"text\": \"wagtail/wagtailadmin/views/pages.py::unlock\\n@require_POST\\ndef unlock(request, page_id):\\n # Get the page\\n page = get_object_or_404(Page, id=page_id).specific\\n\\n # Check permissions\\n if not page.permissions_for_user(request.user).can_lock():\\n raise PermissionDenied\\n\\n # Unlock the page\\n if page.locked:\\n page.locked = False\\n page.save()\\n\\n messages.success(request, _(\\\"Page '{0}' is now unlocked.\\\").format(page.title))\\n\\n # Redirect\\n redirect_to = request.POST.get('next', None)\\n if redirect_to and is_safe_url(url=redirect_to, host=request.get_host()):\\n return redirect(redirect_to)\\n else:\\n return redirect('wagtailadmin_explore', page.get_parent().id)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::SiteManager.get_by_natural_key\",\n \"text\": \"wagtail/wagtailcore/models.py::SiteManager.get_by_natural_key\\n def get_by_natural_key(self, hostname, port):\\n return self.get(hostname=hostname, port=port)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.natural_key\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.natural_key\\n def natural_key(self):\\n return (self.hostname, self.port)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.__str__\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.__str__\\n def __str__(self):\\n return self.hostname + (\\\"\\\" if self.port == 80 else (\\\":%d\\\" % self.port)) + (\\\" [default]\\\" if self.is_default_site else \\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.find_for_request\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.find_for_request\\n @staticmethod\\n def find_for_request(request):\\n \\\"\\\"\\\"\\n Find the site object responsible for responding to this HTTP\\n request object. Try:\\n - unique hostname first\\n - then hostname and port\\n - if there is no matching hostname at all, or no matching\\n hostname:port combination, fall back to the unique default site,\\n or raise an exception\\n NB this means that high-numbered ports on an extant hostname may\\n still be routed to a different hostname which is set as the default\\n \\\"\\\"\\\"\\n try:\\n hostname = request.META['HTTP_HOST'].split(':')[0] # KeyError here goes to the final except clause\\n try:\\n # find a Site matching this specific hostname\\n return Site.objects.get(hostname=hostname) # Site.DoesNotExist here goes to the final except clause\\n except Site.MultipleObjectsReturned:\\n # as there were more than one, try matching by port too\\n port = request.META['SERVER_PORT'] # KeyError here goes to the final except clause\\n return Site.objects.get(hostname=hostname, port=int(port)) # Site.DoesNotExist here goes to the final except clause\\n except (Site.DoesNotExist, KeyError):\\n # If no matching site exists, or request does not specify an HTTP_HOST (which\\n # will often be the case for the Django test client), look for a catch-all Site.\\n # If that fails, let the Site.DoesNotExist propagate back to the caller\\n return Site.objects.get(is_default_site=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.root_url\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.root_url\\n @property\\n def root_url(self):\\n if self.port == 80:\\n return 'http://%s' % self.hostname\\n elif self.port == 443:\\n return 'https://%s' % self.hostname\\n else:\\n return 'http://%s:%d' % (self.hostname, self.port)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.clean_fields\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.clean_fields\\n def clean_fields(self, exclude=None):\\n super(Site, self).clean_fields(exclude)\\n # Only one site can have the is_default_site flag set\\n try:\\n default = Site.objects.get(is_default_site=True)\\n except Site.DoesNotExist:\\n pass\\n except Site.MultipleObjectsReturned:\\n raise\\n else:\\n if self.is_default_site and self.pk != default.pk:\\n raise ValidationError(\\n {'is_default_site': [\\n _(\\\"%(hostname)s is already configured as the default site. You must unset that before you can save this site as default.\\\")\\n % {'hostname': default.hostname}\\n ]}\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Site.get_site_root_paths\",\n \"text\": \"wagtail/wagtailcore/models.py::Site.get_site_root_paths\\n @staticmethod\\n def get_site_root_paths():\\n \\\"\\\"\\\"\\n Return a list of (root_path, root_url) tuples, most specific path first -\\n used to translate url_paths into actual URLs with hostnames\\n \\\"\\\"\\\"\\n result = cache.get('wagtail_site_root_paths')\\n\\n if result is None:\\n result = [\\n (site.id, site.root_page.url_path, site.root_url)\\n for site in Site.objects.select_related('root_page').order_by('-root_page__url_path')\\n ]\\n cache.set('wagtail_site_root_paths', result, 3600)\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::clear_site_root_paths_on_save\",\n \"text\": \"wagtail/wagtailcore/models.py::clear_site_root_paths_on_save\\n@receiver(post_save, sender=Site)\\ndef clear_site_root_paths_on_save(sender, instance, **kwargs):\\n cache.delete('wagtail_site_root_paths')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::clear_site_root_paths_on_delete\",\n \"text\": \"wagtail/wagtailcore/models.py::clear_site_root_paths_on_delete\\n@receiver(post_delete, sender=Site)\\ndef clear_site_root_paths_on_delete(sender, instance, **kwargs):\\n cache.delete('wagtail_site_root_paths')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::get_page_types\",\n \"text\": \"wagtail/wagtailcore/models.py::get_page_types\\ndef get_page_types():\\n global _PAGE_CONTENT_TYPES\\n if len(_PAGE_CONTENT_TYPES) != len(PAGE_MODEL_CLASSES):\\n _PAGE_CONTENT_TYPES = [\\n ContentType.objects.get_for_model(cls) for cls in PAGE_MODEL_CLASSES\\n ]\\n return _PAGE_CONTENT_TYPES\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.get_queryset\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.get_queryset\\n def get_queryset(self):\\n return PageQuerySet(self.model).order_by('path')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.live\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.live\\n def live(self):\\n return self.get_queryset().live()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_live\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_live\\n def not_live(self):\\n return self.get_queryset().not_live()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.in_menu\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.in_menu\\n def in_menu(self):\\n return self.get_queryset().in_menu()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_in_menu\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_in_menu\\n def not_in_menu(self):\\n return self.get_queryset().not_in_menu()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.page\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.page\\n def page(self, other):\\n return self.get_queryset().page(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_page\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_page\\n def not_page(self, other):\\n return self.get_queryset().not_page(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.descendant_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.descendant_of\\n def descendant_of(self, other, inclusive=False):\\n return self.get_queryset().descendant_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_descendant_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_descendant_of\\n def not_descendant_of(self, other, inclusive=False):\\n return self.get_queryset().not_descendant_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.child_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.child_of\\n def child_of(self, other):\\n return self.get_queryset().child_of(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_child_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_child_of\\n def not_child_of(self, other):\\n return self.get_queryset().not_child_of(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.ancestor_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.ancestor_of\\n def ancestor_of(self, other, inclusive=False):\\n return self.get_queryset().ancestor_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_ancestor_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_ancestor_of\\n def not_ancestor_of(self, other, inclusive=False):\\n return self.get_queryset().not_ancestor_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.parent_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.parent_of\\n def parent_of(self, other):\\n return self.get_queryset().parent_of(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_parent_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_parent_of\\n def not_parent_of(self, other):\\n return self.get_queryset().not_parent_of(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.sibling_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.sibling_of\\n def sibling_of(self, other, inclusive=False):\\n return self.get_queryset().sibling_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_sibling_of\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_sibling_of\\n def not_sibling_of(self, other, inclusive=False):\\n return self.get_queryset().not_sibling_of(other, inclusive)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.type\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.type\\n def type(self, model):\\n return self.get_queryset().type(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_type\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_type\\n def not_type(self, model):\\n return self.get_queryset().not_type(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.public\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.public\\n def public(self):\\n return self.get_queryset().public()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.not_public\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.not_public\\n def not_public(self):\\n return self.get_queryset().not_public()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageManager.search\",\n \"text\": \"wagtail/wagtailcore/models.py::PageManager.search\\n def search(self, query_string, fields=None, backend='default'):\\n return self.get_queryset().search(query_string, fields=fields, backend=backend)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::PageBase.__init__\",\n \"text\": \"wagtail/wagtailcore/models.py::PageBase.__init__\\n def __init__(cls, name, bases, dct):\\n super(PageBase, cls).__init__(name, bases, dct)\\n\\n if cls._deferred:\\n # this is an internal class built for Django's deferred-attribute mechanism;\\n # don't proceed with all this page type registration stuff\\n return\\n\\n # Add page manager\\n PageManager().contribute_to_class(cls, 'objects')\\n\\n if 'template' not in dct:\\n # Define a default template path derived from the app name and model name\\n cls.template = \\\"%s/%s.html\\\" % (cls._meta.app_label, camelcase_to_underscore(name))\\n\\n if 'ajax_template' not in dct:\\n cls.ajax_template = None\\n\\n cls._clean_subpage_types = None # to be filled in on first call to cls.clean_subpage_types\\n cls._clean_parent_page_types = None # to be filled in on first call to cls.clean_parent_page_types\\n\\n if not dct.get('is_abstract'):\\n # subclasses are only abstract if the subclass itself defines itself so\\n cls.is_abstract = False\\n\\n if not cls.is_abstract:\\n # register this type in the list of page content types\\n PAGE_MODEL_CLASSES.append(cls)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.__init__\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.__init__\\n def __init__(self, *args, **kwargs):\\n super(Page, self).__init__(*args, **kwargs)\\n if not self.id and not self.content_type_id:\\n # this model is being newly created rather than retrieved from the db;\\n # set content type to correctly represent the model class that this was\\n # created as\\n self.content_type = ContentType.objects.get_for_model(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.__str__\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.__str__\\n def __str__(self):\\n return self.title\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.set_url_path\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.set_url_path\\n def set_url_path(self, parent):\\n \\\"\\\"\\\"\\n Populate the url_path field based on this page's slug and the specified parent page.\\n (We pass a parent in here, rather than retrieving it via get_parent, so that we can give\\n new unsaved pages a meaningful URL when previewing them; at that point the page has not\\n been assigned a position in the tree, as far as treebeard is concerned.\\n \\\"\\\"\\\"\\n if parent:\\n self.url_path = parent.url_path + self.slug + '/'\\n else:\\n # a page without a parent is the tree root, which always has a url_path of '/'\\n self.url_path = '/'\\n\\n return self.url_path\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.save\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.save\\n @transaction.atomic # ensure that changes are only committed when we have updated all descendant URL paths, to preserve consistency\\n def save(self, *args, **kwargs):\\n update_descendant_url_paths = False\\n is_new = self.id is None\\n\\n if is_new:\\n # we are creating a record. If we're doing things properly, this should happen\\n # through a treebeard method like add_child, in which case the 'path' field\\n # has been set and so we can safely call get_parent\\n self.set_url_path(self.get_parent())\\n else:\\n # Check that we are committing the slug to the database\\n # Basically: If update_fields has been specified, and slug is not included, skip this step\\n if not ('update_fields' in kwargs and 'slug' not in kwargs['update_fields']):\\n # see if the slug has changed from the record in the db, in which case we need to\\n # update url_path of self and all descendants\\n old_record = Page.objects.get(id=self.id)\\n if old_record.slug != self.slug:\\n self.set_url_path(self.get_parent())\\n update_descendant_url_paths = True\\n old_url_path = old_record.url_path\\n new_url_path = self.url_path\\n\\n result = super(Page, self).save(*args, **kwargs)\\n\\n if update_descendant_url_paths:\\n self._update_descendant_url_paths(old_url_path, new_url_path)\\n\\n # Check if this is a root page of any sites and clear the 'wagtail_site_root_paths' key if so\\n if Site.objects.filter(root_page=self).exists():\\n cache.delete('wagtail_site_root_paths')\\n\\n # Log\\n if is_new:\\n cls = type(self)\\n logger.info(\\\"Page created: \\\\\\\"%s\\\\\\\" id=%d content_type=%s.%s path=%s\\\", self.title, self.id, cls._meta.app_label, cls.__name__, self.url_path)\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.check\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.check\\n @classmethod\\n def check(cls, **kwargs):\\n errors = super(Page, cls).check(**kwargs)\\n\\n # Check that foreign keys from pages are not configured to cascade\\n # This is the default Django behaviour which must be explicitly overridden\\n # to prevent pages disappearing unexpectedly and the tree being corrupted\\n\\n # get names of foreign keys pointing to parent classes (such as page_ptr)\\n field_exceptions = [field.name\\n for model in [cls] + list(cls._meta.get_parent_list())\\n for field in model._meta.parents.values()]\\n\\n field_exceptions += ['content_type']\\n\\n\\n for field in cls._meta.fields:\\n if isinstance(field, models.ForeignKey) and field.name not in field_exceptions:\\n if field.rel.on_delete == models.CASCADE:\\n errors.append(\\n checks.Warning(\\n \\\"Field hasn't specified on_delete action\\\",\\n hint=\\\"Set on_delete=models.SET_NULL and make sure the field is nullable.\\\",\\n obj=field,\\n id='wagtailcore.W001',\\n )\\n )\\n\\n return errors\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page._update_descendant_url_paths\",\n \"text\": \"wagtail/wagtailcore/models.py::Page._update_descendant_url_paths\\n def _update_descendant_url_paths(self, old_url_path, new_url_path):\\n cursor = connection.cursor()\\n if connection.vendor == 'sqlite':\\n update_statement = \\\"\\\"\\\"\\n UPDATE wagtailcore_page\\n SET url_path = %s || substr(url_path, %s)\\n WHERE path LIKE %s AND id <> %s\\n \\\"\\\"\\\"\\n elif connection.vendor == 'mysql':\\n update_statement = \\\"\\\"\\\"\\n UPDATE wagtailcore_page\\n SET url_path= CONCAT(%s, substring(url_path, %s))\\n WHERE path LIKE %s AND id <> %s\\n \\\"\\\"\\\"\\n else:\\n update_statement = \\\"\\\"\\\"\\n UPDATE wagtailcore_page\\n SET url_path = %s || substring(url_path from %s)\\n WHERE path LIKE %s AND id <> %s\\n \\\"\\\"\\\"\\n cursor.execute(update_statement, [new_url_path, len(old_url_path) + 1, self.path + '%', self.id])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/wagtailcore/models.py::Page.specific\",\n \"text\": \"wagtail/wagtailcore/models.py::Page.specific\\n @cached_property\\n def specific(self):\\n \\\"\\\"\\\"\\n Return this page in its most specific subclassed form.\\n \\\"\\\"\\\"\\n # the ContentType.objects manager keeps a cache, so this should potentially\\n # avoid a database lookup over doing self.content_type. I think.\\n content_type = ContentType.objects.get_for_id(self.content_type_id)\\n if isinstance(self, content_type.model_class()):\\n # self is already the an instance of the most specific class\\n return self\\n else:\\n return content_type.get_object_for_this_type(id=self.id)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":268,"cells":{"instance_id":{"kind":"string","value":"PrefectHQ__prefect-2885"},"repo":{"kind":"string","value":"PrefectHQ/prefect"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Users of ECR and the Fargate Agent unable to use AWS Docker auth\n## Description\r\n\r\nContinuing to work on the branch @joshmeek did in https://github.com/PrefectHQ/prefect/pull/2877 (same diagnostics as https://github.com/PrefectHQ/prefect/issues/2875), I came across an invalidation error for not providing Fargate Agent with a `repositoryCredentials` (with a `credentialsParameter`).\r\n\r\n```\r\nMissing required parameter in containerDefinitions[0].repositoryCredentials: \"credentialsParameter\" \r\n```\r\n\r\n\r\n## Expected Behavior\r\n\r\nDespite [what the boto3 docs say](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/ecs.html#ECS.Client.register_task_definition), I believe this shouldn't be a required field to run. One can see it's not required in [the main API](https://docs.aws.amazon.com/AmazonECS/latest/APIReference/API_ContainerDefinition.html) (also [here](https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-ecs-taskdefinition-containerdefinitions.html#cfn-ecs-taskdefinition-containerdefinition-repositorycredentials)).\r\n\r\nBecause users of ECR should not use credentials like these, auth is handled automatically as long as the task execution role is allowed to use the ECR repo. Amazon covers this in a warning here: https://docs.aws.amazon.com/AmazonECS/latest/developerguide/private-auth.html (`If your task definition references an image stored in Amazon ECR, this topic does not apply`). The auth I mention is covered [here](https://docs.aws.amazon.com/AmazonECR/latest/userguide/ECR_on_ECS.html).\r\n\r\nI tested just altering your `fargate/agent.py` to remove all settings of `repositoryCredentials`, dropping the below lines:\r\n\r\n* https://github.com/PrefectHQ/prefect/blob/master/src/prefect/agent/fargate/agent.py#L343\r\n* https://github.com/PrefectHQ/prefect/blob/master/src/prefect/agent/fargate/agent.py#L642-L644\r\n\r\nAnd the error went away, proceeding to do the boto call [without errors].\r\n\n"},"gold_files":{"kind":"list like","value":["src/prefect/agent/fargate/agent.py"],"string":"[\n \"src/prefect/agent/fargate/agent.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["src/prefect/agent/fargate/agent.py::FargateAgent._create_task_definition","src/prefect/agent/fargate/agent.py::FargateAgent.validate_configuration"],"string":"[\n \"src/prefect/agent/fargate/agent.py::FargateAgent._create_task_definition\",\n \"src/prefect/agent/fargate/agent.py::FargateAgent.validate_configuration\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"src/prefect/agent/fargate/agent.py","text":"src/prefect/agent/fargate/agent.py\nclass FargateAgent # Agent which deploys flow runs as tasks using Fargate. This agent can run anywher\n def __init__(self, name: str=None, labels: Iterable[str]=None, env_vars: dict=None, max_polls: int=None, agent_address: str=None, no_cloud_logs: bool=False, launch_type: str='FARGATE', aws_access_key_id: str=None, aws_secret_access_key: str=None, aws_session_token: str=None, region_name: str=None, botocore_config: dict=None, enable_task_revisions: bool=False, use_external_kwargs: bool=False, external_kwargs_s3_bucket: str=None, external_kwargs_s3_key: str=None, **kwargs)\n def _override_kwargs(self, flow_run: GraphQLResult, flow_task_definition_kwargs: dict, flow_task_run_kwargs: dict, flow_container_definitions_kwargs: dict) # Return new kwargs updated from external kwargs file.\n def _add_flow_tags(self, flow_run: GraphQLResult, flow_task_definition_kwargs: dict) # Add tags to task definition kwargs to\n def _parse_kwargs(self, user_kwargs: dict, check_envars: bool=False) # Parse the kwargs passed in and separate them out for `register_task_definition`\n def deploy_flow(self, flow_run: GraphQLResult) # Deploy flow runs to Fargate\n def _verify_task_definition_exists(self, flow_run: GraphQLResult, task_definition_dict: dict) # Check if a task definition already exists for the flow\n def _create_task_definition(self, image: str, flow_task_definition_kwargs: dict, container_definitions_kwargs: dict, task_definition_name: str) # Create a task definition for the flow that each flow run will use. This function\n def _run_task(self, flow_run: GraphQLResult, flow_task_run_kwargs: dict, task_definition_name: str) # Run a task using the flow run.\n def validate_configuration(self) # Utility function for testing Agent's configuration. This function is helpful in","label":1},{"cid":"src/prefect/agent/kubernetes/agent.py","text":"src/prefect/agent/kubernetes/agent.py\nclass KubernetesAgent # Agent which deploys flow runs as Kubernetes jobs. Currently this is required to \n def __init__(self, namespace: str=None, name: str=None, labels: Iterable[str]=None, env_vars: dict=None, max_polls: int=None, agent_address: str=None, no_cloud_logs: bool=False)\n def deploy_flow(self, flow_run: GraphQLResult) # Deploy flow runs on to a k8s cluster as jobs\n def replace_job_spec_yaml(self, flow_run: GraphQLResult, image: str) # Populate metadata and variables in the job_spec.yaml file for flow runs\n def generate_deployment_yaml(token: str=None, api: str=None, namespace: str=None, image_pull_secrets: str=None, resource_manager_enabled: bool=False, rbac: bool=False, latest: bool=False, mem_request: str=None, mem_limit: str=None, cpu_request: str=None, cpu_limit: str=None, image_pull_policy: str=None, service_account_name: str=None, labels: Iterable[str]=None, env_vars: dict=None, backend: str=None) # Generate and output an installable YAML spec for the agent.","label":0},{"cid":"src/prefect/cli/agent.py","text":"src/prefect/cli/agent.py\ndef agent() # Manage Prefect agents.\ndef start(ctx, agent_option, token, api, name, verbose, label, env, namespace, no_pull, no_cloud_logs, base_url, import_path, show_flow_logs, volume, network, no_docker_interface, max_polls, agent_address) # Start an agent.\ndef install(name, token, api, namespace, image_pull_secrets, resource_manager, rbac, latest, mem_request, mem_limit, cpu_request, cpu_limit, image_pull_policy, service_account_name, label, env, import_path, show_flow_logs, backend) # Install an agent. Outputs configuration text which can be used to install on var","label":0},{"cid":"src/prefect/environments/execution/dask/cloud_provider.py","text":"src/prefect/environments/execution/dask/cloud_provider.py\nclass DaskCloudProviderEnvironment # DaskCloudProviderEnvironment creates Dask clusters using the Dask Cloud Provider\n def __init__(self, provider_class: Type[Cluster], adaptive_min_workers: int=None, adaptive_max_workers: int=None, security: Security=None, executor_kwargs: Dict[str, Any]=None, labels: List[str]=None, on_execute: Callable[[Dict[str, Any], Dict[str, Any]], None]=None, on_start: Callable=None, on_exit: Callable=None, metadata: dict=None, **kwargs)\n def dependencies(self)\n def _create_dask_cluster(self)\n def execute(self, flow: 'Flow', **kwargs: Any)","label":0},{"cid":"src/prefect/environments/execution/fargate/fargate_task.py","text":"src/prefect/environments/execution/fargate/fargate_task.py\nclass FargateTaskEnvironment # FargateTaskEnvironment is an environment which deploys your flow as a Fargate ta\n def __init__(self, launch_type: str='FARGATE', aws_access_key_id: str=None, aws_secret_access_key: str=None, aws_session_token: str=None, region_name: str=None, executor: 'prefect.engine.executors.Executor'=None, executor_kwargs: dict=None, labels: List[str]=None, on_start: Callable=None, on_exit: Callable=None, metadata: dict=None, **kwargs)\n def _parse_kwargs(self, user_kwargs: dict) # Parse the kwargs passed in and separate them out for `register_task_definition`\n def dependencies(self)\n def setup(self, flow: 'Flow') # Register the task definition if it does not already exist.\n def execute(self, flow: 'Flow') # Run the Fargate task that was defined for this flow.","label":0},{"cid":"src/prefect/tasks/aws/s3.py","text":"src/prefect/tasks/aws/s3.py\nclass S3Download # Task for downloading data from an S3 bucket and returning it as a string.\n def __init__(self, bucket: str=None, **kwargs)\n def run(self, key: str, credentials: str=None, bucket: str=None) # Task run method.\nclass S3Upload # Task for uploading string data (e.g., a JSON string) to an S3 bucket.\n def __init__(self, bucket: str=None, **kwargs)\n def run(self, data: str, key: str=None, credentials: dict=None, bucket: str=None) # Task run method.","label":0}],"string":"[\n {\n \"cid\": \"src/prefect/agent/fargate/agent.py\",\n \"text\": \"src/prefect/agent/fargate/agent.py\\nclass FargateAgent # Agent which deploys flow runs as tasks using Fargate. This agent can run anywher\\n def __init__(self, name: str=None, labels: Iterable[str]=None, env_vars: dict=None, max_polls: int=None, agent_address: str=None, no_cloud_logs: bool=False, launch_type: str='FARGATE', aws_access_key_id: str=None, aws_secret_access_key: str=None, aws_session_token: str=None, region_name: str=None, botocore_config: dict=None, enable_task_revisions: bool=False, use_external_kwargs: bool=False, external_kwargs_s3_bucket: str=None, external_kwargs_s3_key: str=None, **kwargs)\\n def _override_kwargs(self, flow_run: GraphQLResult, flow_task_definition_kwargs: dict, flow_task_run_kwargs: dict, flow_container_definitions_kwargs: dict) # Return new kwargs updated from external kwargs file.\\n def _add_flow_tags(self, flow_run: GraphQLResult, flow_task_definition_kwargs: dict) # Add tags to task definition kwargs to\\n def _parse_kwargs(self, user_kwargs: dict, check_envars: bool=False) # Parse the kwargs passed in and separate them out for `register_task_definition`\\n def deploy_flow(self, flow_run: GraphQLResult) # Deploy flow runs to Fargate\\n def _verify_task_definition_exists(self, flow_run: GraphQLResult, task_definition_dict: dict) # Check if a task definition already exists for the flow\\n def _create_task_definition(self, image: str, flow_task_definition_kwargs: dict, container_definitions_kwargs: dict, task_definition_name: str) # Create a task definition for the flow that each flow run will use. This function\\n def _run_task(self, flow_run: GraphQLResult, flow_task_run_kwargs: dict, task_definition_name: str) # Run a task using the flow run.\\n def validate_configuration(self) # Utility function for testing Agent's configuration. This function is helpful in\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/agent/kubernetes/agent.py\",\n \"text\": \"src/prefect/agent/kubernetes/agent.py\\nclass KubernetesAgent # Agent which deploys flow runs as Kubernetes jobs. Currently this is required to \\n def __init__(self, namespace: str=None, name: str=None, labels: Iterable[str]=None, env_vars: dict=None, max_polls: int=None, agent_address: str=None, no_cloud_logs: bool=False)\\n def deploy_flow(self, flow_run: GraphQLResult) # Deploy flow runs on to a k8s cluster as jobs\\n def replace_job_spec_yaml(self, flow_run: GraphQLResult, image: str) # Populate metadata and variables in the job_spec.yaml file for flow runs\\n def generate_deployment_yaml(token: str=None, api: str=None, namespace: str=None, image_pull_secrets: str=None, resource_manager_enabled: bool=False, rbac: bool=False, latest: bool=False, mem_request: str=None, mem_limit: str=None, cpu_request: str=None, cpu_limit: str=None, image_pull_policy: str=None, service_account_name: str=None, labels: Iterable[str]=None, env_vars: dict=None, backend: str=None) # Generate and output an installable YAML spec for the agent.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/cli/agent.py\",\n \"text\": \"src/prefect/cli/agent.py\\ndef agent() # Manage Prefect agents.\\ndef start(ctx, agent_option, token, api, name, verbose, label, env, namespace, no_pull, no_cloud_logs, base_url, import_path, show_flow_logs, volume, network, no_docker_interface, max_polls, agent_address) # Start an agent.\\ndef install(name, token, api, namespace, image_pull_secrets, resource_manager, rbac, latest, mem_request, mem_limit, cpu_request, cpu_limit, image_pull_policy, service_account_name, label, env, import_path, show_flow_logs, backend) # Install an agent. Outputs configuration text which can be used to install on var\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/dask/cloud_provider.py\",\n \"text\": \"src/prefect/environments/execution/dask/cloud_provider.py\\nclass DaskCloudProviderEnvironment # DaskCloudProviderEnvironment creates Dask clusters using the Dask Cloud Provider\\n def __init__(self, provider_class: Type[Cluster], adaptive_min_workers: int=None, adaptive_max_workers: int=None, security: Security=None, executor_kwargs: Dict[str, Any]=None, labels: List[str]=None, on_execute: Callable[[Dict[str, Any], Dict[str, Any]], None]=None, on_start: Callable=None, on_exit: Callable=None, metadata: dict=None, **kwargs)\\n def dependencies(self)\\n def _create_dask_cluster(self)\\n def execute(self, flow: 'Flow', **kwargs: Any)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/fargate/fargate_task.py\",\n \"text\": \"src/prefect/environments/execution/fargate/fargate_task.py\\nclass FargateTaskEnvironment # FargateTaskEnvironment is an environment which deploys your flow as a Fargate ta\\n def __init__(self, launch_type: str='FARGATE', aws_access_key_id: str=None, aws_secret_access_key: str=None, aws_session_token: str=None, region_name: str=None, executor: 'prefect.engine.executors.Executor'=None, executor_kwargs: dict=None, labels: List[str]=None, on_start: Callable=None, on_exit: Callable=None, metadata: dict=None, **kwargs)\\n def _parse_kwargs(self, user_kwargs: dict) # Parse the kwargs passed in and separate them out for `register_task_definition`\\n def dependencies(self)\\n def setup(self, flow: 'Flow') # Register the task definition if it does not already exist.\\n def execute(self, flow: 'Flow') # Run the Fargate task that was defined for this flow.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/aws/s3.py\",\n \"text\": \"src/prefect/tasks/aws/s3.py\\nclass S3Download # Task for downloading data from an S3 bucket and returning it as a string.\\n def __init__(self, bucket: str=None, **kwargs)\\n def run(self, key: str, credentials: str=None, bucket: str=None) # Task run method.\\nclass S3Upload # Task for uploading string data (e.g., a JSON string) to an S3 bucket.\\n def __init__(self, bucket: str=None, **kwargs)\\n def run(self, data: str, key: str=None, credentials: dict=None, bucket: str=None) # Task run method.\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"src/prefect/agent/fargate/agent.py::FargateAgent.__init__","text":"src/prefect/agent/fargate/agent.py::FargateAgent.__init__\n def __init__( # type: ignore\n self,\n name: str = None,\n labels: Iterable[str] = None,\n env_vars: dict = None,\n max_polls: int = None,\n agent_address: str = None,\n no_cloud_logs: bool = False,\n launch_type: str = \"FARGATE\",\n aws_access_key_id: str = None,\n aws_secret_access_key: str = None,\n aws_session_token: str = None,\n region_name: str = None,\n botocore_config: dict = None,\n enable_task_revisions: bool = False,\n use_external_kwargs: bool = False,\n external_kwargs_s3_bucket: str = None,\n external_kwargs_s3_key: str = None,\n **kwargs,\n ) -> None:\n super().__init__(\n name=name,\n labels=labels,\n env_vars=env_vars,\n max_polls=max_polls,\n agent_address=agent_address,\n no_cloud_logs=no_cloud_logs,\n )\n\n from boto3 import client as boto3_client\n from boto3 import resource as boto3_resource\n from botocore.config import Config\n\n # Config used for boto3 client initialization\n aws_access_key_id = aws_access_key_id or os.getenv(\"AWS_ACCESS_KEY_ID\")\n aws_secret_access_key = aws_secret_access_key or os.getenv(\n \"AWS_SECRET_ACCESS_KEY\"\n )\n aws_session_token = aws_session_token or os.getenv(\"AWS_SESSION_TOKEN\")\n region_name = region_name or os.getenv(\"REGION_NAME\")\n botocore_config = botocore_config or {}\n\n # revisions and kwargs configurations\n self.enable_task_revisions = enable_task_revisions\n self.use_external_kwargs = use_external_kwargs\n self.external_kwargs_s3_bucket = external_kwargs_s3_bucket\n self.external_kwargs_s3_key = external_kwargs_s3_key\n self.launch_type = launch_type\n\n # Parse accepted kwargs for task definition, run, and container definitions key of task\n # de","label":0},{"cid":"src/prefect/agent/fargate/agent.py::FargateAgent._override_kwargs","text":"src/prefect/agent/fargate/agent.py::FargateAgent._override_kwargs\n def _override_kwargs(\n self,\n flow_run: GraphQLResult,\n flow_task_definition_kwargs: dict,\n flow_task_run_kwargs: dict,\n flow_container_definitions_kwargs: dict,\n ) -> None:\n \"\"\"\n Return new kwargs updated from external kwargs file.\n\n Args:\n - flow_run (GraphQLResult): A GraphQLResult flow run object\n - flow_task_definition_kwargs (dict): task_definition_kwargs to update\n - flow_task_run_kwargs (dict): task_run_kwargs to update\n - flow_container_definitions_kwargs (dict): container_definitions_kwargs to update\n \"\"\"\n from botocore.exceptions import ClientError\n\n # get external kwargs from S3\n try:\n self.logger.info(\"Fetching external kwargs from S3\")\n obj = self.s3_resource.Object(\n self.external_kwargs_s3_bucket,\n os.path.join( # type: ignore\n self.external_kwargs_s3_key, # type: ignore\n slugify(flow_run.flow.name), # type: ignore\n \"{}.json\".format(flow_run.flow.id[:8]), # type: ignore\n ), # type: ignore\n )\n body = obj.get()[\"Body\"].read().decode(\"utf-8\")\n except ClientError:\n self.logger.info(\n \"Flow id {} does not have external kwargs.\".format(flow_run.flow.id[:8])\n )\n body = \"{}\"\n self.logger.debug(\"External kwargs:\\n{}\".format(body))\n\n # update kwargs from with external kwargs\n self.logger.info(\"Updating default kwargs with external\")\n external_kwargs = json.loads(body)\n # parse external kwargs\n (\n ext_task_definition_kwargs,\n ext_task_run_kwargs,\n ext_container_definitions_kwargs,\n ) = self._parse_kwargs(external_kwargs)\n self.logger.debug(\n \"External task defini","label":0},{"cid":"src/prefect/agent/fargate/agent.py::FargateAgent._add_flow_tags","text":"src/prefect/agent/fargate/agent.py::FargateAgent._add_flow_tags\n def _add_flow_tags(\n self, flow_run: GraphQLResult, flow_task_definition_kwargs: dict\n ) -> None:\n \"\"\"\n Add tags to task definition kwargs to\n\n Args:\n - flow_run (GraphQLResult): A GraphQLResult flow run object\n - flow_task_definition_kwargs (dict): task_definition_kwargs to add tags to\n \"\"\"\n # add flow id and version to definition tags\n self.logger.info(\"Adding tags for flow_id and version.\")\n if not flow_task_definition_kwargs.get(\"tags\"):\n flow_task_definition_kwargs[\"tags\"] = []\n else:\n flow_task_definition_kwargs[\"tags\"] = copy.deepcopy(\n flow_task_definition_kwargs[\"tags\"]\n )\n append_tag = True\n for i in flow_task_definition_kwargs[\"tags\"]:\n if i[\"key\"] == \"PrefectFlowId\":\n i[\"value\"] = flow_run.flow.id[:8]\n append_tag = False\n if append_tag:\n flow_task_definition_kwargs[\"tags\"].append(\n {\"key\": \"PrefectFlowId\", \"value\": flow_run.flow.id[:8]}\n )\n append_tag = True\n for i in flow_task_definition_kwargs[\"tags\"]:\n if i[\"key\"] == \"PrefectFlowVersion\":\n i[\"value\"] = str(flow_run.flow.version)\n append_tag = False\n if append_tag:\n flow_task_definition_kwargs[\"tags\"].append(\n {\"key\": \"PrefectFlowVersion\", \"value\": str(flow_run.flow.version)}\n )","label":0},{"cid":"src/prefect/agent/fargate/agent.py::FargateAgent._parse_kwargs","text":"src/prefect/agent/fargate/agent.py::FargateAgent._parse_kwargs\n def _parse_kwargs(self, user_kwargs: dict, check_envars: bool = False) -> tuple:\n \"\"\"\n Parse the kwargs passed in and separate them out for `register_task_definition`\n and `run_task`. This is required because boto3 does not allow extra kwargs\n and if they are provided it will raise botocore.exceptions.ParamValidationError.\n\n Args:\n - user_kwargs (dict): The kwargs passed to the initialization of the environment\n - check_envars (bool): Whether to check envars for kwargs\n\n Returns:\n tuple: a tuple of three dictionaries (task_definition_kwargs, task_run_kwargs,\n container_definitions_kwargs)\n \"\"\"\n definition_kwarg_list = [\n \"taskRoleArn\",\n \"executionRoleArn\",\n \"volumes\",\n \"placementConstraints\",\n \"cpu\",\n \"memory\",\n \"tags\",\n \"pidMode\",\n \"ipcMode\",\n \"proxyConfiguration\",\n \"inferenceAccelerators\",\n ]\n\n definition_kwarg_list_no_eval = [\"cpu\", \"memory\"]\n\n run_kwarg_list = [\n \"cluster\",\n \"count\",\n \"startedBy\",\n \"group\",\n \"placementConstraints\",\n \"placementStrategy\",\n \"platformVersion\",\n \"networkConfiguration\",\n \"tags\",\n \"enableECSManagedTags\",\n \"propagateTags\",\n ]\n\n container_definitions_kwarg_list = [\n \"mountPoints\",\n \"secrets\",\n \"environment\",\n \"logConfiguration\",\n \"repositoryCredentials\",\n ]\n\n task_definition_kwargs = {}\n definition_kwarg_list_eval = {\n i: (i not in definition_kwarg_list_no_eval) for i in definition_kwarg_list\n }\n for key, item in user_kwargs.items():\n if key in definition_kwarg_list:\n if defi","label":0},{"cid":"src/prefect/agent/fargate/agent.py::FargateAgent.deploy_flow","text":"src/prefect/agent/fargate/agent.py::FargateAgent.deploy_flow\n def deploy_flow(self, flow_run: GraphQLResult) -> str:\n \"\"\"\n Deploy flow runs to Fargate\n\n Args:\n - flow_run (GraphQLResult): A GraphQLResult flow run object\n\n Returns:\n - str: Information about the deployment\n \"\"\"\n self.logger.info(\n \"Deploying flow run {}\".format(flow_run.id) # type: ignore\n )\n\n # create copies of kwargs to apply overrides as needed\n flow_task_definition_kwargs = copy.deepcopy(self.task_definition_kwargs)\n flow_task_run_kwargs = copy.deepcopy(self.task_run_kwargs)\n flow_container_definitions_kwargs = copy.deepcopy(\n self.container_definitions_kwargs\n )\n\n # create task_definition_name dict for passing into verify method\n task_definition_dict = {}\n\n if self.use_external_kwargs:\n # override from external kwargs\n self._override_kwargs(\n flow_run,\n flow_task_definition_kwargs,\n flow_task_run_kwargs,\n flow_container_definitions_kwargs,\n )\n\n # set proper task_definition_name and tags based on enable_task_revisions flag\n if self.enable_task_revisions:\n # set task definition name\n task_definition_dict[\"task_definition_name\"] = slugify(flow_run.flow.name)\n self._add_flow_tags(flow_run, flow_task_definition_kwargs)\n\n else:\n task_definition_dict[\"task_definition_name\"] = \"prefect-task-{}\".format( # type: ignore\n flow_run.flow.id[:8] # type: ignore\n ) # type: ignore\n\n image = get_flow_image(flow_run=flow_run)\n\n # check if task definition exists\n self.logger.debug(\"Checking for task definition\")\n if not self._verify_task_definition_exists(flow_run, task_definition_dict):\n self.logger.debug(\"No task definition found\")\n ","label":0},{"cid":"src/prefect/agent/fargate/agent.py::FargateAgent._verify_task_definition_exists","text":"src/prefect/agent/fargate/agent.py::FargateAgent._verify_task_definition_exists\n def _verify_task_definition_exists(\n self, flow_run: GraphQLResult, task_definition_dict: dict\n ) -> bool:\n \"\"\"\n Check if a task definition already exists for the flow\n\n Args:\n - flow_run (GraphQLResult): A GraphQLResult representing a flow run object\n - task_definition_dict(dict): Dictionary containing task definition name to update\n if needed.\n\n Returns:\n - bool: whether or not a preexisting task definition is found for this flow\n \"\"\"\n from botocore.exceptions import ClientError\n\n try:\n definition_exists = True\n task_definition_name = task_definition_dict[\"task_definition_name\"]\n definition_response = self.boto3_client.describe_task_definition(\n taskDefinition=task_definition_name, include=[\"TAGS\"]\n )\n # if current active task definition has current flow id, then exists\n if self.enable_task_revisions:\n definition_exists = False\n tag_dict = {x[\"key\"]: x[\"value\"] for x in definition_response[\"tags\"]}\n current_flow_id = tag_dict.get(\"PrefectFlowId\")\n current_flow_version = int(tag_dict.get(\"PrefectFlowVersion\", 0))\n if current_flow_id == flow_run.flow.id[:8]:\n self.logger.debug(\n \"Active task definition for {} already exists\".format(\n flow_run.flow.id[:8]\n ) # type: ignore\n )\n definition_exists = True\n elif flow_run.flow.version < current_flow_version:\n tag_search = self.boto3_client_tags.get_resources(\n TagFilters=[\n {\"Key\": \"PrefectFlowId\", \"Values\": [flow_run.flow.id[:8]]}\n ],\n ","label":0},{"cid":"src/prefect/agent/fargate/agent.py::FargateAgent._create_task_definition","text":"src/prefect/agent/fargate/agent.py::FargateAgent._create_task_definition\n def _create_task_definition(\n self,\n image: str,\n flow_task_definition_kwargs: dict,\n container_definitions_kwargs: dict,\n task_definition_name: str,\n ) -> None:\n \"\"\"\n Create a task definition for the flow that each flow run will use. This function\n is only called when a flow is run for the first time.\n\n Args:\n - image (str): The full name of an image to use for this task definition\n - flow_task_definition_kwargs (dict): kwargs to use for registration\n - container_definitions_kwargs (dict): container definitions kwargs to use for\n registration\n - task_definition_name (str): task definition name to use\n \"\"\"\n self.logger.debug(\"Using image {} for task definition\".format(image))\n container_definitions = [\n {\n \"name\": \"flow\",\n \"image\": image,\n \"command\": [\"/bin/sh\", \"-c\", \"prefect execute cloud-flow\"],\n \"environment\": [\n {\n \"name\": \"PREFECT__CLOUD__API\",\n \"value\": config.cloud.api or \"https://api.prefect.io\",\n },\n {\n \"name\": \"PREFECT__CLOUD__AGENT__LABELS\",\n \"value\": str(self.labels),\n },\n {\"name\": \"PREFECT__CLOUD__USE_LOCAL_SECRETS\", \"value\": \"false\"},\n {\n \"name\": \"PREFECT__LOGGING__LOG_TO_CLOUD\",\n \"value\": str(self.log_to_cloud).lower(),\n },\n {\"name\": \"PREFECT__LOGGING__LEVEL\", \"value\": \"DEBUG\"},\n {\n \"name\": \"PREFECT__ENGINE__FLOW_RUNNER__DEFAULT_CLASS\",\n \"value\": \"prefect.engine.cloud.CloudFlowRunner\",\n },\n ","label":1},{"cid":"src/prefect/agent/fargate/agent.py::FargateAgent._run_task","text":"src/prefect/agent/fargate/agent.py::FargateAgent._run_task\n def _run_task(\n self,\n flow_run: GraphQLResult,\n flow_task_run_kwargs: dict,\n task_definition_name: str,\n ) -> str:\n \"\"\"\n Run a task using the flow run.\n\n Args:\n - flow_run (GraphQLResult): A GraphQLResult flow run object\n - flow_task_run_kwargs (dict): kwargs to use for task run\n - task_definition_name (str): task definition name to use\n \"\"\"\n container_overrides = [\n {\n \"name\": \"flow\",\n \"environment\": [\n {\n \"name\": \"PREFECT__CLOUD__AUTH_TOKEN\",\n \"value\": config.cloud.agent.auth_token,\n },\n {\n \"name\": \"PREFECT__CONTEXT__FLOW_RUN_ID\",\n \"value\": flow_run.id, # type: ignore\n },\n {\n \"name\": \"PREFECT__CONTEXT__FLOW_ID\",\n \"value\": flow_run.flow.id, # type: ignore\n },\n ],\n }\n ]\n\n # Run task\n self.logger.debug(\n \"Running task using task definition {}\".format(\n task_definition_name # type: ignore\n )\n )\n\n if self.launch_type:\n flow_task_run_kwargs[\"launchType\"] = self.launch_type\n task = self.boto3_client.run_task(\n taskDefinition=task_definition_name,\n overrides={\"containerOverrides\": container_overrides},\n **flow_task_run_kwargs,\n )\n\n return task[\"tasks\"][0].get(\"taskArn\")","label":0},{"cid":"src/prefect/agent/fargate/agent.py::FargateAgent.validate_configuration","text":"src/prefect/agent/fargate/agent.py::FargateAgent.validate_configuration\n def validate_configuration(self) -> None:\n \"\"\"\n Utility function for testing Agent's configuration. This function is helpful in\n determining if the provided configuration for the Agent is able to register a\n task definition and then subsequently run the task.\n \"\"\"\n task_name = f\"prefect-test-task-{str(uuid.uuid4())[:8]}\"\n\n # Populate container definition with provided kwargs\n flow_container_definitions_kwargs = copy.deepcopy(\n self.container_definitions_kwargs\n )\n\n container_definitions = [\n {\n \"name\": \"test-container\",\n \"image\": \"busybox\",\n \"command\": [\"/bin/sh\", \"-c\", \"echo 'I am alive!'\"],\n \"environment\": [],\n \"secrets\": [],\n \"mountPoints\": [],\n \"logConfiguration\": {},\n \"essential\": True,\n }\n ]\n\n base_envar_keys = [\n x[\"name\"] for x in container_definitions[0][\"environment\"] # type: ignore\n ]\n container_definitions_environment = [\n x\n for x in flow_container_definitions_kwargs.get(\"environment\", [])\n if x[\"name\"] not in base_envar_keys\n ]\n container_definitions[0][\"environment\"].extend( # type: ignore\n container_definitions_environment\n )\n container_definitions[0][\"secrets\"] = flow_container_definitions_kwargs.get(\n \"secrets\", []\n )\n container_definitions[0][\"mountPoints\"] = flow_container_definitions_kwargs.get(\n \"mountPoints\", []\n )\n container_definitions[0][\n \"logConfiguration\"\n ] = flow_container_definitions_kwargs.get(\"logConfiguration\", {})\n\n # Register task definition\n flow_task_definition_kwargs = copy.deepcopy(self.task_definition_kwargs)\n\n if self.launch_type:\n ","label":1},{"cid":"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.__init__","text":"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.__init__\n def __init__(\n self,\n namespace: str = None,\n name: str = None,\n labels: Iterable[str] = None,\n env_vars: dict = None,\n max_polls: int = None,\n agent_address: str = None,\n no_cloud_logs: bool = False,\n ) -> None:\n super().__init__(\n name=name,\n labels=labels,\n env_vars=env_vars,\n max_polls=max_polls,\n agent_address=agent_address,\n no_cloud_logs=no_cloud_logs,\n )\n\n self.namespace = namespace\n\n from kubernetes import client, config\n\n try:\n self.logger.debug(\"Loading incluster configuration\")\n config.load_incluster_config()\n except config.config_exception.ConfigException as exc:\n self.logger.warning(\n \"{} Using out of cluster configuration option.\".format(exc)\n )\n self.logger.debug(\"Loading out of cluster configuration\")\n config.load_kube_config()\n\n self.batch_client = client.BatchV1Api()\n\n self.logger.debug(f\"Namespace: {self.namespace}\")","label":0},{"cid":"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.deploy_flow","text":"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.deploy_flow\n def deploy_flow(self, flow_run: GraphQLResult) -> str:\n \"\"\"\n Deploy flow runs on to a k8s cluster as jobs\n\n Args:\n - flow_run (GraphQLResult): A GraphQLResult flow run object\n\n Returns:\n - str: Information about the deployment\n \"\"\"\n self.logger.info(\n \"Deploying flow run {}\".format(flow_run.id) # type: ignore\n )\n\n image = get_flow_image(flow_run=flow_run)\n\n job_spec = self.replace_job_spec_yaml(flow_run, image)\n\n self.logger.debug(\n \"Creating namespaced job {}\".format(job_spec[\"metadata\"][\"name\"])\n )\n job = self.batch_client.create_namespaced_job(\n namespace=self.namespace or os.getenv(\"NAMESPACE\", \"default\"), body=job_spec\n )\n\n self.logger.debug(\"Job {} created\".format(job.metadata.name))\n\n return \"Job {}\".format(job.metadata.name)","label":0},{"cid":"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.replace_job_spec_yaml","text":"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.replace_job_spec_yaml\n def replace_job_spec_yaml(self, flow_run: GraphQLResult, image: str) -> dict:\n \"\"\"\n Populate metadata and variables in the job_spec.yaml file for flow runs\n\n Args:\n - flow_run (GraphQLResult): A flow run object\n - image (str): The full name of an image to use for the job\n\n Returns:\n - dict: a dictionary representing the populated yaml object\n \"\"\"\n with open(path.join(path.dirname(__file__), \"job_spec.yaml\"), \"r\") as job_file:\n job = yaml.safe_load(job_file)\n\n identifier = str(uuid.uuid4())[:8]\n job_name = \"prefect-job-{}\".format(identifier)\n\n # Populate job metadata for identification\n k8s_labels = {\n \"prefect.io/identifier\": identifier,\n \"prefect.io/flow_run_id\": flow_run.id, # type: ignore\n \"prefect.io/flow_id\": flow_run.flow.id, # type: ignore\n }\n job[\"metadata\"][\"name\"] = job_name\n job[\"metadata\"][\"labels\"].update(**k8s_labels)\n job[\"spec\"][\"template\"][\"metadata\"][\"labels\"].update(**k8s_labels)\n\n # Use provided image for job\n job[\"spec\"][\"template\"][\"spec\"][\"containers\"][0][\"image\"] = image\n\n self.logger.debug(\"Using image {} for job\".format(image))\n\n # Populate environment variables for flow run execution\n env = job[\"spec\"][\"template\"][\"spec\"][\"containers\"][0][\"env\"]\n\n env[0][\"value\"] = config.cloud.api or \"https://api.prefect.io\"\n env[1][\"value\"] = config.cloud.agent.auth_token\n env[2][\"value\"] = flow_run.id # type: ignore\n env[3][\"value\"] = flow_run.flow.id # type: ignore\n env[4][\"value\"] = os.getenv(\"NAMESPACE\", \"default\")\n env[5][\"value\"] = str(self.labels)\n env[6][\"value\"] = str(self.log_to_cloud).lower()\n\n # append all user provided values\n for key, value in self.env_vars.items():\n env.append(","label":0},{"cid":"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.generate_deployment_yaml","text":"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.generate_deployment_yaml\n @staticmethod\n def generate_deployment_yaml(\n token: str = None,\n api: str = None,\n namespace: str = None,\n image_pull_secrets: str = None,\n resource_manager_enabled: bool = False,\n rbac: bool = False,\n latest: bool = False,\n mem_request: str = None,\n mem_limit: str = None,\n cpu_request: str = None,\n cpu_limit: str = None,\n image_pull_policy: str = None,\n service_account_name: str = None,\n labels: Iterable[str] = None,\n env_vars: dict = None,\n backend: str = None,\n ) -> str:\n \"\"\"\n Generate and output an installable YAML spec for the agent.\n\n Args:\n - token (str, optional): A `RUNNER` token to give the agent\n - api (str, optional): A URL pointing to the Prefect API. Defaults to\n `https://api.prefect.io`\n - namespace (str, optional): The namespace to create Prefect jobs in. Defaults\n to `default`\n - image_pull_secrets (str, optional): The name of an image pull secret to use\n for Prefect jobs\n - resource_manager_enabled (bool, optional): Whether to include the resource\n manager as part of the YAML. Defaults to `False`\n - rbac (bool, optional): Whether to include default RBAC configuration as\n part of the YAML. Defaults to `False`\n - latest (bool, optional): Whether to use the `latest` Prefect image.\n Defaults to `False`\n - mem_request (str, optional): Requested memory for Prefect init job.\n - mem_limit (str, optional): Limit memory for Prefect init job.\n - cpu_request (str, optional): Requested CPU for Prefect init job.\n - cpu_limit (str, optional): Limit CPU for Prefect init job.\n - image_pull_policy (str, optional): imagePullPolic","label":0},{"cid":"src/prefect/cli/agent.py::agent","text":"src/prefect/cli/agent.py::agent\n@click.group(hidden=True)\ndef agent():\n \"\"\"\n Manage Prefect agents.\n\n \\b\n Usage:\n $ prefect agent [COMMAND]\n\n \\b\n Arguments:\n start Start a Prefect agent\n install Output platform-specific agent installation configs\n\n \\b\n Examples:\n $ prefect agent start\n ...agent begins running in process...\n\n \\b\n $ prefect agent start kubernetes --token MY_TOKEN\n ...agent begins running in process...\n\n \\b\n $ prefect agent install kubernetes --token MY_TOKEN --namespace metrics\n ...k8s yaml output...\n \"\"\"","label":0},{"cid":"src/prefect/cli/agent.py::start","text":"src/prefect/cli/agent.py::start\n@agent.command(\n hidden=True,\n context_settings=dict(ignore_unknown_options=True, allow_extra_args=True),\n)\n@click.argument(\"agent-option\", default=\"local\")\n@click.option(\n \"--token\", \"-t\", required=False, help=\"A Prefect Cloud API token.\", hidden=True\n)\n@click.option(\"--api\", \"-a\", required=False, help=\"A Prefect API URL.\", hidden=True)\n@click.option(\n \"--name\",\n \"-n\",\n required=False,\n help=\"A name to use for the agent\",\n hidden=True,\n default=None,\n)\n@click.option(\n \"--verbose\", \"-v\", is_flag=True, help=\"Enable verbose agent logs.\", hidden=True\n)\n@click.option(\n \"--label\",\n \"-l\",\n multiple=True,\n help=\"Labels the agent will use to query for flow runs.\",\n hidden=True,\n)\n@click.option(\n \"--env\",\n \"-e\",\n multiple=True,\n help=\"Environment variables to set on each submitted flow run.\",\n hidden=True,\n)\n@click.option(\n \"--max-polls\",\n required=False,\n help=\"Maximum number of polls for the agent\",\n hidden=True,\n type=int,\n)\n@click.option(\n \"--agent-address\",\n required=False,\n help=\"Address to serve internal api server at. Defaults to no server.\",\n hidden=True,\n type=str,\n default=\"\",\n)\n@click.option(\n \"--namespace\",\n required=False,\n help=\"Kubernetes namespace to create jobs.\",\n hidden=True,\n)\n@click.option(\n \"--import-path\",\n \"-p\",\n multiple=True,\n help=\"Import paths the local agent will add to all flow runs.\",\n hidden=True,\n)\n@click.option(\n \"--show-flow-logs\",\n \"-f\",\n help=\"Display logging output from flows run by the agent.\",\n hidden=True,\n is_flag=True,\n)\n@click.option(\"--no-pull\", is_flag=True, help=\"Pull images flag.\", hidden=True)\n@click.option(\n \"--no-cloud-logs\",\n is_flag=True,\n help=\"Turn off logging for all flows run through this agent.\",\n hidden=True,\n)\n@click.option(\"--base-url\", \"-b\", help=\"Docker daemon base URL.\", hidden=True)\n@click.option(\n \"--volume\",\n multiple=True,\n ","label":0},{"cid":"src/prefect/cli/agent.py::install","text":"src/prefect/cli/agent.py::install\n@agent.command(hidden=True)\n@click.argument(\"name\")\n@click.option(\n \"--token\", \"-t\", required=False, help=\"A Prefect Cloud API token.\", hidden=True\n)\n@click.option(\"--api\", \"-a\", required=False, help=\"A Prefect API URL.\", hidden=True)\n@click.option(\n \"--namespace\",\n \"-n\",\n required=False,\n help=\"Agent namespace to launch workloads.\",\n hidden=True,\n)\n@click.option(\n \"--image-pull-secrets\",\n \"-i\",\n required=False,\n help=\"Name of image pull secrets to use for workloads.\",\n hidden=True,\n)\n@click.option(\n \"--resource-manager\", is_flag=True, help=\"Enable resource manager.\", hidden=True\n)\n@click.option(\"--rbac\", is_flag=True, help=\"Enable default RBAC.\", hidden=True)\n@click.option(\n \"--latest\", is_flag=True, help=\"Use the latest Prefect image.\", hidden=True\n)\n@click.option(\n \"--mem-request\",\n required=False,\n help=\"Requested memory for Prefect init job.\",\n hidden=True,\n)\n@click.option(\n \"--mem-limit\",\n required=False,\n help=\"Limit memory for Prefect init job.\",\n hidden=True,\n)\n@click.option(\n \"--cpu-request\",\n required=False,\n help=\"Requested CPU for Prefect init job.\",\n hidden=True,\n)\n@click.option(\n \"--cpu-limit\", required=False, help=\"Limit CPU for Prefect init job.\", hidden=True\n)\n@click.option(\n \"--image-pull-policy\",\n required=False,\n help=\"imagePullPolicy for Prefect init job\",\n hidden=True,\n)\n@click.option(\n \"--service-account-name\",\n required=False,\n help=\"Name of Service Account for Prefect init job\",\n hidden=True,\n)\n@click.option(\n \"--label\",\n \"-l\",\n multiple=True,\n help=\"Labels the agent will use to query for flow runs.\",\n hidden=True,\n)\n@click.option(\n \"--env\",\n \"-e\",\n multiple=True,\n help=\"Environment variables to set on each submitted flow run.\",\n hidden=True,\n)\n@click.option(\n \"--import-path\",\n \"-p\",\n multiple=True,\n help=\"Import paths the local agent will add to all flow runs.\",\n ","label":0},{"cid":"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment.__init__","text":"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment.__init__\n def __init__( # type: ignore\n self,\n provider_class: Type[Cluster],\n adaptive_min_workers: int = None,\n adaptive_max_workers: int = None,\n security: Security = None,\n executor_kwargs: Dict[str, Any] = None,\n labels: List[str] = None,\n on_execute: Callable[[Dict[str, Any], Dict[str, Any]], None] = None,\n on_start: Callable = None,\n on_exit: Callable = None,\n metadata: dict = None,\n **kwargs\n ) -> None:\n self._provider_class = provider_class\n self._adaptive_min_workers = adaptive_min_workers\n self._adaptive_max_workers = adaptive_max_workers\n self._on_execute = on_execute\n self._provider_kwargs = kwargs\n if \"skip_cleanup\" not in self._provider_kwargs:\n # Prefer this default (if not provided) to avoid deregistering task definitions See\n # this issue in Dask Cloud Provider:\n # https://github.com/dask/dask-cloudprovider/issues/94\n self._provider_kwargs[\"skip_cleanup\"] = True\n self._security = security\n if self._security:\n # We'll use the security config object both for our Dask Client connection *and*\n # for the particular Dask Cloud Provider (e.g. Fargate) to use with *its* Dask\n # Client when it connects to the scheduler after cluster creation. So we\n # put it in _provider_kwargs so it gets passed to the Dask Cloud Provider's constructor\n self._provider_kwargs[\"security\"] = self._security\n self.cluster = None\n super().__init__(\n address=\"\", # The scheduler address will be set after cluster creation\n executor_kwargs=executor_kwargs,\n labels=labels,\n on_start=on_start,\n on_exit=on_exit,\n metadata=metadata,\n security=self._security,\n )","label":0},{"cid":"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment.dependencies","text":"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment.dependencies\n @property\n def dependencies(self) -> list:\n return [\"dask_cloudprovider\"]","label":0},{"cid":"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment._create_dask_cluster","text":"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment._create_dask_cluster\n def _create_dask_cluster(self) -> None:\n self.logger.info(\"Creating Dask cluster using {}\".format(self._provider_class))\n self.cluster = self._provider_class(**self._provider_kwargs)\n if self.cluster and self.cluster.scheduler and self.cluster.scheduler.address:\n self.logger.info(\n \"Dask cluster created. Sheduler address: {} Dashboard: http://{}:8787 \"\n \"(unless port was changed from default of 8787)\".format(\n self.cluster.scheduler.address,\n urlparse(self.cluster.scheduler.address).hostname,\n ) # TODO submit PR to Dask Cloud Provider allowing discovery of dashboard port\n )\n\n self.executor_kwargs[\"address\"] = self.cluster.scheduler.address # type: ignore\n else:\n if self.cluster:\n self.cluster.close()\n raise Exception(\n \"Unable to determine the Dask scheduler address after cluster creation. \"\n \"Tearting down cluster and terminating setup.\"\n )\n if self._adaptive_min_workers:\n self.logger.info(\n \"Enabling adaptive mode with min_workers={} max_workers={}\".format(\n self._adaptive_min_workers, self._adaptive_max_workers\n )\n )\n self.cluster.adapt( # type: ignore\n minimum=self._adaptive_min_workers, maximum=self._adaptive_max_workers\n )","label":0},{"cid":"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment.execute","text":"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment.execute\n def execute( # type: ignore\n self, flow: \"Flow\", **kwargs: Any # type: ignore\n ) -> None:\n flow_run_info = None\n flow_run_id = prefect.context.get(\"flow_run_id\")\n if self._on_execute:\n # If an on_execute Callable has been provided, retrieve the flow run parameters\n # and then allow the Callable a chance to update _provider_kwargs. This allows\n # better sizing of the cluster resources based on parameters for this Flow run.\n try:\n client = Client()\n flow_run_info = client.get_flow_run_info(flow_run_id)\n parameters = flow_run_info.parameters or {} # type: ignore\n self._on_execute(parameters, self._provider_kwargs)\n except Exception as exc:\n self.logger.info(\n \"Failed to retrieve flow run info with error: {}\".format(repr(exc))\n )\n if \"image\" not in self._provider_kwargs or not self._provider_kwargs.get(\n \"image\"\n ):\n # If image is not specified, use the Flow's image so that dependencies are\n # identical on all containers: Flow runner, Dask scheduler, and Dask workers\n flow_id = prefect.context.get(\"flow_id\")\n try:\n client = Client()\n if not flow_id: # We've observed cases where flow_id is None\n if not flow_run_info:\n flow_run_info = client.get_flow_run_info(flow_run_id)\n flow_id = flow_run_info.flow_id\n flow_info = client.graphql(\n \"\"\"query {\n flow(where: {id: {_eq: \"%s\"}}) {\n storage\n }\n }\"\"\"\n % flow_id\n )\n storage_info = flow_info[\"data\"][\"flow\"][0][\"storage\"]\n ","label":0},{"cid":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.__init__","text":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.__init__\n def __init__( # type: ignore\n self,\n launch_type: str = \"FARGATE\",\n aws_access_key_id: str = None,\n aws_secret_access_key: str = None,\n aws_session_token: str = None,\n region_name: str = None,\n executor: \"prefect.engine.executors.Executor\" = None,\n executor_kwargs: dict = None,\n labels: List[str] = None,\n on_start: Callable = None,\n on_exit: Callable = None,\n metadata: dict = None,\n **kwargs,\n ) -> None:\n self.launch_type = launch_type\n # Not serialized, only stored on the object\n self.aws_access_key_id = aws_access_key_id or os.getenv(\"AWS_ACCESS_KEY_ID\")\n self.aws_secret_access_key = aws_secret_access_key or os.getenv(\n \"AWS_SECRET_ACCESS_KEY\"\n )\n self.aws_session_token = aws_session_token or os.getenv(\"AWS_SESSION_TOKEN\")\n self.region_name = region_name or os.getenv(\"REGION_NAME\")\n\n # Parse accepted kwargs for definition and run\n self.task_definition_kwargs, self.task_run_kwargs = self._parse_kwargs(kwargs)\n\n if executor_kwargs is not None:\n warnings.warn(\"`executor_kwargs` is deprecated, use `executor` instead\")\n if executor is None:\n executor = prefect.engine.get_default_executor_class()(\n **(executor_kwargs or {})\n )\n elif not isinstance(executor, prefect.engine.executors.Executor):\n raise TypeError(\n f\"`executor` must be an `Executor` or `None`, got `{executor}`\"\n )\n self.executor = executor\n\n super().__init__(\n labels=labels, on_start=on_start, on_exit=on_exit, metadata=metadata\n )","label":0},{"cid":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment._parse_kwargs","text":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment._parse_kwargs\n def _parse_kwargs(self, user_kwargs: dict) -> tuple:\n \"\"\"\n Parse the kwargs passed in and separate them out for `register_task_definition`\n and `run_task`. This is required because boto3 does not allow extra kwargs\n and if they are provided it will raise botocore.exceptions.ParamValidationError.\n\n Args:\n - user_kwargs (dict): The kwargs passed to the initialization of the environment\n\n Returns:\n tuple: a tuple of two dictionaries (task_definition_kwargs, task_run_kwargs)\n \"\"\"\n definition_kwarg_list = [\n \"family\",\n \"taskRoleArn\",\n \"executionRoleArn\",\n \"networkMode\",\n \"containerDefinitions\",\n \"volumes\",\n \"placementConstraints\",\n \"requiresCompatibilities\",\n \"cpu\",\n \"memory\",\n \"tags\",\n \"pidMode\",\n \"ipcMode\",\n \"proxyConfiguration\",\n \"inferenceAccelerators\",\n ]\n\n run_kwarg_list = [\n \"cluster\",\n \"taskDefinition\",\n \"count\",\n \"startedBy\",\n \"group\",\n \"placementConstraints\",\n \"placementStrategy\",\n \"platformVersion\",\n \"networkConfiguration\",\n \"tags\",\n \"enableECSManagedTags\",\n \"propagateTags\",\n ]\n\n task_definition_kwargs = {}\n for key, item in user_kwargs.items():\n if key in definition_kwarg_list:\n task_definition_kwargs.update({key: item})\n\n task_run_kwargs = {}\n for key, item in user_kwargs.items():\n if key in run_kwarg_list:\n task_run_kwargs.update({key: item})\n\n return task_definition_kwargs, task_run_kwargs","label":0},{"cid":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.dependencies","text":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.dependencies\n @property\n def dependencies(self) -> list:\n return [\"boto3\", \"botocore\"]","label":0},{"cid":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.setup","text":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.setup\n def setup(self, flow: \"Flow\") -> None: # type: ignore\n \"\"\"\n Register the task definition if it does not already exist.\n\n Args:\n - flow (Flow): the Flow object\n \"\"\"\n from boto3 import client as boto3_client\n from botocore.exceptions import ClientError\n\n boto3_c = boto3_client(\n \"ecs\",\n aws_access_key_id=self.aws_access_key_id,\n aws_secret_access_key=self.aws_secret_access_key,\n aws_session_token=self.aws_session_token,\n region_name=self.region_name,\n )\n\n definition_exists = True\n try:\n boto3_c.describe_task_definition(\n taskDefinition=self.task_definition_kwargs.get(\"family\")\n )\n except ClientError:\n definition_exists = False\n\n if not definition_exists:\n env_values = [\n {\"name\": \"PREFECT__CLOUD__GRAPHQL\", \"value\": config.cloud.graphql},\n {\"name\": \"PREFECT__CLOUD__USE_LOCAL_SECRETS\", \"value\": \"false\"},\n {\n \"name\": \"PREFECT__ENGINE__FLOW_RUNNER__DEFAULT_CLASS\",\n \"value\": \"prefect.engine.cloud.CloudFlowRunner\",\n },\n {\n \"name\": \"PREFECT__ENGINE__TASK_RUNNER__DEFAULT_CLASS\",\n \"value\": \"prefect.engine.cloud.CloudTaskRunner\",\n },\n {\"name\": \"PREFECT__LOGGING__LOG_TO_CLOUD\", \"value\": \"true\"},\n {\n \"name\": \"PREFECT__LOGGING__EXTRA_LOGGERS\",\n \"value\": str(config.logging.extra_loggers),\n },\n ]\n\n # create containerDefinitions if they do not exist\n if not self.task_definition_kwargs.get(\"containerDefinitions\"):\n self.task_definition_kwargs[\"containerDefinitions\"] = []\n self.task","label":0},{"cid":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.execute","text":"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.execute\n def execute(self, flow: \"Flow\") -> None: # type: ignore\n \"\"\"\n Run the Fargate task that was defined for this flow.\n\n Args:\n - flow (Flow): the Flow object\n \"\"\"\n from boto3 import client as boto3_client\n\n flow_run_id = prefect.context.get(\"flow_run_id\", \"unknown\")\n container_overrides = [\n {\n \"name\": \"flow-container\",\n \"environment\": [\n {\n \"name\": \"PREFECT__CLOUD__AUTH_TOKEN\",\n \"value\": config.cloud.agent.auth_token\n or config.cloud.auth_token,\n },\n {\"name\": \"PREFECT__CONTEXT__FLOW_RUN_ID\", \"value\": flow_run_id},\n {\"name\": \"PREFECT__CONTEXT__IMAGE\", \"value\": get_flow_image(flow)},\n ],\n }\n ]\n\n boto3_c = boto3_client(\n \"ecs\",\n aws_access_key_id=self.aws_access_key_id,\n aws_secret_access_key=self.aws_secret_access_key,\n aws_session_token=self.aws_session_token,\n region_name=self.region_name,\n )\n\n boto3_c.run_task(\n overrides={\"containerOverrides\": container_overrides},\n launchType=self.launch_type,\n **self.task_run_kwargs,\n )","label":0},{"cid":"src/prefect/tasks/aws/s3.py::S3Download.__init__","text":"src/prefect/tasks/aws/s3.py::S3Download.__init__\n def __init__(self, bucket: str = None, **kwargs):\n self.bucket = bucket\n super().__init__(**kwargs)","label":0},{"cid":"src/prefect/tasks/aws/s3.py::S3Download.run","text":"src/prefect/tasks/aws/s3.py::S3Download.run\n @defaults_from_attrs(\"bucket\")\n def run(\n self, key: str, credentials: str = None, bucket: str = None,\n ):\n \"\"\"\n Task run method.\n\n Args:\n - key (str): the name of the Key within this bucket to retrieve\n - credentials (dict, optional): your AWS credentials passed from an upstream\n Secret task; this Secret must be a JSON string\n with two keys: `ACCESS_KEY` and `SECRET_ACCESS_KEY` which will be\n passed directly to `boto3`. If not provided here or in context, `boto3`\n will fall back on standard AWS rules for authentication.\n - bucket (str, optional): the name of the S3 Bucket to download from\n\n Returns:\n - str: the contents of this Key / Bucket, as a string\n \"\"\"\n if bucket is None:\n raise ValueError(\"A bucket name must be provided.\")\n\n s3_client = get_boto_client(\"s3\", credentials=credentials)\n\n stream = io.BytesIO()\n\n # download\n s3_client.download_fileobj(Bucket=bucket, Key=key, Fileobj=stream)\n\n # prepare data and return\n stream.seek(0)\n output = stream.read()\n return output.decode()","label":0},{"cid":"src/prefect/tasks/aws/s3.py::S3Upload.__init__","text":"src/prefect/tasks/aws/s3.py::S3Upload.__init__\n def __init__(self, bucket: str = None, **kwargs):\n self.bucket = bucket\n super().__init__(**kwargs)","label":0},{"cid":"src/prefect/tasks/aws/s3.py::S3Upload.run","text":"src/prefect/tasks/aws/s3.py::S3Upload.run\n @defaults_from_attrs(\"bucket\")\n def run(\n self, data: str, key: str = None, credentials: dict = None, bucket: str = None,\n ):\n \"\"\"\n Task run method.\n\n Args:\n - data (str): the data payload to upload\n - key (str, optional): the Key to upload the data under; if not\n provided, a random `uuid` will be created\n - credentials (dict, optional): your AWS credentials passed from an upstream\n Secret task; this Secret must be a JSON string\n with two keys: `ACCESS_KEY` and `SECRET_ACCESS_KEY` which will be\n passed directly to `boto3`. If not provided here or in context, `boto3`\n will fall back on standard AWS rules for authentication.\n - bucket (str, optional): the name of the S3 Bucket to upload to\n\n Returns:\n - str: the name of the Key the data payload was uploaded to\n \"\"\"\n if bucket is None:\n raise ValueError(\"A bucket name must be provided.\")\n\n s3_client = get_boto_client(\"s3\", credentials=credentials)\n\n # prepare data\n try:\n stream = io.BytesIO(data)\n except TypeError:\n stream = io.BytesIO(data.encode())\n\n # create key if not provided\n if key is None:\n key = str(uuid.uuid4())\n\n # upload\n s3_client.upload_fileobj(stream, Bucket=bucket, Key=key)\n return key","label":0}],"string":"[\n {\n \"cid\": \"src/prefect/agent/fargate/agent.py::FargateAgent.__init__\",\n \"text\": \"src/prefect/agent/fargate/agent.py::FargateAgent.__init__\\n def __init__( # type: ignore\\n self,\\n name: str = None,\\n labels: Iterable[str] = None,\\n env_vars: dict = None,\\n max_polls: int = None,\\n agent_address: str = None,\\n no_cloud_logs: bool = False,\\n launch_type: str = \\\"FARGATE\\\",\\n aws_access_key_id: str = None,\\n aws_secret_access_key: str = None,\\n aws_session_token: str = None,\\n region_name: str = None,\\n botocore_config: dict = None,\\n enable_task_revisions: bool = False,\\n use_external_kwargs: bool = False,\\n external_kwargs_s3_bucket: str = None,\\n external_kwargs_s3_key: str = None,\\n **kwargs,\\n ) -> None:\\n super().__init__(\\n name=name,\\n labels=labels,\\n env_vars=env_vars,\\n max_polls=max_polls,\\n agent_address=agent_address,\\n no_cloud_logs=no_cloud_logs,\\n )\\n\\n from boto3 import client as boto3_client\\n from boto3 import resource as boto3_resource\\n from botocore.config import Config\\n\\n # Config used for boto3 client initialization\\n aws_access_key_id = aws_access_key_id or os.getenv(\\\"AWS_ACCESS_KEY_ID\\\")\\n aws_secret_access_key = aws_secret_access_key or os.getenv(\\n \\\"AWS_SECRET_ACCESS_KEY\\\"\\n )\\n aws_session_token = aws_session_token or os.getenv(\\\"AWS_SESSION_TOKEN\\\")\\n region_name = region_name or os.getenv(\\\"REGION_NAME\\\")\\n botocore_config = botocore_config or {}\\n\\n # revisions and kwargs configurations\\n self.enable_task_revisions = enable_task_revisions\\n self.use_external_kwargs = use_external_kwargs\\n self.external_kwargs_s3_bucket = external_kwargs_s3_bucket\\n self.external_kwargs_s3_key = external_kwargs_s3_key\\n self.launch_type = launch_type\\n\\n # Parse accepted kwargs for task definition, run, and container definitions key of task\\n # de\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/fargate/agent.py::FargateAgent._override_kwargs\",\n \"text\": \"src/prefect/agent/fargate/agent.py::FargateAgent._override_kwargs\\n def _override_kwargs(\\n self,\\n flow_run: GraphQLResult,\\n flow_task_definition_kwargs: dict,\\n flow_task_run_kwargs: dict,\\n flow_container_definitions_kwargs: dict,\\n ) -> None:\\n \\\"\\\"\\\"\\n Return new kwargs updated from external kwargs file.\\n\\n Args:\\n - flow_run (GraphQLResult): A GraphQLResult flow run object\\n - flow_task_definition_kwargs (dict): task_definition_kwargs to update\\n - flow_task_run_kwargs (dict): task_run_kwargs to update\\n - flow_container_definitions_kwargs (dict): container_definitions_kwargs to update\\n \\\"\\\"\\\"\\n from botocore.exceptions import ClientError\\n\\n # get external kwargs from S3\\n try:\\n self.logger.info(\\\"Fetching external kwargs from S3\\\")\\n obj = self.s3_resource.Object(\\n self.external_kwargs_s3_bucket,\\n os.path.join( # type: ignore\\n self.external_kwargs_s3_key, # type: ignore\\n slugify(flow_run.flow.name), # type: ignore\\n \\\"{}.json\\\".format(flow_run.flow.id[:8]), # type: ignore\\n ), # type: ignore\\n )\\n body = obj.get()[\\\"Body\\\"].read().decode(\\\"utf-8\\\")\\n except ClientError:\\n self.logger.info(\\n \\\"Flow id {} does not have external kwargs.\\\".format(flow_run.flow.id[:8])\\n )\\n body = \\\"{}\\\"\\n self.logger.debug(\\\"External kwargs:\\\\n{}\\\".format(body))\\n\\n # update kwargs from with external kwargs\\n self.logger.info(\\\"Updating default kwargs with external\\\")\\n external_kwargs = json.loads(body)\\n # parse external kwargs\\n (\\n ext_task_definition_kwargs,\\n ext_task_run_kwargs,\\n ext_container_definitions_kwargs,\\n ) = self._parse_kwargs(external_kwargs)\\n self.logger.debug(\\n \\\"External task defini\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/fargate/agent.py::FargateAgent._add_flow_tags\",\n \"text\": \"src/prefect/agent/fargate/agent.py::FargateAgent._add_flow_tags\\n def _add_flow_tags(\\n self, flow_run: GraphQLResult, flow_task_definition_kwargs: dict\\n ) -> None:\\n \\\"\\\"\\\"\\n Add tags to task definition kwargs to\\n\\n Args:\\n - flow_run (GraphQLResult): A GraphQLResult flow run object\\n - flow_task_definition_kwargs (dict): task_definition_kwargs to add tags to\\n \\\"\\\"\\\"\\n # add flow id and version to definition tags\\n self.logger.info(\\\"Adding tags for flow_id and version.\\\")\\n if not flow_task_definition_kwargs.get(\\\"tags\\\"):\\n flow_task_definition_kwargs[\\\"tags\\\"] = []\\n else:\\n flow_task_definition_kwargs[\\\"tags\\\"] = copy.deepcopy(\\n flow_task_definition_kwargs[\\\"tags\\\"]\\n )\\n append_tag = True\\n for i in flow_task_definition_kwargs[\\\"tags\\\"]:\\n if i[\\\"key\\\"] == \\\"PrefectFlowId\\\":\\n i[\\\"value\\\"] = flow_run.flow.id[:8]\\n append_tag = False\\n if append_tag:\\n flow_task_definition_kwargs[\\\"tags\\\"].append(\\n {\\\"key\\\": \\\"PrefectFlowId\\\", \\\"value\\\": flow_run.flow.id[:8]}\\n )\\n append_tag = True\\n for i in flow_task_definition_kwargs[\\\"tags\\\"]:\\n if i[\\\"key\\\"] == \\\"PrefectFlowVersion\\\":\\n i[\\\"value\\\"] = str(flow_run.flow.version)\\n append_tag = False\\n if append_tag:\\n flow_task_definition_kwargs[\\\"tags\\\"].append(\\n {\\\"key\\\": \\\"PrefectFlowVersion\\\", \\\"value\\\": str(flow_run.flow.version)}\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/fargate/agent.py::FargateAgent._parse_kwargs\",\n \"text\": \"src/prefect/agent/fargate/agent.py::FargateAgent._parse_kwargs\\n def _parse_kwargs(self, user_kwargs: dict, check_envars: bool = False) -> tuple:\\n \\\"\\\"\\\"\\n Parse the kwargs passed in and separate them out for `register_task_definition`\\n and `run_task`. This is required because boto3 does not allow extra kwargs\\n and if they are provided it will raise botocore.exceptions.ParamValidationError.\\n\\n Args:\\n - user_kwargs (dict): The kwargs passed to the initialization of the environment\\n - check_envars (bool): Whether to check envars for kwargs\\n\\n Returns:\\n tuple: a tuple of three dictionaries (task_definition_kwargs, task_run_kwargs,\\n container_definitions_kwargs)\\n \\\"\\\"\\\"\\n definition_kwarg_list = [\\n \\\"taskRoleArn\\\",\\n \\\"executionRoleArn\\\",\\n \\\"volumes\\\",\\n \\\"placementConstraints\\\",\\n \\\"cpu\\\",\\n \\\"memory\\\",\\n \\\"tags\\\",\\n \\\"pidMode\\\",\\n \\\"ipcMode\\\",\\n \\\"proxyConfiguration\\\",\\n \\\"inferenceAccelerators\\\",\\n ]\\n\\n definition_kwarg_list_no_eval = [\\\"cpu\\\", \\\"memory\\\"]\\n\\n run_kwarg_list = [\\n \\\"cluster\\\",\\n \\\"count\\\",\\n \\\"startedBy\\\",\\n \\\"group\\\",\\n \\\"placementConstraints\\\",\\n \\\"placementStrategy\\\",\\n \\\"platformVersion\\\",\\n \\\"networkConfiguration\\\",\\n \\\"tags\\\",\\n \\\"enableECSManagedTags\\\",\\n \\\"propagateTags\\\",\\n ]\\n\\n container_definitions_kwarg_list = [\\n \\\"mountPoints\\\",\\n \\\"secrets\\\",\\n \\\"environment\\\",\\n \\\"logConfiguration\\\",\\n \\\"repositoryCredentials\\\",\\n ]\\n\\n task_definition_kwargs = {}\\n definition_kwarg_list_eval = {\\n i: (i not in definition_kwarg_list_no_eval) for i in definition_kwarg_list\\n }\\n for key, item in user_kwargs.items():\\n if key in definition_kwarg_list:\\n if defi\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/fargate/agent.py::FargateAgent.deploy_flow\",\n \"text\": \"src/prefect/agent/fargate/agent.py::FargateAgent.deploy_flow\\n def deploy_flow(self, flow_run: GraphQLResult) -> str:\\n \\\"\\\"\\\"\\n Deploy flow runs to Fargate\\n\\n Args:\\n - flow_run (GraphQLResult): A GraphQLResult flow run object\\n\\n Returns:\\n - str: Information about the deployment\\n \\\"\\\"\\\"\\n self.logger.info(\\n \\\"Deploying flow run {}\\\".format(flow_run.id) # type: ignore\\n )\\n\\n # create copies of kwargs to apply overrides as needed\\n flow_task_definition_kwargs = copy.deepcopy(self.task_definition_kwargs)\\n flow_task_run_kwargs = copy.deepcopy(self.task_run_kwargs)\\n flow_container_definitions_kwargs = copy.deepcopy(\\n self.container_definitions_kwargs\\n )\\n\\n # create task_definition_name dict for passing into verify method\\n task_definition_dict = {}\\n\\n if self.use_external_kwargs:\\n # override from external kwargs\\n self._override_kwargs(\\n flow_run,\\n flow_task_definition_kwargs,\\n flow_task_run_kwargs,\\n flow_container_definitions_kwargs,\\n )\\n\\n # set proper task_definition_name and tags based on enable_task_revisions flag\\n if self.enable_task_revisions:\\n # set task definition name\\n task_definition_dict[\\\"task_definition_name\\\"] = slugify(flow_run.flow.name)\\n self._add_flow_tags(flow_run, flow_task_definition_kwargs)\\n\\n else:\\n task_definition_dict[\\\"task_definition_name\\\"] = \\\"prefect-task-{}\\\".format( # type: ignore\\n flow_run.flow.id[:8] # type: ignore\\n ) # type: ignore\\n\\n image = get_flow_image(flow_run=flow_run)\\n\\n # check if task definition exists\\n self.logger.debug(\\\"Checking for task definition\\\")\\n if not self._verify_task_definition_exists(flow_run, task_definition_dict):\\n self.logger.debug(\\\"No task definition found\\\")\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/fargate/agent.py::FargateAgent._verify_task_definition_exists\",\n \"text\": \"src/prefect/agent/fargate/agent.py::FargateAgent._verify_task_definition_exists\\n def _verify_task_definition_exists(\\n self, flow_run: GraphQLResult, task_definition_dict: dict\\n ) -> bool:\\n \\\"\\\"\\\"\\n Check if a task definition already exists for the flow\\n\\n Args:\\n - flow_run (GraphQLResult): A GraphQLResult representing a flow run object\\n - task_definition_dict(dict): Dictionary containing task definition name to update\\n if needed.\\n\\n Returns:\\n - bool: whether or not a preexisting task definition is found for this flow\\n \\\"\\\"\\\"\\n from botocore.exceptions import ClientError\\n\\n try:\\n definition_exists = True\\n task_definition_name = task_definition_dict[\\\"task_definition_name\\\"]\\n definition_response = self.boto3_client.describe_task_definition(\\n taskDefinition=task_definition_name, include=[\\\"TAGS\\\"]\\n )\\n # if current active task definition has current flow id, then exists\\n if self.enable_task_revisions:\\n definition_exists = False\\n tag_dict = {x[\\\"key\\\"]: x[\\\"value\\\"] for x in definition_response[\\\"tags\\\"]}\\n current_flow_id = tag_dict.get(\\\"PrefectFlowId\\\")\\n current_flow_version = int(tag_dict.get(\\\"PrefectFlowVersion\\\", 0))\\n if current_flow_id == flow_run.flow.id[:8]:\\n self.logger.debug(\\n \\\"Active task definition for {} already exists\\\".format(\\n flow_run.flow.id[:8]\\n ) # type: ignore\\n )\\n definition_exists = True\\n elif flow_run.flow.version < current_flow_version:\\n tag_search = self.boto3_client_tags.get_resources(\\n TagFilters=[\\n {\\\"Key\\\": \\\"PrefectFlowId\\\", \\\"Values\\\": [flow_run.flow.id[:8]]}\\n ],\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/fargate/agent.py::FargateAgent._create_task_definition\",\n \"text\": \"src/prefect/agent/fargate/agent.py::FargateAgent._create_task_definition\\n def _create_task_definition(\\n self,\\n image: str,\\n flow_task_definition_kwargs: dict,\\n container_definitions_kwargs: dict,\\n task_definition_name: str,\\n ) -> None:\\n \\\"\\\"\\\"\\n Create a task definition for the flow that each flow run will use. This function\\n is only called when a flow is run for the first time.\\n\\n Args:\\n - image (str): The full name of an image to use for this task definition\\n - flow_task_definition_kwargs (dict): kwargs to use for registration\\n - container_definitions_kwargs (dict): container definitions kwargs to use for\\n registration\\n - task_definition_name (str): task definition name to use\\n \\\"\\\"\\\"\\n self.logger.debug(\\\"Using image {} for task definition\\\".format(image))\\n container_definitions = [\\n {\\n \\\"name\\\": \\\"flow\\\",\\n \\\"image\\\": image,\\n \\\"command\\\": [\\\"/bin/sh\\\", \\\"-c\\\", \\\"prefect execute cloud-flow\\\"],\\n \\\"environment\\\": [\\n {\\n \\\"name\\\": \\\"PREFECT__CLOUD__API\\\",\\n \\\"value\\\": config.cloud.api or \\\"https://api.prefect.io\\\",\\n },\\n {\\n \\\"name\\\": \\\"PREFECT__CLOUD__AGENT__LABELS\\\",\\n \\\"value\\\": str(self.labels),\\n },\\n {\\\"name\\\": \\\"PREFECT__CLOUD__USE_LOCAL_SECRETS\\\", \\\"value\\\": \\\"false\\\"},\\n {\\n \\\"name\\\": \\\"PREFECT__LOGGING__LOG_TO_CLOUD\\\",\\n \\\"value\\\": str(self.log_to_cloud).lower(),\\n },\\n {\\\"name\\\": \\\"PREFECT__LOGGING__LEVEL\\\", \\\"value\\\": \\\"DEBUG\\\"},\\n {\\n \\\"name\\\": \\\"PREFECT__ENGINE__FLOW_RUNNER__DEFAULT_CLASS\\\",\\n \\\"value\\\": \\\"prefect.engine.cloud.CloudFlowRunner\\\",\\n },\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/agent/fargate/agent.py::FargateAgent._run_task\",\n \"text\": \"src/prefect/agent/fargate/agent.py::FargateAgent._run_task\\n def _run_task(\\n self,\\n flow_run: GraphQLResult,\\n flow_task_run_kwargs: dict,\\n task_definition_name: str,\\n ) -> str:\\n \\\"\\\"\\\"\\n Run a task using the flow run.\\n\\n Args:\\n - flow_run (GraphQLResult): A GraphQLResult flow run object\\n - flow_task_run_kwargs (dict): kwargs to use for task run\\n - task_definition_name (str): task definition name to use\\n \\\"\\\"\\\"\\n container_overrides = [\\n {\\n \\\"name\\\": \\\"flow\\\",\\n \\\"environment\\\": [\\n {\\n \\\"name\\\": \\\"PREFECT__CLOUD__AUTH_TOKEN\\\",\\n \\\"value\\\": config.cloud.agent.auth_token,\\n },\\n {\\n \\\"name\\\": \\\"PREFECT__CONTEXT__FLOW_RUN_ID\\\",\\n \\\"value\\\": flow_run.id, # type: ignore\\n },\\n {\\n \\\"name\\\": \\\"PREFECT__CONTEXT__FLOW_ID\\\",\\n \\\"value\\\": flow_run.flow.id, # type: ignore\\n },\\n ],\\n }\\n ]\\n\\n # Run task\\n self.logger.debug(\\n \\\"Running task using task definition {}\\\".format(\\n task_definition_name # type: ignore\\n )\\n )\\n\\n if self.launch_type:\\n flow_task_run_kwargs[\\\"launchType\\\"] = self.launch_type\\n task = self.boto3_client.run_task(\\n taskDefinition=task_definition_name,\\n overrides={\\\"containerOverrides\\\": container_overrides},\\n **flow_task_run_kwargs,\\n )\\n\\n return task[\\\"tasks\\\"][0].get(\\\"taskArn\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/fargate/agent.py::FargateAgent.validate_configuration\",\n \"text\": \"src/prefect/agent/fargate/agent.py::FargateAgent.validate_configuration\\n def validate_configuration(self) -> None:\\n \\\"\\\"\\\"\\n Utility function for testing Agent's configuration. This function is helpful in\\n determining if the provided configuration for the Agent is able to register a\\n task definition and then subsequently run the task.\\n \\\"\\\"\\\"\\n task_name = f\\\"prefect-test-task-{str(uuid.uuid4())[:8]}\\\"\\n\\n # Populate container definition with provided kwargs\\n flow_container_definitions_kwargs = copy.deepcopy(\\n self.container_definitions_kwargs\\n )\\n\\n container_definitions = [\\n {\\n \\\"name\\\": \\\"test-container\\\",\\n \\\"image\\\": \\\"busybox\\\",\\n \\\"command\\\": [\\\"/bin/sh\\\", \\\"-c\\\", \\\"echo 'I am alive!'\\\"],\\n \\\"environment\\\": [],\\n \\\"secrets\\\": [],\\n \\\"mountPoints\\\": [],\\n \\\"logConfiguration\\\": {},\\n \\\"essential\\\": True,\\n }\\n ]\\n\\n base_envar_keys = [\\n x[\\\"name\\\"] for x in container_definitions[0][\\\"environment\\\"] # type: ignore\\n ]\\n container_definitions_environment = [\\n x\\n for x in flow_container_definitions_kwargs.get(\\\"environment\\\", [])\\n if x[\\\"name\\\"] not in base_envar_keys\\n ]\\n container_definitions[0][\\\"environment\\\"].extend( # type: ignore\\n container_definitions_environment\\n )\\n container_definitions[0][\\\"secrets\\\"] = flow_container_definitions_kwargs.get(\\n \\\"secrets\\\", []\\n )\\n container_definitions[0][\\\"mountPoints\\\"] = flow_container_definitions_kwargs.get(\\n \\\"mountPoints\\\", []\\n )\\n container_definitions[0][\\n \\\"logConfiguration\\\"\\n ] = flow_container_definitions_kwargs.get(\\\"logConfiguration\\\", {})\\n\\n # Register task definition\\n flow_task_definition_kwargs = copy.deepcopy(self.task_definition_kwargs)\\n\\n if self.launch_type:\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.__init__\",\n \"text\": \"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.__init__\\n def __init__(\\n self,\\n namespace: str = None,\\n name: str = None,\\n labels: Iterable[str] = None,\\n env_vars: dict = None,\\n max_polls: int = None,\\n agent_address: str = None,\\n no_cloud_logs: bool = False,\\n ) -> None:\\n super().__init__(\\n name=name,\\n labels=labels,\\n env_vars=env_vars,\\n max_polls=max_polls,\\n agent_address=agent_address,\\n no_cloud_logs=no_cloud_logs,\\n )\\n\\n self.namespace = namespace\\n\\n from kubernetes import client, config\\n\\n try:\\n self.logger.debug(\\\"Loading incluster configuration\\\")\\n config.load_incluster_config()\\n except config.config_exception.ConfigException as exc:\\n self.logger.warning(\\n \\\"{} Using out of cluster configuration option.\\\".format(exc)\\n )\\n self.logger.debug(\\\"Loading out of cluster configuration\\\")\\n config.load_kube_config()\\n\\n self.batch_client = client.BatchV1Api()\\n\\n self.logger.debug(f\\\"Namespace: {self.namespace}\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.deploy_flow\",\n \"text\": \"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.deploy_flow\\n def deploy_flow(self, flow_run: GraphQLResult) -> str:\\n \\\"\\\"\\\"\\n Deploy flow runs on to a k8s cluster as jobs\\n\\n Args:\\n - flow_run (GraphQLResult): A GraphQLResult flow run object\\n\\n Returns:\\n - str: Information about the deployment\\n \\\"\\\"\\\"\\n self.logger.info(\\n \\\"Deploying flow run {}\\\".format(flow_run.id) # type: ignore\\n )\\n\\n image = get_flow_image(flow_run=flow_run)\\n\\n job_spec = self.replace_job_spec_yaml(flow_run, image)\\n\\n self.logger.debug(\\n \\\"Creating namespaced job {}\\\".format(job_spec[\\\"metadata\\\"][\\\"name\\\"])\\n )\\n job = self.batch_client.create_namespaced_job(\\n namespace=self.namespace or os.getenv(\\\"NAMESPACE\\\", \\\"default\\\"), body=job_spec\\n )\\n\\n self.logger.debug(\\\"Job {} created\\\".format(job.metadata.name))\\n\\n return \\\"Job {}\\\".format(job.metadata.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.replace_job_spec_yaml\",\n \"text\": \"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.replace_job_spec_yaml\\n def replace_job_spec_yaml(self, flow_run: GraphQLResult, image: str) -> dict:\\n \\\"\\\"\\\"\\n Populate metadata and variables in the job_spec.yaml file for flow runs\\n\\n Args:\\n - flow_run (GraphQLResult): A flow run object\\n - image (str): The full name of an image to use for the job\\n\\n Returns:\\n - dict: a dictionary representing the populated yaml object\\n \\\"\\\"\\\"\\n with open(path.join(path.dirname(__file__), \\\"job_spec.yaml\\\"), \\\"r\\\") as job_file:\\n job = yaml.safe_load(job_file)\\n\\n identifier = str(uuid.uuid4())[:8]\\n job_name = \\\"prefect-job-{}\\\".format(identifier)\\n\\n # Populate job metadata for identification\\n k8s_labels = {\\n \\\"prefect.io/identifier\\\": identifier,\\n \\\"prefect.io/flow_run_id\\\": flow_run.id, # type: ignore\\n \\\"prefect.io/flow_id\\\": flow_run.flow.id, # type: ignore\\n }\\n job[\\\"metadata\\\"][\\\"name\\\"] = job_name\\n job[\\\"metadata\\\"][\\\"labels\\\"].update(**k8s_labels)\\n job[\\\"spec\\\"][\\\"template\\\"][\\\"metadata\\\"][\\\"labels\\\"].update(**k8s_labels)\\n\\n # Use provided image for job\\n job[\\\"spec\\\"][\\\"template\\\"][\\\"spec\\\"][\\\"containers\\\"][0][\\\"image\\\"] = image\\n\\n self.logger.debug(\\\"Using image {} for job\\\".format(image))\\n\\n # Populate environment variables for flow run execution\\n env = job[\\\"spec\\\"][\\\"template\\\"][\\\"spec\\\"][\\\"containers\\\"][0][\\\"env\\\"]\\n\\n env[0][\\\"value\\\"] = config.cloud.api or \\\"https://api.prefect.io\\\"\\n env[1][\\\"value\\\"] = config.cloud.agent.auth_token\\n env[2][\\\"value\\\"] = flow_run.id # type: ignore\\n env[3][\\\"value\\\"] = flow_run.flow.id # type: ignore\\n env[4][\\\"value\\\"] = os.getenv(\\\"NAMESPACE\\\", \\\"default\\\")\\n env[5][\\\"value\\\"] = str(self.labels)\\n env[6][\\\"value\\\"] = str(self.log_to_cloud).lower()\\n\\n # append all user provided values\\n for key, value in self.env_vars.items():\\n env.append(\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.generate_deployment_yaml\",\n \"text\": \"src/prefect/agent/kubernetes/agent.py::KubernetesAgent.generate_deployment_yaml\\n @staticmethod\\n def generate_deployment_yaml(\\n token: str = None,\\n api: str = None,\\n namespace: str = None,\\n image_pull_secrets: str = None,\\n resource_manager_enabled: bool = False,\\n rbac: bool = False,\\n latest: bool = False,\\n mem_request: str = None,\\n mem_limit: str = None,\\n cpu_request: str = None,\\n cpu_limit: str = None,\\n image_pull_policy: str = None,\\n service_account_name: str = None,\\n labels: Iterable[str] = None,\\n env_vars: dict = None,\\n backend: str = None,\\n ) -> str:\\n \\\"\\\"\\\"\\n Generate and output an installable YAML spec for the agent.\\n\\n Args:\\n - token (str, optional): A `RUNNER` token to give the agent\\n - api (str, optional): A URL pointing to the Prefect API. Defaults to\\n `https://api.prefect.io`\\n - namespace (str, optional): The namespace to create Prefect jobs in. Defaults\\n to `default`\\n - image_pull_secrets (str, optional): The name of an image pull secret to use\\n for Prefect jobs\\n - resource_manager_enabled (bool, optional): Whether to include the resource\\n manager as part of the YAML. Defaults to `False`\\n - rbac (bool, optional): Whether to include default RBAC configuration as\\n part of the YAML. Defaults to `False`\\n - latest (bool, optional): Whether to use the `latest` Prefect image.\\n Defaults to `False`\\n - mem_request (str, optional): Requested memory for Prefect init job.\\n - mem_limit (str, optional): Limit memory for Prefect init job.\\n - cpu_request (str, optional): Requested CPU for Prefect init job.\\n - cpu_limit (str, optional): Limit CPU for Prefect init job.\\n - image_pull_policy (str, optional): imagePullPolic\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/cli/agent.py::agent\",\n \"text\": \"src/prefect/cli/agent.py::agent\\n@click.group(hidden=True)\\ndef agent():\\n \\\"\\\"\\\"\\n Manage Prefect agents.\\n\\n \\\\b\\n Usage:\\n $ prefect agent [COMMAND]\\n\\n \\\\b\\n Arguments:\\n start Start a Prefect agent\\n install Output platform-specific agent installation configs\\n\\n \\\\b\\n Examples:\\n $ prefect agent start\\n ...agent begins running in process...\\n\\n \\\\b\\n $ prefect agent start kubernetes --token MY_TOKEN\\n ...agent begins running in process...\\n\\n \\\\b\\n $ prefect agent install kubernetes --token MY_TOKEN --namespace metrics\\n ...k8s yaml output...\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/cli/agent.py::start\",\n \"text\": \"src/prefect/cli/agent.py::start\\n@agent.command(\\n hidden=True,\\n context_settings=dict(ignore_unknown_options=True, allow_extra_args=True),\\n)\\n@click.argument(\\\"agent-option\\\", default=\\\"local\\\")\\n@click.option(\\n \\\"--token\\\", \\\"-t\\\", required=False, help=\\\"A Prefect Cloud API token.\\\", hidden=True\\n)\\n@click.option(\\\"--api\\\", \\\"-a\\\", required=False, help=\\\"A Prefect API URL.\\\", hidden=True)\\n@click.option(\\n \\\"--name\\\",\\n \\\"-n\\\",\\n required=False,\\n help=\\\"A name to use for the agent\\\",\\n hidden=True,\\n default=None,\\n)\\n@click.option(\\n \\\"--verbose\\\", \\\"-v\\\", is_flag=True, help=\\\"Enable verbose agent logs.\\\", hidden=True\\n)\\n@click.option(\\n \\\"--label\\\",\\n \\\"-l\\\",\\n multiple=True,\\n help=\\\"Labels the agent will use to query for flow runs.\\\",\\n hidden=True,\\n)\\n@click.option(\\n \\\"--env\\\",\\n \\\"-e\\\",\\n multiple=True,\\n help=\\\"Environment variables to set on each submitted flow run.\\\",\\n hidden=True,\\n)\\n@click.option(\\n \\\"--max-polls\\\",\\n required=False,\\n help=\\\"Maximum number of polls for the agent\\\",\\n hidden=True,\\n type=int,\\n)\\n@click.option(\\n \\\"--agent-address\\\",\\n required=False,\\n help=\\\"Address to serve internal api server at. Defaults to no server.\\\",\\n hidden=True,\\n type=str,\\n default=\\\"\\\",\\n)\\n@click.option(\\n \\\"--namespace\\\",\\n required=False,\\n help=\\\"Kubernetes namespace to create jobs.\\\",\\n hidden=True,\\n)\\n@click.option(\\n \\\"--import-path\\\",\\n \\\"-p\\\",\\n multiple=True,\\n help=\\\"Import paths the local agent will add to all flow runs.\\\",\\n hidden=True,\\n)\\n@click.option(\\n \\\"--show-flow-logs\\\",\\n \\\"-f\\\",\\n help=\\\"Display logging output from flows run by the agent.\\\",\\n hidden=True,\\n is_flag=True,\\n)\\n@click.option(\\\"--no-pull\\\", is_flag=True, help=\\\"Pull images flag.\\\", hidden=True)\\n@click.option(\\n \\\"--no-cloud-logs\\\",\\n is_flag=True,\\n help=\\\"Turn off logging for all flows run through this agent.\\\",\\n hidden=True,\\n)\\n@click.option(\\\"--base-url\\\", \\\"-b\\\", help=\\\"Docker daemon base URL.\\\", hidden=True)\\n@click.option(\\n \\\"--volume\\\",\\n multiple=True,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/cli/agent.py::install\",\n \"text\": \"src/prefect/cli/agent.py::install\\n@agent.command(hidden=True)\\n@click.argument(\\\"name\\\")\\n@click.option(\\n \\\"--token\\\", \\\"-t\\\", required=False, help=\\\"A Prefect Cloud API token.\\\", hidden=True\\n)\\n@click.option(\\\"--api\\\", \\\"-a\\\", required=False, help=\\\"A Prefect API URL.\\\", hidden=True)\\n@click.option(\\n \\\"--namespace\\\",\\n \\\"-n\\\",\\n required=False,\\n help=\\\"Agent namespace to launch workloads.\\\",\\n hidden=True,\\n)\\n@click.option(\\n \\\"--image-pull-secrets\\\",\\n \\\"-i\\\",\\n required=False,\\n help=\\\"Name of image pull secrets to use for workloads.\\\",\\n hidden=True,\\n)\\n@click.option(\\n \\\"--resource-manager\\\", is_flag=True, help=\\\"Enable resource manager.\\\", hidden=True\\n)\\n@click.option(\\\"--rbac\\\", is_flag=True, help=\\\"Enable default RBAC.\\\", hidden=True)\\n@click.option(\\n \\\"--latest\\\", is_flag=True, help=\\\"Use the latest Prefect image.\\\", hidden=True\\n)\\n@click.option(\\n \\\"--mem-request\\\",\\n required=False,\\n help=\\\"Requested memory for Prefect init job.\\\",\\n hidden=True,\\n)\\n@click.option(\\n \\\"--mem-limit\\\",\\n required=False,\\n help=\\\"Limit memory for Prefect init job.\\\",\\n hidden=True,\\n)\\n@click.option(\\n \\\"--cpu-request\\\",\\n required=False,\\n help=\\\"Requested CPU for Prefect init job.\\\",\\n hidden=True,\\n)\\n@click.option(\\n \\\"--cpu-limit\\\", required=False, help=\\\"Limit CPU for Prefect init job.\\\", hidden=True\\n)\\n@click.option(\\n \\\"--image-pull-policy\\\",\\n required=False,\\n help=\\\"imagePullPolicy for Prefect init job\\\",\\n hidden=True,\\n)\\n@click.option(\\n \\\"--service-account-name\\\",\\n required=False,\\n help=\\\"Name of Service Account for Prefect init job\\\",\\n hidden=True,\\n)\\n@click.option(\\n \\\"--label\\\",\\n \\\"-l\\\",\\n multiple=True,\\n help=\\\"Labels the agent will use to query for flow runs.\\\",\\n hidden=True,\\n)\\n@click.option(\\n \\\"--env\\\",\\n \\\"-e\\\",\\n multiple=True,\\n help=\\\"Environment variables to set on each submitted flow run.\\\",\\n hidden=True,\\n)\\n@click.option(\\n \\\"--import-path\\\",\\n \\\"-p\\\",\\n multiple=True,\\n help=\\\"Import paths the local agent will add to all flow runs.\\\",\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment.__init__\",\n \"text\": \"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment.__init__\\n def __init__( # type: ignore\\n self,\\n provider_class: Type[Cluster],\\n adaptive_min_workers: int = None,\\n adaptive_max_workers: int = None,\\n security: Security = None,\\n executor_kwargs: Dict[str, Any] = None,\\n labels: List[str] = None,\\n on_execute: Callable[[Dict[str, Any], Dict[str, Any]], None] = None,\\n on_start: Callable = None,\\n on_exit: Callable = None,\\n metadata: dict = None,\\n **kwargs\\n ) -> None:\\n self._provider_class = provider_class\\n self._adaptive_min_workers = adaptive_min_workers\\n self._adaptive_max_workers = adaptive_max_workers\\n self._on_execute = on_execute\\n self._provider_kwargs = kwargs\\n if \\\"skip_cleanup\\\" not in self._provider_kwargs:\\n # Prefer this default (if not provided) to avoid deregistering task definitions See\\n # this issue in Dask Cloud Provider:\\n # https://github.com/dask/dask-cloudprovider/issues/94\\n self._provider_kwargs[\\\"skip_cleanup\\\"] = True\\n self._security = security\\n if self._security:\\n # We'll use the security config object both for our Dask Client connection *and*\\n # for the particular Dask Cloud Provider (e.g. Fargate) to use with *its* Dask\\n # Client when it connects to the scheduler after cluster creation. So we\\n # put it in _provider_kwargs so it gets passed to the Dask Cloud Provider's constructor\\n self._provider_kwargs[\\\"security\\\"] = self._security\\n self.cluster = None\\n super().__init__(\\n address=\\\"\\\", # The scheduler address will be set after cluster creation\\n executor_kwargs=executor_kwargs,\\n labels=labels,\\n on_start=on_start,\\n on_exit=on_exit,\\n metadata=metadata,\\n security=self._security,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment.dependencies\",\n \"text\": \"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment.dependencies\\n @property\\n def dependencies(self) -> list:\\n return [\\\"dask_cloudprovider\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment._create_dask_cluster\",\n \"text\": \"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment._create_dask_cluster\\n def _create_dask_cluster(self) -> None:\\n self.logger.info(\\\"Creating Dask cluster using {}\\\".format(self._provider_class))\\n self.cluster = self._provider_class(**self._provider_kwargs)\\n if self.cluster and self.cluster.scheduler and self.cluster.scheduler.address:\\n self.logger.info(\\n \\\"Dask cluster created. Sheduler address: {} Dashboard: http://{}:8787 \\\"\\n \\\"(unless port was changed from default of 8787)\\\".format(\\n self.cluster.scheduler.address,\\n urlparse(self.cluster.scheduler.address).hostname,\\n ) # TODO submit PR to Dask Cloud Provider allowing discovery of dashboard port\\n )\\n\\n self.executor_kwargs[\\\"address\\\"] = self.cluster.scheduler.address # type: ignore\\n else:\\n if self.cluster:\\n self.cluster.close()\\n raise Exception(\\n \\\"Unable to determine the Dask scheduler address after cluster creation. \\\"\\n \\\"Tearting down cluster and terminating setup.\\\"\\n )\\n if self._adaptive_min_workers:\\n self.logger.info(\\n \\\"Enabling adaptive mode with min_workers={} max_workers={}\\\".format(\\n self._adaptive_min_workers, self._adaptive_max_workers\\n )\\n )\\n self.cluster.adapt( # type: ignore\\n minimum=self._adaptive_min_workers, maximum=self._adaptive_max_workers\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment.execute\",\n \"text\": \"src/prefect/environments/execution/dask/cloud_provider.py::DaskCloudProviderEnvironment.execute\\n def execute( # type: ignore\\n self, flow: \\\"Flow\\\", **kwargs: Any # type: ignore\\n ) -> None:\\n flow_run_info = None\\n flow_run_id = prefect.context.get(\\\"flow_run_id\\\")\\n if self._on_execute:\\n # If an on_execute Callable has been provided, retrieve the flow run parameters\\n # and then allow the Callable a chance to update _provider_kwargs. This allows\\n # better sizing of the cluster resources based on parameters for this Flow run.\\n try:\\n client = Client()\\n flow_run_info = client.get_flow_run_info(flow_run_id)\\n parameters = flow_run_info.parameters or {} # type: ignore\\n self._on_execute(parameters, self._provider_kwargs)\\n except Exception as exc:\\n self.logger.info(\\n \\\"Failed to retrieve flow run info with error: {}\\\".format(repr(exc))\\n )\\n if \\\"image\\\" not in self._provider_kwargs or not self._provider_kwargs.get(\\n \\\"image\\\"\\n ):\\n # If image is not specified, use the Flow's image so that dependencies are\\n # identical on all containers: Flow runner, Dask scheduler, and Dask workers\\n flow_id = prefect.context.get(\\\"flow_id\\\")\\n try:\\n client = Client()\\n if not flow_id: # We've observed cases where flow_id is None\\n if not flow_run_info:\\n flow_run_info = client.get_flow_run_info(flow_run_id)\\n flow_id = flow_run_info.flow_id\\n flow_info = client.graphql(\\n \\\"\\\"\\\"query {\\n flow(where: {id: {_eq: \\\"%s\\\"}}) {\\n storage\\n }\\n }\\\"\\\"\\\"\\n % flow_id\\n )\\n storage_info = flow_info[\\\"data\\\"][\\\"flow\\\"][0][\\\"storage\\\"]\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.__init__\",\n \"text\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.__init__\\n def __init__( # type: ignore\\n self,\\n launch_type: str = \\\"FARGATE\\\",\\n aws_access_key_id: str = None,\\n aws_secret_access_key: str = None,\\n aws_session_token: str = None,\\n region_name: str = None,\\n executor: \\\"prefect.engine.executors.Executor\\\" = None,\\n executor_kwargs: dict = None,\\n labels: List[str] = None,\\n on_start: Callable = None,\\n on_exit: Callable = None,\\n metadata: dict = None,\\n **kwargs,\\n ) -> None:\\n self.launch_type = launch_type\\n # Not serialized, only stored on the object\\n self.aws_access_key_id = aws_access_key_id or os.getenv(\\\"AWS_ACCESS_KEY_ID\\\")\\n self.aws_secret_access_key = aws_secret_access_key or os.getenv(\\n \\\"AWS_SECRET_ACCESS_KEY\\\"\\n )\\n self.aws_session_token = aws_session_token or os.getenv(\\\"AWS_SESSION_TOKEN\\\")\\n self.region_name = region_name or os.getenv(\\\"REGION_NAME\\\")\\n\\n # Parse accepted kwargs for definition and run\\n self.task_definition_kwargs, self.task_run_kwargs = self._parse_kwargs(kwargs)\\n\\n if executor_kwargs is not None:\\n warnings.warn(\\\"`executor_kwargs` is deprecated, use `executor` instead\\\")\\n if executor is None:\\n executor = prefect.engine.get_default_executor_class()(\\n **(executor_kwargs or {})\\n )\\n elif not isinstance(executor, prefect.engine.executors.Executor):\\n raise TypeError(\\n f\\\"`executor` must be an `Executor` or `None`, got `{executor}`\\\"\\n )\\n self.executor = executor\\n\\n super().__init__(\\n labels=labels, on_start=on_start, on_exit=on_exit, metadata=metadata\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment._parse_kwargs\",\n \"text\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment._parse_kwargs\\n def _parse_kwargs(self, user_kwargs: dict) -> tuple:\\n \\\"\\\"\\\"\\n Parse the kwargs passed in and separate them out for `register_task_definition`\\n and `run_task`. This is required because boto3 does not allow extra kwargs\\n and if they are provided it will raise botocore.exceptions.ParamValidationError.\\n\\n Args:\\n - user_kwargs (dict): The kwargs passed to the initialization of the environment\\n\\n Returns:\\n tuple: a tuple of two dictionaries (task_definition_kwargs, task_run_kwargs)\\n \\\"\\\"\\\"\\n definition_kwarg_list = [\\n \\\"family\\\",\\n \\\"taskRoleArn\\\",\\n \\\"executionRoleArn\\\",\\n \\\"networkMode\\\",\\n \\\"containerDefinitions\\\",\\n \\\"volumes\\\",\\n \\\"placementConstraints\\\",\\n \\\"requiresCompatibilities\\\",\\n \\\"cpu\\\",\\n \\\"memory\\\",\\n \\\"tags\\\",\\n \\\"pidMode\\\",\\n \\\"ipcMode\\\",\\n \\\"proxyConfiguration\\\",\\n \\\"inferenceAccelerators\\\",\\n ]\\n\\n run_kwarg_list = [\\n \\\"cluster\\\",\\n \\\"taskDefinition\\\",\\n \\\"count\\\",\\n \\\"startedBy\\\",\\n \\\"group\\\",\\n \\\"placementConstraints\\\",\\n \\\"placementStrategy\\\",\\n \\\"platformVersion\\\",\\n \\\"networkConfiguration\\\",\\n \\\"tags\\\",\\n \\\"enableECSManagedTags\\\",\\n \\\"propagateTags\\\",\\n ]\\n\\n task_definition_kwargs = {}\\n for key, item in user_kwargs.items():\\n if key in definition_kwarg_list:\\n task_definition_kwargs.update({key: item})\\n\\n task_run_kwargs = {}\\n for key, item in user_kwargs.items():\\n if key in run_kwarg_list:\\n task_run_kwargs.update({key: item})\\n\\n return task_definition_kwargs, task_run_kwargs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.dependencies\",\n \"text\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.dependencies\\n @property\\n def dependencies(self) -> list:\\n return [\\\"boto3\\\", \\\"botocore\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.setup\",\n \"text\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.setup\\n def setup(self, flow: \\\"Flow\\\") -> None: # type: ignore\\n \\\"\\\"\\\"\\n Register the task definition if it does not already exist.\\n\\n Args:\\n - flow (Flow): the Flow object\\n \\\"\\\"\\\"\\n from boto3 import client as boto3_client\\n from botocore.exceptions import ClientError\\n\\n boto3_c = boto3_client(\\n \\\"ecs\\\",\\n aws_access_key_id=self.aws_access_key_id,\\n aws_secret_access_key=self.aws_secret_access_key,\\n aws_session_token=self.aws_session_token,\\n region_name=self.region_name,\\n )\\n\\n definition_exists = True\\n try:\\n boto3_c.describe_task_definition(\\n taskDefinition=self.task_definition_kwargs.get(\\\"family\\\")\\n )\\n except ClientError:\\n definition_exists = False\\n\\n if not definition_exists:\\n env_values = [\\n {\\\"name\\\": \\\"PREFECT__CLOUD__GRAPHQL\\\", \\\"value\\\": config.cloud.graphql},\\n {\\\"name\\\": \\\"PREFECT__CLOUD__USE_LOCAL_SECRETS\\\", \\\"value\\\": \\\"false\\\"},\\n {\\n \\\"name\\\": \\\"PREFECT__ENGINE__FLOW_RUNNER__DEFAULT_CLASS\\\",\\n \\\"value\\\": \\\"prefect.engine.cloud.CloudFlowRunner\\\",\\n },\\n {\\n \\\"name\\\": \\\"PREFECT__ENGINE__TASK_RUNNER__DEFAULT_CLASS\\\",\\n \\\"value\\\": \\\"prefect.engine.cloud.CloudTaskRunner\\\",\\n },\\n {\\\"name\\\": \\\"PREFECT__LOGGING__LOG_TO_CLOUD\\\", \\\"value\\\": \\\"true\\\"},\\n {\\n \\\"name\\\": \\\"PREFECT__LOGGING__EXTRA_LOGGERS\\\",\\n \\\"value\\\": str(config.logging.extra_loggers),\\n },\\n ]\\n\\n # create containerDefinitions if they do not exist\\n if not self.task_definition_kwargs.get(\\\"containerDefinitions\\\"):\\n self.task_definition_kwargs[\\\"containerDefinitions\\\"] = []\\n self.task\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.execute\",\n \"text\": \"src/prefect/environments/execution/fargate/fargate_task.py::FargateTaskEnvironment.execute\\n def execute(self, flow: \\\"Flow\\\") -> None: # type: ignore\\n \\\"\\\"\\\"\\n Run the Fargate task that was defined for this flow.\\n\\n Args:\\n - flow (Flow): the Flow object\\n \\\"\\\"\\\"\\n from boto3 import client as boto3_client\\n\\n flow_run_id = prefect.context.get(\\\"flow_run_id\\\", \\\"unknown\\\")\\n container_overrides = [\\n {\\n \\\"name\\\": \\\"flow-container\\\",\\n \\\"environment\\\": [\\n {\\n \\\"name\\\": \\\"PREFECT__CLOUD__AUTH_TOKEN\\\",\\n \\\"value\\\": config.cloud.agent.auth_token\\n or config.cloud.auth_token,\\n },\\n {\\\"name\\\": \\\"PREFECT__CONTEXT__FLOW_RUN_ID\\\", \\\"value\\\": flow_run_id},\\n {\\\"name\\\": \\\"PREFECT__CONTEXT__IMAGE\\\", \\\"value\\\": get_flow_image(flow)},\\n ],\\n }\\n ]\\n\\n boto3_c = boto3_client(\\n \\\"ecs\\\",\\n aws_access_key_id=self.aws_access_key_id,\\n aws_secret_access_key=self.aws_secret_access_key,\\n aws_session_token=self.aws_session_token,\\n region_name=self.region_name,\\n )\\n\\n boto3_c.run_task(\\n overrides={\\\"containerOverrides\\\": container_overrides},\\n launchType=self.launch_type,\\n **self.task_run_kwargs,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/aws/s3.py::S3Download.__init__\",\n \"text\": \"src/prefect/tasks/aws/s3.py::S3Download.__init__\\n def __init__(self, bucket: str = None, **kwargs):\\n self.bucket = bucket\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/aws/s3.py::S3Download.run\",\n \"text\": \"src/prefect/tasks/aws/s3.py::S3Download.run\\n @defaults_from_attrs(\\\"bucket\\\")\\n def run(\\n self, key: str, credentials: str = None, bucket: str = None,\\n ):\\n \\\"\\\"\\\"\\n Task run method.\\n\\n Args:\\n - key (str): the name of the Key within this bucket to retrieve\\n - credentials (dict, optional): your AWS credentials passed from an upstream\\n Secret task; this Secret must be a JSON string\\n with two keys: `ACCESS_KEY` and `SECRET_ACCESS_KEY` which will be\\n passed directly to `boto3`. If not provided here or in context, `boto3`\\n will fall back on standard AWS rules for authentication.\\n - bucket (str, optional): the name of the S3 Bucket to download from\\n\\n Returns:\\n - str: the contents of this Key / Bucket, as a string\\n \\\"\\\"\\\"\\n if bucket is None:\\n raise ValueError(\\\"A bucket name must be provided.\\\")\\n\\n s3_client = get_boto_client(\\\"s3\\\", credentials=credentials)\\n\\n stream = io.BytesIO()\\n\\n # download\\n s3_client.download_fileobj(Bucket=bucket, Key=key, Fileobj=stream)\\n\\n # prepare data and return\\n stream.seek(0)\\n output = stream.read()\\n return output.decode()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/aws/s3.py::S3Upload.__init__\",\n \"text\": \"src/prefect/tasks/aws/s3.py::S3Upload.__init__\\n def __init__(self, bucket: str = None, **kwargs):\\n self.bucket = bucket\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/aws/s3.py::S3Upload.run\",\n \"text\": \"src/prefect/tasks/aws/s3.py::S3Upload.run\\n @defaults_from_attrs(\\\"bucket\\\")\\n def run(\\n self, data: str, key: str = None, credentials: dict = None, bucket: str = None,\\n ):\\n \\\"\\\"\\\"\\n Task run method.\\n\\n Args:\\n - data (str): the data payload to upload\\n - key (str, optional): the Key to upload the data under; if not\\n provided, a random `uuid` will be created\\n - credentials (dict, optional): your AWS credentials passed from an upstream\\n Secret task; this Secret must be a JSON string\\n with two keys: `ACCESS_KEY` and `SECRET_ACCESS_KEY` which will be\\n passed directly to `boto3`. If not provided here or in context, `boto3`\\n will fall back on standard AWS rules for authentication.\\n - bucket (str, optional): the name of the S3 Bucket to upload to\\n\\n Returns:\\n - str: the name of the Key the data payload was uploaded to\\n \\\"\\\"\\\"\\n if bucket is None:\\n raise ValueError(\\\"A bucket name must be provided.\\\")\\n\\n s3_client = get_boto_client(\\\"s3\\\", credentials=credentials)\\n\\n # prepare data\\n try:\\n stream = io.BytesIO(data)\\n except TypeError:\\n stream = io.BytesIO(data.encode())\\n\\n # create key if not provided\\n if key is None:\\n key = str(uuid.uuid4())\\n\\n # upload\\n s3_client.upload_fileobj(stream, Bucket=bucket, Key=key)\\n return key\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":269,"cells":{"instance_id":{"kind":"string","value":"dagster-io__dagster-13080"},"repo":{"kind":"string","value":"dagster-io/dagster"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"[helm] dagit command overrides\n## Use Case\r\n\r\nTo be able to for example, set `--path-prefix` in the `dagit` invocation to allow it to be hosted on a non `/` path \r\n\n"},"gold_files":{"kind":"list like","value":["helm/dagster/schema/schema/charts/dagster/subschema/dagit.py"],"string":"[\n \"helm/dagster/schema/schema/charts/dagster/subschema/dagit.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["helm/dagster/schema/schema/charts/dagster/subschema/dagit.py::<module>"],"string":"[\n \"helm/dagster/schema/schema/charts/dagster/subschema/dagit.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py","text":".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py\ndef _infer_package_type(directory: str)\nclass PackageSpec # Main spec for testing Dagster Python packages using tox.\n def __post_init__(self)\n def build_steps(self)\n def requirements(self)\n def skip_reason(self)","label":0},{"cid":"docs/sphinx/conf.py","text":"docs/sphinx/conf.py","label":0},{"cid":"helm/dagster/schema/schema/charts/dagster/subschema/dagit.py","text":"helm/dagster/schema/schema/charts/dagster/subschema/dagit.py\nclass Server\nclass Workspace\nclass Dagit\n class Config","label":1},{"cid":"helm/dagster/schema/schema/charts/dagster/subschema/ingress.py","text":"helm/dagster/schema/schema/charts/dagster/subschema/ingress.py\nclass IngressPathType\nclass IngressTLSConfiguration\nclass IngressPath\nclass DagitIngressConfiguration\nclass FlowerIngressConfiguration\nclass Ingress","label":0},{"cid":"python_modules/dagit/dagit/cli.py","text":"python_modules/dagit/dagit/cli.py\ndef create_dagit_cli()\ndef dagit(host: str, port: int, path_prefix: str, db_statement_timeout: int, db_pool_recycle: int, read_only: bool, suppress_warnings: bool, log_level: str, code_server_log_level: str, instance_ref: Optional[str], **kwargs: ClickArgValue)\ndef host_dagit_ui_with_workspace_process_context(workspace_process_context: WorkspaceProcessContext, host: Optional[str], port: Optional[int], path_prefix: str, log_level: str)\ndef main()","label":0},{"cid":"python_modules/dagster/dagster/_cli/dev.py","text":"python_modules/dagster/dagster/_cli/dev.py\ndef dev_command_options(f)\ndef dev_command(code_server_log_level: str, dagit_port: Optional[str], dagit_host: Optional[str], **kwargs: ClickArgValue)","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py","text":"python_modules/dagster/dagster/_utils/__init__.py\ndef library_version_from_core_version(core_version: str)\ndef parse_package_version(version_str: str)\ndef convert_dagster_submodule_name(name: str, mode: Literal['private', 'public']) # This function was introduced when all Dagster submodules were marked private by\ndef file_relative_path(dunderfile: str, relative_path: str) # Get a path relative to the currently executing Python file.\ndef script_relative_path(file_path: str) # Useful for testing with local files. Use a path relative to where the\ndef camelcase(string: str)\ndef ensure_single_item(ddict: Mapping[T, U])\ndef pushd(path: str)\ndef safe_isfile(path: str) # Backport of Python 3.8 os.path.isfile behavior.\ndef mkdir_p(path: str)\nclass frozendict\n def __readonly__(self, *args, **kwargs)\n def __reduce__(self)\n def __setstate__(self, state)\n def __hash__(self)\nclass frozenlist\n def __readonly__(self, *args, **kwargs)\n def __reduce__(self)\n def __setstate__(self, state)\n def __hash__(self)\ndef make_readonly_value(value: List[T])\ndef make_readonly_value(value: Dict[T, U])\ndef make_readonly_value(value: T)\ndef make_readonly_value(value: Any)\ndef get_prop_or_key(elem, key)\ndef list_pull(alist, key)\ndef all_none(kwargs)\ndef check_script(path, return_code=0)\ndef check_cli_execute_file_pipeline(path, pipeline_fn_name, env_file=None)\ndef safe_tempfile_path_unmanaged()\ndef safe_tempfile_path()\ndef ensure_gen(thing_or_gen: Generator[T, Any, Any])\ndef ensure_gen(thing_or_gen: T)\ndef ensure_gen(thing_or_gen: Union[T, Iterator[T], Generator[T, Any, Any]])\ndef ensure_dir(file_path: str)\ndef ensure_file(path: str)\ndef touch_file(path)\ndef _kill_on_event(termination_event)\ndef send_interrupt()\ndef start_termination_thread(termination_event)\ndef iterate_with_context(context_fn: Callable[[], ContextManager[Any]], iterator: Iterator[T])\ndef datetime_as_float(dt: datetime.datetime)\nclass frozentags\n def __init__(self, *args, **kwargs)\n def __hash__(self)\n def updated_with(self, new_tags)\nclass EventGenerationManager # Utility class that wraps an event generator function, that also yields a single \n def __init__(self, generator: Iterator[Union['DagsterEvent', T_GeneratedContext]], object_cls: Type[T_GeneratedContext], require_object: Optional[bool]=True)\n def generate_setup_events(self)\n def get_object(self)\n def generate_teardown_events(self)\ndef utc_datetime_from_timestamp(timestamp: float)\ndef utc_datetime_from_naive(dt: datetime.datetime)\ndef is_enum_value(value: object)\ndef git_repository_root()\ndef segfault() # Reliable cross-Python version segfault.\ndef find_free_port()\ndef is_port_in_use(host, port)\ndef alter_sys_path(to_add: Sequence[str], to_remove: Sequence[str])\ndef restore_sys_modules()\ndef process_is_alive(pid: int)\ndef compose(*args) # Compose python functions args such that compose(f, g)(x) is equivalent to f(g(x)\ndef dict_without_keys(ddict, *keys)\nclass Counter\n def __init__(self)\n def incre","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py\nclass DatabricksError\nclass DatabricksClient # A thin wrapper over the Databricks REST API.\n def __init__(self, host: str, token: str, workspace_id: Optional[str]=None)\n def __setup_user_agent(self, client: ApiClient) # Overrides the user agent for the Databricks API client.\n def api_client(self) # Retrieve a reference to the underlying Databricks API client. For more informati\n def read_file(self, dbfs_path: str, block_size: int=1024 ** 2) # Read a file from DBFS to a **byte string**.\n def put_file(self, file_obj, dbfs_path: str, overwrite: bool=False, block_size: int=1024 ** 2) # Upload an arbitrary large file to DBFS.\n def get_run_state(self, databricks_run_id: int) # Get the state of a run by Databricks run ID.\n def poll_run_state(self, logger: logging.Logger, start_poll_time: float, databricks_run_id: int, max_wait_time_sec: float, verbose_logs: bool=True)\n def wait_for_run_to_complete(self, logger: logging.Logger, databricks_run_id: int, poll_interval_sec: float, max_wait_time_sec: int, verbose_logs: bool=True)\nclass DatabricksJobRunner # Submits jobs created using Dagster config to Databricks, and monitors their prog\n def __init__(self, host: str, token: str, poll_interval_sec: float=5, max_wait_time_sec: int=DEFAULT_RUN_MAX_WAIT_TIME_SEC)\n def client(self) # Return the underlying `DatabricksClient` object.\n def submit_run(self, run_config: Mapping[str, Any], task: Mapping[str, Any]) # Submit a new run using the 'Runs submit' API.\n def retrieve_logs_for_run_id(self, log: logging.Logger, databricks_run_id: int) # Retrieve the stdout and stderr logs for a run.\n def wait_for_dbfs_logs(self, log: logging.Logger, prefix, cluster_id, filename, waiter_delay: int=10, waiter_max_attempts: int=10) # Attempt up to `waiter_max_attempts` attempts to get logs from DBFS.","label":0},{"cid":"python_modules/libraries/dagster-dbt/dagster_dbt/cli/constants.py","text":"python_modules/libraries/dagster-dbt/dagster_dbt/cli/constants.py","label":0},{"cid":"python_modules/libraries/dagster-pyspark/dagster_pyspark/types.py","text":"python_modules/libraries/dagster-pyspark/dagster_pyspark/types.py\ndef dataframe_loader(_context, config)","label":0}],"string":"[\n {\n \"cid\": \".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py\",\n \"text\": \".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py\\ndef _infer_package_type(directory: str)\\nclass PackageSpec # Main spec for testing Dagster Python packages using tox.\\n def __post_init__(self)\\n def build_steps(self)\\n def requirements(self)\\n def skip_reason(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/sphinx/conf.py\",\n \"text\": \"docs/sphinx/conf.py\",\n \"label\": 0\n },\n {\n \"cid\": \"helm/dagster/schema/schema/charts/dagster/subschema/dagit.py\",\n \"text\": \"helm/dagster/schema/schema/charts/dagster/subschema/dagit.py\\nclass Server\\nclass Workspace\\nclass Dagit\\n class Config\",\n \"label\": 1\n },\n {\n \"cid\": \"helm/dagster/schema/schema/charts/dagster/subschema/ingress.py\",\n \"text\": \"helm/dagster/schema/schema/charts/dagster/subschema/ingress.py\\nclass IngressPathType\\nclass IngressTLSConfiguration\\nclass IngressPath\\nclass DagitIngressConfiguration\\nclass FlowerIngressConfiguration\\nclass Ingress\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagit/dagit/cli.py\",\n \"text\": \"python_modules/dagit/dagit/cli.py\\ndef create_dagit_cli()\\ndef dagit(host: str, port: int, path_prefix: str, db_statement_timeout: int, db_pool_recycle: int, read_only: bool, suppress_warnings: bool, log_level: str, code_server_log_level: str, instance_ref: Optional[str], **kwargs: ClickArgValue)\\ndef host_dagit_ui_with_workspace_process_context(workspace_process_context: WorkspaceProcessContext, host: Optional[str], port: Optional[int], path_prefix: str, log_level: str)\\ndef main()\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_cli/dev.py\",\n \"text\": \"python_modules/dagster/dagster/_cli/dev.py\\ndef dev_command_options(f)\\ndef dev_command(code_server_log_level: str, dagit_port: Optional[str], dagit_host: Optional[str], **kwargs: ClickArgValue)\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py\\ndef library_version_from_core_version(core_version: str)\\ndef parse_package_version(version_str: str)\\ndef convert_dagster_submodule_name(name: str, mode: Literal['private', 'public']) # This function was introduced when all Dagster submodules were marked private by\\ndef file_relative_path(dunderfile: str, relative_path: str) # Get a path relative to the currently executing Python file.\\ndef script_relative_path(file_path: str) # Useful for testing with local files. Use a path relative to where the\\ndef camelcase(string: str)\\ndef ensure_single_item(ddict: Mapping[T, U])\\ndef pushd(path: str)\\ndef safe_isfile(path: str) # Backport of Python 3.8 os.path.isfile behavior.\\ndef mkdir_p(path: str)\\nclass frozendict\\n def __readonly__(self, *args, **kwargs)\\n def __reduce__(self)\\n def __setstate__(self, state)\\n def __hash__(self)\\nclass frozenlist\\n def __readonly__(self, *args, **kwargs)\\n def __reduce__(self)\\n def __setstate__(self, state)\\n def __hash__(self)\\ndef make_readonly_value(value: List[T])\\ndef make_readonly_value(value: Dict[T, U])\\ndef make_readonly_value(value: T)\\ndef make_readonly_value(value: Any)\\ndef get_prop_or_key(elem, key)\\ndef list_pull(alist, key)\\ndef all_none(kwargs)\\ndef check_script(path, return_code=0)\\ndef check_cli_execute_file_pipeline(path, pipeline_fn_name, env_file=None)\\ndef safe_tempfile_path_unmanaged()\\ndef safe_tempfile_path()\\ndef ensure_gen(thing_or_gen: Generator[T, Any, Any])\\ndef ensure_gen(thing_or_gen: T)\\ndef ensure_gen(thing_or_gen: Union[T, Iterator[T], Generator[T, Any, Any]])\\ndef ensure_dir(file_path: str)\\ndef ensure_file(path: str)\\ndef touch_file(path)\\ndef _kill_on_event(termination_event)\\ndef send_interrupt()\\ndef start_termination_thread(termination_event)\\ndef iterate_with_context(context_fn: Callable[[], ContextManager[Any]], iterator: Iterator[T])\\ndef datetime_as_float(dt: datetime.datetime)\\nclass frozentags\\n def __init__(self, *args, **kwargs)\\n def __hash__(self)\\n def updated_with(self, new_tags)\\nclass EventGenerationManager # Utility class that wraps an event generator function, that also yields a single \\n def __init__(self, generator: Iterator[Union['DagsterEvent', T_GeneratedContext]], object_cls: Type[T_GeneratedContext], require_object: Optional[bool]=True)\\n def generate_setup_events(self)\\n def get_object(self)\\n def generate_teardown_events(self)\\ndef utc_datetime_from_timestamp(timestamp: float)\\ndef utc_datetime_from_naive(dt: datetime.datetime)\\ndef is_enum_value(value: object)\\ndef git_repository_root()\\ndef segfault() # Reliable cross-Python version segfault.\\ndef find_free_port()\\ndef is_port_in_use(host, port)\\ndef alter_sys_path(to_add: Sequence[str], to_remove: Sequence[str])\\ndef restore_sys_modules()\\ndef process_is_alive(pid: int)\\ndef compose(*args) # Compose python functions args such that compose(f, g)(x) is equivalent to f(g(x)\\ndef dict_without_keys(ddict, *keys)\\nclass Counter\\n def __init__(self)\\n def incre\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py\\nclass DatabricksError\\nclass DatabricksClient # A thin wrapper over the Databricks REST API.\\n def __init__(self, host: str, token: str, workspace_id: Optional[str]=None)\\n def __setup_user_agent(self, client: ApiClient) # Overrides the user agent for the Databricks API client.\\n def api_client(self) # Retrieve a reference to the underlying Databricks API client. For more informati\\n def read_file(self, dbfs_path: str, block_size: int=1024 ** 2) # Read a file from DBFS to a **byte string**.\\n def put_file(self, file_obj, dbfs_path: str, overwrite: bool=False, block_size: int=1024 ** 2) # Upload an arbitrary large file to DBFS.\\n def get_run_state(self, databricks_run_id: int) # Get the state of a run by Databricks run ID.\\n def poll_run_state(self, logger: logging.Logger, start_poll_time: float, databricks_run_id: int, max_wait_time_sec: float, verbose_logs: bool=True)\\n def wait_for_run_to_complete(self, logger: logging.Logger, databricks_run_id: int, poll_interval_sec: float, max_wait_time_sec: int, verbose_logs: bool=True)\\nclass DatabricksJobRunner # Submits jobs created using Dagster config to Databricks, and monitors their prog\\n def __init__(self, host: str, token: str, poll_interval_sec: float=5, max_wait_time_sec: int=DEFAULT_RUN_MAX_WAIT_TIME_SEC)\\n def client(self) # Return the underlying `DatabricksClient` object.\\n def submit_run(self, run_config: Mapping[str, Any], task: Mapping[str, Any]) # Submit a new run using the 'Runs submit' API.\\n def retrieve_logs_for_run_id(self, log: logging.Logger, databricks_run_id: int) # Retrieve the stdout and stderr logs for a run.\\n def wait_for_dbfs_logs(self, log: logging.Logger, prefix, cluster_id, filename, waiter_delay: int=10, waiter_max_attempts: int=10) # Attempt up to `waiter_max_attempts` attempts to get logs from DBFS.\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-dbt/dagster_dbt/cli/constants.py\",\n \"text\": \"python_modules/libraries/dagster-dbt/dagster_dbt/cli/constants.py\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-pyspark/dagster_pyspark/types.py\",\n \"text\": \"python_modules/libraries/dagster-pyspark/dagster_pyspark/types.py\\ndef dataframe_loader(_context, config)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::_infer_package_type","text":".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::_infer_package_type\ndef _infer_package_type(directory: str) -> str:\n if directory in _CORE_PACKAGES:\n return \"core\"\n elif directory.startswith(\"examples/\"):\n return \"example\"\n elif directory.startswith(\"python_modules/libraries/\"):\n return \"extension\"\n elif directory in _INFRASTRUCTURE_PACKAGES or directory.startswith(\"integration_tests\"):\n return \"infrastructure\"\n else:\n return \"unknown\"","label":0},{"cid":".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.__post_init__","text":".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.__post_init__\n def __post_init__(self):\n if not self.name:\n self.name = os.path.basename(self.directory)\n\n if not self.package_type:\n self.package_type = _infer_package_type(self.directory)\n\n self._should_skip = None\n self._skip_reason = None","label":0},{"cid":".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.build_steps","text":".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.build_steps\n def build_steps(self) -> List[BuildkiteTopLevelStep]:\n base_name = self.name or os.path.basename(self.directory)\n steps: List[BuildkiteLeafStep] = []\n\n if self.run_pytest:\n default_python_versions = AvailablePythonVersion.get_pytest_defaults()\n\n tox_factors: List[Optional[str]] = (\n [f.lstrip(\"-\") for f in self.pytest_tox_factors]\n if self.pytest_tox_factors\n else [None]\n )\n\n for other_factor in tox_factors:\n if callable(self.unsupported_python_versions):\n unsupported_python_versions = self.unsupported_python_versions(other_factor)\n else:\n unsupported_python_versions = self.unsupported_python_versions or []\n\n supported_python_versions = [\n v\n for v in AvailablePythonVersion.get_all()\n if v not in unsupported_python_versions\n ]\n\n pytest_python_versions = sorted(\n list(set(default_python_versions) - set(unsupported_python_versions))\n )\n # Use lowest supported python version if no defaults match.\n if len(pytest_python_versions) == 0:\n pytest_python_versions = [supported_python_versions[0]]\n\n for py_version in pytest_python_versions:\n version_factor = AvailablePythonVersion.to_tox_factor(py_version)\n if other_factor is None:\n tox_env = version_factor\n else:\n tox_env = f\"{version_factor}-{other_factor}\"\n\n if isinstance(self.pytest_extra_cmds, list):\n extra_commands_pre = self.pytest_extra_cmds\n elif callable(self.pytest_extra_cmds):\n ","label":0},{"cid":".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.requirements","text":".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.requirements\n @property\n def requirements(self):\n # First try to infer requirements from the python package\n package = PythonPackages.get(self.name)\n if package:\n return set.union(package.install_requires, *package.extras_require.values())\n\n # If we don't have a distribution (like many of our integration test suites)\n # we can use a buildkite_deps.txt file to capture requirements\n buildkite_deps_txt = Path(self.directory) / \"buildkite_deps.txt\"\n if buildkite_deps_txt.exists():\n parsed = pkg_resources.parse_requirements(buildkite_deps_txt.read_text())\n return [requirement for requirement in parsed]\n\n # Otherwise return nothing\n return []","label":0},{"cid":".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.skip_reason","text":".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.skip_reason\n @property\n def skip_reason(self) -> Optional[str]:\n # Memoize so we don't log twice\n if self._should_skip is False:\n return None\n\n if self.always_run_if and self.always_run_if():\n self._should_skip = False\n return None\n\n if self._skip_reason:\n return self._skip_reason\n\n if message_contains(\"NO_SKIP\"):\n logging.info(f\"Building {self.name} because NO_SKIP set\")\n self._should_skip = False\n return None\n\n if not is_feature_branch(os.getenv(\"BUILDKITE_BRANCH\", \"\")):\n logging.info(f\"Building {self.name} we're not on a feature branch\")\n self._should_skip = False\n return None\n\n for change in ChangedFiles.all:\n if (\n # Our change is in this package's directory\n (Path(self.directory) in change.parents)\n # The file can alter behavior - exclude things like README changes\n # which we tend to include in .md files\n and change.suffix in changed_filetypes\n ):\n logging.info(f\"Building {self.name} because it has changed\")\n self._should_skip = False\n return None\n\n # Consider anything required by install or an extra to be in scope.\n # We might one day narrow this down to specific extras.\n for requirement in self.requirements:\n in_scope_changes = PythonPackages.with_changes.intersection(\n PythonPackages.walk_dependencies(requirement)\n )\n if in_scope_changes:\n logging.info(f\"Building {self.name} because of changes to {in_scope_changes}\")\n self._should_skip = False\n return None\n\n self._skip_reason = \"Package unaffected by these changes\"\n self._should_skip = True\n return","label":0},{"cid":"python_modules/dagit/dagit/cli.py::create_dagit_cli","text":"python_modules/dagit/dagit/cli.py::create_dagit_cli\ndef create_dagit_cli():\n return dagit","label":0},{"cid":"python_modules/dagit/dagit/cli.py::dagit","text":"python_modules/dagit/dagit/cli.py::dagit\n@click.command(\n name=\"dagit\",\n help=(\n \"Run dagit. Loads a repository or pipeline/job.\\n\\n{warning}\".format(\n warning=WORKSPACE_TARGET_WARNING\n )\n + (\n \"\\n\\nExamples:\"\n \"\\n\\n1. dagit (works if .{default_filename} exists)\"\n \"\\n\\n2. dagit -w path/to/{default_filename}\"\n \"\\n\\n3. dagit -f path/to/file.py\"\n \"\\n\\n4. dagit -f path/to/file.py -d path/to/working_directory\"\n \"\\n\\n5. dagit -m some_module\"\n \"\\n\\n6. dagit -f path/to/file.py -a define_repo\"\n \"\\n\\n7. dagit -m some_module -a define_repo\"\n \"\\n\\n8. dagit -p 3333\"\n \"\\n\\nOptions can also provide arguments via environment variables prefixed with DAGIT\"\n \"\\n\\nFor example, DAGIT_PORT=3333 dagit\"\n ).format(default_filename=DEFAULT_WORKSPACE_YAML_FILENAME)\n ),\n)\n@workspace_target_argument\n@click.option(\n \"--host\",\n \"-h\",\n type=click.STRING,\n default=DEFAULT_DAGIT_HOST,\n help=\"Host to run server on\",\n show_default=True,\n)\n@click.option(\n \"--port\",\n \"-p\",\n type=click.INT,\n help=f\"Port to run server on - defaults to {DEFAULT_DAGIT_PORT}\",\n default=None,\n show_default=True,\n)\n@click.option(\n \"--path-prefix\",\n \"-l\",\n type=click.STRING,\n default=\"\",\n help=\"The path prefix where Dagit will be hosted (eg: /dagit)\",\n show_default=True,\n)\n@click.option(\n \"--db-statement-timeout\",\n help=(\n \"The timeout in milliseconds to set on database statements sent \"\n \"to the DagsterInstance. Not respected in all configurations.\"\n ),\n default=DEFAULT_DB_STATEMENT_TIMEOUT,\n type=click.INT,\n show_default=True,\n)\n@click.option(\n \"--db-pool-recycle\",\n help=(\n \"The maximum age of a connection to use from the sqlalchemy pool without connection\"\n \" recycling. Set to -1 to disable. Not respected in all configurations.\"\n ),\n default=DEFAUL","label":0},{"cid":"python_modules/dagit/dagit/cli.py::host_dagit_ui_with_workspace_process_context","text":"python_modules/dagit/dagit/cli.py::host_dagit_ui_with_workspace_process_context\ndef host_dagit_ui_with_workspace_process_context(\n workspace_process_context: WorkspaceProcessContext,\n host: Optional[str],\n port: Optional[int],\n path_prefix: str,\n log_level: str,\n):\n check.inst_param(\n workspace_process_context, \"workspace_process_context\", WorkspaceProcessContext\n )\n host = check.opt_str_param(host, \"host\", \"127.0.0.1\")\n check.opt_int_param(port, \"port\")\n check.str_param(path_prefix, \"path_prefix\")\n\n logger = logging.getLogger(\"dagit\")\n\n app = create_app_from_workspace_process_context(workspace_process_context, path_prefix)\n\n if not port:\n if is_port_in_use(host, DEFAULT_DAGIT_PORT):\n port = find_free_port()\n logger.warning(f\"Port {DEFAULT_DAGIT_PORT} is in use - using port {port} instead\")\n else:\n port = DEFAULT_DAGIT_PORT\n\n logger.info(\n \"Serving dagit on http://{host}:{port}{path_prefix} in process {pid}\".format(\n host=host, port=port, path_prefix=path_prefix, pid=os.getpid()\n )\n )\n log_action(workspace_process_context.instance, START_DAGIT_WEBSERVER)\n with uploading_logging_thread():\n uvicorn.run(\n app,\n host=host,\n port=port,\n log_level=log_level,\n )","label":0},{"cid":"python_modules/dagit/dagit/cli.py::main","text":"python_modules/dagit/dagit/cli.py::main\ndef main():\n # click magic\n cli(auto_envvar_prefix=\"DAGIT\") # pylint:disable=E1120","label":0},{"cid":"python_modules/dagster/dagster/_cli/dev.py::dev_command_options","text":"python_modules/dagster/dagster/_cli/dev.py::dev_command_options\ndef dev_command_options(f):\n return apply_click_params(\n f,\n workspace_option(),\n python_file_option(allow_multiple=True),\n python_module_option(allow_multiple=True),\n working_directory_option(),\n )","label":0},{"cid":"python_modules/dagster/dagster/_cli/dev.py::dev_command","text":"python_modules/dagster/dagster/_cli/dev.py::dev_command\n@click.command(\n name=\"dev\",\n help=(\n \"Start a local deployment of Dagster, including dagit running on localhost and the\"\n \" dagster-daemon running in the background\"\n ),\n)\n@dev_command_options\n@click.option(\n \"--code-server-log-level\",\n help=\"Set the log level for code servers spun up by dagster services.\",\n show_default=True,\n default=\"warning\",\n type=click.Choice(\n [\"critical\", \"error\", \"warning\", \"info\", \"debug\", \"trace\"], case_sensitive=False\n ),\n)\n@click.option(\"--dagit-port\", \"-p\", help=\"Port to use for the Dagit UI.\", required=False)\n@click.option(\"--dagit-host\", \"-h\", help=\"Host to use for the Dagit UI.\", required=False)\ndef dev_command(\n code_server_log_level: str,\n dagit_port: Optional[str],\n dagit_host: Optional[str],\n **kwargs: ClickArgValue,\n) -> None:\n # check if dagit installed, crash if not\n try:\n import dagit # # noqa: F401\n except ImportError:\n raise click.UsageError(\n \"The dagit Python package must be installed in order to use the dagster dev command. If\"\n ' you\\'re using pip, you can install the dagit package by running \"pip install dagit\"'\n \" in your Python environment.\"\n )\n\n configure_loggers()\n logger = logging.getLogger(\"dagster\")\n\n # Sanity check workspace args\n get_workspace_load_target(kwargs)\n\n dagster_home_path = os.getenv(\"DAGSTER_HOME\")\n\n dagster_yaml_path = os.path.join(os.getcwd(), \"dagster.yaml\")\n\n has_local_dagster_yaml = os.path.exists(dagster_yaml_path)\n if dagster_home_path:\n if has_local_dagster_yaml and Path(os.getcwd()) != Path(dagster_home_path):\n logger.warning(\n \"Found a dagster instance configuration value (dagster.yaml) in the current\"\n \" folder, but your DAGSTER_HOME environment variable is set to\"\n f\" {dagster_home_path}. The dagster.yaml file will not be","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::library_version_from_core_version","text":"python_modules/dagster/dagster/_utils/__init__.py::library_version_from_core_version\ndef library_version_from_core_version(core_version: str) -> str:\n parsed_version = parse_package_version(core_version)\n\n release = parsed_version.release\n if release[0] >= 1:\n library_version = \".\".join([\"0\", str(16 + release[1]), str(release[2])])\n\n if parsed_version.is_prerelease:\n library_version = library_version + \"\".join(\n [str(pre) for pre in check.not_none(parsed_version.pre)]\n )\n\n if parsed_version.is_postrelease:\n library_version = library_version + \"post\" + str(parsed_version.post)\n\n return library_version\n else:\n return core_version","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::parse_package_version","text":"python_modules/dagster/dagster/_utils/__init__.py::parse_package_version\ndef parse_package_version(version_str: str) -> packaging.version.Version:\n parsed_version = packaging.version.parse(version_str)\n assert isinstance(parsed_version, packaging.version.Version)\n return parsed_version","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::convert_dagster_submodule_name","text":"python_modules/dagster/dagster/_utils/__init__.py::convert_dagster_submodule_name\ndef convert_dagster_submodule_name(name: str, mode: Literal[\"private\", \"public\"]) -> str:\n \"\"\"This function was introduced when all Dagster submodules were marked private by\n underscore-prefixing the root submodules (e.g. `dagster._core`). The function provides\n backcompatibility by converting modules between the old and new (i.e. public and private) forms.\n This is needed when reading older data or communicating with older versions of Dagster.\n \"\"\"\n if mode == \"private\":\n return re.sub(r\"^dagster\\.([^_])\", r\"dagster._\\1\", name)\n elif mode == \"public\":\n return re.sub(r\"^dagster._\", \"dagster.\", name)\n else:\n check.failed(\"`mode` must be 'private' or 'public'\")","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::file_relative_path","text":"python_modules/dagster/dagster/_utils/__init__.py::file_relative_path\ndef file_relative_path(dunderfile: str, relative_path: str) -> str:\n \"\"\"Get a path relative to the currently executing Python file.\n\n This function is useful when one needs to load a file that is relative to the position of\n the current file. (Such as when you encode a configuration file path in source file and want\n in runnable in any current working directory)\n\n Args:\n dunderfile (str): Should always be ``__file__``.\n relative_path (str): Path to get relative to the currently executing file.\n\n **Examples**:\n\n .. code-block:: python\n\n file_relative_path(__file__, 'path/relative/to/file')\n\n \"\"\"\n check.str_param(dunderfile, \"dunderfile\")\n check.str_param(relative_path, \"relative_path\")\n\n return os.path.join(os.path.dirname(dunderfile), relative_path)","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::script_relative_path","text":"python_modules/dagster/dagster/_utils/__init__.py::script_relative_path\ndef script_relative_path(file_path: str) -> str:\n \"\"\"Useful for testing with local files. Use a path relative to where the\n test resides and this function will return the absolute path\n of that file. Otherwise it will be relative to script that\n ran the test.\n\n Note: this is function is very, very expensive (on the order of 1\n millisecond per invocation) so this should only be used in performance\n insensitive contexts. Prefer file_relative_path for anything with\n performance constraints.\n\n \"\"\"\n # from http://bit.ly/2snyC6s\n\n check.str_param(file_path, \"file_path\")\n scriptdir = inspect.stack()[1][1]\n return os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(scriptdir)), file_path))","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::camelcase","text":"python_modules/dagster/dagster/_utils/__init__.py::camelcase\ndef camelcase(string: str) -> str:\n check.str_param(string, \"string\")\n\n string = re.sub(r\"^[\\-_\\.]\", \"\", str(string))\n if not string:\n return string\n return str(string[0]).upper() + re.sub(\n r\"[\\-_\\.\\s]([a-z])\", lambda matched: str(matched.group(1)).upper(), string[1:]\n )","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::ensure_single_item","text":"python_modules/dagster/dagster/_utils/__init__.py::ensure_single_item\ndef ensure_single_item(ddict: Mapping[T, U]) -> Tuple[T, U]:\n check.mapping_param(ddict, \"ddict\")\n check.param_invariant(len(ddict) == 1, \"ddict\", \"Expected dict with single item\")\n return list(ddict.items())[0]","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::pushd","text":"python_modules/dagster/dagster/_utils/__init__.py::pushd\n@contextlib.contextmanager\ndef pushd(path: str) -> Iterator[str]:\n old_cwd = os.getcwd()\n os.chdir(path)\n try:\n yield path\n finally:\n os.chdir(old_cwd)","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::safe_isfile","text":"python_modules/dagster/dagster/_utils/__init__.py::safe_isfile\ndef safe_isfile(path: str) -> bool:\n \"\"\"Backport of Python 3.8 os.path.isfile behavior.\n\n This is intended to backport https://docs.python.org/dev/whatsnew/3.8.html#os-path. I'm not\n sure that there are other ways to provoke this behavior on Unix other than the null byte,\n but there are certainly other ways to do it on Windows. Afaict, we won't mask other\n ValueErrors, and the behavior in the status quo ante is rough because we risk throwing an\n unexpected, uncaught ValueError from very deep in our logic.\n \"\"\"\n try:\n return os.path.isfile(path)\n except ValueError:\n return False","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::mkdir_p","text":"python_modules/dagster/dagster/_utils/__init__.py::mkdir_p\ndef mkdir_p(path: str) -> str:\n try:\n os.makedirs(path)\n return path\n except OSError as exc: # Python >2.5\n if exc.errno == errno.EEXIST and os.path.isdir(path):\n return path\n else:\n raise","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__readonly__","text":"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__readonly__\n def __readonly__(self, *args, **kwargs):\n raise RuntimeError(\"Cannot modify ReadOnlyDict\")","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__reduce__","text":"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__reduce__\n def __reduce__(self):\n return (frozendict, (), dict(self))","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__setstate__","text":"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__setstate__\n def __setstate__(self, state):\n self.__init__(state)","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__hash__","text":"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__hash__\n def __hash__(self):\n return hash(tuple(sorted(self.items())))","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__readonly__","text":"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__readonly__\n def __readonly__(self, *args, **kwargs):\n raise RuntimeError(\"Cannot modify ReadOnlyList\")","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__reduce__","text":"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__reduce__\n def __reduce__(self):\n return (frozenlist, (), list(self))","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__setstate__","text":"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__setstate__\n def __setstate__(self, state):\n self.__init__(state)","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__hash__","text":"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__hash__\n def __hash__(self):\n return hash(tuple(self))","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value","text":"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value\n@overload\ndef make_readonly_value(value: List[T]) -> Sequence[T]:\n ...","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value","text":"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value\n@overload\ndef make_readonly_value(value: Dict[T, U]) -> Mapping[T, U]:\n ...","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value","text":"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value\n@overload\ndef make_readonly_value(value: T) -> T:\n ...","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value","text":"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value\ndef make_readonly_value(value: Any) -> Any:\n if isinstance(value, list):\n return frozenlist(list(map(make_readonly_value, value)))\n elif isinstance(value, dict):\n return frozendict({key: make_readonly_value(value) for key, value in value.items()})\n elif isinstance(value, set):\n return frozenset(map(make_readonly_value, value))\n else:\n return value","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::get_prop_or_key","text":"python_modules/dagster/dagster/_utils/__init__.py::get_prop_or_key\ndef get_prop_or_key(elem, key):\n if isinstance(elem, Mapping):\n return elem.get(key)\n else:\n return getattr(elem, key)","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::list_pull","text":"python_modules/dagster/dagster/_utils/__init__.py::list_pull\ndef list_pull(alist, key):\n return list(map(lambda elem: get_prop_or_key(elem, key), alist))","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::all_none","text":"python_modules/dagster/dagster/_utils/__init__.py::all_none\ndef all_none(kwargs):\n for value in kwargs.values():\n if value is not None:\n return False\n return True","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::check_script","text":"python_modules/dagster/dagster/_utils/__init__.py::check_script\ndef check_script(path, return_code=0):\n try:\n subprocess.check_output([sys.executable, path])\n except subprocess.CalledProcessError as exc:\n if return_code != 0:\n if exc.returncode == return_code:\n return\n raise","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::check_cli_execute_file_pipeline","text":"python_modules/dagster/dagster/_utils/__init__.py::check_cli_execute_file_pipeline\ndef check_cli_execute_file_pipeline(path, pipeline_fn_name, env_file=None):\n from dagster._core.test_utils import instance_for_test\n\n with instance_for_test():\n cli_cmd = [\n sys.executable,\n \"-m\",\n \"dagster\",\n \"pipeline\",\n \"execute\",\n \"-f\",\n path,\n \"-a\",\n pipeline_fn_name,\n ]\n\n if env_file:\n cli_cmd.append(\"-c\")\n cli_cmd.append(env_file)\n\n try:\n subprocess.check_output(cli_cmd)\n except subprocess.CalledProcessError as cpe:\n print(cpe) # noqa: T201\n raise cpe","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::safe_tempfile_path_unmanaged","text":"python_modules/dagster/dagster/_utils/__init__.py::safe_tempfile_path_unmanaged\ndef safe_tempfile_path_unmanaged() -> str:\n # This gets a valid temporary file path in the safest possible way, although there is still no\n # guarantee that another process will not create a file at this path. The NamedTemporaryFile is\n # deleted when the context manager exits and the file object is closed.\n #\n # This is preferable to using NamedTemporaryFile as a context manager and passing the name\n # attribute of the file object around because NamedTemporaryFiles cannot be opened a second time\n # if already open on Windows NT or later:\n # https://docs.python.org/3.8/library/tempfile.html#tempfile.NamedTemporaryFile\n # https://github.com/dagster-io/dagster/issues/1582\n with tempfile.NamedTemporaryFile() as fd:\n path = fd.name\n return Path(path).as_posix()","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::safe_tempfile_path","text":"python_modules/dagster/dagster/_utils/__init__.py::safe_tempfile_path\n@contextlib.contextmanager\ndef safe_tempfile_path() -> Iterator[str]:\n path = None\n try:\n path = safe_tempfile_path_unmanaged()\n yield path\n finally:\n if path is not None and os.path.exists(path):\n os.unlink(path)","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::ensure_gen","text":"python_modules/dagster/dagster/_utils/__init__.py::ensure_gen\n@overload\ndef ensure_gen(thing_or_gen: Generator[T, Any, Any]) -> Generator[T, Any, Any]:\n pass","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::ensure_gen","text":"python_modules/dagster/dagster/_utils/__init__.py::ensure_gen\n@overload\ndef ensure_gen(thing_or_gen: T) -> Generator[T, Any, Any]:\n pass","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::ensure_gen","text":"python_modules/dagster/dagster/_utils/__init__.py::ensure_gen\ndef ensure_gen(\n thing_or_gen: Union[T, Iterator[T], Generator[T, Any, Any]]\n) -> Generator[T, Any, Any]:\n if not inspect.isgenerator(thing_or_gen):\n thing_or_gen = cast(T, thing_or_gen)\n\n def _gen_thing():\n yield thing_or_gen\n\n return _gen_thing()\n\n return thing_or_gen","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::ensure_dir","text":"python_modules/dagster/dagster/_utils/__init__.py::ensure_dir\ndef ensure_dir(file_path: str) -> str:\n try:\n os.makedirs(file_path)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise\n return file_path","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::ensure_file","text":"python_modules/dagster/dagster/_utils/__init__.py::ensure_file\ndef ensure_file(path: str) -> str:\n ensure_dir(os.path.dirname(path))\n if not os.path.exists(path):\n touch_file(path)\n return path","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::touch_file","text":"python_modules/dagster/dagster/_utils/__init__.py::touch_file\ndef touch_file(path):\n ensure_dir(os.path.dirname(path))\n with open(path, \"a\", encoding=\"utf8\"):\n os.utime(path, None)","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::_kill_on_event","text":"python_modules/dagster/dagster/_utils/__init__.py::_kill_on_event\ndef _kill_on_event(termination_event):\n termination_event.wait()\n send_interrupt()","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::send_interrupt","text":"python_modules/dagster/dagster/_utils/__init__.py::send_interrupt\ndef send_interrupt():\n if seven.IS_WINDOWS:\n # This will raise a KeyboardInterrupt in python land - meaning this wont be able to\n # interrupt things like sleep()\n thread.interrupt_main()\n else:\n # If on unix send an os level signal to interrupt any situation we may be stuck in\n os.kill(os.getpid(), signal.SIGINT)","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::start_termination_thread","text":"python_modules/dagster/dagster/_utils/__init__.py::start_termination_thread\ndef start_termination_thread(termination_event):\n check.inst_param(termination_event, \"termination_event\", ttype=type(multiprocessing.Event()))\n\n int_thread = threading.Thread(\n target=_kill_on_event, args=(termination_event,), name=\"kill-on-event\"\n )\n int_thread.daemon = True\n int_thread.start()","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::iterate_with_context","text":"python_modules/dagster/dagster/_utils/__init__.py::iterate_with_context\ndef iterate_with_context(\n context_fn: Callable[[], ContextManager[Any]], iterator: Iterator[T]\n) -> Iterator[T]:\n while True:\n # Allow interrupts during user code so that we can terminate slow/hanging steps\n with context_fn():\n try:\n next_output = next(iterator)\n except StopIteration:\n return\n\n yield next_output","label":0},{"cid":"python_modules/dagster/dagster/_utils/__init__.py::datetime_as_float","text":"python_modules/dagster/dagster/_utils/__init__.py::datetime_as_float\ndef datetime_as_float(dt: datetime.datetime) -> float:\n check.inst_param(dt, \"dt\", datetime.datetime)\n return float((dt - EPOCH).total_seconds())","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.__init__","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.__init__\n def __init__(self, host: str, token: str, workspace_id: Optional[str] = None):\n self.host = host\n self.workspace_id = workspace_id\n\n # TODO: This is the old shim client that we were previously using. Arguably this is\n # confusing for users to use since this is an unofficial wrapper around the documented\n # Databricks REST API. We should consider removing this in the future.\n self.client = DatabricksAPI(host=host, token=token)\n self.__setup_user_agent(self.client.client)\n\n # Expose an interface directly to the official Databricks API client.\n self._api_client = ApiClient(host=host, token=token)\n self.__setup_user_agent(self._api_client)","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.__setup_user_agent","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.__setup_user_agent\n def __setup_user_agent(self, client: ApiClient) -> None:\n \"\"\"Overrides the user agent for the Databricks API client.\"\"\"\n client.default_headers[\"user-agent\"] = f\"dagster-databricks/{__version__}\"","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.api_client","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.api_client\n @public\n @property\n def api_client(self) -> ApiClient:\n \"\"\"Retrieve a reference to the underlying Databricks API client. For more information,\n see the `Databricks Python API <https://docs.databricks.com/dev-tools/python-api.html>`_.\n\n **Examples:**\n\n .. code-block:: python\n\n from dagster import op\n from databricks_cli.jobs.api import JobsApi\n from databricks_cli.runs.api import RunsApi\n\n @op(required_resource_keys={\"databricks_client\"})\n def op1(context):\n # Initialize the Databricks Jobs API\n jobs_client = JobsApi(context.resources.databricks_client.api_client)\n runs_client = RunsApi(context.resources.databricks_client.api_client)\n\n # Example 1: Run a Databricks job with some parameters.\n jobs_client.run_now(...)\n\n # Example 2: Trigger a one-time run of a Databricks workload.\n runs_client.submit_run(...)\n\n # Example 3: Get an existing run.\n runs_client.get_run(...)\n\n # Example 4: Cancel a run.\n runs_client.cancel_run(...)\n\n Returns:\n ApiClient: The authenticated Databricks API client.\n \"\"\"\n return self._api_client","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.read_file","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.read_file\n def read_file(self, dbfs_path: str, block_size: int = 1024**2) -> bytes:\n \"\"\"Read a file from DBFS to a **byte string**.\"\"\"\n if dbfs_path.startswith(\"dbfs://\"):\n dbfs_path = dbfs_path[7:]\n\n data = b\"\"\n bytes_read = 0\n dbfs_service = DbfsService(self.api_client)\n\n jdoc = dbfs_service.read(path=dbfs_path, length=block_size)\n data += base64.b64decode(jdoc[\"data\"])\n while jdoc[\"bytes_read\"] == block_size:\n bytes_read += jdoc[\"bytes_read\"]\n jdoc = dbfs_service.read(path=dbfs_path, offset=bytes_read, length=block_size)\n data += base64.b64decode(jdoc[\"data\"])\n\n return data","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.put_file","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.put_file\n def put_file(\n self, file_obj, dbfs_path: str, overwrite: bool = False, block_size: int = 1024**2\n ) -> None:\n \"\"\"Upload an arbitrary large file to DBFS.\n\n This doesn't use the DBFS `Put` API because that endpoint is limited to 1MB.\n \"\"\"\n if dbfs_path.startswith(\"dbfs://\"):\n dbfs_path = dbfs_path[7:]\n\n dbfs_service = DbfsService(self.api_client)\n\n create_response = dbfs_service.create(path=dbfs_path, overwrite=overwrite)\n handle = create_response[\"handle\"]\n\n block = file_obj.read(block_size)\n while block:\n data = base64.b64encode(block).decode(\"utf-8\")\n dbfs_service.add_block(data=data, handle=handle)\n block = file_obj.read(block_size)\n\n dbfs_service.close(handle=handle)","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.get_run_state","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.get_run_state\n def get_run_state(self, databricks_run_id: int) -> \"DatabricksRunState\":\n \"\"\"Get the state of a run by Databricks run ID.\n\n Return a `DatabricksRunState` object. Note that the `result_state`\n attribute may be `None` if the run hasn't yet terminated.\n \"\"\"\n run = JobsService(self.api_client).get_run(databricks_run_id)\n state = run[\"state\"]\n result_state = (\n DatabricksRunResultState(state.get(\"result_state\"))\n if state.get(\"result_state\")\n else None\n )\n\n return DatabricksRunState(\n life_cycle_state=DatabricksRunLifeCycleState(state[\"life_cycle_state\"]),\n result_state=result_state,\n state_message=state[\"state_message\"],\n )","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.poll_run_state","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.poll_run_state\n def poll_run_state(\n self,\n logger: logging.Logger,\n start_poll_time: float,\n databricks_run_id: int,\n max_wait_time_sec: float,\n verbose_logs: bool = True,\n ) -> bool:\n run_state = self.get_run_state(databricks_run_id)\n\n if run_state.has_terminated():\n if run_state.is_successful():\n logger.info(f\"Run `{databricks_run_id}` completed successfully.\")\n return True\n else:\n error_message = (\n f\"Run `{databricks_run_id}` failed with result state:\"\n f\" `{run_state.result_state}`. Message: {run_state.state_message}.\"\n )\n logger.error(error_message)\n raise DatabricksError(error_message)\n else:\n if verbose_logs:\n logger.debug(f\"Run `{databricks_run_id}` in state {run_state}.\")\n if time.time() - start_poll_time > max_wait_time_sec:\n raise DatabricksError(\n f\"Run `{databricks_run_id}` took more than {max_wait_time_sec}s to complete.\"\n \" Failing the run.\"\n )\n return False","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.wait_for_run_to_complete","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.wait_for_run_to_complete\n def wait_for_run_to_complete(\n self,\n logger: logging.Logger,\n databricks_run_id: int,\n poll_interval_sec: float,\n max_wait_time_sec: int,\n verbose_logs: bool = True,\n ) -> None:\n logger.info(f\"Waiting for Databricks run `{databricks_run_id}` to complete...\")\n\n start_poll_time = time.time()\n while True:\n if self.poll_run_state(\n logger=logger,\n start_poll_time=start_poll_time,\n databricks_run_id=databricks_run_id,\n max_wait_time_sec=max_wait_time_sec,\n verbose_logs=verbose_logs,\n ):\n return\n\n time.sleep(poll_interval_sec)","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.__init__","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.__init__\n def __init__(\n self,\n host: str,\n token: str,\n poll_interval_sec: float = 5,\n max_wait_time_sec: int = DEFAULT_RUN_MAX_WAIT_TIME_SEC,\n ):\n self.host = check.str_param(host, \"host\")\n self.token = check.str_param(token, \"token\")\n self.poll_interval_sec = check.numeric_param(poll_interval_sec, \"poll_interval_sec\")\n self.max_wait_time_sec = check.int_param(max_wait_time_sec, \"max_wait_time_sec\")\n\n self._client: DatabricksClient = DatabricksClient(host=self.host, token=self.token)","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.client","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.client\n @property\n def client(self) -> DatabricksClient:\n \"\"\"Return the underlying `DatabricksClient` object.\"\"\"\n return self._client","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.submit_run","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.submit_run\n def submit_run(self, run_config: Mapping[str, Any], task: Mapping[str, Any]) -> int:\n \"\"\"Submit a new run using the 'Runs submit' API.\"\"\"\n existing_cluster_id = run_config[\"cluster\"].get(\"existing\")\n\n new_cluster = run_config[\"cluster\"].get(\"new\")\n\n # The Databricks API needs different keys to be present in API calls depending\n # on new/existing cluster, so we need to process the new_cluster\n # config first.\n if new_cluster:\n new_cluster = new_cluster.copy()\n\n nodes = new_cluster.pop(\"nodes\")\n if \"instance_pool_id\" in nodes:\n new_cluster[\"instance_pool_id\"] = nodes[\"instance_pool_id\"]\n else:\n node_types = nodes[\"node_types\"]\n new_cluster[\"node_type_id\"] = node_types[\"node_type_id\"]\n if \"driver_node_type_id\" in node_types:\n new_cluster[\"driver_node_type_id\"] = node_types[\"driver_node_type_id\"]\n\n cluster_size = new_cluster.pop(\"size\")\n if \"num_workers\" in cluster_size:\n new_cluster[\"num_workers\"] = cluster_size[\"num_workers\"]\n else:\n new_cluster[\"autoscale\"] = cluster_size[\"autoscale\"]\n\n tags = new_cluster.get(\"custom_tags\", [])\n tags.append({\"key\": \"__dagster_version\", \"value\": dagster.__version__})\n new_cluster[\"custom_tags\"] = tags\n\n check.invariant(\n existing_cluster_id is not None or new_cluster is not None,\n \"Invalid value for run_config.cluster\",\n )\n\n # We'll always need some libraries, namely dagster/dagster_databricks/dagster_pyspark,\n # since they're imported by our scripts.\n # Add them if they're not already added by users in config.\n libraries = list(run_config.get(\"libraries\", []))\n install_default_libraries = r","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.retrieve_logs_for_run_id","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.retrieve_logs_for_run_id\n def retrieve_logs_for_run_id(self, log: logging.Logger, databricks_run_id: int):\n \"\"\"Retrieve the stdout and stderr logs for a run.\"\"\"\n api_client = self.client.api_client\n\n run = JobsService(api_client).get_run(databricks_run_id)\n cluster = ClusterService(api_client).get_cluster(run[\"cluster_instance\"][\"cluster_id\"])\n log_config = cluster.get(\"cluster_log_conf\")\n if log_config is None:\n log.warn(\n \"Logs not configured for cluster {cluster} used for run {run}\".format(\n cluster=cluster[\"cluster_id\"], run=databricks_run_id\n )\n )\n return None\n if \"s3\" in log_config:\n logs_prefix = log_config[\"s3\"][\"destination\"]\n log.warn(\"Retrieving S3 logs not yet implemented\")\n return None\n elif \"dbfs\" in log_config:\n logs_prefix = log_config[\"dbfs\"][\"destination\"]\n stdout = self.wait_for_dbfs_logs(log, logs_prefix, cluster[\"cluster_id\"], \"stdout\")\n stderr = self.wait_for_dbfs_logs(log, logs_prefix, cluster[\"cluster_id\"], \"stderr\")\n return stdout, stderr","label":0},{"cid":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.wait_for_dbfs_logs","text":"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.wait_for_dbfs_logs\n def wait_for_dbfs_logs(\n self,\n log: logging.Logger,\n prefix,\n cluster_id,\n filename,\n waiter_delay: int = 10,\n waiter_max_attempts: int = 10,\n ) -> Optional[str]:\n \"\"\"Attempt up to `waiter_max_attempts` attempts to get logs from DBFS.\"\"\"\n path = \"/\".join([prefix, cluster_id, \"driver\", filename])\n log.info(\"Retrieving logs from {}\".format(path))\n num_attempts = 0\n while num_attempts <= waiter_max_attempts:\n try:\n logs = self.client.read_file(path)\n return logs.decode(\"utf-8\")\n except requests.exceptions.HTTPError:\n num_attempts += 1\n time.sleep(waiter_delay)\n log.warn(\"Could not retrieve cluster logs!\")","label":0},{"cid":"python_modules/libraries/dagster-pyspark/dagster_pyspark/types.py::dataframe_loader","text":"python_modules/libraries/dagster-pyspark/dagster_pyspark/types.py::dataframe_loader\n@dagster_type_loader(\n config_schema=Selector(\n {\n \"csv\": Permissive(\n {\n \"path\": Field(\n Any,\n is_required=True,\n description=\"\"\"\n string, or list of strings, for input path(s),\n or RDD of Strings storing CSV rows.\n \"\"\",\n ),\n \"schema\": Field(\n Any,\n is_required=False,\n description=\"\"\"\n an optional :class:`pyspark.sql.types.StructType` for the input schema\n or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).\n \"\"\",\n ),\n \"sep\": Field(\n String,\n is_required=False,\n description=\"\"\"\n sets a separator (one or more characters) for each field and value.\n If None is set, it uses the default value, ``,``.\n \"\"\",\n ),\n \"encoding\": Field(\n String,\n is_required=False,\n description=\"\"\"\n decodes the CSV files by the given encoding type.\n If None is set, it uses the default value, ``UTF-8``.\n \"\"\",\n ),\n \"quote\": Field(\n String,\n is_required=False,\n description=\"\"\"\n sets a single character used for escaping quoted values\n where the separator can be part of the value.\n ","label":0}],"string":"[\n {\n \"cid\": \".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::_infer_package_type\",\n \"text\": \".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::_infer_package_type\\ndef _infer_package_type(directory: str) -> str:\\n if directory in _CORE_PACKAGES:\\n return \\\"core\\\"\\n elif directory.startswith(\\\"examples/\\\"):\\n return \\\"example\\\"\\n elif directory.startswith(\\\"python_modules/libraries/\\\"):\\n return \\\"extension\\\"\\n elif directory in _INFRASTRUCTURE_PACKAGES or directory.startswith(\\\"integration_tests\\\"):\\n return \\\"infrastructure\\\"\\n else:\\n return \\\"unknown\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.__post_init__\",\n \"text\": \".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.__post_init__\\n def __post_init__(self):\\n if not self.name:\\n self.name = os.path.basename(self.directory)\\n\\n if not self.package_type:\\n self.package_type = _infer_package_type(self.directory)\\n\\n self._should_skip = None\\n self._skip_reason = None\",\n \"label\": 0\n },\n {\n \"cid\": \".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.build_steps\",\n \"text\": \".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.build_steps\\n def build_steps(self) -> List[BuildkiteTopLevelStep]:\\n base_name = self.name or os.path.basename(self.directory)\\n steps: List[BuildkiteLeafStep] = []\\n\\n if self.run_pytest:\\n default_python_versions = AvailablePythonVersion.get_pytest_defaults()\\n\\n tox_factors: List[Optional[str]] = (\\n [f.lstrip(\\\"-\\\") for f in self.pytest_tox_factors]\\n if self.pytest_tox_factors\\n else [None]\\n )\\n\\n for other_factor in tox_factors:\\n if callable(self.unsupported_python_versions):\\n unsupported_python_versions = self.unsupported_python_versions(other_factor)\\n else:\\n unsupported_python_versions = self.unsupported_python_versions or []\\n\\n supported_python_versions = [\\n v\\n for v in AvailablePythonVersion.get_all()\\n if v not in unsupported_python_versions\\n ]\\n\\n pytest_python_versions = sorted(\\n list(set(default_python_versions) - set(unsupported_python_versions))\\n )\\n # Use lowest supported python version if no defaults match.\\n if len(pytest_python_versions) == 0:\\n pytest_python_versions = [supported_python_versions[0]]\\n\\n for py_version in pytest_python_versions:\\n version_factor = AvailablePythonVersion.to_tox_factor(py_version)\\n if other_factor is None:\\n tox_env = version_factor\\n else:\\n tox_env = f\\\"{version_factor}-{other_factor}\\\"\\n\\n if isinstance(self.pytest_extra_cmds, list):\\n extra_commands_pre = self.pytest_extra_cmds\\n elif callable(self.pytest_extra_cmds):\\n \",\n \"label\": 0\n },\n {\n \"cid\": \".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.requirements\",\n \"text\": \".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.requirements\\n @property\\n def requirements(self):\\n # First try to infer requirements from the python package\\n package = PythonPackages.get(self.name)\\n if package:\\n return set.union(package.install_requires, *package.extras_require.values())\\n\\n # If we don't have a distribution (like many of our integration test suites)\\n # we can use a buildkite_deps.txt file to capture requirements\\n buildkite_deps_txt = Path(self.directory) / \\\"buildkite_deps.txt\\\"\\n if buildkite_deps_txt.exists():\\n parsed = pkg_resources.parse_requirements(buildkite_deps_txt.read_text())\\n return [requirement for requirement in parsed]\\n\\n # Otherwise return nothing\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.skip_reason\",\n \"text\": \".buildkite/dagster-buildkite/dagster_buildkite/package_spec.py::PackageSpec.skip_reason\\n @property\\n def skip_reason(self) -> Optional[str]:\\n # Memoize so we don't log twice\\n if self._should_skip is False:\\n return None\\n\\n if self.always_run_if and self.always_run_if():\\n self._should_skip = False\\n return None\\n\\n if self._skip_reason:\\n return self._skip_reason\\n\\n if message_contains(\\\"NO_SKIP\\\"):\\n logging.info(f\\\"Building {self.name} because NO_SKIP set\\\")\\n self._should_skip = False\\n return None\\n\\n if not is_feature_branch(os.getenv(\\\"BUILDKITE_BRANCH\\\", \\\"\\\")):\\n logging.info(f\\\"Building {self.name} we're not on a feature branch\\\")\\n self._should_skip = False\\n return None\\n\\n for change in ChangedFiles.all:\\n if (\\n # Our change is in this package's directory\\n (Path(self.directory) in change.parents)\\n # The file can alter behavior - exclude things like README changes\\n # which we tend to include in .md files\\n and change.suffix in changed_filetypes\\n ):\\n logging.info(f\\\"Building {self.name} because it has changed\\\")\\n self._should_skip = False\\n return None\\n\\n # Consider anything required by install or an extra to be in scope.\\n # We might one day narrow this down to specific extras.\\n for requirement in self.requirements:\\n in_scope_changes = PythonPackages.with_changes.intersection(\\n PythonPackages.walk_dependencies(requirement)\\n )\\n if in_scope_changes:\\n logging.info(f\\\"Building {self.name} because of changes to {in_scope_changes}\\\")\\n self._should_skip = False\\n return None\\n\\n self._skip_reason = \\\"Package unaffected by these changes\\\"\\n self._should_skip = True\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagit/dagit/cli.py::create_dagit_cli\",\n \"text\": \"python_modules/dagit/dagit/cli.py::create_dagit_cli\\ndef create_dagit_cli():\\n return dagit\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagit/dagit/cli.py::dagit\",\n \"text\": \"python_modules/dagit/dagit/cli.py::dagit\\n@click.command(\\n name=\\\"dagit\\\",\\n help=(\\n \\\"Run dagit. Loads a repository or pipeline/job.\\\\n\\\\n{warning}\\\".format(\\n warning=WORKSPACE_TARGET_WARNING\\n )\\n + (\\n \\\"\\\\n\\\\nExamples:\\\"\\n \\\"\\\\n\\\\n1. dagit (works if .{default_filename} exists)\\\"\\n \\\"\\\\n\\\\n2. dagit -w path/to/{default_filename}\\\"\\n \\\"\\\\n\\\\n3. dagit -f path/to/file.py\\\"\\n \\\"\\\\n\\\\n4. dagit -f path/to/file.py -d path/to/working_directory\\\"\\n \\\"\\\\n\\\\n5. dagit -m some_module\\\"\\n \\\"\\\\n\\\\n6. dagit -f path/to/file.py -a define_repo\\\"\\n \\\"\\\\n\\\\n7. dagit -m some_module -a define_repo\\\"\\n \\\"\\\\n\\\\n8. dagit -p 3333\\\"\\n \\\"\\\\n\\\\nOptions can also provide arguments via environment variables prefixed with DAGIT\\\"\\n \\\"\\\\n\\\\nFor example, DAGIT_PORT=3333 dagit\\\"\\n ).format(default_filename=DEFAULT_WORKSPACE_YAML_FILENAME)\\n ),\\n)\\n@workspace_target_argument\\n@click.option(\\n \\\"--host\\\",\\n \\\"-h\\\",\\n type=click.STRING,\\n default=DEFAULT_DAGIT_HOST,\\n help=\\\"Host to run server on\\\",\\n show_default=True,\\n)\\n@click.option(\\n \\\"--port\\\",\\n \\\"-p\\\",\\n type=click.INT,\\n help=f\\\"Port to run server on - defaults to {DEFAULT_DAGIT_PORT}\\\",\\n default=None,\\n show_default=True,\\n)\\n@click.option(\\n \\\"--path-prefix\\\",\\n \\\"-l\\\",\\n type=click.STRING,\\n default=\\\"\\\",\\n help=\\\"The path prefix where Dagit will be hosted (eg: /dagit)\\\",\\n show_default=True,\\n)\\n@click.option(\\n \\\"--db-statement-timeout\\\",\\n help=(\\n \\\"The timeout in milliseconds to set on database statements sent \\\"\\n \\\"to the DagsterInstance. Not respected in all configurations.\\\"\\n ),\\n default=DEFAULT_DB_STATEMENT_TIMEOUT,\\n type=click.INT,\\n show_default=True,\\n)\\n@click.option(\\n \\\"--db-pool-recycle\\\",\\n help=(\\n \\\"The maximum age of a connection to use from the sqlalchemy pool without connection\\\"\\n \\\" recycling. Set to -1 to disable. Not respected in all configurations.\\\"\\n ),\\n default=DEFAUL\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagit/dagit/cli.py::host_dagit_ui_with_workspace_process_context\",\n \"text\": \"python_modules/dagit/dagit/cli.py::host_dagit_ui_with_workspace_process_context\\ndef host_dagit_ui_with_workspace_process_context(\\n workspace_process_context: WorkspaceProcessContext,\\n host: Optional[str],\\n port: Optional[int],\\n path_prefix: str,\\n log_level: str,\\n):\\n check.inst_param(\\n workspace_process_context, \\\"workspace_process_context\\\", WorkspaceProcessContext\\n )\\n host = check.opt_str_param(host, \\\"host\\\", \\\"127.0.0.1\\\")\\n check.opt_int_param(port, \\\"port\\\")\\n check.str_param(path_prefix, \\\"path_prefix\\\")\\n\\n logger = logging.getLogger(\\\"dagit\\\")\\n\\n app = create_app_from_workspace_process_context(workspace_process_context, path_prefix)\\n\\n if not port:\\n if is_port_in_use(host, DEFAULT_DAGIT_PORT):\\n port = find_free_port()\\n logger.warning(f\\\"Port {DEFAULT_DAGIT_PORT} is in use - using port {port} instead\\\")\\n else:\\n port = DEFAULT_DAGIT_PORT\\n\\n logger.info(\\n \\\"Serving dagit on http://{host}:{port}{path_prefix} in process {pid}\\\".format(\\n host=host, port=port, path_prefix=path_prefix, pid=os.getpid()\\n )\\n )\\n log_action(workspace_process_context.instance, START_DAGIT_WEBSERVER)\\n with uploading_logging_thread():\\n uvicorn.run(\\n app,\\n host=host,\\n port=port,\\n log_level=log_level,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagit/dagit/cli.py::main\",\n \"text\": \"python_modules/dagit/dagit/cli.py::main\\ndef main():\\n # click magic\\n cli(auto_envvar_prefix=\\\"DAGIT\\\") # pylint:disable=E1120\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_cli/dev.py::dev_command_options\",\n \"text\": \"python_modules/dagster/dagster/_cli/dev.py::dev_command_options\\ndef dev_command_options(f):\\n return apply_click_params(\\n f,\\n workspace_option(),\\n python_file_option(allow_multiple=True),\\n python_module_option(allow_multiple=True),\\n working_directory_option(),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_cli/dev.py::dev_command\",\n \"text\": \"python_modules/dagster/dagster/_cli/dev.py::dev_command\\n@click.command(\\n name=\\\"dev\\\",\\n help=(\\n \\\"Start a local deployment of Dagster, including dagit running on localhost and the\\\"\\n \\\" dagster-daemon running in the background\\\"\\n ),\\n)\\n@dev_command_options\\n@click.option(\\n \\\"--code-server-log-level\\\",\\n help=\\\"Set the log level for code servers spun up by dagster services.\\\",\\n show_default=True,\\n default=\\\"warning\\\",\\n type=click.Choice(\\n [\\\"critical\\\", \\\"error\\\", \\\"warning\\\", \\\"info\\\", \\\"debug\\\", \\\"trace\\\"], case_sensitive=False\\n ),\\n)\\n@click.option(\\\"--dagit-port\\\", \\\"-p\\\", help=\\\"Port to use for the Dagit UI.\\\", required=False)\\n@click.option(\\\"--dagit-host\\\", \\\"-h\\\", help=\\\"Host to use for the Dagit UI.\\\", required=False)\\ndef dev_command(\\n code_server_log_level: str,\\n dagit_port: Optional[str],\\n dagit_host: Optional[str],\\n **kwargs: ClickArgValue,\\n) -> None:\\n # check if dagit installed, crash if not\\n try:\\n import dagit # # noqa: F401\\n except ImportError:\\n raise click.UsageError(\\n \\\"The dagit Python package must be installed in order to use the dagster dev command. If\\\"\\n ' you\\\\'re using pip, you can install the dagit package by running \\\"pip install dagit\\\"'\\n \\\" in your Python environment.\\\"\\n )\\n\\n configure_loggers()\\n logger = logging.getLogger(\\\"dagster\\\")\\n\\n # Sanity check workspace args\\n get_workspace_load_target(kwargs)\\n\\n dagster_home_path = os.getenv(\\\"DAGSTER_HOME\\\")\\n\\n dagster_yaml_path = os.path.join(os.getcwd(), \\\"dagster.yaml\\\")\\n\\n has_local_dagster_yaml = os.path.exists(dagster_yaml_path)\\n if dagster_home_path:\\n if has_local_dagster_yaml and Path(os.getcwd()) != Path(dagster_home_path):\\n logger.warning(\\n \\\"Found a dagster instance configuration value (dagster.yaml) in the current\\\"\\n \\\" folder, but your DAGSTER_HOME environment variable is set to\\\"\\n f\\\" {dagster_home_path}. The dagster.yaml file will not be\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::library_version_from_core_version\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::library_version_from_core_version\\ndef library_version_from_core_version(core_version: str) -> str:\\n parsed_version = parse_package_version(core_version)\\n\\n release = parsed_version.release\\n if release[0] >= 1:\\n library_version = \\\".\\\".join([\\\"0\\\", str(16 + release[1]), str(release[2])])\\n\\n if parsed_version.is_prerelease:\\n library_version = library_version + \\\"\\\".join(\\n [str(pre) for pre in check.not_none(parsed_version.pre)]\\n )\\n\\n if parsed_version.is_postrelease:\\n library_version = library_version + \\\"post\\\" + str(parsed_version.post)\\n\\n return library_version\\n else:\\n return core_version\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::parse_package_version\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::parse_package_version\\ndef parse_package_version(version_str: str) -> packaging.version.Version:\\n parsed_version = packaging.version.parse(version_str)\\n assert isinstance(parsed_version, packaging.version.Version)\\n return parsed_version\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::convert_dagster_submodule_name\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::convert_dagster_submodule_name\\ndef convert_dagster_submodule_name(name: str, mode: Literal[\\\"private\\\", \\\"public\\\"]) -> str:\\n \\\"\\\"\\\"This function was introduced when all Dagster submodules were marked private by\\n underscore-prefixing the root submodules (e.g. `dagster._core`). The function provides\\n backcompatibility by converting modules between the old and new (i.e. public and private) forms.\\n This is needed when reading older data or communicating with older versions of Dagster.\\n \\\"\\\"\\\"\\n if mode == \\\"private\\\":\\n return re.sub(r\\\"^dagster\\\\.([^_])\\\", r\\\"dagster._\\\\1\\\", name)\\n elif mode == \\\"public\\\":\\n return re.sub(r\\\"^dagster._\\\", \\\"dagster.\\\", name)\\n else:\\n check.failed(\\\"`mode` must be 'private' or 'public'\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::file_relative_path\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::file_relative_path\\ndef file_relative_path(dunderfile: str, relative_path: str) -> str:\\n \\\"\\\"\\\"Get a path relative to the currently executing Python file.\\n\\n This function is useful when one needs to load a file that is relative to the position of\\n the current file. (Such as when you encode a configuration file path in source file and want\\n in runnable in any current working directory)\\n\\n Args:\\n dunderfile (str): Should always be ``__file__``.\\n relative_path (str): Path to get relative to the currently executing file.\\n\\n **Examples**:\\n\\n .. code-block:: python\\n\\n file_relative_path(__file__, 'path/relative/to/file')\\n\\n \\\"\\\"\\\"\\n check.str_param(dunderfile, \\\"dunderfile\\\")\\n check.str_param(relative_path, \\\"relative_path\\\")\\n\\n return os.path.join(os.path.dirname(dunderfile), relative_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::script_relative_path\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::script_relative_path\\ndef script_relative_path(file_path: str) -> str:\\n \\\"\\\"\\\"Useful for testing with local files. Use a path relative to where the\\n test resides and this function will return the absolute path\\n of that file. Otherwise it will be relative to script that\\n ran the test.\\n\\n Note: this is function is very, very expensive (on the order of 1\\n millisecond per invocation) so this should only be used in performance\\n insensitive contexts. Prefer file_relative_path for anything with\\n performance constraints.\\n\\n \\\"\\\"\\\"\\n # from http://bit.ly/2snyC6s\\n\\n check.str_param(file_path, \\\"file_path\\\")\\n scriptdir = inspect.stack()[1][1]\\n return os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(scriptdir)), file_path))\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::camelcase\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::camelcase\\ndef camelcase(string: str) -> str:\\n check.str_param(string, \\\"string\\\")\\n\\n string = re.sub(r\\\"^[\\\\-_\\\\.]\\\", \\\"\\\", str(string))\\n if not string:\\n return string\\n return str(string[0]).upper() + re.sub(\\n r\\\"[\\\\-_\\\\.\\\\s]([a-z])\\\", lambda matched: str(matched.group(1)).upper(), string[1:]\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::ensure_single_item\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::ensure_single_item\\ndef ensure_single_item(ddict: Mapping[T, U]) -> Tuple[T, U]:\\n check.mapping_param(ddict, \\\"ddict\\\")\\n check.param_invariant(len(ddict) == 1, \\\"ddict\\\", \\\"Expected dict with single item\\\")\\n return list(ddict.items())[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::pushd\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::pushd\\n@contextlib.contextmanager\\ndef pushd(path: str) -> Iterator[str]:\\n old_cwd = os.getcwd()\\n os.chdir(path)\\n try:\\n yield path\\n finally:\\n os.chdir(old_cwd)\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::safe_isfile\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::safe_isfile\\ndef safe_isfile(path: str) -> bool:\\n \\\"\\\"\\\"Backport of Python 3.8 os.path.isfile behavior.\\n\\n This is intended to backport https://docs.python.org/dev/whatsnew/3.8.html#os-path. I'm not\\n sure that there are other ways to provoke this behavior on Unix other than the null byte,\\n but there are certainly other ways to do it on Windows. Afaict, we won't mask other\\n ValueErrors, and the behavior in the status quo ante is rough because we risk throwing an\\n unexpected, uncaught ValueError from very deep in our logic.\\n \\\"\\\"\\\"\\n try:\\n return os.path.isfile(path)\\n except ValueError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::mkdir_p\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::mkdir_p\\ndef mkdir_p(path: str) -> str:\\n try:\\n os.makedirs(path)\\n return path\\n except OSError as exc: # Python >2.5\\n if exc.errno == errno.EEXIST and os.path.isdir(path):\\n return path\\n else:\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__readonly__\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__readonly__\\n def __readonly__(self, *args, **kwargs):\\n raise RuntimeError(\\\"Cannot modify ReadOnlyDict\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__reduce__\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__reduce__\\n def __reduce__(self):\\n return (frozendict, (), dict(self))\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__setstate__\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__setstate__\\n def __setstate__(self, state):\\n self.__init__(state)\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__hash__\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::frozendict.__hash__\\n def __hash__(self):\\n return hash(tuple(sorted(self.items())))\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__readonly__\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__readonly__\\n def __readonly__(self, *args, **kwargs):\\n raise RuntimeError(\\\"Cannot modify ReadOnlyList\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__reduce__\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__reduce__\\n def __reduce__(self):\\n return (frozenlist, (), list(self))\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__setstate__\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__setstate__\\n def __setstate__(self, state):\\n self.__init__(state)\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__hash__\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::frozenlist.__hash__\\n def __hash__(self):\\n return hash(tuple(self))\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value\\n@overload\\ndef make_readonly_value(value: List[T]) -> Sequence[T]:\\n ...\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value\\n@overload\\ndef make_readonly_value(value: Dict[T, U]) -> Mapping[T, U]:\\n ...\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value\\n@overload\\ndef make_readonly_value(value: T) -> T:\\n ...\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::make_readonly_value\\ndef make_readonly_value(value: Any) -> Any:\\n if isinstance(value, list):\\n return frozenlist(list(map(make_readonly_value, value)))\\n elif isinstance(value, dict):\\n return frozendict({key: make_readonly_value(value) for key, value in value.items()})\\n elif isinstance(value, set):\\n return frozenset(map(make_readonly_value, value))\\n else:\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::get_prop_or_key\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::get_prop_or_key\\ndef get_prop_or_key(elem, key):\\n if isinstance(elem, Mapping):\\n return elem.get(key)\\n else:\\n return getattr(elem, key)\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::list_pull\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::list_pull\\ndef list_pull(alist, key):\\n return list(map(lambda elem: get_prop_or_key(elem, key), alist))\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::all_none\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::all_none\\ndef all_none(kwargs):\\n for value in kwargs.values():\\n if value is not None:\\n return False\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::check_script\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::check_script\\ndef check_script(path, return_code=0):\\n try:\\n subprocess.check_output([sys.executable, path])\\n except subprocess.CalledProcessError as exc:\\n if return_code != 0:\\n if exc.returncode == return_code:\\n return\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::check_cli_execute_file_pipeline\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::check_cli_execute_file_pipeline\\ndef check_cli_execute_file_pipeline(path, pipeline_fn_name, env_file=None):\\n from dagster._core.test_utils import instance_for_test\\n\\n with instance_for_test():\\n cli_cmd = [\\n sys.executable,\\n \\\"-m\\\",\\n \\\"dagster\\\",\\n \\\"pipeline\\\",\\n \\\"execute\\\",\\n \\\"-f\\\",\\n path,\\n \\\"-a\\\",\\n pipeline_fn_name,\\n ]\\n\\n if env_file:\\n cli_cmd.append(\\\"-c\\\")\\n cli_cmd.append(env_file)\\n\\n try:\\n subprocess.check_output(cli_cmd)\\n except subprocess.CalledProcessError as cpe:\\n print(cpe) # noqa: T201\\n raise cpe\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::safe_tempfile_path_unmanaged\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::safe_tempfile_path_unmanaged\\ndef safe_tempfile_path_unmanaged() -> str:\\n # This gets a valid temporary file path in the safest possible way, although there is still no\\n # guarantee that another process will not create a file at this path. The NamedTemporaryFile is\\n # deleted when the context manager exits and the file object is closed.\\n #\\n # This is preferable to using NamedTemporaryFile as a context manager and passing the name\\n # attribute of the file object around because NamedTemporaryFiles cannot be opened a second time\\n # if already open on Windows NT or later:\\n # https://docs.python.org/3.8/library/tempfile.html#tempfile.NamedTemporaryFile\\n # https://github.com/dagster-io/dagster/issues/1582\\n with tempfile.NamedTemporaryFile() as fd:\\n path = fd.name\\n return Path(path).as_posix()\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::safe_tempfile_path\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::safe_tempfile_path\\n@contextlib.contextmanager\\ndef safe_tempfile_path() -> Iterator[str]:\\n path = None\\n try:\\n path = safe_tempfile_path_unmanaged()\\n yield path\\n finally:\\n if path is not None and os.path.exists(path):\\n os.unlink(path)\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::ensure_gen\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::ensure_gen\\n@overload\\ndef ensure_gen(thing_or_gen: Generator[T, Any, Any]) -> Generator[T, Any, Any]:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::ensure_gen\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::ensure_gen\\n@overload\\ndef ensure_gen(thing_or_gen: T) -> Generator[T, Any, Any]:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::ensure_gen\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::ensure_gen\\ndef ensure_gen(\\n thing_or_gen: Union[T, Iterator[T], Generator[T, Any, Any]]\\n) -> Generator[T, Any, Any]:\\n if not inspect.isgenerator(thing_or_gen):\\n thing_or_gen = cast(T, thing_or_gen)\\n\\n def _gen_thing():\\n yield thing_or_gen\\n\\n return _gen_thing()\\n\\n return thing_or_gen\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::ensure_dir\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::ensure_dir\\ndef ensure_dir(file_path: str) -> str:\\n try:\\n os.makedirs(file_path)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise\\n return file_path\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::ensure_file\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::ensure_file\\ndef ensure_file(path: str) -> str:\\n ensure_dir(os.path.dirname(path))\\n if not os.path.exists(path):\\n touch_file(path)\\n return path\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::touch_file\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::touch_file\\ndef touch_file(path):\\n ensure_dir(os.path.dirname(path))\\n with open(path, \\\"a\\\", encoding=\\\"utf8\\\"):\\n os.utime(path, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::_kill_on_event\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::_kill_on_event\\ndef _kill_on_event(termination_event):\\n termination_event.wait()\\n send_interrupt()\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::send_interrupt\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::send_interrupt\\ndef send_interrupt():\\n if seven.IS_WINDOWS:\\n # This will raise a KeyboardInterrupt in python land - meaning this wont be able to\\n # interrupt things like sleep()\\n thread.interrupt_main()\\n else:\\n # If on unix send an os level signal to interrupt any situation we may be stuck in\\n os.kill(os.getpid(), signal.SIGINT)\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::start_termination_thread\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::start_termination_thread\\ndef start_termination_thread(termination_event):\\n check.inst_param(termination_event, \\\"termination_event\\\", ttype=type(multiprocessing.Event()))\\n\\n int_thread = threading.Thread(\\n target=_kill_on_event, args=(termination_event,), name=\\\"kill-on-event\\\"\\n )\\n int_thread.daemon = True\\n int_thread.start()\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::iterate_with_context\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::iterate_with_context\\ndef iterate_with_context(\\n context_fn: Callable[[], ContextManager[Any]], iterator: Iterator[T]\\n) -> Iterator[T]:\\n while True:\\n # Allow interrupts during user code so that we can terminate slow/hanging steps\\n with context_fn():\\n try:\\n next_output = next(iterator)\\n except StopIteration:\\n return\\n\\n yield next_output\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/dagster/dagster/_utils/__init__.py::datetime_as_float\",\n \"text\": \"python_modules/dagster/dagster/_utils/__init__.py::datetime_as_float\\ndef datetime_as_float(dt: datetime.datetime) -> float:\\n check.inst_param(dt, \\\"dt\\\", datetime.datetime)\\n return float((dt - EPOCH).total_seconds())\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.__init__\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.__init__\\n def __init__(self, host: str, token: str, workspace_id: Optional[str] = None):\\n self.host = host\\n self.workspace_id = workspace_id\\n\\n # TODO: This is the old shim client that we were previously using. Arguably this is\\n # confusing for users to use since this is an unofficial wrapper around the documented\\n # Databricks REST API. We should consider removing this in the future.\\n self.client = DatabricksAPI(host=host, token=token)\\n self.__setup_user_agent(self.client.client)\\n\\n # Expose an interface directly to the official Databricks API client.\\n self._api_client = ApiClient(host=host, token=token)\\n self.__setup_user_agent(self._api_client)\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.__setup_user_agent\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.__setup_user_agent\\n def __setup_user_agent(self, client: ApiClient) -> None:\\n \\\"\\\"\\\"Overrides the user agent for the Databricks API client.\\\"\\\"\\\"\\n client.default_headers[\\\"user-agent\\\"] = f\\\"dagster-databricks/{__version__}\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.api_client\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.api_client\\n @public\\n @property\\n def api_client(self) -> ApiClient:\\n \\\"\\\"\\\"Retrieve a reference to the underlying Databricks API client. For more information,\\n see the `Databricks Python API <https://docs.databricks.com/dev-tools/python-api.html>`_.\\n\\n **Examples:**\\n\\n .. code-block:: python\\n\\n from dagster import op\\n from databricks_cli.jobs.api import JobsApi\\n from databricks_cli.runs.api import RunsApi\\n\\n @op(required_resource_keys={\\\"databricks_client\\\"})\\n def op1(context):\\n # Initialize the Databricks Jobs API\\n jobs_client = JobsApi(context.resources.databricks_client.api_client)\\n runs_client = RunsApi(context.resources.databricks_client.api_client)\\n\\n # Example 1: Run a Databricks job with some parameters.\\n jobs_client.run_now(...)\\n\\n # Example 2: Trigger a one-time run of a Databricks workload.\\n runs_client.submit_run(...)\\n\\n # Example 3: Get an existing run.\\n runs_client.get_run(...)\\n\\n # Example 4: Cancel a run.\\n runs_client.cancel_run(...)\\n\\n Returns:\\n ApiClient: The authenticated Databricks API client.\\n \\\"\\\"\\\"\\n return self._api_client\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.read_file\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.read_file\\n def read_file(self, dbfs_path: str, block_size: int = 1024**2) -> bytes:\\n \\\"\\\"\\\"Read a file from DBFS to a **byte string**.\\\"\\\"\\\"\\n if dbfs_path.startswith(\\\"dbfs://\\\"):\\n dbfs_path = dbfs_path[7:]\\n\\n data = b\\\"\\\"\\n bytes_read = 0\\n dbfs_service = DbfsService(self.api_client)\\n\\n jdoc = dbfs_service.read(path=dbfs_path, length=block_size)\\n data += base64.b64decode(jdoc[\\\"data\\\"])\\n while jdoc[\\\"bytes_read\\\"] == block_size:\\n bytes_read += jdoc[\\\"bytes_read\\\"]\\n jdoc = dbfs_service.read(path=dbfs_path, offset=bytes_read, length=block_size)\\n data += base64.b64decode(jdoc[\\\"data\\\"])\\n\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.put_file\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.put_file\\n def put_file(\\n self, file_obj, dbfs_path: str, overwrite: bool = False, block_size: int = 1024**2\\n ) -> None:\\n \\\"\\\"\\\"Upload an arbitrary large file to DBFS.\\n\\n This doesn't use the DBFS `Put` API because that endpoint is limited to 1MB.\\n \\\"\\\"\\\"\\n if dbfs_path.startswith(\\\"dbfs://\\\"):\\n dbfs_path = dbfs_path[7:]\\n\\n dbfs_service = DbfsService(self.api_client)\\n\\n create_response = dbfs_service.create(path=dbfs_path, overwrite=overwrite)\\n handle = create_response[\\\"handle\\\"]\\n\\n block = file_obj.read(block_size)\\n while block:\\n data = base64.b64encode(block).decode(\\\"utf-8\\\")\\n dbfs_service.add_block(data=data, handle=handle)\\n block = file_obj.read(block_size)\\n\\n dbfs_service.close(handle=handle)\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.get_run_state\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.get_run_state\\n def get_run_state(self, databricks_run_id: int) -> \\\"DatabricksRunState\\\":\\n \\\"\\\"\\\"Get the state of a run by Databricks run ID.\\n\\n Return a `DatabricksRunState` object. Note that the `result_state`\\n attribute may be `None` if the run hasn't yet terminated.\\n \\\"\\\"\\\"\\n run = JobsService(self.api_client).get_run(databricks_run_id)\\n state = run[\\\"state\\\"]\\n result_state = (\\n DatabricksRunResultState(state.get(\\\"result_state\\\"))\\n if state.get(\\\"result_state\\\")\\n else None\\n )\\n\\n return DatabricksRunState(\\n life_cycle_state=DatabricksRunLifeCycleState(state[\\\"life_cycle_state\\\"]),\\n result_state=result_state,\\n state_message=state[\\\"state_message\\\"],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.poll_run_state\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.poll_run_state\\n def poll_run_state(\\n self,\\n logger: logging.Logger,\\n start_poll_time: float,\\n databricks_run_id: int,\\n max_wait_time_sec: float,\\n verbose_logs: bool = True,\\n ) -> bool:\\n run_state = self.get_run_state(databricks_run_id)\\n\\n if run_state.has_terminated():\\n if run_state.is_successful():\\n logger.info(f\\\"Run `{databricks_run_id}` completed successfully.\\\")\\n return True\\n else:\\n error_message = (\\n f\\\"Run `{databricks_run_id}` failed with result state:\\\"\\n f\\\" `{run_state.result_state}`. Message: {run_state.state_message}.\\\"\\n )\\n logger.error(error_message)\\n raise DatabricksError(error_message)\\n else:\\n if verbose_logs:\\n logger.debug(f\\\"Run `{databricks_run_id}` in state {run_state}.\\\")\\n if time.time() - start_poll_time > max_wait_time_sec:\\n raise DatabricksError(\\n f\\\"Run `{databricks_run_id}` took more than {max_wait_time_sec}s to complete.\\\"\\n \\\" Failing the run.\\\"\\n )\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.wait_for_run_to_complete\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksClient.wait_for_run_to_complete\\n def wait_for_run_to_complete(\\n self,\\n logger: logging.Logger,\\n databricks_run_id: int,\\n poll_interval_sec: float,\\n max_wait_time_sec: int,\\n verbose_logs: bool = True,\\n ) -> None:\\n logger.info(f\\\"Waiting for Databricks run `{databricks_run_id}` to complete...\\\")\\n\\n start_poll_time = time.time()\\n while True:\\n if self.poll_run_state(\\n logger=logger,\\n start_poll_time=start_poll_time,\\n databricks_run_id=databricks_run_id,\\n max_wait_time_sec=max_wait_time_sec,\\n verbose_logs=verbose_logs,\\n ):\\n return\\n\\n time.sleep(poll_interval_sec)\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.__init__\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.__init__\\n def __init__(\\n self,\\n host: str,\\n token: str,\\n poll_interval_sec: float = 5,\\n max_wait_time_sec: int = DEFAULT_RUN_MAX_WAIT_TIME_SEC,\\n ):\\n self.host = check.str_param(host, \\\"host\\\")\\n self.token = check.str_param(token, \\\"token\\\")\\n self.poll_interval_sec = check.numeric_param(poll_interval_sec, \\\"poll_interval_sec\\\")\\n self.max_wait_time_sec = check.int_param(max_wait_time_sec, \\\"max_wait_time_sec\\\")\\n\\n self._client: DatabricksClient = DatabricksClient(host=self.host, token=self.token)\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.client\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.client\\n @property\\n def client(self) -> DatabricksClient:\\n \\\"\\\"\\\"Return the underlying `DatabricksClient` object.\\\"\\\"\\\"\\n return self._client\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.submit_run\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.submit_run\\n def submit_run(self, run_config: Mapping[str, Any], task: Mapping[str, Any]) -> int:\\n \\\"\\\"\\\"Submit a new run using the 'Runs submit' API.\\\"\\\"\\\"\\n existing_cluster_id = run_config[\\\"cluster\\\"].get(\\\"existing\\\")\\n\\n new_cluster = run_config[\\\"cluster\\\"].get(\\\"new\\\")\\n\\n # The Databricks API needs different keys to be present in API calls depending\\n # on new/existing cluster, so we need to process the new_cluster\\n # config first.\\n if new_cluster:\\n new_cluster = new_cluster.copy()\\n\\n nodes = new_cluster.pop(\\\"nodes\\\")\\n if \\\"instance_pool_id\\\" in nodes:\\n new_cluster[\\\"instance_pool_id\\\"] = nodes[\\\"instance_pool_id\\\"]\\n else:\\n node_types = nodes[\\\"node_types\\\"]\\n new_cluster[\\\"node_type_id\\\"] = node_types[\\\"node_type_id\\\"]\\n if \\\"driver_node_type_id\\\" in node_types:\\n new_cluster[\\\"driver_node_type_id\\\"] = node_types[\\\"driver_node_type_id\\\"]\\n\\n cluster_size = new_cluster.pop(\\\"size\\\")\\n if \\\"num_workers\\\" in cluster_size:\\n new_cluster[\\\"num_workers\\\"] = cluster_size[\\\"num_workers\\\"]\\n else:\\n new_cluster[\\\"autoscale\\\"] = cluster_size[\\\"autoscale\\\"]\\n\\n tags = new_cluster.get(\\\"custom_tags\\\", [])\\n tags.append({\\\"key\\\": \\\"__dagster_version\\\", \\\"value\\\": dagster.__version__})\\n new_cluster[\\\"custom_tags\\\"] = tags\\n\\n check.invariant(\\n existing_cluster_id is not None or new_cluster is not None,\\n \\\"Invalid value for run_config.cluster\\\",\\n )\\n\\n # We'll always need some libraries, namely dagster/dagster_databricks/dagster_pyspark,\\n # since they're imported by our scripts.\\n # Add them if they're not already added by users in config.\\n libraries = list(run_config.get(\\\"libraries\\\", []))\\n install_default_libraries = r\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.retrieve_logs_for_run_id\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.retrieve_logs_for_run_id\\n def retrieve_logs_for_run_id(self, log: logging.Logger, databricks_run_id: int):\\n \\\"\\\"\\\"Retrieve the stdout and stderr logs for a run.\\\"\\\"\\\"\\n api_client = self.client.api_client\\n\\n run = JobsService(api_client).get_run(databricks_run_id)\\n cluster = ClusterService(api_client).get_cluster(run[\\\"cluster_instance\\\"][\\\"cluster_id\\\"])\\n log_config = cluster.get(\\\"cluster_log_conf\\\")\\n if log_config is None:\\n log.warn(\\n \\\"Logs not configured for cluster {cluster} used for run {run}\\\".format(\\n cluster=cluster[\\\"cluster_id\\\"], run=databricks_run_id\\n )\\n )\\n return None\\n if \\\"s3\\\" in log_config:\\n logs_prefix = log_config[\\\"s3\\\"][\\\"destination\\\"]\\n log.warn(\\\"Retrieving S3 logs not yet implemented\\\")\\n return None\\n elif \\\"dbfs\\\" in log_config:\\n logs_prefix = log_config[\\\"dbfs\\\"][\\\"destination\\\"]\\n stdout = self.wait_for_dbfs_logs(log, logs_prefix, cluster[\\\"cluster_id\\\"], \\\"stdout\\\")\\n stderr = self.wait_for_dbfs_logs(log, logs_prefix, cluster[\\\"cluster_id\\\"], \\\"stderr\\\")\\n return stdout, stderr\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.wait_for_dbfs_logs\",\n \"text\": \"python_modules/libraries/dagster-databricks/dagster_databricks/databricks.py::DatabricksJobRunner.wait_for_dbfs_logs\\n def wait_for_dbfs_logs(\\n self,\\n log: logging.Logger,\\n prefix,\\n cluster_id,\\n filename,\\n waiter_delay: int = 10,\\n waiter_max_attempts: int = 10,\\n ) -> Optional[str]:\\n \\\"\\\"\\\"Attempt up to `waiter_max_attempts` attempts to get logs from DBFS.\\\"\\\"\\\"\\n path = \\\"/\\\".join([prefix, cluster_id, \\\"driver\\\", filename])\\n log.info(\\\"Retrieving logs from {}\\\".format(path))\\n num_attempts = 0\\n while num_attempts <= waiter_max_attempts:\\n try:\\n logs = self.client.read_file(path)\\n return logs.decode(\\\"utf-8\\\")\\n except requests.exceptions.HTTPError:\\n num_attempts += 1\\n time.sleep(waiter_delay)\\n log.warn(\\\"Could not retrieve cluster logs!\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python_modules/libraries/dagster-pyspark/dagster_pyspark/types.py::dataframe_loader\",\n \"text\": \"python_modules/libraries/dagster-pyspark/dagster_pyspark/types.py::dataframe_loader\\n@dagster_type_loader(\\n config_schema=Selector(\\n {\\n \\\"csv\\\": Permissive(\\n {\\n \\\"path\\\": Field(\\n Any,\\n is_required=True,\\n description=\\\"\\\"\\\"\\n string, or list of strings, for input path(s),\\n or RDD of Strings storing CSV rows.\\n \\\"\\\"\\\",\\n ),\\n \\\"schema\\\": Field(\\n Any,\\n is_required=False,\\n description=\\\"\\\"\\\"\\n an optional :class:`pyspark.sql.types.StructType` for the input schema\\n or a DDL-formatted string (For example ``col0 INT, col1 DOUBLE``).\\n \\\"\\\"\\\",\\n ),\\n \\\"sep\\\": Field(\\n String,\\n is_required=False,\\n description=\\\"\\\"\\\"\\n sets a separator (one or more characters) for each field and value.\\n If None is set, it uses the default value, ``,``.\\n \\\"\\\"\\\",\\n ),\\n \\\"encoding\\\": Field(\\n String,\\n is_required=False,\\n description=\\\"\\\"\\\"\\n decodes the CSV files by the given encoding type.\\n If None is set, it uses the default value, ``UTF-8``.\\n \\\"\\\"\\\",\\n ),\\n \\\"quote\\\": Field(\\n String,\\n is_required=False,\\n description=\\\"\\\"\\\"\\n sets a single character used for escaping quoted values\\n where the separator can be part of the value.\\n \",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":270,"cells":{"instance_id":{"kind":"string","value":"tensorflow__models-4087"},"repo":{"kind":"string","value":"tensorflow/models"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"\"No module named matplotlib\" when running Pets on Google Cloud tutorial\nI'm following the [\"Distributed Training on the Oxford-IIIT Pets Dataset on Google Cloud\" tutorial](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_pets.md).\r\n\r\nFollowing the step of starting training, about 5 minutes later, I get this termination error from the logs:\r\n\r\n> Termination reason: Error. Traceback (most recent call last): File \"/usr/lib/python2.7/runpy.py\", line 174, in _run_module_as_main \"main\", fname, loader, pkg_name) File \"/usr/lib/python2.7/runpy.py\", line 72, in _run_code exec code in run_globals File \"/root/.local/lib/python2.7/site-packages/object_detection/train.py\", line 51, in from object_detection.builders import model_builder File \"/root/.local/lib/python2.7/site-packages/object_detection/builders/model_builder.py\", line 29, in from object_detection.meta_architectures import ssd_meta_arch File \"/root/.local/lib/python2.7/site-packages/object_detection/meta_architectures/ssd_meta_arch.py\", line 32, in from object_detection.utils import visualization_utils File \"/root/.local/lib/python2.7/site-packages/object_detection/utils/visualization_utils.py\", line 25, in import matplotlib; matplotlib.use('Agg') # pylint: disable=multiple-statements ImportError: No module named matplotlib\r\n\r\nAfter some Googling, it was suggested that I add a requirement for matplotlib to the required packages line of setup.py:\r\n`REQUIRED_PACKAGES = ['Pillow>=1.0', 'matplotlib']`\r\n\r\nThis worked, [until I ran into a later error](https://github.com/tensorflow/models/issues/4058).\r\n\r\nI'm wondering if this is an error in the library (which is up to date on the master branch), an error in the tutorial perhaps caused by an update to the library or Google Cloud, or something that I did wrong.\n"},"gold_files":{"kind":"list like","value":["research/setup.py"],"string":"[\n \"research/setup.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["research/setup.py::<module>"],"string":"[\n \"research/setup.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"official/utils/logs/benchmark_uploader.py","text":"official/utils/logs/benchmark_uploader.py\nclass BigQueryUploader # Upload the benchmark and metric info to BigQuery.\n def __init__(self, logging_dir, gcp_project=None, credentials=None) # Initialized BigQueryUploader with proper setting.\n def upload_benchmark_run(self, dataset_name, table_name, run_id) # Upload benchmark run information to Bigquery.\n def upload_metric(self, dataset_name, table_name, run_id) # Upload metric information to Bigquery.\ndef main(argv)","label":0},{"cid":"research/brain_coder/single_task/ga_train.py","text":"research/brain_coder/single_task/ga_train.py\ndef define_tuner_hparam_space(hparam_space_type) # Define tunable hparams for grid search.\ndef write_hparams_to_config(config, hparams, hparam_space_type) # Write hparams given by the tuner into the Config object.\nclass CheckpointWriter # Manages loading and saving GA populations to disk.\n def __init__(self, checkpoint_dir, population_size)\n def write(self, gen, population, halloffame) # Write GA state to disk.\n def load(self) # Loads GA state from disk.\n def has_checkpoint(self) # Checks if a checkpoint exists on disk, and if so returns True.\ndef run_training(config=None, tuner=None, logdir=None, trial_name=None, is_chief=True) # Do all training runs.\ndef run_random_search(max_num_programs, checkpoint_dir, task_eval_fn, timestep_limit) # Run uniform random search routine.","label":0},{"cid":"research/brain_coder/single_task/pg_train.py","text":"research/brain_coder/single_task/pg_train.py\ndef define_tuner_hparam_space(hparam_space_type) # Define tunable hparams for grid search.\ndef write_hparams_to_config(config, hparams, hparam_space_type) # Write hparams given by the tuner into the Config object.\ndef make_initialized_variable(value, name, shape=None, dtype=tf.float32) # Create a tf.Variable with a constant initializer.\nclass AsyncTrainer # Manages graph creation and training.\n def __init__(self, config, task_id, ps_tasks, num_workers, is_chief=True, summary_writer=None, dtype=tf.float32, summary_interval=1, run_number=0, logging_dir='/tmp', model_v=0)\n def _global_to_local_scope(name)\n def initialize(self, session) # Run initialization ops.\n def update_global_model(self, session) # Run an update step.\n def _local_step_summary(self) # Compute number of local steps per time increment.\n def maybe_save_best_model(self, session, saver, checkpoint_file) # Check if this model got the highest reward and save to disk if so.\n def save_replay_buffer(self) # Save replay buffer to disk.\n def delete_replay_buffer(self) # Delete replay buffer from disk.\n def save_topk_buffer(self) # Save top-k buffer to disk.\ndef managed_session(sv, master='', config=None, start_standard_services=True, close_summary_writer=True, max_wait_secs=7200)\ndef train(config, is_chief, tuner=None, run_dir=None, run_number=0, results_writer=None) # Run training loop.\n def init_fn(unused_sess)\ndef run_training(config=None, tuner=None, logdir=None, trial_name=None, is_chief=True) # Do all training runs.","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py","text":"research/compression/entropy_coder/lib/block_base.py\nclass BlockBase # Base class for transform wrappers of Tensorflow.\n def __init__(self, name)\n def __repr__(self)\n def _OptionalNameScope(self, scope_str)\n def _BlockScope(self) # Context manager that handles graph, namescope, and nested blocks.\n def __call__(self, *args, **kwargs)\n def _Apply(self, *args, **kwargs) # Implementation of __call__().\n def NewVar(self, value, **kwargs) # Creates a new variable.\n def initialized(self) # Returns bool if the block is initialized.\n def AssertInitialized(self) # Asserts initialized property.\n def VariableList(self) # Returns the list of all tensorflow variables used inside this block.\n def _VariableList(self) # Returns the list of all tensorflow variables owned by this block.\n def CreateWeightLoss(self) # Returns L2 loss list of (almost) all variables used inside this block.\n def _CreateWeightLoss(self) # Returns weight loss list of variables that belong to this block.\n def CreateUpdateOps(self) # Creates update operations for this block and its sub-blocks.\n def _CreateUpdateOps(self) # Creates update operations for this block.\n def MarkAsNonTrainable(self) # Mark all the variables of this block as non-trainable.\ndef CreateWeightLoss() # Returns all weight losses from the blocks in the graph.\ndef CreateBlockUpdates() # Combines all updates from the blocks in the graph.","label":0},{"cid":"research/real_nvp/lsun_formatting.py","text":"research/real_nvp/lsun_formatting.py\ndef _int64_feature(value)\ndef _bytes_feature(value)\ndef main() # Main converter function.","label":0},{"cid":"research/setup.py","text":"research/setup.py","label":1},{"cid":"research/syntaxnet/syntaxnet/util/registry.py","text":"research/syntaxnet/syntaxnet/util/registry.py\ndef _GetClass(name) # Looks up a class by name.\ndef _Create(baseclass, subclass_name, *args, **kwargs) # Creates an instance of a named subclass.\ndef _ResolveAndCreate(baseclass, path, subclass_name, *args, **kwargs) # Resolves the name of a subclass and creates an instance of it.\ndef RegisteredClass(baseclass) # Decorates the |baseclass| with a static Create() method.\n def Create(subclass_name, *args, **kwargs) # A wrapper around _Create() that curries the |baseclass|.","label":0},{"cid":"research/tcn/dataset/webcam.py","text":"research/tcn/dataset/webcam.py\nclass ImageQueue # An image queue holding each stream's most recent image.\n def __init__(self)\n def append(self, data)\n def get(self)\n def empty(self)\n def close(self)\nclass WebcamViewer # A class which displays a live stream from the webcams.\n def __init__(self, display_queues) # Create a WebcamViewer instance.\n def _get_next_images(self) # Gets the next image to display.\n def run(self) # Displays the Kcam live stream in a window.\n def update_figure(frame_index) # Animation function for matplotlib FuncAnimation. Updates the image.\ndef reconcile(queues, write_queue) # Gets a list of concurrent images from each view queue.\ndef persist(write_queue, view_dirs) # Pulls lists of concurrent images off a write queue, writes them to disk.\ndef get_image(camera) # Captures a single image from the camera and returns it in PIL format.\ndef capture_webcam(camera, display_queue, reconcile_queue) # Captures images from simultaneous webcams, writes them to queues.\ndef timer(start, end) # Returns a formatted time elapsed.\ndef display_webcams(display_queues) # Builds an WebcamViewer to animate incoming images, runs it.\ndef create_vids(view_dirs, seqname) # Creates one video per view per sequence.\ndef setup_paths() # Sets up the necessary paths to collect videos.\ndef get_view_dirs(vidbase, tmp_imagedir) # Creates and returns one view directory per webcam.\ndef get_cameras() # Opens cameras using cv2, ensures they can take images.\ndef launch_images_to_videos(view_dirs, vid_paths, debug_path) # Launch job in separate process to convert images to videos.\ndef main(_)","label":0},{"cid":"tutorials/image/mnist/convolutional.py","text":"tutorials/image/mnist/convolutional.py\ndef data_type() # Return the type of the activations, weights, and placeholder variables.\ndef maybe_download(filename) # Download the data from Yann's website, unless it's already here.\ndef extract_data(filename, num_images) # Extract the images into a 4D tensor [image index, y, x, channels].\ndef extract_labels(filename, num_images) # Extract the labels into a vector of int64 label IDs.\ndef fake_data(num_images) # Generate a fake dataset that matches the dimensions of MNIST.\ndef error_rate(predictions, labels) # Return the error rate based on dense predictions and sparse labels.\ndef main(_)\n def model(data, train=False) # The Model definition.\n def eval_in_batches(data, sess) # Get all predictions for a dataset by running it in small batches.","label":0}],"string":"[\n {\n \"cid\": \"official/utils/logs/benchmark_uploader.py\",\n \"text\": \"official/utils/logs/benchmark_uploader.py\\nclass BigQueryUploader # Upload the benchmark and metric info to BigQuery.\\n def __init__(self, logging_dir, gcp_project=None, credentials=None) # Initialized BigQueryUploader with proper setting.\\n def upload_benchmark_run(self, dataset_name, table_name, run_id) # Upload benchmark run information to Bigquery.\\n def upload_metric(self, dataset_name, table_name, run_id) # Upload metric information to Bigquery.\\ndef main(argv)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/ga_train.py\",\n \"text\": \"research/brain_coder/single_task/ga_train.py\\ndef define_tuner_hparam_space(hparam_space_type) # Define tunable hparams for grid search.\\ndef write_hparams_to_config(config, hparams, hparam_space_type) # Write hparams given by the tuner into the Config object.\\nclass CheckpointWriter # Manages loading and saving GA populations to disk.\\n def __init__(self, checkpoint_dir, population_size)\\n def write(self, gen, population, halloffame) # Write GA state to disk.\\n def load(self) # Loads GA state from disk.\\n def has_checkpoint(self) # Checks if a checkpoint exists on disk, and if so returns True.\\ndef run_training(config=None, tuner=None, logdir=None, trial_name=None, is_chief=True) # Do all training runs.\\ndef run_random_search(max_num_programs, checkpoint_dir, task_eval_fn, timestep_limit) # Run uniform random search routine.\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py\",\n \"text\": \"research/brain_coder/single_task/pg_train.py\\ndef define_tuner_hparam_space(hparam_space_type) # Define tunable hparams for grid search.\\ndef write_hparams_to_config(config, hparams, hparam_space_type) # Write hparams given by the tuner into the Config object.\\ndef make_initialized_variable(value, name, shape=None, dtype=tf.float32) # Create a tf.Variable with a constant initializer.\\nclass AsyncTrainer # Manages graph creation and training.\\n def __init__(self, config, task_id, ps_tasks, num_workers, is_chief=True, summary_writer=None, dtype=tf.float32, summary_interval=1, run_number=0, logging_dir='/tmp', model_v=0)\\n def _global_to_local_scope(name)\\n def initialize(self, session) # Run initialization ops.\\n def update_global_model(self, session) # Run an update step.\\n def _local_step_summary(self) # Compute number of local steps per time increment.\\n def maybe_save_best_model(self, session, saver, checkpoint_file) # Check if this model got the highest reward and save to disk if so.\\n def save_replay_buffer(self) # Save replay buffer to disk.\\n def delete_replay_buffer(self) # Delete replay buffer from disk.\\n def save_topk_buffer(self) # Save top-k buffer to disk.\\ndef managed_session(sv, master='', config=None, start_standard_services=True, close_summary_writer=True, max_wait_secs=7200)\\ndef train(config, is_chief, tuner=None, run_dir=None, run_number=0, results_writer=None) # Run training loop.\\n def init_fn(unused_sess)\\ndef run_training(config=None, tuner=None, logdir=None, trial_name=None, is_chief=True) # Do all training runs.\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py\\nclass BlockBase # Base class for transform wrappers of Tensorflow.\\n def __init__(self, name)\\n def __repr__(self)\\n def _OptionalNameScope(self, scope_str)\\n def _BlockScope(self) # Context manager that handles graph, namescope, and nested blocks.\\n def __call__(self, *args, **kwargs)\\n def _Apply(self, *args, **kwargs) # Implementation of __call__().\\n def NewVar(self, value, **kwargs) # Creates a new variable.\\n def initialized(self) # Returns bool if the block is initialized.\\n def AssertInitialized(self) # Asserts initialized property.\\n def VariableList(self) # Returns the list of all tensorflow variables used inside this block.\\n def _VariableList(self) # Returns the list of all tensorflow variables owned by this block.\\n def CreateWeightLoss(self) # Returns L2 loss list of (almost) all variables used inside this block.\\n def _CreateWeightLoss(self) # Returns weight loss list of variables that belong to this block.\\n def CreateUpdateOps(self) # Creates update operations for this block and its sub-blocks.\\n def _CreateUpdateOps(self) # Creates update operations for this block.\\n def MarkAsNonTrainable(self) # Mark all the variables of this block as non-trainable.\\ndef CreateWeightLoss() # Returns all weight losses from the blocks in the graph.\\ndef CreateBlockUpdates() # Combines all updates from the blocks in the graph.\",\n \"label\": 0\n },\n {\n \"cid\": \"research/real_nvp/lsun_formatting.py\",\n \"text\": \"research/real_nvp/lsun_formatting.py\\ndef _int64_feature(value)\\ndef _bytes_feature(value)\\ndef main() # Main converter function.\",\n \"label\": 0\n },\n {\n \"cid\": \"research/setup.py\",\n \"text\": \"research/setup.py\",\n \"label\": 1\n },\n {\n \"cid\": \"research/syntaxnet/syntaxnet/util/registry.py\",\n \"text\": \"research/syntaxnet/syntaxnet/util/registry.py\\ndef _GetClass(name) # Looks up a class by name.\\ndef _Create(baseclass, subclass_name, *args, **kwargs) # Creates an instance of a named subclass.\\ndef _ResolveAndCreate(baseclass, path, subclass_name, *args, **kwargs) # Resolves the name of a subclass and creates an instance of it.\\ndef RegisteredClass(baseclass) # Decorates the |baseclass| with a static Create() method.\\n def Create(subclass_name, *args, **kwargs) # A wrapper around _Create() that curries the |baseclass|.\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py\",\n \"text\": \"research/tcn/dataset/webcam.py\\nclass ImageQueue # An image queue holding each stream's most recent image.\\n def __init__(self)\\n def append(self, data)\\n def get(self)\\n def empty(self)\\n def close(self)\\nclass WebcamViewer # A class which displays a live stream from the webcams.\\n def __init__(self, display_queues) # Create a WebcamViewer instance.\\n def _get_next_images(self) # Gets the next image to display.\\n def run(self) # Displays the Kcam live stream in a window.\\n def update_figure(frame_index) # Animation function for matplotlib FuncAnimation. Updates the image.\\ndef reconcile(queues, write_queue) # Gets a list of concurrent images from each view queue.\\ndef persist(write_queue, view_dirs) # Pulls lists of concurrent images off a write queue, writes them to disk.\\ndef get_image(camera) # Captures a single image from the camera and returns it in PIL format.\\ndef capture_webcam(camera, display_queue, reconcile_queue) # Captures images from simultaneous webcams, writes them to queues.\\ndef timer(start, end) # Returns a formatted time elapsed.\\ndef display_webcams(display_queues) # Builds an WebcamViewer to animate incoming images, runs it.\\ndef create_vids(view_dirs, seqname) # Creates one video per view per sequence.\\ndef setup_paths() # Sets up the necessary paths to collect videos.\\ndef get_view_dirs(vidbase, tmp_imagedir) # Creates and returns one view directory per webcam.\\ndef get_cameras() # Opens cameras using cv2, ensures they can take images.\\ndef launch_images_to_videos(view_dirs, vid_paths, debug_path) # Launch job in separate process to convert images to videos.\\ndef main(_)\",\n \"label\": 0\n },\n {\n \"cid\": \"tutorials/image/mnist/convolutional.py\",\n \"text\": \"tutorials/image/mnist/convolutional.py\\ndef data_type() # Return the type of the activations, weights, and placeholder variables.\\ndef maybe_download(filename) # Download the data from Yann's website, unless it's already here.\\ndef extract_data(filename, num_images) # Extract the images into a 4D tensor [image index, y, x, channels].\\ndef extract_labels(filename, num_images) # Extract the labels into a vector of int64 label IDs.\\ndef fake_data(num_images) # Generate a fake dataset that matches the dimensions of MNIST.\\ndef error_rate(predictions, labels) # Return the error rate based on dense predictions and sparse labels.\\ndef main(_)\\n def model(data, train=False) # The Model definition.\\n def eval_in_batches(data, sess) # Get all predictions for a dataset by running it in small batches.\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"official/utils/logs/benchmark_uploader.py::BigQueryUploader.__init__","text":"official/utils/logs/benchmark_uploader.py::BigQueryUploader.__init__\n def __init__(self, logging_dir, gcp_project=None, credentials=None):\n \"\"\"Initialized BigQueryUploader with proper setting.\n\n Args:\n logging_dir: string, logging directory that contains the benchmark log.\n gcp_project: string, the name of the GCP project that the log will be\n uploaded to. The default project name will be detected from local\n environment if no value is provided.\n credentials: google.auth.credentials. The credential to access the\n BigQuery service. The default service account credential will be\n detected from local environment if no value is provided. Please use\n google.oauth2.service_account.Credentials to load credential from local\n file for the case that the test is run out side of GCP.\n \"\"\"\n self._logging_dir = logging_dir\n self._bq_client = bigquery.Client(\n project=gcp_project, credentials=credentials)","label":0},{"cid":"official/utils/logs/benchmark_uploader.py::BigQueryUploader.upload_benchmark_run","text":"official/utils/logs/benchmark_uploader.py::BigQueryUploader.upload_benchmark_run\n def upload_benchmark_run(self, dataset_name, table_name, run_id):\n \"\"\"Upload benchmark run information to Bigquery.\n\n Args:\n dataset_name: string, the name of bigquery dataset where the data will be\n uploaded.\n table_name: string, the name of bigquery table under the dataset where\n the data will be uploaded.\n run_id: string, a unique ID that will be attached to the data, usually\n this is a UUID4 format.\n \"\"\"\n expected_file = os.path.join(\n self._logging_dir, logger.BENCHMARK_RUN_LOG_FILE_NAME)\n with tf.gfile.GFile(expected_file) as f:\n benchmark_json = json.load(f)\n benchmark_json[\"model_id\"] = run_id\n table_ref = self._bq_client.dataset(dataset_name).table(table_name)\n errors = self._bq_client.insert_rows_json(table_ref, [benchmark_json])\n if errors:\n tf.logging.error(\n \"Failed to upload benchmark info to bigquery: {}\".format(errors))","label":0},{"cid":"official/utils/logs/benchmark_uploader.py::BigQueryUploader.upload_metric","text":"official/utils/logs/benchmark_uploader.py::BigQueryUploader.upload_metric\n def upload_metric(self, dataset_name, table_name, run_id):\n \"\"\"Upload metric information to Bigquery.\n\n Args:\n dataset_name: string, the name of bigquery dataset where the data will be\n uploaded.\n table_name: string, the name of bigquery table under the dataset where\n the metric data will be uploaded. This is different from the\n benchmark_run table.\n run_id: string, a unique ID that will be attached to the data, usually\n this is a UUID4 format. This should be the same as the benchmark run_id.\n \"\"\"\n expected_file = os.path.join(\n self._logging_dir, logger.METRIC_LOG_FILE_NAME)\n with tf.gfile.GFile(expected_file) as f:\n lines = f.readlines()\n metrics = []\n for line in filter(lambda l: l.strip(), lines):\n metric = json.loads(line)\n metric[\"run_id\"] = run_id\n metrics.append(metric)\n table_ref = self._bq_client.dataset(dataset_name).table(table_name)\n errors = self._bq_client.insert_rows_json(table_ref, metrics)\n if errors:\n tf.logging.error(\n \"Failed to upload benchmark info to bigquery: {}\".format(errors))","label":0},{"cid":"official/utils/logs/benchmark_uploader.py::main","text":"official/utils/logs/benchmark_uploader.py::main\ndef main(argv):\n parser = parsers.BenchmarkParser()\n flags = parser.parse_args(args=argv[1:])\n if not flags.benchmark_log_dir:\n print(\"Usage: benchmark_uploader.py --benchmark_log_dir=/some/dir\")\n sys.exit(1)\n\n uploader = BigQueryUploader(\n flags.benchmark_log_dir,\n gcp_project=flags.gcp_project)\n run_id = str(uuid.uuid4())\n uploader.upload_benchmark_run(\n flags.bigquery_data_set, flags.bigquery_run_table, run_id)\n uploader.upload_metric(\n flags.bigquery_data_set, flags.bigquery_metric_table, run_id)","label":0},{"cid":"research/brain_coder/single_task/ga_train.py::define_tuner_hparam_space","text":"research/brain_coder/single_task/ga_train.py::define_tuner_hparam_space\ndef define_tuner_hparam_space(hparam_space_type):\n \"\"\"Define tunable hparams for grid search.\"\"\"\n if hparam_space_type != 'ga':\n raise ValueError('Hparam space is not valid: \"%s\"' % hparam_space_type)\n return {\n 'population_size': [10, 25, 50, 100, 500],\n 'crossover_rate': [0.2, 0.5, 0.7, 0.9, 0.95],\n 'mutation_rate': [0.01, 0.03, 0.05, 0.1, 0.15]}","label":0},{"cid":"research/brain_coder/single_task/ga_train.py::write_hparams_to_config","text":"research/brain_coder/single_task/ga_train.py::write_hparams_to_config\ndef write_hparams_to_config(config, hparams, hparam_space_type):\n \"\"\"Write hparams given by the tuner into the Config object.\"\"\"\n if hparam_space_type != 'ga':\n raise ValueError('Hparam space is not valid: \"%s\"' % hparam_space_type)\n config.batch_size = hparams.population_size\n config.agent.crossover_rate = hparams.crossover_rate\n config.agent.mutation_rate = hparams.mutation_rate","label":0},{"cid":"research/brain_coder/single_task/ga_train.py::CheckpointWriter.__init__","text":"research/brain_coder/single_task/ga_train.py::CheckpointWriter.__init__\n def __init__(self, checkpoint_dir, population_size):\n self.checkpoint_file = os.path.join(checkpoint_dir, 'checkpoint.pickle')\n self.population_size = population_size","label":0},{"cid":"research/brain_coder/single_task/ga_train.py::CheckpointWriter.write","text":"research/brain_coder/single_task/ga_train.py::CheckpointWriter.write\n def write(self, gen, population, halloffame):\n \"\"\"Write GA state to disk.\n\n Overwrites previous saved state.\n\n Args:\n gen: Generation number.\n population: List of Individual objects.\n halloffame: Hall-of-fame buffer. Typically a priority queue.\n \"\"\"\n raw = cPickle.dumps((gen, population, halloffame))\n with tf.gfile.FastGFile(self.checkpoint_file, 'w') as f:\n f.write(raw)","label":0},{"cid":"research/brain_coder/single_task/ga_train.py::CheckpointWriter.load","text":"research/brain_coder/single_task/ga_train.py::CheckpointWriter.load\n def load(self):\n \"\"\"Loads GA state from disk.\n\n Loads whatever is on disk, which will be whatever the most recent call\n to `write` wrote.\n\n Returns:\n gen: Generation number.\n population: List of Individual objects.\n halloffame: Hall-of-fame buffer. Typically a priority queue.\n \"\"\"\n with tf.gfile.FastGFile(self.checkpoint_file, 'r') as f:\n raw = f.read()\n objs = cPickle.loads(raw)\n # Validate data.\n assert isinstance(objs, tuple) and len(objs) == 3, (\n 'Expecting a 3-tuple, but got %s instead.' % (objs,))\n gen, population, halloffame = objs\n assert isinstance(gen, int), (\n 'Expecting `gen` to be an integer, got %s' % (gen,))\n assert (\n isinstance(population, list)\n and len(population) == self.population_size\n ), (\n 'Expecting `population` to be a list with size %d, got %s'\n % (self.population_size, population))\n assert halloffame is None or len(halloffame) == 2, (\n 'Expecting hall-of-fame object to have length two, got length %d'\n % len(halloffame))\n logging.info('Loaded pop from checkpoint file: \"%s\".',\n self.checkpoint_file)\n return gen, population, halloffame","label":0},{"cid":"research/brain_coder/single_task/ga_train.py::CheckpointWriter.has_checkpoint","text":"research/brain_coder/single_task/ga_train.py::CheckpointWriter.has_checkpoint\n def has_checkpoint(self):\n \"\"\"Checks if a checkpoint exists on disk, and if so returns True.\"\"\"\n return tf.gfile.Exists(self.checkpoint_file)","label":0},{"cid":"research/brain_coder/single_task/ga_train.py::run_training","text":"research/brain_coder/single_task/ga_train.py::run_training\ndef run_training(config=None, tuner=None, logdir=None, trial_name=None, # pylint: disable=unused-argument\n is_chief=True):\n \"\"\"Do all training runs.\n\n This is the top level training function for policy gradient based models.\n Run this from the main function.\n\n Args:\n config: config_lib.Config instance containing global config (agent and\n environment hparams). If None, config will be parsed from FLAGS.config.\n tuner: (unused) A tuner instance. Leave as None if not tuning.\n logdir: Parent directory where all data from all runs will be written. If\n None, FLAGS.logdir will be used.\n trial_name: (unused) If tuning, set this to a unique string that identifies\n this trial. If `tuner` is not None, this also must be set.\n is_chief: True if this worker is the chief.\n\n Returns:\n List of results dicts which were written to disk. Each training run gets a\n results dict. Results dict contains metrics, i.e. (name, value) pairs which\n give information about the training run.\n\n Raises:\n ValueError: If FLAGS.num_workers does not divide FLAGS.num_repetitions.\n ValueError: If results dicts read from disk contain invalid data.\n \"\"\"\n if not config:\n # If custom config is not given, get it from flags.\n config = defaults.default_config_with_updates(FLAGS.config)\n if not logdir:\n logdir = FLAGS.logdir\n\n if FLAGS.num_repetitions % FLAGS.num_workers != 0:\n raise ValueError('Number of workers must divide number of repetitions')\n num_local_reps = FLAGS.num_repetitions // FLAGS.num_workers\n logging.info('Running %d reps globally.', FLAGS.num_repetitions)\n logging.info('This worker will run %d local reps.', num_local_reps)\n if FLAGS.max_npe:\n max_generations = FLAGS.max_npe // config.batch_size\n logging.info('Max samples per rep: %d', FLAGS.max_npe)\n logging.info('Max generations per rep: %d', max_generations)\n else:\n max_generations ","label":0},{"cid":"research/brain_coder/single_task/ga_train.py::run_random_search","text":"research/brain_coder/single_task/ga_train.py::run_random_search\ndef run_random_search(max_num_programs, checkpoint_dir, task_eval_fn,\n timestep_limit):\n \"\"\"Run uniform random search routine.\n\n Randomly samples programs from a uniform distribution until either a valid\n program is found, or the maximum NPE is reached. Results are written to disk\n and returned.\n\n Args:\n max_num_programs: Maximum NPE (number of programs executed). If no solution\n is found after this many programs are tried, the run is stopped and\n considered a failure.\n checkpoint_dir: Where to save state during the run.\n task_eval_fn: Function that maps code string to result containing total\n reward and info about success.\n timestep_limit: Maximum length of code strings.\n\n Returns:\n ga_lib.GaResult namedtuple instance. This contains the best code and highest\n reward found.\n \"\"\"\n checkpoint_file = os.path.join(checkpoint_dir, 'random_search.txt')\n num_programs_seen = 0\n found_solution = False\n best_code = ''\n best_reward = 0.0\n if tf.gfile.Exists(checkpoint_file):\n try:\n with tf.gfile.FastGFile(checkpoint_file, 'r') as f:\n lines = list(f)\n num_programs_seen = int(lines[0])\n found_solution = bool(int(lines[1]))\n if found_solution:\n best_code = lines[2]\n best_reward = float(lines[3])\n except: # pylint: disable=bare-except\n pass\n\n while not found_solution and num_programs_seen < max_num_programs:\n if num_programs_seen % 1000 == 0:\n logging.info('num_programs_seen = %d', num_programs_seen)\n with tf.gfile.FastGFile(checkpoint_file, 'w') as f:\n f.write(str(num_programs_seen) + '\\n')\n f.write(str(int(found_solution)) + '\\n')\n\n code = np.random.choice(ga_lib.GENES, timestep_limit).tolist()\n res = task_eval_fn(code)\n found_solution = res.correct\n num_programs_seen += 1\n\n if found_solution:\n best_code = ''.join(code)\n best_rewa","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::define_tuner_hparam_space","text":"research/brain_coder/single_task/pg_train.py::define_tuner_hparam_space\ndef define_tuner_hparam_space(hparam_space_type):\n \"\"\"Define tunable hparams for grid search.\"\"\"\n if hparam_space_type not in ('pg', 'pg-topk', 'topk', 'is'):\n raise ValueError('Hparam space is not valid: \"%s\"' % hparam_space_type)\n\n # Discrete hparam space is stored as a dict from hparam name to discrete\n # values.\n hparam_space = {}\n\n if hparam_space_type in ('pg', 'pg-topk', 'is'):\n # Add a floating point parameter named learning rate.\n hparam_space['lr'] = [1e-5, 1e-4, 1e-3]\n hparam_space['entropy_beta'] = [0.005, 0.01, 0.05, 0.10]\n else: # 'topk'\n # Add a floating point parameter named learning rate.\n hparam_space['lr'] = [1e-5, 1e-4, 1e-3]\n hparam_space['entropy_beta'] = [0.0, 0.005, 0.01, 0.05, 0.10]\n\n if hparam_space_type in ('topk', 'pg-topk'):\n # topk tuning will be enabled.\n hparam_space['topk'] = [10]\n hparam_space['topk_loss_hparam'] = [1.0, 10.0, 50.0, 200.0]\n\n elif hparam_space_type == 'is':\n # importance sampling tuning will be enabled.\n hparam_space['replay_temperature'] = [0.25, 0.5, 1.0, 2.0]\n hparam_space['alpha'] = [0.5, 0.75, 63/64.]\n\n return hparam_space","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::write_hparams_to_config","text":"research/brain_coder/single_task/pg_train.py::write_hparams_to_config\ndef write_hparams_to_config(config, hparams, hparam_space_type):\n \"\"\"Write hparams given by the tuner into the Config object.\"\"\"\n if hparam_space_type not in ('pg', 'pg-topk', 'topk', 'is'):\n raise ValueError('Hparam space is not valid: \"%s\"' % hparam_space_type)\n\n config.agent.lr = hparams.lr\n config.agent.entropy_beta = hparams.entropy_beta\n\n if hparam_space_type in ('topk', 'pg-topk'):\n # topk tuning will be enabled.\n config.agent.topk = hparams.topk\n config.agent.topk_loss_hparam = hparams.topk_loss_hparam\n elif hparam_space_type == 'is':\n # importance sampling tuning will be enabled.\n config.agent.replay_temperature = hparams.replay_temperature\n config.agent.alpha = hparams.alpha","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::make_initialized_variable","text":"research/brain_coder/single_task/pg_train.py::make_initialized_variable\ndef make_initialized_variable(value, name, shape=None, dtype=tf.float32):\n \"\"\"Create a tf.Variable with a constant initializer.\n\n Args:\n value: Constant value to initialize the variable with. This is the value\n that the variable starts with.\n name: Name of the variable in the TF graph.\n shape: Shape of the variable. If None, variable will be a scalar.\n dtype: Data type of the variable. Should be a TF dtype. Defaults to\n tf.float32.\n\n Returns:\n tf.Variable instance.\n \"\"\"\n if shape is None:\n shape = []\n return tf.get_variable(\n name=name, shape=shape, initializer=tf.constant_initializer(value),\n dtype=dtype, trainable=False)","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.__init__","text":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.__init__\n def __init__(self, config, task_id, ps_tasks, num_workers, is_chief=True,\n summary_writer=None,\n dtype=tf.float32,\n summary_interval=1,\n run_number=0,\n logging_dir='/tmp', model_v=0):\n self.config = config\n self.data_manager = data.DataManager(\n config, run_number=run_number,\n do_code_simplification=not FLAGS.stop_on_success)\n self.task_id = task_id\n self.ps_tasks = ps_tasks\n self.is_chief = is_chief\n if ps_tasks == 0:\n assert task_id == 0, 'No parameter servers specified. Expecting 1 task.'\n assert num_workers == 1, (\n 'No parameter servers specified. Expecting 1 task.')\n worker_device = '/job:localhost/replica:%d/task:0/cpu:0' % task_id\n # worker_device = '/cpu:0'\n # ps_device = '/cpu:0'\n else:\n assert num_workers > 0, 'There must be at least 1 training worker.'\n worker_device = '/job:worker/replica:%d/task:0/cpu:0' % task_id\n # ps_device = '/job:ps/replica:0/task:0/cpu:0'\n logging.info('worker_device: %s', worker_device)\n\n logging_file = os.path.join(\n logging_dir, 'solutions_%d.txt' % task_id)\n experience_replay_file = os.path.join(\n logging_dir, 'replay_buffer_%d.pickle' % task_id)\n self.topk_file = os.path.join(\n logging_dir, 'topk_buffer_%d.pickle' % task_id)\n\n tf.get_variable_scope().set_use_resource(True)\n\n # global model\n with tf.device(tf.train.replica_device_setter(ps_tasks,\n ps_device='/job:ps/replica:0',\n worker_device=worker_device)):\n with tf.variable_scope('global'):\n global_model = agent_lib.LMAgent(config, dtype=dtype, is_local=False)\n global_params_dict = {p.name: p\n for p in global_model.sync_variables}\n self.global_model = global_mod","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.__init__._global_to_local_scope","text":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.__init__._global_to_local_scope\n def _global_to_local_scope(name):\n assert name.startswith('global/')\n return 'local' + name[6:]","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.initialize","text":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.initialize\n def initialize(self, session):\n \"\"\"Run initialization ops.\"\"\"\n session.run(self.local_init_op)\n session.run(self.sync_op)\n self.cached_global_step, self.cached_global_npe = session.run(\n [self.global_step, self.program_count])","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.update_global_model","text":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.update_global_model\n def update_global_model(self, session):\n \"\"\"Run an update step.\n\n 1) Asynchronously copy global weights to local model.\n 2) Call into local model's update_step method, which does the following:\n a) Sample batch of programs from policy.\n b) Compute rewards.\n c) Compute gradients and update the global model asynchronously.\n 3) Write tensorboard summaries to disk.\n\n Args:\n session: tf.Session instance.\n \"\"\"\n session.run(self.sync_op) # Copy weights from global to local.\n\n with session.as_default():\n result = self.model.update_step(\n session, self.data_manager.sample_rl_batch(), self.train_op,\n self.global_step)\n global_step = result.global_step\n global_npe = result.global_npe\n summaries = result.summaries_list\n self.cached_global_step = global_step\n self.cached_global_npe = global_npe\n self.local_step += 1\n\n if self.summary_writer and self.local_step % self.summary_interval == 0:\n if not isinstance(summaries, (tuple, list)):\n summaries = [summaries]\n summaries.append(self._local_step_summary())\n if self.is_chief:\n (global_best_reward,\n found_solution_flag,\n program_count) = session.run(\n [self.global_best_reward,\n self.found_solution_flag,\n self.program_count])\n summaries.append(\n tf.Summary(\n value=[tf.Summary.Value(\n tag='model/best_reward',\n simple_value=global_best_reward)]))\n summaries.append(\n tf.Summary(\n value=[tf.Summary.Value(\n tag='model/solution_found',\n simple_value=int(found_solution_flag))]))\n summaries.append(\n tf.Summary(\n value=[tf.Summary.Value(\n tag='model/program_count',\n simple_","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::AsyncTrainer._local_step_summary","text":"research/brain_coder/single_task/pg_train.py::AsyncTrainer._local_step_summary\n def _local_step_summary(self):\n \"\"\"Compute number of local steps per time increment.\"\"\"\n dt = time.time() - self.last_summary_time\n steps_per_time = self.summary_interval / float(dt)\n return tf.Summary(value=[\n tf.Summary.Value(\n tag='local_step/per_sec',\n simple_value=steps_per_time),\n tf.Summary.Value(\n tag='local_step/step',\n simple_value=self.local_step)])","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.maybe_save_best_model","text":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.maybe_save_best_model\n def maybe_save_best_model(self, session, saver, checkpoint_file):\n \"\"\"Check if this model got the highest reward and save to disk if so.\"\"\"\n if self.is_chief and session.run(self.is_best_model):\n logging.info('Saving best model to \"%s\"', checkpoint_file)\n saver.save(session, checkpoint_file)\n session.run(self.reset_is_best_model)","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.save_replay_buffer","text":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.save_replay_buffer\n def save_replay_buffer(self):\n \"\"\"Save replay buffer to disk.\n\n Call this periodically so that training can recover if jobs go down.\n \"\"\"\n if self.model.experience_replay is not None:\n logging.info('Saving experience replay buffer to \"%s\".',\n self.model.experience_replay.save_file)\n self.model.experience_replay.incremental_save(True)","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.delete_replay_buffer","text":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.delete_replay_buffer\n def delete_replay_buffer(self):\n \"\"\"Delete replay buffer from disk.\n\n Call this at the end of training to clean up. Replay buffer can get very\n large.\n \"\"\"\n if self.model.experience_replay is not None:\n logging.info('Deleting experience replay buffer at \"%s\".',\n self.model.experience_replay.save_file)\n tf.gfile.Remove(self.model.experience_replay.save_file)","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.save_topk_buffer","text":"research/brain_coder/single_task/pg_train.py::AsyncTrainer.save_topk_buffer\n def save_topk_buffer(self):\n \"\"\"Save top-k buffer to disk.\n\n Call this periodically so that training can recover if jobs go down.\n \"\"\"\n if self.model.top_episodes is not None:\n logging.info('Saving top-k buffer to \"%s\".', self.topk_file)\n # Overwrite previous data each time.\n with tf.gfile.FastGFile(self.topk_file, 'w') as f:\n f.write(cPickle.dumps(self.model.top_episodes))","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::managed_session","text":"research/brain_coder/single_task/pg_train.py::managed_session\n@contextlib.contextmanager\ndef managed_session(sv, master='', config=None,\n start_standard_services=True,\n close_summary_writer=True,\n max_wait_secs=7200):\n # Same as Supervisor.managed_session, but with configurable timeout.\n try:\n sess = sv.prepare_or_wait_for_session(\n master=master, config=config,\n start_standard_services=start_standard_services,\n max_wait_secs=max_wait_secs)\n yield sess\n except tf.errors.DeadlineExceededError:\n raise\n except Exception as e: # pylint: disable=broad-except\n sv.request_stop(e)\n finally:\n try:\n # Request all the threads to stop and wait for them to do so. Any\n # exception raised by the threads is raised again from stop().\n # Passing stop_grace_period_secs is for blocked enqueue/dequeue\n # threads which are not checking for `should_stop()`. They\n # will be stopped when we close the session further down.\n sv.stop(close_summary_writer=close_summary_writer)\n finally:\n # Close the session to finish up all pending calls. We do not care\n # about exceptions raised when closing. This takes care of\n # blocked enqueue/dequeue calls.\n try:\n sess.close()\n except Exception: # pylint: disable=broad-except\n # Silently ignore exceptions raised by close().\n pass","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::train","text":"research/brain_coder/single_task/pg_train.py::train\ndef train(config, is_chief, tuner=None, run_dir=None, run_number=0,\n results_writer=None):\n \"\"\"Run training loop.\n\n Args:\n config: config_lib.Config instance containing global config (agent and env).\n is_chief: True if this worker is chief. Chief worker manages writing some\n data to disk and initialization of the global model.\n tuner: A tuner instance. If not tuning, leave as None.\n run_dir: Directory where all data for this run will be written. If None,\n run_dir = FLAGS.logdir. Set this argument when doing multiple runs.\n run_number: Which run is this.\n results_writer: Managest writing training results to disk. Results are a\n dict of metric names and values.\n\n Returns:\n The trainer object used to run training updates.\n \"\"\"\n logging.info('Will run asynchronous training.')\n\n if run_dir is None:\n run_dir = FLAGS.logdir\n train_dir = os.path.join(run_dir, 'train')\n best_model_checkpoint = os.path.join(train_dir, 'best.ckpt')\n events_dir = '%s/events_%d' % (run_dir, FLAGS.task_id)\n logging.info('Events directory: %s', events_dir)\n\n logging_dir = os.path.join(run_dir, 'logs')\n if not tf.gfile.Exists(logging_dir):\n tf.gfile.MakeDirs(logging_dir)\n status_file = os.path.join(logging_dir, 'status.txt')\n\n if FLAGS.summary_tasks and FLAGS.task_id < FLAGS.summary_tasks:\n summary_writer = tf.summary.FileWriter(events_dir)\n else:\n summary_writer = None\n\n # Only profile task 0.\n if FLAGS.do_profiling:\n logging.info('Profiling enabled')\n profiler = cProfile.Profile()\n profiler.enable()\n else:\n profiler = None\n\n trainer = AsyncTrainer(\n config, FLAGS.task_id, FLAGS.ps_tasks, FLAGS.num_workers,\n is_chief=is_chief,\n summary_interval=FLAGS.summary_interval,\n summary_writer=summary_writer,\n logging_dir=logging_dir,\n run_number=run_number,\n model_v=FLAGS.model_v)\n\n variables_to_save = [v for v in tf.global_var","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::train.init_fn","text":"research/brain_coder/single_task/pg_train.py::train.init_fn\n def init_fn(unused_sess):\n logging.info('No checkpoint found. Initialized global params.')","label":0},{"cid":"research/brain_coder/single_task/pg_train.py::run_training","text":"research/brain_coder/single_task/pg_train.py::run_training\ndef run_training(config=None, tuner=None, logdir=None, trial_name=None,\n is_chief=True):\n \"\"\"Do all training runs.\n\n This is the top level training function for policy gradient based models.\n Run this from the main function.\n\n Args:\n config: config_lib.Config instance containing global config (agent and\n environment hparams). If None, config will be parsed from FLAGS.config.\n tuner: A tuner instance. Leave as None if not tuning.\n logdir: Parent directory where all data from all runs will be written. If\n None, FLAGS.logdir will be used.\n trial_name: If tuning, set this to a unique string that identifies this\n trial. If `tuner` is not None, this also must be set.\n is_chief: True if this worker is the chief.\n\n Returns:\n List of results dicts which were written to disk. Each training run gets a\n results dict. Results dict contains metrics, i.e. (name, value) pairs which\n give information about the training run.\n\n Raises:\n ValueError: If results dicts read from disk contain invalid data.\n \"\"\"\n if not config:\n # If custom config is not given, get it from flags.\n config = defaults.default_config_with_updates(FLAGS.config)\n if not logdir:\n logdir = FLAGS.logdir\n if not tf.gfile.Exists(logdir):\n tf.gfile.MakeDirs(logdir)\n assert FLAGS.num_repetitions > 0\n results = results_lib.Results(logdir)\n results_list, _ = results.read_all()\n\n logging.info('Starting experiment. Directory: \"%s\"', logdir)\n\n if results_list:\n if results_list[0]['max_npe'] != FLAGS.max_npe:\n raise ValueError(\n 'Cannot resume training. Max-NPE changed. Was %s, now %s',\n results_list[0]['max_npe'], FLAGS.max_npe)\n if results_list[0]['max_global_repetitions'] != FLAGS.num_repetitions:\n raise ValueError(\n 'Cannot resume training. Number of repetitions changed. Was %s, '\n 'now %s',\n results_list[0]['max_g","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase.__init__","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase.__init__\n def __init__(self, name):\n self._variables = []\n self._subblocks = []\n self._called = False\n\n # Intentionally distinguishing empty string and None.\n # If name is an empty string, then do not use name scope.\n self.name = name if name is not None else self.__class__.__name__\n self._graph = tf.get_default_graph()\n\n if self.name:\n # Capture the scope string at the init time.\n with self._graph.name_scope(self.name) as scope:\n self._scope_str = scope\n else:\n self._scope_str = ''\n\n # Maintain hierarchy structure of blocks.\n self._stack = _block_stacks[self._graph]\n if self.__class__ is BlockBase:\n # This code is only executed to create the root, which starts in the\n # initialized state.\n assert not self._stack\n self._parent = None\n self._called = True # The root is initialized.\n return\n\n # Create a fake root if a root is not already present.\n if not self._stack:\n self._stack.append(BlockBase('NoOpRoot'))\n\n self._parent = self._stack[-1]\n self._parent._subblocks.append(self) # pylint: disable=protected-access","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase.__repr__","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase.__repr__\n def __repr__(self):\n return '\"{}\" ({})'.format(self._scope_str, self.__class__.__name__)","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase._OptionalNameScope","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase._OptionalNameScope\n @contextlib.contextmanager\n def _OptionalNameScope(self, scope_str):\n if scope_str:\n with self._graph.name_scope(scope_str):\n yield\n else:\n yield","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase._BlockScope","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase._BlockScope\n @contextlib.contextmanager\n def _BlockScope(self):\n \"\"\"Context manager that handles graph, namescope, and nested blocks.\"\"\"\n self._stack.append(self)\n\n try:\n with self._graph.as_default():\n with self._OptionalNameScope(self._scope_str):\n yield self\n finally: # Pop from the stack no matter exception is raised or not.\n # The following line is executed when leaving 'with self._BlockScope()'\n self._stack.pop()","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase.__call__","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase.__call__\n def __call__(self, *args, **kwargs):\n assert self._stack is _block_stacks[self._graph]\n\n with self._BlockScope():\n ret = self._Apply(*args, **kwargs)\n\n self._called = True\n return ret","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase._Apply","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase._Apply\n def _Apply(self, *args, **kwargs):\n \"\"\"Implementation of __call__().\"\"\"\n raise NotImplementedError()","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase.NewVar","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase.NewVar\n def NewVar(self, value, **kwargs):\n \"\"\"Creates a new variable.\n\n This function creates a variable, then returns a local copy created by\n Identity operation. To get the Variable class object, use LookupRef()\n method.\n\n Note that each time Variable class object is used as an input to an\n operation, Tensorflow will create a new Send/Recv pair. This hurts\n performance.\n\n If not for assign operations, use the local copy returned by this method.\n\n Args:\n value: Initialization value of the variable. The shape and the data type\n of the variable is determined by this initial value.\n **kwargs: Extra named arguments passed to Variable.__init__().\n\n Returns:\n A local copy of the new variable.\n \"\"\"\n v = tf.Variable(value, **kwargs)\n\n self._variables.append(v)\n return v","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase.initialized","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase.initialized\n @property\n def initialized(self):\n \"\"\"Returns bool if the block is initialized.\n\n By default, BlockBase assumes that a block is initialized when __call__()\n is executed for the first time. If this is an incorrect assumption for some\n subclasses, override this property in those subclasses.\n\n Returns:\n True if initialized, False otherwise.\n \"\"\"\n return self._called","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase.AssertInitialized","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase.AssertInitialized\n def AssertInitialized(self):\n \"\"\"Asserts initialized property.\"\"\"\n if not self.initialized:\n raise RuntimeError('{} has not been initialized.'.format(self))","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase.VariableList","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase.VariableList\n def VariableList(self):\n \"\"\"Returns the list of all tensorflow variables used inside this block.\"\"\"\n variables = list(itertools.chain(\n itertools.chain.from_iterable(\n t.VariableList() for t in self._subblocks),\n self._VariableList()))\n return variables","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase._VariableList","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase._VariableList\n def _VariableList(self):\n \"\"\"Returns the list of all tensorflow variables owned by this block.\"\"\"\n self.AssertInitialized()\n return self._variables","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase.CreateWeightLoss","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase.CreateWeightLoss\n def CreateWeightLoss(self):\n \"\"\"Returns L2 loss list of (almost) all variables used inside this block.\n\n When this method needs to be overridden, there are two choices.\n\n 1. Override CreateWeightLoss() to change the weight loss of all variables\n that belong to this block, both directly and indirectly.\n 2. Override _CreateWeightLoss() to change the weight loss of all\n variables that directly belong to this block but not to the sub-blocks.\n\n Returns:\n A Tensor object or None.\n \"\"\"\n losses = list(itertools.chain(\n itertools.chain.from_iterable(\n t.CreateWeightLoss() for t in self._subblocks),\n self._CreateWeightLoss()))\n return losses","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase._CreateWeightLoss","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase._CreateWeightLoss\n def _CreateWeightLoss(self):\n \"\"\"Returns weight loss list of variables that belong to this block.\"\"\"\n self.AssertInitialized()\n with self._BlockScope():\n return [tf.nn.l2_loss(v) for v in self._variables]","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase.CreateUpdateOps","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase.CreateUpdateOps\n def CreateUpdateOps(self):\n \"\"\"Creates update operations for this block and its sub-blocks.\"\"\"\n ops = list(itertools.chain(\n itertools.chain.from_iterable(\n t.CreateUpdateOps() for t in self._subblocks),\n self._CreateUpdateOps()))\n return ops","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase._CreateUpdateOps","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase._CreateUpdateOps\n def _CreateUpdateOps(self):\n \"\"\"Creates update operations for this block.\"\"\"\n self.AssertInitialized()\n return []","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::BlockBase.MarkAsNonTrainable","text":"research/compression/entropy_coder/lib/block_base.py::BlockBase.MarkAsNonTrainable\n def MarkAsNonTrainable(self):\n \"\"\"Mark all the variables of this block as non-trainable.\n\n All the variables owned directly or indirectly (through subblocks) are\n marked as non trainable.\n\n This function along with CheckpointInitOp can be used to load a pretrained\n model that consists in only one part of the whole graph.\n \"\"\"\n assert self._called\n\n all_variables = self.VariableList()\n collection = tf.get_collection_ref(tf.GraphKeys.TRAINABLE_VARIABLES)\n for v in all_variables:\n if v in collection:\n collection.remove(v)","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::CreateWeightLoss","text":"research/compression/entropy_coder/lib/block_base.py::CreateWeightLoss\ndef CreateWeightLoss():\n \"\"\"Returns all weight losses from the blocks in the graph.\"\"\"\n stack = _block_stacks[tf.get_default_graph()]\n if not stack:\n return []\n return stack[0].CreateWeightLoss()","label":0},{"cid":"research/compression/entropy_coder/lib/block_base.py::CreateBlockUpdates","text":"research/compression/entropy_coder/lib/block_base.py::CreateBlockUpdates\ndef CreateBlockUpdates():\n \"\"\"Combines all updates from the blocks in the graph.\"\"\"\n stack = _block_stacks[tf.get_default_graph()]\n if not stack:\n return []\n return stack[0].CreateUpdateOps()","label":0},{"cid":"research/real_nvp/lsun_formatting.py::_int64_feature","text":"research/real_nvp/lsun_formatting.py::_int64_feature\ndef _int64_feature(value):\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))","label":0},{"cid":"research/real_nvp/lsun_formatting.py::_bytes_feature","text":"research/real_nvp/lsun_formatting.py::_bytes_feature\ndef _bytes_feature(value):\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))","label":0},{"cid":"research/real_nvp/lsun_formatting.py::main","text":"research/real_nvp/lsun_formatting.py::main\ndef main():\n \"\"\"Main converter function.\"\"\"\n fn_root = FLAGS.fn_root\n img_fn_list = os.listdir(fn_root)\n img_fn_list = [img_fn for img_fn in img_fn_list\n if img_fn.endswith('.webp')]\n num_examples = len(img_fn_list)\n\n n_examples_per_file = 10000\n for example_idx, img_fn in enumerate(img_fn_list):\n if example_idx % n_examples_per_file == 0:\n file_out = \"%s_%05d.tfrecords\"\n file_out = file_out % (FLAGS.file_out,\n example_idx // n_examples_per_file)\n print(\"Writing on:\", file_out)\n writer = tf.python_io.TFRecordWriter(file_out)\n if example_idx % 1000 == 0:\n print(example_idx, \"/\", num_examples)\n image_raw = numpy.array(Image.open(os.path.join(fn_root, img_fn)))\n rows = image_raw.shape[0]\n cols = image_raw.shape[1]\n depth = image_raw.shape[2]\n downscale = min(rows / 96., cols / 96.)\n image_raw = skimage.transform.pyramid_reduce(image_raw, downscale)\n image_raw *= 255.\n image_raw = image_raw.astype(\"uint8\")\n rows = image_raw.shape[0]\n cols = image_raw.shape[1]\n depth = image_raw.shape[2]\n image_raw = image_raw.tostring()\n example = tf.train.Example(\n features=tf.train.Features(\n feature={\n \"height\": _int64_feature(rows),\n \"width\": _int64_feature(cols),\n \"depth\": _int64_feature(depth),\n \"image_raw\": _bytes_feature(image_raw)\n }\n )\n )\n writer.write(example.SerializeToString())\n if example_idx % n_examples_per_file == (n_examples_per_file - 1):\n writer.close()\n writer.close()","label":0},{"cid":"research/syntaxnet/syntaxnet/util/registry.py::_GetClass","text":"research/syntaxnet/syntaxnet/util/registry.py::_GetClass\ndef _GetClass(name):\n \"\"\"Looks up a class by name.\n\n Args:\n name: The fully-qualified type name of the class to return.\n\n Returns:\n The class associated with the |name|, or None on error.\n \"\"\"\n elements = name.split('.')\n\n # Need at least \"module.Class\".\n if len(elements) < 2:\n logging.info('Malformed type: \"%s\"', name)\n return None\n module_path = '.'.join(elements[:-1])\n class_name = elements[-1]\n\n # Import the module.\n try:\n __import__(module_path)\n except ImportError as e:\n logging.info('Unable to find module \"%s\": \"%s\"', module_path, e)\n return None\n module = sys.modules[module_path]\n\n # Look up the class.\n if not hasattr(module, class_name):\n logging.info('Name \"%s\" not found in module: \"%s\"', class_name, module_path)\n return None\n class_obj = getattr(module, class_name)\n\n # Check that it is actually a class.\n if not inspect.isclass(class_obj):\n logging.info('Name does not refer to a class: \"%s\"', name)\n return None\n return class_obj","label":0},{"cid":"research/syntaxnet/syntaxnet/util/registry.py::_Create","text":"research/syntaxnet/syntaxnet/util/registry.py::_Create\ndef _Create(baseclass, subclass_name, *args, **kwargs):\n \"\"\"Creates an instance of a named subclass.\n\n Args:\n baseclass: The expected base class.\n subclass_name: The fully-qualified type name of the subclass to create.\n *args: Passed to the subclass constructor.\n **kwargs: Passed to the subclass constructor.\n\n Returns:\n An instance of the named subclass, or None on error.\n \"\"\"\n subclass = _GetClass(subclass_name)\n if subclass is None:\n return None # _GetClass() already logged an error\n if not issubclass(subclass, baseclass):\n logging.info('Class \"%s\" is not a subclass of \"%s\"', subclass_name,\n baseclass.__name__)\n return None\n return subclass(*args, **kwargs)","label":0},{"cid":"research/syntaxnet/syntaxnet/util/registry.py::_ResolveAndCreate","text":"research/syntaxnet/syntaxnet/util/registry.py::_ResolveAndCreate\ndef _ResolveAndCreate(baseclass, path, subclass_name, *args, **kwargs):\n \"\"\"Resolves the name of a subclass and creates an instance of it.\n\n The subclass is resolved with respect to a package path in an inside-out\n manner. For example, if |path| is 'syntaxnet.foo.bar' and |subclass_name| is\n 'baz.ClassName', then attempts are made to create instances of the following\n fully-qualified class names:\n\n 'syntaxnet.foo.bar.baz.ClassName'\n 'syntaxnet.foo.baz.ClassName'\n 'syntaxnet.baz.ClassName'\n 'baz.ClassName'\n\n An instance corresponding to the first successful attempt is returned.\n\n Args:\n baseclass: The expected base class.\n path: The path to use to resolve the subclass.\n subclass_name: The name of the subclass to create.\n *args: Passed to the subclass constructor.\n **kwargs: Passed to the subclass constructor.\n\n Returns:\n An instance of the named subclass corresponding to the inner-most successful\n name resolution, or None if the name could not be resolved.\n\n Raises:\n ValueError: If the subclass cannot be resolved and created.\n \"\"\"\n elements = path.split('.')\n while True:\n resolved_subclass_name = '.'.join(elements + [subclass_name])\n logging.info('Attempting to instantiate \"%s\"', resolved_subclass_name)\n subclass = _Create(baseclass, resolved_subclass_name, *args, **kwargs)\n if subclass:\n return subclass # success\n if not elements:\n break # no more paths to try\n elements.pop() # try resolving against the next-outer path\n raise ValueError(\n 'Failed to create subclass \"%s\" of base class %s using path %s' %\n (subclass_name, baseclass.__name__, path))","label":0},{"cid":"research/syntaxnet/syntaxnet/util/registry.py::RegisteredClass","text":"research/syntaxnet/syntaxnet/util/registry.py::RegisteredClass\ndef RegisteredClass(baseclass):\n \"\"\"Decorates the |baseclass| with a static Create() method.\"\"\"\n assert not hasattr(baseclass, 'Create')\n\n def Create(subclass_name, *args, **kwargs):\n \"\"\"A wrapper around _Create() that curries the |baseclass|.\"\"\"\n path = inspect.getmodule(baseclass).__name__\n return _ResolveAndCreate(baseclass, path, subclass_name, *args, **kwargs)\n\n baseclass.Create = staticmethod(Create)\n return baseclass","label":0},{"cid":"research/syntaxnet/syntaxnet/util/registry.py::RegisteredClass.Create","text":"research/syntaxnet/syntaxnet/util/registry.py::RegisteredClass.Create\n def Create(subclass_name, *args, **kwargs):\n \"\"\"A wrapper around _Create() that curries the |baseclass|.\"\"\"\n path = inspect.getmodule(baseclass).__name__\n return _ResolveAndCreate(baseclass, path, subclass_name, *args, **kwargs)","label":0},{"cid":"research/tcn/dataset/webcam.py::ImageQueue.__init__","text":"research/tcn/dataset/webcam.py::ImageQueue.__init__\n def __init__(self):\n self.lock = multiprocessing.Lock()\n self._queue = multiprocessing.Queue(maxsize=1)","label":0},{"cid":"research/tcn/dataset/webcam.py::ImageQueue.append","text":"research/tcn/dataset/webcam.py::ImageQueue.append\n def append(self, data):\n with self.lock:\n if self._queue.full():\n # Pop the first element.\n _ = self._queue.get()\n self._queue.put(data)","label":0},{"cid":"research/tcn/dataset/webcam.py::ImageQueue.get","text":"research/tcn/dataset/webcam.py::ImageQueue.get\n def get(self):\n with self.lock:\n return self._queue.get()","label":0},{"cid":"research/tcn/dataset/webcam.py::ImageQueue.empty","text":"research/tcn/dataset/webcam.py::ImageQueue.empty\n def empty(self):\n return self._queue.empty()","label":0},{"cid":"research/tcn/dataset/webcam.py::ImageQueue.close","text":"research/tcn/dataset/webcam.py::ImageQueue.close\n def close(self):\n return self._queue.close()","label":0},{"cid":"research/tcn/dataset/webcam.py::WebcamViewer.__init__","text":"research/tcn/dataset/webcam.py::WebcamViewer.__init__\n def __init__(self, display_queues):\n \"\"\"Create a WebcamViewer instance.\"\"\"\n self.height = FLAGS.height\n self.width = FLAGS.width\n self.queues = display_queues","label":0},{"cid":"research/tcn/dataset/webcam.py::WebcamViewer._get_next_images","text":"research/tcn/dataset/webcam.py::WebcamViewer._get_next_images\n def _get_next_images(self):\n \"\"\"Gets the next image to display.\"\"\"\n # Wait for one image per view.\n not_found = True\n while not_found:\n if True in [q.empty() for q in self.queues]:\n # At least one image queue is empty; wait.\n continue\n else:\n # Retrieve the images.\n latest = [q.get() for q in self.queues]\n combined = np.concatenate(latest, axis=1)\n not_found = False\n return combined","label":0},{"cid":"research/tcn/dataset/webcam.py::WebcamViewer.run","text":"research/tcn/dataset/webcam.py::WebcamViewer.run\n def run(self):\n \"\"\"Displays the Kcam live stream in a window.\n\n This function blocks until the window is closed.\n \"\"\"\n fig, rgb_axis = plt.subplots()\n\n image_rows = self.height\n image_cols = self.width * FLAGS.num_views\n initial_image = np.zeros((image_rows, image_cols, 3))\n rgb_image = rgb_axis.imshow(initial_image, interpolation='nearest')\n\n def update_figure(frame_index):\n \"\"\"Animation function for matplotlib FuncAnimation. Updates the image.\n\n Args:\n frame_index: The frame number.\n Returns:\n An iterable of matplotlib drawables to clear.\n \"\"\"\n _ = frame_index\n images = self._get_next_images()\n images = images[..., [2, 1, 0]]\n rgb_image.set_array(images)\n return rgb_image,\n\n # We must keep a reference to this animation in order for it to work.\n unused_animation = animation.FuncAnimation(\n fig, update_figure, interval=50, blit=True)\n mng = plt.get_current_fig_manager()\n mng.resize(*mng.window.maxsize())\n plt.show()","label":0},{"cid":"research/tcn/dataset/webcam.py::WebcamViewer.run.update_figure","text":"research/tcn/dataset/webcam.py::WebcamViewer.run.update_figure\n def update_figure(frame_index):\n \"\"\"Animation function for matplotlib FuncAnimation. Updates the image.\n\n Args:\n frame_index: The frame number.\n Returns:\n An iterable of matplotlib drawables to clear.\n \"\"\"\n _ = frame_index\n images = self._get_next_images()\n images = images[..., [2, 1, 0]]\n rgb_image.set_array(images)\n return rgb_image,","label":0},{"cid":"research/tcn/dataset/webcam.py::reconcile","text":"research/tcn/dataset/webcam.py::reconcile\ndef reconcile(queues, write_queue):\n \"\"\"Gets a list of concurrent images from each view queue.\n\n This waits for latest images to be available in all view queues,\n then continuously:\n - Creates a list of current images for each view.\n - Writes the list to a queue of image lists to write to disk.\n Args:\n queues: A list of `ImageQueues`, holding the latest image from each webcam.\n write_queue: A multiprocessing.Queue holding lists of concurrent images.\n \"\"\"\n # Loop forever.\n while True:\n # Wait till all queues have an image.\n if True in [q.empty() for q in queues]:\n continue\n else:\n # Retrieve all views' images.\n latest = [q.get() for q in queues]\n # Copy the list of all concurrent images to the write queue.\n write_queue.put(latest)","label":0},{"cid":"research/tcn/dataset/webcam.py::persist","text":"research/tcn/dataset/webcam.py::persist\ndef persist(write_queue, view_dirs):\n \"\"\"Pulls lists of concurrent images off a write queue, writes them to disk.\n\n Args:\n write_queue: A multiprocessing.Queue holding lists of concurrent images;\n one image per view.\n view_dirs: A list of strings, holding the output image directories for each\n view.\n \"\"\"\n timestep = 0\n while True:\n # Wait till there is work in the queue.\n if write_queue.empty():\n continue\n # Get a list of concurrent images to write to disk.\n view_ims = write_queue.get()\n for view_idx, image in enumerate(view_ims):\n view_base = view_dirs[view_idx]\n # Assign all concurrent view images the same sequence timestep.\n fname = os.path.join(view_base, '%s.png' % str(timestep).zfill(10))\n cv2.imwrite(fname, image)\n # Move to the next timestep.\n timestep += 1","label":0},{"cid":"research/tcn/dataset/webcam.py::get_image","text":"research/tcn/dataset/webcam.py::get_image\ndef get_image(camera):\n \"\"\"Captures a single image from the camera and returns it in PIL format.\"\"\"\n data = camera.read()\n _, im = data\n return im","label":0},{"cid":"research/tcn/dataset/webcam.py::capture_webcam","text":"research/tcn/dataset/webcam.py::capture_webcam\ndef capture_webcam(camera, display_queue, reconcile_queue):\n \"\"\"Captures images from simultaneous webcams, writes them to queues.\n\n Args:\n camera: A cv2.VideoCapture object representing an open webcam stream.\n display_queue: An ImageQueue.\n reconcile_queue: An ImageQueue.\n \"\"\"\n # Take some ramp images to allow cams to adjust for brightness etc.\n for i in range(60):\n tf.logging.info('Taking ramp image %d.' % i)\n get_image(camera)\n\n cnt = 0\n start = time.time()\n while True:\n # Get images for all cameras.\n im = get_image(camera)\n # Replace the current image in the display and reconcile queues.\n display_queue.append(im)\n reconcile_queue.append(im)\n cnt += 1\n current = time.time()\n if cnt % 100 == 0:\n tf.logging.info('Collected %s of video, %d frames at ~%.2f fps.' % (\n timer(start, current), cnt, cnt/(current-start)))","label":0},{"cid":"research/tcn/dataset/webcam.py::timer","text":"research/tcn/dataset/webcam.py::timer\ndef timer(start, end):\n \"\"\"Returns a formatted time elapsed.\"\"\"\n hours, rem = divmod(end-start, 3600)\n minutes, seconds = divmod(rem, 60)\n return '{:0>2}:{:0>2}:{:05.2f}'.format(int(hours), int(minutes), seconds)","label":0},{"cid":"research/tcn/dataset/webcam.py::display_webcams","text":"research/tcn/dataset/webcam.py::display_webcams\ndef display_webcams(display_queues):\n \"\"\"Builds an WebcamViewer to animate incoming images, runs it.\"\"\"\n viewer = WebcamViewer(display_queues)\n viewer.run()","label":0},{"cid":"research/tcn/dataset/webcam.py::create_vids","text":"research/tcn/dataset/webcam.py::create_vids\ndef create_vids(view_dirs, seqname):\n \"\"\"Creates one video per view per sequence.\"\"\"\n vidbase = os.path.join(FLAGS.viddir, FLAGS.dataset, FLAGS.mode)\n if not os.path.exists(vidbase):\n os.makedirs(vidbase)\n vidpaths = []\n for idx, view_dir in enumerate(view_dirs):\n vidname = os.path.join(vidbase, '%s_view%d.mp4' % (seqname, idx))\n encode_vid_cmd = r'mencoder mf://%s/*.png \\\n -mf fps=29:type=png \\\n -ovc lavc -lavcopts vcodec=mpeg4:mbd=2:trell \\\n -oac copy -o %s' % (view_dir, vidname)\n os.system(encode_vid_cmd)\n vidpaths.append(vidname)\n\n debugpath = None\n if FLAGS.debug_vids:\n lhs = vidpaths[FLAGS.debug_lhs_view]\n rhs = vidpaths[FLAGS.debug_rhs_view]\n debug_base = os.path.join('%s_debug' % FLAGS.viddir, FLAGS.dataset,\n FLAGS.mode)\n if not os.path.exists(debug_base):\n os.makedirs(debug_base)\n debugpath = '%s/%s.mp4' % (debug_base, seqname)\n os.system(r\"avconv \\\n -i %s \\\n -i %s \\\n -filter_complex '[0:v]pad=iw*2:ih[int];[int][1:v]overlay=W/2:0[vid]' \\\n -map [vid] \\\n -c:v libx264 \\\n -crf 23 \\\n -preset veryfast \\\n %s\" % (lhs, rhs, debugpath))\n\n return vidpaths, debugpath","label":0},{"cid":"research/tcn/dataset/webcam.py::setup_paths","text":"research/tcn/dataset/webcam.py::setup_paths\ndef setup_paths():\n \"\"\"Sets up the necessary paths to collect videos.\"\"\"\n assert FLAGS.dataset\n assert FLAGS.mode\n assert FLAGS.num_views\n\n # Setup directory for final images used to create videos for this sequence.\n tmp_imagedir = os.path.join(FLAGS.tmp_imagedir, FLAGS.dataset, FLAGS.mode)\n if not os.path.exists(tmp_imagedir):\n os.makedirs(tmp_imagedir)\n\n # Create a base directory to hold all sequence videos if it doesn't exist.\n vidbase = os.path.join(FLAGS.viddir, FLAGS.dataset, FLAGS.mode)\n if not os.path.exists(vidbase):\n os.makedirs(vidbase)\n\n # Get one directory per concurrent view and a sequence name.\n view_dirs, seqname = get_view_dirs(vidbase, tmp_imagedir)\n\n # Get an output path to each view's video.\n vid_paths = []\n for idx, _ in enumerate(view_dirs):\n vid_path = os.path.join(vidbase, '%s_view%d.mp4' % (seqname, idx))\n vid_paths.append(vid_path)\n\n # Optionally build paths to debug_videos.\n debug_path = None\n if FLAGS.debug_vids:\n debug_base = os.path.join('%s_debug' % FLAGS.viddir, FLAGS.dataset,\n FLAGS.mode)\n if not os.path.exists(debug_base):\n os.makedirs(debug_base)\n debug_path = '%s/%s.mp4' % (debug_base, seqname)\n\n return view_dirs, vid_paths, debug_path","label":0},{"cid":"research/tcn/dataset/webcam.py::get_view_dirs","text":"research/tcn/dataset/webcam.py::get_view_dirs\ndef get_view_dirs(vidbase, tmp_imagedir):\n \"\"\"Creates and returns one view directory per webcam.\"\"\"\n # Create and append a sequence name.\n if FLAGS.seqname:\n seqname = FLAGS.seqname\n else:\n # If there's no video directory, this is the first sequence.\n if not os.listdir(vidbase):\n seqname = '0'\n else:\n # Otherwise, get the latest sequence name and increment it.\n seq_names = [i.split('_')[0] for i in os.listdir(vidbase)]\n latest_seq = sorted(map(int, seq_names), reverse=True)[0]\n seqname = str(latest_seq+1)\n tf.logging.info('No seqname specified, using: %s' % seqname)\n view_dirs = [os.path.join(\n tmp_imagedir, '%s_view%d' % (seqname, v)) for v in range(FLAGS.num_views)]\n for d in view_dirs:\n if not os.path.exists(d):\n os.makedirs(d)\n return view_dirs, seqname","label":0},{"cid":"research/tcn/dataset/webcam.py::get_cameras","text":"research/tcn/dataset/webcam.py::get_cameras\ndef get_cameras():\n \"\"\"Opens cameras using cv2, ensures they can take images.\"\"\"\n # Try to get free webcam ports.\n if FLAGS.webcam_ports:\n ports = map(int, FLAGS.webcam_ports.split(','))\n else:\n ports = range(FLAGS.num_views)\n cameras = [cv2.VideoCapture(i) for i in ports]\n\n if not all([i.isOpened() for i in cameras]):\n try:\n # Try to find and kill hanging cv2 process_ids.\n output = subprocess.check_output(['lsof -t /dev/video*'], shell=True)\n tf.logging.info('Found hanging cv2 process_ids: \\n')\n tf.logging.info(output)\n tf.logging.info('Killing hanging processes...')\n for process_id in output.split('\\n')[:-1]:\n subprocess.call(['kill %s' % process_id], shell=True)\n time.sleep(3)\n # Recapture webcams.\n cameras = [cv2.VideoCapture(i) for i in ports]\n except subprocess.CalledProcessError:\n raise ValueError(\n 'Cannot connect to cameras. Try running: \\n'\n 'ls -ltrh /dev/video* \\n '\n 'to see which ports your webcams are connected to. Then hand those '\n 'ports as a comma-separated list to --webcam_ports, e.g. '\n '--webcam_ports 0,1')\n\n # Verify each camera is able to capture images.\n ims = map(get_image, cameras)\n assert False not in [i is not None for i in ims]\n return cameras","label":0},{"cid":"research/tcn/dataset/webcam.py::launch_images_to_videos","text":"research/tcn/dataset/webcam.py::launch_images_to_videos\ndef launch_images_to_videos(view_dirs, vid_paths, debug_path):\n \"\"\"Launch job in separate process to convert images to videos.\"\"\"\n\n f = 'learning/brain/research/tcn/dataset/images_to_videos.py'\n cmd = ['python %s ' % f]\n cmd += ['--view_dirs %s ' % ','.join(i for i in view_dirs)]\n cmd += ['--vid_paths %s ' % ','.join(i for i in vid_paths)]\n cmd += ['--debug_path %s ' % debug_path]\n cmd += ['--debug_lhs_view %s ' % FLAGS.debug_lhs_view]\n cmd += ['--debug_rhs_view %s ' % FLAGS.debug_rhs_view]\n cmd += [' & ']\n cmd = ''.join(i for i in cmd)\n\n # Call images_to_videos asynchronously.\n fnull = open(os.devnull, 'w')\n subprocess.Popen([cmd], stdout=fnull, stderr=subprocess.STDOUT, shell=True)\n\n for p in vid_paths:\n tf.logging.info('Writing final video to: %s' % p)\n if debug_path:\n tf.logging.info('Writing debug video to: %s' % debug_path)","label":0},{"cid":"research/tcn/dataset/webcam.py::main","text":"research/tcn/dataset/webcam.py::main\ndef main(_):\n # Initialize the camera capture objects.\n cameras = get_cameras()\n # Get one output directory per view.\n view_dirs, vid_paths, debug_path = setup_paths()\n try:\n # Wait for user input.\n try:\n tf.logging.info('About to write to:')\n for v in view_dirs:\n tf.logging.info(v)\n input('Press Enter to continue...')\n except SyntaxError:\n pass\n\n # Create a queue per view for displaying and saving images.\n display_queues = [ImageQueue() for _ in range(FLAGS.num_views)]\n reconcile_queues = [ImageQueue() for _ in range(FLAGS.num_views)]\n\n # Create a queue for collecting all tuples of multi-view images to write to\n # disk.\n write_queue = multiprocessing.Queue()\n\n processes = []\n # Create a process to display collected images in real time.\n processes.append(Process(target=display_webcams, args=(display_queues,)))\n # Create a process to collect the latest simultaneous images from each view.\n processes.append(Process(\n target=reconcile, args=(reconcile_queues, write_queue,)))\n # Create a process to collect the latest simultaneous images from each view.\n processes.append(Process(\n target=persist, args=(write_queue, view_dirs,)))\n\n for (cam, dq, rq) in zip(cameras, display_queues, reconcile_queues):\n processes.append(Process(\n target=capture_webcam, args=(cam, dq, rq,)))\n\n for p in processes:\n p.start()\n for p in processes:\n p.join()\n\n except KeyboardInterrupt:\n # Close the queues.\n for q in display_queues + reconcile_queues:\n q.close()\n # Release the cameras.\n for cam in cameras:\n cam.release()\n\n # Launch images_to_videos script asynchronously.\n launch_images_to_videos(view_dirs, vid_paths, debug_path)\n\n try:\n sys.exit(0)\n except SystemExit:\n os._exit(0) # pylint: disable=protected-access","label":0},{"cid":"tutorials/image/mnist/convolutional.py::data_type","text":"tutorials/image/mnist/convolutional.py::data_type\ndef data_type():\n \"\"\"Return the type of the activations, weights, and placeholder variables.\"\"\"\n if FLAGS.use_fp16:\n return tf.float16\n else:\n return tf.float32","label":0},{"cid":"tutorials/image/mnist/convolutional.py::maybe_download","text":"tutorials/image/mnist/convolutional.py::maybe_download\ndef maybe_download(filename):\n \"\"\"Download the data from Yann's website, unless it's already here.\"\"\"\n if not tf.gfile.Exists(WORK_DIRECTORY):\n tf.gfile.MakeDirs(WORK_DIRECTORY)\n filepath = os.path.join(WORK_DIRECTORY, filename)\n if not tf.gfile.Exists(filepath):\n filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)\n with tf.gfile.GFile(filepath) as f:\n size = f.size()\n print('Successfully downloaded', filename, size, 'bytes.')\n return filepath","label":0},{"cid":"tutorials/image/mnist/convolutional.py::extract_data","text":"tutorials/image/mnist/convolutional.py::extract_data\ndef extract_data(filename, num_images):\n \"\"\"Extract the images into a 4D tensor [image index, y, x, channels].\n\n Values are rescaled from [0, 255] down to [-0.5, 0.5].\n \"\"\"\n print('Extracting', filename)\n with gzip.open(filename) as bytestream:\n bytestream.read(16)\n buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS)\n data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)\n data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH\n data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)\n return data","label":0},{"cid":"tutorials/image/mnist/convolutional.py::extract_labels","text":"tutorials/image/mnist/convolutional.py::extract_labels\ndef extract_labels(filename, num_images):\n \"\"\"Extract the labels into a vector of int64 label IDs.\"\"\"\n print('Extracting', filename)\n with gzip.open(filename) as bytestream:\n bytestream.read(8)\n buf = bytestream.read(1 * num_images)\n labels = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.int64)\n return labels","label":0},{"cid":"tutorials/image/mnist/convolutional.py::fake_data","text":"tutorials/image/mnist/convolutional.py::fake_data\ndef fake_data(num_images):\n \"\"\"Generate a fake dataset that matches the dimensions of MNIST.\"\"\"\n data = numpy.ndarray(\n shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),\n dtype=numpy.float32)\n labels = numpy.zeros(shape=(num_images,), dtype=numpy.int64)\n for image in xrange(num_images):\n label = image % 2\n data[image, :, :, 0] = label - 0.5\n labels[image] = label\n return data, labels","label":0},{"cid":"tutorials/image/mnist/convolutional.py::error_rate","text":"tutorials/image/mnist/convolutional.py::error_rate\ndef error_rate(predictions, labels):\n \"\"\"Return the error rate based on dense predictions and sparse labels.\"\"\"\n return 100.0 - (\n 100.0 *\n numpy.sum(numpy.argmax(predictions, 1) == labels) /\n predictions.shape[0])","label":0},{"cid":"tutorials/image/mnist/convolutional.py::main","text":"tutorials/image/mnist/convolutional.py::main\ndef main(_):\n if FLAGS.self_test:\n print('Running self-test.')\n train_data, train_labels = fake_data(256)\n validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE)\n test_data, test_labels = fake_data(EVAL_BATCH_SIZE)\n num_epochs = 1\n else:\n # Get the data.\n train_data_filename = maybe_download('train-images-idx3-ubyte.gz')\n train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')\n test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')\n test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')\n\n # Extract it into numpy arrays.\n train_data = extract_data(train_data_filename, 60000)\n train_labels = extract_labels(train_labels_filename, 60000)\n test_data = extract_data(test_data_filename, 10000)\n test_labels = extract_labels(test_labels_filename, 10000)\n\n # Generate a validation set.\n validation_data = train_data[:VALIDATION_SIZE, ...]\n validation_labels = train_labels[:VALIDATION_SIZE]\n train_data = train_data[VALIDATION_SIZE:, ...]\n train_labels = train_labels[VALIDATION_SIZE:]\n num_epochs = NUM_EPOCHS\n train_size = train_labels.shape[0]\n\n # This is where training samples and labels are fed to the graph.\n # These placeholder nodes will be fed a batch of training data at each\n # training step using the {feed_dict} argument to the Run() call below.\n train_data_node = tf.placeholder(\n data_type(),\n shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))\n train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,))\n eval_data = tf.placeholder(\n data_type(),\n shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))\n\n # The variables below hold all the trainable weights. They are passed an\n # initial value which will be assigned when we call:\n # {tf.global_variables_initializer().run()}\n conv1_weights = tf.Variable(\n tf.truncated_normal([5, 5, NUM_CHANNELS, 32], # 5x5 filter, dep","label":0},{"cid":"tutorials/image/mnist/convolutional.py::main.model","text":"tutorials/image/mnist/convolutional.py::main.model\n def model(data, train=False):\n \"\"\"The Model definition.\"\"\"\n # 2D convolution, with 'SAME' padding (i.e. the output feature map has\n # the same size as the input). Note that {strides} is a 4D array whose\n # shape matches the data layout: [image index, y, x, depth].\n conv = tf.nn.conv2d(data,\n conv1_weights,\n strides=[1, 1, 1, 1],\n padding='SAME')\n # Bias and rectified linear non-linearity.\n relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))\n # Max pooling. The kernel size spec {ksize} also follows the layout of\n # the data. Here we have a pooling window of 2, and a stride of 2.\n pool = tf.nn.max_pool(relu,\n ksize=[1, 2, 2, 1],\n strides=[1, 2, 2, 1],\n padding='SAME')\n conv = tf.nn.conv2d(pool,\n conv2_weights,\n strides=[1, 1, 1, 1],\n padding='SAME')\n relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))\n pool = tf.nn.max_pool(relu,\n ksize=[1, 2, 2, 1],\n strides=[1, 2, 2, 1],\n padding='SAME')\n # Reshape the feature map cuboid into a 2D matrix to feed it to the\n # fully connected layers.\n pool_shape = pool.get_shape().as_list()\n reshape = tf.reshape(\n pool,\n [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])\n # Fully connected layer. Note that the '+' operation automatically\n # broadcasts the biases.\n hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)\n # Add a 50% dropout during training only. Dropout also scales\n # activations such that no rescaling is needed at evaluation time.\n if train:\n hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)\n return tf.matmul(hidden, fc2_weights) + fc2_biases","label":0},{"cid":"tutorials/image/mnist/convolutional.py::main.eval_in_batches","text":"tutorials/image/mnist/convolutional.py::main.eval_in_batches\n def eval_in_batches(data, sess):\n \"\"\"Get all predictions for a dataset by running it in small batches.\"\"\"\n size = data.shape[0]\n if size < EVAL_BATCH_SIZE:\n raise ValueError(\"batch size for evals larger than dataset: %d\" % size)\n predictions = numpy.ndarray(shape=(size, NUM_LABELS), dtype=numpy.float32)\n for begin in xrange(0, size, EVAL_BATCH_SIZE):\n end = begin + EVAL_BATCH_SIZE\n if end <= size:\n predictions[begin:end, :] = sess.run(\n eval_prediction,\n feed_dict={eval_data: data[begin:end, ...]})\n else:\n batch_predictions = sess.run(\n eval_prediction,\n feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]})\n predictions[begin:, :] = batch_predictions[begin - size:, :]\n return predictions","label":0}],"string":"[\n {\n \"cid\": \"official/utils/logs/benchmark_uploader.py::BigQueryUploader.__init__\",\n \"text\": \"official/utils/logs/benchmark_uploader.py::BigQueryUploader.__init__\\n def __init__(self, logging_dir, gcp_project=None, credentials=None):\\n \\\"\\\"\\\"Initialized BigQueryUploader with proper setting.\\n\\n Args:\\n logging_dir: string, logging directory that contains the benchmark log.\\n gcp_project: string, the name of the GCP project that the log will be\\n uploaded to. The default project name will be detected from local\\n environment if no value is provided.\\n credentials: google.auth.credentials. The credential to access the\\n BigQuery service. The default service account credential will be\\n detected from local environment if no value is provided. Please use\\n google.oauth2.service_account.Credentials to load credential from local\\n file for the case that the test is run out side of GCP.\\n \\\"\\\"\\\"\\n self._logging_dir = logging_dir\\n self._bq_client = bigquery.Client(\\n project=gcp_project, credentials=credentials)\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/benchmark_uploader.py::BigQueryUploader.upload_benchmark_run\",\n \"text\": \"official/utils/logs/benchmark_uploader.py::BigQueryUploader.upload_benchmark_run\\n def upload_benchmark_run(self, dataset_name, table_name, run_id):\\n \\\"\\\"\\\"Upload benchmark run information to Bigquery.\\n\\n Args:\\n dataset_name: string, the name of bigquery dataset where the data will be\\n uploaded.\\n table_name: string, the name of bigquery table under the dataset where\\n the data will be uploaded.\\n run_id: string, a unique ID that will be attached to the data, usually\\n this is a UUID4 format.\\n \\\"\\\"\\\"\\n expected_file = os.path.join(\\n self._logging_dir, logger.BENCHMARK_RUN_LOG_FILE_NAME)\\n with tf.gfile.GFile(expected_file) as f:\\n benchmark_json = json.load(f)\\n benchmark_json[\\\"model_id\\\"] = run_id\\n table_ref = self._bq_client.dataset(dataset_name).table(table_name)\\n errors = self._bq_client.insert_rows_json(table_ref, [benchmark_json])\\n if errors:\\n tf.logging.error(\\n \\\"Failed to upload benchmark info to bigquery: {}\\\".format(errors))\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/benchmark_uploader.py::BigQueryUploader.upload_metric\",\n \"text\": \"official/utils/logs/benchmark_uploader.py::BigQueryUploader.upload_metric\\n def upload_metric(self, dataset_name, table_name, run_id):\\n \\\"\\\"\\\"Upload metric information to Bigquery.\\n\\n Args:\\n dataset_name: string, the name of bigquery dataset where the data will be\\n uploaded.\\n table_name: string, the name of bigquery table under the dataset where\\n the metric data will be uploaded. This is different from the\\n benchmark_run table.\\n run_id: string, a unique ID that will be attached to the data, usually\\n this is a UUID4 format. This should be the same as the benchmark run_id.\\n \\\"\\\"\\\"\\n expected_file = os.path.join(\\n self._logging_dir, logger.METRIC_LOG_FILE_NAME)\\n with tf.gfile.GFile(expected_file) as f:\\n lines = f.readlines()\\n metrics = []\\n for line in filter(lambda l: l.strip(), lines):\\n metric = json.loads(line)\\n metric[\\\"run_id\\\"] = run_id\\n metrics.append(metric)\\n table_ref = self._bq_client.dataset(dataset_name).table(table_name)\\n errors = self._bq_client.insert_rows_json(table_ref, metrics)\\n if errors:\\n tf.logging.error(\\n \\\"Failed to upload benchmark info to bigquery: {}\\\".format(errors))\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/benchmark_uploader.py::main\",\n \"text\": \"official/utils/logs/benchmark_uploader.py::main\\ndef main(argv):\\n parser = parsers.BenchmarkParser()\\n flags = parser.parse_args(args=argv[1:])\\n if not flags.benchmark_log_dir:\\n print(\\\"Usage: benchmark_uploader.py --benchmark_log_dir=/some/dir\\\")\\n sys.exit(1)\\n\\n uploader = BigQueryUploader(\\n flags.benchmark_log_dir,\\n gcp_project=flags.gcp_project)\\n run_id = str(uuid.uuid4())\\n uploader.upload_benchmark_run(\\n flags.bigquery_data_set, flags.bigquery_run_table, run_id)\\n uploader.upload_metric(\\n flags.bigquery_data_set, flags.bigquery_metric_table, run_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/ga_train.py::define_tuner_hparam_space\",\n \"text\": \"research/brain_coder/single_task/ga_train.py::define_tuner_hparam_space\\ndef define_tuner_hparam_space(hparam_space_type):\\n \\\"\\\"\\\"Define tunable hparams for grid search.\\\"\\\"\\\"\\n if hparam_space_type != 'ga':\\n raise ValueError('Hparam space is not valid: \\\"%s\\\"' % hparam_space_type)\\n return {\\n 'population_size': [10, 25, 50, 100, 500],\\n 'crossover_rate': [0.2, 0.5, 0.7, 0.9, 0.95],\\n 'mutation_rate': [0.01, 0.03, 0.05, 0.1, 0.15]}\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/ga_train.py::write_hparams_to_config\",\n \"text\": \"research/brain_coder/single_task/ga_train.py::write_hparams_to_config\\ndef write_hparams_to_config(config, hparams, hparam_space_type):\\n \\\"\\\"\\\"Write hparams given by the tuner into the Config object.\\\"\\\"\\\"\\n if hparam_space_type != 'ga':\\n raise ValueError('Hparam space is not valid: \\\"%s\\\"' % hparam_space_type)\\n config.batch_size = hparams.population_size\\n config.agent.crossover_rate = hparams.crossover_rate\\n config.agent.mutation_rate = hparams.mutation_rate\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/ga_train.py::CheckpointWriter.__init__\",\n \"text\": \"research/brain_coder/single_task/ga_train.py::CheckpointWriter.__init__\\n def __init__(self, checkpoint_dir, population_size):\\n self.checkpoint_file = os.path.join(checkpoint_dir, 'checkpoint.pickle')\\n self.population_size = population_size\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/ga_train.py::CheckpointWriter.write\",\n \"text\": \"research/brain_coder/single_task/ga_train.py::CheckpointWriter.write\\n def write(self, gen, population, halloffame):\\n \\\"\\\"\\\"Write GA state to disk.\\n\\n Overwrites previous saved state.\\n\\n Args:\\n gen: Generation number.\\n population: List of Individual objects.\\n halloffame: Hall-of-fame buffer. Typically a priority queue.\\n \\\"\\\"\\\"\\n raw = cPickle.dumps((gen, population, halloffame))\\n with tf.gfile.FastGFile(self.checkpoint_file, 'w') as f:\\n f.write(raw)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/ga_train.py::CheckpointWriter.load\",\n \"text\": \"research/brain_coder/single_task/ga_train.py::CheckpointWriter.load\\n def load(self):\\n \\\"\\\"\\\"Loads GA state from disk.\\n\\n Loads whatever is on disk, which will be whatever the most recent call\\n to `write` wrote.\\n\\n Returns:\\n gen: Generation number.\\n population: List of Individual objects.\\n halloffame: Hall-of-fame buffer. Typically a priority queue.\\n \\\"\\\"\\\"\\n with tf.gfile.FastGFile(self.checkpoint_file, 'r') as f:\\n raw = f.read()\\n objs = cPickle.loads(raw)\\n # Validate data.\\n assert isinstance(objs, tuple) and len(objs) == 3, (\\n 'Expecting a 3-tuple, but got %s instead.' % (objs,))\\n gen, population, halloffame = objs\\n assert isinstance(gen, int), (\\n 'Expecting `gen` to be an integer, got %s' % (gen,))\\n assert (\\n isinstance(population, list)\\n and len(population) == self.population_size\\n ), (\\n 'Expecting `population` to be a list with size %d, got %s'\\n % (self.population_size, population))\\n assert halloffame is None or len(halloffame) == 2, (\\n 'Expecting hall-of-fame object to have length two, got length %d'\\n % len(halloffame))\\n logging.info('Loaded pop from checkpoint file: \\\"%s\\\".',\\n self.checkpoint_file)\\n return gen, population, halloffame\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/ga_train.py::CheckpointWriter.has_checkpoint\",\n \"text\": \"research/brain_coder/single_task/ga_train.py::CheckpointWriter.has_checkpoint\\n def has_checkpoint(self):\\n \\\"\\\"\\\"Checks if a checkpoint exists on disk, and if so returns True.\\\"\\\"\\\"\\n return tf.gfile.Exists(self.checkpoint_file)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/ga_train.py::run_training\",\n \"text\": \"research/brain_coder/single_task/ga_train.py::run_training\\ndef run_training(config=None, tuner=None, logdir=None, trial_name=None, # pylint: disable=unused-argument\\n is_chief=True):\\n \\\"\\\"\\\"Do all training runs.\\n\\n This is the top level training function for policy gradient based models.\\n Run this from the main function.\\n\\n Args:\\n config: config_lib.Config instance containing global config (agent and\\n environment hparams). If None, config will be parsed from FLAGS.config.\\n tuner: (unused) A tuner instance. Leave as None if not tuning.\\n logdir: Parent directory where all data from all runs will be written. If\\n None, FLAGS.logdir will be used.\\n trial_name: (unused) If tuning, set this to a unique string that identifies\\n this trial. If `tuner` is not None, this also must be set.\\n is_chief: True if this worker is the chief.\\n\\n Returns:\\n List of results dicts which were written to disk. Each training run gets a\\n results dict. Results dict contains metrics, i.e. (name, value) pairs which\\n give information about the training run.\\n\\n Raises:\\n ValueError: If FLAGS.num_workers does not divide FLAGS.num_repetitions.\\n ValueError: If results dicts read from disk contain invalid data.\\n \\\"\\\"\\\"\\n if not config:\\n # If custom config is not given, get it from flags.\\n config = defaults.default_config_with_updates(FLAGS.config)\\n if not logdir:\\n logdir = FLAGS.logdir\\n\\n if FLAGS.num_repetitions % FLAGS.num_workers != 0:\\n raise ValueError('Number of workers must divide number of repetitions')\\n num_local_reps = FLAGS.num_repetitions // FLAGS.num_workers\\n logging.info('Running %d reps globally.', FLAGS.num_repetitions)\\n logging.info('This worker will run %d local reps.', num_local_reps)\\n if FLAGS.max_npe:\\n max_generations = FLAGS.max_npe // config.batch_size\\n logging.info('Max samples per rep: %d', FLAGS.max_npe)\\n logging.info('Max generations per rep: %d', max_generations)\\n else:\\n max_generations \",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/ga_train.py::run_random_search\",\n \"text\": \"research/brain_coder/single_task/ga_train.py::run_random_search\\ndef run_random_search(max_num_programs, checkpoint_dir, task_eval_fn,\\n timestep_limit):\\n \\\"\\\"\\\"Run uniform random search routine.\\n\\n Randomly samples programs from a uniform distribution until either a valid\\n program is found, or the maximum NPE is reached. Results are written to disk\\n and returned.\\n\\n Args:\\n max_num_programs: Maximum NPE (number of programs executed). If no solution\\n is found after this many programs are tried, the run is stopped and\\n considered a failure.\\n checkpoint_dir: Where to save state during the run.\\n task_eval_fn: Function that maps code string to result containing total\\n reward and info about success.\\n timestep_limit: Maximum length of code strings.\\n\\n Returns:\\n ga_lib.GaResult namedtuple instance. This contains the best code and highest\\n reward found.\\n \\\"\\\"\\\"\\n checkpoint_file = os.path.join(checkpoint_dir, 'random_search.txt')\\n num_programs_seen = 0\\n found_solution = False\\n best_code = ''\\n best_reward = 0.0\\n if tf.gfile.Exists(checkpoint_file):\\n try:\\n with tf.gfile.FastGFile(checkpoint_file, 'r') as f:\\n lines = list(f)\\n num_programs_seen = int(lines[0])\\n found_solution = bool(int(lines[1]))\\n if found_solution:\\n best_code = lines[2]\\n best_reward = float(lines[3])\\n except: # pylint: disable=bare-except\\n pass\\n\\n while not found_solution and num_programs_seen < max_num_programs:\\n if num_programs_seen % 1000 == 0:\\n logging.info('num_programs_seen = %d', num_programs_seen)\\n with tf.gfile.FastGFile(checkpoint_file, 'w') as f:\\n f.write(str(num_programs_seen) + '\\\\n')\\n f.write(str(int(found_solution)) + '\\\\n')\\n\\n code = np.random.choice(ga_lib.GENES, timestep_limit).tolist()\\n res = task_eval_fn(code)\\n found_solution = res.correct\\n num_programs_seen += 1\\n\\n if found_solution:\\n best_code = ''.join(code)\\n best_rewa\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::define_tuner_hparam_space\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::define_tuner_hparam_space\\ndef define_tuner_hparam_space(hparam_space_type):\\n \\\"\\\"\\\"Define tunable hparams for grid search.\\\"\\\"\\\"\\n if hparam_space_type not in ('pg', 'pg-topk', 'topk', 'is'):\\n raise ValueError('Hparam space is not valid: \\\"%s\\\"' % hparam_space_type)\\n\\n # Discrete hparam space is stored as a dict from hparam name to discrete\\n # values.\\n hparam_space = {}\\n\\n if hparam_space_type in ('pg', 'pg-topk', 'is'):\\n # Add a floating point parameter named learning rate.\\n hparam_space['lr'] = [1e-5, 1e-4, 1e-3]\\n hparam_space['entropy_beta'] = [0.005, 0.01, 0.05, 0.10]\\n else: # 'topk'\\n # Add a floating point parameter named learning rate.\\n hparam_space['lr'] = [1e-5, 1e-4, 1e-3]\\n hparam_space['entropy_beta'] = [0.0, 0.005, 0.01, 0.05, 0.10]\\n\\n if hparam_space_type in ('topk', 'pg-topk'):\\n # topk tuning will be enabled.\\n hparam_space['topk'] = [10]\\n hparam_space['topk_loss_hparam'] = [1.0, 10.0, 50.0, 200.0]\\n\\n elif hparam_space_type == 'is':\\n # importance sampling tuning will be enabled.\\n hparam_space['replay_temperature'] = [0.25, 0.5, 1.0, 2.0]\\n hparam_space['alpha'] = [0.5, 0.75, 63/64.]\\n\\n return hparam_space\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::write_hparams_to_config\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::write_hparams_to_config\\ndef write_hparams_to_config(config, hparams, hparam_space_type):\\n \\\"\\\"\\\"Write hparams given by the tuner into the Config object.\\\"\\\"\\\"\\n if hparam_space_type not in ('pg', 'pg-topk', 'topk', 'is'):\\n raise ValueError('Hparam space is not valid: \\\"%s\\\"' % hparam_space_type)\\n\\n config.agent.lr = hparams.lr\\n config.agent.entropy_beta = hparams.entropy_beta\\n\\n if hparam_space_type in ('topk', 'pg-topk'):\\n # topk tuning will be enabled.\\n config.agent.topk = hparams.topk\\n config.agent.topk_loss_hparam = hparams.topk_loss_hparam\\n elif hparam_space_type == 'is':\\n # importance sampling tuning will be enabled.\\n config.agent.replay_temperature = hparams.replay_temperature\\n config.agent.alpha = hparams.alpha\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::make_initialized_variable\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::make_initialized_variable\\ndef make_initialized_variable(value, name, shape=None, dtype=tf.float32):\\n \\\"\\\"\\\"Create a tf.Variable with a constant initializer.\\n\\n Args:\\n value: Constant value to initialize the variable with. This is the value\\n that the variable starts with.\\n name: Name of the variable in the TF graph.\\n shape: Shape of the variable. If None, variable will be a scalar.\\n dtype: Data type of the variable. Should be a TF dtype. Defaults to\\n tf.float32.\\n\\n Returns:\\n tf.Variable instance.\\n \\\"\\\"\\\"\\n if shape is None:\\n shape = []\\n return tf.get_variable(\\n name=name, shape=shape, initializer=tf.constant_initializer(value),\\n dtype=dtype, trainable=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.__init__\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.__init__\\n def __init__(self, config, task_id, ps_tasks, num_workers, is_chief=True,\\n summary_writer=None,\\n dtype=tf.float32,\\n summary_interval=1,\\n run_number=0,\\n logging_dir='/tmp', model_v=0):\\n self.config = config\\n self.data_manager = data.DataManager(\\n config, run_number=run_number,\\n do_code_simplification=not FLAGS.stop_on_success)\\n self.task_id = task_id\\n self.ps_tasks = ps_tasks\\n self.is_chief = is_chief\\n if ps_tasks == 0:\\n assert task_id == 0, 'No parameter servers specified. Expecting 1 task.'\\n assert num_workers == 1, (\\n 'No parameter servers specified. Expecting 1 task.')\\n worker_device = '/job:localhost/replica:%d/task:0/cpu:0' % task_id\\n # worker_device = '/cpu:0'\\n # ps_device = '/cpu:0'\\n else:\\n assert num_workers > 0, 'There must be at least 1 training worker.'\\n worker_device = '/job:worker/replica:%d/task:0/cpu:0' % task_id\\n # ps_device = '/job:ps/replica:0/task:0/cpu:0'\\n logging.info('worker_device: %s', worker_device)\\n\\n logging_file = os.path.join(\\n logging_dir, 'solutions_%d.txt' % task_id)\\n experience_replay_file = os.path.join(\\n logging_dir, 'replay_buffer_%d.pickle' % task_id)\\n self.topk_file = os.path.join(\\n logging_dir, 'topk_buffer_%d.pickle' % task_id)\\n\\n tf.get_variable_scope().set_use_resource(True)\\n\\n # global model\\n with tf.device(tf.train.replica_device_setter(ps_tasks,\\n ps_device='/job:ps/replica:0',\\n worker_device=worker_device)):\\n with tf.variable_scope('global'):\\n global_model = agent_lib.LMAgent(config, dtype=dtype, is_local=False)\\n global_params_dict = {p.name: p\\n for p in global_model.sync_variables}\\n self.global_model = global_mod\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.__init__._global_to_local_scope\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.__init__._global_to_local_scope\\n def _global_to_local_scope(name):\\n assert name.startswith('global/')\\n return 'local' + name[6:]\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.initialize\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.initialize\\n def initialize(self, session):\\n \\\"\\\"\\\"Run initialization ops.\\\"\\\"\\\"\\n session.run(self.local_init_op)\\n session.run(self.sync_op)\\n self.cached_global_step, self.cached_global_npe = session.run(\\n [self.global_step, self.program_count])\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.update_global_model\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.update_global_model\\n def update_global_model(self, session):\\n \\\"\\\"\\\"Run an update step.\\n\\n 1) Asynchronously copy global weights to local model.\\n 2) Call into local model's update_step method, which does the following:\\n a) Sample batch of programs from policy.\\n b) Compute rewards.\\n c) Compute gradients and update the global model asynchronously.\\n 3) Write tensorboard summaries to disk.\\n\\n Args:\\n session: tf.Session instance.\\n \\\"\\\"\\\"\\n session.run(self.sync_op) # Copy weights from global to local.\\n\\n with session.as_default():\\n result = self.model.update_step(\\n session, self.data_manager.sample_rl_batch(), self.train_op,\\n self.global_step)\\n global_step = result.global_step\\n global_npe = result.global_npe\\n summaries = result.summaries_list\\n self.cached_global_step = global_step\\n self.cached_global_npe = global_npe\\n self.local_step += 1\\n\\n if self.summary_writer and self.local_step % self.summary_interval == 0:\\n if not isinstance(summaries, (tuple, list)):\\n summaries = [summaries]\\n summaries.append(self._local_step_summary())\\n if self.is_chief:\\n (global_best_reward,\\n found_solution_flag,\\n program_count) = session.run(\\n [self.global_best_reward,\\n self.found_solution_flag,\\n self.program_count])\\n summaries.append(\\n tf.Summary(\\n value=[tf.Summary.Value(\\n tag='model/best_reward',\\n simple_value=global_best_reward)]))\\n summaries.append(\\n tf.Summary(\\n value=[tf.Summary.Value(\\n tag='model/solution_found',\\n simple_value=int(found_solution_flag))]))\\n summaries.append(\\n tf.Summary(\\n value=[tf.Summary.Value(\\n tag='model/program_count',\\n simple_\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer._local_step_summary\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer._local_step_summary\\n def _local_step_summary(self):\\n \\\"\\\"\\\"Compute number of local steps per time increment.\\\"\\\"\\\"\\n dt = time.time() - self.last_summary_time\\n steps_per_time = self.summary_interval / float(dt)\\n return tf.Summary(value=[\\n tf.Summary.Value(\\n tag='local_step/per_sec',\\n simple_value=steps_per_time),\\n tf.Summary.Value(\\n tag='local_step/step',\\n simple_value=self.local_step)])\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.maybe_save_best_model\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.maybe_save_best_model\\n def maybe_save_best_model(self, session, saver, checkpoint_file):\\n \\\"\\\"\\\"Check if this model got the highest reward and save to disk if so.\\\"\\\"\\\"\\n if self.is_chief and session.run(self.is_best_model):\\n logging.info('Saving best model to \\\"%s\\\"', checkpoint_file)\\n saver.save(session, checkpoint_file)\\n session.run(self.reset_is_best_model)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.save_replay_buffer\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.save_replay_buffer\\n def save_replay_buffer(self):\\n \\\"\\\"\\\"Save replay buffer to disk.\\n\\n Call this periodically so that training can recover if jobs go down.\\n \\\"\\\"\\\"\\n if self.model.experience_replay is not None:\\n logging.info('Saving experience replay buffer to \\\"%s\\\".',\\n self.model.experience_replay.save_file)\\n self.model.experience_replay.incremental_save(True)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.delete_replay_buffer\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.delete_replay_buffer\\n def delete_replay_buffer(self):\\n \\\"\\\"\\\"Delete replay buffer from disk.\\n\\n Call this at the end of training to clean up. Replay buffer can get very\\n large.\\n \\\"\\\"\\\"\\n if self.model.experience_replay is not None:\\n logging.info('Deleting experience replay buffer at \\\"%s\\\".',\\n self.model.experience_replay.save_file)\\n tf.gfile.Remove(self.model.experience_replay.save_file)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.save_topk_buffer\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::AsyncTrainer.save_topk_buffer\\n def save_topk_buffer(self):\\n \\\"\\\"\\\"Save top-k buffer to disk.\\n\\n Call this periodically so that training can recover if jobs go down.\\n \\\"\\\"\\\"\\n if self.model.top_episodes is not None:\\n logging.info('Saving top-k buffer to \\\"%s\\\".', self.topk_file)\\n # Overwrite previous data each time.\\n with tf.gfile.FastGFile(self.topk_file, 'w') as f:\\n f.write(cPickle.dumps(self.model.top_episodes))\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::managed_session\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::managed_session\\n@contextlib.contextmanager\\ndef managed_session(sv, master='', config=None,\\n start_standard_services=True,\\n close_summary_writer=True,\\n max_wait_secs=7200):\\n # Same as Supervisor.managed_session, but with configurable timeout.\\n try:\\n sess = sv.prepare_or_wait_for_session(\\n master=master, config=config,\\n start_standard_services=start_standard_services,\\n max_wait_secs=max_wait_secs)\\n yield sess\\n except tf.errors.DeadlineExceededError:\\n raise\\n except Exception as e: # pylint: disable=broad-except\\n sv.request_stop(e)\\n finally:\\n try:\\n # Request all the threads to stop and wait for them to do so. Any\\n # exception raised by the threads is raised again from stop().\\n # Passing stop_grace_period_secs is for blocked enqueue/dequeue\\n # threads which are not checking for `should_stop()`. They\\n # will be stopped when we close the session further down.\\n sv.stop(close_summary_writer=close_summary_writer)\\n finally:\\n # Close the session to finish up all pending calls. We do not care\\n # about exceptions raised when closing. This takes care of\\n # blocked enqueue/dequeue calls.\\n try:\\n sess.close()\\n except Exception: # pylint: disable=broad-except\\n # Silently ignore exceptions raised by close().\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::train\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::train\\ndef train(config, is_chief, tuner=None, run_dir=None, run_number=0,\\n results_writer=None):\\n \\\"\\\"\\\"Run training loop.\\n\\n Args:\\n config: config_lib.Config instance containing global config (agent and env).\\n is_chief: True if this worker is chief. Chief worker manages writing some\\n data to disk and initialization of the global model.\\n tuner: A tuner instance. If not tuning, leave as None.\\n run_dir: Directory where all data for this run will be written. If None,\\n run_dir = FLAGS.logdir. Set this argument when doing multiple runs.\\n run_number: Which run is this.\\n results_writer: Managest writing training results to disk. Results are a\\n dict of metric names and values.\\n\\n Returns:\\n The trainer object used to run training updates.\\n \\\"\\\"\\\"\\n logging.info('Will run asynchronous training.')\\n\\n if run_dir is None:\\n run_dir = FLAGS.logdir\\n train_dir = os.path.join(run_dir, 'train')\\n best_model_checkpoint = os.path.join(train_dir, 'best.ckpt')\\n events_dir = '%s/events_%d' % (run_dir, FLAGS.task_id)\\n logging.info('Events directory: %s', events_dir)\\n\\n logging_dir = os.path.join(run_dir, 'logs')\\n if not tf.gfile.Exists(logging_dir):\\n tf.gfile.MakeDirs(logging_dir)\\n status_file = os.path.join(logging_dir, 'status.txt')\\n\\n if FLAGS.summary_tasks and FLAGS.task_id < FLAGS.summary_tasks:\\n summary_writer = tf.summary.FileWriter(events_dir)\\n else:\\n summary_writer = None\\n\\n # Only profile task 0.\\n if FLAGS.do_profiling:\\n logging.info('Profiling enabled')\\n profiler = cProfile.Profile()\\n profiler.enable()\\n else:\\n profiler = None\\n\\n trainer = AsyncTrainer(\\n config, FLAGS.task_id, FLAGS.ps_tasks, FLAGS.num_workers,\\n is_chief=is_chief,\\n summary_interval=FLAGS.summary_interval,\\n summary_writer=summary_writer,\\n logging_dir=logging_dir,\\n run_number=run_number,\\n model_v=FLAGS.model_v)\\n\\n variables_to_save = [v for v in tf.global_var\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::train.init_fn\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::train.init_fn\\n def init_fn(unused_sess):\\n logging.info('No checkpoint found. Initialized global params.')\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/pg_train.py::run_training\",\n \"text\": \"research/brain_coder/single_task/pg_train.py::run_training\\ndef run_training(config=None, tuner=None, logdir=None, trial_name=None,\\n is_chief=True):\\n \\\"\\\"\\\"Do all training runs.\\n\\n This is the top level training function for policy gradient based models.\\n Run this from the main function.\\n\\n Args:\\n config: config_lib.Config instance containing global config (agent and\\n environment hparams). If None, config will be parsed from FLAGS.config.\\n tuner: A tuner instance. Leave as None if not tuning.\\n logdir: Parent directory where all data from all runs will be written. If\\n None, FLAGS.logdir will be used.\\n trial_name: If tuning, set this to a unique string that identifies this\\n trial. If `tuner` is not None, this also must be set.\\n is_chief: True if this worker is the chief.\\n\\n Returns:\\n List of results dicts which were written to disk. Each training run gets a\\n results dict. Results dict contains metrics, i.e. (name, value) pairs which\\n give information about the training run.\\n\\n Raises:\\n ValueError: If results dicts read from disk contain invalid data.\\n \\\"\\\"\\\"\\n if not config:\\n # If custom config is not given, get it from flags.\\n config = defaults.default_config_with_updates(FLAGS.config)\\n if not logdir:\\n logdir = FLAGS.logdir\\n if not tf.gfile.Exists(logdir):\\n tf.gfile.MakeDirs(logdir)\\n assert FLAGS.num_repetitions > 0\\n results = results_lib.Results(logdir)\\n results_list, _ = results.read_all()\\n\\n logging.info('Starting experiment. Directory: \\\"%s\\\"', logdir)\\n\\n if results_list:\\n if results_list[0]['max_npe'] != FLAGS.max_npe:\\n raise ValueError(\\n 'Cannot resume training. Max-NPE changed. Was %s, now %s',\\n results_list[0]['max_npe'], FLAGS.max_npe)\\n if results_list[0]['max_global_repetitions'] != FLAGS.num_repetitions:\\n raise ValueError(\\n 'Cannot resume training. Number of repetitions changed. Was %s, '\\n 'now %s',\\n results_list[0]['max_g\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.__init__\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.__init__\\n def __init__(self, name):\\n self._variables = []\\n self._subblocks = []\\n self._called = False\\n\\n # Intentionally distinguishing empty string and None.\\n # If name is an empty string, then do not use name scope.\\n self.name = name if name is not None else self.__class__.__name__\\n self._graph = tf.get_default_graph()\\n\\n if self.name:\\n # Capture the scope string at the init time.\\n with self._graph.name_scope(self.name) as scope:\\n self._scope_str = scope\\n else:\\n self._scope_str = ''\\n\\n # Maintain hierarchy structure of blocks.\\n self._stack = _block_stacks[self._graph]\\n if self.__class__ is BlockBase:\\n # This code is only executed to create the root, which starts in the\\n # initialized state.\\n assert not self._stack\\n self._parent = None\\n self._called = True # The root is initialized.\\n return\\n\\n # Create a fake root if a root is not already present.\\n if not self._stack:\\n self._stack.append(BlockBase('NoOpRoot'))\\n\\n self._parent = self._stack[-1]\\n self._parent._subblocks.append(self) # pylint: disable=protected-access\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.__repr__\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.__repr__\\n def __repr__(self):\\n return '\\\"{}\\\" ({})'.format(self._scope_str, self.__class__.__name__)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase._OptionalNameScope\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase._OptionalNameScope\\n @contextlib.contextmanager\\n def _OptionalNameScope(self, scope_str):\\n if scope_str:\\n with self._graph.name_scope(scope_str):\\n yield\\n else:\\n yield\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase._BlockScope\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase._BlockScope\\n @contextlib.contextmanager\\n def _BlockScope(self):\\n \\\"\\\"\\\"Context manager that handles graph, namescope, and nested blocks.\\\"\\\"\\\"\\n self._stack.append(self)\\n\\n try:\\n with self._graph.as_default():\\n with self._OptionalNameScope(self._scope_str):\\n yield self\\n finally: # Pop from the stack no matter exception is raised or not.\\n # The following line is executed when leaving 'with self._BlockScope()'\\n self._stack.pop()\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.__call__\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.__call__\\n def __call__(self, *args, **kwargs):\\n assert self._stack is _block_stacks[self._graph]\\n\\n with self._BlockScope():\\n ret = self._Apply(*args, **kwargs)\\n\\n self._called = True\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase._Apply\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase._Apply\\n def _Apply(self, *args, **kwargs):\\n \\\"\\\"\\\"Implementation of __call__().\\\"\\\"\\\"\\n raise NotImplementedError()\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.NewVar\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.NewVar\\n def NewVar(self, value, **kwargs):\\n \\\"\\\"\\\"Creates a new variable.\\n\\n This function creates a variable, then returns a local copy created by\\n Identity operation. To get the Variable class object, use LookupRef()\\n method.\\n\\n Note that each time Variable class object is used as an input to an\\n operation, Tensorflow will create a new Send/Recv pair. This hurts\\n performance.\\n\\n If not for assign operations, use the local copy returned by this method.\\n\\n Args:\\n value: Initialization value of the variable. The shape and the data type\\n of the variable is determined by this initial value.\\n **kwargs: Extra named arguments passed to Variable.__init__().\\n\\n Returns:\\n A local copy of the new variable.\\n \\\"\\\"\\\"\\n v = tf.Variable(value, **kwargs)\\n\\n self._variables.append(v)\\n return v\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.initialized\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.initialized\\n @property\\n def initialized(self):\\n \\\"\\\"\\\"Returns bool if the block is initialized.\\n\\n By default, BlockBase assumes that a block is initialized when __call__()\\n is executed for the first time. If this is an incorrect assumption for some\\n subclasses, override this property in those subclasses.\\n\\n Returns:\\n True if initialized, False otherwise.\\n \\\"\\\"\\\"\\n return self._called\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.AssertInitialized\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.AssertInitialized\\n def AssertInitialized(self):\\n \\\"\\\"\\\"Asserts initialized property.\\\"\\\"\\\"\\n if not self.initialized:\\n raise RuntimeError('{} has not been initialized.'.format(self))\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.VariableList\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.VariableList\\n def VariableList(self):\\n \\\"\\\"\\\"Returns the list of all tensorflow variables used inside this block.\\\"\\\"\\\"\\n variables = list(itertools.chain(\\n itertools.chain.from_iterable(\\n t.VariableList() for t in self._subblocks),\\n self._VariableList()))\\n return variables\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase._VariableList\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase._VariableList\\n def _VariableList(self):\\n \\\"\\\"\\\"Returns the list of all tensorflow variables owned by this block.\\\"\\\"\\\"\\n self.AssertInitialized()\\n return self._variables\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.CreateWeightLoss\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.CreateWeightLoss\\n def CreateWeightLoss(self):\\n \\\"\\\"\\\"Returns L2 loss list of (almost) all variables used inside this block.\\n\\n When this method needs to be overridden, there are two choices.\\n\\n 1. Override CreateWeightLoss() to change the weight loss of all variables\\n that belong to this block, both directly and indirectly.\\n 2. Override _CreateWeightLoss() to change the weight loss of all\\n variables that directly belong to this block but not to the sub-blocks.\\n\\n Returns:\\n A Tensor object or None.\\n \\\"\\\"\\\"\\n losses = list(itertools.chain(\\n itertools.chain.from_iterable(\\n t.CreateWeightLoss() for t in self._subblocks),\\n self._CreateWeightLoss()))\\n return losses\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase._CreateWeightLoss\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase._CreateWeightLoss\\n def _CreateWeightLoss(self):\\n \\\"\\\"\\\"Returns weight loss list of variables that belong to this block.\\\"\\\"\\\"\\n self.AssertInitialized()\\n with self._BlockScope():\\n return [tf.nn.l2_loss(v) for v in self._variables]\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.CreateUpdateOps\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.CreateUpdateOps\\n def CreateUpdateOps(self):\\n \\\"\\\"\\\"Creates update operations for this block and its sub-blocks.\\\"\\\"\\\"\\n ops = list(itertools.chain(\\n itertools.chain.from_iterable(\\n t.CreateUpdateOps() for t in self._subblocks),\\n self._CreateUpdateOps()))\\n return ops\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase._CreateUpdateOps\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase._CreateUpdateOps\\n def _CreateUpdateOps(self):\\n \\\"\\\"\\\"Creates update operations for this block.\\\"\\\"\\\"\\n self.AssertInitialized()\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.MarkAsNonTrainable\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::BlockBase.MarkAsNonTrainable\\n def MarkAsNonTrainable(self):\\n \\\"\\\"\\\"Mark all the variables of this block as non-trainable.\\n\\n All the variables owned directly or indirectly (through subblocks) are\\n marked as non trainable.\\n\\n This function along with CheckpointInitOp can be used to load a pretrained\\n model that consists in only one part of the whole graph.\\n \\\"\\\"\\\"\\n assert self._called\\n\\n all_variables = self.VariableList()\\n collection = tf.get_collection_ref(tf.GraphKeys.TRAINABLE_VARIABLES)\\n for v in all_variables:\\n if v in collection:\\n collection.remove(v)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::CreateWeightLoss\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::CreateWeightLoss\\ndef CreateWeightLoss():\\n \\\"\\\"\\\"Returns all weight losses from the blocks in the graph.\\\"\\\"\\\"\\n stack = _block_stacks[tf.get_default_graph()]\\n if not stack:\\n return []\\n return stack[0].CreateWeightLoss()\",\n \"label\": 0\n },\n {\n \"cid\": \"research/compression/entropy_coder/lib/block_base.py::CreateBlockUpdates\",\n \"text\": \"research/compression/entropy_coder/lib/block_base.py::CreateBlockUpdates\\ndef CreateBlockUpdates():\\n \\\"\\\"\\\"Combines all updates from the blocks in the graph.\\\"\\\"\\\"\\n stack = _block_stacks[tf.get_default_graph()]\\n if not stack:\\n return []\\n return stack[0].CreateUpdateOps()\",\n \"label\": 0\n },\n {\n \"cid\": \"research/real_nvp/lsun_formatting.py::_int64_feature\",\n \"text\": \"research/real_nvp/lsun_formatting.py::_int64_feature\\ndef _int64_feature(value):\\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\",\n \"label\": 0\n },\n {\n \"cid\": \"research/real_nvp/lsun_formatting.py::_bytes_feature\",\n \"text\": \"research/real_nvp/lsun_formatting.py::_bytes_feature\\ndef _bytes_feature(value):\\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\",\n \"label\": 0\n },\n {\n \"cid\": \"research/real_nvp/lsun_formatting.py::main\",\n \"text\": \"research/real_nvp/lsun_formatting.py::main\\ndef main():\\n \\\"\\\"\\\"Main converter function.\\\"\\\"\\\"\\n fn_root = FLAGS.fn_root\\n img_fn_list = os.listdir(fn_root)\\n img_fn_list = [img_fn for img_fn in img_fn_list\\n if img_fn.endswith('.webp')]\\n num_examples = len(img_fn_list)\\n\\n n_examples_per_file = 10000\\n for example_idx, img_fn in enumerate(img_fn_list):\\n if example_idx % n_examples_per_file == 0:\\n file_out = \\\"%s_%05d.tfrecords\\\"\\n file_out = file_out % (FLAGS.file_out,\\n example_idx // n_examples_per_file)\\n print(\\\"Writing on:\\\", file_out)\\n writer = tf.python_io.TFRecordWriter(file_out)\\n if example_idx % 1000 == 0:\\n print(example_idx, \\\"/\\\", num_examples)\\n image_raw = numpy.array(Image.open(os.path.join(fn_root, img_fn)))\\n rows = image_raw.shape[0]\\n cols = image_raw.shape[1]\\n depth = image_raw.shape[2]\\n downscale = min(rows / 96., cols / 96.)\\n image_raw = skimage.transform.pyramid_reduce(image_raw, downscale)\\n image_raw *= 255.\\n image_raw = image_raw.astype(\\\"uint8\\\")\\n rows = image_raw.shape[0]\\n cols = image_raw.shape[1]\\n depth = image_raw.shape[2]\\n image_raw = image_raw.tostring()\\n example = tf.train.Example(\\n features=tf.train.Features(\\n feature={\\n \\\"height\\\": _int64_feature(rows),\\n \\\"width\\\": _int64_feature(cols),\\n \\\"depth\\\": _int64_feature(depth),\\n \\\"image_raw\\\": _bytes_feature(image_raw)\\n }\\n )\\n )\\n writer.write(example.SerializeToString())\\n if example_idx % n_examples_per_file == (n_examples_per_file - 1):\\n writer.close()\\n writer.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/syntaxnet/util/registry.py::_GetClass\",\n \"text\": \"research/syntaxnet/syntaxnet/util/registry.py::_GetClass\\ndef _GetClass(name):\\n \\\"\\\"\\\"Looks up a class by name.\\n\\n Args:\\n name: The fully-qualified type name of the class to return.\\n\\n Returns:\\n The class associated with the |name|, or None on error.\\n \\\"\\\"\\\"\\n elements = name.split('.')\\n\\n # Need at least \\\"module.Class\\\".\\n if len(elements) < 2:\\n logging.info('Malformed type: \\\"%s\\\"', name)\\n return None\\n module_path = '.'.join(elements[:-1])\\n class_name = elements[-1]\\n\\n # Import the module.\\n try:\\n __import__(module_path)\\n except ImportError as e:\\n logging.info('Unable to find module \\\"%s\\\": \\\"%s\\\"', module_path, e)\\n return None\\n module = sys.modules[module_path]\\n\\n # Look up the class.\\n if not hasattr(module, class_name):\\n logging.info('Name \\\"%s\\\" not found in module: \\\"%s\\\"', class_name, module_path)\\n return None\\n class_obj = getattr(module, class_name)\\n\\n # Check that it is actually a class.\\n if not inspect.isclass(class_obj):\\n logging.info('Name does not refer to a class: \\\"%s\\\"', name)\\n return None\\n return class_obj\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/syntaxnet/util/registry.py::_Create\",\n \"text\": \"research/syntaxnet/syntaxnet/util/registry.py::_Create\\ndef _Create(baseclass, subclass_name, *args, **kwargs):\\n \\\"\\\"\\\"Creates an instance of a named subclass.\\n\\n Args:\\n baseclass: The expected base class.\\n subclass_name: The fully-qualified type name of the subclass to create.\\n *args: Passed to the subclass constructor.\\n **kwargs: Passed to the subclass constructor.\\n\\n Returns:\\n An instance of the named subclass, or None on error.\\n \\\"\\\"\\\"\\n subclass = _GetClass(subclass_name)\\n if subclass is None:\\n return None # _GetClass() already logged an error\\n if not issubclass(subclass, baseclass):\\n logging.info('Class \\\"%s\\\" is not a subclass of \\\"%s\\\"', subclass_name,\\n baseclass.__name__)\\n return None\\n return subclass(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/syntaxnet/util/registry.py::_ResolveAndCreate\",\n \"text\": \"research/syntaxnet/syntaxnet/util/registry.py::_ResolveAndCreate\\ndef _ResolveAndCreate(baseclass, path, subclass_name, *args, **kwargs):\\n \\\"\\\"\\\"Resolves the name of a subclass and creates an instance of it.\\n\\n The subclass is resolved with respect to a package path in an inside-out\\n manner. For example, if |path| is 'syntaxnet.foo.bar' and |subclass_name| is\\n 'baz.ClassName', then attempts are made to create instances of the following\\n fully-qualified class names:\\n\\n 'syntaxnet.foo.bar.baz.ClassName'\\n 'syntaxnet.foo.baz.ClassName'\\n 'syntaxnet.baz.ClassName'\\n 'baz.ClassName'\\n\\n An instance corresponding to the first successful attempt is returned.\\n\\n Args:\\n baseclass: The expected base class.\\n path: The path to use to resolve the subclass.\\n subclass_name: The name of the subclass to create.\\n *args: Passed to the subclass constructor.\\n **kwargs: Passed to the subclass constructor.\\n\\n Returns:\\n An instance of the named subclass corresponding to the inner-most successful\\n name resolution, or None if the name could not be resolved.\\n\\n Raises:\\n ValueError: If the subclass cannot be resolved and created.\\n \\\"\\\"\\\"\\n elements = path.split('.')\\n while True:\\n resolved_subclass_name = '.'.join(elements + [subclass_name])\\n logging.info('Attempting to instantiate \\\"%s\\\"', resolved_subclass_name)\\n subclass = _Create(baseclass, resolved_subclass_name, *args, **kwargs)\\n if subclass:\\n return subclass # success\\n if not elements:\\n break # no more paths to try\\n elements.pop() # try resolving against the next-outer path\\n raise ValueError(\\n 'Failed to create subclass \\\"%s\\\" of base class %s using path %s' %\\n (subclass_name, baseclass.__name__, path))\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/syntaxnet/util/registry.py::RegisteredClass\",\n \"text\": \"research/syntaxnet/syntaxnet/util/registry.py::RegisteredClass\\ndef RegisteredClass(baseclass):\\n \\\"\\\"\\\"Decorates the |baseclass| with a static Create() method.\\\"\\\"\\\"\\n assert not hasattr(baseclass, 'Create')\\n\\n def Create(subclass_name, *args, **kwargs):\\n \\\"\\\"\\\"A wrapper around _Create() that curries the |baseclass|.\\\"\\\"\\\"\\n path = inspect.getmodule(baseclass).__name__\\n return _ResolveAndCreate(baseclass, path, subclass_name, *args, **kwargs)\\n\\n baseclass.Create = staticmethod(Create)\\n return baseclass\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/syntaxnet/util/registry.py::RegisteredClass.Create\",\n \"text\": \"research/syntaxnet/syntaxnet/util/registry.py::RegisteredClass.Create\\n def Create(subclass_name, *args, **kwargs):\\n \\\"\\\"\\\"A wrapper around _Create() that curries the |baseclass|.\\\"\\\"\\\"\\n path = inspect.getmodule(baseclass).__name__\\n return _ResolveAndCreate(baseclass, path, subclass_name, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::ImageQueue.__init__\",\n \"text\": \"research/tcn/dataset/webcam.py::ImageQueue.__init__\\n def __init__(self):\\n self.lock = multiprocessing.Lock()\\n self._queue = multiprocessing.Queue(maxsize=1)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::ImageQueue.append\",\n \"text\": \"research/tcn/dataset/webcam.py::ImageQueue.append\\n def append(self, data):\\n with self.lock:\\n if self._queue.full():\\n # Pop the first element.\\n _ = self._queue.get()\\n self._queue.put(data)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::ImageQueue.get\",\n \"text\": \"research/tcn/dataset/webcam.py::ImageQueue.get\\n def get(self):\\n with self.lock:\\n return self._queue.get()\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::ImageQueue.empty\",\n \"text\": \"research/tcn/dataset/webcam.py::ImageQueue.empty\\n def empty(self):\\n return self._queue.empty()\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::ImageQueue.close\",\n \"text\": \"research/tcn/dataset/webcam.py::ImageQueue.close\\n def close(self):\\n return self._queue.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::WebcamViewer.__init__\",\n \"text\": \"research/tcn/dataset/webcam.py::WebcamViewer.__init__\\n def __init__(self, display_queues):\\n \\\"\\\"\\\"Create a WebcamViewer instance.\\\"\\\"\\\"\\n self.height = FLAGS.height\\n self.width = FLAGS.width\\n self.queues = display_queues\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::WebcamViewer._get_next_images\",\n \"text\": \"research/tcn/dataset/webcam.py::WebcamViewer._get_next_images\\n def _get_next_images(self):\\n \\\"\\\"\\\"Gets the next image to display.\\\"\\\"\\\"\\n # Wait for one image per view.\\n not_found = True\\n while not_found:\\n if True in [q.empty() for q in self.queues]:\\n # At least one image queue is empty; wait.\\n continue\\n else:\\n # Retrieve the images.\\n latest = [q.get() for q in self.queues]\\n combined = np.concatenate(latest, axis=1)\\n not_found = False\\n return combined\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::WebcamViewer.run\",\n \"text\": \"research/tcn/dataset/webcam.py::WebcamViewer.run\\n def run(self):\\n \\\"\\\"\\\"Displays the Kcam live stream in a window.\\n\\n This function blocks until the window is closed.\\n \\\"\\\"\\\"\\n fig, rgb_axis = plt.subplots()\\n\\n image_rows = self.height\\n image_cols = self.width * FLAGS.num_views\\n initial_image = np.zeros((image_rows, image_cols, 3))\\n rgb_image = rgb_axis.imshow(initial_image, interpolation='nearest')\\n\\n def update_figure(frame_index):\\n \\\"\\\"\\\"Animation function for matplotlib FuncAnimation. Updates the image.\\n\\n Args:\\n frame_index: The frame number.\\n Returns:\\n An iterable of matplotlib drawables to clear.\\n \\\"\\\"\\\"\\n _ = frame_index\\n images = self._get_next_images()\\n images = images[..., [2, 1, 0]]\\n rgb_image.set_array(images)\\n return rgb_image,\\n\\n # We must keep a reference to this animation in order for it to work.\\n unused_animation = animation.FuncAnimation(\\n fig, update_figure, interval=50, blit=True)\\n mng = plt.get_current_fig_manager()\\n mng.resize(*mng.window.maxsize())\\n plt.show()\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::WebcamViewer.run.update_figure\",\n \"text\": \"research/tcn/dataset/webcam.py::WebcamViewer.run.update_figure\\n def update_figure(frame_index):\\n \\\"\\\"\\\"Animation function for matplotlib FuncAnimation. Updates the image.\\n\\n Args:\\n frame_index: The frame number.\\n Returns:\\n An iterable of matplotlib drawables to clear.\\n \\\"\\\"\\\"\\n _ = frame_index\\n images = self._get_next_images()\\n images = images[..., [2, 1, 0]]\\n rgb_image.set_array(images)\\n return rgb_image,\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::reconcile\",\n \"text\": \"research/tcn/dataset/webcam.py::reconcile\\ndef reconcile(queues, write_queue):\\n \\\"\\\"\\\"Gets a list of concurrent images from each view queue.\\n\\n This waits for latest images to be available in all view queues,\\n then continuously:\\n - Creates a list of current images for each view.\\n - Writes the list to a queue of image lists to write to disk.\\n Args:\\n queues: A list of `ImageQueues`, holding the latest image from each webcam.\\n write_queue: A multiprocessing.Queue holding lists of concurrent images.\\n \\\"\\\"\\\"\\n # Loop forever.\\n while True:\\n # Wait till all queues have an image.\\n if True in [q.empty() for q in queues]:\\n continue\\n else:\\n # Retrieve all views' images.\\n latest = [q.get() for q in queues]\\n # Copy the list of all concurrent images to the write queue.\\n write_queue.put(latest)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::persist\",\n \"text\": \"research/tcn/dataset/webcam.py::persist\\ndef persist(write_queue, view_dirs):\\n \\\"\\\"\\\"Pulls lists of concurrent images off a write queue, writes them to disk.\\n\\n Args:\\n write_queue: A multiprocessing.Queue holding lists of concurrent images;\\n one image per view.\\n view_dirs: A list of strings, holding the output image directories for each\\n view.\\n \\\"\\\"\\\"\\n timestep = 0\\n while True:\\n # Wait till there is work in the queue.\\n if write_queue.empty():\\n continue\\n # Get a list of concurrent images to write to disk.\\n view_ims = write_queue.get()\\n for view_idx, image in enumerate(view_ims):\\n view_base = view_dirs[view_idx]\\n # Assign all concurrent view images the same sequence timestep.\\n fname = os.path.join(view_base, '%s.png' % str(timestep).zfill(10))\\n cv2.imwrite(fname, image)\\n # Move to the next timestep.\\n timestep += 1\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::get_image\",\n \"text\": \"research/tcn/dataset/webcam.py::get_image\\ndef get_image(camera):\\n \\\"\\\"\\\"Captures a single image from the camera and returns it in PIL format.\\\"\\\"\\\"\\n data = camera.read()\\n _, im = data\\n return im\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::capture_webcam\",\n \"text\": \"research/tcn/dataset/webcam.py::capture_webcam\\ndef capture_webcam(camera, display_queue, reconcile_queue):\\n \\\"\\\"\\\"Captures images from simultaneous webcams, writes them to queues.\\n\\n Args:\\n camera: A cv2.VideoCapture object representing an open webcam stream.\\n display_queue: An ImageQueue.\\n reconcile_queue: An ImageQueue.\\n \\\"\\\"\\\"\\n # Take some ramp images to allow cams to adjust for brightness etc.\\n for i in range(60):\\n tf.logging.info('Taking ramp image %d.' % i)\\n get_image(camera)\\n\\n cnt = 0\\n start = time.time()\\n while True:\\n # Get images for all cameras.\\n im = get_image(camera)\\n # Replace the current image in the display and reconcile queues.\\n display_queue.append(im)\\n reconcile_queue.append(im)\\n cnt += 1\\n current = time.time()\\n if cnt % 100 == 0:\\n tf.logging.info('Collected %s of video, %d frames at ~%.2f fps.' % (\\n timer(start, current), cnt, cnt/(current-start)))\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::timer\",\n \"text\": \"research/tcn/dataset/webcam.py::timer\\ndef timer(start, end):\\n \\\"\\\"\\\"Returns a formatted time elapsed.\\\"\\\"\\\"\\n hours, rem = divmod(end-start, 3600)\\n minutes, seconds = divmod(rem, 60)\\n return '{:0>2}:{:0>2}:{:05.2f}'.format(int(hours), int(minutes), seconds)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::display_webcams\",\n \"text\": \"research/tcn/dataset/webcam.py::display_webcams\\ndef display_webcams(display_queues):\\n \\\"\\\"\\\"Builds an WebcamViewer to animate incoming images, runs it.\\\"\\\"\\\"\\n viewer = WebcamViewer(display_queues)\\n viewer.run()\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::create_vids\",\n \"text\": \"research/tcn/dataset/webcam.py::create_vids\\ndef create_vids(view_dirs, seqname):\\n \\\"\\\"\\\"Creates one video per view per sequence.\\\"\\\"\\\"\\n vidbase = os.path.join(FLAGS.viddir, FLAGS.dataset, FLAGS.mode)\\n if not os.path.exists(vidbase):\\n os.makedirs(vidbase)\\n vidpaths = []\\n for idx, view_dir in enumerate(view_dirs):\\n vidname = os.path.join(vidbase, '%s_view%d.mp4' % (seqname, idx))\\n encode_vid_cmd = r'mencoder mf://%s/*.png \\\\\\n -mf fps=29:type=png \\\\\\n -ovc lavc -lavcopts vcodec=mpeg4:mbd=2:trell \\\\\\n -oac copy -o %s' % (view_dir, vidname)\\n os.system(encode_vid_cmd)\\n vidpaths.append(vidname)\\n\\n debugpath = None\\n if FLAGS.debug_vids:\\n lhs = vidpaths[FLAGS.debug_lhs_view]\\n rhs = vidpaths[FLAGS.debug_rhs_view]\\n debug_base = os.path.join('%s_debug' % FLAGS.viddir, FLAGS.dataset,\\n FLAGS.mode)\\n if not os.path.exists(debug_base):\\n os.makedirs(debug_base)\\n debugpath = '%s/%s.mp4' % (debug_base, seqname)\\n os.system(r\\\"avconv \\\\\\n -i %s \\\\\\n -i %s \\\\\\n -filter_complex '[0:v]pad=iw*2:ih[int];[int][1:v]overlay=W/2:0[vid]' \\\\\\n -map [vid] \\\\\\n -c:v libx264 \\\\\\n -crf 23 \\\\\\n -preset veryfast \\\\\\n %s\\\" % (lhs, rhs, debugpath))\\n\\n return vidpaths, debugpath\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::setup_paths\",\n \"text\": \"research/tcn/dataset/webcam.py::setup_paths\\ndef setup_paths():\\n \\\"\\\"\\\"Sets up the necessary paths to collect videos.\\\"\\\"\\\"\\n assert FLAGS.dataset\\n assert FLAGS.mode\\n assert FLAGS.num_views\\n\\n # Setup directory for final images used to create videos for this sequence.\\n tmp_imagedir = os.path.join(FLAGS.tmp_imagedir, FLAGS.dataset, FLAGS.mode)\\n if not os.path.exists(tmp_imagedir):\\n os.makedirs(tmp_imagedir)\\n\\n # Create a base directory to hold all sequence videos if it doesn't exist.\\n vidbase = os.path.join(FLAGS.viddir, FLAGS.dataset, FLAGS.mode)\\n if not os.path.exists(vidbase):\\n os.makedirs(vidbase)\\n\\n # Get one directory per concurrent view and a sequence name.\\n view_dirs, seqname = get_view_dirs(vidbase, tmp_imagedir)\\n\\n # Get an output path to each view's video.\\n vid_paths = []\\n for idx, _ in enumerate(view_dirs):\\n vid_path = os.path.join(vidbase, '%s_view%d.mp4' % (seqname, idx))\\n vid_paths.append(vid_path)\\n\\n # Optionally build paths to debug_videos.\\n debug_path = None\\n if FLAGS.debug_vids:\\n debug_base = os.path.join('%s_debug' % FLAGS.viddir, FLAGS.dataset,\\n FLAGS.mode)\\n if not os.path.exists(debug_base):\\n os.makedirs(debug_base)\\n debug_path = '%s/%s.mp4' % (debug_base, seqname)\\n\\n return view_dirs, vid_paths, debug_path\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::get_view_dirs\",\n \"text\": \"research/tcn/dataset/webcam.py::get_view_dirs\\ndef get_view_dirs(vidbase, tmp_imagedir):\\n \\\"\\\"\\\"Creates and returns one view directory per webcam.\\\"\\\"\\\"\\n # Create and append a sequence name.\\n if FLAGS.seqname:\\n seqname = FLAGS.seqname\\n else:\\n # If there's no video directory, this is the first sequence.\\n if not os.listdir(vidbase):\\n seqname = '0'\\n else:\\n # Otherwise, get the latest sequence name and increment it.\\n seq_names = [i.split('_')[0] for i in os.listdir(vidbase)]\\n latest_seq = sorted(map(int, seq_names), reverse=True)[0]\\n seqname = str(latest_seq+1)\\n tf.logging.info('No seqname specified, using: %s' % seqname)\\n view_dirs = [os.path.join(\\n tmp_imagedir, '%s_view%d' % (seqname, v)) for v in range(FLAGS.num_views)]\\n for d in view_dirs:\\n if not os.path.exists(d):\\n os.makedirs(d)\\n return view_dirs, seqname\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::get_cameras\",\n \"text\": \"research/tcn/dataset/webcam.py::get_cameras\\ndef get_cameras():\\n \\\"\\\"\\\"Opens cameras using cv2, ensures they can take images.\\\"\\\"\\\"\\n # Try to get free webcam ports.\\n if FLAGS.webcam_ports:\\n ports = map(int, FLAGS.webcam_ports.split(','))\\n else:\\n ports = range(FLAGS.num_views)\\n cameras = [cv2.VideoCapture(i) for i in ports]\\n\\n if not all([i.isOpened() for i in cameras]):\\n try:\\n # Try to find and kill hanging cv2 process_ids.\\n output = subprocess.check_output(['lsof -t /dev/video*'], shell=True)\\n tf.logging.info('Found hanging cv2 process_ids: \\\\n')\\n tf.logging.info(output)\\n tf.logging.info('Killing hanging processes...')\\n for process_id in output.split('\\\\n')[:-1]:\\n subprocess.call(['kill %s' % process_id], shell=True)\\n time.sleep(3)\\n # Recapture webcams.\\n cameras = [cv2.VideoCapture(i) for i in ports]\\n except subprocess.CalledProcessError:\\n raise ValueError(\\n 'Cannot connect to cameras. Try running: \\\\n'\\n 'ls -ltrh /dev/video* \\\\n '\\n 'to see which ports your webcams are connected to. Then hand those '\\n 'ports as a comma-separated list to --webcam_ports, e.g. '\\n '--webcam_ports 0,1')\\n\\n # Verify each camera is able to capture images.\\n ims = map(get_image, cameras)\\n assert False not in [i is not None for i in ims]\\n return cameras\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::launch_images_to_videos\",\n \"text\": \"research/tcn/dataset/webcam.py::launch_images_to_videos\\ndef launch_images_to_videos(view_dirs, vid_paths, debug_path):\\n \\\"\\\"\\\"Launch job in separate process to convert images to videos.\\\"\\\"\\\"\\n\\n f = 'learning/brain/research/tcn/dataset/images_to_videos.py'\\n cmd = ['python %s ' % f]\\n cmd += ['--view_dirs %s ' % ','.join(i for i in view_dirs)]\\n cmd += ['--vid_paths %s ' % ','.join(i for i in vid_paths)]\\n cmd += ['--debug_path %s ' % debug_path]\\n cmd += ['--debug_lhs_view %s ' % FLAGS.debug_lhs_view]\\n cmd += ['--debug_rhs_view %s ' % FLAGS.debug_rhs_view]\\n cmd += [' & ']\\n cmd = ''.join(i for i in cmd)\\n\\n # Call images_to_videos asynchronously.\\n fnull = open(os.devnull, 'w')\\n subprocess.Popen([cmd], stdout=fnull, stderr=subprocess.STDOUT, shell=True)\\n\\n for p in vid_paths:\\n tf.logging.info('Writing final video to: %s' % p)\\n if debug_path:\\n tf.logging.info('Writing debug video to: %s' % debug_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/tcn/dataset/webcam.py::main\",\n \"text\": \"research/tcn/dataset/webcam.py::main\\ndef main(_):\\n # Initialize the camera capture objects.\\n cameras = get_cameras()\\n # Get one output directory per view.\\n view_dirs, vid_paths, debug_path = setup_paths()\\n try:\\n # Wait for user input.\\n try:\\n tf.logging.info('About to write to:')\\n for v in view_dirs:\\n tf.logging.info(v)\\n input('Press Enter to continue...')\\n except SyntaxError:\\n pass\\n\\n # Create a queue per view for displaying and saving images.\\n display_queues = [ImageQueue() for _ in range(FLAGS.num_views)]\\n reconcile_queues = [ImageQueue() for _ in range(FLAGS.num_views)]\\n\\n # Create a queue for collecting all tuples of multi-view images to write to\\n # disk.\\n write_queue = multiprocessing.Queue()\\n\\n processes = []\\n # Create a process to display collected images in real time.\\n processes.append(Process(target=display_webcams, args=(display_queues,)))\\n # Create a process to collect the latest simultaneous images from each view.\\n processes.append(Process(\\n target=reconcile, args=(reconcile_queues, write_queue,)))\\n # Create a process to collect the latest simultaneous images from each view.\\n processes.append(Process(\\n target=persist, args=(write_queue, view_dirs,)))\\n\\n for (cam, dq, rq) in zip(cameras, display_queues, reconcile_queues):\\n processes.append(Process(\\n target=capture_webcam, args=(cam, dq, rq,)))\\n\\n for p in processes:\\n p.start()\\n for p in processes:\\n p.join()\\n\\n except KeyboardInterrupt:\\n # Close the queues.\\n for q in display_queues + reconcile_queues:\\n q.close()\\n # Release the cameras.\\n for cam in cameras:\\n cam.release()\\n\\n # Launch images_to_videos script asynchronously.\\n launch_images_to_videos(view_dirs, vid_paths, debug_path)\\n\\n try:\\n sys.exit(0)\\n except SystemExit:\\n os._exit(0) # pylint: disable=protected-access\",\n \"label\": 0\n },\n {\n \"cid\": \"tutorials/image/mnist/convolutional.py::data_type\",\n \"text\": \"tutorials/image/mnist/convolutional.py::data_type\\ndef data_type():\\n \\\"\\\"\\\"Return the type of the activations, weights, and placeholder variables.\\\"\\\"\\\"\\n if FLAGS.use_fp16:\\n return tf.float16\\n else:\\n return tf.float32\",\n \"label\": 0\n },\n {\n \"cid\": \"tutorials/image/mnist/convolutional.py::maybe_download\",\n \"text\": \"tutorials/image/mnist/convolutional.py::maybe_download\\ndef maybe_download(filename):\\n \\\"\\\"\\\"Download the data from Yann's website, unless it's already here.\\\"\\\"\\\"\\n if not tf.gfile.Exists(WORK_DIRECTORY):\\n tf.gfile.MakeDirs(WORK_DIRECTORY)\\n filepath = os.path.join(WORK_DIRECTORY, filename)\\n if not tf.gfile.Exists(filepath):\\n filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)\\n with tf.gfile.GFile(filepath) as f:\\n size = f.size()\\n print('Successfully downloaded', filename, size, 'bytes.')\\n return filepath\",\n \"label\": 0\n },\n {\n \"cid\": \"tutorials/image/mnist/convolutional.py::extract_data\",\n \"text\": \"tutorials/image/mnist/convolutional.py::extract_data\\ndef extract_data(filename, num_images):\\n \\\"\\\"\\\"Extract the images into a 4D tensor [image index, y, x, channels].\\n\\n Values are rescaled from [0, 255] down to [-0.5, 0.5].\\n \\\"\\\"\\\"\\n print('Extracting', filename)\\n with gzip.open(filename) as bytestream:\\n bytestream.read(16)\\n buf = bytestream.read(IMAGE_SIZE * IMAGE_SIZE * num_images * NUM_CHANNELS)\\n data = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.float32)\\n data = (data - (PIXEL_DEPTH / 2.0)) / PIXEL_DEPTH\\n data = data.reshape(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS)\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"tutorials/image/mnist/convolutional.py::extract_labels\",\n \"text\": \"tutorials/image/mnist/convolutional.py::extract_labels\\ndef extract_labels(filename, num_images):\\n \\\"\\\"\\\"Extract the labels into a vector of int64 label IDs.\\\"\\\"\\\"\\n print('Extracting', filename)\\n with gzip.open(filename) as bytestream:\\n bytestream.read(8)\\n buf = bytestream.read(1 * num_images)\\n labels = numpy.frombuffer(buf, dtype=numpy.uint8).astype(numpy.int64)\\n return labels\",\n \"label\": 0\n },\n {\n \"cid\": \"tutorials/image/mnist/convolutional.py::fake_data\",\n \"text\": \"tutorials/image/mnist/convolutional.py::fake_data\\ndef fake_data(num_images):\\n \\\"\\\"\\\"Generate a fake dataset that matches the dimensions of MNIST.\\\"\\\"\\\"\\n data = numpy.ndarray(\\n shape=(num_images, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS),\\n dtype=numpy.float32)\\n labels = numpy.zeros(shape=(num_images,), dtype=numpy.int64)\\n for image in xrange(num_images):\\n label = image % 2\\n data[image, :, :, 0] = label - 0.5\\n labels[image] = label\\n return data, labels\",\n \"label\": 0\n },\n {\n \"cid\": \"tutorials/image/mnist/convolutional.py::error_rate\",\n \"text\": \"tutorials/image/mnist/convolutional.py::error_rate\\ndef error_rate(predictions, labels):\\n \\\"\\\"\\\"Return the error rate based on dense predictions and sparse labels.\\\"\\\"\\\"\\n return 100.0 - (\\n 100.0 *\\n numpy.sum(numpy.argmax(predictions, 1) == labels) /\\n predictions.shape[0])\",\n \"label\": 0\n },\n {\n \"cid\": \"tutorials/image/mnist/convolutional.py::main\",\n \"text\": \"tutorials/image/mnist/convolutional.py::main\\ndef main(_):\\n if FLAGS.self_test:\\n print('Running self-test.')\\n train_data, train_labels = fake_data(256)\\n validation_data, validation_labels = fake_data(EVAL_BATCH_SIZE)\\n test_data, test_labels = fake_data(EVAL_BATCH_SIZE)\\n num_epochs = 1\\n else:\\n # Get the data.\\n train_data_filename = maybe_download('train-images-idx3-ubyte.gz')\\n train_labels_filename = maybe_download('train-labels-idx1-ubyte.gz')\\n test_data_filename = maybe_download('t10k-images-idx3-ubyte.gz')\\n test_labels_filename = maybe_download('t10k-labels-idx1-ubyte.gz')\\n\\n # Extract it into numpy arrays.\\n train_data = extract_data(train_data_filename, 60000)\\n train_labels = extract_labels(train_labels_filename, 60000)\\n test_data = extract_data(test_data_filename, 10000)\\n test_labels = extract_labels(test_labels_filename, 10000)\\n\\n # Generate a validation set.\\n validation_data = train_data[:VALIDATION_SIZE, ...]\\n validation_labels = train_labels[:VALIDATION_SIZE]\\n train_data = train_data[VALIDATION_SIZE:, ...]\\n train_labels = train_labels[VALIDATION_SIZE:]\\n num_epochs = NUM_EPOCHS\\n train_size = train_labels.shape[0]\\n\\n # This is where training samples and labels are fed to the graph.\\n # These placeholder nodes will be fed a batch of training data at each\\n # training step using the {feed_dict} argument to the Run() call below.\\n train_data_node = tf.placeholder(\\n data_type(),\\n shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))\\n train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,))\\n eval_data = tf.placeholder(\\n data_type(),\\n shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))\\n\\n # The variables below hold all the trainable weights. They are passed an\\n # initial value which will be assigned when we call:\\n # {tf.global_variables_initializer().run()}\\n conv1_weights = tf.Variable(\\n tf.truncated_normal([5, 5, NUM_CHANNELS, 32], # 5x5 filter, dep\",\n \"label\": 0\n },\n {\n \"cid\": \"tutorials/image/mnist/convolutional.py::main.model\",\n \"text\": \"tutorials/image/mnist/convolutional.py::main.model\\n def model(data, train=False):\\n \\\"\\\"\\\"The Model definition.\\\"\\\"\\\"\\n # 2D convolution, with 'SAME' padding (i.e. the output feature map has\\n # the same size as the input). Note that {strides} is a 4D array whose\\n # shape matches the data layout: [image index, y, x, depth].\\n conv = tf.nn.conv2d(data,\\n conv1_weights,\\n strides=[1, 1, 1, 1],\\n padding='SAME')\\n # Bias and rectified linear non-linearity.\\n relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))\\n # Max pooling. The kernel size spec {ksize} also follows the layout of\\n # the data. Here we have a pooling window of 2, and a stride of 2.\\n pool = tf.nn.max_pool(relu,\\n ksize=[1, 2, 2, 1],\\n strides=[1, 2, 2, 1],\\n padding='SAME')\\n conv = tf.nn.conv2d(pool,\\n conv2_weights,\\n strides=[1, 1, 1, 1],\\n padding='SAME')\\n relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))\\n pool = tf.nn.max_pool(relu,\\n ksize=[1, 2, 2, 1],\\n strides=[1, 2, 2, 1],\\n padding='SAME')\\n # Reshape the feature map cuboid into a 2D matrix to feed it to the\\n # fully connected layers.\\n pool_shape = pool.get_shape().as_list()\\n reshape = tf.reshape(\\n pool,\\n [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])\\n # Fully connected layer. Note that the '+' operation automatically\\n # broadcasts the biases.\\n hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)\\n # Add a 50% dropout during training only. Dropout also scales\\n # activations such that no rescaling is needed at evaluation time.\\n if train:\\n hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)\\n return tf.matmul(hidden, fc2_weights) + fc2_biases\",\n \"label\": 0\n },\n {\n \"cid\": \"tutorials/image/mnist/convolutional.py::main.eval_in_batches\",\n \"text\": \"tutorials/image/mnist/convolutional.py::main.eval_in_batches\\n def eval_in_batches(data, sess):\\n \\\"\\\"\\\"Get all predictions for a dataset by running it in small batches.\\\"\\\"\\\"\\n size = data.shape[0]\\n if size < EVAL_BATCH_SIZE:\\n raise ValueError(\\\"batch size for evals larger than dataset: %d\\\" % size)\\n predictions = numpy.ndarray(shape=(size, NUM_LABELS), dtype=numpy.float32)\\n for begin in xrange(0, size, EVAL_BATCH_SIZE):\\n end = begin + EVAL_BATCH_SIZE\\n if end <= size:\\n predictions[begin:end, :] = sess.run(\\n eval_prediction,\\n feed_dict={eval_data: data[begin:end, ...]})\\n else:\\n batch_predictions = sess.run(\\n eval_prediction,\\n feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]})\\n predictions[begin:, :] = batch_predictions[begin - size:, :]\\n return predictions\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":271,"cells":{"instance_id":{"kind":"string","value":"tensorflow__models-4554"},"repo":{"kind":"string","value":"tensorflow/models"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"astronet: failed provided unit test \"kepler_spline_test\"\nI have all the package required as instructed, and verified by \"pip list\". When I ran this:\r\n\r\nbazel test astronet/... light_curve_util/... third_party/...\r\n\r\neverything passed but the last one:\r\n\r\n//third_party/kepler_spline:kepler_spline_test FAILED in 9.9s\r\n\r\n\n"},"gold_files":{"kind":"list like","value":["research/astronet/light_curve_util/util.py","research/astronet/third_party/kepler_spline/kepler_spline.py"],"string":"[\n \"research/astronet/light_curve_util/util.py\",\n \"research/astronet/third_party/kepler_spline/kepler_spline.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["research/astronet/light_curve_util/util.py::<module>","research/astronet/light_curve_util/util.py::interpolate_masked_spline","research/astronet/light_curve_util/util.py::remove_events","research/astronet/light_curve_util/util.py::split","research/astronet/third_party/kepler_spline/kepler_spline.py::<module>","research/astronet/third_party/kepler_spline/kepler_spline.py::choose_kepler_spline","research/astronet/third_party/kepler_spline/kepler_spline.py::kepler_spline"],"string":"[\n \"research/astronet/light_curve_util/util.py::<module>\",\n \"research/astronet/light_curve_util/util.py::interpolate_masked_spline\",\n \"research/astronet/light_curve_util/util.py::remove_events\",\n \"research/astronet/light_curve_util/util.py::split\",\n \"research/astronet/third_party/kepler_spline/kepler_spline.py::<module>\",\n \"research/astronet/third_party/kepler_spline/kepler_spline.py::choose_kepler_spline\",\n \"research/astronet/third_party/kepler_spline/kepler_spline.py::kepler_spline\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"official/utils/logs/logger.py","text":"official/utils/logs/logger.py\ndef config_benchmark_logger(flag_obj=None) # Config the global benchmark logger.\ndef get_benchmark_logger()\ndef benchmark_context(flag_obj) # Context of benchmark, which will update status of the run accordingly.\nclass BaseBenchmarkLogger # Class to log the benchmark information to STDOUT.\n def log_evaluation_result(self, eval_results) # Log the evaluation result.\n def log_metric(self, name, value, unit=None, global_step=None, extras=None) # Log the benchmark metric information to local file.\n def log_run_info(self, model_name, dataset_name, run_params, test_id=None)\n def on_finish(self, status)\nclass BenchmarkFileLogger # Class to log the benchmark information to local disk.\n def __init__(self, logging_dir)\n def log_metric(self, name, value, unit=None, global_step=None, extras=None) # Log the benchmark metric information to local file.\n def log_run_info(self, model_name, dataset_name, run_params, test_id=None) # Collect most of the TF runtime information for the local env.\n def on_finish(self, status)\nclass BenchmarkBigQueryLogger # Class to log the benchmark information to BigQuery data store.\n def __init__(self, bigquery_uploader, bigquery_data_set, bigquery_run_table, bigquery_run_status_table, bigquery_metric_table, run_id)\n def log_metric(self, name, value, unit=None, global_step=None, extras=None) # Log the benchmark metric information to bigquery.\n def log_run_info(self, model_name, dataset_name, run_params, test_id=None) # Collect most of the TF runtime information for the local env.\n def on_finish(self, status)\ndef _gather_run_info(model_name, dataset_name, run_params, test_id) # Collect the benchmark run information for the local environment.\ndef _process_metric_to_json(name, value, unit=None, global_step=None, extras=None) # Validate the metric data and generate JSON for insert.\ndef _collect_tensorflow_info(run_info)\ndef _collect_run_params(run_info, run_params) # Log the parameter information for the benchmark run.\n def process_param(name, value)\ndef _collect_tensorflow_environment_variables(run_info)\ndef _collect_cpu_info(run_info) # Collect the CPU information for the local environment.\ndef _collect_gpu_info(run_info) # Collect local GPU information by TF device library.\ndef _collect_memory_info(run_info)\ndef _collect_test_environment(run_info) # Detect the local environment, eg GCE, AWS or DGX, etc.\ndef _parse_gpu_model(physical_device_desc)\ndef _convert_to_json_dict(input_dict)","label":0},{"cid":"research/astronet/astronet/data/preprocess.py","text":"research/astronet/astronet/data/preprocess.py\ndef read_and_process_light_curve(kepid, kepler_data_dir, max_gap_width=0.75) # Reads a light curve, fits a B-spline and divides the curve by the spline.\ndef phase_fold_and_sort_light_curve(time, flux, period, t0) # Phase folds a light curve and sorts by ascending time.\ndef generate_view(time, flux, num_bins, bin_width, t_min, t_max, normalize=True) # Generates a view of a phase-folded light curve using a median filter.\ndef global_view(time, flux, period, num_bins=2001, bin_width_factor=1 / 2001) # Generates a 'global view' of a phase folded light curve.\ndef local_view(time, flux, period, duration, num_bins=201, bin_width_factor=0.16, num_durations=4) # Generates a 'local view' of a phase folded light curve.","label":0},{"cid":"research/astronet/light_curve_util/util.py","text":"research/astronet/light_curve_util/util.py\ndef phase_fold_time(time, period, t0) # Creates a phase-folded time vector.\ndef split(all_time, all_flux, gap_width=0.75) # Splits a light curve on discontinuities (gaps).\ndef remove_events(all_time, all_flux, events, width_factor=1.0) # Removes events from a light curve.\ndef interpolate_masked_spline(all_time, all_masked_time, all_masked_spline) # Linearly interpolates spline values across masked points.\ndef count_transit_points(time, event) # Computes the number of points in each transit of a given event.","label":1},{"cid":"research/astronet/third_party/kepler_spline/kepler_spline.py","text":"research/astronet/third_party/kepler_spline/kepler_spline.py\nclass SplineError # Error when fitting a Kepler spline.\ndef kepler_spline(time, flux, bkspace=1.5, maxiter=5, outlier_cut=3) # Computes a best-fit spline curve for a light curve segment.\ndef choose_kepler_spline(all_time, all_flux, bkspaces, maxiter=5, penalty_coeff=1.0, verbose=True) # Computes the best-fit Kepler spline across a break-point spacings.","label":1},{"cid":"research/brain_coder/single_task/aggregate_experiment_results.py","text":"research/brain_coder/single_task/aggregate_experiment_results.py\ndef make_csv_string(table) # Convert 2D list to CSV string.\ndef process_results(metrics) # Extract useful information from given metrics.\ndef get_results_for_experiment(models_dir, task_name, model_type='pg', max_npe='5M', desc='v0', name_prefix='bf_rl_paper', extra_desc='') # Get and process results for a given experiment.\nclass BestCodeResultError\ndef get_best_code_for_experiment(models_dir, task_name, model_type='pg', max_npe='5M', desc=0, name_prefix='bf_rl_paper', extra_desc='') # Like `get_results_for_experiment`, but fetches the code solutions.\ndef make_results_table(models=None, tasks=None, max_npe='5M', name_prefix='bf_rl_paper', extra_desc='', models_dir='/tmp') # Creates a table of results: algorithm + version by tasks.\n def info(stats)\ndef print_results_table(results_table) # Print human readable results table to stdout.\n def info_str(info_row)\ndef main(argv)","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py","text":"research/differential_privacy/privacy_accountant/tf/accountant.py\nclass AmortizedAccountant # Keep track of privacy spending in an amortized way.\n def __init__(self, total_examples) # Initialization. Currently only support amortized tracking.\n def accumulate_privacy_spending(self, eps_delta, unused_sigma, num_examples) # Accumulate the privacy spending.\n def get_privacy_spent(self, sess, target_eps=None) # Report the spending so far.\nclass MomentsAccountant # Privacy accountant which keeps track of moments of privacy loss.\n def __init__(self, total_examples, moment_orders=32) # Initialize a MomentsAccountant.\n def _compute_log_moment(self, sigma, q, moment_order) # Compute high moment of privacy loss.\n def accumulate_privacy_spending(self, unused_eps_delta, sigma, num_examples) # Accumulate privacy spending.\n def _compute_delta(self, log_moments, eps) # Compute delta for given log_moments and eps.\n def _compute_eps(self, log_moments, delta)\n def get_privacy_spent(self, sess, target_eps=None, target_deltas=None) # Compute privacy spending in (e, d)-DP form for a single or list of eps.\nclass GaussianMomentsAccountant # MomentsAccountant which assumes Gaussian noise.\n def __init__(self, total_examples, moment_orders=32) # Initialization.\n def _differential_moments(self, sigma, s, t) # Compute 0 to t-th differential moments for Gaussian variable.\n def _compute_log_moment(self, sigma, q, moment_order) # Compute high moment of privacy loss.\nclass DummyAccountant # An accountant that does no accounting.\n def accumulate_privacy_spending(self, *unused_args)\n def get_privacy_spent(self, unused_sess, **unused_kwargs)","label":0},{"cid":"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py","text":"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py\ndef run_eval(run_dir, checkpoint_dir, hparams) # Runs the eval loop.\ndef to_degrees(log_quaternion_loss) # Converts a log quaternion distance to an angle.\ndef create_metrics(end_points, source_labels, target_labels, hparams) # Create metrics for the model.\ndef main(_)","label":0},{"cid":"research/syntaxnet/dragnn/python/runtime_support.py","text":"research/syntaxnet/dragnn/python/runtime_support.py\ndef add_hooks(component, cell_subgraph_spec) # Adds \"hook\" nodes to the graph, for use by the runtime.\ndef _blocked_and_dtype_transformations(tensor) # Yields variants of a tensor, for standard blocking/dtype variants.\ndef _add_hooks_for_linked_embedding_matrix(component, channel_id) # Adds runtime hooks for a linked embedding matrix.\ndef _add_hooks_for_fixed_embedding_matrix(component, channel_id) # Adds runtime hooks for a fixed embedding matrix.\ndef _add_hooks_for_derived_parameter(getter) # Adds hooks for derived parameters.\n def _hook_name(base_name) # Returns a hook node name constructed from a base name.\ndef _add_hooks_for_trainable_params(component, params) # Adds runtime hooks for a variable of trainable parameters.\n def _hook_name(base_name) # Returns a hook node name constructed from a base name.\ndef make_padded_blocked_matrix(matrix, block_size) # Converts a matrix to padded column-blocked format.\ndef bfloat16_permutation(tensor) # Permutes values in the last dimension of a tensor.\ndef _get_hook_name(component, variable_name, suffix) # Builds the name of a hook node.\ndef _add_hook_node(tensor, fully_qualified_name) # Adds a hook node that outputs a tensor with a fully-qualified name.","label":0}],"string":"[\n {\n \"cid\": \"official/utils/logs/logger.py\",\n \"text\": \"official/utils/logs/logger.py\\ndef config_benchmark_logger(flag_obj=None) # Config the global benchmark logger.\\ndef get_benchmark_logger()\\ndef benchmark_context(flag_obj) # Context of benchmark, which will update status of the run accordingly.\\nclass BaseBenchmarkLogger # Class to log the benchmark information to STDOUT.\\n def log_evaluation_result(self, eval_results) # Log the evaluation result.\\n def log_metric(self, name, value, unit=None, global_step=None, extras=None) # Log the benchmark metric information to local file.\\n def log_run_info(self, model_name, dataset_name, run_params, test_id=None)\\n def on_finish(self, status)\\nclass BenchmarkFileLogger # Class to log the benchmark information to local disk.\\n def __init__(self, logging_dir)\\n def log_metric(self, name, value, unit=None, global_step=None, extras=None) # Log the benchmark metric information to local file.\\n def log_run_info(self, model_name, dataset_name, run_params, test_id=None) # Collect most of the TF runtime information for the local env.\\n def on_finish(self, status)\\nclass BenchmarkBigQueryLogger # Class to log the benchmark information to BigQuery data store.\\n def __init__(self, bigquery_uploader, bigquery_data_set, bigquery_run_table, bigquery_run_status_table, bigquery_metric_table, run_id)\\n def log_metric(self, name, value, unit=None, global_step=None, extras=None) # Log the benchmark metric information to bigquery.\\n def log_run_info(self, model_name, dataset_name, run_params, test_id=None) # Collect most of the TF runtime information for the local env.\\n def on_finish(self, status)\\ndef _gather_run_info(model_name, dataset_name, run_params, test_id) # Collect the benchmark run information for the local environment.\\ndef _process_metric_to_json(name, value, unit=None, global_step=None, extras=None) # Validate the metric data and generate JSON for insert.\\ndef _collect_tensorflow_info(run_info)\\ndef _collect_run_params(run_info, run_params) # Log the parameter information for the benchmark run.\\n def process_param(name, value)\\ndef _collect_tensorflow_environment_variables(run_info)\\ndef _collect_cpu_info(run_info) # Collect the CPU information for the local environment.\\ndef _collect_gpu_info(run_info) # Collect local GPU information by TF device library.\\ndef _collect_memory_info(run_info)\\ndef _collect_test_environment(run_info) # Detect the local environment, eg GCE, AWS or DGX, etc.\\ndef _parse_gpu_model(physical_device_desc)\\ndef _convert_to_json_dict(input_dict)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/astronet/astronet/data/preprocess.py\",\n \"text\": \"research/astronet/astronet/data/preprocess.py\\ndef read_and_process_light_curve(kepid, kepler_data_dir, max_gap_width=0.75) # Reads a light curve, fits a B-spline and divides the curve by the spline.\\ndef phase_fold_and_sort_light_curve(time, flux, period, t0) # Phase folds a light curve and sorts by ascending time.\\ndef generate_view(time, flux, num_bins, bin_width, t_min, t_max, normalize=True) # Generates a view of a phase-folded light curve using a median filter.\\ndef global_view(time, flux, period, num_bins=2001, bin_width_factor=1 / 2001) # Generates a 'global view' of a phase folded light curve.\\ndef local_view(time, flux, period, duration, num_bins=201, bin_width_factor=0.16, num_durations=4) # Generates a 'local view' of a phase folded light curve.\",\n \"label\": 0\n },\n {\n \"cid\": \"research/astronet/light_curve_util/util.py\",\n \"text\": \"research/astronet/light_curve_util/util.py\\ndef phase_fold_time(time, period, t0) # Creates a phase-folded time vector.\\ndef split(all_time, all_flux, gap_width=0.75) # Splits a light curve on discontinuities (gaps).\\ndef remove_events(all_time, all_flux, events, width_factor=1.0) # Removes events from a light curve.\\ndef interpolate_masked_spline(all_time, all_masked_time, all_masked_spline) # Linearly interpolates spline values across masked points.\\ndef count_transit_points(time, event) # Computes the number of points in each transit of a given event.\",\n \"label\": 1\n },\n {\n \"cid\": \"research/astronet/third_party/kepler_spline/kepler_spline.py\",\n \"text\": \"research/astronet/third_party/kepler_spline/kepler_spline.py\\nclass SplineError # Error when fitting a Kepler spline.\\ndef kepler_spline(time, flux, bkspace=1.5, maxiter=5, outlier_cut=3) # Computes a best-fit spline curve for a light curve segment.\\ndef choose_kepler_spline(all_time, all_flux, bkspaces, maxiter=5, penalty_coeff=1.0, verbose=True) # Computes the best-fit Kepler spline across a break-point spacings.\",\n \"label\": 1\n },\n {\n \"cid\": \"research/brain_coder/single_task/aggregate_experiment_results.py\",\n \"text\": \"research/brain_coder/single_task/aggregate_experiment_results.py\\ndef make_csv_string(table) # Convert 2D list to CSV string.\\ndef process_results(metrics) # Extract useful information from given metrics.\\ndef get_results_for_experiment(models_dir, task_name, model_type='pg', max_npe='5M', desc='v0', name_prefix='bf_rl_paper', extra_desc='') # Get and process results for a given experiment.\\nclass BestCodeResultError\\ndef get_best_code_for_experiment(models_dir, task_name, model_type='pg', max_npe='5M', desc=0, name_prefix='bf_rl_paper', extra_desc='') # Like `get_results_for_experiment`, but fetches the code solutions.\\ndef make_results_table(models=None, tasks=None, max_npe='5M', name_prefix='bf_rl_paper', extra_desc='', models_dir='/tmp') # Creates a table of results: algorithm + version by tasks.\\n def info(stats)\\ndef print_results_table(results_table) # Print human readable results table to stdout.\\n def info_str(info_row)\\ndef main(argv)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py\\nclass AmortizedAccountant # Keep track of privacy spending in an amortized way.\\n def __init__(self, total_examples) # Initialization. Currently only support amortized tracking.\\n def accumulate_privacy_spending(self, eps_delta, unused_sigma, num_examples) # Accumulate the privacy spending.\\n def get_privacy_spent(self, sess, target_eps=None) # Report the spending so far.\\nclass MomentsAccountant # Privacy accountant which keeps track of moments of privacy loss.\\n def __init__(self, total_examples, moment_orders=32) # Initialize a MomentsAccountant.\\n def _compute_log_moment(self, sigma, q, moment_order) # Compute high moment of privacy loss.\\n def accumulate_privacy_spending(self, unused_eps_delta, sigma, num_examples) # Accumulate privacy spending.\\n def _compute_delta(self, log_moments, eps) # Compute delta for given log_moments and eps.\\n def _compute_eps(self, log_moments, delta)\\n def get_privacy_spent(self, sess, target_eps=None, target_deltas=None) # Compute privacy spending in (e, d)-DP form for a single or list of eps.\\nclass GaussianMomentsAccountant # MomentsAccountant which assumes Gaussian noise.\\n def __init__(self, total_examples, moment_orders=32) # Initialization.\\n def _differential_moments(self, sigma, s, t) # Compute 0 to t-th differential moments for Gaussian variable.\\n def _compute_log_moment(self, sigma, q, moment_order) # Compute high moment of privacy loss.\\nclass DummyAccountant # An accountant that does no accounting.\\n def accumulate_privacy_spending(self, *unused_args)\\n def get_privacy_spent(self, unused_sess, **unused_kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py\",\n \"text\": \"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py\\ndef run_eval(run_dir, checkpoint_dir, hparams) # Runs the eval loop.\\ndef to_degrees(log_quaternion_loss) # Converts a log quaternion distance to an angle.\\ndef create_metrics(end_points, source_labels, target_labels, hparams) # Create metrics for the model.\\ndef main(_)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/dragnn/python/runtime_support.py\",\n \"text\": \"research/syntaxnet/dragnn/python/runtime_support.py\\ndef add_hooks(component, cell_subgraph_spec) # Adds \\\"hook\\\" nodes to the graph, for use by the runtime.\\ndef _blocked_and_dtype_transformations(tensor) # Yields variants of a tensor, for standard blocking/dtype variants.\\ndef _add_hooks_for_linked_embedding_matrix(component, channel_id) # Adds runtime hooks for a linked embedding matrix.\\ndef _add_hooks_for_fixed_embedding_matrix(component, channel_id) # Adds runtime hooks for a fixed embedding matrix.\\ndef _add_hooks_for_derived_parameter(getter) # Adds hooks for derived parameters.\\n def _hook_name(base_name) # Returns a hook node name constructed from a base name.\\ndef _add_hooks_for_trainable_params(component, params) # Adds runtime hooks for a variable of trainable parameters.\\n def _hook_name(base_name) # Returns a hook node name constructed from a base name.\\ndef make_padded_blocked_matrix(matrix, block_size) # Converts a matrix to padded column-blocked format.\\ndef bfloat16_permutation(tensor) # Permutes values in the last dimension of a tensor.\\ndef _get_hook_name(component, variable_name, suffix) # Builds the name of a hook node.\\ndef _add_hook_node(tensor, fully_qualified_name) # Adds a hook node that outputs a tensor with a fully-qualified name.\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"official/utils/logs/logger.py::config_benchmark_logger","text":"official/utils/logs/logger.py::config_benchmark_logger\ndef config_benchmark_logger(flag_obj=None):\n \"\"\"Config the global benchmark logger.\"\"\"\n _logger_lock.acquire()\n try:\n global _benchmark_logger\n if not flag_obj:\n flag_obj = FLAGS\n\n if (not hasattr(flag_obj, \"benchmark_logger_type\") or\n flag_obj.benchmark_logger_type == \"BaseBenchmarkLogger\"):\n _benchmark_logger = BaseBenchmarkLogger()\n elif flag_obj.benchmark_logger_type == \"BenchmarkFileLogger\":\n _benchmark_logger = BenchmarkFileLogger(flag_obj.benchmark_log_dir)\n elif flag_obj.benchmark_logger_type == \"BenchmarkBigQueryLogger\":\n from official.benchmark import benchmark_uploader as bu # pylint: disable=g-import-not-at-top\n bq_uploader = bu.BigQueryUploader(gcp_project=flag_obj.gcp_project)\n _benchmark_logger = BenchmarkBigQueryLogger(\n bigquery_uploader=bq_uploader,\n bigquery_data_set=flag_obj.bigquery_data_set,\n bigquery_run_table=flag_obj.bigquery_run_table,\n bigquery_run_status_table=flag_obj.bigquery_run_status_table,\n bigquery_metric_table=flag_obj.bigquery_metric_table,\n run_id=str(uuid.uuid4()))\n else:\n raise ValueError(\"Unrecognized benchmark_logger_type: %s\"\n % flag_obj.benchmark_logger_type)\n\n finally:\n _logger_lock.release()\n return _benchmark_logger","label":0},{"cid":"official/utils/logs/logger.py::get_benchmark_logger","text":"official/utils/logs/logger.py::get_benchmark_logger\ndef get_benchmark_logger():\n if not _benchmark_logger:\n config_benchmark_logger()\n return _benchmark_logger","label":0},{"cid":"official/utils/logs/logger.py::benchmark_context","text":"official/utils/logs/logger.py::benchmark_context\n@contextlib.contextmanager\ndef benchmark_context(flag_obj):\n \"\"\"Context of benchmark, which will update status of the run accordingly.\"\"\"\n benchmark_logger = config_benchmark_logger(flag_obj)\n try:\n yield\n benchmark_logger.on_finish(RUN_STATUS_SUCCESS)\n except Exception: # pylint: disable=broad-except\n # Catch all the exception, update the run status to be failure, and re-raise\n benchmark_logger.on_finish(RUN_STATUS_FAILURE)\n raise","label":0},{"cid":"official/utils/logs/logger.py::BaseBenchmarkLogger.log_evaluation_result","text":"official/utils/logs/logger.py::BaseBenchmarkLogger.log_evaluation_result\n def log_evaluation_result(self, eval_results):\n \"\"\"Log the evaluation result.\n\n The evaluate result is a dictionary that contains metrics defined in\n model_fn. It also contains a entry for global_step which contains the value\n of the global step when evaluation was performed.\n\n Args:\n eval_results: dict, the result of evaluate.\n \"\"\"\n if not isinstance(eval_results, dict):\n tf.logging.warning(\"eval_results should be dictionary for logging. \"\n \"Got %s\", type(eval_results))\n return\n global_step = eval_results[tf.GraphKeys.GLOBAL_STEP]\n for key in sorted(eval_results):\n if key != tf.GraphKeys.GLOBAL_STEP:\n self.log_metric(key, eval_results[key], global_step=global_step)","label":0},{"cid":"official/utils/logs/logger.py::BaseBenchmarkLogger.log_metric","text":"official/utils/logs/logger.py::BaseBenchmarkLogger.log_metric\n def log_metric(self, name, value, unit=None, global_step=None, extras=None):\n \"\"\"Log the benchmark metric information to local file.\n\n Currently the logging is done in a synchronized way. This should be updated\n to log asynchronously.\n\n Args:\n name: string, the name of the metric to log.\n value: number, the value of the metric. The value will not be logged if it\n is not a number type.\n unit: string, the unit of the metric, E.g \"image per second\".\n global_step: int, the global_step when the metric is logged.\n extras: map of string:string, the extra information about the metric.\n \"\"\"\n metric = _process_metric_to_json(name, value, unit, global_step, extras)\n if metric:\n tf.logging.info(\"Benchmark metric: %s\", metric)","label":0},{"cid":"official/utils/logs/logger.py::BaseBenchmarkLogger.log_run_info","text":"official/utils/logs/logger.py::BaseBenchmarkLogger.log_run_info\n def log_run_info(self, model_name, dataset_name, run_params, test_id=None):\n tf.logging.info(\"Benchmark run: %s\",\n _gather_run_info(model_name, dataset_name, run_params,\n test_id))","label":0},{"cid":"official/utils/logs/logger.py::BaseBenchmarkLogger.on_finish","text":"official/utils/logs/logger.py::BaseBenchmarkLogger.on_finish\n def on_finish(self, status):\n pass","label":0},{"cid":"official/utils/logs/logger.py::BenchmarkFileLogger.__init__","text":"official/utils/logs/logger.py::BenchmarkFileLogger.__init__\n def __init__(self, logging_dir):\n super(BenchmarkFileLogger, self).__init__()\n self._logging_dir = logging_dir\n if not tf.gfile.IsDirectory(self._logging_dir):\n tf.gfile.MakeDirs(self._logging_dir)\n self._metric_file_handler = tf.gfile.GFile(\n os.path.join(self._logging_dir, METRIC_LOG_FILE_NAME), \"a\")","label":0},{"cid":"official/utils/logs/logger.py::BenchmarkFileLogger.log_metric","text":"official/utils/logs/logger.py::BenchmarkFileLogger.log_metric\n def log_metric(self, name, value, unit=None, global_step=None, extras=None):\n \"\"\"Log the benchmark metric information to local file.\n\n Currently the logging is done in a synchronized way. This should be updated\n to log asynchronously.\n\n Args:\n name: string, the name of the metric to log.\n value: number, the value of the metric. The value will not be logged if it\n is not a number type.\n unit: string, the unit of the metric, E.g \"image per second\".\n global_step: int, the global_step when the metric is logged.\n extras: map of string:string, the extra information about the metric.\n \"\"\"\n metric = _process_metric_to_json(name, value, unit, global_step, extras)\n if metric:\n try:\n json.dump(metric, self._metric_file_handler)\n self._metric_file_handler.write(\"\\n\")\n self._metric_file_handler.flush()\n except (TypeError, ValueError) as e:\n tf.logging.warning(\"Failed to dump metric to log file: \"\n \"name %s, value %s, error %s\", name, value, e)","label":0},{"cid":"official/utils/logs/logger.py::BenchmarkFileLogger.log_run_info","text":"official/utils/logs/logger.py::BenchmarkFileLogger.log_run_info\n def log_run_info(self, model_name, dataset_name, run_params, test_id=None):\n \"\"\"Collect most of the TF runtime information for the local env.\n\n The schema of the run info follows official/benchmark/datastore/schema.\n\n Args:\n model_name: string, the name of the model.\n dataset_name: string, the name of dataset for training and evaluation.\n run_params: dict, the dictionary of parameters for the run, it could\n include hyperparameters or other params that are important for the run.\n test_id: string, the unique name of the test run by the combination of key\n parameters, eg batch size, num of GPU. It is hardware independent.\n \"\"\"\n run_info = _gather_run_info(model_name, dataset_name, run_params, test_id)\n\n with tf.gfile.GFile(os.path.join(\n self._logging_dir, BENCHMARK_RUN_LOG_FILE_NAME), \"w\") as f:\n try:\n json.dump(run_info, f)\n f.write(\"\\n\")\n except (TypeError, ValueError) as e:\n tf.logging.warning(\"Failed to dump benchmark run info to log file: %s\",\n e)","label":0},{"cid":"official/utils/logs/logger.py::BenchmarkFileLogger.on_finish","text":"official/utils/logs/logger.py::BenchmarkFileLogger.on_finish\n def on_finish(self, status):\n self._metric_file_handler.flush()\n self._metric_file_handler.close()","label":0},{"cid":"official/utils/logs/logger.py::BenchmarkBigQueryLogger.__init__","text":"official/utils/logs/logger.py::BenchmarkBigQueryLogger.__init__\n def __init__(self,\n bigquery_uploader,\n bigquery_data_set,\n bigquery_run_table,\n bigquery_run_status_table,\n bigquery_metric_table,\n run_id):\n super(BenchmarkBigQueryLogger, self).__init__()\n self._bigquery_uploader = bigquery_uploader\n self._bigquery_data_set = bigquery_data_set\n self._bigquery_run_table = bigquery_run_table\n self._bigquery_run_status_table = bigquery_run_status_table\n self._bigquery_metric_table = bigquery_metric_table\n self._run_id = run_id","label":0},{"cid":"official/utils/logs/logger.py::BenchmarkBigQueryLogger.log_metric","text":"official/utils/logs/logger.py::BenchmarkBigQueryLogger.log_metric\n def log_metric(self, name, value, unit=None, global_step=None, extras=None):\n \"\"\"Log the benchmark metric information to bigquery.\n\n Args:\n name: string, the name of the metric to log.\n value: number, the value of the metric. The value will not be logged if it\n is not a number type.\n unit: string, the unit of the metric, E.g \"image per second\".\n global_step: int, the global_step when the metric is logged.\n extras: map of string:string, the extra information about the metric.\n \"\"\"\n metric = _process_metric_to_json(name, value, unit, global_step, extras)\n if metric:\n # Starting new thread for bigquery upload in case it might take long time\n # and impact the benchmark and performance measurement. Starting a new\n # thread might have potential performance impact for model that run on\n # CPU.\n thread.start_new_thread(\n self._bigquery_uploader.upload_benchmark_metric_json,\n (self._bigquery_data_set,\n self._bigquery_metric_table,\n self._run_id,\n [metric]))","label":0},{"cid":"official/utils/logs/logger.py::BenchmarkBigQueryLogger.log_run_info","text":"official/utils/logs/logger.py::BenchmarkBigQueryLogger.log_run_info\n def log_run_info(self, model_name, dataset_name, run_params, test_id=None):\n \"\"\"Collect most of the TF runtime information for the local env.\n\n The schema of the run info follows official/benchmark/datastore/schema.\n\n Args:\n model_name: string, the name of the model.\n dataset_name: string, the name of dataset for training and evaluation.\n run_params: dict, the dictionary of parameters for the run, it could\n include hyperparameters or other params that are important for the run.\n test_id: string, the unique name of the test run by the combination of key\n parameters, eg batch size, num of GPU. It is hardware independent.\n \"\"\"\n run_info = _gather_run_info(model_name, dataset_name, run_params, test_id)\n # Starting new thread for bigquery upload in case it might take long time\n # and impact the benchmark and performance measurement. Starting a new\n # thread might have potential performance impact for model that run on CPU.\n thread.start_new_thread(\n self._bigquery_uploader.upload_benchmark_run_json,\n (self._bigquery_data_set,\n self._bigquery_run_table,\n self._run_id,\n run_info))\n thread.start_new_thread(\n self._bigquery_uploader.insert_run_status,\n (self._bigquery_data_set,\n self._bigquery_run_status_table,\n self._run_id,\n RUN_STATUS_RUNNING))","label":0},{"cid":"official/utils/logs/logger.py::BenchmarkBigQueryLogger.on_finish","text":"official/utils/logs/logger.py::BenchmarkBigQueryLogger.on_finish\n def on_finish(self, status):\n thread.start_new_thread(\n self._bigquery_uploader.update_run_status,\n (self._bigquery_data_set,\n self._bigquery_run_status_table,\n self._run_id,\n status))","label":0},{"cid":"official/utils/logs/logger.py::_gather_run_info","text":"official/utils/logs/logger.py::_gather_run_info\ndef _gather_run_info(model_name, dataset_name, run_params, test_id):\n \"\"\"Collect the benchmark run information for the local environment.\"\"\"\n run_info = {\n \"model_name\": model_name,\n \"dataset\": {\"name\": dataset_name},\n \"machine_config\": {},\n \"test_id\": test_id,\n \"run_date\": datetime.datetime.utcnow().strftime(\n _DATE_TIME_FORMAT_PATTERN)}\n _collect_tensorflow_info(run_info)\n _collect_tensorflow_environment_variables(run_info)\n _collect_run_params(run_info, run_params)\n _collect_cpu_info(run_info)\n _collect_gpu_info(run_info)\n _collect_memory_info(run_info)\n _collect_test_environment(run_info)\n return run_info","label":0},{"cid":"official/utils/logs/logger.py::_process_metric_to_json","text":"official/utils/logs/logger.py::_process_metric_to_json\ndef _process_metric_to_json(\n name, value, unit=None, global_step=None, extras=None):\n \"\"\"Validate the metric data and generate JSON for insert.\"\"\"\n if not isinstance(value, numbers.Number):\n tf.logging.warning(\n \"Metric value to log should be a number. Got %s\", type(value))\n return None\n\n extras = _convert_to_json_dict(extras)\n return {\n \"name\": name,\n \"value\": float(value),\n \"unit\": unit,\n \"global_step\": global_step,\n \"timestamp\": datetime.datetime.utcnow().strftime(\n _DATE_TIME_FORMAT_PATTERN),\n \"extras\": extras}","label":0},{"cid":"official/utils/logs/logger.py::_collect_tensorflow_info","text":"official/utils/logs/logger.py::_collect_tensorflow_info\ndef _collect_tensorflow_info(run_info):\n run_info[\"tensorflow_version\"] = {\n \"version\": tf.VERSION, \"git_hash\": tf.GIT_VERSION}","label":0},{"cid":"official/utils/logs/logger.py::_collect_run_params","text":"official/utils/logs/logger.py::_collect_run_params\ndef _collect_run_params(run_info, run_params):\n \"\"\"Log the parameter information for the benchmark run.\"\"\"\n def process_param(name, value):\n type_check = {\n str: {\"name\": name, \"string_value\": value},\n int: {\"name\": name, \"long_value\": value},\n bool: {\"name\": name, \"bool_value\": str(value)},\n float: {\"name\": name, \"float_value\": value},\n }\n return type_check.get(type(value),\n {\"name\": name, \"string_value\": str(value)})\n if run_params:\n run_info[\"run_parameters\"] = [\n process_param(k, v) for k, v in sorted(run_params.items())]","label":0},{"cid":"official/utils/logs/logger.py::_collect_run_params.process_param","text":"official/utils/logs/logger.py::_collect_run_params.process_param\n def process_param(name, value):\n type_check = {\n str: {\"name\": name, \"string_value\": value},\n int: {\"name\": name, \"long_value\": value},\n bool: {\"name\": name, \"bool_value\": str(value)},\n float: {\"name\": name, \"float_value\": value},\n }\n return type_check.get(type(value),\n {\"name\": name, \"string_value\": str(value)})","label":0},{"cid":"official/utils/logs/logger.py::_collect_tensorflow_environment_variables","text":"official/utils/logs/logger.py::_collect_tensorflow_environment_variables\ndef _collect_tensorflow_environment_variables(run_info):\n run_info[\"tensorflow_environment_variables\"] = [\n {\"name\": k, \"value\": v}\n for k, v in sorted(os.environ.items()) if k.startswith(\"TF_\")]","label":0},{"cid":"official/utils/logs/logger.py::_collect_cpu_info","text":"official/utils/logs/logger.py::_collect_cpu_info\ndef _collect_cpu_info(run_info):\n \"\"\"Collect the CPU information for the local environment.\"\"\"\n cpu_info = {}\n\n cpu_info[\"num_cores\"] = multiprocessing.cpu_count()\n\n try:\n # Note: cpuinfo is not installed in the TensorFlow OSS tree.\n # It is installable via pip.\n import cpuinfo # pylint: disable=g-import-not-at-top\n\n info = cpuinfo.get_cpu_info()\n cpu_info[\"cpu_info\"] = info[\"brand\"]\n cpu_info[\"mhz_per_cpu\"] = info[\"hz_advertised_raw\"][0] / 1.0e6\n\n run_info[\"machine_config\"][\"cpu_info\"] = cpu_info\n except ImportError:\n tf.logging.warn(\"'cpuinfo' not imported. CPU info will not be logged.\")","label":0},{"cid":"official/utils/logs/logger.py::_collect_gpu_info","text":"official/utils/logs/logger.py::_collect_gpu_info\ndef _collect_gpu_info(run_info):\n \"\"\"Collect local GPU information by TF device library.\"\"\"\n gpu_info = {}\n local_device_protos = device_lib.list_local_devices()\n\n gpu_info[\"count\"] = len([d for d in local_device_protos\n if d.device_type == \"GPU\"])\n # The device description usually is a JSON string, which contains the GPU\n # model info, eg:\n # \"device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0\"\n for d in local_device_protos:\n if d.device_type == \"GPU\":\n gpu_info[\"model\"] = _parse_gpu_model(d.physical_device_desc)\n # Assume all the GPU connected are same model\n break\n run_info[\"machine_config\"][\"gpu_info\"] = gpu_info","label":0},{"cid":"official/utils/logs/logger.py::_collect_memory_info","text":"official/utils/logs/logger.py::_collect_memory_info\ndef _collect_memory_info(run_info):\n try:\n # Note: psutil is not installed in the TensorFlow OSS tree.\n # It is installable via pip.\n import psutil # pylint: disable=g-import-not-at-top\n vmem = psutil.virtual_memory()\n run_info[\"machine_config\"][\"memory_total\"] = vmem.total\n run_info[\"machine_config\"][\"memory_available\"] = vmem.available\n except ImportError:\n tf.logging.warn(\"'psutil' not imported. Memory info will not be logged.\")","label":0},{"cid":"official/utils/logs/logger.py::_collect_test_environment","text":"official/utils/logs/logger.py::_collect_test_environment\ndef _collect_test_environment(run_info):\n \"\"\"Detect the local environment, eg GCE, AWS or DGX, etc.\"\"\"\n if cloud_lib.on_gcp():\n run_info[\"test_environment\"] = GCP_TEST_ENV","label":0},{"cid":"official/utils/logs/logger.py::_parse_gpu_model","text":"official/utils/logs/logger.py::_parse_gpu_model\ndef _parse_gpu_model(physical_device_desc):\n # Assume all the GPU connected are same model\n for kv in physical_device_desc.split(\",\"):\n k, _, v = kv.partition(\":\")\n if k.strip() == \"name\":\n return v.strip()\n return None","label":0},{"cid":"official/utils/logs/logger.py::_convert_to_json_dict","text":"official/utils/logs/logger.py::_convert_to_json_dict\ndef _convert_to_json_dict(input_dict):\n if input_dict:\n return [{\"name\": k, \"value\": v} for k, v in sorted(input_dict.items())]\n else:\n return []","label":0},{"cid":"research/astronet/astronet/data/preprocess.py::read_and_process_light_curve","text":"research/astronet/astronet/data/preprocess.py::read_and_process_light_curve\ndef read_and_process_light_curve(kepid, kepler_data_dir, max_gap_width=0.75):\n \"\"\"Reads a light curve, fits a B-spline and divides the curve by the spline.\n\n Args:\n kepid: Kepler id of the target star.\n kepler_data_dir: Base directory containing Kepler data. See\n kepler_io.kepler_filenames().\n max_gap_width: Gap size (in days) above which the light curve is split for\n the fitting of B-splines.\n\n Returns:\n time: 1D NumPy array; the time values of the light curve.\n flux: 1D NumPy array; the normalized flux values of the light curve.\n\n Raises:\n IOError: If the light curve files for this Kepler ID cannot be found.\n ValueError: If the spline could not be fit.\n \"\"\"\n # Read the Kepler light curve.\n file_names = kepler_io.kepler_filenames(kepler_data_dir, kepid)\n if not file_names:\n raise IOError(\"Failed to find .fits files in %s for Kepler ID %s\" %\n (kepler_data_dir, kepid))\n\n all_time, all_flux = kepler_io.read_kepler_light_curve(file_names)\n\n # Split on gaps.\n all_time, all_flux = util.split(all_time, all_flux, gap_width=max_gap_width)\n\n # Logarithmically sample candidate break point spacings between 0.5 and 20\n # days.\n bkspaces = np.logspace(np.log10(0.5), np.log10(20), num=20)\n\n # Generate spline.\n spline = kepler_spline.choose_kepler_spline(\n all_time, all_flux, bkspaces, penalty_coeff=1.0, verbose=False)[0]\n\n if spline is None:\n raise ValueError(\"Failed to fit spline with Kepler ID %s\", kepid)\n\n # Concatenate the piecewise light curve and spline.\n time = np.concatenate(all_time)\n flux = np.concatenate(all_flux)\n spline = np.concatenate(spline)\n\n # In rare cases the piecewise spline contains NaNs in places the spline could\n # not be fit. We can't normalize those points if the spline isn't defined\n # there. Instead we just remove them.\n finite_i = np.isfinite(spline)\n if not np.all(finite_i):\n tf.loggin","label":0},{"cid":"research/astronet/astronet/data/preprocess.py::phase_fold_and_sort_light_curve","text":"research/astronet/astronet/data/preprocess.py::phase_fold_and_sort_light_curve\ndef phase_fold_and_sort_light_curve(time, flux, period, t0):\n \"\"\"Phase folds a light curve and sorts by ascending time.\n\n Args:\n time: 1D NumPy array of time values.\n flux: 1D NumPy array of flux values.\n period: A positive real scalar; the period to fold over.\n t0: The center of the resulting folded vector; this value is mapped to 0.\n\n Returns:\n folded_time: 1D NumPy array of phase folded time values in\n [-period / 2, period / 2), where 0 corresponds to t0 in the original\n time array. Values are sorted in ascending order.\n folded_flux: 1D NumPy array. Values are the same as the original input\n array, but sorted by folded_time.\n \"\"\"\n # Phase fold time.\n time = util.phase_fold_time(time, period, t0)\n\n # Sort by ascending time.\n sorted_i = np.argsort(time)\n time = time[sorted_i]\n flux = flux[sorted_i]\n\n return time, flux","label":0},{"cid":"research/astronet/astronet/data/preprocess.py::generate_view","text":"research/astronet/astronet/data/preprocess.py::generate_view\ndef generate_view(time, flux, num_bins, bin_width, t_min, t_max,\n normalize=True):\n \"\"\"Generates a view of a phase-folded light curve using a median filter.\n\n Args:\n time: 1D array of time values, sorted in ascending order.\n flux: 1D array of flux values.\n num_bins: The number of intervals to divide the time axis into.\n bin_width: The width of each bin on the time axis.\n t_min: The inclusive leftmost value to consider on the time axis.\n t_max: The exclusive rightmost value to consider on the time axis.\n normalize: Whether to center the median at 0 and minimum value at -1.\n\n Returns:\n 1D NumPy array of size num_bins containing the median flux values of\n uniformly spaced bins on the phase-folded time axis.\n \"\"\"\n view = median_filter.median_filter(time, flux, num_bins, bin_width, t_min,\n t_max)\n\n if normalize:\n view -= np.median(view)\n view /= np.abs(np.min(view))\n\n return view","label":0},{"cid":"research/astronet/astronet/data/preprocess.py::global_view","text":"research/astronet/astronet/data/preprocess.py::global_view\ndef global_view(time, flux, period, num_bins=2001, bin_width_factor=1 / 2001):\n \"\"\"Generates a 'global view' of a phase folded light curve.\n\n See Section 3.3 of Shallue & Vanderburg, 2018, The Astronomical Journal.\n http://iopscience.iop.org/article/10.3847/1538-3881/aa9e09/meta\n\n Args:\n time: 1D array of time values, sorted in ascending order.\n flux: 1D array of flux values.\n period: The period of the event (in days).\n num_bins: The number of intervals to divide the time axis into.\n bin_width_factor: Width of the bins, as a fraction of period.\n\n Returns:\n 1D NumPy array of size num_bins containing the median flux values of\n uniformly spaced bins on the phase-folded time axis.\n \"\"\"\n return generate_view(\n time,\n flux,\n num_bins=num_bins,\n bin_width=period * bin_width_factor,\n t_min=-period / 2,\n t_max=period / 2)","label":0},{"cid":"research/astronet/astronet/data/preprocess.py::local_view","text":"research/astronet/astronet/data/preprocess.py::local_view\ndef local_view(time,\n flux,\n period,\n duration,\n num_bins=201,\n bin_width_factor=0.16,\n num_durations=4):\n \"\"\"Generates a 'local view' of a phase folded light curve.\n\n See Section 3.3 of Shallue & Vanderburg, 2018, The Astronomical Journal.\n http://iopscience.iop.org/article/10.3847/1538-3881/aa9e09/meta\n\n Args:\n time: 1D array of time values, sorted in ascending order.\n flux: 1D array of flux values.\n period: The period of the event (in days).\n duration: The duration of the event (in days).\n num_bins: The number of intervals to divide the time axis into.\n bin_width_factor: Width of the bins, as a fraction of duration.\n num_durations: The number of durations to consider on either side of 0 (the\n event is assumed to be centered at 0).\n\n Returns:\n 1D NumPy array of size num_bins containing the median flux values of\n uniformly spaced bins on the phase-folded time axis.\n \"\"\"\n return generate_view(\n time,\n flux,\n num_bins=num_bins,\n bin_width=duration * bin_width_factor,\n t_min=max(-period / 2, -duration * num_durations),\n t_max=min(period / 2, duration * num_durations))","label":0},{"cid":"research/astronet/light_curve_util/util.py::phase_fold_time","text":"research/astronet/light_curve_util/util.py::phase_fold_time\ndef phase_fold_time(time, period, t0):\n \"\"\"Creates a phase-folded time vector.\n\n result[i] is the unique number in [-period / 2, period / 2)\n such that result[i] = time[i] - t0 + k_i * period, for some integer k_i.\n\n Args:\n time: 1D numpy array of time values.\n period: A positive real scalar; the period to fold over.\n t0: The center of the resulting folded vector; this value is mapped to 0.\n\n Returns:\n A 1D numpy array.\n \"\"\"\n half_period = period / 2\n result = np.mod(time + (half_period - t0), period)\n result -= half_period\n return result","label":0},{"cid":"research/astronet/light_curve_util/util.py::split","text":"research/astronet/light_curve_util/util.py::split\ndef split(all_time, all_flux, gap_width=0.75):\n \"\"\"Splits a light curve on discontinuities (gaps).\n\n This function accepts a light curve that is either a single segment, or is\n piecewise defined (e.g. split by quarter breaks or gaps in the in the data).\n\n Args:\n all_time: Numpy array or list of numpy arrays; each is a sequence of time\n values.\n all_flux: Numpy array or list of numpy arrays; each is a sequence of flux\n values of the corresponding time array.\n gap_width: Minimum gap size (in time units) for a split.\n\n Returns:\n out_time: List of numpy arrays; the split time arrays.\n out_flux: List of numpy arrays; the split flux arrays.\n \"\"\"\n # Handle single-segment inputs.\n # We must use an explicit length test on all_time because implicit conversion\n # to bool fails if all_time is a numpy array, and all_time.size is not defined\n # if all_time is a list of numpy arrays.\n if len(all_time) > 0 and not isinstance(all_time[0], collections.Iterable): # pylint:disable=g-explicit-length-test\n all_time = [all_time]\n all_flux = [all_flux]\n\n out_time = []\n out_flux = []\n for time, flux in zip(all_time, all_flux):\n start = 0\n for end in range(1, len(time) + 1):\n # Choose the largest endpoint such that time[start:end] has no gaps.\n if end == len(time) or time[end] - time[end - 1] > gap_width:\n out_time.append(time[start:end])\n out_flux.append(flux[start:end])\n start = end\n\n return out_time, out_flux","label":1},{"cid":"research/astronet/light_curve_util/util.py::remove_events","text":"research/astronet/light_curve_util/util.py::remove_events\ndef remove_events(all_time, all_flux, events, width_factor=1.0):\n \"\"\"Removes events from a light curve.\n\n This function accepts either a single-segment or piecewise-defined light\n curve (e.g. one that is split by quarter breaks or gaps in the in the data).\n\n Args:\n all_time: Numpy array or list of numpy arrays; each is a sequence of time\n values.\n all_flux: Numpy array or list of numpy arrays; each is a sequence of flux\n values of the corresponding time array.\n events: List of Event objects to remove.\n width_factor: Fractional multiplier of the duration of each event to remove.\n\n Returns:\n output_time: Numpy array or list of numpy arrays; the time arrays with\n events removed.\n output_flux: Numpy array or list of numpy arrays; the flux arrays with\n events removed.\n \"\"\"\n # Handle single-segment inputs.\n # We must use an explicit length test on all_time because implicit conversion\n # to bool fails if all_time is a numpy array and all_time.size is not defined\n # if all_time is a list of numpy arrays.\n if len(all_time) > 0 and not isinstance(all_time[0], collections.Iterable): # pylint:disable=g-explicit-length-test\n all_time = [all_time]\n all_flux = [all_flux]\n single_segment = True\n else:\n single_segment = False\n\n output_time = []\n output_flux = []\n for time, flux in zip(all_time, all_flux):\n mask = np.ones_like(time, dtype=np.bool)\n for event in events:\n transit_dist = np.abs(phase_fold_time(time, event.period, event.t0))\n mask = np.logical_and(mask,\n transit_dist > 0.5 * width_factor * event.duration)\n\n if single_segment:\n output_time = time[mask]\n output_flux = flux[mask]\n else:\n output_time.append(time[mask])\n output_flux.append(flux[mask])\n\n return output_time, output_flux","label":1},{"cid":"research/astronet/light_curve_util/util.py::interpolate_masked_spline","text":"research/astronet/light_curve_util/util.py::interpolate_masked_spline\ndef interpolate_masked_spline(all_time, all_masked_time, all_masked_spline):\n \"\"\"Linearly interpolates spline values across masked points.\n\n Args:\n all_time: List of numpy arrays; each is a sequence of time values.\n all_masked_time: List of numpy arrays; each is a sequence of time values\n with some values missing (masked).\n all_masked_spline: List of numpy arrays; the masked spline values\n corresponding to all_masked_time.\n\n Returns:\n interp_spline: List of numpy arrays; each is the masked spline with missing\n points linearly interpolated.\n \"\"\"\n interp_spline = []\n for time, masked_time, masked_spline in zip(\n all_time, all_masked_time, all_masked_spline):\n if len(masked_time) > 0: # pylint:disable=g-explicit-length-test\n interp_spline.append(np.interp(time, masked_time, masked_spline))\n else:\n interp_spline.append(np.full_like(time, np.nan))\n return interp_spline","label":1},{"cid":"research/astronet/light_curve_util/util.py::count_transit_points","text":"research/astronet/light_curve_util/util.py::count_transit_points\ndef count_transit_points(time, event):\n \"\"\"Computes the number of points in each transit of a given event.\n\n Args:\n time: Sorted numpy array of time values.\n event: An Event object.\n\n Returns:\n A numpy array containing the number of time points \"in transit\" for each\n transit occurring between the first and last time values.\n\n Raises:\n ValueError: If there are more than 10**6 transits.\n \"\"\"\n t_min = np.min(time)\n t_max = np.max(time)\n\n # Tiny periods or erroneous time values could make this loop take forever.\n if (t_max - t_min) / event.period > 10**6:\n raise ValueError(\n \"Too many transits! Time range is [%.2f, %.2f] and period is %.2e.\" %\n (t_min, t_max, event.period))\n\n # Make sure t0 is in [t_min, t_min + period).\n t0 = np.mod(event.t0 - t_min, event.period) + t_min\n\n # Prepare loop variables.\n points_in_transit = []\n i, j = 0, 0\n\n for transit_midpoint in np.arange(t0, t_max, event.period):\n transit_begin = transit_midpoint - event.duration / 2\n transit_end = transit_midpoint + event.duration / 2\n\n # Move time[i] to the first point >= transit_begin.\n while time[i] < transit_begin:\n # transit_begin is guaranteed to be < np.max(t) (provided duration >= 0).\n # Therefore, i cannot go out of range.\n i += 1\n\n # Move time[j] to the first point > transit_end.\n while time[j] <= transit_end:\n j += 1\n # j went out of range. We're finished.\n if j >= len(time):\n break\n\n # The points in the current transit duration are precisely time[i:j].\n # Since j is an exclusive index, there are exactly j-i points in transit.\n points_in_transit.append(j - i)\n\n return np.array(points_in_transit)","label":0},{"cid":"research/astronet/third_party/kepler_spline/kepler_spline.py::kepler_spline","text":"research/astronet/third_party/kepler_spline/kepler_spline.py::kepler_spline\ndef kepler_spline(time, flux, bkspace=1.5, maxiter=5, outlier_cut=3):\n \"\"\"Computes a best-fit spline curve for a light curve segment.\n\n The spline is fit using an iterative process to remove outliers that may cause\n the spline to be \"pulled\" by discrepent points. In each iteration the spline\n is fit, and if there are any points where the absolute deviation from the\n median residual is at least 3*sigma (where sigma is a robust estimate of the\n standard deviation of the residuals), those points are removed and the spline\n is re-fit.\n\n Args:\n time: Numpy array; the time values of the light curve.\n flux: Numpy array; the flux (brightness) values of the light curve.\n bkspace: Spline break point spacing in time units.\n maxiter: Maximum number of attempts to fit the spline after removing badly\n fit points.\n outlier_cut: The maximum number of standard deviations from the median\n spline residual before a point is considered an outlier.\n\n Returns:\n spline: The values of the fitted spline corresponding to the input time\n values.\n mask: Boolean mask indicating the points used to fit the final spline.\n \"\"\"\n # Rescale time into [0, 1].\n t_min = np.min(time)\n t_max = np.max(time)\n time = (time - t_min) / (t_max - t_min)\n bkspace /= (t_max - t_min) # Rescale bucket spacing.\n\n # Values of the best fitting spline evaluated at the time points.\n spline = None\n\n # Mask indicating the points used to fit the spline.\n mask = None\n\n for _ in range(maxiter):\n if spline is None:\n mask = np.ones_like(time, dtype=np.bool) # Try to fit all points.\n else:\n # Choose points where the absolute deviation from the median residual is\n # less than 3*sigma, where sigma is a robust estimate of the standard\n # deviation of the residuals from the previous spline.\n residuals = flux - spline\n _, _, new_mask = robust_mean.robust_mean(res","label":1},{"cid":"research/astronet/third_party/kepler_spline/kepler_spline.py::choose_kepler_spline","text":"research/astronet/third_party/kepler_spline/kepler_spline.py::choose_kepler_spline\ndef choose_kepler_spline(all_time,\n all_flux,\n bkspaces,\n maxiter=5,\n penalty_coeff=1.0,\n verbose=True):\n \"\"\"Computes the best-fit Kepler spline across a break-point spacings.\n\n Some Kepler light curves have low-frequency variability, while others have\n very high-frequency variability (e.g. due to rapid rotation). Therefore, it is\n suboptimal to use the same break-point spacing for every star. This function\n computes the best-fit spline by fitting splines with different break-point\n spacings, calculating the Bayesian Information Criterion (BIC) for each\n spline, and choosing the break-point spacing that minimizes the BIC.\n\n This function assumes a piecewise light curve, that is, a light curve that is\n divided into different segments (e.g. split by quarter breaks or gaps in the\n in the data). A separate spline is fit for each segment.\n\n Args:\n all_time: List of 1D numpy arrays; the time values of the light curve.\n all_flux: List of 1D numpy arrays; the flux (brightness) values of the light\n curve.\n bkspaces: List of break-point spacings to try.\n maxiter: Maximum number of attempts to fit each spline after removing badly\n fit points.\n penalty_coeff: Coefficient of the penalty term for using more parameters in\n the Bayesian Information Criterion. Decreasing this value will allow\n more parameters to be used (i.e. smaller break-point spacing), and\n vice-versa.\n verbose: Whether to log individual spline errors. Note that if bkspaces\n contains many values (particularly small ones) then this may cause\n logging pollution if calling this function for many light curves.\n\n Returns:\n spline: List of numpy arrays; values of the best-fit spline corresponding to\n to the input flux arrays.\n sp","label":1},{"cid":"research/brain_coder/single_task/aggregate_experiment_results.py::make_csv_string","text":"research/brain_coder/single_task/aggregate_experiment_results.py::make_csv_string\ndef make_csv_string(table):\n \"\"\"Convert 2D list to CSV string.\"\"\"\n s = StringIO.StringIO()\n writer = csv.writer(s)\n writer.writerows(table)\n value = s.getvalue()\n s.close()\n return value","label":0},{"cid":"research/brain_coder/single_task/aggregate_experiment_results.py::process_results","text":"research/brain_coder/single_task/aggregate_experiment_results.py::process_results\ndef process_results(metrics):\n \"\"\"Extract useful information from given metrics.\n\n Args:\n metrics: List of results dicts. These should have been written to disk by\n training jobs.\n\n Returns:\n Dict mapping stats names to values.\n\n Raises:\n ValueError: If max_npe or max_global_repetitions values are inconsistant\n across dicts in the `metrics` list.\n \"\"\"\n count = len(metrics)\n success_count = 0\n total_npe = 0 # Counting NPE across all runs.\n success_npe = 0 # Counting NPE in successful runs only.\n max_npe = 0\n max_repetitions = 0\n for metric_dict in metrics:\n if not max_npe:\n max_npe = metric_dict['max_npe']\n elif max_npe != metric_dict['max_npe']:\n raise ValueError(\n 'Invalid experiment. Different reps have different max-NPE settings.')\n if not max_repetitions:\n max_repetitions = metric_dict['max_global_repetitions']\n elif max_repetitions != metric_dict['max_global_repetitions']:\n raise ValueError(\n 'Invalid experiment. Different reps have different num-repetition '\n 'settings.')\n if metric_dict['found_solution']:\n success_count += 1\n success_npe += metric_dict['npe']\n total_npe += metric_dict['npe']\n stats = {}\n stats['max_npe'] = max_npe\n stats['max_repetitions'] = max_repetitions\n stats['repetitions'] = count\n stats['successes'] = success_count # successful reps\n stats['failures'] = count - success_count # failed reps\n stats['success_npe'] = success_npe\n stats['total_npe'] = total_npe\n if success_count:\n # Only successful runs counted.\n stats['avg_success_npe'] = stats['success_npe'] / float(success_count)\n else:\n stats['avg_success_npe'] = 0.0\n if count:\n stats['success_rate'] = success_count / float(count)\n stats['avg_total_npe'] = stats['total_npe'] / float(count)\n else:\n stats['success_rate'] = 0.0\n stats['avg_total_npe'] = 0.0\n\n retur","label":0},{"cid":"research/brain_coder/single_task/aggregate_experiment_results.py::get_results_for_experiment","text":"research/brain_coder/single_task/aggregate_experiment_results.py::get_results_for_experiment\ndef get_results_for_experiment(\n models_dir, task_name, model_type='pg', max_npe='5M', desc='v0',\n name_prefix='bf_rl_paper', extra_desc=''):\n \"\"\"Get and process results for a given experiment.\n\n An experiment is a set of runs with the same hyperparameters and environment.\n It is uniquely specified by a (task_name, model_type, max_npe) triple, as\n well as an optional description.\n\n We assume that each experiment has a folder with the same name as the job that\n ran the experiment. The name is computed by\n \"%name_prefix%.%desc%-%max_npe%_%task_name%\".\n\n Args:\n models_dir: Parent directory containing experiment folders.\n task_name: String name of task (the coding env). See code_tasks.py or\n run_eval_tasks.py\n model_type: Name of the algorithm, such as 'pg', 'topk', 'ga', 'rand'.\n max_npe: String SI unit representation of the maximum NPE threshold for the\n experiment. For example, \"5M\" means 5 million.\n desc: Description.\n name_prefix: Prefix of job names. Normally leave this as default.\n extra_desc: Optional extra description at the end of the job name.\n\n Returns:\n ProcessedResults namedtuple instance, containing\n metrics: Raw dicts read from disk.\n processed: Stats computed by `process_results`.\n\n Raises:\n ValueError: If max_npe in the metrics does not match NPE in the experiment\n folder name.\n \"\"\"\n folder = name_prefix + '.{0}.{1}-{2}_{3}'.format(desc, model_type, max_npe,\n task_name)\n if extra_desc:\n folder += '.' + extra_desc\n\n results = results_lib.Results(os.path.join(models_dir, folder))\n metrics, _ = results.read_all()\n processed = process_results(metrics)\n if (not np.isclose(processed['max_npe'], misc.si_to_int(max_npe))\n and processed['repetitions']):\n raise ValueError(\n 'Invalid experiment. Max-NPE setting does not match exp","label":0},{"cid":"research/brain_coder/single_task/aggregate_experiment_results.py::get_best_code_for_experiment","text":"research/brain_coder/single_task/aggregate_experiment_results.py::get_best_code_for_experiment\ndef get_best_code_for_experiment(\n models_dir, task_name, model_type='pg', max_npe='5M', desc=0,\n name_prefix='bf_rl_paper', extra_desc=''):\n \"\"\"Like `get_results_for_experiment`, but fetches the code solutions.\"\"\"\n folder = name_prefix + '.{0}.{1}-{2}_{3}'.format(desc, model_type, max_npe,\n task_name)\n if extra_desc:\n folder += '.' + extra_desc\n\n log_dir = os.path.join(models_dir, folder, 'logs')\n search_regex = r'^solutions_([0-9])+\\.txt$'\n try:\n all_children = tf.gfile.ListDirectory(log_dir)\n except tf.errors.NotFoundError:\n return BestCodeResults(\n code=None, reward=0.0, npe=0, folder=folder, finished=False,\n error=BestCodeResultError.experiment_does_not_exist)\n solution_files = [\n fname for fname in all_children if re.search(search_regex, fname)]\n max_reward = 0.0\n npe = 0\n best_code = None\n for fname in solution_files:\n with tf.gfile.FastGFile(os.path.join(log_dir, fname), 'r') as reader:\n results = [ast.literal_eval(entry) for entry in reader]\n for res in results:\n if res['reward'] > max_reward:\n best_code = res['code']\n max_reward = res['reward']\n npe = res['npe']\n error = (\n BestCodeResultError.success if best_code\n else BestCodeResultError.no_solution_found)\n try:\n # If there is a status.txt file, check if it contains the status of the job.\n with tf.gfile.FastGFile(os.path.join(log_dir, 'status.txt'), 'r') as f:\n # Job is done, so mark this experiment as finished.\n finished = f.read().lower().strip() == 'done'\n except tf.errors.NotFoundError:\n # No status file has been written, so the experiment is not done. No need to\n # report an error here, because we do not require that experiment jobs write\n # out a status.txt file until they have finished.\n finished = False\n return BestCodeResults(\n ","label":0},{"cid":"research/brain_coder/single_task/aggregate_experiment_results.py::make_results_table","text":"research/brain_coder/single_task/aggregate_experiment_results.py::make_results_table\ndef make_results_table(\n models=None,\n tasks=None,\n max_npe='5M',\n name_prefix='bf_rl_paper',\n extra_desc='',\n models_dir='/tmp'):\n \"\"\"Creates a table of results: algorithm + version by tasks.\n\n Args:\n models: The table columns. A list of (algorithm, desc) tuples.\n tasks: The table rows. List of task names.\n max_npe: String SI unit representation of the maximum NPE threshold for the\n experiment. For example, \"5M\" means 5 million. All entries in the table\n share the same max-NPE.\n name_prefix: Name prefix used in logging directory for the experiment.\n extra_desc: Extra description added to name of logging directory for the\n experiment.\n models_dir: Parent directory containing all experiment folders.\n\n Returns:\n A 2D list holding the table cells.\n \"\"\"\n if models is None:\n models = DEFAULT_MODELS\n if tasks is None:\n tasks = DEFAULT_TASKS\n model_results = {}\n for model_type, desc in models:\n model_results[model_type] = {\n tname: get_results_for_experiment(\n models_dir, tname, model_type, max_npe, desc,\n name_prefix=name_prefix, extra_desc=extra_desc\n ).processed\n for tname in tasks}\n\n def info(stats):\n return [str(stats['repetitions']),\n '%.2f' % stats['success_rate'],\n str(int(stats['avg_total_npe']))]\n\n rows = [['max NPE: ' + max_npe]\n + misc.flatten([['{0} ({1})'.format(m, d), '', '']\n for m, d in models])]\n rows.append(\n [''] + misc.flatten([['reps', 'success rate', 'avg NPE']\n for _ in models]))\n for tname in tasks:\n rows.append(\n [tname]\n + misc.flatten([info(model_results[model][tname])\n for model, _ in models]))\n\n return rows","label":0},{"cid":"research/brain_coder/single_task/aggregate_experiment_results.py::make_results_table.info","text":"research/brain_coder/single_task/aggregate_experiment_results.py::make_results_table.info\n def info(stats):\n return [str(stats['repetitions']),\n '%.2f' % stats['success_rate'],\n str(int(stats['avg_total_npe']))]","label":0},{"cid":"research/brain_coder/single_task/aggregate_experiment_results.py::print_results_table","text":"research/brain_coder/single_task/aggregate_experiment_results.py::print_results_table\ndef print_results_table(results_table):\n \"\"\"Print human readable results table to stdout.\"\"\"\n print('')\n print('=== Results Table ===')\n print('Format: # reps [success rate, avg total NPE]')\n\n def info_str(info_row):\n # num_runs (success_rate, avg_total_npe)\n if not info_row[0]:\n return '0'\n return '%s [%s, %s]' % (str(info_row[0]).ljust(2), info_row[1], info_row[2])\n\n nc = len(results_table[0]) # num cols\n out_table = [\n [results_table[0][0]] + [results_table[0][i] for i in range(1, nc, 3)]]\n for row in results_table[2:]:\n out_table.append([row[0]] + [info_str(row[i:i+3]) for i in range(1, nc, 3)])\n\n nc = len(out_table[0]) # num cols\n col_widths = [max(len(row[col]) for row in out_table) for col in range(nc)]\n\n table_string = ''\n for row in out_table:\n table_string += ''.join(\n [row[c].ljust(col_widths[c] + 2) for c in range(nc)]) + '\\n'\n\n print(table_string)","label":0},{"cid":"research/brain_coder/single_task/aggregate_experiment_results.py::print_results_table.info_str","text":"research/brain_coder/single_task/aggregate_experiment_results.py::print_results_table.info_str\n def info_str(info_row):\n # num_runs (success_rate, avg_total_npe)\n if not info_row[0]:\n return '0'\n return '%s [%s, %s]' % (str(info_row[0]).ljust(2), info_row[1], info_row[2])","label":0},{"cid":"research/brain_coder/single_task/aggregate_experiment_results.py::main","text":"research/brain_coder/single_task/aggregate_experiment_results.py::main\ndef main(argv):\n del argv # Unused.\n\n name_prefix = FLAGS.exp_prefix\n print('Experiments prefix: %s' % name_prefix)\n\n model_types = ast.literal_eval(FLAGS.model_types)\n\n if FLAGS.data == 'success_rates':\n results_table = make_results_table(\n models=model_types, tasks=FLAGS.task_list, max_npe=FLAGS.max_npe,\n models_dir=FLAGS.models_dir,\n name_prefix=name_prefix, extra_desc='')\n with tf.gfile.FastGFile(FLAGS.csv_file, 'w') as f:\n f.write(make_csv_string(results_table))\n\n print_results_table(results_table)\n else:\n # Best code\n print('* = experiment is still running')\n print('')\n print('=== Best Synthesized Code ===')\n for model_type, desc in model_types:\n print('%s (%s)' % (model_type, desc))\n sys.stdout.flush()\n for tname in FLAGS.task_list:\n res = get_best_code_for_experiment(\n FLAGS.models_dir, tname, model_type, FLAGS.max_npe, desc,\n name_prefix=name_prefix, extra_desc='')\n unfinished_mark = '' if res.finished else ' *'\n tname += unfinished_mark\n if res.error == BestCodeResultError.success:\n print(' %s' % tname)\n print(' %s' % res.code)\n print(' R=%.6f, NPE=%s' % (res.reward, misc.int_to_si(res.npe)))\n elif res.error == BestCodeResultError.experiment_does_not_exist:\n print(' Experiment does not exist. Check arguments.')\n print(' Experiment folder: %s' % res.folder)\n break\n else:\n print(' %s' % tname)\n print(' (none)')\n sys.stdout.flush()","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::AmortizedAccountant.__init__","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::AmortizedAccountant.__init__\n def __init__(self, total_examples):\n \"\"\"Initialization. Currently only support amortized tracking.\n\n Args:\n total_examples: total number of examples.\n \"\"\"\n\n assert total_examples > 0\n self._total_examples = total_examples\n self._eps_squared_sum = tf.Variable(tf.zeros([1]), trainable=False,\n name=\"eps_squared_sum\")\n self._delta_sum = tf.Variable(tf.zeros([1]), trainable=False,\n name=\"delta_sum\")","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::AmortizedAccountant.accumulate_privacy_spending","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::AmortizedAccountant.accumulate_privacy_spending\n def accumulate_privacy_spending(self, eps_delta, unused_sigma,\n num_examples):\n \"\"\"Accumulate the privacy spending.\n\n Currently only support approximate privacy. Here we assume we use Gaussian\n noise on randomly sampled batch so we get better composition: 1. the per\n batch privacy is computed using privacy amplication via sampling bound;\n 2. the composition is done using the composition with Gaussian noise.\n TODO(liqzhang) Add a link to a document that describes the bounds used.\n\n Args:\n eps_delta: EpsDelta pair which can be tensors.\n unused_sigma: the noise sigma. Unused for this accountant.\n num_examples: the number of examples involved.\n Returns:\n a TensorFlow operation for updating the privacy spending.\n \"\"\"\n\n eps, delta = eps_delta\n with tf.control_dependencies(\n [tf.Assert(tf.greater(delta, 0),\n [\"delta needs to be greater than 0\"])]):\n amortize_ratio = (tf.cast(num_examples, tf.float32) * 1.0 /\n self._total_examples)\n # Use privacy amplification via sampling bound.\n # See Lemma 2.2 in http://arxiv.org/pdf/1405.7085v2.pdf\n # TODO(liqzhang) Add a link to a document with formal statement\n # and proof.\n amortize_eps = tf.reshape(tf.log(1.0 + amortize_ratio * (\n tf.exp(eps) - 1.0)), [1])\n amortize_delta = tf.reshape(amortize_ratio * delta, [1])\n return tf.group(*[tf.assign_add(self._eps_squared_sum,\n tf.square(amortize_eps)),\n tf.assign_add(self._delta_sum, amortize_delta)])","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::AmortizedAccountant.get_privacy_spent","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::AmortizedAccountant.get_privacy_spent\n def get_privacy_spent(self, sess, target_eps=None):\n \"\"\"Report the spending so far.\n\n Args:\n sess: the session to run the tensor.\n target_eps: the target epsilon. Unused.\n Returns:\n the list containing a single EpsDelta, with values as Python floats (as\n opposed to numpy.float64). This is to be consistent with\n MomentAccountant which can return a list of (eps, delta) pair.\n \"\"\"\n\n # pylint: disable=unused-argument\n unused_target_eps = target_eps\n eps_squared_sum, delta_sum = sess.run([self._eps_squared_sum,\n self._delta_sum])\n return [EpsDelta(math.sqrt(eps_squared_sum), float(delta_sum))]","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant.__init__","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant.__init__\n def __init__(self, total_examples, moment_orders=32):\n \"\"\"Initialize a MomentsAccountant.\n\n Args:\n total_examples: total number of examples.\n moment_orders: the order of moments to keep.\n \"\"\"\n\n assert total_examples > 0\n self._total_examples = total_examples\n self._moment_orders = (moment_orders\n if isinstance(moment_orders, (list, tuple))\n else range(1, moment_orders + 1))\n self._max_moment_order = max(self._moment_orders)\n assert self._max_moment_order < 100, \"The moment order is too large.\"\n self._log_moments = [tf.Variable(numpy.float64(0.0),\n trainable=False,\n name=(\"log_moments-%d\" % moment_order))\n for moment_order in self._moment_orders]","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant._compute_log_moment","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant._compute_log_moment\n @abc.abstractmethod\n def _compute_log_moment(self, sigma, q, moment_order):\n \"\"\"Compute high moment of privacy loss.\n\n Args:\n sigma: the noise sigma, in the multiples of the sensitivity.\n q: the sampling ratio.\n moment_order: the order of moment.\n Returns:\n log E[exp(moment_order * X)]\n \"\"\"\n pass","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant.accumulate_privacy_spending","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant.accumulate_privacy_spending\n def accumulate_privacy_spending(self, unused_eps_delta,\n sigma, num_examples):\n \"\"\"Accumulate privacy spending.\n\n In particular, accounts for privacy spending when we assume there\n are num_examples, and we are releasing the vector\n (sum_{i=1}^{num_examples} x_i) + Normal(0, stddev=l2norm_bound*sigma)\n where l2norm_bound is the maximum l2_norm of each example x_i, and\n the num_examples have been randomly selected out of a pool of\n self.total_examples.\n\n Args:\n unused_eps_delta: EpsDelta pair which can be tensors. Unused\n in this accountant.\n sigma: the noise sigma, in the multiples of the sensitivity (that is,\n if the l2norm sensitivity is k, then the caller must have added\n Gaussian noise with stddev=k*sigma to the result of the query).\n num_examples: the number of examples involved.\n Returns:\n a TensorFlow operation for updating the privacy spending.\n \"\"\"\n q = tf.cast(num_examples, tf.float64) * 1.0 / self._total_examples\n\n moments_accum_ops = []\n for i in range(len(self._log_moments)):\n moment = self._compute_log_moment(sigma, q, self._moment_orders[i])\n moments_accum_ops.append(tf.assign_add(self._log_moments[i], moment))\n return tf.group(*moments_accum_ops)","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant._compute_delta","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant._compute_delta\n def _compute_delta(self, log_moments, eps):\n \"\"\"Compute delta for given log_moments and eps.\n\n Args:\n log_moments: the log moments of privacy loss, in the form of pairs\n of (moment_order, log_moment)\n eps: the target epsilon.\n Returns:\n delta\n \"\"\"\n min_delta = 1.0\n for moment_order, log_moment in log_moments:\n if math.isinf(log_moment) or math.isnan(log_moment):\n sys.stderr.write(\"The %d-th order is inf or Nan\\n\" % moment_order)\n continue\n if log_moment < moment_order * eps:\n min_delta = min(min_delta,\n math.exp(log_moment - moment_order * eps))\n return min_delta","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant._compute_eps","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant._compute_eps\n def _compute_eps(self, log_moments, delta):\n min_eps = float(\"inf\")\n for moment_order, log_moment in log_moments:\n if math.isinf(log_moment) or math.isnan(log_moment):\n sys.stderr.write(\"The %d-th order is inf or Nan\\n\" % moment_order)\n continue\n min_eps = min(min_eps, (log_moment - math.log(delta)) / moment_order)\n return min_eps","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant.get_privacy_spent","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant.get_privacy_spent\n def get_privacy_spent(self, sess, target_eps=None, target_deltas=None):\n \"\"\"Compute privacy spending in (e, d)-DP form for a single or list of eps.\n\n Args:\n sess: the session to run the tensor.\n target_eps: a list of target epsilon's for which we would like to\n compute corresponding delta value.\n target_deltas: a list of target deltas for which we would like to\n compute the corresponding eps value. Caller must specify\n either target_eps or target_delta.\n Returns:\n A list of EpsDelta pairs.\n \"\"\"\n assert (target_eps is None) ^ (target_deltas is None)\n eps_deltas = []\n log_moments = sess.run(self._log_moments)\n log_moments_with_order = zip(self._moment_orders, log_moments)\n if target_eps is not None:\n for eps in target_eps:\n eps_deltas.append(\n EpsDelta(eps, self._compute_delta(log_moments_with_order, eps)))\n else:\n assert target_deltas\n for delta in target_deltas:\n eps_deltas.append(\n EpsDelta(self._compute_eps(log_moments_with_order, delta), delta))\n return eps_deltas","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::GaussianMomentsAccountant.__init__","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::GaussianMomentsAccountant.__init__\n def __init__(self, total_examples, moment_orders=32):\n \"\"\"Initialization.\n\n Args:\n total_examples: total number of examples.\n moment_orders: the order of moments to keep.\n \"\"\"\n super(self.__class__, self).__init__(total_examples, moment_orders)\n self._binomial_table = utils.GenerateBinomialTable(self._max_moment_order)","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::GaussianMomentsAccountant._differential_moments","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::GaussianMomentsAccountant._differential_moments\n def _differential_moments(self, sigma, s, t):\n \"\"\"Compute 0 to t-th differential moments for Gaussian variable.\n\n E[(P(x+s)/P(x+s-1)-1)^t]\n = sum_{i=0}^t (t choose i) (-1)^{t-i} E[(P(x+s)/P(x+s-1))^i]\n = sum_{i=0}^t (t choose i) (-1)^{t-i} E[exp(-i*(2*x+2*s-1)/(2*sigma^2))]\n = sum_{i=0}^t (t choose i) (-1)^{t-i} exp(i(i+1-2*s)/(2 sigma^2))\n Args:\n sigma: the noise sigma, in the multiples of the sensitivity.\n s: the shift.\n t: 0 to t-th moment.\n Returns:\n 0 to t-th moment as a tensor of shape [t+1].\n \"\"\"\n assert t <= self._max_moment_order, (\"The order of %d is out \"\n \"of the upper bound %d.\"\n % (t, self._max_moment_order))\n binomial = tf.slice(self._binomial_table, [0, 0],\n [t + 1, t + 1])\n signs = numpy.zeros((t + 1, t + 1), dtype=numpy.float64)\n for i in range(t + 1):\n for j in range(t + 1):\n signs[i, j] = 1.0 - 2 * ((i - j) % 2)\n exponents = tf.constant([j * (j + 1.0 - 2.0 * s) / (2.0 * sigma * sigma)\n for j in range(t + 1)], dtype=tf.float64)\n # x[i, j] = binomial[i, j] * signs[i, j] = (i choose j) * (-1)^{i-j}\n x = tf.multiply(binomial, signs)\n # y[i, j] = x[i, j] * exp(exponents[j])\n # = (i choose j) * (-1)^{i-j} * exp(j(j-1)/(2 sigma^2))\n # Note: this computation is done by broadcasting pointwise multiplication\n # between [t+1, t+1] tensor and [t+1] tensor.\n y = tf.multiply(x, tf.exp(exponents))\n # z[i] = sum_j y[i, j]\n # = sum_j (i choose j) * (-1)^{i-j} * exp(j(j-1)/(2 sigma^2))\n z = tf.reduce_sum(y, 1)\n return z","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::GaussianMomentsAccountant._compute_log_moment","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::GaussianMomentsAccountant._compute_log_moment\n def _compute_log_moment(self, sigma, q, moment_order):\n \"\"\"Compute high moment of privacy loss.\n\n Args:\n sigma: the noise sigma, in the multiples of the sensitivity.\n q: the sampling ratio.\n moment_order: the order of moment.\n Returns:\n log E[exp(moment_order * X)]\n \"\"\"\n assert moment_order <= self._max_moment_order, (\"The order of %d is out \"\n \"of the upper bound %d.\"\n % (moment_order,\n self._max_moment_order))\n binomial_table = tf.slice(self._binomial_table, [moment_order, 0],\n [1, moment_order + 1])\n # qs = [1 q q^2 ... q^L] = exp([0 1 2 ... L] * log(q))\n qs = tf.exp(tf.constant([i * 1.0 for i in range(moment_order + 1)],\n dtype=tf.float64) * tf.cast(\n tf.log(q), dtype=tf.float64))\n moments0 = self._differential_moments(sigma, 0.0, moment_order)\n term0 = tf.reduce_sum(binomial_table * qs * moments0)\n moments1 = self._differential_moments(sigma, 1.0, moment_order)\n term1 = tf.reduce_sum(binomial_table * qs * moments1)\n return tf.squeeze(tf.log(tf.cast(q * term0 + (1.0 - q) * term1,\n tf.float64)))","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::DummyAccountant.accumulate_privacy_spending","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::DummyAccountant.accumulate_privacy_spending\n def accumulate_privacy_spending(self, *unused_args):\n return tf.no_op()","label":0},{"cid":"research/differential_privacy/privacy_accountant/tf/accountant.py::DummyAccountant.get_privacy_spent","text":"research/differential_privacy/privacy_accountant/tf/accountant.py::DummyAccountant.get_privacy_spent\n def get_privacy_spent(self, unused_sess, **unused_kwargs):\n return [EpsDelta(numpy.inf, 1.0)]","label":0},{"cid":"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::run_eval","text":"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::run_eval\ndef run_eval(run_dir, checkpoint_dir, hparams):\n \"\"\"Runs the eval loop.\n\n Args:\n run_dir: The directory where eval specific logs are placed\n checkpoint_dir: The directory where the checkpoints are stored\n hparams: The hyperparameters struct.\n\n Raises:\n ValueError: if hparams.arch is not recognized.\n \"\"\"\n for checkpoint_path in slim.evaluation.checkpoints_iterator(\n checkpoint_dir, FLAGS.eval_interval_secs):\n with tf.Graph().as_default():\n #########################\n # Preprocess the inputs #\n #########################\n target_dataset = dataset_factory.get_dataset(\n FLAGS.target_dataset,\n split_name=FLAGS.target_split_name,\n dataset_dir=FLAGS.dataset_dir)\n target_images, target_labels = dataset_factory.provide_batch(\n FLAGS.target_dataset, FLAGS.target_split_name, FLAGS.dataset_dir,\n FLAGS.num_readers, hparams.batch_size,\n FLAGS.num_preprocessing_threads)\n num_target_classes = target_dataset.num_classes\n target_labels['class'] = tf.argmax(target_labels['classes'], 1)\n del target_labels['classes']\n\n if hparams.arch not in ['dcgan']:\n source_dataset = dataset_factory.get_dataset(\n FLAGS.source_dataset,\n split_name=FLAGS.source_split_name,\n dataset_dir=FLAGS.dataset_dir)\n num_source_classes = source_dataset.num_classes\n source_images, source_labels = dataset_factory.provide_batch(\n FLAGS.source_dataset, FLAGS.source_split_name, FLAGS.dataset_dir,\n FLAGS.num_readers, hparams.batch_size,\n FLAGS.num_preprocessing_threads)\n source_labels['class'] = tf.argmax(source_labels['classes'], 1)\n del source_labels['classes']\n if num_source_classes != num_target_classes:\n raise ValueError(\n 'Input and output datasets must have same number of classes')\n el","label":0},{"cid":"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::to_degrees","text":"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::to_degrees\ndef to_degrees(log_quaternion_loss):\n \"\"\"Converts a log quaternion distance to an angle.\n\n Args:\n log_quaternion_loss: The log quaternion distance between two\n unit quaternions (or a batch of pairs of quaternions).\n\n Returns:\n The angle in degrees of the implied angle-axis representation.\n \"\"\"\n return tf.acos(-(tf.exp(log_quaternion_loss) - 1)) * 2 * 180 / math.pi","label":0},{"cid":"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::create_metrics","text":"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::create_metrics\ndef create_metrics(end_points, source_labels, target_labels, hparams):\n \"\"\"Create metrics for the model.\n\n Args:\n end_points: A dictionary of end point name to tensor\n source_labels: Labels for source images. batch_size x 1\n target_labels: Labels for target images. batch_size x 1\n hparams: The hyperparameters struct.\n\n Returns:\n Tuple of (names_to_values, names_to_updates), dictionaries that map a metric\n name to its value and update op, respectively\n\n \"\"\"\n ###########################################\n # Evaluate the Domain Prediction Accuracy #\n ###########################################\n batch_size = hparams.batch_size\n names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({\n ('eval/Domain_Accuracy-Transferred'):\n tf.contrib.metrics.streaming_accuracy(\n tf.to_int32(\n tf.round(tf.sigmoid(end_points[\n 'transferred_domain_logits']))),\n tf.zeros(batch_size, dtype=tf.int32)),\n ('eval/Domain_Accuracy-Target'):\n tf.contrib.metrics.streaming_accuracy(\n tf.to_int32(\n tf.round(tf.sigmoid(end_points['target_domain_logits']))),\n tf.ones(batch_size, dtype=tf.int32))\n })\n\n ################################\n # Evaluate the task classifier #\n ################################\n if 'source_task_logits' in end_points:\n metric_name = 'eval/Task_Accuracy-Source'\n names_to_values[metric_name], names_to_updates[\n metric_name] = tf.contrib.metrics.streaming_accuracy(\n tf.argmax(end_points['source_task_logits'], 1),\n source_labels['class'])\n\n if 'transferred_task_logits' in end_points:\n metric_name = 'eval/Task_Accuracy-Transferred'\n names_to_values[metric_name], names_to_updates[\n metric_name] = tf.contrib.metrics.streaming_accuracy(\n tf.argmax(end_points['transferred_t","label":0},{"cid":"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::main","text":"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::main\ndef main(_):\n tf.logging.set_verbosity(tf.logging.INFO)\n hparams = create_hparams(FLAGS.hparams)\n run_eval(\n run_dir=FLAGS.eval_dir,\n checkpoint_dir=FLAGS.checkpoint_dir,\n hparams=hparams)","label":0},{"cid":"research/syntaxnet/dragnn/python/runtime_support.py::add_hooks","text":"research/syntaxnet/dragnn/python/runtime_support.py::add_hooks\ndef add_hooks(component, cell_subgraph_spec):\n \"\"\"Adds \"hook\" nodes to the graph, for use by the runtime.\n\n The runtime hook nodes are not on the path to any required output, and will\n not be called when running TF-based DRAGNN. As long as the TF graph is not\n pruned, however, the DRAGNN runtime can call them.\n\n Runtime hook nodes can perform any TF computation. Possible uses include:\n * Applying stable names to existing tensors (e.g., via tf.identity()).\n * Converting variable data from a TF-friendly or training-friendly format\n into a runtime-friendly format.\n\n NB: There are several restrictions on the context in which this function is\n called. In brief, call ComponentBuilderBase._add_runtime_hooks() at the top\n of each ComponentBuilderSubclass.build_*() method. In detail, this:\n * Must be called in the variable scope of the |component|, so variable\n references in component.get_variable() work.\n * Must be called, possibly transitively, from one of the |component|'s\n build_*() methods, so MasterBuilder.read_from_avg is set properly for\n component.get_variable().\n * Must not be called from within a tf.while_loop(), or the hook nodes will\n not work. In particular, NetworkUnitInterface.create() is called from a\n tf.while_loop() in DynamicComponentBuilder.\n\n Args:\n component: Component for which to add hooks.\n cell_subgraph_spec: CellSubgraphSpec for which to add hooks.\n \"\"\"\n for channel_id, feature_spec in enumerate(component.spec.linked_feature):\n if feature_spec.embedding_dim != -1:\n _add_hooks_for_linked_embedding_matrix(component, channel_id)\n\n for channel_id, feature_spec in enumerate(component.spec.fixed_feature):\n if feature_spec.embedding_dim != -1:\n _add_hooks_for_fixed_embedding_matrix(component, channel_id)\n\n for params in component.network.params:\n _add_hooks_for_trainable_params(component, params)\n\n for pa","label":0},{"cid":"research/syntaxnet/dragnn/python/runtime_support.py::_blocked_and_dtype_transformations","text":"research/syntaxnet/dragnn/python/runtime_support.py::_blocked_and_dtype_transformations\ndef _blocked_and_dtype_transformations(tensor):\n \"\"\"Yields variants of a tensor, for standard blocking/dtype variants.\n\n Args:\n tensor (tf.Tensor): Input tensor.\n\n Yields:\n (modified_tensor, suffix) pairs, where `modified_tensor` is a transformed\n version of the input, and `suffix` is a string like \"/blocked32\".\n \"\"\"\n for blocking_level in (32, 48):\n blocked = make_padded_blocked_matrix(tensor, blocking_level)\n bfloat16_blocked = tf.to_bfloat16(bfloat16_permutation(blocked))\n yield blocked, '/blocked{}'.format(blocking_level)\n yield bfloat16_blocked, '/blocked{}/bfloat16'.format(blocking_level)","label":0},{"cid":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_linked_embedding_matrix","text":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_linked_embedding_matrix\ndef _add_hooks_for_linked_embedding_matrix(component, channel_id):\n \"\"\"Adds runtime hooks for a linked embedding matrix.\n\n The computation performed by network_units.pass_through_embedding_matrix() is\n equivalent to the following:\n\n for i in range(stride):\n if step_idx[i] == -1:\n outputs[i,:] = out_of_bounds_vector\n else:\n outputs[i,:] = tf.matmul(act_block[i,:], weight_matrix)\n\n The implementation uses clever arithmetic to do this in one matmul per batch.\n Specifically, the weight_matrix is extended with the out_of_bounds_vector and\n each activation vector is extended with a 0/1 out-of-bounds indicator. Then,\n multiplying the two suffices, assuming that act_block[i,:] is set to zero for\n out-of-bounds links.\n\n While this works well for training and high-throughput batched computation, it\n isn't the best for the runtime:\n * Appending a 0/1 indicator to the input activation vector requires a copy.\n Ideally, we could use the input activation vector by reference alone.\n * In order to access to the |out_of_bounds_vector| as a contiguous array,\n the runtime must load the linked embedding matrix in row-major format,\n which may not be the fastest format for arithmetic.\n * The dimensions of the extended-by-1 matrix and vector are likely to be\n pessimal. Most dimensions are specified as 2^n, and adding one element\n produces maximal padding on the trailing elements, which in turn wastes\n memory, reduces cache utilization, etc.\n\n Therefore, in the runtime we split the linked embedding matrix into a separate\n weight matrix and out-of-bounds vector.\n\n Args:\n component: Component for which to add hooks.\n channel_id: Linked embedding channel for which to add hooks.\n \"\"\"\n var_name = network_units.linked_embeddings_name(channel_id)\n extended_matrix = component.get_variable(var_name)\n extended_num_rows =","label":0},{"cid":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_fixed_embedding_matrix","text":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_fixed_embedding_matrix\ndef _add_hooks_for_fixed_embedding_matrix(component, channel_id):\n \"\"\"Adds runtime hooks for a fixed embedding matrix.\n\n The hooks remove the last row from the embedding matrix. The extra row was\n probably intended for out-of-vocabulary items, but those are handled in the\n feature system and the extra row is never used.\n\n Args:\n component: Component for which to add hooks.\n channel_id: Fixed embedding channel for which to add hooks.\n \"\"\"\n var_name = network_units.fixed_embeddings_name(channel_id)\n extended_matrix = component.get_variable(var_name)\n extended_num_rows = tf.shape(extended_matrix)[0]\n matrix = tf.slice(extended_matrix, [0, 0], [extended_num_rows - 1, -1])\n\n # TODO(googleuser): If the extra row is removed from the variable itself, remove\n # the tf.slice() and point the hook directly at the variable.\n _add_hook_node(matrix, _get_hook_name(component, var_name, '/trimmed'))","label":0},{"cid":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_derived_parameter","text":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_derived_parameter\ndef _add_hooks_for_derived_parameter(getter):\n \"\"\"Adds hooks for derived parameters.\n\n Derived parameters are typically slight format modifications of regular\n parameters, exposed because doing the computation in Python is more convenient\n than as VariableStore wrappers.\n\n Args:\n getter: Function which, when called, will return the derived tensor.\n \"\"\"\n parameter = getter()\n full_name = parameter.op.name\n\n def _hook_name(base_name):\n \"\"\"Returns a hook node name constructed from a base name.\"\"\"\n return full_name + base_name\n\n if parameter.shape.ndims != 2:\n tf.logging.info('Not adding matrix hooks for derived parameter %s',\n full_name)\n return\n\n _add_hook_node(tf.transpose(parameter), _hook_name('/transposed'))\n for blocked, blocked_suffix in _blocked_and_dtype_transformations(parameter):\n _add_hook_node(blocked, _hook_name('/matrix' + blocked_suffix))","label":0},{"cid":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_derived_parameter._hook_name","text":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_derived_parameter._hook_name\n def _hook_name(base_name):\n \"\"\"Returns a hook node name constructed from a base name.\"\"\"\n return full_name + base_name","label":0},{"cid":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_trainable_params","text":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_trainable_params\ndef _add_hooks_for_trainable_params(component, params):\n \"\"\"Adds runtime hooks for a variable of trainable parameters.\n\n Ignores parameters that are not statically-deducible as matrices.\n\n Args:\n component: Component for which to add hooks.\n params: Variable for which to add hooks.\n \"\"\"\n full_name = params.op.name\n matrix = component.get_variable(var_params=params)\n\n # Only add hooks for tensors that are statically-deducible as matrices.\n if params.shape.ndims != 2:\n tf.logging.info('Not adding hooks for trainable params %s', full_name)\n return\n\n # Infer the suffix to append to variable names, if any, based on whether the\n # possibly-averaged |matrix| is named differently than the |params|.\n suffix = re.sub('^' + re.escape(full_name), '', matrix.op.name)\n check.Ne(suffix, matrix.op.name,\n 'Failed to find suffix for params %s' % full_name)\n\n def _hook_name(base_name):\n \"\"\"Returns a hook node name constructed from a base name.\"\"\"\n return full_name + base_name + suffix\n\n # Add the matrix and its transpose.\n transposed = tf.transpose(matrix)\n _add_hook_node(matrix, _hook_name('/matrix'))\n _add_hook_node(transposed, _hook_name('/transposed'))\n\n # Add blocked versions of the matrix and its transpose.\n for blocked, blocked_suffix in _blocked_and_dtype_transformations(matrix):\n _add_hook_node(blocked, _hook_name('/matrix' + blocked_suffix))\n for blocked, blocked_suffix in _blocked_and_dtype_transformations(transposed):\n _add_hook_node(blocked, _hook_name('/transposed' + blocked_suffix))\n\n # Also add hooks for the original shapes, which are obscured by padding.\n _add_hook_node(tf.shape(matrix), _hook_name('/matrix/shape'))\n _add_hook_node(tf.shape(transposed), _hook_name('/transposed/shape'))","label":0},{"cid":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_trainable_params._hook_name","text":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_trainable_params._hook_name\n def _hook_name(base_name):\n \"\"\"Returns a hook node name constructed from a base name.\"\"\"\n return full_name + base_name + suffix","label":0},{"cid":"research/syntaxnet/dragnn/python/runtime_support.py::make_padded_blocked_matrix","text":"research/syntaxnet/dragnn/python/runtime_support.py::make_padded_blocked_matrix\ndef make_padded_blocked_matrix(matrix, block_size):\n \"\"\"Converts a matrix to padded column-blocked format.\n\n For example, given a [64,127] matrix and block_size=16, this function returns\n an [8,64,16] tensor where the 8 inner sub-matrices, when concatenated left to\n right, re-constitute the original matrix. Note that the 8th sub-matrix has a\n final column of padding.\n\n Args:\n matrix: The matrix to convert.\n block_size: The number of columns per block.\n\n Returns:\n Padded column-blocked matrix.\n \"\"\"\n shape = tf.shape(matrix)\n num_rows = shape[0]\n num_columns = shape[1]\n\n # Compute the amount of padding and resulting number of blocks.\n last_block_size = num_columns % block_size\n padding_size = (block_size - last_block_size) % block_size\n num_blocks = (num_columns + padding_size) // block_size\n\n # Somehow the obvious approach based on tf.split() and tf.stack() doesn't work\n # (seems that the number of splits needs to be statically-known), but this\n # alternative based on tf.transpose() and tf.reshape() does. Continuing the\n # example from the docstring...\n padded = tf.pad(matrix, [[0, 0], [0, padding_size]]) # [64,127] => [64,128]\n transposed = tf.transpose(padded) # => [128,64]\n blocked = tf.reshape(transposed, [num_blocks, block_size,\n num_rows]) # => [8,16,64]\n return tf.transpose(blocked, [0, 2, 1]) # => [8,64,16]","label":0},{"cid":"research/syntaxnet/dragnn/python/runtime_support.py::bfloat16_permutation","text":"research/syntaxnet/dragnn/python/runtime_support.py::bfloat16_permutation\ndef bfloat16_permutation(tensor):\n \"\"\"Permutes values in the last dimension of a tensor.\n\n This permutation is used so that we can directly use unpacklo/unpackhi AVX2\n instructions on the matrix coefficients. These unpacking instructions\n effectively permute the data. See FastUnpackPermutation() and\n AvxFloatVecArray::Load(const TruncatedFloat16 *) in avx_vector_array.h for\n more details.\n\n Args:\n tensor: Blocked matrix, the result of make_padded_blocked_matrix(). Must\n have its last dimension a multiple of 16.\n\n Returns:\n Permuted matrix, suitable for calling tf.to_bfloat16() on. For testing\n convenience we don't do so in this method.\n\n Raises:\n ValueError: If the matrix's block dimension is not a multiple of 16.\n \"\"\"\n orig_shape = tensor.shape\n if tensor.shape[-1] % 16 != 0:\n raise ValueError('Bad block dimension, must be divisible by 16')\n permutation = [0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15]\n indices = tf.constant(\n [16 * (i // 16) + permutation[i % 16] for i in xrange(orig_shape[-1])])\n return tf.gather(tensor, indices, axis=len(orig_shape) - 1)","label":0},{"cid":"research/syntaxnet/dragnn/python/runtime_support.py::_get_hook_name","text":"research/syntaxnet/dragnn/python/runtime_support.py::_get_hook_name\ndef _get_hook_name(component, variable_name, suffix):\n \"\"\"Builds the name of a hook node.\n\n Specifically, the name of the hook node is:\n\n <component.name>/<variable_name><suffix><remainder>\n\n where <remainder> is whatever follows <variable_name> in the name of the op\n that produces the named variable. Recall that component.get_variable() may\n return either the original variable or its moving average. These might have\n names like:\n\n foo_component/bar_variable\n foo_component/bar_variable/ExponentialMovingAverage\n\n In the examples above, the <remainder> is \"\" for the original variable and\n \"/ExponentialMovingAverage\" for its moving average. Calling this function\n with suffix=\"/baz_suffix\" in either case would add hook nodes named:\n\n foo_component/bar_variable/baz_suffix\n foo_component/bar_variable/baz_suffix/ExponentialMovingAverage\n\n Note that the suffix is inserted after the variable name, not necessarily at\n the end of the entire op name.\n\n Args:\n component: Component that the hook node belongs to.\n variable_name: Variable that the hook node name is based on.\n suffix: Suffix to append to the variable name.\n\n Returns:\n Name of the hook node.\n \"\"\"\n variable = component.get_variable(variable_name)\n full_name = variable.op.name\n prefix = component.name + '/' + variable_name\n hook_name = re.sub('^' + re.escape(prefix), prefix + suffix, full_name)\n\n # If re.sub() did not match anything, it returns the unmodified input (i.e.,\n # |full_name|). Enforce that some change was made.\n check.Ne(\n full_name, hook_name,\n 'Failed to match expected variable prefix \"{}\" in variable \"{}\"'.format(\n prefix, full_name))\n\n return hook_name","label":0},{"cid":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hook_node","text":"research/syntaxnet/dragnn/python/runtime_support.py::_add_hook_node\ndef _add_hook_node(tensor, fully_qualified_name):\n \"\"\"Adds a hook node that outputs a tensor with a fully-qualified name.\"\"\"\n # Since the name is fully-qualified, insert the hook node into the top-level\n # name scope.\n with tf.name_scope(None):\n tf.identity(tensor, name=fully_qualified_name)","label":0}],"string":"[\n {\n \"cid\": \"official/utils/logs/logger.py::config_benchmark_logger\",\n \"text\": \"official/utils/logs/logger.py::config_benchmark_logger\\ndef config_benchmark_logger(flag_obj=None):\\n \\\"\\\"\\\"Config the global benchmark logger.\\\"\\\"\\\"\\n _logger_lock.acquire()\\n try:\\n global _benchmark_logger\\n if not flag_obj:\\n flag_obj = FLAGS\\n\\n if (not hasattr(flag_obj, \\\"benchmark_logger_type\\\") or\\n flag_obj.benchmark_logger_type == \\\"BaseBenchmarkLogger\\\"):\\n _benchmark_logger = BaseBenchmarkLogger()\\n elif flag_obj.benchmark_logger_type == \\\"BenchmarkFileLogger\\\":\\n _benchmark_logger = BenchmarkFileLogger(flag_obj.benchmark_log_dir)\\n elif flag_obj.benchmark_logger_type == \\\"BenchmarkBigQueryLogger\\\":\\n from official.benchmark import benchmark_uploader as bu # pylint: disable=g-import-not-at-top\\n bq_uploader = bu.BigQueryUploader(gcp_project=flag_obj.gcp_project)\\n _benchmark_logger = BenchmarkBigQueryLogger(\\n bigquery_uploader=bq_uploader,\\n bigquery_data_set=flag_obj.bigquery_data_set,\\n bigquery_run_table=flag_obj.bigquery_run_table,\\n bigquery_run_status_table=flag_obj.bigquery_run_status_table,\\n bigquery_metric_table=flag_obj.bigquery_metric_table,\\n run_id=str(uuid.uuid4()))\\n else:\\n raise ValueError(\\\"Unrecognized benchmark_logger_type: %s\\\"\\n % flag_obj.benchmark_logger_type)\\n\\n finally:\\n _logger_lock.release()\\n return _benchmark_logger\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::get_benchmark_logger\",\n \"text\": \"official/utils/logs/logger.py::get_benchmark_logger\\ndef get_benchmark_logger():\\n if not _benchmark_logger:\\n config_benchmark_logger()\\n return _benchmark_logger\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::benchmark_context\",\n \"text\": \"official/utils/logs/logger.py::benchmark_context\\n@contextlib.contextmanager\\ndef benchmark_context(flag_obj):\\n \\\"\\\"\\\"Context of benchmark, which will update status of the run accordingly.\\\"\\\"\\\"\\n benchmark_logger = config_benchmark_logger(flag_obj)\\n try:\\n yield\\n benchmark_logger.on_finish(RUN_STATUS_SUCCESS)\\n except Exception: # pylint: disable=broad-except\\n # Catch all the exception, update the run status to be failure, and re-raise\\n benchmark_logger.on_finish(RUN_STATUS_FAILURE)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::BaseBenchmarkLogger.log_evaluation_result\",\n \"text\": \"official/utils/logs/logger.py::BaseBenchmarkLogger.log_evaluation_result\\n def log_evaluation_result(self, eval_results):\\n \\\"\\\"\\\"Log the evaluation result.\\n\\n The evaluate result is a dictionary that contains metrics defined in\\n model_fn. It also contains a entry for global_step which contains the value\\n of the global step when evaluation was performed.\\n\\n Args:\\n eval_results: dict, the result of evaluate.\\n \\\"\\\"\\\"\\n if not isinstance(eval_results, dict):\\n tf.logging.warning(\\\"eval_results should be dictionary for logging. \\\"\\n \\\"Got %s\\\", type(eval_results))\\n return\\n global_step = eval_results[tf.GraphKeys.GLOBAL_STEP]\\n for key in sorted(eval_results):\\n if key != tf.GraphKeys.GLOBAL_STEP:\\n self.log_metric(key, eval_results[key], global_step=global_step)\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::BaseBenchmarkLogger.log_metric\",\n \"text\": \"official/utils/logs/logger.py::BaseBenchmarkLogger.log_metric\\n def log_metric(self, name, value, unit=None, global_step=None, extras=None):\\n \\\"\\\"\\\"Log the benchmark metric information to local file.\\n\\n Currently the logging is done in a synchronized way. This should be updated\\n to log asynchronously.\\n\\n Args:\\n name: string, the name of the metric to log.\\n value: number, the value of the metric. The value will not be logged if it\\n is not a number type.\\n unit: string, the unit of the metric, E.g \\\"image per second\\\".\\n global_step: int, the global_step when the metric is logged.\\n extras: map of string:string, the extra information about the metric.\\n \\\"\\\"\\\"\\n metric = _process_metric_to_json(name, value, unit, global_step, extras)\\n if metric:\\n tf.logging.info(\\\"Benchmark metric: %s\\\", metric)\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::BaseBenchmarkLogger.log_run_info\",\n \"text\": \"official/utils/logs/logger.py::BaseBenchmarkLogger.log_run_info\\n def log_run_info(self, model_name, dataset_name, run_params, test_id=None):\\n tf.logging.info(\\\"Benchmark run: %s\\\",\\n _gather_run_info(model_name, dataset_name, run_params,\\n test_id))\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::BaseBenchmarkLogger.on_finish\",\n \"text\": \"official/utils/logs/logger.py::BaseBenchmarkLogger.on_finish\\n def on_finish(self, status):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::BenchmarkFileLogger.__init__\",\n \"text\": \"official/utils/logs/logger.py::BenchmarkFileLogger.__init__\\n def __init__(self, logging_dir):\\n super(BenchmarkFileLogger, self).__init__()\\n self._logging_dir = logging_dir\\n if not tf.gfile.IsDirectory(self._logging_dir):\\n tf.gfile.MakeDirs(self._logging_dir)\\n self._metric_file_handler = tf.gfile.GFile(\\n os.path.join(self._logging_dir, METRIC_LOG_FILE_NAME), \\\"a\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::BenchmarkFileLogger.log_metric\",\n \"text\": \"official/utils/logs/logger.py::BenchmarkFileLogger.log_metric\\n def log_metric(self, name, value, unit=None, global_step=None, extras=None):\\n \\\"\\\"\\\"Log the benchmark metric information to local file.\\n\\n Currently the logging is done in a synchronized way. This should be updated\\n to log asynchronously.\\n\\n Args:\\n name: string, the name of the metric to log.\\n value: number, the value of the metric. The value will not be logged if it\\n is not a number type.\\n unit: string, the unit of the metric, E.g \\\"image per second\\\".\\n global_step: int, the global_step when the metric is logged.\\n extras: map of string:string, the extra information about the metric.\\n \\\"\\\"\\\"\\n metric = _process_metric_to_json(name, value, unit, global_step, extras)\\n if metric:\\n try:\\n json.dump(metric, self._metric_file_handler)\\n self._metric_file_handler.write(\\\"\\\\n\\\")\\n self._metric_file_handler.flush()\\n except (TypeError, ValueError) as e:\\n tf.logging.warning(\\\"Failed to dump metric to log file: \\\"\\n \\\"name %s, value %s, error %s\\\", name, value, e)\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::BenchmarkFileLogger.log_run_info\",\n \"text\": \"official/utils/logs/logger.py::BenchmarkFileLogger.log_run_info\\n def log_run_info(self, model_name, dataset_name, run_params, test_id=None):\\n \\\"\\\"\\\"Collect most of the TF runtime information for the local env.\\n\\n The schema of the run info follows official/benchmark/datastore/schema.\\n\\n Args:\\n model_name: string, the name of the model.\\n dataset_name: string, the name of dataset for training and evaluation.\\n run_params: dict, the dictionary of parameters for the run, it could\\n include hyperparameters or other params that are important for the run.\\n test_id: string, the unique name of the test run by the combination of key\\n parameters, eg batch size, num of GPU. It is hardware independent.\\n \\\"\\\"\\\"\\n run_info = _gather_run_info(model_name, dataset_name, run_params, test_id)\\n\\n with tf.gfile.GFile(os.path.join(\\n self._logging_dir, BENCHMARK_RUN_LOG_FILE_NAME), \\\"w\\\") as f:\\n try:\\n json.dump(run_info, f)\\n f.write(\\\"\\\\n\\\")\\n except (TypeError, ValueError) as e:\\n tf.logging.warning(\\\"Failed to dump benchmark run info to log file: %s\\\",\\n e)\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::BenchmarkFileLogger.on_finish\",\n \"text\": \"official/utils/logs/logger.py::BenchmarkFileLogger.on_finish\\n def on_finish(self, status):\\n self._metric_file_handler.flush()\\n self._metric_file_handler.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::BenchmarkBigQueryLogger.__init__\",\n \"text\": \"official/utils/logs/logger.py::BenchmarkBigQueryLogger.__init__\\n def __init__(self,\\n bigquery_uploader,\\n bigquery_data_set,\\n bigquery_run_table,\\n bigquery_run_status_table,\\n bigquery_metric_table,\\n run_id):\\n super(BenchmarkBigQueryLogger, self).__init__()\\n self._bigquery_uploader = bigquery_uploader\\n self._bigquery_data_set = bigquery_data_set\\n self._bigquery_run_table = bigquery_run_table\\n self._bigquery_run_status_table = bigquery_run_status_table\\n self._bigquery_metric_table = bigquery_metric_table\\n self._run_id = run_id\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::BenchmarkBigQueryLogger.log_metric\",\n \"text\": \"official/utils/logs/logger.py::BenchmarkBigQueryLogger.log_metric\\n def log_metric(self, name, value, unit=None, global_step=None, extras=None):\\n \\\"\\\"\\\"Log the benchmark metric information to bigquery.\\n\\n Args:\\n name: string, the name of the metric to log.\\n value: number, the value of the metric. The value will not be logged if it\\n is not a number type.\\n unit: string, the unit of the metric, E.g \\\"image per second\\\".\\n global_step: int, the global_step when the metric is logged.\\n extras: map of string:string, the extra information about the metric.\\n \\\"\\\"\\\"\\n metric = _process_metric_to_json(name, value, unit, global_step, extras)\\n if metric:\\n # Starting new thread for bigquery upload in case it might take long time\\n # and impact the benchmark and performance measurement. Starting a new\\n # thread might have potential performance impact for model that run on\\n # CPU.\\n thread.start_new_thread(\\n self._bigquery_uploader.upload_benchmark_metric_json,\\n (self._bigquery_data_set,\\n self._bigquery_metric_table,\\n self._run_id,\\n [metric]))\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::BenchmarkBigQueryLogger.log_run_info\",\n \"text\": \"official/utils/logs/logger.py::BenchmarkBigQueryLogger.log_run_info\\n def log_run_info(self, model_name, dataset_name, run_params, test_id=None):\\n \\\"\\\"\\\"Collect most of the TF runtime information for the local env.\\n\\n The schema of the run info follows official/benchmark/datastore/schema.\\n\\n Args:\\n model_name: string, the name of the model.\\n dataset_name: string, the name of dataset for training and evaluation.\\n run_params: dict, the dictionary of parameters for the run, it could\\n include hyperparameters or other params that are important for the run.\\n test_id: string, the unique name of the test run by the combination of key\\n parameters, eg batch size, num of GPU. It is hardware independent.\\n \\\"\\\"\\\"\\n run_info = _gather_run_info(model_name, dataset_name, run_params, test_id)\\n # Starting new thread for bigquery upload in case it might take long time\\n # and impact the benchmark and performance measurement. Starting a new\\n # thread might have potential performance impact for model that run on CPU.\\n thread.start_new_thread(\\n self._bigquery_uploader.upload_benchmark_run_json,\\n (self._bigquery_data_set,\\n self._bigquery_run_table,\\n self._run_id,\\n run_info))\\n thread.start_new_thread(\\n self._bigquery_uploader.insert_run_status,\\n (self._bigquery_data_set,\\n self._bigquery_run_status_table,\\n self._run_id,\\n RUN_STATUS_RUNNING))\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::BenchmarkBigQueryLogger.on_finish\",\n \"text\": \"official/utils/logs/logger.py::BenchmarkBigQueryLogger.on_finish\\n def on_finish(self, status):\\n thread.start_new_thread(\\n self._bigquery_uploader.update_run_status,\\n (self._bigquery_data_set,\\n self._bigquery_run_status_table,\\n self._run_id,\\n status))\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::_gather_run_info\",\n \"text\": \"official/utils/logs/logger.py::_gather_run_info\\ndef _gather_run_info(model_name, dataset_name, run_params, test_id):\\n \\\"\\\"\\\"Collect the benchmark run information for the local environment.\\\"\\\"\\\"\\n run_info = {\\n \\\"model_name\\\": model_name,\\n \\\"dataset\\\": {\\\"name\\\": dataset_name},\\n \\\"machine_config\\\": {},\\n \\\"test_id\\\": test_id,\\n \\\"run_date\\\": datetime.datetime.utcnow().strftime(\\n _DATE_TIME_FORMAT_PATTERN)}\\n _collect_tensorflow_info(run_info)\\n _collect_tensorflow_environment_variables(run_info)\\n _collect_run_params(run_info, run_params)\\n _collect_cpu_info(run_info)\\n _collect_gpu_info(run_info)\\n _collect_memory_info(run_info)\\n _collect_test_environment(run_info)\\n return run_info\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::_process_metric_to_json\",\n \"text\": \"official/utils/logs/logger.py::_process_metric_to_json\\ndef _process_metric_to_json(\\n name, value, unit=None, global_step=None, extras=None):\\n \\\"\\\"\\\"Validate the metric data and generate JSON for insert.\\\"\\\"\\\"\\n if not isinstance(value, numbers.Number):\\n tf.logging.warning(\\n \\\"Metric value to log should be a number. Got %s\\\", type(value))\\n return None\\n\\n extras = _convert_to_json_dict(extras)\\n return {\\n \\\"name\\\": name,\\n \\\"value\\\": float(value),\\n \\\"unit\\\": unit,\\n \\\"global_step\\\": global_step,\\n \\\"timestamp\\\": datetime.datetime.utcnow().strftime(\\n _DATE_TIME_FORMAT_PATTERN),\\n \\\"extras\\\": extras}\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::_collect_tensorflow_info\",\n \"text\": \"official/utils/logs/logger.py::_collect_tensorflow_info\\ndef _collect_tensorflow_info(run_info):\\n run_info[\\\"tensorflow_version\\\"] = {\\n \\\"version\\\": tf.VERSION, \\\"git_hash\\\": tf.GIT_VERSION}\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::_collect_run_params\",\n \"text\": \"official/utils/logs/logger.py::_collect_run_params\\ndef _collect_run_params(run_info, run_params):\\n \\\"\\\"\\\"Log the parameter information for the benchmark run.\\\"\\\"\\\"\\n def process_param(name, value):\\n type_check = {\\n str: {\\\"name\\\": name, \\\"string_value\\\": value},\\n int: {\\\"name\\\": name, \\\"long_value\\\": value},\\n bool: {\\\"name\\\": name, \\\"bool_value\\\": str(value)},\\n float: {\\\"name\\\": name, \\\"float_value\\\": value},\\n }\\n return type_check.get(type(value),\\n {\\\"name\\\": name, \\\"string_value\\\": str(value)})\\n if run_params:\\n run_info[\\\"run_parameters\\\"] = [\\n process_param(k, v) for k, v in sorted(run_params.items())]\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::_collect_run_params.process_param\",\n \"text\": \"official/utils/logs/logger.py::_collect_run_params.process_param\\n def process_param(name, value):\\n type_check = {\\n str: {\\\"name\\\": name, \\\"string_value\\\": value},\\n int: {\\\"name\\\": name, \\\"long_value\\\": value},\\n bool: {\\\"name\\\": name, \\\"bool_value\\\": str(value)},\\n float: {\\\"name\\\": name, \\\"float_value\\\": value},\\n }\\n return type_check.get(type(value),\\n {\\\"name\\\": name, \\\"string_value\\\": str(value)})\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::_collect_tensorflow_environment_variables\",\n \"text\": \"official/utils/logs/logger.py::_collect_tensorflow_environment_variables\\ndef _collect_tensorflow_environment_variables(run_info):\\n run_info[\\\"tensorflow_environment_variables\\\"] = [\\n {\\\"name\\\": k, \\\"value\\\": v}\\n for k, v in sorted(os.environ.items()) if k.startswith(\\\"TF_\\\")]\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::_collect_cpu_info\",\n \"text\": \"official/utils/logs/logger.py::_collect_cpu_info\\ndef _collect_cpu_info(run_info):\\n \\\"\\\"\\\"Collect the CPU information for the local environment.\\\"\\\"\\\"\\n cpu_info = {}\\n\\n cpu_info[\\\"num_cores\\\"] = multiprocessing.cpu_count()\\n\\n try:\\n # Note: cpuinfo is not installed in the TensorFlow OSS tree.\\n # It is installable via pip.\\n import cpuinfo # pylint: disable=g-import-not-at-top\\n\\n info = cpuinfo.get_cpu_info()\\n cpu_info[\\\"cpu_info\\\"] = info[\\\"brand\\\"]\\n cpu_info[\\\"mhz_per_cpu\\\"] = info[\\\"hz_advertised_raw\\\"][0] / 1.0e6\\n\\n run_info[\\\"machine_config\\\"][\\\"cpu_info\\\"] = cpu_info\\n except ImportError:\\n tf.logging.warn(\\\"'cpuinfo' not imported. CPU info will not be logged.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::_collect_gpu_info\",\n \"text\": \"official/utils/logs/logger.py::_collect_gpu_info\\ndef _collect_gpu_info(run_info):\\n \\\"\\\"\\\"Collect local GPU information by TF device library.\\\"\\\"\\\"\\n gpu_info = {}\\n local_device_protos = device_lib.list_local_devices()\\n\\n gpu_info[\\\"count\\\"] = len([d for d in local_device_protos\\n if d.device_type == \\\"GPU\\\"])\\n # The device description usually is a JSON string, which contains the GPU\\n # model info, eg:\\n # \\\"device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0\\\"\\n for d in local_device_protos:\\n if d.device_type == \\\"GPU\\\":\\n gpu_info[\\\"model\\\"] = _parse_gpu_model(d.physical_device_desc)\\n # Assume all the GPU connected are same model\\n break\\n run_info[\\\"machine_config\\\"][\\\"gpu_info\\\"] = gpu_info\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::_collect_memory_info\",\n \"text\": \"official/utils/logs/logger.py::_collect_memory_info\\ndef _collect_memory_info(run_info):\\n try:\\n # Note: psutil is not installed in the TensorFlow OSS tree.\\n # It is installable via pip.\\n import psutil # pylint: disable=g-import-not-at-top\\n vmem = psutil.virtual_memory()\\n run_info[\\\"machine_config\\\"][\\\"memory_total\\\"] = vmem.total\\n run_info[\\\"machine_config\\\"][\\\"memory_available\\\"] = vmem.available\\n except ImportError:\\n tf.logging.warn(\\\"'psutil' not imported. Memory info will not be logged.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::_collect_test_environment\",\n \"text\": \"official/utils/logs/logger.py::_collect_test_environment\\ndef _collect_test_environment(run_info):\\n \\\"\\\"\\\"Detect the local environment, eg GCE, AWS or DGX, etc.\\\"\\\"\\\"\\n if cloud_lib.on_gcp():\\n run_info[\\\"test_environment\\\"] = GCP_TEST_ENV\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::_parse_gpu_model\",\n \"text\": \"official/utils/logs/logger.py::_parse_gpu_model\\ndef _parse_gpu_model(physical_device_desc):\\n # Assume all the GPU connected are same model\\n for kv in physical_device_desc.split(\\\",\\\"):\\n k, _, v = kv.partition(\\\":\\\")\\n if k.strip() == \\\"name\\\":\\n return v.strip()\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"official/utils/logs/logger.py::_convert_to_json_dict\",\n \"text\": \"official/utils/logs/logger.py::_convert_to_json_dict\\ndef _convert_to_json_dict(input_dict):\\n if input_dict:\\n return [{\\\"name\\\": k, \\\"value\\\": v} for k, v in sorted(input_dict.items())]\\n else:\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"research/astronet/astronet/data/preprocess.py::read_and_process_light_curve\",\n \"text\": \"research/astronet/astronet/data/preprocess.py::read_and_process_light_curve\\ndef read_and_process_light_curve(kepid, kepler_data_dir, max_gap_width=0.75):\\n \\\"\\\"\\\"Reads a light curve, fits a B-spline and divides the curve by the spline.\\n\\n Args:\\n kepid: Kepler id of the target star.\\n kepler_data_dir: Base directory containing Kepler data. See\\n kepler_io.kepler_filenames().\\n max_gap_width: Gap size (in days) above which the light curve is split for\\n the fitting of B-splines.\\n\\n Returns:\\n time: 1D NumPy array; the time values of the light curve.\\n flux: 1D NumPy array; the normalized flux values of the light curve.\\n\\n Raises:\\n IOError: If the light curve files for this Kepler ID cannot be found.\\n ValueError: If the spline could not be fit.\\n \\\"\\\"\\\"\\n # Read the Kepler light curve.\\n file_names = kepler_io.kepler_filenames(kepler_data_dir, kepid)\\n if not file_names:\\n raise IOError(\\\"Failed to find .fits files in %s for Kepler ID %s\\\" %\\n (kepler_data_dir, kepid))\\n\\n all_time, all_flux = kepler_io.read_kepler_light_curve(file_names)\\n\\n # Split on gaps.\\n all_time, all_flux = util.split(all_time, all_flux, gap_width=max_gap_width)\\n\\n # Logarithmically sample candidate break point spacings between 0.5 and 20\\n # days.\\n bkspaces = np.logspace(np.log10(0.5), np.log10(20), num=20)\\n\\n # Generate spline.\\n spline = kepler_spline.choose_kepler_spline(\\n all_time, all_flux, bkspaces, penalty_coeff=1.0, verbose=False)[0]\\n\\n if spline is None:\\n raise ValueError(\\\"Failed to fit spline with Kepler ID %s\\\", kepid)\\n\\n # Concatenate the piecewise light curve and spline.\\n time = np.concatenate(all_time)\\n flux = np.concatenate(all_flux)\\n spline = np.concatenate(spline)\\n\\n # In rare cases the piecewise spline contains NaNs in places the spline could\\n # not be fit. We can't normalize those points if the spline isn't defined\\n # there. Instead we just remove them.\\n finite_i = np.isfinite(spline)\\n if not np.all(finite_i):\\n tf.loggin\",\n \"label\": 0\n },\n {\n \"cid\": \"research/astronet/astronet/data/preprocess.py::phase_fold_and_sort_light_curve\",\n \"text\": \"research/astronet/astronet/data/preprocess.py::phase_fold_and_sort_light_curve\\ndef phase_fold_and_sort_light_curve(time, flux, period, t0):\\n \\\"\\\"\\\"Phase folds a light curve and sorts by ascending time.\\n\\n Args:\\n time: 1D NumPy array of time values.\\n flux: 1D NumPy array of flux values.\\n period: A positive real scalar; the period to fold over.\\n t0: The center of the resulting folded vector; this value is mapped to 0.\\n\\n Returns:\\n folded_time: 1D NumPy array of phase folded time values in\\n [-period / 2, period / 2), where 0 corresponds to t0 in the original\\n time array. Values are sorted in ascending order.\\n folded_flux: 1D NumPy array. Values are the same as the original input\\n array, but sorted by folded_time.\\n \\\"\\\"\\\"\\n # Phase fold time.\\n time = util.phase_fold_time(time, period, t0)\\n\\n # Sort by ascending time.\\n sorted_i = np.argsort(time)\\n time = time[sorted_i]\\n flux = flux[sorted_i]\\n\\n return time, flux\",\n \"label\": 0\n },\n {\n \"cid\": \"research/astronet/astronet/data/preprocess.py::generate_view\",\n \"text\": \"research/astronet/astronet/data/preprocess.py::generate_view\\ndef generate_view(time, flux, num_bins, bin_width, t_min, t_max,\\n normalize=True):\\n \\\"\\\"\\\"Generates a view of a phase-folded light curve using a median filter.\\n\\n Args:\\n time: 1D array of time values, sorted in ascending order.\\n flux: 1D array of flux values.\\n num_bins: The number of intervals to divide the time axis into.\\n bin_width: The width of each bin on the time axis.\\n t_min: The inclusive leftmost value to consider on the time axis.\\n t_max: The exclusive rightmost value to consider on the time axis.\\n normalize: Whether to center the median at 0 and minimum value at -1.\\n\\n Returns:\\n 1D NumPy array of size num_bins containing the median flux values of\\n uniformly spaced bins on the phase-folded time axis.\\n \\\"\\\"\\\"\\n view = median_filter.median_filter(time, flux, num_bins, bin_width, t_min,\\n t_max)\\n\\n if normalize:\\n view -= np.median(view)\\n view /= np.abs(np.min(view))\\n\\n return view\",\n \"label\": 0\n },\n {\n \"cid\": \"research/astronet/astronet/data/preprocess.py::global_view\",\n \"text\": \"research/astronet/astronet/data/preprocess.py::global_view\\ndef global_view(time, flux, period, num_bins=2001, bin_width_factor=1 / 2001):\\n \\\"\\\"\\\"Generates a 'global view' of a phase folded light curve.\\n\\n See Section 3.3 of Shallue & Vanderburg, 2018, The Astronomical Journal.\\n http://iopscience.iop.org/article/10.3847/1538-3881/aa9e09/meta\\n\\n Args:\\n time: 1D array of time values, sorted in ascending order.\\n flux: 1D array of flux values.\\n period: The period of the event (in days).\\n num_bins: The number of intervals to divide the time axis into.\\n bin_width_factor: Width of the bins, as a fraction of period.\\n\\n Returns:\\n 1D NumPy array of size num_bins containing the median flux values of\\n uniformly spaced bins on the phase-folded time axis.\\n \\\"\\\"\\\"\\n return generate_view(\\n time,\\n flux,\\n num_bins=num_bins,\\n bin_width=period * bin_width_factor,\\n t_min=-period / 2,\\n t_max=period / 2)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/astronet/astronet/data/preprocess.py::local_view\",\n \"text\": \"research/astronet/astronet/data/preprocess.py::local_view\\ndef local_view(time,\\n flux,\\n period,\\n duration,\\n num_bins=201,\\n bin_width_factor=0.16,\\n num_durations=4):\\n \\\"\\\"\\\"Generates a 'local view' of a phase folded light curve.\\n\\n See Section 3.3 of Shallue & Vanderburg, 2018, The Astronomical Journal.\\n http://iopscience.iop.org/article/10.3847/1538-3881/aa9e09/meta\\n\\n Args:\\n time: 1D array of time values, sorted in ascending order.\\n flux: 1D array of flux values.\\n period: The period of the event (in days).\\n duration: The duration of the event (in days).\\n num_bins: The number of intervals to divide the time axis into.\\n bin_width_factor: Width of the bins, as a fraction of duration.\\n num_durations: The number of durations to consider on either side of 0 (the\\n event is assumed to be centered at 0).\\n\\n Returns:\\n 1D NumPy array of size num_bins containing the median flux values of\\n uniformly spaced bins on the phase-folded time axis.\\n \\\"\\\"\\\"\\n return generate_view(\\n time,\\n flux,\\n num_bins=num_bins,\\n bin_width=duration * bin_width_factor,\\n t_min=max(-period / 2, -duration * num_durations),\\n t_max=min(period / 2, duration * num_durations))\",\n \"label\": 0\n },\n {\n \"cid\": \"research/astronet/light_curve_util/util.py::phase_fold_time\",\n \"text\": \"research/astronet/light_curve_util/util.py::phase_fold_time\\ndef phase_fold_time(time, period, t0):\\n \\\"\\\"\\\"Creates a phase-folded time vector.\\n\\n result[i] is the unique number in [-period / 2, period / 2)\\n such that result[i] = time[i] - t0 + k_i * period, for some integer k_i.\\n\\n Args:\\n time: 1D numpy array of time values.\\n period: A positive real scalar; the period to fold over.\\n t0: The center of the resulting folded vector; this value is mapped to 0.\\n\\n Returns:\\n A 1D numpy array.\\n \\\"\\\"\\\"\\n half_period = period / 2\\n result = np.mod(time + (half_period - t0), period)\\n result -= half_period\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"research/astronet/light_curve_util/util.py::split\",\n \"text\": \"research/astronet/light_curve_util/util.py::split\\ndef split(all_time, all_flux, gap_width=0.75):\\n \\\"\\\"\\\"Splits a light curve on discontinuities (gaps).\\n\\n This function accepts a light curve that is either a single segment, or is\\n piecewise defined (e.g. split by quarter breaks or gaps in the in the data).\\n\\n Args:\\n all_time: Numpy array or list of numpy arrays; each is a sequence of time\\n values.\\n all_flux: Numpy array or list of numpy arrays; each is a sequence of flux\\n values of the corresponding time array.\\n gap_width: Minimum gap size (in time units) for a split.\\n\\n Returns:\\n out_time: List of numpy arrays; the split time arrays.\\n out_flux: List of numpy arrays; the split flux arrays.\\n \\\"\\\"\\\"\\n # Handle single-segment inputs.\\n # We must use an explicit length test on all_time because implicit conversion\\n # to bool fails if all_time is a numpy array, and all_time.size is not defined\\n # if all_time is a list of numpy arrays.\\n if len(all_time) > 0 and not isinstance(all_time[0], collections.Iterable): # pylint:disable=g-explicit-length-test\\n all_time = [all_time]\\n all_flux = [all_flux]\\n\\n out_time = []\\n out_flux = []\\n for time, flux in zip(all_time, all_flux):\\n start = 0\\n for end in range(1, len(time) + 1):\\n # Choose the largest endpoint such that time[start:end] has no gaps.\\n if end == len(time) or time[end] - time[end - 1] > gap_width:\\n out_time.append(time[start:end])\\n out_flux.append(flux[start:end])\\n start = end\\n\\n return out_time, out_flux\",\n \"label\": 1\n },\n {\n \"cid\": \"research/astronet/light_curve_util/util.py::remove_events\",\n \"text\": \"research/astronet/light_curve_util/util.py::remove_events\\ndef remove_events(all_time, all_flux, events, width_factor=1.0):\\n \\\"\\\"\\\"Removes events from a light curve.\\n\\n This function accepts either a single-segment or piecewise-defined light\\n curve (e.g. one that is split by quarter breaks or gaps in the in the data).\\n\\n Args:\\n all_time: Numpy array or list of numpy arrays; each is a sequence of time\\n values.\\n all_flux: Numpy array or list of numpy arrays; each is a sequence of flux\\n values of the corresponding time array.\\n events: List of Event objects to remove.\\n width_factor: Fractional multiplier of the duration of each event to remove.\\n\\n Returns:\\n output_time: Numpy array or list of numpy arrays; the time arrays with\\n events removed.\\n output_flux: Numpy array or list of numpy arrays; the flux arrays with\\n events removed.\\n \\\"\\\"\\\"\\n # Handle single-segment inputs.\\n # We must use an explicit length test on all_time because implicit conversion\\n # to bool fails if all_time is a numpy array and all_time.size is not defined\\n # if all_time is a list of numpy arrays.\\n if len(all_time) > 0 and not isinstance(all_time[0], collections.Iterable): # pylint:disable=g-explicit-length-test\\n all_time = [all_time]\\n all_flux = [all_flux]\\n single_segment = True\\n else:\\n single_segment = False\\n\\n output_time = []\\n output_flux = []\\n for time, flux in zip(all_time, all_flux):\\n mask = np.ones_like(time, dtype=np.bool)\\n for event in events:\\n transit_dist = np.abs(phase_fold_time(time, event.period, event.t0))\\n mask = np.logical_and(mask,\\n transit_dist > 0.5 * width_factor * event.duration)\\n\\n if single_segment:\\n output_time = time[mask]\\n output_flux = flux[mask]\\n else:\\n output_time.append(time[mask])\\n output_flux.append(flux[mask])\\n\\n return output_time, output_flux\",\n \"label\": 1\n },\n {\n \"cid\": \"research/astronet/light_curve_util/util.py::interpolate_masked_spline\",\n \"text\": \"research/astronet/light_curve_util/util.py::interpolate_masked_spline\\ndef interpolate_masked_spline(all_time, all_masked_time, all_masked_spline):\\n \\\"\\\"\\\"Linearly interpolates spline values across masked points.\\n\\n Args:\\n all_time: List of numpy arrays; each is a sequence of time values.\\n all_masked_time: List of numpy arrays; each is a sequence of time values\\n with some values missing (masked).\\n all_masked_spline: List of numpy arrays; the masked spline values\\n corresponding to all_masked_time.\\n\\n Returns:\\n interp_spline: List of numpy arrays; each is the masked spline with missing\\n points linearly interpolated.\\n \\\"\\\"\\\"\\n interp_spline = []\\n for time, masked_time, masked_spline in zip(\\n all_time, all_masked_time, all_masked_spline):\\n if len(masked_time) > 0: # pylint:disable=g-explicit-length-test\\n interp_spline.append(np.interp(time, masked_time, masked_spline))\\n else:\\n interp_spline.append(np.full_like(time, np.nan))\\n return interp_spline\",\n \"label\": 1\n },\n {\n \"cid\": \"research/astronet/light_curve_util/util.py::count_transit_points\",\n \"text\": \"research/astronet/light_curve_util/util.py::count_transit_points\\ndef count_transit_points(time, event):\\n \\\"\\\"\\\"Computes the number of points in each transit of a given event.\\n\\n Args:\\n time: Sorted numpy array of time values.\\n event: An Event object.\\n\\n Returns:\\n A numpy array containing the number of time points \\\"in transit\\\" for each\\n transit occurring between the first and last time values.\\n\\n Raises:\\n ValueError: If there are more than 10**6 transits.\\n \\\"\\\"\\\"\\n t_min = np.min(time)\\n t_max = np.max(time)\\n\\n # Tiny periods or erroneous time values could make this loop take forever.\\n if (t_max - t_min) / event.period > 10**6:\\n raise ValueError(\\n \\\"Too many transits! Time range is [%.2f, %.2f] and period is %.2e.\\\" %\\n (t_min, t_max, event.period))\\n\\n # Make sure t0 is in [t_min, t_min + period).\\n t0 = np.mod(event.t0 - t_min, event.period) + t_min\\n\\n # Prepare loop variables.\\n points_in_transit = []\\n i, j = 0, 0\\n\\n for transit_midpoint in np.arange(t0, t_max, event.period):\\n transit_begin = transit_midpoint - event.duration / 2\\n transit_end = transit_midpoint + event.duration / 2\\n\\n # Move time[i] to the first point >= transit_begin.\\n while time[i] < transit_begin:\\n # transit_begin is guaranteed to be < np.max(t) (provided duration >= 0).\\n # Therefore, i cannot go out of range.\\n i += 1\\n\\n # Move time[j] to the first point > transit_end.\\n while time[j] <= transit_end:\\n j += 1\\n # j went out of range. We're finished.\\n if j >= len(time):\\n break\\n\\n # The points in the current transit duration are precisely time[i:j].\\n # Since j is an exclusive index, there are exactly j-i points in transit.\\n points_in_transit.append(j - i)\\n\\n return np.array(points_in_transit)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/astronet/third_party/kepler_spline/kepler_spline.py::kepler_spline\",\n \"text\": \"research/astronet/third_party/kepler_spline/kepler_spline.py::kepler_spline\\ndef kepler_spline(time, flux, bkspace=1.5, maxiter=5, outlier_cut=3):\\n \\\"\\\"\\\"Computes a best-fit spline curve for a light curve segment.\\n\\n The spline is fit using an iterative process to remove outliers that may cause\\n the spline to be \\\"pulled\\\" by discrepent points. In each iteration the spline\\n is fit, and if there are any points where the absolute deviation from the\\n median residual is at least 3*sigma (where sigma is a robust estimate of the\\n standard deviation of the residuals), those points are removed and the spline\\n is re-fit.\\n\\n Args:\\n time: Numpy array; the time values of the light curve.\\n flux: Numpy array; the flux (brightness) values of the light curve.\\n bkspace: Spline break point spacing in time units.\\n maxiter: Maximum number of attempts to fit the spline after removing badly\\n fit points.\\n outlier_cut: The maximum number of standard deviations from the median\\n spline residual before a point is considered an outlier.\\n\\n Returns:\\n spline: The values of the fitted spline corresponding to the input time\\n values.\\n mask: Boolean mask indicating the points used to fit the final spline.\\n \\\"\\\"\\\"\\n # Rescale time into [0, 1].\\n t_min = np.min(time)\\n t_max = np.max(time)\\n time = (time - t_min) / (t_max - t_min)\\n bkspace /= (t_max - t_min) # Rescale bucket spacing.\\n\\n # Values of the best fitting spline evaluated at the time points.\\n spline = None\\n\\n # Mask indicating the points used to fit the spline.\\n mask = None\\n\\n for _ in range(maxiter):\\n if spline is None:\\n mask = np.ones_like(time, dtype=np.bool) # Try to fit all points.\\n else:\\n # Choose points where the absolute deviation from the median residual is\\n # less than 3*sigma, where sigma is a robust estimate of the standard\\n # deviation of the residuals from the previous spline.\\n residuals = flux - spline\\n _, _, new_mask = robust_mean.robust_mean(res\",\n \"label\": 1\n },\n {\n \"cid\": \"research/astronet/third_party/kepler_spline/kepler_spline.py::choose_kepler_spline\",\n \"text\": \"research/astronet/third_party/kepler_spline/kepler_spline.py::choose_kepler_spline\\ndef choose_kepler_spline(all_time,\\n all_flux,\\n bkspaces,\\n maxiter=5,\\n penalty_coeff=1.0,\\n verbose=True):\\n \\\"\\\"\\\"Computes the best-fit Kepler spline across a break-point spacings.\\n\\n Some Kepler light curves have low-frequency variability, while others have\\n very high-frequency variability (e.g. due to rapid rotation). Therefore, it is\\n suboptimal to use the same break-point spacing for every star. This function\\n computes the best-fit spline by fitting splines with different break-point\\n spacings, calculating the Bayesian Information Criterion (BIC) for each\\n spline, and choosing the break-point spacing that minimizes the BIC.\\n\\n This function assumes a piecewise light curve, that is, a light curve that is\\n divided into different segments (e.g. split by quarter breaks or gaps in the\\n in the data). A separate spline is fit for each segment.\\n\\n Args:\\n all_time: List of 1D numpy arrays; the time values of the light curve.\\n all_flux: List of 1D numpy arrays; the flux (brightness) values of the light\\n curve.\\n bkspaces: List of break-point spacings to try.\\n maxiter: Maximum number of attempts to fit each spline after removing badly\\n fit points.\\n penalty_coeff: Coefficient of the penalty term for using more parameters in\\n the Bayesian Information Criterion. Decreasing this value will allow\\n more parameters to be used (i.e. smaller break-point spacing), and\\n vice-versa.\\n verbose: Whether to log individual spline errors. Note that if bkspaces\\n contains many values (particularly small ones) then this may cause\\n logging pollution if calling this function for many light curves.\\n\\n Returns:\\n spline: List of numpy arrays; values of the best-fit spline corresponding to\\n to the input flux arrays.\\n sp\",\n \"label\": 1\n },\n {\n \"cid\": \"research/brain_coder/single_task/aggregate_experiment_results.py::make_csv_string\",\n \"text\": \"research/brain_coder/single_task/aggregate_experiment_results.py::make_csv_string\\ndef make_csv_string(table):\\n \\\"\\\"\\\"Convert 2D list to CSV string.\\\"\\\"\\\"\\n s = StringIO.StringIO()\\n writer = csv.writer(s)\\n writer.writerows(table)\\n value = s.getvalue()\\n s.close()\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/aggregate_experiment_results.py::process_results\",\n \"text\": \"research/brain_coder/single_task/aggregate_experiment_results.py::process_results\\ndef process_results(metrics):\\n \\\"\\\"\\\"Extract useful information from given metrics.\\n\\n Args:\\n metrics: List of results dicts. These should have been written to disk by\\n training jobs.\\n\\n Returns:\\n Dict mapping stats names to values.\\n\\n Raises:\\n ValueError: If max_npe or max_global_repetitions values are inconsistant\\n across dicts in the `metrics` list.\\n \\\"\\\"\\\"\\n count = len(metrics)\\n success_count = 0\\n total_npe = 0 # Counting NPE across all runs.\\n success_npe = 0 # Counting NPE in successful runs only.\\n max_npe = 0\\n max_repetitions = 0\\n for metric_dict in metrics:\\n if not max_npe:\\n max_npe = metric_dict['max_npe']\\n elif max_npe != metric_dict['max_npe']:\\n raise ValueError(\\n 'Invalid experiment. Different reps have different max-NPE settings.')\\n if not max_repetitions:\\n max_repetitions = metric_dict['max_global_repetitions']\\n elif max_repetitions != metric_dict['max_global_repetitions']:\\n raise ValueError(\\n 'Invalid experiment. Different reps have different num-repetition '\\n 'settings.')\\n if metric_dict['found_solution']:\\n success_count += 1\\n success_npe += metric_dict['npe']\\n total_npe += metric_dict['npe']\\n stats = {}\\n stats['max_npe'] = max_npe\\n stats['max_repetitions'] = max_repetitions\\n stats['repetitions'] = count\\n stats['successes'] = success_count # successful reps\\n stats['failures'] = count - success_count # failed reps\\n stats['success_npe'] = success_npe\\n stats['total_npe'] = total_npe\\n if success_count:\\n # Only successful runs counted.\\n stats['avg_success_npe'] = stats['success_npe'] / float(success_count)\\n else:\\n stats['avg_success_npe'] = 0.0\\n if count:\\n stats['success_rate'] = success_count / float(count)\\n stats['avg_total_npe'] = stats['total_npe'] / float(count)\\n else:\\n stats['success_rate'] = 0.0\\n stats['avg_total_npe'] = 0.0\\n\\n retur\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/aggregate_experiment_results.py::get_results_for_experiment\",\n \"text\": \"research/brain_coder/single_task/aggregate_experiment_results.py::get_results_for_experiment\\ndef get_results_for_experiment(\\n models_dir, task_name, model_type='pg', max_npe='5M', desc='v0',\\n name_prefix='bf_rl_paper', extra_desc=''):\\n \\\"\\\"\\\"Get and process results for a given experiment.\\n\\n An experiment is a set of runs with the same hyperparameters and environment.\\n It is uniquely specified by a (task_name, model_type, max_npe) triple, as\\n well as an optional description.\\n\\n We assume that each experiment has a folder with the same name as the job that\\n ran the experiment. The name is computed by\\n \\\"%name_prefix%.%desc%-%max_npe%_%task_name%\\\".\\n\\n Args:\\n models_dir: Parent directory containing experiment folders.\\n task_name: String name of task (the coding env). See code_tasks.py or\\n run_eval_tasks.py\\n model_type: Name of the algorithm, such as 'pg', 'topk', 'ga', 'rand'.\\n max_npe: String SI unit representation of the maximum NPE threshold for the\\n experiment. For example, \\\"5M\\\" means 5 million.\\n desc: Description.\\n name_prefix: Prefix of job names. Normally leave this as default.\\n extra_desc: Optional extra description at the end of the job name.\\n\\n Returns:\\n ProcessedResults namedtuple instance, containing\\n metrics: Raw dicts read from disk.\\n processed: Stats computed by `process_results`.\\n\\n Raises:\\n ValueError: If max_npe in the metrics does not match NPE in the experiment\\n folder name.\\n \\\"\\\"\\\"\\n folder = name_prefix + '.{0}.{1}-{2}_{3}'.format(desc, model_type, max_npe,\\n task_name)\\n if extra_desc:\\n folder += '.' + extra_desc\\n\\n results = results_lib.Results(os.path.join(models_dir, folder))\\n metrics, _ = results.read_all()\\n processed = process_results(metrics)\\n if (not np.isclose(processed['max_npe'], misc.si_to_int(max_npe))\\n and processed['repetitions']):\\n raise ValueError(\\n 'Invalid experiment. Max-NPE setting does not match exp\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/aggregate_experiment_results.py::get_best_code_for_experiment\",\n \"text\": \"research/brain_coder/single_task/aggregate_experiment_results.py::get_best_code_for_experiment\\ndef get_best_code_for_experiment(\\n models_dir, task_name, model_type='pg', max_npe='5M', desc=0,\\n name_prefix='bf_rl_paper', extra_desc=''):\\n \\\"\\\"\\\"Like `get_results_for_experiment`, but fetches the code solutions.\\\"\\\"\\\"\\n folder = name_prefix + '.{0}.{1}-{2}_{3}'.format(desc, model_type, max_npe,\\n task_name)\\n if extra_desc:\\n folder += '.' + extra_desc\\n\\n log_dir = os.path.join(models_dir, folder, 'logs')\\n search_regex = r'^solutions_([0-9])+\\\\.txt$'\\n try:\\n all_children = tf.gfile.ListDirectory(log_dir)\\n except tf.errors.NotFoundError:\\n return BestCodeResults(\\n code=None, reward=0.0, npe=0, folder=folder, finished=False,\\n error=BestCodeResultError.experiment_does_not_exist)\\n solution_files = [\\n fname for fname in all_children if re.search(search_regex, fname)]\\n max_reward = 0.0\\n npe = 0\\n best_code = None\\n for fname in solution_files:\\n with tf.gfile.FastGFile(os.path.join(log_dir, fname), 'r') as reader:\\n results = [ast.literal_eval(entry) for entry in reader]\\n for res in results:\\n if res['reward'] > max_reward:\\n best_code = res['code']\\n max_reward = res['reward']\\n npe = res['npe']\\n error = (\\n BestCodeResultError.success if best_code\\n else BestCodeResultError.no_solution_found)\\n try:\\n # If there is a status.txt file, check if it contains the status of the job.\\n with tf.gfile.FastGFile(os.path.join(log_dir, 'status.txt'), 'r') as f:\\n # Job is done, so mark this experiment as finished.\\n finished = f.read().lower().strip() == 'done'\\n except tf.errors.NotFoundError:\\n # No status file has been written, so the experiment is not done. No need to\\n # report an error here, because we do not require that experiment jobs write\\n # out a status.txt file until they have finished.\\n finished = False\\n return BestCodeResults(\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/aggregate_experiment_results.py::make_results_table\",\n \"text\": \"research/brain_coder/single_task/aggregate_experiment_results.py::make_results_table\\ndef make_results_table(\\n models=None,\\n tasks=None,\\n max_npe='5M',\\n name_prefix='bf_rl_paper',\\n extra_desc='',\\n models_dir='/tmp'):\\n \\\"\\\"\\\"Creates a table of results: algorithm + version by tasks.\\n\\n Args:\\n models: The table columns. A list of (algorithm, desc) tuples.\\n tasks: The table rows. List of task names.\\n max_npe: String SI unit representation of the maximum NPE threshold for the\\n experiment. For example, \\\"5M\\\" means 5 million. All entries in the table\\n share the same max-NPE.\\n name_prefix: Name prefix used in logging directory for the experiment.\\n extra_desc: Extra description added to name of logging directory for the\\n experiment.\\n models_dir: Parent directory containing all experiment folders.\\n\\n Returns:\\n A 2D list holding the table cells.\\n \\\"\\\"\\\"\\n if models is None:\\n models = DEFAULT_MODELS\\n if tasks is None:\\n tasks = DEFAULT_TASKS\\n model_results = {}\\n for model_type, desc in models:\\n model_results[model_type] = {\\n tname: get_results_for_experiment(\\n models_dir, tname, model_type, max_npe, desc,\\n name_prefix=name_prefix, extra_desc=extra_desc\\n ).processed\\n for tname in tasks}\\n\\n def info(stats):\\n return [str(stats['repetitions']),\\n '%.2f' % stats['success_rate'],\\n str(int(stats['avg_total_npe']))]\\n\\n rows = [['max NPE: ' + max_npe]\\n + misc.flatten([['{0} ({1})'.format(m, d), '', '']\\n for m, d in models])]\\n rows.append(\\n [''] + misc.flatten([['reps', 'success rate', 'avg NPE']\\n for _ in models]))\\n for tname in tasks:\\n rows.append(\\n [tname]\\n + misc.flatten([info(model_results[model][tname])\\n for model, _ in models]))\\n\\n return rows\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/aggregate_experiment_results.py::make_results_table.info\",\n \"text\": \"research/brain_coder/single_task/aggregate_experiment_results.py::make_results_table.info\\n def info(stats):\\n return [str(stats['repetitions']),\\n '%.2f' % stats['success_rate'],\\n str(int(stats['avg_total_npe']))]\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/aggregate_experiment_results.py::print_results_table\",\n \"text\": \"research/brain_coder/single_task/aggregate_experiment_results.py::print_results_table\\ndef print_results_table(results_table):\\n \\\"\\\"\\\"Print human readable results table to stdout.\\\"\\\"\\\"\\n print('')\\n print('=== Results Table ===')\\n print('Format: # reps [success rate, avg total NPE]')\\n\\n def info_str(info_row):\\n # num_runs (success_rate, avg_total_npe)\\n if not info_row[0]:\\n return '0'\\n return '%s [%s, %s]' % (str(info_row[0]).ljust(2), info_row[1], info_row[2])\\n\\n nc = len(results_table[0]) # num cols\\n out_table = [\\n [results_table[0][0]] + [results_table[0][i] for i in range(1, nc, 3)]]\\n for row in results_table[2:]:\\n out_table.append([row[0]] + [info_str(row[i:i+3]) for i in range(1, nc, 3)])\\n\\n nc = len(out_table[0]) # num cols\\n col_widths = [max(len(row[col]) for row in out_table) for col in range(nc)]\\n\\n table_string = ''\\n for row in out_table:\\n table_string += ''.join(\\n [row[c].ljust(col_widths[c] + 2) for c in range(nc)]) + '\\\\n'\\n\\n print(table_string)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/aggregate_experiment_results.py::print_results_table.info_str\",\n \"text\": \"research/brain_coder/single_task/aggregate_experiment_results.py::print_results_table.info_str\\n def info_str(info_row):\\n # num_runs (success_rate, avg_total_npe)\\n if not info_row[0]:\\n return '0'\\n return '%s [%s, %s]' % (str(info_row[0]).ljust(2), info_row[1], info_row[2])\",\n \"label\": 0\n },\n {\n \"cid\": \"research/brain_coder/single_task/aggregate_experiment_results.py::main\",\n \"text\": \"research/brain_coder/single_task/aggregate_experiment_results.py::main\\ndef main(argv):\\n del argv # Unused.\\n\\n name_prefix = FLAGS.exp_prefix\\n print('Experiments prefix: %s' % name_prefix)\\n\\n model_types = ast.literal_eval(FLAGS.model_types)\\n\\n if FLAGS.data == 'success_rates':\\n results_table = make_results_table(\\n models=model_types, tasks=FLAGS.task_list, max_npe=FLAGS.max_npe,\\n models_dir=FLAGS.models_dir,\\n name_prefix=name_prefix, extra_desc='')\\n with tf.gfile.FastGFile(FLAGS.csv_file, 'w') as f:\\n f.write(make_csv_string(results_table))\\n\\n print_results_table(results_table)\\n else:\\n # Best code\\n print('* = experiment is still running')\\n print('')\\n print('=== Best Synthesized Code ===')\\n for model_type, desc in model_types:\\n print('%s (%s)' % (model_type, desc))\\n sys.stdout.flush()\\n for tname in FLAGS.task_list:\\n res = get_best_code_for_experiment(\\n FLAGS.models_dir, tname, model_type, FLAGS.max_npe, desc,\\n name_prefix=name_prefix, extra_desc='')\\n unfinished_mark = '' if res.finished else ' *'\\n tname += unfinished_mark\\n if res.error == BestCodeResultError.success:\\n print(' %s' % tname)\\n print(' %s' % res.code)\\n print(' R=%.6f, NPE=%s' % (res.reward, misc.int_to_si(res.npe)))\\n elif res.error == BestCodeResultError.experiment_does_not_exist:\\n print(' Experiment does not exist. Check arguments.')\\n print(' Experiment folder: %s' % res.folder)\\n break\\n else:\\n print(' %s' % tname)\\n print(' (none)')\\n sys.stdout.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::AmortizedAccountant.__init__\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::AmortizedAccountant.__init__\\n def __init__(self, total_examples):\\n \\\"\\\"\\\"Initialization. Currently only support amortized tracking.\\n\\n Args:\\n total_examples: total number of examples.\\n \\\"\\\"\\\"\\n\\n assert total_examples > 0\\n self._total_examples = total_examples\\n self._eps_squared_sum = tf.Variable(tf.zeros([1]), trainable=False,\\n name=\\\"eps_squared_sum\\\")\\n self._delta_sum = tf.Variable(tf.zeros([1]), trainable=False,\\n name=\\\"delta_sum\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::AmortizedAccountant.accumulate_privacy_spending\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::AmortizedAccountant.accumulate_privacy_spending\\n def accumulate_privacy_spending(self, eps_delta, unused_sigma,\\n num_examples):\\n \\\"\\\"\\\"Accumulate the privacy spending.\\n\\n Currently only support approximate privacy. Here we assume we use Gaussian\\n noise on randomly sampled batch so we get better composition: 1. the per\\n batch privacy is computed using privacy amplication via sampling bound;\\n 2. the composition is done using the composition with Gaussian noise.\\n TODO(liqzhang) Add a link to a document that describes the bounds used.\\n\\n Args:\\n eps_delta: EpsDelta pair which can be tensors.\\n unused_sigma: the noise sigma. Unused for this accountant.\\n num_examples: the number of examples involved.\\n Returns:\\n a TensorFlow operation for updating the privacy spending.\\n \\\"\\\"\\\"\\n\\n eps, delta = eps_delta\\n with tf.control_dependencies(\\n [tf.Assert(tf.greater(delta, 0),\\n [\\\"delta needs to be greater than 0\\\"])]):\\n amortize_ratio = (tf.cast(num_examples, tf.float32) * 1.0 /\\n self._total_examples)\\n # Use privacy amplification via sampling bound.\\n # See Lemma 2.2 in http://arxiv.org/pdf/1405.7085v2.pdf\\n # TODO(liqzhang) Add a link to a document with formal statement\\n # and proof.\\n amortize_eps = tf.reshape(tf.log(1.0 + amortize_ratio * (\\n tf.exp(eps) - 1.0)), [1])\\n amortize_delta = tf.reshape(amortize_ratio * delta, [1])\\n return tf.group(*[tf.assign_add(self._eps_squared_sum,\\n tf.square(amortize_eps)),\\n tf.assign_add(self._delta_sum, amortize_delta)])\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::AmortizedAccountant.get_privacy_spent\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::AmortizedAccountant.get_privacy_spent\\n def get_privacy_spent(self, sess, target_eps=None):\\n \\\"\\\"\\\"Report the spending so far.\\n\\n Args:\\n sess: the session to run the tensor.\\n target_eps: the target epsilon. Unused.\\n Returns:\\n the list containing a single EpsDelta, with values as Python floats (as\\n opposed to numpy.float64). This is to be consistent with\\n MomentAccountant which can return a list of (eps, delta) pair.\\n \\\"\\\"\\\"\\n\\n # pylint: disable=unused-argument\\n unused_target_eps = target_eps\\n eps_squared_sum, delta_sum = sess.run([self._eps_squared_sum,\\n self._delta_sum])\\n return [EpsDelta(math.sqrt(eps_squared_sum), float(delta_sum))]\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant.__init__\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant.__init__\\n def __init__(self, total_examples, moment_orders=32):\\n \\\"\\\"\\\"Initialize a MomentsAccountant.\\n\\n Args:\\n total_examples: total number of examples.\\n moment_orders: the order of moments to keep.\\n \\\"\\\"\\\"\\n\\n assert total_examples > 0\\n self._total_examples = total_examples\\n self._moment_orders = (moment_orders\\n if isinstance(moment_orders, (list, tuple))\\n else range(1, moment_orders + 1))\\n self._max_moment_order = max(self._moment_orders)\\n assert self._max_moment_order < 100, \\\"The moment order is too large.\\\"\\n self._log_moments = [tf.Variable(numpy.float64(0.0),\\n trainable=False,\\n name=(\\\"log_moments-%d\\\" % moment_order))\\n for moment_order in self._moment_orders]\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant._compute_log_moment\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant._compute_log_moment\\n @abc.abstractmethod\\n def _compute_log_moment(self, sigma, q, moment_order):\\n \\\"\\\"\\\"Compute high moment of privacy loss.\\n\\n Args:\\n sigma: the noise sigma, in the multiples of the sensitivity.\\n q: the sampling ratio.\\n moment_order: the order of moment.\\n Returns:\\n log E[exp(moment_order * X)]\\n \\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant.accumulate_privacy_spending\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant.accumulate_privacy_spending\\n def accumulate_privacy_spending(self, unused_eps_delta,\\n sigma, num_examples):\\n \\\"\\\"\\\"Accumulate privacy spending.\\n\\n In particular, accounts for privacy spending when we assume there\\n are num_examples, and we are releasing the vector\\n (sum_{i=1}^{num_examples} x_i) + Normal(0, stddev=l2norm_bound*sigma)\\n where l2norm_bound is the maximum l2_norm of each example x_i, and\\n the num_examples have been randomly selected out of a pool of\\n self.total_examples.\\n\\n Args:\\n unused_eps_delta: EpsDelta pair which can be tensors. Unused\\n in this accountant.\\n sigma: the noise sigma, in the multiples of the sensitivity (that is,\\n if the l2norm sensitivity is k, then the caller must have added\\n Gaussian noise with stddev=k*sigma to the result of the query).\\n num_examples: the number of examples involved.\\n Returns:\\n a TensorFlow operation for updating the privacy spending.\\n \\\"\\\"\\\"\\n q = tf.cast(num_examples, tf.float64) * 1.0 / self._total_examples\\n\\n moments_accum_ops = []\\n for i in range(len(self._log_moments)):\\n moment = self._compute_log_moment(sigma, q, self._moment_orders[i])\\n moments_accum_ops.append(tf.assign_add(self._log_moments[i], moment))\\n return tf.group(*moments_accum_ops)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant._compute_delta\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant._compute_delta\\n def _compute_delta(self, log_moments, eps):\\n \\\"\\\"\\\"Compute delta for given log_moments and eps.\\n\\n Args:\\n log_moments: the log moments of privacy loss, in the form of pairs\\n of (moment_order, log_moment)\\n eps: the target epsilon.\\n Returns:\\n delta\\n \\\"\\\"\\\"\\n min_delta = 1.0\\n for moment_order, log_moment in log_moments:\\n if math.isinf(log_moment) or math.isnan(log_moment):\\n sys.stderr.write(\\\"The %d-th order is inf or Nan\\\\n\\\" % moment_order)\\n continue\\n if log_moment < moment_order * eps:\\n min_delta = min(min_delta,\\n math.exp(log_moment - moment_order * eps))\\n return min_delta\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant._compute_eps\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant._compute_eps\\n def _compute_eps(self, log_moments, delta):\\n min_eps = float(\\\"inf\\\")\\n for moment_order, log_moment in log_moments:\\n if math.isinf(log_moment) or math.isnan(log_moment):\\n sys.stderr.write(\\\"The %d-th order is inf or Nan\\\\n\\\" % moment_order)\\n continue\\n min_eps = min(min_eps, (log_moment - math.log(delta)) / moment_order)\\n return min_eps\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant.get_privacy_spent\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::MomentsAccountant.get_privacy_spent\\n def get_privacy_spent(self, sess, target_eps=None, target_deltas=None):\\n \\\"\\\"\\\"Compute privacy spending in (e, d)-DP form for a single or list of eps.\\n\\n Args:\\n sess: the session to run the tensor.\\n target_eps: a list of target epsilon's for which we would like to\\n compute corresponding delta value.\\n target_deltas: a list of target deltas for which we would like to\\n compute the corresponding eps value. Caller must specify\\n either target_eps or target_delta.\\n Returns:\\n A list of EpsDelta pairs.\\n \\\"\\\"\\\"\\n assert (target_eps is None) ^ (target_deltas is None)\\n eps_deltas = []\\n log_moments = sess.run(self._log_moments)\\n log_moments_with_order = zip(self._moment_orders, log_moments)\\n if target_eps is not None:\\n for eps in target_eps:\\n eps_deltas.append(\\n EpsDelta(eps, self._compute_delta(log_moments_with_order, eps)))\\n else:\\n assert target_deltas\\n for delta in target_deltas:\\n eps_deltas.append(\\n EpsDelta(self._compute_eps(log_moments_with_order, delta), delta))\\n return eps_deltas\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::GaussianMomentsAccountant.__init__\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::GaussianMomentsAccountant.__init__\\n def __init__(self, total_examples, moment_orders=32):\\n \\\"\\\"\\\"Initialization.\\n\\n Args:\\n total_examples: total number of examples.\\n moment_orders: the order of moments to keep.\\n \\\"\\\"\\\"\\n super(self.__class__, self).__init__(total_examples, moment_orders)\\n self._binomial_table = utils.GenerateBinomialTable(self._max_moment_order)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::GaussianMomentsAccountant._differential_moments\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::GaussianMomentsAccountant._differential_moments\\n def _differential_moments(self, sigma, s, t):\\n \\\"\\\"\\\"Compute 0 to t-th differential moments for Gaussian variable.\\n\\n E[(P(x+s)/P(x+s-1)-1)^t]\\n = sum_{i=0}^t (t choose i) (-1)^{t-i} E[(P(x+s)/P(x+s-1))^i]\\n = sum_{i=0}^t (t choose i) (-1)^{t-i} E[exp(-i*(2*x+2*s-1)/(2*sigma^2))]\\n = sum_{i=0}^t (t choose i) (-1)^{t-i} exp(i(i+1-2*s)/(2 sigma^2))\\n Args:\\n sigma: the noise sigma, in the multiples of the sensitivity.\\n s: the shift.\\n t: 0 to t-th moment.\\n Returns:\\n 0 to t-th moment as a tensor of shape [t+1].\\n \\\"\\\"\\\"\\n assert t <= self._max_moment_order, (\\\"The order of %d is out \\\"\\n \\\"of the upper bound %d.\\\"\\n % (t, self._max_moment_order))\\n binomial = tf.slice(self._binomial_table, [0, 0],\\n [t + 1, t + 1])\\n signs = numpy.zeros((t + 1, t + 1), dtype=numpy.float64)\\n for i in range(t + 1):\\n for j in range(t + 1):\\n signs[i, j] = 1.0 - 2 * ((i - j) % 2)\\n exponents = tf.constant([j * (j + 1.0 - 2.0 * s) / (2.0 * sigma * sigma)\\n for j in range(t + 1)], dtype=tf.float64)\\n # x[i, j] = binomial[i, j] * signs[i, j] = (i choose j) * (-1)^{i-j}\\n x = tf.multiply(binomial, signs)\\n # y[i, j] = x[i, j] * exp(exponents[j])\\n # = (i choose j) * (-1)^{i-j} * exp(j(j-1)/(2 sigma^2))\\n # Note: this computation is done by broadcasting pointwise multiplication\\n # between [t+1, t+1] tensor and [t+1] tensor.\\n y = tf.multiply(x, tf.exp(exponents))\\n # z[i] = sum_j y[i, j]\\n # = sum_j (i choose j) * (-1)^{i-j} * exp(j(j-1)/(2 sigma^2))\\n z = tf.reduce_sum(y, 1)\\n return z\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::GaussianMomentsAccountant._compute_log_moment\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::GaussianMomentsAccountant._compute_log_moment\\n def _compute_log_moment(self, sigma, q, moment_order):\\n \\\"\\\"\\\"Compute high moment of privacy loss.\\n\\n Args:\\n sigma: the noise sigma, in the multiples of the sensitivity.\\n q: the sampling ratio.\\n moment_order: the order of moment.\\n Returns:\\n log E[exp(moment_order * X)]\\n \\\"\\\"\\\"\\n assert moment_order <= self._max_moment_order, (\\\"The order of %d is out \\\"\\n \\\"of the upper bound %d.\\\"\\n % (moment_order,\\n self._max_moment_order))\\n binomial_table = tf.slice(self._binomial_table, [moment_order, 0],\\n [1, moment_order + 1])\\n # qs = [1 q q^2 ... q^L] = exp([0 1 2 ... L] * log(q))\\n qs = tf.exp(tf.constant([i * 1.0 for i in range(moment_order + 1)],\\n dtype=tf.float64) * tf.cast(\\n tf.log(q), dtype=tf.float64))\\n moments0 = self._differential_moments(sigma, 0.0, moment_order)\\n term0 = tf.reduce_sum(binomial_table * qs * moments0)\\n moments1 = self._differential_moments(sigma, 1.0, moment_order)\\n term1 = tf.reduce_sum(binomial_table * qs * moments1)\\n return tf.squeeze(tf.log(tf.cast(q * term0 + (1.0 - q) * term1,\\n tf.float64)))\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::DummyAccountant.accumulate_privacy_spending\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::DummyAccountant.accumulate_privacy_spending\\n def accumulate_privacy_spending(self, *unused_args):\\n return tf.no_op()\",\n \"label\": 0\n },\n {\n \"cid\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::DummyAccountant.get_privacy_spent\",\n \"text\": \"research/differential_privacy/privacy_accountant/tf/accountant.py::DummyAccountant.get_privacy_spent\\n def get_privacy_spent(self, unused_sess, **unused_kwargs):\\n return [EpsDelta(numpy.inf, 1.0)]\",\n \"label\": 0\n },\n {\n \"cid\": \"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::run_eval\",\n \"text\": \"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::run_eval\\ndef run_eval(run_dir, checkpoint_dir, hparams):\\n \\\"\\\"\\\"Runs the eval loop.\\n\\n Args:\\n run_dir: The directory where eval specific logs are placed\\n checkpoint_dir: The directory where the checkpoints are stored\\n hparams: The hyperparameters struct.\\n\\n Raises:\\n ValueError: if hparams.arch is not recognized.\\n \\\"\\\"\\\"\\n for checkpoint_path in slim.evaluation.checkpoints_iterator(\\n checkpoint_dir, FLAGS.eval_interval_secs):\\n with tf.Graph().as_default():\\n #########################\\n # Preprocess the inputs #\\n #########################\\n target_dataset = dataset_factory.get_dataset(\\n FLAGS.target_dataset,\\n split_name=FLAGS.target_split_name,\\n dataset_dir=FLAGS.dataset_dir)\\n target_images, target_labels = dataset_factory.provide_batch(\\n FLAGS.target_dataset, FLAGS.target_split_name, FLAGS.dataset_dir,\\n FLAGS.num_readers, hparams.batch_size,\\n FLAGS.num_preprocessing_threads)\\n num_target_classes = target_dataset.num_classes\\n target_labels['class'] = tf.argmax(target_labels['classes'], 1)\\n del target_labels['classes']\\n\\n if hparams.arch not in ['dcgan']:\\n source_dataset = dataset_factory.get_dataset(\\n FLAGS.source_dataset,\\n split_name=FLAGS.source_split_name,\\n dataset_dir=FLAGS.dataset_dir)\\n num_source_classes = source_dataset.num_classes\\n source_images, source_labels = dataset_factory.provide_batch(\\n FLAGS.source_dataset, FLAGS.source_split_name, FLAGS.dataset_dir,\\n FLAGS.num_readers, hparams.batch_size,\\n FLAGS.num_preprocessing_threads)\\n source_labels['class'] = tf.argmax(source_labels['classes'], 1)\\n del source_labels['classes']\\n if num_source_classes != num_target_classes:\\n raise ValueError(\\n 'Input and output datasets must have same number of classes')\\n el\",\n \"label\": 0\n },\n {\n \"cid\": \"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::to_degrees\",\n \"text\": \"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::to_degrees\\ndef to_degrees(log_quaternion_loss):\\n \\\"\\\"\\\"Converts a log quaternion distance to an angle.\\n\\n Args:\\n log_quaternion_loss: The log quaternion distance between two\\n unit quaternions (or a batch of pairs of quaternions).\\n\\n Returns:\\n The angle in degrees of the implied angle-axis representation.\\n \\\"\\\"\\\"\\n return tf.acos(-(tf.exp(log_quaternion_loss) - 1)) * 2 * 180 / math.pi\",\n \"label\": 0\n },\n {\n \"cid\": \"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::create_metrics\",\n \"text\": \"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::create_metrics\\ndef create_metrics(end_points, source_labels, target_labels, hparams):\\n \\\"\\\"\\\"Create metrics for the model.\\n\\n Args:\\n end_points: A dictionary of end point name to tensor\\n source_labels: Labels for source images. batch_size x 1\\n target_labels: Labels for target images. batch_size x 1\\n hparams: The hyperparameters struct.\\n\\n Returns:\\n Tuple of (names_to_values, names_to_updates), dictionaries that map a metric\\n name to its value and update op, respectively\\n\\n \\\"\\\"\\\"\\n ###########################################\\n # Evaluate the Domain Prediction Accuracy #\\n ###########################################\\n batch_size = hparams.batch_size\\n names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({\\n ('eval/Domain_Accuracy-Transferred'):\\n tf.contrib.metrics.streaming_accuracy(\\n tf.to_int32(\\n tf.round(tf.sigmoid(end_points[\\n 'transferred_domain_logits']))),\\n tf.zeros(batch_size, dtype=tf.int32)),\\n ('eval/Domain_Accuracy-Target'):\\n tf.contrib.metrics.streaming_accuracy(\\n tf.to_int32(\\n tf.round(tf.sigmoid(end_points['target_domain_logits']))),\\n tf.ones(batch_size, dtype=tf.int32))\\n })\\n\\n ################################\\n # Evaluate the task classifier #\\n ################################\\n if 'source_task_logits' in end_points:\\n metric_name = 'eval/Task_Accuracy-Source'\\n names_to_values[metric_name], names_to_updates[\\n metric_name] = tf.contrib.metrics.streaming_accuracy(\\n tf.argmax(end_points['source_task_logits'], 1),\\n source_labels['class'])\\n\\n if 'transferred_task_logits' in end_points:\\n metric_name = 'eval/Task_Accuracy-Transferred'\\n names_to_values[metric_name], names_to_updates[\\n metric_name] = tf.contrib.metrics.streaming_accuracy(\\n tf.argmax(end_points['transferred_t\",\n \"label\": 0\n },\n {\n \"cid\": \"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::main\",\n \"text\": \"research/domain_adaptation/pixel_domain_adaptation/pixelda_eval.py::main\\ndef main(_):\\n tf.logging.set_verbosity(tf.logging.INFO)\\n hparams = create_hparams(FLAGS.hparams)\\n run_eval(\\n run_dir=FLAGS.eval_dir,\\n checkpoint_dir=FLAGS.checkpoint_dir,\\n hparams=hparams)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/dragnn/python/runtime_support.py::add_hooks\",\n \"text\": \"research/syntaxnet/dragnn/python/runtime_support.py::add_hooks\\ndef add_hooks(component, cell_subgraph_spec):\\n \\\"\\\"\\\"Adds \\\"hook\\\" nodes to the graph, for use by the runtime.\\n\\n The runtime hook nodes are not on the path to any required output, and will\\n not be called when running TF-based DRAGNN. As long as the TF graph is not\\n pruned, however, the DRAGNN runtime can call them.\\n\\n Runtime hook nodes can perform any TF computation. Possible uses include:\\n * Applying stable names to existing tensors (e.g., via tf.identity()).\\n * Converting variable data from a TF-friendly or training-friendly format\\n into a runtime-friendly format.\\n\\n NB: There are several restrictions on the context in which this function is\\n called. In brief, call ComponentBuilderBase._add_runtime_hooks() at the top\\n of each ComponentBuilderSubclass.build_*() method. In detail, this:\\n * Must be called in the variable scope of the |component|, so variable\\n references in component.get_variable() work.\\n * Must be called, possibly transitively, from one of the |component|'s\\n build_*() methods, so MasterBuilder.read_from_avg is set properly for\\n component.get_variable().\\n * Must not be called from within a tf.while_loop(), or the hook nodes will\\n not work. In particular, NetworkUnitInterface.create() is called from a\\n tf.while_loop() in DynamicComponentBuilder.\\n\\n Args:\\n component: Component for which to add hooks.\\n cell_subgraph_spec: CellSubgraphSpec for which to add hooks.\\n \\\"\\\"\\\"\\n for channel_id, feature_spec in enumerate(component.spec.linked_feature):\\n if feature_spec.embedding_dim != -1:\\n _add_hooks_for_linked_embedding_matrix(component, channel_id)\\n\\n for channel_id, feature_spec in enumerate(component.spec.fixed_feature):\\n if feature_spec.embedding_dim != -1:\\n _add_hooks_for_fixed_embedding_matrix(component, channel_id)\\n\\n for params in component.network.params:\\n _add_hooks_for_trainable_params(component, params)\\n\\n for pa\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/dragnn/python/runtime_support.py::_blocked_and_dtype_transformations\",\n \"text\": \"research/syntaxnet/dragnn/python/runtime_support.py::_blocked_and_dtype_transformations\\ndef _blocked_and_dtype_transformations(tensor):\\n \\\"\\\"\\\"Yields variants of a tensor, for standard blocking/dtype variants.\\n\\n Args:\\n tensor (tf.Tensor): Input tensor.\\n\\n Yields:\\n (modified_tensor, suffix) pairs, where `modified_tensor` is a transformed\\n version of the input, and `suffix` is a string like \\\"/blocked32\\\".\\n \\\"\\\"\\\"\\n for blocking_level in (32, 48):\\n blocked = make_padded_blocked_matrix(tensor, blocking_level)\\n bfloat16_blocked = tf.to_bfloat16(bfloat16_permutation(blocked))\\n yield blocked, '/blocked{}'.format(blocking_level)\\n yield bfloat16_blocked, '/blocked{}/bfloat16'.format(blocking_level)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_linked_embedding_matrix\",\n \"text\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_linked_embedding_matrix\\ndef _add_hooks_for_linked_embedding_matrix(component, channel_id):\\n \\\"\\\"\\\"Adds runtime hooks for a linked embedding matrix.\\n\\n The computation performed by network_units.pass_through_embedding_matrix() is\\n equivalent to the following:\\n\\n for i in range(stride):\\n if step_idx[i] == -1:\\n outputs[i,:] = out_of_bounds_vector\\n else:\\n outputs[i,:] = tf.matmul(act_block[i,:], weight_matrix)\\n\\n The implementation uses clever arithmetic to do this in one matmul per batch.\\n Specifically, the weight_matrix is extended with the out_of_bounds_vector and\\n each activation vector is extended with a 0/1 out-of-bounds indicator. Then,\\n multiplying the two suffices, assuming that act_block[i,:] is set to zero for\\n out-of-bounds links.\\n\\n While this works well for training and high-throughput batched computation, it\\n isn't the best for the runtime:\\n * Appending a 0/1 indicator to the input activation vector requires a copy.\\n Ideally, we could use the input activation vector by reference alone.\\n * In order to access to the |out_of_bounds_vector| as a contiguous array,\\n the runtime must load the linked embedding matrix in row-major format,\\n which may not be the fastest format for arithmetic.\\n * The dimensions of the extended-by-1 matrix and vector are likely to be\\n pessimal. Most dimensions are specified as 2^n, and adding one element\\n produces maximal padding on the trailing elements, which in turn wastes\\n memory, reduces cache utilization, etc.\\n\\n Therefore, in the runtime we split the linked embedding matrix into a separate\\n weight matrix and out-of-bounds vector.\\n\\n Args:\\n component: Component for which to add hooks.\\n channel_id: Linked embedding channel for which to add hooks.\\n \\\"\\\"\\\"\\n var_name = network_units.linked_embeddings_name(channel_id)\\n extended_matrix = component.get_variable(var_name)\\n extended_num_rows =\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_fixed_embedding_matrix\",\n \"text\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_fixed_embedding_matrix\\ndef _add_hooks_for_fixed_embedding_matrix(component, channel_id):\\n \\\"\\\"\\\"Adds runtime hooks for a fixed embedding matrix.\\n\\n The hooks remove the last row from the embedding matrix. The extra row was\\n probably intended for out-of-vocabulary items, but those are handled in the\\n feature system and the extra row is never used.\\n\\n Args:\\n component: Component for which to add hooks.\\n channel_id: Fixed embedding channel for which to add hooks.\\n \\\"\\\"\\\"\\n var_name = network_units.fixed_embeddings_name(channel_id)\\n extended_matrix = component.get_variable(var_name)\\n extended_num_rows = tf.shape(extended_matrix)[0]\\n matrix = tf.slice(extended_matrix, [0, 0], [extended_num_rows - 1, -1])\\n\\n # TODO(googleuser): If the extra row is removed from the variable itself, remove\\n # the tf.slice() and point the hook directly at the variable.\\n _add_hook_node(matrix, _get_hook_name(component, var_name, '/trimmed'))\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_derived_parameter\",\n \"text\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_derived_parameter\\ndef _add_hooks_for_derived_parameter(getter):\\n \\\"\\\"\\\"Adds hooks for derived parameters.\\n\\n Derived parameters are typically slight format modifications of regular\\n parameters, exposed because doing the computation in Python is more convenient\\n than as VariableStore wrappers.\\n\\n Args:\\n getter: Function which, when called, will return the derived tensor.\\n \\\"\\\"\\\"\\n parameter = getter()\\n full_name = parameter.op.name\\n\\n def _hook_name(base_name):\\n \\\"\\\"\\\"Returns a hook node name constructed from a base name.\\\"\\\"\\\"\\n return full_name + base_name\\n\\n if parameter.shape.ndims != 2:\\n tf.logging.info('Not adding matrix hooks for derived parameter %s',\\n full_name)\\n return\\n\\n _add_hook_node(tf.transpose(parameter), _hook_name('/transposed'))\\n for blocked, blocked_suffix in _blocked_and_dtype_transformations(parameter):\\n _add_hook_node(blocked, _hook_name('/matrix' + blocked_suffix))\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_derived_parameter._hook_name\",\n \"text\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_derived_parameter._hook_name\\n def _hook_name(base_name):\\n \\\"\\\"\\\"Returns a hook node name constructed from a base name.\\\"\\\"\\\"\\n return full_name + base_name\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_trainable_params\",\n \"text\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_trainable_params\\ndef _add_hooks_for_trainable_params(component, params):\\n \\\"\\\"\\\"Adds runtime hooks for a variable of trainable parameters.\\n\\n Ignores parameters that are not statically-deducible as matrices.\\n\\n Args:\\n component: Component for which to add hooks.\\n params: Variable for which to add hooks.\\n \\\"\\\"\\\"\\n full_name = params.op.name\\n matrix = component.get_variable(var_params=params)\\n\\n # Only add hooks for tensors that are statically-deducible as matrices.\\n if params.shape.ndims != 2:\\n tf.logging.info('Not adding hooks for trainable params %s', full_name)\\n return\\n\\n # Infer the suffix to append to variable names, if any, based on whether the\\n # possibly-averaged |matrix| is named differently than the |params|.\\n suffix = re.sub('^' + re.escape(full_name), '', matrix.op.name)\\n check.Ne(suffix, matrix.op.name,\\n 'Failed to find suffix for params %s' % full_name)\\n\\n def _hook_name(base_name):\\n \\\"\\\"\\\"Returns a hook node name constructed from a base name.\\\"\\\"\\\"\\n return full_name + base_name + suffix\\n\\n # Add the matrix and its transpose.\\n transposed = tf.transpose(matrix)\\n _add_hook_node(matrix, _hook_name('/matrix'))\\n _add_hook_node(transposed, _hook_name('/transposed'))\\n\\n # Add blocked versions of the matrix and its transpose.\\n for blocked, blocked_suffix in _blocked_and_dtype_transformations(matrix):\\n _add_hook_node(blocked, _hook_name('/matrix' + blocked_suffix))\\n for blocked, blocked_suffix in _blocked_and_dtype_transformations(transposed):\\n _add_hook_node(blocked, _hook_name('/transposed' + blocked_suffix))\\n\\n # Also add hooks for the original shapes, which are obscured by padding.\\n _add_hook_node(tf.shape(matrix), _hook_name('/matrix/shape'))\\n _add_hook_node(tf.shape(transposed), _hook_name('/transposed/shape'))\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_trainable_params._hook_name\",\n \"text\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hooks_for_trainable_params._hook_name\\n def _hook_name(base_name):\\n \\\"\\\"\\\"Returns a hook node name constructed from a base name.\\\"\\\"\\\"\\n return full_name + base_name + suffix\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/dragnn/python/runtime_support.py::make_padded_blocked_matrix\",\n \"text\": \"research/syntaxnet/dragnn/python/runtime_support.py::make_padded_blocked_matrix\\ndef make_padded_blocked_matrix(matrix, block_size):\\n \\\"\\\"\\\"Converts a matrix to padded column-blocked format.\\n\\n For example, given a [64,127] matrix and block_size=16, this function returns\\n an [8,64,16] tensor where the 8 inner sub-matrices, when concatenated left to\\n right, re-constitute the original matrix. Note that the 8th sub-matrix has a\\n final column of padding.\\n\\n Args:\\n matrix: The matrix to convert.\\n block_size: The number of columns per block.\\n\\n Returns:\\n Padded column-blocked matrix.\\n \\\"\\\"\\\"\\n shape = tf.shape(matrix)\\n num_rows = shape[0]\\n num_columns = shape[1]\\n\\n # Compute the amount of padding and resulting number of blocks.\\n last_block_size = num_columns % block_size\\n padding_size = (block_size - last_block_size) % block_size\\n num_blocks = (num_columns + padding_size) // block_size\\n\\n # Somehow the obvious approach based on tf.split() and tf.stack() doesn't work\\n # (seems that the number of splits needs to be statically-known), but this\\n # alternative based on tf.transpose() and tf.reshape() does. Continuing the\\n # example from the docstring...\\n padded = tf.pad(matrix, [[0, 0], [0, padding_size]]) # [64,127] => [64,128]\\n transposed = tf.transpose(padded) # => [128,64]\\n blocked = tf.reshape(transposed, [num_blocks, block_size,\\n num_rows]) # => [8,16,64]\\n return tf.transpose(blocked, [0, 2, 1]) # => [8,64,16]\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/dragnn/python/runtime_support.py::bfloat16_permutation\",\n \"text\": \"research/syntaxnet/dragnn/python/runtime_support.py::bfloat16_permutation\\ndef bfloat16_permutation(tensor):\\n \\\"\\\"\\\"Permutes values in the last dimension of a tensor.\\n\\n This permutation is used so that we can directly use unpacklo/unpackhi AVX2\\n instructions on the matrix coefficients. These unpacking instructions\\n effectively permute the data. See FastUnpackPermutation() and\\n AvxFloatVecArray::Load(const TruncatedFloat16 *) in avx_vector_array.h for\\n more details.\\n\\n Args:\\n tensor: Blocked matrix, the result of make_padded_blocked_matrix(). Must\\n have its last dimension a multiple of 16.\\n\\n Returns:\\n Permuted matrix, suitable for calling tf.to_bfloat16() on. For testing\\n convenience we don't do so in this method.\\n\\n Raises:\\n ValueError: If the matrix's block dimension is not a multiple of 16.\\n \\\"\\\"\\\"\\n orig_shape = tensor.shape\\n if tensor.shape[-1] % 16 != 0:\\n raise ValueError('Bad block dimension, must be divisible by 16')\\n permutation = [0, 1, 2, 3, 8, 9, 10, 11, 4, 5, 6, 7, 12, 13, 14, 15]\\n indices = tf.constant(\\n [16 * (i // 16) + permutation[i % 16] for i in xrange(orig_shape[-1])])\\n return tf.gather(tensor, indices, axis=len(orig_shape) - 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/dragnn/python/runtime_support.py::_get_hook_name\",\n \"text\": \"research/syntaxnet/dragnn/python/runtime_support.py::_get_hook_name\\ndef _get_hook_name(component, variable_name, suffix):\\n \\\"\\\"\\\"Builds the name of a hook node.\\n\\n Specifically, the name of the hook node is:\\n\\n <component.name>/<variable_name><suffix><remainder>\\n\\n where <remainder> is whatever follows <variable_name> in the name of the op\\n that produces the named variable. Recall that component.get_variable() may\\n return either the original variable or its moving average. These might have\\n names like:\\n\\n foo_component/bar_variable\\n foo_component/bar_variable/ExponentialMovingAverage\\n\\n In the examples above, the <remainder> is \\\"\\\" for the original variable and\\n \\\"/ExponentialMovingAverage\\\" for its moving average. Calling this function\\n with suffix=\\\"/baz_suffix\\\" in either case would add hook nodes named:\\n\\n foo_component/bar_variable/baz_suffix\\n foo_component/bar_variable/baz_suffix/ExponentialMovingAverage\\n\\n Note that the suffix is inserted after the variable name, not necessarily at\\n the end of the entire op name.\\n\\n Args:\\n component: Component that the hook node belongs to.\\n variable_name: Variable that the hook node name is based on.\\n suffix: Suffix to append to the variable name.\\n\\n Returns:\\n Name of the hook node.\\n \\\"\\\"\\\"\\n variable = component.get_variable(variable_name)\\n full_name = variable.op.name\\n prefix = component.name + '/' + variable_name\\n hook_name = re.sub('^' + re.escape(prefix), prefix + suffix, full_name)\\n\\n # If re.sub() did not match anything, it returns the unmodified input (i.e.,\\n # |full_name|). Enforce that some change was made.\\n check.Ne(\\n full_name, hook_name,\\n 'Failed to match expected variable prefix \\\"{}\\\" in variable \\\"{}\\\"'.format(\\n prefix, full_name))\\n\\n return hook_name\",\n \"label\": 0\n },\n {\n \"cid\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hook_node\",\n \"text\": \"research/syntaxnet/dragnn/python/runtime_support.py::_add_hook_node\\ndef _add_hook_node(tensor, fully_qualified_name):\\n \\\"\\\"\\\"Adds a hook node that outputs a tensor with a fully-qualified name.\\\"\\\"\\\"\\n # Since the name is fully-qualified, insert the hook node into the top-level\\n # name scope.\\n with tf.name_scope(None):\\n tf.identity(tensor, name=fully_qualified_name)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":272,"cells":{"instance_id":{"kind":"string","value":"docker__compose-2206"},"repo":{"kind":"string","value":"docker/compose"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"\"extends\" and multiple yml files don't work together\nI'm currently trying to use the new \"multiple files\" (#2051) feature and got stuck on extends not working anymore when used together.\n\nExample:\n\n```\n# common.yml\nbaseservice:\n labels:\n - \"my.label=my-value\"\n environment:\n MY_ENV: \"MY_VALUE\"\n\n# base.yml\nservice1:\n extends:\n file: common.yml\n service: baseservice\n...more stuff...\n\n# dev.yml\nservice2:\n extends:\n file: common.yml\n service: baseservice\n...more stuff...\n```\n\nWhen started with:\n\n```\ndocker-compose -f base.yml -f dev.yml up\n```\n\nthe services do not include the labels and env vars from \"baseservice\".\n\nJust for testing purposes, I tried to use a random name for the extends/file field in dev.yml and it did not complain about not found files, which looks like it doesn't even try to open the file.\n\n"},"gold_files":{"kind":"list like","value":["compose/config/config.py"],"string":"[\n \"compose/config/config.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["compose/config/config.py::<module>","compose/config/config.py::ServiceLoader.validate_and_construct_extends","compose/config/config.py::load.merge_services"],"string":"[\n \"compose/config/config.py::<module>\",\n \"compose/config/config.py::ServiceLoader.validate_and_construct_extends\",\n \"compose/config/config.py::load.merge_services\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"compose/cli/command.py","text":"compose/cli/command.py\ndef friendly_error_message()\ndef project_from_options(base_dir, options)\ndef get_config_path(file_option)\ndef get_client(verbose=False, version=None)\ndef get_project(base_dir, config_path=None, project_name=None, verbose=False, use_networking=False, network_driver=None)\ndef get_project_name(working_dir, project_name=None)\n def normalize_name(name)","label":0},{"cid":"compose/cli/main.py","text":"compose/cli/main.py\ndef main()\ndef setup_logging()\ndef setup_console_handler(verbose)\ndef parse_doc_section(name, source)\nclass TopLevelCommand # Define and run multi-container applications with Docker.\n def docopt_options(self)\n def perform_command(self, options, handler, command_options)\n def build(self, project, options) # Build or rebuild services.\n def help(self, project, options) # Get help on a command.\n def kill(self, project, options) # Force stop service containers.\n def logs(self, project, options) # View output from containers.\n def pause(self, project, options) # Pause services.\n def port(self, project, options) # Print the public port for a port binding.\n def ps(self, project, options) # List containers.\n def pull(self, project, options) # Pulls images for services.\n def rm(self, project, options) # Remove stopped service containers.\n def run(self, project, options) # Run a one-off command on a service.\n def scale(self, project, options) # Set number of containers to run for a service.\n def start(self, project, options) # Start existing containers.\n def stop(self, project, options) # Stop running containers without removing them.\n def restart(self, project, options) # Restart running containers.\n def unpause(self, project, options) # Unpause services.\n def up(self, project, options) # Builds, (re)creates, starts, and attaches to containers for a service.\n def migrate_to_labels(self, project, _options) # Recreate containers to add labels\n def version(self, project, options) # Show version informations\ndef convergence_strategy_from_opts(options)\ndef build_log_printer(containers, service_names, monochrome)\ndef attach_to_logs(project, log_printer, service_names, timeout)\ndef list_containers(containers)","label":0},{"cid":"compose/config/config.py","text":"compose/config/config.py\nclass ConfigDetails # :param working_dir: the directory to use for relative paths in the config\nclass ConfigFile # :param filename: filename of the config file\ndef find(base_dir, filenames)\ndef get_default_config_files(base_dir)\ndef get_default_override_file(path)\ndef find_candidates_in_parent_dirs(filenames, path) # Given a directory path to start, looks for filenames in the\ndef load(config_details) # Load the configuration from a working directory and a list of\n def build_service(filename, service_name, service_dict)\n def load_file(filename, config)\n def merge_services(base, override)\nclass ServiceLoader\n def __init__(self, working_dir, filename, service_name, service_dict, already_seen=None)\n def detect_cycle(self, name)\n def make_service_dict(self)\n def resolve_environment(self) # Unpack any environment variables from an env_file, if set.\n def validate_and_construct_extends(self)\n def resolve_extends(self)\n def get_extended_config_path(self, extends_options) # Service we are extending either has a value for 'file' set, which we\n def signature(self, name)\ndef validate_extended_service_dict(service_dict, filename, service)\ndef process_container_options(service_dict, working_dir=None)\ndef merge_service_dicts(base, override)\ndef merge_environment(base, override)\ndef get_env_files(options, working_dir=None)\ndef parse_environment(environment)\ndef split_env(env)\ndef resolve_env_var(key, val)\ndef env_vars_from_file(filename) # Read in a line delimited file of environment variables.\ndef resolve_volume_paths(service_dict, working_dir=None)\ndef resolve_volume_path(volume, working_dir, service_name)\ndef resolve_build_path(build_path, working_dir=None)\ndef validate_paths(service_dict)\ndef merge_path_mappings(base, override)\ndef dict_from_path_mappings(path_mappings)\ndef path_mappings_from_dict(d)\ndef split_path_mapping(volume_path) # Ascertain if the volume_path contains a host path as well as a container\ndef join_path_mapping(pair)\ndef merge_labels(base, override)\ndef parse_labels(labels)\ndef split_label(label)\ndef expand_path(working_dir, path)\ndef to_list(value)\ndef get_service_name_from_net(net_config)\ndef load_yaml(filename)","label":1},{"cid":"compose/config/errors.py","text":"compose/config/errors.py\nclass ConfigurationError\n def __init__(self, msg)\n def __str__(self)\nclass CircularReference\n def __init__(self, trail)\n def msg(self)\nclass ComposeFileNotFound\n def __init__(self, supported_filenames)","label":0},{"cid":"compose/config/validation.py","text":"compose/config/validation.py\ndef format_ports(instance)\ndef format_boolean_in_environment(instance) # Check if there is a boolean in the environment and display a warning.\ndef validate_service_names(config)\ndef validate_top_level_object(config)\ndef validate_extends_file_path(service_name, extends_options, filename) # The service to be extended must either be defined in the config key 'file',\ndef validate_extended_service_exists(extended_service_name, full_extended_config, extended_config_path)\ndef get_unsupported_config_msg(service_name, error_key)\ndef anglicize_validator(validator)\ndef process_errors(errors, service_name=None) # jsonschema gives us an error tree full of information to explain what has\n def _parse_key_from_error_msg(error)\n def _clean_error_message(message)\n def _parse_valid_types_from_validator(validator) # A validator value can be either an array of valid types or a string of\n def _parse_oneof_validator(error) # oneOf has multiple schemas, so we need to reason about which schema, sub\ndef validate_against_fields_schema(config)\ndef validate_against_service_schema(config, service_name)\ndef _validate_against_schema(config, schema_filename, format_checker=[], service_name=None)","label":0},{"cid":"compose/container.py","text":"compose/container.py\nclass Container # Represents a Docker container, constructed from the output of\n def __init__(self, client, dictionary, has_been_inspected=False)\n def from_ps(cls, client, dictionary, **kwargs) # Construct a container object from the output of GET /containers/json.\n def from_id(cls, client, id)\n def create(cls, client, **options)\n def id(self)\n def image(self)\n def image_config(self)\n def short_id(self)\n def name(self)\n def service(self)\n def name_without_project(self)\n def number(self)\n def ports(self)\n def human_readable_ports(self)\n def format_port(private, public)\n def labels(self)\n def log_config(self)\n def human_readable_state(self)\n def human_readable_command(self)\n def environment(self)\n def is_running(self)\n def is_paused(self)\n def log_driver(self)\n def has_api_logs(self)\n def get(self, key) # Return a value from the container or None if the value is not set.\n def get_value(dictionary, key)\n def get_local_port(self, port, protocol='tcp')\n def start(self, **options)\n def stop(self, **options)\n def pause(self, **options)\n def unpause(self, **options)\n def kill(self, **options)\n def restart(self, **options)\n def remove(self, **options)\n def inspect_if_not_inspected(self)\n def wait(self)\n def logs(self, *args, **kwargs)\n def inspect(self)\n def links(self)\n def attach(self, *args, **kwargs)\n def __repr__(self)\n def __eq__(self, other)\n def __hash__(self)\ndef get_container_name(container)","label":0},{"cid":"compose/legacy.py","text":"compose/legacy.py\ndef check_for_legacy_containers(client, project, services, allow_one_off=True) # Check if there are containers named using the old naming convention\nclass LegacyError\n def __unicode__(self)\nclass LegacyContainersError\n def __init__(self, names, one_off_names)\nclass LegacyOneOffContainersError\n def __init__(self, one_off_names)\ndef add_labels(project, container)\ndef migrate_project_to_labels(project)\ndef get_legacy_containers(client, project, services, one_off=False)\ndef _get_legacy_containers_iter(client, project, services, one_off=False)\ndef has_container(project, service, name, one_off=False)\ndef is_valid_name(name, one_off=False)\ndef parse_name(name)","label":0},{"cid":"compose/service.py","text":"compose/service.py\nclass BuildError\n def __init__(self, service, reason)\nclass ConfigError\nclass NeedsBuildError\n def __init__(self, service)\nclass NoSuchImageError\nclass ConvergenceStrategy # Enumeration for all possible convergence strategies. Values refer to\n def allows_recreate(self)\nclass Service\n def __init__(self, name, client=None, project='default', use_networking=False, links=None, volumes_from=None, net=None, **options)\n def containers(self, stopped=False, one_off=False, filters={})\n def get_container(self, number=1) # Return a :class:`compose.container.Container` for this service. The\n def start(self, **options)\n def stop(self, **options)\n def pause(self, **options)\n def unpause(self, **options)\n def kill(self, **options)\n def restart(self, **options)\n def scale(self, desired_num, timeout=DEFAULT_TIMEOUT) # Adjusts the number of containers to the specified number and ensures\n def create_and_start(service, number)\n def remove_stopped(self, **options)\n def create_container(self, one_off=False, do_build=True, previous_container=None, number=None, quiet=False, **override_options) # Create a container for this service. If the image doesn't exist, attempt to pull\n def ensure_image_exists(self, do_build=True)\n def image(self)\n def image_name(self)\n def convergence_plan(self, strategy=ConvergenceStrategy.changed)\n def _containers_have_diverged(self, containers)\n def execute_convergence_plan(self, plan, do_build=True, timeout=DEFAULT_TIMEOUT)\n def recreate_container(self, container, timeout=DEFAULT_TIMEOUT) # Recreate a container.\n def start_container_if_stopped(self, container)\n def start_container(self, container)\n def remove_duplicate_containers(self, timeout=DEFAULT_TIMEOUT)\n def duplicate_containers(self)\n def config_hash(self)\n def config_dict(self)\n def get_dependency_names(self)\n def get_linked_service_names(self)\n def get_link_names(self)\n def get_volumes_from_names(self)\n def get_container_name(self, number, one_off=False)\n def _next_container_number(self, one_off=False)\n def _get_links(self, link_to_self)\n def _get_volumes_from(self)\n def _get_container_create_options(self, override_options, number, one_off=False, previous_container=None)\n def _get_container_host_config(self, override_options, one_off=False)\n def build(self, no_cache=False, pull=False)\n def can_be_built(self)\n def full_name(self) # The tag to give to images built for this service.\n def labels(self, one_off=False)\n def custom_container_name(self)\n def specifies_host_port(self)\n def pull(self, ignore_pull_failures=False)\nclass Net # A `standard` network mode (ex: host, bridge)\n def __init__(self, net)\n def id(self)\nclass ContainerNet # A network mode that uses a container's network stack.\n def __init__(self, container)\n def id(self)\n def mode(self)\nclass ServiceNet # A network mode that uses a service's n","label":0}],"string":"[\n {\n \"cid\": \"compose/cli/command.py\",\n \"text\": \"compose/cli/command.py\\ndef friendly_error_message()\\ndef project_from_options(base_dir, options)\\ndef get_config_path(file_option)\\ndef get_client(verbose=False, version=None)\\ndef get_project(base_dir, config_path=None, project_name=None, verbose=False, use_networking=False, network_driver=None)\\ndef get_project_name(working_dir, project_name=None)\\n def normalize_name(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py\",\n \"text\": \"compose/cli/main.py\\ndef main()\\ndef setup_logging()\\ndef setup_console_handler(verbose)\\ndef parse_doc_section(name, source)\\nclass TopLevelCommand # Define and run multi-container applications with Docker.\\n def docopt_options(self)\\n def perform_command(self, options, handler, command_options)\\n def build(self, project, options) # Build or rebuild services.\\n def help(self, project, options) # Get help on a command.\\n def kill(self, project, options) # Force stop service containers.\\n def logs(self, project, options) # View output from containers.\\n def pause(self, project, options) # Pause services.\\n def port(self, project, options) # Print the public port for a port binding.\\n def ps(self, project, options) # List containers.\\n def pull(self, project, options) # Pulls images for services.\\n def rm(self, project, options) # Remove stopped service containers.\\n def run(self, project, options) # Run a one-off command on a service.\\n def scale(self, project, options) # Set number of containers to run for a service.\\n def start(self, project, options) # Start existing containers.\\n def stop(self, project, options) # Stop running containers without removing them.\\n def restart(self, project, options) # Restart running containers.\\n def unpause(self, project, options) # Unpause services.\\n def up(self, project, options) # Builds, (re)creates, starts, and attaches to containers for a service.\\n def migrate_to_labels(self, project, _options) # Recreate containers to add labels\\n def version(self, project, options) # Show version informations\\ndef convergence_strategy_from_opts(options)\\ndef build_log_printer(containers, service_names, monochrome)\\ndef attach_to_logs(project, log_printer, service_names, timeout)\\ndef list_containers(containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py\",\n \"text\": \"compose/config/config.py\\nclass ConfigDetails # :param working_dir: the directory to use for relative paths in the config\\nclass ConfigFile # :param filename: filename of the config file\\ndef find(base_dir, filenames)\\ndef get_default_config_files(base_dir)\\ndef get_default_override_file(path)\\ndef find_candidates_in_parent_dirs(filenames, path) # Given a directory path to start, looks for filenames in the\\ndef load(config_details) # Load the configuration from a working directory and a list of\\n def build_service(filename, service_name, service_dict)\\n def load_file(filename, config)\\n def merge_services(base, override)\\nclass ServiceLoader\\n def __init__(self, working_dir, filename, service_name, service_dict, already_seen=None)\\n def detect_cycle(self, name)\\n def make_service_dict(self)\\n def resolve_environment(self) # Unpack any environment variables from an env_file, if set.\\n def validate_and_construct_extends(self)\\n def resolve_extends(self)\\n def get_extended_config_path(self, extends_options) # Service we are extending either has a value for 'file' set, which we\\n def signature(self, name)\\ndef validate_extended_service_dict(service_dict, filename, service)\\ndef process_container_options(service_dict, working_dir=None)\\ndef merge_service_dicts(base, override)\\ndef merge_environment(base, override)\\ndef get_env_files(options, working_dir=None)\\ndef parse_environment(environment)\\ndef split_env(env)\\ndef resolve_env_var(key, val)\\ndef env_vars_from_file(filename) # Read in a line delimited file of environment variables.\\ndef resolve_volume_paths(service_dict, working_dir=None)\\ndef resolve_volume_path(volume, working_dir, service_name)\\ndef resolve_build_path(build_path, working_dir=None)\\ndef validate_paths(service_dict)\\ndef merge_path_mappings(base, override)\\ndef dict_from_path_mappings(path_mappings)\\ndef path_mappings_from_dict(d)\\ndef split_path_mapping(volume_path) # Ascertain if the volume_path contains a host path as well as a container\\ndef join_path_mapping(pair)\\ndef merge_labels(base, override)\\ndef parse_labels(labels)\\ndef split_label(label)\\ndef expand_path(working_dir, path)\\ndef to_list(value)\\ndef get_service_name_from_net(net_config)\\ndef load_yaml(filename)\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/config/errors.py\",\n \"text\": \"compose/config/errors.py\\nclass ConfigurationError\\n def __init__(self, msg)\\n def __str__(self)\\nclass CircularReference\\n def __init__(self, trail)\\n def msg(self)\\nclass ComposeFileNotFound\\n def __init__(self, supported_filenames)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py\",\n \"text\": \"compose/config/validation.py\\ndef format_ports(instance)\\ndef format_boolean_in_environment(instance) # Check if there is a boolean in the environment and display a warning.\\ndef validate_service_names(config)\\ndef validate_top_level_object(config)\\ndef validate_extends_file_path(service_name, extends_options, filename) # The service to be extended must either be defined in the config key 'file',\\ndef validate_extended_service_exists(extended_service_name, full_extended_config, extended_config_path)\\ndef get_unsupported_config_msg(service_name, error_key)\\ndef anglicize_validator(validator)\\ndef process_errors(errors, service_name=None) # jsonschema gives us an error tree full of information to explain what has\\n def _parse_key_from_error_msg(error)\\n def _clean_error_message(message)\\n def _parse_valid_types_from_validator(validator) # A validator value can be either an array of valid types or a string of\\n def _parse_oneof_validator(error) # oneOf has multiple schemas, so we need to reason about which schema, sub\\ndef validate_against_fields_schema(config)\\ndef validate_against_service_schema(config, service_name)\\ndef _validate_against_schema(config, schema_filename, format_checker=[], service_name=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py\",\n \"text\": \"compose/container.py\\nclass Container # Represents a Docker container, constructed from the output of\\n def __init__(self, client, dictionary, has_been_inspected=False)\\n def from_ps(cls, client, dictionary, **kwargs) # Construct a container object from the output of GET /containers/json.\\n def from_id(cls, client, id)\\n def create(cls, client, **options)\\n def id(self)\\n def image(self)\\n def image_config(self)\\n def short_id(self)\\n def name(self)\\n def service(self)\\n def name_without_project(self)\\n def number(self)\\n def ports(self)\\n def human_readable_ports(self)\\n def format_port(private, public)\\n def labels(self)\\n def log_config(self)\\n def human_readable_state(self)\\n def human_readable_command(self)\\n def environment(self)\\n def is_running(self)\\n def is_paused(self)\\n def log_driver(self)\\n def has_api_logs(self)\\n def get(self, key) # Return a value from the container or None if the value is not set.\\n def get_value(dictionary, key)\\n def get_local_port(self, port, protocol='tcp')\\n def start(self, **options)\\n def stop(self, **options)\\n def pause(self, **options)\\n def unpause(self, **options)\\n def kill(self, **options)\\n def restart(self, **options)\\n def remove(self, **options)\\n def inspect_if_not_inspected(self)\\n def wait(self)\\n def logs(self, *args, **kwargs)\\n def inspect(self)\\n def links(self)\\n def attach(self, *args, **kwargs)\\n def __repr__(self)\\n def __eq__(self, other)\\n def __hash__(self)\\ndef get_container_name(container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py\",\n \"text\": \"compose/legacy.py\\ndef check_for_legacy_containers(client, project, services, allow_one_off=True) # Check if there are containers named using the old naming convention\\nclass LegacyError\\n def __unicode__(self)\\nclass LegacyContainersError\\n def __init__(self, names, one_off_names)\\nclass LegacyOneOffContainersError\\n def __init__(self, one_off_names)\\ndef add_labels(project, container)\\ndef migrate_project_to_labels(project)\\ndef get_legacy_containers(client, project, services, one_off=False)\\ndef _get_legacy_containers_iter(client, project, services, one_off=False)\\ndef has_container(project, service, name, one_off=False)\\ndef is_valid_name(name, one_off=False)\\ndef parse_name(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py\",\n \"text\": \"compose/service.py\\nclass BuildError\\n def __init__(self, service, reason)\\nclass ConfigError\\nclass NeedsBuildError\\n def __init__(self, service)\\nclass NoSuchImageError\\nclass ConvergenceStrategy # Enumeration for all possible convergence strategies. Values refer to\\n def allows_recreate(self)\\nclass Service\\n def __init__(self, name, client=None, project='default', use_networking=False, links=None, volumes_from=None, net=None, **options)\\n def containers(self, stopped=False, one_off=False, filters={})\\n def get_container(self, number=1) # Return a :class:`compose.container.Container` for this service. The\\n def start(self, **options)\\n def stop(self, **options)\\n def pause(self, **options)\\n def unpause(self, **options)\\n def kill(self, **options)\\n def restart(self, **options)\\n def scale(self, desired_num, timeout=DEFAULT_TIMEOUT) # Adjusts the number of containers to the specified number and ensures\\n def create_and_start(service, number)\\n def remove_stopped(self, **options)\\n def create_container(self, one_off=False, do_build=True, previous_container=None, number=None, quiet=False, **override_options) # Create a container for this service. If the image doesn't exist, attempt to pull\\n def ensure_image_exists(self, do_build=True)\\n def image(self)\\n def image_name(self)\\n def convergence_plan(self, strategy=ConvergenceStrategy.changed)\\n def _containers_have_diverged(self, containers)\\n def execute_convergence_plan(self, plan, do_build=True, timeout=DEFAULT_TIMEOUT)\\n def recreate_container(self, container, timeout=DEFAULT_TIMEOUT) # Recreate a container.\\n def start_container_if_stopped(self, container)\\n def start_container(self, container)\\n def remove_duplicate_containers(self, timeout=DEFAULT_TIMEOUT)\\n def duplicate_containers(self)\\n def config_hash(self)\\n def config_dict(self)\\n def get_dependency_names(self)\\n def get_linked_service_names(self)\\n def get_link_names(self)\\n def get_volumes_from_names(self)\\n def get_container_name(self, number, one_off=False)\\n def _next_container_number(self, one_off=False)\\n def _get_links(self, link_to_self)\\n def _get_volumes_from(self)\\n def _get_container_create_options(self, override_options, number, one_off=False, previous_container=None)\\n def _get_container_host_config(self, override_options, one_off=False)\\n def build(self, no_cache=False, pull=False)\\n def can_be_built(self)\\n def full_name(self) # The tag to give to images built for this service.\\n def labels(self, one_off=False)\\n def custom_container_name(self)\\n def specifies_host_port(self)\\n def pull(self, ignore_pull_failures=False)\\nclass Net # A `standard` network mode (ex: host, bridge)\\n def __init__(self, net)\\n def id(self)\\nclass ContainerNet # A network mode that uses a container's network stack.\\n def __init__(self, container)\\n def id(self)\\n def mode(self)\\nclass ServiceNet # A network mode that uses a service's n\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"compose/cli/command.py::friendly_error_message","text":"compose/cli/command.py::friendly_error_message\n@contextlib.contextmanager\ndef friendly_error_message():\n try:\n yield\n except SSLError as e:\n raise errors.UserError('SSL error: %s' % e)\n except ConnectionError:\n if call_silently(['which', 'docker']) != 0:\n if is_mac():\n raise errors.DockerNotFoundMac()\n elif is_ubuntu():\n raise errors.DockerNotFoundUbuntu()\n else:\n raise errors.DockerNotFoundGeneric()\n elif call_silently(['which', 'docker-machine']) == 0:\n raise errors.ConnectionErrorDockerMachine()\n else:\n raise errors.ConnectionErrorGeneric(get_client().base_url)","label":0},{"cid":"compose/cli/command.py::project_from_options","text":"compose/cli/command.py::project_from_options\ndef project_from_options(base_dir, options):\n return get_project(\n base_dir,\n get_config_path(options.get('--file')),\n project_name=options.get('--project-name'),\n verbose=options.get('--verbose'),\n use_networking=options.get('--x-networking'),\n network_driver=options.get('--x-network-driver'),\n )","label":0},{"cid":"compose/cli/command.py::get_config_path","text":"compose/cli/command.py::get_config_path\ndef get_config_path(file_option):\n if file_option:\n return file_option\n\n if 'FIG_FILE' in os.environ:\n log.warn('The FIG_FILE environment variable is deprecated.')\n log.warn('Please use COMPOSE_FILE instead.')\n\n config_file = os.environ.get('COMPOSE_FILE') or os.environ.get('FIG_FILE')\n return [config_file] if config_file else None","label":0},{"cid":"compose/cli/command.py::get_client","text":"compose/cli/command.py::get_client\ndef get_client(verbose=False, version=None):\n client = docker_client(version=version)\n if verbose:\n version_info = six.iteritems(client.version())\n log.info(\"Compose version %s\", __version__)\n log.info(\"Docker base_url: %s\", client.base_url)\n log.info(\"Docker version: %s\",\n \", \".join(\"%s=%s\" % item for item in version_info))\n return verbose_proxy.VerboseProxy('docker', client)\n return client","label":0},{"cid":"compose/cli/command.py::get_project","text":"compose/cli/command.py::get_project\ndef get_project(base_dir, config_path=None, project_name=None, verbose=False,\n use_networking=False, network_driver=None):\n config_details = config.find(base_dir, config_path)\n\n api_version = '1.21' if use_networking else None\n try:\n return Project.from_dicts(\n get_project_name(config_details.working_dir, project_name),\n config.load(config_details),\n get_client(verbose=verbose, version=api_version),\n use_networking=use_networking,\n network_driver=network_driver,\n )\n except ConfigError as e:\n raise errors.UserError(six.text_type(e))","label":0},{"cid":"compose/cli/command.py::get_project_name","text":"compose/cli/command.py::get_project_name\ndef get_project_name(working_dir, project_name=None):\n def normalize_name(name):\n return re.sub(r'[^a-z0-9]', '', name.lower())\n\n if 'FIG_PROJECT_NAME' in os.environ:\n log.warn('The FIG_PROJECT_NAME environment variable is deprecated.')\n log.warn('Please use COMPOSE_PROJECT_NAME instead.')\n\n project_name = (\n project_name or\n os.environ.get('COMPOSE_PROJECT_NAME') or\n os.environ.get('FIG_PROJECT_NAME'))\n if project_name is not None:\n return normalize_name(project_name)\n\n project = os.path.basename(os.path.abspath(working_dir))\n if project:\n return normalize_name(project)\n\n return 'default'","label":0},{"cid":"compose/cli/command.py::get_project_name.normalize_name","text":"compose/cli/command.py::get_project_name.normalize_name\n def normalize_name(name):\n return re.sub(r'[^a-z0-9]', '', name.lower())","label":0},{"cid":"compose/cli/main.py::main","text":"compose/cli/main.py::main\ndef main():\n setup_logging()\n try:\n command = TopLevelCommand()\n command.sys_dispatch()\n except KeyboardInterrupt:\n log.error(\"\\nAborting.\")\n sys.exit(1)\n except (UserError, NoSuchService, ConfigurationError, legacy.LegacyError) as e:\n log.error(e.msg)\n sys.exit(1)\n except NoSuchCommand as e:\n log.error(\"No such command: %s\", e.command)\n log.error(\"\")\n log.error(\"\\n\".join(parse_doc_section(\"commands:\", getdoc(e.supercommand))))\n sys.exit(1)\n except APIError as e:\n log.error(e.explanation)\n sys.exit(1)\n except BuildError as e:\n log.error(\"Service '%s' failed to build: %s\" % (e.service.name, e.reason))\n sys.exit(1)\n except StreamOutputError as e:\n log.error(e)\n sys.exit(1)\n except NeedsBuildError as e:\n log.error(\"Service '%s' needs to be built, but --no-build was passed.\" % e.service.name)\n sys.exit(1)\n except ReadTimeout as e:\n log.error(\n \"An HTTP request took too long to complete. Retry with --verbose to obtain debug information.\\n\"\n \"If you encounter this issue regularly because of slow network conditions, consider setting \"\n \"COMPOSE_HTTP_TIMEOUT to a higher value (current value: %s).\" % HTTP_TIMEOUT\n )","label":0},{"cid":"compose/cli/main.py::setup_logging","text":"compose/cli/main.py::setup_logging\ndef setup_logging():\n root_logger = logging.getLogger()\n root_logger.addHandler(console_handler)\n root_logger.setLevel(logging.DEBUG)\n\n # Disable requests logging\n logging.getLogger(\"requests\").propagate = False","label":0},{"cid":"compose/cli/main.py::setup_console_handler","text":"compose/cli/main.py::setup_console_handler\ndef setup_console_handler(verbose):\n if verbose:\n console_handler.setFormatter(logging.Formatter('%(name)s.%(funcName)s: %(message)s'))\n console_handler.setLevel(logging.DEBUG)\n else:\n console_handler.setFormatter(logging.Formatter())\n console_handler.setLevel(logging.INFO)","label":0},{"cid":"compose/cli/main.py::parse_doc_section","text":"compose/cli/main.py::parse_doc_section\ndef parse_doc_section(name, source):\n pattern = re.compile('^([^\\n]*' + name + '[^\\n]*\\n?(?:[ \\t].*?(?:\\n|$))*)',\n re.IGNORECASE | re.MULTILINE)\n return [s.strip() for s in pattern.findall(source)]","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.docopt_options","text":"compose/cli/main.py::TopLevelCommand.docopt_options\n def docopt_options(self):\n options = super(TopLevelCommand, self).docopt_options()\n options['version'] = get_version_info('compose')\n return options","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.perform_command","text":"compose/cli/main.py::TopLevelCommand.perform_command\n def perform_command(self, options, handler, command_options):\n setup_console_handler(options.get('--verbose'))\n\n if options['COMMAND'] in ('help', 'version'):\n # Skip looking up the compose file.\n handler(None, command_options)\n return\n\n project = project_from_options(self.base_dir, options)\n with friendly_error_message():\n handler(project, command_options)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.build","text":"compose/cli/main.py::TopLevelCommand.build\n def build(self, project, options):\n \"\"\"\n Build or rebuild services.\n\n Services are built once and then tagged as `project_service`,\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\n contents of its build directory, you can run `docker-compose build` to rebuild it.\n\n Usage: build [options] [SERVICE...]\n\n Options:\n --no-cache Do not use cache when building the image.\n --pull Always attempt to pull a newer version of the image.\n \"\"\"\n no_cache = bool(options.get('--no-cache', False))\n pull = bool(options.get('--pull', False))\n project.build(service_names=options['SERVICE'], no_cache=no_cache, pull=pull)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.help","text":"compose/cli/main.py::TopLevelCommand.help\n def help(self, project, options):\n \"\"\"\n Get help on a command.\n\n Usage: help COMMAND\n \"\"\"\n handler = self.get_handler(options['COMMAND'])\n raise SystemExit(getdoc(handler))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.kill","text":"compose/cli/main.py::TopLevelCommand.kill\n def kill(self, project, options):\n \"\"\"\n Force stop service containers.\n\n Usage: kill [options] [SERVICE...]\n\n Options:\n -s SIGNAL SIGNAL to send to the container.\n Default signal is SIGKILL.\n \"\"\"\n signal = options.get('-s', 'SIGKILL')\n\n project.kill(service_names=options['SERVICE'], signal=signal)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.logs","text":"compose/cli/main.py::TopLevelCommand.logs\n def logs(self, project, options):\n \"\"\"\n View output from containers.\n\n Usage: logs [options] [SERVICE...]\n\n Options:\n --no-color Produce monochrome output.\n \"\"\"\n containers = project.containers(service_names=options['SERVICE'], stopped=True)\n\n monochrome = options['--no-color']\n print(\"Attaching to\", list_containers(containers))\n LogPrinter(containers, monochrome=monochrome).run()","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.pause","text":"compose/cli/main.py::TopLevelCommand.pause\n def pause(self, project, options):\n \"\"\"\n Pause services.\n\n Usage: pause [SERVICE...]\n \"\"\"\n project.pause(service_names=options['SERVICE'])","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.port","text":"compose/cli/main.py::TopLevelCommand.port\n def port(self, project, options):\n \"\"\"\n Print the public port for a port binding.\n\n Usage: port [options] SERVICE PRIVATE_PORT\n\n Options:\n --protocol=proto tcp or udp [default: tcp]\n --index=index index of the container if there are multiple\n instances of a service [default: 1]\n \"\"\"\n index = int(options.get('--index'))\n service = project.get_service(options['SERVICE'])\n try:\n container = service.get_container(number=index)\n except ValueError as e:\n raise UserError(str(e))\n print(container.get_local_port(\n options['PRIVATE_PORT'],\n protocol=options.get('--protocol') or 'tcp') or '')","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.ps","text":"compose/cli/main.py::TopLevelCommand.ps\n def ps(self, project, options):\n \"\"\"\n List containers.\n\n Usage: ps [options] [SERVICE...]\n\n Options:\n -q Only display IDs\n \"\"\"\n containers = sorted(\n project.containers(service_names=options['SERVICE'], stopped=True) +\n project.containers(service_names=options['SERVICE'], one_off=True),\n key=attrgetter('name'))\n\n if options['-q']:\n for container in containers:\n print(container.id)\n else:\n headers = [\n 'Name',\n 'Command',\n 'State',\n 'Ports',\n ]\n rows = []\n for container in containers:\n command = container.human_readable_command\n if len(command) > 30:\n command = '%s ...' % command[:26]\n rows.append([\n container.name,\n command,\n container.human_readable_state,\n container.human_readable_ports,\n ])\n print(Formatter().table(headers, rows))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.pull","text":"compose/cli/main.py::TopLevelCommand.pull\n def pull(self, project, options):\n \"\"\"\n Pulls images for services.\n\n Usage: pull [options] [SERVICE...]\n\n Options:\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\n --allow-insecure-ssl Deprecated - no effect.\n \"\"\"\n if options['--allow-insecure-ssl']:\n log.warn(INSECURE_SSL_WARNING)\n\n project.pull(\n service_names=options['SERVICE'],\n ignore_pull_failures=options.get('--ignore-pull-failures')\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.rm","text":"compose/cli/main.py::TopLevelCommand.rm\n def rm(self, project, options):\n \"\"\"\n Remove stopped service containers.\n\n Usage: rm [options] [SERVICE...]\n\n Options:\n -f, --force Don't ask to confirm removal\n -v Remove volumes associated with containers\n \"\"\"\n all_containers = project.containers(service_names=options['SERVICE'], stopped=True)\n stopped_containers = [c for c in all_containers if not c.is_running]\n\n if len(stopped_containers) > 0:\n print(\"Going to remove\", list_containers(stopped_containers))\n if options.get('--force') \\\n or yesno(\"Are you sure? [yN] \", default=False):\n project.remove_stopped(\n service_names=options['SERVICE'],\n v=options.get('-v', False)\n )\n else:\n print(\"No stopped containers\")","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.run","text":"compose/cli/main.py::TopLevelCommand.run\n def run(self, project, options):\n \"\"\"\n Run a one-off command on a service.\n\n For example:\n\n $ docker-compose run web python manage.py shell\n\n By default, linked services will be started, unless they are already\n running. If you do not want to start linked services, use\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\n\n Usage: run [options] [-p PORT...] [-e KEY=VAL...] SERVICE [COMMAND] [ARGS...]\n\n Options:\n --allow-insecure-ssl Deprecated - no effect.\n -d Detached mode: Run container in the background, print\n new container name.\n --name NAME Assign a name to the container\n --entrypoint CMD Override the entrypoint of the image.\n -e KEY=VAL Set an environment variable (can be used multiple times)\n -u, --user=\"\" Run as specified username or uid\n --no-deps Don't start linked services.\n --rm Remove container after run. Ignored in detached mode.\n -p, --publish=[] Publish a container's port(s) to the host\n --service-ports Run command with the service's ports enabled and mapped\n to the host.\n -T Disable pseudo-tty allocation. By default `docker-compose run`\n allocates a TTY.\n \"\"\"\n service = project.get_service(options['SERVICE'])\n\n detach = options['-d']\n\n if IS_WINDOWS_PLATFORM and not detach:\n raise UserError(\n \"Interactive mode is not yet supported on Windows.\\n\"\n \"Please pass the -d flag when using `docker-compose run`.\"\n )\n\n if options['--allow-insecure-ssl']:\n log.warn(INSECURE_SSL_WARNING)\n\n if not options['--no-deps'","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.scale","text":"compose/cli/main.py::TopLevelCommand.scale\n def scale(self, project, options):\n \"\"\"\n Set number of containers to run for a service.\n\n Numbers are specified in the form `service=num` as arguments.\n For example:\n\n $ docker-compose scale web=2 worker=3\n\n Usage: scale [options] [SERVICE=NUM...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\n\n for s in options['SERVICE=NUM']:\n if '=' not in s:\n raise UserError('Arguments to scale should be in the form service=num')\n service_name, num = s.split('=', 1)\n try:\n num = int(num)\n except ValueError:\n raise UserError('Number of containers for service \"%s\" is not a '\n 'number' % service_name)\n project.get_service(service_name).scale(num, timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.start","text":"compose/cli/main.py::TopLevelCommand.start\n def start(self, project, options):\n \"\"\"\n Start existing containers.\n\n Usage: start [SERVICE...]\n \"\"\"\n project.start(service_names=options['SERVICE'])","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.stop","text":"compose/cli/main.py::TopLevelCommand.stop\n def stop(self, project, options):\n \"\"\"\n Stop running containers without removing them.\n\n They can be started again with `docker-compose start`.\n\n Usage: stop [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\n project.stop(service_names=options['SERVICE'], timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.restart","text":"compose/cli/main.py::TopLevelCommand.restart\n def restart(self, project, options):\n \"\"\"\n Restart running containers.\n\n Usage: restart [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\n project.restart(service_names=options['SERVICE'], timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.unpause","text":"compose/cli/main.py::TopLevelCommand.unpause\n def unpause(self, project, options):\n \"\"\"\n Unpause services.\n\n Usage: unpause [SERVICE...]\n \"\"\"\n project.unpause(service_names=options['SERVICE'])","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.up","text":"compose/cli/main.py::TopLevelCommand.up\n def up(self, project, options):\n \"\"\"\n Builds, (re)creates, starts, and attaches to containers for a service.\n\n Unless they are already running, this command also starts any linked services.\n\n The `docker-compose up` command aggregates the output of each container. When\n the command exits, all containers are stopped. Running `docker-compose up -d`\n starts the containers in the background and leaves them running.\n\n If there are existing containers for a service, and the service's configuration\n or image was changed after the container's creation, `docker-compose up` picks\n up the changes by stopping and recreating the containers (preserving mounted\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\n flag.\n\n If you want to force Compose to stop and recreate all containers, use the\n `--force-recreate` flag.\n\n Usage: up [options] [SERVICE...]\n\n Options:\n --allow-insecure-ssl Deprecated - no effect.\n -d Detached mode: Run containers in the background,\n print new container names.\n --no-color Produce monochrome output.\n --no-deps Don't start linked services.\n --force-recreate Recreate containers even if their configuration and\n image haven't changed. Incompatible with --no-recreate.\n --no-recreate If containers already exist, don't recreate them.\n Incompatible with --force-recreate.\n --no-build Don't build an image, even if it's missing\n -t, --timeout TIMEOUT Use this timeout in seconds for container shutdown\n when attached or when containers are already\n running. (default:","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.migrate_to_labels","text":"compose/cli/main.py::TopLevelCommand.migrate_to_labels\n def migrate_to_labels(self, project, _options):\n \"\"\"\n Recreate containers to add labels\n\n If you're coming from Compose 1.2 or earlier, you'll need to remove or\n migrate your existing containers after upgrading Compose. This is\n because, as of version 1.3, Compose uses Docker labels to keep track\n of containers, and so they need to be recreated with labels added.\n\n If Compose detects containers that were created without labels, it\n will refuse to run so that you don't end up with two sets of them. If\n you want to keep using your existing containers (for example, because\n they have data volumes you want to preserve) you can migrate them with\n the following command:\n\n docker-compose migrate-to-labels\n\n Alternatively, if you're not worried about keeping them, you can\n remove them - Compose will just create new ones.\n\n docker rm -f myapp_web_1 myapp_db_1 ...\n\n Usage: migrate-to-labels\n \"\"\"\n legacy.migrate_project_to_labels(project)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.version","text":"compose/cli/main.py::TopLevelCommand.version\n def version(self, project, options):\n \"\"\"\n Show version informations\n\n Usage: version [--short]\n\n Options:\n --short Shows only Compose's version number.\n \"\"\"\n if options['--short']:\n print(__version__)\n else:\n print(get_version_info('full'))","label":0},{"cid":"compose/cli/main.py::convergence_strategy_from_opts","text":"compose/cli/main.py::convergence_strategy_from_opts\ndef convergence_strategy_from_opts(options):\n no_recreate = options['--no-recreate']\n force_recreate = options['--force-recreate']\n if force_recreate and no_recreate:\n raise UserError(\"--force-recreate and --no-recreate cannot be combined.\")\n\n if force_recreate:\n return ConvergenceStrategy.always\n\n if no_recreate:\n return ConvergenceStrategy.never\n\n return ConvergenceStrategy.changed","label":0},{"cid":"compose/cli/main.py::build_log_printer","text":"compose/cli/main.py::build_log_printer\ndef build_log_printer(containers, service_names, monochrome):\n if service_names:\n containers = [c for c in containers if c.service in service_names]\n return LogPrinter(containers, monochrome=monochrome)","label":0},{"cid":"compose/cli/main.py::attach_to_logs","text":"compose/cli/main.py::attach_to_logs\ndef attach_to_logs(project, log_printer, service_names, timeout):\n print(\"Attaching to\", list_containers(log_printer.containers))\n try:\n log_printer.run()\n finally:\n def handler(signal, frame):\n project.kill(service_names=service_names)\n sys.exit(0)\n signal.signal(signal.SIGINT, handler)\n\n print(\"Gracefully stopping... (press Ctrl+C again to force)\")\n project.stop(service_names=service_names, timeout=timeout)","label":0},{"cid":"compose/cli/main.py::list_containers","text":"compose/cli/main.py::list_containers\ndef list_containers(containers):\n return \", \".join(c.name for c in containers)","label":0},{"cid":"compose/config/config.py::find","text":"compose/config/config.py::find\ndef find(base_dir, filenames):\n if filenames == ['-']:\n return ConfigDetails(\n os.getcwd(),\n [ConfigFile(None, yaml.safe_load(sys.stdin))])\n\n if filenames:\n filenames = [os.path.join(base_dir, f) for f in filenames]\n else:\n filenames = get_default_config_files(base_dir)\n\n log.debug(\"Using configuration files: {}\".format(\",\".join(filenames)))\n return ConfigDetails(\n os.path.dirname(filenames[0]),\n [ConfigFile(f, load_yaml(f)) for f in filenames])","label":0},{"cid":"compose/config/config.py::get_default_config_files","text":"compose/config/config.py::get_default_config_files\ndef get_default_config_files(base_dir):\n (candidates, path) = find_candidates_in_parent_dirs(SUPPORTED_FILENAMES, base_dir)\n\n if not candidates:\n raise ComposeFileNotFound(SUPPORTED_FILENAMES)\n\n winner = candidates[0]\n\n if len(candidates) > 1:\n log.warn(\"Found multiple config files with supported names: %s\", \", \".join(candidates))\n log.warn(\"Using %s\\n\", winner)\n\n if winner == 'docker-compose.yaml':\n log.warn(\"Please be aware that .yml is the expected extension \"\n \"in most cases, and using .yaml can cause compatibility \"\n \"issues in future.\\n\")\n\n if winner.startswith(\"fig.\"):\n log.warn(\"%s is deprecated and will not be supported in future. \"\n \"Please rename your config file to docker-compose.yml\\n\" % winner)\n\n return [os.path.join(path, winner)] + get_default_override_file(path)","label":0},{"cid":"compose/config/config.py::get_default_override_file","text":"compose/config/config.py::get_default_override_file\ndef get_default_override_file(path):\n override_filename = os.path.join(path, DEFAULT_OVERRIDE_FILENAME)\n return [override_filename] if os.path.exists(override_filename) else []","label":0},{"cid":"compose/config/config.py::find_candidates_in_parent_dirs","text":"compose/config/config.py::find_candidates_in_parent_dirs\ndef find_candidates_in_parent_dirs(filenames, path):\n \"\"\"\n Given a directory path to start, looks for filenames in the\n directory, and then each parent directory successively,\n until found.\n\n Returns tuple (candidates, path).\n \"\"\"\n candidates = [filename for filename in filenames\n if os.path.exists(os.path.join(path, filename))]\n\n if not candidates:\n parent_dir = os.path.join(path, '..')\n if os.path.abspath(parent_dir) != os.path.abspath(path):\n return find_candidates_in_parent_dirs(filenames, parent_dir)\n\n return (candidates, path)","label":0},{"cid":"compose/config/config.py::load","text":"compose/config/config.py::load\ndef load(config_details):\n \"\"\"Load the configuration from a working directory and a list of\n configuration files. Files are loaded in order, and merged on top\n of each other to create the final configuration.\n\n Return a fully interpolated, extended and validated configuration.\n \"\"\"\n\n def build_service(filename, service_name, service_dict):\n loader = ServiceLoader(\n config_details.working_dir,\n filename,\n service_name,\n service_dict)\n service_dict = loader.make_service_dict()\n validate_paths(service_dict)\n return service_dict\n\n def load_file(filename, config):\n processed_config = interpolate_environment_variables(config)\n validate_against_fields_schema(processed_config)\n return [\n build_service(filename, name, service_config)\n for name, service_config in processed_config.items()\n ]\n\n def merge_services(base, override):\n all_service_names = set(base) | set(override)\n return {\n name: merge_service_dicts(base.get(name, {}), override.get(name, {}))\n for name in all_service_names\n }\n\n config_file = config_details.config_files[0]\n validate_top_level_object(config_file.config)\n for next_file in config_details.config_files[1:]:\n validate_top_level_object(next_file.config)\n\n config_file = ConfigFile(\n config_file.filename,\n merge_services(config_file.config, next_file.config))\n\n return load_file(config_file.filename, config_file.config)","label":0},{"cid":"compose/config/config.py::load.build_service","text":"compose/config/config.py::load.build_service\n def build_service(filename, service_name, service_dict):\n loader = ServiceLoader(\n config_details.working_dir,\n filename,\n service_name,\n service_dict)\n service_dict = loader.make_service_dict()\n validate_paths(service_dict)\n return service_dict","label":0},{"cid":"compose/config/config.py::load.load_file","text":"compose/config/config.py::load.load_file\n def load_file(filename, config):\n processed_config = interpolate_environment_variables(config)\n validate_against_fields_schema(processed_config)\n return [\n build_service(filename, name, service_config)\n for name, service_config in processed_config.items()\n ]","label":0},{"cid":"compose/config/config.py::load.merge_services","text":"compose/config/config.py::load.merge_services\n def merge_services(base, override):\n all_service_names = set(base) | set(override)\n return {\n name: merge_service_dicts(base.get(name, {}), override.get(name, {}))\n for name in all_service_names\n }","label":1},{"cid":"compose/config/config.py::ServiceLoader.__init__","text":"compose/config/config.py::ServiceLoader.__init__\n def __init__(self, working_dir, filename, service_name, service_dict, already_seen=None):\n if working_dir is None:\n raise Exception(\"No working_dir passed to ServiceLoader()\")\n\n self.working_dir = os.path.abspath(working_dir)\n\n if filename:\n self.filename = os.path.abspath(filename)\n else:\n self.filename = filename\n self.already_seen = already_seen or []\n self.service_dict = service_dict.copy()\n self.service_name = service_name\n self.service_dict['name'] = service_name","label":0},{"cid":"compose/config/config.py::ServiceLoader.detect_cycle","text":"compose/config/config.py::ServiceLoader.detect_cycle\n def detect_cycle(self, name):\n if self.signature(name) in self.already_seen:\n raise CircularReference(self.already_seen + [self.signature(name)])","label":0},{"cid":"compose/config/config.py::ServiceLoader.make_service_dict","text":"compose/config/config.py::ServiceLoader.make_service_dict\n def make_service_dict(self):\n self.resolve_environment()\n if 'extends' in self.service_dict:\n self.validate_and_construct_extends()\n self.service_dict = self.resolve_extends()\n\n if not self.already_seen:\n validate_against_service_schema(self.service_dict, self.service_name)\n\n return process_container_options(self.service_dict, working_dir=self.working_dir)","label":0},{"cid":"compose/config/config.py::ServiceLoader.resolve_environment","text":"compose/config/config.py::ServiceLoader.resolve_environment\n def resolve_environment(self):\n \"\"\"\n Unpack any environment variables from an env_file, if set.\n Interpolate environment values if set.\n \"\"\"\n if 'environment' not in self.service_dict and 'env_file' not in self.service_dict:\n return\n\n env = {}\n\n if 'env_file' in self.service_dict:\n for f in get_env_files(self.service_dict, working_dir=self.working_dir):\n env.update(env_vars_from_file(f))\n del self.service_dict['env_file']\n\n env.update(parse_environment(self.service_dict.get('environment')))\n env = dict(resolve_env_var(k, v) for k, v in six.iteritems(env))\n\n self.service_dict['environment'] = env","label":0},{"cid":"compose/config/config.py::ServiceLoader.validate_and_construct_extends","text":"compose/config/config.py::ServiceLoader.validate_and_construct_extends\n def validate_and_construct_extends(self):\n extends = self.service_dict['extends']\n if not isinstance(extends, dict):\n extends = {'service': extends}\n\n validate_extends_file_path(\n self.service_name,\n extends,\n self.filename\n )\n self.extended_config_path = self.get_extended_config_path(\n extends\n )\n self.extended_service_name = extends['service']\n\n config = load_yaml(self.extended_config_path)\n validate_top_level_object(config)\n full_extended_config = interpolate_environment_variables(config)\n\n validate_extended_service_exists(\n self.extended_service_name,\n full_extended_config,\n self.extended_config_path\n )\n validate_against_fields_schema(full_extended_config)\n\n self.extended_config = full_extended_config[self.extended_service_name]","label":1},{"cid":"compose/config/config.py::ServiceLoader.resolve_extends","text":"compose/config/config.py::ServiceLoader.resolve_extends\n def resolve_extends(self):\n other_working_dir = os.path.dirname(self.extended_config_path)\n other_already_seen = self.already_seen + [self.signature(self.service_name)]\n\n other_loader = ServiceLoader(\n working_dir=other_working_dir,\n filename=self.extended_config_path,\n service_name=self.service_name,\n service_dict=self.extended_config,\n already_seen=other_already_seen,\n )\n\n other_loader.detect_cycle(self.extended_service_name)\n other_service_dict = other_loader.make_service_dict()\n validate_extended_service_dict(\n other_service_dict,\n filename=self.extended_config_path,\n service=self.extended_service_name,\n )\n\n return merge_service_dicts(other_service_dict, self.service_dict)","label":0},{"cid":"compose/config/config.py::ServiceLoader.get_extended_config_path","text":"compose/config/config.py::ServiceLoader.get_extended_config_path\n def get_extended_config_path(self, extends_options):\n \"\"\"\n Service we are extending either has a value for 'file' set, which we\n need to obtain a full path too or we are extending from a service\n defined in our own file.\n \"\"\"\n if 'file' in extends_options:\n extends_from_filename = extends_options['file']\n return expand_path(self.working_dir, extends_from_filename)\n\n return self.filename","label":0},{"cid":"compose/config/config.py::ServiceLoader.signature","text":"compose/config/config.py::ServiceLoader.signature\n def signature(self, name):\n return (self.filename, name)","label":0},{"cid":"compose/config/config.py::validate_extended_service_dict","text":"compose/config/config.py::validate_extended_service_dict\ndef validate_extended_service_dict(service_dict, filename, service):\n error_prefix = \"Cannot extend service '%s' in %s:\" % (service, filename)\n\n if 'links' in service_dict:\n raise ConfigurationError(\"%s services with 'links' cannot be extended\" % error_prefix)\n\n if 'volumes_from' in service_dict:\n raise ConfigurationError(\"%s services with 'volumes_from' cannot be extended\" % error_prefix)\n\n if 'net' in service_dict:\n if get_service_name_from_net(service_dict['net']) is not None:\n raise ConfigurationError(\"%s services with 'net: container' cannot be extended\" % error_prefix)","label":0},{"cid":"compose/config/config.py::process_container_options","text":"compose/config/config.py::process_container_options\ndef process_container_options(service_dict, working_dir=None):\n service_dict = service_dict.copy()\n\n if 'volumes' in service_dict and service_dict.get('volume_driver') is None:\n service_dict['volumes'] = resolve_volume_paths(service_dict, working_dir=working_dir)\n\n if 'build' in service_dict:\n service_dict['build'] = resolve_build_path(service_dict['build'], working_dir=working_dir)\n\n if 'labels' in service_dict:\n service_dict['labels'] = parse_labels(service_dict['labels'])\n\n return service_dict","label":0},{"cid":"compose/config/config.py::merge_service_dicts","text":"compose/config/config.py::merge_service_dicts\ndef merge_service_dicts(base, override):\n d = base.copy()\n\n if 'environment' in base or 'environment' in override:\n d['environment'] = merge_environment(\n base.get('environment'),\n override.get('environment'),\n )\n\n path_mapping_keys = ['volumes', 'devices']\n\n for key in path_mapping_keys:\n if key in base or key in override:\n d[key] = merge_path_mappings(\n base.get(key),\n override.get(key),\n )\n\n if 'labels' in base or 'labels' in override:\n d['labels'] = merge_labels(\n base.get('labels'),\n override.get('labels'),\n )\n\n if 'image' in override and 'build' in d:\n del d['build']\n\n if 'build' in override and 'image' in d:\n del d['image']\n\n list_keys = ['ports', 'expose', 'external_links']\n\n for key in list_keys:\n if key in base or key in override:\n d[key] = base.get(key, []) + override.get(key, [])\n\n list_or_string_keys = ['dns', 'dns_search']\n\n for key in list_or_string_keys:\n if key in base or key in override:\n d[key] = to_list(base.get(key)) + to_list(override.get(key))\n\n already_merged_keys = ['environment', 'labels'] + path_mapping_keys + list_keys + list_or_string_keys\n\n for k in set(ALLOWED_KEYS) - set(already_merged_keys):\n if k in override:\n d[k] = override[k]\n\n return d","label":0},{"cid":"compose/config/config.py::merge_environment","text":"compose/config/config.py::merge_environment\ndef merge_environment(base, override):\n env = parse_environment(base)\n env.update(parse_environment(override))\n return env","label":0},{"cid":"compose/config/config.py::get_env_files","text":"compose/config/config.py::get_env_files\ndef get_env_files(options, working_dir=None):\n if 'env_file' not in options:\n return {}\n\n env_files = options.get('env_file', [])\n if not isinstance(env_files, list):\n env_files = [env_files]\n\n return [expand_path(working_dir, path) for path in env_files]","label":0},{"cid":"compose/config/config.py::parse_environment","text":"compose/config/config.py::parse_environment\ndef parse_environment(environment):\n if not environment:\n return {}\n\n if isinstance(environment, list):\n return dict(split_env(e) for e in environment)\n\n if isinstance(environment, dict):\n return dict(environment)\n\n raise ConfigurationError(\n \"environment \\\"%s\\\" must be a list or mapping,\" %\n environment\n )","label":0},{"cid":"compose/config/config.py::split_env","text":"compose/config/config.py::split_env\ndef split_env(env):\n if '=' in env:\n return env.split('=', 1)\n else:\n return env, None","label":0},{"cid":"compose/config/config.py::resolve_env_var","text":"compose/config/config.py::resolve_env_var\ndef resolve_env_var(key, val):\n if val is not None:\n return key, val\n elif key in os.environ:\n return key, os.environ[key]\n else:\n return key, ''","label":0},{"cid":"compose/config/config.py::env_vars_from_file","text":"compose/config/config.py::env_vars_from_file\ndef env_vars_from_file(filename):\n \"\"\"\n Read in a line delimited file of environment variables.\n \"\"\"\n if not os.path.exists(filename):\n raise ConfigurationError(\"Couldn't find env file: %s\" % filename)\n env = {}\n for line in open(filename, 'r'):\n line = line.strip()\n if line and not line.startswith('#'):\n k, v = split_env(line)\n env[k] = v\n return env","label":0},{"cid":"compose/config/config.py::resolve_volume_paths","text":"compose/config/config.py::resolve_volume_paths\ndef resolve_volume_paths(service_dict, working_dir=None):\n if working_dir is None:\n raise Exception(\"No working_dir passed to resolve_volume_paths()\")\n\n return [\n resolve_volume_path(v, working_dir, service_dict['name'])\n for v in service_dict['volumes']\n ]","label":0},{"cid":"compose/config/config.py::resolve_volume_path","text":"compose/config/config.py::resolve_volume_path\ndef resolve_volume_path(volume, working_dir, service_name):\n container_path, host_path = split_path_mapping(volume)\n\n if host_path is not None:\n if host_path.startswith('.'):\n host_path = expand_path(working_dir, host_path)\n host_path = os.path.expanduser(host_path)\n return \"{}:{}\".format(host_path, container_path)\n else:\n return container_path","label":0},{"cid":"compose/config/config.py::resolve_build_path","text":"compose/config/config.py::resolve_build_path\ndef resolve_build_path(build_path, working_dir=None):\n if working_dir is None:\n raise Exception(\"No working_dir passed to resolve_build_path\")\n return expand_path(working_dir, build_path)","label":0},{"cid":"compose/config/config.py::validate_paths","text":"compose/config/config.py::validate_paths\ndef validate_paths(service_dict):\n if 'build' in service_dict:\n build_path = service_dict['build']\n if not os.path.exists(build_path) or not os.access(build_path, os.R_OK):\n raise ConfigurationError(\"build path %s either does not exist or is not accessible.\" % build_path)","label":0},{"cid":"compose/config/config.py::merge_path_mappings","text":"compose/config/config.py::merge_path_mappings\ndef merge_path_mappings(base, override):\n d = dict_from_path_mappings(base)\n d.update(dict_from_path_mappings(override))\n return path_mappings_from_dict(d)","label":0},{"cid":"compose/config/config.py::dict_from_path_mappings","text":"compose/config/config.py::dict_from_path_mappings\ndef dict_from_path_mappings(path_mappings):\n if path_mappings:\n return dict(split_path_mapping(v) for v in path_mappings)\n else:\n return {}","label":0},{"cid":"compose/config/config.py::path_mappings_from_dict","text":"compose/config/config.py::path_mappings_from_dict\ndef path_mappings_from_dict(d):\n return [join_path_mapping(v) for v in d.items()]","label":0},{"cid":"compose/config/config.py::split_path_mapping","text":"compose/config/config.py::split_path_mapping\ndef split_path_mapping(volume_path):\n \"\"\"\n Ascertain if the volume_path contains a host path as well as a container\n path. Using splitdrive so windows absolute paths won't cause issues with\n splitting on ':'.\n \"\"\"\n # splitdrive has limitations when it comes to relative paths, so when it's\n # relative, handle special case to set the drive to ''\n if volume_path.startswith('.') or volume_path.startswith('~'):\n drive, volume_config = '', volume_path\n else:\n drive, volume_config = os.path.splitdrive(volume_path)\n\n if ':' in volume_config:\n (host, container) = volume_config.split(':', 1)\n return (container, drive + host)\n else:\n return (volume_path, None)","label":0},{"cid":"compose/config/config.py::join_path_mapping","text":"compose/config/config.py::join_path_mapping\ndef join_path_mapping(pair):\n (container, host) = pair\n if host is None:\n return container\n else:\n return \":\".join((host, container))","label":0},{"cid":"compose/config/config.py::merge_labels","text":"compose/config/config.py::merge_labels\ndef merge_labels(base, override):\n labels = parse_labels(base)\n labels.update(parse_labels(override))\n return labels","label":0},{"cid":"compose/config/config.py::parse_labels","text":"compose/config/config.py::parse_labels\ndef parse_labels(labels):\n if not labels:\n return {}\n\n if isinstance(labels, list):\n return dict(split_label(e) for e in labels)\n\n if isinstance(labels, dict):\n return labels","label":0},{"cid":"compose/config/config.py::split_label","text":"compose/config/config.py::split_label\ndef split_label(label):\n if '=' in label:\n return label.split('=', 1)\n else:\n return label, ''","label":0},{"cid":"compose/config/config.py::expand_path","text":"compose/config/config.py::expand_path\ndef expand_path(working_dir, path):\n return os.path.abspath(os.path.join(working_dir, os.path.expanduser(path)))","label":0},{"cid":"compose/config/config.py::to_list","text":"compose/config/config.py::to_list\ndef to_list(value):\n if value is None:\n return []\n elif isinstance(value, six.string_types):\n return [value]\n else:\n return value","label":0},{"cid":"compose/config/config.py::get_service_name_from_net","text":"compose/config/config.py::get_service_name_from_net\ndef get_service_name_from_net(net_config):\n if not net_config:\n return\n\n if not net_config.startswith('container:'):\n return\n\n _, net_name = net_config.split(':', 1)\n return net_name","label":0},{"cid":"compose/config/config.py::load_yaml","text":"compose/config/config.py::load_yaml\ndef load_yaml(filename):\n try:\n with open(filename, 'r') as fh:\n return yaml.safe_load(fh)\n except (IOError, yaml.YAMLError) as e:\n error_name = getattr(e, '__module__', '') + '.' + e.__class__.__name__\n raise ConfigurationError(u\"{}: {}\".format(error_name, e))","label":0},{"cid":"compose/config/errors.py::ConfigurationError.__init__","text":"compose/config/errors.py::ConfigurationError.__init__\n def __init__(self, msg):\n self.msg = msg","label":0},{"cid":"compose/config/errors.py::ConfigurationError.__str__","text":"compose/config/errors.py::ConfigurationError.__str__\n def __str__(self):\n return self.msg","label":0},{"cid":"compose/config/errors.py::CircularReference.__init__","text":"compose/config/errors.py::CircularReference.__init__\n def __init__(self, trail):\n self.trail = trail","label":0},{"cid":"compose/config/errors.py::CircularReference.msg","text":"compose/config/errors.py::CircularReference.msg\n @property\n def msg(self):\n lines = [\n \"{} in {}\".format(service_name, filename)\n for (filename, service_name) in self.trail\n ]\n return \"Circular reference:\\n {}\".format(\"\\n extends \".join(lines))","label":0},{"cid":"compose/config/errors.py::ComposeFileNotFound.__init__","text":"compose/config/errors.py::ComposeFileNotFound.__init__\n def __init__(self, supported_filenames):\n super(ComposeFileNotFound, self).__init__(\"\"\"\n Can't find a suitable configuration file in this directory or any parent. Are you in the right directory?\n\n Supported filenames: %s\n \"\"\" % \", \".join(supported_filenames))","label":0},{"cid":"compose/config/validation.py::format_ports","text":"compose/config/validation.py::format_ports\n@FormatChecker.cls_checks(\n format=\"ports\",\n raises=ValidationError(\n \"Invalid port formatting, it should be \"\n \"'[[remote_ip:]remote_port:]port[/protocol]'\"))\ndef format_ports(instance):\n try:\n split_port(instance)\n except ValueError:\n return False\n return True","label":0},{"cid":"compose/config/validation.py::format_boolean_in_environment","text":"compose/config/validation.py::format_boolean_in_environment\n@FormatChecker.cls_checks(format=\"environment\")\ndef format_boolean_in_environment(instance):\n \"\"\"\n Check if there is a boolean in the environment and display a warning.\n Always return True here so the validation won't raise an error.\n \"\"\"\n if isinstance(instance, bool):\n log.warn(\n \"Warning: There is a boolean value in the 'environment' key.\\n\"\n \"Environment variables can only be strings.\\nPlease add quotes to any boolean values to make them string \"\n \"(eg, 'True', 'yes', 'N').\\nThis warning will become an error in a future release. \\r\\n\"\n )\n return True","label":0},{"cid":"compose/config/validation.py::validate_service_names","text":"compose/config/validation.py::validate_service_names\ndef validate_service_names(config):\n for service_name in config.keys():\n if not isinstance(service_name, six.string_types):\n raise ConfigurationError(\n \"Service name: {} needs to be a string, eg '{}'\".format(\n service_name,\n service_name))","label":0},{"cid":"compose/config/validation.py::validate_top_level_object","text":"compose/config/validation.py::validate_top_level_object\ndef validate_top_level_object(config):\n if not isinstance(config, dict):\n raise ConfigurationError(\n \"Top level object needs to be a dictionary. Check your .yml file \"\n \"that you have defined a service at the top level.\")\n validate_service_names(config)","label":0},{"cid":"compose/config/validation.py::validate_extends_file_path","text":"compose/config/validation.py::validate_extends_file_path\ndef validate_extends_file_path(service_name, extends_options, filename):\n \"\"\"\n The service to be extended must either be defined in the config key 'file',\n or within 'filename'.\n \"\"\"\n error_prefix = \"Invalid 'extends' configuration for %s:\" % service_name\n\n if 'file' not in extends_options and filename is None:\n raise ConfigurationError(\n \"%s you need to specify a 'file', e.g. 'file: something.yml'\" % error_prefix\n )","label":0},{"cid":"compose/config/validation.py::validate_extended_service_exists","text":"compose/config/validation.py::validate_extended_service_exists\ndef validate_extended_service_exists(extended_service_name, full_extended_config, extended_config_path):\n if extended_service_name not in full_extended_config:\n msg = (\n \"Cannot extend service '%s' in %s: Service not found\"\n ) % (extended_service_name, extended_config_path)\n raise ConfigurationError(msg)","label":0},{"cid":"compose/config/validation.py::get_unsupported_config_msg","text":"compose/config/validation.py::get_unsupported_config_msg\ndef get_unsupported_config_msg(service_name, error_key):\n msg = \"Unsupported config option for '{}' service: '{}'\".format(service_name, error_key)\n if error_key in DOCKER_CONFIG_HINTS:\n msg += \" (did you mean '{}'?)\".format(DOCKER_CONFIG_HINTS[error_key])\n return msg","label":0},{"cid":"compose/config/validation.py::anglicize_validator","text":"compose/config/validation.py::anglicize_validator\ndef anglicize_validator(validator):\n if validator in [\"array\", \"object\"]:\n return 'an ' + validator\n return 'a ' + validator","label":0},{"cid":"compose/config/validation.py::process_errors","text":"compose/config/validation.py::process_errors\ndef process_errors(errors, service_name=None):\n \"\"\"\n jsonschema gives us an error tree full of information to explain what has\n gone wrong. Process each error and pull out relevant information and re-write\n helpful error messages that are relevant.\n \"\"\"\n def _parse_key_from_error_msg(error):\n return error.message.split(\"'\")[1]\n\n def _clean_error_message(message):\n return message.replace(\"u'\", \"'\")\n\n def _parse_valid_types_from_validator(validator):\n \"\"\"\n A validator value can be either an array of valid types or a string of\n a valid type. Parse the valid types and prefix with the correct article.\n \"\"\"\n if isinstance(validator, list):\n if len(validator) >= 2:\n first_type = anglicize_validator(validator[0])\n last_type = anglicize_validator(validator[-1])\n types_from_validator = \"{}{}\".format(first_type, \", \".join(validator[1:-1]))\n\n msg = \"{} or {}\".format(\n types_from_validator,\n last_type\n )\n else:\n msg = \"{}\".format(anglicize_validator(validator[0]))\n else:\n msg = \"{}\".format(anglicize_validator(validator))\n\n return msg\n\n def _parse_oneof_validator(error):\n \"\"\"\n oneOf has multiple schemas, so we need to reason about which schema, sub\n schema or constraint the validation is failing on.\n Inspecting the context value of a ValidationError gives us information about\n which sub schema failed and which kind of error it is.\n \"\"\"\n\n required = [context for context in error.context if context.validator == 'required']\n if required:\n return required[0].message\n\n additionalProperties = [context for context in error.context if context.validator == 'additionalProperties']\n if additionalProperties:\n i","label":0},{"cid":"compose/config/validation.py::process_errors._parse_key_from_error_msg","text":"compose/config/validation.py::process_errors._parse_key_from_error_msg\n def _parse_key_from_error_msg(error):\n return error.message.split(\"'\")[1]","label":0},{"cid":"compose/config/validation.py::process_errors._clean_error_message","text":"compose/config/validation.py::process_errors._clean_error_message\n def _clean_error_message(message):\n return message.replace(\"u'\", \"'\")","label":0},{"cid":"compose/config/validation.py::process_errors._parse_valid_types_from_validator","text":"compose/config/validation.py::process_errors._parse_valid_types_from_validator\n def _parse_valid_types_from_validator(validator):\n \"\"\"\n A validator value can be either an array of valid types or a string of\n a valid type. Parse the valid types and prefix with the correct article.\n \"\"\"\n if isinstance(validator, list):\n if len(validator) >= 2:\n first_type = anglicize_validator(validator[0])\n last_type = anglicize_validator(validator[-1])\n types_from_validator = \"{}{}\".format(first_type, \", \".join(validator[1:-1]))\n\n msg = \"{} or {}\".format(\n types_from_validator,\n last_type\n )\n else:\n msg = \"{}\".format(anglicize_validator(validator[0]))\n else:\n msg = \"{}\".format(anglicize_validator(validator))\n\n return msg","label":0},{"cid":"compose/config/validation.py::process_errors._parse_oneof_validator","text":"compose/config/validation.py::process_errors._parse_oneof_validator\n def _parse_oneof_validator(error):\n \"\"\"\n oneOf has multiple schemas, so we need to reason about which schema, sub\n schema or constraint the validation is failing on.\n Inspecting the context value of a ValidationError gives us information about\n which sub schema failed and which kind of error it is.\n \"\"\"\n\n required = [context for context in error.context if context.validator == 'required']\n if required:\n return required[0].message\n\n additionalProperties = [context for context in error.context if context.validator == 'additionalProperties']\n if additionalProperties:\n invalid_config_key = _parse_key_from_error_msg(additionalProperties[0])\n return \"contains unsupported option: '{}'\".format(invalid_config_key)\n\n constraint = [context for context in error.context if len(context.path) > 0]\n if constraint:\n valid_types = _parse_valid_types_from_validator(constraint[0].validator_value)\n invalid_config_key = \"\".join(\n \"'{}' \".format(fragment) for fragment in constraint[0].path\n if isinstance(fragment, six.string_types)\n )\n msg = \"{}contains {}, which is an invalid type, it should be {}\".format(\n invalid_config_key,\n constraint[0].instance,\n valid_types\n )\n return msg\n\n uniqueness = [context for context in error.context if context.validator == 'uniqueItems']\n if uniqueness:\n msg = \"contains non unique items, please remove duplicates from {}\".format(\n uniqueness[0].instance\n )\n return msg\n\n types = [context.validator_value for context in error.context if context.validator == 'type']\n valid_types = _parse_valid_types_from_validator(types)\n\n msg = \"contains an invalid type, ","label":0},{"cid":"compose/config/validation.py::validate_against_fields_schema","text":"compose/config/validation.py::validate_against_fields_schema\ndef validate_against_fields_schema(config):\n schema_filename = \"fields_schema.json\"\n format_checkers = [\"ports\", \"environment\"]\n return _validate_against_schema(config, schema_filename, format_checkers)","label":0},{"cid":"compose/config/validation.py::validate_against_service_schema","text":"compose/config/validation.py::validate_against_service_schema\ndef validate_against_service_schema(config, service_name):\n schema_filename = \"service_schema.json\"\n format_checkers = [\"ports\"]\n return _validate_against_schema(config, schema_filename, format_checkers, service_name)","label":0},{"cid":"compose/config/validation.py::_validate_against_schema","text":"compose/config/validation.py::_validate_against_schema\ndef _validate_against_schema(config, schema_filename, format_checker=[], service_name=None):\n config_source_dir = os.path.dirname(os.path.abspath(__file__))\n\n if sys.platform == \"win32\":\n file_pre_fix = \"///\"\n config_source_dir = config_source_dir.replace('\\\\', '/')\n else:\n file_pre_fix = \"//\"\n\n resolver_full_path = \"file:{}{}/\".format(file_pre_fix, config_source_dir)\n schema_file = os.path.join(config_source_dir, schema_filename)\n\n with open(schema_file, \"r\") as schema_fh:\n schema = json.load(schema_fh)\n\n resolver = RefResolver(resolver_full_path, schema)\n validation_output = Draft4Validator(schema, resolver=resolver, format_checker=FormatChecker(format_checker))\n\n errors = [error for error in sorted(validation_output.iter_errors(config), key=str)]\n if errors:\n error_msg = process_errors(errors, service_name)\n raise ConfigurationError(\"Validation failed, reason(s):\\n{}\".format(error_msg))","label":0},{"cid":"compose/container.py::Container.__init__","text":"compose/container.py::Container.__init__\n def __init__(self, client, dictionary, has_been_inspected=False):\n self.client = client\n self.dictionary = dictionary\n self.has_been_inspected = has_been_inspected","label":0},{"cid":"compose/container.py::Container.from_ps","text":"compose/container.py::Container.from_ps\n @classmethod\n def from_ps(cls, client, dictionary, **kwargs):\n \"\"\"\n Construct a container object from the output of GET /containers/json.\n \"\"\"\n name = get_container_name(dictionary)\n if name is None:\n return None\n\n new_dictionary = {\n 'Id': dictionary['Id'],\n 'Image': dictionary['Image'],\n 'Name': '/' + name,\n }\n return cls(client, new_dictionary, **kwargs)","label":0},{"cid":"compose/container.py::Container.from_id","text":"compose/container.py::Container.from_id\n @classmethod\n def from_id(cls, client, id):\n return cls(client, client.inspect_container(id))","label":0},{"cid":"compose/container.py::Container.create","text":"compose/container.py::Container.create\n @classmethod\n def create(cls, client, **options):\n response = client.create_container(**options)\n return cls.from_id(client, response['Id'])","label":0},{"cid":"compose/container.py::Container.id","text":"compose/container.py::Container.id\n @property\n def id(self):\n return self.dictionary['Id']","label":0},{"cid":"compose/container.py::Container.image","text":"compose/container.py::Container.image\n @property\n def image(self):\n return self.dictionary['Image']","label":0},{"cid":"compose/container.py::Container.image_config","text":"compose/container.py::Container.image_config\n @property\n def image_config(self):\n return self.client.inspect_image(self.image)","label":0},{"cid":"compose/container.py::Container.short_id","text":"compose/container.py::Container.short_id\n @property\n def short_id(self):\n return self.id[:10]","label":0},{"cid":"compose/container.py::Container.name","text":"compose/container.py::Container.name\n @property\n def name(self):\n return self.dictionary['Name'][1:]","label":0},{"cid":"compose/container.py::Container.service","text":"compose/container.py::Container.service\n @property\n def service(self):\n return self.labels.get(LABEL_SERVICE)","label":0},{"cid":"compose/container.py::Container.name_without_project","text":"compose/container.py::Container.name_without_project\n @property\n def name_without_project(self):\n project = self.labels.get(LABEL_PROJECT)\n\n if self.name.startswith('{0}_{1}'.format(project, self.service)):\n return '{0}_{1}'.format(self.service, self.number)\n else:\n return self.name","label":0},{"cid":"compose/container.py::Container.number","text":"compose/container.py::Container.number\n @property\n def number(self):\n number = self.labels.get(LABEL_CONTAINER_NUMBER)\n if not number:\n raise ValueError(\"Container {0} does not have a {1} label\".format(\n self.short_id, LABEL_CONTAINER_NUMBER))\n return int(number)","label":0},{"cid":"compose/container.py::Container.ports","text":"compose/container.py::Container.ports\n @property\n def ports(self):\n self.inspect_if_not_inspected()\n return self.get('NetworkSettings.Ports') or {}","label":0},{"cid":"compose/container.py::Container.human_readable_ports","text":"compose/container.py::Container.human_readable_ports\n @property\n def human_readable_ports(self):\n def format_port(private, public):\n if not public:\n return private\n return '{HostIp}:{HostPort}->{private}'.format(\n private=private, **public[0])\n\n return ', '.join(format_port(*item)\n for item in sorted(six.iteritems(self.ports)))","label":0},{"cid":"compose/container.py::Container.human_readable_ports.format_port","text":"compose/container.py::Container.human_readable_ports.format_port\n def format_port(private, public):\n if not public:\n return private\n return '{HostIp}:{HostPort}->{private}'.format(\n private=private, **public[0])","label":0},{"cid":"compose/container.py::Container.labels","text":"compose/container.py::Container.labels\n @property\n def labels(self):\n return self.get('Config.Labels') or {}","label":0},{"cid":"compose/container.py::Container.log_config","text":"compose/container.py::Container.log_config\n @property\n def log_config(self):\n return self.get('HostConfig.LogConfig') or None","label":0},{"cid":"compose/container.py::Container.human_readable_state","text":"compose/container.py::Container.human_readable_state\n @property\n def human_readable_state(self):\n if self.is_paused:\n return 'Paused'\n if self.is_running:\n return 'Ghost' if self.get('State.Ghost') else 'Up'\n else:\n return 'Exit %s' % self.get('State.ExitCode')","label":0},{"cid":"compose/container.py::Container.human_readable_command","text":"compose/container.py::Container.human_readable_command\n @property\n def human_readable_command(self):\n entrypoint = self.get('Config.Entrypoint') or []\n cmd = self.get('Config.Cmd') or []\n return ' '.join(entrypoint + cmd)","label":0},{"cid":"compose/container.py::Container.environment","text":"compose/container.py::Container.environment\n @property\n def environment(self):\n return dict(var.split(\"=\", 1) for var in self.get('Config.Env') or [])","label":0},{"cid":"compose/container.py::Container.is_running","text":"compose/container.py::Container.is_running\n @property\n def is_running(self):\n return self.get('State.Running')","label":0},{"cid":"compose/container.py::Container.is_paused","text":"compose/container.py::Container.is_paused\n @property\n def is_paused(self):\n return self.get('State.Paused')","label":0},{"cid":"compose/container.py::Container.log_driver","text":"compose/container.py::Container.log_driver\n @property\n def log_driver(self):\n return self.get('HostConfig.LogConfig.Type')","label":0},{"cid":"compose/container.py::Container.has_api_logs","text":"compose/container.py::Container.has_api_logs\n @property\n def has_api_logs(self):\n log_type = self.log_driver\n return not log_type or log_type == 'json-file'","label":0},{"cid":"compose/container.py::Container.get","text":"compose/container.py::Container.get\n def get(self, key):\n \"\"\"Return a value from the container or None if the value is not set.\n\n :param key: a string using dotted notation for nested dictionary\n lookups\n \"\"\"\n self.inspect_if_not_inspected()\n\n def get_value(dictionary, key):\n return (dictionary or {}).get(key)\n\n return reduce(get_value, key.split('.'), self.dictionary)","label":0},{"cid":"compose/container.py::Container.get.get_value","text":"compose/container.py::Container.get.get_value\n def get_value(dictionary, key):\n return (dictionary or {}).get(key)","label":0},{"cid":"compose/container.py::Container.get_local_port","text":"compose/container.py::Container.get_local_port\n def get_local_port(self, port, protocol='tcp'):\n port = self.ports.get(\"%s/%s\" % (port, protocol))\n return \"{HostIp}:{HostPort}\".format(**port[0]) if port else None","label":0},{"cid":"compose/container.py::Container.start","text":"compose/container.py::Container.start\n def start(self, **options):\n return self.client.start(self.id, **options)","label":0},{"cid":"compose/container.py::Container.stop","text":"compose/container.py::Container.stop\n def stop(self, **options):\n return self.client.stop(self.id, **options)","label":0},{"cid":"compose/container.py::Container.pause","text":"compose/container.py::Container.pause\n def pause(self, **options):\n return self.client.pause(self.id, **options)","label":0},{"cid":"compose/container.py::Container.unpause","text":"compose/container.py::Container.unpause\n def unpause(self, **options):\n return self.client.unpause(self.id, **options)","label":0},{"cid":"compose/container.py::Container.kill","text":"compose/container.py::Container.kill\n def kill(self, **options):\n return self.client.kill(self.id, **options)","label":0},{"cid":"compose/container.py::Container.restart","text":"compose/container.py::Container.restart\n def restart(self, **options):\n return self.client.restart(self.id, **options)","label":0},{"cid":"compose/container.py::Container.remove","text":"compose/container.py::Container.remove\n def remove(self, **options):\n return self.client.remove_container(self.id, **options)","label":0},{"cid":"compose/container.py::Container.inspect_if_not_inspected","text":"compose/container.py::Container.inspect_if_not_inspected\n def inspect_if_not_inspected(self):\n if not self.has_been_inspected:\n self.inspect()","label":0},{"cid":"compose/container.py::Container.wait","text":"compose/container.py::Container.wait\n def wait(self):\n return self.client.wait(self.id)","label":0},{"cid":"compose/container.py::Container.logs","text":"compose/container.py::Container.logs\n def logs(self, *args, **kwargs):\n return self.client.logs(self.id, *args, **kwargs)","label":0},{"cid":"compose/container.py::Container.inspect","text":"compose/container.py::Container.inspect\n def inspect(self):\n self.dictionary = self.client.inspect_container(self.id)\n self.has_been_inspected = True\n return self.dictionary","label":0},{"cid":"compose/container.py::Container.links","text":"compose/container.py::Container.links\n def links(self):\n links = []\n for container in self.client.containers():\n for name in container['Names']:\n bits = name.split('/')\n if len(bits) > 2 and bits[1] == self.name:\n links.append(bits[2])\n return links","label":0},{"cid":"compose/container.py::Container.attach","text":"compose/container.py::Container.attach\n def attach(self, *args, **kwargs):\n return self.client.attach(self.id, *args, **kwargs)","label":0},{"cid":"compose/legacy.py::check_for_legacy_containers","text":"compose/legacy.py::check_for_legacy_containers\ndef check_for_legacy_containers(\n client,\n project,\n services,\n allow_one_off=True):\n \"\"\"Check if there are containers named using the old naming convention\n and warn the user that those containers may need to be migrated to\n using labels, so that compose can find them.\n \"\"\"\n containers = get_legacy_containers(client, project, services, one_off=False)\n\n if containers:\n one_off_containers = get_legacy_containers(client, project, services, one_off=True)\n\n raise LegacyContainersError(\n [c.name for c in containers],\n [c.name for c in one_off_containers],\n )\n\n if not allow_one_off:\n one_off_containers = get_legacy_containers(client, project, services, one_off=True)\n\n if one_off_containers:\n raise LegacyOneOffContainersError(\n [c.name for c in one_off_containers],\n )","label":0},{"cid":"compose/legacy.py::LegacyError.__unicode__","text":"compose/legacy.py::LegacyError.__unicode__\n def __unicode__(self):\n return self.msg","label":0},{"cid":"compose/legacy.py::LegacyContainersError.__init__","text":"compose/legacy.py::LegacyContainersError.__init__\n def __init__(self, names, one_off_names):\n self.names = names\n self.one_off_names = one_off_names\n\n self.msg = ERROR_MESSAGE_FORMAT.format(\n names_list=\"\\n\".join(\" {}\".format(name) for name in names),\n rm_args=\" \".join(names),\n )\n\n if one_off_names:\n self.msg += ONE_OFF_ADDENDUM_FORMAT.format(rm_args=\" \".join(one_off_names))","label":0},{"cid":"compose/legacy.py::LegacyOneOffContainersError.__init__","text":"compose/legacy.py::LegacyOneOffContainersError.__init__\n def __init__(self, one_off_names):\n self.one_off_names = one_off_names\n\n self.msg = ONE_OFF_ERROR_MESSAGE_FORMAT.format(\n names_list=\"\\n\".join(\" {}\".format(name) for name in one_off_names),\n rm_args=\" \".join(one_off_names),\n )","label":0},{"cid":"compose/legacy.py::add_labels","text":"compose/legacy.py::add_labels\ndef add_labels(project, container):\n project_name, service_name, one_off, number = NAME_RE.match(container.name).groups()\n if project_name != project.name or service_name not in project.service_names:\n return\n service = project.get_service(service_name)\n service.recreate_container(container)","label":0},{"cid":"compose/legacy.py::migrate_project_to_labels","text":"compose/legacy.py::migrate_project_to_labels\ndef migrate_project_to_labels(project):\n log.info(\"Running migration to labels for project %s\", project.name)\n\n containers = get_legacy_containers(\n project.client,\n project.name,\n project.service_names,\n one_off=False,\n )\n\n for container in containers:\n add_labels(project, container)","label":0},{"cid":"compose/legacy.py::get_legacy_containers","text":"compose/legacy.py::get_legacy_containers\ndef get_legacy_containers(\n client,\n project,\n services,\n one_off=False):\n\n return list(_get_legacy_containers_iter(\n client,\n project,\n services,\n one_off=one_off,\n ))","label":0},{"cid":"compose/legacy.py::_get_legacy_containers_iter","text":"compose/legacy.py::_get_legacy_containers_iter\ndef _get_legacy_containers_iter(\n client,\n project,\n services,\n one_off=False):\n\n containers = client.containers(all=True)\n\n for service in services:\n for container in containers:\n if LABEL_VERSION in (container.get('Labels') or {}):\n continue\n\n name = get_container_name(container)\n if has_container(project, service, name, one_off=one_off):\n yield Container.from_ps(client, container)","label":0},{"cid":"compose/legacy.py::has_container","text":"compose/legacy.py::has_container\ndef has_container(project, service, name, one_off=False):\n if not name or not is_valid_name(name, one_off):\n return False\n container_project, container_service, _container_number = parse_name(name)\n return container_project == project and container_service == service","label":0},{"cid":"compose/legacy.py::is_valid_name","text":"compose/legacy.py::is_valid_name\ndef is_valid_name(name, one_off=False):\n match = NAME_RE.match(name)\n if match is None:\n return False\n if one_off:\n return match.group(3) == 'run_'\n else:\n return match.group(3) is None","label":0},{"cid":"compose/legacy.py::parse_name","text":"compose/legacy.py::parse_name\ndef parse_name(name):\n match = NAME_RE.match(name)\n (project, service_name, _, suffix) = match.groups()\n return (project, service_name, int(suffix))","label":0},{"cid":"compose/service.py::BuildError.__init__","text":"compose/service.py::BuildError.__init__\n def __init__(self, service, reason):\n self.service = service\n self.reason = reason","label":0},{"cid":"compose/service.py::NeedsBuildError.__init__","text":"compose/service.py::NeedsBuildError.__init__\n def __init__(self, service):\n self.service = service","label":0},{"cid":"compose/service.py::ConvergenceStrategy.allows_recreate","text":"compose/service.py::ConvergenceStrategy.allows_recreate\n @property\n def allows_recreate(self):\n return self is not type(self).never","label":0},{"cid":"compose/service.py::Service.__init__","text":"compose/service.py::Service.__init__\n def __init__(\n self,\n name,\n client=None,\n project='default',\n use_networking=False,\n links=None,\n volumes_from=None,\n net=None,\n **options\n ):\n if not re.match('^%s+$' % VALID_NAME_CHARS, project):\n raise ConfigError('Invalid project name \"%s\" - only %s are allowed' % (project, VALID_NAME_CHARS))\n\n self.name = name\n self.client = client\n self.project = project\n self.use_networking = use_networking\n self.links = links or []\n self.volumes_from = volumes_from or []\n self.net = net or Net(None)\n self.options = options","label":0},{"cid":"compose/service.py::Service.containers","text":"compose/service.py::Service.containers\n def containers(self, stopped=False, one_off=False, filters={}):\n filters.update({'label': self.labels(one_off=one_off)})\n\n containers = list(filter(None, [\n Container.from_ps(self.client, container)\n for container in self.client.containers(\n all=stopped,\n filters=filters)]))\n\n if not containers:\n check_for_legacy_containers(\n self.client,\n self.project,\n [self.name],\n )\n\n return containers","label":0},{"cid":"compose/service.py::Service.get_container","text":"compose/service.py::Service.get_container\n def get_container(self, number=1):\n \"\"\"Return a :class:`compose.container.Container` for this service. The\n container must be active, and match `number`.\n \"\"\"\n labels = self.labels() + ['{0}={1}'.format(LABEL_CONTAINER_NUMBER, number)]\n for container in self.client.containers(filters={'label': labels}):\n return Container.from_ps(self.client, container)\n\n raise ValueError(\"No container found for %s_%s\" % (self.name, number))","label":0},{"cid":"compose/service.py::Service.start","text":"compose/service.py::Service.start\n def start(self, **options):\n for c in self.containers(stopped=True):\n self.start_container_if_stopped(c, **options)","label":0},{"cid":"compose/service.py::Service.stop","text":"compose/service.py::Service.stop\n def stop(self, **options):\n for c in self.containers():\n log.info(\"Stopping %s\" % c.name)\n c.stop(**options)","label":0},{"cid":"compose/service.py::Service.pause","text":"compose/service.py::Service.pause\n def pause(self, **options):\n for c in self.containers(filters={'status': 'running'}):\n log.info(\"Pausing %s\" % c.name)\n c.pause(**options)","label":0},{"cid":"compose/service.py::Service.unpause","text":"compose/service.py::Service.unpause\n def unpause(self, **options):\n for c in self.containers(filters={'status': 'paused'}):\n log.info(\"Unpausing %s\" % c.name)\n c.unpause()","label":0},{"cid":"compose/service.py::Service.kill","text":"compose/service.py::Service.kill\n def kill(self, **options):\n for c in self.containers():\n log.info(\"Killing %s\" % c.name)\n c.kill(**options)","label":0},{"cid":"compose/service.py::Service.restart","text":"compose/service.py::Service.restart\n def restart(self, **options):\n for c in self.containers():\n log.info(\"Restarting %s\" % c.name)\n c.restart(**options)","label":0},{"cid":"compose/service.py::Service.scale","text":"compose/service.py::Service.scale\n def scale(self, desired_num, timeout=DEFAULT_TIMEOUT):\n \"\"\"\n Adjusts the number of containers to the specified number and ensures\n they are running.\n\n - creates containers until there are at least `desired_num`\n - stops containers until there are at most `desired_num` running\n - starts containers until there are at least `desired_num` running\n - removes all stopped containers\n \"\"\"\n if self.custom_container_name() and desired_num > 1:\n log.warn('The \"%s\" service is using the custom container name \"%s\". '\n 'Docker requires each container to have a unique name. '\n 'Remove the custom name to scale the service.'\n % (self.name, self.custom_container_name()))\n\n if self.specifies_host_port():\n log.warn('The \"%s\" service specifies a port on the host. If multiple containers '\n 'for this service are created on a single host, the port will clash.'\n % self.name)\n\n def create_and_start(service, number):\n container = service.create_container(number=number, quiet=True)\n container.start()\n return container\n\n running_containers = self.containers(stopped=False)\n num_running = len(running_containers)\n\n if desired_num == num_running:\n # do nothing as we already have the desired number\n log.info('Desired container number already achieved')\n return\n\n if desired_num > num_running:\n # we need to start/create until we have desired_num\n all_containers = self.containers(stopped=True)\n\n if num_running != len(all_containers):\n # we have some stopped containers, let's start them up again\n stopped_containers = sorted([c for c in all_containers if not c.is_running], key=attrgetter('number'))\n\n num","label":0},{"cid":"compose/service.py::Service.scale.create_and_start","text":"compose/service.py::Service.scale.create_and_start\n def create_and_start(service, number):\n container = service.create_container(number=number, quiet=True)\n container.start()\n return container","label":0},{"cid":"compose/service.py::Service.remove_stopped","text":"compose/service.py::Service.remove_stopped\n def remove_stopped(self, **options):\n containers = [c for c in self.containers(stopped=True) if not c.is_running]\n\n parallel_execute(\n objects=containers,\n obj_callable=lambda c: c.remove(**options),\n msg_index=lambda c: c.name,\n msg=\"Removing\"\n )","label":0},{"cid":"compose/service.py::Service.create_container","text":"compose/service.py::Service.create_container\n def create_container(self,\n one_off=False,\n do_build=True,\n previous_container=None,\n number=None,\n quiet=False,\n **override_options):\n \"\"\"\n Create a container for this service. If the image doesn't exist, attempt to pull\n it.\n \"\"\"\n self.ensure_image_exists(\n do_build=do_build,\n )\n\n container_options = self._get_container_create_options(\n override_options,\n number or self._next_container_number(one_off=one_off),\n one_off=one_off,\n previous_container=previous_container,\n )\n\n if 'name' in container_options and not quiet:\n log.info(\"Creating %s\" % container_options['name'])\n\n return Container.create(self.client, **container_options)","label":0},{"cid":"compose/service.py::Service.ensure_image_exists","text":"compose/service.py::Service.ensure_image_exists\n def ensure_image_exists(self,\n do_build=True):\n\n try:\n self.image()\n return\n except NoSuchImageError:\n pass\n\n if self.can_be_built():\n if do_build:\n self.build()\n else:\n raise NeedsBuildError(self)\n else:\n self.pull()","label":0},{"cid":"compose/service.py::Service.image","text":"compose/service.py::Service.image\n def image(self):\n try:\n return self.client.inspect_image(self.image_name)\n except APIError as e:\n if e.response.status_code == 404 and e.explanation and 'No such image' in str(e.explanation):\n raise NoSuchImageError(\"Image '{}' not found\".format(self.image_name))\n else:\n raise","label":0},{"cid":"compose/service.py::Service.image_name","text":"compose/service.py::Service.image_name\n @property\n def image_name(self):\n if self.can_be_built():\n return self.full_name\n else:\n return self.options['image']","label":0},{"cid":"compose/service.py::Service.convergence_plan","text":"compose/service.py::Service.convergence_plan\n def convergence_plan(self, strategy=ConvergenceStrategy.changed):\n containers = self.containers(stopped=True)\n\n if not containers:\n return ConvergencePlan('create', [])\n\n if strategy is ConvergenceStrategy.never:\n return ConvergencePlan('start', containers)\n\n if (\n strategy is ConvergenceStrategy.always or\n self._containers_have_diverged(containers)\n ):\n return ConvergencePlan('recreate', containers)\n\n stopped = [c for c in containers if not c.is_running]\n\n if stopped:\n return ConvergencePlan('start', stopped)\n\n return ConvergencePlan('noop', containers)","label":0},{"cid":"compose/service.py::Service._containers_have_diverged","text":"compose/service.py::Service._containers_have_diverged\n def _containers_have_diverged(self, containers):\n config_hash = None\n\n try:\n config_hash = self.config_hash\n except NoSuchImageError as e:\n log.debug(\n 'Service %s has diverged: %s',\n self.name, six.text_type(e),\n )\n return True\n\n has_diverged = False\n\n for c in containers:\n container_config_hash = c.labels.get(LABEL_CONFIG_HASH, None)\n if container_config_hash != config_hash:\n log.debug(\n '%s has diverged: %s != %s',\n c.name, container_config_hash, config_hash,\n )\n has_diverged = True\n\n return has_diverged","label":0},{"cid":"compose/service.py::Service.execute_convergence_plan","text":"compose/service.py::Service.execute_convergence_plan\n def execute_convergence_plan(self,\n plan,\n do_build=True,\n timeout=DEFAULT_TIMEOUT):\n (action, containers) = plan\n\n if action == 'create':\n container = self.create_container(\n do_build=do_build,\n )\n self.start_container(container)\n\n return [container]\n\n elif action == 'recreate':\n return [\n self.recreate_container(\n c,\n timeout=timeout\n )\n for c in containers\n ]\n\n elif action == 'start':\n for c in containers:\n self.start_container_if_stopped(c)\n\n return containers\n\n elif action == 'noop':\n for c in containers:\n log.info(\"%s is up-to-date\" % c.name)\n\n return containers\n\n else:\n raise Exception(\"Invalid action: {}\".format(action))","label":0},{"cid":"compose/service.py::Service.recreate_container","text":"compose/service.py::Service.recreate_container\n def recreate_container(self,\n container,\n timeout=DEFAULT_TIMEOUT):\n \"\"\"Recreate a container.\n\n The original container is renamed to a temporary name so that data\n volumes can be copied to the new container, before the original\n container is removed.\n \"\"\"\n log.info(\"Recreating %s\" % container.name)\n try:\n container.stop(timeout=timeout)\n except APIError as e:\n if (e.response.status_code == 500\n and e.explanation\n and 'no such process' in str(e.explanation)):\n pass\n else:\n raise\n\n # Use a hopefully unique container name by prepending the short id\n self.client.rename(\n container.id,\n '%s_%s' % (container.short_id, container.name))\n\n new_container = self.create_container(\n do_build=False,\n previous_container=container,\n number=container.labels.get(LABEL_CONTAINER_NUMBER),\n quiet=True,\n )\n self.start_container(new_container)\n container.remove()\n return new_container","label":0},{"cid":"compose/service.py::Service.start_container_if_stopped","text":"compose/service.py::Service.start_container_if_stopped\n def start_container_if_stopped(self, container):\n if container.is_running:\n return container\n else:\n log.info(\"Starting %s\" % container.name)\n return self.start_container(container)","label":0},{"cid":"compose/service.py::Service.start_container","text":"compose/service.py::Service.start_container\n def start_container(self, container):\n container.start()\n return container","label":0},{"cid":"compose/service.py::Service.remove_duplicate_containers","text":"compose/service.py::Service.remove_duplicate_containers\n def remove_duplicate_containers(self, timeout=DEFAULT_TIMEOUT):\n for c in self.duplicate_containers():\n log.info('Removing %s' % c.name)\n c.stop(timeout=timeout)\n c.remove()","label":0},{"cid":"compose/service.py::Service.duplicate_containers","text":"compose/service.py::Service.duplicate_containers\n def duplicate_containers(self):\n containers = sorted(\n self.containers(stopped=True),\n key=lambda c: c.get('Created'),\n )\n\n numbers = set()\n\n for c in containers:\n if c.number in numbers:\n yield c\n else:\n numbers.add(c.number)","label":0},{"cid":"compose/service.py::Service.config_hash","text":"compose/service.py::Service.config_hash\n @property\n def config_hash(self):\n return json_hash(self.config_dict())","label":0},{"cid":"compose/service.py::Service.config_dict","text":"compose/service.py::Service.config_dict\n def config_dict(self):\n return {\n 'options': self.options,\n 'image_id': self.image()['Id'],\n 'links': self.get_link_names(),\n 'net': self.net.id,\n 'volumes_from': self.get_volumes_from_names(),\n }","label":0},{"cid":"compose/service.py::Service.get_dependency_names","text":"compose/service.py::Service.get_dependency_names\n def get_dependency_names(self):\n net_name = self.net.service_name\n return (self.get_linked_service_names() +\n self.get_volumes_from_names() +\n ([net_name] if net_name else []))","label":0},{"cid":"compose/service.py::Service.get_linked_service_names","text":"compose/service.py::Service.get_linked_service_names\n def get_linked_service_names(self):\n return [service.name for (service, _) in self.links]","label":0},{"cid":"compose/service.py::Service.get_link_names","text":"compose/service.py::Service.get_link_names\n def get_link_names(self):\n return [(service.name, alias) for service, alias in self.links]","label":0},{"cid":"compose/service.py::Service.get_volumes_from_names","text":"compose/service.py::Service.get_volumes_from_names\n def get_volumes_from_names(self):\n return [s.source.name for s in self.volumes_from if isinstance(s.source, Service)]","label":0},{"cid":"compose/service.py::Service.get_container_name","text":"compose/service.py::Service.get_container_name\n def get_container_name(self, number, one_off=False):\n # TODO: Implement issue #652 here\n return build_container_name(self.project, self.name, number, one_off)","label":0},{"cid":"compose/service.py::Service._next_container_number","text":"compose/service.py::Service._next_container_number\n def _next_container_number(self, one_off=False):\n containers = filter(None, [\n Container.from_ps(self.client, container)\n for container in self.client.containers(\n all=True,\n filters={'label': self.labels(one_off=one_off)})\n ])\n numbers = [c.number for c in containers]\n return 1 if not numbers else max(numbers) + 1","label":0},{"cid":"compose/service.py::Service._get_links","text":"compose/service.py::Service._get_links\n def _get_links(self, link_to_self):\n links = []\n for service, link_name in self.links:\n for container in service.containers():\n links.append((container.name, link_name or service.name))\n links.append((container.name, container.name))\n links.append((container.name, container.name_without_project))\n if link_to_self:\n for container in self.containers():\n links.append((container.name, self.name))\n links.append((container.name, container.name))\n links.append((container.name, container.name_without_project))\n for external_link in self.options.get('external_links') or []:\n if ':' not in external_link:\n link_name = external_link\n else:\n external_link, link_name = external_link.split(':')\n links.append((external_link, link_name))\n return links","label":0},{"cid":"compose/service.py::Service._get_volumes_from","text":"compose/service.py::Service._get_volumes_from\n def _get_volumes_from(self):\n volumes_from = []\n for volume_from_spec in self.volumes_from:\n volumes = build_volume_from(volume_from_spec)\n volumes_from.extend(volumes)\n\n return volumes_from","label":0},{"cid":"compose/service.py::Service._get_container_create_options","text":"compose/service.py::Service._get_container_create_options\n def _get_container_create_options(\n self,\n override_options,\n number,\n one_off=False,\n previous_container=None):\n add_config_hash = (not one_off and not override_options)\n\n container_options = dict(\n (k, self.options[k])\n for k in DOCKER_CONFIG_KEYS if k in self.options)\n container_options.update(override_options)\n\n if self.custom_container_name() and not one_off:\n container_options['name'] = self.custom_container_name()\n elif not container_options.get('name'):\n container_options['name'] = self.get_container_name(number, one_off)\n\n if 'detach' not in container_options:\n container_options['detach'] = True\n\n # If a qualified hostname was given, split it into an\n # unqualified hostname and a domainname unless domainname\n # was also given explicitly. This matches the behavior of\n # the official Docker CLI in that scenario.\n if ('hostname' in container_options\n and 'domainname' not in container_options\n and '.' in container_options['hostname']):\n parts = container_options['hostname'].partition('.')\n container_options['hostname'] = parts[0]\n container_options['domainname'] = parts[2]\n\n if 'hostname' not in container_options and self.use_networking:\n container_options['hostname'] = self.name\n\n if 'ports' in container_options or 'expose' in self.options:\n ports = []\n all_ports = container_options.get('ports', []) + self.options.get('expose', [])\n for port_range in all_ports:\n internal_range, _ = split_port(port_range)\n for port in internal_range:\n port = str(port)\n if '/' in port:\n port = tuple(port.split('/'))\n ","label":0},{"cid":"compose/service.py::Service._get_container_host_config","text":"compose/service.py::Service._get_container_host_config\n def _get_container_host_config(self, override_options, one_off=False):\n options = dict(self.options, **override_options)\n port_bindings = build_port_bindings(options.get('ports') or [])\n\n privileged = options.get('privileged', False)\n cap_add = options.get('cap_add', None)\n cap_drop = options.get('cap_drop', None)\n log_config = LogConfig(\n type=options.get('log_driver', \"\"),\n config=options.get('log_opt', None)\n )\n pid = options.get('pid', None)\n security_opt = options.get('security_opt', None)\n\n dns = options.get('dns', None)\n if isinstance(dns, six.string_types):\n dns = [dns]\n\n dns_search = options.get('dns_search', None)\n if isinstance(dns_search, six.string_types):\n dns_search = [dns_search]\n\n restart = parse_restart_spec(options.get('restart', None))\n\n extra_hosts = build_extra_hosts(options.get('extra_hosts', None))\n read_only = options.get('read_only', None)\n\n devices = options.get('devices', None)\n cgroup_parent = options.get('cgroup_parent', None)\n\n return self.client.create_host_config(\n links=self._get_links(link_to_self=one_off),\n port_bindings=port_bindings,\n binds=options.get('binds'),\n volumes_from=self._get_volumes_from(),\n privileged=privileged,\n network_mode=self.net.mode,\n devices=devices,\n dns=dns,\n dns_search=dns_search,\n restart_policy=restart,\n cap_add=cap_add,\n cap_drop=cap_drop,\n mem_limit=options.get('mem_limit'),\n memswap_limit=options.get('memswap_limit'),\n log_config=log_config,\n extra_hosts=extra_hosts,\n read_only=read_only,\n pid_mode=pid,\n security_opt=security_opt,\n ipc_mode=options.get(","label":0},{"cid":"compose/service.py::Service.build","text":"compose/service.py::Service.build\n def build(self, no_cache=False, pull=False):\n log.info('Building %s' % self.name)\n\n path = self.options['build']\n # python2 os.path() doesn't support unicode, so we need to encode it to\n # a byte string\n if not six.PY3:\n path = path.encode('utf8')\n\n build_output = self.client.build(\n path=path,\n tag=self.image_name,\n stream=True,\n rm=True,\n pull=pull,\n nocache=no_cache,\n dockerfile=self.options.get('dockerfile', None),\n )\n\n try:\n all_events = stream_output(build_output, sys.stdout)\n except StreamOutputError as e:\n raise BuildError(self, six.text_type(e))\n\n # Ensure the HTTP connection is not reused for another\n # streaming command, as the Docker daemon can sometimes\n # complain about it\n self.client.close()\n\n image_id = None\n\n for event in all_events:\n if 'stream' in event:\n match = re.search(r'Successfully built ([0-9a-f]+)', event.get('stream', ''))\n if match:\n image_id = match.group(1)\n\n if image_id is None:\n raise BuildError(self, event if all_events else 'Unknown')\n\n return image_id","label":0}],"string":"[\n {\n \"cid\": \"compose/cli/command.py::friendly_error_message\",\n \"text\": \"compose/cli/command.py::friendly_error_message\\n@contextlib.contextmanager\\ndef friendly_error_message():\\n try:\\n yield\\n except SSLError as e:\\n raise errors.UserError('SSL error: %s' % e)\\n except ConnectionError:\\n if call_silently(['which', 'docker']) != 0:\\n if is_mac():\\n raise errors.DockerNotFoundMac()\\n elif is_ubuntu():\\n raise errors.DockerNotFoundUbuntu()\\n else:\\n raise errors.DockerNotFoundGeneric()\\n elif call_silently(['which', 'docker-machine']) == 0:\\n raise errors.ConnectionErrorDockerMachine()\\n else:\\n raise errors.ConnectionErrorGeneric(get_client().base_url)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/command.py::project_from_options\",\n \"text\": \"compose/cli/command.py::project_from_options\\ndef project_from_options(base_dir, options):\\n return get_project(\\n base_dir,\\n get_config_path(options.get('--file')),\\n project_name=options.get('--project-name'),\\n verbose=options.get('--verbose'),\\n use_networking=options.get('--x-networking'),\\n network_driver=options.get('--x-network-driver'),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/command.py::get_config_path\",\n \"text\": \"compose/cli/command.py::get_config_path\\ndef get_config_path(file_option):\\n if file_option:\\n return file_option\\n\\n if 'FIG_FILE' in os.environ:\\n log.warn('The FIG_FILE environment variable is deprecated.')\\n log.warn('Please use COMPOSE_FILE instead.')\\n\\n config_file = os.environ.get('COMPOSE_FILE') or os.environ.get('FIG_FILE')\\n return [config_file] if config_file else None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/command.py::get_client\",\n \"text\": \"compose/cli/command.py::get_client\\ndef get_client(verbose=False, version=None):\\n client = docker_client(version=version)\\n if verbose:\\n version_info = six.iteritems(client.version())\\n log.info(\\\"Compose version %s\\\", __version__)\\n log.info(\\\"Docker base_url: %s\\\", client.base_url)\\n log.info(\\\"Docker version: %s\\\",\\n \\\", \\\".join(\\\"%s=%s\\\" % item for item in version_info))\\n return verbose_proxy.VerboseProxy('docker', client)\\n return client\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/command.py::get_project\",\n \"text\": \"compose/cli/command.py::get_project\\ndef get_project(base_dir, config_path=None, project_name=None, verbose=False,\\n use_networking=False, network_driver=None):\\n config_details = config.find(base_dir, config_path)\\n\\n api_version = '1.21' if use_networking else None\\n try:\\n return Project.from_dicts(\\n get_project_name(config_details.working_dir, project_name),\\n config.load(config_details),\\n get_client(verbose=verbose, version=api_version),\\n use_networking=use_networking,\\n network_driver=network_driver,\\n )\\n except ConfigError as e:\\n raise errors.UserError(six.text_type(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/command.py::get_project_name\",\n \"text\": \"compose/cli/command.py::get_project_name\\ndef get_project_name(working_dir, project_name=None):\\n def normalize_name(name):\\n return re.sub(r'[^a-z0-9]', '', name.lower())\\n\\n if 'FIG_PROJECT_NAME' in os.environ:\\n log.warn('The FIG_PROJECT_NAME environment variable is deprecated.')\\n log.warn('Please use COMPOSE_PROJECT_NAME instead.')\\n\\n project_name = (\\n project_name or\\n os.environ.get('COMPOSE_PROJECT_NAME') or\\n os.environ.get('FIG_PROJECT_NAME'))\\n if project_name is not None:\\n return normalize_name(project_name)\\n\\n project = os.path.basename(os.path.abspath(working_dir))\\n if project:\\n return normalize_name(project)\\n\\n return 'default'\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/command.py::get_project_name.normalize_name\",\n \"text\": \"compose/cli/command.py::get_project_name.normalize_name\\n def normalize_name(name):\\n return re.sub(r'[^a-z0-9]', '', name.lower())\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::main\",\n \"text\": \"compose/cli/main.py::main\\ndef main():\\n setup_logging()\\n try:\\n command = TopLevelCommand()\\n command.sys_dispatch()\\n except KeyboardInterrupt:\\n log.error(\\\"\\\\nAborting.\\\")\\n sys.exit(1)\\n except (UserError, NoSuchService, ConfigurationError, legacy.LegacyError) as e:\\n log.error(e.msg)\\n sys.exit(1)\\n except NoSuchCommand as e:\\n log.error(\\\"No such command: %s\\\", e.command)\\n log.error(\\\"\\\")\\n log.error(\\\"\\\\n\\\".join(parse_doc_section(\\\"commands:\\\", getdoc(e.supercommand))))\\n sys.exit(1)\\n except APIError as e:\\n log.error(e.explanation)\\n sys.exit(1)\\n except BuildError as e:\\n log.error(\\\"Service '%s' failed to build: %s\\\" % (e.service.name, e.reason))\\n sys.exit(1)\\n except StreamOutputError as e:\\n log.error(e)\\n sys.exit(1)\\n except NeedsBuildError as e:\\n log.error(\\\"Service '%s' needs to be built, but --no-build was passed.\\\" % e.service.name)\\n sys.exit(1)\\n except ReadTimeout as e:\\n log.error(\\n \\\"An HTTP request took too long to complete. Retry with --verbose to obtain debug information.\\\\n\\\"\\n \\\"If you encounter this issue regularly because of slow network conditions, consider setting \\\"\\n \\\"COMPOSE_HTTP_TIMEOUT to a higher value (current value: %s).\\\" % HTTP_TIMEOUT\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_logging\",\n \"text\": \"compose/cli/main.py::setup_logging\\ndef setup_logging():\\n root_logger = logging.getLogger()\\n root_logger.addHandler(console_handler)\\n root_logger.setLevel(logging.DEBUG)\\n\\n # Disable requests logging\\n logging.getLogger(\\\"requests\\\").propagate = False\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_console_handler\",\n \"text\": \"compose/cli/main.py::setup_console_handler\\ndef setup_console_handler(verbose):\\n if verbose:\\n console_handler.setFormatter(logging.Formatter('%(name)s.%(funcName)s: %(message)s'))\\n console_handler.setLevel(logging.DEBUG)\\n else:\\n console_handler.setFormatter(logging.Formatter())\\n console_handler.setLevel(logging.INFO)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::parse_doc_section\",\n \"text\": \"compose/cli/main.py::parse_doc_section\\ndef parse_doc_section(name, source):\\n pattern = re.compile('^([^\\\\n]*' + name + '[^\\\\n]*\\\\n?(?:[ \\\\t].*?(?:\\\\n|$))*)',\\n re.IGNORECASE | re.MULTILINE)\\n return [s.strip() for s in pattern.findall(source)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.docopt_options\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.docopt_options\\n def docopt_options(self):\\n options = super(TopLevelCommand, self).docopt_options()\\n options['version'] = get_version_info('compose')\\n return options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.perform_command\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.perform_command\\n def perform_command(self, options, handler, command_options):\\n setup_console_handler(options.get('--verbose'))\\n\\n if options['COMMAND'] in ('help', 'version'):\\n # Skip looking up the compose file.\\n handler(None, command_options)\\n return\\n\\n project = project_from_options(self.base_dir, options)\\n with friendly_error_message():\\n handler(project, command_options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.build\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.build\\n def build(self, project, options):\\n \\\"\\\"\\\"\\n Build or rebuild services.\\n\\n Services are built once and then tagged as `project_service`,\\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\\n contents of its build directory, you can run `docker-compose build` to rebuild it.\\n\\n Usage: build [options] [SERVICE...]\\n\\n Options:\\n --no-cache Do not use cache when building the image.\\n --pull Always attempt to pull a newer version of the image.\\n \\\"\\\"\\\"\\n no_cache = bool(options.get('--no-cache', False))\\n pull = bool(options.get('--pull', False))\\n project.build(service_names=options['SERVICE'], no_cache=no_cache, pull=pull)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.help\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.help\\n def help(self, project, options):\\n \\\"\\\"\\\"\\n Get help on a command.\\n\\n Usage: help COMMAND\\n \\\"\\\"\\\"\\n handler = self.get_handler(options['COMMAND'])\\n raise SystemExit(getdoc(handler))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.kill\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.kill\\n def kill(self, project, options):\\n \\\"\\\"\\\"\\n Force stop service containers.\\n\\n Usage: kill [options] [SERVICE...]\\n\\n Options:\\n -s SIGNAL SIGNAL to send to the container.\\n Default signal is SIGKILL.\\n \\\"\\\"\\\"\\n signal = options.get('-s', 'SIGKILL')\\n\\n project.kill(service_names=options['SERVICE'], signal=signal)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.logs\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.logs\\n def logs(self, project, options):\\n \\\"\\\"\\\"\\n View output from containers.\\n\\n Usage: logs [options] [SERVICE...]\\n\\n Options:\\n --no-color Produce monochrome output.\\n \\\"\\\"\\\"\\n containers = project.containers(service_names=options['SERVICE'], stopped=True)\\n\\n monochrome = options['--no-color']\\n print(\\\"Attaching to\\\", list_containers(containers))\\n LogPrinter(containers, monochrome=monochrome).run()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pause\\n def pause(self, project, options):\\n \\\"\\\"\\\"\\n Pause services.\\n\\n Usage: pause [SERVICE...]\\n \\\"\\\"\\\"\\n project.pause(service_names=options['SERVICE'])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.port\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.port\\n def port(self, project, options):\\n \\\"\\\"\\\"\\n Print the public port for a port binding.\\n\\n Usage: port [options] SERVICE PRIVATE_PORT\\n\\n Options:\\n --protocol=proto tcp or udp [default: tcp]\\n --index=index index of the container if there are multiple\\n instances of a service [default: 1]\\n \\\"\\\"\\\"\\n index = int(options.get('--index'))\\n service = project.get_service(options['SERVICE'])\\n try:\\n container = service.get_container(number=index)\\n except ValueError as e:\\n raise UserError(str(e))\\n print(container.get_local_port(\\n options['PRIVATE_PORT'],\\n protocol=options.get('--protocol') or 'tcp') or '')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.ps\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.ps\\n def ps(self, project, options):\\n \\\"\\\"\\\"\\n List containers.\\n\\n Usage: ps [options] [SERVICE...]\\n\\n Options:\\n -q Only display IDs\\n \\\"\\\"\\\"\\n containers = sorted(\\n project.containers(service_names=options['SERVICE'], stopped=True) +\\n project.containers(service_names=options['SERVICE'], one_off=True),\\n key=attrgetter('name'))\\n\\n if options['-q']:\\n for container in containers:\\n print(container.id)\\n else:\\n headers = [\\n 'Name',\\n 'Command',\\n 'State',\\n 'Ports',\\n ]\\n rows = []\\n for container in containers:\\n command = container.human_readable_command\\n if len(command) > 30:\\n command = '%s ...' % command[:26]\\n rows.append([\\n container.name,\\n command,\\n container.human_readable_state,\\n container.human_readable_ports,\\n ])\\n print(Formatter().table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pull\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pull\\n def pull(self, project, options):\\n \\\"\\\"\\\"\\n Pulls images for services.\\n\\n Usage: pull [options] [SERVICE...]\\n\\n Options:\\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\\n --allow-insecure-ssl Deprecated - no effect.\\n \\\"\\\"\\\"\\n if options['--allow-insecure-ssl']:\\n log.warn(INSECURE_SSL_WARNING)\\n\\n project.pull(\\n service_names=options['SERVICE'],\\n ignore_pull_failures=options.get('--ignore-pull-failures')\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.rm\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.rm\\n def rm(self, project, options):\\n \\\"\\\"\\\"\\n Remove stopped service containers.\\n\\n Usage: rm [options] [SERVICE...]\\n\\n Options:\\n -f, --force Don't ask to confirm removal\\n -v Remove volumes associated with containers\\n \\\"\\\"\\\"\\n all_containers = project.containers(service_names=options['SERVICE'], stopped=True)\\n stopped_containers = [c for c in all_containers if not c.is_running]\\n\\n if len(stopped_containers) > 0:\\n print(\\\"Going to remove\\\", list_containers(stopped_containers))\\n if options.get('--force') \\\\\\n or yesno(\\\"Are you sure? [yN] \\\", default=False):\\n project.remove_stopped(\\n service_names=options['SERVICE'],\\n v=options.get('-v', False)\\n )\\n else:\\n print(\\\"No stopped containers\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.run\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.run\\n def run(self, project, options):\\n \\\"\\\"\\\"\\n Run a one-off command on a service.\\n\\n For example:\\n\\n $ docker-compose run web python manage.py shell\\n\\n By default, linked services will be started, unless they are already\\n running. If you do not want to start linked services, use\\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\\n\\n Usage: run [options] [-p PORT...] [-e KEY=VAL...] SERVICE [COMMAND] [ARGS...]\\n\\n Options:\\n --allow-insecure-ssl Deprecated - no effect.\\n -d Detached mode: Run container in the background, print\\n new container name.\\n --name NAME Assign a name to the container\\n --entrypoint CMD Override the entrypoint of the image.\\n -e KEY=VAL Set an environment variable (can be used multiple times)\\n -u, --user=\\\"\\\" Run as specified username or uid\\n --no-deps Don't start linked services.\\n --rm Remove container after run. Ignored in detached mode.\\n -p, --publish=[] Publish a container's port(s) to the host\\n --service-ports Run command with the service's ports enabled and mapped\\n to the host.\\n -T Disable pseudo-tty allocation. By default `docker-compose run`\\n allocates a TTY.\\n \\\"\\\"\\\"\\n service = project.get_service(options['SERVICE'])\\n\\n detach = options['-d']\\n\\n if IS_WINDOWS_PLATFORM and not detach:\\n raise UserError(\\n \\\"Interactive mode is not yet supported on Windows.\\\\n\\\"\\n \\\"Please pass the -d flag when using `docker-compose run`.\\\"\\n )\\n\\n if options['--allow-insecure-ssl']:\\n log.warn(INSECURE_SSL_WARNING)\\n\\n if not options['--no-deps'\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.scale\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.scale\\n def scale(self, project, options):\\n \\\"\\\"\\\"\\n Set number of containers to run for a service.\\n\\n Numbers are specified in the form `service=num` as arguments.\\n For example:\\n\\n $ docker-compose scale web=2 worker=3\\n\\n Usage: scale [options] [SERVICE=NUM...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\\n\\n for s in options['SERVICE=NUM']:\\n if '=' not in s:\\n raise UserError('Arguments to scale should be in the form service=num')\\n service_name, num = s.split('=', 1)\\n try:\\n num = int(num)\\n except ValueError:\\n raise UserError('Number of containers for service \\\"%s\\\" is not a '\\n 'number' % service_name)\\n project.get_service(service_name).scale(num, timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.start\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.start\\n def start(self, project, options):\\n \\\"\\\"\\\"\\n Start existing containers.\\n\\n Usage: start [SERVICE...]\\n \\\"\\\"\\\"\\n project.start(service_names=options['SERVICE'])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.stop\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.stop\\n def stop(self, project, options):\\n \\\"\\\"\\\"\\n Stop running containers without removing them.\\n\\n They can be started again with `docker-compose start`.\\n\\n Usage: stop [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\\n project.stop(service_names=options['SERVICE'], timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.restart\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.restart\\n def restart(self, project, options):\\n \\\"\\\"\\\"\\n Restart running containers.\\n\\n Usage: restart [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = int(options.get('--timeout') or DEFAULT_TIMEOUT)\\n project.restart(service_names=options['SERVICE'], timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.unpause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.unpause\\n def unpause(self, project, options):\\n \\\"\\\"\\\"\\n Unpause services.\\n\\n Usage: unpause [SERVICE...]\\n \\\"\\\"\\\"\\n project.unpause(service_names=options['SERVICE'])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.up\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.up\\n def up(self, project, options):\\n \\\"\\\"\\\"\\n Builds, (re)creates, starts, and attaches to containers for a service.\\n\\n Unless they are already running, this command also starts any linked services.\\n\\n The `docker-compose up` command aggregates the output of each container. When\\n the command exits, all containers are stopped. Running `docker-compose up -d`\\n starts the containers in the background and leaves them running.\\n\\n If there are existing containers for a service, and the service's configuration\\n or image was changed after the container's creation, `docker-compose up` picks\\n up the changes by stopping and recreating the containers (preserving mounted\\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\\n flag.\\n\\n If you want to force Compose to stop and recreate all containers, use the\\n `--force-recreate` flag.\\n\\n Usage: up [options] [SERVICE...]\\n\\n Options:\\n --allow-insecure-ssl Deprecated - no effect.\\n -d Detached mode: Run containers in the background,\\n print new container names.\\n --no-color Produce monochrome output.\\n --no-deps Don't start linked services.\\n --force-recreate Recreate containers even if their configuration and\\n image haven't changed. Incompatible with --no-recreate.\\n --no-recreate If containers already exist, don't recreate them.\\n Incompatible with --force-recreate.\\n --no-build Don't build an image, even if it's missing\\n -t, --timeout TIMEOUT Use this timeout in seconds for container shutdown\\n when attached or when containers are already\\n running. (default:\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.migrate_to_labels\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.migrate_to_labels\\n def migrate_to_labels(self, project, _options):\\n \\\"\\\"\\\"\\n Recreate containers to add labels\\n\\n If you're coming from Compose 1.2 or earlier, you'll need to remove or\\n migrate your existing containers after upgrading Compose. This is\\n because, as of version 1.3, Compose uses Docker labels to keep track\\n of containers, and so they need to be recreated with labels added.\\n\\n If Compose detects containers that were created without labels, it\\n will refuse to run so that you don't end up with two sets of them. If\\n you want to keep using your existing containers (for example, because\\n they have data volumes you want to preserve) you can migrate them with\\n the following command:\\n\\n docker-compose migrate-to-labels\\n\\n Alternatively, if you're not worried about keeping them, you can\\n remove them - Compose will just create new ones.\\n\\n docker rm -f myapp_web_1 myapp_db_1 ...\\n\\n Usage: migrate-to-labels\\n \\\"\\\"\\\"\\n legacy.migrate_project_to_labels(project)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.version\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.version\\n def version(self, project, options):\\n \\\"\\\"\\\"\\n Show version informations\\n\\n Usage: version [--short]\\n\\n Options:\\n --short Shows only Compose's version number.\\n \\\"\\\"\\\"\\n if options['--short']:\\n print(__version__)\\n else:\\n print(get_version_info('full'))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::convergence_strategy_from_opts\",\n \"text\": \"compose/cli/main.py::convergence_strategy_from_opts\\ndef convergence_strategy_from_opts(options):\\n no_recreate = options['--no-recreate']\\n force_recreate = options['--force-recreate']\\n if force_recreate and no_recreate:\\n raise UserError(\\\"--force-recreate and --no-recreate cannot be combined.\\\")\\n\\n if force_recreate:\\n return ConvergenceStrategy.always\\n\\n if no_recreate:\\n return ConvergenceStrategy.never\\n\\n return ConvergenceStrategy.changed\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::build_log_printer\",\n \"text\": \"compose/cli/main.py::build_log_printer\\ndef build_log_printer(containers, service_names, monochrome):\\n if service_names:\\n containers = [c for c in containers if c.service in service_names]\\n return LogPrinter(containers, monochrome=monochrome)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::attach_to_logs\",\n \"text\": \"compose/cli/main.py::attach_to_logs\\ndef attach_to_logs(project, log_printer, service_names, timeout):\\n print(\\\"Attaching to\\\", list_containers(log_printer.containers))\\n try:\\n log_printer.run()\\n finally:\\n def handler(signal, frame):\\n project.kill(service_names=service_names)\\n sys.exit(0)\\n signal.signal(signal.SIGINT, handler)\\n\\n print(\\\"Gracefully stopping... (press Ctrl+C again to force)\\\")\\n project.stop(service_names=service_names, timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::list_containers\",\n \"text\": \"compose/cli/main.py::list_containers\\ndef list_containers(containers):\\n return \\\", \\\".join(c.name for c in containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::find\",\n \"text\": \"compose/config/config.py::find\\ndef find(base_dir, filenames):\\n if filenames == ['-']:\\n return ConfigDetails(\\n os.getcwd(),\\n [ConfigFile(None, yaml.safe_load(sys.stdin))])\\n\\n if filenames:\\n filenames = [os.path.join(base_dir, f) for f in filenames]\\n else:\\n filenames = get_default_config_files(base_dir)\\n\\n log.debug(\\\"Using configuration files: {}\\\".format(\\\",\\\".join(filenames)))\\n return ConfigDetails(\\n os.path.dirname(filenames[0]),\\n [ConfigFile(f, load_yaml(f)) for f in filenames])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::get_default_config_files\",\n \"text\": \"compose/config/config.py::get_default_config_files\\ndef get_default_config_files(base_dir):\\n (candidates, path) = find_candidates_in_parent_dirs(SUPPORTED_FILENAMES, base_dir)\\n\\n if not candidates:\\n raise ComposeFileNotFound(SUPPORTED_FILENAMES)\\n\\n winner = candidates[0]\\n\\n if len(candidates) > 1:\\n log.warn(\\\"Found multiple config files with supported names: %s\\\", \\\", \\\".join(candidates))\\n log.warn(\\\"Using %s\\\\n\\\", winner)\\n\\n if winner == 'docker-compose.yaml':\\n log.warn(\\\"Please be aware that .yml is the expected extension \\\"\\n \\\"in most cases, and using .yaml can cause compatibility \\\"\\n \\\"issues in future.\\\\n\\\")\\n\\n if winner.startswith(\\\"fig.\\\"):\\n log.warn(\\\"%s is deprecated and will not be supported in future. \\\"\\n \\\"Please rename your config file to docker-compose.yml\\\\n\\\" % winner)\\n\\n return [os.path.join(path, winner)] + get_default_override_file(path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::get_default_override_file\",\n \"text\": \"compose/config/config.py::get_default_override_file\\ndef get_default_override_file(path):\\n override_filename = os.path.join(path, DEFAULT_OVERRIDE_FILENAME)\\n return [override_filename] if os.path.exists(override_filename) else []\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::find_candidates_in_parent_dirs\",\n \"text\": \"compose/config/config.py::find_candidates_in_parent_dirs\\ndef find_candidates_in_parent_dirs(filenames, path):\\n \\\"\\\"\\\"\\n Given a directory path to start, looks for filenames in the\\n directory, and then each parent directory successively,\\n until found.\\n\\n Returns tuple (candidates, path).\\n \\\"\\\"\\\"\\n candidates = [filename for filename in filenames\\n if os.path.exists(os.path.join(path, filename))]\\n\\n if not candidates:\\n parent_dir = os.path.join(path, '..')\\n if os.path.abspath(parent_dir) != os.path.abspath(path):\\n return find_candidates_in_parent_dirs(filenames, parent_dir)\\n\\n return (candidates, path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load\",\n \"text\": \"compose/config/config.py::load\\ndef load(config_details):\\n \\\"\\\"\\\"Load the configuration from a working directory and a list of\\n configuration files. Files are loaded in order, and merged on top\\n of each other to create the final configuration.\\n\\n Return a fully interpolated, extended and validated configuration.\\n \\\"\\\"\\\"\\n\\n def build_service(filename, service_name, service_dict):\\n loader = ServiceLoader(\\n config_details.working_dir,\\n filename,\\n service_name,\\n service_dict)\\n service_dict = loader.make_service_dict()\\n validate_paths(service_dict)\\n return service_dict\\n\\n def load_file(filename, config):\\n processed_config = interpolate_environment_variables(config)\\n validate_against_fields_schema(processed_config)\\n return [\\n build_service(filename, name, service_config)\\n for name, service_config in processed_config.items()\\n ]\\n\\n def merge_services(base, override):\\n all_service_names = set(base) | set(override)\\n return {\\n name: merge_service_dicts(base.get(name, {}), override.get(name, {}))\\n for name in all_service_names\\n }\\n\\n config_file = config_details.config_files[0]\\n validate_top_level_object(config_file.config)\\n for next_file in config_details.config_files[1:]:\\n validate_top_level_object(next_file.config)\\n\\n config_file = ConfigFile(\\n config_file.filename,\\n merge_services(config_file.config, next_file.config))\\n\\n return load_file(config_file.filename, config_file.config)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load.build_service\",\n \"text\": \"compose/config/config.py::load.build_service\\n def build_service(filename, service_name, service_dict):\\n loader = ServiceLoader(\\n config_details.working_dir,\\n filename,\\n service_name,\\n service_dict)\\n service_dict = loader.make_service_dict()\\n validate_paths(service_dict)\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load.load_file\",\n \"text\": \"compose/config/config.py::load.load_file\\n def load_file(filename, config):\\n processed_config = interpolate_environment_variables(config)\\n validate_against_fields_schema(processed_config)\\n return [\\n build_service(filename, name, service_config)\\n for name, service_config in processed_config.items()\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load.merge_services\",\n \"text\": \"compose/config/config.py::load.merge_services\\n def merge_services(base, override):\\n all_service_names = set(base) | set(override)\\n return {\\n name: merge_service_dicts(base.get(name, {}), override.get(name, {}))\\n for name in all_service_names\\n }\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/config/config.py::ServiceLoader.__init__\",\n \"text\": \"compose/config/config.py::ServiceLoader.__init__\\n def __init__(self, working_dir, filename, service_name, service_dict, already_seen=None):\\n if working_dir is None:\\n raise Exception(\\\"No working_dir passed to ServiceLoader()\\\")\\n\\n self.working_dir = os.path.abspath(working_dir)\\n\\n if filename:\\n self.filename = os.path.abspath(filename)\\n else:\\n self.filename = filename\\n self.already_seen = already_seen or []\\n self.service_dict = service_dict.copy()\\n self.service_name = service_name\\n self.service_dict['name'] = service_name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceLoader.detect_cycle\",\n \"text\": \"compose/config/config.py::ServiceLoader.detect_cycle\\n def detect_cycle(self, name):\\n if self.signature(name) in self.already_seen:\\n raise CircularReference(self.already_seen + [self.signature(name)])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceLoader.make_service_dict\",\n \"text\": \"compose/config/config.py::ServiceLoader.make_service_dict\\n def make_service_dict(self):\\n self.resolve_environment()\\n if 'extends' in self.service_dict:\\n self.validate_and_construct_extends()\\n self.service_dict = self.resolve_extends()\\n\\n if not self.already_seen:\\n validate_against_service_schema(self.service_dict, self.service_name)\\n\\n return process_container_options(self.service_dict, working_dir=self.working_dir)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceLoader.resolve_environment\",\n \"text\": \"compose/config/config.py::ServiceLoader.resolve_environment\\n def resolve_environment(self):\\n \\\"\\\"\\\"\\n Unpack any environment variables from an env_file, if set.\\n Interpolate environment values if set.\\n \\\"\\\"\\\"\\n if 'environment' not in self.service_dict and 'env_file' not in self.service_dict:\\n return\\n\\n env = {}\\n\\n if 'env_file' in self.service_dict:\\n for f in get_env_files(self.service_dict, working_dir=self.working_dir):\\n env.update(env_vars_from_file(f))\\n del self.service_dict['env_file']\\n\\n env.update(parse_environment(self.service_dict.get('environment')))\\n env = dict(resolve_env_var(k, v) for k, v in six.iteritems(env))\\n\\n self.service_dict['environment'] = env\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceLoader.validate_and_construct_extends\",\n \"text\": \"compose/config/config.py::ServiceLoader.validate_and_construct_extends\\n def validate_and_construct_extends(self):\\n extends = self.service_dict['extends']\\n if not isinstance(extends, dict):\\n extends = {'service': extends}\\n\\n validate_extends_file_path(\\n self.service_name,\\n extends,\\n self.filename\\n )\\n self.extended_config_path = self.get_extended_config_path(\\n extends\\n )\\n self.extended_service_name = extends['service']\\n\\n config = load_yaml(self.extended_config_path)\\n validate_top_level_object(config)\\n full_extended_config = interpolate_environment_variables(config)\\n\\n validate_extended_service_exists(\\n self.extended_service_name,\\n full_extended_config,\\n self.extended_config_path\\n )\\n validate_against_fields_schema(full_extended_config)\\n\\n self.extended_config = full_extended_config[self.extended_service_name]\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/config/config.py::ServiceLoader.resolve_extends\",\n \"text\": \"compose/config/config.py::ServiceLoader.resolve_extends\\n def resolve_extends(self):\\n other_working_dir = os.path.dirname(self.extended_config_path)\\n other_already_seen = self.already_seen + [self.signature(self.service_name)]\\n\\n other_loader = ServiceLoader(\\n working_dir=other_working_dir,\\n filename=self.extended_config_path,\\n service_name=self.service_name,\\n service_dict=self.extended_config,\\n already_seen=other_already_seen,\\n )\\n\\n other_loader.detect_cycle(self.extended_service_name)\\n other_service_dict = other_loader.make_service_dict()\\n validate_extended_service_dict(\\n other_service_dict,\\n filename=self.extended_config_path,\\n service=self.extended_service_name,\\n )\\n\\n return merge_service_dicts(other_service_dict, self.service_dict)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceLoader.get_extended_config_path\",\n \"text\": \"compose/config/config.py::ServiceLoader.get_extended_config_path\\n def get_extended_config_path(self, extends_options):\\n \\\"\\\"\\\"\\n Service we are extending either has a value for 'file' set, which we\\n need to obtain a full path too or we are extending from a service\\n defined in our own file.\\n \\\"\\\"\\\"\\n if 'file' in extends_options:\\n extends_from_filename = extends_options['file']\\n return expand_path(self.working_dir, extends_from_filename)\\n\\n return self.filename\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceLoader.signature\",\n \"text\": \"compose/config/config.py::ServiceLoader.signature\\n def signature(self, name):\\n return (self.filename, name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::validate_extended_service_dict\",\n \"text\": \"compose/config/config.py::validate_extended_service_dict\\ndef validate_extended_service_dict(service_dict, filename, service):\\n error_prefix = \\\"Cannot extend service '%s' in %s:\\\" % (service, filename)\\n\\n if 'links' in service_dict:\\n raise ConfigurationError(\\\"%s services with 'links' cannot be extended\\\" % error_prefix)\\n\\n if 'volumes_from' in service_dict:\\n raise ConfigurationError(\\\"%s services with 'volumes_from' cannot be extended\\\" % error_prefix)\\n\\n if 'net' in service_dict:\\n if get_service_name_from_net(service_dict['net']) is not None:\\n raise ConfigurationError(\\\"%s services with 'net: container' cannot be extended\\\" % error_prefix)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::process_container_options\",\n \"text\": \"compose/config/config.py::process_container_options\\ndef process_container_options(service_dict, working_dir=None):\\n service_dict = service_dict.copy()\\n\\n if 'volumes' in service_dict and service_dict.get('volume_driver') is None:\\n service_dict['volumes'] = resolve_volume_paths(service_dict, working_dir=working_dir)\\n\\n if 'build' in service_dict:\\n service_dict['build'] = resolve_build_path(service_dict['build'], working_dir=working_dir)\\n\\n if 'labels' in service_dict:\\n service_dict['labels'] = parse_labels(service_dict['labels'])\\n\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_service_dicts\",\n \"text\": \"compose/config/config.py::merge_service_dicts\\ndef merge_service_dicts(base, override):\\n d = base.copy()\\n\\n if 'environment' in base or 'environment' in override:\\n d['environment'] = merge_environment(\\n base.get('environment'),\\n override.get('environment'),\\n )\\n\\n path_mapping_keys = ['volumes', 'devices']\\n\\n for key in path_mapping_keys:\\n if key in base or key in override:\\n d[key] = merge_path_mappings(\\n base.get(key),\\n override.get(key),\\n )\\n\\n if 'labels' in base or 'labels' in override:\\n d['labels'] = merge_labels(\\n base.get('labels'),\\n override.get('labels'),\\n )\\n\\n if 'image' in override and 'build' in d:\\n del d['build']\\n\\n if 'build' in override and 'image' in d:\\n del d['image']\\n\\n list_keys = ['ports', 'expose', 'external_links']\\n\\n for key in list_keys:\\n if key in base or key in override:\\n d[key] = base.get(key, []) + override.get(key, [])\\n\\n list_or_string_keys = ['dns', 'dns_search']\\n\\n for key in list_or_string_keys:\\n if key in base or key in override:\\n d[key] = to_list(base.get(key)) + to_list(override.get(key))\\n\\n already_merged_keys = ['environment', 'labels'] + path_mapping_keys + list_keys + list_or_string_keys\\n\\n for k in set(ALLOWED_KEYS) - set(already_merged_keys):\\n if k in override:\\n d[k] = override[k]\\n\\n return d\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_environment\",\n \"text\": \"compose/config/config.py::merge_environment\\ndef merge_environment(base, override):\\n env = parse_environment(base)\\n env.update(parse_environment(override))\\n return env\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::get_env_files\",\n \"text\": \"compose/config/config.py::get_env_files\\ndef get_env_files(options, working_dir=None):\\n if 'env_file' not in options:\\n return {}\\n\\n env_files = options.get('env_file', [])\\n if not isinstance(env_files, list):\\n env_files = [env_files]\\n\\n return [expand_path(working_dir, path) for path in env_files]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::parse_environment\",\n \"text\": \"compose/config/config.py::parse_environment\\ndef parse_environment(environment):\\n if not environment:\\n return {}\\n\\n if isinstance(environment, list):\\n return dict(split_env(e) for e in environment)\\n\\n if isinstance(environment, dict):\\n return dict(environment)\\n\\n raise ConfigurationError(\\n \\\"environment \\\\\\\"%s\\\\\\\" must be a list or mapping,\\\" %\\n environment\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::split_env\",\n \"text\": \"compose/config/config.py::split_env\\ndef split_env(env):\\n if '=' in env:\\n return env.split('=', 1)\\n else:\\n return env, None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_env_var\",\n \"text\": \"compose/config/config.py::resolve_env_var\\ndef resolve_env_var(key, val):\\n if val is not None:\\n return key, val\\n elif key in os.environ:\\n return key, os.environ[key]\\n else:\\n return key, ''\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::env_vars_from_file\",\n \"text\": \"compose/config/config.py::env_vars_from_file\\ndef env_vars_from_file(filename):\\n \\\"\\\"\\\"\\n Read in a line delimited file of environment variables.\\n \\\"\\\"\\\"\\n if not os.path.exists(filename):\\n raise ConfigurationError(\\\"Couldn't find env file: %s\\\" % filename)\\n env = {}\\n for line in open(filename, 'r'):\\n line = line.strip()\\n if line and not line.startswith('#'):\\n k, v = split_env(line)\\n env[k] = v\\n return env\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_volume_paths\",\n \"text\": \"compose/config/config.py::resolve_volume_paths\\ndef resolve_volume_paths(service_dict, working_dir=None):\\n if working_dir is None:\\n raise Exception(\\\"No working_dir passed to resolve_volume_paths()\\\")\\n\\n return [\\n resolve_volume_path(v, working_dir, service_dict['name'])\\n for v in service_dict['volumes']\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_volume_path\",\n \"text\": \"compose/config/config.py::resolve_volume_path\\ndef resolve_volume_path(volume, working_dir, service_name):\\n container_path, host_path = split_path_mapping(volume)\\n\\n if host_path is not None:\\n if host_path.startswith('.'):\\n host_path = expand_path(working_dir, host_path)\\n host_path = os.path.expanduser(host_path)\\n return \\\"{}:{}\\\".format(host_path, container_path)\\n else:\\n return container_path\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_build_path\",\n \"text\": \"compose/config/config.py::resolve_build_path\\ndef resolve_build_path(build_path, working_dir=None):\\n if working_dir is None:\\n raise Exception(\\\"No working_dir passed to resolve_build_path\\\")\\n return expand_path(working_dir, build_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::validate_paths\",\n \"text\": \"compose/config/config.py::validate_paths\\ndef validate_paths(service_dict):\\n if 'build' in service_dict:\\n build_path = service_dict['build']\\n if not os.path.exists(build_path) or not os.access(build_path, os.R_OK):\\n raise ConfigurationError(\\\"build path %s either does not exist or is not accessible.\\\" % build_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_path_mappings\",\n \"text\": \"compose/config/config.py::merge_path_mappings\\ndef merge_path_mappings(base, override):\\n d = dict_from_path_mappings(base)\\n d.update(dict_from_path_mappings(override))\\n return path_mappings_from_dict(d)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::dict_from_path_mappings\",\n \"text\": \"compose/config/config.py::dict_from_path_mappings\\ndef dict_from_path_mappings(path_mappings):\\n if path_mappings:\\n return dict(split_path_mapping(v) for v in path_mappings)\\n else:\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::path_mappings_from_dict\",\n \"text\": \"compose/config/config.py::path_mappings_from_dict\\ndef path_mappings_from_dict(d):\\n return [join_path_mapping(v) for v in d.items()]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::split_path_mapping\",\n \"text\": \"compose/config/config.py::split_path_mapping\\ndef split_path_mapping(volume_path):\\n \\\"\\\"\\\"\\n Ascertain if the volume_path contains a host path as well as a container\\n path. Using splitdrive so windows absolute paths won't cause issues with\\n splitting on ':'.\\n \\\"\\\"\\\"\\n # splitdrive has limitations when it comes to relative paths, so when it's\\n # relative, handle special case to set the drive to ''\\n if volume_path.startswith('.') or volume_path.startswith('~'):\\n drive, volume_config = '', volume_path\\n else:\\n drive, volume_config = os.path.splitdrive(volume_path)\\n\\n if ':' in volume_config:\\n (host, container) = volume_config.split(':', 1)\\n return (container, drive + host)\\n else:\\n return (volume_path, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::join_path_mapping\",\n \"text\": \"compose/config/config.py::join_path_mapping\\ndef join_path_mapping(pair):\\n (container, host) = pair\\n if host is None:\\n return container\\n else:\\n return \\\":\\\".join((host, container))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_labels\",\n \"text\": \"compose/config/config.py::merge_labels\\ndef merge_labels(base, override):\\n labels = parse_labels(base)\\n labels.update(parse_labels(override))\\n return labels\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::parse_labels\",\n \"text\": \"compose/config/config.py::parse_labels\\ndef parse_labels(labels):\\n if not labels:\\n return {}\\n\\n if isinstance(labels, list):\\n return dict(split_label(e) for e in labels)\\n\\n if isinstance(labels, dict):\\n return labels\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::split_label\",\n \"text\": \"compose/config/config.py::split_label\\ndef split_label(label):\\n if '=' in label:\\n return label.split('=', 1)\\n else:\\n return label, ''\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::expand_path\",\n \"text\": \"compose/config/config.py::expand_path\\ndef expand_path(working_dir, path):\\n return os.path.abspath(os.path.join(working_dir, os.path.expanduser(path)))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::to_list\",\n \"text\": \"compose/config/config.py::to_list\\ndef to_list(value):\\n if value is None:\\n return []\\n elif isinstance(value, six.string_types):\\n return [value]\\n else:\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::get_service_name_from_net\",\n \"text\": \"compose/config/config.py::get_service_name_from_net\\ndef get_service_name_from_net(net_config):\\n if not net_config:\\n return\\n\\n if not net_config.startswith('container:'):\\n return\\n\\n _, net_name = net_config.split(':', 1)\\n return net_name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load_yaml\",\n \"text\": \"compose/config/config.py::load_yaml\\ndef load_yaml(filename):\\n try:\\n with open(filename, 'r') as fh:\\n return yaml.safe_load(fh)\\n except (IOError, yaml.YAMLError) as e:\\n error_name = getattr(e, '__module__', '') + '.' + e.__class__.__name__\\n raise ConfigurationError(u\\\"{}: {}\\\".format(error_name, e))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/errors.py::ConfigurationError.__init__\",\n \"text\": \"compose/config/errors.py::ConfigurationError.__init__\\n def __init__(self, msg):\\n self.msg = msg\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/errors.py::ConfigurationError.__str__\",\n \"text\": \"compose/config/errors.py::ConfigurationError.__str__\\n def __str__(self):\\n return self.msg\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/errors.py::CircularReference.__init__\",\n \"text\": \"compose/config/errors.py::CircularReference.__init__\\n def __init__(self, trail):\\n self.trail = trail\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/errors.py::CircularReference.msg\",\n \"text\": \"compose/config/errors.py::CircularReference.msg\\n @property\\n def msg(self):\\n lines = [\\n \\\"{} in {}\\\".format(service_name, filename)\\n for (filename, service_name) in self.trail\\n ]\\n return \\\"Circular reference:\\\\n {}\\\".format(\\\"\\\\n extends \\\".join(lines))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/errors.py::ComposeFileNotFound.__init__\",\n \"text\": \"compose/config/errors.py::ComposeFileNotFound.__init__\\n def __init__(self, supported_filenames):\\n super(ComposeFileNotFound, self).__init__(\\\"\\\"\\\"\\n Can't find a suitable configuration file in this directory or any parent. Are you in the right directory?\\n\\n Supported filenames: %s\\n \\\"\\\"\\\" % \\\", \\\".join(supported_filenames))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::format_ports\",\n \"text\": \"compose/config/validation.py::format_ports\\n@FormatChecker.cls_checks(\\n format=\\\"ports\\\",\\n raises=ValidationError(\\n \\\"Invalid port formatting, it should be \\\"\\n \\\"'[[remote_ip:]remote_port:]port[/protocol]'\\\"))\\ndef format_ports(instance):\\n try:\\n split_port(instance)\\n except ValueError:\\n return False\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::format_boolean_in_environment\",\n \"text\": \"compose/config/validation.py::format_boolean_in_environment\\n@FormatChecker.cls_checks(format=\\\"environment\\\")\\ndef format_boolean_in_environment(instance):\\n \\\"\\\"\\\"\\n Check if there is a boolean in the environment and display a warning.\\n Always return True here so the validation won't raise an error.\\n \\\"\\\"\\\"\\n if isinstance(instance, bool):\\n log.warn(\\n \\\"Warning: There is a boolean value in the 'environment' key.\\\\n\\\"\\n \\\"Environment variables can only be strings.\\\\nPlease add quotes to any boolean values to make them string \\\"\\n \\\"(eg, 'True', 'yes', 'N').\\\\nThis warning will become an error in a future release. \\\\r\\\\n\\\"\\n )\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_service_names\",\n \"text\": \"compose/config/validation.py::validate_service_names\\ndef validate_service_names(config):\\n for service_name in config.keys():\\n if not isinstance(service_name, six.string_types):\\n raise ConfigurationError(\\n \\\"Service name: {} needs to be a string, eg '{}'\\\".format(\\n service_name,\\n service_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_top_level_object\",\n \"text\": \"compose/config/validation.py::validate_top_level_object\\ndef validate_top_level_object(config):\\n if not isinstance(config, dict):\\n raise ConfigurationError(\\n \\\"Top level object needs to be a dictionary. Check your .yml file \\\"\\n \\\"that you have defined a service at the top level.\\\")\\n validate_service_names(config)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_extends_file_path\",\n \"text\": \"compose/config/validation.py::validate_extends_file_path\\ndef validate_extends_file_path(service_name, extends_options, filename):\\n \\\"\\\"\\\"\\n The service to be extended must either be defined in the config key 'file',\\n or within 'filename'.\\n \\\"\\\"\\\"\\n error_prefix = \\\"Invalid 'extends' configuration for %s:\\\" % service_name\\n\\n if 'file' not in extends_options and filename is None:\\n raise ConfigurationError(\\n \\\"%s you need to specify a 'file', e.g. 'file: something.yml'\\\" % error_prefix\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_extended_service_exists\",\n \"text\": \"compose/config/validation.py::validate_extended_service_exists\\ndef validate_extended_service_exists(extended_service_name, full_extended_config, extended_config_path):\\n if extended_service_name not in full_extended_config:\\n msg = (\\n \\\"Cannot extend service '%s' in %s: Service not found\\\"\\n ) % (extended_service_name, extended_config_path)\\n raise ConfigurationError(msg)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::get_unsupported_config_msg\",\n \"text\": \"compose/config/validation.py::get_unsupported_config_msg\\ndef get_unsupported_config_msg(service_name, error_key):\\n msg = \\\"Unsupported config option for '{}' service: '{}'\\\".format(service_name, error_key)\\n if error_key in DOCKER_CONFIG_HINTS:\\n msg += \\\" (did you mean '{}'?)\\\".format(DOCKER_CONFIG_HINTS[error_key])\\n return msg\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::anglicize_validator\",\n \"text\": \"compose/config/validation.py::anglicize_validator\\ndef anglicize_validator(validator):\\n if validator in [\\\"array\\\", \\\"object\\\"]:\\n return 'an ' + validator\\n return 'a ' + validator\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::process_errors\",\n \"text\": \"compose/config/validation.py::process_errors\\ndef process_errors(errors, service_name=None):\\n \\\"\\\"\\\"\\n jsonschema gives us an error tree full of information to explain what has\\n gone wrong. Process each error and pull out relevant information and re-write\\n helpful error messages that are relevant.\\n \\\"\\\"\\\"\\n def _parse_key_from_error_msg(error):\\n return error.message.split(\\\"'\\\")[1]\\n\\n def _clean_error_message(message):\\n return message.replace(\\\"u'\\\", \\\"'\\\")\\n\\n def _parse_valid_types_from_validator(validator):\\n \\\"\\\"\\\"\\n A validator value can be either an array of valid types or a string of\\n a valid type. Parse the valid types and prefix with the correct article.\\n \\\"\\\"\\\"\\n if isinstance(validator, list):\\n if len(validator) >= 2:\\n first_type = anglicize_validator(validator[0])\\n last_type = anglicize_validator(validator[-1])\\n types_from_validator = \\\"{}{}\\\".format(first_type, \\\", \\\".join(validator[1:-1]))\\n\\n msg = \\\"{} or {}\\\".format(\\n types_from_validator,\\n last_type\\n )\\n else:\\n msg = \\\"{}\\\".format(anglicize_validator(validator[0]))\\n else:\\n msg = \\\"{}\\\".format(anglicize_validator(validator))\\n\\n return msg\\n\\n def _parse_oneof_validator(error):\\n \\\"\\\"\\\"\\n oneOf has multiple schemas, so we need to reason about which schema, sub\\n schema or constraint the validation is failing on.\\n Inspecting the context value of a ValidationError gives us information about\\n which sub schema failed and which kind of error it is.\\n \\\"\\\"\\\"\\n\\n required = [context for context in error.context if context.validator == 'required']\\n if required:\\n return required[0].message\\n\\n additionalProperties = [context for context in error.context if context.validator == 'additionalProperties']\\n if additionalProperties:\\n i\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::process_errors._parse_key_from_error_msg\",\n \"text\": \"compose/config/validation.py::process_errors._parse_key_from_error_msg\\n def _parse_key_from_error_msg(error):\\n return error.message.split(\\\"'\\\")[1]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::process_errors._clean_error_message\",\n \"text\": \"compose/config/validation.py::process_errors._clean_error_message\\n def _clean_error_message(message):\\n return message.replace(\\\"u'\\\", \\\"'\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::process_errors._parse_valid_types_from_validator\",\n \"text\": \"compose/config/validation.py::process_errors._parse_valid_types_from_validator\\n def _parse_valid_types_from_validator(validator):\\n \\\"\\\"\\\"\\n A validator value can be either an array of valid types or a string of\\n a valid type. Parse the valid types and prefix with the correct article.\\n \\\"\\\"\\\"\\n if isinstance(validator, list):\\n if len(validator) >= 2:\\n first_type = anglicize_validator(validator[0])\\n last_type = anglicize_validator(validator[-1])\\n types_from_validator = \\\"{}{}\\\".format(first_type, \\\", \\\".join(validator[1:-1]))\\n\\n msg = \\\"{} or {}\\\".format(\\n types_from_validator,\\n last_type\\n )\\n else:\\n msg = \\\"{}\\\".format(anglicize_validator(validator[0]))\\n else:\\n msg = \\\"{}\\\".format(anglicize_validator(validator))\\n\\n return msg\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::process_errors._parse_oneof_validator\",\n \"text\": \"compose/config/validation.py::process_errors._parse_oneof_validator\\n def _parse_oneof_validator(error):\\n \\\"\\\"\\\"\\n oneOf has multiple schemas, so we need to reason about which schema, sub\\n schema or constraint the validation is failing on.\\n Inspecting the context value of a ValidationError gives us information about\\n which sub schema failed and which kind of error it is.\\n \\\"\\\"\\\"\\n\\n required = [context for context in error.context if context.validator == 'required']\\n if required:\\n return required[0].message\\n\\n additionalProperties = [context for context in error.context if context.validator == 'additionalProperties']\\n if additionalProperties:\\n invalid_config_key = _parse_key_from_error_msg(additionalProperties[0])\\n return \\\"contains unsupported option: '{}'\\\".format(invalid_config_key)\\n\\n constraint = [context for context in error.context if len(context.path) > 0]\\n if constraint:\\n valid_types = _parse_valid_types_from_validator(constraint[0].validator_value)\\n invalid_config_key = \\\"\\\".join(\\n \\\"'{}' \\\".format(fragment) for fragment in constraint[0].path\\n if isinstance(fragment, six.string_types)\\n )\\n msg = \\\"{}contains {}, which is an invalid type, it should be {}\\\".format(\\n invalid_config_key,\\n constraint[0].instance,\\n valid_types\\n )\\n return msg\\n\\n uniqueness = [context for context in error.context if context.validator == 'uniqueItems']\\n if uniqueness:\\n msg = \\\"contains non unique items, please remove duplicates from {}\\\".format(\\n uniqueness[0].instance\\n )\\n return msg\\n\\n types = [context.validator_value for context in error.context if context.validator == 'type']\\n valid_types = _parse_valid_types_from_validator(types)\\n\\n msg = \\\"contains an invalid type, \",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_against_fields_schema\",\n \"text\": \"compose/config/validation.py::validate_against_fields_schema\\ndef validate_against_fields_schema(config):\\n schema_filename = \\\"fields_schema.json\\\"\\n format_checkers = [\\\"ports\\\", \\\"environment\\\"]\\n return _validate_against_schema(config, schema_filename, format_checkers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_against_service_schema\",\n \"text\": \"compose/config/validation.py::validate_against_service_schema\\ndef validate_against_service_schema(config, service_name):\\n schema_filename = \\\"service_schema.json\\\"\\n format_checkers = [\\\"ports\\\"]\\n return _validate_against_schema(config, schema_filename, format_checkers, service_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::_validate_against_schema\",\n \"text\": \"compose/config/validation.py::_validate_against_schema\\ndef _validate_against_schema(config, schema_filename, format_checker=[], service_name=None):\\n config_source_dir = os.path.dirname(os.path.abspath(__file__))\\n\\n if sys.platform == \\\"win32\\\":\\n file_pre_fix = \\\"///\\\"\\n config_source_dir = config_source_dir.replace('\\\\\\\\', '/')\\n else:\\n file_pre_fix = \\\"//\\\"\\n\\n resolver_full_path = \\\"file:{}{}/\\\".format(file_pre_fix, config_source_dir)\\n schema_file = os.path.join(config_source_dir, schema_filename)\\n\\n with open(schema_file, \\\"r\\\") as schema_fh:\\n schema = json.load(schema_fh)\\n\\n resolver = RefResolver(resolver_full_path, schema)\\n validation_output = Draft4Validator(schema, resolver=resolver, format_checker=FormatChecker(format_checker))\\n\\n errors = [error for error in sorted(validation_output.iter_errors(config), key=str)]\\n if errors:\\n error_msg = process_errors(errors, service_name)\\n raise ConfigurationError(\\\"Validation failed, reason(s):\\\\n{}\\\".format(error_msg))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.__init__\",\n \"text\": \"compose/container.py::Container.__init__\\n def __init__(self, client, dictionary, has_been_inspected=False):\\n self.client = client\\n self.dictionary = dictionary\\n self.has_been_inspected = has_been_inspected\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.from_ps\",\n \"text\": \"compose/container.py::Container.from_ps\\n @classmethod\\n def from_ps(cls, client, dictionary, **kwargs):\\n \\\"\\\"\\\"\\n Construct a container object from the output of GET /containers/json.\\n \\\"\\\"\\\"\\n name = get_container_name(dictionary)\\n if name is None:\\n return None\\n\\n new_dictionary = {\\n 'Id': dictionary['Id'],\\n 'Image': dictionary['Image'],\\n 'Name': '/' + name,\\n }\\n return cls(client, new_dictionary, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.from_id\",\n \"text\": \"compose/container.py::Container.from_id\\n @classmethod\\n def from_id(cls, client, id):\\n return cls(client, client.inspect_container(id))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.create\",\n \"text\": \"compose/container.py::Container.create\\n @classmethod\\n def create(cls, client, **options):\\n response = client.create_container(**options)\\n return cls.from_id(client, response['Id'])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.id\",\n \"text\": \"compose/container.py::Container.id\\n @property\\n def id(self):\\n return self.dictionary['Id']\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.image\",\n \"text\": \"compose/container.py::Container.image\\n @property\\n def image(self):\\n return self.dictionary['Image']\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.image_config\",\n \"text\": \"compose/container.py::Container.image_config\\n @property\\n def image_config(self):\\n return self.client.inspect_image(self.image)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.short_id\",\n \"text\": \"compose/container.py::Container.short_id\\n @property\\n def short_id(self):\\n return self.id[:10]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.name\",\n \"text\": \"compose/container.py::Container.name\\n @property\\n def name(self):\\n return self.dictionary['Name'][1:]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.service\",\n \"text\": \"compose/container.py::Container.service\\n @property\\n def service(self):\\n return self.labels.get(LABEL_SERVICE)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.name_without_project\",\n \"text\": \"compose/container.py::Container.name_without_project\\n @property\\n def name_without_project(self):\\n project = self.labels.get(LABEL_PROJECT)\\n\\n if self.name.startswith('{0}_{1}'.format(project, self.service)):\\n return '{0}_{1}'.format(self.service, self.number)\\n else:\\n return self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.number\",\n \"text\": \"compose/container.py::Container.number\\n @property\\n def number(self):\\n number = self.labels.get(LABEL_CONTAINER_NUMBER)\\n if not number:\\n raise ValueError(\\\"Container {0} does not have a {1} label\\\".format(\\n self.short_id, LABEL_CONTAINER_NUMBER))\\n return int(number)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.ports\",\n \"text\": \"compose/container.py::Container.ports\\n @property\\n def ports(self):\\n self.inspect_if_not_inspected()\\n return self.get('NetworkSettings.Ports') or {}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.human_readable_ports\",\n \"text\": \"compose/container.py::Container.human_readable_ports\\n @property\\n def human_readable_ports(self):\\n def format_port(private, public):\\n if not public:\\n return private\\n return '{HostIp}:{HostPort}->{private}'.format(\\n private=private, **public[0])\\n\\n return ', '.join(format_port(*item)\\n for item in sorted(six.iteritems(self.ports)))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.human_readable_ports.format_port\",\n \"text\": \"compose/container.py::Container.human_readable_ports.format_port\\n def format_port(private, public):\\n if not public:\\n return private\\n return '{HostIp}:{HostPort}->{private}'.format(\\n private=private, **public[0])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.labels\",\n \"text\": \"compose/container.py::Container.labels\\n @property\\n def labels(self):\\n return self.get('Config.Labels') or {}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.log_config\",\n \"text\": \"compose/container.py::Container.log_config\\n @property\\n def log_config(self):\\n return self.get('HostConfig.LogConfig') or None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.human_readable_state\",\n \"text\": \"compose/container.py::Container.human_readable_state\\n @property\\n def human_readable_state(self):\\n if self.is_paused:\\n return 'Paused'\\n if self.is_running:\\n return 'Ghost' if self.get('State.Ghost') else 'Up'\\n else:\\n return 'Exit %s' % self.get('State.ExitCode')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.human_readable_command\",\n \"text\": \"compose/container.py::Container.human_readable_command\\n @property\\n def human_readable_command(self):\\n entrypoint = self.get('Config.Entrypoint') or []\\n cmd = self.get('Config.Cmd') or []\\n return ' '.join(entrypoint + cmd)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.environment\",\n \"text\": \"compose/container.py::Container.environment\\n @property\\n def environment(self):\\n return dict(var.split(\\\"=\\\", 1) for var in self.get('Config.Env') or [])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.is_running\",\n \"text\": \"compose/container.py::Container.is_running\\n @property\\n def is_running(self):\\n return self.get('State.Running')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.is_paused\",\n \"text\": \"compose/container.py::Container.is_paused\\n @property\\n def is_paused(self):\\n return self.get('State.Paused')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.log_driver\",\n \"text\": \"compose/container.py::Container.log_driver\\n @property\\n def log_driver(self):\\n return self.get('HostConfig.LogConfig.Type')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.has_api_logs\",\n \"text\": \"compose/container.py::Container.has_api_logs\\n @property\\n def has_api_logs(self):\\n log_type = self.log_driver\\n return not log_type or log_type == 'json-file'\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.get\",\n \"text\": \"compose/container.py::Container.get\\n def get(self, key):\\n \\\"\\\"\\\"Return a value from the container or None if the value is not set.\\n\\n :param key: a string using dotted notation for nested dictionary\\n lookups\\n \\\"\\\"\\\"\\n self.inspect_if_not_inspected()\\n\\n def get_value(dictionary, key):\\n return (dictionary or {}).get(key)\\n\\n return reduce(get_value, key.split('.'), self.dictionary)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.get.get_value\",\n \"text\": \"compose/container.py::Container.get.get_value\\n def get_value(dictionary, key):\\n return (dictionary or {}).get(key)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.get_local_port\",\n \"text\": \"compose/container.py::Container.get_local_port\\n def get_local_port(self, port, protocol='tcp'):\\n port = self.ports.get(\\\"%s/%s\\\" % (port, protocol))\\n return \\\"{HostIp}:{HostPort}\\\".format(**port[0]) if port else None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.start\",\n \"text\": \"compose/container.py::Container.start\\n def start(self, **options):\\n return self.client.start(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.stop\",\n \"text\": \"compose/container.py::Container.stop\\n def stop(self, **options):\\n return self.client.stop(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.pause\",\n \"text\": \"compose/container.py::Container.pause\\n def pause(self, **options):\\n return self.client.pause(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.unpause\",\n \"text\": \"compose/container.py::Container.unpause\\n def unpause(self, **options):\\n return self.client.unpause(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.kill\",\n \"text\": \"compose/container.py::Container.kill\\n def kill(self, **options):\\n return self.client.kill(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.restart\",\n \"text\": \"compose/container.py::Container.restart\\n def restart(self, **options):\\n return self.client.restart(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.remove\",\n \"text\": \"compose/container.py::Container.remove\\n def remove(self, **options):\\n return self.client.remove_container(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.inspect_if_not_inspected\",\n \"text\": \"compose/container.py::Container.inspect_if_not_inspected\\n def inspect_if_not_inspected(self):\\n if not self.has_been_inspected:\\n self.inspect()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.wait\",\n \"text\": \"compose/container.py::Container.wait\\n def wait(self):\\n return self.client.wait(self.id)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.logs\",\n \"text\": \"compose/container.py::Container.logs\\n def logs(self, *args, **kwargs):\\n return self.client.logs(self.id, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.inspect\",\n \"text\": \"compose/container.py::Container.inspect\\n def inspect(self):\\n self.dictionary = self.client.inspect_container(self.id)\\n self.has_been_inspected = True\\n return self.dictionary\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.links\",\n \"text\": \"compose/container.py::Container.links\\n def links(self):\\n links = []\\n for container in self.client.containers():\\n for name in container['Names']:\\n bits = name.split('/')\\n if len(bits) > 2 and bits[1] == self.name:\\n links.append(bits[2])\\n return links\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/container.py::Container.attach\",\n \"text\": \"compose/container.py::Container.attach\\n def attach(self, *args, **kwargs):\\n return self.client.attach(self.id, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::check_for_legacy_containers\",\n \"text\": \"compose/legacy.py::check_for_legacy_containers\\ndef check_for_legacy_containers(\\n client,\\n project,\\n services,\\n allow_one_off=True):\\n \\\"\\\"\\\"Check if there are containers named using the old naming convention\\n and warn the user that those containers may need to be migrated to\\n using labels, so that compose can find them.\\n \\\"\\\"\\\"\\n containers = get_legacy_containers(client, project, services, one_off=False)\\n\\n if containers:\\n one_off_containers = get_legacy_containers(client, project, services, one_off=True)\\n\\n raise LegacyContainersError(\\n [c.name for c in containers],\\n [c.name for c in one_off_containers],\\n )\\n\\n if not allow_one_off:\\n one_off_containers = get_legacy_containers(client, project, services, one_off=True)\\n\\n if one_off_containers:\\n raise LegacyOneOffContainersError(\\n [c.name for c in one_off_containers],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::LegacyError.__unicode__\",\n \"text\": \"compose/legacy.py::LegacyError.__unicode__\\n def __unicode__(self):\\n return self.msg\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::LegacyContainersError.__init__\",\n \"text\": \"compose/legacy.py::LegacyContainersError.__init__\\n def __init__(self, names, one_off_names):\\n self.names = names\\n self.one_off_names = one_off_names\\n\\n self.msg = ERROR_MESSAGE_FORMAT.format(\\n names_list=\\\"\\\\n\\\".join(\\\" {}\\\".format(name) for name in names),\\n rm_args=\\\" \\\".join(names),\\n )\\n\\n if one_off_names:\\n self.msg += ONE_OFF_ADDENDUM_FORMAT.format(rm_args=\\\" \\\".join(one_off_names))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::LegacyOneOffContainersError.__init__\",\n \"text\": \"compose/legacy.py::LegacyOneOffContainersError.__init__\\n def __init__(self, one_off_names):\\n self.one_off_names = one_off_names\\n\\n self.msg = ONE_OFF_ERROR_MESSAGE_FORMAT.format(\\n names_list=\\\"\\\\n\\\".join(\\\" {}\\\".format(name) for name in one_off_names),\\n rm_args=\\\" \\\".join(one_off_names),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::add_labels\",\n \"text\": \"compose/legacy.py::add_labels\\ndef add_labels(project, container):\\n project_name, service_name, one_off, number = NAME_RE.match(container.name).groups()\\n if project_name != project.name or service_name not in project.service_names:\\n return\\n service = project.get_service(service_name)\\n service.recreate_container(container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::migrate_project_to_labels\",\n \"text\": \"compose/legacy.py::migrate_project_to_labels\\ndef migrate_project_to_labels(project):\\n log.info(\\\"Running migration to labels for project %s\\\", project.name)\\n\\n containers = get_legacy_containers(\\n project.client,\\n project.name,\\n project.service_names,\\n one_off=False,\\n )\\n\\n for container in containers:\\n add_labels(project, container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::get_legacy_containers\",\n \"text\": \"compose/legacy.py::get_legacy_containers\\ndef get_legacy_containers(\\n client,\\n project,\\n services,\\n one_off=False):\\n\\n return list(_get_legacy_containers_iter(\\n client,\\n project,\\n services,\\n one_off=one_off,\\n ))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::_get_legacy_containers_iter\",\n \"text\": \"compose/legacy.py::_get_legacy_containers_iter\\ndef _get_legacy_containers_iter(\\n client,\\n project,\\n services,\\n one_off=False):\\n\\n containers = client.containers(all=True)\\n\\n for service in services:\\n for container in containers:\\n if LABEL_VERSION in (container.get('Labels') or {}):\\n continue\\n\\n name = get_container_name(container)\\n if has_container(project, service, name, one_off=one_off):\\n yield Container.from_ps(client, container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::has_container\",\n \"text\": \"compose/legacy.py::has_container\\ndef has_container(project, service, name, one_off=False):\\n if not name or not is_valid_name(name, one_off):\\n return False\\n container_project, container_service, _container_number = parse_name(name)\\n return container_project == project and container_service == service\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::is_valid_name\",\n \"text\": \"compose/legacy.py::is_valid_name\\ndef is_valid_name(name, one_off=False):\\n match = NAME_RE.match(name)\\n if match is None:\\n return False\\n if one_off:\\n return match.group(3) == 'run_'\\n else:\\n return match.group(3) is None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/legacy.py::parse_name\",\n \"text\": \"compose/legacy.py::parse_name\\ndef parse_name(name):\\n match = NAME_RE.match(name)\\n (project, service_name, _, suffix) = match.groups()\\n return (project, service_name, int(suffix))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::BuildError.__init__\",\n \"text\": \"compose/service.py::BuildError.__init__\\n def __init__(self, service, reason):\\n self.service = service\\n self.reason = reason\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::NeedsBuildError.__init__\",\n \"text\": \"compose/service.py::NeedsBuildError.__init__\\n def __init__(self, service):\\n self.service = service\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::ConvergenceStrategy.allows_recreate\",\n \"text\": \"compose/service.py::ConvergenceStrategy.allows_recreate\\n @property\\n def allows_recreate(self):\\n return self is not type(self).never\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.__init__\",\n \"text\": \"compose/service.py::Service.__init__\\n def __init__(\\n self,\\n name,\\n client=None,\\n project='default',\\n use_networking=False,\\n links=None,\\n volumes_from=None,\\n net=None,\\n **options\\n ):\\n if not re.match('^%s+$' % VALID_NAME_CHARS, project):\\n raise ConfigError('Invalid project name \\\"%s\\\" - only %s are allowed' % (project, VALID_NAME_CHARS))\\n\\n self.name = name\\n self.client = client\\n self.project = project\\n self.use_networking = use_networking\\n self.links = links or []\\n self.volumes_from = volumes_from or []\\n self.net = net or Net(None)\\n self.options = options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.containers\",\n \"text\": \"compose/service.py::Service.containers\\n def containers(self, stopped=False, one_off=False, filters={}):\\n filters.update({'label': self.labels(one_off=one_off)})\\n\\n containers = list(filter(None, [\\n Container.from_ps(self.client, container)\\n for container in self.client.containers(\\n all=stopped,\\n filters=filters)]))\\n\\n if not containers:\\n check_for_legacy_containers(\\n self.client,\\n self.project,\\n [self.name],\\n )\\n\\n return containers\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_container\",\n \"text\": \"compose/service.py::Service.get_container\\n def get_container(self, number=1):\\n \\\"\\\"\\\"Return a :class:`compose.container.Container` for this service. The\\n container must be active, and match `number`.\\n \\\"\\\"\\\"\\n labels = self.labels() + ['{0}={1}'.format(LABEL_CONTAINER_NUMBER, number)]\\n for container in self.client.containers(filters={'label': labels}):\\n return Container.from_ps(self.client, container)\\n\\n raise ValueError(\\\"No container found for %s_%s\\\" % (self.name, number))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start\",\n \"text\": \"compose/service.py::Service.start\\n def start(self, **options):\\n for c in self.containers(stopped=True):\\n self.start_container_if_stopped(c, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.stop\",\n \"text\": \"compose/service.py::Service.stop\\n def stop(self, **options):\\n for c in self.containers():\\n log.info(\\\"Stopping %s\\\" % c.name)\\n c.stop(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.pause\",\n \"text\": \"compose/service.py::Service.pause\\n def pause(self, **options):\\n for c in self.containers(filters={'status': 'running'}):\\n log.info(\\\"Pausing %s\\\" % c.name)\\n c.pause(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.unpause\",\n \"text\": \"compose/service.py::Service.unpause\\n def unpause(self, **options):\\n for c in self.containers(filters={'status': 'paused'}):\\n log.info(\\\"Unpausing %s\\\" % c.name)\\n c.unpause()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.kill\",\n \"text\": \"compose/service.py::Service.kill\\n def kill(self, **options):\\n for c in self.containers():\\n log.info(\\\"Killing %s\\\" % c.name)\\n c.kill(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.restart\",\n \"text\": \"compose/service.py::Service.restart\\n def restart(self, **options):\\n for c in self.containers():\\n log.info(\\\"Restarting %s\\\" % c.name)\\n c.restart(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.scale\",\n \"text\": \"compose/service.py::Service.scale\\n def scale(self, desired_num, timeout=DEFAULT_TIMEOUT):\\n \\\"\\\"\\\"\\n Adjusts the number of containers to the specified number and ensures\\n they are running.\\n\\n - creates containers until there are at least `desired_num`\\n - stops containers until there are at most `desired_num` running\\n - starts containers until there are at least `desired_num` running\\n - removes all stopped containers\\n \\\"\\\"\\\"\\n if self.custom_container_name() and desired_num > 1:\\n log.warn('The \\\"%s\\\" service is using the custom container name \\\"%s\\\". '\\n 'Docker requires each container to have a unique name. '\\n 'Remove the custom name to scale the service.'\\n % (self.name, self.custom_container_name()))\\n\\n if self.specifies_host_port():\\n log.warn('The \\\"%s\\\" service specifies a port on the host. If multiple containers '\\n 'for this service are created on a single host, the port will clash.'\\n % self.name)\\n\\n def create_and_start(service, number):\\n container = service.create_container(number=number, quiet=True)\\n container.start()\\n return container\\n\\n running_containers = self.containers(stopped=False)\\n num_running = len(running_containers)\\n\\n if desired_num == num_running:\\n # do nothing as we already have the desired number\\n log.info('Desired container number already achieved')\\n return\\n\\n if desired_num > num_running:\\n # we need to start/create until we have desired_num\\n all_containers = self.containers(stopped=True)\\n\\n if num_running != len(all_containers):\\n # we have some stopped containers, let's start them up again\\n stopped_containers = sorted([c for c in all_containers if not c.is_running], key=attrgetter('number'))\\n\\n num\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.scale.create_and_start\",\n \"text\": \"compose/service.py::Service.scale.create_and_start\\n def create_and_start(service, number):\\n container = service.create_container(number=number, quiet=True)\\n container.start()\\n return container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.remove_stopped\",\n \"text\": \"compose/service.py::Service.remove_stopped\\n def remove_stopped(self, **options):\\n containers = [c for c in self.containers(stopped=True) if not c.is_running]\\n\\n parallel_execute(\\n objects=containers,\\n obj_callable=lambda c: c.remove(**options),\\n msg_index=lambda c: c.name,\\n msg=\\\"Removing\\\"\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.create_container\",\n \"text\": \"compose/service.py::Service.create_container\\n def create_container(self,\\n one_off=False,\\n do_build=True,\\n previous_container=None,\\n number=None,\\n quiet=False,\\n **override_options):\\n \\\"\\\"\\\"\\n Create a container for this service. If the image doesn't exist, attempt to pull\\n it.\\n \\\"\\\"\\\"\\n self.ensure_image_exists(\\n do_build=do_build,\\n )\\n\\n container_options = self._get_container_create_options(\\n override_options,\\n number or self._next_container_number(one_off=one_off),\\n one_off=one_off,\\n previous_container=previous_container,\\n )\\n\\n if 'name' in container_options and not quiet:\\n log.info(\\\"Creating %s\\\" % container_options['name'])\\n\\n return Container.create(self.client, **container_options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.ensure_image_exists\",\n \"text\": \"compose/service.py::Service.ensure_image_exists\\n def ensure_image_exists(self,\\n do_build=True):\\n\\n try:\\n self.image()\\n return\\n except NoSuchImageError:\\n pass\\n\\n if self.can_be_built():\\n if do_build:\\n self.build()\\n else:\\n raise NeedsBuildError(self)\\n else:\\n self.pull()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.image\",\n \"text\": \"compose/service.py::Service.image\\n def image(self):\\n try:\\n return self.client.inspect_image(self.image_name)\\n except APIError as e:\\n if e.response.status_code == 404 and e.explanation and 'No such image' in str(e.explanation):\\n raise NoSuchImageError(\\\"Image '{}' not found\\\".format(self.image_name))\\n else:\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.image_name\",\n \"text\": \"compose/service.py::Service.image_name\\n @property\\n def image_name(self):\\n if self.can_be_built():\\n return self.full_name\\n else:\\n return self.options['image']\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.convergence_plan\",\n \"text\": \"compose/service.py::Service.convergence_plan\\n def convergence_plan(self, strategy=ConvergenceStrategy.changed):\\n containers = self.containers(stopped=True)\\n\\n if not containers:\\n return ConvergencePlan('create', [])\\n\\n if strategy is ConvergenceStrategy.never:\\n return ConvergencePlan('start', containers)\\n\\n if (\\n strategy is ConvergenceStrategy.always or\\n self._containers_have_diverged(containers)\\n ):\\n return ConvergencePlan('recreate', containers)\\n\\n stopped = [c for c in containers if not c.is_running]\\n\\n if stopped:\\n return ConvergencePlan('start', stopped)\\n\\n return ConvergencePlan('noop', containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._containers_have_diverged\",\n \"text\": \"compose/service.py::Service._containers_have_diverged\\n def _containers_have_diverged(self, containers):\\n config_hash = None\\n\\n try:\\n config_hash = self.config_hash\\n except NoSuchImageError as e:\\n log.debug(\\n 'Service %s has diverged: %s',\\n self.name, six.text_type(e),\\n )\\n return True\\n\\n has_diverged = False\\n\\n for c in containers:\\n container_config_hash = c.labels.get(LABEL_CONFIG_HASH, None)\\n if container_config_hash != config_hash:\\n log.debug(\\n '%s has diverged: %s != %s',\\n c.name, container_config_hash, config_hash,\\n )\\n has_diverged = True\\n\\n return has_diverged\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.execute_convergence_plan\",\n \"text\": \"compose/service.py::Service.execute_convergence_plan\\n def execute_convergence_plan(self,\\n plan,\\n do_build=True,\\n timeout=DEFAULT_TIMEOUT):\\n (action, containers) = plan\\n\\n if action == 'create':\\n container = self.create_container(\\n do_build=do_build,\\n )\\n self.start_container(container)\\n\\n return [container]\\n\\n elif action == 'recreate':\\n return [\\n self.recreate_container(\\n c,\\n timeout=timeout\\n )\\n for c in containers\\n ]\\n\\n elif action == 'start':\\n for c in containers:\\n self.start_container_if_stopped(c)\\n\\n return containers\\n\\n elif action == 'noop':\\n for c in containers:\\n log.info(\\\"%s is up-to-date\\\" % c.name)\\n\\n return containers\\n\\n else:\\n raise Exception(\\\"Invalid action: {}\\\".format(action))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.recreate_container\",\n \"text\": \"compose/service.py::Service.recreate_container\\n def recreate_container(self,\\n container,\\n timeout=DEFAULT_TIMEOUT):\\n \\\"\\\"\\\"Recreate a container.\\n\\n The original container is renamed to a temporary name so that data\\n volumes can be copied to the new container, before the original\\n container is removed.\\n \\\"\\\"\\\"\\n log.info(\\\"Recreating %s\\\" % container.name)\\n try:\\n container.stop(timeout=timeout)\\n except APIError as e:\\n if (e.response.status_code == 500\\n and e.explanation\\n and 'no such process' in str(e.explanation)):\\n pass\\n else:\\n raise\\n\\n # Use a hopefully unique container name by prepending the short id\\n self.client.rename(\\n container.id,\\n '%s_%s' % (container.short_id, container.name))\\n\\n new_container = self.create_container(\\n do_build=False,\\n previous_container=container,\\n number=container.labels.get(LABEL_CONTAINER_NUMBER),\\n quiet=True,\\n )\\n self.start_container(new_container)\\n container.remove()\\n return new_container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start_container_if_stopped\",\n \"text\": \"compose/service.py::Service.start_container_if_stopped\\n def start_container_if_stopped(self, container):\\n if container.is_running:\\n return container\\n else:\\n log.info(\\\"Starting %s\\\" % container.name)\\n return self.start_container(container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start_container\",\n \"text\": \"compose/service.py::Service.start_container\\n def start_container(self, container):\\n container.start()\\n return container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.remove_duplicate_containers\",\n \"text\": \"compose/service.py::Service.remove_duplicate_containers\\n def remove_duplicate_containers(self, timeout=DEFAULT_TIMEOUT):\\n for c in self.duplicate_containers():\\n log.info('Removing %s' % c.name)\\n c.stop(timeout=timeout)\\n c.remove()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.duplicate_containers\",\n \"text\": \"compose/service.py::Service.duplicate_containers\\n def duplicate_containers(self):\\n containers = sorted(\\n self.containers(stopped=True),\\n key=lambda c: c.get('Created'),\\n )\\n\\n numbers = set()\\n\\n for c in containers:\\n if c.number in numbers:\\n yield c\\n else:\\n numbers.add(c.number)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.config_hash\",\n \"text\": \"compose/service.py::Service.config_hash\\n @property\\n def config_hash(self):\\n return json_hash(self.config_dict())\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.config_dict\",\n \"text\": \"compose/service.py::Service.config_dict\\n def config_dict(self):\\n return {\\n 'options': self.options,\\n 'image_id': self.image()['Id'],\\n 'links': self.get_link_names(),\\n 'net': self.net.id,\\n 'volumes_from': self.get_volumes_from_names(),\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_dependency_names\",\n \"text\": \"compose/service.py::Service.get_dependency_names\\n def get_dependency_names(self):\\n net_name = self.net.service_name\\n return (self.get_linked_service_names() +\\n self.get_volumes_from_names() +\\n ([net_name] if net_name else []))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_linked_service_names\",\n \"text\": \"compose/service.py::Service.get_linked_service_names\\n def get_linked_service_names(self):\\n return [service.name for (service, _) in self.links]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_link_names\",\n \"text\": \"compose/service.py::Service.get_link_names\\n def get_link_names(self):\\n return [(service.name, alias) for service, alias in self.links]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_volumes_from_names\",\n \"text\": \"compose/service.py::Service.get_volumes_from_names\\n def get_volumes_from_names(self):\\n return [s.source.name for s in self.volumes_from if isinstance(s.source, Service)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_container_name\",\n \"text\": \"compose/service.py::Service.get_container_name\\n def get_container_name(self, number, one_off=False):\\n # TODO: Implement issue #652 here\\n return build_container_name(self.project, self.name, number, one_off)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._next_container_number\",\n \"text\": \"compose/service.py::Service._next_container_number\\n def _next_container_number(self, one_off=False):\\n containers = filter(None, [\\n Container.from_ps(self.client, container)\\n for container in self.client.containers(\\n all=True,\\n filters={'label': self.labels(one_off=one_off)})\\n ])\\n numbers = [c.number for c in containers]\\n return 1 if not numbers else max(numbers) + 1\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_links\",\n \"text\": \"compose/service.py::Service._get_links\\n def _get_links(self, link_to_self):\\n links = []\\n for service, link_name in self.links:\\n for container in service.containers():\\n links.append((container.name, link_name or service.name))\\n links.append((container.name, container.name))\\n links.append((container.name, container.name_without_project))\\n if link_to_self:\\n for container in self.containers():\\n links.append((container.name, self.name))\\n links.append((container.name, container.name))\\n links.append((container.name, container.name_without_project))\\n for external_link in self.options.get('external_links') or []:\\n if ':' not in external_link:\\n link_name = external_link\\n else:\\n external_link, link_name = external_link.split(':')\\n links.append((external_link, link_name))\\n return links\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_volumes_from\",\n \"text\": \"compose/service.py::Service._get_volumes_from\\n def _get_volumes_from(self):\\n volumes_from = []\\n for volume_from_spec in self.volumes_from:\\n volumes = build_volume_from(volume_from_spec)\\n volumes_from.extend(volumes)\\n\\n return volumes_from\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_container_create_options\",\n \"text\": \"compose/service.py::Service._get_container_create_options\\n def _get_container_create_options(\\n self,\\n override_options,\\n number,\\n one_off=False,\\n previous_container=None):\\n add_config_hash = (not one_off and not override_options)\\n\\n container_options = dict(\\n (k, self.options[k])\\n for k in DOCKER_CONFIG_KEYS if k in self.options)\\n container_options.update(override_options)\\n\\n if self.custom_container_name() and not one_off:\\n container_options['name'] = self.custom_container_name()\\n elif not container_options.get('name'):\\n container_options['name'] = self.get_container_name(number, one_off)\\n\\n if 'detach' not in container_options:\\n container_options['detach'] = True\\n\\n # If a qualified hostname was given, split it into an\\n # unqualified hostname and a domainname unless domainname\\n # was also given explicitly. This matches the behavior of\\n # the official Docker CLI in that scenario.\\n if ('hostname' in container_options\\n and 'domainname' not in container_options\\n and '.' in container_options['hostname']):\\n parts = container_options['hostname'].partition('.')\\n container_options['hostname'] = parts[0]\\n container_options['domainname'] = parts[2]\\n\\n if 'hostname' not in container_options and self.use_networking:\\n container_options['hostname'] = self.name\\n\\n if 'ports' in container_options or 'expose' in self.options:\\n ports = []\\n all_ports = container_options.get('ports', []) + self.options.get('expose', [])\\n for port_range in all_ports:\\n internal_range, _ = split_port(port_range)\\n for port in internal_range:\\n port = str(port)\\n if '/' in port:\\n port = tuple(port.split('/'))\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_container_host_config\",\n \"text\": \"compose/service.py::Service._get_container_host_config\\n def _get_container_host_config(self, override_options, one_off=False):\\n options = dict(self.options, **override_options)\\n port_bindings = build_port_bindings(options.get('ports') or [])\\n\\n privileged = options.get('privileged', False)\\n cap_add = options.get('cap_add', None)\\n cap_drop = options.get('cap_drop', None)\\n log_config = LogConfig(\\n type=options.get('log_driver', \\\"\\\"),\\n config=options.get('log_opt', None)\\n )\\n pid = options.get('pid', None)\\n security_opt = options.get('security_opt', None)\\n\\n dns = options.get('dns', None)\\n if isinstance(dns, six.string_types):\\n dns = [dns]\\n\\n dns_search = options.get('dns_search', None)\\n if isinstance(dns_search, six.string_types):\\n dns_search = [dns_search]\\n\\n restart = parse_restart_spec(options.get('restart', None))\\n\\n extra_hosts = build_extra_hosts(options.get('extra_hosts', None))\\n read_only = options.get('read_only', None)\\n\\n devices = options.get('devices', None)\\n cgroup_parent = options.get('cgroup_parent', None)\\n\\n return self.client.create_host_config(\\n links=self._get_links(link_to_self=one_off),\\n port_bindings=port_bindings,\\n binds=options.get('binds'),\\n volumes_from=self._get_volumes_from(),\\n privileged=privileged,\\n network_mode=self.net.mode,\\n devices=devices,\\n dns=dns,\\n dns_search=dns_search,\\n restart_policy=restart,\\n cap_add=cap_add,\\n cap_drop=cap_drop,\\n mem_limit=options.get('mem_limit'),\\n memswap_limit=options.get('memswap_limit'),\\n log_config=log_config,\\n extra_hosts=extra_hosts,\\n read_only=read_only,\\n pid_mode=pid,\\n security_opt=security_opt,\\n ipc_mode=options.get(\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.build\",\n \"text\": \"compose/service.py::Service.build\\n def build(self, no_cache=False, pull=False):\\n log.info('Building %s' % self.name)\\n\\n path = self.options['build']\\n # python2 os.path() doesn't support unicode, so we need to encode it to\\n # a byte string\\n if not six.PY3:\\n path = path.encode('utf8')\\n\\n build_output = self.client.build(\\n path=path,\\n tag=self.image_name,\\n stream=True,\\n rm=True,\\n pull=pull,\\n nocache=no_cache,\\n dockerfile=self.options.get('dockerfile', None),\\n )\\n\\n try:\\n all_events = stream_output(build_output, sys.stdout)\\n except StreamOutputError as e:\\n raise BuildError(self, six.text_type(e))\\n\\n # Ensure the HTTP connection is not reused for another\\n # streaming command, as the Docker daemon can sometimes\\n # complain about it\\n self.client.close()\\n\\n image_id = None\\n\\n for event in all_events:\\n if 'stream' in event:\\n match = re.search(r'Successfully built ([0-9a-f]+)', event.get('stream', ''))\\n if match:\\n image_id = match.group(1)\\n\\n if image_id is None:\\n raise BuildError(self, event if all_events else 'Unknown')\\n\\n return image_id\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":273,"cells":{"instance_id":{"kind":"string","value":"docker__compose-367"},"repo":{"kind":"string","value":"docker/compose"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Using == in setup.py from requirements.txt will cause conflicts with other python packages\nrequirements.txt with == operator is meant to be used when you need completely limit the versions of package. fig uses requirements.txt input in setup.py to fill install_requires. If any package installed after fig requires other version or updates the dependency things will break.\n\n```\nfig up -d\nTraceback (most recent call last):\n File \"/usr/local/bin/fig\", line 5, in <module>\n from pkg_resources import load_entry_point\n File \"/usr/lib/python2.7/dist-packages/pkg_resources.py\", line 2749, in <module>\n working_set = WorkingSet._build_master()\n File \"/usr/lib/python2.7/dist-packages/pkg_resources.py\", line 446, in _build_master\n return cls._build_from_requirements(__requires__)\n File \"/usr/lib/python2.7/dist-packages/pkg_resources.py\", line 459, in _build_from_requirements\n dists = ws.resolve(reqs, Environment())\n File \"/usr/lib/python2.7/dist-packages/pkg_resources.py\", line 628, in resolve\n raise DistributionNotFound(req)\npkg_resources.DistributionNotFound: requests==2.2.1\n```\n\nThe latest version of requests package available is 2.3.0 (which was installed in this case) and it's of course backward compatibile. You should either stop relaying on reading requirements.txt or start using >= operator.\n\n"},"gold_files":{"kind":"list like","value":["setup.py"],"string":"[\n \"setup.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["setup.py::<module>"],"string":"[\n \"setup.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"fig/cli/command.py","text":"fig/cli/command.py\nclass Command\n def __init__(self)\n def dispatch(self, *args, **kwargs)\n def perform_command(self, options, *args, **kwargs)\n def client(self)\n def project(self)\n def project_name(self)\n def formatter(self)\n def yaml_path(self)\n def yaml_path(self, value)","label":0},{"cid":"fig/cli/errors.py","text":"fig/cli/errors.py\nclass UserError\n def __init__(self, msg)\n def __unicode__(self)\nclass DockerNotFoundMac\n def __init__(self)\nclass DockerNotFoundUbuntu\n def __init__(self)\nclass DockerNotFoundGeneric\n def __init__(self)\nclass ConnectionErrorDockerOSX\n def __init__(self)\nclass ConnectionErrorGeneric\n def __init__(self, url)\nclass FigFileNotFound\n def __init__(self, filename)","label":0},{"cid":"fig/cli/log_printer.py","text":"fig/cli/log_printer.py\nclass LogPrinter\n def __init__(self, containers, attach_params=None, output=sys.stdout)\n def run(self)\n def _calculate_prefix_width(self, containers) # Calculate the maximum width of container names so we can make the log\n def _make_log_generators(self)\n def _make_log_generator(self, container, color_fn)\n def _generate_prefix(self, container) # Generate the prefix for a log line without colour\n def _attach(self, container)","label":0},{"cid":"fig/cli/main.py","text":"fig/cli/main.py\ndef main()\ndef setup_logging()\ndef parse_doc_section(name, source)\nclass TopLevelCommand # Punctual, lightweight development environments using Docker.\n def docopt_options(self)\n def build(self, options) # Build or rebuild services.\n def help(self, options) # Get help on a command.\n def kill(self, options) # Force stop service containers.\n def logs(self, options) # View output from containers.\n def ps(self, options) # List containers.\n def rm(self, options) # Remove stopped service containers.\n def run(self, options) # Run a one-off command on a service.\n def scale(self, options) # Set number of containers to run for a service.\n def start(self, options) # Start existing containers.\n def stop(self, options) # Stop running containers without removing them.\n def up(self, options) # Build, (re)create, start and attach to containers for a service.\ndef list_containers(containers)","label":0},{"cid":"fig/cli/multiplexer.py","text":"fig/cli/multiplexer.py\nclass Multiplexer\n def __init__(self, generators)\n def loop(self)\n def _init_readers(self)\ndef _enqueue_output(generator, queue)","label":0},{"cid":"fig/cli/utils.py","text":"fig/cli/utils.py\ndef cached_property(f) # returns a cached property that is calculated by function f\n def get(self)\ndef yesno(prompt, default=None) # Prompt the user for a yes or no.\ndef prettydate(d)\ndef mkdir(path, permissions=448)\ndef docker_url()\ndef split_buffer(reader, separator) # Given a generator which yields strings and a separator string,\ndef call_silently(*args, **kwargs) # Like subprocess.call(), but redirects stdout and stderr to /dev/null.\ndef is_mac()\ndef is_ubuntu()","label":0},{"cid":"fig/container.py","text":"fig/container.py\nclass Container # Represents a Docker container, constructed from the output of\n def __init__(self, client, dictionary, has_been_inspected=False)\n def from_ps(cls, client, dictionary, **kwargs) # Construct a container object from the output of GET /containers/json.\n def from_id(cls, client, id)\n def create(cls, client, **options)\n def id(self)\n def image(self)\n def short_id(self)\n def name(self)\n def name_without_project(self)\n def number(self)\n def human_readable_ports(self)\n def human_readable_state(self)\n def human_readable_command(self)\n def environment(self)\n def is_running(self)\n def start(self, **options)\n def stop(self, **options)\n def kill(self)\n def remove(self, **options)\n def inspect_if_not_inspected(self)\n def wait(self)\n def logs(self, *args, **kwargs)\n def inspect(self)\n def links(self)\n def attach(self, *args, **kwargs)\n def attach_socket(self, **kwargs)\n def __repr__(self)\n def __eq__(self, other)","label":0},{"cid":"fig/packages/docker/__init__.py","text":"fig/packages/docker/__init__.py","label":0},{"cid":"fig/packages/docker/auth/auth.py","text":"fig/packages/docker/auth/auth.py\ndef swap_protocol(url)\ndef expand_registry_url(hostname)\ndef resolve_repository_name(repo_name)\ndef resolve_authconfig(authconfig, registry=None) # Return the authentication data from the given auth configuration for a\ndef encode_auth(auth_info)\ndef decode_auth(auth)\ndef encode_header(auth)\ndef encode_full_header(auth) # Returns the given auth block encoded for the X-Registry-Config header.\ndef load_config(root=None) # Loads authentication data from a Docker configuration file in the given","label":0},{"cid":"fig/packages/docker/client.py","text":"fig/packages/docker/client.py\nclass Client\n def __init__(self, base_url=None, version=DEFAULT_DOCKER_API_VERSION, timeout=DEFAULT_TIMEOUT_SECONDS)\n def _set_request_timeout(self, kwargs) # Prepare the kwargs for an HTTP request by inserting the timeout\n def _post(self, url, **kwargs)\n def _get(self, url, **kwargs)\n def _delete(self, url, **kwargs)\n def _url(self, path)\n def _raise_for_status(self, response, explanation=None) # Raises stored :class:`APIError`, if one occurred.\n def _result(self, response, json=False, binary=False)\n def _container_config(self, image, command, hostname=None, user=None, detach=False, stdin_open=False, tty=False, mem_limit=0, ports=None, environment=None, dns=None, volumes=None, volumes_from=None, network_disabled=False, entrypoint=None, cpu_shares=None, working_dir=None, domainname=None, memswap_limit=0)\n def _post_json(self, url, data, **kwargs)\n def _attach_params(self, override=None)\n def _attach_websocket(self, container, params=None)\n def _create_websocket_connection(self, url)\n def _get_raw_response_socket(self, response)\n def _stream_helper(self, response) # Generator for data coming from a chunked-encoded HTTP response.\n def _multiplexed_buffer_helper(self, response) # A generator of multiplexed data blocks read from a buffered\n def _multiplexed_socket_stream_helper(self, response) # A generator of multiplexed data blocks coming from a response\n def recvall(socket, size)\n def attach(self, container, stdout=True, stderr=True, stream=False, logs=False)\n def attach_socket(self, container, params=None, ws=False)\n def build(self, path=None, tag=None, quiet=False, fileobj=None, nocache=False, rm=False, stream=False, timeout=None, custom_context=False, encoding=None)\n def commit(self, container, repository=None, tag=None, message=None, author=None, conf=None)\n def containers(self, quiet=False, all=False, trunc=True, latest=False, since=None, before=None, limit=-1, size=False)\n def copy(self, container, resource)\n def create_container(self, image, command=None, hostname=None, user=None, detach=False, stdin_open=False, tty=False, mem_limit=0, ports=None, environment=None, dns=None, volumes=None, volumes_from=None, network_disabled=False, name=None, entrypoint=None, cpu_shares=None, working_dir=None, domainname=None, memswap_limit=0)\n def create_container_from_config(self, config, name=None)\n def diff(self, container)\n def events(self)\n def export(self, container)\n def get_image(self, image)\n def history(self, image)\n def images(self, name=None, quiet=False, all=False, viz=False)\n def import_image(self, src=None, repository=None, tag=None, image=None)\n def info(self)\n def insert(self, image, url, path)\n def inspect_container(self, container)\n def inspect_image(self, image_id)\n def kill(self, container, signal=None)\n def load_image(self, data)\n def login(self, username, password=None, email=No","label":0},{"cid":"fig/packages/docker/errors.py","text":"fig/packages/docker/errors.py\nclass APIError\n def __init__(self, message, response, explanation=None)\n def __str__(self)\n def is_client_error(self)\n def is_server_error(self)\nclass DockerException\nclass InvalidRepository\nclass InvalidConfigFile\nclass DeprecatedMethod","label":0},{"cid":"fig/packages/docker/unixconn/__init__.py","text":"fig/packages/docker/unixconn/__init__.py","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py","text":"fig/packages/docker/unixconn/unixconn.py\nclass UnixHTTPConnection\n def __init__(self, base_url, unix_socket, timeout=60)\n def connect(self)\n def _extract_path(self, url)\n def request(self, method, url, **kwargs)\nclass UnixHTTPConnectionPool\n def __init__(self, base_url, socket_path, timeout=60)\n def _new_conn(self)\nclass UnixAdapter\n def __init__(self, base_url, timeout=60)\n def get_connection(self, socket_path, proxies=None)","label":0},{"cid":"fig/packages/docker/utils/utils.py","text":"fig/packages/docker/utils/utils.py\ndef mkbuildcontext(dockerfile)\ndef tar(path)\ndef compare_version(v1, v2) # Compare docker versions\ndef ping(url)\ndef _convert_port_binding(binding)\ndef convert_port_bindings(port_bindings)\ndef convert_volume_binds(binds)\ndef parse_repository_tag(repo)","label":0},{"cid":"fig/packages/docker/version.py","text":"fig/packages/docker/version.py","label":0},{"cid":"fig/packages/six.py","text":"fig/packages/six.py\ndef _add_doc(func, doc) # Add documentation to a function.\ndef _import_module(name) # Import module, returning the module after the last dot.\nclass _LazyDescr\n def __init__(self, name)\n def __get__(self, obj, tp)\nclass MovedModule\n def __init__(self, name, old, new=None)\n def _resolve(self)\nclass MovedAttribute\n def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None)\n def _resolve(self)\nclass _MovedItems # Lazy loading of moved objects\ndef add_move(move) # Add an item to six.moves.\ndef remove_move(name) # Remove item from six.moves.\ndef iterkeys(d, **kw) # Return an iterator over the keys of a dictionary.\ndef itervalues(d, **kw) # Return an iterator over the values of a dictionary.\ndef iteritems(d, **kw) # Return an iterator over the (key, value) pairs of a dictionary.\ndef iterlists(d, **kw) # Return an iterator over the (key, [values]) pairs of a dictionary.\ndef with_metaclass(meta, base=object) # Create a base class with a metaclass.","label":0},{"cid":"fig/progress_stream.py","text":"fig/progress_stream.py\nclass StreamOutputError\ndef stream_output(output, stream)\ndef print_output_event(event, stream, is_terminal)","label":0},{"cid":"fig/project.py","text":"fig/project.py\ndef sort_service_dicts(services)\n def visit(n)\nclass Project # A collection of services.\n def __init__(self, name, services, client)\n def from_dicts(cls, name, service_dicts, client) # Construct a ServiceCollection from a list of dicts representing services.\n def from_config(cls, name, config, client)\n def get_service(self, name) # Retrieve a service by name. Raises NoSuchService\n def get_services(self, service_names=None, include_links=False) # Returns a list of this project's services filtered\n def get_links(self, service_dict)\n def get_volumes_from(self, service_dict)\n def start(self, service_names=None, **options)\n def stop(self, service_names=None, **options)\n def kill(self, service_names=None, **options)\n def build(self, service_names=None, no_cache=False)\n def up(self, service_names=None, start_links=True, recreate=True)\n def remove_stopped(self, service_names=None, **options)\n def containers(self, service_names=None, *args, **kwargs)\n def _inject_links(self, acc, service)\nclass NoSuchService\n def __init__(self, name)\n def __str__(self)\nclass ConfigurationError\n def __init__(self, msg)\n def __str__(self)\nclass DependencyError","label":0},{"cid":"setup.py","text":"setup.py\ndef read(*parts)\ndef find_version(*file_paths)","label":1}],"string":"[\n {\n \"cid\": \"fig/cli/command.py\",\n \"text\": \"fig/cli/command.py\\nclass Command\\n def __init__(self)\\n def dispatch(self, *args, **kwargs)\\n def perform_command(self, options, *args, **kwargs)\\n def client(self)\\n def project(self)\\n def project_name(self)\\n def formatter(self)\\n def yaml_path(self)\\n def yaml_path(self, value)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py\",\n \"text\": \"fig/cli/errors.py\\nclass UserError\\n def __init__(self, msg)\\n def __unicode__(self)\\nclass DockerNotFoundMac\\n def __init__(self)\\nclass DockerNotFoundUbuntu\\n def __init__(self)\\nclass DockerNotFoundGeneric\\n def __init__(self)\\nclass ConnectionErrorDockerOSX\\n def __init__(self)\\nclass ConnectionErrorGeneric\\n def __init__(self, url)\\nclass FigFileNotFound\\n def __init__(self, filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py\",\n \"text\": \"fig/cli/log_printer.py\\nclass LogPrinter\\n def __init__(self, containers, attach_params=None, output=sys.stdout)\\n def run(self)\\n def _calculate_prefix_width(self, containers) # Calculate the maximum width of container names so we can make the log\\n def _make_log_generators(self)\\n def _make_log_generator(self, container, color_fn)\\n def _generate_prefix(self, container) # Generate the prefix for a log line without colour\\n def _attach(self, container)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py\",\n \"text\": \"fig/cli/main.py\\ndef main()\\ndef setup_logging()\\ndef parse_doc_section(name, source)\\nclass TopLevelCommand # Punctual, lightweight development environments using Docker.\\n def docopt_options(self)\\n def build(self, options) # Build or rebuild services.\\n def help(self, options) # Get help on a command.\\n def kill(self, options) # Force stop service containers.\\n def logs(self, options) # View output from containers.\\n def ps(self, options) # List containers.\\n def rm(self, options) # Remove stopped service containers.\\n def run(self, options) # Run a one-off command on a service.\\n def scale(self, options) # Set number of containers to run for a service.\\n def start(self, options) # Start existing containers.\\n def stop(self, options) # Stop running containers without removing them.\\n def up(self, options) # Build, (re)create, start and attach to containers for a service.\\ndef list_containers(containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/multiplexer.py\",\n \"text\": \"fig/cli/multiplexer.py\\nclass Multiplexer\\n def __init__(self, generators)\\n def loop(self)\\n def _init_readers(self)\\ndef _enqueue_output(generator, queue)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/utils.py\",\n \"text\": \"fig/cli/utils.py\\ndef cached_property(f) # returns a cached property that is calculated by function f\\n def get(self)\\ndef yesno(prompt, default=None) # Prompt the user for a yes or no.\\ndef prettydate(d)\\ndef mkdir(path, permissions=448)\\ndef docker_url()\\ndef split_buffer(reader, separator) # Given a generator which yields strings and a separator string,\\ndef call_silently(*args, **kwargs) # Like subprocess.call(), but redirects stdout and stderr to /dev/null.\\ndef is_mac()\\ndef is_ubuntu()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py\",\n \"text\": \"fig/container.py\\nclass Container # Represents a Docker container, constructed from the output of\\n def __init__(self, client, dictionary, has_been_inspected=False)\\n def from_ps(cls, client, dictionary, **kwargs) # Construct a container object from the output of GET /containers/json.\\n def from_id(cls, client, id)\\n def create(cls, client, **options)\\n def id(self)\\n def image(self)\\n def short_id(self)\\n def name(self)\\n def name_without_project(self)\\n def number(self)\\n def human_readable_ports(self)\\n def human_readable_state(self)\\n def human_readable_command(self)\\n def environment(self)\\n def is_running(self)\\n def start(self, **options)\\n def stop(self, **options)\\n def kill(self)\\n def remove(self, **options)\\n def inspect_if_not_inspected(self)\\n def wait(self)\\n def logs(self, *args, **kwargs)\\n def inspect(self)\\n def links(self)\\n def attach(self, *args, **kwargs)\\n def attach_socket(self, **kwargs)\\n def __repr__(self)\\n def __eq__(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/__init__.py\",\n \"text\": \"fig/packages/docker/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py\",\n \"text\": \"fig/packages/docker/auth/auth.py\\ndef swap_protocol(url)\\ndef expand_registry_url(hostname)\\ndef resolve_repository_name(repo_name)\\ndef resolve_authconfig(authconfig, registry=None) # Return the authentication data from the given auth configuration for a\\ndef encode_auth(auth_info)\\ndef decode_auth(auth)\\ndef encode_header(auth)\\ndef encode_full_header(auth) # Returns the given auth block encoded for the X-Registry-Config header.\\ndef load_config(root=None) # Loads authentication data from a Docker configuration file in the given\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py\",\n \"text\": \"fig/packages/docker/client.py\\nclass Client\\n def __init__(self, base_url=None, version=DEFAULT_DOCKER_API_VERSION, timeout=DEFAULT_TIMEOUT_SECONDS)\\n def _set_request_timeout(self, kwargs) # Prepare the kwargs for an HTTP request by inserting the timeout\\n def _post(self, url, **kwargs)\\n def _get(self, url, **kwargs)\\n def _delete(self, url, **kwargs)\\n def _url(self, path)\\n def _raise_for_status(self, response, explanation=None) # Raises stored :class:`APIError`, if one occurred.\\n def _result(self, response, json=False, binary=False)\\n def _container_config(self, image, command, hostname=None, user=None, detach=False, stdin_open=False, tty=False, mem_limit=0, ports=None, environment=None, dns=None, volumes=None, volumes_from=None, network_disabled=False, entrypoint=None, cpu_shares=None, working_dir=None, domainname=None, memswap_limit=0)\\n def _post_json(self, url, data, **kwargs)\\n def _attach_params(self, override=None)\\n def _attach_websocket(self, container, params=None)\\n def _create_websocket_connection(self, url)\\n def _get_raw_response_socket(self, response)\\n def _stream_helper(self, response) # Generator for data coming from a chunked-encoded HTTP response.\\n def _multiplexed_buffer_helper(self, response) # A generator of multiplexed data blocks read from a buffered\\n def _multiplexed_socket_stream_helper(self, response) # A generator of multiplexed data blocks coming from a response\\n def recvall(socket, size)\\n def attach(self, container, stdout=True, stderr=True, stream=False, logs=False)\\n def attach_socket(self, container, params=None, ws=False)\\n def build(self, path=None, tag=None, quiet=False, fileobj=None, nocache=False, rm=False, stream=False, timeout=None, custom_context=False, encoding=None)\\n def commit(self, container, repository=None, tag=None, message=None, author=None, conf=None)\\n def containers(self, quiet=False, all=False, trunc=True, latest=False, since=None, before=None, limit=-1, size=False)\\n def copy(self, container, resource)\\n def create_container(self, image, command=None, hostname=None, user=None, detach=False, stdin_open=False, tty=False, mem_limit=0, ports=None, environment=None, dns=None, volumes=None, volumes_from=None, network_disabled=False, name=None, entrypoint=None, cpu_shares=None, working_dir=None, domainname=None, memswap_limit=0)\\n def create_container_from_config(self, config, name=None)\\n def diff(self, container)\\n def events(self)\\n def export(self, container)\\n def get_image(self, image)\\n def history(self, image)\\n def images(self, name=None, quiet=False, all=False, viz=False)\\n def import_image(self, src=None, repository=None, tag=None, image=None)\\n def info(self)\\n def insert(self, image, url, path)\\n def inspect_container(self, container)\\n def inspect_image(self, image_id)\\n def kill(self, container, signal=None)\\n def load_image(self, data)\\n def login(self, username, password=None, email=No\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/errors.py\",\n \"text\": \"fig/packages/docker/errors.py\\nclass APIError\\n def __init__(self, message, response, explanation=None)\\n def __str__(self)\\n def is_client_error(self)\\n def is_server_error(self)\\nclass DockerException\\nclass InvalidRepository\\nclass InvalidConfigFile\\nclass DeprecatedMethod\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/__init__.py\",\n \"text\": \"fig/packages/docker/unixconn/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py\\nclass UnixHTTPConnection\\n def __init__(self, base_url, unix_socket, timeout=60)\\n def connect(self)\\n def _extract_path(self, url)\\n def request(self, method, url, **kwargs)\\nclass UnixHTTPConnectionPool\\n def __init__(self, base_url, socket_path, timeout=60)\\n def _new_conn(self)\\nclass UnixAdapter\\n def __init__(self, base_url, timeout=60)\\n def get_connection(self, socket_path, proxies=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py\",\n \"text\": \"fig/packages/docker/utils/utils.py\\ndef mkbuildcontext(dockerfile)\\ndef tar(path)\\ndef compare_version(v1, v2) # Compare docker versions\\ndef ping(url)\\ndef _convert_port_binding(binding)\\ndef convert_port_bindings(port_bindings)\\ndef convert_volume_binds(binds)\\ndef parse_repository_tag(repo)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/version.py\",\n \"text\": \"fig/packages/docker/version.py\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py\",\n \"text\": \"fig/packages/six.py\\ndef _add_doc(func, doc) # Add documentation to a function.\\ndef _import_module(name) # Import module, returning the module after the last dot.\\nclass _LazyDescr\\n def __init__(self, name)\\n def __get__(self, obj, tp)\\nclass MovedModule\\n def __init__(self, name, old, new=None)\\n def _resolve(self)\\nclass MovedAttribute\\n def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None)\\n def _resolve(self)\\nclass _MovedItems # Lazy loading of moved objects\\ndef add_move(move) # Add an item to six.moves.\\ndef remove_move(name) # Remove item from six.moves.\\ndef iterkeys(d, **kw) # Return an iterator over the keys of a dictionary.\\ndef itervalues(d, **kw) # Return an iterator over the values of a dictionary.\\ndef iteritems(d, **kw) # Return an iterator over the (key, value) pairs of a dictionary.\\ndef iterlists(d, **kw) # Return an iterator over the (key, [values]) pairs of a dictionary.\\ndef with_metaclass(meta, base=object) # Create a base class with a metaclass.\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/progress_stream.py\",\n \"text\": \"fig/progress_stream.py\\nclass StreamOutputError\\ndef stream_output(output, stream)\\ndef print_output_event(event, stream, is_terminal)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py\",\n \"text\": \"fig/project.py\\ndef sort_service_dicts(services)\\n def visit(n)\\nclass Project # A collection of services.\\n def __init__(self, name, services, client)\\n def from_dicts(cls, name, service_dicts, client) # Construct a ServiceCollection from a list of dicts representing services.\\n def from_config(cls, name, config, client)\\n def get_service(self, name) # Retrieve a service by name. Raises NoSuchService\\n def get_services(self, service_names=None, include_links=False) # Returns a list of this project's services filtered\\n def get_links(self, service_dict)\\n def get_volumes_from(self, service_dict)\\n def start(self, service_names=None, **options)\\n def stop(self, service_names=None, **options)\\n def kill(self, service_names=None, **options)\\n def build(self, service_names=None, no_cache=False)\\n def up(self, service_names=None, start_links=True, recreate=True)\\n def remove_stopped(self, service_names=None, **options)\\n def containers(self, service_names=None, *args, **kwargs)\\n def _inject_links(self, acc, service)\\nclass NoSuchService\\n def __init__(self, name)\\n def __str__(self)\\nclass ConfigurationError\\n def __init__(self, msg)\\n def __str__(self)\\nclass DependencyError\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\\ndef read(*parts)\\ndef find_version(*file_paths)\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"fig/cli/command.py::Command.__init__","text":"fig/cli/command.py::Command.__init__\n def __init__(self):\n self._yaml_path = os.environ.get('FIG_FILE', None)\n self.explicit_project_name = None","label":0},{"cid":"fig/cli/command.py::Command.dispatch","text":"fig/cli/command.py::Command.dispatch\n def dispatch(self, *args, **kwargs):\n try:\n super(Command, self).dispatch(*args, **kwargs)\n except ConnectionError:\n if call_silently(['which', 'docker']) != 0:\n if is_mac():\n raise errors.DockerNotFoundMac()\n elif is_ubuntu():\n raise errors.DockerNotFoundUbuntu()\n else:\n raise errors.DockerNotFoundGeneric()\n elif call_silently(['which', 'docker-osx']) == 0:\n raise errors.ConnectionErrorDockerOSX()\n else:\n raise errors.ConnectionErrorGeneric(self.client.base_url)","label":0},{"cid":"fig/cli/command.py::Command.perform_command","text":"fig/cli/command.py::Command.perform_command\n def perform_command(self, options, *args, **kwargs):\n if options['--file'] is not None:\n self.yaml_path = os.path.join(self.base_dir, options['--file'])\n if options['--project-name'] is not None:\n self.explicit_project_name = options['--project-name']\n return super(Command, self).perform_command(options, *args, **kwargs)","label":0},{"cid":"fig/cli/command.py::Command.client","text":"fig/cli/command.py::Command.client\n @cached_property\n def client(self):\n return Client(docker_url())","label":0},{"cid":"fig/cli/command.py::Command.project","text":"fig/cli/command.py::Command.project\n @cached_property\n def project(self):\n try:\n config = yaml.safe_load(open(self.yaml_path))\n except IOError as e:\n if e.errno == errno.ENOENT:\n raise errors.FigFileNotFound(os.path.basename(e.filename))\n raise errors.UserError(six.text_type(e))\n\n try:\n return Project.from_config(self.project_name, config, self.client)\n except ConfigError as e:\n raise errors.UserError(six.text_type(e))","label":0},{"cid":"fig/cli/command.py::Command.project_name","text":"fig/cli/command.py::Command.project_name\n @cached_property\n def project_name(self):\n project = os.path.basename(os.path.dirname(os.path.abspath(self.yaml_path)))\n if self.explicit_project_name is not None:\n project = self.explicit_project_name\n project = re.sub(r'[^a-zA-Z0-9]', '', project)\n if not project:\n project = 'default'\n return project","label":0},{"cid":"fig/cli/command.py::Command.formatter","text":"fig/cli/command.py::Command.formatter\n @cached_property\n def formatter(self):\n return Formatter()","label":0},{"cid":"fig/cli/command.py::Command.yaml_path","text":"fig/cli/command.py::Command.yaml_path\n @cached_property\n def yaml_path(self):\n if self._yaml_path is not None:\n return self._yaml_path\n elif os.path.exists(os.path.join(self.base_dir, 'fig.yaml')):\n\n log.warning(\"Fig just read the file 'fig.yaml' on startup, rather than 'fig.yml'\")\n log.warning(\"Please be aware that fig.yml the expected extension in most cases, and using .yaml can cause compatibility issues in future\")\n\n return os.path.join(self.base_dir, 'fig.yaml')\n else:\n return os.path.join(self.base_dir, 'fig.yml')","label":0},{"cid":"fig/cli/command.py::Command.yaml_path","text":"fig/cli/command.py::Command.yaml_path\n @yaml_path.setter\n def yaml_path(self, value):\n self._yaml_path = value","label":0},{"cid":"fig/cli/errors.py::UserError.__init__","text":"fig/cli/errors.py::UserError.__init__\n def __init__(self, msg):\n self.msg = dedent(msg).strip()","label":0},{"cid":"fig/cli/errors.py::UserError.__unicode__","text":"fig/cli/errors.py::UserError.__unicode__\n def __unicode__(self):\n return self.msg","label":0},{"cid":"fig/cli/errors.py::DockerNotFoundMac.__init__","text":"fig/cli/errors.py::DockerNotFoundMac.__init__\n def __init__(self):\n super(DockerNotFoundMac, self).__init__(\"\"\"\n Couldn't connect to Docker daemon. You might need to install docker-osx:\n\n https://github.com/noplay/docker-osx\n \"\"\")","label":0},{"cid":"fig/cli/errors.py::DockerNotFoundUbuntu.__init__","text":"fig/cli/errors.py::DockerNotFoundUbuntu.__init__\n def __init__(self):\n super(DockerNotFoundUbuntu, self).__init__(\"\"\"\n Couldn't connect to Docker daemon. You might need to install Docker:\n\n http://docs.docker.io/en/latest/installation/ubuntulinux/\n \"\"\")","label":0},{"cid":"fig/cli/errors.py::DockerNotFoundGeneric.__init__","text":"fig/cli/errors.py::DockerNotFoundGeneric.__init__\n def __init__(self):\n super(DockerNotFoundGeneric, self).__init__(\"\"\"\n Couldn't connect to Docker daemon. You might need to install Docker:\n\n http://docs.docker.io/en/latest/installation/\n \"\"\")","label":0},{"cid":"fig/cli/errors.py::ConnectionErrorDockerOSX.__init__","text":"fig/cli/errors.py::ConnectionErrorDockerOSX.__init__\n def __init__(self):\n super(ConnectionErrorDockerOSX, self).__init__(\"\"\"\n Couldn't connect to Docker daemon - you might need to run `docker-osx shell`.\n \"\"\")","label":0},{"cid":"fig/cli/errors.py::ConnectionErrorGeneric.__init__","text":"fig/cli/errors.py::ConnectionErrorGeneric.__init__\n def __init__(self, url):\n super(ConnectionErrorGeneric, self).__init__(\"\"\"\n Couldn't connect to Docker daemon at %s - is it running?\n\n If it's at a non-standard location, specify the URL with the DOCKER_HOST environment variable.\n \"\"\" % url)","label":0},{"cid":"fig/cli/errors.py::FigFileNotFound.__init__","text":"fig/cli/errors.py::FigFileNotFound.__init__\n def __init__(self, filename):\n super(FigFileNotFound, self).__init__(\"\"\"\n Can't find %s. Are you in the right directory?\n \"\"\" % filename)","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter.__init__","text":"fig/cli/log_printer.py::LogPrinter.__init__\n def __init__(self, containers, attach_params=None, output=sys.stdout):\n self.containers = containers\n self.attach_params = attach_params or {}\n self.prefix_width = self._calculate_prefix_width(containers)\n self.generators = self._make_log_generators()\n self.output = output","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter.run","text":"fig/cli/log_printer.py::LogPrinter.run\n def run(self):\n mux = Multiplexer(self.generators)\n for line in mux.loop():\n self.output.write(line)","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter._calculate_prefix_width","text":"fig/cli/log_printer.py::LogPrinter._calculate_prefix_width\n def _calculate_prefix_width(self, containers):\n \"\"\"\n Calculate the maximum width of container names so we can make the log\n prefixes line up like so:\n\n db_1 | Listening\n web_1 | Listening\n \"\"\"\n prefix_width = 0\n for container in containers:\n prefix_width = max(prefix_width, len(container.name_without_project))\n return prefix_width","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter._make_log_generators","text":"fig/cli/log_printer.py::LogPrinter._make_log_generators\n def _make_log_generators(self):\n color_fns = cycle(colors.rainbow())\n generators = []\n\n for container in self.containers:\n color_fn = color_fns.next()\n generators.append(self._make_log_generator(container, color_fn))\n\n return generators","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter._make_log_generator","text":"fig/cli/log_printer.py::LogPrinter._make_log_generator\n def _make_log_generator(self, container, color_fn):\n prefix = color_fn(self._generate_prefix(container)).encode('utf-8')\n # Attach to container before log printer starts running\n line_generator = split_buffer(self._attach(container), '\\n')\n\n for line in line_generator:\n yield prefix + line\n\n exit_code = container.wait()\n yield color_fn(\"%s exited with code %s\\n\" % (container.name, exit_code))\n yield STOP","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter._generate_prefix","text":"fig/cli/log_printer.py::LogPrinter._generate_prefix\n def _generate_prefix(self, container):\n \"\"\"\n Generate the prefix for a log line without colour\n \"\"\"\n name = container.name_without_project\n padding = ' ' * (self.prefix_width - len(name))\n return ''.join([name, padding, ' | '])","label":0},{"cid":"fig/cli/log_printer.py::LogPrinter._attach","text":"fig/cli/log_printer.py::LogPrinter._attach\n def _attach(self, container):\n params = {\n 'stdout': True,\n 'stderr': True,\n 'stream': True,\n }\n params.update(self.attach_params)\n params = dict((name, 1 if value else 0) for (name, value) in list(params.items()))\n return container.attach(**params)","label":0},{"cid":"fig/cli/main.py::main","text":"fig/cli/main.py::main\ndef main():\n setup_logging()\n try:\n command = TopLevelCommand()\n command.sys_dispatch()\n except KeyboardInterrupt:\n log.error(\"\\nAborting.\")\n sys.exit(1)\n except (UserError, NoSuchService, ConfigurationError) as e:\n log.error(e.msg)\n sys.exit(1)\n except NoSuchCommand as e:\n log.error(\"No such command: %s\", e.command)\n log.error(\"\")\n log.error(\"\\n\".join(parse_doc_section(\"commands:\", getdoc(e.supercommand))))\n sys.exit(1)\n except APIError as e:\n log.error(e.explanation)\n sys.exit(1)\n except BuildError as e:\n log.error(\"Service '%s' failed to build: %s\" % (e.service.name, e.reason))\n sys.exit(1)","label":0},{"cid":"fig/cli/main.py::setup_logging","text":"fig/cli/main.py::setup_logging\ndef setup_logging():\n console_handler = logging.StreamHandler(sys.stderr)\n console_handler.setFormatter(logging.Formatter())\n console_handler.setLevel(logging.INFO)\n root_logger = logging.getLogger()\n root_logger.addHandler(console_handler)\n root_logger.setLevel(logging.DEBUG)\n\n # Disable requests logging\n logging.getLogger(\"requests\").propagate = False","label":0},{"cid":"fig/cli/main.py::parse_doc_section","text":"fig/cli/main.py::parse_doc_section\ndef parse_doc_section(name, source):\n pattern = re.compile('^([^\\n]*' + name + '[^\\n]*\\n?(?:[ \\t].*?(?:\\n|$))*)',\n re.IGNORECASE | re.MULTILINE)\n return [s.strip() for s in pattern.findall(source)]","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.docopt_options","text":"fig/cli/main.py::TopLevelCommand.docopt_options\n def docopt_options(self):\n options = super(TopLevelCommand, self).docopt_options()\n options['version'] = \"fig %s\" % __version__\n return options","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.build","text":"fig/cli/main.py::TopLevelCommand.build\n def build(self, options):\n \"\"\"\n Build or rebuild services.\n\n Services are built once and then tagged as `project_service`,\n e.g. `figtest_db`. If you change a service's `Dockerfile` or the\n contents of its build directory, you can run `fig build` to rebuild it.\n\n Usage: build [options] [SERVICE...]\n\n Options:\n --no-cache Do not use cache when building the image.\n \"\"\"\n no_cache = bool(options.get('--no-cache', False))\n self.project.build(service_names=options['SERVICE'], no_cache=no_cache)","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.help","text":"fig/cli/main.py::TopLevelCommand.help\n def help(self, options):\n \"\"\"\n Get help on a command.\n\n Usage: help COMMAND\n \"\"\"\n command = options['COMMAND']\n if not hasattr(self, command):\n raise NoSuchCommand(command, self)\n raise SystemExit(getdoc(getattr(self, command)))","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.kill","text":"fig/cli/main.py::TopLevelCommand.kill\n def kill(self, options):\n \"\"\"\n Force stop service containers.\n\n Usage: kill [SERVICE...]\n \"\"\"\n self.project.kill(service_names=options['SERVICE'])","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.logs","text":"fig/cli/main.py::TopLevelCommand.logs\n def logs(self, options):\n \"\"\"\n View output from containers.\n\n Usage: logs [SERVICE...]\n \"\"\"\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\n print(\"Attaching to\", list_containers(containers))\n LogPrinter(containers, attach_params={'logs': True}).run()","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.ps","text":"fig/cli/main.py::TopLevelCommand.ps\n def ps(self, options):\n \"\"\"\n List containers.\n\n Usage: ps [options] [SERVICE...]\n\n Options:\n -q Only display IDs\n \"\"\"\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True) + self.project.containers(service_names=options['SERVICE'], one_off=True)\n\n if options['-q']:\n for container in containers:\n print(container.id)\n else:\n headers = [\n 'Name',\n 'Command',\n 'State',\n 'Ports',\n ]\n rows = []\n for container in containers:\n command = container.human_readable_command\n if len(command) > 30:\n command = '%s ...' % command[:26]\n rows.append([\n container.name,\n command,\n container.human_readable_state,\n container.human_readable_ports,\n ])\n print(Formatter().table(headers, rows))","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.rm","text":"fig/cli/main.py::TopLevelCommand.rm\n def rm(self, options):\n \"\"\"\n Remove stopped service containers.\n\n Usage: rm [options] [SERVICE...]\n\n Options:\n --force Don't ask to confirm removal\n -v Remove volumes associated with containers\n \"\"\"\n all_containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\n stopped_containers = [c for c in all_containers if not c.is_running]\n\n if len(stopped_containers) > 0:\n print(\"Going to remove\", list_containers(stopped_containers))\n if options.get('--force') \\\n or yesno(\"Are you sure? [yN] \", default=False):\n self.project.remove_stopped(\n service_names=options['SERVICE'],\n v=options.get('-v', False)\n )\n else:\n print(\"No stopped containers\")","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.run","text":"fig/cli/main.py::TopLevelCommand.run\n def run(self, options):\n \"\"\"\n Run a one-off command on a service.\n\n For example:\n\n $ fig run web python manage.py shell\n\n By default, linked services will be started, unless they are already\n running. If you do not want to start linked services, use\n `fig run --no-deps SERVICE COMMAND [ARGS...]`.\n\n Usage: run [options] SERVICE [COMMAND] [ARGS...]\n\n Options:\n -d Detached mode: Run container in the background, print\n new container name.\n -T Disable pseudo-tty allocation. By default `fig run`\n allocates a TTY.\n --rm Remove container after run. Ignored in detached mode.\n --no-deps Don't start linked services.\n \"\"\"\n\n service = self.project.get_service(options['SERVICE'])\n\n if not options['--no-deps']:\n deps = service.get_linked_names()\n\n if len(deps) > 0:\n self.project.up(\n service_names=deps,\n start_links=True,\n recreate=False,\n )\n\n tty = True\n if options['-d'] or options['-T'] or not sys.stdin.isatty():\n tty = False\n\n if options['COMMAND']:\n command = [options['COMMAND']] + options['ARGS']\n else:\n command = service.options.get('command')\n\n container_options = {\n 'command': command,\n 'tty': tty,\n 'stdin_open': not options['-d'],\n }\n container = service.create_container(one_off=True, **container_options)\n if options['-d']:\n service.start_container(container, ports=None, one_off=True)\n print(container.name)\n else:\n service.start_container(container, ports=None, one_off=True)\n dockerpty.start(self.client, container.id)\n exit_code = container.wait()\n ","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.scale","text":"fig/cli/main.py::TopLevelCommand.scale\n def scale(self, options):\n \"\"\"\n Set number of containers to run for a service.\n\n Numbers are specified in the form `service=num` as arguments.\n For example:\n\n $ fig scale web=2 worker=3\n\n Usage: scale [SERVICE=NUM...]\n \"\"\"\n for s in options['SERVICE=NUM']:\n if '=' not in s:\n raise UserError('Arguments to scale should be in the form service=num')\n service_name, num = s.split('=', 1)\n try:\n num = int(num)\n except ValueError:\n raise UserError('Number of containers for service \"%s\" is not a '\n 'number' % service_name)\n try:\n self.project.get_service(service_name).scale(num)\n except CannotBeScaledError:\n raise UserError('Service \"%s\" cannot be scaled because it specifies a port on the host. If multiple containers for this service were created, the port would clash.\\n\\nRemove the \":\" from the port definition in fig.yml so Docker can choose a random port for each container.' % service_name)","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.start","text":"fig/cli/main.py::TopLevelCommand.start\n def start(self, options):\n \"\"\"\n Start existing containers.\n\n Usage: start [SERVICE...]\n \"\"\"\n self.project.start(service_names=options['SERVICE'])","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.stop","text":"fig/cli/main.py::TopLevelCommand.stop\n def stop(self, options):\n \"\"\"\n Stop running containers without removing them.\n\n They can be started again with `fig start`.\n\n Usage: stop [SERVICE...]\n \"\"\"\n self.project.stop(service_names=options['SERVICE'])","label":0},{"cid":"fig/cli/main.py::TopLevelCommand.up","text":"fig/cli/main.py::TopLevelCommand.up\n def up(self, options):\n \"\"\"\n Build, (re)create, start and attach to containers for a service.\n\n By default, `fig up` will aggregate the output of each container, and\n when it exits, all containers will be stopped. If you run `fig up -d`,\n it'll start the containers in the background and leave them running.\n\n If there are existing containers for a service, `fig up` will stop\n and recreate them (preserving mounted volumes with volumes-from),\n so that changes in `fig.yml` are picked up. If you do not want existing\n containers to be recreated, `fig up --no-recreate` will re-use existing\n containers.\n\n Usage: up [options] [SERVICE...]\n\n Options:\n -d Detached mode: Run containers in the background,\n print new container names.\n --no-deps Don't start linked services.\n --no-recreate If containers already exist, don't recreate them.\n \"\"\"\n detached = options['-d']\n\n start_links = not options['--no-deps']\n recreate = not options['--no-recreate']\n service_names = options['SERVICE']\n\n self.project.up(\n service_names=service_names,\n start_links=start_links,\n recreate=recreate\n )\n\n to_attach = [c for s in self.project.get_services(service_names) for c in s.containers()]\n\n if not detached:\n print(\"Attaching to\", list_containers(to_attach))\n log_printer = LogPrinter(to_attach, attach_params={\"logs\": True})\n\n try:\n log_printer.run()\n finally:\n def handler(signal, frame):\n self.project.kill(service_names=service_names)\n sys.exit(0)\n signal.signal(signal.SIGINT, handler)\n\n print(\"Gracefully stopping... (press Ctrl+C again to force)\")\n self.p","label":0},{"cid":"fig/cli/main.py::list_containers","text":"fig/cli/main.py::list_containers\ndef list_containers(containers):\n return \", \".join(c.name for c in containers)","label":0},{"cid":"fig/cli/multiplexer.py::Multiplexer.__init__","text":"fig/cli/multiplexer.py::Multiplexer.__init__\n def __init__(self, generators):\n self.generators = generators\n self.queue = Queue()","label":0},{"cid":"fig/cli/multiplexer.py::Multiplexer.loop","text":"fig/cli/multiplexer.py::Multiplexer.loop\n def loop(self):\n self._init_readers()\n\n while True:\n try:\n item = self.queue.get(timeout=0.1)\n if item is STOP:\n break\n else:\n yield item\n except Empty:\n pass","label":0},{"cid":"fig/cli/multiplexer.py::Multiplexer._init_readers","text":"fig/cli/multiplexer.py::Multiplexer._init_readers\n def _init_readers(self):\n for generator in self.generators:\n t = Thread(target=_enqueue_output, args=(generator, self.queue))\n t.daemon = True\n t.start()","label":0},{"cid":"fig/cli/multiplexer.py::_enqueue_output","text":"fig/cli/multiplexer.py::_enqueue_output\ndef _enqueue_output(generator, queue):\n for item in generator:\n queue.put(item)","label":0},{"cid":"fig/cli/utils.py::cached_property","text":"fig/cli/utils.py::cached_property\ndef cached_property(f):\n \"\"\"\n returns a cached property that is calculated by function f\n http://code.activestate.com/recipes/576563-cached-property/\n \"\"\"\n def get(self):\n try:\n return self._property_cache[f]\n except AttributeError:\n self._property_cache = {}\n x = self._property_cache[f] = f(self)\n return x\n except KeyError:\n x = self._property_cache[f] = f(self)\n return x\n\n return property(get)","label":0},{"cid":"fig/cli/utils.py::cached_property.get","text":"fig/cli/utils.py::cached_property.get\n def get(self):\n try:\n return self._property_cache[f]\n except AttributeError:\n self._property_cache = {}\n x = self._property_cache[f] = f(self)\n return x\n except KeyError:\n x = self._property_cache[f] = f(self)\n return x","label":0},{"cid":"fig/cli/utils.py::yesno","text":"fig/cli/utils.py::yesno\ndef yesno(prompt, default=None):\n \"\"\"\n Prompt the user for a yes or no.\n\n Can optionally specify a default value, which will only be\n used if they enter a blank line.\n\n Unrecognised input (anything other than \"y\", \"n\", \"yes\",\n \"no\" or \"\") will return None.\n \"\"\"\n answer = raw_input(prompt).strip().lower()\n\n if answer == \"y\" or answer == \"yes\":\n return True\n elif answer == \"n\" or answer == \"no\":\n return False\n elif answer == \"\":\n return default\n else:\n return None","label":0},{"cid":"fig/cli/utils.py::prettydate","text":"fig/cli/utils.py::prettydate\ndef prettydate(d):\n diff = datetime.datetime.utcnow() - d\n s = diff.seconds\n if diff.days > 7 or diff.days < 0:\n return d.strftime('%d %b %y')\n elif diff.days == 1:\n return '1 day ago'\n elif diff.days > 1:\n return '{0} days ago'.format(diff.days)\n elif s <= 1:\n return 'just now'\n elif s < 60:\n return '{0} seconds ago'.format(s)\n elif s < 120:\n return '1 minute ago'\n elif s < 3600:\n return '{0} minutes ago'.format(s / 60)\n elif s < 7200:\n return '1 hour ago'\n else:\n return '{0} hours ago'.format(s / 3600)","label":0},{"cid":"fig/cli/utils.py::mkdir","text":"fig/cli/utils.py::mkdir\ndef mkdir(path, permissions=0o700):\n if not os.path.exists(path):\n os.mkdir(path)\n\n os.chmod(path, permissions)\n\n return path","label":0},{"cid":"fig/cli/utils.py::docker_url","text":"fig/cli/utils.py::docker_url\ndef docker_url():\n return os.environ.get('DOCKER_HOST')","label":0},{"cid":"fig/cli/utils.py::split_buffer","text":"fig/cli/utils.py::split_buffer\ndef split_buffer(reader, separator):\n \"\"\"\n Given a generator which yields strings and a separator string,\n joins all input, splits on the separator and yields each chunk.\n\n Unlike string.split(), each chunk includes the trailing\n separator, except for the last one if none was found on the end\n of the input.\n \"\"\"\n buffered = str('')\n separator = str(separator)\n\n for data in reader:\n buffered += data\n while True:\n index = buffered.find(separator)\n if index == -1:\n break\n yield buffered[:index + 1]\n buffered = buffered[index + 1:]\n\n if len(buffered) > 0:\n yield buffered","label":0},{"cid":"fig/cli/utils.py::call_silently","text":"fig/cli/utils.py::call_silently\ndef call_silently(*args, **kwargs):\n \"\"\"\n Like subprocess.call(), but redirects stdout and stderr to /dev/null.\n \"\"\"\n with open(os.devnull, 'w') as shutup:\n return subprocess.call(*args, stdout=shutup, stderr=shutup, **kwargs)","label":0},{"cid":"fig/cli/utils.py::is_mac","text":"fig/cli/utils.py::is_mac\ndef is_mac():\n return platform.system() == 'Darwin'","label":0},{"cid":"fig/cli/utils.py::is_ubuntu","text":"fig/cli/utils.py::is_ubuntu\ndef is_ubuntu():\n return platform.system() == 'Linux' and platform.linux_distribution()[0] == 'Ubuntu'","label":0},{"cid":"fig/container.py::Container.__init__","text":"fig/container.py::Container.__init__\n def __init__(self, client, dictionary, has_been_inspected=False):\n self.client = client\n self.dictionary = dictionary\n self.has_been_inspected = has_been_inspected","label":0},{"cid":"fig/container.py::Container.from_ps","text":"fig/container.py::Container.from_ps\n @classmethod\n def from_ps(cls, client, dictionary, **kwargs):\n \"\"\"\n Construct a container object from the output of GET /containers/json.\n \"\"\"\n new_dictionary = {\n 'Id': dictionary['Id'],\n 'Image': dictionary['Image'],\n }\n for name in dictionary.get('Names', []):\n if len(name.split('/')) == 2:\n new_dictionary['Name'] = name\n return cls(client, new_dictionary, **kwargs)","label":0},{"cid":"fig/container.py::Container.from_id","text":"fig/container.py::Container.from_id\n @classmethod\n def from_id(cls, client, id):\n return cls(client, client.inspect_container(id))","label":0},{"cid":"fig/container.py::Container.create","text":"fig/container.py::Container.create\n @classmethod\n def create(cls, client, **options):\n response = client.create_container(**options)\n return cls.from_id(client, response['Id'])","label":0},{"cid":"fig/container.py::Container.id","text":"fig/container.py::Container.id\n @property\n def id(self):\n return self.dictionary['Id']","label":0},{"cid":"fig/container.py::Container.image","text":"fig/container.py::Container.image\n @property\n def image(self):\n return self.dictionary['Image']","label":0},{"cid":"fig/container.py::Container.short_id","text":"fig/container.py::Container.short_id\n @property\n def short_id(self):\n return self.id[:10]","label":0},{"cid":"fig/container.py::Container.name","text":"fig/container.py::Container.name\n @property\n def name(self):\n return self.dictionary['Name'][1:]","label":0},{"cid":"fig/container.py::Container.name_without_project","text":"fig/container.py::Container.name_without_project\n @property\n def name_without_project(self):\n return '_'.join(self.dictionary['Name'].split('_')[1:])","label":0},{"cid":"fig/container.py::Container.number","text":"fig/container.py::Container.number\n @property\n def number(self):\n try:\n return int(self.name.split('_')[-1])\n except ValueError:\n return None","label":0},{"cid":"fig/container.py::Container.human_readable_ports","text":"fig/container.py::Container.human_readable_ports\n @property\n def human_readable_ports(self):\n self.inspect_if_not_inspected()\n if not self.dictionary['NetworkSettings']['Ports']:\n return ''\n ports = []\n for private, public in list(self.dictionary['NetworkSettings']['Ports'].items()):\n if public:\n ports.append('%s->%s' % (public[0]['HostPort'], private))\n else:\n ports.append(private)\n return ', '.join(ports)","label":0},{"cid":"fig/container.py::Container.human_readable_state","text":"fig/container.py::Container.human_readable_state\n @property\n def human_readable_state(self):\n self.inspect_if_not_inspected()\n if self.dictionary['State']['Running']:\n if self.dictionary['State'].get('Ghost'):\n return 'Ghost'\n else:\n return 'Up'\n else:\n return 'Exit %s' % self.dictionary['State']['ExitCode']","label":0},{"cid":"fig/container.py::Container.human_readable_command","text":"fig/container.py::Container.human_readable_command\n @property\n def human_readable_command(self):\n self.inspect_if_not_inspected()\n if self.dictionary['Config']['Cmd']:\n return ' '.join(self.dictionary['Config']['Cmd'])\n else:\n return ''","label":0},{"cid":"fig/container.py::Container.environment","text":"fig/container.py::Container.environment\n @property\n def environment(self):\n self.inspect_if_not_inspected()\n out = {}\n for var in self.dictionary.get('Config', {}).get('Env', []):\n k, v = var.split('=', 1)\n out[k] = v\n return out","label":0},{"cid":"fig/container.py::Container.is_running","text":"fig/container.py::Container.is_running\n @property\n def is_running(self):\n self.inspect_if_not_inspected()\n return self.dictionary['State']['Running']","label":0},{"cid":"fig/container.py::Container.start","text":"fig/container.py::Container.start\n def start(self, **options):\n return self.client.start(self.id, **options)","label":0},{"cid":"fig/container.py::Container.stop","text":"fig/container.py::Container.stop\n def stop(self, **options):\n return self.client.stop(self.id, **options)","label":0},{"cid":"fig/container.py::Container.kill","text":"fig/container.py::Container.kill\n def kill(self):\n return self.client.kill(self.id)","label":0},{"cid":"fig/container.py::Container.remove","text":"fig/container.py::Container.remove\n def remove(self, **options):\n return self.client.remove_container(self.id, **options)","label":0},{"cid":"fig/container.py::Container.inspect_if_not_inspected","text":"fig/container.py::Container.inspect_if_not_inspected\n def inspect_if_not_inspected(self):\n if not self.has_been_inspected:\n self.inspect()","label":0},{"cid":"fig/container.py::Container.wait","text":"fig/container.py::Container.wait\n def wait(self):\n return self.client.wait(self.id)","label":0},{"cid":"fig/container.py::Container.logs","text":"fig/container.py::Container.logs\n def logs(self, *args, **kwargs):\n return self.client.logs(self.id, *args, **kwargs)","label":0},{"cid":"fig/container.py::Container.inspect","text":"fig/container.py::Container.inspect\n def inspect(self):\n self.dictionary = self.client.inspect_container(self.id)\n return self.dictionary","label":0},{"cid":"fig/container.py::Container.links","text":"fig/container.py::Container.links\n def links(self):\n links = []\n for container in self.client.containers():\n for name in container['Names']:\n bits = name.split('/')\n if len(bits) > 2 and bits[1] == self.name:\n links.append(bits[2])\n return links","label":0},{"cid":"fig/container.py::Container.attach","text":"fig/container.py::Container.attach\n def attach(self, *args, **kwargs):\n return self.client.attach(self.id, *args, **kwargs)","label":0},{"cid":"fig/container.py::Container.attach_socket","text":"fig/container.py::Container.attach_socket\n def attach_socket(self, **kwargs):\n return self.client.attach_socket(self.id, **kwargs)","label":0},{"cid":"fig/container.py::Container.__repr__","text":"fig/container.py::Container.__repr__\n def __repr__(self):\n return '<Container: %s>' % self.name","label":0},{"cid":"fig/container.py::Container.__eq__","text":"fig/container.py::Container.__eq__\n def __eq__(self, other):\n if type(self) != type(other):\n return False\n return self.id == other.id","label":0},{"cid":"fig/packages/docker/auth/auth.py::swap_protocol","text":"fig/packages/docker/auth/auth.py::swap_protocol\ndef swap_protocol(url):\n if url.startswith('http://'):\n return url.replace('http://', 'https://', 1)\n if url.startswith('https://'):\n return url.replace('https://', 'http://', 1)\n return url","label":0},{"cid":"fig/packages/docker/auth/auth.py::expand_registry_url","text":"fig/packages/docker/auth/auth.py::expand_registry_url\ndef expand_registry_url(hostname):\n if hostname.startswith('http:') or hostname.startswith('https:'):\n if '/' not in hostname[9:]:\n hostname = hostname + '/v1/'\n return hostname\n if utils.ping('https://' + hostname + '/v1/_ping'):\n return 'https://' + hostname + '/v1/'\n return 'http://' + hostname + '/v1/'","label":0},{"cid":"fig/packages/docker/auth/auth.py::resolve_repository_name","text":"fig/packages/docker/auth/auth.py::resolve_repository_name\ndef resolve_repository_name(repo_name):\n if '://' in repo_name:\n raise errors.InvalidRepository(\n 'Repository name cannot contain a scheme ({0})'.format(repo_name))\n parts = repo_name.split('/', 1)\n if '.' not in parts[0] and ':' not in parts[0] and parts[0] != 'localhost':\n # This is a docker index repo (ex: foo/bar or ubuntu)\n return INDEX_URL, repo_name\n if len(parts) < 2:\n raise errors.InvalidRepository(\n 'Invalid repository name ({0})'.format(repo_name))\n\n if 'index.docker.io' in parts[0]:\n raise errors.InvalidRepository(\n 'Invalid repository name, try \"{0}\" instead'.format(parts[1]))\n\n return expand_registry_url(parts[0]), parts[1]","label":0},{"cid":"fig/packages/docker/auth/auth.py::resolve_authconfig","text":"fig/packages/docker/auth/auth.py::resolve_authconfig\ndef resolve_authconfig(authconfig, registry=None):\n \"\"\"Return the authentication data from the given auth configuration for a\n specific registry. We'll do our best to infer the correct URL for the\n registry, trying both http and https schemes. Returns an empty dictionnary\n if no data exists.\"\"\"\n # Default to the public index server\n registry = registry or INDEX_URL\n\n # Ff its not the index server there are three cases:\n #\n # 1. this is a full config url -> it should be used as is\n # 2. it could be a full url, but with the wrong protocol\n # 3. it can be the hostname optionally with a port\n #\n # as there is only one auth entry which is fully qualified we need to start\n # parsing and matching\n if '/' not in registry:\n registry = registry + '/v1/'\n if not registry.startswith('http:') and not registry.startswith('https:'):\n registry = 'https://' + registry\n\n if registry in authconfig:\n return authconfig[registry]\n return authconfig.get(swap_protocol(registry), None)","label":0},{"cid":"fig/packages/docker/auth/auth.py::encode_auth","text":"fig/packages/docker/auth/auth.py::encode_auth\ndef encode_auth(auth_info):\n return base64.b64encode(auth_info.get('username', '') + b':' +\n auth_info.get('password', ''))","label":0},{"cid":"fig/packages/docker/auth/auth.py::decode_auth","text":"fig/packages/docker/auth/auth.py::decode_auth\ndef decode_auth(auth):\n if isinstance(auth, six.string_types):\n auth = auth.encode('ascii')\n s = base64.b64decode(auth)\n login, pwd = s.split(b':')\n return login.decode('ascii'), pwd.decode('ascii')","label":0},{"cid":"fig/packages/docker/auth/auth.py::encode_header","text":"fig/packages/docker/auth/auth.py::encode_header\ndef encode_header(auth):\n auth_json = json.dumps(auth).encode('ascii')\n return base64.b64encode(auth_json)","label":0},{"cid":"fig/packages/docker/auth/auth.py::encode_full_header","text":"fig/packages/docker/auth/auth.py::encode_full_header\ndef encode_full_header(auth):\n \"\"\" Returns the given auth block encoded for the X-Registry-Config header.\n \"\"\"\n return encode_header({'configs': auth})","label":0},{"cid":"fig/packages/docker/auth/auth.py::load_config","text":"fig/packages/docker/auth/auth.py::load_config\ndef load_config(root=None):\n \"\"\"Loads authentication data from a Docker configuration file in the given\n root directory.\"\"\"\n conf = {}\n data = None\n\n config_file = os.path.join(root or os.environ.get('HOME', '.'),\n DOCKER_CONFIG_FILENAME)\n\n # First try as JSON\n try:\n with open(config_file) as f:\n conf = {}\n for registry, entry in six.iteritems(json.load(f)):\n username, password = decode_auth(entry['auth'])\n conf[registry] = {\n 'username': username,\n 'password': password,\n 'email': entry['email'],\n 'serveraddress': registry,\n }\n return conf\n except:\n pass\n\n # If that fails, we assume the configuration file contains a single\n # authentication token for the public registry in the following format:\n #\n # auth = AUTH_TOKEN\n # email = email@domain.com\n try:\n data = []\n for line in fileinput.input(config_file):\n data.append(line.strip().split(' = ')[1])\n if len(data) < 2:\n # Not enough data\n raise errors.InvalidConfigFile(\n 'Invalid or empty configuration file!')\n\n username, password = decode_auth(data[0])\n conf[INDEX_URL] = {\n 'username': username,\n 'password': password,\n 'email': data[1],\n 'serveraddress': INDEX_URL,\n }\n return conf\n except:\n pass\n\n # If all fails, return an empty config\n return {}","label":0},{"cid":"fig/packages/docker/client.py::Client.__init__","text":"fig/packages/docker/client.py::Client.__init__\n def __init__(self, base_url=None, version=DEFAULT_DOCKER_API_VERSION,\n timeout=DEFAULT_TIMEOUT_SECONDS):\n super(Client, self).__init__()\n if base_url is None:\n base_url = \"http+unix://var/run/docker.sock\"\n if 'unix:///' in base_url:\n base_url = base_url.replace('unix:/', 'unix:')\n if base_url.startswith('unix:'):\n base_url = \"http+\" + base_url\n if base_url.startswith('tcp:'):\n base_url = base_url.replace('tcp:', 'http:')\n if base_url.endswith('/'):\n base_url = base_url[:-1]\n self.base_url = base_url\n self._version = version\n self._timeout = timeout\n self._auth_configs = auth.load_config()\n\n self.mount('http+unix://', unixconn.UnixAdapter(base_url, timeout))","label":0},{"cid":"fig/packages/docker/client.py::Client._set_request_timeout","text":"fig/packages/docker/client.py::Client._set_request_timeout\n def _set_request_timeout(self, kwargs):\n \"\"\"Prepare the kwargs for an HTTP request by inserting the timeout\n parameter, if not already present.\"\"\"\n kwargs.setdefault('timeout', self._timeout)\n return kwargs","label":0},{"cid":"fig/packages/docker/client.py::Client._post","text":"fig/packages/docker/client.py::Client._post\n def _post(self, url, **kwargs):\n return self.post(url, **self._set_request_timeout(kwargs))","label":0},{"cid":"fig/packages/docker/client.py::Client._get","text":"fig/packages/docker/client.py::Client._get\n def _get(self, url, **kwargs):\n return self.get(url, **self._set_request_timeout(kwargs))","label":0},{"cid":"fig/packages/docker/client.py::Client._delete","text":"fig/packages/docker/client.py::Client._delete\n def _delete(self, url, **kwargs):\n return self.delete(url, **self._set_request_timeout(kwargs))","label":0},{"cid":"fig/packages/docker/client.py::Client._url","text":"fig/packages/docker/client.py::Client._url\n def _url(self, path):\n return '{0}/v{1}{2}'.format(self.base_url, self._version, path)","label":0},{"cid":"fig/packages/docker/client.py::Client._raise_for_status","text":"fig/packages/docker/client.py::Client._raise_for_status\n def _raise_for_status(self, response, explanation=None):\n \"\"\"Raises stored :class:`APIError`, if one occurred.\"\"\"\n try:\n response.raise_for_status()\n except requests.exceptions.HTTPError as e:\n raise errors.APIError(e, response, explanation=explanation)","label":0},{"cid":"fig/packages/docker/client.py::Client._result","text":"fig/packages/docker/client.py::Client._result\n def _result(self, response, json=False, binary=False):\n assert not (json and binary)\n self._raise_for_status(response)\n\n if json:\n return response.json()\n if binary:\n return response.content\n return response.text","label":0},{"cid":"fig/packages/docker/client.py::Client._container_config","text":"fig/packages/docker/client.py::Client._container_config\n def _container_config(self, image, command, hostname=None, user=None,\n detach=False, stdin_open=False, tty=False,\n mem_limit=0, ports=None, environment=None, dns=None,\n volumes=None, volumes_from=None,\n network_disabled=False, entrypoint=None,\n cpu_shares=None, working_dir=None, domainname=None,\n memswap_limit=0):\n if isinstance(command, six.string_types):\n command = shlex.split(str(command))\n if isinstance(environment, dict):\n environment = [\n '{0}={1}'.format(k, v) for k, v in environment.items()\n ]\n\n if isinstance(ports, list):\n exposed_ports = {}\n for port_definition in ports:\n port = port_definition\n proto = 'tcp'\n if isinstance(port_definition, tuple):\n if len(port_definition) == 2:\n proto = port_definition[1]\n port = port_definition[0]\n exposed_ports['{0}/{1}'.format(port, proto)] = {}\n ports = exposed_ports\n\n if isinstance(volumes, list):\n volumes_dict = {}\n for vol in volumes:\n volumes_dict[vol] = {}\n volumes = volumes_dict\n\n if volumes_from:\n if not isinstance(volumes_from, six.string_types):\n volumes_from = ','.join(volumes_from)\n else:\n # Force None, an empty list or dict causes client.start to fail\n volumes_from = None\n\n attach_stdin = False\n attach_stdout = False\n attach_stderr = False\n stdin_once = False\n\n if not detach:\n attach_stdout = True\n attach_stderr = True\n\n if stdin_open:\n attach_stdin = True\n stdin_once = ","label":0},{"cid":"fig/packages/docker/client.py::Client._post_json","text":"fig/packages/docker/client.py::Client._post_json\n def _post_json(self, url, data, **kwargs):\n # Go <1.1 can't unserialize null to a string\n # so we do this disgusting thing here.\n data2 = {}\n if data is not None:\n for k, v in six.iteritems(data):\n if v is not None:\n data2[k] = v\n\n if 'headers' not in kwargs:\n kwargs['headers'] = {}\n kwargs['headers']['Content-Type'] = 'application/json'\n return self._post(url, data=json.dumps(data2), **kwargs)","label":0},{"cid":"fig/packages/docker/client.py::Client._attach_params","text":"fig/packages/docker/client.py::Client._attach_params\n def _attach_params(self, override=None):\n return override or {\n 'stdout': 1,\n 'stderr': 1,\n 'stream': 1\n }","label":0},{"cid":"fig/packages/docker/client.py::Client._attach_websocket","text":"fig/packages/docker/client.py::Client._attach_websocket\n def _attach_websocket(self, container, params=None):\n if six.PY3:\n raise NotImplementedError(\"This method is not currently supported \"\n \"under python 3\")\n url = self._url(\"/containers/{0}/attach/ws\".format(container))\n req = requests.Request(\"POST\", url, params=self._attach_params(params))\n full_url = req.prepare().url\n full_url = full_url.replace(\"http://\", \"ws://\", 1)\n full_url = full_url.replace(\"https://\", \"wss://\", 1)\n return self._create_websocket_connection(full_url)","label":0},{"cid":"fig/packages/docker/client.py::Client._create_websocket_connection","text":"fig/packages/docker/client.py::Client._create_websocket_connection\n def _create_websocket_connection(self, url):\n return websocket.create_connection(url)","label":0},{"cid":"fig/packages/docker/client.py::Client._get_raw_response_socket","text":"fig/packages/docker/client.py::Client._get_raw_response_socket\n def _get_raw_response_socket(self, response):\n self._raise_for_status(response)\n if six.PY3:\n return response.raw._fp.fp.raw._sock\n else:\n return response.raw._fp.fp._sock","label":0},{"cid":"fig/packages/docker/client.py::Client._stream_helper","text":"fig/packages/docker/client.py::Client._stream_helper\n def _stream_helper(self, response):\n \"\"\"Generator for data coming from a chunked-encoded HTTP response.\"\"\"\n socket_fp = self._get_raw_response_socket(response)\n socket_fp.setblocking(1)\n socket = socket_fp.makefile()\n while True:\n # Because Docker introduced newlines at the end of chunks in v0.9,\n # and only on some API endpoints, we have to cater for both cases.\n size_line = socket.readline()\n if size_line == '\\r\\n':\n size_line = socket.readline()\n\n size = int(size_line, 16)\n if size <= 0:\n break\n data = socket.readline()\n if not data:\n break\n yield data","label":0},{"cid":"fig/packages/docker/client.py::Client._multiplexed_buffer_helper","text":"fig/packages/docker/client.py::Client._multiplexed_buffer_helper\n def _multiplexed_buffer_helper(self, response):\n \"\"\"A generator of multiplexed data blocks read from a buffered\n response.\"\"\"\n buf = self._result(response, binary=True)\n walker = 0\n while True:\n if len(buf[walker:]) < 8:\n break\n _, length = struct.unpack_from('>BxxxL', buf[walker:])\n start = walker + STREAM_HEADER_SIZE_BYTES\n end = start + length\n walker = end\n yield buf[start:end]","label":0},{"cid":"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper","text":"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper\n def _multiplexed_socket_stream_helper(self, response):\n \"\"\"A generator of multiplexed data blocks coming from a response\n socket.\"\"\"\n socket = self._get_raw_response_socket(response)\n\n def recvall(socket, size):\n blocks = []\n while size > 0:\n block = socket.recv(size)\n if not block:\n return None\n\n blocks.append(block)\n size -= len(block)\n\n sep = bytes() if six.PY3 else str()\n data = sep.join(blocks)\n return data\n\n while True:\n socket.settimeout(None)\n header = recvall(socket, STREAM_HEADER_SIZE_BYTES)\n if not header:\n break\n _, length = struct.unpack('>BxxxL', header)\n if not length:\n break\n data = recvall(socket, length)\n if not data:\n break\n yield data","label":0},{"cid":"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper.recvall","text":"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper.recvall\n def recvall(socket, size):\n blocks = []\n while size > 0:\n block = socket.recv(size)\n if not block:\n return None\n\n blocks.append(block)\n size -= len(block)\n\n sep = bytes() if six.PY3 else str()\n data = sep.join(blocks)\n return data","label":0},{"cid":"fig/packages/docker/client.py::Client.attach","text":"fig/packages/docker/client.py::Client.attach\n def attach(self, container, stdout=True, stderr=True,\n stream=False, logs=False):\n if isinstance(container, dict):\n container = container.get('Id')\n params = {\n 'logs': logs and 1 or 0,\n 'stdout': stdout and 1 or 0,\n 'stderr': stderr and 1 or 0,\n 'stream': stream and 1 or 0,\n }\n u = self._url(\"/containers/{0}/attach\".format(container))\n response = self._post(u, params=params, stream=stream)\n\n # Stream multi-plexing was only introduced in API v1.6. Anything before\n # that needs old-style streaming.\n if utils.compare_version('1.6', self._version) < 0:\n def stream_result():\n self._raise_for_status(response)\n for line in response.iter_lines(chunk_size=1,\n decode_unicode=True):\n # filter out keep-alive new lines\n if line:\n yield line\n\n return stream_result() if stream else \\\n self._result(response, binary=True)\n\n sep = bytes() if six.PY3 else str()\n\n return stream and self._multiplexed_socket_stream_helper(response) or \\\n sep.join([x for x in self._multiplexed_buffer_helper(response)])","label":0},{"cid":"fig/packages/docker/client.py::Client.attach_socket","text":"fig/packages/docker/client.py::Client.attach_socket\n def attach_socket(self, container, params=None, ws=False):\n if params is None:\n params = {\n 'stdout': 1,\n 'stderr': 1,\n 'stream': 1\n }\n\n if ws:\n return self._attach_websocket(container, params)\n\n if isinstance(container, dict):\n container = container.get('Id')\n\n u = self._url(\"/containers/{0}/attach\".format(container))\n return self._get_raw_response_socket(self.post(\n u, None, params=self._attach_params(params), stream=True))","label":0},{"cid":"fig/packages/docker/client.py::Client.build","text":"fig/packages/docker/client.py::Client.build\n def build(self, path=None, tag=None, quiet=False, fileobj=None,\n nocache=False, rm=False, stream=False, timeout=None,\n custom_context=False, encoding=None):\n remote = context = headers = None\n if path is None and fileobj is None:\n raise TypeError(\"Either path or fileobj needs to be provided.\")\n\n if custom_context:\n if not fileobj:\n raise TypeError(\"You must specify fileobj with custom_context\")\n context = fileobj\n elif fileobj is not None:\n context = utils.mkbuildcontext(fileobj)\n elif path.startswith(('http://', 'https://',\n 'git://', 'github.com/')):\n remote = path\n else:\n context = utils.tar(path)\n\n if utils.compare_version('1.8', self._version) >= 0:\n stream = True\n\n u = self._url('/build')\n params = {\n 't': tag,\n 'remote': remote,\n 'q': quiet,\n 'nocache': nocache,\n 'rm': rm\n }\n\n if context is not None:\n headers = {'Content-Type': 'application/tar'}\n if encoding:\n headers['Content-Encoding'] = encoding\n\n if utils.compare_version('1.9', self._version) >= 0:\n # If we don't have any auth data so far, try reloading the config\n # file one more time in case anything showed up in there.\n if not self._auth_configs:\n self._auth_configs = auth.load_config()\n\n # Send the full auth configuration (if any exists), since the build\n # could use any (or all) of the registries.\n if self._auth_configs:\n headers['X-Registry-Config'] = auth.encode_full_header(\n self._auth_configs\n )\n\n response = self._post(\n u,\n data=context,\n params=params,\n ","label":0},{"cid":"fig/packages/docker/client.py::Client.commit","text":"fig/packages/docker/client.py::Client.commit\n def commit(self, container, repository=None, tag=None, message=None,\n author=None, conf=None):\n params = {\n 'container': container,\n 'repo': repository,\n 'tag': tag,\n 'comment': message,\n 'author': author\n }\n u = self._url(\"/commit\")\n return self._result(self._post_json(u, data=conf, params=params),\n json=True)","label":0},{"cid":"fig/packages/docker/client.py::Client.containers","text":"fig/packages/docker/client.py::Client.containers\n def containers(self, quiet=False, all=False, trunc=True, latest=False,\n since=None, before=None, limit=-1, size=False):\n params = {\n 'limit': 1 if latest else limit,\n 'all': 1 if all else 0,\n 'size': 1 if size else 0,\n 'trunc_cmd': 1 if trunc else 0,\n 'since': since,\n 'before': before\n }\n u = self._url(\"/containers/json\")\n res = self._result(self._get(u, params=params), True)\n\n if quiet:\n return [{'Id': x['Id']} for x in res]\n return res","label":0},{"cid":"fig/packages/docker/client.py::Client.copy","text":"fig/packages/docker/client.py::Client.copy\n def copy(self, container, resource):\n if isinstance(container, dict):\n container = container.get('Id')\n res = self._post_json(\n self._url(\"/containers/{0}/copy\".format(container)),\n data={\"Resource\": resource},\n stream=True\n )\n self._raise_for_status(res)\n return res.raw","label":0},{"cid":"fig/packages/docker/client.py::Client.create_container","text":"fig/packages/docker/client.py::Client.create_container\n def create_container(self, image, command=None, hostname=None, user=None,\n detach=False, stdin_open=False, tty=False,\n mem_limit=0, ports=None, environment=None, dns=None,\n volumes=None, volumes_from=None,\n network_disabled=False, name=None, entrypoint=None,\n cpu_shares=None, working_dir=None, domainname=None,\n memswap_limit=0):\n\n config = self._container_config(\n image, command, hostname, user, detach, stdin_open, tty, mem_limit,\n ports, environment, dns, volumes, volumes_from, network_disabled,\n entrypoint, cpu_shares, working_dir, domainname, memswap_limit\n )\n return self.create_container_from_config(config, name)","label":0},{"cid":"fig/packages/docker/client.py::Client.create_container_from_config","text":"fig/packages/docker/client.py::Client.create_container_from_config\n def create_container_from_config(self, config, name=None):\n u = self._url(\"/containers/create\")\n params = {\n 'name': name\n }\n res = self._post_json(u, data=config, params=params)\n return self._result(res, True)","label":0},{"cid":"fig/packages/docker/client.py::Client.diff","text":"fig/packages/docker/client.py::Client.diff\n def diff(self, container):\n if isinstance(container, dict):\n container = container.get('Id')\n return self._result(self._get(self._url(\"/containers/{0}/changes\".\n format(container))), True)","label":0},{"cid":"fig/packages/docker/client.py::Client.events","text":"fig/packages/docker/client.py::Client.events\n def events(self):\n return self._stream_helper(self.get(self._url('/events'), stream=True))","label":0},{"cid":"fig/packages/docker/client.py::Client.export","text":"fig/packages/docker/client.py::Client.export\n def export(self, container):\n if isinstance(container, dict):\n container = container.get('Id')\n res = self._get(self._url(\"/containers/{0}/export\".format(container)),\n stream=True)\n self._raise_for_status(res)\n return res.raw","label":0},{"cid":"fig/packages/docker/client.py::Client.get_image","text":"fig/packages/docker/client.py::Client.get_image\n def get_image(self, image):\n res = self._get(self._url(\"/images/{0}/get\".format(image)),\n stream=True)\n self._raise_for_status(res)\n return res.raw","label":0},{"cid":"fig/packages/docker/client.py::Client.history","text":"fig/packages/docker/client.py::Client.history\n def history(self, image):\n res = self._get(self._url(\"/images/{0}/history\".format(image)))\n self._raise_for_status(res)\n return self._result(res)","label":0},{"cid":"fig/packages/docker/client.py::Client.images","text":"fig/packages/docker/client.py::Client.images\n def images(self, name=None, quiet=False, all=False, viz=False):\n if viz:\n if utils.compare_version('1.7', self._version) >= 0:\n raise Exception('Viz output is not supported in API >= 1.7!')\n return self._result(self._get(self._url(\"images/viz\")))\n params = {\n 'filter': name,\n 'only_ids': 1 if quiet else 0,\n 'all': 1 if all else 0,\n }\n res = self._result(self._get(self._url(\"/images/json\"), params=params),\n True)\n if quiet:\n return [x['Id'] for x in res]\n return res","label":0},{"cid":"fig/packages/docker/client.py::Client.import_image","text":"fig/packages/docker/client.py::Client.import_image\n def import_image(self, src=None, repository=None, tag=None, image=None):\n u = self._url(\"/images/create\")\n params = {\n 'repo': repository,\n 'tag': tag\n }\n\n if src:\n try:\n # XXX: this is ways not optimal but the only way\n # for now to import tarballs through the API\n fic = open(src)\n data = fic.read()\n fic.close()\n src = \"-\"\n except IOError:\n # file does not exists or not a file (URL)\n data = None\n if isinstance(src, six.string_types):\n params['fromSrc'] = src\n return self._result(self._post(u, data=data, params=params))\n return self._result(self._post(u, data=src, params=params))\n\n if image:\n params['fromImage'] = image\n return self._result(self._post(u, data=None, params=params))\n\n raise Exception(\"Must specify a src or image\")","label":0},{"cid":"fig/packages/docker/client.py::Client.info","text":"fig/packages/docker/client.py::Client.info\n def info(self):\n return self._result(self._get(self._url(\"/info\")),\n True)","label":0},{"cid":"fig/packages/docker/client.py::Client.insert","text":"fig/packages/docker/client.py::Client.insert\n def insert(self, image, url, path):\n if utils.compare_version('1.12', self._version) >= 0:\n raise errors.DeprecatedMethod(\n 'insert is not available for API version >=1.12'\n )\n api_url = self._url(\"/images/\" + image + \"/insert\")\n params = {\n 'url': url,\n 'path': path\n }\n return self._result(self._post(api_url, params=params))","label":0},{"cid":"fig/packages/docker/client.py::Client.inspect_container","text":"fig/packages/docker/client.py::Client.inspect_container\n def inspect_container(self, container):\n if isinstance(container, dict):\n container = container.get('Id')\n return self._result(\n self._get(self._url(\"/containers/{0}/json\".format(container))),\n True)","label":0},{"cid":"fig/packages/docker/client.py::Client.inspect_image","text":"fig/packages/docker/client.py::Client.inspect_image\n def inspect_image(self, image_id):\n return self._result(\n self._get(self._url(\"/images/{0}/json\".format(image_id))),\n True\n )","label":0},{"cid":"fig/packages/docker/client.py::Client.kill","text":"fig/packages/docker/client.py::Client.kill\n def kill(self, container, signal=None):\n if isinstance(container, dict):\n container = container.get('Id')\n url = self._url(\"/containers/{0}/kill\".format(container))\n params = {}\n if signal is not None:\n params['signal'] = signal\n res = self._post(url, params=params)\n\n self._raise_for_status(res)","label":0},{"cid":"fig/packages/docker/client.py::Client.load_image","text":"fig/packages/docker/client.py::Client.load_image\n def load_image(self, data):\n res = self._post(self._url(\"/images/load\"), data=data)\n self._raise_for_status(res)","label":0},{"cid":"fig/packages/docker/client.py::Client.login","text":"fig/packages/docker/client.py::Client.login\n def login(self, username, password=None, email=None, registry=None,\n reauth=False):\n # If we don't have any auth data so far, try reloading the config file\n # one more time in case anything showed up in there.\n if not self._auth_configs:\n self._auth_configs = auth.load_config()\n\n registry = registry or auth.INDEX_URL\n\n authcfg = auth.resolve_authconfig(self._auth_configs, registry)\n # If we found an existing auth config for this registry and username\n # combination, we can return it immediately unless reauth is requested.\n if authcfg and authcfg.get('username', None) == username \\\n and not reauth:\n return authcfg\n\n req_data = {\n 'username': username,\n 'password': password,\n 'email': email,\n 'serveraddress': registry,\n }\n\n response = self._post_json(self._url('/auth'), data=req_data)\n if response.status_code == 200:\n self._auth_configs[registry] = req_data\n return self._result(response, json=True)","label":0},{"cid":"fig/packages/docker/errors.py::APIError.__init__","text":"fig/packages/docker/errors.py::APIError.__init__\n def __init__(self, message, response, explanation=None):\n # requests 1.2 supports response as a keyword argument, but\n # requests 1.1 doesn't\n super(APIError, self).__init__(message)\n self.response = response\n\n self.explanation = explanation\n\n if self.explanation is None and response.content:\n self.explanation = response.content.strip()","label":0},{"cid":"fig/packages/docker/errors.py::APIError.__str__","text":"fig/packages/docker/errors.py::APIError.__str__\n def __str__(self):\n message = super(APIError, self).__str__()\n\n if self.is_client_error():\n message = '%s Client Error: %s' % (\n self.response.status_code, self.response.reason)\n\n elif self.is_server_error():\n message = '%s Server Error: %s' % (\n self.response.status_code, self.response.reason)\n\n if self.explanation:\n message = '%s (\"%s\")' % (message, self.explanation)\n\n return message","label":0},{"cid":"fig/packages/docker/errors.py::APIError.is_client_error","text":"fig/packages/docker/errors.py::APIError.is_client_error\n def is_client_error(self):\n return 400 <= self.response.status_code < 500","label":0},{"cid":"fig/packages/docker/errors.py::APIError.is_server_error","text":"fig/packages/docker/errors.py::APIError.is_server_error\n def is_server_error(self):\n return 500 <= self.response.status_code < 600","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.__init__","text":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.__init__\n def __init__(self, base_url, unix_socket, timeout=60):\n httplib.HTTPConnection.__init__(self, 'localhost', timeout=timeout)\n self.base_url = base_url\n self.unix_socket = unix_socket\n self.timeout = timeout","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.connect","text":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.connect\n def connect(self):\n sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\n sock.settimeout(self.timeout)\n sock.connect(self.base_url.replace(\"http+unix:/\", \"\"))\n self.sock = sock","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection._extract_path","text":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection._extract_path\n def _extract_path(self, url):\n # remove the base_url entirely..\n return url.replace(self.base_url, \"\")","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.request","text":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.request\n def request(self, method, url, **kwargs):\n url = self._extract_path(self.unix_socket)\n super(UnixHTTPConnection, self).request(method, url, **kwargs)","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool.__init__","text":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool.__init__\n def __init__(self, base_url, socket_path, timeout=60):\n connectionpool.HTTPConnectionPool.__init__(self, 'localhost',\n timeout=timeout)\n self.base_url = base_url\n self.socket_path = socket_path\n self.timeout = timeout","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool._new_conn","text":"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool._new_conn\n def _new_conn(self):\n return UnixHTTPConnection(self.base_url, self.socket_path,\n self.timeout)","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.__init__","text":"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.__init__\n def __init__(self, base_url, timeout=60):\n self.base_url = base_url\n self.timeout = timeout\n super(UnixAdapter, self).__init__()","label":0},{"cid":"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.get_connection","text":"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.get_connection\n def get_connection(self, socket_path, proxies=None):\n return UnixHTTPConnectionPool(self.base_url, socket_path, self.timeout)","label":0},{"cid":"fig/packages/docker/utils/utils.py::mkbuildcontext","text":"fig/packages/docker/utils/utils.py::mkbuildcontext\ndef mkbuildcontext(dockerfile):\n f = tempfile.NamedTemporaryFile()\n t = tarfile.open(mode='w', fileobj=f)\n if isinstance(dockerfile, io.StringIO):\n dfinfo = tarfile.TarInfo('Dockerfile')\n if six.PY3:\n raise TypeError('Please use io.BytesIO to create in-memory '\n 'Dockerfiles with Python 3')\n else:\n dfinfo.size = len(dockerfile.getvalue())\n elif isinstance(dockerfile, io.BytesIO):\n dfinfo = tarfile.TarInfo('Dockerfile')\n dfinfo.size = len(dockerfile.getvalue())\n else:\n dfinfo = t.gettarinfo(fileobj=dockerfile, arcname='Dockerfile')\n t.addfile(dfinfo, dockerfile)\n t.close()\n f.seek(0)\n return f","label":0},{"cid":"fig/packages/docker/utils/utils.py::tar","text":"fig/packages/docker/utils/utils.py::tar\ndef tar(path):\n f = tempfile.NamedTemporaryFile()\n t = tarfile.open(mode='w', fileobj=f)\n t.add(path, arcname='.')\n t.close()\n f.seek(0)\n return f","label":0},{"cid":"fig/packages/docker/utils/utils.py::compare_version","text":"fig/packages/docker/utils/utils.py::compare_version\ndef compare_version(v1, v2):\n \"\"\"Compare docker versions\n\n >>> v1 = '1.9'\n >>> v2 = '1.10'\n >>> compare_version(v1, v2)\n 1\n >>> compare_version(v2, v1)\n -1\n >>> compare_version(v2, v2)\n 0\n \"\"\"\n s1 = StrictVersion(v1)\n s2 = StrictVersion(v2)\n if s1 == s2:\n return 0\n elif s1 > s2:\n return -1\n else:\n return 1","label":0},{"cid":"fig/packages/docker/utils/utils.py::ping","text":"fig/packages/docker/utils/utils.py::ping\ndef ping(url):\n try:\n res = requests.get(url)\n except Exception:\n return False\n else:\n return res.status_code < 400","label":0},{"cid":"fig/packages/docker/utils/utils.py::_convert_port_binding","text":"fig/packages/docker/utils/utils.py::_convert_port_binding\ndef _convert_port_binding(binding):\n result = {'HostIp': '', 'HostPort': ''}\n if isinstance(binding, tuple):\n if len(binding) == 2:\n result['HostPort'] = binding[1]\n result['HostIp'] = binding[0]\n elif isinstance(binding[0], six.string_types):\n result['HostIp'] = binding[0]\n else:\n result['HostPort'] = binding[0]\n elif isinstance(binding, dict):\n if 'HostPort' in binding:\n result['HostPort'] = binding['HostPort']\n if 'HostIp' in binding:\n result['HostIp'] = binding['HostIp']\n else:\n raise ValueError(binding)\n else:\n result['HostPort'] = binding\n\n if result['HostPort'] is None:\n result['HostPort'] = ''\n else:\n result['HostPort'] = str(result['HostPort'])\n\n return result","label":0},{"cid":"fig/packages/docker/utils/utils.py::convert_port_bindings","text":"fig/packages/docker/utils/utils.py::convert_port_bindings\ndef convert_port_bindings(port_bindings):\n result = {}\n for k, v in six.iteritems(port_bindings):\n key = str(k)\n if '/' not in key:\n key = key + '/tcp'\n if isinstance(v, list):\n result[key] = [_convert_port_binding(binding) for binding in v]\n else:\n result[key] = [_convert_port_binding(v)]\n return result","label":0},{"cid":"fig/packages/docker/utils/utils.py::convert_volume_binds","text":"fig/packages/docker/utils/utils.py::convert_volume_binds\ndef convert_volume_binds(binds):\n result = []\n for k, v in binds.items():\n if isinstance(v, dict):\n result.append('%s:%s:%s' % (\n k, v['bind'], 'ro' if v.get('ro', False) else 'rw'\n ))\n else:\n result.append('%s:%s:rw' % (k, v))\n return result","label":0},{"cid":"fig/packages/docker/utils/utils.py::parse_repository_tag","text":"fig/packages/docker/utils/utils.py::parse_repository_tag\ndef parse_repository_tag(repo):\n column_index = repo.rfind(':')\n if column_index < 0:\n return repo, None\n tag = repo[column_index+1:]\n slash_index = tag.find('/')\n if slash_index < 0:\n return repo[:column_index], tag\n\n return repo, None","label":0},{"cid":"fig/packages/six.py::_add_doc","text":"fig/packages/six.py::_add_doc\ndef _add_doc(func, doc):\n \"\"\"Add documentation to a function.\"\"\"\n func.__doc__ = doc","label":0},{"cid":"fig/packages/six.py::_import_module","text":"fig/packages/six.py::_import_module\ndef _import_module(name):\n \"\"\"Import module, returning the module after the last dot.\"\"\"\n __import__(name)\n return sys.modules[name]","label":0},{"cid":"fig/packages/six.py::_LazyDescr.__init__","text":"fig/packages/six.py::_LazyDescr.__init__\n def __init__(self, name):\n self.name = name","label":0},{"cid":"fig/packages/six.py::_LazyDescr.__get__","text":"fig/packages/six.py::_LazyDescr.__get__\n def __get__(self, obj, tp):\n result = self._resolve()\n setattr(obj, self.name, result)\n # This is a bit ugly, but it avoids running this again.\n delattr(tp, self.name)\n return result","label":0},{"cid":"fig/packages/six.py::MovedModule.__init__","text":"fig/packages/six.py::MovedModule.__init__\n def __init__(self, name, old, new=None):\n super(MovedModule, self).__init__(name)\n if PY3:\n if new is None:\n new = name\n self.mod = new\n else:\n self.mod = old","label":0},{"cid":"fig/packages/six.py::MovedModule._resolve","text":"fig/packages/six.py::MovedModule._resolve\n def _resolve(self):\n return _import_module(self.mod)","label":0},{"cid":"fig/packages/six.py::MovedAttribute.__init__","text":"fig/packages/six.py::MovedAttribute.__init__\n def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):\n super(MovedAttribute, self).__init__(name)\n if PY3:\n if new_mod is None:\n new_mod = name\n self.mod = new_mod\n if new_attr is None:\n if old_attr is None:\n new_attr = name\n else:\n new_attr = old_attr\n self.attr = new_attr\n else:\n self.mod = old_mod\n if old_attr is None:\n old_attr = name\n self.attr = old_attr","label":0},{"cid":"fig/packages/six.py::MovedAttribute._resolve","text":"fig/packages/six.py::MovedAttribute._resolve\n def _resolve(self):\n module = _import_module(self.mod)\n return getattr(module, self.attr)","label":0},{"cid":"fig/packages/six.py::add_move","text":"fig/packages/six.py::add_move\ndef add_move(move):\n \"\"\"Add an item to six.moves.\"\"\"\n setattr(_MovedItems, move.name, move)","label":0},{"cid":"fig/packages/six.py::remove_move","text":"fig/packages/six.py::remove_move\ndef remove_move(name):\n \"\"\"Remove item from six.moves.\"\"\"\n try:\n delattr(_MovedItems, name)\n except AttributeError:\n try:\n del moves.__dict__[name]\n except KeyError:\n raise AttributeError(\"no such move, %r\" % (name,))","label":0},{"cid":"fig/packages/six.py::iterkeys","text":"fig/packages/six.py::iterkeys\ndef iterkeys(d, **kw):\n \"\"\"Return an iterator over the keys of a dictionary.\"\"\"\n return iter(getattr(d, _iterkeys)(**kw))","label":0},{"cid":"fig/packages/six.py::itervalues","text":"fig/packages/six.py::itervalues\ndef itervalues(d, **kw):\n \"\"\"Return an iterator over the values of a dictionary.\"\"\"\n return iter(getattr(d, _itervalues)(**kw))","label":0},{"cid":"fig/packages/six.py::iteritems","text":"fig/packages/six.py::iteritems\ndef iteritems(d, **kw):\n \"\"\"Return an iterator over the (key, value) pairs of a dictionary.\"\"\"\n return iter(getattr(d, _iteritems)(**kw))","label":0},{"cid":"fig/packages/six.py::iterlists","text":"fig/packages/six.py::iterlists\ndef iterlists(d, **kw):\n \"\"\"Return an iterator over the (key, [values]) pairs of a dictionary.\"\"\"\n return iter(getattr(d, _iterlists)(**kw))","label":0},{"cid":"fig/packages/six.py::with_metaclass","text":"fig/packages/six.py::with_metaclass\ndef with_metaclass(meta, base=object):\n \"\"\"Create a base class with a metaclass.\"\"\"\n return meta(\"NewBase\", (base,), {})","label":0},{"cid":"fig/progress_stream.py::stream_output","text":"fig/progress_stream.py::stream_output\ndef stream_output(output, stream):\n is_terminal = hasattr(stream, 'fileno') and os.isatty(stream.fileno())\n stream = codecs.getwriter('utf-8')(stream)\n all_events = []\n lines = {}\n diff = 0\n\n for chunk in output:\n event = json.loads(chunk)\n all_events.append(event)\n\n if 'progress' in event or 'progressDetail' in event:\n image_id = event['id']\n\n if image_id in lines:\n diff = len(lines) - lines[image_id]\n else:\n lines[image_id] = len(lines)\n stream.write(\"\\n\")\n diff = 0\n\n if is_terminal:\n # move cursor up `diff` rows\n stream.write(\"%c[%dA\" % (27, diff))\n\n print_output_event(event, stream, is_terminal)\n\n if 'id' in event and is_terminal:\n # move cursor back down\n stream.write(\"%c[%dB\" % (27, diff))\n\n stream.flush()\n\n return all_events","label":0},{"cid":"fig/progress_stream.py::print_output_event","text":"fig/progress_stream.py::print_output_event\ndef print_output_event(event, stream, is_terminal):\n if 'errorDetail' in event:\n raise StreamOutputError(event['errorDetail']['message'])\n\n terminator = ''\n\n if is_terminal and 'stream' not in event:\n # erase current line\n stream.write(\"%c[2K\\r\" % 27)\n terminator = \"\\r\"\n pass\n elif 'progressDetail' in event:\n return\n\n if 'time' in event:\n stream.write(\"[%s] \" % event['time'])\n\n if 'id' in event:\n stream.write(\"%s: \" % event['id'])\n\n if 'from' in event:\n stream.write(\"(from %s) \" % event['from'])\n\n status = event.get('status', '')\n\n if 'progress' in event:\n stream.write(\"%s %s%s\" % (status, event['progress'], terminator))\n elif 'progressDetail' in event:\n detail = event['progressDetail']\n if 'current' in detail:\n percentage = float(detail['current']) / float(detail['total']) * 100\n stream.write('%s (%.1f%%)%s' % (status, percentage, terminator))\n else:\n stream.write('%s%s' % (status, terminator))\n elif 'stream' in event:\n stream.write(\"%s%s\" % (event['stream'], terminator))\n else:\n stream.write(\"%s%s\\n\" % (status, terminator))","label":0},{"cid":"fig/project.py::sort_service_dicts","text":"fig/project.py::sort_service_dicts\ndef sort_service_dicts(services):\n # Topological sort (Cormen/Tarjan algorithm).\n unmarked = services[:]\n temporary_marked = set()\n sorted_services = []\n\n get_service_names = lambda links: [link.split(':')[0] for link in links]\n\n def visit(n):\n if n['name'] in temporary_marked:\n if n['name'] in get_service_names(n.get('links', [])):\n raise DependencyError('A service can not link to itself: %s' % n['name'])\n if n['name'] in n.get('volumes_from', []):\n raise DependencyError('A service can not mount itself as volume: %s' % n['name'])\n else:\n raise DependencyError('Circular import between %s' % ' and '.join(temporary_marked))\n if n in unmarked:\n temporary_marked.add(n['name'])\n dependents = [m for m in services if (n['name'] in get_service_names(m.get('links', []))) or (n['name'] in m.get('volumes_from', []))]\n for m in dependents:\n visit(m)\n temporary_marked.remove(n['name'])\n unmarked.remove(n)\n sorted_services.insert(0, n)\n\n while unmarked:\n visit(unmarked[-1])\n\n return sorted_services","label":0},{"cid":"fig/project.py::sort_service_dicts.visit","text":"fig/project.py::sort_service_dicts.visit\n def visit(n):\n if n['name'] in temporary_marked:\n if n['name'] in get_service_names(n.get('links', [])):\n raise DependencyError('A service can not link to itself: %s' % n['name'])\n if n['name'] in n.get('volumes_from', []):\n raise DependencyError('A service can not mount itself as volume: %s' % n['name'])\n else:\n raise DependencyError('Circular import between %s' % ' and '.join(temporary_marked))\n if n in unmarked:\n temporary_marked.add(n['name'])\n dependents = [m for m in services if (n['name'] in get_service_names(m.get('links', []))) or (n['name'] in m.get('volumes_from', []))]\n for m in dependents:\n visit(m)\n temporary_marked.remove(n['name'])\n unmarked.remove(n)\n sorted_services.insert(0, n)","label":0},{"cid":"fig/project.py::Project.__init__","text":"fig/project.py::Project.__init__\n def __init__(self, name, services, client):\n self.name = name\n self.services = services\n self.client = client","label":0},{"cid":"fig/project.py::Project.from_dicts","text":"fig/project.py::Project.from_dicts\n @classmethod\n def from_dicts(cls, name, service_dicts, client):\n \"\"\"\n Construct a ServiceCollection from a list of dicts representing services.\n \"\"\"\n project = cls(name, [], client)\n for service_dict in sort_service_dicts(service_dicts):\n links = project.get_links(service_dict)\n volumes_from = project.get_volumes_from(service_dict)\n\n project.services.append(Service(client=client, project=name, links=links, volumes_from=volumes_from, **service_dict))\n return project","label":0},{"cid":"fig/project.py::Project.from_config","text":"fig/project.py::Project.from_config\n @classmethod\n def from_config(cls, name, config, client):\n dicts = []\n for service_name, service in list(config.items()):\n if not isinstance(service, dict):\n raise ConfigurationError('Service \"%s\" doesn\\'t have any configuration options. All top level keys in your fig.yml must map to a dictionary of configuration options.')\n service['name'] = service_name\n dicts.append(service)\n return cls.from_dicts(name, dicts, client)","label":0},{"cid":"fig/project.py::Project.get_service","text":"fig/project.py::Project.get_service\n def get_service(self, name):\n \"\"\"\n Retrieve a service by name. Raises NoSuchService\n if the named service does not exist.\n \"\"\"\n for service in self.services:\n if service.name == name:\n return service\n\n raise NoSuchService(name)","label":0},{"cid":"fig/project.py::Project.get_services","text":"fig/project.py::Project.get_services\n def get_services(self, service_names=None, include_links=False):\n \"\"\"\n Returns a list of this project's services filtered\n by the provided list of names, or all services if service_names is None\n or [].\n\n If include_links is specified, returns a list including the links for\n service_names, in order of dependency.\n\n Preserves the original order of self.services where possible,\n reordering as needed to resolve links.\n\n Raises NoSuchService if any of the named services do not exist.\n \"\"\"\n if service_names is None or len(service_names) == 0:\n return self.get_services(\n service_names=[s.name for s in self.services],\n include_links=include_links\n )\n else:\n unsorted = [self.get_service(name) for name in service_names]\n services = [s for s in self.services if s in unsorted]\n\n if include_links:\n services = reduce(self._inject_links, services, [])\n\n uniques = []\n [uniques.append(s) for s in services if s not in uniques]\n return uniques","label":0},{"cid":"fig/project.py::Project.get_links","text":"fig/project.py::Project.get_links\n def get_links(self, service_dict):\n links = []\n if 'links' in service_dict:\n for link in service_dict.get('links', []):\n if ':' in link:\n service_name, link_name = link.split(':', 1)\n else:\n service_name, link_name = link, None\n try:\n links.append((self.get_service(service_name), link_name))\n except NoSuchService:\n raise ConfigurationError('Service \"%s\" has a link to service \"%s\" which does not exist.' % (service_dict['name'], service_name))\n del service_dict['links']\n return links","label":0},{"cid":"fig/project.py::Project.get_volumes_from","text":"fig/project.py::Project.get_volumes_from\n def get_volumes_from(self, service_dict):\n volumes_from = []\n if 'volumes_from' in service_dict:\n for volume_name in service_dict.get('volumes_from', []):\n try:\n service = self.get_service(volume_name)\n volumes_from.append(service)\n except NoSuchService:\n try:\n container = Container.from_id(self.client, volume_name)\n volumes_from.append(container)\n except APIError:\n raise ConfigurationError('Service \"%s\" mounts volumes from \"%s\", which is not the name of a service or container.' % (service_dict['name'], volume_name))\n del service_dict['volumes_from']\n return volumes_from","label":0},{"cid":"fig/project.py::Project.start","text":"fig/project.py::Project.start\n def start(self, service_names=None, **options):\n for service in self.get_services(service_names):\n service.start(**options)","label":0},{"cid":"fig/project.py::Project.stop","text":"fig/project.py::Project.stop\n def stop(self, service_names=None, **options):\n for service in reversed(self.get_services(service_names)):\n service.stop(**options)","label":0},{"cid":"fig/project.py::Project.kill","text":"fig/project.py::Project.kill\n def kill(self, service_names=None, **options):\n for service in reversed(self.get_services(service_names)):\n service.kill(**options)","label":0},{"cid":"fig/project.py::Project.build","text":"fig/project.py::Project.build\n def build(self, service_names=None, no_cache=False):\n for service in self.get_services(service_names):\n if service.can_be_built():\n service.build(no_cache)\n else:\n log.info('%s uses an image, skipping' % service.name)","label":0},{"cid":"fig/project.py::Project.up","text":"fig/project.py::Project.up\n def up(self, service_names=None, start_links=True, recreate=True):\n running_containers = []\n\n for service in self.get_services(service_names, include_links=start_links):\n if recreate:\n for (_, container) in service.recreate_containers():\n running_containers.append(container)\n else:\n for container in service.start_or_create_containers():\n running_containers.append(container)\n\n return running_containers","label":0},{"cid":"fig/project.py::Project.remove_stopped","text":"fig/project.py::Project.remove_stopped\n def remove_stopped(self, service_names=None, **options):\n for service in self.get_services(service_names):\n service.remove_stopped(**options)","label":0},{"cid":"fig/project.py::Project.containers","text":"fig/project.py::Project.containers\n def containers(self, service_names=None, *args, **kwargs):\n l = []\n for service in self.get_services(service_names):\n for container in service.containers(*args, **kwargs):\n l.append(container)\n return l","label":0},{"cid":"fig/project.py::Project._inject_links","text":"fig/project.py::Project._inject_links\n def _inject_links(self, acc, service):\n linked_names = service.get_linked_names()\n\n if len(linked_names) > 0:\n linked_services = self.get_services(\n service_names=linked_names,\n include_links=True\n )\n else:\n linked_services = []\n\n linked_services.append(service)\n return acc + linked_services","label":0},{"cid":"fig/project.py::NoSuchService.__init__","text":"fig/project.py::NoSuchService.__init__\n def __init__(self, name):\n self.name = name\n self.msg = \"No such service: %s\" % self.name","label":0},{"cid":"fig/project.py::NoSuchService.__str__","text":"fig/project.py::NoSuchService.__str__\n def __str__(self):\n return self.msg","label":0},{"cid":"fig/project.py::ConfigurationError.__init__","text":"fig/project.py::ConfigurationError.__init__\n def __init__(self, msg):\n self.msg = msg","label":0},{"cid":"fig/project.py::ConfigurationError.__str__","text":"fig/project.py::ConfigurationError.__str__\n def __str__(self):\n return self.msg","label":0},{"cid":"setup.py::read","text":"setup.py::read\ndef read(*parts):\n path = os.path.join(os.path.dirname(__file__), *parts)\n with codecs.open(path, encoding='utf-8') as fobj:\n return fobj.read()","label":0},{"cid":"setup.py::find_version","text":"setup.py::find_version\ndef find_version(*file_paths):\n version_file = read(*file_paths)\n version_match = re.search(r\"^__version__ = ['\\\"]([^'\\\"]*)['\\\"]\",\n version_file, re.M)\n if version_match:\n return version_match.group(1)\n raise RuntimeError(\"Unable to find version string.\")","label":0}],"string":"[\n {\n \"cid\": \"fig/cli/command.py::Command.__init__\",\n \"text\": \"fig/cli/command.py::Command.__init__\\n def __init__(self):\\n self._yaml_path = os.environ.get('FIG_FILE', None)\\n self.explicit_project_name = None\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.dispatch\",\n \"text\": \"fig/cli/command.py::Command.dispatch\\n def dispatch(self, *args, **kwargs):\\n try:\\n super(Command, self).dispatch(*args, **kwargs)\\n except ConnectionError:\\n if call_silently(['which', 'docker']) != 0:\\n if is_mac():\\n raise errors.DockerNotFoundMac()\\n elif is_ubuntu():\\n raise errors.DockerNotFoundUbuntu()\\n else:\\n raise errors.DockerNotFoundGeneric()\\n elif call_silently(['which', 'docker-osx']) == 0:\\n raise errors.ConnectionErrorDockerOSX()\\n else:\\n raise errors.ConnectionErrorGeneric(self.client.base_url)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.perform_command\",\n \"text\": \"fig/cli/command.py::Command.perform_command\\n def perform_command(self, options, *args, **kwargs):\\n if options['--file'] is not None:\\n self.yaml_path = os.path.join(self.base_dir, options['--file'])\\n if options['--project-name'] is not None:\\n self.explicit_project_name = options['--project-name']\\n return super(Command, self).perform_command(options, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.client\",\n \"text\": \"fig/cli/command.py::Command.client\\n @cached_property\\n def client(self):\\n return Client(docker_url())\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.project\",\n \"text\": \"fig/cli/command.py::Command.project\\n @cached_property\\n def project(self):\\n try:\\n config = yaml.safe_load(open(self.yaml_path))\\n except IOError as e:\\n if e.errno == errno.ENOENT:\\n raise errors.FigFileNotFound(os.path.basename(e.filename))\\n raise errors.UserError(six.text_type(e))\\n\\n try:\\n return Project.from_config(self.project_name, config, self.client)\\n except ConfigError as e:\\n raise errors.UserError(six.text_type(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.project_name\",\n \"text\": \"fig/cli/command.py::Command.project_name\\n @cached_property\\n def project_name(self):\\n project = os.path.basename(os.path.dirname(os.path.abspath(self.yaml_path)))\\n if self.explicit_project_name is not None:\\n project = self.explicit_project_name\\n project = re.sub(r'[^a-zA-Z0-9]', '', project)\\n if not project:\\n project = 'default'\\n return project\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.formatter\",\n \"text\": \"fig/cli/command.py::Command.formatter\\n @cached_property\\n def formatter(self):\\n return Formatter()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.yaml_path\",\n \"text\": \"fig/cli/command.py::Command.yaml_path\\n @cached_property\\n def yaml_path(self):\\n if self._yaml_path is not None:\\n return self._yaml_path\\n elif os.path.exists(os.path.join(self.base_dir, 'fig.yaml')):\\n\\n log.warning(\\\"Fig just read the file 'fig.yaml' on startup, rather than 'fig.yml'\\\")\\n log.warning(\\\"Please be aware that fig.yml the expected extension in most cases, and using .yaml can cause compatibility issues in future\\\")\\n\\n return os.path.join(self.base_dir, 'fig.yaml')\\n else:\\n return os.path.join(self.base_dir, 'fig.yml')\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/command.py::Command.yaml_path\",\n \"text\": \"fig/cli/command.py::Command.yaml_path\\n @yaml_path.setter\\n def yaml_path(self, value):\\n self._yaml_path = value\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::UserError.__init__\",\n \"text\": \"fig/cli/errors.py::UserError.__init__\\n def __init__(self, msg):\\n self.msg = dedent(msg).strip()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::UserError.__unicode__\",\n \"text\": \"fig/cli/errors.py::UserError.__unicode__\\n def __unicode__(self):\\n return self.msg\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::DockerNotFoundMac.__init__\",\n \"text\": \"fig/cli/errors.py::DockerNotFoundMac.__init__\\n def __init__(self):\\n super(DockerNotFoundMac, self).__init__(\\\"\\\"\\\"\\n Couldn't connect to Docker daemon. You might need to install docker-osx:\\n\\n https://github.com/noplay/docker-osx\\n \\\"\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::DockerNotFoundUbuntu.__init__\",\n \"text\": \"fig/cli/errors.py::DockerNotFoundUbuntu.__init__\\n def __init__(self):\\n super(DockerNotFoundUbuntu, self).__init__(\\\"\\\"\\\"\\n Couldn't connect to Docker daemon. You might need to install Docker:\\n\\n http://docs.docker.io/en/latest/installation/ubuntulinux/\\n \\\"\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::DockerNotFoundGeneric.__init__\",\n \"text\": \"fig/cli/errors.py::DockerNotFoundGeneric.__init__\\n def __init__(self):\\n super(DockerNotFoundGeneric, self).__init__(\\\"\\\"\\\"\\n Couldn't connect to Docker daemon. You might need to install Docker:\\n\\n http://docs.docker.io/en/latest/installation/\\n \\\"\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::ConnectionErrorDockerOSX.__init__\",\n \"text\": \"fig/cli/errors.py::ConnectionErrorDockerOSX.__init__\\n def __init__(self):\\n super(ConnectionErrorDockerOSX, self).__init__(\\\"\\\"\\\"\\n Couldn't connect to Docker daemon - you might need to run `docker-osx shell`.\\n \\\"\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::ConnectionErrorGeneric.__init__\",\n \"text\": \"fig/cli/errors.py::ConnectionErrorGeneric.__init__\\n def __init__(self, url):\\n super(ConnectionErrorGeneric, self).__init__(\\\"\\\"\\\"\\n Couldn't connect to Docker daemon at %s - is it running?\\n\\n If it's at a non-standard location, specify the URL with the DOCKER_HOST environment variable.\\n \\\"\\\"\\\" % url)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/errors.py::FigFileNotFound.__init__\",\n \"text\": \"fig/cli/errors.py::FigFileNotFound.__init__\\n def __init__(self, filename):\\n super(FigFileNotFound, self).__init__(\\\"\\\"\\\"\\n Can't find %s. Are you in the right directory?\\n \\\"\\\"\\\" % filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter.__init__\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter.__init__\\n def __init__(self, containers, attach_params=None, output=sys.stdout):\\n self.containers = containers\\n self.attach_params = attach_params or {}\\n self.prefix_width = self._calculate_prefix_width(containers)\\n self.generators = self._make_log_generators()\\n self.output = output\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter.run\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter.run\\n def run(self):\\n mux = Multiplexer(self.generators)\\n for line in mux.loop():\\n self.output.write(line)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter._calculate_prefix_width\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter._calculate_prefix_width\\n def _calculate_prefix_width(self, containers):\\n \\\"\\\"\\\"\\n Calculate the maximum width of container names so we can make the log\\n prefixes line up like so:\\n\\n db_1 | Listening\\n web_1 | Listening\\n \\\"\\\"\\\"\\n prefix_width = 0\\n for container in containers:\\n prefix_width = max(prefix_width, len(container.name_without_project))\\n return prefix_width\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter._make_log_generators\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter._make_log_generators\\n def _make_log_generators(self):\\n color_fns = cycle(colors.rainbow())\\n generators = []\\n\\n for container in self.containers:\\n color_fn = color_fns.next()\\n generators.append(self._make_log_generator(container, color_fn))\\n\\n return generators\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter._make_log_generator\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter._make_log_generator\\n def _make_log_generator(self, container, color_fn):\\n prefix = color_fn(self._generate_prefix(container)).encode('utf-8')\\n # Attach to container before log printer starts running\\n line_generator = split_buffer(self._attach(container), '\\\\n')\\n\\n for line in line_generator:\\n yield prefix + line\\n\\n exit_code = container.wait()\\n yield color_fn(\\\"%s exited with code %s\\\\n\\\" % (container.name, exit_code))\\n yield STOP\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter._generate_prefix\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter._generate_prefix\\n def _generate_prefix(self, container):\\n \\\"\\\"\\\"\\n Generate the prefix for a log line without colour\\n \\\"\\\"\\\"\\n name = container.name_without_project\\n padding = ' ' * (self.prefix_width - len(name))\\n return ''.join([name, padding, ' | '])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/log_printer.py::LogPrinter._attach\",\n \"text\": \"fig/cli/log_printer.py::LogPrinter._attach\\n def _attach(self, container):\\n params = {\\n 'stdout': True,\\n 'stderr': True,\\n 'stream': True,\\n }\\n params.update(self.attach_params)\\n params = dict((name, 1 if value else 0) for (name, value) in list(params.items()))\\n return container.attach(**params)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::main\",\n \"text\": \"fig/cli/main.py::main\\ndef main():\\n setup_logging()\\n try:\\n command = TopLevelCommand()\\n command.sys_dispatch()\\n except KeyboardInterrupt:\\n log.error(\\\"\\\\nAborting.\\\")\\n sys.exit(1)\\n except (UserError, NoSuchService, ConfigurationError) as e:\\n log.error(e.msg)\\n sys.exit(1)\\n except NoSuchCommand as e:\\n log.error(\\\"No such command: %s\\\", e.command)\\n log.error(\\\"\\\")\\n log.error(\\\"\\\\n\\\".join(parse_doc_section(\\\"commands:\\\", getdoc(e.supercommand))))\\n sys.exit(1)\\n except APIError as e:\\n log.error(e.explanation)\\n sys.exit(1)\\n except BuildError as e:\\n log.error(\\\"Service '%s' failed to build: %s\\\" % (e.service.name, e.reason))\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::setup_logging\",\n \"text\": \"fig/cli/main.py::setup_logging\\ndef setup_logging():\\n console_handler = logging.StreamHandler(sys.stderr)\\n console_handler.setFormatter(logging.Formatter())\\n console_handler.setLevel(logging.INFO)\\n root_logger = logging.getLogger()\\n root_logger.addHandler(console_handler)\\n root_logger.setLevel(logging.DEBUG)\\n\\n # Disable requests logging\\n logging.getLogger(\\\"requests\\\").propagate = False\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::parse_doc_section\",\n \"text\": \"fig/cli/main.py::parse_doc_section\\ndef parse_doc_section(name, source):\\n pattern = re.compile('^([^\\\\n]*' + name + '[^\\\\n]*\\\\n?(?:[ \\\\t].*?(?:\\\\n|$))*)',\\n re.IGNORECASE | re.MULTILINE)\\n return [s.strip() for s in pattern.findall(source)]\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.docopt_options\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.docopt_options\\n def docopt_options(self):\\n options = super(TopLevelCommand, self).docopt_options()\\n options['version'] = \\\"fig %s\\\" % __version__\\n return options\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.build\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.build\\n def build(self, options):\\n \\\"\\\"\\\"\\n Build or rebuild services.\\n\\n Services are built once and then tagged as `project_service`,\\n e.g. `figtest_db`. If you change a service's `Dockerfile` or the\\n contents of its build directory, you can run `fig build` to rebuild it.\\n\\n Usage: build [options] [SERVICE...]\\n\\n Options:\\n --no-cache Do not use cache when building the image.\\n \\\"\\\"\\\"\\n no_cache = bool(options.get('--no-cache', False))\\n self.project.build(service_names=options['SERVICE'], no_cache=no_cache)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.help\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.help\\n def help(self, options):\\n \\\"\\\"\\\"\\n Get help on a command.\\n\\n Usage: help COMMAND\\n \\\"\\\"\\\"\\n command = options['COMMAND']\\n if not hasattr(self, command):\\n raise NoSuchCommand(command, self)\\n raise SystemExit(getdoc(getattr(self, command)))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.kill\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.kill\\n def kill(self, options):\\n \\\"\\\"\\\"\\n Force stop service containers.\\n\\n Usage: kill [SERVICE...]\\n \\\"\\\"\\\"\\n self.project.kill(service_names=options['SERVICE'])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.logs\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.logs\\n def logs(self, options):\\n \\\"\\\"\\\"\\n View output from containers.\\n\\n Usage: logs [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\\n print(\\\"Attaching to\\\", list_containers(containers))\\n LogPrinter(containers, attach_params={'logs': True}).run()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.ps\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.ps\\n def ps(self, options):\\n \\\"\\\"\\\"\\n List containers.\\n\\n Usage: ps [options] [SERVICE...]\\n\\n Options:\\n -q Only display IDs\\n \\\"\\\"\\\"\\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True) + self.project.containers(service_names=options['SERVICE'], one_off=True)\\n\\n if options['-q']:\\n for container in containers:\\n print(container.id)\\n else:\\n headers = [\\n 'Name',\\n 'Command',\\n 'State',\\n 'Ports',\\n ]\\n rows = []\\n for container in containers:\\n command = container.human_readable_command\\n if len(command) > 30:\\n command = '%s ...' % command[:26]\\n rows.append([\\n container.name,\\n command,\\n container.human_readable_state,\\n container.human_readable_ports,\\n ])\\n print(Formatter().table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.rm\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.rm\\n def rm(self, options):\\n \\\"\\\"\\\"\\n Remove stopped service containers.\\n\\n Usage: rm [options] [SERVICE...]\\n\\n Options:\\n --force Don't ask to confirm removal\\n -v Remove volumes associated with containers\\n \\\"\\\"\\\"\\n all_containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\\n stopped_containers = [c for c in all_containers if not c.is_running]\\n\\n if len(stopped_containers) > 0:\\n print(\\\"Going to remove\\\", list_containers(stopped_containers))\\n if options.get('--force') \\\\\\n or yesno(\\\"Are you sure? [yN] \\\", default=False):\\n self.project.remove_stopped(\\n service_names=options['SERVICE'],\\n v=options.get('-v', False)\\n )\\n else:\\n print(\\\"No stopped containers\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.run\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.run\\n def run(self, options):\\n \\\"\\\"\\\"\\n Run a one-off command on a service.\\n\\n For example:\\n\\n $ fig run web python manage.py shell\\n\\n By default, linked services will be started, unless they are already\\n running. If you do not want to start linked services, use\\n `fig run --no-deps SERVICE COMMAND [ARGS...]`.\\n\\n Usage: run [options] SERVICE [COMMAND] [ARGS...]\\n\\n Options:\\n -d Detached mode: Run container in the background, print\\n new container name.\\n -T Disable pseudo-tty allocation. By default `fig run`\\n allocates a TTY.\\n --rm Remove container after run. Ignored in detached mode.\\n --no-deps Don't start linked services.\\n \\\"\\\"\\\"\\n\\n service = self.project.get_service(options['SERVICE'])\\n\\n if not options['--no-deps']:\\n deps = service.get_linked_names()\\n\\n if len(deps) > 0:\\n self.project.up(\\n service_names=deps,\\n start_links=True,\\n recreate=False,\\n )\\n\\n tty = True\\n if options['-d'] or options['-T'] or not sys.stdin.isatty():\\n tty = False\\n\\n if options['COMMAND']:\\n command = [options['COMMAND']] + options['ARGS']\\n else:\\n command = service.options.get('command')\\n\\n container_options = {\\n 'command': command,\\n 'tty': tty,\\n 'stdin_open': not options['-d'],\\n }\\n container = service.create_container(one_off=True, **container_options)\\n if options['-d']:\\n service.start_container(container, ports=None, one_off=True)\\n print(container.name)\\n else:\\n service.start_container(container, ports=None, one_off=True)\\n dockerpty.start(self.client, container.id)\\n exit_code = container.wait()\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.scale\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.scale\\n def scale(self, options):\\n \\\"\\\"\\\"\\n Set number of containers to run for a service.\\n\\n Numbers are specified in the form `service=num` as arguments.\\n For example:\\n\\n $ fig scale web=2 worker=3\\n\\n Usage: scale [SERVICE=NUM...]\\n \\\"\\\"\\\"\\n for s in options['SERVICE=NUM']:\\n if '=' not in s:\\n raise UserError('Arguments to scale should be in the form service=num')\\n service_name, num = s.split('=', 1)\\n try:\\n num = int(num)\\n except ValueError:\\n raise UserError('Number of containers for service \\\"%s\\\" is not a '\\n 'number' % service_name)\\n try:\\n self.project.get_service(service_name).scale(num)\\n except CannotBeScaledError:\\n raise UserError('Service \\\"%s\\\" cannot be scaled because it specifies a port on the host. If multiple containers for this service were created, the port would clash.\\\\n\\\\nRemove the \\\":\\\" from the port definition in fig.yml so Docker can choose a random port for each container.' % service_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.start\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.start\\n def start(self, options):\\n \\\"\\\"\\\"\\n Start existing containers.\\n\\n Usage: start [SERVICE...]\\n \\\"\\\"\\\"\\n self.project.start(service_names=options['SERVICE'])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.stop\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.stop\\n def stop(self, options):\\n \\\"\\\"\\\"\\n Stop running containers without removing them.\\n\\n They can be started again with `fig start`.\\n\\n Usage: stop [SERVICE...]\\n \\\"\\\"\\\"\\n self.project.stop(service_names=options['SERVICE'])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::TopLevelCommand.up\",\n \"text\": \"fig/cli/main.py::TopLevelCommand.up\\n def up(self, options):\\n \\\"\\\"\\\"\\n Build, (re)create, start and attach to containers for a service.\\n\\n By default, `fig up` will aggregate the output of each container, and\\n when it exits, all containers will be stopped. If you run `fig up -d`,\\n it'll start the containers in the background and leave them running.\\n\\n If there are existing containers for a service, `fig up` will stop\\n and recreate them (preserving mounted volumes with volumes-from),\\n so that changes in `fig.yml` are picked up. If you do not want existing\\n containers to be recreated, `fig up --no-recreate` will re-use existing\\n containers.\\n\\n Usage: up [options] [SERVICE...]\\n\\n Options:\\n -d Detached mode: Run containers in the background,\\n print new container names.\\n --no-deps Don't start linked services.\\n --no-recreate If containers already exist, don't recreate them.\\n \\\"\\\"\\\"\\n detached = options['-d']\\n\\n start_links = not options['--no-deps']\\n recreate = not options['--no-recreate']\\n service_names = options['SERVICE']\\n\\n self.project.up(\\n service_names=service_names,\\n start_links=start_links,\\n recreate=recreate\\n )\\n\\n to_attach = [c for s in self.project.get_services(service_names) for c in s.containers()]\\n\\n if not detached:\\n print(\\\"Attaching to\\\", list_containers(to_attach))\\n log_printer = LogPrinter(to_attach, attach_params={\\\"logs\\\": True})\\n\\n try:\\n log_printer.run()\\n finally:\\n def handler(signal, frame):\\n self.project.kill(service_names=service_names)\\n sys.exit(0)\\n signal.signal(signal.SIGINT, handler)\\n\\n print(\\\"Gracefully stopping... (press Ctrl+C again to force)\\\")\\n self.p\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/main.py::list_containers\",\n \"text\": \"fig/cli/main.py::list_containers\\ndef list_containers(containers):\\n return \\\", \\\".join(c.name for c in containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/multiplexer.py::Multiplexer.__init__\",\n \"text\": \"fig/cli/multiplexer.py::Multiplexer.__init__\\n def __init__(self, generators):\\n self.generators = generators\\n self.queue = Queue()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/multiplexer.py::Multiplexer.loop\",\n \"text\": \"fig/cli/multiplexer.py::Multiplexer.loop\\n def loop(self):\\n self._init_readers()\\n\\n while True:\\n try:\\n item = self.queue.get(timeout=0.1)\\n if item is STOP:\\n break\\n else:\\n yield item\\n except Empty:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/multiplexer.py::Multiplexer._init_readers\",\n \"text\": \"fig/cli/multiplexer.py::Multiplexer._init_readers\\n def _init_readers(self):\\n for generator in self.generators:\\n t = Thread(target=_enqueue_output, args=(generator, self.queue))\\n t.daemon = True\\n t.start()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/multiplexer.py::_enqueue_output\",\n \"text\": \"fig/cli/multiplexer.py::_enqueue_output\\ndef _enqueue_output(generator, queue):\\n for item in generator:\\n queue.put(item)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/utils.py::cached_property\",\n \"text\": \"fig/cli/utils.py::cached_property\\ndef cached_property(f):\\n \\\"\\\"\\\"\\n returns a cached property that is calculated by function f\\n http://code.activestate.com/recipes/576563-cached-property/\\n \\\"\\\"\\\"\\n def get(self):\\n try:\\n return self._property_cache[f]\\n except AttributeError:\\n self._property_cache = {}\\n x = self._property_cache[f] = f(self)\\n return x\\n except KeyError:\\n x = self._property_cache[f] = f(self)\\n return x\\n\\n return property(get)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/utils.py::cached_property.get\",\n \"text\": \"fig/cli/utils.py::cached_property.get\\n def get(self):\\n try:\\n return self._property_cache[f]\\n except AttributeError:\\n self._property_cache = {}\\n x = self._property_cache[f] = f(self)\\n return x\\n except KeyError:\\n x = self._property_cache[f] = f(self)\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/utils.py::yesno\",\n \"text\": \"fig/cli/utils.py::yesno\\ndef yesno(prompt, default=None):\\n \\\"\\\"\\\"\\n Prompt the user for a yes or no.\\n\\n Can optionally specify a default value, which will only be\\n used if they enter a blank line.\\n\\n Unrecognised input (anything other than \\\"y\\\", \\\"n\\\", \\\"yes\\\",\\n \\\"no\\\" or \\\"\\\") will return None.\\n \\\"\\\"\\\"\\n answer = raw_input(prompt).strip().lower()\\n\\n if answer == \\\"y\\\" or answer == \\\"yes\\\":\\n return True\\n elif answer == \\\"n\\\" or answer == \\\"no\\\":\\n return False\\n elif answer == \\\"\\\":\\n return default\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/utils.py::prettydate\",\n \"text\": \"fig/cli/utils.py::prettydate\\ndef prettydate(d):\\n diff = datetime.datetime.utcnow() - d\\n s = diff.seconds\\n if diff.days > 7 or diff.days < 0:\\n return d.strftime('%d %b %y')\\n elif diff.days == 1:\\n return '1 day ago'\\n elif diff.days > 1:\\n return '{0} days ago'.format(diff.days)\\n elif s <= 1:\\n return 'just now'\\n elif s < 60:\\n return '{0} seconds ago'.format(s)\\n elif s < 120:\\n return '1 minute ago'\\n elif s < 3600:\\n return '{0} minutes ago'.format(s / 60)\\n elif s < 7200:\\n return '1 hour ago'\\n else:\\n return '{0} hours ago'.format(s / 3600)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/utils.py::mkdir\",\n \"text\": \"fig/cli/utils.py::mkdir\\ndef mkdir(path, permissions=0o700):\\n if not os.path.exists(path):\\n os.mkdir(path)\\n\\n os.chmod(path, permissions)\\n\\n return path\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/utils.py::docker_url\",\n \"text\": \"fig/cli/utils.py::docker_url\\ndef docker_url():\\n return os.environ.get('DOCKER_HOST')\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/utils.py::split_buffer\",\n \"text\": \"fig/cli/utils.py::split_buffer\\ndef split_buffer(reader, separator):\\n \\\"\\\"\\\"\\n Given a generator which yields strings and a separator string,\\n joins all input, splits on the separator and yields each chunk.\\n\\n Unlike string.split(), each chunk includes the trailing\\n separator, except for the last one if none was found on the end\\n of the input.\\n \\\"\\\"\\\"\\n buffered = str('')\\n separator = str(separator)\\n\\n for data in reader:\\n buffered += data\\n while True:\\n index = buffered.find(separator)\\n if index == -1:\\n break\\n yield buffered[:index + 1]\\n buffered = buffered[index + 1:]\\n\\n if len(buffered) > 0:\\n yield buffered\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/utils.py::call_silently\",\n \"text\": \"fig/cli/utils.py::call_silently\\ndef call_silently(*args, **kwargs):\\n \\\"\\\"\\\"\\n Like subprocess.call(), but redirects stdout and stderr to /dev/null.\\n \\\"\\\"\\\"\\n with open(os.devnull, 'w') as shutup:\\n return subprocess.call(*args, stdout=shutup, stderr=shutup, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/utils.py::is_mac\",\n \"text\": \"fig/cli/utils.py::is_mac\\ndef is_mac():\\n return platform.system() == 'Darwin'\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/cli/utils.py::is_ubuntu\",\n \"text\": \"fig/cli/utils.py::is_ubuntu\\ndef is_ubuntu():\\n return platform.system() == 'Linux' and platform.linux_distribution()[0] == 'Ubuntu'\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.__init__\",\n \"text\": \"fig/container.py::Container.__init__\\n def __init__(self, client, dictionary, has_been_inspected=False):\\n self.client = client\\n self.dictionary = dictionary\\n self.has_been_inspected = has_been_inspected\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.from_ps\",\n \"text\": \"fig/container.py::Container.from_ps\\n @classmethod\\n def from_ps(cls, client, dictionary, **kwargs):\\n \\\"\\\"\\\"\\n Construct a container object from the output of GET /containers/json.\\n \\\"\\\"\\\"\\n new_dictionary = {\\n 'Id': dictionary['Id'],\\n 'Image': dictionary['Image'],\\n }\\n for name in dictionary.get('Names', []):\\n if len(name.split('/')) == 2:\\n new_dictionary['Name'] = name\\n return cls(client, new_dictionary, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.from_id\",\n \"text\": \"fig/container.py::Container.from_id\\n @classmethod\\n def from_id(cls, client, id):\\n return cls(client, client.inspect_container(id))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.create\",\n \"text\": \"fig/container.py::Container.create\\n @classmethod\\n def create(cls, client, **options):\\n response = client.create_container(**options)\\n return cls.from_id(client, response['Id'])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.id\",\n \"text\": \"fig/container.py::Container.id\\n @property\\n def id(self):\\n return self.dictionary['Id']\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.image\",\n \"text\": \"fig/container.py::Container.image\\n @property\\n def image(self):\\n return self.dictionary['Image']\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.short_id\",\n \"text\": \"fig/container.py::Container.short_id\\n @property\\n def short_id(self):\\n return self.id[:10]\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.name\",\n \"text\": \"fig/container.py::Container.name\\n @property\\n def name(self):\\n return self.dictionary['Name'][1:]\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.name_without_project\",\n \"text\": \"fig/container.py::Container.name_without_project\\n @property\\n def name_without_project(self):\\n return '_'.join(self.dictionary['Name'].split('_')[1:])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.number\",\n \"text\": \"fig/container.py::Container.number\\n @property\\n def number(self):\\n try:\\n return int(self.name.split('_')[-1])\\n except ValueError:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.human_readable_ports\",\n \"text\": \"fig/container.py::Container.human_readable_ports\\n @property\\n def human_readable_ports(self):\\n self.inspect_if_not_inspected()\\n if not self.dictionary['NetworkSettings']['Ports']:\\n return ''\\n ports = []\\n for private, public in list(self.dictionary['NetworkSettings']['Ports'].items()):\\n if public:\\n ports.append('%s->%s' % (public[0]['HostPort'], private))\\n else:\\n ports.append(private)\\n return ', '.join(ports)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.human_readable_state\",\n \"text\": \"fig/container.py::Container.human_readable_state\\n @property\\n def human_readable_state(self):\\n self.inspect_if_not_inspected()\\n if self.dictionary['State']['Running']:\\n if self.dictionary['State'].get('Ghost'):\\n return 'Ghost'\\n else:\\n return 'Up'\\n else:\\n return 'Exit %s' % self.dictionary['State']['ExitCode']\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.human_readable_command\",\n \"text\": \"fig/container.py::Container.human_readable_command\\n @property\\n def human_readable_command(self):\\n self.inspect_if_not_inspected()\\n if self.dictionary['Config']['Cmd']:\\n return ' '.join(self.dictionary['Config']['Cmd'])\\n else:\\n return ''\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.environment\",\n \"text\": \"fig/container.py::Container.environment\\n @property\\n def environment(self):\\n self.inspect_if_not_inspected()\\n out = {}\\n for var in self.dictionary.get('Config', {}).get('Env', []):\\n k, v = var.split('=', 1)\\n out[k] = v\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.is_running\",\n \"text\": \"fig/container.py::Container.is_running\\n @property\\n def is_running(self):\\n self.inspect_if_not_inspected()\\n return self.dictionary['State']['Running']\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.start\",\n \"text\": \"fig/container.py::Container.start\\n def start(self, **options):\\n return self.client.start(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.stop\",\n \"text\": \"fig/container.py::Container.stop\\n def stop(self, **options):\\n return self.client.stop(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.kill\",\n \"text\": \"fig/container.py::Container.kill\\n def kill(self):\\n return self.client.kill(self.id)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.remove\",\n \"text\": \"fig/container.py::Container.remove\\n def remove(self, **options):\\n return self.client.remove_container(self.id, **options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.inspect_if_not_inspected\",\n \"text\": \"fig/container.py::Container.inspect_if_not_inspected\\n def inspect_if_not_inspected(self):\\n if not self.has_been_inspected:\\n self.inspect()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.wait\",\n \"text\": \"fig/container.py::Container.wait\\n def wait(self):\\n return self.client.wait(self.id)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.logs\",\n \"text\": \"fig/container.py::Container.logs\\n def logs(self, *args, **kwargs):\\n return self.client.logs(self.id, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.inspect\",\n \"text\": \"fig/container.py::Container.inspect\\n def inspect(self):\\n self.dictionary = self.client.inspect_container(self.id)\\n return self.dictionary\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.links\",\n \"text\": \"fig/container.py::Container.links\\n def links(self):\\n links = []\\n for container in self.client.containers():\\n for name in container['Names']:\\n bits = name.split('/')\\n if len(bits) > 2 and bits[1] == self.name:\\n links.append(bits[2])\\n return links\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.attach\",\n \"text\": \"fig/container.py::Container.attach\\n def attach(self, *args, **kwargs):\\n return self.client.attach(self.id, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.attach_socket\",\n \"text\": \"fig/container.py::Container.attach_socket\\n def attach_socket(self, **kwargs):\\n return self.client.attach_socket(self.id, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.__repr__\",\n \"text\": \"fig/container.py::Container.__repr__\\n def __repr__(self):\\n return '<Container: %s>' % self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/container.py::Container.__eq__\",\n \"text\": \"fig/container.py::Container.__eq__\\n def __eq__(self, other):\\n if type(self) != type(other):\\n return False\\n return self.id == other.id\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::swap_protocol\",\n \"text\": \"fig/packages/docker/auth/auth.py::swap_protocol\\ndef swap_protocol(url):\\n if url.startswith('http://'):\\n return url.replace('http://', 'https://', 1)\\n if url.startswith('https://'):\\n return url.replace('https://', 'http://', 1)\\n return url\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::expand_registry_url\",\n \"text\": \"fig/packages/docker/auth/auth.py::expand_registry_url\\ndef expand_registry_url(hostname):\\n if hostname.startswith('http:') or hostname.startswith('https:'):\\n if '/' not in hostname[9:]:\\n hostname = hostname + '/v1/'\\n return hostname\\n if utils.ping('https://' + hostname + '/v1/_ping'):\\n return 'https://' + hostname + '/v1/'\\n return 'http://' + hostname + '/v1/'\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::resolve_repository_name\",\n \"text\": \"fig/packages/docker/auth/auth.py::resolve_repository_name\\ndef resolve_repository_name(repo_name):\\n if '://' in repo_name:\\n raise errors.InvalidRepository(\\n 'Repository name cannot contain a scheme ({0})'.format(repo_name))\\n parts = repo_name.split('/', 1)\\n if '.' not in parts[0] and ':' not in parts[0] and parts[0] != 'localhost':\\n # This is a docker index repo (ex: foo/bar or ubuntu)\\n return INDEX_URL, repo_name\\n if len(parts) < 2:\\n raise errors.InvalidRepository(\\n 'Invalid repository name ({0})'.format(repo_name))\\n\\n if 'index.docker.io' in parts[0]:\\n raise errors.InvalidRepository(\\n 'Invalid repository name, try \\\"{0}\\\" instead'.format(parts[1]))\\n\\n return expand_registry_url(parts[0]), parts[1]\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::resolve_authconfig\",\n \"text\": \"fig/packages/docker/auth/auth.py::resolve_authconfig\\ndef resolve_authconfig(authconfig, registry=None):\\n \\\"\\\"\\\"Return the authentication data from the given auth configuration for a\\n specific registry. We'll do our best to infer the correct URL for the\\n registry, trying both http and https schemes. Returns an empty dictionnary\\n if no data exists.\\\"\\\"\\\"\\n # Default to the public index server\\n registry = registry or INDEX_URL\\n\\n # Ff its not the index server there are three cases:\\n #\\n # 1. this is a full config url -> it should be used as is\\n # 2. it could be a full url, but with the wrong protocol\\n # 3. it can be the hostname optionally with a port\\n #\\n # as there is only one auth entry which is fully qualified we need to start\\n # parsing and matching\\n if '/' not in registry:\\n registry = registry + '/v1/'\\n if not registry.startswith('http:') and not registry.startswith('https:'):\\n registry = 'https://' + registry\\n\\n if registry in authconfig:\\n return authconfig[registry]\\n return authconfig.get(swap_protocol(registry), None)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::encode_auth\",\n \"text\": \"fig/packages/docker/auth/auth.py::encode_auth\\ndef encode_auth(auth_info):\\n return base64.b64encode(auth_info.get('username', '') + b':' +\\n auth_info.get('password', ''))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::decode_auth\",\n \"text\": \"fig/packages/docker/auth/auth.py::decode_auth\\ndef decode_auth(auth):\\n if isinstance(auth, six.string_types):\\n auth = auth.encode('ascii')\\n s = base64.b64decode(auth)\\n login, pwd = s.split(b':')\\n return login.decode('ascii'), pwd.decode('ascii')\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::encode_header\",\n \"text\": \"fig/packages/docker/auth/auth.py::encode_header\\ndef encode_header(auth):\\n auth_json = json.dumps(auth).encode('ascii')\\n return base64.b64encode(auth_json)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::encode_full_header\",\n \"text\": \"fig/packages/docker/auth/auth.py::encode_full_header\\ndef encode_full_header(auth):\\n \\\"\\\"\\\" Returns the given auth block encoded for the X-Registry-Config header.\\n \\\"\\\"\\\"\\n return encode_header({'configs': auth})\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/auth/auth.py::load_config\",\n \"text\": \"fig/packages/docker/auth/auth.py::load_config\\ndef load_config(root=None):\\n \\\"\\\"\\\"Loads authentication data from a Docker configuration file in the given\\n root directory.\\\"\\\"\\\"\\n conf = {}\\n data = None\\n\\n config_file = os.path.join(root or os.environ.get('HOME', '.'),\\n DOCKER_CONFIG_FILENAME)\\n\\n # First try as JSON\\n try:\\n with open(config_file) as f:\\n conf = {}\\n for registry, entry in six.iteritems(json.load(f)):\\n username, password = decode_auth(entry['auth'])\\n conf[registry] = {\\n 'username': username,\\n 'password': password,\\n 'email': entry['email'],\\n 'serveraddress': registry,\\n }\\n return conf\\n except:\\n pass\\n\\n # If that fails, we assume the configuration file contains a single\\n # authentication token for the public registry in the following format:\\n #\\n # auth = AUTH_TOKEN\\n # email = email@domain.com\\n try:\\n data = []\\n for line in fileinput.input(config_file):\\n data.append(line.strip().split(' = ')[1])\\n if len(data) < 2:\\n # Not enough data\\n raise errors.InvalidConfigFile(\\n 'Invalid or empty configuration file!')\\n\\n username, password = decode_auth(data[0])\\n conf[INDEX_URL] = {\\n 'username': username,\\n 'password': password,\\n 'email': data[1],\\n 'serveraddress': INDEX_URL,\\n }\\n return conf\\n except:\\n pass\\n\\n # If all fails, return an empty config\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.__init__\",\n \"text\": \"fig/packages/docker/client.py::Client.__init__\\n def __init__(self, base_url=None, version=DEFAULT_DOCKER_API_VERSION,\\n timeout=DEFAULT_TIMEOUT_SECONDS):\\n super(Client, self).__init__()\\n if base_url is None:\\n base_url = \\\"http+unix://var/run/docker.sock\\\"\\n if 'unix:///' in base_url:\\n base_url = base_url.replace('unix:/', 'unix:')\\n if base_url.startswith('unix:'):\\n base_url = \\\"http+\\\" + base_url\\n if base_url.startswith('tcp:'):\\n base_url = base_url.replace('tcp:', 'http:')\\n if base_url.endswith('/'):\\n base_url = base_url[:-1]\\n self.base_url = base_url\\n self._version = version\\n self._timeout = timeout\\n self._auth_configs = auth.load_config()\\n\\n self.mount('http+unix://', unixconn.UnixAdapter(base_url, timeout))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._set_request_timeout\",\n \"text\": \"fig/packages/docker/client.py::Client._set_request_timeout\\n def _set_request_timeout(self, kwargs):\\n \\\"\\\"\\\"Prepare the kwargs for an HTTP request by inserting the timeout\\n parameter, if not already present.\\\"\\\"\\\"\\n kwargs.setdefault('timeout', self._timeout)\\n return kwargs\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._post\",\n \"text\": \"fig/packages/docker/client.py::Client._post\\n def _post(self, url, **kwargs):\\n return self.post(url, **self._set_request_timeout(kwargs))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._get\",\n \"text\": \"fig/packages/docker/client.py::Client._get\\n def _get(self, url, **kwargs):\\n return self.get(url, **self._set_request_timeout(kwargs))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._delete\",\n \"text\": \"fig/packages/docker/client.py::Client._delete\\n def _delete(self, url, **kwargs):\\n return self.delete(url, **self._set_request_timeout(kwargs))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._url\",\n \"text\": \"fig/packages/docker/client.py::Client._url\\n def _url(self, path):\\n return '{0}/v{1}{2}'.format(self.base_url, self._version, path)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._raise_for_status\",\n \"text\": \"fig/packages/docker/client.py::Client._raise_for_status\\n def _raise_for_status(self, response, explanation=None):\\n \\\"\\\"\\\"Raises stored :class:`APIError`, if one occurred.\\\"\\\"\\\"\\n try:\\n response.raise_for_status()\\n except requests.exceptions.HTTPError as e:\\n raise errors.APIError(e, response, explanation=explanation)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._result\",\n \"text\": \"fig/packages/docker/client.py::Client._result\\n def _result(self, response, json=False, binary=False):\\n assert not (json and binary)\\n self._raise_for_status(response)\\n\\n if json:\\n return response.json()\\n if binary:\\n return response.content\\n return response.text\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._container_config\",\n \"text\": \"fig/packages/docker/client.py::Client._container_config\\n def _container_config(self, image, command, hostname=None, user=None,\\n detach=False, stdin_open=False, tty=False,\\n mem_limit=0, ports=None, environment=None, dns=None,\\n volumes=None, volumes_from=None,\\n network_disabled=False, entrypoint=None,\\n cpu_shares=None, working_dir=None, domainname=None,\\n memswap_limit=0):\\n if isinstance(command, six.string_types):\\n command = shlex.split(str(command))\\n if isinstance(environment, dict):\\n environment = [\\n '{0}={1}'.format(k, v) for k, v in environment.items()\\n ]\\n\\n if isinstance(ports, list):\\n exposed_ports = {}\\n for port_definition in ports:\\n port = port_definition\\n proto = 'tcp'\\n if isinstance(port_definition, tuple):\\n if len(port_definition) == 2:\\n proto = port_definition[1]\\n port = port_definition[0]\\n exposed_ports['{0}/{1}'.format(port, proto)] = {}\\n ports = exposed_ports\\n\\n if isinstance(volumes, list):\\n volumes_dict = {}\\n for vol in volumes:\\n volumes_dict[vol] = {}\\n volumes = volumes_dict\\n\\n if volumes_from:\\n if not isinstance(volumes_from, six.string_types):\\n volumes_from = ','.join(volumes_from)\\n else:\\n # Force None, an empty list or dict causes client.start to fail\\n volumes_from = None\\n\\n attach_stdin = False\\n attach_stdout = False\\n attach_stderr = False\\n stdin_once = False\\n\\n if not detach:\\n attach_stdout = True\\n attach_stderr = True\\n\\n if stdin_open:\\n attach_stdin = True\\n stdin_once = \",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._post_json\",\n \"text\": \"fig/packages/docker/client.py::Client._post_json\\n def _post_json(self, url, data, **kwargs):\\n # Go <1.1 can't unserialize null to a string\\n # so we do this disgusting thing here.\\n data2 = {}\\n if data is not None:\\n for k, v in six.iteritems(data):\\n if v is not None:\\n data2[k] = v\\n\\n if 'headers' not in kwargs:\\n kwargs['headers'] = {}\\n kwargs['headers']['Content-Type'] = 'application/json'\\n return self._post(url, data=json.dumps(data2), **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._attach_params\",\n \"text\": \"fig/packages/docker/client.py::Client._attach_params\\n def _attach_params(self, override=None):\\n return override or {\\n 'stdout': 1,\\n 'stderr': 1,\\n 'stream': 1\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._attach_websocket\",\n \"text\": \"fig/packages/docker/client.py::Client._attach_websocket\\n def _attach_websocket(self, container, params=None):\\n if six.PY3:\\n raise NotImplementedError(\\\"This method is not currently supported \\\"\\n \\\"under python 3\\\")\\n url = self._url(\\\"/containers/{0}/attach/ws\\\".format(container))\\n req = requests.Request(\\\"POST\\\", url, params=self._attach_params(params))\\n full_url = req.prepare().url\\n full_url = full_url.replace(\\\"http://\\\", \\\"ws://\\\", 1)\\n full_url = full_url.replace(\\\"https://\\\", \\\"wss://\\\", 1)\\n return self._create_websocket_connection(full_url)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._create_websocket_connection\",\n \"text\": \"fig/packages/docker/client.py::Client._create_websocket_connection\\n def _create_websocket_connection(self, url):\\n return websocket.create_connection(url)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._get_raw_response_socket\",\n \"text\": \"fig/packages/docker/client.py::Client._get_raw_response_socket\\n def _get_raw_response_socket(self, response):\\n self._raise_for_status(response)\\n if six.PY3:\\n return response.raw._fp.fp.raw._sock\\n else:\\n return response.raw._fp.fp._sock\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._stream_helper\",\n \"text\": \"fig/packages/docker/client.py::Client._stream_helper\\n def _stream_helper(self, response):\\n \\\"\\\"\\\"Generator for data coming from a chunked-encoded HTTP response.\\\"\\\"\\\"\\n socket_fp = self._get_raw_response_socket(response)\\n socket_fp.setblocking(1)\\n socket = socket_fp.makefile()\\n while True:\\n # Because Docker introduced newlines at the end of chunks in v0.9,\\n # and only on some API endpoints, we have to cater for both cases.\\n size_line = socket.readline()\\n if size_line == '\\\\r\\\\n':\\n size_line = socket.readline()\\n\\n size = int(size_line, 16)\\n if size <= 0:\\n break\\n data = socket.readline()\\n if not data:\\n break\\n yield data\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._multiplexed_buffer_helper\",\n \"text\": \"fig/packages/docker/client.py::Client._multiplexed_buffer_helper\\n def _multiplexed_buffer_helper(self, response):\\n \\\"\\\"\\\"A generator of multiplexed data blocks read from a buffered\\n response.\\\"\\\"\\\"\\n buf = self._result(response, binary=True)\\n walker = 0\\n while True:\\n if len(buf[walker:]) < 8:\\n break\\n _, length = struct.unpack_from('>BxxxL', buf[walker:])\\n start = walker + STREAM_HEADER_SIZE_BYTES\\n end = start + length\\n walker = end\\n yield buf[start:end]\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper\",\n \"text\": \"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper\\n def _multiplexed_socket_stream_helper(self, response):\\n \\\"\\\"\\\"A generator of multiplexed data blocks coming from a response\\n socket.\\\"\\\"\\\"\\n socket = self._get_raw_response_socket(response)\\n\\n def recvall(socket, size):\\n blocks = []\\n while size > 0:\\n block = socket.recv(size)\\n if not block:\\n return None\\n\\n blocks.append(block)\\n size -= len(block)\\n\\n sep = bytes() if six.PY3 else str()\\n data = sep.join(blocks)\\n return data\\n\\n while True:\\n socket.settimeout(None)\\n header = recvall(socket, STREAM_HEADER_SIZE_BYTES)\\n if not header:\\n break\\n _, length = struct.unpack('>BxxxL', header)\\n if not length:\\n break\\n data = recvall(socket, length)\\n if not data:\\n break\\n yield data\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper.recvall\",\n \"text\": \"fig/packages/docker/client.py::Client._multiplexed_socket_stream_helper.recvall\\n def recvall(socket, size):\\n blocks = []\\n while size > 0:\\n block = socket.recv(size)\\n if not block:\\n return None\\n\\n blocks.append(block)\\n size -= len(block)\\n\\n sep = bytes() if six.PY3 else str()\\n data = sep.join(blocks)\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.attach\",\n \"text\": \"fig/packages/docker/client.py::Client.attach\\n def attach(self, container, stdout=True, stderr=True,\\n stream=False, logs=False):\\n if isinstance(container, dict):\\n container = container.get('Id')\\n params = {\\n 'logs': logs and 1 or 0,\\n 'stdout': stdout and 1 or 0,\\n 'stderr': stderr and 1 or 0,\\n 'stream': stream and 1 or 0,\\n }\\n u = self._url(\\\"/containers/{0}/attach\\\".format(container))\\n response = self._post(u, params=params, stream=stream)\\n\\n # Stream multi-plexing was only introduced in API v1.6. Anything before\\n # that needs old-style streaming.\\n if utils.compare_version('1.6', self._version) < 0:\\n def stream_result():\\n self._raise_for_status(response)\\n for line in response.iter_lines(chunk_size=1,\\n decode_unicode=True):\\n # filter out keep-alive new lines\\n if line:\\n yield line\\n\\n return stream_result() if stream else \\\\\\n self._result(response, binary=True)\\n\\n sep = bytes() if six.PY3 else str()\\n\\n return stream and self._multiplexed_socket_stream_helper(response) or \\\\\\n sep.join([x for x in self._multiplexed_buffer_helper(response)])\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.attach_socket\",\n \"text\": \"fig/packages/docker/client.py::Client.attach_socket\\n def attach_socket(self, container, params=None, ws=False):\\n if params is None:\\n params = {\\n 'stdout': 1,\\n 'stderr': 1,\\n 'stream': 1\\n }\\n\\n if ws:\\n return self._attach_websocket(container, params)\\n\\n if isinstance(container, dict):\\n container = container.get('Id')\\n\\n u = self._url(\\\"/containers/{0}/attach\\\".format(container))\\n return self._get_raw_response_socket(self.post(\\n u, None, params=self._attach_params(params), stream=True))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.build\",\n \"text\": \"fig/packages/docker/client.py::Client.build\\n def build(self, path=None, tag=None, quiet=False, fileobj=None,\\n nocache=False, rm=False, stream=False, timeout=None,\\n custom_context=False, encoding=None):\\n remote = context = headers = None\\n if path is None and fileobj is None:\\n raise TypeError(\\\"Either path or fileobj needs to be provided.\\\")\\n\\n if custom_context:\\n if not fileobj:\\n raise TypeError(\\\"You must specify fileobj with custom_context\\\")\\n context = fileobj\\n elif fileobj is not None:\\n context = utils.mkbuildcontext(fileobj)\\n elif path.startswith(('http://', 'https://',\\n 'git://', 'github.com/')):\\n remote = path\\n else:\\n context = utils.tar(path)\\n\\n if utils.compare_version('1.8', self._version) >= 0:\\n stream = True\\n\\n u = self._url('/build')\\n params = {\\n 't': tag,\\n 'remote': remote,\\n 'q': quiet,\\n 'nocache': nocache,\\n 'rm': rm\\n }\\n\\n if context is not None:\\n headers = {'Content-Type': 'application/tar'}\\n if encoding:\\n headers['Content-Encoding'] = encoding\\n\\n if utils.compare_version('1.9', self._version) >= 0:\\n # If we don't have any auth data so far, try reloading the config\\n # file one more time in case anything showed up in there.\\n if not self._auth_configs:\\n self._auth_configs = auth.load_config()\\n\\n # Send the full auth configuration (if any exists), since the build\\n # could use any (or all) of the registries.\\n if self._auth_configs:\\n headers['X-Registry-Config'] = auth.encode_full_header(\\n self._auth_configs\\n )\\n\\n response = self._post(\\n u,\\n data=context,\\n params=params,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.commit\",\n \"text\": \"fig/packages/docker/client.py::Client.commit\\n def commit(self, container, repository=None, tag=None, message=None,\\n author=None, conf=None):\\n params = {\\n 'container': container,\\n 'repo': repository,\\n 'tag': tag,\\n 'comment': message,\\n 'author': author\\n }\\n u = self._url(\\\"/commit\\\")\\n return self._result(self._post_json(u, data=conf, params=params),\\n json=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.containers\",\n \"text\": \"fig/packages/docker/client.py::Client.containers\\n def containers(self, quiet=False, all=False, trunc=True, latest=False,\\n since=None, before=None, limit=-1, size=False):\\n params = {\\n 'limit': 1 if latest else limit,\\n 'all': 1 if all else 0,\\n 'size': 1 if size else 0,\\n 'trunc_cmd': 1 if trunc else 0,\\n 'since': since,\\n 'before': before\\n }\\n u = self._url(\\\"/containers/json\\\")\\n res = self._result(self._get(u, params=params), True)\\n\\n if quiet:\\n return [{'Id': x['Id']} for x in res]\\n return res\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.copy\",\n \"text\": \"fig/packages/docker/client.py::Client.copy\\n def copy(self, container, resource):\\n if isinstance(container, dict):\\n container = container.get('Id')\\n res = self._post_json(\\n self._url(\\\"/containers/{0}/copy\\\".format(container)),\\n data={\\\"Resource\\\": resource},\\n stream=True\\n )\\n self._raise_for_status(res)\\n return res.raw\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.create_container\",\n \"text\": \"fig/packages/docker/client.py::Client.create_container\\n def create_container(self, image, command=None, hostname=None, user=None,\\n detach=False, stdin_open=False, tty=False,\\n mem_limit=0, ports=None, environment=None, dns=None,\\n volumes=None, volumes_from=None,\\n network_disabled=False, name=None, entrypoint=None,\\n cpu_shares=None, working_dir=None, domainname=None,\\n memswap_limit=0):\\n\\n config = self._container_config(\\n image, command, hostname, user, detach, stdin_open, tty, mem_limit,\\n ports, environment, dns, volumes, volumes_from, network_disabled,\\n entrypoint, cpu_shares, working_dir, domainname, memswap_limit\\n )\\n return self.create_container_from_config(config, name)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.create_container_from_config\",\n \"text\": \"fig/packages/docker/client.py::Client.create_container_from_config\\n def create_container_from_config(self, config, name=None):\\n u = self._url(\\\"/containers/create\\\")\\n params = {\\n 'name': name\\n }\\n res = self._post_json(u, data=config, params=params)\\n return self._result(res, True)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.diff\",\n \"text\": \"fig/packages/docker/client.py::Client.diff\\n def diff(self, container):\\n if isinstance(container, dict):\\n container = container.get('Id')\\n return self._result(self._get(self._url(\\\"/containers/{0}/changes\\\".\\n format(container))), True)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.events\",\n \"text\": \"fig/packages/docker/client.py::Client.events\\n def events(self):\\n return self._stream_helper(self.get(self._url('/events'), stream=True))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.export\",\n \"text\": \"fig/packages/docker/client.py::Client.export\\n def export(self, container):\\n if isinstance(container, dict):\\n container = container.get('Id')\\n res = self._get(self._url(\\\"/containers/{0}/export\\\".format(container)),\\n stream=True)\\n self._raise_for_status(res)\\n return res.raw\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.get_image\",\n \"text\": \"fig/packages/docker/client.py::Client.get_image\\n def get_image(self, image):\\n res = self._get(self._url(\\\"/images/{0}/get\\\".format(image)),\\n stream=True)\\n self._raise_for_status(res)\\n return res.raw\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.history\",\n \"text\": \"fig/packages/docker/client.py::Client.history\\n def history(self, image):\\n res = self._get(self._url(\\\"/images/{0}/history\\\".format(image)))\\n self._raise_for_status(res)\\n return self._result(res)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.images\",\n \"text\": \"fig/packages/docker/client.py::Client.images\\n def images(self, name=None, quiet=False, all=False, viz=False):\\n if viz:\\n if utils.compare_version('1.7', self._version) >= 0:\\n raise Exception('Viz output is not supported in API >= 1.7!')\\n return self._result(self._get(self._url(\\\"images/viz\\\")))\\n params = {\\n 'filter': name,\\n 'only_ids': 1 if quiet else 0,\\n 'all': 1 if all else 0,\\n }\\n res = self._result(self._get(self._url(\\\"/images/json\\\"), params=params),\\n True)\\n if quiet:\\n return [x['Id'] for x in res]\\n return res\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.import_image\",\n \"text\": \"fig/packages/docker/client.py::Client.import_image\\n def import_image(self, src=None, repository=None, tag=None, image=None):\\n u = self._url(\\\"/images/create\\\")\\n params = {\\n 'repo': repository,\\n 'tag': tag\\n }\\n\\n if src:\\n try:\\n # XXX: this is ways not optimal but the only way\\n # for now to import tarballs through the API\\n fic = open(src)\\n data = fic.read()\\n fic.close()\\n src = \\\"-\\\"\\n except IOError:\\n # file does not exists or not a file (URL)\\n data = None\\n if isinstance(src, six.string_types):\\n params['fromSrc'] = src\\n return self._result(self._post(u, data=data, params=params))\\n return self._result(self._post(u, data=src, params=params))\\n\\n if image:\\n params['fromImage'] = image\\n return self._result(self._post(u, data=None, params=params))\\n\\n raise Exception(\\\"Must specify a src or image\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.info\",\n \"text\": \"fig/packages/docker/client.py::Client.info\\n def info(self):\\n return self._result(self._get(self._url(\\\"/info\\\")),\\n True)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.insert\",\n \"text\": \"fig/packages/docker/client.py::Client.insert\\n def insert(self, image, url, path):\\n if utils.compare_version('1.12', self._version) >= 0:\\n raise errors.DeprecatedMethod(\\n 'insert is not available for API version >=1.12'\\n )\\n api_url = self._url(\\\"/images/\\\" + image + \\\"/insert\\\")\\n params = {\\n 'url': url,\\n 'path': path\\n }\\n return self._result(self._post(api_url, params=params))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.inspect_container\",\n \"text\": \"fig/packages/docker/client.py::Client.inspect_container\\n def inspect_container(self, container):\\n if isinstance(container, dict):\\n container = container.get('Id')\\n return self._result(\\n self._get(self._url(\\\"/containers/{0}/json\\\".format(container))),\\n True)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.inspect_image\",\n \"text\": \"fig/packages/docker/client.py::Client.inspect_image\\n def inspect_image(self, image_id):\\n return self._result(\\n self._get(self._url(\\\"/images/{0}/json\\\".format(image_id))),\\n True\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.kill\",\n \"text\": \"fig/packages/docker/client.py::Client.kill\\n def kill(self, container, signal=None):\\n if isinstance(container, dict):\\n container = container.get('Id')\\n url = self._url(\\\"/containers/{0}/kill\\\".format(container))\\n params = {}\\n if signal is not None:\\n params['signal'] = signal\\n res = self._post(url, params=params)\\n\\n self._raise_for_status(res)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.load_image\",\n \"text\": \"fig/packages/docker/client.py::Client.load_image\\n def load_image(self, data):\\n res = self._post(self._url(\\\"/images/load\\\"), data=data)\\n self._raise_for_status(res)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/client.py::Client.login\",\n \"text\": \"fig/packages/docker/client.py::Client.login\\n def login(self, username, password=None, email=None, registry=None,\\n reauth=False):\\n # If we don't have any auth data so far, try reloading the config file\\n # one more time in case anything showed up in there.\\n if not self._auth_configs:\\n self._auth_configs = auth.load_config()\\n\\n registry = registry or auth.INDEX_URL\\n\\n authcfg = auth.resolve_authconfig(self._auth_configs, registry)\\n # If we found an existing auth config for this registry and username\\n # combination, we can return it immediately unless reauth is requested.\\n if authcfg and authcfg.get('username', None) == username \\\\\\n and not reauth:\\n return authcfg\\n\\n req_data = {\\n 'username': username,\\n 'password': password,\\n 'email': email,\\n 'serveraddress': registry,\\n }\\n\\n response = self._post_json(self._url('/auth'), data=req_data)\\n if response.status_code == 200:\\n self._auth_configs[registry] = req_data\\n return self._result(response, json=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/errors.py::APIError.__init__\",\n \"text\": \"fig/packages/docker/errors.py::APIError.__init__\\n def __init__(self, message, response, explanation=None):\\n # requests 1.2 supports response as a keyword argument, but\\n # requests 1.1 doesn't\\n super(APIError, self).__init__(message)\\n self.response = response\\n\\n self.explanation = explanation\\n\\n if self.explanation is None and response.content:\\n self.explanation = response.content.strip()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/errors.py::APIError.__str__\",\n \"text\": \"fig/packages/docker/errors.py::APIError.__str__\\n def __str__(self):\\n message = super(APIError, self).__str__()\\n\\n if self.is_client_error():\\n message = '%s Client Error: %s' % (\\n self.response.status_code, self.response.reason)\\n\\n elif self.is_server_error():\\n message = '%s Server Error: %s' % (\\n self.response.status_code, self.response.reason)\\n\\n if self.explanation:\\n message = '%s (\\\"%s\\\")' % (message, self.explanation)\\n\\n return message\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/errors.py::APIError.is_client_error\",\n \"text\": \"fig/packages/docker/errors.py::APIError.is_client_error\\n def is_client_error(self):\\n return 400 <= self.response.status_code < 500\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/errors.py::APIError.is_server_error\",\n \"text\": \"fig/packages/docker/errors.py::APIError.is_server_error\\n def is_server_error(self):\\n return 500 <= self.response.status_code < 600\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.__init__\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.__init__\\n def __init__(self, base_url, unix_socket, timeout=60):\\n httplib.HTTPConnection.__init__(self, 'localhost', timeout=timeout)\\n self.base_url = base_url\\n self.unix_socket = unix_socket\\n self.timeout = timeout\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.connect\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.connect\\n def connect(self):\\n sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)\\n sock.settimeout(self.timeout)\\n sock.connect(self.base_url.replace(\\\"http+unix:/\\\", \\\"\\\"))\\n self.sock = sock\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection._extract_path\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection._extract_path\\n def _extract_path(self, url):\\n # remove the base_url entirely..\\n return url.replace(self.base_url, \\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.request\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnection.request\\n def request(self, method, url, **kwargs):\\n url = self._extract_path(self.unix_socket)\\n super(UnixHTTPConnection, self).request(method, url, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool.__init__\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool.__init__\\n def __init__(self, base_url, socket_path, timeout=60):\\n connectionpool.HTTPConnectionPool.__init__(self, 'localhost',\\n timeout=timeout)\\n self.base_url = base_url\\n self.socket_path = socket_path\\n self.timeout = timeout\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool._new_conn\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixHTTPConnectionPool._new_conn\\n def _new_conn(self):\\n return UnixHTTPConnection(self.base_url, self.socket_path,\\n self.timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.__init__\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.__init__\\n def __init__(self, base_url, timeout=60):\\n self.base_url = base_url\\n self.timeout = timeout\\n super(UnixAdapter, self).__init__()\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.get_connection\",\n \"text\": \"fig/packages/docker/unixconn/unixconn.py::UnixAdapter.get_connection\\n def get_connection(self, socket_path, proxies=None):\\n return UnixHTTPConnectionPool(self.base_url, socket_path, self.timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::mkbuildcontext\",\n \"text\": \"fig/packages/docker/utils/utils.py::mkbuildcontext\\ndef mkbuildcontext(dockerfile):\\n f = tempfile.NamedTemporaryFile()\\n t = tarfile.open(mode='w', fileobj=f)\\n if isinstance(dockerfile, io.StringIO):\\n dfinfo = tarfile.TarInfo('Dockerfile')\\n if six.PY3:\\n raise TypeError('Please use io.BytesIO to create in-memory '\\n 'Dockerfiles with Python 3')\\n else:\\n dfinfo.size = len(dockerfile.getvalue())\\n elif isinstance(dockerfile, io.BytesIO):\\n dfinfo = tarfile.TarInfo('Dockerfile')\\n dfinfo.size = len(dockerfile.getvalue())\\n else:\\n dfinfo = t.gettarinfo(fileobj=dockerfile, arcname='Dockerfile')\\n t.addfile(dfinfo, dockerfile)\\n t.close()\\n f.seek(0)\\n return f\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::tar\",\n \"text\": \"fig/packages/docker/utils/utils.py::tar\\ndef tar(path):\\n f = tempfile.NamedTemporaryFile()\\n t = tarfile.open(mode='w', fileobj=f)\\n t.add(path, arcname='.')\\n t.close()\\n f.seek(0)\\n return f\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::compare_version\",\n \"text\": \"fig/packages/docker/utils/utils.py::compare_version\\ndef compare_version(v1, v2):\\n \\\"\\\"\\\"Compare docker versions\\n\\n >>> v1 = '1.9'\\n >>> v2 = '1.10'\\n >>> compare_version(v1, v2)\\n 1\\n >>> compare_version(v2, v1)\\n -1\\n >>> compare_version(v2, v2)\\n 0\\n \\\"\\\"\\\"\\n s1 = StrictVersion(v1)\\n s2 = StrictVersion(v2)\\n if s1 == s2:\\n return 0\\n elif s1 > s2:\\n return -1\\n else:\\n return 1\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::ping\",\n \"text\": \"fig/packages/docker/utils/utils.py::ping\\ndef ping(url):\\n try:\\n res = requests.get(url)\\n except Exception:\\n return False\\n else:\\n return res.status_code < 400\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::_convert_port_binding\",\n \"text\": \"fig/packages/docker/utils/utils.py::_convert_port_binding\\ndef _convert_port_binding(binding):\\n result = {'HostIp': '', 'HostPort': ''}\\n if isinstance(binding, tuple):\\n if len(binding) == 2:\\n result['HostPort'] = binding[1]\\n result['HostIp'] = binding[0]\\n elif isinstance(binding[0], six.string_types):\\n result['HostIp'] = binding[0]\\n else:\\n result['HostPort'] = binding[0]\\n elif isinstance(binding, dict):\\n if 'HostPort' in binding:\\n result['HostPort'] = binding['HostPort']\\n if 'HostIp' in binding:\\n result['HostIp'] = binding['HostIp']\\n else:\\n raise ValueError(binding)\\n else:\\n result['HostPort'] = binding\\n\\n if result['HostPort'] is None:\\n result['HostPort'] = ''\\n else:\\n result['HostPort'] = str(result['HostPort'])\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::convert_port_bindings\",\n \"text\": \"fig/packages/docker/utils/utils.py::convert_port_bindings\\ndef convert_port_bindings(port_bindings):\\n result = {}\\n for k, v in six.iteritems(port_bindings):\\n key = str(k)\\n if '/' not in key:\\n key = key + '/tcp'\\n if isinstance(v, list):\\n result[key] = [_convert_port_binding(binding) for binding in v]\\n else:\\n result[key] = [_convert_port_binding(v)]\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::convert_volume_binds\",\n \"text\": \"fig/packages/docker/utils/utils.py::convert_volume_binds\\ndef convert_volume_binds(binds):\\n result = []\\n for k, v in binds.items():\\n if isinstance(v, dict):\\n result.append('%s:%s:%s' % (\\n k, v['bind'], 'ro' if v.get('ro', False) else 'rw'\\n ))\\n else:\\n result.append('%s:%s:rw' % (k, v))\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/docker/utils/utils.py::parse_repository_tag\",\n \"text\": \"fig/packages/docker/utils/utils.py::parse_repository_tag\\ndef parse_repository_tag(repo):\\n column_index = repo.rfind(':')\\n if column_index < 0:\\n return repo, None\\n tag = repo[column_index+1:]\\n slash_index = tag.find('/')\\n if slash_index < 0:\\n return repo[:column_index], tag\\n\\n return repo, None\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::_add_doc\",\n \"text\": \"fig/packages/six.py::_add_doc\\ndef _add_doc(func, doc):\\n \\\"\\\"\\\"Add documentation to a function.\\\"\\\"\\\"\\n func.__doc__ = doc\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::_import_module\",\n \"text\": \"fig/packages/six.py::_import_module\\ndef _import_module(name):\\n \\\"\\\"\\\"Import module, returning the module after the last dot.\\\"\\\"\\\"\\n __import__(name)\\n return sys.modules[name]\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::_LazyDescr.__init__\",\n \"text\": \"fig/packages/six.py::_LazyDescr.__init__\\n def __init__(self, name):\\n self.name = name\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::_LazyDescr.__get__\",\n \"text\": \"fig/packages/six.py::_LazyDescr.__get__\\n def __get__(self, obj, tp):\\n result = self._resolve()\\n setattr(obj, self.name, result)\\n # This is a bit ugly, but it avoids running this again.\\n delattr(tp, self.name)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::MovedModule.__init__\",\n \"text\": \"fig/packages/six.py::MovedModule.__init__\\n def __init__(self, name, old, new=None):\\n super(MovedModule, self).__init__(name)\\n if PY3:\\n if new is None:\\n new = name\\n self.mod = new\\n else:\\n self.mod = old\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::MovedModule._resolve\",\n \"text\": \"fig/packages/six.py::MovedModule._resolve\\n def _resolve(self):\\n return _import_module(self.mod)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::MovedAttribute.__init__\",\n \"text\": \"fig/packages/six.py::MovedAttribute.__init__\\n def __init__(self, name, old_mod, new_mod, old_attr=None, new_attr=None):\\n super(MovedAttribute, self).__init__(name)\\n if PY3:\\n if new_mod is None:\\n new_mod = name\\n self.mod = new_mod\\n if new_attr is None:\\n if old_attr is None:\\n new_attr = name\\n else:\\n new_attr = old_attr\\n self.attr = new_attr\\n else:\\n self.mod = old_mod\\n if old_attr is None:\\n old_attr = name\\n self.attr = old_attr\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::MovedAttribute._resolve\",\n \"text\": \"fig/packages/six.py::MovedAttribute._resolve\\n def _resolve(self):\\n module = _import_module(self.mod)\\n return getattr(module, self.attr)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::add_move\",\n \"text\": \"fig/packages/six.py::add_move\\ndef add_move(move):\\n \\\"\\\"\\\"Add an item to six.moves.\\\"\\\"\\\"\\n setattr(_MovedItems, move.name, move)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::remove_move\",\n \"text\": \"fig/packages/six.py::remove_move\\ndef remove_move(name):\\n \\\"\\\"\\\"Remove item from six.moves.\\\"\\\"\\\"\\n try:\\n delattr(_MovedItems, name)\\n except AttributeError:\\n try:\\n del moves.__dict__[name]\\n except KeyError:\\n raise AttributeError(\\\"no such move, %r\\\" % (name,))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::iterkeys\",\n \"text\": \"fig/packages/six.py::iterkeys\\ndef iterkeys(d, **kw):\\n \\\"\\\"\\\"Return an iterator over the keys of a dictionary.\\\"\\\"\\\"\\n return iter(getattr(d, _iterkeys)(**kw))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::itervalues\",\n \"text\": \"fig/packages/six.py::itervalues\\ndef itervalues(d, **kw):\\n \\\"\\\"\\\"Return an iterator over the values of a dictionary.\\\"\\\"\\\"\\n return iter(getattr(d, _itervalues)(**kw))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::iteritems\",\n \"text\": \"fig/packages/six.py::iteritems\\ndef iteritems(d, **kw):\\n \\\"\\\"\\\"Return an iterator over the (key, value) pairs of a dictionary.\\\"\\\"\\\"\\n return iter(getattr(d, _iteritems)(**kw))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::iterlists\",\n \"text\": \"fig/packages/six.py::iterlists\\ndef iterlists(d, **kw):\\n \\\"\\\"\\\"Return an iterator over the (key, [values]) pairs of a dictionary.\\\"\\\"\\\"\\n return iter(getattr(d, _iterlists)(**kw))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/packages/six.py::with_metaclass\",\n \"text\": \"fig/packages/six.py::with_metaclass\\ndef with_metaclass(meta, base=object):\\n \\\"\\\"\\\"Create a base class with a metaclass.\\\"\\\"\\\"\\n return meta(\\\"NewBase\\\", (base,), {})\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/progress_stream.py::stream_output\",\n \"text\": \"fig/progress_stream.py::stream_output\\ndef stream_output(output, stream):\\n is_terminal = hasattr(stream, 'fileno') and os.isatty(stream.fileno())\\n stream = codecs.getwriter('utf-8')(stream)\\n all_events = []\\n lines = {}\\n diff = 0\\n\\n for chunk in output:\\n event = json.loads(chunk)\\n all_events.append(event)\\n\\n if 'progress' in event or 'progressDetail' in event:\\n image_id = event['id']\\n\\n if image_id in lines:\\n diff = len(lines) - lines[image_id]\\n else:\\n lines[image_id] = len(lines)\\n stream.write(\\\"\\\\n\\\")\\n diff = 0\\n\\n if is_terminal:\\n # move cursor up `diff` rows\\n stream.write(\\\"%c[%dA\\\" % (27, diff))\\n\\n print_output_event(event, stream, is_terminal)\\n\\n if 'id' in event and is_terminal:\\n # move cursor back down\\n stream.write(\\\"%c[%dB\\\" % (27, diff))\\n\\n stream.flush()\\n\\n return all_events\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/progress_stream.py::print_output_event\",\n \"text\": \"fig/progress_stream.py::print_output_event\\ndef print_output_event(event, stream, is_terminal):\\n if 'errorDetail' in event:\\n raise StreamOutputError(event['errorDetail']['message'])\\n\\n terminator = ''\\n\\n if is_terminal and 'stream' not in event:\\n # erase current line\\n stream.write(\\\"%c[2K\\\\r\\\" % 27)\\n terminator = \\\"\\\\r\\\"\\n pass\\n elif 'progressDetail' in event:\\n return\\n\\n if 'time' in event:\\n stream.write(\\\"[%s] \\\" % event['time'])\\n\\n if 'id' in event:\\n stream.write(\\\"%s: \\\" % event['id'])\\n\\n if 'from' in event:\\n stream.write(\\\"(from %s) \\\" % event['from'])\\n\\n status = event.get('status', '')\\n\\n if 'progress' in event:\\n stream.write(\\\"%s %s%s\\\" % (status, event['progress'], terminator))\\n elif 'progressDetail' in event:\\n detail = event['progressDetail']\\n if 'current' in detail:\\n percentage = float(detail['current']) / float(detail['total']) * 100\\n stream.write('%s (%.1f%%)%s' % (status, percentage, terminator))\\n else:\\n stream.write('%s%s' % (status, terminator))\\n elif 'stream' in event:\\n stream.write(\\\"%s%s\\\" % (event['stream'], terminator))\\n else:\\n stream.write(\\\"%s%s\\\\n\\\" % (status, terminator))\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::sort_service_dicts\",\n \"text\": \"fig/project.py::sort_service_dicts\\ndef sort_service_dicts(services):\\n # Topological sort (Cormen/Tarjan algorithm).\\n unmarked = services[:]\\n temporary_marked = set()\\n sorted_services = []\\n\\n get_service_names = lambda links: [link.split(':')[0] for link in links]\\n\\n def visit(n):\\n if n['name'] in temporary_marked:\\n if n['name'] in get_service_names(n.get('links', [])):\\n raise DependencyError('A service can not link to itself: %s' % n['name'])\\n if n['name'] in n.get('volumes_from', []):\\n raise DependencyError('A service can not mount itself as volume: %s' % n['name'])\\n else:\\n raise DependencyError('Circular import between %s' % ' and '.join(temporary_marked))\\n if n in unmarked:\\n temporary_marked.add(n['name'])\\n dependents = [m for m in services if (n['name'] in get_service_names(m.get('links', []))) or (n['name'] in m.get('volumes_from', []))]\\n for m in dependents:\\n visit(m)\\n temporary_marked.remove(n['name'])\\n unmarked.remove(n)\\n sorted_services.insert(0, n)\\n\\n while unmarked:\\n visit(unmarked[-1])\\n\\n return sorted_services\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::sort_service_dicts.visit\",\n \"text\": \"fig/project.py::sort_service_dicts.visit\\n def visit(n):\\n if n['name'] in temporary_marked:\\n if n['name'] in get_service_names(n.get('links', [])):\\n raise DependencyError('A service can not link to itself: %s' % n['name'])\\n if n['name'] in n.get('volumes_from', []):\\n raise DependencyError('A service can not mount itself as volume: %s' % n['name'])\\n else:\\n raise DependencyError('Circular import between %s' % ' and '.join(temporary_marked))\\n if n in unmarked:\\n temporary_marked.add(n['name'])\\n dependents = [m for m in services if (n['name'] in get_service_names(m.get('links', []))) or (n['name'] in m.get('volumes_from', []))]\\n for m in dependents:\\n visit(m)\\n temporary_marked.remove(n['name'])\\n unmarked.remove(n)\\n sorted_services.insert(0, n)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.__init__\",\n \"text\": \"fig/project.py::Project.__init__\\n def __init__(self, name, services, client):\\n self.name = name\\n self.services = services\\n self.client = client\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.from_dicts\",\n \"text\": \"fig/project.py::Project.from_dicts\\n @classmethod\\n def from_dicts(cls, name, service_dicts, client):\\n \\\"\\\"\\\"\\n Construct a ServiceCollection from a list of dicts representing services.\\n \\\"\\\"\\\"\\n project = cls(name, [], client)\\n for service_dict in sort_service_dicts(service_dicts):\\n links = project.get_links(service_dict)\\n volumes_from = project.get_volumes_from(service_dict)\\n\\n project.services.append(Service(client=client, project=name, links=links, volumes_from=volumes_from, **service_dict))\\n return project\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.from_config\",\n \"text\": \"fig/project.py::Project.from_config\\n @classmethod\\n def from_config(cls, name, config, client):\\n dicts = []\\n for service_name, service in list(config.items()):\\n if not isinstance(service, dict):\\n raise ConfigurationError('Service \\\"%s\\\" doesn\\\\'t have any configuration options. All top level keys in your fig.yml must map to a dictionary of configuration options.')\\n service['name'] = service_name\\n dicts.append(service)\\n return cls.from_dicts(name, dicts, client)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.get_service\",\n \"text\": \"fig/project.py::Project.get_service\\n def get_service(self, name):\\n \\\"\\\"\\\"\\n Retrieve a service by name. Raises NoSuchService\\n if the named service does not exist.\\n \\\"\\\"\\\"\\n for service in self.services:\\n if service.name == name:\\n return service\\n\\n raise NoSuchService(name)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.get_services\",\n \"text\": \"fig/project.py::Project.get_services\\n def get_services(self, service_names=None, include_links=False):\\n \\\"\\\"\\\"\\n Returns a list of this project's services filtered\\n by the provided list of names, or all services if service_names is None\\n or [].\\n\\n If include_links is specified, returns a list including the links for\\n service_names, in order of dependency.\\n\\n Preserves the original order of self.services where possible,\\n reordering as needed to resolve links.\\n\\n Raises NoSuchService if any of the named services do not exist.\\n \\\"\\\"\\\"\\n if service_names is None or len(service_names) == 0:\\n return self.get_services(\\n service_names=[s.name for s in self.services],\\n include_links=include_links\\n )\\n else:\\n unsorted = [self.get_service(name) for name in service_names]\\n services = [s for s in self.services if s in unsorted]\\n\\n if include_links:\\n services = reduce(self._inject_links, services, [])\\n\\n uniques = []\\n [uniques.append(s) for s in services if s not in uniques]\\n return uniques\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.get_links\",\n \"text\": \"fig/project.py::Project.get_links\\n def get_links(self, service_dict):\\n links = []\\n if 'links' in service_dict:\\n for link in service_dict.get('links', []):\\n if ':' in link:\\n service_name, link_name = link.split(':', 1)\\n else:\\n service_name, link_name = link, None\\n try:\\n links.append((self.get_service(service_name), link_name))\\n except NoSuchService:\\n raise ConfigurationError('Service \\\"%s\\\" has a link to service \\\"%s\\\" which does not exist.' % (service_dict['name'], service_name))\\n del service_dict['links']\\n return links\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.get_volumes_from\",\n \"text\": \"fig/project.py::Project.get_volumes_from\\n def get_volumes_from(self, service_dict):\\n volumes_from = []\\n if 'volumes_from' in service_dict:\\n for volume_name in service_dict.get('volumes_from', []):\\n try:\\n service = self.get_service(volume_name)\\n volumes_from.append(service)\\n except NoSuchService:\\n try:\\n container = Container.from_id(self.client, volume_name)\\n volumes_from.append(container)\\n except APIError:\\n raise ConfigurationError('Service \\\"%s\\\" mounts volumes from \\\"%s\\\", which is not the name of a service or container.' % (service_dict['name'], volume_name))\\n del service_dict['volumes_from']\\n return volumes_from\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.start\",\n \"text\": \"fig/project.py::Project.start\\n def start(self, service_names=None, **options):\\n for service in self.get_services(service_names):\\n service.start(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.stop\",\n \"text\": \"fig/project.py::Project.stop\\n def stop(self, service_names=None, **options):\\n for service in reversed(self.get_services(service_names)):\\n service.stop(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.kill\",\n \"text\": \"fig/project.py::Project.kill\\n def kill(self, service_names=None, **options):\\n for service in reversed(self.get_services(service_names)):\\n service.kill(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.build\",\n \"text\": \"fig/project.py::Project.build\\n def build(self, service_names=None, no_cache=False):\\n for service in self.get_services(service_names):\\n if service.can_be_built():\\n service.build(no_cache)\\n else:\\n log.info('%s uses an image, skipping' % service.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.up\",\n \"text\": \"fig/project.py::Project.up\\n def up(self, service_names=None, start_links=True, recreate=True):\\n running_containers = []\\n\\n for service in self.get_services(service_names, include_links=start_links):\\n if recreate:\\n for (_, container) in service.recreate_containers():\\n running_containers.append(container)\\n else:\\n for container in service.start_or_create_containers():\\n running_containers.append(container)\\n\\n return running_containers\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.remove_stopped\",\n \"text\": \"fig/project.py::Project.remove_stopped\\n def remove_stopped(self, service_names=None, **options):\\n for service in self.get_services(service_names):\\n service.remove_stopped(**options)\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project.containers\",\n \"text\": \"fig/project.py::Project.containers\\n def containers(self, service_names=None, *args, **kwargs):\\n l = []\\n for service in self.get_services(service_names):\\n for container in service.containers(*args, **kwargs):\\n l.append(container)\\n return l\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::Project._inject_links\",\n \"text\": \"fig/project.py::Project._inject_links\\n def _inject_links(self, acc, service):\\n linked_names = service.get_linked_names()\\n\\n if len(linked_names) > 0:\\n linked_services = self.get_services(\\n service_names=linked_names,\\n include_links=True\\n )\\n else:\\n linked_services = []\\n\\n linked_services.append(service)\\n return acc + linked_services\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::NoSuchService.__init__\",\n \"text\": \"fig/project.py::NoSuchService.__init__\\n def __init__(self, name):\\n self.name = name\\n self.msg = \\\"No such service: %s\\\" % self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::NoSuchService.__str__\",\n \"text\": \"fig/project.py::NoSuchService.__str__\\n def __str__(self):\\n return self.msg\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::ConfigurationError.__init__\",\n \"text\": \"fig/project.py::ConfigurationError.__init__\\n def __init__(self, msg):\\n self.msg = msg\",\n \"label\": 0\n },\n {\n \"cid\": \"fig/project.py::ConfigurationError.__str__\",\n \"text\": \"fig/project.py::ConfigurationError.__str__\\n def __str__(self):\\n return self.msg\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::read\",\n \"text\": \"setup.py::read\\ndef read(*parts):\\n path = os.path.join(os.path.dirname(__file__), *parts)\\n with codecs.open(path, encoding='utf-8') as fobj:\\n return fobj.read()\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::find_version\",\n \"text\": \"setup.py::find_version\\ndef find_version(*file_paths):\\n version_file = read(*file_paths)\\n version_match = re.search(r\\\"^__version__ = ['\\\\\\\"]([^'\\\\\\\"]*)['\\\\\\\"]\\\",\\n version_file, re.M)\\n if version_match:\\n return version_match.group(1)\\n raise RuntimeError(\\\"Unable to find version string.\\\")\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":274,"cells":{"instance_id":{"kind":"string","value":"PrefectHQ__prefect-1059"},"repo":{"kind":"string","value":"PrefectHQ/prefect"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"PIN: Prefect CLI\n# PIN: Prefect CLI\r\n\r\nDate: May 20, 2019\r\nAuthor: Josh Meek\r\n\r\n## Status\r\n\r\nProposed\r\n\r\n## Context\r\n\r\nA command line interface is an incredibly powerful and useful tool for many software products. Having a well rounded CLI in Prefect should be no exception. After this is adopted, Prefect Cloud will have three modes to work with: visually through the UI, programmatically through the GraphQL API, and textually through the CLI.\r\n\r\n## Proposal\r\n\r\nI am proposing the creation of a Prefect CLI that will be used both for Prefect Cloud control as well as local flow execution. The CLI will definitely be more Cloud focused however it will also allow for some control of locally existing flows, storage, and environments.\r\n\r\nAll of the groups and commands do not need to be built out on first pass and instead they should gradually evolve over time to suit user needs. Below I will go into small details about each group. These are not the final groups and are subject to change.\r\n\r\n### Help Texts\r\n\r\nMock of the main help text when running `prefect` from the command line:\r\n```\r\n$ prefect\r\nUsage: prefect [OPTIONS] COMMAND [ARGS]...\r\n\r\n The Prefect CLI for creating, managing, and inspecting your flows.\r\n\r\n Note: a Prefect Cloud auth token is required for all Cloud related commands. If a token\r\n is not set in your Prefect config.toml then run `prefect auth add` to set it.\r\n\r\n Query Commands:\r\n get List high-level object information\r\n describe Retrieve detailed object descriptions\r\n summarize Aggregate query information\r\n\r\n Execution Commands:\r\n execute Execute a flow's environment\r\n run Run a flow\r\n\r\n Setup Commands:\r\n auth Handle Prefect Cloud authorization\r\n\r\n Miscellaneous Commands:\r\n version Get your current Prefect version\r\n config Output your Prefect config\r\n\r\nOptions:\r\n -h, --help Show this message and exit.\r\n```\r\n\r\nMock of a group's commands (from `prefect get`):\r\n```\r\n$ prefect get --help\r\nUsage: prefect get [OPTIONS] COMMAND [ARGS]...\r\n\r\n Get commands that refer to querying Prefect Cloud metadata.\r\n\r\n Usage:\r\n $ prefect get [OBJECT]\r\n\r\n Arguments:\r\n flow-runs Query flow runs\r\n flows Query flows\r\n projects Query projects\r\n tasks Query tasks\r\n\r\n Examples:\r\n $ prefect get flows\r\n NAME VERSION PROJECT NAME AGE\r\n My-Flow 3 My-Project 3 days ago\r\n\r\n $ prefect get flows --project New-Proj --all-versions\r\n NAME VERSION PROJECT NAME AGE\r\n Test-Flow 2 New-Proj 22 hours ago\r\n Test-Flow 1 New-Proj 1 month ago\r\n\r\n $ prefect get tasks --flow-name Test-Flow\r\n NAME FLOW NAME FLOW VERSION AGE MAPPED TYPE\r\n first_task Test-Flow 1 5 days ago False prefect.tasks.core.function.FunctionTask\r\n second_task Test-Flow 1 5 days ago True prefect.tasks.core.function.FunctionTask\r\n\r\nOptions:\r\n -h, --help Show this message and exit.\r\n```\r\n\r\n### Query Commands\r\nThese commands will be strictly cloud focused. Since Prefect Cloud allows rich metadata object manipulation and inspection, the CLI can query for their information in various ways.\r\n\r\n`prefect get` will retrieve table formatted high level information about Prefect objects\r\n\r\n`prefect describe` will retrieve more detailed JSON formatted metadata about Prefect objects\r\n\r\n`prefect summarize` will retrieve summary statistics over time for Prefect objects\r\n\r\n### Execution Commands\r\nThese commands deal with actual execution of code (both Cloud and local).\r\n\r\n`prefect execute` allows users to specify environment information from Flow objects and both the `setup` and `execute` environment functions will be run\r\n\r\n`prefect run` allows users to run their Flows via platforms and storage options such as Cloud, local, Docker, etc...\r\n\r\n### Setup Commands\r\nThese commands enable users to "},"gold_files":{"kind":"list like","value":["src/prefect/cli/__init__.py"],"string":"[\n \"src/prefect/cli/__init__.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["src/prefect/cli/__init__.py::<module>","src/prefect/cli/__init__.py::cli","src/prefect/cli/__init__.py::execute_cloud_flow","src/prefect/cli/__init__.py::execute_flow"],"string":"[\n \"src/prefect/cli/__init__.py::<module>\",\n \"src/prefect/cli/__init__.py::cli\",\n \"src/prefect/cli/__init__.py::execute_cloud_flow\",\n \"src/prefect/cli/__init__.py::execute_flow\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"examples/airflow_tutorial_dag.py","text":"examples/airflow_tutorial_dag.py\ndef add_7()","label":0},{"cid":"src/prefect/cli/__init__.py","text":"src/prefect/cli/__init__.py\ndef cli() # The Prefect CLI\ndef execute_flow(storage_metadata, environment_metadata, flow_location)\ndef execute_cloud_flow()","label":1},{"cid":"src/prefect/environments/storage/docker.py","text":"src/prefect/environments/storage/docker.py\nclass Docker # Docker storage provides a mechanism for storing Prefect flows in Docker images\n def __init__(self, registry_url: str=None, base_image: str=None, python_dependencies: List[str]=None, image_name: str=None, image_tag: str=None, env_vars: dict=None, files: dict=None, base_url: str='unix://var/run/docker.sock', prefect_version: str=None, local_image: bool=False)\n def get_env_runner(self, flow_location: str) # Given a flow_location within this Storage object, returns something with a\n def runner(env: dict) # Given a dictionary of environment variables, calls `flow.run()` with these\n def add_flow(self, flow: 'prefect.core.flow.Flow') # Method for adding a new flow to this Storage object.\n def name(self) # Full name of the Docker image.\n def __contains__(self, obj: Any) # Method for determining whether an object is contained within this storage.\n def build(self, push: bool=True) # Build the Docker storage object.\n def build_image(self, push: bool=True) # Build a Docker image using the docker python library.\n def create_dockerfile_object(self, directory: str=None) # Writes a dockerfile to the provided directory using the specified\n def pull_image(self) # Pull the image specified so it can be built.\n def push_image(self, image_name: str, image_tag: str) # Push this environment to a registry\n def _parse_generator_output(self, generator: Iterable) # Parses and writes a Docker command's output to stdout","label":0},{"cid":"versioneer.py","text":"versioneer.py\nclass VersioneerConfig # Container for Versioneer configuration parameters.\ndef get_root() # Get the project root directory.\ndef get_config_from_root(root) # Read the project setup.cfg file to determine Versioneer config.\n def get(parser, name)\nclass NotThisMethod # Exception raised if a method is not valid for the current scenario.\ndef register_vcs_handler(vcs, method) # Decorator to mark a method as the handler for a particular VCS.\n def decorate(f) # Store f in HANDLERS[vcs][method].\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None) # Call the given command(s).\ndef git_get_keywords(versionfile_abs) # Extract version information from the given file.\ndef git_versions_from_keywords(keywords, tag_prefix, verbose) # Get version information from git keywords.\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command) # Get version from 'git describe' in the root of the source tree.\ndef do_vcs_install(manifest_in, versionfile_source, ipy) # Git-specific installation logic for Versioneer.\ndef versions_from_parentdir(parentdir_prefix, root, verbose) # Try to determine the version from the parent directory name.\ndef versions_from_file(filename) # Try to determine the version from _version.py if present.\ndef write_to_version_file(filename, versions) # Write the given version number to the given _version.py file.\ndef plus_or_dot(pieces) # Return a + if we don't already have one, else return a .\ndef render_pep440(pieces) # Build up version string, with post-release \"local version identifier\".\ndef render_pep440_pre(pieces) # TAG[.post.devDISTANCE] -- No -dirty.\ndef render_pep440_post(pieces) # TAG[.postDISTANCE[.dev0]+gHEX] .\ndef render_pep440_old(pieces) # TAG[.postDISTANCE[.dev0]] .\ndef render_git_describe(pieces) # TAG[-DISTANCE-gHEX][-dirty].\ndef render_git_describe_long(pieces) # TAG-DISTANCE-gHEX[-dirty].\ndef render(pieces, style) # Render the given version pieces into the requested style.\nclass VersioneerBadRootError # The project root directory is unknown or missing key files.\ndef get_versions(verbose=False) # Get the project version from whatever source is available.\ndef get_version() # Get the short version string for this project.\ndef get_cmdclass() # Get the custom setuptools/distutils subclasses used by Versioneer.\n class cmd_version\n def initialize_options(self)\n def finalize_options(self)\n def run(self)\n class cmd_build_py\n def run(self)\n class cmd_sdist\n def run(self)\n def make_release_tree(self, base_dir, files)\ndef do_setup() # Main VCS-independent setup function for installing Versioneer.\ndef scan_setup_py() # Validate the contents of setup.py against Versioneer's expectations.","label":0}],"string":"[\n {\n \"cid\": \"examples/airflow_tutorial_dag.py\",\n \"text\": \"examples/airflow_tutorial_dag.py\\ndef add_7()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/cli/__init__.py\",\n \"text\": \"src/prefect/cli/__init__.py\\ndef cli() # The Prefect CLI\\ndef execute_flow(storage_metadata, environment_metadata, flow_location)\\ndef execute_cloud_flow()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/environments/storage/docker.py\",\n \"text\": \"src/prefect/environments/storage/docker.py\\nclass Docker # Docker storage provides a mechanism for storing Prefect flows in Docker images\\n def __init__(self, registry_url: str=None, base_image: str=None, python_dependencies: List[str]=None, image_name: str=None, image_tag: str=None, env_vars: dict=None, files: dict=None, base_url: str='unix://var/run/docker.sock', prefect_version: str=None, local_image: bool=False)\\n def get_env_runner(self, flow_location: str) # Given a flow_location within this Storage object, returns something with a\\n def runner(env: dict) # Given a dictionary of environment variables, calls `flow.run()` with these\\n def add_flow(self, flow: 'prefect.core.flow.Flow') # Method for adding a new flow to this Storage object.\\n def name(self) # Full name of the Docker image.\\n def __contains__(self, obj: Any) # Method for determining whether an object is contained within this storage.\\n def build(self, push: bool=True) # Build the Docker storage object.\\n def build_image(self, push: bool=True) # Build a Docker image using the docker python library.\\n def create_dockerfile_object(self, directory: str=None) # Writes a dockerfile to the provided directory using the specified\\n def pull_image(self) # Pull the image specified so it can be built.\\n def push_image(self, image_name: str, image_tag: str) # Push this environment to a registry\\n def _parse_generator_output(self, generator: Iterable) # Parses and writes a Docker command's output to stdout\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py\",\n \"text\": \"versioneer.py\\nclass VersioneerConfig # Container for Versioneer configuration parameters.\\ndef get_root() # Get the project root directory.\\ndef get_config_from_root(root) # Read the project setup.cfg file to determine Versioneer config.\\n def get(parser, name)\\nclass NotThisMethod # Exception raised if a method is not valid for the current scenario.\\ndef register_vcs_handler(vcs, method) # Decorator to mark a method as the handler for a particular VCS.\\n def decorate(f) # Store f in HANDLERS[vcs][method].\\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None) # Call the given command(s).\\ndef git_get_keywords(versionfile_abs) # Extract version information from the given file.\\ndef git_versions_from_keywords(keywords, tag_prefix, verbose) # Get version information from git keywords.\\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command) # Get version from 'git describe' in the root of the source tree.\\ndef do_vcs_install(manifest_in, versionfile_source, ipy) # Git-specific installation logic for Versioneer.\\ndef versions_from_parentdir(parentdir_prefix, root, verbose) # Try to determine the version from the parent directory name.\\ndef versions_from_file(filename) # Try to determine the version from _version.py if present.\\ndef write_to_version_file(filename, versions) # Write the given version number to the given _version.py file.\\ndef plus_or_dot(pieces) # Return a + if we don't already have one, else return a .\\ndef render_pep440(pieces) # Build up version string, with post-release \\\"local version identifier\\\".\\ndef render_pep440_pre(pieces) # TAG[.post.devDISTANCE] -- No -dirty.\\ndef render_pep440_post(pieces) # TAG[.postDISTANCE[.dev0]+gHEX] .\\ndef render_pep440_old(pieces) # TAG[.postDISTANCE[.dev0]] .\\ndef render_git_describe(pieces) # TAG[-DISTANCE-gHEX][-dirty].\\ndef render_git_describe_long(pieces) # TAG-DISTANCE-gHEX[-dirty].\\ndef render(pieces, style) # Render the given version pieces into the requested style.\\nclass VersioneerBadRootError # The project root directory is unknown or missing key files.\\ndef get_versions(verbose=False) # Get the project version from whatever source is available.\\ndef get_version() # Get the short version string for this project.\\ndef get_cmdclass() # Get the custom setuptools/distutils subclasses used by Versioneer.\\n class cmd_version\\n def initialize_options(self)\\n def finalize_options(self)\\n def run(self)\\n class cmd_build_py\\n def run(self)\\n class cmd_sdist\\n def run(self)\\n def make_release_tree(self, base_dir, files)\\ndef do_setup() # Main VCS-independent setup function for installing Versioneer.\\ndef scan_setup_py() # Validate the contents of setup.py against Versioneer's expectations.\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"examples/airflow_tutorial_dag.py::add_7","text":"examples/airflow_tutorial_dag.py::add_7\n@task(max_retries=1, retry_delay=retry_delay)\ndef add_7():\n date = prefect.context.get(\"scheduled_start_time\", datetime.utcnow())\n return date + timedelta(days=7)","label":0},{"cid":"src/prefect/cli/__init__.py::cli","text":"src/prefect/cli/__init__.py::cli\n@click.group()\ndef cli():\n \"\"\"\n The Prefect CLI\n \"\"\"\n pass","label":1},{"cid":"src/prefect/cli/__init__.py::execute_flow","text":"src/prefect/cli/__init__.py::execute_flow\n@cli.command()\n@click.argument(\"storage_metadata\")\n@click.argument(\"environment_metadata\")\n@click.argument(\"flow_location\")\ndef execute_flow(storage_metadata, environment_metadata, flow_location):\n \"\"\"\"\"\"\n storage_schema = prefect.serialization.storage.StorageSchema()\n storage = storage_schema.load(json.loads(storage_metadata))\n\n environment_schema = prefect.serialization.environment.EnvironmentSchema()\n environment = environment_schema.load(json.loads(environment_metadata))\n\n environment.setup(storage)\n environment.execute(storage, flow_location)","label":1},{"cid":"src/prefect/cli/__init__.py::execute_cloud_flow","text":"src/prefect/cli/__init__.py::execute_cloud_flow\n@cli.command()\ndef execute_cloud_flow():\n flow_run_id = prefect.context.get(\"flow_run_id\")\n if not flow_run_id:\n click.echo(\"Not currently executing a flow within a cloud context.\")\n return\n\n query = {\n \"query\": {\n with_args(\"flow_run\", {\"where\": {\"id\": {\"_eq\": flow_run_id}}}): {\n \"flow\": {\"name\": True, \"storage\": True, \"environment\": True}\n }\n }\n }\n\n result = Client().graphql(query)\n\n flow_data = result.data.flow_run[0].flow\n\n storage_schema = prefect.serialization.storage.StorageSchema()\n storage = storage_schema.load(flow_data.storage)\n\n environment_schema = prefect.serialization.environment.EnvironmentSchema()\n environment = environment_schema.load(flow_data.environment)\n\n environment.setup(storage=storage)\n environment.execute(storage=storage, flow_location=storage.flows[flow_data.name])","label":1},{"cid":"src/prefect/environments/storage/docker.py::Docker.__init__","text":"src/prefect/environments/storage/docker.py::Docker.__init__\n def __init__(\n self,\n registry_url: str = None,\n base_image: str = None,\n python_dependencies: List[str] = None,\n image_name: str = None,\n image_tag: str = None,\n env_vars: dict = None,\n files: dict = None,\n base_url: str = \"unix://var/run/docker.sock\",\n prefect_version: str = None,\n local_image: bool = False,\n ) -> None:\n self.registry_url = registry_url\n\n if base_image is None:\n python_version = \"{}.{}\".format(\n sys.version_info.major, sys.version_info.minor\n )\n self.base_image = \"python:{}\".format(python_version)\n else:\n self.base_image = base_image\n\n self.image_name = image_name\n self.image_tag = image_tag\n self.python_dependencies = python_dependencies or []\n self.env_vars = env_vars or {}\n self.files = files or {}\n self.flows = dict() # type: Dict[str, str]\n self._flows = dict() # type: Dict[str, \"prefect.core.flow.Flow\"]\n self.base_url = base_url\n self.local_image = local_image\n\n version = prefect.__version__.split(\"+\")\n if prefect_version is None:\n self.prefect_version = \"master\" if len(version) > 1 else version[0]\n else:\n self.prefect_version = prefect_version\n\n not_absolute = [\n file_path for file_path in self.files if not os.path.isabs(file_path)\n ]\n if not_absolute:\n raise ValueError(\n \"Provided paths {} are not absolute file paths, please provide absolute paths only.\".format(\n \", \".join(not_absolute)\n )\n )","label":0},{"cid":"src/prefect/environments/storage/docker.py::Docker.get_env_runner","text":"src/prefect/environments/storage/docker.py::Docker.get_env_runner\n def get_env_runner(self, flow_location: str) -> Callable[[Dict[str, str]], None]:\n \"\"\"\n Given a flow_location within this Storage object, returns something with a\n `run()` method which accepts the standard runner kwargs and can run the flow.\n\n Args:\n - flow_location (str): the location of a flow within this Storage\n\n Returns:\n - a runner interface (something with a `run()` method for running the flow)\n \"\"\"\n\n def runner(env: dict) -> None:\n \"\"\"\n Given a dictionary of environment variables, calls `flow.run()` with these\n environment variables set.\n \"\"\"\n image = \"{}:{}\".format(self.image_name, self.image_tag)\n client = docker.APIClient(base_url=self.base_url, version=\"auto\")\n container = client.create_container(image, command=\"tail -f /dev/null\")\n client.start(container=container.get(\"Id\"))\n python_script = \"import cloudpickle; f = open('{}', 'rb'); flow = cloudpickle.load(f); f.close(); flow.run()\".format(\n flow_location\n )\n try:\n ee = client.exec_create(\n container.get(\"Id\"),\n 'python -c \"{}\"'.format(python_script),\n environment=env,\n )\n output = client.exec_start(exec_id=ee, stream=True)\n for item in output:\n for line in item.decode(\"utf-8\").split(\"\\n\"):\n if line:\n print(line)\n finally:\n client.stop(container=container.get(\"Id\"))\n\n return runner","label":0},{"cid":"src/prefect/environments/storage/docker.py::Docker.get_env_runner.runner","text":"src/prefect/environments/storage/docker.py::Docker.get_env_runner.runner\n def runner(env: dict) -> None:\n \"\"\"\n Given a dictionary of environment variables, calls `flow.run()` with these\n environment variables set.\n \"\"\"\n image = \"{}:{}\".format(self.image_name, self.image_tag)\n client = docker.APIClient(base_url=self.base_url, version=\"auto\")\n container = client.create_container(image, command=\"tail -f /dev/null\")\n client.start(container=container.get(\"Id\"))\n python_script = \"import cloudpickle; f = open('{}', 'rb'); flow = cloudpickle.load(f); f.close(); flow.run()\".format(\n flow_location\n )\n try:\n ee = client.exec_create(\n container.get(\"Id\"),\n 'python -c \"{}\"'.format(python_script),\n environment=env,\n )\n output = client.exec_start(exec_id=ee, stream=True)\n for item in output:\n for line in item.decode(\"utf-8\").split(\"\\n\"):\n if line:\n print(line)\n finally:\n client.stop(container=container.get(\"Id\"))","label":0},{"cid":"src/prefect/environments/storage/docker.py::Docker.add_flow","text":"src/prefect/environments/storage/docker.py::Docker.add_flow\n def add_flow(self, flow: \"prefect.core.flow.Flow\") -> str:\n \"\"\"\n Method for adding a new flow to this Storage object.\n\n Args:\n - flow (Flow): a Prefect Flow to add\n\n Returns:\n - str: the location of the newly added flow in this Storage object\n \"\"\"\n if flow.name in self:\n raise ValueError(\n 'Name conflict: Flow with the name \"{}\" is already present in this storage.'.format(\n flow.name\n )\n )\n flow_path = \"/root/.prefect/{}.prefect\".format(slugify(flow.name))\n self.flows[flow.name] = flow_path\n self._flows[flow.name] = flow # needed prior to build\n return flow_path","label":0},{"cid":"src/prefect/environments/storage/docker.py::Docker.name","text":"src/prefect/environments/storage/docker.py::Docker.name\n @property\n def name(self) -> str:\n \"\"\"\n Full name of the Docker image.\n \"\"\"\n if None in [self.registry_url, self.image_name, self.image_tag]:\n raise ValueError(\"Docker storage is missing required fields\")\n\n return \"{}:{}\".format(\n os.path.join(self.registry_url, self.image_name), # type: ignore\n self.image_tag, # type: ignore\n )","label":0},{"cid":"src/prefect/environments/storage/docker.py::Docker.__contains__","text":"src/prefect/environments/storage/docker.py::Docker.__contains__\n def __contains__(self, obj: Any) -> bool:\n \"\"\"\n Method for determining whether an object is contained within this storage.\n \"\"\"\n if not isinstance(obj, str):\n return False\n return obj in self.flows","label":0},{"cid":"src/prefect/environments/storage/docker.py::Docker.build","text":"src/prefect/environments/storage/docker.py::Docker.build\n def build(self, push: bool = True) -> \"Storage\":\n \"\"\"\n Build the Docker storage object.\n\n Args:\n - push (bool, optional): Whether or not to push the built Docker image, this\n requires the `registry_url` to be set\n\n Returns:\n - Docker: a new Docker storage object that contains information about how and\n where the flow is stored. Image name and tag are generated during the\n build process.\n\n Raises:\n - InterruptedError: if either pushing or pulling the image fails\n \"\"\"\n image_name, image_tag = self.build_image(push=push)\n self.image_name = image_name\n self.image_tag = image_tag\n\n return self","label":0},{"cid":"src/prefect/environments/storage/docker.py::Docker.build_image","text":"src/prefect/environments/storage/docker.py::Docker.build_image\n def build_image(self, push: bool = True) -> tuple:\n \"\"\"\n Build a Docker image using the docker python library.\n\n Args:\n - push (bool, optional): Whether or not to push the built Docker image, this\n requires the `registry_url` to be set\n\n Returns:\n - tuple: generated UUID strings `image_name`, `image_tag`\n\n Raises:\n - InterruptedError: if either pushing or pulling the image fails\n \"\"\"\n image_name = self.image_name or str(uuid.uuid4())\n image_tag = self.image_tag or str(uuid.uuid4())\n\n # Make temporary directory to hold serialized flow, healthcheck script, and dockerfile\n with tempfile.TemporaryDirectory() as tempdir:\n\n # Build the dockerfile\n if self.base_image and not self.local_image:\n self.pull_image()\n\n self.create_dockerfile_object(directory=tempdir)\n client = docker.APIClient(base_url=self.base_url, version=\"auto\")\n\n # Verify that a registry url has been provided for images that should be pushed\n if self.registry_url:\n full_name = os.path.join(self.registry_url, image_name)\n elif push is True:\n raise ValueError(\n \"This environment has no `registry_url`, and cannot be pushed.\"\n )\n else:\n full_name = image_name\n\n # Use the docker client to build the image\n logging.info(\"Building the flow's Docker storage...\")\n output = client.build(\n path=tempdir, tag=\"{}:{}\".format(full_name, image_tag), forcerm=True\n )\n self._parse_generator_output(output)\n\n if len(client.images(name=full_name)) == 0:\n raise SerializationError(\n \"Your flow failed to deserialize in the container; please ensure that all nec","label":0},{"cid":"src/prefect/environments/storage/docker.py::Docker.create_dockerfile_object","text":"src/prefect/environments/storage/docker.py::Docker.create_dockerfile_object\n def create_dockerfile_object(self, directory: str = None) -> None:\n \"\"\"\n Writes a dockerfile to the provided directory using the specified\n arguments on this Docker storage object.\n\n In order for the docker python library to build a container it needs a\n Dockerfile that it can use to define the container. This function takes the\n specified arguments then writes them to a temporary file called Dockerfile.\n\n *Note*: if `files` are added to this container, they will be copied to this directory as well.\n\n Args:\n - directory (str, optional): A directory where the Dockerfile will be created,\n if no directory is specified is will be created in the current working directory\n \"\"\"\n directory = directory or \"./\"\n\n with open(os.path.join(directory, \"Dockerfile\"), \"w+\") as dockerfile:\n\n # Generate RUN pip install commands for python dependencies\n pip_installs = \"\"\n if self.python_dependencies:\n for dependency in self.python_dependencies:\n pip_installs += \"RUN pip install {}\\n\".format(dependency)\n\n # Generate ENV variables to load into the image\n env_vars = \"\"\n if self.env_vars:\n white_space = \" \" * 20\n env_vars = \"ENV \" + \" \\ \\n{}\".format(white_space).join(\n \"{k}={v}\".format(k=k, v=v) for k, v in self.env_vars.items()\n )\n\n # Copy user specified files into the image\n copy_files = \"\"\n if self.files:\n for src, dest in self.files.items():\n fname = os.path.basename(src)\n full_fname = os.path.join(directory, fname)\n if (\n os.path.exists(full_fname)\n and filecmp.cmp(src, full_fname) is False\n ","label":0},{"cid":"src/prefect/environments/storage/docker.py::Docker.pull_image","text":"src/prefect/environments/storage/docker.py::Docker.pull_image\n def pull_image(self) -> None:\n \"\"\"Pull the image specified so it can be built.\n\n In order for the docker python library to use a base image it must be pulled\n from either the main docker registry or a separate registry that must be set as\n `registry_url` on this class.\n\n Raises:\n - InterruptedError: if either pulling the image fails\n \"\"\"\n client = docker.APIClient(base_url=self.base_url, version=\"auto\")\n\n output = client.pull(self.base_image, stream=True, decode=True)\n for line in output:\n if line.get(\"error\"):\n raise InterruptedError(line.get(\"error\"))\n if line.get(\"progress\"):\n print(line.get(\"status\"), line.get(\"progress\"), end=\"\\r\")\n print(\"\")","label":0},{"cid":"src/prefect/environments/storage/docker.py::Docker.push_image","text":"src/prefect/environments/storage/docker.py::Docker.push_image\n def push_image(self, image_name: str, image_tag: str) -> None:\n \"\"\"Push this environment to a registry\n\n Args:\n - image_name (str): Name for the image\n - image_tag (str): Tag for the image\n\n Raises:\n - InterruptedError: if either pushing the image fails\n \"\"\"\n client = docker.APIClient(base_url=self.base_url, version=\"auto\")\n\n logging.info(\"Pushing image to the registry...\")\n\n output = client.push(image_name, tag=image_tag, stream=True, decode=True)\n for line in output:\n if line.get(\"error\"):\n raise InterruptedError(line.get(\"error\"))\n if line.get(\"progress\"):\n print(line.get(\"status\"), line.get(\"progress\"), end=\"\\r\")\n print(\"\")","label":0},{"cid":"src/prefect/environments/storage/docker.py::Docker._parse_generator_output","text":"src/prefect/environments/storage/docker.py::Docker._parse_generator_output\n def _parse_generator_output(self, generator: Iterable) -> None:\n \"\"\"\n Parses and writes a Docker command's output to stdout\n \"\"\"\n for item in generator:\n item = item.decode(\"utf-8\")\n for line in item.split(\"\\n\"):\n if line:\n output = json.loads(line).get(\"stream\")\n if output and output != \"\\n\":\n print(output.strip(\"\\n\"))","label":0},{"cid":"versioneer.py::get_root","text":"versioneer.py::get_root\ndef get_root():\n \"\"\"Get the project root directory.\n\n We require that all commands are run from the project root, i.e. the\n directory that contains setup.py, setup.cfg, and versioneer.py .\n \"\"\"\n root = os.path.realpath(os.path.abspath(os.getcwd()))\n setup_py = os.path.join(root, \"setup.py\")\n versioneer_py = os.path.join(root, \"versioneer.py\")\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n # allow 'python path/to/setup.py COMMAND'\n root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))\n setup_py = os.path.join(root, \"setup.py\")\n versioneer_py = os.path.join(root, \"versioneer.py\")\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n err = (\n \"Versioneer was unable to run the project root directory. \"\n \"Versioneer requires setup.py to be executed from \"\n \"its immediate directory (like 'python setup.py COMMAND'), \"\n \"or in a way that lets it use sys.argv[0] to find the root \"\n \"(like 'python path/to/setup.py COMMAND').\"\n )\n raise VersioneerBadRootError(err)\n try:\n # Certain runtime workflows (setup.py install/develop in a setuptools\n # tree) execute all dependencies in a single python process, so\n # \"versioneer\" may be imported multiple times, and python's shared\n # module-import table will cache the first one. So we can't use\n # os.path.dirname(__file__), as that will find whichever\n # versioneer.py was first imported, even in later projects.\n me = os.path.realpath(os.path.abspath(__file__))\n me_dir = os.path.normcase(os.path.splitext(me)[0])\n vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])\n if me_dir != vsr_dir:\n print(\n \"Warning: build in %s is using versioneer.py from %s\"\n % (os.path.dirname(me), versioneer_py)\n )\n exc","label":0},{"cid":"versioneer.py::get_config_from_root","text":"versioneer.py::get_config_from_root\ndef get_config_from_root(root):\n \"\"\"Read the project setup.cfg file to determine Versioneer config.\"\"\"\n # This might raise EnvironmentError (if setup.cfg is missing), or\n # configparser.NoSectionError (if it lacks a [versioneer] section), or\n # configparser.NoOptionError (if it lacks \"VCS=\"). See the docstring at\n # the top of versioneer.py for instructions on writing your setup.cfg .\n setup_cfg = os.path.join(root, \"setup.cfg\")\n parser = configparser.SafeConfigParser()\n with open(setup_cfg, \"r\") as f:\n parser.readfp(f)\n VCS = parser.get(\"versioneer\", \"VCS\") # mandatory\n\n def get(parser, name):\n if parser.has_option(\"versioneer\", name):\n return parser.get(\"versioneer\", name)\n return None\n\n cfg = VersioneerConfig()\n cfg.VCS = VCS\n cfg.style = get(parser, \"style\") or \"\"\n cfg.versionfile_source = get(parser, \"versionfile_source\")\n cfg.versionfile_build = get(parser, \"versionfile_build\")\n cfg.tag_prefix = get(parser, \"tag_prefix\")\n if cfg.tag_prefix in (\"''\", '\"\"'):\n cfg.tag_prefix = \"\"\n cfg.parentdir_prefix = get(parser, \"parentdir_prefix\")\n cfg.verbose = get(parser, \"verbose\")\n return cfg","label":0},{"cid":"versioneer.py::get_config_from_root.get","text":"versioneer.py::get_config_from_root.get\n def get(parser, name):\n if parser.has_option(\"versioneer\", name):\n return parser.get(\"versioneer\", name)\n return None","label":0},{"cid":"versioneer.py::register_vcs_handler","text":"versioneer.py::register_vcs_handler\ndef register_vcs_handler(vcs, method): # decorator\n \"\"\"Decorator to mark a method as the handler for a particular VCS.\"\"\"\n\n def decorate(f):\n \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n if vcs not in HANDLERS:\n HANDLERS[vcs] = {}\n HANDLERS[vcs][method] = f\n return f\n\n return decorate","label":0},{"cid":"versioneer.py::register_vcs_handler.decorate","text":"versioneer.py::register_vcs_handler.decorate\n def decorate(f):\n \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n if vcs not in HANDLERS:\n HANDLERS[vcs] = {}\n HANDLERS[vcs][method] = f\n return f","label":0},{"cid":"versioneer.py::run_command","text":"versioneer.py::run_command\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):\n \"\"\"Call the given command(s).\"\"\"\n assert isinstance(commands, list)\n p = None\n for c in commands:\n try:\n dispcmd = str([c] + args)\n # remember shell=False, so use git.cmd on windows, not just git\n p = subprocess.Popen(\n [c] + args,\n cwd=cwd,\n env=env,\n stdout=subprocess.PIPE,\n stderr=(subprocess.PIPE if hide_stderr else None),\n )\n break\n except EnvironmentError:\n e = sys.exc_info()[1]\n if e.errno == errno.ENOENT:\n continue\n if verbose:\n print(\"unable to run %s\" % dispcmd)\n print(e)\n return None, None\n else:\n if verbose:\n print(\"unable to find command, tried %s\" % (commands,))\n return None, None\n stdout = p.communicate()[0].strip()\n if sys.version_info[0] >= 3:\n stdout = stdout.decode()\n if p.returncode != 0:\n if verbose:\n print(\"unable to run %s (error)\" % dispcmd)\n print(\"stdout was %s\" % stdout)\n return None, p.returncode\n return stdout, p.returncode","label":0},{"cid":"versioneer.py::git_get_keywords","text":"versioneer.py::git_get_keywords\n@register_vcs_handler(\"git\", \"get_keywords\")\ndef git_get_keywords(versionfile_abs):\n \"\"\"Extract version information from the given file.\"\"\"\n # the code embedded in _version.py can just fetch the value of these\n # keywords. When used from setup.py, we don't want to import _version.py,\n # so we do it with a regexp instead. This function is not used from\n # _version.py.\n keywords = {}\n try:\n f = open(versionfile_abs, \"r\")\n for line in f.readlines():\n if line.strip().startswith(\"git_refnames =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"refnames\"] = mo.group(1)\n if line.strip().startswith(\"git_full =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"full\"] = mo.group(1)\n if line.strip().startswith(\"git_date =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"date\"] = mo.group(1)\n f.close()\n except EnvironmentError:\n pass\n return keywords","label":0},{"cid":"versioneer.py::git_versions_from_keywords","text":"versioneer.py::git_versions_from_keywords\n@register_vcs_handler(\"git\", \"keywords\")\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\n \"\"\"Get version information from git keywords.\"\"\"\n if not keywords:\n raise NotThisMethod(\"no keywords at all, weird\")\n date = keywords.get(\"date\")\n if date is not None:\n # git-2.2.0 added \"%cI\", which expands to an ISO-8601 -compliant\n # datestamp. However we prefer \"%ci\" (which expands to an \"ISO-8601\n # -like\" string, which we must then edit to make compliant), because\n # it's been around since git-1.5.3, and it's too difficult to\n # discover which version we're using, or to work around using an\n # older one.\n date = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n refnames = keywords[\"refnames\"].strip()\n if refnames.startswith(\"$Format\"):\n if verbose:\n print(\"keywords are unexpanded, not using\")\n raise NotThisMethod(\"unexpanded keywords, not a git-archive tarball\")\n refs = set([r.strip() for r in refnames.strip(\"()\").split(\",\")])\n # starting in git-1.8.3, tags are listed as \"tag: foo-1.0\" instead of\n # just \"foo-1.0\". If we see a \"tag: \" prefix, prefer those.\n TAG = \"tag: \"\n tags = set([r[len(TAG) :] for r in refs if r.startswith(TAG)])\n if not tags:\n # Either we're using git < 1.8.3, or there really are no tags. We use\n # a heuristic: assume all version tags have a digit. The old git %d\n # expansion behaves like git log --decorate=short and strips out the\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\n # between branches and tags. By ignoring refnames without digits, we\n # filter out many common branch names like \"release\" and\n # \"stabilization\", as well as \"HEAD\" and \"master\".\n tags = set([r for r in refs if re.search(r\"\\d\", r)])\n if verbose:\n print(\"discarding '%s', no digits\" % \",\".join(refs - tags))\n","label":0},{"cid":"versioneer.py::git_pieces_from_vcs","text":"versioneer.py::git_pieces_from_vcs\n@register_vcs_handler(\"git\", \"pieces_from_vcs\")\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):\n \"\"\"Get version from 'git describe' in the root of the source tree.\n\n This only gets called if the git-archive 'subst' keywords were *not*\n expanded, and _version.py hasn't already been rewritten with a short\n version string, meaning we're inside a checked out source tree.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n\n out, rc = run_command(GITS, [\"rev-parse\", \"--git-dir\"], cwd=root, hide_stderr=True)\n if rc != 0:\n if verbose:\n print(\"Directory %s not under git control\" % root)\n raise NotThisMethod(\"'git rev-parse --git-dir' returned error\")\n\n # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\n # if there isn't one, this yields HEX[-dirty] (no NUM)\n describe_out, rc = run_command(\n GITS,\n [\n \"describe\",\n \"--tags\",\n \"--dirty\",\n \"--always\",\n \"--long\",\n \"--match\",\n \"%s*\" % tag_prefix,\n ],\n cwd=root,\n )\n # --long was added in git-1.5.5\n if describe_out is None:\n raise NotThisMethod(\"'git describe' failed\")\n describe_out = describe_out.strip()\n full_out, rc = run_command(GITS, [\"rev-parse\", \"HEAD\"], cwd=root)\n if full_out is None:\n raise NotThisMethod(\"'git rev-parse' failed\")\n full_out = full_out.strip()\n\n pieces = {}\n pieces[\"long\"] = full_out\n pieces[\"short\"] = full_out[:7] # maybe improved later\n pieces[\"error\"] = None\n\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\n # TAG might have hyphens.\n git_describe = describe_out\n\n # look for -dirty suffix\n dirty = git_describe.endswith(\"-dirty\")\n pieces[\"dirty\"] = dirty\n if dirty:\n git_describe = git_describe[: git_describe.rindex(\"-dirty","label":0},{"cid":"versioneer.py::do_vcs_install","text":"versioneer.py::do_vcs_install\ndef do_vcs_install(manifest_in, versionfile_source, ipy):\n \"\"\"Git-specific installation logic for Versioneer.\n\n For Git, this means creating/changing .gitattributes to mark _version.py\n for export-subst keyword substitution.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n files = [manifest_in, versionfile_source]\n if ipy:\n files.append(ipy)\n try:\n me = __file__\n if me.endswith(\".pyc\") or me.endswith(\".pyo\"):\n me = os.path.splitext(me)[0] + \".py\"\n versioneer_file = os.path.relpath(me)\n except NameError:\n versioneer_file = \"versioneer.py\"\n files.append(versioneer_file)\n present = False\n try:\n f = open(\".gitattributes\", \"r\")\n for line in f.readlines():\n if line.strip().startswith(versionfile_source):\n if \"export-subst\" in line.strip().split()[1:]:\n present = True\n f.close()\n except EnvironmentError:\n pass\n if not present:\n f = open(\".gitattributes\", \"a+\")\n f.write(\"%s export-subst\\n\" % versionfile_source)\n f.close()\n files.append(\".gitattributes\")\n run_command(GITS, [\"add\", \"--\"] + files)","label":0},{"cid":"versioneer.py::versions_from_parentdir","text":"versioneer.py::versions_from_parentdir\ndef versions_from_parentdir(parentdir_prefix, root, verbose):\n \"\"\"Try to determine the version from the parent directory name.\n\n Source tarballs conventionally unpack into a directory that includes both\n the project name and a version string. We will also support searching up\n two directory levels for an appropriately named parent directory\n \"\"\"\n rootdirs = []\n\n for i in range(3):\n dirname = os.path.basename(root)\n if dirname.startswith(parentdir_prefix):\n return {\n \"version\": dirname[len(parentdir_prefix) :],\n \"full-revisionid\": None,\n \"dirty\": False,\n \"error\": None,\n \"date\": None,\n }\n else:\n rootdirs.append(root)\n root = os.path.dirname(root) # up a level\n\n if verbose:\n print(\n \"Tried directories %s but none started with prefix %s\"\n % (str(rootdirs), parentdir_prefix)\n )\n raise NotThisMethod(\"rootdir doesn't start with parentdir_prefix\")","label":0},{"cid":"versioneer.py::versions_from_file","text":"versioneer.py::versions_from_file\ndef versions_from_file(filename):\n \"\"\"Try to determine the version from _version.py if present.\"\"\"\n try:\n with open(filename) as f:\n contents = f.read()\n except EnvironmentError:\n raise NotThisMethod(\"unable to read _version.py\")\n mo = re.search(\n r\"version_json = '''\\n(.*)''' # END VERSION_JSON\", contents, re.M | re.S\n )\n if not mo:\n mo = re.search(\n r\"version_json = '''\\r\\n(.*)''' # END VERSION_JSON\", contents, re.M | re.S\n )\n if not mo:\n raise NotThisMethod(\"no version_json in _version.py\")\n return json.loads(mo.group(1))","label":0},{"cid":"versioneer.py::write_to_version_file","text":"versioneer.py::write_to_version_file\ndef write_to_version_file(filename, versions):\n \"\"\"Write the given version number to the given _version.py file.\"\"\"\n os.unlink(filename)\n contents = json.dumps(versions, sort_keys=True, indent=1, separators=(\",\", \": \"))\n with open(filename, \"w\") as f:\n f.write(SHORT_VERSION_PY % contents)\n\n print(\"set %s to '%s'\" % (filename, versions[\"version\"]))","label":0},{"cid":"versioneer.py::plus_or_dot","text":"versioneer.py::plus_or_dot\ndef plus_or_dot(pieces):\n \"\"\"Return a + if we don't already have one, else return a .\"\"\"\n if \"+\" in pieces.get(\"closest-tag\", \"\"):\n return \".\"\n return \"+\"","label":0},{"cid":"versioneer.py::render_pep440","text":"versioneer.py::render_pep440\ndef render_pep440(pieces):\n \"\"\"Build up version string, with post-release \"local version identifier\".\n\n Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\n get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\n\n Exceptions:\n 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += plus_or_dot(pieces)\n rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0+untagged.%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered","label":0},{"cid":"versioneer.py::render_pep440_pre","text":"versioneer.py::render_pep440_pre\ndef render_pep440_pre(pieces):\n \"\"\"TAG[.post.devDISTANCE] -- No -dirty.\n\n Exceptions:\n 1: no tags. 0.post.devDISTANCE\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \".post.dev%d\" % pieces[\"distance\"]\n else:\n # exception #1\n rendered = \"0.post.dev%d\" % pieces[\"distance\"]\n return rendered","label":0},{"cid":"versioneer.py::render_pep440_post","text":"versioneer.py::render_pep440_post\ndef render_pep440_post(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]+gHEX] .\n\n The \".dev0\" means dirty. Note that .dev0 sorts backwards\n (a dirty tree will appear \"older\" than the corresponding clean one),\n but you shouldn't be releasing software with -dirty anyways.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%s\" % pieces[\"short\"]\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += \"+g%s\" % pieces[\"short\"]\n return rendered","label":0},{"cid":"versioneer.py::render_pep440_old","text":"versioneer.py::render_pep440_old\ndef render_pep440_old(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]] .\n\n The \".dev0\" means dirty.\n\n Eexceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n return rendered","label":0},{"cid":"versioneer.py::render_git_describe","text":"versioneer.py::render_git_describe\ndef render_git_describe(pieces):\n \"\"\"TAG[-DISTANCE-gHEX][-dirty].\n\n Like 'git describe --tags --dirty --always'.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered","label":0},{"cid":"versioneer.py::render_git_describe_long","text":"versioneer.py::render_git_describe_long\ndef render_git_describe_long(pieces):\n \"\"\"TAG-DISTANCE-gHEX[-dirty].\n\n Like 'git describe --tags --dirty --always -long'.\n The distance/hash is unconditional.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered","label":0},{"cid":"versioneer.py::render","text":"versioneer.py::render\ndef render(pieces, style):\n \"\"\"Render the given version pieces into the requested style.\"\"\"\n if pieces[\"error\"]:\n return {\n \"version\": \"unknown\",\n \"full-revisionid\": pieces.get(\"long\"),\n \"dirty\": None,\n \"error\": pieces[\"error\"],\n \"date\": None,\n }\n\n if not style or style == \"default\":\n style = \"pep440\" # the default\n\n if style == \"pep440\":\n rendered = render_pep440(pieces)\n elif style == \"pep440-pre\":\n rendered = render_pep440_pre(pieces)\n elif style == \"pep440-post\":\n rendered = render_pep440_post(pieces)\n elif style == \"pep440-old\":\n rendered = render_pep440_old(pieces)\n elif style == \"git-describe\":\n rendered = render_git_describe(pieces)\n elif style == \"git-describe-long\":\n rendered = render_git_describe_long(pieces)\n else:\n raise ValueError(\"unknown style '%s'\" % style)\n\n return {\n \"version\": rendered,\n \"full-revisionid\": pieces[\"long\"],\n \"dirty\": pieces[\"dirty\"],\n \"error\": None,\n \"date\": pieces.get(\"date\"),\n }","label":0},{"cid":"versioneer.py::get_versions","text":"versioneer.py::get_versions\ndef get_versions(verbose=False):\n \"\"\"Get the project version from whatever source is available.\n\n Returns dict with two keys: 'version' and 'full'.\n \"\"\"\n if \"versioneer\" in sys.modules:\n # see the discussion in cmdclass.py:get_cmdclass()\n del sys.modules[\"versioneer\"]\n\n root = get_root()\n cfg = get_config_from_root(root)\n\n assert cfg.VCS is not None, \"please set [versioneer]VCS= in setup.cfg\"\n handlers = HANDLERS.get(cfg.VCS)\n assert handlers, \"unrecognized VCS '%s'\" % cfg.VCS\n verbose = verbose or cfg.verbose\n assert (\n cfg.versionfile_source is not None\n ), \"please set versioneer.versionfile_source\"\n assert cfg.tag_prefix is not None, \"please set versioneer.tag_prefix\"\n\n versionfile_abs = os.path.join(root, cfg.versionfile_source)\n\n # extract version from first of: _version.py, VCS command (e.g. 'git\n # describe'), parentdir. This is meant to work for developers using a\n # source checkout, for users of a tarball created by 'setup.py sdist',\n # and for users of a tarball/zipball created by 'git archive' or github's\n # download-from-tag feature or the equivalent in other VCSes.\n\n get_keywords_f = handlers.get(\"get_keywords\")\n from_keywords_f = handlers.get(\"keywords\")\n if get_keywords_f and from_keywords_f:\n try:\n keywords = get_keywords_f(versionfile_abs)\n ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)\n if verbose:\n print(\"got version from expanded keyword %s\" % ver)\n return ver\n except NotThisMethod:\n pass\n\n try:\n ver = versions_from_file(versionfile_abs)\n if verbose:\n print(\"got version from file %s %s\" % (versionfile_abs, ver))\n return ver\n except NotThisMethod:\n pass\n\n from_vcs_f = handlers.get(\"pieces_from_vcs\")\n if from_vcs_f:\n try:\n pieces = from_vcs_f(cfg.tag_prefix, root, verbose)\n ","label":0},{"cid":"versioneer.py::get_version","text":"versioneer.py::get_version\ndef get_version():\n \"\"\"Get the short version string for this project.\"\"\"\n return get_versions()[\"version\"]","label":0},{"cid":"versioneer.py::get_cmdclass","text":"versioneer.py::get_cmdclass\ndef get_cmdclass():\n \"\"\"Get the custom setuptools/distutils subclasses used by Versioneer.\"\"\"\n if \"versioneer\" in sys.modules:\n del sys.modules[\"versioneer\"]\n # this fixes the \"python setup.py develop\" case (also 'install' and\n # 'easy_install .'), in which subdependencies of the main project are\n # built (using setup.py bdist_egg) in the same python process. Assume\n # a main project A and a dependency B, which use different versions\n # of Versioneer. A's setup.py imports A's Versioneer, leaving it in\n # sys.modules by the time B's setup.py is executed, causing B to run\n # with the wrong versioneer. Setuptools wraps the sub-dep builds in a\n # sandbox that restores sys.modules to it's pre-build state, so the\n # parent is protected against the child's \"import versioneer\". By\n # removing ourselves from sys.modules here, before the child build\n # happens, we protect the child from the parent's versioneer too.\n # Also see https://github.com/warner/python-versioneer/issues/52\n\n cmds = {}\n\n # we add \"version\" to both distutils and setuptools\n from distutils.core import Command\n\n class cmd_version(Command):\n description = \"report generated version string\"\n user_options = []\n boolean_options = []\n\n def initialize_options(self):\n pass\n\n def finalize_options(self):\n pass\n\n def run(self):\n vers = get_versions(verbose=True)\n print(\"Version: %s\" % vers[\"version\"])\n print(\" full-revisionid: %s\" % vers.get(\"full-revisionid\"))\n print(\" dirty: %s\" % vers.get(\"dirty\"))\n print(\" date: %s\" % vers.get(\"date\"))\n if vers[\"error\"]:\n print(\" error: %s\" % vers[\"error\"])\n\n cmds[\"version\"] = cmd_version\n\n # we override \"build_py\" in both distutils and setuptools\n #\n # most invocation pathways end up runnin","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_version.initialize_options","text":"versioneer.py::get_cmdclass.cmd_version.initialize_options\n def initialize_options(self):\n pass","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_version.finalize_options","text":"versioneer.py::get_cmdclass.cmd_version.finalize_options\n def finalize_options(self):\n pass","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_version.run","text":"versioneer.py::get_cmdclass.cmd_version.run\n def run(self):\n vers = get_versions(verbose=True)\n print(\"Version: %s\" % vers[\"version\"])\n print(\" full-revisionid: %s\" % vers.get(\"full-revisionid\"))\n print(\" dirty: %s\" % vers.get(\"dirty\"))\n print(\" date: %s\" % vers.get(\"date\"))\n if vers[\"error\"]:\n print(\" error: %s\" % vers[\"error\"])","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_build_py.run","text":"versioneer.py::get_cmdclass.cmd_build_py.run\n def run(self):\n root = get_root()\n cfg = get_config_from_root(root)\n versions = get_versions()\n _build_py.run(self)\n # now locate _version.py in the new build/ directory and replace\n # it with an updated value\n if cfg.versionfile_build:\n target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build)\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(target_versionfile, versions)","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_sdist.run","text":"versioneer.py::get_cmdclass.cmd_sdist.run\n def run(self):\n versions = get_versions()\n self._versioneer_generated_versions = versions\n # unless we update this, the command will keep using the old\n # version\n self.distribution.metadata.version = versions[\"version\"]\n return _sdist.run(self)","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_sdist.make_release_tree","text":"versioneer.py::get_cmdclass.cmd_sdist.make_release_tree\n def make_release_tree(self, base_dir, files):\n root = get_root()\n cfg = get_config_from_root(root)\n _sdist.make_release_tree(self, base_dir, files)\n # now locate _version.py in the new base_dir directory\n # (remembering that it may be a hardlink) and replace it with an\n # updated value\n target_versionfile = os.path.join(base_dir, cfg.versionfile_source)\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(\n target_versionfile, self._versioneer_generated_versions\n )","label":0},{"cid":"versioneer.py::do_setup","text":"versioneer.py::do_setup\ndef do_setup():\n \"\"\"Main VCS-independent setup function for installing Versioneer.\"\"\"\n root = get_root()\n try:\n cfg = get_config_from_root(root)\n except (\n EnvironmentError,\n configparser.NoSectionError,\n configparser.NoOptionError,\n ) as e:\n if isinstance(e, (EnvironmentError, configparser.NoSectionError)):\n print(\"Adding sample versioneer config to setup.cfg\", file=sys.stderr)\n with open(os.path.join(root, \"setup.cfg\"), \"a\") as f:\n f.write(SAMPLE_CONFIG)\n print(CONFIG_ERROR, file=sys.stderr)\n return 1\n\n print(\" creating %s\" % cfg.versionfile_source)\n with open(cfg.versionfile_source, \"w\") as f:\n LONG = LONG_VERSION_PY[cfg.VCS]\n f.write(\n LONG\n % {\n \"DOLLAR\": \"$\",\n \"STYLE\": cfg.style,\n \"TAG_PREFIX\": cfg.tag_prefix,\n \"PARENTDIR_PREFIX\": cfg.parentdir_prefix,\n \"VERSIONFILE_SOURCE\": cfg.versionfile_source,\n }\n )\n\n ipy = os.path.join(os.path.dirname(cfg.versionfile_source), \"__init__.py\")\n if os.path.exists(ipy):\n try:\n with open(ipy, \"r\") as f:\n old = f.read()\n except EnvironmentError:\n old = \"\"\n if INIT_PY_SNIPPET not in old:\n print(\" appending to %s\" % ipy)\n with open(ipy, \"a\") as f:\n f.write(INIT_PY_SNIPPET)\n else:\n print(\" %s unmodified\" % ipy)\n else:\n print(\" %s doesn't exist, ok\" % ipy)\n ipy = None\n\n # Make sure both the top-level \"versioneer.py\" and versionfile_source\n # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so\n # they'll be copied into source distributions. Pip won't be able to\n # install the package without this.\n manifest_in = os.path.join(root, \"MANIFEST.in\")\n simple_includes = set()\n try:\n with open(manifest_in, \"r\") a","label":0},{"cid":"versioneer.py::scan_setup_py","text":"versioneer.py::scan_setup_py\ndef scan_setup_py():\n \"\"\"Validate the contents of setup.py against Versioneer's expectations.\"\"\"\n found = set()\n setters = False\n errors = 0\n with open(\"setup.py\", \"r\") as f:\n for line in f.readlines():\n if \"import versioneer\" in line:\n found.add(\"import\")\n if \"versioneer.get_cmdclass()\" in line:\n found.add(\"cmdclass\")\n if \"versioneer.get_version()\" in line:\n found.add(\"get_version\")\n if \"versioneer.VCS\" in line:\n setters = True\n if \"versioneer.versionfile_source\" in line:\n setters = True\n if len(found) != 3:\n print(\"\")\n print(\"Your setup.py appears to be missing some important items\")\n print(\"(but I might be wrong). Please make sure it has something\")\n print(\"roughly like the following:\")\n print(\"\")\n print(\" import versioneer\")\n print(\" setup( version=versioneer.get_version(),\")\n print(\" cmdclass=versioneer.get_cmdclass(), ...)\")\n print(\"\")\n errors += 1\n if setters:\n print(\"You should remove lines like 'versioneer.VCS = ' and\")\n print(\"'versioneer.versionfile_source = ' . This configuration\")\n print(\"now lives in setup.cfg, and should be removed from setup.py\")\n print(\"\")\n errors += 1\n return errors","label":0}],"string":"[\n {\n \"cid\": \"examples/airflow_tutorial_dag.py::add_7\",\n \"text\": \"examples/airflow_tutorial_dag.py::add_7\\n@task(max_retries=1, retry_delay=retry_delay)\\ndef add_7():\\n date = prefect.context.get(\\\"scheduled_start_time\\\", datetime.utcnow())\\n return date + timedelta(days=7)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/cli/__init__.py::cli\",\n \"text\": \"src/prefect/cli/__init__.py::cli\\n@click.group()\\ndef cli():\\n \\\"\\\"\\\"\\n The Prefect CLI\\n \\\"\\\"\\\"\\n pass\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/cli/__init__.py::execute_flow\",\n \"text\": \"src/prefect/cli/__init__.py::execute_flow\\n@cli.command()\\n@click.argument(\\\"storage_metadata\\\")\\n@click.argument(\\\"environment_metadata\\\")\\n@click.argument(\\\"flow_location\\\")\\ndef execute_flow(storage_metadata, environment_metadata, flow_location):\\n \\\"\\\"\\\"\\\"\\\"\\\"\\n storage_schema = prefect.serialization.storage.StorageSchema()\\n storage = storage_schema.load(json.loads(storage_metadata))\\n\\n environment_schema = prefect.serialization.environment.EnvironmentSchema()\\n environment = environment_schema.load(json.loads(environment_metadata))\\n\\n environment.setup(storage)\\n environment.execute(storage, flow_location)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/cli/__init__.py::execute_cloud_flow\",\n \"text\": \"src/prefect/cli/__init__.py::execute_cloud_flow\\n@cli.command()\\ndef execute_cloud_flow():\\n flow_run_id = prefect.context.get(\\\"flow_run_id\\\")\\n if not flow_run_id:\\n click.echo(\\\"Not currently executing a flow within a cloud context.\\\")\\n return\\n\\n query = {\\n \\\"query\\\": {\\n with_args(\\\"flow_run\\\", {\\\"where\\\": {\\\"id\\\": {\\\"_eq\\\": flow_run_id}}}): {\\n \\\"flow\\\": {\\\"name\\\": True, \\\"storage\\\": True, \\\"environment\\\": True}\\n }\\n }\\n }\\n\\n result = Client().graphql(query)\\n\\n flow_data = result.data.flow_run[0].flow\\n\\n storage_schema = prefect.serialization.storage.StorageSchema()\\n storage = storage_schema.load(flow_data.storage)\\n\\n environment_schema = prefect.serialization.environment.EnvironmentSchema()\\n environment = environment_schema.load(flow_data.environment)\\n\\n environment.setup(storage=storage)\\n environment.execute(storage=storage, flow_location=storage.flows[flow_data.name])\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/environments/storage/docker.py::Docker.__init__\",\n \"text\": \"src/prefect/environments/storage/docker.py::Docker.__init__\\n def __init__(\\n self,\\n registry_url: str = None,\\n base_image: str = None,\\n python_dependencies: List[str] = None,\\n image_name: str = None,\\n image_tag: str = None,\\n env_vars: dict = None,\\n files: dict = None,\\n base_url: str = \\\"unix://var/run/docker.sock\\\",\\n prefect_version: str = None,\\n local_image: bool = False,\\n ) -> None:\\n self.registry_url = registry_url\\n\\n if base_image is None:\\n python_version = \\\"{}.{}\\\".format(\\n sys.version_info.major, sys.version_info.minor\\n )\\n self.base_image = \\\"python:{}\\\".format(python_version)\\n else:\\n self.base_image = base_image\\n\\n self.image_name = image_name\\n self.image_tag = image_tag\\n self.python_dependencies = python_dependencies or []\\n self.env_vars = env_vars or {}\\n self.files = files or {}\\n self.flows = dict() # type: Dict[str, str]\\n self._flows = dict() # type: Dict[str, \\\"prefect.core.flow.Flow\\\"]\\n self.base_url = base_url\\n self.local_image = local_image\\n\\n version = prefect.__version__.split(\\\"+\\\")\\n if prefect_version is None:\\n self.prefect_version = \\\"master\\\" if len(version) > 1 else version[0]\\n else:\\n self.prefect_version = prefect_version\\n\\n not_absolute = [\\n file_path for file_path in self.files if not os.path.isabs(file_path)\\n ]\\n if not_absolute:\\n raise ValueError(\\n \\\"Provided paths {} are not absolute file paths, please provide absolute paths only.\\\".format(\\n \\\", \\\".join(not_absolute)\\n )\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/storage/docker.py::Docker.get_env_runner\",\n \"text\": \"src/prefect/environments/storage/docker.py::Docker.get_env_runner\\n def get_env_runner(self, flow_location: str) -> Callable[[Dict[str, str]], None]:\\n \\\"\\\"\\\"\\n Given a flow_location within this Storage object, returns something with a\\n `run()` method which accepts the standard runner kwargs and can run the flow.\\n\\n Args:\\n - flow_location (str): the location of a flow within this Storage\\n\\n Returns:\\n - a runner interface (something with a `run()` method for running the flow)\\n \\\"\\\"\\\"\\n\\n def runner(env: dict) -> None:\\n \\\"\\\"\\\"\\n Given a dictionary of environment variables, calls `flow.run()` with these\\n environment variables set.\\n \\\"\\\"\\\"\\n image = \\\"{}:{}\\\".format(self.image_name, self.image_tag)\\n client = docker.APIClient(base_url=self.base_url, version=\\\"auto\\\")\\n container = client.create_container(image, command=\\\"tail -f /dev/null\\\")\\n client.start(container=container.get(\\\"Id\\\"))\\n python_script = \\\"import cloudpickle; f = open('{}', 'rb'); flow = cloudpickle.load(f); f.close(); flow.run()\\\".format(\\n flow_location\\n )\\n try:\\n ee = client.exec_create(\\n container.get(\\\"Id\\\"),\\n 'python -c \\\"{}\\\"'.format(python_script),\\n environment=env,\\n )\\n output = client.exec_start(exec_id=ee, stream=True)\\n for item in output:\\n for line in item.decode(\\\"utf-8\\\").split(\\\"\\\\n\\\"):\\n if line:\\n print(line)\\n finally:\\n client.stop(container=container.get(\\\"Id\\\"))\\n\\n return runner\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/storage/docker.py::Docker.get_env_runner.runner\",\n \"text\": \"src/prefect/environments/storage/docker.py::Docker.get_env_runner.runner\\n def runner(env: dict) -> None:\\n \\\"\\\"\\\"\\n Given a dictionary of environment variables, calls `flow.run()` with these\\n environment variables set.\\n \\\"\\\"\\\"\\n image = \\\"{}:{}\\\".format(self.image_name, self.image_tag)\\n client = docker.APIClient(base_url=self.base_url, version=\\\"auto\\\")\\n container = client.create_container(image, command=\\\"tail -f /dev/null\\\")\\n client.start(container=container.get(\\\"Id\\\"))\\n python_script = \\\"import cloudpickle; f = open('{}', 'rb'); flow = cloudpickle.load(f); f.close(); flow.run()\\\".format(\\n flow_location\\n )\\n try:\\n ee = client.exec_create(\\n container.get(\\\"Id\\\"),\\n 'python -c \\\"{}\\\"'.format(python_script),\\n environment=env,\\n )\\n output = client.exec_start(exec_id=ee, stream=True)\\n for item in output:\\n for line in item.decode(\\\"utf-8\\\").split(\\\"\\\\n\\\"):\\n if line:\\n print(line)\\n finally:\\n client.stop(container=container.get(\\\"Id\\\"))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/storage/docker.py::Docker.add_flow\",\n \"text\": \"src/prefect/environments/storage/docker.py::Docker.add_flow\\n def add_flow(self, flow: \\\"prefect.core.flow.Flow\\\") -> str:\\n \\\"\\\"\\\"\\n Method for adding a new flow to this Storage object.\\n\\n Args:\\n - flow (Flow): a Prefect Flow to add\\n\\n Returns:\\n - str: the location of the newly added flow in this Storage object\\n \\\"\\\"\\\"\\n if flow.name in self:\\n raise ValueError(\\n 'Name conflict: Flow with the name \\\"{}\\\" is already present in this storage.'.format(\\n flow.name\\n )\\n )\\n flow_path = \\\"/root/.prefect/{}.prefect\\\".format(slugify(flow.name))\\n self.flows[flow.name] = flow_path\\n self._flows[flow.name] = flow # needed prior to build\\n return flow_path\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/storage/docker.py::Docker.name\",\n \"text\": \"src/prefect/environments/storage/docker.py::Docker.name\\n @property\\n def name(self) -> str:\\n \\\"\\\"\\\"\\n Full name of the Docker image.\\n \\\"\\\"\\\"\\n if None in [self.registry_url, self.image_name, self.image_tag]:\\n raise ValueError(\\\"Docker storage is missing required fields\\\")\\n\\n return \\\"{}:{}\\\".format(\\n os.path.join(self.registry_url, self.image_name), # type: ignore\\n self.image_tag, # type: ignore\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/storage/docker.py::Docker.__contains__\",\n \"text\": \"src/prefect/environments/storage/docker.py::Docker.__contains__\\n def __contains__(self, obj: Any) -> bool:\\n \\\"\\\"\\\"\\n Method for determining whether an object is contained within this storage.\\n \\\"\\\"\\\"\\n if not isinstance(obj, str):\\n return False\\n return obj in self.flows\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/storage/docker.py::Docker.build\",\n \"text\": \"src/prefect/environments/storage/docker.py::Docker.build\\n def build(self, push: bool = True) -> \\\"Storage\\\":\\n \\\"\\\"\\\"\\n Build the Docker storage object.\\n\\n Args:\\n - push (bool, optional): Whether or not to push the built Docker image, this\\n requires the `registry_url` to be set\\n\\n Returns:\\n - Docker: a new Docker storage object that contains information about how and\\n where the flow is stored. Image name and tag are generated during the\\n build process.\\n\\n Raises:\\n - InterruptedError: if either pushing or pulling the image fails\\n \\\"\\\"\\\"\\n image_name, image_tag = self.build_image(push=push)\\n self.image_name = image_name\\n self.image_tag = image_tag\\n\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/storage/docker.py::Docker.build_image\",\n \"text\": \"src/prefect/environments/storage/docker.py::Docker.build_image\\n def build_image(self, push: bool = True) -> tuple:\\n \\\"\\\"\\\"\\n Build a Docker image using the docker python library.\\n\\n Args:\\n - push (bool, optional): Whether or not to push the built Docker image, this\\n requires the `registry_url` to be set\\n\\n Returns:\\n - tuple: generated UUID strings `image_name`, `image_tag`\\n\\n Raises:\\n - InterruptedError: if either pushing or pulling the image fails\\n \\\"\\\"\\\"\\n image_name = self.image_name or str(uuid.uuid4())\\n image_tag = self.image_tag or str(uuid.uuid4())\\n\\n # Make temporary directory to hold serialized flow, healthcheck script, and dockerfile\\n with tempfile.TemporaryDirectory() as tempdir:\\n\\n # Build the dockerfile\\n if self.base_image and not self.local_image:\\n self.pull_image()\\n\\n self.create_dockerfile_object(directory=tempdir)\\n client = docker.APIClient(base_url=self.base_url, version=\\\"auto\\\")\\n\\n # Verify that a registry url has been provided for images that should be pushed\\n if self.registry_url:\\n full_name = os.path.join(self.registry_url, image_name)\\n elif push is True:\\n raise ValueError(\\n \\\"This environment has no `registry_url`, and cannot be pushed.\\\"\\n )\\n else:\\n full_name = image_name\\n\\n # Use the docker client to build the image\\n logging.info(\\\"Building the flow's Docker storage...\\\")\\n output = client.build(\\n path=tempdir, tag=\\\"{}:{}\\\".format(full_name, image_tag), forcerm=True\\n )\\n self._parse_generator_output(output)\\n\\n if len(client.images(name=full_name)) == 0:\\n raise SerializationError(\\n \\\"Your flow failed to deserialize in the container; please ensure that all nec\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/storage/docker.py::Docker.create_dockerfile_object\",\n \"text\": \"src/prefect/environments/storage/docker.py::Docker.create_dockerfile_object\\n def create_dockerfile_object(self, directory: str = None) -> None:\\n \\\"\\\"\\\"\\n Writes a dockerfile to the provided directory using the specified\\n arguments on this Docker storage object.\\n\\n In order for the docker python library to build a container it needs a\\n Dockerfile that it can use to define the container. This function takes the\\n specified arguments then writes them to a temporary file called Dockerfile.\\n\\n *Note*: if `files` are added to this container, they will be copied to this directory as well.\\n\\n Args:\\n - directory (str, optional): A directory where the Dockerfile will be created,\\n if no directory is specified is will be created in the current working directory\\n \\\"\\\"\\\"\\n directory = directory or \\\"./\\\"\\n\\n with open(os.path.join(directory, \\\"Dockerfile\\\"), \\\"w+\\\") as dockerfile:\\n\\n # Generate RUN pip install commands for python dependencies\\n pip_installs = \\\"\\\"\\n if self.python_dependencies:\\n for dependency in self.python_dependencies:\\n pip_installs += \\\"RUN pip install {}\\\\n\\\".format(dependency)\\n\\n # Generate ENV variables to load into the image\\n env_vars = \\\"\\\"\\n if self.env_vars:\\n white_space = \\\" \\\" * 20\\n env_vars = \\\"ENV \\\" + \\\" \\\\ \\\\n{}\\\".format(white_space).join(\\n \\\"{k}={v}\\\".format(k=k, v=v) for k, v in self.env_vars.items()\\n )\\n\\n # Copy user specified files into the image\\n copy_files = \\\"\\\"\\n if self.files:\\n for src, dest in self.files.items():\\n fname = os.path.basename(src)\\n full_fname = os.path.join(directory, fname)\\n if (\\n os.path.exists(full_fname)\\n and filecmp.cmp(src, full_fname) is False\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/storage/docker.py::Docker.pull_image\",\n \"text\": \"src/prefect/environments/storage/docker.py::Docker.pull_image\\n def pull_image(self) -> None:\\n \\\"\\\"\\\"Pull the image specified so it can be built.\\n\\n In order for the docker python library to use a base image it must be pulled\\n from either the main docker registry or a separate registry that must be set as\\n `registry_url` on this class.\\n\\n Raises:\\n - InterruptedError: if either pulling the image fails\\n \\\"\\\"\\\"\\n client = docker.APIClient(base_url=self.base_url, version=\\\"auto\\\")\\n\\n output = client.pull(self.base_image, stream=True, decode=True)\\n for line in output:\\n if line.get(\\\"error\\\"):\\n raise InterruptedError(line.get(\\\"error\\\"))\\n if line.get(\\\"progress\\\"):\\n print(line.get(\\\"status\\\"), line.get(\\\"progress\\\"), end=\\\"\\\\r\\\")\\n print(\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/storage/docker.py::Docker.push_image\",\n \"text\": \"src/prefect/environments/storage/docker.py::Docker.push_image\\n def push_image(self, image_name: str, image_tag: str) -> None:\\n \\\"\\\"\\\"Push this environment to a registry\\n\\n Args:\\n - image_name (str): Name for the image\\n - image_tag (str): Tag for the image\\n\\n Raises:\\n - InterruptedError: if either pushing the image fails\\n \\\"\\\"\\\"\\n client = docker.APIClient(base_url=self.base_url, version=\\\"auto\\\")\\n\\n logging.info(\\\"Pushing image to the registry...\\\")\\n\\n output = client.push(image_name, tag=image_tag, stream=True, decode=True)\\n for line in output:\\n if line.get(\\\"error\\\"):\\n raise InterruptedError(line.get(\\\"error\\\"))\\n if line.get(\\\"progress\\\"):\\n print(line.get(\\\"status\\\"), line.get(\\\"progress\\\"), end=\\\"\\\\r\\\")\\n print(\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/environments/storage/docker.py::Docker._parse_generator_output\",\n \"text\": \"src/prefect/environments/storage/docker.py::Docker._parse_generator_output\\n def _parse_generator_output(self, generator: Iterable) -> None:\\n \\\"\\\"\\\"\\n Parses and writes a Docker command's output to stdout\\n \\\"\\\"\\\"\\n for item in generator:\\n item = item.decode(\\\"utf-8\\\")\\n for line in item.split(\\\"\\\\n\\\"):\\n if line:\\n output = json.loads(line).get(\\\"stream\\\")\\n if output and output != \\\"\\\\n\\\":\\n print(output.strip(\\\"\\\\n\\\"))\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_root\",\n \"text\": \"versioneer.py::get_root\\ndef get_root():\\n \\\"\\\"\\\"Get the project root directory.\\n\\n We require that all commands are run from the project root, i.e. the\\n directory that contains setup.py, setup.cfg, and versioneer.py .\\n \\\"\\\"\\\"\\n root = os.path.realpath(os.path.abspath(os.getcwd()))\\n setup_py = os.path.join(root, \\\"setup.py\\\")\\n versioneer_py = os.path.join(root, \\\"versioneer.py\\\")\\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\\n # allow 'python path/to/setup.py COMMAND'\\n root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))\\n setup_py = os.path.join(root, \\\"setup.py\\\")\\n versioneer_py = os.path.join(root, \\\"versioneer.py\\\")\\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\\n err = (\\n \\\"Versioneer was unable to run the project root directory. \\\"\\n \\\"Versioneer requires setup.py to be executed from \\\"\\n \\\"its immediate directory (like 'python setup.py COMMAND'), \\\"\\n \\\"or in a way that lets it use sys.argv[0] to find the root \\\"\\n \\\"(like 'python path/to/setup.py COMMAND').\\\"\\n )\\n raise VersioneerBadRootError(err)\\n try:\\n # Certain runtime workflows (setup.py install/develop in a setuptools\\n # tree) execute all dependencies in a single python process, so\\n # \\\"versioneer\\\" may be imported multiple times, and python's shared\\n # module-import table will cache the first one. So we can't use\\n # os.path.dirname(__file__), as that will find whichever\\n # versioneer.py was first imported, even in later projects.\\n me = os.path.realpath(os.path.abspath(__file__))\\n me_dir = os.path.normcase(os.path.splitext(me)[0])\\n vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])\\n if me_dir != vsr_dir:\\n print(\\n \\\"Warning: build in %s is using versioneer.py from %s\\\"\\n % (os.path.dirname(me), versioneer_py)\\n )\\n exc\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_config_from_root\",\n \"text\": \"versioneer.py::get_config_from_root\\ndef get_config_from_root(root):\\n \\\"\\\"\\\"Read the project setup.cfg file to determine Versioneer config.\\\"\\\"\\\"\\n # This might raise EnvironmentError (if setup.cfg is missing), or\\n # configparser.NoSectionError (if it lacks a [versioneer] section), or\\n # configparser.NoOptionError (if it lacks \\\"VCS=\\\"). See the docstring at\\n # the top of versioneer.py for instructions on writing your setup.cfg .\\n setup_cfg = os.path.join(root, \\\"setup.cfg\\\")\\n parser = configparser.SafeConfigParser()\\n with open(setup_cfg, \\\"r\\\") as f:\\n parser.readfp(f)\\n VCS = parser.get(\\\"versioneer\\\", \\\"VCS\\\") # mandatory\\n\\n def get(parser, name):\\n if parser.has_option(\\\"versioneer\\\", name):\\n return parser.get(\\\"versioneer\\\", name)\\n return None\\n\\n cfg = VersioneerConfig()\\n cfg.VCS = VCS\\n cfg.style = get(parser, \\\"style\\\") or \\\"\\\"\\n cfg.versionfile_source = get(parser, \\\"versionfile_source\\\")\\n cfg.versionfile_build = get(parser, \\\"versionfile_build\\\")\\n cfg.tag_prefix = get(parser, \\\"tag_prefix\\\")\\n if cfg.tag_prefix in (\\\"''\\\", '\\\"\\\"'):\\n cfg.tag_prefix = \\\"\\\"\\n cfg.parentdir_prefix = get(parser, \\\"parentdir_prefix\\\")\\n cfg.verbose = get(parser, \\\"verbose\\\")\\n return cfg\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_config_from_root.get\",\n \"text\": \"versioneer.py::get_config_from_root.get\\n def get(parser, name):\\n if parser.has_option(\\\"versioneer\\\", name):\\n return parser.get(\\\"versioneer\\\", name)\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::register_vcs_handler\",\n \"text\": \"versioneer.py::register_vcs_handler\\ndef register_vcs_handler(vcs, method): # decorator\\n \\\"\\\"\\\"Decorator to mark a method as the handler for a particular VCS.\\\"\\\"\\\"\\n\\n def decorate(f):\\n \\\"\\\"\\\"Store f in HANDLERS[vcs][method].\\\"\\\"\\\"\\n if vcs not in HANDLERS:\\n HANDLERS[vcs] = {}\\n HANDLERS[vcs][method] = f\\n return f\\n\\n return decorate\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::register_vcs_handler.decorate\",\n \"text\": \"versioneer.py::register_vcs_handler.decorate\\n def decorate(f):\\n \\\"\\\"\\\"Store f in HANDLERS[vcs][method].\\\"\\\"\\\"\\n if vcs not in HANDLERS:\\n HANDLERS[vcs] = {}\\n HANDLERS[vcs][method] = f\\n return f\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::run_command\",\n \"text\": \"versioneer.py::run_command\\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):\\n \\\"\\\"\\\"Call the given command(s).\\\"\\\"\\\"\\n assert isinstance(commands, list)\\n p = None\\n for c in commands:\\n try:\\n dispcmd = str([c] + args)\\n # remember shell=False, so use git.cmd on windows, not just git\\n p = subprocess.Popen(\\n [c] + args,\\n cwd=cwd,\\n env=env,\\n stdout=subprocess.PIPE,\\n stderr=(subprocess.PIPE if hide_stderr else None),\\n )\\n break\\n except EnvironmentError:\\n e = sys.exc_info()[1]\\n if e.errno == errno.ENOENT:\\n continue\\n if verbose:\\n print(\\\"unable to run %s\\\" % dispcmd)\\n print(e)\\n return None, None\\n else:\\n if verbose:\\n print(\\\"unable to find command, tried %s\\\" % (commands,))\\n return None, None\\n stdout = p.communicate()[0].strip()\\n if sys.version_info[0] >= 3:\\n stdout = stdout.decode()\\n if p.returncode != 0:\\n if verbose:\\n print(\\\"unable to run %s (error)\\\" % dispcmd)\\n print(\\\"stdout was %s\\\" % stdout)\\n return None, p.returncode\\n return stdout, p.returncode\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::git_get_keywords\",\n \"text\": \"versioneer.py::git_get_keywords\\n@register_vcs_handler(\\\"git\\\", \\\"get_keywords\\\")\\ndef git_get_keywords(versionfile_abs):\\n \\\"\\\"\\\"Extract version information from the given file.\\\"\\\"\\\"\\n # the code embedded in _version.py can just fetch the value of these\\n # keywords. When used from setup.py, we don't want to import _version.py,\\n # so we do it with a regexp instead. This function is not used from\\n # _version.py.\\n keywords = {}\\n try:\\n f = open(versionfile_abs, \\\"r\\\")\\n for line in f.readlines():\\n if line.strip().startswith(\\\"git_refnames =\\\"):\\n mo = re.search(r'=\\\\s*\\\"(.*)\\\"', line)\\n if mo:\\n keywords[\\\"refnames\\\"] = mo.group(1)\\n if line.strip().startswith(\\\"git_full =\\\"):\\n mo = re.search(r'=\\\\s*\\\"(.*)\\\"', line)\\n if mo:\\n keywords[\\\"full\\\"] = mo.group(1)\\n if line.strip().startswith(\\\"git_date =\\\"):\\n mo = re.search(r'=\\\\s*\\\"(.*)\\\"', line)\\n if mo:\\n keywords[\\\"date\\\"] = mo.group(1)\\n f.close()\\n except EnvironmentError:\\n pass\\n return keywords\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::git_versions_from_keywords\",\n \"text\": \"versioneer.py::git_versions_from_keywords\\n@register_vcs_handler(\\\"git\\\", \\\"keywords\\\")\\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\\n \\\"\\\"\\\"Get version information from git keywords.\\\"\\\"\\\"\\n if not keywords:\\n raise NotThisMethod(\\\"no keywords at all, weird\\\")\\n date = keywords.get(\\\"date\\\")\\n if date is not None:\\n # git-2.2.0 added \\\"%cI\\\", which expands to an ISO-8601 -compliant\\n # datestamp. However we prefer \\\"%ci\\\" (which expands to an \\\"ISO-8601\\n # -like\\\" string, which we must then edit to make compliant), because\\n # it's been around since git-1.5.3, and it's too difficult to\\n # discover which version we're using, or to work around using an\\n # older one.\\n date = date.strip().replace(\\\" \\\", \\\"T\\\", 1).replace(\\\" \\\", \\\"\\\", 1)\\n refnames = keywords[\\\"refnames\\\"].strip()\\n if refnames.startswith(\\\"$Format\\\"):\\n if verbose:\\n print(\\\"keywords are unexpanded, not using\\\")\\n raise NotThisMethod(\\\"unexpanded keywords, not a git-archive tarball\\\")\\n refs = set([r.strip() for r in refnames.strip(\\\"()\\\").split(\\\",\\\")])\\n # starting in git-1.8.3, tags are listed as \\\"tag: foo-1.0\\\" instead of\\n # just \\\"foo-1.0\\\". If we see a \\\"tag: \\\" prefix, prefer those.\\n TAG = \\\"tag: \\\"\\n tags = set([r[len(TAG) :] for r in refs if r.startswith(TAG)])\\n if not tags:\\n # Either we're using git < 1.8.3, or there really are no tags. We use\\n # a heuristic: assume all version tags have a digit. The old git %d\\n # expansion behaves like git log --decorate=short and strips out the\\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\\n # between branches and tags. By ignoring refnames without digits, we\\n # filter out many common branch names like \\\"release\\\" and\\n # \\\"stabilization\\\", as well as \\\"HEAD\\\" and \\\"master\\\".\\n tags = set([r for r in refs if re.search(r\\\"\\\\d\\\", r)])\\n if verbose:\\n print(\\\"discarding '%s', no digits\\\" % \\\",\\\".join(refs - tags))\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::git_pieces_from_vcs\",\n \"text\": \"versioneer.py::git_pieces_from_vcs\\n@register_vcs_handler(\\\"git\\\", \\\"pieces_from_vcs\\\")\\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):\\n \\\"\\\"\\\"Get version from 'git describe' in the root of the source tree.\\n\\n This only gets called if the git-archive 'subst' keywords were *not*\\n expanded, and _version.py hasn't already been rewritten with a short\\n version string, meaning we're inside a checked out source tree.\\n \\\"\\\"\\\"\\n GITS = [\\\"git\\\"]\\n if sys.platform == \\\"win32\\\":\\n GITS = [\\\"git.cmd\\\", \\\"git.exe\\\"]\\n\\n out, rc = run_command(GITS, [\\\"rev-parse\\\", \\\"--git-dir\\\"], cwd=root, hide_stderr=True)\\n if rc != 0:\\n if verbose:\\n print(\\\"Directory %s not under git control\\\" % root)\\n raise NotThisMethod(\\\"'git rev-parse --git-dir' returned error\\\")\\n\\n # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\\n # if there isn't one, this yields HEX[-dirty] (no NUM)\\n describe_out, rc = run_command(\\n GITS,\\n [\\n \\\"describe\\\",\\n \\\"--tags\\\",\\n \\\"--dirty\\\",\\n \\\"--always\\\",\\n \\\"--long\\\",\\n \\\"--match\\\",\\n \\\"%s*\\\" % tag_prefix,\\n ],\\n cwd=root,\\n )\\n # --long was added in git-1.5.5\\n if describe_out is None:\\n raise NotThisMethod(\\\"'git describe' failed\\\")\\n describe_out = describe_out.strip()\\n full_out, rc = run_command(GITS, [\\\"rev-parse\\\", \\\"HEAD\\\"], cwd=root)\\n if full_out is None:\\n raise NotThisMethod(\\\"'git rev-parse' failed\\\")\\n full_out = full_out.strip()\\n\\n pieces = {}\\n pieces[\\\"long\\\"] = full_out\\n pieces[\\\"short\\\"] = full_out[:7] # maybe improved later\\n pieces[\\\"error\\\"] = None\\n\\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\\n # TAG might have hyphens.\\n git_describe = describe_out\\n\\n # look for -dirty suffix\\n dirty = git_describe.endswith(\\\"-dirty\\\")\\n pieces[\\\"dirty\\\"] = dirty\\n if dirty:\\n git_describe = git_describe[: git_describe.rindex(\\\"-dirty\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::do_vcs_install\",\n \"text\": \"versioneer.py::do_vcs_install\\ndef do_vcs_install(manifest_in, versionfile_source, ipy):\\n \\\"\\\"\\\"Git-specific installation logic for Versioneer.\\n\\n For Git, this means creating/changing .gitattributes to mark _version.py\\n for export-subst keyword substitution.\\n \\\"\\\"\\\"\\n GITS = [\\\"git\\\"]\\n if sys.platform == \\\"win32\\\":\\n GITS = [\\\"git.cmd\\\", \\\"git.exe\\\"]\\n files = [manifest_in, versionfile_source]\\n if ipy:\\n files.append(ipy)\\n try:\\n me = __file__\\n if me.endswith(\\\".pyc\\\") or me.endswith(\\\".pyo\\\"):\\n me = os.path.splitext(me)[0] + \\\".py\\\"\\n versioneer_file = os.path.relpath(me)\\n except NameError:\\n versioneer_file = \\\"versioneer.py\\\"\\n files.append(versioneer_file)\\n present = False\\n try:\\n f = open(\\\".gitattributes\\\", \\\"r\\\")\\n for line in f.readlines():\\n if line.strip().startswith(versionfile_source):\\n if \\\"export-subst\\\" in line.strip().split()[1:]:\\n present = True\\n f.close()\\n except EnvironmentError:\\n pass\\n if not present:\\n f = open(\\\".gitattributes\\\", \\\"a+\\\")\\n f.write(\\\"%s export-subst\\\\n\\\" % versionfile_source)\\n f.close()\\n files.append(\\\".gitattributes\\\")\\n run_command(GITS, [\\\"add\\\", \\\"--\\\"] + files)\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::versions_from_parentdir\",\n \"text\": \"versioneer.py::versions_from_parentdir\\ndef versions_from_parentdir(parentdir_prefix, root, verbose):\\n \\\"\\\"\\\"Try to determine the version from the parent directory name.\\n\\n Source tarballs conventionally unpack into a directory that includes both\\n the project name and a version string. We will also support searching up\\n two directory levels for an appropriately named parent directory\\n \\\"\\\"\\\"\\n rootdirs = []\\n\\n for i in range(3):\\n dirname = os.path.basename(root)\\n if dirname.startswith(parentdir_prefix):\\n return {\\n \\\"version\\\": dirname[len(parentdir_prefix) :],\\n \\\"full-revisionid\\\": None,\\n \\\"dirty\\\": False,\\n \\\"error\\\": None,\\n \\\"date\\\": None,\\n }\\n else:\\n rootdirs.append(root)\\n root = os.path.dirname(root) # up a level\\n\\n if verbose:\\n print(\\n \\\"Tried directories %s but none started with prefix %s\\\"\\n % (str(rootdirs), parentdir_prefix)\\n )\\n raise NotThisMethod(\\\"rootdir doesn't start with parentdir_prefix\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::versions_from_file\",\n \"text\": \"versioneer.py::versions_from_file\\ndef versions_from_file(filename):\\n \\\"\\\"\\\"Try to determine the version from _version.py if present.\\\"\\\"\\\"\\n try:\\n with open(filename) as f:\\n contents = f.read()\\n except EnvironmentError:\\n raise NotThisMethod(\\\"unable to read _version.py\\\")\\n mo = re.search(\\n r\\\"version_json = '''\\\\n(.*)''' # END VERSION_JSON\\\", contents, re.M | re.S\\n )\\n if not mo:\\n mo = re.search(\\n r\\\"version_json = '''\\\\r\\\\n(.*)''' # END VERSION_JSON\\\", contents, re.M | re.S\\n )\\n if not mo:\\n raise NotThisMethod(\\\"no version_json in _version.py\\\")\\n return json.loads(mo.group(1))\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::write_to_version_file\",\n \"text\": \"versioneer.py::write_to_version_file\\ndef write_to_version_file(filename, versions):\\n \\\"\\\"\\\"Write the given version number to the given _version.py file.\\\"\\\"\\\"\\n os.unlink(filename)\\n contents = json.dumps(versions, sort_keys=True, indent=1, separators=(\\\",\\\", \\\": \\\"))\\n with open(filename, \\\"w\\\") as f:\\n f.write(SHORT_VERSION_PY % contents)\\n\\n print(\\\"set %s to '%s'\\\" % (filename, versions[\\\"version\\\"]))\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::plus_or_dot\",\n \"text\": \"versioneer.py::plus_or_dot\\ndef plus_or_dot(pieces):\\n \\\"\\\"\\\"Return a + if we don't already have one, else return a .\\\"\\\"\\\"\\n if \\\"+\\\" in pieces.get(\\\"closest-tag\\\", \\\"\\\"):\\n return \\\".\\\"\\n return \\\"+\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_pep440\",\n \"text\": \"versioneer.py::render_pep440\\ndef render_pep440(pieces):\\n \\\"\\\"\\\"Build up version string, with post-release \\\"local version identifier\\\".\\n\\n Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\\n get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\\n\\n Exceptions:\\n 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"] or pieces[\\\"dirty\\\"]:\\n rendered += plus_or_dot(pieces)\\n rendered += \\\"%d.g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dirty\\\"\\n else:\\n # exception #1\\n rendered = \\\"0+untagged.%d.g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dirty\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_pep440_pre\",\n \"text\": \"versioneer.py::render_pep440_pre\\ndef render_pep440_pre(pieces):\\n \\\"\\\"\\\"TAG[.post.devDISTANCE] -- No -dirty.\\n\\n Exceptions:\\n 1: no tags. 0.post.devDISTANCE\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"]:\\n rendered += \\\".post.dev%d\\\" % pieces[\\\"distance\\\"]\\n else:\\n # exception #1\\n rendered = \\\"0.post.dev%d\\\" % pieces[\\\"distance\\\"]\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_pep440_post\",\n \"text\": \"versioneer.py::render_pep440_post\\ndef render_pep440_post(pieces):\\n \\\"\\\"\\\"TAG[.postDISTANCE[.dev0]+gHEX] .\\n\\n The \\\".dev0\\\" means dirty. Note that .dev0 sorts backwards\\n (a dirty tree will appear \\\"older\\\" than the corresponding clean one),\\n but you shouldn't be releasing software with -dirty anyways.\\n\\n Exceptions:\\n 1: no tags. 0.postDISTANCE[.dev0]\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"] or pieces[\\\"dirty\\\"]:\\n rendered += \\\".post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n rendered += plus_or_dot(pieces)\\n rendered += \\\"g%s\\\" % pieces[\\\"short\\\"]\\n else:\\n # exception #1\\n rendered = \\\"0.post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n rendered += \\\"+g%s\\\" % pieces[\\\"short\\\"]\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_pep440_old\",\n \"text\": \"versioneer.py::render_pep440_old\\ndef render_pep440_old(pieces):\\n \\\"\\\"\\\"TAG[.postDISTANCE[.dev0]] .\\n\\n The \\\".dev0\\\" means dirty.\\n\\n Eexceptions:\\n 1: no tags. 0.postDISTANCE[.dev0]\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"] or pieces[\\\"dirty\\\"]:\\n rendered += \\\".post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n else:\\n # exception #1\\n rendered = \\\"0.post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_git_describe\",\n \"text\": \"versioneer.py::render_git_describe\\ndef render_git_describe(pieces):\\n \\\"\\\"\\\"TAG[-DISTANCE-gHEX][-dirty].\\n\\n Like 'git describe --tags --dirty --always'.\\n\\n Exceptions:\\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"]:\\n rendered += \\\"-%d-g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n else:\\n # exception #1\\n rendered = pieces[\\\"short\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\"-dirty\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_git_describe_long\",\n \"text\": \"versioneer.py::render_git_describe_long\\ndef render_git_describe_long(pieces):\\n \\\"\\\"\\\"TAG-DISTANCE-gHEX[-dirty].\\n\\n Like 'git describe --tags --dirty --always -long'.\\n The distance/hash is unconditional.\\n\\n Exceptions:\\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n rendered += \\\"-%d-g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n else:\\n # exception #1\\n rendered = pieces[\\\"short\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\"-dirty\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render\",\n \"text\": \"versioneer.py::render\\ndef render(pieces, style):\\n \\\"\\\"\\\"Render the given version pieces into the requested style.\\\"\\\"\\\"\\n if pieces[\\\"error\\\"]:\\n return {\\n \\\"version\\\": \\\"unknown\\\",\\n \\\"full-revisionid\\\": pieces.get(\\\"long\\\"),\\n \\\"dirty\\\": None,\\n \\\"error\\\": pieces[\\\"error\\\"],\\n \\\"date\\\": None,\\n }\\n\\n if not style or style == \\\"default\\\":\\n style = \\\"pep440\\\" # the default\\n\\n if style == \\\"pep440\\\":\\n rendered = render_pep440(pieces)\\n elif style == \\\"pep440-pre\\\":\\n rendered = render_pep440_pre(pieces)\\n elif style == \\\"pep440-post\\\":\\n rendered = render_pep440_post(pieces)\\n elif style == \\\"pep440-old\\\":\\n rendered = render_pep440_old(pieces)\\n elif style == \\\"git-describe\\\":\\n rendered = render_git_describe(pieces)\\n elif style == \\\"git-describe-long\\\":\\n rendered = render_git_describe_long(pieces)\\n else:\\n raise ValueError(\\\"unknown style '%s'\\\" % style)\\n\\n return {\\n \\\"version\\\": rendered,\\n \\\"full-revisionid\\\": pieces[\\\"long\\\"],\\n \\\"dirty\\\": pieces[\\\"dirty\\\"],\\n \\\"error\\\": None,\\n \\\"date\\\": pieces.get(\\\"date\\\"),\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_versions\",\n \"text\": \"versioneer.py::get_versions\\ndef get_versions(verbose=False):\\n \\\"\\\"\\\"Get the project version from whatever source is available.\\n\\n Returns dict with two keys: 'version' and 'full'.\\n \\\"\\\"\\\"\\n if \\\"versioneer\\\" in sys.modules:\\n # see the discussion in cmdclass.py:get_cmdclass()\\n del sys.modules[\\\"versioneer\\\"]\\n\\n root = get_root()\\n cfg = get_config_from_root(root)\\n\\n assert cfg.VCS is not None, \\\"please set [versioneer]VCS= in setup.cfg\\\"\\n handlers = HANDLERS.get(cfg.VCS)\\n assert handlers, \\\"unrecognized VCS '%s'\\\" % cfg.VCS\\n verbose = verbose or cfg.verbose\\n assert (\\n cfg.versionfile_source is not None\\n ), \\\"please set versioneer.versionfile_source\\\"\\n assert cfg.tag_prefix is not None, \\\"please set versioneer.tag_prefix\\\"\\n\\n versionfile_abs = os.path.join(root, cfg.versionfile_source)\\n\\n # extract version from first of: _version.py, VCS command (e.g. 'git\\n # describe'), parentdir. This is meant to work for developers using a\\n # source checkout, for users of a tarball created by 'setup.py sdist',\\n # and for users of a tarball/zipball created by 'git archive' or github's\\n # download-from-tag feature or the equivalent in other VCSes.\\n\\n get_keywords_f = handlers.get(\\\"get_keywords\\\")\\n from_keywords_f = handlers.get(\\\"keywords\\\")\\n if get_keywords_f and from_keywords_f:\\n try:\\n keywords = get_keywords_f(versionfile_abs)\\n ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)\\n if verbose:\\n print(\\\"got version from expanded keyword %s\\\" % ver)\\n return ver\\n except NotThisMethod:\\n pass\\n\\n try:\\n ver = versions_from_file(versionfile_abs)\\n if verbose:\\n print(\\\"got version from file %s %s\\\" % (versionfile_abs, ver))\\n return ver\\n except NotThisMethod:\\n pass\\n\\n from_vcs_f = handlers.get(\\\"pieces_from_vcs\\\")\\n if from_vcs_f:\\n try:\\n pieces = from_vcs_f(cfg.tag_prefix, root, verbose)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_version\",\n \"text\": \"versioneer.py::get_version\\ndef get_version():\\n \\\"\\\"\\\"Get the short version string for this project.\\\"\\\"\\\"\\n return get_versions()[\\\"version\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass\",\n \"text\": \"versioneer.py::get_cmdclass\\ndef get_cmdclass():\\n \\\"\\\"\\\"Get the custom setuptools/distutils subclasses used by Versioneer.\\\"\\\"\\\"\\n if \\\"versioneer\\\" in sys.modules:\\n del sys.modules[\\\"versioneer\\\"]\\n # this fixes the \\\"python setup.py develop\\\" case (also 'install' and\\n # 'easy_install .'), in which subdependencies of the main project are\\n # built (using setup.py bdist_egg) in the same python process. Assume\\n # a main project A and a dependency B, which use different versions\\n # of Versioneer. A's setup.py imports A's Versioneer, leaving it in\\n # sys.modules by the time B's setup.py is executed, causing B to run\\n # with the wrong versioneer. Setuptools wraps the sub-dep builds in a\\n # sandbox that restores sys.modules to it's pre-build state, so the\\n # parent is protected against the child's \\\"import versioneer\\\". By\\n # removing ourselves from sys.modules here, before the child build\\n # happens, we protect the child from the parent's versioneer too.\\n # Also see https://github.com/warner/python-versioneer/issues/52\\n\\n cmds = {}\\n\\n # we add \\\"version\\\" to both distutils and setuptools\\n from distutils.core import Command\\n\\n class cmd_version(Command):\\n description = \\\"report generated version string\\\"\\n user_options = []\\n boolean_options = []\\n\\n def initialize_options(self):\\n pass\\n\\n def finalize_options(self):\\n pass\\n\\n def run(self):\\n vers = get_versions(verbose=True)\\n print(\\\"Version: %s\\\" % vers[\\\"version\\\"])\\n print(\\\" full-revisionid: %s\\\" % vers.get(\\\"full-revisionid\\\"))\\n print(\\\" dirty: %s\\\" % vers.get(\\\"dirty\\\"))\\n print(\\\" date: %s\\\" % vers.get(\\\"date\\\"))\\n if vers[\\\"error\\\"]:\\n print(\\\" error: %s\\\" % vers[\\\"error\\\"])\\n\\n cmds[\\\"version\\\"] = cmd_version\\n\\n # we override \\\"build_py\\\" in both distutils and setuptools\\n #\\n # most invocation pathways end up runnin\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_version.initialize_options\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_version.initialize_options\\n def initialize_options(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_version.finalize_options\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_version.finalize_options\\n def finalize_options(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_version.run\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_version.run\\n def run(self):\\n vers = get_versions(verbose=True)\\n print(\\\"Version: %s\\\" % vers[\\\"version\\\"])\\n print(\\\" full-revisionid: %s\\\" % vers.get(\\\"full-revisionid\\\"))\\n print(\\\" dirty: %s\\\" % vers.get(\\\"dirty\\\"))\\n print(\\\" date: %s\\\" % vers.get(\\\"date\\\"))\\n if vers[\\\"error\\\"]:\\n print(\\\" error: %s\\\" % vers[\\\"error\\\"])\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_build_py.run\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_build_py.run\\n def run(self):\\n root = get_root()\\n cfg = get_config_from_root(root)\\n versions = get_versions()\\n _build_py.run(self)\\n # now locate _version.py in the new build/ directory and replace\\n # it with an updated value\\n if cfg.versionfile_build:\\n target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build)\\n print(\\\"UPDATING %s\\\" % target_versionfile)\\n write_to_version_file(target_versionfile, versions)\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_sdist.run\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_sdist.run\\n def run(self):\\n versions = get_versions()\\n self._versioneer_generated_versions = versions\\n # unless we update this, the command will keep using the old\\n # version\\n self.distribution.metadata.version = versions[\\\"version\\\"]\\n return _sdist.run(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_sdist.make_release_tree\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_sdist.make_release_tree\\n def make_release_tree(self, base_dir, files):\\n root = get_root()\\n cfg = get_config_from_root(root)\\n _sdist.make_release_tree(self, base_dir, files)\\n # now locate _version.py in the new base_dir directory\\n # (remembering that it may be a hardlink) and replace it with an\\n # updated value\\n target_versionfile = os.path.join(base_dir, cfg.versionfile_source)\\n print(\\\"UPDATING %s\\\" % target_versionfile)\\n write_to_version_file(\\n target_versionfile, self._versioneer_generated_versions\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::do_setup\",\n \"text\": \"versioneer.py::do_setup\\ndef do_setup():\\n \\\"\\\"\\\"Main VCS-independent setup function for installing Versioneer.\\\"\\\"\\\"\\n root = get_root()\\n try:\\n cfg = get_config_from_root(root)\\n except (\\n EnvironmentError,\\n configparser.NoSectionError,\\n configparser.NoOptionError,\\n ) as e:\\n if isinstance(e, (EnvironmentError, configparser.NoSectionError)):\\n print(\\\"Adding sample versioneer config to setup.cfg\\\", file=sys.stderr)\\n with open(os.path.join(root, \\\"setup.cfg\\\"), \\\"a\\\") as f:\\n f.write(SAMPLE_CONFIG)\\n print(CONFIG_ERROR, file=sys.stderr)\\n return 1\\n\\n print(\\\" creating %s\\\" % cfg.versionfile_source)\\n with open(cfg.versionfile_source, \\\"w\\\") as f:\\n LONG = LONG_VERSION_PY[cfg.VCS]\\n f.write(\\n LONG\\n % {\\n \\\"DOLLAR\\\": \\\"$\\\",\\n \\\"STYLE\\\": cfg.style,\\n \\\"TAG_PREFIX\\\": cfg.tag_prefix,\\n \\\"PARENTDIR_PREFIX\\\": cfg.parentdir_prefix,\\n \\\"VERSIONFILE_SOURCE\\\": cfg.versionfile_source,\\n }\\n )\\n\\n ipy = os.path.join(os.path.dirname(cfg.versionfile_source), \\\"__init__.py\\\")\\n if os.path.exists(ipy):\\n try:\\n with open(ipy, \\\"r\\\") as f:\\n old = f.read()\\n except EnvironmentError:\\n old = \\\"\\\"\\n if INIT_PY_SNIPPET not in old:\\n print(\\\" appending to %s\\\" % ipy)\\n with open(ipy, \\\"a\\\") as f:\\n f.write(INIT_PY_SNIPPET)\\n else:\\n print(\\\" %s unmodified\\\" % ipy)\\n else:\\n print(\\\" %s doesn't exist, ok\\\" % ipy)\\n ipy = None\\n\\n # Make sure both the top-level \\\"versioneer.py\\\" and versionfile_source\\n # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so\\n # they'll be copied into source distributions. Pip won't be able to\\n # install the package without this.\\n manifest_in = os.path.join(root, \\\"MANIFEST.in\\\")\\n simple_includes = set()\\n try:\\n with open(manifest_in, \\\"r\\\") a\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::scan_setup_py\",\n \"text\": \"versioneer.py::scan_setup_py\\ndef scan_setup_py():\\n \\\"\\\"\\\"Validate the contents of setup.py against Versioneer's expectations.\\\"\\\"\\\"\\n found = set()\\n setters = False\\n errors = 0\\n with open(\\\"setup.py\\\", \\\"r\\\") as f:\\n for line in f.readlines():\\n if \\\"import versioneer\\\" in line:\\n found.add(\\\"import\\\")\\n if \\\"versioneer.get_cmdclass()\\\" in line:\\n found.add(\\\"cmdclass\\\")\\n if \\\"versioneer.get_version()\\\" in line:\\n found.add(\\\"get_version\\\")\\n if \\\"versioneer.VCS\\\" in line:\\n setters = True\\n if \\\"versioneer.versionfile_source\\\" in line:\\n setters = True\\n if len(found) != 3:\\n print(\\\"\\\")\\n print(\\\"Your setup.py appears to be missing some important items\\\")\\n print(\\\"(but I might be wrong). Please make sure it has something\\\")\\n print(\\\"roughly like the following:\\\")\\n print(\\\"\\\")\\n print(\\\" import versioneer\\\")\\n print(\\\" setup( version=versioneer.get_version(),\\\")\\n print(\\\" cmdclass=versioneer.get_cmdclass(), ...)\\\")\\n print(\\\"\\\")\\n errors += 1\\n if setters:\\n print(\\\"You should remove lines like 'versioneer.VCS = ' and\\\")\\n print(\\\"'versioneer.versionfile_source = ' . This configuration\\\")\\n print(\\\"now lives in setup.cfg, and should be removed from setup.py\\\")\\n print(\\\"\\\")\\n errors += 1\\n return errors\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":275,"cells":{"instance_id":{"kind":"string","value":"conan-io__conan-4546"},"repo":{"kind":"string","value":"conan-io/conan"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Add AIX to default_settings_yml\nAdd AIX to os in default settings.yml.\nAdd AIX to default_settings_yml\nAdd AIX to os in default settings.yml.\n"},"gold_files":{"kind":"list like","value":["conans/client/build/autotools_environment.py","conans/client/build/cmake_flags.py","conans/client/build/compiler_flags.py","conans/client/conf/__init__.py","conans/client/conf/detect.py","conans/client/generators/compiler_args.py","conans/client/tools/oss.py"],"string":"[\n \"conans/client/build/autotools_environment.py\",\n \"conans/client/build/cmake_flags.py\",\n \"conans/client/build/compiler_flags.py\",\n \"conans/client/conf/__init__.py\",\n \"conans/client/conf/detect.py\",\n \"conans/client/generators/compiler_args.py\",\n \"conans/client/tools/oss.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_flags","conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_link_flags","conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars","conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars_dict","conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.get_definitions","conans/client/build/compiler_flags.py::<module>","conans/client/build/compiler_flags.py::architecture_flag","conans/client/conf/__init__.py::<module>","conans/client/conf/detect.py::<module>","conans/client/conf/detect.py::_detect_os_arch","conans/client/generators/compiler_args.py::CompilerArgsGenerator.content","conans/client/tools/oss.py::<module>","conans/client/tools/oss.py::OSInfo.__init__","conans/client/tools/oss.py::detected_architecture","conans/client/tools/oss.py::get_gnu_triplet"],"string":"[\n \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_flags\",\n \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_link_flags\",\n \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars\",\n \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars_dict\",\n \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.get_definitions\",\n \"conans/client/build/compiler_flags.py::<module>\",\n \"conans/client/build/compiler_flags.py::architecture_flag\",\n \"conans/client/conf/__init__.py::<module>\",\n \"conans/client/conf/detect.py::<module>\",\n \"conans/client/conf/detect.py::_detect_os_arch\",\n \"conans/client/generators/compiler_args.py::CompilerArgsGenerator.content\",\n \"conans/client/tools/oss.py::<module>\",\n \"conans/client/tools/oss.py::OSInfo.__init__\",\n \"conans/client/tools/oss.py::detected_architecture\",\n \"conans/client/tools/oss.py::get_gnu_triplet\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"conans/build_info/conan_build_info.py","text":"conans/build_info/conan_build_info.py\ndef _extract_uploads_from_conan_trace(path)\ndef _extract_downloads_from_conan_trace(path)\ndef _get_type(file_path)\ndef _get_build_info_artifact(file_doc)\ndef _get_dependency(file_doc, dep_ref)\ndef _get_upload_modules_with_deps(uploaded_files, downloaded_files)\ndef _get_only_downloads_module(downloaded_files) # Gets a BuildInfoModule for the downloaded_files\ndef _build_modules(trace_path)\ndef get_build_info(trace_path)","label":0},{"cid":"conans/client/build/autotools_environment.py","text":"conans/client/build/autotools_environment.py\nclass AutoToolsBuildEnvironment # - CPPFLAGS (C-PreProcesor-Flags NOT related with c++) (-I -D)\n def __init__(self, conanfile, win_bash=False, include_rpath_flags=False) # FIXME: include_rpath_flags CONAN 2.0 to default True? Could break many packages \n def _configure_fpic(self)\n def _get_host_build_target_flags(self) # Based on google search for build/host triplets, it could need a lot\n def configure(self, configure_dir=None, args=None, build=None, host=None, target=None, pkg_config_paths=None, vars=None, use_default_install_dirs=True) # :param pkg_config_paths: Optional paths to locate the *.pc files\n def _configure_help_output(self, configure_path)\n def _adjust_path(self, path)\n def _valid_configure_flag(varname, args, available_flags)\n def _is_flag_in_args(varname, args)\n def make(self, args='', make_program=None, target=None, vars=None)\n def install(self, args='', make_program=None, vars=None)\n def _configure_link_flags(self) # Not the -L\n def _configure_flags(self)\n def _configure_cxx_flags(self)\n def _configure_defines(self)\n def _get_vars(self)\n def append(*args)\n def vars_dict(self)\n def vars(self)\ndef _environ_value_prefix(var_name, prefix=' ')","label":1},{"cid":"conans/client/build/cmake_flags.py","text":"conans/client/build/cmake_flags.py\ndef get_toolset(settings)\ndef get_generator(settings)\ndef get_generator_platform(settings, generator)\ndef is_multi_configuration(generator)\ndef verbose_definition(value)\ndef in_local_cache_definition(value)\ndef runtime_definition(runtime)\ndef build_type_definition(build_type, generator)\nclass CMakeDefinitionsBuilder\n def __init__(self, conanfile, cmake_system_name=True, make_program=None, parallel=True, generator=None, set_cmake_flags=False, forced_build_type=None, output=None)\n def _ss(self, setname) # safe setting\n def _get_cpp_standard_vars(self)\n def _cmake_cross_build_defines(self)\n def _get_make_program_definition(self)\n def get_definitions(self)","label":1},{"cid":"conans/client/build/compiler_flags.py","text":"conans/client/build/compiler_flags.py\ndef rpath_flags(os_build, compiler, lib_paths)\ndef architecture_flag(compiler, arch) # returns flags specific to the target architecture and compiler\ndef libcxx_define(compiler, libcxx)\ndef libcxx_flag(compiler, libcxx) # returns flag specific to the target C++ standard library\ndef pic_flag(compiler=None) # returns PIC (position independent code) flags, such as -fPIC\ndef build_type_flags(compiler, build_type, vs_toolset=None) # returns flags specific to the build type (Debug, Release, etc.)\ndef build_type_define(build_type=None) # returns definitions specific to the build type (Debug, Release, etc.)\ndef adjust_path(path, win_bash=False, subsystem=None, compiler=None) # adjusts path to be safely passed to the compiler command line\ndef sysroot_flag(sysroot, win_bash=False, subsystem=None, compiler=None)\ndef visual_runtime(runtime)\ndef format_defines(defines)\ndef format_include_paths(include_paths, win_bash=False, subsystem=None, compiler=None)\ndef format_library_paths(library_paths, win_bash=False, subsystem=None, compiler=None)\ndef format_libraries(libraries, compiler=None)\ndef parallel_compiler_cl_flag(output=None)","label":1},{"cid":"conans/client/cache/cache.py","text":"conans/client/cache/cache.py\ndef is_case_insensitive_os()\nclass ClientCache # Class to represent/store/compute all the paths involved in the execution\n def __init__(self, base_folder, output)\n def all_refs(self)\n def store(self)\n def package(self, pref, short_paths=False)\n def installed_as_editable(self, ref)\n def config_install_file(self)\n def package_layout(self, ref, short_paths=None, *args, **kwargs)\n def registry_path(self)\n def registry(self)\n def cacert_path(self)\n def _no_locks(self)\n def put_headers_path(self)\n def read_put_headers(self)\n def config(self)\n def localdb(self)\n def conan_conf_path(self)\n def profiles_path(self)\n def settings_path(self)\n def default_profile_path(self)\n def hooks_path(self) # :return: Hooks folder in client cache\n def default_profile(self)\n def settings(self) # Returns {setting: [value, ...]} defining all the possible\n def hooks(self) # Returns a list of hooks inside the hooks folder\n def delete_empty_dirs(self, deleted_refs)\n def remove_locks(self)\n def invalidate(self)\ndef _mix_settings_with_env(settings) # Reads CONAN_ENV_XXXX variables from environment\n def get_env_value(name)\n def get_setting_name(env_name)","label":0},{"cid":"conans/client/cache/editable.py","text":"conans/client/cache/editable.py\nclass EditablePackages\n def __init__(self, cache_folder)\n def edited_refs(self)\n def save(self)\n def get(self, ref)\n def add(self, ref, path, layout)\n def remove(self, ref)\n def override(self, workspace_edited)","label":0},{"cid":"conans/client/cache/remote_registry.py","text":"conans/client/cache/remote_registry.py\ndef load_registry_txt(contents) # Remove in Conan 2.0\ndef load_old_registry_json(contents) # From json\ndef migrate_registry_file(cache, out)\n def add_ref_remote(reference, remotes, remote_name)\n def add_pref_remote(pkg_ref, remotes, remote_name)\nclass Remotes\n def __init__(self)\n def defaults(cls)\n def select(self, remote_name)\n def __bool__(self)\n def __nonzero__(self)\n def clear(self)\n def items(self)\n def values(self)\n def loads(text)\n def dumps(self)\n def save(self, filename)\n def _get_by_url(self, url)\n def rename(self, remote_name, new_remote_name)\n def get_remote(self, remote_name)\n def default(self)\n def __contains__(self, remote_name)\n def get(self, remote_name)\n def __getitem__(self, remote_name)\n def __delitem__(self, remote_name)\n def _upsert(self, remote_name, url, verify_ssl, insert)\n def add(self, remote_name, url, verify_ssl=True, insert=None, force=None)\n def update(self, remote_name, url, verify_ssl=True, insert=None)\n def _add_update(self, remote_name, url, verify_ssl, insert=None)\nclass RemoteRegistry\n def __init__(self, cache)\n def load_remotes(self)\n def add(self, remote_name, url, verify_ssl=True, insert=None, force=None)\n def update(self, remote_name, url, verify_ssl=True, insert=None)\n def clear(self)\n def remove(self, remote_name)\n def define(self, remotes)\n def rename(self, remote_name, new_remote_name)\n def refs_list(self)\n def prefs_list(self)","label":0},{"cid":"conans/client/conf/__init__.py","text":"conans/client/conf/__init__.py\nclass ConanClientConfigParser\n def __init__(self, filename)\n def env_vars(self)\n def _env_c(self, var_name, env_var_name, default_value)\n def get_item(self, item)\n def set_item(self, key, value)\n def rm_item(self, item)\n def get_conf(self, varname) # Gets the section from config file or raises an exception\n def default_profile(self)\n def cache_no_locks(self)\n def storage(self)\n def request_timeout(self)\n def revisions_enabled(self)\n def default_package_id_mode(self)\n def storage_path(self)\n def proxies(self) # optional field, might not exist\n def cacert_path(self)","label":1},{"cid":"conans/client/conf/config_installer.py","text":"conans/client/conf/config_installer.py\ndef _hide_password(resource) # Hide password from url/file path\ndef _handle_remotes(cache, remote_file)\ndef tmp_config_install_folder(cache)\ndef _process_git_repo(config, cache, output)\ndef _process_zip_file(config, zippath, cache, output, tmp_folder, remove=False)\ndef _handle_conan_conf(current_conan_conf, new_conan_conf_path)\ndef _process_folder(config, folder, cache, output)\ndef _process_download(config, cache, output, requester)\nclass _ConfigOrigin\n def __init__(self, data)\n def __eq__(self, other)\n def __ne__(self, other)\n def json(self)\n def from_item(uri, config_type, verify_ssl, args, source_folder, target_folder)\ndef _process_config(config, cache, output, requester)\ndef _save_configs(configs_file, configs)\ndef configuration_install(uri, cache, output, verify_ssl, requester, config_type=None, args=None, source_folder=None, target_folder=None)","label":0},{"cid":"conans/client/conf/detect.py","text":"conans/client/conf/detect.py\ndef _execute(command)\ndef _gcc_compiler(output, compiler_exe='gcc')\ndef _clang_compiler(output, compiler_exe='clang')\ndef _sun_cc_compiler(output, compiler_exe='cc')\ndef _get_default_compiler(output)\ndef _detect_compiler_version(result, output, profile_path)\ndef _detect_os_arch(result, output)\ndef detect_defaults_settings(output, profile_path) # try to deduce current machine values without any constraints at all","label":1},{"cid":"conans/client/generators/compiler_args.py","text":"conans/client/generators/compiler_args.py\nclass CompilerArgsGenerator\n def filename(self)\n def compiler(self)\n def content(self) # With compiler_args you can invoke your compiler:\n def _libcxx_flags(self)","label":1},{"cid":"conans/client/generators/visualstudio_multi.py","text":"conans/client/generators/visualstudio_multi.py\nclass _VSSettings\n def __init__(self, settings)\n def filename(self)\n def condition(self)\nclass VisualStudioMultiGenerator\n def filename(self)\n def content(self)","label":0},{"cid":"conans/client/importer.py","text":"conans/client/importer.py\ndef undo_imports(current_path, output)\ndef _report_save_manifest(copied_files, output, dest_folder, manifest_name)\ndef _make_files_writable(file_names)\ndef run_imports(conanfile, dest_folder)\ndef remove_imports(conanfile, copied_files, output)\ndef run_deploy(conanfile, install_folder)\n def file_copier(*args, **kwargs)\nclass _FileImporter # manages the copy of files, resources, libs from the local store to the user\n def __init__(self, conanfile, dst_folder)\n def __call__(self, pattern, dst='', src='', root_package=None, folder=False, ignore_case=False, excludes=None, keep_path=True) # param pattern: an fnmatch file pattern of the files that should be copied. Eg. *\n def _get_folders(self, pattern) # given the current deps graph, compute a dict {name: store-path} of","label":0},{"cid":"conans/client/manager.py","text":"conans/client/manager.py\nclass ConanManager\n def __init__(self, cache, user_io, remote_manager, recorder, graph_manager, hook_manager)\n def install(self, ref_or_path, install_folder, graph_info, remotes=None, build_modes=None, update=False, manifest_folder=None, manifest_verify=False, manifest_interactive=False, generators=None, no_imports=False, create_reference=None, keep_build=False) # Fetch and build all dependencies for the given reference","label":0},{"cid":"conans/client/migrations.py","text":"conans/client/migrations.py\nclass ClientMigrator\n def __init__(self, cache, current_version, out)\n def _update_settings_yml(self, old_version)\n def save_new()\n def _make_migrations(self, old_version)\ndef _get_refs(cache)\ndef _get_prefs(layout)\ndef _migrate_full_metadata(cache, out)\ndef _migrate_create_metadata(cache, out)\ndef _migrate_lock_files(cache, out)\ndef migrate_config_install(cache)\ndef migrate_to_default_profile(conf_path, default_profile_path)\ndef migrate_c_src_export_source(cache, out)\ndef migrate_plugins_to_hooks(cache, output=None)","label":0},{"cid":"conans/client/tools/oss.py","text":"conans/client/tools/oss.py\ndef args_to_string(args)\ndef cpu_count(output=None)\ndef detected_os()\ndef detected_architecture()\nclass OSInfo # Usage:\n def __init__(self)\n def _get_linux_distro_info(self)\n def with_apt(self)\n def with_yum(self)\n def with_pacman(self)\n def with_zypper(self)\n def get_win_os_version() # Get's the OS major and minor versions. Returns a tuple of\n class _OSVERSIONINFOEXW\n def get_debian_version_name(version)\n def get_win_version_name(version)\n def get_osx_version_name(version)\n def get_freebsd_version()\n def get_solaris_version_name(version)\n def bash_path()\n def uname(options=None)\n def detect_windows_subsystem()\ndef cross_building(settings, self_os=None, self_arch=None)\ndef get_cross_building_settings(settings, self_os=None, self_arch=None)\ndef get_gnu_triplet(os_, arch, compiler=None) # Returns string with <machine>-<vendor>-<op_system> triplet (<vendor> can be omit\ndef check_output(cmd, folder=None, return_code=False, stderr=None)","label":1},{"cid":"conans/client/tools/pkg_config.py","text":"conans/client/tools/pkg_config.py\nclass PkgConfig\n def _cmd_output(command)\n def __init__(self, library, pkg_config_executable='pkg-config', static=False, msvc_syntax=False, variables=None, print_errors=True) # :param library: library (package) name, such as libastral\n def _parse_output(self, option)\n def _get_option(self, option)\n def cflags(self)\n def cflags_only_I(self)\n def cflags_only_other(self)\n def libs(self)\n def libs_only_L(self)\n def libs_only_l(self)\n def libs_only_other(self)\n def provides(self)\n def requires(self)\n def requires_private(self)\n def variables(self)","label":0},{"cid":"conans/model/info.py","text":"conans/model/info.py\nclass RequirementInfo\n def __init__(self, pref, default_package_id_mode, indirect=False)\n def copy(self)\n def dumps(self)\n def sha(self)\n def unrelated_mode(self)\n def semver_direct_mode(self)\n def semver_mode(self)\n def full_version_mode(self)\n def patch_mode(self)\n def base_mode(self)\n def minor_mode(self)\n def major_mode(self)\n def full_recipe_mode(self)\n def full_package_mode(self)\nclass RequirementsInfo\n def __init__(self, prefs, default_package_id_mode)\n def copy(self)\n def clear(self)\n def remove(self, *args)\n def add(self, prefs_indirect, default_package_id_mode) # necessary to propagate from upstream the real\n def refs(self) # used for updating downstream requirements with this\n def _get_key(self, item)\n def __getitem__(self, item) # get by package name\n def pkg_names(self)\n def sha(self)\n def dumps(self)\n def unrelated_mode(self)\n def semver_direct_mode(self)\n def semver_mode(self)\n def patch_mode(self)\n def minor_mode(self)\n def major_mode(self)\n def base_mode(self)\n def full_version_mode(self)\n def full_recipe_mode(self)\n def full_package_mode(self)\nclass _PackageReferenceList\n def loads(text)\n def dumps(self)\n def serialize(self)\nclass ConanInfo\n def copy(self) # Useful for build_id implementation\n def create(settings, options, prefs_direct, prefs_indirect, default_package_id_mode)\n def loads(text)\n def dumps(self)\n def indent(text)\n def __eq__(self, other) # currently just for testing purposes\n def __ne__(self, other)\n def load_file(conan_info_path) # load from file\n def load_from_package(package_folder)\n def package_id(self) # The package_id of a conans is the sha1 of its specific requirements,\n def serialize_min(self) # This info will be shown in search results.\n def header_only(self)\n def vs_toolset_compatible(self) # Default behaviour, same package for toolset v140 with compiler=Visual Studio 15 \n def vs_toolset_incompatible(self) # Will generate different packages for v140 and visual 15 than the visual 14\n def discard_build_settings(self)\n def include_build_settings(self)\n def default_std_matching(self) # If we are building with gcc 7, and we specify -s cppstd=gnu14, it's the default,\n def default_std_non_matching(self)","label":0}],"string":"[\n {\n \"cid\": \"conans/build_info/conan_build_info.py\",\n \"text\": \"conans/build_info/conan_build_info.py\\ndef _extract_uploads_from_conan_trace(path)\\ndef _extract_downloads_from_conan_trace(path)\\ndef _get_type(file_path)\\ndef _get_build_info_artifact(file_doc)\\ndef _get_dependency(file_doc, dep_ref)\\ndef _get_upload_modules_with_deps(uploaded_files, downloaded_files)\\ndef _get_only_downloads_module(downloaded_files) # Gets a BuildInfoModule for the downloaded_files\\ndef _build_modules(trace_path)\\ndef get_build_info(trace_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py\",\n \"text\": \"conans/client/build/autotools_environment.py\\nclass AutoToolsBuildEnvironment # - CPPFLAGS (C-PreProcesor-Flags NOT related with c++) (-I -D)\\n def __init__(self, conanfile, win_bash=False, include_rpath_flags=False) # FIXME: include_rpath_flags CONAN 2.0 to default True? Could break many packages \\n def _configure_fpic(self)\\n def _get_host_build_target_flags(self) # Based on google search for build/host triplets, it could need a lot\\n def configure(self, configure_dir=None, args=None, build=None, host=None, target=None, pkg_config_paths=None, vars=None, use_default_install_dirs=True) # :param pkg_config_paths: Optional paths to locate the *.pc files\\n def _configure_help_output(self, configure_path)\\n def _adjust_path(self, path)\\n def _valid_configure_flag(varname, args, available_flags)\\n def _is_flag_in_args(varname, args)\\n def make(self, args='', make_program=None, target=None, vars=None)\\n def install(self, args='', make_program=None, vars=None)\\n def _configure_link_flags(self) # Not the -L\\n def _configure_flags(self)\\n def _configure_cxx_flags(self)\\n def _configure_defines(self)\\n def _get_vars(self)\\n def append(*args)\\n def vars_dict(self)\\n def vars(self)\\ndef _environ_value_prefix(var_name, prefix=' ')\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py\",\n \"text\": \"conans/client/build/cmake_flags.py\\ndef get_toolset(settings)\\ndef get_generator(settings)\\ndef get_generator_platform(settings, generator)\\ndef is_multi_configuration(generator)\\ndef verbose_definition(value)\\ndef in_local_cache_definition(value)\\ndef runtime_definition(runtime)\\ndef build_type_definition(build_type, generator)\\nclass CMakeDefinitionsBuilder\\n def __init__(self, conanfile, cmake_system_name=True, make_program=None, parallel=True, generator=None, set_cmake_flags=False, forced_build_type=None, output=None)\\n def _ss(self, setname) # safe setting\\n def _get_cpp_standard_vars(self)\\n def _cmake_cross_build_defines(self)\\n def _get_make_program_definition(self)\\n def get_definitions(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py\",\n \"text\": \"conans/client/build/compiler_flags.py\\ndef rpath_flags(os_build, compiler, lib_paths)\\ndef architecture_flag(compiler, arch) # returns flags specific to the target architecture and compiler\\ndef libcxx_define(compiler, libcxx)\\ndef libcxx_flag(compiler, libcxx) # returns flag specific to the target C++ standard library\\ndef pic_flag(compiler=None) # returns PIC (position independent code) flags, such as -fPIC\\ndef build_type_flags(compiler, build_type, vs_toolset=None) # returns flags specific to the build type (Debug, Release, etc.)\\ndef build_type_define(build_type=None) # returns definitions specific to the build type (Debug, Release, etc.)\\ndef adjust_path(path, win_bash=False, subsystem=None, compiler=None) # adjusts path to be safely passed to the compiler command line\\ndef sysroot_flag(sysroot, win_bash=False, subsystem=None, compiler=None)\\ndef visual_runtime(runtime)\\ndef format_defines(defines)\\ndef format_include_paths(include_paths, win_bash=False, subsystem=None, compiler=None)\\ndef format_library_paths(library_paths, win_bash=False, subsystem=None, compiler=None)\\ndef format_libraries(libraries, compiler=None)\\ndef parallel_compiler_cl_flag(output=None)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/cache/cache.py\",\n \"text\": \"conans/client/cache/cache.py\\ndef is_case_insensitive_os()\\nclass ClientCache # Class to represent/store/compute all the paths involved in the execution\\n def __init__(self, base_folder, output)\\n def all_refs(self)\\n def store(self)\\n def package(self, pref, short_paths=False)\\n def installed_as_editable(self, ref)\\n def config_install_file(self)\\n def package_layout(self, ref, short_paths=None, *args, **kwargs)\\n def registry_path(self)\\n def registry(self)\\n def cacert_path(self)\\n def _no_locks(self)\\n def put_headers_path(self)\\n def read_put_headers(self)\\n def config(self)\\n def localdb(self)\\n def conan_conf_path(self)\\n def profiles_path(self)\\n def settings_path(self)\\n def default_profile_path(self)\\n def hooks_path(self) # :return: Hooks folder in client cache\\n def default_profile(self)\\n def settings(self) # Returns {setting: [value, ...]} defining all the possible\\n def hooks(self) # Returns a list of hooks inside the hooks folder\\n def delete_empty_dirs(self, deleted_refs)\\n def remove_locks(self)\\n def invalidate(self)\\ndef _mix_settings_with_env(settings) # Reads CONAN_ENV_XXXX variables from environment\\n def get_env_value(name)\\n def get_setting_name(env_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/editable.py\",\n \"text\": \"conans/client/cache/editable.py\\nclass EditablePackages\\n def __init__(self, cache_folder)\\n def edited_refs(self)\\n def save(self)\\n def get(self, ref)\\n def add(self, ref, path, layout)\\n def remove(self, ref)\\n def override(self, workspace_edited)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py\",\n \"text\": \"conans/client/cache/remote_registry.py\\ndef load_registry_txt(contents) # Remove in Conan 2.0\\ndef load_old_registry_json(contents) # From json\\ndef migrate_registry_file(cache, out)\\n def add_ref_remote(reference, remotes, remote_name)\\n def add_pref_remote(pkg_ref, remotes, remote_name)\\nclass Remotes\\n def __init__(self)\\n def defaults(cls)\\n def select(self, remote_name)\\n def __bool__(self)\\n def __nonzero__(self)\\n def clear(self)\\n def items(self)\\n def values(self)\\n def loads(text)\\n def dumps(self)\\n def save(self, filename)\\n def _get_by_url(self, url)\\n def rename(self, remote_name, new_remote_name)\\n def get_remote(self, remote_name)\\n def default(self)\\n def __contains__(self, remote_name)\\n def get(self, remote_name)\\n def __getitem__(self, remote_name)\\n def __delitem__(self, remote_name)\\n def _upsert(self, remote_name, url, verify_ssl, insert)\\n def add(self, remote_name, url, verify_ssl=True, insert=None, force=None)\\n def update(self, remote_name, url, verify_ssl=True, insert=None)\\n def _add_update(self, remote_name, url, verify_ssl, insert=None)\\nclass RemoteRegistry\\n def __init__(self, cache)\\n def load_remotes(self)\\n def add(self, remote_name, url, verify_ssl=True, insert=None, force=None)\\n def update(self, remote_name, url, verify_ssl=True, insert=None)\\n def clear(self)\\n def remove(self, remote_name)\\n def define(self, remotes)\\n def rename(self, remote_name, new_remote_name)\\n def refs_list(self)\\n def prefs_list(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py\",\n \"text\": \"conans/client/conf/__init__.py\\nclass ConanClientConfigParser\\n def __init__(self, filename)\\n def env_vars(self)\\n def _env_c(self, var_name, env_var_name, default_value)\\n def get_item(self, item)\\n def set_item(self, key, value)\\n def rm_item(self, item)\\n def get_conf(self, varname) # Gets the section from config file or raises an exception\\n def default_profile(self)\\n def cache_no_locks(self)\\n def storage(self)\\n def request_timeout(self)\\n def revisions_enabled(self)\\n def default_package_id_mode(self)\\n def storage_path(self)\\n def proxies(self) # optional field, might not exist\\n def cacert_path(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py\",\n \"text\": \"conans/client/conf/config_installer.py\\ndef _hide_password(resource) # Hide password from url/file path\\ndef _handle_remotes(cache, remote_file)\\ndef tmp_config_install_folder(cache)\\ndef _process_git_repo(config, cache, output)\\ndef _process_zip_file(config, zippath, cache, output, tmp_folder, remove=False)\\ndef _handle_conan_conf(current_conan_conf, new_conan_conf_path)\\ndef _process_folder(config, folder, cache, output)\\ndef _process_download(config, cache, output, requester)\\nclass _ConfigOrigin\\n def __init__(self, data)\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def json(self)\\n def from_item(uri, config_type, verify_ssl, args, source_folder, target_folder)\\ndef _process_config(config, cache, output, requester)\\ndef _save_configs(configs_file, configs)\\ndef configuration_install(uri, cache, output, verify_ssl, requester, config_type=None, args=None, source_folder=None, target_folder=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/detect.py\",\n \"text\": \"conans/client/conf/detect.py\\ndef _execute(command)\\ndef _gcc_compiler(output, compiler_exe='gcc')\\ndef _clang_compiler(output, compiler_exe='clang')\\ndef _sun_cc_compiler(output, compiler_exe='cc')\\ndef _get_default_compiler(output)\\ndef _detect_compiler_version(result, output, profile_path)\\ndef _detect_os_arch(result, output)\\ndef detect_defaults_settings(output, profile_path) # try to deduce current machine values without any constraints at all\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/generators/compiler_args.py\",\n \"text\": \"conans/client/generators/compiler_args.py\\nclass CompilerArgsGenerator\\n def filename(self)\\n def compiler(self)\\n def content(self) # With compiler_args you can invoke your compiler:\\n def _libcxx_flags(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/generators/visualstudio_multi.py\",\n \"text\": \"conans/client/generators/visualstudio_multi.py\\nclass _VSSettings\\n def __init__(self, settings)\\n def filename(self)\\n def condition(self)\\nclass VisualStudioMultiGenerator\\n def filename(self)\\n def content(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/importer.py\",\n \"text\": \"conans/client/importer.py\\ndef undo_imports(current_path, output)\\ndef _report_save_manifest(copied_files, output, dest_folder, manifest_name)\\ndef _make_files_writable(file_names)\\ndef run_imports(conanfile, dest_folder)\\ndef remove_imports(conanfile, copied_files, output)\\ndef run_deploy(conanfile, install_folder)\\n def file_copier(*args, **kwargs)\\nclass _FileImporter # manages the copy of files, resources, libs from the local store to the user\\n def __init__(self, conanfile, dst_folder)\\n def __call__(self, pattern, dst='', src='', root_package=None, folder=False, ignore_case=False, excludes=None, keep_path=True) # param pattern: an fnmatch file pattern of the files that should be copied. Eg. *\\n def _get_folders(self, pattern) # given the current deps graph, compute a dict {name: store-path} of\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py\",\n \"text\": \"conans/client/manager.py\\nclass ConanManager\\n def __init__(self, cache, user_io, remote_manager, recorder, graph_manager, hook_manager)\\n def install(self, ref_or_path, install_folder, graph_info, remotes=None, build_modes=None, update=False, manifest_folder=None, manifest_verify=False, manifest_interactive=False, generators=None, no_imports=False, create_reference=None, keep_build=False) # Fetch and build all dependencies for the given reference\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py\",\n \"text\": \"conans/client/migrations.py\\nclass ClientMigrator\\n def __init__(self, cache, current_version, out)\\n def _update_settings_yml(self, old_version)\\n def save_new()\\n def _make_migrations(self, old_version)\\ndef _get_refs(cache)\\ndef _get_prefs(layout)\\ndef _migrate_full_metadata(cache, out)\\ndef _migrate_create_metadata(cache, out)\\ndef _migrate_lock_files(cache, out)\\ndef migrate_config_install(cache)\\ndef migrate_to_default_profile(conf_path, default_profile_path)\\ndef migrate_c_src_export_source(cache, out)\\ndef migrate_plugins_to_hooks(cache, output=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py\",\n \"text\": \"conans/client/tools/oss.py\\ndef args_to_string(args)\\ndef cpu_count(output=None)\\ndef detected_os()\\ndef detected_architecture()\\nclass OSInfo # Usage:\\n def __init__(self)\\n def _get_linux_distro_info(self)\\n def with_apt(self)\\n def with_yum(self)\\n def with_pacman(self)\\n def with_zypper(self)\\n def get_win_os_version() # Get's the OS major and minor versions. Returns a tuple of\\n class _OSVERSIONINFOEXW\\n def get_debian_version_name(version)\\n def get_win_version_name(version)\\n def get_osx_version_name(version)\\n def get_freebsd_version()\\n def get_solaris_version_name(version)\\n def bash_path()\\n def uname(options=None)\\n def detect_windows_subsystem()\\ndef cross_building(settings, self_os=None, self_arch=None)\\ndef get_cross_building_settings(settings, self_os=None, self_arch=None)\\ndef get_gnu_triplet(os_, arch, compiler=None) # Returns string with <machine>-<vendor>-<op_system> triplet (<vendor> can be omit\\ndef check_output(cmd, folder=None, return_code=False, stderr=None)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py\",\n \"text\": \"conans/client/tools/pkg_config.py\\nclass PkgConfig\\n def _cmd_output(command)\\n def __init__(self, library, pkg_config_executable='pkg-config', static=False, msvc_syntax=False, variables=None, print_errors=True) # :param library: library (package) name, such as libastral\\n def _parse_output(self, option)\\n def _get_option(self, option)\\n def cflags(self)\\n def cflags_only_I(self)\\n def cflags_only_other(self)\\n def libs(self)\\n def libs_only_L(self)\\n def libs_only_l(self)\\n def libs_only_other(self)\\n def provides(self)\\n def requires(self)\\n def requires_private(self)\\n def variables(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py\",\n \"text\": \"conans/model/info.py\\nclass RequirementInfo\\n def __init__(self, pref, default_package_id_mode, indirect=False)\\n def copy(self)\\n def dumps(self)\\n def sha(self)\\n def unrelated_mode(self)\\n def semver_direct_mode(self)\\n def semver_mode(self)\\n def full_version_mode(self)\\n def patch_mode(self)\\n def base_mode(self)\\n def minor_mode(self)\\n def major_mode(self)\\n def full_recipe_mode(self)\\n def full_package_mode(self)\\nclass RequirementsInfo\\n def __init__(self, prefs, default_package_id_mode)\\n def copy(self)\\n def clear(self)\\n def remove(self, *args)\\n def add(self, prefs_indirect, default_package_id_mode) # necessary to propagate from upstream the real\\n def refs(self) # used for updating downstream requirements with this\\n def _get_key(self, item)\\n def __getitem__(self, item) # get by package name\\n def pkg_names(self)\\n def sha(self)\\n def dumps(self)\\n def unrelated_mode(self)\\n def semver_direct_mode(self)\\n def semver_mode(self)\\n def patch_mode(self)\\n def minor_mode(self)\\n def major_mode(self)\\n def base_mode(self)\\n def full_version_mode(self)\\n def full_recipe_mode(self)\\n def full_package_mode(self)\\nclass _PackageReferenceList\\n def loads(text)\\n def dumps(self)\\n def serialize(self)\\nclass ConanInfo\\n def copy(self) # Useful for build_id implementation\\n def create(settings, options, prefs_direct, prefs_indirect, default_package_id_mode)\\n def loads(text)\\n def dumps(self)\\n def indent(text)\\n def __eq__(self, other) # currently just for testing purposes\\n def __ne__(self, other)\\n def load_file(conan_info_path) # load from file\\n def load_from_package(package_folder)\\n def package_id(self) # The package_id of a conans is the sha1 of its specific requirements,\\n def serialize_min(self) # This info will be shown in search results.\\n def header_only(self)\\n def vs_toolset_compatible(self) # Default behaviour, same package for toolset v140 with compiler=Visual Studio 15 \\n def vs_toolset_incompatible(self) # Will generate different packages for v140 and visual 15 than the visual 14\\n def discard_build_settings(self)\\n def include_build_settings(self)\\n def default_std_matching(self) # If we are building with gcc 7, and we specify -s cppstd=gnu14, it's the default,\\n def default_std_non_matching(self)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"conans/build_info/conan_build_info.py::_extract_uploads_from_conan_trace","text":"conans/build_info/conan_build_info.py::_extract_uploads_from_conan_trace\ndef _extract_uploads_from_conan_trace(path):\n modules = defaultdict(dict)\n try:\n with open(path, \"r\") as traces:\n for line in traces.readlines():\n doc = json.loads(line)\n if doc[\"_action\"] in (\"UPLOADED_RECIPE\", \"UPLOADED_PACKAGE\"):\n module_type = \"recipe\" if doc[\"_action\"] == \"UPLOADED_RECIPE\" else \"package\"\n modules[doc[\"_id\"]] = {\"remote\": doc[\"remote\"], \"files\": [], \"type\": module_type}\n modules[doc[\"_id\"]][\"files\"].extend(doc[\"files\"])\n except ValueError as exc:\n raise Exception(\"INVALID TRACE FILE! %s\" % exc)\n\n return modules","label":0},{"cid":"conans/build_info/conan_build_info.py::_extract_downloads_from_conan_trace","text":"conans/build_info/conan_build_info.py::_extract_downloads_from_conan_trace\ndef _extract_downloads_from_conan_trace(path):\n downloaded_modules = defaultdict(dict) # dict of {reference: {\"files\": [], \"remote\": remote }}\n\n with open(path, \"r\") as traces:\n for line in traces.readlines():\n doc = json.loads(line)\n if doc[\"_action\"] in [\"DOWNLOADED_PACKAGE\", \"DOWNLOADED_RECIPE\"]:\n downloaded_modules[doc[\"_id\"]] = {\"files\": doc[\"files\"], \"remote\": doc[\"remote\"]}\n return downloaded_modules","label":0},{"cid":"conans/build_info/conan_build_info.py::_get_type","text":"conans/build_info/conan_build_info.py::_get_type\ndef _get_type(file_path):\n return os.path.splitext(file_path)[1].upper()[1:]","label":0},{"cid":"conans/build_info/conan_build_info.py::_get_build_info_artifact","text":"conans/build_info/conan_build_info.py::_get_build_info_artifact\ndef _get_build_info_artifact(file_doc):\n the_type = _get_type(file_doc[\"path\"])\n ret = BuildInfoModuleArtifact(the_type, file_doc[\"sha1\"], file_doc[\"md5\"], file_doc[\"name\"])\n return ret","label":0},{"cid":"conans/build_info/conan_build_info.py::_get_dependency","text":"conans/build_info/conan_build_info.py::_get_dependency\ndef _get_dependency(file_doc, dep_ref):\n the_type = _get_type(file_doc[\"path\"])\n the_id = \"%s:%s\" % (dep_ref, file_doc[\"name\"])\n ret = BuildInfoModuleDependency(the_id, the_type, file_doc[\"sha1\"], file_doc[\"md5\"])\n return ret","label":0},{"cid":"conans/build_info/conan_build_info.py::_get_upload_modules_with_deps","text":"conans/build_info/conan_build_info.py::_get_upload_modules_with_deps\ndef _get_upload_modules_with_deps(uploaded_files, downloaded_files):\n modules = []\n deps = defaultdict(set) # Reference: [Reference, Reference]\n # Extract needed information\n for module_id, mod_doc in uploaded_files.items():\n ref_or_pref = ConanFileReference.loads(module_id) if mod_doc[\"type\"] == \"recipe\" \\\n else PackageReference.loads(module_id)\n # Store recipe and package dependencies\n if mod_doc[\"type\"] == \"package\":\n conan_infos = [file_doc for file_doc in mod_doc[\"files\"]\n if file_doc[\"name\"] == \"conaninfo.txt\"]\n if conan_infos:\n conan_info = conan_infos[0][\"path\"]\n info = ConanInfo.loads(load(conan_info))\n for pref in info.full_requires:\n deps[str(ref_or_pref.ref)].add(str(pref.ref))\n deps[str(ref_or_pref)].add(str(pref))\n\n # Add the modules\n for module_id, mod_doc in uploaded_files.items():\n module = BuildInfoModule()\n module.id = str(module_id)\n # Add artifacts\n for file_doc in mod_doc[\"files\"]:\n artifact = _get_build_info_artifact(file_doc)\n module.artifacts.append(artifact)\n\n # Add dependencies, for each module dep modules\n for mod_dep_id in deps[module_id]:\n if mod_dep_id in downloaded_files:\n down_module = downloaded_files[mod_dep_id]\n # Check if the remote from the uploaded package matches the remote\n # from the downloaded dependency\n if down_module.get(\"remote\", None) == mod_doc[\"remote\"]:\n for file_doc in down_module[\"files\"]:\n module.dependencies.append(_get_dependency(file_doc, mod_dep_id))\n\n modules.append(module)\n return modules","label":0},{"cid":"conans/build_info/conan_build_info.py::_get_only_downloads_module","text":"conans/build_info/conan_build_info.py::_get_only_downloads_module\ndef _get_only_downloads_module(downloaded_files):\n \"\"\"\n Gets a BuildInfoModule for the downloaded_files\n :param downloaded_files: {reference: {\"files\": [doc_file, doc_file], \"remote\": remote }}\n :return: BuildInfoModule object\n \"\"\"\n ret = BuildInfoModule()\n ret.id = \"DownloadOnly\"\n for _, data in downloaded_files.items():\n files = data[\"files\"]\n for file_doc in files:\n the_type = _get_type(file_doc[\"path\"])\n dep = BuildInfoModuleDependency(file_doc[\"name\"], the_type, file_doc[\"sha1\"],\n file_doc[\"md5\"])\n ret.dependencies.append(dep)\n return ret","label":0},{"cid":"conans/build_info/conan_build_info.py::_build_modules","text":"conans/build_info/conan_build_info.py::_build_modules\ndef _build_modules(trace_path):\n uploaded_files = _extract_uploads_from_conan_trace(trace_path)\n downloaded_files = _extract_downloads_from_conan_trace(trace_path)\n if uploaded_files:\n return _get_upload_modules_with_deps(uploaded_files, downloaded_files)\n else:\n return [_get_only_downloads_module(downloaded_files)]","label":0},{"cid":"conans/build_info/conan_build_info.py::get_build_info","text":"conans/build_info/conan_build_info.py::get_build_info\ndef get_build_info(trace_path):\n bi = BuildInfo()\n modules = _build_modules(trace_path)\n bi.modules.extend(modules)\n return bi","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.__init__","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.__init__\n def __init__(self, conanfile, win_bash=False, include_rpath_flags=False):\n \"\"\"\n FIXME: include_rpath_flags CONAN 2.0 to default True? Could break many packages in center\n \"\"\"\n self._conanfile = conanfile\n self._win_bash = win_bash\n self._include_rpath_flags = include_rpath_flags\n self.subsystem = OSInfo().detect_windows_subsystem() if self._win_bash else None\n self._deps_cpp_info = conanfile.deps_cpp_info\n self._os = conanfile.settings.get_safe(\"os\")\n self._arch = conanfile.settings.get_safe(\"arch\")\n self._build_type = conanfile.settings.get_safe(\"build_type\")\n self._compiler = conanfile.settings.get_safe(\"compiler\")\n self._compiler_version = conanfile.settings.get_safe(\"compiler.version\")\n self._libcxx = conanfile.settings.get_safe(\"compiler.libcxx\")\n self._cppstd = cppstd_from_settings(conanfile.settings)\n\n # Set the generic objects before mapping to env vars to let the user\n # alter some value\n self.libs = copy.copy(self._deps_cpp_info.libs)\n self.include_paths = copy.copy(self._deps_cpp_info.include_paths)\n self.library_paths = copy.copy(self._deps_cpp_info.lib_paths)\n\n self.defines = self._configure_defines()\n # Will go to CFLAGS and CXXFLAGS [\"-m64\" \"-m32\", \"-g\", \"-s\"]\n self.flags = self._configure_flags()\n # Only c++ flags [-stdlib, -library], will go to CXXFLAGS\n self.cxx_flags = self._configure_cxx_flags()\n # cpp standard\n self.cppstd_flag = cppstd_flag(self._compiler, self._compiler_version, self._cppstd)\n # Not -L flags, [\"-m64\" \"-m32\"]\n self.link_flags = self._configure_link_flags() # TEST!\n # Precalculate -fPIC\n self.fpic = self._configure_fpic()\n\n # Precalculate build, host, target triplets\n self.build, self.host, self.target = self._get_hos","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_fpic","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_fpic\n def _configure_fpic(self):\n if not str(self._os).startswith(\"Windows\"):\n fpic = self._conanfile.options.get_safe(\"fPIC\")\n if fpic is not None:\n shared = self._conanfile.options.get_safe(\"shared\")\n return True if (fpic or shared) else None","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_host_build_target_flags","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_host_build_target_flags\n def _get_host_build_target_flags(self):\n \"\"\"Based on google search for build/host triplets, it could need a lot\n and complex verification\"\"\"\n\n arch_detected = detected_architecture() or platform.machine()\n os_detected = detected_os() or platform.system()\n\n if os_detected is None or arch_detected is None or self._arch is None or self._os is None:\n return False, False, False\n if not cross_building(self._conanfile.settings, os_detected, arch_detected):\n return False, False, False\n\n try:\n build = get_gnu_triplet(os_detected, arch_detected, self._compiler)\n except ConanException as exc:\n self._conanfile.output.warn(str(exc))\n build = None\n try:\n host = get_gnu_triplet(self._os, self._arch, self._compiler)\n except ConanException as exc:\n self._conanfile.output.warn(str(exc))\n host = None\n return build, host, None","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.configure","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.configure\n def configure(self, configure_dir=None, args=None, build=None, host=None, target=None,\n pkg_config_paths=None, vars=None, use_default_install_dirs=True):\n \"\"\"\n :param pkg_config_paths: Optional paths to locate the *.pc files\n :param configure_dir: Absolute or relative path to the configure script\n :param args: Optional arguments to pass to configure.\n :param build: In which system the program will be built. \"False\" skips the --build flag\n :param host: In which system the generated program will run. \"False\" skips the --host flag\n :param target: This option is only used to build a cross-compiling toolchain.\n \"False\" skips the --target flag\n When the tool chain generates executable program, in which target system\n the program will run.\n\n http://jingfenghanmax.blogspot.com.es/2010/09/configure-with-host-target-and-build.html\n https://gcc.gnu.org/onlinedocs/gccint/Configure-Terms.html\n :param use_default_install_dirs: Use or not the defaulted installation dirs\n\n \"\"\"\n if not self._conanfile.should_configure:\n return\n if configure_dir:\n configure_dir = configure_dir.rstrip(\"/\")\n else:\n configure_dir = \".\"\n\n triplet_args = []\n\n if build is not False: # Skipped by user\n if build or self.build: # User specified value or automatic\n triplet_args.append(\"--build=%s\" % (build or self.build))\n\n if host is not False: # Skipped by user\n if host or self.host: # User specified value or automatic\n triplet_args.append(\"--host=%s\" % (host or self.host))\n\n if target is not False: # Skipped by user\n if target or self.target: # User specified value or automatic\n triplet_args.append(\"-","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_help_output","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_help_output\n def _configure_help_output(self, configure_path):\n from six import StringIO # Python 2 and 3 compatible\n mybuf = StringIO()\n try:\n self._conanfile.run(\"%s/configure --help\" % configure_path, output=mybuf)\n except ConanException as e:\n self._conanfile.output.warn(\"Error running `configure --help`: %s\" % e)\n return \"\"\n return mybuf.getvalue()","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._adjust_path","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._adjust_path\n def _adjust_path(self, path):\n if self._win_bash:\n path = unix_path(path, path_flavor=self.subsystem)\n return '\"%s\"' % path if \" \" in path else path","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._valid_configure_flag","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._valid_configure_flag\n @staticmethod\n def _valid_configure_flag(varname, args, available_flags):\n return not AutoToolsBuildEnvironment._is_flag_in_args(varname, args) and \\\n varname in available_flags","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._is_flag_in_args","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._is_flag_in_args\n @staticmethod\n def _is_flag_in_args(varname, args):\n flag = \"--%s=\" % varname\n return any([flag in arg for arg in args])","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.make","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.make\n def make(self, args=\"\", make_program=None, target=None, vars=None):\n if not self._conanfile.should_build:\n return\n make_program = os.getenv(\"CONAN_MAKE_PROGRAM\") or make_program or \"make\"\n with environment_append(vars or self.vars):\n str_args = args_to_string(args)\n cpu_count_option = (\"-j%s\" % cpu_count(output=self._conanfile.output)) if \"-j\" not in str_args else None\n self._conanfile.run(\"%s\" % join_arguments([make_program, target, str_args,\n cpu_count_option]),\n win_bash=self._win_bash, subsystem=self.subsystem)","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.install","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.install\n def install(self, args=\"\", make_program=None, vars=None):\n if not self._conanfile.should_install:\n return\n self.make(args=args, make_program=make_program, target=\"install\", vars=vars)","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_link_flags","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_link_flags\n def _configure_link_flags(self):\n \"\"\"Not the -L\"\"\"\n ret = copy.copy(self._deps_cpp_info.sharedlinkflags)\n ret.extend(self._deps_cpp_info.exelinkflags)\n arch_flag = architecture_flag(compiler=self._compiler, arch=self._arch)\n if arch_flag:\n ret.append(arch_flag)\n\n sysf = sysroot_flag(self._deps_cpp_info.sysroot, win_bash=self._win_bash,\n subsystem=self.subsystem,\n compiler=self._compiler)\n if sysf:\n ret.append(sysf)\n\n if self._include_rpath_flags:\n the_os = self._conanfile.settings.get_safe(\"os_build\") or self._os\n ret.extend(rpath_flags(the_os, self._compiler, self._deps_cpp_info.lib_paths))\n\n return ret","label":1},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_flags","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_flags\n def _configure_flags(self):\n ret = copy.copy(self._deps_cpp_info.cflags)\n arch_flag = architecture_flag(compiler=self._compiler, arch=self._arch)\n if arch_flag:\n ret.append(arch_flag)\n btfs = build_type_flags(compiler=self._compiler, build_type=self._build_type,\n vs_toolset=self._conanfile.settings.get_safe(\"compiler.toolset\"))\n if btfs:\n ret.extend(btfs)\n srf = sysroot_flag(self._deps_cpp_info.sysroot, win_bash=self._win_bash,\n subsystem=self.subsystem,\n compiler=self._compiler)\n if srf:\n ret.append(srf)\n\n return ret","label":1},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_cxx_flags","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_cxx_flags\n def _configure_cxx_flags(self):\n ret = copy.copy(self._deps_cpp_info.cxxflags)\n cxxf = libcxx_flag(compiler=self._compiler, libcxx=self._libcxx)\n if cxxf:\n ret.append(cxxf)\n return ret","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_defines","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_defines\n def _configure_defines(self):\n # requires declared defines\n ret = copy.copy(self._deps_cpp_info.defines)\n\n # Debug definition for GCC\n btf = build_type_define(build_type=self._build_type)\n if btf:\n ret.append(btf)\n\n # CXX11 ABI\n abif = libcxx_define(compiler=self._compiler, libcxx=self._libcxx)\n if abif:\n ret.append(abif)\n return ret","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars\n def _get_vars(self):\n def append(*args):\n ret = []\n for arg in args:\n if arg:\n if isinstance(arg, list):\n ret.extend(arg)\n else:\n ret.append(arg)\n return ret\n\n lib_paths = format_library_paths(self.library_paths, win_bash=self._win_bash,\n subsystem=self.subsystem, compiler=self._compiler)\n include_paths = format_include_paths(self.include_paths, win_bash=self._win_bash,\n subsystem=self.subsystem, compiler=self._compiler)\n\n ld_flags = append(self.link_flags, lib_paths)\n cpp_flags = append(include_paths, format_defines(self.defines))\n libs = format_libraries(self.libs, compiler=self._compiler)\n\n tmp_compilation_flags = copy.copy(self.flags)\n if self.fpic:\n tmp_compilation_flags.append(pic_flag(self._compiler))\n\n cxx_flags = append(tmp_compilation_flags, self.cxx_flags, self.cppstd_flag)\n c_flags = tmp_compilation_flags\n\n return ld_flags, cpp_flags, libs, cxx_flags, c_flags","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars.append","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars.append\n def append(*args):\n ret = []\n for arg in args:\n if arg:\n if isinstance(arg, list):\n ret.extend(arg)\n else:\n ret.append(arg)\n return ret","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars_dict","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars_dict\n @property\n def vars_dict(self):\n\n ld_flags, cpp_flags, libs, cxx_flags, c_flags = self._get_vars()\n\n if os.environ.get(\"CPPFLAGS\", None):\n cpp_flags.append(os.environ.get(\"CPPFLAGS\", None))\n\n if os.environ.get(\"CXXFLAGS\", None):\n cxx_flags.append(os.environ.get(\"CXXFLAGS\", None))\n\n if os.environ.get(\"CFLAGS\", None):\n c_flags.append(os.environ.get(\"CFLAGS\", None))\n\n if os.environ.get(\"LDFLAGS\", None):\n ld_flags.append(os.environ.get(\"LDFLAGS\", None))\n\n if os.environ.get(\"LIBS\", None):\n libs.append(os.environ.get(\"LIBS\", None))\n\n ret = {\"CPPFLAGS\": cpp_flags,\n \"CXXFLAGS\": cxx_flags,\n \"CFLAGS\": c_flags,\n \"LDFLAGS\": ld_flags,\n \"LIBS\": libs,\n }\n return ret","label":1},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars\n @property\n def vars(self):\n ld_flags, cpp_flags, libs, cxx_flags, c_flags = self._get_vars()\n\n cpp_flags = \" \".join(cpp_flags) + _environ_value_prefix(\"CPPFLAGS\")\n cxx_flags = \" \".join(cxx_flags) + _environ_value_prefix(\"CXXFLAGS\")\n cflags = \" \".join(c_flags) + _environ_value_prefix(\"CFLAGS\")\n ldflags = \" \".join(ld_flags) + _environ_value_prefix(\"LDFLAGS\")\n libs = \" \".join(libs) + _environ_value_prefix(\"LIBS\")\n\n ret = {\"CPPFLAGS\": cpp_flags.strip(),\n \"CXXFLAGS\": cxx_flags.strip(),\n \"CFLAGS\": cflags.strip(),\n \"LDFLAGS\": ldflags.strip(),\n \"LIBS\": libs.strip(),\n }\n return ret","label":1},{"cid":"conans/client/build/autotools_environment.py::_environ_value_prefix","text":"conans/client/build/autotools_environment.py::_environ_value_prefix\ndef _environ_value_prefix(var_name, prefix=\" \"):\n if os.environ.get(var_name, \"\"):\n return \"%s%s\" % (prefix, os.environ.get(var_name, \"\"))\n else:\n return \"\"","label":0},{"cid":"conans/client/build/cmake_flags.py::get_toolset","text":"conans/client/build/cmake_flags.py::get_toolset\ndef get_toolset(settings):\n if settings.get_safe(\"compiler\") == \"Visual Studio\":\n subs_toolset = settings.get_safe(\"compiler.toolset\")\n if subs_toolset:\n return subs_toolset\n return None","label":0},{"cid":"conans/client/build/cmake_flags.py::get_generator","text":"conans/client/build/cmake_flags.py::get_generator\ndef get_generator(settings):\n if \"CONAN_CMAKE_GENERATOR\" in os.environ:\n return os.environ[\"CONAN_CMAKE_GENERATOR\"]\n\n compiler = settings.get_safe(\"compiler\")\n arch = settings.get_safe(\"arch\")\n compiler_version = settings.get_safe(\"compiler.version\")\n os_build, _, _, _ = get_cross_building_settings(settings)\n os_host = settings.get_safe(\"os\")\n\n if not compiler or not compiler_version or not arch:\n if os_build == \"Windows\":\n logger.warning(\"CMake generator could not be deduced from settings\")\n return None\n return \"Unix Makefiles\"\n\n if compiler == \"Visual Studio\":\n _visuals = {'8': '8 2005',\n '9': '9 2008',\n '10': '10 2010',\n '11': '11 2012',\n '12': '12 2013',\n '14': '14 2015',\n '15': '15 2017',\n '16': '16 2019'}\n base = \"Visual Studio %s\" % _visuals.get(compiler_version,\n \"UnknownVersion %s\" % compiler_version)\n if os_host != \"WindowsCE\" and Version(compiler_version) < \"16\":\n if arch == \"x86_64\":\n base += \" Win64\"\n elif \"arm\" in arch:\n base += \" ARM\"\n return base\n\n # The generator depends on the build machine, not the target\n if os_build == \"Windows\":\n return \"MinGW Makefiles\" # it is valid only under Windows\n\n return \"Unix Makefiles\"","label":0},{"cid":"conans/client/build/cmake_flags.py::get_generator_platform","text":"conans/client/build/cmake_flags.py::get_generator_platform\ndef get_generator_platform(settings, generator):\n if \"CONAN_CMAKE_GENERATOR_PLATFORM\" in os.environ:\n return os.environ[\"CONAN_CMAKE_GENERATOR_PLATFORM\"]\n\n compiler = settings.get_safe(\"compiler\")\n arch = settings.get_safe(\"arch\")\n compiler_version = settings.get_safe(\"compiler.version\")\n\n if settings.get_safe(\"os\") == \"WindowsCE\":\n return settings.get_safe(\"os.platform\")\n\n if compiler == \"Visual Studio\" and Version(compiler_version) >= \"16\" \\\n and \"Visual\" in generator:\n return {\"x86\": \"Win32\",\n \"x86_64\": \"x64\",\n \"armv7\": \"ARM\",\n \"armv8\": \"ARM64\"}.get(arch)\n return None","label":0},{"cid":"conans/client/build/cmake_flags.py::is_multi_configuration","text":"conans/client/build/cmake_flags.py::is_multi_configuration\ndef is_multi_configuration(generator):\n if not generator:\n return False\n return \"Visual\" in generator or \"Xcode\" in generator","label":0},{"cid":"conans/client/build/cmake_flags.py::verbose_definition","text":"conans/client/build/cmake_flags.py::verbose_definition\ndef verbose_definition(value):\n return {verbose_definition_name: \"ON\" if value else \"OFF\"}","label":0},{"cid":"conans/client/build/cmake_flags.py::in_local_cache_definition","text":"conans/client/build/cmake_flags.py::in_local_cache_definition\ndef in_local_cache_definition(value):\n return {cmake_in_local_cache_var_name: \"ON\" if value else \"OFF\"}","label":0},{"cid":"conans/client/build/cmake_flags.py::runtime_definition","text":"conans/client/build/cmake_flags.py::runtime_definition\ndef runtime_definition(runtime):\n return {runtime_definition_var_name: \"/%s\" % runtime} if runtime else {}","label":0},{"cid":"conans/client/build/cmake_flags.py::build_type_definition","text":"conans/client/build/cmake_flags.py::build_type_definition\ndef build_type_definition(build_type, generator):\n if build_type and not is_multi_configuration(generator):\n return {\"CMAKE_BUILD_TYPE\": build_type}\n return {}","label":0},{"cid":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.__init__","text":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.__init__\n def __init__(self, conanfile, cmake_system_name=True, make_program=None,\n parallel=True, generator=None, set_cmake_flags=False,\n forced_build_type=None, output=None):\n self._conanfile = conanfile\n self._forced_cmake_system_name = cmake_system_name\n self._make_program = make_program\n self._parallel = parallel\n self._generator = generator\n self._set_cmake_flags = set_cmake_flags\n self._forced_build_type = forced_build_type\n self._output = output","label":0},{"cid":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._ss","text":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._ss\n def _ss(self, setname):\n \"\"\"safe setting\"\"\"\n return self._conanfile.settings.get_safe(setname)","label":0},{"cid":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_cpp_standard_vars","text":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_cpp_standard_vars\n def _get_cpp_standard_vars(self):\n cppstd = cppstd_from_settings(self._conanfile.settings)\n compiler = self._ss(\"compiler\")\n compiler_version = self._ss(\"compiler.version\")\n\n if not cppstd:\n return {}\n\n ret = {}\n if cppstd.startswith(\"gnu\"):\n ret[\"CONAN_CMAKE_CXX_STANDARD\"] = cppstd[3:]\n ret[\"CONAN_CMAKE_CXX_EXTENSIONS\"] = \"ON\"\n else:\n ret[\"CONAN_CMAKE_CXX_STANDARD\"] = cppstd\n ret[\"CONAN_CMAKE_CXX_EXTENSIONS\"] = \"OFF\"\n\n ret[\"CONAN_STD_CXX_FLAG\"] = cppstd_flag(compiler, compiler_version, cppstd)\n return ret","label":0},{"cid":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._cmake_cross_build_defines","text":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._cmake_cross_build_defines\n def _cmake_cross_build_defines(self):\n\n os_ = self._ss(\"os\")\n arch = self._ss(\"arch\")\n os_ver_str = \"os.api_level\" if os_ == \"Android\" else \"os.version\"\n op_system_version = self._ss(os_ver_str)\n\n env_sn = get_env(\"CONAN_CMAKE_SYSTEM_NAME\", \"\")\n env_sn = {\"False\": False, \"True\": True, \"\": None}.get(env_sn, env_sn)\n cmake_system_name = env_sn or self._forced_cmake_system_name\n\n os_build, _, _, _ = get_cross_building_settings(self._conanfile.settings)\n\n ret = OrderedDict()\n os_ver = get_env(\"CONAN_CMAKE_SYSTEM_VERSION\", op_system_version)\n toolchain_file = get_env(\"CONAN_CMAKE_TOOLCHAIN_FILE\", \"\")\n\n if toolchain_file != \"\":\n logger.info(\"Setting Cross build toolchain file: %s\" % toolchain_file)\n ret[\"CMAKE_TOOLCHAIN_FILE\"] = toolchain_file\n return ret\n\n if cmake_system_name is False:\n return ret\n\n # System name and system version\n if cmake_system_name is not True: # String not empty\n ret[\"CMAKE_SYSTEM_NAME\"] = cmake_system_name\n else: # detect if we are cross building and the system name and version\n if cross_building(self._conanfile.settings): # We are cross building\n if os_ != os_build:\n if os_: # the_os is the host (regular setting)\n ret[\"CMAKE_SYSTEM_NAME\"] = (\"Darwin\" if os_ in [\"iOS\", \"tvOS\", \"watchOS\"]\n else os_)\n else:\n ret[\"CMAKE_SYSTEM_NAME\"] = \"Generic\"\n if os_ver:\n ret[\"CMAKE_SYSTEM_VERSION\"] = os_ver\n if str(os_) == \"Macos\":\n ret[\"CMAKE_OSX_DEPLOYMENT_TARGET\"] = os_ver\n\n # system processor\n cmake_system_processor = os.getenv(\"CONAN_CMAKE_SYSTEM_PROCESSOR\")\n if cmake_system_pro","label":0},{"cid":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_make_program_definition","text":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_make_program_definition\n def _get_make_program_definition(self):\n make_program = os.getenv(\"CONAN_MAKE_PROGRAM\") or self._make_program\n if make_program:\n if not tools.which(make_program):\n self._output.warn(\"The specified make program '%s' cannot be found and will be \"\n \"ignored\" % make_program)\n else:\n self._output.info(\"Using '%s' as CMAKE_MAKE_PROGRAM\" % make_program)\n return {\"CMAKE_MAKE_PROGRAM\": make_program}\n\n return {}","label":0},{"cid":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.get_definitions","text":"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.get_definitions\n def get_definitions(self):\n\n compiler = self._ss(\"compiler\")\n compiler_version = self._ss(\"compiler.version\")\n arch = self._ss(\"arch\")\n os_ = self._ss(\"os\")\n libcxx = self._ss(\"compiler.libcxx\")\n runtime = self._ss(\"compiler.runtime\")\n build_type = self._ss(\"build_type\")\n\n ret = OrderedDict()\n ret.update(runtime_definition(runtime))\n\n if self._forced_build_type and self._forced_build_type != build_type:\n self._output.warn(\"Forced CMake build type ('%s') different from the settings build \"\n \"type ('%s')\" % (self._forced_build_type, build_type))\n build_type = self._forced_build_type\n\n ret.update(build_type_definition(build_type, self._generator))\n\n if str(os_) == \"Macos\":\n if arch == \"x86\":\n ret[\"CMAKE_OSX_ARCHITECTURES\"] = \"i386\"\n\n ret.update(self._cmake_cross_build_defines())\n ret.update(self._get_cpp_standard_vars())\n\n ret[\"CONAN_EXPORTED\"] = \"1\"\n ret.update(in_local_cache_definition(self._conanfile.in_local_cache))\n\n if compiler:\n ret[\"CONAN_COMPILER\"] = compiler\n if compiler_version:\n ret[\"CONAN_COMPILER_VERSION\"] = str(compiler_version)\n\n # C, CXX, LINK FLAGS\n if compiler == \"Visual Studio\":\n if self._parallel:\n flag = parallel_compiler_cl_flag(output=self._output)\n ret['CONAN_CXX_FLAGS'] = flag\n ret['CONAN_C_FLAGS'] = flag\n else: # arch_flag is only set for non Visual Studio\n arch_flag = architecture_flag(compiler=compiler, arch=arch)\n if arch_flag:\n ret['CONAN_CXX_FLAGS'] = arch_flag\n ret['CONAN_SHARED_LINKER_FLAGS'] = arch_flag\n ret['CONAN_C_FLAGS'] = arch_flag\n if self._set_cmake_flags:\n ","label":1},{"cid":"conans/client/build/compiler_flags.py::rpath_flags","text":"conans/client/build/compiler_flags.py::rpath_flags\ndef rpath_flags(os_build, compiler, lib_paths):\n if not os_build:\n return []\n if compiler in (\"clang\", \"apple-clang\", \"gcc\"):\n rpath_separator = \",\" if os_build in [\"Macos\", \"iOS\", \"watchOS\", \"tvOS\"] else \"=\"\n return ['-Wl,-rpath%s\"%s\"' % (rpath_separator, x.replace(\"\\\\\", \"/\"))\n for x in lib_paths if x]\n return []","label":0},{"cid":"conans/client/build/compiler_flags.py::architecture_flag","text":"conans/client/build/compiler_flags.py::architecture_flag\ndef architecture_flag(compiler, arch):\n \"\"\"\n returns flags specific to the target architecture and compiler\n \"\"\"\n if not compiler or not arch:\n return \"\"\n\n if str(compiler) in ['gcc', 'apple-clang', 'clang', 'sun-cc']:\n if str(arch) in ['x86_64', 'sparcv9', 's390x']:\n return '-m64'\n elif str(arch) in ['x86', 'sparc']:\n return '-m32'\n elif str(arch) in ['s390']:\n return '-m31'\n return \"\"","label":1},{"cid":"conans/client/build/compiler_flags.py::libcxx_define","text":"conans/client/build/compiler_flags.py::libcxx_define\ndef libcxx_define(compiler, libcxx):\n\n if not compiler or not libcxx:\n return \"\"\n\n if str(compiler) in ['gcc', 'clang', 'apple-clang']:\n if str(libcxx) == 'libstdc++':\n return '_GLIBCXX_USE_CXX11_ABI=0'\n elif str(libcxx) == 'libstdc++11':\n return '_GLIBCXX_USE_CXX11_ABI=1'\n return \"\"","label":0},{"cid":"conans/client/build/compiler_flags.py::libcxx_flag","text":"conans/client/build/compiler_flags.py::libcxx_flag\ndef libcxx_flag(compiler, libcxx):\n \"\"\"\n returns flag specific to the target C++ standard library\n \"\"\"\n if not compiler or not libcxx:\n return \"\"\n if str(compiler) in ['clang', 'apple-clang']:\n if str(libcxx) in ['libstdc++', 'libstdc++11']:\n return '-stdlib=libstdc++'\n elif str(libcxx) == 'libc++':\n return '-stdlib=libc++'\n elif str(compiler) == 'sun-cc':\n return ({\"libCstd\": \"-library=Cstd\",\n \"libstdcxx\": \"-library=stdcxx4\",\n \"libstlport\": \"-library=stlport4\",\n \"libstdc++\": \"-library=stdcpp\"}.get(libcxx, \"\"))\n return \"\"","label":0},{"cid":"conans/client/build/compiler_flags.py::pic_flag","text":"conans/client/build/compiler_flags.py::pic_flag\ndef pic_flag(compiler=None):\n \"\"\"\n returns PIC (position independent code) flags, such as -fPIC\n \"\"\"\n if not compiler or compiler == 'Visual Studio':\n return \"\"\n return '-fPIC'","label":0},{"cid":"conans/client/build/compiler_flags.py::build_type_flags","text":"conans/client/build/compiler_flags.py::build_type_flags\ndef build_type_flags(compiler, build_type, vs_toolset=None):\n \"\"\"\n returns flags specific to the build type (Debug, Release, etc.)\n (-s, -g, /Zi, etc.)\n \"\"\"\n if not compiler or not build_type:\n return \"\"\n\n # https://github.com/Kitware/CMake/blob/d7af8a34b67026feaee558433db3a835d6007e06/\n # Modules/Platform/Windows-MSVC.cmake\n if str(compiler) == 'Visual Studio':\n if vs_toolset and \"clang\" in str(vs_toolset):\n flags = {\"Debug\": [\"-gline-tables-only\", \"-fno-inline\", \"-O0\"],\n \"Release\": [\"-O2\"],\n \"RelWithDebInfo\": [\"-gline-tables-only\", \"-O2\", \"-fno-inline\"],\n \"MinSizeRel\": []\n }.get(build_type, [\"-O2\", \"-Ob2\"])\n else:\n flags = {\"Debug\": [\"-Zi\", \"-Ob0\", \"-Od\"],\n \"Release\": [\"-O2\", \"-Ob2\"],\n \"RelWithDebInfo\": [\"-Zi\", \"-O2\", \"-Ob1\"],\n \"MinSizeRel\": [\"-O1\", \"-Ob1\"],\n }.get(build_type, [])\n return flags\n else:\n # https://github.com/Kitware/CMake/blob/f3bbb37b253a1f4a26809d6f132b3996aa2e16fc/\n # Modules/Compiler/GNU.cmake\n # clang include the gnu (overriding some things, but not build type) and apple clang\n # overrides clang but it doesn't touch clang either\n if str(compiler) in [\"clang\", \"gcc\", \"apple-clang\"]:\n # FIXME: It is not clear that the \"-s\" is something related with the build type\n # cmake is not adjusting it\n # -s: Remove all symbol table and relocation information from the executable.\n flags = {\"Debug\": [\"-g\"],\n \"Release\": [\"-O3\", \"-s\"] if str(compiler) == \"gcc\" else [\"-O3\"],\n \"RelWithDebInfo\": [\"-O2\", \"-g\"],\n \"MinSizeRel\": [\"-Os\"],\n }.get(build_type, [])\n return flags\n elif str(compiler) == \"sun-cc\":","label":0},{"cid":"conans/client/build/compiler_flags.py::build_type_define","text":"conans/client/build/compiler_flags.py::build_type_define\ndef build_type_define(build_type=None):\n \"\"\"\n returns definitions specific to the build type (Debug, Release, etc.)\n like DEBUG, _DEBUG, NDEBUG\n \"\"\"\n return 'NDEBUG' if build_type == 'Release' else \"\"","label":0},{"cid":"conans/client/build/compiler_flags.py::adjust_path","text":"conans/client/build/compiler_flags.py::adjust_path\ndef adjust_path(path, win_bash=False, subsystem=None, compiler=None):\n \"\"\"\n adjusts path to be safely passed to the compiler command line\n for Windows bash, ensures path is in format according to the subsystem\n for path with spaces, places double quotes around it\n converts slashes to backslashes, or vice versa\n \"\"\"\n if str(compiler) == 'Visual Studio':\n path = path.replace('/', '\\\\')\n else:\n path = path.replace('\\\\', '/')\n if win_bash:\n path = unix_path(path, subsystem)\n return '\"%s\"' % path if ' ' in path else path","label":0},{"cid":"conans/client/build/compiler_flags.py::sysroot_flag","text":"conans/client/build/compiler_flags.py::sysroot_flag\ndef sysroot_flag(sysroot, win_bash=False, subsystem=None, compiler=None):\n if str(compiler) != 'Visual Studio' and sysroot:\n sysroot = adjust_path(sysroot, win_bash=win_bash, subsystem=subsystem, compiler=compiler)\n return '--sysroot=%s' % sysroot\n return \"\"","label":0},{"cid":"conans/client/build/compiler_flags.py::visual_runtime","text":"conans/client/build/compiler_flags.py::visual_runtime\ndef visual_runtime(runtime):\n if runtime:\n return \"-%s\" % runtime\n return \"\"","label":0},{"cid":"conans/client/build/compiler_flags.py::format_defines","text":"conans/client/build/compiler_flags.py::format_defines\ndef format_defines(defines):\n return [\"-D%s\" % define for define in defines if define]","label":0},{"cid":"conans/client/build/compiler_flags.py::format_include_paths","text":"conans/client/build/compiler_flags.py::format_include_paths\ndef format_include_paths(include_paths, win_bash=False, subsystem=None, compiler=None):\n return [\"%s%s\" % (include_path_option, adjust_path(include_path, win_bash=win_bash,\n subsystem=subsystem, compiler=compiler))\n for include_path in include_paths if include_path]","label":0},{"cid":"conans/client/build/compiler_flags.py::format_library_paths","text":"conans/client/build/compiler_flags.py::format_library_paths\ndef format_library_paths(library_paths, win_bash=False, subsystem=None, compiler=None):\n pattern = \"-LIBPATH:%s\" if str(compiler) == 'Visual Studio' else \"-L%s\"\n return [pattern % adjust_path(library_path, win_bash=win_bash,\n subsystem=subsystem, compiler=compiler)\n for library_path in library_paths if library_path]","label":0},{"cid":"conans/client/build/compiler_flags.py::format_libraries","text":"conans/client/build/compiler_flags.py::format_libraries\ndef format_libraries(libraries, compiler=None):\n result = []\n for library in libraries:\n if str(compiler) == 'Visual Studio':\n if not library.endswith(\".lib\"):\n library += \".lib\"\n result.append(library)\n else:\n result.append(\"-l%s\" % library)\n return result","label":0},{"cid":"conans/client/build/compiler_flags.py::parallel_compiler_cl_flag","text":"conans/client/build/compiler_flags.py::parallel_compiler_cl_flag\ndef parallel_compiler_cl_flag(output=None):\n return \"/MP%s\" % cpu_count(output=output)","label":0},{"cid":"conans/client/cache/cache.py::is_case_insensitive_os","text":"conans/client/cache/cache.py::is_case_insensitive_os\ndef is_case_insensitive_os():\n system = platform.system()\n return system != \"Linux\" and system != \"FreeBSD\" and system != \"SunOS\"","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.__init__","text":"conans/client/cache/cache.py::ClientCache.__init__\n def __init__(self, base_folder, output):\n self.conan_folder = join(base_folder, \".conan\")\n self._config = None\n self._output = output\n # Remove this self.conan_folder in Conan 2.0\n self._store_folder = self.config.storage_path or self.conan_folder\n self._no_lock = None\n self.client_cert_path = normpath(join(self.conan_folder, CLIENT_CERT))\n self.client_cert_key_path = normpath(join(self.conan_folder, CLIENT_KEY))\n self._registry = None\n\n self.editable_packages = EditablePackages(self.conan_folder)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.all_refs","text":"conans/client/cache/cache.py::ClientCache.all_refs\n def all_refs(self):\n subdirs = list_folder_subdirs(basedir=self._store_folder, level=4)\n return [ConanFileReference(*folder.split(\"/\")) for folder in subdirs]","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.store","text":"conans/client/cache/cache.py::ClientCache.store\n @property\n def store(self):\n return self._store_folder","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.package","text":"conans/client/cache/cache.py::ClientCache.package\n def package(self, pref, short_paths=False):\n # TODO: This is deprecated, only used in testing\n return self.package_layout(pref.ref, short_paths).package(pref)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.installed_as_editable","text":"conans/client/cache/cache.py::ClientCache.installed_as_editable\n def installed_as_editable(self, ref):\n return isinstance(self.package_layout(ref), PackageEditableLayout)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.config_install_file","text":"conans/client/cache/cache.py::ClientCache.config_install_file\n @property\n def config_install_file(self):\n return os.path.join(self.conan_folder, \"config_install.json\")","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.package_layout","text":"conans/client/cache/cache.py::ClientCache.package_layout\n def package_layout(self, ref, short_paths=None, *args, **kwargs):\n assert isinstance(ref, ConanFileReference), \"It is a {}\".format(type(ref))\n edited_ref = self.editable_packages.get(ref.copy_clear_rev())\n if edited_ref:\n base_path = edited_ref[\"path\"]\n layout_file = edited_ref[\"layout\"]\n return PackageEditableLayout(base_path, layout_file, ref)\n else:\n check_ref_case(ref, self.store)\n base_folder = os.path.normpath(os.path.join(self.store, ref.dir_repr()))\n return PackageCacheLayout(base_folder=base_folder, ref=ref,\n short_paths=short_paths, no_lock=self._no_locks())","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.registry_path","text":"conans/client/cache/cache.py::ClientCache.registry_path\n @property\n def registry_path(self):\n return join(self.conan_folder, REMOTES)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.registry","text":"conans/client/cache/cache.py::ClientCache.registry\n @property\n def registry(self):\n if not self._registry:\n remotes_path = self.registry_path\n if not os.path.exists(remotes_path):\n self._output.warn(\"Remotes registry file missing, \"\n \"creating default one in %s\" % remotes_path)\n remotes = Remotes.defaults()\n remotes.save(remotes_path)\n self._registry = RemoteRegistry(self)\n return self._registry","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.cacert_path","text":"conans/client/cache/cache.py::ClientCache.cacert_path\n @property\n def cacert_path(self):\n return self.config.cacert_path","label":0},{"cid":"conans/client/cache/cache.py::ClientCache._no_locks","text":"conans/client/cache/cache.py::ClientCache._no_locks\n def _no_locks(self):\n if self._no_lock is None:\n self._no_lock = self.config.cache_no_locks\n return self._no_lock","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.put_headers_path","text":"conans/client/cache/cache.py::ClientCache.put_headers_path\n @property\n def put_headers_path(self):\n return join(self.conan_folder, PUT_HEADERS)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.read_put_headers","text":"conans/client/cache/cache.py::ClientCache.read_put_headers\n def read_put_headers(self):\n ret = {}\n if not os.path.exists(self.put_headers_path):\n save(self.put_headers_path, \"\")\n return ret\n try:\n contents = load(self.put_headers_path)\n for line in contents.splitlines():\n if line and not line.strip().startswith(\"#\"):\n tmp = line.split(\"=\", 1)\n if len(tmp) != 2:\n raise Exception()\n name = tmp[0].strip()\n value = tmp[1].strip()\n ret[str(name)] = str(value)\n return ret\n except Exception:\n raise ConanException(\"Invalid %s file!\" % self.put_headers_path)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.config","text":"conans/client/cache/cache.py::ClientCache.config\n @property\n def config(self):\n if not self._config:\n if not os.path.exists(self.conan_conf_path):\n save(self.conan_conf_path, normalize(default_client_conf))\n\n self._config = ConanClientConfigParser(self.conan_conf_path)\n return self._config","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.localdb","text":"conans/client/cache/cache.py::ClientCache.localdb\n @property\n def localdb(self):\n return join(self.conan_folder, LOCALDB)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.conan_conf_path","text":"conans/client/cache/cache.py::ClientCache.conan_conf_path\n @property\n def conan_conf_path(self):\n return join(self.conan_folder, CONAN_CONF)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.profiles_path","text":"conans/client/cache/cache.py::ClientCache.profiles_path\n @property\n def profiles_path(self):\n return join(self.conan_folder, PROFILES_FOLDER)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.settings_path","text":"conans/client/cache/cache.py::ClientCache.settings_path\n @property\n def settings_path(self):\n return join(self.conan_folder, CONAN_SETTINGS)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.default_profile_path","text":"conans/client/cache/cache.py::ClientCache.default_profile_path\n @property\n def default_profile_path(self):\n if os.path.isabs(self.config.default_profile):\n return self.config.default_profile\n else:\n return join(self.conan_folder, PROFILES_FOLDER,\n self.config.default_profile)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.hooks_path","text":"conans/client/cache/cache.py::ClientCache.hooks_path\n @property\n def hooks_path(self):\n \"\"\"\n :return: Hooks folder in client cache\n \"\"\"\n return join(self.conan_folder, HOOKS_FOLDER)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.default_profile","text":"conans/client/cache/cache.py::ClientCache.default_profile\n @property\n def default_profile(self):\n if not os.path.exists(self.default_profile_path):\n self._output.writeln(\"Auto detecting your dev setup to initialize the \"\n \"default profile (%s)\" % self.default_profile_path,\n Color.BRIGHT_YELLOW)\n\n default_settings = detect_defaults_settings(self._output,\n profile_path=self.default_profile_path)\n self._output.writeln(\"Default settings\", Color.BRIGHT_YELLOW)\n self._output.writeln(\"\\n\".join([\"\\t%s=%s\" % (k, v) for (k, v) in default_settings]),\n Color.BRIGHT_YELLOW)\n self._output.writeln(\"*** You can change them in %s ***\" % self.default_profile_path,\n Color.BRIGHT_MAGENTA)\n self._output.writeln(\"*** Or override with -s compiler='other' -s ...s***\\n\\n\",\n Color.BRIGHT_MAGENTA)\n\n default_profile = Profile()\n tmp = OrderedDict(default_settings)\n default_profile.update_settings(tmp)\n save(self.default_profile_path, default_profile.dumps())\n else:\n default_profile, _ = read_profile(self.default_profile_path, get_cwd(),\n self.profiles_path)\n\n # Mix profile settings with environment\n mixed_settings = _mix_settings_with_env(default_profile.settings)\n default_profile.settings = mixed_settings\n return default_profile","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.settings","text":"conans/client/cache/cache.py::ClientCache.settings\n @property\n def settings(self):\n \"\"\"Returns {setting: [value, ...]} defining all the possible\n settings without values\"\"\"\n\n if not os.path.exists(self.settings_path):\n save(self.settings_path, normalize(default_settings_yml))\n settings = Settings.loads(default_settings_yml)\n else:\n content = load(self.settings_path)\n settings = Settings.loads(content)\n\n return settings","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.hooks","text":"conans/client/cache/cache.py::ClientCache.hooks\n @property\n def hooks(self):\n \"\"\"Returns a list of hooks inside the hooks folder\"\"\"\n hooks = []\n for hook_name in os.listdir(self.hooks_path):\n if os.path.isfile(hook_name) and hook_name.endswith(\".py\"):\n hooks.append(hook_name[:-3])\n return hooks","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.delete_empty_dirs","text":"conans/client/cache/cache.py::ClientCache.delete_empty_dirs\n def delete_empty_dirs(self, deleted_refs):\n for ref in deleted_refs:\n ref_path = self.package_layout(ref).base_folder()\n for _ in range(4):\n if os.path.exists(ref_path):\n try: # Take advantage that os.rmdir does not delete non-empty dirs\n os.rmdir(ref_path)\n except OSError:\n break # not empty\n ref_path = os.path.dirname(ref_path)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.remove_locks","text":"conans/client/cache/cache.py::ClientCache.remove_locks\n def remove_locks(self):\n folders = list_folder_subdirs(self._store_folder, 4)\n for folder in folders:\n conan_folder = os.path.join(self._store_folder, folder)\n Lock.clean(conan_folder)\n shutil.rmtree(os.path.join(conan_folder, \"locks\"), ignore_errors=True)","label":0},{"cid":"conans/client/cache/cache.py::ClientCache.invalidate","text":"conans/client/cache/cache.py::ClientCache.invalidate\n def invalidate(self):\n self._config = None\n self._no_lock = None","label":0},{"cid":"conans/client/cache/cache.py::_mix_settings_with_env","text":"conans/client/cache/cache.py::_mix_settings_with_env\ndef _mix_settings_with_env(settings):\n \"\"\"Reads CONAN_ENV_XXXX variables from environment\n and if it's defined uses these value instead of the default\n from conf file. If you specify a compiler with ENV variable you\n need to specify all the subsettings, the file defaulted will be\n ignored\"\"\"\n\n def get_env_value(name):\n env_name = \"CONAN_ENV_%s\" % name.upper().replace(\".\", \"_\")\n return os.getenv(env_name, None)\n\n def get_setting_name(env_name):\n return env_name[10:].lower().replace(\"_\", \".\")\n\n ret = OrderedDict()\n for name, value in settings.items():\n if get_env_value(name):\n ret[name] = get_env_value(name)\n else:\n # being a subsetting, if parent exist in env discard this, because\n # env doesn't define this setting. EX: env=>Visual Studio but\n # env doesn't define compiler.libcxx\n if \".\" not in name or not get_env_value(name.split(\".\")[0]):\n ret[name] = value\n # Now read if there are more env variables\n for env, value in sorted(os.environ.items()):\n if env.startswith(\"CONAN_ENV_\") and get_setting_name(env) not in ret:\n ret[get_setting_name(env)] = value\n return ret","label":0},{"cid":"conans/client/cache/cache.py::_mix_settings_with_env.get_env_value","text":"conans/client/cache/cache.py::_mix_settings_with_env.get_env_value\n def get_env_value(name):\n env_name = \"CONAN_ENV_%s\" % name.upper().replace(\".\", \"_\")\n return os.getenv(env_name, None)","label":0},{"cid":"conans/client/cache/cache.py::_mix_settings_with_env.get_setting_name","text":"conans/client/cache/cache.py::_mix_settings_with_env.get_setting_name\n def get_setting_name(env_name):\n return env_name[10:].lower().replace(\"_\", \".\")","label":0},{"cid":"conans/client/cache/editable.py::EditablePackages.__init__","text":"conans/client/cache/editable.py::EditablePackages.__init__\n def __init__(self, cache_folder):\n self._cache_folder = cache_folder\n self._edited_file = normpath(join(cache_folder, EDITABLE_PACKAGES_FILE))\n if os.path.exists(self._edited_file):\n edited = load(self._edited_file)\n edited_js = json.loads(edited)\n self._edited_refs = {ConanFileReference.loads(r, validate=False): d\n for r, d in edited_js.items()}\n else:\n self._edited_refs = {} # {ref: {\"path\": path, \"layout\": layout}}","label":0},{"cid":"conans/client/cache/editable.py::EditablePackages.edited_refs","text":"conans/client/cache/editable.py::EditablePackages.edited_refs\n @property\n def edited_refs(self):\n return self._edited_refs","label":0},{"cid":"conans/client/cache/editable.py::EditablePackages.save","text":"conans/client/cache/editable.py::EditablePackages.save\n def save(self):\n d = {str(ref): d for ref, d in self._edited_refs.items()}\n save(self._edited_file, json.dumps(d))","label":0},{"cid":"conans/client/cache/editable.py::EditablePackages.get","text":"conans/client/cache/editable.py::EditablePackages.get\n def get(self, ref):\n ref = ref.copy_clear_rev()\n return self._edited_refs.get(ref)","label":0},{"cid":"conans/client/cache/editable.py::EditablePackages.add","text":"conans/client/cache/editable.py::EditablePackages.add\n def add(self, ref, path, layout):\n assert isinstance(ref, ConanFileReference)\n ref = ref.copy_clear_rev()\n self._edited_refs[ref] = {\"path\": path, \"layout\": layout}\n self.save()","label":0},{"cid":"conans/client/cache/editable.py::EditablePackages.remove","text":"conans/client/cache/editable.py::EditablePackages.remove\n def remove(self, ref):\n assert isinstance(ref, ConanFileReference)\n ref = ref.copy_clear_rev()\n if self._edited_refs.pop(ref, None):\n self.save()\n return True\n return False","label":0},{"cid":"conans/client/cache/editable.py::EditablePackages.override","text":"conans/client/cache/editable.py::EditablePackages.override\n def override(self, workspace_edited):\n self._edited_refs = workspace_edited","label":0},{"cid":"conans/client/cache/remote_registry.py::load_registry_txt","text":"conans/client/cache/remote_registry.py::load_registry_txt\ndef load_registry_txt(contents):\n \"\"\"Remove in Conan 2.0\"\"\"\n remotes = Remotes()\n refs = {}\n end_remotes = False\n # Parse the file\n for line in contents.splitlines():\n line = line.strip()\n\n if not line:\n if end_remotes:\n raise ConanException(\"Bad file format, blank line\")\n end_remotes = True\n continue\n chunks = line.split()\n if not end_remotes:\n if len(chunks) == 2: # Retro compatibility\n remote_name, url = chunks\n verify_ssl = \"True\"\n elif len(chunks) == 3:\n remote_name, url, verify_ssl = chunks\n else:\n raise ConanException(\"Bad file format, wrong item numbers in line '%s'\" % line)\n\n verify_ssl = get_bool_from_text_value(verify_ssl)\n remotes.add(remote_name, url, verify_ssl)\n else:\n ref, remote_name = chunks\n refs[ref] = remote_name\n\n return remotes, refs","label":0},{"cid":"conans/client/cache/remote_registry.py::load_old_registry_json","text":"conans/client/cache/remote_registry.py::load_old_registry_json\ndef load_old_registry_json(contents):\n \"\"\"From json\"\"\"\n data = json.loads(contents)\n remotes = Remotes()\n refs = data.get(\"references\", {})\n prefs = data.get(\"package_references\", {})\n for r in data[\"remotes\"]:\n remotes.add(r[\"name\"], r[\"url\"], r[\"verify_ssl\"])\n return remotes, refs, prefs","label":0},{"cid":"conans/client/cache/remote_registry.py::migrate_registry_file","text":"conans/client/cache/remote_registry.py::migrate_registry_file\ndef migrate_registry_file(cache, out):\n folder = cache.conan_folder\n reg_json_path = os.path.join(folder, \"registry.json\")\n reg_txt_path = os.path.join(folder, \"registry.txt\")\n remotes_path = cache.registry_path\n\n def add_ref_remote(reference, remotes, remote_name):\n ref = ConanFileReference.loads(reference, validate=True)\n remote = remotes[remote_name]\n with cache.package_layout(ref).update_metadata() as metadata:\n metadata.recipe.remote = remote.name\n\n def add_pref_remote(pkg_ref, remotes, remote_name):\n pref = PackageReference.loads(pkg_ref, validate=True)\n remote = remotes[remote_name]\n with cache.package_layout(pref.ref).update_metadata() as metadata:\n metadata.packages[pref.id].remote = remote.name\n\n try:\n if os.path.exists(reg_json_path):\n out.warn(\"registry.json has been deprecated. Migrating to remotes.json\")\n remotes, refs, prefs = load_old_registry_json(load(reg_json_path))\n remotes.save(remotes_path)\n for ref, remote_name in refs.items():\n add_ref_remote(ref, remotes, remote_name)\n for pref, remote_name in prefs.items():\n add_pref_remote(pref, remotes, remote_name)\n os.remove(reg_json_path)\n elif os.path.exists(reg_txt_path):\n out.warn(\"registry.txt has been deprecated. Migrating to remotes.json\")\n remotes, refs = load_registry_txt(load(reg_txt_path))\n remotes.save(remotes_path)\n for ref, remote_name in refs.items():\n add_ref_remote(ref, remotes, remote_name)\n os.remove(reg_txt_path)\n\n except Exception as e:\n raise ConanException(\"Cannot migrate old registry: %s\" % str(e))","label":0},{"cid":"conans/client/cache/remote_registry.py::migrate_registry_file.add_ref_remote","text":"conans/client/cache/remote_registry.py::migrate_registry_file.add_ref_remote\n def add_ref_remote(reference, remotes, remote_name):\n ref = ConanFileReference.loads(reference, validate=True)\n remote = remotes[remote_name]\n with cache.package_layout(ref).update_metadata() as metadata:\n metadata.recipe.remote = remote.name","label":0},{"cid":"conans/client/cache/remote_registry.py::migrate_registry_file.add_pref_remote","text":"conans/client/cache/remote_registry.py::migrate_registry_file.add_pref_remote\n def add_pref_remote(pkg_ref, remotes, remote_name):\n pref = PackageReference.loads(pkg_ref, validate=True)\n remote = remotes[remote_name]\n with cache.package_layout(pref.ref).update_metadata() as metadata:\n metadata.packages[pref.id].remote = remote.name","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.__init__","text":"conans/client/cache/remote_registry.py::Remotes.__init__\n def __init__(self):\n self._remotes = OrderedDict()\n self.selected = None","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.defaults","text":"conans/client/cache/remote_registry.py::Remotes.defaults\n @classmethod\n def defaults(cls):\n result = Remotes()\n result._remotes[\"conan-center\"] = Remote(\"conan-center\", \"https://conan.bintray.com\", True)\n return result","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.select","text":"conans/client/cache/remote_registry.py::Remotes.select\n def select(self, remote_name):\n self.selected = self[remote_name] if remote_name is not None else None","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.__bool__","text":"conans/client/cache/remote_registry.py::Remotes.__bool__\n def __bool__(self):\n return bool(self._remotes)","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.__nonzero__","text":"conans/client/cache/remote_registry.py::Remotes.__nonzero__\n def __nonzero__(self):\n return self.__bool__()","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.clear","text":"conans/client/cache/remote_registry.py::Remotes.clear\n def clear(self):\n self._remotes.clear()","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.items","text":"conans/client/cache/remote_registry.py::Remotes.items\n def items(self):\n return self._remotes.items()","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.values","text":"conans/client/cache/remote_registry.py::Remotes.values\n def values(self):\n return self._remotes.values()","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.loads","text":"conans/client/cache/remote_registry.py::Remotes.loads\n @staticmethod\n def loads(text):\n result = Remotes()\n data = json.loads(text)\n for r in data.get(\"remotes\", []):\n result._remotes[r[\"name\"]] = Remote(r[\"name\"], r[\"url\"], r[\"verify_ssl\"])\n\n return result","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.dumps","text":"conans/client/cache/remote_registry.py::Remotes.dumps\n def dumps(self):\n result = []\n for remote in self._remotes.values():\n result.append(\"%s: %s [Verify SSL: %s]\" % (remote.name, remote.url, remote.verify_ssl))\n return \"\\n\".join(result)","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.save","text":"conans/client/cache/remote_registry.py::Remotes.save\n def save(self, filename):\n ret = {\"remotes\": [{\"name\": r, \"url\": u, \"verify_ssl\": v}\n for r, (_, u, v) in self._remotes.items()]}\n save(filename, json.dumps(ret, indent=True))","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes._get_by_url","text":"conans/client/cache/remote_registry.py::Remotes._get_by_url\n def _get_by_url(self, url):\n for remote in self._remotes.values():\n if remote.url == url:\n return remote","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.rename","text":"conans/client/cache/remote_registry.py::Remotes.rename\n def rename(self, remote_name, new_remote_name):\n if new_remote_name in self._remotes:\n raise ConanException(\"Remote '%s' already exists\" % new_remote_name)\n\n remote = self._remotes[remote_name]\n new_remote = Remote(new_remote_name, remote.url, remote.verify_ssl)\n self._remotes = OrderedDict([(new_remote_name, new_remote) if k == remote_name\n else (k, v) for k, v in self._remotes.items()])","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.get_remote","text":"conans/client/cache/remote_registry.py::Remotes.get_remote\n def get_remote(self, remote_name):\n # Returns the remote defined by the name, or the default if is None\n return self[remote_name] if remote_name is not None else self.default","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.default","text":"conans/client/cache/remote_registry.py::Remotes.default\n @property\n def default(self):\n try:\n # This is the python way to get the first element of an OrderedDict\n return self._remotes[next(iter(self._remotes))]\n except StopIteration:\n raise NoRemoteAvailable(\"No default remote defined\")","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.__contains__","text":"conans/client/cache/remote_registry.py::Remotes.__contains__\n def __contains__(self, remote_name):\n return remote_name in self._remotes","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.get","text":"conans/client/cache/remote_registry.py::Remotes.get\n def get(self, remote_name):\n return self._remotes.get(remote_name)","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.__getitem__","text":"conans/client/cache/remote_registry.py::Remotes.__getitem__\n def __getitem__(self, remote_name):\n try:\n return self._remotes[remote_name]\n except KeyError:\n raise NoRemoteAvailable(\"No remote '%s' defined in remotes\" % (remote_name))","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.__delitem__","text":"conans/client/cache/remote_registry.py::Remotes.__delitem__\n def __delitem__(self, remote_name):\n try:\n del self._remotes[remote_name]\n except KeyError:\n raise NoRemoteAvailable(\"No remote '%s' defined in remotes\" % (remote_name))","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes._upsert","text":"conans/client/cache/remote_registry.py::Remotes._upsert\n def _upsert(self, remote_name, url, verify_ssl, insert):\n # Remove duplicates\n updated_remote = Remote(remote_name, url, verify_ssl)\n self._remotes.pop(remote_name, None)\n remotes_list = []\n renamed = None\n\n for name, remote in self._remotes.items():\n if remote.url != url:\n remotes_list.append((name, remote))\n else:\n renamed = name\n\n if insert is not None:\n try:\n insert_index = int(insert)\n except ValueError:\n raise ConanException(\"insert argument must be an integer\")\n remotes_list.insert(insert_index, (remote_name, updated_remote))\n else:\n remotes_list.append((remote_name, updated_remote))\n self._remotes = OrderedDict(remotes_list)\n return renamed","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.add","text":"conans/client/cache/remote_registry.py::Remotes.add\n def add(self, remote_name, url, verify_ssl=True, insert=None, force=None):\n if force:\n return self._upsert(remote_name, url, verify_ssl, insert)\n\n if remote_name in self._remotes:\n raise ConanException(\"Remote '%s' already exists in remotes (use update to modify)\"\n % remote_name)\n self._add_update(remote_name, url, verify_ssl, insert)","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes.update","text":"conans/client/cache/remote_registry.py::Remotes.update\n def update(self, remote_name, url, verify_ssl=True, insert=None):\n if remote_name not in self._remotes:\n raise ConanException(\"Remote '%s' not found in remotes\" % remote_name)\n self._add_update(remote_name, url, verify_ssl, insert)","label":0},{"cid":"conans/client/cache/remote_registry.py::Remotes._add_update","text":"conans/client/cache/remote_registry.py::Remotes._add_update\n def _add_update(self, remote_name, url, verify_ssl, insert=None):\n prev_remote = self._get_by_url(url)\n if prev_remote and verify_ssl == prev_remote.verify_ssl:\n raise ConanException(\"Remote '%s' already exists with same URL\" % prev_remote.name)\n updated_remote = Remote(remote_name, url, verify_ssl)\n if insert is not None:\n try:\n insert_index = int(insert)\n except ValueError:\n raise ConanException(\"insert argument must be an integer\")\n self._remotes.pop(remote_name, None) # Remove if exists (update)\n remotes_list = list(self._remotes.items())\n remotes_list.insert(insert_index, (remote_name, updated_remote))\n self._remotes = OrderedDict(remotes_list)\n else:\n self._remotes[remote_name] = updated_remote","label":0},{"cid":"conans/client/cache/remote_registry.py::RemoteRegistry.__init__","text":"conans/client/cache/remote_registry.py::RemoteRegistry.__init__\n def __init__(self, cache):\n self._cache = cache\n self._filename = cache.registry_path","label":0},{"cid":"conans/client/cache/remote_registry.py::RemoteRegistry.load_remotes","text":"conans/client/cache/remote_registry.py::RemoteRegistry.load_remotes\n def load_remotes(self):\n content = load(self._filename)\n return Remotes.loads(content)","label":0},{"cid":"conans/client/cache/remote_registry.py::RemoteRegistry.add","text":"conans/client/cache/remote_registry.py::RemoteRegistry.add\n def add(self, remote_name, url, verify_ssl=True, insert=None, force=None):\n remotes = self.load_remotes()\n renamed = remotes.add(remote_name, url, verify_ssl, insert, force)\n remotes.save(self._filename)\n if renamed:\n for ref in self._cache.all_refs():\n with self._cache.package_layout(ref).update_metadata() as metadata:\n if metadata.recipe.remote == renamed:\n metadata.recipe.remote = remote_name\n for pkg_metadata in metadata.packages.values():\n if pkg_metadata.remote == renamed:\n pkg_metadata.remote = remote_name","label":0},{"cid":"conans/client/cache/remote_registry.py::RemoteRegistry.update","text":"conans/client/cache/remote_registry.py::RemoteRegistry.update\n def update(self, remote_name, url, verify_ssl=True, insert=None):\n remotes = self.load_remotes()\n remotes.update(remote_name, url, verify_ssl, insert)\n remotes.save(self._filename)","label":0},{"cid":"conans/client/cache/remote_registry.py::RemoteRegistry.clear","text":"conans/client/cache/remote_registry.py::RemoteRegistry.clear\n def clear(self):\n remotes = self.load_remotes()\n remotes.clear()\n for ref in self._cache.all_refs():\n with self._cache.package_layout(ref).update_metadata() as metadata:\n metadata.recipe.remote = None\n for pkg_metadata in metadata.packages.values():\n pkg_metadata.remote = None\n remotes.save(self._filename)","label":0},{"cid":"conans/client/cache/remote_registry.py::RemoteRegistry.remove","text":"conans/client/cache/remote_registry.py::RemoteRegistry.remove\n def remove(self, remote_name):\n remotes = self.load_remotes()\n del remotes[remote_name]\n\n for ref in self._cache.all_refs():\n with self._cache.package_layout(ref).update_metadata() as metadata:\n if metadata.recipe.remote == remote_name:\n metadata.recipe.remote = None\n for pkg_metadata in metadata.packages.values():\n if pkg_metadata.remote == remote_name:\n pkg_metadata.remote = None\n\n remotes.save(self._filename)","label":0},{"cid":"conans/client/cache/remote_registry.py::RemoteRegistry.define","text":"conans/client/cache/remote_registry.py::RemoteRegistry.define\n def define(self, remotes):\n # For definition from conan config install\n for ref in self._cache.all_refs():\n with self._cache.package_layout(ref).update_metadata() as metadata:\n if metadata.recipe.remote not in remotes:\n metadata.recipe.remote = None\n for pkg_metadata in metadata.packages.values():\n if pkg_metadata.remote not in remotes:\n pkg_metadata.remote = None\n\n remotes.save(self._filename)","label":0},{"cid":"conans/client/cache/remote_registry.py::RemoteRegistry.rename","text":"conans/client/cache/remote_registry.py::RemoteRegistry.rename\n def rename(self, remote_name, new_remote_name):\n remotes = self.load_remotes()\n remotes.rename(remote_name, new_remote_name)\n\n for ref in self._cache.all_refs():\n with self._cache.package_layout(ref).update_metadata() as metadata:\n if metadata.recipe.remote == remote_name:\n metadata.recipe.remote = new_remote_name\n for pkg_metadata in metadata.packages.values():\n if pkg_metadata.remote == remote_name:\n pkg_metadata.remote = new_remote_name\n\n remotes.save(self._filename)","label":0},{"cid":"conans/client/cache/remote_registry.py::RemoteRegistry.refs_list","text":"conans/client/cache/remote_registry.py::RemoteRegistry.refs_list\n @property\n def refs_list(self):\n result = {}\n for ref in self._cache.all_refs():\n metadata = self._cache.package_layout(ref).load_metadata()\n if metadata.recipe.remote:\n result[ref] = metadata.recipe.remote\n return result","label":0},{"cid":"conans/client/cache/remote_registry.py::RemoteRegistry.prefs_list","text":"conans/client/cache/remote_registry.py::RemoteRegistry.prefs_list\n @property\n def prefs_list(self):\n result = {}\n for ref in self._cache.all_refs():\n metadata = self._cache.package_layout(ref).load_metadata()\n for pid, pkg_metadata in metadata.packages.items():\n pref = PackageReference(ref, pid)\n result[pref] = pkg_metadata.remote\n return result","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.__init__","text":"conans/client/conf/__init__.py::ConanClientConfigParser.__init__\n def __init__(self, filename):\n ConfigParser.__init__(self, allow_no_value=True)\n self.read(filename)\n self.filename = filename","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.env_vars","text":"conans/client/conf/__init__.py::ConanClientConfigParser.env_vars\n @property\n def env_vars(self):\n ret = {\"CONAN_LOG_RUN_TO_OUTPUT\": self._env_c(\"log.run_to_output\", \"CONAN_LOG_RUN_TO_OUTPUT\", \"True\"),\n \"CONAN_LOG_RUN_TO_FILE\": self._env_c(\"log.run_to_file\", \"CONAN_LOG_RUN_TO_FILE\", \"False\"),\n \"CONAN_LOGGING_LEVEL\": self._env_c(\"log.level\", \"CONAN_LOGGING_LEVEL\", \"50\"),\n \"CONAN_TRACE_FILE\": self._env_c(\"log.trace_file\", \"CONAN_TRACE_FILE\", None),\n \"CONAN_PRINT_RUN_COMMANDS\": self._env_c(\"log.print_run_commands\", \"CONAN_PRINT_RUN_COMMANDS\", \"False\"),\n \"CONAN_COMPRESSION_LEVEL\": self._env_c(\"general.compression_level\", \"CONAN_COMPRESSION_LEVEL\", \"9\"),\n \"CONAN_NON_INTERACTIVE\": self._env_c(\"general.non_interactive\", \"CONAN_NON_INTERACTIVE\", \"False\"),\n \"CONAN_SKIP_BROKEN_SYMLINKS_CHECK\": self._env_c(\"general.skip_broken_symlinks_check\", \"CONAN_SKIP_BROKEN_SYMLINKS_CHECK\", \"False\"),\n \"CONAN_PYLINTRC\": self._env_c(\"general.pylintrc\", \"CONAN_PYLINTRC\", None),\n \"CONAN_CACHE_NO_LOCKS\": self._env_c(\"general.cache_no_locks\", \"CONAN_CACHE_NO_LOCKS\", \"False\"),\n \"CONAN_PYLINT_WERR\": self._env_c(\"general.pylint_werr\", \"CONAN_PYLINT_WERR\", None),\n \"CONAN_SYSREQUIRES_SUDO\": self._env_c(\"general.sysrequires_sudo\", \"CONAN_SYSREQUIRES_SUDO\", \"False\"),\n \"CONAN_SYSREQUIRES_MODE\": self._env_c(\"general.sysrequires_mode\", \"CONAN_SYSREQUIRES_MODE\", \"enabled\"),\n \"CONAN_REQUEST_TIMEOUT\": self._env_c(\"general.request_timeout\", \"CONAN_REQUEST_TIMEOUT\", None),\n \"CONAN_VS_INSTALLATION_PREFERENCE\": self._env_c(\"general.vs_installation_preference\", \"CONAN_VS_INSTALLATION_PREFERENCE\", None),\n \"CONAN_RECIPE_LINTER\": self._env_c(\"general.recipe_linter\", \"CONAN_RECIPE_LINTER\", \"True\"),\n \"CONAN_CPU_COUNT\": self._env_c(\"general.cpu_count\", \"CONAN_CPU_COUNT\", None),\n ","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser._env_c","text":"conans/client/conf/__init__.py::ConanClientConfigParser._env_c\n def _env_c(self, var_name, env_var_name, default_value):\n env = os.environ.get(env_var_name, None)\n if env is not None:\n return env\n try:\n return unquote(self.get_item(var_name))\n except ConanException:\n return default_value","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.get_item","text":"conans/client/conf/__init__.py::ConanClientConfigParser.get_item\n def get_item(self, item):\n if not item:\n return load(self.filename)\n\n tokens = item.split(\".\", 1)\n section_name = tokens[0]\n try:\n section = self.items(section_name)\n except NoSectionError:\n raise ConanException(\"'%s' is not a section of conan.conf\" % section_name)\n if len(tokens) == 1:\n result = []\n if section_name == \"hooks\":\n for key, _ in section:\n result.append(key)\n return \",\".join(result)\n else:\n for section_item in section:\n result.append(\" = \".join(section_item))\n return \"\\n\".join(result)\n else:\n key = tokens[1]\n try:\n value = dict(section)[key]\n if \" #\" in value: # Comments\n value = value[:value.find(\" #\")].strip()\n except KeyError:\n raise ConanException(\"'%s' doesn't exist in [%s]\" % (key, section_name))\n return value","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.set_item","text":"conans/client/conf/__init__.py::ConanClientConfigParser.set_item\n def set_item(self, key, value):\n tokens = key.split(\".\", 1)\n section_name = tokens[0]\n if not self.has_section(section_name):\n self.add_section(section_name)\n\n if len(tokens) == 1: # defining full section\n raise ConanException(\"You can't set a full section, please specify a key=value\")\n\n key = tokens[1]\n try:\n super(ConanClientConfigParser, self).set(section_name, key, value)\n except ValueError:\n # https://github.com/conan-io/conan/issues/4110\n value = value.replace(\"%\", \"%%\")\n super(ConanClientConfigParser, self).set(section_name, key, value)\n\n with open(self.filename, \"w\") as f:\n self.write(f)","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.rm_item","text":"conans/client/conf/__init__.py::ConanClientConfigParser.rm_item\n def rm_item(self, item):\n tokens = item.split(\".\", 1)\n section_name = tokens[0]\n if not self.has_section(section_name):\n raise ConanException(\"'%s' is not a section of conan.conf\" % section_name)\n\n if len(tokens) == 1:\n self.remove_section(tokens[0])\n else:\n key = tokens[1]\n if not self.has_option(section_name, key):\n raise ConanException(\"'%s' doesn't exist in [%s]\" % (key, section_name))\n self.remove_option(section_name, key)\n\n with open(self.filename, \"w\") as f:\n self.write(f)","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.get_conf","text":"conans/client/conf/__init__.py::ConanClientConfigParser.get_conf\n def get_conf(self, varname):\n \"\"\"Gets the section from config file or raises an exception\"\"\"\n try:\n return self.items(varname)\n except NoSectionError:\n raise ConanException(\"Invalid configuration, missing %s\" % varname)","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.default_profile","text":"conans/client/conf/__init__.py::ConanClientConfigParser.default_profile\n @property\n def default_profile(self):\n ret = os.environ.get(\"CONAN_DEFAULT_PROFILE_PATH\", None)\n if ret:\n if not os.path.isabs(ret):\n from conans.client.cache.cache import PROFILES_FOLDER\n profiles_folder = os.path.join(os.path.dirname(self.filename), PROFILES_FOLDER)\n ret = os.path.abspath(os.path.join(profiles_folder, ret))\n\n if not os.path.exists(ret):\n raise ConanException(\"Environment variable 'CONAN_DEFAULT_PROFILE_PATH' \"\n \"must point to an existing profile file.\")\n return ret\n else:\n try:\n return unquote(self.get_item(\"general.default_profile\"))\n except ConanException:\n return DEFAULT_PROFILE_NAME","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.cache_no_locks","text":"conans/client/conf/__init__.py::ConanClientConfigParser.cache_no_locks\n @property\n def cache_no_locks(self):\n try:\n return get_env(\"CONAN_CACHE_NO_LOCKS\", False)\n except ConanException:\n return False","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.storage","text":"conans/client/conf/__init__.py::ConanClientConfigParser.storage\n @property\n def storage(self):\n return dict(self.get_conf(\"storage\"))","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.request_timeout","text":"conans/client/conf/__init__.py::ConanClientConfigParser.request_timeout\n @property\n def request_timeout(self):\n try:\n return self.get_item(\"general.request_timeout\")\n except ConanException:\n return None","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.revisions_enabled","text":"conans/client/conf/__init__.py::ConanClientConfigParser.revisions_enabled\n @property\n def revisions_enabled(self):\n try:\n revisions_enabled = get_env(\"CONAN_REVISIONS_ENABLED\")\n if revisions_enabled is None:\n try:\n revisions_enabled = self.get_item(\"general.revisions_enabled\")\n except ConanException:\n return False\n return revisions_enabled.lower() in (\"1\", \"true\")\n except ConanException:\n return False","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.default_package_id_mode","text":"conans/client/conf/__init__.py::ConanClientConfigParser.default_package_id_mode\n @property\n def default_package_id_mode(self):\n try:\n default_package_id_mode = get_env(\"CONAN_DEFAULT_PACKAGE_ID_MODE\")\n if default_package_id_mode is None:\n default_package_id_mode = self.get_item(\"general.default_package_id_mode\")\n except ConanException:\n return \"semver_direct_mode\"\n return default_package_id_mode","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.storage_path","text":"conans/client/conf/__init__.py::ConanClientConfigParser.storage_path\n @property\n def storage_path(self):\n # Try with CONAN_STORAGE_PATH\n result = get_env('CONAN_STORAGE_PATH', None)\n if not result:\n # Try with conan.conf \"path\"\n try:\n # TODO: Fix this mess for Conan 2.0\n env_conan_user_home = os.getenv(\"CONAN_USER_HOME\")\n current_dir = os.path.dirname(self.filename)\n # if env var is declared, any specified path will be relative to CONAN_USER_HOME\n # even with the ~/\n result = self.storage[\"path\"]\n if result.startswith(\".\"):\n result = os.path.abspath(os.path.join(current_dir, result))\n elif result[:2] == \"~/\":\n if env_conan_user_home:\n result = os.path.join(env_conan_user_home, result[2:])\n except (KeyError, ConanException): # If storage not defined, to return None\n pass\n\n if result:\n result = conan_expand_user(result)\n if not os.path.isabs(result):\n raise ConanException(\"Conan storage path has to be an absolute path\")\n return result","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.proxies","text":"conans/client/conf/__init__.py::ConanClientConfigParser.proxies\n @property\n def proxies(self):\n \"\"\" optional field, might not exist\n \"\"\"\n try:\n proxies = self.get_conf(\"proxies\")\n # If there is proxies section, but empty, it will try to use system proxy\n if not proxies:\n # We don't have evidences that this following line is necessary.\n # If the proxies has been\n # configured at system level, conan will use it, and shouldn't be necessary\n # to return here the proxies read from the system.\n # Furthermore, the urls excluded for use proxies at system level do not work in\n # this case, then the only way is to remove the [proxies] section with\n # conan config remote proxies, then this method will return None and the proxies\n # dict passed to requests will be empty.\n # We don't remove this line because we are afraid to break something, but maybe\n # until now is working because no one is using system-wide proxies or those proxies\n # rules don't contain excluded urls.c #1777\n return urllib.request.getproxies()\n result = {k: (None if v == \"None\" else v) for k, v in proxies}\n return result\n except:\n return None","label":0},{"cid":"conans/client/conf/__init__.py::ConanClientConfigParser.cacert_path","text":"conans/client/conf/__init__.py::ConanClientConfigParser.cacert_path\n @property\n def cacert_path(self):\n try:\n cacert_path = get_env(\"CONAN_CACERT_PATH\")\n if not cacert_path:\n cacert_path = self.get_item(\"general.cacert_path\")\n except ConanException:\n cacert_path = os.path.join(os.path.dirname(self.filename), CACERT_FILE)\n else:\n # For explicit cacert files, the file should already exist\n if not os.path.exists(cacert_path):\n raise ConanException(\"Configured file for 'cacert_path'\"\n \" doesn't exists: '{}'\".format(cacert_path))\n return cacert_path","label":0},{"cid":"conans/client/conf/config_installer.py::_hide_password","text":"conans/client/conf/config_installer.py::_hide_password\ndef _hide_password(resource):\n \"\"\"\n Hide password from url/file path\n\n :param resource: string with url or file path\n :return: resource with hidden password if present\n \"\"\"\n password = urlparse(resource).password\n return resource.replace(password, \"<hidden>\") if password else resource","label":0},{"cid":"conans/client/conf/config_installer.py::_handle_remotes","text":"conans/client/conf/config_installer.py::_handle_remotes\ndef _handle_remotes(cache, remote_file):\n # FIXME: Should we encourage to pass the remotes in json?\n remotes, _ = load_registry_txt(load(remote_file))\n cache.registry.define(remotes)","label":0},{"cid":"conans/client/conf/config_installer.py::tmp_config_install_folder","text":"conans/client/conf/config_installer.py::tmp_config_install_folder\n@contextmanager\ndef tmp_config_install_folder(cache):\n tmp_folder = os.path.join(cache.conan_folder, \"tmp_config_install\")\n # necessary for Mac OSX, where the temp folders in /var/ are symlinks to /private/var/\n tmp_folder = os.path.realpath(tmp_folder)\n mkdir(tmp_folder)\n try:\n yield tmp_folder\n finally:\n rmdir(tmp_folder)","label":0},{"cid":"conans/client/conf/config_installer.py::_process_git_repo","text":"conans/client/conf/config_installer.py::_process_git_repo\ndef _process_git_repo(config, cache, output):\n output.info(\"Trying to clone repo: %s\" % config.uri)\n with tmp_config_install_folder(cache) as tmp_folder:\n with tools.chdir(tmp_folder):\n try:\n args = config.args or \"\"\n git = Git(verify_ssl=config.verify_ssl, output=output)\n git.clone(config.uri, args=args)\n output.info(\"Repo cloned!\")\n except Exception as e:\n raise ConanException(\"Can't clone repo: %s\" % str(e))\n _process_folder(config, tmp_folder, cache, output)","label":0},{"cid":"conans/client/conf/config_installer.py::_process_zip_file","text":"conans/client/conf/config_installer.py::_process_zip_file\ndef _process_zip_file(config, zippath, cache, output, tmp_folder, remove=False):\n unzip(zippath, tmp_folder, output=output)\n if remove:\n os.unlink(zippath)\n _process_folder(config, tmp_folder, cache, output)","label":0},{"cid":"conans/client/conf/config_installer.py::_handle_conan_conf","text":"conans/client/conf/config_installer.py::_handle_conan_conf\ndef _handle_conan_conf(current_conan_conf, new_conan_conf_path):\n current_conan_conf.read(new_conan_conf_path)\n with open(current_conan_conf.filename, \"w\") as f:\n current_conan_conf.write(f)","label":0},{"cid":"conans/client/conf/config_installer.py::_process_folder","text":"conans/client/conf/config_installer.py::_process_folder\ndef _process_folder(config, folder, cache, output):\n if config.source_folder:\n folder = os.path.join(folder, config.source_folder)\n for root, dirs, files in walk(folder):\n dirs[:] = [d for d in dirs if d != \".git\"]\n if \".git\" in root:\n continue\n for f in files:\n if f == \"settings.yml\":\n output.info(\"Installing settings.yml\")\n settings_path = cache.settings_path\n shutil.copy(os.path.join(root, f), settings_path)\n elif f == \"conan.conf\":\n output.info(\"Processing conan.conf\")\n _handle_conan_conf(cache.config, os.path.join(root, f))\n elif f == \"remotes.txt\":\n output.info(\"Defining remotes from remotes.txt\")\n _handle_remotes(cache, os.path.join(root, f))\n elif f in (\"registry.txt\", \"registry.json\"):\n os.remove(cache.registry_path)\n shutil.copy(os.path.join(root, f), cache.conan_folder)\n migrate_registry_file(cache, output)\n elif f == \"remotes.json\":\n # Fix for Conan 2.0\n raise ConanException(\"remotes.json install is not supported yet. Use 'remotes.txt'\")\n else:\n # This is ugly, should be removed in Conan 2.0\n if root == folder and f in (\"README.md\", \"LICENSE.txt\"):\n output.info(\"Skip %s\" % f)\n continue\n relpath = os.path.relpath(root, folder)\n if config.target_folder:\n target_folder = os.path.join(cache.conan_folder, config.target_folder,\n relpath)\n else:\n target_folder = os.path.join(cache.conan_folder, relpath)\n mkdir(target_folder)\n output.info(\"Copying file %s to %s\" % (f, target_folder))\n ","label":0},{"cid":"conans/client/conf/config_installer.py::_process_download","text":"conans/client/conf/config_installer.py::_process_download\ndef _process_download(config, cache, output, requester):\n with tmp_config_install_folder(cache) as tmp_folder:\n output.info(\"Trying to download %s\" % _hide_password(config.uri))\n zippath = os.path.join(tmp_folder, \"config.zip\")\n try:\n tools.download(config.uri, zippath, out=output, verify=config.verify_ssl,\n requester=requester)\n _process_zip_file(config, zippath, cache, output, tmp_folder, remove=True)\n except Exception as e:\n raise ConanException(\"Error while installing config from %s\\n%s\" % (config.uri, str(e)))","label":0},{"cid":"conans/client/conf/config_installer.py::_ConfigOrigin.__init__","text":"conans/client/conf/config_installer.py::_ConfigOrigin.__init__\n def __init__(self, data):\n self.type = data.get(\"type\")\n self.uri = data.get(\"uri\")\n self.verify_ssl = data.get(\"verify_ssl\")\n self.args = data.get(\"args\")\n self.source_folder = data.get(\"source_folder\")\n self.target_folder = data.get(\"target_folder\")","label":0},{"cid":"conans/client/conf/config_installer.py::_ConfigOrigin.__eq__","text":"conans/client/conf/config_installer.py::_ConfigOrigin.__eq__\n def __eq__(self, other):\n return (self.type == other.type and self.uri == other.uri and\n self.args == other.args and self.source_folder == other.source_folder\n and self.target_folder == other.target_folder)","label":0},{"cid":"conans/client/conf/config_installer.py::_ConfigOrigin.__ne__","text":"conans/client/conf/config_installer.py::_ConfigOrigin.__ne__\n def __ne__(self, other):\n return not self.__eq__(other)","label":0},{"cid":"conans/client/conf/config_installer.py::_ConfigOrigin.json","text":"conans/client/conf/config_installer.py::_ConfigOrigin.json\n def json(self):\n return {\"type\": self.type,\n \"uri\": self.uri,\n \"verify_ssl\": self.verify_ssl,\n \"args\": self.args,\n \"source_folder\": self.source_folder,\n \"target_folder\": self.target_folder}","label":0},{"cid":"conans/client/conf/config_installer.py::_ConfigOrigin.from_item","text":"conans/client/conf/config_installer.py::_ConfigOrigin.from_item\n @staticmethod\n def from_item(uri, config_type, verify_ssl, args, source_folder, target_folder):\n config = _ConfigOrigin({})\n if config_type:\n config.type = config_type\n else:\n if uri.endswith(\".git\"):\n config.type = \"git\"\n elif os.path.isdir(uri):\n config.type = \"dir\"\n elif os.path.isfile(uri):\n config.type = \"file\"\n elif uri.startswith(\"http\"):\n config.type = \"url\"\n else:\n raise ConanException(\"Unable to deduce type config install: %s\" % uri)\n config.source_folder = source_folder\n config.target_folder = target_folder\n config.args = args\n config.verify_ssl = verify_ssl\n if os.path.exists(uri):\n uri = os.path.abspath(uri)\n config.uri = uri\n return config","label":0},{"cid":"conans/client/conf/config_installer.py::_process_config","text":"conans/client/conf/config_installer.py::_process_config\ndef _process_config(config, cache, output, requester):\n if config.type == \"git\":\n _process_git_repo(config, cache, output)\n elif config.type == \"dir\":\n _process_folder(config, config.uri, cache, output)\n elif config.type == \"file\":\n with tmp_config_install_folder(cache) as tmp_folder:\n _process_zip_file(config, config.uri, cache, output, tmp_folder)\n elif config.type == \"url\":\n _process_download(config, cache, output, requester=requester)\n else:\n raise ConanException(\"Unable to process config install: %s\" % config.uri)","label":0},{"cid":"conans/client/conf/config_installer.py::_save_configs","text":"conans/client/conf/config_installer.py::_save_configs\ndef _save_configs(configs_file, configs):\n save(configs_file, json.dumps([config.json() for config in configs],\n indent=True))","label":0},{"cid":"conans/client/conf/config_installer.py::configuration_install","text":"conans/client/conf/config_installer.py::configuration_install\ndef configuration_install(uri, cache, output, verify_ssl, requester, config_type=None,\n args=None, source_folder=None, target_folder=None):\n configs = []\n configs_file = cache.config_install_file\n if os.path.isfile(configs_file):\n try:\n configs = json.loads(load(configs_file))\n except Exception as e:\n raise ConanException(\"Error loading configs-install file: %s\\n%\"\n % (configs_file, str(e)))\n configs = [_ConfigOrigin(config) for config in configs]\n if uri is None:\n if config_type or args or not verify_ssl: # Not the defaults\n if not configs:\n raise ConanException(\"Called config install without arguments\")\n # Modify the last one\n config = configs[-1]\n config.config_type = config_type or config.type\n config.args = args or config.args\n config.verify_ssl = verify_ssl or config.verify_ssl\n _process_config(config, cache, output, requester)\n _save_configs(configs_file, configs)\n else:\n if not configs:\n raise ConanException(\"Called config install without arguments\")\n # Execute the previously stored ones\n for config in configs:\n output.info(\"Config install: %s\" % _hide_password(config.uri))\n _process_config(config, cache, output, requester)\n else:\n # Execute and store the new one\n config = _ConfigOrigin.from_item(uri, config_type, verify_ssl, args,\n source_folder, target_folder)\n _process_config(config, cache, output, requester)\n if config not in configs:\n configs.append(config)\n else:\n configs = [(c if c != config else config) for c in configs]\n _save_configs(configs_file, configs)","label":0},{"cid":"conans/client/conf/detect.py::_execute","text":"conans/client/conf/detect.py::_execute\ndef _execute(command):\n proc = Popen(command, shell=True, bufsize=1, stdout=PIPE, stderr=STDOUT)\n\n output_buffer = []\n while True:\n line = proc.stdout.readline()\n if not line:\n break\n # output.write(line)\n output_buffer.append(str(line))\n\n proc.communicate()\n return proc.returncode, \"\".join(output_buffer)","label":0},{"cid":"conans/client/conf/detect.py::_gcc_compiler","text":"conans/client/conf/detect.py::_gcc_compiler\ndef _gcc_compiler(output, compiler_exe=\"gcc\"):\n\n try:\n if platform.system() == \"Darwin\":\n # In Mac OS X check if gcc is a fronted using apple-clang\n _, out = _execute(\"%s --version\" % compiler_exe)\n out = out.lower()\n if \"clang\" in out:\n return None\n\n ret, out = _execute('%s -dumpversion' % compiler_exe)\n if ret != 0:\n return None\n compiler = \"gcc\"\n installed_version = re.search(\"([0-9](\\.[0-9])?)\", out).group()\n # Since GCC 7.1, -dumpversion return the major version number\n # only (\"7\"). We must use -dumpfullversion to get the full version\n # number (\"7.1.1\").\n if installed_version:\n output.success(\"Found %s %s\" % (compiler, installed_version))\n major = installed_version.split(\".\")[0]\n if int(major) >= 5:\n output.info(\"gcc>=5, using the major as version\")\n installed_version = major\n return compiler, installed_version\n except:\n return None","label":0},{"cid":"conans/client/conf/detect.py::_clang_compiler","text":"conans/client/conf/detect.py::_clang_compiler\ndef _clang_compiler(output, compiler_exe=\"clang\"):\n try:\n ret, out = _execute('%s --version' % compiler_exe)\n if ret != 0:\n return None\n if \"Apple\" in out:\n compiler = \"apple-clang\"\n elif \"clang version\" in out:\n compiler = \"clang\"\n installed_version = re.search(\"([0-9]+\\.[0-9])\", out).group()\n if installed_version:\n output.success(\"Found %s %s\" % (compiler, installed_version))\n major = installed_version.split(\".\")[0]\n if int(major) >= 8 and compiler == \"clang\":\n output.info(\"clang>=8, using the major as version\")\n installed_version = major\n return compiler, installed_version\n except:\n return None","label":0},{"cid":"conans/client/conf/detect.py::_sun_cc_compiler","text":"conans/client/conf/detect.py::_sun_cc_compiler\ndef _sun_cc_compiler(output, compiler_exe=\"cc\"):\n try:\n _, out = _execute('%s -V' % compiler_exe)\n compiler = \"sun-cc\"\n installed_version = re.search(\"([0-9]+\\.[0-9]+)\", out).group()\n if installed_version:\n output.success(\"Found %s %s\" % (compiler, installed_version))\n return compiler, installed_version\n except:\n return None","label":0},{"cid":"conans/client/conf/detect.py::_get_default_compiler","text":"conans/client/conf/detect.py::_get_default_compiler\ndef _get_default_compiler(output):\n cc = os.environ.get(\"CC\", \"\")\n cxx = os.environ.get(\"CXX\", \"\")\n if cc or cxx: # Env defined, use them\n output.info(\"CC and CXX: %s, %s \" % (cc or \"None\", cxx or \"None\"))\n command = cc or cxx\n if \"gcc\" in command:\n gcc = _gcc_compiler(output, command)\n if platform.system() == \"Darwin\" and gcc is None:\n output.error(\n \"%s detected as a frontend using apple-clang. Compiler not supported\" % command\n )\n return gcc\n if \"clang\" in command.lower():\n return _clang_compiler(output, command)\n if platform.system() == \"SunOS\" and command.lower() == \"cc\":\n return _sun_cc_compiler(output, command)\n # I am not able to find its version\n output.error(\"Not able to automatically detect '%s' version\" % command)\n return None\n\n if detected_os() == \"Windows\":\n version = latest_visual_studio_version_installed(output)\n vs = ('Visual Studio', version) if version else None\n gcc = _gcc_compiler(output)\n clang = _clang_compiler(output)\n if platform.system() == \"SunOS\":\n sun_cc = _sun_cc_compiler(output)\n\n if detected_os() == \"Windows\":\n return vs or gcc or clang\n elif platform.system() == \"Darwin\":\n return clang or gcc\n elif platform.system() == \"SunOS\":\n return sun_cc or gcc or clang\n else:\n return gcc or clang","label":0},{"cid":"conans/client/conf/detect.py::_detect_compiler_version","text":"conans/client/conf/detect.py::_detect_compiler_version\ndef _detect_compiler_version(result, output, profile_path):\n try:\n compiler, version = _get_default_compiler(output)\n except:\n compiler, version = None, None\n if not compiler or not version:\n output.error(\"Unable to find a working compiler\")\n else:\n result.append((\"compiler\", compiler))\n result.append((\"compiler.version\", version))\n if compiler == \"apple-clang\":\n result.append((\"compiler.libcxx\", \"libc++\"))\n elif compiler == \"gcc\":\n result.append((\"compiler.libcxx\", \"libstdc++\"))\n if Version(version) >= Version(\"5.1\"):\n profile_name = os.path.basename(profile_path)\n msg = \"\"\"\nConan detected a GCC version > 5 but has adjusted the 'compiler.libcxx' setting to\n'libstdc++' for backwards compatibility.\nYour compiler is likely using the new CXX11 ABI by default (libstdc++11).\n\nIf you want Conan to use the new ABI, edit the {profile} profile at:\n\n {profile_path}\n\nadjusting 'compiler.libcxx=libstdc++11'\n\"\"\".format(profile=profile_name, profile_path=profile_path)\n output.writeln(\"\\n************************* WARNING: GCC OLD ABI COMPATIBILITY \"\n \"***********************\\n %s\\n************************************\"\n \"************************************************\\n\\n\\n\" % msg,\n Color.BRIGHT_RED)\n elif compiler == \"cc\":\n if platform.system() == \"SunOS\":\n result.append((\"compiler.libstdcxx\", \"libstdcxx4\"))\n elif compiler == \"clang\":\n if platform.system() == \"FreeBSD\":\n result.append((\"compiler.libcxx\", \"libc++\"))\n else:\n result.append((\"compiler.libcxx\", \"libstdc++\"))\n elif compiler == \"sun-cc\":\n result.append((\"compiler.libcxx\", \"libCstd\"))","label":0},{"cid":"conans/client/conf/detect.py::_detect_os_arch","text":"conans/client/conf/detect.py::_detect_os_arch\ndef _detect_os_arch(result, output):\n architectures = {'i386': 'x86',\n 'i686': 'x86',\n 'i86pc': 'x86',\n 'amd64': 'x86_64',\n 'aarch64': 'armv8',\n 'sun4v': 'sparc'}\n the_os = detected_os()\n result.append((\"os\", the_os))\n result.append((\"os_build\", the_os))\n platform_machine = platform.machine().lower()\n if platform_machine:\n arch = architectures.get(platform_machine, platform_machine)\n if arch.startswith('arm'):\n for a in (\"armv6\", \"armv7hf\", \"armv7\", \"armv8\"):\n if arch.startswith(a):\n arch = a\n break\n else:\n output.error(\"Your ARM '%s' architecture is probably not defined in settings.yml\\n\"\n \"Please check your conan.conf and settings.yml files\" % arch)\n result.append((\"arch\", arch))\n result.append((\"arch_build\", arch))","label":1},{"cid":"conans/client/conf/detect.py::detect_defaults_settings","text":"conans/client/conf/detect.py::detect_defaults_settings\ndef detect_defaults_settings(output, profile_path):\n \"\"\" try to deduce current machine values without any constraints at all\n :param output: Conan Output instance\n :param profile_path: Conan profile file path\n :return: A list with default settings\n \"\"\"\n result = []\n _detect_os_arch(result, output)\n _detect_compiler_version(result, output, profile_path)\n result.append((\"build_type\", \"Release\"))\n\n return result","label":0},{"cid":"conans/client/generators/compiler_args.py::CompilerArgsGenerator.filename","text":"conans/client/generators/compiler_args.py::CompilerArgsGenerator.filename\n @property\n def filename(self):\n return BUILD_INFO_COMPILER_ARGS","label":0},{"cid":"conans/client/generators/compiler_args.py::CompilerArgsGenerator.compiler","text":"conans/client/generators/compiler_args.py::CompilerArgsGenerator.compiler\n @property\n def compiler(self):\n return self.conanfile.settings.get_safe(\"compiler\")","label":0},{"cid":"conans/client/generators/compiler_args.py::CompilerArgsGenerator.content","text":"conans/client/generators/compiler_args.py::CompilerArgsGenerator.content\n @property\n def content(self):\n \"\"\"With compiler_args you can invoke your compiler:\n $ gcc main.c @conanbuildinfo.args -o main\n $ clang main.c @conanbuildinfo.args -o main\n $ cl /EHsc main.c @conanbuildinfo.args\n \"\"\"\n flags = []\n flags.extend(format_defines(self._deps_build_info.defines))\n flags.extend(format_include_paths(self._deps_build_info.include_paths,\n compiler=self.compiler))\n\n flags.extend(self._deps_build_info.cxxflags)\n flags.extend(self._deps_build_info.cflags)\n\n arch_flag = architecture_flag(arch=self.conanfile.settings.get_safe(\"arch\"),\n compiler=self.compiler)\n if arch_flag:\n flags.append(arch_flag)\n\n build_type = self.conanfile.settings.get_safe(\"build_type\")\n btfs = build_type_flags(compiler=self.compiler, build_type=build_type,\n vs_toolset=self.conanfile.settings.get_safe(\"compiler.toolset\"))\n if btfs:\n flags.extend(btfs)\n btd = build_type_define(build_type=build_type)\n if btd:\n flags.extend(format_defines([btd]))\n\n if self.compiler == \"Visual Studio\":\n runtime = visual_runtime(self.conanfile.settings.get_safe(\"compiler.runtime\"))\n if runtime:\n flags.append(runtime)\n # Necessary in the \"cl\" invocation before specify the rest of linker flags\n flags.append(visual_linker_option_separator)\n\n the_os = (self.conanfile.settings.get_safe(\"os_build\") or\n self.conanfile.settings.get_safe(\"os\"))\n flags.extend(rpath_flags(the_os, self.compiler, self._deps_build_info.lib_paths))\n flags.extend(format_library_paths(self._deps_build_info.lib_paths, compiler=self.compiler))\n flags.extend(format_libraries(self._deps_build_","label":1},{"cid":"conans/client/generators/compiler_args.py::CompilerArgsGenerator._libcxx_flags","text":"conans/client/generators/compiler_args.py::CompilerArgsGenerator._libcxx_flags\n def _libcxx_flags(self):\n libcxx = self.conanfile.settings.get_safe(\"compiler.libcxx\")\n compiler = self.conanfile.settings.get_safe(\"compiler\")\n\n lib_flags = []\n if libcxx:\n stdlib_define = libcxx_define(compiler=compiler, libcxx=libcxx)\n lib_flags.extend(format_defines([stdlib_define]))\n cxxf = libcxx_flag(compiler=compiler, libcxx=libcxx)\n if cxxf:\n lib_flags.append(cxxf)\n\n return lib_flags","label":0},{"cid":"conans/client/generators/visualstudio_multi.py::_VSSettings.__init__","text":"conans/client/generators/visualstudio_multi.py::_VSSettings.__init__\n def __init__(self, settings):\n toolset = msvs_toolset(settings)\n if toolset is None:\n raise ConanException(\"Undefined Visual Studio version %s\" %\n settings.get_safe(\"compiler.version\"))\n\n self._props = [(\"Configuration\", settings.get_safe(\"build_type\")),\n (\"Platform\", {'x86': 'Win32',\n 'x86_64': 'x64'}.get(settings.get_safe(\"arch\"))),\n (\"PlatformToolset\", toolset)]","label":0},{"cid":"conans/client/generators/visualstudio_multi.py::_VSSettings.filename","text":"conans/client/generators/visualstudio_multi.py::_VSSettings.filename\n @property\n def filename(self):\n name = \"conanbuildinfo%s.props\" % \"\".join(\"_%s\" % v for _, v in self._props if v)\n return name.lower()","label":0},{"cid":"conans/client/generators/visualstudio_multi.py::_VSSettings.condition","text":"conans/client/generators/visualstudio_multi.py::_VSSettings.condition\n @property\n def condition(self):\n return \" And \".join(\"'$(%s)' == '%s'\" % (k, v) for k, v in self._props if v)","label":0},{"cid":"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.filename","text":"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.filename\n @property\n def filename(self):\n pass","label":0},{"cid":"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.content","text":"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.content\n @property\n def content(self):\n vs_settings = _VSSettings(self.conanfile.settings)\n condition = vs_settings.condition\n name_current = vs_settings.filename\n name_multi = \"conanbuildinfo_multi.props\"\n\n # read the exising mult_filename or use the template if it doesn't exist\n multi_path = os.path.join(self.output_path, name_multi)\n if os.path.isfile(multi_path):\n content_multi = load(multi_path)\n else:\n content_multi = self.multi_content_template\n\n # parse the multi_file and add a new import statement if needed\n dom = minidom.parseString(content_multi)\n import_group = dom.getElementsByTagName('ImportGroup')[0]\n children = import_group.getElementsByTagName(\"Import\")\n for node in children:\n if name_current == node.getAttribute(\"Project\") and condition == node.getAttribute(\"Condition\"):\n # the import statement already exists\n break\n else:\n # create a new import statement\n import_node = dom.createElement('Import')\n import_node.setAttribute('Condition', condition)\n import_node.setAttribute('Project', name_current)\n # add it to the import group\n import_group.appendChild(import_node)\n content_multi = dom.toprettyxml()\n content_multi = \"\\n\".join(line for line in content_multi.splitlines() if line.strip())\n\n return {name_multi: content_multi,\n vs_settings.filename: VisualStudioGenerator(self.conanfile).content}","label":0},{"cid":"conans/client/importer.py::undo_imports","text":"conans/client/importer.py::undo_imports\ndef undo_imports(current_path, output):\n manifest_path = os.path.join(current_path, IMPORTS_MANIFESTS)\n try:\n manifest_content = load(manifest_path)\n except:\n raise ConanException(\"Cannot load file %s\" % manifest_path)\n\n try:\n manifest = FileTreeManifest.loads(manifest_content)\n except:\n raise ConanException(\"Wrong manifest file format %s\" % manifest_path)\n\n not_removed = 0\n files = manifest.files()\n for filepath in files:\n if not os.path.exists(filepath):\n output.warn(\"File doesn't exist: %s\" % filepath)\n continue\n try:\n os.remove(filepath)\n except:\n output.error(\"Cannot remove file (open or busy): %s\" % filepath)\n not_removed += 1\n\n if not_removed:\n raise ConanException(\"Cannot remove %s or more imported files\" % not_removed)\n\n output.success(\"Removed %s imported files\" % (len(files)))\n try:\n os.remove(manifest_path)\n output.success(\"Removed imports manifest file: %s\" % manifest_path)\n except:\n raise ConanException(\"Cannot remove manifest file (open or busy): %s\" % manifest_path)","label":0},{"cid":"conans/client/importer.py::_report_save_manifest","text":"conans/client/importer.py::_report_save_manifest\ndef _report_save_manifest(copied_files, output, dest_folder, manifest_name):\n report_copied_files(copied_files, output)\n if copied_files:\n date = calendar.timegm(time.gmtime())\n file_dict = {}\n for f in copied_files:\n abs_path = os.path.join(dest_folder, f)\n file_dict[f] = md5sum(abs_path)\n manifest = FileTreeManifest(date, file_dict)\n manifest.save(dest_folder, manifest_name)","label":0},{"cid":"conans/client/importer.py::_make_files_writable","text":"conans/client/importer.py::_make_files_writable\ndef _make_files_writable(file_names):\n if not get_env(\"CONAN_READ_ONLY_CACHE\", False):\n return\n\n for file_name in file_names:\n os.chmod(file_name, os.stat(file_name).st_mode | stat.S_IWRITE)","label":0},{"cid":"conans/client/importer.py::run_imports","text":"conans/client/importer.py::run_imports\ndef run_imports(conanfile, dest_folder):\n if not hasattr(conanfile, \"imports\"):\n return []\n file_importer = _FileImporter(conanfile, dest_folder)\n conanfile.copy = file_importer\n conanfile.imports_folder = dest_folder\n with get_env_context_manager(conanfile):\n with tools.chdir(dest_folder):\n conanfile.imports()\n copied_files = file_importer.copied_files\n _make_files_writable(copied_files)\n import_output = ScopedOutput(\"%s imports()\" % conanfile.display_name, conanfile.output)\n _report_save_manifest(copied_files, import_output, dest_folder, IMPORTS_MANIFESTS)\n return copied_files","label":0},{"cid":"conans/client/importer.py::remove_imports","text":"conans/client/importer.py::remove_imports\ndef remove_imports(conanfile, copied_files, output):\n if not getattr(conanfile, \"keep_imports\", False):\n for f in copied_files:\n try:\n os.remove(f)\n except OSError:\n output.warn(\"Unable to remove imported file from build: %s\" % f)","label":0},{"cid":"conans/client/importer.py::run_deploy","text":"conans/client/importer.py::run_deploy\ndef run_deploy(conanfile, install_folder):\n deploy_output = ScopedOutput(\"%s deploy()\" % conanfile.display_name, conanfile.output)\n file_importer = _FileImporter(conanfile, install_folder)\n package_copied = set()\n\n # This is necessary to capture FileCopier full destination paths\n # Maybe could be improved in FileCopier\n def file_copier(*args, **kwargs):\n file_copy = FileCopier([conanfile.package_folder], install_folder)\n copied = file_copy(*args, **kwargs)\n _make_files_writable(copied)\n package_copied.update(copied)\n\n conanfile.copy_deps = file_importer\n conanfile.copy = file_copier\n conanfile.install_folder = install_folder\n with get_env_context_manager(conanfile):\n with tools.chdir(install_folder):\n conanfile.deploy()\n\n copied_files = file_importer.copied_files\n copied_files.update(package_copied)\n _report_save_manifest(copied_files, deploy_output, install_folder, \"deploy_manifest.txt\")","label":0},{"cid":"conans/client/importer.py::run_deploy.file_copier","text":"conans/client/importer.py::run_deploy.file_copier\n def file_copier(*args, **kwargs):\n file_copy = FileCopier([conanfile.package_folder], install_folder)\n copied = file_copy(*args, **kwargs)\n _make_files_writable(copied)\n package_copied.update(copied)","label":0},{"cid":"conans/client/importer.py::_FileImporter.__init__","text":"conans/client/importer.py::_FileImporter.__init__\n def __init__(self, conanfile, dst_folder):\n self._conanfile = conanfile\n self._dst_folder = dst_folder\n self.copied_files = set()","label":0},{"cid":"conans/client/importer.py::_FileImporter.__call__","text":"conans/client/importer.py::_FileImporter.__call__\n def __call__(self, pattern, dst=\"\", src=\"\", root_package=None, folder=False,\n ignore_case=False, excludes=None, keep_path=True):\n \"\"\"\n param pattern: an fnmatch file pattern of the files that should be copied. Eg. *.dll\n param dst: the destination local folder, wrt to current conanfile dir, to which\n the files will be copied. Eg: \"bin\"\n param src: the source folder in which those files will be searched. This folder\n will be stripped from the dst name. Eg.: lib/Debug/x86\n param root_package: fnmatch pattern of the package name (\"OpenCV\", \"Boost\") from\n which files will be copied. Default: all packages in deps\n \"\"\"\n if os.path.isabs(dst):\n real_dst_folder = dst\n else:\n real_dst_folder = os.path.normpath(os.path.join(self._dst_folder, dst))\n\n matching_paths = self._get_folders(root_package)\n for name, matching_path in matching_paths.items():\n final_dst_path = os.path.join(real_dst_folder, name) if folder else real_dst_folder\n file_copier = FileCopier([matching_path], final_dst_path)\n files = file_copier(pattern, src=src, links=True, ignore_case=ignore_case,\n excludes=excludes, keep_path=keep_path)\n self.copied_files.update(files)","label":0},{"cid":"conans/client/importer.py::_FileImporter._get_folders","text":"conans/client/importer.py::_FileImporter._get_folders\n def _get_folders(self, pattern):\n \"\"\" given the current deps graph, compute a dict {name: store-path} of\n each dependency\n \"\"\"\n if not pattern:\n return {pkg: cpp_info.rootpath\n for pkg, cpp_info in self._conanfile.deps_cpp_info.dependencies}\n return {pkg: cpp_info.rootpath\n for pkg, cpp_info in self._conanfile.deps_cpp_info.dependencies\n if fnmatch.fnmatch(pkg, pattern)}","label":0},{"cid":"conans/client/manager.py::ConanManager.__init__","text":"conans/client/manager.py::ConanManager.__init__\n def __init__(self, cache, user_io, remote_manager,\n recorder, graph_manager, hook_manager):\n assert isinstance(user_io, UserIO)\n assert isinstance(cache, ClientCache)\n self._cache = cache\n self._user_io = user_io\n self._remote_manager = remote_manager\n self._recorder = recorder\n self._graph_manager = graph_manager\n self._hook_manager = hook_manager","label":0},{"cid":"conans/client/manager.py::ConanManager.install","text":"conans/client/manager.py::ConanManager.install\n def install(self, ref_or_path, install_folder, graph_info, remotes=None, build_modes=None,\n update=False, manifest_folder=None, manifest_verify=False,\n manifest_interactive=False, generators=None, no_imports=False, create_reference=None,\n keep_build=False):\n \"\"\" Fetch and build all dependencies for the given reference\n @param ref_or_path: ConanFileReference or path to user space conanfile\n @param install_folder: where the output files will be saved\n @param remote_name: install only from that remote\n @param profile: Profile object with both the -s introduced options and profile read values\n @param build_modes: List of build_modes specified\n @param update: Check for updated in the upstream remotes (and update)\n @param manifest_folder: Folder to install the manifests\n @param manifest_verify: Verify dependencies manifests against stored ones\n @param manifest_interactive: Install deps manifests in folder for later verify, asking user\n for confirmation\n @param generators: List of generators from command line. If False, no generator will be\n written\n @param no_imports: Install specified packages but avoid running imports\n \"\"\"\n\n if generators is not False:\n generators = set(generators) if generators else set()\n generators.add(\"txt\") # Add txt generator by default\n\n self._user_io.out.info(\"Configuration:\")\n self._user_io.out.writeln(graph_info.profile.dumps())\n result = self._graph_manager.load_graph(ref_or_path, create_reference, graph_info,\n build_modes, False, update, remotes,\n self._recorder)\n deps_graph, conanfile = result\n\n if conanfile.display_name == \"virtual\":\n self._user_io.out.highlight(\"Installing p","label":0},{"cid":"conans/client/migrations.py::ClientMigrator.__init__","text":"conans/client/migrations.py::ClientMigrator.__init__\n def __init__(self, cache, current_version, out):\n self.cache = cache\n super(ClientMigrator, self).__init__(cache.conan_folder, cache.store,\n current_version, out)","label":0},{"cid":"conans/client/migrations.py::ClientMigrator._update_settings_yml","text":"conans/client/migrations.py::ClientMigrator._update_settings_yml\n def _update_settings_yml(self, old_version):\n\n from conans.client.conf import default_settings_yml\n settings_path = self.cache.settings_path\n if not os.path.exists(settings_path):\n self.out.warn(\"Migration: This conan installation doesn't have settings yet\")\n self.out.warn(\"Nothing to migrate here, settings will be generated automatically\")\n return\n\n var_name = \"settings_{}\".format(old_version.replace(\".\", \"_\"))\n\n def save_new():\n new_path = self.cache.settings_path + \".new\"\n save(new_path, default_settings_yml)\n self.out.warn(\"*\" * 40)\n self.out.warn(\"settings.yml is locally modified, can't be updated\")\n self.out.warn(\"The new settings.yml has been stored in: %s\" % new_path)\n self.out.warn(\"*\" * 40)\n\n self.out.warn(\"Migration: Updating settings.yml\")\n if hasattr(migrations_settings, var_name):\n version_default_contents = getattr(migrations_settings, var_name)\n if version_default_contents != default_settings_yml:\n current_settings = load(self.cache.settings_path)\n if current_settings != version_default_contents:\n save_new()\n else:\n save(self.cache.settings_path, default_settings_yml)\n else:\n self.out.info(\"Migration: Settings already up to date\")\n else:\n # We don't have the value for that version, so don't override\n save_new()","label":0},{"cid":"conans/client/migrations.py::ClientMigrator._update_settings_yml.save_new","text":"conans/client/migrations.py::ClientMigrator._update_settings_yml.save_new\n def save_new():\n new_path = self.cache.settings_path + \".new\"\n save(new_path, default_settings_yml)\n self.out.warn(\"*\" * 40)\n self.out.warn(\"settings.yml is locally modified, can't be updated\")\n self.out.warn(\"The new settings.yml has been stored in: %s\" % new_path)\n self.out.warn(\"*\" * 40)","label":0},{"cid":"conans/client/migrations.py::ClientMigrator._make_migrations","text":"conans/client/migrations.py::ClientMigrator._make_migrations\n def _make_migrations(self, old_version):\n # ############### FILL THIS METHOD WITH THE REQUIRED ACTIONS ##############\n # VERSION 0.1\n if old_version is None:\n return\n\n # Migrate the settings if they were the default for that version\n self._update_settings_yml(old_version)\n\n if old_version < Version(\"0.25\"):\n from conans.paths import DEFAULT_PROFILE_NAME\n default_profile_path = os.path.join(self.cache.conan_folder, PROFILES_FOLDER,\n DEFAULT_PROFILE_NAME)\n if not os.path.exists(default_profile_path):\n self.out.warn(\"Migration: Moving default settings from %s file to %s\"\n % (CONAN_CONF, DEFAULT_PROFILE_NAME))\n conf_path = os.path.join(self.cache.conan_folder, CONAN_CONF)\n\n migrate_to_default_profile(conf_path, default_profile_path)\n\n self.out.warn(\"Migration: export_source cache new layout\")\n migrate_c_src_export_source(self.cache, self.out)\n\n if old_version < Version(\"1.0\"):\n _migrate_lock_files(self.cache, self.out)\n\n if old_version < Version(\"1.12.0\"):\n migrate_plugins_to_hooks(self.cache)\n\n if old_version < Version(\"1.13.0\"):\n # MIGRATE LOCAL CACHE TO GENERATE MISSING METADATA.json\n _migrate_create_metadata(self.cache, self.out)\n\n if old_version < Version(\"1.14.0\"):\n migrate_config_install(self.cache)\n\n if old_version < Version(\"1.14.2\"):\n _migrate_full_metadata(self.cache, self.out)\n\n if old_version < Version(\"1.15.0\"):\n migrate_registry_file(self.cache, self.out)","label":0},{"cid":"conans/client/migrations.py::_get_refs","text":"conans/client/migrations.py::_get_refs\ndef _get_refs(cache):\n folders = list_folder_subdirs(cache.store, 4)\n return [ConanFileReference(*s.split(\"/\")) for s in folders]","label":0},{"cid":"conans/client/migrations.py::_get_prefs","text":"conans/client/migrations.py::_get_prefs\ndef _get_prefs(layout):\n packages_folder = layout.packages()\n folders = list_folder_subdirs(packages_folder, 1)\n return [PackageReference(layout.ref, s) for s in folders]","label":0},{"cid":"conans/client/migrations.py::_migrate_full_metadata","text":"conans/client/migrations.py::_migrate_full_metadata\ndef _migrate_full_metadata(cache, out):\n # Fix for https://github.com/conan-io/conan/issues/4898\n out.warn(\"Running a full revision metadata migration\")\n refs = _get_refs(cache)\n for ref in refs:\n try:\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\n layout = PackageCacheLayout(base_folder=base_folder, ref=ref, short_paths=None,\n no_lock=True)\n with layout.update_metadata() as metadata:\n # Updating the RREV\n if metadata.recipe.revision is None:\n out.warn(\"Package %s metadata had recipe revision None, migrating\" % str(ref))\n folder = layout.export()\n try:\n manifest = FileTreeManifest.load(folder)\n rrev = manifest.summary_hash\n except Exception:\n rrev = DEFAULT_REVISION_V1\n metadata.recipe.revision = rrev\n\n prefs = _get_prefs(layout)\n existing_ids = [pref.id for pref in prefs]\n for pkg_id in list(metadata.packages.keys()):\n if pkg_id not in existing_ids:\n out.warn(\"Package %s metadata had stalled package information %s, removing\"\n % (str(ref), pkg_id))\n del metadata.packages[pkg_id]\n # UPDATING PREVS\n for pref in prefs:\n try:\n pmanifest = FileTreeManifest.load(layout.package(pref))\n prev = pmanifest.summary_hash\n except Exception:\n prev = DEFAULT_REVISION_V1\n metadata.packages[pref.id].revision = prev\n metadata.packages[pref.id].recipe_revision = metadata.recipe.revision\n\n except Exception as ","label":0},{"cid":"conans/client/migrations.py::_migrate_create_metadata","text":"conans/client/migrations.py::_migrate_create_metadata\ndef _migrate_create_metadata(cache, out):\n out.warn(\"Migration: Generating missing metadata files\")\n refs = _get_refs(cache)\n\n for ref in refs:\n try:\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\n # Force using a package cache layout for everything, we want to alter the cache,\n # not the editables\n layout = PackageCacheLayout(base_folder=base_folder, ref=ref, short_paths=False,\n no_lock=True)\n folder = layout.export()\n try:\n manifest = FileTreeManifest.load(folder)\n rrev = manifest.summary_hash\n except Exception:\n rrev = DEFAULT_REVISION_V1\n metadata_path = layout.package_metadata()\n if not os.path.exists(metadata_path):\n out.info(\"Creating {} for {}\".format(PACKAGE_METADATA, ref))\n prefs = _get_prefs(layout)\n metadata = PackageMetadata()\n metadata.recipe.revision = rrev\n for pref in prefs:\n try:\n pmanifest = FileTreeManifest.load(layout.package(pref))\n prev = pmanifest.summary_hash\n except Exception:\n prev = DEFAULT_REVISION_V1\n metadata.packages[pref.id].revision = prev\n metadata.packages[pref.id].recipe_revision = metadata.recipe.revision\n save(metadata_path, metadata.dumps())\n except Exception as e:\n raise ConanException(\"Something went wrong while generating the metadata.json files \"\n \"in the cache, please try to fix the issue or wipe the cache: {}\"\n \":{}\".format(ref, e))\n out.success(\"Migration: Generating missing metadata files finished OK!\\n\")","label":0},{"cid":"conans/client/migrations.py::_migrate_lock_files","text":"conans/client/migrations.py::_migrate_lock_files\ndef _migrate_lock_files(cache, out):\n out.warn(\"Migration: Removing old lock files\")\n base_dir = cache.store\n pkgs = list_folder_subdirs(base_dir, 4)\n for pkg in pkgs:\n out.info(\"Removing locks for %s\" % pkg)\n try:\n count = os.path.join(base_dir, pkg, \"rw.count\")\n if os.path.exists(count):\n os.remove(count)\n count = os.path.join(base_dir, pkg, \"rw.count.lock\")\n if os.path.exists(count):\n os.remove(count)\n locks = os.path.join(base_dir, pkg, \"locks\")\n if os.path.exists(locks):\n shutil.rmtree(locks)\n except Exception as e:\n raise ConanException(\"Something went wrong while removing %s locks\\n\"\n \"Error: %s\\n\"\n \"Please clean your local conan cache manually\"\n % (pkg, str(e)))\n out.warn(\"Migration: Removing old lock files finished\\n\")","label":0},{"cid":"conans/client/migrations.py::migrate_config_install","text":"conans/client/migrations.py::migrate_config_install\ndef migrate_config_install(cache):\n try:\n item = cache.config.get_item(\"general.config_install\")\n items = [r.strip() for r in item.split(\",\")]\n if len(items) == 4:\n config_type, uri, verify_ssl, args = items\n elif len(items) == 1:\n uri = items[0]\n verify_ssl = \"True\"\n args = \"None\"\n config_type = None\n else:\n raise Exception(\"I don't know how to migrate this config install: %s\" % items)\n verify_ssl = \"true\" in verify_ssl.lower()\n args = None if \"none\" in args.lower() else args\n config = _ConfigOrigin.from_item(uri, config_type, verify_ssl, args, None, None)\n _save_configs(cache.config_install_file, [config])\n cache.config.rm_item(\"general.config_install\")\n except ConanException:\n pass","label":0},{"cid":"conans/client/migrations.py::migrate_to_default_profile","text":"conans/client/migrations.py::migrate_to_default_profile\ndef migrate_to_default_profile(conf_path, default_profile_path):\n tag = \"[settings_defaults]\"\n old_conf = load(conf_path)\n if tag not in old_conf:\n return\n tmp = old_conf.find(tag)\n new_conf = old_conf[0:tmp]\n rest = old_conf[tmp + len(tag):]\n if tmp:\n if \"]\" in rest: # More sections after the settings_defaults\n new_conf += rest[rest.find(\"[\"):]\n save(conf_path, new_conf)\n settings = rest[:rest.find(\"[\")].strip()\n else:\n save(conf_path, new_conf)\n settings = rest.strip()\n # Now generate the default profile from the read settings_defaults\n new_profile = \"[settings]\\n%s\" % settings\n save(default_profile_path, new_profile)","label":0},{"cid":"conans/client/migrations.py::migrate_c_src_export_source","text":"conans/client/migrations.py::migrate_c_src_export_source\ndef migrate_c_src_export_source(cache, out):\n package_folders = list_folder_subdirs(cache.store, 4)\n for package in package_folders:\n package_folder = os.path.join(cache.store, package)\n c_src = os.path.join(package_folder, \"export/%s\" % EXPORT_SOURCES_DIR_OLD)\n if os.path.exists(c_src):\n out.warn(\"Migration: Removing package with old export_sources layout: %s\" % package)\n try:\n shutil.rmtree(package_folder)\n except Exception:\n out.warn(\"Migration: Can't remove the '%s' directory, \"\n \"remove it manually\" % package_folder)","label":0},{"cid":"conans/client/migrations.py::migrate_plugins_to_hooks","text":"conans/client/migrations.py::migrate_plugins_to_hooks\ndef migrate_plugins_to_hooks(cache, output=None):\n plugins_path = os.path.join(cache.conan_folder, \"plugins\")\n if os.path.exists(plugins_path) and not os.path.exists(cache.hooks_path):\n os.rename(plugins_path, cache.hooks_path)\n conf_path = cache.conan_conf_path\n replace_in_file(conf_path, \"[plugins]\", \"[hooks]\", strict=False, output=output)","label":0},{"cid":"conans/client/tools/oss.py::args_to_string","text":"conans/client/tools/oss.py::args_to_string\ndef args_to_string(args):\n if not args:\n return \"\"\n if sys.platform == 'win32':\n return subprocess.list2cmdline(args)\n else:\n return \" \".join(\"'\" + arg.replace(\"'\", r\"'\\''\") + \"'\" for arg in args)","label":0},{"cid":"conans/client/tools/oss.py::cpu_count","text":"conans/client/tools/oss.py::cpu_count\ndef cpu_count(output=None):\n output = default_output(output, 'conans.client.tools.oss.cpu_count')\n try:\n env_cpu_count = os.getenv(\"CONAN_CPU_COUNT\", None)\n if env_cpu_count is not None and not env_cpu_count.isdigit():\n raise ConanException(\"Invalid CONAN_CPU_COUNT value '%s', \"\n \"please specify a positive integer\" % env_cpu_count)\n return int(env_cpu_count) if env_cpu_count else multiprocessing.cpu_count()\n except NotImplementedError:\n output.warn(\"multiprocessing.cpu_count() not implemented. Defaulting to 1 cpu\")\n return 1 # Safe guess","label":0},{"cid":"conans/client/tools/oss.py::detected_os","text":"conans/client/tools/oss.py::detected_os\ndef detected_os():\n if OSInfo().is_macos:\n return \"Macos\"\n if OSInfo().is_windows:\n return \"Windows\"\n return platform.system()","label":0},{"cid":"conans/client/tools/oss.py::detected_architecture","text":"conans/client/tools/oss.py::detected_architecture\ndef detected_architecture():\n # FIXME: Very weak check but not very common to run conan in other architectures\n machine = platform.machine()\n if \"ppc64le\" in machine:\n return \"ppc64le\"\n elif \"ppc64\" in machine:\n return \"ppc64\"\n elif \"ppc\" in machine:\n return \"ppc32\"\n elif \"mips64\" in machine:\n return \"mips64\"\n elif \"mips\" in machine:\n return \"mips\"\n elif \"sparc64\" in machine:\n return \"sparcv9\"\n elif \"sparc\" in machine:\n return \"sparc\"\n elif \"aarch64\" in machine:\n return \"armv8\"\n elif \"64\" in machine:\n return \"x86_64\"\n elif \"86\" in machine:\n return \"x86\"\n elif \"armv8\" in machine:\n return \"armv8\"\n elif \"armv7\" in machine:\n return \"armv7\"\n elif \"arm\" in machine:\n return \"armv6\"\n elif \"s390x\" in machine:\n return \"s390x\"\n elif \"s390\" in machine:\n return \"s390\"\n\n return None","label":1},{"cid":"conans/client/tools/oss.py::OSInfo.__init__","text":"conans/client/tools/oss.py::OSInfo.__init__\n def __init__(self):\n system = platform.system()\n self.os_version = None\n self.os_version_name = None\n self.is_linux = system == \"Linux\"\n self.linux_distro = None\n self.is_msys = system.startswith(\"MING\") or system.startswith(\"MSYS_NT\")\n self.is_cygwin = system.startswith(\"CYGWIN_NT\")\n self.is_windows = system == \"Windows\" or self.is_msys or self.is_cygwin\n self.is_macos = system == \"Darwin\"\n self.is_freebsd = system == \"FreeBSD\"\n self.is_solaris = system == \"SunOS\"\n self.is_posix = os.pathsep == ':'\n\n if self.is_linux:\n self._get_linux_distro_info()\n elif self.is_windows:\n self.os_version = self.get_win_os_version()\n self.os_version_name = self.get_win_version_name(self.os_version)\n elif self.is_macos:\n self.os_version = Version(platform.mac_ver()[0])\n self.os_version_name = self.get_osx_version_name(self.os_version)\n elif self.is_freebsd:\n self.os_version = self.get_freebsd_version()\n self.os_version_name = \"FreeBSD %s\" % self.os_version\n elif self.is_solaris:\n self.os_version = Version(platform.release())\n self.os_version_name = self.get_solaris_version_name(self.os_version)","label":1},{"cid":"conans/client/tools/oss.py::OSInfo._get_linux_distro_info","text":"conans/client/tools/oss.py::OSInfo._get_linux_distro_info\n def _get_linux_distro_info(self):\n import distro\n self.linux_distro = distro.id()\n self.os_version = Version(distro.version())\n version_name = distro.codename()\n self.os_version_name = version_name if version_name != \"n/a\" else \"\"\n if not self.os_version_name and self.linux_distro == \"debian\":\n self.os_version_name = self.get_debian_version_name(self.os_version)","label":0},{"cid":"conans/client/tools/oss.py::OSInfo.with_apt","text":"conans/client/tools/oss.py::OSInfo.with_apt\n @property\n def with_apt(self):\n return self.is_linux and self.linux_distro in \\\n (\"debian\", \"ubuntu\", \"knoppix\", \"linuxmint\", \"raspbian\", \"neon\")","label":0},{"cid":"conans/client/tools/oss.py::OSInfo.with_yum","text":"conans/client/tools/oss.py::OSInfo.with_yum\n @property\n def with_yum(self):\n return self.is_linux and self.linux_distro in \\\n (\"centos\", \"redhat\", \"fedora\", \"pidora\", \"scientific\",\n \"xenserver\", \"amazon\", \"oracle\", \"rhel\")","label":0},{"cid":"conans/client/tools/oss.py::OSInfo.with_pacman","text":"conans/client/tools/oss.py::OSInfo.with_pacman\n @property\n def with_pacman(self):\n if self.is_linux:\n return self.linux_distro in [\"arch\", \"manjaro\"]\n elif self.is_windows and which('uname.exe'):\n uname = check_output(['uname.exe', '-s'])\n return uname.startswith('MSYS_NT') and which('pacman.exe')\n return False","label":0},{"cid":"conans/client/tools/oss.py::OSInfo.with_zypper","text":"conans/client/tools/oss.py::OSInfo.with_zypper\n @property\n def with_zypper(self):\n if not self.is_linux:\n return False\n if \"opensuse\" in self.linux_distro or \"sles\" in self.linux_distro:\n return True\n return False","label":0},{"cid":"conans/client/tools/oss.py::OSInfo.get_win_os_version","text":"conans/client/tools/oss.py::OSInfo.get_win_os_version\n @staticmethod\n def get_win_os_version():\n \"\"\"\n Get's the OS major and minor versions. Returns a tuple of\n (OS_MAJOR, OS_MINOR).\n \"\"\"\n import ctypes\n\n class _OSVERSIONINFOEXW(ctypes.Structure):\n _fields_ = [('dwOSVersionInfoSize', ctypes.c_ulong),\n ('dwMajorVersion', ctypes.c_ulong),\n ('dwMinorVersion', ctypes.c_ulong),\n ('dwBuildNumber', ctypes.c_ulong),\n ('dwPlatformId', ctypes.c_ulong),\n ('szCSDVersion', ctypes.c_wchar * 128),\n ('wServicePackMajor', ctypes.c_ushort),\n ('wServicePackMinor', ctypes.c_ushort),\n ('wSuiteMask', ctypes.c_ushort),\n ('wProductType', ctypes.c_byte),\n ('wReserved', ctypes.c_byte)]\n\n os_version = _OSVERSIONINFOEXW()\n os_version.dwOSVersionInfoSize = ctypes.sizeof(os_version)\n if not hasattr(ctypes, \"windll\"):\n return None\n retcode = ctypes.windll.Ntdll.RtlGetVersion(ctypes.byref(os_version))\n if retcode != 0:\n return None\n\n return Version(\"%d.%d\" % (os_version.dwMajorVersion, os_version.dwMinorVersion))","label":0},{"cid":"conans/client/tools/oss.py::OSInfo.get_debian_version_name","text":"conans/client/tools/oss.py::OSInfo.get_debian_version_name\n @staticmethod\n def get_debian_version_name(version):\n if not version:\n return None\n elif version.major() == \"8.Y.Z\":\n return \"jessie\"\n elif version.major() == \"7.Y.Z\":\n return \"wheezy\"\n elif version.major() == \"6.Y.Z\":\n return \"squeeze\"\n elif version.major() == \"5.Y.Z\":\n return \"lenny\"\n elif version.major() == \"4.Y.Z\":\n return \"etch\"\n elif version.minor() == \"3.1.Z\":\n return \"sarge\"\n elif version.minor() == \"3.0.Z\":\n return \"woody\"","label":0},{"cid":"conans/client/tools/oss.py::OSInfo.get_win_version_name","text":"conans/client/tools/oss.py::OSInfo.get_win_version_name\n @staticmethod\n def get_win_version_name(version):\n if not version:\n return None\n elif version.major() == \"5.Y.Z\":\n return \"Windows XP\"\n elif version.minor() == \"6.0.Z\":\n return \"Windows Vista\"\n elif version.minor() == \"6.1.Z\":\n return \"Windows 7\"\n elif version.minor() == \"6.2.Z\":\n return \"Windows 8\"\n elif version.minor() == \"6.3.Z\":\n return \"Windows 8.1\"\n elif version.minor() == \"10.0.Z\":\n return \"Windows 10\"","label":0},{"cid":"conans/client/tools/oss.py::OSInfo.get_osx_version_name","text":"conans/client/tools/oss.py::OSInfo.get_osx_version_name\n @staticmethod\n def get_osx_version_name(version):\n if not version:\n return None\n elif version.minor() == \"10.13.Z\":\n return \"High Sierra\"\n elif version.minor() == \"10.12.Z\":\n return \"Sierra\"\n elif version.minor() == \"10.11.Z\":\n return \"El Capitan\"\n elif version.minor() == \"10.10.Z\":\n return \"Yosemite\"\n elif version.minor() == \"10.9.Z\":\n return \"Mavericks\"\n elif version.minor() == \"10.8.Z\":\n return \"Mountain Lion\"\n elif version.minor() == \"10.7.Z\":\n return \"Lion\"\n elif version.minor() == \"10.6.Z\":\n return \"Snow Leopard\"\n elif version.minor() == \"10.5.Z\":\n return \"Leopard\"\n elif version.minor() == \"10.4.Z\":\n return \"Tiger\"\n elif version.minor() == \"10.3.Z\":\n return \"Panther\"\n elif version.minor() == \"10.2.Z\":\n return \"Jaguar\"\n elif version.minor() == \"10.1.Z\":\n return \"Puma\"\n elif version.minor() == \"10.0.Z\":\n return \"Cheetha\"","label":0},{"cid":"conans/client/tools/oss.py::OSInfo.get_freebsd_version","text":"conans/client/tools/oss.py::OSInfo.get_freebsd_version\n @staticmethod\n def get_freebsd_version():\n return platform.release().split(\"-\")[0]","label":0},{"cid":"conans/client/tools/oss.py::OSInfo.get_solaris_version_name","text":"conans/client/tools/oss.py::OSInfo.get_solaris_version_name\n @staticmethod\n def get_solaris_version_name(version):\n if not version:\n return None\n elif version.minor() == \"5.10\":\n return \"Solaris 10\"\n elif version.minor() == \"5.11\":\n return \"Solaris 11\"","label":0},{"cid":"conans/client/tools/oss.py::OSInfo.bash_path","text":"conans/client/tools/oss.py::OSInfo.bash_path\n @staticmethod\n def bash_path():\n if os.getenv(\"CONAN_BASH_PATH\"):\n return os.getenv(\"CONAN_BASH_PATH\")\n return which(\"bash\")","label":0},{"cid":"conans/client/tools/oss.py::OSInfo.uname","text":"conans/client/tools/oss.py::OSInfo.uname\n @staticmethod\n def uname(options=None):\n options = \" %s\" % options if options else \"\"\n if not OSInfo().is_windows:\n raise ConanException(\"Command only for Windows operating system\")\n custom_bash_path = OSInfo.bash_path()\n if not custom_bash_path:\n raise ConanException(\"bash is not in the path\")\n\n command = '\"%s\" -c \"uname%s\"' % (custom_bash_path, options)\n try:\n # the uname executable is many times located in the same folder as bash.exe\n with environment_append({\"PATH\": [os.path.dirname(custom_bash_path)]}):\n ret = check_output(command).strip().lower()\n return ret\n except Exception:\n return None","label":0},{"cid":"conans/client/tools/oss.py::OSInfo.detect_windows_subsystem","text":"conans/client/tools/oss.py::OSInfo.detect_windows_subsystem\n @staticmethod\n def detect_windows_subsystem():\n from conans.client.tools.win import CYGWIN, MSYS2, MSYS, WSL\n if OSInfo().is_linux:\n try:\n # https://github.com/Microsoft/WSL/issues/423#issuecomment-221627364\n with open(\"/proc/sys/kernel/osrelease\") as f:\n return WSL if f.read().endswith(\"Microsoft\") else None\n except IOError:\n return None\n try:\n output = OSInfo.uname()\n except ConanException:\n return None\n if not output:\n return None\n if \"cygwin\" in output:\n return CYGWIN\n elif \"msys\" in output or \"mingw\" in output:\n version = OSInfo.uname(\"-r\").split('.')\n if version and version[0].isdigit():\n major = int(version[0])\n if major == 1:\n return MSYS\n elif major >= 2:\n return MSYS2\n return None\n elif \"linux\" in output:\n return WSL\n else:\n return None","label":0},{"cid":"conans/client/tools/oss.py::cross_building","text":"conans/client/tools/oss.py::cross_building\ndef cross_building(settings, self_os=None, self_arch=None):\n ret = get_cross_building_settings(settings, self_os, self_arch)\n build_os, build_arch, host_os, host_arch = ret\n\n if host_os is not None and (build_os != host_os):\n return True\n if host_arch is not None and (build_arch != host_arch):\n return True\n\n return False","label":0},{"cid":"conans/client/tools/oss.py::get_cross_building_settings","text":"conans/client/tools/oss.py::get_cross_building_settings\ndef get_cross_building_settings(settings, self_os=None, self_arch=None):\n build_os = self_os or settings.get_safe(\"os_build\") or detected_os()\n build_arch = self_arch or settings.get_safe(\"arch_build\") or detected_architecture()\n host_os = settings.get_safe(\"os\")\n host_arch = settings.get_safe(\"arch\")\n\n return build_os, build_arch, host_os, host_arch","label":0},{"cid":"conans/client/tools/oss.py::get_gnu_triplet","text":"conans/client/tools/oss.py::get_gnu_triplet\ndef get_gnu_triplet(os_, arch, compiler=None):\n \"\"\"\n Returns string with <machine>-<vendor>-<op_system> triplet (<vendor> can be omitted in practice)\n\n :param os_: os to be used to create the triplet\n :param arch: arch to be used to create the triplet\n :param compiler: compiler used to create the triplet (only needed fo windows)\n \"\"\"\n\n if os_ == \"Windows\" and compiler is None:\n raise ConanException(\"'compiler' parameter for 'get_gnu_triplet()' is not specified and \"\n \"needed for os=Windows\")\n\n # Calculate the arch\n machine = {\"x86\": \"i686\" if os_ != \"Linux\" else \"x86\",\n \"x86_64\": \"x86_64\",\n \"armv8\": \"aarch64\",\n \"armv8_32\": \"aarch64\", # https://wiki.linaro.org/Platform/arm64-ilp32\n \"armv8.3\": \"aarch64\",\n \"asm.js\": \"asmjs\",\n \"wasm\": \"wasm32\",\n }.get(arch, None)\n\n if not machine:\n # https://wiki.debian.org/Multiarch/Tuples\n if \"arm\" in arch:\n machine = \"arm\"\n elif \"ppc64le\" in arch:\n machine = \"powerpc64le\"\n elif \"ppc64\" in arch:\n machine = \"powerpc64\"\n elif \"ppc32\" in arch:\n machine = \"powerpc\"\n elif \"mips64\" in arch:\n machine = \"mips64\"\n elif \"mips\" in arch:\n machine = \"mips\"\n elif \"sparcv9\" in arch:\n machine = \"sparc64\"\n elif \"sparc\" in arch:\n machine = \"sparc\"\n elif \"s390x\" in arch:\n machine = \"s390x-ibm\"\n elif \"s390\" in arch:\n machine = \"s390-ibm\"\n\n if machine is None:\n raise ConanException(\"Unknown '%s' machine, Conan doesn't know how to \"\n \"translate it to the GNU triplet, please report at \"\n \" https://github.com/conan-io/conan/issues\" % arch)\n\n # Calculate the OS\n if compiler == \"gcc\":\n windows_o","label":1},{"cid":"conans/client/tools/oss.py::check_output","text":"conans/client/tools/oss.py::check_output\ndef check_output(cmd, folder=None, return_code=False, stderr=None):\n tmp_file = tempfile.mktemp()\n try:\n stderr = stderr or PIPE\n cmd = cmd if isinstance(cmd, six.string_types) else subprocess.list2cmdline(cmd)\n process = subprocess.Popen(\"{} > {}\".format(cmd, tmp_file), shell=True,\n stderr=stderr, cwd=folder)\n process.communicate()\n\n if return_code:\n return process.returncode\n\n if process.returncode:\n raise CalledProcessError(process.returncode, cmd)\n\n output = load(tmp_file)\n return output\n finally:\n try:\n os.unlink(tmp_file)\n except Exception:\n pass","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig._cmd_output","text":"conans/client/tools/pkg_config.py::PkgConfig._cmd_output\n @staticmethod\n def _cmd_output(command):\n return check_output(command).strip()","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig.__init__","text":"conans/client/tools/pkg_config.py::PkgConfig.__init__\n def __init__(self, library, pkg_config_executable='pkg-config', static=False, msvc_syntax=False, variables=None,\n print_errors=True):\n \"\"\"\n :param library: library (package) name, such as libastral\n :param pkg_config_executable: specify custom pkg-config executable (e.g. for cross-compilation)\n :param static: output libraries suitable for static linking (adds --static to pkg-config command line)\n :param msvc_syntax: MSVC compatibility (adds --msvc-syntax to pkg-config command line)\n :param variables: dictionary of pkg-config variables (passed as --define-variable=VARIABLENAME=VARIABLEVALUE)\n :param print_errors: output error messages (adds --print-errors)\n \"\"\"\n self.library = library\n self.pkg_config_executable = pkg_config_executable\n self.static = static\n self.msvc_syntax = msvc_syntax\n self.define_variables = variables\n self.print_errors = print_errors\n\n self._variables = dict()\n self.info = dict()","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig._parse_output","text":"conans/client/tools/pkg_config.py::PkgConfig._parse_output\n def _parse_output(self, option):\n command = [self.pkg_config_executable, '--' + option, self.library]\n if self.static:\n command.append('--static')\n if self.msvc_syntax:\n command.append('--msvc-syntax')\n if self.print_errors:\n command.append('--print-errors')\n if self.define_variables:\n for name, value in self.define_variables.items():\n command.append('--define-variable=%s=%s' % (name, value))\n try:\n return self._cmd_output(command)\n except subprocess.CalledProcessError as e:\n raise ConanException('pkg-config command %s failed with error: %s' % (command, e))","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig._get_option","text":"conans/client/tools/pkg_config.py::PkgConfig._get_option\n def _get_option(self, option):\n if option not in self.info:\n self.info[option] = self._parse_output(option).split()\n return self.info[option]","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig.cflags","text":"conans/client/tools/pkg_config.py::PkgConfig.cflags\n @property\n def cflags(self):\n return self._get_option('cflags')","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig.cflags_only_I","text":"conans/client/tools/pkg_config.py::PkgConfig.cflags_only_I\n @property\n def cflags_only_I(self):\n return self._get_option('cflags-only-I')","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig.cflags_only_other","text":"conans/client/tools/pkg_config.py::PkgConfig.cflags_only_other\n @property\n def cflags_only_other(self):\n return self._get_option('cflags-only-other')","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig.libs","text":"conans/client/tools/pkg_config.py::PkgConfig.libs\n @property\n def libs(self):\n return self._get_option('libs')","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig.libs_only_L","text":"conans/client/tools/pkg_config.py::PkgConfig.libs_only_L\n @property\n def libs_only_L(self):\n return self._get_option('libs-only-L')","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig.libs_only_l","text":"conans/client/tools/pkg_config.py::PkgConfig.libs_only_l\n @property\n def libs_only_l(self):\n return self._get_option('libs-only-l')","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig.libs_only_other","text":"conans/client/tools/pkg_config.py::PkgConfig.libs_only_other\n @property\n def libs_only_other(self):\n return self._get_option('libs-only-other')","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig.provides","text":"conans/client/tools/pkg_config.py::PkgConfig.provides\n @property\n def provides(self):\n return self._get_option('print-provides')","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig.requires","text":"conans/client/tools/pkg_config.py::PkgConfig.requires\n @property\n def requires(self):\n return self._get_option('print-requires')","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig.requires_private","text":"conans/client/tools/pkg_config.py::PkgConfig.requires_private\n @property\n def requires_private(self):\n return self._get_option('print-requires-private')","label":0},{"cid":"conans/client/tools/pkg_config.py::PkgConfig.variables","text":"conans/client/tools/pkg_config.py::PkgConfig.variables\n @property\n def variables(self):\n if not self._variables:\n variable_names = self._parse_output('print-variables').split()\n for name in variable_names:\n self._variables[name] = self._parse_output('variable=%s' % name)\n return self._variables","label":0},{"cid":"conans/model/info.py::RequirementInfo.__init__","text":"conans/model/info.py::RequirementInfo.__init__\n def __init__(self, pref, default_package_id_mode, indirect=False):\n self.package = pref\n self.full_name = pref.ref.name\n self.full_version = pref.ref.version\n self.full_user = pref.ref.user\n self.full_channel = pref.ref.channel\n self.full_package_id = pref.id\n self._indirect = indirect\n\n try:\n getattr(self, default_package_id_mode)()\n except AttributeError:\n raise ConanException(\"'%s' is not a known package_id_mode\" % default_package_id_mode)","label":0},{"cid":"conans/model/info.py::RequirementInfo.copy","text":"conans/model/info.py::RequirementInfo.copy\n def copy(self):\n # Useful for build_id()\n result = RequirementInfo(self.package, \"unrelated_mode\")\n for f in (\"name\", \"version\", \"user\", \"channel\", \"package_id\"):\n setattr(result, f, getattr(self, f))\n f = \"full_%s\" % f\n setattr(result, f, getattr(self, f))\n return result","label":0},{"cid":"conans/model/info.py::RequirementInfo.dumps","text":"conans/model/info.py::RequirementInfo.dumps\n def dumps(self):\n if not self.name:\n return \"\"\n result = [\"%s/%s\" % (self.name, self.version)]\n if self.user or self.channel:\n result.append(\"@%s/%s\" % (self.user, self.channel))\n if self.package_id:\n result.append(\":%s\" % self.package_id)\n return \"\".join(result)","label":0},{"cid":"conans/model/info.py::RequirementInfo.sha","text":"conans/model/info.py::RequirementInfo.sha\n @property\n def sha(self):\n vals = [str(n) for n in (self.name, self.version, self.user, self.channel, self.package_id)]\n # This is done later to NOT affect existing package-IDs (before revisions)\n return \"/\".join(vals)","label":0},{"cid":"conans/model/info.py::RequirementInfo.unrelated_mode","text":"conans/model/info.py::RequirementInfo.unrelated_mode\n def unrelated_mode(self):\n self.name = self.version = self.user = self.channel = self.package_id = None","label":0},{"cid":"conans/model/info.py::RequirementInfo.semver_direct_mode","text":"conans/model/info.py::RequirementInfo.semver_direct_mode\n def semver_direct_mode(self):\n if self._indirect:\n self.unrelated_mode()\n else:\n self.semver_mode()","label":0},{"cid":"conans/model/info.py::RequirementInfo.semver_mode","text":"conans/model/info.py::RequirementInfo.semver_mode\n def semver_mode(self):\n self.name = self.full_name\n self.version = self.full_version.stable()\n self.user = self.channel = self.package_id = None","label":0},{"cid":"conans/model/info.py::RequirementInfo.full_version_mode","text":"conans/model/info.py::RequirementInfo.full_version_mode\n def full_version_mode(self):\n self.name = self.full_name\n self.version = self.full_version\n self.user = self.channel = self.package_id = None","label":0},{"cid":"conans/model/info.py::RequirementInfo.patch_mode","text":"conans/model/info.py::RequirementInfo.patch_mode\n def patch_mode(self):\n self.name = self.full_name\n self.version = self.full_version.patch()\n self.user = self.channel = self.package_id = None","label":0},{"cid":"conans/model/info.py::RequirementInfo.base_mode","text":"conans/model/info.py::RequirementInfo.base_mode\n def base_mode(self):\n self.name = self.full_name\n self.version = self.full_version.base\n self.user = self.channel = self.package_id = None","label":0},{"cid":"conans/model/info.py::RequirementInfo.minor_mode","text":"conans/model/info.py::RequirementInfo.minor_mode\n def minor_mode(self):\n self.name = self.full_name\n self.version = self.full_version.minor()\n self.user = self.channel = self.package_id = None","label":0},{"cid":"conans/model/info.py::RequirementInfo.major_mode","text":"conans/model/info.py::RequirementInfo.major_mode\n def major_mode(self):\n self.name = self.full_name\n self.version = self.full_version.major()\n self.user = self.channel = self.package_id = None","label":0},{"cid":"conans/model/info.py::RequirementInfo.full_recipe_mode","text":"conans/model/info.py::RequirementInfo.full_recipe_mode\n def full_recipe_mode(self):\n self.name = self.full_name\n self.version = self.full_version\n self.user = self.full_user\n self.channel = self.full_channel\n self.package_id = None","label":0},{"cid":"conans/model/info.py::RequirementInfo.full_package_mode","text":"conans/model/info.py::RequirementInfo.full_package_mode\n def full_package_mode(self):\n self.name = self.full_name\n self.version = self.full_version\n self.user = self.full_user\n self.channel = self.full_channel\n self.package_id = self.full_package_id","label":0},{"cid":"conans/model/info.py::RequirementsInfo.__init__","text":"conans/model/info.py::RequirementsInfo.__init__\n def __init__(self, prefs, default_package_id_mode):\n # {PackageReference: RequirementInfo}\n self._data = {pref: RequirementInfo(pref, default_package_id_mode=default_package_id_mode)\n for pref in prefs}","label":0},{"cid":"conans/model/info.py::RequirementsInfo.copy","text":"conans/model/info.py::RequirementsInfo.copy\n def copy(self):\n # For build_id() implementation\n result = RequirementsInfo([], None)\n result._data = {pref: req_info.copy() for pref, req_info in self._data.items()}\n return result","label":0},{"cid":"conans/model/info.py::RequirementsInfo.clear","text":"conans/model/info.py::RequirementsInfo.clear\n def clear(self):\n self._data = {}","label":0},{"cid":"conans/model/info.py::RequirementsInfo.remove","text":"conans/model/info.py::RequirementsInfo.remove\n def remove(self, *args):\n for name in args:\n del self._data[self._get_key(name)]","label":0},{"cid":"conans/model/info.py::RequirementsInfo.add","text":"conans/model/info.py::RequirementsInfo.add\n def add(self, prefs_indirect, default_package_id_mode):\n \"\"\" necessary to propagate from upstream the real\n package requirements\n \"\"\"\n for r in prefs_indirect:\n self._data[r] = RequirementInfo(r, indirect=True,\n default_package_id_mode=default_package_id_mode)","label":0},{"cid":"conans/model/info.py::RequirementsInfo.refs","text":"conans/model/info.py::RequirementsInfo.refs\n def refs(self):\n \"\"\" used for updating downstream requirements with this\n \"\"\"\n return list(self._data.keys())","label":0},{"cid":"conans/model/info.py::RequirementsInfo._get_key","text":"conans/model/info.py::RequirementsInfo._get_key\n def _get_key(self, item):\n for reference in self._data:\n if reference.ref.name == item:\n return reference\n raise ConanException(\"No requirement matching for %s\" % (item))","label":0},{"cid":"conans/model/info.py::RequirementsInfo.__getitem__","text":"conans/model/info.py::RequirementsInfo.__getitem__\n def __getitem__(self, item):\n \"\"\"get by package name\n Necessary to access from conaninfo\n self.requires[\"Boost\"].version = \"2.X\"\n \"\"\"\n return self._data[self._get_key(item)]","label":0},{"cid":"conans/model/info.py::RequirementsInfo.pkg_names","text":"conans/model/info.py::RequirementsInfo.pkg_names\n @property\n def pkg_names(self):\n return [r.ref.name for r in self._data.keys()]","label":0},{"cid":"conans/model/info.py::RequirementsInfo.sha","text":"conans/model/info.py::RequirementsInfo.sha\n @property\n def sha(self):\n result = []\n # Remove requirements without a name, i.e. indirect transitive requirements\n data = {k: v for k, v in self._data.items() if v.name}\n for key in sorted(data):\n s = data[key].sha\n result.append(s)\n return sha1('\\n'.join(result).encode())","label":0},{"cid":"conans/model/info.py::RequirementsInfo.dumps","text":"conans/model/info.py::RequirementsInfo.dumps\n def dumps(self):\n result = []\n for ref in sorted(self._data):\n dumped = self._data[ref].dumps()\n if dumped:\n result.append(dumped)\n return \"\\n\".join(result)","label":0},{"cid":"conans/model/info.py::RequirementsInfo.unrelated_mode","text":"conans/model/info.py::RequirementsInfo.unrelated_mode\n def unrelated_mode(self):\n self.clear()","label":0},{"cid":"conans/model/info.py::RequirementsInfo.semver_direct_mode","text":"conans/model/info.py::RequirementsInfo.semver_direct_mode\n def semver_direct_mode(self):\n for r in self._data.values():\n r.semver_direct_mode()","label":0},{"cid":"conans/model/info.py::RequirementsInfo.semver_mode","text":"conans/model/info.py::RequirementsInfo.semver_mode\n def semver_mode(self):\n for r in self._data.values():\n r.semver_mode()","label":0},{"cid":"conans/model/info.py::RequirementsInfo.patch_mode","text":"conans/model/info.py::RequirementsInfo.patch_mode\n def patch_mode(self):\n for r in self._data.values():\n r.patch_mode()","label":0},{"cid":"conans/model/info.py::RequirementsInfo.minor_mode","text":"conans/model/info.py::RequirementsInfo.minor_mode\n def minor_mode(self):\n for r in self._data.values():\n r.minor_mode()","label":0},{"cid":"conans/model/info.py::RequirementsInfo.major_mode","text":"conans/model/info.py::RequirementsInfo.major_mode\n def major_mode(self):\n for r in self._data.values():\n r.major_mode()","label":0},{"cid":"conans/model/info.py::RequirementsInfo.base_mode","text":"conans/model/info.py::RequirementsInfo.base_mode\n def base_mode(self):\n for r in self._data.values():\n r.base_mode()","label":0},{"cid":"conans/model/info.py::RequirementsInfo.full_version_mode","text":"conans/model/info.py::RequirementsInfo.full_version_mode\n def full_version_mode(self):\n for r in self._data.values():\n r.full_version_mode()","label":0},{"cid":"conans/model/info.py::RequirementsInfo.full_recipe_mode","text":"conans/model/info.py::RequirementsInfo.full_recipe_mode\n def full_recipe_mode(self):\n for r in self._data.values():\n r.full_recipe_mode()","label":0},{"cid":"conans/model/info.py::RequirementsInfo.full_package_mode","text":"conans/model/info.py::RequirementsInfo.full_package_mode\n def full_package_mode(self):\n for r in self._data.values():\n r.full_package_mode()","label":0},{"cid":"conans/model/info.py::_PackageReferenceList.loads","text":"conans/model/info.py::_PackageReferenceList.loads\n @staticmethod\n def loads(text):\n return _PackageReferenceList([PackageReference.loads(package_reference)\n for package_reference in text.splitlines()])","label":0},{"cid":"conans/model/info.py::_PackageReferenceList.dumps","text":"conans/model/info.py::_PackageReferenceList.dumps\n def dumps(self):\n return \"\\n\".join(self.serialize())","label":0},{"cid":"conans/model/info.py::_PackageReferenceList.serialize","text":"conans/model/info.py::_PackageReferenceList.serialize\n def serialize(self):\n return [str(r) for r in sorted(self)]","label":0},{"cid":"conans/model/info.py::ConanInfo.copy","text":"conans/model/info.py::ConanInfo.copy\n def copy(self):\n \"\"\" Useful for build_id implementation\n \"\"\"\n result = ConanInfo()\n result.settings = self.settings.copy()\n result.options = self.options.copy()\n result.requires = self.requires.copy()\n return result","label":0},{"cid":"conans/model/info.py::ConanInfo.create","text":"conans/model/info.py::ConanInfo.create\n @staticmethod\n def create(settings, options, prefs_direct, prefs_indirect, default_package_id_mode):\n result = ConanInfo()\n result.full_settings = settings\n result.settings = settings.copy()\n result.full_options = options\n result.options = options.copy()\n result.options.clear_indirect()\n result.full_requires = _PackageReferenceList(prefs_direct)\n result.requires = RequirementsInfo(prefs_direct, default_package_id_mode)\n result.requires.add(prefs_indirect, default_package_id_mode)\n result.full_requires.extend(prefs_indirect)\n result.recipe_hash = None\n result.env_values = EnvValues()\n result.vs_toolset_compatible()\n result.discard_build_settings()\n result.default_std_matching()\n\n return result","label":0}],"string":"[\n {\n \"cid\": \"conans/build_info/conan_build_info.py::_extract_uploads_from_conan_trace\",\n \"text\": \"conans/build_info/conan_build_info.py::_extract_uploads_from_conan_trace\\ndef _extract_uploads_from_conan_trace(path):\\n modules = defaultdict(dict)\\n try:\\n with open(path, \\\"r\\\") as traces:\\n for line in traces.readlines():\\n doc = json.loads(line)\\n if doc[\\\"_action\\\"] in (\\\"UPLOADED_RECIPE\\\", \\\"UPLOADED_PACKAGE\\\"):\\n module_type = \\\"recipe\\\" if doc[\\\"_action\\\"] == \\\"UPLOADED_RECIPE\\\" else \\\"package\\\"\\n modules[doc[\\\"_id\\\"]] = {\\\"remote\\\": doc[\\\"remote\\\"], \\\"files\\\": [], \\\"type\\\": module_type}\\n modules[doc[\\\"_id\\\"]][\\\"files\\\"].extend(doc[\\\"files\\\"])\\n except ValueError as exc:\\n raise Exception(\\\"INVALID TRACE FILE! %s\\\" % exc)\\n\\n return modules\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/build_info/conan_build_info.py::_extract_downloads_from_conan_trace\",\n \"text\": \"conans/build_info/conan_build_info.py::_extract_downloads_from_conan_trace\\ndef _extract_downloads_from_conan_trace(path):\\n downloaded_modules = defaultdict(dict) # dict of {reference: {\\\"files\\\": [], \\\"remote\\\": remote }}\\n\\n with open(path, \\\"r\\\") as traces:\\n for line in traces.readlines():\\n doc = json.loads(line)\\n if doc[\\\"_action\\\"] in [\\\"DOWNLOADED_PACKAGE\\\", \\\"DOWNLOADED_RECIPE\\\"]:\\n downloaded_modules[doc[\\\"_id\\\"]] = {\\\"files\\\": doc[\\\"files\\\"], \\\"remote\\\": doc[\\\"remote\\\"]}\\n return downloaded_modules\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/build_info/conan_build_info.py::_get_type\",\n \"text\": \"conans/build_info/conan_build_info.py::_get_type\\ndef _get_type(file_path):\\n return os.path.splitext(file_path)[1].upper()[1:]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/build_info/conan_build_info.py::_get_build_info_artifact\",\n \"text\": \"conans/build_info/conan_build_info.py::_get_build_info_artifact\\ndef _get_build_info_artifact(file_doc):\\n the_type = _get_type(file_doc[\\\"path\\\"])\\n ret = BuildInfoModuleArtifact(the_type, file_doc[\\\"sha1\\\"], file_doc[\\\"md5\\\"], file_doc[\\\"name\\\"])\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/build_info/conan_build_info.py::_get_dependency\",\n \"text\": \"conans/build_info/conan_build_info.py::_get_dependency\\ndef _get_dependency(file_doc, dep_ref):\\n the_type = _get_type(file_doc[\\\"path\\\"])\\n the_id = \\\"%s:%s\\\" % (dep_ref, file_doc[\\\"name\\\"])\\n ret = BuildInfoModuleDependency(the_id, the_type, file_doc[\\\"sha1\\\"], file_doc[\\\"md5\\\"])\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/build_info/conan_build_info.py::_get_upload_modules_with_deps\",\n \"text\": \"conans/build_info/conan_build_info.py::_get_upload_modules_with_deps\\ndef _get_upload_modules_with_deps(uploaded_files, downloaded_files):\\n modules = []\\n deps = defaultdict(set) # Reference: [Reference, Reference]\\n # Extract needed information\\n for module_id, mod_doc in uploaded_files.items():\\n ref_or_pref = ConanFileReference.loads(module_id) if mod_doc[\\\"type\\\"] == \\\"recipe\\\" \\\\\\n else PackageReference.loads(module_id)\\n # Store recipe and package dependencies\\n if mod_doc[\\\"type\\\"] == \\\"package\\\":\\n conan_infos = [file_doc for file_doc in mod_doc[\\\"files\\\"]\\n if file_doc[\\\"name\\\"] == \\\"conaninfo.txt\\\"]\\n if conan_infos:\\n conan_info = conan_infos[0][\\\"path\\\"]\\n info = ConanInfo.loads(load(conan_info))\\n for pref in info.full_requires:\\n deps[str(ref_or_pref.ref)].add(str(pref.ref))\\n deps[str(ref_or_pref)].add(str(pref))\\n\\n # Add the modules\\n for module_id, mod_doc in uploaded_files.items():\\n module = BuildInfoModule()\\n module.id = str(module_id)\\n # Add artifacts\\n for file_doc in mod_doc[\\\"files\\\"]:\\n artifact = _get_build_info_artifact(file_doc)\\n module.artifacts.append(artifact)\\n\\n # Add dependencies, for each module dep modules\\n for mod_dep_id in deps[module_id]:\\n if mod_dep_id in downloaded_files:\\n down_module = downloaded_files[mod_dep_id]\\n # Check if the remote from the uploaded package matches the remote\\n # from the downloaded dependency\\n if down_module.get(\\\"remote\\\", None) == mod_doc[\\\"remote\\\"]:\\n for file_doc in down_module[\\\"files\\\"]:\\n module.dependencies.append(_get_dependency(file_doc, mod_dep_id))\\n\\n modules.append(module)\\n return modules\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/build_info/conan_build_info.py::_get_only_downloads_module\",\n \"text\": \"conans/build_info/conan_build_info.py::_get_only_downloads_module\\ndef _get_only_downloads_module(downloaded_files):\\n \\\"\\\"\\\"\\n Gets a BuildInfoModule for the downloaded_files\\n :param downloaded_files: {reference: {\\\"files\\\": [doc_file, doc_file], \\\"remote\\\": remote }}\\n :return: BuildInfoModule object\\n \\\"\\\"\\\"\\n ret = BuildInfoModule()\\n ret.id = \\\"DownloadOnly\\\"\\n for _, data in downloaded_files.items():\\n files = data[\\\"files\\\"]\\n for file_doc in files:\\n the_type = _get_type(file_doc[\\\"path\\\"])\\n dep = BuildInfoModuleDependency(file_doc[\\\"name\\\"], the_type, file_doc[\\\"sha1\\\"],\\n file_doc[\\\"md5\\\"])\\n ret.dependencies.append(dep)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/build_info/conan_build_info.py::_build_modules\",\n \"text\": \"conans/build_info/conan_build_info.py::_build_modules\\ndef _build_modules(trace_path):\\n uploaded_files = _extract_uploads_from_conan_trace(trace_path)\\n downloaded_files = _extract_downloads_from_conan_trace(trace_path)\\n if uploaded_files:\\n return _get_upload_modules_with_deps(uploaded_files, downloaded_files)\\n else:\\n return [_get_only_downloads_module(downloaded_files)]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/build_info/conan_build_info.py::get_build_info\",\n \"text\": \"conans/build_info/conan_build_info.py::get_build_info\\ndef get_build_info(trace_path):\\n bi = BuildInfo()\\n modules = _build_modules(trace_path)\\n bi.modules.extend(modules)\\n return bi\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.__init__\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.__init__\\n def __init__(self, conanfile, win_bash=False, include_rpath_flags=False):\\n \\\"\\\"\\\"\\n FIXME: include_rpath_flags CONAN 2.0 to default True? Could break many packages in center\\n \\\"\\\"\\\"\\n self._conanfile = conanfile\\n self._win_bash = win_bash\\n self._include_rpath_flags = include_rpath_flags\\n self.subsystem = OSInfo().detect_windows_subsystem() if self._win_bash else None\\n self._deps_cpp_info = conanfile.deps_cpp_info\\n self._os = conanfile.settings.get_safe(\\\"os\\\")\\n self._arch = conanfile.settings.get_safe(\\\"arch\\\")\\n self._build_type = conanfile.settings.get_safe(\\\"build_type\\\")\\n self._compiler = conanfile.settings.get_safe(\\\"compiler\\\")\\n self._compiler_version = conanfile.settings.get_safe(\\\"compiler.version\\\")\\n self._libcxx = conanfile.settings.get_safe(\\\"compiler.libcxx\\\")\\n self._cppstd = cppstd_from_settings(conanfile.settings)\\n\\n # Set the generic objects before mapping to env vars to let the user\\n # alter some value\\n self.libs = copy.copy(self._deps_cpp_info.libs)\\n self.include_paths = copy.copy(self._deps_cpp_info.include_paths)\\n self.library_paths = copy.copy(self._deps_cpp_info.lib_paths)\\n\\n self.defines = self._configure_defines()\\n # Will go to CFLAGS and CXXFLAGS [\\\"-m64\\\" \\\"-m32\\\", \\\"-g\\\", \\\"-s\\\"]\\n self.flags = self._configure_flags()\\n # Only c++ flags [-stdlib, -library], will go to CXXFLAGS\\n self.cxx_flags = self._configure_cxx_flags()\\n # cpp standard\\n self.cppstd_flag = cppstd_flag(self._compiler, self._compiler_version, self._cppstd)\\n # Not -L flags, [\\\"-m64\\\" \\\"-m32\\\"]\\n self.link_flags = self._configure_link_flags() # TEST!\\n # Precalculate -fPIC\\n self.fpic = self._configure_fpic()\\n\\n # Precalculate build, host, target triplets\\n self.build, self.host, self.target = self._get_hos\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_fpic\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_fpic\\n def _configure_fpic(self):\\n if not str(self._os).startswith(\\\"Windows\\\"):\\n fpic = self._conanfile.options.get_safe(\\\"fPIC\\\")\\n if fpic is not None:\\n shared = self._conanfile.options.get_safe(\\\"shared\\\")\\n return True if (fpic or shared) else None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_host_build_target_flags\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_host_build_target_flags\\n def _get_host_build_target_flags(self):\\n \\\"\\\"\\\"Based on google search for build/host triplets, it could need a lot\\n and complex verification\\\"\\\"\\\"\\n\\n arch_detected = detected_architecture() or platform.machine()\\n os_detected = detected_os() or platform.system()\\n\\n if os_detected is None or arch_detected is None or self._arch is None or self._os is None:\\n return False, False, False\\n if not cross_building(self._conanfile.settings, os_detected, arch_detected):\\n return False, False, False\\n\\n try:\\n build = get_gnu_triplet(os_detected, arch_detected, self._compiler)\\n except ConanException as exc:\\n self._conanfile.output.warn(str(exc))\\n build = None\\n try:\\n host = get_gnu_triplet(self._os, self._arch, self._compiler)\\n except ConanException as exc:\\n self._conanfile.output.warn(str(exc))\\n host = None\\n return build, host, None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.configure\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.configure\\n def configure(self, configure_dir=None, args=None, build=None, host=None, target=None,\\n pkg_config_paths=None, vars=None, use_default_install_dirs=True):\\n \\\"\\\"\\\"\\n :param pkg_config_paths: Optional paths to locate the *.pc files\\n :param configure_dir: Absolute or relative path to the configure script\\n :param args: Optional arguments to pass to configure.\\n :param build: In which system the program will be built. \\\"False\\\" skips the --build flag\\n :param host: In which system the generated program will run. \\\"False\\\" skips the --host flag\\n :param target: This option is only used to build a cross-compiling toolchain.\\n \\\"False\\\" skips the --target flag\\n When the tool chain generates executable program, in which target system\\n the program will run.\\n\\n http://jingfenghanmax.blogspot.com.es/2010/09/configure-with-host-target-and-build.html\\n https://gcc.gnu.org/onlinedocs/gccint/Configure-Terms.html\\n :param use_default_install_dirs: Use or not the defaulted installation dirs\\n\\n \\\"\\\"\\\"\\n if not self._conanfile.should_configure:\\n return\\n if configure_dir:\\n configure_dir = configure_dir.rstrip(\\\"/\\\")\\n else:\\n configure_dir = \\\".\\\"\\n\\n triplet_args = []\\n\\n if build is not False: # Skipped by user\\n if build or self.build: # User specified value or automatic\\n triplet_args.append(\\\"--build=%s\\\" % (build or self.build))\\n\\n if host is not False: # Skipped by user\\n if host or self.host: # User specified value or automatic\\n triplet_args.append(\\\"--host=%s\\\" % (host or self.host))\\n\\n if target is not False: # Skipped by user\\n if target or self.target: # User specified value or automatic\\n triplet_args.append(\\\"-\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_help_output\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_help_output\\n def _configure_help_output(self, configure_path):\\n from six import StringIO # Python 2 and 3 compatible\\n mybuf = StringIO()\\n try:\\n self._conanfile.run(\\\"%s/configure --help\\\" % configure_path, output=mybuf)\\n except ConanException as e:\\n self._conanfile.output.warn(\\\"Error running `configure --help`: %s\\\" % e)\\n return \\\"\\\"\\n return mybuf.getvalue()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._adjust_path\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._adjust_path\\n def _adjust_path(self, path):\\n if self._win_bash:\\n path = unix_path(path, path_flavor=self.subsystem)\\n return '\\\"%s\\\"' % path if \\\" \\\" in path else path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._valid_configure_flag\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._valid_configure_flag\\n @staticmethod\\n def _valid_configure_flag(varname, args, available_flags):\\n return not AutoToolsBuildEnvironment._is_flag_in_args(varname, args) and \\\\\\n varname in available_flags\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._is_flag_in_args\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._is_flag_in_args\\n @staticmethod\\n def _is_flag_in_args(varname, args):\\n flag = \\\"--%s=\\\" % varname\\n return any([flag in arg for arg in args])\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.make\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.make\\n def make(self, args=\\\"\\\", make_program=None, target=None, vars=None):\\n if not self._conanfile.should_build:\\n return\\n make_program = os.getenv(\\\"CONAN_MAKE_PROGRAM\\\") or make_program or \\\"make\\\"\\n with environment_append(vars or self.vars):\\n str_args = args_to_string(args)\\n cpu_count_option = (\\\"-j%s\\\" % cpu_count(output=self._conanfile.output)) if \\\"-j\\\" not in str_args else None\\n self._conanfile.run(\\\"%s\\\" % join_arguments([make_program, target, str_args,\\n cpu_count_option]),\\n win_bash=self._win_bash, subsystem=self.subsystem)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.install\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.install\\n def install(self, args=\\\"\\\", make_program=None, vars=None):\\n if not self._conanfile.should_install:\\n return\\n self.make(args=args, make_program=make_program, target=\\\"install\\\", vars=vars)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_link_flags\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_link_flags\\n def _configure_link_flags(self):\\n \\\"\\\"\\\"Not the -L\\\"\\\"\\\"\\n ret = copy.copy(self._deps_cpp_info.sharedlinkflags)\\n ret.extend(self._deps_cpp_info.exelinkflags)\\n arch_flag = architecture_flag(compiler=self._compiler, arch=self._arch)\\n if arch_flag:\\n ret.append(arch_flag)\\n\\n sysf = sysroot_flag(self._deps_cpp_info.sysroot, win_bash=self._win_bash,\\n subsystem=self.subsystem,\\n compiler=self._compiler)\\n if sysf:\\n ret.append(sysf)\\n\\n if self._include_rpath_flags:\\n the_os = self._conanfile.settings.get_safe(\\\"os_build\\\") or self._os\\n ret.extend(rpath_flags(the_os, self._compiler, self._deps_cpp_info.lib_paths))\\n\\n return ret\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_flags\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_flags\\n def _configure_flags(self):\\n ret = copy.copy(self._deps_cpp_info.cflags)\\n arch_flag = architecture_flag(compiler=self._compiler, arch=self._arch)\\n if arch_flag:\\n ret.append(arch_flag)\\n btfs = build_type_flags(compiler=self._compiler, build_type=self._build_type,\\n vs_toolset=self._conanfile.settings.get_safe(\\\"compiler.toolset\\\"))\\n if btfs:\\n ret.extend(btfs)\\n srf = sysroot_flag(self._deps_cpp_info.sysroot, win_bash=self._win_bash,\\n subsystem=self.subsystem,\\n compiler=self._compiler)\\n if srf:\\n ret.append(srf)\\n\\n return ret\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_cxx_flags\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_cxx_flags\\n def _configure_cxx_flags(self):\\n ret = copy.copy(self._deps_cpp_info.cxxflags)\\n cxxf = libcxx_flag(compiler=self._compiler, libcxx=self._libcxx)\\n if cxxf:\\n ret.append(cxxf)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_defines\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_defines\\n def _configure_defines(self):\\n # requires declared defines\\n ret = copy.copy(self._deps_cpp_info.defines)\\n\\n # Debug definition for GCC\\n btf = build_type_define(build_type=self._build_type)\\n if btf:\\n ret.append(btf)\\n\\n # CXX11 ABI\\n abif = libcxx_define(compiler=self._compiler, libcxx=self._libcxx)\\n if abif:\\n ret.append(abif)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars\\n def _get_vars(self):\\n def append(*args):\\n ret = []\\n for arg in args:\\n if arg:\\n if isinstance(arg, list):\\n ret.extend(arg)\\n else:\\n ret.append(arg)\\n return ret\\n\\n lib_paths = format_library_paths(self.library_paths, win_bash=self._win_bash,\\n subsystem=self.subsystem, compiler=self._compiler)\\n include_paths = format_include_paths(self.include_paths, win_bash=self._win_bash,\\n subsystem=self.subsystem, compiler=self._compiler)\\n\\n ld_flags = append(self.link_flags, lib_paths)\\n cpp_flags = append(include_paths, format_defines(self.defines))\\n libs = format_libraries(self.libs, compiler=self._compiler)\\n\\n tmp_compilation_flags = copy.copy(self.flags)\\n if self.fpic:\\n tmp_compilation_flags.append(pic_flag(self._compiler))\\n\\n cxx_flags = append(tmp_compilation_flags, self.cxx_flags, self.cppstd_flag)\\n c_flags = tmp_compilation_flags\\n\\n return ld_flags, cpp_flags, libs, cxx_flags, c_flags\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars.append\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars.append\\n def append(*args):\\n ret = []\\n for arg in args:\\n if arg:\\n if isinstance(arg, list):\\n ret.extend(arg)\\n else:\\n ret.append(arg)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars_dict\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars_dict\\n @property\\n def vars_dict(self):\\n\\n ld_flags, cpp_flags, libs, cxx_flags, c_flags = self._get_vars()\\n\\n if os.environ.get(\\\"CPPFLAGS\\\", None):\\n cpp_flags.append(os.environ.get(\\\"CPPFLAGS\\\", None))\\n\\n if os.environ.get(\\\"CXXFLAGS\\\", None):\\n cxx_flags.append(os.environ.get(\\\"CXXFLAGS\\\", None))\\n\\n if os.environ.get(\\\"CFLAGS\\\", None):\\n c_flags.append(os.environ.get(\\\"CFLAGS\\\", None))\\n\\n if os.environ.get(\\\"LDFLAGS\\\", None):\\n ld_flags.append(os.environ.get(\\\"LDFLAGS\\\", None))\\n\\n if os.environ.get(\\\"LIBS\\\", None):\\n libs.append(os.environ.get(\\\"LIBS\\\", None))\\n\\n ret = {\\\"CPPFLAGS\\\": cpp_flags,\\n \\\"CXXFLAGS\\\": cxx_flags,\\n \\\"CFLAGS\\\": c_flags,\\n \\\"LDFLAGS\\\": ld_flags,\\n \\\"LIBS\\\": libs,\\n }\\n return ret\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars\\n @property\\n def vars(self):\\n ld_flags, cpp_flags, libs, cxx_flags, c_flags = self._get_vars()\\n\\n cpp_flags = \\\" \\\".join(cpp_flags) + _environ_value_prefix(\\\"CPPFLAGS\\\")\\n cxx_flags = \\\" \\\".join(cxx_flags) + _environ_value_prefix(\\\"CXXFLAGS\\\")\\n cflags = \\\" \\\".join(c_flags) + _environ_value_prefix(\\\"CFLAGS\\\")\\n ldflags = \\\" \\\".join(ld_flags) + _environ_value_prefix(\\\"LDFLAGS\\\")\\n libs = \\\" \\\".join(libs) + _environ_value_prefix(\\\"LIBS\\\")\\n\\n ret = {\\\"CPPFLAGS\\\": cpp_flags.strip(),\\n \\\"CXXFLAGS\\\": cxx_flags.strip(),\\n \\\"CFLAGS\\\": cflags.strip(),\\n \\\"LDFLAGS\\\": ldflags.strip(),\\n \\\"LIBS\\\": libs.strip(),\\n }\\n return ret\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::_environ_value_prefix\",\n \"text\": \"conans/client/build/autotools_environment.py::_environ_value_prefix\\ndef _environ_value_prefix(var_name, prefix=\\\" \\\"):\\n if os.environ.get(var_name, \\\"\\\"):\\n return \\\"%s%s\\\" % (prefix, os.environ.get(var_name, \\\"\\\"))\\n else:\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::get_toolset\",\n \"text\": \"conans/client/build/cmake_flags.py::get_toolset\\ndef get_toolset(settings):\\n if settings.get_safe(\\\"compiler\\\") == \\\"Visual Studio\\\":\\n subs_toolset = settings.get_safe(\\\"compiler.toolset\\\")\\n if subs_toolset:\\n return subs_toolset\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::get_generator\",\n \"text\": \"conans/client/build/cmake_flags.py::get_generator\\ndef get_generator(settings):\\n if \\\"CONAN_CMAKE_GENERATOR\\\" in os.environ:\\n return os.environ[\\\"CONAN_CMAKE_GENERATOR\\\"]\\n\\n compiler = settings.get_safe(\\\"compiler\\\")\\n arch = settings.get_safe(\\\"arch\\\")\\n compiler_version = settings.get_safe(\\\"compiler.version\\\")\\n os_build, _, _, _ = get_cross_building_settings(settings)\\n os_host = settings.get_safe(\\\"os\\\")\\n\\n if not compiler or not compiler_version or not arch:\\n if os_build == \\\"Windows\\\":\\n logger.warning(\\\"CMake generator could not be deduced from settings\\\")\\n return None\\n return \\\"Unix Makefiles\\\"\\n\\n if compiler == \\\"Visual Studio\\\":\\n _visuals = {'8': '8 2005',\\n '9': '9 2008',\\n '10': '10 2010',\\n '11': '11 2012',\\n '12': '12 2013',\\n '14': '14 2015',\\n '15': '15 2017',\\n '16': '16 2019'}\\n base = \\\"Visual Studio %s\\\" % _visuals.get(compiler_version,\\n \\\"UnknownVersion %s\\\" % compiler_version)\\n if os_host != \\\"WindowsCE\\\" and Version(compiler_version) < \\\"16\\\":\\n if arch == \\\"x86_64\\\":\\n base += \\\" Win64\\\"\\n elif \\\"arm\\\" in arch:\\n base += \\\" ARM\\\"\\n return base\\n\\n # The generator depends on the build machine, not the target\\n if os_build == \\\"Windows\\\":\\n return \\\"MinGW Makefiles\\\" # it is valid only under Windows\\n\\n return \\\"Unix Makefiles\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::get_generator_platform\",\n \"text\": \"conans/client/build/cmake_flags.py::get_generator_platform\\ndef get_generator_platform(settings, generator):\\n if \\\"CONAN_CMAKE_GENERATOR_PLATFORM\\\" in os.environ:\\n return os.environ[\\\"CONAN_CMAKE_GENERATOR_PLATFORM\\\"]\\n\\n compiler = settings.get_safe(\\\"compiler\\\")\\n arch = settings.get_safe(\\\"arch\\\")\\n compiler_version = settings.get_safe(\\\"compiler.version\\\")\\n\\n if settings.get_safe(\\\"os\\\") == \\\"WindowsCE\\\":\\n return settings.get_safe(\\\"os.platform\\\")\\n\\n if compiler == \\\"Visual Studio\\\" and Version(compiler_version) >= \\\"16\\\" \\\\\\n and \\\"Visual\\\" in generator:\\n return {\\\"x86\\\": \\\"Win32\\\",\\n \\\"x86_64\\\": \\\"x64\\\",\\n \\\"armv7\\\": \\\"ARM\\\",\\n \\\"armv8\\\": \\\"ARM64\\\"}.get(arch)\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::is_multi_configuration\",\n \"text\": \"conans/client/build/cmake_flags.py::is_multi_configuration\\ndef is_multi_configuration(generator):\\n if not generator:\\n return False\\n return \\\"Visual\\\" in generator or \\\"Xcode\\\" in generator\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::verbose_definition\",\n \"text\": \"conans/client/build/cmake_flags.py::verbose_definition\\ndef verbose_definition(value):\\n return {verbose_definition_name: \\\"ON\\\" if value else \\\"OFF\\\"}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::in_local_cache_definition\",\n \"text\": \"conans/client/build/cmake_flags.py::in_local_cache_definition\\ndef in_local_cache_definition(value):\\n return {cmake_in_local_cache_var_name: \\\"ON\\\" if value else \\\"OFF\\\"}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::runtime_definition\",\n \"text\": \"conans/client/build/cmake_flags.py::runtime_definition\\ndef runtime_definition(runtime):\\n return {runtime_definition_var_name: \\\"/%s\\\" % runtime} if runtime else {}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::build_type_definition\",\n \"text\": \"conans/client/build/cmake_flags.py::build_type_definition\\ndef build_type_definition(build_type, generator):\\n if build_type and not is_multi_configuration(generator):\\n return {\\\"CMAKE_BUILD_TYPE\\\": build_type}\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.__init__\",\n \"text\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.__init__\\n def __init__(self, conanfile, cmake_system_name=True, make_program=None,\\n parallel=True, generator=None, set_cmake_flags=False,\\n forced_build_type=None, output=None):\\n self._conanfile = conanfile\\n self._forced_cmake_system_name = cmake_system_name\\n self._make_program = make_program\\n self._parallel = parallel\\n self._generator = generator\\n self._set_cmake_flags = set_cmake_flags\\n self._forced_build_type = forced_build_type\\n self._output = output\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._ss\",\n \"text\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._ss\\n def _ss(self, setname):\\n \\\"\\\"\\\"safe setting\\\"\\\"\\\"\\n return self._conanfile.settings.get_safe(setname)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_cpp_standard_vars\",\n \"text\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_cpp_standard_vars\\n def _get_cpp_standard_vars(self):\\n cppstd = cppstd_from_settings(self._conanfile.settings)\\n compiler = self._ss(\\\"compiler\\\")\\n compiler_version = self._ss(\\\"compiler.version\\\")\\n\\n if not cppstd:\\n return {}\\n\\n ret = {}\\n if cppstd.startswith(\\\"gnu\\\"):\\n ret[\\\"CONAN_CMAKE_CXX_STANDARD\\\"] = cppstd[3:]\\n ret[\\\"CONAN_CMAKE_CXX_EXTENSIONS\\\"] = \\\"ON\\\"\\n else:\\n ret[\\\"CONAN_CMAKE_CXX_STANDARD\\\"] = cppstd\\n ret[\\\"CONAN_CMAKE_CXX_EXTENSIONS\\\"] = \\\"OFF\\\"\\n\\n ret[\\\"CONAN_STD_CXX_FLAG\\\"] = cppstd_flag(compiler, compiler_version, cppstd)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._cmake_cross_build_defines\",\n \"text\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._cmake_cross_build_defines\\n def _cmake_cross_build_defines(self):\\n\\n os_ = self._ss(\\\"os\\\")\\n arch = self._ss(\\\"arch\\\")\\n os_ver_str = \\\"os.api_level\\\" if os_ == \\\"Android\\\" else \\\"os.version\\\"\\n op_system_version = self._ss(os_ver_str)\\n\\n env_sn = get_env(\\\"CONAN_CMAKE_SYSTEM_NAME\\\", \\\"\\\")\\n env_sn = {\\\"False\\\": False, \\\"True\\\": True, \\\"\\\": None}.get(env_sn, env_sn)\\n cmake_system_name = env_sn or self._forced_cmake_system_name\\n\\n os_build, _, _, _ = get_cross_building_settings(self._conanfile.settings)\\n\\n ret = OrderedDict()\\n os_ver = get_env(\\\"CONAN_CMAKE_SYSTEM_VERSION\\\", op_system_version)\\n toolchain_file = get_env(\\\"CONAN_CMAKE_TOOLCHAIN_FILE\\\", \\\"\\\")\\n\\n if toolchain_file != \\\"\\\":\\n logger.info(\\\"Setting Cross build toolchain file: %s\\\" % toolchain_file)\\n ret[\\\"CMAKE_TOOLCHAIN_FILE\\\"] = toolchain_file\\n return ret\\n\\n if cmake_system_name is False:\\n return ret\\n\\n # System name and system version\\n if cmake_system_name is not True: # String not empty\\n ret[\\\"CMAKE_SYSTEM_NAME\\\"] = cmake_system_name\\n else: # detect if we are cross building and the system name and version\\n if cross_building(self._conanfile.settings): # We are cross building\\n if os_ != os_build:\\n if os_: # the_os is the host (regular setting)\\n ret[\\\"CMAKE_SYSTEM_NAME\\\"] = (\\\"Darwin\\\" if os_ in [\\\"iOS\\\", \\\"tvOS\\\", \\\"watchOS\\\"]\\n else os_)\\n else:\\n ret[\\\"CMAKE_SYSTEM_NAME\\\"] = \\\"Generic\\\"\\n if os_ver:\\n ret[\\\"CMAKE_SYSTEM_VERSION\\\"] = os_ver\\n if str(os_) == \\\"Macos\\\":\\n ret[\\\"CMAKE_OSX_DEPLOYMENT_TARGET\\\"] = os_ver\\n\\n # system processor\\n cmake_system_processor = os.getenv(\\\"CONAN_CMAKE_SYSTEM_PROCESSOR\\\")\\n if cmake_system_pro\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_make_program_definition\",\n \"text\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder._get_make_program_definition\\n def _get_make_program_definition(self):\\n make_program = os.getenv(\\\"CONAN_MAKE_PROGRAM\\\") or self._make_program\\n if make_program:\\n if not tools.which(make_program):\\n self._output.warn(\\\"The specified make program '%s' cannot be found and will be \\\"\\n \\\"ignored\\\" % make_program)\\n else:\\n self._output.info(\\\"Using '%s' as CMAKE_MAKE_PROGRAM\\\" % make_program)\\n return {\\\"CMAKE_MAKE_PROGRAM\\\": make_program}\\n\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.get_definitions\",\n \"text\": \"conans/client/build/cmake_flags.py::CMakeDefinitionsBuilder.get_definitions\\n def get_definitions(self):\\n\\n compiler = self._ss(\\\"compiler\\\")\\n compiler_version = self._ss(\\\"compiler.version\\\")\\n arch = self._ss(\\\"arch\\\")\\n os_ = self._ss(\\\"os\\\")\\n libcxx = self._ss(\\\"compiler.libcxx\\\")\\n runtime = self._ss(\\\"compiler.runtime\\\")\\n build_type = self._ss(\\\"build_type\\\")\\n\\n ret = OrderedDict()\\n ret.update(runtime_definition(runtime))\\n\\n if self._forced_build_type and self._forced_build_type != build_type:\\n self._output.warn(\\\"Forced CMake build type ('%s') different from the settings build \\\"\\n \\\"type ('%s')\\\" % (self._forced_build_type, build_type))\\n build_type = self._forced_build_type\\n\\n ret.update(build_type_definition(build_type, self._generator))\\n\\n if str(os_) == \\\"Macos\\\":\\n if arch == \\\"x86\\\":\\n ret[\\\"CMAKE_OSX_ARCHITECTURES\\\"] = \\\"i386\\\"\\n\\n ret.update(self._cmake_cross_build_defines())\\n ret.update(self._get_cpp_standard_vars())\\n\\n ret[\\\"CONAN_EXPORTED\\\"] = \\\"1\\\"\\n ret.update(in_local_cache_definition(self._conanfile.in_local_cache))\\n\\n if compiler:\\n ret[\\\"CONAN_COMPILER\\\"] = compiler\\n if compiler_version:\\n ret[\\\"CONAN_COMPILER_VERSION\\\"] = str(compiler_version)\\n\\n # C, CXX, LINK FLAGS\\n if compiler == \\\"Visual Studio\\\":\\n if self._parallel:\\n flag = parallel_compiler_cl_flag(output=self._output)\\n ret['CONAN_CXX_FLAGS'] = flag\\n ret['CONAN_C_FLAGS'] = flag\\n else: # arch_flag is only set for non Visual Studio\\n arch_flag = architecture_flag(compiler=compiler, arch=arch)\\n if arch_flag:\\n ret['CONAN_CXX_FLAGS'] = arch_flag\\n ret['CONAN_SHARED_LINKER_FLAGS'] = arch_flag\\n ret['CONAN_C_FLAGS'] = arch_flag\\n if self._set_cmake_flags:\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::rpath_flags\",\n \"text\": \"conans/client/build/compiler_flags.py::rpath_flags\\ndef rpath_flags(os_build, compiler, lib_paths):\\n if not os_build:\\n return []\\n if compiler in (\\\"clang\\\", \\\"apple-clang\\\", \\\"gcc\\\"):\\n rpath_separator = \\\",\\\" if os_build in [\\\"Macos\\\", \\\"iOS\\\", \\\"watchOS\\\", \\\"tvOS\\\"] else \\\"=\\\"\\n return ['-Wl,-rpath%s\\\"%s\\\"' % (rpath_separator, x.replace(\\\"\\\\\\\\\\\", \\\"/\\\"))\\n for x in lib_paths if x]\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::architecture_flag\",\n \"text\": \"conans/client/build/compiler_flags.py::architecture_flag\\ndef architecture_flag(compiler, arch):\\n \\\"\\\"\\\"\\n returns flags specific to the target architecture and compiler\\n \\\"\\\"\\\"\\n if not compiler or not arch:\\n return \\\"\\\"\\n\\n if str(compiler) in ['gcc', 'apple-clang', 'clang', 'sun-cc']:\\n if str(arch) in ['x86_64', 'sparcv9', 's390x']:\\n return '-m64'\\n elif str(arch) in ['x86', 'sparc']:\\n return '-m32'\\n elif str(arch) in ['s390']:\\n return '-m31'\\n return \\\"\\\"\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::libcxx_define\",\n \"text\": \"conans/client/build/compiler_flags.py::libcxx_define\\ndef libcxx_define(compiler, libcxx):\\n\\n if not compiler or not libcxx:\\n return \\\"\\\"\\n\\n if str(compiler) in ['gcc', 'clang', 'apple-clang']:\\n if str(libcxx) == 'libstdc++':\\n return '_GLIBCXX_USE_CXX11_ABI=0'\\n elif str(libcxx) == 'libstdc++11':\\n return '_GLIBCXX_USE_CXX11_ABI=1'\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::libcxx_flag\",\n \"text\": \"conans/client/build/compiler_flags.py::libcxx_flag\\ndef libcxx_flag(compiler, libcxx):\\n \\\"\\\"\\\"\\n returns flag specific to the target C++ standard library\\n \\\"\\\"\\\"\\n if not compiler or not libcxx:\\n return \\\"\\\"\\n if str(compiler) in ['clang', 'apple-clang']:\\n if str(libcxx) in ['libstdc++', 'libstdc++11']:\\n return '-stdlib=libstdc++'\\n elif str(libcxx) == 'libc++':\\n return '-stdlib=libc++'\\n elif str(compiler) == 'sun-cc':\\n return ({\\\"libCstd\\\": \\\"-library=Cstd\\\",\\n \\\"libstdcxx\\\": \\\"-library=stdcxx4\\\",\\n \\\"libstlport\\\": \\\"-library=stlport4\\\",\\n \\\"libstdc++\\\": \\\"-library=stdcpp\\\"}.get(libcxx, \\\"\\\"))\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::pic_flag\",\n \"text\": \"conans/client/build/compiler_flags.py::pic_flag\\ndef pic_flag(compiler=None):\\n \\\"\\\"\\\"\\n returns PIC (position independent code) flags, such as -fPIC\\n \\\"\\\"\\\"\\n if not compiler or compiler == 'Visual Studio':\\n return \\\"\\\"\\n return '-fPIC'\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::build_type_flags\",\n \"text\": \"conans/client/build/compiler_flags.py::build_type_flags\\ndef build_type_flags(compiler, build_type, vs_toolset=None):\\n \\\"\\\"\\\"\\n returns flags specific to the build type (Debug, Release, etc.)\\n (-s, -g, /Zi, etc.)\\n \\\"\\\"\\\"\\n if not compiler or not build_type:\\n return \\\"\\\"\\n\\n # https://github.com/Kitware/CMake/blob/d7af8a34b67026feaee558433db3a835d6007e06/\\n # Modules/Platform/Windows-MSVC.cmake\\n if str(compiler) == 'Visual Studio':\\n if vs_toolset and \\\"clang\\\" in str(vs_toolset):\\n flags = {\\\"Debug\\\": [\\\"-gline-tables-only\\\", \\\"-fno-inline\\\", \\\"-O0\\\"],\\n \\\"Release\\\": [\\\"-O2\\\"],\\n \\\"RelWithDebInfo\\\": [\\\"-gline-tables-only\\\", \\\"-O2\\\", \\\"-fno-inline\\\"],\\n \\\"MinSizeRel\\\": []\\n }.get(build_type, [\\\"-O2\\\", \\\"-Ob2\\\"])\\n else:\\n flags = {\\\"Debug\\\": [\\\"-Zi\\\", \\\"-Ob0\\\", \\\"-Od\\\"],\\n \\\"Release\\\": [\\\"-O2\\\", \\\"-Ob2\\\"],\\n \\\"RelWithDebInfo\\\": [\\\"-Zi\\\", \\\"-O2\\\", \\\"-Ob1\\\"],\\n \\\"MinSizeRel\\\": [\\\"-O1\\\", \\\"-Ob1\\\"],\\n }.get(build_type, [])\\n return flags\\n else:\\n # https://github.com/Kitware/CMake/blob/f3bbb37b253a1f4a26809d6f132b3996aa2e16fc/\\n # Modules/Compiler/GNU.cmake\\n # clang include the gnu (overriding some things, but not build type) and apple clang\\n # overrides clang but it doesn't touch clang either\\n if str(compiler) in [\\\"clang\\\", \\\"gcc\\\", \\\"apple-clang\\\"]:\\n # FIXME: It is not clear that the \\\"-s\\\" is something related with the build type\\n # cmake is not adjusting it\\n # -s: Remove all symbol table and relocation information from the executable.\\n flags = {\\\"Debug\\\": [\\\"-g\\\"],\\n \\\"Release\\\": [\\\"-O3\\\", \\\"-s\\\"] if str(compiler) == \\\"gcc\\\" else [\\\"-O3\\\"],\\n \\\"RelWithDebInfo\\\": [\\\"-O2\\\", \\\"-g\\\"],\\n \\\"MinSizeRel\\\": [\\\"-Os\\\"],\\n }.get(build_type, [])\\n return flags\\n elif str(compiler) == \\\"sun-cc\\\":\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::build_type_define\",\n \"text\": \"conans/client/build/compiler_flags.py::build_type_define\\ndef build_type_define(build_type=None):\\n \\\"\\\"\\\"\\n returns definitions specific to the build type (Debug, Release, etc.)\\n like DEBUG, _DEBUG, NDEBUG\\n \\\"\\\"\\\"\\n return 'NDEBUG' if build_type == 'Release' else \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::adjust_path\",\n \"text\": \"conans/client/build/compiler_flags.py::adjust_path\\ndef adjust_path(path, win_bash=False, subsystem=None, compiler=None):\\n \\\"\\\"\\\"\\n adjusts path to be safely passed to the compiler command line\\n for Windows bash, ensures path is in format according to the subsystem\\n for path with spaces, places double quotes around it\\n converts slashes to backslashes, or vice versa\\n \\\"\\\"\\\"\\n if str(compiler) == 'Visual Studio':\\n path = path.replace('/', '\\\\\\\\')\\n else:\\n path = path.replace('\\\\\\\\', '/')\\n if win_bash:\\n path = unix_path(path, subsystem)\\n return '\\\"%s\\\"' % path if ' ' in path else path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::sysroot_flag\",\n \"text\": \"conans/client/build/compiler_flags.py::sysroot_flag\\ndef sysroot_flag(sysroot, win_bash=False, subsystem=None, compiler=None):\\n if str(compiler) != 'Visual Studio' and sysroot:\\n sysroot = adjust_path(sysroot, win_bash=win_bash, subsystem=subsystem, compiler=compiler)\\n return '--sysroot=%s' % sysroot\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::visual_runtime\",\n \"text\": \"conans/client/build/compiler_flags.py::visual_runtime\\ndef visual_runtime(runtime):\\n if runtime:\\n return \\\"-%s\\\" % runtime\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::format_defines\",\n \"text\": \"conans/client/build/compiler_flags.py::format_defines\\ndef format_defines(defines):\\n return [\\\"-D%s\\\" % define for define in defines if define]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::format_include_paths\",\n \"text\": \"conans/client/build/compiler_flags.py::format_include_paths\\ndef format_include_paths(include_paths, win_bash=False, subsystem=None, compiler=None):\\n return [\\\"%s%s\\\" % (include_path_option, adjust_path(include_path, win_bash=win_bash,\\n subsystem=subsystem, compiler=compiler))\\n for include_path in include_paths if include_path]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::format_library_paths\",\n \"text\": \"conans/client/build/compiler_flags.py::format_library_paths\\ndef format_library_paths(library_paths, win_bash=False, subsystem=None, compiler=None):\\n pattern = \\\"-LIBPATH:%s\\\" if str(compiler) == 'Visual Studio' else \\\"-L%s\\\"\\n return [pattern % adjust_path(library_path, win_bash=win_bash,\\n subsystem=subsystem, compiler=compiler)\\n for library_path in library_paths if library_path]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::format_libraries\",\n \"text\": \"conans/client/build/compiler_flags.py::format_libraries\\ndef format_libraries(libraries, compiler=None):\\n result = []\\n for library in libraries:\\n if str(compiler) == 'Visual Studio':\\n if not library.endswith(\\\".lib\\\"):\\n library += \\\".lib\\\"\\n result.append(library)\\n else:\\n result.append(\\\"-l%s\\\" % library)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/compiler_flags.py::parallel_compiler_cl_flag\",\n \"text\": \"conans/client/build/compiler_flags.py::parallel_compiler_cl_flag\\ndef parallel_compiler_cl_flag(output=None):\\n return \\\"/MP%s\\\" % cpu_count(output=output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::is_case_insensitive_os\",\n \"text\": \"conans/client/cache/cache.py::is_case_insensitive_os\\ndef is_case_insensitive_os():\\n system = platform.system()\\n return system != \\\"Linux\\\" and system != \\\"FreeBSD\\\" and system != \\\"SunOS\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.__init__\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.__init__\\n def __init__(self, base_folder, output):\\n self.conan_folder = join(base_folder, \\\".conan\\\")\\n self._config = None\\n self._output = output\\n # Remove this self.conan_folder in Conan 2.0\\n self._store_folder = self.config.storage_path or self.conan_folder\\n self._no_lock = None\\n self.client_cert_path = normpath(join(self.conan_folder, CLIENT_CERT))\\n self.client_cert_key_path = normpath(join(self.conan_folder, CLIENT_KEY))\\n self._registry = None\\n\\n self.editable_packages = EditablePackages(self.conan_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.all_refs\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.all_refs\\n def all_refs(self):\\n subdirs = list_folder_subdirs(basedir=self._store_folder, level=4)\\n return [ConanFileReference(*folder.split(\\\"/\\\")) for folder in subdirs]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.store\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.store\\n @property\\n def store(self):\\n return self._store_folder\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.package\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.package\\n def package(self, pref, short_paths=False):\\n # TODO: This is deprecated, only used in testing\\n return self.package_layout(pref.ref, short_paths).package(pref)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.installed_as_editable\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.installed_as_editable\\n def installed_as_editable(self, ref):\\n return isinstance(self.package_layout(ref), PackageEditableLayout)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.config_install_file\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.config_install_file\\n @property\\n def config_install_file(self):\\n return os.path.join(self.conan_folder, \\\"config_install.json\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.package_layout\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.package_layout\\n def package_layout(self, ref, short_paths=None, *args, **kwargs):\\n assert isinstance(ref, ConanFileReference), \\\"It is a {}\\\".format(type(ref))\\n edited_ref = self.editable_packages.get(ref.copy_clear_rev())\\n if edited_ref:\\n base_path = edited_ref[\\\"path\\\"]\\n layout_file = edited_ref[\\\"layout\\\"]\\n return PackageEditableLayout(base_path, layout_file, ref)\\n else:\\n check_ref_case(ref, self.store)\\n base_folder = os.path.normpath(os.path.join(self.store, ref.dir_repr()))\\n return PackageCacheLayout(base_folder=base_folder, ref=ref,\\n short_paths=short_paths, no_lock=self._no_locks())\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.registry_path\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.registry_path\\n @property\\n def registry_path(self):\\n return join(self.conan_folder, REMOTES)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.registry\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.registry\\n @property\\n def registry(self):\\n if not self._registry:\\n remotes_path = self.registry_path\\n if not os.path.exists(remotes_path):\\n self._output.warn(\\\"Remotes registry file missing, \\\"\\n \\\"creating default one in %s\\\" % remotes_path)\\n remotes = Remotes.defaults()\\n remotes.save(remotes_path)\\n self._registry = RemoteRegistry(self)\\n return self._registry\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.cacert_path\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.cacert_path\\n @property\\n def cacert_path(self):\\n return self.config.cacert_path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache._no_locks\",\n \"text\": \"conans/client/cache/cache.py::ClientCache._no_locks\\n def _no_locks(self):\\n if self._no_lock is None:\\n self._no_lock = self.config.cache_no_locks\\n return self._no_lock\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.put_headers_path\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.put_headers_path\\n @property\\n def put_headers_path(self):\\n return join(self.conan_folder, PUT_HEADERS)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.read_put_headers\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.read_put_headers\\n def read_put_headers(self):\\n ret = {}\\n if not os.path.exists(self.put_headers_path):\\n save(self.put_headers_path, \\\"\\\")\\n return ret\\n try:\\n contents = load(self.put_headers_path)\\n for line in contents.splitlines():\\n if line and not line.strip().startswith(\\\"#\\\"):\\n tmp = line.split(\\\"=\\\", 1)\\n if len(tmp) != 2:\\n raise Exception()\\n name = tmp[0].strip()\\n value = tmp[1].strip()\\n ret[str(name)] = str(value)\\n return ret\\n except Exception:\\n raise ConanException(\\\"Invalid %s file!\\\" % self.put_headers_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.config\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.config\\n @property\\n def config(self):\\n if not self._config:\\n if not os.path.exists(self.conan_conf_path):\\n save(self.conan_conf_path, normalize(default_client_conf))\\n\\n self._config = ConanClientConfigParser(self.conan_conf_path)\\n return self._config\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.localdb\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.localdb\\n @property\\n def localdb(self):\\n return join(self.conan_folder, LOCALDB)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.conan_conf_path\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.conan_conf_path\\n @property\\n def conan_conf_path(self):\\n return join(self.conan_folder, CONAN_CONF)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.profiles_path\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.profiles_path\\n @property\\n def profiles_path(self):\\n return join(self.conan_folder, PROFILES_FOLDER)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.settings_path\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.settings_path\\n @property\\n def settings_path(self):\\n return join(self.conan_folder, CONAN_SETTINGS)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.default_profile_path\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.default_profile_path\\n @property\\n def default_profile_path(self):\\n if os.path.isabs(self.config.default_profile):\\n return self.config.default_profile\\n else:\\n return join(self.conan_folder, PROFILES_FOLDER,\\n self.config.default_profile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.hooks_path\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.hooks_path\\n @property\\n def hooks_path(self):\\n \\\"\\\"\\\"\\n :return: Hooks folder in client cache\\n \\\"\\\"\\\"\\n return join(self.conan_folder, HOOKS_FOLDER)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.default_profile\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.default_profile\\n @property\\n def default_profile(self):\\n if not os.path.exists(self.default_profile_path):\\n self._output.writeln(\\\"Auto detecting your dev setup to initialize the \\\"\\n \\\"default profile (%s)\\\" % self.default_profile_path,\\n Color.BRIGHT_YELLOW)\\n\\n default_settings = detect_defaults_settings(self._output,\\n profile_path=self.default_profile_path)\\n self._output.writeln(\\\"Default settings\\\", Color.BRIGHT_YELLOW)\\n self._output.writeln(\\\"\\\\n\\\".join([\\\"\\\\t%s=%s\\\" % (k, v) for (k, v) in default_settings]),\\n Color.BRIGHT_YELLOW)\\n self._output.writeln(\\\"*** You can change them in %s ***\\\" % self.default_profile_path,\\n Color.BRIGHT_MAGENTA)\\n self._output.writeln(\\\"*** Or override with -s compiler='other' -s ...s***\\\\n\\\\n\\\",\\n Color.BRIGHT_MAGENTA)\\n\\n default_profile = Profile()\\n tmp = OrderedDict(default_settings)\\n default_profile.update_settings(tmp)\\n save(self.default_profile_path, default_profile.dumps())\\n else:\\n default_profile, _ = read_profile(self.default_profile_path, get_cwd(),\\n self.profiles_path)\\n\\n # Mix profile settings with environment\\n mixed_settings = _mix_settings_with_env(default_profile.settings)\\n default_profile.settings = mixed_settings\\n return default_profile\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.settings\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.settings\\n @property\\n def settings(self):\\n \\\"\\\"\\\"Returns {setting: [value, ...]} defining all the possible\\n settings without values\\\"\\\"\\\"\\n\\n if not os.path.exists(self.settings_path):\\n save(self.settings_path, normalize(default_settings_yml))\\n settings = Settings.loads(default_settings_yml)\\n else:\\n content = load(self.settings_path)\\n settings = Settings.loads(content)\\n\\n return settings\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.hooks\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.hooks\\n @property\\n def hooks(self):\\n \\\"\\\"\\\"Returns a list of hooks inside the hooks folder\\\"\\\"\\\"\\n hooks = []\\n for hook_name in os.listdir(self.hooks_path):\\n if os.path.isfile(hook_name) and hook_name.endswith(\\\".py\\\"):\\n hooks.append(hook_name[:-3])\\n return hooks\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.delete_empty_dirs\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.delete_empty_dirs\\n def delete_empty_dirs(self, deleted_refs):\\n for ref in deleted_refs:\\n ref_path = self.package_layout(ref).base_folder()\\n for _ in range(4):\\n if os.path.exists(ref_path):\\n try: # Take advantage that os.rmdir does not delete non-empty dirs\\n os.rmdir(ref_path)\\n except OSError:\\n break # not empty\\n ref_path = os.path.dirname(ref_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.remove_locks\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.remove_locks\\n def remove_locks(self):\\n folders = list_folder_subdirs(self._store_folder, 4)\\n for folder in folders:\\n conan_folder = os.path.join(self._store_folder, folder)\\n Lock.clean(conan_folder)\\n shutil.rmtree(os.path.join(conan_folder, \\\"locks\\\"), ignore_errors=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::ClientCache.invalidate\",\n \"text\": \"conans/client/cache/cache.py::ClientCache.invalidate\\n def invalidate(self):\\n self._config = None\\n self._no_lock = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::_mix_settings_with_env\",\n \"text\": \"conans/client/cache/cache.py::_mix_settings_with_env\\ndef _mix_settings_with_env(settings):\\n \\\"\\\"\\\"Reads CONAN_ENV_XXXX variables from environment\\n and if it's defined uses these value instead of the default\\n from conf file. If you specify a compiler with ENV variable you\\n need to specify all the subsettings, the file defaulted will be\\n ignored\\\"\\\"\\\"\\n\\n def get_env_value(name):\\n env_name = \\\"CONAN_ENV_%s\\\" % name.upper().replace(\\\".\\\", \\\"_\\\")\\n return os.getenv(env_name, None)\\n\\n def get_setting_name(env_name):\\n return env_name[10:].lower().replace(\\\"_\\\", \\\".\\\")\\n\\n ret = OrderedDict()\\n for name, value in settings.items():\\n if get_env_value(name):\\n ret[name] = get_env_value(name)\\n else:\\n # being a subsetting, if parent exist in env discard this, because\\n # env doesn't define this setting. EX: env=>Visual Studio but\\n # env doesn't define compiler.libcxx\\n if \\\".\\\" not in name or not get_env_value(name.split(\\\".\\\")[0]):\\n ret[name] = value\\n # Now read if there are more env variables\\n for env, value in sorted(os.environ.items()):\\n if env.startswith(\\\"CONAN_ENV_\\\") and get_setting_name(env) not in ret:\\n ret[get_setting_name(env)] = value\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::_mix_settings_with_env.get_env_value\",\n \"text\": \"conans/client/cache/cache.py::_mix_settings_with_env.get_env_value\\n def get_env_value(name):\\n env_name = \\\"CONAN_ENV_%s\\\" % name.upper().replace(\\\".\\\", \\\"_\\\")\\n return os.getenv(env_name, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/cache.py::_mix_settings_with_env.get_setting_name\",\n \"text\": \"conans/client/cache/cache.py::_mix_settings_with_env.get_setting_name\\n def get_setting_name(env_name):\\n return env_name[10:].lower().replace(\\\"_\\\", \\\".\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/editable.py::EditablePackages.__init__\",\n \"text\": \"conans/client/cache/editable.py::EditablePackages.__init__\\n def __init__(self, cache_folder):\\n self._cache_folder = cache_folder\\n self._edited_file = normpath(join(cache_folder, EDITABLE_PACKAGES_FILE))\\n if os.path.exists(self._edited_file):\\n edited = load(self._edited_file)\\n edited_js = json.loads(edited)\\n self._edited_refs = {ConanFileReference.loads(r, validate=False): d\\n for r, d in edited_js.items()}\\n else:\\n self._edited_refs = {} # {ref: {\\\"path\\\": path, \\\"layout\\\": layout}}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/editable.py::EditablePackages.edited_refs\",\n \"text\": \"conans/client/cache/editable.py::EditablePackages.edited_refs\\n @property\\n def edited_refs(self):\\n return self._edited_refs\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/editable.py::EditablePackages.save\",\n \"text\": \"conans/client/cache/editable.py::EditablePackages.save\\n def save(self):\\n d = {str(ref): d for ref, d in self._edited_refs.items()}\\n save(self._edited_file, json.dumps(d))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/editable.py::EditablePackages.get\",\n \"text\": \"conans/client/cache/editable.py::EditablePackages.get\\n def get(self, ref):\\n ref = ref.copy_clear_rev()\\n return self._edited_refs.get(ref)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/editable.py::EditablePackages.add\",\n \"text\": \"conans/client/cache/editable.py::EditablePackages.add\\n def add(self, ref, path, layout):\\n assert isinstance(ref, ConanFileReference)\\n ref = ref.copy_clear_rev()\\n self._edited_refs[ref] = {\\\"path\\\": path, \\\"layout\\\": layout}\\n self.save()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/editable.py::EditablePackages.remove\",\n \"text\": \"conans/client/cache/editable.py::EditablePackages.remove\\n def remove(self, ref):\\n assert isinstance(ref, ConanFileReference)\\n ref = ref.copy_clear_rev()\\n if self._edited_refs.pop(ref, None):\\n self.save()\\n return True\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/editable.py::EditablePackages.override\",\n \"text\": \"conans/client/cache/editable.py::EditablePackages.override\\n def override(self, workspace_edited):\\n self._edited_refs = workspace_edited\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::load_registry_txt\",\n \"text\": \"conans/client/cache/remote_registry.py::load_registry_txt\\ndef load_registry_txt(contents):\\n \\\"\\\"\\\"Remove in Conan 2.0\\\"\\\"\\\"\\n remotes = Remotes()\\n refs = {}\\n end_remotes = False\\n # Parse the file\\n for line in contents.splitlines():\\n line = line.strip()\\n\\n if not line:\\n if end_remotes:\\n raise ConanException(\\\"Bad file format, blank line\\\")\\n end_remotes = True\\n continue\\n chunks = line.split()\\n if not end_remotes:\\n if len(chunks) == 2: # Retro compatibility\\n remote_name, url = chunks\\n verify_ssl = \\\"True\\\"\\n elif len(chunks) == 3:\\n remote_name, url, verify_ssl = chunks\\n else:\\n raise ConanException(\\\"Bad file format, wrong item numbers in line '%s'\\\" % line)\\n\\n verify_ssl = get_bool_from_text_value(verify_ssl)\\n remotes.add(remote_name, url, verify_ssl)\\n else:\\n ref, remote_name = chunks\\n refs[ref] = remote_name\\n\\n return remotes, refs\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::load_old_registry_json\",\n \"text\": \"conans/client/cache/remote_registry.py::load_old_registry_json\\ndef load_old_registry_json(contents):\\n \\\"\\\"\\\"From json\\\"\\\"\\\"\\n data = json.loads(contents)\\n remotes = Remotes()\\n refs = data.get(\\\"references\\\", {})\\n prefs = data.get(\\\"package_references\\\", {})\\n for r in data[\\\"remotes\\\"]:\\n remotes.add(r[\\\"name\\\"], r[\\\"url\\\"], r[\\\"verify_ssl\\\"])\\n return remotes, refs, prefs\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::migrate_registry_file\",\n \"text\": \"conans/client/cache/remote_registry.py::migrate_registry_file\\ndef migrate_registry_file(cache, out):\\n folder = cache.conan_folder\\n reg_json_path = os.path.join(folder, \\\"registry.json\\\")\\n reg_txt_path = os.path.join(folder, \\\"registry.txt\\\")\\n remotes_path = cache.registry_path\\n\\n def add_ref_remote(reference, remotes, remote_name):\\n ref = ConanFileReference.loads(reference, validate=True)\\n remote = remotes[remote_name]\\n with cache.package_layout(ref).update_metadata() as metadata:\\n metadata.recipe.remote = remote.name\\n\\n def add_pref_remote(pkg_ref, remotes, remote_name):\\n pref = PackageReference.loads(pkg_ref, validate=True)\\n remote = remotes[remote_name]\\n with cache.package_layout(pref.ref).update_metadata() as metadata:\\n metadata.packages[pref.id].remote = remote.name\\n\\n try:\\n if os.path.exists(reg_json_path):\\n out.warn(\\\"registry.json has been deprecated. Migrating to remotes.json\\\")\\n remotes, refs, prefs = load_old_registry_json(load(reg_json_path))\\n remotes.save(remotes_path)\\n for ref, remote_name in refs.items():\\n add_ref_remote(ref, remotes, remote_name)\\n for pref, remote_name in prefs.items():\\n add_pref_remote(pref, remotes, remote_name)\\n os.remove(reg_json_path)\\n elif os.path.exists(reg_txt_path):\\n out.warn(\\\"registry.txt has been deprecated. Migrating to remotes.json\\\")\\n remotes, refs = load_registry_txt(load(reg_txt_path))\\n remotes.save(remotes_path)\\n for ref, remote_name in refs.items():\\n add_ref_remote(ref, remotes, remote_name)\\n os.remove(reg_txt_path)\\n\\n except Exception as e:\\n raise ConanException(\\\"Cannot migrate old registry: %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::migrate_registry_file.add_ref_remote\",\n \"text\": \"conans/client/cache/remote_registry.py::migrate_registry_file.add_ref_remote\\n def add_ref_remote(reference, remotes, remote_name):\\n ref = ConanFileReference.loads(reference, validate=True)\\n remote = remotes[remote_name]\\n with cache.package_layout(ref).update_metadata() as metadata:\\n metadata.recipe.remote = remote.name\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::migrate_registry_file.add_pref_remote\",\n \"text\": \"conans/client/cache/remote_registry.py::migrate_registry_file.add_pref_remote\\n def add_pref_remote(pkg_ref, remotes, remote_name):\\n pref = PackageReference.loads(pkg_ref, validate=True)\\n remote = remotes[remote_name]\\n with cache.package_layout(pref.ref).update_metadata() as metadata:\\n metadata.packages[pref.id].remote = remote.name\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.__init__\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.__init__\\n def __init__(self):\\n self._remotes = OrderedDict()\\n self.selected = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.defaults\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.defaults\\n @classmethod\\n def defaults(cls):\\n result = Remotes()\\n result._remotes[\\\"conan-center\\\"] = Remote(\\\"conan-center\\\", \\\"https://conan.bintray.com\\\", True)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.select\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.select\\n def select(self, remote_name):\\n self.selected = self[remote_name] if remote_name is not None else None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.__bool__\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.__bool__\\n def __bool__(self):\\n return bool(self._remotes)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.__nonzero__\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.__nonzero__\\n def __nonzero__(self):\\n return self.__bool__()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.clear\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.clear\\n def clear(self):\\n self._remotes.clear()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.items\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.items\\n def items(self):\\n return self._remotes.items()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.values\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.values\\n def values(self):\\n return self._remotes.values()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.loads\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.loads\\n @staticmethod\\n def loads(text):\\n result = Remotes()\\n data = json.loads(text)\\n for r in data.get(\\\"remotes\\\", []):\\n result._remotes[r[\\\"name\\\"]] = Remote(r[\\\"name\\\"], r[\\\"url\\\"], r[\\\"verify_ssl\\\"])\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.dumps\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.dumps\\n def dumps(self):\\n result = []\\n for remote in self._remotes.values():\\n result.append(\\\"%s: %s [Verify SSL: %s]\\\" % (remote.name, remote.url, remote.verify_ssl))\\n return \\\"\\\\n\\\".join(result)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.save\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.save\\n def save(self, filename):\\n ret = {\\\"remotes\\\": [{\\\"name\\\": r, \\\"url\\\": u, \\\"verify_ssl\\\": v}\\n for r, (_, u, v) in self._remotes.items()]}\\n save(filename, json.dumps(ret, indent=True))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes._get_by_url\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes._get_by_url\\n def _get_by_url(self, url):\\n for remote in self._remotes.values():\\n if remote.url == url:\\n return remote\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.rename\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.rename\\n def rename(self, remote_name, new_remote_name):\\n if new_remote_name in self._remotes:\\n raise ConanException(\\\"Remote '%s' already exists\\\" % new_remote_name)\\n\\n remote = self._remotes[remote_name]\\n new_remote = Remote(new_remote_name, remote.url, remote.verify_ssl)\\n self._remotes = OrderedDict([(new_remote_name, new_remote) if k == remote_name\\n else (k, v) for k, v in self._remotes.items()])\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.get_remote\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.get_remote\\n def get_remote(self, remote_name):\\n # Returns the remote defined by the name, or the default if is None\\n return self[remote_name] if remote_name is not None else self.default\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.default\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.default\\n @property\\n def default(self):\\n try:\\n # This is the python way to get the first element of an OrderedDict\\n return self._remotes[next(iter(self._remotes))]\\n except StopIteration:\\n raise NoRemoteAvailable(\\\"No default remote defined\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.__contains__\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.__contains__\\n def __contains__(self, remote_name):\\n return remote_name in self._remotes\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.get\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.get\\n def get(self, remote_name):\\n return self._remotes.get(remote_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.__getitem__\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.__getitem__\\n def __getitem__(self, remote_name):\\n try:\\n return self._remotes[remote_name]\\n except KeyError:\\n raise NoRemoteAvailable(\\\"No remote '%s' defined in remotes\\\" % (remote_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.__delitem__\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.__delitem__\\n def __delitem__(self, remote_name):\\n try:\\n del self._remotes[remote_name]\\n except KeyError:\\n raise NoRemoteAvailable(\\\"No remote '%s' defined in remotes\\\" % (remote_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes._upsert\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes._upsert\\n def _upsert(self, remote_name, url, verify_ssl, insert):\\n # Remove duplicates\\n updated_remote = Remote(remote_name, url, verify_ssl)\\n self._remotes.pop(remote_name, None)\\n remotes_list = []\\n renamed = None\\n\\n for name, remote in self._remotes.items():\\n if remote.url != url:\\n remotes_list.append((name, remote))\\n else:\\n renamed = name\\n\\n if insert is not None:\\n try:\\n insert_index = int(insert)\\n except ValueError:\\n raise ConanException(\\\"insert argument must be an integer\\\")\\n remotes_list.insert(insert_index, (remote_name, updated_remote))\\n else:\\n remotes_list.append((remote_name, updated_remote))\\n self._remotes = OrderedDict(remotes_list)\\n return renamed\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.add\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.add\\n def add(self, remote_name, url, verify_ssl=True, insert=None, force=None):\\n if force:\\n return self._upsert(remote_name, url, verify_ssl, insert)\\n\\n if remote_name in self._remotes:\\n raise ConanException(\\\"Remote '%s' already exists in remotes (use update to modify)\\\"\\n % remote_name)\\n self._add_update(remote_name, url, verify_ssl, insert)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes.update\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes.update\\n def update(self, remote_name, url, verify_ssl=True, insert=None):\\n if remote_name not in self._remotes:\\n raise ConanException(\\\"Remote '%s' not found in remotes\\\" % remote_name)\\n self._add_update(remote_name, url, verify_ssl, insert)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::Remotes._add_update\",\n \"text\": \"conans/client/cache/remote_registry.py::Remotes._add_update\\n def _add_update(self, remote_name, url, verify_ssl, insert=None):\\n prev_remote = self._get_by_url(url)\\n if prev_remote and verify_ssl == prev_remote.verify_ssl:\\n raise ConanException(\\\"Remote '%s' already exists with same URL\\\" % prev_remote.name)\\n updated_remote = Remote(remote_name, url, verify_ssl)\\n if insert is not None:\\n try:\\n insert_index = int(insert)\\n except ValueError:\\n raise ConanException(\\\"insert argument must be an integer\\\")\\n self._remotes.pop(remote_name, None) # Remove if exists (update)\\n remotes_list = list(self._remotes.items())\\n remotes_list.insert(insert_index, (remote_name, updated_remote))\\n self._remotes = OrderedDict(remotes_list)\\n else:\\n self._remotes[remote_name] = updated_remote\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::RemoteRegistry.__init__\",\n \"text\": \"conans/client/cache/remote_registry.py::RemoteRegistry.__init__\\n def __init__(self, cache):\\n self._cache = cache\\n self._filename = cache.registry_path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::RemoteRegistry.load_remotes\",\n \"text\": \"conans/client/cache/remote_registry.py::RemoteRegistry.load_remotes\\n def load_remotes(self):\\n content = load(self._filename)\\n return Remotes.loads(content)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::RemoteRegistry.add\",\n \"text\": \"conans/client/cache/remote_registry.py::RemoteRegistry.add\\n def add(self, remote_name, url, verify_ssl=True, insert=None, force=None):\\n remotes = self.load_remotes()\\n renamed = remotes.add(remote_name, url, verify_ssl, insert, force)\\n remotes.save(self._filename)\\n if renamed:\\n for ref in self._cache.all_refs():\\n with self._cache.package_layout(ref).update_metadata() as metadata:\\n if metadata.recipe.remote == renamed:\\n metadata.recipe.remote = remote_name\\n for pkg_metadata in metadata.packages.values():\\n if pkg_metadata.remote == renamed:\\n pkg_metadata.remote = remote_name\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::RemoteRegistry.update\",\n \"text\": \"conans/client/cache/remote_registry.py::RemoteRegistry.update\\n def update(self, remote_name, url, verify_ssl=True, insert=None):\\n remotes = self.load_remotes()\\n remotes.update(remote_name, url, verify_ssl, insert)\\n remotes.save(self._filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::RemoteRegistry.clear\",\n \"text\": \"conans/client/cache/remote_registry.py::RemoteRegistry.clear\\n def clear(self):\\n remotes = self.load_remotes()\\n remotes.clear()\\n for ref in self._cache.all_refs():\\n with self._cache.package_layout(ref).update_metadata() as metadata:\\n metadata.recipe.remote = None\\n for pkg_metadata in metadata.packages.values():\\n pkg_metadata.remote = None\\n remotes.save(self._filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::RemoteRegistry.remove\",\n \"text\": \"conans/client/cache/remote_registry.py::RemoteRegistry.remove\\n def remove(self, remote_name):\\n remotes = self.load_remotes()\\n del remotes[remote_name]\\n\\n for ref in self._cache.all_refs():\\n with self._cache.package_layout(ref).update_metadata() as metadata:\\n if metadata.recipe.remote == remote_name:\\n metadata.recipe.remote = None\\n for pkg_metadata in metadata.packages.values():\\n if pkg_metadata.remote == remote_name:\\n pkg_metadata.remote = None\\n\\n remotes.save(self._filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::RemoteRegistry.define\",\n \"text\": \"conans/client/cache/remote_registry.py::RemoteRegistry.define\\n def define(self, remotes):\\n # For definition from conan config install\\n for ref in self._cache.all_refs():\\n with self._cache.package_layout(ref).update_metadata() as metadata:\\n if metadata.recipe.remote not in remotes:\\n metadata.recipe.remote = None\\n for pkg_metadata in metadata.packages.values():\\n if pkg_metadata.remote not in remotes:\\n pkg_metadata.remote = None\\n\\n remotes.save(self._filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::RemoteRegistry.rename\",\n \"text\": \"conans/client/cache/remote_registry.py::RemoteRegistry.rename\\n def rename(self, remote_name, new_remote_name):\\n remotes = self.load_remotes()\\n remotes.rename(remote_name, new_remote_name)\\n\\n for ref in self._cache.all_refs():\\n with self._cache.package_layout(ref).update_metadata() as metadata:\\n if metadata.recipe.remote == remote_name:\\n metadata.recipe.remote = new_remote_name\\n for pkg_metadata in metadata.packages.values():\\n if pkg_metadata.remote == remote_name:\\n pkg_metadata.remote = new_remote_name\\n\\n remotes.save(self._filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::RemoteRegistry.refs_list\",\n \"text\": \"conans/client/cache/remote_registry.py::RemoteRegistry.refs_list\\n @property\\n def refs_list(self):\\n result = {}\\n for ref in self._cache.all_refs():\\n metadata = self._cache.package_layout(ref).load_metadata()\\n if metadata.recipe.remote:\\n result[ref] = metadata.recipe.remote\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cache/remote_registry.py::RemoteRegistry.prefs_list\",\n \"text\": \"conans/client/cache/remote_registry.py::RemoteRegistry.prefs_list\\n @property\\n def prefs_list(self):\\n result = {}\\n for ref in self._cache.all_refs():\\n metadata = self._cache.package_layout(ref).load_metadata()\\n for pid, pkg_metadata in metadata.packages.items():\\n pref = PackageReference(ref, pid)\\n result[pref] = pkg_metadata.remote\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.__init__\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.__init__\\n def __init__(self, filename):\\n ConfigParser.__init__(self, allow_no_value=True)\\n self.read(filename)\\n self.filename = filename\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.env_vars\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.env_vars\\n @property\\n def env_vars(self):\\n ret = {\\\"CONAN_LOG_RUN_TO_OUTPUT\\\": self._env_c(\\\"log.run_to_output\\\", \\\"CONAN_LOG_RUN_TO_OUTPUT\\\", \\\"True\\\"),\\n \\\"CONAN_LOG_RUN_TO_FILE\\\": self._env_c(\\\"log.run_to_file\\\", \\\"CONAN_LOG_RUN_TO_FILE\\\", \\\"False\\\"),\\n \\\"CONAN_LOGGING_LEVEL\\\": self._env_c(\\\"log.level\\\", \\\"CONAN_LOGGING_LEVEL\\\", \\\"50\\\"),\\n \\\"CONAN_TRACE_FILE\\\": self._env_c(\\\"log.trace_file\\\", \\\"CONAN_TRACE_FILE\\\", None),\\n \\\"CONAN_PRINT_RUN_COMMANDS\\\": self._env_c(\\\"log.print_run_commands\\\", \\\"CONAN_PRINT_RUN_COMMANDS\\\", \\\"False\\\"),\\n \\\"CONAN_COMPRESSION_LEVEL\\\": self._env_c(\\\"general.compression_level\\\", \\\"CONAN_COMPRESSION_LEVEL\\\", \\\"9\\\"),\\n \\\"CONAN_NON_INTERACTIVE\\\": self._env_c(\\\"general.non_interactive\\\", \\\"CONAN_NON_INTERACTIVE\\\", \\\"False\\\"),\\n \\\"CONAN_SKIP_BROKEN_SYMLINKS_CHECK\\\": self._env_c(\\\"general.skip_broken_symlinks_check\\\", \\\"CONAN_SKIP_BROKEN_SYMLINKS_CHECK\\\", \\\"False\\\"),\\n \\\"CONAN_PYLINTRC\\\": self._env_c(\\\"general.pylintrc\\\", \\\"CONAN_PYLINTRC\\\", None),\\n \\\"CONAN_CACHE_NO_LOCKS\\\": self._env_c(\\\"general.cache_no_locks\\\", \\\"CONAN_CACHE_NO_LOCKS\\\", \\\"False\\\"),\\n \\\"CONAN_PYLINT_WERR\\\": self._env_c(\\\"general.pylint_werr\\\", \\\"CONAN_PYLINT_WERR\\\", None),\\n \\\"CONAN_SYSREQUIRES_SUDO\\\": self._env_c(\\\"general.sysrequires_sudo\\\", \\\"CONAN_SYSREQUIRES_SUDO\\\", \\\"False\\\"),\\n \\\"CONAN_SYSREQUIRES_MODE\\\": self._env_c(\\\"general.sysrequires_mode\\\", \\\"CONAN_SYSREQUIRES_MODE\\\", \\\"enabled\\\"),\\n \\\"CONAN_REQUEST_TIMEOUT\\\": self._env_c(\\\"general.request_timeout\\\", \\\"CONAN_REQUEST_TIMEOUT\\\", None),\\n \\\"CONAN_VS_INSTALLATION_PREFERENCE\\\": self._env_c(\\\"general.vs_installation_preference\\\", \\\"CONAN_VS_INSTALLATION_PREFERENCE\\\", None),\\n \\\"CONAN_RECIPE_LINTER\\\": self._env_c(\\\"general.recipe_linter\\\", \\\"CONAN_RECIPE_LINTER\\\", \\\"True\\\"),\\n \\\"CONAN_CPU_COUNT\\\": self._env_c(\\\"general.cpu_count\\\", \\\"CONAN_CPU_COUNT\\\", None),\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser._env_c\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser._env_c\\n def _env_c(self, var_name, env_var_name, default_value):\\n env = os.environ.get(env_var_name, None)\\n if env is not None:\\n return env\\n try:\\n return unquote(self.get_item(var_name))\\n except ConanException:\\n return default_value\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.get_item\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.get_item\\n def get_item(self, item):\\n if not item:\\n return load(self.filename)\\n\\n tokens = item.split(\\\".\\\", 1)\\n section_name = tokens[0]\\n try:\\n section = self.items(section_name)\\n except NoSectionError:\\n raise ConanException(\\\"'%s' is not a section of conan.conf\\\" % section_name)\\n if len(tokens) == 1:\\n result = []\\n if section_name == \\\"hooks\\\":\\n for key, _ in section:\\n result.append(key)\\n return \\\",\\\".join(result)\\n else:\\n for section_item in section:\\n result.append(\\\" = \\\".join(section_item))\\n return \\\"\\\\n\\\".join(result)\\n else:\\n key = tokens[1]\\n try:\\n value = dict(section)[key]\\n if \\\" #\\\" in value: # Comments\\n value = value[:value.find(\\\" #\\\")].strip()\\n except KeyError:\\n raise ConanException(\\\"'%s' doesn't exist in [%s]\\\" % (key, section_name))\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.set_item\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.set_item\\n def set_item(self, key, value):\\n tokens = key.split(\\\".\\\", 1)\\n section_name = tokens[0]\\n if not self.has_section(section_name):\\n self.add_section(section_name)\\n\\n if len(tokens) == 1: # defining full section\\n raise ConanException(\\\"You can't set a full section, please specify a key=value\\\")\\n\\n key = tokens[1]\\n try:\\n super(ConanClientConfigParser, self).set(section_name, key, value)\\n except ValueError:\\n # https://github.com/conan-io/conan/issues/4110\\n value = value.replace(\\\"%\\\", \\\"%%\\\")\\n super(ConanClientConfigParser, self).set(section_name, key, value)\\n\\n with open(self.filename, \\\"w\\\") as f:\\n self.write(f)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.rm_item\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.rm_item\\n def rm_item(self, item):\\n tokens = item.split(\\\".\\\", 1)\\n section_name = tokens[0]\\n if not self.has_section(section_name):\\n raise ConanException(\\\"'%s' is not a section of conan.conf\\\" % section_name)\\n\\n if len(tokens) == 1:\\n self.remove_section(tokens[0])\\n else:\\n key = tokens[1]\\n if not self.has_option(section_name, key):\\n raise ConanException(\\\"'%s' doesn't exist in [%s]\\\" % (key, section_name))\\n self.remove_option(section_name, key)\\n\\n with open(self.filename, \\\"w\\\") as f:\\n self.write(f)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.get_conf\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.get_conf\\n def get_conf(self, varname):\\n \\\"\\\"\\\"Gets the section from config file or raises an exception\\\"\\\"\\\"\\n try:\\n return self.items(varname)\\n except NoSectionError:\\n raise ConanException(\\\"Invalid configuration, missing %s\\\" % varname)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.default_profile\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.default_profile\\n @property\\n def default_profile(self):\\n ret = os.environ.get(\\\"CONAN_DEFAULT_PROFILE_PATH\\\", None)\\n if ret:\\n if not os.path.isabs(ret):\\n from conans.client.cache.cache import PROFILES_FOLDER\\n profiles_folder = os.path.join(os.path.dirname(self.filename), PROFILES_FOLDER)\\n ret = os.path.abspath(os.path.join(profiles_folder, ret))\\n\\n if not os.path.exists(ret):\\n raise ConanException(\\\"Environment variable 'CONAN_DEFAULT_PROFILE_PATH' \\\"\\n \\\"must point to an existing profile file.\\\")\\n return ret\\n else:\\n try:\\n return unquote(self.get_item(\\\"general.default_profile\\\"))\\n except ConanException:\\n return DEFAULT_PROFILE_NAME\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.cache_no_locks\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.cache_no_locks\\n @property\\n def cache_no_locks(self):\\n try:\\n return get_env(\\\"CONAN_CACHE_NO_LOCKS\\\", False)\\n except ConanException:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.storage\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.storage\\n @property\\n def storage(self):\\n return dict(self.get_conf(\\\"storage\\\"))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.request_timeout\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.request_timeout\\n @property\\n def request_timeout(self):\\n try:\\n return self.get_item(\\\"general.request_timeout\\\")\\n except ConanException:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.revisions_enabled\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.revisions_enabled\\n @property\\n def revisions_enabled(self):\\n try:\\n revisions_enabled = get_env(\\\"CONAN_REVISIONS_ENABLED\\\")\\n if revisions_enabled is None:\\n try:\\n revisions_enabled = self.get_item(\\\"general.revisions_enabled\\\")\\n except ConanException:\\n return False\\n return revisions_enabled.lower() in (\\\"1\\\", \\\"true\\\")\\n except ConanException:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.default_package_id_mode\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.default_package_id_mode\\n @property\\n def default_package_id_mode(self):\\n try:\\n default_package_id_mode = get_env(\\\"CONAN_DEFAULT_PACKAGE_ID_MODE\\\")\\n if default_package_id_mode is None:\\n default_package_id_mode = self.get_item(\\\"general.default_package_id_mode\\\")\\n except ConanException:\\n return \\\"semver_direct_mode\\\"\\n return default_package_id_mode\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.storage_path\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.storage_path\\n @property\\n def storage_path(self):\\n # Try with CONAN_STORAGE_PATH\\n result = get_env('CONAN_STORAGE_PATH', None)\\n if not result:\\n # Try with conan.conf \\\"path\\\"\\n try:\\n # TODO: Fix this mess for Conan 2.0\\n env_conan_user_home = os.getenv(\\\"CONAN_USER_HOME\\\")\\n current_dir = os.path.dirname(self.filename)\\n # if env var is declared, any specified path will be relative to CONAN_USER_HOME\\n # even with the ~/\\n result = self.storage[\\\"path\\\"]\\n if result.startswith(\\\".\\\"):\\n result = os.path.abspath(os.path.join(current_dir, result))\\n elif result[:2] == \\\"~/\\\":\\n if env_conan_user_home:\\n result = os.path.join(env_conan_user_home, result[2:])\\n except (KeyError, ConanException): # If storage not defined, to return None\\n pass\\n\\n if result:\\n result = conan_expand_user(result)\\n if not os.path.isabs(result):\\n raise ConanException(\\\"Conan storage path has to be an absolute path\\\")\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.proxies\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.proxies\\n @property\\n def proxies(self):\\n \\\"\\\"\\\" optional field, might not exist\\n \\\"\\\"\\\"\\n try:\\n proxies = self.get_conf(\\\"proxies\\\")\\n # If there is proxies section, but empty, it will try to use system proxy\\n if not proxies:\\n # We don't have evidences that this following line is necessary.\\n # If the proxies has been\\n # configured at system level, conan will use it, and shouldn't be necessary\\n # to return here the proxies read from the system.\\n # Furthermore, the urls excluded for use proxies at system level do not work in\\n # this case, then the only way is to remove the [proxies] section with\\n # conan config remote proxies, then this method will return None and the proxies\\n # dict passed to requests will be empty.\\n # We don't remove this line because we are afraid to break something, but maybe\\n # until now is working because no one is using system-wide proxies or those proxies\\n # rules don't contain excluded urls.c #1777\\n return urllib.request.getproxies()\\n result = {k: (None if v == \\\"None\\\" else v) for k, v in proxies}\\n return result\\n except:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/__init__.py::ConanClientConfigParser.cacert_path\",\n \"text\": \"conans/client/conf/__init__.py::ConanClientConfigParser.cacert_path\\n @property\\n def cacert_path(self):\\n try:\\n cacert_path = get_env(\\\"CONAN_CACERT_PATH\\\")\\n if not cacert_path:\\n cacert_path = self.get_item(\\\"general.cacert_path\\\")\\n except ConanException:\\n cacert_path = os.path.join(os.path.dirname(self.filename), CACERT_FILE)\\n else:\\n # For explicit cacert files, the file should already exist\\n if not os.path.exists(cacert_path):\\n raise ConanException(\\\"Configured file for 'cacert_path'\\\"\\n \\\" doesn't exists: '{}'\\\".format(cacert_path))\\n return cacert_path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_hide_password\",\n \"text\": \"conans/client/conf/config_installer.py::_hide_password\\ndef _hide_password(resource):\\n \\\"\\\"\\\"\\n Hide password from url/file path\\n\\n :param resource: string with url or file path\\n :return: resource with hidden password if present\\n \\\"\\\"\\\"\\n password = urlparse(resource).password\\n return resource.replace(password, \\\"<hidden>\\\") if password else resource\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_handle_remotes\",\n \"text\": \"conans/client/conf/config_installer.py::_handle_remotes\\ndef _handle_remotes(cache, remote_file):\\n # FIXME: Should we encourage to pass the remotes in json?\\n remotes, _ = load_registry_txt(load(remote_file))\\n cache.registry.define(remotes)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::tmp_config_install_folder\",\n \"text\": \"conans/client/conf/config_installer.py::tmp_config_install_folder\\n@contextmanager\\ndef tmp_config_install_folder(cache):\\n tmp_folder = os.path.join(cache.conan_folder, \\\"tmp_config_install\\\")\\n # necessary for Mac OSX, where the temp folders in /var/ are symlinks to /private/var/\\n tmp_folder = os.path.realpath(tmp_folder)\\n mkdir(tmp_folder)\\n try:\\n yield tmp_folder\\n finally:\\n rmdir(tmp_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_process_git_repo\",\n \"text\": \"conans/client/conf/config_installer.py::_process_git_repo\\ndef _process_git_repo(config, cache, output):\\n output.info(\\\"Trying to clone repo: %s\\\" % config.uri)\\n with tmp_config_install_folder(cache) as tmp_folder:\\n with tools.chdir(tmp_folder):\\n try:\\n args = config.args or \\\"\\\"\\n git = Git(verify_ssl=config.verify_ssl, output=output)\\n git.clone(config.uri, args=args)\\n output.info(\\\"Repo cloned!\\\")\\n except Exception as e:\\n raise ConanException(\\\"Can't clone repo: %s\\\" % str(e))\\n _process_folder(config, tmp_folder, cache, output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_process_zip_file\",\n \"text\": \"conans/client/conf/config_installer.py::_process_zip_file\\ndef _process_zip_file(config, zippath, cache, output, tmp_folder, remove=False):\\n unzip(zippath, tmp_folder, output=output)\\n if remove:\\n os.unlink(zippath)\\n _process_folder(config, tmp_folder, cache, output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_handle_conan_conf\",\n \"text\": \"conans/client/conf/config_installer.py::_handle_conan_conf\\ndef _handle_conan_conf(current_conan_conf, new_conan_conf_path):\\n current_conan_conf.read(new_conan_conf_path)\\n with open(current_conan_conf.filename, \\\"w\\\") as f:\\n current_conan_conf.write(f)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_process_folder\",\n \"text\": \"conans/client/conf/config_installer.py::_process_folder\\ndef _process_folder(config, folder, cache, output):\\n if config.source_folder:\\n folder = os.path.join(folder, config.source_folder)\\n for root, dirs, files in walk(folder):\\n dirs[:] = [d for d in dirs if d != \\\".git\\\"]\\n if \\\".git\\\" in root:\\n continue\\n for f in files:\\n if f == \\\"settings.yml\\\":\\n output.info(\\\"Installing settings.yml\\\")\\n settings_path = cache.settings_path\\n shutil.copy(os.path.join(root, f), settings_path)\\n elif f == \\\"conan.conf\\\":\\n output.info(\\\"Processing conan.conf\\\")\\n _handle_conan_conf(cache.config, os.path.join(root, f))\\n elif f == \\\"remotes.txt\\\":\\n output.info(\\\"Defining remotes from remotes.txt\\\")\\n _handle_remotes(cache, os.path.join(root, f))\\n elif f in (\\\"registry.txt\\\", \\\"registry.json\\\"):\\n os.remove(cache.registry_path)\\n shutil.copy(os.path.join(root, f), cache.conan_folder)\\n migrate_registry_file(cache, output)\\n elif f == \\\"remotes.json\\\":\\n # Fix for Conan 2.0\\n raise ConanException(\\\"remotes.json install is not supported yet. Use 'remotes.txt'\\\")\\n else:\\n # This is ugly, should be removed in Conan 2.0\\n if root == folder and f in (\\\"README.md\\\", \\\"LICENSE.txt\\\"):\\n output.info(\\\"Skip %s\\\" % f)\\n continue\\n relpath = os.path.relpath(root, folder)\\n if config.target_folder:\\n target_folder = os.path.join(cache.conan_folder, config.target_folder,\\n relpath)\\n else:\\n target_folder = os.path.join(cache.conan_folder, relpath)\\n mkdir(target_folder)\\n output.info(\\\"Copying file %s to %s\\\" % (f, target_folder))\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_process_download\",\n \"text\": \"conans/client/conf/config_installer.py::_process_download\\ndef _process_download(config, cache, output, requester):\\n with tmp_config_install_folder(cache) as tmp_folder:\\n output.info(\\\"Trying to download %s\\\" % _hide_password(config.uri))\\n zippath = os.path.join(tmp_folder, \\\"config.zip\\\")\\n try:\\n tools.download(config.uri, zippath, out=output, verify=config.verify_ssl,\\n requester=requester)\\n _process_zip_file(config, zippath, cache, output, tmp_folder, remove=True)\\n except Exception as e:\\n raise ConanException(\\\"Error while installing config from %s\\\\n%s\\\" % (config.uri, str(e)))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_ConfigOrigin.__init__\",\n \"text\": \"conans/client/conf/config_installer.py::_ConfigOrigin.__init__\\n def __init__(self, data):\\n self.type = data.get(\\\"type\\\")\\n self.uri = data.get(\\\"uri\\\")\\n self.verify_ssl = data.get(\\\"verify_ssl\\\")\\n self.args = data.get(\\\"args\\\")\\n self.source_folder = data.get(\\\"source_folder\\\")\\n self.target_folder = data.get(\\\"target_folder\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_ConfigOrigin.__eq__\",\n \"text\": \"conans/client/conf/config_installer.py::_ConfigOrigin.__eq__\\n def __eq__(self, other):\\n return (self.type == other.type and self.uri == other.uri and\\n self.args == other.args and self.source_folder == other.source_folder\\n and self.target_folder == other.target_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_ConfigOrigin.__ne__\",\n \"text\": \"conans/client/conf/config_installer.py::_ConfigOrigin.__ne__\\n def __ne__(self, other):\\n return not self.__eq__(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_ConfigOrigin.json\",\n \"text\": \"conans/client/conf/config_installer.py::_ConfigOrigin.json\\n def json(self):\\n return {\\\"type\\\": self.type,\\n \\\"uri\\\": self.uri,\\n \\\"verify_ssl\\\": self.verify_ssl,\\n \\\"args\\\": self.args,\\n \\\"source_folder\\\": self.source_folder,\\n \\\"target_folder\\\": self.target_folder}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_ConfigOrigin.from_item\",\n \"text\": \"conans/client/conf/config_installer.py::_ConfigOrigin.from_item\\n @staticmethod\\n def from_item(uri, config_type, verify_ssl, args, source_folder, target_folder):\\n config = _ConfigOrigin({})\\n if config_type:\\n config.type = config_type\\n else:\\n if uri.endswith(\\\".git\\\"):\\n config.type = \\\"git\\\"\\n elif os.path.isdir(uri):\\n config.type = \\\"dir\\\"\\n elif os.path.isfile(uri):\\n config.type = \\\"file\\\"\\n elif uri.startswith(\\\"http\\\"):\\n config.type = \\\"url\\\"\\n else:\\n raise ConanException(\\\"Unable to deduce type config install: %s\\\" % uri)\\n config.source_folder = source_folder\\n config.target_folder = target_folder\\n config.args = args\\n config.verify_ssl = verify_ssl\\n if os.path.exists(uri):\\n uri = os.path.abspath(uri)\\n config.uri = uri\\n return config\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_process_config\",\n \"text\": \"conans/client/conf/config_installer.py::_process_config\\ndef _process_config(config, cache, output, requester):\\n if config.type == \\\"git\\\":\\n _process_git_repo(config, cache, output)\\n elif config.type == \\\"dir\\\":\\n _process_folder(config, config.uri, cache, output)\\n elif config.type == \\\"file\\\":\\n with tmp_config_install_folder(cache) as tmp_folder:\\n _process_zip_file(config, config.uri, cache, output, tmp_folder)\\n elif config.type == \\\"url\\\":\\n _process_download(config, cache, output, requester=requester)\\n else:\\n raise ConanException(\\\"Unable to process config install: %s\\\" % config.uri)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::_save_configs\",\n \"text\": \"conans/client/conf/config_installer.py::_save_configs\\ndef _save_configs(configs_file, configs):\\n save(configs_file, json.dumps([config.json() for config in configs],\\n indent=True))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/config_installer.py::configuration_install\",\n \"text\": \"conans/client/conf/config_installer.py::configuration_install\\ndef configuration_install(uri, cache, output, verify_ssl, requester, config_type=None,\\n args=None, source_folder=None, target_folder=None):\\n configs = []\\n configs_file = cache.config_install_file\\n if os.path.isfile(configs_file):\\n try:\\n configs = json.loads(load(configs_file))\\n except Exception as e:\\n raise ConanException(\\\"Error loading configs-install file: %s\\\\n%\\\"\\n % (configs_file, str(e)))\\n configs = [_ConfigOrigin(config) for config in configs]\\n if uri is None:\\n if config_type or args or not verify_ssl: # Not the defaults\\n if not configs:\\n raise ConanException(\\\"Called config install without arguments\\\")\\n # Modify the last one\\n config = configs[-1]\\n config.config_type = config_type or config.type\\n config.args = args or config.args\\n config.verify_ssl = verify_ssl or config.verify_ssl\\n _process_config(config, cache, output, requester)\\n _save_configs(configs_file, configs)\\n else:\\n if not configs:\\n raise ConanException(\\\"Called config install without arguments\\\")\\n # Execute the previously stored ones\\n for config in configs:\\n output.info(\\\"Config install: %s\\\" % _hide_password(config.uri))\\n _process_config(config, cache, output, requester)\\n else:\\n # Execute and store the new one\\n config = _ConfigOrigin.from_item(uri, config_type, verify_ssl, args,\\n source_folder, target_folder)\\n _process_config(config, cache, output, requester)\\n if config not in configs:\\n configs.append(config)\\n else:\\n configs = [(c if c != config else config) for c in configs]\\n _save_configs(configs_file, configs)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/detect.py::_execute\",\n \"text\": \"conans/client/conf/detect.py::_execute\\ndef _execute(command):\\n proc = Popen(command, shell=True, bufsize=1, stdout=PIPE, stderr=STDOUT)\\n\\n output_buffer = []\\n while True:\\n line = proc.stdout.readline()\\n if not line:\\n break\\n # output.write(line)\\n output_buffer.append(str(line))\\n\\n proc.communicate()\\n return proc.returncode, \\\"\\\".join(output_buffer)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/detect.py::_gcc_compiler\",\n \"text\": \"conans/client/conf/detect.py::_gcc_compiler\\ndef _gcc_compiler(output, compiler_exe=\\\"gcc\\\"):\\n\\n try:\\n if platform.system() == \\\"Darwin\\\":\\n # In Mac OS X check if gcc is a fronted using apple-clang\\n _, out = _execute(\\\"%s --version\\\" % compiler_exe)\\n out = out.lower()\\n if \\\"clang\\\" in out:\\n return None\\n\\n ret, out = _execute('%s -dumpversion' % compiler_exe)\\n if ret != 0:\\n return None\\n compiler = \\\"gcc\\\"\\n installed_version = re.search(\\\"([0-9](\\\\.[0-9])?)\\\", out).group()\\n # Since GCC 7.1, -dumpversion return the major version number\\n # only (\\\"7\\\"). We must use -dumpfullversion to get the full version\\n # number (\\\"7.1.1\\\").\\n if installed_version:\\n output.success(\\\"Found %s %s\\\" % (compiler, installed_version))\\n major = installed_version.split(\\\".\\\")[0]\\n if int(major) >= 5:\\n output.info(\\\"gcc>=5, using the major as version\\\")\\n installed_version = major\\n return compiler, installed_version\\n except:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/detect.py::_clang_compiler\",\n \"text\": \"conans/client/conf/detect.py::_clang_compiler\\ndef _clang_compiler(output, compiler_exe=\\\"clang\\\"):\\n try:\\n ret, out = _execute('%s --version' % compiler_exe)\\n if ret != 0:\\n return None\\n if \\\"Apple\\\" in out:\\n compiler = \\\"apple-clang\\\"\\n elif \\\"clang version\\\" in out:\\n compiler = \\\"clang\\\"\\n installed_version = re.search(\\\"([0-9]+\\\\.[0-9])\\\", out).group()\\n if installed_version:\\n output.success(\\\"Found %s %s\\\" % (compiler, installed_version))\\n major = installed_version.split(\\\".\\\")[0]\\n if int(major) >= 8 and compiler == \\\"clang\\\":\\n output.info(\\\"clang>=8, using the major as version\\\")\\n installed_version = major\\n return compiler, installed_version\\n except:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/detect.py::_sun_cc_compiler\",\n \"text\": \"conans/client/conf/detect.py::_sun_cc_compiler\\ndef _sun_cc_compiler(output, compiler_exe=\\\"cc\\\"):\\n try:\\n _, out = _execute('%s -V' % compiler_exe)\\n compiler = \\\"sun-cc\\\"\\n installed_version = re.search(\\\"([0-9]+\\\\.[0-9]+)\\\", out).group()\\n if installed_version:\\n output.success(\\\"Found %s %s\\\" % (compiler, installed_version))\\n return compiler, installed_version\\n except:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/detect.py::_get_default_compiler\",\n \"text\": \"conans/client/conf/detect.py::_get_default_compiler\\ndef _get_default_compiler(output):\\n cc = os.environ.get(\\\"CC\\\", \\\"\\\")\\n cxx = os.environ.get(\\\"CXX\\\", \\\"\\\")\\n if cc or cxx: # Env defined, use them\\n output.info(\\\"CC and CXX: %s, %s \\\" % (cc or \\\"None\\\", cxx or \\\"None\\\"))\\n command = cc or cxx\\n if \\\"gcc\\\" in command:\\n gcc = _gcc_compiler(output, command)\\n if platform.system() == \\\"Darwin\\\" and gcc is None:\\n output.error(\\n \\\"%s detected as a frontend using apple-clang. Compiler not supported\\\" % command\\n )\\n return gcc\\n if \\\"clang\\\" in command.lower():\\n return _clang_compiler(output, command)\\n if platform.system() == \\\"SunOS\\\" and command.lower() == \\\"cc\\\":\\n return _sun_cc_compiler(output, command)\\n # I am not able to find its version\\n output.error(\\\"Not able to automatically detect '%s' version\\\" % command)\\n return None\\n\\n if detected_os() == \\\"Windows\\\":\\n version = latest_visual_studio_version_installed(output)\\n vs = ('Visual Studio', version) if version else None\\n gcc = _gcc_compiler(output)\\n clang = _clang_compiler(output)\\n if platform.system() == \\\"SunOS\\\":\\n sun_cc = _sun_cc_compiler(output)\\n\\n if detected_os() == \\\"Windows\\\":\\n return vs or gcc or clang\\n elif platform.system() == \\\"Darwin\\\":\\n return clang or gcc\\n elif platform.system() == \\\"SunOS\\\":\\n return sun_cc or gcc or clang\\n else:\\n return gcc or clang\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/detect.py::_detect_compiler_version\",\n \"text\": \"conans/client/conf/detect.py::_detect_compiler_version\\ndef _detect_compiler_version(result, output, profile_path):\\n try:\\n compiler, version = _get_default_compiler(output)\\n except:\\n compiler, version = None, None\\n if not compiler or not version:\\n output.error(\\\"Unable to find a working compiler\\\")\\n else:\\n result.append((\\\"compiler\\\", compiler))\\n result.append((\\\"compiler.version\\\", version))\\n if compiler == \\\"apple-clang\\\":\\n result.append((\\\"compiler.libcxx\\\", \\\"libc++\\\"))\\n elif compiler == \\\"gcc\\\":\\n result.append((\\\"compiler.libcxx\\\", \\\"libstdc++\\\"))\\n if Version(version) >= Version(\\\"5.1\\\"):\\n profile_name = os.path.basename(profile_path)\\n msg = \\\"\\\"\\\"\\nConan detected a GCC version > 5 but has adjusted the 'compiler.libcxx' setting to\\n'libstdc++' for backwards compatibility.\\nYour compiler is likely using the new CXX11 ABI by default (libstdc++11).\\n\\nIf you want Conan to use the new ABI, edit the {profile} profile at:\\n\\n {profile_path}\\n\\nadjusting 'compiler.libcxx=libstdc++11'\\n\\\"\\\"\\\".format(profile=profile_name, profile_path=profile_path)\\n output.writeln(\\\"\\\\n************************* WARNING: GCC OLD ABI COMPATIBILITY \\\"\\n \\\"***********************\\\\n %s\\\\n************************************\\\"\\n \\\"************************************************\\\\n\\\\n\\\\n\\\" % msg,\\n Color.BRIGHT_RED)\\n elif compiler == \\\"cc\\\":\\n if platform.system() == \\\"SunOS\\\":\\n result.append((\\\"compiler.libstdcxx\\\", \\\"libstdcxx4\\\"))\\n elif compiler == \\\"clang\\\":\\n if platform.system() == \\\"FreeBSD\\\":\\n result.append((\\\"compiler.libcxx\\\", \\\"libc++\\\"))\\n else:\\n result.append((\\\"compiler.libcxx\\\", \\\"libstdc++\\\"))\\n elif compiler == \\\"sun-cc\\\":\\n result.append((\\\"compiler.libcxx\\\", \\\"libCstd\\\"))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conf/detect.py::_detect_os_arch\",\n \"text\": \"conans/client/conf/detect.py::_detect_os_arch\\ndef _detect_os_arch(result, output):\\n architectures = {'i386': 'x86',\\n 'i686': 'x86',\\n 'i86pc': 'x86',\\n 'amd64': 'x86_64',\\n 'aarch64': 'armv8',\\n 'sun4v': 'sparc'}\\n the_os = detected_os()\\n result.append((\\\"os\\\", the_os))\\n result.append((\\\"os_build\\\", the_os))\\n platform_machine = platform.machine().lower()\\n if platform_machine:\\n arch = architectures.get(platform_machine, platform_machine)\\n if arch.startswith('arm'):\\n for a in (\\\"armv6\\\", \\\"armv7hf\\\", \\\"armv7\\\", \\\"armv8\\\"):\\n if arch.startswith(a):\\n arch = a\\n break\\n else:\\n output.error(\\\"Your ARM '%s' architecture is probably not defined in settings.yml\\\\n\\\"\\n \\\"Please check your conan.conf and settings.yml files\\\" % arch)\\n result.append((\\\"arch\\\", arch))\\n result.append((\\\"arch_build\\\", arch))\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/conf/detect.py::detect_defaults_settings\",\n \"text\": \"conans/client/conf/detect.py::detect_defaults_settings\\ndef detect_defaults_settings(output, profile_path):\\n \\\"\\\"\\\" try to deduce current machine values without any constraints at all\\n :param output: Conan Output instance\\n :param profile_path: Conan profile file path\\n :return: A list with default settings\\n \\\"\\\"\\\"\\n result = []\\n _detect_os_arch(result, output)\\n _detect_compiler_version(result, output, profile_path)\\n result.append((\\\"build_type\\\", \\\"Release\\\"))\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/compiler_args.py::CompilerArgsGenerator.filename\",\n \"text\": \"conans/client/generators/compiler_args.py::CompilerArgsGenerator.filename\\n @property\\n def filename(self):\\n return BUILD_INFO_COMPILER_ARGS\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/compiler_args.py::CompilerArgsGenerator.compiler\",\n \"text\": \"conans/client/generators/compiler_args.py::CompilerArgsGenerator.compiler\\n @property\\n def compiler(self):\\n return self.conanfile.settings.get_safe(\\\"compiler\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/compiler_args.py::CompilerArgsGenerator.content\",\n \"text\": \"conans/client/generators/compiler_args.py::CompilerArgsGenerator.content\\n @property\\n def content(self):\\n \\\"\\\"\\\"With compiler_args you can invoke your compiler:\\n $ gcc main.c @conanbuildinfo.args -o main\\n $ clang main.c @conanbuildinfo.args -o main\\n $ cl /EHsc main.c @conanbuildinfo.args\\n \\\"\\\"\\\"\\n flags = []\\n flags.extend(format_defines(self._deps_build_info.defines))\\n flags.extend(format_include_paths(self._deps_build_info.include_paths,\\n compiler=self.compiler))\\n\\n flags.extend(self._deps_build_info.cxxflags)\\n flags.extend(self._deps_build_info.cflags)\\n\\n arch_flag = architecture_flag(arch=self.conanfile.settings.get_safe(\\\"arch\\\"),\\n compiler=self.compiler)\\n if arch_flag:\\n flags.append(arch_flag)\\n\\n build_type = self.conanfile.settings.get_safe(\\\"build_type\\\")\\n btfs = build_type_flags(compiler=self.compiler, build_type=build_type,\\n vs_toolset=self.conanfile.settings.get_safe(\\\"compiler.toolset\\\"))\\n if btfs:\\n flags.extend(btfs)\\n btd = build_type_define(build_type=build_type)\\n if btd:\\n flags.extend(format_defines([btd]))\\n\\n if self.compiler == \\\"Visual Studio\\\":\\n runtime = visual_runtime(self.conanfile.settings.get_safe(\\\"compiler.runtime\\\"))\\n if runtime:\\n flags.append(runtime)\\n # Necessary in the \\\"cl\\\" invocation before specify the rest of linker flags\\n flags.append(visual_linker_option_separator)\\n\\n the_os = (self.conanfile.settings.get_safe(\\\"os_build\\\") or\\n self.conanfile.settings.get_safe(\\\"os\\\"))\\n flags.extend(rpath_flags(the_os, self.compiler, self._deps_build_info.lib_paths))\\n flags.extend(format_library_paths(self._deps_build_info.lib_paths, compiler=self.compiler))\\n flags.extend(format_libraries(self._deps_build_\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/generators/compiler_args.py::CompilerArgsGenerator._libcxx_flags\",\n \"text\": \"conans/client/generators/compiler_args.py::CompilerArgsGenerator._libcxx_flags\\n def _libcxx_flags(self):\\n libcxx = self.conanfile.settings.get_safe(\\\"compiler.libcxx\\\")\\n compiler = self.conanfile.settings.get_safe(\\\"compiler\\\")\\n\\n lib_flags = []\\n if libcxx:\\n stdlib_define = libcxx_define(compiler=compiler, libcxx=libcxx)\\n lib_flags.extend(format_defines([stdlib_define]))\\n cxxf = libcxx_flag(compiler=compiler, libcxx=libcxx)\\n if cxxf:\\n lib_flags.append(cxxf)\\n\\n return lib_flags\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/visualstudio_multi.py::_VSSettings.__init__\",\n \"text\": \"conans/client/generators/visualstudio_multi.py::_VSSettings.__init__\\n def __init__(self, settings):\\n toolset = msvs_toolset(settings)\\n if toolset is None:\\n raise ConanException(\\\"Undefined Visual Studio version %s\\\" %\\n settings.get_safe(\\\"compiler.version\\\"))\\n\\n self._props = [(\\\"Configuration\\\", settings.get_safe(\\\"build_type\\\")),\\n (\\\"Platform\\\", {'x86': 'Win32',\\n 'x86_64': 'x64'}.get(settings.get_safe(\\\"arch\\\"))),\\n (\\\"PlatformToolset\\\", toolset)]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/visualstudio_multi.py::_VSSettings.filename\",\n \"text\": \"conans/client/generators/visualstudio_multi.py::_VSSettings.filename\\n @property\\n def filename(self):\\n name = \\\"conanbuildinfo%s.props\\\" % \\\"\\\".join(\\\"_%s\\\" % v for _, v in self._props if v)\\n return name.lower()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/visualstudio_multi.py::_VSSettings.condition\",\n \"text\": \"conans/client/generators/visualstudio_multi.py::_VSSettings.condition\\n @property\\n def condition(self):\\n return \\\" And \\\".join(\\\"'$(%s)' == '%s'\\\" % (k, v) for k, v in self._props if v)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.filename\",\n \"text\": \"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.filename\\n @property\\n def filename(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.content\",\n \"text\": \"conans/client/generators/visualstudio_multi.py::VisualStudioMultiGenerator.content\\n @property\\n def content(self):\\n vs_settings = _VSSettings(self.conanfile.settings)\\n condition = vs_settings.condition\\n name_current = vs_settings.filename\\n name_multi = \\\"conanbuildinfo_multi.props\\\"\\n\\n # read the exising mult_filename or use the template if it doesn't exist\\n multi_path = os.path.join(self.output_path, name_multi)\\n if os.path.isfile(multi_path):\\n content_multi = load(multi_path)\\n else:\\n content_multi = self.multi_content_template\\n\\n # parse the multi_file and add a new import statement if needed\\n dom = minidom.parseString(content_multi)\\n import_group = dom.getElementsByTagName('ImportGroup')[0]\\n children = import_group.getElementsByTagName(\\\"Import\\\")\\n for node in children:\\n if name_current == node.getAttribute(\\\"Project\\\") and condition == node.getAttribute(\\\"Condition\\\"):\\n # the import statement already exists\\n break\\n else:\\n # create a new import statement\\n import_node = dom.createElement('Import')\\n import_node.setAttribute('Condition', condition)\\n import_node.setAttribute('Project', name_current)\\n # add it to the import group\\n import_group.appendChild(import_node)\\n content_multi = dom.toprettyxml()\\n content_multi = \\\"\\\\n\\\".join(line for line in content_multi.splitlines() if line.strip())\\n\\n return {name_multi: content_multi,\\n vs_settings.filename: VisualStudioGenerator(self.conanfile).content}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/importer.py::undo_imports\",\n \"text\": \"conans/client/importer.py::undo_imports\\ndef undo_imports(current_path, output):\\n manifest_path = os.path.join(current_path, IMPORTS_MANIFESTS)\\n try:\\n manifest_content = load(manifest_path)\\n except:\\n raise ConanException(\\\"Cannot load file %s\\\" % manifest_path)\\n\\n try:\\n manifest = FileTreeManifest.loads(manifest_content)\\n except:\\n raise ConanException(\\\"Wrong manifest file format %s\\\" % manifest_path)\\n\\n not_removed = 0\\n files = manifest.files()\\n for filepath in files:\\n if not os.path.exists(filepath):\\n output.warn(\\\"File doesn't exist: %s\\\" % filepath)\\n continue\\n try:\\n os.remove(filepath)\\n except:\\n output.error(\\\"Cannot remove file (open or busy): %s\\\" % filepath)\\n not_removed += 1\\n\\n if not_removed:\\n raise ConanException(\\\"Cannot remove %s or more imported files\\\" % not_removed)\\n\\n output.success(\\\"Removed %s imported files\\\" % (len(files)))\\n try:\\n os.remove(manifest_path)\\n output.success(\\\"Removed imports manifest file: %s\\\" % manifest_path)\\n except:\\n raise ConanException(\\\"Cannot remove manifest file (open or busy): %s\\\" % manifest_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/importer.py::_report_save_manifest\",\n \"text\": \"conans/client/importer.py::_report_save_manifest\\ndef _report_save_manifest(copied_files, output, dest_folder, manifest_name):\\n report_copied_files(copied_files, output)\\n if copied_files:\\n date = calendar.timegm(time.gmtime())\\n file_dict = {}\\n for f in copied_files:\\n abs_path = os.path.join(dest_folder, f)\\n file_dict[f] = md5sum(abs_path)\\n manifest = FileTreeManifest(date, file_dict)\\n manifest.save(dest_folder, manifest_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/importer.py::_make_files_writable\",\n \"text\": \"conans/client/importer.py::_make_files_writable\\ndef _make_files_writable(file_names):\\n if not get_env(\\\"CONAN_READ_ONLY_CACHE\\\", False):\\n return\\n\\n for file_name in file_names:\\n os.chmod(file_name, os.stat(file_name).st_mode | stat.S_IWRITE)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/importer.py::run_imports\",\n \"text\": \"conans/client/importer.py::run_imports\\ndef run_imports(conanfile, dest_folder):\\n if not hasattr(conanfile, \\\"imports\\\"):\\n return []\\n file_importer = _FileImporter(conanfile, dest_folder)\\n conanfile.copy = file_importer\\n conanfile.imports_folder = dest_folder\\n with get_env_context_manager(conanfile):\\n with tools.chdir(dest_folder):\\n conanfile.imports()\\n copied_files = file_importer.copied_files\\n _make_files_writable(copied_files)\\n import_output = ScopedOutput(\\\"%s imports()\\\" % conanfile.display_name, conanfile.output)\\n _report_save_manifest(copied_files, import_output, dest_folder, IMPORTS_MANIFESTS)\\n return copied_files\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/importer.py::remove_imports\",\n \"text\": \"conans/client/importer.py::remove_imports\\ndef remove_imports(conanfile, copied_files, output):\\n if not getattr(conanfile, \\\"keep_imports\\\", False):\\n for f in copied_files:\\n try:\\n os.remove(f)\\n except OSError:\\n output.warn(\\\"Unable to remove imported file from build: %s\\\" % f)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/importer.py::run_deploy\",\n \"text\": \"conans/client/importer.py::run_deploy\\ndef run_deploy(conanfile, install_folder):\\n deploy_output = ScopedOutput(\\\"%s deploy()\\\" % conanfile.display_name, conanfile.output)\\n file_importer = _FileImporter(conanfile, install_folder)\\n package_copied = set()\\n\\n # This is necessary to capture FileCopier full destination paths\\n # Maybe could be improved in FileCopier\\n def file_copier(*args, **kwargs):\\n file_copy = FileCopier([conanfile.package_folder], install_folder)\\n copied = file_copy(*args, **kwargs)\\n _make_files_writable(copied)\\n package_copied.update(copied)\\n\\n conanfile.copy_deps = file_importer\\n conanfile.copy = file_copier\\n conanfile.install_folder = install_folder\\n with get_env_context_manager(conanfile):\\n with tools.chdir(install_folder):\\n conanfile.deploy()\\n\\n copied_files = file_importer.copied_files\\n copied_files.update(package_copied)\\n _report_save_manifest(copied_files, deploy_output, install_folder, \\\"deploy_manifest.txt\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/importer.py::run_deploy.file_copier\",\n \"text\": \"conans/client/importer.py::run_deploy.file_copier\\n def file_copier(*args, **kwargs):\\n file_copy = FileCopier([conanfile.package_folder], install_folder)\\n copied = file_copy(*args, **kwargs)\\n _make_files_writable(copied)\\n package_copied.update(copied)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/importer.py::_FileImporter.__init__\",\n \"text\": \"conans/client/importer.py::_FileImporter.__init__\\n def __init__(self, conanfile, dst_folder):\\n self._conanfile = conanfile\\n self._dst_folder = dst_folder\\n self.copied_files = set()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/importer.py::_FileImporter.__call__\",\n \"text\": \"conans/client/importer.py::_FileImporter.__call__\\n def __call__(self, pattern, dst=\\\"\\\", src=\\\"\\\", root_package=None, folder=False,\\n ignore_case=False, excludes=None, keep_path=True):\\n \\\"\\\"\\\"\\n param pattern: an fnmatch file pattern of the files that should be copied. Eg. *.dll\\n param dst: the destination local folder, wrt to current conanfile dir, to which\\n the files will be copied. Eg: \\\"bin\\\"\\n param src: the source folder in which those files will be searched. This folder\\n will be stripped from the dst name. Eg.: lib/Debug/x86\\n param root_package: fnmatch pattern of the package name (\\\"OpenCV\\\", \\\"Boost\\\") from\\n which files will be copied. Default: all packages in deps\\n \\\"\\\"\\\"\\n if os.path.isabs(dst):\\n real_dst_folder = dst\\n else:\\n real_dst_folder = os.path.normpath(os.path.join(self._dst_folder, dst))\\n\\n matching_paths = self._get_folders(root_package)\\n for name, matching_path in matching_paths.items():\\n final_dst_path = os.path.join(real_dst_folder, name) if folder else real_dst_folder\\n file_copier = FileCopier([matching_path], final_dst_path)\\n files = file_copier(pattern, src=src, links=True, ignore_case=ignore_case,\\n excludes=excludes, keep_path=keep_path)\\n self.copied_files.update(files)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/importer.py::_FileImporter._get_folders\",\n \"text\": \"conans/client/importer.py::_FileImporter._get_folders\\n def _get_folders(self, pattern):\\n \\\"\\\"\\\" given the current deps graph, compute a dict {name: store-path} of\\n each dependency\\n \\\"\\\"\\\"\\n if not pattern:\\n return {pkg: cpp_info.rootpath\\n for pkg, cpp_info in self._conanfile.deps_cpp_info.dependencies}\\n return {pkg: cpp_info.rootpath\\n for pkg, cpp_info in self._conanfile.deps_cpp_info.dependencies\\n if fnmatch.fnmatch(pkg, pattern)}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.__init__\",\n \"text\": \"conans/client/manager.py::ConanManager.__init__\\n def __init__(self, cache, user_io, remote_manager,\\n recorder, graph_manager, hook_manager):\\n assert isinstance(user_io, UserIO)\\n assert isinstance(cache, ClientCache)\\n self._cache = cache\\n self._user_io = user_io\\n self._remote_manager = remote_manager\\n self._recorder = recorder\\n self._graph_manager = graph_manager\\n self._hook_manager = hook_manager\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/manager.py::ConanManager.install\",\n \"text\": \"conans/client/manager.py::ConanManager.install\\n def install(self, ref_or_path, install_folder, graph_info, remotes=None, build_modes=None,\\n update=False, manifest_folder=None, manifest_verify=False,\\n manifest_interactive=False, generators=None, no_imports=False, create_reference=None,\\n keep_build=False):\\n \\\"\\\"\\\" Fetch and build all dependencies for the given reference\\n @param ref_or_path: ConanFileReference or path to user space conanfile\\n @param install_folder: where the output files will be saved\\n @param remote_name: install only from that remote\\n @param profile: Profile object with both the -s introduced options and profile read values\\n @param build_modes: List of build_modes specified\\n @param update: Check for updated in the upstream remotes (and update)\\n @param manifest_folder: Folder to install the manifests\\n @param manifest_verify: Verify dependencies manifests against stored ones\\n @param manifest_interactive: Install deps manifests in folder for later verify, asking user\\n for confirmation\\n @param generators: List of generators from command line. If False, no generator will be\\n written\\n @param no_imports: Install specified packages but avoid running imports\\n \\\"\\\"\\\"\\n\\n if generators is not False:\\n generators = set(generators) if generators else set()\\n generators.add(\\\"txt\\\") # Add txt generator by default\\n\\n self._user_io.out.info(\\\"Configuration:\\\")\\n self._user_io.out.writeln(graph_info.profile.dumps())\\n result = self._graph_manager.load_graph(ref_or_path, create_reference, graph_info,\\n build_modes, False, update, remotes,\\n self._recorder)\\n deps_graph, conanfile = result\\n\\n if conanfile.display_name == \\\"virtual\\\":\\n self._user_io.out.highlight(\\\"Installing p\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::ClientMigrator.__init__\",\n \"text\": \"conans/client/migrations.py::ClientMigrator.__init__\\n def __init__(self, cache, current_version, out):\\n self.cache = cache\\n super(ClientMigrator, self).__init__(cache.conan_folder, cache.store,\\n current_version, out)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::ClientMigrator._update_settings_yml\",\n \"text\": \"conans/client/migrations.py::ClientMigrator._update_settings_yml\\n def _update_settings_yml(self, old_version):\\n\\n from conans.client.conf import default_settings_yml\\n settings_path = self.cache.settings_path\\n if not os.path.exists(settings_path):\\n self.out.warn(\\\"Migration: This conan installation doesn't have settings yet\\\")\\n self.out.warn(\\\"Nothing to migrate here, settings will be generated automatically\\\")\\n return\\n\\n var_name = \\\"settings_{}\\\".format(old_version.replace(\\\".\\\", \\\"_\\\"))\\n\\n def save_new():\\n new_path = self.cache.settings_path + \\\".new\\\"\\n save(new_path, default_settings_yml)\\n self.out.warn(\\\"*\\\" * 40)\\n self.out.warn(\\\"settings.yml is locally modified, can't be updated\\\")\\n self.out.warn(\\\"The new settings.yml has been stored in: %s\\\" % new_path)\\n self.out.warn(\\\"*\\\" * 40)\\n\\n self.out.warn(\\\"Migration: Updating settings.yml\\\")\\n if hasattr(migrations_settings, var_name):\\n version_default_contents = getattr(migrations_settings, var_name)\\n if version_default_contents != default_settings_yml:\\n current_settings = load(self.cache.settings_path)\\n if current_settings != version_default_contents:\\n save_new()\\n else:\\n save(self.cache.settings_path, default_settings_yml)\\n else:\\n self.out.info(\\\"Migration: Settings already up to date\\\")\\n else:\\n # We don't have the value for that version, so don't override\\n save_new()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::ClientMigrator._update_settings_yml.save_new\",\n \"text\": \"conans/client/migrations.py::ClientMigrator._update_settings_yml.save_new\\n def save_new():\\n new_path = self.cache.settings_path + \\\".new\\\"\\n save(new_path, default_settings_yml)\\n self.out.warn(\\\"*\\\" * 40)\\n self.out.warn(\\\"settings.yml is locally modified, can't be updated\\\")\\n self.out.warn(\\\"The new settings.yml has been stored in: %s\\\" % new_path)\\n self.out.warn(\\\"*\\\" * 40)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::ClientMigrator._make_migrations\",\n \"text\": \"conans/client/migrations.py::ClientMigrator._make_migrations\\n def _make_migrations(self, old_version):\\n # ############### FILL THIS METHOD WITH THE REQUIRED ACTIONS ##############\\n # VERSION 0.1\\n if old_version is None:\\n return\\n\\n # Migrate the settings if they were the default for that version\\n self._update_settings_yml(old_version)\\n\\n if old_version < Version(\\\"0.25\\\"):\\n from conans.paths import DEFAULT_PROFILE_NAME\\n default_profile_path = os.path.join(self.cache.conan_folder, PROFILES_FOLDER,\\n DEFAULT_PROFILE_NAME)\\n if not os.path.exists(default_profile_path):\\n self.out.warn(\\\"Migration: Moving default settings from %s file to %s\\\"\\n % (CONAN_CONF, DEFAULT_PROFILE_NAME))\\n conf_path = os.path.join(self.cache.conan_folder, CONAN_CONF)\\n\\n migrate_to_default_profile(conf_path, default_profile_path)\\n\\n self.out.warn(\\\"Migration: export_source cache new layout\\\")\\n migrate_c_src_export_source(self.cache, self.out)\\n\\n if old_version < Version(\\\"1.0\\\"):\\n _migrate_lock_files(self.cache, self.out)\\n\\n if old_version < Version(\\\"1.12.0\\\"):\\n migrate_plugins_to_hooks(self.cache)\\n\\n if old_version < Version(\\\"1.13.0\\\"):\\n # MIGRATE LOCAL CACHE TO GENERATE MISSING METADATA.json\\n _migrate_create_metadata(self.cache, self.out)\\n\\n if old_version < Version(\\\"1.14.0\\\"):\\n migrate_config_install(self.cache)\\n\\n if old_version < Version(\\\"1.14.2\\\"):\\n _migrate_full_metadata(self.cache, self.out)\\n\\n if old_version < Version(\\\"1.15.0\\\"):\\n migrate_registry_file(self.cache, self.out)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_get_refs\",\n \"text\": \"conans/client/migrations.py::_get_refs\\ndef _get_refs(cache):\\n folders = list_folder_subdirs(cache.store, 4)\\n return [ConanFileReference(*s.split(\\\"/\\\")) for s in folders]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_get_prefs\",\n \"text\": \"conans/client/migrations.py::_get_prefs\\ndef _get_prefs(layout):\\n packages_folder = layout.packages()\\n folders = list_folder_subdirs(packages_folder, 1)\\n return [PackageReference(layout.ref, s) for s in folders]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_migrate_full_metadata\",\n \"text\": \"conans/client/migrations.py::_migrate_full_metadata\\ndef _migrate_full_metadata(cache, out):\\n # Fix for https://github.com/conan-io/conan/issues/4898\\n out.warn(\\\"Running a full revision metadata migration\\\")\\n refs = _get_refs(cache)\\n for ref in refs:\\n try:\\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\\n layout = PackageCacheLayout(base_folder=base_folder, ref=ref, short_paths=None,\\n no_lock=True)\\n with layout.update_metadata() as metadata:\\n # Updating the RREV\\n if metadata.recipe.revision is None:\\n out.warn(\\\"Package %s metadata had recipe revision None, migrating\\\" % str(ref))\\n folder = layout.export()\\n try:\\n manifest = FileTreeManifest.load(folder)\\n rrev = manifest.summary_hash\\n except Exception:\\n rrev = DEFAULT_REVISION_V1\\n metadata.recipe.revision = rrev\\n\\n prefs = _get_prefs(layout)\\n existing_ids = [pref.id for pref in prefs]\\n for pkg_id in list(metadata.packages.keys()):\\n if pkg_id not in existing_ids:\\n out.warn(\\\"Package %s metadata had stalled package information %s, removing\\\"\\n % (str(ref), pkg_id))\\n del metadata.packages[pkg_id]\\n # UPDATING PREVS\\n for pref in prefs:\\n try:\\n pmanifest = FileTreeManifest.load(layout.package(pref))\\n prev = pmanifest.summary_hash\\n except Exception:\\n prev = DEFAULT_REVISION_V1\\n metadata.packages[pref.id].revision = prev\\n metadata.packages[pref.id].recipe_revision = metadata.recipe.revision\\n\\n except Exception as \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_migrate_create_metadata\",\n \"text\": \"conans/client/migrations.py::_migrate_create_metadata\\ndef _migrate_create_metadata(cache, out):\\n out.warn(\\\"Migration: Generating missing metadata files\\\")\\n refs = _get_refs(cache)\\n\\n for ref in refs:\\n try:\\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\\n # Force using a package cache layout for everything, we want to alter the cache,\\n # not the editables\\n layout = PackageCacheLayout(base_folder=base_folder, ref=ref, short_paths=False,\\n no_lock=True)\\n folder = layout.export()\\n try:\\n manifest = FileTreeManifest.load(folder)\\n rrev = manifest.summary_hash\\n except Exception:\\n rrev = DEFAULT_REVISION_V1\\n metadata_path = layout.package_metadata()\\n if not os.path.exists(metadata_path):\\n out.info(\\\"Creating {} for {}\\\".format(PACKAGE_METADATA, ref))\\n prefs = _get_prefs(layout)\\n metadata = PackageMetadata()\\n metadata.recipe.revision = rrev\\n for pref in prefs:\\n try:\\n pmanifest = FileTreeManifest.load(layout.package(pref))\\n prev = pmanifest.summary_hash\\n except Exception:\\n prev = DEFAULT_REVISION_V1\\n metadata.packages[pref.id].revision = prev\\n metadata.packages[pref.id].recipe_revision = metadata.recipe.revision\\n save(metadata_path, metadata.dumps())\\n except Exception as e:\\n raise ConanException(\\\"Something went wrong while generating the metadata.json files \\\"\\n \\\"in the cache, please try to fix the issue or wipe the cache: {}\\\"\\n \\\":{}\\\".format(ref, e))\\n out.success(\\\"Migration: Generating missing metadata files finished OK!\\\\n\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_migrate_lock_files\",\n \"text\": \"conans/client/migrations.py::_migrate_lock_files\\ndef _migrate_lock_files(cache, out):\\n out.warn(\\\"Migration: Removing old lock files\\\")\\n base_dir = cache.store\\n pkgs = list_folder_subdirs(base_dir, 4)\\n for pkg in pkgs:\\n out.info(\\\"Removing locks for %s\\\" % pkg)\\n try:\\n count = os.path.join(base_dir, pkg, \\\"rw.count\\\")\\n if os.path.exists(count):\\n os.remove(count)\\n count = os.path.join(base_dir, pkg, \\\"rw.count.lock\\\")\\n if os.path.exists(count):\\n os.remove(count)\\n locks = os.path.join(base_dir, pkg, \\\"locks\\\")\\n if os.path.exists(locks):\\n shutil.rmtree(locks)\\n except Exception as e:\\n raise ConanException(\\\"Something went wrong while removing %s locks\\\\n\\\"\\n \\\"Error: %s\\\\n\\\"\\n \\\"Please clean your local conan cache manually\\\"\\n % (pkg, str(e)))\\n out.warn(\\\"Migration: Removing old lock files finished\\\\n\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_config_install\",\n \"text\": \"conans/client/migrations.py::migrate_config_install\\ndef migrate_config_install(cache):\\n try:\\n item = cache.config.get_item(\\\"general.config_install\\\")\\n items = [r.strip() for r in item.split(\\\",\\\")]\\n if len(items) == 4:\\n config_type, uri, verify_ssl, args = items\\n elif len(items) == 1:\\n uri = items[0]\\n verify_ssl = \\\"True\\\"\\n args = \\\"None\\\"\\n config_type = None\\n else:\\n raise Exception(\\\"I don't know how to migrate this config install: %s\\\" % items)\\n verify_ssl = \\\"true\\\" in verify_ssl.lower()\\n args = None if \\\"none\\\" in args.lower() else args\\n config = _ConfigOrigin.from_item(uri, config_type, verify_ssl, args, None, None)\\n _save_configs(cache.config_install_file, [config])\\n cache.config.rm_item(\\\"general.config_install\\\")\\n except ConanException:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_to_default_profile\",\n \"text\": \"conans/client/migrations.py::migrate_to_default_profile\\ndef migrate_to_default_profile(conf_path, default_profile_path):\\n tag = \\\"[settings_defaults]\\\"\\n old_conf = load(conf_path)\\n if tag not in old_conf:\\n return\\n tmp = old_conf.find(tag)\\n new_conf = old_conf[0:tmp]\\n rest = old_conf[tmp + len(tag):]\\n if tmp:\\n if \\\"]\\\" in rest: # More sections after the settings_defaults\\n new_conf += rest[rest.find(\\\"[\\\"):]\\n save(conf_path, new_conf)\\n settings = rest[:rest.find(\\\"[\\\")].strip()\\n else:\\n save(conf_path, new_conf)\\n settings = rest.strip()\\n # Now generate the default profile from the read settings_defaults\\n new_profile = \\\"[settings]\\\\n%s\\\" % settings\\n save(default_profile_path, new_profile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_c_src_export_source\",\n \"text\": \"conans/client/migrations.py::migrate_c_src_export_source\\ndef migrate_c_src_export_source(cache, out):\\n package_folders = list_folder_subdirs(cache.store, 4)\\n for package in package_folders:\\n package_folder = os.path.join(cache.store, package)\\n c_src = os.path.join(package_folder, \\\"export/%s\\\" % EXPORT_SOURCES_DIR_OLD)\\n if os.path.exists(c_src):\\n out.warn(\\\"Migration: Removing package with old export_sources layout: %s\\\" % package)\\n try:\\n shutil.rmtree(package_folder)\\n except Exception:\\n out.warn(\\\"Migration: Can't remove the '%s' directory, \\\"\\n \\\"remove it manually\\\" % package_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_plugins_to_hooks\",\n \"text\": \"conans/client/migrations.py::migrate_plugins_to_hooks\\ndef migrate_plugins_to_hooks(cache, output=None):\\n plugins_path = os.path.join(cache.conan_folder, \\\"plugins\\\")\\n if os.path.exists(plugins_path) and not os.path.exists(cache.hooks_path):\\n os.rename(plugins_path, cache.hooks_path)\\n conf_path = cache.conan_conf_path\\n replace_in_file(conf_path, \\\"[plugins]\\\", \\\"[hooks]\\\", strict=False, output=output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::args_to_string\",\n \"text\": \"conans/client/tools/oss.py::args_to_string\\ndef args_to_string(args):\\n if not args:\\n return \\\"\\\"\\n if sys.platform == 'win32':\\n return subprocess.list2cmdline(args)\\n else:\\n return \\\" \\\".join(\\\"'\\\" + arg.replace(\\\"'\\\", r\\\"'\\\\''\\\") + \\\"'\\\" for arg in args)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::cpu_count\",\n \"text\": \"conans/client/tools/oss.py::cpu_count\\ndef cpu_count(output=None):\\n output = default_output(output, 'conans.client.tools.oss.cpu_count')\\n try:\\n env_cpu_count = os.getenv(\\\"CONAN_CPU_COUNT\\\", None)\\n if env_cpu_count is not None and not env_cpu_count.isdigit():\\n raise ConanException(\\\"Invalid CONAN_CPU_COUNT value '%s', \\\"\\n \\\"please specify a positive integer\\\" % env_cpu_count)\\n return int(env_cpu_count) if env_cpu_count else multiprocessing.cpu_count()\\n except NotImplementedError:\\n output.warn(\\\"multiprocessing.cpu_count() not implemented. Defaulting to 1 cpu\\\")\\n return 1 # Safe guess\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::detected_os\",\n \"text\": \"conans/client/tools/oss.py::detected_os\\ndef detected_os():\\n if OSInfo().is_macos:\\n return \\\"Macos\\\"\\n if OSInfo().is_windows:\\n return \\\"Windows\\\"\\n return platform.system()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::detected_architecture\",\n \"text\": \"conans/client/tools/oss.py::detected_architecture\\ndef detected_architecture():\\n # FIXME: Very weak check but not very common to run conan in other architectures\\n machine = platform.machine()\\n if \\\"ppc64le\\\" in machine:\\n return \\\"ppc64le\\\"\\n elif \\\"ppc64\\\" in machine:\\n return \\\"ppc64\\\"\\n elif \\\"ppc\\\" in machine:\\n return \\\"ppc32\\\"\\n elif \\\"mips64\\\" in machine:\\n return \\\"mips64\\\"\\n elif \\\"mips\\\" in machine:\\n return \\\"mips\\\"\\n elif \\\"sparc64\\\" in machine:\\n return \\\"sparcv9\\\"\\n elif \\\"sparc\\\" in machine:\\n return \\\"sparc\\\"\\n elif \\\"aarch64\\\" in machine:\\n return \\\"armv8\\\"\\n elif \\\"64\\\" in machine:\\n return \\\"x86_64\\\"\\n elif \\\"86\\\" in machine:\\n return \\\"x86\\\"\\n elif \\\"armv8\\\" in machine:\\n return \\\"armv8\\\"\\n elif \\\"armv7\\\" in machine:\\n return \\\"armv7\\\"\\n elif \\\"arm\\\" in machine:\\n return \\\"armv6\\\"\\n elif \\\"s390x\\\" in machine:\\n return \\\"s390x\\\"\\n elif \\\"s390\\\" in machine:\\n return \\\"s390\\\"\\n\\n return None\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.__init__\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.__init__\\n def __init__(self):\\n system = platform.system()\\n self.os_version = None\\n self.os_version_name = None\\n self.is_linux = system == \\\"Linux\\\"\\n self.linux_distro = None\\n self.is_msys = system.startswith(\\\"MING\\\") or system.startswith(\\\"MSYS_NT\\\")\\n self.is_cygwin = system.startswith(\\\"CYGWIN_NT\\\")\\n self.is_windows = system == \\\"Windows\\\" or self.is_msys or self.is_cygwin\\n self.is_macos = system == \\\"Darwin\\\"\\n self.is_freebsd = system == \\\"FreeBSD\\\"\\n self.is_solaris = system == \\\"SunOS\\\"\\n self.is_posix = os.pathsep == ':'\\n\\n if self.is_linux:\\n self._get_linux_distro_info()\\n elif self.is_windows:\\n self.os_version = self.get_win_os_version()\\n self.os_version_name = self.get_win_version_name(self.os_version)\\n elif self.is_macos:\\n self.os_version = Version(platform.mac_ver()[0])\\n self.os_version_name = self.get_osx_version_name(self.os_version)\\n elif self.is_freebsd:\\n self.os_version = self.get_freebsd_version()\\n self.os_version_name = \\\"FreeBSD %s\\\" % self.os_version\\n elif self.is_solaris:\\n self.os_version = Version(platform.release())\\n self.os_version_name = self.get_solaris_version_name(self.os_version)\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo._get_linux_distro_info\",\n \"text\": \"conans/client/tools/oss.py::OSInfo._get_linux_distro_info\\n def _get_linux_distro_info(self):\\n import distro\\n self.linux_distro = distro.id()\\n self.os_version = Version(distro.version())\\n version_name = distro.codename()\\n self.os_version_name = version_name if version_name != \\\"n/a\\\" else \\\"\\\"\\n if not self.os_version_name and self.linux_distro == \\\"debian\\\":\\n self.os_version_name = self.get_debian_version_name(self.os_version)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.with_apt\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.with_apt\\n @property\\n def with_apt(self):\\n return self.is_linux and self.linux_distro in \\\\\\n (\\\"debian\\\", \\\"ubuntu\\\", \\\"knoppix\\\", \\\"linuxmint\\\", \\\"raspbian\\\", \\\"neon\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.with_yum\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.with_yum\\n @property\\n def with_yum(self):\\n return self.is_linux and self.linux_distro in \\\\\\n (\\\"centos\\\", \\\"redhat\\\", \\\"fedora\\\", \\\"pidora\\\", \\\"scientific\\\",\\n \\\"xenserver\\\", \\\"amazon\\\", \\\"oracle\\\", \\\"rhel\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.with_pacman\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.with_pacman\\n @property\\n def with_pacman(self):\\n if self.is_linux:\\n return self.linux_distro in [\\\"arch\\\", \\\"manjaro\\\"]\\n elif self.is_windows and which('uname.exe'):\\n uname = check_output(['uname.exe', '-s'])\\n return uname.startswith('MSYS_NT') and which('pacman.exe')\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.with_zypper\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.with_zypper\\n @property\\n def with_zypper(self):\\n if not self.is_linux:\\n return False\\n if \\\"opensuse\\\" in self.linux_distro or \\\"sles\\\" in self.linux_distro:\\n return True\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.get_win_os_version\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.get_win_os_version\\n @staticmethod\\n def get_win_os_version():\\n \\\"\\\"\\\"\\n Get's the OS major and minor versions. Returns a tuple of\\n (OS_MAJOR, OS_MINOR).\\n \\\"\\\"\\\"\\n import ctypes\\n\\n class _OSVERSIONINFOEXW(ctypes.Structure):\\n _fields_ = [('dwOSVersionInfoSize', ctypes.c_ulong),\\n ('dwMajorVersion', ctypes.c_ulong),\\n ('dwMinorVersion', ctypes.c_ulong),\\n ('dwBuildNumber', ctypes.c_ulong),\\n ('dwPlatformId', ctypes.c_ulong),\\n ('szCSDVersion', ctypes.c_wchar * 128),\\n ('wServicePackMajor', ctypes.c_ushort),\\n ('wServicePackMinor', ctypes.c_ushort),\\n ('wSuiteMask', ctypes.c_ushort),\\n ('wProductType', ctypes.c_byte),\\n ('wReserved', ctypes.c_byte)]\\n\\n os_version = _OSVERSIONINFOEXW()\\n os_version.dwOSVersionInfoSize = ctypes.sizeof(os_version)\\n if not hasattr(ctypes, \\\"windll\\\"):\\n return None\\n retcode = ctypes.windll.Ntdll.RtlGetVersion(ctypes.byref(os_version))\\n if retcode != 0:\\n return None\\n\\n return Version(\\\"%d.%d\\\" % (os_version.dwMajorVersion, os_version.dwMinorVersion))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.get_debian_version_name\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.get_debian_version_name\\n @staticmethod\\n def get_debian_version_name(version):\\n if not version:\\n return None\\n elif version.major() == \\\"8.Y.Z\\\":\\n return \\\"jessie\\\"\\n elif version.major() == \\\"7.Y.Z\\\":\\n return \\\"wheezy\\\"\\n elif version.major() == \\\"6.Y.Z\\\":\\n return \\\"squeeze\\\"\\n elif version.major() == \\\"5.Y.Z\\\":\\n return \\\"lenny\\\"\\n elif version.major() == \\\"4.Y.Z\\\":\\n return \\\"etch\\\"\\n elif version.minor() == \\\"3.1.Z\\\":\\n return \\\"sarge\\\"\\n elif version.minor() == \\\"3.0.Z\\\":\\n return \\\"woody\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.get_win_version_name\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.get_win_version_name\\n @staticmethod\\n def get_win_version_name(version):\\n if not version:\\n return None\\n elif version.major() == \\\"5.Y.Z\\\":\\n return \\\"Windows XP\\\"\\n elif version.minor() == \\\"6.0.Z\\\":\\n return \\\"Windows Vista\\\"\\n elif version.minor() == \\\"6.1.Z\\\":\\n return \\\"Windows 7\\\"\\n elif version.minor() == \\\"6.2.Z\\\":\\n return \\\"Windows 8\\\"\\n elif version.minor() == \\\"6.3.Z\\\":\\n return \\\"Windows 8.1\\\"\\n elif version.minor() == \\\"10.0.Z\\\":\\n return \\\"Windows 10\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.get_osx_version_name\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.get_osx_version_name\\n @staticmethod\\n def get_osx_version_name(version):\\n if not version:\\n return None\\n elif version.minor() == \\\"10.13.Z\\\":\\n return \\\"High Sierra\\\"\\n elif version.minor() == \\\"10.12.Z\\\":\\n return \\\"Sierra\\\"\\n elif version.minor() == \\\"10.11.Z\\\":\\n return \\\"El Capitan\\\"\\n elif version.minor() == \\\"10.10.Z\\\":\\n return \\\"Yosemite\\\"\\n elif version.minor() == \\\"10.9.Z\\\":\\n return \\\"Mavericks\\\"\\n elif version.minor() == \\\"10.8.Z\\\":\\n return \\\"Mountain Lion\\\"\\n elif version.minor() == \\\"10.7.Z\\\":\\n return \\\"Lion\\\"\\n elif version.minor() == \\\"10.6.Z\\\":\\n return \\\"Snow Leopard\\\"\\n elif version.minor() == \\\"10.5.Z\\\":\\n return \\\"Leopard\\\"\\n elif version.minor() == \\\"10.4.Z\\\":\\n return \\\"Tiger\\\"\\n elif version.minor() == \\\"10.3.Z\\\":\\n return \\\"Panther\\\"\\n elif version.minor() == \\\"10.2.Z\\\":\\n return \\\"Jaguar\\\"\\n elif version.minor() == \\\"10.1.Z\\\":\\n return \\\"Puma\\\"\\n elif version.minor() == \\\"10.0.Z\\\":\\n return \\\"Cheetha\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.get_freebsd_version\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.get_freebsd_version\\n @staticmethod\\n def get_freebsd_version():\\n return platform.release().split(\\\"-\\\")[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.get_solaris_version_name\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.get_solaris_version_name\\n @staticmethod\\n def get_solaris_version_name(version):\\n if not version:\\n return None\\n elif version.minor() == \\\"5.10\\\":\\n return \\\"Solaris 10\\\"\\n elif version.minor() == \\\"5.11\\\":\\n return \\\"Solaris 11\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.bash_path\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.bash_path\\n @staticmethod\\n def bash_path():\\n if os.getenv(\\\"CONAN_BASH_PATH\\\"):\\n return os.getenv(\\\"CONAN_BASH_PATH\\\")\\n return which(\\\"bash\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.uname\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.uname\\n @staticmethod\\n def uname(options=None):\\n options = \\\" %s\\\" % options if options else \\\"\\\"\\n if not OSInfo().is_windows:\\n raise ConanException(\\\"Command only for Windows operating system\\\")\\n custom_bash_path = OSInfo.bash_path()\\n if not custom_bash_path:\\n raise ConanException(\\\"bash is not in the path\\\")\\n\\n command = '\\\"%s\\\" -c \\\"uname%s\\\"' % (custom_bash_path, options)\\n try:\\n # the uname executable is many times located in the same folder as bash.exe\\n with environment_append({\\\"PATH\\\": [os.path.dirname(custom_bash_path)]}):\\n ret = check_output(command).strip().lower()\\n return ret\\n except Exception:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::OSInfo.detect_windows_subsystem\",\n \"text\": \"conans/client/tools/oss.py::OSInfo.detect_windows_subsystem\\n @staticmethod\\n def detect_windows_subsystem():\\n from conans.client.tools.win import CYGWIN, MSYS2, MSYS, WSL\\n if OSInfo().is_linux:\\n try:\\n # https://github.com/Microsoft/WSL/issues/423#issuecomment-221627364\\n with open(\\\"/proc/sys/kernel/osrelease\\\") as f:\\n return WSL if f.read().endswith(\\\"Microsoft\\\") else None\\n except IOError:\\n return None\\n try:\\n output = OSInfo.uname()\\n except ConanException:\\n return None\\n if not output:\\n return None\\n if \\\"cygwin\\\" in output:\\n return CYGWIN\\n elif \\\"msys\\\" in output or \\\"mingw\\\" in output:\\n version = OSInfo.uname(\\\"-r\\\").split('.')\\n if version and version[0].isdigit():\\n major = int(version[0])\\n if major == 1:\\n return MSYS\\n elif major >= 2:\\n return MSYS2\\n return None\\n elif \\\"linux\\\" in output:\\n return WSL\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::cross_building\",\n \"text\": \"conans/client/tools/oss.py::cross_building\\ndef cross_building(settings, self_os=None, self_arch=None):\\n ret = get_cross_building_settings(settings, self_os, self_arch)\\n build_os, build_arch, host_os, host_arch = ret\\n\\n if host_os is not None and (build_os != host_os):\\n return True\\n if host_arch is not None and (build_arch != host_arch):\\n return True\\n\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::get_cross_building_settings\",\n \"text\": \"conans/client/tools/oss.py::get_cross_building_settings\\ndef get_cross_building_settings(settings, self_os=None, self_arch=None):\\n build_os = self_os or settings.get_safe(\\\"os_build\\\") or detected_os()\\n build_arch = self_arch or settings.get_safe(\\\"arch_build\\\") or detected_architecture()\\n host_os = settings.get_safe(\\\"os\\\")\\n host_arch = settings.get_safe(\\\"arch\\\")\\n\\n return build_os, build_arch, host_os, host_arch\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/oss.py::get_gnu_triplet\",\n \"text\": \"conans/client/tools/oss.py::get_gnu_triplet\\ndef get_gnu_triplet(os_, arch, compiler=None):\\n \\\"\\\"\\\"\\n Returns string with <machine>-<vendor>-<op_system> triplet (<vendor> can be omitted in practice)\\n\\n :param os_: os to be used to create the triplet\\n :param arch: arch to be used to create the triplet\\n :param compiler: compiler used to create the triplet (only needed fo windows)\\n \\\"\\\"\\\"\\n\\n if os_ == \\\"Windows\\\" and compiler is None:\\n raise ConanException(\\\"'compiler' parameter for 'get_gnu_triplet()' is not specified and \\\"\\n \\\"needed for os=Windows\\\")\\n\\n # Calculate the arch\\n machine = {\\\"x86\\\": \\\"i686\\\" if os_ != \\\"Linux\\\" else \\\"x86\\\",\\n \\\"x86_64\\\": \\\"x86_64\\\",\\n \\\"armv8\\\": \\\"aarch64\\\",\\n \\\"armv8_32\\\": \\\"aarch64\\\", # https://wiki.linaro.org/Platform/arm64-ilp32\\n \\\"armv8.3\\\": \\\"aarch64\\\",\\n \\\"asm.js\\\": \\\"asmjs\\\",\\n \\\"wasm\\\": \\\"wasm32\\\",\\n }.get(arch, None)\\n\\n if not machine:\\n # https://wiki.debian.org/Multiarch/Tuples\\n if \\\"arm\\\" in arch:\\n machine = \\\"arm\\\"\\n elif \\\"ppc64le\\\" in arch:\\n machine = \\\"powerpc64le\\\"\\n elif \\\"ppc64\\\" in arch:\\n machine = \\\"powerpc64\\\"\\n elif \\\"ppc32\\\" in arch:\\n machine = \\\"powerpc\\\"\\n elif \\\"mips64\\\" in arch:\\n machine = \\\"mips64\\\"\\n elif \\\"mips\\\" in arch:\\n machine = \\\"mips\\\"\\n elif \\\"sparcv9\\\" in arch:\\n machine = \\\"sparc64\\\"\\n elif \\\"sparc\\\" in arch:\\n machine = \\\"sparc\\\"\\n elif \\\"s390x\\\" in arch:\\n machine = \\\"s390x-ibm\\\"\\n elif \\\"s390\\\" in arch:\\n machine = \\\"s390-ibm\\\"\\n\\n if machine is None:\\n raise ConanException(\\\"Unknown '%s' machine, Conan doesn't know how to \\\"\\n \\\"translate it to the GNU triplet, please report at \\\"\\n \\\" https://github.com/conan-io/conan/issues\\\" % arch)\\n\\n # Calculate the OS\\n if compiler == \\\"gcc\\\":\\n windows_o\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/tools/oss.py::check_output\",\n \"text\": \"conans/client/tools/oss.py::check_output\\ndef check_output(cmd, folder=None, return_code=False, stderr=None):\\n tmp_file = tempfile.mktemp()\\n try:\\n stderr = stderr or PIPE\\n cmd = cmd if isinstance(cmd, six.string_types) else subprocess.list2cmdline(cmd)\\n process = subprocess.Popen(\\\"{} > {}\\\".format(cmd, tmp_file), shell=True,\\n stderr=stderr, cwd=folder)\\n process.communicate()\\n\\n if return_code:\\n return process.returncode\\n\\n if process.returncode:\\n raise CalledProcessError(process.returncode, cmd)\\n\\n output = load(tmp_file)\\n return output\\n finally:\\n try:\\n os.unlink(tmp_file)\\n except Exception:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig._cmd_output\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig._cmd_output\\n @staticmethod\\n def _cmd_output(command):\\n return check_output(command).strip()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig.__init__\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig.__init__\\n def __init__(self, library, pkg_config_executable='pkg-config', static=False, msvc_syntax=False, variables=None,\\n print_errors=True):\\n \\\"\\\"\\\"\\n :param library: library (package) name, such as libastral\\n :param pkg_config_executable: specify custom pkg-config executable (e.g. for cross-compilation)\\n :param static: output libraries suitable for static linking (adds --static to pkg-config command line)\\n :param msvc_syntax: MSVC compatibility (adds --msvc-syntax to pkg-config command line)\\n :param variables: dictionary of pkg-config variables (passed as --define-variable=VARIABLENAME=VARIABLEVALUE)\\n :param print_errors: output error messages (adds --print-errors)\\n \\\"\\\"\\\"\\n self.library = library\\n self.pkg_config_executable = pkg_config_executable\\n self.static = static\\n self.msvc_syntax = msvc_syntax\\n self.define_variables = variables\\n self.print_errors = print_errors\\n\\n self._variables = dict()\\n self.info = dict()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig._parse_output\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig._parse_output\\n def _parse_output(self, option):\\n command = [self.pkg_config_executable, '--' + option, self.library]\\n if self.static:\\n command.append('--static')\\n if self.msvc_syntax:\\n command.append('--msvc-syntax')\\n if self.print_errors:\\n command.append('--print-errors')\\n if self.define_variables:\\n for name, value in self.define_variables.items():\\n command.append('--define-variable=%s=%s' % (name, value))\\n try:\\n return self._cmd_output(command)\\n except subprocess.CalledProcessError as e:\\n raise ConanException('pkg-config command %s failed with error: %s' % (command, e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig._get_option\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig._get_option\\n def _get_option(self, option):\\n if option not in self.info:\\n self.info[option] = self._parse_output(option).split()\\n return self.info[option]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig.cflags\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig.cflags\\n @property\\n def cflags(self):\\n return self._get_option('cflags')\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig.cflags_only_I\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig.cflags_only_I\\n @property\\n def cflags_only_I(self):\\n return self._get_option('cflags-only-I')\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig.cflags_only_other\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig.cflags_only_other\\n @property\\n def cflags_only_other(self):\\n return self._get_option('cflags-only-other')\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig.libs\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig.libs\\n @property\\n def libs(self):\\n return self._get_option('libs')\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig.libs_only_L\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig.libs_only_L\\n @property\\n def libs_only_L(self):\\n return self._get_option('libs-only-L')\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig.libs_only_l\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig.libs_only_l\\n @property\\n def libs_only_l(self):\\n return self._get_option('libs-only-l')\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig.libs_only_other\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig.libs_only_other\\n @property\\n def libs_only_other(self):\\n return self._get_option('libs-only-other')\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig.provides\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig.provides\\n @property\\n def provides(self):\\n return self._get_option('print-provides')\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig.requires\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig.requires\\n @property\\n def requires(self):\\n return self._get_option('print-requires')\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig.requires_private\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig.requires_private\\n @property\\n def requires_private(self):\\n return self._get_option('print-requires-private')\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/tools/pkg_config.py::PkgConfig.variables\",\n \"text\": \"conans/client/tools/pkg_config.py::PkgConfig.variables\\n @property\\n def variables(self):\\n if not self._variables:\\n variable_names = self._parse_output('print-variables').split()\\n for name in variable_names:\\n self._variables[name] = self._parse_output('variable=%s' % name)\\n return self._variables\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.__init__\",\n \"text\": \"conans/model/info.py::RequirementInfo.__init__\\n def __init__(self, pref, default_package_id_mode, indirect=False):\\n self.package = pref\\n self.full_name = pref.ref.name\\n self.full_version = pref.ref.version\\n self.full_user = pref.ref.user\\n self.full_channel = pref.ref.channel\\n self.full_package_id = pref.id\\n self._indirect = indirect\\n\\n try:\\n getattr(self, default_package_id_mode)()\\n except AttributeError:\\n raise ConanException(\\\"'%s' is not a known package_id_mode\\\" % default_package_id_mode)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.copy\",\n \"text\": \"conans/model/info.py::RequirementInfo.copy\\n def copy(self):\\n # Useful for build_id()\\n result = RequirementInfo(self.package, \\\"unrelated_mode\\\")\\n for f in (\\\"name\\\", \\\"version\\\", \\\"user\\\", \\\"channel\\\", \\\"package_id\\\"):\\n setattr(result, f, getattr(self, f))\\n f = \\\"full_%s\\\" % f\\n setattr(result, f, getattr(self, f))\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.dumps\",\n \"text\": \"conans/model/info.py::RequirementInfo.dumps\\n def dumps(self):\\n if not self.name:\\n return \\\"\\\"\\n result = [\\\"%s/%s\\\" % (self.name, self.version)]\\n if self.user or self.channel:\\n result.append(\\\"@%s/%s\\\" % (self.user, self.channel))\\n if self.package_id:\\n result.append(\\\":%s\\\" % self.package_id)\\n return \\\"\\\".join(result)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.sha\",\n \"text\": \"conans/model/info.py::RequirementInfo.sha\\n @property\\n def sha(self):\\n vals = [str(n) for n in (self.name, self.version, self.user, self.channel, self.package_id)]\\n # This is done later to NOT affect existing package-IDs (before revisions)\\n return \\\"/\\\".join(vals)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.unrelated_mode\",\n \"text\": \"conans/model/info.py::RequirementInfo.unrelated_mode\\n def unrelated_mode(self):\\n self.name = self.version = self.user = self.channel = self.package_id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.semver_direct_mode\",\n \"text\": \"conans/model/info.py::RequirementInfo.semver_direct_mode\\n def semver_direct_mode(self):\\n if self._indirect:\\n self.unrelated_mode()\\n else:\\n self.semver_mode()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.semver_mode\",\n \"text\": \"conans/model/info.py::RequirementInfo.semver_mode\\n def semver_mode(self):\\n self.name = self.full_name\\n self.version = self.full_version.stable()\\n self.user = self.channel = self.package_id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.full_version_mode\",\n \"text\": \"conans/model/info.py::RequirementInfo.full_version_mode\\n def full_version_mode(self):\\n self.name = self.full_name\\n self.version = self.full_version\\n self.user = self.channel = self.package_id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.patch_mode\",\n \"text\": \"conans/model/info.py::RequirementInfo.patch_mode\\n def patch_mode(self):\\n self.name = self.full_name\\n self.version = self.full_version.patch()\\n self.user = self.channel = self.package_id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.base_mode\",\n \"text\": \"conans/model/info.py::RequirementInfo.base_mode\\n def base_mode(self):\\n self.name = self.full_name\\n self.version = self.full_version.base\\n self.user = self.channel = self.package_id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.minor_mode\",\n \"text\": \"conans/model/info.py::RequirementInfo.minor_mode\\n def minor_mode(self):\\n self.name = self.full_name\\n self.version = self.full_version.minor()\\n self.user = self.channel = self.package_id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.major_mode\",\n \"text\": \"conans/model/info.py::RequirementInfo.major_mode\\n def major_mode(self):\\n self.name = self.full_name\\n self.version = self.full_version.major()\\n self.user = self.channel = self.package_id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.full_recipe_mode\",\n \"text\": \"conans/model/info.py::RequirementInfo.full_recipe_mode\\n def full_recipe_mode(self):\\n self.name = self.full_name\\n self.version = self.full_version\\n self.user = self.full_user\\n self.channel = self.full_channel\\n self.package_id = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementInfo.full_package_mode\",\n \"text\": \"conans/model/info.py::RequirementInfo.full_package_mode\\n def full_package_mode(self):\\n self.name = self.full_name\\n self.version = self.full_version\\n self.user = self.full_user\\n self.channel = self.full_channel\\n self.package_id = self.full_package_id\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.__init__\",\n \"text\": \"conans/model/info.py::RequirementsInfo.__init__\\n def __init__(self, prefs, default_package_id_mode):\\n # {PackageReference: RequirementInfo}\\n self._data = {pref: RequirementInfo(pref, default_package_id_mode=default_package_id_mode)\\n for pref in prefs}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.copy\",\n \"text\": \"conans/model/info.py::RequirementsInfo.copy\\n def copy(self):\\n # For build_id() implementation\\n result = RequirementsInfo([], None)\\n result._data = {pref: req_info.copy() for pref, req_info in self._data.items()}\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.clear\",\n \"text\": \"conans/model/info.py::RequirementsInfo.clear\\n def clear(self):\\n self._data = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.remove\",\n \"text\": \"conans/model/info.py::RequirementsInfo.remove\\n def remove(self, *args):\\n for name in args:\\n del self._data[self._get_key(name)]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.add\",\n \"text\": \"conans/model/info.py::RequirementsInfo.add\\n def add(self, prefs_indirect, default_package_id_mode):\\n \\\"\\\"\\\" necessary to propagate from upstream the real\\n package requirements\\n \\\"\\\"\\\"\\n for r in prefs_indirect:\\n self._data[r] = RequirementInfo(r, indirect=True,\\n default_package_id_mode=default_package_id_mode)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.refs\",\n \"text\": \"conans/model/info.py::RequirementsInfo.refs\\n def refs(self):\\n \\\"\\\"\\\" used for updating downstream requirements with this\\n \\\"\\\"\\\"\\n return list(self._data.keys())\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo._get_key\",\n \"text\": \"conans/model/info.py::RequirementsInfo._get_key\\n def _get_key(self, item):\\n for reference in self._data:\\n if reference.ref.name == item:\\n return reference\\n raise ConanException(\\\"No requirement matching for %s\\\" % (item))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.__getitem__\",\n \"text\": \"conans/model/info.py::RequirementsInfo.__getitem__\\n def __getitem__(self, item):\\n \\\"\\\"\\\"get by package name\\n Necessary to access from conaninfo\\n self.requires[\\\"Boost\\\"].version = \\\"2.X\\\"\\n \\\"\\\"\\\"\\n return self._data[self._get_key(item)]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.pkg_names\",\n \"text\": \"conans/model/info.py::RequirementsInfo.pkg_names\\n @property\\n def pkg_names(self):\\n return [r.ref.name for r in self._data.keys()]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.sha\",\n \"text\": \"conans/model/info.py::RequirementsInfo.sha\\n @property\\n def sha(self):\\n result = []\\n # Remove requirements without a name, i.e. indirect transitive requirements\\n data = {k: v for k, v in self._data.items() if v.name}\\n for key in sorted(data):\\n s = data[key].sha\\n result.append(s)\\n return sha1('\\\\n'.join(result).encode())\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.dumps\",\n \"text\": \"conans/model/info.py::RequirementsInfo.dumps\\n def dumps(self):\\n result = []\\n for ref in sorted(self._data):\\n dumped = self._data[ref].dumps()\\n if dumped:\\n result.append(dumped)\\n return \\\"\\\\n\\\".join(result)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.unrelated_mode\",\n \"text\": \"conans/model/info.py::RequirementsInfo.unrelated_mode\\n def unrelated_mode(self):\\n self.clear()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.semver_direct_mode\",\n \"text\": \"conans/model/info.py::RequirementsInfo.semver_direct_mode\\n def semver_direct_mode(self):\\n for r in self._data.values():\\n r.semver_direct_mode()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.semver_mode\",\n \"text\": \"conans/model/info.py::RequirementsInfo.semver_mode\\n def semver_mode(self):\\n for r in self._data.values():\\n r.semver_mode()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.patch_mode\",\n \"text\": \"conans/model/info.py::RequirementsInfo.patch_mode\\n def patch_mode(self):\\n for r in self._data.values():\\n r.patch_mode()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.minor_mode\",\n \"text\": \"conans/model/info.py::RequirementsInfo.minor_mode\\n def minor_mode(self):\\n for r in self._data.values():\\n r.minor_mode()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.major_mode\",\n \"text\": \"conans/model/info.py::RequirementsInfo.major_mode\\n def major_mode(self):\\n for r in self._data.values():\\n r.major_mode()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.base_mode\",\n \"text\": \"conans/model/info.py::RequirementsInfo.base_mode\\n def base_mode(self):\\n for r in self._data.values():\\n r.base_mode()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.full_version_mode\",\n \"text\": \"conans/model/info.py::RequirementsInfo.full_version_mode\\n def full_version_mode(self):\\n for r in self._data.values():\\n r.full_version_mode()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.full_recipe_mode\",\n \"text\": \"conans/model/info.py::RequirementsInfo.full_recipe_mode\\n def full_recipe_mode(self):\\n for r in self._data.values():\\n r.full_recipe_mode()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::RequirementsInfo.full_package_mode\",\n \"text\": \"conans/model/info.py::RequirementsInfo.full_package_mode\\n def full_package_mode(self):\\n for r in self._data.values():\\n r.full_package_mode()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::_PackageReferenceList.loads\",\n \"text\": \"conans/model/info.py::_PackageReferenceList.loads\\n @staticmethod\\n def loads(text):\\n return _PackageReferenceList([PackageReference.loads(package_reference)\\n for package_reference in text.splitlines()])\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::_PackageReferenceList.dumps\",\n \"text\": \"conans/model/info.py::_PackageReferenceList.dumps\\n def dumps(self):\\n return \\\"\\\\n\\\".join(self.serialize())\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::_PackageReferenceList.serialize\",\n \"text\": \"conans/model/info.py::_PackageReferenceList.serialize\\n def serialize(self):\\n return [str(r) for r in sorted(self)]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::ConanInfo.copy\",\n \"text\": \"conans/model/info.py::ConanInfo.copy\\n def copy(self):\\n \\\"\\\"\\\" Useful for build_id implementation\\n \\\"\\\"\\\"\\n result = ConanInfo()\\n result.settings = self.settings.copy()\\n result.options = self.options.copy()\\n result.requires = self.requires.copy()\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/info.py::ConanInfo.create\",\n \"text\": \"conans/model/info.py::ConanInfo.create\\n @staticmethod\\n def create(settings, options, prefs_direct, prefs_indirect, default_package_id_mode):\\n result = ConanInfo()\\n result.full_settings = settings\\n result.settings = settings.copy()\\n result.full_options = options\\n result.options = options.copy()\\n result.options.clear_indirect()\\n result.full_requires = _PackageReferenceList(prefs_direct)\\n result.requires = RequirementsInfo(prefs_direct, default_package_id_mode)\\n result.requires.add(prefs_indirect, default_package_id_mode)\\n result.full_requires.extend(prefs_indirect)\\n result.recipe_hash = None\\n result.env_values = EnvValues()\\n result.vs_toolset_compatible()\\n result.discard_build_settings()\\n result.default_std_matching()\\n\\n return result\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":276,"cells":{"instance_id":{"kind":"string","value":"Lightning-AI__lightning-3163"},"repo":{"kind":"string","value":"Lightning-AI/lightning"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Change checkpoint filename without overwriting full filepath\n## 🚀 Feature\r\nBy default, the checkpoint path resolves to `lightning_logs/{version}/checkpoints/{epoch}.ckpt`. It is currently not possible to change the checkpoint filename without replacing the full path.\r\n\r\n### Motivation\r\nThe full path is replacable with the `ModelCheckpoint.filepath` argument. This feature is undisirable because I want to use the default path resolver that uses the options from `Trainer` instead of hardcoding a path.\r\n\r\nA satisfying path construction workaraound based on values in `Trainer` is not possible because the `Trainer` is constructed *after* the `ModelCheckpoint`.\r\n\r\n### Pitch\r\nI would like to add a `filename` parameter to the `ModelCheckpoint` constructor. This parameter is complementary to `filepath` and `filepath` will keep working like it does for backward compatibility.\r\n\r\n- If only `filepath` is provided, the original behaviour is used.\r\n- If both `filepath` and `filename` are provided, the checkpoint path will resolve to `filepath`/`filename`.\r\n- If only `filename` is provided, the checkpoint path will resolve to `resolved directory`/`filename` (instead of `resolved directory`/`epoch`).\nAdd filename parameter to ModelCheckpoint\n## What does this PR do?\r\nAdds filename parameter to ModelCheckpoint. See issue #2583.\n"},"gold_files":{"kind":"list like","value":["pytorch_lightning/callbacks/early_stopping.py","pytorch_lightning/callbacks/model_checkpoint.py"],"string":"[\n \"pytorch_lightning/callbacks/early_stopping.py\",\n \"pytorch_lightning/callbacks/model_checkpoint.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.__warn_deprecated_monitor_key","pytorch_lightning/callbacks/early_stopping.py::EarlyStopping._validate_condition_metric","pytorch_lightning/callbacks/model_checkpoint.py::<module>","pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.__init__","pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.__warn_deprecated_monitor_key","pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._do_check_save","pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.format_checkpoint_name","pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_pretrain_routine_start","pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_validation_end"],"string":"[\n \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.__warn_deprecated_monitor_key\",\n \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping._validate_condition_metric\",\n \"pytorch_lightning/callbacks/model_checkpoint.py::<module>\",\n \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.__init__\",\n \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.__warn_deprecated_monitor_key\",\n \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._do_check_save\",\n \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.format_checkpoint_name\",\n \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_pretrain_routine_start\",\n \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_validation_end\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/source/conf.py","text":"docs/source/conf.py\ndef run_apidoc(_)\ndef setup(app)\ndef package_list_from_file(file)\ndef linkcode_resolve(domain, info)\n def find_source()","label":0},{"cid":"pytorch_lightning/callbacks/early_stopping.py","text":"pytorch_lightning/callbacks/early_stopping.py\nclass EarlyStopping # Args:\n def __init__(self, monitor: str='val_loss', min_delta: float=0.0, patience: int=3, verbose: bool=False, mode: str='auto', strict: bool=True)\n def _validate_condition_metric(self, logs)\n def monitor_op(self)\n def on_save_checkpoint(self, trainer, pl_module)\n def on_load_checkpoint(self, checkpointed_state)\n def on_validation_end(self, trainer, pl_module)\n def on_validation_epoch_end(self, trainer, pl_module)\n def on_train_epoch_end(self, trainer, pl_module)\n def __warn_deprecated_monitor_key(self)\n def _run_early_stopping_check(self, trainer, pl_module)\n def on_train_end(self, trainer, pl_module)","label":1},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py","text":"pytorch_lightning/callbacks/model_checkpoint.py\nclass ModelCheckpoint # Save the model after every epoch if it improves.\n def __init__(self, filepath: Optional[str]=None, monitor: str='val_loss', verbose: bool=False, save_last: bool=False, save_top_k: int=1, save_weights_only: bool=False, mode: str='auto', period: int=1, prefix: str='')\n def best(self)\n def kth_best_model(self)\n def _del_model(self, filepath)\n def _save_model(self, filepath, trainer, pl_module)\n def check_monitor_top_k(self, current)\n def format_checkpoint_name(self, epoch, metrics, ver=None) # Generate a filename according to the defined template.\n def on_pretrain_routine_start(self, trainer, pl_module) # Determines model checkpoint save directory at runtime. References attributes fro\n def __warn_deprecated_monitor_key(self)\n def on_validation_end(self, trainer, pl_module)\n def _do_check_save(self, filepath, current, epoch, trainer, pl_module)\n def on_save_checkpoint(self, trainer, pl_module)\n def on_load_checkpoint(self, checkpointed_state)","label":1},{"cid":"pytorch_lightning/trainer/__init__.py","text":"pytorch_lightning/trainer/__init__.py","label":0},{"cid":"pytorch_lightning/trainer/docstrings/trainer_ds.py","text":"pytorch_lightning/trainer/docstrings/trainer_ds.py","label":0}],"string":"[\n {\n \"cid\": \"docs/source/conf.py\",\n \"text\": \"docs/source/conf.py\\ndef run_apidoc(_)\\ndef setup(app)\\ndef package_list_from_file(file)\\ndef linkcode_resolve(domain, info)\\n def find_source()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/early_stopping.py\",\n \"text\": \"pytorch_lightning/callbacks/early_stopping.py\\nclass EarlyStopping # Args:\\n def __init__(self, monitor: str='val_loss', min_delta: float=0.0, patience: int=3, verbose: bool=False, mode: str='auto', strict: bool=True)\\n def _validate_condition_metric(self, logs)\\n def monitor_op(self)\\n def on_save_checkpoint(self, trainer, pl_module)\\n def on_load_checkpoint(self, checkpointed_state)\\n def on_validation_end(self, trainer, pl_module)\\n def on_validation_epoch_end(self, trainer, pl_module)\\n def on_train_epoch_end(self, trainer, pl_module)\\n def __warn_deprecated_monitor_key(self)\\n def _run_early_stopping_check(self, trainer, pl_module)\\n def on_train_end(self, trainer, pl_module)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py\\nclass ModelCheckpoint # Save the model after every epoch if it improves.\\n def __init__(self, filepath: Optional[str]=None, monitor: str='val_loss', verbose: bool=False, save_last: bool=False, save_top_k: int=1, save_weights_only: bool=False, mode: str='auto', period: int=1, prefix: str='')\\n def best(self)\\n def kth_best_model(self)\\n def _del_model(self, filepath)\\n def _save_model(self, filepath, trainer, pl_module)\\n def check_monitor_top_k(self, current)\\n def format_checkpoint_name(self, epoch, metrics, ver=None) # Generate a filename according to the defined template.\\n def on_pretrain_routine_start(self, trainer, pl_module) # Determines model checkpoint save directory at runtime. References attributes fro\\n def __warn_deprecated_monitor_key(self)\\n def on_validation_end(self, trainer, pl_module)\\n def _do_check_save(self, filepath, current, epoch, trainer, pl_module)\\n def on_save_checkpoint(self, trainer, pl_module)\\n def on_load_checkpoint(self, checkpointed_state)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/__init__.py\",\n \"text\": \"pytorch_lightning/trainer/__init__.py\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/docstrings/trainer_ds.py\",\n \"text\": \"pytorch_lightning/trainer/docstrings/trainer_ds.py\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"docs/source/conf.py::run_apidoc","text":"docs/source/conf.py::run_apidoc\ndef run_apidoc(_):\n sys.path.insert(0, apidoc_output_folder)\n\n # delete api-doc files before generating them\n if os.path.exists(apidoc_output_folder):\n shutil.rmtree(apidoc_output_folder)\n\n for pkg in PACKAGES:\n argv = ['-e',\n '-o', apidoc_output_folder,\n os.path.join(PATH_ROOT, pkg),\n '**/test_*',\n '--force',\n '--private',\n '--module-first']\n\n apidoc.main(argv)","label":0},{"cid":"docs/source/conf.py::setup","text":"docs/source/conf.py::setup\ndef setup(app):\n # this is for hiding doctest decoration,\n # see: http://z4r.github.io/python/2011/12/02/hides-the-prompts-and-output/\n app.add_javascript('copybutton.js')\n app.connect('builder-inited', run_apidoc)","label":0},{"cid":"docs/source/conf.py::package_list_from_file","text":"docs/source/conf.py::package_list_from_file\ndef package_list_from_file(file):\n mocked_packages = []\n with open(file, 'r') as fp:\n for ln in fp.readlines():\n found = [ln.index(ch) for ch in list(',=<>#') if ch in ln]\n pkg = ln[:min(found)] if found else ln\n if pkg.rstrip():\n mocked_packages.append(pkg.rstrip())\n return mocked_packages","label":0},{"cid":"docs/source/conf.py::linkcode_resolve","text":"docs/source/conf.py::linkcode_resolve\ndef linkcode_resolve(domain, info):\n def find_source():\n # try to find the file and line number, based on code from numpy:\n # https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L286\n obj = sys.modules[info['module']]\n for part in info['fullname'].split('.'):\n obj = getattr(obj, part)\n fname = inspect.getsourcefile(obj)\n # https://github.com/rtfd/readthedocs.org/issues/5735\n if any([s in fname for s in ('readthedocs', 'rtfd', 'checkouts')]):\n # /home/docs/checkouts/readthedocs.org/user_builds/pytorch_lightning/checkouts/\n # devel/pytorch_lightning/utilities/cls_experiment.py#L26-L176\n path_top = os.path.abspath(os.path.join('..', '..', '..'))\n fname = os.path.relpath(fname, start=path_top)\n else:\n # Local build, imitate master\n fname = 'master/' + os.path.relpath(fname, start=os.path.abspath('..'))\n source, lineno = inspect.getsourcelines(obj)\n return fname, lineno, lineno + len(source) - 1\n\n if domain != 'py' or not info['module']:\n return None\n try:\n filename = '%s#L%d-L%d' % find_source()\n except Exception:\n filename = info['module'].replace('.', '/') + '.py'\n # import subprocess\n # tag = subprocess.Popen(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE,\n # universal_newlines=True).communicate()[0][:-1]\n branch = filename.split('/')[0]\n # do mapping from latest tags to master\n branch = {'latest': 'master', 'stable': 'master'}.get(branch, branch)\n filename = '/'.join([branch] + filename.split('/')[1:])\n return \"https://github.com/%s/%s/blob/%s\" \\\n % (github_user, github_repo, filename)","label":0},{"cid":"docs/source/conf.py::linkcode_resolve.find_source","text":"docs/source/conf.py::linkcode_resolve.find_source\n def find_source():\n # try to find the file and line number, based on code from numpy:\n # https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L286\n obj = sys.modules[info['module']]\n for part in info['fullname'].split('.'):\n obj = getattr(obj, part)\n fname = inspect.getsourcefile(obj)\n # https://github.com/rtfd/readthedocs.org/issues/5735\n if any([s in fname for s in ('readthedocs', 'rtfd', 'checkouts')]):\n # /home/docs/checkouts/readthedocs.org/user_builds/pytorch_lightning/checkouts/\n # devel/pytorch_lightning/utilities/cls_experiment.py#L26-L176\n path_top = os.path.abspath(os.path.join('..', '..', '..'))\n fname = os.path.relpath(fname, start=path_top)\n else:\n # Local build, imitate master\n fname = 'master/' + os.path.relpath(fname, start=os.path.abspath('..'))\n source, lineno = inspect.getsourcelines(obj)\n return fname, lineno, lineno + len(source) - 1","label":0},{"cid":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.__init__","text":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.__init__\n def __init__(self, monitor: str = 'val_loss', min_delta: float = 0.0, patience: int = 3,\n verbose: bool = False, mode: str = 'auto', strict: bool = True):\n super().__init__()\n self.monitor = monitor\n self.patience = patience\n self.verbose = verbose\n self.strict = strict\n self.min_delta = min_delta\n self.wait_count = 0\n self.stopped_epoch = 0\n self.mode = mode\n self.warned_result_obj = False\n\n if mode not in self.mode_dict:\n if self.verbose > 0:\n log.info(f'EarlyStopping mode {mode} is unknown, fallback to auto mode.')\n self.mode = 'auto'\n\n if self.mode == 'auto':\n if self.monitor == 'acc':\n self.mode = 'max'\n else:\n self.mode = 'min'\n if self.verbose > 0:\n log.info(f'EarlyStopping mode set to {self.mode} for monitoring {self.monitor}.')\n\n self.min_delta *= 1 if self.monitor_op == torch.gt else -1\n self.best_score = torch_inf if self.monitor_op == torch.lt else -torch_inf","label":0},{"cid":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping._validate_condition_metric","text":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping._validate_condition_metric\n def _validate_condition_metric(self, logs):\n monitor_val = logs.get(self.monitor)\n error_msg = (f'Early stopping conditioned on metric `{self.monitor}`'\n f' which is not available. Either add `{self.monitor}` to the return of '\n f' validation_epoch end or modify your EarlyStopping callback to use any of the '\n f'following: `{\"`, `\".join(list(logs.keys()))}`')\n\n if monitor_val is None:\n if self.strict:\n raise RuntimeError(error_msg)\n if self.verbose > 0:\n rank_zero_warn(error_msg, RuntimeWarning)\n\n return False\n\n return True","label":1},{"cid":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.monitor_op","text":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.monitor_op\n @property\n def monitor_op(self):\n return self.mode_dict[self.mode]","label":0},{"cid":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_save_checkpoint","text":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_save_checkpoint\n def on_save_checkpoint(self, trainer, pl_module):\n return {\n 'wait_count': self.wait_count,\n 'stopped_epoch': self.stopped_epoch,\n 'best_score': self.best_score,\n 'patience': self.patience\n }","label":0},{"cid":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_load_checkpoint","text":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_load_checkpoint\n def on_load_checkpoint(self, checkpointed_state):\n self.wait_count = checkpointed_state['wait_count']\n self.stopped_epoch = checkpointed_state['stopped_epoch']\n self.best_score = checkpointed_state['best_score']\n self.patience = checkpointed_state['patience']","label":0},{"cid":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_validation_end","text":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_validation_end\n def on_validation_end(self, trainer, pl_module):\n if trainer.running_sanity_check:\n return\n\n self._run_early_stopping_check(trainer, pl_module)","label":0},{"cid":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_validation_epoch_end","text":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_validation_epoch_end\n def on_validation_epoch_end(self, trainer, pl_module):\n if trainer.running_sanity_check:\n return\n\n self.__warn_deprecated_monitor_key()\n\n val_es_key = 'val_early_stop_on'\n if trainer.logger_connector.callback_metrics.get(val_es_key) is not None:\n self.monitor = val_es_key\n\n # disable strict checking when using structured results\n if val_es_key in trainer.logger_connector.callback_metrics:\n self.strict = False\n\n self._validate_condition_metric(trainer.logger_connector.callback_metrics)","label":0},{"cid":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_train_epoch_end","text":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_train_epoch_end\n def on_train_epoch_end(self, trainer, pl_module):\n # disable early stopping in train loop when there's a val loop\n if self.monitor == 'val_early_stop_on':\n return\n\n # early stopping can also work in the train loop when there is no val loop and when using structured results\n should_check_early_stop = False\n train_es_key = 'early_stop_on'\n if trainer.logger_connector.callback_metrics.get(train_es_key, None) is not None:\n self.monitor = train_es_key\n should_check_early_stop = True\n\n if should_check_early_stop:\n self._run_early_stopping_check(trainer, pl_module)","label":0},{"cid":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.__warn_deprecated_monitor_key","text":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.__warn_deprecated_monitor_key\n def __warn_deprecated_monitor_key(self):\n using_result_obj = os.environ.get('PL_USING_RESULT_OBJ', None)\n invalid_key = self.monitor not in ['val_loss', 'early_stop_on', 'val_early_stop_on', 'loss']\n if using_result_obj and not self.warned_result_obj and invalid_key:\n self.warned_result_obj = True\n m = f\"\"\"\n When using EvalResult(early_stop_on=X) or TrainResult(early_stop_on=X) the\n 'monitor' key of EarlyStopping has no effect.\n Remove EarlyStopping(monitor='{self.monitor}) to fix')\n \"\"\"\n rank_zero_warn(m)","label":1},{"cid":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping._run_early_stopping_check","text":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping._run_early_stopping_check\n def _run_early_stopping_check(self, trainer, pl_module):\n logs = trainer.logger_connector.callback_metrics\n\n if not self._validate_condition_metric(logs):\n return # short circuit if metric not present\n\n current = logs.get(self.monitor)\n\n # when in dev debugging\n trainer.dev_debugger.track_early_stopping_history(current)\n\n if not isinstance(current, torch.Tensor):\n current = torch.tensor(current, device=pl_module.device)\n\n if trainer.use_tpu and XLA_AVAILABLE:\n current = current.cpu()\n\n if self.monitor_op(current - self.min_delta, self.best_score):\n self.best_score = current\n self.wait_count = 0\n else:\n self.wait_count += 1\n should_stop = self.wait_count >= self.patience\n\n if bool(should_stop):\n self.stopped_epoch = trainer.current_epoch\n trainer.should_stop = True\n\n # stop every ddp process if any world process decides to stop\n should_stop = trainer.accelerator_backend.early_stopping_should_stop(pl_module)\n trainer.should_stop = should_stop","label":0},{"cid":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_train_end","text":"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_train_end\n def on_train_end(self, trainer, pl_module):\n if self.stopped_epoch > 0 and self.verbose > 0:\n rank_zero_warn('Displayed epoch numbers by `EarlyStopping` start from \"1\" until v0.6.x,'\n ' but will start from \"0\" in v0.8.0.', DeprecationWarning)\n log.info(f'Epoch {self.stopped_epoch + 1:05d}: early stopping triggered.')","label":0},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.__init__","text":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.__init__\n def __init__(self, filepath: Optional[str] = None, monitor: str = 'val_loss', verbose: bool = False,\n save_last: bool = False, save_top_k: int = 1, save_weights_only: bool = False,\n mode: str = 'auto', period: int = 1, prefix: str = ''):\n super().__init__()\n if filepath:\n self._fs = get_filesystem(filepath)\n else:\n self._fs = get_filesystem(\"\") # will give local fileystem\n if save_top_k > 0 and filepath is not None and self._fs.isdir(filepath) and len(self._fs.ls(filepath)) > 0:\n rank_zero_warn(\n f\"Checkpoint directory {filepath} exists and is not empty with save_top_k != 0.\"\n \"All files in this directory will be deleted when a checkpoint is saved!\"\n )\n self._rank = 0\n\n self.monitor = monitor\n self.verbose = verbose\n if filepath is None: # will be determined by trainer at runtime\n self.dirpath, self.filename = None, None\n else:\n if self._fs.isdir(filepath):\n self.dirpath, self.filename = filepath, \"{epoch}\"\n else:\n if self._fs.protocol == \"file\": # dont normalize remote paths\n filepath = os.path.realpath(filepath)\n self.dirpath, self.filename = os.path.split(filepath)\n self._fs.makedirs(self.dirpath, exist_ok=True)\n self.save_last = save_last\n self.save_top_k = save_top_k\n self.save_weights_only = save_weights_only\n self.period = period\n self.epoch_last_check = None\n self.prefix = prefix\n self.best_k_models = {}\n # {filename: monitor}\n self.kth_best_model_path = ''\n self.best_model_score = 0\n self.best_model_path = ''\n self.save_function = None\n self.warned_result_obj = False\n\n torch_inf = torch.tensor(np.Inf)\n ","label":1},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.best","text":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.best\n @property\n def best(self):\n rank_zero_warn(\"Attribute `best` has been renamed to `best_model_score` since v0.8.0\"\n \" and will be removed in v0.10.0\", DeprecationWarning)\n return self.best_model_score","label":0},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.kth_best_model","text":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.kth_best_model\n @property\n def kth_best_model(self):\n rank_zero_warn(\"Attribute `kth_best_model` has been renamed to `kth_best_model_path` since v0.8.0\"\n \" and will be removed in v0.10.0\", DeprecationWarning)\n return self.kth_best_model_path","label":0},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._del_model","text":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._del_model\n def _del_model(self, filepath):\n if self._fs.exists(filepath):\n self._fs.rm(filepath)","label":0},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._save_model","text":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._save_model\n def _save_model(self, filepath, trainer, pl_module):\n\n # in debugging, track when we save checkpoints\n trainer.dev_debugger.track_checkpointing_history(filepath)\n\n # make paths\n self._fs.makedirs(os.path.dirname(filepath), exist_ok=True)\n\n # delegate the saving to the model\n if self.save_function is not None:\n self.save_function(filepath, self.save_weights_only)\n else:\n raise ValueError(\".save_function() not set\")","label":0},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.check_monitor_top_k","text":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.check_monitor_top_k\n def check_monitor_top_k(self, current):\n less_than_k_models = len(self.best_k_models) < self.save_top_k\n if less_than_k_models:\n return True\n\n if not isinstance(current, torch.Tensor):\n rank_zero_warn(\n f'{current} is supposed to be a `torch.Tensor`. Saving checkpoint may not work correctly.'\n f' HINT: check the value of {self.monitor} in your validation loop', RuntimeWarning\n )\n current = torch.tensor(current)\n\n monitor_op = {\n \"min\": torch.lt,\n \"max\": torch.gt,\n }[self.mode]\n\n return monitor_op(current, self.best_k_models[self.kth_best_model_path])","label":0},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.format_checkpoint_name","text":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.format_checkpoint_name\n def format_checkpoint_name(self, epoch, metrics, ver=None):\n \"\"\"Generate a filename according to the defined template.\n\n Example::\n\n >>> tmpdir = os.path.dirname(__file__)\n >>> ckpt = ModelCheckpoint(os.path.join(tmpdir, '{epoch}'))\n >>> os.path.basename(ckpt.format_checkpoint_name(0, {}))\n 'epoch=0.ckpt'\n >>> ckpt = ModelCheckpoint(os.path.join(tmpdir, '{epoch:03d}'))\n >>> os.path.basename(ckpt.format_checkpoint_name(5, {}))\n 'epoch=005.ckpt'\n >>> ckpt = ModelCheckpoint(os.path.join(tmpdir, '{epoch}-{val_loss:.2f}'))\n >>> os.path.basename(ckpt.format_checkpoint_name(2, dict(val_loss=0.123456)))\n 'epoch=2-val_loss=0.12.ckpt'\n >>> ckpt = ModelCheckpoint(os.path.join(tmpdir, '{missing:d}'))\n >>> os.path.basename(ckpt.format_checkpoint_name(0, {}))\n 'missing=0.ckpt'\n \"\"\"\n # check if user passed in keys to the string\n groups = re.findall(r'(\\{.*?)[:\\}]', self.filename)\n\n if len(groups) == 0:\n # default name\n filename = f'{self.prefix}_ckpt_epoch_{epoch}'\n else:\n metrics['epoch'] = epoch\n filename = self.filename\n for tmp in groups:\n name = tmp[1:]\n filename = filename.replace(tmp, name + '={' + name)\n if name not in metrics:\n metrics[name] = 0\n filename = filename.format(**metrics)\n str_ver = f'_v{ver}' if ver is not None else ''\n filepath = os.path.join(self.dirpath, self.prefix + filename + str_ver + '.ckpt')\n return filepath","label":1},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_pretrain_routine_start","text":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_pretrain_routine_start\n @rank_zero_only\n def on_pretrain_routine_start(self, trainer, pl_module):\n \"\"\"\n Determines model checkpoint save directory at runtime. References attributes from the\n trainer's logger to determine where to save checkpoints.\n The base path for saving weights is set in this priority:\n\n 1. Checkpoint callback's path (if passed in)\n 2. The default_root_dir from trainer if trainer has no logger\n 3. The weights_save_path from trainer, if user provides it\n 4. User provided weights_saved_path\n\n The base path gets extended with logger name and version (if these are available)\n and subfolder \"checkpoints\".\n \"\"\"\n if self.dirpath is not None:\n return # short circuit\n\n self.filename = '{epoch}'\n\n if trainer.logger is not None:\n if trainer.weights_save_path != trainer.default_root_dir:\n # the user has changed weights_save_path, it overrides anything\n save_dir = trainer.weights_save_path\n else:\n save_dir = trainer.logger.save_dir or trainer.default_root_dir\n\n version = trainer.logger.version if isinstance(\n trainer.logger.version, str) else f'version_{trainer.logger.version}'\n ckpt_path = os.path.join(\n save_dir,\n trainer.logger.name,\n version,\n \"checkpoints\"\n )\n else:\n ckpt_path = os.path.join(trainer.weights_save_path, \"checkpoints\")\n\n self.dirpath = ckpt_path\n\n assert trainer.global_rank == 0, \"tried to make a checkpoint from non global_rank=0\"\n self._fs.makedirs(self.dirpath, exist_ok=True)","label":1},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.__warn_deprecated_monitor_key","text":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.__warn_deprecated_monitor_key\n def __warn_deprecated_monitor_key(self):\n using_result_obj = os.environ.get('PL_USING_RESULT_OBJ', None)\n invalid_key = self.monitor not in ['val_loss', 'checkpoint_on', 'loss', 'val_checkpoint_on']\n if using_result_obj and not self.warned_result_obj and invalid_key:\n self.warned_result_obj = True\n m = f\"\"\"\n When using EvalResult(checkpoint_on=X) or TrainResult(checkpoint_on=X) the\n 'monitor' key of ModelCheckpoint has no effect.\n Remove ModelCheckpoint(monitor='{self.monitor}) to fix')\n \"\"\"\n rank_zero_warn(m)","label":1},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_validation_end","text":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_validation_end\n @rank_zero_only\n def on_validation_end(self, trainer, pl_module):\n # only run on main process\n if trainer.global_rank != 0:\n return\n\n if trainer.running_sanity_check:\n return\n\n # TODO: remove when dict results are deprecated\n self.__warn_deprecated_monitor_key()\n\n metrics = trainer.logger_connector.callback_metrics\n epoch = trainer.current_epoch\n\n # support structured results\n if metrics.get('checkpoint_on') is not None:\n self.monitor = 'checkpoint_on'\n\n # conditioned val metrics override conditioned train loop metrics\n if metrics.get('val_checkpoint_on') is not None:\n self.monitor = 'val_checkpoint_on'\n\n if self.save_top_k == 0:\n # no models are saved\n return\n if self.epoch_last_check is not None and (epoch - self.epoch_last_check) < self.period:\n # skipping in this term\n return\n\n self.epoch_last_check = epoch\n\n ckpt_name_metrics = trainer.logger_connector.logged_metrics\n filepath = self.format_checkpoint_name(epoch, ckpt_name_metrics)\n version_cnt = 0\n while self._fs.exists(filepath):\n filepath = self.format_checkpoint_name(epoch, ckpt_name_metrics, ver=version_cnt)\n # this epoch called before\n version_cnt += 1\n\n if self.save_top_k != -1:\n current = metrics.get(self.monitor)\n\n if not isinstance(current, torch.Tensor):\n rank_zero_warn(\n f'The metric you returned {current} must be a `torch.Tensor` instance, checkpoint not saved'\n f' HINT: what is the value of {self.monitor} in validation_epoch_end()?', RuntimeWarning\n )\n if current is not None:\n current = torch.tensor(current)\n\n if current is Non","label":1},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._do_check_save","text":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._do_check_save\n def _do_check_save(self, filepath, current, epoch, trainer, pl_module):\n # remove kth\n\n del_list = []\n if len(self.best_k_models) == self.save_top_k and self.save_top_k > 0:\n delpath = self.kth_best_model_path\n self.best_k_models.pop(self.kth_best_model_path)\n del_list.append(delpath)\n\n self.best_k_models[filepath] = current\n if len(self.best_k_models) == self.save_top_k:\n # monitor dict has reached k elements\n _op = max if self.mode == 'min' else min\n self.kth_best_model_path = _op(self.best_k_models,\n key=self.best_k_models.get)\n self.kth_value = self.best_k_models[self.kth_best_model_path]\n\n _op = min if self.mode == 'min' else max\n self.best_model_path = _op(self.best_k_models, key=self.best_k_models.get)\n self.best_model_score = self.best_k_models[self.best_model_path]\n\n if self.verbose > 0:\n log.info(\n f'\\nEpoch {epoch:05d}: {self.monitor} reached'\n f' {current:0.5f} (best {self.best_model_score:0.5f}), saving model to'\n f' {filepath} as top {self.save_top_k}')\n self._save_model(filepath, trainer, pl_module)\n\n for cur_path in del_list:\n if cur_path != filepath:\n self._del_model(cur_path)","label":1},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_save_checkpoint","text":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_save_checkpoint\n def on_save_checkpoint(self, trainer, pl_module):\n return {\n 'best_model_score': self.best_model_score,\n 'best_model_path': self.best_model_path,\n }","label":0},{"cid":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_load_checkpoint","text":"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_load_checkpoint\n def on_load_checkpoint(self, checkpointed_state):\n self.best_model_score = checkpointed_state['best_model_score']\n self.best_model_path = checkpointed_state['best_model_path']","label":0}],"string":"[\n {\n \"cid\": \"docs/source/conf.py::run_apidoc\",\n \"text\": \"docs/source/conf.py::run_apidoc\\ndef run_apidoc(_):\\n sys.path.insert(0, apidoc_output_folder)\\n\\n # delete api-doc files before generating them\\n if os.path.exists(apidoc_output_folder):\\n shutil.rmtree(apidoc_output_folder)\\n\\n for pkg in PACKAGES:\\n argv = ['-e',\\n '-o', apidoc_output_folder,\\n os.path.join(PATH_ROOT, pkg),\\n '**/test_*',\\n '--force',\\n '--private',\\n '--module-first']\\n\\n apidoc.main(argv)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/source/conf.py::setup\",\n \"text\": \"docs/source/conf.py::setup\\ndef setup(app):\\n # this is for hiding doctest decoration,\\n # see: http://z4r.github.io/python/2011/12/02/hides-the-prompts-and-output/\\n app.add_javascript('copybutton.js')\\n app.connect('builder-inited', run_apidoc)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/source/conf.py::package_list_from_file\",\n \"text\": \"docs/source/conf.py::package_list_from_file\\ndef package_list_from_file(file):\\n mocked_packages = []\\n with open(file, 'r') as fp:\\n for ln in fp.readlines():\\n found = [ln.index(ch) for ch in list(',=<>#') if ch in ln]\\n pkg = ln[:min(found)] if found else ln\\n if pkg.rstrip():\\n mocked_packages.append(pkg.rstrip())\\n return mocked_packages\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/source/conf.py::linkcode_resolve\",\n \"text\": \"docs/source/conf.py::linkcode_resolve\\ndef linkcode_resolve(domain, info):\\n def find_source():\\n # try to find the file and line number, based on code from numpy:\\n # https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L286\\n obj = sys.modules[info['module']]\\n for part in info['fullname'].split('.'):\\n obj = getattr(obj, part)\\n fname = inspect.getsourcefile(obj)\\n # https://github.com/rtfd/readthedocs.org/issues/5735\\n if any([s in fname for s in ('readthedocs', 'rtfd', 'checkouts')]):\\n # /home/docs/checkouts/readthedocs.org/user_builds/pytorch_lightning/checkouts/\\n # devel/pytorch_lightning/utilities/cls_experiment.py#L26-L176\\n path_top = os.path.abspath(os.path.join('..', '..', '..'))\\n fname = os.path.relpath(fname, start=path_top)\\n else:\\n # Local build, imitate master\\n fname = 'master/' + os.path.relpath(fname, start=os.path.abspath('..'))\\n source, lineno = inspect.getsourcelines(obj)\\n return fname, lineno, lineno + len(source) - 1\\n\\n if domain != 'py' or not info['module']:\\n return None\\n try:\\n filename = '%s#L%d-L%d' % find_source()\\n except Exception:\\n filename = info['module'].replace('.', '/') + '.py'\\n # import subprocess\\n # tag = subprocess.Popen(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE,\\n # universal_newlines=True).communicate()[0][:-1]\\n branch = filename.split('/')[0]\\n # do mapping from latest tags to master\\n branch = {'latest': 'master', 'stable': 'master'}.get(branch, branch)\\n filename = '/'.join([branch] + filename.split('/')[1:])\\n return \\\"https://github.com/%s/%s/blob/%s\\\" \\\\\\n % (github_user, github_repo, filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/source/conf.py::linkcode_resolve.find_source\",\n \"text\": \"docs/source/conf.py::linkcode_resolve.find_source\\n def find_source():\\n # try to find the file and line number, based on code from numpy:\\n # https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L286\\n obj = sys.modules[info['module']]\\n for part in info['fullname'].split('.'):\\n obj = getattr(obj, part)\\n fname = inspect.getsourcefile(obj)\\n # https://github.com/rtfd/readthedocs.org/issues/5735\\n if any([s in fname for s in ('readthedocs', 'rtfd', 'checkouts')]):\\n # /home/docs/checkouts/readthedocs.org/user_builds/pytorch_lightning/checkouts/\\n # devel/pytorch_lightning/utilities/cls_experiment.py#L26-L176\\n path_top = os.path.abspath(os.path.join('..', '..', '..'))\\n fname = os.path.relpath(fname, start=path_top)\\n else:\\n # Local build, imitate master\\n fname = 'master/' + os.path.relpath(fname, start=os.path.abspath('..'))\\n source, lineno = inspect.getsourcelines(obj)\\n return fname, lineno, lineno + len(source) - 1\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.__init__\",\n \"text\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.__init__\\n def __init__(self, monitor: str = 'val_loss', min_delta: float = 0.0, patience: int = 3,\\n verbose: bool = False, mode: str = 'auto', strict: bool = True):\\n super().__init__()\\n self.monitor = monitor\\n self.patience = patience\\n self.verbose = verbose\\n self.strict = strict\\n self.min_delta = min_delta\\n self.wait_count = 0\\n self.stopped_epoch = 0\\n self.mode = mode\\n self.warned_result_obj = False\\n\\n if mode not in self.mode_dict:\\n if self.verbose > 0:\\n log.info(f'EarlyStopping mode {mode} is unknown, fallback to auto mode.')\\n self.mode = 'auto'\\n\\n if self.mode == 'auto':\\n if self.monitor == 'acc':\\n self.mode = 'max'\\n else:\\n self.mode = 'min'\\n if self.verbose > 0:\\n log.info(f'EarlyStopping mode set to {self.mode} for monitoring {self.monitor}.')\\n\\n self.min_delta *= 1 if self.monitor_op == torch.gt else -1\\n self.best_score = torch_inf if self.monitor_op == torch.lt else -torch_inf\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping._validate_condition_metric\",\n \"text\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping._validate_condition_metric\\n def _validate_condition_metric(self, logs):\\n monitor_val = logs.get(self.monitor)\\n error_msg = (f'Early stopping conditioned on metric `{self.monitor}`'\\n f' which is not available. Either add `{self.monitor}` to the return of '\\n f' validation_epoch end or modify your EarlyStopping callback to use any of the '\\n f'following: `{\\\"`, `\\\".join(list(logs.keys()))}`')\\n\\n if monitor_val is None:\\n if self.strict:\\n raise RuntimeError(error_msg)\\n if self.verbose > 0:\\n rank_zero_warn(error_msg, RuntimeWarning)\\n\\n return False\\n\\n return True\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.monitor_op\",\n \"text\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.monitor_op\\n @property\\n def monitor_op(self):\\n return self.mode_dict[self.mode]\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_save_checkpoint\",\n \"text\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_save_checkpoint\\n def on_save_checkpoint(self, trainer, pl_module):\\n return {\\n 'wait_count': self.wait_count,\\n 'stopped_epoch': self.stopped_epoch,\\n 'best_score': self.best_score,\\n 'patience': self.patience\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_load_checkpoint\",\n \"text\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_load_checkpoint\\n def on_load_checkpoint(self, checkpointed_state):\\n self.wait_count = checkpointed_state['wait_count']\\n self.stopped_epoch = checkpointed_state['stopped_epoch']\\n self.best_score = checkpointed_state['best_score']\\n self.patience = checkpointed_state['patience']\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_validation_end\",\n \"text\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_validation_end\\n def on_validation_end(self, trainer, pl_module):\\n if trainer.running_sanity_check:\\n return\\n\\n self._run_early_stopping_check(trainer, pl_module)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_validation_epoch_end\",\n \"text\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_validation_epoch_end\\n def on_validation_epoch_end(self, trainer, pl_module):\\n if trainer.running_sanity_check:\\n return\\n\\n self.__warn_deprecated_monitor_key()\\n\\n val_es_key = 'val_early_stop_on'\\n if trainer.logger_connector.callback_metrics.get(val_es_key) is not None:\\n self.monitor = val_es_key\\n\\n # disable strict checking when using structured results\\n if val_es_key in trainer.logger_connector.callback_metrics:\\n self.strict = False\\n\\n self._validate_condition_metric(trainer.logger_connector.callback_metrics)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_train_epoch_end\",\n \"text\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_train_epoch_end\\n def on_train_epoch_end(self, trainer, pl_module):\\n # disable early stopping in train loop when there's a val loop\\n if self.monitor == 'val_early_stop_on':\\n return\\n\\n # early stopping can also work in the train loop when there is no val loop and when using structured results\\n should_check_early_stop = False\\n train_es_key = 'early_stop_on'\\n if trainer.logger_connector.callback_metrics.get(train_es_key, None) is not None:\\n self.monitor = train_es_key\\n should_check_early_stop = True\\n\\n if should_check_early_stop:\\n self._run_early_stopping_check(trainer, pl_module)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.__warn_deprecated_monitor_key\",\n \"text\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.__warn_deprecated_monitor_key\\n def __warn_deprecated_monitor_key(self):\\n using_result_obj = os.environ.get('PL_USING_RESULT_OBJ', None)\\n invalid_key = self.monitor not in ['val_loss', 'early_stop_on', 'val_early_stop_on', 'loss']\\n if using_result_obj and not self.warned_result_obj and invalid_key:\\n self.warned_result_obj = True\\n m = f\\\"\\\"\\\"\\n When using EvalResult(early_stop_on=X) or TrainResult(early_stop_on=X) the\\n 'monitor' key of EarlyStopping has no effect.\\n Remove EarlyStopping(monitor='{self.monitor}) to fix')\\n \\\"\\\"\\\"\\n rank_zero_warn(m)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping._run_early_stopping_check\",\n \"text\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping._run_early_stopping_check\\n def _run_early_stopping_check(self, trainer, pl_module):\\n logs = trainer.logger_connector.callback_metrics\\n\\n if not self._validate_condition_metric(logs):\\n return # short circuit if metric not present\\n\\n current = logs.get(self.monitor)\\n\\n # when in dev debugging\\n trainer.dev_debugger.track_early_stopping_history(current)\\n\\n if not isinstance(current, torch.Tensor):\\n current = torch.tensor(current, device=pl_module.device)\\n\\n if trainer.use_tpu and XLA_AVAILABLE:\\n current = current.cpu()\\n\\n if self.monitor_op(current - self.min_delta, self.best_score):\\n self.best_score = current\\n self.wait_count = 0\\n else:\\n self.wait_count += 1\\n should_stop = self.wait_count >= self.patience\\n\\n if bool(should_stop):\\n self.stopped_epoch = trainer.current_epoch\\n trainer.should_stop = True\\n\\n # stop every ddp process if any world process decides to stop\\n should_stop = trainer.accelerator_backend.early_stopping_should_stop(pl_module)\\n trainer.should_stop = should_stop\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_train_end\",\n \"text\": \"pytorch_lightning/callbacks/early_stopping.py::EarlyStopping.on_train_end\\n def on_train_end(self, trainer, pl_module):\\n if self.stopped_epoch > 0 and self.verbose > 0:\\n rank_zero_warn('Displayed epoch numbers by `EarlyStopping` start from \\\"1\\\" until v0.6.x,'\\n ' but will start from \\\"0\\\" in v0.8.0.', DeprecationWarning)\\n log.info(f'Epoch {self.stopped_epoch + 1:05d}: early stopping triggered.')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.__init__\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.__init__\\n def __init__(self, filepath: Optional[str] = None, monitor: str = 'val_loss', verbose: bool = False,\\n save_last: bool = False, save_top_k: int = 1, save_weights_only: bool = False,\\n mode: str = 'auto', period: int = 1, prefix: str = ''):\\n super().__init__()\\n if filepath:\\n self._fs = get_filesystem(filepath)\\n else:\\n self._fs = get_filesystem(\\\"\\\") # will give local fileystem\\n if save_top_k > 0 and filepath is not None and self._fs.isdir(filepath) and len(self._fs.ls(filepath)) > 0:\\n rank_zero_warn(\\n f\\\"Checkpoint directory {filepath} exists and is not empty with save_top_k != 0.\\\"\\n \\\"All files in this directory will be deleted when a checkpoint is saved!\\\"\\n )\\n self._rank = 0\\n\\n self.monitor = monitor\\n self.verbose = verbose\\n if filepath is None: # will be determined by trainer at runtime\\n self.dirpath, self.filename = None, None\\n else:\\n if self._fs.isdir(filepath):\\n self.dirpath, self.filename = filepath, \\\"{epoch}\\\"\\n else:\\n if self._fs.protocol == \\\"file\\\": # dont normalize remote paths\\n filepath = os.path.realpath(filepath)\\n self.dirpath, self.filename = os.path.split(filepath)\\n self._fs.makedirs(self.dirpath, exist_ok=True)\\n self.save_last = save_last\\n self.save_top_k = save_top_k\\n self.save_weights_only = save_weights_only\\n self.period = period\\n self.epoch_last_check = None\\n self.prefix = prefix\\n self.best_k_models = {}\\n # {filename: monitor}\\n self.kth_best_model_path = ''\\n self.best_model_score = 0\\n self.best_model_path = ''\\n self.save_function = None\\n self.warned_result_obj = False\\n\\n torch_inf = torch.tensor(np.Inf)\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.best\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.best\\n @property\\n def best(self):\\n rank_zero_warn(\\\"Attribute `best` has been renamed to `best_model_score` since v0.8.0\\\"\\n \\\" and will be removed in v0.10.0\\\", DeprecationWarning)\\n return self.best_model_score\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.kth_best_model\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.kth_best_model\\n @property\\n def kth_best_model(self):\\n rank_zero_warn(\\\"Attribute `kth_best_model` has been renamed to `kth_best_model_path` since v0.8.0\\\"\\n \\\" and will be removed in v0.10.0\\\", DeprecationWarning)\\n return self.kth_best_model_path\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._del_model\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._del_model\\n def _del_model(self, filepath):\\n if self._fs.exists(filepath):\\n self._fs.rm(filepath)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._save_model\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._save_model\\n def _save_model(self, filepath, trainer, pl_module):\\n\\n # in debugging, track when we save checkpoints\\n trainer.dev_debugger.track_checkpointing_history(filepath)\\n\\n # make paths\\n self._fs.makedirs(os.path.dirname(filepath), exist_ok=True)\\n\\n # delegate the saving to the model\\n if self.save_function is not None:\\n self.save_function(filepath, self.save_weights_only)\\n else:\\n raise ValueError(\\\".save_function() not set\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.check_monitor_top_k\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.check_monitor_top_k\\n def check_monitor_top_k(self, current):\\n less_than_k_models = len(self.best_k_models) < self.save_top_k\\n if less_than_k_models:\\n return True\\n\\n if not isinstance(current, torch.Tensor):\\n rank_zero_warn(\\n f'{current} is supposed to be a `torch.Tensor`. Saving checkpoint may not work correctly.'\\n f' HINT: check the value of {self.monitor} in your validation loop', RuntimeWarning\\n )\\n current = torch.tensor(current)\\n\\n monitor_op = {\\n \\\"min\\\": torch.lt,\\n \\\"max\\\": torch.gt,\\n }[self.mode]\\n\\n return monitor_op(current, self.best_k_models[self.kth_best_model_path])\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.format_checkpoint_name\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.format_checkpoint_name\\n def format_checkpoint_name(self, epoch, metrics, ver=None):\\n \\\"\\\"\\\"Generate a filename according to the defined template.\\n\\n Example::\\n\\n >>> tmpdir = os.path.dirname(__file__)\\n >>> ckpt = ModelCheckpoint(os.path.join(tmpdir, '{epoch}'))\\n >>> os.path.basename(ckpt.format_checkpoint_name(0, {}))\\n 'epoch=0.ckpt'\\n >>> ckpt = ModelCheckpoint(os.path.join(tmpdir, '{epoch:03d}'))\\n >>> os.path.basename(ckpt.format_checkpoint_name(5, {}))\\n 'epoch=005.ckpt'\\n >>> ckpt = ModelCheckpoint(os.path.join(tmpdir, '{epoch}-{val_loss:.2f}'))\\n >>> os.path.basename(ckpt.format_checkpoint_name(2, dict(val_loss=0.123456)))\\n 'epoch=2-val_loss=0.12.ckpt'\\n >>> ckpt = ModelCheckpoint(os.path.join(tmpdir, '{missing:d}'))\\n >>> os.path.basename(ckpt.format_checkpoint_name(0, {}))\\n 'missing=0.ckpt'\\n \\\"\\\"\\\"\\n # check if user passed in keys to the string\\n groups = re.findall(r'(\\\\{.*?)[:\\\\}]', self.filename)\\n\\n if len(groups) == 0:\\n # default name\\n filename = f'{self.prefix}_ckpt_epoch_{epoch}'\\n else:\\n metrics['epoch'] = epoch\\n filename = self.filename\\n for tmp in groups:\\n name = tmp[1:]\\n filename = filename.replace(tmp, name + '={' + name)\\n if name not in metrics:\\n metrics[name] = 0\\n filename = filename.format(**metrics)\\n str_ver = f'_v{ver}' if ver is not None else ''\\n filepath = os.path.join(self.dirpath, self.prefix + filename + str_ver + '.ckpt')\\n return filepath\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_pretrain_routine_start\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_pretrain_routine_start\\n @rank_zero_only\\n def on_pretrain_routine_start(self, trainer, pl_module):\\n \\\"\\\"\\\"\\n Determines model checkpoint save directory at runtime. References attributes from the\\n trainer's logger to determine where to save checkpoints.\\n The base path for saving weights is set in this priority:\\n\\n 1. Checkpoint callback's path (if passed in)\\n 2. The default_root_dir from trainer if trainer has no logger\\n 3. The weights_save_path from trainer, if user provides it\\n 4. User provided weights_saved_path\\n\\n The base path gets extended with logger name and version (if these are available)\\n and subfolder \\\"checkpoints\\\".\\n \\\"\\\"\\\"\\n if self.dirpath is not None:\\n return # short circuit\\n\\n self.filename = '{epoch}'\\n\\n if trainer.logger is not None:\\n if trainer.weights_save_path != trainer.default_root_dir:\\n # the user has changed weights_save_path, it overrides anything\\n save_dir = trainer.weights_save_path\\n else:\\n save_dir = trainer.logger.save_dir or trainer.default_root_dir\\n\\n version = trainer.logger.version if isinstance(\\n trainer.logger.version, str) else f'version_{trainer.logger.version}'\\n ckpt_path = os.path.join(\\n save_dir,\\n trainer.logger.name,\\n version,\\n \\\"checkpoints\\\"\\n )\\n else:\\n ckpt_path = os.path.join(trainer.weights_save_path, \\\"checkpoints\\\")\\n\\n self.dirpath = ckpt_path\\n\\n assert trainer.global_rank == 0, \\\"tried to make a checkpoint from non global_rank=0\\\"\\n self._fs.makedirs(self.dirpath, exist_ok=True)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.__warn_deprecated_monitor_key\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.__warn_deprecated_monitor_key\\n def __warn_deprecated_monitor_key(self):\\n using_result_obj = os.environ.get('PL_USING_RESULT_OBJ', None)\\n invalid_key = self.monitor not in ['val_loss', 'checkpoint_on', 'loss', 'val_checkpoint_on']\\n if using_result_obj and not self.warned_result_obj and invalid_key:\\n self.warned_result_obj = True\\n m = f\\\"\\\"\\\"\\n When using EvalResult(checkpoint_on=X) or TrainResult(checkpoint_on=X) the\\n 'monitor' key of ModelCheckpoint has no effect.\\n Remove ModelCheckpoint(monitor='{self.monitor}) to fix')\\n \\\"\\\"\\\"\\n rank_zero_warn(m)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_validation_end\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_validation_end\\n @rank_zero_only\\n def on_validation_end(self, trainer, pl_module):\\n # only run on main process\\n if trainer.global_rank != 0:\\n return\\n\\n if trainer.running_sanity_check:\\n return\\n\\n # TODO: remove when dict results are deprecated\\n self.__warn_deprecated_monitor_key()\\n\\n metrics = trainer.logger_connector.callback_metrics\\n epoch = trainer.current_epoch\\n\\n # support structured results\\n if metrics.get('checkpoint_on') is not None:\\n self.monitor = 'checkpoint_on'\\n\\n # conditioned val metrics override conditioned train loop metrics\\n if metrics.get('val_checkpoint_on') is not None:\\n self.monitor = 'val_checkpoint_on'\\n\\n if self.save_top_k == 0:\\n # no models are saved\\n return\\n if self.epoch_last_check is not None and (epoch - self.epoch_last_check) < self.period:\\n # skipping in this term\\n return\\n\\n self.epoch_last_check = epoch\\n\\n ckpt_name_metrics = trainer.logger_connector.logged_metrics\\n filepath = self.format_checkpoint_name(epoch, ckpt_name_metrics)\\n version_cnt = 0\\n while self._fs.exists(filepath):\\n filepath = self.format_checkpoint_name(epoch, ckpt_name_metrics, ver=version_cnt)\\n # this epoch called before\\n version_cnt += 1\\n\\n if self.save_top_k != -1:\\n current = metrics.get(self.monitor)\\n\\n if not isinstance(current, torch.Tensor):\\n rank_zero_warn(\\n f'The metric you returned {current} must be a `torch.Tensor` instance, checkpoint not saved'\\n f' HINT: what is the value of {self.monitor} in validation_epoch_end()?', RuntimeWarning\\n )\\n if current is not None:\\n current = torch.tensor(current)\\n\\n if current is Non\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._do_check_save\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint._do_check_save\\n def _do_check_save(self, filepath, current, epoch, trainer, pl_module):\\n # remove kth\\n\\n del_list = []\\n if len(self.best_k_models) == self.save_top_k and self.save_top_k > 0:\\n delpath = self.kth_best_model_path\\n self.best_k_models.pop(self.kth_best_model_path)\\n del_list.append(delpath)\\n\\n self.best_k_models[filepath] = current\\n if len(self.best_k_models) == self.save_top_k:\\n # monitor dict has reached k elements\\n _op = max if self.mode == 'min' else min\\n self.kth_best_model_path = _op(self.best_k_models,\\n key=self.best_k_models.get)\\n self.kth_value = self.best_k_models[self.kth_best_model_path]\\n\\n _op = min if self.mode == 'min' else max\\n self.best_model_path = _op(self.best_k_models, key=self.best_k_models.get)\\n self.best_model_score = self.best_k_models[self.best_model_path]\\n\\n if self.verbose > 0:\\n log.info(\\n f'\\\\nEpoch {epoch:05d}: {self.monitor} reached'\\n f' {current:0.5f} (best {self.best_model_score:0.5f}), saving model to'\\n f' {filepath} as top {self.save_top_k}')\\n self._save_model(filepath, trainer, pl_module)\\n\\n for cur_path in del_list:\\n if cur_path != filepath:\\n self._del_model(cur_path)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_save_checkpoint\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_save_checkpoint\\n def on_save_checkpoint(self, trainer, pl_module):\\n return {\\n 'best_model_score': self.best_model_score,\\n 'best_model_path': self.best_model_path,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_load_checkpoint\",\n \"text\": \"pytorch_lightning/callbacks/model_checkpoint.py::ModelCheckpoint.on_load_checkpoint\\n def on_load_checkpoint(self, checkpointed_state):\\n self.best_model_score = checkpointed_state['best_model_score']\\n self.best_model_path = checkpointed_state['best_model_path']\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":277,"cells":{"instance_id":{"kind":"string","value":"pantsbuild__pants-5040"},"repo":{"kind":"string","value":"pantsbuild/pants"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"implement support for the `repl` goal w/ the daemon\ncurrently, `repl` is known to not work with the daemon due to an unimplemented bi-directional user input path here: https://github.com/pantsbuild/pants/blob/e3a227601de8c4179eae852e77c6673cc334b075/src/python/pants/bin/daemon_pants_runner.py#L99-L100\r\n\r\nin order to get users using the daemon more generally, we'll need to get this implemented.\r\n\n"},"gold_files":{"kind":"list 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like","value":[{"cid":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py","text":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py\nclass Fetcher # Knows how to interpret remote import paths and fetch code to satisfy them.\n def __init__(self, import_path)\n def import_path(self)\n def root(self) # Returns the root of this fetcher's remote import_path.\n def fetch(self, dest, rev=None) # Fetches the remote library to the given dest dir.\nclass CloningFetcher # A fetcher that gets the remote library by cloning its repo.\n def __init__(self, import_path, meta_tag_reader)\n def root(self)\n def fetch(self, dest, rev=None)\nclass ArchiveFetcher # A fetcher that retrieves and unpacks remote libraries from archive files.\n class UrlInfo # Information about a remote archive.\n def __init__(self, import_path, import_prefix, url_info, archive_retriever)\n def root(self)\n def fetch(self, dest, rev=None)\n def _fetch(self, archive_url, strip_level, dest)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/scala_repl.py","text":"src/python/pants/backend/jvm/tasks/scala_repl.py\nclass ScalaRepl # Operations to create or launch Scala repls.\n def register_options(cls, register)\n def subsystem_dependencies(cls)\n def select_targets(cls, target)\n def setup_repl_session(self, targets)\n def launch_repl(self, classpath)","label":1},{"cid":"src/python/pants/backend/python/tasks2/python_repl.py","text":"src/python/pants/backend/python/tasks2/python_repl.py\nclass PythonRepl # Launch an interactive Python interpreter session.\n def register_options(cls, register)\n def select_targets(cls, target)\n def extra_requirements(self)\n def setup_repl_session(self, targets)\n def launch_repl(self, pex, **pex_run_kwargs)","label":1},{"cid":"src/python/pants/bin/daemon_pants_runner.py","text":"src/python/pants/bin/daemon_pants_runner.py\n# coding=utf-8\n# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\nfrom __future__ import (absolute_import, division, generators, nested_scopes, print_function,\n unicode_literals, with_statement)\n\nimport datetime\nimport os\nimport signal\nimport socket\nimport sys\nfrom contextlib import contextmanager\n\nfrom setproctitle import setproctitle as set_process_title\n\nfrom pants.base.exiter import Exiter\nfrom pants.bin.local_pants_runner import LocalPantsRunner\nfrom pants.init.util import clean_global_runtime_state\nfrom pants.java.nailgun_io import NailgunStreamWriter\nfrom pants.java.nailgun_protocol import ChunkType, NailgunProtocol\nfrom pants.pantsd.process_manager import ProcessManager\nfrom pants.util.contextutil import HardSystemExit, stdio_as\n\n\nclass DaemonExiter(Exiter):\n \"\"\"An Exiter that emits unhandled tracebacks and exit codes via the Nailgun protocol.\"\"\"\n\n def __init__(self, socket):\n super(DaemonExiter, self).__init__()\n self._socket = socket\n\n def _shutdown_socket(self):\n \"\"\"Shutdown and close the connected socket.\"\"\"\n try:\n self._socket.shutdown(socket.SHUT_WR)\n except socket.error:\n pass\n finally:\n self._socket.close()\n\n def exit(self, result=0, msg=None):\n \"\"\"Exit the runtime.\"\"\"\n try:\n # Write a final message to stderr if present.\n if msg:\n NailgunProtocol.write_chunk(self._socket, ChunkType.STDERR, msg)\n\n # Send an Exit chunk with the result.\n NailgunProtocol.write_chunk(self._socket, ChunkType.EXIT, str(result).encode('ascii'))\n\n # Shutdown the connected socket.\n self._shutdown_socket()\n finally:\n # N.B. Assuming a fork()'d child, cause os._exit to be called here to avoid the routine\n # sys.exit behavior (via `pants.util.contextutil.hard_exit_handler()`).\n raise HardSystemExit()\n\n\nclass DaemonPantsRunner(ProcessManager):\n \"\"\"A daemonizing PantsRunner that speaks the nailgun protocol to a remote client.\n\n N.B. this class is primarily used by the PailgunService in pantsd.\n \"\"\"\n\n def __init__(self, socket, exiter, args, env, graph_helper, deferred_exception=None):\n \"\"\"\n :param socket socket: A connected socket capable of speaking the nailgun protocol.\n :param Exiter exiter: The Exiter instance for this run.\n :param list args: The arguments (i.e. sys.argv) for this run.\n :param dict env: The environment (i.e. os.environ) for this run.\n :param LegacyGraphHelper graph_helper: The LegacyGraphHelper instance to use for BuildGraph\n construction. In the event of an exception, this will be\n None.\n :param Exception deferred_exception: A deferred exception from the daemon's graph construction.\n If present, this will be re-raised in the client context.\n \"\"\"\n ","label":1},{"cid":"src/python/pants/bin/goal_runner.py","text":"src/python/pants/bin/goal_runner.py\nclass GoalRunnerFactory\n def __init__(self, root_dir, options, build_config, run_tracker, reporting, daemon_graph_helper=None, exiter=sys.exit) # :param str root_dir: The root directory of the pants workspace (aka the \"build r\n def _handle_help(self, help_request) # Handle requests for `help` information.\n def _init_graph(self, use_engine, pants_ignore_patterns, build_ignore_patterns, exclude_target_regexps, target_specs, workdir, graph_helper=None, subproject_build_roots=None) # Determine the BuildGraph, AddressMapper and spec_roots for a given run.\n def _determine_goals(self, requested_goals) # Check and populate the requested goals for a given run.\n def _specs_to_targets(self, specs) # Populate the BuildGraph and target list from a set of input specs.\n def _maybe_launch_pantsd(self, pantsd_launcher) # Launches pantsd if configured to do so.\n def _should_be_quiet(self, goals)\n def _setup_context(self)\n def setup(self)\nclass GoalRunner # Lists installed goals or else executes a named goal.\n def __init__(self, context, goals, run_tracker, kill_nailguns, exiter=sys.exit) # :param Context context: The global, pre-initialized Context as created by GoalRu\n def subsystems(cls) # Subsystems used outside of any task.\n def _execute_engine(self)\n def run(self)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py","text":"src/python/pants/engine/legacy/structs.py\nclass TargetAdaptor # A Struct to imitate the existing Target.\n def get_sources(self) # Returns target's non-deferred sources if exists or the default sources if define\n def field_adaptors(self) # Returns a tuple of Fields for captured fields which need additional treatment.\n def default_sources(self) # True if this adaptor should use default sources if they are defined.\n def default_sources_globs(self)\n def default_sources_exclude_globs(self)\nclass Field # A marker for Target(Adaptor) fields for which the engine might perform extra con\nclass SourcesField # Represents the `sources` argument for a particular Target.\n def __eq__(self, other)\n def __ne__(self, other)\n def __hash__(self)\n def __repr__(self)\n def __str__(self)\nclass JavaLibraryAdaptor\n def default_sources_globs(self)\n def default_sources_exclude_globs(self)\nclass ScalaLibraryAdaptor\n def default_sources_globs(self)\n def default_sources_exclude_globs(self)\nclass JunitTestsAdaptor\n def default_sources_globs(self)\nclass BundlesField # Represents the `bundles` argument, each of which has a PathGlobs to represent it\n def __eq__(self, other)\n def __ne__(self, other)\n def __hash__(self)\nclass BundleAdaptor # A Struct to capture the args for the `bundle` object.\nclass JvmAppAdaptor\n def __init__(self, bundles=None, **kwargs) # :param list bundles: A list of `BundleAdaptor` objects\n def bundles(self) # The BundleAdaptors for this JvmApp.\n def field_adaptors(self)\n def _construct_bundles_field(self)\nclass RemoteSourcesAdaptor\n def __init__(self, dest=None, **kwargs) # :param dest: A target constructor.\nclass PythonTargetAdaptor\n def field_adaptors(self)\nclass PythonLibraryAdaptor\n def default_sources_globs(self)\n def default_sources_exclude_globs(self)\nclass PythonTestsAdaptor\n def default_sources_globs(self)\nclass GoTargetAdaptor\n def default_sources(self)\n def default_sources_globs(self)\nclass BaseGlobs # An adaptor class to allow BUILD file parsing from ContextAwareObjectFactories.\n def from_sources_field(sources, spec_path) # Return a BaseGlobs for the given sources field.\n def _filespec_for_exclude(raw_exclude, spec_path)\n def path_globs_kwarg(self) # The name of the `PathGlobs` parameter corresponding to this BaseGlobs instance.\n def legacy_globs_class(self) # The corresponding `wrapped_globs` class for this BaseGlobs.\n def __init__(self, *patterns, **kwargs)\n def filespecs(self) # Return a filespecs dict representing both globs and excludes.\n def _exclude_filespecs(self)\n def to_path_globs(self, relpath) # Return two PathGlobs representing the included and excluded Files for these patt\nclass Files\nclass Globs\nclass RGlobs\nclass ZGlobs","label":0},{"cid":"src/python/pants/java/executor.py","text":"src/python/pants/java/executor.py\nclass Executor # Executes java programs.\n def _scrub_args(classpath, main, jvm_options, args, cwd)\n class Error # Indicates an error launching a java program.\n class InvalidDistribution # Indicates an invalid Distribution was used to construct this runner.\n class Runner # A re-usable executor that can run a configured java command line.\n def executor(self) # Returns the executor this runner uses to run itself.\n def cmd(self) # Returns a string representation of the command that will be run.\n def command(self) # Returns a copy of the command line that will be run as a list of command line to\n def run(self, stdout=None, stderr=None, cwd=None) # Runs the configured java command.\n def spawn(self, stdout=None, stderr=None, cwd=None) # Spawns the configured java command.\n def __init__(self, distribution) # Constructs an Executor that can be used to launch java programs.\n def distribution(self) # Returns the `Distribution` this executor runs via.\n def runner(self, classpath, main, jvm_options=None, args=None, cwd=None) # Returns an `Executor.Runner` for the given java command.\n def execute(self, classpath, main, jvm_options=None, args=None, stdout=None, stderr=None, cwd=None) # Launches the java program defined by the classpath and main.\n def _runner(self, classpath, main, jvm_options, args, cwd=None) # Subclasses should return a `Runner` that can execute the given java main.\n def _create_command(self, classpath, main, jvm_options, args, cwd=None)\nclass CommandLineGrabber # Doesn't actually execute anything, just captures the cmd line.\n def __init__(self, distribution)\n def _runner(self, classpath, main, jvm_options, args, cwd=None)\n class Runner\n def executor(_)\n def command(_)\n def run(_, stdout=None, stderr=None)\n def spawn(_, stdout=None, stderr=None)\n def cmd(self)\nclass SubprocessExecutor # Executes java programs by launching a jvm in a subprocess.\n def _maybe_scrubbed_env(cls)\n def __init__(self, distribution)\n def _runner(self, classpath, main, jvm_options, args, cwd=None)\n class Runner\n def executor(_)\n def command(_)\n def spawn(_, stdout=None, stderr=None)\n def run(_, stdout=None, stderr=None)\n def spawn(self, classpath, main, jvm_options=None, args=None, cwd=None, **subprocess_args) # Spawns the java program passing any extra subprocess kwargs on to subprocess.Pop\n def _spawn(self, cmd, cwd, **subprocess_args)","label":1},{"cid":"src/python/pants/java/nailgun_client.py","text":"src/python/pants/java/nailgun_client.py\nclass NailgunClientSession # Handles a single nailgun client session.\n def __init__(self, sock, in_fd, out_fd, err_fd, exit_on_broken_pipe=False)\n def _maybe_start_input_reader(self)\n def _maybe_stop_input_reader(self)\n def _write_flush(self, fd, payload=None) # Write a payload to a given fd (if provided) and flush the fd.\n def _process_session(self) # Process the outputs of the nailgun session.\n def execute(self, working_dir, main_class, *arguments, **environment)\nclass NailgunClient # A python nailgun client (see http://martiansoftware.com/nailgun for more info).\n class NailgunError # Indicates an error interacting with a nailgun server.\n class NailgunConnectionError # Indicates an error upon initial connect to the nailgun server.\n def __init__(self, host=DEFAULT_NG_HOST, port=DEFAULT_NG_PORT, ins=sys.stdin, out=None, err=None, workdir=None, exit_on_broken_pipe=False) # Creates a nailgun client that can be used to issue zero or more nailgun commands\n def try_connect(self) # Creates a socket, connects it to the nailgun and returns the connected socket.\n def send_control_c(self) # Sends SIGINT to a nailgun server using pid information from the active session.\n def execute(self, main_class, cwd=None, *args, **environment) # Executes the given main_class with any supplied args in the given environment.\n def __repr__(self)","label":1},{"cid":"src/python/pants/java/nailgun_executor.py","text":"src/python/pants/java/nailgun_executor.py\nclass NailgunProcessGroup\n def __init__(self, metadata_base_dir=None)\n def _iter_nailgun_instances(self, everywhere=False)\n def predicate(proc)\n def killall(self, everywhere=False) # Kills all nailgun servers started by pants.\nclass NailgunExecutor # Executes java programs by launching them in nailgun server.\n def __init__(self, identity, workdir, nailgun_classpath, distribution, ins=None, connect_timeout=10, connect_attempts=5, metadata_base_dir=None)\n def __str__(self)\n def _create_owner_arg(self, workdir)\n def _create_fingerprint_arg(self, fingerprint)\n def _fingerprint(jvm_options, classpath, java_version) # Compute a fingerprint for this invocation of a Java task.\n def _runner(self, classpath, main, jvm_options, args, cwd=None) # Runner factory. Called via Executor.execute().\n class Runner\n def executor(this)\n def command(self)\n def run(this, stdout=None, stderr=None, cwd=None)\n def _check_nailgun_state(self, new_fingerprint)\n def _get_nailgun_client(self, jvm_options, classpath, stdout, stderr) # This (somewhat unfortunately) is the main entrypoint to this class via the Runne\n def _await_socket(self, timeout) # Blocks for the nailgun subprocess to bind and emit a listening port in the nailg\n def _create_ngclient(self, port, stdout, stderr)\n def ensure_connectable(self, nailgun) # Ensures that a nailgun client is connectable or raises NailgunError.\n def _spawn_nailgun_server(self, fingerprint, jvm_options, classpath, stdout, stderr) # Synchronously spawn a new nailgun server.\n def _check_process_buildroot(self, process) # Matches only processes started from the current buildroot.\n def is_alive(self) # A ProcessManager.is_alive() override that ensures buildroot flags are present in\n def post_fork_child(self, fingerprint, jvm_options, classpath, stdout, stderr) # Post-fork() child callback for ProcessManager.daemon_spawn().","label":1},{"cid":"src/python/pants/java/nailgun_io.py","text":"src/python/pants/java/nailgun_io.py\nclass NailgunStreamReader # Reads input from stdin and emits Nailgun 'stdin' chunks over a socket.\n def __init__(self, in_fd, sock, buf_size=io.DEFAULT_BUFFER_SIZE, select_timeout=SELECT_TIMEOUT) # :param file in_fd: the input file descriptor (e.g. sys.stdin) to read from.\n def is_stopped(self) # Indicates whether or not the instance is stopped.\n def stop(self) # Stops the instance.\n def running(self)\n def run(self)\nclass NailgunStreamWriter # A sys.{stdout,stderr} replacement that writes output to a socket using the nailg\n def __init__(self, sock, chunk_type, isatty=True, mask_broken_pipe=False) # :param socket sock: A connected socket capable of speaking the nailgun protocol.\n def write(self, payload)\n def flush(self)\n def isatty(self)\n def fileno(self)","label":1},{"cid":"src/python/pants/java/util.py","text":"src/python/pants/java/util.py\ndef _get_runner(classpath, main, jvm_options, args, executor, cwd, distribution, create_synthetic_jar, synthetic_jar_dir) # Gets the java runner for execute_java and execute_java_async.\ndef execute_java(classpath, main, jvm_options=None, args=None, executor=None, workunit_factory=None, workunit_name=None, workunit_labels=None, cwd=None, workunit_log_config=None, distribution=None, create_synthetic_jar=True, synthetic_jar_dir=None) # Executes the java program defined by the classpath and main.\ndef execute_java_async(classpath, main, jvm_options=None, args=None, executor=None, workunit_factory=None, workunit_name=None, workunit_labels=None, cwd=None, workunit_log_config=None, distribution=None, create_synthetic_jar=True, synthetic_jar_dir=None) # This is just like execute_java except that it returns a ProcessHandler rather th\ndef execute_runner(runner, workunit_factory=None, workunit_name=None, workunit_labels=None, workunit_log_config=None) # Executes the given java runner.\ndef execute_runner_async(runner, workunit_factory=None, workunit_name=None, workunit_labels=None, workunit_log_config=None) # Executes the given java runner asynchronously.\ndef relativize_classpath(classpath, root_dir, followlinks=True) # Convert into classpath relative to a directory.\n def relativize_url(url, root_dir)\ndef safe_classpath(classpath, synthetic_jar_dir, custom_name=None) # Bundles classpath into one synthetic jar that includes original classpath in its","label":1},{"cid":"src/python/pants/option/global_options.py","text":"src/python/pants/option/global_options.py\nclass GlobalOptionsRegistrar\n def register_bootstrap_options(cls, register) # Register bootstrap options.\n def register_options(cls, register) # Register options not tied to any particular task or subsystem.","label":0},{"cid":"src/python/pants/pantsd/process_manager.py","text":"src/python/pants/pantsd/process_manager.py\ndef swallow_psutil_exceptions() # A contextmanager that swallows standard psutil access exceptions.\nclass ProcessGroup # Wraps a logical group of processes and provides convenient access to ProcessMana\n def __init__(self, name, metadata_base_dir=None)\n def _instance_from_process(self, process) # Default converter from psutil.Process to process instance classes for subclassin\n def iter_processes(self, proc_filter=None) # Yields processes from psutil.process_iter with an optional filter and swallows p\n def iter_instances(self, *args, **kwargs)\nclass ProcessMetadataManager # \"Manages contextual, on-disk process metadata.\n class MetadataError\n class Timeout\n def __init__(self, metadata_base_dir=None) # :param str metadata_base_dir: The base directory for process metadata.\n def _maybe_cast(item, caster) # Given a casting function, attempt to cast to that type while masking common cast\n def _deadline_until(cls, closure, timeout, wait_interval=WAIT_INTERVAL_SEC) # Execute a function/closure repeatedly until a True condition or timeout is met.\n def _wait_for_file(cls, filename, timeout=FILE_WAIT_SEC, want_content=True) # Wait up to timeout seconds for filename to appear with a non-zero size or raise \n def file_waiter()\n def _get_metadata_dir_by_name(self, name) # Retrieve the metadata dir by name.\n def _maybe_init_metadata_dir_by_name(self, name) # Initialize the metadata directory for a named identity if it doesn't exist.\n def read_metadata_by_name(self, name, metadata_key, caster=None) # Read process metadata using a named identity.\n def write_metadata_by_name(self, name, metadata_key, metadata_value) # Write process metadata using a named identity.\n def await_metadata_by_name(self, name, metadata_key, timeout, caster=None) # Block up to a timeout for process metadata to arrive on disk.\n def purge_metadata_by_name(self, name) # Purge a processes metadata directory.\nclass ProcessManager # Subprocess/daemon management mixin/superclass. Not intended to be thread-safe.\n class InvalidCommandOutput\n class NonResponsiveProcess\n class ExecutionError\n def __init__(self, message, output=None)\n def __repr__(self)\n def __init__(self, name, pid=None, socket=None, process_name=None, socket_type=int, metadata_base_dir=None) # :param string name: The process identity/name (e.g. 'pantsd' or 'ng_Zinc').\n def name(self) # The logical name/label of the process.\n def process_name(self) # The logical process name. If defined, this is compared to exe_name for stale pid\n def process_lock(self) # An identity-keyed inter-process lock for safeguarding lifecycle and other operat\n def cmdline(self) # The process commandline. e.g. ['/usr/bin/python2.7', 'pants.pex'].\n def cmd(self) # The first element of the process commandline e.g. '/usr/bin/python2.7'.\n def pid(self) # The running processes pid (or None).\n def sock","label":0},{"cid":"src/python/pants/pantsd/watchman.py","text":"src/python/pants/pantsd/watchman.py\nclass Watchman # Watchman process manager and helper class.\n class WatchmanCrash # Raised when Watchman crashes.\n def __init__(self, watchman_path, work_dir, log_level='1', startup_timeout=STARTUP_TIMEOUT_SECONDS, timeout=SOCKET_TIMEOUT_SECONDS, socket_path_override=None, metadata_base_dir=None) # :param str watchman_path: The path to the watchman binary.\n def client(self)\n def _make_client(self) # Create a new watchman client using the BSER protocol over a UNIX socket.\n def _is_valid_executable(self, binary_path)\n def _normalize_watchman_path(self, watchman_path)\n def _maybe_init_metadata(self)\n def _construct_cmd(self, cmd_parts, state_file, sock_file, log_file, log_level)\n def _parse_pid_from_output(self, output)\n def launch(self) # Launch and synchronously write metadata.\n def _attempt_set_timeout(self, timeout) # Sets a timeout on the inner watchman client's socket.\n def watch_project(self, path) # Issues the watch-project command to watchman to begin watching the buildroot.\n def subscribed(self, build_root, handlers) # Bulk subscribe generator for StreamableWatchmanClient.","label":1},{"cid":"src/python/pants/pantsd/watchman_launcher.py","text":"src/python/pants/pantsd/watchman_launcher.py\nclass WatchmanLauncher # An object that manages the configuration and lifecycle of Watchman.\n def create(cls, bootstrap_options) # :param Options bootstrap_options: The bootstrap options bag.\n def __init__(self, binary_util, workdir, log_level, watchman_version, watchman_supportdir, startup_timeout, socket_timeout, socket_path_override=None, metadata_base_dir=None) # :param binary_util: The BinaryUtil subsystem instance for binary retrieval.\n def _convert_log_level(level) # Convert a given pants log level string into a watchman log level string.\n def watchman(self)\n def maybe_launch(self)\n def terminate(self)","label":1},{"cid":"src/python/pants/scm/git.py","text":"src/python/pants/scm/git.py\nclass Git # An Scm implementation backed by git.\n def detect_worktree(cls, binary='git', subdir=None) # Detect the git working tree above cwd and return it; else, return None.\n def clone(cls, repo_url, dest, binary='git') # Clone the repo at repo_url into dest.\n def _invoke(cls, cmd) # Invoke the given command, and return a tuple of process and raw binary output.\n def _cleanse(cls, output, errors='strict')\n def _check_result(cls, cmd, result, failure_msg=None, raise_type=Scm.ScmException)\n def __init__(self, binary='git', gitdir=None, worktree=None, remote=None, branch=None) # Creates a git scm proxy that assumes the git repository is in the cwd by default\n def current_rev_identifier(self)\n def worktree(self)\n def commit_id(self)\n def server_url(self)\n def origin_urls()\n def tag_name(self)\n def branch_name(self)\n def fix_git_relative_path(self, worktree_path, relative_to)\n def changed_files(self, from_commit=None, include_untracked=False, relative_to=None)\n def changes_in(self, diffspec, relative_to=None)\n def changelog(self, from_commit=None, files=None)\n def merge_base(self, left='master', right='HEAD') # Returns the merge-base of master and HEAD in bash: `git merge-base left right`\n def refresh(self, leave_clean=False) # Attempt to pull-with-rebase from upstream. This is implemented as fetch-plus-re\n def tag(self, name, message=None)\n def commit(self, message)\n def add(self, *paths)\n def commit_date(self, commit_reference)\n def push(self, *refs)\n def set_state(self, rev)\n def _get_upstream(self) # Return the remote and remote merge branch for the current branch\n def _check_call(self, args, failure_msg=None, raise_type=None)\n def _check_output(self, args, failure_msg=None, raise_type=None, errors='strict')\n def _create_git_cmdline(self, args)\n def _log_call(self, cmd)\n def repo_reader(self, rev)\nclass GitRepositoryReader # Allows reading from files and directory information from an arbitrary git\n def __init__(self, scm, rev)\n def _maybe_start_cat_file_process(self)\n class MissingFileException\n def __init__(self, rev, relpath)\n def __str__(self)\n class IsDirException\n def __init__(self, rev, relpath)\n def __str__(self)\n class NotADirException\n def __init__(self, rev, relpath)\n def __str__(self)\n class SymlinkLoopException\n def __init__(self, rev, relpath)\n def __str__(self)\n class ExternalSymlinkException\n def __init__(self, rev, relpath)\n def __str__(self)\n class GitDiedException\n class UnexpectedGitObjectTypeException\n def _safe_realpath(self, relpath)\n def _safe_read_object(self, relpath, max_symlinks)\n def exists(self, relpath)\n def isfile(self, relpath)\n def isdir(self, relpath)\n def lstat(self, relpath)\n def readlink(self, relpath)\n class Symlink\n def __init__(self, name, sha)\n ","label":0}],"string":"[\n {\n \"cid\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py\",\n \"text\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py\\nclass Fetcher # Knows how to interpret remote import paths and fetch code to satisfy them.\\n def __init__(self, import_path)\\n def import_path(self)\\n def root(self) # Returns the root of this fetcher's remote import_path.\\n def fetch(self, dest, rev=None) # Fetches the remote library to the given dest dir.\\nclass CloningFetcher # A fetcher that gets the remote library by cloning its repo.\\n def __init__(self, import_path, meta_tag_reader)\\n def root(self)\\n def fetch(self, dest, rev=None)\\nclass ArchiveFetcher # A fetcher that retrieves and unpacks remote libraries from archive files.\\n class UrlInfo # Information about a remote archive.\\n def __init__(self, import_path, import_prefix, url_info, archive_retriever)\\n def root(self)\\n def fetch(self, dest, rev=None)\\n def _fetch(self, archive_url, strip_level, dest)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/scala_repl.py\",\n \"text\": \"src/python/pants/backend/jvm/tasks/scala_repl.py\\nclass ScalaRepl # Operations to create or launch Scala repls.\\n def register_options(cls, register)\\n def subsystem_dependencies(cls)\\n def select_targets(cls, target)\\n def setup_repl_session(self, targets)\\n def launch_repl(self, classpath)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks2/python_repl.py\",\n \"text\": \"src/python/pants/backend/python/tasks2/python_repl.py\\nclass PythonRepl # Launch an interactive Python interpreter session.\\n def register_options(cls, register)\\n def select_targets(cls, target)\\n def extra_requirements(self)\\n def setup_repl_session(self, targets)\\n def launch_repl(self, pex, **pex_run_kwargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/bin/daemon_pants_runner.py\",\n \"text\": \"src/python/pants/bin/daemon_pants_runner.py\\n# coding=utf-8\\n# Copyright 2015 Pants project contributors (see CONTRIBUTORS.md).\\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\\n\\nfrom __future__ import (absolute_import, division, generators, nested_scopes, print_function,\\n unicode_literals, with_statement)\\n\\nimport datetime\\nimport os\\nimport signal\\nimport socket\\nimport sys\\nfrom contextlib import contextmanager\\n\\nfrom setproctitle import setproctitle as set_process_title\\n\\nfrom pants.base.exiter import Exiter\\nfrom pants.bin.local_pants_runner import LocalPantsRunner\\nfrom pants.init.util import clean_global_runtime_state\\nfrom pants.java.nailgun_io import NailgunStreamWriter\\nfrom pants.java.nailgun_protocol import ChunkType, NailgunProtocol\\nfrom pants.pantsd.process_manager import ProcessManager\\nfrom pants.util.contextutil import HardSystemExit, stdio_as\\n\\n\\nclass DaemonExiter(Exiter):\\n \\\"\\\"\\\"An Exiter that emits unhandled tracebacks and exit codes via the Nailgun protocol.\\\"\\\"\\\"\\n\\n def __init__(self, socket):\\n super(DaemonExiter, self).__init__()\\n self._socket = socket\\n\\n def _shutdown_socket(self):\\n \\\"\\\"\\\"Shutdown and close the connected socket.\\\"\\\"\\\"\\n try:\\n self._socket.shutdown(socket.SHUT_WR)\\n except socket.error:\\n pass\\n finally:\\n self._socket.close()\\n\\n def exit(self, result=0, msg=None):\\n \\\"\\\"\\\"Exit the runtime.\\\"\\\"\\\"\\n try:\\n # Write a final message to stderr if present.\\n if msg:\\n NailgunProtocol.write_chunk(self._socket, ChunkType.STDERR, msg)\\n\\n # Send an Exit chunk with the result.\\n NailgunProtocol.write_chunk(self._socket, ChunkType.EXIT, str(result).encode('ascii'))\\n\\n # Shutdown the connected socket.\\n self._shutdown_socket()\\n finally:\\n # N.B. Assuming a fork()'d child, cause os._exit to be called here to avoid the routine\\n # sys.exit behavior (via `pants.util.contextutil.hard_exit_handler()`).\\n raise HardSystemExit()\\n\\n\\nclass DaemonPantsRunner(ProcessManager):\\n \\\"\\\"\\\"A daemonizing PantsRunner that speaks the nailgun protocol to a remote client.\\n\\n N.B. this class is primarily used by the PailgunService in pantsd.\\n \\\"\\\"\\\"\\n\\n def __init__(self, socket, exiter, args, env, graph_helper, deferred_exception=None):\\n \\\"\\\"\\\"\\n :param socket socket: A connected socket capable of speaking the nailgun protocol.\\n :param Exiter exiter: The Exiter instance for this run.\\n :param list args: The arguments (i.e. sys.argv) for this run.\\n :param dict env: The environment (i.e. os.environ) for this run.\\n :param LegacyGraphHelper graph_helper: The LegacyGraphHelper instance to use for BuildGraph\\n construction. In the event of an exception, this will be\\n None.\\n :param Exception deferred_exception: A deferred exception from the daemon's graph construction.\\n If present, this will be re-raised in the client context.\\n \\\"\\\"\\\"\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py\",\n \"text\": \"src/python/pants/bin/goal_runner.py\\nclass GoalRunnerFactory\\n def __init__(self, root_dir, options, build_config, run_tracker, reporting, daemon_graph_helper=None, exiter=sys.exit) # :param str root_dir: The root directory of the pants workspace (aka the \\\"build r\\n def _handle_help(self, help_request) # Handle requests for `help` information.\\n def _init_graph(self, use_engine, pants_ignore_patterns, build_ignore_patterns, exclude_target_regexps, target_specs, workdir, graph_helper=None, subproject_build_roots=None) # Determine the BuildGraph, AddressMapper and spec_roots for a given run.\\n def _determine_goals(self, requested_goals) # Check and populate the requested goals for a given run.\\n def _specs_to_targets(self, specs) # Populate the BuildGraph and target list from a set of input specs.\\n def _maybe_launch_pantsd(self, pantsd_launcher) # Launches pantsd if configured to do so.\\n def _should_be_quiet(self, goals)\\n def _setup_context(self)\\n def setup(self)\\nclass GoalRunner # Lists installed goals or else executes a named goal.\\n def __init__(self, context, goals, run_tracker, kill_nailguns, exiter=sys.exit) # :param Context context: The global, pre-initialized Context as created by GoalRu\\n def subsystems(cls) # Subsystems used outside of any task.\\n def _execute_engine(self)\\n def run(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py\",\n \"text\": \"src/python/pants/engine/legacy/structs.py\\nclass TargetAdaptor # A Struct to imitate the existing Target.\\n def get_sources(self) # Returns target's non-deferred sources if exists or the default sources if define\\n def field_adaptors(self) # Returns a tuple of Fields for captured fields which need additional treatment.\\n def default_sources(self) # True if this adaptor should use default sources if they are defined.\\n def default_sources_globs(self)\\n def default_sources_exclude_globs(self)\\nclass Field # A marker for Target(Adaptor) fields for which the engine might perform extra con\\nclass SourcesField # Represents the `sources` argument for a particular Target.\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def __hash__(self)\\n def __repr__(self)\\n def __str__(self)\\nclass JavaLibraryAdaptor\\n def default_sources_globs(self)\\n def default_sources_exclude_globs(self)\\nclass ScalaLibraryAdaptor\\n def default_sources_globs(self)\\n def default_sources_exclude_globs(self)\\nclass JunitTestsAdaptor\\n def default_sources_globs(self)\\nclass BundlesField # Represents the `bundles` argument, each of which has a PathGlobs to represent it\\n def __eq__(self, other)\\n def __ne__(self, other)\\n def __hash__(self)\\nclass BundleAdaptor # A Struct to capture the args for the `bundle` object.\\nclass JvmAppAdaptor\\n def __init__(self, bundles=None, **kwargs) # :param list bundles: A list of `BundleAdaptor` objects\\n def bundles(self) # The BundleAdaptors for this JvmApp.\\n def field_adaptors(self)\\n def _construct_bundles_field(self)\\nclass RemoteSourcesAdaptor\\n def __init__(self, dest=None, **kwargs) # :param dest: A target constructor.\\nclass PythonTargetAdaptor\\n def field_adaptors(self)\\nclass PythonLibraryAdaptor\\n def default_sources_globs(self)\\n def default_sources_exclude_globs(self)\\nclass PythonTestsAdaptor\\n def default_sources_globs(self)\\nclass GoTargetAdaptor\\n def default_sources(self)\\n def default_sources_globs(self)\\nclass BaseGlobs # An adaptor class to allow BUILD file parsing from ContextAwareObjectFactories.\\n def from_sources_field(sources, spec_path) # Return a BaseGlobs for the given sources field.\\n def _filespec_for_exclude(raw_exclude, spec_path)\\n def path_globs_kwarg(self) # The name of the `PathGlobs` parameter corresponding to this BaseGlobs instance.\\n def legacy_globs_class(self) # The corresponding `wrapped_globs` class for this BaseGlobs.\\n def __init__(self, *patterns, **kwargs)\\n def filespecs(self) # Return a filespecs dict representing both globs and excludes.\\n def _exclude_filespecs(self)\\n def to_path_globs(self, relpath) # Return two PathGlobs representing the included and excluded Files for these patt\\nclass Files\\nclass Globs\\nclass RGlobs\\nclass ZGlobs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py\",\n \"text\": \"src/python/pants/java/executor.py\\nclass Executor # Executes java programs.\\n def _scrub_args(classpath, main, jvm_options, args, cwd)\\n class Error # Indicates an error launching a java program.\\n class InvalidDistribution # Indicates an invalid Distribution was used to construct this runner.\\n class Runner # A re-usable executor that can run a configured java command line.\\n def executor(self) # Returns the executor this runner uses to run itself.\\n def cmd(self) # Returns a string representation of the command that will be run.\\n def command(self) # Returns a copy of the command line that will be run as a list of command line to\\n def run(self, stdout=None, stderr=None, cwd=None) # Runs the configured java command.\\n def spawn(self, stdout=None, stderr=None, cwd=None) # Spawns the configured java command.\\n def __init__(self, distribution) # Constructs an Executor that can be used to launch java programs.\\n def distribution(self) # Returns the `Distribution` this executor runs via.\\n def runner(self, classpath, main, jvm_options=None, args=None, cwd=None) # Returns an `Executor.Runner` for the given java command.\\n def execute(self, classpath, main, jvm_options=None, args=None, stdout=None, stderr=None, cwd=None) # Launches the java program defined by the classpath and main.\\n def _runner(self, classpath, main, jvm_options, args, cwd=None) # Subclasses should return a `Runner` that can execute the given java main.\\n def _create_command(self, classpath, main, jvm_options, args, cwd=None)\\nclass CommandLineGrabber # Doesn't actually execute anything, just captures the cmd line.\\n def __init__(self, distribution)\\n def _runner(self, classpath, main, jvm_options, args, cwd=None)\\n class Runner\\n def executor(_)\\n def command(_)\\n def run(_, stdout=None, stderr=None)\\n def spawn(_, stdout=None, stderr=None)\\n def cmd(self)\\nclass SubprocessExecutor # Executes java programs by launching a jvm in a subprocess.\\n def _maybe_scrubbed_env(cls)\\n def __init__(self, distribution)\\n def _runner(self, classpath, main, jvm_options, args, cwd=None)\\n class Runner\\n def executor(_)\\n def command(_)\\n def spawn(_, stdout=None, stderr=None)\\n def run(_, stdout=None, stderr=None)\\n def spawn(self, classpath, main, jvm_options=None, args=None, cwd=None, **subprocess_args) # Spawns the java program passing any extra subprocess kwargs on to subprocess.Pop\\n def _spawn(self, cmd, cwd, **subprocess_args)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_client.py\",\n \"text\": \"src/python/pants/java/nailgun_client.py\\nclass NailgunClientSession # Handles a single nailgun client session.\\n def __init__(self, sock, in_fd, out_fd, err_fd, exit_on_broken_pipe=False)\\n def _maybe_start_input_reader(self)\\n def _maybe_stop_input_reader(self)\\n def _write_flush(self, fd, payload=None) # Write a payload to a given fd (if provided) and flush the fd.\\n def _process_session(self) # Process the outputs of the nailgun session.\\n def execute(self, working_dir, main_class, *arguments, **environment)\\nclass NailgunClient # A python nailgun client (see http://martiansoftware.com/nailgun for more info).\\n class NailgunError # Indicates an error interacting with a nailgun server.\\n class NailgunConnectionError # Indicates an error upon initial connect to the nailgun server.\\n def __init__(self, host=DEFAULT_NG_HOST, port=DEFAULT_NG_PORT, ins=sys.stdin, out=None, err=None, workdir=None, exit_on_broken_pipe=False) # Creates a nailgun client that can be used to issue zero or more nailgun commands\\n def try_connect(self) # Creates a socket, connects it to the nailgun and returns the connected socket.\\n def send_control_c(self) # Sends SIGINT to a nailgun server using pid information from the active session.\\n def execute(self, main_class, cwd=None, *args, **environment) # Executes the given main_class with any supplied args in the given environment.\\n def __repr__(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py\",\n \"text\": \"src/python/pants/java/nailgun_executor.py\\nclass NailgunProcessGroup\\n def __init__(self, metadata_base_dir=None)\\n def _iter_nailgun_instances(self, everywhere=False)\\n def predicate(proc)\\n def killall(self, everywhere=False) # Kills all nailgun servers started by pants.\\nclass NailgunExecutor # Executes java programs by launching them in nailgun server.\\n def __init__(self, identity, workdir, nailgun_classpath, distribution, ins=None, connect_timeout=10, connect_attempts=5, metadata_base_dir=None)\\n def __str__(self)\\n def _create_owner_arg(self, workdir)\\n def _create_fingerprint_arg(self, fingerprint)\\n def _fingerprint(jvm_options, classpath, java_version) # Compute a fingerprint for this invocation of a Java task.\\n def _runner(self, classpath, main, jvm_options, args, cwd=None) # Runner factory. Called via Executor.execute().\\n class Runner\\n def executor(this)\\n def command(self)\\n def run(this, stdout=None, stderr=None, cwd=None)\\n def _check_nailgun_state(self, new_fingerprint)\\n def _get_nailgun_client(self, jvm_options, classpath, stdout, stderr) # This (somewhat unfortunately) is the main entrypoint to this class via the Runne\\n def _await_socket(self, timeout) # Blocks for the nailgun subprocess to bind and emit a listening port in the nailg\\n def _create_ngclient(self, port, stdout, stderr)\\n def ensure_connectable(self, nailgun) # Ensures that a nailgun client is connectable or raises NailgunError.\\n def _spawn_nailgun_server(self, fingerprint, jvm_options, classpath, stdout, stderr) # Synchronously spawn a new nailgun server.\\n def _check_process_buildroot(self, process) # Matches only processes started from the current buildroot.\\n def is_alive(self) # A ProcessManager.is_alive() override that ensures buildroot flags are present in\\n def post_fork_child(self, fingerprint, jvm_options, classpath, stdout, stderr) # Post-fork() child callback for ProcessManager.daemon_spawn().\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_io.py\",\n \"text\": \"src/python/pants/java/nailgun_io.py\\nclass NailgunStreamReader # Reads input from stdin and emits Nailgun 'stdin' chunks over a socket.\\n def __init__(self, in_fd, sock, buf_size=io.DEFAULT_BUFFER_SIZE, select_timeout=SELECT_TIMEOUT) # :param file in_fd: the input file descriptor (e.g. sys.stdin) to read from.\\n def is_stopped(self) # Indicates whether or not the instance is stopped.\\n def stop(self) # Stops the instance.\\n def running(self)\\n def run(self)\\nclass NailgunStreamWriter # A sys.{stdout,stderr} replacement that writes output to a socket using the nailg\\n def __init__(self, sock, chunk_type, isatty=True, mask_broken_pipe=False) # :param socket sock: A connected socket capable of speaking the nailgun protocol.\\n def write(self, payload)\\n def flush(self)\\n def isatty(self)\\n def fileno(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/util.py\",\n \"text\": \"src/python/pants/java/util.py\\ndef _get_runner(classpath, main, jvm_options, args, executor, cwd, distribution, create_synthetic_jar, synthetic_jar_dir) # Gets the java runner for execute_java and execute_java_async.\\ndef execute_java(classpath, main, jvm_options=None, args=None, executor=None, workunit_factory=None, workunit_name=None, workunit_labels=None, cwd=None, workunit_log_config=None, distribution=None, create_synthetic_jar=True, synthetic_jar_dir=None) # Executes the java program defined by the classpath and main.\\ndef execute_java_async(classpath, main, jvm_options=None, args=None, executor=None, workunit_factory=None, workunit_name=None, workunit_labels=None, cwd=None, workunit_log_config=None, distribution=None, create_synthetic_jar=True, synthetic_jar_dir=None) # This is just like execute_java except that it returns a ProcessHandler rather th\\ndef execute_runner(runner, workunit_factory=None, workunit_name=None, workunit_labels=None, workunit_log_config=None) # Executes the given java runner.\\ndef execute_runner_async(runner, workunit_factory=None, workunit_name=None, workunit_labels=None, workunit_log_config=None) # Executes the given java runner asynchronously.\\ndef relativize_classpath(classpath, root_dir, followlinks=True) # Convert into classpath relative to a directory.\\n def relativize_url(url, root_dir)\\ndef safe_classpath(classpath, synthetic_jar_dir, custom_name=None) # Bundles classpath into one synthetic jar that includes original classpath in its\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py\",\n \"text\": \"src/python/pants/option/global_options.py\\nclass GlobalOptionsRegistrar\\n def register_bootstrap_options(cls, register) # Register bootstrap options.\\n def register_options(cls, register) # Register options not tied to any particular task or subsystem.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py\",\n \"text\": \"src/python/pants/pantsd/process_manager.py\\ndef swallow_psutil_exceptions() # A contextmanager that swallows standard psutil access exceptions.\\nclass ProcessGroup # Wraps a logical group of processes and provides convenient access to ProcessMana\\n def __init__(self, name, metadata_base_dir=None)\\n def _instance_from_process(self, process) # Default converter from psutil.Process to process instance classes for subclassin\\n def iter_processes(self, proc_filter=None) # Yields processes from psutil.process_iter with an optional filter and swallows p\\n def iter_instances(self, *args, **kwargs)\\nclass ProcessMetadataManager # \\\"Manages contextual, on-disk process metadata.\\n class MetadataError\\n class Timeout\\n def __init__(self, metadata_base_dir=None) # :param str metadata_base_dir: The base directory for process metadata.\\n def _maybe_cast(item, caster) # Given a casting function, attempt to cast to that type while masking common cast\\n def _deadline_until(cls, closure, timeout, wait_interval=WAIT_INTERVAL_SEC) # Execute a function/closure repeatedly until a True condition or timeout is met.\\n def _wait_for_file(cls, filename, timeout=FILE_WAIT_SEC, want_content=True) # Wait up to timeout seconds for filename to appear with a non-zero size or raise \\n def file_waiter()\\n def _get_metadata_dir_by_name(self, name) # Retrieve the metadata dir by name.\\n def _maybe_init_metadata_dir_by_name(self, name) # Initialize the metadata directory for a named identity if it doesn't exist.\\n def read_metadata_by_name(self, name, metadata_key, caster=None) # Read process metadata using a named identity.\\n def write_metadata_by_name(self, name, metadata_key, metadata_value) # Write process metadata using a named identity.\\n def await_metadata_by_name(self, name, metadata_key, timeout, caster=None) # Block up to a timeout for process metadata to arrive on disk.\\n def purge_metadata_by_name(self, name) # Purge a processes metadata directory.\\nclass ProcessManager # Subprocess/daemon management mixin/superclass. Not intended to be thread-safe.\\n class InvalidCommandOutput\\n class NonResponsiveProcess\\n class ExecutionError\\n def __init__(self, message, output=None)\\n def __repr__(self)\\n def __init__(self, name, pid=None, socket=None, process_name=None, socket_type=int, metadata_base_dir=None) # :param string name: The process identity/name (e.g. 'pantsd' or 'ng_Zinc').\\n def name(self) # The logical name/label of the process.\\n def process_name(self) # The logical process name. If defined, this is compared to exe_name for stale pid\\n def process_lock(self) # An identity-keyed inter-process lock for safeguarding lifecycle and other operat\\n def cmdline(self) # The process commandline. e.g. ['/usr/bin/python2.7', 'pants.pex'].\\n def cmd(self) # The first element of the process commandline e.g. '/usr/bin/python2.7'.\\n def pid(self) # The running processes pid (or None).\\n def sock\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py\",\n \"text\": \"src/python/pants/pantsd/watchman.py\\nclass Watchman # Watchman process manager and helper class.\\n class WatchmanCrash # Raised when Watchman crashes.\\n def __init__(self, watchman_path, work_dir, log_level='1', startup_timeout=STARTUP_TIMEOUT_SECONDS, timeout=SOCKET_TIMEOUT_SECONDS, socket_path_override=None, metadata_base_dir=None) # :param str watchman_path: The path to the watchman binary.\\n def client(self)\\n def _make_client(self) # Create a new watchman client using the BSER protocol over a UNIX socket.\\n def _is_valid_executable(self, binary_path)\\n def _normalize_watchman_path(self, watchman_path)\\n def _maybe_init_metadata(self)\\n def _construct_cmd(self, cmd_parts, state_file, sock_file, log_file, log_level)\\n def _parse_pid_from_output(self, output)\\n def launch(self) # Launch and synchronously write metadata.\\n def _attempt_set_timeout(self, timeout) # Sets a timeout on the inner watchman client's socket.\\n def watch_project(self, path) # Issues the watch-project command to watchman to begin watching the buildroot.\\n def subscribed(self, build_root, handlers) # Bulk subscribe generator for StreamableWatchmanClient.\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman_launcher.py\",\n \"text\": \"src/python/pants/pantsd/watchman_launcher.py\\nclass WatchmanLauncher # An object that manages the configuration and lifecycle of Watchman.\\n def create(cls, bootstrap_options) # :param Options bootstrap_options: The bootstrap options bag.\\n def __init__(self, binary_util, workdir, log_level, watchman_version, watchman_supportdir, startup_timeout, socket_timeout, socket_path_override=None, metadata_base_dir=None) # :param binary_util: The BinaryUtil subsystem instance for binary retrieval.\\n def _convert_log_level(level) # Convert a given pants log level string into a watchman log level string.\\n def watchman(self)\\n def maybe_launch(self)\\n def terminate(self)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/scm/git.py\",\n \"text\": \"src/python/pants/scm/git.py\\nclass Git # An Scm implementation backed by git.\\n def detect_worktree(cls, binary='git', subdir=None) # Detect the git working tree above cwd and return it; else, return None.\\n def clone(cls, repo_url, dest, binary='git') # Clone the repo at repo_url into dest.\\n def _invoke(cls, cmd) # Invoke the given command, and return a tuple of process and raw binary output.\\n def _cleanse(cls, output, errors='strict')\\n def _check_result(cls, cmd, result, failure_msg=None, raise_type=Scm.ScmException)\\n def __init__(self, binary='git', gitdir=None, worktree=None, remote=None, branch=None) # Creates a git scm proxy that assumes the git repository is in the cwd by default\\n def current_rev_identifier(self)\\n def worktree(self)\\n def commit_id(self)\\n def server_url(self)\\n def origin_urls()\\n def tag_name(self)\\n def branch_name(self)\\n def fix_git_relative_path(self, worktree_path, relative_to)\\n def changed_files(self, from_commit=None, include_untracked=False, relative_to=None)\\n def changes_in(self, diffspec, relative_to=None)\\n def changelog(self, from_commit=None, files=None)\\n def merge_base(self, left='master', right='HEAD') # Returns the merge-base of master and HEAD in bash: `git merge-base left right`\\n def refresh(self, leave_clean=False) # Attempt to pull-with-rebase from upstream. This is implemented as fetch-plus-re\\n def tag(self, name, message=None)\\n def commit(self, message)\\n def add(self, *paths)\\n def commit_date(self, commit_reference)\\n def push(self, *refs)\\n def set_state(self, rev)\\n def _get_upstream(self) # Return the remote and remote merge branch for the current branch\\n def _check_call(self, args, failure_msg=None, raise_type=None)\\n def _check_output(self, args, failure_msg=None, raise_type=None, errors='strict')\\n def _create_git_cmdline(self, args)\\n def _log_call(self, cmd)\\n def repo_reader(self, rev)\\nclass GitRepositoryReader # Allows reading from files and directory information from an arbitrary git\\n def __init__(self, scm, rev)\\n def _maybe_start_cat_file_process(self)\\n class MissingFileException\\n def __init__(self, rev, relpath)\\n def __str__(self)\\n class IsDirException\\n def __init__(self, rev, relpath)\\n def __str__(self)\\n class NotADirException\\n def __init__(self, rev, relpath)\\n def __str__(self)\\n class SymlinkLoopException\\n def __init__(self, rev, relpath)\\n def __str__(self)\\n class ExternalSymlinkException\\n def __init__(self, rev, relpath)\\n def __str__(self)\\n class GitDiedException\\n class UnexpectedGitObjectTypeException\\n def _safe_realpath(self, relpath)\\n def _safe_read_object(self, relpath, max_symlinks)\\n def exists(self, relpath)\\n def isfile(self, relpath)\\n def isdir(self, relpath)\\n def lstat(self, relpath)\\n def readlink(self, relpath)\\n class Symlink\\n def __init__(self, name, sha)\\n \",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.__init__","text":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.__init__\n def __init__(self, import_path):\n self._import_path = import_path","label":0},{"cid":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.import_path","text":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.import_path\n @property\n def import_path(self):\n return self._import_path","label":0},{"cid":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.root","text":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.root\n @abstractmethod\n def root(self):\n \"\"\"Returns the root of this fetcher's remote import_path.\n\n The root is defined as the portion of the remote import path indicating the associated\n package's remote location; ie: for the remote import path of\n 'github.com/docker/docker/daemon/events' it would be 'github.com/docker/docker'.\n\n Many remote import paths may share the same root; ie: all the 20+ docker packages hosted at\n https://github.com/docker/docker share the 'github.com/docker/docker' root.\n\n This is called the import-prefix in 'https://golang.org/cmd/go/#hdr-Remote_import_paths'\n\n :returns: The root portion of the import path.\n :rtype: string\n :raises: :class:`FetchError` if there was a problem detecting the root.\n \"\"\"","label":0},{"cid":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.fetch","text":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.fetch\n @abstractmethod\n def fetch(self, dest, rev=None):\n \"\"\"Fetches the remote library to the given dest dir.\n\n The dest dir provided will be an existing empty directory.\n\n :param string dest: The path of an existing empty directory to extract package containing the\n remote library's contents to.\n :param string rev: The version to fetch - may be `None` or empty indicating the latest version\n should be fetched.\n :raises: :class:`FetchError` if there was a problem fetching the remote package.\n \"\"\"","label":0},{"cid":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::CloningFetcher.__init__","text":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::CloningFetcher.__init__\n def __init__(self, import_path, meta_tag_reader):\n super(CloningFetcher, self).__init__(import_path)\n self._meta_tag_reader = meta_tag_reader","label":0},{"cid":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::CloningFetcher.root","text":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::CloningFetcher.root\n def root(self):\n imported_repo = self._meta_tag_reader.get_imported_repo(self.import_path)\n if imported_repo:\n return imported_repo.import_prefix\n else:\n raise FetchError('No <meta name=\"go-import\"> tag found at {}'.format(self.import_path))","label":0},{"cid":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::CloningFetcher.fetch","text":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::CloningFetcher.fetch\n def fetch(self, dest, rev=None):\n imported_repo = self._meta_tag_reader.get_imported_repo(self.import_path)\n if not imported_repo:\n raise FetchError('No <meta name=\"go-import\"> tag found, so cannot fetch repo '\n 'at {}'.format(self.import_path))\n if imported_repo.vcs != 'git':\n # TODO: Support other vcs systems as needed.\n raise FetchError(\"Don't know how to fetch for vcs type {}.\".format(imported_repo.vcs))\n # TODO: Do this in a workunit (see https://github.com/pantsbuild/pants/issues/3502).\n logger.info('Cloning {} into {}'.format(imported_repo.url, dest))\n repo = Git.clone(imported_repo.url, dest)\n if rev:\n repo.set_state(rev)","label":0},{"cid":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher.__init__","text":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher.__init__\n def __init__(self, import_path, import_prefix, url_info, archive_retriever):\n super(ArchiveFetcher, self).__init__(import_path)\n self._import_prefix = import_prefix\n self._url_info = url_info\n self._archive_retriver = archive_retriever","label":0},{"cid":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher.root","text":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher.root\n def root(self):\n return self._import_prefix","label":0},{"cid":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher.fetch","text":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher.fetch\n def fetch(self, dest, rev=None):\n pkg = GoRemoteLibrary.remote_package_path(self.root(), self.import_path)\n archive_url = self._url_info.url_format.format(rev=rev or self._url_info.default_rev,\n pkg=pkg, import_prefix=self.root())\n try:\n self._fetch(archive_url, self._url_info.strip_level, dest)\n except FetchError as e:\n # Modify the message to add more information, then reraise with the original traceback.\n e.add_message_prefix('Error while fetching import {}: '.format(self.import_path))\n raise","label":0},{"cid":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher._fetch","text":"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher._fetch\n def _fetch(self, archive_url, strip_level, dest):\n # Note: Broken out into a separate function so we can mock it out easily in tests.\n self._archive_retriver.fetch_archive(archive_url, strip_level, dest)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.register_options","text":"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.register_options\n @classmethod\n def register_options(cls, register):\n super(ScalaRepl, cls).register_options(register)\n register('--main', default='scala.tools.nsc.MainGenericRunner',\n help='The entry point for running the repl.')\n cls.register_jvm_tool(register, 'pants-runner', classpath=[\n JarDependency(org='org.pantsbuild', name='pants-runner', rev='0.0.1'),\n ], main=ScalaRepl._RUNNER_MAIN)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.subsystem_dependencies","text":"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.subsystem_dependencies\n @classmethod\n def subsystem_dependencies(cls):\n return super(ScalaRepl, cls).subsystem_dependencies() + (DistributionLocator, ScalaPlatform)","label":0},{"cid":"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.select_targets","text":"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.select_targets\n @classmethod\n def select_targets(cls, target):\n return isinstance(target, (JarLibrary, JvmTarget))","label":0},{"cid":"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.setup_repl_session","text":"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.setup_repl_session\n def setup_repl_session(self, targets):\n repl_name = ScalaPlatform.global_instance().repl\n return (self.tool_classpath('pants-runner') +\n self.tool_classpath(repl_name, scope=ScalaPlatform.options_scope) +\n self.classpath(targets))","label":0},{"cid":"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.launch_repl","text":"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.launch_repl\n def launch_repl(self, classpath):\n # The scala repl requires -Dscala.usejavacp=true since Scala 2.8 when launching in the way\n # we do here (not passing -classpath as a program arg to scala.tools.nsc.MainGenericRunner).\n jvm_options = self.jvm_options\n if not any(opt.startswith('-Dscala.usejavacp=') for opt in jvm_options):\n jvm_options.append('-Dscala.usejavacp=true')\n\n # NOTE: We execute with no workunit, as capturing REPL output makes it very sluggish.\n #\n # NOTE: Using PantsRunner class because the classLoader used by REPL\n # does not load Class-Path from manifest.\n DistributionLocator.cached().execute_java(classpath=classpath,\n main=ScalaRepl._RUNNER_MAIN,\n jvm_options=jvm_options,\n args=[self.get_options().main] + self.args,\n create_synthetic_jar=True)","label":1},{"cid":"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.register_options","text":"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.register_options\n @classmethod\n def register_options(cls, register):\n super(PythonRepl, cls).register_options(register)\n # TODO: Create a python equivalent of register_jvm_tool, and use that instead of these\n # ad-hoc options.\n register('--ipython', type=bool,\n help='Run an IPython REPL instead of the standard python one.')\n register('--ipython-entry-point', advanced=True, default='IPython:start_ipython',\n help='The IPython REPL entry point.')\n register('--ipython-requirements', advanced=True, type=list, default=['ipython==1.0.0'],\n help='The IPython interpreter version to use.')","label":0},{"cid":"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.select_targets","text":"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.select_targets\n @classmethod\n def select_targets(cls, target):\n return isinstance(target, (PythonTarget, PythonRequirementLibrary))","label":0},{"cid":"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.extra_requirements","text":"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.extra_requirements\n def extra_requirements(self):\n if self.get_options().ipython:\n return [self.get_options().ipython_requirements]\n else:\n return []","label":0},{"cid":"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.setup_repl_session","text":"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.setup_repl_session\n def setup_repl_session(self, targets):\n if self.get_options().ipython:\n entry_point = self.get_options().ipython_entry_point\n else:\n entry_point = 'code:interact'\n pex_info = PexInfo.default()\n pex_info.entry_point = entry_point\n return self.create_pex(pex_info)","label":0},{"cid":"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.launch_repl","text":"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.launch_repl\n def launch_repl(self, pex, **pex_run_kwargs):\n env = pex_run_kwargs.pop('env', os.environ).copy()\n po = pex.run(blocking=False, env=env, **pex_run_kwargs)\n po.wait()","label":1},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.__init__","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.__init__\n def __init__(self, root_dir, options, build_config, run_tracker, reporting,\n daemon_graph_helper=None, exiter=sys.exit):\n \"\"\"\n :param str root_dir: The root directory of the pants workspace (aka the \"build root\").\n :param Options options: The global, pre-initialized Options instance.\n :param BuildConfiguration build_config: A pre-initialized BuildConfiguration instance.\n :param Runtracker run_tracker: The global, pre-initialized/running RunTracker instance.\n :param Reporting reporting: The global, pre-initialized Reporting instance.\n :param LegacyGraphHelper daemon_graph_helper: A LegacyGraphHelper instance for graph reuse. (Optional)\n :param func exiter: A function that accepts an exit code value and exits. (for tests, Optional)\n \"\"\"\n self._root_dir = root_dir\n self._options = options\n self._build_config = build_config\n self._run_tracker = run_tracker\n self._reporting = reporting\n self._daemon_graph_helper = daemon_graph_helper\n self._exiter = exiter\n\n self._requested_goals = self._options.goals\n self._help_request = self._options.help_request\n self._build_file_parser = BuildFileParser(self._build_config, self._root_dir)\n self._build_graph = None\n self._address_mapper = None\n\n self._global_options = options.for_global_scope()\n self._tag = self._global_options.tag\n self._fail_fast = self._global_options.fail_fast\n self._explain = self._global_options.explain\n self._kill_nailguns = self._global_options.kill_nailguns","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._handle_help","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._handle_help\n def _handle_help(self, help_request):\n \"\"\"Handle requests for `help` information.\"\"\"\n if help_request:\n help_printer = HelpPrinter(self._options)\n result = help_printer.print_help()\n self._exiter(result)","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._init_graph","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._init_graph\n def _init_graph(self, use_engine, pants_ignore_patterns, build_ignore_patterns,\n exclude_target_regexps, target_specs, workdir, graph_helper=None,\n subproject_build_roots=None):\n \"\"\"Determine the BuildGraph, AddressMapper and spec_roots for a given run.\n\n :param bool use_engine: Whether or not to use the v2 engine to construct the BuildGraph.\n :param list pants_ignore_patterns: The pants ignore patterns from '--pants-ignore'.\n :param list build_ignore_patterns: The build ignore patterns from '--build-ignore',\n applied during BUILD file searching.\n :param str workdir: The pants workdir.\n :param list exclude_target_regexps: Regular expressions for targets to be excluded.\n :param list target_specs: The original target specs.\n :param LegacyGraphHelper graph_helper: A LegacyGraphHelper to use for graph construction,\n if available. This would usually come from the daemon.\n :returns: A tuple of (BuildGraph, AddressMapper, spec_roots).\n \"\"\"\n # N.B. Use of the daemon implies use of the v2 engine.\n if graph_helper or use_engine:\n # The daemon may provide a `graph_helper`. If that's present, use it for graph construction.\n if not graph_helper:\n native = Native.create(self._global_options)\n native.set_panic_handler()\n graph_helper = EngineInitializer.setup_legacy_graph(\n pants_ignore_patterns,\n workdir,\n native=native,\n build_file_aliases=self._build_config.registered_aliases(),\n build_ignore_patterns=build_ignore_patterns,\n exclude_target_regexps=exclude_target_regexps,\n subproject_roots=subproject_build_roots,\n include_trace_on_error=self._options.for_global_scope().print_exception_stacktrace\n )\n\n target_roots = TargetRoots.create(options=self._opt","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._determine_goals","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._determine_goals\n def _determine_goals(self, requested_goals):\n \"\"\"Check and populate the requested goals for a given run.\"\"\"\n\n spec_parser = CmdLineSpecParser(self._root_dir)\n for goal in requested_goals:\n if self._address_mapper.is_valid_single_address(spec_parser.parse_spec(goal)):\n logger.warning(\"Command-line argument '{0}' is ambiguous and was assumed to be \"\n \"a goal. If this is incorrect, disambiguate it with ./{0}.\".format(goal))\n\n goals = [Goal.by_name(goal) for goal in requested_goals]\n return goals","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._specs_to_targets","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._specs_to_targets\n def _specs_to_targets(self, specs):\n \"\"\"Populate the BuildGraph and target list from a set of input specs.\"\"\"\n with self._run_tracker.new_workunit(name='parse', labels=[WorkUnitLabel.SETUP]):\n def filter_for_tag(tag):\n return lambda target: tag in map(str, target.tags)\n\n tag_filter = wrap_filters(create_filters(self._tag, filter_for_tag))\n\n def generate_targets(specs):\n for address in self._build_graph.inject_specs_closure(specs, self._fail_fast):\n target = self._build_graph.get_target(address)\n if tag_filter(target):\n yield target\n\n return list(generate_targets(specs))","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._maybe_launch_pantsd","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._maybe_launch_pantsd\n def _maybe_launch_pantsd(self, pantsd_launcher):\n \"\"\"Launches pantsd if configured to do so.\"\"\"\n if self._global_options.enable_pantsd:\n # Avoid runtracker output if pantsd is disabled. Otherwise, show up to inform the user its on.\n with self._run_tracker.new_workunit(name='pantsd', labels=[WorkUnitLabel.SETUP]):\n pantsd_launcher.maybe_launch()","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._should_be_quiet","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._should_be_quiet\n def _should_be_quiet(self, goals):\n if self._explain:\n return True\n\n if self._global_options.get_rank('quiet') > RankedValue.HARDCODED:\n return self._global_options.quiet\n\n return any(goal.has_task_of_type(QuietTaskMixin) for goal in goals)","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._setup_context","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._setup_context\n def _setup_context(self):\n with self._run_tracker.new_workunit(name='setup', labels=[WorkUnitLabel.SETUP]):\n self._build_graph, self._address_mapper, spec_roots = self._init_graph(\n self._global_options.enable_v2_engine,\n self._global_options.pants_ignore,\n self._global_options.build_ignore,\n self._global_options.exclude_target_regexp,\n self._options.target_specs,\n self._global_options.pants_workdir,\n self._daemon_graph_helper,\n self._global_options.subproject_roots,\n )\n\n goals = self._determine_goals(self._requested_goals)\n is_quiet = self._should_be_quiet(goals)\n\n target_roots = self._specs_to_targets(spec_roots)\n\n # Now that we've parsed the bootstrap BUILD files, and know about the SCM system.\n self._run_tracker.run_info.add_scm_info()\n\n # Update the Reporting settings now that we have options and goal info.\n invalidation_report = self._reporting.update_reporting(self._global_options,\n is_quiet,\n self._run_tracker)\n\n context = Context(options=self._options,\n run_tracker=self._run_tracker,\n target_roots=target_roots,\n requested_goals=self._requested_goals,\n build_graph=self._build_graph,\n build_file_parser=self._build_file_parser,\n address_mapper=self._address_mapper,\n invalidation_report=invalidation_report)\n return goals, context","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.setup","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.setup\n def setup(self):\n self._handle_help(self._help_request)\n goals, context = self._setup_context()\n return GoalRunner(context=context,\n goals=goals,\n run_tracker=self._run_tracker,\n kill_nailguns=self._kill_nailguns,\n exiter=self._exiter)","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunner.__init__","text":"src/python/pants/bin/goal_runner.py::GoalRunner.__init__\n def __init__(self, context, goals, run_tracker, kill_nailguns, exiter=sys.exit):\n \"\"\"\n :param Context context: The global, pre-initialized Context as created by GoalRunnerFactory.\n :param list[Goal] goals: The list of goals to act on.\n :param Runtracker run_tracker: The global, pre-initialized/running RunTracker instance.\n :param bool kill_nailguns: Whether or not to kill nailguns after the run.\n :param func exiter: A function that accepts an exit code value and exits (for tests, Optional).\n \"\"\"\n self._context = context\n self._goals = goals\n self._run_tracker = run_tracker\n self._kill_nailguns = kill_nailguns\n self._exiter = exiter","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunner.subsystems","text":"src/python/pants/bin/goal_runner.py::GoalRunner.subsystems\n @classmethod\n def subsystems(cls):\n \"\"\"Subsystems used outside of any task.\"\"\"\n return {\n SourceRootConfig,\n Reporting,\n Reproducer,\n RunTracker,\n Changed.Factory,\n BinaryUtil.Factory,\n Subprocess.Factory\n }","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunner._execute_engine","text":"src/python/pants/bin/goal_runner.py::GoalRunner._execute_engine\n def _execute_engine(self):\n workdir = self._context.options.for_global_scope().pants_workdir\n if not workdir.endswith('.pants.d'):\n self._context.log.error('Pants working directory should end with \\'.pants.d\\', currently it is {}\\n'\n .format(workdir))\n return 1\n\n unknown_goals = [goal.name for goal in self._goals if not goal.ordered_task_names()]\n if unknown_goals:\n self._context.log.error('Unknown goal(s): {}\\n'.format(' '.join(unknown_goals)))\n return 1\n\n engine = RoundEngine()\n result = engine.execute(self._context, self._goals)\n\n if self._context.invalidation_report:\n self._context.invalidation_report.report()\n\n return result","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunner.run","text":"src/python/pants/bin/goal_runner.py::GoalRunner.run\n def run(self):\n should_kill_nailguns = self._kill_nailguns\n\n try:\n result = self._execute_engine()\n if result:\n self._run_tracker.set_root_outcome(WorkUnit.FAILURE)\n except KeyboardInterrupt:\n self._run_tracker.set_root_outcome(WorkUnit.FAILURE)\n # On ctrl-c we always kill nailguns, otherwise they might keep running\n # some heavyweight compilation and gum up the system during a subsequent run.\n should_kill_nailguns = True\n raise\n except Exception:\n self._run_tracker.set_root_outcome(WorkUnit.FAILURE)\n raise\n finally:\n # Must kill nailguns only after run_tracker.end() is called, otherwise there may still\n # be pending background work that needs a nailgun.\n if should_kill_nailguns:\n # TODO: This is JVM-specific and really doesn't belong here.\n # TODO: Make this more selective? Only kill nailguns that affect state?\n # E.g., checkstyle may not need to be killed.\n NailgunProcessGroup().killall()\n\n return result","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::TargetAdaptor.get_sources","text":"src/python/pants/engine/legacy/structs.py::TargetAdaptor.get_sources\n def get_sources(self):\n \"\"\"Returns target's non-deferred sources if exists or the default sources if defined.\n\n NB: once ivy is implemented in the engine, we can fetch sources natively here, and/or\n refactor how deferred sources are implemented.\n see: https://github.com/pantsbuild/pants/issues/2997\n \"\"\"\n sources = getattr(self, 'sources', None)\n # N.B. Here we check specifically for `sources is None`, as it's possible for sources\n # to be e.g. an explicit empty list (sources=[]).\n if sources is None and self.default_sources_globs is not None:\n if self.default_sources:\n return Globs(*self.default_sources_globs,\n spec_path=self.address.spec_path,\n exclude=self.default_sources_exclude_globs or [])\n else:\n deprecated_conditional(lambda: True, '1.5.0.dev0',\n 'default empty sources list',\n 'Targets which do not explicitly pass sources will soon get a default set. '\n 'Please pass an explicit set of sources for target: {address}'\n .format(address=self.address.spec))\n return Files(spec_path=self.address.spec_path)\n return sources","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::TargetAdaptor.field_adaptors","text":"src/python/pants/engine/legacy/structs.py::TargetAdaptor.field_adaptors\n @property\n def field_adaptors(self):\n \"\"\"Returns a tuple of Fields for captured fields which need additional treatment.\"\"\"\n with exception_logging(logger, 'Exception in `field_adaptors` property'):\n sources = self.get_sources()\n if not sources:\n return tuple()\n base_globs = BaseGlobs.from_sources_field(sources, self.address.spec_path)\n path_globs = base_globs.to_path_globs(self.address.spec_path)\n return (SourcesField(self.address, 'sources', base_globs.filespecs, path_globs),)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::TargetAdaptor.default_sources","text":"src/python/pants/engine/legacy/structs.py::TargetAdaptor.default_sources\n @property\n def default_sources(self):\n \"\"\"True if this adaptor should use default sources if they are defined.\"\"\"\n return Target.Arguments.global_instance().get_options().implicit_sources","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::TargetAdaptor.default_sources_globs","text":"src/python/pants/engine/legacy/structs.py::TargetAdaptor.default_sources_globs\n @property\n def default_sources_globs(self):\n return None","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::TargetAdaptor.default_sources_exclude_globs","text":"src/python/pants/engine/legacy/structs.py::TargetAdaptor.default_sources_exclude_globs\n @property\n def default_sources_exclude_globs(self):\n return None","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::SourcesField.__eq__","text":"src/python/pants/engine/legacy/structs.py::SourcesField.__eq__\n def __eq__(self, other):\n return type(self) == type(other) and self.address == other.address and self.arg == other.arg","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::SourcesField.__ne__","text":"src/python/pants/engine/legacy/structs.py::SourcesField.__ne__\n def __ne__(self, other):\n return not (self == other)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::SourcesField.__hash__","text":"src/python/pants/engine/legacy/structs.py::SourcesField.__hash__\n def __hash__(self):\n return hash(self.address)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::SourcesField.__repr__","text":"src/python/pants/engine/legacy/structs.py::SourcesField.__repr__\n def __repr__(self):\n return str(self)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::SourcesField.__str__","text":"src/python/pants/engine/legacy/structs.py::SourcesField.__str__\n def __str__(self):\n return 'SourcesField(address={}, arg={}, filespecs={!r})'.format(self.address, self.arg, self.filespecs)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::JavaLibraryAdaptor.default_sources_globs","text":"src/python/pants/engine/legacy/structs.py::JavaLibraryAdaptor.default_sources_globs\n @property\n def default_sources_globs(self):\n return ('*.java',)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::JavaLibraryAdaptor.default_sources_exclude_globs","text":"src/python/pants/engine/legacy/structs.py::JavaLibraryAdaptor.default_sources_exclude_globs\n @property\n def default_sources_exclude_globs(self):\n return JunitTestsAdaptor.java_test_globs","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::ScalaLibraryAdaptor.default_sources_globs","text":"src/python/pants/engine/legacy/structs.py::ScalaLibraryAdaptor.default_sources_globs\n @property\n def default_sources_globs(self):\n return ('*.scala',)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::ScalaLibraryAdaptor.default_sources_exclude_globs","text":"src/python/pants/engine/legacy/structs.py::ScalaLibraryAdaptor.default_sources_exclude_globs\n @property\n def default_sources_exclude_globs(self):\n return JunitTestsAdaptor.scala_test_globs","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::JunitTestsAdaptor.default_sources_globs","text":"src/python/pants/engine/legacy/structs.py::JunitTestsAdaptor.default_sources_globs\n @property\n def default_sources_globs(self):\n return self.java_test_globs + self.scala_test_globs","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::BundlesField.__eq__","text":"src/python/pants/engine/legacy/structs.py::BundlesField.__eq__\n def __eq__(self, other):\n return type(self) == type(other) and self.address == other.address","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::BundlesField.__ne__","text":"src/python/pants/engine/legacy/structs.py::BundlesField.__ne__\n def __ne__(self, other):\n return not (self == other)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::BundlesField.__hash__","text":"src/python/pants/engine/legacy/structs.py::BundlesField.__hash__\n def __hash__(self):\n return hash(self.address)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor.__init__","text":"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor.__init__\n def __init__(self, bundles=None, **kwargs):\n \"\"\"\n :param list bundles: A list of `BundleAdaptor` objects\n \"\"\"\n super(JvmAppAdaptor, self).__init__(**kwargs)\n self.bundles = bundles","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor.bundles","text":"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor.bundles\n @addressable_list(Exactly(BundleAdaptor))\n def bundles(self):\n \"\"\"The BundleAdaptors for this JvmApp.\"\"\"\n return self.bundles","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor.field_adaptors","text":"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor.field_adaptors\n @property\n def field_adaptors(self):\n with exception_logging(logger, 'Exception in `field_adaptors` property'):\n field_adaptors = super(JvmAppAdaptor, self).field_adaptors\n if getattr(self, 'bundles', None) is None:\n return field_adaptors\n\n bundles_field = self._construct_bundles_field()\n return field_adaptors + (bundles_field,)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor._construct_bundles_field","text":"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor._construct_bundles_field\n def _construct_bundles_field(self):\n filespecs_list = []\n path_globs_list = []\n for bundle in self.bundles:\n # NB: if a bundle has a rel_path, then the rel_root of the resulting file globs must be\n # set to that rel_path.\n rel_root = getattr(bundle, 'rel_path', self.address.spec_path)\n\n base_globs = BaseGlobs.from_sources_field(bundle.fileset, rel_root)\n path_globs = base_globs.to_path_globs(rel_root)\n\n filespecs_list.append(base_globs.filespecs)\n path_globs_list.append(path_globs)\n return BundlesField(self.address,\n self.bundles,\n filespecs_list,\n path_globs_list)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::RemoteSourcesAdaptor.__init__","text":"src/python/pants/engine/legacy/structs.py::RemoteSourcesAdaptor.__init__\n def __init__(self, dest=None, **kwargs):\n \"\"\"\n :param dest: A target constructor.\n \"\"\"\n if not isinstance(dest, string_types):\n dest = dest._type_alias\n super(RemoteSourcesAdaptor, self).__init__(dest=dest, **kwargs)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::PythonTargetAdaptor.field_adaptors","text":"src/python/pants/engine/legacy/structs.py::PythonTargetAdaptor.field_adaptors\n @property\n def field_adaptors(self):\n with exception_logging(logger, 'Exception in `field_adaptors` property'):\n field_adaptors = super(PythonTargetAdaptor, self).field_adaptors\n if getattr(self, 'resources', None) is None:\n return field_adaptors\n base_globs = BaseGlobs.from_sources_field(self.resources, self.address.spec_path)\n path_globs = base_globs.to_path_globs(self.address.spec_path)\n sources_field = SourcesField(self.address,\n 'resources',\n base_globs.filespecs,\n path_globs)\n return field_adaptors + (sources_field,)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::PythonLibraryAdaptor.default_sources_globs","text":"src/python/pants/engine/legacy/structs.py::PythonLibraryAdaptor.default_sources_globs\n @property\n def default_sources_globs(self):\n return ('*.py',)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::PythonLibraryAdaptor.default_sources_exclude_globs","text":"src/python/pants/engine/legacy/structs.py::PythonLibraryAdaptor.default_sources_exclude_globs\n @property\n def default_sources_exclude_globs(self):\n return PythonTestsAdaptor.python_test_globs","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::PythonTestsAdaptor.default_sources_globs","text":"src/python/pants/engine/legacy/structs.py::PythonTestsAdaptor.default_sources_globs\n @property\n def default_sources_globs(self):\n return self.python_test_globs","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::GoTargetAdaptor.default_sources","text":"src/python/pants/engine/legacy/structs.py::GoTargetAdaptor.default_sources\n @property\n def default_sources(self):\n # Go has always used implicit_sources: override to ignore the option.\n return True","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::GoTargetAdaptor.default_sources_globs","text":"src/python/pants/engine/legacy/structs.py::GoTargetAdaptor.default_sources_globs\n @property\n def default_sources_globs(self):\n # N.B. Go targets glob on `*` due to the way resources and .c companion files are handled.\n return ('*',)","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::BaseGlobs.from_sources_field","text":"src/python/pants/engine/legacy/structs.py::BaseGlobs.from_sources_field\n @staticmethod\n def from_sources_field(sources, spec_path):\n \"\"\"Return a BaseGlobs for the given sources field.\n\n `sources` may be None, a list/tuple/set, a string or a BaseGlobs instance.\n \"\"\"\n if sources is None:\n return Files(spec_path=spec_path)\n elif isinstance(sources, BaseGlobs):\n return sources\n elif isinstance(sources, string_types):\n return Files(sources, spec_path=spec_path)\n elif isinstance(sources, (set, list, tuple)):\n return Files(*sources, spec_path=spec_path)\n else:\n raise AssertionError('Could not construct PathGlobs from {}'.format(sources))","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::BaseGlobs._filespec_for_exclude","text":"src/python/pants/engine/legacy/structs.py::BaseGlobs._filespec_for_exclude\n @staticmethod\n def _filespec_for_exclude(raw_exclude, spec_path):\n if isinstance(raw_exclude, string_types):\n raise ValueError('Excludes of type `{}` are not supported: got \"{}\"'\n .format(type(raw_exclude).__name__, raw_exclude))\n\n excluded_patterns = []\n for raw_element in raw_exclude:\n exclude_filespecs = BaseGlobs.from_sources_field(raw_element, spec_path).filespecs\n if exclude_filespecs.get('exclude', []):\n raise ValueError('Nested excludes are not supported: got {}'.format(raw_element))\n excluded_patterns.extend(exclude_filespecs.get('globs', []))\n return {'globs': excluded_patterns}","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::BaseGlobs.path_globs_kwarg","text":"src/python/pants/engine/legacy/structs.py::BaseGlobs.path_globs_kwarg\n @abstractproperty\n def path_globs_kwarg(self):\n \"\"\"The name of the `PathGlobs` parameter corresponding to this BaseGlobs instance.\"\"\"","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::BaseGlobs.legacy_globs_class","text":"src/python/pants/engine/legacy/structs.py::BaseGlobs.legacy_globs_class\n @abstractproperty\n def legacy_globs_class(self):\n \"\"\"The corresponding `wrapped_globs` class for this BaseGlobs.\"\"\"","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::BaseGlobs.__init__","text":"src/python/pants/engine/legacy/structs.py::BaseGlobs.__init__\n def __init__(self, *patterns, **kwargs):\n raw_spec_path = kwargs.pop('spec_path')\n self._file_globs = self.legacy_globs_class.to_filespec(patterns).get('globs', [])\n raw_exclude = kwargs.pop('exclude', [])\n self._excluded_file_globs = self._filespec_for_exclude(raw_exclude, raw_spec_path).get('globs', [])\n self._spec_path = raw_spec_path\n\n # `follow_links=True` is the default behavior for wrapped globs, so we pop the old kwarg\n # and warn here to bridge the gap from v1->v2 BUILD files.\n follow_links = kwargs.pop('follow_links', None)\n if follow_links is not None:\n logger.warn(\n 'Ignoring `follow_links={}` kwarg on glob. Default behavior is to follow all links.'\n .format(follow_links)\n )\n\n if kwargs:\n raise ValueError('kwargs not supported for {}. Got: {}'.format(type(self), kwargs))","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::BaseGlobs.filespecs","text":"src/python/pants/engine/legacy/structs.py::BaseGlobs.filespecs\n @property\n def filespecs(self):\n \"\"\"Return a filespecs dict representing both globs and excludes.\"\"\"\n return {'globs': self._file_globs, 'exclude': self._exclude_filespecs}","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::BaseGlobs._exclude_filespecs","text":"src/python/pants/engine/legacy/structs.py::BaseGlobs._exclude_filespecs\n @property\n def _exclude_filespecs(self):\n if self._excluded_file_globs:\n return [{'globs': self._excluded_file_globs}]\n else:\n return []","label":0},{"cid":"src/python/pants/engine/legacy/structs.py::BaseGlobs.to_path_globs","text":"src/python/pants/engine/legacy/structs.py::BaseGlobs.to_path_globs\n def to_path_globs(self, relpath):\n \"\"\"Return two PathGlobs representing the included and excluded Files for these patterns.\"\"\"\n return PathGlobs.create(relpath, self._file_globs, self._excluded_file_globs)","label":0},{"cid":"src/python/pants/java/executor.py::Executor._scrub_args","text":"src/python/pants/java/executor.py::Executor._scrub_args\n @staticmethod\n def _scrub_args(classpath, main, jvm_options, args, cwd):\n classpath = maybe_list(classpath)\n if not isinstance(main, string_types) or not main:\n raise ValueError('A non-empty main classname is required, given: {}'.format(main))\n jvm_options = maybe_list(jvm_options or ())\n args = maybe_list(args or ())\n return classpath, main, jvm_options, args, cwd","label":0},{"cid":"src/python/pants/java/executor.py::Executor.Runner.executor","text":"src/python/pants/java/executor.py::Executor.Runner.executor\n @abstractproperty\n def executor(self):\n \"\"\"Returns the executor this runner uses to run itself.\"\"\"\n raise NotImplementedError","label":0},{"cid":"src/python/pants/java/executor.py::Executor.Runner.cmd","text":"src/python/pants/java/executor.py::Executor.Runner.cmd\n @property\n def cmd(self):\n \"\"\"Returns a string representation of the command that will be run.\"\"\"\n return ' '.join(self.command)","label":0},{"cid":"src/python/pants/java/executor.py::Executor.Runner.command","text":"src/python/pants/java/executor.py::Executor.Runner.command\n @abstractproperty\n def command(self):\n \"\"\"Returns a copy of the command line that will be run as a list of command line tokens.\"\"\"\n raise NotImplementedError","label":0},{"cid":"src/python/pants/java/executor.py::Executor.Runner.run","text":"src/python/pants/java/executor.py::Executor.Runner.run\n @abstractmethod\n def run(self, stdout=None, stderr=None, cwd=None):\n \"\"\"Runs the configured java command.\n\n If there is a problem executing tha java program subclasses should raise Executor.Error.\n Its guaranteed that all arguments are valid as documented in `execute`\n\n :param stdout: An optional stream to pump stdout to; defaults to `sys.stdout`.\n :param stderr: An optional stream to pump stderr to; defaults to `sys.stderr`.\n :param string cwd: optionally set the working directory\n \"\"\"\n raise NotImplementedError","label":1},{"cid":"src/python/pants/java/executor.py::Executor.Runner.spawn","text":"src/python/pants/java/executor.py::Executor.Runner.spawn\n @abstractmethod\n def spawn(self, stdout=None, stderr=None, cwd=None):\n \"\"\"Spawns the configured java command.\n\n :param stdout: An optional stream to pump stdout to; defaults to `sys.stdout`.\n :param stderr: An optional stream to pump stderr to; defaults to `sys.stderr`.\n :param string cwd: optionally set the working directory\n \"\"\"\n raise NotImplementedError","label":1},{"cid":"src/python/pants/java/executor.py::Executor.__init__","text":"src/python/pants/java/executor.py::Executor.__init__\n def __init__(self, distribution):\n \"\"\"Constructs an Executor that can be used to launch java programs.\n\n :param distribution: a validated java distribution to use when launching java programs.\n \"\"\"\n if not hasattr(distribution, 'java') or not hasattr(distribution, 'validate'):\n raise self.InvalidDistribution('A valid distribution is required, given: {}'\n .format(distribution))\n distribution.validate()\n self._distribution = distribution","label":0},{"cid":"src/python/pants/java/executor.py::Executor.distribution","text":"src/python/pants/java/executor.py::Executor.distribution\n @property\n def distribution(self):\n \"\"\"Returns the `Distribution` this executor runs via.\"\"\"\n return self._distribution","label":0},{"cid":"src/python/pants/java/executor.py::Executor.runner","text":"src/python/pants/java/executor.py::Executor.runner\n def runner(self, classpath, main, jvm_options=None, args=None, cwd=None):\n \"\"\"Returns an `Executor.Runner` for the given java command.\"\"\"\n return self._runner(*self._scrub_args(classpath, main, jvm_options, args, cwd=cwd))","label":0},{"cid":"src/python/pants/java/executor.py::Executor.execute","text":"src/python/pants/java/executor.py::Executor.execute\n def execute(self, classpath, main, jvm_options=None, args=None, stdout=None, stderr=None,\n cwd=None):\n \"\"\"Launches the java program defined by the classpath and main.\n\n :param list classpath: the classpath for the java program\n :param string main: the fully qualified class name of the java program's entry point\n :param list jvm_options: an optional sequence of options for the underlying jvm\n :param list args: an optional sequence of args to pass to the java program\n :param string cwd: optionally set the working directory\n\n Returns the exit code of the java program.\n Raises Executor.Error if there was a problem launching java itself.\n \"\"\"\n runner = self.runner(classpath=classpath, main=main, jvm_options=jvm_options, args=args,\n cwd=cwd)\n return runner.run(stdout=stdout, stderr=stderr)","label":0},{"cid":"src/python/pants/java/executor.py::Executor._runner","text":"src/python/pants/java/executor.py::Executor._runner\n @abstractmethod\n def _runner(self, classpath, main, jvm_options, args, cwd=None):\n \"\"\"Subclasses should return a `Runner` that can execute the given java main.\"\"\"","label":0},{"cid":"src/python/pants/java/executor.py::Executor._create_command","text":"src/python/pants/java/executor.py::Executor._create_command\n def _create_command(self, classpath, main, jvm_options, args, cwd=None):\n cmd = [self._distribution.java]\n cmd.extend(jvm_options)\n if cwd:\n classpath = relativize_paths(classpath, cwd)\n cmd.extend(['-cp', os.pathsep.join(classpath), main])\n cmd.extend(args)\n return cmd","label":0},{"cid":"src/python/pants/java/executor.py::CommandLineGrabber.__init__","text":"src/python/pants/java/executor.py::CommandLineGrabber.__init__\n def __init__(self, distribution):\n super(CommandLineGrabber, self).__init__(distribution=distribution)\n self._command = None # Initialized when we run something.","label":0},{"cid":"src/python/pants/java/executor.py::CommandLineGrabber._runner","text":"src/python/pants/java/executor.py::CommandLineGrabber._runner\n def _runner(self, classpath, main, jvm_options, args, cwd=None):\n self._command = self._create_command(classpath, main, jvm_options, args, cwd=cwd)\n\n class Runner(self.Runner):\n @property\n def executor(_):\n return self\n\n @property\n def command(_):\n return list(self._command)\n\n def run(_, stdout=None, stderr=None):\n return 0\n\n def spawn(_, stdout=None, stderr=None):\n return None\n\n return Runner()","label":0},{"cid":"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.executor","text":"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.executor\n @property\n def executor(_):\n return self","label":0},{"cid":"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.command","text":"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.command\n @property\n def command(_):\n return list(self._command)","label":0},{"cid":"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.run","text":"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.run\n def run(_, stdout=None, stderr=None):\n return 0","label":1},{"cid":"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.spawn","text":"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.spawn\n def spawn(_, stdout=None, stderr=None):\n return None","label":1},{"cid":"src/python/pants/java/executor.py::CommandLineGrabber.cmd","text":"src/python/pants/java/executor.py::CommandLineGrabber.cmd\n @property\n def cmd(self):\n return self._command","label":0},{"cid":"src/python/pants/java/executor.py::SubprocessExecutor._maybe_scrubbed_env","text":"src/python/pants/java/executor.py::SubprocessExecutor._maybe_scrubbed_env\n @classmethod\n @contextmanager\n def _maybe_scrubbed_env(cls):\n for env_var in cls._SCRUBBED_ENV:\n value = os.getenv(env_var)\n if value:\n logger.warn('Scrubbing {env_var}={value}'.format(env_var=env_var, value=value))\n with environment_as(**cls._SCRUBBED_ENV):\n yield","label":0},{"cid":"src/python/pants/java/executor.py::SubprocessExecutor.__init__","text":"src/python/pants/java/executor.py::SubprocessExecutor.__init__\n def __init__(self, distribution):\n super(SubprocessExecutor, self).__init__(distribution=distribution)\n self._buildroot = get_buildroot()\n self._process = None","label":0},{"cid":"src/python/pants/java/executor.py::SubprocessExecutor._runner","text":"src/python/pants/java/executor.py::SubprocessExecutor._runner\n def _runner(self, classpath, main, jvm_options, args, cwd=None):\n cwd = cwd or os.getcwd()\n command = self._create_command(classpath, main, jvm_options, args, cwd=cwd)\n\n class Runner(self.Runner):\n @property\n def executor(_):\n return self\n\n @property\n def command(_):\n return list(command)\n\n def spawn(_, stdout=None, stderr=None):\n return self._spawn(command, cwd, stdout=stdout, stderr=stderr)\n\n def run(_, stdout=None, stderr=None):\n return self._spawn(command, cwd, stdout=stdout, stderr=stderr).wait()\n\n return Runner()","label":0},{"cid":"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.executor","text":"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.executor\n @property\n def executor(_):\n return self","label":0},{"cid":"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.command","text":"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.command\n @property\n def command(_):\n return list(command)","label":0},{"cid":"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.spawn","text":"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.spawn\n def spawn(_, stdout=None, stderr=None):\n return self._spawn(command, cwd, stdout=stdout, stderr=stderr)","label":1},{"cid":"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.run","text":"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.run\n def run(_, stdout=None, stderr=None):\n return self._spawn(command, cwd, stdout=stdout, stderr=stderr).wait()","label":1},{"cid":"src/python/pants/java/executor.py::SubprocessExecutor.spawn","text":"src/python/pants/java/executor.py::SubprocessExecutor.spawn\n def spawn(self, classpath, main, jvm_options=None, args=None, cwd=None, **subprocess_args):\n \"\"\"Spawns the java program passing any extra subprocess kwargs on to subprocess.Popen.\n\n Returns the Popen process object handle to the spawned java program subprocess.\n\n :API: public\n\n :raises: :class:`Executor.Error` if there is a problem spawning the subprocess.\n \"\"\"\n cwd = cwd or os.getcwd()\n cmd = self._create_command(*self._scrub_args(classpath, main, jvm_options, args, cwd=cwd))\n return self._spawn(cmd, cwd, **subprocess_args)","label":0},{"cid":"src/python/pants/java/executor.py::SubprocessExecutor._spawn","text":"src/python/pants/java/executor.py::SubprocessExecutor._spawn\n def _spawn(self, cmd, cwd, **subprocess_args):\n with self._maybe_scrubbed_env():\n logger.debug('Executing: {cmd} args={args} at cwd={cwd}'\n .format(cmd=' '.join(cmd), args=subprocess_args, cwd=cwd))\n try:\n return subprocess.Popen(cmd, cwd=cwd, **subprocess_args)\n except OSError as e:\n raise self.Error('Problem executing {0}: {1}'.format(self._distribution.java, e))","label":1},{"cid":"src/python/pants/java/nailgun_client.py::NailgunClientSession.__init__","text":"src/python/pants/java/nailgun_client.py::NailgunClientSession.__init__\n def __init__(self, sock, in_fd, out_fd, err_fd, exit_on_broken_pipe=False):\n self._sock = sock\n self._input_reader = NailgunStreamReader(in_fd, self._sock) if in_fd else None\n self._stdout = out_fd\n self._stderr = err_fd\n self._exit_on_broken_pipe = exit_on_broken_pipe\n self.remote_pid = None","label":1},{"cid":"src/python/pants/java/nailgun_client.py::NailgunClientSession._maybe_start_input_reader","text":"src/python/pants/java/nailgun_client.py::NailgunClientSession._maybe_start_input_reader\n def _maybe_start_input_reader(self):\n if self._input_reader:\n self._input_reader.start()","label":1},{"cid":"src/python/pants/java/nailgun_client.py::NailgunClientSession._maybe_stop_input_reader","text":"src/python/pants/java/nailgun_client.py::NailgunClientSession._maybe_stop_input_reader\n def _maybe_stop_input_reader(self):\n if self._input_reader:\n self._input_reader.stop()","label":1},{"cid":"src/python/pants/java/nailgun_client.py::NailgunClientSession._write_flush","text":"src/python/pants/java/nailgun_client.py::NailgunClientSession._write_flush\n def _write_flush(self, fd, payload=None):\n \"\"\"Write a payload to a given fd (if provided) and flush the fd.\"\"\"\n try:\n if payload:\n fd.write(payload)\n fd.flush()\n except (IOError, OSError) as e:\n # If a `Broken Pipe` is encountered during a stdio fd write, we're headless - bail.\n if e.errno == errno.EPIPE and self._exit_on_broken_pipe:\n sys.exit()\n # Otherwise, re-raise.\n raise","label":0},{"cid":"src/python/pants/java/nailgun_client.py::NailgunClientSession._process_session","text":"src/python/pants/java/nailgun_client.py::NailgunClientSession._process_session\n def _process_session(self):\n \"\"\"Process the outputs of the nailgun session.\"\"\"\n try:\n for chunk_type, payload in self.iter_chunks(self._sock, return_bytes=True):\n if chunk_type == ChunkType.STDOUT:\n self._write_flush(self._stdout, payload)\n elif chunk_type == ChunkType.STDERR:\n self._write_flush(self._stderr, payload)\n elif chunk_type == ChunkType.EXIT:\n self._write_flush(self._stdout)\n self._write_flush(self._stderr)\n return int(payload)\n elif chunk_type == ChunkType.PID:\n self.remote_pid = int(payload)\n elif chunk_type == ChunkType.START_READING_INPUT:\n self._maybe_start_input_reader()\n else:\n raise self.ProtocolError('received unexpected chunk {} -> {}'.format(chunk_type, payload))\n finally:\n # Bad chunk types received from the server can throw NailgunProtocol.ProtocolError in\n # NailgunProtocol.iter_chunks(). This ensures the NailgunStreamReader is always stopped.\n self._maybe_stop_input_reader()","label":1},{"cid":"src/python/pants/java/nailgun_client.py::NailgunClientSession.execute","text":"src/python/pants/java/nailgun_client.py::NailgunClientSession.execute\n def execute(self, working_dir, main_class, *arguments, **environment):\n # Send the nailgun request.\n self.send_request(self._sock, working_dir, main_class, *arguments, **environment)\n\n # Process the remainder of the nailgun session.\n return self._process_session()","label":0},{"cid":"src/python/pants/java/nailgun_client.py::NailgunClient.__init__","text":"src/python/pants/java/nailgun_client.py::NailgunClient.__init__\n def __init__(self, host=DEFAULT_NG_HOST, port=DEFAULT_NG_PORT, ins=sys.stdin, out=None, err=None,\n workdir=None, exit_on_broken_pipe=False):\n \"\"\"Creates a nailgun client that can be used to issue zero or more nailgun commands.\n\n :param string host: the nailgun server to contact (defaults to '127.0.0.1')\n :param int port: the port the nailgun server is listening on (defaults to the default nailgun\n port: 2113)\n :param file ins: a file to read command standard input from (defaults to stdin) - can be None\n in which case no input is read\n :param file out: a stream to write command standard output to (defaults to stdout)\n :param file err: a stream to write command standard error to (defaults to stderr)\n :param string workdir: the default working directory for all nailgun commands (defaults to CWD)\n :param bool exit_on_broken_pipe: whether or not to exit when `Broken Pipe` errors are encountered.\n \"\"\"\n self._host = host\n self._port = port\n self._stdin = ins\n self._stdout = out or sys.stdout\n self._stderr = err or sys.stderr\n self._workdir = workdir or os.path.abspath(os.path.curdir)\n self._exit_on_broken_pipe = exit_on_broken_pipe\n self._session = None","label":0},{"cid":"src/python/pants/java/nailgun_client.py::NailgunClient.try_connect","text":"src/python/pants/java/nailgun_client.py::NailgunClient.try_connect\n def try_connect(self):\n \"\"\"Creates a socket, connects it to the nailgun and returns the connected socket.\n\n :returns: a connected `socket.socket`.\n :raises: `NailgunClient.NailgunConnectionError` on failure to connect.\n \"\"\"\n sock = RecvBufferedSocket(socket.socket(socket.AF_INET, socket.SOCK_STREAM))\n try:\n sock.connect((self._host, self._port))\n except (socket.error, socket.gaierror) as e:\n logger.debug('Encountered socket exception {!r} when attempting connect to nailgun'.format(e))\n sock.close()\n raise self.NailgunConnectionError(\n 'Problem connecting to nailgun server at {}:{}: {!r}'.format(self._host, self._port, e))\n else:\n return sock","label":0},{"cid":"src/python/pants/java/nailgun_client.py::NailgunClient.send_control_c","text":"src/python/pants/java/nailgun_client.py::NailgunClient.send_control_c\n def send_control_c(self):\n \"\"\"Sends SIGINT to a nailgun server using pid information from the active session.\"\"\"\n if self._session and self._session.remote_pid is not None:\n os.kill(self._session.remote_pid, signal.SIGINT)","label":0},{"cid":"src/python/pants/java/nailgun_client.py::NailgunClient.execute","text":"src/python/pants/java/nailgun_client.py::NailgunClient.execute\n def execute(self, main_class, cwd=None, *args, **environment):\n \"\"\"Executes the given main_class with any supplied args in the given environment.\n\n :param string main_class: the fully qualified class name of the main entrypoint\n :param string cwd: Set the working directory for this command\n :param list args: any arguments to pass to the main entrypoint\n :param dict environment: an env mapping made available to native nails via the nail context\n :returns: the exit code of the main_class.\n \"\"\"\n environment = dict(self.ENV_DEFAULTS.items() + environment.items())\n cwd = cwd or self._workdir\n\n # N.B. This can throw NailgunConnectionError (catchable via NailgunError).\n sock = self.try_connect()\n\n self._session = NailgunClientSession(sock,\n self._stdin,\n self._stdout,\n self._stderr,\n self._exit_on_broken_pipe)\n try:\n return self._session.execute(cwd, main_class, *args, **environment)\n except socket.error as e:\n raise self.NailgunError('Problem communicating with nailgun server at {}:{}: {!r}'\n .format(self._host, self._port, e))\n except NailgunProtocol.ProtocolError as e:\n raise self.NailgunError('Problem in nailgun protocol with nailgun server at {}:{}: {!r}'\n .format(self._host, self._port, e))\n finally:\n sock.close()\n self._session = None","label":0},{"cid":"src/python/pants/java/nailgun_client.py::NailgunClient.__repr__","text":"src/python/pants/java/nailgun_client.py::NailgunClient.__repr__\n def __repr__(self):\n return 'NailgunClient(host={!r}, port={!r}, workdir={!r})'.format(self._host,\n self._port,\n self._workdir)","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup.__init__","text":"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup.__init__\n def __init__(self, metadata_base_dir=None):\n super(NailgunProcessGroup, self).__init__(name='nailgun', metadata_base_dir=metadata_base_dir)","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup._iter_nailgun_instances","text":"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup._iter_nailgun_instances\n def _iter_nailgun_instances(self, everywhere=False):\n def predicate(proc):\n if proc.name() == NailgunExecutor._PROCESS_NAME:\n if not everywhere:\n return NailgunExecutor._PANTS_NG_BUILDROOT_ARG in proc.cmdline()\n else:\n return any(arg.startswith(NailgunExecutor._PANTS_NG_ARG_PREFIX) for arg in proc.cmdline())\n\n return self.iter_instances(predicate)","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup._iter_nailgun_instances.predicate","text":"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup._iter_nailgun_instances.predicate\n def predicate(proc):\n if proc.name() == NailgunExecutor._PROCESS_NAME:\n if not everywhere:\n return NailgunExecutor._PANTS_NG_BUILDROOT_ARG in proc.cmdline()\n else:\n return any(arg.startswith(NailgunExecutor._PANTS_NG_ARG_PREFIX) for arg in proc.cmdline())","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup.killall","text":"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup.killall\n def killall(self, everywhere=False):\n \"\"\"Kills all nailgun servers started by pants.\n\n :param bool everywhere: If ``True``, kills all pants-started nailguns on this machine;\n otherwise restricts the nailguns killed to those started for the\n current build root.\n \"\"\"\n with self._NAILGUN_KILL_LOCK:\n for proc in self._iter_nailgun_instances(everywhere):\n logger.info('killing nailgun server pid={pid}'.format(pid=proc.pid))\n proc.terminate()","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor.__init__","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor.__init__\n def __init__(self, identity, workdir, nailgun_classpath, distribution, ins=None,\n connect_timeout=10, connect_attempts=5, metadata_base_dir=None):\n Executor.__init__(self, distribution=distribution)\n FingerprintedProcessManager.__init__(self,\n name=identity,\n process_name=self._PROCESS_NAME,\n metadata_base_dir=metadata_base_dir)\n\n if not isinstance(workdir, string_types):\n raise ValueError('Workdir must be a path string, not: {workdir}'.format(workdir=workdir))\n\n self._identity = identity\n self._workdir = workdir\n self._ng_stdout = os.path.join(workdir, 'stdout')\n self._ng_stderr = os.path.join(workdir, 'stderr')\n self._nailgun_classpath = maybe_list(nailgun_classpath)\n self._ins = ins\n self._connect_timeout = connect_timeout\n self._connect_attempts = connect_attempts","label":1},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor.__str__","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor.__str__\n def __str__(self):\n return 'NailgunExecutor({identity}, dist={dist}, pid={pid} socket={socket})'.format(\n identity=self._identity, dist=self._distribution, pid=self.pid, socket=self.socket)","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._create_owner_arg","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._create_owner_arg\n def _create_owner_arg(self, workdir):\n # Currently the owner is identified via the full path to the workdir.\n return '='.join((self._PANTS_OWNER_ARG_PREFIX, workdir))","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._create_fingerprint_arg","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._create_fingerprint_arg\n def _create_fingerprint_arg(self, fingerprint):\n return '='.join((self.FINGERPRINT_CMD_KEY, fingerprint))","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._fingerprint","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._fingerprint\n @staticmethod\n def _fingerprint(jvm_options, classpath, java_version):\n \"\"\"Compute a fingerprint for this invocation of a Java task.\n\n :param list jvm_options: JVM options passed to the java invocation\n :param list classpath: The -cp arguments passed to the java invocation\n :param Revision java_version: return value from Distribution.version()\n :return: a hexstring representing a fingerprint of the java invocation\n \"\"\"\n digest = hashlib.sha1()\n # TODO(John Sirois): hash classpath contents?\n [digest.update(item) for item in (''.join(sorted(jvm_options)),\n ''.join(sorted(classpath)),\n repr(java_version))]\n return digest.hexdigest()","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner\n def _runner(self, classpath, main, jvm_options, args, cwd=None):\n \"\"\"Runner factory. Called via Executor.execute().\"\"\"\n command = self._create_command(classpath, main, jvm_options, args)\n\n class Runner(self.Runner):\n @property\n def executor(this):\n return self\n\n @property\n def command(self):\n return list(command)\n\n def run(this, stdout=None, stderr=None, cwd=None):\n nailgun = self._get_nailgun_client(jvm_options, classpath, stdout, stderr)\n try:\n logger.debug('Executing via {ng_desc}: {cmd}'.format(ng_desc=nailgun, cmd=this.cmd))\n return nailgun.execute(main, cwd, *args)\n except nailgun.NailgunError as e:\n self.terminate()\n raise self.Error('Problem launching via {ng_desc} command {main} {args}: {msg}'\n .format(ng_desc=nailgun, main=main, args=' '.join(args), msg=e))\n\n return Runner()","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner.Runner.executor","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner.Runner.executor\n @property\n def executor(this):\n return self","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner.Runner.command","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner.Runner.command\n @property\n def command(self):\n return list(command)","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner.Runner.run","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner.Runner.run\n def run(this, stdout=None, stderr=None, cwd=None):\n nailgun = self._get_nailgun_client(jvm_options, classpath, stdout, stderr)\n try:\n logger.debug('Executing via {ng_desc}: {cmd}'.format(ng_desc=nailgun, cmd=this.cmd))\n return nailgun.execute(main, cwd, *args)\n except nailgun.NailgunError as e:\n self.terminate()\n raise self.Error('Problem launching via {ng_desc} command {main} {args}: {msg}'\n .format(ng_desc=nailgun, main=main, args=' '.join(args), msg=e))","label":1},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._check_nailgun_state","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._check_nailgun_state\n def _check_nailgun_state(self, new_fingerprint):\n running = self.is_alive()\n updated = self.needs_restart(new_fingerprint) or self.cmd != self._distribution.java\n logging.debug('Nailgun {nailgun} state: updated={up!s} running={run!s} fingerprint={old_fp} '\n 'new_fingerprint={new_fp} distribution={old_dist} new_distribution={new_dist}'\n .format(nailgun=self._identity, up=updated, run=running,\n old_fp=self.fingerprint, new_fp=new_fingerprint,\n old_dist=self.cmd, new_dist=self._distribution.java))\n return running, updated","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._get_nailgun_client","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._get_nailgun_client\n def _get_nailgun_client(self, jvm_options, classpath, stdout, stderr):\n \"\"\"This (somewhat unfortunately) is the main entrypoint to this class via the Runner. It handles\n creation of the running nailgun server as well as creation of the client.\"\"\"\n classpath = self._nailgun_classpath + classpath\n new_fingerprint = self._fingerprint(jvm_options, classpath, self._distribution.version)\n\n with self._NAILGUN_SPAWN_LOCK:\n running, updated = self._check_nailgun_state(new_fingerprint)\n\n if running and updated:\n logger.debug('Found running nailgun server that needs updating, killing {server}'\n .format(server=self._identity))\n self.terminate()\n\n if (not running) or (running and updated):\n return self._spawn_nailgun_server(new_fingerprint, jvm_options, classpath, stdout, stderr)\n\n return self._create_ngclient(self.socket, stdout, stderr)","label":1},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._await_socket","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._await_socket\n def _await_socket(self, timeout):\n \"\"\"Blocks for the nailgun subprocess to bind and emit a listening port in the nailgun stdout.\"\"\"\n with safe_open(self._ng_stdout, 'r') as ng_stdout:\n start_time = time.time()\n while 1:\n readable, _, _ = select.select([ng_stdout], [], [], self._SELECT_WAIT)\n if readable:\n line = ng_stdout.readline() # TODO: address deadlock risk here.\n try:\n return self._NG_PORT_REGEX.match(line).group(1)\n except AttributeError:\n pass\n\n if (time.time() - start_time) > timeout:\n raise NailgunClient.NailgunError(\n 'Failed to read nailgun output after {sec} seconds!'.format(sec=timeout))","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._create_ngclient","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._create_ngclient\n def _create_ngclient(self, port, stdout, stderr):\n return NailgunClient(port=port, ins=self._ins, out=stdout, err=stderr, workdir=get_buildroot())","label":1},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor.ensure_connectable","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor.ensure_connectable\n def ensure_connectable(self, nailgun):\n \"\"\"Ensures that a nailgun client is connectable or raises NailgunError.\"\"\"\n attempt_count = 1\n while 1:\n try:\n with closing(nailgun.try_connect()) as sock:\n logger.debug('Verified new ng server is connectable at {}'.format(sock.getpeername()))\n return\n except nailgun.NailgunConnectionError:\n if attempt_count >= self._connect_attempts:\n logger.debug('Failed to connect to ng after {} attempts'.format(self._connect_attempts))\n raise # Re-raise the NailgunConnectionError which provides more context to the user.\n\n attempt_count += 1\n time.sleep(self.WAIT_INTERVAL_SEC)","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._spawn_nailgun_server","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._spawn_nailgun_server\n def _spawn_nailgun_server(self, fingerprint, jvm_options, classpath, stdout, stderr):\n \"\"\"Synchronously spawn a new nailgun server.\"\"\"\n # Truncate the nailguns stdout & stderr.\n safe_file_dump(self._ng_stdout, '')\n safe_file_dump(self._ng_stderr, '')\n\n jvm_options = jvm_options + [self._PANTS_NG_BUILDROOT_ARG,\n self._create_owner_arg(self._workdir),\n self._create_fingerprint_arg(fingerprint)]\n\n post_fork_child_opts = dict(fingerprint=fingerprint,\n jvm_options=jvm_options,\n classpath=classpath,\n stdout=stdout,\n stderr=stderr)\n\n logger.debug('Spawning nailgun server {i} with fingerprint={f}, jvm_options={j}, classpath={cp}'\n .format(i=self._identity, f=fingerprint, j=jvm_options, cp=classpath))\n\n self.daemon_spawn(post_fork_child_opts=post_fork_child_opts)\n\n # Wait for and write the port information in the parent so we can bail on exception/timeout.\n self.await_pid(self._connect_timeout)\n self.write_socket(self._await_socket(self._connect_timeout))\n\n logger.debug('Spawned nailgun server {i} with fingerprint={f}, pid={pid} port={port}'\n .format(i=self._identity, f=fingerprint, pid=self.pid, port=self.socket))\n\n client = self._create_ngclient(self.socket, stdout, stderr)\n self.ensure_connectable(client)\n\n return client","label":1},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._check_process_buildroot","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor._check_process_buildroot\n def _check_process_buildroot(self, process):\n \"\"\"Matches only processes started from the current buildroot.\"\"\"\n return self._PANTS_NG_BUILDROOT_ARG in process.cmdline()","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor.is_alive","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor.is_alive\n def is_alive(self):\n \"\"\"A ProcessManager.is_alive() override that ensures buildroot flags are present in the process\n command line arguments.\"\"\"\n return super(NailgunExecutor, self).is_alive(self._check_process_buildroot)","label":0},{"cid":"src/python/pants/java/nailgun_executor.py::NailgunExecutor.post_fork_child","text":"src/python/pants/java/nailgun_executor.py::NailgunExecutor.post_fork_child\n def post_fork_child(self, fingerprint, jvm_options, classpath, stdout, stderr):\n \"\"\"Post-fork() child callback for ProcessManager.daemon_spawn().\"\"\"\n java = SubprocessExecutor(self._distribution)\n\n subproc = java.spawn(classpath=classpath,\n main='com.martiansoftware.nailgun.NGServer',\n jvm_options=jvm_options,\n args=[':0'],\n stdin=safe_open('/dev/null', 'r'),\n stdout=safe_open(self._ng_stdout, 'w'),\n stderr=safe_open(self._ng_stderr, 'w'),\n close_fds=True)\n\n self.write_pid(subproc.pid)","label":0},{"cid":"src/python/pants/java/nailgun_io.py::NailgunStreamReader.__init__","text":"src/python/pants/java/nailgun_io.py::NailgunStreamReader.__init__\n def __init__(self, in_fd, sock, buf_size=io.DEFAULT_BUFFER_SIZE, select_timeout=SELECT_TIMEOUT):\n \"\"\"\n :param file in_fd: the input file descriptor (e.g. sys.stdin) to read from.\n :param socket sock: the socket to emit nailgun protocol chunks over.\n :param int buf_size: the buffer size for reads from the file descriptor.\n :param int select_timeout: the timeout (in seconds) for select.select() calls against the fd.\n \"\"\"\n super(NailgunStreamReader, self).__init__()\n self.daemon = True\n self._stdin = in_fd\n self._socket = sock\n self._buf_size = buf_size\n self._select_timeout = select_timeout\n # N.B. This Event is used as nothing more than a convenient atomic flag - nothing waits on it.\n self._stopped = threading.Event()","label":1},{"cid":"src/python/pants/java/nailgun_io.py::NailgunStreamReader.is_stopped","text":"src/python/pants/java/nailgun_io.py::NailgunStreamReader.is_stopped\n @property\n def is_stopped(self):\n \"\"\"Indicates whether or not the instance is stopped.\"\"\"\n return self._stopped.is_set()","label":0},{"cid":"src/python/pants/java/nailgun_io.py::NailgunStreamReader.stop","text":"src/python/pants/java/nailgun_io.py::NailgunStreamReader.stop\n def stop(self):\n \"\"\"Stops the instance.\"\"\"\n self._stopped.set()","label":0},{"cid":"src/python/pants/java/nailgun_io.py::NailgunStreamReader.running","text":"src/python/pants/java/nailgun_io.py::NailgunStreamReader.running\n @contextmanager\n def running(self):\n self.start()\n yield\n self.stop()","label":1},{"cid":"src/python/pants/java/nailgun_io.py::NailgunStreamReader.run","text":"src/python/pants/java/nailgun_io.py::NailgunStreamReader.run\n def run(self):\n while not self.is_stopped:\n readable, _, errored = select.select([self._stdin], [], [self._stdin], self._select_timeout)\n\n if self._stdin in errored:\n self.stop()\n return\n\n if not self.is_stopped and self._stdin in readable:\n data = os.read(self._stdin.fileno(), self._buf_size)\n\n if not self.is_stopped:\n if data:\n NailgunProtocol.write_chunk(self._socket, ChunkType.STDIN, data)\n else:\n NailgunProtocol.write_chunk(self._socket, ChunkType.STDIN_EOF)\n try:\n self._socket.shutdown(socket.SHUT_WR) # Shutdown socket sends.\n except socket.error: # Can happen if response is quick.\n pass\n finally:\n self.stop()","label":1},{"cid":"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.__init__","text":"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.__init__\n def __init__(self, sock, chunk_type, isatty=True, mask_broken_pipe=False):\n \"\"\"\n :param socket sock: A connected socket capable of speaking the nailgun protocol.\n :param str chunk_type: A ChunkType constant representing the nailgun protocol chunk type.\n :param bool isatty: Whether or not the consumer of this stream has tty capabilities. (Optional)\n :param bool mask_broken_pipe: This will toggle the masking of 'broken pipe' errors when writing\n to the remote socket. This allows for completion of execution in\n the event of a client disconnect (e.g. to support cleanup work).\n \"\"\"\n self._socket = sock\n self._chunk_type = chunk_type\n self._isatty = isatty\n self._mask_broken_pipe = mask_broken_pipe","label":1},{"cid":"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.write","text":"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.write\n def write(self, payload):\n try:\n NailgunProtocol.write_chunk(self._socket, self._chunk_type, payload)\n except IOError as e:\n # If the remote client disconnects and we try to perform a write (e.g. socket.send/sendall),\n # an 'error: [Errno 32] Broken pipe' exception can be thrown. Setting mask_broken_pipe=True\n # safeguards against this case (which is unexpected for most writers of sys.stdout etc) so\n # that we don't awkwardly interrupt the runtime by throwing this exception on writes to\n # stdout/stderr.\n if e.errno == errno.EPIPE and not self._mask_broken_pipe:\n raise","label":1},{"cid":"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.flush","text":"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.flush\n def flush(self):\n return","label":1},{"cid":"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.isatty","text":"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.isatty\n def isatty(self):\n return self._isatty","label":1},{"cid":"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.fileno","text":"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.fileno\n def fileno(self):\n return self._socket.fileno()","label":1},{"cid":"src/python/pants/java/util.py::_get_runner","text":"src/python/pants/java/util.py::_get_runner\ndef _get_runner(classpath, main, jvm_options, args, executor,\n cwd, distribution,\n create_synthetic_jar, synthetic_jar_dir):\n \"\"\"Gets the java runner for execute_java and execute_java_async.\"\"\"\n\n executor = executor or SubprocessExecutor(distribution)\n\n safe_cp = classpath\n if create_synthetic_jar:\n safe_cp = safe_classpath(classpath, synthetic_jar_dir)\n logger.debug('Bundling classpath {} into {}'.format(':'.join(classpath), safe_cp))\n\n return executor.runner(safe_cp, main, args=args, jvm_options=jvm_options, cwd=cwd)","label":0},{"cid":"src/python/pants/java/util.py::execute_java","text":"src/python/pants/java/util.py::execute_java\ndef execute_java(classpath, main, jvm_options=None, args=None, executor=None,\n workunit_factory=None, workunit_name=None, workunit_labels=None,\n cwd=None, workunit_log_config=None, distribution=None,\n create_synthetic_jar=True, synthetic_jar_dir=None):\n \"\"\"Executes the java program defined by the classpath and main.\n\n If `workunit_factory` is supplied, does so in the context of a workunit.\n\n :param list classpath: the classpath for the java program\n :param string main: the fully qualified class name of the java program's entry point\n :param list jvm_options: an optional sequence of options for the underlying jvm\n :param list args: an optional sequence of args to pass to the java program\n :param executor: an optional java executor to use to launch the program; defaults to a subprocess\n spawn of the default java distribution\n :param workunit_factory: an optional callable that can produce a workunit context\n :param string workunit_name: an optional name for the work unit; defaults to the main\n :param list workunit_labels: an optional sequence of labels for the work unit\n :param string cwd: optionally set the working directory\n :param WorkUnit.LogConfig workunit_log_config: an optional tuple of options affecting reporting\n :param bool create_synthetic_jar: whether to create a synthentic jar that includes the original\n classpath in its manifest.\n :param string synthetic_jar_dir: an optional directory to store the synthetic jar, if `None`\n a temporary directory will be provided and cleaned up upon process exit.\n\n Returns the exit code of the java program.\n Raises `pants.java.Executor.Error` if there was a problem launching java itself.\n \"\"\"\n\n runner = _get_runner(classpath, main, jvm_options, args, executor, cwd, distribution,\n create_synthetic_jar, synthetic_jar_dir)\n workunit_name = workunit_name or main\n\n return execute_runner(r","label":1},{"cid":"src/python/pants/java/util.py::execute_java_async","text":"src/python/pants/java/util.py::execute_java_async\ndef execute_java_async(classpath, main, jvm_options=None, args=None, executor=None,\n workunit_factory=None, workunit_name=None, workunit_labels=None,\n cwd=None, workunit_log_config=None, distribution=None,\n create_synthetic_jar=True, synthetic_jar_dir=None):\n \"\"\"This is just like execute_java except that it returns a ProcessHandler rather than a return code.\n\n\n If `workunit_factory` is supplied, does so in the context of a workunit.\n\n :param list classpath: the classpath for the java program\n :param string main: the fully qualified class name of the java program's entry point\n :param list jvm_options: an optional sequence of options for the underlying jvm\n :param list args: an optional sequence of args to pass to the java program\n :param executor: an optional java executor to use to launch the program; defaults to a subprocess\n spawn of the default java distribution\n :param workunit_factory: an optional callable that can produce a workunit context\n :param string workunit_name: an optional name for the work unit; defaults to the main\n :param list workunit_labels: an optional sequence of labels for the work unit\n :param string cwd: optionally set the working directory\n :param WorkUnit.LogConfig workunit_log_config: an optional tuple of options affecting reporting\n :param bool create_synthetic_jar: whether to create a synthentic jar that includes the original\n classpath in its manifest.\n :param string synthetic_jar_dir: an optional directory to store the synthetic jar, if `None`\n a temporary directory will be provided and cleaned up upon process exit.\n\n Returns a ProcessHandler to the java program.\n Raises `pants.java.Executor.Error` if there was a problem launching java itself.\n \"\"\"\n\n runner = _get_runner(classpath, main, jvm_options, args, executor, cwd, distribution,\n create_synthetic_jar, synthetic_jar_","label":0},{"cid":"src/python/pants/java/util.py::execute_runner","text":"src/python/pants/java/util.py::execute_runner\ndef execute_runner(runner, workunit_factory=None, workunit_name=None, workunit_labels=None,\n workunit_log_config=None):\n \"\"\"Executes the given java runner.\n\n If `workunit_factory` is supplied, does so in the context of a workunit.\n\n :param runner: the java runner to run\n :param workunit_factory: an optional callable that can produce a workunit context\n :param string workunit_name: an optional name for the work unit; defaults to the main\n :param list workunit_labels: an optional sequence of labels for the work unit\n :param WorkUnit.LogConfig workunit_log_config: an optional tuple of task options affecting reporting\n\n Returns the exit code of the java runner.\n Raises `pants.java.Executor.Error` if there was a problem launching java itself.\n \"\"\"\n if not isinstance(runner, Executor.Runner):\n raise ValueError('The runner argument must be a java Executor.Runner instance, '\n 'given {} of type {}'.format(runner, type(runner)))\n\n if workunit_factory is None:\n return runner.run()\n else:\n workunit_labels = [\n WorkUnitLabel.TOOL,\n WorkUnitLabel.NAILGUN if isinstance(runner.executor, NailgunExecutor) else WorkUnitLabel.JVM\n ] + (workunit_labels or [])\n\n with workunit_factory(name=workunit_name, labels=workunit_labels,\n cmd=runner.cmd, log_config=workunit_log_config) as workunit:\n ret = runner.run(stdout=workunit.output('stdout'), stderr=workunit.output('stderr'))\n workunit.set_outcome(WorkUnit.FAILURE if ret else WorkUnit.SUCCESS)\n return ret","label":1},{"cid":"src/python/pants/java/util.py::execute_runner_async","text":"src/python/pants/java/util.py::execute_runner_async\ndef execute_runner_async(runner, workunit_factory=None, workunit_name=None, workunit_labels=None,\n workunit_log_config=None):\n \"\"\"Executes the given java runner asynchronously.\n\n We can't use 'with' here because the workunit_generator's __exit__ function\n must be called after the process exits, in the return_code_handler.\n The wrapper around process.wait() needs to handle the same exceptions\n as the contextmanager does, so we have code duplication.\n\n We're basically faking the 'with' call to deal with asynchronous\n results.\n\n If `workunit_factory` is supplied, does so in the context of a workunit.\n\n :param runner: the java runner to run\n :param workunit_factory: an optional callable that can produce a workunit context\n :param string workunit_name: an optional name for the work unit; defaults to the main\n :param list workunit_labels: an optional sequence of labels for the work unit\n :param WorkUnit.LogConfig workunit_log_config: an optional tuple of task options affecting reporting\n\n Returns a ProcessHandler to the java process that is spawned.\n Raises `pants.java.Executor.Error` if there was a problem launching java itself.\n \"\"\"\n\n if not isinstance(runner, Executor.Runner):\n raise ValueError('The runner argument must be a java Executor.Runner instance, '\n 'given {} of type {}'.format(runner, type(runner)))\n\n if workunit_factory is None:\n return SubprocessProcessHandler(runner.spawn())\n else:\n workunit_labels = [\n WorkUnitLabel.TOOL,\n WorkUnitLabel.NAILGUN if isinstance(runner.executor, NailgunExecutor) else WorkUnitLabel.JVM\n ] + (workunit_labels or [])\n\n workunit_generator = workunit_factory(name=workunit_name, labels=workunit_labels,\n cmd=runner.cmd, log_config=workunit_log_config)\n workunit = workunit_generator.__enter__()\n process = ru","label":0},{"cid":"src/python/pants/java/util.py::relativize_classpath","text":"src/python/pants/java/util.py::relativize_classpath\ndef relativize_classpath(classpath, root_dir, followlinks=True):\n \"\"\"Convert into classpath relative to a directory.\n\n This is eventually used by a jar file located in this directory as its manifest\n attribute Class-Path. See\n https://docs.oracle.com/javase/7/docs/technotes/guides/extensions/spec.html#bundled\n\n :param list classpath: Classpath to be relativized.\n :param string root_dir: directory to relativize urls in the classpath, does not\n have to exist yet.\n :param bool followlinks: whether to follow symlinks to calculate relative path.\n\n :returns: Converted classpath of the same size as input classpath.\n :rtype: list of strings\n \"\"\"\n def relativize_url(url, root_dir):\n # When symlink is involed, root_dir concatenated with the returned relpath may not exist.\n # Consider on mac `/var` is a symlink of `/private/var`, the relative path of subdirectories\n # under /var to any other directories under `/` computed by os.path.relpath misses one level\n # of `..`. Use os.path.realpath to guarantee returned relpath can always be located.\n # This is not needed only when path are all relative.\n url = os.path.realpath(url) if followlinks else url\n root_dir = os.path.realpath(root_dir) if followlinks else root_dir\n url_in_bundle = os.path.relpath(url, root_dir)\n # Append '/' for directories, those not ending with '/' are assumed to be jars.\n # Note isdir does what we need here to follow symlinks.\n if os.path.isdir(url):\n url_in_bundle += '/'\n return url_in_bundle\n\n return [relativize_url(url, root_dir) for url in classpath]","label":0},{"cid":"src/python/pants/java/util.py::relativize_classpath.relativize_url","text":"src/python/pants/java/util.py::relativize_classpath.relativize_url\n def relativize_url(url, root_dir):\n # When symlink is involed, root_dir concatenated with the returned relpath may not exist.\n # Consider on mac `/var` is a symlink of `/private/var`, the relative path of subdirectories\n # under /var to any other directories under `/` computed by os.path.relpath misses one level\n # of `..`. Use os.path.realpath to guarantee returned relpath can always be located.\n # This is not needed only when path are all relative.\n url = os.path.realpath(url) if followlinks else url\n root_dir = os.path.realpath(root_dir) if followlinks else root_dir\n url_in_bundle = os.path.relpath(url, root_dir)\n # Append '/' for directories, those not ending with '/' are assumed to be jars.\n # Note isdir does what we need here to follow symlinks.\n if os.path.isdir(url):\n url_in_bundle += '/'\n return url_in_bundle","label":0},{"cid":"src/python/pants/java/util.py::safe_classpath","text":"src/python/pants/java/util.py::safe_classpath\ndef safe_classpath(classpath, synthetic_jar_dir, custom_name=None):\n \"\"\"Bundles classpath into one synthetic jar that includes original classpath in its manifest.\n\n This is to ensure classpath length never exceeds platform ARG_MAX.\n\n :param list classpath: Classpath to be bundled.\n :param string synthetic_jar_dir: directory to store the synthetic jar, if `None`\n a temp directory will be provided and cleaned up upon process exit. Otherwise synthetic\n jar will remain in the supplied directory, only for debugging purpose.\n :param custom_name: filename of the synthetic jar to be created.\n\n :returns: A classpath (singleton list with just the synthetic jar).\n :rtype: list of strings\n \"\"\"\n if synthetic_jar_dir:\n safe_mkdir(synthetic_jar_dir)\n else:\n synthetic_jar_dir = safe_mkdtemp()\n\n bundled_classpath = relativize_classpath(classpath, synthetic_jar_dir)\n\n manifest = Manifest()\n manifest.addentry(Manifest.CLASS_PATH, ' '.join(bundled_classpath))\n\n with temporary_file(root_dir=synthetic_jar_dir, cleanup=False, suffix='.jar') as jar_file:\n with open_zip(jar_file, mode='w', compression=ZIP_STORED) as jar:\n jar.writestr(Manifest.PATH, manifest.contents())\n\n if custom_name:\n custom_path = os.path.join(synthetic_jar_dir, custom_name)\n safe_concurrent_rename(jar_file.name, custom_path)\n return [custom_path]\n else:\n return [jar_file.name]","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options","text":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options\n @classmethod\n def register_bootstrap_options(cls, register):\n \"\"\"Register bootstrap options.\n\n \"Bootstrap options\" are a small set of options whose values are useful when registering other\n options. Therefore we must bootstrap them early, before other options are registered, let\n alone parsed.\n\n Bootstrap option values can be interpolated into the config file, and can be referenced\n programatically in registration code, e.g., as register.bootstrap.pants_workdir.\n\n Note that regular code can also access these options as normal global-scope options. Their\n status as \"bootstrap options\" is only pertinent during option registration.\n \"\"\"\n buildroot = get_buildroot()\n default_distdir_name = 'dist'\n default_distdir = os.path.join(buildroot, default_distdir_name)\n default_rel_distdir = '/{}/'.format(default_distdir_name)\n\n # Although logging supports the WARN level, its not documented and could conceivably be yanked.\n # Since pants has supported 'warn' since inception, leave the 'warn' choice as-is but explicitly\n # setup a 'WARN' logging level name that maps to 'WARNING'.\n logging.addLevelName(logging.WARNING, 'WARN')\n register('-l', '--level', choices=['debug', 'info', 'warn'], default='info', recursive=True,\n help='Set the logging level.')\n register('-q', '--quiet', type=bool, recursive=True, daemon=False,\n help='Squelches most console output. NOTE: Some tasks default to behaving quietly: '\n 'inverting this option supports making them noisier than they would be otherwise.')\n # Not really needed in bootstrap options, but putting it here means it displays right\n # after -l and -q in help output, which is conveniently contextual.\n register('--colors', type=bool, default=sys.stdout.isatty(), recursive=True, daemon=False,\n help='Set whether log messages are displa","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options","text":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options\n @classmethod\n def register_options(cls, register):\n \"\"\"Register options not tied to any particular task or subsystem.\"\"\"\n # The bootstrap options need to be registered on the post-bootstrap Options instance, so it\n # won't choke on them on the command line, and also so we can access their values as regular\n # global-scope options, for convenience.\n cls.register_bootstrap_options(register)\n\n register('-x', '--time', type=bool,\n help='Output a timing report at the end of the run.')\n register('-e', '--explain', type=bool,\n help='Explain the execution of goals.')\n register('--tag', type=list, metavar='[+-]tag1,tag2,...',\n help=\"Include only targets with these tags (optional '+' prefix) or without these \"\n \"tags ('-' prefix). Useful with ::, to find subsets of targets \"\n \"(e.g., integration tests.)\")\n\n register('-t', '--timeout', advanced=True, type=int, metavar='<seconds>',\n help='Number of seconds to wait for http connections.')\n # TODO: After moving to the new options system these abstraction leaks can go away.\n register('-k', '--kill-nailguns', advanced=True, type=bool,\n help='Kill nailguns before exiting')\n register('-i', '--interpreter', advanced=True, default=[], type=list,\n metavar='<requirement>',\n removal_version='1.5.0.dev0',\n removal_hint='Use --interpreter-constraints in scope python-setup instead.',\n help=\"Constrain what Python interpreters to use. Uses Requirement format from \"\n \"pkg_resources, e.g. 'CPython>=2.7,<3' or 'PyPy'. By default, no constraints \"\n \"are used. Multiple constraints may be added. They will be ORed together.\")\n register('--fail-fast', advanced=True, type=bool, recursive=True,\n help='Exit as quickly as possible on error, rat","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::swallow_psutil_exceptions","text":"src/python/pants/pantsd/process_manager.py::swallow_psutil_exceptions\n@contextmanager\ndef swallow_psutil_exceptions():\n \"\"\"A contextmanager that swallows standard psutil access exceptions.\"\"\"\n try:\n yield\n except (psutil.AccessDenied, psutil.NoSuchProcess):\n # This masks common, but usually benign psutil process access exceptions that might be seen\n # when accessing attributes/methods on psutil.Process objects.\n pass","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessGroup.__init__","text":"src/python/pants/pantsd/process_manager.py::ProcessGroup.__init__\n def __init__(self, name, metadata_base_dir=None):\n self._name = name\n self._metadata_base_dir = metadata_base_dir","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessGroup._instance_from_process","text":"src/python/pants/pantsd/process_manager.py::ProcessGroup._instance_from_process\n def _instance_from_process(self, process):\n \"\"\"Default converter from psutil.Process to process instance classes for subclassing.\"\"\"\n return ProcessManager(name=process.name(),\n pid=process.pid,\n process_name=process.name(),\n metadata_base_dir=self._metadata_base_dir)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_processes","text":"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_processes\n def iter_processes(self, proc_filter=None):\n \"\"\"Yields processes from psutil.process_iter with an optional filter and swallows psutil errors.\n\n If a psutil exception is raised during execution of the filter, that process will not be\n yielded but subsequent processes will. On the other hand, if psutil.process_iter raises\n an exception, no more processes will be yielded.\n \"\"\"\n with swallow_psutil_exceptions(): # process_iter may raise\n for proc in psutil.process_iter():\n with swallow_psutil_exceptions(): # proc_filter may raise\n if (proc_filter is None) or proc_filter(proc):\n yield proc","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_instances","text":"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_instances\n def iter_instances(self, *args, **kwargs):\n for item in self.iter_processes(*args, **kwargs):\n yield self._instance_from_process(item)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.__init__","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.__init__\n def __init__(self, metadata_base_dir=None):\n \"\"\"\n :param str metadata_base_dir: The base directory for process metadata.\n \"\"\"\n super(ProcessMetadataManager, self).__init__()\n\n self._metadata_base_dir = (\n metadata_base_dir or\n Subprocess.Factory.global_instance().create().get_subprocess_dir()\n )","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_cast","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_cast\n @staticmethod\n def _maybe_cast(item, caster):\n \"\"\"Given a casting function, attempt to cast to that type while masking common cast exceptions.\n\n N.B. This is mostly suitable for casting string types to numeric types - e.g. a port number\n read from disk into an int.\n\n :param func caster: A casting callable (e.g. `int`).\n :returns: The result of caster(item) or item if TypeError or ValueError are raised during cast.\n \"\"\"\n try:\n return caster(item)\n except (TypeError, ValueError):\n # N.B. the TypeError catch here (already) protects against the case that caster is None.\n return item","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._deadline_until","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._deadline_until\n @classmethod\n def _deadline_until(cls, closure, timeout, wait_interval=WAIT_INTERVAL_SEC):\n \"\"\"Execute a function/closure repeatedly until a True condition or timeout is met.\n\n :param func closure: the function/closure to execute (should not block for long periods of time\n and must return True on success).\n :param float timeout: the maximum amount of time to wait for a true result from the closure in\n seconds. N.B. this is timing based, so won't be exact if the runtime of\n the closure exceeds the timeout.\n :param float wait_interval: the amount of time to sleep between closure invocations.\n :raises: :class:`ProcessManager.Timeout` on execution timeout.\n \"\"\"\n deadline = time.time() + timeout\n while 1:\n if closure():\n return True\n elif time.time() > deadline:\n raise cls.Timeout('exceeded timeout of {} seconds for {}'.format(timeout, closure))\n elif wait_interval:\n time.sleep(wait_interval)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file\n @classmethod\n def _wait_for_file(cls, filename, timeout=FILE_WAIT_SEC, want_content=True):\n \"\"\"Wait up to timeout seconds for filename to appear with a non-zero size or raise Timeout().\"\"\"\n def file_waiter():\n return os.path.exists(filename) and (not want_content or os.path.getsize(filename))\n\n try:\n return cls._deadline_until(file_waiter, timeout)\n except cls.Timeout:\n # Re-raise with a more helpful exception message.\n raise cls.Timeout('exceeded timeout of {} seconds while waiting for file {} to appear'\n .format(timeout, filename))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file.file_waiter","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file.file_waiter\n def file_waiter():\n return os.path.exists(filename) and (not want_content or os.path.getsize(filename))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._get_metadata_dir_by_name","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._get_metadata_dir_by_name\n def _get_metadata_dir_by_name(self, name):\n \"\"\"Retrieve the metadata dir by name.\n\n This should always live outside of the workdir to survive a clean-all.\n \"\"\"\n return os.path.join(self._metadata_base_dir, name)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_init_metadata_dir_by_name","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_init_metadata_dir_by_name\n def _maybe_init_metadata_dir_by_name(self, name):\n \"\"\"Initialize the metadata directory for a named identity if it doesn't exist.\"\"\"\n safe_mkdir(self._get_metadata_dir_by_name(name))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.read_metadata_by_name","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.read_metadata_by_name\n def read_metadata_by_name(self, name, metadata_key, caster=None):\n \"\"\"Read process metadata using a named identity.\n\n :param string name: The ProcessMetadataManager identity/name (e.g. 'pantsd').\n :param string metadata_key: The metadata key (e.g. 'pid').\n :param func caster: A casting callable to apply to the read value (e.g. `int`).\n \"\"\"\n try:\n file_path = os.path.join(self._get_metadata_dir_by_name(name), metadata_key)\n return self._maybe_cast(read_file(file_path).strip(), caster)\n except (IOError, OSError):\n return None","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.write_metadata_by_name","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.write_metadata_by_name\n def write_metadata_by_name(self, name, metadata_key, metadata_value):\n \"\"\"Write process metadata using a named identity.\n\n :param string name: The ProcessMetadataManager identity/name (e.g. 'pantsd').\n :param string metadata_key: The metadata key (e.g. 'pid').\n :param string metadata_value: The metadata value (e.g. '1729').\n \"\"\"\n self._maybe_init_metadata_dir_by_name(name)\n file_path = os.path.join(self._get_metadata_dir_by_name(name), metadata_key)\n safe_file_dump(file_path, metadata_value)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.await_metadata_by_name","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.await_metadata_by_name\n def await_metadata_by_name(self, name, metadata_key, timeout, caster=None):\n \"\"\"Block up to a timeout for process metadata to arrive on disk.\n\n :param string name: The ProcessMetadataManager identity/name (e.g. 'pantsd').\n :param string metadata_key: The metadata key (e.g. 'pid').\n :param int timeout: The deadline to write metadata.\n :param type caster: A type-casting callable to apply to the read value (e.g. int, str).\n :returns: The value of the metadata key (read from disk post-write).\n :raises: :class:`ProcessMetadataManager.Timeout` on timeout.\n \"\"\"\n file_path = os.path.join(self._get_metadata_dir_by_name(name), metadata_key)\n self._wait_for_file(file_path, timeout=timeout)\n return self.read_metadata_by_name(name, metadata_key, caster)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.purge_metadata_by_name","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.purge_metadata_by_name\n def purge_metadata_by_name(self, name):\n \"\"\"Purge a processes metadata directory.\n\n :raises: `ProcessManager.MetadataError` when OSError is encountered on metadata dir removal.\n \"\"\"\n meta_dir = self._get_metadata_dir_by_name(name)\n logger.debug('purging metadata directory: {}'.format(meta_dir))\n try:\n rm_rf(meta_dir)\n except OSError as e:\n raise self.MetadataError('failed to purge metadata directory {}: {!r}'.format(meta_dir, e))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__init__","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__init__\n def __init__(self, message, output=None):\n super(ProcessManager.ExecutionError, self).__init__(message)\n self.message = message\n self.output = output","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__repr__","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__repr__\n def __repr__(self):\n return '{}(message={!r}, output={!r})'.format(type(self).__name__, self.message, self.output)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.__init__","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.__init__\n def __init__(self, name, pid=None, socket=None, process_name=None, socket_type=int,\n metadata_base_dir=None):\n \"\"\"\n :param string name: The process identity/name (e.g. 'pantsd' or 'ng_Zinc').\n :param int pid: The process pid. Overrides fetching of the self.pid @property.\n :param string socket: The socket metadata. Overrides fetching of the self.socket @property.\n :param string process_name: The process name for cmdline executable name matching.\n :param type socket_type: The type to be used for socket type casting (e.g. int).\n :param str metadata_base_dir: The overridden base directory for process metadata.\n \"\"\"\n super(ProcessManager, self).__init__(metadata_base_dir)\n self._name = name.lower().strip()\n self._pid = pid\n self._socket = socket\n self._socket_type = socket_type\n self._process_name = process_name\n self._buildroot = get_buildroot()\n self._process = None","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.name","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.name\n @property\n def name(self):\n \"\"\"The logical name/label of the process.\"\"\"\n return self._name","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.process_name","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.process_name\n @property\n def process_name(self):\n \"\"\"The logical process name. If defined, this is compared to exe_name for stale pid checking.\"\"\"\n return self._process_name","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.process_lock","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.process_lock\n @memoized_property\n def process_lock(self):\n \"\"\"An identity-keyed inter-process lock for safeguarding lifecycle and other operations.\"\"\"\n safe_mkdir(self._metadata_base_dir)\n return OwnerPrintingInterProcessFileLock(\n # N.B. This lock can't key into the actual named metadata dir (e.g. `.pids/pantsd/lock`\n # via `ProcessMetadataManager._get_metadata_dir_by_name()`) because of a need to purge\n # the named metadata dir on startup to avoid stale metadata reads.\n os.path.join(self._metadata_base_dir, '.lock.{}'.format(self._name))\n )","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.cmdline","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.cmdline\n @property\n def cmdline(self):\n \"\"\"The process commandline. e.g. ['/usr/bin/python2.7', 'pants.pex'].\n\n :returns: The command line or else `None` if the underlying process has died.\n \"\"\"\n with swallow_psutil_exceptions():\n process = self._as_process()\n if process:\n return process.cmdline()\n return None","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.cmd","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.cmd\n @property\n def cmd(self):\n \"\"\"The first element of the process commandline e.g. '/usr/bin/python2.7'.\n\n :returns: The first element of the process command line or else `None` if the underlying\n process has died.\n \"\"\"\n return (self.cmdline or [None])[0]","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.pid","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.pid\n @property\n def pid(self):\n \"\"\"The running processes pid (or None).\"\"\"\n return self._pid or self.read_metadata_by_name(self._name, 'pid', int)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.socket","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.socket\n @property\n def socket(self):\n \"\"\"The running processes socket/port information (or None).\"\"\"\n return self._socket or self.read_metadata_by_name(self._name, 'socket', self._socket_type)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.get_subprocess_output","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.get_subprocess_output\n @classmethod\n def get_subprocess_output(cls, command, ignore_stderr=True, **kwargs):\n \"\"\"Get the output of an executed command.\n\n :param command: An iterable representing the command to execute (e.g. ['ls', '-al']).\n :param ignore_stderr: Whether or not to ignore stderr output vs interleave it with stdout.\n :raises: `ProcessManager.ExecutionError` on `OSError` or `CalledProcessError`.\n :returns: The output of the command.\n \"\"\"\n if ignore_stderr is False:\n kwargs.setdefault('stderr', subprocess.STDOUT)\n\n try:\n return subprocess.check_output(command, **kwargs)\n except (OSError, subprocess.CalledProcessError) as e:\n subprocess_output = getattr(e, 'output', '').strip()\n raise cls.ExecutionError(str(e), subprocess_output)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.await_pid","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.await_pid\n def await_pid(self, timeout):\n \"\"\"Wait up to a given timeout for a process to write pid metadata.\"\"\"\n return self.await_metadata_by_name(self._name, 'pid', timeout, int)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.await_socket","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.await_socket\n def await_socket(self, timeout):\n \"\"\"Wait up to a given timeout for a process to write socket info.\"\"\"\n return self.await_metadata_by_name(self._name, 'socket', timeout, self._socket_type)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.write_pid","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.write_pid\n def write_pid(self, pid=None):\n \"\"\"Write the current processes PID to the pidfile location\"\"\"\n pid = pid or os.getpid()\n self.write_metadata_by_name(self._name, 'pid', str(pid))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.write_socket","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.write_socket\n def write_socket(self, socket_info):\n \"\"\"Write the local processes socket information (TCP port or UNIX socket).\"\"\"\n self.write_metadata_by_name(self._name, 'socket', str(socket_info))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.write_named_socket","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.write_named_socket\n def write_named_socket(self, socket_name, socket_info):\n \"\"\"A multi-tenant, named alternative to ProcessManager.write_socket().\"\"\"\n self.write_metadata_by_name(self._name, 'socket_{}'.format(socket_name), str(socket_info))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.read_named_socket","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.read_named_socket\n def read_named_socket(self, socket_name, socket_type):\n \"\"\"A multi-tenant, named alternative to ProcessManager.socket.\"\"\"\n return self.read_metadata_by_name(self._name, 'socket_{}'.format(socket_name), socket_type)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager._as_process","text":"src/python/pants/pantsd/process_manager.py::ProcessManager._as_process\n def _as_process(self):\n \"\"\"Returns a psutil `Process` object wrapping our pid.\n\n NB: Even with a process object in hand, subsequent method calls against it can always raise\n `NoSuchProcess`. Care is needed to document the raises in the public API or else trap them and\n do something sensible for the API.\n\n :returns: a psutil Process object or else None if we have no pid.\n :rtype: :class:`psutil.Process`\n :raises: :class:`psutil.NoSuchProcess` if the process identified by our pid has died.\n \"\"\"\n if self._process is None and self.pid:\n self._process = psutil.Process(self.pid)\n return self._process","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.is_dead","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.is_dead\n def is_dead(self):\n \"\"\"Return a boolean indicating whether the process is dead or not.\"\"\"\n return not self.is_alive()","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.is_alive","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.is_alive\n def is_alive(self, extended_check=None):\n \"\"\"Return a boolean indicating whether the process is running or not.\n\n :param func extended_check: An additional callable that will be invoked to perform an extended\n liveness check. This callable should take a single argument of a\n `psutil.Process` instance representing the context-local process\n and return a boolean True/False to indicate alive vs not alive.\n \"\"\"\n try:\n process = self._as_process()\n return not (\n # Can happen if we don't find our pid.\n (not process) or\n # Check for walkers.\n (process.status() == psutil.STATUS_ZOMBIE) or\n # Check for stale pids.\n (self.process_name and self.process_name != process.name()) or\n # Extended checking.\n (extended_check and not extended_check(process))\n )\n except (psutil.NoSuchProcess, psutil.AccessDenied):\n # On some platforms, accessing attributes of a zombie'd Process results in NoSuchProcess.\n return False","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.purge_metadata","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.purge_metadata\n def purge_metadata(self, force=False):\n \"\"\"Instance-based version of ProcessMetadataManager.purge_metadata_by_name() that checks\n for process liveness before purging metadata.\n\n :param bool force: If True, skip process liveness check before purging metadata.\n :raises: `ProcessManager.MetadataError` when OSError is encountered on metadata dir removal.\n \"\"\"\n if not force and self.is_alive():\n raise self.MetadataError('cannot purge metadata for a running process!')\n\n super(ProcessManager, self).purge_metadata_by_name(self._name)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager._kill","text":"src/python/pants/pantsd/process_manager.py::ProcessManager._kill\n def _kill(self, kill_sig):\n \"\"\"Send a signal to the current process.\"\"\"\n if self.pid:\n os.kill(self.pid, kill_sig)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.terminate","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.terminate\n def terminate(self, signal_chain=KILL_CHAIN, kill_wait=KILL_WAIT_SEC, purge=True):\n \"\"\"Ensure a process is terminated by sending a chain of kill signals (SIGTERM, SIGKILL).\"\"\"\n alive = self.is_alive()\n if alive:\n logger.debug('terminating {}'.format(self._name))\n for signal_type in signal_chain:\n pid = self.pid\n try:\n logger.debug('sending signal {} to pid {}'.format(signal_type, pid))\n self._kill(signal_type)\n except OSError as e:\n logger.warning('caught OSError({e!s}) during attempt to kill -{signal} {pid}!'\n .format(e=e, signal=signal_type, pid=pid))\n\n # Wait up to kill_wait seconds to terminate or move onto the next signal.\n try:\n if self._deadline_until(self.is_dead, kill_wait):\n alive = False\n logger.debug('successfully terminated pid {}'.format(pid))\n break\n except self.Timeout:\n # Loop to the next kill signal on timeout.\n pass\n\n if alive:\n raise self.NonResponsiveProcess('failed to kill pid {pid} with signals {chain}'\n .format(pid=self.pid, chain=signal_chain))\n\n if purge:\n self.purge_metadata(force=True)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.daemonize","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.daemonize\n def daemonize(self, pre_fork_opts=None, post_fork_parent_opts=None, post_fork_child_opts=None,\n write_pid=True):\n \"\"\"Perform a double-fork, execute callbacks and write the child pid file.\n\n The double-fork here is necessary to truly daemonize the subprocess such that it can never\n take control of a tty. The initial fork and setsid() creates a new, isolated process group\n and also makes the first child a session leader (which can still acquire a tty). By forking a\n second time, we ensure that the second child can never acquire a controlling terminal because\n it's no longer a session leader - but it now has its own separate process group.\n\n Additionally, a normal daemon implementation would typically perform an os.umask(0) to reset\n the processes file mode creation mask post-fork. We do not do this here (and in daemon_spawn\n below) due to the fact that the daemons that pants would run are typically personal user\n daemons. Having a disparate umask from pre-vs-post fork causes files written in each phase to\n differ in their permissions without good reason - in this case, we want to inherit the umask.\n \"\"\"\n self.purge_metadata()\n self.pre_fork(**pre_fork_opts or {})\n logger.debug('forking %s', self)\n pid = os.fork()\n if pid == 0:\n os.setsid()\n second_pid = os.fork()\n if second_pid == 0:\n try:\n os.chdir(self._buildroot)\n self.post_fork_child(**post_fork_child_opts or {})\n except Exception:\n logger.critical(traceback.format_exc())\n finally:\n os._exit(0)\n else:\n try:\n if write_pid: self.write_pid(second_pid)\n self.post_fork_parent(**post_fork_parent_opts or {})\n except Exception:\n logger.critical(traceback.format_exc())\n finally:\n os._exit(0)\n else:\n # This prevents un-reaped, throw-away parent proce","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman.__init__","text":"src/python/pants/pantsd/watchman.py::Watchman.__init__\n def __init__(self, watchman_path, work_dir, log_level='1', startup_timeout=STARTUP_TIMEOUT_SECONDS,\n timeout=SOCKET_TIMEOUT_SECONDS, socket_path_override=None, metadata_base_dir=None):\n \"\"\"\n :param str watchman_path: The path to the watchman binary.\n :param str work_dir: The path to the pants work dir.\n :param float startup_timeout: The timeout for the initial `watch-project` query (in seconds).\n :param float timeout: The watchman socket timeout for all subsequent queries (in seconds).\n :param str log_level: The watchman log level. Watchman has 3 log levels: '0' for no logging,\n '1' for standard logging and '2' for verbose logging.\n :param str socket_path_override: The overridden target path of the watchman socket, if any.\n :param str metadata_base_dir: The overriden metadata base dir for `ProcessMetadataManager`.\n \"\"\"\n super(Watchman, self).__init__(name='watchman',\n process_name='watchman',\n socket_type=str,\n metadata_base_dir=metadata_base_dir)\n self._watchman_path = self._normalize_watchman_path(watchman_path)\n self._watchman_work_dir = os.path.join(work_dir, self.name)\n self._log_level = log_level\n self._startup_timeout = startup_timeout\n self._timeout = timeout\n\n self._state_file = os.path.join(self._watchman_work_dir, '{}.state'.format(self.name))\n self._log_file = os.path.join(self._watchman_work_dir, '{}.log'.format(self.name))\n self._sock_file = socket_path_override or os.path.join(self._watchman_work_dir,\n '{}.sock'.format(self.name))\n\n self._logger = logging.getLogger(__name__)\n self._watchman_client = None","label":1},{"cid":"src/python/pants/pantsd/watchman.py::Watchman.client","text":"src/python/pants/pantsd/watchman.py::Watchman.client\n @property\n def client(self):\n if not self._watchman_client:\n self._watchman_client = self._make_client()\n return self._watchman_client","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._make_client","text":"src/python/pants/pantsd/watchman.py::Watchman._make_client\n def _make_client(self):\n \"\"\"Create a new watchman client using the BSER protocol over a UNIX socket.\"\"\"\n self._logger.debug('setting initial watchman timeout to %s', self._startup_timeout)\n return StreamableWatchmanClient(sockpath=self.socket,\n transport='local',\n timeout=self._startup_timeout)","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._is_valid_executable","text":"src/python/pants/pantsd/watchman.py::Watchman._is_valid_executable\n def _is_valid_executable(self, binary_path):\n return os.path.isfile(binary_path) and os.access(binary_path, os.X_OK)","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._normalize_watchman_path","text":"src/python/pants/pantsd/watchman.py::Watchman._normalize_watchman_path\n def _normalize_watchman_path(self, watchman_path):\n if not self._is_valid_executable(watchman_path):\n raise self.ExecutionError('invalid watchman binary at {}!'.format(watchman_path))\n return os.path.abspath(watchman_path)","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._maybe_init_metadata","text":"src/python/pants/pantsd/watchman.py::Watchman._maybe_init_metadata\n def _maybe_init_metadata(self):\n safe_mkdir(self._watchman_work_dir)\n # Initialize watchman with an empty, but valid statefile so it doesn't complain on startup.\n safe_file_dump(self._state_file, '{}')","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._construct_cmd","text":"src/python/pants/pantsd/watchman.py::Watchman._construct_cmd\n def _construct_cmd(self, cmd_parts, state_file, sock_file, log_file, log_level):\n return [part for part in cmd_parts] + ['--no-save-state',\n '--statefile={}'.format(state_file),\n '--sockname={}'.format(sock_file),\n '--logfile={}'.format(log_file),\n '--log-level', log_level]","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._parse_pid_from_output","text":"src/python/pants/pantsd/watchman.py::Watchman._parse_pid_from_output\n def _parse_pid_from_output(self, output):\n try:\n # Parse the watchman pidfile from the cli output (in JSON).\n return json.loads(output)['pid']\n except ValueError:\n # JSON parse failure.\n self._logger.critical('invalid output from watchman!\\n{output!s}'.format(output=output))\n raise self.InvalidCommandOutput(output)\n except KeyError:\n # Key access error on 'pid' - bad output from watchman.\n self._logger.critical('no pid from watchman!')\n raise self.InvalidCommandOutput(output)","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman.launch","text":"src/python/pants/pantsd/watchman.py::Watchman.launch\n def launch(self):\n \"\"\"Launch and synchronously write metadata.\n\n This is possible due to watchman's built-in async server startup - no double-forking required.\n \"\"\"\n cmd = self._construct_cmd((self._watchman_path, 'get-pid'),\n state_file=self._state_file,\n sock_file=self._sock_file,\n log_file=self._log_file,\n log_level=str(self._log_level))\n self._logger.debug('watchman cmd is: {}'.format(' '.join(cmd)))\n self._maybe_init_metadata()\n\n # Watchman is launched via its cli. By running the 'get-pid' command on the client we implicitly\n # launch the Watchman daemon. This approach is somewhat error-prone - in some cases the client\n # can successfully trigger the launch of the Watchman daemon, but fail to return successfully\n # for the 'get-pid' result due to server <-> daemon socket issues - these can look like:\n #\n # 2016-04-01T17:31:23,820: [cli] unable to talk to your watchman\n # on .../watchman.sock! (Permission denied)\n #\n # This results in a subprocess execution failure and leaves us with no pid information to write\n # to the metadata directory - while in reality a Watchman daemon is actually running but now\n # untracked. To safeguard against this, we retry the (idempotent) 'get-pid' command a few times\n # to give the server-side socket setup a few chances to quiesce before potentially orphaning it.\n\n get_output = functools.partial(self.get_subprocess_output, cmd)\n output = retry_on_exception(get_output, 3, (self.ExecutionError,), lambda n: n * .5)\n\n # Parse the watchman PID from the cli output.\n pid = self._parse_pid_from_output(output)\n\n # Write the process metadata to disk.\n self.write_pid(pid)\n self.write_socket(self._sock_file)","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman._attempt_set_timeout","text":"src/python/pants/pantsd/watchman.py::Watchman._attempt_set_timeout\n def _attempt_set_timeout(self, timeout):\n \"\"\"Sets a timeout on the inner watchman client's socket.\"\"\"\n try:\n self.client.setTimeout(timeout)\n except Exception:\n self._logger.debug('failed to set post-startup watchman timeout to %s', self._timeout)\n else:\n self._logger.debug('set post-startup watchman timeout to %s', self._timeout)","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman.watch_project","text":"src/python/pants/pantsd/watchman.py::Watchman.watch_project\n def watch_project(self, path):\n \"\"\"Issues the watch-project command to watchman to begin watching the buildroot.\n\n :param string path: the path to the watchman project root/pants build root.\n \"\"\"\n # TODO(kwlzn): Add a client.query(timeout=X) param to the upstream pywatchman project.\n try:\n return self.client.query('watch-project', os.path.realpath(path))\n finally:\n self._attempt_set_timeout(self._timeout)","label":0},{"cid":"src/python/pants/pantsd/watchman.py::Watchman.subscribed","text":"src/python/pants/pantsd/watchman.py::Watchman.subscribed\n def subscribed(self, build_root, handlers):\n \"\"\"Bulk subscribe generator for StreamableWatchmanClient.\n\n :param str build_root: the build_root for all subscriptions.\n :param iterable handlers: a sequence of Watchman.EventHandler namedtuple objects.\n :yields: a stream of tuples in the form (subscription_name: str, subscription_event: dict).\n \"\"\"\n command_list = [['subscribe',\n build_root,\n handler.name,\n handler.metadata] for handler in handlers]\n\n self._logger.debug('watchman command_list is: {}'.format(command_list))\n\n try:\n for event in self.client.stream_query(command_list):\n if event is None:\n yield None, None\n elif 'subscribe' in event:\n self._logger.info('confirmed watchman subscription: {}'.format(event))\n yield None, None\n elif 'subscription' in event:\n yield event.get('subscription'), event\n else:\n self._logger.warning('encountered non-subscription event: {}'.format(event))\n except self.client.WatchmanError as e:\n raise self.WatchmanCrash(e)","label":0},{"cid":"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.create","text":"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.create\n @classmethod\n def create(cls, bootstrap_options):\n \"\"\"\n :param Options bootstrap_options: The bootstrap options bag.\n \"\"\"\n binary_util = BinaryUtil(\n bootstrap_options.binaries_baseurls,\n bootstrap_options.binaries_fetch_timeout_secs,\n bootstrap_options.pants_bootstrapdir,\n bootstrap_options.binaries_path_by_id\n )\n\n return WatchmanLauncher(\n binary_util,\n bootstrap_options.pants_workdir,\n bootstrap_options.level,\n bootstrap_options.watchman_version,\n bootstrap_options.watchman_supportdir,\n bootstrap_options.watchman_startup_timeout,\n bootstrap_options.watchman_socket_timeout,\n bootstrap_options.watchman_socket_path,\n bootstrap_options.pants_subprocessdir\n )","label":1},{"cid":"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.__init__","text":"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.__init__\n def __init__(self, binary_util, workdir, log_level, watchman_version, watchman_supportdir,\n startup_timeout, socket_timeout, socket_path_override=None, metadata_base_dir=None):\n \"\"\"\n :param binary_util: The BinaryUtil subsystem instance for binary retrieval.\n :param workdir: The current pants workdir.\n :param log_level: The current log level of pants.\n :param watchman_version: The watchman binary version to retrieve using BinaryUtil.\n :param watchman_supportdir: The supportdir for BinaryUtil.\n :param socket_timeout: The watchman client socket timeout (in seconds).\n :param socket_path_override: The overridden target path of the watchman socket, if any.\n :param metadata_base_dir: The ProcessManager metadata base directory.\n \"\"\"\n self._binary_util = binary_util\n self._workdir = workdir\n self._watchman_version = watchman_version\n self._watchman_supportdir = watchman_supportdir\n self._startup_timeout = startup_timeout\n self._socket_timeout = socket_timeout\n self._socket_path_override = socket_path_override\n self._log_level = log_level\n self._logger = logging.getLogger(__name__)\n self._metadata_base_dir = metadata_base_dir","label":1},{"cid":"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher._convert_log_level","text":"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher._convert_log_level\n @staticmethod\n def _convert_log_level(level):\n \"\"\"Convert a given pants log level string into a watchman log level string.\"\"\"\n # N.B. Enabling true Watchman debug logging (log level 2) can generate an absurd amount of log\n # data (10s of gigabytes over the course of an ~hour for an active fs) and is not particularly\n # helpful except for debugging Watchman itself. Thus, here we intentionally avoid this level\n # in the mapping of pants log level -> watchman.\n return {'warn': '0', 'info': '1', 'debug': '1'}.get(level, '1')","label":0},{"cid":"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.watchman","text":"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.watchman\n @testable_memoized_property\n def watchman(self):\n watchman_binary = self._binary_util.select_binary(self._watchman_supportdir,\n self._watchman_version,\n 'watchman')\n return Watchman(\n watchman_binary,\n self._workdir,\n self._convert_log_level(self._log_level),\n self._startup_timeout,\n self._socket_timeout,\n self._socket_path_override,\n metadata_base_dir=self._metadata_base_dir\n )","label":1},{"cid":"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.maybe_launch","text":"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.maybe_launch\n def maybe_launch(self):\n if not self.watchman.is_alive():\n self._logger.debug('launching watchman')\n try:\n self.watchman.launch()\n except (self.watchman.ExecutionError, self.watchman.InvalidCommandOutput) as e:\n self._logger.fatal('failed to launch watchman: {!r})'.format(e))\n raise\n\n self._logger.debug('watchman is running, pid={pid} socket={socket}'\n .format(pid=self.watchman.pid, socket=self.watchman.socket))\n\n return self.watchman","label":0},{"cid":"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.terminate","text":"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.terminate\n def terminate(self):\n self.watchman.terminate()","label":0},{"cid":"src/python/pants/scm/git.py::Git.detect_worktree","text":"src/python/pants/scm/git.py::Git.detect_worktree\n @classmethod\n def detect_worktree(cls, binary='git', subdir=None):\n \"\"\"Detect the git working tree above cwd and return it; else, return None.\n\n :param string binary: The path to the git binary to use, 'git' by default.\n :param string subdir: The path to start searching for a git repo.\n :returns: path to the directory where the git working tree is rooted.\n :rtype: string\n \"\"\"\n # TODO(John Sirois): This is only used as a factory for a Git instance in\n # pants.base.build_environment.get_scm, encapsulate in a true factory method.\n cmd = [binary, 'rev-parse', '--show-toplevel']\n try:\n if subdir:\n with pushd(subdir):\n process, out = cls._invoke(cmd)\n else:\n process, out = cls._invoke(cmd)\n cls._check_result(cmd, process.returncode, raise_type=Scm.ScmException)\n except Scm.ScmException:\n return None\n return cls._cleanse(out)","label":0},{"cid":"src/python/pants/scm/git.py::Git.clone","text":"src/python/pants/scm/git.py::Git.clone\n @classmethod\n def clone(cls, repo_url, dest, binary='git'):\n \"\"\"Clone the repo at repo_url into dest.\n\n :param string binary: The path to the git binary to use, 'git' by default.\n :returns: an instance of this class representing the cloned repo.\n :rtype: Git\n \"\"\"\n cmd = [binary, 'clone', repo_url, dest]\n process, out = cls._invoke(cmd)\n cls._check_result(cmd, process.returncode)\n return cls(binary=binary, worktree=dest)","label":0},{"cid":"src/python/pants/scm/git.py::Git._invoke","text":"src/python/pants/scm/git.py::Git._invoke\n @classmethod\n def _invoke(cls, cmd):\n \"\"\"Invoke the given command, and return a tuple of process and raw binary output.\n\n stderr flows to wherever its currently mapped for the parent process - generally to\n the terminal where the user can see the error.\n\n :param list cmd: The command in the form of a list of strings\n :returns: The completed process object and its standard output.\n :raises: Scm.LocalException if there was a problem exec'ing the command at all.\n \"\"\"\n try:\n process = subprocess.Popen(cmd, stdout=subprocess.PIPE)\n except OSError as e:\n # Binary DNE or is not executable\n raise cls.LocalException('Failed to execute command {}: {}'.format(' '.join(cmd), e))\n out, _ = process.communicate()\n return process, out","label":0},{"cid":"src/python/pants/scm/git.py::Git._cleanse","text":"src/python/pants/scm/git.py::Git._cleanse\n @classmethod\n def _cleanse(cls, output, errors='strict'):\n return output.strip().decode('utf-8', errors=errors)","label":0},{"cid":"src/python/pants/scm/git.py::Git._check_result","text":"src/python/pants/scm/git.py::Git._check_result\n @classmethod\n def _check_result(cls, cmd, result, failure_msg=None, raise_type=Scm.ScmException):\n if result != 0:\n raise raise_type(failure_msg or '{} failed with exit code {}'.format(' '.join(cmd), result))","label":0},{"cid":"src/python/pants/scm/git.py::Git.__init__","text":"src/python/pants/scm/git.py::Git.__init__\n def __init__(self, binary='git', gitdir=None, worktree=None, remote=None, branch=None):\n \"\"\"Creates a git scm proxy that assumes the git repository is in the cwd by default.\n\n binary: The path to the git binary to use, 'git' by default.\n gitdir: The path to the repository's git metadata directory (typically '.git').\n worktree: The path to the git repository working tree directory (typically '.').\n remote: The default remote to use.\n branch: The default remote branch to use.\n \"\"\"\n super(Scm, self).__init__()\n\n self._gitcmd = binary\n self._worktree = os.path.realpath(worktree or os.getcwd())\n self._gitdir = os.path.realpath(gitdir) if gitdir else os.path.join(self._worktree, '.git')\n self._remote = remote\n self._branch = branch","label":0},{"cid":"src/python/pants/scm/git.py::Git.current_rev_identifier","text":"src/python/pants/scm/git.py::Git.current_rev_identifier\n def current_rev_identifier(self):\n return 'HEAD'","label":0},{"cid":"src/python/pants/scm/git.py::Git.worktree","text":"src/python/pants/scm/git.py::Git.worktree\n @property\n def worktree(self):\n return self._worktree","label":0},{"cid":"src/python/pants/scm/git.py::Git.commit_id","text":"src/python/pants/scm/git.py::Git.commit_id\n @property\n def commit_id(self):\n return self._check_output(['rev-parse', 'HEAD'], raise_type=Scm.LocalException)","label":0},{"cid":"src/python/pants/scm/git.py::Git.server_url","text":"src/python/pants/scm/git.py::Git.server_url\n @property\n def server_url(self):\n git_output = self._check_output(['remote', '--verbose'], raise_type=Scm.LocalException)\n\n def origin_urls():\n for line in git_output.splitlines():\n name, url, action = line.split()\n if name == 'origin' and action == '(push)':\n yield url\n\n origins = list(origin_urls())\n if len(origins) != 1:\n raise Scm.LocalException(\"Unable to find remote named 'origin' that accepts pushes \"\n \"amongst:\\n{}\".format(git_output))\n return origins[0]","label":0},{"cid":"src/python/pants/scm/git.py::Git.server_url.origin_urls","text":"src/python/pants/scm/git.py::Git.server_url.origin_urls\n def origin_urls():\n for line in git_output.splitlines():\n name, url, action = line.split()\n if name == 'origin' and action == '(push)':\n yield url","label":0},{"cid":"src/python/pants/scm/git.py::Git.tag_name","text":"src/python/pants/scm/git.py::Git.tag_name\n @property\n def tag_name(self):\n # Calls to git describe can have bad performance on large repos. Be aware\n # of the performance hit if you use this property.\n tag = self._check_output(['describe', '--tags', '--always'], raise_type=Scm.LocalException)\n return None if b'cannot' in tag else tag","label":0},{"cid":"src/python/pants/scm/git.py::Git.branch_name","text":"src/python/pants/scm/git.py::Git.branch_name\n @property\n def branch_name(self):\n branch = self._check_output(['rev-parse', '--abbrev-ref', 'HEAD'],\n raise_type=Scm.LocalException)\n return None if branch == 'HEAD' else branch","label":0},{"cid":"src/python/pants/scm/git.py::Git.fix_git_relative_path","text":"src/python/pants/scm/git.py::Git.fix_git_relative_path\n def fix_git_relative_path(self, worktree_path, relative_to):\n return os.path.relpath(os.path.join(self._worktree, worktree_path), relative_to)","label":0},{"cid":"src/python/pants/scm/git.py::Git.changed_files","text":"src/python/pants/scm/git.py::Git.changed_files\n def changed_files(self, from_commit=None, include_untracked=False, relative_to=None):\n relative_to = relative_to or self._worktree\n rel_suffix = ['--', relative_to]\n uncommitted_changes = self._check_output(['diff', '--name-only', 'HEAD'] + rel_suffix,\n raise_type=Scm.LocalException)\n\n files = set(uncommitted_changes.split())\n if from_commit:\n # Grab the diff from the merge-base to HEAD using ... syntax. This ensures we have just\n # the changes that have occurred on the current branch.\n committed_cmd = ['diff', '--name-only', from_commit + '...HEAD'] + rel_suffix\n committed_changes = self._check_output(committed_cmd,\n raise_type=Scm.LocalException)\n files.update(committed_changes.split())\n if include_untracked:\n untracked_cmd = ['ls-files', '--other', '--exclude-standard'] + rel_suffix\n untracked = self._check_output(untracked_cmd,\n raise_type=Scm.LocalException)\n files.update(untracked.split())\n # git will report changed files relative to the worktree: re-relativize to relative_to\n return set(self.fix_git_relative_path(f, relative_to) for f in files)","label":0},{"cid":"src/python/pants/scm/git.py::Git.changes_in","text":"src/python/pants/scm/git.py::Git.changes_in\n def changes_in(self, diffspec, relative_to=None):\n relative_to = relative_to or self._worktree\n cmd = ['diff-tree', '--no-commit-id', '--name-only', '-r', diffspec]\n files = self._check_output(cmd, raise_type=Scm.LocalException).split()\n return set(self.fix_git_relative_path(f.strip(), relative_to) for f in files)","label":0},{"cid":"src/python/pants/scm/git.py::Git.changelog","text":"src/python/pants/scm/git.py::Git.changelog\n def changelog(self, from_commit=None, files=None):\n # We force the log output encoding to be UTF-8 here since the user may have a git config that\n # overrides the git UTF-8 default log output encoding.\n args = ['log', '--encoding=UTF-8', '--no-merges', '--stat', '--find-renames', '--find-copies']\n if from_commit:\n args.append(from_commit + '..HEAD')\n if files:\n args.append('--')\n args.extend(files)\n\n # There are various circumstances that can lead to git logs that are not transcodeable to utf-8,\n # for example: http://comments.gmane.org/gmane.comp.version-control.git/262685\n # Git will not error in these cases and we do not wish to either. Here we direct byte sequences\n # that can not be utf-8 decoded to be replaced with the utf-8 replacement character.\n return self._check_output(args, raise_type=Scm.LocalException, errors='replace')","label":0},{"cid":"src/python/pants/scm/git.py::Git.merge_base","text":"src/python/pants/scm/git.py::Git.merge_base\n def merge_base(self, left='master', right='HEAD'):\n \"\"\"Returns the merge-base of master and HEAD in bash: `git merge-base left right`\"\"\"\n return self._check_output(['merge-base', left, right], raise_type=Scm.LocalException)","label":0},{"cid":"src/python/pants/scm/git.py::Git.refresh","text":"src/python/pants/scm/git.py::Git.refresh\n def refresh(self, leave_clean=False):\n \"\"\"Attempt to pull-with-rebase from upstream. This is implemented as fetch-plus-rebase\n so that we can distinguish between errors in the fetch stage (likely network errors)\n and errors in the rebase stage (conflicts). If leave_clean is true, then in the event\n of a rebase failure, the branch will be rolled back. Otherwise, it will be left in the\n conflicted state.\n \"\"\"\n remote, merge = self._get_upstream()\n self._check_call(['fetch', '--tags', remote, merge], raise_type=Scm.RemoteException)\n try:\n self._check_call(['rebase', 'FETCH_HEAD'], raise_type=Scm.LocalException)\n except Scm.LocalException as e:\n if leave_clean:\n logger.debug('Cleaning up after failed rebase')\n try:\n self._check_call(['rebase', '--abort'], raise_type=Scm.LocalException)\n except Scm.LocalException as abort_exc:\n logger.debug('Failed to up after failed rebase')\n logger.debug(traceback.format_exc(abort_exc))\n # But let the original exception propagate, since that's the more interesting one\n raise e","label":0},{"cid":"src/python/pants/scm/git.py::Git.tag","text":"src/python/pants/scm/git.py::Git.tag\n def tag(self, name, message=None):\n # We use -a here instead of --annotate to maintain maximum git compatibility.\n # --annotate was only introduced in 1.7.8 via:\n # https://github.com/git/git/commit/c97eff5a95d57a9561b7c7429e7fcc5d0e3a7f5d\n self._check_call(['tag', '-a', '--message=' + (message or ''), name],\n raise_type=Scm.LocalException)\n self.push('refs/tags/' + name)","label":0},{"cid":"src/python/pants/scm/git.py::Git.commit","text":"src/python/pants/scm/git.py::Git.commit\n def commit(self, message):\n self._check_call(['commit', '--all', '--message=' + message], raise_type=Scm.LocalException)","label":0},{"cid":"src/python/pants/scm/git.py::Git.add","text":"src/python/pants/scm/git.py::Git.add\n def add(self, *paths):\n self._check_call(['add'] + list(paths), raise_type=Scm.LocalException)","label":0},{"cid":"src/python/pants/scm/git.py::Git.commit_date","text":"src/python/pants/scm/git.py::Git.commit_date\n def commit_date(self, commit_reference):\n return self._check_output(['log', '-1', '--pretty=tformat:%ci', commit_reference],\n raise_type=Scm.LocalException)","label":0},{"cid":"src/python/pants/scm/git.py::Git.push","text":"src/python/pants/scm/git.py::Git.push\n def push(self, *refs):\n remote, merge = self._get_upstream()\n self._check_call(['push', remote, merge] + list(refs), raise_type=Scm.RemoteException)","label":0},{"cid":"src/python/pants/scm/git.py::Git.set_state","text":"src/python/pants/scm/git.py::Git.set_state\n def set_state(self, rev):\n self._check_call(['checkout', rev])","label":0},{"cid":"src/python/pants/scm/git.py::Git._get_upstream","text":"src/python/pants/scm/git.py::Git._get_upstream\n def _get_upstream(self):\n \"\"\"Return the remote and remote merge branch for the current branch\"\"\"\n if not self._remote or not self._branch:\n branch = self.branch_name\n if not branch:\n raise Scm.LocalException('Failed to determine local branch')\n\n def get_local_config(key):\n value = self._check_output(['config', '--local', '--get', key],\n raise_type=Scm.LocalException)\n return value.strip()\n\n self._remote = self._remote or get_local_config('branch.{}.remote'.format(branch))\n self._branch = self._branch or get_local_config('branch.{}.merge'.format(branch))\n return self._remote, self._branch","label":0},{"cid":"src/python/pants/scm/git.py::Git._check_call","text":"src/python/pants/scm/git.py::Git._check_call\n def _check_call(self, args, failure_msg=None, raise_type=None):\n cmd = self._create_git_cmdline(args)\n self._log_call(cmd)\n result = subprocess.call(cmd)\n self._check_result(cmd, result, failure_msg, raise_type)","label":0},{"cid":"src/python/pants/scm/git.py::Git._check_output","text":"src/python/pants/scm/git.py::Git._check_output\n def _check_output(self, args, failure_msg=None, raise_type=None, errors='strict'):\n cmd = self._create_git_cmdline(args)\n self._log_call(cmd)\n\n process, out = self._invoke(cmd)\n\n self._check_result(cmd, process.returncode, failure_msg, raise_type)\n return self._cleanse(out, errors=errors)","label":0},{"cid":"src/python/pants/scm/git.py::Git._create_git_cmdline","text":"src/python/pants/scm/git.py::Git._create_git_cmdline\n def _create_git_cmdline(self, args):\n return [self._gitcmd, '--git-dir=' + self._gitdir, '--work-tree=' + self._worktree] + args","label":0},{"cid":"src/python/pants/scm/git.py::Git._log_call","text":"src/python/pants/scm/git.py::Git._log_call\n def _log_call(self, cmd):\n logger.debug('Executing: ' + ' '.join(cmd))","label":0},{"cid":"src/python/pants/scm/git.py::Git.repo_reader","text":"src/python/pants/scm/git.py::Git.repo_reader\n def repo_reader(self, rev):\n return GitRepositoryReader(self, rev)","label":0},{"cid":"src/python/pants/scm/git.py::GitRepositoryReader.__init__","text":"src/python/pants/scm/git.py::GitRepositoryReader.__init__\n def __init__(self, scm, rev):\n self.scm = scm\n self.rev = rev\n self._cat_file_process = None\n # Trees is a dict from path to [list of Dir, Symlink or File objects]\n self._trees = {}\n self._realpath_cache = {'.': './', '': './'}","label":0},{"cid":"src/python/pants/scm/git.py::GitRepositoryReader._maybe_start_cat_file_process","text":"src/python/pants/scm/git.py::GitRepositoryReader._maybe_start_cat_file_process\n def _maybe_start_cat_file_process(self):\n if not self._cat_file_process:\n cmdline = self.scm._create_git_cmdline(['cat-file', '--batch'])\n self._cat_file_process = subprocess.Popen(cmdline,\n stdin=subprocess.PIPE, stdout=subprocess.PIPE)","label":0},{"cid":"src/python/pants/scm/git.py::GitRepositoryReader.MissingFileException.__init__","text":"src/python/pants/scm/git.py::GitRepositoryReader.MissingFileException.__init__\n def __init__(self, rev, relpath):\n self.relpath = relpath\n self.rev = rev","label":0},{"cid":"src/python/pants/scm/git.py::GitRepositoryReader.MissingFileException.__str__","text":"src/python/pants/scm/git.py::GitRepositoryReader.MissingFileException.__str__\n def __str__(self):\n return \"MissingFileException({}, {})\".format(self.relpath, self.rev)","label":0},{"cid":"src/python/pants/scm/git.py::GitRepositoryReader.IsDirException.__init__","text":"src/python/pants/scm/git.py::GitRepositoryReader.IsDirException.__init__\n def __init__(self, rev, relpath):\n self.relpath = relpath\n self.rev = rev","label":0},{"cid":"src/python/pants/scm/git.py::GitRepositoryReader.IsDirException.__str__","text":"src/python/pants/scm/git.py::GitRepositoryReader.IsDirException.__str__\n def __str__(self):\n return \"IsDirException({}, {})\".format(self.relpath, self.rev)","label":0},{"cid":"src/python/pants/scm/git.py::GitRepositoryReader.NotADirException.__init__","text":"src/python/pants/scm/git.py::GitRepositoryReader.NotADirException.__init__\n def __init__(self, rev, relpath):\n self.relpath = relpath\n self.rev = rev","label":0},{"cid":"src/python/pants/scm/git.py::GitRepositoryReader.NotADirException.__str__","text":"src/python/pants/scm/git.py::GitRepositoryReader.NotADirException.__str__\n def __str__(self):\n return \"NotADirException({}, {})\".format(self.relpath, self.rev)","label":0},{"cid":"src/python/pants/scm/git.py::GitRepositoryReader.SymlinkLoopException.__init__","text":"src/python/pants/scm/git.py::GitRepositoryReader.SymlinkLoopException.__init__\n def __init__(self, rev, relpath):\n self.relpath = relpath\n self.rev = rev","label":0}],"string":"[\n {\n \"cid\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.__init__\",\n \"text\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.__init__\\n def __init__(self, import_path):\\n self._import_path = import_path\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.import_path\",\n \"text\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.import_path\\n @property\\n def import_path(self):\\n return self._import_path\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.root\",\n \"text\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.root\\n @abstractmethod\\n def root(self):\\n \\\"\\\"\\\"Returns the root of this fetcher's remote import_path.\\n\\n The root is defined as the portion of the remote import path indicating the associated\\n package's remote location; ie: for the remote import path of\\n 'github.com/docker/docker/daemon/events' it would be 'github.com/docker/docker'.\\n\\n Many remote import paths may share the same root; ie: all the 20+ docker packages hosted at\\n https://github.com/docker/docker share the 'github.com/docker/docker' root.\\n\\n This is called the import-prefix in 'https://golang.org/cmd/go/#hdr-Remote_import_paths'\\n\\n :returns: The root portion of the import path.\\n :rtype: string\\n :raises: :class:`FetchError` if there was a problem detecting the root.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.fetch\",\n \"text\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::Fetcher.fetch\\n @abstractmethod\\n def fetch(self, dest, rev=None):\\n \\\"\\\"\\\"Fetches the remote library to the given dest dir.\\n\\n The dest dir provided will be an existing empty directory.\\n\\n :param string dest: The path of an existing empty directory to extract package containing the\\n remote library's contents to.\\n :param string rev: The version to fetch - may be `None` or empty indicating the latest version\\n should be fetched.\\n :raises: :class:`FetchError` if there was a problem fetching the remote package.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::CloningFetcher.__init__\",\n \"text\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::CloningFetcher.__init__\\n def __init__(self, import_path, meta_tag_reader):\\n super(CloningFetcher, self).__init__(import_path)\\n self._meta_tag_reader = meta_tag_reader\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::CloningFetcher.root\",\n \"text\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::CloningFetcher.root\\n def root(self):\\n imported_repo = self._meta_tag_reader.get_imported_repo(self.import_path)\\n if imported_repo:\\n return imported_repo.import_prefix\\n else:\\n raise FetchError('No <meta name=\\\"go-import\\\"> tag found at {}'.format(self.import_path))\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::CloningFetcher.fetch\",\n \"text\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::CloningFetcher.fetch\\n def fetch(self, dest, rev=None):\\n imported_repo = self._meta_tag_reader.get_imported_repo(self.import_path)\\n if not imported_repo:\\n raise FetchError('No <meta name=\\\"go-import\\\"> tag found, so cannot fetch repo '\\n 'at {}'.format(self.import_path))\\n if imported_repo.vcs != 'git':\\n # TODO: Support other vcs systems as needed.\\n raise FetchError(\\\"Don't know how to fetch for vcs type {}.\\\".format(imported_repo.vcs))\\n # TODO: Do this in a workunit (see https://github.com/pantsbuild/pants/issues/3502).\\n logger.info('Cloning {} into {}'.format(imported_repo.url, dest))\\n repo = Git.clone(imported_repo.url, dest)\\n if rev:\\n repo.set_state(rev)\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher.__init__\",\n \"text\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher.__init__\\n def __init__(self, import_path, import_prefix, url_info, archive_retriever):\\n super(ArchiveFetcher, self).__init__(import_path)\\n self._import_prefix = import_prefix\\n self._url_info = url_info\\n self._archive_retriver = archive_retriever\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher.root\",\n \"text\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher.root\\n def root(self):\\n return self._import_prefix\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher.fetch\",\n \"text\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher.fetch\\n def fetch(self, dest, rev=None):\\n pkg = GoRemoteLibrary.remote_package_path(self.root(), self.import_path)\\n archive_url = self._url_info.url_format.format(rev=rev or self._url_info.default_rev,\\n pkg=pkg, import_prefix=self.root())\\n try:\\n self._fetch(archive_url, self._url_info.strip_level, dest)\\n except FetchError as e:\\n # Modify the message to add more information, then reraise with the original traceback.\\n e.add_message_prefix('Error while fetching import {}: '.format(self.import_path))\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher._fetch\",\n \"text\": \"contrib/go/src/python/pants/contrib/go/subsystems/fetcher.py::ArchiveFetcher._fetch\\n def _fetch(self, archive_url, strip_level, dest):\\n # Note: Broken out into a separate function so we can mock it out easily in tests.\\n self._archive_retriver.fetch_archive(archive_url, strip_level, dest)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.register_options\",\n \"text\": \"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.register_options\\n @classmethod\\n def register_options(cls, register):\\n super(ScalaRepl, cls).register_options(register)\\n register('--main', default='scala.tools.nsc.MainGenericRunner',\\n help='The entry point for running the repl.')\\n cls.register_jvm_tool(register, 'pants-runner', classpath=[\\n JarDependency(org='org.pantsbuild', name='pants-runner', rev='0.0.1'),\\n ], main=ScalaRepl._RUNNER_MAIN)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.subsystem_dependencies\",\n \"text\": \"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.subsystem_dependencies\\n @classmethod\\n def subsystem_dependencies(cls):\\n return super(ScalaRepl, cls).subsystem_dependencies() + (DistributionLocator, ScalaPlatform)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.select_targets\",\n \"text\": \"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.select_targets\\n @classmethod\\n def select_targets(cls, target):\\n return isinstance(target, (JarLibrary, JvmTarget))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.setup_repl_session\",\n \"text\": \"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.setup_repl_session\\n def setup_repl_session(self, targets):\\n repl_name = ScalaPlatform.global_instance().repl\\n return (self.tool_classpath('pants-runner') +\\n self.tool_classpath(repl_name, scope=ScalaPlatform.options_scope) +\\n self.classpath(targets))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.launch_repl\",\n \"text\": \"src/python/pants/backend/jvm/tasks/scala_repl.py::ScalaRepl.launch_repl\\n def launch_repl(self, classpath):\\n # The scala repl requires -Dscala.usejavacp=true since Scala 2.8 when launching in the way\\n # we do here (not passing -classpath as a program arg to scala.tools.nsc.MainGenericRunner).\\n jvm_options = self.jvm_options\\n if not any(opt.startswith('-Dscala.usejavacp=') for opt in jvm_options):\\n jvm_options.append('-Dscala.usejavacp=true')\\n\\n # NOTE: We execute with no workunit, as capturing REPL output makes it very sluggish.\\n #\\n # NOTE: Using PantsRunner class because the classLoader used by REPL\\n # does not load Class-Path from manifest.\\n DistributionLocator.cached().execute_java(classpath=classpath,\\n main=ScalaRepl._RUNNER_MAIN,\\n jvm_options=jvm_options,\\n args=[self.get_options().main] + self.args,\\n create_synthetic_jar=True)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.register_options\",\n \"text\": \"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.register_options\\n @classmethod\\n def register_options(cls, register):\\n super(PythonRepl, cls).register_options(register)\\n # TODO: Create a python equivalent of register_jvm_tool, and use that instead of these\\n # ad-hoc options.\\n register('--ipython', type=bool,\\n help='Run an IPython REPL instead of the standard python one.')\\n register('--ipython-entry-point', advanced=True, default='IPython:start_ipython',\\n help='The IPython REPL entry point.')\\n register('--ipython-requirements', advanced=True, type=list, default=['ipython==1.0.0'],\\n help='The IPython interpreter version to use.')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.select_targets\",\n \"text\": \"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.select_targets\\n @classmethod\\n def select_targets(cls, target):\\n return isinstance(target, (PythonTarget, PythonRequirementLibrary))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.extra_requirements\",\n \"text\": \"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.extra_requirements\\n def extra_requirements(self):\\n if self.get_options().ipython:\\n return [self.get_options().ipython_requirements]\\n else:\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.setup_repl_session\",\n \"text\": \"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.setup_repl_session\\n def setup_repl_session(self, targets):\\n if self.get_options().ipython:\\n entry_point = self.get_options().ipython_entry_point\\n else:\\n entry_point = 'code:interact'\\n pex_info = PexInfo.default()\\n pex_info.entry_point = entry_point\\n return self.create_pex(pex_info)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.launch_repl\",\n \"text\": \"src/python/pants/backend/python/tasks2/python_repl.py::PythonRepl.launch_repl\\n def launch_repl(self, pex, **pex_run_kwargs):\\n env = pex_run_kwargs.pop('env', os.environ).copy()\\n po = pex.run(blocking=False, env=env, **pex_run_kwargs)\\n po.wait()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.__init__\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.__init__\\n def __init__(self, root_dir, options, build_config, run_tracker, reporting,\\n daemon_graph_helper=None, exiter=sys.exit):\\n \\\"\\\"\\\"\\n :param str root_dir: The root directory of the pants workspace (aka the \\\"build root\\\").\\n :param Options options: The global, pre-initialized Options instance.\\n :param BuildConfiguration build_config: A pre-initialized BuildConfiguration instance.\\n :param Runtracker run_tracker: The global, pre-initialized/running RunTracker instance.\\n :param Reporting reporting: The global, pre-initialized Reporting instance.\\n :param LegacyGraphHelper daemon_graph_helper: A LegacyGraphHelper instance for graph reuse. (Optional)\\n :param func exiter: A function that accepts an exit code value and exits. (for tests, Optional)\\n \\\"\\\"\\\"\\n self._root_dir = root_dir\\n self._options = options\\n self._build_config = build_config\\n self._run_tracker = run_tracker\\n self._reporting = reporting\\n self._daemon_graph_helper = daemon_graph_helper\\n self._exiter = exiter\\n\\n self._requested_goals = self._options.goals\\n self._help_request = self._options.help_request\\n self._build_file_parser = BuildFileParser(self._build_config, self._root_dir)\\n self._build_graph = None\\n self._address_mapper = None\\n\\n self._global_options = options.for_global_scope()\\n self._tag = self._global_options.tag\\n self._fail_fast = self._global_options.fail_fast\\n self._explain = self._global_options.explain\\n self._kill_nailguns = self._global_options.kill_nailguns\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._handle_help\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._handle_help\\n def _handle_help(self, help_request):\\n \\\"\\\"\\\"Handle requests for `help` information.\\\"\\\"\\\"\\n if help_request:\\n help_printer = HelpPrinter(self._options)\\n result = help_printer.print_help()\\n self._exiter(result)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._init_graph\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._init_graph\\n def _init_graph(self, use_engine, pants_ignore_patterns, build_ignore_patterns,\\n exclude_target_regexps, target_specs, workdir, graph_helper=None,\\n subproject_build_roots=None):\\n \\\"\\\"\\\"Determine the BuildGraph, AddressMapper and spec_roots for a given run.\\n\\n :param bool use_engine: Whether or not to use the v2 engine to construct the BuildGraph.\\n :param list pants_ignore_patterns: The pants ignore patterns from '--pants-ignore'.\\n :param list build_ignore_patterns: The build ignore patterns from '--build-ignore',\\n applied during BUILD file searching.\\n :param str workdir: The pants workdir.\\n :param list exclude_target_regexps: Regular expressions for targets to be excluded.\\n :param list target_specs: The original target specs.\\n :param LegacyGraphHelper graph_helper: A LegacyGraphHelper to use for graph construction,\\n if available. This would usually come from the daemon.\\n :returns: A tuple of (BuildGraph, AddressMapper, spec_roots).\\n \\\"\\\"\\\"\\n # N.B. Use of the daemon implies use of the v2 engine.\\n if graph_helper or use_engine:\\n # The daemon may provide a `graph_helper`. If that's present, use it for graph construction.\\n if not graph_helper:\\n native = Native.create(self._global_options)\\n native.set_panic_handler()\\n graph_helper = EngineInitializer.setup_legacy_graph(\\n pants_ignore_patterns,\\n workdir,\\n native=native,\\n build_file_aliases=self._build_config.registered_aliases(),\\n build_ignore_patterns=build_ignore_patterns,\\n exclude_target_regexps=exclude_target_regexps,\\n subproject_roots=subproject_build_roots,\\n include_trace_on_error=self._options.for_global_scope().print_exception_stacktrace\\n )\\n\\n target_roots = TargetRoots.create(options=self._opt\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._determine_goals\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._determine_goals\\n def _determine_goals(self, requested_goals):\\n \\\"\\\"\\\"Check and populate the requested goals for a given run.\\\"\\\"\\\"\\n\\n spec_parser = CmdLineSpecParser(self._root_dir)\\n for goal in requested_goals:\\n if self._address_mapper.is_valid_single_address(spec_parser.parse_spec(goal)):\\n logger.warning(\\\"Command-line argument '{0}' is ambiguous and was assumed to be \\\"\\n \\\"a goal. If this is incorrect, disambiguate it with ./{0}.\\\".format(goal))\\n\\n goals = [Goal.by_name(goal) for goal in requested_goals]\\n return goals\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._specs_to_targets\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._specs_to_targets\\n def _specs_to_targets(self, specs):\\n \\\"\\\"\\\"Populate the BuildGraph and target list from a set of input specs.\\\"\\\"\\\"\\n with self._run_tracker.new_workunit(name='parse', labels=[WorkUnitLabel.SETUP]):\\n def filter_for_tag(tag):\\n return lambda target: tag in map(str, target.tags)\\n\\n tag_filter = wrap_filters(create_filters(self._tag, filter_for_tag))\\n\\n def generate_targets(specs):\\n for address in self._build_graph.inject_specs_closure(specs, self._fail_fast):\\n target = self._build_graph.get_target(address)\\n if tag_filter(target):\\n yield target\\n\\n return list(generate_targets(specs))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._maybe_launch_pantsd\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._maybe_launch_pantsd\\n def _maybe_launch_pantsd(self, pantsd_launcher):\\n \\\"\\\"\\\"Launches pantsd if configured to do so.\\\"\\\"\\\"\\n if self._global_options.enable_pantsd:\\n # Avoid runtracker output if pantsd is disabled. Otherwise, show up to inform the user its on.\\n with self._run_tracker.new_workunit(name='pantsd', labels=[WorkUnitLabel.SETUP]):\\n pantsd_launcher.maybe_launch()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._should_be_quiet\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._should_be_quiet\\n def _should_be_quiet(self, goals):\\n if self._explain:\\n return True\\n\\n if self._global_options.get_rank('quiet') > RankedValue.HARDCODED:\\n return self._global_options.quiet\\n\\n return any(goal.has_task_of_type(QuietTaskMixin) for goal in goals)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._setup_context\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._setup_context\\n def _setup_context(self):\\n with self._run_tracker.new_workunit(name='setup', labels=[WorkUnitLabel.SETUP]):\\n self._build_graph, self._address_mapper, spec_roots = self._init_graph(\\n self._global_options.enable_v2_engine,\\n self._global_options.pants_ignore,\\n self._global_options.build_ignore,\\n self._global_options.exclude_target_regexp,\\n self._options.target_specs,\\n self._global_options.pants_workdir,\\n self._daemon_graph_helper,\\n self._global_options.subproject_roots,\\n )\\n\\n goals = self._determine_goals(self._requested_goals)\\n is_quiet = self._should_be_quiet(goals)\\n\\n target_roots = self._specs_to_targets(spec_roots)\\n\\n # Now that we've parsed the bootstrap BUILD files, and know about the SCM system.\\n self._run_tracker.run_info.add_scm_info()\\n\\n # Update the Reporting settings now that we have options and goal info.\\n invalidation_report = self._reporting.update_reporting(self._global_options,\\n is_quiet,\\n self._run_tracker)\\n\\n context = Context(options=self._options,\\n run_tracker=self._run_tracker,\\n target_roots=target_roots,\\n requested_goals=self._requested_goals,\\n build_graph=self._build_graph,\\n build_file_parser=self._build_file_parser,\\n address_mapper=self._address_mapper,\\n invalidation_report=invalidation_report)\\n return goals, context\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.setup\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.setup\\n def setup(self):\\n self._handle_help(self._help_request)\\n goals, context = self._setup_context()\\n return GoalRunner(context=context,\\n goals=goals,\\n run_tracker=self._run_tracker,\\n kill_nailguns=self._kill_nailguns,\\n exiter=self._exiter)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunner.__init__\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunner.__init__\\n def __init__(self, context, goals, run_tracker, kill_nailguns, exiter=sys.exit):\\n \\\"\\\"\\\"\\n :param Context context: The global, pre-initialized Context as created by GoalRunnerFactory.\\n :param list[Goal] goals: The list of goals to act on.\\n :param Runtracker run_tracker: The global, pre-initialized/running RunTracker instance.\\n :param bool kill_nailguns: Whether or not to kill nailguns after the run.\\n :param func exiter: A function that accepts an exit code value and exits (for tests, Optional).\\n \\\"\\\"\\\"\\n self._context = context\\n self._goals = goals\\n self._run_tracker = run_tracker\\n self._kill_nailguns = kill_nailguns\\n self._exiter = exiter\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunner.subsystems\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunner.subsystems\\n @classmethod\\n def subsystems(cls):\\n \\\"\\\"\\\"Subsystems used outside of any task.\\\"\\\"\\\"\\n return {\\n SourceRootConfig,\\n Reporting,\\n Reproducer,\\n RunTracker,\\n Changed.Factory,\\n BinaryUtil.Factory,\\n Subprocess.Factory\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunner._execute_engine\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunner._execute_engine\\n def _execute_engine(self):\\n workdir = self._context.options.for_global_scope().pants_workdir\\n if not workdir.endswith('.pants.d'):\\n self._context.log.error('Pants working directory should end with \\\\'.pants.d\\\\', currently it is {}\\\\n'\\n .format(workdir))\\n return 1\\n\\n unknown_goals = [goal.name for goal in self._goals if not goal.ordered_task_names()]\\n if unknown_goals:\\n self._context.log.error('Unknown goal(s): {}\\\\n'.format(' '.join(unknown_goals)))\\n return 1\\n\\n engine = RoundEngine()\\n result = engine.execute(self._context, self._goals)\\n\\n if self._context.invalidation_report:\\n self._context.invalidation_report.report()\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunner.run\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunner.run\\n def run(self):\\n should_kill_nailguns = self._kill_nailguns\\n\\n try:\\n result = self._execute_engine()\\n if result:\\n self._run_tracker.set_root_outcome(WorkUnit.FAILURE)\\n except KeyboardInterrupt:\\n self._run_tracker.set_root_outcome(WorkUnit.FAILURE)\\n # On ctrl-c we always kill nailguns, otherwise they might keep running\\n # some heavyweight compilation and gum up the system during a subsequent run.\\n should_kill_nailguns = True\\n raise\\n except Exception:\\n self._run_tracker.set_root_outcome(WorkUnit.FAILURE)\\n raise\\n finally:\\n # Must kill nailguns only after run_tracker.end() is called, otherwise there may still\\n # be pending background work that needs a nailgun.\\n if should_kill_nailguns:\\n # TODO: This is JVM-specific and really doesn't belong here.\\n # TODO: Make this more selective? Only kill nailguns that affect state?\\n # E.g., checkstyle may not need to be killed.\\n NailgunProcessGroup().killall()\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::TargetAdaptor.get_sources\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::TargetAdaptor.get_sources\\n def get_sources(self):\\n \\\"\\\"\\\"Returns target's non-deferred sources if exists or the default sources if defined.\\n\\n NB: once ivy is implemented in the engine, we can fetch sources natively here, and/or\\n refactor how deferred sources are implemented.\\n see: https://github.com/pantsbuild/pants/issues/2997\\n \\\"\\\"\\\"\\n sources = getattr(self, 'sources', None)\\n # N.B. Here we check specifically for `sources is None`, as it's possible for sources\\n # to be e.g. an explicit empty list (sources=[]).\\n if sources is None and self.default_sources_globs is not None:\\n if self.default_sources:\\n return Globs(*self.default_sources_globs,\\n spec_path=self.address.spec_path,\\n exclude=self.default_sources_exclude_globs or [])\\n else:\\n deprecated_conditional(lambda: True, '1.5.0.dev0',\\n 'default empty sources list',\\n 'Targets which do not explicitly pass sources will soon get a default set. '\\n 'Please pass an explicit set of sources for target: {address}'\\n .format(address=self.address.spec))\\n return Files(spec_path=self.address.spec_path)\\n return sources\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::TargetAdaptor.field_adaptors\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::TargetAdaptor.field_adaptors\\n @property\\n def field_adaptors(self):\\n \\\"\\\"\\\"Returns a tuple of Fields for captured fields which need additional treatment.\\\"\\\"\\\"\\n with exception_logging(logger, 'Exception in `field_adaptors` property'):\\n sources = self.get_sources()\\n if not sources:\\n return tuple()\\n base_globs = BaseGlobs.from_sources_field(sources, self.address.spec_path)\\n path_globs = base_globs.to_path_globs(self.address.spec_path)\\n return (SourcesField(self.address, 'sources', base_globs.filespecs, path_globs),)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::TargetAdaptor.default_sources\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::TargetAdaptor.default_sources\\n @property\\n def default_sources(self):\\n \\\"\\\"\\\"True if this adaptor should use default sources if they are defined.\\\"\\\"\\\"\\n return Target.Arguments.global_instance().get_options().implicit_sources\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::TargetAdaptor.default_sources_globs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::TargetAdaptor.default_sources_globs\\n @property\\n def default_sources_globs(self):\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::TargetAdaptor.default_sources_exclude_globs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::TargetAdaptor.default_sources_exclude_globs\\n @property\\n def default_sources_exclude_globs(self):\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::SourcesField.__eq__\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::SourcesField.__eq__\\n def __eq__(self, other):\\n return type(self) == type(other) and self.address == other.address and self.arg == other.arg\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::SourcesField.__ne__\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::SourcesField.__ne__\\n def __ne__(self, other):\\n return not (self == other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::SourcesField.__hash__\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::SourcesField.__hash__\\n def __hash__(self):\\n return hash(self.address)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::SourcesField.__repr__\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::SourcesField.__repr__\\n def __repr__(self):\\n return str(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::SourcesField.__str__\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::SourcesField.__str__\\n def __str__(self):\\n return 'SourcesField(address={}, arg={}, filespecs={!r})'.format(self.address, self.arg, self.filespecs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::JavaLibraryAdaptor.default_sources_globs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::JavaLibraryAdaptor.default_sources_globs\\n @property\\n def default_sources_globs(self):\\n return ('*.java',)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::JavaLibraryAdaptor.default_sources_exclude_globs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::JavaLibraryAdaptor.default_sources_exclude_globs\\n @property\\n def default_sources_exclude_globs(self):\\n return JunitTestsAdaptor.java_test_globs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::ScalaLibraryAdaptor.default_sources_globs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::ScalaLibraryAdaptor.default_sources_globs\\n @property\\n def default_sources_globs(self):\\n return ('*.scala',)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::ScalaLibraryAdaptor.default_sources_exclude_globs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::ScalaLibraryAdaptor.default_sources_exclude_globs\\n @property\\n def default_sources_exclude_globs(self):\\n return JunitTestsAdaptor.scala_test_globs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::JunitTestsAdaptor.default_sources_globs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::JunitTestsAdaptor.default_sources_globs\\n @property\\n def default_sources_globs(self):\\n return self.java_test_globs + self.scala_test_globs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::BundlesField.__eq__\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::BundlesField.__eq__\\n def __eq__(self, other):\\n return type(self) == type(other) and self.address == other.address\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::BundlesField.__ne__\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::BundlesField.__ne__\\n def __ne__(self, other):\\n return not (self == other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::BundlesField.__hash__\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::BundlesField.__hash__\\n def __hash__(self):\\n return hash(self.address)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor.__init__\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor.__init__\\n def __init__(self, bundles=None, **kwargs):\\n \\\"\\\"\\\"\\n :param list bundles: A list of `BundleAdaptor` objects\\n \\\"\\\"\\\"\\n super(JvmAppAdaptor, self).__init__(**kwargs)\\n self.bundles = bundles\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor.bundles\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor.bundles\\n @addressable_list(Exactly(BundleAdaptor))\\n def bundles(self):\\n \\\"\\\"\\\"The BundleAdaptors for this JvmApp.\\\"\\\"\\\"\\n return self.bundles\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor.field_adaptors\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor.field_adaptors\\n @property\\n def field_adaptors(self):\\n with exception_logging(logger, 'Exception in `field_adaptors` property'):\\n field_adaptors = super(JvmAppAdaptor, self).field_adaptors\\n if getattr(self, 'bundles', None) is None:\\n return field_adaptors\\n\\n bundles_field = self._construct_bundles_field()\\n return field_adaptors + (bundles_field,)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor._construct_bundles_field\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::JvmAppAdaptor._construct_bundles_field\\n def _construct_bundles_field(self):\\n filespecs_list = []\\n path_globs_list = []\\n for bundle in self.bundles:\\n # NB: if a bundle has a rel_path, then the rel_root of the resulting file globs must be\\n # set to that rel_path.\\n rel_root = getattr(bundle, 'rel_path', self.address.spec_path)\\n\\n base_globs = BaseGlobs.from_sources_field(bundle.fileset, rel_root)\\n path_globs = base_globs.to_path_globs(rel_root)\\n\\n filespecs_list.append(base_globs.filespecs)\\n path_globs_list.append(path_globs)\\n return BundlesField(self.address,\\n self.bundles,\\n filespecs_list,\\n path_globs_list)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::RemoteSourcesAdaptor.__init__\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::RemoteSourcesAdaptor.__init__\\n def __init__(self, dest=None, **kwargs):\\n \\\"\\\"\\\"\\n :param dest: A target constructor.\\n \\\"\\\"\\\"\\n if not isinstance(dest, string_types):\\n dest = dest._type_alias\\n super(RemoteSourcesAdaptor, self).__init__(dest=dest, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::PythonTargetAdaptor.field_adaptors\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::PythonTargetAdaptor.field_adaptors\\n @property\\n def field_adaptors(self):\\n with exception_logging(logger, 'Exception in `field_adaptors` property'):\\n field_adaptors = super(PythonTargetAdaptor, self).field_adaptors\\n if getattr(self, 'resources', None) is None:\\n return field_adaptors\\n base_globs = BaseGlobs.from_sources_field(self.resources, self.address.spec_path)\\n path_globs = base_globs.to_path_globs(self.address.spec_path)\\n sources_field = SourcesField(self.address,\\n 'resources',\\n base_globs.filespecs,\\n path_globs)\\n return field_adaptors + (sources_field,)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::PythonLibraryAdaptor.default_sources_globs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::PythonLibraryAdaptor.default_sources_globs\\n @property\\n def default_sources_globs(self):\\n return ('*.py',)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::PythonLibraryAdaptor.default_sources_exclude_globs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::PythonLibraryAdaptor.default_sources_exclude_globs\\n @property\\n def default_sources_exclude_globs(self):\\n return PythonTestsAdaptor.python_test_globs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::PythonTestsAdaptor.default_sources_globs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::PythonTestsAdaptor.default_sources_globs\\n @property\\n def default_sources_globs(self):\\n return self.python_test_globs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::GoTargetAdaptor.default_sources\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::GoTargetAdaptor.default_sources\\n @property\\n def default_sources(self):\\n # Go has always used implicit_sources: override to ignore the option.\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::GoTargetAdaptor.default_sources_globs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::GoTargetAdaptor.default_sources_globs\\n @property\\n def default_sources_globs(self):\\n # N.B. Go targets glob on `*` due to the way resources and .c companion files are handled.\\n return ('*',)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs.from_sources_field\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs.from_sources_field\\n @staticmethod\\n def from_sources_field(sources, spec_path):\\n \\\"\\\"\\\"Return a BaseGlobs for the given sources field.\\n\\n `sources` may be None, a list/tuple/set, a string or a BaseGlobs instance.\\n \\\"\\\"\\\"\\n if sources is None:\\n return Files(spec_path=spec_path)\\n elif isinstance(sources, BaseGlobs):\\n return sources\\n elif isinstance(sources, string_types):\\n return Files(sources, spec_path=spec_path)\\n elif isinstance(sources, (set, list, tuple)):\\n return Files(*sources, spec_path=spec_path)\\n else:\\n raise AssertionError('Could not construct PathGlobs from {}'.format(sources))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs._filespec_for_exclude\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs._filespec_for_exclude\\n @staticmethod\\n def _filespec_for_exclude(raw_exclude, spec_path):\\n if isinstance(raw_exclude, string_types):\\n raise ValueError('Excludes of type `{}` are not supported: got \\\"{}\\\"'\\n .format(type(raw_exclude).__name__, raw_exclude))\\n\\n excluded_patterns = []\\n for raw_element in raw_exclude:\\n exclude_filespecs = BaseGlobs.from_sources_field(raw_element, spec_path).filespecs\\n if exclude_filespecs.get('exclude', []):\\n raise ValueError('Nested excludes are not supported: got {}'.format(raw_element))\\n excluded_patterns.extend(exclude_filespecs.get('globs', []))\\n return {'globs': excluded_patterns}\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs.path_globs_kwarg\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs.path_globs_kwarg\\n @abstractproperty\\n def path_globs_kwarg(self):\\n \\\"\\\"\\\"The name of the `PathGlobs` parameter corresponding to this BaseGlobs instance.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs.legacy_globs_class\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs.legacy_globs_class\\n @abstractproperty\\n def legacy_globs_class(self):\\n \\\"\\\"\\\"The corresponding `wrapped_globs` class for this BaseGlobs.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs.__init__\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs.__init__\\n def __init__(self, *patterns, **kwargs):\\n raw_spec_path = kwargs.pop('spec_path')\\n self._file_globs = self.legacy_globs_class.to_filespec(patterns).get('globs', [])\\n raw_exclude = kwargs.pop('exclude', [])\\n self._excluded_file_globs = self._filespec_for_exclude(raw_exclude, raw_spec_path).get('globs', [])\\n self._spec_path = raw_spec_path\\n\\n # `follow_links=True` is the default behavior for wrapped globs, so we pop the old kwarg\\n # and warn here to bridge the gap from v1->v2 BUILD files.\\n follow_links = kwargs.pop('follow_links', None)\\n if follow_links is not None:\\n logger.warn(\\n 'Ignoring `follow_links={}` kwarg on glob. Default behavior is to follow all links.'\\n .format(follow_links)\\n )\\n\\n if kwargs:\\n raise ValueError('kwargs not supported for {}. Got: {}'.format(type(self), kwargs))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs.filespecs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs.filespecs\\n @property\\n def filespecs(self):\\n \\\"\\\"\\\"Return a filespecs dict representing both globs and excludes.\\\"\\\"\\\"\\n return {'globs': self._file_globs, 'exclude': self._exclude_filespecs}\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs._exclude_filespecs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs._exclude_filespecs\\n @property\\n def _exclude_filespecs(self):\\n if self._excluded_file_globs:\\n return [{'globs': self._excluded_file_globs}]\\n else:\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs.to_path_globs\",\n \"text\": \"src/python/pants/engine/legacy/structs.py::BaseGlobs.to_path_globs\\n def to_path_globs(self, relpath):\\n \\\"\\\"\\\"Return two PathGlobs representing the included and excluded Files for these patterns.\\\"\\\"\\\"\\n return PathGlobs.create(relpath, self._file_globs, self._excluded_file_globs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::Executor._scrub_args\",\n \"text\": \"src/python/pants/java/executor.py::Executor._scrub_args\\n @staticmethod\\n def _scrub_args(classpath, main, jvm_options, args, cwd):\\n classpath = maybe_list(classpath)\\n if not isinstance(main, string_types) or not main:\\n raise ValueError('A non-empty main classname is required, given: {}'.format(main))\\n jvm_options = maybe_list(jvm_options or ())\\n args = maybe_list(args or ())\\n return classpath, main, jvm_options, args, cwd\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::Executor.Runner.executor\",\n \"text\": \"src/python/pants/java/executor.py::Executor.Runner.executor\\n @abstractproperty\\n def executor(self):\\n \\\"\\\"\\\"Returns the executor this runner uses to run itself.\\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::Executor.Runner.cmd\",\n \"text\": \"src/python/pants/java/executor.py::Executor.Runner.cmd\\n @property\\n def cmd(self):\\n \\\"\\\"\\\"Returns a string representation of the command that will be run.\\\"\\\"\\\"\\n return ' '.join(self.command)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::Executor.Runner.command\",\n \"text\": \"src/python/pants/java/executor.py::Executor.Runner.command\\n @abstractproperty\\n def command(self):\\n \\\"\\\"\\\"Returns a copy of the command line that will be run as a list of command line tokens.\\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::Executor.Runner.run\",\n \"text\": \"src/python/pants/java/executor.py::Executor.Runner.run\\n @abstractmethod\\n def run(self, stdout=None, stderr=None, cwd=None):\\n \\\"\\\"\\\"Runs the configured java command.\\n\\n If there is a problem executing tha java program subclasses should raise Executor.Error.\\n Its guaranteed that all arguments are valid as documented in `execute`\\n\\n :param stdout: An optional stream to pump stdout to; defaults to `sys.stdout`.\\n :param stderr: An optional stream to pump stderr to; defaults to `sys.stderr`.\\n :param string cwd: optionally set the working directory\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::Executor.Runner.spawn\",\n \"text\": \"src/python/pants/java/executor.py::Executor.Runner.spawn\\n @abstractmethod\\n def spawn(self, stdout=None, stderr=None, cwd=None):\\n \\\"\\\"\\\"Spawns the configured java command.\\n\\n :param stdout: An optional stream to pump stdout to; defaults to `sys.stdout`.\\n :param stderr: An optional stream to pump stderr to; defaults to `sys.stderr`.\\n :param string cwd: optionally set the working directory\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::Executor.__init__\",\n \"text\": \"src/python/pants/java/executor.py::Executor.__init__\\n def __init__(self, distribution):\\n \\\"\\\"\\\"Constructs an Executor that can be used to launch java programs.\\n\\n :param distribution: a validated java distribution to use when launching java programs.\\n \\\"\\\"\\\"\\n if not hasattr(distribution, 'java') or not hasattr(distribution, 'validate'):\\n raise self.InvalidDistribution('A valid distribution is required, given: {}'\\n .format(distribution))\\n distribution.validate()\\n self._distribution = distribution\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::Executor.distribution\",\n \"text\": \"src/python/pants/java/executor.py::Executor.distribution\\n @property\\n def distribution(self):\\n \\\"\\\"\\\"Returns the `Distribution` this executor runs via.\\\"\\\"\\\"\\n return self._distribution\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::Executor.runner\",\n \"text\": \"src/python/pants/java/executor.py::Executor.runner\\n def runner(self, classpath, main, jvm_options=None, args=None, cwd=None):\\n \\\"\\\"\\\"Returns an `Executor.Runner` for the given java command.\\\"\\\"\\\"\\n return self._runner(*self._scrub_args(classpath, main, jvm_options, args, cwd=cwd))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::Executor.execute\",\n \"text\": \"src/python/pants/java/executor.py::Executor.execute\\n def execute(self, classpath, main, jvm_options=None, args=None, stdout=None, stderr=None,\\n cwd=None):\\n \\\"\\\"\\\"Launches the java program defined by the classpath and main.\\n\\n :param list classpath: the classpath for the java program\\n :param string main: the fully qualified class name of the java program's entry point\\n :param list jvm_options: an optional sequence of options for the underlying jvm\\n :param list args: an optional sequence of args to pass to the java program\\n :param string cwd: optionally set the working directory\\n\\n Returns the exit code of the java program.\\n Raises Executor.Error if there was a problem launching java itself.\\n \\\"\\\"\\\"\\n runner = self.runner(classpath=classpath, main=main, jvm_options=jvm_options, args=args,\\n cwd=cwd)\\n return runner.run(stdout=stdout, stderr=stderr)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::Executor._runner\",\n \"text\": \"src/python/pants/java/executor.py::Executor._runner\\n @abstractmethod\\n def _runner(self, classpath, main, jvm_options, args, cwd=None):\\n \\\"\\\"\\\"Subclasses should return a `Runner` that can execute the given java main.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::Executor._create_command\",\n \"text\": \"src/python/pants/java/executor.py::Executor._create_command\\n def _create_command(self, classpath, main, jvm_options, args, cwd=None):\\n cmd = [self._distribution.java]\\n cmd.extend(jvm_options)\\n if cwd:\\n classpath = relativize_paths(classpath, cwd)\\n cmd.extend(['-cp', os.pathsep.join(classpath), main])\\n cmd.extend(args)\\n return cmd\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::CommandLineGrabber.__init__\",\n \"text\": \"src/python/pants/java/executor.py::CommandLineGrabber.__init__\\n def __init__(self, distribution):\\n super(CommandLineGrabber, self).__init__(distribution=distribution)\\n self._command = None # Initialized when we run something.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::CommandLineGrabber._runner\",\n \"text\": \"src/python/pants/java/executor.py::CommandLineGrabber._runner\\n def _runner(self, classpath, main, jvm_options, args, cwd=None):\\n self._command = self._create_command(classpath, main, jvm_options, args, cwd=cwd)\\n\\n class Runner(self.Runner):\\n @property\\n def executor(_):\\n return self\\n\\n @property\\n def command(_):\\n return list(self._command)\\n\\n def run(_, stdout=None, stderr=None):\\n return 0\\n\\n def spawn(_, stdout=None, stderr=None):\\n return None\\n\\n return Runner()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.executor\",\n \"text\": \"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.executor\\n @property\\n def executor(_):\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.command\",\n \"text\": \"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.command\\n @property\\n def command(_):\\n return list(self._command)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.run\",\n \"text\": \"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.run\\n def run(_, stdout=None, stderr=None):\\n return 0\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.spawn\",\n \"text\": \"src/python/pants/java/executor.py::CommandLineGrabber._runner.Runner.spawn\\n def spawn(_, stdout=None, stderr=None):\\n return None\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::CommandLineGrabber.cmd\",\n \"text\": \"src/python/pants/java/executor.py::CommandLineGrabber.cmd\\n @property\\n def cmd(self):\\n return self._command\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::SubprocessExecutor._maybe_scrubbed_env\",\n \"text\": \"src/python/pants/java/executor.py::SubprocessExecutor._maybe_scrubbed_env\\n @classmethod\\n @contextmanager\\n def _maybe_scrubbed_env(cls):\\n for env_var in cls._SCRUBBED_ENV:\\n value = os.getenv(env_var)\\n if value:\\n logger.warn('Scrubbing {env_var}={value}'.format(env_var=env_var, value=value))\\n with environment_as(**cls._SCRUBBED_ENV):\\n yield\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::SubprocessExecutor.__init__\",\n \"text\": \"src/python/pants/java/executor.py::SubprocessExecutor.__init__\\n def __init__(self, distribution):\\n super(SubprocessExecutor, self).__init__(distribution=distribution)\\n self._buildroot = get_buildroot()\\n self._process = None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::SubprocessExecutor._runner\",\n \"text\": \"src/python/pants/java/executor.py::SubprocessExecutor._runner\\n def _runner(self, classpath, main, jvm_options, args, cwd=None):\\n cwd = cwd or os.getcwd()\\n command = self._create_command(classpath, main, jvm_options, args, cwd=cwd)\\n\\n class Runner(self.Runner):\\n @property\\n def executor(_):\\n return self\\n\\n @property\\n def command(_):\\n return list(command)\\n\\n def spawn(_, stdout=None, stderr=None):\\n return self._spawn(command, cwd, stdout=stdout, stderr=stderr)\\n\\n def run(_, stdout=None, stderr=None):\\n return self._spawn(command, cwd, stdout=stdout, stderr=stderr).wait()\\n\\n return Runner()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.executor\",\n \"text\": \"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.executor\\n @property\\n def executor(_):\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.command\",\n \"text\": \"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.command\\n @property\\n def command(_):\\n return list(command)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.spawn\",\n \"text\": \"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.spawn\\n def spawn(_, stdout=None, stderr=None):\\n return self._spawn(command, cwd, stdout=stdout, stderr=stderr)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.run\",\n \"text\": \"src/python/pants/java/executor.py::SubprocessExecutor._runner.Runner.run\\n def run(_, stdout=None, stderr=None):\\n return self._spawn(command, cwd, stdout=stdout, stderr=stderr).wait()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::SubprocessExecutor.spawn\",\n \"text\": \"src/python/pants/java/executor.py::SubprocessExecutor.spawn\\n def spawn(self, classpath, main, jvm_options=None, args=None, cwd=None, **subprocess_args):\\n \\\"\\\"\\\"Spawns the java program passing any extra subprocess kwargs on to subprocess.Popen.\\n\\n Returns the Popen process object handle to the spawned java program subprocess.\\n\\n :API: public\\n\\n :raises: :class:`Executor.Error` if there is a problem spawning the subprocess.\\n \\\"\\\"\\\"\\n cwd = cwd or os.getcwd()\\n cmd = self._create_command(*self._scrub_args(classpath, main, jvm_options, args, cwd=cwd))\\n return self._spawn(cmd, cwd, **subprocess_args)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/executor.py::SubprocessExecutor._spawn\",\n \"text\": \"src/python/pants/java/executor.py::SubprocessExecutor._spawn\\n def _spawn(self, cmd, cwd, **subprocess_args):\\n with self._maybe_scrubbed_env():\\n logger.debug('Executing: {cmd} args={args} at cwd={cwd}'\\n .format(cmd=' '.join(cmd), args=subprocess_args, cwd=cwd))\\n try:\\n return subprocess.Popen(cmd, cwd=cwd, **subprocess_args)\\n except OSError as e:\\n raise self.Error('Problem executing {0}: {1}'.format(self._distribution.java, e))\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_client.py::NailgunClientSession.__init__\",\n \"text\": \"src/python/pants/java/nailgun_client.py::NailgunClientSession.__init__\\n def __init__(self, sock, in_fd, out_fd, err_fd, exit_on_broken_pipe=False):\\n self._sock = sock\\n self._input_reader = NailgunStreamReader(in_fd, self._sock) if in_fd else None\\n self._stdout = out_fd\\n self._stderr = err_fd\\n self._exit_on_broken_pipe = exit_on_broken_pipe\\n self.remote_pid = None\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_client.py::NailgunClientSession._maybe_start_input_reader\",\n \"text\": \"src/python/pants/java/nailgun_client.py::NailgunClientSession._maybe_start_input_reader\\n def _maybe_start_input_reader(self):\\n if self._input_reader:\\n self._input_reader.start()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_client.py::NailgunClientSession._maybe_stop_input_reader\",\n \"text\": \"src/python/pants/java/nailgun_client.py::NailgunClientSession._maybe_stop_input_reader\\n def _maybe_stop_input_reader(self):\\n if self._input_reader:\\n self._input_reader.stop()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_client.py::NailgunClientSession._write_flush\",\n \"text\": \"src/python/pants/java/nailgun_client.py::NailgunClientSession._write_flush\\n def _write_flush(self, fd, payload=None):\\n \\\"\\\"\\\"Write a payload to a given fd (if provided) and flush the fd.\\\"\\\"\\\"\\n try:\\n if payload:\\n fd.write(payload)\\n fd.flush()\\n except (IOError, OSError) as e:\\n # If a `Broken Pipe` is encountered during a stdio fd write, we're headless - bail.\\n if e.errno == errno.EPIPE and self._exit_on_broken_pipe:\\n sys.exit()\\n # Otherwise, re-raise.\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_client.py::NailgunClientSession._process_session\",\n \"text\": \"src/python/pants/java/nailgun_client.py::NailgunClientSession._process_session\\n def _process_session(self):\\n \\\"\\\"\\\"Process the outputs of the nailgun session.\\\"\\\"\\\"\\n try:\\n for chunk_type, payload in self.iter_chunks(self._sock, return_bytes=True):\\n if chunk_type == ChunkType.STDOUT:\\n self._write_flush(self._stdout, payload)\\n elif chunk_type == ChunkType.STDERR:\\n self._write_flush(self._stderr, payload)\\n elif chunk_type == ChunkType.EXIT:\\n self._write_flush(self._stdout)\\n self._write_flush(self._stderr)\\n return int(payload)\\n elif chunk_type == ChunkType.PID:\\n self.remote_pid = int(payload)\\n elif chunk_type == ChunkType.START_READING_INPUT:\\n self._maybe_start_input_reader()\\n else:\\n raise self.ProtocolError('received unexpected chunk {} -> {}'.format(chunk_type, payload))\\n finally:\\n # Bad chunk types received from the server can throw NailgunProtocol.ProtocolError in\\n # NailgunProtocol.iter_chunks(). This ensures the NailgunStreamReader is always stopped.\\n self._maybe_stop_input_reader()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_client.py::NailgunClientSession.execute\",\n \"text\": \"src/python/pants/java/nailgun_client.py::NailgunClientSession.execute\\n def execute(self, working_dir, main_class, *arguments, **environment):\\n # Send the nailgun request.\\n self.send_request(self._sock, working_dir, main_class, *arguments, **environment)\\n\\n # Process the remainder of the nailgun session.\\n return self._process_session()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_client.py::NailgunClient.__init__\",\n \"text\": \"src/python/pants/java/nailgun_client.py::NailgunClient.__init__\\n def __init__(self, host=DEFAULT_NG_HOST, port=DEFAULT_NG_PORT, ins=sys.stdin, out=None, err=None,\\n workdir=None, exit_on_broken_pipe=False):\\n \\\"\\\"\\\"Creates a nailgun client that can be used to issue zero or more nailgun commands.\\n\\n :param string host: the nailgun server to contact (defaults to '127.0.0.1')\\n :param int port: the port the nailgun server is listening on (defaults to the default nailgun\\n port: 2113)\\n :param file ins: a file to read command standard input from (defaults to stdin) - can be None\\n in which case no input is read\\n :param file out: a stream to write command standard output to (defaults to stdout)\\n :param file err: a stream to write command standard error to (defaults to stderr)\\n :param string workdir: the default working directory for all nailgun commands (defaults to CWD)\\n :param bool exit_on_broken_pipe: whether or not to exit when `Broken Pipe` errors are encountered.\\n \\\"\\\"\\\"\\n self._host = host\\n self._port = port\\n self._stdin = ins\\n self._stdout = out or sys.stdout\\n self._stderr = err or sys.stderr\\n self._workdir = workdir or os.path.abspath(os.path.curdir)\\n self._exit_on_broken_pipe = exit_on_broken_pipe\\n self._session = None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_client.py::NailgunClient.try_connect\",\n \"text\": \"src/python/pants/java/nailgun_client.py::NailgunClient.try_connect\\n def try_connect(self):\\n \\\"\\\"\\\"Creates a socket, connects it to the nailgun and returns the connected socket.\\n\\n :returns: a connected `socket.socket`.\\n :raises: `NailgunClient.NailgunConnectionError` on failure to connect.\\n \\\"\\\"\\\"\\n sock = RecvBufferedSocket(socket.socket(socket.AF_INET, socket.SOCK_STREAM))\\n try:\\n sock.connect((self._host, self._port))\\n except (socket.error, socket.gaierror) as e:\\n logger.debug('Encountered socket exception {!r} when attempting connect to nailgun'.format(e))\\n sock.close()\\n raise self.NailgunConnectionError(\\n 'Problem connecting to nailgun server at {}:{}: {!r}'.format(self._host, self._port, e))\\n else:\\n return sock\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_client.py::NailgunClient.send_control_c\",\n \"text\": \"src/python/pants/java/nailgun_client.py::NailgunClient.send_control_c\\n def send_control_c(self):\\n \\\"\\\"\\\"Sends SIGINT to a nailgun server using pid information from the active session.\\\"\\\"\\\"\\n if self._session and self._session.remote_pid is not None:\\n os.kill(self._session.remote_pid, signal.SIGINT)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_client.py::NailgunClient.execute\",\n \"text\": \"src/python/pants/java/nailgun_client.py::NailgunClient.execute\\n def execute(self, main_class, cwd=None, *args, **environment):\\n \\\"\\\"\\\"Executes the given main_class with any supplied args in the given environment.\\n\\n :param string main_class: the fully qualified class name of the main entrypoint\\n :param string cwd: Set the working directory for this command\\n :param list args: any arguments to pass to the main entrypoint\\n :param dict environment: an env mapping made available to native nails via the nail context\\n :returns: the exit code of the main_class.\\n \\\"\\\"\\\"\\n environment = dict(self.ENV_DEFAULTS.items() + environment.items())\\n cwd = cwd or self._workdir\\n\\n # N.B. This can throw NailgunConnectionError (catchable via NailgunError).\\n sock = self.try_connect()\\n\\n self._session = NailgunClientSession(sock,\\n self._stdin,\\n self._stdout,\\n self._stderr,\\n self._exit_on_broken_pipe)\\n try:\\n return self._session.execute(cwd, main_class, *args, **environment)\\n except socket.error as e:\\n raise self.NailgunError('Problem communicating with nailgun server at {}:{}: {!r}'\\n .format(self._host, self._port, e))\\n except NailgunProtocol.ProtocolError as e:\\n raise self.NailgunError('Problem in nailgun protocol with nailgun server at {}:{}: {!r}'\\n .format(self._host, self._port, e))\\n finally:\\n sock.close()\\n self._session = None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_client.py::NailgunClient.__repr__\",\n \"text\": \"src/python/pants/java/nailgun_client.py::NailgunClient.__repr__\\n def __repr__(self):\\n return 'NailgunClient(host={!r}, port={!r}, workdir={!r})'.format(self._host,\\n self._port,\\n self._workdir)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup.__init__\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup.__init__\\n def __init__(self, metadata_base_dir=None):\\n super(NailgunProcessGroup, self).__init__(name='nailgun', metadata_base_dir=metadata_base_dir)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup._iter_nailgun_instances\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup._iter_nailgun_instances\\n def _iter_nailgun_instances(self, everywhere=False):\\n def predicate(proc):\\n if proc.name() == NailgunExecutor._PROCESS_NAME:\\n if not everywhere:\\n return NailgunExecutor._PANTS_NG_BUILDROOT_ARG in proc.cmdline()\\n else:\\n return any(arg.startswith(NailgunExecutor._PANTS_NG_ARG_PREFIX) for arg in proc.cmdline())\\n\\n return self.iter_instances(predicate)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup._iter_nailgun_instances.predicate\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup._iter_nailgun_instances.predicate\\n def predicate(proc):\\n if proc.name() == NailgunExecutor._PROCESS_NAME:\\n if not everywhere:\\n return NailgunExecutor._PANTS_NG_BUILDROOT_ARG in proc.cmdline()\\n else:\\n return any(arg.startswith(NailgunExecutor._PANTS_NG_ARG_PREFIX) for arg in proc.cmdline())\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup.killall\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunProcessGroup.killall\\n def killall(self, everywhere=False):\\n \\\"\\\"\\\"Kills all nailgun servers started by pants.\\n\\n :param bool everywhere: If ``True``, kills all pants-started nailguns on this machine;\\n otherwise restricts the nailguns killed to those started for the\\n current build root.\\n \\\"\\\"\\\"\\n with self._NAILGUN_KILL_LOCK:\\n for proc in self._iter_nailgun_instances(everywhere):\\n logger.info('killing nailgun server pid={pid}'.format(pid=proc.pid))\\n proc.terminate()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor.__init__\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor.__init__\\n def __init__(self, identity, workdir, nailgun_classpath, distribution, ins=None,\\n connect_timeout=10, connect_attempts=5, metadata_base_dir=None):\\n Executor.__init__(self, distribution=distribution)\\n FingerprintedProcessManager.__init__(self,\\n name=identity,\\n process_name=self._PROCESS_NAME,\\n metadata_base_dir=metadata_base_dir)\\n\\n if not isinstance(workdir, string_types):\\n raise ValueError('Workdir must be a path string, not: {workdir}'.format(workdir=workdir))\\n\\n self._identity = identity\\n self._workdir = workdir\\n self._ng_stdout = os.path.join(workdir, 'stdout')\\n self._ng_stderr = os.path.join(workdir, 'stderr')\\n self._nailgun_classpath = maybe_list(nailgun_classpath)\\n self._ins = ins\\n self._connect_timeout = connect_timeout\\n self._connect_attempts = connect_attempts\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor.__str__\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor.__str__\\n def __str__(self):\\n return 'NailgunExecutor({identity}, dist={dist}, pid={pid} socket={socket})'.format(\\n identity=self._identity, dist=self._distribution, pid=self.pid, socket=self.socket)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._create_owner_arg\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._create_owner_arg\\n def _create_owner_arg(self, workdir):\\n # Currently the owner is identified via the full path to the workdir.\\n return '='.join((self._PANTS_OWNER_ARG_PREFIX, workdir))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._create_fingerprint_arg\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._create_fingerprint_arg\\n def _create_fingerprint_arg(self, fingerprint):\\n return '='.join((self.FINGERPRINT_CMD_KEY, fingerprint))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._fingerprint\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._fingerprint\\n @staticmethod\\n def _fingerprint(jvm_options, classpath, java_version):\\n \\\"\\\"\\\"Compute a fingerprint for this invocation of a Java task.\\n\\n :param list jvm_options: JVM options passed to the java invocation\\n :param list classpath: The -cp arguments passed to the java invocation\\n :param Revision java_version: return value from Distribution.version()\\n :return: a hexstring representing a fingerprint of the java invocation\\n \\\"\\\"\\\"\\n digest = hashlib.sha1()\\n # TODO(John Sirois): hash classpath contents?\\n [digest.update(item) for item in (''.join(sorted(jvm_options)),\\n ''.join(sorted(classpath)),\\n repr(java_version))]\\n return digest.hexdigest()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner\\n def _runner(self, classpath, main, jvm_options, args, cwd=None):\\n \\\"\\\"\\\"Runner factory. Called via Executor.execute().\\\"\\\"\\\"\\n command = self._create_command(classpath, main, jvm_options, args)\\n\\n class Runner(self.Runner):\\n @property\\n def executor(this):\\n return self\\n\\n @property\\n def command(self):\\n return list(command)\\n\\n def run(this, stdout=None, stderr=None, cwd=None):\\n nailgun = self._get_nailgun_client(jvm_options, classpath, stdout, stderr)\\n try:\\n logger.debug('Executing via {ng_desc}: {cmd}'.format(ng_desc=nailgun, cmd=this.cmd))\\n return nailgun.execute(main, cwd, *args)\\n except nailgun.NailgunError as e:\\n self.terminate()\\n raise self.Error('Problem launching via {ng_desc} command {main} {args}: {msg}'\\n .format(ng_desc=nailgun, main=main, args=' '.join(args), msg=e))\\n\\n return Runner()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner.Runner.executor\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner.Runner.executor\\n @property\\n def executor(this):\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner.Runner.command\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner.Runner.command\\n @property\\n def command(self):\\n return list(command)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner.Runner.run\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._runner.Runner.run\\n def run(this, stdout=None, stderr=None, cwd=None):\\n nailgun = self._get_nailgun_client(jvm_options, classpath, stdout, stderr)\\n try:\\n logger.debug('Executing via {ng_desc}: {cmd}'.format(ng_desc=nailgun, cmd=this.cmd))\\n return nailgun.execute(main, cwd, *args)\\n except nailgun.NailgunError as e:\\n self.terminate()\\n raise self.Error('Problem launching via {ng_desc} command {main} {args}: {msg}'\\n .format(ng_desc=nailgun, main=main, args=' '.join(args), msg=e))\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._check_nailgun_state\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._check_nailgun_state\\n def _check_nailgun_state(self, new_fingerprint):\\n running = self.is_alive()\\n updated = self.needs_restart(new_fingerprint) or self.cmd != self._distribution.java\\n logging.debug('Nailgun {nailgun} state: updated={up!s} running={run!s} fingerprint={old_fp} '\\n 'new_fingerprint={new_fp} distribution={old_dist} new_distribution={new_dist}'\\n .format(nailgun=self._identity, up=updated, run=running,\\n old_fp=self.fingerprint, new_fp=new_fingerprint,\\n old_dist=self.cmd, new_dist=self._distribution.java))\\n return running, updated\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._get_nailgun_client\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._get_nailgun_client\\n def _get_nailgun_client(self, jvm_options, classpath, stdout, stderr):\\n \\\"\\\"\\\"This (somewhat unfortunately) is the main entrypoint to this class via the Runner. It handles\\n creation of the running nailgun server as well as creation of the client.\\\"\\\"\\\"\\n classpath = self._nailgun_classpath + classpath\\n new_fingerprint = self._fingerprint(jvm_options, classpath, self._distribution.version)\\n\\n with self._NAILGUN_SPAWN_LOCK:\\n running, updated = self._check_nailgun_state(new_fingerprint)\\n\\n if running and updated:\\n logger.debug('Found running nailgun server that needs updating, killing {server}'\\n .format(server=self._identity))\\n self.terminate()\\n\\n if (not running) or (running and updated):\\n return self._spawn_nailgun_server(new_fingerprint, jvm_options, classpath, stdout, stderr)\\n\\n return self._create_ngclient(self.socket, stdout, stderr)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._await_socket\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._await_socket\\n def _await_socket(self, timeout):\\n \\\"\\\"\\\"Blocks for the nailgun subprocess to bind and emit a listening port in the nailgun stdout.\\\"\\\"\\\"\\n with safe_open(self._ng_stdout, 'r') as ng_stdout:\\n start_time = time.time()\\n while 1:\\n readable, _, _ = select.select([ng_stdout], [], [], self._SELECT_WAIT)\\n if readable:\\n line = ng_stdout.readline() # TODO: address deadlock risk here.\\n try:\\n return self._NG_PORT_REGEX.match(line).group(1)\\n except AttributeError:\\n pass\\n\\n if (time.time() - start_time) > timeout:\\n raise NailgunClient.NailgunError(\\n 'Failed to read nailgun output after {sec} seconds!'.format(sec=timeout))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._create_ngclient\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._create_ngclient\\n def _create_ngclient(self, port, stdout, stderr):\\n return NailgunClient(port=port, ins=self._ins, out=stdout, err=stderr, workdir=get_buildroot())\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor.ensure_connectable\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor.ensure_connectable\\n def ensure_connectable(self, nailgun):\\n \\\"\\\"\\\"Ensures that a nailgun client is connectable or raises NailgunError.\\\"\\\"\\\"\\n attempt_count = 1\\n while 1:\\n try:\\n with closing(nailgun.try_connect()) as sock:\\n logger.debug('Verified new ng server is connectable at {}'.format(sock.getpeername()))\\n return\\n except nailgun.NailgunConnectionError:\\n if attempt_count >= self._connect_attempts:\\n logger.debug('Failed to connect to ng after {} attempts'.format(self._connect_attempts))\\n raise # Re-raise the NailgunConnectionError which provides more context to the user.\\n\\n attempt_count += 1\\n time.sleep(self.WAIT_INTERVAL_SEC)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._spawn_nailgun_server\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._spawn_nailgun_server\\n def _spawn_nailgun_server(self, fingerprint, jvm_options, classpath, stdout, stderr):\\n \\\"\\\"\\\"Synchronously spawn a new nailgun server.\\\"\\\"\\\"\\n # Truncate the nailguns stdout & stderr.\\n safe_file_dump(self._ng_stdout, '')\\n safe_file_dump(self._ng_stderr, '')\\n\\n jvm_options = jvm_options + [self._PANTS_NG_BUILDROOT_ARG,\\n self._create_owner_arg(self._workdir),\\n self._create_fingerprint_arg(fingerprint)]\\n\\n post_fork_child_opts = dict(fingerprint=fingerprint,\\n jvm_options=jvm_options,\\n classpath=classpath,\\n stdout=stdout,\\n stderr=stderr)\\n\\n logger.debug('Spawning nailgun server {i} with fingerprint={f}, jvm_options={j}, classpath={cp}'\\n .format(i=self._identity, f=fingerprint, j=jvm_options, cp=classpath))\\n\\n self.daemon_spawn(post_fork_child_opts=post_fork_child_opts)\\n\\n # Wait for and write the port information in the parent so we can bail on exception/timeout.\\n self.await_pid(self._connect_timeout)\\n self.write_socket(self._await_socket(self._connect_timeout))\\n\\n logger.debug('Spawned nailgun server {i} with fingerprint={f}, pid={pid} port={port}'\\n .format(i=self._identity, f=fingerprint, pid=self.pid, port=self.socket))\\n\\n client = self._create_ngclient(self.socket, stdout, stderr)\\n self.ensure_connectable(client)\\n\\n return client\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._check_process_buildroot\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor._check_process_buildroot\\n def _check_process_buildroot(self, process):\\n \\\"\\\"\\\"Matches only processes started from the current buildroot.\\\"\\\"\\\"\\n return self._PANTS_NG_BUILDROOT_ARG in process.cmdline()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor.is_alive\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor.is_alive\\n def is_alive(self):\\n \\\"\\\"\\\"A ProcessManager.is_alive() override that ensures buildroot flags are present in the process\\n command line arguments.\\\"\\\"\\\"\\n return super(NailgunExecutor, self).is_alive(self._check_process_buildroot)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor.post_fork_child\",\n \"text\": \"src/python/pants/java/nailgun_executor.py::NailgunExecutor.post_fork_child\\n def post_fork_child(self, fingerprint, jvm_options, classpath, stdout, stderr):\\n \\\"\\\"\\\"Post-fork() child callback for ProcessManager.daemon_spawn().\\\"\\\"\\\"\\n java = SubprocessExecutor(self._distribution)\\n\\n subproc = java.spawn(classpath=classpath,\\n main='com.martiansoftware.nailgun.NGServer',\\n jvm_options=jvm_options,\\n args=[':0'],\\n stdin=safe_open('/dev/null', 'r'),\\n stdout=safe_open(self._ng_stdout, 'w'),\\n stderr=safe_open(self._ng_stderr, 'w'),\\n close_fds=True)\\n\\n self.write_pid(subproc.pid)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_io.py::NailgunStreamReader.__init__\",\n \"text\": \"src/python/pants/java/nailgun_io.py::NailgunStreamReader.__init__\\n def __init__(self, in_fd, sock, buf_size=io.DEFAULT_BUFFER_SIZE, select_timeout=SELECT_TIMEOUT):\\n \\\"\\\"\\\"\\n :param file in_fd: the input file descriptor (e.g. sys.stdin) to read from.\\n :param socket sock: the socket to emit nailgun protocol chunks over.\\n :param int buf_size: the buffer size for reads from the file descriptor.\\n :param int select_timeout: the timeout (in seconds) for select.select() calls against the fd.\\n \\\"\\\"\\\"\\n super(NailgunStreamReader, self).__init__()\\n self.daemon = True\\n self._stdin = in_fd\\n self._socket = sock\\n self._buf_size = buf_size\\n self._select_timeout = select_timeout\\n # N.B. This Event is used as nothing more than a convenient atomic flag - nothing waits on it.\\n self._stopped = threading.Event()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_io.py::NailgunStreamReader.is_stopped\",\n \"text\": \"src/python/pants/java/nailgun_io.py::NailgunStreamReader.is_stopped\\n @property\\n def is_stopped(self):\\n \\\"\\\"\\\"Indicates whether or not the instance is stopped.\\\"\\\"\\\"\\n return self._stopped.is_set()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_io.py::NailgunStreamReader.stop\",\n \"text\": \"src/python/pants/java/nailgun_io.py::NailgunStreamReader.stop\\n def stop(self):\\n \\\"\\\"\\\"Stops the instance.\\\"\\\"\\\"\\n self._stopped.set()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_io.py::NailgunStreamReader.running\",\n \"text\": \"src/python/pants/java/nailgun_io.py::NailgunStreamReader.running\\n @contextmanager\\n def running(self):\\n self.start()\\n yield\\n self.stop()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_io.py::NailgunStreamReader.run\",\n \"text\": \"src/python/pants/java/nailgun_io.py::NailgunStreamReader.run\\n def run(self):\\n while not self.is_stopped:\\n readable, _, errored = select.select([self._stdin], [], [self._stdin], self._select_timeout)\\n\\n if self._stdin in errored:\\n self.stop()\\n return\\n\\n if not self.is_stopped and self._stdin in readable:\\n data = os.read(self._stdin.fileno(), self._buf_size)\\n\\n if not self.is_stopped:\\n if data:\\n NailgunProtocol.write_chunk(self._socket, ChunkType.STDIN, data)\\n else:\\n NailgunProtocol.write_chunk(self._socket, ChunkType.STDIN_EOF)\\n try:\\n self._socket.shutdown(socket.SHUT_WR) # Shutdown socket sends.\\n except socket.error: # Can happen if response is quick.\\n pass\\n finally:\\n self.stop()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.__init__\",\n \"text\": \"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.__init__\\n def __init__(self, sock, chunk_type, isatty=True, mask_broken_pipe=False):\\n \\\"\\\"\\\"\\n :param socket sock: A connected socket capable of speaking the nailgun protocol.\\n :param str chunk_type: A ChunkType constant representing the nailgun protocol chunk type.\\n :param bool isatty: Whether or not the consumer of this stream has tty capabilities. (Optional)\\n :param bool mask_broken_pipe: This will toggle the masking of 'broken pipe' errors when writing\\n to the remote socket. This allows for completion of execution in\\n the event of a client disconnect (e.g. to support cleanup work).\\n \\\"\\\"\\\"\\n self._socket = sock\\n self._chunk_type = chunk_type\\n self._isatty = isatty\\n self._mask_broken_pipe = mask_broken_pipe\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.write\",\n \"text\": \"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.write\\n def write(self, payload):\\n try:\\n NailgunProtocol.write_chunk(self._socket, self._chunk_type, payload)\\n except IOError as e:\\n # If the remote client disconnects and we try to perform a write (e.g. socket.send/sendall),\\n # an 'error: [Errno 32] Broken pipe' exception can be thrown. Setting mask_broken_pipe=True\\n # safeguards against this case (which is unexpected for most writers of sys.stdout etc) so\\n # that we don't awkwardly interrupt the runtime by throwing this exception on writes to\\n # stdout/stderr.\\n if e.errno == errno.EPIPE and not self._mask_broken_pipe:\\n raise\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.flush\",\n \"text\": \"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.flush\\n def flush(self):\\n return\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.isatty\",\n \"text\": \"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.isatty\\n def isatty(self):\\n return self._isatty\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.fileno\",\n \"text\": \"src/python/pants/java/nailgun_io.py::NailgunStreamWriter.fileno\\n def fileno(self):\\n return self._socket.fileno()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/util.py::_get_runner\",\n \"text\": \"src/python/pants/java/util.py::_get_runner\\ndef _get_runner(classpath, main, jvm_options, args, executor,\\n cwd, distribution,\\n create_synthetic_jar, synthetic_jar_dir):\\n \\\"\\\"\\\"Gets the java runner for execute_java and execute_java_async.\\\"\\\"\\\"\\n\\n executor = executor or SubprocessExecutor(distribution)\\n\\n safe_cp = classpath\\n if create_synthetic_jar:\\n safe_cp = safe_classpath(classpath, synthetic_jar_dir)\\n logger.debug('Bundling classpath {} into {}'.format(':'.join(classpath), safe_cp))\\n\\n return executor.runner(safe_cp, main, args=args, jvm_options=jvm_options, cwd=cwd)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/util.py::execute_java\",\n \"text\": \"src/python/pants/java/util.py::execute_java\\ndef execute_java(classpath, main, jvm_options=None, args=None, executor=None,\\n workunit_factory=None, workunit_name=None, workunit_labels=None,\\n cwd=None, workunit_log_config=None, distribution=None,\\n create_synthetic_jar=True, synthetic_jar_dir=None):\\n \\\"\\\"\\\"Executes the java program defined by the classpath and main.\\n\\n If `workunit_factory` is supplied, does so in the context of a workunit.\\n\\n :param list classpath: the classpath for the java program\\n :param string main: the fully qualified class name of the java program's entry point\\n :param list jvm_options: an optional sequence of options for the underlying jvm\\n :param list args: an optional sequence of args to pass to the java program\\n :param executor: an optional java executor to use to launch the program; defaults to a subprocess\\n spawn of the default java distribution\\n :param workunit_factory: an optional callable that can produce a workunit context\\n :param string workunit_name: an optional name for the work unit; defaults to the main\\n :param list workunit_labels: an optional sequence of labels for the work unit\\n :param string cwd: optionally set the working directory\\n :param WorkUnit.LogConfig workunit_log_config: an optional tuple of options affecting reporting\\n :param bool create_synthetic_jar: whether to create a synthentic jar that includes the original\\n classpath in its manifest.\\n :param string synthetic_jar_dir: an optional directory to store the synthetic jar, if `None`\\n a temporary directory will be provided and cleaned up upon process exit.\\n\\n Returns the exit code of the java program.\\n Raises `pants.java.Executor.Error` if there was a problem launching java itself.\\n \\\"\\\"\\\"\\n\\n runner = _get_runner(classpath, main, jvm_options, args, executor, cwd, distribution,\\n create_synthetic_jar, synthetic_jar_dir)\\n workunit_name = workunit_name or main\\n\\n return execute_runner(r\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/util.py::execute_java_async\",\n \"text\": \"src/python/pants/java/util.py::execute_java_async\\ndef execute_java_async(classpath, main, jvm_options=None, args=None, executor=None,\\n workunit_factory=None, workunit_name=None, workunit_labels=None,\\n cwd=None, workunit_log_config=None, distribution=None,\\n create_synthetic_jar=True, synthetic_jar_dir=None):\\n \\\"\\\"\\\"This is just like execute_java except that it returns a ProcessHandler rather than a return code.\\n\\n\\n If `workunit_factory` is supplied, does so in the context of a workunit.\\n\\n :param list classpath: the classpath for the java program\\n :param string main: the fully qualified class name of the java program's entry point\\n :param list jvm_options: an optional sequence of options for the underlying jvm\\n :param list args: an optional sequence of args to pass to the java program\\n :param executor: an optional java executor to use to launch the program; defaults to a subprocess\\n spawn of the default java distribution\\n :param workunit_factory: an optional callable that can produce a workunit context\\n :param string workunit_name: an optional name for the work unit; defaults to the main\\n :param list workunit_labels: an optional sequence of labels for the work unit\\n :param string cwd: optionally set the working directory\\n :param WorkUnit.LogConfig workunit_log_config: an optional tuple of options affecting reporting\\n :param bool create_synthetic_jar: whether to create a synthentic jar that includes the original\\n classpath in its manifest.\\n :param string synthetic_jar_dir: an optional directory to store the synthetic jar, if `None`\\n a temporary directory will be provided and cleaned up upon process exit.\\n\\n Returns a ProcessHandler to the java program.\\n Raises `pants.java.Executor.Error` if there was a problem launching java itself.\\n \\\"\\\"\\\"\\n\\n runner = _get_runner(classpath, main, jvm_options, args, executor, cwd, distribution,\\n create_synthetic_jar, synthetic_jar_\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/util.py::execute_runner\",\n \"text\": \"src/python/pants/java/util.py::execute_runner\\ndef execute_runner(runner, workunit_factory=None, workunit_name=None, workunit_labels=None,\\n workunit_log_config=None):\\n \\\"\\\"\\\"Executes the given java runner.\\n\\n If `workunit_factory` is supplied, does so in the context of a workunit.\\n\\n :param runner: the java runner to run\\n :param workunit_factory: an optional callable that can produce a workunit context\\n :param string workunit_name: an optional name for the work unit; defaults to the main\\n :param list workunit_labels: an optional sequence of labels for the work unit\\n :param WorkUnit.LogConfig workunit_log_config: an optional tuple of task options affecting reporting\\n\\n Returns the exit code of the java runner.\\n Raises `pants.java.Executor.Error` if there was a problem launching java itself.\\n \\\"\\\"\\\"\\n if not isinstance(runner, Executor.Runner):\\n raise ValueError('The runner argument must be a java Executor.Runner instance, '\\n 'given {} of type {}'.format(runner, type(runner)))\\n\\n if workunit_factory is None:\\n return runner.run()\\n else:\\n workunit_labels = [\\n WorkUnitLabel.TOOL,\\n WorkUnitLabel.NAILGUN if isinstance(runner.executor, NailgunExecutor) else WorkUnitLabel.JVM\\n ] + (workunit_labels or [])\\n\\n with workunit_factory(name=workunit_name, labels=workunit_labels,\\n cmd=runner.cmd, log_config=workunit_log_config) as workunit:\\n ret = runner.run(stdout=workunit.output('stdout'), stderr=workunit.output('stderr'))\\n workunit.set_outcome(WorkUnit.FAILURE if ret else WorkUnit.SUCCESS)\\n return ret\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/java/util.py::execute_runner_async\",\n \"text\": \"src/python/pants/java/util.py::execute_runner_async\\ndef execute_runner_async(runner, workunit_factory=None, workunit_name=None, workunit_labels=None,\\n workunit_log_config=None):\\n \\\"\\\"\\\"Executes the given java runner asynchronously.\\n\\n We can't use 'with' here because the workunit_generator's __exit__ function\\n must be called after the process exits, in the return_code_handler.\\n The wrapper around process.wait() needs to handle the same exceptions\\n as the contextmanager does, so we have code duplication.\\n\\n We're basically faking the 'with' call to deal with asynchronous\\n results.\\n\\n If `workunit_factory` is supplied, does so in the context of a workunit.\\n\\n :param runner: the java runner to run\\n :param workunit_factory: an optional callable that can produce a workunit context\\n :param string workunit_name: an optional name for the work unit; defaults to the main\\n :param list workunit_labels: an optional sequence of labels for the work unit\\n :param WorkUnit.LogConfig workunit_log_config: an optional tuple of task options affecting reporting\\n\\n Returns a ProcessHandler to the java process that is spawned.\\n Raises `pants.java.Executor.Error` if there was a problem launching java itself.\\n \\\"\\\"\\\"\\n\\n if not isinstance(runner, Executor.Runner):\\n raise ValueError('The runner argument must be a java Executor.Runner instance, '\\n 'given {} of type {}'.format(runner, type(runner)))\\n\\n if workunit_factory is None:\\n return SubprocessProcessHandler(runner.spawn())\\n else:\\n workunit_labels = [\\n WorkUnitLabel.TOOL,\\n WorkUnitLabel.NAILGUN if isinstance(runner.executor, NailgunExecutor) else WorkUnitLabel.JVM\\n ] + (workunit_labels or [])\\n\\n workunit_generator = workunit_factory(name=workunit_name, labels=workunit_labels,\\n cmd=runner.cmd, log_config=workunit_log_config)\\n workunit = workunit_generator.__enter__()\\n process = ru\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/util.py::relativize_classpath\",\n \"text\": \"src/python/pants/java/util.py::relativize_classpath\\ndef relativize_classpath(classpath, root_dir, followlinks=True):\\n \\\"\\\"\\\"Convert into classpath relative to a directory.\\n\\n This is eventually used by a jar file located in this directory as its manifest\\n attribute Class-Path. See\\n https://docs.oracle.com/javase/7/docs/technotes/guides/extensions/spec.html#bundled\\n\\n :param list classpath: Classpath to be relativized.\\n :param string root_dir: directory to relativize urls in the classpath, does not\\n have to exist yet.\\n :param bool followlinks: whether to follow symlinks to calculate relative path.\\n\\n :returns: Converted classpath of the same size as input classpath.\\n :rtype: list of strings\\n \\\"\\\"\\\"\\n def relativize_url(url, root_dir):\\n # When symlink is involed, root_dir concatenated with the returned relpath may not exist.\\n # Consider on mac `/var` is a symlink of `/private/var`, the relative path of subdirectories\\n # under /var to any other directories under `/` computed by os.path.relpath misses one level\\n # of `..`. Use os.path.realpath to guarantee returned relpath can always be located.\\n # This is not needed only when path are all relative.\\n url = os.path.realpath(url) if followlinks else url\\n root_dir = os.path.realpath(root_dir) if followlinks else root_dir\\n url_in_bundle = os.path.relpath(url, root_dir)\\n # Append '/' for directories, those not ending with '/' are assumed to be jars.\\n # Note isdir does what we need here to follow symlinks.\\n if os.path.isdir(url):\\n url_in_bundle += '/'\\n return url_in_bundle\\n\\n return [relativize_url(url, root_dir) for url in classpath]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/util.py::relativize_classpath.relativize_url\",\n \"text\": \"src/python/pants/java/util.py::relativize_classpath.relativize_url\\n def relativize_url(url, root_dir):\\n # When symlink is involed, root_dir concatenated with the returned relpath may not exist.\\n # Consider on mac `/var` is a symlink of `/private/var`, the relative path of subdirectories\\n # under /var to any other directories under `/` computed by os.path.relpath misses one level\\n # of `..`. Use os.path.realpath to guarantee returned relpath can always be located.\\n # This is not needed only when path are all relative.\\n url = os.path.realpath(url) if followlinks else url\\n root_dir = os.path.realpath(root_dir) if followlinks else root_dir\\n url_in_bundle = os.path.relpath(url, root_dir)\\n # Append '/' for directories, those not ending with '/' are assumed to be jars.\\n # Note isdir does what we need here to follow symlinks.\\n if os.path.isdir(url):\\n url_in_bundle += '/'\\n return url_in_bundle\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/java/util.py::safe_classpath\",\n \"text\": \"src/python/pants/java/util.py::safe_classpath\\ndef safe_classpath(classpath, synthetic_jar_dir, custom_name=None):\\n \\\"\\\"\\\"Bundles classpath into one synthetic jar that includes original classpath in its manifest.\\n\\n This is to ensure classpath length never exceeds platform ARG_MAX.\\n\\n :param list classpath: Classpath to be bundled.\\n :param string synthetic_jar_dir: directory to store the synthetic jar, if `None`\\n a temp directory will be provided and cleaned up upon process exit. Otherwise synthetic\\n jar will remain in the supplied directory, only for debugging purpose.\\n :param custom_name: filename of the synthetic jar to be created.\\n\\n :returns: A classpath (singleton list with just the synthetic jar).\\n :rtype: list of strings\\n \\\"\\\"\\\"\\n if synthetic_jar_dir:\\n safe_mkdir(synthetic_jar_dir)\\n else:\\n synthetic_jar_dir = safe_mkdtemp()\\n\\n bundled_classpath = relativize_classpath(classpath, synthetic_jar_dir)\\n\\n manifest = Manifest()\\n manifest.addentry(Manifest.CLASS_PATH, ' '.join(bundled_classpath))\\n\\n with temporary_file(root_dir=synthetic_jar_dir, cleanup=False, suffix='.jar') as jar_file:\\n with open_zip(jar_file, mode='w', compression=ZIP_STORED) as jar:\\n jar.writestr(Manifest.PATH, manifest.contents())\\n\\n if custom_name:\\n custom_path = os.path.join(synthetic_jar_dir, custom_name)\\n safe_concurrent_rename(jar_file.name, custom_path)\\n return [custom_path]\\n else:\\n return [jar_file.name]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options\\n @classmethod\\n def register_bootstrap_options(cls, register):\\n \\\"\\\"\\\"Register bootstrap options.\\n\\n \\\"Bootstrap options\\\" are a small set of options whose values are useful when registering other\\n options. Therefore we must bootstrap them early, before other options are registered, let\\n alone parsed.\\n\\n Bootstrap option values can be interpolated into the config file, and can be referenced\\n programatically in registration code, e.g., as register.bootstrap.pants_workdir.\\n\\n Note that regular code can also access these options as normal global-scope options. Their\\n status as \\\"bootstrap options\\\" is only pertinent during option registration.\\n \\\"\\\"\\\"\\n buildroot = get_buildroot()\\n default_distdir_name = 'dist'\\n default_distdir = os.path.join(buildroot, default_distdir_name)\\n default_rel_distdir = '/{}/'.format(default_distdir_name)\\n\\n # Although logging supports the WARN level, its not documented and could conceivably be yanked.\\n # Since pants has supported 'warn' since inception, leave the 'warn' choice as-is but explicitly\\n # setup a 'WARN' logging level name that maps to 'WARNING'.\\n logging.addLevelName(logging.WARNING, 'WARN')\\n register('-l', '--level', choices=['debug', 'info', 'warn'], default='info', recursive=True,\\n help='Set the logging level.')\\n register('-q', '--quiet', type=bool, recursive=True, daemon=False,\\n help='Squelches most console output. NOTE: Some tasks default to behaving quietly: '\\n 'inverting this option supports making them noisier than they would be otherwise.')\\n # Not really needed in bootstrap options, but putting it here means it displays right\\n # after -l and -q in help output, which is conveniently contextual.\\n register('--colors', type=bool, default=sys.stdout.isatty(), recursive=True, daemon=False,\\n help='Set whether log messages are displa\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options\\n @classmethod\\n def register_options(cls, register):\\n \\\"\\\"\\\"Register options not tied to any particular task or subsystem.\\\"\\\"\\\"\\n # The bootstrap options need to be registered on the post-bootstrap Options instance, so it\\n # won't choke on them on the command line, and also so we can access their values as regular\\n # global-scope options, for convenience.\\n cls.register_bootstrap_options(register)\\n\\n register('-x', '--time', type=bool,\\n help='Output a timing report at the end of the run.')\\n register('-e', '--explain', type=bool,\\n help='Explain the execution of goals.')\\n register('--tag', type=list, metavar='[+-]tag1,tag2,...',\\n help=\\\"Include only targets with these tags (optional '+' prefix) or without these \\\"\\n \\\"tags ('-' prefix). Useful with ::, to find subsets of targets \\\"\\n \\\"(e.g., integration tests.)\\\")\\n\\n register('-t', '--timeout', advanced=True, type=int, metavar='<seconds>',\\n help='Number of seconds to wait for http connections.')\\n # TODO: After moving to the new options system these abstraction leaks can go away.\\n register('-k', '--kill-nailguns', advanced=True, type=bool,\\n help='Kill nailguns before exiting')\\n register('-i', '--interpreter', advanced=True, default=[], type=list,\\n metavar='<requirement>',\\n removal_version='1.5.0.dev0',\\n removal_hint='Use --interpreter-constraints in scope python-setup instead.',\\n help=\\\"Constrain what Python interpreters to use. Uses Requirement format from \\\"\\n \\\"pkg_resources, e.g. 'CPython>=2.7,<3' or 'PyPy'. By default, no constraints \\\"\\n \\\"are used. Multiple constraints may be added. They will be ORed together.\\\")\\n register('--fail-fast', advanced=True, type=bool, recursive=True,\\n help='Exit as quickly as possible on error, rat\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::swallow_psutil_exceptions\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::swallow_psutil_exceptions\\n@contextmanager\\ndef swallow_psutil_exceptions():\\n \\\"\\\"\\\"A contextmanager that swallows standard psutil access exceptions.\\\"\\\"\\\"\\n try:\\n yield\\n except (psutil.AccessDenied, psutil.NoSuchProcess):\\n # This masks common, but usually benign psutil process access exceptions that might be seen\\n # when accessing attributes/methods on psutil.Process objects.\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup.__init__\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup.__init__\\n def __init__(self, name, metadata_base_dir=None):\\n self._name = name\\n self._metadata_base_dir = metadata_base_dir\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup._instance_from_process\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup._instance_from_process\\n def _instance_from_process(self, process):\\n \\\"\\\"\\\"Default converter from psutil.Process to process instance classes for subclassing.\\\"\\\"\\\"\\n return ProcessManager(name=process.name(),\\n pid=process.pid,\\n process_name=process.name(),\\n metadata_base_dir=self._metadata_base_dir)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_processes\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_processes\\n def iter_processes(self, proc_filter=None):\\n \\\"\\\"\\\"Yields processes from psutil.process_iter with an optional filter and swallows psutil errors.\\n\\n If a psutil exception is raised during execution of the filter, that process will not be\\n yielded but subsequent processes will. On the other hand, if psutil.process_iter raises\\n an exception, no more processes will be yielded.\\n \\\"\\\"\\\"\\n with swallow_psutil_exceptions(): # process_iter may raise\\n for proc in psutil.process_iter():\\n with swallow_psutil_exceptions(): # proc_filter may raise\\n if (proc_filter is None) or proc_filter(proc):\\n yield proc\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_instances\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_instances\\n def iter_instances(self, *args, **kwargs):\\n for item in self.iter_processes(*args, **kwargs):\\n yield self._instance_from_process(item)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.__init__\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.__init__\\n def __init__(self, metadata_base_dir=None):\\n \\\"\\\"\\\"\\n :param str metadata_base_dir: The base directory for process metadata.\\n \\\"\\\"\\\"\\n super(ProcessMetadataManager, self).__init__()\\n\\n self._metadata_base_dir = (\\n metadata_base_dir or\\n Subprocess.Factory.global_instance().create().get_subprocess_dir()\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_cast\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_cast\\n @staticmethod\\n def _maybe_cast(item, caster):\\n \\\"\\\"\\\"Given a casting function, attempt to cast to that type while masking common cast exceptions.\\n\\n N.B. This is mostly suitable for casting string types to numeric types - e.g. a port number\\n read from disk into an int.\\n\\n :param func caster: A casting callable (e.g. `int`).\\n :returns: The result of caster(item) or item if TypeError or ValueError are raised during cast.\\n \\\"\\\"\\\"\\n try:\\n return caster(item)\\n except (TypeError, ValueError):\\n # N.B. the TypeError catch here (already) protects against the case that caster is None.\\n return item\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._deadline_until\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._deadline_until\\n @classmethod\\n def _deadline_until(cls, closure, timeout, wait_interval=WAIT_INTERVAL_SEC):\\n \\\"\\\"\\\"Execute a function/closure repeatedly until a True condition or timeout is met.\\n\\n :param func closure: the function/closure to execute (should not block for long periods of time\\n and must return True on success).\\n :param float timeout: the maximum amount of time to wait for a true result from the closure in\\n seconds. N.B. this is timing based, so won't be exact if the runtime of\\n the closure exceeds the timeout.\\n :param float wait_interval: the amount of time to sleep between closure invocations.\\n :raises: :class:`ProcessManager.Timeout` on execution timeout.\\n \\\"\\\"\\\"\\n deadline = time.time() + timeout\\n while 1:\\n if closure():\\n return True\\n elif time.time() > deadline:\\n raise cls.Timeout('exceeded timeout of {} seconds for {}'.format(timeout, closure))\\n elif wait_interval:\\n time.sleep(wait_interval)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file\\n @classmethod\\n def _wait_for_file(cls, filename, timeout=FILE_WAIT_SEC, want_content=True):\\n \\\"\\\"\\\"Wait up to timeout seconds for filename to appear with a non-zero size or raise Timeout().\\\"\\\"\\\"\\n def file_waiter():\\n return os.path.exists(filename) and (not want_content or os.path.getsize(filename))\\n\\n try:\\n return cls._deadline_until(file_waiter, timeout)\\n except cls.Timeout:\\n # Re-raise with a more helpful exception message.\\n raise cls.Timeout('exceeded timeout of {} seconds while waiting for file {} to appear'\\n .format(timeout, filename))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file.file_waiter\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file.file_waiter\\n def file_waiter():\\n return os.path.exists(filename) and (not want_content or os.path.getsize(filename))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._get_metadata_dir_by_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._get_metadata_dir_by_name\\n def _get_metadata_dir_by_name(self, name):\\n \\\"\\\"\\\"Retrieve the metadata dir by name.\\n\\n This should always live outside of the workdir to survive a clean-all.\\n \\\"\\\"\\\"\\n return os.path.join(self._metadata_base_dir, name)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_init_metadata_dir_by_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_init_metadata_dir_by_name\\n def _maybe_init_metadata_dir_by_name(self, name):\\n \\\"\\\"\\\"Initialize the metadata directory for a named identity if it doesn't exist.\\\"\\\"\\\"\\n safe_mkdir(self._get_metadata_dir_by_name(name))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.read_metadata_by_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.read_metadata_by_name\\n def read_metadata_by_name(self, name, metadata_key, caster=None):\\n \\\"\\\"\\\"Read process metadata using a named identity.\\n\\n :param string name: The ProcessMetadataManager identity/name (e.g. 'pantsd').\\n :param string metadata_key: The metadata key (e.g. 'pid').\\n :param func caster: A casting callable to apply to the read value (e.g. `int`).\\n \\\"\\\"\\\"\\n try:\\n file_path = os.path.join(self._get_metadata_dir_by_name(name), metadata_key)\\n return self._maybe_cast(read_file(file_path).strip(), caster)\\n except (IOError, OSError):\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.write_metadata_by_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.write_metadata_by_name\\n def write_metadata_by_name(self, name, metadata_key, metadata_value):\\n \\\"\\\"\\\"Write process metadata using a named identity.\\n\\n :param string name: The ProcessMetadataManager identity/name (e.g. 'pantsd').\\n :param string metadata_key: The metadata key (e.g. 'pid').\\n :param string metadata_value: The metadata value (e.g. '1729').\\n \\\"\\\"\\\"\\n self._maybe_init_metadata_dir_by_name(name)\\n file_path = os.path.join(self._get_metadata_dir_by_name(name), metadata_key)\\n safe_file_dump(file_path, metadata_value)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.await_metadata_by_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.await_metadata_by_name\\n def await_metadata_by_name(self, name, metadata_key, timeout, caster=None):\\n \\\"\\\"\\\"Block up to a timeout for process metadata to arrive on disk.\\n\\n :param string name: The ProcessMetadataManager identity/name (e.g. 'pantsd').\\n :param string metadata_key: The metadata key (e.g. 'pid').\\n :param int timeout: The deadline to write metadata.\\n :param type caster: A type-casting callable to apply to the read value (e.g. int, str).\\n :returns: The value of the metadata key (read from disk post-write).\\n :raises: :class:`ProcessMetadataManager.Timeout` on timeout.\\n \\\"\\\"\\\"\\n file_path = os.path.join(self._get_metadata_dir_by_name(name), metadata_key)\\n self._wait_for_file(file_path, timeout=timeout)\\n return self.read_metadata_by_name(name, metadata_key, caster)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.purge_metadata_by_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.purge_metadata_by_name\\n def purge_metadata_by_name(self, name):\\n \\\"\\\"\\\"Purge a processes metadata directory.\\n\\n :raises: `ProcessManager.MetadataError` when OSError is encountered on metadata dir removal.\\n \\\"\\\"\\\"\\n meta_dir = self._get_metadata_dir_by_name(name)\\n logger.debug('purging metadata directory: {}'.format(meta_dir))\\n try:\\n rm_rf(meta_dir)\\n except OSError as e:\\n raise self.MetadataError('failed to purge metadata directory {}: {!r}'.format(meta_dir, e))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__init__\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__init__\\n def __init__(self, message, output=None):\\n super(ProcessManager.ExecutionError, self).__init__(message)\\n self.message = message\\n self.output = output\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__repr__\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__repr__\\n def __repr__(self):\\n return '{}(message={!r}, output={!r})'.format(type(self).__name__, self.message, self.output)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.__init__\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.__init__\\n def __init__(self, name, pid=None, socket=None, process_name=None, socket_type=int,\\n metadata_base_dir=None):\\n \\\"\\\"\\\"\\n :param string name: The process identity/name (e.g. 'pantsd' or 'ng_Zinc').\\n :param int pid: The process pid. Overrides fetching of the self.pid @property.\\n :param string socket: The socket metadata. Overrides fetching of the self.socket @property.\\n :param string process_name: The process name for cmdline executable name matching.\\n :param type socket_type: The type to be used for socket type casting (e.g. int).\\n :param str metadata_base_dir: The overridden base directory for process metadata.\\n \\\"\\\"\\\"\\n super(ProcessManager, self).__init__(metadata_base_dir)\\n self._name = name.lower().strip()\\n self._pid = pid\\n self._socket = socket\\n self._socket_type = socket_type\\n self._process_name = process_name\\n self._buildroot = get_buildroot()\\n self._process = None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"The logical name/label of the process.\\\"\\\"\\\"\\n return self._name\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.process_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.process_name\\n @property\\n def process_name(self):\\n \\\"\\\"\\\"The logical process name. If defined, this is compared to exe_name for stale pid checking.\\\"\\\"\\\"\\n return self._process_name\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.process_lock\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.process_lock\\n @memoized_property\\n def process_lock(self):\\n \\\"\\\"\\\"An identity-keyed inter-process lock for safeguarding lifecycle and other operations.\\\"\\\"\\\"\\n safe_mkdir(self._metadata_base_dir)\\n return OwnerPrintingInterProcessFileLock(\\n # N.B. This lock can't key into the actual named metadata dir (e.g. `.pids/pantsd/lock`\\n # via `ProcessMetadataManager._get_metadata_dir_by_name()`) because of a need to purge\\n # the named metadata dir on startup to avoid stale metadata reads.\\n os.path.join(self._metadata_base_dir, '.lock.{}'.format(self._name))\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.cmdline\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.cmdline\\n @property\\n def cmdline(self):\\n \\\"\\\"\\\"The process commandline. e.g. ['/usr/bin/python2.7', 'pants.pex'].\\n\\n :returns: The command line or else `None` if the underlying process has died.\\n \\\"\\\"\\\"\\n with swallow_psutil_exceptions():\\n process = self._as_process()\\n if process:\\n return process.cmdline()\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.cmd\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.cmd\\n @property\\n def cmd(self):\\n \\\"\\\"\\\"The first element of the process commandline e.g. '/usr/bin/python2.7'.\\n\\n :returns: The first element of the process command line or else `None` if the underlying\\n process has died.\\n \\\"\\\"\\\"\\n return (self.cmdline or [None])[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.pid\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.pid\\n @property\\n def pid(self):\\n \\\"\\\"\\\"The running processes pid (or None).\\\"\\\"\\\"\\n return self._pid or self.read_metadata_by_name(self._name, 'pid', int)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.socket\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.socket\\n @property\\n def socket(self):\\n \\\"\\\"\\\"The running processes socket/port information (or None).\\\"\\\"\\\"\\n return self._socket or self.read_metadata_by_name(self._name, 'socket', self._socket_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.get_subprocess_output\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.get_subprocess_output\\n @classmethod\\n def get_subprocess_output(cls, command, ignore_stderr=True, **kwargs):\\n \\\"\\\"\\\"Get the output of an executed command.\\n\\n :param command: An iterable representing the command to execute (e.g. ['ls', '-al']).\\n :param ignore_stderr: Whether or not to ignore stderr output vs interleave it with stdout.\\n :raises: `ProcessManager.ExecutionError` on `OSError` or `CalledProcessError`.\\n :returns: The output of the command.\\n \\\"\\\"\\\"\\n if ignore_stderr is False:\\n kwargs.setdefault('stderr', subprocess.STDOUT)\\n\\n try:\\n return subprocess.check_output(command, **kwargs)\\n except (OSError, subprocess.CalledProcessError) as e:\\n subprocess_output = getattr(e, 'output', '').strip()\\n raise cls.ExecutionError(str(e), subprocess_output)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.await_pid\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.await_pid\\n def await_pid(self, timeout):\\n \\\"\\\"\\\"Wait up to a given timeout for a process to write pid metadata.\\\"\\\"\\\"\\n return self.await_metadata_by_name(self._name, 'pid', timeout, int)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.await_socket\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.await_socket\\n def await_socket(self, timeout):\\n \\\"\\\"\\\"Wait up to a given timeout for a process to write socket info.\\\"\\\"\\\"\\n return self.await_metadata_by_name(self._name, 'socket', timeout, self._socket_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.write_pid\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.write_pid\\n def write_pid(self, pid=None):\\n \\\"\\\"\\\"Write the current processes PID to the pidfile location\\\"\\\"\\\"\\n pid = pid or os.getpid()\\n self.write_metadata_by_name(self._name, 'pid', str(pid))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.write_socket\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.write_socket\\n def write_socket(self, socket_info):\\n \\\"\\\"\\\"Write the local processes socket information (TCP port or UNIX socket).\\\"\\\"\\\"\\n self.write_metadata_by_name(self._name, 'socket', str(socket_info))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.write_named_socket\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.write_named_socket\\n def write_named_socket(self, socket_name, socket_info):\\n \\\"\\\"\\\"A multi-tenant, named alternative to ProcessManager.write_socket().\\\"\\\"\\\"\\n self.write_metadata_by_name(self._name, 'socket_{}'.format(socket_name), str(socket_info))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.read_named_socket\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.read_named_socket\\n def read_named_socket(self, socket_name, socket_type):\\n \\\"\\\"\\\"A multi-tenant, named alternative to ProcessManager.socket.\\\"\\\"\\\"\\n return self.read_metadata_by_name(self._name, 'socket_{}'.format(socket_name), socket_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager._as_process\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager._as_process\\n def _as_process(self):\\n \\\"\\\"\\\"Returns a psutil `Process` object wrapping our pid.\\n\\n NB: Even with a process object in hand, subsequent method calls against it can always raise\\n `NoSuchProcess`. Care is needed to document the raises in the public API or else trap them and\\n do something sensible for the API.\\n\\n :returns: a psutil Process object or else None if we have no pid.\\n :rtype: :class:`psutil.Process`\\n :raises: :class:`psutil.NoSuchProcess` if the process identified by our pid has died.\\n \\\"\\\"\\\"\\n if self._process is None and self.pid:\\n self._process = psutil.Process(self.pid)\\n return self._process\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.is_dead\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.is_dead\\n def is_dead(self):\\n \\\"\\\"\\\"Return a boolean indicating whether the process is dead or not.\\\"\\\"\\\"\\n return not self.is_alive()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.is_alive\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.is_alive\\n def is_alive(self, extended_check=None):\\n \\\"\\\"\\\"Return a boolean indicating whether the process is running or not.\\n\\n :param func extended_check: An additional callable that will be invoked to perform an extended\\n liveness check. This callable should take a single argument of a\\n `psutil.Process` instance representing the context-local process\\n and return a boolean True/False to indicate alive vs not alive.\\n \\\"\\\"\\\"\\n try:\\n process = self._as_process()\\n return not (\\n # Can happen if we don't find our pid.\\n (not process) or\\n # Check for walkers.\\n (process.status() == psutil.STATUS_ZOMBIE) or\\n # Check for stale pids.\\n (self.process_name and self.process_name != process.name()) or\\n # Extended checking.\\n (extended_check and not extended_check(process))\\n )\\n except (psutil.NoSuchProcess, psutil.AccessDenied):\\n # On some platforms, accessing attributes of a zombie'd Process results in NoSuchProcess.\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.purge_metadata\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.purge_metadata\\n def purge_metadata(self, force=False):\\n \\\"\\\"\\\"Instance-based version of ProcessMetadataManager.purge_metadata_by_name() that checks\\n for process liveness before purging metadata.\\n\\n :param bool force: If True, skip process liveness check before purging metadata.\\n :raises: `ProcessManager.MetadataError` when OSError is encountered on metadata dir removal.\\n \\\"\\\"\\\"\\n if not force and self.is_alive():\\n raise self.MetadataError('cannot purge metadata for a running process!')\\n\\n super(ProcessManager, self).purge_metadata_by_name(self._name)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager._kill\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager._kill\\n def _kill(self, kill_sig):\\n \\\"\\\"\\\"Send a signal to the current process.\\\"\\\"\\\"\\n if self.pid:\\n os.kill(self.pid, kill_sig)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.terminate\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.terminate\\n def terminate(self, signal_chain=KILL_CHAIN, kill_wait=KILL_WAIT_SEC, purge=True):\\n \\\"\\\"\\\"Ensure a process is terminated by sending a chain of kill signals (SIGTERM, SIGKILL).\\\"\\\"\\\"\\n alive = self.is_alive()\\n if alive:\\n logger.debug('terminating {}'.format(self._name))\\n for signal_type in signal_chain:\\n pid = self.pid\\n try:\\n logger.debug('sending signal {} to pid {}'.format(signal_type, pid))\\n self._kill(signal_type)\\n except OSError as e:\\n logger.warning('caught OSError({e!s}) during attempt to kill -{signal} {pid}!'\\n .format(e=e, signal=signal_type, pid=pid))\\n\\n # Wait up to kill_wait seconds to terminate or move onto the next signal.\\n try:\\n if self._deadline_until(self.is_dead, kill_wait):\\n alive = False\\n logger.debug('successfully terminated pid {}'.format(pid))\\n break\\n except self.Timeout:\\n # Loop to the next kill signal on timeout.\\n pass\\n\\n if alive:\\n raise self.NonResponsiveProcess('failed to kill pid {pid} with signals {chain}'\\n .format(pid=self.pid, chain=signal_chain))\\n\\n if purge:\\n self.purge_metadata(force=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.daemonize\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.daemonize\\n def daemonize(self, pre_fork_opts=None, post_fork_parent_opts=None, post_fork_child_opts=None,\\n write_pid=True):\\n \\\"\\\"\\\"Perform a double-fork, execute callbacks and write the child pid file.\\n\\n The double-fork here is necessary to truly daemonize the subprocess such that it can never\\n take control of a tty. The initial fork and setsid() creates a new, isolated process group\\n and also makes the first child a session leader (which can still acquire a tty). By forking a\\n second time, we ensure that the second child can never acquire a controlling terminal because\\n it's no longer a session leader - but it now has its own separate process group.\\n\\n Additionally, a normal daemon implementation would typically perform an os.umask(0) to reset\\n the processes file mode creation mask post-fork. We do not do this here (and in daemon_spawn\\n below) due to the fact that the daemons that pants would run are typically personal user\\n daemons. Having a disparate umask from pre-vs-post fork causes files written in each phase to\\n differ in their permissions without good reason - in this case, we want to inherit the umask.\\n \\\"\\\"\\\"\\n self.purge_metadata()\\n self.pre_fork(**pre_fork_opts or {})\\n logger.debug('forking %s', self)\\n pid = os.fork()\\n if pid == 0:\\n os.setsid()\\n second_pid = os.fork()\\n if second_pid == 0:\\n try:\\n os.chdir(self._buildroot)\\n self.post_fork_child(**post_fork_child_opts or {})\\n except Exception:\\n logger.critical(traceback.format_exc())\\n finally:\\n os._exit(0)\\n else:\\n try:\\n if write_pid: self.write_pid(second_pid)\\n self.post_fork_parent(**post_fork_parent_opts or {})\\n except Exception:\\n logger.critical(traceback.format_exc())\\n finally:\\n os._exit(0)\\n else:\\n # This prevents un-reaped, throw-away parent proce\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman.__init__\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman.__init__\\n def __init__(self, watchman_path, work_dir, log_level='1', startup_timeout=STARTUP_TIMEOUT_SECONDS,\\n timeout=SOCKET_TIMEOUT_SECONDS, socket_path_override=None, metadata_base_dir=None):\\n \\\"\\\"\\\"\\n :param str watchman_path: The path to the watchman binary.\\n :param str work_dir: The path to the pants work dir.\\n :param float startup_timeout: The timeout for the initial `watch-project` query (in seconds).\\n :param float timeout: The watchman socket timeout for all subsequent queries (in seconds).\\n :param str log_level: The watchman log level. Watchman has 3 log levels: '0' for no logging,\\n '1' for standard logging and '2' for verbose logging.\\n :param str socket_path_override: The overridden target path of the watchman socket, if any.\\n :param str metadata_base_dir: The overriden metadata base dir for `ProcessMetadataManager`.\\n \\\"\\\"\\\"\\n super(Watchman, self).__init__(name='watchman',\\n process_name='watchman',\\n socket_type=str,\\n metadata_base_dir=metadata_base_dir)\\n self._watchman_path = self._normalize_watchman_path(watchman_path)\\n self._watchman_work_dir = os.path.join(work_dir, self.name)\\n self._log_level = log_level\\n self._startup_timeout = startup_timeout\\n self._timeout = timeout\\n\\n self._state_file = os.path.join(self._watchman_work_dir, '{}.state'.format(self.name))\\n self._log_file = os.path.join(self._watchman_work_dir, '{}.log'.format(self.name))\\n self._sock_file = socket_path_override or os.path.join(self._watchman_work_dir,\\n '{}.sock'.format(self.name))\\n\\n self._logger = logging.getLogger(__name__)\\n self._watchman_client = None\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman.client\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman.client\\n @property\\n def client(self):\\n if not self._watchman_client:\\n self._watchman_client = self._make_client()\\n return self._watchman_client\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._make_client\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._make_client\\n def _make_client(self):\\n \\\"\\\"\\\"Create a new watchman client using the BSER protocol over a UNIX socket.\\\"\\\"\\\"\\n self._logger.debug('setting initial watchman timeout to %s', self._startup_timeout)\\n return StreamableWatchmanClient(sockpath=self.socket,\\n transport='local',\\n timeout=self._startup_timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._is_valid_executable\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._is_valid_executable\\n def _is_valid_executable(self, binary_path):\\n return os.path.isfile(binary_path) and os.access(binary_path, os.X_OK)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._normalize_watchman_path\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._normalize_watchman_path\\n def _normalize_watchman_path(self, watchman_path):\\n if not self._is_valid_executable(watchman_path):\\n raise self.ExecutionError('invalid watchman binary at {}!'.format(watchman_path))\\n return os.path.abspath(watchman_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._maybe_init_metadata\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._maybe_init_metadata\\n def _maybe_init_metadata(self):\\n safe_mkdir(self._watchman_work_dir)\\n # Initialize watchman with an empty, but valid statefile so it doesn't complain on startup.\\n safe_file_dump(self._state_file, '{}')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._construct_cmd\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._construct_cmd\\n def _construct_cmd(self, cmd_parts, state_file, sock_file, log_file, log_level):\\n return [part for part in cmd_parts] + ['--no-save-state',\\n '--statefile={}'.format(state_file),\\n '--sockname={}'.format(sock_file),\\n '--logfile={}'.format(log_file),\\n '--log-level', log_level]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._parse_pid_from_output\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._parse_pid_from_output\\n def _parse_pid_from_output(self, output):\\n try:\\n # Parse the watchman pidfile from the cli output (in JSON).\\n return json.loads(output)['pid']\\n except ValueError:\\n # JSON parse failure.\\n self._logger.critical('invalid output from watchman!\\\\n{output!s}'.format(output=output))\\n raise self.InvalidCommandOutput(output)\\n except KeyError:\\n # Key access error on 'pid' - bad output from watchman.\\n self._logger.critical('no pid from watchman!')\\n raise self.InvalidCommandOutput(output)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman.launch\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman.launch\\n def launch(self):\\n \\\"\\\"\\\"Launch and synchronously write metadata.\\n\\n This is possible due to watchman's built-in async server startup - no double-forking required.\\n \\\"\\\"\\\"\\n cmd = self._construct_cmd((self._watchman_path, 'get-pid'),\\n state_file=self._state_file,\\n sock_file=self._sock_file,\\n log_file=self._log_file,\\n log_level=str(self._log_level))\\n self._logger.debug('watchman cmd is: {}'.format(' '.join(cmd)))\\n self._maybe_init_metadata()\\n\\n # Watchman is launched via its cli. By running the 'get-pid' command on the client we implicitly\\n # launch the Watchman daemon. This approach is somewhat error-prone - in some cases the client\\n # can successfully trigger the launch of the Watchman daemon, but fail to return successfully\\n # for the 'get-pid' result due to server <-> daemon socket issues - these can look like:\\n #\\n # 2016-04-01T17:31:23,820: [cli] unable to talk to your watchman\\n # on .../watchman.sock! (Permission denied)\\n #\\n # This results in a subprocess execution failure and leaves us with no pid information to write\\n # to the metadata directory - while in reality a Watchman daemon is actually running but now\\n # untracked. To safeguard against this, we retry the (idempotent) 'get-pid' command a few times\\n # to give the server-side socket setup a few chances to quiesce before potentially orphaning it.\\n\\n get_output = functools.partial(self.get_subprocess_output, cmd)\\n output = retry_on_exception(get_output, 3, (self.ExecutionError,), lambda n: n * .5)\\n\\n # Parse the watchman PID from the cli output.\\n pid = self._parse_pid_from_output(output)\\n\\n # Write the process metadata to disk.\\n self.write_pid(pid)\\n self.write_socket(self._sock_file)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman._attempt_set_timeout\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman._attempt_set_timeout\\n def _attempt_set_timeout(self, timeout):\\n \\\"\\\"\\\"Sets a timeout on the inner watchman client's socket.\\\"\\\"\\\"\\n try:\\n self.client.setTimeout(timeout)\\n except Exception:\\n self._logger.debug('failed to set post-startup watchman timeout to %s', self._timeout)\\n else:\\n self._logger.debug('set post-startup watchman timeout to %s', self._timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman.watch_project\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman.watch_project\\n def watch_project(self, path):\\n \\\"\\\"\\\"Issues the watch-project command to watchman to begin watching the buildroot.\\n\\n :param string path: the path to the watchman project root/pants build root.\\n \\\"\\\"\\\"\\n # TODO(kwlzn): Add a client.query(timeout=X) param to the upstream pywatchman project.\\n try:\\n return self.client.query('watch-project', os.path.realpath(path))\\n finally:\\n self._attempt_set_timeout(self._timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman.py::Watchman.subscribed\",\n \"text\": \"src/python/pants/pantsd/watchman.py::Watchman.subscribed\\n def subscribed(self, build_root, handlers):\\n \\\"\\\"\\\"Bulk subscribe generator for StreamableWatchmanClient.\\n\\n :param str build_root: the build_root for all subscriptions.\\n :param iterable handlers: a sequence of Watchman.EventHandler namedtuple objects.\\n :yields: a stream of tuples in the form (subscription_name: str, subscription_event: dict).\\n \\\"\\\"\\\"\\n command_list = [['subscribe',\\n build_root,\\n handler.name,\\n handler.metadata] for handler in handlers]\\n\\n self._logger.debug('watchman command_list is: {}'.format(command_list))\\n\\n try:\\n for event in self.client.stream_query(command_list):\\n if event is None:\\n yield None, None\\n elif 'subscribe' in event:\\n self._logger.info('confirmed watchman subscription: {}'.format(event))\\n yield None, None\\n elif 'subscription' in event:\\n yield event.get('subscription'), event\\n else:\\n self._logger.warning('encountered non-subscription event: {}'.format(event))\\n except self.client.WatchmanError as e:\\n raise self.WatchmanCrash(e)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.create\",\n \"text\": \"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.create\\n @classmethod\\n def create(cls, bootstrap_options):\\n \\\"\\\"\\\"\\n :param Options bootstrap_options: The bootstrap options bag.\\n \\\"\\\"\\\"\\n binary_util = BinaryUtil(\\n bootstrap_options.binaries_baseurls,\\n bootstrap_options.binaries_fetch_timeout_secs,\\n bootstrap_options.pants_bootstrapdir,\\n bootstrap_options.binaries_path_by_id\\n )\\n\\n return WatchmanLauncher(\\n binary_util,\\n bootstrap_options.pants_workdir,\\n bootstrap_options.level,\\n bootstrap_options.watchman_version,\\n bootstrap_options.watchman_supportdir,\\n bootstrap_options.watchman_startup_timeout,\\n bootstrap_options.watchman_socket_timeout,\\n bootstrap_options.watchman_socket_path,\\n bootstrap_options.pants_subprocessdir\\n )\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.__init__\",\n \"text\": \"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.__init__\\n def __init__(self, binary_util, workdir, log_level, watchman_version, watchman_supportdir,\\n startup_timeout, socket_timeout, socket_path_override=None, metadata_base_dir=None):\\n \\\"\\\"\\\"\\n :param binary_util: The BinaryUtil subsystem instance for binary retrieval.\\n :param workdir: The current pants workdir.\\n :param log_level: The current log level of pants.\\n :param watchman_version: The watchman binary version to retrieve using BinaryUtil.\\n :param watchman_supportdir: The supportdir for BinaryUtil.\\n :param socket_timeout: The watchman client socket timeout (in seconds).\\n :param socket_path_override: The overridden target path of the watchman socket, if any.\\n :param metadata_base_dir: The ProcessManager metadata base directory.\\n \\\"\\\"\\\"\\n self._binary_util = binary_util\\n self._workdir = workdir\\n self._watchman_version = watchman_version\\n self._watchman_supportdir = watchman_supportdir\\n self._startup_timeout = startup_timeout\\n self._socket_timeout = socket_timeout\\n self._socket_path_override = socket_path_override\\n self._log_level = log_level\\n self._logger = logging.getLogger(__name__)\\n self._metadata_base_dir = metadata_base_dir\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher._convert_log_level\",\n \"text\": \"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher._convert_log_level\\n @staticmethod\\n def _convert_log_level(level):\\n \\\"\\\"\\\"Convert a given pants log level string into a watchman log level string.\\\"\\\"\\\"\\n # N.B. Enabling true Watchman debug logging (log level 2) can generate an absurd amount of log\\n # data (10s of gigabytes over the course of an ~hour for an active fs) and is not particularly\\n # helpful except for debugging Watchman itself. Thus, here we intentionally avoid this level\\n # in the mapping of pants log level -> watchman.\\n return {'warn': '0', 'info': '1', 'debug': '1'}.get(level, '1')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.watchman\",\n \"text\": \"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.watchman\\n @testable_memoized_property\\n def watchman(self):\\n watchman_binary = self._binary_util.select_binary(self._watchman_supportdir,\\n self._watchman_version,\\n 'watchman')\\n return Watchman(\\n watchman_binary,\\n self._workdir,\\n self._convert_log_level(self._log_level),\\n self._startup_timeout,\\n self._socket_timeout,\\n self._socket_path_override,\\n metadata_base_dir=self._metadata_base_dir\\n )\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.maybe_launch\",\n \"text\": \"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.maybe_launch\\n def maybe_launch(self):\\n if not self.watchman.is_alive():\\n self._logger.debug('launching watchman')\\n try:\\n self.watchman.launch()\\n except (self.watchman.ExecutionError, self.watchman.InvalidCommandOutput) as e:\\n self._logger.fatal('failed to launch watchman: {!r})'.format(e))\\n raise\\n\\n self._logger.debug('watchman is running, pid={pid} socket={socket}'\\n .format(pid=self.watchman.pid, socket=self.watchman.socket))\\n\\n return self.watchman\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.terminate\",\n \"text\": \"src/python/pants/pantsd/watchman_launcher.py::WatchmanLauncher.terminate\\n def terminate(self):\\n self.watchman.terminate()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.detect_worktree\",\n \"text\": \"src/python/pants/scm/git.py::Git.detect_worktree\\n @classmethod\\n def detect_worktree(cls, binary='git', subdir=None):\\n \\\"\\\"\\\"Detect the git working tree above cwd and return it; else, return None.\\n\\n :param string binary: The path to the git binary to use, 'git' by default.\\n :param string subdir: The path to start searching for a git repo.\\n :returns: path to the directory where the git working tree is rooted.\\n :rtype: string\\n \\\"\\\"\\\"\\n # TODO(John Sirois): This is only used as a factory for a Git instance in\\n # pants.base.build_environment.get_scm, encapsulate in a true factory method.\\n cmd = [binary, 'rev-parse', '--show-toplevel']\\n try:\\n if subdir:\\n with pushd(subdir):\\n process, out = cls._invoke(cmd)\\n else:\\n process, out = cls._invoke(cmd)\\n cls._check_result(cmd, process.returncode, raise_type=Scm.ScmException)\\n except Scm.ScmException:\\n return None\\n return cls._cleanse(out)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.clone\",\n \"text\": \"src/python/pants/scm/git.py::Git.clone\\n @classmethod\\n def clone(cls, repo_url, dest, binary='git'):\\n \\\"\\\"\\\"Clone the repo at repo_url into dest.\\n\\n :param string binary: The path to the git binary to use, 'git' by default.\\n :returns: an instance of this class representing the cloned repo.\\n :rtype: Git\\n \\\"\\\"\\\"\\n cmd = [binary, 'clone', repo_url, dest]\\n process, out = cls._invoke(cmd)\\n cls._check_result(cmd, process.returncode)\\n return cls(binary=binary, worktree=dest)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git._invoke\",\n \"text\": \"src/python/pants/scm/git.py::Git._invoke\\n @classmethod\\n def _invoke(cls, cmd):\\n \\\"\\\"\\\"Invoke the given command, and return a tuple of process and raw binary output.\\n\\n stderr flows to wherever its currently mapped for the parent process - generally to\\n the terminal where the user can see the error.\\n\\n :param list cmd: The command in the form of a list of strings\\n :returns: The completed process object and its standard output.\\n :raises: Scm.LocalException if there was a problem exec'ing the command at all.\\n \\\"\\\"\\\"\\n try:\\n process = subprocess.Popen(cmd, stdout=subprocess.PIPE)\\n except OSError as e:\\n # Binary DNE or is not executable\\n raise cls.LocalException('Failed to execute command {}: {}'.format(' '.join(cmd), e))\\n out, _ = process.communicate()\\n return process, out\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git._cleanse\",\n \"text\": \"src/python/pants/scm/git.py::Git._cleanse\\n @classmethod\\n def _cleanse(cls, output, errors='strict'):\\n return output.strip().decode('utf-8', errors=errors)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git._check_result\",\n \"text\": \"src/python/pants/scm/git.py::Git._check_result\\n @classmethod\\n def _check_result(cls, cmd, result, failure_msg=None, raise_type=Scm.ScmException):\\n if result != 0:\\n raise raise_type(failure_msg or '{} failed with exit code {}'.format(' '.join(cmd), result))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.__init__\",\n \"text\": \"src/python/pants/scm/git.py::Git.__init__\\n def __init__(self, binary='git', gitdir=None, worktree=None, remote=None, branch=None):\\n \\\"\\\"\\\"Creates a git scm proxy that assumes the git repository is in the cwd by default.\\n\\n binary: The path to the git binary to use, 'git' by default.\\n gitdir: The path to the repository's git metadata directory (typically '.git').\\n worktree: The path to the git repository working tree directory (typically '.').\\n remote: The default remote to use.\\n branch: The default remote branch to use.\\n \\\"\\\"\\\"\\n super(Scm, self).__init__()\\n\\n self._gitcmd = binary\\n self._worktree = os.path.realpath(worktree or os.getcwd())\\n self._gitdir = os.path.realpath(gitdir) if gitdir else os.path.join(self._worktree, '.git')\\n self._remote = remote\\n self._branch = branch\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.current_rev_identifier\",\n \"text\": \"src/python/pants/scm/git.py::Git.current_rev_identifier\\n def current_rev_identifier(self):\\n return 'HEAD'\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.worktree\",\n \"text\": \"src/python/pants/scm/git.py::Git.worktree\\n @property\\n def worktree(self):\\n return self._worktree\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.commit_id\",\n \"text\": \"src/python/pants/scm/git.py::Git.commit_id\\n @property\\n def commit_id(self):\\n return self._check_output(['rev-parse', 'HEAD'], raise_type=Scm.LocalException)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.server_url\",\n \"text\": \"src/python/pants/scm/git.py::Git.server_url\\n @property\\n def server_url(self):\\n git_output = self._check_output(['remote', '--verbose'], raise_type=Scm.LocalException)\\n\\n def origin_urls():\\n for line in git_output.splitlines():\\n name, url, action = line.split()\\n if name == 'origin' and action == '(push)':\\n yield url\\n\\n origins = list(origin_urls())\\n if len(origins) != 1:\\n raise Scm.LocalException(\\\"Unable to find remote named 'origin' that accepts pushes \\\"\\n \\\"amongst:\\\\n{}\\\".format(git_output))\\n return origins[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.server_url.origin_urls\",\n \"text\": \"src/python/pants/scm/git.py::Git.server_url.origin_urls\\n def origin_urls():\\n for line in git_output.splitlines():\\n name, url, action = line.split()\\n if name == 'origin' and action == '(push)':\\n yield url\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.tag_name\",\n \"text\": \"src/python/pants/scm/git.py::Git.tag_name\\n @property\\n def tag_name(self):\\n # Calls to git describe can have bad performance on large repos. Be aware\\n # of the performance hit if you use this property.\\n tag = self._check_output(['describe', '--tags', '--always'], raise_type=Scm.LocalException)\\n return None if b'cannot' in tag else tag\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.branch_name\",\n \"text\": \"src/python/pants/scm/git.py::Git.branch_name\\n @property\\n def branch_name(self):\\n branch = self._check_output(['rev-parse', '--abbrev-ref', 'HEAD'],\\n raise_type=Scm.LocalException)\\n return None if branch == 'HEAD' else branch\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.fix_git_relative_path\",\n \"text\": \"src/python/pants/scm/git.py::Git.fix_git_relative_path\\n def fix_git_relative_path(self, worktree_path, relative_to):\\n return os.path.relpath(os.path.join(self._worktree, worktree_path), relative_to)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.changed_files\",\n \"text\": \"src/python/pants/scm/git.py::Git.changed_files\\n def changed_files(self, from_commit=None, include_untracked=False, relative_to=None):\\n relative_to = relative_to or self._worktree\\n rel_suffix = ['--', relative_to]\\n uncommitted_changes = self._check_output(['diff', '--name-only', 'HEAD'] + rel_suffix,\\n raise_type=Scm.LocalException)\\n\\n files = set(uncommitted_changes.split())\\n if from_commit:\\n # Grab the diff from the merge-base to HEAD using ... syntax. This ensures we have just\\n # the changes that have occurred on the current branch.\\n committed_cmd = ['diff', '--name-only', from_commit + '...HEAD'] + rel_suffix\\n committed_changes = self._check_output(committed_cmd,\\n raise_type=Scm.LocalException)\\n files.update(committed_changes.split())\\n if include_untracked:\\n untracked_cmd = ['ls-files', '--other', '--exclude-standard'] + rel_suffix\\n untracked = self._check_output(untracked_cmd,\\n raise_type=Scm.LocalException)\\n files.update(untracked.split())\\n # git will report changed files relative to the worktree: re-relativize to relative_to\\n return set(self.fix_git_relative_path(f, relative_to) for f in files)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.changes_in\",\n \"text\": \"src/python/pants/scm/git.py::Git.changes_in\\n def changes_in(self, diffspec, relative_to=None):\\n relative_to = relative_to or self._worktree\\n cmd = ['diff-tree', '--no-commit-id', '--name-only', '-r', diffspec]\\n files = self._check_output(cmd, raise_type=Scm.LocalException).split()\\n return set(self.fix_git_relative_path(f.strip(), relative_to) for f in files)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.changelog\",\n \"text\": \"src/python/pants/scm/git.py::Git.changelog\\n def changelog(self, from_commit=None, files=None):\\n # We force the log output encoding to be UTF-8 here since the user may have a git config that\\n # overrides the git UTF-8 default log output encoding.\\n args = ['log', '--encoding=UTF-8', '--no-merges', '--stat', '--find-renames', '--find-copies']\\n if from_commit:\\n args.append(from_commit + '..HEAD')\\n if files:\\n args.append('--')\\n args.extend(files)\\n\\n # There are various circumstances that can lead to git logs that are not transcodeable to utf-8,\\n # for example: http://comments.gmane.org/gmane.comp.version-control.git/262685\\n # Git will not error in these cases and we do not wish to either. Here we direct byte sequences\\n # that can not be utf-8 decoded to be replaced with the utf-8 replacement character.\\n return self._check_output(args, raise_type=Scm.LocalException, errors='replace')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.merge_base\",\n \"text\": \"src/python/pants/scm/git.py::Git.merge_base\\n def merge_base(self, left='master', right='HEAD'):\\n \\\"\\\"\\\"Returns the merge-base of master and HEAD in bash: `git merge-base left right`\\\"\\\"\\\"\\n return self._check_output(['merge-base', left, right], raise_type=Scm.LocalException)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.refresh\",\n \"text\": \"src/python/pants/scm/git.py::Git.refresh\\n def refresh(self, leave_clean=False):\\n \\\"\\\"\\\"Attempt to pull-with-rebase from upstream. This is implemented as fetch-plus-rebase\\n so that we can distinguish between errors in the fetch stage (likely network errors)\\n and errors in the rebase stage (conflicts). If leave_clean is true, then in the event\\n of a rebase failure, the branch will be rolled back. Otherwise, it will be left in the\\n conflicted state.\\n \\\"\\\"\\\"\\n remote, merge = self._get_upstream()\\n self._check_call(['fetch', '--tags', remote, merge], raise_type=Scm.RemoteException)\\n try:\\n self._check_call(['rebase', 'FETCH_HEAD'], raise_type=Scm.LocalException)\\n except Scm.LocalException as e:\\n if leave_clean:\\n logger.debug('Cleaning up after failed rebase')\\n try:\\n self._check_call(['rebase', '--abort'], raise_type=Scm.LocalException)\\n except Scm.LocalException as abort_exc:\\n logger.debug('Failed to up after failed rebase')\\n logger.debug(traceback.format_exc(abort_exc))\\n # But let the original exception propagate, since that's the more interesting one\\n raise e\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.tag\",\n \"text\": \"src/python/pants/scm/git.py::Git.tag\\n def tag(self, name, message=None):\\n # We use -a here instead of --annotate to maintain maximum git compatibility.\\n # --annotate was only introduced in 1.7.8 via:\\n # https://github.com/git/git/commit/c97eff5a95d57a9561b7c7429e7fcc5d0e3a7f5d\\n self._check_call(['tag', '-a', '--message=' + (message or ''), name],\\n raise_type=Scm.LocalException)\\n self.push('refs/tags/' + name)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.commit\",\n \"text\": \"src/python/pants/scm/git.py::Git.commit\\n def commit(self, message):\\n self._check_call(['commit', '--all', '--message=' + message], raise_type=Scm.LocalException)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.add\",\n \"text\": \"src/python/pants/scm/git.py::Git.add\\n def add(self, *paths):\\n self._check_call(['add'] + list(paths), raise_type=Scm.LocalException)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.commit_date\",\n \"text\": \"src/python/pants/scm/git.py::Git.commit_date\\n def commit_date(self, commit_reference):\\n return self._check_output(['log', '-1', '--pretty=tformat:%ci', commit_reference],\\n raise_type=Scm.LocalException)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.push\",\n \"text\": \"src/python/pants/scm/git.py::Git.push\\n def push(self, *refs):\\n remote, merge = self._get_upstream()\\n self._check_call(['push', remote, merge] + list(refs), raise_type=Scm.RemoteException)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.set_state\",\n \"text\": \"src/python/pants/scm/git.py::Git.set_state\\n def set_state(self, rev):\\n self._check_call(['checkout', rev])\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git._get_upstream\",\n \"text\": \"src/python/pants/scm/git.py::Git._get_upstream\\n def _get_upstream(self):\\n \\\"\\\"\\\"Return the remote and remote merge branch for the current branch\\\"\\\"\\\"\\n if not self._remote or not self._branch:\\n branch = self.branch_name\\n if not branch:\\n raise Scm.LocalException('Failed to determine local branch')\\n\\n def get_local_config(key):\\n value = self._check_output(['config', '--local', '--get', key],\\n raise_type=Scm.LocalException)\\n return value.strip()\\n\\n self._remote = self._remote or get_local_config('branch.{}.remote'.format(branch))\\n self._branch = self._branch or get_local_config('branch.{}.merge'.format(branch))\\n return self._remote, self._branch\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git._check_call\",\n \"text\": \"src/python/pants/scm/git.py::Git._check_call\\n def _check_call(self, args, failure_msg=None, raise_type=None):\\n cmd = self._create_git_cmdline(args)\\n self._log_call(cmd)\\n result = subprocess.call(cmd)\\n self._check_result(cmd, result, failure_msg, raise_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git._check_output\",\n \"text\": \"src/python/pants/scm/git.py::Git._check_output\\n def _check_output(self, args, failure_msg=None, raise_type=None, errors='strict'):\\n cmd = self._create_git_cmdline(args)\\n self._log_call(cmd)\\n\\n process, out = self._invoke(cmd)\\n\\n self._check_result(cmd, process.returncode, failure_msg, raise_type)\\n return self._cleanse(out, errors=errors)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git._create_git_cmdline\",\n \"text\": \"src/python/pants/scm/git.py::Git._create_git_cmdline\\n def _create_git_cmdline(self, args):\\n return [self._gitcmd, '--git-dir=' + self._gitdir, '--work-tree=' + self._worktree] + args\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git._log_call\",\n \"text\": \"src/python/pants/scm/git.py::Git._log_call\\n def _log_call(self, cmd):\\n logger.debug('Executing: ' + ' '.join(cmd))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::Git.repo_reader\",\n \"text\": \"src/python/pants/scm/git.py::Git.repo_reader\\n def repo_reader(self, rev):\\n return GitRepositoryReader(self, rev)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::GitRepositoryReader.__init__\",\n \"text\": \"src/python/pants/scm/git.py::GitRepositoryReader.__init__\\n def __init__(self, scm, rev):\\n self.scm = scm\\n self.rev = rev\\n self._cat_file_process = None\\n # Trees is a dict from path to [list of Dir, Symlink or File objects]\\n self._trees = {}\\n self._realpath_cache = {'.': './', '': './'}\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::GitRepositoryReader._maybe_start_cat_file_process\",\n \"text\": \"src/python/pants/scm/git.py::GitRepositoryReader._maybe_start_cat_file_process\\n def _maybe_start_cat_file_process(self):\\n if not self._cat_file_process:\\n cmdline = self.scm._create_git_cmdline(['cat-file', '--batch'])\\n self._cat_file_process = subprocess.Popen(cmdline,\\n stdin=subprocess.PIPE, stdout=subprocess.PIPE)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::GitRepositoryReader.MissingFileException.__init__\",\n \"text\": \"src/python/pants/scm/git.py::GitRepositoryReader.MissingFileException.__init__\\n def __init__(self, rev, relpath):\\n self.relpath = relpath\\n self.rev = rev\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::GitRepositoryReader.MissingFileException.__str__\",\n \"text\": \"src/python/pants/scm/git.py::GitRepositoryReader.MissingFileException.__str__\\n def __str__(self):\\n return \\\"MissingFileException({}, {})\\\".format(self.relpath, self.rev)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::GitRepositoryReader.IsDirException.__init__\",\n \"text\": \"src/python/pants/scm/git.py::GitRepositoryReader.IsDirException.__init__\\n def __init__(self, rev, relpath):\\n self.relpath = relpath\\n self.rev = rev\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::GitRepositoryReader.IsDirException.__str__\",\n \"text\": \"src/python/pants/scm/git.py::GitRepositoryReader.IsDirException.__str__\\n def __str__(self):\\n return \\\"IsDirException({}, {})\\\".format(self.relpath, self.rev)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::GitRepositoryReader.NotADirException.__init__\",\n \"text\": \"src/python/pants/scm/git.py::GitRepositoryReader.NotADirException.__init__\\n def __init__(self, rev, relpath):\\n self.relpath = relpath\\n self.rev = rev\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::GitRepositoryReader.NotADirException.__str__\",\n \"text\": \"src/python/pants/scm/git.py::GitRepositoryReader.NotADirException.__str__\\n def __str__(self):\\n return \\\"NotADirException({}, {})\\\".format(self.relpath, self.rev)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/scm/git.py::GitRepositoryReader.SymlinkLoopException.__init__\",\n \"text\": \"src/python/pants/scm/git.py::GitRepositoryReader.SymlinkLoopException.__init__\\n def __init__(self, rev, relpath):\\n self.relpath = relpath\\n self.rev = rev\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":278,"cells":{"instance_id":{"kind":"string","value":"Qiskit__qiskit-8134"},"repo":{"kind":"string","value":"Qiskit/qiskit"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"QuantumCircuit `.clear()` and `.copy_empty_like()` functionality\n### What should we add?\n\nSometimes it can be useful to erase all instructions in a QuantumCircuit. This request is for:\r\n1. [Clear functionality](https://github.com/pedrorrivero/qiskit-terra/blob/794db4792e97adca4a78eb4f3411c04e50665df7/qiskit/circuit/quantumcircuit.py#L2311-L2313) to erase all instructions from a QuantumCircuit → `.clear()`.\r\nWe not only need to clear all data, but (at least) also update the parameter table:\r\n```python\r\nself.data.clear()\r\nself._parameter_table.clear()\r\n```\r\n2. [Copy empty functionality](https://github.com/Qiskit/qiskit-terra/blob/main/qiskit/dagcircuit/dagcircuit.py#L511-L538) to get a cleared copy of a QuantumCircuit → `.copy_empty_like()`.\r\nThis functionality is already implemented for DAGCircuit, and should retain all relevant circuit properties other than the instructions and associated parameters (e.g. registers, bits, name, metadata). Notice that `quantum_circuit.copy().clear()` would give the desired outcome, however `.copy()` performs a deepcopy of the circuit, which we would then erase almost completely when deleting the instructions, so this method should provide a more efficient way to do this.\r\n\r\n\r\nTODO:\r\n- [ ] `.clear()`\r\n- [ ] `.copy_empty_like()`\n"},"gold_files":{"kind":"list like","value":["qiskit/circuit/quantumcircuit.py"],"string":"[\n \"qiskit/circuit/quantumcircuit.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["qiskit/circuit/quantumcircuit.py::<module>","qiskit/circuit/quantumcircuit.py::QuantumCircuit.copy"],"string":"[\n \"qiskit/circuit/quantumcircuit.py::<module>\",\n \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.copy\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"qiskit/circuit/controlflow/builder.py","text":"qiskit/circuit/controlflow/builder.py\nclass InstructionResources # The quantum and classical resources used within a particular instruction.\nclass InstructionPlaceholder # A fake instruction that lies about its number of qubits and clbits.\n def concrete_instruction(self, qubits: FrozenSet[Qubit], clbits: FrozenSet[Clbit]) # Get a concrete, complete instruction that is valid to act over all the given res\n def placeholder_resources(self) # Get the qubit and clbit resources that this placeholder instruction should be co\n def _copy_mutable_properties(self, instruction: Instruction) # Copy mutable properties from ourselves onto a non-placeholder instruction.\n def assemble(self)\n def qasm(self)\n def repeat(self, n)\nclass ControlFlowBuilderBlock # A lightweight scoped block for holding instructions within a control-flow builde\n def __init__(self, qubits: Iterable[Qubit], clbits: Iterable[Clbit], *, registers: Iterable[Register]=(), resource_requester: Callable, allow_jumps: bool=True) # Args:\n def allow_jumps(self) # Whether this builder scope should allow ``break`` and ``continue`` statements wi\n def append(self, operation: Instruction, qubits: Iterable[Qubit], clbits: Iterable[Clbit]) # Add an instruction into the scope, keeping track of the qubits and clbits that h\n def request_classical_resource(self, specifier) # Resolve a single classical resource specifier into a concrete resource, raising \n def peek(self) # Get the value of the most recent instruction tuple in this scope.\n def pop(self) # Get the value of the most recent instruction tuple in this scope, and remove it \n def add_bits(self, bits: Iterable[Union[Qubit, Clbit]]) # Add extra bits to this scope that are not associated with any concrete instructi\n def add_register(self, register: Register) # Add a :obj:`.Register` to the set of resources used by this block, ensuring that\n def build(self, all_qubits: FrozenSet[Qubit], all_clbits: FrozenSet[Clbit]) # Build this scoped block into a complete :obj:`.QuantumCircuit` instance.\n def copy(self) # Return a semi-shallow copy of this builder block.","label":0},{"cid":"qiskit/circuit/quantumcircuit.py","text":"qiskit/circuit/quantumcircuit.py\nclass QuantumCircuit # Create a new circuit.\n def __init__(self, *regs: Union[Register, int, Sequence[Bit]], name: Optional[str]=None, global_phase: ParameterValueType=0, metadata: Optional[Dict]=None)\n def data(self) # Return the circuit data (instructions and context).\n def op_start_times(self) # Return a list of operation start times.\n def data(self, data_input: List[Tuple[Instruction, List[QubitSpecifier], List[ClbitSpecifier]]]) # Sets the circuit data from a list of instructions and context.\n def calibrations(self) # Return calibration dictionary.\n def calibrations(self, calibrations: dict) # Set the circuit calibration data from a dictionary of calibration definition.\n def metadata(self) # The user provided metadata associated with the circuit\n def metadata(self, metadata: Optional[dict]) # Update the circuit metadata\n def __str__(self)\n def __eq__(self, other)\n def _increment_instances(cls)\n def cls_instances(cls) # Return the current number of instances of this class,\n def cls_prefix(cls) # Return the prefix to use for auto naming.\n def _name_update(self) # update name of instance using instance number\n def has_register(self, register: Register) # Test if this circuit has the register r.\n def reverse_ops(self) # Reverse the circuit by reversing the order of instructions.\n def reverse_bits(self) # Return a circuit with the opposite order of wires.\n def inverse(self) # Invert (take adjoint of) this circuit.\n def repeat(self, reps: int) # Repeat this circuit ``reps`` times.\n def power(self, power: float, matrix_power: bool=False) # Raise this circuit to the power of ``power``.\n def control(self, num_ctrl_qubits: int=1, label: Optional[str]=None, ctrl_state: Optional[Union[str, int]]=None) # Control this circuit on ``num_ctrl_qubits`` qubits.\n def combine(self, rhs: 'QuantumCircuit') # DEPRECATED - Returns rhs appended to self if self contains compatible registers.\n def extend(self, rhs: 'QuantumCircuit') # DEPRECATED - Append QuantumCircuit to the RHS if it contains compatible register\n def compose(self, other: Union['QuantumCircuit', Instruction], qubits: Optional[Sequence[Union[Qubit, int]]]=None, clbits: Optional[Sequence[Union[Clbit, int]]]=None, front: bool=False, inplace: bool=False, wrap: bool=False) # Compose circuit with ``other`` circuit or instruction, optionally permuting wire\n def tensor(self, other: 'QuantumCircuit', inplace: bool=False) # Tensor ``self`` with ``other``.\n def qubits(self) # Returns a list of quantum bits in the order that the registers were added.\n def clbits(self) # Returns a list of classical bits in the order that the registers were added.\n def ancillas(self) # Returns a list of ancilla bits in the order that the registers were added.\n def __add__(self, rhs: 'QuantumCircuit') # Overload + to implement self.combine.\n def __iadd__(self, rhs: 'QuantumCircuit') # ","label":1},{"cid":"qiskit/dagcircuit/dagcircuit.py","text":"qiskit/dagcircuit/dagcircuit.py\nclass DAGCircuit # Quantum circuit as a directed acyclic graph.\n def __init__(self) # Create an empty circuit.\n def to_networkx(self) # Returns a copy of the DAGCircuit in networkx format.\n def from_networkx(cls, graph) # Take a networkx MultiDigraph and create a new DAGCircuit.\n def wires(self) # Return a list of the wires in order.\n def node_counter(self) # Returns the number of nodes in the dag.\n def global_phase(self) # Return the global phase of the circuit.\n def global_phase(self, angle) # Set the global phase of the circuit.\n def calibrations(self) # Return calibration dictionary.\n def calibrations(self, calibrations) # Set the circuit calibration data from a dictionary of calibration definition.\n def add_calibration(self, gate, qubits, schedule, params=None) # Register a low-level, custom pulse definition for the given gate.\n def has_calibration_for(self, node) # Return True if the dag has a calibration defined for the node operation. In this\n def remove_all_ops_named(self, opname) # Remove all operation nodes with the given name.\n def add_qubits(self, qubits) # Add individual qubit wires.\n def add_clbits(self, clbits) # Add individual clbit wires.\n def add_qreg(self, qreg) # Add all wires in a quantum register.\n def add_creg(self, creg) # Add all wires in a classical register.\n def _add_wire(self, wire) # Add a qubit or bit to the circuit.\n def remove_clbits(self, *clbits) # Remove classical bits from the circuit. All bits MUST be idle.\n def remove_cregs(self, *cregs) # Remove classical registers from the circuit, leaving underlying bits\n def _is_wire_idle(self, wire) # Check if a wire is idle.\n def _remove_idle_wire(self, wire) # Remove an idle qubit or bit from the circuit.\n def _check_condition(self, name, condition) # Verify that the condition is valid.\n def _check_bits(self, args, amap) # Check the values of a list of (qu)bit arguments.\n def _bits_in_condition(self, cond) # Return a list of bits in the given condition.\n def _increment_op(self, op)\n def _decrement_op(self, op)\n def _add_op_node(self, op, qargs, cargs) # Add a new operation node to the graph and assign properties.\n def _copy_circuit_metadata(self) # DEPRECATED\n def copy_empty_like(self) # Return a copy of self with the same structure but empty.\n def apply_operation_back(self, op, qargs=None, cargs=None) # Apply an operation to the output of the circuit.\n def apply_operation_front(self, op, qargs, cargs) # Apply an operation to the input of the circuit.\n def _check_edgemap_registers(self, inbound_wires, inbound_regs) # Check that wiremap neither fragments nor leaves duplicate registers.\n def _check_wiremap_validity(self, wire_map, keymap, valmap) # Check that the wiremap is consistent.\n def _map_condition(wire_map, condition, target_cregs) # Use the wire_map dict to change the condition tuple's creg name.","label":0}],"string":"[\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py\",\n \"text\": \"qiskit/circuit/controlflow/builder.py\\nclass InstructionResources # The quantum and classical resources used within a particular instruction.\\nclass InstructionPlaceholder # A fake instruction that lies about its number of qubits and clbits.\\n def concrete_instruction(self, qubits: FrozenSet[Qubit], clbits: FrozenSet[Clbit]) # Get a concrete, complete instruction that is valid to act over all the given res\\n def placeholder_resources(self) # Get the qubit and clbit resources that this placeholder instruction should be co\\n def _copy_mutable_properties(self, instruction: Instruction) # Copy mutable properties from ourselves onto a non-placeholder instruction.\\n def assemble(self)\\n def qasm(self)\\n def repeat(self, n)\\nclass ControlFlowBuilderBlock # A lightweight scoped block for holding instructions within a control-flow builde\\n def __init__(self, qubits: Iterable[Qubit], clbits: Iterable[Clbit], *, registers: Iterable[Register]=(), resource_requester: Callable, allow_jumps: bool=True) # Args:\\n def allow_jumps(self) # Whether this builder scope should allow ``break`` and ``continue`` statements wi\\n def append(self, operation: Instruction, qubits: Iterable[Qubit], clbits: Iterable[Clbit]) # Add an instruction into the scope, keeping track of the qubits and clbits that h\\n def request_classical_resource(self, specifier) # Resolve a single classical resource specifier into a concrete resource, raising \\n def peek(self) # Get the value of the most recent instruction tuple in this scope.\\n def pop(self) # Get the value of the most recent instruction tuple in this scope, and remove it \\n def add_bits(self, bits: Iterable[Union[Qubit, Clbit]]) # Add extra bits to this scope that are not associated with any concrete instructi\\n def add_register(self, register: Register) # Add a :obj:`.Register` to the set of resources used by this block, ensuring that\\n def build(self, all_qubits: FrozenSet[Qubit], all_clbits: FrozenSet[Clbit]) # Build this scoped block into a complete :obj:`.QuantumCircuit` instance.\\n def copy(self) # Return a semi-shallow copy of this builder block.\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py\",\n \"text\": \"qiskit/circuit/quantumcircuit.py\\nclass QuantumCircuit # Create a new circuit.\\n def __init__(self, *regs: Union[Register, int, Sequence[Bit]], name: Optional[str]=None, global_phase: ParameterValueType=0, metadata: Optional[Dict]=None)\\n def data(self) # Return the circuit data (instructions and context).\\n def op_start_times(self) # Return a list of operation start times.\\n def data(self, data_input: List[Tuple[Instruction, List[QubitSpecifier], List[ClbitSpecifier]]]) # Sets the circuit data from a list of instructions and context.\\n def calibrations(self) # Return calibration dictionary.\\n def calibrations(self, calibrations: dict) # Set the circuit calibration data from a dictionary of calibration definition.\\n def metadata(self) # The user provided metadata associated with the circuit\\n def metadata(self, metadata: Optional[dict]) # Update the circuit metadata\\n def __str__(self)\\n def __eq__(self, other)\\n def _increment_instances(cls)\\n def cls_instances(cls) # Return the current number of instances of this class,\\n def cls_prefix(cls) # Return the prefix to use for auto naming.\\n def _name_update(self) # update name of instance using instance number\\n def has_register(self, register: Register) # Test if this circuit has the register r.\\n def reverse_ops(self) # Reverse the circuit by reversing the order of instructions.\\n def reverse_bits(self) # Return a circuit with the opposite order of wires.\\n def inverse(self) # Invert (take adjoint of) this circuit.\\n def repeat(self, reps: int) # Repeat this circuit ``reps`` times.\\n def power(self, power: float, matrix_power: bool=False) # Raise this circuit to the power of ``power``.\\n def control(self, num_ctrl_qubits: int=1, label: Optional[str]=None, ctrl_state: Optional[Union[str, int]]=None) # Control this circuit on ``num_ctrl_qubits`` qubits.\\n def combine(self, rhs: 'QuantumCircuit') # DEPRECATED - Returns rhs appended to self if self contains compatible registers.\\n def extend(self, rhs: 'QuantumCircuit') # DEPRECATED - Append QuantumCircuit to the RHS if it contains compatible register\\n def compose(self, other: Union['QuantumCircuit', Instruction], qubits: Optional[Sequence[Union[Qubit, int]]]=None, clbits: Optional[Sequence[Union[Clbit, int]]]=None, front: bool=False, inplace: bool=False, wrap: bool=False) # Compose circuit with ``other`` circuit or instruction, optionally permuting wire\\n def tensor(self, other: 'QuantumCircuit', inplace: bool=False) # Tensor ``self`` with ``other``.\\n def qubits(self) # Returns a list of quantum bits in the order that the registers were added.\\n def clbits(self) # Returns a list of classical bits in the order that the registers were added.\\n def ancillas(self) # Returns a list of ancilla bits in the order that the registers were added.\\n def __add__(self, rhs: 'QuantumCircuit') # Overload + to implement self.combine.\\n def __iadd__(self, rhs: 'QuantumCircuit') # \",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py\\nclass DAGCircuit # Quantum circuit as a directed acyclic graph.\\n def __init__(self) # Create an empty circuit.\\n def to_networkx(self) # Returns a copy of the DAGCircuit in networkx format.\\n def from_networkx(cls, graph) # Take a networkx MultiDigraph and create a new DAGCircuit.\\n def wires(self) # Return a list of the wires in order.\\n def node_counter(self) # Returns the number of nodes in the dag.\\n def global_phase(self) # Return the global phase of the circuit.\\n def global_phase(self, angle) # Set the global phase of the circuit.\\n def calibrations(self) # Return calibration dictionary.\\n def calibrations(self, calibrations) # Set the circuit calibration data from a dictionary of calibration definition.\\n def add_calibration(self, gate, qubits, schedule, params=None) # Register a low-level, custom pulse definition for the given gate.\\n def has_calibration_for(self, node) # Return True if the dag has a calibration defined for the node operation. In this\\n def remove_all_ops_named(self, opname) # Remove all operation nodes with the given name.\\n def add_qubits(self, qubits) # Add individual qubit wires.\\n def add_clbits(self, clbits) # Add individual clbit wires.\\n def add_qreg(self, qreg) # Add all wires in a quantum register.\\n def add_creg(self, creg) # Add all wires in a classical register.\\n def _add_wire(self, wire) # Add a qubit or bit to the circuit.\\n def remove_clbits(self, *clbits) # Remove classical bits from the circuit. All bits MUST be idle.\\n def remove_cregs(self, *cregs) # Remove classical registers from the circuit, leaving underlying bits\\n def _is_wire_idle(self, wire) # Check if a wire is idle.\\n def _remove_idle_wire(self, wire) # Remove an idle qubit or bit from the circuit.\\n def _check_condition(self, name, condition) # Verify that the condition is valid.\\n def _check_bits(self, args, amap) # Check the values of a list of (qu)bit arguments.\\n def _bits_in_condition(self, cond) # Return a list of bits in the given condition.\\n def _increment_op(self, op)\\n def _decrement_op(self, op)\\n def _add_op_node(self, op, qargs, cargs) # Add a new operation node to the graph and assign properties.\\n def _copy_circuit_metadata(self) # DEPRECATED\\n def copy_empty_like(self) # Return a copy of self with the same structure but empty.\\n def apply_operation_back(self, op, qargs=None, cargs=None) # Apply an operation to the output of the circuit.\\n def apply_operation_front(self, op, qargs, cargs) # Apply an operation to the input of the circuit.\\n def _check_edgemap_registers(self, inbound_wires, inbound_regs) # Check that wiremap neither fragments nor leaves duplicate registers.\\n def _check_wiremap_validity(self, wire_map, keymap, valmap) # Check that the wiremap is consistent.\\n def _map_condition(wire_map, condition, target_cregs) # Use the wire_map dict to change the condition tuple's creg name.\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.concrete_instruction","text":"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.concrete_instruction\n @abc.abstractmethod\n def concrete_instruction(\n self, qubits: FrozenSet[Qubit], clbits: FrozenSet[Clbit]\n ) -> Tuple[Instruction, InstructionResources]:\n \"\"\"Get a concrete, complete instruction that is valid to act over all the given resources.\n\n The returned resources may not be the full width of the given resources, but will certainly\n be a subset of them; this can occur if (for example) a placeholder ``if`` statement is\n present, but does not itself contain any placeholder instructions. For resource efficiency,\n the returned :obj:`.IfElseOp` will not unnecessarily span all resources, but only the ones\n that it needs.\n\n Any condition added in by a call to :obj:`.Instruction.c_if` will be propagated through, but\n set properties like ``duration`` will not; it doesn't make sense for control-flow operations\n to have pulse scheduling on them.\n\n Args:\n qubits: The qubits the created instruction should be defined across.\n clbits: The clbits the created instruction should be defined across.\n\n Returns:\n A full version of the relevant control-flow instruction, and the resources that it uses.\n This is a \"proper\" instruction instance, as if it had been defined with the correct\n number of qubits and clbits from the beginning.\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.placeholder_resources","text":"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.placeholder_resources\n @abc.abstractmethod\n def placeholder_resources(self) -> InstructionResources:\n \"\"\"Get the qubit and clbit resources that this placeholder instruction should be considered\n as using before construction.\n\n This will likely not include *all* resources after the block has been built, but using the\n output of this method ensures that all resources will pass through a\n :meth:`.QuantumCircuit.append` call, even if they come from a placeholder, and consequently\n will be tracked by the scope managers.\n\n Returns:\n A collection of the quantum and classical resources this placeholder instruction will\n certainly use.\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder._copy_mutable_properties","text":"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder._copy_mutable_properties\n def _copy_mutable_properties(self, instruction: Instruction) -> Instruction:\n \"\"\"Copy mutable properties from ourselves onto a non-placeholder instruction.\n\n The mutable properties are expected to be things like ``condition``, added onto a\n placeholder by the :meth:`c_if` method. This mutates ``instruction``, and returns the same\n instance that was passed. This is mostly intended to make writing concrete versions of\n :meth:`.concrete_instruction` easy.\n\n The complete list of mutations is:\n\n * ``condition``, added by :meth:`c_if`.\n\n Args:\n instruction: the concrete instruction instance to be mutated.\n\n Returns:\n The same instruction instance that was passed, but mutated to propagate the tracked\n changes to this class.\n \"\"\"\n # In general the tuple creation should be a no-op, because ``tuple(t) is t`` for tuples.\n instruction.condition = None if self.condition is None else tuple(self.condition)\n return instruction","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.assemble","text":"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.assemble\n def assemble(self):\n raise CircuitError(\"Cannot assemble a placeholder instruction.\")","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.qasm","text":"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.qasm\n def qasm(self):\n raise CircuitError(\"Cannot convert a placeholder instruction to OpenQASM 2\")","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.repeat","text":"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.repeat\n def repeat(self, n):\n raise CircuitError(\"Cannot repeat a placeholder instruction.\")","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.__init__","text":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.__init__\n def __init__(\n self,\n qubits: Iterable[Qubit],\n clbits: Iterable[Clbit],\n *,\n registers: Iterable[Register] = (),\n resource_requester: Callable,\n allow_jumps: bool = True,\n ):\n \"\"\"\n Args:\n qubits: Any qubits this scope should consider itself as using from the beginning.\n clbits: Any clbits this scope should consider itself as using from the beginning. Along\n with ``qubits``, this is useful for things such as ``if`` and ``while`` loop\n builders, where the classical condition has associated resources, and is known when\n this scope is created.\n registers: Any registers this scope should consider itself as using from the\n beginning. This is useful for :obj:`.IfElseOp` and :obj:`.WhileLoopOp` instances\n which use a classical register as their condition.\n allow_jumps: Whether this builder scope should allow ``break`` and ``continue``\n statements within it. This is intended to help give sensible error messages when\n dangerous behaviour is encountered, such as using ``break`` inside an ``if`` context\n manager that is not within a ``for`` manager. This can only be safe if the user is\n going to place the resulting :obj:`.QuantumCircuit` inside a :obj:`.ForLoopOp` that\n uses *exactly* the same set of resources. We cannot verify this from within the\n builder interface (and it is too expensive to do when the ``for`` op is made), so we\n fail safe, and require the user to use the more verbose, internal form.\n resource_requester: A callback function that takes in some classical resource specifier,\n and returns a concrete classical resource, if this scope is allowed to access that\n ","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.allow_jumps","text":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.allow_jumps\n @property\n def allow_jumps(self):\n \"\"\"Whether this builder scope should allow ``break`` and ``continue`` statements within it.\n\n This is intended to help give sensible error messages when dangerous behaviour is\n encountered, such as using ``break`` inside an ``if`` context manager that is not within a\n ``for`` manager. This can only be safe if the user is going to place the resulting\n :obj:`.QuantumCircuit` inside a :obj:`.ForLoopOp` that uses *exactly* the same set of\n resources. We cannot verify this from within the builder interface (and it is too expensive\n to do when the ``for`` op is made), so we fail safe, and require the user to use the more\n verbose, internal form.\n \"\"\"\n return self._allow_jumps","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.append","text":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.append\n def append(\n self,\n operation: Instruction,\n qubits: Iterable[Qubit],\n clbits: Iterable[Clbit],\n ) -> Instruction:\n \"\"\"Add an instruction into the scope, keeping track of the qubits and clbits that have been\n used in total.\"\"\"\n if not self._allow_jumps:\n # pylint: disable=cyclic-import\n from .break_loop import BreakLoopOp, BreakLoopPlaceholder\n from .continue_loop import ContinueLoopOp, ContinueLoopPlaceholder\n\n forbidden = (BreakLoopOp, BreakLoopPlaceholder, ContinueLoopOp, ContinueLoopPlaceholder)\n if isinstance(operation, forbidden):\n raise CircuitError(\n f\"The current builder scope cannot take a '{operation.name}'\"\n \" because it is not in a loop.\"\n )\n\n qubits = tuple(qubits)\n clbits = tuple(clbits)\n self.instructions.append((operation, qubits, clbits))\n self.qubits.update(qubits)\n self.clbits.update(clbits)\n return operation","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.request_classical_resource","text":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.request_classical_resource\n def request_classical_resource(self, specifier):\n \"\"\"Resolve a single classical resource specifier into a concrete resource, raising an error\n if the specifier is invalid, and track it as now being used in scope.\n\n Args:\n specifier (Union[Clbit, ClassicalRegister, int]): a specifier of a classical resource\n present in this circuit. An ``int`` will be resolved into a :obj:`.Clbit` using the\n same conventions that measurement operations on this circuit use.\n\n Returns:\n Union[Clbit, ClassicalRegister]: the requested resource, resolved into a concrete\n instance of :obj:`.Clbit` or :obj:`.ClassicalRegister`.\n\n Raises:\n CircuitError: if the resource is not present in this circuit, or if the integer index\n passed is out-of-bounds.\n \"\"\"\n if self._built:\n raise CircuitError(\"Cannot add resources after the scope has been built.\")\n # Allow the inner resolve to propagate exceptions.\n resource = self._resource_requester(specifier)\n if isinstance(resource, Clbit):\n self.add_bits((resource,))\n else:\n self.add_register(resource)\n return resource","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.peek","text":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.peek\n def peek(self) -> Tuple[Instruction, Tuple[Qubit, ...], Tuple[Clbit, ...]]:\n \"\"\"Get the value of the most recent instruction tuple in this scope.\"\"\"\n if not self.instructions:\n raise CircuitError(\"This scope contains no instructions.\")\n return self.instructions[-1]","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.pop","text":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.pop\n def pop(self) -> Tuple[Instruction, Tuple[Qubit, ...], Tuple[Clbit, ...]]:\n \"\"\"Get the value of the most recent instruction tuple in this scope, and remove it from this\n object.\"\"\"\n if not self.instructions:\n raise CircuitError(\"This scope contains no instructions.\")\n operation, qubits, clbits = self.instructions.pop()\n return (operation, qubits, clbits)","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.add_bits","text":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.add_bits\n def add_bits(self, bits: Iterable[Union[Qubit, Clbit]]):\n \"\"\"Add extra bits to this scope that are not associated with any concrete instruction yet.\n\n This is useful for expanding a scope's resource width when it may contain ``break`` or\n ``continue`` statements, or when its width needs to be expanded to match another scope's\n width (as in the case of :obj:`.IfElseOp`).\n\n Args:\n bits: The qubits and clbits that should be added to a scope. It is not an error if\n there are duplicates, either within the iterable or with the bits currently in\n scope.\n\n Raises:\n TypeError: if the provided bit is of an incorrect type.\n \"\"\"\n for bit in bits:\n if isinstance(bit, Qubit):\n self.qubits.add(bit)\n elif isinstance(bit, Clbit):\n self.clbits.add(bit)\n else:\n raise TypeError(f\"Can only add qubits or classical bits, but received '{bit}'.\")","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.add_register","text":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.add_register\n def add_register(self, register: Register):\n \"\"\"Add a :obj:`.Register` to the set of resources used by this block, ensuring that\n all bits contained within are also accounted for.\n\n Args:\n register: the register to add to the block.\n \"\"\"\n if register in self.registers:\n # Fast return to avoid iterating through the bits.\n return\n self.registers.add(register)\n self.add_bits(register)","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.build","text":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.build\n def build(\n self, all_qubits: FrozenSet[Qubit], all_clbits: FrozenSet[Clbit]\n ) -> \"qiskit.circuit.QuantumCircuit\":\n \"\"\"Build this scoped block into a complete :obj:`.QuantumCircuit` instance.\n\n This will build a circuit which contains all of the necessary qubits and clbits and no\n others.\n\n The ``qubits`` and ``clbits`` arguments should be sets that contains all the resources in\n the outer scope; these will be passed down to inner placeholder instructions, so they can\n apply themselves across the whole scope should they need to. The resulting\n :obj:`.QuantumCircuit` will be defined over a (nonstrict) subset of these resources. This\n is used to let ``break`` and ``continue`` span all resources, even if they are nested within\n several :obj:`.IfElsePlaceholder` objects, without requiring :obj:`.IfElsePlaceholder`\n objects *without* any ``break`` or ``continue`` statements to be full-width.\n\n Args:\n all_qubits: all the qubits in the containing scope of this block. The block may expand\n to use some or all of these qubits, but will never gain qubits that are not in this\n set.\n all_clbits: all the clbits in the containing scope of this block. The block may expand\n to use some or all of these clbits, but will never gain clbits that are not in this\n set.\n\n Returns:\n A circuit containing concrete versions of all the instructions that were in the scope,\n and using the minimal set of resources necessary to support them, within the enclosing\n scope.\n \"\"\"\n from qiskit.circuit import QuantumCircuit\n\n # There's actually no real problem with building a scope more than once. This flag is more\n # so _other_ operations, which aren't safe can be forbidden, such as mutating ins","label":0},{"cid":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.copy","text":"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.copy\n def copy(self) -> \"ControlFlowBuilderBlock\":\n \"\"\"Return a semi-shallow copy of this builder block.\n\n The instruction lists and sets of qubits and clbits will be new instances (so mutations will\n not propagate), but any :obj:`.Instruction` instances within them will not be copied.\n\n Returns:\n a semi-shallow copy of this object.\n \"\"\"\n out = type(self).__new__(type(self))\n out.instructions = self.instructions.copy()\n out.qubits = self.qubits.copy()\n out.clbits = self.clbits.copy()\n out.registers = self.registers.copy()\n out._allow_jumps = self._allow_jumps\n return out","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__init__","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__init__\n def __init__(\n self,\n *regs: Union[Register, int, Sequence[Bit]],\n name: Optional[str] = None,\n global_phase: ParameterValueType = 0,\n metadata: Optional[Dict] = None,\n ):\n if any(not isinstance(reg, (list, QuantumRegister, ClassicalRegister)) for reg in regs):\n # check if inputs are integers, but also allow e.g. 2.0\n\n try:\n valid_reg_size = all(reg == int(reg) for reg in regs)\n except (ValueError, TypeError):\n valid_reg_size = False\n\n if not valid_reg_size:\n raise CircuitError(\n \"Circuit args must be Registers or integers. (%s '%s' was \"\n \"provided)\" % ([type(reg).__name__ for reg in regs], regs)\n )\n\n regs = tuple(int(reg) for reg in regs) # cast to int\n self._base_name = None\n if name is None:\n self._base_name = self.cls_prefix()\n self._name_update()\n elif not isinstance(name, str):\n raise CircuitError(\n \"The circuit name should be a string (or None to auto-generate a name).\"\n )\n else:\n self._base_name = name\n self.name = name\n self._increment_instances()\n\n # Data contains a list of instructions and their contexts,\n # in the order they were applied.\n self._data = []\n self._op_start_times = None\n\n # A stack to hold the instruction sets that are being built up during for-, if- and\n # while-block construction. These are stored as a stripped down sequence of instructions,\n # and sets of qubits and clbits, rather than a full QuantumCircuit instance because the\n # builder interfaces need to wait until they are completed before they can fill in things\n # like `break` and `continue`. This is because these instructions need to \"operate\" on","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.data","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.data\n @property\n def data(self) -> QuantumCircuitData:\n \"\"\"Return the circuit data (instructions and context).\n\n Returns:\n QuantumCircuitData: a list-like object containing the tuples for the circuit's data.\n\n Each tuple is in the format ``(instruction, qargs, cargs)``, where instruction is an\n Instruction (or subclass) object, qargs is a list of Qubit objects, and cargs is a\n list of Clbit objects.\n \"\"\"\n return QuantumCircuitData(self)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.op_start_times","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.op_start_times\n @property\n def op_start_times(self) -> List[int]:\n \"\"\"Return a list of operation start times.\n\n This attribute is enabled once one of scheduling analysis passes\n runs on the quantum circuit.\n\n Returns:\n List of integers representing instruction start times.\n The index corresponds to the index of instruction in :attr:`QuantumCircuit.data`.\n\n Raises:\n AttributeError: When circuit is not scheduled.\n \"\"\"\n if self._op_start_times is None:\n raise AttributeError(\n \"This circuit is not scheduled. \"\n \"To schedule it run the circuit through one of the transpiler scheduling passes.\"\n )\n return self._op_start_times","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.data","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.data\n @data.setter\n def data(\n self, data_input: List[Tuple[Instruction, List[QubitSpecifier], List[ClbitSpecifier]]]\n ):\n \"\"\"Sets the circuit data from a list of instructions and context.\n\n Args:\n data_input (list): A list of instructions with context\n in the format (instruction, qargs, cargs), where Instruction\n is an Instruction (or subclass) object, qargs is a list of\n Qubit objects, and cargs is a list of Clbit objects.\n \"\"\"\n\n # If data_input is QuantumCircuitData(self), clearing self._data\n # below will also empty data_input, so make a shallow copy first.\n data_input = data_input.copy()\n self._data = []\n self._parameter_table = ParameterTable()\n\n for inst, qargs, cargs in data_input:\n self.append(inst, qargs, cargs)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.calibrations","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.calibrations\n @property\n def calibrations(self) -> dict:\n \"\"\"Return calibration dictionary.\n\n The custom pulse definition of a given gate is of the form\n {'gate_name': {(qubits, params): schedule}}\n \"\"\"\n return dict(self._calibrations)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.calibrations","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.calibrations\n @calibrations.setter\n def calibrations(self, calibrations: dict):\n \"\"\"Set the circuit calibration data from a dictionary of calibration definition.\n\n Args:\n calibrations (dict): A dictionary of input in the format\n {'gate_name': {(qubits, gate_params): schedule}}\n \"\"\"\n self._calibrations = defaultdict(dict, calibrations)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.metadata","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.metadata\n @property\n def metadata(self) -> dict:\n \"\"\"The user provided metadata associated with the circuit\n\n The metadata for the circuit is a user provided ``dict`` of metadata\n for the circuit. It will not be used to influence the execution or\n operation of the circuit, but it is expected to be passed between\n all transforms of the circuit (ie transpilation) and that providers will\n associate any circuit metadata with the results it returns from\n execution of that circuit.\n \"\"\"\n return self._metadata","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.metadata","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.metadata\n @metadata.setter\n def metadata(self, metadata: Optional[dict]):\n \"\"\"Update the circuit metadata\"\"\"\n if not isinstance(metadata, dict) and metadata is not None:\n raise TypeError(\"Only a dictionary or None is accepted for circuit metadata\")\n self._metadata = metadata","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__str__","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__str__\n def __str__(self) -> str:\n return str(self.draw(output=\"text\"))","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__eq__","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__eq__\n def __eq__(self, other) -> bool:\n if not isinstance(other, QuantumCircuit):\n return False\n\n # TODO: remove the DAG from this function\n from qiskit.converters import circuit_to_dag\n\n return circuit_to_dag(self) == circuit_to_dag(other)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._increment_instances","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._increment_instances\n @classmethod\n def _increment_instances(cls):\n cls.instances += 1","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cls_instances","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cls_instances\n @classmethod\n def cls_instances(cls) -> int:\n \"\"\"Return the current number of instances of this class,\n useful for auto naming.\"\"\"\n return cls.instances","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cls_prefix","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cls_prefix\n @classmethod\n def cls_prefix(cls) -> str:\n \"\"\"Return the prefix to use for auto naming.\"\"\"\n return cls.prefix","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._name_update","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._name_update\n def _name_update(self) -> None:\n \"\"\"update name of instance using instance number\"\"\"\n if not is_main_process():\n pid_name = f\"-{mp.current_process().pid}\"\n else:\n pid_name = \"\"\n\n self.name = f\"{self._base_name}-{self.cls_instances()}{pid_name}\"","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.has_register","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.has_register\n def has_register(self, register: Register) -> bool:\n \"\"\"\n Test if this circuit has the register r.\n\n Args:\n register (Register): a quantum or classical register.\n\n Returns:\n bool: True if the register is contained in this circuit.\n \"\"\"\n has_reg = False\n if isinstance(register, QuantumRegister) and register in self.qregs:\n has_reg = True\n elif isinstance(register, ClassicalRegister) and register in self.cregs:\n has_reg = True\n return has_reg","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.reverse_ops","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.reverse_ops\n def reverse_ops(self) -> \"QuantumCircuit\":\n \"\"\"Reverse the circuit by reversing the order of instructions.\n\n This is done by recursively reversing all instructions.\n It does not invert (adjoint) any gate.\n\n Returns:\n QuantumCircuit: the reversed circuit.\n\n Examples:\n\n input:\n\n .. parsed-literal::\n\n ┌───┐\n q_0: ┤ H ├─────■──────\n └───┘┌────┴─────┐\n q_1: ─────┤ RX(1.57) ├\n └──────────┘\n\n output:\n\n .. parsed-literal::\n\n ┌───┐\n q_0: ─────■──────┤ H ├\n ┌────┴─────┐└───┘\n q_1: ┤ RX(1.57) ├─────\n └──────────┘\n \"\"\"\n reverse_circ = QuantumCircuit(\n self.qubits, self.clbits, *self.qregs, *self.cregs, name=self.name + \"_reverse\"\n )\n\n for inst, qargs, cargs in reversed(self.data):\n reverse_circ._append(inst.reverse_ops(), qargs, cargs)\n\n reverse_circ.duration = self.duration\n reverse_circ.unit = self.unit\n return reverse_circ","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.reverse_bits","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.reverse_bits\n def reverse_bits(self) -> \"QuantumCircuit\":\n \"\"\"Return a circuit with the opposite order of wires.\n\n The circuit is \"vertically\" flipped. If a circuit is\n defined over multiple registers, the resulting circuit will have\n the same registers but with their order flipped.\n\n This method is useful for converting a circuit written in little-endian\n convention to the big-endian equivalent, and vice versa.\n\n Returns:\n QuantumCircuit: the circuit with reversed bit order.\n\n Examples:\n\n input:\n\n .. parsed-literal::\n\n ┌───┐\n a_0: ┤ H ├──■─────────────────\n └───┘┌─┴─┐\n a_1: ─────┤ X ├──■────────────\n └───┘┌─┴─┐\n a_2: ──────────┤ X ├──■───────\n └───┘┌─┴─┐\n b_0: ───────────────┤ X ├──■──\n └───┘┌─┴─┐\n b_1: ────────────────────┤ X ├\n └───┘\n\n output:\n\n .. parsed-literal::\n\n ┌───┐\n b_0: ────────────────────┤ X ├\n ┌───┐└─┬─┘\n b_1: ───────────────┤ X ├──■──\n ┌───┐└─┬─┘\n a_0: ──────────┤ X ├──■───────\n ┌───┐└─┬─┘\n a_1: ─────┤ X ├──■────────────\n ┌───┐└─┬─┘\n a_2: ┤ H ├──■─────────────────\n └───┘\n \"\"\"\n circ = QuantumCircuit(\n list(reversed(self.qubits)),\n list(reversed(self.clbits)),\n name=self.name,\n global_phase=self.global_phase,\n )\n new_qubit_map = circ.qubits[::-1]\n new_clbit_map = circ.clbits[::-1]\n for reg in reversed(self.qregs):\n b","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.inverse","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.inverse\n def inverse(self) -> \"QuantumCircuit\":\n \"\"\"Invert (take adjoint of) this circuit.\n\n This is done by recursively inverting all gates.\n\n Returns:\n QuantumCircuit: the inverted circuit\n\n Raises:\n CircuitError: if the circuit cannot be inverted.\n\n Examples:\n\n input:\n\n .. parsed-literal::\n\n ┌───┐\n q_0: ┤ H ├─────■──────\n └───┘┌────┴─────┐\n q_1: ─────┤ RX(1.57) ├\n └──────────┘\n\n output:\n\n .. parsed-literal::\n\n ┌───┐\n q_0: ──────■──────┤ H ├\n ┌─────┴─────┐└───┘\n q_1: ┤ RX(-1.57) ├─────\n └───────────┘\n \"\"\"\n inverse_circ = QuantumCircuit(\n self.qubits,\n self.clbits,\n *self.qregs,\n *self.cregs,\n name=self.name + \"_dg\",\n global_phase=-self.global_phase,\n )\n\n for inst, qargs, cargs in reversed(self._data):\n inverse_circ._append(inst.inverse(), qargs, cargs)\n return inverse_circ","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.repeat","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.repeat\n def repeat(self, reps: int) -> \"QuantumCircuit\":\n \"\"\"Repeat this circuit ``reps`` times.\n\n Args:\n reps (int): How often this circuit should be repeated.\n\n Returns:\n QuantumCircuit: A circuit containing ``reps`` repetitions of this circuit.\n \"\"\"\n repeated_circ = QuantumCircuit(\n self.qubits, self.clbits, *self.qregs, *self.cregs, name=self.name + f\"**{reps}\"\n )\n\n # benefit of appending instructions: decomposing shows the subparts, i.e. the power\n # is actually `reps` times this circuit, and it is currently much faster than `compose`.\n if reps > 0:\n try: # try to append as gate if possible to not disallow to_gate\n inst: Instruction = self.to_gate()\n except QiskitError:\n inst = self.to_instruction()\n for _ in range(reps):\n repeated_circ._append(inst, self.qubits, self.clbits)\n\n return repeated_circ","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.power","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.power\n def power(self, power: float, matrix_power: bool = False) -> \"QuantumCircuit\":\n \"\"\"Raise this circuit to the power of ``power``.\n\n If ``power`` is a positive integer and ``matrix_power`` is ``False``, this implementation\n defaults to calling ``repeat``. Otherwise, if the circuit is unitary, the matrix is\n computed to calculate the matrix power.\n\n Args:\n power (float): The power to raise this circuit to.\n matrix_power (bool): If True, the circuit is converted to a matrix and then the\n matrix power is computed. If False, and ``power`` is a positive integer,\n the implementation defaults to ``repeat``.\n\n Raises:\n CircuitError: If the circuit needs to be converted to a gate but it is not unitary.\n\n Returns:\n QuantumCircuit: A circuit implementing this circuit raised to the power of ``power``.\n \"\"\"\n if power >= 0 and isinstance(power, (int, np.integer)) and not matrix_power:\n return self.repeat(power)\n\n # attempt conversion to gate\n if self.num_parameters > 0:\n raise CircuitError(\n \"Cannot raise a parameterized circuit to a non-positive power \"\n \"or matrix-power, please bind the free parameters: \"\n \"{}\".format(self.parameters)\n )\n\n try:\n gate = self.to_gate()\n except QiskitError as ex:\n raise CircuitError(\n \"The circuit contains non-unitary operations and cannot be \"\n \"controlled. Note that no qiskit.circuit.Instruction objects may \"\n \"be in the circuit for this operation.\"\n ) from ex\n\n power_circuit = QuantumCircuit(self.qubits, self.clbits, *self.qregs, *self.cregs)\n power_circuit.append(gate.power(power), list(range(gate.num_qubits)))\n return power_circuit","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.control","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.control\n def control(\n self,\n num_ctrl_qubits: int = 1,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> \"QuantumCircuit\":\n \"\"\"Control this circuit on ``num_ctrl_qubits`` qubits.\n\n Args:\n num_ctrl_qubits (int): The number of control qubits.\n label (str): An optional label to give the controlled operation for visualization.\n ctrl_state (str or int): The control state in decimal or as a bitstring\n (e.g. '111'). If None, use ``2**num_ctrl_qubits - 1``.\n\n Returns:\n QuantumCircuit: The controlled version of this circuit.\n\n Raises:\n CircuitError: If the circuit contains a non-unitary operation and cannot be controlled.\n \"\"\"\n try:\n gate = self.to_gate()\n except QiskitError as ex:\n raise CircuitError(\n \"The circuit contains non-unitary operations and cannot be \"\n \"controlled. Note that no qiskit.circuit.Instruction objects may \"\n \"be in the circuit for this operation.\"\n ) from ex\n\n controlled_gate = gate.control(num_ctrl_qubits, label, ctrl_state)\n control_qreg = QuantumRegister(num_ctrl_qubits)\n controlled_circ = QuantumCircuit(\n control_qreg, self.qubits, *self.qregs, name=f\"c_{self.name}\"\n )\n controlled_circ.append(controlled_gate, controlled_circ.qubits)\n\n return controlled_circ","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.combine","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.combine\n @deprecate_function(\n \"The QuantumCircuit.combine() method is being deprecated. \"\n \"Use the compose() method which is more flexible w.r.t \"\n \"circuit register compatibility.\"\n )\n def combine(self, rhs: \"QuantumCircuit\") -> \"QuantumCircuit\":\n \"\"\"DEPRECATED - Returns rhs appended to self if self contains compatible registers.\n\n Two circuits are compatible if they contain the same registers\n or if they contain different registers with unique names. The\n returned circuit will contain all unique registers between both\n circuits.\n\n Return self + rhs as a new object.\n\n Args:\n rhs (QuantumCircuit): The quantum circuit to append to the right hand side.\n\n Returns:\n QuantumCircuit: Returns a new QuantumCircuit object\n\n Raises:\n QiskitError: if the rhs circuit is not compatible\n \"\"\"\n # Check registers in LHS are compatible with RHS\n self._check_compatible_regs(rhs)\n\n # Make new circuit with combined registers\n combined_qregs = copy.deepcopy(self.qregs)\n combined_cregs = copy.deepcopy(self.cregs)\n\n for element in rhs.qregs:\n if element not in self.qregs:\n combined_qregs.append(element)\n for element in rhs.cregs:\n if element not in self.cregs:\n combined_cregs.append(element)\n circuit = QuantumCircuit(*combined_qregs, *combined_cregs)\n for instruction_context in itertools.chain(self.data, rhs.data):\n circuit._append(*instruction_context)\n circuit.global_phase = self.global_phase + rhs.global_phase\n\n for gate, cals in rhs.calibrations.items():\n for key, sched in cals.items():\n circuit.add_calibration(gate, qubits=key[0], schedule=sched, params=key[1])\n\n for gate, cals in self.calibrations.items():\n for key, sched ","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.extend","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.extend\n @deprecate_function(\n \"The QuantumCircuit.extend() method is being deprecated. Use the \"\n \"compose() (potentially with the inplace=True argument) and tensor() \"\n \"methods which are more flexible w.r.t circuit register compatibility.\"\n )\n def extend(self, rhs: \"QuantumCircuit\") -> \"QuantumCircuit\":\n \"\"\"DEPRECATED - Append QuantumCircuit to the RHS if it contains compatible registers.\n\n Two circuits are compatible if they contain the same registers\n or if they contain different registers with unique names. The\n returned circuit will contain all unique registers between both\n circuits.\n\n Modify and return self.\n\n Args:\n rhs (QuantumCircuit): The quantum circuit to append to the right hand side.\n\n Returns:\n QuantumCircuit: Returns this QuantumCircuit object (which has been modified)\n\n Raises:\n QiskitError: if the rhs circuit is not compatible\n \"\"\"\n # Check registers in LHS are compatible with RHS\n self._check_compatible_regs(rhs)\n\n # Add new registers\n for element in rhs.qregs:\n if element not in self.qregs:\n self.add_register(element)\n\n for element in rhs.cregs:\n if element not in self.cregs:\n self.add_register(element)\n\n # Copy the circuit data if rhs and self are the same, otherwise the data of rhs is\n # appended to both self and rhs resulting in an infinite loop\n data = rhs.data.copy() if rhs is self else rhs.data\n\n # Add new gates\n for instruction_context in data:\n self._append(*instruction_context)\n self.global_phase += rhs.global_phase\n\n for gate, cals in rhs.calibrations.items():\n for key, sched in cals.items():\n self.add_calibration(gate, qubits=key[0], schedule=sched, params=key[1])\n\n return self","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.compose","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.compose\n def compose(\n self,\n other: Union[\"QuantumCircuit\", Instruction],\n qubits: Optional[Sequence[Union[Qubit, int]]] = None,\n clbits: Optional[Sequence[Union[Clbit, int]]] = None,\n front: bool = False,\n inplace: bool = False,\n wrap: bool = False,\n ) -> Optional[\"QuantumCircuit\"]:\n \"\"\"Compose circuit with ``other`` circuit or instruction, optionally permuting wires.\n\n ``other`` can be narrower or of equal width to ``self``.\n\n Args:\n other (qiskit.circuit.Instruction or QuantumCircuit):\n (sub)circuit or instruction to compose onto self. If not a :obj:`.QuantumCircuit`,\n this can be anything that :obj:`.append` will accept.\n qubits (list[Qubit|int]): qubits of self to compose onto.\n clbits (list[Clbit|int]): clbits of self to compose onto.\n front (bool): If True, front composition will be performed (not implemented yet).\n inplace (bool): If True, modify the object. Otherwise return composed circuit.\n wrap (bool): If True, wraps the other circuit into a gate (or instruction, depending on\n whether it contains only unitary instructions) before composing it onto self.\n\n Returns:\n QuantumCircuit: the composed circuit (returns None if inplace==True).\n\n Raises:\n CircuitError: if composing on the front.\n QiskitError: if ``other`` is wider or there are duplicate edge mappings.\n\n Examples::\n\n lhs.compose(rhs, qubits=[3, 2], inplace=True)\n\n .. parsed-literal::\n\n ┌───┐ ┌─────┐ ┌───┐\n lqr_1_0: ───┤ H ├─── rqr_0: ──■──┤ Tdg ├ lqr_1_0: ───┤ H ├───────────────\n ├───┤ ┌─┴─┐└─────┘ ├───┤\n lqr_1_1: ───┤ X ├─── rqr_1: ┤ X ├──","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.tensor","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.tensor\n def tensor(self, other: \"QuantumCircuit\", inplace: bool = False) -> Optional[\"QuantumCircuit\"]:\n \"\"\"Tensor ``self`` with ``other``.\n\n Remember that in the little-endian convention the leftmost operation will be at the bottom\n of the circuit. See also\n [the docs](qiskit.org/documentation/tutorials/circuits/3_summary_of_quantum_operations.html)\n for more information.\n\n .. parsed-literal::\n\n ┌────────┐ ┌─────┐ ┌─────┐\n q_0: ┤ bottom ├ ⊗ q_0: ┤ top ├ = q_0: ─┤ top ├──\n └────────┘ └─────┘ ┌┴─────┴─┐\n q_1: ┤ bottom ├\n └────────┘\n\n Args:\n other (QuantumCircuit): The other circuit to tensor this circuit with.\n inplace (bool): If True, modify the object. Otherwise return composed circuit.\n\n Examples:\n\n .. jupyter-execute::\n\n from qiskit import QuantumCircuit\n top = QuantumCircuit(1)\n top.x(0);\n bottom = QuantumCircuit(2)\n bottom.cry(0.2, 0, 1);\n tensored = bottom.tensor(top)\n print(tensored.draw())\n\n Returns:\n QuantumCircuit: The tensored circuit (returns None if inplace==True).\n \"\"\"\n num_qubits = self.num_qubits + other.num_qubits\n num_clbits = self.num_clbits + other.num_clbits\n\n # If a user defined both circuits with via register sizes and not with named registers\n # (e.g. QuantumCircuit(2, 2)) then we have a naming collision, as the registers are by\n # default called \"q\" resp. \"c\". To still allow tensoring we define new registers of the\n # correct sizes.\n if (\n len(self.qregs) == len(other.qregs) == 1\n and self.qregs[0].name == other.qregs[0].name == \"q\"\n ","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubits","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubits\n @property\n def qubits(self) -> List[Qubit]:\n \"\"\"\n Returns a list of quantum bits in the order that the registers were added.\n \"\"\"\n return self._qubits","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.clbits","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.clbits\n @property\n def clbits(self) -> List[Clbit]:\n \"\"\"\n Returns a list of classical bits in the order that the registers were added.\n \"\"\"\n return self._clbits","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ancillas","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ancillas\n @property\n def ancillas(self) -> List[AncillaQubit]:\n \"\"\"\n Returns a list of ancilla bits in the order that the registers were added.\n \"\"\"\n return self._ancillas","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__add__","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__add__\n @deprecate_function(\n \"The QuantumCircuit.__add__() method is being deprecated.\"\n \"Use the compose() method which is more flexible w.r.t \"\n \"circuit register compatibility.\"\n )\n def __add__(self, rhs: \"QuantumCircuit\") -> \"QuantumCircuit\":\n \"\"\"Overload + to implement self.combine.\"\"\"\n return self.combine(rhs)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__iadd__","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__iadd__\n @deprecate_function(\n \"The QuantumCircuit.__iadd__() method is being deprecated. Use the \"\n \"compose() (potentially with the inplace=True argument) and tensor() \"\n \"methods which are more flexible w.r.t circuit register compatibility.\"\n )\n def __iadd__(self, rhs: \"QuantumCircuit\") -> \"QuantumCircuit\":\n \"\"\"Overload += to implement self.extend.\"\"\"\n return self.extend(rhs)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__and__","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__and__\n def __and__(self, rhs: \"QuantumCircuit\") -> \"QuantumCircuit\":\n \"\"\"Overload & to implement self.compose.\"\"\"\n return self.compose(rhs)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__iand__","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__iand__\n def __iand__(self, rhs: \"QuantumCircuit\") -> \"QuantumCircuit\":\n \"\"\"Overload &= to implement self.compose in place.\"\"\"\n self.compose(rhs, inplace=True)\n return self","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__xor__","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__xor__\n def __xor__(self, top: \"QuantumCircuit\") -> \"QuantumCircuit\":\n \"\"\"Overload ^ to implement self.tensor.\"\"\"\n return self.tensor(top)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__ixor__","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__ixor__\n def __ixor__(self, top: \"QuantumCircuit\") -> \"QuantumCircuit\":\n \"\"\"Overload ^= to implement self.tensor in place.\"\"\"\n self.tensor(top, inplace=True)\n return self","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__len__","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__len__\n def __len__(self) -> int:\n \"\"\"Return number of operations in circuit.\"\"\"\n return len(self._data)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__getitem__","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__getitem__\n @typing.overload\n def __getitem__(self, item: int) -> DataElement:\n ...","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__getitem__","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__getitem__\n @typing.overload\n def __getitem__(self, item: slice) -> List[DataElement]:\n ...","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__getitem__","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__getitem__\n def __getitem__(self, item):\n \"\"\"Return indexed operation.\"\"\"\n return self._data[item]","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cast","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cast\n @staticmethod\n def cast(value: S, type_: Callable[..., T]) -> Union[S, T]:\n \"\"\"Best effort to cast value to type. Otherwise, returns the value.\"\"\"\n try:\n return type_(value)\n except (ValueError, TypeError):\n return value","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qbit_argument_conversion","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qbit_argument_conversion\n def qbit_argument_conversion(self, qubit_representation: QubitSpecifier) -> List[Qubit]:\n \"\"\"\n Converts several qubit representations (such as indexes, range, etc.)\n into a list of qubits.\n\n Args:\n qubit_representation (Object): representation to expand\n\n Returns:\n List(Qubit): the resolved instances of the qubits.\n \"\"\"\n return _bit_argument_conversion(\n qubit_representation, self.qubits, self._qubit_indices, Qubit\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cbit_argument_conversion","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cbit_argument_conversion\n def cbit_argument_conversion(self, clbit_representation: ClbitSpecifier) -> List[Clbit]:\n \"\"\"\n Converts several classical bit representations (such as indexes, range, etc.)\n into a list of classical bits.\n\n Args:\n clbit_representation (Object): representation to expand\n\n Returns:\n List(tuple): Where each tuple is a classical bit.\n \"\"\"\n return _bit_argument_conversion(\n clbit_representation, self.clbits, self._clbit_indices, Clbit\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._resolve_classical_resource","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._resolve_classical_resource\n def _resolve_classical_resource(self, specifier):\n \"\"\"Resolve a single classical resource specifier into a concrete resource, raising an error\n if the specifier is invalid.\n\n This is slightly different to :meth:`.cbit_argument_conversion`, because it should not\n unwrap :obj:`.ClassicalRegister` instances into lists, and in general it should not allow\n iterables or broadcasting. It is expected to be used as a callback for things like\n :meth:`.InstructionSet.c_if` to check the validity of their arguments.\n\n Args:\n specifier (Union[Clbit, ClassicalRegister, int]): a specifier of a classical resource\n present in this circuit. An ``int`` will be resolved into a :obj:`.Clbit` using the\n same conventions as measurement operations on this circuit use.\n\n Returns:\n Union[Clbit, ClassicalRegister]: the resolved resource.\n\n Raises:\n CircuitError: if the resource is not present in this circuit, or if the integer index\n passed is out-of-bounds.\n \"\"\"\n if isinstance(specifier, Clbit):\n if specifier not in self._clbit_indices:\n raise CircuitError(f\"Clbit {specifier} is not present in this circuit.\")\n return specifier\n if isinstance(specifier, ClassicalRegister):\n # This is linear complexity for something that should be constant, but QuantumCircuit\n # does not currently keep a hashmap of registers, and requires non-trivial changes to\n # how it exposes its registers publically before such a map can be safely stored so it\n # doesn't miss updates. (Jake, 2021-11-10).\n if specifier not in self.cregs:\n raise CircuitError(f\"Register {specifier} is not present in this circuit.\")\n return specifier\n if isinstance(specifier, int):","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.append","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.append\n def append(\n self,\n instruction: Instruction,\n qargs: Optional[Sequence[QubitSpecifier]] = None,\n cargs: Optional[Sequence[ClbitSpecifier]] = None,\n ) -> InstructionSet:\n \"\"\"Append one or more instructions to the end of the circuit, modifying\n the circuit in place. Expands qargs and cargs.\n\n Args:\n instruction (qiskit.circuit.Instruction): Instruction instance to append\n qargs (list(argument)): qubits to attach instruction to\n cargs (list(argument)): clbits to attach instruction to\n\n Returns:\n qiskit.circuit.Instruction: a handle to the instruction that was just added\n\n Raises:\n CircuitError: if object passed is a subclass of Instruction\n CircuitError: if object passed is neither subclass nor an instance of Instruction\n \"\"\"\n # Convert input to instruction\n if not isinstance(instruction, Instruction) and not hasattr(instruction, \"to_instruction\"):\n if issubclass(instruction, Instruction):\n raise CircuitError(\n \"Object is a subclass of Instruction, please add () to \"\n \"pass an instance of this object.\"\n )\n\n raise CircuitError(\n \"Object to append must be an Instruction or have a to_instruction() method.\"\n )\n if not isinstance(instruction, Instruction) and hasattr(instruction, \"to_instruction\"):\n instruction = instruction.to_instruction()\n if not isinstance(instruction, Instruction):\n raise CircuitError(\"object is not an Instruction.\")\n\n # Make copy of parameterized gate instances\n if hasattr(instruction, \"params\"):\n is_parameter = any(isinstance(param, Parameter) for param in instruction.params)\n if is_parameter:\n instruction = copy.deepcopy(instruction)\n\n ","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._append","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._append\n def _append(\n self,\n instruction: Instruction,\n qargs: Sequence[Qubit],\n cargs: Sequence[Clbit],\n ) -> Instruction:\n \"\"\"Append an instruction to the end of the circuit, modifying the circuit in place.\n\n .. warning::\n\n This is an internal fast-path function, and it is the responsibility of the caller to\n ensure that all the arguments are valid; there is no error checking here. In\n particular, all the qubits and clbits must already exist in the circuit and there can be\n no duplicates in the list.\n\n .. note::\n\n This function may be used by callers other than :obj:`.QuantumCircuit` when the caller\n is sure that all error-checking, broadcasting and scoping has already been performed,\n and the only reference to the circuit the instructions are being appended to is within\n that same function. In particular, it is not safe to call\n :meth:`QuantumCircuit._append` on a circuit that is received by a function argument.\n This is because :meth:`.QuantumCircuit._append` will not recognise the scoping\n constructs of the control-flow builder interface.\n\n Args:\n instruction: Instruction instance to append\n qargs: Qubits to attach the instruction to.\n cargs: Clbits to attach the instruction to.\n\n Returns:\n Instruction: a handle to the instruction that was just added\n\n :meta public:\n \"\"\"\n\n self._data.append((instruction, qargs, cargs))\n self._update_parameter_table(instruction)\n\n # mark as normal circuit if a new instruction is added\n self.duration = None\n self.unit = \"dt\"\n\n return instruction","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._update_parameter_table","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._update_parameter_table\n def _update_parameter_table(self, instruction: Instruction) -> Instruction:\n for param_index, param in enumerate(instruction.params):\n if isinstance(param, (ParameterExpression, QuantumCircuit)):\n # Scoped constructs like the control-flow ops use QuantumCircuit as a parameter.\n atomic_parameters = set(param.parameters)\n else:\n atomic_parameters = set()\n\n for parameter in atomic_parameters:\n if parameter in self._parameter_table:\n self._parameter_table[parameter].add((instruction, param_index))\n else:\n if parameter.name in self._parameter_table.get_names():\n raise CircuitError(f\"Name conflict on adding parameter: {parameter.name}\")\n self._parameter_table[parameter] = ParameterReferences(\n ((instruction, param_index),)\n )\n\n # clear cache if new parameter is added\n self._parameters = None\n\n return instruction","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.add_register","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.add_register\n def add_register(self, *regs: Union[Register, int, Sequence[Bit]]) -> None:\n \"\"\"Add registers.\"\"\"\n if not regs:\n return\n\n if any(isinstance(reg, int) for reg in regs):\n # QuantumCircuit defined without registers\n if len(regs) == 1 and isinstance(regs[0], int):\n # QuantumCircuit with anonymous quantum wires e.g. QuantumCircuit(2)\n if regs[0] == 0:\n regs = tuple()\n else:\n regs = (QuantumRegister(regs[0], \"q\"),)\n elif len(regs) == 2 and all(isinstance(reg, int) for reg in regs):\n # QuantumCircuit with anonymous wires e.g. QuantumCircuit(2, 3)\n if regs[0] == 0:\n qregs = tuple()\n else:\n qregs = (QuantumRegister(regs[0], \"q\"),)\n if regs[1] == 0:\n cregs = tuple()\n else:\n cregs = (ClassicalRegister(regs[1], \"c\"),)\n regs = qregs + cregs\n else:\n raise CircuitError(\n \"QuantumCircuit parameters can be Registers or Integers.\"\n \" If Integers, up to 2 arguments. QuantumCircuit was called\"\n \" with %s.\" % (regs,)\n )\n\n for register in regs:\n if isinstance(register, Register) and any(\n register.name == reg.name for reg in self.qregs + self.cregs\n ):\n raise CircuitError('register name \"%s\" already exists' % register.name)\n\n if isinstance(register, AncillaRegister):\n for bit in register:\n if bit not in self._qubit_indices:\n self._ancillas.append(bit)\n\n if isinstance(register, QuantumRegister):\n self.qregs.append(register)\n\n for idx, bit in enumerate(reg","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.add_bits","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.add_bits\n def add_bits(self, bits: Iterable[Bit]) -> None:\n \"\"\"Add Bits to the circuit.\"\"\"\n duplicate_bits = set(self._qubit_indices).union(self._clbit_indices).intersection(bits)\n if duplicate_bits:\n raise CircuitError(f\"Attempted to add bits found already in circuit: {duplicate_bits}\")\n\n for bit in bits:\n if isinstance(bit, AncillaQubit):\n self._ancillas.append(bit)\n if isinstance(bit, Qubit):\n self._qubits.append(bit)\n self._qubit_indices[bit] = BitLocations(len(self._qubits) - 1, [])\n elif isinstance(bit, Clbit):\n self._clbits.append(bit)\n self._clbit_indices[bit] = BitLocations(len(self._clbits) - 1, [])\n else:\n raise CircuitError(\n \"Expected an instance of Qubit, Clbit, or \"\n \"AncillaQubit, but was passed {}\".format(bit)\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.find_bit","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.find_bit\n def find_bit(self, bit: Bit) -> BitLocations:\n \"\"\"Find locations in the circuit which can be used to reference a given :obj:`~Bit`.\n\n Args:\n bit (Bit): The bit to locate.\n\n Returns:\n namedtuple(int, List[Tuple(Register, int)]): A 2-tuple. The first element (``index``)\n contains the index at which the ``Bit`` can be found (in either\n :obj:`~QuantumCircuit.qubits`, :obj:`~QuantumCircuit.clbits`, depending on its\n type). The second element (``registers``) is a list of ``(register, index)``\n pairs with an entry for each :obj:`~Register` in the circuit which contains the\n :obj:`~Bit` (and the index in the :obj:`~Register` at which it can be found).\n\n Notes:\n The circuit index of an :obj:`~AncillaQubit` will be its index in\n :obj:`~QuantumCircuit.qubits`, not :obj:`~QuantumCircuit.ancillas`.\n\n Raises:\n CircuitError: If the supplied :obj:`~Bit` was of an unknown type.\n CircuitError: If the supplied :obj:`~Bit` could not be found on the circuit.\n \"\"\"\n\n try:\n if isinstance(bit, Qubit):\n return self._qubit_indices[bit]\n elif isinstance(bit, Clbit):\n return self._clbit_indices[bit]\n else:\n raise CircuitError(f\"Could not locate bit of unknown type: {type(bit)}\")\n except KeyError as err:\n raise CircuitError(\n f\"Could not locate provided bit: {bit}. Has it been added to the QuantumCircuit?\"\n ) from err","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._check_dups","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._check_dups\n def _check_dups(self, qubits: Sequence[Qubit]) -> None:\n \"\"\"Raise exception if list of qubits contains duplicates.\"\"\"\n squbits = set(qubits)\n if len(squbits) != len(qubits):\n raise CircuitError(\"duplicate qubit arguments\")","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.to_instruction","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.to_instruction\n def to_instruction(\n self,\n parameter_map: Optional[Dict[Parameter, ParameterValueType]] = None,\n label: Optional[str] = None,\n ) -> Instruction:\n \"\"\"Create an Instruction out of this circuit.\n\n Args:\n parameter_map(dict): For parameterized circuits, a mapping from\n parameters in the circuit to parameters to be used in the\n instruction. If None, existing circuit parameters will also\n parameterize the instruction.\n label (str): Optional gate label.\n\n Returns:\n qiskit.circuit.Instruction: a composite instruction encapsulating this circuit\n (can be decomposed back)\n \"\"\"\n from qiskit.converters.circuit_to_instruction import circuit_to_instruction\n\n return circuit_to_instruction(self, parameter_map, label=label)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.to_gate","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.to_gate\n def to_gate(\n self,\n parameter_map: Optional[Dict[Parameter, ParameterValueType]] = None,\n label: Optional[str] = None,\n ) -> Gate:\n \"\"\"Create a Gate out of this circuit.\n\n Args:\n parameter_map(dict): For parameterized circuits, a mapping from\n parameters in the circuit to parameters to be used in the\n gate. If None, existing circuit parameters will also\n parameterize the gate.\n label (str): Optional gate label.\n\n Returns:\n Gate: a composite gate encapsulating this circuit\n (can be decomposed back)\n \"\"\"\n from qiskit.converters.circuit_to_gate import circuit_to_gate\n\n return circuit_to_gate(self, parameter_map, label=label)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.decompose","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.decompose\n def decompose(\n self,\n gates_to_decompose: Optional[\n Union[Type[Gate], Sequence[Type[Gate]], Sequence[str], str]\n ] = None,\n reps: int = 1,\n ) -> \"QuantumCircuit\":\n \"\"\"Call a decomposition pass on this circuit,\n to decompose one level (shallow decompose).\n\n Args:\n gates_to_decompose (str or list(str)): optional subset of gates to decompose.\n Defaults to all gates in circuit.\n reps (int): Optional number of times the circuit should be decomposed.\n For instance, ``reps=2`` equals calling ``circuit.decompose().decompose()``.\n can decompose specific gates specific time\n\n Returns:\n QuantumCircuit: a circuit one level decomposed\n \"\"\"\n # pylint: disable=cyclic-import\n from qiskit.transpiler.passes.basis.decompose import Decompose\n from qiskit.converters.circuit_to_dag import circuit_to_dag\n from qiskit.converters.dag_to_circuit import dag_to_circuit\n\n pass_ = Decompose(gates_to_decompose)\n dag = circuit_to_dag(self)\n for _ in range(reps):\n dag = pass_.run(dag)\n return dag_to_circuit(dag)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._check_compatible_regs","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._check_compatible_regs\n def _check_compatible_regs(self, rhs: \"QuantumCircuit\") -> None:\n \"\"\"Raise exception if the circuits are defined on incompatible registers\"\"\"\n list1 = self.qregs + self.cregs\n list2 = rhs.qregs + rhs.cregs\n for element1 in list1:\n for element2 in list2:\n if element2.name == element1.name:\n if element1 != element2:\n raise CircuitError(\n \"circuits are not compatible:\"\n f\" registers {element1} and {element2} not compatible\"\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._unique_register_name","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._unique_register_name\n def _unique_register_name(self, prefix: str = \"\") -> str:\n \"\"\"Generate a register name with the given prefix, which is unique within this circuit.\"\"\"\n used = {\n reg.name[len(prefix) :]\n for reg in itertools.chain(self.qregs, self.cregs)\n if reg.name.startswith(prefix)\n }\n characters = (string.digits + string.ascii_letters) if prefix else string.ascii_letters\n for parts in itertools.chain.from_iterable(\n itertools.product(characters, repeat=n) for n in itertools.count(1)\n ):\n name = \"\".join(parts)\n if name not in used:\n return prefix + name\n # This isn't actually reachable because the above loop is infinite.\n return prefix","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qasm","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qasm\n def qasm(\n self,\n formatted: bool = False,\n filename: Optional[str] = None,\n encoding: Optional[str] = None,\n ) -> Optional[str]:\n \"\"\"Return OpenQASM string.\n\n Args:\n formatted (bool): Return formatted Qasm string.\n filename (str): Save Qasm to file with name 'filename'.\n encoding (str): Optionally specify the encoding to use for the\n output file if ``filename`` is specified. By default this is\n set to the system's default encoding (ie whatever\n ``locale.getpreferredencoding()`` returns) and can be set to\n any valid codec or alias from stdlib's\n `codec module <https://docs.python.org/3/library/codecs.html#standard-encodings>`__\n\n Returns:\n str: If formatted=False.\n\n Raises:\n MissingOptionalLibraryError: If pygments is not installed and ``formatted`` is\n ``True``.\n QasmError: If circuit has free parameters.\n \"\"\"\n\n if self.num_parameters > 0:\n raise QasmError(\"Cannot represent circuits with unbound parameters in OpenQASM 2.\")\n\n existing_gate_names = [\n \"barrier\",\n \"measure\",\n \"reset\",\n \"u3\",\n \"u2\",\n \"u1\",\n \"cx\",\n \"id\",\n \"u0\",\n \"u\",\n \"p\",\n \"x\",\n \"y\",\n \"z\",\n \"h\",\n \"s\",\n \"sdg\",\n \"t\",\n \"tdg\",\n \"rx\",\n \"ry\",\n \"rz\",\n \"sx\",\n \"sxdg\",\n \"cz\",\n \"cy\",\n \"swap\",\n \"ch\",\n \"ccx\",\n \"cswap\",\n \"crx\",\n \"cry\",\n \"crz\",\n \"cu1\",\n \"cp\",\n \"cu3\",\n \"csx\",\n \"cu\",\n \"rxx\",\n ","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.draw","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.draw\n def draw(\n self,\n output: Optional[str] = None,\n scale: Optional[float] = None,\n filename: Optional[str] = None,\n style: Optional[Union[dict, str]] = None,\n interactive: bool = False,\n plot_barriers: bool = True,\n reverse_bits: bool = False,\n justify: Optional[str] = None,\n vertical_compression: Optional[str] = \"medium\",\n idle_wires: bool = True,\n with_layout: bool = True,\n fold: Optional[int] = None,\n # The type of ax is matplotlib.axes.Axes, but this is not a fixed dependency, so cannot be\n # safely forward-referenced.\n ax: Optional[typing.Any] = None,\n initial_state: bool = False,\n cregbundle: bool = True,\n ):\n \"\"\"Draw the quantum circuit. Use the output parameter to choose the drawing format:\n\n **text**: ASCII art TextDrawing that can be printed in the console.\n\n **mpl**: images with color rendered purely in Python using matplotlib.\n\n **latex**: high-quality images compiled via latex.\n\n **latex_source**: raw uncompiled latex output.\n\n Args:\n output (str): select the output method to use for drawing the circuit.\n Valid choices are ``text``, ``mpl``, ``latex``, ``latex_source``.\n By default the `text` drawer is used unless the user config file\n (usually ``~/.qiskit/settings.conf``) has an alternative backend set\n as the default. For example, ``circuit_drawer = latex``. If the output\n kwarg is set, that backend will always be used over the default in\n the user config file.\n scale (float): scale of image to draw (shrink if < 1.0). Only used by\n the `mpl`, `latex` and `latex_source` outputs. Defaults to 1.0.\n filename (str): file path to save image to. Defaults to None.\n style (dict or str): dict","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.size","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.size\n def size(self, filter_function: Optional[callable] = lambda x: not x[0]._directive) -> int:\n \"\"\"Returns total number of instructions in circuit.\n\n Args:\n filter_function (callable): a function to filter out some instructions.\n Should take as input a tuple of (Instruction, list(Qubit), list(Clbit)).\n By default filters out \"directives\", such as barrier or snapshot.\n\n Returns:\n int: Total number of gate operations.\n \"\"\"\n return sum(map(filter_function, self._data))","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.depth","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.depth\n def depth(self, filter_function: Optional[callable] = lambda x: not x[0]._directive) -> int:\n \"\"\"Return circuit depth (i.e., length of critical path).\n\n Args:\n filter_function (callable): a function to filter out some instructions.\n Should take as input a tuple of (Instruction, list(Qubit), list(Clbit)).\n By default filters out \"directives\", such as barrier or snapshot.\n\n Returns:\n int: Depth of circuit.\n\n Notes:\n The circuit depth and the DAG depth need not be the\n same.\n \"\"\"\n # Assign each bit in the circuit a unique integer\n # to index into op_stack.\n bit_indices = {bit: idx for idx, bit in enumerate(self.qubits + self.clbits)}\n\n # If no bits, return 0\n if not bit_indices:\n return 0\n\n # A list that holds the height of each qubit\n # and classical bit.\n op_stack = [0] * len(bit_indices)\n\n # Here we are playing a modified version of\n # Tetris where we stack gates, but multi-qubit\n # gates, or measurements have a block for each\n # qubit or cbit that are connected by a virtual\n # line so that they all stacked at the same depth.\n # Conditional gates act on all cbits in the register\n # they are conditioned on.\n # The max stack height is the circuit depth.\n for instr, qargs, cargs in self._data:\n levels = []\n reg_ints = []\n for ind, reg in enumerate(qargs + cargs):\n # Add to the stacks of the qubits and\n # cbits used in the gate.\n reg_ints.append(bit_indices[reg])\n if filter_function((instr, qargs, cargs)):\n levels.append(op_stack[reg_ints[ind]] + 1)\n else:\n levels.append(op_stack[reg_ints[ind]])\n # Assuming here that there ","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.width","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.width\n def width(self) -> int:\n \"\"\"Return number of qubits plus clbits in circuit.\n\n Returns:\n int: Width of circuit.\n\n \"\"\"\n return len(self.qubits) + len(self.clbits)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_qubits","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_qubits\n @property\n def num_qubits(self) -> int:\n \"\"\"Return number of qubits.\"\"\"\n return len(self.qubits)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_ancillas","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_ancillas\n @property\n def num_ancillas(self) -> int:\n \"\"\"Return the number of ancilla qubits.\"\"\"\n return len(self.ancillas)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_clbits","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_clbits\n @property\n def num_clbits(self) -> int:\n \"\"\"Return number of classical bits.\"\"\"\n return len(self.clbits)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.count_ops","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.count_ops\n def count_ops(self) -> \"OrderedDict[Instruction, int]\":\n \"\"\"Count each operation kind in the circuit.\n\n Returns:\n OrderedDict: a breakdown of how many operations of each kind, sorted by amount.\n \"\"\"\n count_ops: Dict[Instruction, int] = {}\n for instr, _, _ in self._data:\n count_ops[instr.name] = count_ops.get(instr.name, 0) + 1\n return OrderedDict(sorted(count_ops.items(), key=lambda kv: kv[1], reverse=True))","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_nonlocal_gates","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_nonlocal_gates\n def num_nonlocal_gates(self) -> int:\n \"\"\"Return number of non-local gates (i.e. involving 2+ qubits).\n\n Conditional nonlocal gates are also included.\n \"\"\"\n multi_qubit_gates = 0\n for instr, _, _ in self._data:\n if instr.num_qubits > 1 and not instr._directive:\n multi_qubit_gates += 1\n return multi_qubit_gates","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.get_instructions","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.get_instructions\n def get_instructions(self, name: str) -> List[DataElement]:\n \"\"\"Get instructions matching name.\n\n Args:\n name (str): The name of instruction to.\n\n Returns:\n list(tuple): list of (instruction, qargs, cargs).\n \"\"\"\n return [match for match in self._data if match[0].name == name]","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_connected_components","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_connected_components\n def num_connected_components(self, unitary_only: bool = False) -> int:\n \"\"\"How many non-entangled subcircuits can the circuit be factored to.\n\n Args:\n unitary_only (bool): Compute only unitary part of graph.\n\n Returns:\n int: Number of connected components in circuit.\n \"\"\"\n # Convert registers to ints (as done in depth).\n bits = self.qubits if unitary_only else (self.qubits + self.clbits)\n bit_indices = {bit: idx for idx, bit in enumerate(bits)}\n\n # Start with each qubit or cbit being its own subgraph.\n sub_graphs = [[bit] for bit in range(len(bit_indices))]\n\n num_sub_graphs = len(sub_graphs)\n\n # Here we are traversing the gates and looking to see\n # which of the sub_graphs the gate joins together.\n for instr, qargs, cargs in self._data:\n if unitary_only:\n args = qargs\n num_qargs = len(args)\n else:\n args = qargs + cargs\n num_qargs = len(args) + (1 if instr.condition else 0)\n\n if num_qargs >= 2 and not instr._directive:\n graphs_touched = []\n num_touched = 0\n # Controls necessarily join all the cbits in the\n # register that they use.\n if not unitary_only:\n for bit in instr.condition_bits:\n idx = bit_indices[bit]\n for k in range(num_sub_graphs):\n if idx in sub_graphs[k]:\n graphs_touched.append(k)\n break\n\n for item in args:\n reg_int = bit_indices[item]\n for k in range(num_sub_graphs):\n if reg_int in sub_graphs[k]:\n if k not in graphs_touched:\n ","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_unitary_factors","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_unitary_factors\n def num_unitary_factors(self) -> int:\n \"\"\"Computes the number of tensor factors in the unitary\n (quantum) part of the circuit only.\n \"\"\"\n return self.num_connected_components(unitary_only=True)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_tensor_factors","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_tensor_factors\n def num_tensor_factors(self) -> int:\n \"\"\"Computes the number of tensor factors in the unitary\n (quantum) part of the circuit only.\n\n Notes:\n This is here for backwards compatibility, and will be\n removed in a future release of Qiskit. You should call\n `num_unitary_factors` instead.\n \"\"\"\n return self.num_unitary_factors()","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.copy","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.copy\n def copy(self, name: Optional[str] = None) -> \"QuantumCircuit\":\n \"\"\"Copy the circuit.\n\n Args:\n name (str): name to be given to the copied circuit. If None, then the name stays the same\n\n Returns:\n QuantumCircuit: a deepcopy of the current circuit, with the specified name\n \"\"\"\n cpy = copy.copy(self)\n # copy registers correctly, in copy.copy they are only copied via reference\n cpy.qregs = self.qregs.copy()\n cpy.cregs = self.cregs.copy()\n cpy._qubits = self._qubits.copy()\n cpy._ancillas = self._ancillas.copy()\n cpy._clbits = self._clbits.copy()\n cpy._qubit_indices = self._qubit_indices.copy()\n cpy._clbit_indices = self._clbit_indices.copy()\n\n instr_instances = {id(instr): instr for instr, _, __ in self._data}\n\n instr_copies = {id_: instr.copy() for id_, instr in instr_instances.items()}\n\n cpy._parameter_table = ParameterTable(\n {\n param: ParameterReferences(\n (instr_copies[id(instr)], param_index)\n for instr, param_index in self._parameter_table[param]\n )\n for param in self._parameter_table\n }\n )\n\n cpy._data = [\n (instr_copies[id(inst)], qargs.copy(), cargs.copy())\n for inst, qargs, cargs in self._data\n ]\n\n cpy._calibrations = copy.deepcopy(self._calibrations)\n cpy._metadata = copy.deepcopy(self._metadata)\n\n if name:\n cpy.name = name\n return cpy","label":1},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._create_creg","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._create_creg\n def _create_creg(self, length: int, name: str) -> ClassicalRegister:\n \"\"\"Creates a creg, checking if ClassicalRegister with same name exists\"\"\"\n if name in [creg.name for creg in self.cregs]:\n save_prefix = ClassicalRegister.prefix\n ClassicalRegister.prefix = name\n new_creg = ClassicalRegister(length)\n ClassicalRegister.prefix = save_prefix\n else:\n new_creg = ClassicalRegister(length, name)\n return new_creg","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._create_qreg","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._create_qreg\n def _create_qreg(self, length: int, name: str) -> QuantumRegister:\n \"\"\"Creates a qreg, checking if QuantumRegister with same name exists\"\"\"\n if name in [qreg.name for qreg in self.qregs]:\n save_prefix = QuantumRegister.prefix\n QuantumRegister.prefix = name\n new_qreg = QuantumRegister(length)\n QuantumRegister.prefix = save_prefix\n else:\n new_qreg = QuantumRegister(length, name)\n return new_qreg","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.reset","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.reset\n def reset(self, qubit: QubitSpecifier) -> InstructionSet:\n \"\"\"Reset the quantum bit(s) to their default state.\n\n Args:\n qubit: qubit(s) to reset.\n\n Returns:\n qiskit.circuit.InstructionSet: handle to the added instruction.\n \"\"\"\n return self.append(Reset(), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.measure","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.measure\n def measure(self, qubit: QubitSpecifier, cbit: ClbitSpecifier) -> InstructionSet:\n \"\"\"Measure quantum bit into classical bit (tuples).\n\n Args:\n qubit: qubit to measure.\n cbit: classical bit to place the measurement in.\n\n Returns:\n qiskit.circuit.InstructionSet: handle to the added instructions.\n\n Raises:\n CircuitError: if arguments have bad format.\n \"\"\"\n return self.append(Measure(), [qubit], [cbit])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.measure_active","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.measure_active\n def measure_active(self, inplace: bool = True) -> Optional[\"QuantumCircuit\"]:\n \"\"\"Adds measurement to all non-idle qubits. Creates a new ClassicalRegister with\n a size equal to the number of non-idle qubits being measured.\n\n Returns a new circuit with measurements if `inplace=False`.\n\n Args:\n inplace (bool): All measurements inplace or return new circuit.\n\n Returns:\n QuantumCircuit: Returns circuit with measurements when `inplace = False`.\n \"\"\"\n from qiskit.converters.circuit_to_dag import circuit_to_dag\n\n if inplace:\n circ = self\n else:\n circ = self.copy()\n dag = circuit_to_dag(circ)\n qubits_to_measure = [qubit for qubit in circ.qubits if qubit not in dag.idle_wires()]\n new_creg = circ._create_creg(len(qubits_to_measure), \"measure\")\n circ.add_register(new_creg)\n circ.barrier()\n circ.measure(qubits_to_measure, new_creg)\n\n if not inplace:\n return circ\n else:\n return None","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.measure_all","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.measure_all\n def measure_all(\n self, inplace: bool = True, add_bits: bool = True\n ) -> Optional[\"QuantumCircuit\"]:\n \"\"\"Adds measurement to all qubits.\n\n By default, adds new classical bits in a :obj:`.ClassicalRegister` to store these\n measurements. If ``add_bits=False``, the results of the measurements will instead be stored\n in the already existing classical bits, with qubit ``n`` being measured into classical bit\n ``n``.\n\n Returns a new circuit with measurements if ``inplace=False``.\n\n Args:\n inplace (bool): All measurements inplace or return new circuit.\n add_bits (bool): Whether to add new bits to store the results.\n\n Returns:\n QuantumCircuit: Returns circuit with measurements when ``inplace=False``.\n\n Raises:\n CircuitError: if ``add_bits=False`` but there are not enough classical bits.\n \"\"\"\n if inplace:\n circ = self\n else:\n circ = self.copy()\n if add_bits:\n new_creg = circ._create_creg(len(circ.qubits), \"meas\")\n circ.add_register(new_creg)\n circ.barrier()\n circ.measure(circ.qubits, new_creg)\n else:\n if len(circ.clbits) < len(circ.qubits):\n raise CircuitError(\n \"The number of classical bits must be equal or greater than \"\n \"the number of qubits.\"\n )\n circ.barrier()\n circ.measure(circ.qubits, circ.clbits[0 : len(circ.qubits)])\n\n if not inplace:\n return circ\n else:\n return None","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.remove_final_measurements","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.remove_final_measurements\n def remove_final_measurements(self, inplace: bool = True) -> Optional[\"QuantumCircuit\"]:\n \"\"\"Removes final measurements and barriers on all qubits if they are present.\n Deletes the classical registers that were used to store the values from these measurements\n that become idle as a result of this operation, and deletes classical bits that are\n referenced only by removed registers, or that aren't referenced at all but have\n become idle as a result of this operation.\n\n Measurements and barriers are considered final if they are\n followed by no other operations (aside from other measurements or barriers.)\n\n Args:\n inplace (bool): All measurements removed inplace or return new circuit.\n\n Returns:\n QuantumCircuit: Returns the resulting circuit when ``inplace=False``, else None.\n \"\"\"\n # pylint: disable=cyclic-import\n from qiskit.transpiler.passes import RemoveFinalMeasurements\n from qiskit.converters import circuit_to_dag\n\n if inplace:\n circ = self\n else:\n circ = self.copy()\n\n dag = circuit_to_dag(circ)\n remove_final_meas = RemoveFinalMeasurements()\n new_dag = remove_final_meas.run(dag)\n kept_cregs = set(new_dag.cregs.values())\n kept_clbits = set(new_dag.clbits)\n\n # Filter only cregs/clbits still in new DAG, preserving original circuit order\n cregs_to_add = [creg for creg in circ.cregs if creg in kept_cregs]\n clbits_to_add = [clbit for clbit in circ._clbits if clbit in kept_clbits]\n\n # Clear cregs and clbits\n circ.cregs = []\n circ._clbits = []\n circ._clbit_indices = {}\n\n # We must add the clbits first to preserve the original circuit\n # order. This way, add_register never adds clbits and just\n # creates registers that point to them.\n circ.","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.from_qasm_file","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.from_qasm_file\n @staticmethod\n def from_qasm_file(path: str) -> \"QuantumCircuit\":\n \"\"\"Take in a QASM file and generate a QuantumCircuit object.\n\n Args:\n path (str): Path to the file for a QASM program\n Return:\n QuantumCircuit: The QuantumCircuit object for the input QASM\n \"\"\"\n qasm = Qasm(filename=path)\n return _circuit_from_qasm(qasm)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.from_qasm_str","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.from_qasm_str\n @staticmethod\n def from_qasm_str(qasm_str: str) -> \"QuantumCircuit\":\n \"\"\"Take in a QASM string and generate a QuantumCircuit object.\n\n Args:\n qasm_str (str): A QASM program string\n Return:\n QuantumCircuit: The QuantumCircuit object for the input QASM\n \"\"\"\n qasm = Qasm(data=qasm_str)\n return _circuit_from_qasm(qasm)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.global_phase","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.global_phase\n @property\n def global_phase(self) -> ParameterValueType:\n \"\"\"Return the global phase of the circuit in radians.\"\"\"\n return self._global_phase","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.global_phase","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.global_phase\n @global_phase.setter\n def global_phase(self, angle: ParameterValueType):\n \"\"\"Set the phase of the circuit.\n\n Args:\n angle (float, ParameterExpression): radians\n \"\"\"\n if isinstance(angle, ParameterExpression) and angle.parameters:\n self._global_phase = angle\n else:\n # Set the phase to the [0, 2π) interval\n angle = float(angle)\n if not angle:\n self._global_phase = 0\n else:\n self._global_phase = angle % (2 * np.pi)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.parameters","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.parameters\n @property\n def parameters(self) -> ParameterView:\n \"\"\"Convenience function to get the parameters defined in the parameter table.\"\"\"\n # parameters from gates\n if self._parameters is None:\n unsorted = self._unsorted_parameters()\n self._parameters = sorted(unsorted, key=functools.cmp_to_key(_compare_parameters))\n\n # return as parameter view, which implements the set and list interface\n return ParameterView(self._parameters)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_parameters","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_parameters\n @property\n def num_parameters(self) -> int:\n \"\"\"Convenience function to get the number of parameter objects in the circuit.\"\"\"\n return len(self._unsorted_parameters())","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._unsorted_parameters","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._unsorted_parameters\n def _unsorted_parameters(self) -> Set[Parameter]:\n \"\"\"Efficiently get all parameters in the circuit, without any sorting overhead.\"\"\"\n parameters = set(self._parameter_table)\n if isinstance(self.global_phase, ParameterExpression):\n parameters.update(self.global_phase.parameters)\n\n return parameters","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.assign_parameters","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.assign_parameters\n def assign_parameters(\n self,\n parameters: Union[Mapping[Parameter, ParameterValueType], Sequence[ParameterValueType]],\n inplace: bool = False,\n ) -> Optional[\"QuantumCircuit\"]:\n \"\"\"Assign parameters to new parameters or values.\n\n The keys of the parameter dictionary must be Parameter instances in the current circuit. The\n values of the dictionary can either be numeric values or new parameter objects.\n The values can be assigned to the current circuit object or to a copy of it.\n\n Args:\n parameters (dict or iterable): Either a dictionary or iterable specifying the new\n parameter values. If a dict, it specifies the mapping from ``current_parameter`` to\n ``new_parameter``, where ``new_parameter`` can be a new parameter object or a\n numeric value. If an iterable, the elements are assigned to the existing parameters\n in the order of ``QuantumCircuit.parameters``.\n inplace (bool): If False, a copy of the circuit with the bound parameters is\n returned. If True the circuit instance itself is modified.\n\n Raises:\n CircuitError: If parameters is a dict and contains parameters not present in the\n circuit.\n ValueError: If parameters is a list/array and the length mismatches the number of free\n parameters in the circuit.\n\n Returns:\n Optional(QuantumCircuit): A copy of the circuit with bound parameters, if\n ``inplace`` is False, otherwise None.\n\n Examples:\n\n Create a parameterized circuit and assign the parameters in-place.\n\n .. jupyter-execute::\n\n from qiskit.circuit import QuantumCircuit, Parameter\n\n circuit = QuantumCircuit(2)\n params = [Parameter('A'), Parameter('B'), Parameter('C')]\n ","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.bind_parameters","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.bind_parameters\n def bind_parameters(\n self, values: Union[Mapping[Parameter, float], Sequence[float]]\n ) -> \"QuantumCircuit\":\n \"\"\"Assign numeric parameters to values yielding a new circuit.\n\n To assign new Parameter objects or bind the values in-place, without yielding a new\n circuit, use the :meth:`assign_parameters` method.\n\n Args:\n values (dict or iterable): {parameter: value, ...} or [value1, value2, ...]\n\n Raises:\n CircuitError: If values is a dict and contains parameters not present in the circuit.\n TypeError: If values contains a ParameterExpression.\n\n Returns:\n QuantumCircuit: copy of self with assignment substitution.\n \"\"\"\n if isinstance(values, dict):\n if any(isinstance(value, ParameterExpression) for value in values.values()):\n raise TypeError(\n \"Found ParameterExpression in values; use assign_parameters() instead.\"\n )\n return self.assign_parameters(values)\n else:\n if any(isinstance(value, ParameterExpression) for value in values):\n raise TypeError(\n \"Found ParameterExpression in values; use assign_parameters() instead.\"\n )\n return self.assign_parameters(values)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._unroll_param_dict","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._unroll_param_dict\n def _unroll_param_dict(\n self, value_dict: Mapping[Parameter, ParameterValueType]\n ) -> Dict[Parameter, ParameterValueType]:\n unrolled_value_dict: Dict[Parameter, ParameterValueType] = {}\n for (param, value) in value_dict.items():\n if isinstance(param, ParameterVector):\n if not len(param) == len(value):\n raise CircuitError(\n \"ParameterVector {} has length {}, which \"\n \"differs from value list {} of \"\n \"len {}\".format(param, len(param), value, len(value))\n )\n unrolled_value_dict.update(zip(param, value))\n # pass anything else except number through. error checking is done in assign_parameter\n elif isinstance(param, (ParameterExpression, str)) or param is None:\n unrolled_value_dict[param] = value\n return unrolled_value_dict","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._assign_parameter","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._assign_parameter\n def _assign_parameter(self, parameter: Parameter, value: ParameterValueType) -> None:\n \"\"\"Update this circuit where instances of ``parameter`` are replaced by ``value``, which\n can be either a numeric value or a new parameter expression.\n\n Args:\n parameter (ParameterExpression): Parameter to be bound\n value (Union(ParameterExpression, float, int)): A numeric or parametric expression to\n replace instances of ``parameter``.\n\n Raises:\n RuntimeError: if some internal logic error has caused the circuit instruction sequence\n and the parameter table to become out of sync, and the table now contains a\n reference to a value that cannot be assigned.\n \"\"\"\n # parameter might be in global phase only\n if parameter in self._parameter_table:\n for instr, param_index in self._parameter_table[parameter]:\n assignee = instr.params[param_index]\n # Normal ParameterExpression.\n if isinstance(assignee, ParameterExpression):\n new_param = assignee.assign(parameter, value)\n # if fully bound, validate\n if len(new_param.parameters) == 0:\n instr.params[param_index] = instr.validate_parameter(new_param)\n else:\n instr.params[param_index] = new_param\n\n self._rebind_definition(instr, parameter, value)\n # Scoped block of a larger instruction.\n elif isinstance(assignee, QuantumCircuit):\n # It's possible that someone may re-use a loop body, so we need to mutate the\n # parameter vector with a new circuit, rather than mutating the body.\n instr.params[param_index] = assignee.assign_parameters({parameter: value})\n else:","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._assign_calibration_parameters","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._assign_calibration_parameters\n def _assign_calibration_parameters(\n self, parameter: Parameter, value: ParameterValueType\n ) -> None:\n \"\"\"Update parameterized pulse gate calibrations, if there are any which contain\n ``parameter``. This updates the calibration mapping as well as the gate definition\n ``Schedule``s, which also may contain ``parameter``.\n \"\"\"\n new_param: ParameterValueType\n for cals in self.calibrations.values():\n for (qubit, cal_params), schedule in copy.copy(cals).items():\n if any(\n isinstance(p, ParameterExpression) and parameter in p.parameters\n for p in cal_params\n ):\n del cals[(qubit, cal_params)]\n new_cal_params = []\n for p in cal_params:\n if isinstance(p, ParameterExpression) and parameter in p.parameters:\n new_param = p.assign(parameter, value)\n if not new_param.parameters:\n new_param = float(new_param)\n new_cal_params.append(new_param)\n else:\n new_cal_params.append(p)\n schedule.assign_parameters({parameter: value})\n cals[(qubit, tuple(new_cal_params))] = schedule","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._rebind_definition","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._rebind_definition\n def _rebind_definition(\n self, instruction: Instruction, parameter: Parameter, value: ParameterValueType\n ) -> None:\n if instruction._definition:\n for op, _, _ in instruction._definition:\n for idx, param in enumerate(op.params):\n if isinstance(param, ParameterExpression) and parameter in param.parameters:\n if isinstance(value, ParameterExpression):\n op.params[idx] = param.subs({parameter: value})\n else:\n op.params[idx] = param.bind({parameter: value})\n self._rebind_definition(op, parameter, value)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.barrier","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.barrier\n def barrier(self, *qargs: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.Barrier`. If qargs is empty, applies to all qubits in the\n circuit.\n\n Returns:\n qiskit.circuit.InstructionSet: handle to the added instructions.\n \"\"\"\n from .barrier import Barrier\n\n qubits: List[QubitSpecifier] = []\n\n if not qargs: # None\n qubits.extend(self.qubits)\n\n for qarg in qargs:\n if isinstance(qarg, QuantumRegister):\n qubits.extend([qarg[j] for j in range(qarg.size)])\n elif isinstance(qarg, list):\n qubits.extend(qarg)\n elif isinstance(qarg, range):\n qubits.extend(list(qarg))\n elif isinstance(qarg, slice):\n qubits.extend(self.qubits[qarg])\n else:\n qubits.append(qarg)\n\n return self.append(Barrier(len(qubits)), qubits, [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.delay","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.delay\n def delay(\n self,\n duration: ParameterValueType,\n qarg: Optional[QubitSpecifier] = None,\n unit: str = \"dt\",\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.Delay`. If qarg is None, applies to all qubits.\n When applying to multiple qubits, delays with the same duration will be created.\n\n Args:\n duration (int or float or ParameterExpression): duration of the delay.\n qarg (Object): qubit argument to apply this delay.\n unit (str): unit of the duration. Supported units: 's', 'ms', 'us', 'ns', 'ps', 'dt'.\n Default is ``dt``, i.e. integer time unit depending on the target backend.\n\n Returns:\n qiskit.circuit.InstructionSet: handle to the added instructions.\n\n Raises:\n CircuitError: if arguments have bad format.\n \"\"\"\n qubits: List[QubitSpecifier] = []\n if qarg is None: # -> apply delays to all qubits\n for q in self.qubits:\n qubits.append(q)\n else:\n if isinstance(qarg, QuantumRegister):\n qubits.extend([qarg[j] for j in range(qarg.size)])\n elif isinstance(qarg, list):\n qubits.extend(qarg)\n elif isinstance(qarg, (range, tuple)):\n qubits.extend(list(qarg))\n elif isinstance(qarg, slice):\n qubits.extend(self.qubits[qarg])\n else:\n qubits.append(qarg)\n\n instructions = InstructionSet(resource_requester=self._resolve_classical_resource)\n for q in qubits:\n inst = (Delay(duration, unit), [q], [])\n self.append(*inst)\n instructions.add(*inst)\n return instructions","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.h","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.h\n def h(self, qubit: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.HGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.h import HGate\n\n return self.append(HGate(), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ch","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ch\n def ch(\n self,\n control_qubit: QubitSpecifier,\n target_qubit: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.CHGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubit: The qubit(s) used as the control.\n target_qubit: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.h import CHGate\n\n return self.append(\n CHGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.i","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.i\n def i(self, qubit: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.IGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.i import IGate\n\n return self.append(IGate(), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.id","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.id\n def id(self, qubit: QubitSpecifier) -> InstructionSet: # pylint: disable=invalid-name\n \"\"\"Apply :class:`~qiskit.circuit.library.IGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n\n See also:\n QuantumCircuit.i: the same function.\n \"\"\"\n return self.i(qubit)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ms","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ms\n def ms(self, theta: ParameterValueType, qubits: Sequence[QubitSpecifier]) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.generalized_gates.gms.MSGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The angle of the rotation.\n qubits: The qubits to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n # pylint: disable=cyclic-import\n from .library.generalized_gates.gms import MSGate\n\n return self.append(MSGate(len(qubits), theta), qubits)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.p","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.p\n def p(self, theta: ParameterValueType, qubit: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.PhaseGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: THe angle of the rotation.\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.p import PhaseGate\n\n return self.append(PhaseGate(theta), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cp","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cp\n def cp(\n self,\n theta: ParameterValueType,\n control_qubit: QubitSpecifier,\n target_qubit: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.CPhaseGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The angle of the rotation.\n control_qubit: The qubit(s) used as the control.\n target_qubit: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.p import CPhaseGate\n\n return self.append(\n CPhaseGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mcp","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mcp\n def mcp(\n self,\n lam: ParameterValueType,\n control_qubits: Sequence[QubitSpecifier],\n target_qubit: QubitSpecifier,\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.MCPhaseGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n lam: The angle of the rotation.\n control_qubits: The qubits used as the controls.\n target_qubit: The qubit(s) targeted by the gate.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.p import MCPhaseGate\n\n num_ctrl_qubits = len(control_qubits)\n return self.append(\n MCPhaseGate(lam, num_ctrl_qubits), control_qubits[:] + [target_qubit], []\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.r","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.r\n def r(\n self, theta: ParameterValueType, phi: ParameterValueType, qubit: QubitSpecifier\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.RGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The angle of the rotation.\n phi: The angle of the axis of rotation in the x-y plane.\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.r import RGate\n\n return self.append(RGate(theta, phi), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rv","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rv\n def rv(\n self,\n vx: ParameterValueType,\n vy: ParameterValueType,\n vz: ParameterValueType,\n qubit: QubitSpecifier,\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.RVGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Rotation around an arbitrary rotation axis :math:`v`, where :math:`|v|` is the angle of\n rotation in radians.\n\n Args:\n vx: x-compenent of the rotation axis.\n vy: y-compenent of the rotation axis.\n vz: z-compenent of the rotation axis.\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.generalized_gates.rv import RVGate\n\n return self.append(RVGate(vx, vy, vz), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rccx","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rccx\n def rccx(\n self,\n control_qubit1: QubitSpecifier,\n control_qubit2: QubitSpecifier,\n target_qubit: QubitSpecifier,\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.RCCXGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubit1: The qubit(s) used as the first control.\n control_qubit2: The qubit(s) used as the second control.\n target_qubit: The qubit(s) targeted by the gate.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.x import RCCXGate\n\n return self.append(RCCXGate(), [control_qubit1, control_qubit2, target_qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rcccx","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rcccx\n def rcccx(\n self,\n control_qubit1: QubitSpecifier,\n control_qubit2: QubitSpecifier,\n control_qubit3: QubitSpecifier,\n target_qubit: QubitSpecifier,\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.RC3XGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubit1: The qubit(s) used as the first control.\n control_qubit2: The qubit(s) used as the second control.\n control_qubit3: The qubit(s) used as the third control.\n target_qubit: The qubit(s) targeted by the gate.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.x import RC3XGate\n\n return self.append(\n RC3XGate(), [control_qubit1, control_qubit2, control_qubit3, target_qubit], []\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rx","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rx\n def rx(\n self, theta: ParameterValueType, qubit: QubitSpecifier, label: Optional[str] = None\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.RXGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The rotation angle of the gate.\n qubit: The qubit(s) to apply the gate to.\n label: The string label of the gate in the circuit.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.rx import RXGate\n\n return self.append(RXGate(theta, label=label), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.crx","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.crx\n def crx(\n self,\n theta: ParameterValueType,\n control_qubit: QubitSpecifier,\n target_qubit: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.CRXGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The angle of the rotation.\n control_qubit: The qubit(s) used as the control.\n target_qubit: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.rx import CRXGate\n\n return self.append(\n CRXGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rxx","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rxx\n def rxx(\n self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.RXXGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The angle of the rotation.\n qubit1: The qubit(s) to apply the gate to.\n qubit2: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.rxx import RXXGate\n\n return self.append(RXXGate(theta), [qubit1, qubit2], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ry","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ry\n def ry(\n self, theta: ParameterValueType, qubit: QubitSpecifier, label: Optional[str] = None\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.RYGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The rotation angle of the gate.\n qubit: The qubit(s) to apply the gate to.\n label: The string label of the gate in the circuit.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.ry import RYGate\n\n return self.append(RYGate(theta, label=label), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cry","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cry\n def cry(\n self,\n theta: ParameterValueType,\n control_qubit: QubitSpecifier,\n target_qubit: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.CRYGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The angle of the rotation.\n control_qubit: The qubit(s) used as the control.\n target_qubit: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.ry import CRYGate\n\n return self.append(\n CRYGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ryy","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ryy\n def ryy(\n self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.RYYGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The rotation angle of the gate.\n qubit1: The qubit(s) to apply the gate to.\n qubit2: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.ryy import RYYGate\n\n return self.append(RYYGate(theta), [qubit1, qubit2], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rz","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rz\n def rz(self, phi: ParameterValueType, qubit: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.RZGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n phi: The rotation angle of the gate.\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.rz import RZGate\n\n return self.append(RZGate(phi), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.crz","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.crz\n def crz(\n self,\n theta: ParameterValueType,\n control_qubit: QubitSpecifier,\n target_qubit: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.CRZGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The angle of the rotation.\n control_qubit: The qubit(s) used as the control.\n target_qubit: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.rz import CRZGate\n\n return self.append(\n CRZGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rzx","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rzx\n def rzx(\n self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.RZXGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The rotation angle of the gate.\n qubit1: The qubit(s) to apply the gate to.\n qubit2: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.rzx import RZXGate\n\n return self.append(RZXGate(theta), [qubit1, qubit2], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rzz","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rzz\n def rzz(\n self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.RZZGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The rotation angle of the gate.\n qubit1: The qubit(s) to apply the gate to.\n qubit2: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.rzz import RZZGate\n\n return self.append(RZZGate(theta), [qubit1, qubit2], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ecr","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ecr\n def ecr(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.ECRGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit1, qubit2: The qubits to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.ecr import ECRGate\n\n return self.append(ECRGate(), [qubit1, qubit2], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.s","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.s\n def s(self, qubit: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.SGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.s import SGate\n\n return self.append(SGate(), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.sdg","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.sdg\n def sdg(self, qubit: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.SdgGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.s import SdgGate\n\n return self.append(SdgGate(), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.swap","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.swap\n def swap(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.SwapGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit1, qubit2: The qubits to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.swap import SwapGate\n\n return self.append(SwapGate(), [qubit1, qubit2], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.iswap","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.iswap\n def iswap(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.iSwapGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit1, qubit2: The qubits to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.iswap import iSwapGate\n\n return self.append(iSwapGate(), [qubit1, qubit2], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cswap","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cswap\n def cswap(\n self,\n control_qubit: QubitSpecifier,\n target_qubit1: QubitSpecifier,\n target_qubit2: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.CSwapGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubit: The qubit(s) used as the control.\n target_qubit1: The qubit(s) targeted by the gate.\n target_qubit2: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.swap import CSwapGate\n\n return self.append(\n CSwapGate(label=label, ctrl_state=ctrl_state),\n [control_qubit, target_qubit1, target_qubit2],\n [],\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.fredkin","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.fredkin\n def fredkin(\n self,\n control_qubit: QubitSpecifier,\n target_qubit1: QubitSpecifier,\n target_qubit2: QubitSpecifier,\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.CSwapGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubit: The qubit(s) used as the control.\n target_qubit1: The qubit(s) targeted by the gate.\n target_qubit2: The qubit(s) targeted by the gate.\n\n Returns:\n A handle to the instructions created.\n\n See Also:\n QuantumCircuit.cswap: the same function with a different name.\n \"\"\"\n return self.cswap(control_qubit, target_qubit1, target_qubit2)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.sx","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.sx\n def sx(self, qubit: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.SXGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.sx import SXGate\n\n return self.append(SXGate(), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.sxdg","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.sxdg\n def sxdg(self, qubit: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.SXdgGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.sx import SXdgGate\n\n return self.append(SXdgGate(), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.csx","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.csx\n def csx(\n self,\n control_qubit: QubitSpecifier,\n target_qubit: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.CSXGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubit: The qubit(s) used as the control.\n target_qubit: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.sx import CSXGate\n\n return self.append(\n CSXGate(label=label, ctrl_state=ctrl_state),\n [control_qubit, target_qubit],\n [],\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.t","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.t\n def t(self, qubit: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.TGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.t import TGate\n\n return self.append(TGate(), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.tdg","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.tdg\n def tdg(self, qubit: QubitSpecifier) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.TdgGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.t import TdgGate\n\n return self.append(TdgGate(), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u\n def u(\n self,\n theta: ParameterValueType,\n phi: ParameterValueType,\n lam: ParameterValueType,\n qubit: QubitSpecifier,\n ) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.UGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The :math:`\\theta` rotation angle of the gate.\n phi: The :math:`\\phi` rotation angle of the gate.\n lam: The :math:`\\lambda` rotation angle of the gate.\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.u import UGate\n\n return self.append(UGate(theta, phi, lam), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cu","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cu\n def cu(\n self,\n theta: ParameterValueType,\n phi: ParameterValueType,\n lam: ParameterValueType,\n gamma: ParameterValueType,\n control_qubit: QubitSpecifier,\n target_qubit: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.CUGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The :math:`\\theta` rotation angle of the gate.\n phi: The :math:`\\phi` rotation angle of the gate.\n lam: The :math:`\\lambda` rotation angle of the gate.\n gamma: The global phase applied of the U gate, if applied.\n control_qubit: The qubit(s) used as the control.\n target_qubit: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.u import CUGate\n\n return self.append(\n CUGate(theta, phi, lam, gamma, label=label, ctrl_state=ctrl_state),\n [control_qubit, target_qubit],\n [],\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u1","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u1\n @deprecate_function(\n \"The QuantumCircuit.u1 method is deprecated as of \"\n \"0.16.0. It will be removed no earlier than 3 months \"\n \"after the release date. You should use the \"\n \"QuantumCircuit.p method instead, which acts \"\n \"identically.\"\n )\n def u1(self, theta: ParameterValueType, qubit: QubitSpecifier) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.U1Gate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The :math:`\\theta` rotation angle of the gate.\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.u1 import U1Gate\n\n return self.append(U1Gate(theta), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cu1","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cu1\n @deprecate_function(\n \"The QuantumCircuit.cu1 method is deprecated as of \"\n \"0.16.0. It will be removed no earlier than 3 months \"\n \"after the release date. You should use the \"\n \"QuantumCircuit.cp method instead, which acts \"\n \"identically.\"\n )\n def cu1(\n self,\n theta: ParameterValueType,\n control_qubit: QubitSpecifier,\n target_qubit: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.CU1Gate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The :math:`\\theta` rotation angle of the gate.\n control_qubit: The qubit(s) used as the control.\n target_qubit: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.u1 import CU1Gate\n\n return self.append(\n CU1Gate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mcu1","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mcu1\n @deprecate_function(\n \"The QuantumCircuit.mcu1 method is deprecated as of \"\n \"0.16.0. It will be removed no earlier than 3 months \"\n \"after the release date. You should use the \"\n \"QuantumCircuit.mcp method instead, which acts \"\n \"identically.\"\n )\n def mcu1(\n self,\n lam: ParameterValueType,\n control_qubits: Sequence[QubitSpecifier],\n target_qubit: QubitSpecifier,\n ) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.MCU1Gate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n lam: The :math:`\\lambda` rotation angle of the gate.\n control_qubits: The qubits used as the controls.\n target_qubit: The qubit(s) targeted by the gate.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.u1 import MCU1Gate\n\n num_ctrl_qubits = len(control_qubits)\n return self.append(MCU1Gate(lam, num_ctrl_qubits), control_qubits[:] + [target_qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u2","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u2\n @deprecate_function(\n \"The QuantumCircuit.u2 method is deprecated as of \"\n \"0.16.0. It will be removed no earlier than 3 months \"\n \"after the release date. You can use the general 1-\"\n \"qubit gate QuantumCircuit.u instead: u2(φ,λ) = \"\n \"u(π/2, φ, λ). Alternatively, you can decompose it in\"\n \"terms of QuantumCircuit.p and QuantumCircuit.sx: \"\n \"u2(φ,λ) = p(π/2+φ) sx p(λ-π/2) (1 pulse on hardware).\"\n )\n def u2(\n self, phi: ParameterValueType, lam: ParameterValueType, qubit: QubitSpecifier\n ) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.U2Gate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n phi: The :math:`\\phi` rotation angle of the gate.\n lam: The :math:`\\lambda` rotation angle of the gate.\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.u2 import U2Gate\n\n return self.append(U2Gate(phi, lam), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u3","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u3\n @deprecate_function(\n \"The QuantumCircuit.u3 method is deprecated as of 0.16.0. It will be \"\n \"removed no earlier than 3 months after the release date. You should use \"\n \"QuantumCircuit.u instead, which acts identically. Alternatively, you can \"\n \"decompose u3 in terms of QuantumCircuit.p and QuantumCircuit.sx: \"\n \"u3(ϴ,φ,λ) = p(φ+π) sx p(ϴ+π) sx p(λ) (2 pulses on hardware).\"\n )\n def u3(\n self,\n theta: ParameterValueType,\n phi: ParameterValueType,\n lam: ParameterValueType,\n qubit: QubitSpecifier,\n ) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.U3Gate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The :math:`\\theta` rotation angle of the gate.\n phi: The :math:`\\phi` rotation angle of the gate.\n lam: The :math:`\\lambda` rotation angle of the gate.\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.u3 import U3Gate\n\n return self.append(U3Gate(theta, phi, lam), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cu3","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cu3\n @deprecate_function(\n \"The QuantumCircuit.cu3 method is deprecated as of 0.16.0. It will be \"\n \"removed no earlier than 3 months after the release date. You should \"\n \"use the QuantumCircuit.cu method instead, where \"\n \"cu3(ϴ,φ,λ) = cu(ϴ,φ,λ,0).\"\n )\n def cu3(\n self,\n theta: ParameterValueType,\n phi: ParameterValueType,\n lam: ParameterValueType,\n control_qubit: QubitSpecifier,\n target_qubit: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.CU3Gate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n theta: The :math:`\\theta` rotation angle of the gate.\n phi: The :math:`\\phi` rotation angle of the gate.\n lam: The :math:`\\lambda` rotation angle of the gate.\n control_qubit: The qubit(s) used as the control.\n target_qubit: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.u3 import CU3Gate\n\n return self.append(\n CU3Gate(theta, phi, lam, label=label, ctrl_state=ctrl_state),\n [control_qubit, target_qubit],\n [],\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.x","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.x\n def x(self, qubit: QubitSpecifier, label: Optional[str] = None) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.XGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit: The qubit(s) to apply the gate to.\n label: The string label of the gate in the circuit.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.x import XGate\n\n return self.append(XGate(label=label), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cx","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cx\n def cx(\n self,\n control_qubit: QubitSpecifier,\n target_qubit: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.CXGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubit: The qubit(s) used as the control.\n target_qubit: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n\n from .library.standard_gates.x import CXGate\n\n return self.append(\n CXGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cnot","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cnot\n def cnot(\n self,\n control_qubit: QubitSpecifier,\n target_qubit: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.CXGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubit: The qubit(s) used as the control.\n target_qubit: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n\n See Also:\n QuantumCircuit.cx: the same function with a different name.\n \"\"\"\n return self.cx(control_qubit, target_qubit, label, ctrl_state)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.dcx","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.dcx\n def dcx(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.DCXGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit1: The qubit(s) to apply the gate to.\n qubit2: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.dcx import DCXGate\n\n return self.append(DCXGate(), [qubit1, qubit2], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ccx","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ccx\n def ccx(\n self,\n control_qubit1: QubitSpecifier,\n control_qubit2: QubitSpecifier,\n target_qubit: QubitSpecifier,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.CCXGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubit1: The qubit(s) used as the first control.\n control_qubit2: The qubit(s) used as the second control.\n target_qubit: The qubit(s) targeted by the gate.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.x import CCXGate\n\n return self.append(\n CCXGate(ctrl_state=ctrl_state),\n [control_qubit1, control_qubit2, target_qubit],\n [],\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.toffoli","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.toffoli\n def toffoli(\n self,\n control_qubit1: QubitSpecifier,\n control_qubit2: QubitSpecifier,\n target_qubit: QubitSpecifier,\n ) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.CCXGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubit1: The qubit(s) used as the first control.\n control_qubit2: The qubit(s) used as the second control.\n target_qubit: The qubit(s) targeted by the gate.\n\n Returns:\n A handle to the instructions created.\n\n See Also:\n QuantumCircuit.ccx: the same gate with a different name.\n \"\"\"\n return self.ccx(control_qubit1, control_qubit2, target_qubit)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mcx","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mcx\n def mcx(\n self,\n control_qubits: Sequence[QubitSpecifier],\n target_qubit: QubitSpecifier,\n ancilla_qubits: Optional[Union[QubitSpecifier, Sequence[QubitSpecifier]]] = None,\n mode: str = \"noancilla\",\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.MCXGate`.\n\n The multi-cX gate can be implemented using different techniques, which use different numbers\n of ancilla qubits and have varying circuit depth. These modes are:\n\n - 'noancilla': Requires 0 ancilla qubits.\n - 'recursion': Requires 1 ancilla qubit if more than 4 controls are used, otherwise 0.\n - 'v-chain': Requires 2 less ancillas than the number of control qubits.\n - 'v-chain-dirty': Same as for the clean ancillas (but the circuit will be longer).\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubits: The qubits used as the controls.\n target_qubit: The qubit(s) targeted by the gate.\n ancilla_qubits: The qubits used as the ancillae, if the mode requires them.\n mode: The choice of mode, explained further above.\n\n Returns:\n A handle to the instructions created.\n\n Raises:\n ValueError: if the given mode is not known, or if too few ancilla qubits are passed.\n AttributeError: if no ancilla qubits are passed, but some are needed.\n \"\"\"\n from .library.standard_gates.x import MCXGrayCode, MCXRecursive, MCXVChain\n\n num_ctrl_qubits = len(control_qubits)\n\n available_implementations = {\n \"noancilla\": MCXGrayCode(num_ctrl_qubits),\n \"recursion\": MCXRecursive(num_ctrl_qubits),\n \"v-chain\": MCXVChain(num_ctrl_qubits, False),\n \"v-chain-dirty\": MCXVChain(num_ctrl_qubits, dirty_ancillas=True),\n # outdated, previous names\n \"advanced\":","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mct","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mct\n def mct(\n self,\n control_qubits: Sequence[QubitSpecifier],\n target_qubit: QubitSpecifier,\n ancilla_qubits: Optional[Union[QubitSpecifier, Sequence[QubitSpecifier]]] = None,\n mode: str = \"noancilla\",\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.MCXGate`.\n\n The multi-cX gate can be implemented using different techniques, which use different numbers\n of ancilla qubits and have varying circuit depth. These modes are:\n\n - 'noancilla': Requires 0 ancilla qubits.\n - 'recursion': Requires 1 ancilla qubit if more than 4 controls are used, otherwise 0.\n - 'v-chain': Requires 2 less ancillas than the number of control qubits.\n - 'v-chain-dirty': Same as for the clean ancillas (but the circuit will be longer).\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubits: The qubits used as the controls.\n target_qubit: The qubit(s) targeted by the gate.\n ancilla_qubits: The qubits used as the ancillae, if the mode requires them.\n mode: The choice of mode, explained further above.\n\n Returns:\n A handle to the instructions created.\n\n Raises:\n ValueError: if the given mode is not known, or if too few ancilla qubits are passed.\n AttributeError: if no ancilla qubits are passed, but some are needed.\n\n See Also:\n QuantumCircuit.mcx: the same gate with a different name.\n \"\"\"\n return self.mcx(control_qubits, target_qubit, ancilla_qubits, mode)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.y","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.y\n def y(self, qubit: QubitSpecifier) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.YGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.y import YGate\n\n return self.append(YGate(), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cy","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cy\n def cy(\n self,\n control_qubit: QubitSpecifier,\n target_qubit: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.CYGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubit: The qubit(s) used as the controls.\n target_qubit: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.y import CYGate\n\n return self.append(\n CYGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.z","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.z\n def z(self, qubit: QubitSpecifier) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.ZGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n qubit: The qubit(s) to apply the gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.z import ZGate\n\n return self.append(ZGate(), [qubit], [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cz","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cz\n def cz(\n self,\n control_qubit: QubitSpecifier,\n target_qubit: QubitSpecifier,\n label: Optional[str] = None,\n ctrl_state: Optional[Union[str, int]] = None,\n ) -> InstructionSet:\n r\"\"\"Apply :class:`~qiskit.circuit.library.CZGate`.\n\n For the full matrix form of this gate, see the underlying gate documentation.\n\n Args:\n control_qubit: The qubit(s) used as the controls.\n target_qubit: The qubit(s) targeted by the gate.\n label: The string label of the gate in the circuit.\n ctrl_state:\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\n on the '1' state.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from .library.standard_gates.z import CZGate\n\n return self.append(\n CZGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.pauli","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.pauli\n def pauli(\n self,\n pauli_string: str,\n qubits: Sequence[QubitSpecifier],\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.library.PauliGate`.\n\n Args:\n pauli_string: A string representing the Pauli operator to apply, e.g. 'XX'.\n qubits: The qubits to apply this gate to.\n\n Returns:\n A handle to the instructions created.\n \"\"\"\n from qiskit.circuit.library.generalized_gates.pauli import PauliGate\n\n return self.append(PauliGate(pauli_string), qubits, [])","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._push_scope","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._push_scope\n def _push_scope(\n self,\n qubits: Iterable[Qubit] = (),\n clbits: Iterable[Clbit] = (),\n registers: Iterable[Register] = (),\n allow_jumps: bool = True,\n ):\n \"\"\"Add a scope for collecting instructions into this circuit.\n\n This should only be done by the control-flow context managers, which will handle cleaning up\n after themselves at the end as well.\n\n Args:\n qubits: Any qubits that this scope should automatically use.\n clbits: Any clbits that this scope should automatically use.\n allow_jumps: Whether this scope allows jumps to be used within it.\n \"\"\"\n # pylint: disable=cyclic-import\n from qiskit.circuit.controlflow.builder import ControlFlowBuilderBlock\n\n # Chain resource requests so things like registers added to inner scopes via conditions are\n # requested in the outer scope as well.\n if self._control_flow_scopes:\n resource_requester = self._control_flow_scopes[-1].request_classical_resource\n else:\n resource_requester = self._resolve_classical_resource\n\n self._control_flow_scopes.append(\n ControlFlowBuilderBlock(\n qubits,\n clbits,\n resource_requester=resource_requester,\n registers=registers,\n allow_jumps=allow_jumps,\n )\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._pop_scope","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._pop_scope\n def _pop_scope(self) -> \"qiskit.circuit.controlflow.builder.ControlFlowBuilderBlock\":\n \"\"\"Finish a scope used in the control-flow builder interface, and return it to the caller.\n\n This should only be done by the control-flow context managers, since they naturally\n synchronise the creation and deletion of stack elements.\"\"\"\n return self._control_flow_scopes.pop()","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._peek_previous_instruction_in_scope","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._peek_previous_instruction_in_scope\n def _peek_previous_instruction_in_scope(\n self,\n ) -> Tuple[Instruction, Sequence[Qubit], Sequence[Clbit]]:\n \"\"\"Return the instruction 3-tuple of the most recent instruction in the current scope, even\n if that scope is currently under construction.\n\n This function is only intended for use by the control-flow ``if``-statement builders, which\n may need to modify a previous instruction.\"\"\"\n if self._control_flow_scopes:\n return self._control_flow_scopes[-1].peek()\n if not self._data:\n raise CircuitError(\"This circuit contains no instructions.\")\n return self._data[-1]","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._pop_previous_instruction_in_scope","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._pop_previous_instruction_in_scope\n def _pop_previous_instruction_in_scope(\n self,\n ) -> Tuple[Instruction, Sequence[Qubit], Sequence[Clbit]]:\n \"\"\"Return the instruction 3-tuple of the most recent instruction in the current scope, even\n if that scope is currently under construction, and remove it from that scope.\n\n This function is only intended for use by the control-flow ``if``-statement builders, which\n may need to replace a previous instruction with another.\n \"\"\"\n if self._control_flow_scopes:\n return self._control_flow_scopes[-1].pop()\n if not self._data:\n raise CircuitError(\"This circuit contains no instructions.\")\n instruction, qubits, clbits = self._data.pop()\n self._update_parameter_table_on_instruction_removal(instruction)\n return instruction, qubits, clbits","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._update_parameter_table_on_instruction_removal","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit._update_parameter_table_on_instruction_removal\n def _update_parameter_table_on_instruction_removal(self, instruction: Instruction) -> None:\n \"\"\"Update the :obj:`.ParameterTable` of this circuit given that an instance of the given\n ``instruction`` has just been removed from the circuit.\n\n .. note::\n\n This does not account for the possibility for the same instruction instance being added\n more than once to the circuit. At the time of writing (2021-11-17, main commit 271a82f)\n there is a defensive ``deepcopy`` of parameterised instructions inside\n :meth:`.QuantumCircuit.append`, so this should be safe. Trying to account for it would\n involve adding a potentially quadratic-scaling loop to check each entry in ``data``.\n \"\"\"\n atomic_parameters = set()\n for parameter in instruction.params:\n if isinstance(parameter, (ParameterExpression, QuantumCircuit)):\n atomic_parameters.update(parameter.parameters)\n for atomic_parameter in atomic_parameters:\n entries = self._parameter_table[atomic_parameter]\n new_entries = ParameterReferences(\n (entry_instruction, entry_index)\n for entry_instruction, entry_index in entries\n if entry_instruction is not instruction\n )\n\n if not new_entries:\n del self._parameter_table[atomic_parameter]\n # Invalidate cache.\n self._parameters = None\n else:\n self._parameter_table[atomic_parameter] = new_entries","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.while_loop","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.while_loop\n @typing.overload\n def while_loop(\n self,\n condition: Tuple[Union[ClassicalRegister, Clbit], int],\n body: None,\n qubits: None,\n clbits: None,\n *,\n label: Optional[str],\n ) -> \"qiskit.circuit.controlflow.while_loop.WhileLoopContext\":\n ...","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.while_loop","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.while_loop\n @typing.overload\n def while_loop(\n self,\n condition: Tuple[Union[ClassicalRegister, Clbit], int],\n body: \"QuantumCircuit\",\n qubits: Sequence[QubitSpecifier],\n clbits: Sequence[ClbitSpecifier],\n *,\n label: Optional[str],\n ) -> InstructionSet:\n ...","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.while_loop","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.while_loop\n def while_loop(self, condition, body=None, qubits=None, clbits=None, *, label=None):\n \"\"\"Create a ``while`` loop on this circuit.\n\n There are two forms for calling this function. If called with all its arguments (with the\n possible exception of ``label``), it will create a\n :obj:`~qiskit.circuit.controlflow.WhileLoopOp` with the given ``body``. If ``body`` (and\n ``qubits`` and ``clbits``) are *not* passed, then this acts as a context manager, which\n will automatically build a :obj:`~qiskit.circuit.controlflow.WhileLoopOp` when the scope\n finishes. In this form, you do not need to keep track of the qubits or clbits you are\n using, because the scope will handle it for you.\n\n Example usage::\n\n from qiskit.circuit import QuantumCircuit, Clbit, Qubit\n bits = [Qubit(), Qubit(), Clbit()]\n qc = QuantumCircuit(bits)\n\n with qc.while_loop((bits[2], 0)):\n qc.h(0)\n qc.cx(0, 1)\n qc.measure(0, 0)\n\n Args:\n condition (Tuple[Union[ClassicalRegister, Clbit], int]): An equality condition to be\n checked prior to executing ``body``. The left-hand side of the condition must be a\n :obj:`~ClassicalRegister` or a :obj:`~Clbit`, and the right-hand side must be an\n integer or boolean.\n body (Optional[QuantumCircuit]): The loop body to be repeatedly executed. Omit this to\n use the context-manager mode.\n qubits (Optional[Sequence[Qubit]]): The circuit qubits over which the loop body should\n be run. Omit this to use the context-manager mode.\n clbits (Optional[Sequence[Clbit]]): The circuit clbits over which the loop body should\n be run. Omit this to use the context-manager mode.\n label (Optional[str]): The string label of the ins","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.for_loop","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.for_loop\n @typing.overload\n def for_loop(\n self,\n indexset: Iterable[int],\n loop_parameter: Optional[Parameter],\n body: None,\n qubits: None,\n clbits: None,\n *,\n label: Optional[str],\n ) -> \"qiskit.circuit.controlflow.for_loop.ForLoopContext\":\n ...","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.for_loop","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.for_loop\n @typing.overload\n def for_loop(\n self,\n indexset: Iterable[int],\n loop_parameter: Union[Parameter, None],\n body: \"QuantumCircuit\",\n qubits: Sequence[QubitSpecifier],\n clbits: Sequence[ClbitSpecifier],\n *,\n label: Optional[str],\n ) -> InstructionSet:\n ...","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.for_loop","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.for_loop\n def for_loop(\n self, indexset, loop_parameter=None, body=None, qubits=None, clbits=None, *, label=None\n ):\n \"\"\"Create a ``for`` loop on this circuit.\n\n There are two forms for calling this function. If called with all its arguments (with the\n possible exception of ``label``), it will create a\n :obj:`~qiskit.circuit.controlflow.ForLoopOp` with the given ``body``. If ``body`` (and\n ``qubits`` and ``clbits``) are *not* passed, then this acts as a context manager, which,\n when entered, provides a loop variable (unless one is given, in which case it will be\n reused) and will automatically build a :obj:`~qiskit.circuit.controlflow.ForLoopOp` when the\n scope finishes. In this form, you do not need to keep track of the qubits or clbits you are\n using, because the scope will handle it for you.\n\n For example::\n\n from qiskit import QuantumCircuit\n qc = QuantumCircuit(2, 1)\n\n with qc.for_loop(range(5)) as i:\n qc.h(0)\n qc.cx(0, 1)\n qc.measure(0, 0)\n qc.break_loop().c_if(0, True)\n\n Args:\n indexset (Iterable[int]): A collection of integers to loop over. Always necessary.\n loop_parameter (Optional[Parameter]): The parameter used within ``body`` to which\n the values from ``indexset`` will be assigned. In the context-manager form, if this\n argument is not supplied, then a loop parameter will be allocated for you and\n returned as the value of the ``with`` statement. This will only be bound into the\n circuit if it is used within the body.\n\n If this argument is ``None`` in the manual form of this method, ``body`` will be\n repeated once for each of the items in ``indexset`` but their values will be\n ignored.\n ","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_test","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_test\n @typing.overload\n def if_test(\n self,\n condition: Tuple[Union[ClassicalRegister, Clbit], int],\n true_body: None,\n qubits: None,\n clbits: None,\n *,\n label: Optional[str],\n ) -> \"qiskit.circuit.controlflow.if_else.IfContext\":\n ...","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_test","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_test\n @typing.overload\n def if_test(\n self,\n condition: Tuple[Union[ClassicalRegister, Clbit], int],\n true_body: \"QuantumCircuit\",\n qubits: Sequence[QubitSpecifier],\n clbits: Sequence[ClbitSpecifier],\n *,\n label: Optional[str] = None,\n ) -> InstructionSet:\n ...","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_test","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_test\n def if_test(\n self,\n condition,\n true_body=None,\n qubits=None,\n clbits=None,\n *,\n label=None,\n ):\n \"\"\"Create an ``if`` statement on this circuit.\n\n There are two forms for calling this function. If called with all its arguments (with the\n possible exception of ``label``), it will create a\n :obj:`~qiskit.circuit.controlflow.IfElseOp` with the given ``true_body``, and there will be\n no branch for the ``false`` condition (see also the :meth:`.if_else` method). However, if\n ``true_body`` (and ``qubits`` and ``clbits``) are *not* passed, then this acts as a context\n manager, which can be used to build ``if`` statements. The return value of the ``with``\n statement is a chainable context manager, which can be used to create subsequent ``else``\n blocks. In this form, you do not need to keep track of the qubits or clbits you are using,\n because the scope will handle it for you.\n\n For example::\n\n from qiskit.circuit import QuantumCircuit, Qubit, Clbit\n bits = [Qubit(), Qubit(), Qubit(), Clbit(), Clbit()]\n qc = QuantumCircuit(bits)\n\n qc.h(0)\n qc.cx(0, 1)\n qc.measure(0, 0)\n qc.h(0)\n qc.cx(0, 1)\n qc.measure(0, 1)\n\n with qc.if_test((bits[3], 0)) as else_:\n qc.x(2)\n with else_:\n qc.h(2)\n qc.z(2)\n\n Args:\n condition (Tuple[Union[ClassicalRegister, Clbit], int]): A condition to be evaluated at\n circuit runtime which, if true, will trigger the evaluation of ``true_body``. Can be\n specified as either a tuple of a ``ClassicalRegister`` to be tested for equality\n with a given ``int``, or as a tuple of a ``Clbit`` to be compared to either a\n ``bool`` or an ``","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_else","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_else\n def if_else(\n self,\n condition: Union[\n Tuple[ClassicalRegister, int],\n Tuple[Clbit, int],\n Tuple[Clbit, bool],\n ],\n true_body: \"QuantumCircuit\",\n false_body: \"QuantumCircuit\",\n qubits: Sequence[QubitSpecifier],\n clbits: Sequence[ClbitSpecifier],\n label: Optional[str] = None,\n ) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.controlflow.IfElseOp`.\n\n .. note::\n\n This method does not have an associated context-manager form, because it is already\n handled by the :meth:`.if_test` method. You can use the ``else`` part of that with\n something such as::\n\n from qiskit.circuit import QuantumCircuit, Qubit, Clbit\n bits = [Qubit(), Qubit(), Clbit()]\n qc = QuantumCircuit(bits)\n qc.h(0)\n qc.cx(0, 1)\n qc.measure(0, 0)\n with qc.if_test((bits[2], 0)) as else_:\n qc.h(0)\n with else_:\n qc.x(0)\n\n Args:\n condition: A condition to be evaluated at circuit runtime which,\n if true, will trigger the evaluation of ``true_body``. Can be\n specified as either a tuple of a ``ClassicalRegister`` to be\n tested for equality with a given ``int``, or as a tuple of a\n ``Clbit`` to be compared to either a ``bool`` or an ``int``.\n true_body: The circuit body to be run if ``condition`` is true.\n false_body: The circuit to be run if ``condition`` is false.\n qubits: The circuit qubits over which the if/else should be run.\n clbits: The circuit clbits over which the if/else should be run.\n label: The string label of the instruction in the circuit.\n\n Raises:\n CircuitError: If the provided condition refer","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.break_loop","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.break_loop\n def break_loop(self) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.controlflow.BreakLoopOp`.\n\n .. warning::\n\n If you are using the context-manager \"builder\" forms of :meth:`.if_test`,\n :meth:`.for_loop` or :meth:`.while_loop`, you can only call this method if you are\n within a loop context, because otherwise the \"resource width\" of the operation cannot be\n determined. This would quickly lead to invalid circuits, and so if you are trying to\n construct a reusable loop body (without the context managers), you must also use the\n non-context-manager form of :meth:`.if_test` and :meth:`.if_else`. Take care that the\n :obj:`.BreakLoopOp` instruction must span all the resources of its containing loop, not\n just the immediate scope.\n\n Returns:\n A handle to the instruction created.\n\n Raises:\n CircuitError: if this method was called within a builder context, but not contained\n within a loop.\n \"\"\"\n # pylint: disable=cyclic-import\n from qiskit.circuit.controlflow.break_loop import BreakLoopOp, BreakLoopPlaceholder\n\n if self._control_flow_scopes:\n operation = BreakLoopPlaceholder()\n resources = operation.placeholder_resources()\n return self.append(operation, resources.qubits, resources.clbits)\n return self.append(BreakLoopOp(self.num_qubits, self.num_clbits), self.qubits, self.clbits)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.continue_loop","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.continue_loop\n def continue_loop(self) -> InstructionSet:\n \"\"\"Apply :class:`~qiskit.circuit.controlflow.ContinueLoopOp`.\n\n .. warning::\n\n If you are using the context-manager \"builder\" forms of :meth:`.if_test`,\n :meth:`.for_loop` or :meth:`.while_loop`, you can only call this method if you are\n within a loop context, because otherwise the \"resource width\" of the operation cannot be\n determined. This would quickly lead to invalid circuits, and so if you are trying to\n construct a reusable loop body (without the context managers), you must also use the\n non-context-manager form of :meth:`.if_test` and :meth:`.if_else`. Take care that the\n :obj:`.ContinueLoopOp` instruction must span all the resources of its containing loop,\n not just the immediate scope.\n\n Returns:\n A handle to the instruction created.\n\n Raises:\n CircuitError: if this method was called within a builder context, but not contained\n within a loop.\n \"\"\"\n # pylint: disable=cyclic-import\n from qiskit.circuit.controlflow.continue_loop import ContinueLoopOp, ContinueLoopPlaceholder\n\n if self._control_flow_scopes:\n operation = ContinueLoopPlaceholder()\n resources = operation.placeholder_resources()\n return self.append(operation, resources.qubits, resources.clbits)\n return self.append(\n ContinueLoopOp(self.num_qubits, self.num_clbits), self.qubits, self.clbits\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.add_calibration","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.add_calibration\n def add_calibration(\n self,\n gate: Union[Gate, str],\n qubits: Sequence[int],\n # Schedule has the type `qiskit.pulse.Schedule`, but `qiskit.pulse` cannot be imported\n # while this module is, and so Sphinx will not accept a forward reference to it. Sphinx\n # needs the types available at runtime, whereas mypy will accept it, because it handles the\n # type checking by static analysis.\n schedule,\n params: Optional[Sequence[ParameterValueType]] = None,\n ) -> None:\n \"\"\"Register a low-level, custom pulse definition for the given gate.\n\n Args:\n gate (Union[Gate, str]): Gate information.\n qubits (Union[int, Tuple[int]]): List of qubits to be measured.\n schedule (Schedule): Schedule information.\n params (Optional[List[Union[float, Parameter]]]): A list of parameters.\n\n Raises:\n Exception: if the gate is of type string and params is None.\n \"\"\"\n if isinstance(gate, Gate):\n self._calibrations[gate.name][(tuple(qubits), tuple(gate.params))] = schedule\n else:\n self._calibrations[gate][(tuple(qubits), tuple(params or []))] = schedule","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubit_duration","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubit_duration\n def qubit_duration(self, *qubits: Union[Qubit, int]) -> float:\n \"\"\"Return the duration between the start and stop time of the first and last instructions,\n excluding delays, over the supplied qubits. Its time unit is ``self.unit``.\n\n Args:\n *qubits: Qubits within ``self`` to include.\n\n Returns:\n Return the duration between the first start and last stop time of non-delay instructions\n \"\"\"\n return self.qubit_stop_time(*qubits) - self.qubit_start_time(*qubits)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubit_start_time","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubit_start_time\n def qubit_start_time(self, *qubits: Union[Qubit, int]) -> float:\n \"\"\"Return the start time of the first instruction, excluding delays,\n over the supplied qubits. Its time unit is ``self.unit``.\n\n Return 0 if there are no instructions over qubits\n\n Args:\n *qubits: Qubits within ``self`` to include. Integers are allowed for qubits, indicating\n indices of ``self.qubits``.\n\n Returns:\n Return the start time of the first instruction, excluding delays, over the qubits\n\n Raises:\n CircuitError: if ``self`` is a not-yet scheduled circuit.\n \"\"\"\n if self.duration is None:\n # circuit has only delays, this is kind of scheduled\n for inst, _, _ in self.data:\n if not isinstance(inst, Delay):\n raise CircuitError(\n \"qubit_start_time undefined. Circuit must be scheduled first.\"\n )\n return 0\n\n qubits = [self.qubits[q] if isinstance(q, int) else q for q in qubits]\n\n starts = {q: 0 for q in qubits}\n dones = {q: False for q in qubits}\n for inst, qargs, _ in self.data:\n for q in qubits:\n if q in qargs:\n if isinstance(inst, Delay):\n if not dones[q]:\n starts[q] += inst.duration\n else:\n dones[q] = True\n if len(qubits) == len([done for done in dones.values() if done]): # all done\n return min(start for start in starts.values())\n\n return 0 # If there are no instructions over bits","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubit_stop_time","text":"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubit_stop_time\n def qubit_stop_time(self, *qubits: Union[Qubit, int]) -> float:\n \"\"\"Return the stop time of the last instruction, excluding delays, over the supplied qubits.\n Its time unit is ``self.unit``.\n\n Return 0 if there are no instructions over qubits\n\n Args:\n *qubits: Qubits within ``self`` to include. Integers are allowed for qubits, indicating\n indices of ``self.qubits``.\n\n Returns:\n Return the stop time of the last instruction, excluding delays, over the qubits\n\n Raises:\n CircuitError: if ``self`` is a not-yet scheduled circuit.\n \"\"\"\n if self.duration is None:\n # circuit has only delays, this is kind of scheduled\n for inst, _, _ in self.data:\n if not isinstance(inst, Delay):\n raise CircuitError(\n \"qubit_stop_time undefined. Circuit must be scheduled first.\"\n )\n return 0\n\n qubits = [self.qubits[q] if isinstance(q, int) else q for q in qubits]\n\n stops = {q: self.duration for q in qubits}\n dones = {q: False for q in qubits}\n for inst, qargs, _ in reversed(self.data):\n for q in qubits:\n if q in qargs:\n if isinstance(inst, Delay):\n if not dones[q]:\n stops[q] -= inst.duration\n else:\n dones[q] = True\n if len(qubits) == len([done for done in dones.values() if done]): # all done\n return max(stop for stop in stops.values())\n\n return 0 # If there are no instructions over bits","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::_circuit_from_qasm","text":"qiskit/circuit/quantumcircuit.py::_circuit_from_qasm\ndef _circuit_from_qasm(qasm: Qasm) -> \"QuantumCircuit\":\n # pylint: disable=cyclic-import\n from qiskit.converters import ast_to_dag\n from qiskit.converters import dag_to_circuit\n\n ast = qasm.parse()\n dag = ast_to_dag(ast)\n return dag_to_circuit(dag)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::_standard_compare","text":"qiskit/circuit/quantumcircuit.py::_standard_compare\ndef _standard_compare(value1, value2):\n if value1 < value2:\n return -1\n if value1 > value2:\n return 1\n return 0","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::_compare_parameters","text":"qiskit/circuit/quantumcircuit.py::_compare_parameters\ndef _compare_parameters(param1: Parameter, param2: Parameter) -> int:\n if isinstance(param1, ParameterVectorElement) and isinstance(param2, ParameterVectorElement):\n # if they belong to a vector with the same name, sort by index\n if param1.vector.name == param2.vector.name:\n return _standard_compare(param1.index, param2.index)\n\n # else sort by name\n return _standard_compare(param1.name, param2.name)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::_add_sub_instruction_to_existing_composite_circuits","text":"qiskit/circuit/quantumcircuit.py::_add_sub_instruction_to_existing_composite_circuits\ndef _add_sub_instruction_to_existing_composite_circuits(\n instruction: Instruction,\n existing_gate_names: List[str],\n existing_composite_circuits: List[Instruction],\n) -> None:\n \"\"\"Recursively add undefined sub-instructions in the definition of the given\n instruction to existing_composite_circuit list.\n \"\"\"\n for sub_instruction, _, _ in instruction.definition:\n # Check instructions names are valid\n if not VALID_QASM2_IDENTIFIER.fullmatch(sub_instruction.name):\n sub_instruction = sub_instruction.copy(\n name=_qasm_escape_gate_name(sub_instruction.name)\n )\n if (\n sub_instruction.name not in existing_gate_names\n and sub_instruction not in existing_composite_circuits\n ):\n existing_composite_circuits.insert(0, sub_instruction)\n _add_sub_instruction_to_existing_composite_circuits(\n sub_instruction, existing_gate_names, existing_composite_circuits\n )","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::_qasm_escape_gate_name","text":"qiskit/circuit/quantumcircuit.py::_qasm_escape_gate_name\ndef _qasm_escape_gate_name(name: str) -> str:\n \"\"\"Returns a valid OpenQASM gate identifier\"\"\"\n # Replace all non-ASCII-word characters with the underscore.\n escaped_name = re.sub(r\"\\W\", \"_\", name, flags=re.ASCII)\n if not escaped_name or escaped_name[0] not in string.ascii_lowercase:\n # Add an arbitrary, guaranteed-to-be-valid prefix.\n escaped_name = \"gate_\" + escaped_name\n\n return escaped_name","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::_get_composite_circuit_qasm_from_instruction","text":"qiskit/circuit/quantumcircuit.py::_get_composite_circuit_qasm_from_instruction\ndef _get_composite_circuit_qasm_from_instruction(instruction: Instruction) -> str:\n \"\"\"Returns OpenQASM string composite circuit given an instruction.\n The given instruction should be the result of composite_circuit.to_instruction().\"\"\"\n\n if instruction.definition is None:\n raise ValueError(f'Instruction \"{instruction.name}\" is not defined.')\n\n gate_parameters = \",\".join([\"param%i\" % num for num in range(len(instruction.params))])\n qubit_parameters = \",\".join([\"q%i\" % num for num in range(instruction.num_qubits)])\n composite_circuit_gates = \"\"\n\n definition = instruction.definition\n definition_bit_labels = {\n bit: idx for bits in (definition.qubits, definition.clbits) for idx, bit in enumerate(bits)\n }\n for sub_instruction, qargs, _ in definition:\n if not VALID_QASM2_IDENTIFIER.fullmatch(sub_instruction.name):\n sub_instruction = sub_instruction.copy(\n name=_qasm_escape_gate_name(sub_instruction.name)\n )\n\n gate_qargs = \",\".join(\n [\"q%i\" % index for index in [definition_bit_labels[qubit] for qubit in qargs]]\n )\n composite_circuit_gates += f\"{sub_instruction.qasm()} {gate_qargs}; \"\n\n if composite_circuit_gates:\n composite_circuit_gates = composite_circuit_gates.rstrip(\" \")\n\n if gate_parameters:\n qasm_string = \"gate {}({}) {} {{ {} }}\".format(\n instruction.name,\n gate_parameters,\n qubit_parameters,\n composite_circuit_gates,\n )\n else:\n qasm_string = \"gate {} {} {{ {} }}\".format(\n instruction.name,\n qubit_parameters,\n composite_circuit_gates,\n )\n\n return qasm_string","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::_insert_composite_gate_definition_qasm","text":"qiskit/circuit/quantumcircuit.py::_insert_composite_gate_definition_qasm\ndef _insert_composite_gate_definition_qasm(\n string_temp: str, existing_composite_circuits: List[Instruction], extension_lib: str\n) -> str:\n \"\"\"Insert composite gate definition QASM code right after extension library in the header\"\"\"\n\n gate_definition_string = \"\"\n\n # Generate gate definition string\n for instruction in existing_composite_circuits:\n if hasattr(instruction, \"_qasm_definition\"):\n qasm_string = instruction._qasm_definition\n else:\n qasm_string = _get_composite_circuit_qasm_from_instruction(instruction)\n gate_definition_string += \"\\n\" + qasm_string\n\n string_temp = string_temp.replace(extension_lib, f\"{extension_lib}{gate_definition_string}\")\n return string_temp","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::_bit_argument_conversion","text":"qiskit/circuit/quantumcircuit.py::_bit_argument_conversion\ndef _bit_argument_conversion(specifier, bit_sequence, bit_set, type_):\n \"\"\"Get the list of bits referred to by the specifier ``specifier``.\n\n Valid types for ``specifier`` are integers, bits of the correct type (as given in ``type_``), or\n iterables of one of those two scalar types. Integers are interpreted as indices into the\n sequence ``bit_sequence``. All allowed bits must be in ``bit_set`` (which should implement\n fast lookup), which is assumed to contain the same bits as ``bit_sequence``.\n\n Returns:\n List[Bit]: a list of the specified bits from ``bits``.\n\n Raises:\n CircuitError: if an incorrect type or index is encountered, if the same bit is specified\n more than once, or if the specifier is to a bit not in the ``bit_set``.\n \"\"\"\n # The duplication between this function and `_bit_argument_conversion_scalar` is so that fast\n # paths return as quickly as possible, and all valid specifiers will resolve without needing to\n # try/catch exceptions (which is too slow for inner-loop code).\n if isinstance(specifier, type_):\n if specifier in bit_set:\n return [specifier]\n raise CircuitError(f\"Bit '{specifier}' is not in the circuit.\")\n if isinstance(specifier, (int, np.integer)):\n try:\n return [bit_sequence[specifier]]\n except IndexError as ex:\n raise CircuitError(\n f\"Index {specifier} out of range for size {len(bit_sequence)}.\"\n ) from ex\n # Slices can't raise IndexError - they just return an empty list.\n if isinstance(specifier, slice):\n return bit_sequence[specifier]\n try:\n return [\n _bit_argument_conversion_scalar(index, bit_sequence, bit_set, type_)\n for index in specifier\n ]\n except TypeError as ex:\n message = (\n f\"Incorrect bit type: expected '{type_.__name__}' but got '{type(specifier)","label":0},{"cid":"qiskit/circuit/quantumcircuit.py::_bit_argument_conversion_scalar","text":"qiskit/circuit/quantumcircuit.py::_bit_argument_conversion_scalar\ndef _bit_argument_conversion_scalar(specifier, bit_sequence, bit_set, type_):\n if isinstance(specifier, type_):\n if specifier in bit_set:\n return specifier\n raise CircuitError(f\"Bit '{specifier}' is not in the circuit.\")\n if isinstance(specifier, (int, np.integer)):\n try:\n return bit_sequence[specifier]\n except IndexError as ex:\n raise CircuitError(\n f\"Index {specifier} out of range for size {len(bit_sequence)}.\"\n ) from ex\n message = (\n f\"Incorrect bit type: expected '{type_.__name__}' but got '{type(specifier).__name__}'\"\n if isinstance(specifier, Bit)\n else f\"Invalid bit index: '{specifier}' of type '{type(specifier)}'\"\n )\n raise CircuitError(message)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__init__","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__init__\n def __init__(self):\n \"\"\"Create an empty circuit.\"\"\"\n\n # Circuit name. Generally, this corresponds to the name\n # of the QuantumCircuit from which the DAG was generated.\n self.name = None\n\n # Circuit metadata\n self.metadata = None\n\n # Set of wires (Register,idx) in the dag\n self._wires = set()\n\n # Map from wire (Register,idx) to input nodes of the graph\n self.input_map = OrderedDict()\n\n # Map from wire (Register,idx) to output nodes of the graph\n self.output_map = OrderedDict()\n\n # Directed multigraph whose nodes are inputs, outputs, or operations.\n # Operation nodes have equal in- and out-degrees and carry\n # additional data about the operation, including the argument order\n # and parameter values.\n # Input nodes have out-degree 1 and output nodes have in-degree 1.\n # Edges carry wire labels (reg,idx) and each operation has\n # corresponding in- and out-edges with the same wire labels.\n self._multi_graph = rx.PyDAG()\n\n # Map of qreg/creg name to Register object.\n self.qregs = OrderedDict()\n self.cregs = OrderedDict()\n\n # List of Qubit/Clbit wires that the DAG acts on.\n self.qubits = []\n self.clbits = []\n\n self._global_phase = 0\n self._calibrations = defaultdict(dict)\n\n self._op_names = {}\n\n self.duration = None\n self.unit = \"dt\"","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.to_networkx","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.to_networkx\n @_optionals.HAS_NETWORKX.require_in_call\n @deprecate_function(\n \"The to_networkx() method is deprecated and will be removed in a future release.\"\n )\n def to_networkx(self):\n \"\"\"Returns a copy of the DAGCircuit in networkx format.\"\"\"\n import networkx as nx\n\n G = nx.MultiDiGraph()\n for node in self._multi_graph.nodes():\n G.add_node(node)\n for node_id in rx.topological_sort(self._multi_graph):\n for source_id, dest_id, edge in self._multi_graph.in_edges(node_id):\n G.add_edge(self._multi_graph[source_id], self._multi_graph[dest_id], wire=edge)\n return G","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.from_networkx","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.from_networkx\n @classmethod\n @_optionals.HAS_NETWORKX.require_in_call\n @deprecate_function(\n \"The from_networkx() method is deprecated and will be removed in a future release.\"\n )\n def from_networkx(cls, graph):\n \"\"\"Take a networkx MultiDigraph and create a new DAGCircuit.\n\n Args:\n graph (networkx.MultiDiGraph): The graph to create a DAGCircuit\n object from. The format of this MultiDiGraph format must be\n in the same format as returned by to_networkx.\n\n Returns:\n DAGCircuit: The dagcircuit object created from the networkx\n MultiDiGraph.\n Raises:\n MissingOptionalLibraryError: If networkx is not installed\n DAGCircuitError: If input networkx graph is malformed\n \"\"\"\n import networkx as nx\n\n dag = DAGCircuit()\n for node in nx.topological_sort(graph):\n if isinstance(node, DAGOutNode):\n continue\n if isinstance(node, DAGInNode):\n if isinstance(node.wire, Qubit):\n dag.add_qubits([node.wire])\n elif isinstance(node.wire, Clbit):\n dag.add_clbits([node.wire])\n else:\n raise DAGCircuitError(f\"unknown node wire type: {node.wire}\")\n elif isinstance(node, DAGOpNode):\n dag.apply_operation_back(node.op.copy(), node.qargs, node.cargs)\n return dag","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.wires","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.wires\n @property\n def wires(self):\n \"\"\"Return a list of the wires in order.\"\"\"\n return self.qubits + self.clbits","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.node_counter","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.node_counter\n @property\n def node_counter(self):\n \"\"\"\n Returns the number of nodes in the dag.\n \"\"\"\n return len(self._multi_graph)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase\n @property\n def global_phase(self):\n \"\"\"Return the global phase of the circuit.\"\"\"\n return self._global_phase","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase\n @global_phase.setter\n def global_phase(self, angle):\n \"\"\"Set the global phase of the circuit.\n\n Args:\n angle (float, ParameterExpression)\n \"\"\"\n if isinstance(angle, ParameterExpression):\n self._global_phase = angle\n else:\n # Set the phase to the [0, 2π) interval\n angle = float(angle)\n if not angle:\n self._global_phase = 0\n else:\n self._global_phase = angle % (2 * math.pi)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations\n @property\n def calibrations(self):\n \"\"\"Return calibration dictionary.\n\n The custom pulse definition of a given gate is of the form\n {'gate_name': {(qubits, params): schedule}}\n \"\"\"\n return dict(self._calibrations)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations\n @calibrations.setter\n def calibrations(self, calibrations):\n \"\"\"Set the circuit calibration data from a dictionary of calibration definition.\n\n Args:\n calibrations (dict): A dictionary of input in the format\n {'gate_name': {(qubits, gate_params): schedule}}\n \"\"\"\n self._calibrations = defaultdict(dict, calibrations)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_calibration","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_calibration\n def add_calibration(self, gate, qubits, schedule, params=None):\n \"\"\"Register a low-level, custom pulse definition for the given gate.\n\n Args:\n gate (Union[Gate, str]): Gate information.\n qubits (Union[int, Tuple[int]]): List of qubits to be measured.\n schedule (Schedule): Schedule information.\n params (Optional[List[Union[float, Parameter]]]): A list of parameters.\n\n Raises:\n Exception: if the gate is of type string and params is None.\n \"\"\"\n if isinstance(gate, Gate):\n self._calibrations[gate.name][\n (tuple(qubits), tuple(float(p) for p in gate.params))\n ] = schedule\n else:\n self._calibrations[gate][(tuple(qubits), tuple(params or []))] = schedule","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.has_calibration_for","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.has_calibration_for\n def has_calibration_for(self, node):\n \"\"\"Return True if the dag has a calibration defined for the node operation. In this\n case, the operation does not need to be translated to the device basis.\n \"\"\"\n if not self.calibrations or node.op.name not in self.calibrations:\n return False\n qubits = tuple(self.qubits.index(qubit) for qubit in node.qargs)\n params = []\n for p in node.op.params:\n if isinstance(p, ParameterExpression) and not p.parameters:\n params.append(float(p))\n else:\n params.append(p)\n params = tuple(params)\n return (qubits, params) in self.calibrations[node.op.name]","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_all_ops_named","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_all_ops_named\n def remove_all_ops_named(self, opname):\n \"\"\"Remove all operation nodes with the given name.\"\"\"\n for n in self.named_nodes(opname):\n self.remove_op_node(n)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qubits","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qubits\n def add_qubits(self, qubits):\n \"\"\"Add individual qubit wires.\"\"\"\n if any(not isinstance(qubit, Qubit) for qubit in qubits):\n raise DAGCircuitError(\"not a Qubit instance.\")\n\n duplicate_qubits = set(self.qubits).intersection(qubits)\n if duplicate_qubits:\n raise DAGCircuitError(\"duplicate qubits %s\" % duplicate_qubits)\n\n self.qubits.extend(qubits)\n for qubit in qubits:\n self._add_wire(qubit)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_clbits","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_clbits\n def add_clbits(self, clbits):\n \"\"\"Add individual clbit wires.\"\"\"\n if any(not isinstance(clbit, Clbit) for clbit in clbits):\n raise DAGCircuitError(\"not a Clbit instance.\")\n\n duplicate_clbits = set(self.clbits).intersection(clbits)\n if duplicate_clbits:\n raise DAGCircuitError(\"duplicate clbits %s\" % duplicate_clbits)\n\n self.clbits.extend(clbits)\n for clbit in clbits:\n self._add_wire(clbit)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qreg","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qreg\n def add_qreg(self, qreg):\n \"\"\"Add all wires in a quantum register.\"\"\"\n if not isinstance(qreg, QuantumRegister):\n raise DAGCircuitError(\"not a QuantumRegister instance.\")\n if qreg.name in self.qregs:\n raise DAGCircuitError(\"duplicate register %s\" % qreg.name)\n self.qregs[qreg.name] = qreg\n existing_qubits = set(self.qubits)\n for j in range(qreg.size):\n if qreg[j] not in existing_qubits:\n self.qubits.append(qreg[j])\n self._add_wire(qreg[j])","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_creg","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_creg\n def add_creg(self, creg):\n \"\"\"Add all wires in a classical register.\"\"\"\n if not isinstance(creg, ClassicalRegister):\n raise DAGCircuitError(\"not a ClassicalRegister instance.\")\n if creg.name in self.cregs:\n raise DAGCircuitError(\"duplicate register %s\" % creg.name)\n self.cregs[creg.name] = creg\n existing_clbits = set(self.clbits)\n for j in range(creg.size):\n if creg[j] not in existing_clbits:\n self.clbits.append(creg[j])\n self._add_wire(creg[j])","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_wire","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_wire\n def _add_wire(self, wire):\n \"\"\"Add a qubit or bit to the circuit.\n\n Args:\n wire (Bit): the wire to be added\n\n This adds a pair of in and out nodes connected by an edge.\n\n Raises:\n DAGCircuitError: if trying to add duplicate wire\n \"\"\"\n if wire not in self._wires:\n self._wires.add(wire)\n\n inp_node = DAGInNode(wire=wire)\n outp_node = DAGOutNode(wire=wire)\n input_map_id, output_map_id = self._multi_graph.add_nodes_from([inp_node, outp_node])\n inp_node._node_id = input_map_id\n outp_node._node_id = output_map_id\n self.input_map[wire] = inp_node\n self.output_map[wire] = outp_node\n self._multi_graph.add_edge(inp_node._node_id, outp_node._node_id, wire)\n else:\n raise DAGCircuitError(f\"duplicate wire {wire}\")","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_clbits","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_clbits\n def remove_clbits(self, *clbits):\n \"\"\"\n Remove classical bits from the circuit. All bits MUST be idle.\n Any registers with references to at least one of the specified bits will\n also be removed.\n\n Args:\n clbits (List[Clbit]): The bits to remove.\n\n Raises:\n DAGCircuitError: a clbit is not a :obj:`.Clbit`, is not in the circuit,\n or is not idle.\n \"\"\"\n if any(not isinstance(clbit, Clbit) for clbit in clbits):\n raise DAGCircuitError(\n \"clbits not of type Clbit: %s\" % [b for b in clbits if not isinstance(b, Clbit)]\n )\n\n clbits = set(clbits)\n unknown_clbits = clbits.difference(self.clbits)\n if unknown_clbits:\n raise DAGCircuitError(\"clbits not in circuit: %s\" % unknown_clbits)\n\n busy_clbits = {bit for bit in clbits if not self._is_wire_idle(bit)}\n if busy_clbits:\n raise DAGCircuitError(\"clbits not idle: %s\" % busy_clbits)\n\n # remove any references to bits\n cregs_to_remove = {creg for creg in self.cregs.values() if not clbits.isdisjoint(creg)}\n self.remove_cregs(*cregs_to_remove)\n\n for clbit in clbits:\n self._remove_idle_wire(clbit)\n self.clbits.remove(clbit)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_cregs","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_cregs\n def remove_cregs(self, *cregs):\n \"\"\"\n Remove classical registers from the circuit, leaving underlying bits\n in place.\n\n Raises:\n DAGCircuitError: a creg is not a ClassicalRegister, or is not in\n the circuit.\n \"\"\"\n if any(not isinstance(creg, ClassicalRegister) for creg in cregs):\n raise DAGCircuitError(\n \"cregs not of type ClassicalRegister: %s\"\n % [r for r in cregs if not isinstance(r, ClassicalRegister)]\n )\n\n unknown_cregs = set(cregs).difference(self.cregs.values())\n if unknown_cregs:\n raise DAGCircuitError(\"cregs not in circuit: %s\" % unknown_cregs)\n\n for creg in cregs:\n del self.cregs[creg.name]","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._is_wire_idle","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._is_wire_idle\n def _is_wire_idle(self, wire):\n \"\"\"Check if a wire is idle.\n\n Args:\n wire (Bit): a wire in the circuit.\n\n Returns:\n bool: true if the wire is idle, false otherwise.\n\n Raises:\n DAGCircuitError: the wire is not in the circuit.\n \"\"\"\n if wire not in self._wires:\n raise DAGCircuitError(\"wire %s not in circuit\" % wire)\n\n try:\n child = next(self.successors(self.input_map[wire]))\n except StopIteration as e:\n raise DAGCircuitError(\n \"Invalid dagcircuit input node %s has no output\" % self.input_map[wire]\n ) from e\n return child is self.output_map[wire]","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._remove_idle_wire","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._remove_idle_wire\n def _remove_idle_wire(self, wire):\n \"\"\"Remove an idle qubit or bit from the circuit.\n\n Args:\n wire (Bit): the wire to be removed, which MUST be idle.\n \"\"\"\n inp_node = self.input_map[wire]\n oup_node = self.output_map[wire]\n\n self._multi_graph.remove_node(inp_node._node_id)\n self._multi_graph.remove_node(oup_node._node_id)\n self._wires.remove(wire)\n del self.input_map[wire]\n del self.output_map[wire]","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_condition","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_condition\n def _check_condition(self, name, condition):\n \"\"\"Verify that the condition is valid.\n\n Args:\n name (string): used for error reporting\n condition (tuple or None): a condition tuple (ClassicalRegister, int) or (Clbit, bool)\n\n Raises:\n DAGCircuitError: if conditioning on an invalid register\n \"\"\"\n if (\n condition is not None\n and condition[0] not in self.clbits\n and condition[0].name not in self.cregs\n ):\n raise DAGCircuitError(\"invalid creg in condition for %s\" % name)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_bits","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_bits\n def _check_bits(self, args, amap):\n \"\"\"Check the values of a list of (qu)bit arguments.\n\n For each element of args, check that amap contains it.\n\n Args:\n args (list[Bit]): the elements to be checked\n amap (dict): a dictionary keyed on Qubits/Clbits\n\n Raises:\n DAGCircuitError: if a qubit is not contained in amap\n \"\"\"\n # Check for each wire\n for wire in args:\n if wire not in amap:\n raise DAGCircuitError(f\"(qu)bit {wire} not found in {amap}\")","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._bits_in_condition","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._bits_in_condition\n def _bits_in_condition(self, cond):\n \"\"\"Return a list of bits in the given condition.\n\n Args:\n cond (tuple or None): optional condition (ClassicalRegister, int) or (Clbit, bool)\n\n Returns:\n list[Clbit]: list of classical bits\n\n Raises:\n CircuitError: if cond[0] is not ClassicalRegister or Clbit\n \"\"\"\n if cond is None:\n return []\n elif isinstance(cond[0], ClassicalRegister):\n # Returns a list of all the cbits in the given creg cond[0].\n return cond[0][:]\n elif isinstance(cond[0], Clbit):\n # Returns a singleton list of the conditional cbit.\n return [cond[0]]\n else:\n raise CircuitError(\"Condition must be used with ClassicalRegister or Clbit.\")","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._increment_op","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._increment_op\n def _increment_op(self, op):\n if op.name in self._op_names:\n self._op_names[op.name] += 1\n else:\n self._op_names[op.name] = 1","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._decrement_op","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._decrement_op\n def _decrement_op(self, op):\n if self._op_names[op.name] == 1:\n del self._op_names[op.name]\n else:\n self._op_names[op.name] -= 1","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_op_node","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_op_node\n def _add_op_node(self, op, qargs, cargs):\n \"\"\"Add a new operation node to the graph and assign properties.\n\n Args:\n op (qiskit.circuit.Instruction): the operation associated with the DAG node\n qargs (list[Qubit]): list of quantum wires to attach to.\n cargs (list[Clbit]): list of classical wires to attach to.\n Returns:\n int: The integer node index for the new op node on the DAG\n \"\"\"\n # Add a new operation node to the graph\n new_node = DAGOpNode(op=op, qargs=qargs, cargs=cargs)\n node_index = self._multi_graph.add_node(new_node)\n new_node._node_id = node_index\n self._increment_op(op)\n return node_index","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._copy_circuit_metadata","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._copy_circuit_metadata\n @deprecate_function(\n \"\"\"The DAGCircuit._copy_circuit_metadata method is deprecated as of 0.20.0. It will be removed\n no earlier than 3 months after the release date. You should use the DAGCircuit.copy_empty_like\n method instead, which acts identically.\n \"\"\"\n )\n def _copy_circuit_metadata(self):\n \"\"\"DEPRECATED\"\"\"\n return self.copy_empty_like()","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.copy_empty_like","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.copy_empty_like\n def copy_empty_like(self):\n \"\"\"Return a copy of self with the same structure but empty.\n\n That structure includes:\n * name and other metadata\n * global phase\n * duration\n * all the qubits and clbits, including the registers.\n\n Returns:\n DAGCircuit: An empty copy of self.\n \"\"\"\n target_dag = DAGCircuit()\n target_dag.name = self.name\n target_dag._global_phase = self._global_phase\n target_dag.duration = self.duration\n target_dag.unit = self.unit\n target_dag.metadata = self.metadata\n\n target_dag.add_qubits(self.qubits)\n target_dag.add_clbits(self.clbits)\n\n for qreg in self.qregs.values():\n target_dag.add_qreg(qreg)\n for creg in self.cregs.values():\n target_dag.add_creg(creg)\n\n return target_dag","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_back","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_back\n def apply_operation_back(self, op, qargs=None, cargs=None):\n \"\"\"Apply an operation to the output of the circuit.\n\n Args:\n op (qiskit.circuit.Instruction): the operation associated with the DAG node\n qargs (list[Qubit]): qubits that op will be applied to\n cargs (list[Clbit]): cbits that op will be applied to\n Returns:\n DAGOpNode: the node for the op that was added to the dag\n\n Raises:\n DAGCircuitError: if a leaf node is connected to multiple outputs\n\n \"\"\"\n qargs = qargs or []\n cargs = cargs or []\n\n all_cbits = self._bits_in_condition(op.condition)\n all_cbits = set(all_cbits).union(cargs)\n\n self._check_condition(op.name, op.condition)\n self._check_bits(qargs, self.output_map)\n self._check_bits(all_cbits, self.output_map)\n\n node_index = self._add_op_node(op, qargs, cargs)\n\n # Add new in-edges from predecessors of the output nodes to the\n # operation node while deleting the old in-edges of the output nodes\n # and adding new edges from the operation node to each output node\n\n al = [qargs, all_cbits]\n self._multi_graph.insert_node_on_in_edges_multiple(\n node_index, [self.output_map[q]._node_id for q in itertools.chain(*al)]\n )\n return self._multi_graph[node_index]","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_front","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_front\n def apply_operation_front(self, op, qargs, cargs):\n \"\"\"Apply an operation to the input of the circuit.\n\n Args:\n op (qiskit.circuit.Instruction): the operation associated with the DAG node\n qargs (list[Qubit]): qubits that op will be applied to\n cargs (list[Clbit]): cbits that op will be applied to\n Returns:\n DAGOpNode: the node for the op that was added to the dag\n\n Raises:\n DAGCircuitError: if initial nodes connected to multiple out edges\n \"\"\"\n all_cbits = self._bits_in_condition(op.condition)\n all_cbits.extend(cargs)\n\n self._check_condition(op.name, op.condition)\n self._check_bits(qargs, self.input_map)\n self._check_bits(all_cbits, self.input_map)\n node_index = self._add_op_node(op, qargs, cargs)\n\n # Add new out-edges to successors of the input nodes from the\n # operation node while deleting the old out-edges of the input nodes\n # and adding new edges to the operation node from each input node\n al = [qargs, all_cbits]\n self._multi_graph.insert_node_on_out_edges_multiple(\n node_index, [self.input_map[q]._node_id for q in itertools.chain(*al)]\n )\n return self._multi_graph[node_index]","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_edgemap_registers","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_edgemap_registers\n def _check_edgemap_registers(self, inbound_wires, inbound_regs):\n \"\"\"Check that wiremap neither fragments nor leaves duplicate registers.\n\n 1. There are no fragmented registers. A register in keyregs\n is fragmented if not all of its (qu)bits are renamed by edge_map.\n 2. There are no duplicate registers. A register is duplicate if\n it appears in both self and keyregs but not in edge_map.\n\n Args:\n inbound_wires (list): a list of wires being mapped from the inbound dag\n inbound_regs (list): a list from registers from the inbound dag\n\n Returns:\n set(Register): the set of regs to add to self\n\n Raises:\n DAGCircuitError: if the wiremap fragments, or duplicates exist\n \"\"\"\n add_regs = set()\n reg_frag_chk = {}\n\n for inbound_reg in inbound_regs:\n reg_frag_chk[inbound_reg] = {reg_bit: False for reg_bit in inbound_reg}\n\n for inbound_bit in inbound_wires:\n for inbound_reg in inbound_regs:\n if inbound_bit in inbound_reg:\n reg_frag_chk[inbound_reg][inbound_bit] = True\n break\n\n for inbound_reg, v in reg_frag_chk.items():\n s = set(v.values())\n if len(s) == 2:\n raise DAGCircuitError(\"inbound_wires fragments reg %s\" % inbound_reg)\n if s == {False}:\n if inbound_reg.name in self.qregs or inbound_reg.name in self.cregs:\n raise DAGCircuitError(\"unmapped duplicate reg %s\" % inbound_reg)\n\n # Add registers that appear only in inbound_regs\n add_regs.add(inbound_reg)\n\n return add_regs","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_wiremap_validity","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_wiremap_validity\n def _check_wiremap_validity(self, wire_map, keymap, valmap):\n \"\"\"Check that the wiremap is consistent.\n\n Check that the wiremap refers to valid wires and that\n those wires have consistent types.\n\n Args:\n wire_map (dict): map from Bit in keymap to Bit in valmap\n keymap (list): a list of wire_map keys\n valmap (dict): a map whose keys are wire_map values\n\n Raises:\n DAGCircuitError: if wire_map not valid\n \"\"\"\n for k, v in wire_map.items():\n\n if k not in keymap:\n raise DAGCircuitError(\"invalid wire mapping key %s\" % k)\n if v not in valmap:\n raise DAGCircuitError(\"invalid wire mapping value %s\" % v)\n # TODO Support mapping from AncillaQubit to Qubit, since AncillaQubits are mapped to\n # Qubits upon being converted to an Instruction. Until this translation is fixed\n # and Instructions have a concept of ancilla qubits, this fix is required.\n if not (isinstance(k, type(v)) or isinstance(v, type(k))):\n raise DAGCircuitError(f\"inconsistent wire_map at ({k},{v})\")","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._map_condition","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._map_condition\n @staticmethod\n def _map_condition(wire_map, condition, target_cregs):\n \"\"\"Use the wire_map dict to change the condition tuple's creg name.\n\n Args:\n wire_map (dict): a map from source wires to destination wires\n condition (tuple or None): (ClassicalRegister,int)\n target_cregs (list[ClassicalRegister]): List of all cregs in the\n target circuit onto which the condition might possibly be mapped.\n Returns:\n tuple(ClassicalRegister,int): new condition\n Raises:\n DAGCircuitError: if condition register not in wire_map, or if\n wire_map maps condition onto more than one creg, or if the\n specified condition is not present in a classical register.\n \"\"\"\n\n if condition is None:\n new_condition = None\n else:\n # if there is a condition, map the condition bits to the\n # composed cregs based on the wire_map\n is_reg = False\n if isinstance(condition[0], Clbit):\n cond_creg = [condition[0]]\n else:\n cond_creg = condition[0]\n is_reg = True\n cond_val = condition[1]\n new_cond_val = 0\n new_creg = None\n bits_in_condcreg = [bit for bit in wire_map if bit in cond_creg]\n for bit in bits_in_condcreg:\n if is_reg:\n try:\n candidate_creg = next(\n creg for creg in target_cregs if wire_map[bit] in creg\n )\n except StopIteration as ex:\n raise DAGCircuitError(\n \"Did not find creg containing mapped clbit in conditional.\"\n ) from ex\n else:\n # If cond is on a single Clbit then the candidate_creg is\n ","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.compose","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.compose\n def compose(self, other, qubits=None, clbits=None, front=False, inplace=True):\n \"\"\"Compose the ``other`` circuit onto the output of this circuit.\n\n A subset of input wires of ``other`` are mapped\n to a subset of output wires of this circuit.\n\n ``other`` can be narrower or of equal width to ``self``.\n\n Args:\n other (DAGCircuit): circuit to compose with self\n qubits (list[Qubit|int]): qubits of self to compose onto.\n clbits (list[Clbit|int]): clbits of self to compose onto.\n front (bool): If True, front composition will be performed (not implemented yet)\n inplace (bool): If True, modify the object. Otherwise return composed circuit.\n\n Returns:\n DAGCircuit: the composed dag (returns None if inplace==True).\n\n Raises:\n DAGCircuitError: if ``other`` is wider or there are duplicate edge mappings.\n \"\"\"\n if front:\n raise DAGCircuitError(\"Front composition not supported yet.\")\n\n if len(other.qubits) > len(self.qubits) or len(other.clbits) > len(self.clbits):\n raise DAGCircuitError(\n \"Trying to compose with another DAGCircuit which has more 'in' edges.\"\n )\n\n # number of qubits and clbits must match number in circuit or None\n identity_qubit_map = dict(zip(other.qubits, self.qubits))\n identity_clbit_map = dict(zip(other.clbits, self.clbits))\n if qubits is None:\n qubit_map = identity_qubit_map\n elif len(qubits) != len(other.qubits):\n raise DAGCircuitError(\n \"Number of items in qubits parameter does not\"\n \" match number of qubits in the circuit.\"\n )\n else:\n qubit_map = {\n other.qubits[i]: (self.qubits[q] if isinstance(q, int) else q)\n for i, q in enumerate(qubits)\n }\n if cl","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.reverse_ops","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.reverse_ops\n def reverse_ops(self):\n \"\"\"Reverse the operations in the ``self`` circuit.\n\n Returns:\n DAGCircuit: the reversed dag.\n \"\"\"\n # TODO: speed up\n # pylint: disable=cyclic-import\n from qiskit.converters import dag_to_circuit, circuit_to_dag\n\n qc = dag_to_circuit(self)\n reversed_qc = qc.reverse_ops()\n reversed_dag = circuit_to_dag(reversed_qc)\n return reversed_dag","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.idle_wires","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.idle_wires\n def idle_wires(self, ignore=None):\n \"\"\"Return idle wires.\n\n Args:\n ignore (list(str)): List of node names to ignore. Default: []\n\n Yields:\n Bit: Bit in idle wire.\n\n Raises:\n DAGCircuitError: If the DAG is invalid\n \"\"\"\n if ignore is None:\n ignore = set()\n ignore_set = set(ignore)\n for wire in self._wires:\n if not ignore:\n if self._is_wire_idle(wire):\n yield wire\n else:\n for node in self.nodes_on_wire(wire, only_ops=True):\n if node.op.name not in ignore_set:\n # If we found an op node outside of ignore we can stop iterating over the wire\n break\n else:\n yield wire","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.size","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.size\n def size(self):\n \"\"\"Return the number of operations.\"\"\"\n return len(self._multi_graph) - 2 * len(self._wires)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.depth","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.depth\n def depth(self):\n \"\"\"Return the circuit depth.\n Returns:\n int: the circuit depth\n Raises:\n DAGCircuitError: if not a directed acyclic graph\n \"\"\"\n try:\n depth = rx.dag_longest_path_length(self._multi_graph) - 1\n except rx.DAGHasCycle as ex:\n raise DAGCircuitError(\"not a DAG\") from ex\n return depth if depth >= 0 else 0","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.width","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.width\n def width(self):\n \"\"\"Return the total number of qubits + clbits used by the circuit.\n This function formerly returned the number of qubits by the calculation\n return len(self._wires) - self.num_clbits()\n but was changed by issue #2564 to return number of qubits + clbits\n with the new function DAGCircuit.num_qubits replacing the former\n semantic of DAGCircuit.width().\n \"\"\"\n return len(self._wires)","label":0}],"string":"[\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.concrete_instruction\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.concrete_instruction\\n @abc.abstractmethod\\n def concrete_instruction(\\n self, qubits: FrozenSet[Qubit], clbits: FrozenSet[Clbit]\\n ) -> Tuple[Instruction, InstructionResources]:\\n \\\"\\\"\\\"Get a concrete, complete instruction that is valid to act over all the given resources.\\n\\n The returned resources may not be the full width of the given resources, but will certainly\\n be a subset of them; this can occur if (for example) a placeholder ``if`` statement is\\n present, but does not itself contain any placeholder instructions. For resource efficiency,\\n the returned :obj:`.IfElseOp` will not unnecessarily span all resources, but only the ones\\n that it needs.\\n\\n Any condition added in by a call to :obj:`.Instruction.c_if` will be propagated through, but\\n set properties like ``duration`` will not; it doesn't make sense for control-flow operations\\n to have pulse scheduling on them.\\n\\n Args:\\n qubits: The qubits the created instruction should be defined across.\\n clbits: The clbits the created instruction should be defined across.\\n\\n Returns:\\n A full version of the relevant control-flow instruction, and the resources that it uses.\\n This is a \\\"proper\\\" instruction instance, as if it had been defined with the correct\\n number of qubits and clbits from the beginning.\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.placeholder_resources\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.placeholder_resources\\n @abc.abstractmethod\\n def placeholder_resources(self) -> InstructionResources:\\n \\\"\\\"\\\"Get the qubit and clbit resources that this placeholder instruction should be considered\\n as using before construction.\\n\\n This will likely not include *all* resources after the block has been built, but using the\\n output of this method ensures that all resources will pass through a\\n :meth:`.QuantumCircuit.append` call, even if they come from a placeholder, and consequently\\n will be tracked by the scope managers.\\n\\n Returns:\\n A collection of the quantum and classical resources this placeholder instruction will\\n certainly use.\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder._copy_mutable_properties\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder._copy_mutable_properties\\n def _copy_mutable_properties(self, instruction: Instruction) -> Instruction:\\n \\\"\\\"\\\"Copy mutable properties from ourselves onto a non-placeholder instruction.\\n\\n The mutable properties are expected to be things like ``condition``, added onto a\\n placeholder by the :meth:`c_if` method. This mutates ``instruction``, and returns the same\\n instance that was passed. This is mostly intended to make writing concrete versions of\\n :meth:`.concrete_instruction` easy.\\n\\n The complete list of mutations is:\\n\\n * ``condition``, added by :meth:`c_if`.\\n\\n Args:\\n instruction: the concrete instruction instance to be mutated.\\n\\n Returns:\\n The same instruction instance that was passed, but mutated to propagate the tracked\\n changes to this class.\\n \\\"\\\"\\\"\\n # In general the tuple creation should be a no-op, because ``tuple(t) is t`` for tuples.\\n instruction.condition = None if self.condition is None else tuple(self.condition)\\n return instruction\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.assemble\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.assemble\\n def assemble(self):\\n raise CircuitError(\\\"Cannot assemble a placeholder instruction.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.qasm\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.qasm\\n def qasm(self):\\n raise CircuitError(\\\"Cannot convert a placeholder instruction to OpenQASM 2\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.repeat\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::InstructionPlaceholder.repeat\\n def repeat(self, n):\\n raise CircuitError(\\\"Cannot repeat a placeholder instruction.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.__init__\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.__init__\\n def __init__(\\n self,\\n qubits: Iterable[Qubit],\\n clbits: Iterable[Clbit],\\n *,\\n registers: Iterable[Register] = (),\\n resource_requester: Callable,\\n allow_jumps: bool = True,\\n ):\\n \\\"\\\"\\\"\\n Args:\\n qubits: Any qubits this scope should consider itself as using from the beginning.\\n clbits: Any clbits this scope should consider itself as using from the beginning. Along\\n with ``qubits``, this is useful for things such as ``if`` and ``while`` loop\\n builders, where the classical condition has associated resources, and is known when\\n this scope is created.\\n registers: Any registers this scope should consider itself as using from the\\n beginning. This is useful for :obj:`.IfElseOp` and :obj:`.WhileLoopOp` instances\\n which use a classical register as their condition.\\n allow_jumps: Whether this builder scope should allow ``break`` and ``continue``\\n statements within it. This is intended to help give sensible error messages when\\n dangerous behaviour is encountered, such as using ``break`` inside an ``if`` context\\n manager that is not within a ``for`` manager. This can only be safe if the user is\\n going to place the resulting :obj:`.QuantumCircuit` inside a :obj:`.ForLoopOp` that\\n uses *exactly* the same set of resources. We cannot verify this from within the\\n builder interface (and it is too expensive to do when the ``for`` op is made), so we\\n fail safe, and require the user to use the more verbose, internal form.\\n resource_requester: A callback function that takes in some classical resource specifier,\\n and returns a concrete classical resource, if this scope is allowed to access that\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.allow_jumps\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.allow_jumps\\n @property\\n def allow_jumps(self):\\n \\\"\\\"\\\"Whether this builder scope should allow ``break`` and ``continue`` statements within it.\\n\\n This is intended to help give sensible error messages when dangerous behaviour is\\n encountered, such as using ``break`` inside an ``if`` context manager that is not within a\\n ``for`` manager. This can only be safe if the user is going to place the resulting\\n :obj:`.QuantumCircuit` inside a :obj:`.ForLoopOp` that uses *exactly* the same set of\\n resources. We cannot verify this from within the builder interface (and it is too expensive\\n to do when the ``for`` op is made), so we fail safe, and require the user to use the more\\n verbose, internal form.\\n \\\"\\\"\\\"\\n return self._allow_jumps\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.append\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.append\\n def append(\\n self,\\n operation: Instruction,\\n qubits: Iterable[Qubit],\\n clbits: Iterable[Clbit],\\n ) -> Instruction:\\n \\\"\\\"\\\"Add an instruction into the scope, keeping track of the qubits and clbits that have been\\n used in total.\\\"\\\"\\\"\\n if not self._allow_jumps:\\n # pylint: disable=cyclic-import\\n from .break_loop import BreakLoopOp, BreakLoopPlaceholder\\n from .continue_loop import ContinueLoopOp, ContinueLoopPlaceholder\\n\\n forbidden = (BreakLoopOp, BreakLoopPlaceholder, ContinueLoopOp, ContinueLoopPlaceholder)\\n if isinstance(operation, forbidden):\\n raise CircuitError(\\n f\\\"The current builder scope cannot take a '{operation.name}'\\\"\\n \\\" because it is not in a loop.\\\"\\n )\\n\\n qubits = tuple(qubits)\\n clbits = tuple(clbits)\\n self.instructions.append((operation, qubits, clbits))\\n self.qubits.update(qubits)\\n self.clbits.update(clbits)\\n return operation\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.request_classical_resource\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.request_classical_resource\\n def request_classical_resource(self, specifier):\\n \\\"\\\"\\\"Resolve a single classical resource specifier into a concrete resource, raising an error\\n if the specifier is invalid, and track it as now being used in scope.\\n\\n Args:\\n specifier (Union[Clbit, ClassicalRegister, int]): a specifier of a classical resource\\n present in this circuit. An ``int`` will be resolved into a :obj:`.Clbit` using the\\n same conventions that measurement operations on this circuit use.\\n\\n Returns:\\n Union[Clbit, ClassicalRegister]: the requested resource, resolved into a concrete\\n instance of :obj:`.Clbit` or :obj:`.ClassicalRegister`.\\n\\n Raises:\\n CircuitError: if the resource is not present in this circuit, or if the integer index\\n passed is out-of-bounds.\\n \\\"\\\"\\\"\\n if self._built:\\n raise CircuitError(\\\"Cannot add resources after the scope has been built.\\\")\\n # Allow the inner resolve to propagate exceptions.\\n resource = self._resource_requester(specifier)\\n if isinstance(resource, Clbit):\\n self.add_bits((resource,))\\n else:\\n self.add_register(resource)\\n return resource\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.peek\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.peek\\n def peek(self) -> Tuple[Instruction, Tuple[Qubit, ...], Tuple[Clbit, ...]]:\\n \\\"\\\"\\\"Get the value of the most recent instruction tuple in this scope.\\\"\\\"\\\"\\n if not self.instructions:\\n raise CircuitError(\\\"This scope contains no instructions.\\\")\\n return self.instructions[-1]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.pop\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.pop\\n def pop(self) -> Tuple[Instruction, Tuple[Qubit, ...], Tuple[Clbit, ...]]:\\n \\\"\\\"\\\"Get the value of the most recent instruction tuple in this scope, and remove it from this\\n object.\\\"\\\"\\\"\\n if not self.instructions:\\n raise CircuitError(\\\"This scope contains no instructions.\\\")\\n operation, qubits, clbits = self.instructions.pop()\\n return (operation, qubits, clbits)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.add_bits\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.add_bits\\n def add_bits(self, bits: Iterable[Union[Qubit, Clbit]]):\\n \\\"\\\"\\\"Add extra bits to this scope that are not associated with any concrete instruction yet.\\n\\n This is useful for expanding a scope's resource width when it may contain ``break`` or\\n ``continue`` statements, or when its width needs to be expanded to match another scope's\\n width (as in the case of :obj:`.IfElseOp`).\\n\\n Args:\\n bits: The qubits and clbits that should be added to a scope. It is not an error if\\n there are duplicates, either within the iterable or with the bits currently in\\n scope.\\n\\n Raises:\\n TypeError: if the provided bit is of an incorrect type.\\n \\\"\\\"\\\"\\n for bit in bits:\\n if isinstance(bit, Qubit):\\n self.qubits.add(bit)\\n elif isinstance(bit, Clbit):\\n self.clbits.add(bit)\\n else:\\n raise TypeError(f\\\"Can only add qubits or classical bits, but received '{bit}'.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.add_register\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.add_register\\n def add_register(self, register: Register):\\n \\\"\\\"\\\"Add a :obj:`.Register` to the set of resources used by this block, ensuring that\\n all bits contained within are also accounted for.\\n\\n Args:\\n register: the register to add to the block.\\n \\\"\\\"\\\"\\n if register in self.registers:\\n # Fast return to avoid iterating through the bits.\\n return\\n self.registers.add(register)\\n self.add_bits(register)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.build\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.build\\n def build(\\n self, all_qubits: FrozenSet[Qubit], all_clbits: FrozenSet[Clbit]\\n ) -> \\\"qiskit.circuit.QuantumCircuit\\\":\\n \\\"\\\"\\\"Build this scoped block into a complete :obj:`.QuantumCircuit` instance.\\n\\n This will build a circuit which contains all of the necessary qubits and clbits and no\\n others.\\n\\n The ``qubits`` and ``clbits`` arguments should be sets that contains all the resources in\\n the outer scope; these will be passed down to inner placeholder instructions, so they can\\n apply themselves across the whole scope should they need to. The resulting\\n :obj:`.QuantumCircuit` will be defined over a (nonstrict) subset of these resources. This\\n is used to let ``break`` and ``continue`` span all resources, even if they are nested within\\n several :obj:`.IfElsePlaceholder` objects, without requiring :obj:`.IfElsePlaceholder`\\n objects *without* any ``break`` or ``continue`` statements to be full-width.\\n\\n Args:\\n all_qubits: all the qubits in the containing scope of this block. The block may expand\\n to use some or all of these qubits, but will never gain qubits that are not in this\\n set.\\n all_clbits: all the clbits in the containing scope of this block. The block may expand\\n to use some or all of these clbits, but will never gain clbits that are not in this\\n set.\\n\\n Returns:\\n A circuit containing concrete versions of all the instructions that were in the scope,\\n and using the minimal set of resources necessary to support them, within the enclosing\\n scope.\\n \\\"\\\"\\\"\\n from qiskit.circuit import QuantumCircuit\\n\\n # There's actually no real problem with building a scope more than once. This flag is more\\n # so _other_ operations, which aren't safe can be forbidden, such as mutating ins\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.copy\",\n \"text\": \"qiskit/circuit/controlflow/builder.py::ControlFlowBuilderBlock.copy\\n def copy(self) -> \\\"ControlFlowBuilderBlock\\\":\\n \\\"\\\"\\\"Return a semi-shallow copy of this builder block.\\n\\n The instruction lists and sets of qubits and clbits will be new instances (so mutations will\\n not propagate), but any :obj:`.Instruction` instances within them will not be copied.\\n\\n Returns:\\n a semi-shallow copy of this object.\\n \\\"\\\"\\\"\\n out = type(self).__new__(type(self))\\n out.instructions = self.instructions.copy()\\n out.qubits = self.qubits.copy()\\n out.clbits = self.clbits.copy()\\n out.registers = self.registers.copy()\\n out._allow_jumps = self._allow_jumps\\n return out\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__init__\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__init__\\n def __init__(\\n self,\\n *regs: Union[Register, int, Sequence[Bit]],\\n name: Optional[str] = None,\\n global_phase: ParameterValueType = 0,\\n metadata: Optional[Dict] = None,\\n ):\\n if any(not isinstance(reg, (list, QuantumRegister, ClassicalRegister)) for reg in regs):\\n # check if inputs are integers, but also allow e.g. 2.0\\n\\n try:\\n valid_reg_size = all(reg == int(reg) for reg in regs)\\n except (ValueError, TypeError):\\n valid_reg_size = False\\n\\n if not valid_reg_size:\\n raise CircuitError(\\n \\\"Circuit args must be Registers or integers. (%s '%s' was \\\"\\n \\\"provided)\\\" % ([type(reg).__name__ for reg in regs], regs)\\n )\\n\\n regs = tuple(int(reg) for reg in regs) # cast to int\\n self._base_name = None\\n if name is None:\\n self._base_name = self.cls_prefix()\\n self._name_update()\\n elif not isinstance(name, str):\\n raise CircuitError(\\n \\\"The circuit name should be a string (or None to auto-generate a name).\\\"\\n )\\n else:\\n self._base_name = name\\n self.name = name\\n self._increment_instances()\\n\\n # Data contains a list of instructions and their contexts,\\n # in the order they were applied.\\n self._data = []\\n self._op_start_times = None\\n\\n # A stack to hold the instruction sets that are being built up during for-, if- and\\n # while-block construction. These are stored as a stripped down sequence of instructions,\\n # and sets of qubits and clbits, rather than a full QuantumCircuit instance because the\\n # builder interfaces need to wait until they are completed before they can fill in things\\n # like `break` and `continue`. This is because these instructions need to \\\"operate\\\" on\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.data\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.data\\n @property\\n def data(self) -> QuantumCircuitData:\\n \\\"\\\"\\\"Return the circuit data (instructions and context).\\n\\n Returns:\\n QuantumCircuitData: a list-like object containing the tuples for the circuit's data.\\n\\n Each tuple is in the format ``(instruction, qargs, cargs)``, where instruction is an\\n Instruction (or subclass) object, qargs is a list of Qubit objects, and cargs is a\\n list of Clbit objects.\\n \\\"\\\"\\\"\\n return QuantumCircuitData(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.op_start_times\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.op_start_times\\n @property\\n def op_start_times(self) -> List[int]:\\n \\\"\\\"\\\"Return a list of operation start times.\\n\\n This attribute is enabled once one of scheduling analysis passes\\n runs on the quantum circuit.\\n\\n Returns:\\n List of integers representing instruction start times.\\n The index corresponds to the index of instruction in :attr:`QuantumCircuit.data`.\\n\\n Raises:\\n AttributeError: When circuit is not scheduled.\\n \\\"\\\"\\\"\\n if self._op_start_times is None:\\n raise AttributeError(\\n \\\"This circuit is not scheduled. \\\"\\n \\\"To schedule it run the circuit through one of the transpiler scheduling passes.\\\"\\n )\\n return self._op_start_times\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.data\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.data\\n @data.setter\\n def data(\\n self, data_input: List[Tuple[Instruction, List[QubitSpecifier], List[ClbitSpecifier]]]\\n ):\\n \\\"\\\"\\\"Sets the circuit data from a list of instructions and context.\\n\\n Args:\\n data_input (list): A list of instructions with context\\n in the format (instruction, qargs, cargs), where Instruction\\n is an Instruction (or subclass) object, qargs is a list of\\n Qubit objects, and cargs is a list of Clbit objects.\\n \\\"\\\"\\\"\\n\\n # If data_input is QuantumCircuitData(self), clearing self._data\\n # below will also empty data_input, so make a shallow copy first.\\n data_input = data_input.copy()\\n self._data = []\\n self._parameter_table = ParameterTable()\\n\\n for inst, qargs, cargs in data_input:\\n self.append(inst, qargs, cargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.calibrations\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.calibrations\\n @property\\n def calibrations(self) -> dict:\\n \\\"\\\"\\\"Return calibration dictionary.\\n\\n The custom pulse definition of a given gate is of the form\\n {'gate_name': {(qubits, params): schedule}}\\n \\\"\\\"\\\"\\n return dict(self._calibrations)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.calibrations\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.calibrations\\n @calibrations.setter\\n def calibrations(self, calibrations: dict):\\n \\\"\\\"\\\"Set the circuit calibration data from a dictionary of calibration definition.\\n\\n Args:\\n calibrations (dict): A dictionary of input in the format\\n {'gate_name': {(qubits, gate_params): schedule}}\\n \\\"\\\"\\\"\\n self._calibrations = defaultdict(dict, calibrations)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.metadata\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.metadata\\n @property\\n def metadata(self) -> dict:\\n \\\"\\\"\\\"The user provided metadata associated with the circuit\\n\\n The metadata for the circuit is a user provided ``dict`` of metadata\\n for the circuit. It will not be used to influence the execution or\\n operation of the circuit, but it is expected to be passed between\\n all transforms of the circuit (ie transpilation) and that providers will\\n associate any circuit metadata with the results it returns from\\n execution of that circuit.\\n \\\"\\\"\\\"\\n return self._metadata\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.metadata\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.metadata\\n @metadata.setter\\n def metadata(self, metadata: Optional[dict]):\\n \\\"\\\"\\\"Update the circuit metadata\\\"\\\"\\\"\\n if not isinstance(metadata, dict) and metadata is not None:\\n raise TypeError(\\\"Only a dictionary or None is accepted for circuit metadata\\\")\\n self._metadata = metadata\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__str__\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__str__\\n def __str__(self) -> str:\\n return str(self.draw(output=\\\"text\\\"))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__eq__\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__eq__\\n def __eq__(self, other) -> bool:\\n if not isinstance(other, QuantumCircuit):\\n return False\\n\\n # TODO: remove the DAG from this function\\n from qiskit.converters import circuit_to_dag\\n\\n return circuit_to_dag(self) == circuit_to_dag(other)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._increment_instances\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._increment_instances\\n @classmethod\\n def _increment_instances(cls):\\n cls.instances += 1\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cls_instances\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cls_instances\\n @classmethod\\n def cls_instances(cls) -> int:\\n \\\"\\\"\\\"Return the current number of instances of this class,\\n useful for auto naming.\\\"\\\"\\\"\\n return cls.instances\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cls_prefix\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cls_prefix\\n @classmethod\\n def cls_prefix(cls) -> str:\\n \\\"\\\"\\\"Return the prefix to use for auto naming.\\\"\\\"\\\"\\n return cls.prefix\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._name_update\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._name_update\\n def _name_update(self) -> None:\\n \\\"\\\"\\\"update name of instance using instance number\\\"\\\"\\\"\\n if not is_main_process():\\n pid_name = f\\\"-{mp.current_process().pid}\\\"\\n else:\\n pid_name = \\\"\\\"\\n\\n self.name = f\\\"{self._base_name}-{self.cls_instances()}{pid_name}\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.has_register\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.has_register\\n def has_register(self, register: Register) -> bool:\\n \\\"\\\"\\\"\\n Test if this circuit has the register r.\\n\\n Args:\\n register (Register): a quantum or classical register.\\n\\n Returns:\\n bool: True if the register is contained in this circuit.\\n \\\"\\\"\\\"\\n has_reg = False\\n if isinstance(register, QuantumRegister) and register in self.qregs:\\n has_reg = True\\n elif isinstance(register, ClassicalRegister) and register in self.cregs:\\n has_reg = True\\n return has_reg\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.reverse_ops\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.reverse_ops\\n def reverse_ops(self) -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Reverse the circuit by reversing the order of instructions.\\n\\n This is done by recursively reversing all instructions.\\n It does not invert (adjoint) any gate.\\n\\n Returns:\\n QuantumCircuit: the reversed circuit.\\n\\n Examples:\\n\\n input:\\n\\n .. parsed-literal::\\n\\n ┌───┐\\n q_0: ┤ H ├─────■──────\\n └───┘┌────┴─────┐\\n q_1: ─────┤ RX(1.57) ├\\n └──────────┘\\n\\n output:\\n\\n .. parsed-literal::\\n\\n ┌───┐\\n q_0: ─────■──────┤ H ├\\n ┌────┴─────┐└───┘\\n q_1: ┤ RX(1.57) ├─────\\n └──────────┘\\n \\\"\\\"\\\"\\n reverse_circ = QuantumCircuit(\\n self.qubits, self.clbits, *self.qregs, *self.cregs, name=self.name + \\\"_reverse\\\"\\n )\\n\\n for inst, qargs, cargs in reversed(self.data):\\n reverse_circ._append(inst.reverse_ops(), qargs, cargs)\\n\\n reverse_circ.duration = self.duration\\n reverse_circ.unit = self.unit\\n return reverse_circ\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.reverse_bits\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.reverse_bits\\n def reverse_bits(self) -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Return a circuit with the opposite order of wires.\\n\\n The circuit is \\\"vertically\\\" flipped. If a circuit is\\n defined over multiple registers, the resulting circuit will have\\n the same registers but with their order flipped.\\n\\n This method is useful for converting a circuit written in little-endian\\n convention to the big-endian equivalent, and vice versa.\\n\\n Returns:\\n QuantumCircuit: the circuit with reversed bit order.\\n\\n Examples:\\n\\n input:\\n\\n .. parsed-literal::\\n\\n ┌───┐\\n a_0: ┤ H ├──■─────────────────\\n └───┘┌─┴─┐\\n a_1: ─────┤ X ├──■────────────\\n └───┘┌─┴─┐\\n a_2: ──────────┤ X ├──■───────\\n └───┘┌─┴─┐\\n b_0: ───────────────┤ X ├──■──\\n └───┘┌─┴─┐\\n b_1: ────────────────────┤ X ├\\n └───┘\\n\\n output:\\n\\n .. parsed-literal::\\n\\n ┌───┐\\n b_0: ────────────────────┤ X ├\\n ┌───┐└─┬─┘\\n b_1: ───────────────┤ X ├──■──\\n ┌───┐└─┬─┘\\n a_0: ──────────┤ X ├──■───────\\n ┌───┐└─┬─┘\\n a_1: ─────┤ X ├──■────────────\\n ┌───┐└─┬─┘\\n a_2: ┤ H ├──■─────────────────\\n └───┘\\n \\\"\\\"\\\"\\n circ = QuantumCircuit(\\n list(reversed(self.qubits)),\\n list(reversed(self.clbits)),\\n name=self.name,\\n global_phase=self.global_phase,\\n )\\n new_qubit_map = circ.qubits[::-1]\\n new_clbit_map = circ.clbits[::-1]\\n for reg in reversed(self.qregs):\\n b\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.inverse\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.inverse\\n def inverse(self) -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Invert (take adjoint of) this circuit.\\n\\n This is done by recursively inverting all gates.\\n\\n Returns:\\n QuantumCircuit: the inverted circuit\\n\\n Raises:\\n CircuitError: if the circuit cannot be inverted.\\n\\n Examples:\\n\\n input:\\n\\n .. parsed-literal::\\n\\n ┌───┐\\n q_0: ┤ H ├─────■──────\\n └───┘┌────┴─────┐\\n q_1: ─────┤ RX(1.57) ├\\n └──────────┘\\n\\n output:\\n\\n .. parsed-literal::\\n\\n ┌───┐\\n q_0: ──────■──────┤ H ├\\n ┌─────┴─────┐└───┘\\n q_1: ┤ RX(-1.57) ├─────\\n └───────────┘\\n \\\"\\\"\\\"\\n inverse_circ = QuantumCircuit(\\n self.qubits,\\n self.clbits,\\n *self.qregs,\\n *self.cregs,\\n name=self.name + \\\"_dg\\\",\\n global_phase=-self.global_phase,\\n )\\n\\n for inst, qargs, cargs in reversed(self._data):\\n inverse_circ._append(inst.inverse(), qargs, cargs)\\n return inverse_circ\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.repeat\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.repeat\\n def repeat(self, reps: int) -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Repeat this circuit ``reps`` times.\\n\\n Args:\\n reps (int): How often this circuit should be repeated.\\n\\n Returns:\\n QuantumCircuit: A circuit containing ``reps`` repetitions of this circuit.\\n \\\"\\\"\\\"\\n repeated_circ = QuantumCircuit(\\n self.qubits, self.clbits, *self.qregs, *self.cregs, name=self.name + f\\\"**{reps}\\\"\\n )\\n\\n # benefit of appending instructions: decomposing shows the subparts, i.e. the power\\n # is actually `reps` times this circuit, and it is currently much faster than `compose`.\\n if reps > 0:\\n try: # try to append as gate if possible to not disallow to_gate\\n inst: Instruction = self.to_gate()\\n except QiskitError:\\n inst = self.to_instruction()\\n for _ in range(reps):\\n repeated_circ._append(inst, self.qubits, self.clbits)\\n\\n return repeated_circ\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.power\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.power\\n def power(self, power: float, matrix_power: bool = False) -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Raise this circuit to the power of ``power``.\\n\\n If ``power`` is a positive integer and ``matrix_power`` is ``False``, this implementation\\n defaults to calling ``repeat``. Otherwise, if the circuit is unitary, the matrix is\\n computed to calculate the matrix power.\\n\\n Args:\\n power (float): The power to raise this circuit to.\\n matrix_power (bool): If True, the circuit is converted to a matrix and then the\\n matrix power is computed. If False, and ``power`` is a positive integer,\\n the implementation defaults to ``repeat``.\\n\\n Raises:\\n CircuitError: If the circuit needs to be converted to a gate but it is not unitary.\\n\\n Returns:\\n QuantumCircuit: A circuit implementing this circuit raised to the power of ``power``.\\n \\\"\\\"\\\"\\n if power >= 0 and isinstance(power, (int, np.integer)) and not matrix_power:\\n return self.repeat(power)\\n\\n # attempt conversion to gate\\n if self.num_parameters > 0:\\n raise CircuitError(\\n \\\"Cannot raise a parameterized circuit to a non-positive power \\\"\\n \\\"or matrix-power, please bind the free parameters: \\\"\\n \\\"{}\\\".format(self.parameters)\\n )\\n\\n try:\\n gate = self.to_gate()\\n except QiskitError as ex:\\n raise CircuitError(\\n \\\"The circuit contains non-unitary operations and cannot be \\\"\\n \\\"controlled. Note that no qiskit.circuit.Instruction objects may \\\"\\n \\\"be in the circuit for this operation.\\\"\\n ) from ex\\n\\n power_circuit = QuantumCircuit(self.qubits, self.clbits, *self.qregs, *self.cregs)\\n power_circuit.append(gate.power(power), list(range(gate.num_qubits)))\\n return power_circuit\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.control\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.control\\n def control(\\n self,\\n num_ctrl_qubits: int = 1,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Control this circuit on ``num_ctrl_qubits`` qubits.\\n\\n Args:\\n num_ctrl_qubits (int): The number of control qubits.\\n label (str): An optional label to give the controlled operation for visualization.\\n ctrl_state (str or int): The control state in decimal or as a bitstring\\n (e.g. '111'). If None, use ``2**num_ctrl_qubits - 1``.\\n\\n Returns:\\n QuantumCircuit: The controlled version of this circuit.\\n\\n Raises:\\n CircuitError: If the circuit contains a non-unitary operation and cannot be controlled.\\n \\\"\\\"\\\"\\n try:\\n gate = self.to_gate()\\n except QiskitError as ex:\\n raise CircuitError(\\n \\\"The circuit contains non-unitary operations and cannot be \\\"\\n \\\"controlled. Note that no qiskit.circuit.Instruction objects may \\\"\\n \\\"be in the circuit for this operation.\\\"\\n ) from ex\\n\\n controlled_gate = gate.control(num_ctrl_qubits, label, ctrl_state)\\n control_qreg = QuantumRegister(num_ctrl_qubits)\\n controlled_circ = QuantumCircuit(\\n control_qreg, self.qubits, *self.qregs, name=f\\\"c_{self.name}\\\"\\n )\\n controlled_circ.append(controlled_gate, controlled_circ.qubits)\\n\\n return controlled_circ\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.combine\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.combine\\n @deprecate_function(\\n \\\"The QuantumCircuit.combine() method is being deprecated. \\\"\\n \\\"Use the compose() method which is more flexible w.r.t \\\"\\n \\\"circuit register compatibility.\\\"\\n )\\n def combine(self, rhs: \\\"QuantumCircuit\\\") -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"DEPRECATED - Returns rhs appended to self if self contains compatible registers.\\n\\n Two circuits are compatible if they contain the same registers\\n or if they contain different registers with unique names. The\\n returned circuit will contain all unique registers between both\\n circuits.\\n\\n Return self + rhs as a new object.\\n\\n Args:\\n rhs (QuantumCircuit): The quantum circuit to append to the right hand side.\\n\\n Returns:\\n QuantumCircuit: Returns a new QuantumCircuit object\\n\\n Raises:\\n QiskitError: if the rhs circuit is not compatible\\n \\\"\\\"\\\"\\n # Check registers in LHS are compatible with RHS\\n self._check_compatible_regs(rhs)\\n\\n # Make new circuit with combined registers\\n combined_qregs = copy.deepcopy(self.qregs)\\n combined_cregs = copy.deepcopy(self.cregs)\\n\\n for element in rhs.qregs:\\n if element not in self.qregs:\\n combined_qregs.append(element)\\n for element in rhs.cregs:\\n if element not in self.cregs:\\n combined_cregs.append(element)\\n circuit = QuantumCircuit(*combined_qregs, *combined_cregs)\\n for instruction_context in itertools.chain(self.data, rhs.data):\\n circuit._append(*instruction_context)\\n circuit.global_phase = self.global_phase + rhs.global_phase\\n\\n for gate, cals in rhs.calibrations.items():\\n for key, sched in cals.items():\\n circuit.add_calibration(gate, qubits=key[0], schedule=sched, params=key[1])\\n\\n for gate, cals in self.calibrations.items():\\n for key, sched \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.extend\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.extend\\n @deprecate_function(\\n \\\"The QuantumCircuit.extend() method is being deprecated. Use the \\\"\\n \\\"compose() (potentially with the inplace=True argument) and tensor() \\\"\\n \\\"methods which are more flexible w.r.t circuit register compatibility.\\\"\\n )\\n def extend(self, rhs: \\\"QuantumCircuit\\\") -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"DEPRECATED - Append QuantumCircuit to the RHS if it contains compatible registers.\\n\\n Two circuits are compatible if they contain the same registers\\n or if they contain different registers with unique names. The\\n returned circuit will contain all unique registers between both\\n circuits.\\n\\n Modify and return self.\\n\\n Args:\\n rhs (QuantumCircuit): The quantum circuit to append to the right hand side.\\n\\n Returns:\\n QuantumCircuit: Returns this QuantumCircuit object (which has been modified)\\n\\n Raises:\\n QiskitError: if the rhs circuit is not compatible\\n \\\"\\\"\\\"\\n # Check registers in LHS are compatible with RHS\\n self._check_compatible_regs(rhs)\\n\\n # Add new registers\\n for element in rhs.qregs:\\n if element not in self.qregs:\\n self.add_register(element)\\n\\n for element in rhs.cregs:\\n if element not in self.cregs:\\n self.add_register(element)\\n\\n # Copy the circuit data if rhs and self are the same, otherwise the data of rhs is\\n # appended to both self and rhs resulting in an infinite loop\\n data = rhs.data.copy() if rhs is self else rhs.data\\n\\n # Add new gates\\n for instruction_context in data:\\n self._append(*instruction_context)\\n self.global_phase += rhs.global_phase\\n\\n for gate, cals in rhs.calibrations.items():\\n for key, sched in cals.items():\\n self.add_calibration(gate, qubits=key[0], schedule=sched, params=key[1])\\n\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.compose\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.compose\\n def compose(\\n self,\\n other: Union[\\\"QuantumCircuit\\\", Instruction],\\n qubits: Optional[Sequence[Union[Qubit, int]]] = None,\\n clbits: Optional[Sequence[Union[Clbit, int]]] = None,\\n front: bool = False,\\n inplace: bool = False,\\n wrap: bool = False,\\n ) -> Optional[\\\"QuantumCircuit\\\"]:\\n \\\"\\\"\\\"Compose circuit with ``other`` circuit or instruction, optionally permuting wires.\\n\\n ``other`` can be narrower or of equal width to ``self``.\\n\\n Args:\\n other (qiskit.circuit.Instruction or QuantumCircuit):\\n (sub)circuit or instruction to compose onto self. If not a :obj:`.QuantumCircuit`,\\n this can be anything that :obj:`.append` will accept.\\n qubits (list[Qubit|int]): qubits of self to compose onto.\\n clbits (list[Clbit|int]): clbits of self to compose onto.\\n front (bool): If True, front composition will be performed (not implemented yet).\\n inplace (bool): If True, modify the object. Otherwise return composed circuit.\\n wrap (bool): If True, wraps the other circuit into a gate (or instruction, depending on\\n whether it contains only unitary instructions) before composing it onto self.\\n\\n Returns:\\n QuantumCircuit: the composed circuit (returns None if inplace==True).\\n\\n Raises:\\n CircuitError: if composing on the front.\\n QiskitError: if ``other`` is wider or there are duplicate edge mappings.\\n\\n Examples::\\n\\n lhs.compose(rhs, qubits=[3, 2], inplace=True)\\n\\n .. parsed-literal::\\n\\n ┌───┐ ┌─────┐ ┌───┐\\n lqr_1_0: ───┤ H ├─── rqr_0: ──■──┤ Tdg ├ lqr_1_0: ───┤ H ├───────────────\\n ├───┤ ┌─┴─┐└─────┘ ├───┤\\n lqr_1_1: ───┤ X ├─── rqr_1: ┤ X ├──\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.tensor\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.tensor\\n def tensor(self, other: \\\"QuantumCircuit\\\", inplace: bool = False) -> Optional[\\\"QuantumCircuit\\\"]:\\n \\\"\\\"\\\"Tensor ``self`` with ``other``.\\n\\n Remember that in the little-endian convention the leftmost operation will be at the bottom\\n of the circuit. See also\\n [the docs](qiskit.org/documentation/tutorials/circuits/3_summary_of_quantum_operations.html)\\n for more information.\\n\\n .. parsed-literal::\\n\\n ┌────────┐ ┌─────┐ ┌─────┐\\n q_0: ┤ bottom ├ ⊗ q_0: ┤ top ├ = q_0: ─┤ top ├──\\n └────────┘ └─────┘ ┌┴─────┴─┐\\n q_1: ┤ bottom ├\\n └────────┘\\n\\n Args:\\n other (QuantumCircuit): The other circuit to tensor this circuit with.\\n inplace (bool): If True, modify the object. Otherwise return composed circuit.\\n\\n Examples:\\n\\n .. jupyter-execute::\\n\\n from qiskit import QuantumCircuit\\n top = QuantumCircuit(1)\\n top.x(0);\\n bottom = QuantumCircuit(2)\\n bottom.cry(0.2, 0, 1);\\n tensored = bottom.tensor(top)\\n print(tensored.draw())\\n\\n Returns:\\n QuantumCircuit: The tensored circuit (returns None if inplace==True).\\n \\\"\\\"\\\"\\n num_qubits = self.num_qubits + other.num_qubits\\n num_clbits = self.num_clbits + other.num_clbits\\n\\n # If a user defined both circuits with via register sizes and not with named registers\\n # (e.g. QuantumCircuit(2, 2)) then we have a naming collision, as the registers are by\\n # default called \\\"q\\\" resp. \\\"c\\\". To still allow tensoring we define new registers of the\\n # correct sizes.\\n if (\\n len(self.qregs) == len(other.qregs) == 1\\n and self.qregs[0].name == other.qregs[0].name == \\\"q\\\"\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubits\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubits\\n @property\\n def qubits(self) -> List[Qubit]:\\n \\\"\\\"\\\"\\n Returns a list of quantum bits in the order that the registers were added.\\n \\\"\\\"\\\"\\n return self._qubits\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.clbits\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.clbits\\n @property\\n def clbits(self) -> List[Clbit]:\\n \\\"\\\"\\\"\\n Returns a list of classical bits in the order that the registers were added.\\n \\\"\\\"\\\"\\n return self._clbits\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ancillas\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ancillas\\n @property\\n def ancillas(self) -> List[AncillaQubit]:\\n \\\"\\\"\\\"\\n Returns a list of ancilla bits in the order that the registers were added.\\n \\\"\\\"\\\"\\n return self._ancillas\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__add__\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__add__\\n @deprecate_function(\\n \\\"The QuantumCircuit.__add__() method is being deprecated.\\\"\\n \\\"Use the compose() method which is more flexible w.r.t \\\"\\n \\\"circuit register compatibility.\\\"\\n )\\n def __add__(self, rhs: \\\"QuantumCircuit\\\") -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Overload + to implement self.combine.\\\"\\\"\\\"\\n return self.combine(rhs)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__iadd__\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__iadd__\\n @deprecate_function(\\n \\\"The QuantumCircuit.__iadd__() method is being deprecated. Use the \\\"\\n \\\"compose() (potentially with the inplace=True argument) and tensor() \\\"\\n \\\"methods which are more flexible w.r.t circuit register compatibility.\\\"\\n )\\n def __iadd__(self, rhs: \\\"QuantumCircuit\\\") -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Overload += to implement self.extend.\\\"\\\"\\\"\\n return self.extend(rhs)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__and__\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__and__\\n def __and__(self, rhs: \\\"QuantumCircuit\\\") -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Overload & to implement self.compose.\\\"\\\"\\\"\\n return self.compose(rhs)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__iand__\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__iand__\\n def __iand__(self, rhs: \\\"QuantumCircuit\\\") -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Overload &= to implement self.compose in place.\\\"\\\"\\\"\\n self.compose(rhs, inplace=True)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__xor__\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__xor__\\n def __xor__(self, top: \\\"QuantumCircuit\\\") -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Overload ^ to implement self.tensor.\\\"\\\"\\\"\\n return self.tensor(top)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__ixor__\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__ixor__\\n def __ixor__(self, top: \\\"QuantumCircuit\\\") -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Overload ^= to implement self.tensor in place.\\\"\\\"\\\"\\n self.tensor(top, inplace=True)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__len__\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__len__\\n def __len__(self) -> int:\\n \\\"\\\"\\\"Return number of operations in circuit.\\\"\\\"\\\"\\n return len(self._data)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__getitem__\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__getitem__\\n @typing.overload\\n def __getitem__(self, item: int) -> DataElement:\\n ...\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__getitem__\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__getitem__\\n @typing.overload\\n def __getitem__(self, item: slice) -> List[DataElement]:\\n ...\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__getitem__\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.__getitem__\\n def __getitem__(self, item):\\n \\\"\\\"\\\"Return indexed operation.\\\"\\\"\\\"\\n return self._data[item]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cast\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cast\\n @staticmethod\\n def cast(value: S, type_: Callable[..., T]) -> Union[S, T]:\\n \\\"\\\"\\\"Best effort to cast value to type. Otherwise, returns the value.\\\"\\\"\\\"\\n try:\\n return type_(value)\\n except (ValueError, TypeError):\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qbit_argument_conversion\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qbit_argument_conversion\\n def qbit_argument_conversion(self, qubit_representation: QubitSpecifier) -> List[Qubit]:\\n \\\"\\\"\\\"\\n Converts several qubit representations (such as indexes, range, etc.)\\n into a list of qubits.\\n\\n Args:\\n qubit_representation (Object): representation to expand\\n\\n Returns:\\n List(Qubit): the resolved instances of the qubits.\\n \\\"\\\"\\\"\\n return _bit_argument_conversion(\\n qubit_representation, self.qubits, self._qubit_indices, Qubit\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cbit_argument_conversion\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cbit_argument_conversion\\n def cbit_argument_conversion(self, clbit_representation: ClbitSpecifier) -> List[Clbit]:\\n \\\"\\\"\\\"\\n Converts several classical bit representations (such as indexes, range, etc.)\\n into a list of classical bits.\\n\\n Args:\\n clbit_representation (Object): representation to expand\\n\\n Returns:\\n List(tuple): Where each tuple is a classical bit.\\n \\\"\\\"\\\"\\n return _bit_argument_conversion(\\n clbit_representation, self.clbits, self._clbit_indices, Clbit\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._resolve_classical_resource\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._resolve_classical_resource\\n def _resolve_classical_resource(self, specifier):\\n \\\"\\\"\\\"Resolve a single classical resource specifier into a concrete resource, raising an error\\n if the specifier is invalid.\\n\\n This is slightly different to :meth:`.cbit_argument_conversion`, because it should not\\n unwrap :obj:`.ClassicalRegister` instances into lists, and in general it should not allow\\n iterables or broadcasting. It is expected to be used as a callback for things like\\n :meth:`.InstructionSet.c_if` to check the validity of their arguments.\\n\\n Args:\\n specifier (Union[Clbit, ClassicalRegister, int]): a specifier of a classical resource\\n present in this circuit. An ``int`` will be resolved into a :obj:`.Clbit` using the\\n same conventions as measurement operations on this circuit use.\\n\\n Returns:\\n Union[Clbit, ClassicalRegister]: the resolved resource.\\n\\n Raises:\\n CircuitError: if the resource is not present in this circuit, or if the integer index\\n passed is out-of-bounds.\\n \\\"\\\"\\\"\\n if isinstance(specifier, Clbit):\\n if specifier not in self._clbit_indices:\\n raise CircuitError(f\\\"Clbit {specifier} is not present in this circuit.\\\")\\n return specifier\\n if isinstance(specifier, ClassicalRegister):\\n # This is linear complexity for something that should be constant, but QuantumCircuit\\n # does not currently keep a hashmap of registers, and requires non-trivial changes to\\n # how it exposes its registers publically before such a map can be safely stored so it\\n # doesn't miss updates. (Jake, 2021-11-10).\\n if specifier not in self.cregs:\\n raise CircuitError(f\\\"Register {specifier} is not present in this circuit.\\\")\\n return specifier\\n if isinstance(specifier, int):\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.append\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.append\\n def append(\\n self,\\n instruction: Instruction,\\n qargs: Optional[Sequence[QubitSpecifier]] = None,\\n cargs: Optional[Sequence[ClbitSpecifier]] = None,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Append one or more instructions to the end of the circuit, modifying\\n the circuit in place. Expands qargs and cargs.\\n\\n Args:\\n instruction (qiskit.circuit.Instruction): Instruction instance to append\\n qargs (list(argument)): qubits to attach instruction to\\n cargs (list(argument)): clbits to attach instruction to\\n\\n Returns:\\n qiskit.circuit.Instruction: a handle to the instruction that was just added\\n\\n Raises:\\n CircuitError: if object passed is a subclass of Instruction\\n CircuitError: if object passed is neither subclass nor an instance of Instruction\\n \\\"\\\"\\\"\\n # Convert input to instruction\\n if not isinstance(instruction, Instruction) and not hasattr(instruction, \\\"to_instruction\\\"):\\n if issubclass(instruction, Instruction):\\n raise CircuitError(\\n \\\"Object is a subclass of Instruction, please add () to \\\"\\n \\\"pass an instance of this object.\\\"\\n )\\n\\n raise CircuitError(\\n \\\"Object to append must be an Instruction or have a to_instruction() method.\\\"\\n )\\n if not isinstance(instruction, Instruction) and hasattr(instruction, \\\"to_instruction\\\"):\\n instruction = instruction.to_instruction()\\n if not isinstance(instruction, Instruction):\\n raise CircuitError(\\\"object is not an Instruction.\\\")\\n\\n # Make copy of parameterized gate instances\\n if hasattr(instruction, \\\"params\\\"):\\n is_parameter = any(isinstance(param, Parameter) for param in instruction.params)\\n if is_parameter:\\n instruction = copy.deepcopy(instruction)\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._append\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._append\\n def _append(\\n self,\\n instruction: Instruction,\\n qargs: Sequence[Qubit],\\n cargs: Sequence[Clbit],\\n ) -> Instruction:\\n \\\"\\\"\\\"Append an instruction to the end of the circuit, modifying the circuit in place.\\n\\n .. warning::\\n\\n This is an internal fast-path function, and it is the responsibility of the caller to\\n ensure that all the arguments are valid; there is no error checking here. In\\n particular, all the qubits and clbits must already exist in the circuit and there can be\\n no duplicates in the list.\\n\\n .. note::\\n\\n This function may be used by callers other than :obj:`.QuantumCircuit` when the caller\\n is sure that all error-checking, broadcasting and scoping has already been performed,\\n and the only reference to the circuit the instructions are being appended to is within\\n that same function. In particular, it is not safe to call\\n :meth:`QuantumCircuit._append` on a circuit that is received by a function argument.\\n This is because :meth:`.QuantumCircuit._append` will not recognise the scoping\\n constructs of the control-flow builder interface.\\n\\n Args:\\n instruction: Instruction instance to append\\n qargs: Qubits to attach the instruction to.\\n cargs: Clbits to attach the instruction to.\\n\\n Returns:\\n Instruction: a handle to the instruction that was just added\\n\\n :meta public:\\n \\\"\\\"\\\"\\n\\n self._data.append((instruction, qargs, cargs))\\n self._update_parameter_table(instruction)\\n\\n # mark as normal circuit if a new instruction is added\\n self.duration = None\\n self.unit = \\\"dt\\\"\\n\\n return instruction\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._update_parameter_table\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._update_parameter_table\\n def _update_parameter_table(self, instruction: Instruction) -> Instruction:\\n for param_index, param in enumerate(instruction.params):\\n if isinstance(param, (ParameterExpression, QuantumCircuit)):\\n # Scoped constructs like the control-flow ops use QuantumCircuit as a parameter.\\n atomic_parameters = set(param.parameters)\\n else:\\n atomic_parameters = set()\\n\\n for parameter in atomic_parameters:\\n if parameter in self._parameter_table:\\n self._parameter_table[parameter].add((instruction, param_index))\\n else:\\n if parameter.name in self._parameter_table.get_names():\\n raise CircuitError(f\\\"Name conflict on adding parameter: {parameter.name}\\\")\\n self._parameter_table[parameter] = ParameterReferences(\\n ((instruction, param_index),)\\n )\\n\\n # clear cache if new parameter is added\\n self._parameters = None\\n\\n return instruction\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.add_register\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.add_register\\n def add_register(self, *regs: Union[Register, int, Sequence[Bit]]) -> None:\\n \\\"\\\"\\\"Add registers.\\\"\\\"\\\"\\n if not regs:\\n return\\n\\n if any(isinstance(reg, int) for reg in regs):\\n # QuantumCircuit defined without registers\\n if len(regs) == 1 and isinstance(regs[0], int):\\n # QuantumCircuit with anonymous quantum wires e.g. QuantumCircuit(2)\\n if regs[0] == 0:\\n regs = tuple()\\n else:\\n regs = (QuantumRegister(regs[0], \\\"q\\\"),)\\n elif len(regs) == 2 and all(isinstance(reg, int) for reg in regs):\\n # QuantumCircuit with anonymous wires e.g. QuantumCircuit(2, 3)\\n if regs[0] == 0:\\n qregs = tuple()\\n else:\\n qregs = (QuantumRegister(regs[0], \\\"q\\\"),)\\n if regs[1] == 0:\\n cregs = tuple()\\n else:\\n cregs = (ClassicalRegister(regs[1], \\\"c\\\"),)\\n regs = qregs + cregs\\n else:\\n raise CircuitError(\\n \\\"QuantumCircuit parameters can be Registers or Integers.\\\"\\n \\\" If Integers, up to 2 arguments. QuantumCircuit was called\\\"\\n \\\" with %s.\\\" % (regs,)\\n )\\n\\n for register in regs:\\n if isinstance(register, Register) and any(\\n register.name == reg.name for reg in self.qregs + self.cregs\\n ):\\n raise CircuitError('register name \\\"%s\\\" already exists' % register.name)\\n\\n if isinstance(register, AncillaRegister):\\n for bit in register:\\n if bit not in self._qubit_indices:\\n self._ancillas.append(bit)\\n\\n if isinstance(register, QuantumRegister):\\n self.qregs.append(register)\\n\\n for idx, bit in enumerate(reg\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.add_bits\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.add_bits\\n def add_bits(self, bits: Iterable[Bit]) -> None:\\n \\\"\\\"\\\"Add Bits to the circuit.\\\"\\\"\\\"\\n duplicate_bits = set(self._qubit_indices).union(self._clbit_indices).intersection(bits)\\n if duplicate_bits:\\n raise CircuitError(f\\\"Attempted to add bits found already in circuit: {duplicate_bits}\\\")\\n\\n for bit in bits:\\n if isinstance(bit, AncillaQubit):\\n self._ancillas.append(bit)\\n if isinstance(bit, Qubit):\\n self._qubits.append(bit)\\n self._qubit_indices[bit] = BitLocations(len(self._qubits) - 1, [])\\n elif isinstance(bit, Clbit):\\n self._clbits.append(bit)\\n self._clbit_indices[bit] = BitLocations(len(self._clbits) - 1, [])\\n else:\\n raise CircuitError(\\n \\\"Expected an instance of Qubit, Clbit, or \\\"\\n \\\"AncillaQubit, but was passed {}\\\".format(bit)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.find_bit\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.find_bit\\n def find_bit(self, bit: Bit) -> BitLocations:\\n \\\"\\\"\\\"Find locations in the circuit which can be used to reference a given :obj:`~Bit`.\\n\\n Args:\\n bit (Bit): The bit to locate.\\n\\n Returns:\\n namedtuple(int, List[Tuple(Register, int)]): A 2-tuple. The first element (``index``)\\n contains the index at which the ``Bit`` can be found (in either\\n :obj:`~QuantumCircuit.qubits`, :obj:`~QuantumCircuit.clbits`, depending on its\\n type). The second element (``registers``) is a list of ``(register, index)``\\n pairs with an entry for each :obj:`~Register` in the circuit which contains the\\n :obj:`~Bit` (and the index in the :obj:`~Register` at which it can be found).\\n\\n Notes:\\n The circuit index of an :obj:`~AncillaQubit` will be its index in\\n :obj:`~QuantumCircuit.qubits`, not :obj:`~QuantumCircuit.ancillas`.\\n\\n Raises:\\n CircuitError: If the supplied :obj:`~Bit` was of an unknown type.\\n CircuitError: If the supplied :obj:`~Bit` could not be found on the circuit.\\n \\\"\\\"\\\"\\n\\n try:\\n if isinstance(bit, Qubit):\\n return self._qubit_indices[bit]\\n elif isinstance(bit, Clbit):\\n return self._clbit_indices[bit]\\n else:\\n raise CircuitError(f\\\"Could not locate bit of unknown type: {type(bit)}\\\")\\n except KeyError as err:\\n raise CircuitError(\\n f\\\"Could not locate provided bit: {bit}. Has it been added to the QuantumCircuit?\\\"\\n ) from err\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._check_dups\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._check_dups\\n def _check_dups(self, qubits: Sequence[Qubit]) -> None:\\n \\\"\\\"\\\"Raise exception if list of qubits contains duplicates.\\\"\\\"\\\"\\n squbits = set(qubits)\\n if len(squbits) != len(qubits):\\n raise CircuitError(\\\"duplicate qubit arguments\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.to_instruction\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.to_instruction\\n def to_instruction(\\n self,\\n parameter_map: Optional[Dict[Parameter, ParameterValueType]] = None,\\n label: Optional[str] = None,\\n ) -> Instruction:\\n \\\"\\\"\\\"Create an Instruction out of this circuit.\\n\\n Args:\\n parameter_map(dict): For parameterized circuits, a mapping from\\n parameters in the circuit to parameters to be used in the\\n instruction. If None, existing circuit parameters will also\\n parameterize the instruction.\\n label (str): Optional gate label.\\n\\n Returns:\\n qiskit.circuit.Instruction: a composite instruction encapsulating this circuit\\n (can be decomposed back)\\n \\\"\\\"\\\"\\n from qiskit.converters.circuit_to_instruction import circuit_to_instruction\\n\\n return circuit_to_instruction(self, parameter_map, label=label)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.to_gate\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.to_gate\\n def to_gate(\\n self,\\n parameter_map: Optional[Dict[Parameter, ParameterValueType]] = None,\\n label: Optional[str] = None,\\n ) -> Gate:\\n \\\"\\\"\\\"Create a Gate out of this circuit.\\n\\n Args:\\n parameter_map(dict): For parameterized circuits, a mapping from\\n parameters in the circuit to parameters to be used in the\\n gate. If None, existing circuit parameters will also\\n parameterize the gate.\\n label (str): Optional gate label.\\n\\n Returns:\\n Gate: a composite gate encapsulating this circuit\\n (can be decomposed back)\\n \\\"\\\"\\\"\\n from qiskit.converters.circuit_to_gate import circuit_to_gate\\n\\n return circuit_to_gate(self, parameter_map, label=label)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.decompose\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.decompose\\n def decompose(\\n self,\\n gates_to_decompose: Optional[\\n Union[Type[Gate], Sequence[Type[Gate]], Sequence[str], str]\\n ] = None,\\n reps: int = 1,\\n ) -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Call a decomposition pass on this circuit,\\n to decompose one level (shallow decompose).\\n\\n Args:\\n gates_to_decompose (str or list(str)): optional subset of gates to decompose.\\n Defaults to all gates in circuit.\\n reps (int): Optional number of times the circuit should be decomposed.\\n For instance, ``reps=2`` equals calling ``circuit.decompose().decompose()``.\\n can decompose specific gates specific time\\n\\n Returns:\\n QuantumCircuit: a circuit one level decomposed\\n \\\"\\\"\\\"\\n # pylint: disable=cyclic-import\\n from qiskit.transpiler.passes.basis.decompose import Decompose\\n from qiskit.converters.circuit_to_dag import circuit_to_dag\\n from qiskit.converters.dag_to_circuit import dag_to_circuit\\n\\n pass_ = Decompose(gates_to_decompose)\\n dag = circuit_to_dag(self)\\n for _ in range(reps):\\n dag = pass_.run(dag)\\n return dag_to_circuit(dag)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._check_compatible_regs\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._check_compatible_regs\\n def _check_compatible_regs(self, rhs: \\\"QuantumCircuit\\\") -> None:\\n \\\"\\\"\\\"Raise exception if the circuits are defined on incompatible registers\\\"\\\"\\\"\\n list1 = self.qregs + self.cregs\\n list2 = rhs.qregs + rhs.cregs\\n for element1 in list1:\\n for element2 in list2:\\n if element2.name == element1.name:\\n if element1 != element2:\\n raise CircuitError(\\n \\\"circuits are not compatible:\\\"\\n f\\\" registers {element1} and {element2} not compatible\\\"\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._unique_register_name\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._unique_register_name\\n def _unique_register_name(self, prefix: str = \\\"\\\") -> str:\\n \\\"\\\"\\\"Generate a register name with the given prefix, which is unique within this circuit.\\\"\\\"\\\"\\n used = {\\n reg.name[len(prefix) :]\\n for reg in itertools.chain(self.qregs, self.cregs)\\n if reg.name.startswith(prefix)\\n }\\n characters = (string.digits + string.ascii_letters) if prefix else string.ascii_letters\\n for parts in itertools.chain.from_iterable(\\n itertools.product(characters, repeat=n) for n in itertools.count(1)\\n ):\\n name = \\\"\\\".join(parts)\\n if name not in used:\\n return prefix + name\\n # This isn't actually reachable because the above loop is infinite.\\n return prefix\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qasm\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qasm\\n def qasm(\\n self,\\n formatted: bool = False,\\n filename: Optional[str] = None,\\n encoding: Optional[str] = None,\\n ) -> Optional[str]:\\n \\\"\\\"\\\"Return OpenQASM string.\\n\\n Args:\\n formatted (bool): Return formatted Qasm string.\\n filename (str): Save Qasm to file with name 'filename'.\\n encoding (str): Optionally specify the encoding to use for the\\n output file if ``filename`` is specified. By default this is\\n set to the system's default encoding (ie whatever\\n ``locale.getpreferredencoding()`` returns) and can be set to\\n any valid codec or alias from stdlib's\\n `codec module <https://docs.python.org/3/library/codecs.html#standard-encodings>`__\\n\\n Returns:\\n str: If formatted=False.\\n\\n Raises:\\n MissingOptionalLibraryError: If pygments is not installed and ``formatted`` is\\n ``True``.\\n QasmError: If circuit has free parameters.\\n \\\"\\\"\\\"\\n\\n if self.num_parameters > 0:\\n raise QasmError(\\\"Cannot represent circuits with unbound parameters in OpenQASM 2.\\\")\\n\\n existing_gate_names = [\\n \\\"barrier\\\",\\n \\\"measure\\\",\\n \\\"reset\\\",\\n \\\"u3\\\",\\n \\\"u2\\\",\\n \\\"u1\\\",\\n \\\"cx\\\",\\n \\\"id\\\",\\n \\\"u0\\\",\\n \\\"u\\\",\\n \\\"p\\\",\\n \\\"x\\\",\\n \\\"y\\\",\\n \\\"z\\\",\\n \\\"h\\\",\\n \\\"s\\\",\\n \\\"sdg\\\",\\n \\\"t\\\",\\n \\\"tdg\\\",\\n \\\"rx\\\",\\n \\\"ry\\\",\\n \\\"rz\\\",\\n \\\"sx\\\",\\n \\\"sxdg\\\",\\n \\\"cz\\\",\\n \\\"cy\\\",\\n \\\"swap\\\",\\n \\\"ch\\\",\\n \\\"ccx\\\",\\n \\\"cswap\\\",\\n \\\"crx\\\",\\n \\\"cry\\\",\\n \\\"crz\\\",\\n \\\"cu1\\\",\\n \\\"cp\\\",\\n \\\"cu3\\\",\\n \\\"csx\\\",\\n \\\"cu\\\",\\n \\\"rxx\\\",\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.draw\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.draw\\n def draw(\\n self,\\n output: Optional[str] = None,\\n scale: Optional[float] = None,\\n filename: Optional[str] = None,\\n style: Optional[Union[dict, str]] = None,\\n interactive: bool = False,\\n plot_barriers: bool = True,\\n reverse_bits: bool = False,\\n justify: Optional[str] = None,\\n vertical_compression: Optional[str] = \\\"medium\\\",\\n idle_wires: bool = True,\\n with_layout: bool = True,\\n fold: Optional[int] = None,\\n # The type of ax is matplotlib.axes.Axes, but this is not a fixed dependency, so cannot be\\n # safely forward-referenced.\\n ax: Optional[typing.Any] = None,\\n initial_state: bool = False,\\n cregbundle: bool = True,\\n ):\\n \\\"\\\"\\\"Draw the quantum circuit. Use the output parameter to choose the drawing format:\\n\\n **text**: ASCII art TextDrawing that can be printed in the console.\\n\\n **mpl**: images with color rendered purely in Python using matplotlib.\\n\\n **latex**: high-quality images compiled via latex.\\n\\n **latex_source**: raw uncompiled latex output.\\n\\n Args:\\n output (str): select the output method to use for drawing the circuit.\\n Valid choices are ``text``, ``mpl``, ``latex``, ``latex_source``.\\n By default the `text` drawer is used unless the user config file\\n (usually ``~/.qiskit/settings.conf``) has an alternative backend set\\n as the default. For example, ``circuit_drawer = latex``. If the output\\n kwarg is set, that backend will always be used over the default in\\n the user config file.\\n scale (float): scale of image to draw (shrink if < 1.0). Only used by\\n the `mpl`, `latex` and `latex_source` outputs. Defaults to 1.0.\\n filename (str): file path to save image to. Defaults to None.\\n style (dict or str): dict\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.size\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.size\\n def size(self, filter_function: Optional[callable] = lambda x: not x[0]._directive) -> int:\\n \\\"\\\"\\\"Returns total number of instructions in circuit.\\n\\n Args:\\n filter_function (callable): a function to filter out some instructions.\\n Should take as input a tuple of (Instruction, list(Qubit), list(Clbit)).\\n By default filters out \\\"directives\\\", such as barrier or snapshot.\\n\\n Returns:\\n int: Total number of gate operations.\\n \\\"\\\"\\\"\\n return sum(map(filter_function, self._data))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.depth\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.depth\\n def depth(self, filter_function: Optional[callable] = lambda x: not x[0]._directive) -> int:\\n \\\"\\\"\\\"Return circuit depth (i.e., length of critical path).\\n\\n Args:\\n filter_function (callable): a function to filter out some instructions.\\n Should take as input a tuple of (Instruction, list(Qubit), list(Clbit)).\\n By default filters out \\\"directives\\\", such as barrier or snapshot.\\n\\n Returns:\\n int: Depth of circuit.\\n\\n Notes:\\n The circuit depth and the DAG depth need not be the\\n same.\\n \\\"\\\"\\\"\\n # Assign each bit in the circuit a unique integer\\n # to index into op_stack.\\n bit_indices = {bit: idx for idx, bit in enumerate(self.qubits + self.clbits)}\\n\\n # If no bits, return 0\\n if not bit_indices:\\n return 0\\n\\n # A list that holds the height of each qubit\\n # and classical bit.\\n op_stack = [0] * len(bit_indices)\\n\\n # Here we are playing a modified version of\\n # Tetris where we stack gates, but multi-qubit\\n # gates, or measurements have a block for each\\n # qubit or cbit that are connected by a virtual\\n # line so that they all stacked at the same depth.\\n # Conditional gates act on all cbits in the register\\n # they are conditioned on.\\n # The max stack height is the circuit depth.\\n for instr, qargs, cargs in self._data:\\n levels = []\\n reg_ints = []\\n for ind, reg in enumerate(qargs + cargs):\\n # Add to the stacks of the qubits and\\n # cbits used in the gate.\\n reg_ints.append(bit_indices[reg])\\n if filter_function((instr, qargs, cargs)):\\n levels.append(op_stack[reg_ints[ind]] + 1)\\n else:\\n levels.append(op_stack[reg_ints[ind]])\\n # Assuming here that there \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.width\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.width\\n def width(self) -> int:\\n \\\"\\\"\\\"Return number of qubits plus clbits in circuit.\\n\\n Returns:\\n int: Width of circuit.\\n\\n \\\"\\\"\\\"\\n return len(self.qubits) + len(self.clbits)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_qubits\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_qubits\\n @property\\n def num_qubits(self) -> int:\\n \\\"\\\"\\\"Return number of qubits.\\\"\\\"\\\"\\n return len(self.qubits)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_ancillas\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_ancillas\\n @property\\n def num_ancillas(self) -> int:\\n \\\"\\\"\\\"Return the number of ancilla qubits.\\\"\\\"\\\"\\n return len(self.ancillas)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_clbits\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_clbits\\n @property\\n def num_clbits(self) -> int:\\n \\\"\\\"\\\"Return number of classical bits.\\\"\\\"\\\"\\n return len(self.clbits)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.count_ops\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.count_ops\\n def count_ops(self) -> \\\"OrderedDict[Instruction, int]\\\":\\n \\\"\\\"\\\"Count each operation kind in the circuit.\\n\\n Returns:\\n OrderedDict: a breakdown of how many operations of each kind, sorted by amount.\\n \\\"\\\"\\\"\\n count_ops: Dict[Instruction, int] = {}\\n for instr, _, _ in self._data:\\n count_ops[instr.name] = count_ops.get(instr.name, 0) + 1\\n return OrderedDict(sorted(count_ops.items(), key=lambda kv: kv[1], reverse=True))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_nonlocal_gates\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_nonlocal_gates\\n def num_nonlocal_gates(self) -> int:\\n \\\"\\\"\\\"Return number of non-local gates (i.e. involving 2+ qubits).\\n\\n Conditional nonlocal gates are also included.\\n \\\"\\\"\\\"\\n multi_qubit_gates = 0\\n for instr, _, _ in self._data:\\n if instr.num_qubits > 1 and not instr._directive:\\n multi_qubit_gates += 1\\n return multi_qubit_gates\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.get_instructions\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.get_instructions\\n def get_instructions(self, name: str) -> List[DataElement]:\\n \\\"\\\"\\\"Get instructions matching name.\\n\\n Args:\\n name (str): The name of instruction to.\\n\\n Returns:\\n list(tuple): list of (instruction, qargs, cargs).\\n \\\"\\\"\\\"\\n return [match for match in self._data if match[0].name == name]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_connected_components\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_connected_components\\n def num_connected_components(self, unitary_only: bool = False) -> int:\\n \\\"\\\"\\\"How many non-entangled subcircuits can the circuit be factored to.\\n\\n Args:\\n unitary_only (bool): Compute only unitary part of graph.\\n\\n Returns:\\n int: Number of connected components in circuit.\\n \\\"\\\"\\\"\\n # Convert registers to ints (as done in depth).\\n bits = self.qubits if unitary_only else (self.qubits + self.clbits)\\n bit_indices = {bit: idx for idx, bit in enumerate(bits)}\\n\\n # Start with each qubit or cbit being its own subgraph.\\n sub_graphs = [[bit] for bit in range(len(bit_indices))]\\n\\n num_sub_graphs = len(sub_graphs)\\n\\n # Here we are traversing the gates and looking to see\\n # which of the sub_graphs the gate joins together.\\n for instr, qargs, cargs in self._data:\\n if unitary_only:\\n args = qargs\\n num_qargs = len(args)\\n else:\\n args = qargs + cargs\\n num_qargs = len(args) + (1 if instr.condition else 0)\\n\\n if num_qargs >= 2 and not instr._directive:\\n graphs_touched = []\\n num_touched = 0\\n # Controls necessarily join all the cbits in the\\n # register that they use.\\n if not unitary_only:\\n for bit in instr.condition_bits:\\n idx = bit_indices[bit]\\n for k in range(num_sub_graphs):\\n if idx in sub_graphs[k]:\\n graphs_touched.append(k)\\n break\\n\\n for item in args:\\n reg_int = bit_indices[item]\\n for k in range(num_sub_graphs):\\n if reg_int in sub_graphs[k]:\\n if k not in graphs_touched:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_unitary_factors\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_unitary_factors\\n def num_unitary_factors(self) -> int:\\n \\\"\\\"\\\"Computes the number of tensor factors in the unitary\\n (quantum) part of the circuit only.\\n \\\"\\\"\\\"\\n return self.num_connected_components(unitary_only=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_tensor_factors\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_tensor_factors\\n def num_tensor_factors(self) -> int:\\n \\\"\\\"\\\"Computes the number of tensor factors in the unitary\\n (quantum) part of the circuit only.\\n\\n Notes:\\n This is here for backwards compatibility, and will be\\n removed in a future release of Qiskit. You should call\\n `num_unitary_factors` instead.\\n \\\"\\\"\\\"\\n return self.num_unitary_factors()\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.copy\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.copy\\n def copy(self, name: Optional[str] = None) -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Copy the circuit.\\n\\n Args:\\n name (str): name to be given to the copied circuit. If None, then the name stays the same\\n\\n Returns:\\n QuantumCircuit: a deepcopy of the current circuit, with the specified name\\n \\\"\\\"\\\"\\n cpy = copy.copy(self)\\n # copy registers correctly, in copy.copy they are only copied via reference\\n cpy.qregs = self.qregs.copy()\\n cpy.cregs = self.cregs.copy()\\n cpy._qubits = self._qubits.copy()\\n cpy._ancillas = self._ancillas.copy()\\n cpy._clbits = self._clbits.copy()\\n cpy._qubit_indices = self._qubit_indices.copy()\\n cpy._clbit_indices = self._clbit_indices.copy()\\n\\n instr_instances = {id(instr): instr for instr, _, __ in self._data}\\n\\n instr_copies = {id_: instr.copy() for id_, instr in instr_instances.items()}\\n\\n cpy._parameter_table = ParameterTable(\\n {\\n param: ParameterReferences(\\n (instr_copies[id(instr)], param_index)\\n for instr, param_index in self._parameter_table[param]\\n )\\n for param in self._parameter_table\\n }\\n )\\n\\n cpy._data = [\\n (instr_copies[id(inst)], qargs.copy(), cargs.copy())\\n for inst, qargs, cargs in self._data\\n ]\\n\\n cpy._calibrations = copy.deepcopy(self._calibrations)\\n cpy._metadata = copy.deepcopy(self._metadata)\\n\\n if name:\\n cpy.name = name\\n return cpy\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._create_creg\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._create_creg\\n def _create_creg(self, length: int, name: str) -> ClassicalRegister:\\n \\\"\\\"\\\"Creates a creg, checking if ClassicalRegister with same name exists\\\"\\\"\\\"\\n if name in [creg.name for creg in self.cregs]:\\n save_prefix = ClassicalRegister.prefix\\n ClassicalRegister.prefix = name\\n new_creg = ClassicalRegister(length)\\n ClassicalRegister.prefix = save_prefix\\n else:\\n new_creg = ClassicalRegister(length, name)\\n return new_creg\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._create_qreg\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._create_qreg\\n def _create_qreg(self, length: int, name: str) -> QuantumRegister:\\n \\\"\\\"\\\"Creates a qreg, checking if QuantumRegister with same name exists\\\"\\\"\\\"\\n if name in [qreg.name for qreg in self.qregs]:\\n save_prefix = QuantumRegister.prefix\\n QuantumRegister.prefix = name\\n new_qreg = QuantumRegister(length)\\n QuantumRegister.prefix = save_prefix\\n else:\\n new_qreg = QuantumRegister(length, name)\\n return new_qreg\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.reset\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.reset\\n def reset(self, qubit: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Reset the quantum bit(s) to their default state.\\n\\n Args:\\n qubit: qubit(s) to reset.\\n\\n Returns:\\n qiskit.circuit.InstructionSet: handle to the added instruction.\\n \\\"\\\"\\\"\\n return self.append(Reset(), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.measure\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.measure\\n def measure(self, qubit: QubitSpecifier, cbit: ClbitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Measure quantum bit into classical bit (tuples).\\n\\n Args:\\n qubit: qubit to measure.\\n cbit: classical bit to place the measurement in.\\n\\n Returns:\\n qiskit.circuit.InstructionSet: handle to the added instructions.\\n\\n Raises:\\n CircuitError: if arguments have bad format.\\n \\\"\\\"\\\"\\n return self.append(Measure(), [qubit], [cbit])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.measure_active\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.measure_active\\n def measure_active(self, inplace: bool = True) -> Optional[\\\"QuantumCircuit\\\"]:\\n \\\"\\\"\\\"Adds measurement to all non-idle qubits. Creates a new ClassicalRegister with\\n a size equal to the number of non-idle qubits being measured.\\n\\n Returns a new circuit with measurements if `inplace=False`.\\n\\n Args:\\n inplace (bool): All measurements inplace or return new circuit.\\n\\n Returns:\\n QuantumCircuit: Returns circuit with measurements when `inplace = False`.\\n \\\"\\\"\\\"\\n from qiskit.converters.circuit_to_dag import circuit_to_dag\\n\\n if inplace:\\n circ = self\\n else:\\n circ = self.copy()\\n dag = circuit_to_dag(circ)\\n qubits_to_measure = [qubit for qubit in circ.qubits if qubit not in dag.idle_wires()]\\n new_creg = circ._create_creg(len(qubits_to_measure), \\\"measure\\\")\\n circ.add_register(new_creg)\\n circ.barrier()\\n circ.measure(qubits_to_measure, new_creg)\\n\\n if not inplace:\\n return circ\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.measure_all\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.measure_all\\n def measure_all(\\n self, inplace: bool = True, add_bits: bool = True\\n ) -> Optional[\\\"QuantumCircuit\\\"]:\\n \\\"\\\"\\\"Adds measurement to all qubits.\\n\\n By default, adds new classical bits in a :obj:`.ClassicalRegister` to store these\\n measurements. If ``add_bits=False``, the results of the measurements will instead be stored\\n in the already existing classical bits, with qubit ``n`` being measured into classical bit\\n ``n``.\\n\\n Returns a new circuit with measurements if ``inplace=False``.\\n\\n Args:\\n inplace (bool): All measurements inplace or return new circuit.\\n add_bits (bool): Whether to add new bits to store the results.\\n\\n Returns:\\n QuantumCircuit: Returns circuit with measurements when ``inplace=False``.\\n\\n Raises:\\n CircuitError: if ``add_bits=False`` but there are not enough classical bits.\\n \\\"\\\"\\\"\\n if inplace:\\n circ = self\\n else:\\n circ = self.copy()\\n if add_bits:\\n new_creg = circ._create_creg(len(circ.qubits), \\\"meas\\\")\\n circ.add_register(new_creg)\\n circ.barrier()\\n circ.measure(circ.qubits, new_creg)\\n else:\\n if len(circ.clbits) < len(circ.qubits):\\n raise CircuitError(\\n \\\"The number of classical bits must be equal or greater than \\\"\\n \\\"the number of qubits.\\\"\\n )\\n circ.barrier()\\n circ.measure(circ.qubits, circ.clbits[0 : len(circ.qubits)])\\n\\n if not inplace:\\n return circ\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.remove_final_measurements\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.remove_final_measurements\\n def remove_final_measurements(self, inplace: bool = True) -> Optional[\\\"QuantumCircuit\\\"]:\\n \\\"\\\"\\\"Removes final measurements and barriers on all qubits if they are present.\\n Deletes the classical registers that were used to store the values from these measurements\\n that become idle as a result of this operation, and deletes classical bits that are\\n referenced only by removed registers, or that aren't referenced at all but have\\n become idle as a result of this operation.\\n\\n Measurements and barriers are considered final if they are\\n followed by no other operations (aside from other measurements or barriers.)\\n\\n Args:\\n inplace (bool): All measurements removed inplace or return new circuit.\\n\\n Returns:\\n QuantumCircuit: Returns the resulting circuit when ``inplace=False``, else None.\\n \\\"\\\"\\\"\\n # pylint: disable=cyclic-import\\n from qiskit.transpiler.passes import RemoveFinalMeasurements\\n from qiskit.converters import circuit_to_dag\\n\\n if inplace:\\n circ = self\\n else:\\n circ = self.copy()\\n\\n dag = circuit_to_dag(circ)\\n remove_final_meas = RemoveFinalMeasurements()\\n new_dag = remove_final_meas.run(dag)\\n kept_cregs = set(new_dag.cregs.values())\\n kept_clbits = set(new_dag.clbits)\\n\\n # Filter only cregs/clbits still in new DAG, preserving original circuit order\\n cregs_to_add = [creg for creg in circ.cregs if creg in kept_cregs]\\n clbits_to_add = [clbit for clbit in circ._clbits if clbit in kept_clbits]\\n\\n # Clear cregs and clbits\\n circ.cregs = []\\n circ._clbits = []\\n circ._clbit_indices = {}\\n\\n # We must add the clbits first to preserve the original circuit\\n # order. This way, add_register never adds clbits and just\\n # creates registers that point to them.\\n circ.\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.from_qasm_file\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.from_qasm_file\\n @staticmethod\\n def from_qasm_file(path: str) -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Take in a QASM file and generate a QuantumCircuit object.\\n\\n Args:\\n path (str): Path to the file for a QASM program\\n Return:\\n QuantumCircuit: The QuantumCircuit object for the input QASM\\n \\\"\\\"\\\"\\n qasm = Qasm(filename=path)\\n return _circuit_from_qasm(qasm)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.from_qasm_str\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.from_qasm_str\\n @staticmethod\\n def from_qasm_str(qasm_str: str) -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Take in a QASM string and generate a QuantumCircuit object.\\n\\n Args:\\n qasm_str (str): A QASM program string\\n Return:\\n QuantumCircuit: The QuantumCircuit object for the input QASM\\n \\\"\\\"\\\"\\n qasm = Qasm(data=qasm_str)\\n return _circuit_from_qasm(qasm)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.global_phase\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.global_phase\\n @property\\n def global_phase(self) -> ParameterValueType:\\n \\\"\\\"\\\"Return the global phase of the circuit in radians.\\\"\\\"\\\"\\n return self._global_phase\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.global_phase\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.global_phase\\n @global_phase.setter\\n def global_phase(self, angle: ParameterValueType):\\n \\\"\\\"\\\"Set the phase of the circuit.\\n\\n Args:\\n angle (float, ParameterExpression): radians\\n \\\"\\\"\\\"\\n if isinstance(angle, ParameterExpression) and angle.parameters:\\n self._global_phase = angle\\n else:\\n # Set the phase to the [0, 2π) interval\\n angle = float(angle)\\n if not angle:\\n self._global_phase = 0\\n else:\\n self._global_phase = angle % (2 * np.pi)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.parameters\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.parameters\\n @property\\n def parameters(self) -> ParameterView:\\n \\\"\\\"\\\"Convenience function to get the parameters defined in the parameter table.\\\"\\\"\\\"\\n # parameters from gates\\n if self._parameters is None:\\n unsorted = self._unsorted_parameters()\\n self._parameters = sorted(unsorted, key=functools.cmp_to_key(_compare_parameters))\\n\\n # return as parameter view, which implements the set and list interface\\n return ParameterView(self._parameters)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_parameters\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.num_parameters\\n @property\\n def num_parameters(self) -> int:\\n \\\"\\\"\\\"Convenience function to get the number of parameter objects in the circuit.\\\"\\\"\\\"\\n return len(self._unsorted_parameters())\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._unsorted_parameters\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._unsorted_parameters\\n def _unsorted_parameters(self) -> Set[Parameter]:\\n \\\"\\\"\\\"Efficiently get all parameters in the circuit, without any sorting overhead.\\\"\\\"\\\"\\n parameters = set(self._parameter_table)\\n if isinstance(self.global_phase, ParameterExpression):\\n parameters.update(self.global_phase.parameters)\\n\\n return parameters\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.assign_parameters\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.assign_parameters\\n def assign_parameters(\\n self,\\n parameters: Union[Mapping[Parameter, ParameterValueType], Sequence[ParameterValueType]],\\n inplace: bool = False,\\n ) -> Optional[\\\"QuantumCircuit\\\"]:\\n \\\"\\\"\\\"Assign parameters to new parameters or values.\\n\\n The keys of the parameter dictionary must be Parameter instances in the current circuit. The\\n values of the dictionary can either be numeric values or new parameter objects.\\n The values can be assigned to the current circuit object or to a copy of it.\\n\\n Args:\\n parameters (dict or iterable): Either a dictionary or iterable specifying the new\\n parameter values. If a dict, it specifies the mapping from ``current_parameter`` to\\n ``new_parameter``, where ``new_parameter`` can be a new parameter object or a\\n numeric value. If an iterable, the elements are assigned to the existing parameters\\n in the order of ``QuantumCircuit.parameters``.\\n inplace (bool): If False, a copy of the circuit with the bound parameters is\\n returned. If True the circuit instance itself is modified.\\n\\n Raises:\\n CircuitError: If parameters is a dict and contains parameters not present in the\\n circuit.\\n ValueError: If parameters is a list/array and the length mismatches the number of free\\n parameters in the circuit.\\n\\n Returns:\\n Optional(QuantumCircuit): A copy of the circuit with bound parameters, if\\n ``inplace`` is False, otherwise None.\\n\\n Examples:\\n\\n Create a parameterized circuit and assign the parameters in-place.\\n\\n .. jupyter-execute::\\n\\n from qiskit.circuit import QuantumCircuit, Parameter\\n\\n circuit = QuantumCircuit(2)\\n params = [Parameter('A'), Parameter('B'), Parameter('C')]\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.bind_parameters\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.bind_parameters\\n def bind_parameters(\\n self, values: Union[Mapping[Parameter, float], Sequence[float]]\\n ) -> \\\"QuantumCircuit\\\":\\n \\\"\\\"\\\"Assign numeric parameters to values yielding a new circuit.\\n\\n To assign new Parameter objects or bind the values in-place, without yielding a new\\n circuit, use the :meth:`assign_parameters` method.\\n\\n Args:\\n values (dict or iterable): {parameter: value, ...} or [value1, value2, ...]\\n\\n Raises:\\n CircuitError: If values is a dict and contains parameters not present in the circuit.\\n TypeError: If values contains a ParameterExpression.\\n\\n Returns:\\n QuantumCircuit: copy of self with assignment substitution.\\n \\\"\\\"\\\"\\n if isinstance(values, dict):\\n if any(isinstance(value, ParameterExpression) for value in values.values()):\\n raise TypeError(\\n \\\"Found ParameterExpression in values; use assign_parameters() instead.\\\"\\n )\\n return self.assign_parameters(values)\\n else:\\n if any(isinstance(value, ParameterExpression) for value in values):\\n raise TypeError(\\n \\\"Found ParameterExpression in values; use assign_parameters() instead.\\\"\\n )\\n return self.assign_parameters(values)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._unroll_param_dict\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._unroll_param_dict\\n def _unroll_param_dict(\\n self, value_dict: Mapping[Parameter, ParameterValueType]\\n ) -> Dict[Parameter, ParameterValueType]:\\n unrolled_value_dict: Dict[Parameter, ParameterValueType] = {}\\n for (param, value) in value_dict.items():\\n if isinstance(param, ParameterVector):\\n if not len(param) == len(value):\\n raise CircuitError(\\n \\\"ParameterVector {} has length {}, which \\\"\\n \\\"differs from value list {} of \\\"\\n \\\"len {}\\\".format(param, len(param), value, len(value))\\n )\\n unrolled_value_dict.update(zip(param, value))\\n # pass anything else except number through. error checking is done in assign_parameter\\n elif isinstance(param, (ParameterExpression, str)) or param is None:\\n unrolled_value_dict[param] = value\\n return unrolled_value_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._assign_parameter\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._assign_parameter\\n def _assign_parameter(self, parameter: Parameter, value: ParameterValueType) -> None:\\n \\\"\\\"\\\"Update this circuit where instances of ``parameter`` are replaced by ``value``, which\\n can be either a numeric value or a new parameter expression.\\n\\n Args:\\n parameter (ParameterExpression): Parameter to be bound\\n value (Union(ParameterExpression, float, int)): A numeric or parametric expression to\\n replace instances of ``parameter``.\\n\\n Raises:\\n RuntimeError: if some internal logic error has caused the circuit instruction sequence\\n and the parameter table to become out of sync, and the table now contains a\\n reference to a value that cannot be assigned.\\n \\\"\\\"\\\"\\n # parameter might be in global phase only\\n if parameter in self._parameter_table:\\n for instr, param_index in self._parameter_table[parameter]:\\n assignee = instr.params[param_index]\\n # Normal ParameterExpression.\\n if isinstance(assignee, ParameterExpression):\\n new_param = assignee.assign(parameter, value)\\n # if fully bound, validate\\n if len(new_param.parameters) == 0:\\n instr.params[param_index] = instr.validate_parameter(new_param)\\n else:\\n instr.params[param_index] = new_param\\n\\n self._rebind_definition(instr, parameter, value)\\n # Scoped block of a larger instruction.\\n elif isinstance(assignee, QuantumCircuit):\\n # It's possible that someone may re-use a loop body, so we need to mutate the\\n # parameter vector with a new circuit, rather than mutating the body.\\n instr.params[param_index] = assignee.assign_parameters({parameter: value})\\n else:\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._assign_calibration_parameters\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._assign_calibration_parameters\\n def _assign_calibration_parameters(\\n self, parameter: Parameter, value: ParameterValueType\\n ) -> None:\\n \\\"\\\"\\\"Update parameterized pulse gate calibrations, if there are any which contain\\n ``parameter``. This updates the calibration mapping as well as the gate definition\\n ``Schedule``s, which also may contain ``parameter``.\\n \\\"\\\"\\\"\\n new_param: ParameterValueType\\n for cals in self.calibrations.values():\\n for (qubit, cal_params), schedule in copy.copy(cals).items():\\n if any(\\n isinstance(p, ParameterExpression) and parameter in p.parameters\\n for p in cal_params\\n ):\\n del cals[(qubit, cal_params)]\\n new_cal_params = []\\n for p in cal_params:\\n if isinstance(p, ParameterExpression) and parameter in p.parameters:\\n new_param = p.assign(parameter, value)\\n if not new_param.parameters:\\n new_param = float(new_param)\\n new_cal_params.append(new_param)\\n else:\\n new_cal_params.append(p)\\n schedule.assign_parameters({parameter: value})\\n cals[(qubit, tuple(new_cal_params))] = schedule\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._rebind_definition\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._rebind_definition\\n def _rebind_definition(\\n self, instruction: Instruction, parameter: Parameter, value: ParameterValueType\\n ) -> None:\\n if instruction._definition:\\n for op, _, _ in instruction._definition:\\n for idx, param in enumerate(op.params):\\n if isinstance(param, ParameterExpression) and parameter in param.parameters:\\n if isinstance(value, ParameterExpression):\\n op.params[idx] = param.subs({parameter: value})\\n else:\\n op.params[idx] = param.bind({parameter: value})\\n self._rebind_definition(op, parameter, value)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.barrier\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.barrier\\n def barrier(self, *qargs: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.Barrier`. If qargs is empty, applies to all qubits in the\\n circuit.\\n\\n Returns:\\n qiskit.circuit.InstructionSet: handle to the added instructions.\\n \\\"\\\"\\\"\\n from .barrier import Barrier\\n\\n qubits: List[QubitSpecifier] = []\\n\\n if not qargs: # None\\n qubits.extend(self.qubits)\\n\\n for qarg in qargs:\\n if isinstance(qarg, QuantumRegister):\\n qubits.extend([qarg[j] for j in range(qarg.size)])\\n elif isinstance(qarg, list):\\n qubits.extend(qarg)\\n elif isinstance(qarg, range):\\n qubits.extend(list(qarg))\\n elif isinstance(qarg, slice):\\n qubits.extend(self.qubits[qarg])\\n else:\\n qubits.append(qarg)\\n\\n return self.append(Barrier(len(qubits)), qubits, [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.delay\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.delay\\n def delay(\\n self,\\n duration: ParameterValueType,\\n qarg: Optional[QubitSpecifier] = None,\\n unit: str = \\\"dt\\\",\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.Delay`. If qarg is None, applies to all qubits.\\n When applying to multiple qubits, delays with the same duration will be created.\\n\\n Args:\\n duration (int or float or ParameterExpression): duration of the delay.\\n qarg (Object): qubit argument to apply this delay.\\n unit (str): unit of the duration. Supported units: 's', 'ms', 'us', 'ns', 'ps', 'dt'.\\n Default is ``dt``, i.e. integer time unit depending on the target backend.\\n\\n Returns:\\n qiskit.circuit.InstructionSet: handle to the added instructions.\\n\\n Raises:\\n CircuitError: if arguments have bad format.\\n \\\"\\\"\\\"\\n qubits: List[QubitSpecifier] = []\\n if qarg is None: # -> apply delays to all qubits\\n for q in self.qubits:\\n qubits.append(q)\\n else:\\n if isinstance(qarg, QuantumRegister):\\n qubits.extend([qarg[j] for j in range(qarg.size)])\\n elif isinstance(qarg, list):\\n qubits.extend(qarg)\\n elif isinstance(qarg, (range, tuple)):\\n qubits.extend(list(qarg))\\n elif isinstance(qarg, slice):\\n qubits.extend(self.qubits[qarg])\\n else:\\n qubits.append(qarg)\\n\\n instructions = InstructionSet(resource_requester=self._resolve_classical_resource)\\n for q in qubits:\\n inst = (Delay(duration, unit), [q], [])\\n self.append(*inst)\\n instructions.add(*inst)\\n return instructions\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.h\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.h\\n def h(self, qubit: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.HGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.h import HGate\\n\\n return self.append(HGate(), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ch\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ch\\n def ch(\\n self,\\n control_qubit: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CHGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubit: The qubit(s) used as the control.\\n target_qubit: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.h import CHGate\\n\\n return self.append(\\n CHGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.i\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.i\\n def i(self, qubit: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.IGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.i import IGate\\n\\n return self.append(IGate(), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.id\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.id\\n def id(self, qubit: QubitSpecifier) -> InstructionSet: # pylint: disable=invalid-name\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.IGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n\\n See also:\\n QuantumCircuit.i: the same function.\\n \\\"\\\"\\\"\\n return self.i(qubit)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ms\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ms\\n def ms(self, theta: ParameterValueType, qubits: Sequence[QubitSpecifier]) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.generalized_gates.gms.MSGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The angle of the rotation.\\n qubits: The qubits to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n # pylint: disable=cyclic-import\\n from .library.generalized_gates.gms import MSGate\\n\\n return self.append(MSGate(len(qubits), theta), qubits)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.p\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.p\\n def p(self, theta: ParameterValueType, qubit: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.PhaseGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: THe angle of the rotation.\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.p import PhaseGate\\n\\n return self.append(PhaseGate(theta), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cp\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cp\\n def cp(\\n self,\\n theta: ParameterValueType,\\n control_qubit: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CPhaseGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The angle of the rotation.\\n control_qubit: The qubit(s) used as the control.\\n target_qubit: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.p import CPhaseGate\\n\\n return self.append(\\n CPhaseGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mcp\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mcp\\n def mcp(\\n self,\\n lam: ParameterValueType,\\n control_qubits: Sequence[QubitSpecifier],\\n target_qubit: QubitSpecifier,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.MCPhaseGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n lam: The angle of the rotation.\\n control_qubits: The qubits used as the controls.\\n target_qubit: The qubit(s) targeted by the gate.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.p import MCPhaseGate\\n\\n num_ctrl_qubits = len(control_qubits)\\n return self.append(\\n MCPhaseGate(lam, num_ctrl_qubits), control_qubits[:] + [target_qubit], []\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.r\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.r\\n def r(\\n self, theta: ParameterValueType, phi: ParameterValueType, qubit: QubitSpecifier\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.RGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The angle of the rotation.\\n phi: The angle of the axis of rotation in the x-y plane.\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.r import RGate\\n\\n return self.append(RGate(theta, phi), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rv\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rv\\n def rv(\\n self,\\n vx: ParameterValueType,\\n vy: ParameterValueType,\\n vz: ParameterValueType,\\n qubit: QubitSpecifier,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.RVGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Rotation around an arbitrary rotation axis :math:`v`, where :math:`|v|` is the angle of\\n rotation in radians.\\n\\n Args:\\n vx: x-compenent of the rotation axis.\\n vy: y-compenent of the rotation axis.\\n vz: z-compenent of the rotation axis.\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.generalized_gates.rv import RVGate\\n\\n return self.append(RVGate(vx, vy, vz), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rccx\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rccx\\n def rccx(\\n self,\\n control_qubit1: QubitSpecifier,\\n control_qubit2: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.RCCXGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubit1: The qubit(s) used as the first control.\\n control_qubit2: The qubit(s) used as the second control.\\n target_qubit: The qubit(s) targeted by the gate.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.x import RCCXGate\\n\\n return self.append(RCCXGate(), [control_qubit1, control_qubit2, target_qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rcccx\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rcccx\\n def rcccx(\\n self,\\n control_qubit1: QubitSpecifier,\\n control_qubit2: QubitSpecifier,\\n control_qubit3: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.RC3XGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubit1: The qubit(s) used as the first control.\\n control_qubit2: The qubit(s) used as the second control.\\n control_qubit3: The qubit(s) used as the third control.\\n target_qubit: The qubit(s) targeted by the gate.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.x import RC3XGate\\n\\n return self.append(\\n RC3XGate(), [control_qubit1, control_qubit2, control_qubit3, target_qubit], []\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rx\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rx\\n def rx(\\n self, theta: ParameterValueType, qubit: QubitSpecifier, label: Optional[str] = None\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.RXGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The rotation angle of the gate.\\n qubit: The qubit(s) to apply the gate to.\\n label: The string label of the gate in the circuit.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.rx import RXGate\\n\\n return self.append(RXGate(theta, label=label), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.crx\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.crx\\n def crx(\\n self,\\n theta: ParameterValueType,\\n control_qubit: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CRXGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The angle of the rotation.\\n control_qubit: The qubit(s) used as the control.\\n target_qubit: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.rx import CRXGate\\n\\n return self.append(\\n CRXGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rxx\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rxx\\n def rxx(\\n self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.RXXGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The angle of the rotation.\\n qubit1: The qubit(s) to apply the gate to.\\n qubit2: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.rxx import RXXGate\\n\\n return self.append(RXXGate(theta), [qubit1, qubit2], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ry\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ry\\n def ry(\\n self, theta: ParameterValueType, qubit: QubitSpecifier, label: Optional[str] = None\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.RYGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The rotation angle of the gate.\\n qubit: The qubit(s) to apply the gate to.\\n label: The string label of the gate in the circuit.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.ry import RYGate\\n\\n return self.append(RYGate(theta, label=label), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cry\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cry\\n def cry(\\n self,\\n theta: ParameterValueType,\\n control_qubit: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CRYGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The angle of the rotation.\\n control_qubit: The qubit(s) used as the control.\\n target_qubit: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.ry import CRYGate\\n\\n return self.append(\\n CRYGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ryy\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ryy\\n def ryy(\\n self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.RYYGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The rotation angle of the gate.\\n qubit1: The qubit(s) to apply the gate to.\\n qubit2: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.ryy import RYYGate\\n\\n return self.append(RYYGate(theta), [qubit1, qubit2], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rz\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rz\\n def rz(self, phi: ParameterValueType, qubit: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.RZGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n phi: The rotation angle of the gate.\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.rz import RZGate\\n\\n return self.append(RZGate(phi), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.crz\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.crz\\n def crz(\\n self,\\n theta: ParameterValueType,\\n control_qubit: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CRZGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The angle of the rotation.\\n control_qubit: The qubit(s) used as the control.\\n target_qubit: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.rz import CRZGate\\n\\n return self.append(\\n CRZGate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rzx\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rzx\\n def rzx(\\n self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.RZXGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The rotation angle of the gate.\\n qubit1: The qubit(s) to apply the gate to.\\n qubit2: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.rzx import RZXGate\\n\\n return self.append(RZXGate(theta), [qubit1, qubit2], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rzz\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.rzz\\n def rzz(\\n self, theta: ParameterValueType, qubit1: QubitSpecifier, qubit2: QubitSpecifier\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.RZZGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The rotation angle of the gate.\\n qubit1: The qubit(s) to apply the gate to.\\n qubit2: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.rzz import RZZGate\\n\\n return self.append(RZZGate(theta), [qubit1, qubit2], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ecr\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ecr\\n def ecr(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.ECRGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit1, qubit2: The qubits to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.ecr import ECRGate\\n\\n return self.append(ECRGate(), [qubit1, qubit2], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.s\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.s\\n def s(self, qubit: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.SGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.s import SGate\\n\\n return self.append(SGate(), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.sdg\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.sdg\\n def sdg(self, qubit: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.SdgGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.s import SdgGate\\n\\n return self.append(SdgGate(), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.swap\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.swap\\n def swap(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.SwapGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit1, qubit2: The qubits to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.swap import SwapGate\\n\\n return self.append(SwapGate(), [qubit1, qubit2], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.iswap\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.iswap\\n def iswap(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.iSwapGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit1, qubit2: The qubits to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.iswap import iSwapGate\\n\\n return self.append(iSwapGate(), [qubit1, qubit2], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cswap\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cswap\\n def cswap(\\n self,\\n control_qubit: QubitSpecifier,\\n target_qubit1: QubitSpecifier,\\n target_qubit2: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CSwapGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubit: The qubit(s) used as the control.\\n target_qubit1: The qubit(s) targeted by the gate.\\n target_qubit2: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.swap import CSwapGate\\n\\n return self.append(\\n CSwapGate(label=label, ctrl_state=ctrl_state),\\n [control_qubit, target_qubit1, target_qubit2],\\n [],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.fredkin\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.fredkin\\n def fredkin(\\n self,\\n control_qubit: QubitSpecifier,\\n target_qubit1: QubitSpecifier,\\n target_qubit2: QubitSpecifier,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CSwapGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubit: The qubit(s) used as the control.\\n target_qubit1: The qubit(s) targeted by the gate.\\n target_qubit2: The qubit(s) targeted by the gate.\\n\\n Returns:\\n A handle to the instructions created.\\n\\n See Also:\\n QuantumCircuit.cswap: the same function with a different name.\\n \\\"\\\"\\\"\\n return self.cswap(control_qubit, target_qubit1, target_qubit2)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.sx\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.sx\\n def sx(self, qubit: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.SXGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.sx import SXGate\\n\\n return self.append(SXGate(), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.sxdg\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.sxdg\\n def sxdg(self, qubit: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.SXdgGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.sx import SXdgGate\\n\\n return self.append(SXdgGate(), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.csx\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.csx\\n def csx(\\n self,\\n control_qubit: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CSXGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubit: The qubit(s) used as the control.\\n target_qubit: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.sx import CSXGate\\n\\n return self.append(\\n CSXGate(label=label, ctrl_state=ctrl_state),\\n [control_qubit, target_qubit],\\n [],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.t\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.t\\n def t(self, qubit: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.TGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.t import TGate\\n\\n return self.append(TGate(), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.tdg\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.tdg\\n def tdg(self, qubit: QubitSpecifier) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.TdgGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.t import TdgGate\\n\\n return self.append(TdgGate(), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u\\n def u(\\n self,\\n theta: ParameterValueType,\\n phi: ParameterValueType,\\n lam: ParameterValueType,\\n qubit: QubitSpecifier,\\n ) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.UGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The :math:`\\\\theta` rotation angle of the gate.\\n phi: The :math:`\\\\phi` rotation angle of the gate.\\n lam: The :math:`\\\\lambda` rotation angle of the gate.\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.u import UGate\\n\\n return self.append(UGate(theta, phi, lam), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cu\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cu\\n def cu(\\n self,\\n theta: ParameterValueType,\\n phi: ParameterValueType,\\n lam: ParameterValueType,\\n gamma: ParameterValueType,\\n control_qubit: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CUGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The :math:`\\\\theta` rotation angle of the gate.\\n phi: The :math:`\\\\phi` rotation angle of the gate.\\n lam: The :math:`\\\\lambda` rotation angle of the gate.\\n gamma: The global phase applied of the U gate, if applied.\\n control_qubit: The qubit(s) used as the control.\\n target_qubit: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.u import CUGate\\n\\n return self.append(\\n CUGate(theta, phi, lam, gamma, label=label, ctrl_state=ctrl_state),\\n [control_qubit, target_qubit],\\n [],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u1\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u1\\n @deprecate_function(\\n \\\"The QuantumCircuit.u1 method is deprecated as of \\\"\\n \\\"0.16.0. It will be removed no earlier than 3 months \\\"\\n \\\"after the release date. You should use the \\\"\\n \\\"QuantumCircuit.p method instead, which acts \\\"\\n \\\"identically.\\\"\\n )\\n def u1(self, theta: ParameterValueType, qubit: QubitSpecifier) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.U1Gate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The :math:`\\\\theta` rotation angle of the gate.\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.u1 import U1Gate\\n\\n return self.append(U1Gate(theta), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cu1\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cu1\\n @deprecate_function(\\n \\\"The QuantumCircuit.cu1 method is deprecated as of \\\"\\n \\\"0.16.0. It will be removed no earlier than 3 months \\\"\\n \\\"after the release date. You should use the \\\"\\n \\\"QuantumCircuit.cp method instead, which acts \\\"\\n \\\"identically.\\\"\\n )\\n def cu1(\\n self,\\n theta: ParameterValueType,\\n control_qubit: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CU1Gate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The :math:`\\\\theta` rotation angle of the gate.\\n control_qubit: The qubit(s) used as the control.\\n target_qubit: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.u1 import CU1Gate\\n\\n return self.append(\\n CU1Gate(theta, label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mcu1\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mcu1\\n @deprecate_function(\\n \\\"The QuantumCircuit.mcu1 method is deprecated as of \\\"\\n \\\"0.16.0. It will be removed no earlier than 3 months \\\"\\n \\\"after the release date. You should use the \\\"\\n \\\"QuantumCircuit.mcp method instead, which acts \\\"\\n \\\"identically.\\\"\\n )\\n def mcu1(\\n self,\\n lam: ParameterValueType,\\n control_qubits: Sequence[QubitSpecifier],\\n target_qubit: QubitSpecifier,\\n ) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.MCU1Gate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n lam: The :math:`\\\\lambda` rotation angle of the gate.\\n control_qubits: The qubits used as the controls.\\n target_qubit: The qubit(s) targeted by the gate.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.u1 import MCU1Gate\\n\\n num_ctrl_qubits = len(control_qubits)\\n return self.append(MCU1Gate(lam, num_ctrl_qubits), control_qubits[:] + [target_qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u2\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u2\\n @deprecate_function(\\n \\\"The QuantumCircuit.u2 method is deprecated as of \\\"\\n \\\"0.16.0. It will be removed no earlier than 3 months \\\"\\n \\\"after the release date. You can use the general 1-\\\"\\n \\\"qubit gate QuantumCircuit.u instead: u2(φ,λ) = \\\"\\n \\\"u(π/2, φ, λ). Alternatively, you can decompose it in\\\"\\n \\\"terms of QuantumCircuit.p and QuantumCircuit.sx: \\\"\\n \\\"u2(φ,λ) = p(π/2+φ) sx p(λ-π/2) (1 pulse on hardware).\\\"\\n )\\n def u2(\\n self, phi: ParameterValueType, lam: ParameterValueType, qubit: QubitSpecifier\\n ) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.U2Gate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n phi: The :math:`\\\\phi` rotation angle of the gate.\\n lam: The :math:`\\\\lambda` rotation angle of the gate.\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.u2 import U2Gate\\n\\n return self.append(U2Gate(phi, lam), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u3\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.u3\\n @deprecate_function(\\n \\\"The QuantumCircuit.u3 method is deprecated as of 0.16.0. It will be \\\"\\n \\\"removed no earlier than 3 months after the release date. You should use \\\"\\n \\\"QuantumCircuit.u instead, which acts identically. Alternatively, you can \\\"\\n \\\"decompose u3 in terms of QuantumCircuit.p and QuantumCircuit.sx: \\\"\\n \\\"u3(ϴ,φ,λ) = p(φ+π) sx p(ϴ+π) sx p(λ) (2 pulses on hardware).\\\"\\n )\\n def u3(\\n self,\\n theta: ParameterValueType,\\n phi: ParameterValueType,\\n lam: ParameterValueType,\\n qubit: QubitSpecifier,\\n ) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.U3Gate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The :math:`\\\\theta` rotation angle of the gate.\\n phi: The :math:`\\\\phi` rotation angle of the gate.\\n lam: The :math:`\\\\lambda` rotation angle of the gate.\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.u3 import U3Gate\\n\\n return self.append(U3Gate(theta, phi, lam), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cu3\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cu3\\n @deprecate_function(\\n \\\"The QuantumCircuit.cu3 method is deprecated as of 0.16.0. It will be \\\"\\n \\\"removed no earlier than 3 months after the release date. You should \\\"\\n \\\"use the QuantumCircuit.cu method instead, where \\\"\\n \\\"cu3(ϴ,φ,λ) = cu(ϴ,φ,λ,0).\\\"\\n )\\n def cu3(\\n self,\\n theta: ParameterValueType,\\n phi: ParameterValueType,\\n lam: ParameterValueType,\\n control_qubit: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CU3Gate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n theta: The :math:`\\\\theta` rotation angle of the gate.\\n phi: The :math:`\\\\phi` rotation angle of the gate.\\n lam: The :math:`\\\\lambda` rotation angle of the gate.\\n control_qubit: The qubit(s) used as the control.\\n target_qubit: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.u3 import CU3Gate\\n\\n return self.append(\\n CU3Gate(theta, phi, lam, label=label, ctrl_state=ctrl_state),\\n [control_qubit, target_qubit],\\n [],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.x\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.x\\n def x(self, qubit: QubitSpecifier, label: Optional[str] = None) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.XGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit: The qubit(s) to apply the gate to.\\n label: The string label of the gate in the circuit.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.x import XGate\\n\\n return self.append(XGate(label=label), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cx\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cx\\n def cx(\\n self,\\n control_qubit: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CXGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubit: The qubit(s) used as the control.\\n target_qubit: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n\\n from .library.standard_gates.x import CXGate\\n\\n return self.append(\\n CXGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cnot\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cnot\\n def cnot(\\n self,\\n control_qubit: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CXGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubit: The qubit(s) used as the control.\\n target_qubit: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n\\n See Also:\\n QuantumCircuit.cx: the same function with a different name.\\n \\\"\\\"\\\"\\n return self.cx(control_qubit, target_qubit, label, ctrl_state)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.dcx\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.dcx\\n def dcx(self, qubit1: QubitSpecifier, qubit2: QubitSpecifier) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.DCXGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit1: The qubit(s) to apply the gate to.\\n qubit2: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.dcx import DCXGate\\n\\n return self.append(DCXGate(), [qubit1, qubit2], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ccx\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.ccx\\n def ccx(\\n self,\\n control_qubit1: QubitSpecifier,\\n control_qubit2: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CCXGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubit1: The qubit(s) used as the first control.\\n control_qubit2: The qubit(s) used as the second control.\\n target_qubit: The qubit(s) targeted by the gate.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.x import CCXGate\\n\\n return self.append(\\n CCXGate(ctrl_state=ctrl_state),\\n [control_qubit1, control_qubit2, target_qubit],\\n [],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.toffoli\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.toffoli\\n def toffoli(\\n self,\\n control_qubit1: QubitSpecifier,\\n control_qubit2: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n ) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CCXGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubit1: The qubit(s) used as the first control.\\n control_qubit2: The qubit(s) used as the second control.\\n target_qubit: The qubit(s) targeted by the gate.\\n\\n Returns:\\n A handle to the instructions created.\\n\\n See Also:\\n QuantumCircuit.ccx: the same gate with a different name.\\n \\\"\\\"\\\"\\n return self.ccx(control_qubit1, control_qubit2, target_qubit)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mcx\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mcx\\n def mcx(\\n self,\\n control_qubits: Sequence[QubitSpecifier],\\n target_qubit: QubitSpecifier,\\n ancilla_qubits: Optional[Union[QubitSpecifier, Sequence[QubitSpecifier]]] = None,\\n mode: str = \\\"noancilla\\\",\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.MCXGate`.\\n\\n The multi-cX gate can be implemented using different techniques, which use different numbers\\n of ancilla qubits and have varying circuit depth. These modes are:\\n\\n - 'noancilla': Requires 0 ancilla qubits.\\n - 'recursion': Requires 1 ancilla qubit if more than 4 controls are used, otherwise 0.\\n - 'v-chain': Requires 2 less ancillas than the number of control qubits.\\n - 'v-chain-dirty': Same as for the clean ancillas (but the circuit will be longer).\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubits: The qubits used as the controls.\\n target_qubit: The qubit(s) targeted by the gate.\\n ancilla_qubits: The qubits used as the ancillae, if the mode requires them.\\n mode: The choice of mode, explained further above.\\n\\n Returns:\\n A handle to the instructions created.\\n\\n Raises:\\n ValueError: if the given mode is not known, or if too few ancilla qubits are passed.\\n AttributeError: if no ancilla qubits are passed, but some are needed.\\n \\\"\\\"\\\"\\n from .library.standard_gates.x import MCXGrayCode, MCXRecursive, MCXVChain\\n\\n num_ctrl_qubits = len(control_qubits)\\n\\n available_implementations = {\\n \\\"noancilla\\\": MCXGrayCode(num_ctrl_qubits),\\n \\\"recursion\\\": MCXRecursive(num_ctrl_qubits),\\n \\\"v-chain\\\": MCXVChain(num_ctrl_qubits, False),\\n \\\"v-chain-dirty\\\": MCXVChain(num_ctrl_qubits, dirty_ancillas=True),\\n # outdated, previous names\\n \\\"advanced\\\":\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mct\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.mct\\n def mct(\\n self,\\n control_qubits: Sequence[QubitSpecifier],\\n target_qubit: QubitSpecifier,\\n ancilla_qubits: Optional[Union[QubitSpecifier, Sequence[QubitSpecifier]]] = None,\\n mode: str = \\\"noancilla\\\",\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.MCXGate`.\\n\\n The multi-cX gate can be implemented using different techniques, which use different numbers\\n of ancilla qubits and have varying circuit depth. These modes are:\\n\\n - 'noancilla': Requires 0 ancilla qubits.\\n - 'recursion': Requires 1 ancilla qubit if more than 4 controls are used, otherwise 0.\\n - 'v-chain': Requires 2 less ancillas than the number of control qubits.\\n - 'v-chain-dirty': Same as for the clean ancillas (but the circuit will be longer).\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubits: The qubits used as the controls.\\n target_qubit: The qubit(s) targeted by the gate.\\n ancilla_qubits: The qubits used as the ancillae, if the mode requires them.\\n mode: The choice of mode, explained further above.\\n\\n Returns:\\n A handle to the instructions created.\\n\\n Raises:\\n ValueError: if the given mode is not known, or if too few ancilla qubits are passed.\\n AttributeError: if no ancilla qubits are passed, but some are needed.\\n\\n See Also:\\n QuantumCircuit.mcx: the same gate with a different name.\\n \\\"\\\"\\\"\\n return self.mcx(control_qubits, target_qubit, ancilla_qubits, mode)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.y\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.y\\n def y(self, qubit: QubitSpecifier) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.YGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.y import YGate\\n\\n return self.append(YGate(), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cy\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cy\\n def cy(\\n self,\\n control_qubit: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CYGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubit: The qubit(s) used as the controls.\\n target_qubit: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.y import CYGate\\n\\n return self.append(\\n CYGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.z\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.z\\n def z(self, qubit: QubitSpecifier) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.ZGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n qubit: The qubit(s) to apply the gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.z import ZGate\\n\\n return self.append(ZGate(), [qubit], [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cz\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.cz\\n def cz(\\n self,\\n control_qubit: QubitSpecifier,\\n target_qubit: QubitSpecifier,\\n label: Optional[str] = None,\\n ctrl_state: Optional[Union[str, int]] = None,\\n ) -> InstructionSet:\\n r\\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.CZGate`.\\n\\n For the full matrix form of this gate, see the underlying gate documentation.\\n\\n Args:\\n control_qubit: The qubit(s) used as the controls.\\n target_qubit: The qubit(s) targeted by the gate.\\n label: The string label of the gate in the circuit.\\n ctrl_state:\\n The control state in decimal, or as a bitstring (e.g. '1'). Defaults to controlling\\n on the '1' state.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from .library.standard_gates.z import CZGate\\n\\n return self.append(\\n CZGate(label=label, ctrl_state=ctrl_state), [control_qubit, target_qubit], []\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.pauli\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.pauli\\n def pauli(\\n self,\\n pauli_string: str,\\n qubits: Sequence[QubitSpecifier],\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.library.PauliGate`.\\n\\n Args:\\n pauli_string: A string representing the Pauli operator to apply, e.g. 'XX'.\\n qubits: The qubits to apply this gate to.\\n\\n Returns:\\n A handle to the instructions created.\\n \\\"\\\"\\\"\\n from qiskit.circuit.library.generalized_gates.pauli import PauliGate\\n\\n return self.append(PauliGate(pauli_string), qubits, [])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._push_scope\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._push_scope\\n def _push_scope(\\n self,\\n qubits: Iterable[Qubit] = (),\\n clbits: Iterable[Clbit] = (),\\n registers: Iterable[Register] = (),\\n allow_jumps: bool = True,\\n ):\\n \\\"\\\"\\\"Add a scope for collecting instructions into this circuit.\\n\\n This should only be done by the control-flow context managers, which will handle cleaning up\\n after themselves at the end as well.\\n\\n Args:\\n qubits: Any qubits that this scope should automatically use.\\n clbits: Any clbits that this scope should automatically use.\\n allow_jumps: Whether this scope allows jumps to be used within it.\\n \\\"\\\"\\\"\\n # pylint: disable=cyclic-import\\n from qiskit.circuit.controlflow.builder import ControlFlowBuilderBlock\\n\\n # Chain resource requests so things like registers added to inner scopes via conditions are\\n # requested in the outer scope as well.\\n if self._control_flow_scopes:\\n resource_requester = self._control_flow_scopes[-1].request_classical_resource\\n else:\\n resource_requester = self._resolve_classical_resource\\n\\n self._control_flow_scopes.append(\\n ControlFlowBuilderBlock(\\n qubits,\\n clbits,\\n resource_requester=resource_requester,\\n registers=registers,\\n allow_jumps=allow_jumps,\\n )\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._pop_scope\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._pop_scope\\n def _pop_scope(self) -> \\\"qiskit.circuit.controlflow.builder.ControlFlowBuilderBlock\\\":\\n \\\"\\\"\\\"Finish a scope used in the control-flow builder interface, and return it to the caller.\\n\\n This should only be done by the control-flow context managers, since they naturally\\n synchronise the creation and deletion of stack elements.\\\"\\\"\\\"\\n return self._control_flow_scopes.pop()\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._peek_previous_instruction_in_scope\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._peek_previous_instruction_in_scope\\n def _peek_previous_instruction_in_scope(\\n self,\\n ) -> Tuple[Instruction, Sequence[Qubit], Sequence[Clbit]]:\\n \\\"\\\"\\\"Return the instruction 3-tuple of the most recent instruction in the current scope, even\\n if that scope is currently under construction.\\n\\n This function is only intended for use by the control-flow ``if``-statement builders, which\\n may need to modify a previous instruction.\\\"\\\"\\\"\\n if self._control_flow_scopes:\\n return self._control_flow_scopes[-1].peek()\\n if not self._data:\\n raise CircuitError(\\\"This circuit contains no instructions.\\\")\\n return self._data[-1]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._pop_previous_instruction_in_scope\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._pop_previous_instruction_in_scope\\n def _pop_previous_instruction_in_scope(\\n self,\\n ) -> Tuple[Instruction, Sequence[Qubit], Sequence[Clbit]]:\\n \\\"\\\"\\\"Return the instruction 3-tuple of the most recent instruction in the current scope, even\\n if that scope is currently under construction, and remove it from that scope.\\n\\n This function is only intended for use by the control-flow ``if``-statement builders, which\\n may need to replace a previous instruction with another.\\n \\\"\\\"\\\"\\n if self._control_flow_scopes:\\n return self._control_flow_scopes[-1].pop()\\n if not self._data:\\n raise CircuitError(\\\"This circuit contains no instructions.\\\")\\n instruction, qubits, clbits = self._data.pop()\\n self._update_parameter_table_on_instruction_removal(instruction)\\n return instruction, qubits, clbits\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._update_parameter_table_on_instruction_removal\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit._update_parameter_table_on_instruction_removal\\n def _update_parameter_table_on_instruction_removal(self, instruction: Instruction) -> None:\\n \\\"\\\"\\\"Update the :obj:`.ParameterTable` of this circuit given that an instance of the given\\n ``instruction`` has just been removed from the circuit.\\n\\n .. note::\\n\\n This does not account for the possibility for the same instruction instance being added\\n more than once to the circuit. At the time of writing (2021-11-17, main commit 271a82f)\\n there is a defensive ``deepcopy`` of parameterised instructions inside\\n :meth:`.QuantumCircuit.append`, so this should be safe. Trying to account for it would\\n involve adding a potentially quadratic-scaling loop to check each entry in ``data``.\\n \\\"\\\"\\\"\\n atomic_parameters = set()\\n for parameter in instruction.params:\\n if isinstance(parameter, (ParameterExpression, QuantumCircuit)):\\n atomic_parameters.update(parameter.parameters)\\n for atomic_parameter in atomic_parameters:\\n entries = self._parameter_table[atomic_parameter]\\n new_entries = ParameterReferences(\\n (entry_instruction, entry_index)\\n for entry_instruction, entry_index in entries\\n if entry_instruction is not instruction\\n )\\n\\n if not new_entries:\\n del self._parameter_table[atomic_parameter]\\n # Invalidate cache.\\n self._parameters = None\\n else:\\n self._parameter_table[atomic_parameter] = new_entries\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.while_loop\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.while_loop\\n @typing.overload\\n def while_loop(\\n self,\\n condition: Tuple[Union[ClassicalRegister, Clbit], int],\\n body: None,\\n qubits: None,\\n clbits: None,\\n *,\\n label: Optional[str],\\n ) -> \\\"qiskit.circuit.controlflow.while_loop.WhileLoopContext\\\":\\n ...\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.while_loop\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.while_loop\\n @typing.overload\\n def while_loop(\\n self,\\n condition: Tuple[Union[ClassicalRegister, Clbit], int],\\n body: \\\"QuantumCircuit\\\",\\n qubits: Sequence[QubitSpecifier],\\n clbits: Sequence[ClbitSpecifier],\\n *,\\n label: Optional[str],\\n ) -> InstructionSet:\\n ...\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.while_loop\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.while_loop\\n def while_loop(self, condition, body=None, qubits=None, clbits=None, *, label=None):\\n \\\"\\\"\\\"Create a ``while`` loop on this circuit.\\n\\n There are two forms for calling this function. If called with all its arguments (with the\\n possible exception of ``label``), it will create a\\n :obj:`~qiskit.circuit.controlflow.WhileLoopOp` with the given ``body``. If ``body`` (and\\n ``qubits`` and ``clbits``) are *not* passed, then this acts as a context manager, which\\n will automatically build a :obj:`~qiskit.circuit.controlflow.WhileLoopOp` when the scope\\n finishes. In this form, you do not need to keep track of the qubits or clbits you are\\n using, because the scope will handle it for you.\\n\\n Example usage::\\n\\n from qiskit.circuit import QuantumCircuit, Clbit, Qubit\\n bits = [Qubit(), Qubit(), Clbit()]\\n qc = QuantumCircuit(bits)\\n\\n with qc.while_loop((bits[2], 0)):\\n qc.h(0)\\n qc.cx(0, 1)\\n qc.measure(0, 0)\\n\\n Args:\\n condition (Tuple[Union[ClassicalRegister, Clbit], int]): An equality condition to be\\n checked prior to executing ``body``. The left-hand side of the condition must be a\\n :obj:`~ClassicalRegister` or a :obj:`~Clbit`, and the right-hand side must be an\\n integer or boolean.\\n body (Optional[QuantumCircuit]): The loop body to be repeatedly executed. Omit this to\\n use the context-manager mode.\\n qubits (Optional[Sequence[Qubit]]): The circuit qubits over which the loop body should\\n be run. Omit this to use the context-manager mode.\\n clbits (Optional[Sequence[Clbit]]): The circuit clbits over which the loop body should\\n be run. Omit this to use the context-manager mode.\\n label (Optional[str]): The string label of the ins\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.for_loop\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.for_loop\\n @typing.overload\\n def for_loop(\\n self,\\n indexset: Iterable[int],\\n loop_parameter: Optional[Parameter],\\n body: None,\\n qubits: None,\\n clbits: None,\\n *,\\n label: Optional[str],\\n ) -> \\\"qiskit.circuit.controlflow.for_loop.ForLoopContext\\\":\\n ...\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.for_loop\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.for_loop\\n @typing.overload\\n def for_loop(\\n self,\\n indexset: Iterable[int],\\n loop_parameter: Union[Parameter, None],\\n body: \\\"QuantumCircuit\\\",\\n qubits: Sequence[QubitSpecifier],\\n clbits: Sequence[ClbitSpecifier],\\n *,\\n label: Optional[str],\\n ) -> InstructionSet:\\n ...\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.for_loop\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.for_loop\\n def for_loop(\\n self, indexset, loop_parameter=None, body=None, qubits=None, clbits=None, *, label=None\\n ):\\n \\\"\\\"\\\"Create a ``for`` loop on this circuit.\\n\\n There are two forms for calling this function. If called with all its arguments (with the\\n possible exception of ``label``), it will create a\\n :obj:`~qiskit.circuit.controlflow.ForLoopOp` with the given ``body``. If ``body`` (and\\n ``qubits`` and ``clbits``) are *not* passed, then this acts as a context manager, which,\\n when entered, provides a loop variable (unless one is given, in which case it will be\\n reused) and will automatically build a :obj:`~qiskit.circuit.controlflow.ForLoopOp` when the\\n scope finishes. In this form, you do not need to keep track of the qubits or clbits you are\\n using, because the scope will handle it for you.\\n\\n For example::\\n\\n from qiskit import QuantumCircuit\\n qc = QuantumCircuit(2, 1)\\n\\n with qc.for_loop(range(5)) as i:\\n qc.h(0)\\n qc.cx(0, 1)\\n qc.measure(0, 0)\\n qc.break_loop().c_if(0, True)\\n\\n Args:\\n indexset (Iterable[int]): A collection of integers to loop over. Always necessary.\\n loop_parameter (Optional[Parameter]): The parameter used within ``body`` to which\\n the values from ``indexset`` will be assigned. In the context-manager form, if this\\n argument is not supplied, then a loop parameter will be allocated for you and\\n returned as the value of the ``with`` statement. This will only be bound into the\\n circuit if it is used within the body.\\n\\n If this argument is ``None`` in the manual form of this method, ``body`` will be\\n repeated once for each of the items in ``indexset`` but their values will be\\n ignored.\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_test\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_test\\n @typing.overload\\n def if_test(\\n self,\\n condition: Tuple[Union[ClassicalRegister, Clbit], int],\\n true_body: None,\\n qubits: None,\\n clbits: None,\\n *,\\n label: Optional[str],\\n ) -> \\\"qiskit.circuit.controlflow.if_else.IfContext\\\":\\n ...\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_test\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_test\\n @typing.overload\\n def if_test(\\n self,\\n condition: Tuple[Union[ClassicalRegister, Clbit], int],\\n true_body: \\\"QuantumCircuit\\\",\\n qubits: Sequence[QubitSpecifier],\\n clbits: Sequence[ClbitSpecifier],\\n *,\\n label: Optional[str] = None,\\n ) -> InstructionSet:\\n ...\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_test\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_test\\n def if_test(\\n self,\\n condition,\\n true_body=None,\\n qubits=None,\\n clbits=None,\\n *,\\n label=None,\\n ):\\n \\\"\\\"\\\"Create an ``if`` statement on this circuit.\\n\\n There are two forms for calling this function. If called with all its arguments (with the\\n possible exception of ``label``), it will create a\\n :obj:`~qiskit.circuit.controlflow.IfElseOp` with the given ``true_body``, and there will be\\n no branch for the ``false`` condition (see also the :meth:`.if_else` method). However, if\\n ``true_body`` (and ``qubits`` and ``clbits``) are *not* passed, then this acts as a context\\n manager, which can be used to build ``if`` statements. The return value of the ``with``\\n statement is a chainable context manager, which can be used to create subsequent ``else``\\n blocks. In this form, you do not need to keep track of the qubits or clbits you are using,\\n because the scope will handle it for you.\\n\\n For example::\\n\\n from qiskit.circuit import QuantumCircuit, Qubit, Clbit\\n bits = [Qubit(), Qubit(), Qubit(), Clbit(), Clbit()]\\n qc = QuantumCircuit(bits)\\n\\n qc.h(0)\\n qc.cx(0, 1)\\n qc.measure(0, 0)\\n qc.h(0)\\n qc.cx(0, 1)\\n qc.measure(0, 1)\\n\\n with qc.if_test((bits[3], 0)) as else_:\\n qc.x(2)\\n with else_:\\n qc.h(2)\\n qc.z(2)\\n\\n Args:\\n condition (Tuple[Union[ClassicalRegister, Clbit], int]): A condition to be evaluated at\\n circuit runtime which, if true, will trigger the evaluation of ``true_body``. Can be\\n specified as either a tuple of a ``ClassicalRegister`` to be tested for equality\\n with a given ``int``, or as a tuple of a ``Clbit`` to be compared to either a\\n ``bool`` or an ``\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_else\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.if_else\\n def if_else(\\n self,\\n condition: Union[\\n Tuple[ClassicalRegister, int],\\n Tuple[Clbit, int],\\n Tuple[Clbit, bool],\\n ],\\n true_body: \\\"QuantumCircuit\\\",\\n false_body: \\\"QuantumCircuit\\\",\\n qubits: Sequence[QubitSpecifier],\\n clbits: Sequence[ClbitSpecifier],\\n label: Optional[str] = None,\\n ) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.controlflow.IfElseOp`.\\n\\n .. note::\\n\\n This method does not have an associated context-manager form, because it is already\\n handled by the :meth:`.if_test` method. You can use the ``else`` part of that with\\n something such as::\\n\\n from qiskit.circuit import QuantumCircuit, Qubit, Clbit\\n bits = [Qubit(), Qubit(), Clbit()]\\n qc = QuantumCircuit(bits)\\n qc.h(0)\\n qc.cx(0, 1)\\n qc.measure(0, 0)\\n with qc.if_test((bits[2], 0)) as else_:\\n qc.h(0)\\n with else_:\\n qc.x(0)\\n\\n Args:\\n condition: A condition to be evaluated at circuit runtime which,\\n if true, will trigger the evaluation of ``true_body``. Can be\\n specified as either a tuple of a ``ClassicalRegister`` to be\\n tested for equality with a given ``int``, or as a tuple of a\\n ``Clbit`` to be compared to either a ``bool`` or an ``int``.\\n true_body: The circuit body to be run if ``condition`` is true.\\n false_body: The circuit to be run if ``condition`` is false.\\n qubits: The circuit qubits over which the if/else should be run.\\n clbits: The circuit clbits over which the if/else should be run.\\n label: The string label of the instruction in the circuit.\\n\\n Raises:\\n CircuitError: If the provided condition refer\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.break_loop\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.break_loop\\n def break_loop(self) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.controlflow.BreakLoopOp`.\\n\\n .. warning::\\n\\n If you are using the context-manager \\\"builder\\\" forms of :meth:`.if_test`,\\n :meth:`.for_loop` or :meth:`.while_loop`, you can only call this method if you are\\n within a loop context, because otherwise the \\\"resource width\\\" of the operation cannot be\\n determined. This would quickly lead to invalid circuits, and so if you are trying to\\n construct a reusable loop body (without the context managers), you must also use the\\n non-context-manager form of :meth:`.if_test` and :meth:`.if_else`. Take care that the\\n :obj:`.BreakLoopOp` instruction must span all the resources of its containing loop, not\\n just the immediate scope.\\n\\n Returns:\\n A handle to the instruction created.\\n\\n Raises:\\n CircuitError: if this method was called within a builder context, but not contained\\n within a loop.\\n \\\"\\\"\\\"\\n # pylint: disable=cyclic-import\\n from qiskit.circuit.controlflow.break_loop import BreakLoopOp, BreakLoopPlaceholder\\n\\n if self._control_flow_scopes:\\n operation = BreakLoopPlaceholder()\\n resources = operation.placeholder_resources()\\n return self.append(operation, resources.qubits, resources.clbits)\\n return self.append(BreakLoopOp(self.num_qubits, self.num_clbits), self.qubits, self.clbits)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.continue_loop\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.continue_loop\\n def continue_loop(self) -> InstructionSet:\\n \\\"\\\"\\\"Apply :class:`~qiskit.circuit.controlflow.ContinueLoopOp`.\\n\\n .. warning::\\n\\n If you are using the context-manager \\\"builder\\\" forms of :meth:`.if_test`,\\n :meth:`.for_loop` or :meth:`.while_loop`, you can only call this method if you are\\n within a loop context, because otherwise the \\\"resource width\\\" of the operation cannot be\\n determined. This would quickly lead to invalid circuits, and so if you are trying to\\n construct a reusable loop body (without the context managers), you must also use the\\n non-context-manager form of :meth:`.if_test` and :meth:`.if_else`. Take care that the\\n :obj:`.ContinueLoopOp` instruction must span all the resources of its containing loop,\\n not just the immediate scope.\\n\\n Returns:\\n A handle to the instruction created.\\n\\n Raises:\\n CircuitError: if this method was called within a builder context, but not contained\\n within a loop.\\n \\\"\\\"\\\"\\n # pylint: disable=cyclic-import\\n from qiskit.circuit.controlflow.continue_loop import ContinueLoopOp, ContinueLoopPlaceholder\\n\\n if self._control_flow_scopes:\\n operation = ContinueLoopPlaceholder()\\n resources = operation.placeholder_resources()\\n return self.append(operation, resources.qubits, resources.clbits)\\n return self.append(\\n ContinueLoopOp(self.num_qubits, self.num_clbits), self.qubits, self.clbits\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.add_calibration\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.add_calibration\\n def add_calibration(\\n self,\\n gate: Union[Gate, str],\\n qubits: Sequence[int],\\n # Schedule has the type `qiskit.pulse.Schedule`, but `qiskit.pulse` cannot be imported\\n # while this module is, and so Sphinx will not accept a forward reference to it. Sphinx\\n # needs the types available at runtime, whereas mypy will accept it, because it handles the\\n # type checking by static analysis.\\n schedule,\\n params: Optional[Sequence[ParameterValueType]] = None,\\n ) -> None:\\n \\\"\\\"\\\"Register a low-level, custom pulse definition for the given gate.\\n\\n Args:\\n gate (Union[Gate, str]): Gate information.\\n qubits (Union[int, Tuple[int]]): List of qubits to be measured.\\n schedule (Schedule): Schedule information.\\n params (Optional[List[Union[float, Parameter]]]): A list of parameters.\\n\\n Raises:\\n Exception: if the gate is of type string and params is None.\\n \\\"\\\"\\\"\\n if isinstance(gate, Gate):\\n self._calibrations[gate.name][(tuple(qubits), tuple(gate.params))] = schedule\\n else:\\n self._calibrations[gate][(tuple(qubits), tuple(params or []))] = schedule\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubit_duration\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubit_duration\\n def qubit_duration(self, *qubits: Union[Qubit, int]) -> float:\\n \\\"\\\"\\\"Return the duration between the start and stop time of the first and last instructions,\\n excluding delays, over the supplied qubits. Its time unit is ``self.unit``.\\n\\n Args:\\n *qubits: Qubits within ``self`` to include.\\n\\n Returns:\\n Return the duration between the first start and last stop time of non-delay instructions\\n \\\"\\\"\\\"\\n return self.qubit_stop_time(*qubits) - self.qubit_start_time(*qubits)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubit_start_time\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubit_start_time\\n def qubit_start_time(self, *qubits: Union[Qubit, int]) -> float:\\n \\\"\\\"\\\"Return the start time of the first instruction, excluding delays,\\n over the supplied qubits. Its time unit is ``self.unit``.\\n\\n Return 0 if there are no instructions over qubits\\n\\n Args:\\n *qubits: Qubits within ``self`` to include. Integers are allowed for qubits, indicating\\n indices of ``self.qubits``.\\n\\n Returns:\\n Return the start time of the first instruction, excluding delays, over the qubits\\n\\n Raises:\\n CircuitError: if ``self`` is a not-yet scheduled circuit.\\n \\\"\\\"\\\"\\n if self.duration is None:\\n # circuit has only delays, this is kind of scheduled\\n for inst, _, _ in self.data:\\n if not isinstance(inst, Delay):\\n raise CircuitError(\\n \\\"qubit_start_time undefined. Circuit must be scheduled first.\\\"\\n )\\n return 0\\n\\n qubits = [self.qubits[q] if isinstance(q, int) else q for q in qubits]\\n\\n starts = {q: 0 for q in qubits}\\n dones = {q: False for q in qubits}\\n for inst, qargs, _ in self.data:\\n for q in qubits:\\n if q in qargs:\\n if isinstance(inst, Delay):\\n if not dones[q]:\\n starts[q] += inst.duration\\n else:\\n dones[q] = True\\n if len(qubits) == len([done for done in dones.values() if done]): # all done\\n return min(start for start in starts.values())\\n\\n return 0 # If there are no instructions over bits\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubit_stop_time\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::QuantumCircuit.qubit_stop_time\\n def qubit_stop_time(self, *qubits: Union[Qubit, int]) -> float:\\n \\\"\\\"\\\"Return the stop time of the last instruction, excluding delays, over the supplied qubits.\\n Its time unit is ``self.unit``.\\n\\n Return 0 if there are no instructions over qubits\\n\\n Args:\\n *qubits: Qubits within ``self`` to include. Integers are allowed for qubits, indicating\\n indices of ``self.qubits``.\\n\\n Returns:\\n Return the stop time of the last instruction, excluding delays, over the qubits\\n\\n Raises:\\n CircuitError: if ``self`` is a not-yet scheduled circuit.\\n \\\"\\\"\\\"\\n if self.duration is None:\\n # circuit has only delays, this is kind of scheduled\\n for inst, _, _ in self.data:\\n if not isinstance(inst, Delay):\\n raise CircuitError(\\n \\\"qubit_stop_time undefined. Circuit must be scheduled first.\\\"\\n )\\n return 0\\n\\n qubits = [self.qubits[q] if isinstance(q, int) else q for q in qubits]\\n\\n stops = {q: self.duration for q in qubits}\\n dones = {q: False for q in qubits}\\n for inst, qargs, _ in reversed(self.data):\\n for q in qubits:\\n if q in qargs:\\n if isinstance(inst, Delay):\\n if not dones[q]:\\n stops[q] -= inst.duration\\n else:\\n dones[q] = True\\n if len(qubits) == len([done for done in dones.values() if done]): # all done\\n return max(stop for stop in stops.values())\\n\\n return 0 # If there are no instructions over bits\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::_circuit_from_qasm\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::_circuit_from_qasm\\ndef _circuit_from_qasm(qasm: Qasm) -> \\\"QuantumCircuit\\\":\\n # pylint: disable=cyclic-import\\n from qiskit.converters import ast_to_dag\\n from qiskit.converters import dag_to_circuit\\n\\n ast = qasm.parse()\\n dag = ast_to_dag(ast)\\n return dag_to_circuit(dag)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::_standard_compare\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::_standard_compare\\ndef _standard_compare(value1, value2):\\n if value1 < value2:\\n return -1\\n if value1 > value2:\\n return 1\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::_compare_parameters\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::_compare_parameters\\ndef _compare_parameters(param1: Parameter, param2: Parameter) -> int:\\n if isinstance(param1, ParameterVectorElement) and isinstance(param2, ParameterVectorElement):\\n # if they belong to a vector with the same name, sort by index\\n if param1.vector.name == param2.vector.name:\\n return _standard_compare(param1.index, param2.index)\\n\\n # else sort by name\\n return _standard_compare(param1.name, param2.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::_add_sub_instruction_to_existing_composite_circuits\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::_add_sub_instruction_to_existing_composite_circuits\\ndef _add_sub_instruction_to_existing_composite_circuits(\\n instruction: Instruction,\\n existing_gate_names: List[str],\\n existing_composite_circuits: List[Instruction],\\n) -> None:\\n \\\"\\\"\\\"Recursively add undefined sub-instructions in the definition of the given\\n instruction to existing_composite_circuit list.\\n \\\"\\\"\\\"\\n for sub_instruction, _, _ in instruction.definition:\\n # Check instructions names are valid\\n if not VALID_QASM2_IDENTIFIER.fullmatch(sub_instruction.name):\\n sub_instruction = sub_instruction.copy(\\n name=_qasm_escape_gate_name(sub_instruction.name)\\n )\\n if (\\n sub_instruction.name not in existing_gate_names\\n and sub_instruction not in existing_composite_circuits\\n ):\\n existing_composite_circuits.insert(0, sub_instruction)\\n _add_sub_instruction_to_existing_composite_circuits(\\n sub_instruction, existing_gate_names, existing_composite_circuits\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::_qasm_escape_gate_name\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::_qasm_escape_gate_name\\ndef _qasm_escape_gate_name(name: str) -> str:\\n \\\"\\\"\\\"Returns a valid OpenQASM gate identifier\\\"\\\"\\\"\\n # Replace all non-ASCII-word characters with the underscore.\\n escaped_name = re.sub(r\\\"\\\\W\\\", \\\"_\\\", name, flags=re.ASCII)\\n if not escaped_name or escaped_name[0] not in string.ascii_lowercase:\\n # Add an arbitrary, guaranteed-to-be-valid prefix.\\n escaped_name = \\\"gate_\\\" + escaped_name\\n\\n return escaped_name\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::_get_composite_circuit_qasm_from_instruction\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::_get_composite_circuit_qasm_from_instruction\\ndef _get_composite_circuit_qasm_from_instruction(instruction: Instruction) -> str:\\n \\\"\\\"\\\"Returns OpenQASM string composite circuit given an instruction.\\n The given instruction should be the result of composite_circuit.to_instruction().\\\"\\\"\\\"\\n\\n if instruction.definition is None:\\n raise ValueError(f'Instruction \\\"{instruction.name}\\\" is not defined.')\\n\\n gate_parameters = \\\",\\\".join([\\\"param%i\\\" % num for num in range(len(instruction.params))])\\n qubit_parameters = \\\",\\\".join([\\\"q%i\\\" % num for num in range(instruction.num_qubits)])\\n composite_circuit_gates = \\\"\\\"\\n\\n definition = instruction.definition\\n definition_bit_labels = {\\n bit: idx for bits in (definition.qubits, definition.clbits) for idx, bit in enumerate(bits)\\n }\\n for sub_instruction, qargs, _ in definition:\\n if not VALID_QASM2_IDENTIFIER.fullmatch(sub_instruction.name):\\n sub_instruction = sub_instruction.copy(\\n name=_qasm_escape_gate_name(sub_instruction.name)\\n )\\n\\n gate_qargs = \\\",\\\".join(\\n [\\\"q%i\\\" % index for index in [definition_bit_labels[qubit] for qubit in qargs]]\\n )\\n composite_circuit_gates += f\\\"{sub_instruction.qasm()} {gate_qargs}; \\\"\\n\\n if composite_circuit_gates:\\n composite_circuit_gates = composite_circuit_gates.rstrip(\\\" \\\")\\n\\n if gate_parameters:\\n qasm_string = \\\"gate {}({}) {} {{ {} }}\\\".format(\\n instruction.name,\\n gate_parameters,\\n qubit_parameters,\\n composite_circuit_gates,\\n )\\n else:\\n qasm_string = \\\"gate {} {} {{ {} }}\\\".format(\\n instruction.name,\\n qubit_parameters,\\n composite_circuit_gates,\\n )\\n\\n return qasm_string\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::_insert_composite_gate_definition_qasm\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::_insert_composite_gate_definition_qasm\\ndef _insert_composite_gate_definition_qasm(\\n string_temp: str, existing_composite_circuits: List[Instruction], extension_lib: str\\n) -> str:\\n \\\"\\\"\\\"Insert composite gate definition QASM code right after extension library in the header\\\"\\\"\\\"\\n\\n gate_definition_string = \\\"\\\"\\n\\n # Generate gate definition string\\n for instruction in existing_composite_circuits:\\n if hasattr(instruction, \\\"_qasm_definition\\\"):\\n qasm_string = instruction._qasm_definition\\n else:\\n qasm_string = _get_composite_circuit_qasm_from_instruction(instruction)\\n gate_definition_string += \\\"\\\\n\\\" + qasm_string\\n\\n string_temp = string_temp.replace(extension_lib, f\\\"{extension_lib}{gate_definition_string}\\\")\\n return string_temp\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::_bit_argument_conversion\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::_bit_argument_conversion\\ndef _bit_argument_conversion(specifier, bit_sequence, bit_set, type_):\\n \\\"\\\"\\\"Get the list of bits referred to by the specifier ``specifier``.\\n\\n Valid types for ``specifier`` are integers, bits of the correct type (as given in ``type_``), or\\n iterables of one of those two scalar types. Integers are interpreted as indices into the\\n sequence ``bit_sequence``. All allowed bits must be in ``bit_set`` (which should implement\\n fast lookup), which is assumed to contain the same bits as ``bit_sequence``.\\n\\n Returns:\\n List[Bit]: a list of the specified bits from ``bits``.\\n\\n Raises:\\n CircuitError: if an incorrect type or index is encountered, if the same bit is specified\\n more than once, or if the specifier is to a bit not in the ``bit_set``.\\n \\\"\\\"\\\"\\n # The duplication between this function and `_bit_argument_conversion_scalar` is so that fast\\n # paths return as quickly as possible, and all valid specifiers will resolve without needing to\\n # try/catch exceptions (which is too slow for inner-loop code).\\n if isinstance(specifier, type_):\\n if specifier in bit_set:\\n return [specifier]\\n raise CircuitError(f\\\"Bit '{specifier}' is not in the circuit.\\\")\\n if isinstance(specifier, (int, np.integer)):\\n try:\\n return [bit_sequence[specifier]]\\n except IndexError as ex:\\n raise CircuitError(\\n f\\\"Index {specifier} out of range for size {len(bit_sequence)}.\\\"\\n ) from ex\\n # Slices can't raise IndexError - they just return an empty list.\\n if isinstance(specifier, slice):\\n return bit_sequence[specifier]\\n try:\\n return [\\n _bit_argument_conversion_scalar(index, bit_sequence, bit_set, type_)\\n for index in specifier\\n ]\\n except TypeError as ex:\\n message = (\\n f\\\"Incorrect bit type: expected '{type_.__name__}' but got '{type(specifier)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/circuit/quantumcircuit.py::_bit_argument_conversion_scalar\",\n \"text\": \"qiskit/circuit/quantumcircuit.py::_bit_argument_conversion_scalar\\ndef _bit_argument_conversion_scalar(specifier, bit_sequence, bit_set, type_):\\n if isinstance(specifier, type_):\\n if specifier in bit_set:\\n return specifier\\n raise CircuitError(f\\\"Bit '{specifier}' is not in the circuit.\\\")\\n if isinstance(specifier, (int, np.integer)):\\n try:\\n return bit_sequence[specifier]\\n except IndexError as ex:\\n raise CircuitError(\\n f\\\"Index {specifier} out of range for size {len(bit_sequence)}.\\\"\\n ) from ex\\n message = (\\n f\\\"Incorrect bit type: expected '{type_.__name__}' but got '{type(specifier).__name__}'\\\"\\n if isinstance(specifier, Bit)\\n else f\\\"Invalid bit index: '{specifier}' of type '{type(specifier)}'\\\"\\n )\\n raise CircuitError(message)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__init__\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__init__\\n def __init__(self):\\n \\\"\\\"\\\"Create an empty circuit.\\\"\\\"\\\"\\n\\n # Circuit name. Generally, this corresponds to the name\\n # of the QuantumCircuit from which the DAG was generated.\\n self.name = None\\n\\n # Circuit metadata\\n self.metadata = None\\n\\n # Set of wires (Register,idx) in the dag\\n self._wires = set()\\n\\n # Map from wire (Register,idx) to input nodes of the graph\\n self.input_map = OrderedDict()\\n\\n # Map from wire (Register,idx) to output nodes of the graph\\n self.output_map = OrderedDict()\\n\\n # Directed multigraph whose nodes are inputs, outputs, or operations.\\n # Operation nodes have equal in- and out-degrees and carry\\n # additional data about the operation, including the argument order\\n # and parameter values.\\n # Input nodes have out-degree 1 and output nodes have in-degree 1.\\n # Edges carry wire labels (reg,idx) and each operation has\\n # corresponding in- and out-edges with the same wire labels.\\n self._multi_graph = rx.PyDAG()\\n\\n # Map of qreg/creg name to Register object.\\n self.qregs = OrderedDict()\\n self.cregs = OrderedDict()\\n\\n # List of Qubit/Clbit wires that the DAG acts on.\\n self.qubits = []\\n self.clbits = []\\n\\n self._global_phase = 0\\n self._calibrations = defaultdict(dict)\\n\\n self._op_names = {}\\n\\n self.duration = None\\n self.unit = \\\"dt\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.to_networkx\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.to_networkx\\n @_optionals.HAS_NETWORKX.require_in_call\\n @deprecate_function(\\n \\\"The to_networkx() method is deprecated and will be removed in a future release.\\\"\\n )\\n def to_networkx(self):\\n \\\"\\\"\\\"Returns a copy of the DAGCircuit in networkx format.\\\"\\\"\\\"\\n import networkx as nx\\n\\n G = nx.MultiDiGraph()\\n for node in self._multi_graph.nodes():\\n G.add_node(node)\\n for node_id in rx.topological_sort(self._multi_graph):\\n for source_id, dest_id, edge in self._multi_graph.in_edges(node_id):\\n G.add_edge(self._multi_graph[source_id], self._multi_graph[dest_id], wire=edge)\\n return G\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.from_networkx\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.from_networkx\\n @classmethod\\n @_optionals.HAS_NETWORKX.require_in_call\\n @deprecate_function(\\n \\\"The from_networkx() method is deprecated and will be removed in a future release.\\\"\\n )\\n def from_networkx(cls, graph):\\n \\\"\\\"\\\"Take a networkx MultiDigraph and create a new DAGCircuit.\\n\\n Args:\\n graph (networkx.MultiDiGraph): The graph to create a DAGCircuit\\n object from. The format of this MultiDiGraph format must be\\n in the same format as returned by to_networkx.\\n\\n Returns:\\n DAGCircuit: The dagcircuit object created from the networkx\\n MultiDiGraph.\\n Raises:\\n MissingOptionalLibraryError: If networkx is not installed\\n DAGCircuitError: If input networkx graph is malformed\\n \\\"\\\"\\\"\\n import networkx as nx\\n\\n dag = DAGCircuit()\\n for node in nx.topological_sort(graph):\\n if isinstance(node, DAGOutNode):\\n continue\\n if isinstance(node, DAGInNode):\\n if isinstance(node.wire, Qubit):\\n dag.add_qubits([node.wire])\\n elif isinstance(node.wire, Clbit):\\n dag.add_clbits([node.wire])\\n else:\\n raise DAGCircuitError(f\\\"unknown node wire type: {node.wire}\\\")\\n elif isinstance(node, DAGOpNode):\\n dag.apply_operation_back(node.op.copy(), node.qargs, node.cargs)\\n return dag\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.wires\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.wires\\n @property\\n def wires(self):\\n \\\"\\\"\\\"Return a list of the wires in order.\\\"\\\"\\\"\\n return self.qubits + self.clbits\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.node_counter\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.node_counter\\n @property\\n def node_counter(self):\\n \\\"\\\"\\\"\\n Returns the number of nodes in the dag.\\n \\\"\\\"\\\"\\n return len(self._multi_graph)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase\\n @property\\n def global_phase(self):\\n \\\"\\\"\\\"Return the global phase of the circuit.\\\"\\\"\\\"\\n return self._global_phase\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase\\n @global_phase.setter\\n def global_phase(self, angle):\\n \\\"\\\"\\\"Set the global phase of the circuit.\\n\\n Args:\\n angle (float, ParameterExpression)\\n \\\"\\\"\\\"\\n if isinstance(angle, ParameterExpression):\\n self._global_phase = angle\\n else:\\n # Set the phase to the [0, 2π) interval\\n angle = float(angle)\\n if not angle:\\n self._global_phase = 0\\n else:\\n self._global_phase = angle % (2 * math.pi)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations\\n @property\\n def calibrations(self):\\n \\\"\\\"\\\"Return calibration dictionary.\\n\\n The custom pulse definition of a given gate is of the form\\n {'gate_name': {(qubits, params): schedule}}\\n \\\"\\\"\\\"\\n return dict(self._calibrations)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations\\n @calibrations.setter\\n def calibrations(self, calibrations):\\n \\\"\\\"\\\"Set the circuit calibration data from a dictionary of calibration definition.\\n\\n Args:\\n calibrations (dict): A dictionary of input in the format\\n {'gate_name': {(qubits, gate_params): schedule}}\\n \\\"\\\"\\\"\\n self._calibrations = defaultdict(dict, calibrations)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_calibration\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_calibration\\n def add_calibration(self, gate, qubits, schedule, params=None):\\n \\\"\\\"\\\"Register a low-level, custom pulse definition for the given gate.\\n\\n Args:\\n gate (Union[Gate, str]): Gate information.\\n qubits (Union[int, Tuple[int]]): List of qubits to be measured.\\n schedule (Schedule): Schedule information.\\n params (Optional[List[Union[float, Parameter]]]): A list of parameters.\\n\\n Raises:\\n Exception: if the gate is of type string and params is None.\\n \\\"\\\"\\\"\\n if isinstance(gate, Gate):\\n self._calibrations[gate.name][\\n (tuple(qubits), tuple(float(p) for p in gate.params))\\n ] = schedule\\n else:\\n self._calibrations[gate][(tuple(qubits), tuple(params or []))] = schedule\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.has_calibration_for\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.has_calibration_for\\n def has_calibration_for(self, node):\\n \\\"\\\"\\\"Return True if the dag has a calibration defined for the node operation. In this\\n case, the operation does not need to be translated to the device basis.\\n \\\"\\\"\\\"\\n if not self.calibrations or node.op.name not in self.calibrations:\\n return False\\n qubits = tuple(self.qubits.index(qubit) for qubit in node.qargs)\\n params = []\\n for p in node.op.params:\\n if isinstance(p, ParameterExpression) and not p.parameters:\\n params.append(float(p))\\n else:\\n params.append(p)\\n params = tuple(params)\\n return (qubits, params) in self.calibrations[node.op.name]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_all_ops_named\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_all_ops_named\\n def remove_all_ops_named(self, opname):\\n \\\"\\\"\\\"Remove all operation nodes with the given name.\\\"\\\"\\\"\\n for n in self.named_nodes(opname):\\n self.remove_op_node(n)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qubits\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qubits\\n def add_qubits(self, qubits):\\n \\\"\\\"\\\"Add individual qubit wires.\\\"\\\"\\\"\\n if any(not isinstance(qubit, Qubit) for qubit in qubits):\\n raise DAGCircuitError(\\\"not a Qubit instance.\\\")\\n\\n duplicate_qubits = set(self.qubits).intersection(qubits)\\n if duplicate_qubits:\\n raise DAGCircuitError(\\\"duplicate qubits %s\\\" % duplicate_qubits)\\n\\n self.qubits.extend(qubits)\\n for qubit in qubits:\\n self._add_wire(qubit)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_clbits\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_clbits\\n def add_clbits(self, clbits):\\n \\\"\\\"\\\"Add individual clbit wires.\\\"\\\"\\\"\\n if any(not isinstance(clbit, Clbit) for clbit in clbits):\\n raise DAGCircuitError(\\\"not a Clbit instance.\\\")\\n\\n duplicate_clbits = set(self.clbits).intersection(clbits)\\n if duplicate_clbits:\\n raise DAGCircuitError(\\\"duplicate clbits %s\\\" % duplicate_clbits)\\n\\n self.clbits.extend(clbits)\\n for clbit in clbits:\\n self._add_wire(clbit)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qreg\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qreg\\n def add_qreg(self, qreg):\\n \\\"\\\"\\\"Add all wires in a quantum register.\\\"\\\"\\\"\\n if not isinstance(qreg, QuantumRegister):\\n raise DAGCircuitError(\\\"not a QuantumRegister instance.\\\")\\n if qreg.name in self.qregs:\\n raise DAGCircuitError(\\\"duplicate register %s\\\" % qreg.name)\\n self.qregs[qreg.name] = qreg\\n existing_qubits = set(self.qubits)\\n for j in range(qreg.size):\\n if qreg[j] not in existing_qubits:\\n self.qubits.append(qreg[j])\\n self._add_wire(qreg[j])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_creg\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_creg\\n def add_creg(self, creg):\\n \\\"\\\"\\\"Add all wires in a classical register.\\\"\\\"\\\"\\n if not isinstance(creg, ClassicalRegister):\\n raise DAGCircuitError(\\\"not a ClassicalRegister instance.\\\")\\n if creg.name in self.cregs:\\n raise DAGCircuitError(\\\"duplicate register %s\\\" % creg.name)\\n self.cregs[creg.name] = creg\\n existing_clbits = set(self.clbits)\\n for j in range(creg.size):\\n if creg[j] not in existing_clbits:\\n self.clbits.append(creg[j])\\n self._add_wire(creg[j])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_wire\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_wire\\n def _add_wire(self, wire):\\n \\\"\\\"\\\"Add a qubit or bit to the circuit.\\n\\n Args:\\n wire (Bit): the wire to be added\\n\\n This adds a pair of in and out nodes connected by an edge.\\n\\n Raises:\\n DAGCircuitError: if trying to add duplicate wire\\n \\\"\\\"\\\"\\n if wire not in self._wires:\\n self._wires.add(wire)\\n\\n inp_node = DAGInNode(wire=wire)\\n outp_node = DAGOutNode(wire=wire)\\n input_map_id, output_map_id = self._multi_graph.add_nodes_from([inp_node, outp_node])\\n inp_node._node_id = input_map_id\\n outp_node._node_id = output_map_id\\n self.input_map[wire] = inp_node\\n self.output_map[wire] = outp_node\\n self._multi_graph.add_edge(inp_node._node_id, outp_node._node_id, wire)\\n else:\\n raise DAGCircuitError(f\\\"duplicate wire {wire}\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_clbits\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_clbits\\n def remove_clbits(self, *clbits):\\n \\\"\\\"\\\"\\n Remove classical bits from the circuit. All bits MUST be idle.\\n Any registers with references to at least one of the specified bits will\\n also be removed.\\n\\n Args:\\n clbits (List[Clbit]): The bits to remove.\\n\\n Raises:\\n DAGCircuitError: a clbit is not a :obj:`.Clbit`, is not in the circuit,\\n or is not idle.\\n \\\"\\\"\\\"\\n if any(not isinstance(clbit, Clbit) for clbit in clbits):\\n raise DAGCircuitError(\\n \\\"clbits not of type Clbit: %s\\\" % [b for b in clbits if not isinstance(b, Clbit)]\\n )\\n\\n clbits = set(clbits)\\n unknown_clbits = clbits.difference(self.clbits)\\n if unknown_clbits:\\n raise DAGCircuitError(\\\"clbits not in circuit: %s\\\" % unknown_clbits)\\n\\n busy_clbits = {bit for bit in clbits if not self._is_wire_idle(bit)}\\n if busy_clbits:\\n raise DAGCircuitError(\\\"clbits not idle: %s\\\" % busy_clbits)\\n\\n # remove any references to bits\\n cregs_to_remove = {creg for creg in self.cregs.values() if not clbits.isdisjoint(creg)}\\n self.remove_cregs(*cregs_to_remove)\\n\\n for clbit in clbits:\\n self._remove_idle_wire(clbit)\\n self.clbits.remove(clbit)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_cregs\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_cregs\\n def remove_cregs(self, *cregs):\\n \\\"\\\"\\\"\\n Remove classical registers from the circuit, leaving underlying bits\\n in place.\\n\\n Raises:\\n DAGCircuitError: a creg is not a ClassicalRegister, or is not in\\n the circuit.\\n \\\"\\\"\\\"\\n if any(not isinstance(creg, ClassicalRegister) for creg in cregs):\\n raise DAGCircuitError(\\n \\\"cregs not of type ClassicalRegister: %s\\\"\\n % [r for r in cregs if not isinstance(r, ClassicalRegister)]\\n )\\n\\n unknown_cregs = set(cregs).difference(self.cregs.values())\\n if unknown_cregs:\\n raise DAGCircuitError(\\\"cregs not in circuit: %s\\\" % unknown_cregs)\\n\\n for creg in cregs:\\n del self.cregs[creg.name]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._is_wire_idle\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._is_wire_idle\\n def _is_wire_idle(self, wire):\\n \\\"\\\"\\\"Check if a wire is idle.\\n\\n Args:\\n wire (Bit): a wire in the circuit.\\n\\n Returns:\\n bool: true if the wire is idle, false otherwise.\\n\\n Raises:\\n DAGCircuitError: the wire is not in the circuit.\\n \\\"\\\"\\\"\\n if wire not in self._wires:\\n raise DAGCircuitError(\\\"wire %s not in circuit\\\" % wire)\\n\\n try:\\n child = next(self.successors(self.input_map[wire]))\\n except StopIteration as e:\\n raise DAGCircuitError(\\n \\\"Invalid dagcircuit input node %s has no output\\\" % self.input_map[wire]\\n ) from e\\n return child is self.output_map[wire]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._remove_idle_wire\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._remove_idle_wire\\n def _remove_idle_wire(self, wire):\\n \\\"\\\"\\\"Remove an idle qubit or bit from the circuit.\\n\\n Args:\\n wire (Bit): the wire to be removed, which MUST be idle.\\n \\\"\\\"\\\"\\n inp_node = self.input_map[wire]\\n oup_node = self.output_map[wire]\\n\\n self._multi_graph.remove_node(inp_node._node_id)\\n self._multi_graph.remove_node(oup_node._node_id)\\n self._wires.remove(wire)\\n del self.input_map[wire]\\n del self.output_map[wire]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_condition\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_condition\\n def _check_condition(self, name, condition):\\n \\\"\\\"\\\"Verify that the condition is valid.\\n\\n Args:\\n name (string): used for error reporting\\n condition (tuple or None): a condition tuple (ClassicalRegister, int) or (Clbit, bool)\\n\\n Raises:\\n DAGCircuitError: if conditioning on an invalid register\\n \\\"\\\"\\\"\\n if (\\n condition is not None\\n and condition[0] not in self.clbits\\n and condition[0].name not in self.cregs\\n ):\\n raise DAGCircuitError(\\\"invalid creg in condition for %s\\\" % name)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_bits\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_bits\\n def _check_bits(self, args, amap):\\n \\\"\\\"\\\"Check the values of a list of (qu)bit arguments.\\n\\n For each element of args, check that amap contains it.\\n\\n Args:\\n args (list[Bit]): the elements to be checked\\n amap (dict): a dictionary keyed on Qubits/Clbits\\n\\n Raises:\\n DAGCircuitError: if a qubit is not contained in amap\\n \\\"\\\"\\\"\\n # Check for each wire\\n for wire in args:\\n if wire not in amap:\\n raise DAGCircuitError(f\\\"(qu)bit {wire} not found in {amap}\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._bits_in_condition\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._bits_in_condition\\n def _bits_in_condition(self, cond):\\n \\\"\\\"\\\"Return a list of bits in the given condition.\\n\\n Args:\\n cond (tuple or None): optional condition (ClassicalRegister, int) or (Clbit, bool)\\n\\n Returns:\\n list[Clbit]: list of classical bits\\n\\n Raises:\\n CircuitError: if cond[0] is not ClassicalRegister or Clbit\\n \\\"\\\"\\\"\\n if cond is None:\\n return []\\n elif isinstance(cond[0], ClassicalRegister):\\n # Returns a list of all the cbits in the given creg cond[0].\\n return cond[0][:]\\n elif isinstance(cond[0], Clbit):\\n # Returns a singleton list of the conditional cbit.\\n return [cond[0]]\\n else:\\n raise CircuitError(\\\"Condition must be used with ClassicalRegister or Clbit.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._increment_op\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._increment_op\\n def _increment_op(self, op):\\n if op.name in self._op_names:\\n self._op_names[op.name] += 1\\n else:\\n self._op_names[op.name] = 1\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._decrement_op\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._decrement_op\\n def _decrement_op(self, op):\\n if self._op_names[op.name] == 1:\\n del self._op_names[op.name]\\n else:\\n self._op_names[op.name] -= 1\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_op_node\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_op_node\\n def _add_op_node(self, op, qargs, cargs):\\n \\\"\\\"\\\"Add a new operation node to the graph and assign properties.\\n\\n Args:\\n op (qiskit.circuit.Instruction): the operation associated with the DAG node\\n qargs (list[Qubit]): list of quantum wires to attach to.\\n cargs (list[Clbit]): list of classical wires to attach to.\\n Returns:\\n int: The integer node index for the new op node on the DAG\\n \\\"\\\"\\\"\\n # Add a new operation node to the graph\\n new_node = DAGOpNode(op=op, qargs=qargs, cargs=cargs)\\n node_index = self._multi_graph.add_node(new_node)\\n new_node._node_id = node_index\\n self._increment_op(op)\\n return node_index\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._copy_circuit_metadata\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._copy_circuit_metadata\\n @deprecate_function(\\n \\\"\\\"\\\"The DAGCircuit._copy_circuit_metadata method is deprecated as of 0.20.0. It will be removed\\n no earlier than 3 months after the release date. You should use the DAGCircuit.copy_empty_like\\n method instead, which acts identically.\\n \\\"\\\"\\\"\\n )\\n def _copy_circuit_metadata(self):\\n \\\"\\\"\\\"DEPRECATED\\\"\\\"\\\"\\n return self.copy_empty_like()\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.copy_empty_like\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.copy_empty_like\\n def copy_empty_like(self):\\n \\\"\\\"\\\"Return a copy of self with the same structure but empty.\\n\\n That structure includes:\\n * name and other metadata\\n * global phase\\n * duration\\n * all the qubits and clbits, including the registers.\\n\\n Returns:\\n DAGCircuit: An empty copy of self.\\n \\\"\\\"\\\"\\n target_dag = DAGCircuit()\\n target_dag.name = self.name\\n target_dag._global_phase = self._global_phase\\n target_dag.duration = self.duration\\n target_dag.unit = self.unit\\n target_dag.metadata = self.metadata\\n\\n target_dag.add_qubits(self.qubits)\\n target_dag.add_clbits(self.clbits)\\n\\n for qreg in self.qregs.values():\\n target_dag.add_qreg(qreg)\\n for creg in self.cregs.values():\\n target_dag.add_creg(creg)\\n\\n return target_dag\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_back\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_back\\n def apply_operation_back(self, op, qargs=None, cargs=None):\\n \\\"\\\"\\\"Apply an operation to the output of the circuit.\\n\\n Args:\\n op (qiskit.circuit.Instruction): the operation associated with the DAG node\\n qargs (list[Qubit]): qubits that op will be applied to\\n cargs (list[Clbit]): cbits that op will be applied to\\n Returns:\\n DAGOpNode: the node for the op that was added to the dag\\n\\n Raises:\\n DAGCircuitError: if a leaf node is connected to multiple outputs\\n\\n \\\"\\\"\\\"\\n qargs = qargs or []\\n cargs = cargs or []\\n\\n all_cbits = self._bits_in_condition(op.condition)\\n all_cbits = set(all_cbits).union(cargs)\\n\\n self._check_condition(op.name, op.condition)\\n self._check_bits(qargs, self.output_map)\\n self._check_bits(all_cbits, self.output_map)\\n\\n node_index = self._add_op_node(op, qargs, cargs)\\n\\n # Add new in-edges from predecessors of the output nodes to the\\n # operation node while deleting the old in-edges of the output nodes\\n # and adding new edges from the operation node to each output node\\n\\n al = [qargs, all_cbits]\\n self._multi_graph.insert_node_on_in_edges_multiple(\\n node_index, [self.output_map[q]._node_id for q in itertools.chain(*al)]\\n )\\n return self._multi_graph[node_index]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_front\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_front\\n def apply_operation_front(self, op, qargs, cargs):\\n \\\"\\\"\\\"Apply an operation to the input of the circuit.\\n\\n Args:\\n op (qiskit.circuit.Instruction): the operation associated with the DAG node\\n qargs (list[Qubit]): qubits that op will be applied to\\n cargs (list[Clbit]): cbits that op will be applied to\\n Returns:\\n DAGOpNode: the node for the op that was added to the dag\\n\\n Raises:\\n DAGCircuitError: if initial nodes connected to multiple out edges\\n \\\"\\\"\\\"\\n all_cbits = self._bits_in_condition(op.condition)\\n all_cbits.extend(cargs)\\n\\n self._check_condition(op.name, op.condition)\\n self._check_bits(qargs, self.input_map)\\n self._check_bits(all_cbits, self.input_map)\\n node_index = self._add_op_node(op, qargs, cargs)\\n\\n # Add new out-edges to successors of the input nodes from the\\n # operation node while deleting the old out-edges of the input nodes\\n # and adding new edges to the operation node from each input node\\n al = [qargs, all_cbits]\\n self._multi_graph.insert_node_on_out_edges_multiple(\\n node_index, [self.input_map[q]._node_id for q in itertools.chain(*al)]\\n )\\n return self._multi_graph[node_index]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_edgemap_registers\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_edgemap_registers\\n def _check_edgemap_registers(self, inbound_wires, inbound_regs):\\n \\\"\\\"\\\"Check that wiremap neither fragments nor leaves duplicate registers.\\n\\n 1. There are no fragmented registers. A register in keyregs\\n is fragmented if not all of its (qu)bits are renamed by edge_map.\\n 2. There are no duplicate registers. A register is duplicate if\\n it appears in both self and keyregs but not in edge_map.\\n\\n Args:\\n inbound_wires (list): a list of wires being mapped from the inbound dag\\n inbound_regs (list): a list from registers from the inbound dag\\n\\n Returns:\\n set(Register): the set of regs to add to self\\n\\n Raises:\\n DAGCircuitError: if the wiremap fragments, or duplicates exist\\n \\\"\\\"\\\"\\n add_regs = set()\\n reg_frag_chk = {}\\n\\n for inbound_reg in inbound_regs:\\n reg_frag_chk[inbound_reg] = {reg_bit: False for reg_bit in inbound_reg}\\n\\n for inbound_bit in inbound_wires:\\n for inbound_reg in inbound_regs:\\n if inbound_bit in inbound_reg:\\n reg_frag_chk[inbound_reg][inbound_bit] = True\\n break\\n\\n for inbound_reg, v in reg_frag_chk.items():\\n s = set(v.values())\\n if len(s) == 2:\\n raise DAGCircuitError(\\\"inbound_wires fragments reg %s\\\" % inbound_reg)\\n if s == {False}:\\n if inbound_reg.name in self.qregs or inbound_reg.name in self.cregs:\\n raise DAGCircuitError(\\\"unmapped duplicate reg %s\\\" % inbound_reg)\\n\\n # Add registers that appear only in inbound_regs\\n add_regs.add(inbound_reg)\\n\\n return add_regs\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_wiremap_validity\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_wiremap_validity\\n def _check_wiremap_validity(self, wire_map, keymap, valmap):\\n \\\"\\\"\\\"Check that the wiremap is consistent.\\n\\n Check that the wiremap refers to valid wires and that\\n those wires have consistent types.\\n\\n Args:\\n wire_map (dict): map from Bit in keymap to Bit in valmap\\n keymap (list): a list of wire_map keys\\n valmap (dict): a map whose keys are wire_map values\\n\\n Raises:\\n DAGCircuitError: if wire_map not valid\\n \\\"\\\"\\\"\\n for k, v in wire_map.items():\\n\\n if k not in keymap:\\n raise DAGCircuitError(\\\"invalid wire mapping key %s\\\" % k)\\n if v not in valmap:\\n raise DAGCircuitError(\\\"invalid wire mapping value %s\\\" % v)\\n # TODO Support mapping from AncillaQubit to Qubit, since AncillaQubits are mapped to\\n # Qubits upon being converted to an Instruction. Until this translation is fixed\\n # and Instructions have a concept of ancilla qubits, this fix is required.\\n if not (isinstance(k, type(v)) or isinstance(v, type(k))):\\n raise DAGCircuitError(f\\\"inconsistent wire_map at ({k},{v})\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._map_condition\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._map_condition\\n @staticmethod\\n def _map_condition(wire_map, condition, target_cregs):\\n \\\"\\\"\\\"Use the wire_map dict to change the condition tuple's creg name.\\n\\n Args:\\n wire_map (dict): a map from source wires to destination wires\\n condition (tuple or None): (ClassicalRegister,int)\\n target_cregs (list[ClassicalRegister]): List of all cregs in the\\n target circuit onto which the condition might possibly be mapped.\\n Returns:\\n tuple(ClassicalRegister,int): new condition\\n Raises:\\n DAGCircuitError: if condition register not in wire_map, or if\\n wire_map maps condition onto more than one creg, or if the\\n specified condition is not present in a classical register.\\n \\\"\\\"\\\"\\n\\n if condition is None:\\n new_condition = None\\n else:\\n # if there is a condition, map the condition bits to the\\n # composed cregs based on the wire_map\\n is_reg = False\\n if isinstance(condition[0], Clbit):\\n cond_creg = [condition[0]]\\n else:\\n cond_creg = condition[0]\\n is_reg = True\\n cond_val = condition[1]\\n new_cond_val = 0\\n new_creg = None\\n bits_in_condcreg = [bit for bit in wire_map if bit in cond_creg]\\n for bit in bits_in_condcreg:\\n if is_reg:\\n try:\\n candidate_creg = next(\\n creg for creg in target_cregs if wire_map[bit] in creg\\n )\\n except StopIteration as ex:\\n raise DAGCircuitError(\\n \\\"Did not find creg containing mapped clbit in conditional.\\\"\\n ) from ex\\n else:\\n # If cond is on a single Clbit then the candidate_creg is\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.compose\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.compose\\n def compose(self, other, qubits=None, clbits=None, front=False, inplace=True):\\n \\\"\\\"\\\"Compose the ``other`` circuit onto the output of this circuit.\\n\\n A subset of input wires of ``other`` are mapped\\n to a subset of output wires of this circuit.\\n\\n ``other`` can be narrower or of equal width to ``self``.\\n\\n Args:\\n other (DAGCircuit): circuit to compose with self\\n qubits (list[Qubit|int]): qubits of self to compose onto.\\n clbits (list[Clbit|int]): clbits of self to compose onto.\\n front (bool): If True, front composition will be performed (not implemented yet)\\n inplace (bool): If True, modify the object. Otherwise return composed circuit.\\n\\n Returns:\\n DAGCircuit: the composed dag (returns None if inplace==True).\\n\\n Raises:\\n DAGCircuitError: if ``other`` is wider or there are duplicate edge mappings.\\n \\\"\\\"\\\"\\n if front:\\n raise DAGCircuitError(\\\"Front composition not supported yet.\\\")\\n\\n if len(other.qubits) > len(self.qubits) or len(other.clbits) > len(self.clbits):\\n raise DAGCircuitError(\\n \\\"Trying to compose with another DAGCircuit which has more 'in' edges.\\\"\\n )\\n\\n # number of qubits and clbits must match number in circuit or None\\n identity_qubit_map = dict(zip(other.qubits, self.qubits))\\n identity_clbit_map = dict(zip(other.clbits, self.clbits))\\n if qubits is None:\\n qubit_map = identity_qubit_map\\n elif len(qubits) != len(other.qubits):\\n raise DAGCircuitError(\\n \\\"Number of items in qubits parameter does not\\\"\\n \\\" match number of qubits in the circuit.\\\"\\n )\\n else:\\n qubit_map = {\\n other.qubits[i]: (self.qubits[q] if isinstance(q, int) else q)\\n for i, q in enumerate(qubits)\\n }\\n if cl\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.reverse_ops\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.reverse_ops\\n def reverse_ops(self):\\n \\\"\\\"\\\"Reverse the operations in the ``self`` circuit.\\n\\n Returns:\\n DAGCircuit: the reversed dag.\\n \\\"\\\"\\\"\\n # TODO: speed up\\n # pylint: disable=cyclic-import\\n from qiskit.converters import dag_to_circuit, circuit_to_dag\\n\\n qc = dag_to_circuit(self)\\n reversed_qc = qc.reverse_ops()\\n reversed_dag = circuit_to_dag(reversed_qc)\\n return reversed_dag\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.idle_wires\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.idle_wires\\n def idle_wires(self, ignore=None):\\n \\\"\\\"\\\"Return idle wires.\\n\\n Args:\\n ignore (list(str)): List of node names to ignore. Default: []\\n\\n Yields:\\n Bit: Bit in idle wire.\\n\\n Raises:\\n DAGCircuitError: If the DAG is invalid\\n \\\"\\\"\\\"\\n if ignore is None:\\n ignore = set()\\n ignore_set = set(ignore)\\n for wire in self._wires:\\n if not ignore:\\n if self._is_wire_idle(wire):\\n yield wire\\n else:\\n for node in self.nodes_on_wire(wire, only_ops=True):\\n if node.op.name not in ignore_set:\\n # If we found an op node outside of ignore we can stop iterating over the wire\\n break\\n else:\\n yield wire\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.size\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.size\\n def size(self):\\n \\\"\\\"\\\"Return the number of operations.\\\"\\\"\\\"\\n return len(self._multi_graph) - 2 * len(self._wires)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.depth\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.depth\\n def depth(self):\\n \\\"\\\"\\\"Return the circuit depth.\\n Returns:\\n int: the circuit depth\\n Raises:\\n DAGCircuitError: if not a directed acyclic graph\\n \\\"\\\"\\\"\\n try:\\n depth = rx.dag_longest_path_length(self._multi_graph) - 1\\n except rx.DAGHasCycle as ex:\\n raise DAGCircuitError(\\\"not a DAG\\\") from ex\\n return depth if depth >= 0 else 0\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.width\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.width\\n def width(self):\\n \\\"\\\"\\\"Return the total number of qubits + clbits used by the circuit.\\n This function formerly returned the number of qubits by the calculation\\n return len(self._wires) - self.num_clbits()\\n but was changed by issue #2564 to return number of qubits + clbits\\n with the new function DAGCircuit.num_qubits replacing the former\\n semantic of DAGCircuit.width().\\n \\\"\\\"\\\"\\n return len(self._wires)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":279,"cells":{"instance_id":{"kind":"string","value":"pandas-dev__pandas-18629"},"repo":{"kind":"string","value":"pandas-dev/pandas"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Support non-default indexes in to_parquet\nCalling `to_parquet` on a DataFrame with a non-default index results in the error below:\r\n```python-traceback\r\nValueError: parquet does not support serializing a non-default index for the index; you can .reset_index() to make the index into column(s)\r\n```\r\nWhile, you can work-around this by calling `reset_index()` as the message says, this loses the information about what columns made up the index so means you can't round-trip a DataFrame with a non-default index.\r\n\r\n\r\n#### Output of ``pd.show_versions()``\r\n\r\n<details>\r\n\r\n```\r\nINSTALLED VERSIONS\r\n------------------\r\ncommit: None\r\npython: 3.6.3.final.0\r\npython-bits: 64\r\nOS: Windows\r\nOS-release: 10\r\nmachine: AMD64\r\nprocessor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel\r\nbyteorder: little\r\nLC_ALL: None\r\nLANG: None\r\nLOCALE: None.None\r\n\r\npandas: 0.21.0\r\npytest: 3.2.5\r\npip: 9.0.1\r\nsetuptools: 37.0.0\r\nCython: 0.27.3\r\nnumpy: 1.13.3\r\nscipy: 1.0.0\r\npyarrow: 0.7.1\r\nxarray: None\r\nIPython: 6.2.1\r\nsphinx: 1.6.5\r\npatsy: 0.4.1\r\ndateutil: 2.6.1\r\npytz: 2017.3\r\nblosc: 1.4.4\r\n```\r\n</details>\r\n\nSupport non-default indexes in to_parquet\nCalling `to_parquet` on a DataFrame with a non-default index results in the error below:\r\n```python-traceback\r\nValueError: parquet does not support serializing a non-default index for the index; you can .reset_index() to make the index into column(s)\r\n```\r\nWhile, you can work-around this by calling `reset_index()` as the message says, this loses the information about what columns made up the index so means you can't round-trip a DataFrame with a non-default index.\r\n\r\n\r\n#### Output of ``pd.show_versions()``\r\n\r\n<details>\r\n\r\n```\r\nINSTALLED VERSIONS\r\n------------------\r\ncommit: None\r\npython: 3.6.3.final.0\r\npython-bits: 64\r\nOS: Windows\r\nOS-release: 10\r\nmachine: AMD64\r\nprocessor: Intel64 Family 6 Model 142 Stepping 9, GenuineIntel\r\nbyteorder: little\r\nLC_ALL: None\r\nLANG: None\r\nLOCALE: None.None\r\n\r\npandas: 0.21.0\r\npytest: 3.2.5\r\npip: 9.0.1\r\nsetuptools: 37.0.0\r\nCython: 0.27.3\r\nnumpy: 1.13.3\r\nscipy: 1.0.0\r\npyarrow: 0.7.1\r\nxarray: None\r\nIPython: 6.2.1\r\nsphinx: 1.6.5\r\npatsy: 0.4.1\r\ndateutil: 2.6.1\r\npytz: 2017.3\r\nblosc: 1.4.4\r\n```\r\n</details>\r\n\n"},"gold_files":{"kind":"list like","value":["pandas/io/parquet.py"],"string":"[\n \"pandas/io/parquet.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pandas/io/parquet.py::<module>","pandas/io/parquet.py::FastParquetImpl.__init__","pandas/io/parquet.py::FastParquetImpl.write","pandas/io/parquet.py::PyArrowImpl.__init__","pandas/io/parquet.py::PyArrowImpl.read","pandas/io/parquet.py::PyArrowImpl.write","pandas/io/parquet.py::get_engine","pandas/io/parquet.py::to_parquet"],"string":"[\n \"pandas/io/parquet.py::<module>\",\n \"pandas/io/parquet.py::FastParquetImpl.__init__\",\n \"pandas/io/parquet.py::FastParquetImpl.write\",\n \"pandas/io/parquet.py::PyArrowImpl.__init__\",\n \"pandas/io/parquet.py::PyArrowImpl.read\",\n \"pandas/io/parquet.py::PyArrowImpl.write\",\n \"pandas/io/parquet.py::get_engine\",\n \"pandas/io/parquet.py::to_parquet\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"pandas/io/parquet.py","text":"pandas/io/parquet.py\ndef get_engine(engine) # return our implementation\nclass PyArrowImpl\n def __init__(self)\n def write(self, df, path, compression='snappy', coerce_timestamps='ms', **kwargs)\n def read(self, path, columns=None, **kwargs)\nclass FastParquetImpl\n def __init__(self)\n def write(self, df, path, compression='snappy', **kwargs)\n def read(self, path, columns=None, **kwargs)\ndef to_parquet(df, path, engine='auto', compression='snappy', **kwargs) # Write a DataFrame to the parquet format.\ndef read_parquet(path, engine='auto', columns=None, **kwargs) # Load a parquet object from the file path, returning a DataFrame.","label":1},{"cid":"pandas/util/_print_versions.py","text":"pandas/util/_print_versions.py\ndef get_sys_info() # Returns system information as a dict\ndef show_versions(as_json=False)\ndef main()","label":0},{"cid":"versioneer.py","text":"versioneer.py\nclass VersioneerConfig\ndef get_root()\ndef get_config_from_root(root)\n def get(parser, name)\nclass NotThisMethod\ndef register_vcs_handler(vcs, method)\n def decorate(f)\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False)\ndef git_get_keywords(versionfile_abs)\ndef git_versions_from_keywords(keywords, tag_prefix, verbose)\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command)\ndef do_vcs_install(manifest_in, versionfile_source, ipy)\ndef versions_from_parentdir(parentdir_prefix, root, verbose)\ndef versions_from_file(filename)\ndef write_to_version_file(filename, versions)\ndef plus_or_dot(pieces)\ndef render_pep440(pieces)\ndef render_pep440_pre(pieces)\ndef render_pep440_post(pieces)\ndef render_pep440_old(pieces)\ndef render_git_describe(pieces)\ndef render_git_describe_long(pieces)\ndef render(pieces, style)\nclass VersioneerBadRootError\ndef get_versions(verbose=False)\ndef get_version()\ndef get_cmdclass()\n class cmd_version\n def initialize_options(self)\n def finalize_options(self)\n def run(self)\n class cmd_build_py\n def run(self)\n class cmd_sdist\n def run(self)\n def make_release_tree(self, base_dir, files)\ndef do_setup()\ndef scan_setup_py()","label":0}],"string":"[\n {\n \"cid\": \"pandas/io/parquet.py\",\n \"text\": \"pandas/io/parquet.py\\ndef get_engine(engine) # return our implementation\\nclass PyArrowImpl\\n def __init__(self)\\n def write(self, df, path, compression='snappy', coerce_timestamps='ms', **kwargs)\\n def read(self, path, columns=None, **kwargs)\\nclass FastParquetImpl\\n def __init__(self)\\n def write(self, df, path, compression='snappy', **kwargs)\\n def read(self, path, columns=None, **kwargs)\\ndef to_parquet(df, path, engine='auto', compression='snappy', **kwargs) # Write a DataFrame to the parquet format.\\ndef read_parquet(path, engine='auto', columns=None, **kwargs) # Load a parquet object from the file path, returning a DataFrame.\",\n \"label\": 1\n },\n {\n \"cid\": \"pandas/util/_print_versions.py\",\n \"text\": \"pandas/util/_print_versions.py\\ndef get_sys_info() # Returns system information as a dict\\ndef show_versions(as_json=False)\\ndef main()\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py\",\n \"text\": \"versioneer.py\\nclass VersioneerConfig\\ndef get_root()\\ndef get_config_from_root(root)\\n def get(parser, name)\\nclass NotThisMethod\\ndef register_vcs_handler(vcs, method)\\n def decorate(f)\\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False)\\ndef git_get_keywords(versionfile_abs)\\ndef git_versions_from_keywords(keywords, tag_prefix, verbose)\\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command)\\ndef do_vcs_install(manifest_in, versionfile_source, ipy)\\ndef versions_from_parentdir(parentdir_prefix, root, verbose)\\ndef versions_from_file(filename)\\ndef write_to_version_file(filename, versions)\\ndef plus_or_dot(pieces)\\ndef render_pep440(pieces)\\ndef render_pep440_pre(pieces)\\ndef render_pep440_post(pieces)\\ndef render_pep440_old(pieces)\\ndef render_git_describe(pieces)\\ndef render_git_describe_long(pieces)\\ndef render(pieces, style)\\nclass VersioneerBadRootError\\ndef get_versions(verbose=False)\\ndef get_version()\\ndef get_cmdclass()\\n class cmd_version\\n def initialize_options(self)\\n def finalize_options(self)\\n def run(self)\\n class cmd_build_py\\n def run(self)\\n class cmd_sdist\\n def run(self)\\n def make_release_tree(self, base_dir, files)\\ndef do_setup()\\ndef scan_setup_py()\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"pandas/io/parquet.py::get_engine","text":"pandas/io/parquet.py::get_engine\ndef get_engine(engine):\n \"\"\" return our implementation \"\"\"\n\n if engine == 'auto':\n engine = get_option('io.parquet.engine')\n\n if engine == 'auto':\n # try engines in this order\n try:\n return PyArrowImpl()\n except ImportError:\n pass\n\n try:\n return FastParquetImpl()\n except ImportError:\n pass\n\n raise ImportError(\"Unable to find a usable engine; \"\n \"tried using: 'pyarrow', 'fastparquet'.\\n\"\n \"pyarrow or fastparquet is required for parquet \"\n \"support\")\n\n if engine not in ['pyarrow', 'fastparquet']:\n raise ValueError(\"engine must be one of 'pyarrow', 'fastparquet'\")\n\n if engine == 'pyarrow':\n return PyArrowImpl()\n elif engine == 'fastparquet':\n return FastParquetImpl()","label":1},{"cid":"pandas/io/parquet.py::PyArrowImpl.__init__","text":"pandas/io/parquet.py::PyArrowImpl.__init__\n def __init__(self):\n # since pandas is a dependency of pyarrow\n # we need to import on first use\n\n try:\n import pyarrow\n import pyarrow.parquet\n except ImportError:\n raise ImportError(\"pyarrow is required for parquet support\\n\\n\"\n \"you can install via conda\\n\"\n \"conda install pyarrow -c conda-forge\\n\"\n \"\\nor via pip\\n\"\n \"pip install -U pyarrow\\n\")\n\n if LooseVersion(pyarrow.__version__) < LooseVersion('0.4.1'):\n raise ImportError(\"pyarrow >= 0.4.1 is required for parquet\"\n \"support\\n\\n\"\n \"you can install via conda\\n\"\n \"conda install pyarrow -c conda-forge\\n\"\n \"\\nor via pip\\n\"\n \"pip install -U pyarrow\\n\")\n\n self._pyarrow_lt_050 = (LooseVersion(pyarrow.__version__) <\n LooseVersion('0.5.0'))\n self._pyarrow_lt_060 = (LooseVersion(pyarrow.__version__) <\n LooseVersion('0.6.0'))\n self.api = pyarrow","label":1},{"cid":"pandas/io/parquet.py::PyArrowImpl.write","text":"pandas/io/parquet.py::PyArrowImpl.write\n def write(self, df, path, compression='snappy',\n coerce_timestamps='ms', **kwargs):\n path, _, _ = get_filepath_or_buffer(path)\n if self._pyarrow_lt_060:\n table = self.api.Table.from_pandas(df, timestamps_to_ms=True)\n self.api.parquet.write_table(\n table, path, compression=compression, **kwargs)\n\n else:\n table = self.api.Table.from_pandas(df)\n self.api.parquet.write_table(\n table, path, compression=compression,\n coerce_timestamps=coerce_timestamps, **kwargs)","label":1},{"cid":"pandas/io/parquet.py::PyArrowImpl.read","text":"pandas/io/parquet.py::PyArrowImpl.read\n def read(self, path, columns=None, **kwargs):\n path, _, _ = get_filepath_or_buffer(path)\n return self.api.parquet.read_table(path, columns=columns,\n **kwargs).to_pandas()","label":1},{"cid":"pandas/io/parquet.py::FastParquetImpl.__init__","text":"pandas/io/parquet.py::FastParquetImpl.__init__\n def __init__(self):\n # since pandas is a dependency of fastparquet\n # we need to import on first use\n\n try:\n import fastparquet\n except ImportError:\n raise ImportError(\"fastparquet is required for parquet support\\n\\n\"\n \"you can install via conda\\n\"\n \"conda install fastparquet -c conda-forge\\n\"\n \"\\nor via pip\\n\"\n \"pip install -U fastparquet\")\n\n if LooseVersion(fastparquet.__version__) < LooseVersion('0.1.0'):\n raise ImportError(\"fastparquet >= 0.1.0 is required for parquet \"\n \"support\\n\\n\"\n \"you can install via conda\\n\"\n \"conda install fastparquet -c conda-forge\\n\"\n \"\\nor via pip\\n\"\n \"pip install -U fastparquet\")\n\n self.api = fastparquet","label":1},{"cid":"pandas/io/parquet.py::FastParquetImpl.write","text":"pandas/io/parquet.py::FastParquetImpl.write\n def write(self, df, path, compression='snappy', **kwargs):\n # thriftpy/protocol/compact.py:339:\n # DeprecationWarning: tostring() is deprecated.\n # Use tobytes() instead.\n path, _, _ = get_filepath_or_buffer(path)\n with catch_warnings(record=True):\n self.api.write(path, df,\n compression=compression, **kwargs)","label":1},{"cid":"pandas/io/parquet.py::FastParquetImpl.read","text":"pandas/io/parquet.py::FastParquetImpl.read\n def read(self, path, columns=None, **kwargs):\n path, _, _ = get_filepath_or_buffer(path)\n return self.api.ParquetFile(path).to_pandas(columns=columns, **kwargs)","label":0},{"cid":"pandas/io/parquet.py::to_parquet","text":"pandas/io/parquet.py::to_parquet\ndef to_parquet(df, path, engine='auto', compression='snappy', **kwargs):\n \"\"\"\n Write a DataFrame to the parquet format.\n\n Parameters\n ----------\n df : DataFrame\n path : string\n File path\n engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'\n Parquet reader library to use. If 'auto', then the option\n 'io.parquet.engine' is used. If 'auto', then the first\n library to be installed is used.\n compression : str, optional, default 'snappy'\n compression method, includes {'gzip', 'snappy', 'brotli'}\n kwargs\n Additional keyword arguments passed to the engine\n \"\"\"\n\n impl = get_engine(engine)\n\n if not isinstance(df, DataFrame):\n raise ValueError(\"to_parquet only support IO with DataFrames\")\n\n valid_types = {'string', 'unicode'}\n\n # validate index\n # --------------\n\n # validate that we have only a default index\n # raise on anything else as we don't serialize the index\n\n if not isinstance(df.index, Int64Index):\n raise ValueError(\"parquet does not support serializing {} \"\n \"for the index; you can .reset_index()\"\n \"to make the index into column(s)\".format(\n type(df.index)))\n\n if not df.index.equals(RangeIndex.from_range(range(len(df)))):\n raise ValueError(\"parquet does not support serializing a \"\n \"non-default index for the index; you \"\n \"can .reset_index() to make the index \"\n \"into column(s)\")\n\n if df.index.name is not None:\n raise ValueError(\"parquet does not serialize index meta-data on a \"\n \"default index\")\n\n # validate columns\n # ----------------\n\n # must have value column names (strings only)\n if df.columns.inferred_type not in valid_types:\n raise ValueError(\"parquet must have string column names\")\n\n return impl.write","label":1},{"cid":"pandas/io/parquet.py::read_parquet","text":"pandas/io/parquet.py::read_parquet\ndef read_parquet(path, engine='auto', columns=None, **kwargs):\n \"\"\"\n Load a parquet object from the file path, returning a DataFrame.\n\n .. versionadded 0.21.0\n\n Parameters\n ----------\n path : string\n File path\n columns: list, default=None\n If not None, only these columns will be read from the file.\n\n .. versionadded 0.21.1\n engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'\n Parquet reader library to use. If 'auto', then the option\n 'io.parquet.engine' is used. If 'auto', then the first\n library to be installed is used.\n kwargs are passed to the engine\n\n Returns\n -------\n DataFrame\n\n \"\"\"\n\n impl = get_engine(engine)\n return impl.read(path, columns=columns, **kwargs)","label":0},{"cid":"pandas/util/_print_versions.py::get_sys_info","text":"pandas/util/_print_versions.py::get_sys_info\ndef get_sys_info():\n \"Returns system information as a dict\"\n\n blob = []\n\n # get full commit hash\n commit = None\n if os.path.isdir(\".git\") and os.path.isdir(\"pandas\"):\n try:\n pipe = subprocess.Popen('git log --format=\"%H\" -n 1'.split(\" \"),\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n so, serr = pipe.communicate()\n except:\n pass\n else:\n if pipe.returncode == 0:\n commit = so\n try:\n commit = so.decode('utf-8')\n except ValueError:\n pass\n commit = commit.strip().strip('\"')\n\n blob.append(('commit', commit))\n\n try:\n (sysname, nodename, release,\n version, machine, processor) = platform.uname()\n blob.extend([\n (\"python\", '.'.join(map(str, sys.version_info))),\n (\"python-bits\", struct.calcsize(\"P\") * 8),\n (\"OS\", \"{sysname}\".format(sysname=sysname)),\n (\"OS-release\", \"{release}\".format(release=release)),\n # (\"Version\", \"{version}\".format(version=version)),\n (\"machine\", \"{machine}\".format(machine=machine)),\n (\"processor\", \"{processor}\".format(processor=processor)),\n (\"byteorder\", \"{byteorder}\".format(byteorder=sys.byteorder)),\n (\"LC_ALL\", \"{lc}\".format(lc=os.environ.get('LC_ALL', \"None\"))),\n (\"LANG\", \"{lang}\".format(lang=os.environ.get('LANG', \"None\"))),\n (\"LOCALE\", '.'.join(map(str, locale.getlocale()))),\n ])\n except:\n pass\n\n return blob","label":0},{"cid":"pandas/util/_print_versions.py::show_versions","text":"pandas/util/_print_versions.py::show_versions\ndef show_versions(as_json=False):\n sys_info = get_sys_info()\n\n deps = [\n # (MODULE_NAME, f(mod) -> mod version)\n (\"pandas\", lambda mod: mod.__version__),\n (\"pytest\", lambda mod: mod.__version__),\n (\"pip\", lambda mod: mod.__version__),\n (\"setuptools\", lambda mod: mod.__version__),\n (\"Cython\", lambda mod: mod.__version__),\n (\"numpy\", lambda mod: mod.version.version),\n (\"scipy\", lambda mod: mod.version.version),\n (\"pyarrow\", lambda mod: mod.__version__),\n (\"xarray\", lambda mod: mod.__version__),\n (\"IPython\", lambda mod: mod.__version__),\n (\"sphinx\", lambda mod: mod.__version__),\n (\"patsy\", lambda mod: mod.__version__),\n (\"dateutil\", lambda mod: mod.__version__),\n (\"pytz\", lambda mod: mod.VERSION),\n (\"blosc\", lambda mod: mod.__version__),\n (\"bottleneck\", lambda mod: mod.__version__),\n (\"tables\", lambda mod: mod.__version__),\n (\"numexpr\", lambda mod: mod.__version__),\n (\"feather\", lambda mod: mod.__version__),\n (\"matplotlib\", lambda mod: mod.__version__),\n (\"openpyxl\", lambda mod: mod.__version__),\n (\"xlrd\", lambda mod: mod.__VERSION__),\n (\"xlwt\", lambda mod: mod.__VERSION__),\n (\"xlsxwriter\", lambda mod: mod.__version__),\n (\"lxml\", lambda mod: mod.etree.__version__),\n (\"bs4\", lambda mod: mod.__version__),\n (\"html5lib\", lambda mod: mod.__version__),\n (\"sqlalchemy\", lambda mod: mod.__version__),\n (\"pymysql\", lambda mod: mod.__version__),\n (\"psycopg2\", lambda mod: mod.__version__),\n (\"jinja2\", lambda mod: mod.__version__),\n (\"s3fs\", lambda mod: mod.__version__),\n (\"fastparquet\", lambda mod: mod.__version__),\n (\"pandas_gbq\", lambda mod: mod.__version__),\n (\"pandas_datareader\", lambda mod: mod.__version__),\n ]\n\n deps_blob = list()\n for (modname, ver_f) in deps:\n ","label":0},{"cid":"pandas/util/_print_versions.py::main","text":"pandas/util/_print_versions.py::main\ndef main():\n from optparse import OptionParser\n parser = OptionParser()\n parser.add_option(\"-j\", \"--json\", metavar=\"FILE\", nargs=1,\n help=\"Save output as JSON into file, pass in \"\n \"'-' to output to stdout\")\n\n (options, args) = parser.parse_args()\n\n if options.json == \"-\":\n options.json = True\n\n show_versions(as_json=options.json)\n\n return 0","label":0},{"cid":"versioneer.py::get_root","text":"versioneer.py::get_root\ndef get_root():\n # we require that all commands are run from the project root, i.e. the\n # directory that contains setup.py, setup.cfg, and versioneer.py .\n root = os.path.realpath(os.path.abspath(os.getcwd()))\n setup_py = os.path.join(root, \"setup.py\")\n versioneer_py = os.path.join(root, \"versioneer.py\")\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n # allow 'python path/to/setup.py COMMAND'\n root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))\n setup_py = os.path.join(root, \"setup.py\")\n versioneer_py = os.path.join(root, \"versioneer.py\")\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n err = (\"Versioneer was unable to run the project root directory. \"\n \"Versioneer requires setup.py to be executed from \"\n \"its immediate directory (like 'python setup.py COMMAND'), \"\n \"or in a way that lets it use sys.argv[0] to find the root \"\n \"(like 'python path/to/setup.py COMMAND').\")\n raise VersioneerBadRootError(err)\n try:\n # Certain runtime workflows (setup.py install/develop in a setuptools\n # tree) execute all dependencies in a single python process, so\n # \"versioneer\" may be imported multiple times, and python's shared\n # module-import table will cache the first one. So we can't use\n # os.path.dirname(__file__), as that will find whichever\n # versioneer.py was first imported, even in later projects.\n me = os.path.realpath(os.path.abspath(__file__))\n if os.path.splitext(me)[0] != os.path.splitext(versioneer_py)[0]:\n print(\"Warning: build in %s is using versioneer.py from %s\"\n % (os.path.dirname(me), versioneer_py))\n except NameError:\n pass\n return root","label":0},{"cid":"versioneer.py::get_config_from_root","text":"versioneer.py::get_config_from_root\ndef get_config_from_root(root):\n # This might raise EnvironmentError (if setup.cfg is missing), or\n # configparser.NoSectionError (if it lacks a [versioneer] section), or\n # configparser.NoOptionError (if it lacks \"VCS=\"). See the docstring at\n # the top of versioneer.py for instructions on writing your setup.cfg .\n setup_cfg = os.path.join(root, \"setup.cfg\")\n parser = configparser.SafeConfigParser()\n with open(setup_cfg, \"r\") as f:\n parser.readfp(f)\n VCS = parser.get(\"versioneer\", \"VCS\") # mandatory\n\n def get(parser, name):\n if parser.has_option(\"versioneer\", name):\n return parser.get(\"versioneer\", name)\n return None\n cfg = VersioneerConfig()\n cfg.VCS = VCS\n cfg.style = get(parser, \"style\") or \"\"\n cfg.versionfile_source = get(parser, \"versionfile_source\")\n cfg.versionfile_build = get(parser, \"versionfile_build\")\n cfg.tag_prefix = get(parser, \"tag_prefix\")\n cfg.parentdir_prefix = get(parser, \"parentdir_prefix\")\n cfg.verbose = get(parser, \"verbose\")\n return cfg","label":0},{"cid":"versioneer.py::get_config_from_root.get","text":"versioneer.py::get_config_from_root.get\n def get(parser, name):\n if parser.has_option(\"versioneer\", name):\n return parser.get(\"versioneer\", name)\n return None","label":0},{"cid":"versioneer.py::register_vcs_handler","text":"versioneer.py::register_vcs_handler\ndef register_vcs_handler(vcs, method): # decorator\n def decorate(f):\n if vcs not in HANDLERS:\n HANDLERS[vcs] = {}\n HANDLERS[vcs][method] = f\n return f\n return decorate","label":0},{"cid":"versioneer.py::register_vcs_handler.decorate","text":"versioneer.py::register_vcs_handler.decorate\n def decorate(f):\n if vcs not in HANDLERS:\n HANDLERS[vcs] = {}\n HANDLERS[vcs][method] = f\n return f","label":0},{"cid":"versioneer.py::run_command","text":"versioneer.py::run_command\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):\n assert isinstance(commands, list)\n p = None\n for c in commands:\n try:\n dispcmd = str([c] + args)\n # remember shell=False, so use git.cmd on windows, not just git\n p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE,\n stderr=(subprocess.PIPE if hide_stderr\n else None))\n break\n except EnvironmentError:\n e = sys.exc_info()[1]\n if e.errno == errno.ENOENT:\n continue\n if verbose:\n print(\"unable to run %s\" % dispcmd)\n print(e)\n return None\n else:\n if verbose:\n print(\"unable to find command, tried %s\" % (commands,))\n return None\n stdout = p.communicate()[0].strip()\n if sys.version_info[0] >= 3:\n stdout = stdout.decode()\n if p.returncode != 0:\n if verbose:\n print(\"unable to run %s (error)\" % dispcmd)\n return None\n return stdout","label":0},{"cid":"versioneer.py::git_get_keywords","text":"versioneer.py::git_get_keywords\n@register_vcs_handler(\"git\", \"get_keywords\")\ndef git_get_keywords(versionfile_abs):\n # the code embedded in _version.py can just fetch the value of these\n # keywords. When used from setup.py, we don't want to import _version.py,\n # so we do it with a regexp instead. This function is not used from\n # _version.py.\n keywords = {}\n try:\n f = open(versionfile_abs, \"r\")\n for line in f.readlines():\n if line.strip().startswith(\"git_refnames =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"refnames\"] = mo.group(1)\n if line.strip().startswith(\"git_full =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"full\"] = mo.group(1)\n f.close()\n except EnvironmentError:\n pass\n return keywords","label":0},{"cid":"versioneer.py::git_versions_from_keywords","text":"versioneer.py::git_versions_from_keywords\n@register_vcs_handler(\"git\", \"keywords\")\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\n if not keywords:\n raise NotThisMethod(\"no keywords at all, weird\")\n refnames = keywords[\"refnames\"].strip()\n if refnames.startswith(\"$Format\"):\n if verbose:\n print(\"keywords are unexpanded, not using\")\n raise NotThisMethod(\"unexpanded keywords, not a git-archive tarball\")\n refs = {r.strip() for r in refnames.strip(\"()\").split(\",\")}\n # starting in git-1.8.3, tags are listed as \"tag: foo-1.0\" instead of\n # just \"foo-1.0\". If we see a \"tag: \" prefix, prefer those.\n TAG = \"tag: \"\n tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}\n if not tags:\n # Either we're using git < 1.8.3, or there really are no tags. We use\n # a heuristic: assume all version tags have a digit. The old git %d\n # expansion behaves like git log --decorate=short and strips out the\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\n # between branches and tags. By ignoring refnames without digits, we\n # filter out many common branch names like \"release\" and\n # \"stabilization\", as well as \"HEAD\" and \"master\".\n tags = {r for r in refs if re.search(r'\\d', r)}\n if verbose:\n print(\"discarding '%s', no digits\" % \",\".join(refs-tags))\n if verbose:\n print(\"likely tags: %s\" % \",\".join(sorted(tags)))\n for ref in sorted(tags):\n # sorting will prefer e.g. \"2.0\" over \"2.0rc1\"\n if ref.startswith(tag_prefix):\n r = ref[len(tag_prefix):]\n if verbose:\n print(\"picking %s\" % r)\n return {\"version\": r,\n \"full-revisionid\": keywords[\"full\"].strip(),\n \"dirty\": False, \"error\": None\n }\n # no suitable tags, so version is \"0+unknown\", but full hex is still there\n if verbose:\n print(\"no suit","label":0},{"cid":"versioneer.py::git_pieces_from_vcs","text":"versioneer.py::git_pieces_from_vcs\n@register_vcs_handler(\"git\", \"pieces_from_vcs\")\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):\n # this runs 'git' from the root of the source tree. This only gets called\n # if the git-archive 'subst' keywords were *not* expanded, and\n # _version.py hasn't already been rewritten with a short version string,\n # meaning we're inside a checked out source tree.\n\n if not os.path.exists(os.path.join(root, \".git\")):\n if verbose:\n print(\"no .git in %s\" % root)\n raise NotThisMethod(\"no .git directory\")\n\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n # if there is a tag, this yields TAG-NUM-gHEX[-dirty]\n # if there are no tags, this yields HEX[-dirty] (no NUM)\n describe_out = run_command(GITS, [\"describe\", \"--tags\", \"--dirty\",\n \"--always\", \"--long\"],\n cwd=root)\n # --long was added in git-1.5.5\n if describe_out is None:\n raise NotThisMethod(\"'git describe' failed\")\n describe_out = describe_out.strip()\n full_out = run_command(GITS, [\"rev-parse\", \"HEAD\"], cwd=root)\n if full_out is None:\n raise NotThisMethod(\"'git rev-parse' failed\")\n full_out = full_out.strip()\n\n pieces = {}\n pieces[\"long\"] = full_out\n pieces[\"short\"] = full_out[:7] # maybe improved later\n pieces[\"error\"] = None\n\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\n # TAG might have hyphens.\n git_describe = describe_out\n\n # look for -dirty suffix\n dirty = git_describe.endswith(\"-dirty\")\n pieces[\"dirty\"] = dirty\n if dirty:\n git_describe = git_describe[:git_describe.rindex(\"-dirty\")]\n\n # now we have TAG-NUM-gHEX or HEX\n\n if \"-\" in git_describe:\n # TAG-NUM-gHEX\n mo = re.search(r'^(.+)-(\\d+)-g([0-9a-f]+)$', git_describe)\n if not mo:\n # unparseable. Maybe git-describe","label":0},{"cid":"versioneer.py::do_vcs_install","text":"versioneer.py::do_vcs_install\ndef do_vcs_install(manifest_in, versionfile_source, ipy):\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n files = [manifest_in, versionfile_source]\n if ipy:\n files.append(ipy)\n try:\n me = __file__\n if me.endswith(\".pyc\") or me.endswith(\".pyo\"):\n me = os.path.splitext(me)[0] + \".py\"\n versioneer_file = os.path.relpath(me)\n except NameError:\n versioneer_file = \"versioneer.py\"\n files.append(versioneer_file)\n present = False\n try:\n f = open(\".gitattributes\", \"r\")\n for line in f.readlines():\n if line.strip().startswith(versionfile_source):\n if \"export-subst\" in line.strip().split()[1:]:\n present = True\n f.close()\n except EnvironmentError:\n pass\n if not present:\n f = open(\".gitattributes\", \"a+\")\n f.write(\"%s export-subst\\n\" % versionfile_source)\n f.close()\n files.append(\".gitattributes\")\n run_command(GITS, [\"add\", \"--\"] + files)","label":0},{"cid":"versioneer.py::versions_from_parentdir","text":"versioneer.py::versions_from_parentdir\ndef versions_from_parentdir(parentdir_prefix, root, verbose):\n # Source tarballs conventionally unpack into a directory that includes\n # both the project name and a version string.\n dirname = os.path.basename(root)\n if not dirname.startswith(parentdir_prefix):\n if verbose:\n print(\"guessing rootdir is '%s', but '%s' doesn't start with \"\n \"prefix '%s'\" % (root, dirname, parentdir_prefix))\n raise NotThisMethod(\"rootdir doesn't start with parentdir_prefix\")\n return {\"version\": dirname[len(parentdir_prefix):],\n \"full-revisionid\": None,\n \"dirty\": False, \"error\": None}","label":0},{"cid":"versioneer.py::versions_from_file","text":"versioneer.py::versions_from_file\ndef versions_from_file(filename):\n try:\n with open(filename) as f:\n contents = f.read()\n except EnvironmentError:\n raise NotThisMethod(\"unable to read _version.py\")\n mo = re.search(r\"version_json = '''\\n(.*)''' # END VERSION_JSON\",\n contents, re.M | re.S)\n if not mo:\n raise NotThisMethod(\"no version_json in _version.py\")\n return json.loads(mo.group(1))","label":0},{"cid":"versioneer.py::write_to_version_file","text":"versioneer.py::write_to_version_file\ndef write_to_version_file(filename, versions):\n os.unlink(filename)\n contents = json.dumps(versions, sort_keys=True,\n indent=1, separators=(\",\", \": \"))\n with open(filename, \"w\") as f:\n f.write(SHORT_VERSION_PY % contents)\n\n print(\"set %s to '%s'\" % (filename, versions[\"version\"]))","label":0},{"cid":"versioneer.py::plus_or_dot","text":"versioneer.py::plus_or_dot\ndef plus_or_dot(pieces):\n if \"+\" in pieces.get(\"closest-tag\", \"\"):\n return \".\"\n return \"+\"","label":0},{"cid":"versioneer.py::render_pep440","text":"versioneer.py::render_pep440\ndef render_pep440(pieces):\n # now build up version string, with post-release \"local version\n # identifier\". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\n # get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\n\n # exceptions:\n # 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\n\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += plus_or_dot(pieces)\n rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0+untagged.%d.g%s\" % (pieces[\"distance\"],\n pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered","label":0},{"cid":"versioneer.py::render_pep440_pre","text":"versioneer.py::render_pep440_pre\ndef render_pep440_pre(pieces):\n # TAG[.post.devDISTANCE] . No -dirty\n\n # exceptions:\n # 1: no tags. 0.post.devDISTANCE\n\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \".post.dev%d\" % pieces[\"distance\"]\n else:\n # exception #1\n rendered = \"0.post.dev%d\" % pieces[\"distance\"]\n return rendered","label":0},{"cid":"versioneer.py::render_pep440_post","text":"versioneer.py::render_pep440_post\ndef render_pep440_post(pieces):\n # TAG[.postDISTANCE[.dev0]+gHEX] . The \".dev0\" means dirty. Note that\n # .dev0 sorts backwards (a dirty tree will appear \"older\" than the\n # corresponding clean one), but you shouldn't be releasing software with\n # -dirty anyways.\n\n # exceptions:\n # 1: no tags. 0.postDISTANCE[.dev0]\n\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%s\" % pieces[\"short\"]\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += \"+g%s\" % pieces[\"short\"]\n return rendered","label":0},{"cid":"versioneer.py::render_pep440_old","text":"versioneer.py::render_pep440_old\ndef render_pep440_old(pieces):\n # TAG[.postDISTANCE[.dev0]] . The \".dev0\" means dirty.\n\n # exceptions:\n # 1: no tags. 0.postDISTANCE[.dev0]\n\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n return rendered","label":0},{"cid":"versioneer.py::render_git_describe","text":"versioneer.py::render_git_describe\ndef render_git_describe(pieces):\n # TAG[-DISTANCE-gHEX][-dirty], like 'git describe --tags --dirty\n # --always'\n\n # exceptions:\n # 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered","label":0},{"cid":"versioneer.py::render_git_describe_long","text":"versioneer.py::render_git_describe_long\ndef render_git_describe_long(pieces):\n # TAG-DISTANCE-gHEX[-dirty], like 'git describe --tags --dirty\n # --always -long'. The distance/hash is unconditional.\n\n # exceptions:\n # 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered","label":0},{"cid":"versioneer.py::render","text":"versioneer.py::render\ndef render(pieces, style):\n if pieces[\"error\"]:\n return {\"version\": \"unknown\",\n \"full-revisionid\": pieces.get(\"long\"),\n \"dirty\": None,\n \"error\": pieces[\"error\"]}\n\n if not style or style == \"default\":\n style = \"pep440\" # the default\n\n if style == \"pep440\":\n rendered = render_pep440(pieces)\n elif style == \"pep440-pre\":\n rendered = render_pep440_pre(pieces)\n elif style == \"pep440-post\":\n rendered = render_pep440_post(pieces)\n elif style == \"pep440-old\":\n rendered = render_pep440_old(pieces)\n elif style == \"git-describe\":\n rendered = render_git_describe(pieces)\n elif style == \"git-describe-long\":\n rendered = render_git_describe_long(pieces)\n else:\n raise ValueError(\"unknown style '%s'\" % style)\n\n return {\"version\": rendered, \"full-revisionid\": pieces[\"long\"],\n \"dirty\": pieces[\"dirty\"], \"error\": None}","label":0},{"cid":"versioneer.py::get_versions","text":"versioneer.py::get_versions\ndef get_versions(verbose=False):\n # returns dict with two keys: 'version' and 'full'\n\n if \"versioneer\" in sys.modules:\n # see the discussion in cmdclass.py:get_cmdclass()\n del sys.modules[\"versioneer\"]\n\n root = get_root()\n cfg = get_config_from_root(root)\n\n assert cfg.VCS is not None, \"please set [versioneer]VCS= in setup.cfg\"\n handlers = HANDLERS.get(cfg.VCS)\n assert handlers, \"unrecognized VCS '%s'\" % cfg.VCS\n verbose = verbose or cfg.verbose\n assert cfg.versionfile_source is not None, \\\n \"please set versioneer.versionfile_source\"\n assert cfg.tag_prefix is not None, \"please set versioneer.tag_prefix\"\n\n versionfile_abs = os.path.join(root, cfg.versionfile_source)\n\n # extract version from first of: _version.py, VCS command (e.g. 'git\n # describe'), parentdir. This is meant to work for developers using a\n # source checkout, for users of a tarball created by 'setup.py sdist',\n # and for users of a tarball/zipball created by 'git archive' or github's\n # download-from-tag feature or the equivalent in other VCSes.\n\n get_keywords_f = handlers.get(\"get_keywords\")\n from_keywords_f = handlers.get(\"keywords\")\n if get_keywords_f and from_keywords_f:\n try:\n keywords = get_keywords_f(versionfile_abs)\n ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)\n if verbose:\n print(\"got version from expanded keyword %s\" % ver)\n return ver\n except NotThisMethod:\n pass\n\n try:\n ver = versions_from_file(versionfile_abs)\n if verbose:\n print(\"got version from file %s %s\" % (versionfile_abs, ver))\n return ver\n except NotThisMethod:\n pass\n\n from_vcs_f = handlers.get(\"pieces_from_vcs\")\n if from_vcs_f:\n try:\n pieces = from_vcs_f(cfg.tag_prefix, root, verbose)\n ver = render(pieces, cfg.style)\n if verbose:\n ","label":0},{"cid":"versioneer.py::get_version","text":"versioneer.py::get_version\ndef get_version():\n return get_versions()[\"version\"]","label":0},{"cid":"versioneer.py::get_cmdclass","text":"versioneer.py::get_cmdclass\ndef get_cmdclass():\n if \"versioneer\" in sys.modules:\n del sys.modules[\"versioneer\"]\n # this fixes the \"python setup.py develop\" case (also 'install' and\n # 'easy_install .'), in which subdependencies of the main project are\n # built (using setup.py bdist_egg) in the same python process. Assume\n # a main project A and a dependency B, which use different versions\n # of Versioneer. A's setup.py imports A's Versioneer, leaving it in\n # sys.modules by the time B's setup.py is executed, causing B to run\n # with the wrong versioneer. Setuptools wraps the sub-dep builds in a\n # sandbox that restores sys.modules to it's pre-build state, so the\n # parent is protected against the child's \"import versioneer\". By\n # removing ourselves from sys.modules here, before the child build\n # happens, we protect the child from the parent's versioneer too.\n # Also see https://github.com/warner/python-versioneer/issues/52\n\n cmds = {}\n\n # we add \"version\" to both distutils and setuptools\n from distutils.core import Command\n\n class cmd_version(Command):\n description = \"report generated version string\"\n user_options = []\n boolean_options = []\n\n def initialize_options(self):\n pass\n\n def finalize_options(self):\n pass\n\n def run(self):\n vers = get_versions(verbose=True)\n print(\"Version: %s\" % vers[\"version\"])\n print(\" full-revisionid: %s\" % vers.get(\"full-revisionid\"))\n print(\" dirty: %s\" % vers.get(\"dirty\"))\n if vers[\"error\"]:\n print(\" error: %s\" % vers[\"error\"])\n cmds[\"version\"] = cmd_version\n\n # we override \"build_py\" in both distutils and setuptools\n #\n # most invocation pathways end up running build_py:\n # distutils/build -> build_py\n # distutils/install -> distutils/build ->..\n # setuptools/bdist_wheel ->","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_version.initialize_options","text":"versioneer.py::get_cmdclass.cmd_version.initialize_options\n def initialize_options(self):\n pass","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_version.finalize_options","text":"versioneer.py::get_cmdclass.cmd_version.finalize_options\n def finalize_options(self):\n pass","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_version.run","text":"versioneer.py::get_cmdclass.cmd_version.run\n def run(self):\n vers = get_versions(verbose=True)\n print(\"Version: %s\" % vers[\"version\"])\n print(\" full-revisionid: %s\" % vers.get(\"full-revisionid\"))\n print(\" dirty: %s\" % vers.get(\"dirty\"))\n if vers[\"error\"]:\n print(\" error: %s\" % vers[\"error\"])","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_build_py.run","text":"versioneer.py::get_cmdclass.cmd_build_py.run\n def run(self):\n root = get_root()\n cfg = get_config_from_root(root)\n versions = get_versions()\n _build_py.run(self)\n # now locate _version.py in the new build/ directory and replace\n # it with an updated value\n if cfg.versionfile_build:\n target_versionfile = os.path.join(self.build_lib,\n cfg.versionfile_build)\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(target_versionfile, versions)","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_sdist.run","text":"versioneer.py::get_cmdclass.cmd_sdist.run\n def run(self):\n versions = get_versions()\n self._versioneer_generated_versions = versions\n # unless we update this, the command will keep using the old\n # version\n self.distribution.metadata.version = versions[\"version\"]\n return _sdist.run(self)","label":0},{"cid":"versioneer.py::get_cmdclass.cmd_sdist.make_release_tree","text":"versioneer.py::get_cmdclass.cmd_sdist.make_release_tree\n def make_release_tree(self, base_dir, files):\n root = get_root()\n cfg = get_config_from_root(root)\n _sdist.make_release_tree(self, base_dir, files)\n # now locate _version.py in the new base_dir directory\n # (remembering that it may be a hardlink) and replace it with an\n # updated value\n target_versionfile = os.path.join(base_dir, cfg.versionfile_source)\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(target_versionfile,\n self._versioneer_generated_versions)","label":0},{"cid":"versioneer.py::do_setup","text":"versioneer.py::do_setup\ndef do_setup():\n root = get_root()\n try:\n cfg = get_config_from_root(root)\n except (EnvironmentError, configparser.NoSectionError,\n configparser.NoOptionError) as e:\n if isinstance(e, (EnvironmentError, configparser.NoSectionError)):\n print(\"Adding sample versioneer config to setup.cfg\",\n file=sys.stderr)\n with open(os.path.join(root, \"setup.cfg\"), \"a\") as f:\n f.write(SAMPLE_CONFIG)\n print(CONFIG_ERROR, file=sys.stderr)\n return 1\n\n print(\" creating %s\" % cfg.versionfile_source)\n with open(cfg.versionfile_source, \"w\") as f:\n LONG = LONG_VERSION_PY[cfg.VCS]\n f.write(LONG % {\"DOLLAR\": \"$\",\n \"STYLE\": cfg.style,\n \"TAG_PREFIX\": cfg.tag_prefix,\n \"PARENTDIR_PREFIX\": cfg.parentdir_prefix,\n \"VERSIONFILE_SOURCE\": cfg.versionfile_source,\n })\n\n ipy = os.path.join(os.path.dirname(cfg.versionfile_source),\n \"__init__.py\")\n if os.path.exists(ipy):\n try:\n with open(ipy, \"r\") as f:\n old = f.read()\n except EnvironmentError:\n old = \"\"\n if INIT_PY_SNIPPET not in old:\n print(\" appending to %s\" % ipy)\n with open(ipy, \"a\") as f:\n f.write(INIT_PY_SNIPPET)\n else:\n print(\" %s unmodified\" % ipy)\n else:\n print(\" %s doesn't exist, ok\" % ipy)\n ipy = None\n\n # Make sure both the top-level \"versioneer.py\" and versionfile_source\n # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so\n # they'll be copied into source distributions. Pip won't be able to\n # install the package without this.\n manifest_in = os.path.join(root, \"MANIFEST.in\")\n simple_includes = set()\n try:\n with open(manifest_in, \"r\") as f:\n for line in f:\n if line.st","label":0},{"cid":"versioneer.py::scan_setup_py","text":"versioneer.py::scan_setup_py\ndef scan_setup_py():\n found = set()\n setters = False\n errors = 0\n with open(\"setup.py\", \"r\") as f:\n for line in f.readlines():\n if \"import versioneer\" in line:\n found.add(\"import\")\n if \"versioneer.get_cmdclass()\" in line:\n found.add(\"cmdclass\")\n if \"versioneer.get_version()\" in line:\n found.add(\"get_version\")\n if \"versioneer.VCS\" in line:\n setters = True\n if \"versioneer.versionfile_source\" in line:\n setters = True\n if len(found) != 3:\n print(\"\")\n print(\"Your setup.py appears to be missing some important items\")\n print(\"(but I might be wrong). Please make sure it has something\")\n print(\"roughly like the following:\")\n print(\"\")\n print(\" import versioneer\")\n print(\" setup( version=versioneer.get_version(),\")\n print(\" cmdclass=versioneer.get_cmdclass(), ...)\")\n print(\"\")\n errors += 1\n if setters:\n print(\"You should remove lines like 'versioneer.VCS = ' and\")\n print(\"'versioneer.versionfile_source = ' . This configuration\")\n print(\"now lives in setup.cfg, and should be removed from setup.py\")\n print(\"\")\n errors += 1\n return errors","label":0}],"string":"[\n {\n \"cid\": \"pandas/io/parquet.py::get_engine\",\n \"text\": \"pandas/io/parquet.py::get_engine\\ndef get_engine(engine):\\n \\\"\\\"\\\" return our implementation \\\"\\\"\\\"\\n\\n if engine == 'auto':\\n engine = get_option('io.parquet.engine')\\n\\n if engine == 'auto':\\n # try engines in this order\\n try:\\n return PyArrowImpl()\\n except ImportError:\\n pass\\n\\n try:\\n return FastParquetImpl()\\n except ImportError:\\n pass\\n\\n raise ImportError(\\\"Unable to find a usable engine; \\\"\\n \\\"tried using: 'pyarrow', 'fastparquet'.\\\\n\\\"\\n \\\"pyarrow or fastparquet is required for parquet \\\"\\n \\\"support\\\")\\n\\n if engine not in ['pyarrow', 'fastparquet']:\\n raise ValueError(\\\"engine must be one of 'pyarrow', 'fastparquet'\\\")\\n\\n if engine == 'pyarrow':\\n return PyArrowImpl()\\n elif engine == 'fastparquet':\\n return FastParquetImpl()\",\n \"label\": 1\n },\n {\n \"cid\": \"pandas/io/parquet.py::PyArrowImpl.__init__\",\n \"text\": \"pandas/io/parquet.py::PyArrowImpl.__init__\\n def __init__(self):\\n # since pandas is a dependency of pyarrow\\n # we need to import on first use\\n\\n try:\\n import pyarrow\\n import pyarrow.parquet\\n except ImportError:\\n raise ImportError(\\\"pyarrow is required for parquet support\\\\n\\\\n\\\"\\n \\\"you can install via conda\\\\n\\\"\\n \\\"conda install pyarrow -c conda-forge\\\\n\\\"\\n \\\"\\\\nor via pip\\\\n\\\"\\n \\\"pip install -U pyarrow\\\\n\\\")\\n\\n if LooseVersion(pyarrow.__version__) < LooseVersion('0.4.1'):\\n raise ImportError(\\\"pyarrow >= 0.4.1 is required for parquet\\\"\\n \\\"support\\\\n\\\\n\\\"\\n \\\"you can install via conda\\\\n\\\"\\n \\\"conda install pyarrow -c conda-forge\\\\n\\\"\\n \\\"\\\\nor via pip\\\\n\\\"\\n \\\"pip install -U pyarrow\\\\n\\\")\\n\\n self._pyarrow_lt_050 = (LooseVersion(pyarrow.__version__) <\\n LooseVersion('0.5.0'))\\n self._pyarrow_lt_060 = (LooseVersion(pyarrow.__version__) <\\n LooseVersion('0.6.0'))\\n self.api = pyarrow\",\n \"label\": 1\n },\n {\n \"cid\": \"pandas/io/parquet.py::PyArrowImpl.write\",\n \"text\": \"pandas/io/parquet.py::PyArrowImpl.write\\n def write(self, df, path, compression='snappy',\\n coerce_timestamps='ms', **kwargs):\\n path, _, _ = get_filepath_or_buffer(path)\\n if self._pyarrow_lt_060:\\n table = self.api.Table.from_pandas(df, timestamps_to_ms=True)\\n self.api.parquet.write_table(\\n table, path, compression=compression, **kwargs)\\n\\n else:\\n table = self.api.Table.from_pandas(df)\\n self.api.parquet.write_table(\\n table, path, compression=compression,\\n coerce_timestamps=coerce_timestamps, **kwargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"pandas/io/parquet.py::PyArrowImpl.read\",\n \"text\": \"pandas/io/parquet.py::PyArrowImpl.read\\n def read(self, path, columns=None, **kwargs):\\n path, _, _ = get_filepath_or_buffer(path)\\n return self.api.parquet.read_table(path, columns=columns,\\n **kwargs).to_pandas()\",\n \"label\": 1\n },\n {\n \"cid\": \"pandas/io/parquet.py::FastParquetImpl.__init__\",\n \"text\": \"pandas/io/parquet.py::FastParquetImpl.__init__\\n def __init__(self):\\n # since pandas is a dependency of fastparquet\\n # we need to import on first use\\n\\n try:\\n import fastparquet\\n except ImportError:\\n raise ImportError(\\\"fastparquet is required for parquet support\\\\n\\\\n\\\"\\n \\\"you can install via conda\\\\n\\\"\\n \\\"conda install fastparquet -c conda-forge\\\\n\\\"\\n \\\"\\\\nor via pip\\\\n\\\"\\n \\\"pip install -U fastparquet\\\")\\n\\n if LooseVersion(fastparquet.__version__) < LooseVersion('0.1.0'):\\n raise ImportError(\\\"fastparquet >= 0.1.0 is required for parquet \\\"\\n \\\"support\\\\n\\\\n\\\"\\n \\\"you can install via conda\\\\n\\\"\\n \\\"conda install fastparquet -c conda-forge\\\\n\\\"\\n \\\"\\\\nor via pip\\\\n\\\"\\n \\\"pip install -U fastparquet\\\")\\n\\n self.api = fastparquet\",\n \"label\": 1\n },\n {\n \"cid\": \"pandas/io/parquet.py::FastParquetImpl.write\",\n \"text\": \"pandas/io/parquet.py::FastParquetImpl.write\\n def write(self, df, path, compression='snappy', **kwargs):\\n # thriftpy/protocol/compact.py:339:\\n # DeprecationWarning: tostring() is deprecated.\\n # Use tobytes() instead.\\n path, _, _ = get_filepath_or_buffer(path)\\n with catch_warnings(record=True):\\n self.api.write(path, df,\\n compression=compression, **kwargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"pandas/io/parquet.py::FastParquetImpl.read\",\n \"text\": \"pandas/io/parquet.py::FastParquetImpl.read\\n def read(self, path, columns=None, **kwargs):\\n path, _, _ = get_filepath_or_buffer(path)\\n return self.api.ParquetFile(path).to_pandas(columns=columns, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/io/parquet.py::to_parquet\",\n \"text\": \"pandas/io/parquet.py::to_parquet\\ndef to_parquet(df, path, engine='auto', compression='snappy', **kwargs):\\n \\\"\\\"\\\"\\n Write a DataFrame to the parquet format.\\n\\n Parameters\\n ----------\\n df : DataFrame\\n path : string\\n File path\\n engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'\\n Parquet reader library to use. If 'auto', then the option\\n 'io.parquet.engine' is used. If 'auto', then the first\\n library to be installed is used.\\n compression : str, optional, default 'snappy'\\n compression method, includes {'gzip', 'snappy', 'brotli'}\\n kwargs\\n Additional keyword arguments passed to the engine\\n \\\"\\\"\\\"\\n\\n impl = get_engine(engine)\\n\\n if not isinstance(df, DataFrame):\\n raise ValueError(\\\"to_parquet only support IO with DataFrames\\\")\\n\\n valid_types = {'string', 'unicode'}\\n\\n # validate index\\n # --------------\\n\\n # validate that we have only a default index\\n # raise on anything else as we don't serialize the index\\n\\n if not isinstance(df.index, Int64Index):\\n raise ValueError(\\\"parquet does not support serializing {} \\\"\\n \\\"for the index; you can .reset_index()\\\"\\n \\\"to make the index into column(s)\\\".format(\\n type(df.index)))\\n\\n if not df.index.equals(RangeIndex.from_range(range(len(df)))):\\n raise ValueError(\\\"parquet does not support serializing a \\\"\\n \\\"non-default index for the index; you \\\"\\n \\\"can .reset_index() to make the index \\\"\\n \\\"into column(s)\\\")\\n\\n if df.index.name is not None:\\n raise ValueError(\\\"parquet does not serialize index meta-data on a \\\"\\n \\\"default index\\\")\\n\\n # validate columns\\n # ----------------\\n\\n # must have value column names (strings only)\\n if df.columns.inferred_type not in valid_types:\\n raise ValueError(\\\"parquet must have string column names\\\")\\n\\n return impl.write\",\n \"label\": 1\n },\n {\n \"cid\": \"pandas/io/parquet.py::read_parquet\",\n \"text\": \"pandas/io/parquet.py::read_parquet\\ndef read_parquet(path, engine='auto', columns=None, **kwargs):\\n \\\"\\\"\\\"\\n Load a parquet object from the file path, returning a DataFrame.\\n\\n .. versionadded 0.21.0\\n\\n Parameters\\n ----------\\n path : string\\n File path\\n columns: list, default=None\\n If not None, only these columns will be read from the file.\\n\\n .. versionadded 0.21.1\\n engine : {'auto', 'pyarrow', 'fastparquet'}, default 'auto'\\n Parquet reader library to use. If 'auto', then the option\\n 'io.parquet.engine' is used. If 'auto', then the first\\n library to be installed is used.\\n kwargs are passed to the engine\\n\\n Returns\\n -------\\n DataFrame\\n\\n \\\"\\\"\\\"\\n\\n impl = get_engine(engine)\\n return impl.read(path, columns=columns, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/util/_print_versions.py::get_sys_info\",\n \"text\": \"pandas/util/_print_versions.py::get_sys_info\\ndef get_sys_info():\\n \\\"Returns system information as a dict\\\"\\n\\n blob = []\\n\\n # get full commit hash\\n commit = None\\n if os.path.isdir(\\\".git\\\") and os.path.isdir(\\\"pandas\\\"):\\n try:\\n pipe = subprocess.Popen('git log --format=\\\"%H\\\" -n 1'.split(\\\" \\\"),\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n so, serr = pipe.communicate()\\n except:\\n pass\\n else:\\n if pipe.returncode == 0:\\n commit = so\\n try:\\n commit = so.decode('utf-8')\\n except ValueError:\\n pass\\n commit = commit.strip().strip('\\\"')\\n\\n blob.append(('commit', commit))\\n\\n try:\\n (sysname, nodename, release,\\n version, machine, processor) = platform.uname()\\n blob.extend([\\n (\\\"python\\\", '.'.join(map(str, sys.version_info))),\\n (\\\"python-bits\\\", struct.calcsize(\\\"P\\\") * 8),\\n (\\\"OS\\\", \\\"{sysname}\\\".format(sysname=sysname)),\\n (\\\"OS-release\\\", \\\"{release}\\\".format(release=release)),\\n # (\\\"Version\\\", \\\"{version}\\\".format(version=version)),\\n (\\\"machine\\\", \\\"{machine}\\\".format(machine=machine)),\\n (\\\"processor\\\", \\\"{processor}\\\".format(processor=processor)),\\n (\\\"byteorder\\\", \\\"{byteorder}\\\".format(byteorder=sys.byteorder)),\\n (\\\"LC_ALL\\\", \\\"{lc}\\\".format(lc=os.environ.get('LC_ALL', \\\"None\\\"))),\\n (\\\"LANG\\\", \\\"{lang}\\\".format(lang=os.environ.get('LANG', \\\"None\\\"))),\\n (\\\"LOCALE\\\", '.'.join(map(str, locale.getlocale()))),\\n ])\\n except:\\n pass\\n\\n return blob\",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/util/_print_versions.py::show_versions\",\n \"text\": \"pandas/util/_print_versions.py::show_versions\\ndef show_versions(as_json=False):\\n sys_info = get_sys_info()\\n\\n deps = [\\n # (MODULE_NAME, f(mod) -> mod version)\\n (\\\"pandas\\\", lambda mod: mod.__version__),\\n (\\\"pytest\\\", lambda mod: mod.__version__),\\n (\\\"pip\\\", lambda mod: mod.__version__),\\n (\\\"setuptools\\\", lambda mod: mod.__version__),\\n (\\\"Cython\\\", lambda mod: mod.__version__),\\n (\\\"numpy\\\", lambda mod: mod.version.version),\\n (\\\"scipy\\\", lambda mod: mod.version.version),\\n (\\\"pyarrow\\\", lambda mod: mod.__version__),\\n (\\\"xarray\\\", lambda mod: mod.__version__),\\n (\\\"IPython\\\", lambda mod: mod.__version__),\\n (\\\"sphinx\\\", lambda mod: mod.__version__),\\n (\\\"patsy\\\", lambda mod: mod.__version__),\\n (\\\"dateutil\\\", lambda mod: mod.__version__),\\n (\\\"pytz\\\", lambda mod: mod.VERSION),\\n (\\\"blosc\\\", lambda mod: mod.__version__),\\n (\\\"bottleneck\\\", lambda mod: mod.__version__),\\n (\\\"tables\\\", lambda mod: mod.__version__),\\n (\\\"numexpr\\\", lambda mod: mod.__version__),\\n (\\\"feather\\\", lambda mod: mod.__version__),\\n (\\\"matplotlib\\\", lambda mod: mod.__version__),\\n (\\\"openpyxl\\\", lambda mod: mod.__version__),\\n (\\\"xlrd\\\", lambda mod: mod.__VERSION__),\\n (\\\"xlwt\\\", lambda mod: mod.__VERSION__),\\n (\\\"xlsxwriter\\\", lambda mod: mod.__version__),\\n (\\\"lxml\\\", lambda mod: mod.etree.__version__),\\n (\\\"bs4\\\", lambda mod: mod.__version__),\\n (\\\"html5lib\\\", lambda mod: mod.__version__),\\n (\\\"sqlalchemy\\\", lambda mod: mod.__version__),\\n (\\\"pymysql\\\", lambda mod: mod.__version__),\\n (\\\"psycopg2\\\", lambda mod: mod.__version__),\\n (\\\"jinja2\\\", lambda mod: mod.__version__),\\n (\\\"s3fs\\\", lambda mod: mod.__version__),\\n (\\\"fastparquet\\\", lambda mod: mod.__version__),\\n (\\\"pandas_gbq\\\", lambda mod: mod.__version__),\\n (\\\"pandas_datareader\\\", lambda mod: mod.__version__),\\n ]\\n\\n deps_blob = list()\\n for (modname, ver_f) in deps:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pandas/util/_print_versions.py::main\",\n \"text\": \"pandas/util/_print_versions.py::main\\ndef main():\\n from optparse import OptionParser\\n parser = OptionParser()\\n parser.add_option(\\\"-j\\\", \\\"--json\\\", metavar=\\\"FILE\\\", nargs=1,\\n help=\\\"Save output as JSON into file, pass in \\\"\\n \\\"'-' to output to stdout\\\")\\n\\n (options, args) = parser.parse_args()\\n\\n if options.json == \\\"-\\\":\\n options.json = True\\n\\n show_versions(as_json=options.json)\\n\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_root\",\n \"text\": \"versioneer.py::get_root\\ndef get_root():\\n # we require that all commands are run from the project root, i.e. the\\n # directory that contains setup.py, setup.cfg, and versioneer.py .\\n root = os.path.realpath(os.path.abspath(os.getcwd()))\\n setup_py = os.path.join(root, \\\"setup.py\\\")\\n versioneer_py = os.path.join(root, \\\"versioneer.py\\\")\\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\\n # allow 'python path/to/setup.py COMMAND'\\n root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))\\n setup_py = os.path.join(root, \\\"setup.py\\\")\\n versioneer_py = os.path.join(root, \\\"versioneer.py\\\")\\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\\n err = (\\\"Versioneer was unable to run the project root directory. \\\"\\n \\\"Versioneer requires setup.py to be executed from \\\"\\n \\\"its immediate directory (like 'python setup.py COMMAND'), \\\"\\n \\\"or in a way that lets it use sys.argv[0] to find the root \\\"\\n \\\"(like 'python path/to/setup.py COMMAND').\\\")\\n raise VersioneerBadRootError(err)\\n try:\\n # Certain runtime workflows (setup.py install/develop in a setuptools\\n # tree) execute all dependencies in a single python process, so\\n # \\\"versioneer\\\" may be imported multiple times, and python's shared\\n # module-import table will cache the first one. So we can't use\\n # os.path.dirname(__file__), as that will find whichever\\n # versioneer.py was first imported, even in later projects.\\n me = os.path.realpath(os.path.abspath(__file__))\\n if os.path.splitext(me)[0] != os.path.splitext(versioneer_py)[0]:\\n print(\\\"Warning: build in %s is using versioneer.py from %s\\\"\\n % (os.path.dirname(me), versioneer_py))\\n except NameError:\\n pass\\n return root\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_config_from_root\",\n \"text\": \"versioneer.py::get_config_from_root\\ndef get_config_from_root(root):\\n # This might raise EnvironmentError (if setup.cfg is missing), or\\n # configparser.NoSectionError (if it lacks a [versioneer] section), or\\n # configparser.NoOptionError (if it lacks \\\"VCS=\\\"). See the docstring at\\n # the top of versioneer.py for instructions on writing your setup.cfg .\\n setup_cfg = os.path.join(root, \\\"setup.cfg\\\")\\n parser = configparser.SafeConfigParser()\\n with open(setup_cfg, \\\"r\\\") as f:\\n parser.readfp(f)\\n VCS = parser.get(\\\"versioneer\\\", \\\"VCS\\\") # mandatory\\n\\n def get(parser, name):\\n if parser.has_option(\\\"versioneer\\\", name):\\n return parser.get(\\\"versioneer\\\", name)\\n return None\\n cfg = VersioneerConfig()\\n cfg.VCS = VCS\\n cfg.style = get(parser, \\\"style\\\") or \\\"\\\"\\n cfg.versionfile_source = get(parser, \\\"versionfile_source\\\")\\n cfg.versionfile_build = get(parser, \\\"versionfile_build\\\")\\n cfg.tag_prefix = get(parser, \\\"tag_prefix\\\")\\n cfg.parentdir_prefix = get(parser, \\\"parentdir_prefix\\\")\\n cfg.verbose = get(parser, \\\"verbose\\\")\\n return cfg\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_config_from_root.get\",\n \"text\": \"versioneer.py::get_config_from_root.get\\n def get(parser, name):\\n if parser.has_option(\\\"versioneer\\\", name):\\n return parser.get(\\\"versioneer\\\", name)\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::register_vcs_handler\",\n \"text\": \"versioneer.py::register_vcs_handler\\ndef register_vcs_handler(vcs, method): # decorator\\n def decorate(f):\\n if vcs not in HANDLERS:\\n HANDLERS[vcs] = {}\\n HANDLERS[vcs][method] = f\\n return f\\n return decorate\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::register_vcs_handler.decorate\",\n \"text\": \"versioneer.py::register_vcs_handler.decorate\\n def decorate(f):\\n if vcs not in HANDLERS:\\n HANDLERS[vcs] = {}\\n HANDLERS[vcs][method] = f\\n return f\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::run_command\",\n \"text\": \"versioneer.py::run_command\\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False):\\n assert isinstance(commands, list)\\n p = None\\n for c in commands:\\n try:\\n dispcmd = str([c] + args)\\n # remember shell=False, so use git.cmd on windows, not just git\\n p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE,\\n stderr=(subprocess.PIPE if hide_stderr\\n else None))\\n break\\n except EnvironmentError:\\n e = sys.exc_info()[1]\\n if e.errno == errno.ENOENT:\\n continue\\n if verbose:\\n print(\\\"unable to run %s\\\" % dispcmd)\\n print(e)\\n return None\\n else:\\n if verbose:\\n print(\\\"unable to find command, tried %s\\\" % (commands,))\\n return None\\n stdout = p.communicate()[0].strip()\\n if sys.version_info[0] >= 3:\\n stdout = stdout.decode()\\n if p.returncode != 0:\\n if verbose:\\n print(\\\"unable to run %s (error)\\\" % dispcmd)\\n return None\\n return stdout\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::git_get_keywords\",\n \"text\": \"versioneer.py::git_get_keywords\\n@register_vcs_handler(\\\"git\\\", \\\"get_keywords\\\")\\ndef git_get_keywords(versionfile_abs):\\n # the code embedded in _version.py can just fetch the value of these\\n # keywords. When used from setup.py, we don't want to import _version.py,\\n # so we do it with a regexp instead. This function is not used from\\n # _version.py.\\n keywords = {}\\n try:\\n f = open(versionfile_abs, \\\"r\\\")\\n for line in f.readlines():\\n if line.strip().startswith(\\\"git_refnames =\\\"):\\n mo = re.search(r'=\\\\s*\\\"(.*)\\\"', line)\\n if mo:\\n keywords[\\\"refnames\\\"] = mo.group(1)\\n if line.strip().startswith(\\\"git_full =\\\"):\\n mo = re.search(r'=\\\\s*\\\"(.*)\\\"', line)\\n if mo:\\n keywords[\\\"full\\\"] = mo.group(1)\\n f.close()\\n except EnvironmentError:\\n pass\\n return keywords\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::git_versions_from_keywords\",\n \"text\": \"versioneer.py::git_versions_from_keywords\\n@register_vcs_handler(\\\"git\\\", \\\"keywords\\\")\\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\\n if not keywords:\\n raise NotThisMethod(\\\"no keywords at all, weird\\\")\\n refnames = keywords[\\\"refnames\\\"].strip()\\n if refnames.startswith(\\\"$Format\\\"):\\n if verbose:\\n print(\\\"keywords are unexpanded, not using\\\")\\n raise NotThisMethod(\\\"unexpanded keywords, not a git-archive tarball\\\")\\n refs = {r.strip() for r in refnames.strip(\\\"()\\\").split(\\\",\\\")}\\n # starting in git-1.8.3, tags are listed as \\\"tag: foo-1.0\\\" instead of\\n # just \\\"foo-1.0\\\". If we see a \\\"tag: \\\" prefix, prefer those.\\n TAG = \\\"tag: \\\"\\n tags = {r[len(TAG):] for r in refs if r.startswith(TAG)}\\n if not tags:\\n # Either we're using git < 1.8.3, or there really are no tags. We use\\n # a heuristic: assume all version tags have a digit. The old git %d\\n # expansion behaves like git log --decorate=short and strips out the\\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\\n # between branches and tags. By ignoring refnames without digits, we\\n # filter out many common branch names like \\\"release\\\" and\\n # \\\"stabilization\\\", as well as \\\"HEAD\\\" and \\\"master\\\".\\n tags = {r for r in refs if re.search(r'\\\\d', r)}\\n if verbose:\\n print(\\\"discarding '%s', no digits\\\" % \\\",\\\".join(refs-tags))\\n if verbose:\\n print(\\\"likely tags: %s\\\" % \\\",\\\".join(sorted(tags)))\\n for ref in sorted(tags):\\n # sorting will prefer e.g. \\\"2.0\\\" over \\\"2.0rc1\\\"\\n if ref.startswith(tag_prefix):\\n r = ref[len(tag_prefix):]\\n if verbose:\\n print(\\\"picking %s\\\" % r)\\n return {\\\"version\\\": r,\\n \\\"full-revisionid\\\": keywords[\\\"full\\\"].strip(),\\n \\\"dirty\\\": False, \\\"error\\\": None\\n }\\n # no suitable tags, so version is \\\"0+unknown\\\", but full hex is still there\\n if verbose:\\n print(\\\"no suit\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::git_pieces_from_vcs\",\n \"text\": \"versioneer.py::git_pieces_from_vcs\\n@register_vcs_handler(\\\"git\\\", \\\"pieces_from_vcs\\\")\\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):\\n # this runs 'git' from the root of the source tree. This only gets called\\n # if the git-archive 'subst' keywords were *not* expanded, and\\n # _version.py hasn't already been rewritten with a short version string,\\n # meaning we're inside a checked out source tree.\\n\\n if not os.path.exists(os.path.join(root, \\\".git\\\")):\\n if verbose:\\n print(\\\"no .git in %s\\\" % root)\\n raise NotThisMethod(\\\"no .git directory\\\")\\n\\n GITS = [\\\"git\\\"]\\n if sys.platform == \\\"win32\\\":\\n GITS = [\\\"git.cmd\\\", \\\"git.exe\\\"]\\n # if there is a tag, this yields TAG-NUM-gHEX[-dirty]\\n # if there are no tags, this yields HEX[-dirty] (no NUM)\\n describe_out = run_command(GITS, [\\\"describe\\\", \\\"--tags\\\", \\\"--dirty\\\",\\n \\\"--always\\\", \\\"--long\\\"],\\n cwd=root)\\n # --long was added in git-1.5.5\\n if describe_out is None:\\n raise NotThisMethod(\\\"'git describe' failed\\\")\\n describe_out = describe_out.strip()\\n full_out = run_command(GITS, [\\\"rev-parse\\\", \\\"HEAD\\\"], cwd=root)\\n if full_out is None:\\n raise NotThisMethod(\\\"'git rev-parse' failed\\\")\\n full_out = full_out.strip()\\n\\n pieces = {}\\n pieces[\\\"long\\\"] = full_out\\n pieces[\\\"short\\\"] = full_out[:7] # maybe improved later\\n pieces[\\\"error\\\"] = None\\n\\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\\n # TAG might have hyphens.\\n git_describe = describe_out\\n\\n # look for -dirty suffix\\n dirty = git_describe.endswith(\\\"-dirty\\\")\\n pieces[\\\"dirty\\\"] = dirty\\n if dirty:\\n git_describe = git_describe[:git_describe.rindex(\\\"-dirty\\\")]\\n\\n # now we have TAG-NUM-gHEX or HEX\\n\\n if \\\"-\\\" in git_describe:\\n # TAG-NUM-gHEX\\n mo = re.search(r'^(.+)-(\\\\d+)-g([0-9a-f]+)$', git_describe)\\n if not mo:\\n # unparseable. Maybe git-describe\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::do_vcs_install\",\n \"text\": \"versioneer.py::do_vcs_install\\ndef do_vcs_install(manifest_in, versionfile_source, ipy):\\n GITS = [\\\"git\\\"]\\n if sys.platform == \\\"win32\\\":\\n GITS = [\\\"git.cmd\\\", \\\"git.exe\\\"]\\n files = [manifest_in, versionfile_source]\\n if ipy:\\n files.append(ipy)\\n try:\\n me = __file__\\n if me.endswith(\\\".pyc\\\") or me.endswith(\\\".pyo\\\"):\\n me = os.path.splitext(me)[0] + \\\".py\\\"\\n versioneer_file = os.path.relpath(me)\\n except NameError:\\n versioneer_file = \\\"versioneer.py\\\"\\n files.append(versioneer_file)\\n present = False\\n try:\\n f = open(\\\".gitattributes\\\", \\\"r\\\")\\n for line in f.readlines():\\n if line.strip().startswith(versionfile_source):\\n if \\\"export-subst\\\" in line.strip().split()[1:]:\\n present = True\\n f.close()\\n except EnvironmentError:\\n pass\\n if not present:\\n f = open(\\\".gitattributes\\\", \\\"a+\\\")\\n f.write(\\\"%s export-subst\\\\n\\\" % versionfile_source)\\n f.close()\\n files.append(\\\".gitattributes\\\")\\n run_command(GITS, [\\\"add\\\", \\\"--\\\"] + files)\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::versions_from_parentdir\",\n \"text\": \"versioneer.py::versions_from_parentdir\\ndef versions_from_parentdir(parentdir_prefix, root, verbose):\\n # Source tarballs conventionally unpack into a directory that includes\\n # both the project name and a version string.\\n dirname = os.path.basename(root)\\n if not dirname.startswith(parentdir_prefix):\\n if verbose:\\n print(\\\"guessing rootdir is '%s', but '%s' doesn't start with \\\"\\n \\\"prefix '%s'\\\" % (root, dirname, parentdir_prefix))\\n raise NotThisMethod(\\\"rootdir doesn't start with parentdir_prefix\\\")\\n return {\\\"version\\\": dirname[len(parentdir_prefix):],\\n \\\"full-revisionid\\\": None,\\n \\\"dirty\\\": False, \\\"error\\\": None}\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::versions_from_file\",\n \"text\": \"versioneer.py::versions_from_file\\ndef versions_from_file(filename):\\n try:\\n with open(filename) as f:\\n contents = f.read()\\n except EnvironmentError:\\n raise NotThisMethod(\\\"unable to read _version.py\\\")\\n mo = re.search(r\\\"version_json = '''\\\\n(.*)''' # END VERSION_JSON\\\",\\n contents, re.M | re.S)\\n if not mo:\\n raise NotThisMethod(\\\"no version_json in _version.py\\\")\\n return json.loads(mo.group(1))\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::write_to_version_file\",\n \"text\": \"versioneer.py::write_to_version_file\\ndef write_to_version_file(filename, versions):\\n os.unlink(filename)\\n contents = json.dumps(versions, sort_keys=True,\\n indent=1, separators=(\\\",\\\", \\\": \\\"))\\n with open(filename, \\\"w\\\") as f:\\n f.write(SHORT_VERSION_PY % contents)\\n\\n print(\\\"set %s to '%s'\\\" % (filename, versions[\\\"version\\\"]))\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::plus_or_dot\",\n \"text\": \"versioneer.py::plus_or_dot\\ndef plus_or_dot(pieces):\\n if \\\"+\\\" in pieces.get(\\\"closest-tag\\\", \\\"\\\"):\\n return \\\".\\\"\\n return \\\"+\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_pep440\",\n \"text\": \"versioneer.py::render_pep440\\ndef render_pep440(pieces):\\n # now build up version string, with post-release \\\"local version\\n # identifier\\\". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\\n # get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\\n\\n # exceptions:\\n # 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\\n\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"] or pieces[\\\"dirty\\\"]:\\n rendered += plus_or_dot(pieces)\\n rendered += \\\"%d.g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dirty\\\"\\n else:\\n # exception #1\\n rendered = \\\"0+untagged.%d.g%s\\\" % (pieces[\\\"distance\\\"],\\n pieces[\\\"short\\\"])\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dirty\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_pep440_pre\",\n \"text\": \"versioneer.py::render_pep440_pre\\ndef render_pep440_pre(pieces):\\n # TAG[.post.devDISTANCE] . No -dirty\\n\\n # exceptions:\\n # 1: no tags. 0.post.devDISTANCE\\n\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"]:\\n rendered += \\\".post.dev%d\\\" % pieces[\\\"distance\\\"]\\n else:\\n # exception #1\\n rendered = \\\"0.post.dev%d\\\" % pieces[\\\"distance\\\"]\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_pep440_post\",\n \"text\": \"versioneer.py::render_pep440_post\\ndef render_pep440_post(pieces):\\n # TAG[.postDISTANCE[.dev0]+gHEX] . The \\\".dev0\\\" means dirty. Note that\\n # .dev0 sorts backwards (a dirty tree will appear \\\"older\\\" than the\\n # corresponding clean one), but you shouldn't be releasing software with\\n # -dirty anyways.\\n\\n # exceptions:\\n # 1: no tags. 0.postDISTANCE[.dev0]\\n\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"] or pieces[\\\"dirty\\\"]:\\n rendered += \\\".post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n rendered += plus_or_dot(pieces)\\n rendered += \\\"g%s\\\" % pieces[\\\"short\\\"]\\n else:\\n # exception #1\\n rendered = \\\"0.post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n rendered += \\\"+g%s\\\" % pieces[\\\"short\\\"]\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_pep440_old\",\n \"text\": \"versioneer.py::render_pep440_old\\ndef render_pep440_old(pieces):\\n # TAG[.postDISTANCE[.dev0]] . The \\\".dev0\\\" means dirty.\\n\\n # exceptions:\\n # 1: no tags. 0.postDISTANCE[.dev0]\\n\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"] or pieces[\\\"dirty\\\"]:\\n rendered += \\\".post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n else:\\n # exception #1\\n rendered = \\\"0.post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_git_describe\",\n \"text\": \"versioneer.py::render_git_describe\\ndef render_git_describe(pieces):\\n # TAG[-DISTANCE-gHEX][-dirty], like 'git describe --tags --dirty\\n # --always'\\n\\n # exceptions:\\n # 1: no tags. HEX[-dirty] (note: no 'g' prefix)\\n\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"]:\\n rendered += \\\"-%d-g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n else:\\n # exception #1\\n rendered = pieces[\\\"short\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\"-dirty\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render_git_describe_long\",\n \"text\": \"versioneer.py::render_git_describe_long\\ndef render_git_describe_long(pieces):\\n # TAG-DISTANCE-gHEX[-dirty], like 'git describe --tags --dirty\\n # --always -long'. The distance/hash is unconditional.\\n\\n # exceptions:\\n # 1: no tags. HEX[-dirty] (note: no 'g' prefix)\\n\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n rendered += \\\"-%d-g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n else:\\n # exception #1\\n rendered = pieces[\\\"short\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\"-dirty\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::render\",\n \"text\": \"versioneer.py::render\\ndef render(pieces, style):\\n if pieces[\\\"error\\\"]:\\n return {\\\"version\\\": \\\"unknown\\\",\\n \\\"full-revisionid\\\": pieces.get(\\\"long\\\"),\\n \\\"dirty\\\": None,\\n \\\"error\\\": pieces[\\\"error\\\"]}\\n\\n if not style or style == \\\"default\\\":\\n style = \\\"pep440\\\" # the default\\n\\n if style == \\\"pep440\\\":\\n rendered = render_pep440(pieces)\\n elif style == \\\"pep440-pre\\\":\\n rendered = render_pep440_pre(pieces)\\n elif style == \\\"pep440-post\\\":\\n rendered = render_pep440_post(pieces)\\n elif style == \\\"pep440-old\\\":\\n rendered = render_pep440_old(pieces)\\n elif style == \\\"git-describe\\\":\\n rendered = render_git_describe(pieces)\\n elif style == \\\"git-describe-long\\\":\\n rendered = render_git_describe_long(pieces)\\n else:\\n raise ValueError(\\\"unknown style '%s'\\\" % style)\\n\\n return {\\\"version\\\": rendered, \\\"full-revisionid\\\": pieces[\\\"long\\\"],\\n \\\"dirty\\\": pieces[\\\"dirty\\\"], \\\"error\\\": None}\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_versions\",\n \"text\": \"versioneer.py::get_versions\\ndef get_versions(verbose=False):\\n # returns dict with two keys: 'version' and 'full'\\n\\n if \\\"versioneer\\\" in sys.modules:\\n # see the discussion in cmdclass.py:get_cmdclass()\\n del sys.modules[\\\"versioneer\\\"]\\n\\n root = get_root()\\n cfg = get_config_from_root(root)\\n\\n assert cfg.VCS is not None, \\\"please set [versioneer]VCS= in setup.cfg\\\"\\n handlers = HANDLERS.get(cfg.VCS)\\n assert handlers, \\\"unrecognized VCS '%s'\\\" % cfg.VCS\\n verbose = verbose or cfg.verbose\\n assert cfg.versionfile_source is not None, \\\\\\n \\\"please set versioneer.versionfile_source\\\"\\n assert cfg.tag_prefix is not None, \\\"please set versioneer.tag_prefix\\\"\\n\\n versionfile_abs = os.path.join(root, cfg.versionfile_source)\\n\\n # extract version from first of: _version.py, VCS command (e.g. 'git\\n # describe'), parentdir. This is meant to work for developers using a\\n # source checkout, for users of a tarball created by 'setup.py sdist',\\n # and for users of a tarball/zipball created by 'git archive' or github's\\n # download-from-tag feature or the equivalent in other VCSes.\\n\\n get_keywords_f = handlers.get(\\\"get_keywords\\\")\\n from_keywords_f = handlers.get(\\\"keywords\\\")\\n if get_keywords_f and from_keywords_f:\\n try:\\n keywords = get_keywords_f(versionfile_abs)\\n ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)\\n if verbose:\\n print(\\\"got version from expanded keyword %s\\\" % ver)\\n return ver\\n except NotThisMethod:\\n pass\\n\\n try:\\n ver = versions_from_file(versionfile_abs)\\n if verbose:\\n print(\\\"got version from file %s %s\\\" % (versionfile_abs, ver))\\n return ver\\n except NotThisMethod:\\n pass\\n\\n from_vcs_f = handlers.get(\\\"pieces_from_vcs\\\")\\n if from_vcs_f:\\n try:\\n pieces = from_vcs_f(cfg.tag_prefix, root, verbose)\\n ver = render(pieces, cfg.style)\\n if verbose:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_version\",\n \"text\": \"versioneer.py::get_version\\ndef get_version():\\n return get_versions()[\\\"version\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass\",\n \"text\": \"versioneer.py::get_cmdclass\\ndef get_cmdclass():\\n if \\\"versioneer\\\" in sys.modules:\\n del sys.modules[\\\"versioneer\\\"]\\n # this fixes the \\\"python setup.py develop\\\" case (also 'install' and\\n # 'easy_install .'), in which subdependencies of the main project are\\n # built (using setup.py bdist_egg) in the same python process. Assume\\n # a main project A and a dependency B, which use different versions\\n # of Versioneer. A's setup.py imports A's Versioneer, leaving it in\\n # sys.modules by the time B's setup.py is executed, causing B to run\\n # with the wrong versioneer. Setuptools wraps the sub-dep builds in a\\n # sandbox that restores sys.modules to it's pre-build state, so the\\n # parent is protected against the child's \\\"import versioneer\\\". By\\n # removing ourselves from sys.modules here, before the child build\\n # happens, we protect the child from the parent's versioneer too.\\n # Also see https://github.com/warner/python-versioneer/issues/52\\n\\n cmds = {}\\n\\n # we add \\\"version\\\" to both distutils and setuptools\\n from distutils.core import Command\\n\\n class cmd_version(Command):\\n description = \\\"report generated version string\\\"\\n user_options = []\\n boolean_options = []\\n\\n def initialize_options(self):\\n pass\\n\\n def finalize_options(self):\\n pass\\n\\n def run(self):\\n vers = get_versions(verbose=True)\\n print(\\\"Version: %s\\\" % vers[\\\"version\\\"])\\n print(\\\" full-revisionid: %s\\\" % vers.get(\\\"full-revisionid\\\"))\\n print(\\\" dirty: %s\\\" % vers.get(\\\"dirty\\\"))\\n if vers[\\\"error\\\"]:\\n print(\\\" error: %s\\\" % vers[\\\"error\\\"])\\n cmds[\\\"version\\\"] = cmd_version\\n\\n # we override \\\"build_py\\\" in both distutils and setuptools\\n #\\n # most invocation pathways end up running build_py:\\n # distutils/build -> build_py\\n # distutils/install -> distutils/build ->..\\n # setuptools/bdist_wheel ->\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_version.initialize_options\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_version.initialize_options\\n def initialize_options(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_version.finalize_options\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_version.finalize_options\\n def finalize_options(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_version.run\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_version.run\\n def run(self):\\n vers = get_versions(verbose=True)\\n print(\\\"Version: %s\\\" % vers[\\\"version\\\"])\\n print(\\\" full-revisionid: %s\\\" % vers.get(\\\"full-revisionid\\\"))\\n print(\\\" dirty: %s\\\" % vers.get(\\\"dirty\\\"))\\n if vers[\\\"error\\\"]:\\n print(\\\" error: %s\\\" % vers[\\\"error\\\"])\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_build_py.run\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_build_py.run\\n def run(self):\\n root = get_root()\\n cfg = get_config_from_root(root)\\n versions = get_versions()\\n _build_py.run(self)\\n # now locate _version.py in the new build/ directory and replace\\n # it with an updated value\\n if cfg.versionfile_build:\\n target_versionfile = os.path.join(self.build_lib,\\n cfg.versionfile_build)\\n print(\\\"UPDATING %s\\\" % target_versionfile)\\n write_to_version_file(target_versionfile, versions)\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_sdist.run\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_sdist.run\\n def run(self):\\n versions = get_versions()\\n self._versioneer_generated_versions = versions\\n # unless we update this, the command will keep using the old\\n # version\\n self.distribution.metadata.version = versions[\\\"version\\\"]\\n return _sdist.run(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::get_cmdclass.cmd_sdist.make_release_tree\",\n \"text\": \"versioneer.py::get_cmdclass.cmd_sdist.make_release_tree\\n def make_release_tree(self, base_dir, files):\\n root = get_root()\\n cfg = get_config_from_root(root)\\n _sdist.make_release_tree(self, base_dir, files)\\n # now locate _version.py in the new base_dir directory\\n # (remembering that it may be a hardlink) and replace it with an\\n # updated value\\n target_versionfile = os.path.join(base_dir, cfg.versionfile_source)\\n print(\\\"UPDATING %s\\\" % target_versionfile)\\n write_to_version_file(target_versionfile,\\n self._versioneer_generated_versions)\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::do_setup\",\n \"text\": \"versioneer.py::do_setup\\ndef do_setup():\\n root = get_root()\\n try:\\n cfg = get_config_from_root(root)\\n except (EnvironmentError, configparser.NoSectionError,\\n configparser.NoOptionError) as e:\\n if isinstance(e, (EnvironmentError, configparser.NoSectionError)):\\n print(\\\"Adding sample versioneer config to setup.cfg\\\",\\n file=sys.stderr)\\n with open(os.path.join(root, \\\"setup.cfg\\\"), \\\"a\\\") as f:\\n f.write(SAMPLE_CONFIG)\\n print(CONFIG_ERROR, file=sys.stderr)\\n return 1\\n\\n print(\\\" creating %s\\\" % cfg.versionfile_source)\\n with open(cfg.versionfile_source, \\\"w\\\") as f:\\n LONG = LONG_VERSION_PY[cfg.VCS]\\n f.write(LONG % {\\\"DOLLAR\\\": \\\"$\\\",\\n \\\"STYLE\\\": cfg.style,\\n \\\"TAG_PREFIX\\\": cfg.tag_prefix,\\n \\\"PARENTDIR_PREFIX\\\": cfg.parentdir_prefix,\\n \\\"VERSIONFILE_SOURCE\\\": cfg.versionfile_source,\\n })\\n\\n ipy = os.path.join(os.path.dirname(cfg.versionfile_source),\\n \\\"__init__.py\\\")\\n if os.path.exists(ipy):\\n try:\\n with open(ipy, \\\"r\\\") as f:\\n old = f.read()\\n except EnvironmentError:\\n old = \\\"\\\"\\n if INIT_PY_SNIPPET not in old:\\n print(\\\" appending to %s\\\" % ipy)\\n with open(ipy, \\\"a\\\") as f:\\n f.write(INIT_PY_SNIPPET)\\n else:\\n print(\\\" %s unmodified\\\" % ipy)\\n else:\\n print(\\\" %s doesn't exist, ok\\\" % ipy)\\n ipy = None\\n\\n # Make sure both the top-level \\\"versioneer.py\\\" and versionfile_source\\n # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so\\n # they'll be copied into source distributions. Pip won't be able to\\n # install the package without this.\\n manifest_in = os.path.join(root, \\\"MANIFEST.in\\\")\\n simple_includes = set()\\n try:\\n with open(manifest_in, \\\"r\\\") as f:\\n for line in f:\\n if line.st\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py::scan_setup_py\",\n \"text\": \"versioneer.py::scan_setup_py\\ndef scan_setup_py():\\n found = set()\\n setters = False\\n errors = 0\\n with open(\\\"setup.py\\\", \\\"r\\\") as f:\\n for line in f.readlines():\\n if \\\"import versioneer\\\" in line:\\n found.add(\\\"import\\\")\\n if \\\"versioneer.get_cmdclass()\\\" in line:\\n found.add(\\\"cmdclass\\\")\\n if \\\"versioneer.get_version()\\\" in line:\\n found.add(\\\"get_version\\\")\\n if \\\"versioneer.VCS\\\" in line:\\n setters = True\\n if \\\"versioneer.versionfile_source\\\" in line:\\n setters = True\\n if len(found) != 3:\\n print(\\\"\\\")\\n print(\\\"Your setup.py appears to be missing some important items\\\")\\n print(\\\"(but I might be wrong). Please make sure it has something\\\")\\n print(\\\"roughly like the following:\\\")\\n print(\\\"\\\")\\n print(\\\" import versioneer\\\")\\n print(\\\" setup( version=versioneer.get_version(),\\\")\\n print(\\\" cmdclass=versioneer.get_cmdclass(), ...)\\\")\\n print(\\\"\\\")\\n errors += 1\\n if setters:\\n print(\\\"You should remove lines like 'versioneer.VCS = ' and\\\")\\n print(\\\"'versioneer.versionfile_source = ' . This configuration\\\")\\n print(\\\"now lives in setup.cfg, and should be removed from setup.py\\\")\\n print(\\\"\\\")\\n errors += 1\\n return errors\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":280,"cells":{"instance_id":{"kind":"string","value":"Lightning-AI__lightning-409"},"repo":{"kind":"string","value":"Lightning-AI/lightning"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Error when model checkpoint and no early stop\n**Describe the bug**\r\nWhen creating a `Trainer`, if we set a `checkpoint_callback=ModelCheckpoint(...)` and `early_stop_callback = False` we get an error at this [line](https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/trainer/trainer_io.py#L157) here. \r\n\r\n**To Reproduce**\r\nSteps to reproduce the behavior:\r\n1. Create a `ckpt = ModelCheckpoint(...)`\r\n2. Create a `Trainer`\r\n3. Setting `checkpoint_callback = ckpt` and `early_stop_callback=False`\r\n4. See the error `AttributeError: 'NoneType' object has no attribute 'wait'`\r\n\r\n**Expected behavior**\r\nIt should be possible to save the model without setting an EarlyStopping condition. Of course one could set an EarlyStopping with the max integer, but changing the condition from an `or` to an `and` solves the problem. \r\n\r\n**Desktop**\r\n - OS: Ubuntu 19.04\r\n - Browser: Firefox Quantum\r\n - Version: 69.0.2\n"},"gold_files":{"kind":"list like","value":["pytorch_lightning/trainer/trainer_io.py"],"string":"[\n \"pytorch_lightning/trainer/trainer_io.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.dump_checkpoint","pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_training_state"],"string":"[\n \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.dump_checkpoint\",\n \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_training_state\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"pytorch_lightning/callbacks/pt_callbacks.py","text":"pytorch_lightning/callbacks/pt_callbacks.py\nclass Callback # Abstract base class used to build new callbacks.\n def __init__(self)\n def set_params(self, params)\n def set_model(self, model)\n def on_epoch_begin(self, epoch, logs=None)\n def on_epoch_end(self, epoch, logs=None)\n def on_batch_begin(self, batch, logs=None)\n def on_batch_end(self, batch, logs=None)\n def on_train_begin(self, logs=None)\n def on_train_end(self, logs=None)\nclass EarlyStopping # Stop training when a monitored quantity has stopped improving.\n def __init__(self, monitor='val_loss', min_delta=0.0, patience=0, verbose=0, mode='auto')\n def on_train_begin(self, logs=None)\n def on_epoch_end(self, epoch, logs=None)\n def on_train_end(self, logs=None)\nclass ModelCheckpoint # Save the model after every epoch.\n def __init__(self, filepath, monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', period=1, prefix='')\n def save_model(self, filepath, overwrite)\n def on_epoch_end(self, epoch, logs=None)\nclass GradientAccumulationScheduler # Change gradient accumulation factor according to scheduling.\n def __init__(self, scheduling: dict)\n def on_epoch_begin(self, epoch, trainer)","label":0},{"cid":"pytorch_lightning/root_module/root_module.py","text":"pytorch_lightning/root_module/root_module.py\nclass LightningModule\n def __init__(self, *args, **kwargs)\n def forward(self, *args, **kwargs) # Expand model in into whatever you need.\n def training_step(self, *args, **kwargs) # return loss, dict with metrics for tqdm\n def validation_step(self, *args, **kwargs) # return whatever outputs will need to be aggregated in validation_end\n def test_step(self, *args, **kwargs) # return whatever outputs will need to be aggregated in test_end\n def validation_end(self, outputs) # Outputs has the appended output after each validation step\n def test_end(self, outputs) # Outputs has the appended output after each test step\n def configure_optimizers(self) # Return a list of optimizers and a list of schedulers (could be empty)\n def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i, second_order_closure=None) # Do something instead of the standard optimizer behavior\n def tng_dataloader(self) # Implement a PyTorch DataLoader\n def train_dataloader(self) # Implement a PyTorch DataLoader\n def test_dataloader(self) # Implement a PyTorch DataLoader\n def val_dataloader(self) # Implement a PyTorch DataLoader\n def load_from_metrics(cls, weights_path, tags_csv) # Primary way of loading model from csv weights path\n def summarize(self, mode)\n def freeze(self)\n def unfreeze(self)","label":0},{"cid":"pytorch_lightning/trainer/amp_mixin.py","text":"pytorch_lightning/trainer/amp_mixin.py\nclass TrainerAMPMixin\n def init_amp(self, use_amp)","label":0},{"cid":"pytorch_lightning/trainer/callback_config_mixin.py","text":"pytorch_lightning/trainer/callback_config_mixin.py\nclass TrainerCallbackConfigMixin\n def configure_checkpoint_callback(self) # Weight path set in this priority:\n def configure_early_stopping(self, early_stop_callback, logger)","label":0},{"cid":"pytorch_lightning/trainer/ddp_mixin.py","text":"pytorch_lightning/trainer/ddp_mixin.py\nclass TrainerDDPMixin\n def set_distributed_mode(self, distributed_backend, nb_gpu_nodes)\n def configure_slurm_ddp(self, nb_gpu_nodes)\n def set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids)\n def ddp_train(self, gpu_nb, model) # Entry point into a DP thread\n def __init_tcp_connection(self) # Connect all procs in the world using the env:// init\n def resolve_root_node_address(self, root_node)","label":0},{"cid":"pytorch_lightning/trainer/dp_mixin.py","text":"pytorch_lightning/trainer/dp_mixin.py\nclass TrainerDPMixin\n def copy_trainer_model_properties(self, model)\n def transfer_batch_to_gpu(self, batch, gpu_id)\n def single_gpu_train(self, model)\n def dp_train(self, model)","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop_mixin.py","text":"pytorch_lightning/trainer/evaluation_loop_mixin.py\nclass TrainerEvaluationLoopMixin\n def evaluate(self, model, dataloaders, max_batches, test=False) # Run evaluation code\n def run_evaluation(self, test=False)\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False)","label":0},{"cid":"pytorch_lightning/trainer/train_loop_mixin.py","text":"pytorch_lightning/trainer/train_loop_mixin.py\nclass TrainerTrainLoopMixin\n def train(self)\n def run_training_epoch(self)\n def run_training_batch(self, batch, batch_nb)\n def training_forward(self, batch, batch_nb, opt_idx) # Handle forward for each training case (distributed, single gpu, etc...)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py","text":"pytorch_lightning/trainer/trainer.py\nclass Trainer\n def __init__(self, logger=True, checkpoint_callback=True, early_stop_callback=True, default_save_path=None, gradient_clip_val=0, gradient_clip=None, process_position=0, nb_gpu_nodes=1, gpus=None, log_gpu_memory=None, show_progress_bar=True, overfit_pct=0.0, track_grad_norm=-1, check_val_every_n_epoch=1, fast_dev_run=False, accumulate_grad_batches=1, max_nb_epochs=1000, min_nb_epochs=1, train_percent_check=1.0, val_percent_check=1.0, test_percent_check=1.0, val_check_interval=1.0, log_save_interval=100, row_log_interval=10, add_row_log_interval=None, distributed_backend=None, use_amp=False, print_nan_grads=False, weights_summary='full', weights_save_path=None, amp_level='O1', nb_sanity_val_steps=5) # :param logger: Logger for experiment tracking\n def slurm_job_id(self)\n def __parse_gpu_ids(self, gpus) # :param gpus: Int, string or list of ids\n def __set_root_gpu(self, gpus)\n def num_gpus(self)\n def data_parallel(self)\n def training_tqdm_dict(self) # Read-only for tqdm metrics\n def tng_tqdm_dic(self) # * Deprecated in v0.5.0. use training_tqdm_dict instead. *\n def fit(self, model)\n def init_optimizers(self, optimizers)\n def run_pretrain_routine(self, model) # Sanity check a few things before starting actual training\n def test(self, model=None)","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py","text":"pytorch_lightning/trainer/trainer_io.py\nclass TrainerIOMixin\n def get_model(self)\n def restore_weights(self, model) # To restore weights we have two cases.\n def restore_state_if_checkpoint_exists(self, model)\n def register_slurm_signal_handlers(self)\n def sig_handler(self, signum, frame)\n def term_handler(self, signum, frame)\n def save_checkpoint(self, filepath)\n def restore(self, checkpoint_path, on_gpu)\n def dump_checkpoint(self)\n def restore_hpc_weights_if_needed(self, model) # If there is a set of hpc weights, use as signal to restore model\n def restore_training_state(self, checkpoint) # Restore trainer state.\n def hpc_save(self, folderpath, logger)\n def hpc_load(self, folderpath, on_gpu)\n def max_ckpt_in_folder(self, path, name_key='ckpt_')\ndef load_hparams_from_tags_csv(tags_csv)\ndef convert(val)","label":1},{"cid":"pytorch_lightning/utilities/arg_parse.py","text":"pytorch_lightning/utilities/arg_parse.py\ndef add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None)","label":0},{"cid":"setup.py","text":"setup.py","label":0}],"string":"[\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py\\nclass Callback # Abstract base class used to build new callbacks.\\n def __init__(self)\\n def set_params(self, params)\\n def set_model(self, model)\\n def on_epoch_begin(self, epoch, logs=None)\\n def on_epoch_end(self, epoch, logs=None)\\n def on_batch_begin(self, batch, logs=None)\\n def on_batch_end(self, batch, logs=None)\\n def on_train_begin(self, logs=None)\\n def on_train_end(self, logs=None)\\nclass EarlyStopping # Stop training when a monitored quantity has stopped improving.\\n def __init__(self, monitor='val_loss', min_delta=0.0, patience=0, verbose=0, mode='auto')\\n def on_train_begin(self, logs=None)\\n def on_epoch_end(self, epoch, logs=None)\\n def on_train_end(self, logs=None)\\nclass ModelCheckpoint # Save the model after every epoch.\\n def __init__(self, filepath, monitor='val_loss', verbose=0, save_best_only=True, save_weights_only=False, mode='auto', period=1, prefix='')\\n def save_model(self, filepath, overwrite)\\n def on_epoch_end(self, epoch, logs=None)\\nclass GradientAccumulationScheduler # Change gradient accumulation factor according to scheduling.\\n def __init__(self, scheduling: dict)\\n def on_epoch_begin(self, epoch, trainer)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py\",\n \"text\": \"pytorch_lightning/root_module/root_module.py\\nclass LightningModule\\n def __init__(self, *args, **kwargs)\\n def forward(self, *args, **kwargs) # Expand model in into whatever you need.\\n def training_step(self, *args, **kwargs) # return loss, dict with metrics for tqdm\\n def validation_step(self, *args, **kwargs) # return whatever outputs will need to be aggregated in validation_end\\n def test_step(self, *args, **kwargs) # return whatever outputs will need to be aggregated in test_end\\n def validation_end(self, outputs) # Outputs has the appended output after each validation step\\n def test_end(self, outputs) # Outputs has the appended output after each test step\\n def configure_optimizers(self) # Return a list of optimizers and a list of schedulers (could be empty)\\n def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i, second_order_closure=None) # Do something instead of the standard optimizer behavior\\n def tng_dataloader(self) # Implement a PyTorch DataLoader\\n def train_dataloader(self) # Implement a PyTorch DataLoader\\n def test_dataloader(self) # Implement a PyTorch DataLoader\\n def val_dataloader(self) # Implement a PyTorch DataLoader\\n def load_from_metrics(cls, weights_path, tags_csv) # Primary way of loading model from csv weights path\\n def summarize(self, mode)\\n def freeze(self)\\n def unfreeze(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/amp_mixin.py\",\n \"text\": \"pytorch_lightning/trainer/amp_mixin.py\\nclass TrainerAMPMixin\\n def init_amp(self, use_amp)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_config_mixin.py\",\n \"text\": \"pytorch_lightning/trainer/callback_config_mixin.py\\nclass TrainerCallbackConfigMixin\\n def configure_checkpoint_callback(self) # Weight path set in this priority:\\n def configure_early_stopping(self, early_stop_callback, logger)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/ddp_mixin.py\",\n \"text\": \"pytorch_lightning/trainer/ddp_mixin.py\\nclass TrainerDDPMixin\\n def set_distributed_mode(self, distributed_backend, nb_gpu_nodes)\\n def configure_slurm_ddp(self, nb_gpu_nodes)\\n def set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids)\\n def ddp_train(self, gpu_nb, model) # Entry point into a DP thread\\n def __init_tcp_connection(self) # Connect all procs in the world using the env:// init\\n def resolve_root_node_address(self, root_node)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/dp_mixin.py\",\n \"text\": \"pytorch_lightning/trainer/dp_mixin.py\\nclass TrainerDPMixin\\n def copy_trainer_model_properties(self, model)\\n def transfer_batch_to_gpu(self, batch, gpu_id)\\n def single_gpu_train(self, model)\\n def dp_train(self, model)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop_mixin.py\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop_mixin.py\\nclass TrainerEvaluationLoopMixin\\n def evaluate(self, model, dataloaders, max_batches, test=False) # Run evaluation code\\n def run_evaluation(self, test=False)\\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/train_loop_mixin.py\",\n \"text\": \"pytorch_lightning/trainer/train_loop_mixin.py\\nclass TrainerTrainLoopMixin\\n def train(self)\\n def run_training_epoch(self)\\n def run_training_batch(self, batch, batch_nb)\\n def training_forward(self, batch, batch_nb, opt_idx) # Handle forward for each training case (distributed, single gpu, etc...)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py\",\n \"text\": \"pytorch_lightning/trainer/trainer.py\\nclass Trainer\\n def __init__(self, logger=True, checkpoint_callback=True, early_stop_callback=True, default_save_path=None, gradient_clip_val=0, gradient_clip=None, process_position=0, nb_gpu_nodes=1, gpus=None, log_gpu_memory=None, show_progress_bar=True, overfit_pct=0.0, track_grad_norm=-1, check_val_every_n_epoch=1, fast_dev_run=False, accumulate_grad_batches=1, max_nb_epochs=1000, min_nb_epochs=1, train_percent_check=1.0, val_percent_check=1.0, test_percent_check=1.0, val_check_interval=1.0, log_save_interval=100, row_log_interval=10, add_row_log_interval=None, distributed_backend=None, use_amp=False, print_nan_grads=False, weights_summary='full', weights_save_path=None, amp_level='O1', nb_sanity_val_steps=5) # :param logger: Logger for experiment tracking\\n def slurm_job_id(self)\\n def __parse_gpu_ids(self, gpus) # :param gpus: Int, string or list of ids\\n def __set_root_gpu(self, gpus)\\n def num_gpus(self)\\n def data_parallel(self)\\n def training_tqdm_dict(self) # Read-only for tqdm metrics\\n def tng_tqdm_dic(self) # * Deprecated in v0.5.0. use training_tqdm_dict instead. *\\n def fit(self, model)\\n def init_optimizers(self, optimizers)\\n def run_pretrain_routine(self, model) # Sanity check a few things before starting actual training\\n def test(self, model=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py\\nclass TrainerIOMixin\\n def get_model(self)\\n def restore_weights(self, model) # To restore weights we have two cases.\\n def restore_state_if_checkpoint_exists(self, model)\\n def register_slurm_signal_handlers(self)\\n def sig_handler(self, signum, frame)\\n def term_handler(self, signum, frame)\\n def save_checkpoint(self, filepath)\\n def restore(self, checkpoint_path, on_gpu)\\n def dump_checkpoint(self)\\n def restore_hpc_weights_if_needed(self, model) # If there is a set of hpc weights, use as signal to restore model\\n def restore_training_state(self, checkpoint) # Restore trainer state.\\n def hpc_save(self, folderpath, logger)\\n def hpc_load(self, folderpath, on_gpu)\\n def max_ckpt_in_folder(self, path, name_key='ckpt_')\\ndef load_hparams_from_tags_csv(tags_csv)\\ndef convert(val)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/utilities/arg_parse.py\",\n \"text\": \"pytorch_lightning/utilities/arg_parse.py\\ndef add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.__init__","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.__init__\n def __init__(self):\n self.validation_data = None\n self.model = None","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_params","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_params\n def set_params(self, params):\n self.params = params","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_model","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_model\n def set_model(self, model):\n if type(model) is LightningDistributedDataParallel:\n model = model.module\n self.model = model","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_begin","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_begin\n def on_epoch_begin(self, epoch, logs=None):\n pass","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_end","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_end\n def on_epoch_end(self, epoch, logs=None):\n pass","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_begin","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_begin\n def on_batch_begin(self, batch, logs=None):\n pass","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_end","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_end\n def on_batch_end(self, batch, logs=None):\n pass","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_begin","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_begin\n def on_train_begin(self, logs=None):\n pass","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_end","text":"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_end\n def on_train_end(self, logs=None):\n pass","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.__init__","text":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.__init__\n def __init__(self, monitor='val_loss',\n min_delta=0.0, patience=0, verbose=0, mode='auto'):\n super(EarlyStopping, self).__init__()\n\n self.monitor = monitor\n self.patience = patience\n self.verbose = verbose\n self.min_delta = min_delta\n self.wait = 0\n self.stopped_epoch = 0\n\n if mode not in ['auto', 'min', 'max']:\n print('EarlyStopping mode %s is unknown, fallback to auto mode.' % mode)\n mode = 'auto'\n\n if mode == 'min':\n self.monitor_op = np.less\n elif mode == 'max':\n self.monitor_op = np.greater\n else:\n if 'acc' in self.monitor:\n self.monitor_op = np.greater\n else:\n self.monitor_op = np.less\n\n if self.monitor_op == np.greater:\n self.min_delta *= 1\n else:\n self.min_delta *= -1\n\n self.on_train_begin()","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_begin","text":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_begin\n def on_train_begin(self, logs=None):\n # Allow instances to be re-used\n self.wait = 0\n self.stopped_epoch = 0\n self.best = np.Inf if self.monitor_op == np.less else -np.Inf","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_epoch_end","text":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_epoch_end\n def on_epoch_end(self, epoch, logs=None):\n current = logs.get(self.monitor)\n stop_training = False\n if current is None:\n print('Early stopping conditioned on metric `%s` '\n 'which is not available. Available metrics are: %s' %\n (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning)\n stop_training = True\n return stop_training\n\n if self.monitor_op(current - self.min_delta, self.best):\n self.best = current\n self.wait = 0\n else:\n self.wait += 1\n if self.wait >= self.patience:\n self.stopped_epoch = epoch\n stop_training = True\n self.on_train_end()\n\n return stop_training","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_end","text":"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_end\n def on_train_end(self, logs=None):\n if self.stopped_epoch > 0 and self.verbose > 0:\n print('Epoch %05d: early stopping' % (self.stopped_epoch + 1))","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.__init__","text":"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.__init__\n def __init__(self, filepath, monitor='val_loss', verbose=0,\n save_best_only=True, save_weights_only=False,\n mode='auto', period=1, prefix=''):\n super(ModelCheckpoint, self).__init__()\n self.monitor = monitor\n self.verbose = verbose\n self.filepath = filepath\n self.save_best_only = save_best_only\n self.save_weights_only = save_weights_only\n self.period = period\n self.epochs_since_last_save = 0\n self.prefix = prefix\n\n if mode not in ['auto', 'min', 'max']:\n print('ModelCheckpoint mode %s is unknown, '\n 'fallback to auto mode.' % (mode), RuntimeWarning)\n mode = 'auto'\n\n if mode == 'min':\n self.monitor_op = np.less\n self.best = np.Inf\n elif mode == 'max':\n self.monitor_op = np.greater\n self.best = -np.Inf\n else:\n if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):\n self.monitor_op = np.greater\n self.best = -np.Inf\n else:\n self.monitor_op = np.less\n self.best = np.Inf","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.save_model","text":"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.save_model\n def save_model(self, filepath, overwrite):\n dirpath = '/'.join(filepath.split('/')[:-1])\n\n # make paths\n os.makedirs(os.path.dirname(filepath), exist_ok=True)\n\n if overwrite:\n for filename in os.listdir(dirpath):\n if self.prefix in filename:\n path_to_delete = os.path.join(dirpath, filename)\n try:\n shutil.rmtree(path_to_delete)\n except OSError:\n os.remove(path_to_delete)\n\n # delegate the saving to the model\n self.save_function(filepath)","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.on_epoch_end","text":"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.on_epoch_end\n def on_epoch_end(self, epoch, logs=None):\n logs = logs or {}\n self.epochs_since_last_save += 1\n if self.epochs_since_last_save >= self.period:\n self.epochs_since_last_save = 0\n filepath = '{}/{}_ckpt_epoch_{}.ckpt'.format(self.filepath, self.prefix, epoch + 1)\n if self.save_best_only:\n current = logs.get(self.monitor)\n if current is None:\n print('Can save best model only with %s available,'\n ' skipping.' % (self.monitor), RuntimeWarning)\n else:\n if self.monitor_op(current, self.best):\n if self.verbose > 0:\n print('\\nEpoch %05d: %s improved from %0.5f to %0.5f,'\n ' saving model to %s'\n % (epoch + 1, self.monitor, self.best,\n current, filepath))\n self.best = current\n self.save_model(filepath, overwrite=True)\n\n else:\n if self.verbose > 0:\n print('\\nEpoch %05d: %s did not improve' %\n (epoch + 1, self.monitor))\n else:\n if self.verbose > 0:\n print('\\nEpoch %05d: saving model to %s' % (epoch + 1, filepath))\n self.save_model(filepath, overwrite=False)","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::GradientAccumulationScheduler.__init__","text":"pytorch_lightning/callbacks/pt_callbacks.py::GradientAccumulationScheduler.__init__\n def __init__(self, scheduling: dict):\n if scheduling == {}: # empty dict error\n raise TypeError(\"Empty dict cannot be interpreted correct\")\n\n for key in scheduling.keys():\n if not isinstance(key, int) or not isinstance(scheduling[key], int):\n raise TypeError(\"All epoches and accumulation factor must be integers\")\n\n minimal_epoch = min(scheduling.keys())\n if minimal_epoch < 1:\n msg = f\"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct\"\n raise IndexError(msg)\n elif minimal_epoch != 1: # if user didnt define first epoch accumulation factor\n scheduling.update({1: 1})\n\n self.scheduling = scheduling\n self.epochs = sorted(scheduling.keys())","label":0},{"cid":"pytorch_lightning/callbacks/pt_callbacks.py::GradientAccumulationScheduler.on_epoch_begin","text":"pytorch_lightning/callbacks/pt_callbacks.py::GradientAccumulationScheduler.on_epoch_begin\n def on_epoch_begin(self, epoch, trainer):\n epoch += 1 # indexing epochs from 1\n for i in reversed(range(len(self.epochs))):\n if epoch >= self.epochs[i]:\n trainer.accumulate_grad_batches = self.scheduling.get(self.epochs[i])\n break","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.__init__","text":"pytorch_lightning/root_module/root_module.py::LightningModule.__init__\n def __init__(self, *args, **kwargs):\n super(LightningModule, self).__init__(*args, **kwargs)\n\n self.dtype = torch.FloatTensor\n self.exp_save_path = None\n self.current_epoch = 0\n self.global_step = 0\n self.loaded_optimizer_states_dict = {}\n self.trainer = None\n self.logger = None\n self.example_input_array = None\n\n # track if gpu was requested for checkpointing\n self.on_gpu = False\n self.use_dp = False\n self.use_ddp = False\n self.use_ddp2 = False\n self.use_amp = False","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.forward","text":"pytorch_lightning/root_module/root_module.py::LightningModule.forward\n def forward(self, *args, **kwargs):\n \"\"\"\n Expand model in into whatever you need.\n Also need to return the target\n :param x:\n :return:\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.training_step","text":"pytorch_lightning/root_module/root_module.py::LightningModule.training_step\n def training_step(self, *args, **kwargs):\n \"\"\"\n return loss, dict with metrics for tqdm\n :param called with batch, batch_nb\n additional: optimizer_i if multiple optimizers used\n :return:\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.validation_step","text":"pytorch_lightning/root_module/root_module.py::LightningModule.validation_step\n def validation_step(self, *args, **kwargs):\n \"\"\"\n return whatever outputs will need to be aggregated in validation_end\n OPTIONAL\n :param called with batch, batch_nb\n additional: dataset_i if multiple val datasets used\n :return:\n \"\"\"\n pass","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.test_step","text":"pytorch_lightning/root_module/root_module.py::LightningModule.test_step\n def test_step(self, *args, **kwargs):\n \"\"\"\n return whatever outputs will need to be aggregated in test_end\n OPTIONAL\n :param called with batch, batch_nb\n additional: dataset_i if multiple val datasets used\n :return:\n \"\"\"\n pass","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.validation_end","text":"pytorch_lightning/root_module/root_module.py::LightningModule.validation_end\n def validation_end(self, outputs):\n \"\"\"\n Outputs has the appended output after each validation step\n OPTIONAL\n :param outputs:\n :return: dic_with_metrics for tqdm\n \"\"\"\n pass","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.test_end","text":"pytorch_lightning/root_module/root_module.py::LightningModule.test_end\n def test_end(self, outputs):\n \"\"\"\n Outputs has the appended output after each test step\n OPTIONAL\n :param outputs:\n :return: dic_with_metrics for tqdm\n \"\"\"\n pass","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.configure_optimizers","text":"pytorch_lightning/root_module/root_module.py::LightningModule.configure_optimizers\n def configure_optimizers(self):\n \"\"\"\n Return a list of optimizers and a list of schedulers (could be empty)\n :return:\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.optimizer_step","text":"pytorch_lightning/root_module/root_module.py::LightningModule.optimizer_step\n def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i, second_order_closure=None):\n \"\"\"\n Do something instead of the standard optimizer behavior\n :param epoch_nb:\n :param batch_nb:\n :param optimizer:\n :param optimizer_i:\n :param second_order_closure: closure for second order methods\n :return:\n \"\"\"\n if isinstance(optimizer, torch.optim.LBFGS):\n optimizer.step(second_order_closure)\n else:\n optimizer.step()\n\n # clear gradients\n optimizer.zero_grad()","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.tng_dataloader","text":"pytorch_lightning/root_module/root_module.py::LightningModule.tng_dataloader\n @data_loader\n def tng_dataloader(self):\n \"\"\"\n Implement a PyTorch DataLoader\n * Deprecated in v0.5.0. use train_dataloader instead. *\n :return:\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.train_dataloader","text":"pytorch_lightning/root_module/root_module.py::LightningModule.train_dataloader\n @data_loader\n def train_dataloader(self):\n \"\"\"\n Implement a PyTorch DataLoader\n :return:\n \"\"\"\n #\n try:\n output = self.tng_dataloader()\n warnings.warn(\"tng_dataloader has been renamed to train_dataloader since v0.5.0\",\n DeprecationWarning)\n return output\n except NotImplementedError:\n raise NotImplementedError","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.test_dataloader","text":"pytorch_lightning/root_module/root_module.py::LightningModule.test_dataloader\n @data_loader\n def test_dataloader(self):\n \"\"\"\n Implement a PyTorch DataLoader\n :return:\n \"\"\"\n return None","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.val_dataloader","text":"pytorch_lightning/root_module/root_module.py::LightningModule.val_dataloader\n @data_loader\n def val_dataloader(self):\n \"\"\"\n Implement a PyTorch DataLoader\n :return:\n \"\"\"\n return None","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.load_from_metrics","text":"pytorch_lightning/root_module/root_module.py::LightningModule.load_from_metrics\n @classmethod\n def load_from_metrics(cls, weights_path, tags_csv):\n \"\"\"\n Primary way of loading model from csv weights path\n :param weights_path:\n :param tags_csv:\n :param map_location: dic for mapping storage {'cuda:1':'cuda:0'}\n :return:\n \"\"\"\n hparams = load_hparams_from_tags_csv(tags_csv)\n hparams.__setattr__('on_gpu', False)\n\n # load on CPU only to avoid OOM issues\n # then its up to user to put back on GPUs\n checkpoint = torch.load(weights_path, map_location=lambda storage, loc: storage)\n\n # load the state_dict on the model automatically\n model = cls(hparams)\n model.load_state_dict(checkpoint['state_dict'])\n\n # give model a chance to load something\n model.on_load_checkpoint(checkpoint)\n\n return model","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.summarize","text":"pytorch_lightning/root_module/root_module.py::LightningModule.summarize\n def summarize(self, mode):\n model_summary = ModelSummary(self, mode=mode)\n print(model_summary)","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.freeze","text":"pytorch_lightning/root_module/root_module.py::LightningModule.freeze\n def freeze(self):\n for param in self.parameters():\n param.requires_grad = False","label":0},{"cid":"pytorch_lightning/root_module/root_module.py::LightningModule.unfreeze","text":"pytorch_lightning/root_module/root_module.py::LightningModule.unfreeze\n def unfreeze(self):\n for param in self.parameters():\n param.requires_grad = True","label":0},{"cid":"pytorch_lightning/trainer/amp_mixin.py::TrainerAMPMixin.init_amp","text":"pytorch_lightning/trainer/amp_mixin.py::TrainerAMPMixin.init_amp\n def init_amp(self, use_amp):\n self.use_amp = use_amp and APEX_AVAILABLE\n if self.use_amp:\n print('using 16bit precision')\n\n if use_amp and not APEX_AVAILABLE: # pragma: no cover\n msg = \"\"\"\n You set use_amp=True but do not have apex installed.\n Install apex first using this guide and rerun with use_amp=True:\n https://github.com/NVIDIA/apex#linux\n\n this run will NOT use 16 bit precision\n \"\"\"\n raise ModuleNotFoundError(msg)","label":0},{"cid":"pytorch_lightning/trainer/callback_config_mixin.py::TrainerCallbackConfigMixin.configure_checkpoint_callback","text":"pytorch_lightning/trainer/callback_config_mixin.py::TrainerCallbackConfigMixin.configure_checkpoint_callback\n def configure_checkpoint_callback(self):\n \"\"\"\n Weight path set in this priority:\n Checkpoint_callback's path (if passed in).\n User provided weights_saved_path\n Otherwise use os.getcwd()\n \"\"\"\n if self.checkpoint_callback is True:\n # init a default one\n if isinstance(self.logger, TestTubeLogger):\n ckpt_path = '{}/{}/version_{}/{}'.format(\n self.default_save_path,\n self.logger.experiment.name,\n self.logger.experiment.version,\n 'checkpoints')\n else:\n ckpt_path = self.default_save_path\n\n self.checkpoint_callback = ModelCheckpoint(\n filepath=ckpt_path\n )\n elif self.checkpoint_callback is False:\n self.checkpoint_callback = None\n\n if self.checkpoint_callback:\n # set the path for the callbacks\n self.checkpoint_callback.save_function = self.save_checkpoint\n\n # if checkpoint callback used, then override the weights path\n self.weights_save_path = self.checkpoint_callback.filepath\n\n # if weights_save_path is still none here, set to current working dir\n if self.weights_save_path is None:\n self.weights_save_path = self.default_save_path","label":0},{"cid":"pytorch_lightning/trainer/callback_config_mixin.py::TrainerCallbackConfigMixin.configure_early_stopping","text":"pytorch_lightning/trainer/callback_config_mixin.py::TrainerCallbackConfigMixin.configure_early_stopping\n def configure_early_stopping(self, early_stop_callback, logger):\n if early_stop_callback is True:\n self.early_stop_callback = EarlyStopping(\n monitor='val_loss',\n patience=3,\n verbose=True,\n mode='min'\n )\n self.enable_early_stop = True\n elif not early_stop_callback:\n self.early_stop_callback = None\n self.enable_early_stop = False\n else:\n self.early_stop_callback = early_stop_callback\n self.enable_early_stop = True\n\n # configure logger\n if logger is True:\n # default logger\n self.logger = TestTubeLogger(\n save_dir=self.default_save_path,\n version=self.slurm_job_id,\n name='lightning_logs'\n )\n self.logger.rank = 0\n elif logger is False:\n self.logger = None\n else:\n self.logger = logger\n self.logger.rank = 0","label":0},{"cid":"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.set_distributed_mode","text":"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.set_distributed_mode\n def set_distributed_mode(self, distributed_backend, nb_gpu_nodes):\n # skip for CPU\n if self.num_gpus == 0:\n return\n\n # single GPU case\n # in single gpu case we allow ddp so we can train on multiple\n # nodes, 1 gpu per node\n if self.num_gpus == 1:\n self.single_gpu = True\n\n if distributed_backend is not None:\n self.use_dp = distributed_backend == 'dp'\n self.use_ddp = distributed_backend == 'ddp'\n self.use_ddp2 = distributed_backend == 'ddp2'\n\n # disable single gpu when using ddp2\n if self.use_ddp2:\n self.single_gpu = False\n\n # multiple GPU case\n elif self.num_gpus > 1:\n if distributed_backend is not None:\n # DP, DDP case\n self.use_dp = distributed_backend == 'dp'\n self.use_ddp = distributed_backend == 'ddp'\n self.use_ddp2 = distributed_backend == 'ddp2'\n\n elif distributed_backend is None:\n m = 'When using multiple GPUs set ' \\\n 'Trainer(distributed_backend=dp) (or ddp)'\n raise MisconfigurationException(m)\n\n # use ddp automatically if nb_gpu_nodes > 1\n if nb_gpu_nodes > 1 and self.use_dp: # pragma: no cover\n self.use_ddp = True\n self.use_dp = False\n w = 'DataParallel does not support nb_gpu_nodes > 1. ' \\\n 'Switching to DistributedDataParallel for you. ' \\\n 'To silence this warning set distributed_backend=ddp'\n warnings.warn(w)\n\n print('gpu available: {}, used: {}'.format(torch.cuda.is_available(), self.on_gpu))","label":0},{"cid":"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.configure_slurm_ddp","text":"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.configure_slurm_ddp\n def configure_slurm_ddp(self, nb_gpu_nodes):\n self.is_slurm_managing_tasks = False\n\n # extract SLURM flag vars\n # whenever we have the correct number of tasks, we let slurm manage processes\n # otherwise we launch the required number of processes\n if self.use_ddp:\n self.nb_requested_gpus = self.num_gpus * nb_gpu_nodes\n self.nb_slurm_tasks = 0\n try:\n self.nb_slurm_tasks = int(os.environ['SLURM_NTASKS'])\n self.is_slurm_managing_tasks = self.nb_slurm_tasks == self.nb_requested_gpus\n\n # in interactive mode we don't manage tasks\n job_name = os.environ['SLURM_JOB_NAME']\n if job_name == 'bash':\n self.is_slurm_managing_tasks = False\n\n except Exception:\n # likely not on slurm, so set the slurm managed flag to false\n self.is_slurm_managing_tasks = False\n\n # used for tests only, set this flag to simulate slurm managing a task\n try:\n should_fake = int(os.environ['FAKE_SLURM_MANAGING_TASKS'])\n if should_fake:\n self.is_slurm_managing_tasks = True\n except Exception as e:\n pass","label":0},{"cid":"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.set_nvidia_flags","text":"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.set_nvidia_flags\n def set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids):\n if data_parallel_device_ids is None:\n return\n\n # set the correct cuda visible devices (using pci order)\n os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n\n # when slurm is managing the task it sets the visible devices\n if not is_slurm_managing_tasks:\n if type(data_parallel_device_ids) is int:\n id_str = ','.join(str(x) for x in list(range(data_parallel_device_ids)))\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = id_str\n else:\n gpu_str = ','.join([str(x) for x in data_parallel_device_ids])\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = gpu_str\n\n print(f'VISIBLE GPUS: {os.environ[\"CUDA_VISIBLE_DEVICES\"]}')","label":0},{"cid":"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.ddp_train","text":"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.ddp_train\n def ddp_train(self, gpu_nb, model):\n \"\"\"\n Entry point into a DP thread\n :param gpu_nb:\n :param model:\n :param cluster_obj:\n :return:\n \"\"\"\n # node rank using relative slurm id\n # otherwise default to node rank 0\n try:\n node_id = os.environ['SLURM_NODEID']\n self.node_rank = int(node_id)\n except Exception:\n self.node_rank = 0\n\n # show progressbar only on progress_rank 0\n self.show_progress_bar = self.show_progress_bar and self.node_rank == 0 and gpu_nb == 0\n\n # determine which process we are and world size\n if self.use_ddp:\n self.proc_rank = self.node_rank * self.num_gpus + gpu_nb\n self.world_size = self.nb_gpu_nodes * self.num_gpus\n\n elif self.use_ddp2:\n self.proc_rank = self.node_rank\n self.world_size = self.nb_gpu_nodes\n\n # let the exp know the rank to avoid overwriting logs\n if self.logger is not None:\n self.logger.rank = self.proc_rank\n\n # set up server using proc 0's ip address\n # try to init for 20 times at max in case ports are taken\n # where to store ip_table\n self.__init_tcp_connection()\n\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n # MODEL\n # copy model to each gpu\n if self.distributed_backend == 'ddp':\n torch.cuda.set_device(gpu_nb)\n model.cuda(gpu_nb)\n\n # set model properties before going into wrapper\n self.copy_trainer_model_properties(model)\n\n # override root GPU\n self.root_gpu = gpu_nb\n\n # AMP\n # run through amp wrapper before going to distributed DP\n if self.use_amp:\n # An example\n model, optimizers = amp.initialize(\n","label":0},{"cid":"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.__init_tcp_connection","text":"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.__init_tcp_connection\n def __init_tcp_connection(self):\n \"\"\"\n Connect all procs in the world using the env:// init\n Use the first node as the root address\n :param port:\n :param tries:\n :return:\n \"\"\"\n\n # use slurm job id for the port number\n # guarantees unique ports across jobs from same grid search\n try:\n # use the last 4 numbers in the job id as the id\n default_port = os.environ['SLURM_JOB_ID']\n default_port = default_port[-4:]\n\n # all ports should be in the 10k+ range\n default_port = int(default_port) + 15000\n\n except Exception as e:\n default_port = 12910\n\n # if user gave a port number, use that one instead\n try:\n default_port = os.environ['MASTER_PORT']\n except Exception:\n os.environ['MASTER_PORT'] = str(default_port)\n\n # figure out the root node addr\n try:\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\n except Exception:\n root_node = '127.0.0.2'\n\n root_node = self.resolve_root_node_address(root_node)\n os.environ['MASTER_ADDR'] = root_node\n dist.init_process_group(\"nccl\", rank=self.proc_rank, world_size=self.world_size)","label":0},{"cid":"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.resolve_root_node_address","text":"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.resolve_root_node_address\n def resolve_root_node_address(self, root_node):\n if '[' in root_node:\n name = root_node.split('[')[0]\n number = root_node.split(',')[0]\n if '-' in number:\n number = number.split('-')[0]\n\n number = re.sub('[^0-9]', '', number)\n root_node = name + number\n\n return root_node","label":0},{"cid":"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.copy_trainer_model_properties","text":"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.copy_trainer_model_properties\n def copy_trainer_model_properties(self, model):\n if isinstance(model, LightningDataParallel):\n ref_model = model.module\n elif isinstance(model, LightningDistributedDataParallel):\n ref_model = model.module\n else:\n ref_model = model\n\n for m in [model, ref_model]:\n m.trainer = self\n m.on_gpu = self.on_gpu\n m.use_dp = self.use_dp\n m.use_ddp2 = self.use_ddp2\n m.use_ddp = self.use_ddp\n m.use_amp = self.use_amp\n m.testing = self.testing\n m.single_gpu = self.single_gpu","label":0},{"cid":"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.transfer_batch_to_gpu","text":"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.transfer_batch_to_gpu\n def transfer_batch_to_gpu(self, batch, gpu_id):\n # base case: object can be directly moved using `cuda` or `to`\n if callable(getattr(batch, 'cuda', None)):\n return batch.cuda(gpu_id)\n\n elif callable(getattr(batch, 'to', None)):\n return batch.to(torch.device('cuda', gpu_id))\n\n # when list\n elif isinstance(batch, list):\n for i, x in enumerate(batch):\n batch[i] = self.transfer_batch_to_gpu(x, gpu_id)\n return batch\n\n # when tuple\n elif isinstance(batch, tuple):\n batch = list(batch)\n for i, x in enumerate(batch):\n batch[i] = self.transfer_batch_to_gpu(x, gpu_id)\n return tuple(batch)\n\n # when dict\n elif isinstance(batch, dict):\n for k, v in batch.items():\n batch[k] = self.transfer_batch_to_gpu(v, gpu_id)\n\n return batch\n\n # nothing matches, return the value as is without transform\n return batch","label":0},{"cid":"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.single_gpu_train","text":"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.single_gpu_train\n def single_gpu_train(self, model):\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n model.cuda(self.root_gpu)\n\n if self.use_amp:\n # An example\n model, optimizers = amp.initialize(\n model, self.optimizers, opt_level=self.amp_level,\n )\n self.optimizers = optimizers\n\n self.run_pretrain_routine(model)","label":0},{"cid":"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.dp_train","text":"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.dp_train\n def dp_train(self, model):\n\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n model.cuda(self.root_gpu)\n\n # check for this bug (amp + dp + !01 doesn't work)\n # https://github.com/NVIDIA/apex/issues/227\n if self.use_dp and self.use_amp:\n m = f\"\"\"\n Amp level {self.amp_level} with DataParallel is not supported.\n See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227.\n We recommend you switch to ddp if you want to use amp\n \"\"\"\n raise MisconfigurationException(m)\n\n # create list of device ids\n device_ids = self.data_parallel_device_ids\n if type(device_ids) is int:\n device_ids = list(range(device_ids))\n\n model = LightningDataParallel(model, device_ids=device_ids)\n\n self.run_pretrain_routine(model)","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop_mixin.py::TrainerEvaluationLoopMixin.evaluate","text":"pytorch_lightning/trainer/evaluation_loop_mixin.py::TrainerEvaluationLoopMixin.evaluate\n def evaluate(self, model, dataloaders, max_batches, test=False):\n \"\"\"\n Run evaluation code\n :param model: PT model\n :param dataloaders: list of PT dataloaders\n :param max_batches: Scalar\n :param test: boolean\n :return:\n \"\"\"\n # enable eval mode\n model.zero_grad()\n model.eval()\n\n # copy properties for forward overrides\n self.copy_trainer_model_properties(model)\n\n # disable gradients to save memory\n torch.set_grad_enabled(False)\n\n # bookkeeping\n outputs = []\n\n # run training\n for dataloader_idx, dataloader in enumerate(dataloaders):\n dl_outputs = []\n for batch_idx, batch in enumerate(dataloader):\n\n if batch is None: # pragma: no cover\n continue\n\n # stop short when on fast_dev_run (sets max_batch=1)\n if batch_idx >= max_batches:\n break\n\n # -----------------\n # RUN EVALUATION STEP\n # -----------------\n output = self.evaluation_forward(model,\n batch,\n batch_idx,\n dataloader_idx,\n test)\n\n # track outputs for collation\n dl_outputs.append(output)\n\n # batch done\n if self.show_progress_bar:\n self.progress_bar.update(1)\n outputs.append(dl_outputs)\n\n eval_results = {}\n\n # with a single dataloader don't pass an array\n if len(dataloaders) == 1:\n outputs = outputs[0]\n\n # give model a chance to do something with the outputs (and method defined)\n model = self.get_model()\n ","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop_mixin.py::TrainerEvaluationLoopMixin.run_evaluation","text":"pytorch_lightning/trainer/evaluation_loop_mixin.py::TrainerEvaluationLoopMixin.run_evaluation\n def run_evaluation(self, test=False):\n # when testing make sure user defined a test step\n can_run_test_step = False\n if test:\n can_run_test_step = self.is_overriden('test_step') and self.is_overriden('test_end')\n if not can_run_test_step:\n m = '''You called .test() without defining a test step or test_end.\n Please define and try again'''\n raise MisconfigurationException(m)\n\n # validate only if model has validation_step defined\n # test only if test_step or validation_step are defined\n run_val_step = self.is_overriden('validation_step')\n\n if run_val_step or can_run_test_step:\n\n # hook\n model = self.get_model()\n model.on_pre_performance_check()\n\n # select dataloaders\n if test:\n dataloaders = self.get_test_dataloaders()\n max_batches = self.nb_test_batches\n else:\n # val\n dataloaders = self.get_val_dataloaders()\n max_batches = self.nb_val_batches\n\n # cap max batches to 1 when using fast_dev_run\n if self.fast_dev_run:\n max_batches = 1\n\n # run evaluation\n eval_results = self.evaluate(self.model,\n dataloaders,\n max_batches,\n test)\n _, prog_bar_metrics, log_metrics, callback_metrics = self.process_output(eval_results)\n\n # add metrics to prog bar\n self.add_tqdm_metrics(prog_bar_metrics)\n\n # log metrics\n self.log_metrics(log_metrics, {})\n\n # track metrics for callbacks\n self.callback_metrics = callback_metrics\n\n # hook\n model.on_post_performance_check()\n\n ","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop_mixin.py::TrainerEvaluationLoopMixin.evaluation_forward","text":"pytorch_lightning/trainer/evaluation_loop_mixin.py::TrainerEvaluationLoopMixin.evaluation_forward\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False):\n # make dataloader_idx arg in validation_step optional\n args = [batch, batch_idx]\n\n if test and len(self.get_test_dataloaders()) > 1:\n args.append(dataloader_idx)\n\n elif not test and len(self.get_val_dataloaders()) > 1:\n args.append(dataloader_idx)\n\n # handle DP, DDP forward\n if self.use_ddp or self.use_dp or self.use_ddp2:\n output = model(*args)\n return output\n\n # single GPU\n if self.single_gpu:\n # for single GPU put inputs on gpu manually\n root_gpu = 0\n if type(self.data_parallel_device_ids) is list:\n root_gpu = self.data_parallel_device_ids[0]\n batch = self.transfer_batch_to_gpu(batch, root_gpu)\n args[0] = batch\n\n # CPU\n if test:\n output = model.test_step(*args)\n else:\n output = model.validation_step(*args)\n\n return output","label":0},{"cid":"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.train","text":"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.train\n def train(self):\n # run all epochs\n for epoch_nb in range(self.current_epoch, self.max_nb_epochs):\n # set seed for distributed sampler (enables shuffling for each epoch)\n if self.use_ddp and hasattr(self.get_train_dataloader().sampler, 'set_epoch'):\n self.get_train_dataloader().sampler.set_epoch(epoch_nb)\n\n # get model\n model = self.get_model()\n\n # update training progress in trainer and model\n model.current_epoch = epoch_nb\n self.current_epoch = epoch_nb\n self.total_batches = self.nb_training_batches + self.nb_val_batches\n self.batch_loss_value = 0 # accumulated grads\n\n # limit the number of batches to 1 in fast_dev_run\n if self.fast_dev_run:\n self.total_batches = 1\n\n # init progress_bar when requested\n if self.show_progress_bar:\n nb_iterations = self.total_batches\n\n # for iterable train loader, the progress bar never ends\n if self.is_iterable_train_dataloader:\n nb_iterations = float('inf')\n self.progress_bar.reset(nb_iterations)\n\n # changing gradient according accumulation_scheduler\n self.accumulation_scheduler.on_epoch_begin(epoch_nb, self)\n\n # -----------------\n # RUN TNG EPOCH\n # -----------------\n self.run_training_epoch()\n\n # update LR schedulers\n if self.lr_schedulers is not None:\n for lr_scheduler in self.lr_schedulers:\n lr_scheduler.step(self.current_epoch)\n\n # early stopping\n met_min_epochs = epoch_nb > self.min_nb_epochs\n if self.enable_early_stop and (met_min_epochs or self.fast_dev_run):\n should_stop = self.early_stop_callback.on_epoch_end(epoch","label":0},{"cid":"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.run_training_epoch","text":"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.run_training_epoch\n def run_training_epoch(self):\n # before epoch hook\n if self.is_function_implemented('on_epoch_start'):\n model = self.get_model()\n model.on_epoch_start()\n\n # run epoch\n for batch_nb, batch in enumerate(self.get_train_dataloader()):\n self.batch_nb = batch_nb\n self.global_step += 1\n\n model = self.get_model()\n model.global_step = self.global_step\n\n # stop when the flag is changed or we've gone past the amount\n # requested in the batches\n self.total_batch_nb += 1\n met_batch_limit = batch_nb >= self.nb_training_batches\n if met_batch_limit:\n break\n\n # ---------------\n # RUN TRAIN STEP\n # ---------------\n output = self.run_training_batch(batch, batch_nb)\n batch_result, grad_norm_dic, batch_step_metrics = output\n early_stop_epoch = batch_result == -1\n\n # ---------------\n # RUN VAL STEP\n # ---------------\n is_val_check_batch = (batch_nb + 1) % self.val_check_batch == 0\n can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0\n should_check_val = ((is_val_check_batch or early_stop_epoch) and can_check_epoch)\n\n # fast_dev_run always forces val checking after train batch\n if self.fast_dev_run or should_check_val:\n self.run_evaluation(test=self.testing)\n\n # when logs should be saved\n should_save_log = (batch_nb + 1) % self.log_save_interval == 0 or early_stop_epoch\n if should_save_log or self.fast_dev_run:\n if self.proc_rank == 0 and self.logger is not None:\n self.logger.save()\n\n # when metrics should be logged\n should_log_metrics = batch_nb % self.row_","label":0},{"cid":"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.run_training_batch","text":"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.run_training_batch\n def run_training_batch(self, batch, batch_nb):\n # track grad norms\n grad_norm_dic = {}\n\n # track all metrics for callbacks\n all_callback_metrics = []\n\n # track metrics to log\n all_log_metrics = []\n\n if batch is None:\n return 0, grad_norm_dic\n\n # hook\n if self.is_function_implemented('on_batch_start'):\n model_ref = self.get_model()\n response = model_ref.on_batch_start(batch)\n\n if response == -1:\n return -1, grad_norm_dic\n\n if self.show_progress_bar:\n self.progress_bar.update(1)\n\n # call training_step once per optimizer\n for opt_idx, optimizer in enumerate(self.optimizers):\n\n # wrap the forward step in a closure so second order methods work\n def optimizer_closure():\n # forward pass\n output = self.training_forward(batch, batch_nb, opt_idx)\n closure_loss, progress_bar_metrics, log_metrics, callback_metrics = output\n\n # track metrics for callbacks\n all_callback_metrics.append(callback_metrics)\n\n # track progress bar metrics\n self.add_tqdm_metrics(progress_bar_metrics)\n all_log_metrics.append(log_metrics)\n\n # accumulate loss\n # (if accumulate_grad_batches = 1 no effect)\n closure_loss = closure_loss / self.accumulate_grad_batches\n\n # backward pass\n if self.use_amp:\n with amp.scale_loss(closure_loss, optimizer) as scaled_loss:\n scaled_loss.backward()\n else:\n closure_loss.backward()\n\n # insert after step hook\n if self.is_function_implemented('on_after_backward'):\n model_ref = self.get_model()\n ","label":0},{"cid":"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.training_forward","text":"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.training_forward\n def training_forward(self, batch, batch_nb, opt_idx):\n \"\"\"\n Handle forward for each training case (distributed, single gpu, etc...)\n :param batch:\n :param batch_nb:\n :return:\n \"\"\"\n # ---------------\n # FORWARD\n # ---------------\n # enable not needing to add opt_idx to training_step\n args = [batch, batch_nb]\n if len(self.optimizers) > 1:\n args.append(opt_idx)\n\n if self.use_ddp or self.use_ddp2:\n output = self.model(*args)\n elif self.use_dp:\n output = self.model(*args)\n elif self.single_gpu:\n gpu_id = 0\n if type(self.data_parallel_device_ids) is list:\n gpu_id = self.data_parallel_device_ids[0]\n batch = self.transfer_batch_to_gpu(batch, gpu_id)\n args[0] = batch\n output = self.model.training_step(*args)\n\n else:\n output = self.model.training_step(*args)\n\n # format and reduce outputs accordingly\n output = self.process_output(output, train=True)\n loss, progress_bar_metrics, log_metrics, callback_metrics = output\n return loss, progress_bar_metrics, log_metrics, callback_metrics","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__init__","text":"pytorch_lightning/trainer/trainer.py::Trainer.__init__\n def __init__(self,\n logger=True,\n checkpoint_callback=True,\n early_stop_callback=True,\n default_save_path=None,\n gradient_clip_val=0,\n gradient_clip=None, # backward compatible\n process_position=0,\n nb_gpu_nodes=1,\n gpus=None,\n log_gpu_memory=None,\n show_progress_bar=True,\n overfit_pct=0.0,\n track_grad_norm=-1,\n check_val_every_n_epoch=1,\n fast_dev_run=False,\n accumulate_grad_batches=1,\n max_nb_epochs=1000,\n min_nb_epochs=1,\n train_percent_check=1.0,\n val_percent_check=1.0,\n test_percent_check=1.0,\n val_check_interval=1.0,\n log_save_interval=100,\n row_log_interval=10,\n add_row_log_interval=None, # backward compatible\n distributed_backend=None,\n use_amp=False,\n print_nan_grads=False,\n weights_summary='full',\n weights_save_path=None,\n amp_level='O1',\n nb_sanity_val_steps=5):\n \"\"\"\n\n :param logger: Logger for experiment tracking\n :param checkpoint_callback: Callback for checkpointing\n :param early_stop_callback: Callback for early stopping\n :param default_save_path: Default path for logs+weights if no logger/ckpt_callback passed\n :param gradient_clip_val: int. 0 means don't clip.\n :param gradient_clip: int. 0 means don't clip. Deprecated.\n :param process_position: shown in the tqdm bar\n :param nb_gpu_nodes: number of GPU nodes\n :param gpus: int. (ie: 2 gpus) OR list to specify which GPUs [0, 1] or '0,1'\n :param log_gpu_memory: str. ","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id","text":"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id\n @property\n def slurm_job_id(self):\n try:\n job_id = os.environ['SLURM_JOB_ID']\n job_id = int(job_id)\n except Exception as e:\n job_id = None\n return job_id","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__parse_gpu_ids","text":"pytorch_lightning/trainer/trainer.py::Trainer.__parse_gpu_ids\n def __parse_gpu_ids(self, gpus):\n \"\"\"\n :param gpus: Int, string or list of ids\n :return:\n \"\"\"\n # if gpus = -1 then use all available devices\n # otherwise, split the string using commas\n if gpus is not None:\n if type(gpus) is list:\n gpus = gpus\n elif type(gpus) is str:\n if gpus == '-1':\n gpus = list(range(0, torch.cuda.device_count()))\n else:\n gpus = [int(x.strip()) for x in gpus.split(',')]\n elif type(gpus) is int:\n gpus = gpus\n else:\n raise Exception('gpus has to be a string, int or list of ints')\n\n return gpus","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__set_root_gpu","text":"pytorch_lightning/trainer/trainer.py::Trainer.__set_root_gpu\n def __set_root_gpu(self, gpus):\n if gpus is None:\n return None\n\n # set root gpu\n root_gpu = 0\n if type(gpus) is list:\n root_gpu = gpus[0]\n\n return root_gpu","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus","text":"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus\n @property\n def num_gpus(self):\n gpus = self.data_parallel_device_ids\n if gpus is None:\n return 0\n\n if type(gpus) is list:\n return len(gpus)\n if type(gpus) is int:\n return gpus\n\n m = 'gpus must be int, none or list of ints'\n raise MisconfigurationException(m)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel","text":"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel\n @property\n def data_parallel(self):\n return self.use_dp or self.use_ddp or self.use_ddp2","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.training_tqdm_dict","text":"pytorch_lightning/trainer/trainer.py::Trainer.training_tqdm_dict\n @property\n def training_tqdm_dict(self):\n \"\"\"\n Read-only for tqdm metrics\n :return:\n \"\"\"\n tqdm_dict = {\n 'loss': '{0:.3f}'.format(self.avg_loss),\n 'epoch': '{}'.format(self.current_epoch),\n 'batch_nb': '{}'.format(self.batch_nb),\n }\n\n if self.logger is not None and self.logger.version is not None:\n tqdm_dict['v_nb'] = self.logger.version\n\n tqdm_dict.update(self.tqdm_metrics)\n\n if self.on_gpu:\n tqdm_dict['gpu'] = '{}'.format(torch.cuda.current_device())\n\n return tqdm_dict","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.tng_tqdm_dic","text":"pytorch_lightning/trainer/trainer.py::Trainer.tng_tqdm_dic\n @property\n def tng_tqdm_dic(self):\n \"\"\"\n * Deprecated in v0.5.0. use training_tqdm_dict instead. *\n :return:\n \"\"\"\n warnings.warn(\"tng_tqdm_dict has renamed to training_tqdm_dict since v0.5.0\",\n DeprecationWarning)\n return self.training_tqdm_dict","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.fit","text":"pytorch_lightning/trainer/trainer.py::Trainer.fit\n def fit(self, model):\n # when using multi-node or DDP within a node start each module in a separate process\n if self.use_ddp2:\n task = int(os.environ['SLURM_LOCALID'])\n self.ddp_train(task, model)\n\n elif self.use_ddp:\n if self.is_slurm_managing_tasks:\n task = int(os.environ['SLURM_LOCALID'])\n self.ddp_train(task, model)\n else:\n mp.spawn(self.ddp_train, nprocs=self.num_gpus, args=(model,))\n\n # 1 gpu or dp option triggers training using DP module\n # easier to avoid NCCL issues\n elif self.use_dp:\n self.dp_train(model)\n\n elif self.single_gpu:\n self.single_gpu_train(model)\n\n # ON CPU\n else:\n # run through amp wrapper\n if self.use_amp:\n raise MisconfigurationException('amp + cpu is not supported.'\n ' Please use a GPU option')\n\n # CHOOSE OPTIMIZER\n # allow for lr schedulers as well\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\n\n self.run_pretrain_routine(model)\n\n # return 1 when finished\n # used for testing or when we need to know that training succeeded\n return 1","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.init_optimizers","text":"pytorch_lightning/trainer/trainer.py::Trainer.init_optimizers\n def init_optimizers(self, optimizers):\n\n # single optimizer\n if isinstance(optimizers, Optimizer):\n return [optimizers], []\n\n # two lists\n elif len(optimizers) == 2 and isinstance(optimizers[0], list):\n optimizers, lr_schedulers = optimizers\n return optimizers, lr_schedulers\n\n # single list or tuple\n elif isinstance(optimizers, list) or isinstance(optimizers, tuple):\n return optimizers, []","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.run_pretrain_routine","text":"pytorch_lightning/trainer/trainer.py::Trainer.run_pretrain_routine\n def run_pretrain_routine(self, model):\n \"\"\"\n Sanity check a few things before starting actual training\n :param model:\n :return:\n \"\"\"\n ref_model = model\n if self.data_parallel:\n ref_model = model.module\n\n # give model convenience properties\n ref_model.trainer = self\n\n # set local properties on the model\n self.copy_trainer_model_properties(ref_model)\n\n # link up experiment object\n if self.logger is not None:\n ref_model.logger = self.logger\n\n # save exp to get started\n if hasattr(ref_model, \"hparams\"):\n self.logger.log_hyperparams(ref_model.hparams)\n\n self.logger.save()\n\n if self.use_ddp or self.use_ddp2:\n dist.barrier()\n\n # set up checkpoint callback\n self.configure_checkpoint_callback()\n\n # register auto-resubmit when on SLURM\n self.register_slurm_signal_handlers()\n\n # transfer data loaders from model\n self.get_dataloaders(ref_model)\n\n # init training constants\n self.layout_bookeeping()\n\n # print model summary\n if self.proc_rank == 0 and self.weights_summary is not None:\n if self.weights_summary in ['full', 'top']:\n ref_model.summarize(mode=self.weights_summary)\n else:\n m = \"weights_summary can be None, 'full' or 'top'\"\n raise MisconfigurationException(m)\n\n # track model now.\n # if cluster resets state, the model will update with the saved weights\n self.model = model\n\n # restore training and model before hpc call\n self.restore_weights(model)\n\n # progress bar init\n if self.show_progress_bar:\n self.progress_bar = tqdm.tqdm(0, position=self.process_position)\n\n # when testing requested only run test and return\n if self","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.test","text":"pytorch_lightning/trainer/trainer.py::Trainer.test\n def test(self, model=None):\n self.testing = True\n if model is not None:\n self.fit(model)\n else:\n self.run_evaluation(test=True)","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.get_model","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.get_model\n def get_model(self):\n is_dp_module = isinstance(self.model, (LightningDistributedDataParallel,\n LightningDataParallel))\n model = self.model.module if is_dp_module else self.model\n return model","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_weights","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_weights\n def restore_weights(self, model):\n \"\"\"\n To restore weights we have two cases.\n First, if we use the same experiment version, then restore the latest ckpt.\n AFTER that, if we find weights from hpc checkpoint, then restore that.\n :param model:\n :return:\n \"\"\"\n # restore weights if same exp version\n self.restore_state_if_checkpoint_exists(model)\n\n # if script called from hpc resubmit, load weights\n self.restore_hpc_weights_if_needed(model)\n\n # wait for all models to restore weights\n if self.use_ddp or self.use_ddp2:\n # wait for all processes to catch up\n dist.barrier()","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_state_if_checkpoint_exists","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_state_if_checkpoint_exists\n def restore_state_if_checkpoint_exists(self, model):\n # do nothing if there's not dir or callback\n no_ckpt_callback = (self.checkpoint_callback is None) or (not self.checkpoint_callback)\n if no_ckpt_callback or not os.path.exists(self.checkpoint_callback.filepath):\n return\n\n # restore trainer state and model if there is a weight for this experiment\n last_epoch = -1\n last_ckpt_name = None\n\n # find last epoch\n checkpoints = os.listdir(self.checkpoint_callback.filepath)\n for name in checkpoints:\n # ignore hpc ckpts\n if 'hpc_' in name:\n continue\n\n if '.ckpt' in name:\n epoch = name.split('epoch_')[1]\n epoch = int(re.sub('[^0-9]', '', epoch))\n\n if epoch > last_epoch:\n last_epoch = epoch\n last_ckpt_name = name\n\n # restore last checkpoint\n if last_ckpt_name is not None:\n last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name)\n self.restore(last_ckpt_path, self.on_gpu)\n print(f'model and trainer restored from checkpoint: {last_ckpt_path}')","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.register_slurm_signal_handlers","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.register_slurm_signal_handlers\n def register_slurm_signal_handlers(self):\n # see if we're using slurm (not interactive)\n on_slurm = False\n try:\n job_name = os.environ['SLURM_JOB_NAME']\n if job_name != 'bash':\n on_slurm = True\n except Exception as e:\n pass\n\n if on_slurm:\n print('set slurm handle signals')\n signal.signal(signal.SIGUSR1, self.sig_handler)\n signal.signal(signal.SIGTERM, self.term_handler)","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.sig_handler","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.sig_handler\n def sig_handler(self, signum, frame):\n if self.proc_rank == 0:\n # save weights\n print('handling SIGUSR1')\n self.hpc_save(self.weights_save_path, self.logger)\n\n # find job id\n job_id = os.environ['SLURM_JOB_ID']\n cmd = 'scontrol requeue {}'.format(job_id)\n\n # requeue job\n print('\\nrequeing job {}...'.format(job_id))\n result = call(cmd, shell=True)\n\n # print result text\n if result == 0:\n print('requeued exp ', job_id)\n else:\n print('requeue failed...')\n\n # close experiment to avoid issues\n self.logger.close()","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.term_handler","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.term_handler\n def term_handler(self, signum, frame):\n # save\n print(\"bypassing sigterm\")","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.save_checkpoint","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.save_checkpoint\n def save_checkpoint(self, filepath):\n checkpoint = self.dump_checkpoint()\n\n # do the actual save\n torch.save(checkpoint, filepath)","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore\n def restore(self, checkpoint_path, on_gpu):\n\n # if on_gpu:\n # checkpoint = torch.load(checkpoint_path)\n # else:\n # load on CPU first\n checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)\n\n # load model state\n model = self.get_model()\n\n # load the state_dict on the model automatically\n model.load_state_dict(checkpoint['state_dict'])\n if on_gpu:\n model.cuda(self.root_gpu)\n\n # load training state (affects trainer only)\n self.restore_training_state(checkpoint)","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.dump_checkpoint","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.dump_checkpoint\n def dump_checkpoint(self):\n\n checkpoint = {\n 'epoch': self.current_epoch,\n 'global_step': self.global_step\n }\n\n if self.checkpoint_callback is not None or self.checkpoint_callback is not False:\n checkpoint['checkpoint_callback_best'] = self.checkpoint_callback.best\n\n if self.early_stop_callback is not None or self.checkpoint_callback is not False:\n checkpoint['early_stop_callback_wait'] = self.early_stop_callback.wait\n checkpoint['early_stop_callback_patience'] = self.early_stop_callback.patience\n\n # save optimizers\n optimizer_states = []\n for i, optimizer in enumerate(self.optimizers):\n optimizer_states.append(optimizer.state_dict())\n\n checkpoint['optimizer_states'] = optimizer_states\n\n # save lr schedulers\n lr_schedulers = []\n for i, scheduler in enumerate(self.lr_schedulers):\n lr_schedulers.append(scheduler.state_dict())\n\n checkpoint['lr_schedulers'] = lr_schedulers\n\n # add the state_dict from the model\n model = self.get_model()\n checkpoint['state_dict'] = model.state_dict()\n\n # give the model a chance to add a few things\n model.on_save_checkpoint(checkpoint)\n\n return checkpoint","label":1},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_hpc_weights_if_needed","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_hpc_weights_if_needed\n def restore_hpc_weights_if_needed(self, model):\n \"\"\"\n If there is a set of hpc weights, use as signal to restore model\n :param model:\n :return:\n \"\"\"\n # look for hpc weights\n folderpath = self.weights_save_path\n if os.path.exists(folderpath):\n files = os.listdir(folderpath)\n hpc_weight_paths = [x for x in files if 'hpc_ckpt' in x]\n\n # if hpc weights exist restore model\n if len(hpc_weight_paths) > 0:\n self.hpc_load(folderpath, self.on_gpu)","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_training_state","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_training_state\n def restore_training_state(self, checkpoint):\n \"\"\"\n Restore trainer state.\n Model will get its change to update\n :param checkpoint:\n :return:\n \"\"\"\n if self.checkpoint_callback is not None or self.checkpoint_callback is not False:\n self.checkpoint_callback.best = checkpoint['checkpoint_callback_best']\n\n if self.early_stop_callback is not None or self.early_stop_callback is not False:\n self.early_stop_callback.wait = checkpoint['early_stop_callback_wait']\n self.early_stop_callback.patience = checkpoint['early_stop_callback_patience']\n\n self.global_step = checkpoint['global_step']\n self.current_epoch = checkpoint['epoch']\n\n # restore the optimizers\n optimizer_states = checkpoint['optimizer_states']\n for optimizer, opt_state in zip(self.optimizers, optimizer_states):\n optimizer.load_state_dict(opt_state)\n\n # move optimizer to GPU 1 weight at a time\n # avoids OOM\n if self.root_gpu is not None:\n for state in optimizer.state.values():\n for k, v in state.items():\n if isinstance(v, torch.Tensor):\n state[k] = v.cuda(self.root_gpu)\n\n # restore the lr schedulers\n lr_schedulers = checkpoint['lr_schedulers']\n for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers):\n scheduler.load_state_dict(lrs_state)","label":1},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.hpc_save","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.hpc_save\n def hpc_save(self, folderpath, logger):\n # make sure the checkpoint folder exists\n os.makedirs(folderpath, exist_ok=True)\n\n # save logger to make sure we get all the metrics\n logger.save()\n\n ckpt_number = self.max_ckpt_in_folder(folderpath) + 1\n\n if not os.path.exists(folderpath):\n os.makedirs(folderpath, exist_ok=True)\n filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, ckpt_number)\n\n # give model a chance to do something on hpc_save\n model = self.get_model()\n checkpoint = self.dump_checkpoint()\n\n model.on_hpc_save(checkpoint)\n\n # do the actual save\n torch.save(checkpoint, filepath)\n\n return filepath","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.hpc_load","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.hpc_load\n def hpc_load(self, folderpath, on_gpu):\n filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, self.max_ckpt_in_folder(folderpath))\n\n # load on CPU first\n checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)\n\n # load model state\n model = self.get_model()\n\n # load the state_dict on the model automatically\n model.load_state_dict(checkpoint['state_dict'])\n\n if self.root_gpu is not None:\n model.cuda(self.root_gpu)\n\n # load training state (affects trainer only)\n self.restore_training_state(checkpoint)\n\n # call model hook\n model.on_hpc_load(checkpoint)\n\n print(f'restored hpc model from: {filepath}')","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.max_ckpt_in_folder","text":"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.max_ckpt_in_folder\n def max_ckpt_in_folder(self, path, name_key='ckpt_'):\n files = os.listdir(path)\n files = [x for x in files if name_key in x]\n if len(files) == 0:\n return 0\n\n ckpt_vs = []\n for name in files:\n name = name.split(name_key)[-1]\n name = re.sub('[^0-9]', '', name)\n ckpt_vs.append(int(name))\n\n return max(ckpt_vs)","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::load_hparams_from_tags_csv","text":"pytorch_lightning/trainer/trainer_io.py::load_hparams_from_tags_csv\ndef load_hparams_from_tags_csv(tags_csv):\n from argparse import Namespace\n import pandas as pd\n\n tags_df = pd.read_csv(tags_csv)\n dic = tags_df.to_dict(orient='records')\n\n ns_dict = {row['key']: convert(row['value']) for row in dic}\n\n ns = Namespace(**ns_dict)\n return ns","label":0},{"cid":"pytorch_lightning/trainer/trainer_io.py::convert","text":"pytorch_lightning/trainer/trainer_io.py::convert\ndef convert(val):\n constructors = [int, float, str]\n\n if type(val) is str:\n if val.lower() == 'true':\n return True\n if val.lower() == 'false':\n return False\n\n for c in constructors:\n try:\n return c(val)\n except ValueError:\n pass\n return val","label":0},{"cid":"pytorch_lightning/utilities/arg_parse.py::add_default_args","text":"pytorch_lightning/utilities/arg_parse.py::add_default_args\ndef add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None):\n # training, test, val check intervals\n parser.add_argument('--eval_test_set', dest='eval_test_set', action='store_true',\n help='true = run test set also')\n parser.add_argument('--check_val_every_n_epoch', default=1, type=int,\n help='check val every n epochs')\n parser.opt_list('--accumulate_grad_batches', default=1, type=int, tunable=False,\n help='accumulates gradients k times before applying update.'\n ' Simulates huge batch size')\n parser.add_argument('--max_nb_epochs', default=200, type=int, help='cap epochs')\n parser.add_argument('--min_nb_epochs', default=2, type=int, help='min epochs')\n parser.add_argument('--train_percent_check', default=1.0, type=float,\n help='how much of training set to check')\n parser.add_argument('--val_percent_check', default=1.0, type=float,\n help='how much of val set to check')\n parser.add_argument('--test_percent_check', default=1.0, type=float,\n help='how much of test set to check')\n\n parser.add_argument('--val_check_interval', default=0.95, type=float,\n help='how much within 1 epoch to check val')\n parser.add_argument('--log_save_interval', default=100, type=int,\n help='how many batches between log saves')\n parser.add_argument('--row_log_interval', default=100, type=int,\n help='add log every k batches')\n\n # early stopping\n parser.add_argument('--disable_early_stop', dest='enable_early_stop', action='store_false')\n parser.add_argument('--early_stop_metric', default='val_acc', type=str)\n parser.add_argument('--early_stop_mode', default='min', type=str)\n parser.add_argument('--early_stop_patience', default=3, type=int,\n ","label":0}],"string":"[\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.__init__\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.__init__\\n def __init__(self):\\n self.validation_data = None\\n self.model = None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_params\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_params\\n def set_params(self, params):\\n self.params = params\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_model\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.set_model\\n def set_model(self, model):\\n if type(model) is LightningDistributedDataParallel:\\n model = model.module\\n self.model = model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_begin\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_begin\\n def on_epoch_begin(self, epoch, logs=None):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_end\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_epoch_end\\n def on_epoch_end(self, epoch, logs=None):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_begin\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_begin\\n def on_batch_begin(self, batch, logs=None):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_end\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_batch_end\\n def on_batch_end(self, batch, logs=None):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_begin\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_begin\\n def on_train_begin(self, logs=None):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_end\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::Callback.on_train_end\\n def on_train_end(self, logs=None):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.__init__\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.__init__\\n def __init__(self, monitor='val_loss',\\n min_delta=0.0, patience=0, verbose=0, mode='auto'):\\n super(EarlyStopping, self).__init__()\\n\\n self.monitor = monitor\\n self.patience = patience\\n self.verbose = verbose\\n self.min_delta = min_delta\\n self.wait = 0\\n self.stopped_epoch = 0\\n\\n if mode not in ['auto', 'min', 'max']:\\n print('EarlyStopping mode %s is unknown, fallback to auto mode.' % mode)\\n mode = 'auto'\\n\\n if mode == 'min':\\n self.monitor_op = np.less\\n elif mode == 'max':\\n self.monitor_op = np.greater\\n else:\\n if 'acc' in self.monitor:\\n self.monitor_op = np.greater\\n else:\\n self.monitor_op = np.less\\n\\n if self.monitor_op == np.greater:\\n self.min_delta *= 1\\n else:\\n self.min_delta *= -1\\n\\n self.on_train_begin()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_begin\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_begin\\n def on_train_begin(self, logs=None):\\n # Allow instances to be re-used\\n self.wait = 0\\n self.stopped_epoch = 0\\n self.best = np.Inf if self.monitor_op == np.less else -np.Inf\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_epoch_end\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_epoch_end\\n def on_epoch_end(self, epoch, logs=None):\\n current = logs.get(self.monitor)\\n stop_training = False\\n if current is None:\\n print('Early stopping conditioned on metric `%s` '\\n 'which is not available. Available metrics are: %s' %\\n (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning)\\n stop_training = True\\n return stop_training\\n\\n if self.monitor_op(current - self.min_delta, self.best):\\n self.best = current\\n self.wait = 0\\n else:\\n self.wait += 1\\n if self.wait >= self.patience:\\n self.stopped_epoch = epoch\\n stop_training = True\\n self.on_train_end()\\n\\n return stop_training\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_end\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::EarlyStopping.on_train_end\\n def on_train_end(self, logs=None):\\n if self.stopped_epoch > 0 and self.verbose > 0:\\n print('Epoch %05d: early stopping' % (self.stopped_epoch + 1))\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.__init__\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.__init__\\n def __init__(self, filepath, monitor='val_loss', verbose=0,\\n save_best_only=True, save_weights_only=False,\\n mode='auto', period=1, prefix=''):\\n super(ModelCheckpoint, self).__init__()\\n self.monitor = monitor\\n self.verbose = verbose\\n self.filepath = filepath\\n self.save_best_only = save_best_only\\n self.save_weights_only = save_weights_only\\n self.period = period\\n self.epochs_since_last_save = 0\\n self.prefix = prefix\\n\\n if mode not in ['auto', 'min', 'max']:\\n print('ModelCheckpoint mode %s is unknown, '\\n 'fallback to auto mode.' % (mode), RuntimeWarning)\\n mode = 'auto'\\n\\n if mode == 'min':\\n self.monitor_op = np.less\\n self.best = np.Inf\\n elif mode == 'max':\\n self.monitor_op = np.greater\\n self.best = -np.Inf\\n else:\\n if 'acc' in self.monitor or self.monitor.startswith('fmeasure'):\\n self.monitor_op = np.greater\\n self.best = -np.Inf\\n else:\\n self.monitor_op = np.less\\n self.best = np.Inf\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.save_model\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.save_model\\n def save_model(self, filepath, overwrite):\\n dirpath = '/'.join(filepath.split('/')[:-1])\\n\\n # make paths\\n os.makedirs(os.path.dirname(filepath), exist_ok=True)\\n\\n if overwrite:\\n for filename in os.listdir(dirpath):\\n if self.prefix in filename:\\n path_to_delete = os.path.join(dirpath, filename)\\n try:\\n shutil.rmtree(path_to_delete)\\n except OSError:\\n os.remove(path_to_delete)\\n\\n # delegate the saving to the model\\n self.save_function(filepath)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.on_epoch_end\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::ModelCheckpoint.on_epoch_end\\n def on_epoch_end(self, epoch, logs=None):\\n logs = logs or {}\\n self.epochs_since_last_save += 1\\n if self.epochs_since_last_save >= self.period:\\n self.epochs_since_last_save = 0\\n filepath = '{}/{}_ckpt_epoch_{}.ckpt'.format(self.filepath, self.prefix, epoch + 1)\\n if self.save_best_only:\\n current = logs.get(self.monitor)\\n if current is None:\\n print('Can save best model only with %s available,'\\n ' skipping.' % (self.monitor), RuntimeWarning)\\n else:\\n if self.monitor_op(current, self.best):\\n if self.verbose > 0:\\n print('\\\\nEpoch %05d: %s improved from %0.5f to %0.5f,'\\n ' saving model to %s'\\n % (epoch + 1, self.monitor, self.best,\\n current, filepath))\\n self.best = current\\n self.save_model(filepath, overwrite=True)\\n\\n else:\\n if self.verbose > 0:\\n print('\\\\nEpoch %05d: %s did not improve' %\\n (epoch + 1, self.monitor))\\n else:\\n if self.verbose > 0:\\n print('\\\\nEpoch %05d: saving model to %s' % (epoch + 1, filepath))\\n self.save_model(filepath, overwrite=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::GradientAccumulationScheduler.__init__\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::GradientAccumulationScheduler.__init__\\n def __init__(self, scheduling: dict):\\n if scheduling == {}: # empty dict error\\n raise TypeError(\\\"Empty dict cannot be interpreted correct\\\")\\n\\n for key in scheduling.keys():\\n if not isinstance(key, int) or not isinstance(scheduling[key], int):\\n raise TypeError(\\\"All epoches and accumulation factor must be integers\\\")\\n\\n minimal_epoch = min(scheduling.keys())\\n if minimal_epoch < 1:\\n msg = f\\\"Epochs indexing from 1, epoch {minimal_epoch} cannot be interpreted correct\\\"\\n raise IndexError(msg)\\n elif minimal_epoch != 1: # if user didnt define first epoch accumulation factor\\n scheduling.update({1: 1})\\n\\n self.scheduling = scheduling\\n self.epochs = sorted(scheduling.keys())\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/callbacks/pt_callbacks.py::GradientAccumulationScheduler.on_epoch_begin\",\n \"text\": \"pytorch_lightning/callbacks/pt_callbacks.py::GradientAccumulationScheduler.on_epoch_begin\\n def on_epoch_begin(self, epoch, trainer):\\n epoch += 1 # indexing epochs from 1\\n for i in reversed(range(len(self.epochs))):\\n if epoch >= self.epochs[i]:\\n trainer.accumulate_grad_batches = self.scheduling.get(self.epochs[i])\\n break\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.__init__\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.__init__\\n def __init__(self, *args, **kwargs):\\n super(LightningModule, self).__init__(*args, **kwargs)\\n\\n self.dtype = torch.FloatTensor\\n self.exp_save_path = None\\n self.current_epoch = 0\\n self.global_step = 0\\n self.loaded_optimizer_states_dict = {}\\n self.trainer = None\\n self.logger = None\\n self.example_input_array = None\\n\\n # track if gpu was requested for checkpointing\\n self.on_gpu = False\\n self.use_dp = False\\n self.use_ddp = False\\n self.use_ddp2 = False\\n self.use_amp = False\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.forward\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.forward\\n def forward(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n Expand model in into whatever you need.\\n Also need to return the target\\n :param x:\\n :return:\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.training_step\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.training_step\\n def training_step(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n return loss, dict with metrics for tqdm\\n :param called with batch, batch_nb\\n additional: optimizer_i if multiple optimizers used\\n :return:\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.validation_step\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.validation_step\\n def validation_step(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n return whatever outputs will need to be aggregated in validation_end\\n OPTIONAL\\n :param called with batch, batch_nb\\n additional: dataset_i if multiple val datasets used\\n :return:\\n \\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.test_step\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.test_step\\n def test_step(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n return whatever outputs will need to be aggregated in test_end\\n OPTIONAL\\n :param called with batch, batch_nb\\n additional: dataset_i if multiple val datasets used\\n :return:\\n \\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.validation_end\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.validation_end\\n def validation_end(self, outputs):\\n \\\"\\\"\\\"\\n Outputs has the appended output after each validation step\\n OPTIONAL\\n :param outputs:\\n :return: dic_with_metrics for tqdm\\n \\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.test_end\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.test_end\\n def test_end(self, outputs):\\n \\\"\\\"\\\"\\n Outputs has the appended output after each test step\\n OPTIONAL\\n :param outputs:\\n :return: dic_with_metrics for tqdm\\n \\\"\\\"\\\"\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.configure_optimizers\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.configure_optimizers\\n def configure_optimizers(self):\\n \\\"\\\"\\\"\\n Return a list of optimizers and a list of schedulers (could be empty)\\n :return:\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.optimizer_step\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.optimizer_step\\n def optimizer_step(self, epoch_nb, batch_nb, optimizer, optimizer_i, second_order_closure=None):\\n \\\"\\\"\\\"\\n Do something instead of the standard optimizer behavior\\n :param epoch_nb:\\n :param batch_nb:\\n :param optimizer:\\n :param optimizer_i:\\n :param second_order_closure: closure for second order methods\\n :return:\\n \\\"\\\"\\\"\\n if isinstance(optimizer, torch.optim.LBFGS):\\n optimizer.step(second_order_closure)\\n else:\\n optimizer.step()\\n\\n # clear gradients\\n optimizer.zero_grad()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.tng_dataloader\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.tng_dataloader\\n @data_loader\\n def tng_dataloader(self):\\n \\\"\\\"\\\"\\n Implement a PyTorch DataLoader\\n * Deprecated in v0.5.0. use train_dataloader instead. *\\n :return:\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.train_dataloader\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.train_dataloader\\n @data_loader\\n def train_dataloader(self):\\n \\\"\\\"\\\"\\n Implement a PyTorch DataLoader\\n :return:\\n \\\"\\\"\\\"\\n #\\n try:\\n output = self.tng_dataloader()\\n warnings.warn(\\\"tng_dataloader has been renamed to train_dataloader since v0.5.0\\\",\\n DeprecationWarning)\\n return output\\n except NotImplementedError:\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.test_dataloader\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.test_dataloader\\n @data_loader\\n def test_dataloader(self):\\n \\\"\\\"\\\"\\n Implement a PyTorch DataLoader\\n :return:\\n \\\"\\\"\\\"\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.val_dataloader\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.val_dataloader\\n @data_loader\\n def val_dataloader(self):\\n \\\"\\\"\\\"\\n Implement a PyTorch DataLoader\\n :return:\\n \\\"\\\"\\\"\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.load_from_metrics\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.load_from_metrics\\n @classmethod\\n def load_from_metrics(cls, weights_path, tags_csv):\\n \\\"\\\"\\\"\\n Primary way of loading model from csv weights path\\n :param weights_path:\\n :param tags_csv:\\n :param map_location: dic for mapping storage {'cuda:1':'cuda:0'}\\n :return:\\n \\\"\\\"\\\"\\n hparams = load_hparams_from_tags_csv(tags_csv)\\n hparams.__setattr__('on_gpu', False)\\n\\n # load on CPU only to avoid OOM issues\\n # then its up to user to put back on GPUs\\n checkpoint = torch.load(weights_path, map_location=lambda storage, loc: storage)\\n\\n # load the state_dict on the model automatically\\n model = cls(hparams)\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n # give model a chance to load something\\n model.on_load_checkpoint(checkpoint)\\n\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.summarize\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.summarize\\n def summarize(self, mode):\\n model_summary = ModelSummary(self, mode=mode)\\n print(model_summary)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.freeze\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.freeze\\n def freeze(self):\\n for param in self.parameters():\\n param.requires_grad = False\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/root_module/root_module.py::LightningModule.unfreeze\",\n \"text\": \"pytorch_lightning/root_module/root_module.py::LightningModule.unfreeze\\n def unfreeze(self):\\n for param in self.parameters():\\n param.requires_grad = True\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/amp_mixin.py::TrainerAMPMixin.init_amp\",\n \"text\": \"pytorch_lightning/trainer/amp_mixin.py::TrainerAMPMixin.init_amp\\n def init_amp(self, use_amp):\\n self.use_amp = use_amp and APEX_AVAILABLE\\n if self.use_amp:\\n print('using 16bit precision')\\n\\n if use_amp and not APEX_AVAILABLE: # pragma: no cover\\n msg = \\\"\\\"\\\"\\n You set use_amp=True but do not have apex installed.\\n Install apex first using this guide and rerun with use_amp=True:\\n https://github.com/NVIDIA/apex#linux\\n\\n this run will NOT use 16 bit precision\\n \\\"\\\"\\\"\\n raise ModuleNotFoundError(msg)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_config_mixin.py::TrainerCallbackConfigMixin.configure_checkpoint_callback\",\n \"text\": \"pytorch_lightning/trainer/callback_config_mixin.py::TrainerCallbackConfigMixin.configure_checkpoint_callback\\n def configure_checkpoint_callback(self):\\n \\\"\\\"\\\"\\n Weight path set in this priority:\\n Checkpoint_callback's path (if passed in).\\n User provided weights_saved_path\\n Otherwise use os.getcwd()\\n \\\"\\\"\\\"\\n if self.checkpoint_callback is True:\\n # init a default one\\n if isinstance(self.logger, TestTubeLogger):\\n ckpt_path = '{}/{}/version_{}/{}'.format(\\n self.default_save_path,\\n self.logger.experiment.name,\\n self.logger.experiment.version,\\n 'checkpoints')\\n else:\\n ckpt_path = self.default_save_path\\n\\n self.checkpoint_callback = ModelCheckpoint(\\n filepath=ckpt_path\\n )\\n elif self.checkpoint_callback is False:\\n self.checkpoint_callback = None\\n\\n if self.checkpoint_callback:\\n # set the path for the callbacks\\n self.checkpoint_callback.save_function = self.save_checkpoint\\n\\n # if checkpoint callback used, then override the weights path\\n self.weights_save_path = self.checkpoint_callback.filepath\\n\\n # if weights_save_path is still none here, set to current working dir\\n if self.weights_save_path is None:\\n self.weights_save_path = self.default_save_path\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/callback_config_mixin.py::TrainerCallbackConfigMixin.configure_early_stopping\",\n \"text\": \"pytorch_lightning/trainer/callback_config_mixin.py::TrainerCallbackConfigMixin.configure_early_stopping\\n def configure_early_stopping(self, early_stop_callback, logger):\\n if early_stop_callback is True:\\n self.early_stop_callback = EarlyStopping(\\n monitor='val_loss',\\n patience=3,\\n verbose=True,\\n mode='min'\\n )\\n self.enable_early_stop = True\\n elif not early_stop_callback:\\n self.early_stop_callback = None\\n self.enable_early_stop = False\\n else:\\n self.early_stop_callback = early_stop_callback\\n self.enable_early_stop = True\\n\\n # configure logger\\n if logger is True:\\n # default logger\\n self.logger = TestTubeLogger(\\n save_dir=self.default_save_path,\\n version=self.slurm_job_id,\\n name='lightning_logs'\\n )\\n self.logger.rank = 0\\n elif logger is False:\\n self.logger = None\\n else:\\n self.logger = logger\\n self.logger.rank = 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.set_distributed_mode\",\n \"text\": \"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.set_distributed_mode\\n def set_distributed_mode(self, distributed_backend, nb_gpu_nodes):\\n # skip for CPU\\n if self.num_gpus == 0:\\n return\\n\\n # single GPU case\\n # in single gpu case we allow ddp so we can train on multiple\\n # nodes, 1 gpu per node\\n if self.num_gpus == 1:\\n self.single_gpu = True\\n\\n if distributed_backend is not None:\\n self.use_dp = distributed_backend == 'dp'\\n self.use_ddp = distributed_backend == 'ddp'\\n self.use_ddp2 = distributed_backend == 'ddp2'\\n\\n # disable single gpu when using ddp2\\n if self.use_ddp2:\\n self.single_gpu = False\\n\\n # multiple GPU case\\n elif self.num_gpus > 1:\\n if distributed_backend is not None:\\n # DP, DDP case\\n self.use_dp = distributed_backend == 'dp'\\n self.use_ddp = distributed_backend == 'ddp'\\n self.use_ddp2 = distributed_backend == 'ddp2'\\n\\n elif distributed_backend is None:\\n m = 'When using multiple GPUs set ' \\\\\\n 'Trainer(distributed_backend=dp) (or ddp)'\\n raise MisconfigurationException(m)\\n\\n # use ddp automatically if nb_gpu_nodes > 1\\n if nb_gpu_nodes > 1 and self.use_dp: # pragma: no cover\\n self.use_ddp = True\\n self.use_dp = False\\n w = 'DataParallel does not support nb_gpu_nodes > 1. ' \\\\\\n 'Switching to DistributedDataParallel for you. ' \\\\\\n 'To silence this warning set distributed_backend=ddp'\\n warnings.warn(w)\\n\\n print('gpu available: {}, used: {}'.format(torch.cuda.is_available(), self.on_gpu))\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.configure_slurm_ddp\",\n \"text\": \"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.configure_slurm_ddp\\n def configure_slurm_ddp(self, nb_gpu_nodes):\\n self.is_slurm_managing_tasks = False\\n\\n # extract SLURM flag vars\\n # whenever we have the correct number of tasks, we let slurm manage processes\\n # otherwise we launch the required number of processes\\n if self.use_ddp:\\n self.nb_requested_gpus = self.num_gpus * nb_gpu_nodes\\n self.nb_slurm_tasks = 0\\n try:\\n self.nb_slurm_tasks = int(os.environ['SLURM_NTASKS'])\\n self.is_slurm_managing_tasks = self.nb_slurm_tasks == self.nb_requested_gpus\\n\\n # in interactive mode we don't manage tasks\\n job_name = os.environ['SLURM_JOB_NAME']\\n if job_name == 'bash':\\n self.is_slurm_managing_tasks = False\\n\\n except Exception:\\n # likely not on slurm, so set the slurm managed flag to false\\n self.is_slurm_managing_tasks = False\\n\\n # used for tests only, set this flag to simulate slurm managing a task\\n try:\\n should_fake = int(os.environ['FAKE_SLURM_MANAGING_TASKS'])\\n if should_fake:\\n self.is_slurm_managing_tasks = True\\n except Exception as e:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.set_nvidia_flags\",\n \"text\": \"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.set_nvidia_flags\\n def set_nvidia_flags(self, is_slurm_managing_tasks, data_parallel_device_ids):\\n if data_parallel_device_ids is None:\\n return\\n\\n # set the correct cuda visible devices (using pci order)\\n os.environ[\\\"CUDA_DEVICE_ORDER\\\"] = \\\"PCI_BUS_ID\\\"\\n\\n # when slurm is managing the task it sets the visible devices\\n if not is_slurm_managing_tasks:\\n if type(data_parallel_device_ids) is int:\\n id_str = ','.join(str(x) for x in list(range(data_parallel_device_ids)))\\n os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = id_str\\n else:\\n gpu_str = ','.join([str(x) for x in data_parallel_device_ids])\\n os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = gpu_str\\n\\n print(f'VISIBLE GPUS: {os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"]}')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.ddp_train\",\n \"text\": \"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.ddp_train\\n def ddp_train(self, gpu_nb, model):\\n \\\"\\\"\\\"\\n Entry point into a DP thread\\n :param gpu_nb:\\n :param model:\\n :param cluster_obj:\\n :return:\\n \\\"\\\"\\\"\\n # node rank using relative slurm id\\n # otherwise default to node rank 0\\n try:\\n node_id = os.environ['SLURM_NODEID']\\n self.node_rank = int(node_id)\\n except Exception:\\n self.node_rank = 0\\n\\n # show progressbar only on progress_rank 0\\n self.show_progress_bar = self.show_progress_bar and self.node_rank == 0 and gpu_nb == 0\\n\\n # determine which process we are and world size\\n if self.use_ddp:\\n self.proc_rank = self.node_rank * self.num_gpus + gpu_nb\\n self.world_size = self.nb_gpu_nodes * self.num_gpus\\n\\n elif self.use_ddp2:\\n self.proc_rank = self.node_rank\\n self.world_size = self.nb_gpu_nodes\\n\\n # let the exp know the rank to avoid overwriting logs\\n if self.logger is not None:\\n self.logger.rank = self.proc_rank\\n\\n # set up server using proc 0's ip address\\n # try to init for 20 times at max in case ports are taken\\n # where to store ip_table\\n self.__init_tcp_connection()\\n\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n # MODEL\\n # copy model to each gpu\\n if self.distributed_backend == 'ddp':\\n torch.cuda.set_device(gpu_nb)\\n model.cuda(gpu_nb)\\n\\n # set model properties before going into wrapper\\n self.copy_trainer_model_properties(model)\\n\\n # override root GPU\\n self.root_gpu = gpu_nb\\n\\n # AMP\\n # run through amp wrapper before going to distributed DP\\n if self.use_amp:\\n # An example\\n model, optimizers = amp.initialize(\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.__init_tcp_connection\",\n \"text\": \"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.__init_tcp_connection\\n def __init_tcp_connection(self):\\n \\\"\\\"\\\"\\n Connect all procs in the world using the env:// init\\n Use the first node as the root address\\n :param port:\\n :param tries:\\n :return:\\n \\\"\\\"\\\"\\n\\n # use slurm job id for the port number\\n # guarantees unique ports across jobs from same grid search\\n try:\\n # use the last 4 numbers in the job id as the id\\n default_port = os.environ['SLURM_JOB_ID']\\n default_port = default_port[-4:]\\n\\n # all ports should be in the 10k+ range\\n default_port = int(default_port) + 15000\\n\\n except Exception as e:\\n default_port = 12910\\n\\n # if user gave a port number, use that one instead\\n try:\\n default_port = os.environ['MASTER_PORT']\\n except Exception:\\n os.environ['MASTER_PORT'] = str(default_port)\\n\\n # figure out the root node addr\\n try:\\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\\n except Exception:\\n root_node = '127.0.0.2'\\n\\n root_node = self.resolve_root_node_address(root_node)\\n os.environ['MASTER_ADDR'] = root_node\\n dist.init_process_group(\\\"nccl\\\", rank=self.proc_rank, world_size=self.world_size)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.resolve_root_node_address\",\n \"text\": \"pytorch_lightning/trainer/ddp_mixin.py::TrainerDDPMixin.resolve_root_node_address\\n def resolve_root_node_address(self, root_node):\\n if '[' in root_node:\\n name = root_node.split('[')[0]\\n number = root_node.split(',')[0]\\n if '-' in number:\\n number = number.split('-')[0]\\n\\n number = re.sub('[^0-9]', '', number)\\n root_node = name + number\\n\\n return root_node\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.copy_trainer_model_properties\",\n \"text\": \"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.copy_trainer_model_properties\\n def copy_trainer_model_properties(self, model):\\n if isinstance(model, LightningDataParallel):\\n ref_model = model.module\\n elif isinstance(model, LightningDistributedDataParallel):\\n ref_model = model.module\\n else:\\n ref_model = model\\n\\n for m in [model, ref_model]:\\n m.trainer = self\\n m.on_gpu = self.on_gpu\\n m.use_dp = self.use_dp\\n m.use_ddp2 = self.use_ddp2\\n m.use_ddp = self.use_ddp\\n m.use_amp = self.use_amp\\n m.testing = self.testing\\n m.single_gpu = self.single_gpu\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.transfer_batch_to_gpu\",\n \"text\": \"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.transfer_batch_to_gpu\\n def transfer_batch_to_gpu(self, batch, gpu_id):\\n # base case: object can be directly moved using `cuda` or `to`\\n if callable(getattr(batch, 'cuda', None)):\\n return batch.cuda(gpu_id)\\n\\n elif callable(getattr(batch, 'to', None)):\\n return batch.to(torch.device('cuda', gpu_id))\\n\\n # when list\\n elif isinstance(batch, list):\\n for i, x in enumerate(batch):\\n batch[i] = self.transfer_batch_to_gpu(x, gpu_id)\\n return batch\\n\\n # when tuple\\n elif isinstance(batch, tuple):\\n batch = list(batch)\\n for i, x in enumerate(batch):\\n batch[i] = self.transfer_batch_to_gpu(x, gpu_id)\\n return tuple(batch)\\n\\n # when dict\\n elif isinstance(batch, dict):\\n for k, v in batch.items():\\n batch[k] = self.transfer_batch_to_gpu(v, gpu_id)\\n\\n return batch\\n\\n # nothing matches, return the value as is without transform\\n return batch\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.single_gpu_train\",\n \"text\": \"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.single_gpu_train\\n def single_gpu_train(self, model):\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n model.cuda(self.root_gpu)\\n\\n if self.use_amp:\\n # An example\\n model, optimizers = amp.initialize(\\n model, self.optimizers, opt_level=self.amp_level,\\n )\\n self.optimizers = optimizers\\n\\n self.run_pretrain_routine(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.dp_train\",\n \"text\": \"pytorch_lightning/trainer/dp_mixin.py::TrainerDPMixin.dp_train\\n def dp_train(self, model):\\n\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n model.cuda(self.root_gpu)\\n\\n # check for this bug (amp + dp + !01 doesn't work)\\n # https://github.com/NVIDIA/apex/issues/227\\n if self.use_dp and self.use_amp:\\n m = f\\\"\\\"\\\"\\n Amp level {self.amp_level} with DataParallel is not supported.\\n See this note from NVIDIA for more info: https://github.com/NVIDIA/apex/issues/227.\\n We recommend you switch to ddp if you want to use amp\\n \\\"\\\"\\\"\\n raise MisconfigurationException(m)\\n\\n # create list of device ids\\n device_ids = self.data_parallel_device_ids\\n if type(device_ids) is int:\\n device_ids = list(range(device_ids))\\n\\n model = LightningDataParallel(model, device_ids=device_ids)\\n\\n self.run_pretrain_routine(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop_mixin.py::TrainerEvaluationLoopMixin.evaluate\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop_mixin.py::TrainerEvaluationLoopMixin.evaluate\\n def evaluate(self, model, dataloaders, max_batches, test=False):\\n \\\"\\\"\\\"\\n Run evaluation code\\n :param model: PT model\\n :param dataloaders: list of PT dataloaders\\n :param max_batches: Scalar\\n :param test: boolean\\n :return:\\n \\\"\\\"\\\"\\n # enable eval mode\\n model.zero_grad()\\n model.eval()\\n\\n # copy properties for forward overrides\\n self.copy_trainer_model_properties(model)\\n\\n # disable gradients to save memory\\n torch.set_grad_enabled(False)\\n\\n # bookkeeping\\n outputs = []\\n\\n # run training\\n for dataloader_idx, dataloader in enumerate(dataloaders):\\n dl_outputs = []\\n for batch_idx, batch in enumerate(dataloader):\\n\\n if batch is None: # pragma: no cover\\n continue\\n\\n # stop short when on fast_dev_run (sets max_batch=1)\\n if batch_idx >= max_batches:\\n break\\n\\n # -----------------\\n # RUN EVALUATION STEP\\n # -----------------\\n output = self.evaluation_forward(model,\\n batch,\\n batch_idx,\\n dataloader_idx,\\n test)\\n\\n # track outputs for collation\\n dl_outputs.append(output)\\n\\n # batch done\\n if self.show_progress_bar:\\n self.progress_bar.update(1)\\n outputs.append(dl_outputs)\\n\\n eval_results = {}\\n\\n # with a single dataloader don't pass an array\\n if len(dataloaders) == 1:\\n outputs = outputs[0]\\n\\n # give model a chance to do something with the outputs (and method defined)\\n model = self.get_model()\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop_mixin.py::TrainerEvaluationLoopMixin.run_evaluation\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop_mixin.py::TrainerEvaluationLoopMixin.run_evaluation\\n def run_evaluation(self, test=False):\\n # when testing make sure user defined a test step\\n can_run_test_step = False\\n if test:\\n can_run_test_step = self.is_overriden('test_step') and self.is_overriden('test_end')\\n if not can_run_test_step:\\n m = '''You called .test() without defining a test step or test_end.\\n Please define and try again'''\\n raise MisconfigurationException(m)\\n\\n # validate only if model has validation_step defined\\n # test only if test_step or validation_step are defined\\n run_val_step = self.is_overriden('validation_step')\\n\\n if run_val_step or can_run_test_step:\\n\\n # hook\\n model = self.get_model()\\n model.on_pre_performance_check()\\n\\n # select dataloaders\\n if test:\\n dataloaders = self.get_test_dataloaders()\\n max_batches = self.nb_test_batches\\n else:\\n # val\\n dataloaders = self.get_val_dataloaders()\\n max_batches = self.nb_val_batches\\n\\n # cap max batches to 1 when using fast_dev_run\\n if self.fast_dev_run:\\n max_batches = 1\\n\\n # run evaluation\\n eval_results = self.evaluate(self.model,\\n dataloaders,\\n max_batches,\\n test)\\n _, prog_bar_metrics, log_metrics, callback_metrics = self.process_output(eval_results)\\n\\n # add metrics to prog bar\\n self.add_tqdm_metrics(prog_bar_metrics)\\n\\n # log metrics\\n self.log_metrics(log_metrics, {})\\n\\n # track metrics for callbacks\\n self.callback_metrics = callback_metrics\\n\\n # hook\\n model.on_post_performance_check()\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop_mixin.py::TrainerEvaluationLoopMixin.evaluation_forward\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop_mixin.py::TrainerEvaluationLoopMixin.evaluation_forward\\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test=False):\\n # make dataloader_idx arg in validation_step optional\\n args = [batch, batch_idx]\\n\\n if test and len(self.get_test_dataloaders()) > 1:\\n args.append(dataloader_idx)\\n\\n elif not test and len(self.get_val_dataloaders()) > 1:\\n args.append(dataloader_idx)\\n\\n # handle DP, DDP forward\\n if self.use_ddp or self.use_dp or self.use_ddp2:\\n output = model(*args)\\n return output\\n\\n # single GPU\\n if self.single_gpu:\\n # for single GPU put inputs on gpu manually\\n root_gpu = 0\\n if type(self.data_parallel_device_ids) is list:\\n root_gpu = self.data_parallel_device_ids[0]\\n batch = self.transfer_batch_to_gpu(batch, root_gpu)\\n args[0] = batch\\n\\n # CPU\\n if test:\\n output = model.test_step(*args)\\n else:\\n output = model.validation_step(*args)\\n\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.train\",\n \"text\": \"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.train\\n def train(self):\\n # run all epochs\\n for epoch_nb in range(self.current_epoch, self.max_nb_epochs):\\n # set seed for distributed sampler (enables shuffling for each epoch)\\n if self.use_ddp and hasattr(self.get_train_dataloader().sampler, 'set_epoch'):\\n self.get_train_dataloader().sampler.set_epoch(epoch_nb)\\n\\n # get model\\n model = self.get_model()\\n\\n # update training progress in trainer and model\\n model.current_epoch = epoch_nb\\n self.current_epoch = epoch_nb\\n self.total_batches = self.nb_training_batches + self.nb_val_batches\\n self.batch_loss_value = 0 # accumulated grads\\n\\n # limit the number of batches to 1 in fast_dev_run\\n if self.fast_dev_run:\\n self.total_batches = 1\\n\\n # init progress_bar when requested\\n if self.show_progress_bar:\\n nb_iterations = self.total_batches\\n\\n # for iterable train loader, the progress bar never ends\\n if self.is_iterable_train_dataloader:\\n nb_iterations = float('inf')\\n self.progress_bar.reset(nb_iterations)\\n\\n # changing gradient according accumulation_scheduler\\n self.accumulation_scheduler.on_epoch_begin(epoch_nb, self)\\n\\n # -----------------\\n # RUN TNG EPOCH\\n # -----------------\\n self.run_training_epoch()\\n\\n # update LR schedulers\\n if self.lr_schedulers is not None:\\n for lr_scheduler in self.lr_schedulers:\\n lr_scheduler.step(self.current_epoch)\\n\\n # early stopping\\n met_min_epochs = epoch_nb > self.min_nb_epochs\\n if self.enable_early_stop and (met_min_epochs or self.fast_dev_run):\\n should_stop = self.early_stop_callback.on_epoch_end(epoch\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.run_training_epoch\",\n \"text\": \"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.run_training_epoch\\n def run_training_epoch(self):\\n # before epoch hook\\n if self.is_function_implemented('on_epoch_start'):\\n model = self.get_model()\\n model.on_epoch_start()\\n\\n # run epoch\\n for batch_nb, batch in enumerate(self.get_train_dataloader()):\\n self.batch_nb = batch_nb\\n self.global_step += 1\\n\\n model = self.get_model()\\n model.global_step = self.global_step\\n\\n # stop when the flag is changed or we've gone past the amount\\n # requested in the batches\\n self.total_batch_nb += 1\\n met_batch_limit = batch_nb >= self.nb_training_batches\\n if met_batch_limit:\\n break\\n\\n # ---------------\\n # RUN TRAIN STEP\\n # ---------------\\n output = self.run_training_batch(batch, batch_nb)\\n batch_result, grad_norm_dic, batch_step_metrics = output\\n early_stop_epoch = batch_result == -1\\n\\n # ---------------\\n # RUN VAL STEP\\n # ---------------\\n is_val_check_batch = (batch_nb + 1) % self.val_check_batch == 0\\n can_check_epoch = (self.current_epoch + 1) % self.check_val_every_n_epoch == 0\\n should_check_val = ((is_val_check_batch or early_stop_epoch) and can_check_epoch)\\n\\n # fast_dev_run always forces val checking after train batch\\n if self.fast_dev_run or should_check_val:\\n self.run_evaluation(test=self.testing)\\n\\n # when logs should be saved\\n should_save_log = (batch_nb + 1) % self.log_save_interval == 0 or early_stop_epoch\\n if should_save_log or self.fast_dev_run:\\n if self.proc_rank == 0 and self.logger is not None:\\n self.logger.save()\\n\\n # when metrics should be logged\\n should_log_metrics = batch_nb % self.row_\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.run_training_batch\",\n \"text\": \"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.run_training_batch\\n def run_training_batch(self, batch, batch_nb):\\n # track grad norms\\n grad_norm_dic = {}\\n\\n # track all metrics for callbacks\\n all_callback_metrics = []\\n\\n # track metrics to log\\n all_log_metrics = []\\n\\n if batch is None:\\n return 0, grad_norm_dic\\n\\n # hook\\n if self.is_function_implemented('on_batch_start'):\\n model_ref = self.get_model()\\n response = model_ref.on_batch_start(batch)\\n\\n if response == -1:\\n return -1, grad_norm_dic\\n\\n if self.show_progress_bar:\\n self.progress_bar.update(1)\\n\\n # call training_step once per optimizer\\n for opt_idx, optimizer in enumerate(self.optimizers):\\n\\n # wrap the forward step in a closure so second order methods work\\n def optimizer_closure():\\n # forward pass\\n output = self.training_forward(batch, batch_nb, opt_idx)\\n closure_loss, progress_bar_metrics, log_metrics, callback_metrics = output\\n\\n # track metrics for callbacks\\n all_callback_metrics.append(callback_metrics)\\n\\n # track progress bar metrics\\n self.add_tqdm_metrics(progress_bar_metrics)\\n all_log_metrics.append(log_metrics)\\n\\n # accumulate loss\\n # (if accumulate_grad_batches = 1 no effect)\\n closure_loss = closure_loss / self.accumulate_grad_batches\\n\\n # backward pass\\n if self.use_amp:\\n with amp.scale_loss(closure_loss, optimizer) as scaled_loss:\\n scaled_loss.backward()\\n else:\\n closure_loss.backward()\\n\\n # insert after step hook\\n if self.is_function_implemented('on_after_backward'):\\n model_ref = self.get_model()\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.training_forward\",\n \"text\": \"pytorch_lightning/trainer/train_loop_mixin.py::TrainerTrainLoopMixin.training_forward\\n def training_forward(self, batch, batch_nb, opt_idx):\\n \\\"\\\"\\\"\\n Handle forward for each training case (distributed, single gpu, etc...)\\n :param batch:\\n :param batch_nb:\\n :return:\\n \\\"\\\"\\\"\\n # ---------------\\n # FORWARD\\n # ---------------\\n # enable not needing to add opt_idx to training_step\\n args = [batch, batch_nb]\\n if len(self.optimizers) > 1:\\n args.append(opt_idx)\\n\\n if self.use_ddp or self.use_ddp2:\\n output = self.model(*args)\\n elif self.use_dp:\\n output = self.model(*args)\\n elif self.single_gpu:\\n gpu_id = 0\\n if type(self.data_parallel_device_ids) is list:\\n gpu_id = self.data_parallel_device_ids[0]\\n batch = self.transfer_batch_to_gpu(batch, gpu_id)\\n args[0] = batch\\n output = self.model.training_step(*args)\\n\\n else:\\n output = self.model.training_step(*args)\\n\\n # format and reduce outputs accordingly\\n output = self.process_output(output, train=True)\\n loss, progress_bar_metrics, log_metrics, callback_metrics = output\\n return loss, progress_bar_metrics, log_metrics, callback_metrics\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__init__\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__init__\\n def __init__(self,\\n logger=True,\\n checkpoint_callback=True,\\n early_stop_callback=True,\\n default_save_path=None,\\n gradient_clip_val=0,\\n gradient_clip=None, # backward compatible\\n process_position=0,\\n nb_gpu_nodes=1,\\n gpus=None,\\n log_gpu_memory=None,\\n show_progress_bar=True,\\n overfit_pct=0.0,\\n track_grad_norm=-1,\\n check_val_every_n_epoch=1,\\n fast_dev_run=False,\\n accumulate_grad_batches=1,\\n max_nb_epochs=1000,\\n min_nb_epochs=1,\\n train_percent_check=1.0,\\n val_percent_check=1.0,\\n test_percent_check=1.0,\\n val_check_interval=1.0,\\n log_save_interval=100,\\n row_log_interval=10,\\n add_row_log_interval=None, # backward compatible\\n distributed_backend=None,\\n use_amp=False,\\n print_nan_grads=False,\\n weights_summary='full',\\n weights_save_path=None,\\n amp_level='O1',\\n nb_sanity_val_steps=5):\\n \\\"\\\"\\\"\\n\\n :param logger: Logger for experiment tracking\\n :param checkpoint_callback: Callback for checkpointing\\n :param early_stop_callback: Callback for early stopping\\n :param default_save_path: Default path for logs+weights if no logger/ckpt_callback passed\\n :param gradient_clip_val: int. 0 means don't clip.\\n :param gradient_clip: int. 0 means don't clip. Deprecated.\\n :param process_position: shown in the tqdm bar\\n :param nb_gpu_nodes: number of GPU nodes\\n :param gpus: int. (ie: 2 gpus) OR list to specify which GPUs [0, 1] or '0,1'\\n :param log_gpu_memory: str. \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id\\n @property\\n def slurm_job_id(self):\\n try:\\n job_id = os.environ['SLURM_JOB_ID']\\n job_id = int(job_id)\\n except Exception as e:\\n job_id = None\\n return job_id\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__parse_gpu_ids\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__parse_gpu_ids\\n def __parse_gpu_ids(self, gpus):\\n \\\"\\\"\\\"\\n :param gpus: Int, string or list of ids\\n :return:\\n \\\"\\\"\\\"\\n # if gpus = -1 then use all available devices\\n # otherwise, split the string using commas\\n if gpus is not None:\\n if type(gpus) is list:\\n gpus = gpus\\n elif type(gpus) is str:\\n if gpus == '-1':\\n gpus = list(range(0, torch.cuda.device_count()))\\n else:\\n gpus = [int(x.strip()) for x in gpus.split(',')]\\n elif type(gpus) is int:\\n gpus = gpus\\n else:\\n raise Exception('gpus has to be a string, int or list of ints')\\n\\n return gpus\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__set_root_gpu\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__set_root_gpu\\n def __set_root_gpu(self, gpus):\\n if gpus is None:\\n return None\\n\\n # set root gpu\\n root_gpu = 0\\n if type(gpus) is list:\\n root_gpu = gpus[0]\\n\\n return root_gpu\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus\\n @property\\n def num_gpus(self):\\n gpus = self.data_parallel_device_ids\\n if gpus is None:\\n return 0\\n\\n if type(gpus) is list:\\n return len(gpus)\\n if type(gpus) is int:\\n return gpus\\n\\n m = 'gpus must be int, none or list of ints'\\n raise MisconfigurationException(m)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel\\n @property\\n def data_parallel(self):\\n return self.use_dp or self.use_ddp or self.use_ddp2\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.training_tqdm_dict\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.training_tqdm_dict\\n @property\\n def training_tqdm_dict(self):\\n \\\"\\\"\\\"\\n Read-only for tqdm metrics\\n :return:\\n \\\"\\\"\\\"\\n tqdm_dict = {\\n 'loss': '{0:.3f}'.format(self.avg_loss),\\n 'epoch': '{}'.format(self.current_epoch),\\n 'batch_nb': '{}'.format(self.batch_nb),\\n }\\n\\n if self.logger is not None and self.logger.version is not None:\\n tqdm_dict['v_nb'] = self.logger.version\\n\\n tqdm_dict.update(self.tqdm_metrics)\\n\\n if self.on_gpu:\\n tqdm_dict['gpu'] = '{}'.format(torch.cuda.current_device())\\n\\n return tqdm_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.tng_tqdm_dic\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.tng_tqdm_dic\\n @property\\n def tng_tqdm_dic(self):\\n \\\"\\\"\\\"\\n * Deprecated in v0.5.0. use training_tqdm_dict instead. *\\n :return:\\n \\\"\\\"\\\"\\n warnings.warn(\\\"tng_tqdm_dict has renamed to training_tqdm_dict since v0.5.0\\\",\\n DeprecationWarning)\\n return self.training_tqdm_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.fit\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.fit\\n def fit(self, model):\\n # when using multi-node or DDP within a node start each module in a separate process\\n if self.use_ddp2:\\n task = int(os.environ['SLURM_LOCALID'])\\n self.ddp_train(task, model)\\n\\n elif self.use_ddp:\\n if self.is_slurm_managing_tasks:\\n task = int(os.environ['SLURM_LOCALID'])\\n self.ddp_train(task, model)\\n else:\\n mp.spawn(self.ddp_train, nprocs=self.num_gpus, args=(model,))\\n\\n # 1 gpu or dp option triggers training using DP module\\n # easier to avoid NCCL issues\\n elif self.use_dp:\\n self.dp_train(model)\\n\\n elif self.single_gpu:\\n self.single_gpu_train(model)\\n\\n # ON CPU\\n else:\\n # run through amp wrapper\\n if self.use_amp:\\n raise MisconfigurationException('amp + cpu is not supported.'\\n ' Please use a GPU option')\\n\\n # CHOOSE OPTIMIZER\\n # allow for lr schedulers as well\\n self.optimizers, self.lr_schedulers = self.init_optimizers(model.configure_optimizers())\\n\\n self.run_pretrain_routine(model)\\n\\n # return 1 when finished\\n # used for testing or when we need to know that training succeeded\\n return 1\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.init_optimizers\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.init_optimizers\\n def init_optimizers(self, optimizers):\\n\\n # single optimizer\\n if isinstance(optimizers, Optimizer):\\n return [optimizers], []\\n\\n # two lists\\n elif len(optimizers) == 2 and isinstance(optimizers[0], list):\\n optimizers, lr_schedulers = optimizers\\n return optimizers, lr_schedulers\\n\\n # single list or tuple\\n elif isinstance(optimizers, list) or isinstance(optimizers, tuple):\\n return optimizers, []\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.run_pretrain_routine\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.run_pretrain_routine\\n def run_pretrain_routine(self, model):\\n \\\"\\\"\\\"\\n Sanity check a few things before starting actual training\\n :param model:\\n :return:\\n \\\"\\\"\\\"\\n ref_model = model\\n if self.data_parallel:\\n ref_model = model.module\\n\\n # give model convenience properties\\n ref_model.trainer = self\\n\\n # set local properties on the model\\n self.copy_trainer_model_properties(ref_model)\\n\\n # link up experiment object\\n if self.logger is not None:\\n ref_model.logger = self.logger\\n\\n # save exp to get started\\n if hasattr(ref_model, \\\"hparams\\\"):\\n self.logger.log_hyperparams(ref_model.hparams)\\n\\n self.logger.save()\\n\\n if self.use_ddp or self.use_ddp2:\\n dist.barrier()\\n\\n # set up checkpoint callback\\n self.configure_checkpoint_callback()\\n\\n # register auto-resubmit when on SLURM\\n self.register_slurm_signal_handlers()\\n\\n # transfer data loaders from model\\n self.get_dataloaders(ref_model)\\n\\n # init training constants\\n self.layout_bookeeping()\\n\\n # print model summary\\n if self.proc_rank == 0 and self.weights_summary is not None:\\n if self.weights_summary in ['full', 'top']:\\n ref_model.summarize(mode=self.weights_summary)\\n else:\\n m = \\\"weights_summary can be None, 'full' or 'top'\\\"\\n raise MisconfigurationException(m)\\n\\n # track model now.\\n # if cluster resets state, the model will update with the saved weights\\n self.model = model\\n\\n # restore training and model before hpc call\\n self.restore_weights(model)\\n\\n # progress bar init\\n if self.show_progress_bar:\\n self.progress_bar = tqdm.tqdm(0, position=self.process_position)\\n\\n # when testing requested only run test and return\\n if self\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.test\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.test\\n def test(self, model=None):\\n self.testing = True\\n if model is not None:\\n self.fit(model)\\n else:\\n self.run_evaluation(test=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.get_model\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.get_model\\n def get_model(self):\\n is_dp_module = isinstance(self.model, (LightningDistributedDataParallel,\\n LightningDataParallel))\\n model = self.model.module if is_dp_module else self.model\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_weights\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_weights\\n def restore_weights(self, model):\\n \\\"\\\"\\\"\\n To restore weights we have two cases.\\n First, if we use the same experiment version, then restore the latest ckpt.\\n AFTER that, if we find weights from hpc checkpoint, then restore that.\\n :param model:\\n :return:\\n \\\"\\\"\\\"\\n # restore weights if same exp version\\n self.restore_state_if_checkpoint_exists(model)\\n\\n # if script called from hpc resubmit, load weights\\n self.restore_hpc_weights_if_needed(model)\\n\\n # wait for all models to restore weights\\n if self.use_ddp or self.use_ddp2:\\n # wait for all processes to catch up\\n dist.barrier()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_state_if_checkpoint_exists\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_state_if_checkpoint_exists\\n def restore_state_if_checkpoint_exists(self, model):\\n # do nothing if there's not dir or callback\\n no_ckpt_callback = (self.checkpoint_callback is None) or (not self.checkpoint_callback)\\n if no_ckpt_callback or not os.path.exists(self.checkpoint_callback.filepath):\\n return\\n\\n # restore trainer state and model if there is a weight for this experiment\\n last_epoch = -1\\n last_ckpt_name = None\\n\\n # find last epoch\\n checkpoints = os.listdir(self.checkpoint_callback.filepath)\\n for name in checkpoints:\\n # ignore hpc ckpts\\n if 'hpc_' in name:\\n continue\\n\\n if '.ckpt' in name:\\n epoch = name.split('epoch_')[1]\\n epoch = int(re.sub('[^0-9]', '', epoch))\\n\\n if epoch > last_epoch:\\n last_epoch = epoch\\n last_ckpt_name = name\\n\\n # restore last checkpoint\\n if last_ckpt_name is not None:\\n last_ckpt_path = os.path.join(self.checkpoint_callback.filepath, last_ckpt_name)\\n self.restore(last_ckpt_path, self.on_gpu)\\n print(f'model and trainer restored from checkpoint: {last_ckpt_path}')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.register_slurm_signal_handlers\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.register_slurm_signal_handlers\\n def register_slurm_signal_handlers(self):\\n # see if we're using slurm (not interactive)\\n on_slurm = False\\n try:\\n job_name = os.environ['SLURM_JOB_NAME']\\n if job_name != 'bash':\\n on_slurm = True\\n except Exception as e:\\n pass\\n\\n if on_slurm:\\n print('set slurm handle signals')\\n signal.signal(signal.SIGUSR1, self.sig_handler)\\n signal.signal(signal.SIGTERM, self.term_handler)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.sig_handler\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.sig_handler\\n def sig_handler(self, signum, frame):\\n if self.proc_rank == 0:\\n # save weights\\n print('handling SIGUSR1')\\n self.hpc_save(self.weights_save_path, self.logger)\\n\\n # find job id\\n job_id = os.environ['SLURM_JOB_ID']\\n cmd = 'scontrol requeue {}'.format(job_id)\\n\\n # requeue job\\n print('\\\\nrequeing job {}...'.format(job_id))\\n result = call(cmd, shell=True)\\n\\n # print result text\\n if result == 0:\\n print('requeued exp ', job_id)\\n else:\\n print('requeue failed...')\\n\\n # close experiment to avoid issues\\n self.logger.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.term_handler\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.term_handler\\n def term_handler(self, signum, frame):\\n # save\\n print(\\\"bypassing sigterm\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.save_checkpoint\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.save_checkpoint\\n def save_checkpoint(self, filepath):\\n checkpoint = self.dump_checkpoint()\\n\\n # do the actual save\\n torch.save(checkpoint, filepath)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore\\n def restore(self, checkpoint_path, on_gpu):\\n\\n # if on_gpu:\\n # checkpoint = torch.load(checkpoint_path)\\n # else:\\n # load on CPU first\\n checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)\\n\\n # load model state\\n model = self.get_model()\\n\\n # load the state_dict on the model automatically\\n model.load_state_dict(checkpoint['state_dict'])\\n if on_gpu:\\n model.cuda(self.root_gpu)\\n\\n # load training state (affects trainer only)\\n self.restore_training_state(checkpoint)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.dump_checkpoint\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.dump_checkpoint\\n def dump_checkpoint(self):\\n\\n checkpoint = {\\n 'epoch': self.current_epoch,\\n 'global_step': self.global_step\\n }\\n\\n if self.checkpoint_callback is not None or self.checkpoint_callback is not False:\\n checkpoint['checkpoint_callback_best'] = self.checkpoint_callback.best\\n\\n if self.early_stop_callback is not None or self.checkpoint_callback is not False:\\n checkpoint['early_stop_callback_wait'] = self.early_stop_callback.wait\\n checkpoint['early_stop_callback_patience'] = self.early_stop_callback.patience\\n\\n # save optimizers\\n optimizer_states = []\\n for i, optimizer in enumerate(self.optimizers):\\n optimizer_states.append(optimizer.state_dict())\\n\\n checkpoint['optimizer_states'] = optimizer_states\\n\\n # save lr schedulers\\n lr_schedulers = []\\n for i, scheduler in enumerate(self.lr_schedulers):\\n lr_schedulers.append(scheduler.state_dict())\\n\\n checkpoint['lr_schedulers'] = lr_schedulers\\n\\n # add the state_dict from the model\\n model = self.get_model()\\n checkpoint['state_dict'] = model.state_dict()\\n\\n # give the model a chance to add a few things\\n model.on_save_checkpoint(checkpoint)\\n\\n return checkpoint\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_hpc_weights_if_needed\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_hpc_weights_if_needed\\n def restore_hpc_weights_if_needed(self, model):\\n \\\"\\\"\\\"\\n If there is a set of hpc weights, use as signal to restore model\\n :param model:\\n :return:\\n \\\"\\\"\\\"\\n # look for hpc weights\\n folderpath = self.weights_save_path\\n if os.path.exists(folderpath):\\n files = os.listdir(folderpath)\\n hpc_weight_paths = [x for x in files if 'hpc_ckpt' in x]\\n\\n # if hpc weights exist restore model\\n if len(hpc_weight_paths) > 0:\\n self.hpc_load(folderpath, self.on_gpu)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_training_state\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.restore_training_state\\n def restore_training_state(self, checkpoint):\\n \\\"\\\"\\\"\\n Restore trainer state.\\n Model will get its change to update\\n :param checkpoint:\\n :return:\\n \\\"\\\"\\\"\\n if self.checkpoint_callback is not None or self.checkpoint_callback is not False:\\n self.checkpoint_callback.best = checkpoint['checkpoint_callback_best']\\n\\n if self.early_stop_callback is not None or self.early_stop_callback is not False:\\n self.early_stop_callback.wait = checkpoint['early_stop_callback_wait']\\n self.early_stop_callback.patience = checkpoint['early_stop_callback_patience']\\n\\n self.global_step = checkpoint['global_step']\\n self.current_epoch = checkpoint['epoch']\\n\\n # restore the optimizers\\n optimizer_states = checkpoint['optimizer_states']\\n for optimizer, opt_state in zip(self.optimizers, optimizer_states):\\n optimizer.load_state_dict(opt_state)\\n\\n # move optimizer to GPU 1 weight at a time\\n # avoids OOM\\n if self.root_gpu is not None:\\n for state in optimizer.state.values():\\n for k, v in state.items():\\n if isinstance(v, torch.Tensor):\\n state[k] = v.cuda(self.root_gpu)\\n\\n # restore the lr schedulers\\n lr_schedulers = checkpoint['lr_schedulers']\\n for scheduler, lrs_state in zip(self.lr_schedulers, lr_schedulers):\\n scheduler.load_state_dict(lrs_state)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.hpc_save\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.hpc_save\\n def hpc_save(self, folderpath, logger):\\n # make sure the checkpoint folder exists\\n os.makedirs(folderpath, exist_ok=True)\\n\\n # save logger to make sure we get all the metrics\\n logger.save()\\n\\n ckpt_number = self.max_ckpt_in_folder(folderpath) + 1\\n\\n if not os.path.exists(folderpath):\\n os.makedirs(folderpath, exist_ok=True)\\n filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, ckpt_number)\\n\\n # give model a chance to do something on hpc_save\\n model = self.get_model()\\n checkpoint = self.dump_checkpoint()\\n\\n model.on_hpc_save(checkpoint)\\n\\n # do the actual save\\n torch.save(checkpoint, filepath)\\n\\n return filepath\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.hpc_load\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.hpc_load\\n def hpc_load(self, folderpath, on_gpu):\\n filepath = '{}/hpc_ckpt_{}.ckpt'.format(folderpath, self.max_ckpt_in_folder(folderpath))\\n\\n # load on CPU first\\n checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)\\n\\n # load model state\\n model = self.get_model()\\n\\n # load the state_dict on the model automatically\\n model.load_state_dict(checkpoint['state_dict'])\\n\\n if self.root_gpu is not None:\\n model.cuda(self.root_gpu)\\n\\n # load training state (affects trainer only)\\n self.restore_training_state(checkpoint)\\n\\n # call model hook\\n model.on_hpc_load(checkpoint)\\n\\n print(f'restored hpc model from: {filepath}')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.max_ckpt_in_folder\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::TrainerIOMixin.max_ckpt_in_folder\\n def max_ckpt_in_folder(self, path, name_key='ckpt_'):\\n files = os.listdir(path)\\n files = [x for x in files if name_key in x]\\n if len(files) == 0:\\n return 0\\n\\n ckpt_vs = []\\n for name in files:\\n name = name.split(name_key)[-1]\\n name = re.sub('[^0-9]', '', name)\\n ckpt_vs.append(int(name))\\n\\n return max(ckpt_vs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::load_hparams_from_tags_csv\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::load_hparams_from_tags_csv\\ndef load_hparams_from_tags_csv(tags_csv):\\n from argparse import Namespace\\n import pandas as pd\\n\\n tags_df = pd.read_csv(tags_csv)\\n dic = tags_df.to_dict(orient='records')\\n\\n ns_dict = {row['key']: convert(row['value']) for row in dic}\\n\\n ns = Namespace(**ns_dict)\\n return ns\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer_io.py::convert\",\n \"text\": \"pytorch_lightning/trainer/trainer_io.py::convert\\ndef convert(val):\\n constructors = [int, float, str]\\n\\n if type(val) is str:\\n if val.lower() == 'true':\\n return True\\n if val.lower() == 'false':\\n return False\\n\\n for c in constructors:\\n try:\\n return c(val)\\n except ValueError:\\n pass\\n return val\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/utilities/arg_parse.py::add_default_args\",\n \"text\": \"pytorch_lightning/utilities/arg_parse.py::add_default_args\\ndef add_default_args(parser, root_dir, rand_seed=None, possible_model_names=None):\\n # training, test, val check intervals\\n parser.add_argument('--eval_test_set', dest='eval_test_set', action='store_true',\\n help='true = run test set also')\\n parser.add_argument('--check_val_every_n_epoch', default=1, type=int,\\n help='check val every n epochs')\\n parser.opt_list('--accumulate_grad_batches', default=1, type=int, tunable=False,\\n help='accumulates gradients k times before applying update.'\\n ' Simulates huge batch size')\\n parser.add_argument('--max_nb_epochs', default=200, type=int, help='cap epochs')\\n parser.add_argument('--min_nb_epochs', default=2, type=int, help='min epochs')\\n parser.add_argument('--train_percent_check', default=1.0, type=float,\\n help='how much of training set to check')\\n parser.add_argument('--val_percent_check', default=1.0, type=float,\\n help='how much of val set to check')\\n parser.add_argument('--test_percent_check', default=1.0, type=float,\\n help='how much of test set to check')\\n\\n parser.add_argument('--val_check_interval', default=0.95, type=float,\\n help='how much within 1 epoch to check val')\\n parser.add_argument('--log_save_interval', default=100, type=int,\\n help='how many batches between log saves')\\n parser.add_argument('--row_log_interval', default=100, type=int,\\n help='add log every k batches')\\n\\n # early stopping\\n parser.add_argument('--disable_early_stop', dest='enable_early_stop', action='store_false')\\n parser.add_argument('--early_stop_metric', default='val_acc', type=str)\\n parser.add_argument('--early_stop_mode', default='min', type=str)\\n parser.add_argument('--early_stop_patience', default=3, type=int,\\n \",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":281,"cells":{"instance_id":{"kind":"string","value":"docker__compose-6390"},"repo":{"kind":"string","value":"docker/compose"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Cannot override network settings.\n<!--\r\nWelcome to the docker-compose issue tracker! Before creating an issue, please heed the following:\r\n\r\n1. This tracker should only be used to report bugs and request features / enhancements to docker-compose\r\n - For questions and general support, use https://forums.docker.com\r\n - For documentation issues, use https://github.com/docker/docker.github.io\r\n - For issues with the `docker stack` commands and the version 3 of the Compose file, use\r\n https://github.com/docker/cli\r\n2. Use the search function before creating a new issue. Duplicates will be closed and directed to\r\n the original discussion.\r\n3. When making a bug report, make sure you provide all required information. The easier it is for\r\n maintainers to reproduce, the faster it'll be fixed.\r\n-->\r\n\r\n## Description of the issue\r\n\r\nConfiguration merge doesn't seem to merge network subsections correctly.\r\nProbably related to #2851.\r\n\r\n\r\n## Context information (for bug reports)\r\n\r\n**Output of `docker-compose version`**\r\n```\r\ndocker-compose version 1.23.1, build unknown\r\ndocker-py version: 3.5.1\r\nCPython version: 3.7.1\r\nOpenSSL version: OpenSSL 1.1.1 11 Sep 2018\r\n```\r\n\r\n**Output of `docker version`**\r\n```\r\nClient:\r\n Version: 18.09.0-ce\r\n API version: 1.39\r\n Go version: go1.11.2\r\n Git commit: 4d60db472b\r\n Built: Fri Nov 9 00:05:34 2018\r\n OS/Arch: linux/amd64\r\n Experimental: false\r\n\r\nServer:\r\n Engine:\r\n Version: 18.09.0-ce\r\n API version: 1.39 (minimum version 1.12)\r\n Go version: go1.11.2\r\n Git commit: 4d60db472b\r\n Built: Fri Nov 9 00:05:11 2018\r\n OS/Arch: linux/amd64\r\n Experimental: false\r\n\r\n```\r\n\r\n## Steps to reproduce the issue\r\n\r\n1. Set an `alias` on a service network.\r\n2. Add an `ip_address` on the override file.\r\n\r\n### Observed result\r\n\r\nThe `alias` is removed\r\n\r\n### Expected result\r\n\r\nBoth the `alias` and the `ip_address` are set.\r\n\r\n### Example\r\n\r\n```\r\n// docker-compose.yml\r\nversion: '3.7'\r\nservices:\r\n foo:\r\n image: scratch\r\n networks:\r\n default:\r\n aliases:\r\n - foo.bar\r\n```\r\n\r\n```\r\n// docker-compose.override.yml\r\nversion: '3.7'\r\nservices:\r\n foo:\r\n networks:\r\n default:\r\n ipv4_address: 123.234.123.234\r\n```\r\n\r\n```\r\n$: docker-compose config\r\nservices:\r\n foo:\r\n image: scratch\r\n networks:\r\n default:\r\n ipv4_address: 123.234.123.234\r\nversion: '3.7'\r\n```\r\n\r\n## Additional information\r\n\r\nOS: Manjaro Linux (kernel 4.18.19-1-MANJARO)\r\n`docker-compose` install method: distro package manager\r\n\n"},"gold_files":{"kind":"list like","value":["compose/config/config.py"],"string":"[\n \"compose/config/config.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["compose/config/config.py::<module>","compose/config/config.py::merge_service_dicts"],"string":"[\n \"compose/config/config.py::<module>\",\n \"compose/config/config.py::merge_service_dicts\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"compose/cli/errors.py","text":"compose/cli/errors.py\nclass UserError\n def __init__(self, msg)\n def __unicode__(self)\nclass ConnectionError\ndef handle_connection_errors(client)\ndef log_windows_pipe_error(exc)\ndef log_timeout_error(timeout)\ndef log_api_error(e, client_version)\ndef exit_with_error(msg)\ndef get_conn_error_message(url)\ndef docker_not_found_msg(problem)\ndef docker_install_url()","label":0},{"cid":"compose/cli/main.py","text":"compose/cli/main.py\ndef main()\ndef dispatch()\ndef perform_command(options, handler, command_options)\ndef setup_logging()\ndef setup_parallel_logger(noansi)\ndef setup_console_handler(handler, verbose, noansi=False, level=None)\ndef parse_doc_section(name, source)\nclass TopLevelCommand # Define and run multi-container applications with Docker.\n def __init__(self, project, options=None)\n def project_dir(self)\n def build(self, options) # Build or rebuild services.\n def bundle(self, options) # Generate a Distributed Application Bundle (DAB) from the Compose file.\n def config(self, options) # Validate and view the Compose file.\n def create(self, options) # Creates containers for a service.\n def down(self, options) # Stops containers and removes containers, networks, volumes, and images\n def events(self, options) # Receive real time events from containers.\n def format_event(event)\n def json_format_event(event)\n def exec_command(self, options) # Execute a command in a running container\n def help(cls, options) # Get help on a command.\n def images(self, options) # List images used by the created containers.\n def add_default_tag(img_name)\n def kill(self, options) # Force stop service containers.\n def logs(self, options) # View output from containers.\n def pause(self, options) # Pause services.\n def port(self, options) # Print the public port for a port binding.\n def ps(self, options) # List containers.\n def pull(self, options) # Pulls images for services defined in a Compose file, but does not start the cont\n def push(self, options) # Pushes images for services.\n def rm(self, options) # Removes stopped service containers.\n def run(self, options) # Run a one-off command on a service.\n def scale(self, options) # Set number of containers to run for a service.\n def start(self, options) # Start existing containers.\n def stop(self, options) # Stop running containers without removing them.\n def restart(self, options) # Restart running containers.\n def top(self, options) # Display the running processes\n def unpause(self, options) # Unpause services.\n def up(self, options) # Builds, (re)creates, starts, and attaches to containers for a service.\n def version(cls, options) # Show version information\ndef compute_service_exit_code(exit_value_from, attached_containers)\ndef compute_exit_code(exit_value_from, attached_containers, cascade_starter, all_containers)\ndef convergence_strategy_from_opts(options)\ndef timeout_from_opts(options)\ndef image_digests_for_project(project, allow_push=False)\ndef exitval_from_opts(options, project)\ndef image_type_from_opt(flag, value)\ndef build_action_from_opts(options)\ndef build_one_off_container_options(options, detach, command)\ndef run_one_off_container(container_options, project, service, options, toplevel_options, project_dir='.')\n def remove_container(force=False)\ndef log_printer_from_project(project, co","label":0},{"cid":"compose/config/config.py","text":"compose/config/config.py\nclass ConfigDetails # :param working_dir: the directory to use for relative paths in the config\n def __new__(cls, working_dir, config_files, environment=None)\nclass ConfigFile # :param filename: filename of the config file\n def from_filename(cls, filename)\n def version(self)\n def get_service(self, name)\n def get_service_dicts(self)\n def get_volumes(self)\n def get_networks(self)\n def get_secrets(self)\n def get_configs(self)\nclass Config # :param version: configuration version\nclass ServiceConfig\n def with_abs_paths(cls, working_dir, filename, name, config)\ndef find(base_dir, filenames, environment, override_dir=None)\ndef validate_config_version(config_files)\ndef get_default_config_files(base_dir)\ndef get_default_override_file(path)\ndef find_candidates_in_parent_dirs(filenames, path) # Given a directory path to start, looks for filenames in the\ndef check_swarm_only_config(service_dicts, compatibility=False)\n def check_swarm_only_key(service_dicts, key)\ndef load(config_details, compatibility=False) # Load the configuration from a working directory and a list of\ndef load_mapping(config_files, get_func, entity_type, working_dir=None)\ndef validate_external(entity_type, name, config, version)\ndef load_services(config_details, config_file, compatibility=False)\n def build_service(service_name, service_dict, service_names)\n def build_services(service_config)\n def merge_services(base, override)\ndef interpolate_config_section(config_file, config, section, environment)\ndef process_config_file(config_file, environment, service_name=None)\nclass ServiceExtendsResolver\n def __init__(self, service_config, config_file, environment, already_seen=None)\n def signature(self)\n def detect_cycle(self)\n def run(self)\n def validate_and_construct_extends(self)\n def resolve_extends(self, extended_config_path, service_dict, service_name)\n def get_extended_config_path(self, extends_options) # Service we are extending either has a value for 'file' set, which we\ndef resolve_environment(service_dict, environment=None) # Unpack any environment variables from an env_file, if set.\ndef resolve_build_args(buildargs, environment)\ndef validate_extended_service_dict(service_dict, filename, service)\ndef validate_service(service_config, service_names, config_file)\ndef process_service(service_config)\ndef process_build_section(service_dict, working_dir)\ndef process_ports(service_dict)\ndef process_depends_on(service_dict)\ndef process_blkio_config(service_dict)\ndef process_healthcheck(service_dict)\ndef finalize_service_volumes(service_dict, environment)\ndef finalize_service(service_config, service_names, version, environment, compatibility)\ndef translate_resource_keys_to_container_config(resources_dict, service_dict)\ndef convert_restart_policy(name)\ndef translate_deploy_keys_to_container_config(service_dict)\ndef normalize_v1_service_format(service_dict)\ndef merge_service_dicts_from_files(base, overrid","label":1},{"cid":"compose/config/validation.py","text":"compose/config/validation.py\ndef format_ports(instance)\ndef format_expose(instance)\ndef format_subnet_ip_address(instance)\ndef match_named_volumes(service_dict, project_volumes)\ndef python_type_to_yaml_type(type_)\ndef validate_config_section(filename, config, section) # Validate the structure of a configuration section. This must be done\ndef validate_top_level_object(config_file)\ndef validate_ulimits(service_config)\ndef validate_extends_file_path(service_name, extends_options, filename) # The service to be extended must either be defined in the config key 'file',\ndef validate_network_mode(service_config, service_names)\ndef validate_pid_mode(service_config, service_names)\ndef validate_links(service_config, service_names)\ndef validate_depends_on(service_config, service_names)\ndef get_unsupported_config_msg(path, error_key)\ndef anglicize_json_type(json_type)\ndef is_service_dict_schema(schema_id)\ndef handle_error_for_schema_with_id(error, path)\ndef handle_generic_error(error, path)\ndef parse_key_from_error_msg(error)\ndef path_string(path)\ndef _parse_valid_types_from_validator(validator) # A validator value can be either an array of valid types or a string of\ndef _parse_oneof_validator(error) # oneOf has multiple schemas, so we need to reason about which schema, sub\ndef process_service_constraint_errors(error, service_name, version)\ndef process_config_schema_errors(error)\ndef validate_against_config_schema(config_file)\ndef validate_service_constraints(config, service_name, config_file)\n def handler(errors)\ndef validate_cpu(service_config)\ndef get_schema_path()\ndef load_jsonschema(config_file)\ndef get_resolver_path()\ndef handle_errors(errors, format_error_func, filename) # jsonschema returns an error tree full of information to explain what has\ndef validate_healthcheck(service_config)","label":0},{"cid":"script/release/release.py","text":"script/release/release.py\ndef create_initial_branch(repository, args)\ndef create_bump_commit(repository, release_branch, bintray_user, bintray_org)\ndef monitor_pr_status(pr_data)\ndef check_pr_mergeable(pr_data)\ndef create_release_draft(repository, version, pr_data, files)\ndef print_final_instructions(args)\ndef distclean()\ndef resume(args)\ndef cancel(args)\ndef start(args)\ndef finalize(args)\ndef main()","label":0},{"cid":"setup.py","text":"setup.py\ndef read(*parts)\ndef find_version(*file_paths)","label":0}],"string":"[\n {\n \"cid\": \"compose/cli/errors.py\",\n \"text\": \"compose/cli/errors.py\\nclass UserError\\n def __init__(self, msg)\\n def __unicode__(self)\\nclass ConnectionError\\ndef handle_connection_errors(client)\\ndef log_windows_pipe_error(exc)\\ndef log_timeout_error(timeout)\\ndef log_api_error(e, client_version)\\ndef exit_with_error(msg)\\ndef get_conn_error_message(url)\\ndef docker_not_found_msg(problem)\\ndef docker_install_url()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py\",\n \"text\": \"compose/cli/main.py\\ndef main()\\ndef dispatch()\\ndef perform_command(options, handler, command_options)\\ndef setup_logging()\\ndef setup_parallel_logger(noansi)\\ndef setup_console_handler(handler, verbose, noansi=False, level=None)\\ndef parse_doc_section(name, source)\\nclass TopLevelCommand # Define and run multi-container applications with Docker.\\n def __init__(self, project, options=None)\\n def project_dir(self)\\n def build(self, options) # Build or rebuild services.\\n def bundle(self, options) # Generate a Distributed Application Bundle (DAB) from the Compose file.\\n def config(self, options) # Validate and view the Compose file.\\n def create(self, options) # Creates containers for a service.\\n def down(self, options) # Stops containers and removes containers, networks, volumes, and images\\n def events(self, options) # Receive real time events from containers.\\n def format_event(event)\\n def json_format_event(event)\\n def exec_command(self, options) # Execute a command in a running container\\n def help(cls, options) # Get help on a command.\\n def images(self, options) # List images used by the created containers.\\n def add_default_tag(img_name)\\n def kill(self, options) # Force stop service containers.\\n def logs(self, options) # View output from containers.\\n def pause(self, options) # Pause services.\\n def port(self, options) # Print the public port for a port binding.\\n def ps(self, options) # List containers.\\n def pull(self, options) # Pulls images for services defined in a Compose file, but does not start the cont\\n def push(self, options) # Pushes images for services.\\n def rm(self, options) # Removes stopped service containers.\\n def run(self, options) # Run a one-off command on a service.\\n def scale(self, options) # Set number of containers to run for a service.\\n def start(self, options) # Start existing containers.\\n def stop(self, options) # Stop running containers without removing them.\\n def restart(self, options) # Restart running containers.\\n def top(self, options) # Display the running processes\\n def unpause(self, options) # Unpause services.\\n def up(self, options) # Builds, (re)creates, starts, and attaches to containers for a service.\\n def version(cls, options) # Show version information\\ndef compute_service_exit_code(exit_value_from, attached_containers)\\ndef compute_exit_code(exit_value_from, attached_containers, cascade_starter, all_containers)\\ndef convergence_strategy_from_opts(options)\\ndef timeout_from_opts(options)\\ndef image_digests_for_project(project, allow_push=False)\\ndef exitval_from_opts(options, project)\\ndef image_type_from_opt(flag, value)\\ndef build_action_from_opts(options)\\ndef build_one_off_container_options(options, detach, command)\\ndef run_one_off_container(container_options, project, service, options, toplevel_options, project_dir='.')\\n def remove_container(force=False)\\ndef log_printer_from_project(project, co\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py\",\n \"text\": \"compose/config/config.py\\nclass ConfigDetails # :param working_dir: the directory to use for relative paths in the config\\n def __new__(cls, working_dir, config_files, environment=None)\\nclass ConfigFile # :param filename: filename of the config file\\n def from_filename(cls, filename)\\n def version(self)\\n def get_service(self, name)\\n def get_service_dicts(self)\\n def get_volumes(self)\\n def get_networks(self)\\n def get_secrets(self)\\n def get_configs(self)\\nclass Config # :param version: configuration version\\nclass ServiceConfig\\n def with_abs_paths(cls, working_dir, filename, name, config)\\ndef find(base_dir, filenames, environment, override_dir=None)\\ndef validate_config_version(config_files)\\ndef get_default_config_files(base_dir)\\ndef get_default_override_file(path)\\ndef find_candidates_in_parent_dirs(filenames, path) # Given a directory path to start, looks for filenames in the\\ndef check_swarm_only_config(service_dicts, compatibility=False)\\n def check_swarm_only_key(service_dicts, key)\\ndef load(config_details, compatibility=False) # Load the configuration from a working directory and a list of\\ndef load_mapping(config_files, get_func, entity_type, working_dir=None)\\ndef validate_external(entity_type, name, config, version)\\ndef load_services(config_details, config_file, compatibility=False)\\n def build_service(service_name, service_dict, service_names)\\n def build_services(service_config)\\n def merge_services(base, override)\\ndef interpolate_config_section(config_file, config, section, environment)\\ndef process_config_file(config_file, environment, service_name=None)\\nclass ServiceExtendsResolver\\n def __init__(self, service_config, config_file, environment, already_seen=None)\\n def signature(self)\\n def detect_cycle(self)\\n def run(self)\\n def validate_and_construct_extends(self)\\n def resolve_extends(self, extended_config_path, service_dict, service_name)\\n def get_extended_config_path(self, extends_options) # Service we are extending either has a value for 'file' set, which we\\ndef resolve_environment(service_dict, environment=None) # Unpack any environment variables from an env_file, if set.\\ndef resolve_build_args(buildargs, environment)\\ndef validate_extended_service_dict(service_dict, filename, service)\\ndef validate_service(service_config, service_names, config_file)\\ndef process_service(service_config)\\ndef process_build_section(service_dict, working_dir)\\ndef process_ports(service_dict)\\ndef process_depends_on(service_dict)\\ndef process_blkio_config(service_dict)\\ndef process_healthcheck(service_dict)\\ndef finalize_service_volumes(service_dict, environment)\\ndef finalize_service(service_config, service_names, version, environment, compatibility)\\ndef translate_resource_keys_to_container_config(resources_dict, service_dict)\\ndef convert_restart_policy(name)\\ndef translate_deploy_keys_to_container_config(service_dict)\\ndef normalize_v1_service_format(service_dict)\\ndef merge_service_dicts_from_files(base, overrid\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/config/validation.py\",\n \"text\": \"compose/config/validation.py\\ndef format_ports(instance)\\ndef format_expose(instance)\\ndef format_subnet_ip_address(instance)\\ndef match_named_volumes(service_dict, project_volumes)\\ndef python_type_to_yaml_type(type_)\\ndef validate_config_section(filename, config, section) # Validate the structure of a configuration section. This must be done\\ndef validate_top_level_object(config_file)\\ndef validate_ulimits(service_config)\\ndef validate_extends_file_path(service_name, extends_options, filename) # The service to be extended must either be defined in the config key 'file',\\ndef validate_network_mode(service_config, service_names)\\ndef validate_pid_mode(service_config, service_names)\\ndef validate_links(service_config, service_names)\\ndef validate_depends_on(service_config, service_names)\\ndef get_unsupported_config_msg(path, error_key)\\ndef anglicize_json_type(json_type)\\ndef is_service_dict_schema(schema_id)\\ndef handle_error_for_schema_with_id(error, path)\\ndef handle_generic_error(error, path)\\ndef parse_key_from_error_msg(error)\\ndef path_string(path)\\ndef _parse_valid_types_from_validator(validator) # A validator value can be either an array of valid types or a string of\\ndef _parse_oneof_validator(error) # oneOf has multiple schemas, so we need to reason about which schema, sub\\ndef process_service_constraint_errors(error, service_name, version)\\ndef process_config_schema_errors(error)\\ndef validate_against_config_schema(config_file)\\ndef validate_service_constraints(config, service_name, config_file)\\n def handler(errors)\\ndef validate_cpu(service_config)\\ndef get_schema_path()\\ndef load_jsonschema(config_file)\\ndef get_resolver_path()\\ndef handle_errors(errors, format_error_func, filename) # jsonschema returns an error tree full of information to explain what has\\ndef validate_healthcheck(service_config)\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py\",\n \"text\": \"script/release/release.py\\ndef create_initial_branch(repository, args)\\ndef create_bump_commit(repository, release_branch, bintray_user, bintray_org)\\ndef monitor_pr_status(pr_data)\\ndef check_pr_mergeable(pr_data)\\ndef create_release_draft(repository, version, pr_data, files)\\ndef print_final_instructions(args)\\ndef distclean()\\ndef resume(args)\\ndef cancel(args)\\ndef start(args)\\ndef finalize(args)\\ndef main()\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\\ndef read(*parts)\\ndef find_version(*file_paths)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"compose/cli/errors.py::UserError.__init__","text":"compose/cli/errors.py::UserError.__init__\n def __init__(self, msg):\n self.msg = dedent(msg).strip()","label":0},{"cid":"compose/cli/errors.py::UserError.__unicode__","text":"compose/cli/errors.py::UserError.__unicode__\n def __unicode__(self):\n return self.msg","label":0},{"cid":"compose/cli/errors.py::handle_connection_errors","text":"compose/cli/errors.py::handle_connection_errors\n@contextlib.contextmanager\ndef handle_connection_errors(client):\n try:\n yield\n except SSLError as e:\n log.error('SSL error: %s' % e)\n raise ConnectionError()\n except RequestsConnectionError as e:\n if e.args and isinstance(e.args[0], ReadTimeoutError):\n log_timeout_error(client.timeout)\n raise ConnectionError()\n exit_with_error(get_conn_error_message(client.base_url))\n except APIError as e:\n log_api_error(e, client.api_version)\n raise ConnectionError()\n except (ReadTimeout, socket.timeout):\n log_timeout_error(client.timeout)\n raise ConnectionError()\n except Exception as e:\n if is_windows():\n import pywintypes\n if isinstance(e, pywintypes.error):\n log_windows_pipe_error(e)\n raise ConnectionError()\n raise","label":0},{"cid":"compose/cli/errors.py::log_windows_pipe_error","text":"compose/cli/errors.py::log_windows_pipe_error\ndef log_windows_pipe_error(exc):\n if exc.winerror == 2:\n log.error(\"Couldn't connect to Docker daemon. You might need to start Docker for Windows.\")\n elif exc.winerror == 232: # https://github.com/docker/compose/issues/5005\n log.error(\n \"The current Compose file version is not compatible with your engine version. \"\n \"Please upgrade your Compose file to a more recent version, or set \"\n \"a COMPOSE_API_VERSION in your environment.\"\n )\n else:\n log.error(\n \"Windows named pipe error: {} (code: {})\".format(\n binarystr_to_unicode(exc.strerror), exc.winerror\n )\n )","label":0},{"cid":"compose/cli/errors.py::log_timeout_error","text":"compose/cli/errors.py::log_timeout_error\ndef log_timeout_error(timeout):\n log.error(\n \"An HTTP request took too long to complete. Retry with --verbose to \"\n \"obtain debug information.\\n\"\n \"If you encounter this issue regularly because of slow network \"\n \"conditions, consider setting COMPOSE_HTTP_TIMEOUT to a higher \"\n \"value (current value: %s).\" % timeout)","label":0},{"cid":"compose/cli/errors.py::log_api_error","text":"compose/cli/errors.py::log_api_error\ndef log_api_error(e, client_version):\n explanation = binarystr_to_unicode(e.explanation)\n\n if 'client is newer than server' not in explanation:\n log.error(explanation)\n return\n\n version = API_VERSION_TO_ENGINE_VERSION.get(client_version)\n if not version:\n # They've set a custom API version\n log.error(explanation)\n return\n\n log.error(\n \"The Docker Engine version is less than the minimum required by \"\n \"Compose. Your current project requires a Docker Engine of \"\n \"version {version} or greater.\".format(version=version)\n )","label":0},{"cid":"compose/cli/errors.py::exit_with_error","text":"compose/cli/errors.py::exit_with_error\ndef exit_with_error(msg):\n log.error(dedent(msg).strip())\n raise ConnectionError()","label":0},{"cid":"compose/cli/errors.py::get_conn_error_message","text":"compose/cli/errors.py::get_conn_error_message\ndef get_conn_error_message(url):\n try:\n if find_executable('docker') is None:\n return docker_not_found_msg(\"Couldn't connect to Docker daemon.\")\n if is_docker_for_mac_installed():\n return conn_error_docker_for_mac\n if find_executable('docker-machine') is not None:\n return conn_error_docker_machine\n except UnicodeDecodeError:\n # https://github.com/docker/compose/issues/5442\n # Ignore the error and print the generic message instead.\n pass\n return conn_error_generic.format(url=url)","label":0},{"cid":"compose/cli/errors.py::docker_not_found_msg","text":"compose/cli/errors.py::docker_not_found_msg\ndef docker_not_found_msg(problem):\n return \"{} You might need to install Docker:\\n\\n{}\".format(\n problem, docker_install_url())","label":0},{"cid":"compose/cli/errors.py::docker_install_url","text":"compose/cli/errors.py::docker_install_url\ndef docker_install_url():\n if is_mac():\n return docker_install_url_mac\n elif is_ubuntu():\n return docker_install_url_ubuntu\n elif is_windows():\n return docker_install_url_windows\n else:\n return docker_install_url_generic","label":0},{"cid":"compose/cli/main.py::main","text":"compose/cli/main.py::main\ndef main():\n signals.ignore_sigpipe()\n try:\n command = dispatch()\n command()\n except (KeyboardInterrupt, signals.ShutdownException):\n log.error(\"Aborting.\")\n sys.exit(1)\n except (UserError, NoSuchService, ConfigurationError,\n ProjectError, OperationFailedError) as e:\n log.error(e.msg)\n sys.exit(1)\n except BuildError as e:\n log.error(\"Service '%s' failed to build: %s\" % (e.service.name, e.reason))\n sys.exit(1)\n except StreamOutputError as e:\n log.error(e)\n sys.exit(1)\n except NeedsBuildError as e:\n log.error(\"Service '%s' needs to be built, but --no-build was passed.\" % e.service.name)\n sys.exit(1)\n except NoSuchCommand as e:\n commands = \"\\n\".join(parse_doc_section(\"commands:\", getdoc(e.supercommand)))\n log.error(\"No such command: %s\\n\\n%s\", e.command, commands)\n sys.exit(1)\n except (errors.ConnectionError, StreamParseError):\n sys.exit(1)","label":0},{"cid":"compose/cli/main.py::dispatch","text":"compose/cli/main.py::dispatch\ndef dispatch():\n setup_logging()\n dispatcher = DocoptDispatcher(\n TopLevelCommand,\n {'options_first': True, 'version': get_version_info('compose')})\n\n options, handler, command_options = dispatcher.parse(sys.argv[1:])\n setup_console_handler(console_handler,\n options.get('--verbose'),\n options.get('--no-ansi'),\n options.get(\"--log-level\"))\n setup_parallel_logger(options.get('--no-ansi'))\n if options.get('--no-ansi'):\n command_options['--no-color'] = True\n return functools.partial(perform_command, options, handler, command_options)","label":0},{"cid":"compose/cli/main.py::perform_command","text":"compose/cli/main.py::perform_command\ndef perform_command(options, handler, command_options):\n if options['COMMAND'] in ('help', 'version'):\n # Skip looking up the compose file.\n handler(command_options)\n return\n\n if options['COMMAND'] == 'config':\n command = TopLevelCommand(None, options=options)\n handler(command, command_options)\n return\n\n project = project_from_options('.', options)\n command = TopLevelCommand(project, options=options)\n with errors.handle_connection_errors(project.client):\n handler(command, command_options)","label":0},{"cid":"compose/cli/main.py::setup_logging","text":"compose/cli/main.py::setup_logging\ndef setup_logging():\n root_logger = logging.getLogger()\n root_logger.addHandler(console_handler)\n root_logger.setLevel(logging.DEBUG)\n\n # Disable requests logging\n logging.getLogger(\"requests\").propagate = False","label":0},{"cid":"compose/cli/main.py::setup_parallel_logger","text":"compose/cli/main.py::setup_parallel_logger\ndef setup_parallel_logger(noansi):\n if noansi:\n import compose.parallel\n compose.parallel.ParallelStreamWriter.set_noansi()","label":0},{"cid":"compose/cli/main.py::setup_console_handler","text":"compose/cli/main.py::setup_console_handler\ndef setup_console_handler(handler, verbose, noansi=False, level=None):\n if handler.stream.isatty() and noansi is False:\n format_class = ConsoleWarningFormatter\n else:\n format_class = logging.Formatter\n\n if verbose:\n handler.setFormatter(format_class('%(name)s.%(funcName)s: %(message)s'))\n loglevel = logging.DEBUG\n else:\n handler.setFormatter(format_class())\n loglevel = logging.INFO\n\n if level is not None:\n levels = {\n 'DEBUG': logging.DEBUG,\n 'INFO': logging.INFO,\n 'WARNING': logging.WARNING,\n 'ERROR': logging.ERROR,\n 'CRITICAL': logging.CRITICAL,\n }\n loglevel = levels.get(level.upper())\n if loglevel is None:\n raise UserError(\n 'Invalid value for --log-level. Expected one of DEBUG, INFO, WARNING, ERROR, CRITICAL.'\n )\n\n handler.setLevel(loglevel)","label":0},{"cid":"compose/cli/main.py::parse_doc_section","text":"compose/cli/main.py::parse_doc_section\ndef parse_doc_section(name, source):\n pattern = re.compile('^([^\\n]*' + name + '[^\\n]*\\n?(?:[ \\t].*?(?:\\n|$))*)',\n re.IGNORECASE | re.MULTILINE)\n return [s.strip() for s in pattern.findall(source)]","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.__init__","text":"compose/cli/main.py::TopLevelCommand.__init__\n def __init__(self, project, options=None):\n self.project = project\n self.toplevel_options = options or {}","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.project_dir","text":"compose/cli/main.py::TopLevelCommand.project_dir\n @property\n def project_dir(self):\n return self.toplevel_options.get('--project-directory') or '.'","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.build","text":"compose/cli/main.py::TopLevelCommand.build\n def build(self, options):\n \"\"\"\n Build or rebuild services.\n\n Services are built once and then tagged as `project_service`,\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\n contents of its build directory, you can run `docker-compose build` to rebuild it.\n\n Usage: build [options] [--build-arg key=val...] [SERVICE...]\n\n Options:\n --compress Compress the build context using gzip.\n --force-rm Always remove intermediate containers.\n --no-cache Do not use cache when building the image.\n --pull Always attempt to pull a newer version of the image.\n -m, --memory MEM Sets memory limit for the build container.\n --build-arg key=val Set build-time variables for services.\n --parallel Build images in parallel.\n \"\"\"\n service_names = options['SERVICE']\n build_args = options.get('--build-arg', None)\n if build_args:\n if not service_names and docker.utils.version_lt(self.project.client.api_version, '1.25'):\n raise UserError(\n '--build-arg is only supported when services are specified for API version < 1.25.'\n ' Please use a Compose file version > 2.2 or specify which services to build.'\n )\n environment = Environment.from_env_file(self.project_dir)\n build_args = resolve_build_args(build_args, environment)\n\n self.project.build(\n service_names=options['SERVICE'],\n no_cache=bool(options.get('--no-cache', False)),\n pull=bool(options.get('--pull', False)),\n force_rm=bool(options.get('--force-rm', False)),\n memory=options.get('--memory'),\n build_args=build_args,\n gzip=options.get('--compress', False),\n ","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.bundle","text":"compose/cli/main.py::TopLevelCommand.bundle\n def bundle(self, options):\n \"\"\"\n Generate a Distributed Application Bundle (DAB) from the Compose file.\n\n Images must have digests stored, which requires interaction with a\n Docker registry. If digests aren't stored for all images, you can fetch\n them with `docker-compose pull` or `docker-compose push`. To push images\n automatically when bundling, pass `--push-images`. Only services with\n a `build` option specified will have their images pushed.\n\n Usage: bundle [options]\n\n Options:\n --push-images Automatically push images for any services\n which have a `build` option specified.\n\n -o, --output PATH Path to write the bundle file to.\n Defaults to \"<project name>.dab\".\n \"\"\"\n compose_config = get_config_from_options('.', self.toplevel_options)\n\n output = options[\"--output\"]\n if not output:\n output = \"{}.dab\".format(self.project.name)\n\n image_digests = image_digests_for_project(self.project, options['--push-images'])\n\n with open(output, 'w') as f:\n f.write(serialize_bundle(compose_config, image_digests))\n\n log.info(\"Wrote bundle to {}\".format(output))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.config","text":"compose/cli/main.py::TopLevelCommand.config\n def config(self, options):\n \"\"\"\n Validate and view the Compose file.\n\n Usage: config [options]\n\n Options:\n --resolve-image-digests Pin image tags to digests.\n -q, --quiet Only validate the configuration, don't print\n anything.\n --services Print the service names, one per line.\n --volumes Print the volume names, one per line.\n --hash=\"*\" Print the service config hash, one per line.\n Set \"service1,service2\" for a list of specified services\n or use the wildcard symbol to display all services\n \"\"\"\n\n compose_config = get_config_from_options('.', self.toplevel_options)\n image_digests = None\n\n if options['--resolve-image-digests']:\n self.project = project_from_options('.', self.toplevel_options)\n with errors.handle_connection_errors(self.project.client):\n image_digests = image_digests_for_project(self.project)\n\n if options['--quiet']:\n return\n\n if options['--services']:\n print('\\n'.join(service['name'] for service in compose_config.services))\n return\n\n if options['--volumes']:\n print('\\n'.join(volume for volume in compose_config.volumes))\n return\n\n if options['--hash'] is not None:\n h = options['--hash']\n self.project = project_from_options('.', self.toplevel_options)\n services = [svc for svc in options['--hash'].split(',')] if h != '*' else None\n with errors.handle_connection_errors(self.project.client):\n for service in self.project.get_services(services):\n print('{} {}'.format(service.name, service.config_hash))\n return\n\n print(serialize_con","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.create","text":"compose/cli/main.py::TopLevelCommand.create\n def create(self, options):\n \"\"\"\n Creates containers for a service.\n This command is deprecated. Use the `up` command with `--no-start` instead.\n\n Usage: create [options] [SERVICE...]\n\n Options:\n --force-recreate Recreate containers even if their configuration and\n image haven't changed. Incompatible with --no-recreate.\n --no-recreate If containers already exist, don't recreate them.\n Incompatible with --force-recreate.\n --no-build Don't build an image, even if it's missing.\n --build Build images before creating containers.\n \"\"\"\n service_names = options['SERVICE']\n\n log.warn(\n 'The create command is deprecated. '\n 'Use the up command with the --no-start flag instead.'\n )\n\n self.project.create(\n service_names=service_names,\n strategy=convergence_strategy_from_opts(options),\n do_build=build_action_from_opts(options),\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.down","text":"compose/cli/main.py::TopLevelCommand.down\n def down(self, options):\n \"\"\"\n Stops containers and removes containers, networks, volumes, and images\n created by `up`.\n\n By default, the only things removed are:\n\n - Containers for services defined in the Compose file\n - Networks defined in the `networks` section of the Compose file\n - The default network, if one is used\n\n Networks and volumes defined as `external` are never removed.\n\n Usage: down [options]\n\n Options:\n --rmi type Remove images. Type must be one of:\n 'all': Remove all images used by any service.\n 'local': Remove only images that don't have a\n custom tag set by the `image` field.\n -v, --volumes Remove named volumes declared in the `volumes`\n section of the Compose file and anonymous volumes\n attached to containers.\n --remove-orphans Remove containers for services not defined in the\n Compose file\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n environment = Environment.from_env_file(self.project_dir)\n ignore_orphans = environment.get_boolean('COMPOSE_IGNORE_ORPHANS')\n\n if ignore_orphans and options['--remove-orphans']:\n raise UserError(\"COMPOSE_IGNORE_ORPHANS and --remove-orphans cannot be combined.\")\n\n image_type = image_type_from_opt('--rmi', options['--rmi'])\n timeout = timeout_from_opts(options)\n self.project.down(\n image_type,\n options['--volumes'],\n options['--remove-orphans'],\n timeout=timeout,\n ignore_orphans=ignore_orphans)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events","text":"compose/cli/main.py::TopLevelCommand.events\n def events(self, options):\n \"\"\"\n Receive real time events from containers.\n\n Usage: events [options] [SERVICE...]\n\n Options:\n --json Output events as a stream of json objects\n \"\"\"\n def format_event(event):\n attributes = [\"%s=%s\" % item for item in event['attributes'].items()]\n return (\"{time} {type} {action} {id} ({attrs})\").format(\n attrs=\", \".join(sorted(attributes)),\n **event)\n\n def json_format_event(event):\n event['time'] = event['time'].isoformat()\n event.pop('container')\n return json.dumps(event)\n\n for event in self.project.events():\n formatter = json_format_event if options['--json'] else format_event\n print(formatter(event))\n sys.stdout.flush()","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events.format_event","text":"compose/cli/main.py::TopLevelCommand.events.format_event\n def format_event(event):\n attributes = [\"%s=%s\" % item for item in event['attributes'].items()]\n return (\"{time} {type} {action} {id} ({attrs})\").format(\n attrs=\", \".join(sorted(attributes)),\n **event)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events.json_format_event","text":"compose/cli/main.py::TopLevelCommand.events.json_format_event\n def json_format_event(event):\n event['time'] = event['time'].isoformat()\n event.pop('container')\n return json.dumps(event)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.exec_command","text":"compose/cli/main.py::TopLevelCommand.exec_command\n def exec_command(self, options):\n \"\"\"\n Execute a command in a running container\n\n Usage: exec [options] [-e KEY=VAL...] SERVICE COMMAND [ARGS...]\n\n Options:\n -d, --detach Detached mode: Run command in the background.\n --privileged Give extended privileges to the process.\n -u, --user USER Run the command as this user.\n -T Disable pseudo-tty allocation. By default `docker-compose exec`\n allocates a TTY.\n --index=index index of the container if there are multiple\n instances of a service [default: 1]\n -e, --env KEY=VAL Set environment variables (can be used multiple times,\n not supported in API < 1.25)\n -w, --workdir DIR Path to workdir directory for this command.\n \"\"\"\n environment = Environment.from_env_file(self.project_dir)\n use_cli = not environment.get_boolean('COMPOSE_INTERACTIVE_NO_CLI')\n index = int(options.get('--index'))\n service = self.project.get_service(options['SERVICE'])\n detach = options.get('--detach')\n\n if options['--env'] and docker.utils.version_lt(self.project.client.api_version, '1.25'):\n raise UserError(\"Setting environment for exec is not supported in API < 1.25 (%s)\"\n % self.project.client.api_version)\n\n if options['--workdir'] and docker.utils.version_lt(self.project.client.api_version, '1.35'):\n raise UserError(\"Setting workdir for exec is not supported in API < 1.35 (%s)\"\n % self.project.client.api_version)\n\n try:\n container = service.get_container(number=index)\n except ValueError as e:\n raise UserError(str(e))\n command = [options['COMMAND']] + options['ARGS']\n tty = not options[\"-T\"]\n\n ","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.help","text":"compose/cli/main.py::TopLevelCommand.help\n @classmethod\n def help(cls, options):\n \"\"\"\n Get help on a command.\n\n Usage: help [COMMAND]\n \"\"\"\n if options['COMMAND']:\n subject = get_handler(cls, options['COMMAND'])\n else:\n subject = cls\n\n print(getdoc(subject))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.images","text":"compose/cli/main.py::TopLevelCommand.images\n def images(self, options):\n \"\"\"\n List images used by the created containers.\n Usage: images [options] [SERVICE...]\n\n Options:\n -q, --quiet Only display IDs\n \"\"\"\n containers = sorted(\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\n key=attrgetter('name'))\n\n if options['--quiet']:\n for image in set(c.image for c in containers):\n print(image.split(':')[1])\n return\n\n def add_default_tag(img_name):\n if ':' not in img_name.split('/')[-1]:\n return '{}:latest'.format(img_name)\n return img_name\n\n headers = [\n 'Container',\n 'Repository',\n 'Tag',\n 'Image Id',\n 'Size'\n ]\n rows = []\n for container in containers:\n image_config = container.image_config\n service = self.project.get_service(container.service)\n index = 0\n img_name = add_default_tag(service.image_name)\n if img_name in image_config['RepoTags']:\n index = image_config['RepoTags'].index(img_name)\n repo_tags = (\n image_config['RepoTags'][index].rsplit(':', 1) if image_config['RepoTags']\n else ('<none>', '<none>')\n )\n\n image_id = image_config['Id'].split(':')[1][:12]\n size = human_readable_file_size(image_config['Size'])\n rows.append([\n container.name,\n repo_tags[0],\n repo_tags[1],\n image_id,\n size\n ])\n print(Formatter().table(headers, rows))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.images.add_default_tag","text":"compose/cli/main.py::TopLevelCommand.images.add_default_tag\n def add_default_tag(img_name):\n if ':' not in img_name.split('/')[-1]:\n return '{}:latest'.format(img_name)\n return img_name","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.kill","text":"compose/cli/main.py::TopLevelCommand.kill\n def kill(self, options):\n \"\"\"\n Force stop service containers.\n\n Usage: kill [options] [SERVICE...]\n\n Options:\n -s SIGNAL SIGNAL to send to the container.\n Default signal is SIGKILL.\n \"\"\"\n signal = options.get('-s', 'SIGKILL')\n\n self.project.kill(service_names=options['SERVICE'], signal=signal)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.logs","text":"compose/cli/main.py::TopLevelCommand.logs\n def logs(self, options):\n \"\"\"\n View output from containers.\n\n Usage: logs [options] [SERVICE...]\n\n Options:\n --no-color Produce monochrome output.\n -f, --follow Follow log output.\n -t, --timestamps Show timestamps.\n --tail=\"all\" Number of lines to show from the end of the logs\n for each container.\n \"\"\"\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\n\n tail = options['--tail']\n if tail is not None:\n if tail.isdigit():\n tail = int(tail)\n elif tail != 'all':\n raise UserError(\"tail flag must be all or a number\")\n log_args = {\n 'follow': options['--follow'],\n 'tail': tail,\n 'timestamps': options['--timestamps']\n }\n print(\"Attaching to\", list_containers(containers))\n log_printer_from_project(\n self.project,\n containers,\n options['--no-color'],\n log_args,\n event_stream=self.project.events(service_names=options['SERVICE'])).run()","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.pause","text":"compose/cli/main.py::TopLevelCommand.pause\n def pause(self, options):\n \"\"\"\n Pause services.\n\n Usage: pause [SERVICE...]\n \"\"\"\n containers = self.project.pause(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to pause', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.port","text":"compose/cli/main.py::TopLevelCommand.port\n def port(self, options):\n \"\"\"\n Print the public port for a port binding.\n\n Usage: port [options] SERVICE PRIVATE_PORT\n\n Options:\n --protocol=proto tcp or udp [default: tcp]\n --index=index index of the container if there are multiple\n instances of a service [default: 1]\n \"\"\"\n index = int(options.get('--index'))\n service = self.project.get_service(options['SERVICE'])\n try:\n container = service.get_container(number=index)\n except ValueError as e:\n raise UserError(str(e))\n print(container.get_local_port(\n options['PRIVATE_PORT'],\n protocol=options.get('--protocol') or 'tcp') or '')","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.ps","text":"compose/cli/main.py::TopLevelCommand.ps\n def ps(self, options):\n \"\"\"\n List containers.\n\n Usage: ps [options] [SERVICE...]\n\n Options:\n -q, --quiet Only display IDs\n --services Display services\n --filter KEY=VAL Filter services by a property\n -a, --all Show all stopped containers (including those created by the run command)\n \"\"\"\n if options['--quiet'] and options['--services']:\n raise UserError('--quiet and --services cannot be combined')\n\n if options['--services']:\n filt = build_filter(options.get('--filter'))\n services = self.project.services\n if filt:\n services = filter_services(filt, services, self.project)\n print('\\n'.join(service.name for service in services))\n return\n\n if options['--all']:\n containers = sorted(self.project.containers(service_names=options['SERVICE'],\n one_off=OneOffFilter.include, stopped=True))\n else:\n containers = sorted(\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\n key=attrgetter('name'))\n\n if options['--quiet']:\n for container in containers:\n print(container.id)\n else:\n headers = [\n 'Name',\n 'Command',\n 'State',\n 'Ports',\n ]\n rows = []\n for container in containers:\n command = container.human_readable_command\n if len(command) > 30:\n command = '%s ...' % command[:26]\n rows.append([\n container.name,\n command,\n container.human_r","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.pull","text":"compose/cli/main.py::TopLevelCommand.pull\n def pull(self, options):\n \"\"\"\n Pulls images for services defined in a Compose file, but does not start the containers.\n\n Usage: pull [options] [SERVICE...]\n\n Options:\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\n --parallel Deprecated, pull multiple images in parallel (enabled by default).\n --no-parallel Disable parallel pulling.\n -q, --quiet Pull without printing progress information\n --include-deps Also pull services declared as dependencies\n \"\"\"\n if options.get('--parallel'):\n log.warn('--parallel option is deprecated and will be removed in future versions.')\n self.project.pull(\n service_names=options['SERVICE'],\n ignore_pull_failures=options.get('--ignore-pull-failures'),\n parallel_pull=not options.get('--no-parallel'),\n silent=options.get('--quiet'),\n include_deps=options.get('--include-deps'),\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.push","text":"compose/cli/main.py::TopLevelCommand.push\n def push(self, options):\n \"\"\"\n Pushes images for services.\n\n Usage: push [options] [SERVICE...]\n\n Options:\n --ignore-push-failures Push what it can and ignores images with push failures.\n \"\"\"\n self.project.push(\n service_names=options['SERVICE'],\n ignore_push_failures=options.get('--ignore-push-failures')\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.rm","text":"compose/cli/main.py::TopLevelCommand.rm\n def rm(self, options):\n \"\"\"\n Removes stopped service containers.\n\n By default, anonymous volumes attached to containers will not be removed. You\n can override this with `-v`. To list all volumes, use `docker volume ls`.\n\n Any data which is not in a volume will be lost.\n\n Usage: rm [options] [SERVICE...]\n\n Options:\n -f, --force Don't ask to confirm removal\n -s, --stop Stop the containers, if required, before removing\n -v Remove any anonymous volumes attached to containers\n -a, --all Deprecated - no effect.\n \"\"\"\n if options.get('--all'):\n log.warn(\n '--all flag is obsolete. This is now the default behavior '\n 'of `docker-compose rm`'\n )\n one_off = OneOffFilter.include\n\n if options.get('--stop'):\n self.project.stop(service_names=options['SERVICE'], one_off=one_off)\n\n all_containers = self.project.containers(\n service_names=options['SERVICE'], stopped=True, one_off=one_off\n )\n stopped_containers = [c for c in all_containers if not c.is_running]\n\n if len(stopped_containers) > 0:\n print(\"Going to remove\", list_containers(stopped_containers))\n if options.get('--force') \\\n or yesno(\"Are you sure? [yN] \", default=False):\n self.project.remove_stopped(\n service_names=options['SERVICE'],\n v=options.get('-v', False),\n one_off=one_off\n )\n else:\n print(\"No stopped containers\")","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.run","text":"compose/cli/main.py::TopLevelCommand.run\n def run(self, options):\n \"\"\"\n Run a one-off command on a service.\n\n For example:\n\n $ docker-compose run web python manage.py shell\n\n By default, linked services will be started, unless they are already\n running. If you do not want to start linked services, use\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\n\n Usage:\n run [options] [-v VOLUME...] [-p PORT...] [-e KEY=VAL...] [-l KEY=VALUE...]\n SERVICE [COMMAND] [ARGS...]\n\n Options:\n -d, --detach Detached mode: Run container in the background, print\n new container name.\n --name NAME Assign a name to the container\n --entrypoint CMD Override the entrypoint of the image.\n -e KEY=VAL Set an environment variable (can be used multiple times)\n -l, --label KEY=VAL Add or override a label (can be used multiple times)\n -u, --user=\"\" Run as specified username or uid\n --no-deps Don't start linked services.\n --rm Remove container after run. Ignored in detached mode.\n -p, --publish=[] Publish a container's port(s) to the host\n --service-ports Run command with the service's ports enabled and mapped\n to the host.\n --use-aliases Use the service's network aliases in the network(s) the\n container connects to.\n -v, --volume=[] Bind mount a volume (default [])\n -T Disable pseudo-tty allocation. By default `docker-compose run`\n allocates a TTY.\n -w, --workdir=\"\" Working directory inside the container\n \"\"\"\n service = self.project.get_service(options['SERVICE'])\n detach = o","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.scale","text":"compose/cli/main.py::TopLevelCommand.scale\n def scale(self, options):\n \"\"\"\n Set number of containers to run for a service.\n\n Numbers are specified in the form `service=num` as arguments.\n For example:\n\n $ docker-compose scale web=2 worker=3\n\n This command is deprecated. Use the up command with the `--scale` flag\n instead.\n\n Usage: scale [options] [SERVICE=NUM...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = timeout_from_opts(options)\n\n if self.project.config_version == V2_2:\n raise UserError(\n 'The scale command is incompatible with the v2.2 format. '\n 'Use the up command with the --scale flag instead.'\n )\n else:\n log.warn(\n 'The scale command is deprecated. '\n 'Use the up command with the --scale flag instead.'\n )\n\n for service_name, num in parse_scale_args(options['SERVICE=NUM']).items():\n self.project.get_service(service_name).scale(num, timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.start","text":"compose/cli/main.py::TopLevelCommand.start\n def start(self, options):\n \"\"\"\n Start existing containers.\n\n Usage: start [SERVICE...]\n \"\"\"\n containers = self.project.start(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to start', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.stop","text":"compose/cli/main.py::TopLevelCommand.stop\n def stop(self, options):\n \"\"\"\n Stop running containers without removing them.\n\n They can be started again with `docker-compose start`.\n\n Usage: stop [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = timeout_from_opts(options)\n self.project.stop(service_names=options['SERVICE'], timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.restart","text":"compose/cli/main.py::TopLevelCommand.restart\n def restart(self, options):\n \"\"\"\n Restart running containers.\n\n Usage: restart [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = timeout_from_opts(options)\n containers = self.project.restart(service_names=options['SERVICE'], timeout=timeout)\n exit_if(not containers, 'No containers to restart', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.top","text":"compose/cli/main.py::TopLevelCommand.top\n def top(self, options):\n \"\"\"\n Display the running processes\n\n Usage: top [SERVICE...]\n\n \"\"\"\n containers = sorted(\n self.project.containers(service_names=options['SERVICE'], stopped=False) +\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\n key=attrgetter('name')\n )\n\n for idx, container in enumerate(containers):\n if idx > 0:\n print()\n\n top_data = self.project.client.top(container.name)\n headers = top_data.get(\"Titles\")\n rows = []\n\n for process in top_data.get(\"Processes\", []):\n rows.append(process)\n\n print(container.name)\n print(Formatter().table(headers, rows))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.unpause","text":"compose/cli/main.py::TopLevelCommand.unpause\n def unpause(self, options):\n \"\"\"\n Unpause services.\n\n Usage: unpause [SERVICE...]\n \"\"\"\n containers = self.project.unpause(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to unpause', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.up","text":"compose/cli/main.py::TopLevelCommand.up\n def up(self, options):\n \"\"\"\n Builds, (re)creates, starts, and attaches to containers for a service.\n\n Unless they are already running, this command also starts any linked services.\n\n The `docker-compose up` command aggregates the output of each container. When\n the command exits, all containers are stopped. Running `docker-compose up -d`\n starts the containers in the background and leaves them running.\n\n If there are existing containers for a service, and the service's configuration\n or image was changed after the container's creation, `docker-compose up` picks\n up the changes by stopping and recreating the containers (preserving mounted\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\n flag.\n\n If you want to force Compose to stop and recreate all containers, use the\n `--force-recreate` flag.\n\n Usage: up [options] [--scale SERVICE=NUM...] [SERVICE...]\n\n Options:\n -d, --detach Detached mode: Run containers in the background,\n print new container names. Incompatible with\n --abort-on-container-exit.\n --no-color Produce monochrome output.\n --quiet-pull Pull without printing progress information\n --no-deps Don't start linked services.\n --force-recreate Recreate containers even if their configuration\n and image haven't changed.\n --always-recreate-deps Recreate dependent containers.\n Incompatible with --no-recreate.\n --no-recreate If containers already exist, don't recreate\n them. Incompatible with --force-recreate and -V.\n --no-build ","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.version","text":"compose/cli/main.py::TopLevelCommand.version\n @classmethod\n def version(cls, options):\n \"\"\"\n Show version information\n\n Usage: version [--short]\n\n Options:\n --short Shows only Compose's version number.\n \"\"\"\n if options['--short']:\n print(__version__)\n else:\n print(get_version_info('full'))","label":0},{"cid":"compose/cli/main.py::compute_service_exit_code","text":"compose/cli/main.py::compute_service_exit_code\ndef compute_service_exit_code(exit_value_from, attached_containers):\n candidates = list(filter(\n lambda c: c.service == exit_value_from,\n attached_containers))\n if not candidates:\n log.error(\n 'No containers matching the spec \"{0}\" '\n 'were run.'.format(exit_value_from)\n )\n return 2\n if len(candidates) > 1:\n exit_values = filter(\n lambda e: e != 0,\n [c.inspect()['State']['ExitCode'] for c in candidates]\n )\n\n return exit_values[0]\n return candidates[0].inspect()['State']['ExitCode']","label":0},{"cid":"compose/cli/main.py::compute_exit_code","text":"compose/cli/main.py::compute_exit_code\ndef compute_exit_code(exit_value_from, attached_containers, cascade_starter, all_containers):\n exit_code = 0\n for e in all_containers:\n if (not e.is_running and cascade_starter == e.name):\n if not e.exit_code == 0:\n exit_code = e.exit_code\n break\n\n return exit_code","label":0},{"cid":"compose/config/config.py::ConfigDetails.__new__","text":"compose/config/config.py::ConfigDetails.__new__\n def __new__(cls, working_dir, config_files, environment=None):\n if environment is None:\n environment = Environment.from_env_file(working_dir)\n return super(ConfigDetails, cls).__new__(\n cls, working_dir, config_files, environment\n )","label":0},{"cid":"compose/config/config.py::ConfigFile.from_filename","text":"compose/config/config.py::ConfigFile.from_filename\n @classmethod\n def from_filename(cls, filename):\n return cls(filename, load_yaml(filename))","label":0},{"cid":"compose/config/config.py::ConfigFile.version","text":"compose/config/config.py::ConfigFile.version\n @cached_property\n def version(self):\n if 'version' not in self.config:\n return V1\n\n version = self.config['version']\n\n if isinstance(version, dict):\n log.warn('Unexpected type for \"version\" key in \"{}\". Assuming '\n '\"version\" is the name of a service, and defaulting to '\n 'Compose file version 1.'.format(self.filename))\n return V1\n\n if not isinstance(version, six.string_types):\n raise ConfigurationError(\n 'Version in \"{}\" is invalid - it should be a string.'\n .format(self.filename))\n\n if version == '1':\n raise ConfigurationError(\n 'Version in \"{}\" is invalid. {}'\n .format(self.filename, VERSION_EXPLANATION)\n )\n\n if version == '2':\n return const.COMPOSEFILE_V2_0\n\n if version == '3':\n return const.COMPOSEFILE_V3_0\n\n return ComposeVersion(version)","label":0},{"cid":"compose/config/config.py::ConfigFile.get_service","text":"compose/config/config.py::ConfigFile.get_service\n def get_service(self, name):\n return self.get_service_dicts()[name]","label":0},{"cid":"compose/config/config.py::ConfigFile.get_service_dicts","text":"compose/config/config.py::ConfigFile.get_service_dicts\n def get_service_dicts(self):\n return self.config if self.version == V1 else self.config.get('services', {})","label":0},{"cid":"compose/config/config.py::ConfigFile.get_volumes","text":"compose/config/config.py::ConfigFile.get_volumes\n def get_volumes(self):\n return {} if self.version == V1 else self.config.get('volumes', {})","label":0},{"cid":"compose/config/config.py::ConfigFile.get_networks","text":"compose/config/config.py::ConfigFile.get_networks\n def get_networks(self):\n return {} if self.version == V1 else self.config.get('networks', {})","label":0},{"cid":"compose/config/config.py::ConfigFile.get_secrets","text":"compose/config/config.py::ConfigFile.get_secrets\n def get_secrets(self):\n return {} if self.version < const.COMPOSEFILE_V3_1 else self.config.get('secrets', {})","label":0},{"cid":"compose/config/config.py::ConfigFile.get_configs","text":"compose/config/config.py::ConfigFile.get_configs\n def get_configs(self):\n return {} if self.version < const.COMPOSEFILE_V3_3 else self.config.get('configs', {})","label":0},{"cid":"compose/config/config.py::ServiceConfig.with_abs_paths","text":"compose/config/config.py::ServiceConfig.with_abs_paths\n @classmethod\n def with_abs_paths(cls, working_dir, filename, name, config):\n if not working_dir:\n raise ValueError(\"No working_dir for ServiceConfig.\")\n\n return cls(\n os.path.abspath(working_dir),\n os.path.abspath(filename) if filename else filename,\n name,\n config)","label":0},{"cid":"compose/config/config.py::find","text":"compose/config/config.py::find\ndef find(base_dir, filenames, environment, override_dir=None):\n if filenames == ['-']:\n return ConfigDetails(\n os.path.abspath(override_dir) if override_dir else os.getcwd(),\n [ConfigFile(None, yaml.safe_load(sys.stdin))],\n environment\n )\n\n if filenames:\n filenames = [os.path.join(base_dir, f) for f in filenames]\n else:\n filenames = get_default_config_files(base_dir)\n\n log.debug(\"Using configuration files: {}\".format(\",\".join(filenames)))\n return ConfigDetails(\n override_dir if override_dir else os.path.dirname(filenames[0]),\n [ConfigFile.from_filename(f) for f in filenames],\n environment\n )","label":0},{"cid":"compose/config/config.py::validate_config_version","text":"compose/config/config.py::validate_config_version\ndef validate_config_version(config_files):\n main_file = config_files[0]\n validate_top_level_object(main_file)\n for next_file in config_files[1:]:\n validate_top_level_object(next_file)\n\n if main_file.version != next_file.version:\n raise ConfigurationError(\n \"Version mismatch: file {0} specifies version {1} but \"\n \"extension file {2} uses version {3}\".format(\n main_file.filename,\n main_file.version,\n next_file.filename,\n next_file.version))","label":0},{"cid":"compose/config/config.py::get_default_config_files","text":"compose/config/config.py::get_default_config_files\ndef get_default_config_files(base_dir):\n (candidates, path) = find_candidates_in_parent_dirs(SUPPORTED_FILENAMES, base_dir)\n\n if not candidates:\n raise ComposeFileNotFound(SUPPORTED_FILENAMES)\n\n winner = candidates[0]\n\n if len(candidates) > 1:\n log.warn(\"Found multiple config files with supported names: %s\", \", \".join(candidates))\n log.warn(\"Using %s\\n\", winner)\n\n return [os.path.join(path, winner)] + get_default_override_file(path)","label":0},{"cid":"compose/config/config.py::get_default_override_file","text":"compose/config/config.py::get_default_override_file\ndef get_default_override_file(path):\n override_files_in_path = [os.path.join(path, override_filename) for override_filename\n in DEFAULT_OVERRIDE_FILENAMES\n if os.path.exists(os.path.join(path, override_filename))]\n if len(override_files_in_path) > 1:\n raise DuplicateOverrideFileFound(override_files_in_path)\n return override_files_in_path","label":0},{"cid":"compose/config/config.py::find_candidates_in_parent_dirs","text":"compose/config/config.py::find_candidates_in_parent_dirs\ndef find_candidates_in_parent_dirs(filenames, path):\n \"\"\"\n Given a directory path to start, looks for filenames in the\n directory, and then each parent directory successively,\n until found.\n\n Returns tuple (candidates, path).\n \"\"\"\n candidates = [filename for filename in filenames\n if os.path.exists(os.path.join(path, filename))]\n\n if not candidates:\n parent_dir = os.path.join(path, '..')\n if os.path.abspath(parent_dir) != os.path.abspath(path):\n return find_candidates_in_parent_dirs(filenames, parent_dir)\n\n return (candidates, path)","label":0},{"cid":"compose/config/config.py::check_swarm_only_config","text":"compose/config/config.py::check_swarm_only_config\ndef check_swarm_only_config(service_dicts, compatibility=False):\n warning_template = (\n \"Some services ({services}) use the '{key}' key, which will be ignored. \"\n \"Compose does not support '{key}' configuration - use \"\n \"`docker stack deploy` to deploy to a swarm.\"\n )\n\n def check_swarm_only_key(service_dicts, key):\n services = [s for s in service_dicts if s.get(key)]\n if services:\n log.warn(\n warning_template.format(\n services=\", \".join(sorted(s['name'] for s in services)),\n key=key\n )\n )\n if not compatibility:\n check_swarm_only_key(service_dicts, 'deploy')\n check_swarm_only_key(service_dicts, 'credential_spec')\n check_swarm_only_key(service_dicts, 'configs')","label":0},{"cid":"compose/config/config.py::check_swarm_only_config.check_swarm_only_key","text":"compose/config/config.py::check_swarm_only_config.check_swarm_only_key\n def check_swarm_only_key(service_dicts, key):\n services = [s for s in service_dicts if s.get(key)]\n if services:\n log.warn(\n warning_template.format(\n services=\", \".join(sorted(s['name'] for s in services)),\n key=key\n )\n )","label":0},{"cid":"compose/config/config.py::load","text":"compose/config/config.py::load\ndef load(config_details, compatibility=False):\n \"\"\"Load the configuration from a working directory and a list of\n configuration files. Files are loaded in order, and merged on top\n of each other to create the final configuration.\n\n Return a fully interpolated, extended and validated configuration.\n \"\"\"\n validate_config_version(config_details.config_files)\n\n processed_files = [\n process_config_file(config_file, config_details.environment)\n for config_file in config_details.config_files\n ]\n config_details = config_details._replace(config_files=processed_files)\n\n main_file = config_details.config_files[0]\n volumes = load_mapping(\n config_details.config_files, 'get_volumes', 'Volume'\n )\n networks = load_mapping(\n config_details.config_files, 'get_networks', 'Network'\n )\n secrets = load_mapping(\n config_details.config_files, 'get_secrets', 'Secret', config_details.working_dir\n )\n configs = load_mapping(\n config_details.config_files, 'get_configs', 'Config', config_details.working_dir\n )\n service_dicts = load_services(config_details, main_file, compatibility)\n\n if main_file.version != V1:\n for service_dict in service_dicts:\n match_named_volumes(service_dict, volumes)\n\n check_swarm_only_config(service_dicts, compatibility)\n\n version = V2_3 if compatibility and main_file.version >= V3_0 else main_file.version\n\n return Config(version, service_dicts, volumes, networks, secrets, configs)","label":0},{"cid":"compose/config/config.py::load_mapping","text":"compose/config/config.py::load_mapping\ndef load_mapping(config_files, get_func, entity_type, working_dir=None):\n mapping = {}\n\n for config_file in config_files:\n for name, config in getattr(config_file, get_func)().items():\n mapping[name] = config or {}\n if not config:\n continue\n\n external = config.get('external')\n if external:\n validate_external(entity_type, name, config, config_file.version)\n if isinstance(external, dict):\n config['name'] = external.get('name')\n elif not config.get('name'):\n config['name'] = name\n\n if 'driver_opts' in config:\n config['driver_opts'] = build_string_dict(\n config['driver_opts']\n )\n\n if 'labels' in config:\n config['labels'] = parse_labels(config['labels'])\n\n if 'file' in config:\n config['file'] = expand_path(working_dir, config['file'])\n\n return mapping","label":0},{"cid":"compose/config/config.py::validate_external","text":"compose/config/config.py::validate_external\ndef validate_external(entity_type, name, config, version):\n if (version < V2_1 or (version >= V3_0 and version < V3_4)) and len(config.keys()) > 1:\n raise ConfigurationError(\n \"{} {} declared as external but specifies additional attributes \"\n \"({}).\".format(\n entity_type, name, ', '.join(k for k in config if k != 'external')))","label":0},{"cid":"compose/config/config.py::load_services","text":"compose/config/config.py::load_services\ndef load_services(config_details, config_file, compatibility=False):\n def build_service(service_name, service_dict, service_names):\n service_config = ServiceConfig.with_abs_paths(\n config_details.working_dir,\n config_file.filename,\n service_name,\n service_dict)\n resolver = ServiceExtendsResolver(\n service_config, config_file, environment=config_details.environment\n )\n service_dict = process_service(resolver.run())\n\n service_config = service_config._replace(config=service_dict)\n validate_service(service_config, service_names, config_file)\n service_dict = finalize_service(\n service_config,\n service_names,\n config_file.version,\n config_details.environment,\n compatibility\n )\n return service_dict\n\n def build_services(service_config):\n service_names = service_config.keys()\n return sort_service_dicts([\n build_service(name, service_dict, service_names)\n for name, service_dict in service_config.items()\n ])\n\n def merge_services(base, override):\n all_service_names = set(base) | set(override)\n return {\n name: merge_service_dicts_from_files(\n base.get(name, {}),\n override.get(name, {}),\n config_file.version)\n for name in all_service_names\n }\n\n service_configs = [\n file.get_service_dicts() for file in config_details.config_files\n ]\n\n service_config = service_configs[0]\n for next_config in service_configs[1:]:\n service_config = merge_services(service_config, next_config)\n\n return build_services(service_config)","label":0},{"cid":"compose/config/config.py::load_services.build_service","text":"compose/config/config.py::load_services.build_service\n def build_service(service_name, service_dict, service_names):\n service_config = ServiceConfig.with_abs_paths(\n config_details.working_dir,\n config_file.filename,\n service_name,\n service_dict)\n resolver = ServiceExtendsResolver(\n service_config, config_file, environment=config_details.environment\n )\n service_dict = process_service(resolver.run())\n\n service_config = service_config._replace(config=service_dict)\n validate_service(service_config, service_names, config_file)\n service_dict = finalize_service(\n service_config,\n service_names,\n config_file.version,\n config_details.environment,\n compatibility\n )\n return service_dict","label":0},{"cid":"compose/config/config.py::load_services.build_services","text":"compose/config/config.py::load_services.build_services\n def build_services(service_config):\n service_names = service_config.keys()\n return sort_service_dicts([\n build_service(name, service_dict, service_names)\n for name, service_dict in service_config.items()\n ])","label":0},{"cid":"compose/config/config.py::load_services.merge_services","text":"compose/config/config.py::load_services.merge_services\n def merge_services(base, override):\n all_service_names = set(base) | set(override)\n return {\n name: merge_service_dicts_from_files(\n base.get(name, {}),\n override.get(name, {}),\n config_file.version)\n for name in all_service_names\n }","label":0},{"cid":"compose/config/config.py::interpolate_config_section","text":"compose/config/config.py::interpolate_config_section\ndef interpolate_config_section(config_file, config, section, environment):\n validate_config_section(config_file.filename, config, section)\n return interpolate_environment_variables(\n config_file.version,\n config,\n section,\n environment\n )","label":0},{"cid":"compose/config/config.py::process_config_file","text":"compose/config/config.py::process_config_file\ndef process_config_file(config_file, environment, service_name=None):\n services = interpolate_config_section(\n config_file,\n config_file.get_service_dicts(),\n 'service',\n environment)\n\n if config_file.version > V1:\n processed_config = dict(config_file.config)\n processed_config['services'] = services\n processed_config['volumes'] = interpolate_config_section(\n config_file,\n config_file.get_volumes(),\n 'volume',\n environment)\n processed_config['networks'] = interpolate_config_section(\n config_file,\n config_file.get_networks(),\n 'network',\n environment)\n if config_file.version >= const.COMPOSEFILE_V3_1:\n processed_config['secrets'] = interpolate_config_section(\n config_file,\n config_file.get_secrets(),\n 'secret',\n environment)\n if config_file.version >= const.COMPOSEFILE_V3_3:\n processed_config['configs'] = interpolate_config_section(\n config_file,\n config_file.get_configs(),\n 'config',\n environment\n )\n else:\n processed_config = services\n\n config_file = config_file._replace(config=processed_config)\n validate_against_config_schema(config_file)\n\n if service_name and service_name not in services:\n raise ConfigurationError(\n \"Cannot extend service '{}' in {}: Service not found\".format(\n service_name, config_file.filename))\n\n return config_file","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.__init__","text":"compose/config/config.py::ServiceExtendsResolver.__init__\n def __init__(self, service_config, config_file, environment, already_seen=None):\n self.service_config = service_config\n self.working_dir = service_config.working_dir\n self.already_seen = already_seen or []\n self.config_file = config_file\n self.environment = environment","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.signature","text":"compose/config/config.py::ServiceExtendsResolver.signature\n @property\n def signature(self):\n return self.service_config.filename, self.service_config.name","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.detect_cycle","text":"compose/config/config.py::ServiceExtendsResolver.detect_cycle\n def detect_cycle(self):\n if self.signature in self.already_seen:\n raise CircularReference(self.already_seen + [self.signature])","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.run","text":"compose/config/config.py::ServiceExtendsResolver.run\n def run(self):\n self.detect_cycle()\n\n if 'extends' in self.service_config.config:\n service_dict = self.resolve_extends(*self.validate_and_construct_extends())\n return self.service_config._replace(config=service_dict)\n\n return self.service_config","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.validate_and_construct_extends","text":"compose/config/config.py::ServiceExtendsResolver.validate_and_construct_extends\n def validate_and_construct_extends(self):\n extends = self.service_config.config['extends']\n if not isinstance(extends, dict):\n extends = {'service': extends}\n\n config_path = self.get_extended_config_path(extends)\n service_name = extends['service']\n\n if config_path == self.config_file.filename:\n try:\n service_config = self.config_file.get_service(service_name)\n except KeyError:\n raise ConfigurationError(\n \"Cannot extend service '{}' in {}: Service not found\".format(\n service_name, config_path)\n )\n else:\n extends_file = ConfigFile.from_filename(config_path)\n validate_config_version([self.config_file, extends_file])\n extended_file = process_config_file(\n extends_file, self.environment, service_name=service_name\n )\n service_config = extended_file.get_service(service_name)\n\n return config_path, service_config, service_name","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.resolve_extends","text":"compose/config/config.py::ServiceExtendsResolver.resolve_extends\n def resolve_extends(self, extended_config_path, service_dict, service_name):\n resolver = ServiceExtendsResolver(\n ServiceConfig.with_abs_paths(\n os.path.dirname(extended_config_path),\n extended_config_path,\n service_name,\n service_dict),\n self.config_file,\n already_seen=self.already_seen + [self.signature],\n environment=self.environment\n )\n\n service_config = resolver.run()\n other_service_dict = process_service(service_config)\n validate_extended_service_dict(\n other_service_dict,\n extended_config_path,\n service_name)\n\n return merge_service_dicts(\n other_service_dict,\n self.service_config.config,\n self.config_file.version)","label":0},{"cid":"compose/config/config.py::ServiceExtendsResolver.get_extended_config_path","text":"compose/config/config.py::ServiceExtendsResolver.get_extended_config_path\n def get_extended_config_path(self, extends_options):\n \"\"\"Service we are extending either has a value for 'file' set, which we\n need to obtain a full path too or we are extending from a service\n defined in our own file.\n \"\"\"\n filename = self.service_config.filename\n validate_extends_file_path(\n self.service_config.name,\n extends_options,\n filename)\n if 'file' in extends_options:\n return expand_path(self.working_dir, extends_options['file'])\n return filename","label":0},{"cid":"compose/config/config.py::resolve_environment","text":"compose/config/config.py::resolve_environment\ndef resolve_environment(service_dict, environment=None):\n \"\"\"Unpack any environment variables from an env_file, if set.\n Interpolate environment values if set.\n \"\"\"\n env = {}\n for env_file in service_dict.get('env_file', []):\n env.update(env_vars_from_file(env_file))\n\n env.update(parse_environment(service_dict.get('environment')))\n return dict(resolve_env_var(k, v, environment) for k, v in six.iteritems(env))","label":0},{"cid":"compose/config/config.py::resolve_build_args","text":"compose/config/config.py::resolve_build_args\ndef resolve_build_args(buildargs, environment):\n args = parse_build_arguments(buildargs)\n return dict(resolve_env_var(k, v, environment) for k, v in six.iteritems(args))","label":0},{"cid":"compose/config/config.py::validate_extended_service_dict","text":"compose/config/config.py::validate_extended_service_dict\ndef validate_extended_service_dict(service_dict, filename, service):\n error_prefix = \"Cannot extend service '%s' in %s:\" % (service, filename)\n\n if 'links' in service_dict:\n raise ConfigurationError(\n \"%s services with 'links' cannot be extended\" % error_prefix)\n\n if 'volumes_from' in service_dict:\n raise ConfigurationError(\n \"%s services with 'volumes_from' cannot be extended\" % error_prefix)\n\n if 'net' in service_dict:\n if get_container_name_from_network_mode(service_dict['net']):\n raise ConfigurationError(\n \"%s services with 'net: container' cannot be extended\" % error_prefix)\n\n if 'network_mode' in service_dict:\n if get_service_name_from_network_mode(service_dict['network_mode']):\n raise ConfigurationError(\n \"%s services with 'network_mode: service' cannot be extended\" % error_prefix)\n\n if 'depends_on' in service_dict:\n raise ConfigurationError(\n \"%s services with 'depends_on' cannot be extended\" % error_prefix)","label":0},{"cid":"compose/config/config.py::validate_service","text":"compose/config/config.py::validate_service\ndef validate_service(service_config, service_names, config_file):\n service_dict, service_name = service_config.config, service_config.name\n validate_service_constraints(service_dict, service_name, config_file)\n validate_paths(service_dict)\n\n validate_cpu(service_config)\n validate_ulimits(service_config)\n validate_network_mode(service_config, service_names)\n validate_pid_mode(service_config, service_names)\n validate_depends_on(service_config, service_names)\n validate_links(service_config, service_names)\n validate_healthcheck(service_config)\n\n if not service_dict.get('image') and has_uppercase(service_name):\n raise ConfigurationError(\n \"Service '{name}' contains uppercase characters which are not valid \"\n \"as part of an image name. Either use a lowercase service name or \"\n \"use the `image` field to set a custom name for the service image.\"\n .format(name=service_name))","label":0},{"cid":"compose/config/config.py::process_service","text":"compose/config/config.py::process_service\ndef process_service(service_config):\n working_dir = service_config.working_dir\n service_dict = dict(service_config.config)\n\n if 'env_file' in service_dict:\n service_dict['env_file'] = [\n expand_path(working_dir, path)\n for path in to_list(service_dict['env_file'])\n ]\n\n if 'build' in service_dict:\n process_build_section(service_dict, working_dir)\n\n if 'volumes' in service_dict and service_dict.get('volume_driver') is None:\n service_dict['volumes'] = resolve_volume_paths(working_dir, service_dict)\n\n if 'sysctls' in service_dict:\n service_dict['sysctls'] = build_string_dict(parse_sysctls(service_dict['sysctls']))\n\n if 'labels' in service_dict:\n service_dict['labels'] = parse_labels(service_dict['labels'])\n\n service_dict = process_depends_on(service_dict)\n\n for field in ['dns', 'dns_search', 'tmpfs']:\n if field in service_dict:\n service_dict[field] = to_list(service_dict[field])\n\n service_dict = process_security_opt(process_blkio_config(process_ports(\n process_healthcheck(service_dict)\n )))\n\n return service_dict","label":0},{"cid":"compose/config/config.py::process_build_section","text":"compose/config/config.py::process_build_section\ndef process_build_section(service_dict, working_dir):\n if isinstance(service_dict['build'], six.string_types):\n service_dict['build'] = resolve_build_path(working_dir, service_dict['build'])\n elif isinstance(service_dict['build'], dict):\n if 'context' in service_dict['build']:\n path = service_dict['build']['context']\n service_dict['build']['context'] = resolve_build_path(working_dir, path)\n if 'labels' in service_dict['build']:\n service_dict['build']['labels'] = parse_labels(service_dict['build']['labels'])","label":0},{"cid":"compose/config/config.py::process_ports","text":"compose/config/config.py::process_ports\ndef process_ports(service_dict):\n if 'ports' not in service_dict:\n return service_dict\n\n ports = []\n for port_definition in service_dict['ports']:\n if isinstance(port_definition, ServicePort):\n ports.append(port_definition)\n else:\n ports.extend(ServicePort.parse(port_definition))\n service_dict['ports'] = ports\n return service_dict","label":0},{"cid":"compose/config/config.py::process_depends_on","text":"compose/config/config.py::process_depends_on\ndef process_depends_on(service_dict):\n if 'depends_on' in service_dict and not isinstance(service_dict['depends_on'], dict):\n service_dict['depends_on'] = dict([\n (svc, {'condition': 'service_started'}) for svc in service_dict['depends_on']\n ])\n return service_dict","label":0},{"cid":"compose/config/config.py::process_blkio_config","text":"compose/config/config.py::process_blkio_config\ndef process_blkio_config(service_dict):\n if not service_dict.get('blkio_config'):\n return service_dict\n\n for field in ['device_read_bps', 'device_write_bps']:\n if field in service_dict['blkio_config']:\n for v in service_dict['blkio_config'].get(field, []):\n rate = v.get('rate', 0)\n v['rate'] = parse_bytes(rate)\n if v['rate'] is None:\n raise ConfigurationError('Invalid format for bytes value: \"{}\"'.format(rate))\n\n for field in ['device_read_iops', 'device_write_iops']:\n if field in service_dict['blkio_config']:\n for v in service_dict['blkio_config'].get(field, []):\n try:\n v['rate'] = int(v.get('rate', 0))\n except ValueError:\n raise ConfigurationError(\n 'Invalid IOPS value: \"{}\". Must be a positive integer.'.format(v.get('rate'))\n )\n\n return service_dict","label":0},{"cid":"compose/config/config.py::process_healthcheck","text":"compose/config/config.py::process_healthcheck\ndef process_healthcheck(service_dict):\n if 'healthcheck' not in service_dict:\n return service_dict\n\n hc = service_dict['healthcheck']\n\n if 'disable' in hc:\n del hc['disable']\n hc['test'] = ['NONE']\n\n for field in ['interval', 'timeout', 'start_period']:\n if field not in hc or isinstance(hc[field], six.integer_types):\n continue\n hc[field] = parse_nanoseconds_int(hc[field])\n\n return service_dict","label":0},{"cid":"compose/config/config.py::finalize_service_volumes","text":"compose/config/config.py::finalize_service_volumes\ndef finalize_service_volumes(service_dict, environment):\n if 'volumes' in service_dict:\n finalized_volumes = []\n normalize = environment.get_boolean('COMPOSE_CONVERT_WINDOWS_PATHS')\n win_host = environment.get_boolean('COMPOSE_FORCE_WINDOWS_HOST')\n for v in service_dict['volumes']:\n if isinstance(v, dict):\n finalized_volumes.append(MountSpec.parse(v, normalize, win_host))\n else:\n finalized_volumes.append(VolumeSpec.parse(v, normalize, win_host))\n service_dict['volumes'] = finalized_volumes\n\n return service_dict","label":0},{"cid":"compose/config/config.py::finalize_service","text":"compose/config/config.py::finalize_service\ndef finalize_service(service_config, service_names, version, environment, compatibility):\n service_dict = dict(service_config.config)\n\n if 'environment' in service_dict or 'env_file' in service_dict:\n service_dict['environment'] = resolve_environment(service_dict, environment)\n service_dict.pop('env_file', None)\n\n if 'volumes_from' in service_dict:\n service_dict['volumes_from'] = [\n VolumeFromSpec.parse(vf, service_names, version)\n for vf in service_dict['volumes_from']\n ]\n\n service_dict = finalize_service_volumes(service_dict, environment)\n\n if 'net' in service_dict:\n network_mode = service_dict.pop('net')\n container_name = get_container_name_from_network_mode(network_mode)\n if container_name and container_name in service_names:\n service_dict['network_mode'] = 'service:{}'.format(container_name)\n else:\n service_dict['network_mode'] = network_mode\n\n if 'networks' in service_dict:\n service_dict['networks'] = parse_networks(service_dict['networks'])\n\n if 'restart' in service_dict:\n service_dict['restart'] = parse_restart_spec(service_dict['restart'])\n\n if 'secrets' in service_dict:\n service_dict['secrets'] = [\n types.ServiceSecret.parse(s) for s in service_dict['secrets']\n ]\n\n if 'configs' in service_dict:\n service_dict['configs'] = [\n types.ServiceConfig.parse(c) for c in service_dict['configs']\n ]\n\n normalize_build(service_dict, service_config.working_dir, environment)\n\n if compatibility:\n service_dict, ignored_keys = translate_deploy_keys_to_container_config(\n service_dict\n )\n if ignored_keys:\n log.warn(\n 'The following deploy sub-keys are not supported in compatibility mode and have'\n ' been ignored: {}'.format(', '.join(ignored_keys))\n )\n\n se","label":0},{"cid":"compose/config/config.py::translate_resource_keys_to_container_config","text":"compose/config/config.py::translate_resource_keys_to_container_config\ndef translate_resource_keys_to_container_config(resources_dict, service_dict):\n if 'limits' in resources_dict:\n service_dict['mem_limit'] = resources_dict['limits'].get('memory')\n if 'cpus' in resources_dict['limits']:\n service_dict['cpus'] = float(resources_dict['limits']['cpus'])\n if 'reservations' in resources_dict:\n service_dict['mem_reservation'] = resources_dict['reservations'].get('memory')\n if 'cpus' in resources_dict['reservations']:\n return ['resources.reservations.cpus']\n return []","label":0},{"cid":"compose/config/config.py::convert_restart_policy","text":"compose/config/config.py::convert_restart_policy\ndef convert_restart_policy(name):\n try:\n return {\n 'any': 'always',\n 'none': 'no',\n 'on-failure': 'on-failure'\n }[name]\n except KeyError:\n raise ConfigurationError('Invalid restart policy \"{}\"'.format(name))","label":0},{"cid":"compose/config/config.py::translate_deploy_keys_to_container_config","text":"compose/config/config.py::translate_deploy_keys_to_container_config\ndef translate_deploy_keys_to_container_config(service_dict):\n if 'credential_spec' in service_dict:\n del service_dict['credential_spec']\n if 'configs' in service_dict:\n del service_dict['configs']\n\n if 'deploy' not in service_dict:\n return service_dict, []\n\n deploy_dict = service_dict['deploy']\n ignored_keys = [\n k for k in ['endpoint_mode', 'labels', 'update_config', 'rollback_config', 'placement']\n if k in deploy_dict\n ]\n\n if 'replicas' in deploy_dict and deploy_dict.get('mode', 'replicated') == 'replicated':\n service_dict['scale'] = deploy_dict['replicas']\n\n if 'restart_policy' in deploy_dict:\n service_dict['restart'] = {\n 'Name': convert_restart_policy(deploy_dict['restart_policy'].get('condition', 'any')),\n 'MaximumRetryCount': deploy_dict['restart_policy'].get('max_attempts', 0)\n }\n for k in deploy_dict['restart_policy'].keys():\n if k != 'condition' and k != 'max_attempts':\n ignored_keys.append('restart_policy.{}'.format(k))\n\n ignored_keys.extend(\n translate_resource_keys_to_container_config(\n deploy_dict.get('resources', {}), service_dict\n )\n )\n\n del service_dict['deploy']\n\n return service_dict, ignored_keys","label":0},{"cid":"compose/config/config.py::normalize_v1_service_format","text":"compose/config/config.py::normalize_v1_service_format\ndef normalize_v1_service_format(service_dict):\n if 'log_driver' in service_dict or 'log_opt' in service_dict:\n if 'logging' not in service_dict:\n service_dict['logging'] = {}\n if 'log_driver' in service_dict:\n service_dict['logging']['driver'] = service_dict['log_driver']\n del service_dict['log_driver']\n if 'log_opt' in service_dict:\n service_dict['logging']['options'] = service_dict['log_opt']\n del service_dict['log_opt']\n\n if 'dockerfile' in service_dict:\n service_dict['build'] = service_dict.get('build', {})\n service_dict['build'].update({\n 'dockerfile': service_dict.pop('dockerfile')\n })\n\n return service_dict","label":0},{"cid":"compose/config/config.py::merge_service_dicts_from_files","text":"compose/config/config.py::merge_service_dicts_from_files\ndef merge_service_dicts_from_files(base, override, version):\n \"\"\"When merging services from multiple files we need to merge the `extends`\n field. This is not handled by `merge_service_dicts()` which is used to\n perform the `extends`.\n \"\"\"\n new_service = merge_service_dicts(base, override, version)\n if 'extends' in override:\n new_service['extends'] = override['extends']\n elif 'extends' in base:\n new_service['extends'] = base['extends']\n return new_service","label":0},{"cid":"compose/config/config.py::MergeDict.__init__","text":"compose/config/config.py::MergeDict.__init__\n def __init__(self, base, override):\n self.base = base\n self.override = override","label":0},{"cid":"compose/config/config.py::MergeDict.needs_merge","text":"compose/config/config.py::MergeDict.needs_merge\n def needs_merge(self, field):\n return field in self.base or field in self.override","label":0},{"cid":"compose/config/config.py::MergeDict.merge_field","text":"compose/config/config.py::MergeDict.merge_field\n def merge_field(self, field, merge_func, default=None):\n if not self.needs_merge(field):\n return\n\n self[field] = merge_func(\n self.base.get(field, default),\n self.override.get(field, default))","label":0},{"cid":"compose/config/config.py::MergeDict.merge_mapping","text":"compose/config/config.py::MergeDict.merge_mapping\n def merge_mapping(self, field, parse_func=None):\n if not self.needs_merge(field):\n return\n\n if parse_func is None:\n def parse_func(m):\n return m or {}\n\n self[field] = parse_func(self.base.get(field))\n self[field].update(parse_func(self.override.get(field)))","label":0},{"cid":"compose/config/config.py::MergeDict.merge_sequence","text":"compose/config/config.py::MergeDict.merge_sequence\n def merge_sequence(self, field, parse_func):\n def parse_sequence_func(seq):\n return to_mapping((parse_func(item) for item in seq), 'merge_field')\n\n if not self.needs_merge(field):\n return\n\n merged = parse_sequence_func(self.base.get(field, []))\n merged.update(parse_sequence_func(self.override.get(field, [])))\n self[field] = [item.repr() for item in sorted(merged.values())]","label":0},{"cid":"compose/config/config.py::MergeDict.merge_sequence.parse_sequence_func","text":"compose/config/config.py::MergeDict.merge_sequence.parse_sequence_func\n def parse_sequence_func(seq):\n return to_mapping((parse_func(item) for item in seq), 'merge_field')","label":0},{"cid":"compose/config/config.py::MergeDict.merge_scalar","text":"compose/config/config.py::MergeDict.merge_scalar\n def merge_scalar(self, field):\n if self.needs_merge(field):\n self[field] = self.override.get(field, self.base.get(field))","label":0},{"cid":"compose/config/config.py::merge_service_dicts","text":"compose/config/config.py::merge_service_dicts\ndef merge_service_dicts(base, override, version):\n md = MergeDict(base, override)\n\n md.merge_mapping('environment', parse_environment)\n md.merge_mapping('labels', parse_labels)\n md.merge_mapping('ulimits', parse_flat_dict)\n md.merge_mapping('networks', parse_networks)\n md.merge_mapping('sysctls', parse_sysctls)\n md.merge_mapping('depends_on', parse_depends_on)\n md.merge_mapping('storage_opt', parse_flat_dict)\n md.merge_sequence('links', ServiceLink.parse)\n md.merge_sequence('secrets', types.ServiceSecret.parse)\n md.merge_sequence('configs', types.ServiceConfig.parse)\n md.merge_sequence('security_opt', types.SecurityOpt.parse)\n md.merge_mapping('extra_hosts', parse_extra_hosts)\n\n for field in ['volumes', 'devices']:\n md.merge_field(field, merge_path_mappings)\n\n for field in [\n 'cap_add', 'cap_drop', 'expose', 'external_links',\n 'volumes_from', 'device_cgroup_rules',\n ]:\n md.merge_field(field, merge_unique_items_lists, default=[])\n\n for field in ['dns', 'dns_search', 'env_file', 'tmpfs']:\n md.merge_field(field, merge_list_or_string)\n\n md.merge_field('logging', merge_logging, default={})\n merge_ports(md, base, override)\n md.merge_field('blkio_config', merge_blkio_config, default={})\n md.merge_field('healthcheck', merge_healthchecks, default={})\n md.merge_field('deploy', merge_deploy, default={})\n\n for field in set(ALLOWED_KEYS) - set(md):\n md.merge_scalar(field)\n\n if version == V1:\n legacy_v1_merge_image_or_build(md, base, override)\n elif md.needs_merge('build'):\n md['build'] = merge_build(md, base, override)\n\n return dict(md)","label":1},{"cid":"compose/config/config.py::merge_unique_items_lists","text":"compose/config/config.py::merge_unique_items_lists\ndef merge_unique_items_lists(base, override):\n override = [str(o) for o in override]\n base = [str(b) for b in base]\n return sorted(set().union(base, override))","label":0},{"cid":"compose/config/config.py::merge_healthchecks","text":"compose/config/config.py::merge_healthchecks\ndef merge_healthchecks(base, override):\n if override.get('disabled') is True:\n return override\n result = base.copy()\n result.update(override)\n return result","label":0},{"cid":"compose/config/config.py::merge_ports","text":"compose/config/config.py::merge_ports\ndef merge_ports(md, base, override):\n def parse_sequence_func(seq):\n acc = []\n for item in seq:\n acc.extend(ServicePort.parse(item))\n return to_mapping(acc, 'merge_field')\n\n field = 'ports'\n\n if not md.needs_merge(field):\n return\n\n merged = parse_sequence_func(md.base.get(field, []))\n merged.update(parse_sequence_func(md.override.get(field, [])))\n md[field] = [item for item in sorted(merged.values(), key=lambda x: x.target)]","label":0},{"cid":"compose/config/config.py::merge_ports.parse_sequence_func","text":"compose/config/config.py::merge_ports.parse_sequence_func\n def parse_sequence_func(seq):\n acc = []\n for item in seq:\n acc.extend(ServicePort.parse(item))\n return to_mapping(acc, 'merge_field')","label":0},{"cid":"compose/config/config.py::merge_build","text":"compose/config/config.py::merge_build\ndef merge_build(output, base, override):\n def to_dict(service):\n build_config = service.get('build', {})\n if isinstance(build_config, six.string_types):\n return {'context': build_config}\n return build_config\n\n md = MergeDict(to_dict(base), to_dict(override))\n md.merge_scalar('context')\n md.merge_scalar('dockerfile')\n md.merge_scalar('network')\n md.merge_scalar('target')\n md.merge_scalar('shm_size')\n md.merge_scalar('isolation')\n md.merge_mapping('args', parse_build_arguments)\n md.merge_field('cache_from', merge_unique_items_lists, default=[])\n md.merge_mapping('labels', parse_labels)\n md.merge_mapping('extra_hosts', parse_extra_hosts)\n return dict(md)","label":0},{"cid":"compose/config/config.py::merge_build.to_dict","text":"compose/config/config.py::merge_build.to_dict\n def to_dict(service):\n build_config = service.get('build', {})\n if isinstance(build_config, six.string_types):\n return {'context': build_config}\n return build_config","label":0},{"cid":"compose/config/config.py::merge_deploy","text":"compose/config/config.py::merge_deploy\ndef merge_deploy(base, override):\n md = MergeDict(base or {}, override or {})\n md.merge_scalar('mode')\n md.merge_scalar('endpoint_mode')\n md.merge_scalar('replicas')\n md.merge_mapping('labels', parse_labels)\n md.merge_mapping('update_config')\n md.merge_mapping('rollback_config')\n md.merge_mapping('restart_policy')\n if md.needs_merge('resources'):\n resources_md = MergeDict(md.base.get('resources') or {}, md.override.get('resources') or {})\n resources_md.merge_mapping('limits')\n resources_md.merge_field('reservations', merge_reservations, default={})\n md['resources'] = dict(resources_md)\n if md.needs_merge('placement'):\n placement_md = MergeDict(md.base.get('placement') or {}, md.override.get('placement') or {})\n placement_md.merge_field('constraints', merge_unique_items_lists, default=[])\n placement_md.merge_field('preferences', merge_unique_objects_lists, default=[])\n md['placement'] = dict(placement_md)\n\n return dict(md)","label":0},{"cid":"compose/config/config.py::merge_reservations","text":"compose/config/config.py::merge_reservations\ndef merge_reservations(base, override):\n md = MergeDict(base, override)\n md.merge_scalar('cpus')\n md.merge_scalar('memory')\n md.merge_sequence('generic_resources', types.GenericResource.parse)\n return dict(md)","label":0},{"cid":"compose/config/config.py::merge_unique_objects_lists","text":"compose/config/config.py::merge_unique_objects_lists\ndef merge_unique_objects_lists(base, override):\n result = dict((json_hash(i), i) for i in base + override)\n return [i[1] for i in sorted([(k, v) for k, v in result.items()], key=lambda x: x[0])]","label":0},{"cid":"compose/config/config.py::merge_blkio_config","text":"compose/config/config.py::merge_blkio_config\ndef merge_blkio_config(base, override):\n md = MergeDict(base, override)\n md.merge_scalar('weight')\n\n def merge_blkio_limits(base, override):\n index = dict((b['path'], b) for b in base)\n for o in override:\n index[o['path']] = o\n\n return sorted(list(index.values()), key=lambda x: x['path'])\n\n for field in [\n \"device_read_bps\", \"device_read_iops\", \"device_write_bps\",\n \"device_write_iops\", \"weight_device\",\n ]:\n md.merge_field(field, merge_blkio_limits, default=[])\n\n return dict(md)","label":0},{"cid":"compose/config/config.py::merge_blkio_config.merge_blkio_limits","text":"compose/config/config.py::merge_blkio_config.merge_blkio_limits\n def merge_blkio_limits(base, override):\n index = dict((b['path'], b) for b in base)\n for o in override:\n index[o['path']] = o\n\n return sorted(list(index.values()), key=lambda x: x['path'])","label":0},{"cid":"compose/config/config.py::merge_logging","text":"compose/config/config.py::merge_logging\ndef merge_logging(base, override):\n md = MergeDict(base, override)\n md.merge_scalar('driver')\n if md.get('driver') == base.get('driver') or base.get('driver') is None:\n md.merge_mapping('options', lambda m: m or {})\n elif override.get('options'):\n md['options'] = override.get('options', {})\n return dict(md)","label":0},{"cid":"compose/config/config.py::legacy_v1_merge_image_or_build","text":"compose/config/config.py::legacy_v1_merge_image_or_build\ndef legacy_v1_merge_image_or_build(output, base, override):\n output.pop('image', None)\n output.pop('build', None)\n if 'image' in override:\n output['image'] = override['image']\n elif 'build' in override:\n output['build'] = override['build']\n elif 'image' in base:\n output['image'] = base['image']\n elif 'build' in base:\n output['build'] = base['build']","label":0},{"cid":"compose/config/config.py::merge_environment","text":"compose/config/config.py::merge_environment\ndef merge_environment(base, override):\n env = parse_environment(base)\n env.update(parse_environment(override))\n return env","label":0},{"cid":"compose/config/config.py::merge_labels","text":"compose/config/config.py::merge_labels\ndef merge_labels(base, override):\n labels = parse_labels(base)\n labels.update(parse_labels(override))\n return labels","label":0},{"cid":"compose/config/config.py::split_kv","text":"compose/config/config.py::split_kv\ndef split_kv(kvpair):\n if '=' in kvpair:\n return kvpair.split('=', 1)\n else:\n return kvpair, ''","label":0},{"cid":"compose/config/config.py::parse_dict_or_list","text":"compose/config/config.py::parse_dict_or_list\ndef parse_dict_or_list(split_func, type_name, arguments):\n if not arguments:\n return {}\n\n if isinstance(arguments, list):\n return dict(split_func(e) for e in arguments)\n\n if isinstance(arguments, dict):\n return dict(arguments)\n\n raise ConfigurationError(\n \"%s \\\"%s\\\" must be a list or mapping,\" %\n (type_name, arguments)\n )","label":0},{"cid":"compose/config/config.py::parse_flat_dict","text":"compose/config/config.py::parse_flat_dict\ndef parse_flat_dict(d):\n if not d:\n return {}\n\n if isinstance(d, dict):\n return dict(d)\n\n raise ConfigurationError(\"Invalid type: expected mapping\")","label":0},{"cid":"compose/config/config.py::resolve_env_var","text":"compose/config/config.py::resolve_env_var\ndef resolve_env_var(key, val, environment):\n if val is not None:\n return key, val\n elif environment and key in environment:\n return key, environment[key]\n else:\n return key, None","label":0},{"cid":"compose/config/config.py::resolve_volume_paths","text":"compose/config/config.py::resolve_volume_paths\ndef resolve_volume_paths(working_dir, service_dict):\n return [\n resolve_volume_path(working_dir, volume)\n for volume in service_dict['volumes']\n ]","label":0},{"cid":"compose/config/config.py::resolve_volume_path","text":"compose/config/config.py::resolve_volume_path\ndef resolve_volume_path(working_dir, volume):\n if isinstance(volume, dict):\n if volume.get('source', '').startswith(('.', '~')) and volume['type'] == 'bind':\n volume['source'] = expand_path(working_dir, volume['source'])\n return volume\n\n mount_params = None\n container_path, mount_params = split_path_mapping(volume)\n\n if mount_params is not None:\n host_path, mode = mount_params\n if host_path is None:\n return container_path\n if host_path.startswith('.'):\n host_path = expand_path(working_dir, host_path)\n host_path = os.path.expanduser(host_path)\n return u\"{}:{}{}\".format(host_path, container_path, (':' + mode if mode else ''))\n\n return container_path","label":0},{"cid":"compose/config/config.py::normalize_build","text":"compose/config/config.py::normalize_build\ndef normalize_build(service_dict, working_dir, environment):\n\n if 'build' in service_dict:\n build = {}\n # Shortcut where specifying a string is treated as the build context\n if isinstance(service_dict['build'], six.string_types):\n build['context'] = service_dict.pop('build')\n else:\n build.update(service_dict['build'])\n if 'args' in build:\n build['args'] = build_string_dict(\n resolve_build_args(build.get('args'), environment)\n )\n\n service_dict['build'] = build","label":0},{"cid":"compose/config/config.py::resolve_build_path","text":"compose/config/config.py::resolve_build_path\ndef resolve_build_path(working_dir, build_path):\n if is_url(build_path):\n return build_path\n return expand_path(working_dir, build_path)","label":0},{"cid":"compose/config/config.py::is_url","text":"compose/config/config.py::is_url\ndef is_url(build_path):\n return build_path.startswith(DOCKER_VALID_URL_PREFIXES)","label":0},{"cid":"compose/config/config.py::validate_paths","text":"compose/config/config.py::validate_paths\ndef validate_paths(service_dict):\n if 'build' in service_dict:\n build = service_dict.get('build', {})\n\n if isinstance(build, six.string_types):\n build_path = build\n elif isinstance(build, dict) and 'context' in build:\n build_path = build['context']\n else:\n # We have a build section but no context, so nothing to validate\n return\n\n if (\n not is_url(build_path) and\n (not os.path.exists(build_path) or not os.access(build_path, os.R_OK))\n ):\n raise ConfigurationError(\n \"build path %s either does not exist, is not accessible, \"\n \"or is not a valid URL.\" % build_path)","label":0},{"cid":"compose/config/config.py::merge_path_mappings","text":"compose/config/config.py::merge_path_mappings\ndef merge_path_mappings(base, override):\n d = dict_from_path_mappings(base)\n d.update(dict_from_path_mappings(override))\n return path_mappings_from_dict(d)","label":0},{"cid":"compose/config/config.py::dict_from_path_mappings","text":"compose/config/config.py::dict_from_path_mappings\ndef dict_from_path_mappings(path_mappings):\n if path_mappings:\n return dict(split_path_mapping(v) for v in path_mappings)\n else:\n return {}","label":0},{"cid":"compose/config/config.py::path_mappings_from_dict","text":"compose/config/config.py::path_mappings_from_dict\ndef path_mappings_from_dict(d):\n return [join_path_mapping(v) for v in sorted(d.items())]","label":0},{"cid":"compose/config/config.py::split_path_mapping","text":"compose/config/config.py::split_path_mapping\ndef split_path_mapping(volume_path):\n \"\"\"\n Ascertain if the volume_path contains a host path as well as a container\n path. Using splitdrive so windows absolute paths won't cause issues with\n splitting on ':'.\n \"\"\"\n if isinstance(volume_path, dict):\n return (volume_path.get('target'), volume_path)\n drive, volume_config = splitdrive(volume_path)\n\n if ':' in volume_config:\n (host, container) = volume_config.split(':', 1)\n container_drive, container_path = splitdrive(container)\n mode = None\n if ':' in container_path:\n container_path, mode = container_path.rsplit(':', 1)\n\n return (container_drive + container_path, (drive + host, mode))\n else:\n return (volume_path, None)","label":0},{"cid":"compose/config/config.py::process_security_opt","text":"compose/config/config.py::process_security_opt\ndef process_security_opt(service_dict):\n security_opts = service_dict.get('security_opt', [])\n result = []\n for value in security_opts:\n result.append(SecurityOpt.parse(value))\n if result:\n service_dict['security_opt'] = result\n return service_dict","label":0},{"cid":"compose/config/config.py::join_path_mapping","text":"compose/config/config.py::join_path_mapping\ndef join_path_mapping(pair):\n (container, host) = pair\n if isinstance(host, dict):\n return host\n elif host is None:\n return container\n else:\n host, mode = host\n result = \":\".join((host, container))\n if mode:\n result += \":\" + mode\n return result","label":0},{"cid":"compose/config/config.py::expand_path","text":"compose/config/config.py::expand_path\ndef expand_path(working_dir, path):\n return os.path.abspath(os.path.join(working_dir, os.path.expanduser(path)))","label":0},{"cid":"compose/config/config.py::merge_list_or_string","text":"compose/config/config.py::merge_list_or_string\ndef merge_list_or_string(base, override):\n return to_list(base) + to_list(override)","label":0},{"cid":"compose/config/config.py::to_list","text":"compose/config/config.py::to_list\ndef to_list(value):\n if value is None:\n return []\n elif isinstance(value, six.string_types):\n return [value]\n else:\n return value","label":0},{"cid":"compose/config/config.py::to_mapping","text":"compose/config/config.py::to_mapping\ndef to_mapping(sequence, key_field):\n return {getattr(item, key_field): item for item in sequence}","label":0},{"cid":"compose/config/config.py::has_uppercase","text":"compose/config/config.py::has_uppercase\ndef has_uppercase(name):\n return any(char in string.ascii_uppercase for char in name)","label":0},{"cid":"compose/config/config.py::load_yaml","text":"compose/config/config.py::load_yaml\ndef load_yaml(filename, encoding=None, binary=True):\n try:\n with io.open(filename, 'rb' if binary else 'r', encoding=encoding) as fh:\n return yaml.safe_load(fh)\n except (IOError, yaml.YAMLError, UnicodeDecodeError) as e:\n if encoding is None:\n # Sometimes the user's locale sets an encoding that doesn't match\n # the YAML files. Im such cases, retry once with the \"default\"\n # UTF-8 encoding\n return load_yaml(filename, encoding='utf-8-sig', binary=False)\n error_name = getattr(e, '__module__', '') + '.' + e.__class__.__name__\n raise ConfigurationError(u\"{}: {}\".format(error_name, e))","label":0},{"cid":"compose/config/validation.py::format_ports","text":"compose/config/validation.py::format_ports\n@FormatChecker.cls_checks(format=\"ports\", raises=ValidationError)\ndef format_ports(instance):\n try:\n split_port(instance)\n except ValueError as e:\n raise ValidationError(six.text_type(e))\n return True","label":0},{"cid":"compose/config/validation.py::format_expose","text":"compose/config/validation.py::format_expose\n@FormatChecker.cls_checks(format=\"expose\", raises=ValidationError)\ndef format_expose(instance):\n if isinstance(instance, six.string_types):\n if not re.match(VALID_EXPOSE_FORMAT, instance):\n raise ValidationError(\n \"should be of the format 'PORT[/PROTOCOL]'\")\n\n return True","label":0},{"cid":"compose/config/validation.py::format_subnet_ip_address","text":"compose/config/validation.py::format_subnet_ip_address\n@FormatChecker.cls_checks(\"subnet_ip_address\", raises=ValidationError)\ndef format_subnet_ip_address(instance):\n if isinstance(instance, six.string_types):\n if not re.match(VALID_REGEX_IPV4_CIDR, instance) and \\\n not re.match(VALID_REGEX_IPV6_CIDR, instance):\n raise ValidationError(\"should use the CIDR format\")\n\n return True","label":0},{"cid":"compose/config/validation.py::match_named_volumes","text":"compose/config/validation.py::match_named_volumes\ndef match_named_volumes(service_dict, project_volumes):\n service_volumes = service_dict.get('volumes', [])\n for volume_spec in service_volumes:\n if volume_spec.is_named_volume and volume_spec.external not in project_volumes:\n raise ConfigurationError(\n 'Named volume \"{0}\" is used in service \"{1}\" but no'\n ' declaration was found in the volumes section.'.format(\n volume_spec.repr(), service_dict.get('name')\n )\n )","label":0},{"cid":"compose/config/validation.py::python_type_to_yaml_type","text":"compose/config/validation.py::python_type_to_yaml_type\ndef python_type_to_yaml_type(type_):\n type_name = type(type_).__name__\n return {\n 'dict': 'mapping',\n 'list': 'array',\n 'int': 'number',\n 'float': 'number',\n 'bool': 'boolean',\n 'unicode': 'string',\n 'str': 'string',\n 'bytes': 'string',\n }.get(type_name, type_name)","label":0},{"cid":"compose/config/validation.py::validate_config_section","text":"compose/config/validation.py::validate_config_section\ndef validate_config_section(filename, config, section):\n \"\"\"Validate the structure of a configuration section. This must be done\n before interpolation so it's separate from schema validation.\n \"\"\"\n if not isinstance(config, dict):\n raise ConfigurationError(\n \"In file '{filename}', {section} must be a mapping, not \"\n \"{type}.\".format(\n filename=filename,\n section=section,\n type=anglicize_json_type(python_type_to_yaml_type(config))))\n\n for key, value in config.items():\n if not isinstance(key, six.string_types):\n raise ConfigurationError(\n \"In file '{filename}', the {section} name {name} must be a \"\n \"quoted string, i.e. '{name}'.\".format(\n filename=filename,\n section=section,\n name=key))\n\n if not isinstance(value, (dict, type(None))):\n raise ConfigurationError(\n \"In file '{filename}', {section} '{name}' must be a mapping not \"\n \"{type}.\".format(\n filename=filename,\n section=section,\n name=key,\n type=anglicize_json_type(python_type_to_yaml_type(value))))","label":0},{"cid":"compose/config/validation.py::validate_top_level_object","text":"compose/config/validation.py::validate_top_level_object\ndef validate_top_level_object(config_file):\n if not isinstance(config_file.config, dict):\n raise ConfigurationError(\n \"Top level object in '{}' needs to be an object not '{}'.\".format(\n config_file.filename,\n type(config_file.config)))","label":0},{"cid":"compose/config/validation.py::validate_ulimits","text":"compose/config/validation.py::validate_ulimits\ndef validate_ulimits(service_config):\n ulimit_config = service_config.config.get('ulimits', {})\n for limit_name, soft_hard_values in six.iteritems(ulimit_config):\n if isinstance(soft_hard_values, dict):\n if not soft_hard_values['soft'] <= soft_hard_values['hard']:\n raise ConfigurationError(\n \"Service '{s.name}' has invalid ulimit '{ulimit}'. \"\n \"'soft' value can not be greater than 'hard' value \".format(\n s=service_config,\n ulimit=ulimit_config))","label":0},{"cid":"compose/config/validation.py::validate_extends_file_path","text":"compose/config/validation.py::validate_extends_file_path\ndef validate_extends_file_path(service_name, extends_options, filename):\n \"\"\"\n The service to be extended must either be defined in the config key 'file',\n or within 'filename'.\n \"\"\"\n error_prefix = \"Invalid 'extends' configuration for %s:\" % service_name\n\n if 'file' not in extends_options and filename is None:\n raise ConfigurationError(\n \"%s you need to specify a 'file', e.g. 'file: something.yml'\" % error_prefix\n )","label":0},{"cid":"compose/config/validation.py::validate_network_mode","text":"compose/config/validation.py::validate_network_mode\ndef validate_network_mode(service_config, service_names):\n network_mode = service_config.config.get('network_mode')\n if not network_mode:\n return\n\n if 'networks' in service_config.config:\n raise ConfigurationError(\"'network_mode' and 'networks' cannot be combined\")\n\n dependency = get_service_name_from_network_mode(network_mode)\n if not dependency:\n return\n\n if dependency not in service_names:\n raise ConfigurationError(\n \"Service '{s.name}' uses the network stack of service '{dep}' which \"\n \"is undefined.\".format(s=service_config, dep=dependency))","label":0},{"cid":"compose/config/validation.py::validate_pid_mode","text":"compose/config/validation.py::validate_pid_mode\ndef validate_pid_mode(service_config, service_names):\n pid_mode = service_config.config.get('pid')\n if not pid_mode:\n return\n\n dependency = get_service_name_from_network_mode(pid_mode)\n if not dependency:\n return\n if dependency not in service_names:\n raise ConfigurationError(\n \"Service '{s.name}' uses the PID namespace of service '{dep}' which \"\n \"is undefined.\".format(s=service_config, dep=dependency)\n )","label":0},{"cid":"compose/config/validation.py::validate_links","text":"compose/config/validation.py::validate_links\ndef validate_links(service_config, service_names):\n for link in service_config.config.get('links', []):\n if link.split(':')[0] not in service_names:\n raise ConfigurationError(\n \"Service '{s.name}' has a link to service '{link}' which is \"\n \"undefined.\".format(s=service_config, link=link))","label":0},{"cid":"compose/config/validation.py::validate_depends_on","text":"compose/config/validation.py::validate_depends_on\ndef validate_depends_on(service_config, service_names):\n deps = service_config.config.get('depends_on', {})\n for dependency in deps.keys():\n if dependency not in service_names:\n raise ConfigurationError(\n \"Service '{s.name}' depends on service '{dep}' which is \"\n \"undefined.\".format(s=service_config, dep=dependency)\n )","label":0},{"cid":"compose/config/validation.py::get_unsupported_config_msg","text":"compose/config/validation.py::get_unsupported_config_msg\ndef get_unsupported_config_msg(path, error_key):\n msg = \"Unsupported config option for {}: '{}'\".format(path_string(path), error_key)\n if error_key in DOCKER_CONFIG_HINTS:\n msg += \" (did you mean '{}'?)\".format(DOCKER_CONFIG_HINTS[error_key])\n return msg","label":0},{"cid":"compose/config/validation.py::anglicize_json_type","text":"compose/config/validation.py::anglicize_json_type\ndef anglicize_json_type(json_type):\n if json_type.startswith(('a', 'e', 'i', 'o', 'u')):\n return 'an ' + json_type\n return 'a ' + json_type","label":0},{"cid":"compose/config/validation.py::is_service_dict_schema","text":"compose/config/validation.py::is_service_dict_schema\ndef is_service_dict_schema(schema_id):\n return schema_id in ('config_schema_v1.json', '#/properties/services')","label":0},{"cid":"compose/config/validation.py::handle_error_for_schema_with_id","text":"compose/config/validation.py::handle_error_for_schema_with_id\ndef handle_error_for_schema_with_id(error, path):\n schema_id = error.schema['id']\n\n if is_service_dict_schema(schema_id) and error.validator == 'additionalProperties':\n return \"Invalid service name '{}' - only {} characters are allowed\".format(\n # The service_name is one of the keys in the json object\n [i for i in list(error.instance) if not i or any(filter(\n lambda c: not re.match(VALID_NAME_CHARS, c), i\n ))][0],\n VALID_NAME_CHARS\n )\n\n if error.validator == 'additionalProperties':\n if schema_id == '#/definitions/service':\n invalid_config_key = parse_key_from_error_msg(error)\n return get_unsupported_config_msg(path, invalid_config_key)\n\n if schema_id.startswith('config_schema_v'):\n invalid_config_key = parse_key_from_error_msg(error)\n return ('Invalid top-level property \"{key}\". Valid top-level '\n 'sections for this Compose file are: {properties}, and '\n 'extensions starting with \"x-\".\\n\\n{explanation}').format(\n key=invalid_config_key,\n properties=', '.join(error.schema['properties'].keys()),\n explanation=VERSION_EXPLANATION\n )\n\n if not error.path:\n return '{}\\n\\n{}'.format(error.message, VERSION_EXPLANATION)","label":0},{"cid":"compose/config/validation.py::handle_generic_error","text":"compose/config/validation.py::handle_generic_error\ndef handle_generic_error(error, path):\n msg_format = None\n error_msg = error.message\n\n if error.validator == 'oneOf':\n msg_format = \"{path} {msg}\"\n config_key, error_msg = _parse_oneof_validator(error)\n if config_key:\n path.append(config_key)\n\n elif error.validator == 'type':\n msg_format = \"{path} contains an invalid type, it should be {msg}\"\n error_msg = _parse_valid_types_from_validator(error.validator_value)\n\n elif error.validator == 'required':\n error_msg = \", \".join(error.validator_value)\n msg_format = \"{path} is invalid, {msg} is required.\"\n\n elif error.validator == 'dependencies':\n config_key = list(error.validator_value.keys())[0]\n required_keys = \",\".join(error.validator_value[config_key])\n\n msg_format = \"{path} is invalid: {msg}\"\n path.append(config_key)\n error_msg = \"when defining '{}' you must set '{}' as well\".format(\n config_key,\n required_keys)\n\n elif error.cause:\n error_msg = six.text_type(error.cause)\n msg_format = \"{path} is invalid: {msg}\"\n\n elif error.path:\n msg_format = \"{path} value {msg}\"\n\n if msg_format:\n return msg_format.format(path=path_string(path), msg=error_msg)\n\n return error.message","label":0},{"cid":"compose/config/validation.py::parse_key_from_error_msg","text":"compose/config/validation.py::parse_key_from_error_msg\ndef parse_key_from_error_msg(error):\n try:\n return error.message.split(\"'\")[1]\n except IndexError:\n return error.message.split('(')[1].split(' ')[0].strip(\"'\")","label":0},{"cid":"compose/config/validation.py::path_string","text":"compose/config/validation.py::path_string\ndef path_string(path):\n return \".\".join(c for c in path if isinstance(c, six.string_types))","label":0},{"cid":"compose/config/validation.py::_parse_valid_types_from_validator","text":"compose/config/validation.py::_parse_valid_types_from_validator\ndef _parse_valid_types_from_validator(validator):\n \"\"\"A validator value can be either an array of valid types or a string of\n a valid type. Parse the valid types and prefix with the correct article.\n \"\"\"\n if not isinstance(validator, list):\n return anglicize_json_type(validator)\n\n if len(validator) == 1:\n return anglicize_json_type(validator[0])\n\n return \"{}, or {}\".format(\n \", \".join([anglicize_json_type(validator[0])] + validator[1:-1]),\n anglicize_json_type(validator[-1]))","label":0},{"cid":"compose/config/validation.py::_parse_oneof_validator","text":"compose/config/validation.py::_parse_oneof_validator\ndef _parse_oneof_validator(error):\n \"\"\"oneOf has multiple schemas, so we need to reason about which schema, sub\n schema or constraint the validation is failing on.\n Inspecting the context value of a ValidationError gives us information about\n which sub schema failed and which kind of error it is.\n \"\"\"\n types = []\n for context in error.context:\n if context.validator == 'oneOf':\n _, error_msg = _parse_oneof_validator(context)\n return path_string(context.path), error_msg\n\n if context.validator == 'required':\n return (None, context.message)\n\n if context.validator == 'additionalProperties':\n invalid_config_key = parse_key_from_error_msg(context)\n return (None, \"contains unsupported option: '{}'\".format(invalid_config_key))\n\n if context.validator == 'uniqueItems':\n return (\n path_string(context.path) if context.path else None,\n \"contains non-unique items, please remove duplicates from {}\".format(\n context.instance),\n )\n\n if context.path:\n return (\n path_string(context.path),\n \"contains {}, which is an invalid type, it should be {}\".format(\n json.dumps(context.instance),\n _parse_valid_types_from_validator(context.validator_value)),\n )\n\n if context.validator == 'type':\n types.append(context.validator_value)\n\n valid_types = _parse_valid_types_from_validator(types)\n return (None, \"contains an invalid type, it should be {}\".format(valid_types))","label":0},{"cid":"compose/config/validation.py::process_service_constraint_errors","text":"compose/config/validation.py::process_service_constraint_errors\ndef process_service_constraint_errors(error, service_name, version):\n if version == V1:\n if 'image' in error.instance and 'build' in error.instance:\n return (\n \"Service {} has both an image and build path specified. \"\n \"A service can either be built to image or use an existing \"\n \"image, not both.\".format(service_name))\n\n if 'image' in error.instance and 'dockerfile' in error.instance:\n return (\n \"Service {} has both an image and alternate Dockerfile. \"\n \"A service can either be built to image or use an existing \"\n \"image, not both.\".format(service_name))\n\n if 'image' not in error.instance and 'build' not in error.instance:\n return (\n \"Service {} has neither an image nor a build context specified. \"\n \"At least one must be provided.\".format(service_name))","label":0},{"cid":"compose/config/validation.py::process_config_schema_errors","text":"compose/config/validation.py::process_config_schema_errors\ndef process_config_schema_errors(error):\n path = list(error.path)\n\n if 'id' in error.schema:\n error_msg = handle_error_for_schema_with_id(error, path)\n if error_msg:\n return error_msg\n\n return handle_generic_error(error, path)","label":0},{"cid":"compose/config/validation.py::validate_against_config_schema","text":"compose/config/validation.py::validate_against_config_schema\ndef validate_against_config_schema(config_file):\n schema = load_jsonschema(config_file)\n format_checker = FormatChecker([\"ports\", \"expose\", \"subnet_ip_address\"])\n validator = Draft4Validator(\n schema,\n resolver=RefResolver(get_resolver_path(), schema),\n format_checker=format_checker)\n handle_errors(\n validator.iter_errors(config_file.config),\n process_config_schema_errors,\n config_file.filename)","label":0},{"cid":"compose/config/validation.py::validate_service_constraints","text":"compose/config/validation.py::validate_service_constraints\ndef validate_service_constraints(config, service_name, config_file):\n def handler(errors):\n return process_service_constraint_errors(\n errors, service_name, config_file.version)\n\n schema = load_jsonschema(config_file)\n validator = Draft4Validator(schema['definitions']['constraints']['service'])\n handle_errors(validator.iter_errors(config), handler, None)","label":0},{"cid":"compose/config/validation.py::validate_service_constraints.handler","text":"compose/config/validation.py::validate_service_constraints.handler\n def handler(errors):\n return process_service_constraint_errors(\n errors, service_name, config_file.version)","label":0},{"cid":"compose/config/validation.py::validate_cpu","text":"compose/config/validation.py::validate_cpu\ndef validate_cpu(service_config):\n cpus = service_config.config.get('cpus')\n if not cpus:\n return\n nano_cpus = cpus * NANOCPUS_SCALE\n if isinstance(nano_cpus, float) and not nano_cpus.is_integer():\n raise ConfigurationError(\n \"cpus must have nine or less digits after decimal point\")","label":0},{"cid":"compose/config/validation.py::get_schema_path","text":"compose/config/validation.py::get_schema_path\ndef get_schema_path():\n return os.path.dirname(os.path.abspath(__file__))","label":0},{"cid":"compose/config/validation.py::load_jsonschema","text":"compose/config/validation.py::load_jsonschema\ndef load_jsonschema(config_file):\n filename = os.path.join(\n get_schema_path(),\n \"config_schema_v{0}.json\".format(config_file.version))\n\n if not os.path.exists(filename):\n raise ConfigurationError(\n 'Version in \"{}\" is unsupported. {}'\n .format(config_file.filename, VERSION_EXPLANATION))\n\n with open(filename, \"r\") as fh:\n return json.load(fh)","label":0},{"cid":"compose/config/validation.py::get_resolver_path","text":"compose/config/validation.py::get_resolver_path\ndef get_resolver_path():\n schema_path = get_schema_path()\n if sys.platform == \"win32\":\n scheme = \"///\"\n # TODO: why is this necessary?\n schema_path = schema_path.replace('\\\\', '/')\n else:\n scheme = \"//\"\n return \"file:{}{}/\".format(scheme, schema_path)","label":0},{"cid":"compose/config/validation.py::handle_errors","text":"compose/config/validation.py::handle_errors\ndef handle_errors(errors, format_error_func, filename):\n \"\"\"jsonschema returns an error tree full of information to explain what has\n gone wrong. Process each error and pull out relevant information and re-write\n helpful error messages that are relevant.\n \"\"\"\n errors = list(sorted(errors, key=str))\n if not errors:\n return\n\n error_msg = '\\n'.join(format_error_func(error) for error in errors)\n raise ConfigurationError(\n \"The Compose file{file_msg} is invalid because:\\n{error_msg}\".format(\n file_msg=\" '{}'\".format(filename) if filename else \"\",\n error_msg=error_msg))","label":0},{"cid":"compose/config/validation.py::validate_healthcheck","text":"compose/config/validation.py::validate_healthcheck\ndef validate_healthcheck(service_config):\n healthcheck = service_config.config.get('healthcheck', {})\n\n if 'test' in healthcheck and isinstance(healthcheck['test'], list):\n if len(healthcheck['test']) == 0:\n raise ConfigurationError(\n 'Service \"{}\" defines an invalid healthcheck: '\n '\"test\" is an empty list'\n .format(service_config.name))\n\n # when disable is true config.py::process_healthcheck adds \"test: ['NONE']\" to service_config\n elif healthcheck['test'][0] == 'NONE' and len(healthcheck) > 1:\n raise ConfigurationError(\n 'Service \"{}\" defines an invalid healthcheck: '\n '\"disable: true\" cannot be combined with other options'\n .format(service_config.name))\n\n elif healthcheck['test'][0] not in ('NONE', 'CMD', 'CMD-SHELL'):\n raise ConfigurationError(\n 'Service \"{}\" defines an invalid healthcheck: '\n 'when \"test\" is a list the first item must be either NONE, CMD or CMD-SHELL'\n .format(service_config.name))","label":0},{"cid":"script/release/release.py::create_initial_branch","text":"script/release/release.py::create_initial_branch\ndef create_initial_branch(repository, args):\n release_branch = repository.create_release_branch(args.release, args.base)\n if args.base and args.cherries:\n print('Detected patch version.')\n cherries = input('Indicate (space-separated) PR numbers to cherry-pick then press Enter:\\n')\n repository.cherry_pick_prs(release_branch, cherries.split())\n\n return create_bump_commit(repository, release_branch, args.bintray_user, args.bintray_org)","label":0},{"cid":"script/release/release.py::create_bump_commit","text":"script/release/release.py::create_bump_commit\ndef create_bump_commit(repository, release_branch, bintray_user, bintray_org):\n with release_branch.config_reader() as cfg:\n release = cfg.get('release')\n print('Updating version info in __init__.py and run.sh')\n update_run_sh_version(release)\n update_init_py_version(release)\n\n input('Please add the release notes to the CHANGELOG.md file, then press Enter to continue.')\n proceed = None\n while not proceed:\n print(repository.diff())\n proceed = yesno('Are these changes ok? y/N ', default=False)\n\n if repository.diff():\n repository.create_bump_commit(release_branch, release)\n repository.push_branch_to_remote(release_branch)\n\n bintray_api = BintrayAPI(os.environ['BINTRAY_TOKEN'], bintray_user)\n if not bintray_api.repository_exists(bintray_org, release_branch.name):\n print('Creating data repository {} on bintray'.format(release_branch.name))\n bintray_api.create_repository(bintray_org, release_branch.name, 'generic')\n else:\n print('Bintray repository {} already exists. Skipping'.format(release_branch.name))","label":0},{"cid":"script/release/release.py::monitor_pr_status","text":"script/release/release.py::monitor_pr_status\ndef monitor_pr_status(pr_data):\n print('Waiting for CI to complete...')\n last_commit = pr_data.get_commits().reversed[0]\n while True:\n status = last_commit.get_combined_status()\n if status.state == 'pending' or status.state == 'failure':\n summary = {\n 'pending': 0,\n 'success': 0,\n 'failure': 0,\n 'error': 0,\n }\n for detail in status.statuses:\n if detail.context == 'dco-signed':\n # dco-signed check breaks on merge remote-tracking ; ignore it\n continue\n if detail.state in summary:\n summary[detail.state] += 1\n print(\n '{pending} pending, {success} successes, {failure} failures, '\n '{error} errors'.format(**summary)\n )\n if summary['failure'] > 0 or summary['error'] > 0:\n raise ScriptError('CI failures detected!')\n elif summary['pending'] == 0 and summary['success'] > 0:\n # This check assumes at least 1 non-DCO CI check to avoid race conditions.\n # If testing on a repo without CI, use --skip-ci-check to avoid looping eternally\n return True\n time.sleep(30)\n elif status.state == 'success':\n print('{} successes: all clear!'.format(status.total_count))\n return True","label":0},{"cid":"script/release/release.py::check_pr_mergeable","text":"script/release/release.py::check_pr_mergeable\ndef check_pr_mergeable(pr_data):\n if pr_data.mergeable is False:\n # mergeable can also be null, in which case the warning would be a false positive.\n print(\n 'WARNING!! PR #{} can not currently be merged. You will need to '\n 'resolve the conflicts manually before finalizing the release.'.format(pr_data.number)\n )\n\n return pr_data.mergeable is True","label":0},{"cid":"script/release/release.py::create_release_draft","text":"script/release/release.py::create_release_draft\ndef create_release_draft(repository, version, pr_data, files):\n print('Creating Github release draft')\n with open(os.path.join(os.path.dirname(__file__), 'release.md.tmpl'), 'r') as f:\n template = Template(f.read())\n print('Rendering release notes based on template')\n release_notes = template.render(\n version=version,\n compat_matrix=compatibility_matrix(),\n integrity=files,\n contributors=get_contributors(pr_data),\n changelog=read_release_notes_from_changelog(),\n )\n gh_release = repository.create_release(\n version, release_notes, draft=True, prerelease='-rc' in version,\n target_commitish='release'\n )\n print('Release draft initialized')\n return gh_release","label":0},{"cid":"script/release/release.py::print_final_instructions","text":"script/release/release.py::print_final_instructions\ndef print_final_instructions(args):\n print(\n \"You're almost done! Please verify that everything is in order and \"\n \"you are ready to make the release public, then run the following \"\n \"command:\\n{exe} -b {user} finalize {version}\".format(\n exe='./script/release/release.sh', user=args.bintray_user, version=args.release\n )\n )","label":0},{"cid":"script/release/release.py::distclean","text":"script/release/release.py::distclean\ndef distclean():\n print('Running distclean...')\n dirs = [\n os.path.join(REPO_ROOT, 'build'), os.path.join(REPO_ROOT, 'dist'),\n os.path.join(REPO_ROOT, 'docker-compose.egg-info')\n ]\n files = []\n for base, dirnames, fnames in os.walk(REPO_ROOT):\n for fname in fnames:\n path = os.path.normpath(os.path.join(base, fname))\n if fname.endswith('.pyc'):\n files.append(path)\n elif fname.startswith('.coverage.'):\n files.append(path)\n for dirname in dirnames:\n path = os.path.normpath(os.path.join(base, dirname))\n if dirname == '__pycache__':\n dirs.append(path)\n elif dirname == '.coverage-binfiles':\n dirs.append(path)\n\n for file in files:\n os.unlink(file)\n\n for folder in dirs:\n shutil.rmtree(folder, ignore_errors=True)","label":0},{"cid":"script/release/release.py::resume","text":"script/release/release.py::resume\ndef resume(args):\n try:\n distclean()\n repository = Repository(REPO_ROOT, args.repo)\n br_name = branch_name(args.release)\n if not repository.branch_exists(br_name):\n raise ScriptError('No local branch exists for this release.')\n gh_release = repository.find_release(args.release)\n if gh_release and not gh_release.draft:\n print('WARNING!! Found non-draft (public) release for this version!')\n proceed = yesno(\n 'Are you sure you wish to proceed? Modifying an already '\n 'released version is dangerous! y/N ', default=False\n )\n if proceed.lower() is not True:\n raise ScriptError('Aborting release')\n\n release_branch = repository.checkout_branch(br_name)\n if args.cherries:\n cherries = input('Indicate (space-separated) PR numbers to cherry-pick then press Enter:\\n')\n repository.cherry_pick_prs(release_branch, cherries.split())\n\n create_bump_commit(repository, release_branch, args.bintray_user, args.bintray_org)\n pr_data = repository.find_release_pr(args.release)\n if not pr_data:\n pr_data = repository.create_release_pull_request(args.release)\n check_pr_mergeable(pr_data)\n if not args.skip_ci:\n monitor_pr_status(pr_data)\n downloader = BinaryDownloader(args.destination)\n files = downloader.download_all(args.release)\n if not gh_release:\n gh_release = create_release_draft(repository, args.release, pr_data, files)\n delete_assets(gh_release)\n upload_assets(gh_release, files)\n img_manager = ImageManager(args.release)\n img_manager.build_images(repository, files)\n except ScriptError as e:\n print(e)\n return 1\n\n print_final_instructions(args)\n return 0","label":0},{"cid":"script/release/release.py::cancel","text":"script/release/release.py::cancel\ndef cancel(args):\n try:\n repository = Repository(REPO_ROOT, args.repo)\n repository.close_release_pr(args.release)\n repository.remove_release(args.release)\n repository.remove_bump_branch(args.release)\n bintray_api = BintrayAPI(os.environ['BINTRAY_TOKEN'], args.bintray_user)\n print('Removing Bintray data repository for {}'.format(args.release))\n bintray_api.delete_repository(args.bintray_org, branch_name(args.release))\n distclean()\n except ScriptError as e:\n print(e)\n return 1\n print('Release cancellation complete.')\n return 0","label":0},{"cid":"script/release/release.py::start","text":"script/release/release.py::start\ndef start(args):\n distclean()\n try:\n repository = Repository(REPO_ROOT, args.repo)\n create_initial_branch(repository, args)\n pr_data = repository.create_release_pull_request(args.release)\n check_pr_mergeable(pr_data)\n if not args.skip_ci:\n monitor_pr_status(pr_data)\n downloader = BinaryDownloader(args.destination)\n files = downloader.download_all(args.release)\n gh_release = create_release_draft(repository, args.release, pr_data, files)\n upload_assets(gh_release, files)\n img_manager = ImageManager(args.release)\n img_manager.build_images(repository, files)\n except ScriptError as e:\n print(e)\n return 1\n\n print_final_instructions(args)\n return 0","label":0},{"cid":"script/release/release.py::finalize","text":"script/release/release.py::finalize\ndef finalize(args):\n distclean()\n try:\n check_pypirc()\n repository = Repository(REPO_ROOT, args.repo)\n img_manager = ImageManager(args.release)\n pr_data = repository.find_release_pr(args.release)\n if not pr_data:\n raise ScriptError('No PR found for {}'.format(args.release))\n if not check_pr_mergeable(pr_data):\n raise ScriptError('Can not finalize release with an unmergeable PR')\n if not img_manager.check_images():\n raise ScriptError('Missing release image')\n br_name = branch_name(args.release)\n if not repository.branch_exists(br_name):\n raise ScriptError('No local branch exists for this release.')\n gh_release = repository.find_release(args.release)\n if not gh_release:\n raise ScriptError('No Github release draft for this version')\n\n repository.checkout_branch(br_name)\n\n run_setup(os.path.join(REPO_ROOT, 'setup.py'), script_args=['sdist', 'bdist_wheel'])\n\n merge_status = pr_data.merge()\n if not merge_status.merged and not args.finalize_resume:\n raise ScriptError(\n 'Unable to merge PR #{}: {}'.format(pr_data.number, merge_status.message)\n )\n\n pypi_upload(args)\n\n img_manager.push_images()\n repository.publish_release(gh_release)\n except ScriptError as e:\n print(e)\n return 1\n\n return 0","label":0},{"cid":"script/release/release.py::main","text":"script/release/release.py::main\ndef main():\n if 'GITHUB_TOKEN' not in os.environ:\n print('GITHUB_TOKEN environment variable must be set')\n return 1\n\n if 'BINTRAY_TOKEN' not in os.environ:\n print('BINTRAY_TOKEN environment variable must be set')\n return 1\n\n parser = argparse.ArgumentParser(\n description='Orchestrate a new release of docker/compose. This tool assumes that you have '\n 'obtained a Github API token and Bintray API key and set the GITHUB_TOKEN and '\n 'BINTRAY_TOKEN environment variables accordingly.',\n epilog=EPILOG, formatter_class=argparse.RawTextHelpFormatter)\n parser.add_argument(\n 'action', choices=ACTIONS, help='The action to be performed for this release'\n )\n parser.add_argument('release', help='Release number, e.g. 1.9.0-rc1, 2.1.1')\n parser.add_argument(\n '--patch', '-p', dest='base',\n help='Which version is being patched by this release'\n )\n parser.add_argument(\n '--repo', '-r', dest='repo', default=NAME,\n help='Start a release for the given repo (default: {})'.format(NAME)\n )\n parser.add_argument(\n '-b', dest='bintray_user', required=True, metavar='USER',\n help='Username associated with the Bintray API key'\n )\n parser.add_argument(\n '--bintray-org', dest='bintray_org', metavar='ORG', default=BINTRAY_ORG,\n help='Organization name on bintray where the data repository will be created.'\n )\n parser.add_argument(\n '--destination', '-o', metavar='DIR', default='binaries',\n help='Directory where release binaries will be downloaded relative to the project root'\n )\n parser.add_argument(\n '--no-cherries', '-C', dest='cherries', action='store_false',\n help='If set, the program will not prompt the user for PR numbers to cherry-pick'\n )\n parser.add_argument(\n '--skip-ci-checks', dest='skip_ci', action='store_true',\n help","label":0},{"cid":"setup.py::read","text":"setup.py::read\ndef read(*parts):\n path = os.path.join(os.path.dirname(__file__), *parts)\n with codecs.open(path, encoding='utf-8') as fobj:\n return fobj.read()","label":0},{"cid":"setup.py::find_version","text":"setup.py::find_version\ndef find_version(*file_paths):\n version_file = read(*file_paths)\n version_match = re.search(r\"^__version__ = ['\\\"]([^'\\\"]*)['\\\"]\",\n version_file, re.M)\n if version_match:\n return version_match.group(1)\n raise RuntimeError(\"Unable to find version string.\")","label":0}],"string":"[\n {\n \"cid\": \"compose/cli/errors.py::UserError.__init__\",\n \"text\": \"compose/cli/errors.py::UserError.__init__\\n def __init__(self, msg):\\n self.msg = dedent(msg).strip()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/errors.py::UserError.__unicode__\",\n \"text\": \"compose/cli/errors.py::UserError.__unicode__\\n def __unicode__(self):\\n return self.msg\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/errors.py::handle_connection_errors\",\n \"text\": \"compose/cli/errors.py::handle_connection_errors\\n@contextlib.contextmanager\\ndef handle_connection_errors(client):\\n try:\\n yield\\n except SSLError as e:\\n log.error('SSL error: %s' % e)\\n raise ConnectionError()\\n except RequestsConnectionError as e:\\n if e.args and isinstance(e.args[0], ReadTimeoutError):\\n log_timeout_error(client.timeout)\\n raise ConnectionError()\\n exit_with_error(get_conn_error_message(client.base_url))\\n except APIError as e:\\n log_api_error(e, client.api_version)\\n raise ConnectionError()\\n except (ReadTimeout, socket.timeout):\\n log_timeout_error(client.timeout)\\n raise ConnectionError()\\n except Exception as e:\\n if is_windows():\\n import pywintypes\\n if isinstance(e, pywintypes.error):\\n log_windows_pipe_error(e)\\n raise ConnectionError()\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/errors.py::log_windows_pipe_error\",\n \"text\": \"compose/cli/errors.py::log_windows_pipe_error\\ndef log_windows_pipe_error(exc):\\n if exc.winerror == 2:\\n log.error(\\\"Couldn't connect to Docker daemon. You might need to start Docker for Windows.\\\")\\n elif exc.winerror == 232: # https://github.com/docker/compose/issues/5005\\n log.error(\\n \\\"The current Compose file version is not compatible with your engine version. \\\"\\n \\\"Please upgrade your Compose file to a more recent version, or set \\\"\\n \\\"a COMPOSE_API_VERSION in your environment.\\\"\\n )\\n else:\\n log.error(\\n \\\"Windows named pipe error: {} (code: {})\\\".format(\\n binarystr_to_unicode(exc.strerror), exc.winerror\\n )\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/errors.py::log_timeout_error\",\n \"text\": \"compose/cli/errors.py::log_timeout_error\\ndef log_timeout_error(timeout):\\n log.error(\\n \\\"An HTTP request took too long to complete. Retry with --verbose to \\\"\\n \\\"obtain debug information.\\\\n\\\"\\n \\\"If you encounter this issue regularly because of slow network \\\"\\n \\\"conditions, consider setting COMPOSE_HTTP_TIMEOUT to a higher \\\"\\n \\\"value (current value: %s).\\\" % timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/errors.py::log_api_error\",\n \"text\": \"compose/cli/errors.py::log_api_error\\ndef log_api_error(e, client_version):\\n explanation = binarystr_to_unicode(e.explanation)\\n\\n if 'client is newer than server' not in explanation:\\n log.error(explanation)\\n return\\n\\n version = API_VERSION_TO_ENGINE_VERSION.get(client_version)\\n if not version:\\n # They've set a custom API version\\n log.error(explanation)\\n return\\n\\n log.error(\\n \\\"The Docker Engine version is less than the minimum required by \\\"\\n \\\"Compose. Your current project requires a Docker Engine of \\\"\\n \\\"version {version} or greater.\\\".format(version=version)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/errors.py::exit_with_error\",\n \"text\": \"compose/cli/errors.py::exit_with_error\\ndef exit_with_error(msg):\\n log.error(dedent(msg).strip())\\n raise ConnectionError()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/errors.py::get_conn_error_message\",\n \"text\": \"compose/cli/errors.py::get_conn_error_message\\ndef get_conn_error_message(url):\\n try:\\n if find_executable('docker') is None:\\n return docker_not_found_msg(\\\"Couldn't connect to Docker daemon.\\\")\\n if is_docker_for_mac_installed():\\n return conn_error_docker_for_mac\\n if find_executable('docker-machine') is not None:\\n return conn_error_docker_machine\\n except UnicodeDecodeError:\\n # https://github.com/docker/compose/issues/5442\\n # Ignore the error and print the generic message instead.\\n pass\\n return conn_error_generic.format(url=url)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/errors.py::docker_not_found_msg\",\n \"text\": \"compose/cli/errors.py::docker_not_found_msg\\ndef docker_not_found_msg(problem):\\n return \\\"{} You might need to install Docker:\\\\n\\\\n{}\\\".format(\\n problem, docker_install_url())\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/errors.py::docker_install_url\",\n \"text\": \"compose/cli/errors.py::docker_install_url\\ndef docker_install_url():\\n if is_mac():\\n return docker_install_url_mac\\n elif is_ubuntu():\\n return docker_install_url_ubuntu\\n elif is_windows():\\n return docker_install_url_windows\\n else:\\n return docker_install_url_generic\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::main\",\n \"text\": \"compose/cli/main.py::main\\ndef main():\\n signals.ignore_sigpipe()\\n try:\\n command = dispatch()\\n command()\\n except (KeyboardInterrupt, signals.ShutdownException):\\n log.error(\\\"Aborting.\\\")\\n sys.exit(1)\\n except (UserError, NoSuchService, ConfigurationError,\\n ProjectError, OperationFailedError) as e:\\n log.error(e.msg)\\n sys.exit(1)\\n except BuildError as e:\\n log.error(\\\"Service '%s' failed to build: %s\\\" % (e.service.name, e.reason))\\n sys.exit(1)\\n except StreamOutputError as e:\\n log.error(e)\\n sys.exit(1)\\n except NeedsBuildError as e:\\n log.error(\\\"Service '%s' needs to be built, but --no-build was passed.\\\" % e.service.name)\\n sys.exit(1)\\n except NoSuchCommand as e:\\n commands = \\\"\\\\n\\\".join(parse_doc_section(\\\"commands:\\\", getdoc(e.supercommand)))\\n log.error(\\\"No such command: %s\\\\n\\\\n%s\\\", e.command, commands)\\n sys.exit(1)\\n except (errors.ConnectionError, StreamParseError):\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::dispatch\",\n \"text\": \"compose/cli/main.py::dispatch\\ndef dispatch():\\n setup_logging()\\n dispatcher = DocoptDispatcher(\\n TopLevelCommand,\\n {'options_first': True, 'version': get_version_info('compose')})\\n\\n options, handler, command_options = dispatcher.parse(sys.argv[1:])\\n setup_console_handler(console_handler,\\n options.get('--verbose'),\\n options.get('--no-ansi'),\\n options.get(\\\"--log-level\\\"))\\n setup_parallel_logger(options.get('--no-ansi'))\\n if options.get('--no-ansi'):\\n command_options['--no-color'] = True\\n return functools.partial(perform_command, options, handler, command_options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::perform_command\",\n \"text\": \"compose/cli/main.py::perform_command\\ndef perform_command(options, handler, command_options):\\n if options['COMMAND'] in ('help', 'version'):\\n # Skip looking up the compose file.\\n handler(command_options)\\n return\\n\\n if options['COMMAND'] == 'config':\\n command = TopLevelCommand(None, options=options)\\n handler(command, command_options)\\n return\\n\\n project = project_from_options('.', options)\\n command = TopLevelCommand(project, options=options)\\n with errors.handle_connection_errors(project.client):\\n handler(command, command_options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_logging\",\n \"text\": \"compose/cli/main.py::setup_logging\\ndef setup_logging():\\n root_logger = logging.getLogger()\\n root_logger.addHandler(console_handler)\\n root_logger.setLevel(logging.DEBUG)\\n\\n # Disable requests logging\\n logging.getLogger(\\\"requests\\\").propagate = False\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_parallel_logger\",\n \"text\": \"compose/cli/main.py::setup_parallel_logger\\ndef setup_parallel_logger(noansi):\\n if noansi:\\n import compose.parallel\\n compose.parallel.ParallelStreamWriter.set_noansi()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_console_handler\",\n \"text\": \"compose/cli/main.py::setup_console_handler\\ndef setup_console_handler(handler, verbose, noansi=False, level=None):\\n if handler.stream.isatty() and noansi is False:\\n format_class = ConsoleWarningFormatter\\n else:\\n format_class = logging.Formatter\\n\\n if verbose:\\n handler.setFormatter(format_class('%(name)s.%(funcName)s: %(message)s'))\\n loglevel = logging.DEBUG\\n else:\\n handler.setFormatter(format_class())\\n loglevel = logging.INFO\\n\\n if level is not None:\\n levels = {\\n 'DEBUG': logging.DEBUG,\\n 'INFO': logging.INFO,\\n 'WARNING': logging.WARNING,\\n 'ERROR': logging.ERROR,\\n 'CRITICAL': logging.CRITICAL,\\n }\\n loglevel = levels.get(level.upper())\\n if loglevel is None:\\n raise UserError(\\n 'Invalid value for --log-level. Expected one of DEBUG, INFO, WARNING, ERROR, CRITICAL.'\\n )\\n\\n handler.setLevel(loglevel)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::parse_doc_section\",\n \"text\": \"compose/cli/main.py::parse_doc_section\\ndef parse_doc_section(name, source):\\n pattern = re.compile('^([^\\\\n]*' + name + '[^\\\\n]*\\\\n?(?:[ \\\\t].*?(?:\\\\n|$))*)',\\n re.IGNORECASE | re.MULTILINE)\\n return [s.strip() for s in pattern.findall(source)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.__init__\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.__init__\\n def __init__(self, project, options=None):\\n self.project = project\\n self.toplevel_options = options or {}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.project_dir\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.project_dir\\n @property\\n def project_dir(self):\\n return self.toplevel_options.get('--project-directory') or '.'\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.build\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.build\\n def build(self, options):\\n \\\"\\\"\\\"\\n Build or rebuild services.\\n\\n Services are built once and then tagged as `project_service`,\\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\\n contents of its build directory, you can run `docker-compose build` to rebuild it.\\n\\n Usage: build [options] [--build-arg key=val...] [SERVICE...]\\n\\n Options:\\n --compress Compress the build context using gzip.\\n --force-rm Always remove intermediate containers.\\n --no-cache Do not use cache when building the image.\\n --pull Always attempt to pull a newer version of the image.\\n -m, --memory MEM Sets memory limit for the build container.\\n --build-arg key=val Set build-time variables for services.\\n --parallel Build images in parallel.\\n \\\"\\\"\\\"\\n service_names = options['SERVICE']\\n build_args = options.get('--build-arg', None)\\n if build_args:\\n if not service_names and docker.utils.version_lt(self.project.client.api_version, '1.25'):\\n raise UserError(\\n '--build-arg is only supported when services are specified for API version < 1.25.'\\n ' Please use a Compose file version > 2.2 or specify which services to build.'\\n )\\n environment = Environment.from_env_file(self.project_dir)\\n build_args = resolve_build_args(build_args, environment)\\n\\n self.project.build(\\n service_names=options['SERVICE'],\\n no_cache=bool(options.get('--no-cache', False)),\\n pull=bool(options.get('--pull', False)),\\n force_rm=bool(options.get('--force-rm', False)),\\n memory=options.get('--memory'),\\n build_args=build_args,\\n gzip=options.get('--compress', False),\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.bundle\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.bundle\\n def bundle(self, options):\\n \\\"\\\"\\\"\\n Generate a Distributed Application Bundle (DAB) from the Compose file.\\n\\n Images must have digests stored, which requires interaction with a\\n Docker registry. If digests aren't stored for all images, you can fetch\\n them with `docker-compose pull` or `docker-compose push`. To push images\\n automatically when bundling, pass `--push-images`. Only services with\\n a `build` option specified will have their images pushed.\\n\\n Usage: bundle [options]\\n\\n Options:\\n --push-images Automatically push images for any services\\n which have a `build` option specified.\\n\\n -o, --output PATH Path to write the bundle file to.\\n Defaults to \\\"<project name>.dab\\\".\\n \\\"\\\"\\\"\\n compose_config = get_config_from_options('.', self.toplevel_options)\\n\\n output = options[\\\"--output\\\"]\\n if not output:\\n output = \\\"{}.dab\\\".format(self.project.name)\\n\\n image_digests = image_digests_for_project(self.project, options['--push-images'])\\n\\n with open(output, 'w') as f:\\n f.write(serialize_bundle(compose_config, image_digests))\\n\\n log.info(\\\"Wrote bundle to {}\\\".format(output))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.config\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.config\\n def config(self, options):\\n \\\"\\\"\\\"\\n Validate and view the Compose file.\\n\\n Usage: config [options]\\n\\n Options:\\n --resolve-image-digests Pin image tags to digests.\\n -q, --quiet Only validate the configuration, don't print\\n anything.\\n --services Print the service names, one per line.\\n --volumes Print the volume names, one per line.\\n --hash=\\\"*\\\" Print the service config hash, one per line.\\n Set \\\"service1,service2\\\" for a list of specified services\\n or use the wildcard symbol to display all services\\n \\\"\\\"\\\"\\n\\n compose_config = get_config_from_options('.', self.toplevel_options)\\n image_digests = None\\n\\n if options['--resolve-image-digests']:\\n self.project = project_from_options('.', self.toplevel_options)\\n with errors.handle_connection_errors(self.project.client):\\n image_digests = image_digests_for_project(self.project)\\n\\n if options['--quiet']:\\n return\\n\\n if options['--services']:\\n print('\\\\n'.join(service['name'] for service in compose_config.services))\\n return\\n\\n if options['--volumes']:\\n print('\\\\n'.join(volume for volume in compose_config.volumes))\\n return\\n\\n if options['--hash'] is not None:\\n h = options['--hash']\\n self.project = project_from_options('.', self.toplevel_options)\\n services = [svc for svc in options['--hash'].split(',')] if h != '*' else None\\n with errors.handle_connection_errors(self.project.client):\\n for service in self.project.get_services(services):\\n print('{} {}'.format(service.name, service.config_hash))\\n return\\n\\n print(serialize_con\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.create\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.create\\n def create(self, options):\\n \\\"\\\"\\\"\\n Creates containers for a service.\\n This command is deprecated. Use the `up` command with `--no-start` instead.\\n\\n Usage: create [options] [SERVICE...]\\n\\n Options:\\n --force-recreate Recreate containers even if their configuration and\\n image haven't changed. Incompatible with --no-recreate.\\n --no-recreate If containers already exist, don't recreate them.\\n Incompatible with --force-recreate.\\n --no-build Don't build an image, even if it's missing.\\n --build Build images before creating containers.\\n \\\"\\\"\\\"\\n service_names = options['SERVICE']\\n\\n log.warn(\\n 'The create command is deprecated. '\\n 'Use the up command with the --no-start flag instead.'\\n )\\n\\n self.project.create(\\n service_names=service_names,\\n strategy=convergence_strategy_from_opts(options),\\n do_build=build_action_from_opts(options),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.down\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.down\\n def down(self, options):\\n \\\"\\\"\\\"\\n Stops containers and removes containers, networks, volumes, and images\\n created by `up`.\\n\\n By default, the only things removed are:\\n\\n - Containers for services defined in the Compose file\\n - Networks defined in the `networks` section of the Compose file\\n - The default network, if one is used\\n\\n Networks and volumes defined as `external` are never removed.\\n\\n Usage: down [options]\\n\\n Options:\\n --rmi type Remove images. Type must be one of:\\n 'all': Remove all images used by any service.\\n 'local': Remove only images that don't have a\\n custom tag set by the `image` field.\\n -v, --volumes Remove named volumes declared in the `volumes`\\n section of the Compose file and anonymous volumes\\n attached to containers.\\n --remove-orphans Remove containers for services not defined in the\\n Compose file\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n environment = Environment.from_env_file(self.project_dir)\\n ignore_orphans = environment.get_boolean('COMPOSE_IGNORE_ORPHANS')\\n\\n if ignore_orphans and options['--remove-orphans']:\\n raise UserError(\\\"COMPOSE_IGNORE_ORPHANS and --remove-orphans cannot be combined.\\\")\\n\\n image_type = image_type_from_opt('--rmi', options['--rmi'])\\n timeout = timeout_from_opts(options)\\n self.project.down(\\n image_type,\\n options['--volumes'],\\n options['--remove-orphans'],\\n timeout=timeout,\\n ignore_orphans=ignore_orphans)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events\\n def events(self, options):\\n \\\"\\\"\\\"\\n Receive real time events from containers.\\n\\n Usage: events [options] [SERVICE...]\\n\\n Options:\\n --json Output events as a stream of json objects\\n \\\"\\\"\\\"\\n def format_event(event):\\n attributes = [\\\"%s=%s\\\" % item for item in event['attributes'].items()]\\n return (\\\"{time} {type} {action} {id} ({attrs})\\\").format(\\n attrs=\\\", \\\".join(sorted(attributes)),\\n **event)\\n\\n def json_format_event(event):\\n event['time'] = event['time'].isoformat()\\n event.pop('container')\\n return json.dumps(event)\\n\\n for event in self.project.events():\\n formatter = json_format_event if options['--json'] else format_event\\n print(formatter(event))\\n sys.stdout.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events.format_event\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events.format_event\\n def format_event(event):\\n attributes = [\\\"%s=%s\\\" % item for item in event['attributes'].items()]\\n return (\\\"{time} {type} {action} {id} ({attrs})\\\").format(\\n attrs=\\\", \\\".join(sorted(attributes)),\\n **event)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events.json_format_event\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events.json_format_event\\n def json_format_event(event):\\n event['time'] = event['time'].isoformat()\\n event.pop('container')\\n return json.dumps(event)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.exec_command\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.exec_command\\n def exec_command(self, options):\\n \\\"\\\"\\\"\\n Execute a command in a running container\\n\\n Usage: exec [options] [-e KEY=VAL...] SERVICE COMMAND [ARGS...]\\n\\n Options:\\n -d, --detach Detached mode: Run command in the background.\\n --privileged Give extended privileges to the process.\\n -u, --user USER Run the command as this user.\\n -T Disable pseudo-tty allocation. By default `docker-compose exec`\\n allocates a TTY.\\n --index=index index of the container if there are multiple\\n instances of a service [default: 1]\\n -e, --env KEY=VAL Set environment variables (can be used multiple times,\\n not supported in API < 1.25)\\n -w, --workdir DIR Path to workdir directory for this command.\\n \\\"\\\"\\\"\\n environment = Environment.from_env_file(self.project_dir)\\n use_cli = not environment.get_boolean('COMPOSE_INTERACTIVE_NO_CLI')\\n index = int(options.get('--index'))\\n service = self.project.get_service(options['SERVICE'])\\n detach = options.get('--detach')\\n\\n if options['--env'] and docker.utils.version_lt(self.project.client.api_version, '1.25'):\\n raise UserError(\\\"Setting environment for exec is not supported in API < 1.25 (%s)\\\"\\n % self.project.client.api_version)\\n\\n if options['--workdir'] and docker.utils.version_lt(self.project.client.api_version, '1.35'):\\n raise UserError(\\\"Setting workdir for exec is not supported in API < 1.35 (%s)\\\"\\n % self.project.client.api_version)\\n\\n try:\\n container = service.get_container(number=index)\\n except ValueError as e:\\n raise UserError(str(e))\\n command = [options['COMMAND']] + options['ARGS']\\n tty = not options[\\\"-T\\\"]\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.help\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.help\\n @classmethod\\n def help(cls, options):\\n \\\"\\\"\\\"\\n Get help on a command.\\n\\n Usage: help [COMMAND]\\n \\\"\\\"\\\"\\n if options['COMMAND']:\\n subject = get_handler(cls, options['COMMAND'])\\n else:\\n subject = cls\\n\\n print(getdoc(subject))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.images\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.images\\n def images(self, options):\\n \\\"\\\"\\\"\\n List images used by the created containers.\\n Usage: images [options] [SERVICE...]\\n\\n Options:\\n -q, --quiet Only display IDs\\n \\\"\\\"\\\"\\n containers = sorted(\\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\\n key=attrgetter('name'))\\n\\n if options['--quiet']:\\n for image in set(c.image for c in containers):\\n print(image.split(':')[1])\\n return\\n\\n def add_default_tag(img_name):\\n if ':' not in img_name.split('/')[-1]:\\n return '{}:latest'.format(img_name)\\n return img_name\\n\\n headers = [\\n 'Container',\\n 'Repository',\\n 'Tag',\\n 'Image Id',\\n 'Size'\\n ]\\n rows = []\\n for container in containers:\\n image_config = container.image_config\\n service = self.project.get_service(container.service)\\n index = 0\\n img_name = add_default_tag(service.image_name)\\n if img_name in image_config['RepoTags']:\\n index = image_config['RepoTags'].index(img_name)\\n repo_tags = (\\n image_config['RepoTags'][index].rsplit(':', 1) if image_config['RepoTags']\\n else ('<none>', '<none>')\\n )\\n\\n image_id = image_config['Id'].split(':')[1][:12]\\n size = human_readable_file_size(image_config['Size'])\\n rows.append([\\n container.name,\\n repo_tags[0],\\n repo_tags[1],\\n image_id,\\n size\\n ])\\n print(Formatter().table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.images.add_default_tag\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.images.add_default_tag\\n def add_default_tag(img_name):\\n if ':' not in img_name.split('/')[-1]:\\n return '{}:latest'.format(img_name)\\n return img_name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.kill\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.kill\\n def kill(self, options):\\n \\\"\\\"\\\"\\n Force stop service containers.\\n\\n Usage: kill [options] [SERVICE...]\\n\\n Options:\\n -s SIGNAL SIGNAL to send to the container.\\n Default signal is SIGKILL.\\n \\\"\\\"\\\"\\n signal = options.get('-s', 'SIGKILL')\\n\\n self.project.kill(service_names=options['SERVICE'], signal=signal)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.logs\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.logs\\n def logs(self, options):\\n \\\"\\\"\\\"\\n View output from containers.\\n\\n Usage: logs [options] [SERVICE...]\\n\\n Options:\\n --no-color Produce monochrome output.\\n -f, --follow Follow log output.\\n -t, --timestamps Show timestamps.\\n --tail=\\\"all\\\" Number of lines to show from the end of the logs\\n for each container.\\n \\\"\\\"\\\"\\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\\n\\n tail = options['--tail']\\n if tail is not None:\\n if tail.isdigit():\\n tail = int(tail)\\n elif tail != 'all':\\n raise UserError(\\\"tail flag must be all or a number\\\")\\n log_args = {\\n 'follow': options['--follow'],\\n 'tail': tail,\\n 'timestamps': options['--timestamps']\\n }\\n print(\\\"Attaching to\\\", list_containers(containers))\\n log_printer_from_project(\\n self.project,\\n containers,\\n options['--no-color'],\\n log_args,\\n event_stream=self.project.events(service_names=options['SERVICE'])).run()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pause\\n def pause(self, options):\\n \\\"\\\"\\\"\\n Pause services.\\n\\n Usage: pause [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.pause(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to pause', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.port\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.port\\n def port(self, options):\\n \\\"\\\"\\\"\\n Print the public port for a port binding.\\n\\n Usage: port [options] SERVICE PRIVATE_PORT\\n\\n Options:\\n --protocol=proto tcp or udp [default: tcp]\\n --index=index index of the container if there are multiple\\n instances of a service [default: 1]\\n \\\"\\\"\\\"\\n index = int(options.get('--index'))\\n service = self.project.get_service(options['SERVICE'])\\n try:\\n container = service.get_container(number=index)\\n except ValueError as e:\\n raise UserError(str(e))\\n print(container.get_local_port(\\n options['PRIVATE_PORT'],\\n protocol=options.get('--protocol') or 'tcp') or '')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.ps\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.ps\\n def ps(self, options):\\n \\\"\\\"\\\"\\n List containers.\\n\\n Usage: ps [options] [SERVICE...]\\n\\n Options:\\n -q, --quiet Only display IDs\\n --services Display services\\n --filter KEY=VAL Filter services by a property\\n -a, --all Show all stopped containers (including those created by the run command)\\n \\\"\\\"\\\"\\n if options['--quiet'] and options['--services']:\\n raise UserError('--quiet and --services cannot be combined')\\n\\n if options['--services']:\\n filt = build_filter(options.get('--filter'))\\n services = self.project.services\\n if filt:\\n services = filter_services(filt, services, self.project)\\n print('\\\\n'.join(service.name for service in services))\\n return\\n\\n if options['--all']:\\n containers = sorted(self.project.containers(service_names=options['SERVICE'],\\n one_off=OneOffFilter.include, stopped=True))\\n else:\\n containers = sorted(\\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\\n key=attrgetter('name'))\\n\\n if options['--quiet']:\\n for container in containers:\\n print(container.id)\\n else:\\n headers = [\\n 'Name',\\n 'Command',\\n 'State',\\n 'Ports',\\n ]\\n rows = []\\n for container in containers:\\n command = container.human_readable_command\\n if len(command) > 30:\\n command = '%s ...' % command[:26]\\n rows.append([\\n container.name,\\n command,\\n container.human_r\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pull\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pull\\n def pull(self, options):\\n \\\"\\\"\\\"\\n Pulls images for services defined in a Compose file, but does not start the containers.\\n\\n Usage: pull [options] [SERVICE...]\\n\\n Options:\\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\\n --parallel Deprecated, pull multiple images in parallel (enabled by default).\\n --no-parallel Disable parallel pulling.\\n -q, --quiet Pull without printing progress information\\n --include-deps Also pull services declared as dependencies\\n \\\"\\\"\\\"\\n if options.get('--parallel'):\\n log.warn('--parallel option is deprecated and will be removed in future versions.')\\n self.project.pull(\\n service_names=options['SERVICE'],\\n ignore_pull_failures=options.get('--ignore-pull-failures'),\\n parallel_pull=not options.get('--no-parallel'),\\n silent=options.get('--quiet'),\\n include_deps=options.get('--include-deps'),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.push\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.push\\n def push(self, options):\\n \\\"\\\"\\\"\\n Pushes images for services.\\n\\n Usage: push [options] [SERVICE...]\\n\\n Options:\\n --ignore-push-failures Push what it can and ignores images with push failures.\\n \\\"\\\"\\\"\\n self.project.push(\\n service_names=options['SERVICE'],\\n ignore_push_failures=options.get('--ignore-push-failures')\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.rm\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.rm\\n def rm(self, options):\\n \\\"\\\"\\\"\\n Removes stopped service containers.\\n\\n By default, anonymous volumes attached to containers will not be removed. You\\n can override this with `-v`. To list all volumes, use `docker volume ls`.\\n\\n Any data which is not in a volume will be lost.\\n\\n Usage: rm [options] [SERVICE...]\\n\\n Options:\\n -f, --force Don't ask to confirm removal\\n -s, --stop Stop the containers, if required, before removing\\n -v Remove any anonymous volumes attached to containers\\n -a, --all Deprecated - no effect.\\n \\\"\\\"\\\"\\n if options.get('--all'):\\n log.warn(\\n '--all flag is obsolete. This is now the default behavior '\\n 'of `docker-compose rm`'\\n )\\n one_off = OneOffFilter.include\\n\\n if options.get('--stop'):\\n self.project.stop(service_names=options['SERVICE'], one_off=one_off)\\n\\n all_containers = self.project.containers(\\n service_names=options['SERVICE'], stopped=True, one_off=one_off\\n )\\n stopped_containers = [c for c in all_containers if not c.is_running]\\n\\n if len(stopped_containers) > 0:\\n print(\\\"Going to remove\\\", list_containers(stopped_containers))\\n if options.get('--force') \\\\\\n or yesno(\\\"Are you sure? [yN] \\\", default=False):\\n self.project.remove_stopped(\\n service_names=options['SERVICE'],\\n v=options.get('-v', False),\\n one_off=one_off\\n )\\n else:\\n print(\\\"No stopped containers\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.run\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.run\\n def run(self, options):\\n \\\"\\\"\\\"\\n Run a one-off command on a service.\\n\\n For example:\\n\\n $ docker-compose run web python manage.py shell\\n\\n By default, linked services will be started, unless they are already\\n running. If you do not want to start linked services, use\\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\\n\\n Usage:\\n run [options] [-v VOLUME...] [-p PORT...] [-e KEY=VAL...] [-l KEY=VALUE...]\\n SERVICE [COMMAND] [ARGS...]\\n\\n Options:\\n -d, --detach Detached mode: Run container in the background, print\\n new container name.\\n --name NAME Assign a name to the container\\n --entrypoint CMD Override the entrypoint of the image.\\n -e KEY=VAL Set an environment variable (can be used multiple times)\\n -l, --label KEY=VAL Add or override a label (can be used multiple times)\\n -u, --user=\\\"\\\" Run as specified username or uid\\n --no-deps Don't start linked services.\\n --rm Remove container after run. Ignored in detached mode.\\n -p, --publish=[] Publish a container's port(s) to the host\\n --service-ports Run command with the service's ports enabled and mapped\\n to the host.\\n --use-aliases Use the service's network aliases in the network(s) the\\n container connects to.\\n -v, --volume=[] Bind mount a volume (default [])\\n -T Disable pseudo-tty allocation. By default `docker-compose run`\\n allocates a TTY.\\n -w, --workdir=\\\"\\\" Working directory inside the container\\n \\\"\\\"\\\"\\n service = self.project.get_service(options['SERVICE'])\\n detach = o\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.scale\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.scale\\n def scale(self, options):\\n \\\"\\\"\\\"\\n Set number of containers to run for a service.\\n\\n Numbers are specified in the form `service=num` as arguments.\\n For example:\\n\\n $ docker-compose scale web=2 worker=3\\n\\n This command is deprecated. Use the up command with the `--scale` flag\\n instead.\\n\\n Usage: scale [options] [SERVICE=NUM...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = timeout_from_opts(options)\\n\\n if self.project.config_version == V2_2:\\n raise UserError(\\n 'The scale command is incompatible with the v2.2 format. '\\n 'Use the up command with the --scale flag instead.'\\n )\\n else:\\n log.warn(\\n 'The scale command is deprecated. '\\n 'Use the up command with the --scale flag instead.'\\n )\\n\\n for service_name, num in parse_scale_args(options['SERVICE=NUM']).items():\\n self.project.get_service(service_name).scale(num, timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.start\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.start\\n def start(self, options):\\n \\\"\\\"\\\"\\n Start existing containers.\\n\\n Usage: start [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.start(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to start', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.stop\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.stop\\n def stop(self, options):\\n \\\"\\\"\\\"\\n Stop running containers without removing them.\\n\\n They can be started again with `docker-compose start`.\\n\\n Usage: stop [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = timeout_from_opts(options)\\n self.project.stop(service_names=options['SERVICE'], timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.restart\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.restart\\n def restart(self, options):\\n \\\"\\\"\\\"\\n Restart running containers.\\n\\n Usage: restart [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = timeout_from_opts(options)\\n containers = self.project.restart(service_names=options['SERVICE'], timeout=timeout)\\n exit_if(not containers, 'No containers to restart', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.top\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.top\\n def top(self, options):\\n \\\"\\\"\\\"\\n Display the running processes\\n\\n Usage: top [SERVICE...]\\n\\n \\\"\\\"\\\"\\n containers = sorted(\\n self.project.containers(service_names=options['SERVICE'], stopped=False) +\\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\\n key=attrgetter('name')\\n )\\n\\n for idx, container in enumerate(containers):\\n if idx > 0:\\n print()\\n\\n top_data = self.project.client.top(container.name)\\n headers = top_data.get(\\\"Titles\\\")\\n rows = []\\n\\n for process in top_data.get(\\\"Processes\\\", []):\\n rows.append(process)\\n\\n print(container.name)\\n print(Formatter().table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.unpause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.unpause\\n def unpause(self, options):\\n \\\"\\\"\\\"\\n Unpause services.\\n\\n Usage: unpause [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.unpause(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to unpause', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.up\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.up\\n def up(self, options):\\n \\\"\\\"\\\"\\n Builds, (re)creates, starts, and attaches to containers for a service.\\n\\n Unless they are already running, this command also starts any linked services.\\n\\n The `docker-compose up` command aggregates the output of each container. When\\n the command exits, all containers are stopped. Running `docker-compose up -d`\\n starts the containers in the background and leaves them running.\\n\\n If there are existing containers for a service, and the service's configuration\\n or image was changed after the container's creation, `docker-compose up` picks\\n up the changes by stopping and recreating the containers (preserving mounted\\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\\n flag.\\n\\n If you want to force Compose to stop and recreate all containers, use the\\n `--force-recreate` flag.\\n\\n Usage: up [options] [--scale SERVICE=NUM...] [SERVICE...]\\n\\n Options:\\n -d, --detach Detached mode: Run containers in the background,\\n print new container names. Incompatible with\\n --abort-on-container-exit.\\n --no-color Produce monochrome output.\\n --quiet-pull Pull without printing progress information\\n --no-deps Don't start linked services.\\n --force-recreate Recreate containers even if their configuration\\n and image haven't changed.\\n --always-recreate-deps Recreate dependent containers.\\n Incompatible with --no-recreate.\\n --no-recreate If containers already exist, don't recreate\\n them. Incompatible with --force-recreate and -V.\\n --no-build \",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.version\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.version\\n @classmethod\\n def version(cls, options):\\n \\\"\\\"\\\"\\n Show version information\\n\\n Usage: version [--short]\\n\\n Options:\\n --short Shows only Compose's version number.\\n \\\"\\\"\\\"\\n if options['--short']:\\n print(__version__)\\n else:\\n print(get_version_info('full'))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::compute_service_exit_code\",\n \"text\": \"compose/cli/main.py::compute_service_exit_code\\ndef compute_service_exit_code(exit_value_from, attached_containers):\\n candidates = list(filter(\\n lambda c: c.service == exit_value_from,\\n attached_containers))\\n if not candidates:\\n log.error(\\n 'No containers matching the spec \\\"{0}\\\" '\\n 'were run.'.format(exit_value_from)\\n )\\n return 2\\n if len(candidates) > 1:\\n exit_values = filter(\\n lambda e: e != 0,\\n [c.inspect()['State']['ExitCode'] for c in candidates]\\n )\\n\\n return exit_values[0]\\n return candidates[0].inspect()['State']['ExitCode']\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::compute_exit_code\",\n \"text\": \"compose/cli/main.py::compute_exit_code\\ndef compute_exit_code(exit_value_from, attached_containers, cascade_starter, all_containers):\\n exit_code = 0\\n for e in all_containers:\\n if (not e.is_running and cascade_starter == e.name):\\n if not e.exit_code == 0:\\n exit_code = e.exit_code\\n break\\n\\n return exit_code\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ConfigDetails.__new__\",\n \"text\": \"compose/config/config.py::ConfigDetails.__new__\\n def __new__(cls, working_dir, config_files, environment=None):\\n if environment is None:\\n environment = Environment.from_env_file(working_dir)\\n return super(ConfigDetails, cls).__new__(\\n cls, working_dir, config_files, environment\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ConfigFile.from_filename\",\n \"text\": \"compose/config/config.py::ConfigFile.from_filename\\n @classmethod\\n def from_filename(cls, filename):\\n return cls(filename, load_yaml(filename))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ConfigFile.version\",\n \"text\": \"compose/config/config.py::ConfigFile.version\\n @cached_property\\n def version(self):\\n if 'version' not in self.config:\\n return V1\\n\\n version = self.config['version']\\n\\n if isinstance(version, dict):\\n log.warn('Unexpected type for \\\"version\\\" key in \\\"{}\\\". Assuming '\\n '\\\"version\\\" is the name of a service, and defaulting to '\\n 'Compose file version 1.'.format(self.filename))\\n return V1\\n\\n if not isinstance(version, six.string_types):\\n raise ConfigurationError(\\n 'Version in \\\"{}\\\" is invalid - it should be a string.'\\n .format(self.filename))\\n\\n if version == '1':\\n raise ConfigurationError(\\n 'Version in \\\"{}\\\" is invalid. {}'\\n .format(self.filename, VERSION_EXPLANATION)\\n )\\n\\n if version == '2':\\n return const.COMPOSEFILE_V2_0\\n\\n if version == '3':\\n return const.COMPOSEFILE_V3_0\\n\\n return ComposeVersion(version)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ConfigFile.get_service\",\n \"text\": \"compose/config/config.py::ConfigFile.get_service\\n def get_service(self, name):\\n return self.get_service_dicts()[name]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ConfigFile.get_service_dicts\",\n \"text\": \"compose/config/config.py::ConfigFile.get_service_dicts\\n def get_service_dicts(self):\\n return self.config if self.version == V1 else self.config.get('services', {})\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ConfigFile.get_volumes\",\n \"text\": \"compose/config/config.py::ConfigFile.get_volumes\\n def get_volumes(self):\\n return {} if self.version == V1 else self.config.get('volumes', {})\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ConfigFile.get_networks\",\n \"text\": \"compose/config/config.py::ConfigFile.get_networks\\n def get_networks(self):\\n return {} if self.version == V1 else self.config.get('networks', {})\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ConfigFile.get_secrets\",\n \"text\": \"compose/config/config.py::ConfigFile.get_secrets\\n def get_secrets(self):\\n return {} if self.version < const.COMPOSEFILE_V3_1 else self.config.get('secrets', {})\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ConfigFile.get_configs\",\n \"text\": \"compose/config/config.py::ConfigFile.get_configs\\n def get_configs(self):\\n return {} if self.version < const.COMPOSEFILE_V3_3 else self.config.get('configs', {})\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceConfig.with_abs_paths\",\n \"text\": \"compose/config/config.py::ServiceConfig.with_abs_paths\\n @classmethod\\n def with_abs_paths(cls, working_dir, filename, name, config):\\n if not working_dir:\\n raise ValueError(\\\"No working_dir for ServiceConfig.\\\")\\n\\n return cls(\\n os.path.abspath(working_dir),\\n os.path.abspath(filename) if filename else filename,\\n name,\\n config)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::find\",\n \"text\": \"compose/config/config.py::find\\ndef find(base_dir, filenames, environment, override_dir=None):\\n if filenames == ['-']:\\n return ConfigDetails(\\n os.path.abspath(override_dir) if override_dir else os.getcwd(),\\n [ConfigFile(None, yaml.safe_load(sys.stdin))],\\n environment\\n )\\n\\n if filenames:\\n filenames = [os.path.join(base_dir, f) for f in filenames]\\n else:\\n filenames = get_default_config_files(base_dir)\\n\\n log.debug(\\\"Using configuration files: {}\\\".format(\\\",\\\".join(filenames)))\\n return ConfigDetails(\\n override_dir if override_dir else os.path.dirname(filenames[0]),\\n [ConfigFile.from_filename(f) for f in filenames],\\n environment\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::validate_config_version\",\n \"text\": \"compose/config/config.py::validate_config_version\\ndef validate_config_version(config_files):\\n main_file = config_files[0]\\n validate_top_level_object(main_file)\\n for next_file in config_files[1:]:\\n validate_top_level_object(next_file)\\n\\n if main_file.version != next_file.version:\\n raise ConfigurationError(\\n \\\"Version mismatch: file {0} specifies version {1} but \\\"\\n \\\"extension file {2} uses version {3}\\\".format(\\n main_file.filename,\\n main_file.version,\\n next_file.filename,\\n next_file.version))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::get_default_config_files\",\n \"text\": \"compose/config/config.py::get_default_config_files\\ndef get_default_config_files(base_dir):\\n (candidates, path) = find_candidates_in_parent_dirs(SUPPORTED_FILENAMES, base_dir)\\n\\n if not candidates:\\n raise ComposeFileNotFound(SUPPORTED_FILENAMES)\\n\\n winner = candidates[0]\\n\\n if len(candidates) > 1:\\n log.warn(\\\"Found multiple config files with supported names: %s\\\", \\\", \\\".join(candidates))\\n log.warn(\\\"Using %s\\\\n\\\", winner)\\n\\n return [os.path.join(path, winner)] + get_default_override_file(path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::get_default_override_file\",\n \"text\": \"compose/config/config.py::get_default_override_file\\ndef get_default_override_file(path):\\n override_files_in_path = [os.path.join(path, override_filename) for override_filename\\n in DEFAULT_OVERRIDE_FILENAMES\\n if os.path.exists(os.path.join(path, override_filename))]\\n if len(override_files_in_path) > 1:\\n raise DuplicateOverrideFileFound(override_files_in_path)\\n return override_files_in_path\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::find_candidates_in_parent_dirs\",\n \"text\": \"compose/config/config.py::find_candidates_in_parent_dirs\\ndef find_candidates_in_parent_dirs(filenames, path):\\n \\\"\\\"\\\"\\n Given a directory path to start, looks for filenames in the\\n directory, and then each parent directory successively,\\n until found.\\n\\n Returns tuple (candidates, path).\\n \\\"\\\"\\\"\\n candidates = [filename for filename in filenames\\n if os.path.exists(os.path.join(path, filename))]\\n\\n if not candidates:\\n parent_dir = os.path.join(path, '..')\\n if os.path.abspath(parent_dir) != os.path.abspath(path):\\n return find_candidates_in_parent_dirs(filenames, parent_dir)\\n\\n return (candidates, path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::check_swarm_only_config\",\n \"text\": \"compose/config/config.py::check_swarm_only_config\\ndef check_swarm_only_config(service_dicts, compatibility=False):\\n warning_template = (\\n \\\"Some services ({services}) use the '{key}' key, which will be ignored. \\\"\\n \\\"Compose does not support '{key}' configuration - use \\\"\\n \\\"`docker stack deploy` to deploy to a swarm.\\\"\\n )\\n\\n def check_swarm_only_key(service_dicts, key):\\n services = [s for s in service_dicts if s.get(key)]\\n if services:\\n log.warn(\\n warning_template.format(\\n services=\\\", \\\".join(sorted(s['name'] for s in services)),\\n key=key\\n )\\n )\\n if not compatibility:\\n check_swarm_only_key(service_dicts, 'deploy')\\n check_swarm_only_key(service_dicts, 'credential_spec')\\n check_swarm_only_key(service_dicts, 'configs')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::check_swarm_only_config.check_swarm_only_key\",\n \"text\": \"compose/config/config.py::check_swarm_only_config.check_swarm_only_key\\n def check_swarm_only_key(service_dicts, key):\\n services = [s for s in service_dicts if s.get(key)]\\n if services:\\n log.warn(\\n warning_template.format(\\n services=\\\", \\\".join(sorted(s['name'] for s in services)),\\n key=key\\n )\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load\",\n \"text\": \"compose/config/config.py::load\\ndef load(config_details, compatibility=False):\\n \\\"\\\"\\\"Load the configuration from a working directory and a list of\\n configuration files. Files are loaded in order, and merged on top\\n of each other to create the final configuration.\\n\\n Return a fully interpolated, extended and validated configuration.\\n \\\"\\\"\\\"\\n validate_config_version(config_details.config_files)\\n\\n processed_files = [\\n process_config_file(config_file, config_details.environment)\\n for config_file in config_details.config_files\\n ]\\n config_details = config_details._replace(config_files=processed_files)\\n\\n main_file = config_details.config_files[0]\\n volumes = load_mapping(\\n config_details.config_files, 'get_volumes', 'Volume'\\n )\\n networks = load_mapping(\\n config_details.config_files, 'get_networks', 'Network'\\n )\\n secrets = load_mapping(\\n config_details.config_files, 'get_secrets', 'Secret', config_details.working_dir\\n )\\n configs = load_mapping(\\n config_details.config_files, 'get_configs', 'Config', config_details.working_dir\\n )\\n service_dicts = load_services(config_details, main_file, compatibility)\\n\\n if main_file.version != V1:\\n for service_dict in service_dicts:\\n match_named_volumes(service_dict, volumes)\\n\\n check_swarm_only_config(service_dicts, compatibility)\\n\\n version = V2_3 if compatibility and main_file.version >= V3_0 else main_file.version\\n\\n return Config(version, service_dicts, volumes, networks, secrets, configs)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load_mapping\",\n \"text\": \"compose/config/config.py::load_mapping\\ndef load_mapping(config_files, get_func, entity_type, working_dir=None):\\n mapping = {}\\n\\n for config_file in config_files:\\n for name, config in getattr(config_file, get_func)().items():\\n mapping[name] = config or {}\\n if not config:\\n continue\\n\\n external = config.get('external')\\n if external:\\n validate_external(entity_type, name, config, config_file.version)\\n if isinstance(external, dict):\\n config['name'] = external.get('name')\\n elif not config.get('name'):\\n config['name'] = name\\n\\n if 'driver_opts' in config:\\n config['driver_opts'] = build_string_dict(\\n config['driver_opts']\\n )\\n\\n if 'labels' in config:\\n config['labels'] = parse_labels(config['labels'])\\n\\n if 'file' in config:\\n config['file'] = expand_path(working_dir, config['file'])\\n\\n return mapping\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::validate_external\",\n \"text\": \"compose/config/config.py::validate_external\\ndef validate_external(entity_type, name, config, version):\\n if (version < V2_1 or (version >= V3_0 and version < V3_4)) and len(config.keys()) > 1:\\n raise ConfigurationError(\\n \\\"{} {} declared as external but specifies additional attributes \\\"\\n \\\"({}).\\\".format(\\n entity_type, name, ', '.join(k for k in config if k != 'external')))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load_services\",\n \"text\": \"compose/config/config.py::load_services\\ndef load_services(config_details, config_file, compatibility=False):\\n def build_service(service_name, service_dict, service_names):\\n service_config = ServiceConfig.with_abs_paths(\\n config_details.working_dir,\\n config_file.filename,\\n service_name,\\n service_dict)\\n resolver = ServiceExtendsResolver(\\n service_config, config_file, environment=config_details.environment\\n )\\n service_dict = process_service(resolver.run())\\n\\n service_config = service_config._replace(config=service_dict)\\n validate_service(service_config, service_names, config_file)\\n service_dict = finalize_service(\\n service_config,\\n service_names,\\n config_file.version,\\n config_details.environment,\\n compatibility\\n )\\n return service_dict\\n\\n def build_services(service_config):\\n service_names = service_config.keys()\\n return sort_service_dicts([\\n build_service(name, service_dict, service_names)\\n for name, service_dict in service_config.items()\\n ])\\n\\n def merge_services(base, override):\\n all_service_names = set(base) | set(override)\\n return {\\n name: merge_service_dicts_from_files(\\n base.get(name, {}),\\n override.get(name, {}),\\n config_file.version)\\n for name in all_service_names\\n }\\n\\n service_configs = [\\n file.get_service_dicts() for file in config_details.config_files\\n ]\\n\\n service_config = service_configs[0]\\n for next_config in service_configs[1:]:\\n service_config = merge_services(service_config, next_config)\\n\\n return build_services(service_config)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load_services.build_service\",\n \"text\": \"compose/config/config.py::load_services.build_service\\n def build_service(service_name, service_dict, service_names):\\n service_config = ServiceConfig.with_abs_paths(\\n config_details.working_dir,\\n config_file.filename,\\n service_name,\\n service_dict)\\n resolver = ServiceExtendsResolver(\\n service_config, config_file, environment=config_details.environment\\n )\\n service_dict = process_service(resolver.run())\\n\\n service_config = service_config._replace(config=service_dict)\\n validate_service(service_config, service_names, config_file)\\n service_dict = finalize_service(\\n service_config,\\n service_names,\\n config_file.version,\\n config_details.environment,\\n compatibility\\n )\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load_services.build_services\",\n \"text\": \"compose/config/config.py::load_services.build_services\\n def build_services(service_config):\\n service_names = service_config.keys()\\n return sort_service_dicts([\\n build_service(name, service_dict, service_names)\\n for name, service_dict in service_config.items()\\n ])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load_services.merge_services\",\n \"text\": \"compose/config/config.py::load_services.merge_services\\n def merge_services(base, override):\\n all_service_names = set(base) | set(override)\\n return {\\n name: merge_service_dicts_from_files(\\n base.get(name, {}),\\n override.get(name, {}),\\n config_file.version)\\n for name in all_service_names\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::interpolate_config_section\",\n \"text\": \"compose/config/config.py::interpolate_config_section\\ndef interpolate_config_section(config_file, config, section, environment):\\n validate_config_section(config_file.filename, config, section)\\n return interpolate_environment_variables(\\n config_file.version,\\n config,\\n section,\\n environment\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::process_config_file\",\n \"text\": \"compose/config/config.py::process_config_file\\ndef process_config_file(config_file, environment, service_name=None):\\n services = interpolate_config_section(\\n config_file,\\n config_file.get_service_dicts(),\\n 'service',\\n environment)\\n\\n if config_file.version > V1:\\n processed_config = dict(config_file.config)\\n processed_config['services'] = services\\n processed_config['volumes'] = interpolate_config_section(\\n config_file,\\n config_file.get_volumes(),\\n 'volume',\\n environment)\\n processed_config['networks'] = interpolate_config_section(\\n config_file,\\n config_file.get_networks(),\\n 'network',\\n environment)\\n if config_file.version >= const.COMPOSEFILE_V3_1:\\n processed_config['secrets'] = interpolate_config_section(\\n config_file,\\n config_file.get_secrets(),\\n 'secret',\\n environment)\\n if config_file.version >= const.COMPOSEFILE_V3_3:\\n processed_config['configs'] = interpolate_config_section(\\n config_file,\\n config_file.get_configs(),\\n 'config',\\n environment\\n )\\n else:\\n processed_config = services\\n\\n config_file = config_file._replace(config=processed_config)\\n validate_against_config_schema(config_file)\\n\\n if service_name and service_name not in services:\\n raise ConfigurationError(\\n \\\"Cannot extend service '{}' in {}: Service not found\\\".format(\\n service_name, config_file.filename))\\n\\n return config_file\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.__init__\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.__init__\\n def __init__(self, service_config, config_file, environment, already_seen=None):\\n self.service_config = service_config\\n self.working_dir = service_config.working_dir\\n self.already_seen = already_seen or []\\n self.config_file = config_file\\n self.environment = environment\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.signature\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.signature\\n @property\\n def signature(self):\\n return self.service_config.filename, self.service_config.name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.detect_cycle\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.detect_cycle\\n def detect_cycle(self):\\n if self.signature in self.already_seen:\\n raise CircularReference(self.already_seen + [self.signature])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.run\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.run\\n def run(self):\\n self.detect_cycle()\\n\\n if 'extends' in self.service_config.config:\\n service_dict = self.resolve_extends(*self.validate_and_construct_extends())\\n return self.service_config._replace(config=service_dict)\\n\\n return self.service_config\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.validate_and_construct_extends\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.validate_and_construct_extends\\n def validate_and_construct_extends(self):\\n extends = self.service_config.config['extends']\\n if not isinstance(extends, dict):\\n extends = {'service': extends}\\n\\n config_path = self.get_extended_config_path(extends)\\n service_name = extends['service']\\n\\n if config_path == self.config_file.filename:\\n try:\\n service_config = self.config_file.get_service(service_name)\\n except KeyError:\\n raise ConfigurationError(\\n \\\"Cannot extend service '{}' in {}: Service not found\\\".format(\\n service_name, config_path)\\n )\\n else:\\n extends_file = ConfigFile.from_filename(config_path)\\n validate_config_version([self.config_file, extends_file])\\n extended_file = process_config_file(\\n extends_file, self.environment, service_name=service_name\\n )\\n service_config = extended_file.get_service(service_name)\\n\\n return config_path, service_config, service_name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.resolve_extends\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.resolve_extends\\n def resolve_extends(self, extended_config_path, service_dict, service_name):\\n resolver = ServiceExtendsResolver(\\n ServiceConfig.with_abs_paths(\\n os.path.dirname(extended_config_path),\\n extended_config_path,\\n service_name,\\n service_dict),\\n self.config_file,\\n already_seen=self.already_seen + [self.signature],\\n environment=self.environment\\n )\\n\\n service_config = resolver.run()\\n other_service_dict = process_service(service_config)\\n validate_extended_service_dict(\\n other_service_dict,\\n extended_config_path,\\n service_name)\\n\\n return merge_service_dicts(\\n other_service_dict,\\n self.service_config.config,\\n self.config_file.version)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::ServiceExtendsResolver.get_extended_config_path\",\n \"text\": \"compose/config/config.py::ServiceExtendsResolver.get_extended_config_path\\n def get_extended_config_path(self, extends_options):\\n \\\"\\\"\\\"Service we are extending either has a value for 'file' set, which we\\n need to obtain a full path too or we are extending from a service\\n defined in our own file.\\n \\\"\\\"\\\"\\n filename = self.service_config.filename\\n validate_extends_file_path(\\n self.service_config.name,\\n extends_options,\\n filename)\\n if 'file' in extends_options:\\n return expand_path(self.working_dir, extends_options['file'])\\n return filename\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_environment\",\n \"text\": \"compose/config/config.py::resolve_environment\\ndef resolve_environment(service_dict, environment=None):\\n \\\"\\\"\\\"Unpack any environment variables from an env_file, if set.\\n Interpolate environment values if set.\\n \\\"\\\"\\\"\\n env = {}\\n for env_file in service_dict.get('env_file', []):\\n env.update(env_vars_from_file(env_file))\\n\\n env.update(parse_environment(service_dict.get('environment')))\\n return dict(resolve_env_var(k, v, environment) for k, v in six.iteritems(env))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_build_args\",\n \"text\": \"compose/config/config.py::resolve_build_args\\ndef resolve_build_args(buildargs, environment):\\n args = parse_build_arguments(buildargs)\\n return dict(resolve_env_var(k, v, environment) for k, v in six.iteritems(args))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::validate_extended_service_dict\",\n \"text\": \"compose/config/config.py::validate_extended_service_dict\\ndef validate_extended_service_dict(service_dict, filename, service):\\n error_prefix = \\\"Cannot extend service '%s' in %s:\\\" % (service, filename)\\n\\n if 'links' in service_dict:\\n raise ConfigurationError(\\n \\\"%s services with 'links' cannot be extended\\\" % error_prefix)\\n\\n if 'volumes_from' in service_dict:\\n raise ConfigurationError(\\n \\\"%s services with 'volumes_from' cannot be extended\\\" % error_prefix)\\n\\n if 'net' in service_dict:\\n if get_container_name_from_network_mode(service_dict['net']):\\n raise ConfigurationError(\\n \\\"%s services with 'net: container' cannot be extended\\\" % error_prefix)\\n\\n if 'network_mode' in service_dict:\\n if get_service_name_from_network_mode(service_dict['network_mode']):\\n raise ConfigurationError(\\n \\\"%s services with 'network_mode: service' cannot be extended\\\" % error_prefix)\\n\\n if 'depends_on' in service_dict:\\n raise ConfigurationError(\\n \\\"%s services with 'depends_on' cannot be extended\\\" % error_prefix)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::validate_service\",\n \"text\": \"compose/config/config.py::validate_service\\ndef validate_service(service_config, service_names, config_file):\\n service_dict, service_name = service_config.config, service_config.name\\n validate_service_constraints(service_dict, service_name, config_file)\\n validate_paths(service_dict)\\n\\n validate_cpu(service_config)\\n validate_ulimits(service_config)\\n validate_network_mode(service_config, service_names)\\n validate_pid_mode(service_config, service_names)\\n validate_depends_on(service_config, service_names)\\n validate_links(service_config, service_names)\\n validate_healthcheck(service_config)\\n\\n if not service_dict.get('image') and has_uppercase(service_name):\\n raise ConfigurationError(\\n \\\"Service '{name}' contains uppercase characters which are not valid \\\"\\n \\\"as part of an image name. Either use a lowercase service name or \\\"\\n \\\"use the `image` field to set a custom name for the service image.\\\"\\n .format(name=service_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::process_service\",\n \"text\": \"compose/config/config.py::process_service\\ndef process_service(service_config):\\n working_dir = service_config.working_dir\\n service_dict = dict(service_config.config)\\n\\n if 'env_file' in service_dict:\\n service_dict['env_file'] = [\\n expand_path(working_dir, path)\\n for path in to_list(service_dict['env_file'])\\n ]\\n\\n if 'build' in service_dict:\\n process_build_section(service_dict, working_dir)\\n\\n if 'volumes' in service_dict and service_dict.get('volume_driver') is None:\\n service_dict['volumes'] = resolve_volume_paths(working_dir, service_dict)\\n\\n if 'sysctls' in service_dict:\\n service_dict['sysctls'] = build_string_dict(parse_sysctls(service_dict['sysctls']))\\n\\n if 'labels' in service_dict:\\n service_dict['labels'] = parse_labels(service_dict['labels'])\\n\\n service_dict = process_depends_on(service_dict)\\n\\n for field in ['dns', 'dns_search', 'tmpfs']:\\n if field in service_dict:\\n service_dict[field] = to_list(service_dict[field])\\n\\n service_dict = process_security_opt(process_blkio_config(process_ports(\\n process_healthcheck(service_dict)\\n )))\\n\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::process_build_section\",\n \"text\": \"compose/config/config.py::process_build_section\\ndef process_build_section(service_dict, working_dir):\\n if isinstance(service_dict['build'], six.string_types):\\n service_dict['build'] = resolve_build_path(working_dir, service_dict['build'])\\n elif isinstance(service_dict['build'], dict):\\n if 'context' in service_dict['build']:\\n path = service_dict['build']['context']\\n service_dict['build']['context'] = resolve_build_path(working_dir, path)\\n if 'labels' in service_dict['build']:\\n service_dict['build']['labels'] = parse_labels(service_dict['build']['labels'])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::process_ports\",\n \"text\": \"compose/config/config.py::process_ports\\ndef process_ports(service_dict):\\n if 'ports' not in service_dict:\\n return service_dict\\n\\n ports = []\\n for port_definition in service_dict['ports']:\\n if isinstance(port_definition, ServicePort):\\n ports.append(port_definition)\\n else:\\n ports.extend(ServicePort.parse(port_definition))\\n service_dict['ports'] = ports\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::process_depends_on\",\n \"text\": \"compose/config/config.py::process_depends_on\\ndef process_depends_on(service_dict):\\n if 'depends_on' in service_dict and not isinstance(service_dict['depends_on'], dict):\\n service_dict['depends_on'] = dict([\\n (svc, {'condition': 'service_started'}) for svc in service_dict['depends_on']\\n ])\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::process_blkio_config\",\n \"text\": \"compose/config/config.py::process_blkio_config\\ndef process_blkio_config(service_dict):\\n if not service_dict.get('blkio_config'):\\n return service_dict\\n\\n for field in ['device_read_bps', 'device_write_bps']:\\n if field in service_dict['blkio_config']:\\n for v in service_dict['blkio_config'].get(field, []):\\n rate = v.get('rate', 0)\\n v['rate'] = parse_bytes(rate)\\n if v['rate'] is None:\\n raise ConfigurationError('Invalid format for bytes value: \\\"{}\\\"'.format(rate))\\n\\n for field in ['device_read_iops', 'device_write_iops']:\\n if field in service_dict['blkio_config']:\\n for v in service_dict['blkio_config'].get(field, []):\\n try:\\n v['rate'] = int(v.get('rate', 0))\\n except ValueError:\\n raise ConfigurationError(\\n 'Invalid IOPS value: \\\"{}\\\". Must be a positive integer.'.format(v.get('rate'))\\n )\\n\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::process_healthcheck\",\n \"text\": \"compose/config/config.py::process_healthcheck\\ndef process_healthcheck(service_dict):\\n if 'healthcheck' not in service_dict:\\n return service_dict\\n\\n hc = service_dict['healthcheck']\\n\\n if 'disable' in hc:\\n del hc['disable']\\n hc['test'] = ['NONE']\\n\\n for field in ['interval', 'timeout', 'start_period']:\\n if field not in hc or isinstance(hc[field], six.integer_types):\\n continue\\n hc[field] = parse_nanoseconds_int(hc[field])\\n\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::finalize_service_volumes\",\n \"text\": \"compose/config/config.py::finalize_service_volumes\\ndef finalize_service_volumes(service_dict, environment):\\n if 'volumes' in service_dict:\\n finalized_volumes = []\\n normalize = environment.get_boolean('COMPOSE_CONVERT_WINDOWS_PATHS')\\n win_host = environment.get_boolean('COMPOSE_FORCE_WINDOWS_HOST')\\n for v in service_dict['volumes']:\\n if isinstance(v, dict):\\n finalized_volumes.append(MountSpec.parse(v, normalize, win_host))\\n else:\\n finalized_volumes.append(VolumeSpec.parse(v, normalize, win_host))\\n service_dict['volumes'] = finalized_volumes\\n\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::finalize_service\",\n \"text\": \"compose/config/config.py::finalize_service\\ndef finalize_service(service_config, service_names, version, environment, compatibility):\\n service_dict = dict(service_config.config)\\n\\n if 'environment' in service_dict or 'env_file' in service_dict:\\n service_dict['environment'] = resolve_environment(service_dict, environment)\\n service_dict.pop('env_file', None)\\n\\n if 'volumes_from' in service_dict:\\n service_dict['volumes_from'] = [\\n VolumeFromSpec.parse(vf, service_names, version)\\n for vf in service_dict['volumes_from']\\n ]\\n\\n service_dict = finalize_service_volumes(service_dict, environment)\\n\\n if 'net' in service_dict:\\n network_mode = service_dict.pop('net')\\n container_name = get_container_name_from_network_mode(network_mode)\\n if container_name and container_name in service_names:\\n service_dict['network_mode'] = 'service:{}'.format(container_name)\\n else:\\n service_dict['network_mode'] = network_mode\\n\\n if 'networks' in service_dict:\\n service_dict['networks'] = parse_networks(service_dict['networks'])\\n\\n if 'restart' in service_dict:\\n service_dict['restart'] = parse_restart_spec(service_dict['restart'])\\n\\n if 'secrets' in service_dict:\\n service_dict['secrets'] = [\\n types.ServiceSecret.parse(s) for s in service_dict['secrets']\\n ]\\n\\n if 'configs' in service_dict:\\n service_dict['configs'] = [\\n types.ServiceConfig.parse(c) for c in service_dict['configs']\\n ]\\n\\n normalize_build(service_dict, service_config.working_dir, environment)\\n\\n if compatibility:\\n service_dict, ignored_keys = translate_deploy_keys_to_container_config(\\n service_dict\\n )\\n if ignored_keys:\\n log.warn(\\n 'The following deploy sub-keys are not supported in compatibility mode and have'\\n ' been ignored: {}'.format(', '.join(ignored_keys))\\n )\\n\\n se\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::translate_resource_keys_to_container_config\",\n \"text\": \"compose/config/config.py::translate_resource_keys_to_container_config\\ndef translate_resource_keys_to_container_config(resources_dict, service_dict):\\n if 'limits' in resources_dict:\\n service_dict['mem_limit'] = resources_dict['limits'].get('memory')\\n if 'cpus' in resources_dict['limits']:\\n service_dict['cpus'] = float(resources_dict['limits']['cpus'])\\n if 'reservations' in resources_dict:\\n service_dict['mem_reservation'] = resources_dict['reservations'].get('memory')\\n if 'cpus' in resources_dict['reservations']:\\n return ['resources.reservations.cpus']\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::convert_restart_policy\",\n \"text\": \"compose/config/config.py::convert_restart_policy\\ndef convert_restart_policy(name):\\n try:\\n return {\\n 'any': 'always',\\n 'none': 'no',\\n 'on-failure': 'on-failure'\\n }[name]\\n except KeyError:\\n raise ConfigurationError('Invalid restart policy \\\"{}\\\"'.format(name))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::translate_deploy_keys_to_container_config\",\n \"text\": \"compose/config/config.py::translate_deploy_keys_to_container_config\\ndef translate_deploy_keys_to_container_config(service_dict):\\n if 'credential_spec' in service_dict:\\n del service_dict['credential_spec']\\n if 'configs' in service_dict:\\n del service_dict['configs']\\n\\n if 'deploy' not in service_dict:\\n return service_dict, []\\n\\n deploy_dict = service_dict['deploy']\\n ignored_keys = [\\n k for k in ['endpoint_mode', 'labels', 'update_config', 'rollback_config', 'placement']\\n if k in deploy_dict\\n ]\\n\\n if 'replicas' in deploy_dict and deploy_dict.get('mode', 'replicated') == 'replicated':\\n service_dict['scale'] = deploy_dict['replicas']\\n\\n if 'restart_policy' in deploy_dict:\\n service_dict['restart'] = {\\n 'Name': convert_restart_policy(deploy_dict['restart_policy'].get('condition', 'any')),\\n 'MaximumRetryCount': deploy_dict['restart_policy'].get('max_attempts', 0)\\n }\\n for k in deploy_dict['restart_policy'].keys():\\n if k != 'condition' and k != 'max_attempts':\\n ignored_keys.append('restart_policy.{}'.format(k))\\n\\n ignored_keys.extend(\\n translate_resource_keys_to_container_config(\\n deploy_dict.get('resources', {}), service_dict\\n )\\n )\\n\\n del service_dict['deploy']\\n\\n return service_dict, ignored_keys\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::normalize_v1_service_format\",\n \"text\": \"compose/config/config.py::normalize_v1_service_format\\ndef normalize_v1_service_format(service_dict):\\n if 'log_driver' in service_dict or 'log_opt' in service_dict:\\n if 'logging' not in service_dict:\\n service_dict['logging'] = {}\\n if 'log_driver' in service_dict:\\n service_dict['logging']['driver'] = service_dict['log_driver']\\n del service_dict['log_driver']\\n if 'log_opt' in service_dict:\\n service_dict['logging']['options'] = service_dict['log_opt']\\n del service_dict['log_opt']\\n\\n if 'dockerfile' in service_dict:\\n service_dict['build'] = service_dict.get('build', {})\\n service_dict['build'].update({\\n 'dockerfile': service_dict.pop('dockerfile')\\n })\\n\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_service_dicts_from_files\",\n \"text\": \"compose/config/config.py::merge_service_dicts_from_files\\ndef merge_service_dicts_from_files(base, override, version):\\n \\\"\\\"\\\"When merging services from multiple files we need to merge the `extends`\\n field. This is not handled by `merge_service_dicts()` which is used to\\n perform the `extends`.\\n \\\"\\\"\\\"\\n new_service = merge_service_dicts(base, override, version)\\n if 'extends' in override:\\n new_service['extends'] = override['extends']\\n elif 'extends' in base:\\n new_service['extends'] = base['extends']\\n return new_service\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::MergeDict.__init__\",\n \"text\": \"compose/config/config.py::MergeDict.__init__\\n def __init__(self, base, override):\\n self.base = base\\n self.override = override\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::MergeDict.needs_merge\",\n \"text\": \"compose/config/config.py::MergeDict.needs_merge\\n def needs_merge(self, field):\\n return field in self.base or field in self.override\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::MergeDict.merge_field\",\n \"text\": \"compose/config/config.py::MergeDict.merge_field\\n def merge_field(self, field, merge_func, default=None):\\n if not self.needs_merge(field):\\n return\\n\\n self[field] = merge_func(\\n self.base.get(field, default),\\n self.override.get(field, default))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::MergeDict.merge_mapping\",\n \"text\": \"compose/config/config.py::MergeDict.merge_mapping\\n def merge_mapping(self, field, parse_func=None):\\n if not self.needs_merge(field):\\n return\\n\\n if parse_func is None:\\n def parse_func(m):\\n return m or {}\\n\\n self[field] = parse_func(self.base.get(field))\\n self[field].update(parse_func(self.override.get(field)))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::MergeDict.merge_sequence\",\n \"text\": \"compose/config/config.py::MergeDict.merge_sequence\\n def merge_sequence(self, field, parse_func):\\n def parse_sequence_func(seq):\\n return to_mapping((parse_func(item) for item in seq), 'merge_field')\\n\\n if not self.needs_merge(field):\\n return\\n\\n merged = parse_sequence_func(self.base.get(field, []))\\n merged.update(parse_sequence_func(self.override.get(field, [])))\\n self[field] = [item.repr() for item in sorted(merged.values())]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::MergeDict.merge_sequence.parse_sequence_func\",\n \"text\": \"compose/config/config.py::MergeDict.merge_sequence.parse_sequence_func\\n def parse_sequence_func(seq):\\n return to_mapping((parse_func(item) for item in seq), 'merge_field')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::MergeDict.merge_scalar\",\n \"text\": \"compose/config/config.py::MergeDict.merge_scalar\\n def merge_scalar(self, field):\\n if self.needs_merge(field):\\n self[field] = self.override.get(field, self.base.get(field))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_service_dicts\",\n \"text\": \"compose/config/config.py::merge_service_dicts\\ndef merge_service_dicts(base, override, version):\\n md = MergeDict(base, override)\\n\\n md.merge_mapping('environment', parse_environment)\\n md.merge_mapping('labels', parse_labels)\\n md.merge_mapping('ulimits', parse_flat_dict)\\n md.merge_mapping('networks', parse_networks)\\n md.merge_mapping('sysctls', parse_sysctls)\\n md.merge_mapping('depends_on', parse_depends_on)\\n md.merge_mapping('storage_opt', parse_flat_dict)\\n md.merge_sequence('links', ServiceLink.parse)\\n md.merge_sequence('secrets', types.ServiceSecret.parse)\\n md.merge_sequence('configs', types.ServiceConfig.parse)\\n md.merge_sequence('security_opt', types.SecurityOpt.parse)\\n md.merge_mapping('extra_hosts', parse_extra_hosts)\\n\\n for field in ['volumes', 'devices']:\\n md.merge_field(field, merge_path_mappings)\\n\\n for field in [\\n 'cap_add', 'cap_drop', 'expose', 'external_links',\\n 'volumes_from', 'device_cgroup_rules',\\n ]:\\n md.merge_field(field, merge_unique_items_lists, default=[])\\n\\n for field in ['dns', 'dns_search', 'env_file', 'tmpfs']:\\n md.merge_field(field, merge_list_or_string)\\n\\n md.merge_field('logging', merge_logging, default={})\\n merge_ports(md, base, override)\\n md.merge_field('blkio_config', merge_blkio_config, default={})\\n md.merge_field('healthcheck', merge_healthchecks, default={})\\n md.merge_field('deploy', merge_deploy, default={})\\n\\n for field in set(ALLOWED_KEYS) - set(md):\\n md.merge_scalar(field)\\n\\n if version == V1:\\n legacy_v1_merge_image_or_build(md, base, override)\\n elif md.needs_merge('build'):\\n md['build'] = merge_build(md, base, override)\\n\\n return dict(md)\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/config/config.py::merge_unique_items_lists\",\n \"text\": \"compose/config/config.py::merge_unique_items_lists\\ndef merge_unique_items_lists(base, override):\\n override = [str(o) for o in override]\\n base = [str(b) for b in base]\\n return sorted(set().union(base, override))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_healthchecks\",\n \"text\": \"compose/config/config.py::merge_healthchecks\\ndef merge_healthchecks(base, override):\\n if override.get('disabled') is True:\\n return override\\n result = base.copy()\\n result.update(override)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_ports\",\n \"text\": \"compose/config/config.py::merge_ports\\ndef merge_ports(md, base, override):\\n def parse_sequence_func(seq):\\n acc = []\\n for item in seq:\\n acc.extend(ServicePort.parse(item))\\n return to_mapping(acc, 'merge_field')\\n\\n field = 'ports'\\n\\n if not md.needs_merge(field):\\n return\\n\\n merged = parse_sequence_func(md.base.get(field, []))\\n merged.update(parse_sequence_func(md.override.get(field, [])))\\n md[field] = [item for item in sorted(merged.values(), key=lambda x: x.target)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_ports.parse_sequence_func\",\n \"text\": \"compose/config/config.py::merge_ports.parse_sequence_func\\n def parse_sequence_func(seq):\\n acc = []\\n for item in seq:\\n acc.extend(ServicePort.parse(item))\\n return to_mapping(acc, 'merge_field')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_build\",\n \"text\": \"compose/config/config.py::merge_build\\ndef merge_build(output, base, override):\\n def to_dict(service):\\n build_config = service.get('build', {})\\n if isinstance(build_config, six.string_types):\\n return {'context': build_config}\\n return build_config\\n\\n md = MergeDict(to_dict(base), to_dict(override))\\n md.merge_scalar('context')\\n md.merge_scalar('dockerfile')\\n md.merge_scalar('network')\\n md.merge_scalar('target')\\n md.merge_scalar('shm_size')\\n md.merge_scalar('isolation')\\n md.merge_mapping('args', parse_build_arguments)\\n md.merge_field('cache_from', merge_unique_items_lists, default=[])\\n md.merge_mapping('labels', parse_labels)\\n md.merge_mapping('extra_hosts', parse_extra_hosts)\\n return dict(md)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_build.to_dict\",\n \"text\": \"compose/config/config.py::merge_build.to_dict\\n def to_dict(service):\\n build_config = service.get('build', {})\\n if isinstance(build_config, six.string_types):\\n return {'context': build_config}\\n return build_config\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_deploy\",\n \"text\": \"compose/config/config.py::merge_deploy\\ndef merge_deploy(base, override):\\n md = MergeDict(base or {}, override or {})\\n md.merge_scalar('mode')\\n md.merge_scalar('endpoint_mode')\\n md.merge_scalar('replicas')\\n md.merge_mapping('labels', parse_labels)\\n md.merge_mapping('update_config')\\n md.merge_mapping('rollback_config')\\n md.merge_mapping('restart_policy')\\n if md.needs_merge('resources'):\\n resources_md = MergeDict(md.base.get('resources') or {}, md.override.get('resources') or {})\\n resources_md.merge_mapping('limits')\\n resources_md.merge_field('reservations', merge_reservations, default={})\\n md['resources'] = dict(resources_md)\\n if md.needs_merge('placement'):\\n placement_md = MergeDict(md.base.get('placement') or {}, md.override.get('placement') or {})\\n placement_md.merge_field('constraints', merge_unique_items_lists, default=[])\\n placement_md.merge_field('preferences', merge_unique_objects_lists, default=[])\\n md['placement'] = dict(placement_md)\\n\\n return dict(md)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_reservations\",\n \"text\": \"compose/config/config.py::merge_reservations\\ndef merge_reservations(base, override):\\n md = MergeDict(base, override)\\n md.merge_scalar('cpus')\\n md.merge_scalar('memory')\\n md.merge_sequence('generic_resources', types.GenericResource.parse)\\n return dict(md)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_unique_objects_lists\",\n \"text\": \"compose/config/config.py::merge_unique_objects_lists\\ndef merge_unique_objects_lists(base, override):\\n result = dict((json_hash(i), i) for i in base + override)\\n return [i[1] for i in sorted([(k, v) for k, v in result.items()], key=lambda x: x[0])]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_blkio_config\",\n \"text\": \"compose/config/config.py::merge_blkio_config\\ndef merge_blkio_config(base, override):\\n md = MergeDict(base, override)\\n md.merge_scalar('weight')\\n\\n def merge_blkio_limits(base, override):\\n index = dict((b['path'], b) for b in base)\\n for o in override:\\n index[o['path']] = o\\n\\n return sorted(list(index.values()), key=lambda x: x['path'])\\n\\n for field in [\\n \\\"device_read_bps\\\", \\\"device_read_iops\\\", \\\"device_write_bps\\\",\\n \\\"device_write_iops\\\", \\\"weight_device\\\",\\n ]:\\n md.merge_field(field, merge_blkio_limits, default=[])\\n\\n return dict(md)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_blkio_config.merge_blkio_limits\",\n \"text\": \"compose/config/config.py::merge_blkio_config.merge_blkio_limits\\n def merge_blkio_limits(base, override):\\n index = dict((b['path'], b) for b in base)\\n for o in override:\\n index[o['path']] = o\\n\\n return sorted(list(index.values()), key=lambda x: x['path'])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_logging\",\n \"text\": \"compose/config/config.py::merge_logging\\ndef merge_logging(base, override):\\n md = MergeDict(base, override)\\n md.merge_scalar('driver')\\n if md.get('driver') == base.get('driver') or base.get('driver') is None:\\n md.merge_mapping('options', lambda m: m or {})\\n elif override.get('options'):\\n md['options'] = override.get('options', {})\\n return dict(md)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::legacy_v1_merge_image_or_build\",\n \"text\": \"compose/config/config.py::legacy_v1_merge_image_or_build\\ndef legacy_v1_merge_image_or_build(output, base, override):\\n output.pop('image', None)\\n output.pop('build', None)\\n if 'image' in override:\\n output['image'] = override['image']\\n elif 'build' in override:\\n output['build'] = override['build']\\n elif 'image' in base:\\n output['image'] = base['image']\\n elif 'build' in base:\\n output['build'] = base['build']\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_environment\",\n \"text\": \"compose/config/config.py::merge_environment\\ndef merge_environment(base, override):\\n env = parse_environment(base)\\n env.update(parse_environment(override))\\n return env\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_labels\",\n \"text\": \"compose/config/config.py::merge_labels\\ndef merge_labels(base, override):\\n labels = parse_labels(base)\\n labels.update(parse_labels(override))\\n return labels\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::split_kv\",\n \"text\": \"compose/config/config.py::split_kv\\ndef split_kv(kvpair):\\n if '=' in kvpair:\\n return kvpair.split('=', 1)\\n else:\\n return kvpair, ''\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::parse_dict_or_list\",\n \"text\": \"compose/config/config.py::parse_dict_or_list\\ndef parse_dict_or_list(split_func, type_name, arguments):\\n if not arguments:\\n return {}\\n\\n if isinstance(arguments, list):\\n return dict(split_func(e) for e in arguments)\\n\\n if isinstance(arguments, dict):\\n return dict(arguments)\\n\\n raise ConfigurationError(\\n \\\"%s \\\\\\\"%s\\\\\\\" must be a list or mapping,\\\" %\\n (type_name, arguments)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::parse_flat_dict\",\n \"text\": \"compose/config/config.py::parse_flat_dict\\ndef parse_flat_dict(d):\\n if not d:\\n return {}\\n\\n if isinstance(d, dict):\\n return dict(d)\\n\\n raise ConfigurationError(\\\"Invalid type: expected mapping\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_env_var\",\n \"text\": \"compose/config/config.py::resolve_env_var\\ndef resolve_env_var(key, val, environment):\\n if val is not None:\\n return key, val\\n elif environment and key in environment:\\n return key, environment[key]\\n else:\\n return key, None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_volume_paths\",\n \"text\": \"compose/config/config.py::resolve_volume_paths\\ndef resolve_volume_paths(working_dir, service_dict):\\n return [\\n resolve_volume_path(working_dir, volume)\\n for volume in service_dict['volumes']\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_volume_path\",\n \"text\": \"compose/config/config.py::resolve_volume_path\\ndef resolve_volume_path(working_dir, volume):\\n if isinstance(volume, dict):\\n if volume.get('source', '').startswith(('.', '~')) and volume['type'] == 'bind':\\n volume['source'] = expand_path(working_dir, volume['source'])\\n return volume\\n\\n mount_params = None\\n container_path, mount_params = split_path_mapping(volume)\\n\\n if mount_params is not None:\\n host_path, mode = mount_params\\n if host_path is None:\\n return container_path\\n if host_path.startswith('.'):\\n host_path = expand_path(working_dir, host_path)\\n host_path = os.path.expanduser(host_path)\\n return u\\\"{}:{}{}\\\".format(host_path, container_path, (':' + mode if mode else ''))\\n\\n return container_path\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::normalize_build\",\n \"text\": \"compose/config/config.py::normalize_build\\ndef normalize_build(service_dict, working_dir, environment):\\n\\n if 'build' in service_dict:\\n build = {}\\n # Shortcut where specifying a string is treated as the build context\\n if isinstance(service_dict['build'], six.string_types):\\n build['context'] = service_dict.pop('build')\\n else:\\n build.update(service_dict['build'])\\n if 'args' in build:\\n build['args'] = build_string_dict(\\n resolve_build_args(build.get('args'), environment)\\n )\\n\\n service_dict['build'] = build\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::resolve_build_path\",\n \"text\": \"compose/config/config.py::resolve_build_path\\ndef resolve_build_path(working_dir, build_path):\\n if is_url(build_path):\\n return build_path\\n return expand_path(working_dir, build_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::is_url\",\n \"text\": \"compose/config/config.py::is_url\\ndef is_url(build_path):\\n return build_path.startswith(DOCKER_VALID_URL_PREFIXES)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::validate_paths\",\n \"text\": \"compose/config/config.py::validate_paths\\ndef validate_paths(service_dict):\\n if 'build' in service_dict:\\n build = service_dict.get('build', {})\\n\\n if isinstance(build, six.string_types):\\n build_path = build\\n elif isinstance(build, dict) and 'context' in build:\\n build_path = build['context']\\n else:\\n # We have a build section but no context, so nothing to validate\\n return\\n\\n if (\\n not is_url(build_path) and\\n (not os.path.exists(build_path) or not os.access(build_path, os.R_OK))\\n ):\\n raise ConfigurationError(\\n \\\"build path %s either does not exist, is not accessible, \\\"\\n \\\"or is not a valid URL.\\\" % build_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_path_mappings\",\n \"text\": \"compose/config/config.py::merge_path_mappings\\ndef merge_path_mappings(base, override):\\n d = dict_from_path_mappings(base)\\n d.update(dict_from_path_mappings(override))\\n return path_mappings_from_dict(d)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::dict_from_path_mappings\",\n \"text\": \"compose/config/config.py::dict_from_path_mappings\\ndef dict_from_path_mappings(path_mappings):\\n if path_mappings:\\n return dict(split_path_mapping(v) for v in path_mappings)\\n else:\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::path_mappings_from_dict\",\n \"text\": \"compose/config/config.py::path_mappings_from_dict\\ndef path_mappings_from_dict(d):\\n return [join_path_mapping(v) for v in sorted(d.items())]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::split_path_mapping\",\n \"text\": \"compose/config/config.py::split_path_mapping\\ndef split_path_mapping(volume_path):\\n \\\"\\\"\\\"\\n Ascertain if the volume_path contains a host path as well as a container\\n path. Using splitdrive so windows absolute paths won't cause issues with\\n splitting on ':'.\\n \\\"\\\"\\\"\\n if isinstance(volume_path, dict):\\n return (volume_path.get('target'), volume_path)\\n drive, volume_config = splitdrive(volume_path)\\n\\n if ':' in volume_config:\\n (host, container) = volume_config.split(':', 1)\\n container_drive, container_path = splitdrive(container)\\n mode = None\\n if ':' in container_path:\\n container_path, mode = container_path.rsplit(':', 1)\\n\\n return (container_drive + container_path, (drive + host, mode))\\n else:\\n return (volume_path, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::process_security_opt\",\n \"text\": \"compose/config/config.py::process_security_opt\\ndef process_security_opt(service_dict):\\n security_opts = service_dict.get('security_opt', [])\\n result = []\\n for value in security_opts:\\n result.append(SecurityOpt.parse(value))\\n if result:\\n service_dict['security_opt'] = result\\n return service_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::join_path_mapping\",\n \"text\": \"compose/config/config.py::join_path_mapping\\ndef join_path_mapping(pair):\\n (container, host) = pair\\n if isinstance(host, dict):\\n return host\\n elif host is None:\\n return container\\n else:\\n host, mode = host\\n result = \\\":\\\".join((host, container))\\n if mode:\\n result += \\\":\\\" + mode\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::expand_path\",\n \"text\": \"compose/config/config.py::expand_path\\ndef expand_path(working_dir, path):\\n return os.path.abspath(os.path.join(working_dir, os.path.expanduser(path)))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::merge_list_or_string\",\n \"text\": \"compose/config/config.py::merge_list_or_string\\ndef merge_list_or_string(base, override):\\n return to_list(base) + to_list(override)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::to_list\",\n \"text\": \"compose/config/config.py::to_list\\ndef to_list(value):\\n if value is None:\\n return []\\n elif isinstance(value, six.string_types):\\n return [value]\\n else:\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::to_mapping\",\n \"text\": \"compose/config/config.py::to_mapping\\ndef to_mapping(sequence, key_field):\\n return {getattr(item, key_field): item for item in sequence}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::has_uppercase\",\n \"text\": \"compose/config/config.py::has_uppercase\\ndef has_uppercase(name):\\n return any(char in string.ascii_uppercase for char in name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/config.py::load_yaml\",\n \"text\": \"compose/config/config.py::load_yaml\\ndef load_yaml(filename, encoding=None, binary=True):\\n try:\\n with io.open(filename, 'rb' if binary else 'r', encoding=encoding) as fh:\\n return yaml.safe_load(fh)\\n except (IOError, yaml.YAMLError, UnicodeDecodeError) as e:\\n if encoding is None:\\n # Sometimes the user's locale sets an encoding that doesn't match\\n # the YAML files. Im such cases, retry once with the \\\"default\\\"\\n # UTF-8 encoding\\n return load_yaml(filename, encoding='utf-8-sig', binary=False)\\n error_name = getattr(e, '__module__', '') + '.' + e.__class__.__name__\\n raise ConfigurationError(u\\\"{}: {}\\\".format(error_name, e))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::format_ports\",\n \"text\": \"compose/config/validation.py::format_ports\\n@FormatChecker.cls_checks(format=\\\"ports\\\", raises=ValidationError)\\ndef format_ports(instance):\\n try:\\n split_port(instance)\\n except ValueError as e:\\n raise ValidationError(six.text_type(e))\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::format_expose\",\n \"text\": \"compose/config/validation.py::format_expose\\n@FormatChecker.cls_checks(format=\\\"expose\\\", raises=ValidationError)\\ndef format_expose(instance):\\n if isinstance(instance, six.string_types):\\n if not re.match(VALID_EXPOSE_FORMAT, instance):\\n raise ValidationError(\\n \\\"should be of the format 'PORT[/PROTOCOL]'\\\")\\n\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::format_subnet_ip_address\",\n \"text\": \"compose/config/validation.py::format_subnet_ip_address\\n@FormatChecker.cls_checks(\\\"subnet_ip_address\\\", raises=ValidationError)\\ndef format_subnet_ip_address(instance):\\n if isinstance(instance, six.string_types):\\n if not re.match(VALID_REGEX_IPV4_CIDR, instance) and \\\\\\n not re.match(VALID_REGEX_IPV6_CIDR, instance):\\n raise ValidationError(\\\"should use the CIDR format\\\")\\n\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::match_named_volumes\",\n \"text\": \"compose/config/validation.py::match_named_volumes\\ndef match_named_volumes(service_dict, project_volumes):\\n service_volumes = service_dict.get('volumes', [])\\n for volume_spec in service_volumes:\\n if volume_spec.is_named_volume and volume_spec.external not in project_volumes:\\n raise ConfigurationError(\\n 'Named volume \\\"{0}\\\" is used in service \\\"{1}\\\" but no'\\n ' declaration was found in the volumes section.'.format(\\n volume_spec.repr(), service_dict.get('name')\\n )\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::python_type_to_yaml_type\",\n \"text\": \"compose/config/validation.py::python_type_to_yaml_type\\ndef python_type_to_yaml_type(type_):\\n type_name = type(type_).__name__\\n return {\\n 'dict': 'mapping',\\n 'list': 'array',\\n 'int': 'number',\\n 'float': 'number',\\n 'bool': 'boolean',\\n 'unicode': 'string',\\n 'str': 'string',\\n 'bytes': 'string',\\n }.get(type_name, type_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_config_section\",\n \"text\": \"compose/config/validation.py::validate_config_section\\ndef validate_config_section(filename, config, section):\\n \\\"\\\"\\\"Validate the structure of a configuration section. This must be done\\n before interpolation so it's separate from schema validation.\\n \\\"\\\"\\\"\\n if not isinstance(config, dict):\\n raise ConfigurationError(\\n \\\"In file '{filename}', {section} must be a mapping, not \\\"\\n \\\"{type}.\\\".format(\\n filename=filename,\\n section=section,\\n type=anglicize_json_type(python_type_to_yaml_type(config))))\\n\\n for key, value in config.items():\\n if not isinstance(key, six.string_types):\\n raise ConfigurationError(\\n \\\"In file '{filename}', the {section} name {name} must be a \\\"\\n \\\"quoted string, i.e. '{name}'.\\\".format(\\n filename=filename,\\n section=section,\\n name=key))\\n\\n if not isinstance(value, (dict, type(None))):\\n raise ConfigurationError(\\n \\\"In file '{filename}', {section} '{name}' must be a mapping not \\\"\\n \\\"{type}.\\\".format(\\n filename=filename,\\n section=section,\\n name=key,\\n type=anglicize_json_type(python_type_to_yaml_type(value))))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_top_level_object\",\n \"text\": \"compose/config/validation.py::validate_top_level_object\\ndef validate_top_level_object(config_file):\\n if not isinstance(config_file.config, dict):\\n raise ConfigurationError(\\n \\\"Top level object in '{}' needs to be an object not '{}'.\\\".format(\\n config_file.filename,\\n type(config_file.config)))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_ulimits\",\n \"text\": \"compose/config/validation.py::validate_ulimits\\ndef validate_ulimits(service_config):\\n ulimit_config = service_config.config.get('ulimits', {})\\n for limit_name, soft_hard_values in six.iteritems(ulimit_config):\\n if isinstance(soft_hard_values, dict):\\n if not soft_hard_values['soft'] <= soft_hard_values['hard']:\\n raise ConfigurationError(\\n \\\"Service '{s.name}' has invalid ulimit '{ulimit}'. \\\"\\n \\\"'soft' value can not be greater than 'hard' value \\\".format(\\n s=service_config,\\n ulimit=ulimit_config))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_extends_file_path\",\n \"text\": \"compose/config/validation.py::validate_extends_file_path\\ndef validate_extends_file_path(service_name, extends_options, filename):\\n \\\"\\\"\\\"\\n The service to be extended must either be defined in the config key 'file',\\n or within 'filename'.\\n \\\"\\\"\\\"\\n error_prefix = \\\"Invalid 'extends' configuration for %s:\\\" % service_name\\n\\n if 'file' not in extends_options and filename is None:\\n raise ConfigurationError(\\n \\\"%s you need to specify a 'file', e.g. 'file: something.yml'\\\" % error_prefix\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_network_mode\",\n \"text\": \"compose/config/validation.py::validate_network_mode\\ndef validate_network_mode(service_config, service_names):\\n network_mode = service_config.config.get('network_mode')\\n if not network_mode:\\n return\\n\\n if 'networks' in service_config.config:\\n raise ConfigurationError(\\\"'network_mode' and 'networks' cannot be combined\\\")\\n\\n dependency = get_service_name_from_network_mode(network_mode)\\n if not dependency:\\n return\\n\\n if dependency not in service_names:\\n raise ConfigurationError(\\n \\\"Service '{s.name}' uses the network stack of service '{dep}' which \\\"\\n \\\"is undefined.\\\".format(s=service_config, dep=dependency))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_pid_mode\",\n \"text\": \"compose/config/validation.py::validate_pid_mode\\ndef validate_pid_mode(service_config, service_names):\\n pid_mode = service_config.config.get('pid')\\n if not pid_mode:\\n return\\n\\n dependency = get_service_name_from_network_mode(pid_mode)\\n if not dependency:\\n return\\n if dependency not in service_names:\\n raise ConfigurationError(\\n \\\"Service '{s.name}' uses the PID namespace of service '{dep}' which \\\"\\n \\\"is undefined.\\\".format(s=service_config, dep=dependency)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_links\",\n \"text\": \"compose/config/validation.py::validate_links\\ndef validate_links(service_config, service_names):\\n for link in service_config.config.get('links', []):\\n if link.split(':')[0] not in service_names:\\n raise ConfigurationError(\\n \\\"Service '{s.name}' has a link to service '{link}' which is \\\"\\n \\\"undefined.\\\".format(s=service_config, link=link))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_depends_on\",\n \"text\": \"compose/config/validation.py::validate_depends_on\\ndef validate_depends_on(service_config, service_names):\\n deps = service_config.config.get('depends_on', {})\\n for dependency in deps.keys():\\n if dependency not in service_names:\\n raise ConfigurationError(\\n \\\"Service '{s.name}' depends on service '{dep}' which is \\\"\\n \\\"undefined.\\\".format(s=service_config, dep=dependency)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::get_unsupported_config_msg\",\n \"text\": \"compose/config/validation.py::get_unsupported_config_msg\\ndef get_unsupported_config_msg(path, error_key):\\n msg = \\\"Unsupported config option for {}: '{}'\\\".format(path_string(path), error_key)\\n if error_key in DOCKER_CONFIG_HINTS:\\n msg += \\\" (did you mean '{}'?)\\\".format(DOCKER_CONFIG_HINTS[error_key])\\n return msg\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::anglicize_json_type\",\n \"text\": \"compose/config/validation.py::anglicize_json_type\\ndef anglicize_json_type(json_type):\\n if json_type.startswith(('a', 'e', 'i', 'o', 'u')):\\n return 'an ' + json_type\\n return 'a ' + json_type\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::is_service_dict_schema\",\n \"text\": \"compose/config/validation.py::is_service_dict_schema\\ndef is_service_dict_schema(schema_id):\\n return schema_id in ('config_schema_v1.json', '#/properties/services')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::handle_error_for_schema_with_id\",\n \"text\": \"compose/config/validation.py::handle_error_for_schema_with_id\\ndef handle_error_for_schema_with_id(error, path):\\n schema_id = error.schema['id']\\n\\n if is_service_dict_schema(schema_id) and error.validator == 'additionalProperties':\\n return \\\"Invalid service name '{}' - only {} characters are allowed\\\".format(\\n # The service_name is one of the keys in the json object\\n [i for i in list(error.instance) if not i or any(filter(\\n lambda c: not re.match(VALID_NAME_CHARS, c), i\\n ))][0],\\n VALID_NAME_CHARS\\n )\\n\\n if error.validator == 'additionalProperties':\\n if schema_id == '#/definitions/service':\\n invalid_config_key = parse_key_from_error_msg(error)\\n return get_unsupported_config_msg(path, invalid_config_key)\\n\\n if schema_id.startswith('config_schema_v'):\\n invalid_config_key = parse_key_from_error_msg(error)\\n return ('Invalid top-level property \\\"{key}\\\". Valid top-level '\\n 'sections for this Compose file are: {properties}, and '\\n 'extensions starting with \\\"x-\\\".\\\\n\\\\n{explanation}').format(\\n key=invalid_config_key,\\n properties=', '.join(error.schema['properties'].keys()),\\n explanation=VERSION_EXPLANATION\\n )\\n\\n if not error.path:\\n return '{}\\\\n\\\\n{}'.format(error.message, VERSION_EXPLANATION)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::handle_generic_error\",\n \"text\": \"compose/config/validation.py::handle_generic_error\\ndef handle_generic_error(error, path):\\n msg_format = None\\n error_msg = error.message\\n\\n if error.validator == 'oneOf':\\n msg_format = \\\"{path} {msg}\\\"\\n config_key, error_msg = _parse_oneof_validator(error)\\n if config_key:\\n path.append(config_key)\\n\\n elif error.validator == 'type':\\n msg_format = \\\"{path} contains an invalid type, it should be {msg}\\\"\\n error_msg = _parse_valid_types_from_validator(error.validator_value)\\n\\n elif error.validator == 'required':\\n error_msg = \\\", \\\".join(error.validator_value)\\n msg_format = \\\"{path} is invalid, {msg} is required.\\\"\\n\\n elif error.validator == 'dependencies':\\n config_key = list(error.validator_value.keys())[0]\\n required_keys = \\\",\\\".join(error.validator_value[config_key])\\n\\n msg_format = \\\"{path} is invalid: {msg}\\\"\\n path.append(config_key)\\n error_msg = \\\"when defining '{}' you must set '{}' as well\\\".format(\\n config_key,\\n required_keys)\\n\\n elif error.cause:\\n error_msg = six.text_type(error.cause)\\n msg_format = \\\"{path} is invalid: {msg}\\\"\\n\\n elif error.path:\\n msg_format = \\\"{path} value {msg}\\\"\\n\\n if msg_format:\\n return msg_format.format(path=path_string(path), msg=error_msg)\\n\\n return error.message\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::parse_key_from_error_msg\",\n \"text\": \"compose/config/validation.py::parse_key_from_error_msg\\ndef parse_key_from_error_msg(error):\\n try:\\n return error.message.split(\\\"'\\\")[1]\\n except IndexError:\\n return error.message.split('(')[1].split(' ')[0].strip(\\\"'\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::path_string\",\n \"text\": \"compose/config/validation.py::path_string\\ndef path_string(path):\\n return \\\".\\\".join(c for c in path if isinstance(c, six.string_types))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::_parse_valid_types_from_validator\",\n \"text\": \"compose/config/validation.py::_parse_valid_types_from_validator\\ndef _parse_valid_types_from_validator(validator):\\n \\\"\\\"\\\"A validator value can be either an array of valid types or a string of\\n a valid type. Parse the valid types and prefix with the correct article.\\n \\\"\\\"\\\"\\n if not isinstance(validator, list):\\n return anglicize_json_type(validator)\\n\\n if len(validator) == 1:\\n return anglicize_json_type(validator[0])\\n\\n return \\\"{}, or {}\\\".format(\\n \\\", \\\".join([anglicize_json_type(validator[0])] + validator[1:-1]),\\n anglicize_json_type(validator[-1]))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::_parse_oneof_validator\",\n \"text\": \"compose/config/validation.py::_parse_oneof_validator\\ndef _parse_oneof_validator(error):\\n \\\"\\\"\\\"oneOf has multiple schemas, so we need to reason about which schema, sub\\n schema or constraint the validation is failing on.\\n Inspecting the context value of a ValidationError gives us information about\\n which sub schema failed and which kind of error it is.\\n \\\"\\\"\\\"\\n types = []\\n for context in error.context:\\n if context.validator == 'oneOf':\\n _, error_msg = _parse_oneof_validator(context)\\n return path_string(context.path), error_msg\\n\\n if context.validator == 'required':\\n return (None, context.message)\\n\\n if context.validator == 'additionalProperties':\\n invalid_config_key = parse_key_from_error_msg(context)\\n return (None, \\\"contains unsupported option: '{}'\\\".format(invalid_config_key))\\n\\n if context.validator == 'uniqueItems':\\n return (\\n path_string(context.path) if context.path else None,\\n \\\"contains non-unique items, please remove duplicates from {}\\\".format(\\n context.instance),\\n )\\n\\n if context.path:\\n return (\\n path_string(context.path),\\n \\\"contains {}, which is an invalid type, it should be {}\\\".format(\\n json.dumps(context.instance),\\n _parse_valid_types_from_validator(context.validator_value)),\\n )\\n\\n if context.validator == 'type':\\n types.append(context.validator_value)\\n\\n valid_types = _parse_valid_types_from_validator(types)\\n return (None, \\\"contains an invalid type, it should be {}\\\".format(valid_types))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::process_service_constraint_errors\",\n \"text\": \"compose/config/validation.py::process_service_constraint_errors\\ndef process_service_constraint_errors(error, service_name, version):\\n if version == V1:\\n if 'image' in error.instance and 'build' in error.instance:\\n return (\\n \\\"Service {} has both an image and build path specified. \\\"\\n \\\"A service can either be built to image or use an existing \\\"\\n \\\"image, not both.\\\".format(service_name))\\n\\n if 'image' in error.instance and 'dockerfile' in error.instance:\\n return (\\n \\\"Service {} has both an image and alternate Dockerfile. \\\"\\n \\\"A service can either be built to image or use an existing \\\"\\n \\\"image, not both.\\\".format(service_name))\\n\\n if 'image' not in error.instance and 'build' not in error.instance:\\n return (\\n \\\"Service {} has neither an image nor a build context specified. \\\"\\n \\\"At least one must be provided.\\\".format(service_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::process_config_schema_errors\",\n \"text\": \"compose/config/validation.py::process_config_schema_errors\\ndef process_config_schema_errors(error):\\n path = list(error.path)\\n\\n if 'id' in error.schema:\\n error_msg = handle_error_for_schema_with_id(error, path)\\n if error_msg:\\n return error_msg\\n\\n return handle_generic_error(error, path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_against_config_schema\",\n \"text\": \"compose/config/validation.py::validate_against_config_schema\\ndef validate_against_config_schema(config_file):\\n schema = load_jsonschema(config_file)\\n format_checker = FormatChecker([\\\"ports\\\", \\\"expose\\\", \\\"subnet_ip_address\\\"])\\n validator = Draft4Validator(\\n schema,\\n resolver=RefResolver(get_resolver_path(), schema),\\n format_checker=format_checker)\\n handle_errors(\\n validator.iter_errors(config_file.config),\\n process_config_schema_errors,\\n config_file.filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_service_constraints\",\n \"text\": \"compose/config/validation.py::validate_service_constraints\\ndef validate_service_constraints(config, service_name, config_file):\\n def handler(errors):\\n return process_service_constraint_errors(\\n errors, service_name, config_file.version)\\n\\n schema = load_jsonschema(config_file)\\n validator = Draft4Validator(schema['definitions']['constraints']['service'])\\n handle_errors(validator.iter_errors(config), handler, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_service_constraints.handler\",\n \"text\": \"compose/config/validation.py::validate_service_constraints.handler\\n def handler(errors):\\n return process_service_constraint_errors(\\n errors, service_name, config_file.version)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_cpu\",\n \"text\": \"compose/config/validation.py::validate_cpu\\ndef validate_cpu(service_config):\\n cpus = service_config.config.get('cpus')\\n if not cpus:\\n return\\n nano_cpus = cpus * NANOCPUS_SCALE\\n if isinstance(nano_cpus, float) and not nano_cpus.is_integer():\\n raise ConfigurationError(\\n \\\"cpus must have nine or less digits after decimal point\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::get_schema_path\",\n \"text\": \"compose/config/validation.py::get_schema_path\\ndef get_schema_path():\\n return os.path.dirname(os.path.abspath(__file__))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::load_jsonschema\",\n \"text\": \"compose/config/validation.py::load_jsonschema\\ndef load_jsonschema(config_file):\\n filename = os.path.join(\\n get_schema_path(),\\n \\\"config_schema_v{0}.json\\\".format(config_file.version))\\n\\n if not os.path.exists(filename):\\n raise ConfigurationError(\\n 'Version in \\\"{}\\\" is unsupported. {}'\\n .format(config_file.filename, VERSION_EXPLANATION))\\n\\n with open(filename, \\\"r\\\") as fh:\\n return json.load(fh)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::get_resolver_path\",\n \"text\": \"compose/config/validation.py::get_resolver_path\\ndef get_resolver_path():\\n schema_path = get_schema_path()\\n if sys.platform == \\\"win32\\\":\\n scheme = \\\"///\\\"\\n # TODO: why is this necessary?\\n schema_path = schema_path.replace('\\\\\\\\', '/')\\n else:\\n scheme = \\\"//\\\"\\n return \\\"file:{}{}/\\\".format(scheme, schema_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::handle_errors\",\n \"text\": \"compose/config/validation.py::handle_errors\\ndef handle_errors(errors, format_error_func, filename):\\n \\\"\\\"\\\"jsonschema returns an error tree full of information to explain what has\\n gone wrong. Process each error and pull out relevant information and re-write\\n helpful error messages that are relevant.\\n \\\"\\\"\\\"\\n errors = list(sorted(errors, key=str))\\n if not errors:\\n return\\n\\n error_msg = '\\\\n'.join(format_error_func(error) for error in errors)\\n raise ConfigurationError(\\n \\\"The Compose file{file_msg} is invalid because:\\\\n{error_msg}\\\".format(\\n file_msg=\\\" '{}'\\\".format(filename) if filename else \\\"\\\",\\n error_msg=error_msg))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_healthcheck\",\n \"text\": \"compose/config/validation.py::validate_healthcheck\\ndef validate_healthcheck(service_config):\\n healthcheck = service_config.config.get('healthcheck', {})\\n\\n if 'test' in healthcheck and isinstance(healthcheck['test'], list):\\n if len(healthcheck['test']) == 0:\\n raise ConfigurationError(\\n 'Service \\\"{}\\\" defines an invalid healthcheck: '\\n '\\\"test\\\" is an empty list'\\n .format(service_config.name))\\n\\n # when disable is true config.py::process_healthcheck adds \\\"test: ['NONE']\\\" to service_config\\n elif healthcheck['test'][0] == 'NONE' and len(healthcheck) > 1:\\n raise ConfigurationError(\\n 'Service \\\"{}\\\" defines an invalid healthcheck: '\\n '\\\"disable: true\\\" cannot be combined with other options'\\n .format(service_config.name))\\n\\n elif healthcheck['test'][0] not in ('NONE', 'CMD', 'CMD-SHELL'):\\n raise ConfigurationError(\\n 'Service \\\"{}\\\" defines an invalid healthcheck: '\\n 'when \\\"test\\\" is a list the first item must be either NONE, CMD or CMD-SHELL'\\n .format(service_config.name))\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::create_initial_branch\",\n \"text\": \"script/release/release.py::create_initial_branch\\ndef create_initial_branch(repository, args):\\n release_branch = repository.create_release_branch(args.release, args.base)\\n if args.base and args.cherries:\\n print('Detected patch version.')\\n cherries = input('Indicate (space-separated) PR numbers to cherry-pick then press Enter:\\\\n')\\n repository.cherry_pick_prs(release_branch, cherries.split())\\n\\n return create_bump_commit(repository, release_branch, args.bintray_user, args.bintray_org)\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::create_bump_commit\",\n \"text\": \"script/release/release.py::create_bump_commit\\ndef create_bump_commit(repository, release_branch, bintray_user, bintray_org):\\n with release_branch.config_reader() as cfg:\\n release = cfg.get('release')\\n print('Updating version info in __init__.py and run.sh')\\n update_run_sh_version(release)\\n update_init_py_version(release)\\n\\n input('Please add the release notes to the CHANGELOG.md file, then press Enter to continue.')\\n proceed = None\\n while not proceed:\\n print(repository.diff())\\n proceed = yesno('Are these changes ok? y/N ', default=False)\\n\\n if repository.diff():\\n repository.create_bump_commit(release_branch, release)\\n repository.push_branch_to_remote(release_branch)\\n\\n bintray_api = BintrayAPI(os.environ['BINTRAY_TOKEN'], bintray_user)\\n if not bintray_api.repository_exists(bintray_org, release_branch.name):\\n print('Creating data repository {} on bintray'.format(release_branch.name))\\n bintray_api.create_repository(bintray_org, release_branch.name, 'generic')\\n else:\\n print('Bintray repository {} already exists. Skipping'.format(release_branch.name))\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::monitor_pr_status\",\n \"text\": \"script/release/release.py::monitor_pr_status\\ndef monitor_pr_status(pr_data):\\n print('Waiting for CI to complete...')\\n last_commit = pr_data.get_commits().reversed[0]\\n while True:\\n status = last_commit.get_combined_status()\\n if status.state == 'pending' or status.state == 'failure':\\n summary = {\\n 'pending': 0,\\n 'success': 0,\\n 'failure': 0,\\n 'error': 0,\\n }\\n for detail in status.statuses:\\n if detail.context == 'dco-signed':\\n # dco-signed check breaks on merge remote-tracking ; ignore it\\n continue\\n if detail.state in summary:\\n summary[detail.state] += 1\\n print(\\n '{pending} pending, {success} successes, {failure} failures, '\\n '{error} errors'.format(**summary)\\n )\\n if summary['failure'] > 0 or summary['error'] > 0:\\n raise ScriptError('CI failures detected!')\\n elif summary['pending'] == 0 and summary['success'] > 0:\\n # This check assumes at least 1 non-DCO CI check to avoid race conditions.\\n # If testing on a repo without CI, use --skip-ci-check to avoid looping eternally\\n return True\\n time.sleep(30)\\n elif status.state == 'success':\\n print('{} successes: all clear!'.format(status.total_count))\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::check_pr_mergeable\",\n \"text\": \"script/release/release.py::check_pr_mergeable\\ndef check_pr_mergeable(pr_data):\\n if pr_data.mergeable is False:\\n # mergeable can also be null, in which case the warning would be a false positive.\\n print(\\n 'WARNING!! PR #{} can not currently be merged. You will need to '\\n 'resolve the conflicts manually before finalizing the release.'.format(pr_data.number)\\n )\\n\\n return pr_data.mergeable is True\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::create_release_draft\",\n \"text\": \"script/release/release.py::create_release_draft\\ndef create_release_draft(repository, version, pr_data, files):\\n print('Creating Github release draft')\\n with open(os.path.join(os.path.dirname(__file__), 'release.md.tmpl'), 'r') as f:\\n template = Template(f.read())\\n print('Rendering release notes based on template')\\n release_notes = template.render(\\n version=version,\\n compat_matrix=compatibility_matrix(),\\n integrity=files,\\n contributors=get_contributors(pr_data),\\n changelog=read_release_notes_from_changelog(),\\n )\\n gh_release = repository.create_release(\\n version, release_notes, draft=True, prerelease='-rc' in version,\\n target_commitish='release'\\n )\\n print('Release draft initialized')\\n return gh_release\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::print_final_instructions\",\n \"text\": \"script/release/release.py::print_final_instructions\\ndef print_final_instructions(args):\\n print(\\n \\\"You're almost done! Please verify that everything is in order and \\\"\\n \\\"you are ready to make the release public, then run the following \\\"\\n \\\"command:\\\\n{exe} -b {user} finalize {version}\\\".format(\\n exe='./script/release/release.sh', user=args.bintray_user, version=args.release\\n )\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::distclean\",\n \"text\": \"script/release/release.py::distclean\\ndef distclean():\\n print('Running distclean...')\\n dirs = [\\n os.path.join(REPO_ROOT, 'build'), os.path.join(REPO_ROOT, 'dist'),\\n os.path.join(REPO_ROOT, 'docker-compose.egg-info')\\n ]\\n files = []\\n for base, dirnames, fnames in os.walk(REPO_ROOT):\\n for fname in fnames:\\n path = os.path.normpath(os.path.join(base, fname))\\n if fname.endswith('.pyc'):\\n files.append(path)\\n elif fname.startswith('.coverage.'):\\n files.append(path)\\n for dirname in dirnames:\\n path = os.path.normpath(os.path.join(base, dirname))\\n if dirname == '__pycache__':\\n dirs.append(path)\\n elif dirname == '.coverage-binfiles':\\n dirs.append(path)\\n\\n for file in files:\\n os.unlink(file)\\n\\n for folder in dirs:\\n shutil.rmtree(folder, ignore_errors=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::resume\",\n \"text\": \"script/release/release.py::resume\\ndef resume(args):\\n try:\\n distclean()\\n repository = Repository(REPO_ROOT, args.repo)\\n br_name = branch_name(args.release)\\n if not repository.branch_exists(br_name):\\n raise ScriptError('No local branch exists for this release.')\\n gh_release = repository.find_release(args.release)\\n if gh_release and not gh_release.draft:\\n print('WARNING!! Found non-draft (public) release for this version!')\\n proceed = yesno(\\n 'Are you sure you wish to proceed? Modifying an already '\\n 'released version is dangerous! y/N ', default=False\\n )\\n if proceed.lower() is not True:\\n raise ScriptError('Aborting release')\\n\\n release_branch = repository.checkout_branch(br_name)\\n if args.cherries:\\n cherries = input('Indicate (space-separated) PR numbers to cherry-pick then press Enter:\\\\n')\\n repository.cherry_pick_prs(release_branch, cherries.split())\\n\\n create_bump_commit(repository, release_branch, args.bintray_user, args.bintray_org)\\n pr_data = repository.find_release_pr(args.release)\\n if not pr_data:\\n pr_data = repository.create_release_pull_request(args.release)\\n check_pr_mergeable(pr_data)\\n if not args.skip_ci:\\n monitor_pr_status(pr_data)\\n downloader = BinaryDownloader(args.destination)\\n files = downloader.download_all(args.release)\\n if not gh_release:\\n gh_release = create_release_draft(repository, args.release, pr_data, files)\\n delete_assets(gh_release)\\n upload_assets(gh_release, files)\\n img_manager = ImageManager(args.release)\\n img_manager.build_images(repository, files)\\n except ScriptError as e:\\n print(e)\\n return 1\\n\\n print_final_instructions(args)\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::cancel\",\n \"text\": \"script/release/release.py::cancel\\ndef cancel(args):\\n try:\\n repository = Repository(REPO_ROOT, args.repo)\\n repository.close_release_pr(args.release)\\n repository.remove_release(args.release)\\n repository.remove_bump_branch(args.release)\\n bintray_api = BintrayAPI(os.environ['BINTRAY_TOKEN'], args.bintray_user)\\n print('Removing Bintray data repository for {}'.format(args.release))\\n bintray_api.delete_repository(args.bintray_org, branch_name(args.release))\\n distclean()\\n except ScriptError as e:\\n print(e)\\n return 1\\n print('Release cancellation complete.')\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::start\",\n \"text\": \"script/release/release.py::start\\ndef start(args):\\n distclean()\\n try:\\n repository = Repository(REPO_ROOT, args.repo)\\n create_initial_branch(repository, args)\\n pr_data = repository.create_release_pull_request(args.release)\\n check_pr_mergeable(pr_data)\\n if not args.skip_ci:\\n monitor_pr_status(pr_data)\\n downloader = BinaryDownloader(args.destination)\\n files = downloader.download_all(args.release)\\n gh_release = create_release_draft(repository, args.release, pr_data, files)\\n upload_assets(gh_release, files)\\n img_manager = ImageManager(args.release)\\n img_manager.build_images(repository, files)\\n except ScriptError as e:\\n print(e)\\n return 1\\n\\n print_final_instructions(args)\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::finalize\",\n \"text\": \"script/release/release.py::finalize\\ndef finalize(args):\\n distclean()\\n try:\\n check_pypirc()\\n repository = Repository(REPO_ROOT, args.repo)\\n img_manager = ImageManager(args.release)\\n pr_data = repository.find_release_pr(args.release)\\n if not pr_data:\\n raise ScriptError('No PR found for {}'.format(args.release))\\n if not check_pr_mergeable(pr_data):\\n raise ScriptError('Can not finalize release with an unmergeable PR')\\n if not img_manager.check_images():\\n raise ScriptError('Missing release image')\\n br_name = branch_name(args.release)\\n if not repository.branch_exists(br_name):\\n raise ScriptError('No local branch exists for this release.')\\n gh_release = repository.find_release(args.release)\\n if not gh_release:\\n raise ScriptError('No Github release draft for this version')\\n\\n repository.checkout_branch(br_name)\\n\\n run_setup(os.path.join(REPO_ROOT, 'setup.py'), script_args=['sdist', 'bdist_wheel'])\\n\\n merge_status = pr_data.merge()\\n if not merge_status.merged and not args.finalize_resume:\\n raise ScriptError(\\n 'Unable to merge PR #{}: {}'.format(pr_data.number, merge_status.message)\\n )\\n\\n pypi_upload(args)\\n\\n img_manager.push_images()\\n repository.publish_release(gh_release)\\n except ScriptError as e:\\n print(e)\\n return 1\\n\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::main\",\n \"text\": \"script/release/release.py::main\\ndef main():\\n if 'GITHUB_TOKEN' not in os.environ:\\n print('GITHUB_TOKEN environment variable must be set')\\n return 1\\n\\n if 'BINTRAY_TOKEN' not in os.environ:\\n print('BINTRAY_TOKEN environment variable must be set')\\n return 1\\n\\n parser = argparse.ArgumentParser(\\n description='Orchestrate a new release of docker/compose. This tool assumes that you have '\\n 'obtained a Github API token and Bintray API key and set the GITHUB_TOKEN and '\\n 'BINTRAY_TOKEN environment variables accordingly.',\\n epilog=EPILOG, formatter_class=argparse.RawTextHelpFormatter)\\n parser.add_argument(\\n 'action', choices=ACTIONS, help='The action to be performed for this release'\\n )\\n parser.add_argument('release', help='Release number, e.g. 1.9.0-rc1, 2.1.1')\\n parser.add_argument(\\n '--patch', '-p', dest='base',\\n help='Which version is being patched by this release'\\n )\\n parser.add_argument(\\n '--repo', '-r', dest='repo', default=NAME,\\n help='Start a release for the given repo (default: {})'.format(NAME)\\n )\\n parser.add_argument(\\n '-b', dest='bintray_user', required=True, metavar='USER',\\n help='Username associated with the Bintray API key'\\n )\\n parser.add_argument(\\n '--bintray-org', dest='bintray_org', metavar='ORG', default=BINTRAY_ORG,\\n help='Organization name on bintray where the data repository will be created.'\\n )\\n parser.add_argument(\\n '--destination', '-o', metavar='DIR', default='binaries',\\n help='Directory where release binaries will be downloaded relative to the project root'\\n )\\n parser.add_argument(\\n '--no-cherries', '-C', dest='cherries', action='store_false',\\n help='If set, the program will not prompt the user for PR numbers to cherry-pick'\\n )\\n parser.add_argument(\\n '--skip-ci-checks', dest='skip_ci', action='store_true',\\n help\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::read\",\n \"text\": \"setup.py::read\\ndef read(*parts):\\n path = os.path.join(os.path.dirname(__file__), *parts)\\n with codecs.open(path, encoding='utf-8') as fobj:\\n return fobj.read()\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::find_version\",\n \"text\": \"setup.py::find_version\\ndef find_version(*file_paths):\\n version_file = read(*file_paths)\\n version_match = re.search(r\\\"^__version__ = ['\\\\\\\"]([^'\\\\\\\"]*)['\\\\\\\"]\\\",\\n version_file, re.M)\\n if version_match:\\n return version_match.group(1)\\n raise RuntimeError(\\\"Unable to find version string.\\\")\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":282,"cells":{"instance_id":{"kind":"string","value":"wagtail__wagtail-4648"},"repo":{"kind":"string","value":"wagtail/wagtail"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"PageTypeField API serializer should gracefully handle missing page models\n### Issue Summary\r\n\r\nIf a page model is deleted while pages of that type still exist, any API calls for page listings that include that page type will fail with `AttributeError: 'NoneType' object has no attribute '_meta'`. This results in a server error when using the explorer popout menu. (Related: #3569, which fixed the page explorer views for this case, but did not address the explorer menu.)\r\n\r\n### Steps to Reproduce\r\n\r\nFrom a new bakerydemo install, delete the `LocationPage` model and run makemigrations / migrate. Use the popout menu to navigate into the Locations section. This fails with \"Server error\" in the UI, and the following stack trace:\r\n\r\n Internal Server Error: /admin/api/v2beta/pages/\r\n Traceback (most recent call last):\r\n File \"/home/vagrant/.virtualenvs/bakerydemo/lib/python3.4/site-packages/django/core/handlers/exception.py\", line 35, in inner\r\n response = get_response(request)\r\n File \"/home/vagrant/.virtualenvs/bakerydemo/lib/python3.4/site-packages/django/core/handlers/base.py\", line 128, in _get_response\r\n response = self.process_exception_by_middleware(e, request)\r\n File \"/home/vagrant/.virtualenvs/bakerydemo/lib/python3.4/site-packages/django/core/handlers/base.py\", line 126, in _get_response\r\n response = wrapped_callback(request, *callback_args, **callback_kwargs)\r\n File \"/home/vagrant/.virtualenvs/bakerydemo/lib/python3.4/site-packages/django/views/decorators/cache.py\", line 31, in _cache_controlled\r\n response = viewfunc(request, *args, **kw)\r\n File \"/vagrant/wagtail/wagtail/admin/urls/__init__.py\", line 102, in wrapper\r\n return view_func(request, *args, **kwargs)\r\n File \"/vagrant/wagtail/wagtail/admin/decorators.py\", line 34, in decorated_view\r\n return view_func(request, *args, **kwargs)\r\n File \"/vagrant/wagtail/wagtail/api/v2/router.py\", line 63, in wrapped\r\n return func(request, *args, **kwargs)\r\n File \"/home/vagrant/.virtualenvs/bakerydemo/lib/python3.4/site-packages/django/views/decorators/csrf.py\", line 54, in wrapped_view\r\n return view_func(*args, **kwargs)\r\n File \"/home/vagrant/.virtualenvs/bakerydemo/lib/python3.4/site-packages/rest_framework/viewsets.py\", line 95, in view\r\n return self.dispatch(request, *args, **kwargs)\r\n File \"/home/vagrant/.virtualenvs/bakerydemo/lib/python3.4/site-packages/rest_framework/views.py\", line 494, in dispatch\r\n response = self.handle_exception(exc)\r\n File \"/vagrant/wagtail/wagtail/api/v2/endpoints.py\", line 116, in handle_exception\r\n return super().handle_exception(exc)\r\n File \"/home/vagrant/.virtualenvs/bakerydemo/lib/python3.4/site-packages/rest_framework/views.py\", line 454, in handle_exception\r\n self.raise_uncaught_exception(exc)\r\n File \"/home/vagrant/.virtualenvs/bakerydemo/lib/python3.4/site-packages/rest_framework/views.py\", line 491, in dispatch\r\n response = handler(request, *args, **kwargs)\r\n File \"/vagrant/wagtail/wagtail/admin/api/endpoints.py\", line 94, in listing_view\r\n response = super().listing_view(request)\r\n File \"/vagrant/wagtail/wagtail/api/v2/endpoints.py\", line 74, in listing_view\r\n return self.get_paginated_response(serializer.data)\r\n File \"/home/vagrant/.virtualenvs/bakerydemo/lib/python3.4/site-packages/rest_framework/serializers.py\", line 742, in data\r\n ret = super(ListSerializer, self).data\r\n File \"/home/vagrant/.virtualenvs/bakerydemo/lib/python3.4/site-packages/rest_framework/serializers.py\", line 262, in data\r\n self._data = self.to_representation(self.instance)\r\n File \"/home/vagrant/.virtualenvs/bakerydemo/lib/python3.4/site-packages/rest_framework/serializers.py\", line 660, in to_representation\r\n self.child.to_representation(item) for item in iterable\r\n File \"/home/vagrant/.virtualenvs/bakerydemo/lib/python3.4/site-packages/rest_framewo"},"gold_files":{"kind":"list like","value":["wagtail/api/v2/serializers.py"],"string":"[\n \"wagtail/api/v2/serializers.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["wagtail/api/v2/serializers.py::PageTypeField.to_representation"],"string":"[\n \"wagtail/api/v2/serializers.py::PageTypeField.to_representation\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/conf.py","text":"docs/conf.py\ndef setup(app)","label":0},{"cid":"wagtail/admin/urls/__init__.py","text":"wagtail/admin/urls/__init__.py\ndef display_custom_404(view_func)\n def wrapper(request, *args, **kwargs)","label":0},{"cid":"wagtail/api/v2/endpoints.py","text":"wagtail/api/v2/endpoints.py\nclass BaseAPIEndpoint\n def __init__(self, *args, **kwargs)\n def get_queryset(self)\n def listing_view(self, request)\n def detail_view(self, request, pk)\n def find_view(self, request)\n def find_object(self, queryset, request) # Override this to implement more find methods.\n def handle_exception(self, exc)\n def _convert_api_fields(cls, fields)\n def get_body_fields(cls, model)\n def get_body_fields_names(cls, model)\n def get_meta_fields(cls, model)\n def get_meta_fields_names(cls, model)\n def get_field_serializer_overrides(cls, model)\n def get_available_fields(cls, model, db_fields_only=False) # Returns a list of all the fields that can be used in the API for the\n def get_detail_default_fields(cls, model)\n def get_listing_default_fields(cls, model)\n def get_nested_default_fields(cls, model)\n def check_query_parameters(self, queryset) # Ensure that only valid query paramters are included in the URL.\n def _get_serializer_class(cls, router, model, fields_config, show_details=False, nested=False)\n def get_serializer_class(self)\n def get_serializer_context(self) # The serialization context differs between listing and detail views.\n def get_renderer_context(self)\n def get_urlpatterns(cls) # This returns a list of URL patterns for the endpoint\n def get_model_listing_urlpath(cls, model, namespace='')\n def get_object_detail_urlpath(cls, model, pk, namespace='')\nclass PagesAPIEndpoint\n def get_queryset(self)\n def get_object(self)\n def find_object(self, queryset, request)","label":0},{"cid":"wagtail/api/v2/serializers.py","text":"wagtail/api/v2/serializers.py\nclass TypeField # Serializes the \"type\" field of each object.\n def get_attribute(self, instance)\n def to_representation(self, obj)\nclass DetailUrlField # Serializes the \"detail_url\" field of each object.\n def get_attribute(self, instance)\n def to_representation(self, url)\nclass PageHtmlUrlField # Serializes the \"html_url\" field for pages.\n def get_attribute(self, instance)\n def to_representation(self, page)\nclass PageTypeField # Serializes the \"type\" field for pages.\n def get_attribute(self, instance)\n def to_representation(self, page)\nclass RelatedField # Serializes related objects (eg, foreign keys).\n def __init__(self, *args, **kwargs)\n def to_representation(self, value)\nclass PageParentField # Serializes the \"parent\" field on Page objects.\n def get_attribute(self, instance)\n def to_representation(self, value)\nclass ChildRelationField # Serializes child relations.\n def __init__(self, *args, **kwargs)\n def to_representation(self, value)\nclass StreamField # Serializes StreamField values.\n def to_representation(self, value)\nclass TagsField # Serializes django-taggit TaggableManager fields.\n def to_representation(self, value)\nclass BaseSerializer\n def to_representation(self, instance)\n def build_property_field(self, field_name, model_class)\n def build_relational_field(self, field_name, relation_info)\nclass PageSerializer\n def build_relational_field(self, field_name, relation_info)\ndef get_serializer_class(model, field_names, meta_fields, field_serializer_overrides=None, child_serializer_classes=None, base=BaseSerializer)\n class Meta","label":1},{"cid":"wagtail/bin/wagtail.py","text":"wagtail/bin/wagtail.py\ndef pluralize(value, arg='s')\nclass Command\n def create_parser(self, command_name=None)\n def add_arguments(self, parser)\n def print_help(self, command_name)\n def execute(self, argv)\nclass CreateProject\n def add_arguments(self, parser)\n def run(self, project_name=None, dest_dir=None)\nclass UpdateModulePaths\n def add_arguments(self, parser)\n def run(self, root_path=None, list_files=False, diff=False, ignored_dirs=None, ignored_patterns=None)\n def _rewrite_line(self, line)\n def _show_diff(self, filename, relative_path=None)\n def _count_changes(self, filename)\n def _rewrite_file(self, filename)\ndef prog_name()\ndef help_index()\ndef unknown_command(command)\ndef main()","label":0},{"cid":"wagtail/contrib/forms/models.py","text":"wagtail/contrib/forms/models.py\nclass AbstractFormSubmission # Data for a form submission.\n def get_data(self) # Returns dict with form data.\n def __str__(self)\n class Meta\nclass FormSubmission # Data for a Form submission.\nclass AbstractFormField # Database Fields required for building a Django Form field.\n def clean_name(self)\n class Meta\nclass AbstractForm # A Form Page. Pages implementing a form should inherit from it\n def __init__(self, *args, **kwargs)\n class Meta\n def get_form_fields(self) # Form page expects `form_fields` to be declared.\n def get_data_fields(self) # Returns a list of tuples with (field_name, field_label).\n def get_form_class(self)\n def get_form_parameters(self)\n def get_form(self, *args, **kwargs)\n def get_landing_page_template(self, request, *args, **kwargs)\n def get_submission_class(self) # Returns submission class.\n def process_form_submission(self, form) # Accepts form instance with submitted data, user and page.\n def render_landing_page(self, request, form_submission=None, *args, **kwargs) # Renders the landing page.\n def serve_submissions_list_view(self, request, *args, **kwargs) # Returns list submissions view for admin.\n def serve(self, request, *args, **kwargs)\n def serve_preview(self, request, mode)\nclass AbstractEmailForm # A Form Page that sends email. Pages implementing a form to be send to an email s\n def process_form_submission(self, form)\n def send_mail(self, form)\n class Meta","label":0},{"cid":"wagtail/core/blocks/base.py","text":"wagtail/core/blocks/base.py\nclass BaseBlock\n def __new__(mcs, name, bases, attrs)\nclass Block\n class Meta\n def __new__(cls, *args, **kwargs)\n def all_blocks(self) # Return a list consisting of self and all block objects that are direct or indire\n def all_media(self)\n def all_html_declarations(self)\n def __init__(self, **kwargs)\n def set_name(self, name)\n def media(self)\n def html_declarations(self) # Return an HTML fragment to be rendered on the form page once per block definitio\n def js_initializer(self) # Returns a Javascript expression string, or None if this block does not require a\n def render_form(self, value, prefix='', errors=None) # Render the HTML for this block with 'value' as its content.\n def value_from_datadict(self, data, files, prefix)\n def value_omitted_from_data(self, data, files, name) # Used only for top-level blocks wrapped by BlockWidget (i.e.: typically only Stre\n def bind(self, value, prefix=None, errors=None) # Return a BoundBlock which represents the association of this block definition wi\n def get_default(self) # Return this block's default value (conventionally found in self.meta.default),\n def prototype_block(self) # Return a BoundBlock that can be used as a basis for new empty block instances to\n def clean(self, value) # Validate value and return a cleaned version of it, or throw a ValidationError if\n def to_python(self, value) # Convert 'value' from a simple (JSON-serialisable) value to a (possibly complex) \n def get_prep_value(self, value) # The reverse of to_python; convert the python value into JSON-serialisable form.\n def get_context(self, value, parent_context=None) # Return a dict of context variables (derived from the block value and combined wi\n def get_template(self, context=None) # Return the template to use for rendering the block if specified on meta class.\n def render(self, value, context=None) # Return a text rendering of 'value', suitable for display on templates. By defaul\n def get_api_representation(self, value, context=None) # Can be used to customise the API response and defaults to the value returned by \n def render_basic(self, value, context=None) # Return a text rendering of 'value', suitable for display on templates. render() \n def get_searchable_content(self, value) # Returns a list of strings containing text content within this block to be used i\n def check(self, **kwargs) # Hook for the Django system checks framework -\n def _check_name(self, **kwargs) # Helper method called by container blocks as part of the system checks framework,\n def id_for_label(self, prefix) # Return the ID to be used as the 'for' attribute of <label> elements that refer t\n def required(self) # Flag used to determine whether labels for this block should display a 'required'\n def deconstruct(self)\n def __eq__(self, other) # Implement equality on block objects so that two blocks with matching definitions","label":0},{"cid":"wagtail/images/views/images.py","text":"wagtail/images/views/images.py\ndef index(request)\ndef edit(request, image_id)\ndef url_generator(request, image_id)\ndef generate_url(request, image_id, filter_spec)\ndef preview(request, image_id, filter_spec)\ndef delete(request, image_id)\ndef add(request)\ndef usage(request, image_id)","label":0}],"string":"[\n {\n \"cid\": \"docs/conf.py\",\n \"text\": \"docs/conf.py\\ndef setup(app)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/urls/__init__.py\",\n \"text\": \"wagtail/admin/urls/__init__.py\\ndef display_custom_404(view_func)\\n def wrapper(request, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py\",\n \"text\": \"wagtail/api/v2/endpoints.py\\nclass BaseAPIEndpoint\\n def __init__(self, *args, **kwargs)\\n def get_queryset(self)\\n def listing_view(self, request)\\n def detail_view(self, request, pk)\\n def find_view(self, request)\\n def find_object(self, queryset, request) # Override this to implement more find methods.\\n def handle_exception(self, exc)\\n def _convert_api_fields(cls, fields)\\n def get_body_fields(cls, model)\\n def get_body_fields_names(cls, model)\\n def get_meta_fields(cls, model)\\n def get_meta_fields_names(cls, model)\\n def get_field_serializer_overrides(cls, model)\\n def get_available_fields(cls, model, db_fields_only=False) # Returns a list of all the fields that can be used in the API for the\\n def get_detail_default_fields(cls, model)\\n def get_listing_default_fields(cls, model)\\n def get_nested_default_fields(cls, model)\\n def check_query_parameters(self, queryset) # Ensure that only valid query paramters are included in the URL.\\n def _get_serializer_class(cls, router, model, fields_config, show_details=False, nested=False)\\n def get_serializer_class(self)\\n def get_serializer_context(self) # The serialization context differs between listing and detail views.\\n def get_renderer_context(self)\\n def get_urlpatterns(cls) # This returns a list of URL patterns for the endpoint\\n def get_model_listing_urlpath(cls, model, namespace='')\\n def get_object_detail_urlpath(cls, model, pk, namespace='')\\nclass PagesAPIEndpoint\\n def get_queryset(self)\\n def get_object(self)\\n def find_object(self, queryset, request)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py\",\n \"text\": \"wagtail/api/v2/serializers.py\\nclass TypeField # Serializes the \\\"type\\\" field of each object.\\n def get_attribute(self, instance)\\n def to_representation(self, obj)\\nclass DetailUrlField # Serializes the \\\"detail_url\\\" field of each object.\\n def get_attribute(self, instance)\\n def to_representation(self, url)\\nclass PageHtmlUrlField # Serializes the \\\"html_url\\\" field for pages.\\n def get_attribute(self, instance)\\n def to_representation(self, page)\\nclass PageTypeField # Serializes the \\\"type\\\" field for pages.\\n def get_attribute(self, instance)\\n def to_representation(self, page)\\nclass RelatedField # Serializes related objects (eg, foreign keys).\\n def __init__(self, *args, **kwargs)\\n def to_representation(self, value)\\nclass PageParentField # Serializes the \\\"parent\\\" field on Page objects.\\n def get_attribute(self, instance)\\n def to_representation(self, value)\\nclass ChildRelationField # Serializes child relations.\\n def __init__(self, *args, **kwargs)\\n def to_representation(self, value)\\nclass StreamField # Serializes StreamField values.\\n def to_representation(self, value)\\nclass TagsField # Serializes django-taggit TaggableManager fields.\\n def to_representation(self, value)\\nclass BaseSerializer\\n def to_representation(self, instance)\\n def build_property_field(self, field_name, model_class)\\n def build_relational_field(self, field_name, relation_info)\\nclass PageSerializer\\n def build_relational_field(self, field_name, relation_info)\\ndef get_serializer_class(model, field_names, meta_fields, field_serializer_overrides=None, child_serializer_classes=None, base=BaseSerializer)\\n class Meta\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py\",\n \"text\": \"wagtail/bin/wagtail.py\\ndef pluralize(value, arg='s')\\nclass Command\\n def create_parser(self, command_name=None)\\n def add_arguments(self, parser)\\n def print_help(self, command_name)\\n def execute(self, argv)\\nclass CreateProject\\n def add_arguments(self, parser)\\n def run(self, project_name=None, dest_dir=None)\\nclass UpdateModulePaths\\n def add_arguments(self, parser)\\n def run(self, root_path=None, list_files=False, diff=False, ignored_dirs=None, ignored_patterns=None)\\n def _rewrite_line(self, line)\\n def _show_diff(self, filename, relative_path=None)\\n def _count_changes(self, filename)\\n def _rewrite_file(self, filename)\\ndef prog_name()\\ndef help_index()\\ndef unknown_command(command)\\ndef main()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py\",\n \"text\": \"wagtail/contrib/forms/models.py\\nclass AbstractFormSubmission # Data for a form submission.\\n def get_data(self) # Returns dict with form data.\\n def __str__(self)\\n class Meta\\nclass FormSubmission # Data for a Form submission.\\nclass AbstractFormField # Database Fields required for building a Django Form field.\\n def clean_name(self)\\n class Meta\\nclass AbstractForm # A Form Page. Pages implementing a form should inherit from it\\n def __init__(self, *args, **kwargs)\\n class Meta\\n def get_form_fields(self) # Form page expects `form_fields` to be declared.\\n def get_data_fields(self) # Returns a list of tuples with (field_name, field_label).\\n def get_form_class(self)\\n def get_form_parameters(self)\\n def get_form(self, *args, **kwargs)\\n def get_landing_page_template(self, request, *args, **kwargs)\\n def get_submission_class(self) # Returns submission class.\\n def process_form_submission(self, form) # Accepts form instance with submitted data, user and page.\\n def render_landing_page(self, request, form_submission=None, *args, **kwargs) # Renders the landing page.\\n def serve_submissions_list_view(self, request, *args, **kwargs) # Returns list submissions view for admin.\\n def serve(self, request, *args, **kwargs)\\n def serve_preview(self, request, mode)\\nclass AbstractEmailForm # A Form Page that sends email. Pages implementing a form to be send to an email s\\n def process_form_submission(self, form)\\n def send_mail(self, form)\\n class Meta\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py\",\n \"text\": \"wagtail/core/blocks/base.py\\nclass BaseBlock\\n def __new__(mcs, name, bases, attrs)\\nclass Block\\n class Meta\\n def __new__(cls, *args, **kwargs)\\n def all_blocks(self) # Return a list consisting of self and all block objects that are direct or indire\\n def all_media(self)\\n def all_html_declarations(self)\\n def __init__(self, **kwargs)\\n def set_name(self, name)\\n def media(self)\\n def html_declarations(self) # Return an HTML fragment to be rendered on the form page once per block definitio\\n def js_initializer(self) # Returns a Javascript expression string, or None if this block does not require a\\n def render_form(self, value, prefix='', errors=None) # Render the HTML for this block with 'value' as its content.\\n def value_from_datadict(self, data, files, prefix)\\n def value_omitted_from_data(self, data, files, name) # Used only for top-level blocks wrapped by BlockWidget (i.e.: typically only Stre\\n def bind(self, value, prefix=None, errors=None) # Return a BoundBlock which represents the association of this block definition wi\\n def get_default(self) # Return this block's default value (conventionally found in self.meta.default),\\n def prototype_block(self) # Return a BoundBlock that can be used as a basis for new empty block instances to\\n def clean(self, value) # Validate value and return a cleaned version of it, or throw a ValidationError if\\n def to_python(self, value) # Convert 'value' from a simple (JSON-serialisable) value to a (possibly complex) \\n def get_prep_value(self, value) # The reverse of to_python; convert the python value into JSON-serialisable form.\\n def get_context(self, value, parent_context=None) # Return a dict of context variables (derived from the block value and combined wi\\n def get_template(self, context=None) # Return the template to use for rendering the block if specified on meta class.\\n def render(self, value, context=None) # Return a text rendering of 'value', suitable for display on templates. By defaul\\n def get_api_representation(self, value, context=None) # Can be used to customise the API response and defaults to the value returned by \\n def render_basic(self, value, context=None) # Return a text rendering of 'value', suitable for display on templates. render() \\n def get_searchable_content(self, value) # Returns a list of strings containing text content within this block to be used i\\n def check(self, **kwargs) # Hook for the Django system checks framework -\\n def _check_name(self, **kwargs) # Helper method called by container blocks as part of the system checks framework,\\n def id_for_label(self, prefix) # Return the ID to be used as the 'for' attribute of <label> elements that refer t\\n def required(self) # Flag used to determine whether labels for this block should display a 'required'\\n def deconstruct(self)\\n def __eq__(self, other) # Implement equality on block objects so that two blocks with matching definitions\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/views/images.py\",\n \"text\": \"wagtail/images/views/images.py\\ndef index(request)\\ndef edit(request, image_id)\\ndef url_generator(request, image_id)\\ndef generate_url(request, image_id, filter_spec)\\ndef preview(request, image_id, filter_spec)\\ndef delete(request, image_id)\\ndef add(request)\\ndef usage(request, image_id)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"docs/conf.py::setup","text":"docs/conf.py::setup\ndef setup(app):\n app.add_stylesheet('css/custom.css')","label":0},{"cid":"wagtail/admin/urls/__init__.py::display_custom_404","text":"wagtail/admin/urls/__init__.py::display_custom_404\ndef display_custom_404(view_func):\n @functools.wraps(view_func)\n def wrapper(request, *args, **kwargs):\n try:\n return view_func(request, *args, **kwargs)\n except Http404:\n return page_not_found(request, '', template_name='wagtailadmin/404.html')\n\n return wrapper","label":0},{"cid":"wagtail/admin/urls/__init__.py::display_custom_404.wrapper","text":"wagtail/admin/urls/__init__.py::display_custom_404.wrapper\n @functools.wraps(view_func)\n def wrapper(request, *args, **kwargs):\n try:\n return view_func(request, *args, **kwargs)\n except Http404:\n return page_not_found(request, '', template_name='wagtailadmin/404.html')","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.__init__","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.__init__\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n # seen_types is a mapping of type name strings (format: \"app_label.ModelName\")\n # to model classes. When an object is serialised in the API, its model\n # is added to this mapping. This is used by the Admin API which appends a\n # summary of the used types to the response.\n self.seen_types = OrderedDict()","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_queryset","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_queryset\n def get_queryset(self):\n return self.model.objects.all().order_by('id')","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.listing_view","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.listing_view\n def listing_view(self, request):\n queryset = self.get_queryset()\n self.check_query_parameters(queryset)\n queryset = self.filter_queryset(queryset)\n queryset = self.paginate_queryset(queryset)\n serializer = self.get_serializer(queryset, many=True)\n return self.get_paginated_response(serializer.data)","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.detail_view","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.detail_view\n def detail_view(self, request, pk):\n instance = self.get_object()\n serializer = self.get_serializer(instance)\n return Response(serializer.data)","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.find_view","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.find_view\n def find_view(self, request):\n queryset = self.get_queryset()\n\n try:\n obj = self.find_object(queryset, request)\n\n if obj is None:\n raise self.model.DoesNotExist\n\n except self.model.DoesNotExist:\n raise Http404(\"not found\")\n\n # Generate redirect\n url = get_object_detail_url(self.request.wagtailapi_router, request, self.model, obj.pk)\n\n if url is None:\n # Shouldn't happen unless this endpoint isn't actually installed in the router\n raise Exception(\"Cannot generate URL to detail view. Is '{}' installed in the API router?\".format(self.__class__.__name__))\n\n return redirect(url)","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.find_object","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.find_object\n def find_object(self, queryset, request):\n \"\"\"\n Override this to implement more find methods.\n \"\"\"\n if 'id' in request.GET:\n return queryset.get(id=request.GET['id'])","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.handle_exception","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.handle_exception\n def handle_exception(self, exc):\n if isinstance(exc, Http404):\n data = {'message': str(exc)}\n return Response(data, status=status.HTTP_404_NOT_FOUND)\n elif isinstance(exc, BadRequestError):\n data = {'message': str(exc)}\n return Response(data, status=status.HTTP_400_BAD_REQUEST)\n return super().handle_exception(exc)","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint._convert_api_fields","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint._convert_api_fields\n @classmethod\n def _convert_api_fields(cls, fields):\n return [field if isinstance(field, APIField) else APIField(field)\n for field in fields]","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_body_fields","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_body_fields\n @classmethod\n def get_body_fields(cls, model):\n return cls._convert_api_fields(cls.body_fields + list(getattr(model, 'api_fields', ())))","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_body_fields_names","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_body_fields_names\n @classmethod\n def get_body_fields_names(cls, model):\n return [field.name for field in cls.get_body_fields(model)]","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_meta_fields","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_meta_fields\n @classmethod\n def get_meta_fields(cls, model):\n return cls._convert_api_fields(cls.meta_fields + list(getattr(model, 'api_meta_fields', ())))","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_meta_fields_names","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_meta_fields_names\n @classmethod\n def get_meta_fields_names(cls, model):\n return [field.name for field in cls.get_meta_fields(model)]","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_field_serializer_overrides","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_field_serializer_overrides\n @classmethod\n def get_field_serializer_overrides(cls, model):\n return {field.name: field.serializer\n for field in cls.get_body_fields(model) + cls.get_meta_fields(model)\n if field.serializer is not None}","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_available_fields","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_available_fields\n @classmethod\n def get_available_fields(cls, model, db_fields_only=False):\n \"\"\"\n Returns a list of all the fields that can be used in the API for the\n specified model class.\n\n Setting db_fields_only to True will remove all fields that do not have\n an underlying column in the database (eg, type/detail_url and any custom\n fields that are callables)\n \"\"\"\n fields = cls.get_body_fields_names(model) + cls.get_meta_fields_names(model)\n\n if db_fields_only:\n # Get list of available database fields then remove any fields in our\n # list that isn't a database field\n database_fields = set()\n for field in model._meta.get_fields():\n database_fields.add(field.name)\n\n if hasattr(field, 'attname'):\n database_fields.add(field.attname)\n\n fields = [field for field in fields if field in database_fields]\n\n return fields","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_detail_default_fields","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_detail_default_fields\n @classmethod\n def get_detail_default_fields(cls, model):\n return cls.get_available_fields(model)","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_listing_default_fields","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_listing_default_fields\n @classmethod\n def get_listing_default_fields(cls, model):\n return cls.listing_default_fields[:]","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_nested_default_fields","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_nested_default_fields\n @classmethod\n def get_nested_default_fields(cls, model):\n return cls.nested_default_fields[:]","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.check_query_parameters","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.check_query_parameters\n def check_query_parameters(self, queryset):\n \"\"\"\n Ensure that only valid query paramters are included in the URL.\n \"\"\"\n query_parameters = set(self.request.GET.keys())\n\n # All query paramters must be either a database field or an operation\n allowed_query_parameters = set(self.get_available_fields(queryset.model, db_fields_only=True)).union(self.known_query_parameters)\n unknown_parameters = query_parameters - allowed_query_parameters\n if unknown_parameters:\n raise BadRequestError(\"query parameter is not an operation or a recognised field: %s\" % ', '.join(sorted(unknown_parameters)))","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint._get_serializer_class","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint._get_serializer_class\n @classmethod\n def _get_serializer_class(cls, router, model, fields_config, show_details=False, nested=False):\n # Get all available fields\n body_fields = cls.get_body_fields_names(model)\n meta_fields = cls.get_meta_fields_names(model)\n all_fields = body_fields + meta_fields\n\n # Remove any duplicates\n all_fields = list(OrderedDict.fromkeys(all_fields))\n\n if not show_details:\n # Remove detail only fields\n for field in cls.detail_only_fields:\n try:\n all_fields.remove(field)\n except KeyError:\n pass\n\n # Get list of configured fields\n if show_details:\n fields = set(cls.get_detail_default_fields(model))\n elif nested:\n fields = set(cls.get_nested_default_fields(model))\n else:\n fields = set(cls.get_listing_default_fields(model))\n\n # If first field is '*' start with all fields\n # If first field is '_' start with no fields\n if fields_config and fields_config[0][0] == '*':\n fields = set(all_fields)\n fields_config = fields_config[1:]\n elif fields_config and fields_config[0][0] == '_':\n fields = set()\n fields_config = fields_config[1:]\n\n mentioned_fields = set()\n sub_fields = {}\n\n for field_name, negated, field_sub_fields in fields_config:\n if negated:\n try:\n fields.remove(field_name)\n except KeyError:\n pass\n else:\n fields.add(field_name)\n if field_sub_fields:\n sub_fields[field_name] = field_sub_fields\n\n mentioned_fields.add(field_name)\n\n unknown_fields = mentioned_fields - set(all_fields)\n\n if unknown_fields:\n raise BadRequestError(\"unknown fields:","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_serializer_class","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_serializer_class\n def get_serializer_class(self):\n request = self.request\n\n # Get model\n if self.action == 'listing_view':\n model = self.get_queryset().model\n else:\n model = type(self.get_object())\n\n # Fields\n if 'fields' in request.GET:\n try:\n fields_config = parse_fields_parameter(request.GET['fields'])\n except ValueError as e:\n raise BadRequestError(\"fields error: %s\" % str(e))\n else:\n # Use default fields\n fields_config = []\n\n # Allow \"detail_only\" (eg parent) fields on detail view\n if self.action == 'listing_view':\n show_details = False\n else:\n show_details = True\n\n return self._get_serializer_class(self.request.wagtailapi_router, model, fields_config, show_details=show_details)","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_serializer_context","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_serializer_context\n def get_serializer_context(self):\n \"\"\"\n The serialization context differs between listing and detail views.\n \"\"\"\n return {\n 'request': self.request,\n 'view': self,\n 'router': self.request.wagtailapi_router\n }","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_renderer_context","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_renderer_context\n def get_renderer_context(self):\n context = super().get_renderer_context()\n context['indent'] = 4\n return context","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_urlpatterns","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_urlpatterns\n @classmethod\n def get_urlpatterns(cls):\n \"\"\"\n This returns a list of URL patterns for the endpoint\n \"\"\"\n return [\n url(r'^$', cls.as_view({'get': 'listing_view'}), name='listing'),\n url(r'^(?P<pk>\\d+)/$', cls.as_view({'get': 'detail_view'}), name='detail'),\n url(r'^find/$', cls.as_view({'get': 'find_view'}), name='find'),\n ]","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_model_listing_urlpath","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_model_listing_urlpath\n @classmethod\n def get_model_listing_urlpath(cls, model, namespace=''):\n if namespace:\n url_name = namespace + ':listing'\n else:\n url_name = 'listing'\n\n return reverse(url_name)","label":0},{"cid":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_object_detail_urlpath","text":"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_object_detail_urlpath\n @classmethod\n def get_object_detail_urlpath(cls, model, pk, namespace=''):\n if namespace:\n url_name = namespace + ':detail'\n else:\n url_name = 'detail'\n\n return reverse(url_name, args=(pk, ))","label":0},{"cid":"wagtail/api/v2/endpoints.py::PagesAPIEndpoint.get_queryset","text":"wagtail/api/v2/endpoints.py::PagesAPIEndpoint.get_queryset\n def get_queryset(self):\n request = self.request\n\n # Allow pages to be filtered to a specific type\n try:\n models = page_models_from_string(request.GET.get('type', 'wagtailcore.Page'))\n except (LookupError, ValueError):\n raise BadRequestError(\"type doesn't exist\")\n\n if not models:\n models = [Page]\n\n if len(models) == 1:\n queryset = models[0].objects.all()\n else:\n queryset = Page.objects.all()\n\n # Filter pages by specified models\n queryset = filter_page_type(queryset, models)\n\n # Get live pages that are not in a private section\n queryset = queryset.public().live()\n\n # Filter by site\n if request.site:\n queryset = queryset.descendant_of(request.site.root_page, inclusive=True)\n else:\n # No sites configured\n queryset = queryset.none()\n\n return queryset","label":0},{"cid":"wagtail/api/v2/endpoints.py::PagesAPIEndpoint.get_object","text":"wagtail/api/v2/endpoints.py::PagesAPIEndpoint.get_object\n def get_object(self):\n base = super().get_object()\n return base.specific","label":0},{"cid":"wagtail/api/v2/endpoints.py::PagesAPIEndpoint.find_object","text":"wagtail/api/v2/endpoints.py::PagesAPIEndpoint.find_object\n def find_object(self, queryset, request):\n if 'html_path' in request.GET and request.site is not None:\n path = request.GET['html_path']\n path_components = [component for component in path.split('/') if component]\n\n try:\n page, _, _ = request.site.root_page.specific.route(request, path_components)\n except Http404:\n return\n\n if queryset.filter(id=page.id).exists():\n return page\n\n return super().find_object(queryset, request)","label":0},{"cid":"wagtail/api/v2/serializers.py::TypeField.get_attribute","text":"wagtail/api/v2/serializers.py::TypeField.get_attribute\n def get_attribute(self, instance):\n return instance","label":0},{"cid":"wagtail/api/v2/serializers.py::TypeField.to_representation","text":"wagtail/api/v2/serializers.py::TypeField.to_representation\n def to_representation(self, obj):\n name = type(obj)._meta.app_label + '.' + type(obj).__name__\n self.context['view'].seen_types[name] = type(obj)\n return name","label":0},{"cid":"wagtail/api/v2/serializers.py::DetailUrlField.get_attribute","text":"wagtail/api/v2/serializers.py::DetailUrlField.get_attribute\n def get_attribute(self, instance):\n url = get_object_detail_url(self.context['router'], self.context['request'], type(instance), instance.pk)\n\n if url:\n return url\n else:\n # Hide the detail_url field if the object doesn't have an endpoint\n raise SkipField","label":0},{"cid":"wagtail/api/v2/serializers.py::DetailUrlField.to_representation","text":"wagtail/api/v2/serializers.py::DetailUrlField.to_representation\n def to_representation(self, url):\n return url","label":0},{"cid":"wagtail/api/v2/serializers.py::PageHtmlUrlField.get_attribute","text":"wagtail/api/v2/serializers.py::PageHtmlUrlField.get_attribute\n def get_attribute(self, instance):\n return instance","label":0},{"cid":"wagtail/api/v2/serializers.py::PageHtmlUrlField.to_representation","text":"wagtail/api/v2/serializers.py::PageHtmlUrlField.to_representation\n def to_representation(self, page):\n try:\n return page.full_url\n except NoReverseMatch:\n return None","label":0},{"cid":"wagtail/api/v2/serializers.py::PageTypeField.get_attribute","text":"wagtail/api/v2/serializers.py::PageTypeField.get_attribute\n def get_attribute(self, instance):\n return instance","label":0},{"cid":"wagtail/api/v2/serializers.py::PageTypeField.to_representation","text":"wagtail/api/v2/serializers.py::PageTypeField.to_representation\n def to_representation(self, page):\n name = page.specific_class._meta.app_label + '.' + page.specific_class.__name__\n self.context['view'].seen_types[name] = page.specific_class\n return name","label":1},{"cid":"wagtail/api/v2/serializers.py::RelatedField.__init__","text":"wagtail/api/v2/serializers.py::RelatedField.__init__\n def __init__(self, *args, **kwargs):\n self.serializer_class = kwargs.pop('serializer_class')\n super().__init__(*args, **kwargs)","label":0},{"cid":"wagtail/api/v2/serializers.py::RelatedField.to_representation","text":"wagtail/api/v2/serializers.py::RelatedField.to_representation\n def to_representation(self, value):\n serializer = self.serializer_class(context=self.context)\n return serializer.to_representation(value)","label":0},{"cid":"wagtail/api/v2/serializers.py::PageParentField.get_attribute","text":"wagtail/api/v2/serializers.py::PageParentField.get_attribute\n def get_attribute(self, instance):\n parent = instance.get_parent()\n\n site_pages = pages_for_site(self.context['request'].site)\n if site_pages.filter(id=parent.id).exists():\n return parent","label":0},{"cid":"wagtail/api/v2/serializers.py::PageParentField.to_representation","text":"wagtail/api/v2/serializers.py::PageParentField.to_representation\n def to_representation(self, value):\n serializer_class = get_serializer_class(value.__class__, ['id', 'type', 'detail_url', 'html_url', 'title'], meta_fields=['type', 'detail_url', 'html_url'], base=PageSerializer)\n serializer = serializer_class(context=self.context)\n return serializer.to_representation(value)","label":0},{"cid":"wagtail/api/v2/serializers.py::ChildRelationField.__init__","text":"wagtail/api/v2/serializers.py::ChildRelationField.__init__\n def __init__(self, *args, **kwargs):\n self.serializer_class = kwargs.pop('serializer_class')\n super().__init__(*args, **kwargs)","label":0},{"cid":"wagtail/api/v2/serializers.py::ChildRelationField.to_representation","text":"wagtail/api/v2/serializers.py::ChildRelationField.to_representation\n def to_representation(self, value):\n serializer = self.serializer_class(context=self.context)\n\n return [\n serializer.to_representation(child_object)\n for child_object in value.all()\n ]","label":0},{"cid":"wagtail/api/v2/serializers.py::StreamField.to_representation","text":"wagtail/api/v2/serializers.py::StreamField.to_representation\n def to_representation(self, value):\n return value.stream_block.get_api_representation(value, self.context)","label":0},{"cid":"wagtail/api/v2/serializers.py::TagsField.to_representation","text":"wagtail/api/v2/serializers.py::TagsField.to_representation\n def to_representation(self, value):\n return list(value.all().order_by('name').values_list('name', flat=True))","label":0},{"cid":"wagtail/api/v2/serializers.py::BaseSerializer.to_representation","text":"wagtail/api/v2/serializers.py::BaseSerializer.to_representation\n def to_representation(self, instance):\n data = OrderedDict()\n fields = [field for field in self.fields.values() if not field.write_only]\n\n # Split meta fields from core fields\n meta_fields = [field for field in fields if field.field_name in self.meta_fields]\n fields = [field for field in fields if field.field_name not in self.meta_fields]\n\n # Make sure id is always first. This will be filled in later\n if 'id' in [field.field_name for field in fields]:\n data['id'] = None\n\n # Serialise meta fields\n meta = OrderedDict()\n for field in meta_fields:\n try:\n attribute = field.get_attribute(instance)\n except SkipField:\n continue\n\n if attribute is None:\n # We skip `to_representation` for `None` values so that\n # fields do not have to explicitly deal with that case.\n meta[field.field_name] = None\n else:\n meta[field.field_name] = field.to_representation(attribute)\n\n if meta:\n data['meta'] = meta\n\n # Serialise core fields\n for field in fields:\n try:\n attribute = field.get_attribute(instance)\n except SkipField:\n continue\n\n if attribute is None:\n # We skip `to_representation` for `None` values so that\n # fields do not have to explicitly deal with that case.\n data[field.field_name] = None\n else:\n data[field.field_name] = field.to_representation(attribute)\n\n return data","label":0},{"cid":"wagtail/api/v2/serializers.py::BaseSerializer.build_property_field","text":"wagtail/api/v2/serializers.py::BaseSerializer.build_property_field\n def build_property_field(self, field_name, model_class):\n # TaggableManager is not a Django field so it gets treated as a property\n field = getattr(model_class, field_name)\n if isinstance(field, _TaggableManager):\n return TagsField, {}\n\n return super().build_property_field(field_name, model_class)","label":0},{"cid":"wagtail/api/v2/serializers.py::BaseSerializer.build_relational_field","text":"wagtail/api/v2/serializers.py::BaseSerializer.build_relational_field\n def build_relational_field(self, field_name, relation_info):\n field_class, field_kwargs = super().build_relational_field(field_name, relation_info)\n field_kwargs['serializer_class'] = self.child_serializer_classes[field_name]\n return field_class, field_kwargs","label":0},{"cid":"wagtail/api/v2/serializers.py::PageSerializer.build_relational_field","text":"wagtail/api/v2/serializers.py::PageSerializer.build_relational_field\n def build_relational_field(self, field_name, relation_info):\n # Find all relation fields that point to child class and make them use\n # the ChildRelationField class.\n if relation_info.to_many:\n model = getattr(self.Meta, 'model')\n child_relations = {\n child_relation.field.remote_field.related_name: child_relation.related_model\n for child_relation in get_all_child_relations(model)\n }\n\n if field_name in child_relations and field_name in self.child_serializer_classes:\n return ChildRelationField, {'serializer_class': self.child_serializer_classes[field_name]}\n\n return super().build_relational_field(field_name, relation_info)","label":0},{"cid":"wagtail/api/v2/serializers.py::get_serializer_class","text":"wagtail/api/v2/serializers.py::get_serializer_class\ndef get_serializer_class(model, field_names, meta_fields, field_serializer_overrides=None, child_serializer_classes=None, base=BaseSerializer):\n model_ = model\n\n class Meta:\n model = model_\n fields = list(field_names)\n\n attrs = {\n 'Meta': Meta,\n 'meta_fields': list(meta_fields),\n 'child_serializer_classes': child_serializer_classes or {},\n }\n\n if field_serializer_overrides:\n attrs.update(field_serializer_overrides)\n\n return type(str(model_.__name__ + 'Serializer'), (base, ), attrs)","label":0},{"cid":"wagtail/bin/wagtail.py::pluralize","text":"wagtail/bin/wagtail.py::pluralize\ndef pluralize(value, arg='s'):\n return '' if value == 1 else arg","label":0},{"cid":"wagtail/bin/wagtail.py::Command.create_parser","text":"wagtail/bin/wagtail.py::Command.create_parser\n def create_parser(self, command_name=None):\n if command_name is None:\n prog = None\n else:\n # hack the prog name as reported to ArgumentParser to include the command\n prog = \"%s %s\" % (prog_name(), command_name)\n\n parser = ArgumentParser(\n description=getattr(self, 'description', None), add_help=False, prog=prog\n )\n self.add_arguments(parser)\n return parser","label":0},{"cid":"wagtail/bin/wagtail.py::Command.add_arguments","text":"wagtail/bin/wagtail.py::Command.add_arguments\n def add_arguments(self, parser):\n pass","label":0},{"cid":"wagtail/bin/wagtail.py::Command.print_help","text":"wagtail/bin/wagtail.py::Command.print_help\n def print_help(self, command_name):\n parser = self.create_parser(command_name=command_name)\n parser.print_help()","label":0},{"cid":"wagtail/bin/wagtail.py::Command.execute","text":"wagtail/bin/wagtail.py::Command.execute\n def execute(self, argv):\n parser = self.create_parser()\n options = parser.parse_args(sys.argv[2:])\n options_dict = vars(options)\n self.run(**options_dict)","label":0},{"cid":"wagtail/bin/wagtail.py::CreateProject.add_arguments","text":"wagtail/bin/wagtail.py::CreateProject.add_arguments\n def add_arguments(self, parser):\n parser.add_argument('project_name', help=\"Name for your Wagtail project\")\n parser.add_argument('dest_dir', nargs='?', help=\"Destination directory inside which to create the project\")","label":0},{"cid":"wagtail/bin/wagtail.py::CreateProject.run","text":"wagtail/bin/wagtail.py::CreateProject.run\n def run(self, project_name=None, dest_dir=None):\n # Make sure given name is not already in use by another python package/module.\n try:\n __import__(project_name)\n except ImportError:\n pass\n else:\n sys.exit(\"'%s' conflicts with the name of an existing \"\n \"Python module and cannot be used as a project \"\n \"name. Please try another name.\" % project_name)\n\n print(\"Creating a Wagtail project called %(project_name)s\" % {'project_name': project_name}) # noqa\n\n # Create the project from the Wagtail template using startapp\n\n # First find the path to Wagtail\n import wagtail\n wagtail_path = os.path.dirname(wagtail.__file__)\n template_path = os.path.join(wagtail_path, 'project_template')\n\n # Call django-admin startproject\n utility_args = ['django-admin.py',\n 'startproject',\n '--template=' + template_path,\n '--ext=html,rst',\n '--name=Dockerfile',\n project_name]\n\n if dest_dir:\n utility_args.append(dest_dir)\n\n utility = ManagementUtility(utility_args)\n utility.execute()\n\n print(\"Success! %(project_name)s has been created\" % {'project_name': project_name}) # noqa","label":0},{"cid":"wagtail/bin/wagtail.py::UpdateModulePaths.add_arguments","text":"wagtail/bin/wagtail.py::UpdateModulePaths.add_arguments\n def add_arguments(self, parser):\n parser.add_argument('root_path', nargs='?', help=\"Path to your project's root\")\n parser.add_argument('--list', action='store_true', dest='list_files', help=\"Show the list of files to change, without modifying them\")\n parser.add_argument('--diff', action='store_true', help=\"Show the changes that would be made, without modifying the files\")\n parser.add_argument(\n '--ignore-dir', action='append', dest='ignored_dirs', metavar='NAME',\n help=\"Ignore files in this directory\"\n )\n parser.add_argument(\n '--ignore-file', action='append', dest='ignored_patterns', metavar='NAME',\n help=\"Ignore files with this name (supports wildcards)\"\n )","label":0},{"cid":"wagtail/bin/wagtail.py::UpdateModulePaths.run","text":"wagtail/bin/wagtail.py::UpdateModulePaths.run\n def run(self, root_path=None, list_files=False, diff=False, ignored_dirs=None, ignored_patterns=None):\n if root_path is None:\n root_path = os.getcwd()\n\n absolute_ignored_dirs = [\n os.path.abspath(dir_path) + os.sep\n for dir_path in (ignored_dirs or [])\n ]\n\n if ignored_patterns is None:\n ignored_patterns = []\n\n checked_file_count = 0\n changed_file_count = 0\n\n for (dirpath, dirnames, filenames) in os.walk(root_path):\n dirpath_with_slash = os.path.abspath(dirpath) + os.sep\n if any(dirpath_with_slash.startswith(ignored_dir) for ignored_dir in absolute_ignored_dirs):\n continue\n\n for filename in filenames:\n if not filename.lower().endswith('.py'):\n continue\n\n if any(fnmatch.fnmatch(filename, pattern) for pattern in ignored_patterns):\n continue\n\n path = os.path.join(dirpath, filename)\n relative_path = os.path.relpath(path, start=root_path)\n checked_file_count += 1\n\n if diff:\n change_count = self._show_diff(path, relative_path=relative_path)\n else:\n if list_files:\n change_count = self._count_changes(path)\n else: # actually update\n change_count = self._rewrite_file(path)\n if change_count:\n print(\"%s - %d change%s\" % (relative_path, change_count, pluralize(change_count))) # NOQA\n\n if change_count:\n changed_file_count += 1\n\n if diff or list_files:\n print(\n \"\\nChecked %d .py file%s, %d file%s to update.\" % (\n checked_file_count, pluralize(checked_file_count),\n changed_file_count, pluralize(changed_file_","label":0},{"cid":"wagtail/bin/wagtail.py::UpdateModulePaths._rewrite_line","text":"wagtail/bin/wagtail.py::UpdateModulePaths._rewrite_line\n def _rewrite_line(self, line):\n for pattern, repl in self.REPLACEMENTS:\n line = re.sub(pattern, repl, line)\n return line","label":0},{"cid":"wagtail/bin/wagtail.py::UpdateModulePaths._show_diff","text":"wagtail/bin/wagtail.py::UpdateModulePaths._show_diff\n def _show_diff(self, filename, relative_path=None):\n change_count = 0\n original = []\n updated = []\n\n with open(filename) as f:\n for original_line in f:\n original.append(original_line)\n\n line = self._rewrite_line(original_line)\n updated.append(line)\n if line != original_line:\n change_count += 1\n\n if change_count:\n relative_path = relative_path or filename\n\n sys.stdout.writelines(unified_diff(\n original, updated, fromfile=\"%s:before\" % relative_path, tofile=\"%s:after\" % relative_path\n ))\n\n return change_count","label":0},{"cid":"wagtail/bin/wagtail.py::UpdateModulePaths._count_changes","text":"wagtail/bin/wagtail.py::UpdateModulePaths._count_changes\n def _count_changes(self, filename):\n change_count = 0\n\n with open(filename) as f:\n for original_line in f:\n line = self._rewrite_line(original_line)\n if line != original_line:\n change_count += 1\n\n return change_count","label":0},{"cid":"wagtail/bin/wagtail.py::UpdateModulePaths._rewrite_file","text":"wagtail/bin/wagtail.py::UpdateModulePaths._rewrite_file\n def _rewrite_file(self, filename):\n change_count = 0\n\n with fileinput.FileInput(filename, inplace=True) as f:\n for original_line in f:\n line = self._rewrite_line(original_line)\n print_(line, end='') # NOQA\n if line != original_line:\n change_count += 1\n\n return change_count","label":0},{"cid":"wagtail/bin/wagtail.py::prog_name","text":"wagtail/bin/wagtail.py::prog_name\ndef prog_name():\n return os.path.basename(sys.argv[0])","label":0},{"cid":"wagtail/bin/wagtail.py::help_index","text":"wagtail/bin/wagtail.py::help_index\ndef help_index():\n print(\"Type '%s help <subcommand>' for help on a specific subcommand.\\n\" % prog_name()) # NOQA\n print(\"Available subcommands:\\n\") # NOQA\n for name, cmd in sorted(COMMANDS.items()):\n print(\" %s%s\" % (name.ljust(20), cmd.description)) # NOQA","label":0},{"cid":"wagtail/bin/wagtail.py::unknown_command","text":"wagtail/bin/wagtail.py::unknown_command\ndef unknown_command(command):\n print(\"Unknown command: '%s'\" % command) # NOQA\n print(\"Type '%s help' for usage.\" % prog_name()) # NOQA\n sys.exit(1)","label":0},{"cid":"wagtail/bin/wagtail.py::main","text":"wagtail/bin/wagtail.py::main\ndef main():\n try:\n command_name = sys.argv[1]\n except IndexError:\n help_index()\n return\n\n if command_name == 'help':\n try:\n help_command_name = sys.argv[2]\n except IndexError:\n help_index()\n return\n\n try:\n command = COMMANDS[help_command_name]\n except KeyError:\n unknown_command(help_command_name)\n return\n\n command.print_help(help_command_name)\n return\n\n try:\n command = COMMANDS[command_name]\n except KeyError:\n unknown_command(command_name)\n return\n\n command.execute(sys.argv)","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractFormSubmission.get_data","text":"wagtail/contrib/forms/models.py::AbstractFormSubmission.get_data\n def get_data(self):\n \"\"\"\n Returns dict with form data.\n\n You can override this method to add additional data.\n \"\"\"\n form_data = json.loads(self.form_data)\n form_data.update({\n 'submit_time': self.submit_time,\n })\n\n return form_data","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractFormSubmission.__str__","text":"wagtail/contrib/forms/models.py::AbstractFormSubmission.__str__\n def __str__(self):\n return self.form_data","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractFormField.clean_name","text":"wagtail/contrib/forms/models.py::AbstractFormField.clean_name\n @property\n def clean_name(self):\n # unidecode will return an ascii string while slugify wants a\n # unicode string on the other hand, slugify returns a safe-string\n # which will be converted to a normal str\n return str(slugify(str(unidecode(self.label))))","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.__init__","text":"wagtail/contrib/forms/models.py::AbstractForm.__init__\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n if not hasattr(self, 'landing_page_template'):\n name, ext = os.path.splitext(self.template)\n self.landing_page_template = name + '_landing' + ext","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_form_fields","text":"wagtail/contrib/forms/models.py::AbstractForm.get_form_fields\n def get_form_fields(self):\n \"\"\"\n Form page expects `form_fields` to be declared.\n If you want to change backwards relation name,\n you need to override this method.\n \"\"\"\n\n return self.form_fields.all()","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_data_fields","text":"wagtail/contrib/forms/models.py::AbstractForm.get_data_fields\n def get_data_fields(self):\n \"\"\"\n Returns a list of tuples with (field_name, field_label).\n \"\"\"\n\n data_fields = [\n ('submit_time', _('Submission date')),\n ]\n data_fields += [\n (field.clean_name, field.label)\n for field in self.get_form_fields()\n ]\n\n return data_fields","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_form_class","text":"wagtail/contrib/forms/models.py::AbstractForm.get_form_class\n def get_form_class(self):\n fb = self.form_builder(self.get_form_fields())\n return fb.get_form_class()","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_form_parameters","text":"wagtail/contrib/forms/models.py::AbstractForm.get_form_parameters\n def get_form_parameters(self):\n return {}","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_form","text":"wagtail/contrib/forms/models.py::AbstractForm.get_form\n def get_form(self, *args, **kwargs):\n form_class = self.get_form_class()\n form_params = self.get_form_parameters()\n form_params.update(kwargs)\n\n return form_class(*args, **form_params)","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_landing_page_template","text":"wagtail/contrib/forms/models.py::AbstractForm.get_landing_page_template\n def get_landing_page_template(self, request, *args, **kwargs):\n return self.landing_page_template","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.get_submission_class","text":"wagtail/contrib/forms/models.py::AbstractForm.get_submission_class\n def get_submission_class(self):\n \"\"\"\n Returns submission class.\n\n You can override this method to provide custom submission class.\n Your class must be inherited from AbstractFormSubmission.\n \"\"\"\n\n return FormSubmission","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.process_form_submission","text":"wagtail/contrib/forms/models.py::AbstractForm.process_form_submission\n def process_form_submission(self, form):\n \"\"\"\n Accepts form instance with submitted data, user and page.\n Creates submission instance.\n\n You can override this method if you want to have custom creation logic.\n For example, if you want to save reference to a user.\n \"\"\"\n\n return self.get_submission_class().objects.create(\n form_data=json.dumps(form.cleaned_data, cls=DjangoJSONEncoder),\n page=self,\n )","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.render_landing_page","text":"wagtail/contrib/forms/models.py::AbstractForm.render_landing_page\n def render_landing_page(self, request, form_submission=None, *args, **kwargs):\n \"\"\"\n Renders the landing page.\n\n You can override this method to return a different HttpResponse as\n landing page. E.g. you could return a redirect to a separate page.\n \"\"\"\n context = self.get_context(request)\n context['form_submission'] = form_submission\n return render(\n request,\n self.get_landing_page_template(request),\n context\n )","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.serve_submissions_list_view","text":"wagtail/contrib/forms/models.py::AbstractForm.serve_submissions_list_view\n def serve_submissions_list_view(self, request, *args, **kwargs):\n \"\"\"\n Returns list submissions view for admin.\n\n `list_submissions_view_class` can bse set to provide custom view class.\n Your class must be inherited from SubmissionsListView.\n \"\"\"\n view = self.submissions_list_view_class.as_view()\n return view(request, form_page=self, *args, **kwargs)","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.serve","text":"wagtail/contrib/forms/models.py::AbstractForm.serve\n def serve(self, request, *args, **kwargs):\n if request.method == 'POST':\n form = self.get_form(request.POST, request.FILES, page=self, user=request.user)\n\n if form.is_valid():\n form_submission = self.process_form_submission(form)\n return self.render_landing_page(request, form_submission, *args, **kwargs)\n else:\n form = self.get_form(page=self, user=request.user)\n\n context = self.get_context(request)\n context['form'] = form\n return render(\n request,\n self.get_template(request),\n context\n )","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractForm.serve_preview","text":"wagtail/contrib/forms/models.py::AbstractForm.serve_preview\n def serve_preview(self, request, mode):\n if mode == 'landing':\n request.is_preview = True\n return self.render_landing_page(request)\n else:\n return super().serve_preview(request, mode)","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractEmailForm.process_form_submission","text":"wagtail/contrib/forms/models.py::AbstractEmailForm.process_form_submission\n def process_form_submission(self, form):\n submission = super().process_form_submission(form)\n if self.to_address:\n self.send_mail(form)\n return submission","label":0},{"cid":"wagtail/contrib/forms/models.py::AbstractEmailForm.send_mail","text":"wagtail/contrib/forms/models.py::AbstractEmailForm.send_mail\n def send_mail(self, form):\n addresses = [x.strip() for x in self.to_address.split(',')]\n content = []\n for field in form:\n value = field.value()\n if isinstance(value, list):\n value = ', '.join(value)\n content.append('{}: {}'.format(field.label, value))\n content = '\\n'.join(content)\n send_mail(self.subject, content, addresses, self.from_address,)","label":0},{"cid":"wagtail/core/blocks/base.py::BaseBlock.__new__","text":"wagtail/core/blocks/base.py::BaseBlock.__new__\n def __new__(mcs, name, bases, attrs):\n meta_class = attrs.pop('Meta', None)\n\n cls = super(BaseBlock, mcs).__new__(mcs, name, bases, attrs)\n\n # Get all the Meta classes from all the bases\n meta_class_bases = [meta_class] + [getattr(base, '_meta_class', None)\n for base in bases]\n meta_class_bases = tuple(filter(bool, meta_class_bases))\n cls._meta_class = type(str(name + 'Meta'), meta_class_bases, {})\n\n return cls","label":0},{"cid":"wagtail/core/blocks/base.py::Block.__new__","text":"wagtail/core/blocks/base.py::Block.__new__\n def __new__(cls, *args, **kwargs):\n # adapted from django.utils.deconstruct.deconstructible; capture the arguments\n # so that we can return them in the 'deconstruct' method\n obj = super(Block, cls).__new__(cls)\n obj._constructor_args = (args, kwargs)\n return obj","label":0},{"cid":"wagtail/core/blocks/base.py::Block.all_blocks","text":"wagtail/core/blocks/base.py::Block.all_blocks\n def all_blocks(self):\n \"\"\"\n Return a list consisting of self and all block objects that are direct or indirect dependencies\n of this block\n \"\"\"\n result = [self]\n for dep in self.dependencies:\n result.extend(dep.all_blocks())\n return result","label":0},{"cid":"wagtail/core/blocks/base.py::Block.all_media","text":"wagtail/core/blocks/base.py::Block.all_media\n def all_media(self):\n media = forms.Media()\n for block in self.all_blocks():\n media += block.media\n return media","label":0},{"cid":"wagtail/core/blocks/base.py::Block.all_html_declarations","text":"wagtail/core/blocks/base.py::Block.all_html_declarations\n def all_html_declarations(self):\n declarations = filter(bool, [block.html_declarations() for block in self.all_blocks()])\n return mark_safe('\\n'.join(declarations))","label":0},{"cid":"wagtail/core/blocks/base.py::Block.__init__","text":"wagtail/core/blocks/base.py::Block.__init__\n def __init__(self, **kwargs):\n self.meta = self._meta_class()\n\n for attr, value in kwargs.items():\n setattr(self.meta, attr, value)\n\n # Increase the creation counter, and save our local copy.\n self.creation_counter = Block.creation_counter\n Block.creation_counter += 1\n self.definition_prefix = 'blockdef-%d' % self.creation_counter\n\n self.label = self.meta.label or ''","label":0},{"cid":"wagtail/core/blocks/base.py::Block.set_name","text":"wagtail/core/blocks/base.py::Block.set_name\n def set_name(self, name):\n self.name = name\n if not self.meta.label:\n self.label = capfirst(force_text(name).replace('_', ' '))","label":0},{"cid":"wagtail/core/blocks/base.py::Block.media","text":"wagtail/core/blocks/base.py::Block.media\n @property\n def media(self):\n return forms.Media()","label":0},{"cid":"wagtail/core/blocks/base.py::Block.html_declarations","text":"wagtail/core/blocks/base.py::Block.html_declarations\n def html_declarations(self):\n \"\"\"\n Return an HTML fragment to be rendered on the form page once per block definition -\n as opposed to once per occurrence of the block. For example, the block definition\n ListBlock(label=\"Shopping list\", CharBlock(label=\"Product\"))\n needs to output a <script type=\"text/template\"></script> block containing the HTML for\n a 'product' text input, to that these can be dynamically added to the list. This\n template block must only occur once in the page, even if there are multiple 'shopping list'\n blocks on the page.\n\n Any element IDs used in this HTML fragment must begin with definition_prefix.\n (More precisely, they must either be definition_prefix itself, or begin with definition_prefix\n followed by a '-' character)\n \"\"\"\n return ''","label":0},{"cid":"wagtail/core/blocks/base.py::Block.js_initializer","text":"wagtail/core/blocks/base.py::Block.js_initializer\n def js_initializer(self):\n \"\"\"\n Returns a Javascript expression string, or None if this block does not require any\n Javascript behaviour. This expression evaluates to an initializer function, a function that\n takes the ID prefix and applies JS behaviour to the block instance with that value and prefix.\n\n The parent block of this block (or the top-level page code) must ensure that this\n expression is not evaluated more than once. (The resulting initializer function can and will be\n called as many times as there are instances of this block, though.)\n \"\"\"\n return None","label":0},{"cid":"wagtail/core/blocks/base.py::Block.render_form","text":"wagtail/core/blocks/base.py::Block.render_form\n def render_form(self, value, prefix='', errors=None):\n \"\"\"\n Render the HTML for this block with 'value' as its content.\n \"\"\"\n raise NotImplementedError('%s.render_form' % self.__class__)","label":0},{"cid":"wagtail/core/blocks/base.py::Block.value_from_datadict","text":"wagtail/core/blocks/base.py::Block.value_from_datadict\n def value_from_datadict(self, data, files, prefix):\n raise NotImplementedError('%s.value_from_datadict' % self.__class__)","label":0},{"cid":"wagtail/core/blocks/base.py::Block.value_omitted_from_data","text":"wagtail/core/blocks/base.py::Block.value_omitted_from_data\n def value_omitted_from_data(self, data, files, name):\n \"\"\"\n Used only for top-level blocks wrapped by BlockWidget (i.e.: typically only StreamBlock)\n to inform ModelForm logic on Django >=1.10.2 whether the field is absent from the form\n submission (and should therefore revert to the field default).\n \"\"\"\n return name not in data","label":0},{"cid":"wagtail/core/blocks/base.py::Block.bind","text":"wagtail/core/blocks/base.py::Block.bind\n def bind(self, value, prefix=None, errors=None):\n \"\"\"\n Return a BoundBlock which represents the association of this block definition with a value\n and a prefix (and optionally, a ValidationError to be rendered).\n BoundBlock primarily exists as a convenience to allow rendering within templates:\n bound_block.render() rather than blockdef.render(value, prefix) which can't be called from\n within a template.\n \"\"\"\n return BoundBlock(self, value, prefix=prefix, errors=errors)","label":0},{"cid":"wagtail/core/blocks/base.py::Block.get_default","text":"wagtail/core/blocks/base.py::Block.get_default\n def get_default(self):\n \"\"\"\n Return this block's default value (conventionally found in self.meta.default),\n converted to the value type expected by this block. This caters for the case\n where that value type is not something that can be expressed statically at\n model definition type (e.g. something like StructValue which incorporates a\n pointer back to the block definion object).\n \"\"\"\n return self.meta.default","label":0},{"cid":"wagtail/core/blocks/base.py::Block.prototype_block","text":"wagtail/core/blocks/base.py::Block.prototype_block\n def prototype_block(self):\n \"\"\"\n Return a BoundBlock that can be used as a basis for new empty block instances to be added on the fly\n (new list items, for example). This will have a prefix of '__PREFIX__' (to be dynamically replaced with\n a real prefix when it's inserted into the page) and a value equal to the block's default value.\n \"\"\"\n return self.bind(self.get_default(), '__PREFIX__')","label":0},{"cid":"wagtail/core/blocks/base.py::Block.clean","text":"wagtail/core/blocks/base.py::Block.clean\n def clean(self, value):\n \"\"\"\n Validate value and return a cleaned version of it, or throw a ValidationError if validation fails.\n The thrown ValidationError instance will subsequently be passed to render() to display the\n error message; the ValidationError must therefore include all detail necessary to perform that\n rendering, such as identifying the specific child block(s) with errors, in the case of nested\n blocks. (It is suggested that you use the 'params' attribute for this; using error_list /\n error_dict is unreliable because Django tends to hack around with these when nested.)\n \"\"\"\n return value","label":0},{"cid":"wagtail/core/blocks/base.py::Block.to_python","text":"wagtail/core/blocks/base.py::Block.to_python\n def to_python(self, value):\n \"\"\"\n Convert 'value' from a simple (JSON-serialisable) value to a (possibly complex) Python value to be\n used in the rest of the block API and within front-end templates . In simple cases this might be\n the value itself; alternatively, it might be a 'smart' version of the value which behaves mostly\n like the original value but provides a native HTML rendering when inserted into a template; or it\n might be something totally different (e.g. an image chooser will use the image ID as the clean\n value, and turn this back into an actual image object here).\n \"\"\"\n return value","label":0},{"cid":"wagtail/core/blocks/base.py::Block.get_prep_value","text":"wagtail/core/blocks/base.py::Block.get_prep_value\n def get_prep_value(self, value):\n \"\"\"\n The reverse of to_python; convert the python value into JSON-serialisable form.\n \"\"\"\n return value","label":0},{"cid":"wagtail/core/blocks/base.py::Block.get_context","text":"wagtail/core/blocks/base.py::Block.get_context\n def get_context(self, value, parent_context=None):\n \"\"\"\n Return a dict of context variables (derived from the block value and combined with the parent_context)\n to be used as the template context when rendering this value through a template.\n \"\"\"\n\n context = parent_context or {}\n context.update({\n 'self': value,\n self.TEMPLATE_VAR: value,\n })\n return context","label":0},{"cid":"wagtail/core/blocks/base.py::Block.get_template","text":"wagtail/core/blocks/base.py::Block.get_template\n def get_template(self, context=None):\n \"\"\"\n Return the template to use for rendering the block if specified on meta class.\n This extraction was added to make dynamic templates possible if you override this method\n \"\"\"\n return getattr(self.meta, 'template', None)","label":0},{"cid":"wagtail/core/blocks/base.py::Block.render","text":"wagtail/core/blocks/base.py::Block.render\n def render(self, value, context=None):\n \"\"\"\n Return a text rendering of 'value', suitable for display on templates. By default, this will\n use a template (with the passed context, supplemented by the result of get_context) if a\n 'template' property is specified on the block, and fall back on render_basic otherwise.\n \"\"\"\n template = self.get_template(context=context)\n if not template:\n return self.render_basic(value, context=context)\n\n if context is None:\n new_context = self.get_context(value)\n else:\n new_context = self.get_context(value, parent_context=dict(context))\n\n return mark_safe(render_to_string(template, new_context))","label":0},{"cid":"wagtail/core/blocks/base.py::Block.get_api_representation","text":"wagtail/core/blocks/base.py::Block.get_api_representation\n def get_api_representation(self, value, context=None):\n \"\"\"\n Can be used to customise the API response and defaults to the value returned by get_prep_value.\n \"\"\"\n return self.get_prep_value(value)","label":0},{"cid":"wagtail/core/blocks/base.py::Block.render_basic","text":"wagtail/core/blocks/base.py::Block.render_basic\n def render_basic(self, value, context=None):\n \"\"\"\n Return a text rendering of 'value', suitable for display on templates. render() will fall back on\n this if the block does not define a 'template' property.\n \"\"\"\n return force_text(value)","label":0},{"cid":"wagtail/core/blocks/base.py::Block.get_searchable_content","text":"wagtail/core/blocks/base.py::Block.get_searchable_content\n def get_searchable_content(self, value):\n \"\"\"\n Returns a list of strings containing text content within this block to be used in a search engine.\n \"\"\"\n return []","label":0},{"cid":"wagtail/core/blocks/base.py::Block.check","text":"wagtail/core/blocks/base.py::Block.check\n def check(self, **kwargs):\n \"\"\"\n Hook for the Django system checks framework -\n returns a list of django.core.checks.Error objects indicating validity errors in the block\n \"\"\"\n return []","label":0},{"cid":"wagtail/core/blocks/base.py::Block._check_name","text":"wagtail/core/blocks/base.py::Block._check_name\n def _check_name(self, **kwargs):\n \"\"\"\n Helper method called by container blocks as part of the system checks framework,\n to validate that this block's name is a valid identifier.\n (Not called universally, because not all blocks need names)\n \"\"\"\n errors = []\n if not self.name:\n errors.append(checks.Error(\n \"Block name %r is invalid\" % self.name,\n hint=\"Block name cannot be empty\",\n obj=kwargs.get('field', self),\n id='wagtailcore.E001',\n ))\n\n if ' ' in self.name:\n errors.append(checks.Error(\n \"Block name %r is invalid\" % self.name,\n hint=\"Block names cannot contain spaces\",\n obj=kwargs.get('field', self),\n id='wagtailcore.E001',\n ))\n\n if '-' in self.name:\n errors.append(checks.Error(\n \"Block name %r is invalid\" % self.name,\n \"Block names cannot contain dashes\",\n obj=kwargs.get('field', self),\n id='wagtailcore.E001',\n ))\n\n if self.name and self.name[0].isdigit():\n errors.append(checks.Error(\n \"Block name %r is invalid\" % self.name,\n \"Block names cannot begin with a digit\",\n obj=kwargs.get('field', self),\n id='wagtailcore.E001',\n ))\n\n return errors","label":0},{"cid":"wagtail/core/blocks/base.py::Block.id_for_label","text":"wagtail/core/blocks/base.py::Block.id_for_label\n def id_for_label(self, prefix):\n \"\"\"\n Return the ID to be used as the 'for' attribute of <label> elements that refer to this block,\n when the given field prefix is in use. Return None if no 'for' attribute should be used.\n \"\"\"\n return None","label":0},{"cid":"wagtail/core/blocks/base.py::Block.required","text":"wagtail/core/blocks/base.py::Block.required\n @property\n def required(self):\n \"\"\"\n Flag used to determine whether labels for this block should display a 'required' asterisk.\n False by default, since Block does not provide any validation of its own - it's up to subclasses\n to define what required-ness means.\n \"\"\"\n return False","label":0},{"cid":"wagtail/core/blocks/base.py::Block.deconstruct","text":"wagtail/core/blocks/base.py::Block.deconstruct\n def deconstruct(self):\n # adapted from django.utils.deconstruct.deconstructible\n module_name = self.__module__\n name = self.__class__.__name__\n\n # Make sure it's actually there and not an inner class\n module = import_module(module_name)\n if not hasattr(module, name):\n raise ValueError(\n \"Could not find object %s in %s.\\n\"\n \"Please note that you cannot serialize things like inner \"\n \"classes. Please move the object into the main module \"\n \"body to use migrations.\\n\"\n % (name, module_name))\n\n # if the module defines a DECONSTRUCT_ALIASES dictionary, see if the class has an entry in there;\n # if so, use that instead of the real path\n try:\n path = module.DECONSTRUCT_ALIASES[self.__class__]\n except (AttributeError, KeyError):\n path = '%s.%s' % (module_name, name)\n\n return (\n path,\n self._constructor_args[0],\n self._constructor_args[1],\n )","label":0},{"cid":"wagtail/core/blocks/base.py::Block.__eq__","text":"wagtail/core/blocks/base.py::Block.__eq__\n def __eq__(self, other):\n \"\"\"\n Implement equality on block objects so that two blocks with matching definitions are considered\n equal. (Block objects are intended to be immutable with the exception of set_name(), so here\n 'matching definitions' means that both the 'name' property and the constructor args/kwargs - as\n captured in _constructor_args - are equal on both blocks.)\n\n This was originally necessary as a workaround for https://code.djangoproject.com/ticket/24340\n in Django <1.9; the deep_deconstruct function used to detect changes for migrations did not\n recurse into the block lists, and left them as Block instances. This __eq__ method therefore\n came into play when identifying changes within migrations.\n\n As of Django >=1.9, this *probably* isn't required any more. However, it may be useful in\n future as a way of identifying blocks that can be re-used within StreamField definitions\n (https://github.com/wagtail/wagtail/issues/4298#issuecomment-367656028).\n \"\"\"\n\n if not isinstance(other, Block):\n # if the other object isn't a block at all, it clearly isn't equal.\n return False\n\n # Note that we do not require the two blocks to be of the exact same class. This is because\n # we may wish the following blocks to be considered equal:\n #\n # class FooBlock(StructBlock):\n # first_name = CharBlock()\n # surname = CharBlock()\n #\n # class BarBlock(StructBlock):\n # first_name = CharBlock()\n # surname = CharBlock()\n #\n # FooBlock() == BarBlock() == StructBlock([('first_name', CharBlock()), ('surname': CharBlock())])\n #\n # For this to work, StructBlock will need to ensure that 'deconstruct' returns the same signature\n # in all of these","label":0},{"cid":"wagtail/core/blocks/base.py::BoundBlock.__init__","text":"wagtail/core/blocks/base.py::BoundBlock.__init__\n def __init__(self, block, value, prefix=None, errors=None):\n self.block = block\n self.value = value\n self.prefix = prefix\n self.errors = errors","label":0},{"cid":"wagtail/core/blocks/base.py::BoundBlock.render_form","text":"wagtail/core/blocks/base.py::BoundBlock.render_form\n def render_form(self):\n return self.block.render_form(self.value, self.prefix, errors=self.errors)","label":0},{"cid":"wagtail/core/blocks/base.py::BoundBlock.render","text":"wagtail/core/blocks/base.py::BoundBlock.render\n def render(self, context=None):\n return self.block.render(self.value, context=context)","label":0},{"cid":"wagtail/core/blocks/base.py::BoundBlock.render_as_block","text":"wagtail/core/blocks/base.py::BoundBlock.render_as_block\n def render_as_block(self, context=None):\n \"\"\"\n Alias for render; the include_block tag will specifically check for the presence of a method\n with this name. (This is because {% include_block %} is just as likely to be invoked on a bare\n value as a BoundBlock. If we looked for a `render` method instead, we'd run the risk of finding\n an unrelated method that just happened to have that name - for example, when called on a\n PageChooserBlock it could end up calling page.render.\n \"\"\"\n return self.block.render(self.value, context=context)","label":0},{"cid":"wagtail/core/blocks/base.py::BoundBlock.id_for_label","text":"wagtail/core/blocks/base.py::BoundBlock.id_for_label\n def id_for_label(self):\n return self.block.id_for_label(self.prefix)","label":0},{"cid":"wagtail/core/blocks/base.py::BoundBlock.__str__","text":"wagtail/core/blocks/base.py::BoundBlock.__str__\n def __str__(self):\n \"\"\"Render the value according to the block's native rendering\"\"\"\n return self.block.render(self.value)","label":0},{"cid":"wagtail/core/blocks/base.py::DeclarativeSubBlocksMetaclass.__new__","text":"wagtail/core/blocks/base.py::DeclarativeSubBlocksMetaclass.__new__\n def __new__(mcs, name, bases, attrs):\n # Collect sub-blocks declared on the current class.\n # These are available on the class as `declared_blocks`\n current_blocks = []\n for key, value in list(attrs.items()):\n if isinstance(value, Block):\n current_blocks.append((key, value))\n value.set_name(key)\n attrs.pop(key)\n current_blocks.sort(key=lambda x: x[1].creation_counter)\n attrs['declared_blocks'] = collections.OrderedDict(current_blocks)\n\n new_class = (super(DeclarativeSubBlocksMetaclass, mcs).__new__(\n mcs, name, bases, attrs))\n\n # Walk through the MRO, collecting all inherited sub-blocks, to make\n # the combined `base_blocks`.\n base_blocks = collections.OrderedDict()\n for base in reversed(new_class.__mro__):\n # Collect sub-blocks from base class.\n if hasattr(base, 'declared_blocks'):\n base_blocks.update(base.declared_blocks)\n\n # Field shadowing.\n for attr, value in base.__dict__.items():\n if value is None and attr in base_blocks:\n base_blocks.pop(attr)\n new_class.base_blocks = base_blocks\n\n return new_class","label":0},{"cid":"wagtail/core/blocks/base.py::BlockWidget.__init__","text":"wagtail/core/blocks/base.py::BlockWidget.__init__\n def __init__(self, block_def, attrs=None):\n super().__init__(attrs=attrs)\n self.block_def = block_def","label":0},{"cid":"wagtail/core/blocks/base.py::BlockWidget.render_with_errors","text":"wagtail/core/blocks/base.py::BlockWidget.render_with_errors\n def render_with_errors(self, name, value, attrs=None, errors=None):\n bound_block = self.block_def.bind(value, prefix=name, errors=errors)\n js_initializer = self.block_def.js_initializer()\n if js_initializer:\n js_snippet = \"\"\"\n <script>\n $(function() {\n var initializer = %s;\n initializer('%s');\n })\n </script>\n \"\"\" % (js_initializer, name)\n else:\n js_snippet = ''\n return mark_safe(bound_block.render_form() + js_snippet)","label":0},{"cid":"wagtail/images/views/images.py::index","text":"wagtail/images/views/images.py::index\n@permission_checker.require_any('add', 'change', 'delete')\n@vary_on_headers('X-Requested-With')\ndef index(request):\n Image = get_image_model()\n\n # Get images (filtered by user permission)\n images = permission_policy.instances_user_has_any_permission_for(\n request.user, ['change', 'delete']\n ).order_by('-created_at')\n\n # Search\n query_string = None\n if 'q' in request.GET:\n form = SearchForm(request.GET, placeholder=_(\"Search images\"))\n if form.is_valid():\n query_string = form.cleaned_data['q']\n\n images = images.search(query_string)\n else:\n form = SearchForm(placeholder=_(\"Search images\"))\n\n # Filter by collection\n current_collection = None\n collection_id = request.GET.get('collection_id')\n if collection_id:\n try:\n current_collection = Collection.objects.get(id=collection_id)\n images = images.filter(collection=current_collection)\n except (ValueError, Collection.DoesNotExist):\n pass\n\n paginator, images = paginate(request, images)\n\n collections = permission_policy.collections_user_has_any_permission_for(\n request.user, ['add', 'change']\n )\n if len(collections) < 2:\n collections = None\n\n # Create response\n if request.is_ajax():\n return render(request, 'wagtailimages/images/results.html', {\n 'images': images,\n 'query_string': query_string,\n 'is_searching': bool(query_string),\n })\n else:\n return render(request, 'wagtailimages/images/index.html', {\n 'images': images,\n 'query_string': query_string,\n 'is_searching': bool(query_string),\n\n 'search_form': form,\n 'popular_tags': popular_tags_for_model(Image),\n 'collections': collections,\n 'current_collection': current_collection,\n 'user_can_add': permission_policy.user_has_permission(requ","label":0},{"cid":"wagtail/images/views/images.py::edit","text":"wagtail/images/views/images.py::edit\n@permission_checker.require('change')\ndef edit(request, image_id):\n Image = get_image_model()\n ImageForm = get_image_form(Image)\n\n image = get_object_or_404(Image, id=image_id)\n\n if not permission_policy.user_has_permission_for_instance(request.user, 'change', image):\n return permission_denied(request)\n\n if request.method == 'POST':\n original_file = image.file\n form = ImageForm(request.POST, request.FILES, instance=image, user=request.user)\n if form.is_valid():\n if 'file' in form.changed_data:\n # Set new image file size\n image.file_size = image.file.size\n\n # Set new image file hash\n image.file.seek(0)\n image._set_file_hash(image.file.read())\n\n form.save()\n\n if 'file' in form.changed_data:\n # if providing a new image file, delete the old one and all renditions.\n # NB Doing this via original_file.delete() clears the file field,\n # which definitely isn't what we want...\n original_file.storage.delete(original_file.name)\n image.renditions.all().delete()\n\n # Reindex the image to make sure all tags are indexed\n search_index.insert_or_update_object(image)\n\n messages.success(request, _(\"Image '{0}' updated.\").format(image.title), buttons=[\n messages.button(reverse('wagtailimages:edit', args=(image.id,)), _('Edit again'))\n ])\n return redirect('wagtailimages:index')\n else:\n messages.error(request, _(\"The image could not be saved due to errors.\"))\n else:\n form = ImageForm(instance=image, user=request.user)\n\n # Check if we should enable the frontend url generator\n try:\n reverse('wagtailimages_serve', args=('foo', '1', 'bar'))\n url_generator_enabled = True\n except NoReverseMatch:\n url_generator_e","label":0},{"cid":"wagtail/images/views/images.py::url_generator","text":"wagtail/images/views/images.py::url_generator\ndef url_generator(request, image_id):\n image = get_object_or_404(get_image_model(), id=image_id)\n\n if not permission_policy.user_has_permission_for_instance(request.user, 'change', image):\n return permission_denied(request)\n\n form = URLGeneratorForm(initial={\n 'filter_method': 'original',\n 'width': image.width,\n 'height': image.height,\n })\n\n return render(request, \"wagtailimages/images/url_generator.html\", {\n 'image': image,\n 'form': form,\n })","label":0},{"cid":"wagtail/images/views/images.py::generate_url","text":"wagtail/images/views/images.py::generate_url\ndef generate_url(request, image_id, filter_spec):\n # Get the image\n Image = get_image_model()\n try:\n image = Image.objects.get(id=image_id)\n except Image.DoesNotExist:\n return JsonResponse({\n 'error': \"Cannot find image.\"\n }, status=404)\n\n # Check if this user has edit permission on this image\n if not permission_policy.user_has_permission_for_instance(request.user, 'change', image):\n return JsonResponse({\n 'error': \"You do not have permission to generate a URL for this image.\"\n }, status=403)\n\n # Parse the filter spec to make sure its valid\n try:\n Filter(spec=filter_spec).operations\n except InvalidFilterSpecError:\n return JsonResponse({\n 'error': \"Invalid filter spec.\"\n }, status=400)\n\n # Generate url\n signature = generate_signature(image_id, filter_spec)\n url = reverse('wagtailimages_serve', args=(signature, image_id, filter_spec))\n\n # Get site root url\n try:\n site_root_url = Site.objects.get(is_default_site=True).root_url\n except Site.DoesNotExist:\n site_root_url = Site.objects.first().root_url\n\n # Generate preview url\n preview_url = reverse('wagtailimages:preview', args=(image_id, filter_spec))\n\n return JsonResponse({'url': site_root_url + url, 'preview_url': preview_url}, status=200)","label":0},{"cid":"wagtail/images/views/images.py::preview","text":"wagtail/images/views/images.py::preview\ndef preview(request, image_id, filter_spec):\n image = get_object_or_404(get_image_model(), id=image_id)\n\n try:\n response = HttpResponse()\n image = Filter(spec=filter_spec).run(image, response)\n response['Content-Type'] = 'image/' + image.format_name\n return response\n except InvalidFilterSpecError:\n return HttpResponse(\"Invalid filter spec: \" + filter_spec, content_type='text/plain', status=400)","label":0},{"cid":"wagtail/images/views/images.py::delete","text":"wagtail/images/views/images.py::delete\n@permission_checker.require('delete')\ndef delete(request, image_id):\n image = get_object_or_404(get_image_model(), id=image_id)\n\n if not permission_policy.user_has_permission_for_instance(request.user, 'delete', image):\n return permission_denied(request)\n\n if request.method == 'POST':\n image.delete()\n messages.success(request, _(\"Image '{0}' deleted.\").format(image.title))\n return redirect('wagtailimages:index')\n\n return render(request, \"wagtailimages/images/confirm_delete.html\", {\n 'image': image,\n })","label":0},{"cid":"wagtail/images/views/images.py::add","text":"wagtail/images/views/images.py::add\n@permission_checker.require('add')\ndef add(request):\n ImageModel = get_image_model()\n ImageForm = get_image_form(ImageModel)\n\n if request.method == 'POST':\n image = ImageModel(uploaded_by_user=request.user)\n form = ImageForm(request.POST, request.FILES, instance=image, user=request.user)\n if form.is_valid():\n # Set image file size\n image.file_size = image.file.size\n\n # Set image file hash\n image.file.seek(0)\n image._set_file_hash(image.file.read())\n\n form.save()\n\n # Reindex the image to make sure all tags are indexed\n search_index.insert_or_update_object(image)\n\n messages.success(request, _(\"Image '{0}' added.\").format(image.title), buttons=[\n messages.button(reverse('wagtailimages:edit', args=(image.id,)), _('Edit'))\n ])\n return redirect('wagtailimages:index')\n else:\n messages.error(request, _(\"The image could not be created due to errors.\"))\n else:\n form = ImageForm(user=request.user)\n\n return render(request, \"wagtailimages/images/add.html\", {\n 'form': form,\n })","label":0},{"cid":"wagtail/images/views/images.py::usage","text":"wagtail/images/views/images.py::usage\ndef usage(request, image_id):\n image = get_object_or_404(get_image_model(), id=image_id)\n\n paginator, used_by = paginate(request, image.get_usage())\n\n return render(request, \"wagtailimages/images/usage.html\", {\n 'image': image,\n 'used_by': used_by\n })","label":0}],"string":"[\n {\n \"cid\": \"docs/conf.py::setup\",\n \"text\": \"docs/conf.py::setup\\ndef setup(app):\\n app.add_stylesheet('css/custom.css')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/urls/__init__.py::display_custom_404\",\n \"text\": \"wagtail/admin/urls/__init__.py::display_custom_404\\ndef display_custom_404(view_func):\\n @functools.wraps(view_func)\\n def wrapper(request, *args, **kwargs):\\n try:\\n return view_func(request, *args, **kwargs)\\n except Http404:\\n return page_not_found(request, '', template_name='wagtailadmin/404.html')\\n\\n return wrapper\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/admin/urls/__init__.py::display_custom_404.wrapper\",\n \"text\": \"wagtail/admin/urls/__init__.py::display_custom_404.wrapper\\n @functools.wraps(view_func)\\n def wrapper(request, *args, **kwargs):\\n try:\\n return view_func(request, *args, **kwargs)\\n except Http404:\\n return page_not_found(request, '', template_name='wagtailadmin/404.html')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.__init__\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.__init__\\n def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n\\n # seen_types is a mapping of type name strings (format: \\\"app_label.ModelName\\\")\\n # to model classes. When an object is serialised in the API, its model\\n # is added to this mapping. This is used by the Admin API which appends a\\n # summary of the used types to the response.\\n self.seen_types = OrderedDict()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_queryset\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_queryset\\n def get_queryset(self):\\n return self.model.objects.all().order_by('id')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.listing_view\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.listing_view\\n def listing_view(self, request):\\n queryset = self.get_queryset()\\n self.check_query_parameters(queryset)\\n queryset = self.filter_queryset(queryset)\\n queryset = self.paginate_queryset(queryset)\\n serializer = self.get_serializer(queryset, many=True)\\n return self.get_paginated_response(serializer.data)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.detail_view\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.detail_view\\n def detail_view(self, request, pk):\\n instance = self.get_object()\\n serializer = self.get_serializer(instance)\\n return Response(serializer.data)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.find_view\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.find_view\\n def find_view(self, request):\\n queryset = self.get_queryset()\\n\\n try:\\n obj = self.find_object(queryset, request)\\n\\n if obj is None:\\n raise self.model.DoesNotExist\\n\\n except self.model.DoesNotExist:\\n raise Http404(\\\"not found\\\")\\n\\n # Generate redirect\\n url = get_object_detail_url(self.request.wagtailapi_router, request, self.model, obj.pk)\\n\\n if url is None:\\n # Shouldn't happen unless this endpoint isn't actually installed in the router\\n raise Exception(\\\"Cannot generate URL to detail view. Is '{}' installed in the API router?\\\".format(self.__class__.__name__))\\n\\n return redirect(url)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.find_object\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.find_object\\n def find_object(self, queryset, request):\\n \\\"\\\"\\\"\\n Override this to implement more find methods.\\n \\\"\\\"\\\"\\n if 'id' in request.GET:\\n return queryset.get(id=request.GET['id'])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.handle_exception\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.handle_exception\\n def handle_exception(self, exc):\\n if isinstance(exc, Http404):\\n data = {'message': str(exc)}\\n return Response(data, status=status.HTTP_404_NOT_FOUND)\\n elif isinstance(exc, BadRequestError):\\n data = {'message': str(exc)}\\n return Response(data, status=status.HTTP_400_BAD_REQUEST)\\n return super().handle_exception(exc)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint._convert_api_fields\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint._convert_api_fields\\n @classmethod\\n def _convert_api_fields(cls, fields):\\n return [field if isinstance(field, APIField) else APIField(field)\\n for field in fields]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_body_fields\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_body_fields\\n @classmethod\\n def get_body_fields(cls, model):\\n return cls._convert_api_fields(cls.body_fields + list(getattr(model, 'api_fields', ())))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_body_fields_names\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_body_fields_names\\n @classmethod\\n def get_body_fields_names(cls, model):\\n return [field.name for field in cls.get_body_fields(model)]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_meta_fields\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_meta_fields\\n @classmethod\\n def get_meta_fields(cls, model):\\n return cls._convert_api_fields(cls.meta_fields + list(getattr(model, 'api_meta_fields', ())))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_meta_fields_names\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_meta_fields_names\\n @classmethod\\n def get_meta_fields_names(cls, model):\\n return [field.name for field in cls.get_meta_fields(model)]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_field_serializer_overrides\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_field_serializer_overrides\\n @classmethod\\n def get_field_serializer_overrides(cls, model):\\n return {field.name: field.serializer\\n for field in cls.get_body_fields(model) + cls.get_meta_fields(model)\\n if field.serializer is not None}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_available_fields\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_available_fields\\n @classmethod\\n def get_available_fields(cls, model, db_fields_only=False):\\n \\\"\\\"\\\"\\n Returns a list of all the fields that can be used in the API for the\\n specified model class.\\n\\n Setting db_fields_only to True will remove all fields that do not have\\n an underlying column in the database (eg, type/detail_url and any custom\\n fields that are callables)\\n \\\"\\\"\\\"\\n fields = cls.get_body_fields_names(model) + cls.get_meta_fields_names(model)\\n\\n if db_fields_only:\\n # Get list of available database fields then remove any fields in our\\n # list that isn't a database field\\n database_fields = set()\\n for field in model._meta.get_fields():\\n database_fields.add(field.name)\\n\\n if hasattr(field, 'attname'):\\n database_fields.add(field.attname)\\n\\n fields = [field for field in fields if field in database_fields]\\n\\n return fields\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_detail_default_fields\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_detail_default_fields\\n @classmethod\\n def get_detail_default_fields(cls, model):\\n return cls.get_available_fields(model)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_listing_default_fields\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_listing_default_fields\\n @classmethod\\n def get_listing_default_fields(cls, model):\\n return cls.listing_default_fields[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_nested_default_fields\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_nested_default_fields\\n @classmethod\\n def get_nested_default_fields(cls, model):\\n return cls.nested_default_fields[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.check_query_parameters\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.check_query_parameters\\n def check_query_parameters(self, queryset):\\n \\\"\\\"\\\"\\n Ensure that only valid query paramters are included in the URL.\\n \\\"\\\"\\\"\\n query_parameters = set(self.request.GET.keys())\\n\\n # All query paramters must be either a database field or an operation\\n allowed_query_parameters = set(self.get_available_fields(queryset.model, db_fields_only=True)).union(self.known_query_parameters)\\n unknown_parameters = query_parameters - allowed_query_parameters\\n if unknown_parameters:\\n raise BadRequestError(\\\"query parameter is not an operation or a recognised field: %s\\\" % ', '.join(sorted(unknown_parameters)))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint._get_serializer_class\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint._get_serializer_class\\n @classmethod\\n def _get_serializer_class(cls, router, model, fields_config, show_details=False, nested=False):\\n # Get all available fields\\n body_fields = cls.get_body_fields_names(model)\\n meta_fields = cls.get_meta_fields_names(model)\\n all_fields = body_fields + meta_fields\\n\\n # Remove any duplicates\\n all_fields = list(OrderedDict.fromkeys(all_fields))\\n\\n if not show_details:\\n # Remove detail only fields\\n for field in cls.detail_only_fields:\\n try:\\n all_fields.remove(field)\\n except KeyError:\\n pass\\n\\n # Get list of configured fields\\n if show_details:\\n fields = set(cls.get_detail_default_fields(model))\\n elif nested:\\n fields = set(cls.get_nested_default_fields(model))\\n else:\\n fields = set(cls.get_listing_default_fields(model))\\n\\n # If first field is '*' start with all fields\\n # If first field is '_' start with no fields\\n if fields_config and fields_config[0][0] == '*':\\n fields = set(all_fields)\\n fields_config = fields_config[1:]\\n elif fields_config and fields_config[0][0] == '_':\\n fields = set()\\n fields_config = fields_config[1:]\\n\\n mentioned_fields = set()\\n sub_fields = {}\\n\\n for field_name, negated, field_sub_fields in fields_config:\\n if negated:\\n try:\\n fields.remove(field_name)\\n except KeyError:\\n pass\\n else:\\n fields.add(field_name)\\n if field_sub_fields:\\n sub_fields[field_name] = field_sub_fields\\n\\n mentioned_fields.add(field_name)\\n\\n unknown_fields = mentioned_fields - set(all_fields)\\n\\n if unknown_fields:\\n raise BadRequestError(\\\"unknown fields:\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_serializer_class\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_serializer_class\\n def get_serializer_class(self):\\n request = self.request\\n\\n # Get model\\n if self.action == 'listing_view':\\n model = self.get_queryset().model\\n else:\\n model = type(self.get_object())\\n\\n # Fields\\n if 'fields' in request.GET:\\n try:\\n fields_config = parse_fields_parameter(request.GET['fields'])\\n except ValueError as e:\\n raise BadRequestError(\\\"fields error: %s\\\" % str(e))\\n else:\\n # Use default fields\\n fields_config = []\\n\\n # Allow \\\"detail_only\\\" (eg parent) fields on detail view\\n if self.action == 'listing_view':\\n show_details = False\\n else:\\n show_details = True\\n\\n return self._get_serializer_class(self.request.wagtailapi_router, model, fields_config, show_details=show_details)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_serializer_context\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_serializer_context\\n def get_serializer_context(self):\\n \\\"\\\"\\\"\\n The serialization context differs between listing and detail views.\\n \\\"\\\"\\\"\\n return {\\n 'request': self.request,\\n 'view': self,\\n 'router': self.request.wagtailapi_router\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_renderer_context\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_renderer_context\\n def get_renderer_context(self):\\n context = super().get_renderer_context()\\n context['indent'] = 4\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_urlpatterns\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_urlpatterns\\n @classmethod\\n def get_urlpatterns(cls):\\n \\\"\\\"\\\"\\n This returns a list of URL patterns for the endpoint\\n \\\"\\\"\\\"\\n return [\\n url(r'^$', cls.as_view({'get': 'listing_view'}), name='listing'),\\n url(r'^(?P<pk>\\\\d+)/$', cls.as_view({'get': 'detail_view'}), name='detail'),\\n url(r'^find/$', cls.as_view({'get': 'find_view'}), name='find'),\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_model_listing_urlpath\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_model_listing_urlpath\\n @classmethod\\n def get_model_listing_urlpath(cls, model, namespace=''):\\n if namespace:\\n url_name = namespace + ':listing'\\n else:\\n url_name = 'listing'\\n\\n return reverse(url_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_object_detail_urlpath\",\n \"text\": \"wagtail/api/v2/endpoints.py::BaseAPIEndpoint.get_object_detail_urlpath\\n @classmethod\\n def get_object_detail_urlpath(cls, model, pk, namespace=''):\\n if namespace:\\n url_name = namespace + ':detail'\\n else:\\n url_name = 'detail'\\n\\n return reverse(url_name, args=(pk, ))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::PagesAPIEndpoint.get_queryset\",\n \"text\": \"wagtail/api/v2/endpoints.py::PagesAPIEndpoint.get_queryset\\n def get_queryset(self):\\n request = self.request\\n\\n # Allow pages to be filtered to a specific type\\n try:\\n models = page_models_from_string(request.GET.get('type', 'wagtailcore.Page'))\\n except (LookupError, ValueError):\\n raise BadRequestError(\\\"type doesn't exist\\\")\\n\\n if not models:\\n models = [Page]\\n\\n if len(models) == 1:\\n queryset = models[0].objects.all()\\n else:\\n queryset = Page.objects.all()\\n\\n # Filter pages by specified models\\n queryset = filter_page_type(queryset, models)\\n\\n # Get live pages that are not in a private section\\n queryset = queryset.public().live()\\n\\n # Filter by site\\n if request.site:\\n queryset = queryset.descendant_of(request.site.root_page, inclusive=True)\\n else:\\n # No sites configured\\n queryset = queryset.none()\\n\\n return queryset\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::PagesAPIEndpoint.get_object\",\n \"text\": \"wagtail/api/v2/endpoints.py::PagesAPIEndpoint.get_object\\n def get_object(self):\\n base = super().get_object()\\n return base.specific\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/endpoints.py::PagesAPIEndpoint.find_object\",\n \"text\": \"wagtail/api/v2/endpoints.py::PagesAPIEndpoint.find_object\\n def find_object(self, queryset, request):\\n if 'html_path' in request.GET and request.site is not None:\\n path = request.GET['html_path']\\n path_components = [component for component in path.split('/') if component]\\n\\n try:\\n page, _, _ = request.site.root_page.specific.route(request, path_components)\\n except Http404:\\n return\\n\\n if queryset.filter(id=page.id).exists():\\n return page\\n\\n return super().find_object(queryset, request)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::TypeField.get_attribute\",\n \"text\": \"wagtail/api/v2/serializers.py::TypeField.get_attribute\\n def get_attribute(self, instance):\\n return instance\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::TypeField.to_representation\",\n \"text\": \"wagtail/api/v2/serializers.py::TypeField.to_representation\\n def to_representation(self, obj):\\n name = type(obj)._meta.app_label + '.' + type(obj).__name__\\n self.context['view'].seen_types[name] = type(obj)\\n return name\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::DetailUrlField.get_attribute\",\n \"text\": \"wagtail/api/v2/serializers.py::DetailUrlField.get_attribute\\n def get_attribute(self, instance):\\n url = get_object_detail_url(self.context['router'], self.context['request'], type(instance), instance.pk)\\n\\n if url:\\n return url\\n else:\\n # Hide the detail_url field if the object doesn't have an endpoint\\n raise SkipField\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::DetailUrlField.to_representation\",\n \"text\": \"wagtail/api/v2/serializers.py::DetailUrlField.to_representation\\n def to_representation(self, url):\\n return url\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::PageHtmlUrlField.get_attribute\",\n \"text\": \"wagtail/api/v2/serializers.py::PageHtmlUrlField.get_attribute\\n def get_attribute(self, instance):\\n return instance\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::PageHtmlUrlField.to_representation\",\n \"text\": \"wagtail/api/v2/serializers.py::PageHtmlUrlField.to_representation\\n def to_representation(self, page):\\n try:\\n return page.full_url\\n except NoReverseMatch:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::PageTypeField.get_attribute\",\n \"text\": \"wagtail/api/v2/serializers.py::PageTypeField.get_attribute\\n def get_attribute(self, instance):\\n return instance\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::PageTypeField.to_representation\",\n \"text\": \"wagtail/api/v2/serializers.py::PageTypeField.to_representation\\n def to_representation(self, page):\\n name = page.specific_class._meta.app_label + '.' + page.specific_class.__name__\\n self.context['view'].seen_types[name] = page.specific_class\\n return name\",\n \"label\": 1\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::RelatedField.__init__\",\n \"text\": \"wagtail/api/v2/serializers.py::RelatedField.__init__\\n def __init__(self, *args, **kwargs):\\n self.serializer_class = kwargs.pop('serializer_class')\\n super().__init__(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::RelatedField.to_representation\",\n \"text\": \"wagtail/api/v2/serializers.py::RelatedField.to_representation\\n def to_representation(self, value):\\n serializer = self.serializer_class(context=self.context)\\n return serializer.to_representation(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::PageParentField.get_attribute\",\n \"text\": \"wagtail/api/v2/serializers.py::PageParentField.get_attribute\\n def get_attribute(self, instance):\\n parent = instance.get_parent()\\n\\n site_pages = pages_for_site(self.context['request'].site)\\n if site_pages.filter(id=parent.id).exists():\\n return parent\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::PageParentField.to_representation\",\n \"text\": \"wagtail/api/v2/serializers.py::PageParentField.to_representation\\n def to_representation(self, value):\\n serializer_class = get_serializer_class(value.__class__, ['id', 'type', 'detail_url', 'html_url', 'title'], meta_fields=['type', 'detail_url', 'html_url'], base=PageSerializer)\\n serializer = serializer_class(context=self.context)\\n return serializer.to_representation(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::ChildRelationField.__init__\",\n \"text\": \"wagtail/api/v2/serializers.py::ChildRelationField.__init__\\n def __init__(self, *args, **kwargs):\\n self.serializer_class = kwargs.pop('serializer_class')\\n super().__init__(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::ChildRelationField.to_representation\",\n \"text\": \"wagtail/api/v2/serializers.py::ChildRelationField.to_representation\\n def to_representation(self, value):\\n serializer = self.serializer_class(context=self.context)\\n\\n return [\\n serializer.to_representation(child_object)\\n for child_object in value.all()\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::StreamField.to_representation\",\n \"text\": \"wagtail/api/v2/serializers.py::StreamField.to_representation\\n def to_representation(self, value):\\n return value.stream_block.get_api_representation(value, self.context)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::TagsField.to_representation\",\n \"text\": \"wagtail/api/v2/serializers.py::TagsField.to_representation\\n def to_representation(self, value):\\n return list(value.all().order_by('name').values_list('name', flat=True))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::BaseSerializer.to_representation\",\n \"text\": \"wagtail/api/v2/serializers.py::BaseSerializer.to_representation\\n def to_representation(self, instance):\\n data = OrderedDict()\\n fields = [field for field in self.fields.values() if not field.write_only]\\n\\n # Split meta fields from core fields\\n meta_fields = [field for field in fields if field.field_name in self.meta_fields]\\n fields = [field for field in fields if field.field_name not in self.meta_fields]\\n\\n # Make sure id is always first. This will be filled in later\\n if 'id' in [field.field_name for field in fields]:\\n data['id'] = None\\n\\n # Serialise meta fields\\n meta = OrderedDict()\\n for field in meta_fields:\\n try:\\n attribute = field.get_attribute(instance)\\n except SkipField:\\n continue\\n\\n if attribute is None:\\n # We skip `to_representation` for `None` values so that\\n # fields do not have to explicitly deal with that case.\\n meta[field.field_name] = None\\n else:\\n meta[field.field_name] = field.to_representation(attribute)\\n\\n if meta:\\n data['meta'] = meta\\n\\n # Serialise core fields\\n for field in fields:\\n try:\\n attribute = field.get_attribute(instance)\\n except SkipField:\\n continue\\n\\n if attribute is None:\\n # We skip `to_representation` for `None` values so that\\n # fields do not have to explicitly deal with that case.\\n data[field.field_name] = None\\n else:\\n data[field.field_name] = field.to_representation(attribute)\\n\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::BaseSerializer.build_property_field\",\n \"text\": \"wagtail/api/v2/serializers.py::BaseSerializer.build_property_field\\n def build_property_field(self, field_name, model_class):\\n # TaggableManager is not a Django field so it gets treated as a property\\n field = getattr(model_class, field_name)\\n if isinstance(field, _TaggableManager):\\n return TagsField, {}\\n\\n return super().build_property_field(field_name, model_class)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::BaseSerializer.build_relational_field\",\n \"text\": \"wagtail/api/v2/serializers.py::BaseSerializer.build_relational_field\\n def build_relational_field(self, field_name, relation_info):\\n field_class, field_kwargs = super().build_relational_field(field_name, relation_info)\\n field_kwargs['serializer_class'] = self.child_serializer_classes[field_name]\\n return field_class, field_kwargs\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::PageSerializer.build_relational_field\",\n \"text\": \"wagtail/api/v2/serializers.py::PageSerializer.build_relational_field\\n def build_relational_field(self, field_name, relation_info):\\n # Find all relation fields that point to child class and make them use\\n # the ChildRelationField class.\\n if relation_info.to_many:\\n model = getattr(self.Meta, 'model')\\n child_relations = {\\n child_relation.field.remote_field.related_name: child_relation.related_model\\n for child_relation in get_all_child_relations(model)\\n }\\n\\n if field_name in child_relations and field_name in self.child_serializer_classes:\\n return ChildRelationField, {'serializer_class': self.child_serializer_classes[field_name]}\\n\\n return super().build_relational_field(field_name, relation_info)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/api/v2/serializers.py::get_serializer_class\",\n \"text\": \"wagtail/api/v2/serializers.py::get_serializer_class\\ndef get_serializer_class(model, field_names, meta_fields, field_serializer_overrides=None, child_serializer_classes=None, base=BaseSerializer):\\n model_ = model\\n\\n class Meta:\\n model = model_\\n fields = list(field_names)\\n\\n attrs = {\\n 'Meta': Meta,\\n 'meta_fields': list(meta_fields),\\n 'child_serializer_classes': child_serializer_classes or {},\\n }\\n\\n if field_serializer_overrides:\\n attrs.update(field_serializer_overrides)\\n\\n return type(str(model_.__name__ + 'Serializer'), (base, ), attrs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::pluralize\",\n \"text\": \"wagtail/bin/wagtail.py::pluralize\\ndef pluralize(value, arg='s'):\\n return '' if value == 1 else arg\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::Command.create_parser\",\n \"text\": \"wagtail/bin/wagtail.py::Command.create_parser\\n def create_parser(self, command_name=None):\\n if command_name is None:\\n prog = None\\n else:\\n # hack the prog name as reported to ArgumentParser to include the command\\n prog = \\\"%s %s\\\" % (prog_name(), command_name)\\n\\n parser = ArgumentParser(\\n description=getattr(self, 'description', None), add_help=False, prog=prog\\n )\\n self.add_arguments(parser)\\n return parser\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::Command.add_arguments\",\n \"text\": \"wagtail/bin/wagtail.py::Command.add_arguments\\n def add_arguments(self, parser):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::Command.print_help\",\n \"text\": \"wagtail/bin/wagtail.py::Command.print_help\\n def print_help(self, command_name):\\n parser = self.create_parser(command_name=command_name)\\n parser.print_help()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::Command.execute\",\n \"text\": \"wagtail/bin/wagtail.py::Command.execute\\n def execute(self, argv):\\n parser = self.create_parser()\\n options = parser.parse_args(sys.argv[2:])\\n options_dict = vars(options)\\n self.run(**options_dict)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::CreateProject.add_arguments\",\n \"text\": \"wagtail/bin/wagtail.py::CreateProject.add_arguments\\n def add_arguments(self, parser):\\n parser.add_argument('project_name', help=\\\"Name for your Wagtail project\\\")\\n parser.add_argument('dest_dir', nargs='?', help=\\\"Destination directory inside which to create the project\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::CreateProject.run\",\n \"text\": \"wagtail/bin/wagtail.py::CreateProject.run\\n def run(self, project_name=None, dest_dir=None):\\n # Make sure given name is not already in use by another python package/module.\\n try:\\n __import__(project_name)\\n except ImportError:\\n pass\\n else:\\n sys.exit(\\\"'%s' conflicts with the name of an existing \\\"\\n \\\"Python module and cannot be used as a project \\\"\\n \\\"name. Please try another name.\\\" % project_name)\\n\\n print(\\\"Creating a Wagtail project called %(project_name)s\\\" % {'project_name': project_name}) # noqa\\n\\n # Create the project from the Wagtail template using startapp\\n\\n # First find the path to Wagtail\\n import wagtail\\n wagtail_path = os.path.dirname(wagtail.__file__)\\n template_path = os.path.join(wagtail_path, 'project_template')\\n\\n # Call django-admin startproject\\n utility_args = ['django-admin.py',\\n 'startproject',\\n '--template=' + template_path,\\n '--ext=html,rst',\\n '--name=Dockerfile',\\n project_name]\\n\\n if dest_dir:\\n utility_args.append(dest_dir)\\n\\n utility = ManagementUtility(utility_args)\\n utility.execute()\\n\\n print(\\\"Success! %(project_name)s has been created\\\" % {'project_name': project_name}) # noqa\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::UpdateModulePaths.add_arguments\",\n \"text\": \"wagtail/bin/wagtail.py::UpdateModulePaths.add_arguments\\n def add_arguments(self, parser):\\n parser.add_argument('root_path', nargs='?', help=\\\"Path to your project's root\\\")\\n parser.add_argument('--list', action='store_true', dest='list_files', help=\\\"Show the list of files to change, without modifying them\\\")\\n parser.add_argument('--diff', action='store_true', help=\\\"Show the changes that would be made, without modifying the files\\\")\\n parser.add_argument(\\n '--ignore-dir', action='append', dest='ignored_dirs', metavar='NAME',\\n help=\\\"Ignore files in this directory\\\"\\n )\\n parser.add_argument(\\n '--ignore-file', action='append', dest='ignored_patterns', metavar='NAME',\\n help=\\\"Ignore files with this name (supports wildcards)\\\"\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::UpdateModulePaths.run\",\n \"text\": \"wagtail/bin/wagtail.py::UpdateModulePaths.run\\n def run(self, root_path=None, list_files=False, diff=False, ignored_dirs=None, ignored_patterns=None):\\n if root_path is None:\\n root_path = os.getcwd()\\n\\n absolute_ignored_dirs = [\\n os.path.abspath(dir_path) + os.sep\\n for dir_path in (ignored_dirs or [])\\n ]\\n\\n if ignored_patterns is None:\\n ignored_patterns = []\\n\\n checked_file_count = 0\\n changed_file_count = 0\\n\\n for (dirpath, dirnames, filenames) in os.walk(root_path):\\n dirpath_with_slash = os.path.abspath(dirpath) + os.sep\\n if any(dirpath_with_slash.startswith(ignored_dir) for ignored_dir in absolute_ignored_dirs):\\n continue\\n\\n for filename in filenames:\\n if not filename.lower().endswith('.py'):\\n continue\\n\\n if any(fnmatch.fnmatch(filename, pattern) for pattern in ignored_patterns):\\n continue\\n\\n path = os.path.join(dirpath, filename)\\n relative_path = os.path.relpath(path, start=root_path)\\n checked_file_count += 1\\n\\n if diff:\\n change_count = self._show_diff(path, relative_path=relative_path)\\n else:\\n if list_files:\\n change_count = self._count_changes(path)\\n else: # actually update\\n change_count = self._rewrite_file(path)\\n if change_count:\\n print(\\\"%s - %d change%s\\\" % (relative_path, change_count, pluralize(change_count))) # NOQA\\n\\n if change_count:\\n changed_file_count += 1\\n\\n if diff or list_files:\\n print(\\n \\\"\\\\nChecked %d .py file%s, %d file%s to update.\\\" % (\\n checked_file_count, pluralize(checked_file_count),\\n changed_file_count, pluralize(changed_file_\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::UpdateModulePaths._rewrite_line\",\n \"text\": \"wagtail/bin/wagtail.py::UpdateModulePaths._rewrite_line\\n def _rewrite_line(self, line):\\n for pattern, repl in self.REPLACEMENTS:\\n line = re.sub(pattern, repl, line)\\n return line\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::UpdateModulePaths._show_diff\",\n \"text\": \"wagtail/bin/wagtail.py::UpdateModulePaths._show_diff\\n def _show_diff(self, filename, relative_path=None):\\n change_count = 0\\n original = []\\n updated = []\\n\\n with open(filename) as f:\\n for original_line in f:\\n original.append(original_line)\\n\\n line = self._rewrite_line(original_line)\\n updated.append(line)\\n if line != original_line:\\n change_count += 1\\n\\n if change_count:\\n relative_path = relative_path or filename\\n\\n sys.stdout.writelines(unified_diff(\\n original, updated, fromfile=\\\"%s:before\\\" % relative_path, tofile=\\\"%s:after\\\" % relative_path\\n ))\\n\\n return change_count\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::UpdateModulePaths._count_changes\",\n \"text\": \"wagtail/bin/wagtail.py::UpdateModulePaths._count_changes\\n def _count_changes(self, filename):\\n change_count = 0\\n\\n with open(filename) as f:\\n for original_line in f:\\n line = self._rewrite_line(original_line)\\n if line != original_line:\\n change_count += 1\\n\\n return change_count\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::UpdateModulePaths._rewrite_file\",\n \"text\": \"wagtail/bin/wagtail.py::UpdateModulePaths._rewrite_file\\n def _rewrite_file(self, filename):\\n change_count = 0\\n\\n with fileinput.FileInput(filename, inplace=True) as f:\\n for original_line in f:\\n line = self._rewrite_line(original_line)\\n print_(line, end='') # NOQA\\n if line != original_line:\\n change_count += 1\\n\\n return change_count\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::prog_name\",\n \"text\": \"wagtail/bin/wagtail.py::prog_name\\ndef prog_name():\\n return os.path.basename(sys.argv[0])\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::help_index\",\n \"text\": \"wagtail/bin/wagtail.py::help_index\\ndef help_index():\\n print(\\\"Type '%s help <subcommand>' for help on a specific subcommand.\\\\n\\\" % prog_name()) # NOQA\\n print(\\\"Available subcommands:\\\\n\\\") # NOQA\\n for name, cmd in sorted(COMMANDS.items()):\\n print(\\\" %s%s\\\" % (name.ljust(20), cmd.description)) # NOQA\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::unknown_command\",\n \"text\": \"wagtail/bin/wagtail.py::unknown_command\\ndef unknown_command(command):\\n print(\\\"Unknown command: '%s'\\\" % command) # NOQA\\n print(\\\"Type '%s help' for usage.\\\" % prog_name()) # NOQA\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/bin/wagtail.py::main\",\n \"text\": \"wagtail/bin/wagtail.py::main\\ndef main():\\n try:\\n command_name = sys.argv[1]\\n except IndexError:\\n help_index()\\n return\\n\\n if command_name == 'help':\\n try:\\n help_command_name = sys.argv[2]\\n except IndexError:\\n help_index()\\n return\\n\\n try:\\n command = COMMANDS[help_command_name]\\n except KeyError:\\n unknown_command(help_command_name)\\n return\\n\\n command.print_help(help_command_name)\\n return\\n\\n try:\\n command = COMMANDS[command_name]\\n except KeyError:\\n unknown_command(command_name)\\n return\\n\\n command.execute(sys.argv)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractFormSubmission.get_data\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractFormSubmission.get_data\\n def get_data(self):\\n \\\"\\\"\\\"\\n Returns dict with form data.\\n\\n You can override this method to add additional data.\\n \\\"\\\"\\\"\\n form_data = json.loads(self.form_data)\\n form_data.update({\\n 'submit_time': self.submit_time,\\n })\\n\\n return form_data\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractFormSubmission.__str__\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractFormSubmission.__str__\\n def __str__(self):\\n return self.form_data\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractFormField.clean_name\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractFormField.clean_name\\n @property\\n def clean_name(self):\\n # unidecode will return an ascii string while slugify wants a\\n # unicode string on the other hand, slugify returns a safe-string\\n # which will be converted to a normal str\\n return str(slugify(str(unidecode(self.label))))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.__init__\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.__init__\\n def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n if not hasattr(self, 'landing_page_template'):\\n name, ext = os.path.splitext(self.template)\\n self.landing_page_template = name + '_landing' + ext\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form_fields\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form_fields\\n def get_form_fields(self):\\n \\\"\\\"\\\"\\n Form page expects `form_fields` to be declared.\\n If you want to change backwards relation name,\\n you need to override this method.\\n \\\"\\\"\\\"\\n\\n return self.form_fields.all()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_data_fields\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_data_fields\\n def get_data_fields(self):\\n \\\"\\\"\\\"\\n Returns a list of tuples with (field_name, field_label).\\n \\\"\\\"\\\"\\n\\n data_fields = [\\n ('submit_time', _('Submission date')),\\n ]\\n data_fields += [\\n (field.clean_name, field.label)\\n for field in self.get_form_fields()\\n ]\\n\\n return data_fields\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form_class\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form_class\\n def get_form_class(self):\\n fb = self.form_builder(self.get_form_fields())\\n return fb.get_form_class()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form_parameters\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form_parameters\\n def get_form_parameters(self):\\n return {}\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_form\\n def get_form(self, *args, **kwargs):\\n form_class = self.get_form_class()\\n form_params = self.get_form_parameters()\\n form_params.update(kwargs)\\n\\n return form_class(*args, **form_params)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_landing_page_template\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_landing_page_template\\n def get_landing_page_template(self, request, *args, **kwargs):\\n return self.landing_page_template\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.get_submission_class\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.get_submission_class\\n def get_submission_class(self):\\n \\\"\\\"\\\"\\n Returns submission class.\\n\\n You can override this method to provide custom submission class.\\n Your class must be inherited from AbstractFormSubmission.\\n \\\"\\\"\\\"\\n\\n return FormSubmission\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.process_form_submission\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.process_form_submission\\n def process_form_submission(self, form):\\n \\\"\\\"\\\"\\n Accepts form instance with submitted data, user and page.\\n Creates submission instance.\\n\\n You can override this method if you want to have custom creation logic.\\n For example, if you want to save reference to a user.\\n \\\"\\\"\\\"\\n\\n return self.get_submission_class().objects.create(\\n form_data=json.dumps(form.cleaned_data, cls=DjangoJSONEncoder),\\n page=self,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.render_landing_page\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.render_landing_page\\n def render_landing_page(self, request, form_submission=None, *args, **kwargs):\\n \\\"\\\"\\\"\\n Renders the landing page.\\n\\n You can override this method to return a different HttpResponse as\\n landing page. E.g. you could return a redirect to a separate page.\\n \\\"\\\"\\\"\\n context = self.get_context(request)\\n context['form_submission'] = form_submission\\n return render(\\n request,\\n self.get_landing_page_template(request),\\n context\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.serve_submissions_list_view\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.serve_submissions_list_view\\n def serve_submissions_list_view(self, request, *args, **kwargs):\\n \\\"\\\"\\\"\\n Returns list submissions view for admin.\\n\\n `list_submissions_view_class` can bse set to provide custom view class.\\n Your class must be inherited from SubmissionsListView.\\n \\\"\\\"\\\"\\n view = self.submissions_list_view_class.as_view()\\n return view(request, form_page=self, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.serve\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.serve\\n def serve(self, request, *args, **kwargs):\\n if request.method == 'POST':\\n form = self.get_form(request.POST, request.FILES, page=self, user=request.user)\\n\\n if form.is_valid():\\n form_submission = self.process_form_submission(form)\\n return self.render_landing_page(request, form_submission, *args, **kwargs)\\n else:\\n form = self.get_form(page=self, user=request.user)\\n\\n context = self.get_context(request)\\n context['form'] = form\\n return render(\\n request,\\n self.get_template(request),\\n context\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractForm.serve_preview\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractForm.serve_preview\\n def serve_preview(self, request, mode):\\n if mode == 'landing':\\n request.is_preview = True\\n return self.render_landing_page(request)\\n else:\\n return super().serve_preview(request, mode)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractEmailForm.process_form_submission\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractEmailForm.process_form_submission\\n def process_form_submission(self, form):\\n submission = super().process_form_submission(form)\\n if self.to_address:\\n self.send_mail(form)\\n return submission\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/contrib/forms/models.py::AbstractEmailForm.send_mail\",\n \"text\": \"wagtail/contrib/forms/models.py::AbstractEmailForm.send_mail\\n def send_mail(self, form):\\n addresses = [x.strip() for x in self.to_address.split(',')]\\n content = []\\n for field in form:\\n value = field.value()\\n if isinstance(value, list):\\n value = ', '.join(value)\\n content.append('{}: {}'.format(field.label, value))\\n content = '\\\\n'.join(content)\\n send_mail(self.subject, content, addresses, self.from_address,)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BaseBlock.__new__\",\n \"text\": \"wagtail/core/blocks/base.py::BaseBlock.__new__\\n def __new__(mcs, name, bases, attrs):\\n meta_class = attrs.pop('Meta', None)\\n\\n cls = super(BaseBlock, mcs).__new__(mcs, name, bases, attrs)\\n\\n # Get all the Meta classes from all the bases\\n meta_class_bases = [meta_class] + [getattr(base, '_meta_class', None)\\n for base in bases]\\n meta_class_bases = tuple(filter(bool, meta_class_bases))\\n cls._meta_class = type(str(name + 'Meta'), meta_class_bases, {})\\n\\n return cls\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.__new__\",\n \"text\": \"wagtail/core/blocks/base.py::Block.__new__\\n def __new__(cls, *args, **kwargs):\\n # adapted from django.utils.deconstruct.deconstructible; capture the arguments\\n # so that we can return them in the 'deconstruct' method\\n obj = super(Block, cls).__new__(cls)\\n obj._constructor_args = (args, kwargs)\\n return obj\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.all_blocks\",\n \"text\": \"wagtail/core/blocks/base.py::Block.all_blocks\\n def all_blocks(self):\\n \\\"\\\"\\\"\\n Return a list consisting of self and all block objects that are direct or indirect dependencies\\n of this block\\n \\\"\\\"\\\"\\n result = [self]\\n for dep in self.dependencies:\\n result.extend(dep.all_blocks())\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.all_media\",\n \"text\": \"wagtail/core/blocks/base.py::Block.all_media\\n def all_media(self):\\n media = forms.Media()\\n for block in self.all_blocks():\\n media += block.media\\n return media\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.all_html_declarations\",\n \"text\": \"wagtail/core/blocks/base.py::Block.all_html_declarations\\n def all_html_declarations(self):\\n declarations = filter(bool, [block.html_declarations() for block in self.all_blocks()])\\n return mark_safe('\\\\n'.join(declarations))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.__init__\",\n \"text\": \"wagtail/core/blocks/base.py::Block.__init__\\n def __init__(self, **kwargs):\\n self.meta = self._meta_class()\\n\\n for attr, value in kwargs.items():\\n setattr(self.meta, attr, value)\\n\\n # Increase the creation counter, and save our local copy.\\n self.creation_counter = Block.creation_counter\\n Block.creation_counter += 1\\n self.definition_prefix = 'blockdef-%d' % self.creation_counter\\n\\n self.label = self.meta.label or ''\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.set_name\",\n \"text\": \"wagtail/core/blocks/base.py::Block.set_name\\n def set_name(self, name):\\n self.name = name\\n if not self.meta.label:\\n self.label = capfirst(force_text(name).replace('_', ' '))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.media\",\n \"text\": \"wagtail/core/blocks/base.py::Block.media\\n @property\\n def media(self):\\n return forms.Media()\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.html_declarations\",\n \"text\": \"wagtail/core/blocks/base.py::Block.html_declarations\\n def html_declarations(self):\\n \\\"\\\"\\\"\\n Return an HTML fragment to be rendered on the form page once per block definition -\\n as opposed to once per occurrence of the block. For example, the block definition\\n ListBlock(label=\\\"Shopping list\\\", CharBlock(label=\\\"Product\\\"))\\n needs to output a <script type=\\\"text/template\\\"></script> block containing the HTML for\\n a 'product' text input, to that these can be dynamically added to the list. This\\n template block must only occur once in the page, even if there are multiple 'shopping list'\\n blocks on the page.\\n\\n Any element IDs used in this HTML fragment must begin with definition_prefix.\\n (More precisely, they must either be definition_prefix itself, or begin with definition_prefix\\n followed by a '-' character)\\n \\\"\\\"\\\"\\n return ''\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.js_initializer\",\n \"text\": \"wagtail/core/blocks/base.py::Block.js_initializer\\n def js_initializer(self):\\n \\\"\\\"\\\"\\n Returns a Javascript expression string, or None if this block does not require any\\n Javascript behaviour. This expression evaluates to an initializer function, a function that\\n takes the ID prefix and applies JS behaviour to the block instance with that value and prefix.\\n\\n The parent block of this block (or the top-level page code) must ensure that this\\n expression is not evaluated more than once. (The resulting initializer function can and will be\\n called as many times as there are instances of this block, though.)\\n \\\"\\\"\\\"\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.render_form\",\n \"text\": \"wagtail/core/blocks/base.py::Block.render_form\\n def render_form(self, value, prefix='', errors=None):\\n \\\"\\\"\\\"\\n Render the HTML for this block with 'value' as its content.\\n \\\"\\\"\\\"\\n raise NotImplementedError('%s.render_form' % self.__class__)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.value_from_datadict\",\n \"text\": \"wagtail/core/blocks/base.py::Block.value_from_datadict\\n def value_from_datadict(self, data, files, prefix):\\n raise NotImplementedError('%s.value_from_datadict' % self.__class__)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.value_omitted_from_data\",\n \"text\": \"wagtail/core/blocks/base.py::Block.value_omitted_from_data\\n def value_omitted_from_data(self, data, files, name):\\n \\\"\\\"\\\"\\n Used only for top-level blocks wrapped by BlockWidget (i.e.: typically only StreamBlock)\\n to inform ModelForm logic on Django >=1.10.2 whether the field is absent from the form\\n submission (and should therefore revert to the field default).\\n \\\"\\\"\\\"\\n return name not in data\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.bind\",\n \"text\": \"wagtail/core/blocks/base.py::Block.bind\\n def bind(self, value, prefix=None, errors=None):\\n \\\"\\\"\\\"\\n Return a BoundBlock which represents the association of this block definition with a value\\n and a prefix (and optionally, a ValidationError to be rendered).\\n BoundBlock primarily exists as a convenience to allow rendering within templates:\\n bound_block.render() rather than blockdef.render(value, prefix) which can't be called from\\n within a template.\\n \\\"\\\"\\\"\\n return BoundBlock(self, value, prefix=prefix, errors=errors)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.get_default\",\n \"text\": \"wagtail/core/blocks/base.py::Block.get_default\\n def get_default(self):\\n \\\"\\\"\\\"\\n Return this block's default value (conventionally found in self.meta.default),\\n converted to the value type expected by this block. This caters for the case\\n where that value type is not something that can be expressed statically at\\n model definition type (e.g. something like StructValue which incorporates a\\n pointer back to the block definion object).\\n \\\"\\\"\\\"\\n return self.meta.default\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.prototype_block\",\n \"text\": \"wagtail/core/blocks/base.py::Block.prototype_block\\n def prototype_block(self):\\n \\\"\\\"\\\"\\n Return a BoundBlock that can be used as a basis for new empty block instances to be added on the fly\\n (new list items, for example). This will have a prefix of '__PREFIX__' (to be dynamically replaced with\\n a real prefix when it's inserted into the page) and a value equal to the block's default value.\\n \\\"\\\"\\\"\\n return self.bind(self.get_default(), '__PREFIX__')\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.clean\",\n \"text\": \"wagtail/core/blocks/base.py::Block.clean\\n def clean(self, value):\\n \\\"\\\"\\\"\\n Validate value and return a cleaned version of it, or throw a ValidationError if validation fails.\\n The thrown ValidationError instance will subsequently be passed to render() to display the\\n error message; the ValidationError must therefore include all detail necessary to perform that\\n rendering, such as identifying the specific child block(s) with errors, in the case of nested\\n blocks. (It is suggested that you use the 'params' attribute for this; using error_list /\\n error_dict is unreliable because Django tends to hack around with these when nested.)\\n \\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.to_python\",\n \"text\": \"wagtail/core/blocks/base.py::Block.to_python\\n def to_python(self, value):\\n \\\"\\\"\\\"\\n Convert 'value' from a simple (JSON-serialisable) value to a (possibly complex) Python value to be\\n used in the rest of the block API and within front-end templates . In simple cases this might be\\n the value itself; alternatively, it might be a 'smart' version of the value which behaves mostly\\n like the original value but provides a native HTML rendering when inserted into a template; or it\\n might be something totally different (e.g. an image chooser will use the image ID as the clean\\n value, and turn this back into an actual image object here).\\n \\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.get_prep_value\",\n \"text\": \"wagtail/core/blocks/base.py::Block.get_prep_value\\n def get_prep_value(self, value):\\n \\\"\\\"\\\"\\n The reverse of to_python; convert the python value into JSON-serialisable form.\\n \\\"\\\"\\\"\\n return value\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.get_context\",\n \"text\": \"wagtail/core/blocks/base.py::Block.get_context\\n def get_context(self, value, parent_context=None):\\n \\\"\\\"\\\"\\n Return a dict of context variables (derived from the block value and combined with the parent_context)\\n to be used as the template context when rendering this value through a template.\\n \\\"\\\"\\\"\\n\\n context = parent_context or {}\\n context.update({\\n 'self': value,\\n self.TEMPLATE_VAR: value,\\n })\\n return context\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.get_template\",\n \"text\": \"wagtail/core/blocks/base.py::Block.get_template\\n def get_template(self, context=None):\\n \\\"\\\"\\\"\\n Return the template to use for rendering the block if specified on meta class.\\n This extraction was added to make dynamic templates possible if you override this method\\n \\\"\\\"\\\"\\n return getattr(self.meta, 'template', None)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.render\",\n \"text\": \"wagtail/core/blocks/base.py::Block.render\\n def render(self, value, context=None):\\n \\\"\\\"\\\"\\n Return a text rendering of 'value', suitable for display on templates. By default, this will\\n use a template (with the passed context, supplemented by the result of get_context) if a\\n 'template' property is specified on the block, and fall back on render_basic otherwise.\\n \\\"\\\"\\\"\\n template = self.get_template(context=context)\\n if not template:\\n return self.render_basic(value, context=context)\\n\\n if context is None:\\n new_context = self.get_context(value)\\n else:\\n new_context = self.get_context(value, parent_context=dict(context))\\n\\n return mark_safe(render_to_string(template, new_context))\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.get_api_representation\",\n \"text\": \"wagtail/core/blocks/base.py::Block.get_api_representation\\n def get_api_representation(self, value, context=None):\\n \\\"\\\"\\\"\\n Can be used to customise the API response and defaults to the value returned by get_prep_value.\\n \\\"\\\"\\\"\\n return self.get_prep_value(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.render_basic\",\n \"text\": \"wagtail/core/blocks/base.py::Block.render_basic\\n def render_basic(self, value, context=None):\\n \\\"\\\"\\\"\\n Return a text rendering of 'value', suitable for display on templates. render() will fall back on\\n this if the block does not define a 'template' property.\\n \\\"\\\"\\\"\\n return force_text(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.get_searchable_content\",\n \"text\": \"wagtail/core/blocks/base.py::Block.get_searchable_content\\n def get_searchable_content(self, value):\\n \\\"\\\"\\\"\\n Returns a list of strings containing text content within this block to be used in a search engine.\\n \\\"\\\"\\\"\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.check\",\n \"text\": \"wagtail/core/blocks/base.py::Block.check\\n def check(self, **kwargs):\\n \\\"\\\"\\\"\\n Hook for the Django system checks framework -\\n returns a list of django.core.checks.Error objects indicating validity errors in the block\\n \\\"\\\"\\\"\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block._check_name\",\n \"text\": \"wagtail/core/blocks/base.py::Block._check_name\\n def _check_name(self, **kwargs):\\n \\\"\\\"\\\"\\n Helper method called by container blocks as part of the system checks framework,\\n to validate that this block's name is a valid identifier.\\n (Not called universally, because not all blocks need names)\\n \\\"\\\"\\\"\\n errors = []\\n if not self.name:\\n errors.append(checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n hint=\\\"Block name cannot be empty\\\",\\n obj=kwargs.get('field', self),\\n id='wagtailcore.E001',\\n ))\\n\\n if ' ' in self.name:\\n errors.append(checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n hint=\\\"Block names cannot contain spaces\\\",\\n obj=kwargs.get('field', self),\\n id='wagtailcore.E001',\\n ))\\n\\n if '-' in self.name:\\n errors.append(checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n \\\"Block names cannot contain dashes\\\",\\n obj=kwargs.get('field', self),\\n id='wagtailcore.E001',\\n ))\\n\\n if self.name and self.name[0].isdigit():\\n errors.append(checks.Error(\\n \\\"Block name %r is invalid\\\" % self.name,\\n \\\"Block names cannot begin with a digit\\\",\\n obj=kwargs.get('field', self),\\n id='wagtailcore.E001',\\n ))\\n\\n return errors\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.id_for_label\",\n \"text\": \"wagtail/core/blocks/base.py::Block.id_for_label\\n def id_for_label(self, prefix):\\n \\\"\\\"\\\"\\n Return the ID to be used as the 'for' attribute of <label> elements that refer to this block,\\n when the given field prefix is in use. Return None if no 'for' attribute should be used.\\n \\\"\\\"\\\"\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.required\",\n \"text\": \"wagtail/core/blocks/base.py::Block.required\\n @property\\n def required(self):\\n \\\"\\\"\\\"\\n Flag used to determine whether labels for this block should display a 'required' asterisk.\\n False by default, since Block does not provide any validation of its own - it's up to subclasses\\n to define what required-ness means.\\n \\\"\\\"\\\"\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.deconstruct\",\n \"text\": \"wagtail/core/blocks/base.py::Block.deconstruct\\n def deconstruct(self):\\n # adapted from django.utils.deconstruct.deconstructible\\n module_name = self.__module__\\n name = self.__class__.__name__\\n\\n # Make sure it's actually there and not an inner class\\n module = import_module(module_name)\\n if not hasattr(module, name):\\n raise ValueError(\\n \\\"Could not find object %s in %s.\\\\n\\\"\\n \\\"Please note that you cannot serialize things like inner \\\"\\n \\\"classes. Please move the object into the main module \\\"\\n \\\"body to use migrations.\\\\n\\\"\\n % (name, module_name))\\n\\n # if the module defines a DECONSTRUCT_ALIASES dictionary, see if the class has an entry in there;\\n # if so, use that instead of the real path\\n try:\\n path = module.DECONSTRUCT_ALIASES[self.__class__]\\n except (AttributeError, KeyError):\\n path = '%s.%s' % (module_name, name)\\n\\n return (\\n path,\\n self._constructor_args[0],\\n self._constructor_args[1],\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::Block.__eq__\",\n \"text\": \"wagtail/core/blocks/base.py::Block.__eq__\\n def __eq__(self, other):\\n \\\"\\\"\\\"\\n Implement equality on block objects so that two blocks with matching definitions are considered\\n equal. (Block objects are intended to be immutable with the exception of set_name(), so here\\n 'matching definitions' means that both the 'name' property and the constructor args/kwargs - as\\n captured in _constructor_args - are equal on both blocks.)\\n\\n This was originally necessary as a workaround for https://code.djangoproject.com/ticket/24340\\n in Django <1.9; the deep_deconstruct function used to detect changes for migrations did not\\n recurse into the block lists, and left them as Block instances. This __eq__ method therefore\\n came into play when identifying changes within migrations.\\n\\n As of Django >=1.9, this *probably* isn't required any more. However, it may be useful in\\n future as a way of identifying blocks that can be re-used within StreamField definitions\\n (https://github.com/wagtail/wagtail/issues/4298#issuecomment-367656028).\\n \\\"\\\"\\\"\\n\\n if not isinstance(other, Block):\\n # if the other object isn't a block at all, it clearly isn't equal.\\n return False\\n\\n # Note that we do not require the two blocks to be of the exact same class. This is because\\n # we may wish the following blocks to be considered equal:\\n #\\n # class FooBlock(StructBlock):\\n # first_name = CharBlock()\\n # surname = CharBlock()\\n #\\n # class BarBlock(StructBlock):\\n # first_name = CharBlock()\\n # surname = CharBlock()\\n #\\n # FooBlock() == BarBlock() == StructBlock([('first_name', CharBlock()), ('surname': CharBlock())])\\n #\\n # For this to work, StructBlock will need to ensure that 'deconstruct' returns the same signature\\n # in all of these\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BoundBlock.__init__\",\n \"text\": \"wagtail/core/blocks/base.py::BoundBlock.__init__\\n def __init__(self, block, value, prefix=None, errors=None):\\n self.block = block\\n self.value = value\\n self.prefix = prefix\\n self.errors = errors\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BoundBlock.render_form\",\n \"text\": \"wagtail/core/blocks/base.py::BoundBlock.render_form\\n def render_form(self):\\n return self.block.render_form(self.value, self.prefix, errors=self.errors)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BoundBlock.render\",\n \"text\": \"wagtail/core/blocks/base.py::BoundBlock.render\\n def render(self, context=None):\\n return self.block.render(self.value, context=context)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BoundBlock.render_as_block\",\n \"text\": \"wagtail/core/blocks/base.py::BoundBlock.render_as_block\\n def render_as_block(self, context=None):\\n \\\"\\\"\\\"\\n Alias for render; the include_block tag will specifically check for the presence of a method\\n with this name. (This is because {% include_block %} is just as likely to be invoked on a bare\\n value as a BoundBlock. If we looked for a `render` method instead, we'd run the risk of finding\\n an unrelated method that just happened to have that name - for example, when called on a\\n PageChooserBlock it could end up calling page.render.\\n \\\"\\\"\\\"\\n return self.block.render(self.value, context=context)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BoundBlock.id_for_label\",\n \"text\": \"wagtail/core/blocks/base.py::BoundBlock.id_for_label\\n def id_for_label(self):\\n return self.block.id_for_label(self.prefix)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BoundBlock.__str__\",\n \"text\": \"wagtail/core/blocks/base.py::BoundBlock.__str__\\n def __str__(self):\\n \\\"\\\"\\\"Render the value according to the block's native rendering\\\"\\\"\\\"\\n return self.block.render(self.value)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::DeclarativeSubBlocksMetaclass.__new__\",\n \"text\": \"wagtail/core/blocks/base.py::DeclarativeSubBlocksMetaclass.__new__\\n def __new__(mcs, name, bases, attrs):\\n # Collect sub-blocks declared on the current class.\\n # These are available on the class as `declared_blocks`\\n current_blocks = []\\n for key, value in list(attrs.items()):\\n if isinstance(value, Block):\\n current_blocks.append((key, value))\\n value.set_name(key)\\n attrs.pop(key)\\n current_blocks.sort(key=lambda x: x[1].creation_counter)\\n attrs['declared_blocks'] = collections.OrderedDict(current_blocks)\\n\\n new_class = (super(DeclarativeSubBlocksMetaclass, mcs).__new__(\\n mcs, name, bases, attrs))\\n\\n # Walk through the MRO, collecting all inherited sub-blocks, to make\\n # the combined `base_blocks`.\\n base_blocks = collections.OrderedDict()\\n for base in reversed(new_class.__mro__):\\n # Collect sub-blocks from base class.\\n if hasattr(base, 'declared_blocks'):\\n base_blocks.update(base.declared_blocks)\\n\\n # Field shadowing.\\n for attr, value in base.__dict__.items():\\n if value is None and attr in base_blocks:\\n base_blocks.pop(attr)\\n new_class.base_blocks = base_blocks\\n\\n return new_class\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BlockWidget.__init__\",\n \"text\": \"wagtail/core/blocks/base.py::BlockWidget.__init__\\n def __init__(self, block_def, attrs=None):\\n super().__init__(attrs=attrs)\\n self.block_def = block_def\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/core/blocks/base.py::BlockWidget.render_with_errors\",\n \"text\": \"wagtail/core/blocks/base.py::BlockWidget.render_with_errors\\n def render_with_errors(self, name, value, attrs=None, errors=None):\\n bound_block = self.block_def.bind(value, prefix=name, errors=errors)\\n js_initializer = self.block_def.js_initializer()\\n if js_initializer:\\n js_snippet = \\\"\\\"\\\"\\n <script>\\n $(function() {\\n var initializer = %s;\\n initializer('%s');\\n })\\n </script>\\n \\\"\\\"\\\" % (js_initializer, name)\\n else:\\n js_snippet = ''\\n return mark_safe(bound_block.render_form() + js_snippet)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/views/images.py::index\",\n \"text\": \"wagtail/images/views/images.py::index\\n@permission_checker.require_any('add', 'change', 'delete')\\n@vary_on_headers('X-Requested-With')\\ndef index(request):\\n Image = get_image_model()\\n\\n # Get images (filtered by user permission)\\n images = permission_policy.instances_user_has_any_permission_for(\\n request.user, ['change', 'delete']\\n ).order_by('-created_at')\\n\\n # Search\\n query_string = None\\n if 'q' in request.GET:\\n form = SearchForm(request.GET, placeholder=_(\\\"Search images\\\"))\\n if form.is_valid():\\n query_string = form.cleaned_data['q']\\n\\n images = images.search(query_string)\\n else:\\n form = SearchForm(placeholder=_(\\\"Search images\\\"))\\n\\n # Filter by collection\\n current_collection = None\\n collection_id = request.GET.get('collection_id')\\n if collection_id:\\n try:\\n current_collection = Collection.objects.get(id=collection_id)\\n images = images.filter(collection=current_collection)\\n except (ValueError, Collection.DoesNotExist):\\n pass\\n\\n paginator, images = paginate(request, images)\\n\\n collections = permission_policy.collections_user_has_any_permission_for(\\n request.user, ['add', 'change']\\n )\\n if len(collections) < 2:\\n collections = None\\n\\n # Create response\\n if request.is_ajax():\\n return render(request, 'wagtailimages/images/results.html', {\\n 'images': images,\\n 'query_string': query_string,\\n 'is_searching': bool(query_string),\\n })\\n else:\\n return render(request, 'wagtailimages/images/index.html', {\\n 'images': images,\\n 'query_string': query_string,\\n 'is_searching': bool(query_string),\\n\\n 'search_form': form,\\n 'popular_tags': popular_tags_for_model(Image),\\n 'collections': collections,\\n 'current_collection': current_collection,\\n 'user_can_add': permission_policy.user_has_permission(requ\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/views/images.py::edit\",\n \"text\": \"wagtail/images/views/images.py::edit\\n@permission_checker.require('change')\\ndef edit(request, image_id):\\n Image = get_image_model()\\n ImageForm = get_image_form(Image)\\n\\n image = get_object_or_404(Image, id=image_id)\\n\\n if not permission_policy.user_has_permission_for_instance(request.user, 'change', image):\\n return permission_denied(request)\\n\\n if request.method == 'POST':\\n original_file = image.file\\n form = ImageForm(request.POST, request.FILES, instance=image, user=request.user)\\n if form.is_valid():\\n if 'file' in form.changed_data:\\n # Set new image file size\\n image.file_size = image.file.size\\n\\n # Set new image file hash\\n image.file.seek(0)\\n image._set_file_hash(image.file.read())\\n\\n form.save()\\n\\n if 'file' in form.changed_data:\\n # if providing a new image file, delete the old one and all renditions.\\n # NB Doing this via original_file.delete() clears the file field,\\n # which definitely isn't what we want...\\n original_file.storage.delete(original_file.name)\\n image.renditions.all().delete()\\n\\n # Reindex the image to make sure all tags are indexed\\n search_index.insert_or_update_object(image)\\n\\n messages.success(request, _(\\\"Image '{0}' updated.\\\").format(image.title), buttons=[\\n messages.button(reverse('wagtailimages:edit', args=(image.id,)), _('Edit again'))\\n ])\\n return redirect('wagtailimages:index')\\n else:\\n messages.error(request, _(\\\"The image could not be saved due to errors.\\\"))\\n else:\\n form = ImageForm(instance=image, user=request.user)\\n\\n # Check if we should enable the frontend url generator\\n try:\\n reverse('wagtailimages_serve', args=('foo', '1', 'bar'))\\n url_generator_enabled = True\\n except NoReverseMatch:\\n url_generator_e\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/views/images.py::url_generator\",\n \"text\": \"wagtail/images/views/images.py::url_generator\\ndef url_generator(request, image_id):\\n image = get_object_or_404(get_image_model(), id=image_id)\\n\\n if not permission_policy.user_has_permission_for_instance(request.user, 'change', image):\\n return permission_denied(request)\\n\\n form = URLGeneratorForm(initial={\\n 'filter_method': 'original',\\n 'width': image.width,\\n 'height': image.height,\\n })\\n\\n return render(request, \\\"wagtailimages/images/url_generator.html\\\", {\\n 'image': image,\\n 'form': form,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/views/images.py::generate_url\",\n \"text\": \"wagtail/images/views/images.py::generate_url\\ndef generate_url(request, image_id, filter_spec):\\n # Get the image\\n Image = get_image_model()\\n try:\\n image = Image.objects.get(id=image_id)\\n except Image.DoesNotExist:\\n return JsonResponse({\\n 'error': \\\"Cannot find image.\\\"\\n }, status=404)\\n\\n # Check if this user has edit permission on this image\\n if not permission_policy.user_has_permission_for_instance(request.user, 'change', image):\\n return JsonResponse({\\n 'error': \\\"You do not have permission to generate a URL for this image.\\\"\\n }, status=403)\\n\\n # Parse the filter spec to make sure its valid\\n try:\\n Filter(spec=filter_spec).operations\\n except InvalidFilterSpecError:\\n return JsonResponse({\\n 'error': \\\"Invalid filter spec.\\\"\\n }, status=400)\\n\\n # Generate url\\n signature = generate_signature(image_id, filter_spec)\\n url = reverse('wagtailimages_serve', args=(signature, image_id, filter_spec))\\n\\n # Get site root url\\n try:\\n site_root_url = Site.objects.get(is_default_site=True).root_url\\n except Site.DoesNotExist:\\n site_root_url = Site.objects.first().root_url\\n\\n # Generate preview url\\n preview_url = reverse('wagtailimages:preview', args=(image_id, filter_spec))\\n\\n return JsonResponse({'url': site_root_url + url, 'preview_url': preview_url}, status=200)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/views/images.py::preview\",\n \"text\": \"wagtail/images/views/images.py::preview\\ndef preview(request, image_id, filter_spec):\\n image = get_object_or_404(get_image_model(), id=image_id)\\n\\n try:\\n response = HttpResponse()\\n image = Filter(spec=filter_spec).run(image, response)\\n response['Content-Type'] = 'image/' + image.format_name\\n return response\\n except InvalidFilterSpecError:\\n return HttpResponse(\\\"Invalid filter spec: \\\" + filter_spec, content_type='text/plain', status=400)\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/views/images.py::delete\",\n \"text\": \"wagtail/images/views/images.py::delete\\n@permission_checker.require('delete')\\ndef delete(request, image_id):\\n image = get_object_or_404(get_image_model(), id=image_id)\\n\\n if not permission_policy.user_has_permission_for_instance(request.user, 'delete', image):\\n return permission_denied(request)\\n\\n if request.method == 'POST':\\n image.delete()\\n messages.success(request, _(\\\"Image '{0}' deleted.\\\").format(image.title))\\n return redirect('wagtailimages:index')\\n\\n return render(request, \\\"wagtailimages/images/confirm_delete.html\\\", {\\n 'image': image,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/views/images.py::add\",\n \"text\": \"wagtail/images/views/images.py::add\\n@permission_checker.require('add')\\ndef add(request):\\n ImageModel = get_image_model()\\n ImageForm = get_image_form(ImageModel)\\n\\n if request.method == 'POST':\\n image = ImageModel(uploaded_by_user=request.user)\\n form = ImageForm(request.POST, request.FILES, instance=image, user=request.user)\\n if form.is_valid():\\n # Set image file size\\n image.file_size = image.file.size\\n\\n # Set image file hash\\n image.file.seek(0)\\n image._set_file_hash(image.file.read())\\n\\n form.save()\\n\\n # Reindex the image to make sure all tags are indexed\\n search_index.insert_or_update_object(image)\\n\\n messages.success(request, _(\\\"Image '{0}' added.\\\").format(image.title), buttons=[\\n messages.button(reverse('wagtailimages:edit', args=(image.id,)), _('Edit'))\\n ])\\n return redirect('wagtailimages:index')\\n else:\\n messages.error(request, _(\\\"The image could not be created due to errors.\\\"))\\n else:\\n form = ImageForm(user=request.user)\\n\\n return render(request, \\\"wagtailimages/images/add.html\\\", {\\n 'form': form,\\n })\",\n \"label\": 0\n },\n {\n \"cid\": \"wagtail/images/views/images.py::usage\",\n \"text\": \"wagtail/images/views/images.py::usage\\ndef usage(request, image_id):\\n image = get_object_or_404(get_image_model(), id=image_id)\\n\\n paginator, used_by = paginate(request, image.get_usage())\\n\\n return render(request, \\\"wagtailimages/images/usage.html\\\", {\\n 'image': image,\\n 'used_by': used_by\\n })\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":283,"cells":{"instance_id":{"kind":"string","value":"ray-project__ray-9547"},"repo":{"kind":"string","value":"ray-project/ray"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"[Core] Turning off log redirection to files breaks all Ray service startups.\n<!--Please include [tune], [rllib], [autoscaler] etc. in the issue title if relevant-->\r\n\r\n### What is the problem?\r\n\r\nTurning off log redirection to files breaks all Ray process startups (redis, GCS server, log monitor, reporter, plasma store, raylet, etc.)\r\n\r\n```\r\n File \"/app/descarteslabs/services/workbench/deploy/head-image-dev.binary.runfiles/requirements_py3_pypi__ray_0_9_0_dev0/ray-0.9.0.dev0.data/purelib/ray/scripts/scripts.py\", line 470, in start\r\n ray_params, head=True, shutdown_at_exit=block, spawn_reaper=block)\r\n File \"/app/descarteslabs/services/workbench/deploy/head-image-dev.binary.runfiles/requirements_py3_pypi__ray_0_9_0_dev0/ray-0.9.0.dev0.data/purelib/ray/node.py\", line 194, in __init__\r\n self.start_head_processes()\r\n File \"/app/descarteslabs/services/workbench/deploy/head-image-dev.binary.runfiles/requirements_py3_pypi__ray_0_9_0_dev0/ray-0.9.0.dev0.data/purelib/ray/node.py\", line 746, in start_head_processes\r\n self.start_redis()\r\n File \"/app/descarteslabs/services/workbench/deploy/head-image-dev.binary.runfiles/requirements_py3_pypi__ray_0_9_0_dev0/ray-0.9.0.dev0.data/purelib/ray/node.py\", line 506, in start_redis\r\n redis_log_files = [(open_log(redis_out_name),\r\n File \"/app/descarteslabs/services/workbench/deploy/head-image-dev.binary.runfiles/requirements_py3_pypi__ray_0_9_0_dev0/ray-0.9.0.dev0.data/purelib/ray/utils.py\", line 397, in open_log\r\n return open(path, **kwargs)\r\nTypeError: expected str, bytes or os.PathLike object, not NoneType\r\n```\r\n\r\n\r\n*Ray version and other system information (Python version, TensorFlow version, OS):*\r\n\r\nThis bug was introduced in [this PR](https://github.com/ray-project/ray/pull/8885), so it shouldn't be affecting any releases.\r\n\r\nThe issue is that `get_log_file_names` will return [`None, None` if output redirection is turned off](https://github.com/ray-project/ray/blob/f080aa6ce33ed34f686e868d97b85632c8a8f894/python/ray/node.py#L414-L415), while all callers of that function now pass the returned log file names directly to `open_log`, e.g. [here](https://github.com/ray-project/ray/blob/f080aa6ce33ed34f686e868d97b85632c8a8f894/python/ray/node.py#L506-L507).\r\n\r\n### Reproduction:\r\n\r\nStart Ray with the environment variable `GLOG_logtostderr` set to 1, or with `redirect_output` or `redirect_worker_output` set to `False`. I've confirmed that this bug exists on the latest build off master.\r\n\r\nIf we cannot run your script, we cannot fix your issue.\r\n\r\n- [x] I have verified my script runs in a clean environment and reproduces the issue.\r\n- [x] I have verified the issue also occurs with the [latest wheels](https://docs.ray.io/en/latest/installation.html).\r\n\n"},"gold_files":{"kind":"list like","value":["python/ray/node.py","python/ray/utils.py"],"string":"[\n \"python/ray/node.py\",\n \"python/ray/utils.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["python/ray/node.py::Node.get_job_redirected_log_file","python/ray/node.py::Node.get_log_file_names","python/ray/node.py::Node.start_dashboard","python/ray/node.py::Node.start_gcs_server","python/ray/node.py::Node.start_log_monitor","python/ray/node.py::Node.start_monitor","python/ray/node.py::Node.start_plasma_store","python/ray/node.py::Node.start_raylet","python/ray/node.py::Node.start_redis","python/ray/node.py::Node.start_reporter","python/ray/utils.py::open_log"],"string":"[\n \"python/ray/node.py::Node.get_job_redirected_log_file\",\n \"python/ray/node.py::Node.get_log_file_names\",\n \"python/ray/node.py::Node.start_dashboard\",\n \"python/ray/node.py::Node.start_gcs_server\",\n \"python/ray/node.py::Node.start_log_monitor\",\n \"python/ray/node.py::Node.start_monitor\",\n \"python/ray/node.py::Node.start_plasma_store\",\n \"python/ray/node.py::Node.start_raylet\",\n \"python/ray/node.py::Node.start_redis\",\n \"python/ray/node.py::Node.start_reporter\",\n \"python/ray/utils.py::open_log\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"doc/source/conf.py","text":"doc/source/conf.py\ndef feedback_form_url(project, page) # Create a URL for feedback on a particular page in a project.\ndef update_context(app, pagename, templatename, context, doctree) # Update the page rendering context to include ``feedback_form_url``.\ndef setup(app)","label":0},{"cid":"python/ray/node.py","text":"python/ray/node.py\nclass Node # An encapsulation of the Ray processes on a single node.\n def __init__(self, ray_params, head=False, shutdown_at_exit=True, spawn_reaper=True, connect_only=False) # Start a node.\n def _register_shutdown_hooks(self)\n def atexit_handler(*args)\n def sigterm_handler(signum, frame)\n def _init_temp(self, redis_client)\n def get_resource_spec(self) # Resolve and return the current resource spec for the node.\n def node_ip_address(self) # Get the IP address of this node.\n def raylet_ip_address(self) # Get the IP address of the raylet that this node connects to.\n def address(self) # Get the cluster address.\n def redis_address(self) # Get the cluster Redis address.\n def redis_password(self) # Get the cluster Redis password\n def load_code_from_local(self)\n def object_ref_seed(self) # Get the seed for deterministic generation of object refs\n def plasma_store_socket_name(self) # Get the node's plasma store socket name.\n def unique_id(self) # Get a unique identifier for this node.\n def webui_url(self) # Get the cluster's web UI url.\n def raylet_socket_name(self) # Get the node's raylet socket name.\n def node_manager_port(self) # Get the node manager's port.\n def socket(self) # Get the socket reserving the node manager's port\n def address_info(self) # Get a dictionary of addresses.\n def create_redis_client(self) # Create a redis client.\n def get_temp_dir_path(self) # Get the path of the temporary directory.\n def get_session_dir_path(self) # Get the path of the session directory.\n def get_logs_dir_path(self) # Get the path of the log files directory.\n def get_sockets_dir_path(self) # Get the path of the sockets directory.\n def _make_inc_temp(self, suffix='', prefix='', directory_name=None) # Return a incremental temporary file name. The file is not created.\n def get_log_file_names(self, name, unique=False) # Generate partially randomized filenames for log files.\n def _get_unused_port(self, close_on_exit=True)\n def _prepare_socket_file(self, socket_path, default_prefix) # Prepare the socket file for raylet and plasma.\n def start_reaper_process(self) # Start the reaper process.\n def start_redis(self) # Start the Redis servers.\n def start_log_monitor(self) # Start the log monitor.\n def start_reporter(self) # Start the reporter.\n def start_dashboard(self, require_dashboard) # Start the dashboard.\n def start_plasma_store(self) # Start the plasma store.\n def start_gcs_server(self) # Start the gcs server.\n def start_raylet(self, use_valgrind=False, use_profiler=False) # Start the raylet.\n def get_job_redirected_log_file(self, worker_id: bytes, job_id: bytes=None) # Determines (but does not create) logging files for workers to\n def start_worker(self) # Start a worker process.\n def start_monitor(self) # Start the monitor.\n def start_head_processes(self) # Start head processes on","label":1},{"cid":"python/ray/utils.py","text":"python/ray/utils.py\ndef get_user_temp_dir()\ndef get_ray_temp_dir()\ndef _random_string()\ndef format_error_message(exception_message, task_exception=False) # Improve the formatting of an exception thrown by a remote function.\ndef push_error_to_driver(worker, error_type, message, job_id=None) # Push an error message to the driver to be printed in the background.\ndef push_error_to_driver_through_redis(redis_client, error_type, message, job_id=None) # Push an error message to the driver to be printed in the background.\ndef is_cython(obj) # Check if an object is a Cython function or method\n def check_cython(x)\ndef is_function_or_method(obj) # Check if an object is a function or method.\ndef is_class_method(f) # Returns whether the given method is a class_method.\ndef is_static_method(cls, f_name) # Returns whether the class has a static method with the given name.\ndef random_string() # Generate a random string to use as an ID.\ndef decode(byte_str, allow_none=False) # Make this unicode in Python 3, otherwise leave it as bytes.\ndef ensure_str(s, encoding='utf-8', errors='strict') # Coerce *s* to `str`.\ndef binary_to_object_ref(binary_object_ref)\ndef binary_to_task_id(binary_task_id)\ndef binary_to_hex(identifier)\ndef hex_to_binary(hex_identifier)\ndef compute_job_id_from_driver(driver_id)\ndef compute_driver_id_from_job(job_id)\ndef get_cuda_visible_devices() # Get the device IDs in the CUDA_VISIBLE_DEVICES environment variable.\ndef set_cuda_visible_devices(gpu_ids) # Set the CUDA_VISIBLE_DEVICES environment variable.\ndef resources_from_resource_arguments(default_num_cpus, default_num_gpus, default_memory, default_object_store_memory, default_resources, runtime_num_cpus, runtime_num_gpus, runtime_memory, runtime_object_store_memory, runtime_resources) # Determine a task's resource requirements.\ndef setup_logger(logging_level, logging_format) # Setup default logging for ray.\ndef open_log(path, **kwargs)\ndef create_and_init_new_worker_log(path, worker_pid) # Opens a path (or creates if necessary) for a log.\ndef get_system_memory() # Return the total amount of system memory in bytes.\ndef get_used_memory() # Return the currently used system memory in bytes\ndef estimate_available_memory() # Return the currently available amount of system memory in bytes.\ndef get_shared_memory_bytes() # Get the size of the shared memory file system.\ndef check_oversized_pickle(pickled, name, obj_type, worker) # Send a warning message if the pickled object is too large.\ndef is_main_thread()\ndef detect_fate_sharing_support_win32()\ndef detect_fate_sharing_support_linux()\ndef detect_fate_sharing_support()\ndef set_kill_on_parent_death_linux() # Ensures this process dies if its parent dies (fate-sharing).\ndef set_kill_child_on_death_win32(child_proc) # Ensures the child process dies if this process dies (fate-sharing).\ndef set_sigterm_handler(sigterm_handler) # Registers a handler for SIGTERM in a platform-compatible manner.\ndef try_make_directory_shared(directory","label":1},{"cid":"python/ray/worker.py","text":"python/ray/worker.py\nclass ActorCheckpointInfo # Information used to maintain actor checkpoints.\n def __init__(self, num_tasks_since_last_checkpoint, last_checkpoint_timestamp, checkpoint_ids)\nclass Worker # A class used to define the control flow of a worker process.\n def __init__(self) # Initialize a Worker object.\n def connected(self)\n def node_ip_address(self)\n def load_code_from_local(self)\n def current_job_id(self)\n def actor_id(self)\n def current_task_id(self)\n def current_session_and_job(self) # Get the current session index and job id as pair.\n def mark_actor_init_failed(self, error) # Called to mark this actor as failed during initialization.\n def reraise_actor_init_error(self) # Raises any previous actor initialization error.\n def get_serialization_context(self, job_id=None) # Get the SerializationContext of the job that this worker is processing.\n def check_connected(self) # Check if the worker is connected.\n def set_mode(self, mode) # Set the mode of the worker.\n def put_object(self, value, object_ref=None, pin_object=True) # Put value in the local object store with object reference `object_ref`.\n def deserialize_objects(self, data_metadata_pairs, object_refs)\n def get_objects(self, object_refs, timeout=None) # Get the values in the object store associated with the IDs.\n def run_function_on_all_workers(self, function, run_on_other_drivers=False) # Run arbitrary code on all of the workers.\n def main_loop(self) # The main loop a worker runs to receive and execute tasks.\n def sigterm_handler(signum, frame)\ndef get_gpu_ids() # Get the IDs of the GPUs that are available to the worker.\ndef get_resource_ids() # Get the IDs of the resources that are available to the worker.\ndef get_webui_url() # Get the URL to access the web UI.\ndef print_failed_task(task_status) # Print information about failed tasks.\ndef init(address=None, redis_address=None, redis_port=None, num_cpus=None, num_gpus=None, memory=None, object_store_memory=None, resources=None, driver_object_store_memory=None, redis_max_memory=None, log_to_driver=True, node_ip_address=ray_constants.NODE_DEFAULT_IP, object_ref_seed=None, local_mode=False, redirect_worker_output=None, redirect_output=None, ignore_reinit_error=False, num_redis_shards=None, redis_max_clients=None, redis_password=ray_constants.REDIS_DEFAULT_PASSWORD, plasma_directory=None, huge_pages=False, include_java=False, include_dashboard=None, dashboard_host='localhost', dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT, job_id=None, configure_logging=True, logging_level=logging.INFO, logging_format=ray_constants.LOGGER_FORMAT, plasma_store_socket_name=None, raylet_socket_name=None, temp_dir=None, load_code_from_local=False, java_worker_options=None, use_pickle=True, _internal_config=None, lru_evict=False, enable_object_reconstruction=False) # Connect to an existing Ray cluster or start one and connect to it.\ndef shutdown(exiting_interprete","label":0}],"string":"[\n {\n \"cid\": \"doc/source/conf.py\",\n \"text\": \"doc/source/conf.py\\ndef feedback_form_url(project, page) # Create a URL for feedback on a particular page in a project.\\ndef update_context(app, pagename, templatename, context, doctree) # Update the page rendering context to include ``feedback_form_url``.\\ndef setup(app)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py\",\n \"text\": \"python/ray/node.py\\nclass Node # An encapsulation of the Ray processes on a single node.\\n def __init__(self, ray_params, head=False, shutdown_at_exit=True, spawn_reaper=True, connect_only=False) # Start a node.\\n def _register_shutdown_hooks(self)\\n def atexit_handler(*args)\\n def sigterm_handler(signum, frame)\\n def _init_temp(self, redis_client)\\n def get_resource_spec(self) # Resolve and return the current resource spec for the node.\\n def node_ip_address(self) # Get the IP address of this node.\\n def raylet_ip_address(self) # Get the IP address of the raylet that this node connects to.\\n def address(self) # Get the cluster address.\\n def redis_address(self) # Get the cluster Redis address.\\n def redis_password(self) # Get the cluster Redis password\\n def load_code_from_local(self)\\n def object_ref_seed(self) # Get the seed for deterministic generation of object refs\\n def plasma_store_socket_name(self) # Get the node's plasma store socket name.\\n def unique_id(self) # Get a unique identifier for this node.\\n def webui_url(self) # Get the cluster's web UI url.\\n def raylet_socket_name(self) # Get the node's raylet socket name.\\n def node_manager_port(self) # Get the node manager's port.\\n def socket(self) # Get the socket reserving the node manager's port\\n def address_info(self) # Get a dictionary of addresses.\\n def create_redis_client(self) # Create a redis client.\\n def get_temp_dir_path(self) # Get the path of the temporary directory.\\n def get_session_dir_path(self) # Get the path of the session directory.\\n def get_logs_dir_path(self) # Get the path of the log files directory.\\n def get_sockets_dir_path(self) # Get the path of the sockets directory.\\n def _make_inc_temp(self, suffix='', prefix='', directory_name=None) # Return a incremental temporary file name. The file is not created.\\n def get_log_file_names(self, name, unique=False) # Generate partially randomized filenames for log files.\\n def _get_unused_port(self, close_on_exit=True)\\n def _prepare_socket_file(self, socket_path, default_prefix) # Prepare the socket file for raylet and plasma.\\n def start_reaper_process(self) # Start the reaper process.\\n def start_redis(self) # Start the Redis servers.\\n def start_log_monitor(self) # Start the log monitor.\\n def start_reporter(self) # Start the reporter.\\n def start_dashboard(self, require_dashboard) # Start the dashboard.\\n def start_plasma_store(self) # Start the plasma store.\\n def start_gcs_server(self) # Start the gcs server.\\n def start_raylet(self, use_valgrind=False, use_profiler=False) # Start the raylet.\\n def get_job_redirected_log_file(self, worker_id: bytes, job_id: bytes=None) # Determines (but does not create) logging files for workers to\\n def start_worker(self) # Start a worker process.\\n def start_monitor(self) # Start the monitor.\\n def start_head_processes(self) # Start head processes on\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/utils.py\",\n \"text\": \"python/ray/utils.py\\ndef get_user_temp_dir()\\ndef get_ray_temp_dir()\\ndef _random_string()\\ndef format_error_message(exception_message, task_exception=False) # Improve the formatting of an exception thrown by a remote function.\\ndef push_error_to_driver(worker, error_type, message, job_id=None) # Push an error message to the driver to be printed in the background.\\ndef push_error_to_driver_through_redis(redis_client, error_type, message, job_id=None) # Push an error message to the driver to be printed in the background.\\ndef is_cython(obj) # Check if an object is a Cython function or method\\n def check_cython(x)\\ndef is_function_or_method(obj) # Check if an object is a function or method.\\ndef is_class_method(f) # Returns whether the given method is a class_method.\\ndef is_static_method(cls, f_name) # Returns whether the class has a static method with the given name.\\ndef random_string() # Generate a random string to use as an ID.\\ndef decode(byte_str, allow_none=False) # Make this unicode in Python 3, otherwise leave it as bytes.\\ndef ensure_str(s, encoding='utf-8', errors='strict') # Coerce *s* to `str`.\\ndef binary_to_object_ref(binary_object_ref)\\ndef binary_to_task_id(binary_task_id)\\ndef binary_to_hex(identifier)\\ndef hex_to_binary(hex_identifier)\\ndef compute_job_id_from_driver(driver_id)\\ndef compute_driver_id_from_job(job_id)\\ndef get_cuda_visible_devices() # Get the device IDs in the CUDA_VISIBLE_DEVICES environment variable.\\ndef set_cuda_visible_devices(gpu_ids) # Set the CUDA_VISIBLE_DEVICES environment variable.\\ndef resources_from_resource_arguments(default_num_cpus, default_num_gpus, default_memory, default_object_store_memory, default_resources, runtime_num_cpus, runtime_num_gpus, runtime_memory, runtime_object_store_memory, runtime_resources) # Determine a task's resource requirements.\\ndef setup_logger(logging_level, logging_format) # Setup default logging for ray.\\ndef open_log(path, **kwargs)\\ndef create_and_init_new_worker_log(path, worker_pid) # Opens a path (or creates if necessary) for a log.\\ndef get_system_memory() # Return the total amount of system memory in bytes.\\ndef get_used_memory() # Return the currently used system memory in bytes\\ndef estimate_available_memory() # Return the currently available amount of system memory in bytes.\\ndef get_shared_memory_bytes() # Get the size of the shared memory file system.\\ndef check_oversized_pickle(pickled, name, obj_type, worker) # Send a warning message if the pickled object is too large.\\ndef is_main_thread()\\ndef detect_fate_sharing_support_win32()\\ndef detect_fate_sharing_support_linux()\\ndef detect_fate_sharing_support()\\ndef set_kill_on_parent_death_linux() # Ensures this process dies if its parent dies (fate-sharing).\\ndef set_kill_child_on_death_win32(child_proc) # Ensures the child process dies if this process dies (fate-sharing).\\ndef set_sigterm_handler(sigterm_handler) # Registers a handler for SIGTERM in a platform-compatible manner.\\ndef try_make_directory_shared(directory\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/worker.py\",\n \"text\": \"python/ray/worker.py\\nclass ActorCheckpointInfo # Information used to maintain actor checkpoints.\\n def __init__(self, num_tasks_since_last_checkpoint, last_checkpoint_timestamp, checkpoint_ids)\\nclass Worker # A class used to define the control flow of a worker process.\\n def __init__(self) # Initialize a Worker object.\\n def connected(self)\\n def node_ip_address(self)\\n def load_code_from_local(self)\\n def current_job_id(self)\\n def actor_id(self)\\n def current_task_id(self)\\n def current_session_and_job(self) # Get the current session index and job id as pair.\\n def mark_actor_init_failed(self, error) # Called to mark this actor as failed during initialization.\\n def reraise_actor_init_error(self) # Raises any previous actor initialization error.\\n def get_serialization_context(self, job_id=None) # Get the SerializationContext of the job that this worker is processing.\\n def check_connected(self) # Check if the worker is connected.\\n def set_mode(self, mode) # Set the mode of the worker.\\n def put_object(self, value, object_ref=None, pin_object=True) # Put value in the local object store with object reference `object_ref`.\\n def deserialize_objects(self, data_metadata_pairs, object_refs)\\n def get_objects(self, object_refs, timeout=None) # Get the values in the object store associated with the IDs.\\n def run_function_on_all_workers(self, function, run_on_other_drivers=False) # Run arbitrary code on all of the workers.\\n def main_loop(self) # The main loop a worker runs to receive and execute tasks.\\n def sigterm_handler(signum, frame)\\ndef get_gpu_ids() # Get the IDs of the GPUs that are available to the worker.\\ndef get_resource_ids() # Get the IDs of the resources that are available to the worker.\\ndef get_webui_url() # Get the URL to access the web UI.\\ndef print_failed_task(task_status) # Print information about failed tasks.\\ndef init(address=None, redis_address=None, redis_port=None, num_cpus=None, num_gpus=None, memory=None, object_store_memory=None, resources=None, driver_object_store_memory=None, redis_max_memory=None, log_to_driver=True, node_ip_address=ray_constants.NODE_DEFAULT_IP, object_ref_seed=None, local_mode=False, redirect_worker_output=None, redirect_output=None, ignore_reinit_error=False, num_redis_shards=None, redis_max_clients=None, redis_password=ray_constants.REDIS_DEFAULT_PASSWORD, plasma_directory=None, huge_pages=False, include_java=False, include_dashboard=None, dashboard_host='localhost', dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT, job_id=None, configure_logging=True, logging_level=logging.INFO, logging_format=ray_constants.LOGGER_FORMAT, plasma_store_socket_name=None, raylet_socket_name=None, temp_dir=None, load_code_from_local=False, java_worker_options=None, use_pickle=True, _internal_config=None, lru_evict=False, enable_object_reconstruction=False) # Connect to an existing Ray cluster or start one and connect to it.\\ndef shutdown(exiting_interprete\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"doc/source/conf.py::feedback_form_url","text":"doc/source/conf.py::feedback_form_url\ndef feedback_form_url(project, page):\n \"\"\"Create a URL for feedback on a particular page in a project.\"\"\"\n return FEEDBACK_FORM_FMT.format(\n title=urllib.parse.quote(\n \"[docs] Issue on `{page}.rst`\".format(page=page)),\n body=urllib.parse.quote(\n \"# Documentation Problem/Question/Comment\\n\"\n \"<!-- Describe your issue/question/comment below. -->\\n\"\n \"<!-- If there are typos or errors in the docs, feel free to create a pull-request. -->\\n\"\n \"\\n\\n\\n\\n\"\n \"(Created directly from the docs)\\n\"))","label":0},{"cid":"doc/source/conf.py::update_context","text":"doc/source/conf.py::update_context\ndef update_context(app, pagename, templatename, context, doctree):\n \"\"\"Update the page rendering context to include ``feedback_form_url``.\"\"\"\n context['feedback_form_url'] = feedback_form_url(app.config.project,\n pagename)","label":0},{"cid":"doc/source/conf.py::setup","text":"doc/source/conf.py::setup\ndef setup(app):\n app.connect('html-page-context', update_context)\n app.add_stylesheet('css/custom.css')\n # Custom directives\n app.add_directive('customgalleryitem', CustomGalleryItemDirective)","label":0},{"cid":"python/ray/node.py::Node.__init__","text":"python/ray/node.py::Node.__init__\n def __init__(self,\n ray_params,\n head=False,\n shutdown_at_exit=True,\n spawn_reaper=True,\n connect_only=False):\n \"\"\"Start a node.\n\n Args:\n ray_params (ray.params.RayParams): The parameters to use to\n configure the node.\n head (bool): True if this is the head node, which means it will\n start additional processes like the Redis servers, monitor\n processes, and web UI.\n shutdown_at_exit (bool): If true, spawned processes will be cleaned\n up if this process exits normally.\n spawn_reaper (bool): If true, spawns a process that will clean up\n other spawned processes if this process dies unexpectedly.\n connect_only (bool): If true, connect to the node without starting\n new processes.\n \"\"\"\n if shutdown_at_exit:\n if connect_only:\n raise ValueError(\"'shutdown_at_exit' and 'connect_only' \"\n \"cannot both be true.\")\n self._register_shutdown_hooks()\n\n self.head = head\n self.kernel_fate_share = bool(\n spawn_reaper and ray.utils.detect_fate_sharing_support())\n self.all_processes = {}\n\n # Try to get node IP address with the parameters.\n if ray_params.node_ip_address:\n node_ip_address = ray_params.node_ip_address\n elif ray_params.redis_address:\n node_ip_address = ray.services.get_node_ip_address(\n ray_params.redis_address)\n else:\n node_ip_address = ray.services.get_node_ip_address()\n self._node_ip_address = node_ip_address\n\n if ray_params.raylet_ip_address:\n raylet_ip_address = ray_params.raylet_ip_address\n else:\n raylet_ip_address = node_ip_address\n\n if raylet_ip_address != no","label":0},{"cid":"python/ray/node.py::Node._register_shutdown_hooks","text":"python/ray/node.py::Node._register_shutdown_hooks\n def _register_shutdown_hooks(self):\n # Register the atexit handler. In this case, we shouldn't call sys.exit\n # as we're already in the exit procedure.\n def atexit_handler(*args):\n self.kill_all_processes(check_alive=False, allow_graceful=True)\n\n atexit.register(atexit_handler)\n\n # Register the handler to be called if we get a SIGTERM.\n # In this case, we want to exit with an error code (1) after\n # cleaning up child processes.\n def sigterm_handler(signum, frame):\n self.kill_all_processes(check_alive=False, allow_graceful=True)\n sys.exit(1)\n\n ray.utils.set_sigterm_handler(sigterm_handler)","label":0},{"cid":"python/ray/node.py::Node._register_shutdown_hooks.atexit_handler","text":"python/ray/node.py::Node._register_shutdown_hooks.atexit_handler\n def atexit_handler(*args):\n self.kill_all_processes(check_alive=False, allow_graceful=True)","label":0},{"cid":"python/ray/node.py::Node._register_shutdown_hooks.sigterm_handler","text":"python/ray/node.py::Node._register_shutdown_hooks.sigterm_handler\n def sigterm_handler(signum, frame):\n self.kill_all_processes(check_alive=False, allow_graceful=True)\n sys.exit(1)","label":0},{"cid":"python/ray/node.py::Node._init_temp","text":"python/ray/node.py::Node._init_temp\n def _init_temp(self, redis_client):\n # Create an dictionary to store temp file index.\n self._incremental_dict = collections.defaultdict(lambda: 0)\n\n if self.head:\n self._temp_dir = self._ray_params.temp_dir\n else:\n self._temp_dir = ray.utils.decode(redis_client.get(\"temp_dir\"))\n\n try_to_create_directory(self._temp_dir)\n\n if self.head:\n self._session_dir = os.path.join(self._temp_dir, self.session_name)\n else:\n self._session_dir = ray.utils.decode(\n redis_client.get(\"session_dir\"))\n session_symlink = os.path.join(self._temp_dir, SESSION_LATEST)\n\n # Send a warning message if the session exists.\n try_to_create_directory(self._session_dir)\n try_to_symlink(session_symlink, self._session_dir)\n # Create a directory to be used for socket files.\n self._sockets_dir = os.path.join(self._session_dir, \"sockets\")\n try_to_create_directory(self._sockets_dir)\n # Create a directory to be used for process log files.\n self._logs_dir = os.path.join(self._session_dir, \"logs\")\n try_to_create_directory(self._logs_dir)\n old_logs_dir = os.path.join(self._logs_dir, \"old\")\n try_to_create_directory(old_logs_dir)","label":0},{"cid":"python/ray/node.py::Node.get_resource_spec","text":"python/ray/node.py::Node.get_resource_spec\n def get_resource_spec(self):\n \"\"\"Resolve and return the current resource spec for the node.\"\"\"\n if not self._resource_spec:\n self._resource_spec = ResourceSpec(\n self._ray_params.num_cpus, self._ray_params.num_gpus,\n self._ray_params.memory, self._ray_params.object_store_memory,\n self._ray_params.resources,\n self._ray_params.redis_max_memory).resolve(\n is_head=self.head, node_ip_address=self.node_ip_address)\n return self._resource_spec","label":0},{"cid":"python/ray/node.py::Node.node_ip_address","text":"python/ray/node.py::Node.node_ip_address\n @property\n def node_ip_address(self):\n \"\"\"Get the IP address of this node.\"\"\"\n return self._node_ip_address","label":0},{"cid":"python/ray/node.py::Node.raylet_ip_address","text":"python/ray/node.py::Node.raylet_ip_address\n @property\n def raylet_ip_address(self):\n \"\"\"Get the IP address of the raylet that this node connects to.\"\"\"\n return self._raylet_ip_address","label":0},{"cid":"python/ray/node.py::Node.address","text":"python/ray/node.py::Node.address\n @property\n def address(self):\n \"\"\"Get the cluster address.\"\"\"\n return self._redis_address","label":0},{"cid":"python/ray/node.py::Node.redis_address","text":"python/ray/node.py::Node.redis_address\n @property\n def redis_address(self):\n \"\"\"Get the cluster Redis address.\"\"\"\n return self._redis_address","label":0},{"cid":"python/ray/node.py::Node.redis_password","text":"python/ray/node.py::Node.redis_password\n @property\n def redis_password(self):\n \"\"\"Get the cluster Redis password\"\"\"\n return self._ray_params.redis_password","label":0},{"cid":"python/ray/node.py::Node.load_code_from_local","text":"python/ray/node.py::Node.load_code_from_local\n @property\n def load_code_from_local(self):\n return self._ray_params.load_code_from_local","label":0},{"cid":"python/ray/node.py::Node.object_ref_seed","text":"python/ray/node.py::Node.object_ref_seed\n @property\n def object_ref_seed(self):\n \"\"\"Get the seed for deterministic generation of object refs\"\"\"\n return self._ray_params.object_ref_seed","label":0},{"cid":"python/ray/node.py::Node.plasma_store_socket_name","text":"python/ray/node.py::Node.plasma_store_socket_name\n @property\n def plasma_store_socket_name(self):\n \"\"\"Get the node's plasma store socket name.\"\"\"\n return self._plasma_store_socket_name","label":0},{"cid":"python/ray/node.py::Node.unique_id","text":"python/ray/node.py::Node.unique_id\n @property\n def unique_id(self):\n \"\"\"Get a unique identifier for this node.\"\"\"\n return \"{}:{}\".format(self.node_ip_address,\n self._plasma_store_socket_name)","label":0},{"cid":"python/ray/node.py::Node.webui_url","text":"python/ray/node.py::Node.webui_url\n @property\n def webui_url(self):\n \"\"\"Get the cluster's web UI url.\"\"\"\n return self._webui_url","label":0},{"cid":"python/ray/node.py::Node.raylet_socket_name","text":"python/ray/node.py::Node.raylet_socket_name\n @property\n def raylet_socket_name(self):\n \"\"\"Get the node's raylet socket name.\"\"\"\n return self._raylet_socket_name","label":0},{"cid":"python/ray/node.py::Node.node_manager_port","text":"python/ray/node.py::Node.node_manager_port\n @property\n def node_manager_port(self):\n \"\"\"Get the node manager's port.\"\"\"\n return self._ray_params.node_manager_port","label":0},{"cid":"python/ray/node.py::Node.socket","text":"python/ray/node.py::Node.socket\n @property\n def socket(self):\n \"\"\"Get the socket reserving the node manager's port\"\"\"\n try:\n return self._socket\n except AttributeError:\n return None","label":0},{"cid":"python/ray/node.py::Node.address_info","text":"python/ray/node.py::Node.address_info\n @property\n def address_info(self):\n \"\"\"Get a dictionary of addresses.\"\"\"\n return {\n \"node_ip_address\": self._node_ip_address,\n \"raylet_ip_address\": self._raylet_ip_address,\n \"redis_address\": self._redis_address,\n \"object_store_address\": self._plasma_store_socket_name,\n \"raylet_socket_name\": self._raylet_socket_name,\n \"webui_url\": self._webui_url,\n \"session_dir\": self._session_dir,\n }","label":0},{"cid":"python/ray/node.py::Node.create_redis_client","text":"python/ray/node.py::Node.create_redis_client\n def create_redis_client(self):\n \"\"\"Create a redis client.\"\"\"\n return ray.services.create_redis_client(\n self._redis_address, self._ray_params.redis_password)","label":0},{"cid":"python/ray/node.py::Node.get_temp_dir_path","text":"python/ray/node.py::Node.get_temp_dir_path\n def get_temp_dir_path(self):\n \"\"\"Get the path of the temporary directory.\"\"\"\n return self._temp_dir","label":0},{"cid":"python/ray/node.py::Node.get_session_dir_path","text":"python/ray/node.py::Node.get_session_dir_path\n def get_session_dir_path(self):\n \"\"\"Get the path of the session directory.\"\"\"\n return self._session_dir","label":0},{"cid":"python/ray/node.py::Node.get_logs_dir_path","text":"python/ray/node.py::Node.get_logs_dir_path\n def get_logs_dir_path(self):\n \"\"\"Get the path of the log files directory.\"\"\"\n return self._logs_dir","label":0},{"cid":"python/ray/node.py::Node.get_sockets_dir_path","text":"python/ray/node.py::Node.get_sockets_dir_path\n def get_sockets_dir_path(self):\n \"\"\"Get the path of the sockets directory.\"\"\"\n return self._sockets_dir","label":0},{"cid":"python/ray/node.py::Node._make_inc_temp","text":"python/ray/node.py::Node._make_inc_temp\n def _make_inc_temp(self, suffix=\"\", prefix=\"\", directory_name=None):\n \"\"\"Return a incremental temporary file name. The file is not created.\n\n Args:\n suffix (str): The suffix of the temp file.\n prefix (str): The prefix of the temp file.\n directory_name (str) : The base directory of the temp file.\n\n Returns:\n A string of file name. If there existing a file having\n the same name, the returned name will look like\n \"{directory_name}/{prefix}.{unique_index}{suffix}\"\n \"\"\"\n if directory_name is None:\n directory_name = ray.utils.get_ray_temp_dir()\n directory_name = os.path.expanduser(directory_name)\n index = self._incremental_dict[suffix, prefix, directory_name]\n # `tempfile.TMP_MAX` could be extremely large,\n # so using `range` in Python2.x should be avoided.\n while index < tempfile.TMP_MAX:\n if index == 0:\n filename = os.path.join(directory_name, prefix + suffix)\n else:\n filename = os.path.join(directory_name,\n prefix + \".\" + str(index) + suffix)\n index += 1\n if not os.path.exists(filename):\n # Save the index.\n self._incremental_dict[suffix, prefix, directory_name] = index\n return filename\n\n raise FileExistsError(errno.EEXIST,\n \"No usable temporary filename found\")","label":0},{"cid":"python/ray/node.py::Node.get_log_file_names","text":"python/ray/node.py::Node.get_log_file_names\n def get_log_file_names(self, name, unique=False):\n \"\"\"Generate partially randomized filenames for log files.\n\n Args:\n name (str): descriptive string for this log file.\n unique (bool): if true, a counter will be attached to `name` to\n ensure the returned filename is not already used.\n\n Returns:\n A tuple of two file names for redirecting (stdout, stderr).\n \"\"\"\n redirect_output = self._ray_params.redirect_output\n\n if redirect_output is None:\n # Make the default behavior match that of glog.\n redirect_output = os.getenv(\"GLOG_logtostderr\") != \"1\"\n\n if not redirect_output:\n return None, None\n\n if unique:\n log_stdout = self._make_inc_temp(\n suffix=\".out\", prefix=name, directory_name=self._logs_dir)\n log_stderr = self._make_inc_temp(\n suffix=\".err\", prefix=name, directory_name=self._logs_dir)\n else:\n log_stdout = os.path.join(self._logs_dir, \"{}.out\".format(name))\n log_stderr = os.path.join(self._logs_dir, \"{}.err\".format(name))\n return log_stdout, log_stderr","label":1},{"cid":"python/ray/node.py::Node._get_unused_port","text":"python/ray/node.py::Node._get_unused_port\n def _get_unused_port(self, close_on_exit=True):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.bind((\"\", 0))\n port = s.getsockname()[1]\n\n # Try to generate a port that is far above the 'next available' one.\n # This solves issue #8254 where GRPC fails because the port assigned\n # from this method has been used by a different process.\n for _ in range(NUMBER_OF_PORT_RETRIES):\n new_port = random.randint(port, 65535)\n new_s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n try:\n new_s.bind((\"\", new_port))\n except OSError:\n new_s.close()\n continue\n s.close()\n if close_on_exit:\n new_s.close()\n return new_port, new_s\n logger.error(\"Unable to succeed in selecting a random port.\")\n if close_on_exit:\n s.close()\n return port, s","label":0},{"cid":"python/ray/node.py::Node._prepare_socket_file","text":"python/ray/node.py::Node._prepare_socket_file\n def _prepare_socket_file(self, socket_path, default_prefix):\n \"\"\"Prepare the socket file for raylet and plasma.\n\n This method helps to prepare a socket file.\n 1. Make the directory if the directory does not exist.\n 2. If the socket file exists, raise exception.\n\n Args:\n socket_path (string): the socket file to prepare.\n \"\"\"\n result = socket_path\n is_mac = sys.platform.startswith(\"darwin\")\n if sys.platform == \"win32\":\n if socket_path is None:\n result = \"tcp://{}:{}\".format(self._localhost,\n self._get_unused_port()[0])\n else:\n if socket_path is None:\n result = self._make_inc_temp(\n prefix=default_prefix, directory_name=self._sockets_dir)\n else:\n if os.path.exists(socket_path):\n raise RuntimeError(\n \"Socket file {} exists!\".format(socket_path))\n try_to_create_directory(os.path.dirname(socket_path))\n\n # Check socket path length to make sure it's short enough\n maxlen = (104 if is_mac else 108) - 1 # sockaddr_un->sun_path\n if len(result.split(\"://\", 1)[-1].encode(\"utf-8\")) > maxlen:\n raise OSError(\"AF_UNIX path length cannot exceed \"\n \"{} bytes: {!r}\".format(maxlen, result))\n return result","label":0},{"cid":"python/ray/node.py::Node.start_reaper_process","text":"python/ray/node.py::Node.start_reaper_process\n def start_reaper_process(self):\n \"\"\"\n Start the reaper process.\n\n This must be the first process spawned and should only be called when\n ray processes should be cleaned up if this process dies.\n \"\"\"\n assert not self.kernel_fate_share, (\n \"a reaper should not be used with kernel fate-sharing\")\n process_info = ray.services.start_reaper(fate_share=False)\n assert ray_constants.PROCESS_TYPE_REAPER not in self.all_processes\n if process_info is not None:\n self.all_processes[ray_constants.PROCESS_TYPE_REAPER] = [\n process_info,\n ]","label":0},{"cid":"python/ray/node.py::Node.start_redis","text":"python/ray/node.py::Node.start_redis\n def start_redis(self):\n \"\"\"Start the Redis servers.\"\"\"\n assert self._redis_address is None\n redis_out_name, redis_err_name = self.get_log_file_names(\n \"redis\", unique=True)\n redis_log_files = [(open_log(redis_out_name),\n open_log(redis_err_name))]\n for i in range(self._ray_params.num_redis_shards):\n shard_out_name, shard_err_name = self.get_log_file_names(\n \"redis-shard_{}\".format(i), unique=True)\n redis_log_files.append((open_log(shard_out_name),\n open_log(shard_err_name)))\n\n (self._redis_address, redis_shards,\n process_infos) = ray.services.start_redis(\n self._node_ip_address,\n redis_log_files,\n self.get_resource_spec(),\n port=self._ray_params.redis_port,\n redis_shard_ports=self._ray_params.redis_shard_ports,\n num_redis_shards=self._ray_params.num_redis_shards,\n redis_max_clients=self._ray_params.redis_max_clients,\n redirect_worker_output=True,\n password=self._ray_params.redis_password,\n include_java=self._ray_params.include_java,\n fate_share=self.kernel_fate_share)\n assert (\n ray_constants.PROCESS_TYPE_REDIS_SERVER not in self.all_processes)\n self.all_processes[ray_constants.PROCESS_TYPE_REDIS_SERVER] = (\n process_infos)","label":1},{"cid":"python/ray/node.py::Node.start_log_monitor","text":"python/ray/node.py::Node.start_log_monitor\n def start_log_monitor(self):\n \"\"\"Start the log monitor.\"\"\"\n log_out_name, log_err_name = self.get_log_file_names(\n \"log_monitor\", unique=True)\n stdout_file, stderr_file = open_log(log_out_name), open_log(\n log_err_name)\n process_info = ray.services.start_log_monitor(\n self.redis_address,\n self._logs_dir,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n redis_password=self._ray_params.redis_password,\n fate_share=self.kernel_fate_share)\n assert ray_constants.PROCESS_TYPE_LOG_MONITOR not in self.all_processes\n self.all_processes[ray_constants.PROCESS_TYPE_LOG_MONITOR] = [\n process_info,\n ]","label":1},{"cid":"python/ray/node.py::Node.start_reporter","text":"python/ray/node.py::Node.start_reporter\n def start_reporter(self):\n \"\"\"Start the reporter.\"\"\"\n reporter_out_name, reporter_err_name = self.get_log_file_names(\n \"reporter\", unique=True)\n stdout_file, stderr_file = (open_log(reporter_out_name),\n open_log(reporter_err_name))\n process_info = ray.services.start_reporter(\n self.redis_address,\n self._ray_params.metrics_agent_port,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n redis_password=self._ray_params.redis_password,\n fate_share=self.kernel_fate_share)\n assert ray_constants.PROCESS_TYPE_REPORTER not in self.all_processes\n if process_info is not None:\n self.all_processes[ray_constants.PROCESS_TYPE_REPORTER] = [\n process_info,\n ]","label":1},{"cid":"python/ray/node.py::Node.start_dashboard","text":"python/ray/node.py::Node.start_dashboard\n def start_dashboard(self, require_dashboard):\n \"\"\"Start the dashboard.\n\n Args:\n require_dashboard (bool): If true, this will raise an exception\n if we fail to start the dashboard. Otherwise it will print\n a warning if we fail to start the dashboard.\n \"\"\"\n dashboard_out_name, dashboard_err_name = self.get_log_file_names(\n \"dashboard\", unique=True)\n stdout_file, stderr_file = (open_log(dashboard_out_name),\n open_log(dashboard_err_name))\n self._webui_url, process_info = ray.services.start_dashboard(\n require_dashboard,\n self._ray_params.dashboard_host,\n self.redis_address,\n self._temp_dir,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n redis_password=self._ray_params.redis_password,\n fate_share=self.kernel_fate_share,\n port=self._ray_params.dashboard_port)\n assert ray_constants.PROCESS_TYPE_DASHBOARD not in self.all_processes\n if process_info is not None:\n self.all_processes[ray_constants.PROCESS_TYPE_DASHBOARD] = [\n process_info,\n ]\n redis_client = self.create_redis_client()\n redis_client.hmset(\"webui\", {\"url\": self._webui_url})","label":1},{"cid":"python/ray/node.py::Node.start_plasma_store","text":"python/ray/node.py::Node.start_plasma_store\n def start_plasma_store(self):\n \"\"\"Start the plasma store.\"\"\"\n plasma_out_name, plasma_err_name = self.get_log_file_names(\n \"plasma_store\", unique=True)\n stdout_file, stderr_file = (open_log(plasma_out_name),\n open_log(plasma_err_name))\n process_info = ray.services.start_plasma_store(\n self.get_resource_spec(),\n self._plasma_store_socket_name,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n plasma_directory=self._ray_params.plasma_directory,\n huge_pages=self._ray_params.huge_pages,\n keep_idle=bool(self._config.get(\"plasma_store_as_thread\")),\n fate_share=self.kernel_fate_share)\n assert (\n ray_constants.PROCESS_TYPE_PLASMA_STORE not in self.all_processes)\n self.all_processes[ray_constants.PROCESS_TYPE_PLASMA_STORE] = [\n process_info,\n ]","label":1},{"cid":"python/ray/node.py::Node.start_gcs_server","text":"python/ray/node.py::Node.start_gcs_server\n def start_gcs_server(self):\n \"\"\"Start the gcs server.\n \"\"\"\n gcs_out_name, gcs_err_name = self.get_log_file_names(\n \"gcs_server\", unique=True)\n stdout_file, stderr_file = (open_log(gcs_out_name),\n open_log(gcs_err_name))\n process_info = ray.services.start_gcs_server(\n self._redis_address,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n redis_password=self._ray_params.redis_password,\n config=self._config,\n fate_share=self.kernel_fate_share,\n gcs_server_port=self._ray_params.gcs_server_port)\n assert (\n ray_constants.PROCESS_TYPE_GCS_SERVER not in self.all_processes)\n self.all_processes[ray_constants.PROCESS_TYPE_GCS_SERVER] = [\n process_info,\n ]","label":1},{"cid":"python/ray/node.py::Node.start_raylet","text":"python/ray/node.py::Node.start_raylet\n def start_raylet(self, use_valgrind=False, use_profiler=False):\n \"\"\"Start the raylet.\n\n Args:\n use_valgrind (bool): True if we should start the process in\n valgrind.\n use_profiler (bool): True if we should start the process in the\n valgrind profiler.\n \"\"\"\n raylet_out_name, raylet_err_name = self.get_log_file_names(\n \"raylet\", unique=True)\n stdout_file, stderr_file = (open_log(raylet_out_name),\n open_log(raylet_err_name))\n process_info = ray.services.start_raylet(\n self._redis_address,\n self._node_ip_address,\n self._ray_params.node_manager_port,\n self._raylet_socket_name,\n self._plasma_store_socket_name,\n self._ray_params.worker_path,\n self._temp_dir,\n self._session_dir,\n self.get_resource_spec(),\n self._ray_params.min_worker_port,\n self._ray_params.max_worker_port,\n self._ray_params.object_manager_port,\n self._ray_params.redis_password,\n self._ray_params.metrics_agent_port,\n use_valgrind=use_valgrind,\n use_profiler=use_profiler,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n config=self._config,\n include_java=self._ray_params.include_java,\n java_worker_options=self._ray_params.java_worker_options,\n load_code_from_local=self._ray_params.load_code_from_local,\n plasma_directory=self._ray_params.plasma_directory,\n huge_pages=self._ray_params.huge_pages,\n fate_share=self.kernel_fate_share,\n socket_to_use=self.socket,\n head_node=self.head)\n assert ray_constants.PROCESS_TYPE_RAYLET not in self.all_processes\n self.all_processes[ray_constants.PROCESS_TYPE_RAYLET] = [process_info]","label":1},{"cid":"python/ray/node.py::Node.get_job_redirected_log_file","text":"python/ray/node.py::Node.get_job_redirected_log_file\n def get_job_redirected_log_file(self,\n worker_id: bytes,\n job_id: bytes = None):\n \"\"\"Determines (but does not create) logging files for workers to\n redirect its output.\n\n Args:\n worker_id (bytes): A byte representation of the worker id.\n job_id (bytes): A byte representation of the job id. If None,\n provides a generic log file for the worker.\n\n Returns:\n (tuple) The stdout and stderr file names that the job should be\n redirected to.\n \"\"\"\n redirect_output = self._ray_params.redirect_output\n\n if redirect_output is None:\n # Make the default behavior match that of glog.\n redirect_output = os.getenv(\"GLOG_logtostderr\") != \"1\"\n\n if not redirect_output:\n return None, None\n\n if job_id is not None:\n name = \"worker-{}-{}\".format(\n ray.utils.binary_to_hex(worker_id),\n ray.utils.binary_to_hex(job_id))\n else:\n name = \"worker-{}\".format(ray.utils.binary_to_hex(worker_id))\n\n worker_stdout_file, worker_stderr_file = self.get_log_file_names(\n name, unique=False)\n return worker_stdout_file, worker_stderr_file","label":1},{"cid":"python/ray/node.py::Node.start_worker","text":"python/ray/node.py::Node.start_worker\n def start_worker(self):\n \"\"\"Start a worker process.\"\"\"\n raise NotImplementedError","label":0},{"cid":"python/ray/node.py::Node.start_monitor","text":"python/ray/node.py::Node.start_monitor\n def start_monitor(self):\n \"\"\"Start the monitor.\"\"\"\n monitor_out_name, monitor_err_name = self.get_log_file_names(\n \"monitor\", unique=True)\n stdout_file, stderr_file = (open_log(monitor_out_name),\n open_log(monitor_err_name))\n process_info = ray.services.start_monitor(\n self._redis_address,\n stdout_file=stdout_file,\n stderr_file=stderr_file,\n autoscaling_config=self._ray_params.autoscaling_config,\n redis_password=self._ray_params.redis_password,\n fate_share=self.kernel_fate_share)\n assert ray_constants.PROCESS_TYPE_MONITOR not in self.all_processes\n self.all_processes[ray_constants.PROCESS_TYPE_MONITOR] = [process_info]","label":1},{"cid":"python/ray/node.py::Node.start_head_processes","text":"python/ray/node.py::Node.start_head_processes\n def start_head_processes(self):\n \"\"\"Start head processes on the node.\"\"\"\n logger.debug(\n \"Process STDOUT and STDERR is being redirected to {}.\".format(\n self._logs_dir))\n assert self._redis_address is None\n # If this is the head node, start the relevant head node processes.\n self.start_redis()\n\n self.start_gcs_server()\n\n self.start_monitor()\n\n if self._ray_params.include_dashboard:\n self.start_dashboard(require_dashboard=True)\n elif self._ray_params.include_dashboard is None:\n self.start_dashboard(require_dashboard=False)","label":0},{"cid":"python/ray/node.py::Node.start_ray_processes","text":"python/ray/node.py::Node.start_ray_processes\n def start_ray_processes(self):\n \"\"\"Start all of the processes on the node.\"\"\"\n logger.debug(\n \"Process STDOUT and STDERR is being redirected to {}.\".format(\n self._logs_dir))\n\n self.start_plasma_store()\n self.start_raylet()\n self.start_reporter()\n\n if self._ray_params.include_log_monitor:\n self.start_log_monitor()","label":0},{"cid":"python/ray/node.py::Node._kill_process_type","text":"python/ray/node.py::Node._kill_process_type\n def _kill_process_type(self,\n process_type,\n allow_graceful=False,\n check_alive=True,\n wait=False):\n \"\"\"Kill a process of a given type.\n\n If the process type is PROCESS_TYPE_REDIS_SERVER, then we will kill all\n of the Redis servers.\n\n If the process was started in valgrind, then we will raise an exception\n if the process has a non-zero exit code.\n\n Args:\n process_type: The type of the process to kill.\n allow_graceful (bool): Send a SIGTERM first and give the process\n time to exit gracefully. If that doesn't work, then use\n SIGKILL. We usually want to do this outside of tests.\n check_alive (bool): If true, then we expect the process to be alive\n and will raise an exception if the process is already dead.\n wait (bool): If true, then this method will not return until the\n process in question has exited.\n\n Raises:\n This process raises an exception in the following cases:\n 1. The process had already died and check_alive is true.\n 2. The process had been started in valgrind and had a non-zero\n exit code.\n \"\"\"\n process_infos = self.all_processes[process_type]\n if process_type != ray_constants.PROCESS_TYPE_REDIS_SERVER:\n assert len(process_infos) == 1\n for process_info in process_infos:\n process = process_info.process\n # Handle the case where the process has already exited.\n if process.poll() is not None:\n if check_alive:\n raise RuntimeError(\n \"Attempting to kill a process of type \"\n \"'{}', but this process is already dead.\"\n .format(process_typ","label":0},{"cid":"python/ray/node.py::Node.kill_redis","text":"python/ray/node.py::Node.kill_redis\n def kill_redis(self, check_alive=True):\n \"\"\"Kill the Redis servers.\n\n Args:\n check_alive (bool): Raise an exception if any of the processes\n were already dead.\n \"\"\"\n self._kill_process_type(\n ray_constants.PROCESS_TYPE_REDIS_SERVER, check_alive=check_alive)","label":0},{"cid":"python/ray/node.py::Node.kill_plasma_store","text":"python/ray/node.py::Node.kill_plasma_store\n def kill_plasma_store(self, check_alive=True):\n \"\"\"Kill the plasma store.\n\n Args:\n check_alive (bool): Raise an exception if the process was already\n dead.\n \"\"\"\n self._kill_process_type(\n ray_constants.PROCESS_TYPE_PLASMA_STORE, check_alive=check_alive)","label":0},{"cid":"python/ray/node.py::Node.kill_raylet","text":"python/ray/node.py::Node.kill_raylet\n def kill_raylet(self, check_alive=True):\n \"\"\"Kill the raylet.\n\n Args:\n check_alive (bool): Raise an exception if the process was already\n dead.\n \"\"\"\n self._kill_process_type(\n ray_constants.PROCESS_TYPE_RAYLET, check_alive=check_alive)","label":0},{"cid":"python/ray/node.py::Node.kill_log_monitor","text":"python/ray/node.py::Node.kill_log_monitor\n def kill_log_monitor(self, check_alive=True):\n \"\"\"Kill the log monitor.\n\n Args:\n check_alive (bool): Raise an exception if the process was already\n dead.\n \"\"\"\n self._kill_process_type(\n ray_constants.PROCESS_TYPE_LOG_MONITOR, check_alive=check_alive)","label":0},{"cid":"python/ray/node.py::Node.kill_reporter","text":"python/ray/node.py::Node.kill_reporter\n def kill_reporter(self, check_alive=True):\n \"\"\"Kill the reporter.\n\n Args:\n check_alive (bool): Raise an exception if the process was already\n dead.\n \"\"\"\n self._kill_process_type(\n ray_constants.PROCESS_TYPE_REPORTER, check_alive=check_alive)","label":0},{"cid":"python/ray/node.py::Node.kill_dashboard","text":"python/ray/node.py::Node.kill_dashboard\n def kill_dashboard(self, check_alive=True):\n \"\"\"Kill the dashboard.\n\n Args:\n check_alive (bool): Raise an exception if the process was already\n dead.\n \"\"\"\n self._kill_process_type(\n ray_constants.PROCESS_TYPE_DASHBOARD, check_alive=check_alive)","label":0},{"cid":"python/ray/node.py::Node.kill_monitor","text":"python/ray/node.py::Node.kill_monitor\n def kill_monitor(self, check_alive=True):\n \"\"\"Kill the monitor.\n\n Args:\n check_alive (bool): Raise an exception if the process was already\n dead.\n \"\"\"\n self._kill_process_type(\n ray_constants.PROCESS_TYPE_MONITOR, check_alive=check_alive)","label":0},{"cid":"python/ray/node.py::Node.kill_gcs_server","text":"python/ray/node.py::Node.kill_gcs_server\n def kill_gcs_server(self, check_alive=True):\n \"\"\"Kill the gcs server.\n Args:\n check_alive (bool): Raise an exception if the process was already\n dead.\n \"\"\"\n self._kill_process_type(\n ray_constants.PROCESS_TYPE_GCS_SERVER, check_alive=check_alive)","label":0},{"cid":"python/ray/node.py::Node.kill_reaper","text":"python/ray/node.py::Node.kill_reaper\n def kill_reaper(self, check_alive=True):\n \"\"\"Kill the reaper process.\n\n Args:\n check_alive (bool): Raise an exception if the process was already\n dead.\n \"\"\"\n self._kill_process_type(\n ray_constants.PROCESS_TYPE_REAPER, check_alive=check_alive)","label":0},{"cid":"python/ray/node.py::Node.kill_all_processes","text":"python/ray/node.py::Node.kill_all_processes\n def kill_all_processes(self, check_alive=True, allow_graceful=False):\n \"\"\"Kill all of the processes.\n\n Note that This is slower than necessary because it calls kill, wait,\n kill, wait, ... instead of kill, kill, ..., wait, wait, ...\n\n Args:\n check_alive (bool): Raise an exception if any of the processes were\n already dead.\n \"\"\"\n # Kill the raylet first. This is important for suppressing errors at\n # shutdown because we give the raylet a chance to exit gracefully and\n # clean up its child worker processes. If we were to kill the plasma\n # store (or Redis) first, that could cause the raylet to exit\n # ungracefully, leading to more verbose output from the workers.\n if ray_constants.PROCESS_TYPE_RAYLET in self.all_processes:\n self._kill_process_type(\n ray_constants.PROCESS_TYPE_RAYLET,\n check_alive=check_alive,\n allow_graceful=allow_graceful)\n\n if ray_constants.PROCESS_TYPE_GCS_SERVER in self.all_processes:\n self._kill_process_type(\n ray_constants.PROCESS_TYPE_GCS_SERVER,\n check_alive=check_alive,\n allow_graceful=allow_graceful)\n\n # We call \"list\" to copy the keys because we are modifying the\n # dictionary while iterating over it.\n for process_type in list(self.all_processes.keys()):\n # Need to kill the reaper process last in case we die unexpectedly\n # while cleaning up.\n if process_type != ray_constants.PROCESS_TYPE_REAPER:\n self._kill_process_type(\n process_type,\n check_alive=check_alive,\n allow_graceful=allow_graceful)\n\n if ray_constants.PROCESS_TYPE_REAPER in self.all_processes:\n self._kill_process_type(\n ray_constants.PROCESS_TYPE_REAPER,\n ","label":0},{"cid":"python/ray/node.py::Node.live_processes","text":"python/ray/node.py::Node.live_processes\n def live_processes(self):\n \"\"\"Return a list of the live processes.\n\n Returns:\n A list of the live processes.\n \"\"\"\n result = []\n for process_type, process_infos in self.all_processes.items():\n for process_info in process_infos:\n if process_info.process.poll() is None:\n result.append((process_type, process_info.process))\n return result","label":0},{"cid":"python/ray/node.py::Node.dead_processes","text":"python/ray/node.py::Node.dead_processes\n def dead_processes(self):\n \"\"\"Return a list of the dead processes.\n\n Note that this ignores processes that have been explicitly killed,\n e.g., via a command like node.kill_raylet().\n\n Returns:\n A list of the dead processes ignoring the ones that have been\n explicitly killed.\n \"\"\"\n result = []\n for process_type, process_infos in self.all_processes.items():\n for process_info in process_infos:\n if process_info.process.poll() is not None:\n result.append((process_type, process_info.process))\n return result","label":0},{"cid":"python/ray/node.py::Node.any_processes_alive","text":"python/ray/node.py::Node.any_processes_alive\n def any_processes_alive(self):\n \"\"\"Return true if any processes are still alive.\n\n Returns:\n True if any process is still alive.\n \"\"\"\n return any(self.live_processes())","label":0},{"cid":"python/ray/node.py::Node.remaining_processes_alive","text":"python/ray/node.py::Node.remaining_processes_alive\n def remaining_processes_alive(self):\n \"\"\"Return true if all remaining processes are still alive.\n\n Note that this ignores processes that have been explicitly killed,\n e.g., via a command like node.kill_raylet().\n\n Returns:\n True if any process that wasn't explicitly killed is still alive.\n \"\"\"\n return not any(self.dead_processes())","label":0},{"cid":"python/ray/utils.py::get_user_temp_dir","text":"python/ray/utils.py::get_user_temp_dir\ndef get_user_temp_dir():\n if sys.platform.startswith(\"darwin\") or sys.platform.startswith(\"linux\"):\n # Ideally we wouldn't need this fallback, but keep it for now for\n # for compatibility\n tempdir = os.path.join(os.sep, \"tmp\")\n else:\n tempdir = tempfile.gettempdir()\n return tempdir","label":0},{"cid":"python/ray/utils.py::get_ray_temp_dir","text":"python/ray/utils.py::get_ray_temp_dir\ndef get_ray_temp_dir():\n return os.path.join(get_user_temp_dir(), \"ray\")","label":0},{"cid":"python/ray/utils.py::_random_string","text":"python/ray/utils.py::_random_string\ndef _random_string():\n id_hash = hashlib.sha1()\n id_hash.update(uuid.uuid4().bytes)\n id_bytes = id_hash.digest()\n assert len(id_bytes) == ray_constants.ID_SIZE\n return id_bytes","label":0},{"cid":"python/ray/utils.py::format_error_message","text":"python/ray/utils.py::format_error_message\ndef format_error_message(exception_message, task_exception=False):\n \"\"\"Improve the formatting of an exception thrown by a remote function.\n\n This method takes a traceback from an exception and makes it nicer by\n removing a few uninformative lines and adding some space to indent the\n remaining lines nicely.\n\n Args:\n exception_message (str): A message generated by traceback.format_exc().\n\n Returns:\n A string of the formatted exception message.\n \"\"\"\n lines = exception_message.split(\"\\n\")\n if task_exception:\n # For errors that occur inside of tasks, remove lines 1 and 2 which are\n # always the same, they just contain information about the worker code.\n lines = lines[0:1] + lines[3:]\n pass\n return \"\\n\".join(lines)","label":0},{"cid":"python/ray/utils.py::push_error_to_driver","text":"python/ray/utils.py::push_error_to_driver\ndef push_error_to_driver(worker, error_type, message, job_id=None):\n \"\"\"Push an error message to the driver to be printed in the background.\n\n Args:\n worker: The worker to use.\n error_type (str): The type of the error.\n message (str): The message that will be printed in the background\n on the driver.\n job_id: The ID of the driver to push the error message to. If this\n is None, then the message will be pushed to all drivers.\n \"\"\"\n if job_id is None:\n job_id = ray.JobID.nil()\n assert isinstance(job_id, ray.JobID)\n worker.core_worker.push_error(job_id, error_type, message, time.time())","label":0},{"cid":"python/ray/utils.py::push_error_to_driver_through_redis","text":"python/ray/utils.py::push_error_to_driver_through_redis\ndef push_error_to_driver_through_redis(redis_client,\n error_type,\n message,\n job_id=None):\n \"\"\"Push an error message to the driver to be printed in the background.\n\n Normally the push_error_to_driver function should be used. However, in some\n instances, the raylet client is not available, e.g., because the\n error happens in Python before the driver or worker has connected to the\n backend processes.\n\n Args:\n redis_client: The redis client to use.\n error_type (str): The type of the error.\n message (str): The message that will be printed in the background\n on the driver.\n job_id: The ID of the driver to push the error message to. If this\n is None, then the message will be pushed to all drivers.\n \"\"\"\n if job_id is None:\n job_id = ray.JobID.nil()\n assert isinstance(job_id, ray.JobID)\n # Do everything in Python and through the Python Redis client instead\n # of through the raylet.\n error_data = ray.gcs_utils.construct_error_message(job_id, error_type,\n message, time.time())\n redis_client.execute_command(\n \"RAY.TABLE_APPEND\", ray.gcs_utils.TablePrefix.Value(\"ERROR_INFO\"),\n ray.gcs_utils.TablePubsub.Value(\"ERROR_INFO_PUBSUB\"), job_id.binary(),\n error_data)","label":0},{"cid":"python/ray/utils.py::is_cython","text":"python/ray/utils.py::is_cython\ndef is_cython(obj):\n \"\"\"Check if an object is a Cython function or method\"\"\"\n\n # TODO(suo): We could split these into two functions, one for Cython\n # functions and another for Cython methods.\n # TODO(suo): There doesn't appear to be a Cython function 'type' we can\n # check against via isinstance. Please correct me if I'm wrong.\n def check_cython(x):\n return type(x).__name__ == \"cython_function_or_method\"\n\n # Check if function or method, respectively\n return check_cython(obj) or \\\n (hasattr(obj, \"__func__\") and check_cython(obj.__func__))","label":0},{"cid":"python/ray/utils.py::is_cython.check_cython","text":"python/ray/utils.py::is_cython.check_cython\n def check_cython(x):\n return type(x).__name__ == \"cython_function_or_method\"","label":0},{"cid":"python/ray/utils.py::is_function_or_method","text":"python/ray/utils.py::is_function_or_method\ndef is_function_or_method(obj):\n \"\"\"Check if an object is a function or method.\n\n Args:\n obj: The Python object in question.\n\n Returns:\n True if the object is an function or method.\n \"\"\"\n return inspect.isfunction(obj) or inspect.ismethod(obj) or is_cython(obj)","label":0},{"cid":"python/ray/utils.py::is_class_method","text":"python/ray/utils.py::is_class_method\ndef is_class_method(f):\n \"\"\"Returns whether the given method is a class_method.\"\"\"\n return hasattr(f, \"__self__\") and f.__self__ is not None","label":0},{"cid":"python/ray/utils.py::is_static_method","text":"python/ray/utils.py::is_static_method\ndef is_static_method(cls, f_name):\n \"\"\"Returns whether the class has a static method with the given name.\n\n Args:\n cls: The Python class (i.e. object of type `type`) to\n search for the method in.\n f_name: The name of the method to look up in this class\n and check whether or not it is static.\n \"\"\"\n for cls in inspect.getmro(cls):\n if f_name in cls.__dict__:\n return isinstance(cls.__dict__[f_name], staticmethod)\n return False","label":0},{"cid":"python/ray/utils.py::random_string","text":"python/ray/utils.py::random_string\ndef random_string():\n \"\"\"Generate a random string to use as an ID.\n\n Note that users may seed numpy, which could cause this function to generate\n duplicate IDs. Therefore, we need to seed numpy ourselves, but we can't\n interfere with the state of the user's random number generator, so we\n extract the state of the random number generator and reset it after we are\n done.\n\n TODO(rkn): If we want to later guarantee that these are generated in a\n deterministic manner, then we will need to make some changes here.\n\n Returns:\n A random byte string of length ray_constants.ID_SIZE.\n \"\"\"\n # Get the state of the numpy random number generator.\n numpy_state = np.random.get_state()\n # Try to use true randomness.\n np.random.seed(None)\n # Generate the random ID.\n random_id = np.random.bytes(ray_constants.ID_SIZE)\n # Reset the state of the numpy random number generator.\n np.random.set_state(numpy_state)\n return random_id","label":0},{"cid":"python/ray/utils.py::decode","text":"python/ray/utils.py::decode\ndef decode(byte_str, allow_none=False):\n \"\"\"Make this unicode in Python 3, otherwise leave it as bytes.\n\n Args:\n byte_str: The byte string to decode.\n allow_none: If true, then we will allow byte_str to be None in which\n case we will return an empty string. TODO(rkn): Remove this flag.\n This is only here to simplify upgrading to flatbuffers 1.10.0.\n\n Returns:\n A byte string in Python 2 and a unicode string in Python 3.\n \"\"\"\n if byte_str is None and allow_none:\n return \"\"\n\n if not isinstance(byte_str, bytes):\n raise ValueError(\n \"The argument {} must be a bytes object.\".format(byte_str))\n if sys.version_info >= (3, 0):\n return byte_str.decode(\"ascii\")\n else:\n return byte_str","label":0},{"cid":"python/ray/utils.py::ensure_str","text":"python/ray/utils.py::ensure_str\ndef ensure_str(s, encoding=\"utf-8\", errors=\"strict\"):\n \"\"\"Coerce *s* to `str`.\n\n - `str` -> `str`\n - `bytes` -> decoded to `str`\n \"\"\"\n if isinstance(s, str):\n return s\n else:\n assert isinstance(s, bytes)\n return s.decode(encoding, errors)","label":0},{"cid":"python/ray/utils.py::binary_to_object_ref","text":"python/ray/utils.py::binary_to_object_ref\ndef binary_to_object_ref(binary_object_ref):\n return ray.ObjectRef(binary_object_ref)","label":0},{"cid":"python/ray/utils.py::binary_to_task_id","text":"python/ray/utils.py::binary_to_task_id\ndef binary_to_task_id(binary_task_id):\n return ray.TaskID(binary_task_id)","label":0},{"cid":"python/ray/utils.py::binary_to_hex","text":"python/ray/utils.py::binary_to_hex\ndef binary_to_hex(identifier):\n hex_identifier = binascii.hexlify(identifier)\n if sys.version_info >= (3, 0):\n hex_identifier = hex_identifier.decode()\n return hex_identifier","label":0},{"cid":"python/ray/utils.py::hex_to_binary","text":"python/ray/utils.py::hex_to_binary\ndef hex_to_binary(hex_identifier):\n return binascii.unhexlify(hex_identifier)","label":0},{"cid":"python/ray/utils.py::compute_job_id_from_driver","text":"python/ray/utils.py::compute_job_id_from_driver\ndef compute_job_id_from_driver(driver_id):\n assert isinstance(driver_id, ray.WorkerID)\n return ray.JobID(driver_id.binary()[0:ray.JobID.size()])","label":0},{"cid":"python/ray/utils.py::compute_driver_id_from_job","text":"python/ray/utils.py::compute_driver_id_from_job\ndef compute_driver_id_from_job(job_id):\n assert isinstance(job_id, ray.JobID)\n rest_length = ray_constants.ID_SIZE - job_id.size()\n driver_id_str = job_id.binary() + (rest_length * b\"\\xff\")\n return ray.WorkerID(driver_id_str)","label":0},{"cid":"python/ray/utils.py::get_cuda_visible_devices","text":"python/ray/utils.py::get_cuda_visible_devices\ndef get_cuda_visible_devices():\n \"\"\"Get the device IDs in the CUDA_VISIBLE_DEVICES environment variable.\n\n Returns:\n if CUDA_VISIBLE_DEVICES is set, this returns a list of integers with\n the IDs of the GPUs. If it is not set or is set to NoDevFiles,\n this returns None.\n \"\"\"\n gpu_ids_str = os.environ.get(\"CUDA_VISIBLE_DEVICES\", None)\n\n if gpu_ids_str is None:\n return None\n\n if gpu_ids_str == \"\":\n return []\n\n if gpu_ids_str == \"NoDevFiles\":\n return []\n\n return [int(i) for i in gpu_ids_str.split(\",\")]","label":0},{"cid":"python/ray/utils.py::set_cuda_visible_devices","text":"python/ray/utils.py::set_cuda_visible_devices\ndef set_cuda_visible_devices(gpu_ids):\n \"\"\"Set the CUDA_VISIBLE_DEVICES environment variable.\n\n Args:\n gpu_ids: This is a list of integers representing GPU IDs.\n \"\"\"\n\n global last_set_gpu_ids\n if last_set_gpu_ids == gpu_ids:\n return # optimization: already set\n\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = \",\".join([str(i) for i in gpu_ids])\n last_set_gpu_ids = gpu_ids","label":0},{"cid":"python/ray/utils.py::resources_from_resource_arguments","text":"python/ray/utils.py::resources_from_resource_arguments\ndef resources_from_resource_arguments(\n default_num_cpus, default_num_gpus, default_memory,\n default_object_store_memory, default_resources, runtime_num_cpus,\n runtime_num_gpus, runtime_memory, runtime_object_store_memory,\n runtime_resources):\n \"\"\"Determine a task's resource requirements.\n\n Args:\n default_num_cpus: The default number of CPUs required by this function\n or actor method.\n default_num_gpus: The default number of GPUs required by this function\n or actor method.\n default_memory: The default heap memory required by this function\n or actor method.\n default_object_store_memory: The default object store memory required\n by this function or actor method.\n default_resources: The default custom resources required by this\n function or actor method.\n runtime_num_cpus: The number of CPUs requested when the task was\n invoked.\n runtime_num_gpus: The number of GPUs requested when the task was\n invoked.\n runtime_memory: The heap memory requested when the task was invoked.\n runtime_object_store_memory: The object store memory requested when\n the task was invoked.\n runtime_resources: The custom resources requested when the task was\n invoked.\n\n Returns:\n A dictionary of the resource requirements for the task.\n \"\"\"\n if runtime_resources is not None:\n resources = runtime_resources.copy()\n elif default_resources is not None:\n resources = default_resources.copy()\n else:\n resources = {}\n\n if \"CPU\" in resources or \"GPU\" in resources:\n raise ValueError(\"The resources dictionary must not \"\n \"contain the key 'CPU' or 'GPU'\")\n elif \"memory\" in resources or \"object_store_memory\" in resources:\n raise ValueError(\"The resources dictionary must not","label":0},{"cid":"python/ray/utils.py::setup_logger","text":"python/ray/utils.py::setup_logger\ndef setup_logger(logging_level, logging_format):\n \"\"\"Setup default logging for ray.\"\"\"\n logger = logging.getLogger(\"ray\")\n if type(logging_level) is str:\n logging_level = logging.getLevelName(logging_level.upper())\n logger.setLevel(logging_level)\n global _default_handler\n if _default_handler is None:\n _default_handler = logging.StreamHandler()\n logger.addHandler(_default_handler)\n _default_handler.setFormatter(logging.Formatter(logging_format))\n logger.propagate = False","label":0},{"cid":"python/ray/utils.py::open_log","text":"python/ray/utils.py::open_log\ndef open_log(path, **kwargs):\n kwargs.setdefault(\"buffering\", 1)\n kwargs.setdefault(\"mode\", \"a\")\n kwargs.setdefault(\"encoding\", \"utf-8\")\n return open(path, **kwargs)","label":1},{"cid":"python/ray/utils.py::create_and_init_new_worker_log","text":"python/ray/utils.py::create_and_init_new_worker_log\ndef create_and_init_new_worker_log(path, worker_pid):\n \"\"\"\n Opens a path (or creates if necessary) for a log.\n\n Args:\n path (str): The name/path of the file to be opened.\n worker_pid (int): The pid of the worker process.\n\n Returns:\n A file-like object which can be written to.\n \"\"\"\n # TODO (Alex): We should eventually be able to replace this with\n # named-pipes.\n f = open_log(path)\n # Check to see if we're creating this file. No one else should ever write\n # to this file, so we don't have to worry about TOCTOU.\n if f.tell() == 0:\n # This should always be the first message to appear in the worker's\n # stdout and stderr log files. The string \"Ray worker pid:\" is\n # parsed in the log monitor process.\n print(\"Ray worker pid: {}\".format(worker_pid), file=f)\n return f","label":0},{"cid":"python/ray/utils.py::get_system_memory","text":"python/ray/utils.py::get_system_memory\ndef get_system_memory():\n \"\"\"Return the total amount of system memory in bytes.\n\n Returns:\n The total amount of system memory in bytes.\n \"\"\"\n # Try to accurately figure out the memory limit if we are in a docker\n # container. Note that this file is not specific to Docker and its value is\n # often much larger than the actual amount of memory.\n docker_limit = None\n memory_limit_filename = \"/sys/fs/cgroup/memory/memory.limit_in_bytes\"\n if os.path.exists(memory_limit_filename):\n with open(memory_limit_filename, \"r\") as f:\n docker_limit = int(f.read())\n\n # Use psutil if it is available.\n psutil_memory_in_bytes = psutil.virtual_memory().total\n\n if docker_limit is not None:\n # We take the min because the cgroup limit is very large if we aren't\n # in Docker.\n return min(docker_limit, psutil_memory_in_bytes)\n\n return psutil_memory_in_bytes","label":0},{"cid":"python/ray/utils.py::get_used_memory","text":"python/ray/utils.py::get_used_memory\ndef get_used_memory():\n \"\"\"Return the currently used system memory in bytes\n\n Returns:\n The total amount of used memory\n \"\"\"\n # Try to accurately figure out the memory usage if we are in a docker\n # container.\n docker_usage = None\n memory_usage_filename = \"/sys/fs/cgroup/memory/memory.usage_in_bytes\"\n if os.path.exists(memory_usage_filename):\n with open(memory_usage_filename, \"r\") as f:\n docker_usage = int(f.read())\n\n # Use psutil if it is available.\n psutil_memory_in_bytes = psutil.virtual_memory().used\n\n if docker_usage is not None:\n # We take the min because the cgroup limit is very large if we aren't\n # in Docker.\n return min(docker_usage, psutil_memory_in_bytes)\n\n return psutil_memory_in_bytes","label":0},{"cid":"python/ray/utils.py::estimate_available_memory","text":"python/ray/utils.py::estimate_available_memory\ndef estimate_available_memory():\n \"\"\"Return the currently available amount of system memory in bytes.\n\n Returns:\n The total amount of available memory in bytes. Based on the used\n and total memory.\n\n \"\"\"\n return get_system_memory() - get_used_memory()","label":0},{"cid":"python/ray/utils.py::get_shared_memory_bytes","text":"python/ray/utils.py::get_shared_memory_bytes\ndef get_shared_memory_bytes():\n \"\"\"Get the size of the shared memory file system.\n\n Returns:\n The size of the shared memory file system in bytes.\n \"\"\"\n # Make sure this is only called on Linux.\n assert sys.platform == \"linux\" or sys.platform == \"linux2\"\n\n shm_fd = os.open(\"/dev/shm\", os.O_RDONLY)\n try:\n shm_fs_stats = os.fstatvfs(shm_fd)\n # The value shm_fs_stats.f_bsize is the block size and the\n # value shm_fs_stats.f_bavail is the number of available\n # blocks.\n shm_avail = shm_fs_stats.f_bsize * shm_fs_stats.f_bavail\n finally:\n os.close(shm_fd)\n\n return shm_avail","label":0},{"cid":"python/ray/utils.py::check_oversized_pickle","text":"python/ray/utils.py::check_oversized_pickle\ndef check_oversized_pickle(pickled, name, obj_type, worker):\n \"\"\"Send a warning message if the pickled object is too large.\n\n Args:\n pickled: the pickled object.\n name: name of the pickled object.\n obj_type: type of the pickled object, can be 'function',\n 'remote function', 'actor', or 'object'.\n worker: the worker used to send warning message.\n \"\"\"\n length = len(pickled)\n if length <= ray_constants.PICKLE_OBJECT_WARNING_SIZE:\n return\n warning_message = (\n \"Warning: The {} {} has size {} when pickled. \"\n \"It will be stored in Redis, which could cause memory issues. \"\n \"This may mean that its definition uses a large array or other object.\"\n ).format(obj_type, name, length)\n push_error_to_driver(\n worker,\n ray_constants.PICKLING_LARGE_OBJECT_PUSH_ERROR,\n warning_message,\n job_id=worker.current_job_id)","label":0},{"cid":"python/ray/utils.py::is_main_thread","text":"python/ray/utils.py::is_main_thread\ndef is_main_thread():\n return threading.current_thread().getName() == \"MainThread\"","label":0},{"cid":"python/ray/utils.py::detect_fate_sharing_support_win32","text":"python/ray/utils.py::detect_fate_sharing_support_win32\ndef detect_fate_sharing_support_win32():\n global win32_job, win32_AssignProcessToJobObject\n if win32_job is None and sys.platform == \"win32\":\n import ctypes\n try:\n from ctypes.wintypes import BOOL, DWORD, HANDLE, LPVOID, LPCWSTR\n kernel32 = ctypes.WinDLL(\"kernel32\")\n kernel32.CreateJobObjectW.argtypes = (LPVOID, LPCWSTR)\n kernel32.CreateJobObjectW.restype = HANDLE\n sijo_argtypes = (HANDLE, ctypes.c_int, LPVOID, DWORD)\n kernel32.SetInformationJobObject.argtypes = sijo_argtypes\n kernel32.SetInformationJobObject.restype = BOOL\n kernel32.AssignProcessToJobObject.argtypes = (HANDLE, HANDLE)\n kernel32.AssignProcessToJobObject.restype = BOOL\n kernel32.IsDebuggerPresent.argtypes = ()\n kernel32.IsDebuggerPresent.restype = BOOL\n except (AttributeError, TypeError, ImportError):\n kernel32 = None\n job = kernel32.CreateJobObjectW(None, None) if kernel32 else None\n job = subprocess.Handle(job) if job else job\n if job:\n from ctypes.wintypes import DWORD, LARGE_INTEGER, ULARGE_INTEGER\n\n class JOBOBJECT_BASIC_LIMIT_INFORMATION(ctypes.Structure):\n _fields_ = [\n (\"PerProcessUserTimeLimit\", LARGE_INTEGER),\n (\"PerJobUserTimeLimit\", LARGE_INTEGER),\n (\"LimitFlags\", DWORD),\n (\"MinimumWorkingSetSize\", ctypes.c_size_t),\n (\"MaximumWorkingSetSize\", ctypes.c_size_t),\n (\"ActiveProcessLimit\", DWORD),\n (\"Affinity\", ctypes.c_size_t),\n (\"PriorityClass\", DWORD),\n (\"SchedulingClass\", DWORD),\n ]\n\n class IO_COUNTERS(ctypes.Structure):\n _fields_ = [\n (\"ReadOperationCount\", ULARGE_INTEGER),\n (\"","label":0},{"cid":"python/ray/utils.py::detect_fate_sharing_support_linux","text":"python/ray/utils.py::detect_fate_sharing_support_linux\ndef detect_fate_sharing_support_linux():\n global linux_prctl\n if linux_prctl is None and sys.platform.startswith(\"linux\"):\n try:\n from ctypes import c_int, c_ulong, CDLL\n prctl = CDLL(None).prctl\n prctl.restype = c_int\n prctl.argtypes = [c_int, c_ulong, c_ulong, c_ulong, c_ulong]\n except (AttributeError, TypeError):\n prctl = None\n linux_prctl = prctl if prctl else False\n return bool(linux_prctl)","label":0},{"cid":"python/ray/utils.py::detect_fate_sharing_support","text":"python/ray/utils.py::detect_fate_sharing_support\ndef detect_fate_sharing_support():\n result = None\n if sys.platform == \"win32\":\n result = detect_fate_sharing_support_win32()\n elif sys.platform.startswith(\"linux\"):\n result = detect_fate_sharing_support_linux()\n return result","label":0},{"cid":"python/ray/utils.py::set_kill_on_parent_death_linux","text":"python/ray/utils.py::set_kill_on_parent_death_linux\ndef set_kill_on_parent_death_linux():\n \"\"\"Ensures this process dies if its parent dies (fate-sharing).\n\n Linux-only. Must be called in preexec_fn (i.e. by the child).\n \"\"\"\n if detect_fate_sharing_support_linux():\n import signal\n PR_SET_PDEATHSIG = 1\n if linux_prctl(PR_SET_PDEATHSIG, signal.SIGKILL, 0, 0, 0) != 0:\n import ctypes\n raise OSError(ctypes.get_errno(), \"prctl(PR_SET_PDEATHSIG) failed\")\n else:\n assert False, \"PR_SET_PDEATHSIG used despite being unavailable\"","label":0},{"cid":"python/ray/utils.py::set_kill_child_on_death_win32","text":"python/ray/utils.py::set_kill_child_on_death_win32\ndef set_kill_child_on_death_win32(child_proc):\n \"\"\"Ensures the child process dies if this process dies (fate-sharing).\n\n Windows-only. Must be called by the parent, after spawning the child.\n\n Args:\n child_proc: The subprocess.Popen or subprocess.Handle object.\n \"\"\"\n\n if isinstance(child_proc, subprocess.Popen):\n child_proc = child_proc._handle\n assert isinstance(child_proc, subprocess.Handle)\n\n if detect_fate_sharing_support_win32():\n if not win32_AssignProcessToJobObject(win32_job, int(child_proc)):\n import ctypes\n raise OSError(ctypes.get_last_error(),\n \"AssignProcessToJobObject() failed\")\n else:\n assert False, \"AssignProcessToJobObject used despite being unavailable\"","label":0},{"cid":"python/ray/utils.py::set_sigterm_handler","text":"python/ray/utils.py::set_sigterm_handler\ndef set_sigterm_handler(sigterm_handler):\n \"\"\"Registers a handler for SIGTERM in a platform-compatible manner.\"\"\"\n if sys.platform == \"win32\":\n # Note that these signal handlers only work for console applications.\n # TODO(mehrdadn): implement graceful process termination mechanism\n # SIGINT is Ctrl+C, SIGBREAK is Ctrl+Break.\n signal.signal(signal.SIGBREAK, sigterm_handler)\n else:\n signal.signal(signal.SIGTERM, sigterm_handler)","label":0},{"cid":"python/ray/utils.py::try_make_directory_shared","text":"python/ray/utils.py::try_make_directory_shared\ndef try_make_directory_shared(directory_path):\n try:\n os.chmod(directory_path, 0o0777)\n except OSError as e:\n # Silently suppress the PermissionError that is thrown by the chmod.\n # This is done because the user attempting to change the permissions\n # on a directory may not own it. The chmod is attempted whether the\n # directory is new or not to avoid race conditions.\n # ray-project/ray/#3591\n if e.errno in [errno.EACCES, errno.EPERM]:\n pass\n else:\n raise","label":0},{"cid":"python/ray/utils.py::try_to_create_directory","text":"python/ray/utils.py::try_to_create_directory\ndef try_to_create_directory(directory_path):\n \"\"\"Attempt to create a directory that is globally readable/writable.\n\n Args:\n directory_path: The path of the directory to create.\n \"\"\"\n directory_path = os.path.expanduser(directory_path)\n os.makedirs(directory_path, exist_ok=True)\n # Change the log directory permissions so others can use it. This is\n # important when multiple people are using the same machine.\n try_make_directory_shared(directory_path)","label":0},{"cid":"python/ray/utils.py::try_to_symlink","text":"python/ray/utils.py::try_to_symlink\ndef try_to_symlink(symlink_path, target_path):\n \"\"\"Attempt to create a symlink.\n\n If the symlink path exists and isn't a symlink, the symlink will not be\n created. If a symlink exists in the path, it will be attempted to be\n removed and replaced.\n\n Args:\n symlink_path: The path at which to create the symlink.\n target_path: The path the symlink should point to.\n \"\"\"\n symlink_path = os.path.expanduser(symlink_path)\n target_path = os.path.expanduser(target_path)\n\n if os.path.exists(symlink_path):\n if os.path.islink(symlink_path):\n # Try to remove existing symlink.\n try:\n os.remove(symlink_path)\n except OSError:\n return\n else:\n # There's an existing non-symlink file, don't overwrite it.\n return\n\n try:\n os.symlink(target_path, symlink_path)\n except OSError:\n return","label":0},{"cid":"python/ray/worker.py::ActorCheckpointInfo.__init__","text":"python/ray/worker.py::ActorCheckpointInfo.__init__\n def __init__(self, num_tasks_since_last_checkpoint,\n last_checkpoint_timestamp, checkpoint_ids):\n self.num_tasks_since_last_checkpoint = num_tasks_since_last_checkpoint\n self.last_checkpoint_timestamp = last_checkpoint_timestamp\n self.checkpoint_ids = checkpoint_ids","label":0},{"cid":"python/ray/worker.py::Worker.__init__","text":"python/ray/worker.py::Worker.__init__\n def __init__(self):\n \"\"\"Initialize a Worker object.\"\"\"\n self.node = None\n self.mode = None\n self.cached_functions_to_run = []\n self.actor_init_error = None\n self.actors = {}\n # Information used to maintain actor checkpoints.\n self.actor_checkpoint_info = {}\n self.actor_task_counter = 0\n # When the worker is constructed. Record the original value of the\n # CUDA_VISIBLE_DEVICES environment variable.\n self.original_gpu_ids = ray.utils.get_cuda_visible_devices()\n self.memory_monitor = memory_monitor.MemoryMonitor()\n # A dictionary that maps from driver id to SerializationContext\n # TODO: clean up the SerializationContext once the job finished.\n self.serialization_context_map = {}\n self.function_actor_manager = FunctionActorManager(self)\n # This event is checked regularly by all of the threads so that they\n # know when to exit.\n self.threads_stopped = threading.Event()\n # Index of the current session. This number will\n # increment every time when `ray.shutdown` is called.\n self._session_index = 0\n # Functions to run to process the values returned by ray.get. Each\n # postprocessor must take two arguments (\"object_refs\", and \"values\").\n self._post_get_hooks = []","label":0},{"cid":"python/ray/worker.py::Worker.connected","text":"python/ray/worker.py::Worker.connected\n @property\n def connected(self):\n return self.node is not None","label":0},{"cid":"python/ray/worker.py::Worker.node_ip_address","text":"python/ray/worker.py::Worker.node_ip_address\n @property\n def node_ip_address(self):\n self.check_connected()\n return self.node.node_ip_address","label":0},{"cid":"python/ray/worker.py::Worker.load_code_from_local","text":"python/ray/worker.py::Worker.load_code_from_local\n @property\n def load_code_from_local(self):\n self.check_connected()\n return self.node.load_code_from_local","label":0},{"cid":"python/ray/worker.py::Worker.current_job_id","text":"python/ray/worker.py::Worker.current_job_id\n @property\n def current_job_id(self):\n if hasattr(self, \"core_worker\"):\n return self.core_worker.get_current_job_id()\n return JobID.nil()","label":0},{"cid":"python/ray/worker.py::Worker.actor_id","text":"python/ray/worker.py::Worker.actor_id\n @property\n def actor_id(self):\n if hasattr(self, \"core_worker\"):\n return self.core_worker.get_actor_id()\n return ActorID.nil()","label":0},{"cid":"python/ray/worker.py::Worker.current_task_id","text":"python/ray/worker.py::Worker.current_task_id\n @property\n def current_task_id(self):\n return self.core_worker.get_current_task_id()","label":0},{"cid":"python/ray/worker.py::Worker.current_session_and_job","text":"python/ray/worker.py::Worker.current_session_and_job\n @property\n def current_session_and_job(self):\n \"\"\"Get the current session index and job id as pair.\"\"\"\n assert isinstance(self._session_index, int)\n assert isinstance(self.current_job_id, ray.JobID)\n return self._session_index, self.current_job_id","label":0},{"cid":"python/ray/worker.py::Worker.mark_actor_init_failed","text":"python/ray/worker.py::Worker.mark_actor_init_failed\n def mark_actor_init_failed(self, error):\n \"\"\"Called to mark this actor as failed during initialization.\"\"\"\n\n self.actor_init_error = error","label":0},{"cid":"python/ray/worker.py::Worker.reraise_actor_init_error","text":"python/ray/worker.py::Worker.reraise_actor_init_error\n def reraise_actor_init_error(self):\n \"\"\"Raises any previous actor initialization error.\"\"\"\n\n if self.actor_init_error is not None:\n raise self.actor_init_error","label":0},{"cid":"python/ray/worker.py::Worker.get_serialization_context","text":"python/ray/worker.py::Worker.get_serialization_context\n def get_serialization_context(self, job_id=None):\n \"\"\"Get the SerializationContext of the job that this worker is processing.\n\n Args:\n job_id: The ID of the job that indicates which job to get\n the serialization context for.\n\n Returns:\n The serialization context of the given job.\n \"\"\"\n # This function needs to be protected by a lock, because it will be\n # called by`register_class_for_serialization`, as well as the import\n # thread, from different threads. Also, this function will recursively\n # call itself, so we use RLock here.\n if job_id is None:\n job_id = self.current_job_id\n with self.lock:\n if job_id not in self.serialization_context_map:\n self.serialization_context_map[\n job_id] = serialization.SerializationContext(self)\n return self.serialization_context_map[job_id]","label":0},{"cid":"python/ray/worker.py::Worker.check_connected","text":"python/ray/worker.py::Worker.check_connected\n def check_connected(self):\n \"\"\"Check if the worker is connected.\n\n Raises:\n Exception: An exception is raised if the worker is not connected.\n \"\"\"\n if not self.connected:\n raise RayConnectionError(\"Ray has not been started yet. You can \"\n \"start Ray with 'ray.init()'.\")","label":0},{"cid":"python/ray/worker.py::Worker.set_mode","text":"python/ray/worker.py::Worker.set_mode\n def set_mode(self, mode):\n \"\"\"Set the mode of the worker.\n\n The mode SCRIPT_MODE should be used if this Worker is a driver that is\n being run as a Python script or interactively in a shell. It will print\n information about task failures.\n\n The mode WORKER_MODE should be used if this Worker is not a driver. It\n will not print information about tasks.\n\n The mode LOCAL_MODE should be used if this Worker is a driver and if\n you want to run the driver in a manner equivalent to serial Python for\n debugging purposes. It will not send remote function calls to the\n scheduler and will instead execute them in a blocking fashion.\n\n Args:\n mode: One of SCRIPT_MODE, WORKER_MODE, and LOCAL_MODE.\n \"\"\"\n self.mode = mode","label":0},{"cid":"python/ray/worker.py::Worker.put_object","text":"python/ray/worker.py::Worker.put_object\n def put_object(self, value, object_ref=None, pin_object=True):\n \"\"\"Put value in the local object store with object reference `object_ref`.\n\n This assumes that the value for `object_ref` has not yet been placed in\n the local object store. If the plasma store is full, the worker will\n automatically retry up to DEFAULT_PUT_OBJECT_RETRIES times. Each\n retry will delay for an exponentially doubling amount of time,\n starting with DEFAULT_PUT_OBJECT_DELAY. After this, exception\n will be raised.\n\n Args:\n value: The value to put in the object store.\n object_ref (ObjectRef): The object ref of the value to be\n put. If None, one will be generated.\n pin_object: If set, the object will be pinned at the raylet.\n\n Returns:\n ObjectRef: The object ref the object was put under.\n\n Raises:\n ray.exceptions.ObjectStoreFullError: This is raised if the attempt\n to store the object fails because the object store is full even\n after multiple retries.\n \"\"\"\n # Make sure that the value is not an object ref.\n if isinstance(value, ObjectRef):\n raise TypeError(\n \"Calling 'put' on an ray.ObjectRef is not allowed \"\n \"(similarly, returning an ray.ObjectRef from a remote \"\n \"function is not allowed). If you really want to \"\n \"do this, you can wrap the ray.ObjectRef in a list and \"\n \"call 'put' on it (or return it).\")\n\n if self.mode == LOCAL_MODE:\n assert object_ref is None, (\"Local Mode does not support \"\n \"inserting with an ObjectRef\")\n\n serialized_value = self.get_serialization_context().serialize(value)\n # This *must* be the first place that we construct this python\n # ObjectRef because an entry with 0 local","label":0},{"cid":"python/ray/worker.py::Worker.deserialize_objects","text":"python/ray/worker.py::Worker.deserialize_objects\n def deserialize_objects(self, data_metadata_pairs, object_refs):\n context = self.get_serialization_context()\n return context.deserialize_objects(data_metadata_pairs, object_refs)","label":0},{"cid":"python/ray/worker.py::Worker.get_objects","text":"python/ray/worker.py::Worker.get_objects\n def get_objects(self, object_refs, timeout=None):\n \"\"\"Get the values in the object store associated with the IDs.\n\n Return the values from the local object store for object_refs. This\n will block until all the values for object_refs have been written to\n the local object store.\n\n Args:\n object_refs (List[object_ref.ObjectRef]): A list of the object refs\n whose values should be retrieved.\n timeout (float): timeout (float): The maximum amount of time in\n seconds to wait before returning.\n \"\"\"\n # Make sure that the values are object refs.\n for object_ref in object_refs:\n if not isinstance(object_ref, ObjectRef):\n raise TypeError(\n \"Attempting to call `get` on the value {}, \"\n \"which is not an ray.ObjectRef.\".format(object_ref))\n\n timeout_ms = int(timeout * 1000) if timeout else -1\n data_metadata_pairs = self.core_worker.get_objects(\n object_refs, self.current_task_id, timeout_ms)\n return self.deserialize_objects(data_metadata_pairs, object_refs)","label":0},{"cid":"python/ray/worker.py::Worker.run_function_on_all_workers","text":"python/ray/worker.py::Worker.run_function_on_all_workers\n def run_function_on_all_workers(self, function,\n run_on_other_drivers=False):\n \"\"\"Run arbitrary code on all of the workers.\n\n This function will first be run on the driver, and then it will be\n exported to all of the workers to be run. It will also be run on any\n new workers that register later. If ray.init has not been called yet,\n then cache the function and export it later.\n\n Args:\n function (Callable): The function to run on all of the workers. It\n takes only one argument, a worker info dict. If it returns\n anything, its return values will not be used.\n run_on_other_drivers: The boolean that indicates whether we want to\n run this function on other drivers. One case is we may need to\n share objects across drivers.\n \"\"\"\n # If ray.init has not been called yet, then cache the function and\n # export it when connect is called. Otherwise, run the function on all\n # workers.\n if self.mode is None:\n self.cached_functions_to_run.append(function)\n else:\n # Attempt to pickle the function before we need it. This could\n # fail, and it is more convenient if the failure happens before we\n # actually run the function locally.\n pickled_function = pickle.dumps(function)\n\n function_to_run_id = hashlib.sha1(pickled_function).digest()\n key = b\"FunctionsToRun:\" + function_to_run_id\n # First run the function on the driver.\n # We always run the task locally.\n function({\"worker\": self})\n # Check if the function has already been put into redis.\n function_exported = self.redis_client.setnx(b\"Lock:\" + key, 1)\n if not function_exported:\n # In this case, the function has already b","label":0},{"cid":"python/ray/worker.py::Worker.main_loop","text":"python/ray/worker.py::Worker.main_loop\n def main_loop(self):\n \"\"\"The main loop a worker runs to receive and execute tasks.\"\"\"\n\n def sigterm_handler(signum, frame):\n shutdown(True)\n sys.exit(1)\n\n ray.utils.set_sigterm_handler(sigterm_handler)\n self.core_worker.run_task_loop()\n sys.exit(0)","label":0},{"cid":"python/ray/worker.py::Worker.main_loop.sigterm_handler","text":"python/ray/worker.py::Worker.main_loop.sigterm_handler\n def sigterm_handler(signum, frame):\n shutdown(True)\n sys.exit(1)","label":0},{"cid":"python/ray/worker.py::get_gpu_ids","text":"python/ray/worker.py::get_gpu_ids\ndef get_gpu_ids():\n \"\"\"Get the IDs of the GPUs that are available to the worker.\n\n If the CUDA_VISIBLE_DEVICES environment variable was set when the worker\n started up, then the IDs returned by this method will be a subset of the\n IDs in CUDA_VISIBLE_DEVICES. If not, the IDs will fall in the range\n [0, NUM_GPUS - 1], where NUM_GPUS is the number of GPUs that the node has.\n\n Returns:\n A list of GPU IDs.\n \"\"\"\n\n # TODO(ilr) Handle inserting resources in local mode\n all_resource_ids = global_worker.core_worker.resource_ids()\n assigned_ids = [\n resource_id for resource_id, _ in all_resource_ids.get(\"GPU\", [])\n ]\n # If the user had already set CUDA_VISIBLE_DEVICES, then respect that (in\n # the sense that only GPU IDs that appear in CUDA_VISIBLE_DEVICES should be\n # returned).\n if global_worker.original_gpu_ids is not None:\n assigned_ids = [\n global_worker.original_gpu_ids[gpu_id] for gpu_id in assigned_ids\n ]\n # Give all GPUs in local_mode.\n if global_worker.mode == LOCAL_MODE:\n max_gpus = global_worker.node.get_resource_spec().num_gpus\n return global_worker.original_gpu_ids[:max_gpus]\n\n return assigned_ids","label":0},{"cid":"python/ray/worker.py::get_resource_ids","text":"python/ray/worker.py::get_resource_ids\ndef get_resource_ids():\n \"\"\"Get the IDs of the resources that are available to the worker.\n\n Returns:\n A dictionary mapping the name of a resource to a list of pairs, where\n each pair consists of the ID of a resource and the fraction of that\n resource reserved for this worker.\n \"\"\"\n if _mode() == LOCAL_MODE:\n raise RuntimeError(\"ray.get_resource_ids() currently does not work in \"\n \"local_mode.\")\n\n return global_worker.core_worker.resource_ids()","label":0},{"cid":"python/ray/worker.py::get_webui_url","text":"python/ray/worker.py::get_webui_url\ndef get_webui_url():\n \"\"\"Get the URL to access the web UI.\n\n Note that the URL does not specify which node the web UI is on.\n\n Returns:\n The URL of the web UI as a string.\n \"\"\"\n if _global_node is None:\n raise RuntimeError(\"Ray has not been initialized/connected.\")\n return _global_node.webui_url","label":0},{"cid":"python/ray/worker.py::print_failed_task","text":"python/ray/worker.py::print_failed_task\ndef print_failed_task(task_status):\n \"\"\"Print information about failed tasks.\n\n Args:\n task_status (Dict): A dictionary containing the name, operationid, and\n error message for a failed task.\n \"\"\"\n logger.error(\"\"\"\n Error: Task failed\n Function Name: {}\n Task ID: {}\n Error Message: \\n{}\n \"\"\".format(task_status[\"function_name\"], task_status[\"operationid\"],\n task_status[\"error_message\"]))","label":0},{"cid":"python/ray/worker.py::init","text":"python/ray/worker.py::init\ndef init(address=None,\n redis_address=None,\n redis_port=None,\n num_cpus=None,\n num_gpus=None,\n memory=None,\n object_store_memory=None,\n resources=None,\n driver_object_store_memory=None,\n redis_max_memory=None,\n log_to_driver=True,\n node_ip_address=ray_constants.NODE_DEFAULT_IP,\n object_ref_seed=None,\n local_mode=False,\n redirect_worker_output=None,\n redirect_output=None,\n ignore_reinit_error=False,\n num_redis_shards=None,\n redis_max_clients=None,\n redis_password=ray_constants.REDIS_DEFAULT_PASSWORD,\n plasma_directory=None,\n huge_pages=False,\n include_java=False,\n include_dashboard=None,\n dashboard_host=\"localhost\",\n dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT,\n job_id=None,\n configure_logging=True,\n logging_level=logging.INFO,\n logging_format=ray_constants.LOGGER_FORMAT,\n plasma_store_socket_name=None,\n raylet_socket_name=None,\n temp_dir=None,\n load_code_from_local=False,\n java_worker_options=None,\n use_pickle=True,\n _internal_config=None,\n lru_evict=False,\n enable_object_reconstruction=False):\n \"\"\"\n Connect to an existing Ray cluster or start one and connect to it.\n\n This method handles two cases; either a Ray cluster already exists and we\n just attach this driver to it or we start all of the processes associated\n with a Ray cluster and attach to the newly started cluster.\n\n To start Ray and all of the relevant processes, use this as follows:\n\n .. code-block:: python\n\n ray.init()\n\n To connect to an existing Ray cluster, use this as follows (substituting\n in the appropriate address):\n\n .. code-block:: python\n\n ray.init(address=\"123.45.67.89:6379\")\n\n You can also define an environment var","label":0},{"cid":"python/ray/worker.py::shutdown","text":"python/ray/worker.py::shutdown\ndef shutdown(exiting_interpreter=False):\n \"\"\"Disconnect the worker, and terminate processes started by ray.init().\n\n This will automatically run at the end when a Python process that uses Ray\n exits. It is ok to run this twice in a row. The primary use case for this\n function is to cleanup state between tests.\n\n Note that this will clear any remote function definitions, actor\n definitions, and existing actors, so if you wish to use any previously\n defined remote functions or actors after calling ray.shutdown(), then you\n need to redefine them. If they were defined in an imported module, then you\n will need to reload the module.\n\n Args:\n exiting_interpreter (bool): True if this is called by the atexit hook\n and false otherwise. If we are exiting the interpreter, we will\n wait a little while to print any extra error messages.\n \"\"\"\n if exiting_interpreter and global_worker.mode == SCRIPT_MODE:\n # This is a duration to sleep before shutting down everything in order\n # to make sure that log messages finish printing.\n time.sleep(0.5)\n\n disconnect(exiting_interpreter)\n\n # We need to destruct the core worker here because after this function,\n # we will tear down any processes spawned by ray.init() and the background\n # IO thread in the core worker doesn't currently handle that gracefully.\n if hasattr(global_worker, \"core_worker\"):\n del global_worker.core_worker\n\n # Disconnect global state from GCS.\n ray.state.state.disconnect()\n\n # Shut down the Ray processes.\n global _global_node\n if _global_node is not None:\n _global_node.kill_all_processes(check_alive=False, allow_graceful=True)\n _global_node = None\n\n # TODO(rkn): Instead of manually resetting some of the worker fields, we\n # should simply set \"global_worker\" to equal \"None\" or something like that.\n global_worker.set_mode(None)\n global_worker._","label":0},{"cid":"python/ray/worker.py::sigterm_handler","text":"python/ray/worker.py::sigterm_handler\ndef sigterm_handler(signum, frame):\n sys.exit(signum)","label":0},{"cid":"python/ray/worker.py::custom_excepthook","text":"python/ray/worker.py::custom_excepthook\ndef custom_excepthook(type, value, tb):\n # If this is a driver, push the exception to GCS worker table.\n if global_worker.mode == SCRIPT_MODE:\n error_message = \"\".join(traceback.format_tb(tb))\n worker_id = global_worker.worker_id\n worker_type = ray.gcs_utils.DRIVER\n worker_info = {\"exception\": error_message}\n\n ray.state.state.add_worker(worker_id, worker_type, worker_info)\n # Call the normal excepthook.\n normal_excepthook(type, value, tb)","label":0},{"cid":"python/ray/worker.py::_set_log_file","text":"python/ray/worker.py::_set_log_file\ndef _set_log_file(file_name, worker_pid, old_obj, setter_func):\n # Line-buffer the output (mode 1).\n f = create_and_init_new_worker_log(file_name, worker_pid)\n\n # TODO (Alex): Python seems to always flush when writing. If that is no\n # longer true, then we need to manually flush the old buffer.\n # old_obj.flush()\n\n # TODO (Alex): Flush the c/c++ userspace buffers if necessary.\n # `fflush(stdout); cout.flush();`\n\n fileno = old_obj.fileno()\n\n # C++ logging requires redirecting the stdout file descriptor. Note that\n # dup2 will automatically close the old file descriptor before overriding\n # it.\n os.dup2(f.fileno(), fileno)\n\n # We also manually set sys.stdout and sys.stderr because that seems to\n # have an effect on the output buffering. Without doing this, stdout\n # and stderr are heavily buffered resulting in seemingly lost logging\n # statements. We never want to close the stdout file descriptor, dup2 will\n # close it when necessary and we don't want python's GC to close it.\n setter_func(open_log(fileno, closefd=False))\n\n return os.path.abspath(f.name)","label":0},{"cid":"python/ray/worker.py::set_log_file","text":"python/ray/worker.py::set_log_file\ndef set_log_file(stdout_name, stderr_name):\n \"\"\"Sets up logging for the current worker, creating the (fd backed) file and\n flushing buffers as is necessary.\n\n Args:\n stdout_name (str): The file name that stdout should be written to.\n stderr_name(str): The file name that stderr should be written to.\n\n Returns:\n (tuple) The absolute paths of the files that stdout and stderr will be\n written to.\n\n \"\"\"\n stdout_path = \"\"\n stderr_path = \"\"\n worker_pid = os.getpid()\n\n # lambda cannot contain assignment\n def stdout_setter(x):\n sys.stdout = x\n\n def stderr_setter(x):\n sys.stderr = x\n\n if stdout_name:\n _set_log_file(stdout_name, worker_pid, sys.stdout, stdout_setter)\n\n # The stderr case should be analogous to the stdout case\n if stderr_name:\n _set_log_file(stderr_name, worker_pid, sys.stderr, stderr_setter)\n\n return stdout_path, stderr_path","label":0},{"cid":"python/ray/worker.py::set_log_file.stdout_setter","text":"python/ray/worker.py::set_log_file.stdout_setter\n def stdout_setter(x):\n sys.stdout = x","label":0},{"cid":"python/ray/worker.py::set_log_file.stderr_setter","text":"python/ray/worker.py::set_log_file.stderr_setter\n def stderr_setter(x):\n sys.stderr = x","label":0},{"cid":"python/ray/worker.py::print_logs","text":"python/ray/worker.py::print_logs\ndef print_logs(redis_client, threads_stopped, job_id):\n \"\"\"Prints log messages from workers on all of the nodes.\n\n Args:\n redis_client: A client to the primary Redis shard.\n threads_stopped (threading.Event): A threading event used to signal to\n the thread that it should exit.\n job_id (JobID): The id of the driver's job\n\n \"\"\"\n pubsub_client = redis_client.pubsub(ignore_subscribe_messages=True)\n pubsub_client.subscribe(ray.gcs_utils.LOG_FILE_CHANNEL)\n localhost = services.get_node_ip_address()\n try:\n # Keep track of the number of consecutive log messages that have been\n # received with no break in between. If this number grows continually,\n # then the worker is probably not able to process the log messages as\n # rapidly as they are coming in.\n num_consecutive_messages_received = 0\n while True:\n # Exit if we received a signal that we should stop.\n if threads_stopped.is_set():\n return\n\n msg = pubsub_client.get_message()\n if msg is None:\n num_consecutive_messages_received = 0\n threads_stopped.wait(timeout=0.01)\n continue\n num_consecutive_messages_received += 1\n if (num_consecutive_messages_received % 100 == 0\n and num_consecutive_messages_received > 0):\n logger.warning(\n \"The driver may not be able to keep up with the \"\n \"stdout/stderr of the workers. To avoid forwarding logs \"\n \"to the driver, use 'ray.init(log_to_driver=False)'.\")\n\n data = json.loads(ray.utils.decode(msg[\"data\"]))\n\n # Don't show logs from other drivers.\n if data[\"job\"] and ray.utils.binary_to_hex(\n job_id.binary()) != data[\"job\"]:\n continue\n\n def color_for(data):\n if data","label":0},{"cid":"python/ray/worker.py::print_error_messages_raylet","text":"python/ray/worker.py::print_error_messages_raylet\ndef print_error_messages_raylet(task_error_queue, threads_stopped):\n \"\"\"Prints message received in the given output queue.\n\n This checks periodically if any un-raised errors occured in the background.\n\n Args:\n task_error_queue (queue.Queue): A queue used to receive errors from the\n thread that listens to Redis.\n threads_stopped (threading.Event): A threading event used to signal to\n the thread that it should exit.\n \"\"\"\n\n while True:\n # Exit if we received a signal that we should stop.\n if threads_stopped.is_set():\n return\n\n try:\n error, t = task_error_queue.get(block=False)\n except queue.Empty:\n threads_stopped.wait(timeout=0.01)\n continue\n # Delay errors a little bit of time to attempt to suppress redundant\n # messages originating from the worker.\n while t + UNCAUGHT_ERROR_GRACE_PERIOD > time.time():\n threads_stopped.wait(timeout=1)\n if threads_stopped.is_set():\n break\n if t < last_task_error_raise_time + UNCAUGHT_ERROR_GRACE_PERIOD:\n logger.debug(\"Suppressing error from worker: {}\".format(error))\n else:\n logger.error(\n \"Possible unhandled error from worker: {}\".format(error))","label":0},{"cid":"python/ray/worker.py::listen_error_messages_raylet","text":"python/ray/worker.py::listen_error_messages_raylet\ndef listen_error_messages_raylet(worker, task_error_queue, threads_stopped):\n \"\"\"Listen to error messages in the background on the driver.\n\n This runs in a separate thread on the driver and pushes (error, time)\n tuples to the output queue.\n\n Args:\n worker: The worker class that this thread belongs to.\n task_error_queue (queue.Queue): A queue used to communicate with the\n thread that prints the errors found by this thread.\n threads_stopped (threading.Event): A threading event used to signal to\n the thread that it should exit.\n \"\"\"\n worker.error_message_pubsub_client = worker.redis_client.pubsub(\n ignore_subscribe_messages=True)\n # Exports that are published after the call to\n # error_message_pubsub_client.subscribe and before the call to\n # error_message_pubsub_client.listen will still be processed in the loop.\n\n # Really we should just subscribe to the errors for this specific job.\n # However, currently all errors seem to be published on the same channel.\n error_pubsub_channel = str(\n ray.gcs_utils.TablePubsub.Value(\"ERROR_INFO_PUBSUB\")).encode(\"ascii\")\n worker.error_message_pubsub_client.subscribe(error_pubsub_channel)\n # worker.error_message_pubsub_client.psubscribe(\"*\")\n\n try:\n # Get the errors that occurred before the call to subscribe.\n error_messages = ray.errors()\n for error_message in error_messages:\n logger.error(error_message)\n\n while True:\n # Exit if we received a signal that we should stop.\n if threads_stopped.is_set():\n return\n\n msg = worker.error_message_pubsub_client.get_message()\n if msg is None:\n threads_stopped.wait(timeout=0.01)\n continue\n gcs_entry = ray.gcs_utils.GcsEntry.FromString(msg[\"data\"])\n assert len(gcs_entry.entries) == 1\n error","label":0},{"cid":"python/ray/worker.py::is_initialized","text":"python/ray/worker.py::is_initialized\ndef is_initialized():\n \"\"\"Check if ray.init has been called yet.\n\n Returns:\n True if ray.init has already been called and false otherwise.\n \"\"\"\n return ray.worker.global_worker.connected","label":0},{"cid":"python/ray/worker.py::connect","text":"python/ray/worker.py::connect\ndef connect(node,\n mode=WORKER_MODE,\n log_to_driver=False,\n worker=global_worker,\n driver_object_store_memory=None,\n job_id=None):\n \"\"\"Connect this worker to the raylet, to Plasma, and to Redis.\n\n Args:\n node (ray.node.Node): The node to connect.\n mode: The mode of the worker. One of SCRIPT_MODE, WORKER_MODE, and\n LOCAL_MODE.\n log_to_driver (bool): If true, then output from all of the worker\n processes on all nodes will be directed to the driver.\n worker: The ray.Worker instance.\n driver_object_store_memory: Limit the amount of memory the driver can\n use in the object store when creating objects.\n job_id: The ID of job. If it's None, then we will generate one.\n \"\"\"\n # Do some basic checking to make sure we didn't call ray.init twice.\n error_message = \"Perhaps you called ray.init twice by accident?\"\n assert not worker.connected, error_message\n assert worker.cached_functions_to_run is not None, error_message\n\n # Enable nice stack traces on SIGSEGV etc.\n try:\n if not faulthandler.is_enabled():\n faulthandler.enable(all_threads=False)\n except io.UnsupportedOperation:\n pass # ignore\n\n # Create a Redis client to primary.\n # The Redis client can safely be shared between threads. However,\n # that is not true of Redis pubsub clients. See the documentation at\n # https://github.com/andymccurdy/redis-py#thread-safety.\n worker.redis_client = node.create_redis_client()\n\n # Initialize some fields.\n if mode is WORKER_MODE:\n # We should not specify the job_id if it's `WORKER_MODE`.\n assert job_id is None\n job_id = JobID.nil()\n # TODO(qwang): Rename this to `worker_id_str` or type to `WorkerID`\n worker.worker_id = _random_string()\n else:\n # This is the code path of driver mode.\n if job_id is None:\n ","label":0},{"cid":"python/ray/worker.py::disconnect","text":"python/ray/worker.py::disconnect\ndef disconnect(exiting_interpreter=False):\n \"\"\"Disconnect this worker from the raylet and object store.\"\"\"\n # Reset the list of cached remote functions and actors so that if more\n # remote functions or actors are defined and then connect is called again,\n # the remote functions will be exported. This is mostly relevant for the\n # tests.\n worker = global_worker\n if worker.connected:\n # Shutdown all of the threads that we've started. TODO(rkn): This\n # should be handled cleanly in the worker object's destructor and not\n # in this disconnect method.\n worker.threads_stopped.set()\n if hasattr(worker, \"import_thread\"):\n worker.import_thread.join_import_thread()\n if hasattr(worker, \"listener_thread\"):\n worker.listener_thread.join()\n if hasattr(worker, \"printer_thread\"):\n worker.printer_thread.join()\n if hasattr(worker, \"logger_thread\"):\n worker.logger_thread.join()\n worker.threads_stopped.clear()\n worker._session_index += 1\n\n worker.node = None # Disconnect the worker from the node.\n worker.cached_functions_to_run = []\n worker.serialization_context_map.clear()\n try:\n ray_actor = ray.actor\n except AttributeError:\n ray_actor = None # This can occur during program termination\n if ray_actor is not None:\n ray_actor.ActorClassMethodMetadata.reset_cache()","label":0},{"cid":"python/ray/worker.py::_changeproctitle","text":"python/ray/worker.py::_changeproctitle\n@contextmanager\ndef _changeproctitle(title, next_title):\n setproctitle.setproctitle(title)\n try:\n yield\n finally:\n setproctitle.setproctitle(next_title)","label":0},{"cid":"python/ray/worker.py::register_custom_serializer","text":"python/ray/worker.py::register_custom_serializer\ndef register_custom_serializer(cls,\n serializer,\n deserializer,\n use_pickle=False,\n use_dict=False,\n class_id=None):\n \"\"\"Registers custom functions for efficient object serialization.\n\n The serializer and deserializer are used when transferring objects of\n `cls` across processes and nodes. This can be significantly faster than\n the Ray default fallbacks. Wraps `register_custom_serializer` underneath.\n\n Args:\n cls (type): The class that ray should use this custom serializer for.\n serializer: The custom serializer that takes in a cls instance and\n outputs a serialized representation. use_pickle and use_dict\n must be False if provided.\n deserializer: The custom deserializer that takes in a serialized\n representation of the cls and outputs a cls instance. use_pickle\n and use_dict must be False if provided.\n use_pickle: Deprecated.\n use_dict: Deprecated.\n class_id (str): Unique ID of the class. Autogenerated if None.\n \"\"\"\n worker = global_worker\n worker.check_connected()\n\n if use_pickle:\n raise DeprecationWarning(\n \"`use_pickle` is no longer a valid parameter and will be removed \"\n \"in future versions of Ray. If this breaks your application, \"\n \"see `SerializationContext.register_custom_serializer`.\")\n if use_dict:\n raise DeprecationWarning(\n \"`use_pickle` is no longer a valid parameter and will be removed \"\n \"in future versions of Ray. If this breaks your application, \"\n \"see `SerializationContext.register_custom_serializer`.\")\n assert serializer is not None and deserializer is not None\n context = global_worker.get_serialization_context()\n context.register_custom_serializer","label":0}],"string":"[\n {\n \"cid\": \"doc/source/conf.py::feedback_form_url\",\n \"text\": \"doc/source/conf.py::feedback_form_url\\ndef feedback_form_url(project, page):\\n \\\"\\\"\\\"Create a URL for feedback on a particular page in a project.\\\"\\\"\\\"\\n return FEEDBACK_FORM_FMT.format(\\n title=urllib.parse.quote(\\n \\\"[docs] Issue on `{page}.rst`\\\".format(page=page)),\\n body=urllib.parse.quote(\\n \\\"# Documentation Problem/Question/Comment\\\\n\\\"\\n \\\"<!-- Describe your issue/question/comment below. -->\\\\n\\\"\\n \\\"<!-- If there are typos or errors in the docs, feel free to create a pull-request. -->\\\\n\\\"\\n \\\"\\\\n\\\\n\\\\n\\\\n\\\"\\n \\\"(Created directly from the docs)\\\\n\\\"))\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/source/conf.py::update_context\",\n \"text\": \"doc/source/conf.py::update_context\\ndef update_context(app, pagename, templatename, context, doctree):\\n \\\"\\\"\\\"Update the page rendering context to include ``feedback_form_url``.\\\"\\\"\\\"\\n context['feedback_form_url'] = feedback_form_url(app.config.project,\\n pagename)\",\n \"label\": 0\n },\n {\n \"cid\": \"doc/source/conf.py::setup\",\n \"text\": \"doc/source/conf.py::setup\\ndef setup(app):\\n app.connect('html-page-context', update_context)\\n app.add_stylesheet('css/custom.css')\\n # Custom directives\\n app.add_directive('customgalleryitem', CustomGalleryItemDirective)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.__init__\",\n \"text\": \"python/ray/node.py::Node.__init__\\n def __init__(self,\\n ray_params,\\n head=False,\\n shutdown_at_exit=True,\\n spawn_reaper=True,\\n connect_only=False):\\n \\\"\\\"\\\"Start a node.\\n\\n Args:\\n ray_params (ray.params.RayParams): The parameters to use to\\n configure the node.\\n head (bool): True if this is the head node, which means it will\\n start additional processes like the Redis servers, monitor\\n processes, and web UI.\\n shutdown_at_exit (bool): If true, spawned processes will be cleaned\\n up if this process exits normally.\\n spawn_reaper (bool): If true, spawns a process that will clean up\\n other spawned processes if this process dies unexpectedly.\\n connect_only (bool): If true, connect to the node without starting\\n new processes.\\n \\\"\\\"\\\"\\n if shutdown_at_exit:\\n if connect_only:\\n raise ValueError(\\\"'shutdown_at_exit' and 'connect_only' \\\"\\n \\\"cannot both be true.\\\")\\n self._register_shutdown_hooks()\\n\\n self.head = head\\n self.kernel_fate_share = bool(\\n spawn_reaper and ray.utils.detect_fate_sharing_support())\\n self.all_processes = {}\\n\\n # Try to get node IP address with the parameters.\\n if ray_params.node_ip_address:\\n node_ip_address = ray_params.node_ip_address\\n elif ray_params.redis_address:\\n node_ip_address = ray.services.get_node_ip_address(\\n ray_params.redis_address)\\n else:\\n node_ip_address = ray.services.get_node_ip_address()\\n self._node_ip_address = node_ip_address\\n\\n if ray_params.raylet_ip_address:\\n raylet_ip_address = ray_params.raylet_ip_address\\n else:\\n raylet_ip_address = node_ip_address\\n\\n if raylet_ip_address != no\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node._register_shutdown_hooks\",\n \"text\": \"python/ray/node.py::Node._register_shutdown_hooks\\n def _register_shutdown_hooks(self):\\n # Register the atexit handler. In this case, we shouldn't call sys.exit\\n # as we're already in the exit procedure.\\n def atexit_handler(*args):\\n self.kill_all_processes(check_alive=False, allow_graceful=True)\\n\\n atexit.register(atexit_handler)\\n\\n # Register the handler to be called if we get a SIGTERM.\\n # In this case, we want to exit with an error code (1) after\\n # cleaning up child processes.\\n def sigterm_handler(signum, frame):\\n self.kill_all_processes(check_alive=False, allow_graceful=True)\\n sys.exit(1)\\n\\n ray.utils.set_sigterm_handler(sigterm_handler)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node._register_shutdown_hooks.atexit_handler\",\n \"text\": \"python/ray/node.py::Node._register_shutdown_hooks.atexit_handler\\n def atexit_handler(*args):\\n self.kill_all_processes(check_alive=False, allow_graceful=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node._register_shutdown_hooks.sigterm_handler\",\n \"text\": \"python/ray/node.py::Node._register_shutdown_hooks.sigterm_handler\\n def sigterm_handler(signum, frame):\\n self.kill_all_processes(check_alive=False, allow_graceful=True)\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node._init_temp\",\n \"text\": \"python/ray/node.py::Node._init_temp\\n def _init_temp(self, redis_client):\\n # Create an dictionary to store temp file index.\\n self._incremental_dict = collections.defaultdict(lambda: 0)\\n\\n if self.head:\\n self._temp_dir = self._ray_params.temp_dir\\n else:\\n self._temp_dir = ray.utils.decode(redis_client.get(\\\"temp_dir\\\"))\\n\\n try_to_create_directory(self._temp_dir)\\n\\n if self.head:\\n self._session_dir = os.path.join(self._temp_dir, self.session_name)\\n else:\\n self._session_dir = ray.utils.decode(\\n redis_client.get(\\\"session_dir\\\"))\\n session_symlink = os.path.join(self._temp_dir, SESSION_LATEST)\\n\\n # Send a warning message if the session exists.\\n try_to_create_directory(self._session_dir)\\n try_to_symlink(session_symlink, self._session_dir)\\n # Create a directory to be used for socket files.\\n self._sockets_dir = os.path.join(self._session_dir, \\\"sockets\\\")\\n try_to_create_directory(self._sockets_dir)\\n # Create a directory to be used for process log files.\\n self._logs_dir = os.path.join(self._session_dir, \\\"logs\\\")\\n try_to_create_directory(self._logs_dir)\\n old_logs_dir = os.path.join(self._logs_dir, \\\"old\\\")\\n try_to_create_directory(old_logs_dir)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.get_resource_spec\",\n \"text\": \"python/ray/node.py::Node.get_resource_spec\\n def get_resource_spec(self):\\n \\\"\\\"\\\"Resolve and return the current resource spec for the node.\\\"\\\"\\\"\\n if not self._resource_spec:\\n self._resource_spec = ResourceSpec(\\n self._ray_params.num_cpus, self._ray_params.num_gpus,\\n self._ray_params.memory, self._ray_params.object_store_memory,\\n self._ray_params.resources,\\n self._ray_params.redis_max_memory).resolve(\\n is_head=self.head, node_ip_address=self.node_ip_address)\\n return self._resource_spec\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.node_ip_address\",\n \"text\": \"python/ray/node.py::Node.node_ip_address\\n @property\\n def node_ip_address(self):\\n \\\"\\\"\\\"Get the IP address of this node.\\\"\\\"\\\"\\n return self._node_ip_address\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.raylet_ip_address\",\n \"text\": \"python/ray/node.py::Node.raylet_ip_address\\n @property\\n def raylet_ip_address(self):\\n \\\"\\\"\\\"Get the IP address of the raylet that this node connects to.\\\"\\\"\\\"\\n return self._raylet_ip_address\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.address\",\n \"text\": \"python/ray/node.py::Node.address\\n @property\\n def address(self):\\n \\\"\\\"\\\"Get the cluster address.\\\"\\\"\\\"\\n return self._redis_address\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.redis_address\",\n \"text\": \"python/ray/node.py::Node.redis_address\\n @property\\n def redis_address(self):\\n \\\"\\\"\\\"Get the cluster Redis address.\\\"\\\"\\\"\\n return self._redis_address\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.redis_password\",\n \"text\": \"python/ray/node.py::Node.redis_password\\n @property\\n def redis_password(self):\\n \\\"\\\"\\\"Get the cluster Redis password\\\"\\\"\\\"\\n return self._ray_params.redis_password\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.load_code_from_local\",\n \"text\": \"python/ray/node.py::Node.load_code_from_local\\n @property\\n def load_code_from_local(self):\\n return self._ray_params.load_code_from_local\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.object_ref_seed\",\n \"text\": \"python/ray/node.py::Node.object_ref_seed\\n @property\\n def object_ref_seed(self):\\n \\\"\\\"\\\"Get the seed for deterministic generation of object refs\\\"\\\"\\\"\\n return self._ray_params.object_ref_seed\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.plasma_store_socket_name\",\n \"text\": \"python/ray/node.py::Node.plasma_store_socket_name\\n @property\\n def plasma_store_socket_name(self):\\n \\\"\\\"\\\"Get the node's plasma store socket name.\\\"\\\"\\\"\\n return self._plasma_store_socket_name\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.unique_id\",\n \"text\": \"python/ray/node.py::Node.unique_id\\n @property\\n def unique_id(self):\\n \\\"\\\"\\\"Get a unique identifier for this node.\\\"\\\"\\\"\\n return \\\"{}:{}\\\".format(self.node_ip_address,\\n self._plasma_store_socket_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.webui_url\",\n \"text\": \"python/ray/node.py::Node.webui_url\\n @property\\n def webui_url(self):\\n \\\"\\\"\\\"Get the cluster's web UI url.\\\"\\\"\\\"\\n return self._webui_url\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.raylet_socket_name\",\n \"text\": \"python/ray/node.py::Node.raylet_socket_name\\n @property\\n def raylet_socket_name(self):\\n \\\"\\\"\\\"Get the node's raylet socket name.\\\"\\\"\\\"\\n return self._raylet_socket_name\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.node_manager_port\",\n \"text\": \"python/ray/node.py::Node.node_manager_port\\n @property\\n def node_manager_port(self):\\n \\\"\\\"\\\"Get the node manager's port.\\\"\\\"\\\"\\n return self._ray_params.node_manager_port\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.socket\",\n \"text\": \"python/ray/node.py::Node.socket\\n @property\\n def socket(self):\\n \\\"\\\"\\\"Get the socket reserving the node manager's port\\\"\\\"\\\"\\n try:\\n return self._socket\\n except AttributeError:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.address_info\",\n \"text\": \"python/ray/node.py::Node.address_info\\n @property\\n def address_info(self):\\n \\\"\\\"\\\"Get a dictionary of addresses.\\\"\\\"\\\"\\n return {\\n \\\"node_ip_address\\\": self._node_ip_address,\\n \\\"raylet_ip_address\\\": self._raylet_ip_address,\\n \\\"redis_address\\\": self._redis_address,\\n \\\"object_store_address\\\": self._plasma_store_socket_name,\\n \\\"raylet_socket_name\\\": self._raylet_socket_name,\\n \\\"webui_url\\\": self._webui_url,\\n \\\"session_dir\\\": self._session_dir,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.create_redis_client\",\n \"text\": \"python/ray/node.py::Node.create_redis_client\\n def create_redis_client(self):\\n \\\"\\\"\\\"Create a redis client.\\\"\\\"\\\"\\n return ray.services.create_redis_client(\\n self._redis_address, self._ray_params.redis_password)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.get_temp_dir_path\",\n \"text\": \"python/ray/node.py::Node.get_temp_dir_path\\n def get_temp_dir_path(self):\\n \\\"\\\"\\\"Get the path of the temporary directory.\\\"\\\"\\\"\\n return self._temp_dir\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.get_session_dir_path\",\n \"text\": \"python/ray/node.py::Node.get_session_dir_path\\n def get_session_dir_path(self):\\n \\\"\\\"\\\"Get the path of the session directory.\\\"\\\"\\\"\\n return self._session_dir\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.get_logs_dir_path\",\n \"text\": \"python/ray/node.py::Node.get_logs_dir_path\\n def get_logs_dir_path(self):\\n \\\"\\\"\\\"Get the path of the log files directory.\\\"\\\"\\\"\\n return self._logs_dir\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.get_sockets_dir_path\",\n \"text\": \"python/ray/node.py::Node.get_sockets_dir_path\\n def get_sockets_dir_path(self):\\n \\\"\\\"\\\"Get the path of the sockets directory.\\\"\\\"\\\"\\n return self._sockets_dir\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node._make_inc_temp\",\n \"text\": \"python/ray/node.py::Node._make_inc_temp\\n def _make_inc_temp(self, suffix=\\\"\\\", prefix=\\\"\\\", directory_name=None):\\n \\\"\\\"\\\"Return a incremental temporary file name. The file is not created.\\n\\n Args:\\n suffix (str): The suffix of the temp file.\\n prefix (str): The prefix of the temp file.\\n directory_name (str) : The base directory of the temp file.\\n\\n Returns:\\n A string of file name. If there existing a file having\\n the same name, the returned name will look like\\n \\\"{directory_name}/{prefix}.{unique_index}{suffix}\\\"\\n \\\"\\\"\\\"\\n if directory_name is None:\\n directory_name = ray.utils.get_ray_temp_dir()\\n directory_name = os.path.expanduser(directory_name)\\n index = self._incremental_dict[suffix, prefix, directory_name]\\n # `tempfile.TMP_MAX` could be extremely large,\\n # so using `range` in Python2.x should be avoided.\\n while index < tempfile.TMP_MAX:\\n if index == 0:\\n filename = os.path.join(directory_name, prefix + suffix)\\n else:\\n filename = os.path.join(directory_name,\\n prefix + \\\".\\\" + str(index) + suffix)\\n index += 1\\n if not os.path.exists(filename):\\n # Save the index.\\n self._incremental_dict[suffix, prefix, directory_name] = index\\n return filename\\n\\n raise FileExistsError(errno.EEXIST,\\n \\\"No usable temporary filename found\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.get_log_file_names\",\n \"text\": \"python/ray/node.py::Node.get_log_file_names\\n def get_log_file_names(self, name, unique=False):\\n \\\"\\\"\\\"Generate partially randomized filenames for log files.\\n\\n Args:\\n name (str): descriptive string for this log file.\\n unique (bool): if true, a counter will be attached to `name` to\\n ensure the returned filename is not already used.\\n\\n Returns:\\n A tuple of two file names for redirecting (stdout, stderr).\\n \\\"\\\"\\\"\\n redirect_output = self._ray_params.redirect_output\\n\\n if redirect_output is None:\\n # Make the default behavior match that of glog.\\n redirect_output = os.getenv(\\\"GLOG_logtostderr\\\") != \\\"1\\\"\\n\\n if not redirect_output:\\n return None, None\\n\\n if unique:\\n log_stdout = self._make_inc_temp(\\n suffix=\\\".out\\\", prefix=name, directory_name=self._logs_dir)\\n log_stderr = self._make_inc_temp(\\n suffix=\\\".err\\\", prefix=name, directory_name=self._logs_dir)\\n else:\\n log_stdout = os.path.join(self._logs_dir, \\\"{}.out\\\".format(name))\\n log_stderr = os.path.join(self._logs_dir, \\\"{}.err\\\".format(name))\\n return log_stdout, log_stderr\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/node.py::Node._get_unused_port\",\n \"text\": \"python/ray/node.py::Node._get_unused_port\\n def _get_unused_port(self, close_on_exit=True):\\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n s.bind((\\\"\\\", 0))\\n port = s.getsockname()[1]\\n\\n # Try to generate a port that is far above the 'next available' one.\\n # This solves issue #8254 where GRPC fails because the port assigned\\n # from this method has been used by a different process.\\n for _ in range(NUMBER_OF_PORT_RETRIES):\\n new_port = random.randint(port, 65535)\\n new_s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\\n try:\\n new_s.bind((\\\"\\\", new_port))\\n except OSError:\\n new_s.close()\\n continue\\n s.close()\\n if close_on_exit:\\n new_s.close()\\n return new_port, new_s\\n logger.error(\\\"Unable to succeed in selecting a random port.\\\")\\n if close_on_exit:\\n s.close()\\n return port, s\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node._prepare_socket_file\",\n \"text\": \"python/ray/node.py::Node._prepare_socket_file\\n def _prepare_socket_file(self, socket_path, default_prefix):\\n \\\"\\\"\\\"Prepare the socket file for raylet and plasma.\\n\\n This method helps to prepare a socket file.\\n 1. Make the directory if the directory does not exist.\\n 2. If the socket file exists, raise exception.\\n\\n Args:\\n socket_path (string): the socket file to prepare.\\n \\\"\\\"\\\"\\n result = socket_path\\n is_mac = sys.platform.startswith(\\\"darwin\\\")\\n if sys.platform == \\\"win32\\\":\\n if socket_path is None:\\n result = \\\"tcp://{}:{}\\\".format(self._localhost,\\n self._get_unused_port()[0])\\n else:\\n if socket_path is None:\\n result = self._make_inc_temp(\\n prefix=default_prefix, directory_name=self._sockets_dir)\\n else:\\n if os.path.exists(socket_path):\\n raise RuntimeError(\\n \\\"Socket file {} exists!\\\".format(socket_path))\\n try_to_create_directory(os.path.dirname(socket_path))\\n\\n # Check socket path length to make sure it's short enough\\n maxlen = (104 if is_mac else 108) - 1 # sockaddr_un->sun_path\\n if len(result.split(\\\"://\\\", 1)[-1].encode(\\\"utf-8\\\")) > maxlen:\\n raise OSError(\\\"AF_UNIX path length cannot exceed \\\"\\n \\\"{} bytes: {!r}\\\".format(maxlen, result))\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.start_reaper_process\",\n \"text\": \"python/ray/node.py::Node.start_reaper_process\\n def start_reaper_process(self):\\n \\\"\\\"\\\"\\n Start the reaper process.\\n\\n This must be the first process spawned and should only be called when\\n ray processes should be cleaned up if this process dies.\\n \\\"\\\"\\\"\\n assert not self.kernel_fate_share, (\\n \\\"a reaper should not be used with kernel fate-sharing\\\")\\n process_info = ray.services.start_reaper(fate_share=False)\\n assert ray_constants.PROCESS_TYPE_REAPER not in self.all_processes\\n if process_info is not None:\\n self.all_processes[ray_constants.PROCESS_TYPE_REAPER] = [\\n process_info,\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.start_redis\",\n \"text\": \"python/ray/node.py::Node.start_redis\\n def start_redis(self):\\n \\\"\\\"\\\"Start the Redis servers.\\\"\\\"\\\"\\n assert self._redis_address is None\\n redis_out_name, redis_err_name = self.get_log_file_names(\\n \\\"redis\\\", unique=True)\\n redis_log_files = [(open_log(redis_out_name),\\n open_log(redis_err_name))]\\n for i in range(self._ray_params.num_redis_shards):\\n shard_out_name, shard_err_name = self.get_log_file_names(\\n \\\"redis-shard_{}\\\".format(i), unique=True)\\n redis_log_files.append((open_log(shard_out_name),\\n open_log(shard_err_name)))\\n\\n (self._redis_address, redis_shards,\\n process_infos) = ray.services.start_redis(\\n self._node_ip_address,\\n redis_log_files,\\n self.get_resource_spec(),\\n port=self._ray_params.redis_port,\\n redis_shard_ports=self._ray_params.redis_shard_ports,\\n num_redis_shards=self._ray_params.num_redis_shards,\\n redis_max_clients=self._ray_params.redis_max_clients,\\n redirect_worker_output=True,\\n password=self._ray_params.redis_password,\\n include_java=self._ray_params.include_java,\\n fate_share=self.kernel_fate_share)\\n assert (\\n ray_constants.PROCESS_TYPE_REDIS_SERVER not in self.all_processes)\\n self.all_processes[ray_constants.PROCESS_TYPE_REDIS_SERVER] = (\\n process_infos)\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/node.py::Node.start_log_monitor\",\n \"text\": \"python/ray/node.py::Node.start_log_monitor\\n def start_log_monitor(self):\\n \\\"\\\"\\\"Start the log monitor.\\\"\\\"\\\"\\n log_out_name, log_err_name = self.get_log_file_names(\\n \\\"log_monitor\\\", unique=True)\\n stdout_file, stderr_file = open_log(log_out_name), open_log(\\n log_err_name)\\n process_info = ray.services.start_log_monitor(\\n self.redis_address,\\n self._logs_dir,\\n stdout_file=stdout_file,\\n stderr_file=stderr_file,\\n redis_password=self._ray_params.redis_password,\\n fate_share=self.kernel_fate_share)\\n assert ray_constants.PROCESS_TYPE_LOG_MONITOR not in self.all_processes\\n self.all_processes[ray_constants.PROCESS_TYPE_LOG_MONITOR] = [\\n process_info,\\n ]\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/node.py::Node.start_reporter\",\n \"text\": \"python/ray/node.py::Node.start_reporter\\n def start_reporter(self):\\n \\\"\\\"\\\"Start the reporter.\\\"\\\"\\\"\\n reporter_out_name, reporter_err_name = self.get_log_file_names(\\n \\\"reporter\\\", unique=True)\\n stdout_file, stderr_file = (open_log(reporter_out_name),\\n open_log(reporter_err_name))\\n process_info = ray.services.start_reporter(\\n self.redis_address,\\n self._ray_params.metrics_agent_port,\\n stdout_file=stdout_file,\\n stderr_file=stderr_file,\\n redis_password=self._ray_params.redis_password,\\n fate_share=self.kernel_fate_share)\\n assert ray_constants.PROCESS_TYPE_REPORTER not in self.all_processes\\n if process_info is not None:\\n self.all_processes[ray_constants.PROCESS_TYPE_REPORTER] = [\\n process_info,\\n ]\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/node.py::Node.start_dashboard\",\n \"text\": \"python/ray/node.py::Node.start_dashboard\\n def start_dashboard(self, require_dashboard):\\n \\\"\\\"\\\"Start the dashboard.\\n\\n Args:\\n require_dashboard (bool): If true, this will raise an exception\\n if we fail to start the dashboard. Otherwise it will print\\n a warning if we fail to start the dashboard.\\n \\\"\\\"\\\"\\n dashboard_out_name, dashboard_err_name = self.get_log_file_names(\\n \\\"dashboard\\\", unique=True)\\n stdout_file, stderr_file = (open_log(dashboard_out_name),\\n open_log(dashboard_err_name))\\n self._webui_url, process_info = ray.services.start_dashboard(\\n require_dashboard,\\n self._ray_params.dashboard_host,\\n self.redis_address,\\n self._temp_dir,\\n stdout_file=stdout_file,\\n stderr_file=stderr_file,\\n redis_password=self._ray_params.redis_password,\\n fate_share=self.kernel_fate_share,\\n port=self._ray_params.dashboard_port)\\n assert ray_constants.PROCESS_TYPE_DASHBOARD not in self.all_processes\\n if process_info is not None:\\n self.all_processes[ray_constants.PROCESS_TYPE_DASHBOARD] = [\\n process_info,\\n ]\\n redis_client = self.create_redis_client()\\n redis_client.hmset(\\\"webui\\\", {\\\"url\\\": self._webui_url})\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/node.py::Node.start_plasma_store\",\n \"text\": \"python/ray/node.py::Node.start_plasma_store\\n def start_plasma_store(self):\\n \\\"\\\"\\\"Start the plasma store.\\\"\\\"\\\"\\n plasma_out_name, plasma_err_name = self.get_log_file_names(\\n \\\"plasma_store\\\", unique=True)\\n stdout_file, stderr_file = (open_log(plasma_out_name),\\n open_log(plasma_err_name))\\n process_info = ray.services.start_plasma_store(\\n self.get_resource_spec(),\\n self._plasma_store_socket_name,\\n stdout_file=stdout_file,\\n stderr_file=stderr_file,\\n plasma_directory=self._ray_params.plasma_directory,\\n huge_pages=self._ray_params.huge_pages,\\n keep_idle=bool(self._config.get(\\\"plasma_store_as_thread\\\")),\\n fate_share=self.kernel_fate_share)\\n assert (\\n ray_constants.PROCESS_TYPE_PLASMA_STORE not in self.all_processes)\\n self.all_processes[ray_constants.PROCESS_TYPE_PLASMA_STORE] = [\\n process_info,\\n ]\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/node.py::Node.start_gcs_server\",\n \"text\": \"python/ray/node.py::Node.start_gcs_server\\n def start_gcs_server(self):\\n \\\"\\\"\\\"Start the gcs server.\\n \\\"\\\"\\\"\\n gcs_out_name, gcs_err_name = self.get_log_file_names(\\n \\\"gcs_server\\\", unique=True)\\n stdout_file, stderr_file = (open_log(gcs_out_name),\\n open_log(gcs_err_name))\\n process_info = ray.services.start_gcs_server(\\n self._redis_address,\\n stdout_file=stdout_file,\\n stderr_file=stderr_file,\\n redis_password=self._ray_params.redis_password,\\n config=self._config,\\n fate_share=self.kernel_fate_share,\\n gcs_server_port=self._ray_params.gcs_server_port)\\n assert (\\n ray_constants.PROCESS_TYPE_GCS_SERVER not in self.all_processes)\\n self.all_processes[ray_constants.PROCESS_TYPE_GCS_SERVER] = [\\n process_info,\\n ]\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/node.py::Node.start_raylet\",\n \"text\": \"python/ray/node.py::Node.start_raylet\\n def start_raylet(self, use_valgrind=False, use_profiler=False):\\n \\\"\\\"\\\"Start the raylet.\\n\\n Args:\\n use_valgrind (bool): True if we should start the process in\\n valgrind.\\n use_profiler (bool): True if we should start the process in the\\n valgrind profiler.\\n \\\"\\\"\\\"\\n raylet_out_name, raylet_err_name = self.get_log_file_names(\\n \\\"raylet\\\", unique=True)\\n stdout_file, stderr_file = (open_log(raylet_out_name),\\n open_log(raylet_err_name))\\n process_info = ray.services.start_raylet(\\n self._redis_address,\\n self._node_ip_address,\\n self._ray_params.node_manager_port,\\n self._raylet_socket_name,\\n self._plasma_store_socket_name,\\n self._ray_params.worker_path,\\n self._temp_dir,\\n self._session_dir,\\n self.get_resource_spec(),\\n self._ray_params.min_worker_port,\\n self._ray_params.max_worker_port,\\n self._ray_params.object_manager_port,\\n self._ray_params.redis_password,\\n self._ray_params.metrics_agent_port,\\n use_valgrind=use_valgrind,\\n use_profiler=use_profiler,\\n stdout_file=stdout_file,\\n stderr_file=stderr_file,\\n config=self._config,\\n include_java=self._ray_params.include_java,\\n java_worker_options=self._ray_params.java_worker_options,\\n load_code_from_local=self._ray_params.load_code_from_local,\\n plasma_directory=self._ray_params.plasma_directory,\\n huge_pages=self._ray_params.huge_pages,\\n fate_share=self.kernel_fate_share,\\n socket_to_use=self.socket,\\n head_node=self.head)\\n assert ray_constants.PROCESS_TYPE_RAYLET not in self.all_processes\\n self.all_processes[ray_constants.PROCESS_TYPE_RAYLET] = [process_info]\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/node.py::Node.get_job_redirected_log_file\",\n \"text\": \"python/ray/node.py::Node.get_job_redirected_log_file\\n def get_job_redirected_log_file(self,\\n worker_id: bytes,\\n job_id: bytes = None):\\n \\\"\\\"\\\"Determines (but does not create) logging files for workers to\\n redirect its output.\\n\\n Args:\\n worker_id (bytes): A byte representation of the worker id.\\n job_id (bytes): A byte representation of the job id. If None,\\n provides a generic log file for the worker.\\n\\n Returns:\\n (tuple) The stdout and stderr file names that the job should be\\n redirected to.\\n \\\"\\\"\\\"\\n redirect_output = self._ray_params.redirect_output\\n\\n if redirect_output is None:\\n # Make the default behavior match that of glog.\\n redirect_output = os.getenv(\\\"GLOG_logtostderr\\\") != \\\"1\\\"\\n\\n if not redirect_output:\\n return None, None\\n\\n if job_id is not None:\\n name = \\\"worker-{}-{}\\\".format(\\n ray.utils.binary_to_hex(worker_id),\\n ray.utils.binary_to_hex(job_id))\\n else:\\n name = \\\"worker-{}\\\".format(ray.utils.binary_to_hex(worker_id))\\n\\n worker_stdout_file, worker_stderr_file = self.get_log_file_names(\\n name, unique=False)\\n return worker_stdout_file, worker_stderr_file\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/node.py::Node.start_worker\",\n \"text\": \"python/ray/node.py::Node.start_worker\\n def start_worker(self):\\n \\\"\\\"\\\"Start a worker process.\\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.start_monitor\",\n \"text\": \"python/ray/node.py::Node.start_monitor\\n def start_monitor(self):\\n \\\"\\\"\\\"Start the monitor.\\\"\\\"\\\"\\n monitor_out_name, monitor_err_name = self.get_log_file_names(\\n \\\"monitor\\\", unique=True)\\n stdout_file, stderr_file = (open_log(monitor_out_name),\\n open_log(monitor_err_name))\\n process_info = ray.services.start_monitor(\\n self._redis_address,\\n stdout_file=stdout_file,\\n stderr_file=stderr_file,\\n autoscaling_config=self._ray_params.autoscaling_config,\\n redis_password=self._ray_params.redis_password,\\n fate_share=self.kernel_fate_share)\\n assert ray_constants.PROCESS_TYPE_MONITOR not in self.all_processes\\n self.all_processes[ray_constants.PROCESS_TYPE_MONITOR] = [process_info]\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/node.py::Node.start_head_processes\",\n \"text\": \"python/ray/node.py::Node.start_head_processes\\n def start_head_processes(self):\\n \\\"\\\"\\\"Start head processes on the node.\\\"\\\"\\\"\\n logger.debug(\\n \\\"Process STDOUT and STDERR is being redirected to {}.\\\".format(\\n self._logs_dir))\\n assert self._redis_address is None\\n # If this is the head node, start the relevant head node processes.\\n self.start_redis()\\n\\n self.start_gcs_server()\\n\\n self.start_monitor()\\n\\n if self._ray_params.include_dashboard:\\n self.start_dashboard(require_dashboard=True)\\n elif self._ray_params.include_dashboard is None:\\n self.start_dashboard(require_dashboard=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.start_ray_processes\",\n \"text\": \"python/ray/node.py::Node.start_ray_processes\\n def start_ray_processes(self):\\n \\\"\\\"\\\"Start all of the processes on the node.\\\"\\\"\\\"\\n logger.debug(\\n \\\"Process STDOUT and STDERR is being redirected to {}.\\\".format(\\n self._logs_dir))\\n\\n self.start_plasma_store()\\n self.start_raylet()\\n self.start_reporter()\\n\\n if self._ray_params.include_log_monitor:\\n self.start_log_monitor()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node._kill_process_type\",\n \"text\": \"python/ray/node.py::Node._kill_process_type\\n def _kill_process_type(self,\\n process_type,\\n allow_graceful=False,\\n check_alive=True,\\n wait=False):\\n \\\"\\\"\\\"Kill a process of a given type.\\n\\n If the process type is PROCESS_TYPE_REDIS_SERVER, then we will kill all\\n of the Redis servers.\\n\\n If the process was started in valgrind, then we will raise an exception\\n if the process has a non-zero exit code.\\n\\n Args:\\n process_type: The type of the process to kill.\\n allow_graceful (bool): Send a SIGTERM first and give the process\\n time to exit gracefully. If that doesn't work, then use\\n SIGKILL. We usually want to do this outside of tests.\\n check_alive (bool): If true, then we expect the process to be alive\\n and will raise an exception if the process is already dead.\\n wait (bool): If true, then this method will not return until the\\n process in question has exited.\\n\\n Raises:\\n This process raises an exception in the following cases:\\n 1. The process had already died and check_alive is true.\\n 2. The process had been started in valgrind and had a non-zero\\n exit code.\\n \\\"\\\"\\\"\\n process_infos = self.all_processes[process_type]\\n if process_type != ray_constants.PROCESS_TYPE_REDIS_SERVER:\\n assert len(process_infos) == 1\\n for process_info in process_infos:\\n process = process_info.process\\n # Handle the case where the process has already exited.\\n if process.poll() is not None:\\n if check_alive:\\n raise RuntimeError(\\n \\\"Attempting to kill a process of type \\\"\\n \\\"'{}', but this process is already dead.\\\"\\n .format(process_typ\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.kill_redis\",\n \"text\": \"python/ray/node.py::Node.kill_redis\\n def kill_redis(self, check_alive=True):\\n \\\"\\\"\\\"Kill the Redis servers.\\n\\n Args:\\n check_alive (bool): Raise an exception if any of the processes\\n were already dead.\\n \\\"\\\"\\\"\\n self._kill_process_type(\\n ray_constants.PROCESS_TYPE_REDIS_SERVER, check_alive=check_alive)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.kill_plasma_store\",\n \"text\": \"python/ray/node.py::Node.kill_plasma_store\\n def kill_plasma_store(self, check_alive=True):\\n \\\"\\\"\\\"Kill the plasma store.\\n\\n Args:\\n check_alive (bool): Raise an exception if the process was already\\n dead.\\n \\\"\\\"\\\"\\n self._kill_process_type(\\n ray_constants.PROCESS_TYPE_PLASMA_STORE, check_alive=check_alive)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.kill_raylet\",\n \"text\": \"python/ray/node.py::Node.kill_raylet\\n def kill_raylet(self, check_alive=True):\\n \\\"\\\"\\\"Kill the raylet.\\n\\n Args:\\n check_alive (bool): Raise an exception if the process was already\\n dead.\\n \\\"\\\"\\\"\\n self._kill_process_type(\\n ray_constants.PROCESS_TYPE_RAYLET, check_alive=check_alive)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.kill_log_monitor\",\n \"text\": \"python/ray/node.py::Node.kill_log_monitor\\n def kill_log_monitor(self, check_alive=True):\\n \\\"\\\"\\\"Kill the log monitor.\\n\\n Args:\\n check_alive (bool): Raise an exception if the process was already\\n dead.\\n \\\"\\\"\\\"\\n self._kill_process_type(\\n ray_constants.PROCESS_TYPE_LOG_MONITOR, check_alive=check_alive)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.kill_reporter\",\n \"text\": \"python/ray/node.py::Node.kill_reporter\\n def kill_reporter(self, check_alive=True):\\n \\\"\\\"\\\"Kill the reporter.\\n\\n Args:\\n check_alive (bool): Raise an exception if the process was already\\n dead.\\n \\\"\\\"\\\"\\n self._kill_process_type(\\n ray_constants.PROCESS_TYPE_REPORTER, check_alive=check_alive)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.kill_dashboard\",\n \"text\": \"python/ray/node.py::Node.kill_dashboard\\n def kill_dashboard(self, check_alive=True):\\n \\\"\\\"\\\"Kill the dashboard.\\n\\n Args:\\n check_alive (bool): Raise an exception if the process was already\\n dead.\\n \\\"\\\"\\\"\\n self._kill_process_type(\\n ray_constants.PROCESS_TYPE_DASHBOARD, check_alive=check_alive)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.kill_monitor\",\n \"text\": \"python/ray/node.py::Node.kill_monitor\\n def kill_monitor(self, check_alive=True):\\n \\\"\\\"\\\"Kill the monitor.\\n\\n Args:\\n check_alive (bool): Raise an exception if the process was already\\n dead.\\n \\\"\\\"\\\"\\n self._kill_process_type(\\n ray_constants.PROCESS_TYPE_MONITOR, check_alive=check_alive)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.kill_gcs_server\",\n \"text\": \"python/ray/node.py::Node.kill_gcs_server\\n def kill_gcs_server(self, check_alive=True):\\n \\\"\\\"\\\"Kill the gcs server.\\n Args:\\n check_alive (bool): Raise an exception if the process was already\\n dead.\\n \\\"\\\"\\\"\\n self._kill_process_type(\\n ray_constants.PROCESS_TYPE_GCS_SERVER, check_alive=check_alive)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.kill_reaper\",\n \"text\": \"python/ray/node.py::Node.kill_reaper\\n def kill_reaper(self, check_alive=True):\\n \\\"\\\"\\\"Kill the reaper process.\\n\\n Args:\\n check_alive (bool): Raise an exception if the process was already\\n dead.\\n \\\"\\\"\\\"\\n self._kill_process_type(\\n ray_constants.PROCESS_TYPE_REAPER, check_alive=check_alive)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.kill_all_processes\",\n \"text\": \"python/ray/node.py::Node.kill_all_processes\\n def kill_all_processes(self, check_alive=True, allow_graceful=False):\\n \\\"\\\"\\\"Kill all of the processes.\\n\\n Note that This is slower than necessary because it calls kill, wait,\\n kill, wait, ... instead of kill, kill, ..., wait, wait, ...\\n\\n Args:\\n check_alive (bool): Raise an exception if any of the processes were\\n already dead.\\n \\\"\\\"\\\"\\n # Kill the raylet first. This is important for suppressing errors at\\n # shutdown because we give the raylet a chance to exit gracefully and\\n # clean up its child worker processes. If we were to kill the plasma\\n # store (or Redis) first, that could cause the raylet to exit\\n # ungracefully, leading to more verbose output from the workers.\\n if ray_constants.PROCESS_TYPE_RAYLET in self.all_processes:\\n self._kill_process_type(\\n ray_constants.PROCESS_TYPE_RAYLET,\\n check_alive=check_alive,\\n allow_graceful=allow_graceful)\\n\\n if ray_constants.PROCESS_TYPE_GCS_SERVER in self.all_processes:\\n self._kill_process_type(\\n ray_constants.PROCESS_TYPE_GCS_SERVER,\\n check_alive=check_alive,\\n allow_graceful=allow_graceful)\\n\\n # We call \\\"list\\\" to copy the keys because we are modifying the\\n # dictionary while iterating over it.\\n for process_type in list(self.all_processes.keys()):\\n # Need to kill the reaper process last in case we die unexpectedly\\n # while cleaning up.\\n if process_type != ray_constants.PROCESS_TYPE_REAPER:\\n self._kill_process_type(\\n process_type,\\n check_alive=check_alive,\\n allow_graceful=allow_graceful)\\n\\n if ray_constants.PROCESS_TYPE_REAPER in self.all_processes:\\n self._kill_process_type(\\n ray_constants.PROCESS_TYPE_REAPER,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.live_processes\",\n \"text\": \"python/ray/node.py::Node.live_processes\\n def live_processes(self):\\n \\\"\\\"\\\"Return a list of the live processes.\\n\\n Returns:\\n A list of the live processes.\\n \\\"\\\"\\\"\\n result = []\\n for process_type, process_infos in self.all_processes.items():\\n for process_info in process_infos:\\n if process_info.process.poll() is None:\\n result.append((process_type, process_info.process))\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.dead_processes\",\n \"text\": \"python/ray/node.py::Node.dead_processes\\n def dead_processes(self):\\n \\\"\\\"\\\"Return a list of the dead processes.\\n\\n Note that this ignores processes that have been explicitly killed,\\n e.g., via a command like node.kill_raylet().\\n\\n Returns:\\n A list of the dead processes ignoring the ones that have been\\n explicitly killed.\\n \\\"\\\"\\\"\\n result = []\\n for process_type, process_infos in self.all_processes.items():\\n for process_info in process_infos:\\n if process_info.process.poll() is not None:\\n result.append((process_type, process_info.process))\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.any_processes_alive\",\n \"text\": \"python/ray/node.py::Node.any_processes_alive\\n def any_processes_alive(self):\\n \\\"\\\"\\\"Return true if any processes are still alive.\\n\\n Returns:\\n True if any process is still alive.\\n \\\"\\\"\\\"\\n return any(self.live_processes())\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/node.py::Node.remaining_processes_alive\",\n \"text\": \"python/ray/node.py::Node.remaining_processes_alive\\n def remaining_processes_alive(self):\\n \\\"\\\"\\\"Return true if all remaining processes are still alive.\\n\\n Note that this ignores processes that have been explicitly killed,\\n e.g., via a command like node.kill_raylet().\\n\\n Returns:\\n True if any process that wasn't explicitly killed is still alive.\\n \\\"\\\"\\\"\\n return not any(self.dead_processes())\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::get_user_temp_dir\",\n \"text\": \"python/ray/utils.py::get_user_temp_dir\\ndef get_user_temp_dir():\\n if sys.platform.startswith(\\\"darwin\\\") or sys.platform.startswith(\\\"linux\\\"):\\n # Ideally we wouldn't need this fallback, but keep it for now for\\n # for compatibility\\n tempdir = os.path.join(os.sep, \\\"tmp\\\")\\n else:\\n tempdir = tempfile.gettempdir()\\n return tempdir\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::get_ray_temp_dir\",\n \"text\": \"python/ray/utils.py::get_ray_temp_dir\\ndef get_ray_temp_dir():\\n return os.path.join(get_user_temp_dir(), \\\"ray\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::_random_string\",\n \"text\": \"python/ray/utils.py::_random_string\\ndef _random_string():\\n id_hash = hashlib.sha1()\\n id_hash.update(uuid.uuid4().bytes)\\n id_bytes = id_hash.digest()\\n assert len(id_bytes) == ray_constants.ID_SIZE\\n return id_bytes\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::format_error_message\",\n \"text\": \"python/ray/utils.py::format_error_message\\ndef format_error_message(exception_message, task_exception=False):\\n \\\"\\\"\\\"Improve the formatting of an exception thrown by a remote function.\\n\\n This method takes a traceback from an exception and makes it nicer by\\n removing a few uninformative lines and adding some space to indent the\\n remaining lines nicely.\\n\\n Args:\\n exception_message (str): A message generated by traceback.format_exc().\\n\\n Returns:\\n A string of the formatted exception message.\\n \\\"\\\"\\\"\\n lines = exception_message.split(\\\"\\\\n\\\")\\n if task_exception:\\n # For errors that occur inside of tasks, remove lines 1 and 2 which are\\n # always the same, they just contain information about the worker code.\\n lines = lines[0:1] + lines[3:]\\n pass\\n return \\\"\\\\n\\\".join(lines)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::push_error_to_driver\",\n \"text\": \"python/ray/utils.py::push_error_to_driver\\ndef push_error_to_driver(worker, error_type, message, job_id=None):\\n \\\"\\\"\\\"Push an error message to the driver to be printed in the background.\\n\\n Args:\\n worker: The worker to use.\\n error_type (str): The type of the error.\\n message (str): The message that will be printed in the background\\n on the driver.\\n job_id: The ID of the driver to push the error message to. If this\\n is None, then the message will be pushed to all drivers.\\n \\\"\\\"\\\"\\n if job_id is None:\\n job_id = ray.JobID.nil()\\n assert isinstance(job_id, ray.JobID)\\n worker.core_worker.push_error(job_id, error_type, message, time.time())\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::push_error_to_driver_through_redis\",\n \"text\": \"python/ray/utils.py::push_error_to_driver_through_redis\\ndef push_error_to_driver_through_redis(redis_client,\\n error_type,\\n message,\\n job_id=None):\\n \\\"\\\"\\\"Push an error message to the driver to be printed in the background.\\n\\n Normally the push_error_to_driver function should be used. However, in some\\n instances, the raylet client is not available, e.g., because the\\n error happens in Python before the driver or worker has connected to the\\n backend processes.\\n\\n Args:\\n redis_client: The redis client to use.\\n error_type (str): The type of the error.\\n message (str): The message that will be printed in the background\\n on the driver.\\n job_id: The ID of the driver to push the error message to. If this\\n is None, then the message will be pushed to all drivers.\\n \\\"\\\"\\\"\\n if job_id is None:\\n job_id = ray.JobID.nil()\\n assert isinstance(job_id, ray.JobID)\\n # Do everything in Python and through the Python Redis client instead\\n # of through the raylet.\\n error_data = ray.gcs_utils.construct_error_message(job_id, error_type,\\n message, time.time())\\n redis_client.execute_command(\\n \\\"RAY.TABLE_APPEND\\\", ray.gcs_utils.TablePrefix.Value(\\\"ERROR_INFO\\\"),\\n ray.gcs_utils.TablePubsub.Value(\\\"ERROR_INFO_PUBSUB\\\"), job_id.binary(),\\n error_data)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::is_cython\",\n \"text\": \"python/ray/utils.py::is_cython\\ndef is_cython(obj):\\n \\\"\\\"\\\"Check if an object is a Cython function or method\\\"\\\"\\\"\\n\\n # TODO(suo): We could split these into two functions, one for Cython\\n # functions and another for Cython methods.\\n # TODO(suo): There doesn't appear to be a Cython function 'type' we can\\n # check against via isinstance. Please correct me if I'm wrong.\\n def check_cython(x):\\n return type(x).__name__ == \\\"cython_function_or_method\\\"\\n\\n # Check if function or method, respectively\\n return check_cython(obj) or \\\\\\n (hasattr(obj, \\\"__func__\\\") and check_cython(obj.__func__))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::is_cython.check_cython\",\n \"text\": \"python/ray/utils.py::is_cython.check_cython\\n def check_cython(x):\\n return type(x).__name__ == \\\"cython_function_or_method\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::is_function_or_method\",\n \"text\": \"python/ray/utils.py::is_function_or_method\\ndef is_function_or_method(obj):\\n \\\"\\\"\\\"Check if an object is a function or method.\\n\\n Args:\\n obj: The Python object in question.\\n\\n Returns:\\n True if the object is an function or method.\\n \\\"\\\"\\\"\\n return inspect.isfunction(obj) or inspect.ismethod(obj) or is_cython(obj)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::is_class_method\",\n \"text\": \"python/ray/utils.py::is_class_method\\ndef is_class_method(f):\\n \\\"\\\"\\\"Returns whether the given method is a class_method.\\\"\\\"\\\"\\n return hasattr(f, \\\"__self__\\\") and f.__self__ is not None\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::is_static_method\",\n \"text\": \"python/ray/utils.py::is_static_method\\ndef is_static_method(cls, f_name):\\n \\\"\\\"\\\"Returns whether the class has a static method with the given name.\\n\\n Args:\\n cls: The Python class (i.e. object of type `type`) to\\n search for the method in.\\n f_name: The name of the method to look up in this class\\n and check whether or not it is static.\\n \\\"\\\"\\\"\\n for cls in inspect.getmro(cls):\\n if f_name in cls.__dict__:\\n return isinstance(cls.__dict__[f_name], staticmethod)\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::random_string\",\n \"text\": \"python/ray/utils.py::random_string\\ndef random_string():\\n \\\"\\\"\\\"Generate a random string to use as an ID.\\n\\n Note that users may seed numpy, which could cause this function to generate\\n duplicate IDs. Therefore, we need to seed numpy ourselves, but we can't\\n interfere with the state of the user's random number generator, so we\\n extract the state of the random number generator and reset it after we are\\n done.\\n\\n TODO(rkn): If we want to later guarantee that these are generated in a\\n deterministic manner, then we will need to make some changes here.\\n\\n Returns:\\n A random byte string of length ray_constants.ID_SIZE.\\n \\\"\\\"\\\"\\n # Get the state of the numpy random number generator.\\n numpy_state = np.random.get_state()\\n # Try to use true randomness.\\n np.random.seed(None)\\n # Generate the random ID.\\n random_id = np.random.bytes(ray_constants.ID_SIZE)\\n # Reset the state of the numpy random number generator.\\n np.random.set_state(numpy_state)\\n return random_id\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::decode\",\n \"text\": \"python/ray/utils.py::decode\\ndef decode(byte_str, allow_none=False):\\n \\\"\\\"\\\"Make this unicode in Python 3, otherwise leave it as bytes.\\n\\n Args:\\n byte_str: The byte string to decode.\\n allow_none: If true, then we will allow byte_str to be None in which\\n case we will return an empty string. TODO(rkn): Remove this flag.\\n This is only here to simplify upgrading to flatbuffers 1.10.0.\\n\\n Returns:\\n A byte string in Python 2 and a unicode string in Python 3.\\n \\\"\\\"\\\"\\n if byte_str is None and allow_none:\\n return \\\"\\\"\\n\\n if not isinstance(byte_str, bytes):\\n raise ValueError(\\n \\\"The argument {} must be a bytes object.\\\".format(byte_str))\\n if sys.version_info >= (3, 0):\\n return byte_str.decode(\\\"ascii\\\")\\n else:\\n return byte_str\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::ensure_str\",\n \"text\": \"python/ray/utils.py::ensure_str\\ndef ensure_str(s, encoding=\\\"utf-8\\\", errors=\\\"strict\\\"):\\n \\\"\\\"\\\"Coerce *s* to `str`.\\n\\n - `str` -> `str`\\n - `bytes` -> decoded to `str`\\n \\\"\\\"\\\"\\n if isinstance(s, str):\\n return s\\n else:\\n assert isinstance(s, bytes)\\n return s.decode(encoding, errors)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::binary_to_object_ref\",\n \"text\": \"python/ray/utils.py::binary_to_object_ref\\ndef binary_to_object_ref(binary_object_ref):\\n return ray.ObjectRef(binary_object_ref)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::binary_to_task_id\",\n \"text\": \"python/ray/utils.py::binary_to_task_id\\ndef binary_to_task_id(binary_task_id):\\n return ray.TaskID(binary_task_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::binary_to_hex\",\n \"text\": \"python/ray/utils.py::binary_to_hex\\ndef binary_to_hex(identifier):\\n hex_identifier = binascii.hexlify(identifier)\\n if sys.version_info >= (3, 0):\\n hex_identifier = hex_identifier.decode()\\n return hex_identifier\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::hex_to_binary\",\n \"text\": \"python/ray/utils.py::hex_to_binary\\ndef hex_to_binary(hex_identifier):\\n return binascii.unhexlify(hex_identifier)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::compute_job_id_from_driver\",\n \"text\": \"python/ray/utils.py::compute_job_id_from_driver\\ndef compute_job_id_from_driver(driver_id):\\n assert isinstance(driver_id, ray.WorkerID)\\n return ray.JobID(driver_id.binary()[0:ray.JobID.size()])\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::compute_driver_id_from_job\",\n \"text\": \"python/ray/utils.py::compute_driver_id_from_job\\ndef compute_driver_id_from_job(job_id):\\n assert isinstance(job_id, ray.JobID)\\n rest_length = ray_constants.ID_SIZE - job_id.size()\\n driver_id_str = job_id.binary() + (rest_length * b\\\"\\\\xff\\\")\\n return ray.WorkerID(driver_id_str)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::get_cuda_visible_devices\",\n \"text\": \"python/ray/utils.py::get_cuda_visible_devices\\ndef get_cuda_visible_devices():\\n \\\"\\\"\\\"Get the device IDs in the CUDA_VISIBLE_DEVICES environment variable.\\n\\n Returns:\\n if CUDA_VISIBLE_DEVICES is set, this returns a list of integers with\\n the IDs of the GPUs. If it is not set or is set to NoDevFiles,\\n this returns None.\\n \\\"\\\"\\\"\\n gpu_ids_str = os.environ.get(\\\"CUDA_VISIBLE_DEVICES\\\", None)\\n\\n if gpu_ids_str is None:\\n return None\\n\\n if gpu_ids_str == \\\"\\\":\\n return []\\n\\n if gpu_ids_str == \\\"NoDevFiles\\\":\\n return []\\n\\n return [int(i) for i in gpu_ids_str.split(\\\",\\\")]\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::set_cuda_visible_devices\",\n \"text\": \"python/ray/utils.py::set_cuda_visible_devices\\ndef set_cuda_visible_devices(gpu_ids):\\n \\\"\\\"\\\"Set the CUDA_VISIBLE_DEVICES environment variable.\\n\\n Args:\\n gpu_ids: This is a list of integers representing GPU IDs.\\n \\\"\\\"\\\"\\n\\n global last_set_gpu_ids\\n if last_set_gpu_ids == gpu_ids:\\n return # optimization: already set\\n\\n os.environ[\\\"CUDA_VISIBLE_DEVICES\\\"] = \\\",\\\".join([str(i) for i in gpu_ids])\\n last_set_gpu_ids = gpu_ids\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::resources_from_resource_arguments\",\n \"text\": \"python/ray/utils.py::resources_from_resource_arguments\\ndef resources_from_resource_arguments(\\n default_num_cpus, default_num_gpus, default_memory,\\n default_object_store_memory, default_resources, runtime_num_cpus,\\n runtime_num_gpus, runtime_memory, runtime_object_store_memory,\\n runtime_resources):\\n \\\"\\\"\\\"Determine a task's resource requirements.\\n\\n Args:\\n default_num_cpus: The default number of CPUs required by this function\\n or actor method.\\n default_num_gpus: The default number of GPUs required by this function\\n or actor method.\\n default_memory: The default heap memory required by this function\\n or actor method.\\n default_object_store_memory: The default object store memory required\\n by this function or actor method.\\n default_resources: The default custom resources required by this\\n function or actor method.\\n runtime_num_cpus: The number of CPUs requested when the task was\\n invoked.\\n runtime_num_gpus: The number of GPUs requested when the task was\\n invoked.\\n runtime_memory: The heap memory requested when the task was invoked.\\n runtime_object_store_memory: The object store memory requested when\\n the task was invoked.\\n runtime_resources: The custom resources requested when the task was\\n invoked.\\n\\n Returns:\\n A dictionary of the resource requirements for the task.\\n \\\"\\\"\\\"\\n if runtime_resources is not None:\\n resources = runtime_resources.copy()\\n elif default_resources is not None:\\n resources = default_resources.copy()\\n else:\\n resources = {}\\n\\n if \\\"CPU\\\" in resources or \\\"GPU\\\" in resources:\\n raise ValueError(\\\"The resources dictionary must not \\\"\\n \\\"contain the key 'CPU' or 'GPU'\\\")\\n elif \\\"memory\\\" in resources or \\\"object_store_memory\\\" in resources:\\n raise ValueError(\\\"The resources dictionary must not\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::setup_logger\",\n \"text\": \"python/ray/utils.py::setup_logger\\ndef setup_logger(logging_level, logging_format):\\n \\\"\\\"\\\"Setup default logging for ray.\\\"\\\"\\\"\\n logger = logging.getLogger(\\\"ray\\\")\\n if type(logging_level) is str:\\n logging_level = logging.getLevelName(logging_level.upper())\\n logger.setLevel(logging_level)\\n global _default_handler\\n if _default_handler is None:\\n _default_handler = logging.StreamHandler()\\n logger.addHandler(_default_handler)\\n _default_handler.setFormatter(logging.Formatter(logging_format))\\n logger.propagate = False\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::open_log\",\n \"text\": \"python/ray/utils.py::open_log\\ndef open_log(path, **kwargs):\\n kwargs.setdefault(\\\"buffering\\\", 1)\\n kwargs.setdefault(\\\"mode\\\", \\\"a\\\")\\n kwargs.setdefault(\\\"encoding\\\", \\\"utf-8\\\")\\n return open(path, **kwargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"python/ray/utils.py::create_and_init_new_worker_log\",\n \"text\": \"python/ray/utils.py::create_and_init_new_worker_log\\ndef create_and_init_new_worker_log(path, worker_pid):\\n \\\"\\\"\\\"\\n Opens a path (or creates if necessary) for a log.\\n\\n Args:\\n path (str): The name/path of the file to be opened.\\n worker_pid (int): The pid of the worker process.\\n\\n Returns:\\n A file-like object which can be written to.\\n \\\"\\\"\\\"\\n # TODO (Alex): We should eventually be able to replace this with\\n # named-pipes.\\n f = open_log(path)\\n # Check to see if we're creating this file. No one else should ever write\\n # to this file, so we don't have to worry about TOCTOU.\\n if f.tell() == 0:\\n # This should always be the first message to appear in the worker's\\n # stdout and stderr log files. The string \\\"Ray worker pid:\\\" is\\n # parsed in the log monitor process.\\n print(\\\"Ray worker pid: {}\\\".format(worker_pid), file=f)\\n return f\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::get_system_memory\",\n \"text\": \"python/ray/utils.py::get_system_memory\\ndef get_system_memory():\\n \\\"\\\"\\\"Return the total amount of system memory in bytes.\\n\\n Returns:\\n The total amount of system memory in bytes.\\n \\\"\\\"\\\"\\n # Try to accurately figure out the memory limit if we are in a docker\\n # container. Note that this file is not specific to Docker and its value is\\n # often much larger than the actual amount of memory.\\n docker_limit = None\\n memory_limit_filename = \\\"/sys/fs/cgroup/memory/memory.limit_in_bytes\\\"\\n if os.path.exists(memory_limit_filename):\\n with open(memory_limit_filename, \\\"r\\\") as f:\\n docker_limit = int(f.read())\\n\\n # Use psutil if it is available.\\n psutil_memory_in_bytes = psutil.virtual_memory().total\\n\\n if docker_limit is not None:\\n # We take the min because the cgroup limit is very large if we aren't\\n # in Docker.\\n return min(docker_limit, psutil_memory_in_bytes)\\n\\n return psutil_memory_in_bytes\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::get_used_memory\",\n \"text\": \"python/ray/utils.py::get_used_memory\\ndef get_used_memory():\\n \\\"\\\"\\\"Return the currently used system memory in bytes\\n\\n Returns:\\n The total amount of used memory\\n \\\"\\\"\\\"\\n # Try to accurately figure out the memory usage if we are in a docker\\n # container.\\n docker_usage = None\\n memory_usage_filename = \\\"/sys/fs/cgroup/memory/memory.usage_in_bytes\\\"\\n if os.path.exists(memory_usage_filename):\\n with open(memory_usage_filename, \\\"r\\\") as f:\\n docker_usage = int(f.read())\\n\\n # Use psutil if it is available.\\n psutil_memory_in_bytes = psutil.virtual_memory().used\\n\\n if docker_usage is not None:\\n # We take the min because the cgroup limit is very large if we aren't\\n # in Docker.\\n return min(docker_usage, psutil_memory_in_bytes)\\n\\n return psutil_memory_in_bytes\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::estimate_available_memory\",\n \"text\": \"python/ray/utils.py::estimate_available_memory\\ndef estimate_available_memory():\\n \\\"\\\"\\\"Return the currently available amount of system memory in bytes.\\n\\n Returns:\\n The total amount of available memory in bytes. Based on the used\\n and total memory.\\n\\n \\\"\\\"\\\"\\n return get_system_memory() - get_used_memory()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::get_shared_memory_bytes\",\n \"text\": \"python/ray/utils.py::get_shared_memory_bytes\\ndef get_shared_memory_bytes():\\n \\\"\\\"\\\"Get the size of the shared memory file system.\\n\\n Returns:\\n The size of the shared memory file system in bytes.\\n \\\"\\\"\\\"\\n # Make sure this is only called on Linux.\\n assert sys.platform == \\\"linux\\\" or sys.platform == \\\"linux2\\\"\\n\\n shm_fd = os.open(\\\"/dev/shm\\\", os.O_RDONLY)\\n try:\\n shm_fs_stats = os.fstatvfs(shm_fd)\\n # The value shm_fs_stats.f_bsize is the block size and the\\n # value shm_fs_stats.f_bavail is the number of available\\n # blocks.\\n shm_avail = shm_fs_stats.f_bsize * shm_fs_stats.f_bavail\\n finally:\\n os.close(shm_fd)\\n\\n return shm_avail\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::check_oversized_pickle\",\n \"text\": \"python/ray/utils.py::check_oversized_pickle\\ndef check_oversized_pickle(pickled, name, obj_type, worker):\\n \\\"\\\"\\\"Send a warning message if the pickled object is too large.\\n\\n Args:\\n pickled: the pickled object.\\n name: name of the pickled object.\\n obj_type: type of the pickled object, can be 'function',\\n 'remote function', 'actor', or 'object'.\\n worker: the worker used to send warning message.\\n \\\"\\\"\\\"\\n length = len(pickled)\\n if length <= ray_constants.PICKLE_OBJECT_WARNING_SIZE:\\n return\\n warning_message = (\\n \\\"Warning: The {} {} has size {} when pickled. \\\"\\n \\\"It will be stored in Redis, which could cause memory issues. \\\"\\n \\\"This may mean that its definition uses a large array or other object.\\\"\\n ).format(obj_type, name, length)\\n push_error_to_driver(\\n worker,\\n ray_constants.PICKLING_LARGE_OBJECT_PUSH_ERROR,\\n warning_message,\\n job_id=worker.current_job_id)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::is_main_thread\",\n \"text\": \"python/ray/utils.py::is_main_thread\\ndef is_main_thread():\\n return threading.current_thread().getName() == \\\"MainThread\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::detect_fate_sharing_support_win32\",\n \"text\": \"python/ray/utils.py::detect_fate_sharing_support_win32\\ndef detect_fate_sharing_support_win32():\\n global win32_job, win32_AssignProcessToJobObject\\n if win32_job is None and sys.platform == \\\"win32\\\":\\n import ctypes\\n try:\\n from ctypes.wintypes import BOOL, DWORD, HANDLE, LPVOID, LPCWSTR\\n kernel32 = ctypes.WinDLL(\\\"kernel32\\\")\\n kernel32.CreateJobObjectW.argtypes = (LPVOID, LPCWSTR)\\n kernel32.CreateJobObjectW.restype = HANDLE\\n sijo_argtypes = (HANDLE, ctypes.c_int, LPVOID, DWORD)\\n kernel32.SetInformationJobObject.argtypes = sijo_argtypes\\n kernel32.SetInformationJobObject.restype = BOOL\\n kernel32.AssignProcessToJobObject.argtypes = (HANDLE, HANDLE)\\n kernel32.AssignProcessToJobObject.restype = BOOL\\n kernel32.IsDebuggerPresent.argtypes = ()\\n kernel32.IsDebuggerPresent.restype = BOOL\\n except (AttributeError, TypeError, ImportError):\\n kernel32 = None\\n job = kernel32.CreateJobObjectW(None, None) if kernel32 else None\\n job = subprocess.Handle(job) if job else job\\n if job:\\n from ctypes.wintypes import DWORD, LARGE_INTEGER, ULARGE_INTEGER\\n\\n class JOBOBJECT_BASIC_LIMIT_INFORMATION(ctypes.Structure):\\n _fields_ = [\\n (\\\"PerProcessUserTimeLimit\\\", LARGE_INTEGER),\\n (\\\"PerJobUserTimeLimit\\\", LARGE_INTEGER),\\n (\\\"LimitFlags\\\", DWORD),\\n (\\\"MinimumWorkingSetSize\\\", ctypes.c_size_t),\\n (\\\"MaximumWorkingSetSize\\\", ctypes.c_size_t),\\n (\\\"ActiveProcessLimit\\\", DWORD),\\n (\\\"Affinity\\\", ctypes.c_size_t),\\n (\\\"PriorityClass\\\", DWORD),\\n (\\\"SchedulingClass\\\", DWORD),\\n ]\\n\\n class IO_COUNTERS(ctypes.Structure):\\n _fields_ = [\\n (\\\"ReadOperationCount\\\", ULARGE_INTEGER),\\n (\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::detect_fate_sharing_support_linux\",\n \"text\": \"python/ray/utils.py::detect_fate_sharing_support_linux\\ndef detect_fate_sharing_support_linux():\\n global linux_prctl\\n if linux_prctl is None and sys.platform.startswith(\\\"linux\\\"):\\n try:\\n from ctypes import c_int, c_ulong, CDLL\\n prctl = CDLL(None).prctl\\n prctl.restype = c_int\\n prctl.argtypes = [c_int, c_ulong, c_ulong, c_ulong, c_ulong]\\n except (AttributeError, TypeError):\\n prctl = None\\n linux_prctl = prctl if prctl else False\\n return bool(linux_prctl)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::detect_fate_sharing_support\",\n \"text\": \"python/ray/utils.py::detect_fate_sharing_support\\ndef detect_fate_sharing_support():\\n result = None\\n if sys.platform == \\\"win32\\\":\\n result = detect_fate_sharing_support_win32()\\n elif sys.platform.startswith(\\\"linux\\\"):\\n result = detect_fate_sharing_support_linux()\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::set_kill_on_parent_death_linux\",\n \"text\": \"python/ray/utils.py::set_kill_on_parent_death_linux\\ndef set_kill_on_parent_death_linux():\\n \\\"\\\"\\\"Ensures this process dies if its parent dies (fate-sharing).\\n\\n Linux-only. Must be called in preexec_fn (i.e. by the child).\\n \\\"\\\"\\\"\\n if detect_fate_sharing_support_linux():\\n import signal\\n PR_SET_PDEATHSIG = 1\\n if linux_prctl(PR_SET_PDEATHSIG, signal.SIGKILL, 0, 0, 0) != 0:\\n import ctypes\\n raise OSError(ctypes.get_errno(), \\\"prctl(PR_SET_PDEATHSIG) failed\\\")\\n else:\\n assert False, \\\"PR_SET_PDEATHSIG used despite being unavailable\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::set_kill_child_on_death_win32\",\n \"text\": \"python/ray/utils.py::set_kill_child_on_death_win32\\ndef set_kill_child_on_death_win32(child_proc):\\n \\\"\\\"\\\"Ensures the child process dies if this process dies (fate-sharing).\\n\\n Windows-only. Must be called by the parent, after spawning the child.\\n\\n Args:\\n child_proc: The subprocess.Popen or subprocess.Handle object.\\n \\\"\\\"\\\"\\n\\n if isinstance(child_proc, subprocess.Popen):\\n child_proc = child_proc._handle\\n assert isinstance(child_proc, subprocess.Handle)\\n\\n if detect_fate_sharing_support_win32():\\n if not win32_AssignProcessToJobObject(win32_job, int(child_proc)):\\n import ctypes\\n raise OSError(ctypes.get_last_error(),\\n \\\"AssignProcessToJobObject() failed\\\")\\n else:\\n assert False, \\\"AssignProcessToJobObject used despite being unavailable\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::set_sigterm_handler\",\n \"text\": \"python/ray/utils.py::set_sigterm_handler\\ndef set_sigterm_handler(sigterm_handler):\\n \\\"\\\"\\\"Registers a handler for SIGTERM in a platform-compatible manner.\\\"\\\"\\\"\\n if sys.platform == \\\"win32\\\":\\n # Note that these signal handlers only work for console applications.\\n # TODO(mehrdadn): implement graceful process termination mechanism\\n # SIGINT is Ctrl+C, SIGBREAK is Ctrl+Break.\\n signal.signal(signal.SIGBREAK, sigterm_handler)\\n else:\\n signal.signal(signal.SIGTERM, sigterm_handler)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::try_make_directory_shared\",\n \"text\": \"python/ray/utils.py::try_make_directory_shared\\ndef try_make_directory_shared(directory_path):\\n try:\\n os.chmod(directory_path, 0o0777)\\n except OSError as e:\\n # Silently suppress the PermissionError that is thrown by the chmod.\\n # This is done because the user attempting to change the permissions\\n # on a directory may not own it. The chmod is attempted whether the\\n # directory is new or not to avoid race conditions.\\n # ray-project/ray/#3591\\n if e.errno in [errno.EACCES, errno.EPERM]:\\n pass\\n else:\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::try_to_create_directory\",\n \"text\": \"python/ray/utils.py::try_to_create_directory\\ndef try_to_create_directory(directory_path):\\n \\\"\\\"\\\"Attempt to create a directory that is globally readable/writable.\\n\\n Args:\\n directory_path: The path of the directory to create.\\n \\\"\\\"\\\"\\n directory_path = os.path.expanduser(directory_path)\\n os.makedirs(directory_path, exist_ok=True)\\n # Change the log directory permissions so others can use it. This is\\n # important when multiple people are using the same machine.\\n try_make_directory_shared(directory_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/utils.py::try_to_symlink\",\n \"text\": \"python/ray/utils.py::try_to_symlink\\ndef try_to_symlink(symlink_path, target_path):\\n \\\"\\\"\\\"Attempt to create a symlink.\\n\\n If the symlink path exists and isn't a symlink, the symlink will not be\\n created. If a symlink exists in the path, it will be attempted to be\\n removed and replaced.\\n\\n Args:\\n symlink_path: The path at which to create the symlink.\\n target_path: The path the symlink should point to.\\n \\\"\\\"\\\"\\n symlink_path = os.path.expanduser(symlink_path)\\n target_path = os.path.expanduser(target_path)\\n\\n if os.path.exists(symlink_path):\\n if os.path.islink(symlink_path):\\n # Try to remove existing symlink.\\n try:\\n os.remove(symlink_path)\\n except OSError:\\n return\\n else:\\n # There's an existing non-symlink file, don't overwrite it.\\n return\\n\\n try:\\n os.symlink(target_path, symlink_path)\\n except OSError:\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::ActorCheckpointInfo.__init__\",\n \"text\": \"python/ray/worker.py::ActorCheckpointInfo.__init__\\n def __init__(self, num_tasks_since_last_checkpoint,\\n last_checkpoint_timestamp, checkpoint_ids):\\n self.num_tasks_since_last_checkpoint = num_tasks_since_last_checkpoint\\n self.last_checkpoint_timestamp = last_checkpoint_timestamp\\n self.checkpoint_ids = checkpoint_ids\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.__init__\",\n \"text\": \"python/ray/worker.py::Worker.__init__\\n def __init__(self):\\n \\\"\\\"\\\"Initialize a Worker object.\\\"\\\"\\\"\\n self.node = None\\n self.mode = None\\n self.cached_functions_to_run = []\\n self.actor_init_error = None\\n self.actors = {}\\n # Information used to maintain actor checkpoints.\\n self.actor_checkpoint_info = {}\\n self.actor_task_counter = 0\\n # When the worker is constructed. Record the original value of the\\n # CUDA_VISIBLE_DEVICES environment variable.\\n self.original_gpu_ids = ray.utils.get_cuda_visible_devices()\\n self.memory_monitor = memory_monitor.MemoryMonitor()\\n # A dictionary that maps from driver id to SerializationContext\\n # TODO: clean up the SerializationContext once the job finished.\\n self.serialization_context_map = {}\\n self.function_actor_manager = FunctionActorManager(self)\\n # This event is checked regularly by all of the threads so that they\\n # know when to exit.\\n self.threads_stopped = threading.Event()\\n # Index of the current session. This number will\\n # increment every time when `ray.shutdown` is called.\\n self._session_index = 0\\n # Functions to run to process the values returned by ray.get. Each\\n # postprocessor must take two arguments (\\\"object_refs\\\", and \\\"values\\\").\\n self._post_get_hooks = []\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.connected\",\n \"text\": \"python/ray/worker.py::Worker.connected\\n @property\\n def connected(self):\\n return self.node is not None\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.node_ip_address\",\n \"text\": \"python/ray/worker.py::Worker.node_ip_address\\n @property\\n def node_ip_address(self):\\n self.check_connected()\\n return self.node.node_ip_address\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.load_code_from_local\",\n \"text\": \"python/ray/worker.py::Worker.load_code_from_local\\n @property\\n def load_code_from_local(self):\\n self.check_connected()\\n return self.node.load_code_from_local\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.current_job_id\",\n \"text\": \"python/ray/worker.py::Worker.current_job_id\\n @property\\n def current_job_id(self):\\n if hasattr(self, \\\"core_worker\\\"):\\n return self.core_worker.get_current_job_id()\\n return JobID.nil()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.actor_id\",\n \"text\": \"python/ray/worker.py::Worker.actor_id\\n @property\\n def actor_id(self):\\n if hasattr(self, \\\"core_worker\\\"):\\n return self.core_worker.get_actor_id()\\n return ActorID.nil()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.current_task_id\",\n \"text\": \"python/ray/worker.py::Worker.current_task_id\\n @property\\n def current_task_id(self):\\n return self.core_worker.get_current_task_id()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.current_session_and_job\",\n \"text\": \"python/ray/worker.py::Worker.current_session_and_job\\n @property\\n def current_session_and_job(self):\\n \\\"\\\"\\\"Get the current session index and job id as pair.\\\"\\\"\\\"\\n assert isinstance(self._session_index, int)\\n assert isinstance(self.current_job_id, ray.JobID)\\n return self._session_index, self.current_job_id\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.mark_actor_init_failed\",\n \"text\": \"python/ray/worker.py::Worker.mark_actor_init_failed\\n def mark_actor_init_failed(self, error):\\n \\\"\\\"\\\"Called to mark this actor as failed during initialization.\\\"\\\"\\\"\\n\\n self.actor_init_error = error\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.reraise_actor_init_error\",\n \"text\": \"python/ray/worker.py::Worker.reraise_actor_init_error\\n def reraise_actor_init_error(self):\\n \\\"\\\"\\\"Raises any previous actor initialization error.\\\"\\\"\\\"\\n\\n if self.actor_init_error is not None:\\n raise self.actor_init_error\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.get_serialization_context\",\n \"text\": \"python/ray/worker.py::Worker.get_serialization_context\\n def get_serialization_context(self, job_id=None):\\n \\\"\\\"\\\"Get the SerializationContext of the job that this worker is processing.\\n\\n Args:\\n job_id: The ID of the job that indicates which job to get\\n the serialization context for.\\n\\n Returns:\\n The serialization context of the given job.\\n \\\"\\\"\\\"\\n # This function needs to be protected by a lock, because it will be\\n # called by`register_class_for_serialization`, as well as the import\\n # thread, from different threads. Also, this function will recursively\\n # call itself, so we use RLock here.\\n if job_id is None:\\n job_id = self.current_job_id\\n with self.lock:\\n if job_id not in self.serialization_context_map:\\n self.serialization_context_map[\\n job_id] = serialization.SerializationContext(self)\\n return self.serialization_context_map[job_id]\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.check_connected\",\n \"text\": \"python/ray/worker.py::Worker.check_connected\\n def check_connected(self):\\n \\\"\\\"\\\"Check if the worker is connected.\\n\\n Raises:\\n Exception: An exception is raised if the worker is not connected.\\n \\\"\\\"\\\"\\n if not self.connected:\\n raise RayConnectionError(\\\"Ray has not been started yet. You can \\\"\\n \\\"start Ray with 'ray.init()'.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.set_mode\",\n \"text\": \"python/ray/worker.py::Worker.set_mode\\n def set_mode(self, mode):\\n \\\"\\\"\\\"Set the mode of the worker.\\n\\n The mode SCRIPT_MODE should be used if this Worker is a driver that is\\n being run as a Python script or interactively in a shell. It will print\\n information about task failures.\\n\\n The mode WORKER_MODE should be used if this Worker is not a driver. It\\n will not print information about tasks.\\n\\n The mode LOCAL_MODE should be used if this Worker is a driver and if\\n you want to run the driver in a manner equivalent to serial Python for\\n debugging purposes. It will not send remote function calls to the\\n scheduler and will instead execute them in a blocking fashion.\\n\\n Args:\\n mode: One of SCRIPT_MODE, WORKER_MODE, and LOCAL_MODE.\\n \\\"\\\"\\\"\\n self.mode = mode\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.put_object\",\n \"text\": \"python/ray/worker.py::Worker.put_object\\n def put_object(self, value, object_ref=None, pin_object=True):\\n \\\"\\\"\\\"Put value in the local object store with object reference `object_ref`.\\n\\n This assumes that the value for `object_ref` has not yet been placed in\\n the local object store. If the plasma store is full, the worker will\\n automatically retry up to DEFAULT_PUT_OBJECT_RETRIES times. Each\\n retry will delay for an exponentially doubling amount of time,\\n starting with DEFAULT_PUT_OBJECT_DELAY. After this, exception\\n will be raised.\\n\\n Args:\\n value: The value to put in the object store.\\n object_ref (ObjectRef): The object ref of the value to be\\n put. If None, one will be generated.\\n pin_object: If set, the object will be pinned at the raylet.\\n\\n Returns:\\n ObjectRef: The object ref the object was put under.\\n\\n Raises:\\n ray.exceptions.ObjectStoreFullError: This is raised if the attempt\\n to store the object fails because the object store is full even\\n after multiple retries.\\n \\\"\\\"\\\"\\n # Make sure that the value is not an object ref.\\n if isinstance(value, ObjectRef):\\n raise TypeError(\\n \\\"Calling 'put' on an ray.ObjectRef is not allowed \\\"\\n \\\"(similarly, returning an ray.ObjectRef from a remote \\\"\\n \\\"function is not allowed). If you really want to \\\"\\n \\\"do this, you can wrap the ray.ObjectRef in a list and \\\"\\n \\\"call 'put' on it (or return it).\\\")\\n\\n if self.mode == LOCAL_MODE:\\n assert object_ref is None, (\\\"Local Mode does not support \\\"\\n \\\"inserting with an ObjectRef\\\")\\n\\n serialized_value = self.get_serialization_context().serialize(value)\\n # This *must* be the first place that we construct this python\\n # ObjectRef because an entry with 0 local\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.deserialize_objects\",\n \"text\": \"python/ray/worker.py::Worker.deserialize_objects\\n def deserialize_objects(self, data_metadata_pairs, object_refs):\\n context = self.get_serialization_context()\\n return context.deserialize_objects(data_metadata_pairs, object_refs)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.get_objects\",\n \"text\": \"python/ray/worker.py::Worker.get_objects\\n def get_objects(self, object_refs, timeout=None):\\n \\\"\\\"\\\"Get the values in the object store associated with the IDs.\\n\\n Return the values from the local object store for object_refs. This\\n will block until all the values for object_refs have been written to\\n the local object store.\\n\\n Args:\\n object_refs (List[object_ref.ObjectRef]): A list of the object refs\\n whose values should be retrieved.\\n timeout (float): timeout (float): The maximum amount of time in\\n seconds to wait before returning.\\n \\\"\\\"\\\"\\n # Make sure that the values are object refs.\\n for object_ref in object_refs:\\n if not isinstance(object_ref, ObjectRef):\\n raise TypeError(\\n \\\"Attempting to call `get` on the value {}, \\\"\\n \\\"which is not an ray.ObjectRef.\\\".format(object_ref))\\n\\n timeout_ms = int(timeout * 1000) if timeout else -1\\n data_metadata_pairs = self.core_worker.get_objects(\\n object_refs, self.current_task_id, timeout_ms)\\n return self.deserialize_objects(data_metadata_pairs, object_refs)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.run_function_on_all_workers\",\n \"text\": \"python/ray/worker.py::Worker.run_function_on_all_workers\\n def run_function_on_all_workers(self, function,\\n run_on_other_drivers=False):\\n \\\"\\\"\\\"Run arbitrary code on all of the workers.\\n\\n This function will first be run on the driver, and then it will be\\n exported to all of the workers to be run. It will also be run on any\\n new workers that register later. If ray.init has not been called yet,\\n then cache the function and export it later.\\n\\n Args:\\n function (Callable): The function to run on all of the workers. It\\n takes only one argument, a worker info dict. If it returns\\n anything, its return values will not be used.\\n run_on_other_drivers: The boolean that indicates whether we want to\\n run this function on other drivers. One case is we may need to\\n share objects across drivers.\\n \\\"\\\"\\\"\\n # If ray.init has not been called yet, then cache the function and\\n # export it when connect is called. Otherwise, run the function on all\\n # workers.\\n if self.mode is None:\\n self.cached_functions_to_run.append(function)\\n else:\\n # Attempt to pickle the function before we need it. This could\\n # fail, and it is more convenient if the failure happens before we\\n # actually run the function locally.\\n pickled_function = pickle.dumps(function)\\n\\n function_to_run_id = hashlib.sha1(pickled_function).digest()\\n key = b\\\"FunctionsToRun:\\\" + function_to_run_id\\n # First run the function on the driver.\\n # We always run the task locally.\\n function({\\\"worker\\\": self})\\n # Check if the function has already been put into redis.\\n function_exported = self.redis_client.setnx(b\\\"Lock:\\\" + key, 1)\\n if not function_exported:\\n # In this case, the function has already b\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.main_loop\",\n \"text\": \"python/ray/worker.py::Worker.main_loop\\n def main_loop(self):\\n \\\"\\\"\\\"The main loop a worker runs to receive and execute tasks.\\\"\\\"\\\"\\n\\n def sigterm_handler(signum, frame):\\n shutdown(True)\\n sys.exit(1)\\n\\n ray.utils.set_sigterm_handler(sigterm_handler)\\n self.core_worker.run_task_loop()\\n sys.exit(0)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::Worker.main_loop.sigterm_handler\",\n \"text\": \"python/ray/worker.py::Worker.main_loop.sigterm_handler\\n def sigterm_handler(signum, frame):\\n shutdown(True)\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::get_gpu_ids\",\n \"text\": \"python/ray/worker.py::get_gpu_ids\\ndef get_gpu_ids():\\n \\\"\\\"\\\"Get the IDs of the GPUs that are available to the worker.\\n\\n If the CUDA_VISIBLE_DEVICES environment variable was set when the worker\\n started up, then the IDs returned by this method will be a subset of the\\n IDs in CUDA_VISIBLE_DEVICES. If not, the IDs will fall in the range\\n [0, NUM_GPUS - 1], where NUM_GPUS is the number of GPUs that the node has.\\n\\n Returns:\\n A list of GPU IDs.\\n \\\"\\\"\\\"\\n\\n # TODO(ilr) Handle inserting resources in local mode\\n all_resource_ids = global_worker.core_worker.resource_ids()\\n assigned_ids = [\\n resource_id for resource_id, _ in all_resource_ids.get(\\\"GPU\\\", [])\\n ]\\n # If the user had already set CUDA_VISIBLE_DEVICES, then respect that (in\\n # the sense that only GPU IDs that appear in CUDA_VISIBLE_DEVICES should be\\n # returned).\\n if global_worker.original_gpu_ids is not None:\\n assigned_ids = [\\n global_worker.original_gpu_ids[gpu_id] for gpu_id in assigned_ids\\n ]\\n # Give all GPUs in local_mode.\\n if global_worker.mode == LOCAL_MODE:\\n max_gpus = global_worker.node.get_resource_spec().num_gpus\\n return global_worker.original_gpu_ids[:max_gpus]\\n\\n return assigned_ids\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::get_resource_ids\",\n \"text\": \"python/ray/worker.py::get_resource_ids\\ndef get_resource_ids():\\n \\\"\\\"\\\"Get the IDs of the resources that are available to the worker.\\n\\n Returns:\\n A dictionary mapping the name of a resource to a list of pairs, where\\n each pair consists of the ID of a resource and the fraction of that\\n resource reserved for this worker.\\n \\\"\\\"\\\"\\n if _mode() == LOCAL_MODE:\\n raise RuntimeError(\\\"ray.get_resource_ids() currently does not work in \\\"\\n \\\"local_mode.\\\")\\n\\n return global_worker.core_worker.resource_ids()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::get_webui_url\",\n \"text\": \"python/ray/worker.py::get_webui_url\\ndef get_webui_url():\\n \\\"\\\"\\\"Get the URL to access the web UI.\\n\\n Note that the URL does not specify which node the web UI is on.\\n\\n Returns:\\n The URL of the web UI as a string.\\n \\\"\\\"\\\"\\n if _global_node is None:\\n raise RuntimeError(\\\"Ray has not been initialized/connected.\\\")\\n return _global_node.webui_url\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::print_failed_task\",\n \"text\": \"python/ray/worker.py::print_failed_task\\ndef print_failed_task(task_status):\\n \\\"\\\"\\\"Print information about failed tasks.\\n\\n Args:\\n task_status (Dict): A dictionary containing the name, operationid, and\\n error message for a failed task.\\n \\\"\\\"\\\"\\n logger.error(\\\"\\\"\\\"\\n Error: Task failed\\n Function Name: {}\\n Task ID: {}\\n Error Message: \\\\n{}\\n \\\"\\\"\\\".format(task_status[\\\"function_name\\\"], task_status[\\\"operationid\\\"],\\n task_status[\\\"error_message\\\"]))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::init\",\n \"text\": \"python/ray/worker.py::init\\ndef init(address=None,\\n redis_address=None,\\n redis_port=None,\\n num_cpus=None,\\n num_gpus=None,\\n memory=None,\\n object_store_memory=None,\\n resources=None,\\n driver_object_store_memory=None,\\n redis_max_memory=None,\\n log_to_driver=True,\\n node_ip_address=ray_constants.NODE_DEFAULT_IP,\\n object_ref_seed=None,\\n local_mode=False,\\n redirect_worker_output=None,\\n redirect_output=None,\\n ignore_reinit_error=False,\\n num_redis_shards=None,\\n redis_max_clients=None,\\n redis_password=ray_constants.REDIS_DEFAULT_PASSWORD,\\n plasma_directory=None,\\n huge_pages=False,\\n include_java=False,\\n include_dashboard=None,\\n dashboard_host=\\\"localhost\\\",\\n dashboard_port=ray_constants.DEFAULT_DASHBOARD_PORT,\\n job_id=None,\\n configure_logging=True,\\n logging_level=logging.INFO,\\n logging_format=ray_constants.LOGGER_FORMAT,\\n plasma_store_socket_name=None,\\n raylet_socket_name=None,\\n temp_dir=None,\\n load_code_from_local=False,\\n java_worker_options=None,\\n use_pickle=True,\\n _internal_config=None,\\n lru_evict=False,\\n enable_object_reconstruction=False):\\n \\\"\\\"\\\"\\n Connect to an existing Ray cluster or start one and connect to it.\\n\\n This method handles two cases; either a Ray cluster already exists and we\\n just attach this driver to it or we start all of the processes associated\\n with a Ray cluster and attach to the newly started cluster.\\n\\n To start Ray and all of the relevant processes, use this as follows:\\n\\n .. code-block:: python\\n\\n ray.init()\\n\\n To connect to an existing Ray cluster, use this as follows (substituting\\n in the appropriate address):\\n\\n .. code-block:: python\\n\\n ray.init(address=\\\"123.45.67.89:6379\\\")\\n\\n You can also define an environment var\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::shutdown\",\n \"text\": \"python/ray/worker.py::shutdown\\ndef shutdown(exiting_interpreter=False):\\n \\\"\\\"\\\"Disconnect the worker, and terminate processes started by ray.init().\\n\\n This will automatically run at the end when a Python process that uses Ray\\n exits. It is ok to run this twice in a row. The primary use case for this\\n function is to cleanup state between tests.\\n\\n Note that this will clear any remote function definitions, actor\\n definitions, and existing actors, so if you wish to use any previously\\n defined remote functions or actors after calling ray.shutdown(), then you\\n need to redefine them. If they were defined in an imported module, then you\\n will need to reload the module.\\n\\n Args:\\n exiting_interpreter (bool): True if this is called by the atexit hook\\n and false otherwise. If we are exiting the interpreter, we will\\n wait a little while to print any extra error messages.\\n \\\"\\\"\\\"\\n if exiting_interpreter and global_worker.mode == SCRIPT_MODE:\\n # This is a duration to sleep before shutting down everything in order\\n # to make sure that log messages finish printing.\\n time.sleep(0.5)\\n\\n disconnect(exiting_interpreter)\\n\\n # We need to destruct the core worker here because after this function,\\n # we will tear down any processes spawned by ray.init() and the background\\n # IO thread in the core worker doesn't currently handle that gracefully.\\n if hasattr(global_worker, \\\"core_worker\\\"):\\n del global_worker.core_worker\\n\\n # Disconnect global state from GCS.\\n ray.state.state.disconnect()\\n\\n # Shut down the Ray processes.\\n global _global_node\\n if _global_node is not None:\\n _global_node.kill_all_processes(check_alive=False, allow_graceful=True)\\n _global_node = None\\n\\n # TODO(rkn): Instead of manually resetting some of the worker fields, we\\n # should simply set \\\"global_worker\\\" to equal \\\"None\\\" or something like that.\\n global_worker.set_mode(None)\\n global_worker._\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::sigterm_handler\",\n \"text\": \"python/ray/worker.py::sigterm_handler\\ndef sigterm_handler(signum, frame):\\n sys.exit(signum)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::custom_excepthook\",\n \"text\": \"python/ray/worker.py::custom_excepthook\\ndef custom_excepthook(type, value, tb):\\n # If this is a driver, push the exception to GCS worker table.\\n if global_worker.mode == SCRIPT_MODE:\\n error_message = \\\"\\\".join(traceback.format_tb(tb))\\n worker_id = global_worker.worker_id\\n worker_type = ray.gcs_utils.DRIVER\\n worker_info = {\\\"exception\\\": error_message}\\n\\n ray.state.state.add_worker(worker_id, worker_type, worker_info)\\n # Call the normal excepthook.\\n normal_excepthook(type, value, tb)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::_set_log_file\",\n \"text\": \"python/ray/worker.py::_set_log_file\\ndef _set_log_file(file_name, worker_pid, old_obj, setter_func):\\n # Line-buffer the output (mode 1).\\n f = create_and_init_new_worker_log(file_name, worker_pid)\\n\\n # TODO (Alex): Python seems to always flush when writing. If that is no\\n # longer true, then we need to manually flush the old buffer.\\n # old_obj.flush()\\n\\n # TODO (Alex): Flush the c/c++ userspace buffers if necessary.\\n # `fflush(stdout); cout.flush();`\\n\\n fileno = old_obj.fileno()\\n\\n # C++ logging requires redirecting the stdout file descriptor. Note that\\n # dup2 will automatically close the old file descriptor before overriding\\n # it.\\n os.dup2(f.fileno(), fileno)\\n\\n # We also manually set sys.stdout and sys.stderr because that seems to\\n # have an effect on the output buffering. Without doing this, stdout\\n # and stderr are heavily buffered resulting in seemingly lost logging\\n # statements. We never want to close the stdout file descriptor, dup2 will\\n # close it when necessary and we don't want python's GC to close it.\\n setter_func(open_log(fileno, closefd=False))\\n\\n return os.path.abspath(f.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::set_log_file\",\n \"text\": \"python/ray/worker.py::set_log_file\\ndef set_log_file(stdout_name, stderr_name):\\n \\\"\\\"\\\"Sets up logging for the current worker, creating the (fd backed) file and\\n flushing buffers as is necessary.\\n\\n Args:\\n stdout_name (str): The file name that stdout should be written to.\\n stderr_name(str): The file name that stderr should be written to.\\n\\n Returns:\\n (tuple) The absolute paths of the files that stdout and stderr will be\\n written to.\\n\\n \\\"\\\"\\\"\\n stdout_path = \\\"\\\"\\n stderr_path = \\\"\\\"\\n worker_pid = os.getpid()\\n\\n # lambda cannot contain assignment\\n def stdout_setter(x):\\n sys.stdout = x\\n\\n def stderr_setter(x):\\n sys.stderr = x\\n\\n if stdout_name:\\n _set_log_file(stdout_name, worker_pid, sys.stdout, stdout_setter)\\n\\n # The stderr case should be analogous to the stdout case\\n if stderr_name:\\n _set_log_file(stderr_name, worker_pid, sys.stderr, stderr_setter)\\n\\n return stdout_path, stderr_path\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::set_log_file.stdout_setter\",\n \"text\": \"python/ray/worker.py::set_log_file.stdout_setter\\n def stdout_setter(x):\\n sys.stdout = x\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::set_log_file.stderr_setter\",\n \"text\": \"python/ray/worker.py::set_log_file.stderr_setter\\n def stderr_setter(x):\\n sys.stderr = x\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::print_logs\",\n \"text\": \"python/ray/worker.py::print_logs\\ndef print_logs(redis_client, threads_stopped, job_id):\\n \\\"\\\"\\\"Prints log messages from workers on all of the nodes.\\n\\n Args:\\n redis_client: A client to the primary Redis shard.\\n threads_stopped (threading.Event): A threading event used to signal to\\n the thread that it should exit.\\n job_id (JobID): The id of the driver's job\\n\\n \\\"\\\"\\\"\\n pubsub_client = redis_client.pubsub(ignore_subscribe_messages=True)\\n pubsub_client.subscribe(ray.gcs_utils.LOG_FILE_CHANNEL)\\n localhost = services.get_node_ip_address()\\n try:\\n # Keep track of the number of consecutive log messages that have been\\n # received with no break in between. If this number grows continually,\\n # then the worker is probably not able to process the log messages as\\n # rapidly as they are coming in.\\n num_consecutive_messages_received = 0\\n while True:\\n # Exit if we received a signal that we should stop.\\n if threads_stopped.is_set():\\n return\\n\\n msg = pubsub_client.get_message()\\n if msg is None:\\n num_consecutive_messages_received = 0\\n threads_stopped.wait(timeout=0.01)\\n continue\\n num_consecutive_messages_received += 1\\n if (num_consecutive_messages_received % 100 == 0\\n and num_consecutive_messages_received > 0):\\n logger.warning(\\n \\\"The driver may not be able to keep up with the \\\"\\n \\\"stdout/stderr of the workers. To avoid forwarding logs \\\"\\n \\\"to the driver, use 'ray.init(log_to_driver=False)'.\\\")\\n\\n data = json.loads(ray.utils.decode(msg[\\\"data\\\"]))\\n\\n # Don't show logs from other drivers.\\n if data[\\\"job\\\"] and ray.utils.binary_to_hex(\\n job_id.binary()) != data[\\\"job\\\"]:\\n continue\\n\\n def color_for(data):\\n if data\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::print_error_messages_raylet\",\n \"text\": \"python/ray/worker.py::print_error_messages_raylet\\ndef print_error_messages_raylet(task_error_queue, threads_stopped):\\n \\\"\\\"\\\"Prints message received in the given output queue.\\n\\n This checks periodically if any un-raised errors occured in the background.\\n\\n Args:\\n task_error_queue (queue.Queue): A queue used to receive errors from the\\n thread that listens to Redis.\\n threads_stopped (threading.Event): A threading event used to signal to\\n the thread that it should exit.\\n \\\"\\\"\\\"\\n\\n while True:\\n # Exit if we received a signal that we should stop.\\n if threads_stopped.is_set():\\n return\\n\\n try:\\n error, t = task_error_queue.get(block=False)\\n except queue.Empty:\\n threads_stopped.wait(timeout=0.01)\\n continue\\n # Delay errors a little bit of time to attempt to suppress redundant\\n # messages originating from the worker.\\n while t + UNCAUGHT_ERROR_GRACE_PERIOD > time.time():\\n threads_stopped.wait(timeout=1)\\n if threads_stopped.is_set():\\n break\\n if t < last_task_error_raise_time + UNCAUGHT_ERROR_GRACE_PERIOD:\\n logger.debug(\\\"Suppressing error from worker: {}\\\".format(error))\\n else:\\n logger.error(\\n \\\"Possible unhandled error from worker: {}\\\".format(error))\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::listen_error_messages_raylet\",\n \"text\": \"python/ray/worker.py::listen_error_messages_raylet\\ndef listen_error_messages_raylet(worker, task_error_queue, threads_stopped):\\n \\\"\\\"\\\"Listen to error messages in the background on the driver.\\n\\n This runs in a separate thread on the driver and pushes (error, time)\\n tuples to the output queue.\\n\\n Args:\\n worker: The worker class that this thread belongs to.\\n task_error_queue (queue.Queue): A queue used to communicate with the\\n thread that prints the errors found by this thread.\\n threads_stopped (threading.Event): A threading event used to signal to\\n the thread that it should exit.\\n \\\"\\\"\\\"\\n worker.error_message_pubsub_client = worker.redis_client.pubsub(\\n ignore_subscribe_messages=True)\\n # Exports that are published after the call to\\n # error_message_pubsub_client.subscribe and before the call to\\n # error_message_pubsub_client.listen will still be processed in the loop.\\n\\n # Really we should just subscribe to the errors for this specific job.\\n # However, currently all errors seem to be published on the same channel.\\n error_pubsub_channel = str(\\n ray.gcs_utils.TablePubsub.Value(\\\"ERROR_INFO_PUBSUB\\\")).encode(\\\"ascii\\\")\\n worker.error_message_pubsub_client.subscribe(error_pubsub_channel)\\n # worker.error_message_pubsub_client.psubscribe(\\\"*\\\")\\n\\n try:\\n # Get the errors that occurred before the call to subscribe.\\n error_messages = ray.errors()\\n for error_message in error_messages:\\n logger.error(error_message)\\n\\n while True:\\n # Exit if we received a signal that we should stop.\\n if threads_stopped.is_set():\\n return\\n\\n msg = worker.error_message_pubsub_client.get_message()\\n if msg is None:\\n threads_stopped.wait(timeout=0.01)\\n continue\\n gcs_entry = ray.gcs_utils.GcsEntry.FromString(msg[\\\"data\\\"])\\n assert len(gcs_entry.entries) == 1\\n error\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::is_initialized\",\n \"text\": \"python/ray/worker.py::is_initialized\\ndef is_initialized():\\n \\\"\\\"\\\"Check if ray.init has been called yet.\\n\\n Returns:\\n True if ray.init has already been called and false otherwise.\\n \\\"\\\"\\\"\\n return ray.worker.global_worker.connected\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::connect\",\n \"text\": \"python/ray/worker.py::connect\\ndef connect(node,\\n mode=WORKER_MODE,\\n log_to_driver=False,\\n worker=global_worker,\\n driver_object_store_memory=None,\\n job_id=None):\\n \\\"\\\"\\\"Connect this worker to the raylet, to Plasma, and to Redis.\\n\\n Args:\\n node (ray.node.Node): The node to connect.\\n mode: The mode of the worker. One of SCRIPT_MODE, WORKER_MODE, and\\n LOCAL_MODE.\\n log_to_driver (bool): If true, then output from all of the worker\\n processes on all nodes will be directed to the driver.\\n worker: The ray.Worker instance.\\n driver_object_store_memory: Limit the amount of memory the driver can\\n use in the object store when creating objects.\\n job_id: The ID of job. If it's None, then we will generate one.\\n \\\"\\\"\\\"\\n # Do some basic checking to make sure we didn't call ray.init twice.\\n error_message = \\\"Perhaps you called ray.init twice by accident?\\\"\\n assert not worker.connected, error_message\\n assert worker.cached_functions_to_run is not None, error_message\\n\\n # Enable nice stack traces on SIGSEGV etc.\\n try:\\n if not faulthandler.is_enabled():\\n faulthandler.enable(all_threads=False)\\n except io.UnsupportedOperation:\\n pass # ignore\\n\\n # Create a Redis client to primary.\\n # The Redis client can safely be shared between threads. However,\\n # that is not true of Redis pubsub clients. See the documentation at\\n # https://github.com/andymccurdy/redis-py#thread-safety.\\n worker.redis_client = node.create_redis_client()\\n\\n # Initialize some fields.\\n if mode is WORKER_MODE:\\n # We should not specify the job_id if it's `WORKER_MODE`.\\n assert job_id is None\\n job_id = JobID.nil()\\n # TODO(qwang): Rename this to `worker_id_str` or type to `WorkerID`\\n worker.worker_id = _random_string()\\n else:\\n # This is the code path of driver mode.\\n if job_id is None:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::disconnect\",\n \"text\": \"python/ray/worker.py::disconnect\\ndef disconnect(exiting_interpreter=False):\\n \\\"\\\"\\\"Disconnect this worker from the raylet and object store.\\\"\\\"\\\"\\n # Reset the list of cached remote functions and actors so that if more\\n # remote functions or actors are defined and then connect is called again,\\n # the remote functions will be exported. This is mostly relevant for the\\n # tests.\\n worker = global_worker\\n if worker.connected:\\n # Shutdown all of the threads that we've started. TODO(rkn): This\\n # should be handled cleanly in the worker object's destructor and not\\n # in this disconnect method.\\n worker.threads_stopped.set()\\n if hasattr(worker, \\\"import_thread\\\"):\\n worker.import_thread.join_import_thread()\\n if hasattr(worker, \\\"listener_thread\\\"):\\n worker.listener_thread.join()\\n if hasattr(worker, \\\"printer_thread\\\"):\\n worker.printer_thread.join()\\n if hasattr(worker, \\\"logger_thread\\\"):\\n worker.logger_thread.join()\\n worker.threads_stopped.clear()\\n worker._session_index += 1\\n\\n worker.node = None # Disconnect the worker from the node.\\n worker.cached_functions_to_run = []\\n worker.serialization_context_map.clear()\\n try:\\n ray_actor = ray.actor\\n except AttributeError:\\n ray_actor = None # This can occur during program termination\\n if ray_actor is not None:\\n ray_actor.ActorClassMethodMetadata.reset_cache()\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::_changeproctitle\",\n \"text\": \"python/ray/worker.py::_changeproctitle\\n@contextmanager\\ndef _changeproctitle(title, next_title):\\n setproctitle.setproctitle(title)\\n try:\\n yield\\n finally:\\n setproctitle.setproctitle(next_title)\",\n \"label\": 0\n },\n {\n \"cid\": \"python/ray/worker.py::register_custom_serializer\",\n \"text\": \"python/ray/worker.py::register_custom_serializer\\ndef register_custom_serializer(cls,\\n serializer,\\n deserializer,\\n use_pickle=False,\\n use_dict=False,\\n class_id=None):\\n \\\"\\\"\\\"Registers custom functions for efficient object serialization.\\n\\n The serializer and deserializer are used when transferring objects of\\n `cls` across processes and nodes. This can be significantly faster than\\n the Ray default fallbacks. Wraps `register_custom_serializer` underneath.\\n\\n Args:\\n cls (type): The class that ray should use this custom serializer for.\\n serializer: The custom serializer that takes in a cls instance and\\n outputs a serialized representation. use_pickle and use_dict\\n must be False if provided.\\n deserializer: The custom deserializer that takes in a serialized\\n representation of the cls and outputs a cls instance. use_pickle\\n and use_dict must be False if provided.\\n use_pickle: Deprecated.\\n use_dict: Deprecated.\\n class_id (str): Unique ID of the class. Autogenerated if None.\\n \\\"\\\"\\\"\\n worker = global_worker\\n worker.check_connected()\\n\\n if use_pickle:\\n raise DeprecationWarning(\\n \\\"`use_pickle` is no longer a valid parameter and will be removed \\\"\\n \\\"in future versions of Ray. If this breaks your application, \\\"\\n \\\"see `SerializationContext.register_custom_serializer`.\\\")\\n if use_dict:\\n raise DeprecationWarning(\\n \\\"`use_pickle` is no longer a valid parameter and will be removed \\\"\\n \\\"in future versions of Ray. If this breaks your application, \\\"\\n \\\"see `SerializationContext.register_custom_serializer`.\\\")\\n assert serializer is not None and deserializer is not None\\n context = global_worker.get_serialization_context()\\n context.register_custom_serializer\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":284,"cells":{"instance_id":{"kind":"string","value":"jupyterlab__jupyterlab-1954"},"repo":{"kind":"string","value":"jupyterlab/jupyterlab"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Remove outdated version compatibility check for extensions\nThis [check](https://github.com/jupyterlab/jupyterlab/blob/53a60f7016a96787765ba40851a4200d8cd5a463/jupyterlab/labextensions.py#L502) needs to be removed in light of the big split until we have a replacement system.\n"},"gold_files":{"kind":"list like","value":["jupyterlab/labextensions.py"],"string":"[\n \"jupyterlab/labextensions.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["jupyterlab/labextensions.py::<module>","jupyterlab/labextensions.py::validate_labextension_folder"],"string":"[\n \"jupyterlab/labextensions.py::<module>\",\n \"jupyterlab/labextensions.py::validate_labextension_folder\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"jupyterlab/__init__.py","text":"jupyterlab/__init__.py\ndef _jupyter_server_extension_paths()","label":0},{"cid":"jupyterlab/_version.py","text":"jupyterlab/_version.py","label":0},{"cid":"jupyterlab/labextensions.py","text":"jupyterlab/labextensions.py\nclass ArgumentConflict\ndef check_labextension(files, user=False, prefix=None, labextensions_dir=None, sys_prefix=False) # Check whether labextension files have been installed\ndef install_labextension(path, name, overwrite=False, symlink=False, user=False, prefix=None, labextensions_dir=None, logger=None, sys_prefix=False) # Install a Javascript extension for JupyterLab\ndef install_labextension_python(module, overwrite=False, symlink=False, user=False, sys_prefix=False, prefix=None, labextensions_dir=None, logger=None) # Install a labextension bundled in a Python package.\ndef uninstall_labextension(name, user=False, sys_prefix=False, prefix=None, labextensions_dir=None, logger=None) # Uninstall a Javascript extension of JupyterLab\ndef uninstall_labextension_python(module, user=False, sys_prefix=False, prefix=None, labextensions_dir=None, logger=None) # Uninstall a labextension bundled in a Python package.\ndef _set_labextension_state(name, state, user=True, sys_prefix=False, logger=None, python_module=None) # Set whether the JupyterLab frontend should use the named labextension\ndef _set_labextension_state_python(state, module, user, sys_prefix, logger=None) # Enable or disable some labextensions stored in a Python package\ndef enable_labextension(name, user=True, sys_prefix=False, logger=None) # Enable a named labextension\ndef disable_labextension(name, user=True, sys_prefix=False, logger=None) # Disable a named labextension\ndef enable_labextension_python(module, user=True, sys_prefix=False, logger=None) # Enable some labextensions associated with a Python module.\ndef disable_labextension_python(module, user=True, sys_prefix=False, logger=None) # Disable some labextensions associated with a Python module.\ndef find_labextension(name) # Find a labextension path\ndef validate_labextension_folder(name, full_dest, logger=None) # Assess the health of an installed labextension\ndef get_labextension_manifest_data_by_folder(folder) # Get the manifest data for a given lab extension folder\ndef get_labextension_manifest_data_by_name(name) # Get the manifest data for a given lab extension folder\ndef get_labextension_config_python(module) # Get the labextension configuration data associated with a Python module. \nclass BaseLabExtensionApp # Base labextension installer app\n def _log_format_default(self) # A default format for messages\nclass InstallLabExtensionApp # Entry point for installing JupyterLab extensions\n def _config_file_name_default(self) # The default config file name.\n def install_extensions(self) # Perform the installation of labextension(s)\n def start(self) # Perform the App's function as configured\nclass UninstallLabExtensionApp # Entry point for uninstalling JupyterLab extensions\n def _config_file_name_default(self) # The default config file name.\n def uninstall_extensions(self) # Uninstall some labextensions\n def start(self)\nclass ToggleLabExtensionApp # A base class for ap","label":1},{"cid":"jupyterlab/semver.py","text":"jupyterlab/semver.py\nclass _R\n def __init__(self, i)\n def __call__(self)\n def value(self)\nclass Extendlist\n def __setitem__(self, i, v)\ndef list_get(xs, i)\ndef parse(version, loose)\ndef valid(version, loose)\ndef clean(version, loose)\ndef semver(version, loose)\nclass SemVer\n def __init__(self, version, loose)\n def format(self)\n def __repr__(self)\n def __str__(self)\n def compare(self, other)\n def compare_main(self, other)\n def compare_pre(self, other)\n def inc(self, release)\n def _inc(self, release)\ndef inc(version, release, loose)\ndef compare_identifiers(a, b)\ndef rcompare_identifiers(a, b)\ndef compare(a, b, loose)\ndef compare_loose(a, b)\ndef rcompare(a, b, loose)\ndef sort(list, loose)\ndef rsort(list, loose)\ndef gt(a, b, loose)\ndef lt(a, b, loose)\ndef eq(a, b, loose)\ndef neq(a, b, loose)\ndef gte(a, b, loose)\ndef lte(a, b, loose)\ndef cmp(a, op, b, loose)\ndef comparator(comp, loose)\nclass Comparator\n def __init__(self, comp, loose)\n def parse(self, comp)\n def __repr__(self)\n def __str__(self)\n def test(self, version)\ndef make_range(range_, loose)\nclass Range\n def __init__(self, range_, loose)\n def __repr__(self)\n def format(self)\n def __str__(self)\n def parse_range(self, range_)\n def test(self, version)\ndef to_comparators(range_, loose)\ndef parse_comparator(comp, loose)\ndef is_x(id)\ndef replace_tildes(comp, loose)\ndef replace_tilde(comp, loose)\n def repl(mob)\ndef replace_carets(comp, loose)\ndef replace_caret(comp, loose)\n def repl(mob)\ndef replace_xranges(comp, loose)\ndef replace_xrange(comp, loose)\n def repl(mob)\ndef replace_stars(comp, loose)\ndef hyphen_replace(mob)\ndef test_set(set_, version)\ndef satisfies(version, range_, loose)\ndef max_satisfying(versions, range_, loose)\ndef valid_range(range_, loose)\ndef ltr(version, range_, loose)\ndef rtr(version, range_, loose)\ndef outside(version, range_, hilo, loose)","label":0},{"cid":"setup.py","text":"setup.py","label":0},{"cid":"tutorial/conf.py","text":"tutorial/conf.py\ndef setup(app)","label":0}],"string":"[\n {\n \"cid\": \"jupyterlab/__init__.py\",\n \"text\": \"jupyterlab/__init__.py\\ndef _jupyter_server_extension_paths()\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/_version.py\",\n \"text\": \"jupyterlab/_version.py\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py\",\n \"text\": \"jupyterlab/labextensions.py\\nclass ArgumentConflict\\ndef check_labextension(files, user=False, prefix=None, labextensions_dir=None, sys_prefix=False) # Check whether labextension files have been installed\\ndef install_labextension(path, name, overwrite=False, symlink=False, user=False, prefix=None, labextensions_dir=None, logger=None, sys_prefix=False) # Install a Javascript extension for JupyterLab\\ndef install_labextension_python(module, overwrite=False, symlink=False, user=False, sys_prefix=False, prefix=None, labextensions_dir=None, logger=None) # Install a labextension bundled in a Python package.\\ndef uninstall_labextension(name, user=False, sys_prefix=False, prefix=None, labextensions_dir=None, logger=None) # Uninstall a Javascript extension of JupyterLab\\ndef uninstall_labextension_python(module, user=False, sys_prefix=False, prefix=None, labextensions_dir=None, logger=None) # Uninstall a labextension bundled in a Python package.\\ndef _set_labextension_state(name, state, user=True, sys_prefix=False, logger=None, python_module=None) # Set whether the JupyterLab frontend should use the named labextension\\ndef _set_labextension_state_python(state, module, user, sys_prefix, logger=None) # Enable or disable some labextensions stored in a Python package\\ndef enable_labextension(name, user=True, sys_prefix=False, logger=None) # Enable a named labextension\\ndef disable_labextension(name, user=True, sys_prefix=False, logger=None) # Disable a named labextension\\ndef enable_labextension_python(module, user=True, sys_prefix=False, logger=None) # Enable some labextensions associated with a Python module.\\ndef disable_labextension_python(module, user=True, sys_prefix=False, logger=None) # Disable some labextensions associated with a Python module.\\ndef find_labextension(name) # Find a labextension path\\ndef validate_labextension_folder(name, full_dest, logger=None) # Assess the health of an installed labextension\\ndef get_labextension_manifest_data_by_folder(folder) # Get the manifest data for a given lab extension folder\\ndef get_labextension_manifest_data_by_name(name) # Get the manifest data for a given lab extension folder\\ndef get_labextension_config_python(module) # Get the labextension configuration data associated with a Python module. \\nclass BaseLabExtensionApp # Base labextension installer app\\n def _log_format_default(self) # A default format for messages\\nclass InstallLabExtensionApp # Entry point for installing JupyterLab extensions\\n def _config_file_name_default(self) # The default config file name.\\n def install_extensions(self) # Perform the installation of labextension(s)\\n def start(self) # Perform the App's function as configured\\nclass UninstallLabExtensionApp # Entry point for uninstalling JupyterLab extensions\\n def _config_file_name_default(self) # The default config file name.\\n def uninstall_extensions(self) # Uninstall some labextensions\\n def start(self)\\nclass ToggleLabExtensionApp # A base class for ap\",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/semver.py\",\n \"text\": \"jupyterlab/semver.py\\nclass _R\\n def __init__(self, i)\\n def __call__(self)\\n def value(self)\\nclass Extendlist\\n def __setitem__(self, i, v)\\ndef list_get(xs, i)\\ndef parse(version, loose)\\ndef valid(version, loose)\\ndef clean(version, loose)\\ndef semver(version, loose)\\nclass SemVer\\n def __init__(self, version, loose)\\n def format(self)\\n def __repr__(self)\\n def __str__(self)\\n def compare(self, other)\\n def compare_main(self, other)\\n def compare_pre(self, other)\\n def inc(self, release)\\n def _inc(self, release)\\ndef inc(version, release, loose)\\ndef compare_identifiers(a, b)\\ndef rcompare_identifiers(a, b)\\ndef compare(a, b, loose)\\ndef compare_loose(a, b)\\ndef rcompare(a, b, loose)\\ndef sort(list, loose)\\ndef rsort(list, loose)\\ndef gt(a, b, loose)\\ndef lt(a, b, loose)\\ndef eq(a, b, loose)\\ndef neq(a, b, loose)\\ndef gte(a, b, loose)\\ndef lte(a, b, loose)\\ndef cmp(a, op, b, loose)\\ndef comparator(comp, loose)\\nclass Comparator\\n def __init__(self, comp, loose)\\n def parse(self, comp)\\n def __repr__(self)\\n def __str__(self)\\n def test(self, version)\\ndef make_range(range_, loose)\\nclass Range\\n def __init__(self, range_, loose)\\n def __repr__(self)\\n def format(self)\\n def __str__(self)\\n def parse_range(self, range_)\\n def test(self, version)\\ndef to_comparators(range_, loose)\\ndef parse_comparator(comp, loose)\\ndef is_x(id)\\ndef replace_tildes(comp, loose)\\ndef replace_tilde(comp, loose)\\n def repl(mob)\\ndef replace_carets(comp, loose)\\ndef replace_caret(comp, loose)\\n def repl(mob)\\ndef replace_xranges(comp, loose)\\ndef replace_xrange(comp, loose)\\n def repl(mob)\\ndef replace_stars(comp, loose)\\ndef hyphen_replace(mob)\\ndef test_set(set_, version)\\ndef satisfies(version, range_, loose)\\ndef max_satisfying(versions, range_, loose)\\ndef valid_range(range_, loose)\\ndef ltr(version, range_, loose)\\ndef rtr(version, range_, loose)\\ndef outside(version, range_, hilo, loose)\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\",\n \"label\": 0\n },\n {\n \"cid\": \"tutorial/conf.py\",\n \"text\": \"tutorial/conf.py\\ndef setup(app)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"jupyterlab/__init__.py::_jupyter_server_extension_paths","text":"jupyterlab/__init__.py::_jupyter_server_extension_paths\ndef _jupyter_server_extension_paths():\n return [{\n \"module\": \"jupyterlab\"\n }]","label":0},{"cid":"jupyterlab/labextensions.py::check_labextension","text":"jupyterlab/labextensions.py::check_labextension\ndef check_labextension(files, user=False, prefix=None, labextensions_dir=None, sys_prefix=False):\n \"\"\"Check whether labextension files have been installed\n \n Returns True if all files are found, False if any are missing.\n\n Parameters\n ----------\n files : list(paths)\n a list of relative paths within labextensions.\n user : bool [default: False]\n Whether to check the user's .jupyter/labextensions directory.\n Otherwise check a system-wide install (e.g. /usr/local/share/jupyter/labextensions).\n prefix : str [optional]\n Specify install prefix, if it should differ from default (e.g. /usr/local).\n Will check prefix/share/jupyter/labextensions\n labextensions_dir : str [optional]\n Specify absolute path of labextensions directory explicitly.\n sys_prefix : bool [default: False]\n Install into the sys.prefix, i.e. environment\n \"\"\"\n labext = _get_labextension_dir(user=user, sys_prefix=sys_prefix, prefix=prefix, labextensions_dir=labextensions_dir)\n # make sure labextensions dir exists\n if not os.path.exists(labext):\n return False\n \n if isinstance(files, string_types):\n # one file given, turn it into a list\n files = [files]\n \n return all(os.path.exists(pjoin(labext, f)) for f in files)","label":0},{"cid":"jupyterlab/labextensions.py::install_labextension","text":"jupyterlab/labextensions.py::install_labextension\ndef install_labextension(path, name, overwrite=False, symlink=False,\n user=False, prefix=None, labextensions_dir=None,\n logger=None, sys_prefix=False\n ):\n \"\"\"Install a Javascript extension for JupyterLab\n \n Stages files and/or directories into the labextensions directory.\n By default, this compares modification time, and only stages files that need updating.\n If `overwrite` is specified, matching files are purged before proceeding.\n \n Parameters\n ----------\n path : path to file, directory, zip or tarball archive, or URL to install\n Archives (zip or tarballs) will be extracted into the labextensions directory.\n name : str\n name the labextension is installed to. For example, if name is 'foo', then\n the source file will be installed to 'labextensions/foo'.\n overwrite : bool [default: False]\n If True, always install the files, regardless of what may already be installed.\n symlink : bool [default: False]\n If True, create a symlink in labextensions, rather than copying files.\n Not allowed with URLs or archives. Windows support for symlinks requires\n Vista or above, Python 3, and a permission bit which only admin users\n have by default, so don't rely on it.\n user : bool [default: False]\n Whether to install to the user's labextensions directory.\n Otherwise do a system-wide install (e.g. /usr/local/share/jupyter/labextensions).\n prefix : str [optional]\n Specify install prefix, if it should differ from default (e.g. /usr/local).\n Will install to ``<prefix>/share/jupyter/labextensions``\n labextensions_dir : str [optional]\n Specify absolute path of labextensions directory explicitly.\n logger : Jupyter logger [optional]\n Logger instance to use\n sys_prefix : bool [default: False]\n Install into the sys.prefix,","label":0},{"cid":"jupyterlab/labextensions.py::install_labextension_python","text":"jupyterlab/labextensions.py::install_labextension_python\ndef install_labextension_python(module, overwrite=False, symlink=False,\n user=False, sys_prefix=False, prefix=None, labextensions_dir=None, logger=None):\n \"\"\"Install a labextension bundled in a Python package.\n\n Returns a list of installed/updated directories.\n\n See install_labextension for parameter information.\"\"\"\n m, labexts = _get_labextension_metadata(module)\n base_path = os.path.split(m.__file__)[0]\n\n full_dests = []\n\n for labext in labexts:\n src = os.path.join(base_path, labext['src'])\n name = labext['name']\n\n if logger:\n logger.info(\"Installing %s -> %s\" % (src, name))\n full_dest = install_labextension(\n src, name=name, overwrite=overwrite, symlink=symlink,\n user=user, sys_prefix=sys_prefix, prefix=prefix, labextensions_dir=labextensions_dir,\n logger=logger\n )\n validate_labextension_folder(name, full_dest, logger)\n full_dests.append(full_dest)\n\n return full_dests","label":0},{"cid":"jupyterlab/labextensions.py::uninstall_labextension","text":"jupyterlab/labextensions.py::uninstall_labextension\ndef uninstall_labextension(name, user=False, sys_prefix=False, prefix=None, \n labextensions_dir=None, logger=None):\n \"\"\"Uninstall a Javascript extension of JupyterLab\n \n Removes staged files and/or directories in the labextensions directory and \n removes the extension from the frontend config.\n \n Parameters\n ----------\n name: str\n The name of the labextension.\n user : bool [default: False]\n Whether to uninstall from the user's labextensions directory.\n Otherwise do a system-wide uninstall (e.g. /usr/local/share/jupyter/labextensions).\n sys_prefix : bool [default: False]\n Uninstall from the sys.prefix, i.e. environment\n prefix : str [optional]\n Specify prefix, if it should differ from default (e.g. /usr/local).\n Will uninstall from ``<prefix>/share/jupyter/labextensions``\n labextensions_dir : str [optional]\n Specify absolute path of labextensions directory explicitly.\n logger : Jupyter logger [optional]\n Logger instance to use\n \"\"\"\n labext = _get_labextension_dir(user=user, sys_prefix=sys_prefix, prefix=prefix, labextensions_dir=labextensions_dir)\n dest = cast_unicode_py2(name)\n full_dest = pjoin(labext, dest)\n if os.path.lexists(full_dest):\n if logger:\n logger.info(\"Removing: %s\" % full_dest)\n if os.path.isdir(full_dest) and not os.path.islink(full_dest):\n shutil.rmtree(full_dest)\n else:\n os.remove(full_dest)\n disable_labextension(name, user=user, sys_prefix=sys_prefix,\n logger=logger)","label":0},{"cid":"jupyterlab/labextensions.py::uninstall_labextension_python","text":"jupyterlab/labextensions.py::uninstall_labextension_python\ndef uninstall_labextension_python(module,\n user=False, sys_prefix=False, prefix=None, labextensions_dir=None,\n logger=None):\n \"\"\"Uninstall a labextension bundled in a Python package.\n \n See parameters of `install_labextension_python`\n \"\"\"\n m, labexts = _get_labextension_metadata(module)\n for labext in labexts:\n name = labext['name']\n if logger:\n logger.info(\"Uninstalling {}\".format(name))\n uninstall_labextension(name, user=user, sys_prefix=sys_prefix, \n prefix=prefix, labextensions_dir=labextensions_dir, logger=logger)","label":0},{"cid":"jupyterlab/labextensions.py::_set_labextension_state","text":"jupyterlab/labextensions.py::_set_labextension_state\ndef _set_labextension_state(name, state,\n user=True, sys_prefix=False, logger=None,\n python_module=None):\n \"\"\"Set whether the JupyterLab frontend should use the named labextension\n\n Returns True if the final state is the one requested.\n\n Parameters\n name : string\n The name of the extension.\n state : bool\n The state in which to leave the extension\n user : bool [default: True]\n Whether to update the user's .jupyter/labextensions directory\n sys_prefix : bool [default: False]\n Whether to update the sys.prefix, i.e. environment. Will override\n `user`.\n logger : Jupyter logger [optional]\n Logger instance to use [optional]\n python_module: string\n The name of the python module associated with the extension.\n \"\"\"\n user = False if sys_prefix else user\n extensions = _read_config_data('labextensions', user=user,\n sys_prefix=sys_prefix)\n if logger:\n logger.info(\"{} extension {}...\".format(\n \"Enabling\" if state else \"Disabling\",\n name\n ))\n\n old_state = extensions.get(name, None)\n if old_state is None:\n old_state = dict(enabled=False)\n elif isinstance(old_state, bool):\n old_state = dict(enabled=old_state)\n old_enabled = old_state['enabled']\n new_enabled = state if state is not None else not old_enabled\n\n if logger:\n if new_enabled:\n logger.info(u\"Enabling: %s\" % (name))\n else:\n logger.info(u\"Disabling: %s\" % (name))\n\n extensions[name] = dict(\n enabled=new_enabled,\n python_module=python_module\n )\n\n _write_config_data('labextensions', extensions, user=user,\n sys_prefix=sys_prefix, logger=logger)\n\n if new_enabled:\n full_dest = find_labextension(name)\n validate_labextension_folder(name, full_dest, l","label":0},{"cid":"jupyterlab/labextensions.py::_set_labextension_state_python","text":"jupyterlab/labextensions.py::_set_labextension_state_python\ndef _set_labextension_state_python(state, module, user, sys_prefix,\n logger=None):\n \"\"\"Enable or disable some labextensions stored in a Python package\n\n Returns a list of whether the state was achieved (i.e. changed, or was\n already right)\n\n Parameters\n ----------\n\n state : Bool\n Whether the extensions should be enabled\n module : str\n Importable Python module exposing the\n magic-named `_jupyter_labextension_paths` function\n user : bool\n Whether to enable in the user's labextensions directory.\n sys_prefix : bool\n Enable/disable in the sys.prefix, i.e. environment\n logger : Jupyter logger [optional]\n Logger instance to use\n \"\"\"\n m, labexts = _get_labextension_metadata(module)\n return [_set_labextension_state(name=labext[\"name\"],\n state=state,\n user=user, sys_prefix=sys_prefix,\n logger=logger,\n python_module=module)\n for labext in labexts]","label":0},{"cid":"jupyterlab/labextensions.py::enable_labextension","text":"jupyterlab/labextensions.py::enable_labextension\ndef enable_labextension(name, user=True, sys_prefix=False,\n logger=None):\n \"\"\"Enable a named labextension\n\n Returns True if the final state is the one requested.\n\n Parameters\n ----------\n name : string\n The name of the extension.\n user : bool [default: True]\n Whether to enable in the user's labextensions directory.\n sys_prefix : bool [default: False]\n Whether to enable in the sys.prefix, i.e. environment. Will override\n `user`\n logger : Jupyter logger [optional]\n Logger instance to use\n \"\"\"\n return _set_labextension_state(name=name,\n state=True,\n user=user, sys_prefix=sys_prefix,\n logger=logger)","label":0},{"cid":"jupyterlab/labextensions.py::disable_labextension","text":"jupyterlab/labextensions.py::disable_labextension\ndef disable_labextension(name, user=True, sys_prefix=False,\n logger=None):\n \"\"\"Disable a named labextension\n \n Returns True if the final state is the one requested.\n\n Parameters\n ----------\n name : string\n The name of the extension.\n user : bool [default: True]\n Whether to enable in the user's labextensions directory.\n sys_prefix : bool [default: False]\n Whether to enable in the sys.prefix, i.e. environment. Will override\n `user`.\n logger : Jupyter logger [optional]\n Logger instance to use\n \"\"\"\n return _set_labextension_state(name=name,\n state=False,\n user=user, sys_prefix=sys_prefix,\n logger=logger)","label":0},{"cid":"jupyterlab/labextensions.py::enable_labextension_python","text":"jupyterlab/labextensions.py::enable_labextension_python\ndef enable_labextension_python(module, user=True, sys_prefix=False,\n logger=None):\n \"\"\"Enable some labextensions associated with a Python module.\n\n Returns a list of whether the state was achieved (i.e. changed, or was\n already right)\n\n Parameters\n ----------\n\n module : str\n Importable Python module exposing the\n magic-named `_jupyter_labextension_paths` function\n user : bool [default: True]\n Whether to enable in the user's labextensions directory.\n sys_prefix : bool [default: False]\n Whether to enable in the sys.prefix, i.e. environment. Will override\n `user`\n logger : Jupyter logger [optional]\n Logger instance to use\n \"\"\"\n return _set_labextension_state_python(True, module, user, sys_prefix,\n logger=logger)","label":0},{"cid":"jupyterlab/labextensions.py::disable_labextension_python","text":"jupyterlab/labextensions.py::disable_labextension_python\ndef disable_labextension_python(module, user=True, sys_prefix=False,\n logger=None):\n \"\"\"Disable some labextensions associated with a Python module.\n\n Returns True if the final state is the one requested.\n\n Parameters\n ----------\n module : str\n Importable Python module exposing the\n magic-named `_jupyter_labextension_paths` function\n user : bool [default: True]\n Whether to enable in the user's labextensions directory.\n sys_prefix : bool [default: False]\n Whether to enable in the sys.prefix, i.e. environment\n logger : Jupyter logger [optional]\n Logger instance to use\n \"\"\"\n return _set_labextension_state_python(False, module, user, sys_prefix,\n logger=logger)","label":0},{"cid":"jupyterlab/labextensions.py::find_labextension","text":"jupyterlab/labextensions.py::find_labextension\ndef find_labextension(name):\n \"\"\"Find a labextension path\n\n Looks across all of the labextension directories.\n\n Returns the first path where the extension is found,\n or None if not found.\n\n Parameters\n ----------\n name : str\n The name of the extension.\n \"\"\"\n for exts in jupyter_path('labextensions'):\n full_dest = os.path.join(exts, name)\n if os.path.exists(full_dest):\n return full_dest\n return None","label":0},{"cid":"jupyterlab/labextensions.py::validate_labextension_folder","text":"jupyterlab/labextensions.py::validate_labextension_folder\ndef validate_labextension_folder(name, full_dest, logger=None):\n \"\"\"Assess the health of an installed labextension\n\n Returns a list of warnings.\n\n Parameters\n ----------\n full_dest : str\n The on-disk location of the installed labextension: this should end\n with `labextensions/<name>`\n logger : Jupyter logger [optional]\n Logger instance to use\n \"\"\"\n infos = []\n warnings = []\n\n has_files = []\n has_entry = False\n version_compatible = []\n\n data = get_labextension_manifest_data_by_folder(full_dest)\n for manifest in data.values():\n if ('entry' in manifest and 'modules' in manifest):\n if (manifest['entry'] in manifest['modules']):\n has_entry = True\n files = manifest.get('files', [])\n if not files:\n has_files.append(\"No 'files' key in manifest\")\n else:\n for fname in files:\n path = os.path.join(full_dest, fname)\n if not os.path.exists(path):\n has_files.append(\"File in manifest does not exist: {}\".format(path))\n\n for (mod, deps) in manifest.get('modules', {}).items():\n # ignore jupyterlab modules since\n # they are only semver compatibile with their own version\n if mod.startswith('jupyterlab@'):\n continue\n for dep in deps:\n if dep.startswith('jupyterlab@'):\n dep = dep.split('/')[0].split('@')[-1]\n if not satisfies(__version__, dep, False):\n version_compatible.append('Expects JupyterLab version %s from packaged module %s'%(dep, mod))\n break\n\n indent = \" \"\n subindent = indent + \" \"\n entry_msg = indent + u\"{} has entry point in manifest\"\n name = os.path.basename(full_dest)\n if has_entry:\n infos.append(entry_msg.format(GREEN_OK))\n else:\n ","label":1},{"cid":"jupyterlab/labextensions.py::get_labextension_manifest_data_by_folder","text":"jupyterlab/labextensions.py::get_labextension_manifest_data_by_folder\ndef get_labextension_manifest_data_by_folder(folder):\n \"\"\"Get the manifest data for a given lab extension folder\n \"\"\"\n manifest_files = glob.glob(os.path.join(folder, '*.manifest'))\n manifests = {}\n for file in manifest_files:\n with open(file) as fid:\n manifest = json.load(fid)\n manifests[manifest['name']] = manifest\n return manifests","label":0},{"cid":"jupyterlab/labextensions.py::get_labextension_manifest_data_by_name","text":"jupyterlab/labextensions.py::get_labextension_manifest_data_by_name\ndef get_labextension_manifest_data_by_name(name):\n \"\"\"Get the manifest data for a given lab extension folder\n \"\"\"\n for exts in jupyter_path('labextensions'):\n full_dest = os.path.join(exts, name)\n if os.path.exists(full_dest):\n return get_labextension_manifest_data_by_folder(full_dest)","label":0},{"cid":"jupyterlab/labextensions.py::get_labextension_config_python","text":"jupyterlab/labextensions.py::get_labextension_config_python\ndef get_labextension_config_python(module):\n \"\"\"Get the labextension configuration data associated with a Python module. \n\n Parameters\n -----------\n module : str\n Importable Python module exposing the\n magic-named `_jupyter_labextension_config` function\n \"\"\"\n m = import_item(module)\n if not hasattr(m, '_jupyter_labextension_config'):\n return {}\n return m._jupyter_labextension_config()","label":0},{"cid":"jupyterlab/labextensions.py::BaseLabExtensionApp._log_format_default","text":"jupyterlab/labextensions.py::BaseLabExtensionApp._log_format_default\n def _log_format_default(self):\n \"\"\"A default format for messages\"\"\"\n return \"%(message)s\"","label":0},{"cid":"jupyterlab/labextensions.py::InstallLabExtensionApp._config_file_name_default","text":"jupyterlab/labextensions.py::InstallLabExtensionApp._config_file_name_default\n def _config_file_name_default(self):\n \"\"\"The default config file name.\"\"\"\n return 'jupyterlab_config'","label":0},{"cid":"jupyterlab/labextensions.py::InstallLabExtensionApp.install_extensions","text":"jupyterlab/labextensions.py::InstallLabExtensionApp.install_extensions\n def install_extensions(self):\n \"\"\"Perform the installation of labextension(s)\"\"\"\n if self.python:\n if len(self.extra_args) > 1:\n raise ValueError(\"Only one labextension allowed at a time. \"\n \"Call multiple times to install multiple extensions.\")\n install = install_labextension_python\n kwargs = {}\n else:\n if len(self.extra_args) > 2:\n raise ValueError(\"Only one labextension allowed at a time. \"\n \"Call multiple times to install multiple extensions.\")\n install = install_labextension\n kwargs = {'name': self.extra_args[1]}\n \n full_dests = install(self.extra_args[0],\n overwrite=self.overwrite,\n symlink=self.symlink,\n user=self.user,\n sys_prefix=self.sys_prefix,\n prefix=self.prefix,\n labextensions_dir=self.labextensions_dir,\n logger=self.log,\n **kwargs\n )\n\n if full_dests:\n self.log.info(\n u\"\\nTo enable this labextension in the browser every time\"\n \" JupyterLab loads:\\n\\n\"\n \" jupyter labextension enable {}{}{}{}\\n\".format(\n self.extra_args[0] if self.python else self.extra_args[1],\n \" --user\" if self.user else \"\",\n \" --py\" if self.python else \"\",\n \" --sys-prefix\" if self.sys_prefix else \"\"\n )\n )","label":0},{"cid":"jupyterlab/labextensions.py::InstallLabExtensionApp.start","text":"jupyterlab/labextensions.py::InstallLabExtensionApp.start\n def start(self):\n \"\"\"Perform the App's function as configured\"\"\"\n if not self.extra_args:\n sys.exit('Please specify a labextension to install')\n else:\n try:\n self.install_extensions()\n except ArgumentConflict as e:\n sys.exit(str(e))","label":0},{"cid":"jupyterlab/labextensions.py::UninstallLabExtensionApp._config_file_name_default","text":"jupyterlab/labextensions.py::UninstallLabExtensionApp._config_file_name_default\n def _config_file_name_default(self):\n \"\"\"The default config file name.\"\"\"\n return 'jupyterlab_config'","label":0},{"cid":"jupyterlab/labextensions.py::UninstallLabExtensionApp.uninstall_extensions","text":"jupyterlab/labextensions.py::UninstallLabExtensionApp.uninstall_extensions\n def uninstall_extensions(self):\n \"\"\"Uninstall some labextensions\"\"\"\n kwargs = {\n 'user': self.user,\n 'sys_prefix': self.sys_prefix,\n 'prefix': self.prefix,\n 'labextensions_dir': self.labextensions_dir,\n 'logger': self.log\n }\n \n arg_count = 1\n if len(self.extra_args) > arg_count:\n raise ValueError(\"only one labextension allowed at a time. Call multiple times to uninstall multiple extensions.\")\n if len(self.extra_args) < arg_count:\n raise ValueError(\"not enough arguments\")\n \n if self.python:\n uninstall_labextension_python(self.extra_args[0], **kwargs)\n else:\n uninstall_labextension(self.extra_args[0], **kwargs)","label":0},{"cid":"jupyterlab/labextensions.py::UninstallLabExtensionApp.start","text":"jupyterlab/labextensions.py::UninstallLabExtensionApp.start\n def start(self):\n if not self.extra_args:\n sys.exit('Please specify a labextension to uninstall')\n else:\n try:\n self.uninstall_extensions()\n except ArgumentConflict as e:\n sys.exit(str(e))","label":0},{"cid":"jupyterlab/labextensions.py::ToggleLabExtensionApp._config_file_name_default","text":"jupyterlab/labextensions.py::ToggleLabExtensionApp._config_file_name_default\n def _config_file_name_default(self):\n \"\"\"The default config file name.\"\"\"\n return 'jupyterlab_config'","label":0},{"cid":"jupyterlab/labextensions.py::ToggleLabExtensionApp.toggle_labextension_python","text":"jupyterlab/labextensions.py::ToggleLabExtensionApp.toggle_labextension_python\n def toggle_labextension_python(self, module):\n \"\"\"Toggle some extensions in an importable Python module.\n\n Returns a list of booleans indicating whether the state was changed as\n requested.\n\n Parameters\n ----------\n module : str\n Importable Python module exposing the\n magic-named `_jupyter_labextension_paths` function\n \"\"\"\n toggle = (enable_labextension_python if self._toggle_value\n else disable_labextension_python)\n return toggle(module,\n user=self.user,\n sys_prefix=self.sys_prefix,\n logger=self.log)","label":0},{"cid":"jupyterlab/labextensions.py::ToggleLabExtensionApp.toggle_labextension","text":"jupyterlab/labextensions.py::ToggleLabExtensionApp.toggle_labextension\n def toggle_labextension(self, name):\n \"\"\"Toggle some a named labextension by require-able AMD module.\n\n Returns whether the state was changed as requested.\n\n Parameters\n ----------\n require : str\n require.js path used to load the labextension\n \"\"\"\n toggle = (enable_labextension if self._toggle_value\n else disable_labextension)\n return toggle(name,\n user=self.user, sys_prefix=self.sys_prefix,\n logger=self.log)","label":0},{"cid":"jupyterlab/labextensions.py::ToggleLabExtensionApp.start","text":"jupyterlab/labextensions.py::ToggleLabExtensionApp.start\n def start(self):\n if not self.extra_args:\n sys.exit('Please specify a labextension/package to enable or disable')\n elif len(self.extra_args) > 1:\n sys.exit('Please specify one labextension/package at a time')\n if self.python:\n self.toggle_labextension_python(self.extra_args[0])\n else:\n self.toggle_labextension(self.extra_args[0])","label":0},{"cid":"jupyterlab/labextensions.py::ListLabExtensionsApp.list_labextensions","text":"jupyterlab/labextensions.py::ListLabExtensionsApp.list_labextensions\n def list_labextensions(self):\n \"\"\"List all the labextensions\"\"\"\n print(\"Known labextensions:\")\n seen = False\n for config_dir in jupyter_config_path():\n config_dir = os.path.join(config_dir, CONFIG_DIR)\n cm = BaseJSONConfigManager(parent=self, config_dir=config_dir)\n labextensions = cm.get('labextensions')\n if labextensions:\n print(u'config dir: {}'.format(config_dir))\n seen = True\n for name, config in sorted(labextensions.items()):\n if isinstance(config, bool):\n config = dict(enabled=config)\n enabled = config['enabled']\n full_dest = find_labextension(name)\n print(u' {} {}: {}'.format(\n name,\n GREEN_ENABLED if enabled else RED_DISABLED,\n full_dest if not None else RED_X+\" Files not found\"\n ))\n if full_dest is not None:\n validate_labextension_folder(name, full_dest, self.log)\n if not seen:\n print('....None found!')","label":0},{"cid":"jupyterlab/labextensions.py::ListLabExtensionsApp.start","text":"jupyterlab/labextensions.py::ListLabExtensionsApp.start\n def start(self):\n \"\"\"Perform the App's functions as configured\"\"\"\n self.list_labextensions()","label":0},{"cid":"jupyterlab/labextensions.py::LabExtensionApp.start","text":"jupyterlab/labextensions.py::LabExtensionApp.start\n def start(self):\n \"\"\"Perform the App's functions as configured\"\"\"\n super(LabExtensionApp, self).start()\n\n # The above should have called a subcommand and raised NoStart; if we\n # get here, it didn't, so we should self.log.info a message.\n subcmds = \", \".join(sorted(self.subcommands))\n sys.exit(\"Please supply at least one subcommand: %s\" % subcmds)","label":0},{"cid":"jupyterlab/labextensions.py::_should_copy","text":"jupyterlab/labextensions.py::_should_copy\ndef _should_copy(src, dest, logger=None):\n \"\"\"Should a file be copied, if it doesn't exist, or is newer?\n\n Returns whether the file needs to be updated.\n\n Parameters\n ----------\n\n src : string\n A path that should exist from which to copy a file\n src : string\n A path that might exist to which to copy a file\n logger : Jupyter logger [optional]\n Logger instance to use\n \"\"\"\n if not os.path.exists(dest):\n return True\n if os.stat(src).st_mtime - os.stat(dest).st_mtime > 1e-6:\n # we add a fudge factor to work around a bug in python 2.x\n # that was fixed in python 3.x: http://bugs.python.org/issue12904\n if logger:\n logger.warn(\"Out of date: %s\" % dest)\n return True\n if logger:\n logger.info(\"Up to date: %s\" % dest)\n return False","label":0},{"cid":"jupyterlab/labextensions.py::_maybe_copy","text":"jupyterlab/labextensions.py::_maybe_copy\ndef _maybe_copy(src, dest, logger=None):\n \"\"\"Copy a file if it needs updating.\n\n Parameters\n ----------\n\n src : string\n A path that should exist from which to copy a file\n src : string\n A path that might exist to which to copy a file\n logger : Jupyter logger [optional]\n Logger instance to use\n \"\"\"\n if _should_copy(src, dest, logger=logger):\n if logger:\n logger.info(\"Copying: %s -> %s\" % (src, dest))\n shutil.copy2(src, dest)","label":0},{"cid":"jupyterlab/labextensions.py::_safe_is_tarfile","text":"jupyterlab/labextensions.py::_safe_is_tarfile\ndef _safe_is_tarfile(path):\n \"\"\"Safe version of is_tarfile, return False on IOError.\n\n Returns whether the file exists and is a tarfile.\n\n Parameters\n ----------\n\n path : string\n A path that might not exist and or be a tarfile\n \"\"\"\n try:\n return tarfile.is_tarfile(path)\n except IOError:\n return False","label":0},{"cid":"jupyterlab/labextensions.py::_get_labextension_dir","text":"jupyterlab/labextensions.py::_get_labextension_dir\ndef _get_labextension_dir(user=False, sys_prefix=False, prefix=None, labextensions_dir=None):\n \"\"\"Return the labextension directory specified\n\n Parameters\n ----------\n\n user : bool [default: False]\n Get the user's .jupyter/labextensions directory\n sys_prefix : bool [default: False]\n Get sys.prefix, i.e. ~/.envs/my-env/share/jupyter/labextensions\n prefix : str [optional]\n Get custom prefix\n labextensions_dir : str [optional]\n Get what you put in\n \"\"\"\n if sum(map(bool, [user, prefix, labextensions_dir, sys_prefix])) > 1:\n raise ArgumentConflict(\"cannot specify more than one of user, sys_prefix, prefix, or labextensions_dir\")\n if user:\n labext = pjoin(jupyter_data_dir(), u'labextensions')\n elif sys_prefix:\n labext = pjoin(ENV_JUPYTER_PATH[0], u'labextensions')\n elif prefix:\n labext = pjoin(prefix, 'share', 'jupyter', 'labextensions')\n elif labextensions_dir:\n labext = labextensions_dir\n else:\n labext = pjoin(SYSTEM_JUPYTER_PATH[0], 'labextensions')\n return labext","label":0},{"cid":"jupyterlab/labextensions.py::_get_config_dir","text":"jupyterlab/labextensions.py::_get_config_dir\ndef _get_config_dir(user=False, sys_prefix=False):\n \"\"\"Get the location of config files for the current context\n\n Returns the string to the enviornment\n\n Parameters\n ----------\n\n user : bool [default: False]\n Get the user's .jupyter config directory\n sys_prefix : bool [default: False]\n Get sys.prefix, i.e. ~/.envs/my-env/etc/jupyter\n \"\"\"\n user = False if sys_prefix else user\n if user and sys_prefix:\n raise ArgumentConflict(\"Cannot specify more than one of user or sys_prefix\")\n if user:\n labext = jupyter_config_dir()\n elif sys_prefix:\n labext = ENV_CONFIG_PATH[0]\n else:\n labext = SYSTEM_CONFIG_PATH[0]\n return os.path.join(labext, CONFIG_DIR)","label":0},{"cid":"jupyterlab/labextensions.py::_get_labextension_metadata","text":"jupyterlab/labextensions.py::_get_labextension_metadata\ndef _get_labextension_metadata(module):\n \"\"\"Get the list of labextension paths associated with a Python module.\n\n Returns a tuple of (the module, [{\n 'name': 'mockextension',\n 'src': 'static',\n }])\n\n Parameters\n ----------\n\n module : str\n Importable Python module exposing the\n magic-named `_jupyter_labextension_paths` function\n \"\"\"\n m = import_item(module)\n if not hasattr(m, '_jupyter_labextension_paths'):\n raise KeyError('The Python module {} is not a valid labextension'.format(module))\n labexts = m._jupyter_labextension_paths()\n return m, labexts","label":0},{"cid":"jupyterlab/labextensions.py::_read_config_data","text":"jupyterlab/labextensions.py::_read_config_data\ndef _read_config_data(section, user=False, sys_prefix=False):\n \"\"\"Get the config for the current context\n\n Returns the string to the enviornment\n\n Parameters\n ----------\n section: string\n The section of config to read.\n user : bool [default: False]\n Get the user's .jupyter config directory\n sys_prefix : bool [default: False]\n Get sys.prefix, i.e. ~/.envs/my-env/etc/jupyter\n \"\"\"\n config_dir = _get_config_dir(user=user, sys_prefix=sys_prefix)\n return BaseJSONConfigManager(config_dir=config_dir).get(section)","label":0},{"cid":"jupyterlab/labextensions.py::_write_config_data","text":"jupyterlab/labextensions.py::_write_config_data\ndef _write_config_data(section, data, user=False, sys_prefix=False, logger=None):\n \"\"\"Update the config for the current context\n\n Parameters\n ----------\n section: string\n The section of data to update.\n data : object\n An object which can be accepted by ConfigManager.update\n user : bool [default: False]\n Get the user's .jupyter config directory\n sys_prefix : bool [default: False]\n Get sys.prefix, i.e. ~/.envs/my-env/etc/jupyter\n logger: logger instance\n The logger instance.\n \"\"\"\n config_dir = _get_config_dir(user=user, sys_prefix=sys_prefix)\n if logger:\n logger.info(u\"- Writing config: {}\".format(config_dir))\n config_man = BaseJSONConfigManager(config_dir=config_dir)\n config_man.update(section, data)","label":0},{"cid":"jupyterlab/semver.py::_R.__init__","text":"jupyterlab/semver.py::_R.__init__\n def __init__(self, i):\n self.i = i","label":0},{"cid":"jupyterlab/semver.py::_R.__call__","text":"jupyterlab/semver.py::_R.__call__\n def __call__(self):\n v = self.i\n self.i += 1\n return v","label":0},{"cid":"jupyterlab/semver.py::_R.value","text":"jupyterlab/semver.py::_R.value\n def value(self):\n return self.i","label":0},{"cid":"jupyterlab/semver.py::Extendlist.__setitem__","text":"jupyterlab/semver.py::Extendlist.__setitem__\n def __setitem__(self, i, v):\n try:\n list.__setitem__(self, i, v)\n except IndexError:\n if len(self) == i:\n self.append(v)\n else:\n raise","label":0},{"cid":"jupyterlab/semver.py::list_get","text":"jupyterlab/semver.py::list_get\ndef list_get(xs, i):\n try:\n return xs[i]\n except IndexError:\n return None","label":0},{"cid":"jupyterlab/semver.py::parse","text":"jupyterlab/semver.py::parse\ndef parse(version, loose):\n if loose:\n r = regexp[LOOSE]\n else:\n r = regexp[FULL]\n m = r.search(version)\n if m:\n return semver(version, loose)\n else:\n return None","label":0},{"cid":"jupyterlab/semver.py::valid","text":"jupyterlab/semver.py::valid\ndef valid(version, loose):\n v = parse(version, loose)\n if v.version:\n return v\n else:\n return None","label":0},{"cid":"jupyterlab/semver.py::clean","text":"jupyterlab/semver.py::clean\ndef clean(version, loose):\n s = parse(version, loose)\n if s:\n return s.version\n else:\n return None","label":0},{"cid":"jupyterlab/semver.py::semver","text":"jupyterlab/semver.py::semver\ndef semver(version, loose):\n if isinstance(version, SemVer):\n if version.loose == loose:\n return version\n else:\n version = version.version\n elif not isinstance(version, str): # xxx:\n raise ValueError(\"Invalid Version: {}\".format(version))\n\n \"\"\"\n if (!(this instanceof SemVer))\n return new SemVer(version, loose);\n \"\"\"\n return SemVer(version, loose)","label":0},{"cid":"jupyterlab/semver.py::SemVer.__init__","text":"jupyterlab/semver.py::SemVer.__init__\n def __init__(self, version, loose):\n logger.debug(\"SemVer %s, %s\", version, loose)\n self.loose = loose\n self.raw = version\n\n m = regexp[LOOSE if loose else FULL].search(version.strip())\n if not m:\n if not loose:\n raise ValueError(\"Invalid Version: {}\".format(version))\n m = regexp[RECOVERYVERSIONNAME].search(version.strip())\n self.major = int(m.group(1)) if m.group(1) else 0\n self.minor = int(m.group(2)) if m.group(2) else 0\n self.patch = 0\n if not m.group(3):\n self.prerelease = []\n else:\n self.prerelease = [(int(id) if NUMERIC.search(id) else id)\n for id in m.group(3).split(\".\")]\n else:\n # these are actually numbers\n self.major = int(m.group(1))\n self.minor = int(m.group(2))\n self.patch = int(m.group(3))\n # numberify any prerelease numeric ids\n if not m.group(4):\n self.prerelease = []\n else:\n\n self.prerelease = [(int(id) if NUMERIC.search(id) else id)\n for id in m.group(4).split(\".\")]\n if m.group(5):\n self.build = m.group(5).split(\".\")\n else:\n self.build = []\n\n self.format() # xxx:","label":0},{"cid":"jupyterlab/semver.py::SemVer.format","text":"jupyterlab/semver.py::SemVer.format\n def format(self):\n self.version = \"{}.{}.{}\".format(self.major, self.minor, self.patch)\n if len(self.prerelease) > 0:\n self.version += (\"-{}\".format(\".\".join(str(v) for v in self.prerelease)))\n return self.version","label":0},{"cid":"jupyterlab/semver.py::SemVer.__repr__","text":"jupyterlab/semver.py::SemVer.__repr__\n def __repr__(self):\n return \"<SemVer {!r} >\".format(self)","label":0},{"cid":"jupyterlab/semver.py::SemVer.__str__","text":"jupyterlab/semver.py::SemVer.__str__\n def __str__(self):\n return self.version","label":0},{"cid":"jupyterlab/semver.py::SemVer.compare","text":"jupyterlab/semver.py::SemVer.compare\n def compare(self, other):\n logger.debug('SemVer.compare %s %s %s', self.version, self.loose, other)\n if not isinstance(other, SemVer):\n other = make_semver(other, self.loose)\n result = self.compare_main(other) or self.compare_pre(other)\n logger.debug(\"compare result %s\", result)\n return result","label":0},{"cid":"jupyterlab/semver.py::SemVer.compare_main","text":"jupyterlab/semver.py::SemVer.compare_main\n def compare_main(self, other):\n if not isinstance(other, SemVer):\n other = make_semver(other, self.loose)\n\n return (compare_identifiers(str(self.major), str(other.major)) or\n compare_identifiers(str(self.minor), str(other.minor)) or\n compare_identifiers(str(self.patch), str(other.patch)))","label":0},{"cid":"jupyterlab/semver.py::SemVer.compare_pre","text":"jupyterlab/semver.py::SemVer.compare_pre\n def compare_pre(self, other):\n if not isinstance(other, SemVer):\n other = make_semver(other, self.loose)\n\n # NOT having a prerelease is > having one\n is_self_more_than_zero = len(self.prerelease) > 0\n is_other_more_than_zero = len(other.prerelease) > 0\n\n if not is_self_more_than_zero and is_other_more_than_zero:\n return 1\n elif is_self_more_than_zero and not is_other_more_than_zero:\n return -1\n elif not is_self_more_than_zero and not is_other_more_than_zero:\n return 0\n\n i = 0\n while True:\n a = list_get(self.prerelease, i)\n b = list_get(other.prerelease, i)\n logger.debug(\"prerelease compare %s: %s %s\", i, a, b)\n i += 1\n if a is None and b is None:\n return 0\n elif b is None:\n return 1\n elif a is None:\n return -1\n elif a == b:\n continue\n else:\n return compare_identifiers(str(a), str(b))","label":0},{"cid":"jupyterlab/semver.py::SemVer.inc","text":"jupyterlab/semver.py::SemVer.inc\n def inc(self, release):\n self._inc(release)\n i = -1\n while len(self.prerelease) > 1 and self.prerelease[i] == 0:\n self.prerelease.pop()\n self.format()\n return self","label":0},{"cid":"jupyterlab/semver.py::SemVer._inc","text":"jupyterlab/semver.py::SemVer._inc\n def _inc(self, release):\n logger.debug(\"inc release %s %s\", self.prerelease, release)\n if release == 'premajor':\n self._inc(\"major\")\n self._inc(\"pre\")\n elif release == \"preminor\":\n self._inc(\"minor\")\n self._inc(\"pre\")\n elif release == \"prepatch\":\n self._inc(\"patch\")\n self._inc(\"pre\")\n elif release == 'prerelease':\n if len(self.prerelease) == 0:\n self._inc(\"patch\")\n self._inc(\"pre\")\n elif release == \"major\":\n self.major += 1\n self.minor = -1\n self.minor += 1\n self.patch = 0\n self.prerelease = []\n elif release == \"minor\":\n self.minor += 1\n self.patch = 0\n self.prerelease = []\n elif release == \"patch\":\n # If this is not a pre-release version, it will increment the patch.\n # If it is a pre-release it will bump up to the same patch version.\n # 1.2.0-5 patches to 1.2.0\n # 1.2.0 patches to 1.2.1\n if len(self.prerelease) == 0:\n self.patch += 1\n self.prerelease = []\n elif release == \"pre\":\n # This probably shouldn't be used publically.\n # 1.0.0 \"pre\" would become 1.0.0-0 which is the wrong direction.\n logger.debug(\"inc prerelease %s\", self.prerelease)\n if len(self.prerelease) == 0:\n self.prerelease = [0]\n else:\n i = len(self.prerelease) - 1\n while i >= 0:\n if isinstance(self.prerelease[i], int):\n self.prerelease[i] += 1\n i -= 2\n i -= 1\n if i == -1: # didn't increment anything\n self.prerelease.append(0)\n else:\n raise ValueError('invalid increment argument: {}'.format(rele","label":0},{"cid":"jupyterlab/semver.py::inc","text":"jupyterlab/semver.py::inc\ndef inc(version, release, loose): # wow!\n try:\n return make_semver(version, loose).inc(release).version\n except Exception as e:\n logger.debug(e, exc_info=5)\n return None","label":0},{"cid":"jupyterlab/semver.py::compare_identifiers","text":"jupyterlab/semver.py::compare_identifiers\ndef compare_identifiers(a, b):\n anum = NUMERIC.search(a)\n bnum = NUMERIC.search(b)\n\n if anum and bnum:\n a = int(a)\n b = int(b)\n\n if anum and not bnum:\n return -1\n elif bnum and not anum:\n return 1\n elif a < b:\n return -1\n elif a > b:\n return 1\n else:\n return 0","label":0},{"cid":"jupyterlab/semver.py::rcompare_identifiers","text":"jupyterlab/semver.py::rcompare_identifiers\ndef rcompare_identifiers(a, b):\n return compare_identifiers(b, a)","label":0},{"cid":"jupyterlab/semver.py::compare","text":"jupyterlab/semver.py::compare\ndef compare(a, b, loose):\n return make_semver(a, loose).compare(b)","label":0},{"cid":"jupyterlab/semver.py::compare_loose","text":"jupyterlab/semver.py::compare_loose\ndef compare_loose(a, b):\n return compare(a, b, True)","label":0},{"cid":"jupyterlab/semver.py::rcompare","text":"jupyterlab/semver.py::rcompare\ndef rcompare(a, b, loose):\n return compare(b, a, loose)","label":0},{"cid":"jupyterlab/semver.py::sort","text":"jupyterlab/semver.py::sort\ndef sort(list, loose):\n list.sort(lambda a, b: compare(a, b, loose))\n return list","label":0},{"cid":"jupyterlab/semver.py::rsort","text":"jupyterlab/semver.py::rsort\ndef rsort(list, loose):\n list.sort(lambda a, b: rcompare(a, b, loose))\n return list","label":0},{"cid":"jupyterlab/semver.py::gt","text":"jupyterlab/semver.py::gt\ndef gt(a, b, loose):\n return compare(a, b, loose) > 0","label":0},{"cid":"jupyterlab/semver.py::lt","text":"jupyterlab/semver.py::lt\ndef lt(a, b, loose):\n return compare(a, b, loose) < 0","label":0},{"cid":"jupyterlab/semver.py::eq","text":"jupyterlab/semver.py::eq\ndef eq(a, b, loose):\n return compare(a, b, loose) == 0","label":0},{"cid":"jupyterlab/semver.py::neq","text":"jupyterlab/semver.py::neq\ndef neq(a, b, loose):\n return compare(a, b, loose) != 0","label":0},{"cid":"jupyterlab/semver.py::gte","text":"jupyterlab/semver.py::gte\ndef gte(a, b, loose):\n return compare(a, b, loose) >= 0","label":0},{"cid":"jupyterlab/semver.py::lte","text":"jupyterlab/semver.py::lte\ndef lte(a, b, loose):\n return compare(a, b, loose) <= 0","label":0},{"cid":"jupyterlab/semver.py::cmp","text":"jupyterlab/semver.py::cmp\ndef cmp(a, op, b, loose):\n logger.debug(\"cmp: %s\", op)\n if op == \"===\":\n return a == b\n elif op == \"!==\":\n return a != b\n elif op == \"\" or op == \"=\" or op == \"==\":\n return eq(a, b, loose)\n elif op == \"!=\":\n return neq(a, b, loose)\n elif op == \">\":\n return gt(a, b, loose)\n elif op == \">=\":\n return gte(a, b, loose)\n elif op == \"<\":\n return lt(a, b, loose)\n elif op == \"<=\":\n return lte(a, b, loose)\n else:\n raise ValueError(\"Invalid operator: {}\".format(op))","label":0},{"cid":"jupyterlab/semver.py::comparator","text":"jupyterlab/semver.py::comparator\ndef comparator(comp, loose):\n if isinstance(comp, Comparator):\n if(comp.loose == loose):\n return comp\n else:\n comp = comp.value\n\n # if (!(this instanceof Comparator))\n # return new Comparator(comp, loose)\n return Comparator(comp, loose)","label":0},{"cid":"jupyterlab/semver.py::Comparator.__init__","text":"jupyterlab/semver.py::Comparator.__init__\n def __init__(self, comp, loose):\n logger.debug(\"comparator: %s %s\", comp, loose)\n self.loose = loose\n self.parse(comp)\n\n if self.semver == ANY:\n self.value = \"\"\n else:\n self.value = self.operator + self.semver.version","label":0},{"cid":"jupyterlab/semver.py::Comparator.parse","text":"jupyterlab/semver.py::Comparator.parse\n def parse(self, comp):\n if self.loose:\n r = regexp[COMPARATORLOOSE]\n else:\n r = regexp[COMPARATOR]\n logger.debug(\"parse comp=%s\", comp)\n m = r.search(comp)\n\n if m is None:\n raise ValueError(\"Invalid comparator: {}\".format(comp))\n\n self.operator = m.group(1)\n # if it literally is just '>' or '' then allow anything.\n if m.group(2) is None:\n self.semver = ANY\n else:\n self.semver = semver(m.group(2), self.loose)\n # <1.2.3-rc DOES allow 1.2.3-beta (has prerelease)\n # >=1.2.3 DOES NOT allow 1.2.3-beta\n # <=1.2.3 DOES allow 1.2.3-beta\n # However, <1.2.3 does NOT allow 1.2.3-beta,\n # even though `1.2.3-beta < 1.2.3`\n # The assumption is that the 1.2.3 version has something you\n # *don't* want, so we push the prerelease down to the minimum.\n if (self.operator == '<' and len(self.semver.prerelease) >= 0):\n self.semver.prerelease = [\"0\"]\n self.semver.format()\n logger.debug(\"Comparator.parse semver %s\", self.semver)","label":0},{"cid":"jupyterlab/semver.py::Comparator.__repr__","text":"jupyterlab/semver.py::Comparator.__repr__\n def __repr__(self):\n return '<SemVer Comparator \"{}\">'.format(self)","label":0},{"cid":"jupyterlab/semver.py::Comparator.__str__","text":"jupyterlab/semver.py::Comparator.__str__\n def __str__(self):\n return self.value","label":0},{"cid":"jupyterlab/semver.py::Comparator.test","text":"jupyterlab/semver.py::Comparator.test\n def test(self, version):\n logger.debug('Comparator, test %s, %s', version, self.loose)\n if self.semver == ANY:\n return True\n else:\n return cmp(version, self.operator, self.semver, self.loose)","label":0},{"cid":"jupyterlab/semver.py::make_range","text":"jupyterlab/semver.py::make_range\ndef make_range(range_, loose):\n if isinstance(range_, Range) and range_.loose == loose:\n return range_\n\n # if (!(this instanceof Range))\n # return new Range(range, loose);\n return Range(range_, loose)","label":0},{"cid":"tutorial/conf.py::setup","text":"tutorial/conf.py::setup\ndef setup(app):\n app.add_config_value('recommonmark_config', {\n 'enable_auto_doc_ref': True\n }, True)\n app.add_transform(AutoStructify)","label":0}],"string":"[\n {\n \"cid\": \"jupyterlab/__init__.py::_jupyter_server_extension_paths\",\n \"text\": \"jupyterlab/__init__.py::_jupyter_server_extension_paths\\ndef _jupyter_server_extension_paths():\\n return [{\\n \\\"module\\\": \\\"jupyterlab\\\"\\n }]\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::check_labextension\",\n \"text\": \"jupyterlab/labextensions.py::check_labextension\\ndef check_labextension(files, user=False, prefix=None, labextensions_dir=None, sys_prefix=False):\\n \\\"\\\"\\\"Check whether labextension files have been installed\\n \\n Returns True if all files are found, False if any are missing.\\n\\n Parameters\\n ----------\\n files : list(paths)\\n a list of relative paths within labextensions.\\n user : bool [default: False]\\n Whether to check the user's .jupyter/labextensions directory.\\n Otherwise check a system-wide install (e.g. /usr/local/share/jupyter/labextensions).\\n prefix : str [optional]\\n Specify install prefix, if it should differ from default (e.g. /usr/local).\\n Will check prefix/share/jupyter/labextensions\\n labextensions_dir : str [optional]\\n Specify absolute path of labextensions directory explicitly.\\n sys_prefix : bool [default: False]\\n Install into the sys.prefix, i.e. environment\\n \\\"\\\"\\\"\\n labext = _get_labextension_dir(user=user, sys_prefix=sys_prefix, prefix=prefix, labextensions_dir=labextensions_dir)\\n # make sure labextensions dir exists\\n if not os.path.exists(labext):\\n return False\\n \\n if isinstance(files, string_types):\\n # one file given, turn it into a list\\n files = [files]\\n \\n return all(os.path.exists(pjoin(labext, f)) for f in files)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::install_labextension\",\n \"text\": \"jupyterlab/labextensions.py::install_labextension\\ndef install_labextension(path, name, overwrite=False, symlink=False,\\n user=False, prefix=None, labextensions_dir=None,\\n logger=None, sys_prefix=False\\n ):\\n \\\"\\\"\\\"Install a Javascript extension for JupyterLab\\n \\n Stages files and/or directories into the labextensions directory.\\n By default, this compares modification time, and only stages files that need updating.\\n If `overwrite` is specified, matching files are purged before proceeding.\\n \\n Parameters\\n ----------\\n path : path to file, directory, zip or tarball archive, or URL to install\\n Archives (zip or tarballs) will be extracted into the labextensions directory.\\n name : str\\n name the labextension is installed to. For example, if name is 'foo', then\\n the source file will be installed to 'labextensions/foo'.\\n overwrite : bool [default: False]\\n If True, always install the files, regardless of what may already be installed.\\n symlink : bool [default: False]\\n If True, create a symlink in labextensions, rather than copying files.\\n Not allowed with URLs or archives. Windows support for symlinks requires\\n Vista or above, Python 3, and a permission bit which only admin users\\n have by default, so don't rely on it.\\n user : bool [default: False]\\n Whether to install to the user's labextensions directory.\\n Otherwise do a system-wide install (e.g. /usr/local/share/jupyter/labextensions).\\n prefix : str [optional]\\n Specify install prefix, if it should differ from default (e.g. /usr/local).\\n Will install to ``<prefix>/share/jupyter/labextensions``\\n labextensions_dir : str [optional]\\n Specify absolute path of labextensions directory explicitly.\\n logger : Jupyter logger [optional]\\n Logger instance to use\\n sys_prefix : bool [default: False]\\n Install into the sys.prefix,\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::install_labextension_python\",\n \"text\": \"jupyterlab/labextensions.py::install_labextension_python\\ndef install_labextension_python(module, overwrite=False, symlink=False,\\n user=False, sys_prefix=False, prefix=None, labextensions_dir=None, logger=None):\\n \\\"\\\"\\\"Install a labextension bundled in a Python package.\\n\\n Returns a list of installed/updated directories.\\n\\n See install_labextension for parameter information.\\\"\\\"\\\"\\n m, labexts = _get_labextension_metadata(module)\\n base_path = os.path.split(m.__file__)[0]\\n\\n full_dests = []\\n\\n for labext in labexts:\\n src = os.path.join(base_path, labext['src'])\\n name = labext['name']\\n\\n if logger:\\n logger.info(\\\"Installing %s -> %s\\\" % (src, name))\\n full_dest = install_labextension(\\n src, name=name, overwrite=overwrite, symlink=symlink,\\n user=user, sys_prefix=sys_prefix, prefix=prefix, labextensions_dir=labextensions_dir,\\n logger=logger\\n )\\n validate_labextension_folder(name, full_dest, logger)\\n full_dests.append(full_dest)\\n\\n return full_dests\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::uninstall_labextension\",\n \"text\": \"jupyterlab/labextensions.py::uninstall_labextension\\ndef uninstall_labextension(name, user=False, sys_prefix=False, prefix=None, \\n labextensions_dir=None, logger=None):\\n \\\"\\\"\\\"Uninstall a Javascript extension of JupyterLab\\n \\n Removes staged files and/or directories in the labextensions directory and \\n removes the extension from the frontend config.\\n \\n Parameters\\n ----------\\n name: str\\n The name of the labextension.\\n user : bool [default: False]\\n Whether to uninstall from the user's labextensions directory.\\n Otherwise do a system-wide uninstall (e.g. /usr/local/share/jupyter/labextensions).\\n sys_prefix : bool [default: False]\\n Uninstall from the sys.prefix, i.e. environment\\n prefix : str [optional]\\n Specify prefix, if it should differ from default (e.g. /usr/local).\\n Will uninstall from ``<prefix>/share/jupyter/labextensions``\\n labextensions_dir : str [optional]\\n Specify absolute path of labextensions directory explicitly.\\n logger : Jupyter logger [optional]\\n Logger instance to use\\n \\\"\\\"\\\"\\n labext = _get_labextension_dir(user=user, sys_prefix=sys_prefix, prefix=prefix, labextensions_dir=labextensions_dir)\\n dest = cast_unicode_py2(name)\\n full_dest = pjoin(labext, dest)\\n if os.path.lexists(full_dest):\\n if logger:\\n logger.info(\\\"Removing: %s\\\" % full_dest)\\n if os.path.isdir(full_dest) and not os.path.islink(full_dest):\\n shutil.rmtree(full_dest)\\n else:\\n os.remove(full_dest)\\n disable_labextension(name, user=user, sys_prefix=sys_prefix,\\n logger=logger)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::uninstall_labextension_python\",\n \"text\": \"jupyterlab/labextensions.py::uninstall_labextension_python\\ndef uninstall_labextension_python(module,\\n user=False, sys_prefix=False, prefix=None, labextensions_dir=None,\\n logger=None):\\n \\\"\\\"\\\"Uninstall a labextension bundled in a Python package.\\n \\n See parameters of `install_labextension_python`\\n \\\"\\\"\\\"\\n m, labexts = _get_labextension_metadata(module)\\n for labext in labexts:\\n name = labext['name']\\n if logger:\\n logger.info(\\\"Uninstalling {}\\\".format(name))\\n uninstall_labextension(name, user=user, sys_prefix=sys_prefix, \\n prefix=prefix, labextensions_dir=labextensions_dir, logger=logger)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::_set_labextension_state\",\n \"text\": \"jupyterlab/labextensions.py::_set_labextension_state\\ndef _set_labextension_state(name, state,\\n user=True, sys_prefix=False, logger=None,\\n python_module=None):\\n \\\"\\\"\\\"Set whether the JupyterLab frontend should use the named labextension\\n\\n Returns True if the final state is the one requested.\\n\\n Parameters\\n name : string\\n The name of the extension.\\n state : bool\\n The state in which to leave the extension\\n user : bool [default: True]\\n Whether to update the user's .jupyter/labextensions directory\\n sys_prefix : bool [default: False]\\n Whether to update the sys.prefix, i.e. environment. Will override\\n `user`.\\n logger : Jupyter logger [optional]\\n Logger instance to use [optional]\\n python_module: string\\n The name of the python module associated with the extension.\\n \\\"\\\"\\\"\\n user = False if sys_prefix else user\\n extensions = _read_config_data('labextensions', user=user,\\n sys_prefix=sys_prefix)\\n if logger:\\n logger.info(\\\"{} extension {}...\\\".format(\\n \\\"Enabling\\\" if state else \\\"Disabling\\\",\\n name\\n ))\\n\\n old_state = extensions.get(name, None)\\n if old_state is None:\\n old_state = dict(enabled=False)\\n elif isinstance(old_state, bool):\\n old_state = dict(enabled=old_state)\\n old_enabled = old_state['enabled']\\n new_enabled = state if state is not None else not old_enabled\\n\\n if logger:\\n if new_enabled:\\n logger.info(u\\\"Enabling: %s\\\" % (name))\\n else:\\n logger.info(u\\\"Disabling: %s\\\" % (name))\\n\\n extensions[name] = dict(\\n enabled=new_enabled,\\n python_module=python_module\\n )\\n\\n _write_config_data('labextensions', extensions, user=user,\\n sys_prefix=sys_prefix, logger=logger)\\n\\n if new_enabled:\\n full_dest = find_labextension(name)\\n validate_labextension_folder(name, full_dest, l\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::_set_labextension_state_python\",\n \"text\": \"jupyterlab/labextensions.py::_set_labextension_state_python\\ndef _set_labextension_state_python(state, module, user, sys_prefix,\\n logger=None):\\n \\\"\\\"\\\"Enable or disable some labextensions stored in a Python package\\n\\n Returns a list of whether the state was achieved (i.e. changed, or was\\n already right)\\n\\n Parameters\\n ----------\\n\\n state : Bool\\n Whether the extensions should be enabled\\n module : str\\n Importable Python module exposing the\\n magic-named `_jupyter_labextension_paths` function\\n user : bool\\n Whether to enable in the user's labextensions directory.\\n sys_prefix : bool\\n Enable/disable in the sys.prefix, i.e. environment\\n logger : Jupyter logger [optional]\\n Logger instance to use\\n \\\"\\\"\\\"\\n m, labexts = _get_labextension_metadata(module)\\n return [_set_labextension_state(name=labext[\\\"name\\\"],\\n state=state,\\n user=user, sys_prefix=sys_prefix,\\n logger=logger,\\n python_module=module)\\n for labext in labexts]\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::enable_labextension\",\n \"text\": \"jupyterlab/labextensions.py::enable_labextension\\ndef enable_labextension(name, user=True, sys_prefix=False,\\n logger=None):\\n \\\"\\\"\\\"Enable a named labextension\\n\\n Returns True if the final state is the one requested.\\n\\n Parameters\\n ----------\\n name : string\\n The name of the extension.\\n user : bool [default: True]\\n Whether to enable in the user's labextensions directory.\\n sys_prefix : bool [default: False]\\n Whether to enable in the sys.prefix, i.e. environment. Will override\\n `user`\\n logger : Jupyter logger [optional]\\n Logger instance to use\\n \\\"\\\"\\\"\\n return _set_labextension_state(name=name,\\n state=True,\\n user=user, sys_prefix=sys_prefix,\\n logger=logger)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::disable_labextension\",\n \"text\": \"jupyterlab/labextensions.py::disable_labextension\\ndef disable_labextension(name, user=True, sys_prefix=False,\\n logger=None):\\n \\\"\\\"\\\"Disable a named labextension\\n \\n Returns True if the final state is the one requested.\\n\\n Parameters\\n ----------\\n name : string\\n The name of the extension.\\n user : bool [default: True]\\n Whether to enable in the user's labextensions directory.\\n sys_prefix : bool [default: False]\\n Whether to enable in the sys.prefix, i.e. environment. Will override\\n `user`.\\n logger : Jupyter logger [optional]\\n Logger instance to use\\n \\\"\\\"\\\"\\n return _set_labextension_state(name=name,\\n state=False,\\n user=user, sys_prefix=sys_prefix,\\n logger=logger)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::enable_labextension_python\",\n \"text\": \"jupyterlab/labextensions.py::enable_labextension_python\\ndef enable_labextension_python(module, user=True, sys_prefix=False,\\n logger=None):\\n \\\"\\\"\\\"Enable some labextensions associated with a Python module.\\n\\n Returns a list of whether the state was achieved (i.e. changed, or was\\n already right)\\n\\n Parameters\\n ----------\\n\\n module : str\\n Importable Python module exposing the\\n magic-named `_jupyter_labextension_paths` function\\n user : bool [default: True]\\n Whether to enable in the user's labextensions directory.\\n sys_prefix : bool [default: False]\\n Whether to enable in the sys.prefix, i.e. environment. Will override\\n `user`\\n logger : Jupyter logger [optional]\\n Logger instance to use\\n \\\"\\\"\\\"\\n return _set_labextension_state_python(True, module, user, sys_prefix,\\n logger=logger)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::disable_labextension_python\",\n \"text\": \"jupyterlab/labextensions.py::disable_labextension_python\\ndef disable_labextension_python(module, user=True, sys_prefix=False,\\n logger=None):\\n \\\"\\\"\\\"Disable some labextensions associated with a Python module.\\n\\n Returns True if the final state is the one requested.\\n\\n Parameters\\n ----------\\n module : str\\n Importable Python module exposing the\\n magic-named `_jupyter_labextension_paths` function\\n user : bool [default: True]\\n Whether to enable in the user's labextensions directory.\\n sys_prefix : bool [default: False]\\n Whether to enable in the sys.prefix, i.e. environment\\n logger : Jupyter logger [optional]\\n Logger instance to use\\n \\\"\\\"\\\"\\n return _set_labextension_state_python(False, module, user, sys_prefix,\\n logger=logger)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::find_labextension\",\n \"text\": \"jupyterlab/labextensions.py::find_labextension\\ndef find_labextension(name):\\n \\\"\\\"\\\"Find a labextension path\\n\\n Looks across all of the labextension directories.\\n\\n Returns the first path where the extension is found,\\n or None if not found.\\n\\n Parameters\\n ----------\\n name : str\\n The name of the extension.\\n \\\"\\\"\\\"\\n for exts in jupyter_path('labextensions'):\\n full_dest = os.path.join(exts, name)\\n if os.path.exists(full_dest):\\n return full_dest\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::validate_labextension_folder\",\n \"text\": \"jupyterlab/labextensions.py::validate_labextension_folder\\ndef validate_labextension_folder(name, full_dest, logger=None):\\n \\\"\\\"\\\"Assess the health of an installed labextension\\n\\n Returns a list of warnings.\\n\\n Parameters\\n ----------\\n full_dest : str\\n The on-disk location of the installed labextension: this should end\\n with `labextensions/<name>`\\n logger : Jupyter logger [optional]\\n Logger instance to use\\n \\\"\\\"\\\"\\n infos = []\\n warnings = []\\n\\n has_files = []\\n has_entry = False\\n version_compatible = []\\n\\n data = get_labextension_manifest_data_by_folder(full_dest)\\n for manifest in data.values():\\n if ('entry' in manifest and 'modules' in manifest):\\n if (manifest['entry'] in manifest['modules']):\\n has_entry = True\\n files = manifest.get('files', [])\\n if not files:\\n has_files.append(\\\"No 'files' key in manifest\\\")\\n else:\\n for fname in files:\\n path = os.path.join(full_dest, fname)\\n if not os.path.exists(path):\\n has_files.append(\\\"File in manifest does not exist: {}\\\".format(path))\\n\\n for (mod, deps) in manifest.get('modules', {}).items():\\n # ignore jupyterlab modules since\\n # they are only semver compatibile with their own version\\n if mod.startswith('jupyterlab@'):\\n continue\\n for dep in deps:\\n if dep.startswith('jupyterlab@'):\\n dep = dep.split('/')[0].split('@')[-1]\\n if not satisfies(__version__, dep, False):\\n version_compatible.append('Expects JupyterLab version %s from packaged module %s'%(dep, mod))\\n break\\n\\n indent = \\\" \\\"\\n subindent = indent + \\\" \\\"\\n entry_msg = indent + u\\\"{} has entry point in manifest\\\"\\n name = os.path.basename(full_dest)\\n if has_entry:\\n infos.append(entry_msg.format(GREEN_OK))\\n else:\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::get_labextension_manifest_data_by_folder\",\n \"text\": \"jupyterlab/labextensions.py::get_labextension_manifest_data_by_folder\\ndef get_labextension_manifest_data_by_folder(folder):\\n \\\"\\\"\\\"Get the manifest data for a given lab extension folder\\n \\\"\\\"\\\"\\n manifest_files = glob.glob(os.path.join(folder, '*.manifest'))\\n manifests = {}\\n for file in manifest_files:\\n with open(file) as fid:\\n manifest = json.load(fid)\\n manifests[manifest['name']] = manifest\\n return manifests\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::get_labextension_manifest_data_by_name\",\n \"text\": \"jupyterlab/labextensions.py::get_labextension_manifest_data_by_name\\ndef get_labextension_manifest_data_by_name(name):\\n \\\"\\\"\\\"Get the manifest data for a given lab extension folder\\n \\\"\\\"\\\"\\n for exts in jupyter_path('labextensions'):\\n full_dest = os.path.join(exts, name)\\n if os.path.exists(full_dest):\\n return get_labextension_manifest_data_by_folder(full_dest)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::get_labextension_config_python\",\n \"text\": \"jupyterlab/labextensions.py::get_labextension_config_python\\ndef get_labextension_config_python(module):\\n \\\"\\\"\\\"Get the labextension configuration data associated with a Python module. \\n\\n Parameters\\n -----------\\n module : str\\n Importable Python module exposing the\\n magic-named `_jupyter_labextension_config` function\\n \\\"\\\"\\\"\\n m = import_item(module)\\n if not hasattr(m, '_jupyter_labextension_config'):\\n return {}\\n return m._jupyter_labextension_config()\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::BaseLabExtensionApp._log_format_default\",\n \"text\": \"jupyterlab/labextensions.py::BaseLabExtensionApp._log_format_default\\n def _log_format_default(self):\\n \\\"\\\"\\\"A default format for messages\\\"\\\"\\\"\\n return \\\"%(message)s\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::InstallLabExtensionApp._config_file_name_default\",\n \"text\": \"jupyterlab/labextensions.py::InstallLabExtensionApp._config_file_name_default\\n def _config_file_name_default(self):\\n \\\"\\\"\\\"The default config file name.\\\"\\\"\\\"\\n return 'jupyterlab_config'\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::InstallLabExtensionApp.install_extensions\",\n \"text\": \"jupyterlab/labextensions.py::InstallLabExtensionApp.install_extensions\\n def install_extensions(self):\\n \\\"\\\"\\\"Perform the installation of labextension(s)\\\"\\\"\\\"\\n if self.python:\\n if len(self.extra_args) > 1:\\n raise ValueError(\\\"Only one labextension allowed at a time. \\\"\\n \\\"Call multiple times to install multiple extensions.\\\")\\n install = install_labextension_python\\n kwargs = {}\\n else:\\n if len(self.extra_args) > 2:\\n raise ValueError(\\\"Only one labextension allowed at a time. \\\"\\n \\\"Call multiple times to install multiple extensions.\\\")\\n install = install_labextension\\n kwargs = {'name': self.extra_args[1]}\\n \\n full_dests = install(self.extra_args[0],\\n overwrite=self.overwrite,\\n symlink=self.symlink,\\n user=self.user,\\n sys_prefix=self.sys_prefix,\\n prefix=self.prefix,\\n labextensions_dir=self.labextensions_dir,\\n logger=self.log,\\n **kwargs\\n )\\n\\n if full_dests:\\n self.log.info(\\n u\\\"\\\\nTo enable this labextension in the browser every time\\\"\\n \\\" JupyterLab loads:\\\\n\\\\n\\\"\\n \\\" jupyter labextension enable {}{}{}{}\\\\n\\\".format(\\n self.extra_args[0] if self.python else self.extra_args[1],\\n \\\" --user\\\" if self.user else \\\"\\\",\\n \\\" --py\\\" if self.python else \\\"\\\",\\n \\\" --sys-prefix\\\" if self.sys_prefix else \\\"\\\"\\n )\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::InstallLabExtensionApp.start\",\n \"text\": \"jupyterlab/labextensions.py::InstallLabExtensionApp.start\\n def start(self):\\n \\\"\\\"\\\"Perform the App's function as configured\\\"\\\"\\\"\\n if not self.extra_args:\\n sys.exit('Please specify a labextension to install')\\n else:\\n try:\\n self.install_extensions()\\n except ArgumentConflict as e:\\n sys.exit(str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::UninstallLabExtensionApp._config_file_name_default\",\n \"text\": \"jupyterlab/labextensions.py::UninstallLabExtensionApp._config_file_name_default\\n def _config_file_name_default(self):\\n \\\"\\\"\\\"The default config file name.\\\"\\\"\\\"\\n return 'jupyterlab_config'\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::UninstallLabExtensionApp.uninstall_extensions\",\n \"text\": \"jupyterlab/labextensions.py::UninstallLabExtensionApp.uninstall_extensions\\n def uninstall_extensions(self):\\n \\\"\\\"\\\"Uninstall some labextensions\\\"\\\"\\\"\\n kwargs = {\\n 'user': self.user,\\n 'sys_prefix': self.sys_prefix,\\n 'prefix': self.prefix,\\n 'labextensions_dir': self.labextensions_dir,\\n 'logger': self.log\\n }\\n \\n arg_count = 1\\n if len(self.extra_args) > arg_count:\\n raise ValueError(\\\"only one labextension allowed at a time. Call multiple times to uninstall multiple extensions.\\\")\\n if len(self.extra_args) < arg_count:\\n raise ValueError(\\\"not enough arguments\\\")\\n \\n if self.python:\\n uninstall_labextension_python(self.extra_args[0], **kwargs)\\n else:\\n uninstall_labextension(self.extra_args[0], **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::UninstallLabExtensionApp.start\",\n \"text\": \"jupyterlab/labextensions.py::UninstallLabExtensionApp.start\\n def start(self):\\n if not self.extra_args:\\n sys.exit('Please specify a labextension to uninstall')\\n else:\\n try:\\n self.uninstall_extensions()\\n except ArgumentConflict as e:\\n sys.exit(str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::ToggleLabExtensionApp._config_file_name_default\",\n \"text\": \"jupyterlab/labextensions.py::ToggleLabExtensionApp._config_file_name_default\\n def _config_file_name_default(self):\\n \\\"\\\"\\\"The default config file name.\\\"\\\"\\\"\\n return 'jupyterlab_config'\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::ToggleLabExtensionApp.toggle_labextension_python\",\n \"text\": \"jupyterlab/labextensions.py::ToggleLabExtensionApp.toggle_labextension_python\\n def toggle_labextension_python(self, module):\\n \\\"\\\"\\\"Toggle some extensions in an importable Python module.\\n\\n Returns a list of booleans indicating whether the state was changed as\\n requested.\\n\\n Parameters\\n ----------\\n module : str\\n Importable Python module exposing the\\n magic-named `_jupyter_labextension_paths` function\\n \\\"\\\"\\\"\\n toggle = (enable_labextension_python if self._toggle_value\\n else disable_labextension_python)\\n return toggle(module,\\n user=self.user,\\n sys_prefix=self.sys_prefix,\\n logger=self.log)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::ToggleLabExtensionApp.toggle_labextension\",\n \"text\": \"jupyterlab/labextensions.py::ToggleLabExtensionApp.toggle_labextension\\n def toggle_labextension(self, name):\\n \\\"\\\"\\\"Toggle some a named labextension by require-able AMD module.\\n\\n Returns whether the state was changed as requested.\\n\\n Parameters\\n ----------\\n require : str\\n require.js path used to load the labextension\\n \\\"\\\"\\\"\\n toggle = (enable_labextension if self._toggle_value\\n else disable_labextension)\\n return toggle(name,\\n user=self.user, sys_prefix=self.sys_prefix,\\n logger=self.log)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::ToggleLabExtensionApp.start\",\n \"text\": \"jupyterlab/labextensions.py::ToggleLabExtensionApp.start\\n def start(self):\\n if not self.extra_args:\\n sys.exit('Please specify a labextension/package to enable or disable')\\n elif len(self.extra_args) > 1:\\n sys.exit('Please specify one labextension/package at a time')\\n if self.python:\\n self.toggle_labextension_python(self.extra_args[0])\\n else:\\n self.toggle_labextension(self.extra_args[0])\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::ListLabExtensionsApp.list_labextensions\",\n \"text\": \"jupyterlab/labextensions.py::ListLabExtensionsApp.list_labextensions\\n def list_labextensions(self):\\n \\\"\\\"\\\"List all the labextensions\\\"\\\"\\\"\\n print(\\\"Known labextensions:\\\")\\n seen = False\\n for config_dir in jupyter_config_path():\\n config_dir = os.path.join(config_dir, CONFIG_DIR)\\n cm = BaseJSONConfigManager(parent=self, config_dir=config_dir)\\n labextensions = cm.get('labextensions')\\n if labextensions:\\n print(u'config dir: {}'.format(config_dir))\\n seen = True\\n for name, config in sorted(labextensions.items()):\\n if isinstance(config, bool):\\n config = dict(enabled=config)\\n enabled = config['enabled']\\n full_dest = find_labextension(name)\\n print(u' {} {}: {}'.format(\\n name,\\n GREEN_ENABLED if enabled else RED_DISABLED,\\n full_dest if not None else RED_X+\\\" Files not found\\\"\\n ))\\n if full_dest is not None:\\n validate_labextension_folder(name, full_dest, self.log)\\n if not seen:\\n print('....None found!')\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::ListLabExtensionsApp.start\",\n \"text\": \"jupyterlab/labextensions.py::ListLabExtensionsApp.start\\n def start(self):\\n \\\"\\\"\\\"Perform the App's functions as configured\\\"\\\"\\\"\\n self.list_labextensions()\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::LabExtensionApp.start\",\n \"text\": \"jupyterlab/labextensions.py::LabExtensionApp.start\\n def start(self):\\n \\\"\\\"\\\"Perform the App's functions as configured\\\"\\\"\\\"\\n super(LabExtensionApp, self).start()\\n\\n # The above should have called a subcommand and raised NoStart; if we\\n # get here, it didn't, so we should self.log.info a message.\\n subcmds = \\\", \\\".join(sorted(self.subcommands))\\n sys.exit(\\\"Please supply at least one subcommand: %s\\\" % subcmds)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::_should_copy\",\n \"text\": \"jupyterlab/labextensions.py::_should_copy\\ndef _should_copy(src, dest, logger=None):\\n \\\"\\\"\\\"Should a file be copied, if it doesn't exist, or is newer?\\n\\n Returns whether the file needs to be updated.\\n\\n Parameters\\n ----------\\n\\n src : string\\n A path that should exist from which to copy a file\\n src : string\\n A path that might exist to which to copy a file\\n logger : Jupyter logger [optional]\\n Logger instance to use\\n \\\"\\\"\\\"\\n if not os.path.exists(dest):\\n return True\\n if os.stat(src).st_mtime - os.stat(dest).st_mtime > 1e-6:\\n # we add a fudge factor to work around a bug in python 2.x\\n # that was fixed in python 3.x: http://bugs.python.org/issue12904\\n if logger:\\n logger.warn(\\\"Out of date: %s\\\" % dest)\\n return True\\n if logger:\\n logger.info(\\\"Up to date: %s\\\" % dest)\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::_maybe_copy\",\n \"text\": \"jupyterlab/labextensions.py::_maybe_copy\\ndef _maybe_copy(src, dest, logger=None):\\n \\\"\\\"\\\"Copy a file if it needs updating.\\n\\n Parameters\\n ----------\\n\\n src : string\\n A path that should exist from which to copy a file\\n src : string\\n A path that might exist to which to copy a file\\n logger : Jupyter logger [optional]\\n Logger instance to use\\n \\\"\\\"\\\"\\n if _should_copy(src, dest, logger=logger):\\n if logger:\\n logger.info(\\\"Copying: %s -> %s\\\" % (src, dest))\\n shutil.copy2(src, dest)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::_safe_is_tarfile\",\n \"text\": \"jupyterlab/labextensions.py::_safe_is_tarfile\\ndef _safe_is_tarfile(path):\\n \\\"\\\"\\\"Safe version of is_tarfile, return False on IOError.\\n\\n Returns whether the file exists and is a tarfile.\\n\\n Parameters\\n ----------\\n\\n path : string\\n A path that might not exist and or be a tarfile\\n \\\"\\\"\\\"\\n try:\\n return tarfile.is_tarfile(path)\\n except IOError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::_get_labextension_dir\",\n \"text\": \"jupyterlab/labextensions.py::_get_labextension_dir\\ndef _get_labextension_dir(user=False, sys_prefix=False, prefix=None, labextensions_dir=None):\\n \\\"\\\"\\\"Return the labextension directory specified\\n\\n Parameters\\n ----------\\n\\n user : bool [default: False]\\n Get the user's .jupyter/labextensions directory\\n sys_prefix : bool [default: False]\\n Get sys.prefix, i.e. ~/.envs/my-env/share/jupyter/labextensions\\n prefix : str [optional]\\n Get custom prefix\\n labextensions_dir : str [optional]\\n Get what you put in\\n \\\"\\\"\\\"\\n if sum(map(bool, [user, prefix, labextensions_dir, sys_prefix])) > 1:\\n raise ArgumentConflict(\\\"cannot specify more than one of user, sys_prefix, prefix, or labextensions_dir\\\")\\n if user:\\n labext = pjoin(jupyter_data_dir(), u'labextensions')\\n elif sys_prefix:\\n labext = pjoin(ENV_JUPYTER_PATH[0], u'labextensions')\\n elif prefix:\\n labext = pjoin(prefix, 'share', 'jupyter', 'labextensions')\\n elif labextensions_dir:\\n labext = labextensions_dir\\n else:\\n labext = pjoin(SYSTEM_JUPYTER_PATH[0], 'labextensions')\\n return labext\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::_get_config_dir\",\n \"text\": \"jupyterlab/labextensions.py::_get_config_dir\\ndef _get_config_dir(user=False, sys_prefix=False):\\n \\\"\\\"\\\"Get the location of config files for the current context\\n\\n Returns the string to the enviornment\\n\\n Parameters\\n ----------\\n\\n user : bool [default: False]\\n Get the user's .jupyter config directory\\n sys_prefix : bool [default: False]\\n Get sys.prefix, i.e. ~/.envs/my-env/etc/jupyter\\n \\\"\\\"\\\"\\n user = False if sys_prefix else user\\n if user and sys_prefix:\\n raise ArgumentConflict(\\\"Cannot specify more than one of user or sys_prefix\\\")\\n if user:\\n labext = jupyter_config_dir()\\n elif sys_prefix:\\n labext = ENV_CONFIG_PATH[0]\\n else:\\n labext = SYSTEM_CONFIG_PATH[0]\\n return os.path.join(labext, CONFIG_DIR)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::_get_labextension_metadata\",\n \"text\": \"jupyterlab/labextensions.py::_get_labextension_metadata\\ndef _get_labextension_metadata(module):\\n \\\"\\\"\\\"Get the list of labextension paths associated with a Python module.\\n\\n Returns a tuple of (the module, [{\\n 'name': 'mockextension',\\n 'src': 'static',\\n }])\\n\\n Parameters\\n ----------\\n\\n module : str\\n Importable Python module exposing the\\n magic-named `_jupyter_labextension_paths` function\\n \\\"\\\"\\\"\\n m = import_item(module)\\n if not hasattr(m, '_jupyter_labextension_paths'):\\n raise KeyError('The Python module {} is not a valid labextension'.format(module))\\n labexts = m._jupyter_labextension_paths()\\n return m, labexts\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::_read_config_data\",\n \"text\": \"jupyterlab/labextensions.py::_read_config_data\\ndef _read_config_data(section, user=False, sys_prefix=False):\\n \\\"\\\"\\\"Get the config for the current context\\n\\n Returns the string to the enviornment\\n\\n Parameters\\n ----------\\n section: string\\n The section of config to read.\\n user : bool [default: False]\\n Get the user's .jupyter config directory\\n sys_prefix : bool [default: False]\\n Get sys.prefix, i.e. ~/.envs/my-env/etc/jupyter\\n \\\"\\\"\\\"\\n config_dir = _get_config_dir(user=user, sys_prefix=sys_prefix)\\n return BaseJSONConfigManager(config_dir=config_dir).get(section)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/labextensions.py::_write_config_data\",\n \"text\": \"jupyterlab/labextensions.py::_write_config_data\\ndef _write_config_data(section, data, user=False, sys_prefix=False, logger=None):\\n \\\"\\\"\\\"Update the config for the current context\\n\\n Parameters\\n ----------\\n section: string\\n The section of data to update.\\n data : object\\n An object which can be accepted by ConfigManager.update\\n user : bool [default: False]\\n Get the user's .jupyter config directory\\n sys_prefix : bool [default: False]\\n Get sys.prefix, i.e. ~/.envs/my-env/etc/jupyter\\n logger: logger instance\\n The logger instance.\\n \\\"\\\"\\\"\\n config_dir = _get_config_dir(user=user, sys_prefix=sys_prefix)\\n if logger:\\n logger.info(u\\\"- Writing config: {}\\\".format(config_dir))\\n config_man = BaseJSONConfigManager(config_dir=config_dir)\\n config_man.update(section, data)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::_R.__init__\",\n \"text\": \"jupyterlab/semver.py::_R.__init__\\n def __init__(self, i):\\n self.i = i\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::_R.__call__\",\n \"text\": \"jupyterlab/semver.py::_R.__call__\\n def __call__(self):\\n v = self.i\\n self.i += 1\\n return v\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::_R.value\",\n \"text\": \"jupyterlab/semver.py::_R.value\\n def value(self):\\n return self.i\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::Extendlist.__setitem__\",\n \"text\": \"jupyterlab/semver.py::Extendlist.__setitem__\\n def __setitem__(self, i, v):\\n try:\\n list.__setitem__(self, i, v)\\n except IndexError:\\n if len(self) == i:\\n self.append(v)\\n else:\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::list_get\",\n \"text\": \"jupyterlab/semver.py::list_get\\ndef list_get(xs, i):\\n try:\\n return xs[i]\\n except IndexError:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::parse\",\n \"text\": \"jupyterlab/semver.py::parse\\ndef parse(version, loose):\\n if loose:\\n r = regexp[LOOSE]\\n else:\\n r = regexp[FULL]\\n m = r.search(version)\\n if m:\\n return semver(version, loose)\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::valid\",\n \"text\": \"jupyterlab/semver.py::valid\\ndef valid(version, loose):\\n v = parse(version, loose)\\n if v.version:\\n return v\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::clean\",\n \"text\": \"jupyterlab/semver.py::clean\\ndef clean(version, loose):\\n s = parse(version, loose)\\n if s:\\n return s.version\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::semver\",\n \"text\": \"jupyterlab/semver.py::semver\\ndef semver(version, loose):\\n if isinstance(version, SemVer):\\n if version.loose == loose:\\n return version\\n else:\\n version = version.version\\n elif not isinstance(version, str): # xxx:\\n raise ValueError(\\\"Invalid Version: {}\\\".format(version))\\n\\n \\\"\\\"\\\"\\n if (!(this instanceof SemVer))\\n return new SemVer(version, loose);\\n \\\"\\\"\\\"\\n return SemVer(version, loose)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::SemVer.__init__\",\n \"text\": \"jupyterlab/semver.py::SemVer.__init__\\n def __init__(self, version, loose):\\n logger.debug(\\\"SemVer %s, %s\\\", version, loose)\\n self.loose = loose\\n self.raw = version\\n\\n m = regexp[LOOSE if loose else FULL].search(version.strip())\\n if not m:\\n if not loose:\\n raise ValueError(\\\"Invalid Version: {}\\\".format(version))\\n m = regexp[RECOVERYVERSIONNAME].search(version.strip())\\n self.major = int(m.group(1)) if m.group(1) else 0\\n self.minor = int(m.group(2)) if m.group(2) else 0\\n self.patch = 0\\n if not m.group(3):\\n self.prerelease = []\\n else:\\n self.prerelease = [(int(id) if NUMERIC.search(id) else id)\\n for id in m.group(3).split(\\\".\\\")]\\n else:\\n # these are actually numbers\\n self.major = int(m.group(1))\\n self.minor = int(m.group(2))\\n self.patch = int(m.group(3))\\n # numberify any prerelease numeric ids\\n if not m.group(4):\\n self.prerelease = []\\n else:\\n\\n self.prerelease = [(int(id) if NUMERIC.search(id) else id)\\n for id in m.group(4).split(\\\".\\\")]\\n if m.group(5):\\n self.build = m.group(5).split(\\\".\\\")\\n else:\\n self.build = []\\n\\n self.format() # xxx:\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::SemVer.format\",\n \"text\": \"jupyterlab/semver.py::SemVer.format\\n def format(self):\\n self.version = \\\"{}.{}.{}\\\".format(self.major, self.minor, self.patch)\\n if len(self.prerelease) > 0:\\n self.version += (\\\"-{}\\\".format(\\\".\\\".join(str(v) for v in self.prerelease)))\\n return self.version\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::SemVer.__repr__\",\n \"text\": \"jupyterlab/semver.py::SemVer.__repr__\\n def __repr__(self):\\n return \\\"<SemVer {!r} >\\\".format(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::SemVer.__str__\",\n \"text\": \"jupyterlab/semver.py::SemVer.__str__\\n def __str__(self):\\n return self.version\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::SemVer.compare\",\n \"text\": \"jupyterlab/semver.py::SemVer.compare\\n def compare(self, other):\\n logger.debug('SemVer.compare %s %s %s', self.version, self.loose, other)\\n if not isinstance(other, SemVer):\\n other = make_semver(other, self.loose)\\n result = self.compare_main(other) or self.compare_pre(other)\\n logger.debug(\\\"compare result %s\\\", result)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::SemVer.compare_main\",\n \"text\": \"jupyterlab/semver.py::SemVer.compare_main\\n def compare_main(self, other):\\n if not isinstance(other, SemVer):\\n other = make_semver(other, self.loose)\\n\\n return (compare_identifiers(str(self.major), str(other.major)) or\\n compare_identifiers(str(self.minor), str(other.minor)) or\\n compare_identifiers(str(self.patch), str(other.patch)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::SemVer.compare_pre\",\n \"text\": \"jupyterlab/semver.py::SemVer.compare_pre\\n def compare_pre(self, other):\\n if not isinstance(other, SemVer):\\n other = make_semver(other, self.loose)\\n\\n # NOT having a prerelease is > having one\\n is_self_more_than_zero = len(self.prerelease) > 0\\n is_other_more_than_zero = len(other.prerelease) > 0\\n\\n if not is_self_more_than_zero and is_other_more_than_zero:\\n return 1\\n elif is_self_more_than_zero and not is_other_more_than_zero:\\n return -1\\n elif not is_self_more_than_zero and not is_other_more_than_zero:\\n return 0\\n\\n i = 0\\n while True:\\n a = list_get(self.prerelease, i)\\n b = list_get(other.prerelease, i)\\n logger.debug(\\\"prerelease compare %s: %s %s\\\", i, a, b)\\n i += 1\\n if a is None and b is None:\\n return 0\\n elif b is None:\\n return 1\\n elif a is None:\\n return -1\\n elif a == b:\\n continue\\n else:\\n return compare_identifiers(str(a), str(b))\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::SemVer.inc\",\n \"text\": \"jupyterlab/semver.py::SemVer.inc\\n def inc(self, release):\\n self._inc(release)\\n i = -1\\n while len(self.prerelease) > 1 and self.prerelease[i] == 0:\\n self.prerelease.pop()\\n self.format()\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::SemVer._inc\",\n \"text\": \"jupyterlab/semver.py::SemVer._inc\\n def _inc(self, release):\\n logger.debug(\\\"inc release %s %s\\\", self.prerelease, release)\\n if release == 'premajor':\\n self._inc(\\\"major\\\")\\n self._inc(\\\"pre\\\")\\n elif release == \\\"preminor\\\":\\n self._inc(\\\"minor\\\")\\n self._inc(\\\"pre\\\")\\n elif release == \\\"prepatch\\\":\\n self._inc(\\\"patch\\\")\\n self._inc(\\\"pre\\\")\\n elif release == 'prerelease':\\n if len(self.prerelease) == 0:\\n self._inc(\\\"patch\\\")\\n self._inc(\\\"pre\\\")\\n elif release == \\\"major\\\":\\n self.major += 1\\n self.minor = -1\\n self.minor += 1\\n self.patch = 0\\n self.prerelease = []\\n elif release == \\\"minor\\\":\\n self.minor += 1\\n self.patch = 0\\n self.prerelease = []\\n elif release == \\\"patch\\\":\\n # If this is not a pre-release version, it will increment the patch.\\n # If it is a pre-release it will bump up to the same patch version.\\n # 1.2.0-5 patches to 1.2.0\\n # 1.2.0 patches to 1.2.1\\n if len(self.prerelease) == 0:\\n self.patch += 1\\n self.prerelease = []\\n elif release == \\\"pre\\\":\\n # This probably shouldn't be used publically.\\n # 1.0.0 \\\"pre\\\" would become 1.0.0-0 which is the wrong direction.\\n logger.debug(\\\"inc prerelease %s\\\", self.prerelease)\\n if len(self.prerelease) == 0:\\n self.prerelease = [0]\\n else:\\n i = len(self.prerelease) - 1\\n while i >= 0:\\n if isinstance(self.prerelease[i], int):\\n self.prerelease[i] += 1\\n i -= 2\\n i -= 1\\n if i == -1: # didn't increment anything\\n self.prerelease.append(0)\\n else:\\n raise ValueError('invalid increment argument: {}'.format(rele\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::inc\",\n \"text\": \"jupyterlab/semver.py::inc\\ndef inc(version, release, loose): # wow!\\n try:\\n return make_semver(version, loose).inc(release).version\\n except Exception as e:\\n logger.debug(e, exc_info=5)\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::compare_identifiers\",\n \"text\": \"jupyterlab/semver.py::compare_identifiers\\ndef compare_identifiers(a, b):\\n anum = NUMERIC.search(a)\\n bnum = NUMERIC.search(b)\\n\\n if anum and bnum:\\n a = int(a)\\n b = int(b)\\n\\n if anum and not bnum:\\n return -1\\n elif bnum and not anum:\\n return 1\\n elif a < b:\\n return -1\\n elif a > b:\\n return 1\\n else:\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::rcompare_identifiers\",\n \"text\": \"jupyterlab/semver.py::rcompare_identifiers\\ndef rcompare_identifiers(a, b):\\n return compare_identifiers(b, a)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::compare\",\n \"text\": \"jupyterlab/semver.py::compare\\ndef compare(a, b, loose):\\n return make_semver(a, loose).compare(b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::compare_loose\",\n \"text\": \"jupyterlab/semver.py::compare_loose\\ndef compare_loose(a, b):\\n return compare(a, b, True)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::rcompare\",\n \"text\": \"jupyterlab/semver.py::rcompare\\ndef rcompare(a, b, loose):\\n return compare(b, a, loose)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::sort\",\n \"text\": \"jupyterlab/semver.py::sort\\ndef sort(list, loose):\\n list.sort(lambda a, b: compare(a, b, loose))\\n return list\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::rsort\",\n \"text\": \"jupyterlab/semver.py::rsort\\ndef rsort(list, loose):\\n list.sort(lambda a, b: rcompare(a, b, loose))\\n return list\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::gt\",\n \"text\": \"jupyterlab/semver.py::gt\\ndef gt(a, b, loose):\\n return compare(a, b, loose) > 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::lt\",\n \"text\": \"jupyterlab/semver.py::lt\\ndef lt(a, b, loose):\\n return compare(a, b, loose) < 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::eq\",\n \"text\": \"jupyterlab/semver.py::eq\\ndef eq(a, b, loose):\\n return compare(a, b, loose) == 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::neq\",\n \"text\": \"jupyterlab/semver.py::neq\\ndef neq(a, b, loose):\\n return compare(a, b, loose) != 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::gte\",\n \"text\": \"jupyterlab/semver.py::gte\\ndef gte(a, b, loose):\\n return compare(a, b, loose) >= 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::lte\",\n \"text\": \"jupyterlab/semver.py::lte\\ndef lte(a, b, loose):\\n return compare(a, b, loose) <= 0\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::cmp\",\n \"text\": \"jupyterlab/semver.py::cmp\\ndef cmp(a, op, b, loose):\\n logger.debug(\\\"cmp: %s\\\", op)\\n if op == \\\"===\\\":\\n return a == b\\n elif op == \\\"!==\\\":\\n return a != b\\n elif op == \\\"\\\" or op == \\\"=\\\" or op == \\\"==\\\":\\n return eq(a, b, loose)\\n elif op == \\\"!=\\\":\\n return neq(a, b, loose)\\n elif op == \\\">\\\":\\n return gt(a, b, loose)\\n elif op == \\\">=\\\":\\n return gte(a, b, loose)\\n elif op == \\\"<\\\":\\n return lt(a, b, loose)\\n elif op == \\\"<=\\\":\\n return lte(a, b, loose)\\n else:\\n raise ValueError(\\\"Invalid operator: {}\\\".format(op))\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::comparator\",\n \"text\": \"jupyterlab/semver.py::comparator\\ndef comparator(comp, loose):\\n if isinstance(comp, Comparator):\\n if(comp.loose == loose):\\n return comp\\n else:\\n comp = comp.value\\n\\n # if (!(this instanceof Comparator))\\n # return new Comparator(comp, loose)\\n return Comparator(comp, loose)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::Comparator.__init__\",\n \"text\": \"jupyterlab/semver.py::Comparator.__init__\\n def __init__(self, comp, loose):\\n logger.debug(\\\"comparator: %s %s\\\", comp, loose)\\n self.loose = loose\\n self.parse(comp)\\n\\n if self.semver == ANY:\\n self.value = \\\"\\\"\\n else:\\n self.value = self.operator + self.semver.version\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::Comparator.parse\",\n \"text\": \"jupyterlab/semver.py::Comparator.parse\\n def parse(self, comp):\\n if self.loose:\\n r = regexp[COMPARATORLOOSE]\\n else:\\n r = regexp[COMPARATOR]\\n logger.debug(\\\"parse comp=%s\\\", comp)\\n m = r.search(comp)\\n\\n if m is None:\\n raise ValueError(\\\"Invalid comparator: {}\\\".format(comp))\\n\\n self.operator = m.group(1)\\n # if it literally is just '>' or '' then allow anything.\\n if m.group(2) is None:\\n self.semver = ANY\\n else:\\n self.semver = semver(m.group(2), self.loose)\\n # <1.2.3-rc DOES allow 1.2.3-beta (has prerelease)\\n # >=1.2.3 DOES NOT allow 1.2.3-beta\\n # <=1.2.3 DOES allow 1.2.3-beta\\n # However, <1.2.3 does NOT allow 1.2.3-beta,\\n # even though `1.2.3-beta < 1.2.3`\\n # The assumption is that the 1.2.3 version has something you\\n # *don't* want, so we push the prerelease down to the minimum.\\n if (self.operator == '<' and len(self.semver.prerelease) >= 0):\\n self.semver.prerelease = [\\\"0\\\"]\\n self.semver.format()\\n logger.debug(\\\"Comparator.parse semver %s\\\", self.semver)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::Comparator.__repr__\",\n \"text\": \"jupyterlab/semver.py::Comparator.__repr__\\n def __repr__(self):\\n return '<SemVer Comparator \\\"{}\\\">'.format(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::Comparator.__str__\",\n \"text\": \"jupyterlab/semver.py::Comparator.__str__\\n def __str__(self):\\n return self.value\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::Comparator.test\",\n \"text\": \"jupyterlab/semver.py::Comparator.test\\n def test(self, version):\\n logger.debug('Comparator, test %s, %s', version, self.loose)\\n if self.semver == ANY:\\n return True\\n else:\\n return cmp(version, self.operator, self.semver, self.loose)\",\n \"label\": 0\n },\n {\n \"cid\": \"jupyterlab/semver.py::make_range\",\n \"text\": \"jupyterlab/semver.py::make_range\\ndef make_range(range_, loose):\\n if isinstance(range_, Range) and range_.loose == loose:\\n return range_\\n\\n # if (!(this instanceof Range))\\n # return new Range(range, loose);\\n return Range(range_, loose)\",\n \"label\": 0\n },\n {\n \"cid\": \"tutorial/conf.py::setup\",\n \"text\": \"tutorial/conf.py::setup\\ndef setup(app):\\n app.add_config_value('recommonmark_config', {\\n 'enable_auto_doc_ref': True\\n }, True)\\n app.add_transform(AutoStructify)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":285,"cells":{"instance_id":{"kind":"string","value":"conan-io__conan-3715"},"repo":{"kind":"string","value":"conan-io/conan"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"[bug] 1.8.0 uses --sbin instead --sbindir\nAfter updating to conan 1.8.0, I ran into an issue building a [librdkafka/0.11.4-1](https://github.com/ess-dmsc/conan-librdkafka) package on CentOS due to the ./configure statement using `--sbin` as a parameter instead of `--sbindir`:\r\n\r\n\r\n```\r\nlibrdkafka/0.11.4-1@<asdf>/stable: Calling:\r\n ./configure '--prefix=' '--disable-sasl' '--diable-lz4' '--disable-optimization' '--bindir=${prefix}/bin' '--sbin=${prefix}/bin' '--libexec=${prefix}/bin' '--libdir=${prefix}/lib' '--includedir=${prefix}/include'\r\nUnknown option sbin=${prefix}/bin\r\n```\r\n\r\n\r\nThe GNU Coding Standard states that the variable for the [`sbin` installation directory is `sbindir`](https://www.gnu.org/prep/standards/html_node/Directory-Variables.html).\r\n\r\nIt looks like the issue came from #3599 and the [librdkafka/0.11.4-1](https://github.com/ess-dmsc/conan-librdkafka) package builds fine on conan 1.6.1.\r\n\r\n\r\n\n"},"gold_files":{"kind":"list like","value":["conans/client/build/autotools_environment.py"],"string":"[\n \"conans/client/build/autotools_environment.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.configure"],"string":"[\n \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.configure\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"conans/client/build/autotools_environment.py","text":"conans/client/build/autotools_environment.py\nclass AutoToolsBuildEnvironment # - CPPFLAGS (C-PreProcesor-Flags NOT related with c++) (-I -D)\n def __init__(self, conanfile, win_bash=False, include_rpath_flags=False) # FIXME: include_rpath_flags CONAN 2.0 to default True? Could break many packages \n def _configure_fpic(self)\n def _get_host_build_target_flags(self) # Based on google search for build/host triplets, it could need a lot\n def configure(self, configure_dir=None, args=None, build=None, host=None, target=None, pkg_config_paths=None, vars=None, use_default_install_dirs=True) # :param pkg_config_paths: Optional paths to locate the *.pc files\n def _configure_help_output(self, configure_path)\n def _adjust_path(self, path)\n def _valid_configure_flag(varname, args, available_flags)\n def _is_flag_in_args(varname, args)\n def make(self, args='', make_program=None, target=None, vars=None)\n def install(self, args='', make_program=None, vars=None)\n def _configure_link_flags(self) # Not the -L\n def _configure_flags(self)\n def _configure_cxx_flags(self)\n def _configure_defines(self)\n def _get_vars(self)\n def append(*args)\n def vars_dict(self)\n def vars(self)\ndef _environ_value_prefix(var_name, prefix=' ')","label":1},{"cid":"conans/client/command.py","text":"conans/client/command.py\nclass Extender # Allows to use the same flag several times in a command and creates a list with t\n def __call__(self, parser, namespace, values, option_strings=None)\nclass OnceArgument # Allows to declare a parameter that can have only one value, by default argparse \n def __call__(self, parser, namespace, values, option_string=None)\nclass Command # A single command of the conan application, with all the first level commands. Ma\n def __init__(self, conan_api, client_cache, user_io, outputer)\n def help(self, *args) # Show help of a specific commmand.\n def new(self, *args) # Creates a new package recipe template with a 'conanfile.py' and optionally,\n def inspect(self, *args) # Displays conanfile attributes, like name, version, options\n def test(self, *args) # Test a package consuming it from a conanfile.py with a test() method.\n def create(self, *args) # Builds a binary package for a recipe (conanfile.py).\n def download(self, *args) # Downloads recipe and binaries to the local cache, without using settings.\n def install(self, *args) # Installs the requirements specified in a recipe (conanfile.py or conanfile.txt).\n def config(self, *args) # Manages Conan configuration.\n def info(self, *args) # Gets information about the dependency graph of a recipe.\n def source(self, *args) # Calls your local conanfile.py 'source()' method.\n def build(self, *args) # Calls your local conanfile.py 'build()' method.\n def package(self, *args) # Calls your local conanfile.py 'package()' method.\n def imports(self, *args) # Calls your local conanfile.py or conanfile.txt 'imports' method.\n def export_pkg(self, *args) # Exports a recipe, then creates a package from local source and build folders.\n def export(self, *args) # Copies the recipe (conanfile.py & associated files) to your local cache.\n def remove(self, *args) # Removes packages or binaries matching pattern from local cache or remote.\n def copy(self, *args) # Copies conan recipes and packages to another user/channel.\n def user(self, *args) # Authenticates against a remote with user/pass, caching the auth token.\n def search(self, *args) # Searches package recipes and binaries in the local cache or in a remote.\n def upload(self, *args) # Uploads a recipe and binary packages to a remote.\n def remote(self, *args) # Manages the remote list and the package recipes associated to a remote.\n def profile(self, *args) # Lists profiles in the '.conan/profiles' folder, or shows profile details.\n def get(self, *args) # Gets a file or list a directory of a given reference or package.\n def alias(self, *args) # Creates and exports an 'alias package recipe'.\n def _show_help(self) # Prints a summary of all commands\n def check_all_commands_listed() # Keep updated the main directory, raise if don't\n def _commands(self) # returns a list of available commands\n def _warn_python2(self)\n d","label":0},{"cid":"conans/client/generators/pkg_config.py","text":"conans/client/generators/pkg_config.py\nclass PkgConfigGenerator\n def filename(self)\n def compiler(self)\n def content(self)\n def single_pc_file_contents(self, name, cpp_info)\ndef _concat_if_not_empty(groups)\ndef _generate_dir_lines(prefix_path, varname, dirs)","label":0}],"string":"[\n {\n \"cid\": \"conans/client/build/autotools_environment.py\",\n \"text\": \"conans/client/build/autotools_environment.py\\nclass AutoToolsBuildEnvironment # - CPPFLAGS (C-PreProcesor-Flags NOT related with c++) (-I -D)\\n def __init__(self, conanfile, win_bash=False, include_rpath_flags=False) # FIXME: include_rpath_flags CONAN 2.0 to default True? Could break many packages \\n def _configure_fpic(self)\\n def _get_host_build_target_flags(self) # Based on google search for build/host triplets, it could need a lot\\n def configure(self, configure_dir=None, args=None, build=None, host=None, target=None, pkg_config_paths=None, vars=None, use_default_install_dirs=True) # :param pkg_config_paths: Optional paths to locate the *.pc files\\n def _configure_help_output(self, configure_path)\\n def _adjust_path(self, path)\\n def _valid_configure_flag(varname, args, available_flags)\\n def _is_flag_in_args(varname, args)\\n def make(self, args='', make_program=None, target=None, vars=None)\\n def install(self, args='', make_program=None, vars=None)\\n def _configure_link_flags(self) # Not the -L\\n def _configure_flags(self)\\n def _configure_cxx_flags(self)\\n def _configure_defines(self)\\n def _get_vars(self)\\n def append(*args)\\n def vars_dict(self)\\n def vars(self)\\ndef _environ_value_prefix(var_name, prefix=' ')\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/command.py\",\n \"text\": \"conans/client/command.py\\nclass Extender # Allows to use the same flag several times in a command and creates a list with t\\n def __call__(self, parser, namespace, values, option_strings=None)\\nclass OnceArgument # Allows to declare a parameter that can have only one value, by default argparse \\n def __call__(self, parser, namespace, values, option_string=None)\\nclass Command # A single command of the conan application, with all the first level commands. Ma\\n def __init__(self, conan_api, client_cache, user_io, outputer)\\n def help(self, *args) # Show help of a specific commmand.\\n def new(self, *args) # Creates a new package recipe template with a 'conanfile.py' and optionally,\\n def inspect(self, *args) # Displays conanfile attributes, like name, version, options\\n def test(self, *args) # Test a package consuming it from a conanfile.py with a test() method.\\n def create(self, *args) # Builds a binary package for a recipe (conanfile.py).\\n def download(self, *args) # Downloads recipe and binaries to the local cache, without using settings.\\n def install(self, *args) # Installs the requirements specified in a recipe (conanfile.py or conanfile.txt).\\n def config(self, *args) # Manages Conan configuration.\\n def info(self, *args) # Gets information about the dependency graph of a recipe.\\n def source(self, *args) # Calls your local conanfile.py 'source()' method.\\n def build(self, *args) # Calls your local conanfile.py 'build()' method.\\n def package(self, *args) # Calls your local conanfile.py 'package()' method.\\n def imports(self, *args) # Calls your local conanfile.py or conanfile.txt 'imports' method.\\n def export_pkg(self, *args) # Exports a recipe, then creates a package from local source and build folders.\\n def export(self, *args) # Copies the recipe (conanfile.py & associated files) to your local cache.\\n def remove(self, *args) # Removes packages or binaries matching pattern from local cache or remote.\\n def copy(self, *args) # Copies conan recipes and packages to another user/channel.\\n def user(self, *args) # Authenticates against a remote with user/pass, caching the auth token.\\n def search(self, *args) # Searches package recipes and binaries in the local cache or in a remote.\\n def upload(self, *args) # Uploads a recipe and binary packages to a remote.\\n def remote(self, *args) # Manages the remote list and the package recipes associated to a remote.\\n def profile(self, *args) # Lists profiles in the '.conan/profiles' folder, or shows profile details.\\n def get(self, *args) # Gets a file or list a directory of a given reference or package.\\n def alias(self, *args) # Creates and exports an 'alias package recipe'.\\n def _show_help(self) # Prints a summary of all commands\\n def check_all_commands_listed() # Keep updated the main directory, raise if don't\\n def _commands(self) # returns a list of available commands\\n def _warn_python2(self)\\n d\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/pkg_config.py\",\n \"text\": \"conans/client/generators/pkg_config.py\\nclass PkgConfigGenerator\\n def filename(self)\\n def compiler(self)\\n def content(self)\\n def single_pc_file_contents(self, name, cpp_info)\\ndef _concat_if_not_empty(groups)\\ndef _generate_dir_lines(prefix_path, varname, dirs)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.__init__","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.__init__\n def __init__(self, conanfile, win_bash=False, include_rpath_flags=False):\n \"\"\"\n FIXME: include_rpath_flags CONAN 2.0 to default True? Could break many packages in center\n \"\"\"\n self._conanfile = conanfile\n self._win_bash = win_bash\n self._include_rpath_flags = include_rpath_flags\n self.subsystem = OSInfo().detect_windows_subsystem() if self._win_bash else None\n self._deps_cpp_info = conanfile.deps_cpp_info\n self._os = conanfile.settings.get_safe(\"os\")\n self._arch = conanfile.settings.get_safe(\"arch\")\n self._build_type = conanfile.settings.get_safe(\"build_type\")\n self._compiler = conanfile.settings.get_safe(\"compiler\")\n self._compiler_version = conanfile.settings.get_safe(\"compiler.version\")\n self._libcxx = conanfile.settings.get_safe(\"compiler.libcxx\")\n self._cppstd = conanfile.settings.get_safe(\"cppstd\")\n\n # Set the generic objects before mapping to env vars to let the user\n # alter some value\n self.libs = copy.copy(self._deps_cpp_info.libs)\n self.include_paths = copy.copy(self._deps_cpp_info.include_paths)\n self.library_paths = copy.copy(self._deps_cpp_info.lib_paths)\n\n self.defines = self._configure_defines()\n # Will go to CFLAGS and CXXFLAGS [\"-m64\" \"-m32\", \"-g\", \"-s\"]\n self.flags = self._configure_flags()\n # Only c++ flags [-stdlib, -library], will go to CXXFLAGS\n self.cxx_flags = self._configure_cxx_flags()\n # cpp standard\n self.cppstd_flag = cppstd_flag(self._compiler, self._compiler_version, self._cppstd)\n # Not -L flags, [\"-m64\" \"-m32\"]\n self.link_flags = self._configure_link_flags() # TEST!\n # Precalculate -fPIC\n self.fpic = self._configure_fpic()\n\n # Precalculate build, host, target triplets\n self.build, self.host, self.target = self._get_host_b","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_fpic","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_fpic\n def _configure_fpic(self):\n if str(self._os) not in [\"Windows\", \"WindowsStore\"]:\n fpic = self._conanfile.options.get_safe(\"fPIC\")\n if fpic is not None:\n shared = self._conanfile.options.get_safe(\"shared\")\n return True if (fpic or shared) else None","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_host_build_target_flags","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_host_build_target_flags\n def _get_host_build_target_flags(self):\n \"\"\"Based on google search for build/host triplets, it could need a lot\n and complex verification\"\"\"\n\n arch_detected = detected_architecture() or platform.machine()\n os_detected = platform.system()\n\n if os_detected is None or arch_detected is None or self._arch is None or self._os is None:\n return False, False, False\n if not cross_building(self._conanfile.settings, os_detected, arch_detected):\n return False, False, False\n\n try:\n build = get_gnu_triplet(os_detected, arch_detected, self._compiler)\n except ConanException as exc:\n self._conanfile.output.warn(str(exc))\n build = None\n try:\n host = get_gnu_triplet(self._os, self._arch, self._compiler)\n except ConanException as exc:\n self._conanfile.output.warn(str(exc))\n host = None\n return build, host, None","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.configure","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.configure\n def configure(self, configure_dir=None, args=None, build=None, host=None, target=None,\n pkg_config_paths=None, vars=None, use_default_install_dirs=True):\n \"\"\"\n :param pkg_config_paths: Optional paths to locate the *.pc files\n :param configure_dir: Absolute or relative path to the configure script\n :param args: Optional arguments to pass to configure.\n :param build: In which system the program will be built. \"False\" skips the --build flag\n :param host: In which system the generated program will run. \"False\" skips the --host flag\n :param target: This option is only used to build a cross-compiling toolchain.\n \"False\" skips the --target flag\n When the tool chain generates executable program, in which target system\n the program will run.\n\n http://jingfenghanmax.blogspot.com.es/2010/09/configure-with-host-target-and-build.html\n https://gcc.gnu.org/onlinedocs/gccint/Configure-Terms.html\n :param use_default_install_dirs: Use or not the defaulted installation dirs\n\n \"\"\"\n if not self._conanfile.should_configure:\n return\n if configure_dir:\n configure_dir = configure_dir.rstrip(\"/\")\n else:\n configure_dir = \".\"\n\n triplet_args = []\n\n if build is not False: # Skipped by user\n if build or self.build: # User specified value or automatic\n triplet_args.append(\"--build=%s\" % (build or self.build))\n\n if host is not False: # Skipped by user\n if host or self.host: # User specified value or automatic\n triplet_args.append(\"--host=%s\" % (host or self.host))\n\n if target is not False: # Skipped by user\n if target or self.target: # User specified value or automatic\n triplet_args.append(\"-","label":1},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_help_output","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_help_output\n def _configure_help_output(self, configure_path):\n from six import StringIO # Python 2 and 3 compatible\n mybuf = StringIO()\n try:\n self._conanfile.run(\"%s/configure --help\" % configure_path, output=mybuf)\n except ConanException as e:\n self._conanfile.output.warn(\"Error running `configure --help`: %s\" % e)\n return \"\"\n return mybuf.getvalue()","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._adjust_path","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._adjust_path\n def _adjust_path(self, path):\n if self._win_bash:\n path = unix_path(path, path_flavor=self.subsystem)\n return '\"%s\"' % path if \" \" in path else path","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._valid_configure_flag","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._valid_configure_flag\n @staticmethod\n def _valid_configure_flag(varname, args, available_flags):\n return not AutoToolsBuildEnvironment._is_flag_in_args(varname, args) and \\\n varname in available_flags","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._is_flag_in_args","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._is_flag_in_args\n @staticmethod\n def _is_flag_in_args(varname, args):\n flag = \"--%s=\" % varname\n return any([flag in arg for arg in args])","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.make","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.make\n def make(self, args=\"\", make_program=None, target=None, vars=None):\n if not self._conanfile.should_build:\n return\n make_program = os.getenv(\"CONAN_MAKE_PROGRAM\") or make_program or \"make\"\n with environment_append(vars or self.vars):\n str_args = args_to_string(args)\n cpu_count_option = (\"-j%s\" % cpu_count()) if \"-j\" not in str_args else None\n self._conanfile.run(\"%s\" % join_arguments([make_program, target, str_args,\n cpu_count_option]),\n win_bash=self._win_bash, subsystem=self.subsystem)","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.install","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.install\n def install(self, args=\"\", make_program=None, vars=None):\n if not self._conanfile.should_install:\n return\n self.make(args=args, make_program=make_program, target=\"install\", vars=vars)","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_link_flags","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_link_flags\n def _configure_link_flags(self):\n \"\"\"Not the -L\"\"\"\n ret = copy.copy(self._deps_cpp_info.sharedlinkflags)\n ret.extend(self._deps_cpp_info.exelinkflags)\n arch_flag = architecture_flag(compiler=self._compiler, arch=self._arch)\n if arch_flag:\n ret.append(arch_flag)\n\n sysf = sysroot_flag(self._deps_cpp_info.sysroot, win_bash=self._win_bash,\n subsystem=self.subsystem,\n compiler=self._compiler)\n if sysf:\n ret.append(sysf)\n\n if self._include_rpath_flags:\n the_os = self._conanfile.settings.get_safe(\"os_build\") or self._os\n ret.extend(rpath_flags(the_os, self._compiler, self._deps_cpp_info.lib_paths))\n\n return ret","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_flags","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_flags\n def _configure_flags(self):\n ret = copy.copy(self._deps_cpp_info.cflags)\n arch_flag = architecture_flag(compiler=self._compiler, arch=self._arch)\n if arch_flag:\n ret.append(arch_flag)\n btfs = build_type_flags(compiler=self._compiler, build_type=self._build_type,\n vs_toolset=self._conanfile.settings.get_safe(\"compiler.toolset\"))\n if btfs:\n ret.extend(btfs)\n srf = sysroot_flag(self._deps_cpp_info.sysroot, win_bash=self._win_bash,\n subsystem=self.subsystem,\n compiler=self._compiler)\n if srf:\n ret.append(srf)\n\n return ret","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_cxx_flags","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_cxx_flags\n def _configure_cxx_flags(self):\n ret = copy.copy(self._deps_cpp_info.cppflags)\n cxxf = libcxx_flag(compiler=self._compiler, libcxx=self._libcxx)\n if cxxf:\n ret.append(cxxf)\n return ret","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_defines","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_defines\n def _configure_defines(self):\n # requires declared defines\n ret = copy.copy(self._deps_cpp_info.defines)\n\n # Debug definition for GCC\n btf = build_type_define(build_type=self._build_type)\n if btf:\n ret.append(btf)\n\n # CXX11 ABI\n abif = libcxx_define(compiler=self._compiler, libcxx=self._libcxx)\n if abif:\n ret.append(abif)\n return ret","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars\n def _get_vars(self):\n def append(*args):\n ret = []\n for arg in args:\n if arg:\n if isinstance(arg, list):\n ret.extend(arg)\n else:\n ret.append(arg)\n return ret\n\n lib_paths = format_library_paths(self.library_paths, win_bash=self._win_bash,\n subsystem=self.subsystem, compiler=self._compiler)\n include_paths = format_include_paths(self.include_paths, win_bash=self._win_bash,\n subsystem=self.subsystem, compiler=self._compiler)\n\n ld_flags = append(self.link_flags, lib_paths)\n cpp_flags = append(include_paths, format_defines(self.defines))\n libs = format_libraries(self.libs, compiler=self._compiler)\n\n tmp_compilation_flags = copy.copy(self.flags)\n if self.fpic:\n tmp_compilation_flags.append(pic_flag(self._compiler))\n\n cxx_flags = append(tmp_compilation_flags, self.cxx_flags, self.cppstd_flag)\n c_flags = tmp_compilation_flags\n\n return ld_flags, cpp_flags, libs, cxx_flags, c_flags","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars.append","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars.append\n def append(*args):\n ret = []\n for arg in args:\n if arg:\n if isinstance(arg, list):\n ret.extend(arg)\n else:\n ret.append(arg)\n return ret","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars_dict","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars_dict\n @property\n def vars_dict(self):\n\n ld_flags, cpp_flags, libs, cxx_flags, c_flags = self._get_vars()\n\n if os.environ.get(\"CPPFLAGS\", None):\n cpp_flags.append(os.environ.get(\"CPPFLAGS\", None))\n\n if os.environ.get(\"CXXFLAGS\", None):\n cxx_flags.append(os.environ.get(\"CXXFLAGS\", None))\n\n if os.environ.get(\"CFLAGS\", None):\n c_flags.append(os.environ.get(\"CFLAGS\", None))\n\n if os.environ.get(\"LDFLAGS\", None):\n ld_flags.append(os.environ.get(\"LDFLAGS\", None))\n\n if os.environ.get(\"LIBS\", None):\n libs.append(os.environ.get(\"LIBS\", None))\n\n ret = {\"CPPFLAGS\": cpp_flags,\n \"CXXFLAGS\": cxx_flags,\n \"CFLAGS\": c_flags,\n \"LDFLAGS\": ld_flags,\n \"LIBS\": libs,\n }\n return ret","label":0},{"cid":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars","text":"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars\n @property\n def vars(self):\n ld_flags, cpp_flags, libs, cxx_flags, c_flags = self._get_vars()\n\n cpp_flags = \" \".join(cpp_flags) + _environ_value_prefix(\"CPPFLAGS\")\n cxx_flags = \" \".join(cxx_flags) + _environ_value_prefix(\"CXXFLAGS\")\n cflags = \" \".join(c_flags) + _environ_value_prefix(\"CFLAGS\")\n ldflags = \" \".join(ld_flags) + _environ_value_prefix(\"LDFLAGS\")\n libs = \" \".join(libs) + _environ_value_prefix(\"LIBS\")\n\n ret = {\"CPPFLAGS\": cpp_flags.strip(),\n \"CXXFLAGS\": cxx_flags.strip(),\n \"CFLAGS\": cflags.strip(),\n \"LDFLAGS\": ldflags.strip(),\n \"LIBS\": libs.strip(),\n }\n return ret","label":0},{"cid":"conans/client/build/autotools_environment.py::_environ_value_prefix","text":"conans/client/build/autotools_environment.py::_environ_value_prefix\ndef _environ_value_prefix(var_name, prefix=\" \"):\n if os.environ.get(var_name, \"\"):\n return \"%s%s\" % (prefix, os.environ.get(var_name, \"\"))\n else:\n return \"\"","label":0},{"cid":"conans/client/command.py::Extender.__call__","text":"conans/client/command.py::Extender.__call__\n def __call__(self, parser, namespace, values, option_strings=None): # @UnusedVariable\n # Need None here incase `argparse.SUPPRESS` was supplied for `dest`\n dest = getattr(namespace, self.dest, None)\n if not hasattr(dest, 'extend') or dest == self.default:\n dest = []\n setattr(namespace, self.dest, dest)\n # if default isn't set to None, this method might be called\n # with the default as `values` for other arguments which\n # share this destination.\n parser.set_defaults(**{self.dest: None})\n\n if isinstance(values, str):\n dest.append(values)\n elif values:\n try:\n dest.extend(values)\n except ValueError:\n dest.append(values)","label":0},{"cid":"conans/client/command.py::OnceArgument.__call__","text":"conans/client/command.py::OnceArgument.__call__\n def __call__(self, parser, namespace, values, option_string=None):\n if getattr(namespace, self.dest) is not None and self.default is None:\n msg = '{o} can only be specified once'.format(o=option_string)\n raise argparse.ArgumentError(None, msg)\n setattr(namespace, self.dest, values)","label":0},{"cid":"conans/client/command.py::Command.__init__","text":"conans/client/command.py::Command.__init__\n def __init__(self, conan_api, client_cache, user_io, outputer):\n assert isinstance(conan_api, Conan)\n self._conan = conan_api\n self._client_cache = client_cache\n self._user_io = user_io\n self._outputer = outputer","label":0},{"cid":"conans/client/command.py::Command.help","text":"conans/client/command.py::Command.help\n def help(self, *args):\n \"\"\"Show help of a specific commmand.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.help.__doc__, prog=\"conan help\")\n parser.add_argument(\"command\", help='command', nargs=\"?\")\n args = parser.parse_args(*args)\n if not args.command:\n self._show_help()\n return\n try:\n commands = self._commands()\n method = commands[args.command]\n self._warn_python2()\n method([\"--help\"])\n except KeyError:\n raise ConanException(\"Unknown command '%s'\" % args.command)","label":0},{"cid":"conans/client/command.py::Command.new","text":"conans/client/command.py::Command.new\n def new(self, *args):\n \"\"\"Creates a new package recipe template with a 'conanfile.py' and optionally,\n 'test_package' testing files.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.new.__doc__, prog=\"conan new\")\n parser.add_argument(\"name\", help='Package name, e.g.: \"Poco/1.7.3\" or complete reference'\n ' for CI scripts: \"Poco/1.7.3@conan/stable\"')\n parser.add_argument(\"-t\", \"--test\", action='store_true', default=False,\n help='Create test_package skeleton to test package')\n parser.add_argument(\"-i\", \"--header\", action='store_true', default=False,\n help='Create a headers only package template')\n parser.add_argument(\"-c\", \"--pure-c\", action='store_true', default=False,\n help='Create a C language package only package, '\n 'deleting \"self.settings.compiler.libcxx\" setting '\n 'in the configure method')\n parser.add_argument(\"-s\", \"--sources\", action='store_true', default=False,\n help='Create a package with embedded sources in \"src\" folder, '\n 'using \"exports_sources\" instead of retrieving external code with '\n 'the \"source()\" method')\n parser.add_argument(\"-b\", \"--bare\", action='store_true', default=False,\n help='Create the minimum package recipe, without build() method'\n 'Useful in combination with \"export-pkg\" command')\n parser.add_argument(\"-cis\", \"--ci-shared\", action='store_true',\n default=False,\n help='Package will have a \"shared\" option to be used in CI')\n parser.add_argument(\"-cilg\", \"--ci-travis-gcc\", action='store_true',\n default","label":0},{"cid":"conans/client/command.py::Command.inspect","text":"conans/client/command.py::Command.inspect\n def inspect(self, *args):\n \"\"\"Displays conanfile attributes, like name, version, options\n Works both locally, in local cache and remote\n \"\"\"\n parser = argparse.ArgumentParser(description=self.inspect.__doc__, prog=\"conan inspect\")\n parser.add_argument(\"path_or_reference\", help=\"Path to a folder containing a recipe\"\n \" (conanfile.py) or to a recipe file. e.g., \"\n \"./my_project/conanfile.py. It could also be a reference\")\n parser.add_argument(\"-a\", \"--attribute\", help='The attribute to be displayed, e.g \"name\"',\n nargs=\"?\", action=Extender)\n parser.add_argument(\"-r\", \"--remote\", help='look in the specified remote server',\n action=OnceArgument)\n parser.add_argument(\"-j\", \"--json\", default=None, action=OnceArgument,\n help='json output file')\n\n args = parser.parse_args(*args)\n result = self._conan.inspect(args.path_or_reference, args.attribute, args.remote)\n Printer(self._user_io.out).print_inspect(result)\n if args.json:\n json_output = json.dumps(result)\n if not os.path.isabs(args.json):\n json_output_file = os.path.join(get_cwd(), args.json)\n else:\n json_output_file = args.json\n save(json_output_file, json_output)","label":0},{"cid":"conans/client/command.py::Command.test","text":"conans/client/command.py::Command.test\n def test(self, *args):\n \"\"\"Test a package consuming it from a conanfile.py with a test() method.\n\n This command installs the conanfile dependencies (including the tested\n package), calls a 'conan build' to build test apps and finally executes\n the test() method. The testing recipe does not require name or version,\n neither definition of package() or package_info() methods. The package\n to be tested must exist in the local cache or in any configured remote.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.test.__doc__, prog=\"conan test\")\n parser.add_argument(\"path\", help='Path to the \"testing\" folder containing a conanfile.py or'\n ' to a recipe file with test() method'\n 'e.g. conan test_package/conanfile.py pkg/version@user/channel')\n parser.add_argument(\"reference\",\n help='pkg/version@user/channel of the package to be tested')\n parser.add_argument(\"-tbf\", \"--test-build-folder\", action=OnceArgument,\n help=\"Working directory of the build process.\")\n\n _add_common_install_arguments(parser, build_help=_help_build_policies)\n args = parser.parse_args(*args)\n self._warn_python2()\n return self._conan.test(args.path, args.reference, args.profile, args.settings,\n args.options, args.env, args.remote, args.update,\n build_modes=args.build, test_build_folder=args.test_build_folder)","label":0},{"cid":"conans/client/command.py::Command.create","text":"conans/client/command.py::Command.create\n def create(self, *args):\n \"\"\"Builds a binary package for a recipe (conanfile.py).\n\n Uses the specified configuration in a profile or in -s settings, -o\n options etc. If a 'test_package' folder (the name can be configured\n with -tf) is found, the command will run the consumer project to ensure\n that the package has been created correctly. Check 'conan test' command\n to know more about 'test_folder' project.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.create.__doc__, prog=\"conan create\")\n parser.add_argument(\"path\", help=_PATH_HELP)\n parser.add_argument(\"reference\",\n help='user/channel or pkg/version@user/channel (if name and version not'\n ' declared in conanfile.py) where the package will be created')\n parser.add_argument(\"-j\", \"--json\", default=None, action=OnceArgument,\n help='json file path where the install information will be written to')\n parser.add_argument('-k', '-ks', '--keep-source', default=False, action='store_true',\n help=_KEEP_SOURCE_HELP)\n parser.add_argument('-kb', '--keep-build', default=False, action='store_true',\n help='Do not remove the build folder in local cache. Implies --keep-source. '\n 'Use this for testing purposes only')\n parser.add_argument(\"-ne\", \"--not-export\", default=False, action='store_true',\n help='Do not export the conanfile.py')\n parser.add_argument(\"-tbf\", \"--test-build-folder\", action=OnceArgument,\n help='Working directory for the build of the test project.')\n parser.add_argument(\"-tf\", \"--test-folder\", action=OnceArgument,\n help='Alternative test folder name. By default it is \"test_package\". '\n ","label":0},{"cid":"conans/client/command.py::Command.download","text":"conans/client/command.py::Command.download\n def download(self, *args):\n \"\"\"Downloads recipe and binaries to the local cache, without using settings.\n\n It works specifying the recipe reference and package ID to be\n installed. Not transitive, requirements of the specified reference will\n NOT be retrieved. Useful together with 'conan copy' to automate the\n promotion of packages to a different user/channel. Only if a reference\n is specified, it will download all packages from the specified remote.\n If no remote is specified, it will use the default remote.\n \"\"\"\n\n parser = argparse.ArgumentParser(description=self.download.__doc__, prog=\"conan download\")\n parser.add_argument(\"reference\",\n help='pkg/version@user/channel')\n parser.add_argument(\"-p\", \"--package\", nargs=1, action=Extender,\n help='Force install specified package ID (ignore settings/options)')\n parser.add_argument(\"-r\", \"--remote\", help='look in the specified remote server',\n action=OnceArgument)\n parser.add_argument(\"-re\", \"--recipe\", help='Downloads only the recipe', default=False,\n action=\"store_true\")\n\n args = parser.parse_args(*args)\n\n self._warn_python2()\n return self._conan.download(reference=args.reference, package=args.package,\n remote_name=args.remote, recipe=args.recipe)","label":0},{"cid":"conans/client/command.py::Command.install","text":"conans/client/command.py::Command.install\n def install(self, *args):\n \"\"\"Installs the requirements specified in a recipe (conanfile.py or conanfile.txt).\n\n It can also be used to install a concrete package specifying a\n reference. If any requirement is not found in the local cache, it will\n retrieve the recipe from a remote, looking for it sequentially in the\n configured remotes. When the recipes have been downloaded it will try\n to download a binary package matching the specified settings, only from\n the remote from which the recipe was retrieved. If no binary package is\n found, it can be build from sources using the '--build' option. When\n the package is installed, Conan will write the files for the specified\n generators.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.install.__doc__, prog=\"conan install\")\n parser.add_argument(\"path_or_reference\", help=\"Path to a folder containing a recipe\"\n \" (conanfile.py or conanfile.txt) or to a recipe file. e.g., \"\n \"./my_project/conanfile.txt. It could also be a reference\")\n parser.add_argument(\"-g\", \"--generator\", nargs=1, action=Extender,\n help='Generators to use')\n parser.add_argument(\"-if\", \"--install-folder\", action=OnceArgument,\n help='Use this directory as the directory where to put the generator'\n 'files. e.g., conaninfo/conanbuildinfo.txt')\n\n _add_manifests_arguments(parser)\n\n parser.add_argument(\"--no-imports\", action='store_true', default=False,\n help='Install specified packages but avoid running imports')\n parser.add_argument(\"-j\", \"--json\", default=None, action=OnceArgument,\n help='Path to a json file where the install information will be '\n 'written')\n\n ","label":0},{"cid":"conans/client/command.py::Command.config","text":"conans/client/command.py::Command.config\n def config(self, *args):\n \"\"\"Manages Conan configuration.\n\n Used to edit conan.conf, or install config files.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.config.__doc__, prog=\"conan config\")\n\n subparsers = parser.add_subparsers(dest='subcommand', help='sub-command help')\n rm_subparser = subparsers.add_parser('rm', help='Remove an existing config element')\n set_subparser = subparsers.add_parser('set', help='Set a value for a configuration item')\n get_subparser = subparsers.add_parser('get', help='Get the value of configuration item')\n install_subparser = subparsers.add_parser('install', help='install a full configuration '\n 'from a local or remote zip file')\n rm_subparser.add_argument(\"item\", help=\"Item to remove\")\n get_subparser.add_argument(\"item\", nargs=\"?\", help=\"Item to print\")\n set_subparser.add_argument(\"item\", help=\"'item=value' to set\")\n install_subparser.add_argument(\"item\", nargs=\"?\", help=\"Configuration file or directory to use\")\n\n install_subparser.add_argument(\"--verify-ssl\", nargs=\"?\", default=\"True\",\n help='Verify SSL connection when downloading file')\n install_subparser.add_argument(\"--type\", \"-t\", choices=[\"git\"],\n help='Type of remote config')\n install_subparser.add_argument(\"--args\", \"-a\",\n help='String with extra arguments for \"git clone\"')\n\n args = parser.parse_args(*args)\n\n if args.subcommand == \"set\":\n try:\n key, value = args.item.split(\"=\", 1)\n except ValueError:\n if \"plugins.\" in args.item:\n key, value = args.item.split(\"=\", 1)[0], None\n else:\n raise ConanException(\"Please specify 'k","label":0},{"cid":"conans/client/command.py::Command.info","text":"conans/client/command.py::Command.info\n def info(self, *args):\n \"\"\"Gets information about the dependency graph of a recipe.\n\n It can be used with a recipe or a reference for any existing package in\n your local cache.\n \"\"\"\n\n info_only_options = [\"id\", \"build_id\", \"remote\", \"url\", \"license\", \"requires\", \"update\",\n \"required\", \"date\", \"author\", \"None\"]\n path_only_options = [\"export_folder\", \"build_folder\", \"package_folder\", \"source_folder\"]\n str_path_only_options = \", \".join(['\"%s\"' % field for field in path_only_options])\n str_only_options = \", \".join(['\"%s\"' % field for field in info_only_options])\n\n parser = argparse.ArgumentParser(description=self.info.__doc__, prog=\"conan info\")\n parser.add_argument(\"path_or_reference\", help=\"Path to a folder containing a recipe\"\n \" (conanfile.py or conanfile.txt) or to a recipe file. e.g., \"\n \"./my_project/conanfile.txt. It could also be a reference\")\n parser.add_argument(\"--paths\", action='store_true', default=False,\n help='Show package paths in local cache')\n parser.add_argument(\"-bo\", \"--build-order\",\n help='given a modified reference, return an ordered list to build (CI)',\n nargs=1, action=Extender)\n parser.add_argument(\"-g\", \"--graph\", action=OnceArgument,\n help='Creates file with project dependencies graph. It will generate '\n 'a DOT or HTML file depending on the filename extension')\n parser.add_argument(\"-if\", \"--install-folder\", action=OnceArgument,\n help=\"local folder containing the conaninfo.txt and conanbuildinfo.txt \"\n \"files (from a previous conan install execution). Defaulted to \"\n \"current folder, unless --profile, -s or -o","label":0},{"cid":"conans/client/command.py::Command.source","text":"conans/client/command.py::Command.source\n def source(self, *args):\n \"\"\" Calls your local conanfile.py 'source()' method.\n\n Usually downloads and uncompresses the package sources.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.source.__doc__, prog=\"conan source\")\n parser.add_argument(\"path\", help=_PATH_HELP)\n parser.add_argument(\"-sf\", \"--source-folder\", action=OnceArgument,\n help='Destination directory. Defaulted to current directory')\n parser.add_argument(\"-if\", \"--install-folder\", action=OnceArgument,\n help=_INSTALL_FOLDER_HELP + \" Optional, source method will run without \"\n \"the information retrieved from the conaninfo.txt and \"\n \"conanbuildinfo.txt, only required when using conditional source() \"\n \"based on settings, options, env_info and user_info\")\n args = parser.parse_args(*args)\n\n try:\n if \"@\" in args.path and ConanFileReference.loads(args.path):\n raise ArgumentError(None,\n \"'conan source' doesn't accept a reference anymore. \"\n \"If you were using it as a concurrency workaround, \"\n \"you can call 'conan install' simultaneously from several \"\n \"different processes, the concurrency is now natively supported\"\n \". The path parameter should be a folder containing a \"\n \"conanfile.py file.\")\n except ConanException:\n pass\n\n self._warn_python2()\n return self._conan.source(args.path, args.source_folder, args.install_folder)","label":0},{"cid":"conans/client/command.py::Command.build","text":"conans/client/command.py::Command.build\n def build(self, *args):\n \"\"\"Calls your local conanfile.py 'build()' method.\n\n The recipe will be built in the local directory specified by\n --build-folder, reading the sources from --source-folder. If you are\n using a build helper, like CMake(), the --package-folder will be\n configured as destination folder for the install step.\n \"\"\"\n\n parser = argparse.ArgumentParser(description=self.build.__doc__, prog=\"conan build\")\n parser.add_argument(\"path\", help=_PATH_HELP)\n parser.add_argument(\"-b\", \"--build\", default=None, action=\"store_true\",\n help=\"Execute the build step (variable should_build=True). When \"\n \"specified, configure/install/test won't run unless \"\n \"--configure/--install/--test specified\")\n parser.add_argument(\"-bf\", \"--build-folder\", action=OnceArgument, help=_BUILD_FOLDER_HELP)\n parser.add_argument(\"-c\", \"--configure\", default=None, action=\"store_true\",\n help=\"Execute the configuration step (variable should_configure=True). \"\n \"When specified, build/install/test won't run unless \"\n \"--build/--install/--test specified\")\n parser.add_argument(\"-i\", \"--install\", default=None, action=\"store_true\",\n help=\"Execute the install step (variable should_install=True). When \"\n \"specified, configure/build/test won't run unless \"\n \"--configure/--build/--test specified\")\n parser.add_argument(\"-t\", \"--test\", default=None, action=\"store_true\",\n help=\"Execute the test step (variable should_test=True). When \"\n \"specified, configure/build/install won't run unless \"\n \"--configure/--build/--install specified\")\n parser.add_","label":0},{"cid":"conans/client/command.py::Command.package","text":"conans/client/command.py::Command.package\n def package(self, *args):\n \"\"\" Calls your local conanfile.py 'package()' method.\n\n This command works in the user space and it will copy artifacts from\n the --build-folder and --source-folder folder to the --package-folder\n one. It won't create a new package in the local cache, if you want to\n do it, use 'conan create' or 'conan export-pkg' after a 'conan build'\n command.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.package.__doc__, prog=\"conan package\")\n parser.add_argument(\"path\", help=_PATH_HELP)\n parser.add_argument(\"-bf\", \"--build-folder\", action=OnceArgument, help=_BUILD_FOLDER_HELP)\n parser.add_argument(\"-if\", \"--install-folder\", action=OnceArgument,\n help=_INSTALL_FOLDER_HELP)\n parser.add_argument(\"-pf\", \"--package-folder\", action=OnceArgument,\n help=\"folder to install the package. Defaulted to the \"\n \"'{build_folder}/package' folder. A relative path can be specified\"\n \" (relative to the current directory). Also an absolute path\"\n \" is allowed.\")\n parser.add_argument(\"-sf\", \"--source-folder\", action=OnceArgument, help=_SOURCE_FOLDER_HELP)\n args = parser.parse_args(*args)\n try:\n if \"@\" in args.path and ConanFileReference.loads(args.path):\n raise ArgumentError(None,\n \"'conan package' doesn't accept a reference anymore. \"\n \"The path parameter should be a conanfile.py or a folder \"\n \"containing one. If you were using the 'conan package' \"\n \"command for development purposes we recommend to use \"\n \"the local development commands: 'conan build' + \"\n ","label":0},{"cid":"conans/client/command.py::Command.imports","text":"conans/client/command.py::Command.imports\n def imports(self, *args):\n \"\"\" Calls your local conanfile.py or conanfile.txt 'imports' method.\n\n It requires to have been previously installed and have a\n conanbuildinfo.txt generated file in the --install-folder (defaulted to\n current directory).\n \"\"\"\n parser = argparse.ArgumentParser(description=self.imports.__doc__, prog=\"conan imports\")\n parser.add_argument(\"path\",\n help=_PATH_HELP + \" With --undo option, this parameter is the folder \"\n \"containing the conan_imports_manifest.txt file generated in a previous \"\n \"execution. e.g.: conan imports ./imported_files --undo \")\n parser.add_argument(\"-if\", \"--install-folder\", action=OnceArgument,\n help=_INSTALL_FOLDER_HELP)\n parser.add_argument(\"-imf\", \"--import-folder\", action=OnceArgument,\n help=\"Directory to copy the artifacts to. By default it will be the\"\n \" current directory\")\n parser.add_argument(\"-u\", \"--undo\", default=False, action=\"store_true\",\n help=\"Undo imports. Remove imported files\")\n args = parser.parse_args(*args)\n\n if args.undo:\n return self._conan.imports_undo(args.path)\n\n try:\n if \"@\" in args.path and ConanFileReference.loads(args.path):\n raise ArgumentError(None, \"Parameter 'path' cannot be a reference. Use a folder \"\n \"containing a conanfile.py or conanfile.txt file.\")\n except ConanException:\n pass\n self._warn_python2()\n return self._conan.imports(args.path, args.import_folder, args.install_folder)","label":0},{"cid":"conans/client/command.py::Command.export_pkg","text":"conans/client/command.py::Command.export_pkg\n def export_pkg(self, *args):\n \"\"\"Exports a recipe, then creates a package from local source and build folders.\n\n The package is created by calling the package() method applied to the\n local folders '--source-folder' and '--build-folder' It's created in\n the local cache for the specified 'reference' and for the specified\n '--settings', '--options' and or '--profile'.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.export_pkg.__doc__,\n prog=\"conan export-pkg\")\n parser.add_argument(\"path\", help=_PATH_HELP)\n parser.add_argument(\"reference\", help=\"user/channel or pkg/version@user/channel \"\n \"(if name and version are not declared in the \"\n \"conanfile.py)\")\n parser.add_argument(\"-bf\", \"--build-folder\", action=OnceArgument, help=_BUILD_FOLDER_HELP)\n parser.add_argument(\"-e\", \"--env\", nargs=1, action=Extender,\n help='Environment variables that will be set during the package build, '\n '-e CXX=/usr/bin/clang++')\n parser.add_argument('-f', '--force', default=False, action='store_true',\n help='Overwrite existing package if existing')\n parser.add_argument(\"-if\", \"--install-folder\", action=OnceArgument,\n help=_INSTALL_FOLDER_HELP + \" If these files are found in the specified\"\n \" folder and any of '-e', '-o', '-pr' or '-s' arguments are used, it \"\n \"will raise an error.\")\n parser.add_argument(\"-o\", \"--options\", nargs=1, action=Extender,\n help='Define options values, e.g., -o pkg:with_qt=true')\n parser.add_argument(\"-pr\", \"--profile\", action=OnceArgument,\n help='Profile for this ","label":0},{"cid":"conans/client/command.py::Command.export","text":"conans/client/command.py::Command.export\n def export(self, *args):\n \"\"\"Copies the recipe (conanfile.py & associated files) to your local cache.\n\n Use the 'reference' param to specify a user and channel where to export\n it. Once the recipe is in the local cache it can be shared, reused and\n to any remote with the 'conan upload' command.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.export.__doc__, prog=\"conan export\")\n parser.add_argument(\"path\", help=_PATH_HELP)\n parser.add_argument(\"reference\", help=\"user/channel, or Pkg/version@user/channel (if name \"\n \"and version are not declared in the conanfile.py\")\n parser.add_argument('-k', '-ks', '--keep-source', default=False, action='store_true',\n help=_KEEP_SOURCE_HELP)\n\n args = parser.parse_args(*args)\n self._warn_python2()\n name, version, user, channel = get_reference_fields(args.reference)\n return self._conan.export(path=args.path,\n name=name, version=version, user=user, channel=channel,\n keep_source=args.keep_source)","label":0},{"cid":"conans/client/command.py::Command.remove","text":"conans/client/command.py::Command.remove\n def remove(self, *args):\n \"\"\"Removes packages or binaries matching pattern from local cache or remote.\n\n It can also be used to remove temporary source or build folders in the\n local conan cache. If no remote is specified, the removal will be done\n by default in the local conan cache.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.remove.__doc__, prog=\"conan remove\")\n parser.add_argument('pattern_or_reference', nargs=\"?\", help=_PATTERN_OR_REFERENCE_HELP)\n parser.add_argument('-b', '--builds', nargs=\"*\", action=Extender,\n help=(\"By default, remove all the build folders or select one, \"\n \"specifying the package ID\"))\n parser.add_argument('-f', '--force', default=False, action='store_true',\n help='Remove without requesting a confirmation')\n parser.add_argument(\"-o\", \"--outdated\", default=False, action=\"store_true\",\n help=\"Remove only outdated from recipe packages. \" \\\n \"This flag can only be used with a reference\")\n parser.add_argument('-p', '--packages', nargs=\"*\", action=Extender,\n help=\"Select package to remove specifying the package ID\")\n parser.add_argument('-q', '--query', default=None, action=OnceArgument, help=_QUERY_HELP)\n parser.add_argument('-r', '--remote', action=OnceArgument,\n help='Will remove from the specified remote')\n parser.add_argument('-s', '--src', default=False, action=\"store_true\",\n help='Remove source folders')\n parser.add_argument(\"-l\", \"--locks\", default=False, action=\"store_true\",\n help=\"Remove locks\")\n args = parser.parse_args(*args)\n\n self._warn_python2()\n\n # NOTE: returns the expanded pattern (if a pattern was g","label":0},{"cid":"conans/client/command.py::Command.copy","text":"conans/client/command.py::Command.copy\n def copy(self, *args):\n \"\"\"Copies conan recipes and packages to another user/channel.\n\n Useful to promote packages (e.g. from \"beta\" to \"stable\") or transfer\n them from one user to another.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.copy.__doc__, prog=\"conan copy\")\n parser.add_argument(\"reference\", default=\"\",\n help='package reference. e.g., MyPackage/1.2@user/channel')\n parser.add_argument(\"user_channel\", default=\"\",\n help='Destination user/channel. e.g., lasote/testing')\n parser.add_argument(\"-p\", \"--package\", nargs=1, action=Extender,\n help='copy specified package ID')\n parser.add_argument(\"--all\", action='store_true', default=False,\n help='Copy all packages from the specified package recipe')\n parser.add_argument(\"--force\", action='store_true', default=False,\n help='Override destination packages and the package recipe')\n args = parser.parse_args(*args)\n\n if args.all and args.package:\n raise ConanException(\"Cannot specify both --all and --package\")\n\n self._warn_python2()\n\n return self._conan.copy(reference=args.reference, user_channel=args.user_channel,\n force=args.force, packages=args.package or args.all)","label":0},{"cid":"conans/client/command.py::Command.user","text":"conans/client/command.py::Command.user\n def user(self, *args):\n \"\"\"Authenticates against a remote with user/pass, caching the auth token.\n\n Useful to avoid the user and password being requested later. e.g. while\n you're uploading a package. You can have one user for each remote.\n Changing the user, or introducing the password is only necessary to\n perform changes in remote packages.\n \"\"\"\n # FIXME: Difficult and confusing CLI. Better with:\n # - conan user clean -> clean users\n # - conan user list ('remote') -> list users (of a remote)\n # - conan user auth 'remote' ('user') ('password') -> login a remote (w/o user or pass)\n # - conan user set 'user' 'remote' -> set user for a remote (not login) necessary??\n parser = argparse.ArgumentParser(description=self.user.__doc__, prog=\"conan user\")\n parser.add_argument(\"name\", nargs='?', default=None,\n help='Username you want to use. If no name is provided it will show the'\n ' current user')\n parser.add_argument('-c', '--clean', default=False, action='store_true',\n help='Remove user and tokens for all remotes')\n parser.add_argument(\"-p\", \"--password\", nargs='?', const=\"\", type=str, action=OnceArgument,\n help='User password. Use double quotes if password with spacing, '\n 'and escape quotes if existing. If empty, the password is '\n 'requested interactively (not exposed)')\n parser.add_argument(\"-r\", \"--remote\", help='Use the specified remote server',\n action=OnceArgument)\n parser.add_argument(\"-j\", \"--json\", default=None, action=OnceArgument,\n help='json file path where the user list will be written to')\n args = parser.parse_args(*args)\n\n if args.clean and any((args.name, ar","label":0},{"cid":"conans/client/command.py::Command.search","text":"conans/client/command.py::Command.search\n def search(self, *args):\n \"\"\"Searches package recipes and binaries in the local cache or in a remote.\n\n If you provide a pattern, then it will search for existing package\n recipes matching it. If a full reference is provided\n (pkg/0.1@user/channel) then the existing binary packages for that\n reference will be displayed. If no remote is specified, the search\n will be done in the local cache. Search is case sensitive, exact case\n has to be used. For case insensitive file systems, like Windows, case\n sensitive search can be forced with '--case-sensitive'.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.search.__doc__, prog=\"conan search\")\n parser.add_argument('pattern_or_reference', nargs='?', help=_PATTERN_OR_REFERENCE_HELP)\n parser.add_argument('-o', '--outdated', default=False, action='store_true',\n help=\"Show only outdated from recipe packages. \" \\\n \"This flag can only be used with a reference\")\n parser.add_argument('-q', '--query', default=None, action=OnceArgument, help=_QUERY_HELP)\n parser.add_argument('-r', '--remote', action=OnceArgument,\n help=\"Remote to search in. '-r all' searches all remotes\")\n parser.add_argument('--case-sensitive', default=False, action='store_true',\n help='Make a case-sensitive search. Use it to guarantee case-sensitive '\n 'search in Windows or other case-insensitive file systems')\n parser.add_argument('--raw', default=False, action='store_true',\n help='Print just the list of recipes')\n parser.add_argument('--table', action=OnceArgument,\n help=\"Outputs html file with a table of binaries. Only valid for a \"\n \"reference search\")\n parser.add_ar","label":0},{"cid":"conans/client/command.py::Command.upload","text":"conans/client/command.py::Command.upload\n def upload(self, *args):\n \"\"\"Uploads a recipe and binary packages to a remote.\n\n If no remote is specified, the first configured remote (by default conan-center, use\n 'conan remote list' to list the remotes) will be used.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.upload.__doc__,\n prog=\"conan upload\")\n parser.add_argument('pattern_or_reference', help=_PATTERN_OR_REFERENCE_HELP)\n parser.add_argument(\"-p\", \"--package\", default=None, action=OnceArgument,\n help='package ID to upload')\n parser.add_argument('-q', '--query', default=None, action=OnceArgument,\n help=\"Only upload packages matching a specific query. \" + _QUERY_HELP)\n parser.add_argument(\"-r\", \"--remote\", action=OnceArgument,\n help='upload to this specific remote')\n parser.add_argument(\"--all\", action='store_true', default=False,\n help='Upload both package recipe and packages')\n parser.add_argument(\"--skip-upload\", action='store_true', default=False,\n help='Do not upload anything, just run the checks and the compression')\n parser.add_argument(\"--force\", action='store_true', default=False,\n help='Do not check conan recipe date, override remote with local')\n parser.add_argument(\"--check\", action='store_true', default=False,\n help='Perform an integrity check, using the manifests, before upload')\n parser.add_argument('-c', '--confirm', default=False, action='store_true',\n help='Upload all matching recipes without confirmation')\n parser.add_argument('--retry', default=2, type=int, action=OnceArgument,\n help=\"In case of fail retries to upload again the specified times. \"\n ","label":0},{"cid":"conans/client/command.py::Command.remote","text":"conans/client/command.py::Command.remote\n def remote(self, *args):\n \"\"\"Manages the remote list and the package recipes associated to a remote.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.remote.__doc__, prog=\"conan remote\")\n subparsers = parser.add_subparsers(dest='subcommand', help='sub-command help')\n\n # create the parser for the \"a\" command\n parser_list = subparsers.add_parser('list', help='List current remotes')\n parser_list.add_argument(\"-raw\", \"--raw\", action='store_true', default=False,\n help='Raw format. Valid for \"remotes.txt\" file for \"conan config install\"')\n parser_add = subparsers.add_parser('add', help='Add a remote')\n parser_add.add_argument('remote', help='Name of the remote')\n parser_add.add_argument('url', help='URL of the remote')\n parser_add.add_argument('verify_ssl', nargs=\"?\", default=\"True\",\n help='Verify SSL certificated. Default True')\n parser_add.add_argument(\"-i\", \"--insert\", nargs=\"?\", const=0, type=int, action=OnceArgument,\n help=\"insert remote at specific index\")\n parser_add.add_argument(\"-f\", \"--force\", default=False, action='store_true',\n help=\"Force addition, will update if existing\")\n parser_rm = subparsers.add_parser('remove', help='Remove a remote')\n parser_rm.add_argument('remote', help='Name of the remote')\n parser_upd = subparsers.add_parser('update', help='Update the remote url')\n parser_upd.add_argument('remote', help='Name of the remote')\n\n parser_upd.add_argument('url', help='URL')\n parser_upd.add_argument('verify_ssl', nargs=\"?\", default=\"True\",\n help='Verify SSL certificated. Default True')\n parser_upd.add_argument(\"-i\", \"--insert\", nargs=\"?\", const=0, type=int, action=OnceArgument,\n he","label":0},{"cid":"conans/client/command.py::Command.profile","text":"conans/client/command.py::Command.profile\n def profile(self, *args):\n \"\"\" Lists profiles in the '.conan/profiles' folder, or shows profile details.\n The 'list' subcommand will always use the default user 'conan/profiles' folder. But the\n 'show' subcommand is able to resolve absolute and relative paths, as well as to map names to\n '.conan/profiles' folder, in the same way as the '--profile' install argument.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.profile.__doc__, prog=\"conan profile\")\n subparsers = parser.add_subparsers(dest='subcommand')\n\n # create the parser for the \"profile\" command\n subparsers.add_parser('list', help='List current profiles')\n parser_show = subparsers.add_parser('show', help='Show the values defined for a profile')\n parser_show.add_argument('profile', help=\"name of the profile in the '.conan/profiles' \"\n \"folder or path to a profile file\")\n\n parser_new = subparsers.add_parser('new', help='Creates a new empty profile')\n parser_new.add_argument('profile', help=\"Name for the profile in the '.conan/profiles' \"\n \"folder or path and name for a profile file\")\n parser_new.add_argument(\"--detect\", action='store_true', default=False,\n help='Autodetect settings and fill [settings] section')\n\n parser_update = subparsers.add_parser('update', help='Update a profile with desired value')\n parser_update.add_argument('item',\n help=\"'item=value' to update. e.g., settings.compiler=gcc\")\n parser_update.add_argument('profile', help=\"Name of the profile in the '.conan/profiles' \"\n \"folder or path to a profile file\")\n\n parser_get = subparsers.add_parser('get', help='Get a profile key')\n parser_get.add_argument('ite","label":0},{"cid":"conans/client/command.py::Command.get","text":"conans/client/command.py::Command.get\n def get(self, *args):\n \"\"\" Gets a file or list a directory of a given reference or package.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.get.__doc__,\n prog=\"conan get\")\n parser.add_argument('reference', help='package recipe reference')\n parser.add_argument('path',\n help='Path to the file or directory. If not specified will get the '\n 'conanfile if only a reference is specified and a conaninfo.txt '\n 'file contents if the package is also specified',\n default=None, nargs=\"?\")\n parser.add_argument(\"-p\", \"--package\", default=None, help='Package ID',\n action=OnceArgument)\n parser.add_argument(\"-r\", \"--remote\", action=OnceArgument,\n help='Get from this specific remote')\n parser.add_argument(\"-raw\", \"--raw\", action='store_true', default=False,\n help='Do not decorate the text')\n args = parser.parse_args(*args)\n\n ret, path = self._conan.get_path(args.reference, args.package, args.path, args.remote)\n if isinstance(ret, list):\n self._outputer.print_dir_list(ret, path, args.raw)\n else:\n self._outputer.print_file_contents(ret, path, args.raw)\n\n return","label":0},{"cid":"conans/client/command.py::Command.alias","text":"conans/client/command.py::Command.alias\n def alias(self, *args):\n \"\"\"Creates and exports an 'alias package recipe'.\n\n An \"alias\" package is a symbolic name (reference) for another package\n (target). When some package depends on an alias, the target one will be\n retrieved and used instead, so the alias reference, the symbolic name,\n does not appear in the final dependency graph.\n \"\"\"\n parser = argparse.ArgumentParser(description=self.alias.__doc__,\n prog=\"conan alias\")\n parser.add_argument('reference', help='Alias reference. e.g.: mylib/1.X@user/channel')\n parser.add_argument('target', help='Target reference. e.g.: mylib/1.12@user/channel')\n args = parser.parse_args(*args)\n\n self._warn_python2()\n\n self._conan.export_alias(args.reference, args.target)","label":0},{"cid":"conans/client/command.py::Command._show_help","text":"conans/client/command.py::Command._show_help\n def _show_help(self):\n \"\"\"Prints a summary of all commands\n \"\"\"\n grps = [(\"Consumer commands\", (\"install\", \"config\", \"get\", \"info\", \"search\")),\n (\"Creator commands\", (\"new\", \"create\", \"upload\", \"export\", \"export-pkg\", \"test\")),\n (\"Package development commands\", (\"source\", \"build\", \"package\")),\n (\"Misc commands\", (\"profile\", \"remote\", \"user\", \"imports\", \"copy\", \"remove\",\n \"alias\", \"download\", \"inspect\", \"help\"))]\n\n def check_all_commands_listed():\n \"\"\"Keep updated the main directory, raise if don't\"\"\"\n all_commands = self._commands()\n all_in_grps = [command for _, command_list in grps for command in command_list]\n if set(all_in_grps) != set(all_commands):\n diff = set(all_commands) - set(all_in_grps)\n raise Exception(\"Some command is missing in the main help: %s\" % \",\".join(diff))\n return all_commands\n\n commands = check_all_commands_listed()\n max_len = max((len(c) for c in commands)) + 1\n fmt = ' %-{}s'.format(max_len)\n\n for group_name, comm_names in grps:\n self._user_io.out.writeln(group_name, Color.BRIGHT_MAGENTA)\n for name in comm_names:\n # future-proof way to ensure tabular formatting\n self._user_io.out.write(fmt % name, Color.GREEN)\n self._user_io.out.writeln(commands[name].__doc__.split('\\n', 1)[0].strip())\n\n self._user_io.out.writeln(\"\")\n self._user_io.out.writeln('Conan commands. Type \"conan <command> -h\" for help',\n Color.BRIGHT_YELLOW)","label":0},{"cid":"conans/client/command.py::Command._show_help.check_all_commands_listed","text":"conans/client/command.py::Command._show_help.check_all_commands_listed\n def check_all_commands_listed():\n \"\"\"Keep updated the main directory, raise if don't\"\"\"\n all_commands = self._commands()\n all_in_grps = [command for _, command_list in grps for command in command_list]\n if set(all_in_grps) != set(all_commands):\n diff = set(all_commands) - set(all_in_grps)\n raise Exception(\"Some command is missing in the main help: %s\" % \",\".join(diff))\n return all_commands","label":0},{"cid":"conans/client/command.py::Command._commands","text":"conans/client/command.py::Command._commands\n def _commands(self):\n \"\"\" returns a list of available commands\n \"\"\"\n result = {}\n for m in inspect.getmembers(self, predicate=inspect.ismethod):\n method_name = m[0]\n if not method_name.startswith('_'):\n if \"export_pkg\" == method_name:\n method_name = \"export-pkg\"\n method = m[1]\n if method.__doc__ and not method.__doc__.startswith('HIDDEN'):\n result[method_name] = method\n return result","label":0},{"cid":"conans/client/command.py::Command._warn_python2","text":"conans/client/command.py::Command._warn_python2\n def _warn_python2(self):\n if six.PY2:\n self._user_io.out.writeln(\"\")\n self._user_io.out.writeln(\"Python 2 will soon be deprecated. It is strongly \"\n \"recommended to use Python 3 with Conan:\",\n front=Color.BRIGHT_YELLOW)\n self._user_io.out.writeln(\"https://docs.conan.io/en/latest/installation.html\"\n \"#python-2-deprecation-notice\", front=Color.BRIGHT_YELLOW)\n self._user_io.out.writeln(\"\")","label":0},{"cid":"conans/client/command.py::Command._check_query_parameter_and_get_reference","text":"conans/client/command.py::Command._check_query_parameter_and_get_reference\n @staticmethod\n def _check_query_parameter_and_get_reference(pattern, query):\n reference = None\n if pattern:\n try:\n reference = ConanFileReference.loads(pattern)\n except ConanException:\n if query is not None:\n raise ConanException(\"-q parameter only allowed with a valid recipe \"\n \"reference as search pattern. e.g. conan search \"\n \"MyPackage/1.2@user/channel -q \\\"os=Windows\\\"\")\n return reference","label":0},{"cid":"conans/client/command.py::Command.run","text":"conans/client/command.py::Command.run\n def run(self, *args):\n \"\"\"HIDDEN: entry point for executing commands, dispatcher to class\n methods\n \"\"\"\n ret_code = SUCCESS\n try:\n try:\n command = args[0][0]\n commands = self._commands()\n method = commands[command]\n except KeyError as exc:\n if command in [\"-v\", \"--version\"]:\n self._user_io.out.success(\"Conan version %s\" % client_version)\n return False\n self._warn_python2()\n self._show_help()\n if command in [\"-h\", \"--help\"]:\n return False\n raise ConanException(\"Unknown command %s\" % str(exc))\n except IndexError: # No parameters\n self._show_help()\n return False\n method(args[0][1:])\n except KeyboardInterrupt as exc:\n logger.error(exc)\n ret_code = SUCCESS\n except SystemExit as exc:\n if exc.code != 0:\n logger.error(exc)\n self._user_io.out.error(\"Exiting with code: %d\" % exc.code)\n ret_code = exc.code\n except ConanInvalidConfiguration as exc:\n ret_code = ERROR_INVALID_CONFIGURATION\n msg = exception_message_safe(exc)\n self._user_io.out.error(msg)\n except ConanException as exc:\n ret_code = ERROR_GENERAL\n msg = exception_message_safe(exc)\n self._user_io.out.error(msg)\n except Exception as exc:\n import traceback\n print(traceback.format_exc())\n ret_code = ERROR_GENERAL\n msg = exception_message_safe(exc)\n self._user_io.out.error(msg)\n\n return ret_code","label":0},{"cid":"conans/client/command.py::get_reference_fields","text":"conans/client/command.py::get_reference_fields\ndef get_reference_fields(arg_reference):\n \"\"\"\n :param arg_reference: String with a complete reference, or only user/channel\n :return: name, version, user and channel, in a tuple\n \"\"\"\n\n if not arg_reference:\n return None, None, None, None\n\n try:\n name_version, user_channel = arg_reference.split(\"@\")\n name, version = name_version.split(\"/\")\n user, channel = user_channel.split(\"/\")\n except ValueError:\n name, version = None, None\n try:\n user, channel = arg_reference.split(\"/\")\n except ValueError:\n raise ConanException(\"Invalid parameter '%s', specify the full reference or \"\n \"user/channel\" % arg_reference)\n\n return name, version, user, channel","label":0},{"cid":"conans/client/command.py::_add_manifests_arguments","text":"conans/client/command.py::_add_manifests_arguments\ndef _add_manifests_arguments(parser):\n parser.add_argument(\"-m\", \"--manifests\", const=default_manifest_folder, nargs=\"?\",\n help='Install dependencies manifests in folder for later verify.'\n ' Default folder is .conan_manifests, but can be changed',\n action=OnceArgument)\n parser.add_argument(\"-mi\", \"--manifests-interactive\", const=default_manifest_folder,\n nargs=\"?\",\n help='Install dependencies manifests in folder for later verify, '\n 'asking user for confirmation. '\n 'Default folder is .conan_manifests, but can be changed',\n action=OnceArgument)\n parser.add_argument(\"-v\", \"--verify\", const=default_manifest_folder, nargs=\"?\",\n help='Verify dependencies manifests against stored ones',\n action=OnceArgument)","label":0},{"cid":"conans/client/command.py::_add_common_install_arguments","text":"conans/client/command.py::_add_common_install_arguments\ndef _add_common_install_arguments(parser, build_help):\n if build_help:\n parser.add_argument(\"-b\", \"--build\", action=Extender, nargs=\"?\", help=build_help)\n\n parser.add_argument(\"-e\", \"--env\", nargs=1, action=Extender,\n help='Environment variables that will be set during the package build, '\n '-e CXX=/usr/bin/clang++')\n parser.add_argument(\"-o\", \"--options\", nargs=1, action=Extender,\n help='Define options values, e.g., -o Pkg:with_qt=true')\n parser.add_argument(\"-pr\", \"--profile\", default=None, action=OnceArgument,\n help='Apply the specified profile to the install command')\n parser.add_argument(\"-r\", \"--remote\", action=OnceArgument,\n help='Look in the specified remote server')\n parser.add_argument(\"-s\", \"--settings\", nargs=1, action=Extender,\n help='Settings to build the package, overwriting the defaults. e.g., '\n '-s compiler=gcc')\n parser.add_argument(\"-u\", \"--update\", action='store_true', default=False,\n help=\"Check updates exist from upstream remotes\")","label":0},{"cid":"conans/client/command.py::main","text":"conans/client/command.py::main\ndef main(args):\n \"\"\" main entry point of the conan application, using a Command to\n parse parameters\n\n Exit codes for conan command:\n\n 0: Success (done)\n 1: General ConanException error (done)\n 2: Migration error\n 3: Ctrl+C\n 4: Ctrl+Break\n 5: SIGTERM\n 6: Invalid configuration (done)\n \"\"\"\n try:\n conan_api, client_cache, user_io = Conan.factory()\n except ConanException: # Error migrating\n sys.exit(ERROR_MIGRATION)\n\n outputer = CommandOutputer(user_io, client_cache)\n command = Command(conan_api, client_cache, user_io, outputer)\n current_dir = get_cwd()\n try:\n import signal\n\n def ctrl_c_handler(_, __):\n print('You pressed Ctrl+C!')\n sys.exit(USER_CTRL_C)\n\n def sigterm_handler(_, __):\n print('Received SIGTERM!')\n sys.exit(ERROR_SIGTERM)\n\n def ctrl_break_handler(_, __):\n print('You pressed Ctrl+Break!')\n sys.exit(USER_CTRL_BREAK)\n\n signal.signal(signal.SIGINT, ctrl_c_handler)\n signal.signal(signal.SIGTERM, sigterm_handler)\n\n if sys.platform == 'win32':\n signal.signal(signal.SIGBREAK, ctrl_break_handler)\n error = command.run(args)\n finally:\n os.chdir(current_dir)\n sys.exit(error)","label":0},{"cid":"conans/client/generators/pkg_config.py::PkgConfigGenerator.filename","text":"conans/client/generators/pkg_config.py::PkgConfigGenerator.filename\n @property\n def filename(self):\n pass","label":0},{"cid":"conans/client/generators/pkg_config.py::PkgConfigGenerator.compiler","text":"conans/client/generators/pkg_config.py::PkgConfigGenerator.compiler\n @property\n def compiler(self):\n return self.conanfile.settings.get_safe(\"compiler\")","label":0},{"cid":"conans/client/generators/pkg_config.py::PkgConfigGenerator.content","text":"conans/client/generators/pkg_config.py::PkgConfigGenerator.content\n @property\n def content(self):\n ret = {}\n for depname, cpp_info in self.deps_build_info.dependencies:\n ret[\"%s.pc\" % depname] = self.single_pc_file_contents(depname, cpp_info)\n return ret","label":0},{"cid":"conans/client/generators/pkg_config.py::PkgConfigGenerator.single_pc_file_contents","text":"conans/client/generators/pkg_config.py::PkgConfigGenerator.single_pc_file_contents\n def single_pc_file_contents(self, name, cpp_info):\n prefix_path = cpp_info.rootpath.replace(\"\\\\\", \"/\")\n lines = ['prefix=%s' % prefix_path]\n\n libdir_vars = []\n dir_lines, varnames = _generate_dir_lines(prefix_path, \"libdir\", cpp_info.libdirs)\n if dir_lines:\n libdir_vars = varnames\n lines.extend(dir_lines)\n\n includedir_vars = []\n dir_lines, varnames = _generate_dir_lines(prefix_path, \"includedir\", cpp_info.includedirs)\n if dir_lines:\n includedir_vars = varnames\n lines.extend(dir_lines)\n\n lines.append(\"\")\n lines.append(\"Name: %s\" % name)\n description = cpp_info.description or \"Conan package: %s\" % name\n lines.append(\"Description: %s\" % description)\n lines.append(\"Version: %s\" % cpp_info.version)\n libdirs_flags = [\"-L${%s}\" % name for name in libdir_vars]\n libnames_flags = [\"-l%s \" % name for name in cpp_info.libs]\n shared_flags = cpp_info.sharedlinkflags + cpp_info.exelinkflags\n the_os = (self.conanfile.settings.get_safe(\"os_build\") or\n self.conanfile.settings.get_safe(\"os\"))\n rpaths = rpath_flags(the_os, self.compiler, [\"${%s}\" % libdir for libdir in libdir_vars])\n lines.append(\"Libs: %s\" % _concat_if_not_empty([libdirs_flags,\n libnames_flags,\n shared_flags,\n rpaths]))\n include_dirs_flags = [\"-I${%s}\" % name for name in includedir_vars]\n\n lines.append(\"Cflags: %s\" % _concat_if_not_empty(\n [include_dirs_flags,\n cpp_info.cppflags,\n cpp_info.cflags,\n [\"-D%s\" % d for d in cpp_info.defines]]))\n\n if cpp_info.public_deps:\n public_deps = \" \".join(cpp_info.publ","label":0},{"cid":"conans/client/generators/pkg_config.py::_concat_if_not_empty","text":"conans/client/generators/pkg_config.py::_concat_if_not_empty\ndef _concat_if_not_empty(groups):\n return \" \".join([param for group in groups for param in group if param and param.strip()])","label":0},{"cid":"conans/client/generators/pkg_config.py::_generate_dir_lines","text":"conans/client/generators/pkg_config.py::_generate_dir_lines\ndef _generate_dir_lines(prefix_path, varname, dirs):\n lines = []\n varnames = []\n for i, directory in enumerate(dirs):\n directory = os.path.normpath(directory).replace(\"\\\\\", \"/\")\n varname = varname if i == 0 else \"%s%d\" % (varname, (i + 2))\n prefix = \"\"\n if not os.path.isabs(directory):\n prefix = \"${prefix}/\"\n elif directory.startswith(prefix_path):\n prefix = \"${prefix}/\"\n directory = os.path.relpath(directory, prefix_path)\n lines.append(\"%s=%s%s\" % (varname, prefix, directory))\n varnames.append(varname)\n return lines, varnames","label":0}],"string":"[\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.__init__\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.__init__\\n def __init__(self, conanfile, win_bash=False, include_rpath_flags=False):\\n \\\"\\\"\\\"\\n FIXME: include_rpath_flags CONAN 2.0 to default True? Could break many packages in center\\n \\\"\\\"\\\"\\n self._conanfile = conanfile\\n self._win_bash = win_bash\\n self._include_rpath_flags = include_rpath_flags\\n self.subsystem = OSInfo().detect_windows_subsystem() if self._win_bash else None\\n self._deps_cpp_info = conanfile.deps_cpp_info\\n self._os = conanfile.settings.get_safe(\\\"os\\\")\\n self._arch = conanfile.settings.get_safe(\\\"arch\\\")\\n self._build_type = conanfile.settings.get_safe(\\\"build_type\\\")\\n self._compiler = conanfile.settings.get_safe(\\\"compiler\\\")\\n self._compiler_version = conanfile.settings.get_safe(\\\"compiler.version\\\")\\n self._libcxx = conanfile.settings.get_safe(\\\"compiler.libcxx\\\")\\n self._cppstd = conanfile.settings.get_safe(\\\"cppstd\\\")\\n\\n # Set the generic objects before mapping to env vars to let the user\\n # alter some value\\n self.libs = copy.copy(self._deps_cpp_info.libs)\\n self.include_paths = copy.copy(self._deps_cpp_info.include_paths)\\n self.library_paths = copy.copy(self._deps_cpp_info.lib_paths)\\n\\n self.defines = self._configure_defines()\\n # Will go to CFLAGS and CXXFLAGS [\\\"-m64\\\" \\\"-m32\\\", \\\"-g\\\", \\\"-s\\\"]\\n self.flags = self._configure_flags()\\n # Only c++ flags [-stdlib, -library], will go to CXXFLAGS\\n self.cxx_flags = self._configure_cxx_flags()\\n # cpp standard\\n self.cppstd_flag = cppstd_flag(self._compiler, self._compiler_version, self._cppstd)\\n # Not -L flags, [\\\"-m64\\\" \\\"-m32\\\"]\\n self.link_flags = self._configure_link_flags() # TEST!\\n # Precalculate -fPIC\\n self.fpic = self._configure_fpic()\\n\\n # Precalculate build, host, target triplets\\n self.build, self.host, self.target = self._get_host_b\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_fpic\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_fpic\\n def _configure_fpic(self):\\n if str(self._os) not in [\\\"Windows\\\", \\\"WindowsStore\\\"]:\\n fpic = self._conanfile.options.get_safe(\\\"fPIC\\\")\\n if fpic is not None:\\n shared = self._conanfile.options.get_safe(\\\"shared\\\")\\n return True if (fpic or shared) else None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_host_build_target_flags\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_host_build_target_flags\\n def _get_host_build_target_flags(self):\\n \\\"\\\"\\\"Based on google search for build/host triplets, it could need a lot\\n and complex verification\\\"\\\"\\\"\\n\\n arch_detected = detected_architecture() or platform.machine()\\n os_detected = platform.system()\\n\\n if os_detected is None or arch_detected is None or self._arch is None or self._os is None:\\n return False, False, False\\n if not cross_building(self._conanfile.settings, os_detected, arch_detected):\\n return False, False, False\\n\\n try:\\n build = get_gnu_triplet(os_detected, arch_detected, self._compiler)\\n except ConanException as exc:\\n self._conanfile.output.warn(str(exc))\\n build = None\\n try:\\n host = get_gnu_triplet(self._os, self._arch, self._compiler)\\n except ConanException as exc:\\n self._conanfile.output.warn(str(exc))\\n host = None\\n return build, host, None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.configure\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.configure\\n def configure(self, configure_dir=None, args=None, build=None, host=None, target=None,\\n pkg_config_paths=None, vars=None, use_default_install_dirs=True):\\n \\\"\\\"\\\"\\n :param pkg_config_paths: Optional paths to locate the *.pc files\\n :param configure_dir: Absolute or relative path to the configure script\\n :param args: Optional arguments to pass to configure.\\n :param build: In which system the program will be built. \\\"False\\\" skips the --build flag\\n :param host: In which system the generated program will run. \\\"False\\\" skips the --host flag\\n :param target: This option is only used to build a cross-compiling toolchain.\\n \\\"False\\\" skips the --target flag\\n When the tool chain generates executable program, in which target system\\n the program will run.\\n\\n http://jingfenghanmax.blogspot.com.es/2010/09/configure-with-host-target-and-build.html\\n https://gcc.gnu.org/onlinedocs/gccint/Configure-Terms.html\\n :param use_default_install_dirs: Use or not the defaulted installation dirs\\n\\n \\\"\\\"\\\"\\n if not self._conanfile.should_configure:\\n return\\n if configure_dir:\\n configure_dir = configure_dir.rstrip(\\\"/\\\")\\n else:\\n configure_dir = \\\".\\\"\\n\\n triplet_args = []\\n\\n if build is not False: # Skipped by user\\n if build or self.build: # User specified value or automatic\\n triplet_args.append(\\\"--build=%s\\\" % (build or self.build))\\n\\n if host is not False: # Skipped by user\\n if host or self.host: # User specified value or automatic\\n triplet_args.append(\\\"--host=%s\\\" % (host or self.host))\\n\\n if target is not False: # Skipped by user\\n if target or self.target: # User specified value or automatic\\n triplet_args.append(\\\"-\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_help_output\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_help_output\\n def _configure_help_output(self, configure_path):\\n from six import StringIO # Python 2 and 3 compatible\\n mybuf = StringIO()\\n try:\\n self._conanfile.run(\\\"%s/configure --help\\\" % configure_path, output=mybuf)\\n except ConanException as e:\\n self._conanfile.output.warn(\\\"Error running `configure --help`: %s\\\" % e)\\n return \\\"\\\"\\n return mybuf.getvalue()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._adjust_path\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._adjust_path\\n def _adjust_path(self, path):\\n if self._win_bash:\\n path = unix_path(path, path_flavor=self.subsystem)\\n return '\\\"%s\\\"' % path if \\\" \\\" in path else path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._valid_configure_flag\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._valid_configure_flag\\n @staticmethod\\n def _valid_configure_flag(varname, args, available_flags):\\n return not AutoToolsBuildEnvironment._is_flag_in_args(varname, args) and \\\\\\n varname in available_flags\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._is_flag_in_args\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._is_flag_in_args\\n @staticmethod\\n def _is_flag_in_args(varname, args):\\n flag = \\\"--%s=\\\" % varname\\n return any([flag in arg for arg in args])\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.make\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.make\\n def make(self, args=\\\"\\\", make_program=None, target=None, vars=None):\\n if not self._conanfile.should_build:\\n return\\n make_program = os.getenv(\\\"CONAN_MAKE_PROGRAM\\\") or make_program or \\\"make\\\"\\n with environment_append(vars or self.vars):\\n str_args = args_to_string(args)\\n cpu_count_option = (\\\"-j%s\\\" % cpu_count()) if \\\"-j\\\" not in str_args else None\\n self._conanfile.run(\\\"%s\\\" % join_arguments([make_program, target, str_args,\\n cpu_count_option]),\\n win_bash=self._win_bash, subsystem=self.subsystem)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.install\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.install\\n def install(self, args=\\\"\\\", make_program=None, vars=None):\\n if not self._conanfile.should_install:\\n return\\n self.make(args=args, make_program=make_program, target=\\\"install\\\", vars=vars)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_link_flags\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_link_flags\\n def _configure_link_flags(self):\\n \\\"\\\"\\\"Not the -L\\\"\\\"\\\"\\n ret = copy.copy(self._deps_cpp_info.sharedlinkflags)\\n ret.extend(self._deps_cpp_info.exelinkflags)\\n arch_flag = architecture_flag(compiler=self._compiler, arch=self._arch)\\n if arch_flag:\\n ret.append(arch_flag)\\n\\n sysf = sysroot_flag(self._deps_cpp_info.sysroot, win_bash=self._win_bash,\\n subsystem=self.subsystem,\\n compiler=self._compiler)\\n if sysf:\\n ret.append(sysf)\\n\\n if self._include_rpath_flags:\\n the_os = self._conanfile.settings.get_safe(\\\"os_build\\\") or self._os\\n ret.extend(rpath_flags(the_os, self._compiler, self._deps_cpp_info.lib_paths))\\n\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_flags\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_flags\\n def _configure_flags(self):\\n ret = copy.copy(self._deps_cpp_info.cflags)\\n arch_flag = architecture_flag(compiler=self._compiler, arch=self._arch)\\n if arch_flag:\\n ret.append(arch_flag)\\n btfs = build_type_flags(compiler=self._compiler, build_type=self._build_type,\\n vs_toolset=self._conanfile.settings.get_safe(\\\"compiler.toolset\\\"))\\n if btfs:\\n ret.extend(btfs)\\n srf = sysroot_flag(self._deps_cpp_info.sysroot, win_bash=self._win_bash,\\n subsystem=self.subsystem,\\n compiler=self._compiler)\\n if srf:\\n ret.append(srf)\\n\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_cxx_flags\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_cxx_flags\\n def _configure_cxx_flags(self):\\n ret = copy.copy(self._deps_cpp_info.cppflags)\\n cxxf = libcxx_flag(compiler=self._compiler, libcxx=self._libcxx)\\n if cxxf:\\n ret.append(cxxf)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_defines\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._configure_defines\\n def _configure_defines(self):\\n # requires declared defines\\n ret = copy.copy(self._deps_cpp_info.defines)\\n\\n # Debug definition for GCC\\n btf = build_type_define(build_type=self._build_type)\\n if btf:\\n ret.append(btf)\\n\\n # CXX11 ABI\\n abif = libcxx_define(compiler=self._compiler, libcxx=self._libcxx)\\n if abif:\\n ret.append(abif)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars\\n def _get_vars(self):\\n def append(*args):\\n ret = []\\n for arg in args:\\n if arg:\\n if isinstance(arg, list):\\n ret.extend(arg)\\n else:\\n ret.append(arg)\\n return ret\\n\\n lib_paths = format_library_paths(self.library_paths, win_bash=self._win_bash,\\n subsystem=self.subsystem, compiler=self._compiler)\\n include_paths = format_include_paths(self.include_paths, win_bash=self._win_bash,\\n subsystem=self.subsystem, compiler=self._compiler)\\n\\n ld_flags = append(self.link_flags, lib_paths)\\n cpp_flags = append(include_paths, format_defines(self.defines))\\n libs = format_libraries(self.libs, compiler=self._compiler)\\n\\n tmp_compilation_flags = copy.copy(self.flags)\\n if self.fpic:\\n tmp_compilation_flags.append(pic_flag(self._compiler))\\n\\n cxx_flags = append(tmp_compilation_flags, self.cxx_flags, self.cppstd_flag)\\n c_flags = tmp_compilation_flags\\n\\n return ld_flags, cpp_flags, libs, cxx_flags, c_flags\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars.append\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment._get_vars.append\\n def append(*args):\\n ret = []\\n for arg in args:\\n if arg:\\n if isinstance(arg, list):\\n ret.extend(arg)\\n else:\\n ret.append(arg)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars_dict\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars_dict\\n @property\\n def vars_dict(self):\\n\\n ld_flags, cpp_flags, libs, cxx_flags, c_flags = self._get_vars()\\n\\n if os.environ.get(\\\"CPPFLAGS\\\", None):\\n cpp_flags.append(os.environ.get(\\\"CPPFLAGS\\\", None))\\n\\n if os.environ.get(\\\"CXXFLAGS\\\", None):\\n cxx_flags.append(os.environ.get(\\\"CXXFLAGS\\\", None))\\n\\n if os.environ.get(\\\"CFLAGS\\\", None):\\n c_flags.append(os.environ.get(\\\"CFLAGS\\\", None))\\n\\n if os.environ.get(\\\"LDFLAGS\\\", None):\\n ld_flags.append(os.environ.get(\\\"LDFLAGS\\\", None))\\n\\n if os.environ.get(\\\"LIBS\\\", None):\\n libs.append(os.environ.get(\\\"LIBS\\\", None))\\n\\n ret = {\\\"CPPFLAGS\\\": cpp_flags,\\n \\\"CXXFLAGS\\\": cxx_flags,\\n \\\"CFLAGS\\\": c_flags,\\n \\\"LDFLAGS\\\": ld_flags,\\n \\\"LIBS\\\": libs,\\n }\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars\",\n \"text\": \"conans/client/build/autotools_environment.py::AutoToolsBuildEnvironment.vars\\n @property\\n def vars(self):\\n ld_flags, cpp_flags, libs, cxx_flags, c_flags = self._get_vars()\\n\\n cpp_flags = \\\" \\\".join(cpp_flags) + _environ_value_prefix(\\\"CPPFLAGS\\\")\\n cxx_flags = \\\" \\\".join(cxx_flags) + _environ_value_prefix(\\\"CXXFLAGS\\\")\\n cflags = \\\" \\\".join(c_flags) + _environ_value_prefix(\\\"CFLAGS\\\")\\n ldflags = \\\" \\\".join(ld_flags) + _environ_value_prefix(\\\"LDFLAGS\\\")\\n libs = \\\" \\\".join(libs) + _environ_value_prefix(\\\"LIBS\\\")\\n\\n ret = {\\\"CPPFLAGS\\\": cpp_flags.strip(),\\n \\\"CXXFLAGS\\\": cxx_flags.strip(),\\n \\\"CFLAGS\\\": cflags.strip(),\\n \\\"LDFLAGS\\\": ldflags.strip(),\\n \\\"LIBS\\\": libs.strip(),\\n }\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/autotools_environment.py::_environ_value_prefix\",\n \"text\": \"conans/client/build/autotools_environment.py::_environ_value_prefix\\ndef _environ_value_prefix(var_name, prefix=\\\" \\\"):\\n if os.environ.get(var_name, \\\"\\\"):\\n return \\\"%s%s\\\" % (prefix, os.environ.get(var_name, \\\"\\\"))\\n else:\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Extender.__call__\",\n \"text\": \"conans/client/command.py::Extender.__call__\\n def __call__(self, parser, namespace, values, option_strings=None): # @UnusedVariable\\n # Need None here incase `argparse.SUPPRESS` was supplied for `dest`\\n dest = getattr(namespace, self.dest, None)\\n if not hasattr(dest, 'extend') or dest == self.default:\\n dest = []\\n setattr(namespace, self.dest, dest)\\n # if default isn't set to None, this method might be called\\n # with the default as `values` for other arguments which\\n # share this destination.\\n parser.set_defaults(**{self.dest: None})\\n\\n if isinstance(values, str):\\n dest.append(values)\\n elif values:\\n try:\\n dest.extend(values)\\n except ValueError:\\n dest.append(values)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::OnceArgument.__call__\",\n \"text\": \"conans/client/command.py::OnceArgument.__call__\\n def __call__(self, parser, namespace, values, option_string=None):\\n if getattr(namespace, self.dest) is not None and self.default is None:\\n msg = '{o} can only be specified once'.format(o=option_string)\\n raise argparse.ArgumentError(None, msg)\\n setattr(namespace, self.dest, values)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.__init__\",\n \"text\": \"conans/client/command.py::Command.__init__\\n def __init__(self, conan_api, client_cache, user_io, outputer):\\n assert isinstance(conan_api, Conan)\\n self._conan = conan_api\\n self._client_cache = client_cache\\n self._user_io = user_io\\n self._outputer = outputer\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.help\",\n \"text\": \"conans/client/command.py::Command.help\\n def help(self, *args):\\n \\\"\\\"\\\"Show help of a specific commmand.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.help.__doc__, prog=\\\"conan help\\\")\\n parser.add_argument(\\\"command\\\", help='command', nargs=\\\"?\\\")\\n args = parser.parse_args(*args)\\n if not args.command:\\n self._show_help()\\n return\\n try:\\n commands = self._commands()\\n method = commands[args.command]\\n self._warn_python2()\\n method([\\\"--help\\\"])\\n except KeyError:\\n raise ConanException(\\\"Unknown command '%s'\\\" % args.command)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.new\",\n \"text\": \"conans/client/command.py::Command.new\\n def new(self, *args):\\n \\\"\\\"\\\"Creates a new package recipe template with a 'conanfile.py' and optionally,\\n 'test_package' testing files.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.new.__doc__, prog=\\\"conan new\\\")\\n parser.add_argument(\\\"name\\\", help='Package name, e.g.: \\\"Poco/1.7.3\\\" or complete reference'\\n ' for CI scripts: \\\"Poco/1.7.3@conan/stable\\\"')\\n parser.add_argument(\\\"-t\\\", \\\"--test\\\", action='store_true', default=False,\\n help='Create test_package skeleton to test package')\\n parser.add_argument(\\\"-i\\\", \\\"--header\\\", action='store_true', default=False,\\n help='Create a headers only package template')\\n parser.add_argument(\\\"-c\\\", \\\"--pure-c\\\", action='store_true', default=False,\\n help='Create a C language package only package, '\\n 'deleting \\\"self.settings.compiler.libcxx\\\" setting '\\n 'in the configure method')\\n parser.add_argument(\\\"-s\\\", \\\"--sources\\\", action='store_true', default=False,\\n help='Create a package with embedded sources in \\\"src\\\" folder, '\\n 'using \\\"exports_sources\\\" instead of retrieving external code with '\\n 'the \\\"source()\\\" method')\\n parser.add_argument(\\\"-b\\\", \\\"--bare\\\", action='store_true', default=False,\\n help='Create the minimum package recipe, without build() method'\\n 'Useful in combination with \\\"export-pkg\\\" command')\\n parser.add_argument(\\\"-cis\\\", \\\"--ci-shared\\\", action='store_true',\\n default=False,\\n help='Package will have a \\\"shared\\\" option to be used in CI')\\n parser.add_argument(\\\"-cilg\\\", \\\"--ci-travis-gcc\\\", action='store_true',\\n default\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.inspect\",\n \"text\": \"conans/client/command.py::Command.inspect\\n def inspect(self, *args):\\n \\\"\\\"\\\"Displays conanfile attributes, like name, version, options\\n Works both locally, in local cache and remote\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.inspect.__doc__, prog=\\\"conan inspect\\\")\\n parser.add_argument(\\\"path_or_reference\\\", help=\\\"Path to a folder containing a recipe\\\"\\n \\\" (conanfile.py) or to a recipe file. e.g., \\\"\\n \\\"./my_project/conanfile.py. It could also be a reference\\\")\\n parser.add_argument(\\\"-a\\\", \\\"--attribute\\\", help='The attribute to be displayed, e.g \\\"name\\\"',\\n nargs=\\\"?\\\", action=Extender)\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", help='look in the specified remote server',\\n action=OnceArgument)\\n parser.add_argument(\\\"-j\\\", \\\"--json\\\", default=None, action=OnceArgument,\\n help='json output file')\\n\\n args = parser.parse_args(*args)\\n result = self._conan.inspect(args.path_or_reference, args.attribute, args.remote)\\n Printer(self._user_io.out).print_inspect(result)\\n if args.json:\\n json_output = json.dumps(result)\\n if not os.path.isabs(args.json):\\n json_output_file = os.path.join(get_cwd(), args.json)\\n else:\\n json_output_file = args.json\\n save(json_output_file, json_output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.test\",\n \"text\": \"conans/client/command.py::Command.test\\n def test(self, *args):\\n \\\"\\\"\\\"Test a package consuming it from a conanfile.py with a test() method.\\n\\n This command installs the conanfile dependencies (including the tested\\n package), calls a 'conan build' to build test apps and finally executes\\n the test() method. The testing recipe does not require name or version,\\n neither definition of package() or package_info() methods. The package\\n to be tested must exist in the local cache or in any configured remote.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.test.__doc__, prog=\\\"conan test\\\")\\n parser.add_argument(\\\"path\\\", help='Path to the \\\"testing\\\" folder containing a conanfile.py or'\\n ' to a recipe file with test() method'\\n 'e.g. conan test_package/conanfile.py pkg/version@user/channel')\\n parser.add_argument(\\\"reference\\\",\\n help='pkg/version@user/channel of the package to be tested')\\n parser.add_argument(\\\"-tbf\\\", \\\"--test-build-folder\\\", action=OnceArgument,\\n help=\\\"Working directory of the build process.\\\")\\n\\n _add_common_install_arguments(parser, build_help=_help_build_policies)\\n args = parser.parse_args(*args)\\n self._warn_python2()\\n return self._conan.test(args.path, args.reference, args.profile, args.settings,\\n args.options, args.env, args.remote, args.update,\\n build_modes=args.build, test_build_folder=args.test_build_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.create\",\n \"text\": \"conans/client/command.py::Command.create\\n def create(self, *args):\\n \\\"\\\"\\\"Builds a binary package for a recipe (conanfile.py).\\n\\n Uses the specified configuration in a profile or in -s settings, -o\\n options etc. If a 'test_package' folder (the name can be configured\\n with -tf) is found, the command will run the consumer project to ensure\\n that the package has been created correctly. Check 'conan test' command\\n to know more about 'test_folder' project.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.create.__doc__, prog=\\\"conan create\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"reference\\\",\\n help='user/channel or pkg/version@user/channel (if name and version not'\\n ' declared in conanfile.py) where the package will be created')\\n parser.add_argument(\\\"-j\\\", \\\"--json\\\", default=None, action=OnceArgument,\\n help='json file path where the install information will be written to')\\n parser.add_argument('-k', '-ks', '--keep-source', default=False, action='store_true',\\n help=_KEEP_SOURCE_HELP)\\n parser.add_argument('-kb', '--keep-build', default=False, action='store_true',\\n help='Do not remove the build folder in local cache. Implies --keep-source. '\\n 'Use this for testing purposes only')\\n parser.add_argument(\\\"-ne\\\", \\\"--not-export\\\", default=False, action='store_true',\\n help='Do not export the conanfile.py')\\n parser.add_argument(\\\"-tbf\\\", \\\"--test-build-folder\\\", action=OnceArgument,\\n help='Working directory for the build of the test project.')\\n parser.add_argument(\\\"-tf\\\", \\\"--test-folder\\\", action=OnceArgument,\\n help='Alternative test folder name. By default it is \\\"test_package\\\". '\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.download\",\n \"text\": \"conans/client/command.py::Command.download\\n def download(self, *args):\\n \\\"\\\"\\\"Downloads recipe and binaries to the local cache, without using settings.\\n\\n It works specifying the recipe reference and package ID to be\\n installed. Not transitive, requirements of the specified reference will\\n NOT be retrieved. Useful together with 'conan copy' to automate the\\n promotion of packages to a different user/channel. Only if a reference\\n is specified, it will download all packages from the specified remote.\\n If no remote is specified, it will use the default remote.\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(description=self.download.__doc__, prog=\\\"conan download\\\")\\n parser.add_argument(\\\"reference\\\",\\n help='pkg/version@user/channel')\\n parser.add_argument(\\\"-p\\\", \\\"--package\\\", nargs=1, action=Extender,\\n help='Force install specified package ID (ignore settings/options)')\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", help='look in the specified remote server',\\n action=OnceArgument)\\n parser.add_argument(\\\"-re\\\", \\\"--recipe\\\", help='Downloads only the recipe', default=False,\\n action=\\\"store_true\\\")\\n\\n args = parser.parse_args(*args)\\n\\n self._warn_python2()\\n return self._conan.download(reference=args.reference, package=args.package,\\n remote_name=args.remote, recipe=args.recipe)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.install\",\n \"text\": \"conans/client/command.py::Command.install\\n def install(self, *args):\\n \\\"\\\"\\\"Installs the requirements specified in a recipe (conanfile.py or conanfile.txt).\\n\\n It can also be used to install a concrete package specifying a\\n reference. If any requirement is not found in the local cache, it will\\n retrieve the recipe from a remote, looking for it sequentially in the\\n configured remotes. When the recipes have been downloaded it will try\\n to download a binary package matching the specified settings, only from\\n the remote from which the recipe was retrieved. If no binary package is\\n found, it can be build from sources using the '--build' option. When\\n the package is installed, Conan will write the files for the specified\\n generators.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.install.__doc__, prog=\\\"conan install\\\")\\n parser.add_argument(\\\"path_or_reference\\\", help=\\\"Path to a folder containing a recipe\\\"\\n \\\" (conanfile.py or conanfile.txt) or to a recipe file. e.g., \\\"\\n \\\"./my_project/conanfile.txt. It could also be a reference\\\")\\n parser.add_argument(\\\"-g\\\", \\\"--generator\\\", nargs=1, action=Extender,\\n help='Generators to use')\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help='Use this directory as the directory where to put the generator'\\n 'files. e.g., conaninfo/conanbuildinfo.txt')\\n\\n _add_manifests_arguments(parser)\\n\\n parser.add_argument(\\\"--no-imports\\\", action='store_true', default=False,\\n help='Install specified packages but avoid running imports')\\n parser.add_argument(\\\"-j\\\", \\\"--json\\\", default=None, action=OnceArgument,\\n help='Path to a json file where the install information will be '\\n 'written')\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.config\",\n \"text\": \"conans/client/command.py::Command.config\\n def config(self, *args):\\n \\\"\\\"\\\"Manages Conan configuration.\\n\\n Used to edit conan.conf, or install config files.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.config.__doc__, prog=\\\"conan config\\\")\\n\\n subparsers = parser.add_subparsers(dest='subcommand', help='sub-command help')\\n rm_subparser = subparsers.add_parser('rm', help='Remove an existing config element')\\n set_subparser = subparsers.add_parser('set', help='Set a value for a configuration item')\\n get_subparser = subparsers.add_parser('get', help='Get the value of configuration item')\\n install_subparser = subparsers.add_parser('install', help='install a full configuration '\\n 'from a local or remote zip file')\\n rm_subparser.add_argument(\\\"item\\\", help=\\\"Item to remove\\\")\\n get_subparser.add_argument(\\\"item\\\", nargs=\\\"?\\\", help=\\\"Item to print\\\")\\n set_subparser.add_argument(\\\"item\\\", help=\\\"'item=value' to set\\\")\\n install_subparser.add_argument(\\\"item\\\", nargs=\\\"?\\\", help=\\\"Configuration file or directory to use\\\")\\n\\n install_subparser.add_argument(\\\"--verify-ssl\\\", nargs=\\\"?\\\", default=\\\"True\\\",\\n help='Verify SSL connection when downloading file')\\n install_subparser.add_argument(\\\"--type\\\", \\\"-t\\\", choices=[\\\"git\\\"],\\n help='Type of remote config')\\n install_subparser.add_argument(\\\"--args\\\", \\\"-a\\\",\\n help='String with extra arguments for \\\"git clone\\\"')\\n\\n args = parser.parse_args(*args)\\n\\n if args.subcommand == \\\"set\\\":\\n try:\\n key, value = args.item.split(\\\"=\\\", 1)\\n except ValueError:\\n if \\\"plugins.\\\" in args.item:\\n key, value = args.item.split(\\\"=\\\", 1)[0], None\\n else:\\n raise ConanException(\\\"Please specify 'k\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.info\",\n \"text\": \"conans/client/command.py::Command.info\\n def info(self, *args):\\n \\\"\\\"\\\"Gets information about the dependency graph of a recipe.\\n\\n It can be used with a recipe or a reference for any existing package in\\n your local cache.\\n \\\"\\\"\\\"\\n\\n info_only_options = [\\\"id\\\", \\\"build_id\\\", \\\"remote\\\", \\\"url\\\", \\\"license\\\", \\\"requires\\\", \\\"update\\\",\\n \\\"required\\\", \\\"date\\\", \\\"author\\\", \\\"None\\\"]\\n path_only_options = [\\\"export_folder\\\", \\\"build_folder\\\", \\\"package_folder\\\", \\\"source_folder\\\"]\\n str_path_only_options = \\\", \\\".join(['\\\"%s\\\"' % field for field in path_only_options])\\n str_only_options = \\\", \\\".join(['\\\"%s\\\"' % field for field in info_only_options])\\n\\n parser = argparse.ArgumentParser(description=self.info.__doc__, prog=\\\"conan info\\\")\\n parser.add_argument(\\\"path_or_reference\\\", help=\\\"Path to a folder containing a recipe\\\"\\n \\\" (conanfile.py or conanfile.txt) or to a recipe file. e.g., \\\"\\n \\\"./my_project/conanfile.txt. It could also be a reference\\\")\\n parser.add_argument(\\\"--paths\\\", action='store_true', default=False,\\n help='Show package paths in local cache')\\n parser.add_argument(\\\"-bo\\\", \\\"--build-order\\\",\\n help='given a modified reference, return an ordered list to build (CI)',\\n nargs=1, action=Extender)\\n parser.add_argument(\\\"-g\\\", \\\"--graph\\\", action=OnceArgument,\\n help='Creates file with project dependencies graph. It will generate '\\n 'a DOT or HTML file depending on the filename extension')\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=\\\"local folder containing the conaninfo.txt and conanbuildinfo.txt \\\"\\n \\\"files (from a previous conan install execution). Defaulted to \\\"\\n \\\"current folder, unless --profile, -s or -o\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.source\",\n \"text\": \"conans/client/command.py::Command.source\\n def source(self, *args):\\n \\\"\\\"\\\" Calls your local conanfile.py 'source()' method.\\n\\n Usually downloads and uncompresses the package sources.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.source.__doc__, prog=\\\"conan source\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"-sf\\\", \\\"--source-folder\\\", action=OnceArgument,\\n help='Destination directory. Defaulted to current directory')\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=_INSTALL_FOLDER_HELP + \\\" Optional, source method will run without \\\"\\n \\\"the information retrieved from the conaninfo.txt and \\\"\\n \\\"conanbuildinfo.txt, only required when using conditional source() \\\"\\n \\\"based on settings, options, env_info and user_info\\\")\\n args = parser.parse_args(*args)\\n\\n try:\\n if \\\"@\\\" in args.path and ConanFileReference.loads(args.path):\\n raise ArgumentError(None,\\n \\\"'conan source' doesn't accept a reference anymore. \\\"\\n \\\"If you were using it as a concurrency workaround, \\\"\\n \\\"you can call 'conan install' simultaneously from several \\\"\\n \\\"different processes, the concurrency is now natively supported\\\"\\n \\\". The path parameter should be a folder containing a \\\"\\n \\\"conanfile.py file.\\\")\\n except ConanException:\\n pass\\n\\n self._warn_python2()\\n return self._conan.source(args.path, args.source_folder, args.install_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.build\",\n \"text\": \"conans/client/command.py::Command.build\\n def build(self, *args):\\n \\\"\\\"\\\"Calls your local conanfile.py 'build()' method.\\n\\n The recipe will be built in the local directory specified by\\n --build-folder, reading the sources from --source-folder. If you are\\n using a build helper, like CMake(), the --package-folder will be\\n configured as destination folder for the install step.\\n \\\"\\\"\\\"\\n\\n parser = argparse.ArgumentParser(description=self.build.__doc__, prog=\\\"conan build\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"-b\\\", \\\"--build\\\", default=None, action=\\\"store_true\\\",\\n help=\\\"Execute the build step (variable should_build=True). When \\\"\\n \\\"specified, configure/install/test won't run unless \\\"\\n \\\"--configure/--install/--test specified\\\")\\n parser.add_argument(\\\"-bf\\\", \\\"--build-folder\\\", action=OnceArgument, help=_BUILD_FOLDER_HELP)\\n parser.add_argument(\\\"-c\\\", \\\"--configure\\\", default=None, action=\\\"store_true\\\",\\n help=\\\"Execute the configuration step (variable should_configure=True). \\\"\\n \\\"When specified, build/install/test won't run unless \\\"\\n \\\"--build/--install/--test specified\\\")\\n parser.add_argument(\\\"-i\\\", \\\"--install\\\", default=None, action=\\\"store_true\\\",\\n help=\\\"Execute the install step (variable should_install=True). When \\\"\\n \\\"specified, configure/build/test won't run unless \\\"\\n \\\"--configure/--build/--test specified\\\")\\n parser.add_argument(\\\"-t\\\", \\\"--test\\\", default=None, action=\\\"store_true\\\",\\n help=\\\"Execute the test step (variable should_test=True). When \\\"\\n \\\"specified, configure/build/install won't run unless \\\"\\n \\\"--configure/--build/--install specified\\\")\\n parser.add_\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.package\",\n \"text\": \"conans/client/command.py::Command.package\\n def package(self, *args):\\n \\\"\\\"\\\" Calls your local conanfile.py 'package()' method.\\n\\n This command works in the user space and it will copy artifacts from\\n the --build-folder and --source-folder folder to the --package-folder\\n one. It won't create a new package in the local cache, if you want to\\n do it, use 'conan create' or 'conan export-pkg' after a 'conan build'\\n command.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.package.__doc__, prog=\\\"conan package\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"-bf\\\", \\\"--build-folder\\\", action=OnceArgument, help=_BUILD_FOLDER_HELP)\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=_INSTALL_FOLDER_HELP)\\n parser.add_argument(\\\"-pf\\\", \\\"--package-folder\\\", action=OnceArgument,\\n help=\\\"folder to install the package. Defaulted to the \\\"\\n \\\"'{build_folder}/package' folder. A relative path can be specified\\\"\\n \\\" (relative to the current directory). Also an absolute path\\\"\\n \\\" is allowed.\\\")\\n parser.add_argument(\\\"-sf\\\", \\\"--source-folder\\\", action=OnceArgument, help=_SOURCE_FOLDER_HELP)\\n args = parser.parse_args(*args)\\n try:\\n if \\\"@\\\" in args.path and ConanFileReference.loads(args.path):\\n raise ArgumentError(None,\\n \\\"'conan package' doesn't accept a reference anymore. \\\"\\n \\\"The path parameter should be a conanfile.py or a folder \\\"\\n \\\"containing one. If you were using the 'conan package' \\\"\\n \\\"command for development purposes we recommend to use \\\"\\n \\\"the local development commands: 'conan build' + \\\"\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.imports\",\n \"text\": \"conans/client/command.py::Command.imports\\n def imports(self, *args):\\n \\\"\\\"\\\" Calls your local conanfile.py or conanfile.txt 'imports' method.\\n\\n It requires to have been previously installed and have a\\n conanbuildinfo.txt generated file in the --install-folder (defaulted to\\n current directory).\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.imports.__doc__, prog=\\\"conan imports\\\")\\n parser.add_argument(\\\"path\\\",\\n help=_PATH_HELP + \\\" With --undo option, this parameter is the folder \\\"\\n \\\"containing the conan_imports_manifest.txt file generated in a previous \\\"\\n \\\"execution. e.g.: conan imports ./imported_files --undo \\\")\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=_INSTALL_FOLDER_HELP)\\n parser.add_argument(\\\"-imf\\\", \\\"--import-folder\\\", action=OnceArgument,\\n help=\\\"Directory to copy the artifacts to. By default it will be the\\\"\\n \\\" current directory\\\")\\n parser.add_argument(\\\"-u\\\", \\\"--undo\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"Undo imports. Remove imported files\\\")\\n args = parser.parse_args(*args)\\n\\n if args.undo:\\n return self._conan.imports_undo(args.path)\\n\\n try:\\n if \\\"@\\\" in args.path and ConanFileReference.loads(args.path):\\n raise ArgumentError(None, \\\"Parameter 'path' cannot be a reference. Use a folder \\\"\\n \\\"containing a conanfile.py or conanfile.txt file.\\\")\\n except ConanException:\\n pass\\n self._warn_python2()\\n return self._conan.imports(args.path, args.import_folder, args.install_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.export_pkg\",\n \"text\": \"conans/client/command.py::Command.export_pkg\\n def export_pkg(self, *args):\\n \\\"\\\"\\\"Exports a recipe, then creates a package from local source and build folders.\\n\\n The package is created by calling the package() method applied to the\\n local folders '--source-folder' and '--build-folder' It's created in\\n the local cache for the specified 'reference' and for the specified\\n '--settings', '--options' and or '--profile'.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.export_pkg.__doc__,\\n prog=\\\"conan export-pkg\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"reference\\\", help=\\\"user/channel or pkg/version@user/channel \\\"\\n \\\"(if name and version are not declared in the \\\"\\n \\\"conanfile.py)\\\")\\n parser.add_argument(\\\"-bf\\\", \\\"--build-folder\\\", action=OnceArgument, help=_BUILD_FOLDER_HELP)\\n parser.add_argument(\\\"-e\\\", \\\"--env\\\", nargs=1, action=Extender,\\n help='Environment variables that will be set during the package build, '\\n '-e CXX=/usr/bin/clang++')\\n parser.add_argument('-f', '--force', default=False, action='store_true',\\n help='Overwrite existing package if existing')\\n parser.add_argument(\\\"-if\\\", \\\"--install-folder\\\", action=OnceArgument,\\n help=_INSTALL_FOLDER_HELP + \\\" If these files are found in the specified\\\"\\n \\\" folder and any of '-e', '-o', '-pr' or '-s' arguments are used, it \\\"\\n \\\"will raise an error.\\\")\\n parser.add_argument(\\\"-o\\\", \\\"--options\\\", nargs=1, action=Extender,\\n help='Define options values, e.g., -o pkg:with_qt=true')\\n parser.add_argument(\\\"-pr\\\", \\\"--profile\\\", action=OnceArgument,\\n help='Profile for this \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.export\",\n \"text\": \"conans/client/command.py::Command.export\\n def export(self, *args):\\n \\\"\\\"\\\"Copies the recipe (conanfile.py & associated files) to your local cache.\\n\\n Use the 'reference' param to specify a user and channel where to export\\n it. Once the recipe is in the local cache it can be shared, reused and\\n to any remote with the 'conan upload' command.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.export.__doc__, prog=\\\"conan export\\\")\\n parser.add_argument(\\\"path\\\", help=_PATH_HELP)\\n parser.add_argument(\\\"reference\\\", help=\\\"user/channel, or Pkg/version@user/channel (if name \\\"\\n \\\"and version are not declared in the conanfile.py\\\")\\n parser.add_argument('-k', '-ks', '--keep-source', default=False, action='store_true',\\n help=_KEEP_SOURCE_HELP)\\n\\n args = parser.parse_args(*args)\\n self._warn_python2()\\n name, version, user, channel = get_reference_fields(args.reference)\\n return self._conan.export(path=args.path,\\n name=name, version=version, user=user, channel=channel,\\n keep_source=args.keep_source)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.remove\",\n \"text\": \"conans/client/command.py::Command.remove\\n def remove(self, *args):\\n \\\"\\\"\\\"Removes packages or binaries matching pattern from local cache or remote.\\n\\n It can also be used to remove temporary source or build folders in the\\n local conan cache. If no remote is specified, the removal will be done\\n by default in the local conan cache.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.remove.__doc__, prog=\\\"conan remove\\\")\\n parser.add_argument('pattern_or_reference', nargs=\\\"?\\\", help=_PATTERN_OR_REFERENCE_HELP)\\n parser.add_argument('-b', '--builds', nargs=\\\"*\\\", action=Extender,\\n help=(\\\"By default, remove all the build folders or select one, \\\"\\n \\\"specifying the package ID\\\"))\\n parser.add_argument('-f', '--force', default=False, action='store_true',\\n help='Remove without requesting a confirmation')\\n parser.add_argument(\\\"-o\\\", \\\"--outdated\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"Remove only outdated from recipe packages. \\\" \\\\\\n \\\"This flag can only be used with a reference\\\")\\n parser.add_argument('-p', '--packages', nargs=\\\"*\\\", action=Extender,\\n help=\\\"Select package to remove specifying the package ID\\\")\\n parser.add_argument('-q', '--query', default=None, action=OnceArgument, help=_QUERY_HELP)\\n parser.add_argument('-r', '--remote', action=OnceArgument,\\n help='Will remove from the specified remote')\\n parser.add_argument('-s', '--src', default=False, action=\\\"store_true\\\",\\n help='Remove source folders')\\n parser.add_argument(\\\"-l\\\", \\\"--locks\\\", default=False, action=\\\"store_true\\\",\\n help=\\\"Remove locks\\\")\\n args = parser.parse_args(*args)\\n\\n self._warn_python2()\\n\\n # NOTE: returns the expanded pattern (if a pattern was g\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.copy\",\n \"text\": \"conans/client/command.py::Command.copy\\n def copy(self, *args):\\n \\\"\\\"\\\"Copies conan recipes and packages to another user/channel.\\n\\n Useful to promote packages (e.g. from \\\"beta\\\" to \\\"stable\\\") or transfer\\n them from one user to another.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.copy.__doc__, prog=\\\"conan copy\\\")\\n parser.add_argument(\\\"reference\\\", default=\\\"\\\",\\n help='package reference. e.g., MyPackage/1.2@user/channel')\\n parser.add_argument(\\\"user_channel\\\", default=\\\"\\\",\\n help='Destination user/channel. e.g., lasote/testing')\\n parser.add_argument(\\\"-p\\\", \\\"--package\\\", nargs=1, action=Extender,\\n help='copy specified package ID')\\n parser.add_argument(\\\"--all\\\", action='store_true', default=False,\\n help='Copy all packages from the specified package recipe')\\n parser.add_argument(\\\"--force\\\", action='store_true', default=False,\\n help='Override destination packages and the package recipe')\\n args = parser.parse_args(*args)\\n\\n if args.all and args.package:\\n raise ConanException(\\\"Cannot specify both --all and --package\\\")\\n\\n self._warn_python2()\\n\\n return self._conan.copy(reference=args.reference, user_channel=args.user_channel,\\n force=args.force, packages=args.package or args.all)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.user\",\n \"text\": \"conans/client/command.py::Command.user\\n def user(self, *args):\\n \\\"\\\"\\\"Authenticates against a remote with user/pass, caching the auth token.\\n\\n Useful to avoid the user and password being requested later. e.g. while\\n you're uploading a package. You can have one user for each remote.\\n Changing the user, or introducing the password is only necessary to\\n perform changes in remote packages.\\n \\\"\\\"\\\"\\n # FIXME: Difficult and confusing CLI. Better with:\\n # - conan user clean -> clean users\\n # - conan user list ('remote') -> list users (of a remote)\\n # - conan user auth 'remote' ('user') ('password') -> login a remote (w/o user or pass)\\n # - conan user set 'user' 'remote' -> set user for a remote (not login) necessary??\\n parser = argparse.ArgumentParser(description=self.user.__doc__, prog=\\\"conan user\\\")\\n parser.add_argument(\\\"name\\\", nargs='?', default=None,\\n help='Username you want to use. If no name is provided it will show the'\\n ' current user')\\n parser.add_argument('-c', '--clean', default=False, action='store_true',\\n help='Remove user and tokens for all remotes')\\n parser.add_argument(\\\"-p\\\", \\\"--password\\\", nargs='?', const=\\\"\\\", type=str, action=OnceArgument,\\n help='User password. Use double quotes if password with spacing, '\\n 'and escape quotes if existing. If empty, the password is '\\n 'requested interactively (not exposed)')\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", help='Use the specified remote server',\\n action=OnceArgument)\\n parser.add_argument(\\\"-j\\\", \\\"--json\\\", default=None, action=OnceArgument,\\n help='json file path where the user list will be written to')\\n args = parser.parse_args(*args)\\n\\n if args.clean and any((args.name, ar\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.search\",\n \"text\": \"conans/client/command.py::Command.search\\n def search(self, *args):\\n \\\"\\\"\\\"Searches package recipes and binaries in the local cache or in a remote.\\n\\n If you provide a pattern, then it will search for existing package\\n recipes matching it. If a full reference is provided\\n (pkg/0.1@user/channel) then the existing binary packages for that\\n reference will be displayed. If no remote is specified, the search\\n will be done in the local cache. Search is case sensitive, exact case\\n has to be used. For case insensitive file systems, like Windows, case\\n sensitive search can be forced with '--case-sensitive'.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.search.__doc__, prog=\\\"conan search\\\")\\n parser.add_argument('pattern_or_reference', nargs='?', help=_PATTERN_OR_REFERENCE_HELP)\\n parser.add_argument('-o', '--outdated', default=False, action='store_true',\\n help=\\\"Show only outdated from recipe packages. \\\" \\\\\\n \\\"This flag can only be used with a reference\\\")\\n parser.add_argument('-q', '--query', default=None, action=OnceArgument, help=_QUERY_HELP)\\n parser.add_argument('-r', '--remote', action=OnceArgument,\\n help=\\\"Remote to search in. '-r all' searches all remotes\\\")\\n parser.add_argument('--case-sensitive', default=False, action='store_true',\\n help='Make a case-sensitive search. Use it to guarantee case-sensitive '\\n 'search in Windows or other case-insensitive file systems')\\n parser.add_argument('--raw', default=False, action='store_true',\\n help='Print just the list of recipes')\\n parser.add_argument('--table', action=OnceArgument,\\n help=\\\"Outputs html file with a table of binaries. Only valid for a \\\"\\n \\\"reference search\\\")\\n parser.add_ar\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.upload\",\n \"text\": \"conans/client/command.py::Command.upload\\n def upload(self, *args):\\n \\\"\\\"\\\"Uploads a recipe and binary packages to a remote.\\n\\n If no remote is specified, the first configured remote (by default conan-center, use\\n 'conan remote list' to list the remotes) will be used.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.upload.__doc__,\\n prog=\\\"conan upload\\\")\\n parser.add_argument('pattern_or_reference', help=_PATTERN_OR_REFERENCE_HELP)\\n parser.add_argument(\\\"-p\\\", \\\"--package\\\", default=None, action=OnceArgument,\\n help='package ID to upload')\\n parser.add_argument('-q', '--query', default=None, action=OnceArgument,\\n help=\\\"Only upload packages matching a specific query. \\\" + _QUERY_HELP)\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", action=OnceArgument,\\n help='upload to this specific remote')\\n parser.add_argument(\\\"--all\\\", action='store_true', default=False,\\n help='Upload both package recipe and packages')\\n parser.add_argument(\\\"--skip-upload\\\", action='store_true', default=False,\\n help='Do not upload anything, just run the checks and the compression')\\n parser.add_argument(\\\"--force\\\", action='store_true', default=False,\\n help='Do not check conan recipe date, override remote with local')\\n parser.add_argument(\\\"--check\\\", action='store_true', default=False,\\n help='Perform an integrity check, using the manifests, before upload')\\n parser.add_argument('-c', '--confirm', default=False, action='store_true',\\n help='Upload all matching recipes without confirmation')\\n parser.add_argument('--retry', default=2, type=int, action=OnceArgument,\\n help=\\\"In case of fail retries to upload again the specified times. \\\"\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.remote\",\n \"text\": \"conans/client/command.py::Command.remote\\n def remote(self, *args):\\n \\\"\\\"\\\"Manages the remote list and the package recipes associated to a remote.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.remote.__doc__, prog=\\\"conan remote\\\")\\n subparsers = parser.add_subparsers(dest='subcommand', help='sub-command help')\\n\\n # create the parser for the \\\"a\\\" command\\n parser_list = subparsers.add_parser('list', help='List current remotes')\\n parser_list.add_argument(\\\"-raw\\\", \\\"--raw\\\", action='store_true', default=False,\\n help='Raw format. Valid for \\\"remotes.txt\\\" file for \\\"conan config install\\\"')\\n parser_add = subparsers.add_parser('add', help='Add a remote')\\n parser_add.add_argument('remote', help='Name of the remote')\\n parser_add.add_argument('url', help='URL of the remote')\\n parser_add.add_argument('verify_ssl', nargs=\\\"?\\\", default=\\\"True\\\",\\n help='Verify SSL certificated. Default True')\\n parser_add.add_argument(\\\"-i\\\", \\\"--insert\\\", nargs=\\\"?\\\", const=0, type=int, action=OnceArgument,\\n help=\\\"insert remote at specific index\\\")\\n parser_add.add_argument(\\\"-f\\\", \\\"--force\\\", default=False, action='store_true',\\n help=\\\"Force addition, will update if existing\\\")\\n parser_rm = subparsers.add_parser('remove', help='Remove a remote')\\n parser_rm.add_argument('remote', help='Name of the remote')\\n parser_upd = subparsers.add_parser('update', help='Update the remote url')\\n parser_upd.add_argument('remote', help='Name of the remote')\\n\\n parser_upd.add_argument('url', help='URL')\\n parser_upd.add_argument('verify_ssl', nargs=\\\"?\\\", default=\\\"True\\\",\\n help='Verify SSL certificated. Default True')\\n parser_upd.add_argument(\\\"-i\\\", \\\"--insert\\\", nargs=\\\"?\\\", const=0, type=int, action=OnceArgument,\\n he\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.profile\",\n \"text\": \"conans/client/command.py::Command.profile\\n def profile(self, *args):\\n \\\"\\\"\\\" Lists profiles in the '.conan/profiles' folder, or shows profile details.\\n The 'list' subcommand will always use the default user 'conan/profiles' folder. But the\\n 'show' subcommand is able to resolve absolute and relative paths, as well as to map names to\\n '.conan/profiles' folder, in the same way as the '--profile' install argument.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.profile.__doc__, prog=\\\"conan profile\\\")\\n subparsers = parser.add_subparsers(dest='subcommand')\\n\\n # create the parser for the \\\"profile\\\" command\\n subparsers.add_parser('list', help='List current profiles')\\n parser_show = subparsers.add_parser('show', help='Show the values defined for a profile')\\n parser_show.add_argument('profile', help=\\\"name of the profile in the '.conan/profiles' \\\"\\n \\\"folder or path to a profile file\\\")\\n\\n parser_new = subparsers.add_parser('new', help='Creates a new empty profile')\\n parser_new.add_argument('profile', help=\\\"Name for the profile in the '.conan/profiles' \\\"\\n \\\"folder or path and name for a profile file\\\")\\n parser_new.add_argument(\\\"--detect\\\", action='store_true', default=False,\\n help='Autodetect settings and fill [settings] section')\\n\\n parser_update = subparsers.add_parser('update', help='Update a profile with desired value')\\n parser_update.add_argument('item',\\n help=\\\"'item=value' to update. e.g., settings.compiler=gcc\\\")\\n parser_update.add_argument('profile', help=\\\"Name of the profile in the '.conan/profiles' \\\"\\n \\\"folder or path to a profile file\\\")\\n\\n parser_get = subparsers.add_parser('get', help='Get a profile key')\\n parser_get.add_argument('ite\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.get\",\n \"text\": \"conans/client/command.py::Command.get\\n def get(self, *args):\\n \\\"\\\"\\\" Gets a file or list a directory of a given reference or package.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.get.__doc__,\\n prog=\\\"conan get\\\")\\n parser.add_argument('reference', help='package recipe reference')\\n parser.add_argument('path',\\n help='Path to the file or directory. If not specified will get the '\\n 'conanfile if only a reference is specified and a conaninfo.txt '\\n 'file contents if the package is also specified',\\n default=None, nargs=\\\"?\\\")\\n parser.add_argument(\\\"-p\\\", \\\"--package\\\", default=None, help='Package ID',\\n action=OnceArgument)\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", action=OnceArgument,\\n help='Get from this specific remote')\\n parser.add_argument(\\\"-raw\\\", \\\"--raw\\\", action='store_true', default=False,\\n help='Do not decorate the text')\\n args = parser.parse_args(*args)\\n\\n ret, path = self._conan.get_path(args.reference, args.package, args.path, args.remote)\\n if isinstance(ret, list):\\n self._outputer.print_dir_list(ret, path, args.raw)\\n else:\\n self._outputer.print_file_contents(ret, path, args.raw)\\n\\n return\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.alias\",\n \"text\": \"conans/client/command.py::Command.alias\\n def alias(self, *args):\\n \\\"\\\"\\\"Creates and exports an 'alias package recipe'.\\n\\n An \\\"alias\\\" package is a symbolic name (reference) for another package\\n (target). When some package depends on an alias, the target one will be\\n retrieved and used instead, so the alias reference, the symbolic name,\\n does not appear in the final dependency graph.\\n \\\"\\\"\\\"\\n parser = argparse.ArgumentParser(description=self.alias.__doc__,\\n prog=\\\"conan alias\\\")\\n parser.add_argument('reference', help='Alias reference. e.g.: mylib/1.X@user/channel')\\n parser.add_argument('target', help='Target reference. e.g.: mylib/1.12@user/channel')\\n args = parser.parse_args(*args)\\n\\n self._warn_python2()\\n\\n self._conan.export_alias(args.reference, args.target)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._show_help\",\n \"text\": \"conans/client/command.py::Command._show_help\\n def _show_help(self):\\n \\\"\\\"\\\"Prints a summary of all commands\\n \\\"\\\"\\\"\\n grps = [(\\\"Consumer commands\\\", (\\\"install\\\", \\\"config\\\", \\\"get\\\", \\\"info\\\", \\\"search\\\")),\\n (\\\"Creator commands\\\", (\\\"new\\\", \\\"create\\\", \\\"upload\\\", \\\"export\\\", \\\"export-pkg\\\", \\\"test\\\")),\\n (\\\"Package development commands\\\", (\\\"source\\\", \\\"build\\\", \\\"package\\\")),\\n (\\\"Misc commands\\\", (\\\"profile\\\", \\\"remote\\\", \\\"user\\\", \\\"imports\\\", \\\"copy\\\", \\\"remove\\\",\\n \\\"alias\\\", \\\"download\\\", \\\"inspect\\\", \\\"help\\\"))]\\n\\n def check_all_commands_listed():\\n \\\"\\\"\\\"Keep updated the main directory, raise if don't\\\"\\\"\\\"\\n all_commands = self._commands()\\n all_in_grps = [command for _, command_list in grps for command in command_list]\\n if set(all_in_grps) != set(all_commands):\\n diff = set(all_commands) - set(all_in_grps)\\n raise Exception(\\\"Some command is missing in the main help: %s\\\" % \\\",\\\".join(diff))\\n return all_commands\\n\\n commands = check_all_commands_listed()\\n max_len = max((len(c) for c in commands)) + 1\\n fmt = ' %-{}s'.format(max_len)\\n\\n for group_name, comm_names in grps:\\n self._user_io.out.writeln(group_name, Color.BRIGHT_MAGENTA)\\n for name in comm_names:\\n # future-proof way to ensure tabular formatting\\n self._user_io.out.write(fmt % name, Color.GREEN)\\n self._user_io.out.writeln(commands[name].__doc__.split('\\\\n', 1)[0].strip())\\n\\n self._user_io.out.writeln(\\\"\\\")\\n self._user_io.out.writeln('Conan commands. Type \\\"conan <command> -h\\\" for help',\\n Color.BRIGHT_YELLOW)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._show_help.check_all_commands_listed\",\n \"text\": \"conans/client/command.py::Command._show_help.check_all_commands_listed\\n def check_all_commands_listed():\\n \\\"\\\"\\\"Keep updated the main directory, raise if don't\\\"\\\"\\\"\\n all_commands = self._commands()\\n all_in_grps = [command for _, command_list in grps for command in command_list]\\n if set(all_in_grps) != set(all_commands):\\n diff = set(all_commands) - set(all_in_grps)\\n raise Exception(\\\"Some command is missing in the main help: %s\\\" % \\\",\\\".join(diff))\\n return all_commands\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._commands\",\n \"text\": \"conans/client/command.py::Command._commands\\n def _commands(self):\\n \\\"\\\"\\\" returns a list of available commands\\n \\\"\\\"\\\"\\n result = {}\\n for m in inspect.getmembers(self, predicate=inspect.ismethod):\\n method_name = m[0]\\n if not method_name.startswith('_'):\\n if \\\"export_pkg\\\" == method_name:\\n method_name = \\\"export-pkg\\\"\\n method = m[1]\\n if method.__doc__ and not method.__doc__.startswith('HIDDEN'):\\n result[method_name] = method\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._warn_python2\",\n \"text\": \"conans/client/command.py::Command._warn_python2\\n def _warn_python2(self):\\n if six.PY2:\\n self._user_io.out.writeln(\\\"\\\")\\n self._user_io.out.writeln(\\\"Python 2 will soon be deprecated. It is strongly \\\"\\n \\\"recommended to use Python 3 with Conan:\\\",\\n front=Color.BRIGHT_YELLOW)\\n self._user_io.out.writeln(\\\"https://docs.conan.io/en/latest/installation.html\\\"\\n \\\"#python-2-deprecation-notice\\\", front=Color.BRIGHT_YELLOW)\\n self._user_io.out.writeln(\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command._check_query_parameter_and_get_reference\",\n \"text\": \"conans/client/command.py::Command._check_query_parameter_and_get_reference\\n @staticmethod\\n def _check_query_parameter_and_get_reference(pattern, query):\\n reference = None\\n if pattern:\\n try:\\n reference = ConanFileReference.loads(pattern)\\n except ConanException:\\n if query is not None:\\n raise ConanException(\\\"-q parameter only allowed with a valid recipe \\\"\\n \\\"reference as search pattern. e.g. conan search \\\"\\n \\\"MyPackage/1.2@user/channel -q \\\\\\\"os=Windows\\\\\\\"\\\")\\n return reference\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::Command.run\",\n \"text\": \"conans/client/command.py::Command.run\\n def run(self, *args):\\n \\\"\\\"\\\"HIDDEN: entry point for executing commands, dispatcher to class\\n methods\\n \\\"\\\"\\\"\\n ret_code = SUCCESS\\n try:\\n try:\\n command = args[0][0]\\n commands = self._commands()\\n method = commands[command]\\n except KeyError as exc:\\n if command in [\\\"-v\\\", \\\"--version\\\"]:\\n self._user_io.out.success(\\\"Conan version %s\\\" % client_version)\\n return False\\n self._warn_python2()\\n self._show_help()\\n if command in [\\\"-h\\\", \\\"--help\\\"]:\\n return False\\n raise ConanException(\\\"Unknown command %s\\\" % str(exc))\\n except IndexError: # No parameters\\n self._show_help()\\n return False\\n method(args[0][1:])\\n except KeyboardInterrupt as exc:\\n logger.error(exc)\\n ret_code = SUCCESS\\n except SystemExit as exc:\\n if exc.code != 0:\\n logger.error(exc)\\n self._user_io.out.error(\\\"Exiting with code: %d\\\" % exc.code)\\n ret_code = exc.code\\n except ConanInvalidConfiguration as exc:\\n ret_code = ERROR_INVALID_CONFIGURATION\\n msg = exception_message_safe(exc)\\n self._user_io.out.error(msg)\\n except ConanException as exc:\\n ret_code = ERROR_GENERAL\\n msg = exception_message_safe(exc)\\n self._user_io.out.error(msg)\\n except Exception as exc:\\n import traceback\\n print(traceback.format_exc())\\n ret_code = ERROR_GENERAL\\n msg = exception_message_safe(exc)\\n self._user_io.out.error(msg)\\n\\n return ret_code\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::get_reference_fields\",\n \"text\": \"conans/client/command.py::get_reference_fields\\ndef get_reference_fields(arg_reference):\\n \\\"\\\"\\\"\\n :param arg_reference: String with a complete reference, or only user/channel\\n :return: name, version, user and channel, in a tuple\\n \\\"\\\"\\\"\\n\\n if not arg_reference:\\n return None, None, None, None\\n\\n try:\\n name_version, user_channel = arg_reference.split(\\\"@\\\")\\n name, version = name_version.split(\\\"/\\\")\\n user, channel = user_channel.split(\\\"/\\\")\\n except ValueError:\\n name, version = None, None\\n try:\\n user, channel = arg_reference.split(\\\"/\\\")\\n except ValueError:\\n raise ConanException(\\\"Invalid parameter '%s', specify the full reference or \\\"\\n \\\"user/channel\\\" % arg_reference)\\n\\n return name, version, user, channel\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::_add_manifests_arguments\",\n \"text\": \"conans/client/command.py::_add_manifests_arguments\\ndef _add_manifests_arguments(parser):\\n parser.add_argument(\\\"-m\\\", \\\"--manifests\\\", const=default_manifest_folder, nargs=\\\"?\\\",\\n help='Install dependencies manifests in folder for later verify.'\\n ' Default folder is .conan_manifests, but can be changed',\\n action=OnceArgument)\\n parser.add_argument(\\\"-mi\\\", \\\"--manifests-interactive\\\", const=default_manifest_folder,\\n nargs=\\\"?\\\",\\n help='Install dependencies manifests in folder for later verify, '\\n 'asking user for confirmation. '\\n 'Default folder is .conan_manifests, but can be changed',\\n action=OnceArgument)\\n parser.add_argument(\\\"-v\\\", \\\"--verify\\\", const=default_manifest_folder, nargs=\\\"?\\\",\\n help='Verify dependencies manifests against stored ones',\\n action=OnceArgument)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::_add_common_install_arguments\",\n \"text\": \"conans/client/command.py::_add_common_install_arguments\\ndef _add_common_install_arguments(parser, build_help):\\n if build_help:\\n parser.add_argument(\\\"-b\\\", \\\"--build\\\", action=Extender, nargs=\\\"?\\\", help=build_help)\\n\\n parser.add_argument(\\\"-e\\\", \\\"--env\\\", nargs=1, action=Extender,\\n help='Environment variables that will be set during the package build, '\\n '-e CXX=/usr/bin/clang++')\\n parser.add_argument(\\\"-o\\\", \\\"--options\\\", nargs=1, action=Extender,\\n help='Define options values, e.g., -o Pkg:with_qt=true')\\n parser.add_argument(\\\"-pr\\\", \\\"--profile\\\", default=None, action=OnceArgument,\\n help='Apply the specified profile to the install command')\\n parser.add_argument(\\\"-r\\\", \\\"--remote\\\", action=OnceArgument,\\n help='Look in the specified remote server')\\n parser.add_argument(\\\"-s\\\", \\\"--settings\\\", nargs=1, action=Extender,\\n help='Settings to build the package, overwriting the defaults. e.g., '\\n '-s compiler=gcc')\\n parser.add_argument(\\\"-u\\\", \\\"--update\\\", action='store_true', default=False,\\n help=\\\"Check updates exist from upstream remotes\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/command.py::main\",\n \"text\": \"conans/client/command.py::main\\ndef main(args):\\n \\\"\\\"\\\" main entry point of the conan application, using a Command to\\n parse parameters\\n\\n Exit codes for conan command:\\n\\n 0: Success (done)\\n 1: General ConanException error (done)\\n 2: Migration error\\n 3: Ctrl+C\\n 4: Ctrl+Break\\n 5: SIGTERM\\n 6: Invalid configuration (done)\\n \\\"\\\"\\\"\\n try:\\n conan_api, client_cache, user_io = Conan.factory()\\n except ConanException: # Error migrating\\n sys.exit(ERROR_MIGRATION)\\n\\n outputer = CommandOutputer(user_io, client_cache)\\n command = Command(conan_api, client_cache, user_io, outputer)\\n current_dir = get_cwd()\\n try:\\n import signal\\n\\n def ctrl_c_handler(_, __):\\n print('You pressed Ctrl+C!')\\n sys.exit(USER_CTRL_C)\\n\\n def sigterm_handler(_, __):\\n print('Received SIGTERM!')\\n sys.exit(ERROR_SIGTERM)\\n\\n def ctrl_break_handler(_, __):\\n print('You pressed Ctrl+Break!')\\n sys.exit(USER_CTRL_BREAK)\\n\\n signal.signal(signal.SIGINT, ctrl_c_handler)\\n signal.signal(signal.SIGTERM, sigterm_handler)\\n\\n if sys.platform == 'win32':\\n signal.signal(signal.SIGBREAK, ctrl_break_handler)\\n error = command.run(args)\\n finally:\\n os.chdir(current_dir)\\n sys.exit(error)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/pkg_config.py::PkgConfigGenerator.filename\",\n \"text\": \"conans/client/generators/pkg_config.py::PkgConfigGenerator.filename\\n @property\\n def filename(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/pkg_config.py::PkgConfigGenerator.compiler\",\n \"text\": \"conans/client/generators/pkg_config.py::PkgConfigGenerator.compiler\\n @property\\n def compiler(self):\\n return self.conanfile.settings.get_safe(\\\"compiler\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/pkg_config.py::PkgConfigGenerator.content\",\n \"text\": \"conans/client/generators/pkg_config.py::PkgConfigGenerator.content\\n @property\\n def content(self):\\n ret = {}\\n for depname, cpp_info in self.deps_build_info.dependencies:\\n ret[\\\"%s.pc\\\" % depname] = self.single_pc_file_contents(depname, cpp_info)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/pkg_config.py::PkgConfigGenerator.single_pc_file_contents\",\n \"text\": \"conans/client/generators/pkg_config.py::PkgConfigGenerator.single_pc_file_contents\\n def single_pc_file_contents(self, name, cpp_info):\\n prefix_path = cpp_info.rootpath.replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\n lines = ['prefix=%s' % prefix_path]\\n\\n libdir_vars = []\\n dir_lines, varnames = _generate_dir_lines(prefix_path, \\\"libdir\\\", cpp_info.libdirs)\\n if dir_lines:\\n libdir_vars = varnames\\n lines.extend(dir_lines)\\n\\n includedir_vars = []\\n dir_lines, varnames = _generate_dir_lines(prefix_path, \\\"includedir\\\", cpp_info.includedirs)\\n if dir_lines:\\n includedir_vars = varnames\\n lines.extend(dir_lines)\\n\\n lines.append(\\\"\\\")\\n lines.append(\\\"Name: %s\\\" % name)\\n description = cpp_info.description or \\\"Conan package: %s\\\" % name\\n lines.append(\\\"Description: %s\\\" % description)\\n lines.append(\\\"Version: %s\\\" % cpp_info.version)\\n libdirs_flags = [\\\"-L${%s}\\\" % name for name in libdir_vars]\\n libnames_flags = [\\\"-l%s \\\" % name for name in cpp_info.libs]\\n shared_flags = cpp_info.sharedlinkflags + cpp_info.exelinkflags\\n the_os = (self.conanfile.settings.get_safe(\\\"os_build\\\") or\\n self.conanfile.settings.get_safe(\\\"os\\\"))\\n rpaths = rpath_flags(the_os, self.compiler, [\\\"${%s}\\\" % libdir for libdir in libdir_vars])\\n lines.append(\\\"Libs: %s\\\" % _concat_if_not_empty([libdirs_flags,\\n libnames_flags,\\n shared_flags,\\n rpaths]))\\n include_dirs_flags = [\\\"-I${%s}\\\" % name for name in includedir_vars]\\n\\n lines.append(\\\"Cflags: %s\\\" % _concat_if_not_empty(\\n [include_dirs_flags,\\n cpp_info.cppflags,\\n cpp_info.cflags,\\n [\\\"-D%s\\\" % d for d in cpp_info.defines]]))\\n\\n if cpp_info.public_deps:\\n public_deps = \\\" \\\".join(cpp_info.publ\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/pkg_config.py::_concat_if_not_empty\",\n \"text\": \"conans/client/generators/pkg_config.py::_concat_if_not_empty\\ndef _concat_if_not_empty(groups):\\n return \\\" \\\".join([param for group in groups for param in group if param and param.strip()])\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/generators/pkg_config.py::_generate_dir_lines\",\n \"text\": \"conans/client/generators/pkg_config.py::_generate_dir_lines\\ndef _generate_dir_lines(prefix_path, varname, dirs):\\n lines = []\\n varnames = []\\n for i, directory in enumerate(dirs):\\n directory = os.path.normpath(directory).replace(\\\"\\\\\\\\\\\", \\\"/\\\")\\n varname = varname if i == 0 else \\\"%s%d\\\" % (varname, (i + 2))\\n prefix = \\\"\\\"\\n if not os.path.isabs(directory):\\n prefix = \\\"${prefix}/\\\"\\n elif directory.startswith(prefix_path):\\n prefix = \\\"${prefix}/\\\"\\n directory = os.path.relpath(directory, prefix_path)\\n lines.append(\\\"%s=%s%s\\\" % (varname, prefix, directory))\\n varnames.append(varname)\\n return lines, varnames\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":286,"cells":{"instance_id":{"kind":"string","value":"docker__compose-6973"},"repo":{"kind":"string","value":"docker/compose"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"--no-color does not disable colors on ERROR and WARNING logs\n`docker-compose up -d --no-color` still displays ERROR messages with the word \"ERROR\" in red and the word \"WARNING\" in yellow.\r\n\r\nIt would also be nice if compose respected the `CLICOLOR=0` env variable as a synonym for `--no-color`.\n"},"gold_files":{"kind":"list like","value":["compose/cli/main.py"],"string":"[\n \"compose/cli/main.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["compose/cli/main.py::<module>","compose/cli/main.py::TopLevelCommand.logs","compose/cli/main.py::TopLevelCommand.up","compose/cli/main.py::dispatch","compose/cli/main.py::warn_for_swarm_mode"],"string":"[\n \"compose/cli/main.py::<module>\",\n \"compose/cli/main.py::TopLevelCommand.logs\",\n \"compose/cli/main.py::TopLevelCommand.up\",\n \"compose/cli/main.py::dispatch\",\n \"compose/cli/main.py::warn_for_swarm_mode\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"compose/cli/colors.py","text":"compose/cli/colors.py\ndef get_pairs()\ndef ansi(code)\ndef ansi_color(code, s)\ndef make_color_fn(code)\ndef rainbow()","label":0},{"cid":"compose/cli/formatter.py","text":"compose/cli/formatter.py\ndef get_tty_width()\nclass Formatter # Format tabular data for printing.\n def table(headers, rows)\nclass ConsoleWarningFormatter # A logging.Formatter which prints WARNING and ERROR messages with\n def get_level_message(self, record)\n def format(self, record)","label":0},{"cid":"compose/cli/log_printer.py","text":"compose/cli/log_printer.py\nclass LogPresenter\n def __init__(self, prefix_width, color_func)\n def present(self, container, line)\ndef build_log_presenters(service_names, monochrome) # Return an iterable of functions.\n def no_color(text)\ndef max_name_width(service_names, max_index_width=3) # Calculate the maximum width of container names so we can make the log\nclass LogPrinter # Print logs from many containers to a single output stream.\n def __init__(self, containers, presenters, event_stream, output=sys.stdout, cascade_stop=False, log_args=None)\n def run(self)\n def write(self, line)\ndef remove_stopped_threads(thread_map)\ndef build_thread(container, presenter, queue, log_args)\ndef build_thread_map(initial_containers, presenters, thread_args)\nclass QueueItem\n def new(cls, item)\n def exception(cls, exc)\n def stop(cls, item=None)\ndef tail_container_logs(container, presenter, queue, log_args)\ndef get_log_generator(container)\ndef build_no_log_generator(container, log_args) # Return a generator that prints a warning about logs and waits for\ndef build_log_generator(container, log_args)\ndef wait_on_exit(container)\ndef start_producer_thread(thread_args)\ndef watch_events(thread_map, event_stream, presenters, thread_args)\ndef consume_queue(queue, cascade_stop) # Consume the queue by reading lines off of it and yielding them.","label":0},{"cid":"compose/cli/main.py","text":"compose/cli/main.py\ndef main()\ndef dispatch()\ndef perform_command(options, handler, command_options)\ndef setup_logging()\ndef setup_parallel_logger(noansi)\ndef setup_console_handler(handler, verbose, noansi=False, level=None)\ndef parse_doc_section(name, source)\nclass TopLevelCommand # Define and run multi-container applications with Docker.\n def __init__(self, project, options=None)\n def project_dir(self)\n def toplevel_environment(self)\n def build(self, options) # Build or rebuild services.\n def bundle(self, options) # Generate a Distributed Application Bundle (DAB) from the Compose file.\n def config(self, options) # Validate and view the Compose file.\n def create(self, options) # Creates containers for a service.\n def down(self, options) # Stops containers and removes containers, networks, volumes, and images\n def events(self, options) # Receive real time events from containers.\n def format_event(event)\n def json_format_event(event)\n def exec_command(self, options) # Execute a command in a running container\n def help(cls, options) # Get help on a command.\n def images(self, options) # List images used by the created containers.\n def add_default_tag(img_name)\n def kill(self, options) # Force stop service containers.\n def logs(self, options) # View output from containers.\n def pause(self, options) # Pause services.\n def port(self, options) # Print the public port for a port binding.\n def ps(self, options) # List containers.\n def pull(self, options) # Pulls images for services defined in a Compose file, but does not start the cont\n def push(self, options) # Pushes images for services.\n def rm(self, options) # Removes stopped service containers.\n def run(self, options) # Run a one-off command on a service.\n def scale(self, options) # Set number of containers to run for a service.\n def start(self, options) # Start existing containers.\n def stop(self, options) # Stop running containers without removing them.\n def restart(self, options) # Restart running containers.\n def top(self, options) # Display the running processes\n def unpause(self, options) # Unpause services.\n def up(self, options) # Builds, (re)creates, starts, and attaches to containers for a service.\n def version(cls, options) # Show version information\ndef compute_service_exit_code(exit_value_from, attached_containers)\ndef compute_exit_code(exit_value_from, attached_containers, cascade_starter, all_containers)\ndef convergence_strategy_from_opts(options)\ndef timeout_from_opts(options)\ndef image_digests_for_project(project, allow_push=False)\ndef exitval_from_opts(options, project)\ndef image_type_from_opt(flag, value)\ndef build_action_from_opts(options)\ndef build_one_off_container_options(options, detach, command)\ndef run_one_off_container(container_options, project, service, options, toplevel_options, toplevel_environment)\n def remove_container(force=False)\n","label":1},{"cid":"compose/config/environment.py","text":"compose/config/environment.py\ndef split_env(env)\ndef env_vars_from_file(filename) # Read in a line delimited file of environment variables.\nclass Environment\n def __init__(self, *args, **kwargs)\n def from_env_file(cls, base_dir, env_file=None)\n def _initialize()\n def from_command_line(cls, parsed_env_opts)\n def __getitem__(self, key)\n def __contains__(self, key)\n def get(self, key, *args, **kwargs)\n def get_boolean(self, key)","label":0},{"cid":"compose/parallel.py","text":"compose/parallel.py\nclass GlobalLimit # Simple class to hold a global semaphore limiter for a project. This class\n def set_global_limit(cls, value)\ndef parallel_execute_watch(events, writer, errors, results, msg, get_name, fail_check) # Watch events from a parallel execution, update status and fill errors and result\ndef parallel_execute(objects, func, get_name, msg, get_deps=None, limit=None, fail_check=None) # Runs func on objects in parallel while ensuring that func is\ndef _no_deps(x)\nclass State # Holds the state of a partially-complete parallel operation.\n def __init__(self, objects)\n def is_done(self)\n def pending(self)\nclass NoLimit\n def __enter__(self)\n def __exit__(self, *ex)\ndef parallel_execute_iter(objects, func, get_deps, limit) # Runs func on objects in parallel while ensuring that func is\ndef producer(obj, func, results, limiter) # The entry point for a producer thread which runs func on a single object.\ndef feed_queue(objects, func, get_deps, results, state, limiter) # Starts producer threads for any objects which are ready to be processed\nclass UpstreamError\nclass ParallelStreamWriter # Write out messages for operations happening in parallel.\n def set_noansi(cls, value=True)\n def __init__(self, stream)\n def add_object(self, msg, obj_index)\n def write_initial(self, msg, obj_index)\n def _write_ansi(self, msg, obj_index, status)\n def _write_noansi(self, msg, obj_index, status)\n def write(self, msg, obj_index, status, color_func)\ndef get_stream_writer()\ndef parallel_operation(containers, operation, options, message)\ndef parallel_remove(containers, options)\ndef parallel_pause(containers, options)\ndef parallel_unpause(containers, options)\ndef parallel_kill(containers, options)","label":0},{"cid":"compose/progress_stream.py","text":"compose/progress_stream.py\nclass StreamOutputError\ndef write_to_stream(s, stream)\ndef stream_output(output, stream)\ndef print_output_event(event, stream, is_terminal)\ndef get_digest_from_pull(events)\ndef get_digest_from_push(events)","label":0},{"cid":"script/release/release.py","text":"script/release/release.py\ndef create_initial_branch(repository, args)\ndef create_bump_commit(repository, release_branch, bintray_user, bintray_org)\ndef monitor_pr_status(pr_data)\ndef check_pr_mergeable(pr_data)\ndef create_release_draft(repository, version, pr_data, files)\ndef print_final_instructions(args)\ndef distclean()\ndef resume(args)\ndef cancel(args)\ndef start(args)\ndef finalize(args)\ndef main()","label":0}],"string":"[\n {\n \"cid\": \"compose/cli/colors.py\",\n \"text\": \"compose/cli/colors.py\\ndef get_pairs()\\ndef ansi(code)\\ndef ansi_color(code, s)\\ndef make_color_fn(code)\\ndef rainbow()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/formatter.py\",\n \"text\": \"compose/cli/formatter.py\\ndef get_tty_width()\\nclass Formatter # Format tabular data for printing.\\n def table(headers, rows)\\nclass ConsoleWarningFormatter # A logging.Formatter which prints WARNING and ERROR messages with\\n def get_level_message(self, record)\\n def format(self, record)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py\",\n \"text\": \"compose/cli/log_printer.py\\nclass LogPresenter\\n def __init__(self, prefix_width, color_func)\\n def present(self, container, line)\\ndef build_log_presenters(service_names, monochrome) # Return an iterable of functions.\\n def no_color(text)\\ndef max_name_width(service_names, max_index_width=3) # Calculate the maximum width of container names so we can make the log\\nclass LogPrinter # Print logs from many containers to a single output stream.\\n def __init__(self, containers, presenters, event_stream, output=sys.stdout, cascade_stop=False, log_args=None)\\n def run(self)\\n def write(self, line)\\ndef remove_stopped_threads(thread_map)\\ndef build_thread(container, presenter, queue, log_args)\\ndef build_thread_map(initial_containers, presenters, thread_args)\\nclass QueueItem\\n def new(cls, item)\\n def exception(cls, exc)\\n def stop(cls, item=None)\\ndef tail_container_logs(container, presenter, queue, log_args)\\ndef get_log_generator(container)\\ndef build_no_log_generator(container, log_args) # Return a generator that prints a warning about logs and waits for\\ndef build_log_generator(container, log_args)\\ndef wait_on_exit(container)\\ndef start_producer_thread(thread_args)\\ndef watch_events(thread_map, event_stream, presenters, thread_args)\\ndef consume_queue(queue, cascade_stop) # Consume the queue by reading lines off of it and yielding them.\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py\",\n \"text\": \"compose/cli/main.py\\ndef main()\\ndef dispatch()\\ndef perform_command(options, handler, command_options)\\ndef setup_logging()\\ndef setup_parallel_logger(noansi)\\ndef setup_console_handler(handler, verbose, noansi=False, level=None)\\ndef parse_doc_section(name, source)\\nclass TopLevelCommand # Define and run multi-container applications with Docker.\\n def __init__(self, project, options=None)\\n def project_dir(self)\\n def toplevel_environment(self)\\n def build(self, options) # Build or rebuild services.\\n def bundle(self, options) # Generate a Distributed Application Bundle (DAB) from the Compose file.\\n def config(self, options) # Validate and view the Compose file.\\n def create(self, options) # Creates containers for a service.\\n def down(self, options) # Stops containers and removes containers, networks, volumes, and images\\n def events(self, options) # Receive real time events from containers.\\n def format_event(event)\\n def json_format_event(event)\\n def exec_command(self, options) # Execute a command in a running container\\n def help(cls, options) # Get help on a command.\\n def images(self, options) # List images used by the created containers.\\n def add_default_tag(img_name)\\n def kill(self, options) # Force stop service containers.\\n def logs(self, options) # View output from containers.\\n def pause(self, options) # Pause services.\\n def port(self, options) # Print the public port for a port binding.\\n def ps(self, options) # List containers.\\n def pull(self, options) # Pulls images for services defined in a Compose file, but does not start the cont\\n def push(self, options) # Pushes images for services.\\n def rm(self, options) # Removes stopped service containers.\\n def run(self, options) # Run a one-off command on a service.\\n def scale(self, options) # Set number of containers to run for a service.\\n def start(self, options) # Start existing containers.\\n def stop(self, options) # Stop running containers without removing them.\\n def restart(self, options) # Restart running containers.\\n def top(self, options) # Display the running processes\\n def unpause(self, options) # Unpause services.\\n def up(self, options) # Builds, (re)creates, starts, and attaches to containers for a service.\\n def version(cls, options) # Show version information\\ndef compute_service_exit_code(exit_value_from, attached_containers)\\ndef compute_exit_code(exit_value_from, attached_containers, cascade_starter, all_containers)\\ndef convergence_strategy_from_opts(options)\\ndef timeout_from_opts(options)\\ndef image_digests_for_project(project, allow_push=False)\\ndef exitval_from_opts(options, project)\\ndef image_type_from_opt(flag, value)\\ndef build_action_from_opts(options)\\ndef build_one_off_container_options(options, detach, command)\\ndef run_one_off_container(container_options, project, service, options, toplevel_options, toplevel_environment)\\n def remove_container(force=False)\\n\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/config/environment.py\",\n \"text\": \"compose/config/environment.py\\ndef split_env(env)\\ndef env_vars_from_file(filename) # Read in a line delimited file of environment variables.\\nclass Environment\\n def __init__(self, *args, **kwargs)\\n def from_env_file(cls, base_dir, env_file=None)\\n def _initialize()\\n def from_command_line(cls, parsed_env_opts)\\n def __getitem__(self, key)\\n def __contains__(self, key)\\n def get(self, key, *args, **kwargs)\\n def get_boolean(self, key)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py\",\n \"text\": \"compose/parallel.py\\nclass GlobalLimit # Simple class to hold a global semaphore limiter for a project. This class\\n def set_global_limit(cls, value)\\ndef parallel_execute_watch(events, writer, errors, results, msg, get_name, fail_check) # Watch events from a parallel execution, update status and fill errors and result\\ndef parallel_execute(objects, func, get_name, msg, get_deps=None, limit=None, fail_check=None) # Runs func on objects in parallel while ensuring that func is\\ndef _no_deps(x)\\nclass State # Holds the state of a partially-complete parallel operation.\\n def __init__(self, objects)\\n def is_done(self)\\n def pending(self)\\nclass NoLimit\\n def __enter__(self)\\n def __exit__(self, *ex)\\ndef parallel_execute_iter(objects, func, get_deps, limit) # Runs func on objects in parallel while ensuring that func is\\ndef producer(obj, func, results, limiter) # The entry point for a producer thread which runs func on a single object.\\ndef feed_queue(objects, func, get_deps, results, state, limiter) # Starts producer threads for any objects which are ready to be processed\\nclass UpstreamError\\nclass ParallelStreamWriter # Write out messages for operations happening in parallel.\\n def set_noansi(cls, value=True)\\n def __init__(self, stream)\\n def add_object(self, msg, obj_index)\\n def write_initial(self, msg, obj_index)\\n def _write_ansi(self, msg, obj_index, status)\\n def _write_noansi(self, msg, obj_index, status)\\n def write(self, msg, obj_index, status, color_func)\\ndef get_stream_writer()\\ndef parallel_operation(containers, operation, options, message)\\ndef parallel_remove(containers, options)\\ndef parallel_pause(containers, options)\\ndef parallel_unpause(containers, options)\\ndef parallel_kill(containers, options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/progress_stream.py\",\n \"text\": \"compose/progress_stream.py\\nclass StreamOutputError\\ndef write_to_stream(s, stream)\\ndef stream_output(output, stream)\\ndef print_output_event(event, stream, is_terminal)\\ndef get_digest_from_pull(events)\\ndef get_digest_from_push(events)\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py\",\n \"text\": \"script/release/release.py\\ndef create_initial_branch(repository, args)\\ndef create_bump_commit(repository, release_branch, bintray_user, bintray_org)\\ndef monitor_pr_status(pr_data)\\ndef check_pr_mergeable(pr_data)\\ndef create_release_draft(repository, version, pr_data, files)\\ndef print_final_instructions(args)\\ndef distclean()\\ndef resume(args)\\ndef cancel(args)\\ndef start(args)\\ndef finalize(args)\\ndef main()\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"compose/cli/colors.py::get_pairs","text":"compose/cli/colors.py::get_pairs\ndef get_pairs():\n for i, name in enumerate(NAMES):\n yield(name, str(30 + i))\n yield('intense_' + name, str(30 + i) + ';1')","label":0},{"cid":"compose/cli/colors.py::ansi","text":"compose/cli/colors.py::ansi\ndef ansi(code):\n return '\\033[{0}m'.format(code)","label":0},{"cid":"compose/cli/colors.py::ansi_color","text":"compose/cli/colors.py::ansi_color\ndef ansi_color(code, s):\n return '{0}{1}{2}'.format(ansi(code), s, ansi(0))","label":0},{"cid":"compose/cli/colors.py::make_color_fn","text":"compose/cli/colors.py::make_color_fn\ndef make_color_fn(code):\n return lambda s: ansi_color(code, s)","label":0},{"cid":"compose/cli/colors.py::rainbow","text":"compose/cli/colors.py::rainbow\ndef rainbow():\n cs = ['cyan', 'yellow', 'green', 'magenta', 'red', 'blue',\n 'intense_cyan', 'intense_yellow', 'intense_green',\n 'intense_magenta', 'intense_red', 'intense_blue']\n\n for c in cs:\n yield globals()[c]","label":0},{"cid":"compose/cli/formatter.py::get_tty_width","text":"compose/cli/formatter.py::get_tty_width\ndef get_tty_width():\n try:\n width, _ = get_terminal_size()\n return int(width)\n except OSError:\n return 0","label":0},{"cid":"compose/cli/formatter.py::Formatter.table","text":"compose/cli/formatter.py::Formatter.table\n @staticmethod\n def table(headers, rows):\n table = texttable.Texttable(max_width=get_tty_width())\n table.set_cols_dtype(['t' for h in headers])\n table.add_rows([headers] + rows)\n table.set_deco(table.HEADER)\n table.set_chars(['-', '|', '+', '-'])\n\n return table.draw()","label":0},{"cid":"compose/cli/formatter.py::ConsoleWarningFormatter.get_level_message","text":"compose/cli/formatter.py::ConsoleWarningFormatter.get_level_message\n def get_level_message(self, record):\n separator = ': '\n if record.levelno == logging.WARNING:\n return colors.yellow(record.levelname) + separator\n if record.levelno == logging.ERROR:\n return colors.red(record.levelname) + separator\n\n return ''","label":0},{"cid":"compose/cli/formatter.py::ConsoleWarningFormatter.format","text":"compose/cli/formatter.py::ConsoleWarningFormatter.format\n def format(self, record):\n if isinstance(record.msg, six.binary_type):\n record.msg = record.msg.decode('utf-8')\n message = super(ConsoleWarningFormatter, self).format(record)\n return '{0}{1}'.format(self.get_level_message(record), message)","label":0},{"cid":"compose/cli/log_printer.py::LogPresenter.__init__","text":"compose/cli/log_printer.py::LogPresenter.__init__\n def __init__(self, prefix_width, color_func):\n self.prefix_width = prefix_width\n self.color_func = color_func","label":0},{"cid":"compose/cli/log_printer.py::LogPresenter.present","text":"compose/cli/log_printer.py::LogPresenter.present\n def present(self, container, line):\n prefix = container.name_without_project.ljust(self.prefix_width)\n return '{prefix} {line}'.format(\n prefix=self.color_func(prefix + ' |'),\n line=line)","label":0},{"cid":"compose/cli/log_printer.py::build_log_presenters","text":"compose/cli/log_printer.py::build_log_presenters\ndef build_log_presenters(service_names, monochrome):\n \"\"\"Return an iterable of functions.\n\n Each function can be used to format the logs output of a container.\n \"\"\"\n prefix_width = max_name_width(service_names)\n\n def no_color(text):\n return text\n\n for color_func in cycle([no_color] if monochrome else colors.rainbow()):\n yield LogPresenter(prefix_width, color_func)","label":0},{"cid":"compose/cli/log_printer.py::build_log_presenters.no_color","text":"compose/cli/log_printer.py::build_log_presenters.no_color\n def no_color(text):\n return text","label":0},{"cid":"compose/cli/log_printer.py::max_name_width","text":"compose/cli/log_printer.py::max_name_width\ndef max_name_width(service_names, max_index_width=3):\n \"\"\"Calculate the maximum width of container names so we can make the log\n prefixes line up like so:\n\n db_1 | Listening\n web_1 | Listening\n \"\"\"\n return max(len(name) for name in service_names) + max_index_width","label":0},{"cid":"compose/cli/log_printer.py::LogPrinter.__init__","text":"compose/cli/log_printer.py::LogPrinter.__init__\n def __init__(self,\n containers,\n presenters,\n event_stream,\n output=sys.stdout,\n cascade_stop=False,\n log_args=None):\n self.containers = containers\n self.presenters = presenters\n self.event_stream = event_stream\n self.output = utils.get_output_stream(output)\n self.cascade_stop = cascade_stop\n self.log_args = log_args or {}","label":0},{"cid":"compose/cli/log_printer.py::LogPrinter.run","text":"compose/cli/log_printer.py::LogPrinter.run\n def run(self):\n if not self.containers:\n return\n\n queue = Queue()\n thread_args = queue, self.log_args\n thread_map = build_thread_map(self.containers, self.presenters, thread_args)\n start_producer_thread((\n thread_map,\n self.event_stream,\n self.presenters,\n thread_args))\n\n for line in consume_queue(queue, self.cascade_stop):\n remove_stopped_threads(thread_map)\n\n if self.cascade_stop:\n matching_container = [cont.name for cont in self.containers if cont.name == line]\n if line in matching_container:\n # Returning the name of the container that started the\n # the cascade_stop so we can return the correct exit code\n return line\n\n if not line:\n if not thread_map:\n # There are no running containers left to tail, so exit\n return\n # We got an empty line because of a timeout, but there are still\n # active containers to tail, so continue\n continue\n\n self.write(line)","label":0},{"cid":"compose/cli/log_printer.py::LogPrinter.write","text":"compose/cli/log_printer.py::LogPrinter.write\n def write(self, line):\n try:\n self.output.write(line)\n except UnicodeEncodeError:\n # This may happen if the user's locale settings don't support UTF-8\n # and UTF-8 characters are present in the log line. The following\n # will output a \"degraded\" log with unsupported characters\n # replaced by `?`\n self.output.write(line.encode('ascii', 'replace').decode())\n self.output.flush()","label":0},{"cid":"compose/cli/log_printer.py::remove_stopped_threads","text":"compose/cli/log_printer.py::remove_stopped_threads\ndef remove_stopped_threads(thread_map):\n for container_id, tailer_thread in list(thread_map.items()):\n if not tailer_thread.is_alive():\n thread_map.pop(container_id, None)","label":0},{"cid":"compose/cli/log_printer.py::build_thread","text":"compose/cli/log_printer.py::build_thread\ndef build_thread(container, presenter, queue, log_args):\n tailer = Thread(\n target=tail_container_logs,\n args=(container, presenter, queue, log_args))\n tailer.daemon = True\n tailer.start()\n return tailer","label":0},{"cid":"compose/cli/log_printer.py::build_thread_map","text":"compose/cli/log_printer.py::build_thread_map\ndef build_thread_map(initial_containers, presenters, thread_args):\n return {\n container.id: build_thread(container, next(presenters), *thread_args)\n for container in initial_containers\n }","label":0},{"cid":"compose/cli/log_printer.py::QueueItem.new","text":"compose/cli/log_printer.py::QueueItem.new\n @classmethod\n def new(cls, item):\n return cls(item, None, None)","label":0},{"cid":"compose/cli/log_printer.py::QueueItem.exception","text":"compose/cli/log_printer.py::QueueItem.exception\n @classmethod\n def exception(cls, exc):\n return cls(None, None, exc)","label":0},{"cid":"compose/cli/log_printer.py::QueueItem.stop","text":"compose/cli/log_printer.py::QueueItem.stop\n @classmethod\n def stop(cls, item=None):\n return cls(item, True, None)","label":0},{"cid":"compose/cli/log_printer.py::tail_container_logs","text":"compose/cli/log_printer.py::tail_container_logs\ndef tail_container_logs(container, presenter, queue, log_args):\n generator = get_log_generator(container)\n\n try:\n for item in generator(container, log_args):\n queue.put(QueueItem.new(presenter.present(container, item)))\n except Exception as e:\n queue.put(QueueItem.exception(e))\n return\n if log_args.get('follow'):\n queue.put(QueueItem.new(presenter.color_func(wait_on_exit(container))))\n queue.put(QueueItem.stop(container.name))","label":0},{"cid":"compose/cli/log_printer.py::get_log_generator","text":"compose/cli/log_printer.py::get_log_generator\ndef get_log_generator(container):\n if container.has_api_logs:\n return build_log_generator\n return build_no_log_generator","label":0},{"cid":"compose/cli/log_printer.py::build_no_log_generator","text":"compose/cli/log_printer.py::build_no_log_generator\ndef build_no_log_generator(container, log_args):\n \"\"\"Return a generator that prints a warning about logs and waits for\n container to exit.\n \"\"\"\n yield \"WARNING: no logs are available with the '{}' log driver\\n\".format(\n container.log_driver)","label":0},{"cid":"compose/cli/log_printer.py::build_log_generator","text":"compose/cli/log_printer.py::build_log_generator\ndef build_log_generator(container, log_args):\n # if the container doesn't have a log_stream we need to attach to container\n # before log printer starts running\n if container.log_stream is None:\n stream = container.logs(stdout=True, stderr=True, stream=True, **log_args)\n else:\n stream = container.log_stream\n\n return split_buffer(stream)","label":0},{"cid":"compose/cli/log_printer.py::wait_on_exit","text":"compose/cli/log_printer.py::wait_on_exit\ndef wait_on_exit(container):\n try:\n exit_code = container.wait()\n return \"%s exited with code %s\\n\" % (container.name, exit_code)\n except APIError as e:\n return \"Unexpected API error for %s (HTTP code %s)\\nResponse body:\\n%s\\n\" % (\n container.name, e.response.status_code,\n e.response.text or '[empty]'\n )","label":0},{"cid":"compose/cli/log_printer.py::start_producer_thread","text":"compose/cli/log_printer.py::start_producer_thread\ndef start_producer_thread(thread_args):\n producer = Thread(target=watch_events, args=thread_args)\n producer.daemon = True\n producer.start()","label":0},{"cid":"compose/cli/log_printer.py::watch_events","text":"compose/cli/log_printer.py::watch_events\ndef watch_events(thread_map, event_stream, presenters, thread_args):\n crashed_containers = set()\n for event in event_stream:\n if event['action'] == 'stop':\n thread_map.pop(event['id'], None)\n\n if event['action'] == 'die':\n thread_map.pop(event['id'], None)\n crashed_containers.add(event['id'])\n\n if event['action'] != 'start':\n continue\n\n if event['id'] in thread_map:\n if thread_map[event['id']].is_alive():\n continue\n # Container was stopped and started, we need a new thread\n thread_map.pop(event['id'], None)\n\n # Container crashed so we should reattach to it\n if event['id'] in crashed_containers:\n container = event['container']\n if not container.is_restarting:\n try:\n container.attach_log_stream()\n except APIError:\n # Just ignore errors when reattaching to already crashed containers\n pass\n crashed_containers.remove(event['id'])\n\n thread_map[event['id']] = build_thread(\n event['container'],\n next(presenters),\n *thread_args\n )","label":0},{"cid":"compose/cli/log_printer.py::consume_queue","text":"compose/cli/log_printer.py::consume_queue\ndef consume_queue(queue, cascade_stop):\n \"\"\"Consume the queue by reading lines off of it and yielding them.\"\"\"\n while True:\n try:\n item = queue.get(timeout=0.1)\n except Empty:\n yield None\n continue\n # See https://github.com/docker/compose/issues/189\n except thread.error:\n raise ShutdownException()\n\n if item.exc:\n raise item.exc\n\n if item.is_stop and not cascade_stop:\n continue\n\n yield item.item","label":0},{"cid":"compose/cli/main.py::main","text":"compose/cli/main.py::main\ndef main():\n signals.ignore_sigpipe()\n try:\n command = dispatch()\n command()\n except (KeyboardInterrupt, signals.ShutdownException):\n log.error(\"Aborting.\")\n sys.exit(1)\n except (UserError, NoSuchService, ConfigurationError,\n ProjectError, OperationFailedError) as e:\n log.error(e.msg)\n sys.exit(1)\n except BuildError as e:\n log.error(\"Service '%s' failed to build: %s\" % (e.service.name, e.reason))\n sys.exit(1)\n except StreamOutputError as e:\n log.error(e)\n sys.exit(1)\n except NeedsBuildError as e:\n log.error(\"Service '%s' needs to be built, but --no-build was passed.\" % e.service.name)\n sys.exit(1)\n except NoSuchCommand as e:\n commands = \"\\n\".join(parse_doc_section(\"commands:\", getdoc(e.supercommand)))\n log.error(\"No such command: %s\\n\\n%s\", e.command, commands)\n sys.exit(1)\n except (errors.ConnectionError, StreamParseError):\n sys.exit(1)","label":0},{"cid":"compose/cli/main.py::dispatch","text":"compose/cli/main.py::dispatch\ndef dispatch():\n setup_logging()\n dispatcher = DocoptDispatcher(\n TopLevelCommand,\n {'options_first': True, 'version': get_version_info('compose')})\n\n options, handler, command_options = dispatcher.parse(sys.argv[1:])\n setup_console_handler(console_handler,\n options.get('--verbose'),\n options.get('--no-ansi'),\n options.get(\"--log-level\"))\n setup_parallel_logger(options.get('--no-ansi'))\n if options.get('--no-ansi'):\n command_options['--no-color'] = True\n return functools.partial(perform_command, options, handler, command_options)","label":1},{"cid":"compose/cli/main.py::perform_command","text":"compose/cli/main.py::perform_command\ndef perform_command(options, handler, command_options):\n if options['COMMAND'] in ('help', 'version'):\n # Skip looking up the compose file.\n handler(command_options)\n return\n\n if options['COMMAND'] == 'config':\n command = TopLevelCommand(None, options=options)\n handler(command, command_options)\n return\n\n project = project_from_options('.', options)\n command = TopLevelCommand(project, options=options)\n with errors.handle_connection_errors(project.client):\n handler(command, command_options)","label":0},{"cid":"compose/cli/main.py::setup_logging","text":"compose/cli/main.py::setup_logging\ndef setup_logging():\n root_logger = logging.getLogger()\n root_logger.addHandler(console_handler)\n root_logger.setLevel(logging.DEBUG)\n\n # Disable requests logging\n logging.getLogger(\"requests\").propagate = False","label":0},{"cid":"compose/cli/main.py::setup_parallel_logger","text":"compose/cli/main.py::setup_parallel_logger\ndef setup_parallel_logger(noansi):\n if noansi:\n import compose.parallel\n compose.parallel.ParallelStreamWriter.set_noansi()","label":0},{"cid":"compose/cli/main.py::setup_console_handler","text":"compose/cli/main.py::setup_console_handler\ndef setup_console_handler(handler, verbose, noansi=False, level=None):\n if handler.stream.isatty() and noansi is False:\n format_class = ConsoleWarningFormatter\n else:\n format_class = logging.Formatter\n\n if verbose:\n handler.setFormatter(format_class('%(name)s.%(funcName)s: %(message)s'))\n loglevel = logging.DEBUG\n else:\n handler.setFormatter(format_class())\n loglevel = logging.INFO\n\n if level is not None:\n levels = {\n 'DEBUG': logging.DEBUG,\n 'INFO': logging.INFO,\n 'WARNING': logging.WARNING,\n 'ERROR': logging.ERROR,\n 'CRITICAL': logging.CRITICAL,\n }\n loglevel = levels.get(level.upper())\n if loglevel is None:\n raise UserError(\n 'Invalid value for --log-level. Expected one of DEBUG, INFO, WARNING, ERROR, CRITICAL.'\n )\n\n handler.setLevel(loglevel)","label":0},{"cid":"compose/cli/main.py::parse_doc_section","text":"compose/cli/main.py::parse_doc_section\ndef parse_doc_section(name, source):\n pattern = re.compile('^([^\\n]*' + name + '[^\\n]*\\n?(?:[ \\t].*?(?:\\n|$))*)',\n re.IGNORECASE | re.MULTILINE)\n return [s.strip() for s in pattern.findall(source)]","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.__init__","text":"compose/cli/main.py::TopLevelCommand.__init__\n def __init__(self, project, options=None):\n self.project = project\n self.toplevel_options = options or {}","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.project_dir","text":"compose/cli/main.py::TopLevelCommand.project_dir\n @property\n def project_dir(self):\n return self.toplevel_options.get('--project-directory') or '.'","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.toplevel_environment","text":"compose/cli/main.py::TopLevelCommand.toplevel_environment\n @property\n def toplevel_environment(self):\n environment_file = self.toplevel_options.get('--env-file')\n return Environment.from_env_file(self.project_dir, environment_file)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.build","text":"compose/cli/main.py::TopLevelCommand.build\n def build(self, options):\n \"\"\"\n Build or rebuild services.\n\n Services are built once and then tagged as `project_service`,\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\n contents of its build directory, you can run `docker-compose build` to rebuild it.\n\n Usage: build [options] [--build-arg key=val...] [SERVICE...]\n\n Options:\n --build-arg key=val Set build-time variables for services.\n --compress Compress the build context using gzip.\n --force-rm Always remove intermediate containers.\n -m, --memory MEM Set memory limit for the build container.\n --no-cache Do not use cache when building the image.\n --no-rm Do not remove intermediate containers after a successful build.\n --parallel Build images in parallel.\n --progress string Set type of progress output (auto, plain, tty).\n EXPERIMENTAL flag for native builder.\n To enable, run with COMPOSE_DOCKER_CLI_BUILD=1)\n --pull Always attempt to pull a newer version of the image.\n -q, --quiet Don't print anything to STDOUT\n \"\"\"\n service_names = options['SERVICE']\n build_args = options.get('--build-arg', None)\n if build_args:\n if not service_names and docker.utils.version_lt(self.project.client.api_version, '1.25'):\n raise UserError(\n '--build-arg is only supported when services are specified for API version < 1.25.'\n ' Please use a Compose file version > 2.2 or specify which services to build.'\n )\n build_args = resolve_build_args(build_args, self.toplevel_environment)\n\n native_builder = self.toplevel_","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.bundle","text":"compose/cli/main.py::TopLevelCommand.bundle\n def bundle(self, options):\n \"\"\"\n Generate a Distributed Application Bundle (DAB) from the Compose file.\n\n Images must have digests stored, which requires interaction with a\n Docker registry. If digests aren't stored for all images, you can fetch\n them with `docker-compose pull` or `docker-compose push`. To push images\n automatically when bundling, pass `--push-images`. Only services with\n a `build` option specified will have their images pushed.\n\n Usage: bundle [options]\n\n Options:\n --push-images Automatically push images for any services\n which have a `build` option specified.\n\n -o, --output PATH Path to write the bundle file to.\n Defaults to \"<project name>.dab\".\n \"\"\"\n compose_config = get_config_from_options('.', self.toplevel_options)\n\n output = options[\"--output\"]\n if not output:\n output = \"{}.dab\".format(self.project.name)\n\n image_digests = image_digests_for_project(self.project, options['--push-images'])\n\n with open(output, 'w') as f:\n f.write(serialize_bundle(compose_config, image_digests))\n\n log.info(\"Wrote bundle to {}\".format(output))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.config","text":"compose/cli/main.py::TopLevelCommand.config\n def config(self, options):\n \"\"\"\n Validate and view the Compose file.\n\n Usage: config [options]\n\n Options:\n --resolve-image-digests Pin image tags to digests.\n --no-interpolate Don't interpolate environment variables\n -q, --quiet Only validate the configuration, don't print\n anything.\n --services Print the service names, one per line.\n --volumes Print the volume names, one per line.\n --hash=\"*\" Print the service config hash, one per line.\n Set \"service1,service2\" for a list of specified services\n or use the wildcard symbol to display all services\n \"\"\"\n\n additional_options = {'--no-interpolate': options.get('--no-interpolate')}\n compose_config = get_config_from_options('.', self.toplevel_options, additional_options)\n image_digests = None\n\n if options['--resolve-image-digests']:\n self.project = project_from_options('.', self.toplevel_options, additional_options)\n with errors.handle_connection_errors(self.project.client):\n image_digests = image_digests_for_project(self.project)\n\n if options['--quiet']:\n return\n\n if options['--services']:\n print('\\n'.join(service['name'] for service in compose_config.services))\n return\n\n if options['--volumes']:\n print('\\n'.join(volume for volume in compose_config.volumes))\n return\n\n if options['--hash'] is not None:\n h = options['--hash']\n self.project = project_from_options('.', self.toplevel_options, additional_options)\n services = [svc for svc in options['--hash'].split(',')] if h != '*' else None\n with errors.handle_connection_e","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.create","text":"compose/cli/main.py::TopLevelCommand.create\n def create(self, options):\n \"\"\"\n Creates containers for a service.\n This command is deprecated. Use the `up` command with `--no-start` instead.\n\n Usage: create [options] [SERVICE...]\n\n Options:\n --force-recreate Recreate containers even if their configuration and\n image haven't changed. Incompatible with --no-recreate.\n --no-recreate If containers already exist, don't recreate them.\n Incompatible with --force-recreate.\n --no-build Don't build an image, even if it's missing.\n --build Build images before creating containers.\n \"\"\"\n service_names = options['SERVICE']\n\n log.warning(\n 'The create command is deprecated. '\n 'Use the up command with the --no-start flag instead.'\n )\n\n self.project.create(\n service_names=service_names,\n strategy=convergence_strategy_from_opts(options),\n do_build=build_action_from_opts(options),\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.down","text":"compose/cli/main.py::TopLevelCommand.down\n def down(self, options):\n \"\"\"\n Stops containers and removes containers, networks, volumes, and images\n created by `up`.\n\n By default, the only things removed are:\n\n - Containers for services defined in the Compose file\n - Networks defined in the `networks` section of the Compose file\n - The default network, if one is used\n\n Networks and volumes defined as `external` are never removed.\n\n Usage: down [options]\n\n Options:\n --rmi type Remove images. Type must be one of:\n 'all': Remove all images used by any service.\n 'local': Remove only images that don't have a\n custom tag set by the `image` field.\n -v, --volumes Remove named volumes declared in the `volumes`\n section of the Compose file and anonymous volumes\n attached to containers.\n --remove-orphans Remove containers for services not defined in the\n Compose file\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n ignore_orphans = self.toplevel_environment.get_boolean('COMPOSE_IGNORE_ORPHANS')\n\n if ignore_orphans and options['--remove-orphans']:\n raise UserError(\"COMPOSE_IGNORE_ORPHANS and --remove-orphans cannot be combined.\")\n\n image_type = image_type_from_opt('--rmi', options['--rmi'])\n timeout = timeout_from_opts(options)\n self.project.down(\n image_type,\n options['--volumes'],\n options['--remove-orphans'],\n timeout=timeout,\n ignore_orphans=ignore_orphans)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events","text":"compose/cli/main.py::TopLevelCommand.events\n def events(self, options):\n \"\"\"\n Receive real time events from containers.\n\n Usage: events [options] [SERVICE...]\n\n Options:\n --json Output events as a stream of json objects\n \"\"\"\n def format_event(event):\n attributes = [\"%s=%s\" % item for item in event['attributes'].items()]\n return (\"{time} {type} {action} {id} ({attrs})\").format(\n attrs=\", \".join(sorted(attributes)),\n **event)\n\n def json_format_event(event):\n event['time'] = event['time'].isoformat()\n event.pop('container')\n return json.dumps(event)\n\n for event in self.project.events():\n formatter = json_format_event if options['--json'] else format_event\n print(formatter(event))\n sys.stdout.flush()","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events.format_event","text":"compose/cli/main.py::TopLevelCommand.events.format_event\n def format_event(event):\n attributes = [\"%s=%s\" % item for item in event['attributes'].items()]\n return (\"{time} {type} {action} {id} ({attrs})\").format(\n attrs=\", \".join(sorted(attributes)),\n **event)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events.json_format_event","text":"compose/cli/main.py::TopLevelCommand.events.json_format_event\n def json_format_event(event):\n event['time'] = event['time'].isoformat()\n event.pop('container')\n return json.dumps(event)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.exec_command","text":"compose/cli/main.py::TopLevelCommand.exec_command\n def exec_command(self, options):\n \"\"\"\n Execute a command in a running container\n\n Usage: exec [options] [-e KEY=VAL...] SERVICE COMMAND [ARGS...]\n\n Options:\n -d, --detach Detached mode: Run command in the background.\n --privileged Give extended privileges to the process.\n -u, --user USER Run the command as this user.\n -T Disable pseudo-tty allocation. By default `docker-compose exec`\n allocates a TTY.\n --index=index index of the container if there are multiple\n instances of a service [default: 1]\n -e, --env KEY=VAL Set environment variables (can be used multiple times,\n not supported in API < 1.25)\n -w, --workdir DIR Path to workdir directory for this command.\n \"\"\"\n use_cli = not self.toplevel_environment.get_boolean('COMPOSE_INTERACTIVE_NO_CLI')\n index = int(options.get('--index'))\n service = self.project.get_service(options['SERVICE'])\n detach = options.get('--detach')\n\n if options['--env'] and docker.utils.version_lt(self.project.client.api_version, '1.25'):\n raise UserError(\"Setting environment for exec is not supported in API < 1.25 (%s)\"\n % self.project.client.api_version)\n\n if options['--workdir'] and docker.utils.version_lt(self.project.client.api_version, '1.35'):\n raise UserError(\"Setting workdir for exec is not supported in API < 1.35 (%s)\"\n % self.project.client.api_version)\n\n try:\n container = service.get_container(number=index)\n except ValueError as e:\n raise UserError(str(e))\n command = [options['COMMAND']] + options['ARGS']\n tty = not options[\"-T\"]\n\n if IS_WINDOWS_PLATFORM or use_cli and not detach:\n","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.help","text":"compose/cli/main.py::TopLevelCommand.help\n @classmethod\n def help(cls, options):\n \"\"\"\n Get help on a command.\n\n Usage: help [COMMAND]\n \"\"\"\n if options['COMMAND']:\n subject = get_handler(cls, options['COMMAND'])\n else:\n subject = cls\n\n print(getdoc(subject))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.images","text":"compose/cli/main.py::TopLevelCommand.images\n def images(self, options):\n \"\"\"\n List images used by the created containers.\n Usage: images [options] [SERVICE...]\n\n Options:\n -q, --quiet Only display IDs\n \"\"\"\n containers = sorted(\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\n key=attrgetter('name'))\n\n if options['--quiet']:\n for image in set(c.image for c in containers):\n print(image.split(':')[1])\n return\n\n def add_default_tag(img_name):\n if ':' not in img_name.split('/')[-1]:\n return '{}:latest'.format(img_name)\n return img_name\n\n headers = [\n 'Container',\n 'Repository',\n 'Tag',\n 'Image Id',\n 'Size'\n ]\n rows = []\n for container in containers:\n image_config = container.image_config\n service = self.project.get_service(container.service)\n index = 0\n img_name = add_default_tag(service.image_name)\n if img_name in image_config['RepoTags']:\n index = image_config['RepoTags'].index(img_name)\n repo_tags = (\n image_config['RepoTags'][index].rsplit(':', 1) if image_config['RepoTags']\n else ('<none>', '<none>')\n )\n\n image_id = image_config['Id'].split(':')[1][:12]\n size = human_readable_file_size(image_config['Size'])\n rows.append([\n container.name,\n repo_tags[0],\n repo_tags[1],\n image_id,\n size\n ])\n print(Formatter.table(headers, rows))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.images.add_default_tag","text":"compose/cli/main.py::TopLevelCommand.images.add_default_tag\n def add_default_tag(img_name):\n if ':' not in img_name.split('/')[-1]:\n return '{}:latest'.format(img_name)\n return img_name","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.kill","text":"compose/cli/main.py::TopLevelCommand.kill\n def kill(self, options):\n \"\"\"\n Force stop service containers.\n\n Usage: kill [options] [SERVICE...]\n\n Options:\n -s SIGNAL SIGNAL to send to the container.\n Default signal is SIGKILL.\n \"\"\"\n signal = options.get('-s', 'SIGKILL')\n\n self.project.kill(service_names=options['SERVICE'], signal=signal)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.logs","text":"compose/cli/main.py::TopLevelCommand.logs\n def logs(self, options):\n \"\"\"\n View output from containers.\n\n Usage: logs [options] [SERVICE...]\n\n Options:\n --no-color Produce monochrome output.\n -f, --follow Follow log output.\n -t, --timestamps Show timestamps.\n --tail=\"all\" Number of lines to show from the end of the logs\n for each container.\n \"\"\"\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\n\n tail = options['--tail']\n if tail is not None:\n if tail.isdigit():\n tail = int(tail)\n elif tail != 'all':\n raise UserError(\"tail flag must be all or a number\")\n log_args = {\n 'follow': options['--follow'],\n 'tail': tail,\n 'timestamps': options['--timestamps']\n }\n print(\"Attaching to\", list_containers(containers))\n log_printer_from_project(\n self.project,\n containers,\n options['--no-color'],\n log_args,\n event_stream=self.project.events(service_names=options['SERVICE'])).run()","label":1},{"cid":"compose/cli/main.py::TopLevelCommand.pause","text":"compose/cli/main.py::TopLevelCommand.pause\n def pause(self, options):\n \"\"\"\n Pause services.\n\n Usage: pause [SERVICE...]\n \"\"\"\n containers = self.project.pause(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to pause', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.port","text":"compose/cli/main.py::TopLevelCommand.port\n def port(self, options):\n \"\"\"\n Print the public port for a port binding.\n\n Usage: port [options] SERVICE PRIVATE_PORT\n\n Options:\n --protocol=proto tcp or udp [default: tcp]\n --index=index index of the container if there are multiple\n instances of a service [default: 1]\n \"\"\"\n index = int(options.get('--index'))\n service = self.project.get_service(options['SERVICE'])\n try:\n container = service.get_container(number=index)\n except ValueError as e:\n raise UserError(str(e))\n print(container.get_local_port(\n options['PRIVATE_PORT'],\n protocol=options.get('--protocol') or 'tcp') or '')","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.ps","text":"compose/cli/main.py::TopLevelCommand.ps\n def ps(self, options):\n \"\"\"\n List containers.\n\n Usage: ps [options] [SERVICE...]\n\n Options:\n -q, --quiet Only display IDs\n --services Display services\n --filter KEY=VAL Filter services by a property\n -a, --all Show all stopped containers (including those created by the run command)\n \"\"\"\n if options['--quiet'] and options['--services']:\n raise UserError('--quiet and --services cannot be combined')\n\n if options['--services']:\n filt = build_filter(options.get('--filter'))\n services = self.project.services\n if filt:\n services = filter_services(filt, services, self.project)\n print('\\n'.join(service.name for service in services))\n return\n\n if options['--all']:\n containers = sorted(self.project.containers(service_names=options['SERVICE'],\n one_off=OneOffFilter.include, stopped=True),\n key=attrgetter('name'))\n else:\n containers = sorted(\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\n key=attrgetter('name'))\n\n if options['--quiet']:\n for container in containers:\n print(container.id)\n else:\n headers = [\n 'Name',\n 'Command',\n 'State',\n 'Ports',\n ]\n rows = []\n for container in containers:\n command = container.human_readable_command\n if len(command) > 30:\n command = '%s ...' % command[:26]\n rows.append([\n container.name,\n ","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.pull","text":"compose/cli/main.py::TopLevelCommand.pull\n def pull(self, options):\n \"\"\"\n Pulls images for services defined in a Compose file, but does not start the containers.\n\n Usage: pull [options] [SERVICE...]\n\n Options:\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\n --parallel Deprecated, pull multiple images in parallel (enabled by default).\n --no-parallel Disable parallel pulling.\n -q, --quiet Pull without printing progress information\n --include-deps Also pull services declared as dependencies\n \"\"\"\n if options.get('--parallel'):\n log.warning('--parallel option is deprecated and will be removed in future versions.')\n self.project.pull(\n service_names=options['SERVICE'],\n ignore_pull_failures=options.get('--ignore-pull-failures'),\n parallel_pull=not options.get('--no-parallel'),\n silent=options.get('--quiet'),\n include_deps=options.get('--include-deps'),\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.push","text":"compose/cli/main.py::TopLevelCommand.push\n def push(self, options):\n \"\"\"\n Pushes images for services.\n\n Usage: push [options] [SERVICE...]\n\n Options:\n --ignore-push-failures Push what it can and ignores images with push failures.\n \"\"\"\n self.project.push(\n service_names=options['SERVICE'],\n ignore_push_failures=options.get('--ignore-push-failures')\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.rm","text":"compose/cli/main.py::TopLevelCommand.rm\n def rm(self, options):\n \"\"\"\n Removes stopped service containers.\n\n By default, anonymous volumes attached to containers will not be removed. You\n can override this with `-v`. To list all volumes, use `docker volume ls`.\n\n Any data which is not in a volume will be lost.\n\n Usage: rm [options] [SERVICE...]\n\n Options:\n -f, --force Don't ask to confirm removal\n -s, --stop Stop the containers, if required, before removing\n -v Remove any anonymous volumes attached to containers\n -a, --all Deprecated - no effect.\n \"\"\"\n if options.get('--all'):\n log.warning(\n '--all flag is obsolete. This is now the default behavior '\n 'of `docker-compose rm`'\n )\n one_off = OneOffFilter.include\n\n if options.get('--stop'):\n self.project.stop(service_names=options['SERVICE'], one_off=one_off)\n\n all_containers = self.project.containers(\n service_names=options['SERVICE'], stopped=True, one_off=one_off\n )\n stopped_containers = [c for c in all_containers if not c.is_running]\n\n if len(stopped_containers) > 0:\n print(\"Going to remove\", list_containers(stopped_containers))\n if options.get('--force') \\\n or yesno(\"Are you sure? [yN] \", default=False):\n self.project.remove_stopped(\n service_names=options['SERVICE'],\n v=options.get('-v', False),\n one_off=one_off\n )\n else:\n print(\"No stopped containers\")","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.run","text":"compose/cli/main.py::TopLevelCommand.run\n def run(self, options):\n \"\"\"\n Run a one-off command on a service.\n\n For example:\n\n $ docker-compose run web python manage.py shell\n\n By default, linked services will be started, unless they are already\n running. If you do not want to start linked services, use\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\n\n Usage:\n run [options] [-v VOLUME...] [-p PORT...] [-e KEY=VAL...] [-l KEY=VALUE...]\n SERVICE [COMMAND] [ARGS...]\n\n Options:\n -d, --detach Detached mode: Run container in the background, print\n new container name.\n --name NAME Assign a name to the container\n --entrypoint CMD Override the entrypoint of the image.\n -e KEY=VAL Set an environment variable (can be used multiple times)\n -l, --label KEY=VAL Add or override a label (can be used multiple times)\n -u, --user=\"\" Run as specified username or uid\n --no-deps Don't start linked services.\n --rm Remove container after run. Ignored in detached mode.\n -p, --publish=[] Publish a container's port(s) to the host\n --service-ports Run command with the service's ports enabled and mapped\n to the host.\n --use-aliases Use the service's network aliases in the network(s) the\n container connects to.\n -v, --volume=[] Bind mount a volume (default [])\n -T Disable pseudo-tty allocation. By default `docker-compose run`\n allocates a TTY.\n -w, --workdir=\"\" Working directory inside the container\n \"\"\"\n service = self.project.get_service(options['SERVICE'])\n detach = o","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.scale","text":"compose/cli/main.py::TopLevelCommand.scale\n def scale(self, options):\n \"\"\"\n Set number of containers to run for a service.\n\n Numbers are specified in the form `service=num` as arguments.\n For example:\n\n $ docker-compose scale web=2 worker=3\n\n This command is deprecated. Use the up command with the `--scale` flag\n instead.\n\n Usage: scale [options] [SERVICE=NUM...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = timeout_from_opts(options)\n\n if self.project.config_version == V2_2:\n raise UserError(\n 'The scale command is incompatible with the v2.2 format. '\n 'Use the up command with the --scale flag instead.'\n )\n else:\n log.warning(\n 'The scale command is deprecated. '\n 'Use the up command with the --scale flag instead.'\n )\n\n for service_name, num in parse_scale_args(options['SERVICE=NUM']).items():\n self.project.get_service(service_name).scale(num, timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.start","text":"compose/cli/main.py::TopLevelCommand.start\n def start(self, options):\n \"\"\"\n Start existing containers.\n\n Usage: start [SERVICE...]\n \"\"\"\n containers = self.project.start(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to start', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.stop","text":"compose/cli/main.py::TopLevelCommand.stop\n def stop(self, options):\n \"\"\"\n Stop running containers without removing them.\n\n They can be started again with `docker-compose start`.\n\n Usage: stop [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = timeout_from_opts(options)\n self.project.stop(service_names=options['SERVICE'], timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.restart","text":"compose/cli/main.py::TopLevelCommand.restart\n def restart(self, options):\n \"\"\"\n Restart running containers.\n\n Usage: restart [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = timeout_from_opts(options)\n containers = self.project.restart(service_names=options['SERVICE'], timeout=timeout)\n exit_if(not containers, 'No containers to restart', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.top","text":"compose/cli/main.py::TopLevelCommand.top\n def top(self, options):\n \"\"\"\n Display the running processes\n\n Usage: top [SERVICE...]\n\n \"\"\"\n containers = sorted(\n self.project.containers(service_names=options['SERVICE'], stopped=False) +\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\n key=attrgetter('name')\n )\n\n for idx, container in enumerate(containers):\n if idx > 0:\n print()\n\n top_data = self.project.client.top(container.name)\n headers = top_data.get(\"Titles\")\n rows = []\n\n for process in top_data.get(\"Processes\", []):\n rows.append(process)\n\n print(container.name)\n print(Formatter.table(headers, rows))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.unpause","text":"compose/cli/main.py::TopLevelCommand.unpause\n def unpause(self, options):\n \"\"\"\n Unpause services.\n\n Usage: unpause [SERVICE...]\n \"\"\"\n containers = self.project.unpause(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to unpause', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.up","text":"compose/cli/main.py::TopLevelCommand.up\n def up(self, options):\n \"\"\"\n Builds, (re)creates, starts, and attaches to containers for a service.\n\n Unless they are already running, this command also starts any linked services.\n\n The `docker-compose up` command aggregates the output of each container. When\n the command exits, all containers are stopped. Running `docker-compose up -d`\n starts the containers in the background and leaves them running.\n\n If there are existing containers for a service, and the service's configuration\n or image was changed after the container's creation, `docker-compose up` picks\n up the changes by stopping and recreating the containers (preserving mounted\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\n flag.\n\n If you want to force Compose to stop and recreate all containers, use the\n `--force-recreate` flag.\n\n Usage: up [options] [--scale SERVICE=NUM...] [SERVICE...]\n\n Options:\n -d, --detach Detached mode: Run containers in the background,\n print new container names. Incompatible with\n --abort-on-container-exit.\n --no-color Produce monochrome output.\n --quiet-pull Pull without printing progress information\n --no-deps Don't start linked services.\n --force-recreate Recreate containers even if their configuration\n and image haven't changed.\n --always-recreate-deps Recreate dependent containers.\n Incompatible with --no-recreate.\n --no-recreate If containers already exist, don't recreate\n them. Incompatible with --force-recreate and -V.\n --no-build ","label":1},{"cid":"compose/cli/main.py::TopLevelCommand.version","text":"compose/cli/main.py::TopLevelCommand.version\n @classmethod\n def version(cls, options):\n \"\"\"\n Show version information\n\n Usage: version [--short]\n\n Options:\n --short Shows only Compose's version number.\n \"\"\"\n if options['--short']:\n print(__version__)\n else:\n print(get_version_info('full'))","label":0},{"cid":"compose/cli/main.py::compute_service_exit_code","text":"compose/cli/main.py::compute_service_exit_code\ndef compute_service_exit_code(exit_value_from, attached_containers):\n candidates = list(filter(\n lambda c: c.service == exit_value_from,\n attached_containers))\n if not candidates:\n log.error(\n 'No containers matching the spec \"{0}\" '\n 'were run.'.format(exit_value_from)\n )\n return 2\n if len(candidates) > 1:\n exit_values = filter(\n lambda e: e != 0,\n [c.inspect()['State']['ExitCode'] for c in candidates]\n )\n\n return exit_values[0]\n return candidates[0].inspect()['State']['ExitCode']","label":0},{"cid":"compose/cli/main.py::compute_exit_code","text":"compose/cli/main.py::compute_exit_code\ndef compute_exit_code(exit_value_from, attached_containers, cascade_starter, all_containers):\n exit_code = 0\n for e in all_containers:\n if (not e.is_running and cascade_starter == e.name):\n if not e.exit_code == 0:\n exit_code = e.exit_code\n break\n\n return exit_code","label":0},{"cid":"compose/cli/main.py::convergence_strategy_from_opts","text":"compose/cli/main.py::convergence_strategy_from_opts\ndef convergence_strategy_from_opts(options):\n no_recreate = options['--no-recreate']\n force_recreate = options['--force-recreate']\n renew_anonymous_volumes = options.get('--renew-anon-volumes')\n if force_recreate and no_recreate:\n raise UserError(\"--force-recreate and --no-recreate cannot be combined.\")\n\n if no_recreate and renew_anonymous_volumes:\n raise UserError('--no-recreate and --renew-anon-volumes cannot be combined.')\n\n if force_recreate or renew_anonymous_volumes:\n return ConvergenceStrategy.always\n\n if no_recreate:\n return ConvergenceStrategy.never\n\n return ConvergenceStrategy.changed","label":0},{"cid":"compose/cli/main.py::timeout_from_opts","text":"compose/cli/main.py::timeout_from_opts\ndef timeout_from_opts(options):\n timeout = options.get('--timeout')\n return None if timeout is None else int(timeout)","label":0},{"cid":"compose/cli/main.py::image_digests_for_project","text":"compose/cli/main.py::image_digests_for_project\ndef image_digests_for_project(project, allow_push=False):\n try:\n return get_image_digests(\n project,\n allow_push=allow_push\n )\n except MissingDigests as e:\n def list_images(images):\n return \"\\n\".join(\" {}\".format(name) for name in sorted(images))\n\n paras = [\"Some images are missing digests.\"]\n\n if e.needs_push:\n command_hint = (\n \"Use `docker-compose push {}` to push them. \"\n .format(\" \".join(sorted(e.needs_push)))\n )\n paras += [\n \"The following images can be pushed:\",\n list_images(e.needs_push),\n command_hint,\n ]\n\n if e.needs_pull:\n command_hint = (\n \"Use `docker-compose pull {}` to pull them. \"\n .format(\" \".join(sorted(e.needs_pull)))\n )\n\n paras += [\n \"The following images need to be pulled:\",\n list_images(e.needs_pull),\n command_hint,\n ]\n\n raise UserError(\"\\n\\n\".join(paras))","label":0},{"cid":"compose/cli/main.py::exitval_from_opts","text":"compose/cli/main.py::exitval_from_opts\ndef exitval_from_opts(options, project):\n exit_value_from = options.get('--exit-code-from')\n if exit_value_from:\n if not options.get('--abort-on-container-exit'):\n log.warning('using --exit-code-from implies --abort-on-container-exit')\n options['--abort-on-container-exit'] = True\n if exit_value_from not in [s.name for s in project.get_services()]:\n log.error('No service named \"%s\" was found in your compose file.',\n exit_value_from)\n sys.exit(2)\n return exit_value_from","label":0},{"cid":"compose/cli/main.py::image_type_from_opt","text":"compose/cli/main.py::image_type_from_opt\ndef image_type_from_opt(flag, value):\n if not value:\n return ImageType.none\n try:\n return ImageType[value]\n except KeyError:\n raise UserError(\"%s flag must be one of: all, local\" % flag)","label":0},{"cid":"compose/cli/main.py::build_action_from_opts","text":"compose/cli/main.py::build_action_from_opts\ndef build_action_from_opts(options):\n if options['--build'] and options['--no-build']:\n raise UserError(\"--build and --no-build can not be combined.\")\n\n if options['--build']:\n return BuildAction.force\n\n if options['--no-build']:\n return BuildAction.skip\n\n return BuildAction.none","label":0},{"cid":"compose/cli/main.py::build_one_off_container_options","text":"compose/cli/main.py::build_one_off_container_options\ndef build_one_off_container_options(options, detach, command):\n container_options = {\n 'command': command,\n 'tty': not (detach or options['-T'] or not sys.stdin.isatty()),\n 'stdin_open': options.get('stdin_open'),\n 'detach': detach,\n }\n\n if options['-e']:\n container_options['environment'] = Environment.from_command_line(\n parse_environment(options['-e'])\n )\n\n if options['--label']:\n container_options['labels'] = parse_labels(options['--label'])\n\n if options.get('--entrypoint') is not None:\n container_options['entrypoint'] = (\n [\"\"] if options['--entrypoint'] == '' else options['--entrypoint']\n )\n\n # Ensure that run command remains one-off (issue #6302)\n container_options['restart'] = None\n\n if options['--user']:\n container_options['user'] = options.get('--user')\n\n if not options['--service-ports']:\n container_options['ports'] = []\n\n if options['--publish']:\n container_options['ports'] = options.get('--publish')\n\n if options['--name']:\n container_options['name'] = options['--name']\n\n if options['--workdir']:\n container_options['working_dir'] = options['--workdir']\n\n if options['--volume']:\n volumes = [VolumeSpec.parse(i) for i in options['--volume']]\n container_options['volumes'] = volumes\n\n return container_options","label":0},{"cid":"compose/cli/main.py::run_one_off_container","text":"compose/cli/main.py::run_one_off_container\ndef run_one_off_container(container_options, project, service, options, toplevel_options,\n toplevel_environment):\n if not options['--no-deps']:\n deps = service.get_dependency_names()\n if deps:\n project.up(\n service_names=deps,\n start_deps=True,\n strategy=ConvergenceStrategy.never,\n rescale=False\n )\n\n project.initialize()\n\n container = service.create_container(\n quiet=True,\n one_off=True,\n **container_options)\n\n use_network_aliases = options['--use-aliases']\n\n if options.get('--detach'):\n service.start_container(container, use_network_aliases)\n print(container.name)\n return\n\n def remove_container(force=False):\n if options['--rm']:\n project.client.remove_container(container.id, force=True, v=True)\n\n use_cli = not toplevel_environment.get_boolean('COMPOSE_INTERACTIVE_NO_CLI')\n\n signals.set_signal_handler_to_shutdown()\n signals.set_signal_handler_to_hang_up()\n try:\n try:\n if IS_WINDOWS_PLATFORM or use_cli:\n service.connect_container_to_networks(container, use_network_aliases)\n exit_code = call_docker(\n get_docker_start_call(container_options, container.id),\n toplevel_options, toplevel_environment\n )\n else:\n operation = RunOperation(\n project.client,\n container.id,\n interactive=not options['-T'],\n logs=False,\n )\n pty = PseudoTerminal(project.client, operation)\n sockets = pty.sockets()\n service.start_container(container, use_network_aliases)\n pty.start(sockets)\n exit_code = container.wait()\n except (signals.ShutdownException):\n ","label":0},{"cid":"compose/cli/main.py::run_one_off_container.remove_container","text":"compose/cli/main.py::run_one_off_container.remove_container\n def remove_container(force=False):\n if options['--rm']:\n project.client.remove_container(container.id, force=True, v=True)","label":0},{"cid":"compose/cli/main.py::get_docker_start_call","text":"compose/cli/main.py::get_docker_start_call\ndef get_docker_start_call(container_options, container_id):\n docker_call = [\"start\"]\n if not container_options.get('detach'):\n docker_call.append(\"--attach\")\n if container_options.get('stdin_open'):\n docker_call.append(\"--interactive\")\n docker_call.append(container_id)\n return docker_call","label":0},{"cid":"compose/cli/main.py::log_printer_from_project","text":"compose/cli/main.py::log_printer_from_project\ndef log_printer_from_project(\n project,\n containers,\n monochrome,\n log_args,\n cascade_stop=False,\n event_stream=None,\n):\n return LogPrinter(\n containers,\n build_log_presenters(project.service_names, monochrome),\n event_stream or project.events(),\n cascade_stop=cascade_stop,\n log_args=log_args)","label":0},{"cid":"compose/cli/main.py::filter_containers_to_service_names","text":"compose/cli/main.py::filter_containers_to_service_names\ndef filter_containers_to_service_names(containers, service_names):\n if not service_names:\n return containers\n\n return [\n container\n for container in containers if container.service in service_names\n ]","label":0},{"cid":"compose/cli/main.py::up_shutdown_context","text":"compose/cli/main.py::up_shutdown_context\n@contextlib.contextmanager\ndef up_shutdown_context(project, service_names, timeout, detached):\n if detached:\n yield\n return\n\n signals.set_signal_handler_to_shutdown()\n try:\n try:\n yield\n except signals.ShutdownException:\n print(\"Gracefully stopping... (press Ctrl+C again to force)\")\n project.stop(service_names=service_names, timeout=timeout)\n except signals.ShutdownException:\n project.kill(service_names=service_names)\n sys.exit(2)","label":0},{"cid":"compose/cli/main.py::list_containers","text":"compose/cli/main.py::list_containers\ndef list_containers(containers):\n return \", \".join(c.name for c in containers)","label":0},{"cid":"compose/cli/main.py::exit_if","text":"compose/cli/main.py::exit_if\ndef exit_if(condition, message, exit_code):\n if condition:\n log.error(message)\n raise SystemExit(exit_code)","label":0},{"cid":"compose/cli/main.py::call_docker","text":"compose/cli/main.py::call_docker\ndef call_docker(args, dockeropts, environment):\n executable_path = find_executable('docker')\n if not executable_path:\n raise UserError(errors.docker_not_found_msg(\"Couldn't find `docker` binary.\"))\n\n tls = dockeropts.get('--tls', False)\n ca_cert = dockeropts.get('--tlscacert')\n cert = dockeropts.get('--tlscert')\n key = dockeropts.get('--tlskey')\n verify = dockeropts.get('--tlsverify')\n host = dockeropts.get('--host')\n tls_options = []\n if tls:\n tls_options.append('--tls')\n if ca_cert:\n tls_options.extend(['--tlscacert', ca_cert])\n if cert:\n tls_options.extend(['--tlscert', cert])\n if key:\n tls_options.extend(['--tlskey', key])\n if verify:\n tls_options.append('--tlsverify')\n if host:\n tls_options.extend(\n ['--host', re.sub(r'^https?://', 'tcp://', host.lstrip('='))]\n )\n\n args = [executable_path] + tls_options + args\n log.debug(\" \".join(map(pipes.quote, args)))\n\n return subprocess.call(args, env=environment)","label":0},{"cid":"compose/cli/main.py::parse_scale_args","text":"compose/cli/main.py::parse_scale_args\ndef parse_scale_args(options):\n res = {}\n for s in options:\n if '=' not in s:\n raise UserError('Arguments to scale should be in the form service=num')\n service_name, num = s.split('=', 1)\n try:\n num = int(num)\n except ValueError:\n raise UserError(\n 'Number of containers for service \"%s\" is not a number' % service_name\n )\n res[service_name] = num\n return res","label":0},{"cid":"compose/cli/main.py::build_exec_command","text":"compose/cli/main.py::build_exec_command\ndef build_exec_command(options, container_id, command):\n args = [\"exec\"]\n\n if options[\"--detach\"]:\n args += [\"--detach\"]\n else:\n args += [\"--interactive\"]\n\n if not options[\"-T\"]:\n args += [\"--tty\"]\n\n if options[\"--privileged\"]:\n args += [\"--privileged\"]\n\n if options[\"--user\"]:\n args += [\"--user\", options[\"--user\"]]\n\n if options[\"--env\"]:\n for env_variable in options[\"--env\"]:\n args += [\"--env\", env_variable]\n\n if options[\"--workdir\"]:\n args += [\"--workdir\", options[\"--workdir\"]]\n\n args += [container_id]\n args += command\n return args","label":0},{"cid":"compose/cli/main.py::has_container_with_state","text":"compose/cli/main.py::has_container_with_state\ndef has_container_with_state(containers, state):\n states = {\n 'running': lambda c: c.is_running,\n 'stopped': lambda c: not c.is_running,\n 'paused': lambda c: c.is_paused,\n 'restarting': lambda c: c.is_restarting,\n }\n for container in containers:\n if state not in states:\n raise UserError(\"Invalid state: %s\" % state)\n if states[state](container):\n return True","label":0},{"cid":"compose/cli/main.py::filter_services","text":"compose/cli/main.py::filter_services\ndef filter_services(filt, services, project):\n def should_include(service):\n for f in filt:\n if f == 'status':\n state = filt[f]\n containers = project.containers([service.name], stopped=True)\n if not has_container_with_state(containers, state):\n return False\n elif f == 'source':\n source = filt[f]\n if source == 'image' or source == 'build':\n if source not in service.options:\n return False\n else:\n raise UserError(\"Invalid value for source filter: %s\" % source)\n else:\n raise UserError(\"Invalid filter: %s\" % f)\n return True\n\n return filter(should_include, services)","label":0},{"cid":"compose/cli/main.py::filter_services.should_include","text":"compose/cli/main.py::filter_services.should_include\n def should_include(service):\n for f in filt:\n if f == 'status':\n state = filt[f]\n containers = project.containers([service.name], stopped=True)\n if not has_container_with_state(containers, state):\n return False\n elif f == 'source':\n source = filt[f]\n if source == 'image' or source == 'build':\n if source not in service.options:\n return False\n else:\n raise UserError(\"Invalid value for source filter: %s\" % source)\n else:\n raise UserError(\"Invalid filter: %s\" % f)\n return True","label":0},{"cid":"compose/cli/main.py::build_filter","text":"compose/cli/main.py::build_filter\ndef build_filter(arg):\n filt = {}\n if arg is not None:\n if '=' not in arg:\n raise UserError(\"Arguments to --filter should be in form KEY=VAL\")\n key, val = arg.split('=', 1)\n filt[key] = val\n return filt","label":0},{"cid":"compose/cli/main.py::warn_for_swarm_mode","text":"compose/cli/main.py::warn_for_swarm_mode\ndef warn_for_swarm_mode(client):\n info = client.info()\n if info.get('Swarm', {}).get('LocalNodeState') == 'active':\n if info.get('ServerVersion', '').startswith('ucp'):\n # UCP does multi-node scheduling with traditional Compose files.\n return\n\n log.warning(\n \"The Docker Engine you're using is running in swarm mode.\\n\\n\"\n \"Compose does not use swarm mode to deploy services to multiple nodes in a swarm. \"\n \"All containers will be scheduled on the current node.\\n\\n\"\n \"To deploy your application across the swarm, \"\n \"use `docker stack deploy`.\\n\"\n )","label":1},{"cid":"compose/config/environment.py::split_env","text":"compose/config/environment.py::split_env\ndef split_env(env):\n if isinstance(env, six.binary_type):\n env = env.decode('utf-8', 'replace')\n key = value = None\n if '=' in env:\n key, value = env.split('=', 1)\n else:\n key = env\n if re.search(r'\\s', key):\n raise ConfigurationError(\n \"environment variable name '{}' may not contain whitespace.\".format(key)\n )\n return key, value","label":0},{"cid":"compose/config/environment.py::env_vars_from_file","text":"compose/config/environment.py::env_vars_from_file\ndef env_vars_from_file(filename):\n \"\"\"\n Read in a line delimited file of environment variables.\n \"\"\"\n if not os.path.exists(filename):\n raise EnvFileNotFound(\"Couldn't find env file: {}\".format(filename))\n elif not os.path.isfile(filename):\n raise EnvFileNotFound(\"{} is not a file.\".format(filename))\n env = {}\n with contextlib.closing(codecs.open(filename, 'r', 'utf-8-sig')) as fileobj:\n for line in fileobj:\n line = line.strip()\n if line and not line.startswith('#'):\n try:\n k, v = split_env(line)\n env[k] = v\n except ConfigurationError as e:\n raise ConfigurationError('In file {}: {}'.format(filename, e.msg))\n return env","label":0},{"cid":"compose/config/environment.py::Environment.__init__","text":"compose/config/environment.py::Environment.__init__\n def __init__(self, *args, **kwargs):\n super(Environment, self).__init__(*args, **kwargs)\n self.missing_keys = []\n self.silent = False","label":0},{"cid":"compose/config/environment.py::Environment.from_env_file","text":"compose/config/environment.py::Environment.from_env_file\n @classmethod\n def from_env_file(cls, base_dir, env_file=None):\n def _initialize():\n result = cls()\n if base_dir is None:\n return result\n if env_file:\n env_file_path = os.path.join(base_dir, env_file)\n else:\n env_file_path = os.path.join(base_dir, '.env')\n try:\n return cls(env_vars_from_file(env_file_path))\n except EnvFileNotFound:\n pass\n return result\n\n instance = _initialize()\n instance.update(os.environ)\n return instance","label":0},{"cid":"compose/config/environment.py::Environment.from_env_file._initialize","text":"compose/config/environment.py::Environment.from_env_file._initialize\n def _initialize():\n result = cls()\n if base_dir is None:\n return result\n if env_file:\n env_file_path = os.path.join(base_dir, env_file)\n else:\n env_file_path = os.path.join(base_dir, '.env')\n try:\n return cls(env_vars_from_file(env_file_path))\n except EnvFileNotFound:\n pass\n return result","label":0},{"cid":"compose/config/environment.py::Environment.from_command_line","text":"compose/config/environment.py::Environment.from_command_line\n @classmethod\n def from_command_line(cls, parsed_env_opts):\n result = cls()\n for k, v in parsed_env_opts.items():\n # Values from the command line take priority, unless they're unset\n # in which case they take the value from the system's environment\n if v is None and k in os.environ:\n result[k] = os.environ[k]\n else:\n result[k] = v\n return result","label":0},{"cid":"compose/config/environment.py::Environment.__getitem__","text":"compose/config/environment.py::Environment.__getitem__\n def __getitem__(self, key):\n try:\n return super(Environment, self).__getitem__(key)\n except KeyError:\n if IS_WINDOWS_PLATFORM:\n try:\n return super(Environment, self).__getitem__(key.upper())\n except KeyError:\n pass\n if not self.silent and key not in self.missing_keys:\n log.warning(\n \"The {} variable is not set. Defaulting to a blank string.\"\n .format(key)\n )\n self.missing_keys.append(key)\n\n return \"\"","label":0},{"cid":"compose/config/environment.py::Environment.__contains__","text":"compose/config/environment.py::Environment.__contains__\n def __contains__(self, key):\n result = super(Environment, self).__contains__(key)\n if IS_WINDOWS_PLATFORM:\n return (\n result or super(Environment, self).__contains__(key.upper())\n )\n return result","label":0},{"cid":"compose/config/environment.py::Environment.get","text":"compose/config/environment.py::Environment.get\n def get(self, key, *args, **kwargs):\n if IS_WINDOWS_PLATFORM:\n return super(Environment, self).get(\n key,\n super(Environment, self).get(key.upper(), *args, **kwargs)\n )\n return super(Environment, self).get(key, *args, **kwargs)","label":0},{"cid":"compose/config/environment.py::Environment.get_boolean","text":"compose/config/environment.py::Environment.get_boolean\n def get_boolean(self, key):\n # Convert a value to a boolean using \"common sense\" rules.\n # Unset, empty, \"0\" and \"false\" (i-case) yield False.\n # All other values yield True.\n value = self.get(key)\n if not value:\n return False\n if value.lower() in ['0', 'false']:\n return False\n return True","label":0},{"cid":"compose/parallel.py::GlobalLimit.set_global_limit","text":"compose/parallel.py::GlobalLimit.set_global_limit\n @classmethod\n def set_global_limit(cls, value):\n if value is None:\n value = PARALLEL_LIMIT\n cls.global_limiter = Semaphore(value)","label":0},{"cid":"compose/parallel.py::parallel_execute_watch","text":"compose/parallel.py::parallel_execute_watch\ndef parallel_execute_watch(events, writer, errors, results, msg, get_name, fail_check):\n \"\"\" Watch events from a parallel execution, update status and fill errors and results.\n Returns exception to re-raise.\n \"\"\"\n error_to_reraise = None\n for obj, result, exception in events:\n if exception is None:\n if fail_check is not None and fail_check(obj):\n writer.write(msg, get_name(obj), 'failed', red)\n else:\n writer.write(msg, get_name(obj), 'done', green)\n results.append(result)\n elif isinstance(exception, ImageNotFound):\n # This is to bubble up ImageNotFound exceptions to the client so we\n # can prompt the user if they want to rebuild.\n errors[get_name(obj)] = exception.explanation\n writer.write(msg, get_name(obj), 'error', red)\n error_to_reraise = exception\n elif isinstance(exception, APIError):\n errors[get_name(obj)] = exception.explanation\n writer.write(msg, get_name(obj), 'error', red)\n elif isinstance(exception, (OperationFailedError, HealthCheckFailed, NoHealthCheckConfigured)):\n errors[get_name(obj)] = exception.msg\n writer.write(msg, get_name(obj), 'error', red)\n elif isinstance(exception, UpstreamError):\n writer.write(msg, get_name(obj), 'error', red)\n else:\n errors[get_name(obj)] = exception\n error_to_reraise = exception\n return error_to_reraise","label":0},{"cid":"compose/parallel.py::parallel_execute","text":"compose/parallel.py::parallel_execute\ndef parallel_execute(objects, func, get_name, msg, get_deps=None, limit=None, fail_check=None):\n \"\"\"Runs func on objects in parallel while ensuring that func is\n ran on object only after it is ran on all its dependencies.\n\n get_deps called on object must return a collection with its dependencies.\n get_name called on object must return its name.\n fail_check is an additional failure check for cases that should display as a failure\n in the CLI logs, but don't raise an exception (such as attempting to start 0 containers)\n \"\"\"\n objects = list(objects)\n stream = get_output_stream(sys.stderr)\n\n if ParallelStreamWriter.instance:\n writer = ParallelStreamWriter.instance\n else:\n writer = ParallelStreamWriter(stream)\n\n for obj in objects:\n writer.add_object(msg, get_name(obj))\n for obj in objects:\n writer.write_initial(msg, get_name(obj))\n\n events = parallel_execute_iter(objects, func, get_deps, limit)\n\n errors = {}\n results = []\n error_to_reraise = parallel_execute_watch(\n events, writer, errors, results, msg, get_name, fail_check\n )\n\n for obj_name, error in errors.items():\n stream.write(\"\\nERROR: for {} {}\\n\".format(obj_name, error))\n\n if error_to_reraise:\n raise error_to_reraise\n\n return results, errors","label":0},{"cid":"compose/parallel.py::_no_deps","text":"compose/parallel.py::_no_deps\ndef _no_deps(x):\n return []","label":0},{"cid":"compose/parallel.py::State.__init__","text":"compose/parallel.py::State.__init__\n def __init__(self, objects):\n self.objects = objects\n\n self.started = set()\n self.finished = set()\n self.failed = set()","label":0},{"cid":"compose/parallel.py::State.is_done","text":"compose/parallel.py::State.is_done\n def is_done(self):\n return len(self.finished) + len(self.failed) >= len(self.objects)","label":0},{"cid":"compose/parallel.py::State.pending","text":"compose/parallel.py::State.pending\n def pending(self):\n return set(self.objects) - self.started - self.finished - self.failed","label":0},{"cid":"compose/parallel.py::NoLimit.__enter__","text":"compose/parallel.py::NoLimit.__enter__\n def __enter__(self):\n pass","label":0},{"cid":"compose/parallel.py::NoLimit.__exit__","text":"compose/parallel.py::NoLimit.__exit__\n def __exit__(self, *ex):\n pass","label":0},{"cid":"compose/parallel.py::parallel_execute_iter","text":"compose/parallel.py::parallel_execute_iter\ndef parallel_execute_iter(objects, func, get_deps, limit):\n \"\"\"\n Runs func on objects in parallel while ensuring that func is\n ran on object only after it is ran on all its dependencies.\n\n Returns an iterator of tuples which look like:\n\n # if func returned normally when run on object\n (object, result, None)\n\n # if func raised an exception when run on object\n (object, None, exception)\n\n # if func raised an exception when run on one of object's dependencies\n (object, None, UpstreamError())\n \"\"\"\n if get_deps is None:\n get_deps = _no_deps\n\n if limit is None:\n limiter = NoLimit()\n else:\n limiter = Semaphore(limit)\n\n results = Queue()\n state = State(objects)\n\n while True:\n feed_queue(objects, func, get_deps, results, state, limiter)\n\n try:\n event = results.get(timeout=0.1)\n except Empty:\n continue\n # See https://github.com/docker/compose/issues/189\n except thread.error:\n raise ShutdownException()\n\n if event is STOP:\n break\n\n obj, _, exception = event\n if exception is None:\n log.debug('Finished processing: {}'.format(obj))\n state.finished.add(obj)\n else:\n log.debug('Failed: {}'.format(obj))\n state.failed.add(obj)\n\n yield event","label":0},{"cid":"compose/parallel.py::producer","text":"compose/parallel.py::producer\ndef producer(obj, func, results, limiter):\n \"\"\"\n The entry point for a producer thread which runs func on a single object.\n Places a tuple on the results queue once func has either returned or raised.\n \"\"\"\n with limiter, GlobalLimit.global_limiter:\n try:\n result = func(obj)\n results.put((obj, result, None))\n except Exception as e:\n results.put((obj, None, e))","label":0},{"cid":"compose/parallel.py::feed_queue","text":"compose/parallel.py::feed_queue\ndef feed_queue(objects, func, get_deps, results, state, limiter):\n \"\"\"\n Starts producer threads for any objects which are ready to be processed\n (i.e. they have no dependencies which haven't been successfully processed).\n\n Shortcuts any objects whose dependencies have failed and places an\n (object, None, UpstreamError()) tuple on the results queue.\n \"\"\"\n pending = state.pending()\n log.debug('Pending: {}'.format(pending))\n\n for obj in pending:\n deps = get_deps(obj)\n try:\n if any(dep[0] in state.failed for dep in deps):\n log.debug('{} has upstream errors - not processing'.format(obj))\n results.put((obj, None, UpstreamError()))\n state.failed.add(obj)\n elif all(\n dep not in objects or (\n dep in state.finished and (not ready_check or ready_check(dep))\n ) for dep, ready_check in deps\n ):\n log.debug('Starting producer thread for {}'.format(obj))\n t = Thread(target=producer, args=(obj, func, results, limiter))\n t.daemon = True\n t.start()\n state.started.add(obj)\n except (HealthCheckFailed, NoHealthCheckConfigured) as e:\n log.debug(\n 'Healthcheck for service(s) upstream of {} failed - '\n 'not processing'.format(obj)\n )\n results.put((obj, None, e))\n\n if state.is_done():\n results.put(STOP)","label":0},{"cid":"compose/parallel.py::ParallelStreamWriter.set_noansi","text":"compose/parallel.py::ParallelStreamWriter.set_noansi\n @classmethod\n def set_noansi(cls, value=True):\n cls.noansi = value","label":0},{"cid":"compose/parallel.py::ParallelStreamWriter.__init__","text":"compose/parallel.py::ParallelStreamWriter.__init__\n def __init__(self, stream):\n self.stream = stream\n self.lines = []\n self.width = 0\n ParallelStreamWriter.instance = self","label":0},{"cid":"compose/parallel.py::ParallelStreamWriter.add_object","text":"compose/parallel.py::ParallelStreamWriter.add_object\n def add_object(self, msg, obj_index):\n if msg is None:\n return\n self.lines.append(msg + obj_index)\n self.width = max(self.width, len(msg + ' ' + obj_index))","label":0},{"cid":"compose/parallel.py::ParallelStreamWriter.write_initial","text":"compose/parallel.py::ParallelStreamWriter.write_initial\n def write_initial(self, msg, obj_index):\n if msg is None:\n return\n return self._write_noansi(msg, obj_index, '')","label":0},{"cid":"compose/parallel.py::ParallelStreamWriter._write_ansi","text":"compose/parallel.py::ParallelStreamWriter._write_ansi\n def _write_ansi(self, msg, obj_index, status):\n self.lock.acquire()\n position = self.lines.index(msg + obj_index)\n diff = len(self.lines) - position\n # move up\n self.stream.write(\"%c[%dA\" % (27, diff))\n # erase\n self.stream.write(\"%c[2K\\r\" % 27)\n self.stream.write(\"{:<{width}} ... {}\\r\".format(msg + ' ' + obj_index,\n status, width=self.width))\n # move back down\n self.stream.write(\"%c[%dB\" % (27, diff))\n self.stream.flush()\n self.lock.release()","label":0},{"cid":"compose/parallel.py::ParallelStreamWriter._write_noansi","text":"compose/parallel.py::ParallelStreamWriter._write_noansi\n def _write_noansi(self, msg, obj_index, status):\n self.stream.write(\n \"{:<{width}} ... {}\\r\\n\".format(\n msg + ' ' + obj_index, status, width=self.width\n )\n )\n self.stream.flush()","label":0},{"cid":"compose/parallel.py::ParallelStreamWriter.write","text":"compose/parallel.py::ParallelStreamWriter.write\n def write(self, msg, obj_index, status, color_func):\n if msg is None:\n return\n if self.noansi:\n self._write_noansi(msg, obj_index, status)\n else:\n self._write_ansi(msg, obj_index, color_func(status))","label":0},{"cid":"compose/parallel.py::get_stream_writer","text":"compose/parallel.py::get_stream_writer\ndef get_stream_writer():\n instance = ParallelStreamWriter.instance\n if instance is None:\n raise RuntimeError('ParallelStreamWriter has not yet been instantiated')\n return instance","label":0},{"cid":"compose/parallel.py::parallel_operation","text":"compose/parallel.py::parallel_operation\ndef parallel_operation(containers, operation, options, message):\n parallel_execute(\n containers,\n operator.methodcaller(operation, **options),\n operator.attrgetter('name'),\n message,\n )","label":0},{"cid":"compose/parallel.py::parallel_remove","text":"compose/parallel.py::parallel_remove\ndef parallel_remove(containers, options):\n stopped_containers = [c for c in containers if not c.is_running]\n parallel_operation(stopped_containers, 'remove', options, 'Removing')","label":0},{"cid":"compose/parallel.py::parallel_pause","text":"compose/parallel.py::parallel_pause\ndef parallel_pause(containers, options):\n parallel_operation(containers, 'pause', options, 'Pausing')","label":0},{"cid":"compose/parallel.py::parallel_unpause","text":"compose/parallel.py::parallel_unpause\ndef parallel_unpause(containers, options):\n parallel_operation(containers, 'unpause', options, 'Unpausing')","label":0},{"cid":"compose/parallel.py::parallel_kill","text":"compose/parallel.py::parallel_kill\ndef parallel_kill(containers, options):\n parallel_operation(containers, 'kill', options, 'Killing')","label":0},{"cid":"compose/progress_stream.py::write_to_stream","text":"compose/progress_stream.py::write_to_stream\ndef write_to_stream(s, stream):\n try:\n stream.write(s)\n except UnicodeEncodeError:\n encoding = getattr(stream, 'encoding', 'ascii')\n stream.write(s.encode(encoding, errors='replace').decode(encoding))","label":0},{"cid":"compose/progress_stream.py::stream_output","text":"compose/progress_stream.py::stream_output\ndef stream_output(output, stream):\n is_terminal = hasattr(stream, 'isatty') and stream.isatty()\n stream = utils.get_output_stream(stream)\n lines = {}\n diff = 0\n\n for event in utils.json_stream(output):\n yield event\n is_progress_event = 'progress' in event or 'progressDetail' in event\n\n if not is_progress_event:\n print_output_event(event, stream, is_terminal)\n stream.flush()\n continue\n\n if not is_terminal:\n continue\n\n # if it's a progress event and we have a terminal, then display the progress bars\n image_id = event.get('id')\n if not image_id:\n continue\n\n if image_id not in lines:\n lines[image_id] = len(lines)\n write_to_stream(\"\\n\", stream)\n\n diff = len(lines) - lines[image_id]\n\n # move cursor up `diff` rows\n write_to_stream(\"%c[%dA\" % (27, diff), stream)\n\n print_output_event(event, stream, is_terminal)\n\n if 'id' in event:\n # move cursor back down\n write_to_stream(\"%c[%dB\" % (27, diff), stream)\n\n stream.flush()","label":0},{"cid":"compose/progress_stream.py::print_output_event","text":"compose/progress_stream.py::print_output_event\ndef print_output_event(event, stream, is_terminal):\n if 'errorDetail' in event:\n raise StreamOutputError(event['errorDetail']['message'])\n\n terminator = ''\n\n if is_terminal and 'stream' not in event:\n # erase current line\n write_to_stream(\"%c[2K\\r\" % 27, stream)\n terminator = \"\\r\"\n elif 'progressDetail' in event:\n return\n\n if 'time' in event:\n write_to_stream(\"[%s] \" % event['time'], stream)\n\n if 'id' in event:\n write_to_stream(\"%s: \" % event['id'], stream)\n\n if 'from' in event:\n write_to_stream(\"(from %s) \" % event['from'], stream)\n\n status = event.get('status', '')\n\n if 'progress' in event:\n write_to_stream(\"%s %s%s\" % (status, event['progress'], terminator), stream)\n elif 'progressDetail' in event:\n detail = event['progressDetail']\n total = detail.get('total')\n if 'current' in detail and total:\n percentage = float(detail['current']) / float(total) * 100\n write_to_stream('%s (%.1f%%)%s' % (status, percentage, terminator), stream)\n else:\n write_to_stream('%s%s' % (status, terminator), stream)\n elif 'stream' in event:\n write_to_stream(\"%s%s\" % (event['stream'], terminator), stream)\n else:\n write_to_stream(\"%s%s\\n\" % (status, terminator), stream)","label":0},{"cid":"compose/progress_stream.py::get_digest_from_pull","text":"compose/progress_stream.py::get_digest_from_pull\ndef get_digest_from_pull(events):\n digest = None\n for event in events:\n status = event.get('status')\n if not status or 'Digest' not in status:\n continue\n else:\n digest = status.split(':', 1)[1].strip()\n return digest","label":0},{"cid":"compose/progress_stream.py::get_digest_from_push","text":"compose/progress_stream.py::get_digest_from_push\ndef get_digest_from_push(events):\n for event in events:\n digest = event.get('aux', {}).get('Digest')\n if digest:\n return digest\n return None","label":0},{"cid":"script/release/release.py::create_initial_branch","text":"script/release/release.py::create_initial_branch\ndef create_initial_branch(repository, args):\n release_branch = repository.create_release_branch(args.release, args.base)\n if args.base and args.cherries:\n print('Detected patch version.')\n cherries = input('Indicate (space-separated) PR numbers to cherry-pick then press Enter:\\n')\n repository.cherry_pick_prs(release_branch, cherries.split())\n\n return create_bump_commit(repository, release_branch, args.bintray_user, args.bintray_org)","label":0},{"cid":"script/release/release.py::create_bump_commit","text":"script/release/release.py::create_bump_commit\ndef create_bump_commit(repository, release_branch, bintray_user, bintray_org):\n with release_branch.config_reader() as cfg:\n release = cfg.get('release')\n print('Updating version info in __init__.py and run.sh')\n update_run_sh_version(release)\n update_init_py_version(release)\n\n input('Please add the release notes to the CHANGELOG.md file, then press Enter to continue.')\n proceed = None\n while not proceed:\n print(repository.diff())\n proceed = yesno('Are these changes ok? y/N ', default=False)\n\n if repository.diff():\n repository.create_bump_commit(release_branch, release)\n repository.push_branch_to_remote(release_branch)\n\n bintray_api = BintrayAPI(os.environ['BINTRAY_TOKEN'], bintray_user)\n if not bintray_api.repository_exists(bintray_org, release_branch.name):\n print('Creating data repository {} on bintray'.format(release_branch.name))\n bintray_api.create_repository(bintray_org, release_branch.name, 'generic')\n else:\n print('Bintray repository {} already exists. Skipping'.format(release_branch.name))","label":0},{"cid":"script/release/release.py::monitor_pr_status","text":"script/release/release.py::monitor_pr_status\ndef monitor_pr_status(pr_data):\n print('Waiting for CI to complete...')\n last_commit = pr_data.get_commits().reversed[0]\n while True:\n status = last_commit.get_combined_status()\n if status.state == 'pending' or status.state == 'failure':\n summary = {\n 'pending': 0,\n 'success': 0,\n 'failure': 0,\n 'error': 0,\n }\n for detail in status.statuses:\n if detail.context == 'dco-signed':\n # dco-signed check breaks on merge remote-tracking ; ignore it\n continue\n if detail.state in summary:\n summary[detail.state] += 1\n print(\n '{pending} pending, {success} successes, {failure} failures, '\n '{error} errors'.format(**summary)\n )\n if summary['failure'] > 0 or summary['error'] > 0:\n raise ScriptError('CI failures detected!')\n elif summary['pending'] == 0 and summary['success'] > 0:\n # This check assumes at least 1 non-DCO CI check to avoid race conditions.\n # If testing on a repo without CI, use --skip-ci-check to avoid looping eternally\n return True\n time.sleep(30)\n elif status.state == 'success':\n print('{} successes: all clear!'.format(status.total_count))\n return True","label":0},{"cid":"script/release/release.py::check_pr_mergeable","text":"script/release/release.py::check_pr_mergeable\ndef check_pr_mergeable(pr_data):\n if pr_data.mergeable is False:\n # mergeable can also be null, in which case the warning would be a false positive.\n print(\n 'WARNING!! PR #{} can not currently be merged. You will need to '\n 'resolve the conflicts manually before finalizing the release.'.format(pr_data.number)\n )\n\n return pr_data.mergeable is True","label":0},{"cid":"script/release/release.py::create_release_draft","text":"script/release/release.py::create_release_draft\ndef create_release_draft(repository, version, pr_data, files):\n print('Creating Github release draft')\n with open(os.path.join(os.path.dirname(__file__), 'release.md.tmpl'), 'r') as f:\n template = Template(f.read())\n print('Rendering release notes based on template')\n release_notes = template.render(\n version=version,\n compat_matrix=compatibility_matrix(),\n integrity=files,\n contributors=get_contributors(pr_data),\n changelog=read_release_notes_from_changelog(),\n )\n gh_release = repository.create_release(\n version, release_notes, draft=True, prerelease='-rc' in version,\n target_commitish='release'\n )\n print('Release draft initialized')\n return gh_release","label":0},{"cid":"script/release/release.py::print_final_instructions","text":"script/release/release.py::print_final_instructions\ndef print_final_instructions(args):\n print(\n \"You're almost done! Please verify that everything is in order and \"\n \"you are ready to make the release public, then run the following \"\n \"command:\\n{exe} -b {user} finalize {version}\".format(\n exe='./script/release/release.sh', user=args.bintray_user, version=args.release\n )\n )","label":0},{"cid":"script/release/release.py::distclean","text":"script/release/release.py::distclean\ndef distclean():\n print('Running distclean...')\n dirs = [\n os.path.join(REPO_ROOT, 'build'), os.path.join(REPO_ROOT, 'dist'),\n os.path.join(REPO_ROOT, 'docker-compose.egg-info')\n ]\n files = []\n for base, dirnames, fnames in os.walk(REPO_ROOT):\n for fname in fnames:\n path = os.path.normpath(os.path.join(base, fname))\n if fname.endswith('.pyc'):\n files.append(path)\n elif fname.startswith('.coverage.'):\n files.append(path)\n for dirname in dirnames:\n path = os.path.normpath(os.path.join(base, dirname))\n if dirname == '__pycache__':\n dirs.append(path)\n elif dirname == '.coverage-binfiles':\n dirs.append(path)\n\n for file in files:\n os.unlink(file)\n\n for folder in dirs:\n shutil.rmtree(folder, ignore_errors=True)","label":0},{"cid":"script/release/release.py::resume","text":"script/release/release.py::resume\ndef resume(args):\n try:\n distclean()\n repository = Repository(REPO_ROOT, args.repo)\n br_name = branch_name(args.release)\n if not repository.branch_exists(br_name):\n raise ScriptError('No local branch exists for this release.')\n gh_release = repository.find_release(args.release)\n if gh_release and not gh_release.draft:\n print('WARNING!! Found non-draft (public) release for this version!')\n proceed = yesno(\n 'Are you sure you wish to proceed? Modifying an already '\n 'released version is dangerous! y/N ', default=False\n )\n if proceed.lower() is not True:\n raise ScriptError('Aborting release')\n\n release_branch = repository.checkout_branch(br_name)\n if args.cherries:\n cherries = input('Indicate (space-separated) PR numbers to cherry-pick then press Enter:\\n')\n repository.cherry_pick_prs(release_branch, cherries.split())\n\n create_bump_commit(repository, release_branch, args.bintray_user, args.bintray_org)\n pr_data = repository.find_release_pr(args.release)\n if not pr_data:\n pr_data = repository.create_release_pull_request(args.release)\n check_pr_mergeable(pr_data)\n if not args.skip_ci:\n monitor_pr_status(pr_data)\n downloader = BinaryDownloader(args.destination)\n files = downloader.download_all(args.release)\n if not gh_release:\n gh_release = create_release_draft(repository, args.release, pr_data, files)\n delete_assets(gh_release)\n upload_assets(gh_release, files)\n img_manager = ImageManager(args.release)\n img_manager.build_images(repository)\n except ScriptError as e:\n print(e)\n return 1\n\n print_final_instructions(args)\n return 0","label":0},{"cid":"script/release/release.py::cancel","text":"script/release/release.py::cancel\ndef cancel(args):\n try:\n repository = Repository(REPO_ROOT, args.repo)\n repository.close_release_pr(args.release)\n repository.remove_release(args.release)\n repository.remove_bump_branch(args.release)\n bintray_api = BintrayAPI(os.environ['BINTRAY_TOKEN'], args.bintray_user)\n print('Removing Bintray data repository for {}'.format(args.release))\n bintray_api.delete_repository(args.bintray_org, branch_name(args.release))\n distclean()\n except ScriptError as e:\n print(e)\n return 1\n print('Release cancellation complete.')\n return 0","label":0},{"cid":"script/release/release.py::start","text":"script/release/release.py::start\ndef start(args):\n distclean()\n try:\n repository = Repository(REPO_ROOT, args.repo)\n create_initial_branch(repository, args)\n pr_data = repository.create_release_pull_request(args.release)\n check_pr_mergeable(pr_data)\n if not args.skip_ci:\n monitor_pr_status(pr_data)\n downloader = BinaryDownloader(args.destination)\n files = downloader.download_all(args.release)\n gh_release = create_release_draft(repository, args.release, pr_data, files)\n upload_assets(gh_release, files)\n img_manager = ImageManager(args.release)\n img_manager.build_images(repository)\n except ScriptError as e:\n print(e)\n return 1\n\n print_final_instructions(args)\n return 0","label":0},{"cid":"script/release/release.py::finalize","text":"script/release/release.py::finalize\ndef finalize(args):\n distclean()\n try:\n check_pypirc()\n repository = Repository(REPO_ROOT, args.repo)\n tag_as_latest = is_tag_latest(args.release)\n img_manager = ImageManager(args.release, tag_as_latest)\n pr_data = repository.find_release_pr(args.release)\n if not pr_data:\n raise ScriptError('No PR found for {}'.format(args.release))\n if not check_pr_mergeable(pr_data):\n raise ScriptError('Can not finalize release with an unmergeable PR')\n if not img_manager.check_images():\n raise ScriptError('Missing release image')\n br_name = branch_name(args.release)\n if not repository.branch_exists(br_name):\n raise ScriptError('No local branch exists for this release.')\n gh_release = repository.find_release(args.release)\n if not gh_release:\n raise ScriptError('No Github release draft for this version')\n\n repository.checkout_branch(br_name)\n\n os.system('python {setup_script} sdist bdist_wheel'.format(\n setup_script=os.path.join(REPO_ROOT, 'setup.py')))\n\n merge_status = pr_data.merge()\n if not merge_status.merged and not args.finalize_resume:\n raise ScriptError(\n 'Unable to merge PR #{}: {}'.format(pr_data.number, merge_status.message)\n )\n\n pypi_upload(args)\n\n img_manager.push_images()\n repository.publish_release(gh_release)\n except ScriptError as e:\n print(e)\n return 1\n\n return 0","label":0},{"cid":"script/release/release.py::main","text":"script/release/release.py::main\ndef main():\n if 'GITHUB_TOKEN' not in os.environ:\n print('GITHUB_TOKEN environment variable must be set')\n return 1\n\n if 'BINTRAY_TOKEN' not in os.environ:\n print('BINTRAY_TOKEN environment variable must be set')\n return 1\n\n parser = argparse.ArgumentParser(\n description='Orchestrate a new release of docker/compose. This tool assumes that you have '\n 'obtained a Github API token and Bintray API key and set the GITHUB_TOKEN and '\n 'BINTRAY_TOKEN environment variables accordingly.',\n epilog=EPILOG, formatter_class=argparse.RawTextHelpFormatter)\n parser.add_argument(\n 'action', choices=ACTIONS, help='The action to be performed for this release'\n )\n parser.add_argument('release', help='Release number, e.g. 1.9.0-rc1, 2.1.1')\n parser.add_argument(\n '--patch', '-p', dest='base',\n help='Which version is being patched by this release'\n )\n parser.add_argument(\n '--repo', '-r', dest='repo', default=NAME,\n help='Start a release for the given repo (default: {})'.format(NAME)\n )\n parser.add_argument(\n '-b', dest='bintray_user', required=True, metavar='USER',\n help='Username associated with the Bintray API key'\n )\n parser.add_argument(\n '--bintray-org', dest='bintray_org', metavar='ORG', default=BINTRAY_ORG,\n help='Organization name on bintray where the data repository will be created.'\n )\n parser.add_argument(\n '--destination', '-o', metavar='DIR', default='binaries',\n help='Directory where release binaries will be downloaded relative to the project root'\n )\n parser.add_argument(\n '--no-cherries', '-C', dest='cherries', action='store_false',\n help='If set, the program will not prompt the user for PR numbers to cherry-pick'\n )\n parser.add_argument(\n '--skip-ci-checks', dest='skip_ci', action='store_true',\n help","label":0}],"string":"[\n {\n \"cid\": \"compose/cli/colors.py::get_pairs\",\n \"text\": \"compose/cli/colors.py::get_pairs\\ndef get_pairs():\\n for i, name in enumerate(NAMES):\\n yield(name, str(30 + i))\\n yield('intense_' + name, str(30 + i) + ';1')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/colors.py::ansi\",\n \"text\": \"compose/cli/colors.py::ansi\\ndef ansi(code):\\n return '\\\\033[{0}m'.format(code)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/colors.py::ansi_color\",\n \"text\": \"compose/cli/colors.py::ansi_color\\ndef ansi_color(code, s):\\n return '{0}{1}{2}'.format(ansi(code), s, ansi(0))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/colors.py::make_color_fn\",\n \"text\": \"compose/cli/colors.py::make_color_fn\\ndef make_color_fn(code):\\n return lambda s: ansi_color(code, s)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/colors.py::rainbow\",\n \"text\": \"compose/cli/colors.py::rainbow\\ndef rainbow():\\n cs = ['cyan', 'yellow', 'green', 'magenta', 'red', 'blue',\\n 'intense_cyan', 'intense_yellow', 'intense_green',\\n 'intense_magenta', 'intense_red', 'intense_blue']\\n\\n for c in cs:\\n yield globals()[c]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/formatter.py::get_tty_width\",\n \"text\": \"compose/cli/formatter.py::get_tty_width\\ndef get_tty_width():\\n try:\\n width, _ = get_terminal_size()\\n return int(width)\\n except OSError:\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/formatter.py::Formatter.table\",\n \"text\": \"compose/cli/formatter.py::Formatter.table\\n @staticmethod\\n def table(headers, rows):\\n table = texttable.Texttable(max_width=get_tty_width())\\n table.set_cols_dtype(['t' for h in headers])\\n table.add_rows([headers] + rows)\\n table.set_deco(table.HEADER)\\n table.set_chars(['-', '|', '+', '-'])\\n\\n return table.draw()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/formatter.py::ConsoleWarningFormatter.get_level_message\",\n \"text\": \"compose/cli/formatter.py::ConsoleWarningFormatter.get_level_message\\n def get_level_message(self, record):\\n separator = ': '\\n if record.levelno == logging.WARNING:\\n return colors.yellow(record.levelname) + separator\\n if record.levelno == logging.ERROR:\\n return colors.red(record.levelname) + separator\\n\\n return ''\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/formatter.py::ConsoleWarningFormatter.format\",\n \"text\": \"compose/cli/formatter.py::ConsoleWarningFormatter.format\\n def format(self, record):\\n if isinstance(record.msg, six.binary_type):\\n record.msg = record.msg.decode('utf-8')\\n message = super(ConsoleWarningFormatter, self).format(record)\\n return '{0}{1}'.format(self.get_level_message(record), message)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::LogPresenter.__init__\",\n \"text\": \"compose/cli/log_printer.py::LogPresenter.__init__\\n def __init__(self, prefix_width, color_func):\\n self.prefix_width = prefix_width\\n self.color_func = color_func\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::LogPresenter.present\",\n \"text\": \"compose/cli/log_printer.py::LogPresenter.present\\n def present(self, container, line):\\n prefix = container.name_without_project.ljust(self.prefix_width)\\n return '{prefix} {line}'.format(\\n prefix=self.color_func(prefix + ' |'),\\n line=line)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::build_log_presenters\",\n \"text\": \"compose/cli/log_printer.py::build_log_presenters\\ndef build_log_presenters(service_names, monochrome):\\n \\\"\\\"\\\"Return an iterable of functions.\\n\\n Each function can be used to format the logs output of a container.\\n \\\"\\\"\\\"\\n prefix_width = max_name_width(service_names)\\n\\n def no_color(text):\\n return text\\n\\n for color_func in cycle([no_color] if monochrome else colors.rainbow()):\\n yield LogPresenter(prefix_width, color_func)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::build_log_presenters.no_color\",\n \"text\": \"compose/cli/log_printer.py::build_log_presenters.no_color\\n def no_color(text):\\n return text\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::max_name_width\",\n \"text\": \"compose/cli/log_printer.py::max_name_width\\ndef max_name_width(service_names, max_index_width=3):\\n \\\"\\\"\\\"Calculate the maximum width of container names so we can make the log\\n prefixes line up like so:\\n\\n db_1 | Listening\\n web_1 | Listening\\n \\\"\\\"\\\"\\n return max(len(name) for name in service_names) + max_index_width\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::LogPrinter.__init__\",\n \"text\": \"compose/cli/log_printer.py::LogPrinter.__init__\\n def __init__(self,\\n containers,\\n presenters,\\n event_stream,\\n output=sys.stdout,\\n cascade_stop=False,\\n log_args=None):\\n self.containers = containers\\n self.presenters = presenters\\n self.event_stream = event_stream\\n self.output = utils.get_output_stream(output)\\n self.cascade_stop = cascade_stop\\n self.log_args = log_args or {}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::LogPrinter.run\",\n \"text\": \"compose/cli/log_printer.py::LogPrinter.run\\n def run(self):\\n if not self.containers:\\n return\\n\\n queue = Queue()\\n thread_args = queue, self.log_args\\n thread_map = build_thread_map(self.containers, self.presenters, thread_args)\\n start_producer_thread((\\n thread_map,\\n self.event_stream,\\n self.presenters,\\n thread_args))\\n\\n for line in consume_queue(queue, self.cascade_stop):\\n remove_stopped_threads(thread_map)\\n\\n if self.cascade_stop:\\n matching_container = [cont.name for cont in self.containers if cont.name == line]\\n if line in matching_container:\\n # Returning the name of the container that started the\\n # the cascade_stop so we can return the correct exit code\\n return line\\n\\n if not line:\\n if not thread_map:\\n # There are no running containers left to tail, so exit\\n return\\n # We got an empty line because of a timeout, but there are still\\n # active containers to tail, so continue\\n continue\\n\\n self.write(line)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::LogPrinter.write\",\n \"text\": \"compose/cli/log_printer.py::LogPrinter.write\\n def write(self, line):\\n try:\\n self.output.write(line)\\n except UnicodeEncodeError:\\n # This may happen if the user's locale settings don't support UTF-8\\n # and UTF-8 characters are present in the log line. The following\\n # will output a \\\"degraded\\\" log with unsupported characters\\n # replaced by `?`\\n self.output.write(line.encode('ascii', 'replace').decode())\\n self.output.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::remove_stopped_threads\",\n \"text\": \"compose/cli/log_printer.py::remove_stopped_threads\\ndef remove_stopped_threads(thread_map):\\n for container_id, tailer_thread in list(thread_map.items()):\\n if not tailer_thread.is_alive():\\n thread_map.pop(container_id, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::build_thread\",\n \"text\": \"compose/cli/log_printer.py::build_thread\\ndef build_thread(container, presenter, queue, log_args):\\n tailer = Thread(\\n target=tail_container_logs,\\n args=(container, presenter, queue, log_args))\\n tailer.daemon = True\\n tailer.start()\\n return tailer\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::build_thread_map\",\n \"text\": \"compose/cli/log_printer.py::build_thread_map\\ndef build_thread_map(initial_containers, presenters, thread_args):\\n return {\\n container.id: build_thread(container, next(presenters), *thread_args)\\n for container in initial_containers\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::QueueItem.new\",\n \"text\": \"compose/cli/log_printer.py::QueueItem.new\\n @classmethod\\n def new(cls, item):\\n return cls(item, None, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::QueueItem.exception\",\n \"text\": \"compose/cli/log_printer.py::QueueItem.exception\\n @classmethod\\n def exception(cls, exc):\\n return cls(None, None, exc)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::QueueItem.stop\",\n \"text\": \"compose/cli/log_printer.py::QueueItem.stop\\n @classmethod\\n def stop(cls, item=None):\\n return cls(item, True, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::tail_container_logs\",\n \"text\": \"compose/cli/log_printer.py::tail_container_logs\\ndef tail_container_logs(container, presenter, queue, log_args):\\n generator = get_log_generator(container)\\n\\n try:\\n for item in generator(container, log_args):\\n queue.put(QueueItem.new(presenter.present(container, item)))\\n except Exception as e:\\n queue.put(QueueItem.exception(e))\\n return\\n if log_args.get('follow'):\\n queue.put(QueueItem.new(presenter.color_func(wait_on_exit(container))))\\n queue.put(QueueItem.stop(container.name))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::get_log_generator\",\n \"text\": \"compose/cli/log_printer.py::get_log_generator\\ndef get_log_generator(container):\\n if container.has_api_logs:\\n return build_log_generator\\n return build_no_log_generator\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::build_no_log_generator\",\n \"text\": \"compose/cli/log_printer.py::build_no_log_generator\\ndef build_no_log_generator(container, log_args):\\n \\\"\\\"\\\"Return a generator that prints a warning about logs and waits for\\n container to exit.\\n \\\"\\\"\\\"\\n yield \\\"WARNING: no logs are available with the '{}' log driver\\\\n\\\".format(\\n container.log_driver)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::build_log_generator\",\n \"text\": \"compose/cli/log_printer.py::build_log_generator\\ndef build_log_generator(container, log_args):\\n # if the container doesn't have a log_stream we need to attach to container\\n # before log printer starts running\\n if container.log_stream is None:\\n stream = container.logs(stdout=True, stderr=True, stream=True, **log_args)\\n else:\\n stream = container.log_stream\\n\\n return split_buffer(stream)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::wait_on_exit\",\n \"text\": \"compose/cli/log_printer.py::wait_on_exit\\ndef wait_on_exit(container):\\n try:\\n exit_code = container.wait()\\n return \\\"%s exited with code %s\\\\n\\\" % (container.name, exit_code)\\n except APIError as e:\\n return \\\"Unexpected API error for %s (HTTP code %s)\\\\nResponse body:\\\\n%s\\\\n\\\" % (\\n container.name, e.response.status_code,\\n e.response.text or '[empty]'\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::start_producer_thread\",\n \"text\": \"compose/cli/log_printer.py::start_producer_thread\\ndef start_producer_thread(thread_args):\\n producer = Thread(target=watch_events, args=thread_args)\\n producer.daemon = True\\n producer.start()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::watch_events\",\n \"text\": \"compose/cli/log_printer.py::watch_events\\ndef watch_events(thread_map, event_stream, presenters, thread_args):\\n crashed_containers = set()\\n for event in event_stream:\\n if event['action'] == 'stop':\\n thread_map.pop(event['id'], None)\\n\\n if event['action'] == 'die':\\n thread_map.pop(event['id'], None)\\n crashed_containers.add(event['id'])\\n\\n if event['action'] != 'start':\\n continue\\n\\n if event['id'] in thread_map:\\n if thread_map[event['id']].is_alive():\\n continue\\n # Container was stopped and started, we need a new thread\\n thread_map.pop(event['id'], None)\\n\\n # Container crashed so we should reattach to it\\n if event['id'] in crashed_containers:\\n container = event['container']\\n if not container.is_restarting:\\n try:\\n container.attach_log_stream()\\n except APIError:\\n # Just ignore errors when reattaching to already crashed containers\\n pass\\n crashed_containers.remove(event['id'])\\n\\n thread_map[event['id']] = build_thread(\\n event['container'],\\n next(presenters),\\n *thread_args\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/log_printer.py::consume_queue\",\n \"text\": \"compose/cli/log_printer.py::consume_queue\\ndef consume_queue(queue, cascade_stop):\\n \\\"\\\"\\\"Consume the queue by reading lines off of it and yielding them.\\\"\\\"\\\"\\n while True:\\n try:\\n item = queue.get(timeout=0.1)\\n except Empty:\\n yield None\\n continue\\n # See https://github.com/docker/compose/issues/189\\n except thread.error:\\n raise ShutdownException()\\n\\n if item.exc:\\n raise item.exc\\n\\n if item.is_stop and not cascade_stop:\\n continue\\n\\n yield item.item\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::main\",\n \"text\": \"compose/cli/main.py::main\\ndef main():\\n signals.ignore_sigpipe()\\n try:\\n command = dispatch()\\n command()\\n except (KeyboardInterrupt, signals.ShutdownException):\\n log.error(\\\"Aborting.\\\")\\n sys.exit(1)\\n except (UserError, NoSuchService, ConfigurationError,\\n ProjectError, OperationFailedError) as e:\\n log.error(e.msg)\\n sys.exit(1)\\n except BuildError as e:\\n log.error(\\\"Service '%s' failed to build: %s\\\" % (e.service.name, e.reason))\\n sys.exit(1)\\n except StreamOutputError as e:\\n log.error(e)\\n sys.exit(1)\\n except NeedsBuildError as e:\\n log.error(\\\"Service '%s' needs to be built, but --no-build was passed.\\\" % e.service.name)\\n sys.exit(1)\\n except NoSuchCommand as e:\\n commands = \\\"\\\\n\\\".join(parse_doc_section(\\\"commands:\\\", getdoc(e.supercommand)))\\n log.error(\\\"No such command: %s\\\\n\\\\n%s\\\", e.command, commands)\\n sys.exit(1)\\n except (errors.ConnectionError, StreamParseError):\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::dispatch\",\n \"text\": \"compose/cli/main.py::dispatch\\ndef dispatch():\\n setup_logging()\\n dispatcher = DocoptDispatcher(\\n TopLevelCommand,\\n {'options_first': True, 'version': get_version_info('compose')})\\n\\n options, handler, command_options = dispatcher.parse(sys.argv[1:])\\n setup_console_handler(console_handler,\\n options.get('--verbose'),\\n options.get('--no-ansi'),\\n options.get(\\\"--log-level\\\"))\\n setup_parallel_logger(options.get('--no-ansi'))\\n if options.get('--no-ansi'):\\n command_options['--no-color'] = True\\n return functools.partial(perform_command, options, handler, command_options)\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/cli/main.py::perform_command\",\n \"text\": \"compose/cli/main.py::perform_command\\ndef perform_command(options, handler, command_options):\\n if options['COMMAND'] in ('help', 'version'):\\n # Skip looking up the compose file.\\n handler(command_options)\\n return\\n\\n if options['COMMAND'] == 'config':\\n command = TopLevelCommand(None, options=options)\\n handler(command, command_options)\\n return\\n\\n project = project_from_options('.', options)\\n command = TopLevelCommand(project, options=options)\\n with errors.handle_connection_errors(project.client):\\n handler(command, command_options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_logging\",\n \"text\": \"compose/cli/main.py::setup_logging\\ndef setup_logging():\\n root_logger = logging.getLogger()\\n root_logger.addHandler(console_handler)\\n root_logger.setLevel(logging.DEBUG)\\n\\n # Disable requests logging\\n logging.getLogger(\\\"requests\\\").propagate = False\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_parallel_logger\",\n \"text\": \"compose/cli/main.py::setup_parallel_logger\\ndef setup_parallel_logger(noansi):\\n if noansi:\\n import compose.parallel\\n compose.parallel.ParallelStreamWriter.set_noansi()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_console_handler\",\n \"text\": \"compose/cli/main.py::setup_console_handler\\ndef setup_console_handler(handler, verbose, noansi=False, level=None):\\n if handler.stream.isatty() and noansi is False:\\n format_class = ConsoleWarningFormatter\\n else:\\n format_class = logging.Formatter\\n\\n if verbose:\\n handler.setFormatter(format_class('%(name)s.%(funcName)s: %(message)s'))\\n loglevel = logging.DEBUG\\n else:\\n handler.setFormatter(format_class())\\n loglevel = logging.INFO\\n\\n if level is not None:\\n levels = {\\n 'DEBUG': logging.DEBUG,\\n 'INFO': logging.INFO,\\n 'WARNING': logging.WARNING,\\n 'ERROR': logging.ERROR,\\n 'CRITICAL': logging.CRITICAL,\\n }\\n loglevel = levels.get(level.upper())\\n if loglevel is None:\\n raise UserError(\\n 'Invalid value for --log-level. Expected one of DEBUG, INFO, WARNING, ERROR, CRITICAL.'\\n )\\n\\n handler.setLevel(loglevel)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::parse_doc_section\",\n \"text\": \"compose/cli/main.py::parse_doc_section\\ndef parse_doc_section(name, source):\\n pattern = re.compile('^([^\\\\n]*' + name + '[^\\\\n]*\\\\n?(?:[ \\\\t].*?(?:\\\\n|$))*)',\\n re.IGNORECASE | re.MULTILINE)\\n return [s.strip() for s in pattern.findall(source)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.__init__\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.__init__\\n def __init__(self, project, options=None):\\n self.project = project\\n self.toplevel_options = options or {}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.project_dir\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.project_dir\\n @property\\n def project_dir(self):\\n return self.toplevel_options.get('--project-directory') or '.'\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.toplevel_environment\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.toplevel_environment\\n @property\\n def toplevel_environment(self):\\n environment_file = self.toplevel_options.get('--env-file')\\n return Environment.from_env_file(self.project_dir, environment_file)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.build\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.build\\n def build(self, options):\\n \\\"\\\"\\\"\\n Build or rebuild services.\\n\\n Services are built once and then tagged as `project_service`,\\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\\n contents of its build directory, you can run `docker-compose build` to rebuild it.\\n\\n Usage: build [options] [--build-arg key=val...] [SERVICE...]\\n\\n Options:\\n --build-arg key=val Set build-time variables for services.\\n --compress Compress the build context using gzip.\\n --force-rm Always remove intermediate containers.\\n -m, --memory MEM Set memory limit for the build container.\\n --no-cache Do not use cache when building the image.\\n --no-rm Do not remove intermediate containers after a successful build.\\n --parallel Build images in parallel.\\n --progress string Set type of progress output (auto, plain, tty).\\n EXPERIMENTAL flag for native builder.\\n To enable, run with COMPOSE_DOCKER_CLI_BUILD=1)\\n --pull Always attempt to pull a newer version of the image.\\n -q, --quiet Don't print anything to STDOUT\\n \\\"\\\"\\\"\\n service_names = options['SERVICE']\\n build_args = options.get('--build-arg', None)\\n if build_args:\\n if not service_names and docker.utils.version_lt(self.project.client.api_version, '1.25'):\\n raise UserError(\\n '--build-arg is only supported when services are specified for API version < 1.25.'\\n ' Please use a Compose file version > 2.2 or specify which services to build.'\\n )\\n build_args = resolve_build_args(build_args, self.toplevel_environment)\\n\\n native_builder = self.toplevel_\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.bundle\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.bundle\\n def bundle(self, options):\\n \\\"\\\"\\\"\\n Generate a Distributed Application Bundle (DAB) from the Compose file.\\n\\n Images must have digests stored, which requires interaction with a\\n Docker registry. If digests aren't stored for all images, you can fetch\\n them with `docker-compose pull` or `docker-compose push`. To push images\\n automatically when bundling, pass `--push-images`. Only services with\\n a `build` option specified will have their images pushed.\\n\\n Usage: bundle [options]\\n\\n Options:\\n --push-images Automatically push images for any services\\n which have a `build` option specified.\\n\\n -o, --output PATH Path to write the bundle file to.\\n Defaults to \\\"<project name>.dab\\\".\\n \\\"\\\"\\\"\\n compose_config = get_config_from_options('.', self.toplevel_options)\\n\\n output = options[\\\"--output\\\"]\\n if not output:\\n output = \\\"{}.dab\\\".format(self.project.name)\\n\\n image_digests = image_digests_for_project(self.project, options['--push-images'])\\n\\n with open(output, 'w') as f:\\n f.write(serialize_bundle(compose_config, image_digests))\\n\\n log.info(\\\"Wrote bundle to {}\\\".format(output))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.config\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.config\\n def config(self, options):\\n \\\"\\\"\\\"\\n Validate and view the Compose file.\\n\\n Usage: config [options]\\n\\n Options:\\n --resolve-image-digests Pin image tags to digests.\\n --no-interpolate Don't interpolate environment variables\\n -q, --quiet Only validate the configuration, don't print\\n anything.\\n --services Print the service names, one per line.\\n --volumes Print the volume names, one per line.\\n --hash=\\\"*\\\" Print the service config hash, one per line.\\n Set \\\"service1,service2\\\" for a list of specified services\\n or use the wildcard symbol to display all services\\n \\\"\\\"\\\"\\n\\n additional_options = {'--no-interpolate': options.get('--no-interpolate')}\\n compose_config = get_config_from_options('.', self.toplevel_options, additional_options)\\n image_digests = None\\n\\n if options['--resolve-image-digests']:\\n self.project = project_from_options('.', self.toplevel_options, additional_options)\\n with errors.handle_connection_errors(self.project.client):\\n image_digests = image_digests_for_project(self.project)\\n\\n if options['--quiet']:\\n return\\n\\n if options['--services']:\\n print('\\\\n'.join(service['name'] for service in compose_config.services))\\n return\\n\\n if options['--volumes']:\\n print('\\\\n'.join(volume for volume in compose_config.volumes))\\n return\\n\\n if options['--hash'] is not None:\\n h = options['--hash']\\n self.project = project_from_options('.', self.toplevel_options, additional_options)\\n services = [svc for svc in options['--hash'].split(',')] if h != '*' else None\\n with errors.handle_connection_e\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.create\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.create\\n def create(self, options):\\n \\\"\\\"\\\"\\n Creates containers for a service.\\n This command is deprecated. Use the `up` command with `--no-start` instead.\\n\\n Usage: create [options] [SERVICE...]\\n\\n Options:\\n --force-recreate Recreate containers even if their configuration and\\n image haven't changed. Incompatible with --no-recreate.\\n --no-recreate If containers already exist, don't recreate them.\\n Incompatible with --force-recreate.\\n --no-build Don't build an image, even if it's missing.\\n --build Build images before creating containers.\\n \\\"\\\"\\\"\\n service_names = options['SERVICE']\\n\\n log.warning(\\n 'The create command is deprecated. '\\n 'Use the up command with the --no-start flag instead.'\\n )\\n\\n self.project.create(\\n service_names=service_names,\\n strategy=convergence_strategy_from_opts(options),\\n do_build=build_action_from_opts(options),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.down\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.down\\n def down(self, options):\\n \\\"\\\"\\\"\\n Stops containers and removes containers, networks, volumes, and images\\n created by `up`.\\n\\n By default, the only things removed are:\\n\\n - Containers for services defined in the Compose file\\n - Networks defined in the `networks` section of the Compose file\\n - The default network, if one is used\\n\\n Networks and volumes defined as `external` are never removed.\\n\\n Usage: down [options]\\n\\n Options:\\n --rmi type Remove images. Type must be one of:\\n 'all': Remove all images used by any service.\\n 'local': Remove only images that don't have a\\n custom tag set by the `image` field.\\n -v, --volumes Remove named volumes declared in the `volumes`\\n section of the Compose file and anonymous volumes\\n attached to containers.\\n --remove-orphans Remove containers for services not defined in the\\n Compose file\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n ignore_orphans = self.toplevel_environment.get_boolean('COMPOSE_IGNORE_ORPHANS')\\n\\n if ignore_orphans and options['--remove-orphans']:\\n raise UserError(\\\"COMPOSE_IGNORE_ORPHANS and --remove-orphans cannot be combined.\\\")\\n\\n image_type = image_type_from_opt('--rmi', options['--rmi'])\\n timeout = timeout_from_opts(options)\\n self.project.down(\\n image_type,\\n options['--volumes'],\\n options['--remove-orphans'],\\n timeout=timeout,\\n ignore_orphans=ignore_orphans)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events\\n def events(self, options):\\n \\\"\\\"\\\"\\n Receive real time events from containers.\\n\\n Usage: events [options] [SERVICE...]\\n\\n Options:\\n --json Output events as a stream of json objects\\n \\\"\\\"\\\"\\n def format_event(event):\\n attributes = [\\\"%s=%s\\\" % item for item in event['attributes'].items()]\\n return (\\\"{time} {type} {action} {id} ({attrs})\\\").format(\\n attrs=\\\", \\\".join(sorted(attributes)),\\n **event)\\n\\n def json_format_event(event):\\n event['time'] = event['time'].isoformat()\\n event.pop('container')\\n return json.dumps(event)\\n\\n for event in self.project.events():\\n formatter = json_format_event if options['--json'] else format_event\\n print(formatter(event))\\n sys.stdout.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events.format_event\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events.format_event\\n def format_event(event):\\n attributes = [\\\"%s=%s\\\" % item for item in event['attributes'].items()]\\n return (\\\"{time} {type} {action} {id} ({attrs})\\\").format(\\n attrs=\\\", \\\".join(sorted(attributes)),\\n **event)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events.json_format_event\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events.json_format_event\\n def json_format_event(event):\\n event['time'] = event['time'].isoformat()\\n event.pop('container')\\n return json.dumps(event)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.exec_command\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.exec_command\\n def exec_command(self, options):\\n \\\"\\\"\\\"\\n Execute a command in a running container\\n\\n Usage: exec [options] [-e KEY=VAL...] SERVICE COMMAND [ARGS...]\\n\\n Options:\\n -d, --detach Detached mode: Run command in the background.\\n --privileged Give extended privileges to the process.\\n -u, --user USER Run the command as this user.\\n -T Disable pseudo-tty allocation. By default `docker-compose exec`\\n allocates a TTY.\\n --index=index index of the container if there are multiple\\n instances of a service [default: 1]\\n -e, --env KEY=VAL Set environment variables (can be used multiple times,\\n not supported in API < 1.25)\\n -w, --workdir DIR Path to workdir directory for this command.\\n \\\"\\\"\\\"\\n use_cli = not self.toplevel_environment.get_boolean('COMPOSE_INTERACTIVE_NO_CLI')\\n index = int(options.get('--index'))\\n service = self.project.get_service(options['SERVICE'])\\n detach = options.get('--detach')\\n\\n if options['--env'] and docker.utils.version_lt(self.project.client.api_version, '1.25'):\\n raise UserError(\\\"Setting environment for exec is not supported in API < 1.25 (%s)\\\"\\n % self.project.client.api_version)\\n\\n if options['--workdir'] and docker.utils.version_lt(self.project.client.api_version, '1.35'):\\n raise UserError(\\\"Setting workdir for exec is not supported in API < 1.35 (%s)\\\"\\n % self.project.client.api_version)\\n\\n try:\\n container = service.get_container(number=index)\\n except ValueError as e:\\n raise UserError(str(e))\\n command = [options['COMMAND']] + options['ARGS']\\n tty = not options[\\\"-T\\\"]\\n\\n if IS_WINDOWS_PLATFORM or use_cli and not detach:\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.help\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.help\\n @classmethod\\n def help(cls, options):\\n \\\"\\\"\\\"\\n Get help on a command.\\n\\n Usage: help [COMMAND]\\n \\\"\\\"\\\"\\n if options['COMMAND']:\\n subject = get_handler(cls, options['COMMAND'])\\n else:\\n subject = cls\\n\\n print(getdoc(subject))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.images\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.images\\n def images(self, options):\\n \\\"\\\"\\\"\\n List images used by the created containers.\\n Usage: images [options] [SERVICE...]\\n\\n Options:\\n -q, --quiet Only display IDs\\n \\\"\\\"\\\"\\n containers = sorted(\\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\\n key=attrgetter('name'))\\n\\n if options['--quiet']:\\n for image in set(c.image for c in containers):\\n print(image.split(':')[1])\\n return\\n\\n def add_default_tag(img_name):\\n if ':' not in img_name.split('/')[-1]:\\n return '{}:latest'.format(img_name)\\n return img_name\\n\\n headers = [\\n 'Container',\\n 'Repository',\\n 'Tag',\\n 'Image Id',\\n 'Size'\\n ]\\n rows = []\\n for container in containers:\\n image_config = container.image_config\\n service = self.project.get_service(container.service)\\n index = 0\\n img_name = add_default_tag(service.image_name)\\n if img_name in image_config['RepoTags']:\\n index = image_config['RepoTags'].index(img_name)\\n repo_tags = (\\n image_config['RepoTags'][index].rsplit(':', 1) if image_config['RepoTags']\\n else ('<none>', '<none>')\\n )\\n\\n image_id = image_config['Id'].split(':')[1][:12]\\n size = human_readable_file_size(image_config['Size'])\\n rows.append([\\n container.name,\\n repo_tags[0],\\n repo_tags[1],\\n image_id,\\n size\\n ])\\n print(Formatter.table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.images.add_default_tag\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.images.add_default_tag\\n def add_default_tag(img_name):\\n if ':' not in img_name.split('/')[-1]:\\n return '{}:latest'.format(img_name)\\n return img_name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.kill\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.kill\\n def kill(self, options):\\n \\\"\\\"\\\"\\n Force stop service containers.\\n\\n Usage: kill [options] [SERVICE...]\\n\\n Options:\\n -s SIGNAL SIGNAL to send to the container.\\n Default signal is SIGKILL.\\n \\\"\\\"\\\"\\n signal = options.get('-s', 'SIGKILL')\\n\\n self.project.kill(service_names=options['SERVICE'], signal=signal)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.logs\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.logs\\n def logs(self, options):\\n \\\"\\\"\\\"\\n View output from containers.\\n\\n Usage: logs [options] [SERVICE...]\\n\\n Options:\\n --no-color Produce monochrome output.\\n -f, --follow Follow log output.\\n -t, --timestamps Show timestamps.\\n --tail=\\\"all\\\" Number of lines to show from the end of the logs\\n for each container.\\n \\\"\\\"\\\"\\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\\n\\n tail = options['--tail']\\n if tail is not None:\\n if tail.isdigit():\\n tail = int(tail)\\n elif tail != 'all':\\n raise UserError(\\\"tail flag must be all or a number\\\")\\n log_args = {\\n 'follow': options['--follow'],\\n 'tail': tail,\\n 'timestamps': options['--timestamps']\\n }\\n print(\\\"Attaching to\\\", list_containers(containers))\\n log_printer_from_project(\\n self.project,\\n containers,\\n options['--no-color'],\\n log_args,\\n event_stream=self.project.events(service_names=options['SERVICE'])).run()\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pause\\n def pause(self, options):\\n \\\"\\\"\\\"\\n Pause services.\\n\\n Usage: pause [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.pause(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to pause', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.port\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.port\\n def port(self, options):\\n \\\"\\\"\\\"\\n Print the public port for a port binding.\\n\\n Usage: port [options] SERVICE PRIVATE_PORT\\n\\n Options:\\n --protocol=proto tcp or udp [default: tcp]\\n --index=index index of the container if there are multiple\\n instances of a service [default: 1]\\n \\\"\\\"\\\"\\n index = int(options.get('--index'))\\n service = self.project.get_service(options['SERVICE'])\\n try:\\n container = service.get_container(number=index)\\n except ValueError as e:\\n raise UserError(str(e))\\n print(container.get_local_port(\\n options['PRIVATE_PORT'],\\n protocol=options.get('--protocol') or 'tcp') or '')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.ps\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.ps\\n def ps(self, options):\\n \\\"\\\"\\\"\\n List containers.\\n\\n Usage: ps [options] [SERVICE...]\\n\\n Options:\\n -q, --quiet Only display IDs\\n --services Display services\\n --filter KEY=VAL Filter services by a property\\n -a, --all Show all stopped containers (including those created by the run command)\\n \\\"\\\"\\\"\\n if options['--quiet'] and options['--services']:\\n raise UserError('--quiet and --services cannot be combined')\\n\\n if options['--services']:\\n filt = build_filter(options.get('--filter'))\\n services = self.project.services\\n if filt:\\n services = filter_services(filt, services, self.project)\\n print('\\\\n'.join(service.name for service in services))\\n return\\n\\n if options['--all']:\\n containers = sorted(self.project.containers(service_names=options['SERVICE'],\\n one_off=OneOffFilter.include, stopped=True),\\n key=attrgetter('name'))\\n else:\\n containers = sorted(\\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\\n key=attrgetter('name'))\\n\\n if options['--quiet']:\\n for container in containers:\\n print(container.id)\\n else:\\n headers = [\\n 'Name',\\n 'Command',\\n 'State',\\n 'Ports',\\n ]\\n rows = []\\n for container in containers:\\n command = container.human_readable_command\\n if len(command) > 30:\\n command = '%s ...' % command[:26]\\n rows.append([\\n container.name,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pull\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pull\\n def pull(self, options):\\n \\\"\\\"\\\"\\n Pulls images for services defined in a Compose file, but does not start the containers.\\n\\n Usage: pull [options] [SERVICE...]\\n\\n Options:\\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\\n --parallel Deprecated, pull multiple images in parallel (enabled by default).\\n --no-parallel Disable parallel pulling.\\n -q, --quiet Pull without printing progress information\\n --include-deps Also pull services declared as dependencies\\n \\\"\\\"\\\"\\n if options.get('--parallel'):\\n log.warning('--parallel option is deprecated and will be removed in future versions.')\\n self.project.pull(\\n service_names=options['SERVICE'],\\n ignore_pull_failures=options.get('--ignore-pull-failures'),\\n parallel_pull=not options.get('--no-parallel'),\\n silent=options.get('--quiet'),\\n include_deps=options.get('--include-deps'),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.push\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.push\\n def push(self, options):\\n \\\"\\\"\\\"\\n Pushes images for services.\\n\\n Usage: push [options] [SERVICE...]\\n\\n Options:\\n --ignore-push-failures Push what it can and ignores images with push failures.\\n \\\"\\\"\\\"\\n self.project.push(\\n service_names=options['SERVICE'],\\n ignore_push_failures=options.get('--ignore-push-failures')\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.rm\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.rm\\n def rm(self, options):\\n \\\"\\\"\\\"\\n Removes stopped service containers.\\n\\n By default, anonymous volumes attached to containers will not be removed. You\\n can override this with `-v`. To list all volumes, use `docker volume ls`.\\n\\n Any data which is not in a volume will be lost.\\n\\n Usage: rm [options] [SERVICE...]\\n\\n Options:\\n -f, --force Don't ask to confirm removal\\n -s, --stop Stop the containers, if required, before removing\\n -v Remove any anonymous volumes attached to containers\\n -a, --all Deprecated - no effect.\\n \\\"\\\"\\\"\\n if options.get('--all'):\\n log.warning(\\n '--all flag is obsolete. This is now the default behavior '\\n 'of `docker-compose rm`'\\n )\\n one_off = OneOffFilter.include\\n\\n if options.get('--stop'):\\n self.project.stop(service_names=options['SERVICE'], one_off=one_off)\\n\\n all_containers = self.project.containers(\\n service_names=options['SERVICE'], stopped=True, one_off=one_off\\n )\\n stopped_containers = [c for c in all_containers if not c.is_running]\\n\\n if len(stopped_containers) > 0:\\n print(\\\"Going to remove\\\", list_containers(stopped_containers))\\n if options.get('--force') \\\\\\n or yesno(\\\"Are you sure? [yN] \\\", default=False):\\n self.project.remove_stopped(\\n service_names=options['SERVICE'],\\n v=options.get('-v', False),\\n one_off=one_off\\n )\\n else:\\n print(\\\"No stopped containers\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.run\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.run\\n def run(self, options):\\n \\\"\\\"\\\"\\n Run a one-off command on a service.\\n\\n For example:\\n\\n $ docker-compose run web python manage.py shell\\n\\n By default, linked services will be started, unless they are already\\n running. If you do not want to start linked services, use\\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\\n\\n Usage:\\n run [options] [-v VOLUME...] [-p PORT...] [-e KEY=VAL...] [-l KEY=VALUE...]\\n SERVICE [COMMAND] [ARGS...]\\n\\n Options:\\n -d, --detach Detached mode: Run container in the background, print\\n new container name.\\n --name NAME Assign a name to the container\\n --entrypoint CMD Override the entrypoint of the image.\\n -e KEY=VAL Set an environment variable (can be used multiple times)\\n -l, --label KEY=VAL Add or override a label (can be used multiple times)\\n -u, --user=\\\"\\\" Run as specified username or uid\\n --no-deps Don't start linked services.\\n --rm Remove container after run. Ignored in detached mode.\\n -p, --publish=[] Publish a container's port(s) to the host\\n --service-ports Run command with the service's ports enabled and mapped\\n to the host.\\n --use-aliases Use the service's network aliases in the network(s) the\\n container connects to.\\n -v, --volume=[] Bind mount a volume (default [])\\n -T Disable pseudo-tty allocation. By default `docker-compose run`\\n allocates a TTY.\\n -w, --workdir=\\\"\\\" Working directory inside the container\\n \\\"\\\"\\\"\\n service = self.project.get_service(options['SERVICE'])\\n detach = o\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.scale\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.scale\\n def scale(self, options):\\n \\\"\\\"\\\"\\n Set number of containers to run for a service.\\n\\n Numbers are specified in the form `service=num` as arguments.\\n For example:\\n\\n $ docker-compose scale web=2 worker=3\\n\\n This command is deprecated. Use the up command with the `--scale` flag\\n instead.\\n\\n Usage: scale [options] [SERVICE=NUM...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = timeout_from_opts(options)\\n\\n if self.project.config_version == V2_2:\\n raise UserError(\\n 'The scale command is incompatible with the v2.2 format. '\\n 'Use the up command with the --scale flag instead.'\\n )\\n else:\\n log.warning(\\n 'The scale command is deprecated. '\\n 'Use the up command with the --scale flag instead.'\\n )\\n\\n for service_name, num in parse_scale_args(options['SERVICE=NUM']).items():\\n self.project.get_service(service_name).scale(num, timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.start\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.start\\n def start(self, options):\\n \\\"\\\"\\\"\\n Start existing containers.\\n\\n Usage: start [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.start(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to start', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.stop\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.stop\\n def stop(self, options):\\n \\\"\\\"\\\"\\n Stop running containers without removing them.\\n\\n They can be started again with `docker-compose start`.\\n\\n Usage: stop [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = timeout_from_opts(options)\\n self.project.stop(service_names=options['SERVICE'], timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.restart\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.restart\\n def restart(self, options):\\n \\\"\\\"\\\"\\n Restart running containers.\\n\\n Usage: restart [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = timeout_from_opts(options)\\n containers = self.project.restart(service_names=options['SERVICE'], timeout=timeout)\\n exit_if(not containers, 'No containers to restart', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.top\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.top\\n def top(self, options):\\n \\\"\\\"\\\"\\n Display the running processes\\n\\n Usage: top [SERVICE...]\\n\\n \\\"\\\"\\\"\\n containers = sorted(\\n self.project.containers(service_names=options['SERVICE'], stopped=False) +\\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\\n key=attrgetter('name')\\n )\\n\\n for idx, container in enumerate(containers):\\n if idx > 0:\\n print()\\n\\n top_data = self.project.client.top(container.name)\\n headers = top_data.get(\\\"Titles\\\")\\n rows = []\\n\\n for process in top_data.get(\\\"Processes\\\", []):\\n rows.append(process)\\n\\n print(container.name)\\n print(Formatter.table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.unpause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.unpause\\n def unpause(self, options):\\n \\\"\\\"\\\"\\n Unpause services.\\n\\n Usage: unpause [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.unpause(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to unpause', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.up\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.up\\n def up(self, options):\\n \\\"\\\"\\\"\\n Builds, (re)creates, starts, and attaches to containers for a service.\\n\\n Unless they are already running, this command also starts any linked services.\\n\\n The `docker-compose up` command aggregates the output of each container. When\\n the command exits, all containers are stopped. Running `docker-compose up -d`\\n starts the containers in the background and leaves them running.\\n\\n If there are existing containers for a service, and the service's configuration\\n or image was changed after the container's creation, `docker-compose up` picks\\n up the changes by stopping and recreating the containers (preserving mounted\\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\\n flag.\\n\\n If you want to force Compose to stop and recreate all containers, use the\\n `--force-recreate` flag.\\n\\n Usage: up [options] [--scale SERVICE=NUM...] [SERVICE...]\\n\\n Options:\\n -d, --detach Detached mode: Run containers in the background,\\n print new container names. Incompatible with\\n --abort-on-container-exit.\\n --no-color Produce monochrome output.\\n --quiet-pull Pull without printing progress information\\n --no-deps Don't start linked services.\\n --force-recreate Recreate containers even if their configuration\\n and image haven't changed.\\n --always-recreate-deps Recreate dependent containers.\\n Incompatible with --no-recreate.\\n --no-recreate If containers already exist, don't recreate\\n them. Incompatible with --force-recreate and -V.\\n --no-build \",\n \"label\": 1\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.version\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.version\\n @classmethod\\n def version(cls, options):\\n \\\"\\\"\\\"\\n Show version information\\n\\n Usage: version [--short]\\n\\n Options:\\n --short Shows only Compose's version number.\\n \\\"\\\"\\\"\\n if options['--short']:\\n print(__version__)\\n else:\\n print(get_version_info('full'))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::compute_service_exit_code\",\n \"text\": \"compose/cli/main.py::compute_service_exit_code\\ndef compute_service_exit_code(exit_value_from, attached_containers):\\n candidates = list(filter(\\n lambda c: c.service == exit_value_from,\\n attached_containers))\\n if not candidates:\\n log.error(\\n 'No containers matching the spec \\\"{0}\\\" '\\n 'were run.'.format(exit_value_from)\\n )\\n return 2\\n if len(candidates) > 1:\\n exit_values = filter(\\n lambda e: e != 0,\\n [c.inspect()['State']['ExitCode'] for c in candidates]\\n )\\n\\n return exit_values[0]\\n return candidates[0].inspect()['State']['ExitCode']\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::compute_exit_code\",\n \"text\": \"compose/cli/main.py::compute_exit_code\\ndef compute_exit_code(exit_value_from, attached_containers, cascade_starter, all_containers):\\n exit_code = 0\\n for e in all_containers:\\n if (not e.is_running and cascade_starter == e.name):\\n if not e.exit_code == 0:\\n exit_code = e.exit_code\\n break\\n\\n return exit_code\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::convergence_strategy_from_opts\",\n \"text\": \"compose/cli/main.py::convergence_strategy_from_opts\\ndef convergence_strategy_from_opts(options):\\n no_recreate = options['--no-recreate']\\n force_recreate = options['--force-recreate']\\n renew_anonymous_volumes = options.get('--renew-anon-volumes')\\n if force_recreate and no_recreate:\\n raise UserError(\\\"--force-recreate and --no-recreate cannot be combined.\\\")\\n\\n if no_recreate and renew_anonymous_volumes:\\n raise UserError('--no-recreate and --renew-anon-volumes cannot be combined.')\\n\\n if force_recreate or renew_anonymous_volumes:\\n return ConvergenceStrategy.always\\n\\n if no_recreate:\\n return ConvergenceStrategy.never\\n\\n return ConvergenceStrategy.changed\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::timeout_from_opts\",\n \"text\": \"compose/cli/main.py::timeout_from_opts\\ndef timeout_from_opts(options):\\n timeout = options.get('--timeout')\\n return None if timeout is None else int(timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::image_digests_for_project\",\n \"text\": \"compose/cli/main.py::image_digests_for_project\\ndef image_digests_for_project(project, allow_push=False):\\n try:\\n return get_image_digests(\\n project,\\n allow_push=allow_push\\n )\\n except MissingDigests as e:\\n def list_images(images):\\n return \\\"\\\\n\\\".join(\\\" {}\\\".format(name) for name in sorted(images))\\n\\n paras = [\\\"Some images are missing digests.\\\"]\\n\\n if e.needs_push:\\n command_hint = (\\n \\\"Use `docker-compose push {}` to push them. \\\"\\n .format(\\\" \\\".join(sorted(e.needs_push)))\\n )\\n paras += [\\n \\\"The following images can be pushed:\\\",\\n list_images(e.needs_push),\\n command_hint,\\n ]\\n\\n if e.needs_pull:\\n command_hint = (\\n \\\"Use `docker-compose pull {}` to pull them. \\\"\\n .format(\\\" \\\".join(sorted(e.needs_pull)))\\n )\\n\\n paras += [\\n \\\"The following images need to be pulled:\\\",\\n list_images(e.needs_pull),\\n command_hint,\\n ]\\n\\n raise UserError(\\\"\\\\n\\\\n\\\".join(paras))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::exitval_from_opts\",\n \"text\": \"compose/cli/main.py::exitval_from_opts\\ndef exitval_from_opts(options, project):\\n exit_value_from = options.get('--exit-code-from')\\n if exit_value_from:\\n if not options.get('--abort-on-container-exit'):\\n log.warning('using --exit-code-from implies --abort-on-container-exit')\\n options['--abort-on-container-exit'] = True\\n if exit_value_from not in [s.name for s in project.get_services()]:\\n log.error('No service named \\\"%s\\\" was found in your compose file.',\\n exit_value_from)\\n sys.exit(2)\\n return exit_value_from\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::image_type_from_opt\",\n \"text\": \"compose/cli/main.py::image_type_from_opt\\ndef image_type_from_opt(flag, value):\\n if not value:\\n return ImageType.none\\n try:\\n return ImageType[value]\\n except KeyError:\\n raise UserError(\\\"%s flag must be one of: all, local\\\" % flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::build_action_from_opts\",\n \"text\": \"compose/cli/main.py::build_action_from_opts\\ndef build_action_from_opts(options):\\n if options['--build'] and options['--no-build']:\\n raise UserError(\\\"--build and --no-build can not be combined.\\\")\\n\\n if options['--build']:\\n return BuildAction.force\\n\\n if options['--no-build']:\\n return BuildAction.skip\\n\\n return BuildAction.none\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::build_one_off_container_options\",\n \"text\": \"compose/cli/main.py::build_one_off_container_options\\ndef build_one_off_container_options(options, detach, command):\\n container_options = {\\n 'command': command,\\n 'tty': not (detach or options['-T'] or not sys.stdin.isatty()),\\n 'stdin_open': options.get('stdin_open'),\\n 'detach': detach,\\n }\\n\\n if options['-e']:\\n container_options['environment'] = Environment.from_command_line(\\n parse_environment(options['-e'])\\n )\\n\\n if options['--label']:\\n container_options['labels'] = parse_labels(options['--label'])\\n\\n if options.get('--entrypoint') is not None:\\n container_options['entrypoint'] = (\\n [\\\"\\\"] if options['--entrypoint'] == '' else options['--entrypoint']\\n )\\n\\n # Ensure that run command remains one-off (issue #6302)\\n container_options['restart'] = None\\n\\n if options['--user']:\\n container_options['user'] = options.get('--user')\\n\\n if not options['--service-ports']:\\n container_options['ports'] = []\\n\\n if options['--publish']:\\n container_options['ports'] = options.get('--publish')\\n\\n if options['--name']:\\n container_options['name'] = options['--name']\\n\\n if options['--workdir']:\\n container_options['working_dir'] = options['--workdir']\\n\\n if options['--volume']:\\n volumes = [VolumeSpec.parse(i) for i in options['--volume']]\\n container_options['volumes'] = volumes\\n\\n return container_options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::run_one_off_container\",\n \"text\": \"compose/cli/main.py::run_one_off_container\\ndef run_one_off_container(container_options, project, service, options, toplevel_options,\\n toplevel_environment):\\n if not options['--no-deps']:\\n deps = service.get_dependency_names()\\n if deps:\\n project.up(\\n service_names=deps,\\n start_deps=True,\\n strategy=ConvergenceStrategy.never,\\n rescale=False\\n )\\n\\n project.initialize()\\n\\n container = service.create_container(\\n quiet=True,\\n one_off=True,\\n **container_options)\\n\\n use_network_aliases = options['--use-aliases']\\n\\n if options.get('--detach'):\\n service.start_container(container, use_network_aliases)\\n print(container.name)\\n return\\n\\n def remove_container(force=False):\\n if options['--rm']:\\n project.client.remove_container(container.id, force=True, v=True)\\n\\n use_cli = not toplevel_environment.get_boolean('COMPOSE_INTERACTIVE_NO_CLI')\\n\\n signals.set_signal_handler_to_shutdown()\\n signals.set_signal_handler_to_hang_up()\\n try:\\n try:\\n if IS_WINDOWS_PLATFORM or use_cli:\\n service.connect_container_to_networks(container, use_network_aliases)\\n exit_code = call_docker(\\n get_docker_start_call(container_options, container.id),\\n toplevel_options, toplevel_environment\\n )\\n else:\\n operation = RunOperation(\\n project.client,\\n container.id,\\n interactive=not options['-T'],\\n logs=False,\\n )\\n pty = PseudoTerminal(project.client, operation)\\n sockets = pty.sockets()\\n service.start_container(container, use_network_aliases)\\n pty.start(sockets)\\n exit_code = container.wait()\\n except (signals.ShutdownException):\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::run_one_off_container.remove_container\",\n \"text\": \"compose/cli/main.py::run_one_off_container.remove_container\\n def remove_container(force=False):\\n if options['--rm']:\\n project.client.remove_container(container.id, force=True, v=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::get_docker_start_call\",\n \"text\": \"compose/cli/main.py::get_docker_start_call\\ndef get_docker_start_call(container_options, container_id):\\n docker_call = [\\\"start\\\"]\\n if not container_options.get('detach'):\\n docker_call.append(\\\"--attach\\\")\\n if container_options.get('stdin_open'):\\n docker_call.append(\\\"--interactive\\\")\\n docker_call.append(container_id)\\n return docker_call\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::log_printer_from_project\",\n \"text\": \"compose/cli/main.py::log_printer_from_project\\ndef log_printer_from_project(\\n project,\\n containers,\\n monochrome,\\n log_args,\\n cascade_stop=False,\\n event_stream=None,\\n):\\n return LogPrinter(\\n containers,\\n build_log_presenters(project.service_names, monochrome),\\n event_stream or project.events(),\\n cascade_stop=cascade_stop,\\n log_args=log_args)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::filter_containers_to_service_names\",\n \"text\": \"compose/cli/main.py::filter_containers_to_service_names\\ndef filter_containers_to_service_names(containers, service_names):\\n if not service_names:\\n return containers\\n\\n return [\\n container\\n for container in containers if container.service in service_names\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::up_shutdown_context\",\n \"text\": \"compose/cli/main.py::up_shutdown_context\\n@contextlib.contextmanager\\ndef up_shutdown_context(project, service_names, timeout, detached):\\n if detached:\\n yield\\n return\\n\\n signals.set_signal_handler_to_shutdown()\\n try:\\n try:\\n yield\\n except signals.ShutdownException:\\n print(\\\"Gracefully stopping... (press Ctrl+C again to force)\\\")\\n project.stop(service_names=service_names, timeout=timeout)\\n except signals.ShutdownException:\\n project.kill(service_names=service_names)\\n sys.exit(2)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::list_containers\",\n \"text\": \"compose/cli/main.py::list_containers\\ndef list_containers(containers):\\n return \\\", \\\".join(c.name for c in containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::exit_if\",\n \"text\": \"compose/cli/main.py::exit_if\\ndef exit_if(condition, message, exit_code):\\n if condition:\\n log.error(message)\\n raise SystemExit(exit_code)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::call_docker\",\n \"text\": \"compose/cli/main.py::call_docker\\ndef call_docker(args, dockeropts, environment):\\n executable_path = find_executable('docker')\\n if not executable_path:\\n raise UserError(errors.docker_not_found_msg(\\\"Couldn't find `docker` binary.\\\"))\\n\\n tls = dockeropts.get('--tls', False)\\n ca_cert = dockeropts.get('--tlscacert')\\n cert = dockeropts.get('--tlscert')\\n key = dockeropts.get('--tlskey')\\n verify = dockeropts.get('--tlsverify')\\n host = dockeropts.get('--host')\\n tls_options = []\\n if tls:\\n tls_options.append('--tls')\\n if ca_cert:\\n tls_options.extend(['--tlscacert', ca_cert])\\n if cert:\\n tls_options.extend(['--tlscert', cert])\\n if key:\\n tls_options.extend(['--tlskey', key])\\n if verify:\\n tls_options.append('--tlsverify')\\n if host:\\n tls_options.extend(\\n ['--host', re.sub(r'^https?://', 'tcp://', host.lstrip('='))]\\n )\\n\\n args = [executable_path] + tls_options + args\\n log.debug(\\\" \\\".join(map(pipes.quote, args)))\\n\\n return subprocess.call(args, env=environment)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::parse_scale_args\",\n \"text\": \"compose/cli/main.py::parse_scale_args\\ndef parse_scale_args(options):\\n res = {}\\n for s in options:\\n if '=' not in s:\\n raise UserError('Arguments to scale should be in the form service=num')\\n service_name, num = s.split('=', 1)\\n try:\\n num = int(num)\\n except ValueError:\\n raise UserError(\\n 'Number of containers for service \\\"%s\\\" is not a number' % service_name\\n )\\n res[service_name] = num\\n return res\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::build_exec_command\",\n \"text\": \"compose/cli/main.py::build_exec_command\\ndef build_exec_command(options, container_id, command):\\n args = [\\\"exec\\\"]\\n\\n if options[\\\"--detach\\\"]:\\n args += [\\\"--detach\\\"]\\n else:\\n args += [\\\"--interactive\\\"]\\n\\n if not options[\\\"-T\\\"]:\\n args += [\\\"--tty\\\"]\\n\\n if options[\\\"--privileged\\\"]:\\n args += [\\\"--privileged\\\"]\\n\\n if options[\\\"--user\\\"]:\\n args += [\\\"--user\\\", options[\\\"--user\\\"]]\\n\\n if options[\\\"--env\\\"]:\\n for env_variable in options[\\\"--env\\\"]:\\n args += [\\\"--env\\\", env_variable]\\n\\n if options[\\\"--workdir\\\"]:\\n args += [\\\"--workdir\\\", options[\\\"--workdir\\\"]]\\n\\n args += [container_id]\\n args += command\\n return args\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::has_container_with_state\",\n \"text\": \"compose/cli/main.py::has_container_with_state\\ndef has_container_with_state(containers, state):\\n states = {\\n 'running': lambda c: c.is_running,\\n 'stopped': lambda c: not c.is_running,\\n 'paused': lambda c: c.is_paused,\\n 'restarting': lambda c: c.is_restarting,\\n }\\n for container in containers:\\n if state not in states:\\n raise UserError(\\\"Invalid state: %s\\\" % state)\\n if states[state](container):\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::filter_services\",\n \"text\": \"compose/cli/main.py::filter_services\\ndef filter_services(filt, services, project):\\n def should_include(service):\\n for f in filt:\\n if f == 'status':\\n state = filt[f]\\n containers = project.containers([service.name], stopped=True)\\n if not has_container_with_state(containers, state):\\n return False\\n elif f == 'source':\\n source = filt[f]\\n if source == 'image' or source == 'build':\\n if source not in service.options:\\n return False\\n else:\\n raise UserError(\\\"Invalid value for source filter: %s\\\" % source)\\n else:\\n raise UserError(\\\"Invalid filter: %s\\\" % f)\\n return True\\n\\n return filter(should_include, services)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::filter_services.should_include\",\n \"text\": \"compose/cli/main.py::filter_services.should_include\\n def should_include(service):\\n for f in filt:\\n if f == 'status':\\n state = filt[f]\\n containers = project.containers([service.name], stopped=True)\\n if not has_container_with_state(containers, state):\\n return False\\n elif f == 'source':\\n source = filt[f]\\n if source == 'image' or source == 'build':\\n if source not in service.options:\\n return False\\n else:\\n raise UserError(\\\"Invalid value for source filter: %s\\\" % source)\\n else:\\n raise UserError(\\\"Invalid filter: %s\\\" % f)\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::build_filter\",\n \"text\": \"compose/cli/main.py::build_filter\\ndef build_filter(arg):\\n filt = {}\\n if arg is not None:\\n if '=' not in arg:\\n raise UserError(\\\"Arguments to --filter should be in form KEY=VAL\\\")\\n key, val = arg.split('=', 1)\\n filt[key] = val\\n return filt\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::warn_for_swarm_mode\",\n \"text\": \"compose/cli/main.py::warn_for_swarm_mode\\ndef warn_for_swarm_mode(client):\\n info = client.info()\\n if info.get('Swarm', {}).get('LocalNodeState') == 'active':\\n if info.get('ServerVersion', '').startswith('ucp'):\\n # UCP does multi-node scheduling with traditional Compose files.\\n return\\n\\n log.warning(\\n \\\"The Docker Engine you're using is running in swarm mode.\\\\n\\\\n\\\"\\n \\\"Compose does not use swarm mode to deploy services to multiple nodes in a swarm. \\\"\\n \\\"All containers will be scheduled on the current node.\\\\n\\\\n\\\"\\n \\\"To deploy your application across the swarm, \\\"\\n \\\"use `docker stack deploy`.\\\\n\\\"\\n )\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/config/environment.py::split_env\",\n \"text\": \"compose/config/environment.py::split_env\\ndef split_env(env):\\n if isinstance(env, six.binary_type):\\n env = env.decode('utf-8', 'replace')\\n key = value = None\\n if '=' in env:\\n key, value = env.split('=', 1)\\n else:\\n key = env\\n if re.search(r'\\\\s', key):\\n raise ConfigurationError(\\n \\\"environment variable name '{}' may not contain whitespace.\\\".format(key)\\n )\\n return key, value\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::env_vars_from_file\",\n \"text\": \"compose/config/environment.py::env_vars_from_file\\ndef env_vars_from_file(filename):\\n \\\"\\\"\\\"\\n Read in a line delimited file of environment variables.\\n \\\"\\\"\\\"\\n if not os.path.exists(filename):\\n raise EnvFileNotFound(\\\"Couldn't find env file: {}\\\".format(filename))\\n elif not os.path.isfile(filename):\\n raise EnvFileNotFound(\\\"{} is not a file.\\\".format(filename))\\n env = {}\\n with contextlib.closing(codecs.open(filename, 'r', 'utf-8-sig')) as fileobj:\\n for line in fileobj:\\n line = line.strip()\\n if line and not line.startswith('#'):\\n try:\\n k, v = split_env(line)\\n env[k] = v\\n except ConfigurationError as e:\\n raise ConfigurationError('In file {}: {}'.format(filename, e.msg))\\n return env\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.__init__\",\n \"text\": \"compose/config/environment.py::Environment.__init__\\n def __init__(self, *args, **kwargs):\\n super(Environment, self).__init__(*args, **kwargs)\\n self.missing_keys = []\\n self.silent = False\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.from_env_file\",\n \"text\": \"compose/config/environment.py::Environment.from_env_file\\n @classmethod\\n def from_env_file(cls, base_dir, env_file=None):\\n def _initialize():\\n result = cls()\\n if base_dir is None:\\n return result\\n if env_file:\\n env_file_path = os.path.join(base_dir, env_file)\\n else:\\n env_file_path = os.path.join(base_dir, '.env')\\n try:\\n return cls(env_vars_from_file(env_file_path))\\n except EnvFileNotFound:\\n pass\\n return result\\n\\n instance = _initialize()\\n instance.update(os.environ)\\n return instance\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.from_env_file._initialize\",\n \"text\": \"compose/config/environment.py::Environment.from_env_file._initialize\\n def _initialize():\\n result = cls()\\n if base_dir is None:\\n return result\\n if env_file:\\n env_file_path = os.path.join(base_dir, env_file)\\n else:\\n env_file_path = os.path.join(base_dir, '.env')\\n try:\\n return cls(env_vars_from_file(env_file_path))\\n except EnvFileNotFound:\\n pass\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.from_command_line\",\n \"text\": \"compose/config/environment.py::Environment.from_command_line\\n @classmethod\\n def from_command_line(cls, parsed_env_opts):\\n result = cls()\\n for k, v in parsed_env_opts.items():\\n # Values from the command line take priority, unless they're unset\\n # in which case they take the value from the system's environment\\n if v is None and k in os.environ:\\n result[k] = os.environ[k]\\n else:\\n result[k] = v\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.__getitem__\",\n \"text\": \"compose/config/environment.py::Environment.__getitem__\\n def __getitem__(self, key):\\n try:\\n return super(Environment, self).__getitem__(key)\\n except KeyError:\\n if IS_WINDOWS_PLATFORM:\\n try:\\n return super(Environment, self).__getitem__(key.upper())\\n except KeyError:\\n pass\\n if not self.silent and key not in self.missing_keys:\\n log.warning(\\n \\\"The {} variable is not set. Defaulting to a blank string.\\\"\\n .format(key)\\n )\\n self.missing_keys.append(key)\\n\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.__contains__\",\n \"text\": \"compose/config/environment.py::Environment.__contains__\\n def __contains__(self, key):\\n result = super(Environment, self).__contains__(key)\\n if IS_WINDOWS_PLATFORM:\\n return (\\n result or super(Environment, self).__contains__(key.upper())\\n )\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.get\",\n \"text\": \"compose/config/environment.py::Environment.get\\n def get(self, key, *args, **kwargs):\\n if IS_WINDOWS_PLATFORM:\\n return super(Environment, self).get(\\n key,\\n super(Environment, self).get(key.upper(), *args, **kwargs)\\n )\\n return super(Environment, self).get(key, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/environment.py::Environment.get_boolean\",\n \"text\": \"compose/config/environment.py::Environment.get_boolean\\n def get_boolean(self, key):\\n # Convert a value to a boolean using \\\"common sense\\\" rules.\\n # Unset, empty, \\\"0\\\" and \\\"false\\\" (i-case) yield False.\\n # All other values yield True.\\n value = self.get(key)\\n if not value:\\n return False\\n if value.lower() in ['0', 'false']:\\n return False\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::GlobalLimit.set_global_limit\",\n \"text\": \"compose/parallel.py::GlobalLimit.set_global_limit\\n @classmethod\\n def set_global_limit(cls, value):\\n if value is None:\\n value = PARALLEL_LIMIT\\n cls.global_limiter = Semaphore(value)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::parallel_execute_watch\",\n \"text\": \"compose/parallel.py::parallel_execute_watch\\ndef parallel_execute_watch(events, writer, errors, results, msg, get_name, fail_check):\\n \\\"\\\"\\\" Watch events from a parallel execution, update status and fill errors and results.\\n Returns exception to re-raise.\\n \\\"\\\"\\\"\\n error_to_reraise = None\\n for obj, result, exception in events:\\n if exception is None:\\n if fail_check is not None and fail_check(obj):\\n writer.write(msg, get_name(obj), 'failed', red)\\n else:\\n writer.write(msg, get_name(obj), 'done', green)\\n results.append(result)\\n elif isinstance(exception, ImageNotFound):\\n # This is to bubble up ImageNotFound exceptions to the client so we\\n # can prompt the user if they want to rebuild.\\n errors[get_name(obj)] = exception.explanation\\n writer.write(msg, get_name(obj), 'error', red)\\n error_to_reraise = exception\\n elif isinstance(exception, APIError):\\n errors[get_name(obj)] = exception.explanation\\n writer.write(msg, get_name(obj), 'error', red)\\n elif isinstance(exception, (OperationFailedError, HealthCheckFailed, NoHealthCheckConfigured)):\\n errors[get_name(obj)] = exception.msg\\n writer.write(msg, get_name(obj), 'error', red)\\n elif isinstance(exception, UpstreamError):\\n writer.write(msg, get_name(obj), 'error', red)\\n else:\\n errors[get_name(obj)] = exception\\n error_to_reraise = exception\\n return error_to_reraise\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::parallel_execute\",\n \"text\": \"compose/parallel.py::parallel_execute\\ndef parallel_execute(objects, func, get_name, msg, get_deps=None, limit=None, fail_check=None):\\n \\\"\\\"\\\"Runs func on objects in parallel while ensuring that func is\\n ran on object only after it is ran on all its dependencies.\\n\\n get_deps called on object must return a collection with its dependencies.\\n get_name called on object must return its name.\\n fail_check is an additional failure check for cases that should display as a failure\\n in the CLI logs, but don't raise an exception (such as attempting to start 0 containers)\\n \\\"\\\"\\\"\\n objects = list(objects)\\n stream = get_output_stream(sys.stderr)\\n\\n if ParallelStreamWriter.instance:\\n writer = ParallelStreamWriter.instance\\n else:\\n writer = ParallelStreamWriter(stream)\\n\\n for obj in objects:\\n writer.add_object(msg, get_name(obj))\\n for obj in objects:\\n writer.write_initial(msg, get_name(obj))\\n\\n events = parallel_execute_iter(objects, func, get_deps, limit)\\n\\n errors = {}\\n results = []\\n error_to_reraise = parallel_execute_watch(\\n events, writer, errors, results, msg, get_name, fail_check\\n )\\n\\n for obj_name, error in errors.items():\\n stream.write(\\\"\\\\nERROR: for {} {}\\\\n\\\".format(obj_name, error))\\n\\n if error_to_reraise:\\n raise error_to_reraise\\n\\n return results, errors\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::_no_deps\",\n \"text\": \"compose/parallel.py::_no_deps\\ndef _no_deps(x):\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::State.__init__\",\n \"text\": \"compose/parallel.py::State.__init__\\n def __init__(self, objects):\\n self.objects = objects\\n\\n self.started = set()\\n self.finished = set()\\n self.failed = set()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::State.is_done\",\n \"text\": \"compose/parallel.py::State.is_done\\n def is_done(self):\\n return len(self.finished) + len(self.failed) >= len(self.objects)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::State.pending\",\n \"text\": \"compose/parallel.py::State.pending\\n def pending(self):\\n return set(self.objects) - self.started - self.finished - self.failed\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::NoLimit.__enter__\",\n \"text\": \"compose/parallel.py::NoLimit.__enter__\\n def __enter__(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::NoLimit.__exit__\",\n \"text\": \"compose/parallel.py::NoLimit.__exit__\\n def __exit__(self, *ex):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::parallel_execute_iter\",\n \"text\": \"compose/parallel.py::parallel_execute_iter\\ndef parallel_execute_iter(objects, func, get_deps, limit):\\n \\\"\\\"\\\"\\n Runs func on objects in parallel while ensuring that func is\\n ran on object only after it is ran on all its dependencies.\\n\\n Returns an iterator of tuples which look like:\\n\\n # if func returned normally when run on object\\n (object, result, None)\\n\\n # if func raised an exception when run on object\\n (object, None, exception)\\n\\n # if func raised an exception when run on one of object's dependencies\\n (object, None, UpstreamError())\\n \\\"\\\"\\\"\\n if get_deps is None:\\n get_deps = _no_deps\\n\\n if limit is None:\\n limiter = NoLimit()\\n else:\\n limiter = Semaphore(limit)\\n\\n results = Queue()\\n state = State(objects)\\n\\n while True:\\n feed_queue(objects, func, get_deps, results, state, limiter)\\n\\n try:\\n event = results.get(timeout=0.1)\\n except Empty:\\n continue\\n # See https://github.com/docker/compose/issues/189\\n except thread.error:\\n raise ShutdownException()\\n\\n if event is STOP:\\n break\\n\\n obj, _, exception = event\\n if exception is None:\\n log.debug('Finished processing: {}'.format(obj))\\n state.finished.add(obj)\\n else:\\n log.debug('Failed: {}'.format(obj))\\n state.failed.add(obj)\\n\\n yield event\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::producer\",\n \"text\": \"compose/parallel.py::producer\\ndef producer(obj, func, results, limiter):\\n \\\"\\\"\\\"\\n The entry point for a producer thread which runs func on a single object.\\n Places a tuple on the results queue once func has either returned or raised.\\n \\\"\\\"\\\"\\n with limiter, GlobalLimit.global_limiter:\\n try:\\n result = func(obj)\\n results.put((obj, result, None))\\n except Exception as e:\\n results.put((obj, None, e))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::feed_queue\",\n \"text\": \"compose/parallel.py::feed_queue\\ndef feed_queue(objects, func, get_deps, results, state, limiter):\\n \\\"\\\"\\\"\\n Starts producer threads for any objects which are ready to be processed\\n (i.e. they have no dependencies which haven't been successfully processed).\\n\\n Shortcuts any objects whose dependencies have failed and places an\\n (object, None, UpstreamError()) tuple on the results queue.\\n \\\"\\\"\\\"\\n pending = state.pending()\\n log.debug('Pending: {}'.format(pending))\\n\\n for obj in pending:\\n deps = get_deps(obj)\\n try:\\n if any(dep[0] in state.failed for dep in deps):\\n log.debug('{} has upstream errors - not processing'.format(obj))\\n results.put((obj, None, UpstreamError()))\\n state.failed.add(obj)\\n elif all(\\n dep not in objects or (\\n dep in state.finished and (not ready_check or ready_check(dep))\\n ) for dep, ready_check in deps\\n ):\\n log.debug('Starting producer thread for {}'.format(obj))\\n t = Thread(target=producer, args=(obj, func, results, limiter))\\n t.daemon = True\\n t.start()\\n state.started.add(obj)\\n except (HealthCheckFailed, NoHealthCheckConfigured) as e:\\n log.debug(\\n 'Healthcheck for service(s) upstream of {} failed - '\\n 'not processing'.format(obj)\\n )\\n results.put((obj, None, e))\\n\\n if state.is_done():\\n results.put(STOP)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::ParallelStreamWriter.set_noansi\",\n \"text\": \"compose/parallel.py::ParallelStreamWriter.set_noansi\\n @classmethod\\n def set_noansi(cls, value=True):\\n cls.noansi = value\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::ParallelStreamWriter.__init__\",\n \"text\": \"compose/parallel.py::ParallelStreamWriter.__init__\\n def __init__(self, stream):\\n self.stream = stream\\n self.lines = []\\n self.width = 0\\n ParallelStreamWriter.instance = self\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::ParallelStreamWriter.add_object\",\n \"text\": \"compose/parallel.py::ParallelStreamWriter.add_object\\n def add_object(self, msg, obj_index):\\n if msg is None:\\n return\\n self.lines.append(msg + obj_index)\\n self.width = max(self.width, len(msg + ' ' + obj_index))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::ParallelStreamWriter.write_initial\",\n \"text\": \"compose/parallel.py::ParallelStreamWriter.write_initial\\n def write_initial(self, msg, obj_index):\\n if msg is None:\\n return\\n return self._write_noansi(msg, obj_index, '')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::ParallelStreamWriter._write_ansi\",\n \"text\": \"compose/parallel.py::ParallelStreamWriter._write_ansi\\n def _write_ansi(self, msg, obj_index, status):\\n self.lock.acquire()\\n position = self.lines.index(msg + obj_index)\\n diff = len(self.lines) - position\\n # move up\\n self.stream.write(\\\"%c[%dA\\\" % (27, diff))\\n # erase\\n self.stream.write(\\\"%c[2K\\\\r\\\" % 27)\\n self.stream.write(\\\"{:<{width}} ... {}\\\\r\\\".format(msg + ' ' + obj_index,\\n status, width=self.width))\\n # move back down\\n self.stream.write(\\\"%c[%dB\\\" % (27, diff))\\n self.stream.flush()\\n self.lock.release()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::ParallelStreamWriter._write_noansi\",\n \"text\": \"compose/parallel.py::ParallelStreamWriter._write_noansi\\n def _write_noansi(self, msg, obj_index, status):\\n self.stream.write(\\n \\\"{:<{width}} ... {}\\\\r\\\\n\\\".format(\\n msg + ' ' + obj_index, status, width=self.width\\n )\\n )\\n self.stream.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::ParallelStreamWriter.write\",\n \"text\": \"compose/parallel.py::ParallelStreamWriter.write\\n def write(self, msg, obj_index, status, color_func):\\n if msg is None:\\n return\\n if self.noansi:\\n self._write_noansi(msg, obj_index, status)\\n else:\\n self._write_ansi(msg, obj_index, color_func(status))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::get_stream_writer\",\n \"text\": \"compose/parallel.py::get_stream_writer\\ndef get_stream_writer():\\n instance = ParallelStreamWriter.instance\\n if instance is None:\\n raise RuntimeError('ParallelStreamWriter has not yet been instantiated')\\n return instance\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::parallel_operation\",\n \"text\": \"compose/parallel.py::parallel_operation\\ndef parallel_operation(containers, operation, options, message):\\n parallel_execute(\\n containers,\\n operator.methodcaller(operation, **options),\\n operator.attrgetter('name'),\\n message,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::parallel_remove\",\n \"text\": \"compose/parallel.py::parallel_remove\\ndef parallel_remove(containers, options):\\n stopped_containers = [c for c in containers if not c.is_running]\\n parallel_operation(stopped_containers, 'remove', options, 'Removing')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::parallel_pause\",\n \"text\": \"compose/parallel.py::parallel_pause\\ndef parallel_pause(containers, options):\\n parallel_operation(containers, 'pause', options, 'Pausing')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::parallel_unpause\",\n \"text\": \"compose/parallel.py::parallel_unpause\\ndef parallel_unpause(containers, options):\\n parallel_operation(containers, 'unpause', options, 'Unpausing')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/parallel.py::parallel_kill\",\n \"text\": \"compose/parallel.py::parallel_kill\\ndef parallel_kill(containers, options):\\n parallel_operation(containers, 'kill', options, 'Killing')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/progress_stream.py::write_to_stream\",\n \"text\": \"compose/progress_stream.py::write_to_stream\\ndef write_to_stream(s, stream):\\n try:\\n stream.write(s)\\n except UnicodeEncodeError:\\n encoding = getattr(stream, 'encoding', 'ascii')\\n stream.write(s.encode(encoding, errors='replace').decode(encoding))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/progress_stream.py::stream_output\",\n \"text\": \"compose/progress_stream.py::stream_output\\ndef stream_output(output, stream):\\n is_terminal = hasattr(stream, 'isatty') and stream.isatty()\\n stream = utils.get_output_stream(stream)\\n lines = {}\\n diff = 0\\n\\n for event in utils.json_stream(output):\\n yield event\\n is_progress_event = 'progress' in event or 'progressDetail' in event\\n\\n if not is_progress_event:\\n print_output_event(event, stream, is_terminal)\\n stream.flush()\\n continue\\n\\n if not is_terminal:\\n continue\\n\\n # if it's a progress event and we have a terminal, then display the progress bars\\n image_id = event.get('id')\\n if not image_id:\\n continue\\n\\n if image_id not in lines:\\n lines[image_id] = len(lines)\\n write_to_stream(\\\"\\\\n\\\", stream)\\n\\n diff = len(lines) - lines[image_id]\\n\\n # move cursor up `diff` rows\\n write_to_stream(\\\"%c[%dA\\\" % (27, diff), stream)\\n\\n print_output_event(event, stream, is_terminal)\\n\\n if 'id' in event:\\n # move cursor back down\\n write_to_stream(\\\"%c[%dB\\\" % (27, diff), stream)\\n\\n stream.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/progress_stream.py::print_output_event\",\n \"text\": \"compose/progress_stream.py::print_output_event\\ndef print_output_event(event, stream, is_terminal):\\n if 'errorDetail' in event:\\n raise StreamOutputError(event['errorDetail']['message'])\\n\\n terminator = ''\\n\\n if is_terminal and 'stream' not in event:\\n # erase current line\\n write_to_stream(\\\"%c[2K\\\\r\\\" % 27, stream)\\n terminator = \\\"\\\\r\\\"\\n elif 'progressDetail' in event:\\n return\\n\\n if 'time' in event:\\n write_to_stream(\\\"[%s] \\\" % event['time'], stream)\\n\\n if 'id' in event:\\n write_to_stream(\\\"%s: \\\" % event['id'], stream)\\n\\n if 'from' in event:\\n write_to_stream(\\\"(from %s) \\\" % event['from'], stream)\\n\\n status = event.get('status', '')\\n\\n if 'progress' in event:\\n write_to_stream(\\\"%s %s%s\\\" % (status, event['progress'], terminator), stream)\\n elif 'progressDetail' in event:\\n detail = event['progressDetail']\\n total = detail.get('total')\\n if 'current' in detail and total:\\n percentage = float(detail['current']) / float(total) * 100\\n write_to_stream('%s (%.1f%%)%s' % (status, percentage, terminator), stream)\\n else:\\n write_to_stream('%s%s' % (status, terminator), stream)\\n elif 'stream' in event:\\n write_to_stream(\\\"%s%s\\\" % (event['stream'], terminator), stream)\\n else:\\n write_to_stream(\\\"%s%s\\\\n\\\" % (status, terminator), stream)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/progress_stream.py::get_digest_from_pull\",\n \"text\": \"compose/progress_stream.py::get_digest_from_pull\\ndef get_digest_from_pull(events):\\n digest = None\\n for event in events:\\n status = event.get('status')\\n if not status or 'Digest' not in status:\\n continue\\n else:\\n digest = status.split(':', 1)[1].strip()\\n return digest\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/progress_stream.py::get_digest_from_push\",\n \"text\": \"compose/progress_stream.py::get_digest_from_push\\ndef get_digest_from_push(events):\\n for event in events:\\n digest = event.get('aux', {}).get('Digest')\\n if digest:\\n return digest\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::create_initial_branch\",\n \"text\": \"script/release/release.py::create_initial_branch\\ndef create_initial_branch(repository, args):\\n release_branch = repository.create_release_branch(args.release, args.base)\\n if args.base and args.cherries:\\n print('Detected patch version.')\\n cherries = input('Indicate (space-separated) PR numbers to cherry-pick then press Enter:\\\\n')\\n repository.cherry_pick_prs(release_branch, cherries.split())\\n\\n return create_bump_commit(repository, release_branch, args.bintray_user, args.bintray_org)\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::create_bump_commit\",\n \"text\": \"script/release/release.py::create_bump_commit\\ndef create_bump_commit(repository, release_branch, bintray_user, bintray_org):\\n with release_branch.config_reader() as cfg:\\n release = cfg.get('release')\\n print('Updating version info in __init__.py and run.sh')\\n update_run_sh_version(release)\\n update_init_py_version(release)\\n\\n input('Please add the release notes to the CHANGELOG.md file, then press Enter to continue.')\\n proceed = None\\n while not proceed:\\n print(repository.diff())\\n proceed = yesno('Are these changes ok? y/N ', default=False)\\n\\n if repository.diff():\\n repository.create_bump_commit(release_branch, release)\\n repository.push_branch_to_remote(release_branch)\\n\\n bintray_api = BintrayAPI(os.environ['BINTRAY_TOKEN'], bintray_user)\\n if not bintray_api.repository_exists(bintray_org, release_branch.name):\\n print('Creating data repository {} on bintray'.format(release_branch.name))\\n bintray_api.create_repository(bintray_org, release_branch.name, 'generic')\\n else:\\n print('Bintray repository {} already exists. Skipping'.format(release_branch.name))\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::monitor_pr_status\",\n \"text\": \"script/release/release.py::monitor_pr_status\\ndef monitor_pr_status(pr_data):\\n print('Waiting for CI to complete...')\\n last_commit = pr_data.get_commits().reversed[0]\\n while True:\\n status = last_commit.get_combined_status()\\n if status.state == 'pending' or status.state == 'failure':\\n summary = {\\n 'pending': 0,\\n 'success': 0,\\n 'failure': 0,\\n 'error': 0,\\n }\\n for detail in status.statuses:\\n if detail.context == 'dco-signed':\\n # dco-signed check breaks on merge remote-tracking ; ignore it\\n continue\\n if detail.state in summary:\\n summary[detail.state] += 1\\n print(\\n '{pending} pending, {success} successes, {failure} failures, '\\n '{error} errors'.format(**summary)\\n )\\n if summary['failure'] > 0 or summary['error'] > 0:\\n raise ScriptError('CI failures detected!')\\n elif summary['pending'] == 0 and summary['success'] > 0:\\n # This check assumes at least 1 non-DCO CI check to avoid race conditions.\\n # If testing on a repo without CI, use --skip-ci-check to avoid looping eternally\\n return True\\n time.sleep(30)\\n elif status.state == 'success':\\n print('{} successes: all clear!'.format(status.total_count))\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::check_pr_mergeable\",\n \"text\": \"script/release/release.py::check_pr_mergeable\\ndef check_pr_mergeable(pr_data):\\n if pr_data.mergeable is False:\\n # mergeable can also be null, in which case the warning would be a false positive.\\n print(\\n 'WARNING!! PR #{} can not currently be merged. You will need to '\\n 'resolve the conflicts manually before finalizing the release.'.format(pr_data.number)\\n )\\n\\n return pr_data.mergeable is True\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::create_release_draft\",\n \"text\": \"script/release/release.py::create_release_draft\\ndef create_release_draft(repository, version, pr_data, files):\\n print('Creating Github release draft')\\n with open(os.path.join(os.path.dirname(__file__), 'release.md.tmpl'), 'r') as f:\\n template = Template(f.read())\\n print('Rendering release notes based on template')\\n release_notes = template.render(\\n version=version,\\n compat_matrix=compatibility_matrix(),\\n integrity=files,\\n contributors=get_contributors(pr_data),\\n changelog=read_release_notes_from_changelog(),\\n )\\n gh_release = repository.create_release(\\n version, release_notes, draft=True, prerelease='-rc' in version,\\n target_commitish='release'\\n )\\n print('Release draft initialized')\\n return gh_release\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::print_final_instructions\",\n \"text\": \"script/release/release.py::print_final_instructions\\ndef print_final_instructions(args):\\n print(\\n \\\"You're almost done! Please verify that everything is in order and \\\"\\n \\\"you are ready to make the release public, then run the following \\\"\\n \\\"command:\\\\n{exe} -b {user} finalize {version}\\\".format(\\n exe='./script/release/release.sh', user=args.bintray_user, version=args.release\\n )\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::distclean\",\n \"text\": \"script/release/release.py::distclean\\ndef distclean():\\n print('Running distclean...')\\n dirs = [\\n os.path.join(REPO_ROOT, 'build'), os.path.join(REPO_ROOT, 'dist'),\\n os.path.join(REPO_ROOT, 'docker-compose.egg-info')\\n ]\\n files = []\\n for base, dirnames, fnames in os.walk(REPO_ROOT):\\n for fname in fnames:\\n path = os.path.normpath(os.path.join(base, fname))\\n if fname.endswith('.pyc'):\\n files.append(path)\\n elif fname.startswith('.coverage.'):\\n files.append(path)\\n for dirname in dirnames:\\n path = os.path.normpath(os.path.join(base, dirname))\\n if dirname == '__pycache__':\\n dirs.append(path)\\n elif dirname == '.coverage-binfiles':\\n dirs.append(path)\\n\\n for file in files:\\n os.unlink(file)\\n\\n for folder in dirs:\\n shutil.rmtree(folder, ignore_errors=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::resume\",\n \"text\": \"script/release/release.py::resume\\ndef resume(args):\\n try:\\n distclean()\\n repository = Repository(REPO_ROOT, args.repo)\\n br_name = branch_name(args.release)\\n if not repository.branch_exists(br_name):\\n raise ScriptError('No local branch exists for this release.')\\n gh_release = repository.find_release(args.release)\\n if gh_release and not gh_release.draft:\\n print('WARNING!! Found non-draft (public) release for this version!')\\n proceed = yesno(\\n 'Are you sure you wish to proceed? Modifying an already '\\n 'released version is dangerous! y/N ', default=False\\n )\\n if proceed.lower() is not True:\\n raise ScriptError('Aborting release')\\n\\n release_branch = repository.checkout_branch(br_name)\\n if args.cherries:\\n cherries = input('Indicate (space-separated) PR numbers to cherry-pick then press Enter:\\\\n')\\n repository.cherry_pick_prs(release_branch, cherries.split())\\n\\n create_bump_commit(repository, release_branch, args.bintray_user, args.bintray_org)\\n pr_data = repository.find_release_pr(args.release)\\n if not pr_data:\\n pr_data = repository.create_release_pull_request(args.release)\\n check_pr_mergeable(pr_data)\\n if not args.skip_ci:\\n monitor_pr_status(pr_data)\\n downloader = BinaryDownloader(args.destination)\\n files = downloader.download_all(args.release)\\n if not gh_release:\\n gh_release = create_release_draft(repository, args.release, pr_data, files)\\n delete_assets(gh_release)\\n upload_assets(gh_release, files)\\n img_manager = ImageManager(args.release)\\n img_manager.build_images(repository)\\n except ScriptError as e:\\n print(e)\\n return 1\\n\\n print_final_instructions(args)\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::cancel\",\n \"text\": \"script/release/release.py::cancel\\ndef cancel(args):\\n try:\\n repository = Repository(REPO_ROOT, args.repo)\\n repository.close_release_pr(args.release)\\n repository.remove_release(args.release)\\n repository.remove_bump_branch(args.release)\\n bintray_api = BintrayAPI(os.environ['BINTRAY_TOKEN'], args.bintray_user)\\n print('Removing Bintray data repository for {}'.format(args.release))\\n bintray_api.delete_repository(args.bintray_org, branch_name(args.release))\\n distclean()\\n except ScriptError as e:\\n print(e)\\n return 1\\n print('Release cancellation complete.')\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::start\",\n \"text\": \"script/release/release.py::start\\ndef start(args):\\n distclean()\\n try:\\n repository = Repository(REPO_ROOT, args.repo)\\n create_initial_branch(repository, args)\\n pr_data = repository.create_release_pull_request(args.release)\\n check_pr_mergeable(pr_data)\\n if not args.skip_ci:\\n monitor_pr_status(pr_data)\\n downloader = BinaryDownloader(args.destination)\\n files = downloader.download_all(args.release)\\n gh_release = create_release_draft(repository, args.release, pr_data, files)\\n upload_assets(gh_release, files)\\n img_manager = ImageManager(args.release)\\n img_manager.build_images(repository)\\n except ScriptError as e:\\n print(e)\\n return 1\\n\\n print_final_instructions(args)\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::finalize\",\n \"text\": \"script/release/release.py::finalize\\ndef finalize(args):\\n distclean()\\n try:\\n check_pypirc()\\n repository = Repository(REPO_ROOT, args.repo)\\n tag_as_latest = is_tag_latest(args.release)\\n img_manager = ImageManager(args.release, tag_as_latest)\\n pr_data = repository.find_release_pr(args.release)\\n if not pr_data:\\n raise ScriptError('No PR found for {}'.format(args.release))\\n if not check_pr_mergeable(pr_data):\\n raise ScriptError('Can not finalize release with an unmergeable PR')\\n if not img_manager.check_images():\\n raise ScriptError('Missing release image')\\n br_name = branch_name(args.release)\\n if not repository.branch_exists(br_name):\\n raise ScriptError('No local branch exists for this release.')\\n gh_release = repository.find_release(args.release)\\n if not gh_release:\\n raise ScriptError('No Github release draft for this version')\\n\\n repository.checkout_branch(br_name)\\n\\n os.system('python {setup_script} sdist bdist_wheel'.format(\\n setup_script=os.path.join(REPO_ROOT, 'setup.py')))\\n\\n merge_status = pr_data.merge()\\n if not merge_status.merged and not args.finalize_resume:\\n raise ScriptError(\\n 'Unable to merge PR #{}: {}'.format(pr_data.number, merge_status.message)\\n )\\n\\n pypi_upload(args)\\n\\n img_manager.push_images()\\n repository.publish_release(gh_release)\\n except ScriptError as e:\\n print(e)\\n return 1\\n\\n return 0\",\n \"label\": 0\n },\n {\n \"cid\": \"script/release/release.py::main\",\n \"text\": \"script/release/release.py::main\\ndef main():\\n if 'GITHUB_TOKEN' not in os.environ:\\n print('GITHUB_TOKEN environment variable must be set')\\n return 1\\n\\n if 'BINTRAY_TOKEN' not in os.environ:\\n print('BINTRAY_TOKEN environment variable must be set')\\n return 1\\n\\n parser = argparse.ArgumentParser(\\n description='Orchestrate a new release of docker/compose. This tool assumes that you have '\\n 'obtained a Github API token and Bintray API key and set the GITHUB_TOKEN and '\\n 'BINTRAY_TOKEN environment variables accordingly.',\\n epilog=EPILOG, formatter_class=argparse.RawTextHelpFormatter)\\n parser.add_argument(\\n 'action', choices=ACTIONS, help='The action to be performed for this release'\\n )\\n parser.add_argument('release', help='Release number, e.g. 1.9.0-rc1, 2.1.1')\\n parser.add_argument(\\n '--patch', '-p', dest='base',\\n help='Which version is being patched by this release'\\n )\\n parser.add_argument(\\n '--repo', '-r', dest='repo', default=NAME,\\n help='Start a release for the given repo (default: {})'.format(NAME)\\n )\\n parser.add_argument(\\n '-b', dest='bintray_user', required=True, metavar='USER',\\n help='Username associated with the Bintray API key'\\n )\\n parser.add_argument(\\n '--bintray-org', dest='bintray_org', metavar='ORG', default=BINTRAY_ORG,\\n help='Organization name on bintray where the data repository will be created.'\\n )\\n parser.add_argument(\\n '--destination', '-o', metavar='DIR', default='binaries',\\n help='Directory where release binaries will be downloaded relative to the project root'\\n )\\n parser.add_argument(\\n '--no-cherries', '-C', dest='cherries', action='store_false',\\n help='If set, the program will not prompt the user for PR numbers to cherry-pick'\\n )\\n parser.add_argument(\\n '--skip-ci-checks', dest='skip_ci', action='store_true',\\n help\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":287,"cells":{"instance_id":{"kind":"string","value":"PrefectHQ__prefect-2546"},"repo":{"kind":"string","value":"PrefectHQ/prefect"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Make new conditional API compatible with the imperative API\n## Current behavior\r\n*Please describe how the feature works today*\r\nIn #2443, @jcrist added a new conditional API to Prefect, which is 💯. However, at this moment, it can only be used with the functional API (as it [requires](https://github.com/PrefectHQ/prefect/blob/12662a3a821b52dca0c0d39ced1bbbbba0674a85/src/prefect/tasks/control_flow/case.py#L87) a `flow` in the Prefect context). \r\n\r\n\r\n## Proposed behavior\r\n*Please describe your proposed change to the current behavior*\r\n\r\nThe check for `case` in context currently [takes place](https://github.com/PrefectHQ/prefect/blob/12662a3a821b52dca0c0d39ced1bbbbba0674a85/src/prefect/core/task.py#L463) in `Task.bind()`, which is called by the functional API but not common imperative patterns like `Task.set_upstream()`, `Task.set_downstream()`, `Task.set_dependencies()`, `Flow.add_task()`, or `Flow.set_dependencies()`. \r\n\r\nI believe `Flow.add_task()` is the lowest-level API (called by all the others mentioned above) and consequently might be an opportune place to move the check for `case` in context. This way, adding a task to a flow while in a conditional block would automatically add it to the `case` class as well. Since the flow is (obviously) available when `Flow.add_task()` is called, it can be used inside the `case` context manager when calling `bind()` and `set_upstream()`. \r\n\r\nI think this would enable the following imperative syntax, while still using the existing case context managers:\r\n\r\n```\r\nSwitch = Task()\r\nA = Task()\r\nB = Task()\r\nC = Task()\r\nf = Flow('imperative flow')\r\n\r\nswitch = Switch()\r\nf.add_task(switch)\r\n\r\nwith case(switch, 'foo'):\r\n f.add_task(A())\r\n\r\nwith case(switch, 'bar'):\r\n b = B()\r\n c = C()\r\n c.set_upstream(b, flow=f)\r\n```\r\n\r\n\r\n## Example\r\n*Please give an example of how the enhancement would be useful*\r\n\n"},"gold_files":{"kind":"list like","value":["src/prefect/core/flow.py","src/prefect/core/task.py","src/prefect/tasks/control_flow/case.py"],"string":"[\n \"src/prefect/core/flow.py\",\n \"src/prefect/core/task.py\",\n \"src/prefect/tasks/control_flow/case.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["src/prefect/core/flow.py::Flow.add_task","src/prefect/core/task.py::Task.bind","src/prefect/tasks/control_flow/case.py::<module>","src/prefect/tasks/control_flow/case.py::case.__exit__","src/prefect/tasks/control_flow/case.py::case.__init__","src/prefect/tasks/control_flow/case.py::case.add_task"],"string":"[\n \"src/prefect/core/flow.py::Flow.add_task\",\n \"src/prefect/core/task.py::Task.bind\",\n \"src/prefect/tasks/control_flow/case.py::<module>\",\n \"src/prefect/tasks/control_flow/case.py::case.__exit__\",\n \"src/prefect/tasks/control_flow/case.py::case.__init__\",\n \"src/prefect/tasks/control_flow/case.py::case.add_task\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"server/versioneer.py","text":"server/versioneer.py\nclass VersioneerConfig # Container for Versioneer configuration parameters.\ndef get_root() # Get the project root directory.\ndef get_config_from_root(root) # Read the project setup.cfg file to determine Versioneer config.\n def get(parser, name)\nclass NotThisMethod # Exception raised if a method is not valid for the current scenario.\ndef register_vcs_handler(vcs, method) # Decorator to mark a method as the handler for a particular VCS.\n def decorate(f) # Store f in HANDLERS[vcs][method].\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None) # Call the given command(s).\ndef git_get_keywords(versionfile_abs) # Extract version information from the given file.\ndef git_versions_from_keywords(keywords, tag_prefix, verbose) # Get version information from git keywords.\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command) # Get version from 'git describe' in the root of the source tree.\ndef do_vcs_install(manifest_in, versionfile_source, ipy) # Git-specific installation logic for Versioneer.\ndef versions_from_parentdir(parentdir_prefix, root, verbose) # Try to determine the version from the parent directory name.\ndef versions_from_file(filename) # Try to determine the version from _version.py if present.\ndef write_to_version_file(filename, versions) # Write the given version number to the given _version.py file.\ndef plus_or_dot(pieces) # Return a + if we don't already have one, else return a .\ndef render_pep440(pieces) # Build up version string, with post-release \"local version identifier\".\ndef render_pep440_pre(pieces) # TAG[.post.devDISTANCE] -- No -dirty.\ndef render_pep440_post(pieces) # TAG[.postDISTANCE[.dev0]+gHEX] .\ndef render_pep440_old(pieces) # TAG[.postDISTANCE[.dev0]] .\ndef render_git_describe(pieces) # TAG[-DISTANCE-gHEX][-dirty].\ndef render_git_describe_long(pieces) # TAG-DISTANCE-gHEX[-dirty].\ndef render(pieces, style) # Render the given version pieces into the requested style.\nclass VersioneerBadRootError # The project root directory is unknown or missing key files.\ndef get_versions(verbose=False) # Get the project version from whatever source is available.\ndef get_version() # Get the short version string for this project.\ndef get_cmdclass() # Get the custom setuptools/distutils subclasses used by Versioneer.\n class cmd_version\n def initialize_options(self)\n def finalize_options(self)\n def run(self)\n class cmd_build_py\n def run(self)\n class cmd_sdist\n def run(self)\n def make_release_tree(self, base_dir, files)\ndef do_setup() # Main VCS-independent setup function for installing Versioneer.\ndef scan_setup_py() # Validate the contents of setup.py against Versioneer's expectations.","label":0},{"cid":"src/prefect/core/flow.py","text":"src/prefect/core/flow.py\ndef cache(method: Callable) # Decorator for caching Flow methods.\n def wrapper(self, *args, **kwargs)\nclass Flow # The Flow class is used as the representation of a collection of dependent Tasks.\n def __init__(self, name: str, schedule: prefect.schedules.Schedule=None, environment: Environment=None, storage: Storage=None, tasks: Iterable[Task]=None, edges: Iterable[Edge]=None, reference_tasks: Iterable[Task]=None, state_handlers: List[Callable]=None, on_failure: Callable=None, validate: bool=None, result_handler: Optional[ResultHandler]=None, result: Optional[Result]=None)\n def __eq__(self, other: Any)\n def __repr__(self)\n def __iter__(self)\n def copy(self) # Create and returns a copy of the current Flow.\n def get_tasks(self, name: str=None, slug: str=None, tags: Iterable[str]=None, task_type: type=None) # Helper method for retrieving tasks from this flow based on certain attributes.\n def sieve(t: Task)\n def replace(self, old: Task, new: Task, validate: bool=True) # Performs an inplace replacement of the old task with the provided new task.\n def __enter__(self)\n def __exit__(self, _type, _value, _tb)\n def root_tasks(self) # Get the tasks in the flow that have no upstream dependencies; these are\n def terminal_tasks(self) # Get the tasks in the flow that have no downstream dependencies\n def parameters(self) # Returns any parameters of the flow.\n def reference_tasks(self) # A flow's \"reference tasks\" are used to determine its state when it runs. If all \n def set_reference_tasks(self, tasks: Iterable[Task]) # Sets the `reference_tasks` for the flow. See `flow.reference_tasks` for more det\n def add_task(self, task: Task) # Add a task to the flow if the task does not already exist. The tasks are\n def add_edge(self, upstream_task: Task, downstream_task: Task, key: str=None, mapped: bool=False, validate: bool=None) # Add an edge in the flow between two tasks. All edges are directed beginning with\n def chain(self, *tasks: Task, validate: bool=None) # Adds a sequence of dependent tasks to the flow; each task should be provided\n def update(self, flow: 'Flow', validate: bool=None) # Take all tasks and edges in another flow and add it to this flow\n def all_upstream_edges(self) # Returns a dictionary relating each task in the Flow to the set of\n def all_downstream_edges(self) # Returns a dictionary relating each task in the Flow to the set of\n def edges_to(self, task: Task) # Get all of the edges leading to a task (i.e., the upstream edges)\n def edges_from(self, task: Task) # Get all of the edges leading from a task (i.e., the downstream edges)\n def upstream_tasks(self, task: Task) # Get all of the tasks upstream of a task\n def downstream_tasks(self, task: Task) # Get all of the tasks downstream of a task\n def validate(self) # Checks that the flow is valid.\n def sorted_tasks(self, root_tasks: Iterable[Task]=None) # Get the task","label":1},{"cid":"src/prefect/core/task.py","text":"src/prefect/core/task.py\ndef _validate_run_signature(run: Callable)\nclass SignatureValidator\n def __new__(cls, name: str, parents: tuple, methods: dict)\nclass Task # The Task class which is used as the full representation of a unit of work.\n def __init__(self, name: str=None, slug: str=None, tags: Iterable[str]=None, max_retries: int=None, retry_delay: timedelta=None, timeout: int=None, trigger: Callable[[Dict['Edge', 'State']], bool]=None, skip_on_upstream_skip: bool=True, cache_for: timedelta=None, cache_validator: Callable=None, cache_key: str=None, checkpoint: bool=None, result_handler: 'ResultHandler'=None, state_handlers: List[Callable]=None, on_failure: Callable=None, log_stdout: bool=False, result: 'Result'=None, target: str=None)\n def __repr__(self)\n def __hash__(self)\n def run(self) # The `run()` method is called (with arguments, if appropriate) to run a task.\n def copy(self, **task_args: Any) # Creates and returns a copy of the current Task.\n def __call__(self, *args: Any, mapped: bool=False, task_args: dict=None, upstream_tasks: Iterable[Any]=None, flow: 'Flow'=None, **kwargs: Any) # Calling a Task instance will first create a _copy_ of the instance, and then\n def bind(self, *args: Any, mapped: bool=False, upstream_tasks: Iterable[Any]=None, flow: 'Flow'=None, **kwargs: Any) # Binding a task to (keyword) arguments creates a _keyed_ edge in the active Flow\n def map(self, *args: Any, upstream_tasks: Iterable[Any]=None, flow: 'Flow'=None, task_args: dict=None, **kwargs: Any) # Map the Task elementwise across one or more Tasks. Arguments that should _not_ b\n def set_dependencies(self, flow: 'Flow'=None, upstream_tasks: Iterable[object]=None, downstream_tasks: Iterable[object]=None, keyword_tasks: Mapping[str, object]=None, mapped: bool=False, validate: bool=None) # Set dependencies for a flow either specified or in the current context using thi\n def set_upstream(self, task: object, flow: 'Flow'=None, key: str=None, mapped: bool=False) # Sets the provided task as an upstream dependency of this task.\n def set_downstream(self, task: 'Task', flow: 'Flow'=None, key: str=None, mapped: bool=False) # Sets the provided task as a downstream dependency of this task.\n def inputs(self) # Describe the inputs for this task. The result is a dictionary that maps each inp\n def outputs(self) # Get the output types for this task.\n def serialize(self) # Creates a serialized representation of this task\n def is_equal(self, other: object) # Produces a Task that evaluates `self == other`\n def is_not_equal(self, other: object) # Produces a Task that evaluates `self != other`\n def not_(self) # Produces a Task that evaluates `not self`\n def or_(self, other: object) # Produces a Task that evaluates `self or other`\n def __getitem__(self, key: Any) # Produces a Task that evaluates `self[key]`\n def __or__(self, other: object) # Creates a state dependency between `self` and `other`\n def _","label":1},{"cid":"src/prefect/tasks/control_flow/case.py","text":"src/prefect/tasks/control_flow/case.py\nclass case # A conditional block in a flow definition.\n def __init__(self, task: Task, value: Any)\n def add_task(self, task: Task) # Add a new task under the case statement.\n def __enter__(self)\n def __exit__(self, *args)","label":1},{"cid":"src/prefect/tasks/control_flow/conditional.py","text":"src/prefect/tasks/control_flow/conditional.py\nclass Merge\n def __init__(self, **kwargs)\n def run(self, **task_results: Any)\nclass CompareValue # This task stores a `value` at initialization and compares it to a `value` receiv\n def __init__(self, value: Any, **kwargs: Any)\n def run(self, value: Any) # Raises a SKIP signal if the passed value does not match the task's match value;\ndef switch(condition: Task, cases: Dict[Any, Task]) # Adds a SWITCH to a workflow.\ndef ifelse(condition: Task, true_task: Task, false_task: Task) # Builds a conditional branch into a workflow.\n def as_bool(x)\ndef merge(*tasks: Task) # Merges conditional branches back together.","label":0}],"string":"[\n {\n \"cid\": \"server/versioneer.py\",\n \"text\": \"server/versioneer.py\\nclass VersioneerConfig # Container for Versioneer configuration parameters.\\ndef get_root() # Get the project root directory.\\ndef get_config_from_root(root) # Read the project setup.cfg file to determine Versioneer config.\\n def get(parser, name)\\nclass NotThisMethod # Exception raised if a method is not valid for the current scenario.\\ndef register_vcs_handler(vcs, method) # Decorator to mark a method as the handler for a particular VCS.\\n def decorate(f) # Store f in HANDLERS[vcs][method].\\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None) # Call the given command(s).\\ndef git_get_keywords(versionfile_abs) # Extract version information from the given file.\\ndef git_versions_from_keywords(keywords, tag_prefix, verbose) # Get version information from git keywords.\\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command) # Get version from 'git describe' in the root of the source tree.\\ndef do_vcs_install(manifest_in, versionfile_source, ipy) # Git-specific installation logic for Versioneer.\\ndef versions_from_parentdir(parentdir_prefix, root, verbose) # Try to determine the version from the parent directory name.\\ndef versions_from_file(filename) # Try to determine the version from _version.py if present.\\ndef write_to_version_file(filename, versions) # Write the given version number to the given _version.py file.\\ndef plus_or_dot(pieces) # Return a + if we don't already have one, else return a .\\ndef render_pep440(pieces) # Build up version string, with post-release \\\"local version identifier\\\".\\ndef render_pep440_pre(pieces) # TAG[.post.devDISTANCE] -- No -dirty.\\ndef render_pep440_post(pieces) # TAG[.postDISTANCE[.dev0]+gHEX] .\\ndef render_pep440_old(pieces) # TAG[.postDISTANCE[.dev0]] .\\ndef render_git_describe(pieces) # TAG[-DISTANCE-gHEX][-dirty].\\ndef render_git_describe_long(pieces) # TAG-DISTANCE-gHEX[-dirty].\\ndef render(pieces, style) # Render the given version pieces into the requested style.\\nclass VersioneerBadRootError # The project root directory is unknown or missing key files.\\ndef get_versions(verbose=False) # Get the project version from whatever source is available.\\ndef get_version() # Get the short version string for this project.\\ndef get_cmdclass() # Get the custom setuptools/distutils subclasses used by Versioneer.\\n class cmd_version\\n def initialize_options(self)\\n def finalize_options(self)\\n def run(self)\\n class cmd_build_py\\n def run(self)\\n class cmd_sdist\\n def run(self)\\n def make_release_tree(self, base_dir, files)\\ndef do_setup() # Main VCS-independent setup function for installing Versioneer.\\ndef scan_setup_py() # Validate the contents of setup.py against Versioneer's expectations.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py\",\n \"text\": \"src/prefect/core/flow.py\\ndef cache(method: Callable) # Decorator for caching Flow methods.\\n def wrapper(self, *args, **kwargs)\\nclass Flow # The Flow class is used as the representation of a collection of dependent Tasks.\\n def __init__(self, name: str, schedule: prefect.schedules.Schedule=None, environment: Environment=None, storage: Storage=None, tasks: Iterable[Task]=None, edges: Iterable[Edge]=None, reference_tasks: Iterable[Task]=None, state_handlers: List[Callable]=None, on_failure: Callable=None, validate: bool=None, result_handler: Optional[ResultHandler]=None, result: Optional[Result]=None)\\n def __eq__(self, other: Any)\\n def __repr__(self)\\n def __iter__(self)\\n def copy(self) # Create and returns a copy of the current Flow.\\n def get_tasks(self, name: str=None, slug: str=None, tags: Iterable[str]=None, task_type: type=None) # Helper method for retrieving tasks from this flow based on certain attributes.\\n def sieve(t: Task)\\n def replace(self, old: Task, new: Task, validate: bool=True) # Performs an inplace replacement of the old task with the provided new task.\\n def __enter__(self)\\n def __exit__(self, _type, _value, _tb)\\n def root_tasks(self) # Get the tasks in the flow that have no upstream dependencies; these are\\n def terminal_tasks(self) # Get the tasks in the flow that have no downstream dependencies\\n def parameters(self) # Returns any parameters of the flow.\\n def reference_tasks(self) # A flow's \\\"reference tasks\\\" are used to determine its state when it runs. If all \\n def set_reference_tasks(self, tasks: Iterable[Task]) # Sets the `reference_tasks` for the flow. See `flow.reference_tasks` for more det\\n def add_task(self, task: Task) # Add a task to the flow if the task does not already exist. The tasks are\\n def add_edge(self, upstream_task: Task, downstream_task: Task, key: str=None, mapped: bool=False, validate: bool=None) # Add an edge in the flow between two tasks. All edges are directed beginning with\\n def chain(self, *tasks: Task, validate: bool=None) # Adds a sequence of dependent tasks to the flow; each task should be provided\\n def update(self, flow: 'Flow', validate: bool=None) # Take all tasks and edges in another flow and add it to this flow\\n def all_upstream_edges(self) # Returns a dictionary relating each task in the Flow to the set of\\n def all_downstream_edges(self) # Returns a dictionary relating each task in the Flow to the set of\\n def edges_to(self, task: Task) # Get all of the edges leading to a task (i.e., the upstream edges)\\n def edges_from(self, task: Task) # Get all of the edges leading from a task (i.e., the downstream edges)\\n def upstream_tasks(self, task: Task) # Get all of the tasks upstream of a task\\n def downstream_tasks(self, task: Task) # Get all of the tasks downstream of a task\\n def validate(self) # Checks that the flow is valid.\\n def sorted_tasks(self, root_tasks: Iterable[Task]=None) # Get the task\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/core/task.py\",\n \"text\": \"src/prefect/core/task.py\\ndef _validate_run_signature(run: Callable)\\nclass SignatureValidator\\n def __new__(cls, name: str, parents: tuple, methods: dict)\\nclass Task # The Task class which is used as the full representation of a unit of work.\\n def __init__(self, name: str=None, slug: str=None, tags: Iterable[str]=None, max_retries: int=None, retry_delay: timedelta=None, timeout: int=None, trigger: Callable[[Dict['Edge', 'State']], bool]=None, skip_on_upstream_skip: bool=True, cache_for: timedelta=None, cache_validator: Callable=None, cache_key: str=None, checkpoint: bool=None, result_handler: 'ResultHandler'=None, state_handlers: List[Callable]=None, on_failure: Callable=None, log_stdout: bool=False, result: 'Result'=None, target: str=None)\\n def __repr__(self)\\n def __hash__(self)\\n def run(self) # The `run()` method is called (with arguments, if appropriate) to run a task.\\n def copy(self, **task_args: Any) # Creates and returns a copy of the current Task.\\n def __call__(self, *args: Any, mapped: bool=False, task_args: dict=None, upstream_tasks: Iterable[Any]=None, flow: 'Flow'=None, **kwargs: Any) # Calling a Task instance will first create a _copy_ of the instance, and then\\n def bind(self, *args: Any, mapped: bool=False, upstream_tasks: Iterable[Any]=None, flow: 'Flow'=None, **kwargs: Any) # Binding a task to (keyword) arguments creates a _keyed_ edge in the active Flow\\n def map(self, *args: Any, upstream_tasks: Iterable[Any]=None, flow: 'Flow'=None, task_args: dict=None, **kwargs: Any) # Map the Task elementwise across one or more Tasks. Arguments that should _not_ b\\n def set_dependencies(self, flow: 'Flow'=None, upstream_tasks: Iterable[object]=None, downstream_tasks: Iterable[object]=None, keyword_tasks: Mapping[str, object]=None, mapped: bool=False, validate: bool=None) # Set dependencies for a flow either specified or in the current context using thi\\n def set_upstream(self, task: object, flow: 'Flow'=None, key: str=None, mapped: bool=False) # Sets the provided task as an upstream dependency of this task.\\n def set_downstream(self, task: 'Task', flow: 'Flow'=None, key: str=None, mapped: bool=False) # Sets the provided task as a downstream dependency of this task.\\n def inputs(self) # Describe the inputs for this task. The result is a dictionary that maps each inp\\n def outputs(self) # Get the output types for this task.\\n def serialize(self) # Creates a serialized representation of this task\\n def is_equal(self, other: object) # Produces a Task that evaluates `self == other`\\n def is_not_equal(self, other: object) # Produces a Task that evaluates `self != other`\\n def not_(self) # Produces a Task that evaluates `not self`\\n def or_(self, other: object) # Produces a Task that evaluates `self or other`\\n def __getitem__(self, key: Any) # Produces a Task that evaluates `self[key]`\\n def __or__(self, other: object) # Creates a state dependency between `self` and `other`\\n def _\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/case.py\",\n \"text\": \"src/prefect/tasks/control_flow/case.py\\nclass case # A conditional block in a flow definition.\\n def __init__(self, task: Task, value: Any)\\n def add_task(self, task: Task) # Add a new task under the case statement.\\n def __enter__(self)\\n def __exit__(self, *args)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/conditional.py\",\n \"text\": \"src/prefect/tasks/control_flow/conditional.py\\nclass Merge\\n def __init__(self, **kwargs)\\n def run(self, **task_results: Any)\\nclass CompareValue # This task stores a `value` at initialization and compares it to a `value` receiv\\n def __init__(self, value: Any, **kwargs: Any)\\n def run(self, value: Any) # Raises a SKIP signal if the passed value does not match the task's match value;\\ndef switch(condition: Task, cases: Dict[Any, Task]) # Adds a SWITCH to a workflow.\\ndef ifelse(condition: Task, true_task: Task, false_task: Task) # Builds a conditional branch into a workflow.\\n def as_bool(x)\\ndef merge(*tasks: Task) # Merges conditional branches back together.\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"server/versioneer.py::get_root","text":"server/versioneer.py::get_root\ndef get_root():\n \"\"\"Get the project root directory.\n\n We require that all commands are run from the project root, i.e. the\n directory that contains setup.py, setup.cfg, and versioneer.py .\n \"\"\"\n root = os.path.realpath(os.path.abspath(os.getcwd()))\n setup_py = os.path.join(root, \"setup.py\")\n versioneer_py = os.path.join(root, \"versioneer.py\")\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n # allow 'python path/to/setup.py COMMAND'\n root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))\n setup_py = os.path.join(root, \"setup.py\")\n versioneer_py = os.path.join(root, \"versioneer.py\")\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\n err = (\n \"Versioneer was unable to run the project root directory. \"\n \"Versioneer requires setup.py to be executed from \"\n \"its immediate directory (like 'python setup.py COMMAND'), \"\n \"or in a way that lets it use sys.argv[0] to find the root \"\n \"(like 'python path/to/setup.py COMMAND').\"\n )\n raise VersioneerBadRootError(err)\n try:\n # Certain runtime workflows (setup.py install/develop in a setuptools\n # tree) execute all dependencies in a single python process, so\n # \"versioneer\" may be imported multiple times, and python's shared\n # module-import table will cache the first one. So we can't use\n # os.path.dirname(__file__), as that will find whichever\n # versioneer.py was first imported, even in later projects.\n me = os.path.realpath(os.path.abspath(__file__))\n me_dir = os.path.normcase(os.path.splitext(me)[0])\n vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])\n if me_dir != vsr_dir:\n print(\n \"Warning: build in %s is using versioneer.py from %s\"\n % (os.path.dirname(me), versioneer_py)\n )\n","label":0},{"cid":"server/versioneer.py::get_config_from_root","text":"server/versioneer.py::get_config_from_root\ndef get_config_from_root(root):\n \"\"\"Read the project setup.cfg file to determine Versioneer config.\"\"\"\n # This might raise EnvironmentError (if setup.cfg is missing), or\n # configparser.NoSectionError (if it lacks a [versioneer] section), or\n # configparser.NoOptionError (if it lacks \"VCS=\"). See the docstring at\n # the top of versioneer.py for instructions on writing your setup.cfg .\n setup_cfg = os.path.join(root, \"setup.cfg\")\n parser = configparser.SafeConfigParser()\n with open(setup_cfg, \"r\") as f:\n parser.readfp(f)\n VCS = parser.get(\"versioneer\", \"VCS\") # mandatory\n\n def get(parser, name):\n if parser.has_option(\"versioneer\", name):\n return parser.get(\"versioneer\", name)\n return None\n\n cfg = VersioneerConfig()\n cfg.VCS = VCS\n cfg.style = get(parser, \"style\") or \"\"\n cfg.versionfile_source = get(parser, \"versionfile_source\")\n cfg.versionfile_build = get(parser, \"versionfile_build\")\n cfg.tag_prefix = get(parser, \"tag_prefix\")\n if cfg.tag_prefix in (\"''\", '\"\"'):\n cfg.tag_prefix = \"\"\n cfg.parentdir_prefix = get(parser, \"parentdir_prefix\")\n cfg.verbose = get(parser, \"verbose\")\n return cfg","label":0},{"cid":"server/versioneer.py::get_config_from_root.get","text":"server/versioneer.py::get_config_from_root.get\n def get(parser, name):\n if parser.has_option(\"versioneer\", name):\n return parser.get(\"versioneer\", name)\n return None","label":0},{"cid":"server/versioneer.py::register_vcs_handler","text":"server/versioneer.py::register_vcs_handler\ndef register_vcs_handler(vcs, method): # decorator\n \"\"\"Decorator to mark a method as the handler for a particular VCS.\"\"\"\n\n def decorate(f):\n \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n if vcs not in HANDLERS:\n HANDLERS[vcs] = {}\n HANDLERS[vcs][method] = f\n return f\n\n return decorate","label":0},{"cid":"server/versioneer.py::register_vcs_handler.decorate","text":"server/versioneer.py::register_vcs_handler.decorate\n def decorate(f):\n \"\"\"Store f in HANDLERS[vcs][method].\"\"\"\n if vcs not in HANDLERS:\n HANDLERS[vcs] = {}\n HANDLERS[vcs][method] = f\n return f","label":0},{"cid":"server/versioneer.py::run_command","text":"server/versioneer.py::run_command\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):\n \"\"\"Call the given command(s).\"\"\"\n assert isinstance(commands, list)\n p = None\n for c in commands:\n try:\n dispcmd = str([c] + args)\n # remember shell=False, so use git.cmd on windows, not just git\n p = subprocess.Popen(\n [c] + args,\n cwd=cwd,\n env=env,\n stdout=subprocess.PIPE,\n stderr=(subprocess.PIPE if hide_stderr else None),\n )\n break\n except EnvironmentError:\n e = sys.exc_info()[1]\n if e.errno == errno.ENOENT:\n continue\n if verbose:\n print(\"unable to run %s\" % dispcmd)\n print(e)\n return None, None\n else:\n if verbose:\n print(\"unable to find command, tried {}\".format(commands))\n return None, None\n stdout = p.communicate()[0].strip()\n if sys.version_info[0] >= 3:\n stdout = stdout.decode()\n if p.returncode != 0:\n if verbose:\n print(\"unable to run %s (error)\" % dispcmd)\n print(\"stdout was %s\" % stdout)\n return None, p.returncode\n return stdout, p.returncode","label":0},{"cid":"server/versioneer.py::git_get_keywords","text":"server/versioneer.py::git_get_keywords\n@register_vcs_handler(\"git\", \"get_keywords\")\ndef git_get_keywords(versionfile_abs):\n \"\"\"Extract version information from the given file.\"\"\"\n # the code embedded in _version.py can just fetch the value of these\n # keywords. When used from setup.py, we don't want to import _version.py,\n # so we do it with a regexp instead. This function is not used from\n # _version.py.\n keywords = {}\n try:\n f = open(versionfile_abs, \"r\")\n for line in f.readlines():\n if line.strip().startswith(\"git_refnames =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"refnames\"] = mo.group(1)\n if line.strip().startswith(\"git_full =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"full\"] = mo.group(1)\n if line.strip().startswith(\"git_date =\"):\n mo = re.search(r'=\\s*\"(.*)\"', line)\n if mo:\n keywords[\"date\"] = mo.group(1)\n f.close()\n except EnvironmentError:\n pass\n return keywords","label":0},{"cid":"server/versioneer.py::git_versions_from_keywords","text":"server/versioneer.py::git_versions_from_keywords\n@register_vcs_handler(\"git\", \"keywords\")\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\n \"\"\"Get version information from git keywords.\"\"\"\n if not keywords:\n raise NotThisMethod(\"no keywords at all, weird\")\n date = keywords.get(\"date\")\n if date is not None:\n # git-2.2.0 added \"%cI\", which expands to an ISO-8601 -compliant\n # datestamp. However we prefer \"%ci\" (which expands to an \"ISO-8601\n # -like\" string, which we must then edit to make compliant), because\n # it's been around since git-1.5.3, and it's too difficult to\n # discover which version we're using, or to work around using an\n # older one.\n date = date.strip().replace(\" \", \"T\", 1).replace(\" \", \"\", 1)\n refnames = keywords[\"refnames\"].strip()\n if refnames.startswith(\"$Format\"):\n if verbose:\n print(\"keywords are unexpanded, not using\")\n raise NotThisMethod(\"unexpanded keywords, not a git-archive tarball\")\n refs = {r.strip() for r in refnames.strip(\"()\").split(\",\")}\n # starting in git-1.8.3, tags are listed as \"tag: foo-1.0\" instead of\n # just \"foo-1.0\". If we see a \"tag: \" prefix, prefer those.\n TAG = \"tag: \"\n tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)}\n if not tags:\n # Either we're using git < 1.8.3, or there really are no tags. We use\n # a heuristic: assume all version tags have a digit. The old git %d\n # expansion behaves like git log --decorate=short and strips out the\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\n # between branches and tags. By ignoring refnames without digits, we\n # filter out many common branch names like \"release\" and\n # \"stabilization\", as well as \"HEAD\" and \"master\".\n tags = {r for r in refs if re.search(r\"\\d\", r)}\n if verbose:\n print(\"discarding '%s', no digits\" % \",\".join(refs - tags))\n if v","label":0},{"cid":"server/versioneer.py::git_pieces_from_vcs","text":"server/versioneer.py::git_pieces_from_vcs\n@register_vcs_handler(\"git\", \"pieces_from_vcs\")\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):\n \"\"\"Get version from 'git describe' in the root of the source tree.\n\n This only gets called if the git-archive 'subst' keywords were *not*\n expanded, and _version.py hasn't already been rewritten with a short\n version string, meaning we're inside a checked out source tree.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n\n out, rc = run_command(GITS, [\"rev-parse\", \"--git-dir\"], cwd=root, hide_stderr=True)\n if rc != 0:\n if verbose:\n print(\"Directory %s not under git control\" % root)\n raise NotThisMethod(\"'git rev-parse --git-dir' returned error\")\n\n # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\n # if there isn't one, this yields HEX[-dirty] (no NUM)\n describe_out, rc = run_command(\n GITS,\n [\n \"describe\",\n \"--tags\",\n \"--dirty\",\n \"--always\",\n \"--long\",\n \"--match\",\n \"%s*\" % tag_prefix,\n ],\n cwd=root,\n )\n # --long was added in git-1.5.5\n if describe_out is None:\n raise NotThisMethod(\"'git describe' failed\")\n describe_out = describe_out.strip()\n full_out, rc = run_command(GITS, [\"rev-parse\", \"HEAD\"], cwd=root)\n if full_out is None:\n raise NotThisMethod(\"'git rev-parse' failed\")\n full_out = full_out.strip()\n\n pieces = {}\n pieces[\"long\"] = full_out\n pieces[\"short\"] = full_out[:7] # maybe improved later\n pieces[\"error\"] = None\n\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\n # TAG might have hyphens.\n git_describe = describe_out\n\n # look for -dirty suffix\n dirty = git_describe.endswith(\"-dirty\")\n pieces[\"dirty\"] = dirty\n if dirty:\n git_describe = git_describe[: git_describe.rindex(","label":0},{"cid":"server/versioneer.py::do_vcs_install","text":"server/versioneer.py::do_vcs_install\ndef do_vcs_install(manifest_in, versionfile_source, ipy):\n \"\"\"Git-specific installation logic for Versioneer.\n\n For Git, this means creating/changing .gitattributes to mark _version.py\n for export-subst keyword substitution.\n \"\"\"\n GITS = [\"git\"]\n if sys.platform == \"win32\":\n GITS = [\"git.cmd\", \"git.exe\"]\n files = [manifest_in, versionfile_source]\n if ipy:\n files.append(ipy)\n try:\n me = __file__\n if me.endswith(\".pyc\") or me.endswith(\".pyo\"):\n me = os.path.splitext(me)[0] + \".py\"\n versioneer_file = os.path.relpath(me)\n except NameError:\n versioneer_file = \"versioneer.py\"\n files.append(versioneer_file)\n present = False\n try:\n f = open(\".gitattributes\", \"r\")\n for line in f.readlines():\n if line.strip().startswith(versionfile_source):\n if \"export-subst\" in line.strip().split()[1:]:\n present = True\n f.close()\n except EnvironmentError:\n pass\n if not present:\n f = open(\".gitattributes\", \"a+\")\n f.write(\"%s export-subst\\n\" % versionfile_source)\n f.close()\n files.append(\".gitattributes\")\n run_command(GITS, [\"add\", \"--\"] + files)","label":0},{"cid":"server/versioneer.py::versions_from_parentdir","text":"server/versioneer.py::versions_from_parentdir\ndef versions_from_parentdir(parentdir_prefix, root, verbose):\n \"\"\"Try to determine the version from the parent directory name.\n\n Source tarballs conventionally unpack into a directory that includes both\n the project name and a version string. We will also support searching up\n two directory levels for an appropriately named parent directory\n \"\"\"\n rootdirs = []\n\n for i in range(3):\n dirname = os.path.basename(root)\n if dirname.startswith(parentdir_prefix):\n return {\n \"version\": dirname[len(parentdir_prefix) :],\n \"full-revisionid\": None,\n \"dirty\": False,\n \"error\": None,\n \"date\": None,\n }\n else:\n rootdirs.append(root)\n root = os.path.dirname(root) # up a level\n\n if verbose:\n print(\n \"Tried directories %s but none started with prefix %s\"\n % (str(rootdirs), parentdir_prefix)\n )\n raise NotThisMethod(\"rootdir doesn't start with parentdir_prefix\")","label":0},{"cid":"server/versioneer.py::versions_from_file","text":"server/versioneer.py::versions_from_file\ndef versions_from_file(filename):\n \"\"\"Try to determine the version from _version.py if present.\"\"\"\n try:\n with open(filename) as f:\n contents = f.read()\n except EnvironmentError:\n raise NotThisMethod(\"unable to read _version.py\")\n mo = re.search(\n r\"version_json = '''\\n(.*)''' # END VERSION_JSON\", contents, re.M | re.S\n )\n if not mo:\n mo = re.search(\n r\"version_json = '''\\r\\n(.*)''' # END VERSION_JSON\", contents, re.M | re.S\n )\n if not mo:\n raise NotThisMethod(\"no version_json in _version.py\")\n return json.loads(mo.group(1))","label":0},{"cid":"server/versioneer.py::write_to_version_file","text":"server/versioneer.py::write_to_version_file\ndef write_to_version_file(filename, versions):\n \"\"\"Write the given version number to the given _version.py file.\"\"\"\n os.unlink(filename)\n contents = json.dumps(versions, sort_keys=True, indent=1, separators=(\",\", \": \"))\n with open(filename, \"w\") as f:\n f.write(SHORT_VERSION_PY % contents)\n\n print(\"set {} to '{}'\".format(filename, versions[\"version\"]))","label":0},{"cid":"server/versioneer.py::plus_or_dot","text":"server/versioneer.py::plus_or_dot\ndef plus_or_dot(pieces):\n \"\"\"Return a + if we don't already have one, else return a .\"\"\"\n if \"+\" in pieces.get(\"closest-tag\", \"\"):\n return \".\"\n return \"+\"","label":0},{"cid":"server/versioneer.py::render_pep440","text":"server/versioneer.py::render_pep440\ndef render_pep440(pieces):\n \"\"\"Build up version string, with post-release \"local version identifier\".\n\n Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\n get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\n\n Exceptions:\n 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += plus_or_dot(pieces)\n rendered += \"%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n else:\n # exception #1\n rendered = \"0+untagged.%d.g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n if pieces[\"dirty\"]:\n rendered += \".dirty\"\n return rendered","label":0},{"cid":"server/versioneer.py::render_pep440_pre","text":"server/versioneer.py::render_pep440_pre\ndef render_pep440_pre(pieces):\n \"\"\"TAG[.post.devDISTANCE] -- No -dirty.\n\n Exceptions:\n 1: no tags. 0.post.devDISTANCE\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \".post.dev%d\" % pieces[\"distance\"]\n else:\n # exception #1\n rendered = \"0.post.dev%d\" % pieces[\"distance\"]\n return rendered","label":0},{"cid":"server/versioneer.py::render_pep440_post","text":"server/versioneer.py::render_pep440_post\ndef render_pep440_post(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]+gHEX] .\n\n The \".dev0\" means dirty. Note that .dev0 sorts backwards\n (a dirty tree will appear \"older\" than the corresponding clean one),\n but you shouldn't be releasing software with -dirty anyways.\n\n Exceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += plus_or_dot(pieces)\n rendered += \"g%s\" % pieces[\"short\"]\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n rendered += \"+g%s\" % pieces[\"short\"]\n return rendered","label":0},{"cid":"server/versioneer.py::render_pep440_old","text":"server/versioneer.py::render_pep440_old\ndef render_pep440_old(pieces):\n \"\"\"TAG[.postDISTANCE[.dev0]] .\n\n The \".dev0\" means dirty.\n\n Eexceptions:\n 1: no tags. 0.postDISTANCE[.dev0]\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"] or pieces[\"dirty\"]:\n rendered += \".post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n else:\n # exception #1\n rendered = \"0.post%d\" % pieces[\"distance\"]\n if pieces[\"dirty\"]:\n rendered += \".dev0\"\n return rendered","label":0},{"cid":"server/versioneer.py::render_git_describe","text":"server/versioneer.py::render_git_describe\ndef render_git_describe(pieces):\n \"\"\"TAG[-DISTANCE-gHEX][-dirty].\n\n Like 'git describe --tags --dirty --always'.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n if pieces[\"distance\"]:\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered","label":0},{"cid":"server/versioneer.py::render_git_describe_long","text":"server/versioneer.py::render_git_describe_long\ndef render_git_describe_long(pieces):\n \"\"\"TAG-DISTANCE-gHEX[-dirty].\n\n Like 'git describe --tags --dirty --always -long'.\n The distance/hash is unconditional.\n\n Exceptions:\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\n \"\"\"\n if pieces[\"closest-tag\"]:\n rendered = pieces[\"closest-tag\"]\n rendered += \"-%d-g%s\" % (pieces[\"distance\"], pieces[\"short\"])\n else:\n # exception #1\n rendered = pieces[\"short\"]\n if pieces[\"dirty\"]:\n rendered += \"-dirty\"\n return rendered","label":0},{"cid":"server/versioneer.py::render","text":"server/versioneer.py::render\ndef render(pieces, style):\n \"\"\"Render the given version pieces into the requested style.\"\"\"\n if pieces[\"error\"]:\n return {\n \"version\": \"unknown\",\n \"full-revisionid\": pieces.get(\"long\"),\n \"dirty\": None,\n \"error\": pieces[\"error\"],\n \"date\": None,\n }\n\n if not style or style == \"default\":\n style = \"pep440\" # the default\n\n if style == \"pep440\":\n rendered = render_pep440(pieces)\n elif style == \"pep440-pre\":\n rendered = render_pep440_pre(pieces)\n elif style == \"pep440-post\":\n rendered = render_pep440_post(pieces)\n elif style == \"pep440-old\":\n rendered = render_pep440_old(pieces)\n elif style == \"git-describe\":\n rendered = render_git_describe(pieces)\n elif style == \"git-describe-long\":\n rendered = render_git_describe_long(pieces)\n else:\n raise ValueError(\"unknown style '%s'\" % style)\n\n return {\n \"version\": rendered,\n \"full-revisionid\": pieces[\"long\"],\n \"dirty\": pieces[\"dirty\"],\n \"error\": None,\n \"date\": pieces.get(\"date\"),\n }","label":0},{"cid":"server/versioneer.py::get_versions","text":"server/versioneer.py::get_versions\ndef get_versions(verbose=False):\n \"\"\"Get the project version from whatever source is available.\n\n Returns dict with two keys: 'version' and 'full'.\n \"\"\"\n if \"versioneer\" in sys.modules:\n # see the discussion in cmdclass.py:get_cmdclass()\n del sys.modules[\"versioneer\"]\n\n root = get_root()\n cfg = get_config_from_root(root)\n\n assert cfg.VCS is not None, \"please set [versioneer]VCS= in setup.cfg\"\n handlers = HANDLERS.get(cfg.VCS)\n assert handlers, \"unrecognized VCS '%s'\" % cfg.VCS\n verbose = verbose or cfg.verbose\n assert (\n cfg.versionfile_source is not None\n ), \"please set versioneer.versionfile_source\"\n assert cfg.tag_prefix is not None, \"please set versioneer.tag_prefix\"\n\n versionfile_abs = os.path.join(root, cfg.versionfile_source)\n\n # extract version from first of: _version.py, VCS command (e.g. 'git\n # describe'), parentdir. This is meant to work for developers using a\n # source checkout, for users of a tarball created by 'setup.py sdist',\n # and for users of a tarball/zipball created by 'git archive' or github's\n # download-from-tag feature or the equivalent in other VCSes.\n\n get_keywords_f = handlers.get(\"get_keywords\")\n from_keywords_f = handlers.get(\"keywords\")\n if get_keywords_f and from_keywords_f:\n try:\n keywords = get_keywords_f(versionfile_abs)\n ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)\n if verbose:\n print(\"got version from expanded keyword %s\" % ver)\n return ver\n except NotThisMethod:\n pass\n\n try:\n ver = versions_from_file(versionfile_abs)\n if verbose:\n print(\"got version from file {} {}\".format(versionfile_abs, ver))\n return ver\n except NotThisMethod:\n pass\n\n from_vcs_f = handlers.get(\"pieces_from_vcs\")\n if from_vcs_f:\n try:\n pieces = from_vcs_f(cfg.tag_prefix, root, ","label":0},{"cid":"server/versioneer.py::get_version","text":"server/versioneer.py::get_version\ndef get_version():\n \"\"\"Get the short version string for this project.\"\"\"\n return get_versions()[\"version\"]","label":0},{"cid":"server/versioneer.py::get_cmdclass","text":"server/versioneer.py::get_cmdclass\ndef get_cmdclass():\n \"\"\"Get the custom setuptools/distutils subclasses used by Versioneer.\"\"\"\n if \"versioneer\" in sys.modules:\n del sys.modules[\"versioneer\"]\n # this fixes the \"python setup.py develop\" case (also 'install' and\n # 'easy_install .'), in which subdependencies of the main project are\n # built (using setup.py bdist_egg) in the same python process. Assume\n # a main project A and a dependency B, which use different versions\n # of Versioneer. A's setup.py imports A's Versioneer, leaving it in\n # sys.modules by the time B's setup.py is executed, causing B to run\n # with the wrong versioneer. Setuptools wraps the sub-dep builds in a\n # sandbox that restores sys.modules to it's pre-build state, so the\n # parent is protected against the child's \"import versioneer\". By\n # removing ourselves from sys.modules here, before the child build\n # happens, we protect the child from the parent's versioneer too.\n # Also see https://github.com/warner/python-versioneer/issues/52\n\n cmds = {}\n\n # we add \"version\" to both distutils and setuptools\n from distutils.core import Command\n\n class cmd_version(Command):\n description = \"report generated version string\"\n user_options = []\n boolean_options = []\n\n def initialize_options(self):\n pass\n\n def finalize_options(self):\n pass\n\n def run(self):\n vers = get_versions(verbose=True)\n print(\"Version: %s\" % vers[\"version\"])\n print(\" full-revisionid: %s\" % vers.get(\"full-revisionid\"))\n print(\" dirty: %s\" % vers.get(\"dirty\"))\n print(\" date: %s\" % vers.get(\"date\"))\n if vers[\"error\"]:\n print(\" error: %s\" % vers[\"error\"])\n\n cmds[\"version\"] = cmd_version\n\n # we override \"build_py\" in both distutils and setuptools\n #\n # most invocation pathways end up","label":0},{"cid":"server/versioneer.py::get_cmdclass.cmd_version.initialize_options","text":"server/versioneer.py::get_cmdclass.cmd_version.initialize_options\n def initialize_options(self):\n pass","label":0},{"cid":"server/versioneer.py::get_cmdclass.cmd_version.finalize_options","text":"server/versioneer.py::get_cmdclass.cmd_version.finalize_options\n def finalize_options(self):\n pass","label":0},{"cid":"server/versioneer.py::get_cmdclass.cmd_version.run","text":"server/versioneer.py::get_cmdclass.cmd_version.run\n def run(self):\n vers = get_versions(verbose=True)\n print(\"Version: %s\" % vers[\"version\"])\n print(\" full-revisionid: %s\" % vers.get(\"full-revisionid\"))\n print(\" dirty: %s\" % vers.get(\"dirty\"))\n print(\" date: %s\" % vers.get(\"date\"))\n if vers[\"error\"]:\n print(\" error: %s\" % vers[\"error\"])","label":0},{"cid":"server/versioneer.py::get_cmdclass.cmd_build_py.run","text":"server/versioneer.py::get_cmdclass.cmd_build_py.run\n def run(self):\n root = get_root()\n cfg = get_config_from_root(root)\n versions = get_versions()\n _build_py.run(self)\n # now locate _version.py in the new build/ directory and replace\n # it with an updated value\n if cfg.versionfile_build:\n target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build)\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(target_versionfile, versions)","label":0},{"cid":"server/versioneer.py::get_cmdclass.cmd_sdist.run","text":"server/versioneer.py::get_cmdclass.cmd_sdist.run\n def run(self):\n versions = get_versions()\n self._versioneer_generated_versions = versions\n # unless we update this, the command will keep using the old\n # version\n self.distribution.metadata.version = versions[\"version\"]\n return _sdist.run(self)","label":0},{"cid":"server/versioneer.py::get_cmdclass.cmd_sdist.make_release_tree","text":"server/versioneer.py::get_cmdclass.cmd_sdist.make_release_tree\n def make_release_tree(self, base_dir, files):\n root = get_root()\n cfg = get_config_from_root(root)\n _sdist.make_release_tree(self, base_dir, files)\n # now locate _version.py in the new base_dir directory\n # (remembering that it may be a hardlink) and replace it with an\n # updated value\n target_versionfile = os.path.join(base_dir, cfg.versionfile_source)\n print(\"UPDATING %s\" % target_versionfile)\n write_to_version_file(\n target_versionfile, self._versioneer_generated_versions\n )","label":0},{"cid":"server/versioneer.py::do_setup","text":"server/versioneer.py::do_setup\ndef do_setup():\n \"\"\"Main VCS-independent setup function for installing Versioneer.\"\"\"\n root = get_root()\n try:\n cfg = get_config_from_root(root)\n except (\n EnvironmentError,\n configparser.NoSectionError,\n configparser.NoOptionError,\n ) as e:\n if isinstance(e, (EnvironmentError, configparser.NoSectionError)):\n print(\"Adding sample versioneer config to setup.cfg\", file=sys.stderr)\n with open(os.path.join(root, \"setup.cfg\"), \"a\") as f:\n f.write(SAMPLE_CONFIG)\n print(CONFIG_ERROR, file=sys.stderr)\n return 1\n\n print(\" creating %s\" % cfg.versionfile_source)\n with open(cfg.versionfile_source, \"w\") as f:\n LONG = LONG_VERSION_PY[cfg.VCS]\n f.write(\n LONG\n % {\n \"DOLLAR\": \"$\",\n \"STYLE\": cfg.style,\n \"TAG_PREFIX\": cfg.tag_prefix,\n \"PARENTDIR_PREFIX\": cfg.parentdir_prefix,\n \"VERSIONFILE_SOURCE\": cfg.versionfile_source,\n }\n )\n\n ipy = os.path.join(os.path.dirname(cfg.versionfile_source), \"__init__.py\")\n if os.path.exists(ipy):\n try:\n with open(ipy, \"r\") as f:\n old = f.read()\n except EnvironmentError:\n old = \"\"\n if INIT_PY_SNIPPET not in old:\n print(\" appending to %s\" % ipy)\n with open(ipy, \"a\") as f:\n f.write(INIT_PY_SNIPPET)\n else:\n print(\" %s unmodified\" % ipy)\n else:\n print(\" %s doesn't exist, ok\" % ipy)\n ipy = None\n\n # Make sure both the top-level \"versioneer.py\" and versionfile_source\n # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so\n # they'll be copied into source distributions. Pip won't be able to\n # install the package without this.\n manifest_in = os.path.join(root, \"MANIFEST.in\")\n simple_includes = set()\n try:\n with open(manifest_in,","label":0},{"cid":"server/versioneer.py::scan_setup_py","text":"server/versioneer.py::scan_setup_py\ndef scan_setup_py():\n \"\"\"Validate the contents of setup.py against Versioneer's expectations.\"\"\"\n found = set()\n setters = False\n errors = 0\n with open(\"setup.py\", \"r\") as f:\n for line in f.readlines():\n if \"import versioneer\" in line:\n found.add(\"import\")\n if \"versioneer.get_cmdclass()\" in line:\n found.add(\"cmdclass\")\n if \"versioneer.get_version()\" in line:\n found.add(\"get_version\")\n if \"versioneer.VCS\" in line:\n setters = True\n if \"versioneer.versionfile_source\" in line:\n setters = True\n if len(found) != 3:\n print(\"\")\n print(\"Your setup.py appears to be missing some important items\")\n print(\"(but I might be wrong). Please make sure it has something\")\n print(\"roughly like the following:\")\n print(\"\")\n print(\" import versioneer\")\n print(\" setup( version=versioneer.get_version(),\")\n print(\" cmdclass=versioneer.get_cmdclass(), ...)\")\n print(\"\")\n errors += 1\n if setters:\n print(\"You should remove lines like 'versioneer.VCS = ' and\")\n print(\"'versioneer.versionfile_source = ' . This configuration\")\n print(\"now lives in setup.cfg, and should be removed from setup.py\")\n print(\"\")\n errors += 1\n return errors","label":0},{"cid":"src/prefect/core/flow.py::cache","text":"src/prefect/core/flow.py::cache\ndef cache(method: Callable) -> Callable:\n \"\"\"\n Decorator for caching Flow methods.\n\n Each Flow has a _cache dict that can be used to memoize expensive functions. This\n decorator automatically compares a hash of the Flow's current tasks, edges, and reference_tasks\n to a cached hash; if the hash is the same, it attempts to retrieve a value from the cache.\n If the hash is different, it invalidates the cache.\n \"\"\"\n\n @functools.wraps(method)\n def wrapper(self, *args, **kwargs): # type: ignore\n\n cache_check = dict(\n tasks=self.tasks.copy(),\n edges=self.edges.copy(),\n reference_tasks=copy.copy(self._reference_tasks),\n )\n if any(self._cache.get(k) != v for k, v in cache_check.items()):\n self._cache.clear()\n self._cache.update(cache_check)\n\n callargs = inspect.signature(method).bind(self, *args, **kwargs).arguments\n key = (method.__name__, tuple(callargs.items())[1:])\n if key not in self._cache:\n self._cache[key] = method(self, *args, **kwargs)\n return self._cache[key]\n\n return wrapper","label":0},{"cid":"src/prefect/core/flow.py::cache.wrapper","text":"src/prefect/core/flow.py::cache.wrapper\n @functools.wraps(method)\n def wrapper(self, *args, **kwargs): # type: ignore\n\n cache_check = dict(\n tasks=self.tasks.copy(),\n edges=self.edges.copy(),\n reference_tasks=copy.copy(self._reference_tasks),\n )\n if any(self._cache.get(k) != v for k, v in cache_check.items()):\n self._cache.clear()\n self._cache.update(cache_check)\n\n callargs = inspect.signature(method).bind(self, *args, **kwargs).arguments\n key = (method.__name__, tuple(callargs.items())[1:])\n if key not in self._cache:\n self._cache[key] = method(self, *args, **kwargs)\n return self._cache[key]","label":0},{"cid":"src/prefect/core/flow.py::Flow.__init__","text":"src/prefect/core/flow.py::Flow.__init__\n def __init__(\n self,\n name: str,\n schedule: prefect.schedules.Schedule = None,\n environment: Environment = None,\n storage: Storage = None,\n tasks: Iterable[Task] = None,\n edges: Iterable[Edge] = None,\n reference_tasks: Iterable[Task] = None,\n state_handlers: List[Callable] = None,\n on_failure: Callable = None,\n validate: bool = None,\n result_handler: Optional[ResultHandler] = None,\n result: Optional[Result] = None,\n ):\n self._cache = {} # type: dict\n\n if not name:\n raise ValueError(\"A name must be provided for the flow.\")\n\n self.name = name\n self.logger = logging.get_logger(self.name)\n self.schedule = schedule\n self.environment = environment or prefect.environments.RemoteEnvironment()\n self.storage = storage\n if result_handler:\n warnings.warn(\n \"Result Handlers are deprecated; please use the new style Result classes instead.\"\n )\n self.result = ResultHandlerResult.from_result_handler(\n result_handler\n ) # type: Optional[Result]\n else:\n self.result = result\n\n self.tasks = set() # type: Set[Task]\n self.edges = set() # type: Set[Edge]\n self.constants = collections.defaultdict(\n dict\n ) # type: Dict[Task, Dict[str, Any]]\n\n for t in tasks or []:\n self.add_task(t)\n\n self.set_reference_tasks(reference_tasks or [])\n for e in edges or []:\n self.add_edge(\n upstream_task=e.upstream_task,\n downstream_task=e.downstream_task,\n key=e.key,\n mapped=e.mapped,\n validate=validate,\n )\n\n self._prefect_version = prefect.__version__\n\n if state_handlers and not isinstance(state_handlers, collections.abc.Sequence):\n ","label":0},{"cid":"src/prefect/core/flow.py::Flow.__eq__","text":"src/prefect/core/flow.py::Flow.__eq__\n def __eq__(self, other: Any) -> bool:\n if type(self) == type(other):\n s = (self.name, self.tasks, self.edges, self.reference_tasks())\n o = (other.name, other.tasks, other.edges, other.reference_tasks())\n return s == o\n return False","label":0},{"cid":"src/prefect/core/flow.py::Flow.__repr__","text":"src/prefect/core/flow.py::Flow.__repr__\n def __repr__(self) -> str:\n template = '<{cls}: name=\"{self.name}\">'\n return template.format(cls=type(self).__name__, self=self)","label":0},{"cid":"src/prefect/core/flow.py::Flow.__iter__","text":"src/prefect/core/flow.py::Flow.__iter__\n def __iter__(self) -> Iterable[Task]:\n yield from self.sorted_tasks()","label":0},{"cid":"src/prefect/core/flow.py::Flow.copy","text":"src/prefect/core/flow.py::Flow.copy\n def copy(self) -> \"Flow\":\n \"\"\"\n Create and returns a copy of the current Flow.\n \"\"\"\n new = copy.copy(self)\n # create a new cache\n new._cache = dict()\n new.tasks = self.tasks.copy()\n new.edges = self.edges.copy()\n new.set_reference_tasks(self._reference_tasks)\n return new","label":0},{"cid":"src/prefect/core/flow.py::Flow.get_tasks","text":"src/prefect/core/flow.py::Flow.get_tasks\n def get_tasks(\n self,\n name: str = None,\n slug: str = None,\n tags: Iterable[str] = None,\n task_type: type = None,\n ) -> List[Task]:\n \"\"\"\n Helper method for retrieving tasks from this flow based on certain attributes.\n The _intersection_ of all provided attributes is taken, i.e., only those tasks\n which match _all_ provided conditions are returned.\n\n Args:\n - name (str, optional): the name of the task\n - slug (str, optional): the slug of the task\n - tags ([str], optional): an iterable of task tags\n - task_type (type, optional): a possible task class type\n\n Returns:\n - [Task]: a list of tasks that meet the required conditions\n \"\"\"\n\n def sieve(t: Task) -> bool:\n keep = True\n if name is not None:\n keep &= t.name == name\n if slug is not None:\n keep &= t.slug == slug\n if tags is not None:\n keep &= t.tags.issuperset(tags)\n if task_type is not None:\n keep &= isinstance(t, task_type)\n return keep\n\n keep_tasks = filter(sieve, self.tasks)\n return list(keep_tasks)","label":0},{"cid":"src/prefect/core/flow.py::Flow.get_tasks.sieve","text":"src/prefect/core/flow.py::Flow.get_tasks.sieve\n def sieve(t: Task) -> bool:\n keep = True\n if name is not None:\n keep &= t.name == name\n if slug is not None:\n keep &= t.slug == slug\n if tags is not None:\n keep &= t.tags.issuperset(tags)\n if task_type is not None:\n keep &= isinstance(t, task_type)\n return keep","label":0},{"cid":"src/prefect/core/flow.py::Flow.replace","text":"src/prefect/core/flow.py::Flow.replace\n def replace(self, old: Task, new: Task, validate: bool = True) -> None:\n \"\"\"\n Performs an inplace replacement of the old task with the provided new task.\n\n Args:\n - old (Task): the old task to replace\n - new (Task): the new task to replace the old with; if not a Prefect\n Task, Prefect will attempt to convert it to one\n - validate (boolean, optional): whether to validate the Flow after\n the replace has been completed; defaults to `True`\n\n Raises:\n - ValueError: if the `old` task is not a part of this flow\n \"\"\"\n if old not in self.tasks:\n raise ValueError(\"Task {t} was not found in Flow {f}\".format(t=old, f=self))\n\n new = as_task(new, flow=self)\n\n # update tasks\n self.tasks.remove(old)\n self.add_task(new)\n\n self._cache.clear()\n\n affected_edges = {e for e in self.edges if old in e.tasks}\n\n # remove old edges\n for edge in affected_edges:\n self.edges.remove(edge)\n\n # replace with new edges\n for edge in affected_edges:\n upstream = new if edge.upstream_task == old else edge.upstream_task\n downstream = new if edge.downstream_task == old else edge.downstream_task\n self.add_edge(\n upstream_task=upstream,\n downstream_task=downstream,\n key=edge.key,\n mapped=edge.mapped,\n validate=False,\n )\n\n # update auxiliary task collections\n ref_tasks = self.reference_tasks()\n new_refs = [t for t in ref_tasks if t != old] + (\n [new] if old in ref_tasks else []\n )\n self.set_reference_tasks(new_refs)\n\n if validate:\n self.validate()","label":0},{"cid":"src/prefect/core/flow.py::Flow.__enter__","text":"src/prefect/core/flow.py::Flow.__enter__\n def __enter__(self) -> \"Flow\":\n self.__previous_flow = prefect.context.get(\"flow\")\n prefect.context.update(flow=self)\n return self","label":0},{"cid":"src/prefect/core/flow.py::Flow.__exit__","text":"src/prefect/core/flow.py::Flow.__exit__\n def __exit__(self, _type, _value, _tb) -> None: # type: ignore\n del prefect.context.flow\n if self.__previous_flow is not None:\n prefect.context.update(flow=self.__previous_flow)\n\n del self.__previous_flow","label":0},{"cid":"src/prefect/core/flow.py::Flow.root_tasks","text":"src/prefect/core/flow.py::Flow.root_tasks\n @cache\n def root_tasks(self) -> Set[Task]:\n \"\"\"\n Get the tasks in the flow that have no upstream dependencies; these are\n the tasks that, by default, flow execution begins with.\n\n Returns:\n - set of Task objects that have no upstream dependencies\n \"\"\"\n return set(t for t in self.tasks if not self.edges_to(t))","label":0},{"cid":"src/prefect/core/flow.py::Flow.terminal_tasks","text":"src/prefect/core/flow.py::Flow.terminal_tasks\n @cache\n def terminal_tasks(self) -> Set[Task]:\n \"\"\"\n Get the tasks in the flow that have no downstream dependencies\n\n Returns:\n - set of Task objects that have no downstream dependencies\n \"\"\"\n return set(t for t in self.tasks if not self.edges_from(t))","label":0},{"cid":"src/prefect/core/flow.py::Flow.parameters","text":"src/prefect/core/flow.py::Flow.parameters\n def parameters(self) -> Set[Parameter]:\n \"\"\"\n Returns any parameters of the flow.\n\n Returns:\n - set: a set of any Parameters in this flow\n \"\"\"\n return {p for p in self.tasks if isinstance(p, Parameter)}","label":0},{"cid":"src/prefect/core/flow.py::Flow.reference_tasks","text":"src/prefect/core/flow.py::Flow.reference_tasks\n def reference_tasks(self) -> Set[Task]:\n \"\"\"\n A flow's \"reference tasks\" are used to determine its state when it runs. If all the reference\n tasks are successful, then the flow run is considered successful. However, if\n any of the reference tasks fail, the flow is considered to fail. (Note that skips are\n counted as successes; see [the state documentation](../engine/state.html) for a full description\n of what is considered failure, success, etc.)\n\n By default, a flow's reference tasks are its terminal tasks. This means the state of a\n flow is determined by those tasks that have no downstream dependencies.\n\n In some situations, users may want to customize this behavior; for example, if a\n flow's terminal tasks are \"clean up\" tasks for the rest of the flow that only run\n if certain (more relevant) tasks fail, we might not want them determining the overall\n state of the flow run. The `flow.set_reference_tasks()` method can be used to set such custom `reference_tasks`.\n\n Please note that even if `reference_tasks` are provided that are not terminal tasks, the flow\n will not be considered \"finished\" until all terminal tasks have completed. Only then\n will state be determined, using the reference tasks.\n\n Returns:\n - set of Task objects which are the reference tasks in the flow\n \"\"\"\n if self._reference_tasks:\n return set(self._reference_tasks)\n else:\n return self.terminal_tasks()","label":0},{"cid":"src/prefect/core/flow.py::Flow.set_reference_tasks","text":"src/prefect/core/flow.py::Flow.set_reference_tasks\n def set_reference_tasks(self, tasks: Iterable[Task]) -> None:\n \"\"\"\n Sets the `reference_tasks` for the flow. See `flow.reference_tasks` for more details.\n\n Args:\n - tasks ([Task]): the tasks that should be set as a flow's reference tasks\n\n Returns:\n - None\n \"\"\"\n self._cache.clear()\n reference_tasks = set(tasks)\n if any(t not in self.tasks for t in reference_tasks):\n raise ValueError(\"reference tasks must be part of the flow.\")\n self._reference_tasks = reference_tasks","label":0},{"cid":"src/prefect/core/flow.py::Flow.add_task","text":"src/prefect/core/flow.py::Flow.add_task\n def add_task(self, task: Task) -> Task:\n \"\"\"\n Add a task to the flow if the task does not already exist. The tasks are\n uniquely identified by their `slug`.\n\n Args:\n - task (Task): the new Task to be added to the flow\n\n Returns:\n - Task: the `Task` object passed in if the task was successfully added\n\n Raises:\n - TypeError: if the `task` is not of type `Task`\n - ValueError: if the `task.slug` matches that of a task already in the flow\n \"\"\"\n if not isinstance(task, Task):\n raise TypeError(\n \"Tasks must be Task instances (received {})\".format(type(task))\n )\n elif task not in self.tasks:\n if task.slug and any(task.slug == t.slug for t in self.tasks):\n raise ValueError(\n 'A task with the slug \"{}\" already exists in this '\n \"flow.\".format(task.slug)\n )\n\n if task not in self.tasks:\n self.tasks.add(task)\n self._cache.clear()\n\n return task","label":1},{"cid":"src/prefect/core/flow.py::Flow.add_edge","text":"src/prefect/core/flow.py::Flow.add_edge\n def add_edge(\n self,\n upstream_task: Task,\n downstream_task: Task,\n key: str = None,\n mapped: bool = False,\n validate: bool = None,\n ) -> Edge:\n \"\"\"\n Add an edge in the flow between two tasks. All edges are directed beginning with\n an upstream task and ending with a downstream task.\n\n Args:\n - upstream_task (Task): The task that the edge should start from\n - downstream_task (Task): The task that the edge should end with\n - key (str, optional): The key to be set for the new edge; the result of the upstream task\n will be passed to the downstream task's `run()` method under this keyword argument\n - mapped (bool, optional): Whether this edge represents a call to `Task.map()`; defaults to `False`\n - validate (bool, optional): Whether or not to check the validity of\n the flow (e.g., presence of cycles and illegal keys). Defaults to the value\n of `eager_edge_validation` in your prefect configuration file.\n\n Returns:\n - prefect.core.edge.Edge: The `Edge` object that was successfully added to the flow\n\n Raises:\n - ValueError: if the `downstream_task` is of type `Parameter`\n - ValueError: if the edge exists with this `key` and `downstream_task`\n \"\"\"\n if validate is None:\n validate = cast(bool, prefect.config.flows.eager_edge_validation)\n if isinstance(downstream_task, Parameter):\n raise ValueError(\n \"Parameters must be root tasks and can not have upstream dependencies.\"\n )\n\n self.add_task(upstream_task)\n self.add_task(downstream_task)\n\n # we can only check the downstream task's edges once it has been added to the\n # flow, so we need to perform this check here and not earlier.\n if validate and key and key in {e.key for ","label":0},{"cid":"src/prefect/core/flow.py::Flow.chain","text":"src/prefect/core/flow.py::Flow.chain\n def chain(self, *tasks: Task, validate: bool = None) -> List[Edge]:\n \"\"\"\n Adds a sequence of dependent tasks to the flow; each task should be provided\n as an argument (or splatted from a list).\n\n Args:\n - *tasks (list): A list of tasks to chain together\n - validate (bool, optional): Whether or not to check the validity of\n the flow (e.g., presence of cycles). Defaults to the value of `eager_edge_validation`\n in your prefect configuration file.\n\n Returns:\n - A list of Edge objects added to the flow\n \"\"\"\n edges = []\n for u_task, d_task in zip(tasks, tasks[1:]):\n edges.append(\n self.add_edge(\n upstream_task=u_task, downstream_task=d_task, validate=validate\n )\n )\n return edges","label":0},{"cid":"src/prefect/core/flow.py::Flow.update","text":"src/prefect/core/flow.py::Flow.update\n def update(self, flow: \"Flow\", validate: bool = None) -> None:\n \"\"\"\n Take all tasks and edges in another flow and add it to this flow\n\n Args:\n - flow (Flow): A flow which is used to update this flow\n - validate (bool, optional): Whether or not to check the validity of the flow\n\n Returns:\n - None\n \"\"\"\n for task in flow.tasks:\n if task not in self.tasks:\n self.add_task(task)\n\n for edge in flow.edges:\n if edge not in self.edges:\n self.add_edge(\n upstream_task=edge.upstream_task,\n downstream_task=edge.downstream_task,\n key=edge.key,\n mapped=edge.mapped,\n validate=validate,\n )\n\n self.constants.update(flow.constants or {})","label":0},{"cid":"src/prefect/core/flow.py::Flow.all_upstream_edges","text":"src/prefect/core/flow.py::Flow.all_upstream_edges\n @cache\n def all_upstream_edges(self) -> Dict[Task, Set[Edge]]:\n \"\"\"\n Returns a dictionary relating each task in the Flow to the set of\n all _upstream_ edges for the task\n\n Returns:\n - dict with the key as tasks and the value as a set of upstream edges\n \"\"\"\n edges = {t: set() for t in self.tasks} # type: Dict[Task, Set[Edge]]\n for edge in self.edges:\n edges[edge.downstream_task].add(edge)\n return edges","label":0},{"cid":"src/prefect/core/flow.py::Flow.all_downstream_edges","text":"src/prefect/core/flow.py::Flow.all_downstream_edges\n @cache\n def all_downstream_edges(self) -> Dict[Task, Set[Edge]]:\n \"\"\"\n Returns a dictionary relating each task in the Flow to the set of\n all _downstream_ edges for the task\n\n Returns:\n - dict with the key as tasks and the value as a set of downstream edges\n \"\"\"\n edges = {t: set() for t in self.tasks} # type: Dict[Task, Set[Edge]]\n for edge in self.edges:\n edges[edge.upstream_task].add(edge)\n return edges","label":0},{"cid":"src/prefect/core/flow.py::Flow.edges_to","text":"src/prefect/core/flow.py::Flow.edges_to\n def edges_to(self, task: Task) -> Set[Edge]:\n \"\"\"\n Get all of the edges leading to a task (i.e., the upstream edges)\n\n Args:\n - task (Task): The task that we want to find edges leading to\n\n Returns:\n - dict with the key as the task passed in and the value as a set of all edges\n leading to that task\n\n Raises:\n - ValueError: if `task` is not found in this flow\n \"\"\"\n if task not in self.tasks:\n raise ValueError(\n \"Task {t} was not found in Flow {f}\".format(t=task, f=self)\n )\n return self.all_upstream_edges()[task]","label":0},{"cid":"src/prefect/core/flow.py::Flow.edges_from","text":"src/prefect/core/flow.py::Flow.edges_from\n def edges_from(self, task: Task) -> Set[Edge]:\n \"\"\"\n Get all of the edges leading from a task (i.e., the downstream edges)\n\n Args:\n - task (Task): The task that we want to find edges leading from\n\n Returns:\n - dict with the key as the task passed in and the value as a set of all edges\n leading from that task\n\n Raises:\n - ValueError: if `task` is not found in this flow\n \"\"\"\n if task not in self.tasks:\n raise ValueError(\n \"Task {t} was not found in Flow {f}\".format(t=task, f=self)\n )\n return self.all_downstream_edges()[task]","label":0},{"cid":"src/prefect/core/flow.py::Flow.upstream_tasks","text":"src/prefect/core/flow.py::Flow.upstream_tasks\n def upstream_tasks(self, task: Task) -> Set[Task]:\n \"\"\"\n Get all of the tasks upstream of a task\n\n Args:\n - task (Task): The task that we want to find upstream tasks of\n\n Returns:\n - set of Task objects which are upstream of `task`\n \"\"\"\n return set(e.upstream_task for e in self.edges_to(task))","label":0},{"cid":"src/prefect/core/flow.py::Flow.downstream_tasks","text":"src/prefect/core/flow.py::Flow.downstream_tasks\n def downstream_tasks(self, task: Task) -> Set[Task]:\n \"\"\"\n Get all of the tasks downstream of a task\n\n Args:\n - task (Task): The task that we want to find downstream tasks from\n\n Returns:\n - set of Task objects which are downstream of `task`\n \"\"\"\n return set(e.downstream_task for e in self.edges_from(task))","label":0},{"cid":"src/prefect/core/flow.py::Flow.validate","text":"src/prefect/core/flow.py::Flow.validate\n def validate(self) -> None:\n \"\"\"\n Checks that the flow is valid.\n\n Returns:\n - None\n\n Raises:\n - ValueError: if edges refer to tasks that are not in this flow\n - ValueError: if specified reference tasks are not in this flow\n - ValueError: if any tasks do not have assigned IDs\n \"\"\"\n\n self._cache.clear()\n\n if any(e.upstream_task not in self.tasks for e in self.edges) or any(\n e.downstream_task not in self.tasks for e in self.edges\n ):\n raise ValueError(\"Some edges refer to tasks not contained in this flow.\")\n\n self.sorted_tasks()\n\n if any(t not in self.tasks for t in self.reference_tasks()):\n raise ValueError(\"Some reference tasks are not contained in this flow.\")","label":0},{"cid":"src/prefect/core/flow.py::Flow.sorted_tasks","text":"src/prefect/core/flow.py::Flow.sorted_tasks\n def sorted_tasks(self, root_tasks: Iterable[Task] = None) -> Tuple[Task, ...]:\n \"\"\"\n Get the tasks in this flow in a sorted manner. This allows us to find if any\n cycles exist in this flow's DAG.\n\n Args:\n - root_tasks ([Tasks], optional): an `Iterable` of `Task` objects to\n start the sorting from\n\n Returns:\n - tuple of task objects that were sorted\n\n Raises:\n - ValueError: if a cycle is found in the flow's DAG\n \"\"\"\n return self._sorted_tasks(root_tasks=tuple(root_tasks or []))","label":0},{"cid":"src/prefect/core/flow.py::Flow._sorted_tasks","text":"src/prefect/core/flow.py::Flow._sorted_tasks\n @cache\n def _sorted_tasks(self, root_tasks: Tuple[Task, ...] = None) -> Tuple[Task, ...]:\n \"\"\"\n Computes a topological sort of the flow's tasks.\n\n Flow.sorted_tasks() can accept non-hashable arguments and therefore can't be\n cached, so this private method is called and cached instead.\n \"\"\"\n\n # begin by getting all tasks under consideration (root tasks and all\n # downstream tasks)\n if root_tasks:\n tasks = set(root_tasks)\n seen = set() # type: Set[Task]\n\n # while the set of tasks is different from the seen tasks...\n while tasks.difference(seen):\n # iterate over the new tasks...\n for t in list(tasks.difference(seen)):\n # add its downstream tasks to the task list\n tasks.update(self.downstream_tasks(t))\n # mark it as seen\n seen.add(t)\n else:\n tasks = self.tasks\n\n # build the list of sorted tasks\n remaining_tasks = list(tasks)\n sorted_tasks = []\n while remaining_tasks:\n # mark the flow as cyclic unless we prove otherwise\n cyclic = True\n\n # iterate over each remaining task\n for task in remaining_tasks.copy():\n # check all the upstream tasks of that task\n for upstream_task in self.upstream_tasks(task):\n # if the upstream task is also remaining, it means it\n # hasn't been sorted, so we can't sort this task either\n if upstream_task in remaining_tasks:\n break\n else:\n # but if all upstream tasks have been sorted, we can sort\n # this one too. We note that we found no cycle this time.\n cyclic = False\n remaining_tasks.remove(task)\n ","label":0},{"cid":"src/prefect/core/flow.py::Flow.set_dependencies","text":"src/prefect/core/flow.py::Flow.set_dependencies\n def set_dependencies(\n self,\n task: object,\n upstream_tasks: Iterable[object] = None,\n downstream_tasks: Iterable[object] = None,\n keyword_tasks: Mapping[str, object] = None,\n mapped: bool = False,\n validate: bool = None,\n ) -> None:\n \"\"\"\n Convenience function for adding task dependencies.\n\n Args:\n - task (object): a Task that will become part of the Flow. If the task is not a\n Task subclass, Prefect will attempt to convert it to one.\n - upstream_tasks ([object], optional): Tasks that will run before the task runs. If any task\n is not a Task subclass, Prefect will attempt to convert it to one.\n - downstream_tasks ([object], optional): Tasks that will run after the task runs. If any task\n is not a Task subclass, Prefect will attempt to convert it to one.\n - keyword_tasks ({key: object}, optional): The results of these tasks\n will be provided to the task under the specified keyword\n arguments. If any task is not a Task subclass, Prefect will attempt to\n convert it to one.\n - mapped (bool, optional): Whether the upstream tasks (both keyed\n and non-keyed) should be mapped over; defaults to `False`. If `True`, any\n tasks wrapped in the `prefect.utilities.tasks.unmapped` container will\n _not_ be mapped over.\n - validate (bool, optional): Whether or not to check the validity of\n the flow (e.g., presence of cycles). Defaults to the value of `eager_edge_validation`\n in your Prefect configuration file.\n\n Returns:\n - None\n \"\"\"\n\n task = as_task(task, flow=self)\n assert isinstance(task, Task) # mypy assert\n\n # add the main task (in case it was called with no arguments)\n self.","label":0},{"cid":"src/prefect/core/flow.py::Flow._run","text":"src/prefect/core/flow.py::Flow._run\n def _run(\n self,\n parameters: Dict[str, Any],\n runner_cls: type,\n run_on_schedule: bool = True,\n **kwargs: Any\n ) -> \"prefect.engine.state.State\":\n\n base_parameters = parameters or dict()\n\n ## determine time of first run\n try:\n if run_on_schedule and self.schedule is not None:\n next_run_event = self.schedule.next(1, return_events=True)[0]\n next_run_time = next_run_event.start_time # type: ignore\n parameters = base_parameters.copy()\n parameters.update(next_run_event.parameter_defaults) # type: ignore\n else:\n next_run_time = pendulum.now(\"utc\")\n except IndexError:\n raise ValueError(\"Flow has no more scheduled runs.\") from None\n\n ## setup initial states\n flow_state = prefect.engine.state.Scheduled(start_time=next_run_time, result={})\n flow_state = kwargs.pop(\"state\", flow_state)\n if not isinstance(flow_state.result, dict):\n flow_state.result = {}\n task_states = kwargs.pop(\"task_states\", {})\n flow_state.result.update(task_states)\n\n # set context for this flow run\n flow_run_context = kwargs.pop(\n \"context\", {}\n ).copy() # copy to avoid modification\n\n ## run this flow indefinitely, so long as its schedule has future dates\n while True:\n\n flow_run_context.update(scheduled_start_time=next_run_time)\n\n if flow_state.is_scheduled():\n next_run_time = flow_state.start_time\n now = pendulum.now(\"utc\")\n naptime = max((next_run_time - now).total_seconds(), 0)\n if naptime > 0:\n self.logger.info(\n \"Waiting for next scheduled run at {}\".format(next_run_time)\n )\n time.sleep(naptime)\n\n error = False\n\n ","label":0},{"cid":"src/prefect/core/flow.py::Flow.run","text":"src/prefect/core/flow.py::Flow.run\n def run(\n self,\n parameters: Dict[str, Any] = None,\n run_on_schedule: bool = None,\n runner_cls: type = None,\n **kwargs: Any\n ) -> \"prefect.engine.state.State\":\n \"\"\"\n Run the flow on its schedule using an instance of a FlowRunner. If the Flow has no schedule,\n a single stateful run will occur (including retries).\n\n Note that this command will block and run this Flow on its schedule indefinitely (if it has one);\n all task states will be stored in memory, and task retries will not occur until every Task in the Flow has had a chance\n to run.\n\n Args:\n - parameters (Dict[str, Any], optional): values to pass into the runner\n - run_on_schedule (bool, optional): whether to run this flow on its schedule, or run a single execution;\n if not provided, will default to the value set in your user config\n - runner_cls (type): an optional FlowRunner class (will use the default if not provided)\n - **kwargs: additional keyword arguments; if any provided keywords\n match known parameter names, they will be used as such. Otherwise they will be passed to the\n `FlowRunner.run()` method\n\n Raises:\n - ValueError: if this Flow has a Schedule with no more scheduled runs\n - ValueError: if the `return_tasks` keyword argument is provided\n\n Returns:\n - State: the state of the flow after its final run\n \"\"\"\n # protect against old behavior\n if \"return_tasks\" in kwargs:\n raise ValueError(\n \"The `return_tasks` keyword cannot be provided to `flow.run()`; \"\n \"all task states are always returned. If you want to receive a subset \"\n \"of task states, use a FlowRunner directly.\"\n )\n\n if runner_cls is None:\n runner_cls = prefect.engine.get_default_fl","label":0},{"cid":"src/prefect/core/flow.py::Flow.visualize","text":"src/prefect/core/flow.py::Flow.visualize\n def visualize(\n self,\n flow_state: \"prefect.engine.state.State\" = None,\n filename: str = None,\n format: str = None,\n ) -> object:\n \"\"\"\n Creates graphviz object for representing the current flow; this graphviz\n object will be rendered inline if called from an IPython notebook, otherwise\n it will be rendered in a new window. If a `filename` is provided, the object\n will not be rendered and instead saved to the location specified.\n\n Args:\n - flow_state (State, optional): flow state object used to optionally color the nodes\n - filename (str, optional): a filename specifying a location to save this visualization to; if provided,\n the visualization will not be rendered automatically\n - format (str, optional): a format specifying the output file type; defaults to 'pdf'.\n Refer to http://www.graphviz.org/doc/info/output.html for valid formats\n\n Raises:\n - ImportError: if `graphviz` is not installed\n \"\"\"\n\n try:\n import graphviz\n except ImportError:\n msg = (\n \"This feature requires graphviz.\\n\"\n \"Try re-installing prefect with `pip install 'prefect[viz]'`\"\n )\n raise ImportError(msg)\n\n def get_color(task: Task, map_index: int = None) -> str:\n assert flow_state\n assert isinstance(flow_state.result, dict)\n\n if map_index is not None:\n state = flow_state.result[task].map_states[map_index]\n else:\n state = flow_state.result.get(task)\n if state is not None:\n assert state is not None # mypy assert\n return state.color + \"80\"\n return \"#00000080\"\n\n graph = graphviz.Digraph()\n\n for t in self.tasks:\n is_mapped = any(edge.mapped for edge in self.e","label":0},{"cid":"src/prefect/core/flow.py::Flow.visualize.get_color","text":"src/prefect/core/flow.py::Flow.visualize.get_color\n def get_color(task: Task, map_index: int = None) -> str:\n assert flow_state\n assert isinstance(flow_state.result, dict)\n\n if map_index is not None:\n state = flow_state.result[task].map_states[map_index]\n else:\n state = flow_state.result.get(task)\n if state is not None:\n assert state is not None # mypy assert\n return state.color + \"80\"\n return \"#00000080\"","label":0},{"cid":"src/prefect/core/flow.py::Flow.serialize","text":"src/prefect/core/flow.py::Flow.serialize\n def serialize(self, build: bool = False) -> dict:\n \"\"\"\n Creates a serialized representation of the flow.\n\n Args:\n - build (bool, optional): if `True`, the flow's environment is built\n prior to serialization\n\n Returns:\n - dict representing the flow\n\n Raises:\n - ValueError: if `build=True` and the flow has no storage\n \"\"\"\n\n self.validate()\n schema = prefect.serialization.flow.FlowSchema\n serialized = schema(exclude=[\"storage\"]).dump(self)\n\n if build:\n if not self.storage:\n raise ValueError(\"This flow has no storage to build\")\n if self.name not in self.storage:\n self.storage.add_flow(self)\n else:\n warnings.warn(\n \"A flow with the same name is already contained in storage; if you changed your Flow since\"\n \" the last build, you might experience unexpected issues and should re-create your storage object.\"\n )\n storage = self.storage.build() # type: Optional[Storage]\n else:\n storage = self.storage\n\n serialized.update(schema(only=[\"storage\"]).dump({\"storage\": storage}))\n\n return serialized","label":0},{"cid":"src/prefect/core/flow.py::Flow.diagnostics","text":"src/prefect/core/flow.py::Flow.diagnostics\n def diagnostics(self, include_secret_names: bool = False) -> str:\n \"\"\"\n Get flow and Prefect diagnostic information\n\n Args:\n - include_secret_names (bool, optional): toggle output of Secret names, defaults to False.\n Note: Secret values are never returned, only their names.\n \"\"\"\n return diagnostics.diagnostic_info(self, include_secret_names)","label":0},{"cid":"src/prefect/core/flow.py::Flow.load","text":"src/prefect/core/flow.py::Flow.load\n @classmethod\n def load(cls, fpath: str) -> \"Flow\":\n \"\"\"\n Reads a Flow from a file that was created with `flow.save()`.\n\n Args:\n - fpath (str): either the absolute filepath where your Flow will be loaded from,\n or the name of the Flow you wish to load\n \"\"\"\n if not os.path.isabs(fpath):\n path = \"{home}/flows\".format(home=prefect.context.config.home_dir) # type: ignore\n fpath = Path(os.path.expanduser(path)) / \"{}.prefect\".format( # type: ignore\n slugify(fpath)\n ) # type: ignore\n with open(str(fpath), \"rb\") as f:\n return cloudpickle.load(f)","label":0},{"cid":"src/prefect/core/flow.py::Flow.save","text":"src/prefect/core/flow.py::Flow.save\n def save(self, fpath: str = None) -> str:\n \"\"\"\n Saves the Flow to a file by serializing it with cloudpickle. This method is\n recommended if you wish to separate out the building of your Flow from its registration.\n\n Args:\n - fpath (str, optional): the filepath where your Flow will be saved; defaults to\n `~/.prefect/flows/FLOW-NAME.prefect`\n\n Returns:\n - str: the full location the Flow was saved to\n \"\"\"\n if fpath is None:\n path = \"{home}/flows\".format(home=prefect.context.config.home_dir) # type: ignore\n fpath = Path( # type: ignore\n os.path.expanduser(path) # type: ignore\n ) / \"{}.prefect\".format( # type: ignore\n slugify(self.name)\n )\n assert fpath is not None # mypy assert\n fpath.parent.mkdir(exist_ok=True, parents=True)\n with open(str(fpath), \"wb\") as f:\n cloudpickle.dump(self, f)\n\n return str(fpath)","label":0},{"cid":"src/prefect/core/flow.py::Flow.run_agent","text":"src/prefect/core/flow.py::Flow.run_agent\n def run_agent(\n self, token: str = None, show_flow_logs: bool = False, log_to_cloud: bool = True\n ) -> None:\n \"\"\"\n Runs a Cloud agent for this Flow in-process.\n\n Args:\n - token (str, optional): A Prefect Cloud API token with a RUNNER scope;\n will default to the token found in `config.cloud.agent.auth_token`\n - show_flow_logs (bool, optional): a boolean specifying whether the agent should re-route Flow run logs\n to stdout; defaults to `False`\n - log_to_cloud (bool, optional): a boolean specifying whether Flow run logs should be sent to Prefect Cloud;\n defaults to `True`\n \"\"\"\n temp_config = {\n \"cloud.agent.auth_token\": token or prefect.config.cloud.agent.auth_token,\n \"logging.log_to_cloud\": log_to_cloud,\n }\n with set_temporary_config(temp_config):\n labels = self.environment.labels\n agent = prefect.agent.local.LocalAgent(\n labels=labels, show_flow_logs=show_flow_logs\n )\n agent.start()","label":0},{"cid":"src/prefect/core/flow.py::Flow.register","text":"src/prefect/core/flow.py::Flow.register\n def register(\n self,\n project_name: str = None,\n build: bool = True,\n labels: List[str] = None,\n set_schedule_active: bool = True,\n version_group_id: str = None,\n no_url: bool = False,\n **kwargs: Any\n ) -> str:\n \"\"\"\n Register the flow with Prefect Cloud; if no storage is present on the Flow, the default value from your config\n will be used and initialized with `**kwargs`.\n\n Args:\n - project_name (str, optional): the project that should contain this flow.\n - build (bool, optional): if `True`, the flow's environment is built\n prior to serialization; defaults to `True`\n - labels (List[str], optional): a list of labels to add to this Flow's environment; useful for\n associating Flows with individual Agents; see http://docs.prefect.io/orchestration/agents/overview.html#flow-affinity-labels\n - set_schedule_active (bool, optional): if `False`, will set the\n schedule to inactive in the database to prevent auto-scheduling runs (if the Flow has a schedule).\n Defaults to `True`. This can be changed later.\n - version_group_id (str, optional): the UUID version group ID to use for versioning this Flow\n in Cloud; if not provided, the version group ID associated with this Flow's project and name\n will be used.\n - no_url (bool, optional): if `True`, the stdout from this function will not contain the\n URL link to the newly-registered flow in the Cloud UI\n - **kwargs (Any): if instantiating a Storage object from default settings, these keyword arguments\n will be passed to the initialization method of the default Storage class\n\n Returns:\n - str: the ID of the flow that was registered\n \"\"\"\n if self.storage is None:\n self.st","label":0},{"cid":"src/prefect/core/flow.py::Flow.__mifflin__","text":"src/prefect/core/flow.py::Flow.__mifflin__\n def __mifflin__(self) -> None: # coverage: ignore\n \"Calls Dunder Mifflin\"\n import webbrowser\n\n webbrowser.open(\"https://cicdw.github.io/welcome.html\")","label":0},{"cid":"src/prefect/core/task.py::_validate_run_signature","text":"src/prefect/core/task.py::_validate_run_signature\ndef _validate_run_signature(run: Callable) -> None:\n func = getattr(run, \"__wrapped__\", run)\n try:\n run_sig = inspect.getfullargspec(func)\n except TypeError as exc:\n if str(exc) == \"unsupported callable\":\n raise ValueError(\n \"This function can not be inspected (this is common \"\n \"with `builtin` and `numpy` functions). In order to \"\n \"use it as a task, please wrap it in a standard \"\n \"Python function. For more detail, see \"\n \"https://docs.prefect.io/core/advanced_tutorials/task-guide.html#the-task-decorator\"\n )\n raise\n\n if run_sig.varargs:\n raise ValueError(\n \"Tasks with variable positional arguments (*args) are not \"\n \"supported, because all Prefect arguments are stored as \"\n \"keywords. As a workaround, consider modifying the run() \"\n \"method to accept **kwargs and feeding the values \"\n \"to *args.\"\n )\n\n reserved_kwargs = [\"upstream_tasks\", \"mapped\", \"task_args\", \"flow\"]\n violations = [kw for kw in reserved_kwargs if kw in run_sig.args]\n if violations:\n msg = \"Tasks cannot have the following argument names: {}.\".format(\n \", \".join(violations)\n )\n msg += \" These are reserved keyword arguments.\"\n raise ValueError(msg)","label":0},{"cid":"src/prefect/core/task.py::SignatureValidator.__new__","text":"src/prefect/core/task.py::SignatureValidator.__new__\n def __new__(cls, name: str, parents: tuple, methods: dict) -> \"SignatureValidator\":\n run = methods.get(\"run\", lambda: None)\n _validate_run_signature(run)\n\n # necessary to ensure classes that inherit from parent class\n # also get passed through __new__\n return type.__new__(cls, name, parents, methods) # type: ignore","label":0},{"cid":"src/prefect/core/task.py::Task.__init__","text":"src/prefect/core/task.py::Task.__init__\n def __init__(\n self,\n name: str = None,\n slug: str = None,\n tags: Iterable[str] = None,\n max_retries: int = None,\n retry_delay: timedelta = None,\n timeout: int = None,\n trigger: Callable[[Dict[\"Edge\", \"State\"]], bool] = None,\n skip_on_upstream_skip: bool = True,\n cache_for: timedelta = None,\n cache_validator: Callable = None,\n cache_key: str = None,\n checkpoint: bool = None,\n result_handler: \"ResultHandler\" = None,\n state_handlers: List[Callable] = None,\n on_failure: Callable = None,\n log_stdout: bool = False,\n result: \"Result\" = None,\n target: str = None,\n ):\n self.name = name or type(self).__name__\n self.slug = slug or str(uuid.uuid4())\n\n self.logger = logging.get_logger(self.name)\n\n # avoid silently iterating over a string\n if isinstance(tags, str):\n raise TypeError(\"Tags should be a set of tags, not a string.\")\n current_tags = set(prefect.context.get(\"tags\", set()))\n self.tags = (set(tags) if tags is not None else set()) | current_tags\n\n max_retries = (\n max_retries\n if max_retries is not None\n else prefect.config.tasks.defaults.max_retries\n )\n retry_delay = (\n retry_delay\n if retry_delay is not None\n else prefect.config.tasks.defaults.retry_delay\n )\n timeout = (\n timeout if timeout is not None else prefect.config.tasks.defaults.timeout\n )\n\n if max_retries > 0 and retry_delay is None:\n raise ValueError(\n \"A datetime.timedelta `retry_delay` must be provided if max_retries > 0\"\n )\n # specify not max retries because the default is false\n if retry_delay is not None and not max_retries:\n raise ValueError(\n \"A `max_retries` argument ","label":0},{"cid":"src/prefect/core/task.py::Task.__repr__","text":"src/prefect/core/task.py::Task.__repr__\n def __repr__(self) -> str:\n return \"<Task: {self.name}>\".format(self=self)","label":0},{"cid":"src/prefect/core/task.py::Task.__hash__","text":"src/prefect/core/task.py::Task.__hash__\n def __hash__(self) -> int:\n return id(self)","label":0},{"cid":"src/prefect/core/task.py::Task.run","text":"src/prefect/core/task.py::Task.run\n def run(self) -> None:\n \"\"\"\n The `run()` method is called (with arguments, if appropriate) to run a task.\n\n *Note:* The implemented `run` method cannot have `*args` in its signature. In addition,\n the following keywords are reserved: `upstream_tasks`, `task_args` and `mapped`.\n\n If a task has arguments in its `run()` method, these can be bound either by using the functional\n API and _calling_ the task instance, or by using `self.bind` directly.\n\n In addition to running arbitrary functions, tasks can interact with Prefect in a few ways:\n <ul><li> Return an optional result. When this function runs successfully,\n the task is considered successful and the result (if any) can be\n made available to downstream tasks. </li>\n <li> Raise an error. Errors are interpreted as failure. </li>\n <li> Raise a [signal](../engine/signals.html). Signals can include `FAIL`, `SUCCESS`, `RETRY`, `SKIP`, etc.\n and indicate that the task should be put in the indicated state.\n <ul>\n <li> `FAIL` will lead to retries if appropriate </li>\n <li> `SUCCESS` will cause the task to be marked successful </li>\n <li> `RETRY` will cause the task to be marked for retry, even if `max_retries`\n has been exceeded </li>\n <li> `SKIP` will skip the task and possibly propogate the skip state through the\n flow, depending on whether downstream tasks have `skip_on_upstream_skip=True`. </li></ul>\n </li></ul>\n \"\"\"","label":0},{"cid":"src/prefect/core/task.py::Task.copy","text":"src/prefect/core/task.py::Task.copy\n def copy(self, **task_args: Any) -> \"Task\":\n \"\"\"\n Creates and returns a copy of the current Task.\n\n Args:\n - **task_args (dict, optional): a dictionary of task attribute keyword arguments, these attributes\n will be set on the new copy\n\n Raises:\n - AttributeError: if any passed `task_args` are not attributes of the original\n\n Returns:\n - Task: a copy of the current Task, with any attributes updated from `task_args`\n \"\"\"\n\n flow = prefect.context.get(\"flow\", None)\n if (\n flow\n and self in flow.tasks\n and (flow.edges_to(self) or flow.edges_from(self))\n ):\n warnings.warn(\n \"You are making a copy of a task that has dependencies on or to other tasks \"\n \"in the active flow context. The copy will not retain those dependencies.\"\n )\n\n new = copy.copy(self)\n\n # ensure new slug is provided\n if \"slug\" not in task_args:\n task_args[\"slug\"] = str(uuid.uuid4())\n\n # check task_args\n for attr, val in task_args.items():\n if not hasattr(new, attr):\n raise AttributeError(\n \"{0} does not have {1} as an attribute\".format(self, attr)\n )\n else:\n setattr(new, attr, val)\n\n new.tags = copy.deepcopy(self.tags).union(set(new.tags))\n tags = set(prefect.context.get(\"tags\", set()))\n new.tags.update(tags)\n\n return new","label":0},{"cid":"src/prefect/core/task.py::Task.__call__","text":"src/prefect/core/task.py::Task.__call__\n def __call__(\n self,\n *args: Any,\n mapped: bool = False,\n task_args: dict = None,\n upstream_tasks: Iterable[Any] = None,\n flow: \"Flow\" = None,\n **kwargs: Any\n ) -> \"Task\":\n \"\"\"\n Calling a Task instance will first create a _copy_ of the instance, and then\n bind any passed `args` / `kwargs` to the run method of the copy. This new task\n is then returned.\n\n Args:\n - *args: arguments to bind to the new Task's `run` method\n - **kwargs: keyword arguments to bind to the new Task's `run` method\n - mapped (bool, optional): Whether the results of these tasks should be mapped over\n with the specified keyword arguments; defaults to `False`.\n If `True`, any arguments contained within a `prefect.utilities.tasks.unmapped`\n container will _not_ be mapped over.\n - task_args (dict, optional): a dictionary of task attribute keyword arguments, these attributes\n will be set on the new copy\n - upstream_tasks ([Task], optional): a list of upstream dependencies\n for the new task. This kwarg can be used to functionally specify\n dependencies without binding their result to `run()`\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\n flow in context if no flow is specified\n\n Returns:\n - Task: a new Task instance\n \"\"\"\n new = self.copy(**(task_args or {}))\n new.bind(\n *args, mapped=mapped, upstream_tasks=upstream_tasks, flow=flow, **kwargs\n )\n return new","label":0},{"cid":"src/prefect/core/task.py::Task.bind","text":"src/prefect/core/task.py::Task.bind\n def bind(\n self,\n *args: Any,\n mapped: bool = False,\n upstream_tasks: Iterable[Any] = None,\n flow: \"Flow\" = None,\n **kwargs: Any\n ) -> \"Task\":\n \"\"\"\n Binding a task to (keyword) arguments creates a _keyed_ edge in the active Flow\n that will pass data from the arguments (whether Tasks or constants) to the\n Task's `run` method under the appropriate key. Once a Task is bound in this\n manner, the same task instance cannot be bound a second time in the same Flow.\n\n To bind arguments to a _copy_ of this Task instance, see `__call__`.\n Additionally, non-keyed edges can be created by passing any upstream\n dependencies through `upstream_tasks`.\n\n Args:\n - *args: arguments to bind to the current Task's `run` method\n - mapped (bool, optional): Whether the results of these tasks should be mapped over\n with the specified keyword arguments; defaults to `False`.\n If `True`, any arguments contained within a `prefect.utilities.tasks.unmapped`\n container will _not_ be mapped over.\n - upstream_tasks ([Task], optional): a list of upstream dependencies for the\n current task.\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\n flow in context if no flow is specified\n - **kwargs: keyword arguments to bind to the current Task's `run` method\n\n Returns:\n - Task: the current Task instance\n \"\"\"\n\n # this will raise an error if callargs weren't all provided\n signature = inspect.signature(self.run)\n callargs = dict(signature.bind(*args, **kwargs).arguments) # type: Dict\n\n # bind() compresses all variable keyword arguments under the ** argument name,\n # so we expand them explicitly\n var_kw_arg = next(\n (p for p in si","label":1},{"cid":"src/prefect/core/task.py::Task.map","text":"src/prefect/core/task.py::Task.map\n def map(\n self,\n *args: Any,\n upstream_tasks: Iterable[Any] = None,\n flow: \"Flow\" = None,\n task_args: dict = None,\n **kwargs: Any\n ) -> \"Task\":\n \"\"\"\n Map the Task elementwise across one or more Tasks. Arguments that should _not_ be mapped over\n should be placed in the `prefect.utilities.tasks.unmapped` container.\n\n For example:\n ```\n task.map(x=X, y=unmapped(Y))\n ```\n will map over the values of `X`, but not over the values of `Y`\n\n\n Args:\n - *args: arguments to map over, which will elementwise be bound to the Task's `run` method\n - upstream_tasks ([Task], optional): a list of upstream dependencies\n to map over\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\n flow in context if no flow is specified\n - task_args (dict, optional): a dictionary of task attribute keyword arguments,\n these attributes will be set on the new copy\n - **kwargs: keyword arguments to map over, which will elementwise be bound to the Task's `run` method\n\n Raises:\n - AttributeError: if any passed `task_args` are not attributes of the original\n\n Returns:\n - Task: a new Task instance\n \"\"\"\n for arg in args:\n if not hasattr(arg, \"__getitem__\") and not isinstance(arg, unmapped):\n raise TypeError(\n \"Cannot map over unsubscriptable object of type {t}: {preview}...\".format(\n t=type(arg), preview=repr(arg)[:10]\n )\n )\n new = self.copy(**(task_args or {}))\n return new.bind(\n *args, mapped=True, upstream_tasks=upstream_tasks, flow=flow, **kwargs\n )","label":0},{"cid":"src/prefect/core/task.py::Task.set_dependencies","text":"src/prefect/core/task.py::Task.set_dependencies\n def set_dependencies(\n self,\n flow: \"Flow\" = None,\n upstream_tasks: Iterable[object] = None,\n downstream_tasks: Iterable[object] = None,\n keyword_tasks: Mapping[str, object] = None,\n mapped: bool = False,\n validate: bool = None,\n ) -> None:\n \"\"\"\n Set dependencies for a flow either specified or in the current context using this task\n\n Args:\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\n flow in context if no flow is specified\n - upstream_tasks ([object], optional): A list of upstream tasks for this task\n - downstream_tasks ([object], optional): A list of downtream tasks for this task\n - keyword_tasks ({str, object}}, optional): The results of these tasks will be provided\n to this task under the specified keyword arguments.\n - mapped (bool, optional): Whether the results of the _upstream_ tasks should be mapped over\n with the specified keyword arguments\n - validate (bool, optional): Whether or not to check the validity of the flow. If not\n provided, defaults to the value of `eager_edge_validation` in your Prefect\n configuration file.\n\n Returns:\n - None\n\n Raises:\n - ValueError: if no flow is specified and no flow can be found in the current context\n \"\"\"\n flow = flow or prefect.context.get(\"flow\", None)\n if not flow:\n raise ValueError(\n \"No Flow was passed, and could not infer an active Flow context.\"\n )\n\n flow.set_dependencies(\n task=self,\n upstream_tasks=upstream_tasks,\n downstream_tasks=downstream_tasks,\n keyword_tasks=keyword_tasks,\n validate=validate,\n mapped=mapped,\n )","label":0},{"cid":"src/prefect/core/task.py::Task.set_upstream","text":"src/prefect/core/task.py::Task.set_upstream\n def set_upstream(\n self, task: object, flow: \"Flow\" = None, key: str = None, mapped: bool = False\n ) -> None:\n \"\"\"\n Sets the provided task as an upstream dependency of this task.\n\n Args:\n - task (object): A task or object that will be converted to a task that will be set\n as a upstream dependency of this task.\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\n flow in context if no flow is specified\n - key (str, optional): The key to be set for the new edge; the result of the upstream task\n will be passed to this task's `run()` method under this keyword argument.\n - mapped (bool, optional): Whether this dependency is mapped; defaults to `False`\n\n Raises:\n - ValueError: if no flow is specified and no flow can be found in the current context\n \"\"\"\n if key is not None:\n keyword_tasks = {key: task}\n self.set_dependencies(flow=flow, keyword_tasks=keyword_tasks, mapped=mapped)\n else:\n self.set_dependencies(flow=flow, upstream_tasks=[task], mapped=mapped)","label":0},{"cid":"src/prefect/core/task.py::Task.set_downstream","text":"src/prefect/core/task.py::Task.set_downstream\n def set_downstream(\n self, task: \"Task\", flow: \"Flow\" = None, key: str = None, mapped: bool = False\n ) -> None:\n \"\"\"\n Sets the provided task as a downstream dependency of this task.\n\n Args:\n - task (Task): A task that will be set as a downstream dependency of this task.\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\n flow in context if no flow is specified\n - key (str, optional): The key to be set for the new edge; the result of this task\n will be passed to the downstream task's `run()` method under this keyword argument.\n - mapped (bool, optional): Whether this dependency is mapped; defaults to `False`\n\n Raises:\n - ValueError: if no flow is specified and no flow can be found in the current context\n \"\"\"\n if key is not None:\n keyword_tasks = {key: self}\n task.set_dependencies( # type: ignore\n flow=flow, keyword_tasks=keyword_tasks, mapped=mapped\n ) # type: ignore\n else:\n task.set_dependencies(flow=flow, upstream_tasks=[self], mapped=mapped)","label":0},{"cid":"src/prefect/core/task.py::Task.inputs","text":"src/prefect/core/task.py::Task.inputs\n def inputs(self) -> Dict[str, Dict]:\n \"\"\"\n Describe the inputs for this task. The result is a dictionary that maps each input to\n a `type`, `required`, and `default`. All values are inferred from the `run()`\n signature; this method can be overloaded for more precise control.\n\n Returns:\n - dict\n \"\"\"\n inputs = {}\n for name, parameter in inspect.signature(self.run).parameters.items():\n input_type = parameter.annotation\n if input_type is inspect._empty: # type: ignore\n input_type = Any\n\n input_default = parameter.default\n input_required = False\n if input_default is inspect._empty: # type: ignore\n input_required = True\n input_default = None\n\n inputs[name] = dict(\n type=input_type, default=input_default, required=input_required\n )\n\n return inputs","label":0},{"cid":"src/prefect/core/task.py::Task.outputs","text":"src/prefect/core/task.py::Task.outputs\n def outputs(self) -> Any:\n \"\"\"\n Get the output types for this task.\n\n Returns:\n - Any\n \"\"\"\n return_annotation = inspect.signature(self.run).return_annotation\n if return_annotation is inspect._empty: # type: ignore\n return_annotation = Any\n return return_annotation","label":0},{"cid":"src/prefect/core/task.py::Task.serialize","text":"src/prefect/core/task.py::Task.serialize\n def serialize(self) -> Dict[str, Any]:\n \"\"\"\n Creates a serialized representation of this task\n\n Returns:\n - dict representing this task\n \"\"\"\n return prefect.serialization.task.TaskSchema().dump(self)","label":0},{"cid":"src/prefect/core/task.py::Task.is_equal","text":"src/prefect/core/task.py::Task.is_equal\n def is_equal(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self == other`\n\n This can't be implemented as the __eq__() magic method because of Task\n comparisons.\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Equal().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.is_not_equal","text":"src/prefect/core/task.py::Task.is_not_equal\n def is_not_equal(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self != other`\n\n This can't be implemented as the __neq__() magic method because of Task\n comparisons.\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.NotEqual().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.not_","text":"src/prefect/core/task.py::Task.not_\n def not_(self) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `not self`\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Not().bind(self)","label":0},{"cid":"src/prefect/core/task.py::Task.or_","text":"src/prefect/core/task.py::Task.or_\n def or_(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self or other`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Or().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.__getitem__","text":"src/prefect/core/task.py::Task.__getitem__\n def __getitem__(self, key: Any) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self[key]`\n\n Args:\n - key (object): the object to use an an index for this task. It will be converted\n to a Task if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.GetItem().bind(self, key)","label":0},{"cid":"src/prefect/core/task.py::Task.__or__","text":"src/prefect/core/task.py::Task.__or__\n def __or__(self, other: object) -> object:\n \"\"\"\n Creates a state dependency between `self` and `other`\n `self | other --> self.set_dependencies(downstream_tasks=[other])`\n\n Args:\n - other (object): An object that will be converted to a Task (if it isn't one already)\n and set as a downstream dependency of this Task.\n\n Returns:\n - Task\n \"\"\"\n self.set_dependencies(downstream_tasks=[other])\n return other","label":0},{"cid":"src/prefect/core/task.py::Task.__mifflin__","text":"src/prefect/core/task.py::Task.__mifflin__\n def __mifflin__(self) -> None: # coverage: ignore\n \"Calls Dunder Mifflin\"\n import webbrowser\n\n webbrowser.open(\"https://cicdw.github.io/welcome.html\")","label":0},{"cid":"src/prefect/core/task.py::Task.__ror__","text":"src/prefect/core/task.py::Task.__ror__\n def __ror__(self, other: object) -> \"Task\":\n \"\"\"\n Creates a state dependency between `self` and `other`:\n `other | self --> self.set_dependencies(upstream_tasks=[other])`\n\n Args:\n - other (object): An object that will be converted to a Task and set as an\n upstream dependency of this Task.\n\n Returns:\n - Task\n \"\"\"\n self.set_dependencies(upstream_tasks=[other])\n return self","label":0},{"cid":"src/prefect/core/task.py::Task.__add__","text":"src/prefect/core/task.py::Task.__add__\n def __add__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self + other`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Add().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.__sub__","text":"src/prefect/core/task.py::Task.__sub__\n def __sub__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self - other`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Sub().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.__mul__","text":"src/prefect/core/task.py::Task.__mul__\n def __mul__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self * other`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Mul().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.__truediv__","text":"src/prefect/core/task.py::Task.__truediv__\n def __truediv__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self / other`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Div().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.__floordiv__","text":"src/prefect/core/task.py::Task.__floordiv__\n def __floordiv__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self // other`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.FloorDiv().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.__mod__","text":"src/prefect/core/task.py::Task.__mod__\n def __mod__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self % other`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Mod().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.__pow__","text":"src/prefect/core/task.py::Task.__pow__\n def __pow__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self ** other`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Pow().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.__and__","text":"src/prefect/core/task.py::Task.__and__\n def __and__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self & other`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.And().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.__radd__","text":"src/prefect/core/task.py::Task.__radd__\n def __radd__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `other + self`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Add().bind(other, self)","label":0},{"cid":"src/prefect/core/task.py::Task.__rsub__","text":"src/prefect/core/task.py::Task.__rsub__\n def __rsub__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `other - self`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Sub().bind(other, self)","label":0},{"cid":"src/prefect/core/task.py::Task.__rmul__","text":"src/prefect/core/task.py::Task.__rmul__\n def __rmul__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `other * self`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Mul().bind(other, self)","label":0},{"cid":"src/prefect/core/task.py::Task.__rtruediv__","text":"src/prefect/core/task.py::Task.__rtruediv__\n def __rtruediv__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `other / self`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Div().bind(other, self)","label":0},{"cid":"src/prefect/core/task.py::Task.__rfloordiv__","text":"src/prefect/core/task.py::Task.__rfloordiv__\n def __rfloordiv__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `other // self`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.FloorDiv().bind(other, self)","label":0},{"cid":"src/prefect/core/task.py::Task.__rmod__","text":"src/prefect/core/task.py::Task.__rmod__\n def __rmod__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `other % self`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Mod().bind(other, self)","label":0},{"cid":"src/prefect/core/task.py::Task.__rpow__","text":"src/prefect/core/task.py::Task.__rpow__\n def __rpow__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `other ** self`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.Pow().bind(other, self)","label":0},{"cid":"src/prefect/core/task.py::Task.__rand__","text":"src/prefect/core/task.py::Task.__rand__\n def __rand__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `other & self`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.And().bind(other, self)","label":0},{"cid":"src/prefect/core/task.py::Task.__gt__","text":"src/prefect/core/task.py::Task.__gt__\n def __gt__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self > other`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.GreaterThan().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.__ge__","text":"src/prefect/core/task.py::Task.__ge__\n def __ge__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self >= other`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.GreaterThanOrEqual().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.__lt__","text":"src/prefect/core/task.py::Task.__lt__\n def __lt__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self < other`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.LessThan().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Task.__le__","text":"src/prefect/core/task.py::Task.__le__\n def __le__(self, other: object) -> \"Task\":\n \"\"\"\n Produces a Task that evaluates `self <= other`\n\n Args:\n - other (object): the other operand of the operator. It will be converted to a Task\n if it isn't one already.\n\n Returns:\n - Task\n \"\"\"\n return prefect.tasks.core.operators.LessThanOrEqual().bind(self, other)","label":0},{"cid":"src/prefect/core/task.py::Parameter.__init__","text":"src/prefect/core/task.py::Parameter.__init__\n def __init__(\n self,\n name: str,\n default: Any = None,\n required: bool = True,\n tags: Iterable[str] = None,\n ):\n if default is not None:\n required = False\n\n self.required = required\n self.default = default\n\n super().__init__(\n name=name, slug=name, tags=tags, result=PrefectResult(), checkpoint=True,\n )","label":0},{"cid":"src/prefect/core/task.py::Parameter.__repr__","text":"src/prefect/core/task.py::Parameter.__repr__\n def __repr__(self) -> str:\n return \"<Parameter: {self.name}>\".format(self=self)","label":0},{"cid":"src/prefect/core/task.py::Parameter.__call__","text":"src/prefect/core/task.py::Parameter.__call__\n def __call__(self, flow: \"Flow\" = None) -> \"Parameter\": # type: ignore\n \"\"\"\n Calling a Parameter adds it to a flow.\n\n Args:\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\n flow in context if no flow is specified\n\n Returns:\n - Task: a new Task instance\n\n \"\"\"\n result = super().bind(flow=flow)\n assert isinstance(result, Parameter) # mypy assert\n return result","label":0},{"cid":"src/prefect/core/task.py::Parameter.copy","text":"src/prefect/core/task.py::Parameter.copy\n def copy(self, name: str, **task_args: Any) -> \"Task\": # type: ignore\n \"\"\"\n Creates a copy of the Parameter with a new name.\n\n Args:\n - name (str): the new Parameter name\n - **task_args (dict, optional): a dictionary of task attribute keyword arguments,\n these attributes will be set on the new copy\n\n Raises:\n - AttributeError: if any passed `task_args` are not attributes of the original\n\n Returns:\n - Parameter: a copy of the current Parameter, with a new name and any attributes\n updated from `task_args`\n \"\"\"\n return super().copy(name=name, slug=name, **task_args)","label":0},{"cid":"src/prefect/core/task.py::Parameter.run","text":"src/prefect/core/task.py::Parameter.run\n def run(self) -> Any:\n params = prefect.context.get(\"parameters\") or {}\n if self.required and self.name not in params:\n self.logger.debug(\n 'Parameter \"{}\" was required but not provided.'.format(self.name)\n )\n raise prefect.engine.signals.FAIL(\n 'Parameter \"{}\" was required but not provided.'.format(self.name)\n )\n return params.get(self.name, self.default)","label":0},{"cid":"src/prefect/core/task.py::Parameter.serialize","text":"src/prefect/core/task.py::Parameter.serialize\n def serialize(self) -> Dict[str, Any]:\n \"\"\"\n Creates a serialized representation of this parameter\n\n Returns:\n - dict representing this parameter\n \"\"\"\n return prefect.serialization.task.ParameterSchema().dump(self)","label":0},{"cid":"src/prefect/tasks/control_flow/case.py::case.__init__","text":"src/prefect/tasks/control_flow/case.py::case.__init__\n def __init__(self, task: Task, value: Any):\n if isinstance(value, Task):\n raise TypeError(\"`value` cannot be a task\")\n\n self.task = task\n self.value = value\n self._tasks = set()","label":1},{"cid":"src/prefect/tasks/control_flow/case.py::case.add_task","text":"src/prefect/tasks/control_flow/case.py::case.add_task\n def add_task(self, task: Task) -> None:\n \"\"\"Add a new task under the case statement.\n\n Args:\n - task (Task): the task to add\n \"\"\"\n self._tasks.add(task)","label":1},{"cid":"src/prefect/tasks/control_flow/case.py::case.__enter__","text":"src/prefect/tasks/control_flow/case.py::case.__enter__\n def __enter__(self):\n self.__prev_case = prefect.context.get(\"case\")\n prefect.context.update(case=self)","label":0},{"cid":"src/prefect/tasks/control_flow/case.py::case.__exit__","text":"src/prefect/tasks/control_flow/case.py::case.__exit__\n def __exit__(self, *args):\n if self.__prev_case is None:\n prefect.context.pop(\"case\", None)\n else:\n prefect.context.update(case=self.__prev_case)\n\n if self._tasks:\n\n flow = prefect.context.get(\"flow\", None)\n if not flow:\n raise ValueError(\"Could not infer an active Flow context.\")\n\n cond = CompareValue(self.value, name=f\"case({self.value})\",).bind(\n value=self.task\n )\n\n for child in self._tasks:\n # If a task has no upstream tasks created in this case block,\n # the case conditional should be set as an upstream task.\n if not self._tasks.intersection(flow.upstream_tasks(child)):\n child.set_upstream(cond)","label":1},{"cid":"src/prefect/tasks/control_flow/conditional.py::Merge.__init__","text":"src/prefect/tasks/control_flow/conditional.py::Merge.__init__\n def __init__(self, **kwargs) -> None:\n if kwargs.setdefault(\"skip_on_upstream_skip\", False):\n raise ValueError(\"Merge tasks must have `skip_on_upstream_skip=False`.\")\n kwargs.setdefault(\"trigger\", prefect.triggers.not_all_skipped)\n super().__init__(**kwargs)","label":0},{"cid":"src/prefect/tasks/control_flow/conditional.py::Merge.run","text":"src/prefect/tasks/control_flow/conditional.py::Merge.run\n def run(self, **task_results: Any) -> Any:\n return next(\n (v for k, v in sorted(task_results.items()) if v is not None), None,\n )","label":0},{"cid":"src/prefect/tasks/control_flow/conditional.py::CompareValue.__init__","text":"src/prefect/tasks/control_flow/conditional.py::CompareValue.__init__\n def __init__(self, value: Any, **kwargs: Any):\n self.value = value\n kwargs.setdefault(\"name\", 'CompareValue: \"{}\"'.format(value))\n super().__init__(**kwargs)","label":0},{"cid":"src/prefect/tasks/control_flow/conditional.py::CompareValue.run","text":"src/prefect/tasks/control_flow/conditional.py::CompareValue.run\n def run(self, value: Any) -> None:\n \"\"\"\n Raises a SKIP signal if the passed value does not match the task's match value;\n succeeds silently otherwise.\n\n Args:\n - value (Any): the value that will be matched against the task's value.\n \"\"\"\n if value != self.value:\n raise signals.SKIP(\n 'Provided value \"{}\" did not match \"{}\"'.format(value, self.value)\n )","label":0},{"cid":"src/prefect/tasks/control_flow/conditional.py::switch","text":"src/prefect/tasks/control_flow/conditional.py::switch\ndef switch(condition: Task, cases: Dict[Any, Task]) -> None:\n \"\"\"\n Adds a SWITCH to a workflow.\n\n The condition task is evaluated and the result is compared to the keys of the cases\n dictionary. The task corresponding to the matching key is run; all other tasks are\n skipped. Any tasks downstream of the skipped tasks are also skipped unless they set\n `skip_on_upstream_skip=False`.\n\n Example:\n ```python\n @task\n def condition():\n return \"b\" # returning 'b' will take the b_branch\n\n @task\n def a_branch():\n return \"A Branch\"\n\n @task\n def b_branch():\n return \"B Branch\"\n\n with Flow(\"switch-flow\") as flow:\n switch(condition, dict(a=a_branch, b=b_branch))\n ```\n\n Args:\n - condition (Task): a task whose result forms the condition for the switch\n - cases (Dict[Any, Task]): a dict representing the \"case\" statements of the switch.\n The value of the `condition` task will be compared to the keys of this dict, and\n the matching task will be executed.\n\n Raises:\n - PrefectWarning: if any of the tasks in \"cases\" have upstream dependencies,\n then this task will warn that those upstream tasks may run whether or not the switch condition matches their branch. The most common cause of this\n is passing a list of tasks as one of the cases, which adds the `List` task\n to the switch condition but leaves the tasks themselves upstream.\n \"\"\"\n\n with prefect.tags(\"switch\"):\n for value, task in cases.items():\n task = prefect.utilities.tasks.as_task(task)\n match_condition = CompareValue(value=value).bind(value=condition)\n task.set_dependencies(upstream_tasks=[match_condition])","label":0},{"cid":"src/prefect/tasks/control_flow/conditional.py::ifelse","text":"src/prefect/tasks/control_flow/conditional.py::ifelse\ndef ifelse(condition: Task, true_task: Task, false_task: Task) -> None:\n \"\"\"\n Builds a conditional branch into a workflow.\n\n If the condition evaluates True(ish), the true_task will run. If it\n evaluates False(ish), the false_task will run. The task doesn't run is Skipped, as are\n all downstream tasks that don't set `skip_on_upstream_skip=False`.\n\n Args:\n - condition (Task): a task whose boolean result forms the condition for the ifelse\n - true_task (Task): a task that will be executed if the condition is True\n - false_task (Task): a task that will be executed if the condition is False\n \"\"\"\n\n @prefect.task\n def as_bool(x):\n return bool(x)\n\n cases = {c: t for c, t in [(True, true_task), (False, false_task)] if t is not None}\n if cases:\n switch(condition=as_bool(condition), cases=cases)","label":0},{"cid":"src/prefect/tasks/control_flow/conditional.py::ifelse.as_bool","text":"src/prefect/tasks/control_flow/conditional.py::ifelse.as_bool\n @prefect.task\n def as_bool(x):\n return bool(x)","label":0},{"cid":"src/prefect/tasks/control_flow/conditional.py::merge","text":"src/prefect/tasks/control_flow/conditional.py::merge\ndef merge(*tasks: Task) -> Task:\n \"\"\"\n Merges conditional branches back together.\n\n A conditional branch in a flow results in one or more tasks proceeding and one or\n more tasks skipping. It is often convenient to merge those branches back into a\n single result. This function is a simple way to achieve that goal. By default this\n task will skip if all its upstream dependencies are also skipped.\n\n The merge will return the first real result it encounters, or `None`. If multiple\n tasks might return a result, group them with a list.\n\n Example:\n ```python\n with Flow(\"My Flow\"):\n true_branch = ActionIfTrue()\n false_branch = ActionIfFalse()\n ifelse(CheckCondition(), true_branch, false_branch)\n\n merged_result = merge(true_branch, false_branch)\n ```\n\n Args:\n - *tasks (Task): tasks whose results should be merged into a single result. The tasks are\n assumed to all sit downstream of different `switch` branches, such that only\n one of them will contain a result and the others will all be skipped.\n\n Returns:\n - Task: a Task representing the merged result.\n\n \"\"\"\n return Merge().bind(**{\"task_{}\".format(i + 1): t for i, t in enumerate(tasks)})","label":0}],"string":"[\n {\n \"cid\": \"server/versioneer.py::get_root\",\n \"text\": \"server/versioneer.py::get_root\\ndef get_root():\\n \\\"\\\"\\\"Get the project root directory.\\n\\n We require that all commands are run from the project root, i.e. the\\n directory that contains setup.py, setup.cfg, and versioneer.py .\\n \\\"\\\"\\\"\\n root = os.path.realpath(os.path.abspath(os.getcwd()))\\n setup_py = os.path.join(root, \\\"setup.py\\\")\\n versioneer_py = os.path.join(root, \\\"versioneer.py\\\")\\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\\n # allow 'python path/to/setup.py COMMAND'\\n root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0])))\\n setup_py = os.path.join(root, \\\"setup.py\\\")\\n versioneer_py = os.path.join(root, \\\"versioneer.py\\\")\\n if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)):\\n err = (\\n \\\"Versioneer was unable to run the project root directory. \\\"\\n \\\"Versioneer requires setup.py to be executed from \\\"\\n \\\"its immediate directory (like 'python setup.py COMMAND'), \\\"\\n \\\"or in a way that lets it use sys.argv[0] to find the root \\\"\\n \\\"(like 'python path/to/setup.py COMMAND').\\\"\\n )\\n raise VersioneerBadRootError(err)\\n try:\\n # Certain runtime workflows (setup.py install/develop in a setuptools\\n # tree) execute all dependencies in a single python process, so\\n # \\\"versioneer\\\" may be imported multiple times, and python's shared\\n # module-import table will cache the first one. So we can't use\\n # os.path.dirname(__file__), as that will find whichever\\n # versioneer.py was first imported, even in later projects.\\n me = os.path.realpath(os.path.abspath(__file__))\\n me_dir = os.path.normcase(os.path.splitext(me)[0])\\n vsr_dir = os.path.normcase(os.path.splitext(versioneer_py)[0])\\n if me_dir != vsr_dir:\\n print(\\n \\\"Warning: build in %s is using versioneer.py from %s\\\"\\n % (os.path.dirname(me), versioneer_py)\\n )\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::get_config_from_root\",\n \"text\": \"server/versioneer.py::get_config_from_root\\ndef get_config_from_root(root):\\n \\\"\\\"\\\"Read the project setup.cfg file to determine Versioneer config.\\\"\\\"\\\"\\n # This might raise EnvironmentError (if setup.cfg is missing), or\\n # configparser.NoSectionError (if it lacks a [versioneer] section), or\\n # configparser.NoOptionError (if it lacks \\\"VCS=\\\"). See the docstring at\\n # the top of versioneer.py for instructions on writing your setup.cfg .\\n setup_cfg = os.path.join(root, \\\"setup.cfg\\\")\\n parser = configparser.SafeConfigParser()\\n with open(setup_cfg, \\\"r\\\") as f:\\n parser.readfp(f)\\n VCS = parser.get(\\\"versioneer\\\", \\\"VCS\\\") # mandatory\\n\\n def get(parser, name):\\n if parser.has_option(\\\"versioneer\\\", name):\\n return parser.get(\\\"versioneer\\\", name)\\n return None\\n\\n cfg = VersioneerConfig()\\n cfg.VCS = VCS\\n cfg.style = get(parser, \\\"style\\\") or \\\"\\\"\\n cfg.versionfile_source = get(parser, \\\"versionfile_source\\\")\\n cfg.versionfile_build = get(parser, \\\"versionfile_build\\\")\\n cfg.tag_prefix = get(parser, \\\"tag_prefix\\\")\\n if cfg.tag_prefix in (\\\"''\\\", '\\\"\\\"'):\\n cfg.tag_prefix = \\\"\\\"\\n cfg.parentdir_prefix = get(parser, \\\"parentdir_prefix\\\")\\n cfg.verbose = get(parser, \\\"verbose\\\")\\n return cfg\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::get_config_from_root.get\",\n \"text\": \"server/versioneer.py::get_config_from_root.get\\n def get(parser, name):\\n if parser.has_option(\\\"versioneer\\\", name):\\n return parser.get(\\\"versioneer\\\", name)\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::register_vcs_handler\",\n \"text\": \"server/versioneer.py::register_vcs_handler\\ndef register_vcs_handler(vcs, method): # decorator\\n \\\"\\\"\\\"Decorator to mark a method as the handler for a particular VCS.\\\"\\\"\\\"\\n\\n def decorate(f):\\n \\\"\\\"\\\"Store f in HANDLERS[vcs][method].\\\"\\\"\\\"\\n if vcs not in HANDLERS:\\n HANDLERS[vcs] = {}\\n HANDLERS[vcs][method] = f\\n return f\\n\\n return decorate\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::register_vcs_handler.decorate\",\n \"text\": \"server/versioneer.py::register_vcs_handler.decorate\\n def decorate(f):\\n \\\"\\\"\\\"Store f in HANDLERS[vcs][method].\\\"\\\"\\\"\\n if vcs not in HANDLERS:\\n HANDLERS[vcs] = {}\\n HANDLERS[vcs][method] = f\\n return f\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::run_command\",\n \"text\": \"server/versioneer.py::run_command\\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None):\\n \\\"\\\"\\\"Call the given command(s).\\\"\\\"\\\"\\n assert isinstance(commands, list)\\n p = None\\n for c in commands:\\n try:\\n dispcmd = str([c] + args)\\n # remember shell=False, so use git.cmd on windows, not just git\\n p = subprocess.Popen(\\n [c] + args,\\n cwd=cwd,\\n env=env,\\n stdout=subprocess.PIPE,\\n stderr=(subprocess.PIPE if hide_stderr else None),\\n )\\n break\\n except EnvironmentError:\\n e = sys.exc_info()[1]\\n if e.errno == errno.ENOENT:\\n continue\\n if verbose:\\n print(\\\"unable to run %s\\\" % dispcmd)\\n print(e)\\n return None, None\\n else:\\n if verbose:\\n print(\\\"unable to find command, tried {}\\\".format(commands))\\n return None, None\\n stdout = p.communicate()[0].strip()\\n if sys.version_info[0] >= 3:\\n stdout = stdout.decode()\\n if p.returncode != 0:\\n if verbose:\\n print(\\\"unable to run %s (error)\\\" % dispcmd)\\n print(\\\"stdout was %s\\\" % stdout)\\n return None, p.returncode\\n return stdout, p.returncode\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::git_get_keywords\",\n \"text\": \"server/versioneer.py::git_get_keywords\\n@register_vcs_handler(\\\"git\\\", \\\"get_keywords\\\")\\ndef git_get_keywords(versionfile_abs):\\n \\\"\\\"\\\"Extract version information from the given file.\\\"\\\"\\\"\\n # the code embedded in _version.py can just fetch the value of these\\n # keywords. When used from setup.py, we don't want to import _version.py,\\n # so we do it with a regexp instead. This function is not used from\\n # _version.py.\\n keywords = {}\\n try:\\n f = open(versionfile_abs, \\\"r\\\")\\n for line in f.readlines():\\n if line.strip().startswith(\\\"git_refnames =\\\"):\\n mo = re.search(r'=\\\\s*\\\"(.*)\\\"', line)\\n if mo:\\n keywords[\\\"refnames\\\"] = mo.group(1)\\n if line.strip().startswith(\\\"git_full =\\\"):\\n mo = re.search(r'=\\\\s*\\\"(.*)\\\"', line)\\n if mo:\\n keywords[\\\"full\\\"] = mo.group(1)\\n if line.strip().startswith(\\\"git_date =\\\"):\\n mo = re.search(r'=\\\\s*\\\"(.*)\\\"', line)\\n if mo:\\n keywords[\\\"date\\\"] = mo.group(1)\\n f.close()\\n except EnvironmentError:\\n pass\\n return keywords\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::git_versions_from_keywords\",\n \"text\": \"server/versioneer.py::git_versions_from_keywords\\n@register_vcs_handler(\\\"git\\\", \\\"keywords\\\")\\ndef git_versions_from_keywords(keywords, tag_prefix, verbose):\\n \\\"\\\"\\\"Get version information from git keywords.\\\"\\\"\\\"\\n if not keywords:\\n raise NotThisMethod(\\\"no keywords at all, weird\\\")\\n date = keywords.get(\\\"date\\\")\\n if date is not None:\\n # git-2.2.0 added \\\"%cI\\\", which expands to an ISO-8601 -compliant\\n # datestamp. However we prefer \\\"%ci\\\" (which expands to an \\\"ISO-8601\\n # -like\\\" string, which we must then edit to make compliant), because\\n # it's been around since git-1.5.3, and it's too difficult to\\n # discover which version we're using, or to work around using an\\n # older one.\\n date = date.strip().replace(\\\" \\\", \\\"T\\\", 1).replace(\\\" \\\", \\\"\\\", 1)\\n refnames = keywords[\\\"refnames\\\"].strip()\\n if refnames.startswith(\\\"$Format\\\"):\\n if verbose:\\n print(\\\"keywords are unexpanded, not using\\\")\\n raise NotThisMethod(\\\"unexpanded keywords, not a git-archive tarball\\\")\\n refs = {r.strip() for r in refnames.strip(\\\"()\\\").split(\\\",\\\")}\\n # starting in git-1.8.3, tags are listed as \\\"tag: foo-1.0\\\" instead of\\n # just \\\"foo-1.0\\\". If we see a \\\"tag: \\\" prefix, prefer those.\\n TAG = \\\"tag: \\\"\\n tags = {r[len(TAG) :] for r in refs if r.startswith(TAG)}\\n if not tags:\\n # Either we're using git < 1.8.3, or there really are no tags. We use\\n # a heuristic: assume all version tags have a digit. The old git %d\\n # expansion behaves like git log --decorate=short and strips out the\\n # refs/heads/ and refs/tags/ prefixes that would let us distinguish\\n # between branches and tags. By ignoring refnames without digits, we\\n # filter out many common branch names like \\\"release\\\" and\\n # \\\"stabilization\\\", as well as \\\"HEAD\\\" and \\\"master\\\".\\n tags = {r for r in refs if re.search(r\\\"\\\\d\\\", r)}\\n if verbose:\\n print(\\\"discarding '%s', no digits\\\" % \\\",\\\".join(refs - tags))\\n if v\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::git_pieces_from_vcs\",\n \"text\": \"server/versioneer.py::git_pieces_from_vcs\\n@register_vcs_handler(\\\"git\\\", \\\"pieces_from_vcs\\\")\\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):\\n \\\"\\\"\\\"Get version from 'git describe' in the root of the source tree.\\n\\n This only gets called if the git-archive 'subst' keywords were *not*\\n expanded, and _version.py hasn't already been rewritten with a short\\n version string, meaning we're inside a checked out source tree.\\n \\\"\\\"\\\"\\n GITS = [\\\"git\\\"]\\n if sys.platform == \\\"win32\\\":\\n GITS = [\\\"git.cmd\\\", \\\"git.exe\\\"]\\n\\n out, rc = run_command(GITS, [\\\"rev-parse\\\", \\\"--git-dir\\\"], cwd=root, hide_stderr=True)\\n if rc != 0:\\n if verbose:\\n print(\\\"Directory %s not under git control\\\" % root)\\n raise NotThisMethod(\\\"'git rev-parse --git-dir' returned error\\\")\\n\\n # if there is a tag matching tag_prefix, this yields TAG-NUM-gHEX[-dirty]\\n # if there isn't one, this yields HEX[-dirty] (no NUM)\\n describe_out, rc = run_command(\\n GITS,\\n [\\n \\\"describe\\\",\\n \\\"--tags\\\",\\n \\\"--dirty\\\",\\n \\\"--always\\\",\\n \\\"--long\\\",\\n \\\"--match\\\",\\n \\\"%s*\\\" % tag_prefix,\\n ],\\n cwd=root,\\n )\\n # --long was added in git-1.5.5\\n if describe_out is None:\\n raise NotThisMethod(\\\"'git describe' failed\\\")\\n describe_out = describe_out.strip()\\n full_out, rc = run_command(GITS, [\\\"rev-parse\\\", \\\"HEAD\\\"], cwd=root)\\n if full_out is None:\\n raise NotThisMethod(\\\"'git rev-parse' failed\\\")\\n full_out = full_out.strip()\\n\\n pieces = {}\\n pieces[\\\"long\\\"] = full_out\\n pieces[\\\"short\\\"] = full_out[:7] # maybe improved later\\n pieces[\\\"error\\\"] = None\\n\\n # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty]\\n # TAG might have hyphens.\\n git_describe = describe_out\\n\\n # look for -dirty suffix\\n dirty = git_describe.endswith(\\\"-dirty\\\")\\n pieces[\\\"dirty\\\"] = dirty\\n if dirty:\\n git_describe = git_describe[: git_describe.rindex(\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::do_vcs_install\",\n \"text\": \"server/versioneer.py::do_vcs_install\\ndef do_vcs_install(manifest_in, versionfile_source, ipy):\\n \\\"\\\"\\\"Git-specific installation logic for Versioneer.\\n\\n For Git, this means creating/changing .gitattributes to mark _version.py\\n for export-subst keyword substitution.\\n \\\"\\\"\\\"\\n GITS = [\\\"git\\\"]\\n if sys.platform == \\\"win32\\\":\\n GITS = [\\\"git.cmd\\\", \\\"git.exe\\\"]\\n files = [manifest_in, versionfile_source]\\n if ipy:\\n files.append(ipy)\\n try:\\n me = __file__\\n if me.endswith(\\\".pyc\\\") or me.endswith(\\\".pyo\\\"):\\n me = os.path.splitext(me)[0] + \\\".py\\\"\\n versioneer_file = os.path.relpath(me)\\n except NameError:\\n versioneer_file = \\\"versioneer.py\\\"\\n files.append(versioneer_file)\\n present = False\\n try:\\n f = open(\\\".gitattributes\\\", \\\"r\\\")\\n for line in f.readlines():\\n if line.strip().startswith(versionfile_source):\\n if \\\"export-subst\\\" in line.strip().split()[1:]:\\n present = True\\n f.close()\\n except EnvironmentError:\\n pass\\n if not present:\\n f = open(\\\".gitattributes\\\", \\\"a+\\\")\\n f.write(\\\"%s export-subst\\\\n\\\" % versionfile_source)\\n f.close()\\n files.append(\\\".gitattributes\\\")\\n run_command(GITS, [\\\"add\\\", \\\"--\\\"] + files)\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::versions_from_parentdir\",\n \"text\": \"server/versioneer.py::versions_from_parentdir\\ndef versions_from_parentdir(parentdir_prefix, root, verbose):\\n \\\"\\\"\\\"Try to determine the version from the parent directory name.\\n\\n Source tarballs conventionally unpack into a directory that includes both\\n the project name and a version string. We will also support searching up\\n two directory levels for an appropriately named parent directory\\n \\\"\\\"\\\"\\n rootdirs = []\\n\\n for i in range(3):\\n dirname = os.path.basename(root)\\n if dirname.startswith(parentdir_prefix):\\n return {\\n \\\"version\\\": dirname[len(parentdir_prefix) :],\\n \\\"full-revisionid\\\": None,\\n \\\"dirty\\\": False,\\n \\\"error\\\": None,\\n \\\"date\\\": None,\\n }\\n else:\\n rootdirs.append(root)\\n root = os.path.dirname(root) # up a level\\n\\n if verbose:\\n print(\\n \\\"Tried directories %s but none started with prefix %s\\\"\\n % (str(rootdirs), parentdir_prefix)\\n )\\n raise NotThisMethod(\\\"rootdir doesn't start with parentdir_prefix\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::versions_from_file\",\n \"text\": \"server/versioneer.py::versions_from_file\\ndef versions_from_file(filename):\\n \\\"\\\"\\\"Try to determine the version from _version.py if present.\\\"\\\"\\\"\\n try:\\n with open(filename) as f:\\n contents = f.read()\\n except EnvironmentError:\\n raise NotThisMethod(\\\"unable to read _version.py\\\")\\n mo = re.search(\\n r\\\"version_json = '''\\\\n(.*)''' # END VERSION_JSON\\\", contents, re.M | re.S\\n )\\n if not mo:\\n mo = re.search(\\n r\\\"version_json = '''\\\\r\\\\n(.*)''' # END VERSION_JSON\\\", contents, re.M | re.S\\n )\\n if not mo:\\n raise NotThisMethod(\\\"no version_json in _version.py\\\")\\n return json.loads(mo.group(1))\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::write_to_version_file\",\n \"text\": \"server/versioneer.py::write_to_version_file\\ndef write_to_version_file(filename, versions):\\n \\\"\\\"\\\"Write the given version number to the given _version.py file.\\\"\\\"\\\"\\n os.unlink(filename)\\n contents = json.dumps(versions, sort_keys=True, indent=1, separators=(\\\",\\\", \\\": \\\"))\\n with open(filename, \\\"w\\\") as f:\\n f.write(SHORT_VERSION_PY % contents)\\n\\n print(\\\"set {} to '{}'\\\".format(filename, versions[\\\"version\\\"]))\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::plus_or_dot\",\n \"text\": \"server/versioneer.py::plus_or_dot\\ndef plus_or_dot(pieces):\\n \\\"\\\"\\\"Return a + if we don't already have one, else return a .\\\"\\\"\\\"\\n if \\\"+\\\" in pieces.get(\\\"closest-tag\\\", \\\"\\\"):\\n return \\\".\\\"\\n return \\\"+\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::render_pep440\",\n \"text\": \"server/versioneer.py::render_pep440\\ndef render_pep440(pieces):\\n \\\"\\\"\\\"Build up version string, with post-release \\\"local version identifier\\\".\\n\\n Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you\\n get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty\\n\\n Exceptions:\\n 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty]\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"] or pieces[\\\"dirty\\\"]:\\n rendered += plus_or_dot(pieces)\\n rendered += \\\"%d.g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dirty\\\"\\n else:\\n # exception #1\\n rendered = \\\"0+untagged.%d.g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dirty\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::render_pep440_pre\",\n \"text\": \"server/versioneer.py::render_pep440_pre\\ndef render_pep440_pre(pieces):\\n \\\"\\\"\\\"TAG[.post.devDISTANCE] -- No -dirty.\\n\\n Exceptions:\\n 1: no tags. 0.post.devDISTANCE\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"]:\\n rendered += \\\".post.dev%d\\\" % pieces[\\\"distance\\\"]\\n else:\\n # exception #1\\n rendered = \\\"0.post.dev%d\\\" % pieces[\\\"distance\\\"]\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::render_pep440_post\",\n \"text\": \"server/versioneer.py::render_pep440_post\\ndef render_pep440_post(pieces):\\n \\\"\\\"\\\"TAG[.postDISTANCE[.dev0]+gHEX] .\\n\\n The \\\".dev0\\\" means dirty. Note that .dev0 sorts backwards\\n (a dirty tree will appear \\\"older\\\" than the corresponding clean one),\\n but you shouldn't be releasing software with -dirty anyways.\\n\\n Exceptions:\\n 1: no tags. 0.postDISTANCE[.dev0]\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"] or pieces[\\\"dirty\\\"]:\\n rendered += \\\".post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n rendered += plus_or_dot(pieces)\\n rendered += \\\"g%s\\\" % pieces[\\\"short\\\"]\\n else:\\n # exception #1\\n rendered = \\\"0.post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n rendered += \\\"+g%s\\\" % pieces[\\\"short\\\"]\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::render_pep440_old\",\n \"text\": \"server/versioneer.py::render_pep440_old\\ndef render_pep440_old(pieces):\\n \\\"\\\"\\\"TAG[.postDISTANCE[.dev0]] .\\n\\n The \\\".dev0\\\" means dirty.\\n\\n Eexceptions:\\n 1: no tags. 0.postDISTANCE[.dev0]\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"] or pieces[\\\"dirty\\\"]:\\n rendered += \\\".post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n else:\\n # exception #1\\n rendered = \\\"0.post%d\\\" % pieces[\\\"distance\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\".dev0\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::render_git_describe\",\n \"text\": \"server/versioneer.py::render_git_describe\\ndef render_git_describe(pieces):\\n \\\"\\\"\\\"TAG[-DISTANCE-gHEX][-dirty].\\n\\n Like 'git describe --tags --dirty --always'.\\n\\n Exceptions:\\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n if pieces[\\\"distance\\\"]:\\n rendered += \\\"-%d-g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n else:\\n # exception #1\\n rendered = pieces[\\\"short\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\"-dirty\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::render_git_describe_long\",\n \"text\": \"server/versioneer.py::render_git_describe_long\\ndef render_git_describe_long(pieces):\\n \\\"\\\"\\\"TAG-DISTANCE-gHEX[-dirty].\\n\\n Like 'git describe --tags --dirty --always -long'.\\n The distance/hash is unconditional.\\n\\n Exceptions:\\n 1: no tags. HEX[-dirty] (note: no 'g' prefix)\\n \\\"\\\"\\\"\\n if pieces[\\\"closest-tag\\\"]:\\n rendered = pieces[\\\"closest-tag\\\"]\\n rendered += \\\"-%d-g%s\\\" % (pieces[\\\"distance\\\"], pieces[\\\"short\\\"])\\n else:\\n # exception #1\\n rendered = pieces[\\\"short\\\"]\\n if pieces[\\\"dirty\\\"]:\\n rendered += \\\"-dirty\\\"\\n return rendered\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::render\",\n \"text\": \"server/versioneer.py::render\\ndef render(pieces, style):\\n \\\"\\\"\\\"Render the given version pieces into the requested style.\\\"\\\"\\\"\\n if pieces[\\\"error\\\"]:\\n return {\\n \\\"version\\\": \\\"unknown\\\",\\n \\\"full-revisionid\\\": pieces.get(\\\"long\\\"),\\n \\\"dirty\\\": None,\\n \\\"error\\\": pieces[\\\"error\\\"],\\n \\\"date\\\": None,\\n }\\n\\n if not style or style == \\\"default\\\":\\n style = \\\"pep440\\\" # the default\\n\\n if style == \\\"pep440\\\":\\n rendered = render_pep440(pieces)\\n elif style == \\\"pep440-pre\\\":\\n rendered = render_pep440_pre(pieces)\\n elif style == \\\"pep440-post\\\":\\n rendered = render_pep440_post(pieces)\\n elif style == \\\"pep440-old\\\":\\n rendered = render_pep440_old(pieces)\\n elif style == \\\"git-describe\\\":\\n rendered = render_git_describe(pieces)\\n elif style == \\\"git-describe-long\\\":\\n rendered = render_git_describe_long(pieces)\\n else:\\n raise ValueError(\\\"unknown style '%s'\\\" % style)\\n\\n return {\\n \\\"version\\\": rendered,\\n \\\"full-revisionid\\\": pieces[\\\"long\\\"],\\n \\\"dirty\\\": pieces[\\\"dirty\\\"],\\n \\\"error\\\": None,\\n \\\"date\\\": pieces.get(\\\"date\\\"),\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::get_versions\",\n \"text\": \"server/versioneer.py::get_versions\\ndef get_versions(verbose=False):\\n \\\"\\\"\\\"Get the project version from whatever source is available.\\n\\n Returns dict with two keys: 'version' and 'full'.\\n \\\"\\\"\\\"\\n if \\\"versioneer\\\" in sys.modules:\\n # see the discussion in cmdclass.py:get_cmdclass()\\n del sys.modules[\\\"versioneer\\\"]\\n\\n root = get_root()\\n cfg = get_config_from_root(root)\\n\\n assert cfg.VCS is not None, \\\"please set [versioneer]VCS= in setup.cfg\\\"\\n handlers = HANDLERS.get(cfg.VCS)\\n assert handlers, \\\"unrecognized VCS '%s'\\\" % cfg.VCS\\n verbose = verbose or cfg.verbose\\n assert (\\n cfg.versionfile_source is not None\\n ), \\\"please set versioneer.versionfile_source\\\"\\n assert cfg.tag_prefix is not None, \\\"please set versioneer.tag_prefix\\\"\\n\\n versionfile_abs = os.path.join(root, cfg.versionfile_source)\\n\\n # extract version from first of: _version.py, VCS command (e.g. 'git\\n # describe'), parentdir. This is meant to work for developers using a\\n # source checkout, for users of a tarball created by 'setup.py sdist',\\n # and for users of a tarball/zipball created by 'git archive' or github's\\n # download-from-tag feature or the equivalent in other VCSes.\\n\\n get_keywords_f = handlers.get(\\\"get_keywords\\\")\\n from_keywords_f = handlers.get(\\\"keywords\\\")\\n if get_keywords_f and from_keywords_f:\\n try:\\n keywords = get_keywords_f(versionfile_abs)\\n ver = from_keywords_f(keywords, cfg.tag_prefix, verbose)\\n if verbose:\\n print(\\\"got version from expanded keyword %s\\\" % ver)\\n return ver\\n except NotThisMethod:\\n pass\\n\\n try:\\n ver = versions_from_file(versionfile_abs)\\n if verbose:\\n print(\\\"got version from file {} {}\\\".format(versionfile_abs, ver))\\n return ver\\n except NotThisMethod:\\n pass\\n\\n from_vcs_f = handlers.get(\\\"pieces_from_vcs\\\")\\n if from_vcs_f:\\n try:\\n pieces = from_vcs_f(cfg.tag_prefix, root, \",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::get_version\",\n \"text\": \"server/versioneer.py::get_version\\ndef get_version():\\n \\\"\\\"\\\"Get the short version string for this project.\\\"\\\"\\\"\\n return get_versions()[\\\"version\\\"]\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::get_cmdclass\",\n \"text\": \"server/versioneer.py::get_cmdclass\\ndef get_cmdclass():\\n \\\"\\\"\\\"Get the custom setuptools/distutils subclasses used by Versioneer.\\\"\\\"\\\"\\n if \\\"versioneer\\\" in sys.modules:\\n del sys.modules[\\\"versioneer\\\"]\\n # this fixes the \\\"python setup.py develop\\\" case (also 'install' and\\n # 'easy_install .'), in which subdependencies of the main project are\\n # built (using setup.py bdist_egg) in the same python process. Assume\\n # a main project A and a dependency B, which use different versions\\n # of Versioneer. A's setup.py imports A's Versioneer, leaving it in\\n # sys.modules by the time B's setup.py is executed, causing B to run\\n # with the wrong versioneer. Setuptools wraps the sub-dep builds in a\\n # sandbox that restores sys.modules to it's pre-build state, so the\\n # parent is protected against the child's \\\"import versioneer\\\". By\\n # removing ourselves from sys.modules here, before the child build\\n # happens, we protect the child from the parent's versioneer too.\\n # Also see https://github.com/warner/python-versioneer/issues/52\\n\\n cmds = {}\\n\\n # we add \\\"version\\\" to both distutils and setuptools\\n from distutils.core import Command\\n\\n class cmd_version(Command):\\n description = \\\"report generated version string\\\"\\n user_options = []\\n boolean_options = []\\n\\n def initialize_options(self):\\n pass\\n\\n def finalize_options(self):\\n pass\\n\\n def run(self):\\n vers = get_versions(verbose=True)\\n print(\\\"Version: %s\\\" % vers[\\\"version\\\"])\\n print(\\\" full-revisionid: %s\\\" % vers.get(\\\"full-revisionid\\\"))\\n print(\\\" dirty: %s\\\" % vers.get(\\\"dirty\\\"))\\n print(\\\" date: %s\\\" % vers.get(\\\"date\\\"))\\n if vers[\\\"error\\\"]:\\n print(\\\" error: %s\\\" % vers[\\\"error\\\"])\\n\\n cmds[\\\"version\\\"] = cmd_version\\n\\n # we override \\\"build_py\\\" in both distutils and setuptools\\n #\\n # most invocation pathways end up\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::get_cmdclass.cmd_version.initialize_options\",\n \"text\": \"server/versioneer.py::get_cmdclass.cmd_version.initialize_options\\n def initialize_options(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::get_cmdclass.cmd_version.finalize_options\",\n \"text\": \"server/versioneer.py::get_cmdclass.cmd_version.finalize_options\\n def finalize_options(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::get_cmdclass.cmd_version.run\",\n \"text\": \"server/versioneer.py::get_cmdclass.cmd_version.run\\n def run(self):\\n vers = get_versions(verbose=True)\\n print(\\\"Version: %s\\\" % vers[\\\"version\\\"])\\n print(\\\" full-revisionid: %s\\\" % vers.get(\\\"full-revisionid\\\"))\\n print(\\\" dirty: %s\\\" % vers.get(\\\"dirty\\\"))\\n print(\\\" date: %s\\\" % vers.get(\\\"date\\\"))\\n if vers[\\\"error\\\"]:\\n print(\\\" error: %s\\\" % vers[\\\"error\\\"])\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::get_cmdclass.cmd_build_py.run\",\n \"text\": \"server/versioneer.py::get_cmdclass.cmd_build_py.run\\n def run(self):\\n root = get_root()\\n cfg = get_config_from_root(root)\\n versions = get_versions()\\n _build_py.run(self)\\n # now locate _version.py in the new build/ directory and replace\\n # it with an updated value\\n if cfg.versionfile_build:\\n target_versionfile = os.path.join(self.build_lib, cfg.versionfile_build)\\n print(\\\"UPDATING %s\\\" % target_versionfile)\\n write_to_version_file(target_versionfile, versions)\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::get_cmdclass.cmd_sdist.run\",\n \"text\": \"server/versioneer.py::get_cmdclass.cmd_sdist.run\\n def run(self):\\n versions = get_versions()\\n self._versioneer_generated_versions = versions\\n # unless we update this, the command will keep using the old\\n # version\\n self.distribution.metadata.version = versions[\\\"version\\\"]\\n return _sdist.run(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::get_cmdclass.cmd_sdist.make_release_tree\",\n \"text\": \"server/versioneer.py::get_cmdclass.cmd_sdist.make_release_tree\\n def make_release_tree(self, base_dir, files):\\n root = get_root()\\n cfg = get_config_from_root(root)\\n _sdist.make_release_tree(self, base_dir, files)\\n # now locate _version.py in the new base_dir directory\\n # (remembering that it may be a hardlink) and replace it with an\\n # updated value\\n target_versionfile = os.path.join(base_dir, cfg.versionfile_source)\\n print(\\\"UPDATING %s\\\" % target_versionfile)\\n write_to_version_file(\\n target_versionfile, self._versioneer_generated_versions\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::do_setup\",\n \"text\": \"server/versioneer.py::do_setup\\ndef do_setup():\\n \\\"\\\"\\\"Main VCS-independent setup function for installing Versioneer.\\\"\\\"\\\"\\n root = get_root()\\n try:\\n cfg = get_config_from_root(root)\\n except (\\n EnvironmentError,\\n configparser.NoSectionError,\\n configparser.NoOptionError,\\n ) as e:\\n if isinstance(e, (EnvironmentError, configparser.NoSectionError)):\\n print(\\\"Adding sample versioneer config to setup.cfg\\\", file=sys.stderr)\\n with open(os.path.join(root, \\\"setup.cfg\\\"), \\\"a\\\") as f:\\n f.write(SAMPLE_CONFIG)\\n print(CONFIG_ERROR, file=sys.stderr)\\n return 1\\n\\n print(\\\" creating %s\\\" % cfg.versionfile_source)\\n with open(cfg.versionfile_source, \\\"w\\\") as f:\\n LONG = LONG_VERSION_PY[cfg.VCS]\\n f.write(\\n LONG\\n % {\\n \\\"DOLLAR\\\": \\\"$\\\",\\n \\\"STYLE\\\": cfg.style,\\n \\\"TAG_PREFIX\\\": cfg.tag_prefix,\\n \\\"PARENTDIR_PREFIX\\\": cfg.parentdir_prefix,\\n \\\"VERSIONFILE_SOURCE\\\": cfg.versionfile_source,\\n }\\n )\\n\\n ipy = os.path.join(os.path.dirname(cfg.versionfile_source), \\\"__init__.py\\\")\\n if os.path.exists(ipy):\\n try:\\n with open(ipy, \\\"r\\\") as f:\\n old = f.read()\\n except EnvironmentError:\\n old = \\\"\\\"\\n if INIT_PY_SNIPPET not in old:\\n print(\\\" appending to %s\\\" % ipy)\\n with open(ipy, \\\"a\\\") as f:\\n f.write(INIT_PY_SNIPPET)\\n else:\\n print(\\\" %s unmodified\\\" % ipy)\\n else:\\n print(\\\" %s doesn't exist, ok\\\" % ipy)\\n ipy = None\\n\\n # Make sure both the top-level \\\"versioneer.py\\\" and versionfile_source\\n # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so\\n # they'll be copied into source distributions. Pip won't be able to\\n # install the package without this.\\n manifest_in = os.path.join(root, \\\"MANIFEST.in\\\")\\n simple_includes = set()\\n try:\\n with open(manifest_in,\",\n \"label\": 0\n },\n {\n \"cid\": \"server/versioneer.py::scan_setup_py\",\n \"text\": \"server/versioneer.py::scan_setup_py\\ndef scan_setup_py():\\n \\\"\\\"\\\"Validate the contents of setup.py against Versioneer's expectations.\\\"\\\"\\\"\\n found = set()\\n setters = False\\n errors = 0\\n with open(\\\"setup.py\\\", \\\"r\\\") as f:\\n for line in f.readlines():\\n if \\\"import versioneer\\\" in line:\\n found.add(\\\"import\\\")\\n if \\\"versioneer.get_cmdclass()\\\" in line:\\n found.add(\\\"cmdclass\\\")\\n if \\\"versioneer.get_version()\\\" in line:\\n found.add(\\\"get_version\\\")\\n if \\\"versioneer.VCS\\\" in line:\\n setters = True\\n if \\\"versioneer.versionfile_source\\\" in line:\\n setters = True\\n if len(found) != 3:\\n print(\\\"\\\")\\n print(\\\"Your setup.py appears to be missing some important items\\\")\\n print(\\\"(but I might be wrong). Please make sure it has something\\\")\\n print(\\\"roughly like the following:\\\")\\n print(\\\"\\\")\\n print(\\\" import versioneer\\\")\\n print(\\\" setup( version=versioneer.get_version(),\\\")\\n print(\\\" cmdclass=versioneer.get_cmdclass(), ...)\\\")\\n print(\\\"\\\")\\n errors += 1\\n if setters:\\n print(\\\"You should remove lines like 'versioneer.VCS = ' and\\\")\\n print(\\\"'versioneer.versionfile_source = ' . This configuration\\\")\\n print(\\\"now lives in setup.cfg, and should be removed from setup.py\\\")\\n print(\\\"\\\")\\n errors += 1\\n return errors\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::cache\",\n \"text\": \"src/prefect/core/flow.py::cache\\ndef cache(method: Callable) -> Callable:\\n \\\"\\\"\\\"\\n Decorator for caching Flow methods.\\n\\n Each Flow has a _cache dict that can be used to memoize expensive functions. This\\n decorator automatically compares a hash of the Flow's current tasks, edges, and reference_tasks\\n to a cached hash; if the hash is the same, it attempts to retrieve a value from the cache.\\n If the hash is different, it invalidates the cache.\\n \\\"\\\"\\\"\\n\\n @functools.wraps(method)\\n def wrapper(self, *args, **kwargs): # type: ignore\\n\\n cache_check = dict(\\n tasks=self.tasks.copy(),\\n edges=self.edges.copy(),\\n reference_tasks=copy.copy(self._reference_tasks),\\n )\\n if any(self._cache.get(k) != v for k, v in cache_check.items()):\\n self._cache.clear()\\n self._cache.update(cache_check)\\n\\n callargs = inspect.signature(method).bind(self, *args, **kwargs).arguments\\n key = (method.__name__, tuple(callargs.items())[1:])\\n if key not in self._cache:\\n self._cache[key] = method(self, *args, **kwargs)\\n return self._cache[key]\\n\\n return wrapper\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::cache.wrapper\",\n \"text\": \"src/prefect/core/flow.py::cache.wrapper\\n @functools.wraps(method)\\n def wrapper(self, *args, **kwargs): # type: ignore\\n\\n cache_check = dict(\\n tasks=self.tasks.copy(),\\n edges=self.edges.copy(),\\n reference_tasks=copy.copy(self._reference_tasks),\\n )\\n if any(self._cache.get(k) != v for k, v in cache_check.items()):\\n self._cache.clear()\\n self._cache.update(cache_check)\\n\\n callargs = inspect.signature(method).bind(self, *args, **kwargs).arguments\\n key = (method.__name__, tuple(callargs.items())[1:])\\n if key not in self._cache:\\n self._cache[key] = method(self, *args, **kwargs)\\n return self._cache[key]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.__init__\",\n \"text\": \"src/prefect/core/flow.py::Flow.__init__\\n def __init__(\\n self,\\n name: str,\\n schedule: prefect.schedules.Schedule = None,\\n environment: Environment = None,\\n storage: Storage = None,\\n tasks: Iterable[Task] = None,\\n edges: Iterable[Edge] = None,\\n reference_tasks: Iterable[Task] = None,\\n state_handlers: List[Callable] = None,\\n on_failure: Callable = None,\\n validate: bool = None,\\n result_handler: Optional[ResultHandler] = None,\\n result: Optional[Result] = None,\\n ):\\n self._cache = {} # type: dict\\n\\n if not name:\\n raise ValueError(\\\"A name must be provided for the flow.\\\")\\n\\n self.name = name\\n self.logger = logging.get_logger(self.name)\\n self.schedule = schedule\\n self.environment = environment or prefect.environments.RemoteEnvironment()\\n self.storage = storage\\n if result_handler:\\n warnings.warn(\\n \\\"Result Handlers are deprecated; please use the new style Result classes instead.\\\"\\n )\\n self.result = ResultHandlerResult.from_result_handler(\\n result_handler\\n ) # type: Optional[Result]\\n else:\\n self.result = result\\n\\n self.tasks = set() # type: Set[Task]\\n self.edges = set() # type: Set[Edge]\\n self.constants = collections.defaultdict(\\n dict\\n ) # type: Dict[Task, Dict[str, Any]]\\n\\n for t in tasks or []:\\n self.add_task(t)\\n\\n self.set_reference_tasks(reference_tasks or [])\\n for e in edges or []:\\n self.add_edge(\\n upstream_task=e.upstream_task,\\n downstream_task=e.downstream_task,\\n key=e.key,\\n mapped=e.mapped,\\n validate=validate,\\n )\\n\\n self._prefect_version = prefect.__version__\\n\\n if state_handlers and not isinstance(state_handlers, collections.abc.Sequence):\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.__eq__\",\n \"text\": \"src/prefect/core/flow.py::Flow.__eq__\\n def __eq__(self, other: Any) -> bool:\\n if type(self) == type(other):\\n s = (self.name, self.tasks, self.edges, self.reference_tasks())\\n o = (other.name, other.tasks, other.edges, other.reference_tasks())\\n return s == o\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.__repr__\",\n \"text\": \"src/prefect/core/flow.py::Flow.__repr__\\n def __repr__(self) -> str:\\n template = '<{cls}: name=\\\"{self.name}\\\">'\\n return template.format(cls=type(self).__name__, self=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.__iter__\",\n \"text\": \"src/prefect/core/flow.py::Flow.__iter__\\n def __iter__(self) -> Iterable[Task]:\\n yield from self.sorted_tasks()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.copy\",\n \"text\": \"src/prefect/core/flow.py::Flow.copy\\n def copy(self) -> \\\"Flow\\\":\\n \\\"\\\"\\\"\\n Create and returns a copy of the current Flow.\\n \\\"\\\"\\\"\\n new = copy.copy(self)\\n # create a new cache\\n new._cache = dict()\\n new.tasks = self.tasks.copy()\\n new.edges = self.edges.copy()\\n new.set_reference_tasks(self._reference_tasks)\\n return new\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.get_tasks\",\n \"text\": \"src/prefect/core/flow.py::Flow.get_tasks\\n def get_tasks(\\n self,\\n name: str = None,\\n slug: str = None,\\n tags: Iterable[str] = None,\\n task_type: type = None,\\n ) -> List[Task]:\\n \\\"\\\"\\\"\\n Helper method for retrieving tasks from this flow based on certain attributes.\\n The _intersection_ of all provided attributes is taken, i.e., only those tasks\\n which match _all_ provided conditions are returned.\\n\\n Args:\\n - name (str, optional): the name of the task\\n - slug (str, optional): the slug of the task\\n - tags ([str], optional): an iterable of task tags\\n - task_type (type, optional): a possible task class type\\n\\n Returns:\\n - [Task]: a list of tasks that meet the required conditions\\n \\\"\\\"\\\"\\n\\n def sieve(t: Task) -> bool:\\n keep = True\\n if name is not None:\\n keep &= t.name == name\\n if slug is not None:\\n keep &= t.slug == slug\\n if tags is not None:\\n keep &= t.tags.issuperset(tags)\\n if task_type is not None:\\n keep &= isinstance(t, task_type)\\n return keep\\n\\n keep_tasks = filter(sieve, self.tasks)\\n return list(keep_tasks)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.get_tasks.sieve\",\n \"text\": \"src/prefect/core/flow.py::Flow.get_tasks.sieve\\n def sieve(t: Task) -> bool:\\n keep = True\\n if name is not None:\\n keep &= t.name == name\\n if slug is not None:\\n keep &= t.slug == slug\\n if tags is not None:\\n keep &= t.tags.issuperset(tags)\\n if task_type is not None:\\n keep &= isinstance(t, task_type)\\n return keep\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.replace\",\n \"text\": \"src/prefect/core/flow.py::Flow.replace\\n def replace(self, old: Task, new: Task, validate: bool = True) -> None:\\n \\\"\\\"\\\"\\n Performs an inplace replacement of the old task with the provided new task.\\n\\n Args:\\n - old (Task): the old task to replace\\n - new (Task): the new task to replace the old with; if not a Prefect\\n Task, Prefect will attempt to convert it to one\\n - validate (boolean, optional): whether to validate the Flow after\\n the replace has been completed; defaults to `True`\\n\\n Raises:\\n - ValueError: if the `old` task is not a part of this flow\\n \\\"\\\"\\\"\\n if old not in self.tasks:\\n raise ValueError(\\\"Task {t} was not found in Flow {f}\\\".format(t=old, f=self))\\n\\n new = as_task(new, flow=self)\\n\\n # update tasks\\n self.tasks.remove(old)\\n self.add_task(new)\\n\\n self._cache.clear()\\n\\n affected_edges = {e for e in self.edges if old in e.tasks}\\n\\n # remove old edges\\n for edge in affected_edges:\\n self.edges.remove(edge)\\n\\n # replace with new edges\\n for edge in affected_edges:\\n upstream = new if edge.upstream_task == old else edge.upstream_task\\n downstream = new if edge.downstream_task == old else edge.downstream_task\\n self.add_edge(\\n upstream_task=upstream,\\n downstream_task=downstream,\\n key=edge.key,\\n mapped=edge.mapped,\\n validate=False,\\n )\\n\\n # update auxiliary task collections\\n ref_tasks = self.reference_tasks()\\n new_refs = [t for t in ref_tasks if t != old] + (\\n [new] if old in ref_tasks else []\\n )\\n self.set_reference_tasks(new_refs)\\n\\n if validate:\\n self.validate()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.__enter__\",\n \"text\": \"src/prefect/core/flow.py::Flow.__enter__\\n def __enter__(self) -> \\\"Flow\\\":\\n self.__previous_flow = prefect.context.get(\\\"flow\\\")\\n prefect.context.update(flow=self)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.__exit__\",\n \"text\": \"src/prefect/core/flow.py::Flow.__exit__\\n def __exit__(self, _type, _value, _tb) -> None: # type: ignore\\n del prefect.context.flow\\n if self.__previous_flow is not None:\\n prefect.context.update(flow=self.__previous_flow)\\n\\n del self.__previous_flow\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.root_tasks\",\n \"text\": \"src/prefect/core/flow.py::Flow.root_tasks\\n @cache\\n def root_tasks(self) -> Set[Task]:\\n \\\"\\\"\\\"\\n Get the tasks in the flow that have no upstream dependencies; these are\\n the tasks that, by default, flow execution begins with.\\n\\n Returns:\\n - set of Task objects that have no upstream dependencies\\n \\\"\\\"\\\"\\n return set(t for t in self.tasks if not self.edges_to(t))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.terminal_tasks\",\n \"text\": \"src/prefect/core/flow.py::Flow.terminal_tasks\\n @cache\\n def terminal_tasks(self) -> Set[Task]:\\n \\\"\\\"\\\"\\n Get the tasks in the flow that have no downstream dependencies\\n\\n Returns:\\n - set of Task objects that have no downstream dependencies\\n \\\"\\\"\\\"\\n return set(t for t in self.tasks if not self.edges_from(t))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.parameters\",\n \"text\": \"src/prefect/core/flow.py::Flow.parameters\\n def parameters(self) -> Set[Parameter]:\\n \\\"\\\"\\\"\\n Returns any parameters of the flow.\\n\\n Returns:\\n - set: a set of any Parameters in this flow\\n \\\"\\\"\\\"\\n return {p for p in self.tasks if isinstance(p, Parameter)}\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.reference_tasks\",\n \"text\": \"src/prefect/core/flow.py::Flow.reference_tasks\\n def reference_tasks(self) -> Set[Task]:\\n \\\"\\\"\\\"\\n A flow's \\\"reference tasks\\\" are used to determine its state when it runs. If all the reference\\n tasks are successful, then the flow run is considered successful. However, if\\n any of the reference tasks fail, the flow is considered to fail. (Note that skips are\\n counted as successes; see [the state documentation](../engine/state.html) for a full description\\n of what is considered failure, success, etc.)\\n\\n By default, a flow's reference tasks are its terminal tasks. This means the state of a\\n flow is determined by those tasks that have no downstream dependencies.\\n\\n In some situations, users may want to customize this behavior; for example, if a\\n flow's terminal tasks are \\\"clean up\\\" tasks for the rest of the flow that only run\\n if certain (more relevant) tasks fail, we might not want them determining the overall\\n state of the flow run. The `flow.set_reference_tasks()` method can be used to set such custom `reference_tasks`.\\n\\n Please note that even if `reference_tasks` are provided that are not terminal tasks, the flow\\n will not be considered \\\"finished\\\" until all terminal tasks have completed. Only then\\n will state be determined, using the reference tasks.\\n\\n Returns:\\n - set of Task objects which are the reference tasks in the flow\\n \\\"\\\"\\\"\\n if self._reference_tasks:\\n return set(self._reference_tasks)\\n else:\\n return self.terminal_tasks()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.set_reference_tasks\",\n \"text\": \"src/prefect/core/flow.py::Flow.set_reference_tasks\\n def set_reference_tasks(self, tasks: Iterable[Task]) -> None:\\n \\\"\\\"\\\"\\n Sets the `reference_tasks` for the flow. See `flow.reference_tasks` for more details.\\n\\n Args:\\n - tasks ([Task]): the tasks that should be set as a flow's reference tasks\\n\\n Returns:\\n - None\\n \\\"\\\"\\\"\\n self._cache.clear()\\n reference_tasks = set(tasks)\\n if any(t not in self.tasks for t in reference_tasks):\\n raise ValueError(\\\"reference tasks must be part of the flow.\\\")\\n self._reference_tasks = reference_tasks\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.add_task\",\n \"text\": \"src/prefect/core/flow.py::Flow.add_task\\n def add_task(self, task: Task) -> Task:\\n \\\"\\\"\\\"\\n Add a task to the flow if the task does not already exist. The tasks are\\n uniquely identified by their `slug`.\\n\\n Args:\\n - task (Task): the new Task to be added to the flow\\n\\n Returns:\\n - Task: the `Task` object passed in if the task was successfully added\\n\\n Raises:\\n - TypeError: if the `task` is not of type `Task`\\n - ValueError: if the `task.slug` matches that of a task already in the flow\\n \\\"\\\"\\\"\\n if not isinstance(task, Task):\\n raise TypeError(\\n \\\"Tasks must be Task instances (received {})\\\".format(type(task))\\n )\\n elif task not in self.tasks:\\n if task.slug and any(task.slug == t.slug for t in self.tasks):\\n raise ValueError(\\n 'A task with the slug \\\"{}\\\" already exists in this '\\n \\\"flow.\\\".format(task.slug)\\n )\\n\\n if task not in self.tasks:\\n self.tasks.add(task)\\n self._cache.clear()\\n\\n return task\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.add_edge\",\n \"text\": \"src/prefect/core/flow.py::Flow.add_edge\\n def add_edge(\\n self,\\n upstream_task: Task,\\n downstream_task: Task,\\n key: str = None,\\n mapped: bool = False,\\n validate: bool = None,\\n ) -> Edge:\\n \\\"\\\"\\\"\\n Add an edge in the flow between two tasks. All edges are directed beginning with\\n an upstream task and ending with a downstream task.\\n\\n Args:\\n - upstream_task (Task): The task that the edge should start from\\n - downstream_task (Task): The task that the edge should end with\\n - key (str, optional): The key to be set for the new edge; the result of the upstream task\\n will be passed to the downstream task's `run()` method under this keyword argument\\n - mapped (bool, optional): Whether this edge represents a call to `Task.map()`; defaults to `False`\\n - validate (bool, optional): Whether or not to check the validity of\\n the flow (e.g., presence of cycles and illegal keys). Defaults to the value\\n of `eager_edge_validation` in your prefect configuration file.\\n\\n Returns:\\n - prefect.core.edge.Edge: The `Edge` object that was successfully added to the flow\\n\\n Raises:\\n - ValueError: if the `downstream_task` is of type `Parameter`\\n - ValueError: if the edge exists with this `key` and `downstream_task`\\n \\\"\\\"\\\"\\n if validate is None:\\n validate = cast(bool, prefect.config.flows.eager_edge_validation)\\n if isinstance(downstream_task, Parameter):\\n raise ValueError(\\n \\\"Parameters must be root tasks and can not have upstream dependencies.\\\"\\n )\\n\\n self.add_task(upstream_task)\\n self.add_task(downstream_task)\\n\\n # we can only check the downstream task's edges once it has been added to the\\n # flow, so we need to perform this check here and not earlier.\\n if validate and key and key in {e.key for \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.chain\",\n \"text\": \"src/prefect/core/flow.py::Flow.chain\\n def chain(self, *tasks: Task, validate: bool = None) -> List[Edge]:\\n \\\"\\\"\\\"\\n Adds a sequence of dependent tasks to the flow; each task should be provided\\n as an argument (or splatted from a list).\\n\\n Args:\\n - *tasks (list): A list of tasks to chain together\\n - validate (bool, optional): Whether or not to check the validity of\\n the flow (e.g., presence of cycles). Defaults to the value of `eager_edge_validation`\\n in your prefect configuration file.\\n\\n Returns:\\n - A list of Edge objects added to the flow\\n \\\"\\\"\\\"\\n edges = []\\n for u_task, d_task in zip(tasks, tasks[1:]):\\n edges.append(\\n self.add_edge(\\n upstream_task=u_task, downstream_task=d_task, validate=validate\\n )\\n )\\n return edges\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.update\",\n \"text\": \"src/prefect/core/flow.py::Flow.update\\n def update(self, flow: \\\"Flow\\\", validate: bool = None) -> None:\\n \\\"\\\"\\\"\\n Take all tasks and edges in another flow and add it to this flow\\n\\n Args:\\n - flow (Flow): A flow which is used to update this flow\\n - validate (bool, optional): Whether or not to check the validity of the flow\\n\\n Returns:\\n - None\\n \\\"\\\"\\\"\\n for task in flow.tasks:\\n if task not in self.tasks:\\n self.add_task(task)\\n\\n for edge in flow.edges:\\n if edge not in self.edges:\\n self.add_edge(\\n upstream_task=edge.upstream_task,\\n downstream_task=edge.downstream_task,\\n key=edge.key,\\n mapped=edge.mapped,\\n validate=validate,\\n )\\n\\n self.constants.update(flow.constants or {})\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.all_upstream_edges\",\n \"text\": \"src/prefect/core/flow.py::Flow.all_upstream_edges\\n @cache\\n def all_upstream_edges(self) -> Dict[Task, Set[Edge]]:\\n \\\"\\\"\\\"\\n Returns a dictionary relating each task in the Flow to the set of\\n all _upstream_ edges for the task\\n\\n Returns:\\n - dict with the key as tasks and the value as a set of upstream edges\\n \\\"\\\"\\\"\\n edges = {t: set() for t in self.tasks} # type: Dict[Task, Set[Edge]]\\n for edge in self.edges:\\n edges[edge.downstream_task].add(edge)\\n return edges\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.all_downstream_edges\",\n \"text\": \"src/prefect/core/flow.py::Flow.all_downstream_edges\\n @cache\\n def all_downstream_edges(self) -> Dict[Task, Set[Edge]]:\\n \\\"\\\"\\\"\\n Returns a dictionary relating each task in the Flow to the set of\\n all _downstream_ edges for the task\\n\\n Returns:\\n - dict with the key as tasks and the value as a set of downstream edges\\n \\\"\\\"\\\"\\n edges = {t: set() for t in self.tasks} # type: Dict[Task, Set[Edge]]\\n for edge in self.edges:\\n edges[edge.upstream_task].add(edge)\\n return edges\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.edges_to\",\n \"text\": \"src/prefect/core/flow.py::Flow.edges_to\\n def edges_to(self, task: Task) -> Set[Edge]:\\n \\\"\\\"\\\"\\n Get all of the edges leading to a task (i.e., the upstream edges)\\n\\n Args:\\n - task (Task): The task that we want to find edges leading to\\n\\n Returns:\\n - dict with the key as the task passed in and the value as a set of all edges\\n leading to that task\\n\\n Raises:\\n - ValueError: if `task` is not found in this flow\\n \\\"\\\"\\\"\\n if task not in self.tasks:\\n raise ValueError(\\n \\\"Task {t} was not found in Flow {f}\\\".format(t=task, f=self)\\n )\\n return self.all_upstream_edges()[task]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.edges_from\",\n \"text\": \"src/prefect/core/flow.py::Flow.edges_from\\n def edges_from(self, task: Task) -> Set[Edge]:\\n \\\"\\\"\\\"\\n Get all of the edges leading from a task (i.e., the downstream edges)\\n\\n Args:\\n - task (Task): The task that we want to find edges leading from\\n\\n Returns:\\n - dict with the key as the task passed in and the value as a set of all edges\\n leading from that task\\n\\n Raises:\\n - ValueError: if `task` is not found in this flow\\n \\\"\\\"\\\"\\n if task not in self.tasks:\\n raise ValueError(\\n \\\"Task {t} was not found in Flow {f}\\\".format(t=task, f=self)\\n )\\n return self.all_downstream_edges()[task]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.upstream_tasks\",\n \"text\": \"src/prefect/core/flow.py::Flow.upstream_tasks\\n def upstream_tasks(self, task: Task) -> Set[Task]:\\n \\\"\\\"\\\"\\n Get all of the tasks upstream of a task\\n\\n Args:\\n - task (Task): The task that we want to find upstream tasks of\\n\\n Returns:\\n - set of Task objects which are upstream of `task`\\n \\\"\\\"\\\"\\n return set(e.upstream_task for e in self.edges_to(task))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.downstream_tasks\",\n \"text\": \"src/prefect/core/flow.py::Flow.downstream_tasks\\n def downstream_tasks(self, task: Task) -> Set[Task]:\\n \\\"\\\"\\\"\\n Get all of the tasks downstream of a task\\n\\n Args:\\n - task (Task): The task that we want to find downstream tasks from\\n\\n Returns:\\n - set of Task objects which are downstream of `task`\\n \\\"\\\"\\\"\\n return set(e.downstream_task for e in self.edges_from(task))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.validate\",\n \"text\": \"src/prefect/core/flow.py::Flow.validate\\n def validate(self) -> None:\\n \\\"\\\"\\\"\\n Checks that the flow is valid.\\n\\n Returns:\\n - None\\n\\n Raises:\\n - ValueError: if edges refer to tasks that are not in this flow\\n - ValueError: if specified reference tasks are not in this flow\\n - ValueError: if any tasks do not have assigned IDs\\n \\\"\\\"\\\"\\n\\n self._cache.clear()\\n\\n if any(e.upstream_task not in self.tasks for e in self.edges) or any(\\n e.downstream_task not in self.tasks for e in self.edges\\n ):\\n raise ValueError(\\\"Some edges refer to tasks not contained in this flow.\\\")\\n\\n self.sorted_tasks()\\n\\n if any(t not in self.tasks for t in self.reference_tasks()):\\n raise ValueError(\\\"Some reference tasks are not contained in this flow.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.sorted_tasks\",\n \"text\": \"src/prefect/core/flow.py::Flow.sorted_tasks\\n def sorted_tasks(self, root_tasks: Iterable[Task] = None) -> Tuple[Task, ...]:\\n \\\"\\\"\\\"\\n Get the tasks in this flow in a sorted manner. This allows us to find if any\\n cycles exist in this flow's DAG.\\n\\n Args:\\n - root_tasks ([Tasks], optional): an `Iterable` of `Task` objects to\\n start the sorting from\\n\\n Returns:\\n - tuple of task objects that were sorted\\n\\n Raises:\\n - ValueError: if a cycle is found in the flow's DAG\\n \\\"\\\"\\\"\\n return self._sorted_tasks(root_tasks=tuple(root_tasks or []))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow._sorted_tasks\",\n \"text\": \"src/prefect/core/flow.py::Flow._sorted_tasks\\n @cache\\n def _sorted_tasks(self, root_tasks: Tuple[Task, ...] = None) -> Tuple[Task, ...]:\\n \\\"\\\"\\\"\\n Computes a topological sort of the flow's tasks.\\n\\n Flow.sorted_tasks() can accept non-hashable arguments and therefore can't be\\n cached, so this private method is called and cached instead.\\n \\\"\\\"\\\"\\n\\n # begin by getting all tasks under consideration (root tasks and all\\n # downstream tasks)\\n if root_tasks:\\n tasks = set(root_tasks)\\n seen = set() # type: Set[Task]\\n\\n # while the set of tasks is different from the seen tasks...\\n while tasks.difference(seen):\\n # iterate over the new tasks...\\n for t in list(tasks.difference(seen)):\\n # add its downstream tasks to the task list\\n tasks.update(self.downstream_tasks(t))\\n # mark it as seen\\n seen.add(t)\\n else:\\n tasks = self.tasks\\n\\n # build the list of sorted tasks\\n remaining_tasks = list(tasks)\\n sorted_tasks = []\\n while remaining_tasks:\\n # mark the flow as cyclic unless we prove otherwise\\n cyclic = True\\n\\n # iterate over each remaining task\\n for task in remaining_tasks.copy():\\n # check all the upstream tasks of that task\\n for upstream_task in self.upstream_tasks(task):\\n # if the upstream task is also remaining, it means it\\n # hasn't been sorted, so we can't sort this task either\\n if upstream_task in remaining_tasks:\\n break\\n else:\\n # but if all upstream tasks have been sorted, we can sort\\n # this one too. We note that we found no cycle this time.\\n cyclic = False\\n remaining_tasks.remove(task)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.set_dependencies\",\n \"text\": \"src/prefect/core/flow.py::Flow.set_dependencies\\n def set_dependencies(\\n self,\\n task: object,\\n upstream_tasks: Iterable[object] = None,\\n downstream_tasks: Iterable[object] = None,\\n keyword_tasks: Mapping[str, object] = None,\\n mapped: bool = False,\\n validate: bool = None,\\n ) -> None:\\n \\\"\\\"\\\"\\n Convenience function for adding task dependencies.\\n\\n Args:\\n - task (object): a Task that will become part of the Flow. If the task is not a\\n Task subclass, Prefect will attempt to convert it to one.\\n - upstream_tasks ([object], optional): Tasks that will run before the task runs. If any task\\n is not a Task subclass, Prefect will attempt to convert it to one.\\n - downstream_tasks ([object], optional): Tasks that will run after the task runs. If any task\\n is not a Task subclass, Prefect will attempt to convert it to one.\\n - keyword_tasks ({key: object}, optional): The results of these tasks\\n will be provided to the task under the specified keyword\\n arguments. If any task is not a Task subclass, Prefect will attempt to\\n convert it to one.\\n - mapped (bool, optional): Whether the upstream tasks (both keyed\\n and non-keyed) should be mapped over; defaults to `False`. If `True`, any\\n tasks wrapped in the `prefect.utilities.tasks.unmapped` container will\\n _not_ be mapped over.\\n - validate (bool, optional): Whether or not to check the validity of\\n the flow (e.g., presence of cycles). Defaults to the value of `eager_edge_validation`\\n in your Prefect configuration file.\\n\\n Returns:\\n - None\\n \\\"\\\"\\\"\\n\\n task = as_task(task, flow=self)\\n assert isinstance(task, Task) # mypy assert\\n\\n # add the main task (in case it was called with no arguments)\\n self.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow._run\",\n \"text\": \"src/prefect/core/flow.py::Flow._run\\n def _run(\\n self,\\n parameters: Dict[str, Any],\\n runner_cls: type,\\n run_on_schedule: bool = True,\\n **kwargs: Any\\n ) -> \\\"prefect.engine.state.State\\\":\\n\\n base_parameters = parameters or dict()\\n\\n ## determine time of first run\\n try:\\n if run_on_schedule and self.schedule is not None:\\n next_run_event = self.schedule.next(1, return_events=True)[0]\\n next_run_time = next_run_event.start_time # type: ignore\\n parameters = base_parameters.copy()\\n parameters.update(next_run_event.parameter_defaults) # type: ignore\\n else:\\n next_run_time = pendulum.now(\\\"utc\\\")\\n except IndexError:\\n raise ValueError(\\\"Flow has no more scheduled runs.\\\") from None\\n\\n ## setup initial states\\n flow_state = prefect.engine.state.Scheduled(start_time=next_run_time, result={})\\n flow_state = kwargs.pop(\\\"state\\\", flow_state)\\n if not isinstance(flow_state.result, dict):\\n flow_state.result = {}\\n task_states = kwargs.pop(\\\"task_states\\\", {})\\n flow_state.result.update(task_states)\\n\\n # set context for this flow run\\n flow_run_context = kwargs.pop(\\n \\\"context\\\", {}\\n ).copy() # copy to avoid modification\\n\\n ## run this flow indefinitely, so long as its schedule has future dates\\n while True:\\n\\n flow_run_context.update(scheduled_start_time=next_run_time)\\n\\n if flow_state.is_scheduled():\\n next_run_time = flow_state.start_time\\n now = pendulum.now(\\\"utc\\\")\\n naptime = max((next_run_time - now).total_seconds(), 0)\\n if naptime > 0:\\n self.logger.info(\\n \\\"Waiting for next scheduled run at {}\\\".format(next_run_time)\\n )\\n time.sleep(naptime)\\n\\n error = False\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.run\",\n \"text\": \"src/prefect/core/flow.py::Flow.run\\n def run(\\n self,\\n parameters: Dict[str, Any] = None,\\n run_on_schedule: bool = None,\\n runner_cls: type = None,\\n **kwargs: Any\\n ) -> \\\"prefect.engine.state.State\\\":\\n \\\"\\\"\\\"\\n Run the flow on its schedule using an instance of a FlowRunner. If the Flow has no schedule,\\n a single stateful run will occur (including retries).\\n\\n Note that this command will block and run this Flow on its schedule indefinitely (if it has one);\\n all task states will be stored in memory, and task retries will not occur until every Task in the Flow has had a chance\\n to run.\\n\\n Args:\\n - parameters (Dict[str, Any], optional): values to pass into the runner\\n - run_on_schedule (bool, optional): whether to run this flow on its schedule, or run a single execution;\\n if not provided, will default to the value set in your user config\\n - runner_cls (type): an optional FlowRunner class (will use the default if not provided)\\n - **kwargs: additional keyword arguments; if any provided keywords\\n match known parameter names, they will be used as such. Otherwise they will be passed to the\\n `FlowRunner.run()` method\\n\\n Raises:\\n - ValueError: if this Flow has a Schedule with no more scheduled runs\\n - ValueError: if the `return_tasks` keyword argument is provided\\n\\n Returns:\\n - State: the state of the flow after its final run\\n \\\"\\\"\\\"\\n # protect against old behavior\\n if \\\"return_tasks\\\" in kwargs:\\n raise ValueError(\\n \\\"The `return_tasks` keyword cannot be provided to `flow.run()`; \\\"\\n \\\"all task states are always returned. If you want to receive a subset \\\"\\n \\\"of task states, use a FlowRunner directly.\\\"\\n )\\n\\n if runner_cls is None:\\n runner_cls = prefect.engine.get_default_fl\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.visualize\",\n \"text\": \"src/prefect/core/flow.py::Flow.visualize\\n def visualize(\\n self,\\n flow_state: \\\"prefect.engine.state.State\\\" = None,\\n filename: str = None,\\n format: str = None,\\n ) -> object:\\n \\\"\\\"\\\"\\n Creates graphviz object for representing the current flow; this graphviz\\n object will be rendered inline if called from an IPython notebook, otherwise\\n it will be rendered in a new window. If a `filename` is provided, the object\\n will not be rendered and instead saved to the location specified.\\n\\n Args:\\n - flow_state (State, optional): flow state object used to optionally color the nodes\\n - filename (str, optional): a filename specifying a location to save this visualization to; if provided,\\n the visualization will not be rendered automatically\\n - format (str, optional): a format specifying the output file type; defaults to 'pdf'.\\n Refer to http://www.graphviz.org/doc/info/output.html for valid formats\\n\\n Raises:\\n - ImportError: if `graphviz` is not installed\\n \\\"\\\"\\\"\\n\\n try:\\n import graphviz\\n except ImportError:\\n msg = (\\n \\\"This feature requires graphviz.\\\\n\\\"\\n \\\"Try re-installing prefect with `pip install 'prefect[viz]'`\\\"\\n )\\n raise ImportError(msg)\\n\\n def get_color(task: Task, map_index: int = None) -> str:\\n assert flow_state\\n assert isinstance(flow_state.result, dict)\\n\\n if map_index is not None:\\n state = flow_state.result[task].map_states[map_index]\\n else:\\n state = flow_state.result.get(task)\\n if state is not None:\\n assert state is not None # mypy assert\\n return state.color + \\\"80\\\"\\n return \\\"#00000080\\\"\\n\\n graph = graphviz.Digraph()\\n\\n for t in self.tasks:\\n is_mapped = any(edge.mapped for edge in self.e\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.visualize.get_color\",\n \"text\": \"src/prefect/core/flow.py::Flow.visualize.get_color\\n def get_color(task: Task, map_index: int = None) -> str:\\n assert flow_state\\n assert isinstance(flow_state.result, dict)\\n\\n if map_index is not None:\\n state = flow_state.result[task].map_states[map_index]\\n else:\\n state = flow_state.result.get(task)\\n if state is not None:\\n assert state is not None # mypy assert\\n return state.color + \\\"80\\\"\\n return \\\"#00000080\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.serialize\",\n \"text\": \"src/prefect/core/flow.py::Flow.serialize\\n def serialize(self, build: bool = False) -> dict:\\n \\\"\\\"\\\"\\n Creates a serialized representation of the flow.\\n\\n Args:\\n - build (bool, optional): if `True`, the flow's environment is built\\n prior to serialization\\n\\n Returns:\\n - dict representing the flow\\n\\n Raises:\\n - ValueError: if `build=True` and the flow has no storage\\n \\\"\\\"\\\"\\n\\n self.validate()\\n schema = prefect.serialization.flow.FlowSchema\\n serialized = schema(exclude=[\\\"storage\\\"]).dump(self)\\n\\n if build:\\n if not self.storage:\\n raise ValueError(\\\"This flow has no storage to build\\\")\\n if self.name not in self.storage:\\n self.storage.add_flow(self)\\n else:\\n warnings.warn(\\n \\\"A flow with the same name is already contained in storage; if you changed your Flow since\\\"\\n \\\" the last build, you might experience unexpected issues and should re-create your storage object.\\\"\\n )\\n storage = self.storage.build() # type: Optional[Storage]\\n else:\\n storage = self.storage\\n\\n serialized.update(schema(only=[\\\"storage\\\"]).dump({\\\"storage\\\": storage}))\\n\\n return serialized\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.diagnostics\",\n \"text\": \"src/prefect/core/flow.py::Flow.diagnostics\\n def diagnostics(self, include_secret_names: bool = False) -> str:\\n \\\"\\\"\\\"\\n Get flow and Prefect diagnostic information\\n\\n Args:\\n - include_secret_names (bool, optional): toggle output of Secret names, defaults to False.\\n Note: Secret values are never returned, only their names.\\n \\\"\\\"\\\"\\n return diagnostics.diagnostic_info(self, include_secret_names)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.load\",\n \"text\": \"src/prefect/core/flow.py::Flow.load\\n @classmethod\\n def load(cls, fpath: str) -> \\\"Flow\\\":\\n \\\"\\\"\\\"\\n Reads a Flow from a file that was created with `flow.save()`.\\n\\n Args:\\n - fpath (str): either the absolute filepath where your Flow will be loaded from,\\n or the name of the Flow you wish to load\\n \\\"\\\"\\\"\\n if not os.path.isabs(fpath):\\n path = \\\"{home}/flows\\\".format(home=prefect.context.config.home_dir) # type: ignore\\n fpath = Path(os.path.expanduser(path)) / \\\"{}.prefect\\\".format( # type: ignore\\n slugify(fpath)\\n ) # type: ignore\\n with open(str(fpath), \\\"rb\\\") as f:\\n return cloudpickle.load(f)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.save\",\n \"text\": \"src/prefect/core/flow.py::Flow.save\\n def save(self, fpath: str = None) -> str:\\n \\\"\\\"\\\"\\n Saves the Flow to a file by serializing it with cloudpickle. This method is\\n recommended if you wish to separate out the building of your Flow from its registration.\\n\\n Args:\\n - fpath (str, optional): the filepath where your Flow will be saved; defaults to\\n `~/.prefect/flows/FLOW-NAME.prefect`\\n\\n Returns:\\n - str: the full location the Flow was saved to\\n \\\"\\\"\\\"\\n if fpath is None:\\n path = \\\"{home}/flows\\\".format(home=prefect.context.config.home_dir) # type: ignore\\n fpath = Path( # type: ignore\\n os.path.expanduser(path) # type: ignore\\n ) / \\\"{}.prefect\\\".format( # type: ignore\\n slugify(self.name)\\n )\\n assert fpath is not None # mypy assert\\n fpath.parent.mkdir(exist_ok=True, parents=True)\\n with open(str(fpath), \\\"wb\\\") as f:\\n cloudpickle.dump(self, f)\\n\\n return str(fpath)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.run_agent\",\n \"text\": \"src/prefect/core/flow.py::Flow.run_agent\\n def run_agent(\\n self, token: str = None, show_flow_logs: bool = False, log_to_cloud: bool = True\\n ) -> None:\\n \\\"\\\"\\\"\\n Runs a Cloud agent for this Flow in-process.\\n\\n Args:\\n - token (str, optional): A Prefect Cloud API token with a RUNNER scope;\\n will default to the token found in `config.cloud.agent.auth_token`\\n - show_flow_logs (bool, optional): a boolean specifying whether the agent should re-route Flow run logs\\n to stdout; defaults to `False`\\n - log_to_cloud (bool, optional): a boolean specifying whether Flow run logs should be sent to Prefect Cloud;\\n defaults to `True`\\n \\\"\\\"\\\"\\n temp_config = {\\n \\\"cloud.agent.auth_token\\\": token or prefect.config.cloud.agent.auth_token,\\n \\\"logging.log_to_cloud\\\": log_to_cloud,\\n }\\n with set_temporary_config(temp_config):\\n labels = self.environment.labels\\n agent = prefect.agent.local.LocalAgent(\\n labels=labels, show_flow_logs=show_flow_logs\\n )\\n agent.start()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.register\",\n \"text\": \"src/prefect/core/flow.py::Flow.register\\n def register(\\n self,\\n project_name: str = None,\\n build: bool = True,\\n labels: List[str] = None,\\n set_schedule_active: bool = True,\\n version_group_id: str = None,\\n no_url: bool = False,\\n **kwargs: Any\\n ) -> str:\\n \\\"\\\"\\\"\\n Register the flow with Prefect Cloud; if no storage is present on the Flow, the default value from your config\\n will be used and initialized with `**kwargs`.\\n\\n Args:\\n - project_name (str, optional): the project that should contain this flow.\\n - build (bool, optional): if `True`, the flow's environment is built\\n prior to serialization; defaults to `True`\\n - labels (List[str], optional): a list of labels to add to this Flow's environment; useful for\\n associating Flows with individual Agents; see http://docs.prefect.io/orchestration/agents/overview.html#flow-affinity-labels\\n - set_schedule_active (bool, optional): if `False`, will set the\\n schedule to inactive in the database to prevent auto-scheduling runs (if the Flow has a schedule).\\n Defaults to `True`. This can be changed later.\\n - version_group_id (str, optional): the UUID version group ID to use for versioning this Flow\\n in Cloud; if not provided, the version group ID associated with this Flow's project and name\\n will be used.\\n - no_url (bool, optional): if `True`, the stdout from this function will not contain the\\n URL link to the newly-registered flow in the Cloud UI\\n - **kwargs (Any): if instantiating a Storage object from default settings, these keyword arguments\\n will be passed to the initialization method of the default Storage class\\n\\n Returns:\\n - str: the ID of the flow that was registered\\n \\\"\\\"\\\"\\n if self.storage is None:\\n self.st\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/flow.py::Flow.__mifflin__\",\n \"text\": \"src/prefect/core/flow.py::Flow.__mifflin__\\n def __mifflin__(self) -> None: # coverage: ignore\\n \\\"Calls Dunder Mifflin\\\"\\n import webbrowser\\n\\n webbrowser.open(\\\"https://cicdw.github.io/welcome.html\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::_validate_run_signature\",\n \"text\": \"src/prefect/core/task.py::_validate_run_signature\\ndef _validate_run_signature(run: Callable) -> None:\\n func = getattr(run, \\\"__wrapped__\\\", run)\\n try:\\n run_sig = inspect.getfullargspec(func)\\n except TypeError as exc:\\n if str(exc) == \\\"unsupported callable\\\":\\n raise ValueError(\\n \\\"This function can not be inspected (this is common \\\"\\n \\\"with `builtin` and `numpy` functions). In order to \\\"\\n \\\"use it as a task, please wrap it in a standard \\\"\\n \\\"Python function. For more detail, see \\\"\\n \\\"https://docs.prefect.io/core/advanced_tutorials/task-guide.html#the-task-decorator\\\"\\n )\\n raise\\n\\n if run_sig.varargs:\\n raise ValueError(\\n \\\"Tasks with variable positional arguments (*args) are not \\\"\\n \\\"supported, because all Prefect arguments are stored as \\\"\\n \\\"keywords. As a workaround, consider modifying the run() \\\"\\n \\\"method to accept **kwargs and feeding the values \\\"\\n \\\"to *args.\\\"\\n )\\n\\n reserved_kwargs = [\\\"upstream_tasks\\\", \\\"mapped\\\", \\\"task_args\\\", \\\"flow\\\"]\\n violations = [kw for kw in reserved_kwargs if kw in run_sig.args]\\n if violations:\\n msg = \\\"Tasks cannot have the following argument names: {}.\\\".format(\\n \\\", \\\".join(violations)\\n )\\n msg += \\\" These are reserved keyword arguments.\\\"\\n raise ValueError(msg)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::SignatureValidator.__new__\",\n \"text\": \"src/prefect/core/task.py::SignatureValidator.__new__\\n def __new__(cls, name: str, parents: tuple, methods: dict) -> \\\"SignatureValidator\\\":\\n run = methods.get(\\\"run\\\", lambda: None)\\n _validate_run_signature(run)\\n\\n # necessary to ensure classes that inherit from parent class\\n # also get passed through __new__\\n return type.__new__(cls, name, parents, methods) # type: ignore\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__init__\",\n \"text\": \"src/prefect/core/task.py::Task.__init__\\n def __init__(\\n self,\\n name: str = None,\\n slug: str = None,\\n tags: Iterable[str] = None,\\n max_retries: int = None,\\n retry_delay: timedelta = None,\\n timeout: int = None,\\n trigger: Callable[[Dict[\\\"Edge\\\", \\\"State\\\"]], bool] = None,\\n skip_on_upstream_skip: bool = True,\\n cache_for: timedelta = None,\\n cache_validator: Callable = None,\\n cache_key: str = None,\\n checkpoint: bool = None,\\n result_handler: \\\"ResultHandler\\\" = None,\\n state_handlers: List[Callable] = None,\\n on_failure: Callable = None,\\n log_stdout: bool = False,\\n result: \\\"Result\\\" = None,\\n target: str = None,\\n ):\\n self.name = name or type(self).__name__\\n self.slug = slug or str(uuid.uuid4())\\n\\n self.logger = logging.get_logger(self.name)\\n\\n # avoid silently iterating over a string\\n if isinstance(tags, str):\\n raise TypeError(\\\"Tags should be a set of tags, not a string.\\\")\\n current_tags = set(prefect.context.get(\\\"tags\\\", set()))\\n self.tags = (set(tags) if tags is not None else set()) | current_tags\\n\\n max_retries = (\\n max_retries\\n if max_retries is not None\\n else prefect.config.tasks.defaults.max_retries\\n )\\n retry_delay = (\\n retry_delay\\n if retry_delay is not None\\n else prefect.config.tasks.defaults.retry_delay\\n )\\n timeout = (\\n timeout if timeout is not None else prefect.config.tasks.defaults.timeout\\n )\\n\\n if max_retries > 0 and retry_delay is None:\\n raise ValueError(\\n \\\"A datetime.timedelta `retry_delay` must be provided if max_retries > 0\\\"\\n )\\n # specify not max retries because the default is false\\n if retry_delay is not None and not max_retries:\\n raise ValueError(\\n \\\"A `max_retries` argument \",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__repr__\",\n \"text\": \"src/prefect/core/task.py::Task.__repr__\\n def __repr__(self) -> str:\\n return \\\"<Task: {self.name}>\\\".format(self=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__hash__\",\n \"text\": \"src/prefect/core/task.py::Task.__hash__\\n def __hash__(self) -> int:\\n return id(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.run\",\n \"text\": \"src/prefect/core/task.py::Task.run\\n def run(self) -> None:\\n \\\"\\\"\\\"\\n The `run()` method is called (with arguments, if appropriate) to run a task.\\n\\n *Note:* The implemented `run` method cannot have `*args` in its signature. In addition,\\n the following keywords are reserved: `upstream_tasks`, `task_args` and `mapped`.\\n\\n If a task has arguments in its `run()` method, these can be bound either by using the functional\\n API and _calling_ the task instance, or by using `self.bind` directly.\\n\\n In addition to running arbitrary functions, tasks can interact with Prefect in a few ways:\\n <ul><li> Return an optional result. When this function runs successfully,\\n the task is considered successful and the result (if any) can be\\n made available to downstream tasks. </li>\\n <li> Raise an error. Errors are interpreted as failure. </li>\\n <li> Raise a [signal](../engine/signals.html). Signals can include `FAIL`, `SUCCESS`, `RETRY`, `SKIP`, etc.\\n and indicate that the task should be put in the indicated state.\\n <ul>\\n <li> `FAIL` will lead to retries if appropriate </li>\\n <li> `SUCCESS` will cause the task to be marked successful </li>\\n <li> `RETRY` will cause the task to be marked for retry, even if `max_retries`\\n has been exceeded </li>\\n <li> `SKIP` will skip the task and possibly propogate the skip state through the\\n flow, depending on whether downstream tasks have `skip_on_upstream_skip=True`. </li></ul>\\n </li></ul>\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.copy\",\n \"text\": \"src/prefect/core/task.py::Task.copy\\n def copy(self, **task_args: Any) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Creates and returns a copy of the current Task.\\n\\n Args:\\n - **task_args (dict, optional): a dictionary of task attribute keyword arguments, these attributes\\n will be set on the new copy\\n\\n Raises:\\n - AttributeError: if any passed `task_args` are not attributes of the original\\n\\n Returns:\\n - Task: a copy of the current Task, with any attributes updated from `task_args`\\n \\\"\\\"\\\"\\n\\n flow = prefect.context.get(\\\"flow\\\", None)\\n if (\\n flow\\n and self in flow.tasks\\n and (flow.edges_to(self) or flow.edges_from(self))\\n ):\\n warnings.warn(\\n \\\"You are making a copy of a task that has dependencies on or to other tasks \\\"\\n \\\"in the active flow context. The copy will not retain those dependencies.\\\"\\n )\\n\\n new = copy.copy(self)\\n\\n # ensure new slug is provided\\n if \\\"slug\\\" not in task_args:\\n task_args[\\\"slug\\\"] = str(uuid.uuid4())\\n\\n # check task_args\\n for attr, val in task_args.items():\\n if not hasattr(new, attr):\\n raise AttributeError(\\n \\\"{0} does not have {1} as an attribute\\\".format(self, attr)\\n )\\n else:\\n setattr(new, attr, val)\\n\\n new.tags = copy.deepcopy(self.tags).union(set(new.tags))\\n tags = set(prefect.context.get(\\\"tags\\\", set()))\\n new.tags.update(tags)\\n\\n return new\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__call__\",\n \"text\": \"src/prefect/core/task.py::Task.__call__\\n def __call__(\\n self,\\n *args: Any,\\n mapped: bool = False,\\n task_args: dict = None,\\n upstream_tasks: Iterable[Any] = None,\\n flow: \\\"Flow\\\" = None,\\n **kwargs: Any\\n ) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Calling a Task instance will first create a _copy_ of the instance, and then\\n bind any passed `args` / `kwargs` to the run method of the copy. This new task\\n is then returned.\\n\\n Args:\\n - *args: arguments to bind to the new Task's `run` method\\n - **kwargs: keyword arguments to bind to the new Task's `run` method\\n - mapped (bool, optional): Whether the results of these tasks should be mapped over\\n with the specified keyword arguments; defaults to `False`.\\n If `True`, any arguments contained within a `prefect.utilities.tasks.unmapped`\\n container will _not_ be mapped over.\\n - task_args (dict, optional): a dictionary of task attribute keyword arguments, these attributes\\n will be set on the new copy\\n - upstream_tasks ([Task], optional): a list of upstream dependencies\\n for the new task. This kwarg can be used to functionally specify\\n dependencies without binding their result to `run()`\\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\\n flow in context if no flow is specified\\n\\n Returns:\\n - Task: a new Task instance\\n \\\"\\\"\\\"\\n new = self.copy(**(task_args or {}))\\n new.bind(\\n *args, mapped=mapped, upstream_tasks=upstream_tasks, flow=flow, **kwargs\\n )\\n return new\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.bind\",\n \"text\": \"src/prefect/core/task.py::Task.bind\\n def bind(\\n self,\\n *args: Any,\\n mapped: bool = False,\\n upstream_tasks: Iterable[Any] = None,\\n flow: \\\"Flow\\\" = None,\\n **kwargs: Any\\n ) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Binding a task to (keyword) arguments creates a _keyed_ edge in the active Flow\\n that will pass data from the arguments (whether Tasks or constants) to the\\n Task's `run` method under the appropriate key. Once a Task is bound in this\\n manner, the same task instance cannot be bound a second time in the same Flow.\\n\\n To bind arguments to a _copy_ of this Task instance, see `__call__`.\\n Additionally, non-keyed edges can be created by passing any upstream\\n dependencies through `upstream_tasks`.\\n\\n Args:\\n - *args: arguments to bind to the current Task's `run` method\\n - mapped (bool, optional): Whether the results of these tasks should be mapped over\\n with the specified keyword arguments; defaults to `False`.\\n If `True`, any arguments contained within a `prefect.utilities.tasks.unmapped`\\n container will _not_ be mapped over.\\n - upstream_tasks ([Task], optional): a list of upstream dependencies for the\\n current task.\\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\\n flow in context if no flow is specified\\n - **kwargs: keyword arguments to bind to the current Task's `run` method\\n\\n Returns:\\n - Task: the current Task instance\\n \\\"\\\"\\\"\\n\\n # this will raise an error if callargs weren't all provided\\n signature = inspect.signature(self.run)\\n callargs = dict(signature.bind(*args, **kwargs).arguments) # type: Dict\\n\\n # bind() compresses all variable keyword arguments under the ** argument name,\\n # so we expand them explicitly\\n var_kw_arg = next(\\n (p for p in si\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.map\",\n \"text\": \"src/prefect/core/task.py::Task.map\\n def map(\\n self,\\n *args: Any,\\n upstream_tasks: Iterable[Any] = None,\\n flow: \\\"Flow\\\" = None,\\n task_args: dict = None,\\n **kwargs: Any\\n ) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Map the Task elementwise across one or more Tasks. Arguments that should _not_ be mapped over\\n should be placed in the `prefect.utilities.tasks.unmapped` container.\\n\\n For example:\\n ```\\n task.map(x=X, y=unmapped(Y))\\n ```\\n will map over the values of `X`, but not over the values of `Y`\\n\\n\\n Args:\\n - *args: arguments to map over, which will elementwise be bound to the Task's `run` method\\n - upstream_tasks ([Task], optional): a list of upstream dependencies\\n to map over\\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\\n flow in context if no flow is specified\\n - task_args (dict, optional): a dictionary of task attribute keyword arguments,\\n these attributes will be set on the new copy\\n - **kwargs: keyword arguments to map over, which will elementwise be bound to the Task's `run` method\\n\\n Raises:\\n - AttributeError: if any passed `task_args` are not attributes of the original\\n\\n Returns:\\n - Task: a new Task instance\\n \\\"\\\"\\\"\\n for arg in args:\\n if not hasattr(arg, \\\"__getitem__\\\") and not isinstance(arg, unmapped):\\n raise TypeError(\\n \\\"Cannot map over unsubscriptable object of type {t}: {preview}...\\\".format(\\n t=type(arg), preview=repr(arg)[:10]\\n )\\n )\\n new = self.copy(**(task_args or {}))\\n return new.bind(\\n *args, mapped=True, upstream_tasks=upstream_tasks, flow=flow, **kwargs\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.set_dependencies\",\n \"text\": \"src/prefect/core/task.py::Task.set_dependencies\\n def set_dependencies(\\n self,\\n flow: \\\"Flow\\\" = None,\\n upstream_tasks: Iterable[object] = None,\\n downstream_tasks: Iterable[object] = None,\\n keyword_tasks: Mapping[str, object] = None,\\n mapped: bool = False,\\n validate: bool = None,\\n ) -> None:\\n \\\"\\\"\\\"\\n Set dependencies for a flow either specified or in the current context using this task\\n\\n Args:\\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\\n flow in context if no flow is specified\\n - upstream_tasks ([object], optional): A list of upstream tasks for this task\\n - downstream_tasks ([object], optional): A list of downtream tasks for this task\\n - keyword_tasks ({str, object}}, optional): The results of these tasks will be provided\\n to this task under the specified keyword arguments.\\n - mapped (bool, optional): Whether the results of the _upstream_ tasks should be mapped over\\n with the specified keyword arguments\\n - validate (bool, optional): Whether or not to check the validity of the flow. If not\\n provided, defaults to the value of `eager_edge_validation` in your Prefect\\n configuration file.\\n\\n Returns:\\n - None\\n\\n Raises:\\n - ValueError: if no flow is specified and no flow can be found in the current context\\n \\\"\\\"\\\"\\n flow = flow or prefect.context.get(\\\"flow\\\", None)\\n if not flow:\\n raise ValueError(\\n \\\"No Flow was passed, and could not infer an active Flow context.\\\"\\n )\\n\\n flow.set_dependencies(\\n task=self,\\n upstream_tasks=upstream_tasks,\\n downstream_tasks=downstream_tasks,\\n keyword_tasks=keyword_tasks,\\n validate=validate,\\n mapped=mapped,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.set_upstream\",\n \"text\": \"src/prefect/core/task.py::Task.set_upstream\\n def set_upstream(\\n self, task: object, flow: \\\"Flow\\\" = None, key: str = None, mapped: bool = False\\n ) -> None:\\n \\\"\\\"\\\"\\n Sets the provided task as an upstream dependency of this task.\\n\\n Args:\\n - task (object): A task or object that will be converted to a task that will be set\\n as a upstream dependency of this task.\\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\\n flow in context if no flow is specified\\n - key (str, optional): The key to be set for the new edge; the result of the upstream task\\n will be passed to this task's `run()` method under this keyword argument.\\n - mapped (bool, optional): Whether this dependency is mapped; defaults to `False`\\n\\n Raises:\\n - ValueError: if no flow is specified and no flow can be found in the current context\\n \\\"\\\"\\\"\\n if key is not None:\\n keyword_tasks = {key: task}\\n self.set_dependencies(flow=flow, keyword_tasks=keyword_tasks, mapped=mapped)\\n else:\\n self.set_dependencies(flow=flow, upstream_tasks=[task], mapped=mapped)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.set_downstream\",\n \"text\": \"src/prefect/core/task.py::Task.set_downstream\\n def set_downstream(\\n self, task: \\\"Task\\\", flow: \\\"Flow\\\" = None, key: str = None, mapped: bool = False\\n ) -> None:\\n \\\"\\\"\\\"\\n Sets the provided task as a downstream dependency of this task.\\n\\n Args:\\n - task (Task): A task that will be set as a downstream dependency of this task.\\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\\n flow in context if no flow is specified\\n - key (str, optional): The key to be set for the new edge; the result of this task\\n will be passed to the downstream task's `run()` method under this keyword argument.\\n - mapped (bool, optional): Whether this dependency is mapped; defaults to `False`\\n\\n Raises:\\n - ValueError: if no flow is specified and no flow can be found in the current context\\n \\\"\\\"\\\"\\n if key is not None:\\n keyword_tasks = {key: self}\\n task.set_dependencies( # type: ignore\\n flow=flow, keyword_tasks=keyword_tasks, mapped=mapped\\n ) # type: ignore\\n else:\\n task.set_dependencies(flow=flow, upstream_tasks=[self], mapped=mapped)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.inputs\",\n \"text\": \"src/prefect/core/task.py::Task.inputs\\n def inputs(self) -> Dict[str, Dict]:\\n \\\"\\\"\\\"\\n Describe the inputs for this task. The result is a dictionary that maps each input to\\n a `type`, `required`, and `default`. All values are inferred from the `run()`\\n signature; this method can be overloaded for more precise control.\\n\\n Returns:\\n - dict\\n \\\"\\\"\\\"\\n inputs = {}\\n for name, parameter in inspect.signature(self.run).parameters.items():\\n input_type = parameter.annotation\\n if input_type is inspect._empty: # type: ignore\\n input_type = Any\\n\\n input_default = parameter.default\\n input_required = False\\n if input_default is inspect._empty: # type: ignore\\n input_required = True\\n input_default = None\\n\\n inputs[name] = dict(\\n type=input_type, default=input_default, required=input_required\\n )\\n\\n return inputs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.outputs\",\n \"text\": \"src/prefect/core/task.py::Task.outputs\\n def outputs(self) -> Any:\\n \\\"\\\"\\\"\\n Get the output types for this task.\\n\\n Returns:\\n - Any\\n \\\"\\\"\\\"\\n return_annotation = inspect.signature(self.run).return_annotation\\n if return_annotation is inspect._empty: # type: ignore\\n return_annotation = Any\\n return return_annotation\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.serialize\",\n \"text\": \"src/prefect/core/task.py::Task.serialize\\n def serialize(self) -> Dict[str, Any]:\\n \\\"\\\"\\\"\\n Creates a serialized representation of this task\\n\\n Returns:\\n - dict representing this task\\n \\\"\\\"\\\"\\n return prefect.serialization.task.TaskSchema().dump(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.is_equal\",\n \"text\": \"src/prefect/core/task.py::Task.is_equal\\n def is_equal(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self == other`\\n\\n This can't be implemented as the __eq__() magic method because of Task\\n comparisons.\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Equal().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.is_not_equal\",\n \"text\": \"src/prefect/core/task.py::Task.is_not_equal\\n def is_not_equal(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self != other`\\n\\n This can't be implemented as the __neq__() magic method because of Task\\n comparisons.\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.NotEqual().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.not_\",\n \"text\": \"src/prefect/core/task.py::Task.not_\\n def not_(self) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `not self`\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Not().bind(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.or_\",\n \"text\": \"src/prefect/core/task.py::Task.or_\\n def or_(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self or other`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Or().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__getitem__\",\n \"text\": \"src/prefect/core/task.py::Task.__getitem__\\n def __getitem__(self, key: Any) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self[key]`\\n\\n Args:\\n - key (object): the object to use an an index for this task. It will be converted\\n to a Task if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.GetItem().bind(self, key)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__or__\",\n \"text\": \"src/prefect/core/task.py::Task.__or__\\n def __or__(self, other: object) -> object:\\n \\\"\\\"\\\"\\n Creates a state dependency between `self` and `other`\\n `self | other --> self.set_dependencies(downstream_tasks=[other])`\\n\\n Args:\\n - other (object): An object that will be converted to a Task (if it isn't one already)\\n and set as a downstream dependency of this Task.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n self.set_dependencies(downstream_tasks=[other])\\n return other\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__mifflin__\",\n \"text\": \"src/prefect/core/task.py::Task.__mifflin__\\n def __mifflin__(self) -> None: # coverage: ignore\\n \\\"Calls Dunder Mifflin\\\"\\n import webbrowser\\n\\n webbrowser.open(\\\"https://cicdw.github.io/welcome.html\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__ror__\",\n \"text\": \"src/prefect/core/task.py::Task.__ror__\\n def __ror__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Creates a state dependency between `self` and `other`:\\n `other | self --> self.set_dependencies(upstream_tasks=[other])`\\n\\n Args:\\n - other (object): An object that will be converted to a Task and set as an\\n upstream dependency of this Task.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n self.set_dependencies(upstream_tasks=[other])\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__add__\",\n \"text\": \"src/prefect/core/task.py::Task.__add__\\n def __add__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self + other`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Add().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__sub__\",\n \"text\": \"src/prefect/core/task.py::Task.__sub__\\n def __sub__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self - other`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Sub().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__mul__\",\n \"text\": \"src/prefect/core/task.py::Task.__mul__\\n def __mul__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self * other`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Mul().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__truediv__\",\n \"text\": \"src/prefect/core/task.py::Task.__truediv__\\n def __truediv__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self / other`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Div().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__floordiv__\",\n \"text\": \"src/prefect/core/task.py::Task.__floordiv__\\n def __floordiv__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self // other`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.FloorDiv().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__mod__\",\n \"text\": \"src/prefect/core/task.py::Task.__mod__\\n def __mod__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self % other`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Mod().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__pow__\",\n \"text\": \"src/prefect/core/task.py::Task.__pow__\\n def __pow__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self ** other`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Pow().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__and__\",\n \"text\": \"src/prefect/core/task.py::Task.__and__\\n def __and__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self & other`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.And().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__radd__\",\n \"text\": \"src/prefect/core/task.py::Task.__radd__\\n def __radd__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `other + self`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Add().bind(other, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__rsub__\",\n \"text\": \"src/prefect/core/task.py::Task.__rsub__\\n def __rsub__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `other - self`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Sub().bind(other, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__rmul__\",\n \"text\": \"src/prefect/core/task.py::Task.__rmul__\\n def __rmul__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `other * self`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Mul().bind(other, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__rtruediv__\",\n \"text\": \"src/prefect/core/task.py::Task.__rtruediv__\\n def __rtruediv__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `other / self`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Div().bind(other, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__rfloordiv__\",\n \"text\": \"src/prefect/core/task.py::Task.__rfloordiv__\\n def __rfloordiv__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `other // self`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.FloorDiv().bind(other, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__rmod__\",\n \"text\": \"src/prefect/core/task.py::Task.__rmod__\\n def __rmod__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `other % self`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Mod().bind(other, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__rpow__\",\n \"text\": \"src/prefect/core/task.py::Task.__rpow__\\n def __rpow__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `other ** self`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.Pow().bind(other, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__rand__\",\n \"text\": \"src/prefect/core/task.py::Task.__rand__\\n def __rand__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `other & self`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.And().bind(other, self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__gt__\",\n \"text\": \"src/prefect/core/task.py::Task.__gt__\\n def __gt__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self > other`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.GreaterThan().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__ge__\",\n \"text\": \"src/prefect/core/task.py::Task.__ge__\\n def __ge__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self >= other`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.GreaterThanOrEqual().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__lt__\",\n \"text\": \"src/prefect/core/task.py::Task.__lt__\\n def __lt__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self < other`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.LessThan().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Task.__le__\",\n \"text\": \"src/prefect/core/task.py::Task.__le__\\n def __le__(self, other: object) -> \\\"Task\\\":\\n \\\"\\\"\\\"\\n Produces a Task that evaluates `self <= other`\\n\\n Args:\\n - other (object): the other operand of the operator. It will be converted to a Task\\n if it isn't one already.\\n\\n Returns:\\n - Task\\n \\\"\\\"\\\"\\n return prefect.tasks.core.operators.LessThanOrEqual().bind(self, other)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Parameter.__init__\",\n \"text\": \"src/prefect/core/task.py::Parameter.__init__\\n def __init__(\\n self,\\n name: str,\\n default: Any = None,\\n required: bool = True,\\n tags: Iterable[str] = None,\\n ):\\n if default is not None:\\n required = False\\n\\n self.required = required\\n self.default = default\\n\\n super().__init__(\\n name=name, slug=name, tags=tags, result=PrefectResult(), checkpoint=True,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Parameter.__repr__\",\n \"text\": \"src/prefect/core/task.py::Parameter.__repr__\\n def __repr__(self) -> str:\\n return \\\"<Parameter: {self.name}>\\\".format(self=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Parameter.__call__\",\n \"text\": \"src/prefect/core/task.py::Parameter.__call__\\n def __call__(self, flow: \\\"Flow\\\" = None) -> \\\"Parameter\\\": # type: ignore\\n \\\"\\\"\\\"\\n Calling a Parameter adds it to a flow.\\n\\n Args:\\n - flow (Flow, optional): The flow to set dependencies on, defaults to the current\\n flow in context if no flow is specified\\n\\n Returns:\\n - Task: a new Task instance\\n\\n \\\"\\\"\\\"\\n result = super().bind(flow=flow)\\n assert isinstance(result, Parameter) # mypy assert\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Parameter.copy\",\n \"text\": \"src/prefect/core/task.py::Parameter.copy\\n def copy(self, name: str, **task_args: Any) -> \\\"Task\\\": # type: ignore\\n \\\"\\\"\\\"\\n Creates a copy of the Parameter with a new name.\\n\\n Args:\\n - name (str): the new Parameter name\\n - **task_args (dict, optional): a dictionary of task attribute keyword arguments,\\n these attributes will be set on the new copy\\n\\n Raises:\\n - AttributeError: if any passed `task_args` are not attributes of the original\\n\\n Returns:\\n - Parameter: a copy of the current Parameter, with a new name and any attributes\\n updated from `task_args`\\n \\\"\\\"\\\"\\n return super().copy(name=name, slug=name, **task_args)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Parameter.run\",\n \"text\": \"src/prefect/core/task.py::Parameter.run\\n def run(self) -> Any:\\n params = prefect.context.get(\\\"parameters\\\") or {}\\n if self.required and self.name not in params:\\n self.logger.debug(\\n 'Parameter \\\"{}\\\" was required but not provided.'.format(self.name)\\n )\\n raise prefect.engine.signals.FAIL(\\n 'Parameter \\\"{}\\\" was required but not provided.'.format(self.name)\\n )\\n return params.get(self.name, self.default)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/core/task.py::Parameter.serialize\",\n \"text\": \"src/prefect/core/task.py::Parameter.serialize\\n def serialize(self) -> Dict[str, Any]:\\n \\\"\\\"\\\"\\n Creates a serialized representation of this parameter\\n\\n Returns:\\n - dict representing this parameter\\n \\\"\\\"\\\"\\n return prefect.serialization.task.ParameterSchema().dump(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/case.py::case.__init__\",\n \"text\": \"src/prefect/tasks/control_flow/case.py::case.__init__\\n def __init__(self, task: Task, value: Any):\\n if isinstance(value, Task):\\n raise TypeError(\\\"`value` cannot be a task\\\")\\n\\n self.task = task\\n self.value = value\\n self._tasks = set()\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/case.py::case.add_task\",\n \"text\": \"src/prefect/tasks/control_flow/case.py::case.add_task\\n def add_task(self, task: Task) -> None:\\n \\\"\\\"\\\"Add a new task under the case statement.\\n\\n Args:\\n - task (Task): the task to add\\n \\\"\\\"\\\"\\n self._tasks.add(task)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/case.py::case.__enter__\",\n \"text\": \"src/prefect/tasks/control_flow/case.py::case.__enter__\\n def __enter__(self):\\n self.__prev_case = prefect.context.get(\\\"case\\\")\\n prefect.context.update(case=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/case.py::case.__exit__\",\n \"text\": \"src/prefect/tasks/control_flow/case.py::case.__exit__\\n def __exit__(self, *args):\\n if self.__prev_case is None:\\n prefect.context.pop(\\\"case\\\", None)\\n else:\\n prefect.context.update(case=self.__prev_case)\\n\\n if self._tasks:\\n\\n flow = prefect.context.get(\\\"flow\\\", None)\\n if not flow:\\n raise ValueError(\\\"Could not infer an active Flow context.\\\")\\n\\n cond = CompareValue(self.value, name=f\\\"case({self.value})\\\",).bind(\\n value=self.task\\n )\\n\\n for child in self._tasks:\\n # If a task has no upstream tasks created in this case block,\\n # the case conditional should be set as an upstream task.\\n if not self._tasks.intersection(flow.upstream_tasks(child)):\\n child.set_upstream(cond)\",\n \"label\": 1\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/conditional.py::Merge.__init__\",\n \"text\": \"src/prefect/tasks/control_flow/conditional.py::Merge.__init__\\n def __init__(self, **kwargs) -> None:\\n if kwargs.setdefault(\\\"skip_on_upstream_skip\\\", False):\\n raise ValueError(\\\"Merge tasks must have `skip_on_upstream_skip=False`.\\\")\\n kwargs.setdefault(\\\"trigger\\\", prefect.triggers.not_all_skipped)\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/conditional.py::Merge.run\",\n \"text\": \"src/prefect/tasks/control_flow/conditional.py::Merge.run\\n def run(self, **task_results: Any) -> Any:\\n return next(\\n (v for k, v in sorted(task_results.items()) if v is not None), None,\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/conditional.py::CompareValue.__init__\",\n \"text\": \"src/prefect/tasks/control_flow/conditional.py::CompareValue.__init__\\n def __init__(self, value: Any, **kwargs: Any):\\n self.value = value\\n kwargs.setdefault(\\\"name\\\", 'CompareValue: \\\"{}\\\"'.format(value))\\n super().__init__(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/conditional.py::CompareValue.run\",\n \"text\": \"src/prefect/tasks/control_flow/conditional.py::CompareValue.run\\n def run(self, value: Any) -> None:\\n \\\"\\\"\\\"\\n Raises a SKIP signal if the passed value does not match the task's match value;\\n succeeds silently otherwise.\\n\\n Args:\\n - value (Any): the value that will be matched against the task's value.\\n \\\"\\\"\\\"\\n if value != self.value:\\n raise signals.SKIP(\\n 'Provided value \\\"{}\\\" did not match \\\"{}\\\"'.format(value, self.value)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/conditional.py::switch\",\n \"text\": \"src/prefect/tasks/control_flow/conditional.py::switch\\ndef switch(condition: Task, cases: Dict[Any, Task]) -> None:\\n \\\"\\\"\\\"\\n Adds a SWITCH to a workflow.\\n\\n The condition task is evaluated and the result is compared to the keys of the cases\\n dictionary. The task corresponding to the matching key is run; all other tasks are\\n skipped. Any tasks downstream of the skipped tasks are also skipped unless they set\\n `skip_on_upstream_skip=False`.\\n\\n Example:\\n ```python\\n @task\\n def condition():\\n return \\\"b\\\" # returning 'b' will take the b_branch\\n\\n @task\\n def a_branch():\\n return \\\"A Branch\\\"\\n\\n @task\\n def b_branch():\\n return \\\"B Branch\\\"\\n\\n with Flow(\\\"switch-flow\\\") as flow:\\n switch(condition, dict(a=a_branch, b=b_branch))\\n ```\\n\\n Args:\\n - condition (Task): a task whose result forms the condition for the switch\\n - cases (Dict[Any, Task]): a dict representing the \\\"case\\\" statements of the switch.\\n The value of the `condition` task will be compared to the keys of this dict, and\\n the matching task will be executed.\\n\\n Raises:\\n - PrefectWarning: if any of the tasks in \\\"cases\\\" have upstream dependencies,\\n then this task will warn that those upstream tasks may run whether or not the switch condition matches their branch. The most common cause of this\\n is passing a list of tasks as one of the cases, which adds the `List` task\\n to the switch condition but leaves the tasks themselves upstream.\\n \\\"\\\"\\\"\\n\\n with prefect.tags(\\\"switch\\\"):\\n for value, task in cases.items():\\n task = prefect.utilities.tasks.as_task(task)\\n match_condition = CompareValue(value=value).bind(value=condition)\\n task.set_dependencies(upstream_tasks=[match_condition])\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/conditional.py::ifelse\",\n \"text\": \"src/prefect/tasks/control_flow/conditional.py::ifelse\\ndef ifelse(condition: Task, true_task: Task, false_task: Task) -> None:\\n \\\"\\\"\\\"\\n Builds a conditional branch into a workflow.\\n\\n If the condition evaluates True(ish), the true_task will run. If it\\n evaluates False(ish), the false_task will run. The task doesn't run is Skipped, as are\\n all downstream tasks that don't set `skip_on_upstream_skip=False`.\\n\\n Args:\\n - condition (Task): a task whose boolean result forms the condition for the ifelse\\n - true_task (Task): a task that will be executed if the condition is True\\n - false_task (Task): a task that will be executed if the condition is False\\n \\\"\\\"\\\"\\n\\n @prefect.task\\n def as_bool(x):\\n return bool(x)\\n\\n cases = {c: t for c, t in [(True, true_task), (False, false_task)] if t is not None}\\n if cases:\\n switch(condition=as_bool(condition), cases=cases)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/conditional.py::ifelse.as_bool\",\n \"text\": \"src/prefect/tasks/control_flow/conditional.py::ifelse.as_bool\\n @prefect.task\\n def as_bool(x):\\n return bool(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/prefect/tasks/control_flow/conditional.py::merge\",\n \"text\": \"src/prefect/tasks/control_flow/conditional.py::merge\\ndef merge(*tasks: Task) -> Task:\\n \\\"\\\"\\\"\\n Merges conditional branches back together.\\n\\n A conditional branch in a flow results in one or more tasks proceeding and one or\\n more tasks skipping. It is often convenient to merge those branches back into a\\n single result. This function is a simple way to achieve that goal. By default this\\n task will skip if all its upstream dependencies are also skipped.\\n\\n The merge will return the first real result it encounters, or `None`. If multiple\\n tasks might return a result, group them with a list.\\n\\n Example:\\n ```python\\n with Flow(\\\"My Flow\\\"):\\n true_branch = ActionIfTrue()\\n false_branch = ActionIfFalse()\\n ifelse(CheckCondition(), true_branch, false_branch)\\n\\n merged_result = merge(true_branch, false_branch)\\n ```\\n\\n Args:\\n - *tasks (Task): tasks whose results should be merged into a single result. The tasks are\\n assumed to all sit downstream of different `switch` branches, such that only\\n one of them will contain a result and the others will all be skipped.\\n\\n Returns:\\n - Task: a Task representing the merged result.\\n\\n \\\"\\\"\\\"\\n return Merge().bind(**{\\\"task_{}\\\".format(i + 1): t for i, t in enumerate(tasks)})\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":288,"cells":{"instance_id":{"kind":"string","value":"Lightning-AI__lightning-2986"},"repo":{"kind":"string","value":"Lightning-AI/lightning"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Neptune logger with a validation epoch end conflict due to the 'epoch' key added on the fly.\nHi everybody! First thanks for this lib, it is very handy!\r\n\r\n## 🐛 Bug\r\n\r\nWhen using pytorch lightning in conjunction with the neptune logger, one can see this kind of error popping every time an epoch ends:\r\n`\r\nneptune.api_exceptions.ChannelsValuesSendBatchError: Received batch errors sending channels' values to experiment SOC-114. Cause: Error(code=400, message='X-coordinates must be strictly increasing for channel: e4e2635d-b707-46fa-9a1b-996dd009790f. Invalid point: InputChannelValue(timestamp=2020-08-13T11:55:38.422Z, x=5.0, numericValue=2.0, textValue=null, image', type=None) (metricId: 'e4e2635d-b707-46fa-9a1b-996dd009790f', x: 5.0) Skipping 1 values.\r\n`\r\n\r\nthe import part in this error is the following line: `X-coordinates must be strictly increasing`\r\n\r\nThis is because, in `trainer/logging.py`, the `epoch` key is added on the fly on line 69: \r\n```python\r\nscalar_metrics['epoch'] = self.current_epoch\r\n```\r\n\r\nBut why does Neptune complains?\r\n\r\nIf you log all the timesteps (using `row_log_interval = 1`), at the end of an epoch, 2 calls are emitted to the logger: One to log the training logs and one for the validation logs. \r\nBoth of those have the same `step` value which is the current training `step` value. Since the key `epoch` is duplicated in both those calls, Neptune receives the key `epoch` twice with the same `step` value leading to the exception.\r\n\r\n### To Reproduce\r\n\r\nlaunch training with:\r\n- Neptune logger\r\n- training logs\r\n- validation logs\r\n- row_log_interval=1\r\n\r\n### Expected behaviour\r\n\r\nDon't add the `epoch` key on the fly which force the logger to log it.\r\n\r\n\n"},"gold_files":{"kind":"list like","value":["pytorch_lightning/trainer/evaluation_loop.py","pytorch_lightning/trainer/logging.py"],"string":"[\n \"pytorch_lightning/trainer/evaluation_loop.py\",\n \"pytorch_lightning/trainer/logging.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__eval_add_step_metrics","pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin._evaluate","pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics","pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.log_metrics"],"string":"[\n \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__eval_add_step_metrics\",\n \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin._evaluate\",\n \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics\",\n \"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.log_metrics\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"pytorch_lightning/core/lightning.py","text":"pytorch_lightning/core/lightning.py\nclass LightningModule\n def __init__(self, *args, **kwargs)\n def example_input_array(self)\n def example_input_array(self, example: Any)\n def datamodule(self)\n def datamodule(self, datamodule: Any)\n def on_gpu(self) # True if your model is currently running on GPUs.\n def print(self, *args, **kwargs) # Prints only from process 0. Use this in any distributed mode to log only once.\n def forward(self, *args, **kwargs) # Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to\n def training_step(self, *args, **kwargs) # Here you compute and return the training loss and some additional metrics for e.\n def training_end(self, *args, **kwargs) # Warnings:\n def training_step_end(self, *args, **kwargs) # Use this when training with dp or ddp2 because :meth:`training_step`\n def training_epoch_end(self, outputs: Union[TrainResult, List[TrainResult]]) # Called at the end of the training epoch with the outputs of all training steps.\n def validation_step(self, *args, **kwargs) # Operates on a single batch of data from the validation set.\n def validation_step_end(self, *args, **kwargs) # Use this when validating with dp or ddp2 because :meth:`validation_step`\n def validation_end(self, outputs) # Warnings:\n def validation_epoch_end(self, outputs: Union[EvalResult, List[EvalResult]]) # Called at the end of the validation epoch with the outputs of all validation ste\n def test_step(self, *args, **kwargs) # Operates on a single batch of data from the test set.\n def test_step_end(self, *args, **kwargs) # Use this when testing with dp or ddp2 because :meth:`test_step` will operate\n def test_end(self, outputs) # Warnings:\n def test_epoch_end(self, outputs: Union[EvalResult, List[EvalResult]]) # Called at the end of a test epoch with the output of all test steps.\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) # Override to init DDP in your own way or with your own wrapper.\n def _init_slurm_connection(self)\n def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool=True) # Override to define your custom way of setting up a distributed environment.\n def configure_sync_batchnorm(self, model: 'LightningModule') # Add global batchnorm for a model spread across multiple GPUs and nodes.\n def configure_apex(self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str) # Override to init AMP your own way.\n def configure_optimizers(self) # Choose what optimizers and learning-rate schedulers to use in your optimization.\n def optimizer_step(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int, second_order_closure: Optional[Callable]=None, on_tpu: bool=False, using_native_amp: bool=False, using_lbfgs: bool=False) # Override this method to adjust the default way the\n def optimizer_zero_grad(self, epoch: int, batc","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py","text":"pytorch_lightning/trainer/evaluation_loop.py\nclass TrainerEvaluationLoopMixin\n def copy_trainer_model_properties(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def add_progress_bar_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_test_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def reset_val_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\n def __call_eval_loop_hook_start(self, test_mode) # on_validation/test_epoch_start\n def __call_eval_loop_hook_end(self, test_mode) # on_validation/test_epoch_end\n def __call_eval_loop_hook_evt(self, test_mode, epoch_event)\n def _evaluate(self, model: LightningModule, dataloaders: List[DataLoader], max_batches: Union[int, List[int]], test_mode: bool=False) # Run evaluation code.\n def __update_callback_metrics(self, eval_results, using_eval_result)\n def __run_eval_epoch_end(self, test_mode, outputs, dataloaders, using_eval_result)\n def __gather_epoch_end_eval_results(self, outputs)\n def __eval_add_step_metrics(self, output)\n def __auto_reduce_result_objs(self, outputs)\n def run_evaluation(self, test_mode: bool=False)\n def __log_evaluation_epoch_metrics(self, eval_results, test_mode)\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode: bool=False)","label":1},{"cid":"pytorch_lightning/trainer/logging.py","text":"pytorch_lightning/trainer/logging.py\nclass TrainerLoggingMixin\n def configure_logger(self, logger)\n def log_metrics(self, metrics, grad_norm_dic, step=None) # Logs the metric dict passed in.\n def add_progress_bar_metrics(self, metrics)\n def metrics_to_scalars(self, metrics)\n def process_output(self, output, train=False) # Reduces output according to the training mode.\n def reduce_distributed_output(self, output, num_gpus)","label":1}],"string":"[\n {\n \"cid\": \"pytorch_lightning/core/lightning.py\",\n \"text\": \"pytorch_lightning/core/lightning.py\\nclass LightningModule\\n def __init__(self, *args, **kwargs)\\n def example_input_array(self)\\n def example_input_array(self, example: Any)\\n def datamodule(self)\\n def datamodule(self, datamodule: Any)\\n def on_gpu(self) # True if your model is currently running on GPUs.\\n def print(self, *args, **kwargs) # Prints only from process 0. Use this in any distributed mode to log only once.\\n def forward(self, *args, **kwargs) # Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to\\n def training_step(self, *args, **kwargs) # Here you compute and return the training loss and some additional metrics for e.\\n def training_end(self, *args, **kwargs) # Warnings:\\n def training_step_end(self, *args, **kwargs) # Use this when training with dp or ddp2 because :meth:`training_step`\\n def training_epoch_end(self, outputs: Union[TrainResult, List[TrainResult]]) # Called at the end of the training epoch with the outputs of all training steps.\\n def validation_step(self, *args, **kwargs) # Operates on a single batch of data from the validation set.\\n def validation_step_end(self, *args, **kwargs) # Use this when validating with dp or ddp2 because :meth:`validation_step`\\n def validation_end(self, outputs) # Warnings:\\n def validation_epoch_end(self, outputs: Union[EvalResult, List[EvalResult]]) # Called at the end of the validation epoch with the outputs of all validation ste\\n def test_step(self, *args, **kwargs) # Operates on a single batch of data from the test set.\\n def test_step_end(self, *args, **kwargs) # Use this when testing with dp or ddp2 because :meth:`test_step` will operate\\n def test_end(self, outputs) # Warnings:\\n def test_epoch_end(self, outputs: Union[EvalResult, List[EvalResult]]) # Called at the end of a test epoch with the output of all test steps.\\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) # Override to init DDP in your own way or with your own wrapper.\\n def _init_slurm_connection(self)\\n def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool=True) # Override to define your custom way of setting up a distributed environment.\\n def configure_sync_batchnorm(self, model: 'LightningModule') # Add global batchnorm for a model spread across multiple GPUs and nodes.\\n def configure_apex(self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str) # Override to init AMP your own way.\\n def configure_optimizers(self) # Choose what optimizers and learning-rate schedulers to use in your optimization.\\n def optimizer_step(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int, second_order_closure: Optional[Callable]=None, on_tpu: bool=False, using_native_amp: bool=False, using_lbfgs: bool=False) # Override this method to adjust the default way the\\n def optimizer_zero_grad(self, epoch: int, batc\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py\\nclass TrainerEvaluationLoopMixin\\n def copy_trainer_model_properties(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def get_model(self) # Warning: this is just empty shell for code implemented in other class.\\n def is_overridden(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_tpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def transfer_batch_to_gpu(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def add_progress_bar_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def log_metrics(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_test_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def reset_val_dataloader(self, *args) # Warning: this is just empty shell for code implemented in other class.\\n def __call_eval_loop_hook_start(self, test_mode) # on_validation/test_epoch_start\\n def __call_eval_loop_hook_end(self, test_mode) # on_validation/test_epoch_end\\n def __call_eval_loop_hook_evt(self, test_mode, epoch_event)\\n def _evaluate(self, model: LightningModule, dataloaders: List[DataLoader], max_batches: Union[int, List[int]], test_mode: bool=False) # Run evaluation code.\\n def __update_callback_metrics(self, eval_results, using_eval_result)\\n def __run_eval_epoch_end(self, test_mode, outputs, dataloaders, using_eval_result)\\n def __gather_epoch_end_eval_results(self, outputs)\\n def __eval_add_step_metrics(self, output)\\n def __auto_reduce_result_objs(self, outputs)\\n def run_evaluation(self, test_mode: bool=False)\\n def __log_evaluation_epoch_metrics(self, eval_results, test_mode)\\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode: bool=False)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/logging.py\",\n \"text\": \"pytorch_lightning/trainer/logging.py\\nclass TrainerLoggingMixin\\n def configure_logger(self, logger)\\n def log_metrics(self, metrics, grad_norm_dic, step=None) # Logs the metric dict passed in.\\n def add_progress_bar_metrics(self, metrics)\\n def metrics_to_scalars(self, metrics)\\n def process_output(self, output, train=False) # Reduces output according to the training mode.\\n def reduce_distributed_output(self, output, num_gpus)\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"pytorch_lightning/core/lightning.py::LightningModule.__init__","text":"pytorch_lightning/core/lightning.py::LightningModule.__init__\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n self.exp_save_path = None\n\n #: The current epoch\n self.current_epoch = 0\n\n #: Total training batches seen across all epochs\n self.global_step = 0\n\n self.loaded_optimizer_states_dict = {}\n\n #: Pointer to the trainer object\n self.trainer = None\n\n #: Pointer to the logger object\n self.logger = None\n\n #: True if using dp\n self.use_dp = False\n\n #: True if using ddp\n self.use_ddp = False\n\n #: True if using ddp2\n self.use_ddp2 = False\n\n # True if on tpu\n self.use_tpu = False\n\n #: True if using amp\n self.use_amp = False\n\n #: The precision used\n self.precision = 32\n\n # optionally can be set by user\n self._example_input_array = None\n self._datamodule = None","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.example_input_array","text":"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\n @property\n def example_input_array(self) -> Any:\n return self._example_input_array","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.example_input_array","text":"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\n @example_input_array.setter\n def example_input_array(self, example: Any) -> None:\n self._example_input_array = example","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.datamodule","text":"pytorch_lightning/core/lightning.py::LightningModule.datamodule\n @property\n def datamodule(self) -> Any:\n return self._datamodule","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.datamodule","text":"pytorch_lightning/core/lightning.py::LightningModule.datamodule\n @datamodule.setter\n def datamodule(self, datamodule: Any) -> None:\n self._datamodule = datamodule","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_gpu","text":"pytorch_lightning/core/lightning.py::LightningModule.on_gpu\n @property\n def on_gpu(self):\n \"\"\"\n True if your model is currently running on GPUs.\n Useful to set flags around the LightningModule for different CPU vs GPU behavior.\n \"\"\"\n return self.device.type == 'cuda'","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.print","text":"pytorch_lightning/core/lightning.py::LightningModule.print\n def print(self, *args, **kwargs) -> None:\n r\"\"\"\n Prints only from process 0. Use this in any distributed mode to log only once.\n\n Args:\n *args: The thing to print. Will be passed to Python's built-in print function.\n **kwargs: Will be passed to Python's built-in print function.\n\n Example:\n\n .. code-block:: python\n\n def forward(self, x):\n self.print(x, 'in forward')\n\n \"\"\"\n if self.trainer.is_global_zero:\n print(*args, **kwargs)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.forward","text":"pytorch_lightning/core/lightning.py::LightningModule.forward\n def forward(self, *args, **kwargs):\n r\"\"\"\n Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define\n the operations you want to use for prediction (i.e.: on a server or as a feature extractor).\n\n Normally you'd call ``self()`` from your :meth:`training_step` method.\n This makes it easy to write a complex system for training with the outputs\n you'd want in a prediction setting.\n\n You may also find the :func:`~pytorch_lightning.core.decorators.auto_move_data` decorator useful\n when using the module outside Lightning in a production setting.\n\n Args:\n *args: Whatever you decide to pass into the forward method.\n **kwargs: Keyword arguments are also possible.\n\n Return:\n Predicted output\n\n Examples:\n .. code-block:: python\n\n # example if we were using this model as a feature extractor\n def forward(self, x):\n feature_maps = self.convnet(x)\n return feature_maps\n\n def training_step(self, batch, batch_idx):\n x, y = batch\n feature_maps = self(x)\n logits = self.classifier(feature_maps)\n\n # ...\n return loss\n\n # splitting it this way allows model to be used a feature extractor\n model = MyModelAbove()\n\n inputs = server.get_request()\n results = model(inputs)\n server.write_results(results)\n\n # -------------\n # This is in stark contrast to torch.nn.Module where normally you would have this:\n def forward(self, batch):\n x, y = batch\n feature_maps = self.convnet(x)\n logits = self.classifier(feature_maps)\n re","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_step","text":"pytorch_lightning/core/lightning.py::LightningModule.training_step\n def training_step(self, *args, **kwargs):\n r\"\"\"\n Here you compute and return the training loss and some additional metrics for e.g.\n the progress bar or logger.\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): Integer displaying index of this batch\n optimizer_idx (int): When using multiple optimizers, this argument will also be present.\n hiddens(:class:`~torch.Tensor`): Passed in if\n :paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0.\n\n Return:\n :class:`~pytorch_lightning.core.step_result.TrainResult`\n\n .. note:: :class:`~pytorch_lightning.core.step_result.TrainResult` is simply a Dict with convenient\n functions for logging, distributed sync and error checking.\n\n In this step you'd normally do the forward pass and calculate the loss for a batch.\n You can also do fancier things like multiple forward passes or something model specific.\n\n Example::\n\n def training_step(self, batch, batch_idx):\n x, y, z = batch\n\n # implement your own\n out = self(x)\n loss = self.loss(out, x)\n\n # TrainResult auto-detaches the loss after the optimization steps are complete\n result = pl.TrainResult(minimize=loss)\n\n The return object :class:`~pytorch_lightning.core.step_result.TrainResult` controls where to log,\n when to log (step or epoch) and syncing with multiple GPUs.\n\n .. code-block:: python\n\n # log to progress bar and logger\n result.log('train_loss', loss, prog_bar=True, logger=True)\n\n # sync metric value across GPUs ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_end\n def training_end(self, *args, **kwargs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`training_step_end` instead.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\n def training_step_end(self, *args, **kwargs):\n \"\"\"\n Use this when training with dp or ddp2 because :meth:`training_step`\n will operate on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]\n training_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in `training_step` for each batch part.\n\n Return:\n :class:`~pytorch_lightning.core.step_result.TrainResult`\n\n .. note:: :class:`~pytorch_lightning.core.step_result.TrainResult` is simply a Dict with convenient\n functions for logging, distributed sync and error checking.\n\n When using dp/ddp2 distributed backends, only a portion of the batch is inside the training_step:\n\n .. code-block:: python\n\n def training_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n\n # softmax uses only a portion of the batch in the denomintaor\n loss = self.softmax(out)\n loss = nce_loss(loss)\n return pl.TrainResult(loss)\n\n If you wish to do something with all the parts of the batch, then use this method to do it:\n\n .. code-block:: python\n\n def training_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n result = pl.TrainResult()\n result.out = o","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\n def training_epoch_end(\n self, outputs: Union[TrainResult, List[TrainResult]]\n ):\n \"\"\"\n Called at the end of the training epoch with the outputs of all training steps.\n Use this in case you need to do something with all the outputs for every training_step.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n train_outs = []\n for train_batch in train_data:\n out = training_step(train_batch)\n train_outs.append(out)\n training_epoch_end(train_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`training_step`, or if there are\n multiple dataloaders, a list containing a list of outputs for each dataloader.\n\n Return:\n :class:`~pytorch_lightning.core.step_result.TrainResult`\n\n .. note:: :class:`~pytorch_lightning.core.step_result.TrainResult` is simply a Dict with convenient\n functions for logging, distributed sync and error checking.\n\n Note:\n If this method is not overridden, this won't be called.\n\n Example::\n\n def training_epoch_end(self, training_step_outputs):\n # do something with all training_step outputs\n return result\n\n With multiple dataloaders, ``outputs`` will be a list of lists. The outer list contains\n one entry per dataloader, while the inner list contains the individual outputs of\n each training step for that dataloader.\n\n .. code-block:: python\n\n def training_epoch_end(self, outputs):\n epoch_result = pl.TrainResult()\n for train_result in outputs:\n all_losses = train_result.minimize\n # do something with all losses\n return results\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_step","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_step\n def validation_step(self, *args, **kwargs) -> EvalResult:\n r\"\"\"\n Operates on a single batch of data from the validation set.\n In this step you'd might generate examples or calculate anything of interest like accuracy.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n val_outs = []\n for val_batch in val_data:\n out = validation_step(train_batch)\n val_outs.append(out)\n validation_epoch_end(val_outs)\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): The index of this batch\n dataloader_idx (int): The index of the dataloader that produced this batch\n (only if multiple val datasets used)\n\n Return:\n :class:`~pytorch_lightning.core.step_result.TrainResult`\n\n .. code-block:: python\n\n # pseudocode of order\n out = validation_step()\n if defined('validation_step_end'):\n out = validation_step_end(out)\n out = validation_epoch_end(out)\n\n\n .. code-block:: python\n\n # if you have one val dataloader:\n def validation_step(self, batch, batch_idx)\n\n # if you have multiple val dataloaders:\n def validation_step(self, batch, batch_idx, dataloader_idx)\n\n Examples:\n .. code-block:: python\n\n # CASE 1: A single validation dataset\n def validation_step(self, batch, batch_idx):\n x, y = batch\n\n # implement your own\n out = self(x)\n loss = self.loss(out, y)\n\n # log 6 example images\n # or generated text","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\n def validation_step_end(self, *args, **kwargs) -> EvalResult:\n \"\"\"\n Use this when validating with dp or ddp2 because :meth:`validation_step`\n will operate on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code.\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]\n validation_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in :meth:`validation_step`\n for each batch part.\n\n Return:\n :class:`~pytorch_lightning.core.step_result.TrainResult`\n\n .. code-block:: python\n\n # WITHOUT validation_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def validation_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n result = pl.EvalResult()\n result.log('val_loss', loss)\n return result\n\n # --------------\n # with validation_step_end to do softmax over the full batch\n def validation_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n result = pl.EvalResult()\n result.out = out\n return result\n\n def validation_epoch_end(self, output_results):\n # this out is now the full size of the batch\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_end\n def validation_end(self, outputs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`validation_epoch_end` instead.\n Will be removed in 1.0.0.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\n def validation_epoch_end(\n self, outputs: Union[EvalResult, List[EvalResult]]\n ) -> EvalResult:\n \"\"\"\n Called at the end of the validation epoch with the outputs of all validation steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n val_outs = []\n for val_batch in val_data:\n out = validation_step(val_batch)\n val_outs.append(out)\n validation_epoch_end(val_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`validation_step`, or if there\n are multiple dataloaders, a list containing a list of outputs for each dataloader.\n\n Return:\n :class:`~pytorch_lightning.core.step_result.TrainResult`\n\n Note:\n If you didn't define a :meth:`validation_step`, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def validation_epoch_end(self, val_step_outputs):\n # do something with the outputs of all val batches\n all_val_preds = val_step_outputs.predictions\n\n val_step_outputs.some_result = calc_all_results(all_val_preds)\n return val_step_outputs\n\n With multiple dataloaders, `outputs` will be a list of lists. The outer list contains\n one entry per dataloader, while the inner list contains the individual outputs of\n each validation step for that dataloader.\n\n .. code-block:: python\n\n def validation_epoch_end(self, outputs):\n for dataloader_output_result in outputs:\n dataloader_outs = dataloader_output_result.dataloader_i_ou","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_step","text":"pytorch_lightning/core/lightning.py::LightningModule.test_step\n def test_step(self, *args, **kwargs) -> EvalResult:\n r\"\"\"\n Operates on a single batch of data from the test set.\n In this step you'd normally generate examples or calculate anything of interest\n such as accuracy.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n test_outs = []\n for test_batch in test_data:\n out = test_step(test_batch)\n test_outs.append(out)\n test_epoch_end(test_outs)\n\n Args:\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\n batch_idx (int): The index of this batch.\n dataloader_idx (int): The index of the dataloader that produced this batch\n (only if multiple test datasets used).\n\n Return:\n :class:`~pytorch_lightning.core.step_result.TrainResult`\n\n .. code-block:: python\n\n # if you have one test dataloader:\n def test_step(self, batch, batch_idx)\n\n # if you have multiple test dataloaders:\n def test_step(self, batch, batch_idx, dataloader_idx)\n\n Examples:\n .. code-block:: python\n\n # CASE 1: A single test dataset\n def test_step(self, batch, batch_idx):\n x, y = batch\n\n # implement your own\n out = self(x)\n loss = self.loss(out, y)\n\n # log 6 example images\n # or generated text... or whatever\n sample_imgs = x[:6]\n grid = torchvision.utils.make_grid(sample_imgs)\n self.logger.experiment.add_image('example_images', grid, 0)\n\n # calculate acc\n labels_hat = tor","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_step_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\n def test_step_end(self, *args, **kwargs) -> EvalResult:\n \"\"\"\n Use this when testing with dp or ddp2 because :meth:`test_step` will operate\n on only part of the batch. However, this is still optional\n and only needed for things like softmax or NCE loss.\n\n Note:\n If you later switch to ddp or some other mode, this will still be called\n so that you don't have to change your code.\n\n .. code-block:: python\n\n # pseudocode\n sub_batches = split_batches_for_dp(batch)\n batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]\n test_step_end(batch_parts_outputs)\n\n Args:\n batch_parts_outputs: What you return in :meth:`test_step` for each batch part.\n\n Return:\n :class:`~pytorch_lightning.core.step_result.TrainResult`\n\n .. code-block:: python\n\n # WITHOUT test_step_end\n # if used in DP or DDP2, this batch is 1/num_gpus large\n def test_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n loss = self.softmax(out)\n loss = nce_loss(loss)\n result = pl.EvalResult()\n result.log('test_loss', loss)\n return result\n\n # --------------\n # with test_step_end to do softmax over the full batch\n def test_step(self, batch, batch_idx):\n # batch is 1/num_gpus big\n x, y = batch\n\n out = self(x)\n result = pl.EvalResult()\n result.out = out\n return result\n\n def test_epoch_end(self, output_results):\n # this out is now the full size of the batch\n all_test_step_outs = output_results.out\n loss = nce_loss(all_test_st","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_end\n def test_end(self, outputs):\n \"\"\"\n Warnings:\n Deprecated in v0.7.0. Use :meth:`test_epoch_end` instead.\n Will be removed in 1.0.0.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end","text":"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\n def test_epoch_end(\n self, outputs: Union[EvalResult, List[EvalResult]]\n ) -> EvalResult:\n\n \"\"\"\n Called at the end of a test epoch with the output of all test steps.\n\n .. code-block:: python\n\n # the pseudocode for these calls\n test_outs = []\n for test_batch in test_data:\n out = test_step(test_batch)\n test_outs.append(out)\n test_epoch_end(test_outs)\n\n Args:\n outputs: List of outputs you defined in :meth:`test_step_end`, or if there\n are multiple dataloaders, a list containing a list of outputs for each dataloader\n\n Return:\n :class:`~pytorch_lightning.core.step_result.TrainResult`\n\n Note:\n If you didn't define a :meth:`test_step`, this won't be called.\n\n - The outputs here are strictly for logging or progress bar.\n - If you don't need to display anything, don't return anything.\n\n Examples:\n With a single dataloader:\n\n .. code-block:: python\n\n def test_epoch_end(self, outputs):\n # do something with the outputs of all test batches\n all_test_preds = test_step_outputs.predictions\n\n test_step_outputs.some_result = calc_all_results(all_test_preds)\n return test_step_outputs\n\n With multiple dataloaders, `outputs` will be a list of lists. The outer list contains\n one entry per dataloader, while the inner list contains the individual outputs of\n each test step for that dataloader.\n\n .. code-block:: python\n\n def test_epoch_end(self, outputs):\n for dataloader_output_result in outputs:\n dataloader_outs = dataloader_output_result.dataloader_i_outputs\n\n result = pl.EvalResult()\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) -> DistributedDataParallel:\n r\"\"\"\n Override to init DDP in your own way or with your own wrapper.\n The only requirements are that:\n\n 1. On a validation batch, the call goes to ``model.validation_step``.\n 2. On a training batch, the call goes to ``model.training_step``.\n 3. On a testing batch, the call goes to ``model.test_step``.\n\n Args:\n model: the :class:`LightningModule` currently being optimized.\n device_ids: the list of GPU ids.\n\n Return:\n DDP wrapped model\n\n Examples:\n .. code-block:: python\n\n # default implementation used in Trainer\n def configure_ddp(self, model, device_ids):\n # Lightning DDP simply routes to test_step, val_step, etc...\n model = LightningDistributedDataParallel(\n model,\n device_ids=device_ids,\n find_unused_parameters=True\n )\n return model\n\n \"\"\"\n model = LightningDistributedDataParallel(model, device_ids=device_ids, find_unused_parameters=True)\n return model","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection","text":"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection\n def _init_slurm_connection(self) -> None:\n \"\"\"\"\"\"\n \"\"\"\n Sets up environment variables necessary for pytorch distributed communications\n based on slurm environment.\n \"\"\"\n # use slurm job id for the port number\n # guarantees unique ports across jobs from same grid search\n try:\n # use the last 4 numbers in the job id as the id\n default_port = os.environ['SLURM_JOB_ID']\n default_port = default_port[-4:]\n\n # all ports should be in the 10k+ range\n default_port = int(default_port) + 15000\n\n except Exception:\n default_port = 12910\n\n # if user gave a port number, use that one instead\n try:\n default_port = os.environ['MASTER_PORT']\n except Exception:\n os.environ['MASTER_PORT'] = str(default_port)\n\n # figure out the root node addr\n try:\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\n except Exception:\n root_node = '127.0.0.1'\n\n root_node = self.trainer.resolve_root_node_address(root_node)\n os.environ['MASTER_ADDR'] = root_node","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection","text":"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\n def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool = True) -> None:\n \"\"\"\n Override to define your custom way of setting up a distributed environment.\n\n Lightning's implementation uses env:// init by default and sets the first node as root\n for SLURM managed cluster.\n\n Args:\n global_rank: The global process idx.\n world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus).\n is_slurm_managing_tasks: is cluster managed by SLURM.\n\n \"\"\"\n if is_slurm_managing_tasks:\n self._init_slurm_connection()\n\n if 'MASTER_ADDR' not in os.environ:\n rank_zero_warn(\"MASTER_ADDR environment variable is not defined. Set as localhost\")\n os.environ['MASTER_ADDR'] = '127.0.0.1'\n log.debug(f\"MASTER_ADDR: {os.environ['MASTER_ADDR']}\")\n\n if 'MASTER_PORT' not in os.environ:\n rank_zero_warn(\"MASTER_PORT environment variable is not defined. Set as 12910\")\n os.environ['MASTER_PORT'] = '12910'\n log.debug(f\"MASTER_PORT: {os.environ['MASTER_PORT']}\")\n\n if 'WORLD_SIZE' in os.environ and int(os.environ['WORLD_SIZE']) != world_size:\n rank_zero_warn(\n f\"WORLD_SIZE environment variable ({os.environ['WORLD_SIZE']}) \"\n f\"is not equal to the computed world size ({world_size}). Ignored.\"\n )\n\n torch_backend = \"nccl\" if self.trainer.on_gpu else \"gloo\"\n log.info(f\"initializing ddp: GLOBAL_RANK: {global_rank}, MEMBER: {global_rank+1}/{world_size}\")\n torch_distrib.init_process_group(torch_backend, rank=global_rank, world_size=world_size)","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_sync_batchnorm","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_sync_batchnorm\n def configure_sync_batchnorm(self, model: 'LightningModule') -> 'LightningModule':\n \"\"\"\n Add global batchnorm for a model spread across multiple GPUs and nodes.\n\n Override to synchronize batchnorm between specific process groups instead\n of the whole world or use a different sync_bn like `apex`'s version.\n\n Args:\n model: pointer to current :class:`LightningModule`.\n\n Return:\n LightningModule with batchnorm layers synchronized between process groups\n \"\"\"\n model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model, process_group=None)\n\n return model","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_apex","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\n def configure_apex(\n self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str\n ) -> Tuple['LightningModule', List[Optimizer]]:\n r\"\"\"\n Override to init AMP your own way.\n Must return a model and list of optimizers.\n\n Args:\n amp: pointer to amp library object.\n model: pointer to current :class:`LightningModule`.\n optimizers: list of optimizers passed in :meth:`configure_optimizers`.\n amp_level: AMP mode chosen ('O1', 'O2', etc...)\n\n Return:\n Apex wrapped model and optimizers\n\n Examples:\n .. code-block:: python\n\n # Default implementation used by Trainer.\n def configure_apex(self, amp, model, optimizers, amp_level):\n model, optimizers = amp.initialize(\n model, optimizers, opt_level=amp_level,\n )\n\n return model, optimizers\n \"\"\"\n model, optimizers = amp.initialize(model, optimizers, opt_level=amp_level)\n\n return model, optimizers","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers","text":"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\n def configure_optimizers(\n self,\n ) -> Optional[Union[Optimizer, Sequence[Optimizer], Dict, Sequence[Dict], Tuple[List, List]]]:\n r\"\"\"\n Choose what optimizers and learning-rate schedulers to use in your optimization.\n Normally you'd need one. But in the case of GANs or similar you might have multiple.\n\n Return:\n Any of these 6 options.\n\n - Single optimizer.\n - List or Tuple - List of optimizers.\n - Two lists - The first list has multiple optimizers, the second a list of LR schedulers (or lr_dict).\n - Dictionary, with an 'optimizer' key, and (optionally) a 'lr_scheduler' key which value is a single LR scheduler or lr_dict.\n - Tuple of dictionaries as described, with an optional 'frequency' key.\n - None - Fit will run without any optimizer.\n\n Note:\n The 'frequency' value is an int corresponding to the number of sequential batches\n optimized with the specific optimizer. It should be given to none or to all of the optimizers.\n There is a difference between passing multiple optimizers in a list,\n and passing multiple optimizers in dictionaries with a frequency of 1:\n In the former case, all optimizers will operate on the given batch in each optimization step.\n In the latter, only one optimizer will operate on the given batch at every step.\n\n The lr_dict is a dictionary which contains scheduler and its associated configuration.\n It has five keys. The default configuration is shown below.\n\n .. code-block:: python\n\n {\n 'scheduler': lr_scheduler, # The LR schduler\n 'interval': 'epoch', # The unit of the scheduler's step size\n 'frequency': 1, # The frequency of the scheduler\n 'reduce_on_platea","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step","text":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\n def optimizer_step(\n self,\n epoch: int,\n batch_idx: int,\n optimizer: Optimizer,\n optimizer_idx: int,\n second_order_closure: Optional[Callable] = None,\n on_tpu: bool = False,\n using_native_amp: bool = False,\n using_lbfgs: bool = False,\n ) -> None:\n r\"\"\"\n Override this method to adjust the default way the\n :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.\n By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example\n once per optimizer.\n\n Args:\n epoch: Current epoch\n batch_idx: Index of current batch\n optimizer: A PyTorch optimizer\n optimizer_idx: If you used multiple optimizers this indexes into that list.\n second_order_closure: closure for second order methods\n on_tpu: true if TPU backward is required\n using_native_amp: True if using native amp\n using_lbfgs: True if the matching optimizer is lbfgs\n\n Examples:\n .. code-block:: python\n\n # DEFAULT\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\n second_order_closure, on_tpu, using_native_amp, using_lbfgs):\n optimizer.step()\n\n # Alternating schedule for optimizer steps (i.e.: GANs)\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\n second_order_closure, on_tpu, using_native_amp, using_lbfgs):\n # update generator opt every 2 steps\n if optimizer_idx == 0:\n if batch_idx % 2 == 0 :\n optimizer.step()\n optimizer.zero_grad()\n\n # update discriminator opt every 4 steps\n ","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_zero_grad","text":"pytorch_lightning/core/lightning.py::LightningModule.optimizer_zero_grad\n def optimizer_zero_grad(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int):\n optimizer.zero_grad()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch","text":"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\n def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list:\n r\"\"\"\n When using truncated backpropagation through time, each batch must be split along the\n time dimension. Lightning handles this by default, but for custom behavior override\n this function.\n\n Args:\n batch: Current batch\n split_size: The size of the split\n\n Return:\n List of batch splits. Each split will be passed to :meth:`training_step` to enable truncated\n back propagation through time. The default implementation splits root level Tensors and\n Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.\n\n Examples:\n .. code-block:: python\n\n def tbptt_split_batch(self, batch, split_size):\n splits = []\n for t in range(0, time_dims[0], split_size):\n batch_split = []\n for i, x in enumerate(batch):\n if isinstance(x, torch.Tensor):\n split_x = x[:, t:t + split_size]\n elif isinstance(x, collections.Sequence):\n split_x = [None] * len(x)\n for batch_idx in range(len(x)):\n split_x[batch_idx] = x[batch_idx][t:t + split_size]\n\n batch_split.append(split_x)\n\n splits.append(batch_split)\n\n return splits\n\n Note:\n Called in the training loop after\n :meth:`~pytorch_lightning.callbacks.base.Callback.on_batch_start`\n if :paramref:`~pytorch_lightning.trainer.Trainer.truncated_bptt_steps` > 0.\n Each returned batch split is passed separately to :meth:`training_step`.\n\n \"\"\"\n time_dims = [len(x[0]) for x in batch if isinstance(x","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.prepare_data","text":"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\n def prepare_data(self) -> None:\n \"\"\"\n Use this to download and prepare data.\n\n .. warning:: DO NOT set state to the model (use `setup` instead)\n since this is NOT called on every GPU in DDP/TPU\n\n Example::\n\n def prepare_data(self):\n # good\n download_data()\n tokenize()\n etc()\n\n # bad\n self.split = data_split\n self.some_state = some_other_state()\n\n In DDP prepare_data can be called in two ways (using Trainer(prepare_data_per_node)):\n\n 1. Once per node. This is the default and is only called on LOCAL_RANK=0.\n 2. Once in total. Only called on GLOBAL_RANK=0.\n\n Example::\n\n # DEFAULT\n # called once per node on LOCAL_RANK=0 of that node\n Trainer(prepare_data_per_node=True)\n\n # call on GLOBAL_RANK=0 (great for shared file systems)\n Trainer(prepare_data_per_node=False)\n\n This is called before requesting the dataloaders:\n\n .. code-block:: python\n\n model.prepare_data()\n if ddp/tpu: init()\n model.setup(stage)\n model.train_dataloader()\n model.val_dataloader()\n model.test_dataloader()\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\n def train_dataloader(self) -> DataLoader:\n \"\"\"\n Implement a PyTorch DataLoader for training.\n\n Return:\n Single PyTorch :class:`~torch.utils.data.DataLoader`.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n For data processing use the following pattern:\n\n - download in :meth:`prepare_data`\n - process and split in :meth:`setup`\n\n However, the above are only necessary for distributed processing.\n\n .. warning:: do not assign state in prepare_data\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`setup`\n - :meth:`train_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware.\n There is no need to set it yourself.\n\n Example:\n .. code-block:: python\n\n def train_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,\n download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.batch_size,\n shuffle=True\n )\n return loader\n\n \"\"\"\n rank_zero_warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\n def tng_dataloader(self): # todo: remove in v1.0.0\n \"\"\"\n Warnings:\n Deprecated in v0.5.0. Use :meth:`train_dataloader` instead. Will be removed in 1.0.0.\n \"\"\"\n output = self.train_dataloader()\n rank_zero_warn(\n \"`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0.\"\n \" and this method will be removed in v1.0.0\",\n DeprecationWarning,\n )\n return output","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\n def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\n r\"\"\"\n Implement one or multiple PyTorch DataLoaders for testing.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n For data processing use the following pattern:\n\n - download in :meth:`prepare_data`\n - process and split in :meth:`setup`\n\n However, the above are only necessary for distributed processing.\n\n .. warning:: do not assign state in prepare_data\n\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`setup`\n - :meth:`train_dataloader`\n - :meth:`val_dataloader`\n - :meth:`test_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware.\n There is no need to set it yourself.\n\n Return:\n Single or multiple PyTorch DataLoaders.\n\n Example:\n .. code-block:: python\n\n def test_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,\n download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.batch_size,\n shuffle=False\n )\n\n return loader\n\n # can also return multiple dataloaders\n def test_dataloader(self):\n return [loader_a, loader_b, ..., loader_n]\n\n Note:\n If you don't need a te","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader","text":"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\n def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\n r\"\"\"\n Implement one or multiple PyTorch DataLoaders for validation.\n\n The dataloader you return will not be called every epoch unless you set\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\n\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\n\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\n - ...\n - :meth:`prepare_data`\n - :meth:`train_dataloader`\n - :meth:`val_dataloader`\n - :meth:`test_dataloader`\n\n Note:\n Lightning adds the correct sampler for distributed and arbitrary hardware\n There is no need to set it yourself.\n\n Return:\n Single or multiple PyTorch DataLoaders.\n\n Examples:\n .. code-block:: python\n\n def val_dataloader(self):\n transform = transforms.Compose([transforms.ToTensor(),\n transforms.Normalize((0.5,), (1.0,))])\n dataset = MNIST(root='/path/to/mnist/', train=False,\n transform=transform, download=True)\n loader = torch.utils.data.DataLoader(\n dataset=dataset,\n batch_size=self.batch_size,\n shuffle=False\n )\n\n return loader\n\n # can also return multiple dataloaders\n def val_dataloader(self):\n return [loader_a, loader_b, ..., loader_n]\n\n Note:\n If you don't need a validation dataset and a :meth:`validation_step`, you don't need to\n implement this method.\n\n Note:\n In the case where you return multiple validation dataloaders, the :meth:`validation_st","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.summarize","text":"pytorch_lightning/core/lightning.py::LightningModule.summarize\n def summarize(self, mode: str = ModelSummary.MODE_DEFAULT) -> ModelSummary:\n model_summary = ModelSummary(self, mode=mode)\n log.info('\\n' + str(model_summary))\n return model_summary","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.freeze","text":"pytorch_lightning/core/lightning.py::LightningModule.freeze\n def freeze(self) -> None:\n r\"\"\"\n Freeze all params for inference.\n\n Example:\n .. code-block:: python\n\n model = MyLightningModule(...)\n model.freeze()\n\n \"\"\"\n for param in self.parameters():\n param.requires_grad = False\n\n self.eval()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.unfreeze","text":"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\n def unfreeze(self) -> None:\n \"\"\"\n Unfreeze all parameters for training.\n\n .. code-block:: python\n\n model = MyLightningModule(...)\n model.unfreeze()\n\n \"\"\"\n for param in self.parameters():\n param.requires_grad = True\n\n self.train()","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\n def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\n r\"\"\"\n Called by Lightning to restore your model.\n If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.\n\n Args:\n checkpoint: Loaded checkpoint\n\n\n Example:\n .. code-block:: python\n\n def on_load_checkpoint(self, checkpoint):\n # 99% of the time you don't need to implement this method\n self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']\n\n Note:\n Lightning auto-restores global step, epoch, and train state including amp scaling.\n There is no need for you to restore anything regarding training.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint","text":"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\n def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\n r\"\"\"\n Called by Lightning when saving a checkpoint to give you a chance to store anything\n else you might want to save.\n\n Args:\n checkpoint: Checkpoint to be saved\n\n Example:\n .. code-block:: python\n\n def on_save_checkpoint(self, checkpoint):\n # 99% of use cases you don't need to implement this method\n checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object\n\n Note:\n Lightning saves all aspects of training (epoch, global step, etc...)\n including amp scaling.\n There is no need for you to store anything about training.\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict","text":"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict\n def get_progress_bar_dict(self) -> Dict[str, Union[int, str]]:\n r\"\"\"\n Implement this to override the default items displayed in the progress bar.\n By default it includes the average loss value, split index of BPTT (if used)\n and the version of the experiment when using a logger.\n\n .. code-block::\n\n Epoch 1: 4%|▎ | 40/1095 [00:03<01:37, 10.84it/s, loss=4.501, v_num=10]\n\n Here is an example how to override the defaults:\n\n .. code-block:: python\n\n def get_progress_bar_dict(self):\n # don't show the version number\n items = super().get_progress_bar_dict()\n items.pop(\"v_num\", None)\n return items\n\n Return:\n Dictionary with the items to be displayed in the progress bar.\n \"\"\"\n # call .item() only once but store elements without graphs\n running_train_loss = self.trainer.running_loss.mean()\n avg_training_loss = running_train_loss.cpu().item() if running_train_loss is not None else float('NaN')\n tqdm_dict = {'loss': '{:.3f}'.format(avg_training_loss)}\n\n if self.trainer.truncated_bptt_steps is not None:\n tqdm_dict['split_idx'] = self.trainer.split_idx\n\n if self.trainer.logger is not None and self.trainer.logger.version is not None:\n version = self.trainer.logger.version\n # show last 4 places of long version strings\n version = version[-4:] if isinstance(version, str) else version\n tqdm_dict['v_num'] = version\n\n return tqdm_dict","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.copy_trainer_model_properties","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.copy_trainer_model_properties\n @abstractmethod\n def copy_trainer_model_properties(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.get_model","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.get_model\n @abstractmethod\n def get_model(self) -> LightningModule:\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.is_overridden","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.is_overridden\n @abstractmethod\n def is_overridden(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_tpu","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_tpu\n @abstractmethod\n def transfer_batch_to_tpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_gpu","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_gpu\n @abstractmethod\n def transfer_batch_to_gpu(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.add_progress_bar_metrics","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.add_progress_bar_metrics\n @abstractmethod\n def add_progress_bar_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics\n @abstractmethod\n def log_metrics(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":1},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_test_dataloader","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_test_dataloader\n @abstractmethod\n def reset_test_dataloader(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_val_dataloader","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_val_dataloader\n @abstractmethod\n def reset_val_dataloader(self, *args):\n \"\"\"Warning: this is just empty shell for code implemented in other class.\"\"\"","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_start","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_start\n def __call_eval_loop_hook_start(self, test_mode):\n \"\"\"on_validation/test_epoch_start\"\"\"\n self.__call_eval_loop_hook_evt(test_mode, 'start')","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_end","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_end\n def __call_eval_loop_hook_end(self, test_mode):\n \"\"\"on_validation/test_epoch_end\"\"\"\n self.__call_eval_loop_hook_evt(test_mode, 'end')","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_evt","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_evt\n def __call_eval_loop_hook_evt(self, test_mode, epoch_event):\n model = self.get_model()\n\n # on_[train/validation]_epoch_start hook\n hook_root_name = 'test' if test_mode else 'validation'\n hook_name = f'on_{hook_root_name}_epoch_{epoch_event}'\n with self.profiler.profile(hook_name):\n # call hook\n getattr(self, hook_name)()\n\n # model hooks\n if self.is_function_implemented(hook_name):\n getattr(model, hook_name)()","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin._evaluate","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin._evaluate\n def _evaluate(\n self,\n model: LightningModule,\n dataloaders: List[DataLoader],\n max_batches: Union[int, List[int]],\n test_mode: bool = False\n ):\n \"\"\"Run evaluation code.\n\n Args:\n model: The model to evaluate.\n dataloaders: A list of PyTorch dataloaders.\n max_batches: An integer or list of integers with length of the number of dataloaders. Each\n entry is the number of batches to process in the corresponding dataloader.\n test_mode:\n \"\"\"\n # enable eval mode\n model.zero_grad()\n model.eval()\n\n # copy properties for forward overrides\n self.copy_trainer_model_properties(model)\n\n # disable gradients to save memory\n torch.set_grad_enabled(False)\n\n # bookkeeping\n outputs = []\n predictions = PredictionCollection(self.global_rank, self.world_size)\n\n # convert max_batches to list\n if isinstance(max_batches, int):\n max_batches = [max_batches] * len(dataloaders)\n\n # --------------------------\n # ON_EVAL_EPOCH_START hook\n # --------------------------\n self.__call_eval_loop_hook_start(test_mode)\n\n # run validation\n for dataloader_idx, dataloader in enumerate(dataloaders):\n dl_outputs = []\n\n # on TPU we have to wrap it under the ParallelLoader\n if self.use_tpu:\n device = xm.xla_device(self.tpu_id)\n dataloader = xla_pl.ParallelLoader(dataloader, [device])\n dataloader = dataloader.per_device_loader(device)\n\n # each dataloader has a max num batches\n dl_max_batches = max_batches[dataloader_idx]\n\n for batch_idx, batch in enumerate(dataloader):\n if batch is None:\n continue\n\n # stop short when runn","label":1},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__update_callback_metrics","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__update_callback_metrics\n def __update_callback_metrics(self, eval_results, using_eval_result):\n if using_eval_result:\n if isinstance(eval_results, list):\n for eval_result in eval_results:\n self.callback_metrics = eval_result.callback_metrics\n else:\n self.callback_metrics = eval_results.callback_metrics\n else:\n if isinstance(eval_results, list):\n for eval_result in eval_results:\n # with a scalar return, auto set it to \"val_loss\" for callbacks\n if isinstance(eval_result, torch.Tensor):\n flat = {'val_loss': eval_result}\n else:\n flat = flatten_dict(eval_result)\n self.callback_metrics.update(flat)\n else:\n # with a scalar return, auto set it to \"val_loss\" for callbacks\n if isinstance(eval_results, torch.Tensor):\n flat = {'val_loss': eval_results}\n else:\n flat = flatten_dict(eval_results)\n self.callback_metrics.update(flat)","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__run_eval_epoch_end","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__run_eval_epoch_end\n def __run_eval_epoch_end(self, test_mode, outputs, dataloaders, using_eval_result):\n model = self.get_model()\n\n # with a single dataloader don't pass an array\n eval_results = outputs\n if len(dataloaders) == 1:\n eval_results = outputs[0]\n\n user_reduced = False\n\n if test_mode:\n if self.is_overridden('test_end', model=model):\n # TODO: remove in v1.0.0\n if using_eval_result:\n eval_results = self.__gather_epoch_end_eval_results(outputs)\n\n eval_results = model.test_end(eval_results)\n user_reduced = True\n rank_zero_warn('Method `test_end` was deprecated in v0.7 and will be removed in v1.0.'\n ' Use `test_epoch_end` instead.', DeprecationWarning)\n\n elif self.is_overridden('test_epoch_end', model=model):\n if using_eval_result:\n eval_results = self.__gather_epoch_end_eval_results(outputs)\n\n eval_results = model.test_epoch_end(eval_results)\n user_reduced = True\n\n else:\n if self.is_overridden('validation_end', model=model):\n # TODO: remove in v1.0.0\n if using_eval_result:\n eval_results = self.__gather_epoch_end_eval_results(outputs)\n\n eval_results = model.validation_end(eval_results)\n user_reduced = True\n rank_zero_warn('Method `validation_end` was deprecated in v0.7 and will be removed in v1.0.'\n ' Use `validation_epoch_end` instead.', DeprecationWarning)\n\n elif self.is_overridden('validation_epoch_end', model=model):\n if using_eval_result:\n eval_results = self.__gather_epoch_end_eval_results(outputs)\n\n eval_results = model.v","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__gather_epoch_end_eval_results","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__gather_epoch_end_eval_results\n def __gather_epoch_end_eval_results(self, outputs):\n eval_results = []\n for epoch_output in outputs:\n result = epoch_output[0].__class__.gather(epoch_output)\n if 'checkpoint_on' in result:\n result.checkpoint_on = result.checkpoint_on.mean()\n if 'early_stop_on' in result:\n result.early_stop_on = result.early_stop_on.mean()\n\n eval_results.append(result)\n\n # with 1 dataloader don't pass in a list\n if len(eval_results) == 1:\n eval_results = eval_results[0]\n return eval_results","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__eval_add_step_metrics","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__eval_add_step_metrics\n def __eval_add_step_metrics(self, output):\n # track step level metrics\n if isinstance(output, EvalResult) and not self.running_sanity_check:\n step_log_metrics = output.batch_log_metrics\n step_pbar_metrics = output.batch_pbar_metrics\n\n if len(step_log_metrics) > 0:\n self.log_metrics(step_log_metrics, {})\n\n if len(step_pbar_metrics) > 0:\n self.add_progress_bar_metrics(step_pbar_metrics)","label":1},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__auto_reduce_result_objs","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__auto_reduce_result_objs\n def __auto_reduce_result_objs(self, outputs):\n # outputs has a list of results per dataloader\n eval_results = []\n for dl_output in outputs:\n result = dl_output[0]\n result = result.__class__.reduce_on_epoch_end(dl_output)\n if 'checkpoint_on' in result:\n result.checkpoint_on = result.checkpoint_on.mean()\n if 'early_stop_on' in result:\n result.early_stop_on = result.early_stop_on.mean()\n eval_results.append(result)\n\n return eval_results","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation\n def run_evaluation(self, test_mode: bool = False):\n # hook\n model = self.get_model()\n model.on_pre_performance_check()\n\n # select dataloaders\n if test_mode:\n self.reset_test_dataloader(model)\n\n dataloaders = self.test_dataloaders\n max_batches = self.num_test_batches\n else:\n # val\n if self.val_dataloaders is None:\n self.reset_val_dataloader(model)\n\n dataloaders = self.val_dataloaders\n max_batches = self.num_val_batches\n\n if dataloaders is None:\n return [], []\n\n # Validation/Test begin callbacks\n if test_mode:\n self.on_test_start()\n else:\n self.on_validation_start()\n\n # enable disabling validation step with limit_val_batches = 0\n should_skip = sum(max_batches) == 0\n if should_skip:\n return [], []\n\n # run evaluation (val_step + val_step_end + val_epoch_end)\n eval_results = self._evaluate(self.model, dataloaders, max_batches, test_mode)\n\n # log the final eval loop metrics\n eval_loop_results = self.__log_evaluation_epoch_metrics(eval_results, test_mode)\n\n # hook\n model.on_post_performance_check()\n\n # eventual dataset reloading\n if test_mode:\n if self.reload_dataloaders_every_epoch:\n self.reset_test_dataloader(model)\n else:\n # val\n if self.reload_dataloaders_every_epoch:\n self.reset_val_dataloader(model)\n\n # Validation/Test end callbacks\n if test_mode:\n self.on_test_end()\n else:\n self.on_validation_end()\n\n return eval_loop_results, eval_results","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__log_evaluation_epoch_metrics","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__log_evaluation_epoch_metrics\n def __log_evaluation_epoch_metrics(self, eval_results, test_mode):\n eval_loop_results = []\n if eval_results is not None and len(eval_results) > 0:\n\n # in eval, the user may return something at every validation step without final reduction\n if not isinstance(eval_results, list):\n eval_results = [eval_results]\n\n for result_idx, result in enumerate(eval_results):\n if isinstance(result, EvalResult):\n prog_bar_metrics = result.epoch_pbar_metrics\n log_metrics = result.epoch_log_metrics\n callback_metrics = result.callback_metrics\n else:\n _, prog_bar_metrics, log_metrics, callback_metrics, _ = self.process_output(result)\n\n # eval loop returns all metrics\n dataloader_result_metrics = {**prog_bar_metrics, **log_metrics, **callback_metrics}\n\n # add metrics to prog bar\n self.add_progress_bar_metrics(prog_bar_metrics)\n\n # log metrics\n self.log_metrics(log_metrics, {})\n\n # track metrics for callbacks\n self.callback_metrics.update(callback_metrics)\n\n if len(dataloader_result_metrics) > 0:\n eval_loop_results.append(dataloader_result_metrics)\n\n # log results of test\n if test_mode and self.is_global_zero and self.verbose_test:\n print('-' * 80)\n for result_idx, results in enumerate(eval_loop_results):\n print(f'DATALOADER:{result_idx} TEST RESULTS')\n pprint(results)\n print('-' * 80)\n\n return eval_loop_results","label":0},{"cid":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluation_forward","text":"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluation_forward\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode: bool = False):\n # make dataloader_idx arg in validation_step optional\n args = [batch, batch_idx]\n\n if (test_mode and len(self.test_dataloaders) > 1) \\\n or (not test_mode and len(self.val_dataloaders) > 1):\n args.append(dataloader_idx)\n\n # handle DP, DDP forward\n if self.use_ddp or self.use_dp or self.use_ddp2:\n output = model(*args)\n return output\n\n # Horovod\n if self.use_horovod and self.on_gpu:\n batch = self.transfer_batch_to_gpu(batch, hvd.local_rank())\n args[0] = batch\n\n # single GPU data transfer\n if self.use_single_gpu:\n # for single GPU put inputs on gpu manually\n root_gpu = 0\n if isinstance(self.data_parallel_device_ids, list):\n root_gpu = self.data_parallel_device_ids[0]\n batch = self.transfer_batch_to_gpu(batch, root_gpu)\n args[0] = batch\n\n # TPU data transfer\n if self.use_tpu:\n batch = self.transfer_batch_to_tpu(batch, self.tpu_id)\n args[0] = batch\n\n # CPU, TPU or gpu step\n if test_mode:\n output = model.test_step(*args)\n else:\n output = model.validation_step(*args)\n\n return output","label":0},{"cid":"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.configure_logger","text":"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.configure_logger\n def configure_logger(self, logger):\n if logger is True:\n # default logger\n self.logger = TensorBoardLogger(\n save_dir=self.default_root_dir,\n version=self.slurm_job_id,\n name='lightning_logs'\n )\n elif logger is False:\n self.logger = None\n else:\n if isinstance(logger, Iterable):\n self.logger = LoggerCollection(logger)\n else:\n self.logger = logger","label":0},{"cid":"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.log_metrics","text":"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.log_metrics\n def log_metrics(self, metrics, grad_norm_dic, step=None):\n \"\"\"Logs the metric dict passed in.\n If `step` parameter is None and `step` key is presented is metrics,\n uses metrics[\"step\"] as a step\n\n Args:\n metrics (dict): Metric values\n grad_norm_dic (dict): Gradient norms\n step (int): Step for which metrics should be logged. Default value corresponds to `self.global_step`\n \"\"\"\n # add gpu memory\n if self.on_gpu and self.log_gpu_memory:\n mem_map = memory.get_memory_profile(self.log_gpu_memory)\n metrics.update(mem_map)\n\n # add norms\n metrics.update(grad_norm_dic)\n\n # turn all tensors to scalars\n scalar_metrics = self.metrics_to_scalars(metrics)\n\n if \"step\" in scalar_metrics and step is None:\n step = scalar_metrics.pop(\"step\")\n else:\n # added metrics by Lightning for convenience\n scalar_metrics['epoch'] = self.current_epoch\n step = step if step is not None else self.global_step\n # log actual metrics\n if self.is_global_zero and self.logger is not None:\n self.logger.agg_and_log_metrics(scalar_metrics, step=step)\n self.logger.save()\n\n self.dev_debugger.track_logged_metrics_history(scalar_metrics)","label":1},{"cid":"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.add_progress_bar_metrics","text":"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.add_progress_bar_metrics\n def add_progress_bar_metrics(self, metrics):\n for k, v in metrics.items():\n if isinstance(v, torch.Tensor):\n v = v.item()\n\n self.progress_bar_metrics[k] = v\n\n self.dev_debugger.track_pbar_metrics_history(metrics)","label":0},{"cid":"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.metrics_to_scalars","text":"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.metrics_to_scalars\n def metrics_to_scalars(self, metrics):\n new_metrics = {}\n for k, v in metrics.items():\n if isinstance(v, torch.Tensor):\n v = v.item()\n\n if isinstance(v, dict):\n v = self.metrics_to_scalars(v)\n\n new_metrics[k] = v\n\n return new_metrics","label":0},{"cid":"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.process_output","text":"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.process_output\n def process_output(self, output, train=False):\n \"\"\"Reduces output according to the training mode.\n\n Separates loss from logging and progress bar metrics\n \"\"\"\n # --------------------------\n # handle single scalar only\n # --------------------------\n # single scalar returned from a xx_step\n if isinstance(output, torch.Tensor):\n progress_bar_metrics = {}\n log_metrics = {}\n callback_metrics = {}\n hiddens = None\n return output, progress_bar_metrics, log_metrics, callback_metrics, hiddens\n\n # ---------------\n # EXTRACT CALLBACK KEYS\n # ---------------\n # all keys not progress_bar or log are candidates for callbacks\n callback_metrics = {}\n for k, v in output.items():\n if k not in ['progress_bar', 'log', 'hiddens']:\n callback_metrics[k] = v\n\n if train and (self.use_dp or self.use_ddp2):\n num_gpus = self.num_gpus\n callback_metrics = self.reduce_distributed_output(callback_metrics, num_gpus)\n\n # ---------------\n # EXTRACT PROGRESS BAR KEYS\n # ---------------\n try:\n progress_output = output['progress_bar']\n\n # reduce progress metrics for progress bar when using dp\n if train and (self.use_dp or self.use_ddp2):\n num_gpus = self.num_gpus\n progress_output = self.reduce_distributed_output(progress_output, num_gpus)\n\n progress_bar_metrics = progress_output\n except Exception:\n progress_bar_metrics = {}\n\n # ---------------\n # EXTRACT LOGGING KEYS\n # ---------------\n # extract metrics to log to experiment\n try:\n log_output = output['log']\n\n # reduce progress metrics for progress bar when using dp\n if train and (self.use_dp","label":0},{"cid":"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.reduce_distributed_output","text":"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.reduce_distributed_output\n def reduce_distributed_output(self, output, num_gpus):\n if num_gpus <= 1:\n return output\n\n # when using DP, we get one output per gpu\n # average outputs and return\n if isinstance(output, torch.Tensor):\n return output.mean()\n\n for k, v in output.items():\n # recurse on nested dics\n if isinstance(output[k], dict):\n output[k] = self.reduce_distributed_output(output[k], num_gpus)\n\n # compute the average of scalars\n elif isinstance(output[k], list):\n output[k] = sum(output[k]) / len(output[k])\n\n # do nothing when there's a scalar\n elif isinstance(output[k], torch.Tensor) and output[k].dim() == 0:\n pass\n\n # do not reduce metrics that have batch size > num gpus\n elif output[k].size(0) <= num_gpus:\n output[k] = torch.mean(output[k])\n\n return output","label":0}],"string":"[\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.__init__\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.__init__\\n def __init__(self, *args, **kwargs):\\n super().__init__(*args, **kwargs)\\n\\n self.exp_save_path = None\\n\\n #: The current epoch\\n self.current_epoch = 0\\n\\n #: Total training batches seen across all epochs\\n self.global_step = 0\\n\\n self.loaded_optimizer_states_dict = {}\\n\\n #: Pointer to the trainer object\\n self.trainer = None\\n\\n #: Pointer to the logger object\\n self.logger = None\\n\\n #: True if using dp\\n self.use_dp = False\\n\\n #: True if using ddp\\n self.use_ddp = False\\n\\n #: True if using ddp2\\n self.use_ddp2 = False\\n\\n # True if on tpu\\n self.use_tpu = False\\n\\n #: True if using amp\\n self.use_amp = False\\n\\n #: The precision used\\n self.precision = 32\\n\\n # optionally can be set by user\\n self._example_input_array = None\\n self._datamodule = None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\\n @property\\n def example_input_array(self) -> Any:\\n return self._example_input_array\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.example_input_array\\n @example_input_array.setter\\n def example_input_array(self, example: Any) -> None:\\n self._example_input_array = example\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.datamodule\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.datamodule\\n @property\\n def datamodule(self) -> Any:\\n return self._datamodule\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.datamodule\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.datamodule\\n @datamodule.setter\\n def datamodule(self, datamodule: Any) -> None:\\n self._datamodule = datamodule\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.on_gpu\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.on_gpu\\n @property\\n def on_gpu(self):\\n \\\"\\\"\\\"\\n True if your model is currently running on GPUs.\\n Useful to set flags around the LightningModule for different CPU vs GPU behavior.\\n \\\"\\\"\\\"\\n return self.device.type == 'cuda'\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.print\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.print\\n def print(self, *args, **kwargs) -> None:\\n r\\\"\\\"\\\"\\n Prints only from process 0. Use this in any distributed mode to log only once.\\n\\n Args:\\n *args: The thing to print. Will be passed to Python's built-in print function.\\n **kwargs: Will be passed to Python's built-in print function.\\n\\n Example:\\n\\n .. code-block:: python\\n\\n def forward(self, x):\\n self.print(x, 'in forward')\\n\\n \\\"\\\"\\\"\\n if self.trainer.is_global_zero:\\n print(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.forward\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.forward\\n def forward(self, *args, **kwargs):\\n r\\\"\\\"\\\"\\n Same as :meth:`torch.nn.Module.forward()`, however in Lightning you want this to define\\n the operations you want to use for prediction (i.e.: on a server or as a feature extractor).\\n\\n Normally you'd call ``self()`` from your :meth:`training_step` method.\\n This makes it easy to write a complex system for training with the outputs\\n you'd want in a prediction setting.\\n\\n You may also find the :func:`~pytorch_lightning.core.decorators.auto_move_data` decorator useful\\n when using the module outside Lightning in a production setting.\\n\\n Args:\\n *args: Whatever you decide to pass into the forward method.\\n **kwargs: Keyword arguments are also possible.\\n\\n Return:\\n Predicted output\\n\\n Examples:\\n .. code-block:: python\\n\\n # example if we were using this model as a feature extractor\\n def forward(self, x):\\n feature_maps = self.convnet(x)\\n return feature_maps\\n\\n def training_step(self, batch, batch_idx):\\n x, y = batch\\n feature_maps = self(x)\\n logits = self.classifier(feature_maps)\\n\\n # ...\\n return loss\\n\\n # splitting it this way allows model to be used a feature extractor\\n model = MyModelAbove()\\n\\n inputs = server.get_request()\\n results = model(inputs)\\n server.write_results(results)\\n\\n # -------------\\n # This is in stark contrast to torch.nn.Module where normally you would have this:\\n def forward(self, batch):\\n x, y = batch\\n feature_maps = self.convnet(x)\\n logits = self.classifier(feature_maps)\\n re\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step\\n def training_step(self, *args, **kwargs):\\n r\\\"\\\"\\\"\\n Here you compute and return the training loss and some additional metrics for e.g.\\n the progress bar or logger.\\n\\n Args:\\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\\n batch_idx (int): Integer displaying index of this batch\\n optimizer_idx (int): When using multiple optimizers, this argument will also be present.\\n hiddens(:class:`~torch.Tensor`): Passed in if\\n :paramref:`~pytorch_lightning.trainer.trainer.Trainer.truncated_bptt_steps` > 0.\\n\\n Return:\\n :class:`~pytorch_lightning.core.step_result.TrainResult`\\n\\n .. note:: :class:`~pytorch_lightning.core.step_result.TrainResult` is simply a Dict with convenient\\n functions for logging, distributed sync and error checking.\\n\\n In this step you'd normally do the forward pass and calculate the loss for a batch.\\n You can also do fancier things like multiple forward passes or something model specific.\\n\\n Example::\\n\\n def training_step(self, batch, batch_idx):\\n x, y, z = batch\\n\\n # implement your own\\n out = self(x)\\n loss = self.loss(out, x)\\n\\n # TrainResult auto-detaches the loss after the optimization steps are complete\\n result = pl.TrainResult(minimize=loss)\\n\\n The return object :class:`~pytorch_lightning.core.step_result.TrainResult` controls where to log,\\n when to log (step or epoch) and syncing with multiple GPUs.\\n\\n .. code-block:: python\\n\\n # log to progress bar and logger\\n result.log('train_loss', loss, prog_bar=True, logger=True)\\n\\n # sync metric value across GPUs \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_end\\n def training_end(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. Use :meth:`training_step_end` instead.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_step_end\\n def training_step_end(self, *args, **kwargs):\\n \\\"\\\"\\\"\\n Use this when training with dp or ddp2 because :meth:`training_step`\\n will operate on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n Note:\\n If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [training_step(sub_batch) for sub_batch in sub_batches]\\n training_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in `training_step` for each batch part.\\n\\n Return:\\n :class:`~pytorch_lightning.core.step_result.TrainResult`\\n\\n .. note:: :class:`~pytorch_lightning.core.step_result.TrainResult` is simply a Dict with convenient\\n functions for logging, distributed sync and error checking.\\n\\n When using dp/ddp2 distributed backends, only a portion of the batch is inside the training_step:\\n\\n .. code-block:: python\\n\\n def training_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n\\n # softmax uses only a portion of the batch in the denomintaor\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n return pl.TrainResult(loss)\\n\\n If you wish to do something with all the parts of the batch, then use this method to do it:\\n\\n .. code-block:: python\\n\\n def training_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n result = pl.TrainResult()\\n result.out = o\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.training_epoch_end\\n def training_epoch_end(\\n self, outputs: Union[TrainResult, List[TrainResult]]\\n ):\\n \\\"\\\"\\\"\\n Called at the end of the training epoch with the outputs of all training steps.\\n Use this in case you need to do something with all the outputs for every training_step.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n train_outs = []\\n for train_batch in train_data:\\n out = training_step(train_batch)\\n train_outs.append(out)\\n training_epoch_end(train_outs)\\n\\n Args:\\n outputs: List of outputs you defined in :meth:`training_step`, or if there are\\n multiple dataloaders, a list containing a list of outputs for each dataloader.\\n\\n Return:\\n :class:`~pytorch_lightning.core.step_result.TrainResult`\\n\\n .. note:: :class:`~pytorch_lightning.core.step_result.TrainResult` is simply a Dict with convenient\\n functions for logging, distributed sync and error checking.\\n\\n Note:\\n If this method is not overridden, this won't be called.\\n\\n Example::\\n\\n def training_epoch_end(self, training_step_outputs):\\n # do something with all training_step outputs\\n return result\\n\\n With multiple dataloaders, ``outputs`` will be a list of lists. The outer list contains\\n one entry per dataloader, while the inner list contains the individual outputs of\\n each training step for that dataloader.\\n\\n .. code-block:: python\\n\\n def training_epoch_end(self, outputs):\\n epoch_result = pl.TrainResult()\\n for train_result in outputs:\\n all_losses = train_result.minimize\\n # do something with all losses\\n return results\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step\\n def validation_step(self, *args, **kwargs) -> EvalResult:\\n r\\\"\\\"\\\"\\n Operates on a single batch of data from the validation set.\\n In this step you'd might generate examples or calculate anything of interest like accuracy.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n val_outs = []\\n for val_batch in val_data:\\n out = validation_step(train_batch)\\n val_outs.append(out)\\n validation_epoch_end(val_outs)\\n\\n Args:\\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\\n batch_idx (int): The index of this batch\\n dataloader_idx (int): The index of the dataloader that produced this batch\\n (only if multiple val datasets used)\\n\\n Return:\\n :class:`~pytorch_lightning.core.step_result.TrainResult`\\n\\n .. code-block:: python\\n\\n # pseudocode of order\\n out = validation_step()\\n if defined('validation_step_end'):\\n out = validation_step_end(out)\\n out = validation_epoch_end(out)\\n\\n\\n .. code-block:: python\\n\\n # if you have one val dataloader:\\n def validation_step(self, batch, batch_idx)\\n\\n # if you have multiple val dataloaders:\\n def validation_step(self, batch, batch_idx, dataloader_idx)\\n\\n Examples:\\n .. code-block:: python\\n\\n # CASE 1: A single validation dataset\\n def validation_step(self, batch, batch_idx):\\n x, y = batch\\n\\n # implement your own\\n out = self(x)\\n loss = self.loss(out, y)\\n\\n # log 6 example images\\n # or generated text\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_step_end\\n def validation_step_end(self, *args, **kwargs) -> EvalResult:\\n \\\"\\\"\\\"\\n Use this when validating with dp or ddp2 because :meth:`validation_step`\\n will operate on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n Note:\\n If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code.\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [validation_step(sub_batch) for sub_batch in sub_batches]\\n validation_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in :meth:`validation_step`\\n for each batch part.\\n\\n Return:\\n :class:`~pytorch_lightning.core.step_result.TrainResult`\\n\\n .. code-block:: python\\n\\n # WITHOUT validation_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def validation_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n result = pl.EvalResult()\\n result.log('val_loss', loss)\\n return result\\n\\n # --------------\\n # with validation_step_end to do softmax over the full batch\\n def validation_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n result = pl.EvalResult()\\n result.out = out\\n return result\\n\\n def validation_epoch_end(self, output_results):\\n # this out is now the full size of the batch\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_end\\n def validation_end(self, outputs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. Use :meth:`validation_epoch_end` instead.\\n Will be removed in 1.0.0.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.validation_epoch_end\\n def validation_epoch_end(\\n self, outputs: Union[EvalResult, List[EvalResult]]\\n ) -> EvalResult:\\n \\\"\\\"\\\"\\n Called at the end of the validation epoch with the outputs of all validation steps.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n val_outs = []\\n for val_batch in val_data:\\n out = validation_step(val_batch)\\n val_outs.append(out)\\n validation_epoch_end(val_outs)\\n\\n Args:\\n outputs: List of outputs you defined in :meth:`validation_step`, or if there\\n are multiple dataloaders, a list containing a list of outputs for each dataloader.\\n\\n Return:\\n :class:`~pytorch_lightning.core.step_result.TrainResult`\\n\\n Note:\\n If you didn't define a :meth:`validation_step`, this won't be called.\\n\\n - The outputs here are strictly for logging or progress bar.\\n - If you don't need to display anything, don't return anything.\\n\\n Examples:\\n With a single dataloader:\\n\\n .. code-block:: python\\n\\n def validation_epoch_end(self, val_step_outputs):\\n # do something with the outputs of all val batches\\n all_val_preds = val_step_outputs.predictions\\n\\n val_step_outputs.some_result = calc_all_results(all_val_preds)\\n return val_step_outputs\\n\\n With multiple dataloaders, `outputs` will be a list of lists. The outer list contains\\n one entry per dataloader, while the inner list contains the individual outputs of\\n each validation step for that dataloader.\\n\\n .. code-block:: python\\n\\n def validation_epoch_end(self, outputs):\\n for dataloader_output_result in outputs:\\n dataloader_outs = dataloader_output_result.dataloader_i_ou\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step\\n def test_step(self, *args, **kwargs) -> EvalResult:\\n r\\\"\\\"\\\"\\n Operates on a single batch of data from the test set.\\n In this step you'd normally generate examples or calculate anything of interest\\n such as accuracy.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n test_outs = []\\n for test_batch in test_data:\\n out = test_step(test_batch)\\n test_outs.append(out)\\n test_epoch_end(test_outs)\\n\\n Args:\\n batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):\\n The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.\\n batch_idx (int): The index of this batch.\\n dataloader_idx (int): The index of the dataloader that produced this batch\\n (only if multiple test datasets used).\\n\\n Return:\\n :class:`~pytorch_lightning.core.step_result.TrainResult`\\n\\n .. code-block:: python\\n\\n # if you have one test dataloader:\\n def test_step(self, batch, batch_idx)\\n\\n # if you have multiple test dataloaders:\\n def test_step(self, batch, batch_idx, dataloader_idx)\\n\\n Examples:\\n .. code-block:: python\\n\\n # CASE 1: A single test dataset\\n def test_step(self, batch, batch_idx):\\n x, y = batch\\n\\n # implement your own\\n out = self(x)\\n loss = self.loss(out, y)\\n\\n # log 6 example images\\n # or generated text... or whatever\\n sample_imgs = x[:6]\\n grid = torchvision.utils.make_grid(sample_imgs)\\n self.logger.experiment.add_image('example_images', grid, 0)\\n\\n # calculate acc\\n labels_hat = tor\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_step_end\\n def test_step_end(self, *args, **kwargs) -> EvalResult:\\n \\\"\\\"\\\"\\n Use this when testing with dp or ddp2 because :meth:`test_step` will operate\\n on only part of the batch. However, this is still optional\\n and only needed for things like softmax or NCE loss.\\n\\n Note:\\n If you later switch to ddp or some other mode, this will still be called\\n so that you don't have to change your code.\\n\\n .. code-block:: python\\n\\n # pseudocode\\n sub_batches = split_batches_for_dp(batch)\\n batch_parts_outputs = [test_step(sub_batch) for sub_batch in sub_batches]\\n test_step_end(batch_parts_outputs)\\n\\n Args:\\n batch_parts_outputs: What you return in :meth:`test_step` for each batch part.\\n\\n Return:\\n :class:`~pytorch_lightning.core.step_result.TrainResult`\\n\\n .. code-block:: python\\n\\n # WITHOUT test_step_end\\n # if used in DP or DDP2, this batch is 1/num_gpus large\\n def test_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n loss = self.softmax(out)\\n loss = nce_loss(loss)\\n result = pl.EvalResult()\\n result.log('test_loss', loss)\\n return result\\n\\n # --------------\\n # with test_step_end to do softmax over the full batch\\n def test_step(self, batch, batch_idx):\\n # batch is 1/num_gpus big\\n x, y = batch\\n\\n out = self(x)\\n result = pl.EvalResult()\\n result.out = out\\n return result\\n\\n def test_epoch_end(self, output_results):\\n # this out is now the full size of the batch\\n all_test_step_outs = output_results.out\\n loss = nce_loss(all_test_st\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_end\\n def test_end(self, outputs):\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.7.0. Use :meth:`test_epoch_end` instead.\\n Will be removed in 1.0.0.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_epoch_end\\n def test_epoch_end(\\n self, outputs: Union[EvalResult, List[EvalResult]]\\n ) -> EvalResult:\\n\\n \\\"\\\"\\\"\\n Called at the end of a test epoch with the output of all test steps.\\n\\n .. code-block:: python\\n\\n # the pseudocode for these calls\\n test_outs = []\\n for test_batch in test_data:\\n out = test_step(test_batch)\\n test_outs.append(out)\\n test_epoch_end(test_outs)\\n\\n Args:\\n outputs: List of outputs you defined in :meth:`test_step_end`, or if there\\n are multiple dataloaders, a list containing a list of outputs for each dataloader\\n\\n Return:\\n :class:`~pytorch_lightning.core.step_result.TrainResult`\\n\\n Note:\\n If you didn't define a :meth:`test_step`, this won't be called.\\n\\n - The outputs here are strictly for logging or progress bar.\\n - If you don't need to display anything, don't return anything.\\n\\n Examples:\\n With a single dataloader:\\n\\n .. code-block:: python\\n\\n def test_epoch_end(self, outputs):\\n # do something with the outputs of all test batches\\n all_test_preds = test_step_outputs.predictions\\n\\n test_step_outputs.some_result = calc_all_results(all_test_preds)\\n return test_step_outputs\\n\\n With multiple dataloaders, `outputs` will be a list of lists. The outer list contains\\n one entry per dataloader, while the inner list contains the individual outputs of\\n each test step for that dataloader.\\n\\n .. code-block:: python\\n\\n def test_epoch_end(self, outputs):\\n for dataloader_output_result in outputs:\\n dataloader_outs = dataloader_output_result.dataloader_i_outputs\\n\\n result = pl.EvalResult()\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_ddp\\n def configure_ddp(self, model: 'LightningModule', device_ids: List[int]) -> DistributedDataParallel:\\n r\\\"\\\"\\\"\\n Override to init DDP in your own way or with your own wrapper.\\n The only requirements are that:\\n\\n 1. On a validation batch, the call goes to ``model.validation_step``.\\n 2. On a training batch, the call goes to ``model.training_step``.\\n 3. On a testing batch, the call goes to ``model.test_step``.\\n\\n Args:\\n model: the :class:`LightningModule` currently being optimized.\\n device_ids: the list of GPU ids.\\n\\n Return:\\n DDP wrapped model\\n\\n Examples:\\n .. code-block:: python\\n\\n # default implementation used in Trainer\\n def configure_ddp(self, model, device_ids):\\n # Lightning DDP simply routes to test_step, val_step, etc...\\n model = LightningDistributedDataParallel(\\n model,\\n device_ids=device_ids,\\n find_unused_parameters=True\\n )\\n return model\\n\\n \\\"\\\"\\\"\\n model = LightningDistributedDataParallel(model, device_ids=device_ids, find_unused_parameters=True)\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule._init_slurm_connection\\n def _init_slurm_connection(self) -> None:\\n \\\"\\\"\\\"\\\"\\\"\\\"\\n \\\"\\\"\\\"\\n Sets up environment variables necessary for pytorch distributed communications\\n based on slurm environment.\\n \\\"\\\"\\\"\\n # use slurm job id for the port number\\n # guarantees unique ports across jobs from same grid search\\n try:\\n # use the last 4 numbers in the job id as the id\\n default_port = os.environ['SLURM_JOB_ID']\\n default_port = default_port[-4:]\\n\\n # all ports should be in the 10k+ range\\n default_port = int(default_port) + 15000\\n\\n except Exception:\\n default_port = 12910\\n\\n # if user gave a port number, use that one instead\\n try:\\n default_port = os.environ['MASTER_PORT']\\n except Exception:\\n os.environ['MASTER_PORT'] = str(default_port)\\n\\n # figure out the root node addr\\n try:\\n root_node = os.environ['SLURM_NODELIST'].split(' ')[0]\\n except Exception:\\n root_node = '127.0.0.1'\\n\\n root_node = self.trainer.resolve_root_node_address(root_node)\\n os.environ['MASTER_ADDR'] = root_node\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.init_ddp_connection\\n def init_ddp_connection(self, global_rank: int, world_size: int, is_slurm_managing_tasks: bool = True) -> None:\\n \\\"\\\"\\\"\\n Override to define your custom way of setting up a distributed environment.\\n\\n Lightning's implementation uses env:// init by default and sets the first node as root\\n for SLURM managed cluster.\\n\\n Args:\\n global_rank: The global process idx.\\n world_size: Number of GPUs being use across all nodes. (num_nodes * num_gpus).\\n is_slurm_managing_tasks: is cluster managed by SLURM.\\n\\n \\\"\\\"\\\"\\n if is_slurm_managing_tasks:\\n self._init_slurm_connection()\\n\\n if 'MASTER_ADDR' not in os.environ:\\n rank_zero_warn(\\\"MASTER_ADDR environment variable is not defined. Set as localhost\\\")\\n os.environ['MASTER_ADDR'] = '127.0.0.1'\\n log.debug(f\\\"MASTER_ADDR: {os.environ['MASTER_ADDR']}\\\")\\n\\n if 'MASTER_PORT' not in os.environ:\\n rank_zero_warn(\\\"MASTER_PORT environment variable is not defined. Set as 12910\\\")\\n os.environ['MASTER_PORT'] = '12910'\\n log.debug(f\\\"MASTER_PORT: {os.environ['MASTER_PORT']}\\\")\\n\\n if 'WORLD_SIZE' in os.environ and int(os.environ['WORLD_SIZE']) != world_size:\\n rank_zero_warn(\\n f\\\"WORLD_SIZE environment variable ({os.environ['WORLD_SIZE']}) \\\"\\n f\\\"is not equal to the computed world size ({world_size}). Ignored.\\\"\\n )\\n\\n torch_backend = \\\"nccl\\\" if self.trainer.on_gpu else \\\"gloo\\\"\\n log.info(f\\\"initializing ddp: GLOBAL_RANK: {global_rank}, MEMBER: {global_rank+1}/{world_size}\\\")\\n torch_distrib.init_process_group(torch_backend, rank=global_rank, world_size=world_size)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_sync_batchnorm\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_sync_batchnorm\\n def configure_sync_batchnorm(self, model: 'LightningModule') -> 'LightningModule':\\n \\\"\\\"\\\"\\n Add global batchnorm for a model spread across multiple GPUs and nodes.\\n\\n Override to synchronize batchnorm between specific process groups instead\\n of the whole world or use a different sync_bn like `apex`'s version.\\n\\n Args:\\n model: pointer to current :class:`LightningModule`.\\n\\n Return:\\n LightningModule with batchnorm layers synchronized between process groups\\n \\\"\\\"\\\"\\n model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model, process_group=None)\\n\\n return model\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_apex\\n def configure_apex(\\n self, amp: object, model: 'LightningModule', optimizers: List[Optimizer], amp_level: str\\n ) -> Tuple['LightningModule', List[Optimizer]]:\\n r\\\"\\\"\\\"\\n Override to init AMP your own way.\\n Must return a model and list of optimizers.\\n\\n Args:\\n amp: pointer to amp library object.\\n model: pointer to current :class:`LightningModule`.\\n optimizers: list of optimizers passed in :meth:`configure_optimizers`.\\n amp_level: AMP mode chosen ('O1', 'O2', etc...)\\n\\n Return:\\n Apex wrapped model and optimizers\\n\\n Examples:\\n .. code-block:: python\\n\\n # Default implementation used by Trainer.\\n def configure_apex(self, amp, model, optimizers, amp_level):\\n model, optimizers = amp.initialize(\\n model, optimizers, opt_level=amp_level,\\n )\\n\\n return model, optimizers\\n \\\"\\\"\\\"\\n model, optimizers = amp.initialize(model, optimizers, opt_level=amp_level)\\n\\n return model, optimizers\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.configure_optimizers\\n def configure_optimizers(\\n self,\\n ) -> Optional[Union[Optimizer, Sequence[Optimizer], Dict, Sequence[Dict], Tuple[List, List]]]:\\n r\\\"\\\"\\\"\\n Choose what optimizers and learning-rate schedulers to use in your optimization.\\n Normally you'd need one. But in the case of GANs or similar you might have multiple.\\n\\n Return:\\n Any of these 6 options.\\n\\n - Single optimizer.\\n - List or Tuple - List of optimizers.\\n - Two lists - The first list has multiple optimizers, the second a list of LR schedulers (or lr_dict).\\n - Dictionary, with an 'optimizer' key, and (optionally) a 'lr_scheduler' key which value is a single LR scheduler or lr_dict.\\n - Tuple of dictionaries as described, with an optional 'frequency' key.\\n - None - Fit will run without any optimizer.\\n\\n Note:\\n The 'frequency' value is an int corresponding to the number of sequential batches\\n optimized with the specific optimizer. It should be given to none or to all of the optimizers.\\n There is a difference between passing multiple optimizers in a list,\\n and passing multiple optimizers in dictionaries with a frequency of 1:\\n In the former case, all optimizers will operate on the given batch in each optimization step.\\n In the latter, only one optimizer will operate on the given batch at every step.\\n\\n The lr_dict is a dictionary which contains scheduler and its associated configuration.\\n It has five keys. The default configuration is shown below.\\n\\n .. code-block:: python\\n\\n {\\n 'scheduler': lr_scheduler, # The LR schduler\\n 'interval': 'epoch', # The unit of the scheduler's step size\\n 'frequency': 1, # The frequency of the scheduler\\n 'reduce_on_platea\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_step\\n def optimizer_step(\\n self,\\n epoch: int,\\n batch_idx: int,\\n optimizer: Optimizer,\\n optimizer_idx: int,\\n second_order_closure: Optional[Callable] = None,\\n on_tpu: bool = False,\\n using_native_amp: bool = False,\\n using_lbfgs: bool = False,\\n ) -> None:\\n r\\\"\\\"\\\"\\n Override this method to adjust the default way the\\n :class:`~pytorch_lightning.trainer.trainer.Trainer` calls each optimizer.\\n By default, Lightning calls ``step()`` and ``zero_grad()`` as shown in the example\\n once per optimizer.\\n\\n Args:\\n epoch: Current epoch\\n batch_idx: Index of current batch\\n optimizer: A PyTorch optimizer\\n optimizer_idx: If you used multiple optimizers this indexes into that list.\\n second_order_closure: closure for second order methods\\n on_tpu: true if TPU backward is required\\n using_native_amp: True if using native amp\\n using_lbfgs: True if the matching optimizer is lbfgs\\n\\n Examples:\\n .. code-block:: python\\n\\n # DEFAULT\\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\\n second_order_closure, on_tpu, using_native_amp, using_lbfgs):\\n optimizer.step()\\n\\n # Alternating schedule for optimizer steps (i.e.: GANs)\\n def optimizer_step(self, current_epoch, batch_idx, optimizer, optimizer_idx,\\n second_order_closure, on_tpu, using_native_amp, using_lbfgs):\\n # update generator opt every 2 steps\\n if optimizer_idx == 0:\\n if batch_idx % 2 == 0 :\\n optimizer.step()\\n optimizer.zero_grad()\\n\\n # update discriminator opt every 4 steps\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_zero_grad\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.optimizer_zero_grad\\n def optimizer_zero_grad(self, epoch: int, batch_idx: int, optimizer: Optimizer, optimizer_idx: int):\\n optimizer.zero_grad()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.tbptt_split_batch\\n def tbptt_split_batch(self, batch: Tensor, split_size: int) -> list:\\n r\\\"\\\"\\\"\\n When using truncated backpropagation through time, each batch must be split along the\\n time dimension. Lightning handles this by default, but for custom behavior override\\n this function.\\n\\n Args:\\n batch: Current batch\\n split_size: The size of the split\\n\\n Return:\\n List of batch splits. Each split will be passed to :meth:`training_step` to enable truncated\\n back propagation through time. The default implementation splits root level Tensors and\\n Sequences at dim=1 (i.e. time dim). It assumes that each time dim is the same length.\\n\\n Examples:\\n .. code-block:: python\\n\\n def tbptt_split_batch(self, batch, split_size):\\n splits = []\\n for t in range(0, time_dims[0], split_size):\\n batch_split = []\\n for i, x in enumerate(batch):\\n if isinstance(x, torch.Tensor):\\n split_x = x[:, t:t + split_size]\\n elif isinstance(x, collections.Sequence):\\n split_x = [None] * len(x)\\n for batch_idx in range(len(x)):\\n split_x[batch_idx] = x[batch_idx][t:t + split_size]\\n\\n batch_split.append(split_x)\\n\\n splits.append(batch_split)\\n\\n return splits\\n\\n Note:\\n Called in the training loop after\\n :meth:`~pytorch_lightning.callbacks.base.Callback.on_batch_start`\\n if :paramref:`~pytorch_lightning.trainer.Trainer.truncated_bptt_steps` > 0.\\n Each returned batch split is passed separately to :meth:`training_step`.\\n\\n \\\"\\\"\\\"\\n time_dims = [len(x[0]) for x in batch if isinstance(x\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.prepare_data\\n def prepare_data(self) -> None:\\n \\\"\\\"\\\"\\n Use this to download and prepare data.\\n\\n .. warning:: DO NOT set state to the model (use `setup` instead)\\n since this is NOT called on every GPU in DDP/TPU\\n\\n Example::\\n\\n def prepare_data(self):\\n # good\\n download_data()\\n tokenize()\\n etc()\\n\\n # bad\\n self.split = data_split\\n self.some_state = some_other_state()\\n\\n In DDP prepare_data can be called in two ways (using Trainer(prepare_data_per_node)):\\n\\n 1. Once per node. This is the default and is only called on LOCAL_RANK=0.\\n 2. Once in total. Only called on GLOBAL_RANK=0.\\n\\n Example::\\n\\n # DEFAULT\\n # called once per node on LOCAL_RANK=0 of that node\\n Trainer(prepare_data_per_node=True)\\n\\n # call on GLOBAL_RANK=0 (great for shared file systems)\\n Trainer(prepare_data_per_node=False)\\n\\n This is called before requesting the dataloaders:\\n\\n .. code-block:: python\\n\\n model.prepare_data()\\n if ddp/tpu: init()\\n model.setup(stage)\\n model.train_dataloader()\\n model.val_dataloader()\\n model.test_dataloader()\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.train_dataloader\\n def train_dataloader(self) -> DataLoader:\\n \\\"\\\"\\\"\\n Implement a PyTorch DataLoader for training.\\n\\n Return:\\n Single PyTorch :class:`~torch.utils.data.DataLoader`.\\n\\n The dataloader you return will not be called every epoch unless you set\\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\\n\\n For data processing use the following pattern:\\n\\n - download in :meth:`prepare_data`\\n - process and split in :meth:`setup`\\n\\n However, the above are only necessary for distributed processing.\\n\\n .. warning:: do not assign state in prepare_data\\n\\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\\n - ...\\n - :meth:`prepare_data`\\n - :meth:`setup`\\n - :meth:`train_dataloader`\\n\\n Note:\\n Lightning adds the correct sampler for distributed and arbitrary hardware.\\n There is no need to set it yourself.\\n\\n Example:\\n .. code-block:: python\\n\\n def train_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform,\\n download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.batch_size,\\n shuffle=True\\n )\\n return loader\\n\\n \\\"\\\"\\\"\\n rank_zero_warn('`train_dataloader` must be implemented to be used with the Lightning Trainer')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.tng_dataloader\\n def tng_dataloader(self): # todo: remove in v1.0.0\\n \\\"\\\"\\\"\\n Warnings:\\n Deprecated in v0.5.0. Use :meth:`train_dataloader` instead. Will be removed in 1.0.0.\\n \\\"\\\"\\\"\\n output = self.train_dataloader()\\n rank_zero_warn(\\n \\\"`tng_dataloader` has been renamed to `train_dataloader` since v0.5.0.\\\"\\n \\\" and this method will be removed in v1.0.0\\\",\\n DeprecationWarning,\\n )\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.test_dataloader\\n def test_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\\n r\\\"\\\"\\\"\\n Implement one or multiple PyTorch DataLoaders for testing.\\n\\n The dataloader you return will not be called every epoch unless you set\\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\\n\\n For data processing use the following pattern:\\n\\n - download in :meth:`prepare_data`\\n - process and split in :meth:`setup`\\n\\n However, the above are only necessary for distributed processing.\\n\\n .. warning:: do not assign state in prepare_data\\n\\n\\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\\n - ...\\n - :meth:`prepare_data`\\n - :meth:`setup`\\n - :meth:`train_dataloader`\\n - :meth:`val_dataloader`\\n - :meth:`test_dataloader`\\n\\n Note:\\n Lightning adds the correct sampler for distributed and arbitrary hardware.\\n There is no need to set it yourself.\\n\\n Return:\\n Single or multiple PyTorch DataLoaders.\\n\\n Example:\\n .. code-block:: python\\n\\n def test_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform,\\n download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.batch_size,\\n shuffle=False\\n )\\n\\n return loader\\n\\n # can also return multiple dataloaders\\n def test_dataloader(self):\\n return [loader_a, loader_b, ..., loader_n]\\n\\n Note:\\n If you don't need a te\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.val_dataloader\\n def val_dataloader(self) -> Union[DataLoader, List[DataLoader]]:\\n r\\\"\\\"\\\"\\n Implement one or multiple PyTorch DataLoaders for validation.\\n\\n The dataloader you return will not be called every epoch unless you set\\n :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_epoch` to ``True``.\\n\\n It's recommended that all data downloads and preparation happen in :meth:`prepare_data`.\\n\\n - :meth:`~pytorch_lightning.trainer.Trainer.fit`\\n - ...\\n - :meth:`prepare_data`\\n - :meth:`train_dataloader`\\n - :meth:`val_dataloader`\\n - :meth:`test_dataloader`\\n\\n Note:\\n Lightning adds the correct sampler for distributed and arbitrary hardware\\n There is no need to set it yourself.\\n\\n Return:\\n Single or multiple PyTorch DataLoaders.\\n\\n Examples:\\n .. code-block:: python\\n\\n def val_dataloader(self):\\n transform = transforms.Compose([transforms.ToTensor(),\\n transforms.Normalize((0.5,), (1.0,))])\\n dataset = MNIST(root='/path/to/mnist/', train=False,\\n transform=transform, download=True)\\n loader = torch.utils.data.DataLoader(\\n dataset=dataset,\\n batch_size=self.batch_size,\\n shuffle=False\\n )\\n\\n return loader\\n\\n # can also return multiple dataloaders\\n def val_dataloader(self):\\n return [loader_a, loader_b, ..., loader_n]\\n\\n Note:\\n If you don't need a validation dataset and a :meth:`validation_step`, you don't need to\\n implement this method.\\n\\n Note:\\n In the case where you return multiple validation dataloaders, the :meth:`validation_st\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.summarize\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.summarize\\n def summarize(self, mode: str = ModelSummary.MODE_DEFAULT) -> ModelSummary:\\n model_summary = ModelSummary(self, mode=mode)\\n log.info('\\\\n' + str(model_summary))\\n return model_summary\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.freeze\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.freeze\\n def freeze(self) -> None:\\n r\\\"\\\"\\\"\\n Freeze all params for inference.\\n\\n Example:\\n .. code-block:: python\\n\\n model = MyLightningModule(...)\\n model.freeze()\\n\\n \\\"\\\"\\\"\\n for param in self.parameters():\\n param.requires_grad = False\\n\\n self.eval()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.unfreeze\\n def unfreeze(self) -> None:\\n \\\"\\\"\\\"\\n Unfreeze all parameters for training.\\n\\n .. code-block:: python\\n\\n model = MyLightningModule(...)\\n model.unfreeze()\\n\\n \\\"\\\"\\\"\\n for param in self.parameters():\\n param.requires_grad = True\\n\\n self.train()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.on_load_checkpoint\\n def on_load_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\\n r\\\"\\\"\\\"\\n Called by Lightning to restore your model.\\n If you saved something with :meth:`on_save_checkpoint` this is your chance to restore this.\\n\\n Args:\\n checkpoint: Loaded checkpoint\\n\\n\\n Example:\\n .. code-block:: python\\n\\n def on_load_checkpoint(self, checkpoint):\\n # 99% of the time you don't need to implement this method\\n self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']\\n\\n Note:\\n Lightning auto-restores global step, epoch, and train state including amp scaling.\\n There is no need for you to restore anything regarding training.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.on_save_checkpoint\\n def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:\\n r\\\"\\\"\\\"\\n Called by Lightning when saving a checkpoint to give you a chance to store anything\\n else you might want to save.\\n\\n Args:\\n checkpoint: Checkpoint to be saved\\n\\n Example:\\n .. code-block:: python\\n\\n def on_save_checkpoint(self, checkpoint):\\n # 99% of use cases you don't need to implement this method\\n checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object\\n\\n Note:\\n Lightning saves all aspects of training (epoch, global step, etc...)\\n including amp scaling.\\n There is no need for you to store anything about training.\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict\",\n \"text\": \"pytorch_lightning/core/lightning.py::LightningModule.get_progress_bar_dict\\n def get_progress_bar_dict(self) -> Dict[str, Union[int, str]]:\\n r\\\"\\\"\\\"\\n Implement this to override the default items displayed in the progress bar.\\n By default it includes the average loss value, split index of BPTT (if used)\\n and the version of the experiment when using a logger.\\n\\n .. code-block::\\n\\n Epoch 1: 4%|▎ | 40/1095 [00:03<01:37, 10.84it/s, loss=4.501, v_num=10]\\n\\n Here is an example how to override the defaults:\\n\\n .. code-block:: python\\n\\n def get_progress_bar_dict(self):\\n # don't show the version number\\n items = super().get_progress_bar_dict()\\n items.pop(\\\"v_num\\\", None)\\n return items\\n\\n Return:\\n Dictionary with the items to be displayed in the progress bar.\\n \\\"\\\"\\\"\\n # call .item() only once but store elements without graphs\\n running_train_loss = self.trainer.running_loss.mean()\\n avg_training_loss = running_train_loss.cpu().item() if running_train_loss is not None else float('NaN')\\n tqdm_dict = {'loss': '{:.3f}'.format(avg_training_loss)}\\n\\n if self.trainer.truncated_bptt_steps is not None:\\n tqdm_dict['split_idx'] = self.trainer.split_idx\\n\\n if self.trainer.logger is not None and self.trainer.logger.version is not None:\\n version = self.trainer.logger.version\\n # show last 4 places of long version strings\\n version = version[-4:] if isinstance(version, str) else version\\n tqdm_dict['v_num'] = version\\n\\n return tqdm_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.copy_trainer_model_properties\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.copy_trainer_model_properties\\n @abstractmethod\\n def copy_trainer_model_properties(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.get_model\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.get_model\\n @abstractmethod\\n def get_model(self) -> LightningModule:\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.is_overridden\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.is_overridden\\n @abstractmethod\\n def is_overridden(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_tpu\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_tpu\\n @abstractmethod\\n def transfer_batch_to_tpu(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_gpu\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.transfer_batch_to_gpu\\n @abstractmethod\\n def transfer_batch_to_gpu(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.add_progress_bar_metrics\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.add_progress_bar_metrics\\n @abstractmethod\\n def add_progress_bar_metrics(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.log_metrics\\n @abstractmethod\\n def log_metrics(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_test_dataloader\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_test_dataloader\\n @abstractmethod\\n def reset_test_dataloader(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_val_dataloader\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.reset_val_dataloader\\n @abstractmethod\\n def reset_val_dataloader(self, *args):\\n \\\"\\\"\\\"Warning: this is just empty shell for code implemented in other class.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_start\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_start\\n def __call_eval_loop_hook_start(self, test_mode):\\n \\\"\\\"\\\"on_validation/test_epoch_start\\\"\\\"\\\"\\n self.__call_eval_loop_hook_evt(test_mode, 'start')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_end\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_end\\n def __call_eval_loop_hook_end(self, test_mode):\\n \\\"\\\"\\\"on_validation/test_epoch_end\\\"\\\"\\\"\\n self.__call_eval_loop_hook_evt(test_mode, 'end')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_evt\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__call_eval_loop_hook_evt\\n def __call_eval_loop_hook_evt(self, test_mode, epoch_event):\\n model = self.get_model()\\n\\n # on_[train/validation]_epoch_start hook\\n hook_root_name = 'test' if test_mode else 'validation'\\n hook_name = f'on_{hook_root_name}_epoch_{epoch_event}'\\n with self.profiler.profile(hook_name):\\n # call hook\\n getattr(self, hook_name)()\\n\\n # model hooks\\n if self.is_function_implemented(hook_name):\\n getattr(model, hook_name)()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin._evaluate\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin._evaluate\\n def _evaluate(\\n self,\\n model: LightningModule,\\n dataloaders: List[DataLoader],\\n max_batches: Union[int, List[int]],\\n test_mode: bool = False\\n ):\\n \\\"\\\"\\\"Run evaluation code.\\n\\n Args:\\n model: The model to evaluate.\\n dataloaders: A list of PyTorch dataloaders.\\n max_batches: An integer or list of integers with length of the number of dataloaders. Each\\n entry is the number of batches to process in the corresponding dataloader.\\n test_mode:\\n \\\"\\\"\\\"\\n # enable eval mode\\n model.zero_grad()\\n model.eval()\\n\\n # copy properties for forward overrides\\n self.copy_trainer_model_properties(model)\\n\\n # disable gradients to save memory\\n torch.set_grad_enabled(False)\\n\\n # bookkeeping\\n outputs = []\\n predictions = PredictionCollection(self.global_rank, self.world_size)\\n\\n # convert max_batches to list\\n if isinstance(max_batches, int):\\n max_batches = [max_batches] * len(dataloaders)\\n\\n # --------------------------\\n # ON_EVAL_EPOCH_START hook\\n # --------------------------\\n self.__call_eval_loop_hook_start(test_mode)\\n\\n # run validation\\n for dataloader_idx, dataloader in enumerate(dataloaders):\\n dl_outputs = []\\n\\n # on TPU we have to wrap it under the ParallelLoader\\n if self.use_tpu:\\n device = xm.xla_device(self.tpu_id)\\n dataloader = xla_pl.ParallelLoader(dataloader, [device])\\n dataloader = dataloader.per_device_loader(device)\\n\\n # each dataloader has a max num batches\\n dl_max_batches = max_batches[dataloader_idx]\\n\\n for batch_idx, batch in enumerate(dataloader):\\n if batch is None:\\n continue\\n\\n # stop short when runn\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__update_callback_metrics\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__update_callback_metrics\\n def __update_callback_metrics(self, eval_results, using_eval_result):\\n if using_eval_result:\\n if isinstance(eval_results, list):\\n for eval_result in eval_results:\\n self.callback_metrics = eval_result.callback_metrics\\n else:\\n self.callback_metrics = eval_results.callback_metrics\\n else:\\n if isinstance(eval_results, list):\\n for eval_result in eval_results:\\n # with a scalar return, auto set it to \\\"val_loss\\\" for callbacks\\n if isinstance(eval_result, torch.Tensor):\\n flat = {'val_loss': eval_result}\\n else:\\n flat = flatten_dict(eval_result)\\n self.callback_metrics.update(flat)\\n else:\\n # with a scalar return, auto set it to \\\"val_loss\\\" for callbacks\\n if isinstance(eval_results, torch.Tensor):\\n flat = {'val_loss': eval_results}\\n else:\\n flat = flatten_dict(eval_results)\\n self.callback_metrics.update(flat)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__run_eval_epoch_end\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__run_eval_epoch_end\\n def __run_eval_epoch_end(self, test_mode, outputs, dataloaders, using_eval_result):\\n model = self.get_model()\\n\\n # with a single dataloader don't pass an array\\n eval_results = outputs\\n if len(dataloaders) == 1:\\n eval_results = outputs[0]\\n\\n user_reduced = False\\n\\n if test_mode:\\n if self.is_overridden('test_end', model=model):\\n # TODO: remove in v1.0.0\\n if using_eval_result:\\n eval_results = self.__gather_epoch_end_eval_results(outputs)\\n\\n eval_results = model.test_end(eval_results)\\n user_reduced = True\\n rank_zero_warn('Method `test_end` was deprecated in v0.7 and will be removed in v1.0.'\\n ' Use `test_epoch_end` instead.', DeprecationWarning)\\n\\n elif self.is_overridden('test_epoch_end', model=model):\\n if using_eval_result:\\n eval_results = self.__gather_epoch_end_eval_results(outputs)\\n\\n eval_results = model.test_epoch_end(eval_results)\\n user_reduced = True\\n\\n else:\\n if self.is_overridden('validation_end', model=model):\\n # TODO: remove in v1.0.0\\n if using_eval_result:\\n eval_results = self.__gather_epoch_end_eval_results(outputs)\\n\\n eval_results = model.validation_end(eval_results)\\n user_reduced = True\\n rank_zero_warn('Method `validation_end` was deprecated in v0.7 and will be removed in v1.0.'\\n ' Use `validation_epoch_end` instead.', DeprecationWarning)\\n\\n elif self.is_overridden('validation_epoch_end', model=model):\\n if using_eval_result:\\n eval_results = self.__gather_epoch_end_eval_results(outputs)\\n\\n eval_results = model.v\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__gather_epoch_end_eval_results\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__gather_epoch_end_eval_results\\n def __gather_epoch_end_eval_results(self, outputs):\\n eval_results = []\\n for epoch_output in outputs:\\n result = epoch_output[0].__class__.gather(epoch_output)\\n if 'checkpoint_on' in result:\\n result.checkpoint_on = result.checkpoint_on.mean()\\n if 'early_stop_on' in result:\\n result.early_stop_on = result.early_stop_on.mean()\\n\\n eval_results.append(result)\\n\\n # with 1 dataloader don't pass in a list\\n if len(eval_results) == 1:\\n eval_results = eval_results[0]\\n return eval_results\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__eval_add_step_metrics\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__eval_add_step_metrics\\n def __eval_add_step_metrics(self, output):\\n # track step level metrics\\n if isinstance(output, EvalResult) and not self.running_sanity_check:\\n step_log_metrics = output.batch_log_metrics\\n step_pbar_metrics = output.batch_pbar_metrics\\n\\n if len(step_log_metrics) > 0:\\n self.log_metrics(step_log_metrics, {})\\n\\n if len(step_pbar_metrics) > 0:\\n self.add_progress_bar_metrics(step_pbar_metrics)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__auto_reduce_result_objs\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__auto_reduce_result_objs\\n def __auto_reduce_result_objs(self, outputs):\\n # outputs has a list of results per dataloader\\n eval_results = []\\n for dl_output in outputs:\\n result = dl_output[0]\\n result = result.__class__.reduce_on_epoch_end(dl_output)\\n if 'checkpoint_on' in result:\\n result.checkpoint_on = result.checkpoint_on.mean()\\n if 'early_stop_on' in result:\\n result.early_stop_on = result.early_stop_on.mean()\\n eval_results.append(result)\\n\\n return eval_results\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.run_evaluation\\n def run_evaluation(self, test_mode: bool = False):\\n # hook\\n model = self.get_model()\\n model.on_pre_performance_check()\\n\\n # select dataloaders\\n if test_mode:\\n self.reset_test_dataloader(model)\\n\\n dataloaders = self.test_dataloaders\\n max_batches = self.num_test_batches\\n else:\\n # val\\n if self.val_dataloaders is None:\\n self.reset_val_dataloader(model)\\n\\n dataloaders = self.val_dataloaders\\n max_batches = self.num_val_batches\\n\\n if dataloaders is None:\\n return [], []\\n\\n # Validation/Test begin callbacks\\n if test_mode:\\n self.on_test_start()\\n else:\\n self.on_validation_start()\\n\\n # enable disabling validation step with limit_val_batches = 0\\n should_skip = sum(max_batches) == 0\\n if should_skip:\\n return [], []\\n\\n # run evaluation (val_step + val_step_end + val_epoch_end)\\n eval_results = self._evaluate(self.model, dataloaders, max_batches, test_mode)\\n\\n # log the final eval loop metrics\\n eval_loop_results = self.__log_evaluation_epoch_metrics(eval_results, test_mode)\\n\\n # hook\\n model.on_post_performance_check()\\n\\n # eventual dataset reloading\\n if test_mode:\\n if self.reload_dataloaders_every_epoch:\\n self.reset_test_dataloader(model)\\n else:\\n # val\\n if self.reload_dataloaders_every_epoch:\\n self.reset_val_dataloader(model)\\n\\n # Validation/Test end callbacks\\n if test_mode:\\n self.on_test_end()\\n else:\\n self.on_validation_end()\\n\\n return eval_loop_results, eval_results\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__log_evaluation_epoch_metrics\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.__log_evaluation_epoch_metrics\\n def __log_evaluation_epoch_metrics(self, eval_results, test_mode):\\n eval_loop_results = []\\n if eval_results is not None and len(eval_results) > 0:\\n\\n # in eval, the user may return something at every validation step without final reduction\\n if not isinstance(eval_results, list):\\n eval_results = [eval_results]\\n\\n for result_idx, result in enumerate(eval_results):\\n if isinstance(result, EvalResult):\\n prog_bar_metrics = result.epoch_pbar_metrics\\n log_metrics = result.epoch_log_metrics\\n callback_metrics = result.callback_metrics\\n else:\\n _, prog_bar_metrics, log_metrics, callback_metrics, _ = self.process_output(result)\\n\\n # eval loop returns all metrics\\n dataloader_result_metrics = {**prog_bar_metrics, **log_metrics, **callback_metrics}\\n\\n # add metrics to prog bar\\n self.add_progress_bar_metrics(prog_bar_metrics)\\n\\n # log metrics\\n self.log_metrics(log_metrics, {})\\n\\n # track metrics for callbacks\\n self.callback_metrics.update(callback_metrics)\\n\\n if len(dataloader_result_metrics) > 0:\\n eval_loop_results.append(dataloader_result_metrics)\\n\\n # log results of test\\n if test_mode and self.is_global_zero and self.verbose_test:\\n print('-' * 80)\\n for result_idx, results in enumerate(eval_loop_results):\\n print(f'DATALOADER:{result_idx} TEST RESULTS')\\n pprint(results)\\n print('-' * 80)\\n\\n return eval_loop_results\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluation_forward\",\n \"text\": \"pytorch_lightning/trainer/evaluation_loop.py::TrainerEvaluationLoopMixin.evaluation_forward\\n def evaluation_forward(self, model, batch, batch_idx, dataloader_idx, test_mode: bool = False):\\n # make dataloader_idx arg in validation_step optional\\n args = [batch, batch_idx]\\n\\n if (test_mode and len(self.test_dataloaders) > 1) \\\\\\n or (not test_mode and len(self.val_dataloaders) > 1):\\n args.append(dataloader_idx)\\n\\n # handle DP, DDP forward\\n if self.use_ddp or self.use_dp or self.use_ddp2:\\n output = model(*args)\\n return output\\n\\n # Horovod\\n if self.use_horovod and self.on_gpu:\\n batch = self.transfer_batch_to_gpu(batch, hvd.local_rank())\\n args[0] = batch\\n\\n # single GPU data transfer\\n if self.use_single_gpu:\\n # for single GPU put inputs on gpu manually\\n root_gpu = 0\\n if isinstance(self.data_parallel_device_ids, list):\\n root_gpu = self.data_parallel_device_ids[0]\\n batch = self.transfer_batch_to_gpu(batch, root_gpu)\\n args[0] = batch\\n\\n # TPU data transfer\\n if self.use_tpu:\\n batch = self.transfer_batch_to_tpu(batch, self.tpu_id)\\n args[0] = batch\\n\\n # CPU, TPU or gpu step\\n if test_mode:\\n output = model.test_step(*args)\\n else:\\n output = model.validation_step(*args)\\n\\n return output\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.configure_logger\",\n \"text\": \"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.configure_logger\\n def configure_logger(self, logger):\\n if logger is True:\\n # default logger\\n self.logger = TensorBoardLogger(\\n save_dir=self.default_root_dir,\\n version=self.slurm_job_id,\\n name='lightning_logs'\\n )\\n elif logger is False:\\n self.logger = None\\n else:\\n if isinstance(logger, Iterable):\\n self.logger = LoggerCollection(logger)\\n else:\\n self.logger = logger\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.log_metrics\",\n \"text\": \"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.log_metrics\\n def log_metrics(self, metrics, grad_norm_dic, step=None):\\n \\\"\\\"\\\"Logs the metric dict passed in.\\n If `step` parameter is None and `step` key is presented is metrics,\\n uses metrics[\\\"step\\\"] as a step\\n\\n Args:\\n metrics (dict): Metric values\\n grad_norm_dic (dict): Gradient norms\\n step (int): Step for which metrics should be logged. Default value corresponds to `self.global_step`\\n \\\"\\\"\\\"\\n # add gpu memory\\n if self.on_gpu and self.log_gpu_memory:\\n mem_map = memory.get_memory_profile(self.log_gpu_memory)\\n metrics.update(mem_map)\\n\\n # add norms\\n metrics.update(grad_norm_dic)\\n\\n # turn all tensors to scalars\\n scalar_metrics = self.metrics_to_scalars(metrics)\\n\\n if \\\"step\\\" in scalar_metrics and step is None:\\n step = scalar_metrics.pop(\\\"step\\\")\\n else:\\n # added metrics by Lightning for convenience\\n scalar_metrics['epoch'] = self.current_epoch\\n step = step if step is not None else self.global_step\\n # log actual metrics\\n if self.is_global_zero and self.logger is not None:\\n self.logger.agg_and_log_metrics(scalar_metrics, step=step)\\n self.logger.save()\\n\\n self.dev_debugger.track_logged_metrics_history(scalar_metrics)\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.add_progress_bar_metrics\",\n \"text\": \"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.add_progress_bar_metrics\\n def add_progress_bar_metrics(self, metrics):\\n for k, v in metrics.items():\\n if isinstance(v, torch.Tensor):\\n v = v.item()\\n\\n self.progress_bar_metrics[k] = v\\n\\n self.dev_debugger.track_pbar_metrics_history(metrics)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.metrics_to_scalars\",\n \"text\": \"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.metrics_to_scalars\\n def metrics_to_scalars(self, metrics):\\n new_metrics = {}\\n for k, v in metrics.items():\\n if isinstance(v, torch.Tensor):\\n v = v.item()\\n\\n if isinstance(v, dict):\\n v = self.metrics_to_scalars(v)\\n\\n new_metrics[k] = v\\n\\n return new_metrics\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.process_output\",\n \"text\": \"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.process_output\\n def process_output(self, output, train=False):\\n \\\"\\\"\\\"Reduces output according to the training mode.\\n\\n Separates loss from logging and progress bar metrics\\n \\\"\\\"\\\"\\n # --------------------------\\n # handle single scalar only\\n # --------------------------\\n # single scalar returned from a xx_step\\n if isinstance(output, torch.Tensor):\\n progress_bar_metrics = {}\\n log_metrics = {}\\n callback_metrics = {}\\n hiddens = None\\n return output, progress_bar_metrics, log_metrics, callback_metrics, hiddens\\n\\n # ---------------\\n # EXTRACT CALLBACK KEYS\\n # ---------------\\n # all keys not progress_bar or log are candidates for callbacks\\n callback_metrics = {}\\n for k, v in output.items():\\n if k not in ['progress_bar', 'log', 'hiddens']:\\n callback_metrics[k] = v\\n\\n if train and (self.use_dp or self.use_ddp2):\\n num_gpus = self.num_gpus\\n callback_metrics = self.reduce_distributed_output(callback_metrics, num_gpus)\\n\\n # ---------------\\n # EXTRACT PROGRESS BAR KEYS\\n # ---------------\\n try:\\n progress_output = output['progress_bar']\\n\\n # reduce progress metrics for progress bar when using dp\\n if train and (self.use_dp or self.use_ddp2):\\n num_gpus = self.num_gpus\\n progress_output = self.reduce_distributed_output(progress_output, num_gpus)\\n\\n progress_bar_metrics = progress_output\\n except Exception:\\n progress_bar_metrics = {}\\n\\n # ---------------\\n # EXTRACT LOGGING KEYS\\n # ---------------\\n # extract metrics to log to experiment\\n try:\\n log_output = output['log']\\n\\n # reduce progress metrics for progress bar when using dp\\n if train and (self.use_dp\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.reduce_distributed_output\",\n \"text\": \"pytorch_lightning/trainer/logging.py::TrainerLoggingMixin.reduce_distributed_output\\n def reduce_distributed_output(self, output, num_gpus):\\n if num_gpus <= 1:\\n return output\\n\\n # when using DP, we get one output per gpu\\n # average outputs and return\\n if isinstance(output, torch.Tensor):\\n return output.mean()\\n\\n for k, v in output.items():\\n # recurse on nested dics\\n if isinstance(output[k], dict):\\n output[k] = self.reduce_distributed_output(output[k], num_gpus)\\n\\n # compute the average of scalars\\n elif isinstance(output[k], list):\\n output[k] = sum(output[k]) / len(output[k])\\n\\n # do nothing when there's a scalar\\n elif isinstance(output[k], torch.Tensor) and output[k].dim() == 0:\\n pass\\n\\n # do not reduce metrics that have batch size > num gpus\\n elif output[k].size(0) <= num_gpus:\\n output[k] = torch.mean(output[k])\\n\\n return output\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":289,"cells":{"instance_id":{"kind":"string","value":"Qiskit__qiskit-6199"},"repo":{"kind":"string","value":"Qiskit/qiskit"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Convert DAGNode.name attribute to property\nAn unrelated thing here is I think maybe we need a similar property lookup as to DAGNode.condtion so for op nodes we just pull the name directly from the op node. something like:\r\n```python3\r\n@property\r\ndef name(self):\r\n if self._name:\r\n return self._name\r\n return self.op.name\r\n```\r\njust because it seems like there is a lot of mixing of node.name and node.op.name and while they should always be the same thing, it's probably good to actually enforce that.\r\n\r\n_Originally posted by @mtreinish in https://github.com/Qiskit/qiskit-terra/pull/6187#discussion_r610764534_\n"},"gold_files":{"kind":"list like","value":["qiskit/dagcircuit/dagcircuit.py","qiskit/dagcircuit/dagnode.py"],"string":"[\n \"qiskit/dagcircuit/dagcircuit.py\",\n \"qiskit/dagcircuit/dagnode.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_op_node","qiskit/dagcircuit/dagnode.py::<module>","qiskit/dagcircuit/dagnode.py::DAGNode.__init__"],"string":"[\n \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_op_node\",\n \"qiskit/dagcircuit/dagnode.py::<module>\",\n \"qiskit/dagcircuit/dagnode.py::DAGNode.__init__\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"qiskit/dagcircuit/dagcircuit.py","text":"qiskit/dagcircuit/dagcircuit.py\nclass DAGCircuit # Quantum circuit as a directed acyclic graph.\n def __init__(self) # Create an empty circuit.\n def to_networkx(self) # Returns a copy of the DAGCircuit in networkx format.\n def from_networkx(cls, graph) # Take a networkx MultiDigraph and create a new DAGCircuit.\n def wires(self) # Return a list of the wires in order.\n def node_counter(self) # Returns the number of nodes in the dag.\n def global_phase(self) # Return the global phase of the circuit.\n def global_phase(self, angle) # Set the global phase of the circuit.\n def calibrations(self) # Return calibration dictionary.\n def calibrations(self, calibrations) # Set the circuit calibration data from a dictionary of calibration definition.\n def add_calibration(self, gate, qubits, schedule, params=None) # Register a low-level, custom pulse definition for the given gate.\n def has_calibration_for(self, node) # Return True if the dag has a calibration defined for the node operation. In this\n def remove_all_ops_named(self, opname) # Remove all operation nodes with the given name.\n def add_qubits(self, qubits) # Add individual qubit wires.\n def add_clbits(self, clbits) # Add individual clbit wires.\n def add_qreg(self, qreg) # Add all wires in a quantum register.\n def add_creg(self, creg) # Add all wires in a classical register.\n def _add_wire(self, wire) # Add a qubit or bit to the circuit.\n def _check_condition(self, name, condition) # Verify that the condition is valid.\n def _check_bits(self, args, amap) # Check the values of a list of (qu)bit arguments.\n def _bits_in_condition(self, cond) # Return a list of bits in the given condition.\n def _add_op_node(self, op, qargs, cargs) # Add a new operation node to the graph and assign properties.\n def _copy_circuit_metadata(self) # Return a copy of source_dag with metadata but empty.\n def apply_operation_back(self, op, qargs=None, cargs=None, condition=None) # Apply an operation to the output of the circuit.\n def apply_operation_front(self, op, qargs, cargs, condition=None) # Apply an operation to the input of the circuit.\n def _check_edgemap_registers(self, inbound_wires, inbound_regs) # Check that wiremap neither fragments nor leaves duplicate registers.\n def _check_wiremap_validity(self, wire_map, keymap, valmap) # Check that the wiremap is consistent.\n def _map_condition(wire_map, condition, target_cregs) # Use the wire_map dict to change the condition tuple's creg name.\n def extend_back(self, dag, edge_map=None) # DEPRECATED: Add `dag` at the end of `self`, using `edge_map`.\n def compose_back(self, input_circuit, edge_map=None) # DEPRECATED: use DAGCircuit.compose() instead.\n def compose(self, other, edge_map=None, qubits=None, clbits=None, front=False, inplace=True) # Compose the ``other`` circuit onto the output of this circuit.\n def reverse_ops(self) # Reverse the operations in the","label":1},{"cid":"qiskit/dagcircuit/dagnode.py","text":"qiskit/dagcircuit/dagnode.py\nclass DAGNode # Object to represent the information at a node in the DAGCircuit.\n def __init__(self, type=None, op=None, name=None, qargs=None, cargs=None, wire=None, nid=-1) # Create a node\n def op(self) # Returns the Instruction object corresponding to the op for the node, else None\n def op(self, data)\n def condition(self) # Returns the condition of the node.op\n def condition(self, new_condition) # Sets the node.condition which sets the node.op.condition.\n def qargs(self) # Returns list of Qubit, else an empty list.\n def qargs(self, new_qargs) # Sets the qargs to be the given list of qargs.\n def wire(self) # Returns the Bit object, else None.\n def wire(self, data)\n def __lt__(self, other)\n def __gt__(self, other)\n def __str__(self)\n def semantic_eq(node1, node2, bit_indices1=None, bit_indices2=None) # Check if DAG nodes are considered equivalent, e.g., as a node_match for nx.is_is","label":1},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py","text":"qiskit/opflow/state_fns/operator_state_fn.py\nclass OperatorStateFn # A class for state functions and measurements which are defined by a density Oper\n def __init__(self, primitive: OperatorBase, coeff: Union[complex, ParameterExpression]=1.0, is_measurement: bool=False) # Args:\n def primitive_strings(self)\n def num_qubits(self)\n def add(self, other: OperatorBase)\n def adjoint(self)\n def _expand_dim(self, num_qubits: int)\n def permute(self, permutation: List[int])\n def tensor(self, other: OperatorBase)\n def to_density_matrix(self, massive: bool=False) # Return numpy matrix of density operator, warn if more than 16 qubits\n def to_matrix_op(self, massive: bool=False) # Return a MatrixOp for this operator.\n def to_matrix(self, massive: bool=False) # Note: this does not return a density matrix, it returns a classical matrix\n def diag_over_tree(op)\n def to_circuit_op(self) # Return ``StateFnCircuit`` corresponding to this StateFn. Ignore for now because \n def __str__(self)\n def eval(self, front: Optional[Union[str, dict, np.ndarray, OperatorBase, Statevector]]=None)\n def sample(self, shots: int=1024, massive: bool=False, reverse_endianness: bool=False)","label":0},{"cid":"qiskit/providers/__init__.py","text":"qiskit/providers/__init__.py","label":0}],"string":"[\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py\\nclass DAGCircuit # Quantum circuit as a directed acyclic graph.\\n def __init__(self) # Create an empty circuit.\\n def to_networkx(self) # Returns a copy of the DAGCircuit in networkx format.\\n def from_networkx(cls, graph) # Take a networkx MultiDigraph and create a new DAGCircuit.\\n def wires(self) # Return a list of the wires in order.\\n def node_counter(self) # Returns the number of nodes in the dag.\\n def global_phase(self) # Return the global phase of the circuit.\\n def global_phase(self, angle) # Set the global phase of the circuit.\\n def calibrations(self) # Return calibration dictionary.\\n def calibrations(self, calibrations) # Set the circuit calibration data from a dictionary of calibration definition.\\n def add_calibration(self, gate, qubits, schedule, params=None) # Register a low-level, custom pulse definition for the given gate.\\n def has_calibration_for(self, node) # Return True if the dag has a calibration defined for the node operation. In this\\n def remove_all_ops_named(self, opname) # Remove all operation nodes with the given name.\\n def add_qubits(self, qubits) # Add individual qubit wires.\\n def add_clbits(self, clbits) # Add individual clbit wires.\\n def add_qreg(self, qreg) # Add all wires in a quantum register.\\n def add_creg(self, creg) # Add all wires in a classical register.\\n def _add_wire(self, wire) # Add a qubit or bit to the circuit.\\n def _check_condition(self, name, condition) # Verify that the condition is valid.\\n def _check_bits(self, args, amap) # Check the values of a list of (qu)bit arguments.\\n def _bits_in_condition(self, cond) # Return a list of bits in the given condition.\\n def _add_op_node(self, op, qargs, cargs) # Add a new operation node to the graph and assign properties.\\n def _copy_circuit_metadata(self) # Return a copy of source_dag with metadata but empty.\\n def apply_operation_back(self, op, qargs=None, cargs=None, condition=None) # Apply an operation to the output of the circuit.\\n def apply_operation_front(self, op, qargs, cargs, condition=None) # Apply an operation to the input of the circuit.\\n def _check_edgemap_registers(self, inbound_wires, inbound_regs) # Check that wiremap neither fragments nor leaves duplicate registers.\\n def _check_wiremap_validity(self, wire_map, keymap, valmap) # Check that the wiremap is consistent.\\n def _map_condition(wire_map, condition, target_cregs) # Use the wire_map dict to change the condition tuple's creg name.\\n def extend_back(self, dag, edge_map=None) # DEPRECATED: Add `dag` at the end of `self`, using `edge_map`.\\n def compose_back(self, input_circuit, edge_map=None) # DEPRECATED: use DAGCircuit.compose() instead.\\n def compose(self, other, edge_map=None, qubits=None, clbits=None, front=False, inplace=True) # Compose the ``other`` circuit onto the output of this circuit.\\n def reverse_ops(self) # Reverse the operations in the\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py\",\n \"text\": \"qiskit/dagcircuit/dagnode.py\\nclass DAGNode # Object to represent the information at a node in the DAGCircuit.\\n def __init__(self, type=None, op=None, name=None, qargs=None, cargs=None, wire=None, nid=-1) # Create a node\\n def op(self) # Returns the Instruction object corresponding to the op for the node, else None\\n def op(self, data)\\n def condition(self) # Returns the condition of the node.op\\n def condition(self, new_condition) # Sets the node.condition which sets the node.op.condition.\\n def qargs(self) # Returns list of Qubit, else an empty list.\\n def qargs(self, new_qargs) # Sets the qargs to be the given list of qargs.\\n def wire(self) # Returns the Bit object, else None.\\n def wire(self, data)\\n def __lt__(self, other)\\n def __gt__(self, other)\\n def __str__(self)\\n def semantic_eq(node1, node2, bit_indices1=None, bit_indices2=None) # Check if DAG nodes are considered equivalent, e.g., as a node_match for nx.is_is\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py\\nclass OperatorStateFn # A class for state functions and measurements which are defined by a density Oper\\n def __init__(self, primitive: OperatorBase, coeff: Union[complex, ParameterExpression]=1.0, is_measurement: bool=False) # Args:\\n def primitive_strings(self)\\n def num_qubits(self)\\n def add(self, other: OperatorBase)\\n def adjoint(self)\\n def _expand_dim(self, num_qubits: int)\\n def permute(self, permutation: List[int])\\n def tensor(self, other: OperatorBase)\\n def to_density_matrix(self, massive: bool=False) # Return numpy matrix of density operator, warn if more than 16 qubits\\n def to_matrix_op(self, massive: bool=False) # Return a MatrixOp for this operator.\\n def to_matrix(self, massive: bool=False) # Note: this does not return a density matrix, it returns a classical matrix\\n def diag_over_tree(op)\\n def to_circuit_op(self) # Return ``StateFnCircuit`` corresponding to this StateFn. Ignore for now because \\n def __str__(self)\\n def eval(self, front: Optional[Union[str, dict, np.ndarray, OperatorBase, Statevector]]=None)\\n def sample(self, shots: int=1024, massive: bool=False, reverse_endianness: bool=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/providers/__init__.py\",\n \"text\": \"qiskit/providers/__init__.py\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__init__","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__init__\n def __init__(self):\n \"\"\"Create an empty circuit.\"\"\"\n\n # Circuit name. Generally, this corresponds to the name\n # of the QuantumCircuit from which the DAG was generated.\n self.name = None\n\n # Circuit metadata\n self.metadata = None\n\n # Set of wires (Register,idx) in the dag\n self._wires = set()\n\n # Map from wire (Register,idx) to input nodes of the graph\n self.input_map = OrderedDict()\n\n # Map from wire (Register,idx) to output nodes of the graph\n self.output_map = OrderedDict()\n\n # Directed multigraph whose nodes are inputs, outputs, or operations.\n # Operation nodes have equal in- and out-degrees and carry\n # additional data about the operation, including the argument order\n # and parameter values.\n # Input nodes have out-degree 1 and output nodes have in-degree 1.\n # Edges carry wire labels (reg,idx) and each operation has\n # corresponding in- and out-edges with the same wire labels.\n self._multi_graph = rx.PyDAG()\n\n # Map of qreg/creg name to Register object.\n self.qregs = OrderedDict()\n self.cregs = OrderedDict()\n\n # List of Qubit/Clbit wires that the DAG acts on.\n self.qubits = []\n self.clbits = []\n\n self._global_phase = 0\n self._calibrations = defaultdict(dict)\n\n self.duration = None\n self.unit = 'dt'","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.to_networkx","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.to_networkx\n def to_networkx(self):\n \"\"\"Returns a copy of the DAGCircuit in networkx format.\"\"\"\n try:\n import networkx as nx\n except ImportError as ex:\n raise ImportError(\"Networkx is needed to use to_networkx(). It \"\n \"can be installed with 'pip install networkx'\") from ex\n G = nx.MultiDiGraph()\n for node in self._multi_graph.nodes():\n G.add_node(node)\n for node_id in rx.topological_sort(self._multi_graph):\n for source_id, dest_id, edge in self._multi_graph.in_edges(node_id):\n G.add_edge(self._multi_graph[source_id],\n self._multi_graph[dest_id],\n wire=edge)\n return G","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.from_networkx","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.from_networkx\n @classmethod\n def from_networkx(cls, graph):\n \"\"\"Take a networkx MultiDigraph and create a new DAGCircuit.\n\n Args:\n graph (networkx.MultiDiGraph): The graph to create a DAGCircuit\n object from. The format of this MultiDiGraph format must be\n in the same format as returned by to_networkx.\n\n Returns:\n DAGCircuit: The dagcircuit object created from the networkx\n MultiDiGraph.\n Raises:\n ImportError: If networkx is not installed\n DAGCircuitError: If input networkx graph is malformed\n \"\"\"\n try:\n import networkx as nx\n except ImportError as ex:\n raise ImportError(\"Networkx is needed to use from_networkx(). It \"\n \"can be installed with 'pip install networkx'\") from ex\n dag = DAGCircuit()\n for node in nx.topological_sort(graph):\n if node.type == 'out':\n continue\n if node.type == 'in':\n if isinstance(node.wire, Qubit):\n dag.add_qubits([node.wire])\n elif isinstance(node.wire, Clbit):\n dag.add_clbits([node.wire])\n else:\n raise DAGCircuitError('unknown node wire type: {}'.format(\n node.wire))\n elif node.type == 'op':\n dag.apply_operation_back(node.op.copy(), node.qargs, node.cargs)\n return dag","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.wires","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.wires\n @property\n def wires(self):\n \"\"\"Return a list of the wires in order.\"\"\"\n return self.qubits + self.clbits","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.node_counter","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.node_counter\n @property\n def node_counter(self):\n \"\"\"\n Returns the number of nodes in the dag.\n \"\"\"\n return len(self._multi_graph)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase\n @property\n def global_phase(self):\n \"\"\"Return the global phase of the circuit.\"\"\"\n return self._global_phase","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase\n @global_phase.setter\n def global_phase(self, angle):\n \"\"\"Set the global phase of the circuit.\n\n Args:\n angle (float, ParameterExpression)\n \"\"\"\n if isinstance(angle, ParameterExpression):\n self._global_phase = angle\n else:\n # Set the phase to the [0, 2π) interval\n angle = float(angle)\n if not angle:\n self._global_phase = 0\n else:\n self._global_phase = angle % (2 * math.pi)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations\n @property\n def calibrations(self):\n \"\"\"Return calibration dictionary.\n\n The custom pulse definition of a given gate is of the form\n {'gate_name': {(qubits, params): schedule}}\n \"\"\"\n return dict(self._calibrations)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations\n @calibrations.setter\n def calibrations(self, calibrations):\n \"\"\"Set the circuit calibration data from a dictionary of calibration definition.\n\n Args:\n calibrations (dict): A dictionary of input in the format\n {'gate_name': {(qubits, gate_params): schedule}}\n \"\"\"\n self._calibrations = defaultdict(dict, calibrations)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_calibration","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_calibration\n def add_calibration(self, gate, qubits, schedule, params=None):\n \"\"\"Register a low-level, custom pulse definition for the given gate.\n\n Args:\n gate (Union[Gate, str]): Gate information.\n qubits (Union[int, Tuple[int]]): List of qubits to be measured.\n schedule (Schedule): Schedule information.\n params (Optional[List[Union[float, Parameter]]]): A list of parameters.\n\n Raises:\n Exception: if the gate is of type string and params is None.\n \"\"\"\n if isinstance(gate, Gate):\n self._calibrations[gate.name][(tuple(qubits), tuple(gate.params))] = schedule\n else:\n self._calibrations[gate][(tuple(qubits), tuple(params or []))] = schedule","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.has_calibration_for","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.has_calibration_for\n def has_calibration_for(self, node):\n \"\"\"Return True if the dag has a calibration defined for the node operation. In this\n case, the operation does not need to be translated to the device basis.\n \"\"\"\n if not self.calibrations or node.name not in self.calibrations:\n return False\n qubits = tuple(self.qubits.index(qubit) for qubit in node.qargs)\n params = []\n for p in node.op.params:\n if isinstance(p, ParameterExpression) and not p.parameters:\n params.append(float(p))\n else:\n params.append(p)\n params = tuple(params)\n return (qubits, params) in self.calibrations[node.name]","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_all_ops_named","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_all_ops_named\n def remove_all_ops_named(self, opname):\n \"\"\"Remove all operation nodes with the given name.\"\"\"\n for n in self.named_nodes(opname):\n self.remove_op_node(n)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qubits","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qubits\n def add_qubits(self, qubits):\n \"\"\"Add individual qubit wires.\"\"\"\n if any(not isinstance(qubit, Qubit) for qubit in qubits):\n raise DAGCircuitError(\"not a Qubit instance.\")\n\n duplicate_qubits = set(self.qubits).intersection(qubits)\n if duplicate_qubits:\n raise DAGCircuitError(\"duplicate qubits %s\" % duplicate_qubits)\n\n self.qubits.extend(qubits)\n for qubit in qubits:\n self._add_wire(qubit)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_clbits","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_clbits\n def add_clbits(self, clbits):\n \"\"\"Add individual clbit wires.\"\"\"\n if any(not isinstance(clbit, Clbit) for clbit in clbits):\n raise DAGCircuitError(\"not a Clbit instance.\")\n\n duplicate_clbits = set(self.clbits).intersection(clbits)\n if duplicate_clbits:\n raise DAGCircuitError(\"duplicate clbits %s\" % duplicate_clbits)\n\n self.clbits.extend(clbits)\n for clbit in clbits:\n self._add_wire(clbit)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qreg","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qreg\n def add_qreg(self, qreg):\n \"\"\"Add all wires in a quantum register.\"\"\"\n if not isinstance(qreg, QuantumRegister):\n raise DAGCircuitError(\"not a QuantumRegister instance.\")\n if qreg.name in self.qregs:\n raise DAGCircuitError(\"duplicate register %s\" % qreg.name)\n self.qregs[qreg.name] = qreg\n existing_qubits = set(self.qubits)\n for j in range(qreg.size):\n if qreg[j] not in existing_qubits:\n self.qubits.append(qreg[j])\n self._add_wire(qreg[j])","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_creg","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_creg\n def add_creg(self, creg):\n \"\"\"Add all wires in a classical register.\"\"\"\n if not isinstance(creg, ClassicalRegister):\n raise DAGCircuitError(\"not a ClassicalRegister instance.\")\n if creg.name in self.cregs:\n raise DAGCircuitError(\"duplicate register %s\" % creg.name)\n self.cregs[creg.name] = creg\n existing_clbits = set(self.clbits)\n for j in range(creg.size):\n if creg[j] not in existing_clbits:\n self.clbits.append(creg[j])\n self._add_wire(creg[j])","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_wire","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_wire\n def _add_wire(self, wire):\n \"\"\"Add a qubit or bit to the circuit.\n\n Args:\n wire (Bit): the wire to be added\n\n This adds a pair of in and out nodes connected by an edge.\n\n Raises:\n DAGCircuitError: if trying to add duplicate wire\n \"\"\"\n if wire not in self._wires:\n self._wires.add(wire)\n\n inp_node = DAGNode(type='in', wire=wire)\n outp_node = DAGNode(type='out', wire=wire)\n input_map_id, output_map_id = self._multi_graph.add_nodes_from(\n [inp_node, outp_node])\n inp_node._node_id = input_map_id\n outp_node._node_id = output_map_id\n self.input_map[wire] = inp_node\n self.output_map[wire] = outp_node\n self._multi_graph.add_edge(inp_node._node_id,\n outp_node._node_id,\n wire)\n else:\n raise DAGCircuitError(\"duplicate wire %s\" % (wire,))","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_condition","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_condition\n def _check_condition(self, name, condition):\n \"\"\"Verify that the condition is valid.\n\n Args:\n name (string): used for error reporting\n condition (tuple or None): a condition tuple (ClassicalRegister,int)\n\n Raises:\n DAGCircuitError: if conditioning on an invalid register\n \"\"\"\n # Verify creg exists\n if condition is not None and condition[0].name not in self.cregs:\n raise DAGCircuitError(\"invalid creg in condition for %s\" % name)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_bits","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_bits\n def _check_bits(self, args, amap):\n \"\"\"Check the values of a list of (qu)bit arguments.\n\n For each element of args, check that amap contains it.\n\n Args:\n args (list[Bit]): the elements to be checked\n amap (dict): a dictionary keyed on Qubits/Clbits\n\n Raises:\n DAGCircuitError: if a qubit is not contained in amap\n \"\"\"\n # Check for each wire\n for wire in args:\n if wire not in amap:\n raise DAGCircuitError(\"(qu)bit %s not found in %s\" %\n (wire, amap))","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._bits_in_condition","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._bits_in_condition\n def _bits_in_condition(self, cond):\n \"\"\"Return a list of bits in the given condition.\n\n Args:\n cond (tuple or None): optional condition (ClassicalRegister, int)\n\n Returns:\n list[Clbit]: list of classical bits\n \"\"\"\n return [] if cond is None else list(cond[0])","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_op_node","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_op_node\n def _add_op_node(self, op, qargs, cargs):\n \"\"\"Add a new operation node to the graph and assign properties.\n\n Args:\n op (qiskit.circuit.Instruction): the operation associated with the DAG node\n qargs (list[Qubit]): list of quantum wires to attach to.\n cargs (list[Clbit]): list of classical wires to attach to.\n Returns:\n int: The integer node index for the new op node on the DAG\n \"\"\"\n # Add a new operation node to the graph\n new_node = DAGNode(type=\"op\", op=op, name=op.name, qargs=qargs,\n cargs=cargs)\n node_index = self._multi_graph.add_node(new_node)\n new_node._node_id = node_index\n return node_index","label":1},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._copy_circuit_metadata","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._copy_circuit_metadata\n def _copy_circuit_metadata(self):\n \"\"\"Return a copy of source_dag with metadata but empty.\"\"\"\n target_dag = DAGCircuit()\n target_dag.name = self.name\n target_dag._global_phase = self._global_phase\n target_dag.metadata = self.metadata\n\n target_dag.add_qubits(self.qubits)\n target_dag.add_clbits(self.clbits)\n\n for qreg in self.qregs.values():\n target_dag.add_qreg(qreg)\n for creg in self.cregs.values():\n target_dag.add_creg(creg)\n\n return target_dag","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_back","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_back\n def apply_operation_back(self, op, qargs=None, cargs=None, condition=None):\n \"\"\"Apply an operation to the output of the circuit.\n\n Args:\n op (qiskit.circuit.Instruction): the operation associated with the DAG node\n qargs (list[Qubit]): qubits that op will be applied to\n cargs (list[Clbit]): cbits that op will be applied to\n condition (tuple or None): DEPRECATED optional condition (ClassicalRegister, int)\n Returns:\n DAGNode: the current max node\n\n Raises:\n DAGCircuitError: if a leaf node is connected to multiple outputs\n\n \"\"\"\n if condition:\n warnings.warn(\"Use of condition arg is deprecated, set condition in instruction\",\n DeprecationWarning)\n op.condition = condition if op.condition is None else op.condition\n\n qargs = qargs or []\n cargs = cargs or []\n\n all_cbits = self._bits_in_condition(op.condition)\n all_cbits = set(all_cbits).union(cargs)\n\n self._check_condition(op.name, op.condition)\n self._check_bits(qargs, self.output_map)\n self._check_bits(all_cbits, self.output_map)\n\n node_index = self._add_op_node(op, qargs, cargs)\n\n # Add new in-edges from predecessors of the output nodes to the\n # operation node while deleting the old in-edges of the output nodes\n # and adding new edges from the operation node to each output node\n\n al = [qargs, all_cbits]\n self._multi_graph.insert_node_on_in_edges_multiple(\n node_index,\n [self.output_map[q]._node_id for q in itertools.chain(*al)])\n return self._multi_graph[node_index]","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_front","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_front\n def apply_operation_front(self, op, qargs, cargs, condition=None):\n \"\"\"Apply an operation to the input of the circuit.\n\n Args:\n op (qiskit.circuit.Instruction): the operation associated with the DAG node\n qargs (list[Qubit]): qubits that op will be applied to\n cargs (list[Clbit]): cbits that op will be applied to\n condition (tuple or None): DEPRECATED optional condition (ClassicalRegister, int)\n Returns:\n DAGNode: the current max node\n\n Raises:\n DAGCircuitError: if initial nodes connected to multiple out edges\n \"\"\"\n if condition:\n warnings.warn(\"Use of condition arg is deprecated, set condition in instruction\",\n DeprecationWarning)\n\n op.condition = condition if op.condition is None else op.condition\n all_cbits = self._bits_in_condition(op.condition)\n all_cbits.extend(cargs)\n\n self._check_condition(op.name, op.condition)\n self._check_bits(qargs, self.input_map)\n self._check_bits(all_cbits, self.input_map)\n node_index = self._add_op_node(op, qargs, cargs)\n\n # Add new out-edges to successors of the input nodes from the\n # operation node while deleting the old out-edges of the input nodes\n # and adding new edges to the operation node from each input node\n al = [qargs, all_cbits]\n self._multi_graph.insert_node_on_out_edges_multiple(\n node_index,\n [self.input_map[q]._node_id for q in itertools.chain(*al)])\n return self._multi_graph[node_index]","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_edgemap_registers","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_edgemap_registers\n def _check_edgemap_registers(self, inbound_wires, inbound_regs):\n \"\"\"Check that wiremap neither fragments nor leaves duplicate registers.\n\n 1. There are no fragmented registers. A register in keyregs\n is fragmented if not all of its (qu)bits are renamed by edge_map.\n 2. There are no duplicate registers. A register is duplicate if\n it appears in both self and keyregs but not in edge_map.\n\n Args:\n inbound_wires (list): a list of wires being mapped from the inbound dag\n inbound_regs (list): a list from registers from the inbound dag\n\n Returns:\n set(Register): the set of regs to add to self\n\n Raises:\n DAGCircuitError: if the wiremap fragments, or duplicates exist\n \"\"\"\n add_regs = set()\n reg_frag_chk = {}\n\n for inbound_reg in inbound_regs:\n reg_frag_chk[inbound_reg] = {reg_bit: False for reg_bit in inbound_reg}\n\n for inbound_bit in inbound_wires:\n for inbound_reg in inbound_regs:\n if inbound_bit in inbound_reg:\n reg_frag_chk[inbound_reg][inbound_bit] = True\n break\n\n for inbound_reg, v in reg_frag_chk.items():\n s = set(v.values())\n if len(s) == 2:\n raise DAGCircuitError(\"inbound_wires fragments reg %s\" % inbound_reg)\n if s == {False}:\n if (\n inbound_reg.name in self.qregs\n or inbound_reg.name in self.cregs\n ):\n raise DAGCircuitError(\"unmapped duplicate reg %s\" % inbound_reg)\n\n # Add registers that appear only in inbound_regs\n add_regs.add(inbound_reg)\n\n return add_regs","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_wiremap_validity","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_wiremap_validity\n def _check_wiremap_validity(self, wire_map, keymap, valmap):\n \"\"\"Check that the wiremap is consistent.\n\n Check that the wiremap refers to valid wires and that\n those wires have consistent types.\n\n Args:\n wire_map (dict): map from Bit in keymap to Bit in valmap\n keymap (list): a list of wire_map keys\n valmap (dict): a map whose keys are wire_map values\n\n Raises:\n DAGCircuitError: if wire_map not valid\n \"\"\"\n for k, v in wire_map.items():\n\n if k not in keymap:\n raise DAGCircuitError(\"invalid wire mapping key %s\" % k)\n if v not in valmap:\n raise DAGCircuitError(\"invalid wire mapping value %s\" % v)\n # TODO Support mapping from AncillaQubit to Qubit, since AncillaQubits are mapped to\n # Qubits upon being converted to an Instruction. Until this translation is fixed\n # and Instructions have a concept of ancilla qubits, this fix is required.\n if not (isinstance(k, type(v)) or isinstance(v, type(k))):\n raise DAGCircuitError(\"inconsistent wire_map at (%s,%s)\" % (k, v))","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._map_condition","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._map_condition\n @staticmethod\n def _map_condition(wire_map, condition, target_cregs):\n \"\"\"Use the wire_map dict to change the condition tuple's creg name.\n\n Args:\n wire_map (dict): a map from source wires to destination wires\n condition (tuple or None): (ClassicalRegister,int)\n target_cregs (list[ClassicalRegister]): List of all cregs in the\n target circuit onto which the condition might possibly be mapped.\n Returns:\n tuple(ClassicalRegister,int): new condition\n Raises:\n DAGCircuitError: if condition register not in wire_map, or if\n wire_map maps condition onto more than one creg, or if the\n specified condition is not present in a classical register.\n \"\"\"\n\n if condition is None:\n new_condition = None\n else:\n # if there is a condition, map the condition bits to the\n # composed cregs based on the wire_map\n cond_creg = condition[0]\n cond_val = condition[1]\n new_cond_val = 0\n new_creg = None\n for bit in wire_map:\n if bit in cond_creg:\n try:\n candidate_creg = next(creg\n for creg in target_cregs\n if wire_map[bit] in creg)\n except StopIteration as ex:\n raise DAGCircuitError('Did not find creg containing '\n 'mapped clbit in conditional.') from ex\n\n if new_creg is None:\n new_creg = candidate_creg\n elif new_creg != candidate_creg:\n # Raise if wire_map maps condition creg on to more than one\n # creg in target DAG.\n raise DAGCircuitError('wi","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.extend_back","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.extend_back\n def extend_back(self, dag, edge_map=None):\n \"\"\"DEPRECATED: Add `dag` at the end of `self`, using `edge_map`.\n \"\"\"\n warnings.warn(\"dag.extend_back is deprecated, please use dag.compose.\",\n DeprecationWarning, stacklevel=2)\n edge_map = edge_map or {}\n for qreg in dag.qregs.values():\n if qreg.name not in self.qregs:\n self.add_qreg(QuantumRegister(qreg.size, qreg.name))\n edge_map.update([(qbit, qbit) for qbit in qreg if qbit not in edge_map])\n\n for creg in dag.cregs.values():\n if creg.name not in self.cregs:\n self.add_creg(ClassicalRegister(creg.size, creg.name))\n edge_map.update([(cbit, cbit) for cbit in creg if cbit not in edge_map])\n\n self.compose_back(dag, edge_map)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.compose_back","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.compose_back\n def compose_back(self, input_circuit, edge_map=None):\n \"\"\"DEPRECATED: use DAGCircuit.compose() instead.\n \"\"\"\n warnings.warn(\"dag.compose_back is deprecated, please use dag.compose.\",\n DeprecationWarning, stacklevel=2)\n self.compose(input_circuit, edge_map)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.compose","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.compose\n def compose(self, other, edge_map=None, qubits=None, clbits=None, front=False, inplace=True):\n \"\"\"Compose the ``other`` circuit onto the output of this circuit.\n\n A subset of input wires of ``other`` are mapped\n to a subset of output wires of this circuit.\n\n ``other`` can be narrower or of equal width to ``self``.\n\n Args:\n other (DAGCircuit): circuit to compose with self\n edge_map (dict): DEPRECATED - a {Bit: Bit} map from input wires of other\n to output wires of self (i.e. rhs->lhs).\n The key, value pairs can be either Qubit or Clbit mappings.\n qubits (list[Qubit|int]): qubits of self to compose onto.\n clbits (list[Clbit|int]): clbits of self to compose onto.\n front (bool): If True, front composition will be performed (not implemented yet)\n inplace (bool): If True, modify the object. Otherwise return composed circuit.\n\n Returns:\n DAGCircuit: the composed dag (returns None if inplace==True).\n\n Raises:\n DAGCircuitError: if ``other`` is wider or there are duplicate edge mappings.\n \"\"\"\n if front:\n raise DAGCircuitError(\"Front composition not supported yet.\")\n\n if len(other.qubits) > len(self.qubits) or \\\n len(other.clbits) > len(self.clbits):\n raise DAGCircuitError(\"Trying to compose with another DAGCircuit \"\n \"which has more 'in' edges.\")\n\n if edge_map is not None:\n warnings.warn(\"edge_map arg as a dictionary is deprecated. \"\n \"Use qubits and clbits args to specify a list of \"\n \"self edges to compose onto.\", DeprecationWarning,\n stacklevel=2)\n\n # number of qubits and clbits must match number in circuit or None\n identity_qubit_map = dict(zip(other.qubits, self.","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.reverse_ops","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.reverse_ops\n def reverse_ops(self):\n \"\"\"Reverse the operations in the ``self`` circuit.\n\n Returns:\n DAGCircuit: the reversed dag.\n \"\"\"\n # TODO: speed up\n # pylint: disable=cyclic-import\n from qiskit.converters import dag_to_circuit, circuit_to_dag\n qc = dag_to_circuit(self)\n reversed_qc = qc.reverse_ops()\n reversed_dag = circuit_to_dag(reversed_qc)\n return reversed_dag","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.idle_wires","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.idle_wires\n def idle_wires(self, ignore=None):\n \"\"\"Return idle wires.\n\n Args:\n ignore (list(str)): List of node names to ignore. Default: []\n\n Yields:\n Bit: Bit in idle wire.\n \"\"\"\n if ignore is None:\n ignore = []\n for wire in self._wires:\n nodes = [node for node in self.nodes_on_wire(wire, only_ops=False)\n if node.name not in ignore]\n if len(nodes) == 2:\n yield wire","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.size","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.size\n def size(self):\n \"\"\"Return the number of operations.\"\"\"\n return len(self._multi_graph) - 2 * len(self._wires)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.depth","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.depth\n def depth(self):\n \"\"\"Return the circuit depth.\n Returns:\n int: the circuit depth\n Raises:\n DAGCircuitError: if not a directed acyclic graph\n \"\"\"\n try:\n depth = rx.dag_longest_path_length(self._multi_graph) - 1\n except rx.DAGHasCycle as ex:\n raise DAGCircuitError(\"not a DAG\") from ex\n return depth if depth >= 0 else 0","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.width","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.width\n def width(self):\n \"\"\"Return the total number of qubits + clbits used by the circuit.\n This function formerly returned the number of qubits by the calculation\n return len(self._wires) - self.num_clbits()\n but was changed by issue #2564 to return number of qubits + clbits\n with the new function DAGCircuit.num_qubits replacing the former\n semantic of DAGCircuit.width().\n \"\"\"\n return len(self._wires)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.num_qubits","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.num_qubits\n def num_qubits(self):\n \"\"\"Return the total number of qubits used by the circuit.\n num_qubits() replaces former use of width().\n DAGCircuit.width() now returns qubits + clbits for\n consistency with Circuit.width() [qiskit-terra #2564].\n \"\"\"\n return len(self.qubits)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.num_clbits","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.num_clbits\n def num_clbits(self):\n \"\"\"Return the total number of classical bits used by the circuit.\"\"\"\n return len(self.clbits)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.num_tensor_factors","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.num_tensor_factors\n def num_tensor_factors(self):\n \"\"\"Compute how many components the circuit can decompose into.\"\"\"\n return rx.number_weakly_connected_components(self._multi_graph)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_wires_list","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_wires_list\n def _check_wires_list(self, wires, node):\n \"\"\"Check that a list of wires is compatible with a node to be replaced.\n\n - no duplicate names\n - correct length for operation\n Raise an exception otherwise.\n\n Args:\n wires (list[Bit]): gives an order for (qu)bits\n in the input circuit that is replacing the node.\n node (DAGNode): a node in the dag\n\n Raises:\n DAGCircuitError: if check doesn't pass.\n \"\"\"\n if len(set(wires)) != len(wires):\n raise DAGCircuitError(\"duplicate wires\")\n\n wire_tot = len(node.qargs) + len(node.cargs)\n if node.op.condition is not None:\n wire_tot += node.op.condition[0].size\n\n if len(wires) != wire_tot:\n raise DAGCircuitError(\"expected %d wires, got %d\"\n % (wire_tot, len(wires)))","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._make_pred_succ_maps","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._make_pred_succ_maps\n def _make_pred_succ_maps(self, node):\n \"\"\"Return predecessor and successor dictionaries.\n\n Args:\n node (DAGNode): reference to multi_graph node\n\n Returns:\n tuple(dict): tuple(predecessor_map, successor_map)\n These map from wire (Register, int) to the node ids for the\n predecessor (successor) nodes of the input node.\n \"\"\"\n\n pred_map = {e[2]: e[0] for e in\n self._multi_graph.in_edges(node._node_id)}\n succ_map = {e[2]: e[1] for e in\n self._multi_graph.out_edges(node._node_id)}\n return pred_map, succ_map","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._full_pred_succ_maps","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._full_pred_succ_maps\n def _full_pred_succ_maps(self, pred_map, succ_map, input_circuit,\n wire_map):\n \"\"\"Map all wires of the input circuit.\n\n Map all wires of the input circuit to predecessor and\n successor nodes in self, keyed on wires in self.\n\n Args:\n pred_map (dict): comes from _make_pred_succ_maps\n succ_map (dict): comes from _make_pred_succ_maps\n input_circuit (DAGCircuit): the input circuit\n wire_map (dict): the map from wires of input_circuit to wires of self\n\n Returns:\n tuple: full_pred_map, full_succ_map (dict, dict)\n\n Raises:\n DAGCircuitError: if more than one predecessor for output nodes\n \"\"\"\n full_pred_map = {}\n full_succ_map = {}\n for w in input_circuit.input_map:\n # If w is wire mapped, find the corresponding predecessor\n # of the node\n if w in wire_map:\n full_pred_map[wire_map[w]] = pred_map[wire_map[w]]\n full_succ_map[wire_map[w]] = succ_map[wire_map[w]]\n else:\n # Otherwise, use the corresponding output nodes of self\n # and compute the predecessor.\n full_succ_map[w] = self.output_map[w]\n full_pred_map[w] = self._multi_graph.predecessors(\n self.output_map[w])[0]\n if len(self._multi_graph.predecessors(self.output_map[w])) != 1:\n raise DAGCircuitError(\"too many predecessors for %s[%d] \"\n \"output node\" % (w.register, w.index))\n\n return full_pred_map, full_succ_map","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__eq__","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__eq__\n def __eq__(self, other):\n # Try to convert to float, but in case of unbound ParameterExpressions\n # a TypeError will be raise, fallback to normal equality in those\n # cases\n\n try:\n self_phase = float(self.global_phase)\n other_phase = float(other.global_phase)\n if abs((self_phase - other_phase + np.pi) % (2*np.pi) - np.pi) > 1.E-10: # TODO: atol?\n return False\n except TypeError:\n if self.global_phase != other.global_phase:\n return False\n if self.calibrations != other.calibrations:\n return False\n\n self_bit_indices = {bit: idx\n for idx, bit in\n enumerate(self.qubits + self.clbits)}\n other_bit_indices = {bit: idx\n for idx, bit in\n enumerate(other.qubits + other.clbits)}\n\n self_qreg_indices = [(regname, [self_bit_indices[bit] for bit in reg])\n for regname, reg in self.qregs.items()]\n self_creg_indices = [(regname, [self_bit_indices[bit] for bit in reg])\n for regname, reg in self.cregs.items()]\n\n other_qreg_indices = [(regname, [other_bit_indices[bit] for bit in reg])\n for regname, reg in other.qregs.items()]\n other_creg_indices = [(regname, [other_bit_indices[bit] for bit in reg])\n for regname, reg in other.cregs.items()]\n if (\n self_qreg_indices != other_qreg_indices\n or self_creg_indices != other_creg_indices\n ):\n return False\n\n def node_eq(node_self, node_other):\n return DAGNode.semantic_eq(node_self, node_other,\n self_bit_indices, other_bit_indices)\n\n return rx.is_isomorphic_node_match(self._multi_graph,\n ","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__eq__.node_eq","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__eq__.node_eq\n def node_eq(node_self, node_other):\n return DAGNode.semantic_eq(node_self, node_other,\n self_bit_indices, other_bit_indices)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.topological_nodes","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.topological_nodes\n def topological_nodes(self):\n \"\"\"\n Yield nodes in topological order.\n\n Returns:\n generator(DAGNode): node in topological order\n \"\"\"\n\n def _key(x):\n return x.sort_key\n\n return iter(rx.lexicographical_topological_sort(\n self._multi_graph, key=_key))","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.topological_nodes._key","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.topological_nodes._key\n def _key(x):\n return x.sort_key","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.topological_op_nodes","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.topological_op_nodes\n def topological_op_nodes(self):\n \"\"\"\n Yield op nodes in topological order.\n\n Returns:\n generator(DAGNode): op node in topological order\n \"\"\"\n return (nd for nd in self.topological_nodes() if nd.type == 'op')","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.substitute_node_with_dag","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.substitute_node_with_dag\n def substitute_node_with_dag(self, node, input_dag, wires=None):\n \"\"\"Replace one node with dag.\n\n Args:\n node (DAGNode): node to substitute\n input_dag (DAGCircuit): circuit that will substitute the node\n wires (list[Bit]): gives an order for (qu)bits\n in the input circuit. This order gets matched to the node wires\n by qargs first, then cargs, then conditions.\n\n Raises:\n DAGCircuitError: if met with unexpected predecessor/successors\n \"\"\"\n in_dag = input_dag\n condition = None if node.type != 'op' else node.op.condition\n\n # the dag must be amended if used in a\n # conditional context. delete the op nodes and replay\n # them with the condition.\n if condition:\n in_dag = copy.deepcopy(input_dag)\n in_dag.add_creg(condition[0])\n to_replay = []\n for sorted_node in in_dag.topological_nodes():\n if sorted_node.type == \"op\":\n sorted_node.op.condition = condition\n to_replay.append(sorted_node)\n for input_node in in_dag.op_nodes():\n in_dag.remove_op_node(input_node)\n for replay_node in to_replay:\n in_dag.apply_operation_back(replay_node.op, replay_node.qargs,\n replay_node.cargs)\n\n if in_dag.global_phase:\n from sympy import evaluate\n with evaluate(False):\n self.global_phase += in_dag.global_phase\n\n if wires is None:\n wires = in_dag.wires\n\n self._check_wires_list(wires, node)\n\n # Create a proxy wire_map to identify fragments and duplicates\n # and determine what registers need to be added to self\n add_qregs = self._check_edgemap_registers(wires, in_dag.qregs.values())\n for qreg in add_qreg","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.substitute_node","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.substitute_node\n def substitute_node(self, node, op, inplace=False):\n \"\"\"Replace a DAGNode with a single instruction. qargs, cargs and\n conditions for the new instruction will be inferred from the node to be\n replaced. The new instruction will be checked to match the shape of the\n replaced instruction.\n\n Args:\n node (DAGNode): Node to be replaced\n op (qiskit.circuit.Instruction): The :class:`qiskit.circuit.Instruction`\n instance to be added to the DAG\n inplace (bool): Optional, default False. If True, existing DAG node\n will be modified to include op. Otherwise, a new DAG node will\n be used.\n\n Returns:\n DAGNode: the new node containing the added instruction.\n\n Raises:\n DAGCircuitError: If replacement instruction was incompatible with\n location of target node.\n \"\"\"\n\n if node.type != 'op':\n raise DAGCircuitError('Only DAGNodes of type \"op\" can be replaced.')\n\n if (\n node.op.num_qubits != op.num_qubits\n or node.op.num_clbits != op.num_clbits\n ):\n raise DAGCircuitError(\n 'Cannot replace node of width ({} qubits, {} clbits) with '\n 'instruction of mismatched width ({} qubits, {} clbits).'.format(\n node.op.num_qubits, node.op.num_clbits,\n op.num_qubits, op.num_clbits))\n\n if inplace:\n save_condition = node.op.condition\n node.op = op\n node.name = op.name\n node.op.condition = save_condition\n return node\n\n new_node = copy.copy(node)\n save_condition = new_node.op.condition\n new_node.op = op\n new_node.name = op.name\n new_node.op.condition = save_condition\n self._multi_graph[node._node_id] = new_node\n return new_node","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.node","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.node\n def node(self, node_id):\n \"\"\"Get the node in the dag.\n\n Args:\n node_id(int): Node identifier.\n\n Returns:\n node: the node.\n \"\"\"\n return self._multi_graph[node_id]","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.nodes","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.nodes\n def nodes(self):\n \"\"\"Iterator for node values.\n\n Yield:\n node: the node.\n \"\"\"\n yield from self._multi_graph.nodes()","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.edges","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.edges\n def edges(self, nodes=None):\n \"\"\"Iterator for edge values and source and dest node\n\n This works by returning the output edges from the specified nodes. If\n no nodes are specified all edges from the graph are returned.\n\n Args:\n nodes(DAGNode|list(DAGNode): Either a list of nodes or a single\n input node. If none is specified all edges are returned from\n the graph.\n\n Yield:\n edge: the edge in the same format as out_edges the tuple\n (source node, destination node, edge data)\n \"\"\"\n if nodes is None:\n nodes = self._multi_graph.nodes()\n\n elif isinstance(nodes, DAGNode):\n nodes = [nodes]\n for node in nodes:\n raw_nodes = self._multi_graph.out_edges(node._node_id)\n for source, dest, edge in raw_nodes:\n yield (self._multi_graph[source],\n self._multi_graph[dest],\n edge)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.op_nodes","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.op_nodes\n def op_nodes(self, op=None, include_directives=True):\n \"\"\"Get the list of \"op\" nodes in the dag.\n\n Args:\n op (Type): :class:`qiskit.circuit.Instruction` subclass op nodes to\n return. If None, return all op nodes.\n include_directives (bool): include `barrier`, `snapshot` etc.\n\n Returns:\n list[DAGNode]: the list of node ids containing the given op.\n \"\"\"\n nodes = []\n for node in self._multi_graph.nodes():\n if node.type == \"op\":\n if not include_directives and node.op._directive:\n continue\n if op is None or isinstance(node.op, op):\n nodes.append(node)\n return nodes","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.gate_nodes","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.gate_nodes\n def gate_nodes(self):\n \"\"\"Get the list of gate nodes in the dag.\n\n Returns:\n list[DAGNode]: the list of DAGNodes that represent gates.\n \"\"\"\n nodes = []\n for node in self.op_nodes():\n if isinstance(node.op, Gate):\n nodes.append(node)\n return nodes","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.named_nodes","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.named_nodes\n def named_nodes(self, *names):\n \"\"\"Get the set of \"op\" nodes with the given name.\"\"\"\n named_nodes = []\n for node in self._multi_graph.nodes():\n if node.type == 'op' and node.op.name in names:\n named_nodes.append(node)\n return named_nodes","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.twoQ_gates","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.twoQ_gates\n def twoQ_gates(self):\n \"\"\"Get list of 2-qubit gates. Ignore snapshot, barriers, and the like.\"\"\"\n warnings.warn('deprecated function, use dag.two_qubit_ops(). '\n 'filter output by isinstance(op, Gate) to only get unitary Gates.',\n DeprecationWarning, stacklevel=2)\n two_q_gates = []\n for node in self.gate_nodes():\n if len(node.qargs) == 2:\n two_q_gates.append(node)\n return two_q_gates","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.threeQ_or_more_gates","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.threeQ_or_more_gates\n def threeQ_or_more_gates(self):\n \"\"\"Get list of 3-or-more-qubit gates: (id, data).\"\"\"\n warnings.warn('deprecated function, use dag.multi_qubit_ops(). '\n 'filter output by isinstance(op, Gate) to only get unitary Gates.',\n DeprecationWarning, stacklevel=2)\n three_q_gates = []\n for node in self.gate_nodes():\n if len(node.qargs) >= 3:\n three_q_gates.append(node)\n return three_q_gates","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.two_qubit_ops","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.two_qubit_ops\n def two_qubit_ops(self):\n \"\"\"Get list of 2 qubit operations. Ignore directives like snapshot and barrier.\"\"\"\n ops = []\n for node in self.op_nodes(include_directives=False):\n if len(node.qargs) == 2:\n ops.append(node)\n return ops","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.multi_qubit_ops","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.multi_qubit_ops\n def multi_qubit_ops(self):\n \"\"\"Get list of 3+ qubit operations. Ignore directives like snapshot and barrier.\"\"\"\n ops = []\n for node in self.op_nodes(include_directives=False):\n if len(node.qargs) >= 3:\n ops.append(node)\n return ops","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.longest_path","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.longest_path\n def longest_path(self):\n \"\"\"Returns the longest path in the dag as a list of DAGNodes.\"\"\"\n return [self._multi_graph[x] for x in rx.dag_longest_path(self._multi_graph)]","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.successors","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.successors\n def successors(self, node):\n \"\"\"Returns iterator of the successors of a node as DAGNodes.\"\"\"\n return iter(self._multi_graph.successors(node._node_id))","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.predecessors","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.predecessors\n def predecessors(self, node):\n \"\"\"Returns iterator of the predecessors of a node as DAGNodes.\"\"\"\n return iter(self._multi_graph.predecessors(node._node_id))","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.quantum_predecessors","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.quantum_predecessors\n def quantum_predecessors(self, node):\n \"\"\"Returns iterator of the predecessors of a node that are\n connected by a quantum edge as DAGNodes.\"\"\"\n for predecessor in self.predecessors(node):\n if any(isinstance(edge_data, Qubit) for edge_data in\n self._multi_graph.get_all_edge_data(\n predecessor._node_id, node._node_id)):\n yield predecessor","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.ancestors","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.ancestors\n def ancestors(self, node):\n \"\"\"Returns set of the ancestors of a node as DAGNodes.\"\"\"\n return {\n self._multi_graph[x] for x in rx.ancestors(\n self._multi_graph, node._node_id)}","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.descendants","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.descendants\n def descendants(self, node):\n \"\"\"Returns set of the descendants of a node as DAGNodes.\"\"\"\n return {\n self._multi_graph[x] for x in rx.descendants(\n self._multi_graph, node._node_id)}","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.bfs_successors","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.bfs_successors\n def bfs_successors(self, node):\n \"\"\"\n Returns an iterator of tuples of (DAGNode, [DAGNodes]) where the DAGNode is the current node\n and [DAGNode] is its successors in BFS order.\n \"\"\"\n return iter(rx.bfs_successors(self._multi_graph, node._node_id))","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.quantum_successors","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.quantum_successors\n def quantum_successors(self, node):\n \"\"\"Returns iterator of the successors of a node that are\n connected by a quantum edge as DAGNodes.\"\"\"\n for successor in self.successors(node):\n if any(isinstance(edge_data, Qubit) for edge_data in\n self._multi_graph.get_all_edge_data(\n node._node_id, successor._node_id)):\n yield successor","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_op_node","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_op_node\n def remove_op_node(self, node):\n \"\"\"Remove an operation node n.\n\n Add edges from predecessors to successors.\n \"\"\"\n if node.type != 'op':\n raise DAGCircuitError('The method remove_op_node only works on op node types. An \"%s\" '\n 'node type was wrongly provided.' % node.type)\n\n self._multi_graph.remove_node_retain_edges(\n node._node_id, use_outgoing=False,\n condition=lambda edge1, edge2: edge1 == edge2)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_ancestors_of","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_ancestors_of\n def remove_ancestors_of(self, node):\n \"\"\"Remove all of the ancestor operation nodes of node.\"\"\"\n anc = rx.ancestors(self._multi_graph, node)\n # TODO: probably better to do all at once using\n # multi_graph.remove_nodes_from; same for related functions ...\n\n for anc_node in anc:\n if anc_node.type == \"op\":\n self.remove_op_node(anc_node)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_descendants_of","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_descendants_of\n def remove_descendants_of(self, node):\n \"\"\"Remove all of the descendant operation nodes of node.\"\"\"\n desc = rx.descendants(self._multi_graph, node)\n for desc_node in desc:\n if desc_node.type == \"op\":\n self.remove_op_node(desc_node)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_nonancestors_of","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_nonancestors_of\n def remove_nonancestors_of(self, node):\n \"\"\"Remove all of the non-ancestors operation nodes of node.\"\"\"\n anc = rx.ancestors(self._multi_graph, node)\n comp = list(set(self._multi_graph.nodes()) - set(anc))\n for n in comp:\n if n.type == \"op\":\n self.remove_op_node(n)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_nondescendants_of","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_nondescendants_of\n def remove_nondescendants_of(self, node):\n \"\"\"Remove all of the non-descendants operation nodes of node.\"\"\"\n dec = rx.descendants(self._multi_graph, node)\n comp = list(set(self._multi_graph.nodes()) - set(dec))\n for n in comp:\n if n.type == \"op\":\n self.remove_op_node(n)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.front_layer","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.front_layer\n def front_layer(self):\n \"\"\"Return a list of op nodes in the first layer of this dag.\n \"\"\"\n graph_layers = self.multigraph_layers()\n try:\n next(graph_layers) # Remove input nodes\n except StopIteration:\n return []\n\n op_nodes = [node for node in next(graph_layers) if node.type == \"op\"]\n\n return op_nodes","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.layers","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.layers\n def layers(self):\n \"\"\"Yield a shallow view on a layer of this DAGCircuit for all d layers of this circuit.\n\n A layer is a circuit whose gates act on disjoint qubits, i.e.,\n a layer has depth 1. The total number of layers equals the\n circuit depth d. The layers are indexed from 0 to d-1 with the\n earliest layer at index 0. The layers are constructed using a\n greedy algorithm. Each returned layer is a dict containing\n {\"graph\": circuit graph, \"partition\": list of qubit lists}.\n\n The returned layer contains new (but semantically equivalent) DAGNodes.\n These are not the same as nodes of the original dag, but are equivalent\n via DAGNode.semantic_eq(node1, node2).\n\n TODO: Gates that use the same cbits will end up in different\n layers as this is currently implemented. This may not be\n the desired behavior.\n \"\"\"\n graph_layers = self.multigraph_layers()\n try:\n next(graph_layers) # Remove input nodes\n except StopIteration:\n return\n\n for graph_layer in graph_layers:\n\n # Get the op nodes from the layer, removing any input and output nodes.\n op_nodes = [node for node in graph_layer if node.type == \"op\"]\n\n # Sort to make sure they are in the order they were added to the original DAG\n # It has to be done by node_id as graph_layer is just a list of nodes\n # with no implied topology\n # Drawing tools rely on _node_id to infer order of node creation\n # so we need this to be preserved by layers()\n op_nodes.sort(key=lambda nd: nd._node_id)\n\n # Stop yielding once there are no more op_nodes in a layer.\n if not op_nodes:\n return\n\n # Construct a shallow copy of self\n new_layer = self._copy_circuit_metadata()\n\n for node in op_nodes:\n ","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.serial_layers","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.serial_layers\n def serial_layers(self):\n \"\"\"Yield a layer for all gates of this circuit.\n\n A serial layer is a circuit with one gate. The layers have the\n same structure as in layers().\n \"\"\"\n for next_node in self.topological_op_nodes():\n new_layer = self._copy_circuit_metadata()\n\n # Save the support of the operation we add to the layer\n support_list = []\n # Operation data\n op = copy.copy(next_node.op)\n qa = copy.copy(next_node.qargs)\n ca = copy.copy(next_node.cargs)\n co = copy.copy(next_node.op.condition)\n _ = self._bits_in_condition(co)\n\n # Add node to new_layer\n new_layer.apply_operation_back(op, qa, ca)\n # Add operation to partition\n if not next_node.op._directive:\n support_list.append(list(qa))\n l_dict = {\"graph\": new_layer, \"partition\": support_list}\n yield l_dict","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.multigraph_layers","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.multigraph_layers\n def multigraph_layers(self):\n \"\"\"Yield layers of the multigraph.\"\"\"\n first_layer = [x._node_id for x in self.input_map.values()]\n return iter(rx.layers(self._multi_graph, first_layer))","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_runs","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_runs\n def collect_runs(self, namelist):\n \"\"\"Return a set of non-conditional runs of \"op\" nodes with the given names.\n\n For example, \"... h q[0]; cx q[0],q[1]; cx q[0],q[1]; h q[1]; ..\"\n would produce the tuple of cx nodes as an element of the set returned\n from a call to collect_runs([\"cx\"]). If instead the cx nodes were\n \"cx q[0],q[1]; cx q[1],q[0];\", the method would still return the\n pair in a tuple. The namelist can contain names that are not\n in the circuit's basis.\n\n Nodes must have only one successor to continue the run.\n \"\"\"\n\n def filter_fn(node):\n return node.type == \"op\" and node.name in namelist and node.op.condition is None\n\n group_list = rx.collect_runs(self._multi_graph, filter_fn)\n return set(tuple(x) for x in group_list)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_runs.filter_fn","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_runs.filter_fn\n def filter_fn(node):\n return node.type == \"op\" and node.name in namelist and node.op.condition is None","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_1q_runs","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_1q_runs\n def collect_1q_runs(self):\n \"\"\"Return a set of non-conditional runs of 1q \"op\" nodes.\"\"\"\n\n def filter_fn(node):\n return node.type == 'op' and len(node.qargs) == 1 \\\n and len(node.cargs) == 0 and node.op.condition is None \\\n and not node.op.is_parameterized() \\\n and isinstance(node.op, Gate) \\\n and hasattr(node.op, '__array__')\n\n return rx.collect_runs(self._multi_graph, filter_fn)","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_1q_runs.filter_fn","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_1q_runs.filter_fn\n def filter_fn(node):\n return node.type == 'op' and len(node.qargs) == 1 \\\n and len(node.cargs) == 0 and node.op.condition is None \\\n and not node.op.is_parameterized() \\\n and isinstance(node.op, Gate) \\\n and hasattr(node.op, '__array__')","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.nodes_on_wire","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.nodes_on_wire\n def nodes_on_wire(self, wire, only_ops=False):\n \"\"\"\n Iterator for nodes that affect a given wire.\n\n Args:\n wire (Bit): the wire to be looked at.\n only_ops (bool): True if only the ops nodes are wanted;\n otherwise, all nodes are returned.\n Yield:\n DAGNode: the successive ops on the given wire\n\n Raises:\n DAGCircuitError: if the given wire doesn't exist in the DAG\n \"\"\"\n current_node = self.input_map.get(wire, None)\n\n if not current_node:\n raise DAGCircuitError('The given wire %s is not present in the circuit'\n % str(wire))\n\n more_nodes = True\n while more_nodes:\n more_nodes = False\n # allow user to just get ops on the wire - not the input/output nodes\n if current_node.type == 'op' or not only_ops:\n yield current_node\n\n try:\n current_node = self._multi_graph.find_adjacent_node_by_edge(\n current_node._node_id, lambda x: wire == x)\n more_nodes = True\n except rx.NoSuitableNeighbors:\n pass","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.count_ops","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.count_ops\n def count_ops(self):\n \"\"\"Count the occurrences of operation names.\n\n Returns a dictionary of counts keyed on the operation name.\n \"\"\"\n op_dict = {}\n for node in self.topological_op_nodes():\n name = node.name\n if name not in op_dict:\n op_dict[name] = 1\n else:\n op_dict[name] += 1\n return op_dict","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.count_ops_longest_path","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.count_ops_longest_path\n def count_ops_longest_path(self):\n \"\"\"Count the occurrences of operation names on the longest path.\n\n Returns a dictionary of counts keyed on the operation name.\n \"\"\"\n op_dict = {}\n path = self.longest_path()\n path = path[1:-1] # remove qubits at beginning and end of path\n for node in path:\n name = node.name\n if name not in op_dict:\n op_dict[name] = 1\n else:\n op_dict[name] += 1\n return op_dict","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.properties","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.properties\n def properties(self):\n \"\"\"Return a dictionary of circuit properties.\"\"\"\n summary = {\"size\": self.size(),\n \"depth\": self.depth(),\n \"width\": self.width(),\n \"qubits\": self.num_qubits(),\n \"bits\": self.num_clbits(),\n \"factors\": self.num_tensor_factors(),\n \"operations\": self.count_ops()}\n return summary","label":0},{"cid":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.draw","text":"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.draw\n def draw(self, scale=0.7, filename=None, style='color'):\n \"\"\"\n Draws the dag circuit.\n\n This function needs `pydot <https://github.com/erocarrera/pydot>`_, which in turn needs\n `Graphviz <https://www.graphviz.org/>`_ to be installed.\n\n Args:\n scale (float): scaling factor\n filename (str): file path to save image to (format inferred from name)\n style (str):\n 'plain': B&W graph;\n 'color' (default): color input/output/op nodes\n\n Returns:\n Ipython.display.Image: if in Jupyter notebook and not saving to file,\n otherwise None.\n \"\"\"\n from qiskit.visualization.dag_visualization import dag_drawer\n return dag_drawer(dag=self, scale=scale, filename=filename, style=style)","label":0},{"cid":"qiskit/dagcircuit/dagnode.py::DAGNode.__init__","text":"qiskit/dagcircuit/dagnode.py::DAGNode.__init__\n def __init__(self, type=None, op=None, name=None, qargs=None, cargs=None,\n wire=None, nid=-1):\n \"\"\"Create a node \"\"\"\n self.type = type\n self._op = op\n self.name = name\n self._qargs = qargs if qargs is not None else []\n self.cargs = cargs if cargs is not None else []\n self._wire = wire\n self._node_id = nid\n self.sort_key = str(self._qargs)","label":1},{"cid":"qiskit/dagcircuit/dagnode.py::DAGNode.op","text":"qiskit/dagcircuit/dagnode.py::DAGNode.op\n @property\n def op(self):\n \"\"\"Returns the Instruction object corresponding to the op for the node, else None\"\"\"\n if not self.type or self.type != 'op':\n raise QiskitError(\"The node %s is not an op node\" % (str(self)))\n return self._op","label":0},{"cid":"qiskit/dagcircuit/dagnode.py::DAGNode.op","text":"qiskit/dagcircuit/dagnode.py::DAGNode.op\n @op.setter\n def op(self, data):\n self._op = data","label":0},{"cid":"qiskit/dagcircuit/dagnode.py::DAGNode.condition","text":"qiskit/dagcircuit/dagnode.py::DAGNode.condition\n @property\n def condition(self):\n \"\"\"Returns the condition of the node.op\"\"\"\n if not self.type or self.type != 'op':\n raise QiskitError(\"The node %s is not an op node\" % (str(self)))\n warnings.warn(\n \"The DAGNode 'condition' attribute is deprecated as of 0.18.0 and \"\n \"will be removed no earlier than 3 months after the release date. \"\n \"You can use 'DAGNode.op.condition' if the DAGNode is of type 'op'.\",\n DeprecationWarning, 2)\n return self._op.condition","label":0},{"cid":"qiskit/dagcircuit/dagnode.py::DAGNode.condition","text":"qiskit/dagcircuit/dagnode.py::DAGNode.condition\n @condition.setter\n def condition(self, new_condition):\n \"\"\"Sets the node.condition which sets the node.op.condition.\"\"\"\n if not self.type or self.type != 'op':\n raise QiskitError(\"The node %s is not an op node\" % (str(self)))\n warnings.warn(\n \"The DAGNode 'condition' attribute is deprecated as of 0.18.0 and \"\n \"will be removed no earlier than 3 months after the release date. \"\n \"You can use 'DAGNode.op.condition' if the DAGNode is of type 'op'.\",\n DeprecationWarning, 2)\n self._op.condition = new_condition","label":0},{"cid":"qiskit/dagcircuit/dagnode.py::DAGNode.qargs","text":"qiskit/dagcircuit/dagnode.py::DAGNode.qargs\n @property\n def qargs(self):\n \"\"\"\n Returns list of Qubit, else an empty list.\n \"\"\"\n return self._qargs","label":0},{"cid":"qiskit/dagcircuit/dagnode.py::DAGNode.qargs","text":"qiskit/dagcircuit/dagnode.py::DAGNode.qargs\n @qargs.setter\n def qargs(self, new_qargs):\n \"\"\"Sets the qargs to be the given list of qargs.\"\"\"\n self._qargs = new_qargs\n self.sort_key = str(new_qargs)","label":0},{"cid":"qiskit/dagcircuit/dagnode.py::DAGNode.wire","text":"qiskit/dagcircuit/dagnode.py::DAGNode.wire\n @property\n def wire(self):\n \"\"\"\n Returns the Bit object, else None.\n \"\"\"\n if self.type not in ['in', 'out']:\n raise QiskitError('The node %s is not an input/output node' % str(self))\n return self._wire","label":0},{"cid":"qiskit/dagcircuit/dagnode.py::DAGNode.wire","text":"qiskit/dagcircuit/dagnode.py::DAGNode.wire\n @wire.setter\n def wire(self, data):\n self._wire = data","label":0},{"cid":"qiskit/dagcircuit/dagnode.py::DAGNode.__lt__","text":"qiskit/dagcircuit/dagnode.py::DAGNode.__lt__\n def __lt__(self, other):\n return self._node_id < other._node_id","label":0},{"cid":"qiskit/dagcircuit/dagnode.py::DAGNode.__gt__","text":"qiskit/dagcircuit/dagnode.py::DAGNode.__gt__\n def __gt__(self, other):\n return self._node_id > other._node_id","label":0},{"cid":"qiskit/dagcircuit/dagnode.py::DAGNode.__str__","text":"qiskit/dagcircuit/dagnode.py::DAGNode.__str__\n def __str__(self):\n # TODO is this used anywhere other than in DAG drawing?\n # needs to be unique as it is what pydot uses to distinguish nodes\n return str(id(self))","label":0},{"cid":"qiskit/dagcircuit/dagnode.py::DAGNode.semantic_eq","text":"qiskit/dagcircuit/dagnode.py::DAGNode.semantic_eq\n @staticmethod\n def semantic_eq(node1, node2, bit_indices1=None, bit_indices2=None):\n \"\"\"\n Check if DAG nodes are considered equivalent, e.g., as a node_match for nx.is_isomorphic.\n\n Args:\n node1 (DAGNode): A node to compare.\n node2 (DAGNode): The other node to compare.\n bit_indices1 (dict): Dictionary mapping Bit instances to their index\n within the circuit containing node1\n bit_indices2 (dict): Dictionary mapping Bit instances to their index\n within the circuit containing node2\n\n Return:\n Bool: If node1 == node2\n \"\"\"\n if bit_indices1 is None or bit_indices2 is None:\n warnings.warn(\n 'DAGNode.semantic_eq now expects two bit-to-circuit index '\n 'mappings as arguments. To ease the transition, these will be '\n 'pre-populated based on the values found in Bit.index and '\n 'Bit.register. However, this behavior is deprecated and a future '\n 'release will require the mappings to be provided as arguments.',\n DeprecationWarning)\n\n bit_indices1 = {arg: arg for arg in node1.qargs + node1.cargs}\n bit_indices2 = {arg: arg for arg in node2.qargs + node2.cargs}\n\n node1_qargs = [bit_indices1[qarg] for qarg in node1.qargs]\n node1_cargs = [bit_indices1[carg] for carg in node1.cargs]\n\n node2_qargs = [bit_indices2[qarg] for qarg in node2.qargs]\n node2_cargs = [bit_indices2[carg] for carg in node2.cargs]\n\n # For barriers, qarg order is not significant so compare as sets\n if 'barrier' == node1.name == node2.name:\n return set(node1_qargs) == set(node2_qargs)\n\n if node1.type == node2.type:\n if node1._op == node2._op:\n if node1.name == node2.name:\n if node1_qargs == node2_qargs:\n ","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.__init__","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.__init__\n def __init__(self,\n primitive: OperatorBase,\n coeff: Union[complex, ParameterExpression] = 1.0,\n is_measurement: bool = False) -> None:\n \"\"\"\n Args:\n primitive: The ``OperatorBase`` which defines the behavior of the underlying State\n function.\n coeff: A coefficient by which to multiply the state function\n is_measurement: Whether the StateFn is a measurement operator\n \"\"\"\n\n super().__init__(primitive, coeff=coeff, is_measurement=is_measurement)","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.primitive_strings","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.primitive_strings\n def primitive_strings(self) -> Set[str]:\n return self.primitive.primitive_strings()","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.num_qubits","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.num_qubits\n @property\n def num_qubits(self) -> int:\n return self.primitive.num_qubits","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.add","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.add\n def add(self, other: OperatorBase) -> Union[\"OperatorStateFn\", SummedOp]:\n if not self.num_qubits == other.num_qubits:\n raise ValueError(\n 'Sum over statefns with different numbers of qubits, {} and {}, is not well '\n 'defined'.format(self.num_qubits, other.num_qubits))\n\n # Right now doesn't make sense to add a StateFn to a Measurement\n if isinstance(other, OperatorStateFn) and self.is_measurement == other.is_measurement:\n if isinstance(other.primitive, OperatorBase) and self.primitive == other.primitive:\n return OperatorStateFn(\n self.primitive,\n coeff=self.coeff + other.coeff,\n is_measurement=self.is_measurement,\n )\n # Covers Statevector and custom.\n elif isinstance(other, OperatorStateFn):\n # Also assumes scalar multiplication is available\n return OperatorStateFn(\n (self.coeff * self.primitive).add(other.primitive * other.coeff),\n is_measurement=self._is_measurement)\n\n return SummedOp([self, other])","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.adjoint","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.adjoint\n def adjoint(self) -> \"OperatorStateFn\":\n return OperatorStateFn(self.primitive.adjoint(),\n coeff=self.coeff.conjugate(),\n is_measurement=(not self.is_measurement))","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn._expand_dim","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn._expand_dim\n def _expand_dim(self, num_qubits: int) -> 'OperatorStateFn':\n return OperatorStateFn(self.primitive._expand_dim(num_qubits),\n coeff=self.coeff,\n is_measurement=self.is_measurement)","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.permute","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.permute\n def permute(self, permutation: List[int]) -> 'OperatorStateFn':\n return OperatorStateFn(self.primitive.permute(permutation),\n coeff=self.coeff,\n is_measurement=self.is_measurement)","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.tensor","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.tensor\n def tensor(self, other: OperatorBase) -> Union[\"OperatorStateFn\", TensoredOp]:\n if isinstance(other, OperatorStateFn):\n return OperatorStateFn(\n self.primitive.tensor(other.primitive),\n coeff=self.coeff * other.coeff,\n is_measurement=self.is_measurement,\n )\n\n return TensoredOp([self, other])","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_density_matrix","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_density_matrix\n def to_density_matrix(self, massive: bool = False) -> np.ndarray:\n \"\"\" Return numpy matrix of density operator, warn if more than 16 qubits\n to force the user to set\n massive=True if they want such a large matrix. Generally big methods like\n this should require the use of a\n converter, but in this case a convenience method for quick hacking and\n access to classical tools is\n appropriate. \"\"\"\n OperatorBase._check_massive('to_density_matrix', True, self.num_qubits, massive)\n return self.primitive.to_matrix() * self.coeff","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_matrix_op","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_matrix_op\n def to_matrix_op(self, massive: bool = False) -> \"OperatorStateFn\":\n \"\"\" Return a MatrixOp for this operator. \"\"\"\n return OperatorStateFn(self.primitive.to_matrix_op(massive=massive) * self.coeff,\n is_measurement=self.is_measurement)","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_matrix","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_matrix\n def to_matrix(self, massive: bool = False) -> np.ndarray:\n r\"\"\"\n Note: this does not return a density matrix, it returns a classical matrix\n containing the quantum or classical vector representing the evaluation of the state\n function on each binary basis state. Do not assume this is is a normalized quantum or\n classical probability vector. If we allowed this to return a density matrix,\n then we would need to change the definition of composition to be ~Op @ StateFn @ Op for\n those cases, whereas by this methodology we can ensure that composition always means Op\n @ StateFn.\n\n Return numpy vector of state vector, warn if more than 16 qubits to force the user to set\n massive=True if they want such a large vector.\n\n Args:\n massive: Whether to allow large conversions, e.g. creating a matrix representing\n over 16 qubits.\n\n Returns:\n np.ndarray: Vector of state vector\n\n Raises:\n ValueError: Invalid parameters.\n \"\"\"\n OperatorBase._check_massive('to_matrix', False, self.num_qubits, massive)\n # Operator - return diagonal (real values, not complex),\n # not rank 1 decomposition (statevector)!\n mat = self.primitive.to_matrix(massive=massive)\n # TODO change to weighted sum of eigenvectors' StateFns?\n\n # ListOp primitives can return lists of matrices (or trees for nested ListOps),\n # so we need to recurse over the\n # possible tree.\n def diag_over_tree(op):\n if isinstance(op, list):\n return [diag_over_tree(o) for o in op]\n else:\n vec = np.diag(op) * self.coeff\n # Reshape for measurements so np.dot still works for composition.\n return vec if not self.is_measurement else vec.reshape(1, -1)\n\n return diag_over_tree(mat","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_matrix.diag_over_tree","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_matrix.diag_over_tree\n def diag_over_tree(op):\n if isinstance(op, list):\n return [diag_over_tree(o) for o in op]\n else:\n vec = np.diag(op) * self.coeff\n # Reshape for measurements so np.dot still works for composition.\n return vec if not self.is_measurement else vec.reshape(1, -1)","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_circuit_op","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_circuit_op\n def to_circuit_op(self):\n r\"\"\" Return ``StateFnCircuit`` corresponding to this StateFn. Ignore for now because this is\n undefined. TODO maybe call to_pauli_op and diagonalize here, but that could be very\n inefficient, e.g. splitting one Stabilizer measurement into hundreds of 1 qubit Paulis.\"\"\"\n raise NotImplementedError","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.__str__","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.__str__\n def __str__(self) -> str:\n prim_str = str(self.primitive)\n if self.coeff == 1.0:\n return \"{}({})\".format('OperatorStateFn' if not self.is_measurement\n else 'OperatorMeasurement', prim_str)\n else:\n return \"{}({}) * {}\".format(\n 'OperatorStateFn' if not self.is_measurement else 'OperatorMeasurement',\n prim_str,\n self.coeff)","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.eval","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.eval\n def eval(\n self, front: Optional[Union[str, dict, np.ndarray, OperatorBase, Statevector]] = None\n ) -> Union[OperatorBase, complex]:\n if front is None:\n matrix = cast(MatrixOp, self.primitive.to_matrix_op()).primitive.data\n # pylint: disable=cyclic-import\n from .vector_state_fn import VectorStateFn\n return VectorStateFn(matrix[0, :])\n\n if not self.is_measurement and isinstance(front, OperatorBase):\n raise ValueError(\n 'Cannot compute overlap with StateFn or Operator if not Measurement. Try taking '\n 'sf.adjoint() first to convert to measurement.')\n\n if not isinstance(front, OperatorBase):\n front = StateFn(front)\n\n if isinstance(self.primitive, ListOp) and self.primitive.distributive:\n evals = [OperatorStateFn(op, is_measurement=self.is_measurement).eval(\n front) for op in self.primitive.oplist]\n result = self.primitive.combo_fn(evals)\n if isinstance(result, list):\n multiplied = self.primitive.coeff * self.coeff * np.array(result)\n return multiplied.tolist()\n return result * self.coeff * self.primitive.coeff\n\n # pylint: disable=cyclic-import\n from .vector_state_fn import VectorStateFn\n if isinstance(self.primitive, PauliSumOp) and isinstance(front, VectorStateFn):\n return (\n front.primitive.expectation_value(self.primitive.primitive)\n * self.coeff\n * front.coeff\n )\n\n # Need an ListOp-specific carve-out here to make sure measurement over a ListOp doesn't\n # produce two-dimensional ListOp from composing from both sides of primitive.\n # Can't use isinstance because this would include subclasses.\n # pylint: disable=unidiomatic-typecheck\n if isinstance(front, Lis","label":0},{"cid":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.sample","text":"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.sample\n def sample(self,\n shots: int = 1024,\n massive: bool = False,\n reverse_endianness: bool = False):\n raise NotImplementedError","label":0}],"string":"[\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__init__\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__init__\\n def __init__(self):\\n \\\"\\\"\\\"Create an empty circuit.\\\"\\\"\\\"\\n\\n # Circuit name. Generally, this corresponds to the name\\n # of the QuantumCircuit from which the DAG was generated.\\n self.name = None\\n\\n # Circuit metadata\\n self.metadata = None\\n\\n # Set of wires (Register,idx) in the dag\\n self._wires = set()\\n\\n # Map from wire (Register,idx) to input nodes of the graph\\n self.input_map = OrderedDict()\\n\\n # Map from wire (Register,idx) to output nodes of the graph\\n self.output_map = OrderedDict()\\n\\n # Directed multigraph whose nodes are inputs, outputs, or operations.\\n # Operation nodes have equal in- and out-degrees and carry\\n # additional data about the operation, including the argument order\\n # and parameter values.\\n # Input nodes have out-degree 1 and output nodes have in-degree 1.\\n # Edges carry wire labels (reg,idx) and each operation has\\n # corresponding in- and out-edges with the same wire labels.\\n self._multi_graph = rx.PyDAG()\\n\\n # Map of qreg/creg name to Register object.\\n self.qregs = OrderedDict()\\n self.cregs = OrderedDict()\\n\\n # List of Qubit/Clbit wires that the DAG acts on.\\n self.qubits = []\\n self.clbits = []\\n\\n self._global_phase = 0\\n self._calibrations = defaultdict(dict)\\n\\n self.duration = None\\n self.unit = 'dt'\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.to_networkx\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.to_networkx\\n def to_networkx(self):\\n \\\"\\\"\\\"Returns a copy of the DAGCircuit in networkx format.\\\"\\\"\\\"\\n try:\\n import networkx as nx\\n except ImportError as ex:\\n raise ImportError(\\\"Networkx is needed to use to_networkx(). It \\\"\\n \\\"can be installed with 'pip install networkx'\\\") from ex\\n G = nx.MultiDiGraph()\\n for node in self._multi_graph.nodes():\\n G.add_node(node)\\n for node_id in rx.topological_sort(self._multi_graph):\\n for source_id, dest_id, edge in self._multi_graph.in_edges(node_id):\\n G.add_edge(self._multi_graph[source_id],\\n self._multi_graph[dest_id],\\n wire=edge)\\n return G\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.from_networkx\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.from_networkx\\n @classmethod\\n def from_networkx(cls, graph):\\n \\\"\\\"\\\"Take a networkx MultiDigraph and create a new DAGCircuit.\\n\\n Args:\\n graph (networkx.MultiDiGraph): The graph to create a DAGCircuit\\n object from. The format of this MultiDiGraph format must be\\n in the same format as returned by to_networkx.\\n\\n Returns:\\n DAGCircuit: The dagcircuit object created from the networkx\\n MultiDiGraph.\\n Raises:\\n ImportError: If networkx is not installed\\n DAGCircuitError: If input networkx graph is malformed\\n \\\"\\\"\\\"\\n try:\\n import networkx as nx\\n except ImportError as ex:\\n raise ImportError(\\\"Networkx is needed to use from_networkx(). It \\\"\\n \\\"can be installed with 'pip install networkx'\\\") from ex\\n dag = DAGCircuit()\\n for node in nx.topological_sort(graph):\\n if node.type == 'out':\\n continue\\n if node.type == 'in':\\n if isinstance(node.wire, Qubit):\\n dag.add_qubits([node.wire])\\n elif isinstance(node.wire, Clbit):\\n dag.add_clbits([node.wire])\\n else:\\n raise DAGCircuitError('unknown node wire type: {}'.format(\\n node.wire))\\n elif node.type == 'op':\\n dag.apply_operation_back(node.op.copy(), node.qargs, node.cargs)\\n return dag\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.wires\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.wires\\n @property\\n def wires(self):\\n \\\"\\\"\\\"Return a list of the wires in order.\\\"\\\"\\\"\\n return self.qubits + self.clbits\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.node_counter\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.node_counter\\n @property\\n def node_counter(self):\\n \\\"\\\"\\\"\\n Returns the number of nodes in the dag.\\n \\\"\\\"\\\"\\n return len(self._multi_graph)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase\\n @property\\n def global_phase(self):\\n \\\"\\\"\\\"Return the global phase of the circuit.\\\"\\\"\\\"\\n return self._global_phase\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.global_phase\\n @global_phase.setter\\n def global_phase(self, angle):\\n \\\"\\\"\\\"Set the global phase of the circuit.\\n\\n Args:\\n angle (float, ParameterExpression)\\n \\\"\\\"\\\"\\n if isinstance(angle, ParameterExpression):\\n self._global_phase = angle\\n else:\\n # Set the phase to the [0, 2π) interval\\n angle = float(angle)\\n if not angle:\\n self._global_phase = 0\\n else:\\n self._global_phase = angle % (2 * math.pi)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations\\n @property\\n def calibrations(self):\\n \\\"\\\"\\\"Return calibration dictionary.\\n\\n The custom pulse definition of a given gate is of the form\\n {'gate_name': {(qubits, params): schedule}}\\n \\\"\\\"\\\"\\n return dict(self._calibrations)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.calibrations\\n @calibrations.setter\\n def calibrations(self, calibrations):\\n \\\"\\\"\\\"Set the circuit calibration data from a dictionary of calibration definition.\\n\\n Args:\\n calibrations (dict): A dictionary of input in the format\\n {'gate_name': {(qubits, gate_params): schedule}}\\n \\\"\\\"\\\"\\n self._calibrations = defaultdict(dict, calibrations)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_calibration\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_calibration\\n def add_calibration(self, gate, qubits, schedule, params=None):\\n \\\"\\\"\\\"Register a low-level, custom pulse definition for the given gate.\\n\\n Args:\\n gate (Union[Gate, str]): Gate information.\\n qubits (Union[int, Tuple[int]]): List of qubits to be measured.\\n schedule (Schedule): Schedule information.\\n params (Optional[List[Union[float, Parameter]]]): A list of parameters.\\n\\n Raises:\\n Exception: if the gate is of type string and params is None.\\n \\\"\\\"\\\"\\n if isinstance(gate, Gate):\\n self._calibrations[gate.name][(tuple(qubits), tuple(gate.params))] = schedule\\n else:\\n self._calibrations[gate][(tuple(qubits), tuple(params or []))] = schedule\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.has_calibration_for\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.has_calibration_for\\n def has_calibration_for(self, node):\\n \\\"\\\"\\\"Return True if the dag has a calibration defined for the node operation. In this\\n case, the operation does not need to be translated to the device basis.\\n \\\"\\\"\\\"\\n if not self.calibrations or node.name not in self.calibrations:\\n return False\\n qubits = tuple(self.qubits.index(qubit) for qubit in node.qargs)\\n params = []\\n for p in node.op.params:\\n if isinstance(p, ParameterExpression) and not p.parameters:\\n params.append(float(p))\\n else:\\n params.append(p)\\n params = tuple(params)\\n return (qubits, params) in self.calibrations[node.name]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_all_ops_named\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_all_ops_named\\n def remove_all_ops_named(self, opname):\\n \\\"\\\"\\\"Remove all operation nodes with the given name.\\\"\\\"\\\"\\n for n in self.named_nodes(opname):\\n self.remove_op_node(n)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qubits\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qubits\\n def add_qubits(self, qubits):\\n \\\"\\\"\\\"Add individual qubit wires.\\\"\\\"\\\"\\n if any(not isinstance(qubit, Qubit) for qubit in qubits):\\n raise DAGCircuitError(\\\"not a Qubit instance.\\\")\\n\\n duplicate_qubits = set(self.qubits).intersection(qubits)\\n if duplicate_qubits:\\n raise DAGCircuitError(\\\"duplicate qubits %s\\\" % duplicate_qubits)\\n\\n self.qubits.extend(qubits)\\n for qubit in qubits:\\n self._add_wire(qubit)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_clbits\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_clbits\\n def add_clbits(self, clbits):\\n \\\"\\\"\\\"Add individual clbit wires.\\\"\\\"\\\"\\n if any(not isinstance(clbit, Clbit) for clbit in clbits):\\n raise DAGCircuitError(\\\"not a Clbit instance.\\\")\\n\\n duplicate_clbits = set(self.clbits).intersection(clbits)\\n if duplicate_clbits:\\n raise DAGCircuitError(\\\"duplicate clbits %s\\\" % duplicate_clbits)\\n\\n self.clbits.extend(clbits)\\n for clbit in clbits:\\n self._add_wire(clbit)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qreg\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_qreg\\n def add_qreg(self, qreg):\\n \\\"\\\"\\\"Add all wires in a quantum register.\\\"\\\"\\\"\\n if not isinstance(qreg, QuantumRegister):\\n raise DAGCircuitError(\\\"not a QuantumRegister instance.\\\")\\n if qreg.name in self.qregs:\\n raise DAGCircuitError(\\\"duplicate register %s\\\" % qreg.name)\\n self.qregs[qreg.name] = qreg\\n existing_qubits = set(self.qubits)\\n for j in range(qreg.size):\\n if qreg[j] not in existing_qubits:\\n self.qubits.append(qreg[j])\\n self._add_wire(qreg[j])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_creg\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.add_creg\\n def add_creg(self, creg):\\n \\\"\\\"\\\"Add all wires in a classical register.\\\"\\\"\\\"\\n if not isinstance(creg, ClassicalRegister):\\n raise DAGCircuitError(\\\"not a ClassicalRegister instance.\\\")\\n if creg.name in self.cregs:\\n raise DAGCircuitError(\\\"duplicate register %s\\\" % creg.name)\\n self.cregs[creg.name] = creg\\n existing_clbits = set(self.clbits)\\n for j in range(creg.size):\\n if creg[j] not in existing_clbits:\\n self.clbits.append(creg[j])\\n self._add_wire(creg[j])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_wire\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_wire\\n def _add_wire(self, wire):\\n \\\"\\\"\\\"Add a qubit or bit to the circuit.\\n\\n Args:\\n wire (Bit): the wire to be added\\n\\n This adds a pair of in and out nodes connected by an edge.\\n\\n Raises:\\n DAGCircuitError: if trying to add duplicate wire\\n \\\"\\\"\\\"\\n if wire not in self._wires:\\n self._wires.add(wire)\\n\\n inp_node = DAGNode(type='in', wire=wire)\\n outp_node = DAGNode(type='out', wire=wire)\\n input_map_id, output_map_id = self._multi_graph.add_nodes_from(\\n [inp_node, outp_node])\\n inp_node._node_id = input_map_id\\n outp_node._node_id = output_map_id\\n self.input_map[wire] = inp_node\\n self.output_map[wire] = outp_node\\n self._multi_graph.add_edge(inp_node._node_id,\\n outp_node._node_id,\\n wire)\\n else:\\n raise DAGCircuitError(\\\"duplicate wire %s\\\" % (wire,))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_condition\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_condition\\n def _check_condition(self, name, condition):\\n \\\"\\\"\\\"Verify that the condition is valid.\\n\\n Args:\\n name (string): used for error reporting\\n condition (tuple or None): a condition tuple (ClassicalRegister,int)\\n\\n Raises:\\n DAGCircuitError: if conditioning on an invalid register\\n \\\"\\\"\\\"\\n # Verify creg exists\\n if condition is not None and condition[0].name not in self.cregs:\\n raise DAGCircuitError(\\\"invalid creg in condition for %s\\\" % name)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_bits\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_bits\\n def _check_bits(self, args, amap):\\n \\\"\\\"\\\"Check the values of a list of (qu)bit arguments.\\n\\n For each element of args, check that amap contains it.\\n\\n Args:\\n args (list[Bit]): the elements to be checked\\n amap (dict): a dictionary keyed on Qubits/Clbits\\n\\n Raises:\\n DAGCircuitError: if a qubit is not contained in amap\\n \\\"\\\"\\\"\\n # Check for each wire\\n for wire in args:\\n if wire not in amap:\\n raise DAGCircuitError(\\\"(qu)bit %s not found in %s\\\" %\\n (wire, amap))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._bits_in_condition\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._bits_in_condition\\n def _bits_in_condition(self, cond):\\n \\\"\\\"\\\"Return a list of bits in the given condition.\\n\\n Args:\\n cond (tuple or None): optional condition (ClassicalRegister, int)\\n\\n Returns:\\n list[Clbit]: list of classical bits\\n \\\"\\\"\\\"\\n return [] if cond is None else list(cond[0])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_op_node\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._add_op_node\\n def _add_op_node(self, op, qargs, cargs):\\n \\\"\\\"\\\"Add a new operation node to the graph and assign properties.\\n\\n Args:\\n op (qiskit.circuit.Instruction): the operation associated with the DAG node\\n qargs (list[Qubit]): list of quantum wires to attach to.\\n cargs (list[Clbit]): list of classical wires to attach to.\\n Returns:\\n int: The integer node index for the new op node on the DAG\\n \\\"\\\"\\\"\\n # Add a new operation node to the graph\\n new_node = DAGNode(type=\\\"op\\\", op=op, name=op.name, qargs=qargs,\\n cargs=cargs)\\n node_index = self._multi_graph.add_node(new_node)\\n new_node._node_id = node_index\\n return node_index\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._copy_circuit_metadata\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._copy_circuit_metadata\\n def _copy_circuit_metadata(self):\\n \\\"\\\"\\\"Return a copy of source_dag with metadata but empty.\\\"\\\"\\\"\\n target_dag = DAGCircuit()\\n target_dag.name = self.name\\n target_dag._global_phase = self._global_phase\\n target_dag.metadata = self.metadata\\n\\n target_dag.add_qubits(self.qubits)\\n target_dag.add_clbits(self.clbits)\\n\\n for qreg in self.qregs.values():\\n target_dag.add_qreg(qreg)\\n for creg in self.cregs.values():\\n target_dag.add_creg(creg)\\n\\n return target_dag\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_back\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_back\\n def apply_operation_back(self, op, qargs=None, cargs=None, condition=None):\\n \\\"\\\"\\\"Apply an operation to the output of the circuit.\\n\\n Args:\\n op (qiskit.circuit.Instruction): the operation associated with the DAG node\\n qargs (list[Qubit]): qubits that op will be applied to\\n cargs (list[Clbit]): cbits that op will be applied to\\n condition (tuple or None): DEPRECATED optional condition (ClassicalRegister, int)\\n Returns:\\n DAGNode: the current max node\\n\\n Raises:\\n DAGCircuitError: if a leaf node is connected to multiple outputs\\n\\n \\\"\\\"\\\"\\n if condition:\\n warnings.warn(\\\"Use of condition arg is deprecated, set condition in instruction\\\",\\n DeprecationWarning)\\n op.condition = condition if op.condition is None else op.condition\\n\\n qargs = qargs or []\\n cargs = cargs or []\\n\\n all_cbits = self._bits_in_condition(op.condition)\\n all_cbits = set(all_cbits).union(cargs)\\n\\n self._check_condition(op.name, op.condition)\\n self._check_bits(qargs, self.output_map)\\n self._check_bits(all_cbits, self.output_map)\\n\\n node_index = self._add_op_node(op, qargs, cargs)\\n\\n # Add new in-edges from predecessors of the output nodes to the\\n # operation node while deleting the old in-edges of the output nodes\\n # and adding new edges from the operation node to each output node\\n\\n al = [qargs, all_cbits]\\n self._multi_graph.insert_node_on_in_edges_multiple(\\n node_index,\\n [self.output_map[q]._node_id for q in itertools.chain(*al)])\\n return self._multi_graph[node_index]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_front\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.apply_operation_front\\n def apply_operation_front(self, op, qargs, cargs, condition=None):\\n \\\"\\\"\\\"Apply an operation to the input of the circuit.\\n\\n Args:\\n op (qiskit.circuit.Instruction): the operation associated with the DAG node\\n qargs (list[Qubit]): qubits that op will be applied to\\n cargs (list[Clbit]): cbits that op will be applied to\\n condition (tuple or None): DEPRECATED optional condition (ClassicalRegister, int)\\n Returns:\\n DAGNode: the current max node\\n\\n Raises:\\n DAGCircuitError: if initial nodes connected to multiple out edges\\n \\\"\\\"\\\"\\n if condition:\\n warnings.warn(\\\"Use of condition arg is deprecated, set condition in instruction\\\",\\n DeprecationWarning)\\n\\n op.condition = condition if op.condition is None else op.condition\\n all_cbits = self._bits_in_condition(op.condition)\\n all_cbits.extend(cargs)\\n\\n self._check_condition(op.name, op.condition)\\n self._check_bits(qargs, self.input_map)\\n self._check_bits(all_cbits, self.input_map)\\n node_index = self._add_op_node(op, qargs, cargs)\\n\\n # Add new out-edges to successors of the input nodes from the\\n # operation node while deleting the old out-edges of the input nodes\\n # and adding new edges to the operation node from each input node\\n al = [qargs, all_cbits]\\n self._multi_graph.insert_node_on_out_edges_multiple(\\n node_index,\\n [self.input_map[q]._node_id for q in itertools.chain(*al)])\\n return self._multi_graph[node_index]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_edgemap_registers\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_edgemap_registers\\n def _check_edgemap_registers(self, inbound_wires, inbound_regs):\\n \\\"\\\"\\\"Check that wiremap neither fragments nor leaves duplicate registers.\\n\\n 1. There are no fragmented registers. A register in keyregs\\n is fragmented if not all of its (qu)bits are renamed by edge_map.\\n 2. There are no duplicate registers. A register is duplicate if\\n it appears in both self and keyregs but not in edge_map.\\n\\n Args:\\n inbound_wires (list): a list of wires being mapped from the inbound dag\\n inbound_regs (list): a list from registers from the inbound dag\\n\\n Returns:\\n set(Register): the set of regs to add to self\\n\\n Raises:\\n DAGCircuitError: if the wiremap fragments, or duplicates exist\\n \\\"\\\"\\\"\\n add_regs = set()\\n reg_frag_chk = {}\\n\\n for inbound_reg in inbound_regs:\\n reg_frag_chk[inbound_reg] = {reg_bit: False for reg_bit in inbound_reg}\\n\\n for inbound_bit in inbound_wires:\\n for inbound_reg in inbound_regs:\\n if inbound_bit in inbound_reg:\\n reg_frag_chk[inbound_reg][inbound_bit] = True\\n break\\n\\n for inbound_reg, v in reg_frag_chk.items():\\n s = set(v.values())\\n if len(s) == 2:\\n raise DAGCircuitError(\\\"inbound_wires fragments reg %s\\\" % inbound_reg)\\n if s == {False}:\\n if (\\n inbound_reg.name in self.qregs\\n or inbound_reg.name in self.cregs\\n ):\\n raise DAGCircuitError(\\\"unmapped duplicate reg %s\\\" % inbound_reg)\\n\\n # Add registers that appear only in inbound_regs\\n add_regs.add(inbound_reg)\\n\\n return add_regs\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_wiremap_validity\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_wiremap_validity\\n def _check_wiremap_validity(self, wire_map, keymap, valmap):\\n \\\"\\\"\\\"Check that the wiremap is consistent.\\n\\n Check that the wiremap refers to valid wires and that\\n those wires have consistent types.\\n\\n Args:\\n wire_map (dict): map from Bit in keymap to Bit in valmap\\n keymap (list): a list of wire_map keys\\n valmap (dict): a map whose keys are wire_map values\\n\\n Raises:\\n DAGCircuitError: if wire_map not valid\\n \\\"\\\"\\\"\\n for k, v in wire_map.items():\\n\\n if k not in keymap:\\n raise DAGCircuitError(\\\"invalid wire mapping key %s\\\" % k)\\n if v not in valmap:\\n raise DAGCircuitError(\\\"invalid wire mapping value %s\\\" % v)\\n # TODO Support mapping from AncillaQubit to Qubit, since AncillaQubits are mapped to\\n # Qubits upon being converted to an Instruction. Until this translation is fixed\\n # and Instructions have a concept of ancilla qubits, this fix is required.\\n if not (isinstance(k, type(v)) or isinstance(v, type(k))):\\n raise DAGCircuitError(\\\"inconsistent wire_map at (%s,%s)\\\" % (k, v))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._map_condition\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._map_condition\\n @staticmethod\\n def _map_condition(wire_map, condition, target_cregs):\\n \\\"\\\"\\\"Use the wire_map dict to change the condition tuple's creg name.\\n\\n Args:\\n wire_map (dict): a map from source wires to destination wires\\n condition (tuple or None): (ClassicalRegister,int)\\n target_cregs (list[ClassicalRegister]): List of all cregs in the\\n target circuit onto which the condition might possibly be mapped.\\n Returns:\\n tuple(ClassicalRegister,int): new condition\\n Raises:\\n DAGCircuitError: if condition register not in wire_map, or if\\n wire_map maps condition onto more than one creg, or if the\\n specified condition is not present in a classical register.\\n \\\"\\\"\\\"\\n\\n if condition is None:\\n new_condition = None\\n else:\\n # if there is a condition, map the condition bits to the\\n # composed cregs based on the wire_map\\n cond_creg = condition[0]\\n cond_val = condition[1]\\n new_cond_val = 0\\n new_creg = None\\n for bit in wire_map:\\n if bit in cond_creg:\\n try:\\n candidate_creg = next(creg\\n for creg in target_cregs\\n if wire_map[bit] in creg)\\n except StopIteration as ex:\\n raise DAGCircuitError('Did not find creg containing '\\n 'mapped clbit in conditional.') from ex\\n\\n if new_creg is None:\\n new_creg = candidate_creg\\n elif new_creg != candidate_creg:\\n # Raise if wire_map maps condition creg on to more than one\\n # creg in target DAG.\\n raise DAGCircuitError('wi\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.extend_back\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.extend_back\\n def extend_back(self, dag, edge_map=None):\\n \\\"\\\"\\\"DEPRECATED: Add `dag` at the end of `self`, using `edge_map`.\\n \\\"\\\"\\\"\\n warnings.warn(\\\"dag.extend_back is deprecated, please use dag.compose.\\\",\\n DeprecationWarning, stacklevel=2)\\n edge_map = edge_map or {}\\n for qreg in dag.qregs.values():\\n if qreg.name not in self.qregs:\\n self.add_qreg(QuantumRegister(qreg.size, qreg.name))\\n edge_map.update([(qbit, qbit) for qbit in qreg if qbit not in edge_map])\\n\\n for creg in dag.cregs.values():\\n if creg.name not in self.cregs:\\n self.add_creg(ClassicalRegister(creg.size, creg.name))\\n edge_map.update([(cbit, cbit) for cbit in creg if cbit not in edge_map])\\n\\n self.compose_back(dag, edge_map)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.compose_back\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.compose_back\\n def compose_back(self, input_circuit, edge_map=None):\\n \\\"\\\"\\\"DEPRECATED: use DAGCircuit.compose() instead.\\n \\\"\\\"\\\"\\n warnings.warn(\\\"dag.compose_back is deprecated, please use dag.compose.\\\",\\n DeprecationWarning, stacklevel=2)\\n self.compose(input_circuit, edge_map)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.compose\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.compose\\n def compose(self, other, edge_map=None, qubits=None, clbits=None, front=False, inplace=True):\\n \\\"\\\"\\\"Compose the ``other`` circuit onto the output of this circuit.\\n\\n A subset of input wires of ``other`` are mapped\\n to a subset of output wires of this circuit.\\n\\n ``other`` can be narrower or of equal width to ``self``.\\n\\n Args:\\n other (DAGCircuit): circuit to compose with self\\n edge_map (dict): DEPRECATED - a {Bit: Bit} map from input wires of other\\n to output wires of self (i.e. rhs->lhs).\\n The key, value pairs can be either Qubit or Clbit mappings.\\n qubits (list[Qubit|int]): qubits of self to compose onto.\\n clbits (list[Clbit|int]): clbits of self to compose onto.\\n front (bool): If True, front composition will be performed (not implemented yet)\\n inplace (bool): If True, modify the object. Otherwise return composed circuit.\\n\\n Returns:\\n DAGCircuit: the composed dag (returns None if inplace==True).\\n\\n Raises:\\n DAGCircuitError: if ``other`` is wider or there are duplicate edge mappings.\\n \\\"\\\"\\\"\\n if front:\\n raise DAGCircuitError(\\\"Front composition not supported yet.\\\")\\n\\n if len(other.qubits) > len(self.qubits) or \\\\\\n len(other.clbits) > len(self.clbits):\\n raise DAGCircuitError(\\\"Trying to compose with another DAGCircuit \\\"\\n \\\"which has more 'in' edges.\\\")\\n\\n if edge_map is not None:\\n warnings.warn(\\\"edge_map arg as a dictionary is deprecated. \\\"\\n \\\"Use qubits and clbits args to specify a list of \\\"\\n \\\"self edges to compose onto.\\\", DeprecationWarning,\\n stacklevel=2)\\n\\n # number of qubits and clbits must match number in circuit or None\\n identity_qubit_map = dict(zip(other.qubits, self.\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.reverse_ops\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.reverse_ops\\n def reverse_ops(self):\\n \\\"\\\"\\\"Reverse the operations in the ``self`` circuit.\\n\\n Returns:\\n DAGCircuit: the reversed dag.\\n \\\"\\\"\\\"\\n # TODO: speed up\\n # pylint: disable=cyclic-import\\n from qiskit.converters import dag_to_circuit, circuit_to_dag\\n qc = dag_to_circuit(self)\\n reversed_qc = qc.reverse_ops()\\n reversed_dag = circuit_to_dag(reversed_qc)\\n return reversed_dag\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.idle_wires\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.idle_wires\\n def idle_wires(self, ignore=None):\\n \\\"\\\"\\\"Return idle wires.\\n\\n Args:\\n ignore (list(str)): List of node names to ignore. Default: []\\n\\n Yields:\\n Bit: Bit in idle wire.\\n \\\"\\\"\\\"\\n if ignore is None:\\n ignore = []\\n for wire in self._wires:\\n nodes = [node for node in self.nodes_on_wire(wire, only_ops=False)\\n if node.name not in ignore]\\n if len(nodes) == 2:\\n yield wire\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.size\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.size\\n def size(self):\\n \\\"\\\"\\\"Return the number of operations.\\\"\\\"\\\"\\n return len(self._multi_graph) - 2 * len(self._wires)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.depth\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.depth\\n def depth(self):\\n \\\"\\\"\\\"Return the circuit depth.\\n Returns:\\n int: the circuit depth\\n Raises:\\n DAGCircuitError: if not a directed acyclic graph\\n \\\"\\\"\\\"\\n try:\\n depth = rx.dag_longest_path_length(self._multi_graph) - 1\\n except rx.DAGHasCycle as ex:\\n raise DAGCircuitError(\\\"not a DAG\\\") from ex\\n return depth if depth >= 0 else 0\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.width\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.width\\n def width(self):\\n \\\"\\\"\\\"Return the total number of qubits + clbits used by the circuit.\\n This function formerly returned the number of qubits by the calculation\\n return len(self._wires) - self.num_clbits()\\n but was changed by issue #2564 to return number of qubits + clbits\\n with the new function DAGCircuit.num_qubits replacing the former\\n semantic of DAGCircuit.width().\\n \\\"\\\"\\\"\\n return len(self._wires)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.num_qubits\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.num_qubits\\n def num_qubits(self):\\n \\\"\\\"\\\"Return the total number of qubits used by the circuit.\\n num_qubits() replaces former use of width().\\n DAGCircuit.width() now returns qubits + clbits for\\n consistency with Circuit.width() [qiskit-terra #2564].\\n \\\"\\\"\\\"\\n return len(self.qubits)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.num_clbits\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.num_clbits\\n def num_clbits(self):\\n \\\"\\\"\\\"Return the total number of classical bits used by the circuit.\\\"\\\"\\\"\\n return len(self.clbits)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.num_tensor_factors\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.num_tensor_factors\\n def num_tensor_factors(self):\\n \\\"\\\"\\\"Compute how many components the circuit can decompose into.\\\"\\\"\\\"\\n return rx.number_weakly_connected_components(self._multi_graph)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_wires_list\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._check_wires_list\\n def _check_wires_list(self, wires, node):\\n \\\"\\\"\\\"Check that a list of wires is compatible with a node to be replaced.\\n\\n - no duplicate names\\n - correct length for operation\\n Raise an exception otherwise.\\n\\n Args:\\n wires (list[Bit]): gives an order for (qu)bits\\n in the input circuit that is replacing the node.\\n node (DAGNode): a node in the dag\\n\\n Raises:\\n DAGCircuitError: if check doesn't pass.\\n \\\"\\\"\\\"\\n if len(set(wires)) != len(wires):\\n raise DAGCircuitError(\\\"duplicate wires\\\")\\n\\n wire_tot = len(node.qargs) + len(node.cargs)\\n if node.op.condition is not None:\\n wire_tot += node.op.condition[0].size\\n\\n if len(wires) != wire_tot:\\n raise DAGCircuitError(\\\"expected %d wires, got %d\\\"\\n % (wire_tot, len(wires)))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._make_pred_succ_maps\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._make_pred_succ_maps\\n def _make_pred_succ_maps(self, node):\\n \\\"\\\"\\\"Return predecessor and successor dictionaries.\\n\\n Args:\\n node (DAGNode): reference to multi_graph node\\n\\n Returns:\\n tuple(dict): tuple(predecessor_map, successor_map)\\n These map from wire (Register, int) to the node ids for the\\n predecessor (successor) nodes of the input node.\\n \\\"\\\"\\\"\\n\\n pred_map = {e[2]: e[0] for e in\\n self._multi_graph.in_edges(node._node_id)}\\n succ_map = {e[2]: e[1] for e in\\n self._multi_graph.out_edges(node._node_id)}\\n return pred_map, succ_map\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._full_pred_succ_maps\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit._full_pred_succ_maps\\n def _full_pred_succ_maps(self, pred_map, succ_map, input_circuit,\\n wire_map):\\n \\\"\\\"\\\"Map all wires of the input circuit.\\n\\n Map all wires of the input circuit to predecessor and\\n successor nodes in self, keyed on wires in self.\\n\\n Args:\\n pred_map (dict): comes from _make_pred_succ_maps\\n succ_map (dict): comes from _make_pred_succ_maps\\n input_circuit (DAGCircuit): the input circuit\\n wire_map (dict): the map from wires of input_circuit to wires of self\\n\\n Returns:\\n tuple: full_pred_map, full_succ_map (dict, dict)\\n\\n Raises:\\n DAGCircuitError: if more than one predecessor for output nodes\\n \\\"\\\"\\\"\\n full_pred_map = {}\\n full_succ_map = {}\\n for w in input_circuit.input_map:\\n # If w is wire mapped, find the corresponding predecessor\\n # of the node\\n if w in wire_map:\\n full_pred_map[wire_map[w]] = pred_map[wire_map[w]]\\n full_succ_map[wire_map[w]] = succ_map[wire_map[w]]\\n else:\\n # Otherwise, use the corresponding output nodes of self\\n # and compute the predecessor.\\n full_succ_map[w] = self.output_map[w]\\n full_pred_map[w] = self._multi_graph.predecessors(\\n self.output_map[w])[0]\\n if len(self._multi_graph.predecessors(self.output_map[w])) != 1:\\n raise DAGCircuitError(\\\"too many predecessors for %s[%d] \\\"\\n \\\"output node\\\" % (w.register, w.index))\\n\\n return full_pred_map, full_succ_map\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__eq__\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__eq__\\n def __eq__(self, other):\\n # Try to convert to float, but in case of unbound ParameterExpressions\\n # a TypeError will be raise, fallback to normal equality in those\\n # cases\\n\\n try:\\n self_phase = float(self.global_phase)\\n other_phase = float(other.global_phase)\\n if abs((self_phase - other_phase + np.pi) % (2*np.pi) - np.pi) > 1.E-10: # TODO: atol?\\n return False\\n except TypeError:\\n if self.global_phase != other.global_phase:\\n return False\\n if self.calibrations != other.calibrations:\\n return False\\n\\n self_bit_indices = {bit: idx\\n for idx, bit in\\n enumerate(self.qubits + self.clbits)}\\n other_bit_indices = {bit: idx\\n for idx, bit in\\n enumerate(other.qubits + other.clbits)}\\n\\n self_qreg_indices = [(regname, [self_bit_indices[bit] for bit in reg])\\n for regname, reg in self.qregs.items()]\\n self_creg_indices = [(regname, [self_bit_indices[bit] for bit in reg])\\n for regname, reg in self.cregs.items()]\\n\\n other_qreg_indices = [(regname, [other_bit_indices[bit] for bit in reg])\\n for regname, reg in other.qregs.items()]\\n other_creg_indices = [(regname, [other_bit_indices[bit] for bit in reg])\\n for regname, reg in other.cregs.items()]\\n if (\\n self_qreg_indices != other_qreg_indices\\n or self_creg_indices != other_creg_indices\\n ):\\n return False\\n\\n def node_eq(node_self, node_other):\\n return DAGNode.semantic_eq(node_self, node_other,\\n self_bit_indices, other_bit_indices)\\n\\n return rx.is_isomorphic_node_match(self._multi_graph,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__eq__.node_eq\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.__eq__.node_eq\\n def node_eq(node_self, node_other):\\n return DAGNode.semantic_eq(node_self, node_other,\\n self_bit_indices, other_bit_indices)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.topological_nodes\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.topological_nodes\\n def topological_nodes(self):\\n \\\"\\\"\\\"\\n Yield nodes in topological order.\\n\\n Returns:\\n generator(DAGNode): node in topological order\\n \\\"\\\"\\\"\\n\\n def _key(x):\\n return x.sort_key\\n\\n return iter(rx.lexicographical_topological_sort(\\n self._multi_graph, key=_key))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.topological_nodes._key\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.topological_nodes._key\\n def _key(x):\\n return x.sort_key\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.topological_op_nodes\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.topological_op_nodes\\n def topological_op_nodes(self):\\n \\\"\\\"\\\"\\n Yield op nodes in topological order.\\n\\n Returns:\\n generator(DAGNode): op node in topological order\\n \\\"\\\"\\\"\\n return (nd for nd in self.topological_nodes() if nd.type == 'op')\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.substitute_node_with_dag\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.substitute_node_with_dag\\n def substitute_node_with_dag(self, node, input_dag, wires=None):\\n \\\"\\\"\\\"Replace one node with dag.\\n\\n Args:\\n node (DAGNode): node to substitute\\n input_dag (DAGCircuit): circuit that will substitute the node\\n wires (list[Bit]): gives an order for (qu)bits\\n in the input circuit. This order gets matched to the node wires\\n by qargs first, then cargs, then conditions.\\n\\n Raises:\\n DAGCircuitError: if met with unexpected predecessor/successors\\n \\\"\\\"\\\"\\n in_dag = input_dag\\n condition = None if node.type != 'op' else node.op.condition\\n\\n # the dag must be amended if used in a\\n # conditional context. delete the op nodes and replay\\n # them with the condition.\\n if condition:\\n in_dag = copy.deepcopy(input_dag)\\n in_dag.add_creg(condition[0])\\n to_replay = []\\n for sorted_node in in_dag.topological_nodes():\\n if sorted_node.type == \\\"op\\\":\\n sorted_node.op.condition = condition\\n to_replay.append(sorted_node)\\n for input_node in in_dag.op_nodes():\\n in_dag.remove_op_node(input_node)\\n for replay_node in to_replay:\\n in_dag.apply_operation_back(replay_node.op, replay_node.qargs,\\n replay_node.cargs)\\n\\n if in_dag.global_phase:\\n from sympy import evaluate\\n with evaluate(False):\\n self.global_phase += in_dag.global_phase\\n\\n if wires is None:\\n wires = in_dag.wires\\n\\n self._check_wires_list(wires, node)\\n\\n # Create a proxy wire_map to identify fragments and duplicates\\n # and determine what registers need to be added to self\\n add_qregs = self._check_edgemap_registers(wires, in_dag.qregs.values())\\n for qreg in add_qreg\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.substitute_node\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.substitute_node\\n def substitute_node(self, node, op, inplace=False):\\n \\\"\\\"\\\"Replace a DAGNode with a single instruction. qargs, cargs and\\n conditions for the new instruction will be inferred from the node to be\\n replaced. The new instruction will be checked to match the shape of the\\n replaced instruction.\\n\\n Args:\\n node (DAGNode): Node to be replaced\\n op (qiskit.circuit.Instruction): The :class:`qiskit.circuit.Instruction`\\n instance to be added to the DAG\\n inplace (bool): Optional, default False. If True, existing DAG node\\n will be modified to include op. Otherwise, a new DAG node will\\n be used.\\n\\n Returns:\\n DAGNode: the new node containing the added instruction.\\n\\n Raises:\\n DAGCircuitError: If replacement instruction was incompatible with\\n location of target node.\\n \\\"\\\"\\\"\\n\\n if node.type != 'op':\\n raise DAGCircuitError('Only DAGNodes of type \\\"op\\\" can be replaced.')\\n\\n if (\\n node.op.num_qubits != op.num_qubits\\n or node.op.num_clbits != op.num_clbits\\n ):\\n raise DAGCircuitError(\\n 'Cannot replace node of width ({} qubits, {} clbits) with '\\n 'instruction of mismatched width ({} qubits, {} clbits).'.format(\\n node.op.num_qubits, node.op.num_clbits,\\n op.num_qubits, op.num_clbits))\\n\\n if inplace:\\n save_condition = node.op.condition\\n node.op = op\\n node.name = op.name\\n node.op.condition = save_condition\\n return node\\n\\n new_node = copy.copy(node)\\n save_condition = new_node.op.condition\\n new_node.op = op\\n new_node.name = op.name\\n new_node.op.condition = save_condition\\n self._multi_graph[node._node_id] = new_node\\n return new_node\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.node\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.node\\n def node(self, node_id):\\n \\\"\\\"\\\"Get the node in the dag.\\n\\n Args:\\n node_id(int): Node identifier.\\n\\n Returns:\\n node: the node.\\n \\\"\\\"\\\"\\n return self._multi_graph[node_id]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.nodes\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.nodes\\n def nodes(self):\\n \\\"\\\"\\\"Iterator for node values.\\n\\n Yield:\\n node: the node.\\n \\\"\\\"\\\"\\n yield from self._multi_graph.nodes()\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.edges\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.edges\\n def edges(self, nodes=None):\\n \\\"\\\"\\\"Iterator for edge values and source and dest node\\n\\n This works by returning the output edges from the specified nodes. If\\n no nodes are specified all edges from the graph are returned.\\n\\n Args:\\n nodes(DAGNode|list(DAGNode): Either a list of nodes or a single\\n input node. If none is specified all edges are returned from\\n the graph.\\n\\n Yield:\\n edge: the edge in the same format as out_edges the tuple\\n (source node, destination node, edge data)\\n \\\"\\\"\\\"\\n if nodes is None:\\n nodes = self._multi_graph.nodes()\\n\\n elif isinstance(nodes, DAGNode):\\n nodes = [nodes]\\n for node in nodes:\\n raw_nodes = self._multi_graph.out_edges(node._node_id)\\n for source, dest, edge in raw_nodes:\\n yield (self._multi_graph[source],\\n self._multi_graph[dest],\\n edge)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.op_nodes\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.op_nodes\\n def op_nodes(self, op=None, include_directives=True):\\n \\\"\\\"\\\"Get the list of \\\"op\\\" nodes in the dag.\\n\\n Args:\\n op (Type): :class:`qiskit.circuit.Instruction` subclass op nodes to\\n return. If None, return all op nodes.\\n include_directives (bool): include `barrier`, `snapshot` etc.\\n\\n Returns:\\n list[DAGNode]: the list of node ids containing the given op.\\n \\\"\\\"\\\"\\n nodes = []\\n for node in self._multi_graph.nodes():\\n if node.type == \\\"op\\\":\\n if not include_directives and node.op._directive:\\n continue\\n if op is None or isinstance(node.op, op):\\n nodes.append(node)\\n return nodes\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.gate_nodes\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.gate_nodes\\n def gate_nodes(self):\\n \\\"\\\"\\\"Get the list of gate nodes in the dag.\\n\\n Returns:\\n list[DAGNode]: the list of DAGNodes that represent gates.\\n \\\"\\\"\\\"\\n nodes = []\\n for node in self.op_nodes():\\n if isinstance(node.op, Gate):\\n nodes.append(node)\\n return nodes\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.named_nodes\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.named_nodes\\n def named_nodes(self, *names):\\n \\\"\\\"\\\"Get the set of \\\"op\\\" nodes with the given name.\\\"\\\"\\\"\\n named_nodes = []\\n for node in self._multi_graph.nodes():\\n if node.type == 'op' and node.op.name in names:\\n named_nodes.append(node)\\n return named_nodes\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.twoQ_gates\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.twoQ_gates\\n def twoQ_gates(self):\\n \\\"\\\"\\\"Get list of 2-qubit gates. Ignore snapshot, barriers, and the like.\\\"\\\"\\\"\\n warnings.warn('deprecated function, use dag.two_qubit_ops(). '\\n 'filter output by isinstance(op, Gate) to only get unitary Gates.',\\n DeprecationWarning, stacklevel=2)\\n two_q_gates = []\\n for node in self.gate_nodes():\\n if len(node.qargs) == 2:\\n two_q_gates.append(node)\\n return two_q_gates\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.threeQ_or_more_gates\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.threeQ_or_more_gates\\n def threeQ_or_more_gates(self):\\n \\\"\\\"\\\"Get list of 3-or-more-qubit gates: (id, data).\\\"\\\"\\\"\\n warnings.warn('deprecated function, use dag.multi_qubit_ops(). '\\n 'filter output by isinstance(op, Gate) to only get unitary Gates.',\\n DeprecationWarning, stacklevel=2)\\n three_q_gates = []\\n for node in self.gate_nodes():\\n if len(node.qargs) >= 3:\\n three_q_gates.append(node)\\n return three_q_gates\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.two_qubit_ops\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.two_qubit_ops\\n def two_qubit_ops(self):\\n \\\"\\\"\\\"Get list of 2 qubit operations. Ignore directives like snapshot and barrier.\\\"\\\"\\\"\\n ops = []\\n for node in self.op_nodes(include_directives=False):\\n if len(node.qargs) == 2:\\n ops.append(node)\\n return ops\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.multi_qubit_ops\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.multi_qubit_ops\\n def multi_qubit_ops(self):\\n \\\"\\\"\\\"Get list of 3+ qubit operations. Ignore directives like snapshot and barrier.\\\"\\\"\\\"\\n ops = []\\n for node in self.op_nodes(include_directives=False):\\n if len(node.qargs) >= 3:\\n ops.append(node)\\n return ops\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.longest_path\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.longest_path\\n def longest_path(self):\\n \\\"\\\"\\\"Returns the longest path in the dag as a list of DAGNodes.\\\"\\\"\\\"\\n return [self._multi_graph[x] for x in rx.dag_longest_path(self._multi_graph)]\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.successors\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.successors\\n def successors(self, node):\\n \\\"\\\"\\\"Returns iterator of the successors of a node as DAGNodes.\\\"\\\"\\\"\\n return iter(self._multi_graph.successors(node._node_id))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.predecessors\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.predecessors\\n def predecessors(self, node):\\n \\\"\\\"\\\"Returns iterator of the predecessors of a node as DAGNodes.\\\"\\\"\\\"\\n return iter(self._multi_graph.predecessors(node._node_id))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.quantum_predecessors\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.quantum_predecessors\\n def quantum_predecessors(self, node):\\n \\\"\\\"\\\"Returns iterator of the predecessors of a node that are\\n connected by a quantum edge as DAGNodes.\\\"\\\"\\\"\\n for predecessor in self.predecessors(node):\\n if any(isinstance(edge_data, Qubit) for edge_data in\\n self._multi_graph.get_all_edge_data(\\n predecessor._node_id, node._node_id)):\\n yield predecessor\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.ancestors\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.ancestors\\n def ancestors(self, node):\\n \\\"\\\"\\\"Returns set of the ancestors of a node as DAGNodes.\\\"\\\"\\\"\\n return {\\n self._multi_graph[x] for x in rx.ancestors(\\n self._multi_graph, node._node_id)}\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.descendants\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.descendants\\n def descendants(self, node):\\n \\\"\\\"\\\"Returns set of the descendants of a node as DAGNodes.\\\"\\\"\\\"\\n return {\\n self._multi_graph[x] for x in rx.descendants(\\n self._multi_graph, node._node_id)}\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.bfs_successors\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.bfs_successors\\n def bfs_successors(self, node):\\n \\\"\\\"\\\"\\n Returns an iterator of tuples of (DAGNode, [DAGNodes]) where the DAGNode is the current node\\n and [DAGNode] is its successors in BFS order.\\n \\\"\\\"\\\"\\n return iter(rx.bfs_successors(self._multi_graph, node._node_id))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.quantum_successors\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.quantum_successors\\n def quantum_successors(self, node):\\n \\\"\\\"\\\"Returns iterator of the successors of a node that are\\n connected by a quantum edge as DAGNodes.\\\"\\\"\\\"\\n for successor in self.successors(node):\\n if any(isinstance(edge_data, Qubit) for edge_data in\\n self._multi_graph.get_all_edge_data(\\n node._node_id, successor._node_id)):\\n yield successor\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_op_node\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_op_node\\n def remove_op_node(self, node):\\n \\\"\\\"\\\"Remove an operation node n.\\n\\n Add edges from predecessors to successors.\\n \\\"\\\"\\\"\\n if node.type != 'op':\\n raise DAGCircuitError('The method remove_op_node only works on op node types. An \\\"%s\\\" '\\n 'node type was wrongly provided.' % node.type)\\n\\n self._multi_graph.remove_node_retain_edges(\\n node._node_id, use_outgoing=False,\\n condition=lambda edge1, edge2: edge1 == edge2)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_ancestors_of\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_ancestors_of\\n def remove_ancestors_of(self, node):\\n \\\"\\\"\\\"Remove all of the ancestor operation nodes of node.\\\"\\\"\\\"\\n anc = rx.ancestors(self._multi_graph, node)\\n # TODO: probably better to do all at once using\\n # multi_graph.remove_nodes_from; same for related functions ...\\n\\n for anc_node in anc:\\n if anc_node.type == \\\"op\\\":\\n self.remove_op_node(anc_node)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_descendants_of\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_descendants_of\\n def remove_descendants_of(self, node):\\n \\\"\\\"\\\"Remove all of the descendant operation nodes of node.\\\"\\\"\\\"\\n desc = rx.descendants(self._multi_graph, node)\\n for desc_node in desc:\\n if desc_node.type == \\\"op\\\":\\n self.remove_op_node(desc_node)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_nonancestors_of\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_nonancestors_of\\n def remove_nonancestors_of(self, node):\\n \\\"\\\"\\\"Remove all of the non-ancestors operation nodes of node.\\\"\\\"\\\"\\n anc = rx.ancestors(self._multi_graph, node)\\n comp = list(set(self._multi_graph.nodes()) - set(anc))\\n for n in comp:\\n if n.type == \\\"op\\\":\\n self.remove_op_node(n)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_nondescendants_of\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.remove_nondescendants_of\\n def remove_nondescendants_of(self, node):\\n \\\"\\\"\\\"Remove all of the non-descendants operation nodes of node.\\\"\\\"\\\"\\n dec = rx.descendants(self._multi_graph, node)\\n comp = list(set(self._multi_graph.nodes()) - set(dec))\\n for n in comp:\\n if n.type == \\\"op\\\":\\n self.remove_op_node(n)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.front_layer\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.front_layer\\n def front_layer(self):\\n \\\"\\\"\\\"Return a list of op nodes in the first layer of this dag.\\n \\\"\\\"\\\"\\n graph_layers = self.multigraph_layers()\\n try:\\n next(graph_layers) # Remove input nodes\\n except StopIteration:\\n return []\\n\\n op_nodes = [node for node in next(graph_layers) if node.type == \\\"op\\\"]\\n\\n return op_nodes\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.layers\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.layers\\n def layers(self):\\n \\\"\\\"\\\"Yield a shallow view on a layer of this DAGCircuit for all d layers of this circuit.\\n\\n A layer is a circuit whose gates act on disjoint qubits, i.e.,\\n a layer has depth 1. The total number of layers equals the\\n circuit depth d. The layers are indexed from 0 to d-1 with the\\n earliest layer at index 0. The layers are constructed using a\\n greedy algorithm. Each returned layer is a dict containing\\n {\\\"graph\\\": circuit graph, \\\"partition\\\": list of qubit lists}.\\n\\n The returned layer contains new (but semantically equivalent) DAGNodes.\\n These are not the same as nodes of the original dag, but are equivalent\\n via DAGNode.semantic_eq(node1, node2).\\n\\n TODO: Gates that use the same cbits will end up in different\\n layers as this is currently implemented. This may not be\\n the desired behavior.\\n \\\"\\\"\\\"\\n graph_layers = self.multigraph_layers()\\n try:\\n next(graph_layers) # Remove input nodes\\n except StopIteration:\\n return\\n\\n for graph_layer in graph_layers:\\n\\n # Get the op nodes from the layer, removing any input and output nodes.\\n op_nodes = [node for node in graph_layer if node.type == \\\"op\\\"]\\n\\n # Sort to make sure they are in the order they were added to the original DAG\\n # It has to be done by node_id as graph_layer is just a list of nodes\\n # with no implied topology\\n # Drawing tools rely on _node_id to infer order of node creation\\n # so we need this to be preserved by layers()\\n op_nodes.sort(key=lambda nd: nd._node_id)\\n\\n # Stop yielding once there are no more op_nodes in a layer.\\n if not op_nodes:\\n return\\n\\n # Construct a shallow copy of self\\n new_layer = self._copy_circuit_metadata()\\n\\n for node in op_nodes:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.serial_layers\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.serial_layers\\n def serial_layers(self):\\n \\\"\\\"\\\"Yield a layer for all gates of this circuit.\\n\\n A serial layer is a circuit with one gate. The layers have the\\n same structure as in layers().\\n \\\"\\\"\\\"\\n for next_node in self.topological_op_nodes():\\n new_layer = self._copy_circuit_metadata()\\n\\n # Save the support of the operation we add to the layer\\n support_list = []\\n # Operation data\\n op = copy.copy(next_node.op)\\n qa = copy.copy(next_node.qargs)\\n ca = copy.copy(next_node.cargs)\\n co = copy.copy(next_node.op.condition)\\n _ = self._bits_in_condition(co)\\n\\n # Add node to new_layer\\n new_layer.apply_operation_back(op, qa, ca)\\n # Add operation to partition\\n if not next_node.op._directive:\\n support_list.append(list(qa))\\n l_dict = {\\\"graph\\\": new_layer, \\\"partition\\\": support_list}\\n yield l_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.multigraph_layers\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.multigraph_layers\\n def multigraph_layers(self):\\n \\\"\\\"\\\"Yield layers of the multigraph.\\\"\\\"\\\"\\n first_layer = [x._node_id for x in self.input_map.values()]\\n return iter(rx.layers(self._multi_graph, first_layer))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_runs\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_runs\\n def collect_runs(self, namelist):\\n \\\"\\\"\\\"Return a set of non-conditional runs of \\\"op\\\" nodes with the given names.\\n\\n For example, \\\"... h q[0]; cx q[0],q[1]; cx q[0],q[1]; h q[1]; ..\\\"\\n would produce the tuple of cx nodes as an element of the set returned\\n from a call to collect_runs([\\\"cx\\\"]). If instead the cx nodes were\\n \\\"cx q[0],q[1]; cx q[1],q[0];\\\", the method would still return the\\n pair in a tuple. The namelist can contain names that are not\\n in the circuit's basis.\\n\\n Nodes must have only one successor to continue the run.\\n \\\"\\\"\\\"\\n\\n def filter_fn(node):\\n return node.type == \\\"op\\\" and node.name in namelist and node.op.condition is None\\n\\n group_list = rx.collect_runs(self._multi_graph, filter_fn)\\n return set(tuple(x) for x in group_list)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_runs.filter_fn\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_runs.filter_fn\\n def filter_fn(node):\\n return node.type == \\\"op\\\" and node.name in namelist and node.op.condition is None\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_1q_runs\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_1q_runs\\n def collect_1q_runs(self):\\n \\\"\\\"\\\"Return a set of non-conditional runs of 1q \\\"op\\\" nodes.\\\"\\\"\\\"\\n\\n def filter_fn(node):\\n return node.type == 'op' and len(node.qargs) == 1 \\\\\\n and len(node.cargs) == 0 and node.op.condition is None \\\\\\n and not node.op.is_parameterized() \\\\\\n and isinstance(node.op, Gate) \\\\\\n and hasattr(node.op, '__array__')\\n\\n return rx.collect_runs(self._multi_graph, filter_fn)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_1q_runs.filter_fn\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.collect_1q_runs.filter_fn\\n def filter_fn(node):\\n return node.type == 'op' and len(node.qargs) == 1 \\\\\\n and len(node.cargs) == 0 and node.op.condition is None \\\\\\n and not node.op.is_parameterized() \\\\\\n and isinstance(node.op, Gate) \\\\\\n and hasattr(node.op, '__array__')\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.nodes_on_wire\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.nodes_on_wire\\n def nodes_on_wire(self, wire, only_ops=False):\\n \\\"\\\"\\\"\\n Iterator for nodes that affect a given wire.\\n\\n Args:\\n wire (Bit): the wire to be looked at.\\n only_ops (bool): True if only the ops nodes are wanted;\\n otherwise, all nodes are returned.\\n Yield:\\n DAGNode: the successive ops on the given wire\\n\\n Raises:\\n DAGCircuitError: if the given wire doesn't exist in the DAG\\n \\\"\\\"\\\"\\n current_node = self.input_map.get(wire, None)\\n\\n if not current_node:\\n raise DAGCircuitError('The given wire %s is not present in the circuit'\\n % str(wire))\\n\\n more_nodes = True\\n while more_nodes:\\n more_nodes = False\\n # allow user to just get ops on the wire - not the input/output nodes\\n if current_node.type == 'op' or not only_ops:\\n yield current_node\\n\\n try:\\n current_node = self._multi_graph.find_adjacent_node_by_edge(\\n current_node._node_id, lambda x: wire == x)\\n more_nodes = True\\n except rx.NoSuitableNeighbors:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.count_ops\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.count_ops\\n def count_ops(self):\\n \\\"\\\"\\\"Count the occurrences of operation names.\\n\\n Returns a dictionary of counts keyed on the operation name.\\n \\\"\\\"\\\"\\n op_dict = {}\\n for node in self.topological_op_nodes():\\n name = node.name\\n if name not in op_dict:\\n op_dict[name] = 1\\n else:\\n op_dict[name] += 1\\n return op_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.count_ops_longest_path\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.count_ops_longest_path\\n def count_ops_longest_path(self):\\n \\\"\\\"\\\"Count the occurrences of operation names on the longest path.\\n\\n Returns a dictionary of counts keyed on the operation name.\\n \\\"\\\"\\\"\\n op_dict = {}\\n path = self.longest_path()\\n path = path[1:-1] # remove qubits at beginning and end of path\\n for node in path:\\n name = node.name\\n if name not in op_dict:\\n op_dict[name] = 1\\n else:\\n op_dict[name] += 1\\n return op_dict\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.properties\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.properties\\n def properties(self):\\n \\\"\\\"\\\"Return a dictionary of circuit properties.\\\"\\\"\\\"\\n summary = {\\\"size\\\": self.size(),\\n \\\"depth\\\": self.depth(),\\n \\\"width\\\": self.width(),\\n \\\"qubits\\\": self.num_qubits(),\\n \\\"bits\\\": self.num_clbits(),\\n \\\"factors\\\": self.num_tensor_factors(),\\n \\\"operations\\\": self.count_ops()}\\n return summary\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.draw\",\n \"text\": \"qiskit/dagcircuit/dagcircuit.py::DAGCircuit.draw\\n def draw(self, scale=0.7, filename=None, style='color'):\\n \\\"\\\"\\\"\\n Draws the dag circuit.\\n\\n This function needs `pydot <https://github.com/erocarrera/pydot>`_, which in turn needs\\n `Graphviz <https://www.graphviz.org/>`_ to be installed.\\n\\n Args:\\n scale (float): scaling factor\\n filename (str): file path to save image to (format inferred from name)\\n style (str):\\n 'plain': B&W graph;\\n 'color' (default): color input/output/op nodes\\n\\n Returns:\\n Ipython.display.Image: if in Jupyter notebook and not saving to file,\\n otherwise None.\\n \\\"\\\"\\\"\\n from qiskit.visualization.dag_visualization import dag_drawer\\n return dag_drawer(dag=self, scale=scale, filename=filename, style=style)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py::DAGNode.__init__\",\n \"text\": \"qiskit/dagcircuit/dagnode.py::DAGNode.__init__\\n def __init__(self, type=None, op=None, name=None, qargs=None, cargs=None,\\n wire=None, nid=-1):\\n \\\"\\\"\\\"Create a node \\\"\\\"\\\"\\n self.type = type\\n self._op = op\\n self.name = name\\n self._qargs = qargs if qargs is not None else []\\n self.cargs = cargs if cargs is not None else []\\n self._wire = wire\\n self._node_id = nid\\n self.sort_key = str(self._qargs)\",\n \"label\": 1\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py::DAGNode.op\",\n \"text\": \"qiskit/dagcircuit/dagnode.py::DAGNode.op\\n @property\\n def op(self):\\n \\\"\\\"\\\"Returns the Instruction object corresponding to the op for the node, else None\\\"\\\"\\\"\\n if not self.type or self.type != 'op':\\n raise QiskitError(\\\"The node %s is not an op node\\\" % (str(self)))\\n return self._op\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py::DAGNode.op\",\n \"text\": \"qiskit/dagcircuit/dagnode.py::DAGNode.op\\n @op.setter\\n def op(self, data):\\n self._op = data\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py::DAGNode.condition\",\n \"text\": \"qiskit/dagcircuit/dagnode.py::DAGNode.condition\\n @property\\n def condition(self):\\n \\\"\\\"\\\"Returns the condition of the node.op\\\"\\\"\\\"\\n if not self.type or self.type != 'op':\\n raise QiskitError(\\\"The node %s is not an op node\\\" % (str(self)))\\n warnings.warn(\\n \\\"The DAGNode 'condition' attribute is deprecated as of 0.18.0 and \\\"\\n \\\"will be removed no earlier than 3 months after the release date. \\\"\\n \\\"You can use 'DAGNode.op.condition' if the DAGNode is of type 'op'.\\\",\\n DeprecationWarning, 2)\\n return self._op.condition\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py::DAGNode.condition\",\n \"text\": \"qiskit/dagcircuit/dagnode.py::DAGNode.condition\\n @condition.setter\\n def condition(self, new_condition):\\n \\\"\\\"\\\"Sets the node.condition which sets the node.op.condition.\\\"\\\"\\\"\\n if not self.type or self.type != 'op':\\n raise QiskitError(\\\"The node %s is not an op node\\\" % (str(self)))\\n warnings.warn(\\n \\\"The DAGNode 'condition' attribute is deprecated as of 0.18.0 and \\\"\\n \\\"will be removed no earlier than 3 months after the release date. \\\"\\n \\\"You can use 'DAGNode.op.condition' if the DAGNode is of type 'op'.\\\",\\n DeprecationWarning, 2)\\n self._op.condition = new_condition\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py::DAGNode.qargs\",\n \"text\": \"qiskit/dagcircuit/dagnode.py::DAGNode.qargs\\n @property\\n def qargs(self):\\n \\\"\\\"\\\"\\n Returns list of Qubit, else an empty list.\\n \\\"\\\"\\\"\\n return self._qargs\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py::DAGNode.qargs\",\n \"text\": \"qiskit/dagcircuit/dagnode.py::DAGNode.qargs\\n @qargs.setter\\n def qargs(self, new_qargs):\\n \\\"\\\"\\\"Sets the qargs to be the given list of qargs.\\\"\\\"\\\"\\n self._qargs = new_qargs\\n self.sort_key = str(new_qargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py::DAGNode.wire\",\n \"text\": \"qiskit/dagcircuit/dagnode.py::DAGNode.wire\\n @property\\n def wire(self):\\n \\\"\\\"\\\"\\n Returns the Bit object, else None.\\n \\\"\\\"\\\"\\n if self.type not in ['in', 'out']:\\n raise QiskitError('The node %s is not an input/output node' % str(self))\\n return self._wire\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py::DAGNode.wire\",\n \"text\": \"qiskit/dagcircuit/dagnode.py::DAGNode.wire\\n @wire.setter\\n def wire(self, data):\\n self._wire = data\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py::DAGNode.__lt__\",\n \"text\": \"qiskit/dagcircuit/dagnode.py::DAGNode.__lt__\\n def __lt__(self, other):\\n return self._node_id < other._node_id\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py::DAGNode.__gt__\",\n \"text\": \"qiskit/dagcircuit/dagnode.py::DAGNode.__gt__\\n def __gt__(self, other):\\n return self._node_id > other._node_id\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py::DAGNode.__str__\",\n \"text\": \"qiskit/dagcircuit/dagnode.py::DAGNode.__str__\\n def __str__(self):\\n # TODO is this used anywhere other than in DAG drawing?\\n # needs to be unique as it is what pydot uses to distinguish nodes\\n return str(id(self))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/dagcircuit/dagnode.py::DAGNode.semantic_eq\",\n \"text\": \"qiskit/dagcircuit/dagnode.py::DAGNode.semantic_eq\\n @staticmethod\\n def semantic_eq(node1, node2, bit_indices1=None, bit_indices2=None):\\n \\\"\\\"\\\"\\n Check if DAG nodes are considered equivalent, e.g., as a node_match for nx.is_isomorphic.\\n\\n Args:\\n node1 (DAGNode): A node to compare.\\n node2 (DAGNode): The other node to compare.\\n bit_indices1 (dict): Dictionary mapping Bit instances to their index\\n within the circuit containing node1\\n bit_indices2 (dict): Dictionary mapping Bit instances to their index\\n within the circuit containing node2\\n\\n Return:\\n Bool: If node1 == node2\\n \\\"\\\"\\\"\\n if bit_indices1 is None or bit_indices2 is None:\\n warnings.warn(\\n 'DAGNode.semantic_eq now expects two bit-to-circuit index '\\n 'mappings as arguments. To ease the transition, these will be '\\n 'pre-populated based on the values found in Bit.index and '\\n 'Bit.register. However, this behavior is deprecated and a future '\\n 'release will require the mappings to be provided as arguments.',\\n DeprecationWarning)\\n\\n bit_indices1 = {arg: arg for arg in node1.qargs + node1.cargs}\\n bit_indices2 = {arg: arg for arg in node2.qargs + node2.cargs}\\n\\n node1_qargs = [bit_indices1[qarg] for qarg in node1.qargs]\\n node1_cargs = [bit_indices1[carg] for carg in node1.cargs]\\n\\n node2_qargs = [bit_indices2[qarg] for qarg in node2.qargs]\\n node2_cargs = [bit_indices2[carg] for carg in node2.cargs]\\n\\n # For barriers, qarg order is not significant so compare as sets\\n if 'barrier' == node1.name == node2.name:\\n return set(node1_qargs) == set(node2_qargs)\\n\\n if node1.type == node2.type:\\n if node1._op == node2._op:\\n if node1.name == node2.name:\\n if node1_qargs == node2_qargs:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.__init__\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.__init__\\n def __init__(self,\\n primitive: OperatorBase,\\n coeff: Union[complex, ParameterExpression] = 1.0,\\n is_measurement: bool = False) -> None:\\n \\\"\\\"\\\"\\n Args:\\n primitive: The ``OperatorBase`` which defines the behavior of the underlying State\\n function.\\n coeff: A coefficient by which to multiply the state function\\n is_measurement: Whether the StateFn is a measurement operator\\n \\\"\\\"\\\"\\n\\n super().__init__(primitive, coeff=coeff, is_measurement=is_measurement)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.primitive_strings\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.primitive_strings\\n def primitive_strings(self) -> Set[str]:\\n return self.primitive.primitive_strings()\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.num_qubits\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.num_qubits\\n @property\\n def num_qubits(self) -> int:\\n return self.primitive.num_qubits\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.add\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.add\\n def add(self, other: OperatorBase) -> Union[\\\"OperatorStateFn\\\", SummedOp]:\\n if not self.num_qubits == other.num_qubits:\\n raise ValueError(\\n 'Sum over statefns with different numbers of qubits, {} and {}, is not well '\\n 'defined'.format(self.num_qubits, other.num_qubits))\\n\\n # Right now doesn't make sense to add a StateFn to a Measurement\\n if isinstance(other, OperatorStateFn) and self.is_measurement == other.is_measurement:\\n if isinstance(other.primitive, OperatorBase) and self.primitive == other.primitive:\\n return OperatorStateFn(\\n self.primitive,\\n coeff=self.coeff + other.coeff,\\n is_measurement=self.is_measurement,\\n )\\n # Covers Statevector and custom.\\n elif isinstance(other, OperatorStateFn):\\n # Also assumes scalar multiplication is available\\n return OperatorStateFn(\\n (self.coeff * self.primitive).add(other.primitive * other.coeff),\\n is_measurement=self._is_measurement)\\n\\n return SummedOp([self, other])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.adjoint\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.adjoint\\n def adjoint(self) -> \\\"OperatorStateFn\\\":\\n return OperatorStateFn(self.primitive.adjoint(),\\n coeff=self.coeff.conjugate(),\\n is_measurement=(not self.is_measurement))\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn._expand_dim\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn._expand_dim\\n def _expand_dim(self, num_qubits: int) -> 'OperatorStateFn':\\n return OperatorStateFn(self.primitive._expand_dim(num_qubits),\\n coeff=self.coeff,\\n is_measurement=self.is_measurement)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.permute\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.permute\\n def permute(self, permutation: List[int]) -> 'OperatorStateFn':\\n return OperatorStateFn(self.primitive.permute(permutation),\\n coeff=self.coeff,\\n is_measurement=self.is_measurement)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.tensor\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.tensor\\n def tensor(self, other: OperatorBase) -> Union[\\\"OperatorStateFn\\\", TensoredOp]:\\n if isinstance(other, OperatorStateFn):\\n return OperatorStateFn(\\n self.primitive.tensor(other.primitive),\\n coeff=self.coeff * other.coeff,\\n is_measurement=self.is_measurement,\\n )\\n\\n return TensoredOp([self, other])\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_density_matrix\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_density_matrix\\n def to_density_matrix(self, massive: bool = False) -> np.ndarray:\\n \\\"\\\"\\\" Return numpy matrix of density operator, warn if more than 16 qubits\\n to force the user to set\\n massive=True if they want such a large matrix. Generally big methods like\\n this should require the use of a\\n converter, but in this case a convenience method for quick hacking and\\n access to classical tools is\\n appropriate. \\\"\\\"\\\"\\n OperatorBase._check_massive('to_density_matrix', True, self.num_qubits, massive)\\n return self.primitive.to_matrix() * self.coeff\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_matrix_op\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_matrix_op\\n def to_matrix_op(self, massive: bool = False) -> \\\"OperatorStateFn\\\":\\n \\\"\\\"\\\" Return a MatrixOp for this operator. \\\"\\\"\\\"\\n return OperatorStateFn(self.primitive.to_matrix_op(massive=massive) * self.coeff,\\n is_measurement=self.is_measurement)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_matrix\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_matrix\\n def to_matrix(self, massive: bool = False) -> np.ndarray:\\n r\\\"\\\"\\\"\\n Note: this does not return a density matrix, it returns a classical matrix\\n containing the quantum or classical vector representing the evaluation of the state\\n function on each binary basis state. Do not assume this is is a normalized quantum or\\n classical probability vector. If we allowed this to return a density matrix,\\n then we would need to change the definition of composition to be ~Op @ StateFn @ Op for\\n those cases, whereas by this methodology we can ensure that composition always means Op\\n @ StateFn.\\n\\n Return numpy vector of state vector, warn if more than 16 qubits to force the user to set\\n massive=True if they want such a large vector.\\n\\n Args:\\n massive: Whether to allow large conversions, e.g. creating a matrix representing\\n over 16 qubits.\\n\\n Returns:\\n np.ndarray: Vector of state vector\\n\\n Raises:\\n ValueError: Invalid parameters.\\n \\\"\\\"\\\"\\n OperatorBase._check_massive('to_matrix', False, self.num_qubits, massive)\\n # Operator - return diagonal (real values, not complex),\\n # not rank 1 decomposition (statevector)!\\n mat = self.primitive.to_matrix(massive=massive)\\n # TODO change to weighted sum of eigenvectors' StateFns?\\n\\n # ListOp primitives can return lists of matrices (or trees for nested ListOps),\\n # so we need to recurse over the\\n # possible tree.\\n def diag_over_tree(op):\\n if isinstance(op, list):\\n return [diag_over_tree(o) for o in op]\\n else:\\n vec = np.diag(op) * self.coeff\\n # Reshape for measurements so np.dot still works for composition.\\n return vec if not self.is_measurement else vec.reshape(1, -1)\\n\\n return diag_over_tree(mat\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_matrix.diag_over_tree\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_matrix.diag_over_tree\\n def diag_over_tree(op):\\n if isinstance(op, list):\\n return [diag_over_tree(o) for o in op]\\n else:\\n vec = np.diag(op) * self.coeff\\n # Reshape for measurements so np.dot still works for composition.\\n return vec if not self.is_measurement else vec.reshape(1, -1)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_circuit_op\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.to_circuit_op\\n def to_circuit_op(self):\\n r\\\"\\\"\\\" Return ``StateFnCircuit`` corresponding to this StateFn. Ignore for now because this is\\n undefined. TODO maybe call to_pauli_op and diagonalize here, but that could be very\\n inefficient, e.g. splitting one Stabilizer measurement into hundreds of 1 qubit Paulis.\\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.__str__\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.__str__\\n def __str__(self) -> str:\\n prim_str = str(self.primitive)\\n if self.coeff == 1.0:\\n return \\\"{}({})\\\".format('OperatorStateFn' if not self.is_measurement\\n else 'OperatorMeasurement', prim_str)\\n else:\\n return \\\"{}({}) * {}\\\".format(\\n 'OperatorStateFn' if not self.is_measurement else 'OperatorMeasurement',\\n prim_str,\\n self.coeff)\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.eval\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.eval\\n def eval(\\n self, front: Optional[Union[str, dict, np.ndarray, OperatorBase, Statevector]] = None\\n ) -> Union[OperatorBase, complex]:\\n if front is None:\\n matrix = cast(MatrixOp, self.primitive.to_matrix_op()).primitive.data\\n # pylint: disable=cyclic-import\\n from .vector_state_fn import VectorStateFn\\n return VectorStateFn(matrix[0, :])\\n\\n if not self.is_measurement and isinstance(front, OperatorBase):\\n raise ValueError(\\n 'Cannot compute overlap with StateFn or Operator if not Measurement. Try taking '\\n 'sf.adjoint() first to convert to measurement.')\\n\\n if not isinstance(front, OperatorBase):\\n front = StateFn(front)\\n\\n if isinstance(self.primitive, ListOp) and self.primitive.distributive:\\n evals = [OperatorStateFn(op, is_measurement=self.is_measurement).eval(\\n front) for op in self.primitive.oplist]\\n result = self.primitive.combo_fn(evals)\\n if isinstance(result, list):\\n multiplied = self.primitive.coeff * self.coeff * np.array(result)\\n return multiplied.tolist()\\n return result * self.coeff * self.primitive.coeff\\n\\n # pylint: disable=cyclic-import\\n from .vector_state_fn import VectorStateFn\\n if isinstance(self.primitive, PauliSumOp) and isinstance(front, VectorStateFn):\\n return (\\n front.primitive.expectation_value(self.primitive.primitive)\\n * self.coeff\\n * front.coeff\\n )\\n\\n # Need an ListOp-specific carve-out here to make sure measurement over a ListOp doesn't\\n # produce two-dimensional ListOp from composing from both sides of primitive.\\n # Can't use isinstance because this would include subclasses.\\n # pylint: disable=unidiomatic-typecheck\\n if isinstance(front, Lis\",\n \"label\": 0\n },\n {\n \"cid\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.sample\",\n \"text\": \"qiskit/opflow/state_fns/operator_state_fn.py::OperatorStateFn.sample\\n def sample(self,\\n shots: int = 1024,\\n massive: bool = False,\\n reverse_endianness: bool = False):\\n raise NotImplementedError\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":290,"cells":{"instance_id":{"kind":"string","value":"conan-io__conan-6720"},"repo":{"kind":"string","value":"conan-io/conan"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"[bug] conan export-pkg doesn't fail when exporting a package folder that does not exist\n### Environment Details\r\n * Operating System+version: Debian unstable\r\n * Compiler+version: N/A\r\n * Conan version: 1.22.0\r\n * Python version: 3.7.5\r\n\r\n### Steps to reproduce\r\n\r\n- Assuming you have a conan repository with a conanfile.py in ~/myproject\r\n- Run `conan export-pkg --force --package-folder /this/folder/does/not/exist ~/myproject`\r\n- conan will just warn `WARN: No files in this package!` and exit successfully\r\n\r\nIt is ok to export an empty package, but I would have expected conan to show an error since the folder doesn't exist at all.\r\n\r\n### Logs\r\n\r\n```\r\n$ conan export-pkg --package-folder /this/folder/does/not/exist ~/work/tanker/conan/cppcodec \r\nExporting package recipe\r\ncppcodec/edf46ab: A new conanfile.py version was exported\r\ncppcodec/edf46ab: Folder: /home/philippe/.conan/data/cppcodec/edf46ab/_/_/export\r\ncppcodec/edf46ab: Using the exported files summary hash as the recipe revision: b068bd011732194d966f09f269d94124 \r\ncppcodec/edf46ab: Exported revision: b068bd011732194d966f09f269d94124\r\ncppcodec/edf46ab: Forced build from source\r\nPackaging to 5ab84d6acfe1f23c4fae0ab88f26e3a396351ac9\r\ncppcodec/edf46ab: Exporting to cache existing package from user folder\r\ncppcodec/edf46ab: Package folder /home/philippe/.conan/data/cppcodec/edf46ab/_/_/package/5ab84d6acfe1f23c4fae0ab88f26e3a396351ac9\r\ncppcodec/edf46ab: WARN: No files in this package!\r\ncppcodec/edf46ab: Package '5ab84d6acfe1f23c4fae0ab88f26e3a396351ac9' created\r\ncppcodec/edf46ab: Created package revision 62acfcc1c48ee02e1a4300df700347a4\r\n```\r\n\n"},"gold_files":{"kind":"list like","value":["conans/client/conan_api.py"],"string":"[\n \"conans/client/conan_api.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["conans/client/conan_api.py::ConanAPIV1.export_pkg"],"string":"[\n \"conans/client/conan_api.py::ConanAPIV1.export_pkg\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"conans/client/build/cmake.py","text":"conans/client/build/cmake.py\nclass CMake\n def __init__(self, conanfile, generator=None, cmake_system_name=True, parallel=True, build_type=None, toolset=None, make_program=None, set_cmake_flags=False, msbuild_verbosity='minimal', cmake_program=None, generator_platform=None, append_vcvars=False) # :param conanfile: Conanfile instance\n def generator(self)\n def generator(self, value)\n def generator_platform(self)\n def generator_platform(self, value)\n def build_folder(self)\n def build_folder(self, value)\n def build_type(self)\n def build_type(self, build_type)\n def in_local_cache(self)\n def runtime(self)\n def flags(self)\n def is_multi_configuration(self)\n def command_line(self)\n def build_config(self) # cmake --build tool have a --config option for Multi-configuration IDEs\n def _get_dirs(self, source_folder, build_folder, source_dir, build_dir, cache_build_folder)\n def get_dir(folder, origin)\n def _run(self, command)\n def configure(self, args=None, defs=None, source_dir=None, build_dir=None, source_folder=None, build_folder=None, cache_build_folder=None, pkg_config_paths=None)\n def build(self, args=None, build_dir=None, target=None)\n def _build(self, args=None, build_dir=None, target=None)\n def install(self, args=None, build_dir=None)\n def test(self, args=None, build_dir=None, target=None, output_on_failure=False)\n def verbose(self)\n def verbose(self, value)\n def patch_config_paths(self) # changes references to the absolute path of the installed package and its depende\n def get_version()","label":0},{"cid":"conans/client/cmd/export.py","text":"conans/client/cmd/export.py\ndef export_alias(package_layout, target_ref, output, revisions_enabled)\ndef check_casing_conflict(cache, ref)\ndef cmd_export(app, conanfile_path, name, version, user, channel, keep_source, export=True, graph_lock=None, ignore_dirty=False) # Export the recipe\ndef _check_settings_for_warnings(conanfile, output)\ndef _capture_scm_auto_fields(conanfile, conanfile_dir, package_layout, output, ignore_dirty, scm_to_conandata) # Deduce the values for the scm auto fields or functions assigned to 'url' or 'rev\ndef _replace_scm_data_in_recipe(package_layout, scm_data, scm_to_conandata)\ndef _replace_scm_data_in_conanfile(conanfile_path, scm_data)\ndef _detect_scm_revision(path)\ndef _update_revision_in_metadata(package_layout, revisions_enabled, output, path, manifest, revision_mode)\ndef _recreate_folders(destination_folder)\ndef _classify_patterns(patterns)\ndef _export_scm(scm_data, origin_folder, scm_sources_folder, output) # Copy the local folder to the scm_sources folder in the cache, this enables to wo\ndef export_source(conanfile, origin_folder, destination_source_folder)\ndef export_recipe(conanfile, origin_folder, destination_folder)","label":0},{"cid":"conans/client/cmd/export_pkg.py","text":"conans/client/cmd/export_pkg.py\ndef export_pkg(app, recorder, full_ref, source_folder, build_folder, package_folder, install_folder, graph_info, force, remotes)\n def _init_conanfile_infos()","label":0},{"cid":"conans/client/cmd/uploader.py","text":"conans/client/cmd/uploader.py\nclass CmdUpload # This class is responsible for uploading packages to remotes. The flow is:\n def __init__(self, cache, user_io, remote_manager, loader, hook_manager)\n def upload(self, reference_or_pattern, remotes, upload_recorder, package_id=None, all_packages=None, confirm=False, retry=None, retry_wait=None, integrity_check=False, policy=None, query=None, parallel_upload=False)\n def _collects_refs_to_upload(self, package_id, reference_or_pattern, confirm) # validate inputs and compute the refs (without revisions) to be uploaded\n def _collect_packages_to_upload(self, refs, confirm, remotes, all_packages, query, package_id) # compute the references with revisions and the package_ids to be uploaded\n def _upload_ref(self, conanfile, ref, prefs, retry, retry_wait, integrity_check, policy, recipe_remote, upload_recorder, remotes) # Uploads the recipes and binaries identified by ref\n def _upload_recipe(self, ref, conanfile, retry, retry_wait, policy, remote, remotes)\n def _upload_package(self, pref, retry=None, retry_wait=None, integrity_check=False, policy=None, p_remote=None)\n def _compress_recipe_files(self, ref)\n def _compress_package_files(self, pref, integrity_check)\n def _recipe_files_to_upload(self, ref, policy, the_files, remote, remote_manifest, local_manifest)\n def _package_files_to_upload(self, pref, policy, the_files, remote)\n def _upload_recipe_end_msg(self, ref, remote)\n def _package_integrity_check(self, pref, files, package_folder)\n def _check_recipe_date(self, ref, remote, local_manifest)\n def _print_manifest_information(self, remote_recipe_manifest, local_manifest, ref, remote)\ndef _compress_recipe_files(files, symlinks, src_files, src_symlinks, dest_folder, output)\n def add_tgz(tgz_name, tgz_path, tgz_files, tgz_symlinks, msg)\ndef _compress_package_files(files, symlinks, dest_folder, output)\ndef compress_files(files, symlinks, name, dest_dir, output=None)","label":0},{"cid":"conans/client/conan_api.py","text":"conans/client/conan_api.py\ndef api_method(f)\n def wrapper(api, *args, **kwargs)\ndef _make_abs_path(path, cwd=None, default=None) # convert 'path' to absolute if necessary (could be already absolute)\ndef _get_conanfile_path(path, cwd, py) # param py= True: Must be .py, False: Must be .txt, None: Try .py, then .txt\nclass ConanApp\n def __init__(self, cache_folder, user_io, http_requester=None, runner=None, quiet_output=None)\n def load_remotes(self, remote_name=None, update=False, check_updates=False)\nclass ConanAPIV1\n def factory(cls)\n def __init__(self, cache_folder=None, output=None, user_io=None, http_requester=None, runner=None)\n def create_app(self, quiet_output=None)\n def new(self, name, header=False, pure_c=False, test=False, exports_sources=False, bare=False, cwd=None, visual_versions=None, linux_gcc_versions=None, linux_clang_versions=None, osx_clang_versions=None, shared=None, upload_url=None, gitignore=None, gitlab_gcc_versions=None, gitlab_clang_versions=None, circleci_gcc_versions=None, circleci_clang_versions=None, circleci_osx_versions=None, template=None)\n def inspect(self, path, attributes, remote_name=None)\n def test(self, path, reference, profile_names=None, settings=None, options=None, env=None, remote_name=None, update=False, build_modes=None, cwd=None, test_build_folder=None, lockfile=None)\n def create(self, conanfile_path, name=None, version=None, user=None, channel=None, profile_names=None, settings=None, options=None, env=None, test_folder=None, not_export=False, build_modes=None, keep_source=False, keep_build=False, verify=None, manifests=None, manifests_interactive=None, remote_name=None, update=False, cwd=None, test_build_folder=None, lockfile=None, ignore_dirty=False) # API method to create a conan package\n def export_pkg(self, conanfile_path, name, channel, source_folder=None, build_folder=None, package_folder=None, install_folder=None, profile_names=None, settings=None, options=None, env=None, force=False, user=None, version=None, cwd=None, lockfile=None, ignore_dirty=False)\n def download(self, reference, remote_name=None, packages=None, recipe=False)\n def workspace_install(self, path, settings=None, options=None, env=None, remote_name=None, build=None, profile_name=None, update=False, cwd=None, install_folder=None)\n def install_reference(self, reference, settings=None, options=None, env=None, remote_name=None, verify=None, manifests=None, manifests_interactive=None, build=None, profile_names=None, update=False, generators=None, install_folder=None, cwd=None, lockfile=None)\n def install(self, path='', name=None, version=None, user=None, channel=None, settings=None, options=None, env=None, remote_name=None, verify=None, manifests=None, manifests_interactive=None, build=None, profile_names=None, update=False, generators=None, no_imports=False, install_folder=None, cwd=None, lockfile=None)\n def config_get(self, item)\n def config_set(self, item, value)\n def config_rm","label":1},{"cid":"conans/client/migrations.py","text":"conans/client/migrations.py\nclass ClientMigrator\n def __init__(self, cache_folder, current_version, out)\n def _update_settings_yml(self, old_version)\n def save_new()\n def _make_migrations(self, old_version)\ndef _get_refs(cache)\ndef _get_prefs(layout)\ndef migrate_localdb_refresh_token(cache, out)\ndef _migrate_full_metadata(cache, out)\ndef _migrate_create_metadata(cache, out)\ndef _migrate_lock_files(cache, out)\ndef migrate_config_install(cache)\ndef migrate_to_default_profile(conf_path, default_profile_path)\ndef migrate_c_src_export_source(cache, out)\ndef migrate_plugins_to_hooks(cache, output=None)","label":0},{"cid":"conans/model/conan_file.py","text":"conans/model/conan_file.py\ndef create_options(conanfile)\ndef create_requirements(conanfile)\ndef create_settings(conanfile, settings)\ndef _env_and_python(conanfile)\ndef get_env_context_manager(conanfile, without_python=False)\nclass ConanFile # The base class for all package recipes\n def __init__(self, output, runner, display_name='', user=None, channel=None)\n def initialize(self, settings, env)\n def env(self) # Apply the self.deps_env_info into a copy of self._conan_env_values (will priorit\n def channel(self)\n def user(self)\n def collect_libs(self, folder=None)\n def build_policy_missing(self)\n def build_policy_always(self)\n def source(self)\n def system_requirements(self) # this method can be overwritten to implement logic for system package\n def config_options(self) # modify options, probably conditioned to some settings. This call is executed\n def configure(self) # modify settings, probably conditioned to some options. This call is executed\n def build(self) # build your project calling the desired build tools as done in the command line.\n def package(self) # package the needed files from source and build folders.\n def package_info(self) # define cpp_build_info, flags, etc\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True, ignore_errors=False, run_environment=False, with_login=True)\n def _run()\n def package_id(self) # modify the binary info, typically to narrow values\n def test(self) # test the generated executable.\n def __repr__(self)","label":0}],"string":"[\n {\n \"cid\": \"conans/client/build/cmake.py\",\n \"text\": \"conans/client/build/cmake.py\\nclass CMake\\n def __init__(self, conanfile, generator=None, cmake_system_name=True, parallel=True, build_type=None, toolset=None, make_program=None, set_cmake_flags=False, msbuild_verbosity='minimal', cmake_program=None, generator_platform=None, append_vcvars=False) # :param conanfile: Conanfile instance\\n def generator(self)\\n def generator(self, value)\\n def generator_platform(self)\\n def generator_platform(self, value)\\n def build_folder(self)\\n def build_folder(self, value)\\n def build_type(self)\\n def build_type(self, build_type)\\n def in_local_cache(self)\\n def runtime(self)\\n def flags(self)\\n def is_multi_configuration(self)\\n def command_line(self)\\n def build_config(self) # cmake --build tool have a --config option for Multi-configuration IDEs\\n def _get_dirs(self, source_folder, build_folder, source_dir, build_dir, cache_build_folder)\\n def get_dir(folder, origin)\\n def _run(self, command)\\n def configure(self, args=None, defs=None, source_dir=None, build_dir=None, source_folder=None, build_folder=None, cache_build_folder=None, pkg_config_paths=None)\\n def build(self, args=None, build_dir=None, target=None)\\n def _build(self, args=None, build_dir=None, target=None)\\n def install(self, args=None, build_dir=None)\\n def test(self, args=None, build_dir=None, target=None, output_on_failure=False)\\n def verbose(self)\\n def verbose(self, value)\\n def patch_config_paths(self) # changes references to the absolute path of the installed package and its depende\\n def get_version()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py\",\n \"text\": \"conans/client/cmd/export.py\\ndef export_alias(package_layout, target_ref, output, revisions_enabled)\\ndef check_casing_conflict(cache, ref)\\ndef cmd_export(app, conanfile_path, name, version, user, channel, keep_source, export=True, graph_lock=None, ignore_dirty=False) # Export the recipe\\ndef _check_settings_for_warnings(conanfile, output)\\ndef _capture_scm_auto_fields(conanfile, conanfile_dir, package_layout, output, ignore_dirty, scm_to_conandata) # Deduce the values for the scm auto fields or functions assigned to 'url' or 'rev\\ndef _replace_scm_data_in_recipe(package_layout, scm_data, scm_to_conandata)\\ndef _replace_scm_data_in_conanfile(conanfile_path, scm_data)\\ndef _detect_scm_revision(path)\\ndef _update_revision_in_metadata(package_layout, revisions_enabled, output, path, manifest, revision_mode)\\ndef _recreate_folders(destination_folder)\\ndef _classify_patterns(patterns)\\ndef _export_scm(scm_data, origin_folder, scm_sources_folder, output) # Copy the local folder to the scm_sources folder in the cache, this enables to wo\\ndef export_source(conanfile, origin_folder, destination_source_folder)\\ndef export_recipe(conanfile, origin_folder, destination_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export_pkg.py\",\n \"text\": \"conans/client/cmd/export_pkg.py\\ndef export_pkg(app, recorder, full_ref, source_folder, build_folder, package_folder, install_folder, graph_info, force, remotes)\\n def _init_conanfile_infos()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py\",\n \"text\": \"conans/client/cmd/uploader.py\\nclass CmdUpload # This class is responsible for uploading packages to remotes. The flow is:\\n def __init__(self, cache, user_io, remote_manager, loader, hook_manager)\\n def upload(self, reference_or_pattern, remotes, upload_recorder, package_id=None, all_packages=None, confirm=False, retry=None, retry_wait=None, integrity_check=False, policy=None, query=None, parallel_upload=False)\\n def _collects_refs_to_upload(self, package_id, reference_or_pattern, confirm) # validate inputs and compute the refs (without revisions) to be uploaded\\n def _collect_packages_to_upload(self, refs, confirm, remotes, all_packages, query, package_id) # compute the references with revisions and the package_ids to be uploaded\\n def _upload_ref(self, conanfile, ref, prefs, retry, retry_wait, integrity_check, policy, recipe_remote, upload_recorder, remotes) # Uploads the recipes and binaries identified by ref\\n def _upload_recipe(self, ref, conanfile, retry, retry_wait, policy, remote, remotes)\\n def _upload_package(self, pref, retry=None, retry_wait=None, integrity_check=False, policy=None, p_remote=None)\\n def _compress_recipe_files(self, ref)\\n def _compress_package_files(self, pref, integrity_check)\\n def _recipe_files_to_upload(self, ref, policy, the_files, remote, remote_manifest, local_manifest)\\n def _package_files_to_upload(self, pref, policy, the_files, remote)\\n def _upload_recipe_end_msg(self, ref, remote)\\n def _package_integrity_check(self, pref, files, package_folder)\\n def _check_recipe_date(self, ref, remote, local_manifest)\\n def _print_manifest_information(self, remote_recipe_manifest, local_manifest, ref, remote)\\ndef _compress_recipe_files(files, symlinks, src_files, src_symlinks, dest_folder, output)\\n def add_tgz(tgz_name, tgz_path, tgz_files, tgz_symlinks, msg)\\ndef _compress_package_files(files, symlinks, dest_folder, output)\\ndef compress_files(files, symlinks, name, dest_dir, output=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py\",\n \"text\": \"conans/client/conan_api.py\\ndef api_method(f)\\n def wrapper(api, *args, **kwargs)\\ndef _make_abs_path(path, cwd=None, default=None) # convert 'path' to absolute if necessary (could be already absolute)\\ndef _get_conanfile_path(path, cwd, py) # param py= True: Must be .py, False: Must be .txt, None: Try .py, then .txt\\nclass ConanApp\\n def __init__(self, cache_folder, user_io, http_requester=None, runner=None, quiet_output=None)\\n def load_remotes(self, remote_name=None, update=False, check_updates=False)\\nclass ConanAPIV1\\n def factory(cls)\\n def __init__(self, cache_folder=None, output=None, user_io=None, http_requester=None, runner=None)\\n def create_app(self, quiet_output=None)\\n def new(self, name, header=False, pure_c=False, test=False, exports_sources=False, bare=False, cwd=None, visual_versions=None, linux_gcc_versions=None, linux_clang_versions=None, osx_clang_versions=None, shared=None, upload_url=None, gitignore=None, gitlab_gcc_versions=None, gitlab_clang_versions=None, circleci_gcc_versions=None, circleci_clang_versions=None, circleci_osx_versions=None, template=None)\\n def inspect(self, path, attributes, remote_name=None)\\n def test(self, path, reference, profile_names=None, settings=None, options=None, env=None, remote_name=None, update=False, build_modes=None, cwd=None, test_build_folder=None, lockfile=None)\\n def create(self, conanfile_path, name=None, version=None, user=None, channel=None, profile_names=None, settings=None, options=None, env=None, test_folder=None, not_export=False, build_modes=None, keep_source=False, keep_build=False, verify=None, manifests=None, manifests_interactive=None, remote_name=None, update=False, cwd=None, test_build_folder=None, lockfile=None, ignore_dirty=False) # API method to create a conan package\\n def export_pkg(self, conanfile_path, name, channel, source_folder=None, build_folder=None, package_folder=None, install_folder=None, profile_names=None, settings=None, options=None, env=None, force=False, user=None, version=None, cwd=None, lockfile=None, ignore_dirty=False)\\n def download(self, reference, remote_name=None, packages=None, recipe=False)\\n def workspace_install(self, path, settings=None, options=None, env=None, remote_name=None, build=None, profile_name=None, update=False, cwd=None, install_folder=None)\\n def install_reference(self, reference, settings=None, options=None, env=None, remote_name=None, verify=None, manifests=None, manifests_interactive=None, build=None, profile_names=None, update=False, generators=None, install_folder=None, cwd=None, lockfile=None)\\n def install(self, path='', name=None, version=None, user=None, channel=None, settings=None, options=None, env=None, remote_name=None, verify=None, manifests=None, manifests_interactive=None, build=None, profile_names=None, update=False, generators=None, no_imports=False, install_folder=None, cwd=None, lockfile=None)\\n def config_get(self, item)\\n def config_set(self, item, value)\\n def config_rm\",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/migrations.py\",\n \"text\": \"conans/client/migrations.py\\nclass ClientMigrator\\n def __init__(self, cache_folder, current_version, out)\\n def _update_settings_yml(self, old_version)\\n def save_new()\\n def _make_migrations(self, old_version)\\ndef _get_refs(cache)\\ndef _get_prefs(layout)\\ndef migrate_localdb_refresh_token(cache, out)\\ndef _migrate_full_metadata(cache, out)\\ndef _migrate_create_metadata(cache, out)\\ndef _migrate_lock_files(cache, out)\\ndef migrate_config_install(cache)\\ndef migrate_to_default_profile(conf_path, default_profile_path)\\ndef migrate_c_src_export_source(cache, out)\\ndef migrate_plugins_to_hooks(cache, output=None)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py\",\n \"text\": \"conans/model/conan_file.py\\ndef create_options(conanfile)\\ndef create_requirements(conanfile)\\ndef create_settings(conanfile, settings)\\ndef _env_and_python(conanfile)\\ndef get_env_context_manager(conanfile, without_python=False)\\nclass ConanFile # The base class for all package recipes\\n def __init__(self, output, runner, display_name='', user=None, channel=None)\\n def initialize(self, settings, env)\\n def env(self) # Apply the self.deps_env_info into a copy of self._conan_env_values (will priorit\\n def channel(self)\\n def user(self)\\n def collect_libs(self, folder=None)\\n def build_policy_missing(self)\\n def build_policy_always(self)\\n def source(self)\\n def system_requirements(self) # this method can be overwritten to implement logic for system package\\n def config_options(self) # modify options, probably conditioned to some settings. This call is executed\\n def configure(self) # modify settings, probably conditioned to some options. This call is executed\\n def build(self) # build your project calling the desired build tools as done in the command line.\\n def package(self) # package the needed files from source and build folders.\\n def package_info(self) # define cpp_build_info, flags, etc\\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True, ignore_errors=False, run_environment=False, with_login=True)\\n def _run()\\n def package_id(self) # modify the binary info, typically to narrow values\\n def test(self) # test the generated executable.\\n def __repr__(self)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"conans/client/build/cmake.py::CMake.__init__","text":"conans/client/build/cmake.py::CMake.__init__\n def __init__(self, conanfile, generator=None, cmake_system_name=True,\n parallel=True, build_type=None, toolset=None, make_program=None,\n set_cmake_flags=False, msbuild_verbosity=\"minimal\", cmake_program=None,\n generator_platform=None, append_vcvars=False):\n \"\"\"\n :param conanfile: Conanfile instance\n :param generator: Generator name to use or none to autodetect\n :param cmake_system_name: False to not use CMAKE_SYSTEM_NAME variable,\n True for auto-detect or directly a string with the system name\n :param parallel: Try to build with multiple cores if available\n :param build_type: Overrides default build type coming from settings\n :param toolset: Toolset name to use (such as llvm-vs2014) or none for default one,\n applies only to certain generators (e.g. Visual Studio)\n :param set_cmake_flags: whether or not to set CMake flags like CMAKE_CXX_FLAGS,\n CMAKE_C_FLAGS, etc. it's vital to set for certain projects\n (e.g. using CMAKE_SIZEOF_VOID_P or CMAKE_LIBRARY_ARCHITECTURE)\n :param msbuild_verbosity: verbosity level for MSBuild (in case of Visual Studio generator)\n :param cmake_program: Path to the custom cmake executable\n :param generator_platform: Generator platform name or none to autodetect (-A cmake option)\n \"\"\"\n if not isinstance(conanfile, ConanFile):\n raise ConanException(\"First argument of CMake() has to be ConanFile. Use CMake(self)\")\n\n self._append_vcvars = append_vcvars\n self._conanfile = conanfile\n self._settings = conanfile.settings\n self._build_type = build_type or conanfile.settings.get_safe(\"build_type\")\n self._cmake_program = os.getenv(\"CONAN_CMAKE_PROGRAM\") or cmake_program or \"cmake\"\n\n self.generator_platform = generator_platform\n self.generator = generat","label":0},{"cid":"conans/client/build/cmake.py::CMake.generator","text":"conans/client/build/cmake.py::CMake.generator\n @property\n def generator(self):\n return self._generator","label":0},{"cid":"conans/client/build/cmake.py::CMake.generator","text":"conans/client/build/cmake.py::CMake.generator\n @generator.setter\n def generator(self, value):\n self._generator = value\n if not self._generator_platform_is_assigned:\n self._generator_platform = get_generator_platform(self._settings, self._generator)","label":0},{"cid":"conans/client/build/cmake.py::CMake.generator_platform","text":"conans/client/build/cmake.py::CMake.generator_platform\n @property\n def generator_platform(self):\n return self._generator_platform","label":0},{"cid":"conans/client/build/cmake.py::CMake.generator_platform","text":"conans/client/build/cmake.py::CMake.generator_platform\n @generator_platform.setter\n def generator_platform(self, value):\n self._generator_platform = value\n self._generator_platform_is_assigned = bool(value is not None)","label":0},{"cid":"conans/client/build/cmake.py::CMake.build_folder","text":"conans/client/build/cmake.py::CMake.build_folder\n @property\n def build_folder(self):\n return self.build_dir","label":0},{"cid":"conans/client/build/cmake.py::CMake.build_folder","text":"conans/client/build/cmake.py::CMake.build_folder\n @build_folder.setter\n def build_folder(self, value):\n self.build_dir = value","label":0},{"cid":"conans/client/build/cmake.py::CMake.build_type","text":"conans/client/build/cmake.py::CMake.build_type\n @property\n def build_type(self):\n return self._build_type","label":0},{"cid":"conans/client/build/cmake.py::CMake.build_type","text":"conans/client/build/cmake.py::CMake.build_type\n @build_type.setter\n def build_type(self, build_type):\n settings_build_type = self._settings.get_safe(\"build_type\")\n self.definitions.pop(\"CMAKE_BUILD_TYPE\", None)\n self.definitions.update(build_type_definition(build_type, settings_build_type,\n self.generator, self._conanfile.output))\n self._build_type = build_type","label":0},{"cid":"conans/client/build/cmake.py::CMake.in_local_cache","text":"conans/client/build/cmake.py::CMake.in_local_cache\n @property\n def in_local_cache(self):\n try:\n in_local_cache = self.definitions[cmake_in_local_cache_var_name]\n return get_bool_from_text(str(in_local_cache))\n except KeyError:\n return False","label":0},{"cid":"conans/client/build/cmake.py::CMake.runtime","text":"conans/client/build/cmake.py::CMake.runtime\n @property\n def runtime(self):\n return defs_to_string(self.definitions.get(runtime_definition_var_name))","label":0},{"cid":"conans/client/build/cmake.py::CMake.flags","text":"conans/client/build/cmake.py::CMake.flags\n @property\n def flags(self):\n return defs_to_string(self.definitions)","label":0},{"cid":"conans/client/build/cmake.py::CMake.is_multi_configuration","text":"conans/client/build/cmake.py::CMake.is_multi_configuration\n @property\n def is_multi_configuration(self):\n return is_multi_configuration(self.generator)","label":0},{"cid":"conans/client/build/cmake.py::CMake.command_line","text":"conans/client/build/cmake.py::CMake.command_line\n @property\n def command_line(self):\n if self.generator_platform and not is_generator_platform_supported(self.generator):\n raise ConanException('CMake does not support generator platform with generator '\n '\"%s:. Please check your conan profile to either remove the '\n 'generator platform, or change the CMake generator.'\n % self.generator)\n\n if self.toolset and not is_toolset_supported(self.generator):\n raise ConanException('CMake does not support toolsets with generator \"%s:.'\n 'Please check your conan profile to either remove the toolset,'\n ' or change the CMake generator.' % self.generator)\n\n generator = self.generator\n generator_platform = self.generator_platform\n\n if self.generator_platform and 'Visual Studio' in generator:\n # FIXME: Conan 2.0 We are adding the platform to the generator instead of using the -A argument\n # to keep previous implementation, but any modern CMake will support (and recommend) passing the\n # platform in its own argument.\n compiler_version = self._settings.get_safe(\"compiler.version\")\n if Version(compiler_version) < \"16\" and self._settings.get_safe(\"os\") != \"WindowsCE\":\n if self.generator_platform == \"x64\":\n generator += \" Win64\" if not generator.endswith(\" Win64\") else \"\"\n generator_platform = None\n elif self.generator_platform == \"ARM\":\n generator += \" ARM\" if not generator.endswith(\" ARM\") else \"\"\n generator_platform = None\n elif self.generator_platform == \"Win32\":\n generator_platform = None\n\n args = ['-G \"{}\"'.format(generator)] if generator else []\n if gener","label":0},{"cid":"conans/client/build/cmake.py::CMake.build_config","text":"conans/client/build/cmake.py::CMake.build_config\n @property\n def build_config(self):\n \"\"\" cmake --build tool have a --config option for Multi-configuration IDEs\n \"\"\"\n if self._build_type and self.is_multi_configuration:\n return \"--config %s\" % self._build_type\n return \"\"","label":0},{"cid":"conans/client/build/cmake.py::CMake._get_dirs","text":"conans/client/build/cmake.py::CMake._get_dirs\n def _get_dirs(self, source_folder, build_folder, source_dir, build_dir, cache_build_folder):\n if (source_folder or build_folder) and (source_dir or build_dir):\n raise ConanException(\"Use 'build_folder'/'source_folder' arguments\")\n\n def get_dir(folder, origin):\n if folder:\n if os.path.isabs(folder):\n return folder\n return os.path.join(origin, folder)\n return origin\n\n if source_dir or build_dir: # OLD MODE\n build_ret = build_dir or self.build_dir or self._conanfile.build_folder\n source_ret = source_dir or self._conanfile.source_folder\n else:\n build_ret = get_dir(build_folder, self._conanfile.build_folder)\n source_ret = get_dir(source_folder, self._conanfile.source_folder)\n\n if self._conanfile.in_local_cache and cache_build_folder:\n build_ret = get_dir(cache_build_folder, self._conanfile.build_folder)\n\n return source_ret, build_ret","label":0},{"cid":"conans/client/build/cmake.py::CMake._get_dirs.get_dir","text":"conans/client/build/cmake.py::CMake._get_dirs.get_dir\n def get_dir(folder, origin):\n if folder:\n if os.path.isabs(folder):\n return folder\n return os.path.join(origin, folder)\n return origin","label":0},{"cid":"conans/client/build/cmake.py::CMake._run","text":"conans/client/build/cmake.py::CMake._run\n def _run(self, command):\n compiler = self._settings.get_safe(\"compiler\")\n the_os = self._settings.get_safe(\"os\")\n is_clangcl = the_os == \"Windows\" and compiler == \"clang\"\n is_msvc = compiler == \"Visual Studio\"\n if ((is_msvc or is_clangcl) and platform.system() == \"Windows\" and\n self.generator in [\"Ninja\", \"NMake Makefiles\", \"NMake Makefiles JOM\"]):\n vcvars_dict = tools.vcvars_dict(self._settings, force=True, filter_known_paths=False,\n output=self._conanfile.output)\n with _environment_add(vcvars_dict, post=self._append_vcvars):\n self._conanfile.run(command)\n else:\n self._conanfile.run(command)","label":0},{"cid":"conans/client/build/cmake.py::CMake.configure","text":"conans/client/build/cmake.py::CMake.configure\n def configure(self, args=None, defs=None, source_dir=None, build_dir=None,\n source_folder=None, build_folder=None, cache_build_folder=None,\n pkg_config_paths=None):\n\n # TODO: Deprecate source_dir and build_dir in favor of xxx_folder\n if not self._conanfile.should_configure:\n return\n args = args or []\n defs = defs or {}\n source_dir, self.build_dir = self._get_dirs(source_folder, build_folder,\n source_dir, build_dir,\n cache_build_folder)\n mkdir(self.build_dir)\n arg_list = join_arguments([\n self.command_line,\n args_to_string(args),\n defs_to_string(defs),\n args_to_string([source_dir])\n ])\n\n if pkg_config_paths:\n pkg_env = {\"PKG_CONFIG_PATH\":\n os.pathsep.join(get_abs_path(f, self._conanfile.install_folder)\n for f in pkg_config_paths)}\n else:\n # If we are using pkg_config generator automate the pcs location, otherwise it could\n # read wrong files\n set_env = \"pkg_config\" in self._conanfile.generators \\\n and \"PKG_CONFIG_PATH\" not in os.environ\n pkg_env = {\"PKG_CONFIG_PATH\": self._conanfile.install_folder} if set_env else None\n\n with environment_append(pkg_env):\n command = \"cd %s && %s %s\" % (args_to_string([self.build_dir]), self._cmake_program,\n arg_list)\n if platform.system() == \"Windows\" and self.generator == \"MinGW Makefiles\":\n with tools.remove_from_path(\"sh\"):\n self._run(command)\n else:\n self._run(command)","label":0},{"cid":"conans/client/build/cmake.py::CMake.build","text":"conans/client/build/cmake.py::CMake.build\n def build(self, args=None, build_dir=None, target=None):\n if not self._conanfile.should_build:\n return\n self._build(args, build_dir, target)","label":0},{"cid":"conans/client/build/cmake.py::CMake._build","text":"conans/client/build/cmake.py::CMake._build\n def _build(self, args=None, build_dir=None, target=None):\n args = args or []\n build_dir = build_dir or self.build_dir or self._conanfile.build_folder\n if target is not None:\n args = [\"--target\", target] + args\n\n compiler_version = self._settings.get_safe(\"compiler.version\")\n if self.generator and self.parallel:\n if (\"Makefiles\" in self.generator or \"Ninja\" in self.generator) and \\\n \"NMake\" not in self.generator:\n if \"--\" not in args:\n args.append(\"--\")\n args.append(\"-j%i\" % cpu_count(self._conanfile.output))\n elif \"Visual Studio\" in self.generator and \\\n compiler_version and Version(compiler_version) >= \"10\":\n if \"--\" not in args:\n args.append(\"--\")\n # Parallel for building projects in the solution\n args.append(\"/m:%i\" % cpu_count(output=self._conanfile.output))\n\n if self.generator and self.msbuild_verbosity:\n if \"Visual Studio\" in self.generator and \\\n compiler_version and Version(compiler_version) >= \"10\":\n if \"--\" not in args:\n args.append(\"--\")\n args.append(\"/verbosity:%s\" % self.msbuild_verbosity)\n\n arg_list = join_arguments([\n args_to_string([build_dir]),\n self.build_config,\n args_to_string(args)\n ])\n command = \"%s --build %s\" % (self._cmake_program, arg_list)\n self._run(command)","label":0},{"cid":"conans/client/build/cmake.py::CMake.install","text":"conans/client/build/cmake.py::CMake.install\n def install(self, args=None, build_dir=None):\n if not self._conanfile.should_install:\n return\n mkdir(self._conanfile.package_folder)\n if not self.definitions.get(cmake_install_prefix_var_name):\n raise ConanException(\"%s not defined for 'cmake.install()'\\n\"\n \"Make sure 'package_folder' is \"\n \"defined\" % cmake_install_prefix_var_name)\n self._build(args=args, build_dir=build_dir, target=\"install\")","label":0},{"cid":"conans/client/build/cmake.py::CMake.test","text":"conans/client/build/cmake.py::CMake.test\n def test(self, args=None, build_dir=None, target=None, output_on_failure=False):\n if not self._conanfile.should_test:\n return\n if not target:\n target = \"RUN_TESTS\" if self.is_multi_configuration else \"test\"\n\n test_env = {'CTEST_OUTPUT_ON_FAILURE': '1' if output_on_failure else '0'}\n if self.parallel:\n test_env['CTEST_PARALLEL_LEVEL'] = str(cpu_count(self._conanfile.output))\n with environment_append(test_env):\n self._build(args=args, build_dir=build_dir, target=target)","label":0},{"cid":"conans/client/build/cmake.py::CMake.verbose","text":"conans/client/build/cmake.py::CMake.verbose\n @property\n def verbose(self):\n try:\n verbose = self.definitions[verbose_definition_name]\n return get_bool_from_text(str(verbose))\n except KeyError:\n return False","label":0},{"cid":"conans/client/build/cmake.py::CMake.verbose","text":"conans/client/build/cmake.py::CMake.verbose\n @verbose.setter\n def verbose(self, value):\n self.definitions.update(verbose_definition(value))","label":0},{"cid":"conans/client/build/cmake.py::CMake.patch_config_paths","text":"conans/client/build/cmake.py::CMake.patch_config_paths\n def patch_config_paths(self):\n \"\"\"\n changes references to the absolute path of the installed package and its dependencies in\n exported cmake config files to the appropriate conan variable. This makes\n most (sensible) cmake config files portable.\n\n For example, if a package foo installs a file called \"fooConfig.cmake\" to\n be used by cmake's find_package method, normally this file will contain\n absolute paths to the installed package folder, for example it will contain\n a line such as:\n\n SET(Foo_INSTALL_DIR /home/developer/.conan/data/Foo/1.0.0/...)\n\n This will cause cmake find_package() method to fail when someone else\n installs the package via conan.\n\n This function will replace such mentions to\n\n SET(Foo_INSTALL_DIR ${CONAN_FOO_ROOT})\n\n which is a variable that is set by conanbuildinfo.cmake, so that find_package()\n now correctly works on this conan package.\n\n For dependent packages, if a package foo installs a file called \"fooConfig.cmake\" to\n be used by cmake's find_package method and if it depends to a package bar,\n normally this file will contain absolute paths to the bar package folder,\n for example it will contain a line such as:\n\n SET_TARGET_PROPERTIES(foo PROPERTIES\n INTERFACE_INCLUDE_DIRECTORIES\n \"/home/developer/.conan/data/Bar/1.0.0/user/channel/id/include\")\n\n This function will replace such mentions to\n\n SET_TARGET_PROPERTIES(foo PROPERTIES\n INTERFACE_INCLUDE_DIRECTORIES\n \"${CONAN_BAR_ROOT}/include\")\n\n If the install() method of the CMake object in the conan file is used, this\n function should be called _after_ that invocation. For example:\n\n def build(self):\n cmake = CMake(self)\n cmake.configure()\n ","label":0},{"cid":"conans/client/build/cmake.py::CMake.get_version","text":"conans/client/build/cmake.py::CMake.get_version\n @staticmethod\n def get_version():\n try:\n out = version_runner([\"cmake\", \"--version\"])\n version_line = decode_text(out).split('\\n', 1)[0]\n version_str = version_line.rsplit(' ', 1)[-1]\n return Version(version_str)\n except Exception as e:\n raise ConanException(\"Error retrieving CMake version: '{}'\".format(e))","label":0},{"cid":"conans/client/cmd/export.py::export_alias","text":"conans/client/cmd/export.py::export_alias\ndef export_alias(package_layout, target_ref, output, revisions_enabled):\n revision_mode = \"hash\"\n conanfile = \"\"\"\nfrom conans import ConanFile\n\nclass AliasConanfile(ConanFile):\n alias = \"%s\"\n revision_mode = \"%s\"\n\"\"\" % (target_ref.full_str(), revision_mode)\n\n save(package_layout.conanfile(), conanfile)\n manifest = FileTreeManifest.create(package_layout.export())\n manifest.save(folder=package_layout.export())\n\n # Create the metadata for the alias\n _update_revision_in_metadata(package_layout=package_layout, revisions_enabled=revisions_enabled,\n output=output, path=None, manifest=manifest,\n revision_mode=revision_mode)","label":0},{"cid":"conans/client/cmd/export.py::check_casing_conflict","text":"conans/client/cmd/export.py::check_casing_conflict\ndef check_casing_conflict(cache, ref):\n # Check for casing conflict\n # Maybe a platform check could be added, but depends on disk partition\n refs = search_recipes(cache, ref, ignorecase=True)\n refs2 = [ConanFileReference(r.name, r.version, r.user if ref.user else None,\n r.channel if ref.channel else None, validate=False) for r in refs]\n\n if refs and ref not in refs2:\n raise ConanException(\"Cannot export package with same name but different case\\n\"\n \"You exported '%s' but already existing '%s'\"\n % (str(ref), \" \".join(str(s) for s in refs)))","label":0},{"cid":"conans/client/cmd/export.py::cmd_export","text":"conans/client/cmd/export.py::cmd_export\ndef cmd_export(app, conanfile_path, name, version, user, channel, keep_source,\n export=True, graph_lock=None, ignore_dirty=False):\n \"\"\" Export the recipe\n param conanfile_path: the original source directory of the user containing a\n conanfile.py\n \"\"\"\n loader, cache, hook_manager, output = app.loader, app.cache, app.hook_manager, app.out\n revisions_enabled = app.config.revisions_enabled\n scm_to_conandata = app.config.scm_to_conandata\n conanfile = loader.load_export(conanfile_path, name, version, user, channel)\n\n # FIXME: Conan 2.0, deprecate CONAN_USER AND CONAN_CHANNEL and remove this try excepts\n # Take the default from the env vars if they exist to not break behavior\n try:\n user = conanfile.user\n except ConanV2Exception:\n raise\n except ConanException:\n user = None\n\n try:\n channel = conanfile.channel\n except ConanV2Exception:\n raise\n except ConanException:\n channel = None\n\n ref = ConanFileReference(conanfile.name, conanfile.version, user, channel)\n\n # If we receive lock information, python_requires could have been locked\n if graph_lock:\n node_id = graph_lock.get_node(ref)\n python_requires = graph_lock.python_requires(node_id)\n # TODO: check that the locked python_requires are different from the loaded ones\n app.range_resolver.clear_output() # invalidate previous version range output\n conanfile = loader.load_export(conanfile_path, conanfile.name, conanfile.version,\n ref.user, ref.channel, python_requires)\n\n check_casing_conflict(cache=cache, ref=ref)\n package_layout = cache.package_layout(ref, short_paths=conanfile.short_paths)\n if not export:\n metadata = package_layout.load_metadata()\n recipe_revision = metadata.recipe.revision\n ref = ref.copy_with_rev(recipe_revision)\n if graph_lock:\n ","label":0},{"cid":"conans/client/cmd/export.py::_check_settings_for_warnings","text":"conans/client/cmd/export.py::_check_settings_for_warnings\ndef _check_settings_for_warnings(conanfile, output):\n if not conanfile.settings:\n return\n try:\n if 'os_build' not in conanfile.settings:\n return\n if 'os' not in conanfile.settings:\n return\n\n output.writeln(\"*\"*60, front=Color.BRIGHT_RED)\n output.writeln(\" This package defines both 'os' and 'os_build' \",\n front=Color.BRIGHT_RED)\n output.writeln(\" Please use 'os' for libraries and 'os_build'\",\n front=Color.BRIGHT_RED)\n output.writeln(\" only for build-requires used for cross-building\",\n front=Color.BRIGHT_RED)\n output.writeln(\"*\"*60, front=Color.BRIGHT_RED)\n except ConanException:\n pass","label":0},{"cid":"conans/client/cmd/export.py::_capture_scm_auto_fields","text":"conans/client/cmd/export.py::_capture_scm_auto_fields\ndef _capture_scm_auto_fields(conanfile, conanfile_dir, package_layout, output, ignore_dirty, scm_to_conandata):\n \"\"\"Deduce the values for the scm auto fields or functions assigned to 'url' or 'revision'\n and replace the conanfile.py contents.\n Returns a tuple with (scm_data, path_to_scm_local_directory)\"\"\"\n scm_data = get_scm_data(conanfile)\n if not scm_data:\n return None, None\n\n # Resolve SCMData in the user workspace (someone may want to access CVS or import some py)\n scm = SCM(scm_data, conanfile_dir, output)\n captured = scm_data.capture_origin or scm_data.capture_revision\n\n if not captured:\n # We replace not only \"auto\" values, also evaluated functions (e.g from a python_require)\n _replace_scm_data_in_recipe(package_layout, scm_data, scm_to_conandata)\n return scm_data, None\n\n if not scm.is_pristine() and not ignore_dirty:\n output.warn(\"There are uncommitted changes, skipping the replacement of 'scm.url' and \"\n \"'scm.revision' auto fields. Use --ignore-dirty to force it. The 'conan \"\n \"upload' command will prevent uploading recipes with 'auto' values in these \"\n \"fields.\")\n origin = scm.get_qualified_remote_url(remove_credentials=True)\n local_src_path = scm.get_local_path_to_url(origin)\n return scm_data, local_src_path\n\n if scm_data.url == \"auto\":\n origin = scm.get_qualified_remote_url(remove_credentials=True)\n if not origin:\n raise ConanException(\"Repo origin cannot be deduced\")\n if scm.is_local_repository():\n output.warn(\"Repo origin looks like a local path: %s\" % origin)\n output.success(\"Repo origin deduced by 'auto': %s\" % origin)\n scm_data.url = origin\n\n if scm_data.revision == \"auto\":\n # If it is pristine by default we don't replace the \"auto\" unless forcing\n # This prevents the rec","label":0},{"cid":"conans/client/cmd/export.py::_replace_scm_data_in_recipe","text":"conans/client/cmd/export.py::_replace_scm_data_in_recipe\ndef _replace_scm_data_in_recipe(package_layout, scm_data, scm_to_conandata):\n if scm_to_conandata:\n conandata_path = os.path.join(package_layout.export(), DATA_YML)\n conandata_yml = {}\n if os.path.exists(conandata_path):\n conandata_yml = yaml.safe_load(load(conandata_path))\n if '.conan' in conandata_yml:\n raise ConanException(\"Field '.conan' inside '{}' file is reserved to Conan usage.\".format(DATA_YML))\n scm_data_copied = scm_data.as_dict()\n scm_data_copied.pop('username', None)\n scm_data_copied.pop('password', None)\n conandata_yml['.conan'] = {'scm': scm_data_copied}\n\n save(conandata_path, yaml.safe_dump(conandata_yml, default_flow_style=False))\n else:\n _replace_scm_data_in_conanfile(package_layout.conanfile(), scm_data)","label":0},{"cid":"conans/client/cmd/export.py::_replace_scm_data_in_conanfile","text":"conans/client/cmd/export.py::_replace_scm_data_in_conanfile\ndef _replace_scm_data_in_conanfile(conanfile_path, scm_data):\n # Parsing and replacing the SCM field\n content = load(conanfile_path)\n headers = []\n\n if six.PY2:\n # Workaround for https://bugs.python.org/issue22221\n lines_without_headers = []\n lines = content.splitlines(True)\n for line in lines:\n if not lines_without_headers and line.startswith(\"#\"):\n headers.append(line)\n else:\n lines_without_headers.append(line)\n content = ''.join(lines_without_headers)\n\n lines = content.splitlines(True)\n tree = ast.parse(content)\n to_replace = []\n comments = []\n class_line = None\n tab_size = 4\n for i_body, item in enumerate(tree.body):\n if isinstance(item, ast.ClassDef):\n statements = item.body\n class_line = item.lineno\n for i, stmt in enumerate(item.body):\n if isinstance(stmt, ast.Assign) and len(stmt.targets) == 1:\n line = lines[stmt.lineno - 1]\n tab_size = len(line) - len(line.lstrip())\n if isinstance(stmt.targets[0], ast.Name) and stmt.targets[0].id == \"scm\":\n try:\n if i + 1 == len(statements): # Last statement in my ClassDef\n if i_body + 1 == len(tree.body): # Last statement over all\n next_line = len(lines)\n else:\n next_line = tree.body[i_body+1].lineno - 1\n else:\n # Next statement can be a comment or anything else\n next_statement = statements[i+1]\n if isPY38 and isinstance(next_statement, ast.Expr):\n # Python 3.8 properly parses multiline comments with sta","label":0},{"cid":"conans/client/cmd/export.py::_detect_scm_revision","text":"conans/client/cmd/export.py::_detect_scm_revision\ndef _detect_scm_revision(path):\n if not path:\n raise ConanException(\"Not path supplied\")\n\n repo_type = SCM.detect_scm(path)\n if not repo_type:\n raise ConanException(\"'{}' repository not detected\".format(repo_type))\n\n repo_obj = SCM.availables.get(repo_type)(path)\n return repo_obj.get_revision(), repo_type, repo_obj.is_pristine()","label":0},{"cid":"conans/client/cmd/export.py::_update_revision_in_metadata","text":"conans/client/cmd/export.py::_update_revision_in_metadata\ndef _update_revision_in_metadata(package_layout, revisions_enabled, output, path, manifest,\n revision_mode):\n if revision_mode not in [\"scm\", \"hash\"]:\n raise ConanException(\"Revision mode should be one of 'hash' (default) or 'scm'\")\n\n # Use the proper approach depending on 'revision_mode'\n if revision_mode == \"hash\":\n revision = manifest.summary_hash\n if revisions_enabled:\n output.info(\"Using the exported files summary hash as the recipe\"\n \" revision: {} \".format(revision))\n else:\n try:\n rev_detected, repo_type, is_pristine = _detect_scm_revision(path)\n except Exception as exc:\n error_msg = \"Cannot detect revision using '{}' mode from repository at \" \\\n \"'{}'\".format(revision_mode, path)\n raise ConanException(\"{}: {}\".format(error_msg, exc))\n\n revision = rev_detected\n\n if revisions_enabled:\n output.info(\"Using %s commit as the recipe revision: %s\" % (repo_type, revision))\n if not is_pristine:\n output.warn(\"Repo status is not pristine: there might be modified files\")\n\n with package_layout.update_metadata() as metadata:\n metadata.recipe.revision = revision\n\n return revision","label":0},{"cid":"conans/client/cmd/export.py::_recreate_folders","text":"conans/client/cmd/export.py::_recreate_folders\ndef _recreate_folders(destination_folder):\n try:\n if os.path.exists(destination_folder):\n rmdir(destination_folder)\n os.makedirs(destination_folder)\n except Exception as e:\n raise ConanException(\"Unable to create folder %s\\n%s\" % (destination_folder, str(e)))","label":0},{"cid":"conans/client/cmd/export.py::_classify_patterns","text":"conans/client/cmd/export.py::_classify_patterns\ndef _classify_patterns(patterns):\n patterns = patterns or []\n included, excluded = [], []\n for p in patterns:\n if p.startswith(\"!\"):\n excluded.append(p[1:])\n else:\n included.append(p)\n return included, excluded","label":0},{"cid":"conans/client/cmd/export.py::_export_scm","text":"conans/client/cmd/export.py::_export_scm\ndef _export_scm(scm_data, origin_folder, scm_sources_folder, output):\n \"\"\" Copy the local folder to the scm_sources folder in the cache, this enables to work\n with local sources without committing and pushing changes to the scm remote.\n https://github.com/conan-io/conan/issues/5195\"\"\"\n excluded = SCM(scm_data, origin_folder, output).excluded_files\n excluded.append(\"conanfile.py\")\n output.info(\"SCM: Getting sources from folder: %s\" % origin_folder)\n merge_directories(origin_folder, scm_sources_folder, excluded=excluded)","label":0},{"cid":"conans/client/cmd/export.py::export_source","text":"conans/client/cmd/export.py::export_source\ndef export_source(conanfile, origin_folder, destination_source_folder):\n if isinstance(conanfile.exports_sources, str):\n conanfile.exports_sources = (conanfile.exports_sources, )\n\n included_sources, excluded_sources = _classify_patterns(conanfile.exports_sources)\n copier = FileCopier([origin_folder], destination_source_folder)\n for pattern in included_sources:\n copier(pattern, links=True, excludes=excluded_sources)\n output = conanfile.output\n package_output = ScopedOutput(\"%s exports_sources\" % output.scope, output)\n copier.report(package_output)","label":0},{"cid":"conans/client/cmd/export.py::export_recipe","text":"conans/client/cmd/export.py::export_recipe\ndef export_recipe(conanfile, origin_folder, destination_folder):\n if isinstance(conanfile.exports, str):\n conanfile.exports = (conanfile.exports, )\n\n output = conanfile.output\n package_output = ScopedOutput(\"%s exports\" % output.scope, output)\n\n if os.path.exists(os.path.join(origin_folder, DATA_YML)):\n package_output.info(\"File '{}' found. Exporting it...\".format(DATA_YML))\n tmp = [DATA_YML]\n if conanfile.exports:\n tmp.extend(conanfile.exports) # conanfile.exports could be a tuple (immutable)\n conanfile.exports = tmp\n\n included_exports, excluded_exports = _classify_patterns(conanfile.exports)\n\n try:\n os.unlink(os.path.join(origin_folder, CONANFILE + 'c'))\n except OSError:\n pass\n\n copier = FileCopier([origin_folder], destination_folder)\n for pattern in included_exports:\n copier(pattern, links=True, excludes=excluded_exports)\n\n copier.report(package_output)","label":0},{"cid":"conans/client/cmd/export_pkg.py::export_pkg","text":"conans/client/cmd/export_pkg.py::export_pkg\ndef export_pkg(app, recorder, full_ref, source_folder, build_folder, package_folder, install_folder,\n graph_info, force, remotes):\n ref = full_ref.copy_clear_rev()\n cache, output, hook_manager = app.cache, app.out, app.hook_manager\n graph_manager = app.graph_manager\n conan_file_path = cache.package_layout(ref).conanfile()\n if not os.path.exists(conan_file_path):\n raise ConanException(\"Package recipe '%s' does not exist\" % str(ref))\n\n # The graph has to be loaded with build_mode=[ref.name], so that node is not tried\n # to be downloaded from remotes\n # passing here the create_reference=ref argument is useful so the recipe is in \"develop\",\n # because the \"package()\" method is in develop=True already\n deps_graph = graph_manager.load_graph(ref, ref, graph_info=graph_info, build_mode=[ref.name],\n check_updates=False, update=False, remotes=remotes,\n recorder=recorder, apply_build_requires=False)\n # this is a bit tricky, but works. The root (virtual), has only 1 neighbor,\n # which is the exported pkg\n nodes = deps_graph.root.neighbors()\n pkg_node = nodes[0]\n conanfile = pkg_node.conanfile\n\n def _init_conanfile_infos():\n node_order = [n for n in pkg_node.public_closure if n.binary != BINARY_SKIP]\n subtree_libnames = [node.ref.name for node in node_order]\n add_env_conaninfo(conanfile, subtree_libnames)\n\n _init_conanfile_infos()\n from conans.client.conan_api import existing_info_files\n if install_folder and existing_info_files(install_folder):\n load_deps_info(install_folder, conanfile, required=True)\n package_id = pkg_node.package_id\n output.info(\"Packaging to %s\" % package_id)\n pref = PackageReference(ref, package_id)\n layout = cache.package_layout(ref, short_paths=conanfile.short_paths)\n dest_package_folder = layout.package(pref)\n\n ","label":0},{"cid":"conans/client/cmd/export_pkg.py::export_pkg._init_conanfile_infos","text":"conans/client/cmd/export_pkg.py::export_pkg._init_conanfile_infos\n def _init_conanfile_infos():\n node_order = [n for n in pkg_node.public_closure if n.binary != BINARY_SKIP]\n subtree_libnames = [node.ref.name for node in node_order]\n add_env_conaninfo(conanfile, subtree_libnames)","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload.__init__","text":"conans/client/cmd/uploader.py::CmdUpload.__init__\n def __init__(self, cache, user_io, remote_manager, loader, hook_manager):\n self._cache = cache\n self._user_io = user_io\n self._output = progress_bar.ProgressOutput(self._user_io.out)\n self._remote_manager = remote_manager\n self._loader = loader\n self._hook_manager = hook_manager\n self._upload_thread_pool = None\n self._exceptions_list = []","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload.upload","text":"conans/client/cmd/uploader.py::CmdUpload.upload\n def upload(self, reference_or_pattern, remotes, upload_recorder, package_id=None,\n all_packages=None, confirm=False, retry=None, retry_wait=None, integrity_check=False,\n policy=None, query=None, parallel_upload=False):\n t1 = time.time()\n refs, confirm = self._collects_refs_to_upload(package_id, reference_or_pattern, confirm)\n refs_by_remote = self._collect_packages_to_upload(refs, confirm, remotes, all_packages,\n query, package_id)\n\n if parallel_upload:\n self._upload_thread_pool = ThreadPool(8)\n self._user_io.disable_input()\n else:\n self._upload_thread_pool = ThreadPool(1)\n\n for remote, refs in refs_by_remote.items():\n self._output.info(\"Uploading to remote '{}':\".format(remote.name))\n\n def upload_ref(ref_conanfile_prefs):\n _ref, _conanfile, _prefs = ref_conanfile_prefs\n try:\n self._upload_ref(_conanfile, _ref, _prefs, retry, retry_wait,\n integrity_check, policy, remote, upload_recorder, remotes)\n except BaseException as base_exception:\n base_trace = traceback.format_exc()\n self._exceptions_list.append((base_exception, _ref, base_trace))\n\n self._upload_thread_pool.map(upload_ref,\n [(ref, conanfile, prefs) for (ref, conanfile, prefs) in\n refs])\n self._upload_thread_pool.close()\n self._upload_thread_pool.join()\n\n if len(self._exceptions_list) > 0:\n for exc, ref, trace in self._exceptions_list:\n t = \"recipe\" if isinstance(ref, ConanFileReference) else \"package\"\n msg = \"%s: Upload %s to '%s' failed: %s\\n\" % (str(ref), t, remote.na","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload._collects_refs_to_upload","text":"conans/client/cmd/uploader.py::CmdUpload._collects_refs_to_upload\n def _collects_refs_to_upload(self, package_id, reference_or_pattern, confirm):\n \"\"\" validate inputs and compute the refs (without revisions) to be uploaded\n \"\"\"\n if package_id and not check_valid_ref(reference_or_pattern, strict_mode=False):\n raise ConanException(\"-p parameter only allowed with a valid recipe reference, \"\n \"not with a pattern\")\n\n if package_id or check_valid_ref(reference_or_pattern):\n # Upload package\n ref = ConanFileReference.loads(reference_or_pattern)\n if ref.revision and not self._cache.config.revisions_enabled:\n raise ConanException(\"Revisions not enabled in the client, specify a \"\n \"reference without revision\")\n refs = [ref, ]\n confirm = True\n else:\n refs = search_recipes(self._cache, reference_or_pattern)\n if not refs:\n raise NotFoundException((\"No packages found matching pattern '%s'\" %\n reference_or_pattern))\n return refs, confirm","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload._collect_packages_to_upload","text":"conans/client/cmd/uploader.py::CmdUpload._collect_packages_to_upload\n def _collect_packages_to_upload(self, refs, confirm, remotes, all_packages, query, package_id):\n \"\"\" compute the references with revisions and the package_ids to be uploaded\n \"\"\"\n # Group recipes by remote\n refs_by_remote = defaultdict(list)\n\n for ref in refs:\n metadata = self._cache.package_layout(ref).load_metadata()\n if ref.revision and ref.revision != metadata.recipe.revision:\n raise ConanException(\"Recipe revision {} does not match the one stored in the \"\n \"cache {}\".format(ref.revision, metadata.recipe.revision))\n ref = ref.copy_with_rev(metadata.recipe.revision)\n remote = remotes.selected\n if remote:\n ref_remote = remote\n else:\n ref_remote = metadata.recipe.remote\n ref_remote = remotes.get_remote(ref_remote)\n\n upload = True\n if not confirm:\n msg = \"Are you sure you want to upload '%s' to '%s'?\" % (str(ref), ref_remote.name)\n upload = self._user_io.request_boolean(msg)\n if upload:\n try:\n conanfile_path = self._cache.package_layout(ref).conanfile()\n conanfile = self._loader.load_basic(conanfile_path)\n except NotFoundException:\n raise NotFoundException((\"There is no local conanfile exported as %s\" %\n str(ref)))\n\n # TODO: This search of binary packages has to be improved, more robust\n # So only real packages are retrieved\n if all_packages or query:\n if all_packages:\n query = None\n # better to do a search, that will retrieve real packages with ConanInfo\n # Not only \"package_id\" folders ","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload._upload_ref","text":"conans/client/cmd/uploader.py::CmdUpload._upload_ref\n def _upload_ref(self, conanfile, ref, prefs, retry, retry_wait, integrity_check, policy,\n recipe_remote, upload_recorder, remotes):\n \"\"\" Uploads the recipes and binaries identified by ref\n \"\"\"\n assert (ref.revision is not None), \"Cannot upload a recipe without RREV\"\n conanfile_path = self._cache.package_layout(ref).conanfile()\n # FIXME: I think it makes no sense to specify a remote to \"pre_upload\"\n # FIXME: because the recipe can have one and the package a different one\n self._hook_manager.execute(\"pre_upload\", conanfile_path=conanfile_path,\n reference=ref, remote=recipe_remote)\n msg = \"\\rUploading %s to remote '%s'\" % (str(ref), recipe_remote.name)\n self._output.info(left_justify_message(msg))\n self._upload_recipe(ref, conanfile, retry, retry_wait, policy, recipe_remote, remotes)\n upload_recorder.add_recipe(ref, recipe_remote.name, recipe_remote.url)\n\n # Now the binaries\n if prefs:\n total = len(prefs)\n p_remote = recipe_remote\n\n def upload_package_index(index_pref):\n index, pref = index_pref\n try:\n up_msg = \"\\rUploading package %d/%d: %s to '%s'\" % (index + 1, total,\n str(pref.id),\n p_remote.name)\n self._output.info(left_justify_message(up_msg))\n self._upload_package(pref, retry, retry_wait, integrity_check, policy, p_remote)\n upload_recorder.add_package(pref, p_remote.name, p_remote.url)\n except BaseException as pkg_exc:\n trace = traceback.format_exc()\n return pkg_exc, pref, trace\n\n def upload_package_callback(ret):\n ","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload._upload_recipe","text":"conans/client/cmd/uploader.py::CmdUpload._upload_recipe\n def _upload_recipe(self, ref, conanfile, retry, retry_wait, policy, remote, remotes):\n\n current_remote_name = self._cache.package_layout(ref).load_metadata().recipe.remote\n\n if remote.name != current_remote_name:\n complete_recipe_sources(self._remote_manager, self._cache, conanfile, ref, remotes)\n\n conanfile_path = self._cache.package_layout(ref).conanfile()\n self._hook_manager.execute(\"pre_upload_recipe\", conanfile_path=conanfile_path,\n reference=ref, remote=remote)\n\n t1 = time.time()\n the_files = self._compress_recipe_files(ref)\n\n with self._cache.package_layout(ref).update_metadata() as metadata:\n metadata.recipe.checksums = calc_files_checksum(the_files)\n\n local_manifest = FileTreeManifest.loads(load(the_files[\"conanmanifest.txt\"]))\n\n remote_manifest = None\n if policy != UPLOAD_POLICY_FORCE:\n # Check SCM data for auto fields\n if hasattr(conanfile, \"scm\") and (\n conanfile.scm.get(\"url\") == \"auto\" or conanfile.scm.get(\"revision\") == \"auto\"):\n raise ConanException(\"The recipe has 'scm.url' or 'scm.revision' with 'auto' \"\n \"values. Use '--force' to ignore this error or export again \"\n \"the recipe ('conan export' or 'conan create') in a \"\n \"repository with no-uncommitted changes or by \"\n \"using the '--ignore-dirty' option\")\n\n remote_manifest = self._check_recipe_date(ref, remote, local_manifest)\n if policy == UPLOAD_POLICY_SKIP:\n return ref\n\n files_to_upload, deleted = self._recipe_files_to_upload(ref, policy, the_files,\n remote, remote_manifest,\n ","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload._upload_package","text":"conans/client/cmd/uploader.py::CmdUpload._upload_package\n def _upload_package(self, pref, retry=None, retry_wait=None, integrity_check=False,\n policy=None, p_remote=None):\n\n assert (pref.revision is not None), \"Cannot upload a package without PREV\"\n assert (pref.ref.revision is not None), \"Cannot upload a package without RREV\"\n\n pkg_layout = self._cache.package_layout(pref.ref)\n conanfile_path = pkg_layout.conanfile()\n self._hook_manager.execute(\"pre_upload_package\", conanfile_path=conanfile_path,\n reference=pref.ref,\n package_id=pref.id,\n remote=p_remote)\n\n t1 = time.time()\n the_files = self._compress_package_files(pref, integrity_check)\n\n if policy == UPLOAD_POLICY_SKIP:\n return None\n files_to_upload, deleted = self._package_files_to_upload(pref, policy, the_files, p_remote)\n\n if files_to_upload or deleted:\n self._remote_manager.upload_package(pref, files_to_upload, deleted, p_remote, retry,\n retry_wait)\n logger.debug(\"UPLOAD: Time upload package: %f\" % (time.time() - t1))\n else:\n self._output.info(\"Package is up to date, upload skipped\")\n\n duration = time.time() - t1\n log_package_upload(pref, duration, the_files, p_remote)\n self._hook_manager.execute(\"post_upload_package\", conanfile_path=conanfile_path,\n reference=pref.ref, package_id=pref.id, remote=p_remote)\n\n logger.debug(\"UPLOAD: Time uploader upload_package: %f\" % (time.time() - t1))\n\n # Update the package metadata\n checksums = calc_files_checksum(the_files)\n with pkg_layout.update_metadata() as metadata:\n cur_package_remote = metadata.packages[pref.id].remote\n if not cur_package_remote:\n metadata.pa","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload._compress_recipe_files","text":"conans/client/cmd/uploader.py::CmdUpload._compress_recipe_files\n def _compress_recipe_files(self, ref):\n export_folder = self._cache.package_layout(ref).export()\n\n for f in (EXPORT_TGZ_NAME, EXPORT_SOURCES_TGZ_NAME):\n tgz_path = os.path.join(export_folder, f)\n if is_dirty(tgz_path):\n self._output.warn(\"%s: Removing %s, marked as dirty\" % (str(ref), f))\n os.remove(tgz_path)\n clean_dirty(tgz_path)\n\n files, symlinks = gather_files(export_folder)\n if CONANFILE not in files or CONAN_MANIFEST not in files:\n raise ConanException(\"Cannot upload corrupted recipe '%s'\" % str(ref))\n export_src_folder = self._cache.package_layout(ref).export_sources()\n src_files, src_symlinks = gather_files(export_src_folder)\n the_files = _compress_recipe_files(files, symlinks, src_files, src_symlinks, export_folder,\n self._output)\n\n return the_files","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload._compress_package_files","text":"conans/client/cmd/uploader.py::CmdUpload._compress_package_files\n def _compress_package_files(self, pref, integrity_check):\n\n t1 = time.time()\n # existing package, will use short paths if defined\n package_folder = self._cache.package_layout(pref.ref, short_paths=None).package(pref)\n\n if is_dirty(package_folder):\n raise ConanException(\"Package %s is corrupted, aborting upload.\\n\"\n \"Remove it with 'conan remove %s -p=%s'\"\n % (pref, pref.ref, pref.id))\n tgz_path = os.path.join(package_folder, PACKAGE_TGZ_NAME)\n if is_dirty(tgz_path):\n self._output.warn(\"%s: Removing %s, marked as dirty\" % (str(pref), PACKAGE_TGZ_NAME))\n os.remove(tgz_path)\n clean_dirty(tgz_path)\n # Get all the files in that directory\n files, symlinks = gather_files(package_folder)\n\n if CONANINFO not in files or CONAN_MANIFEST not in files:\n logger.error(\"Missing info or manifest in uploading files: %s\" % (str(files)))\n raise ConanException(\"Cannot upload corrupted package '%s'\" % str(pref))\n\n logger.debug(\"UPLOAD: Time remote_manager build_files_set : %f\" % (time.time() - t1))\n if integrity_check:\n self._package_integrity_check(pref, files, package_folder)\n logger.debug(\"UPLOAD: Time remote_manager check package integrity : %f\"\n % (time.time() - t1))\n\n the_files = _compress_package_files(files, symlinks, package_folder, self._output)\n return the_files","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload._recipe_files_to_upload","text":"conans/client/cmd/uploader.py::CmdUpload._recipe_files_to_upload\n def _recipe_files_to_upload(self, ref, policy, the_files, remote, remote_manifest,\n local_manifest):\n self._remote_manager.check_credentials(remote)\n remote_snapshot = self._remote_manager.get_recipe_snapshot(ref, remote)\n files_to_upload = {filename.replace(\"\\\\\", \"/\"): path\n for filename, path in the_files.items()}\n if not remote_snapshot:\n return files_to_upload, set()\n\n deleted = set(remote_snapshot).difference(the_files)\n if policy != UPLOAD_POLICY_FORCE:\n if remote_manifest is None:\n # This is the weird scenario, we have a snapshot but don't have a manifest.\n # Can be due to concurrency issues, so we can try retrieve it now\n try:\n remote_manifest, _ = self._remote_manager.get_recipe_manifest(ref, remote)\n except NotFoundException:\n # This is weird, the manifest still not there, better upload everything\n self._output.warn(\"The remote recipe doesn't have the 'conanmanifest.txt' \"\n \"file and will be uploaded: '{}'\".format(ref))\n return files_to_upload, deleted\n\n if remote_manifest == local_manifest:\n return None, None\n\n if policy in (UPLOAD_POLICY_NO_OVERWRITE, UPLOAD_POLICY_NO_OVERWRITE_RECIPE):\n raise ConanException(\"Local recipe is different from the remote recipe. \"\n \"Forbidden overwrite.\")\n\n return files_to_upload, deleted","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload._package_files_to_upload","text":"conans/client/cmd/uploader.py::CmdUpload._package_files_to_upload\n def _package_files_to_upload(self, pref, policy, the_files, remote):\n self._remote_manager.check_credentials(remote)\n remote_snapshot = self._remote_manager.get_package_snapshot(pref, remote)\n\n if remote_snapshot and policy != UPLOAD_POLICY_FORCE:\n if not is_package_snapshot_complete(remote_snapshot):\n return the_files, set([])\n remote_manifest, _ = self._remote_manager.get_package_manifest(pref, remote)\n local_manifest = FileTreeManifest.loads(load(the_files[\"conanmanifest.txt\"]))\n if remote_manifest == local_manifest:\n return None, None\n if policy == UPLOAD_POLICY_NO_OVERWRITE:\n raise ConanException(\"Local package is different from the remote package. Forbidden\"\n \" overwrite.\")\n deleted = set(remote_snapshot).difference(the_files)\n return the_files, deleted","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload._upload_recipe_end_msg","text":"conans/client/cmd/uploader.py::CmdUpload._upload_recipe_end_msg\n def _upload_recipe_end_msg(self, ref, remote):\n msg = \"\\rUploaded conan recipe '%s' to '%s'\" % (str(ref), remote.name)\n url = remote.url.replace(\"https://api.bintray.com/conan\", \"https://bintray.com\")\n msg += \": %s\" % url\n self._output.info(left_justify_message(msg))","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload._package_integrity_check","text":"conans/client/cmd/uploader.py::CmdUpload._package_integrity_check\n def _package_integrity_check(self, pref, files, package_folder):\n # If package has been modified remove tgz to regenerate it\n self._output.rewrite_line(\"Checking package integrity...\")\n\n # short_paths = None is enough if there exist short_paths\n layout = self._cache.package_layout(pref.ref, short_paths=None)\n read_manifest, expected_manifest = layout.package_manifests(pref)\n\n if read_manifest != expected_manifest:\n self._output.writeln(\"\")\n diff = read_manifest.difference(expected_manifest)\n for fname, (h1, h2) in diff.items():\n self._output.warn(\"Mismatched checksum '%s' (manifest: %s, file: %s)\"\n % (fname, h1, h2))\n\n if PACKAGE_TGZ_NAME in files:\n tgz_path = os.path.join(package_folder, PACKAGE_TGZ_NAME)\n try:\n os.unlink(tgz_path)\n except OSError:\n pass\n error_msg = os.linesep.join(\"Mismatched checksum '%s' (manifest: %s, file: %s)\"\n % (fname, h1, h2) for fname, (h1, h2) in diff.items())\n logger.error(\"Manifests doesn't match!\\n%s\" % error_msg)\n raise ConanException(\"Cannot upload corrupted package '%s'\" % str(pref))\n else:\n self._output.rewrite_line(\"Package integrity OK!\")\n self._output.writeln(\"\")","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload._check_recipe_date","text":"conans/client/cmd/uploader.py::CmdUpload._check_recipe_date\n def _check_recipe_date(self, ref, remote, local_manifest):\n try:\n remote_recipe_manifest, ref = self._remote_manager.get_recipe_manifest(ref, remote)\n except NotFoundException:\n return # First time uploading this package\n\n if (remote_recipe_manifest != local_manifest and\n remote_recipe_manifest.time > local_manifest.time):\n self._print_manifest_information(remote_recipe_manifest, local_manifest, ref, remote)\n raise ConanException(\"Remote recipe is newer than local recipe: \"\n \"\\n Remote date: %s\\n Local date: %s\" %\n (remote_recipe_manifest.time, local_manifest.time))\n\n return remote_recipe_manifest","label":0},{"cid":"conans/client/cmd/uploader.py::CmdUpload._print_manifest_information","text":"conans/client/cmd/uploader.py::CmdUpload._print_manifest_information\n def _print_manifest_information(self, remote_recipe_manifest, local_manifest, ref, remote):\n try:\n self._output.info(\"\\n%s\" % (\"-\"*40))\n self._output.info(\"Remote manifest:\")\n self._output.info(remote_recipe_manifest)\n self._output.info(\"Local manifest:\")\n self._output.info(local_manifest)\n difference = remote_recipe_manifest.difference(local_manifest)\n if \"conanfile.py\" in difference:\n contents = load(self._cache.package_layout(ref).conanfile())\n endlines = \"\\\\r\\\\n\" if \"\\r\\n\" in contents else \"\\\\n\"\n self._output.info(\"Local 'conanfile.py' using '%s' line-ends\" % endlines)\n remote_contents = self._remote_manager.get_recipe_path(ref, path=\"conanfile.py\",\n remote=remote)\n endlines = \"\\\\r\\\\n\" if \"\\r\\n\" in remote_contents else \"\\\\n\"\n self._output.info(\"Remote 'conanfile.py' using '%s' line-ends\" % endlines)\n self._output.info(\"\\n%s\" % (\"-\"*40))\n except Exception as e:\n self._output.info(\"Error printing information about the diff: %s\" % str(e))","label":0},{"cid":"conans/client/cmd/uploader.py::_compress_recipe_files","text":"conans/client/cmd/uploader.py::_compress_recipe_files\ndef _compress_recipe_files(files, symlinks, src_files, src_symlinks, dest_folder, output):\n # This is the minimum recipe\n result = {CONANFILE: files.pop(CONANFILE),\n CONAN_MANIFEST: files.pop(CONAN_MANIFEST)}\n\n export_tgz_path = files.pop(EXPORT_TGZ_NAME, None)\n sources_tgz_path = files.pop(EXPORT_SOURCES_TGZ_NAME, None)\n\n def add_tgz(tgz_name, tgz_path, tgz_files, tgz_symlinks, msg):\n if tgz_path:\n result[tgz_name] = tgz_path\n elif tgz_files:\n if output and not output.is_terminal:\n output.writeln(msg)\n tgz_path = compress_files(tgz_files, tgz_symlinks, tgz_name, dest_folder, output)\n result[tgz_name] = tgz_path\n\n add_tgz(EXPORT_TGZ_NAME, export_tgz_path, files, symlinks, \"Compressing recipe...\")\n add_tgz(EXPORT_SOURCES_TGZ_NAME, sources_tgz_path, src_files, src_symlinks,\n \"Compressing recipe sources...\")\n\n return result","label":0},{"cid":"conans/client/cmd/uploader.py::_compress_recipe_files.add_tgz","text":"conans/client/cmd/uploader.py::_compress_recipe_files.add_tgz\n def add_tgz(tgz_name, tgz_path, tgz_files, tgz_symlinks, msg):\n if tgz_path:\n result[tgz_name] = tgz_path\n elif tgz_files:\n if output and not output.is_terminal:\n output.writeln(msg)\n tgz_path = compress_files(tgz_files, tgz_symlinks, tgz_name, dest_folder, output)\n result[tgz_name] = tgz_path","label":0},{"cid":"conans/client/cmd/uploader.py::_compress_package_files","text":"conans/client/cmd/uploader.py::_compress_package_files\ndef _compress_package_files(files, symlinks, dest_folder, output):\n tgz_path = files.get(PACKAGE_TGZ_NAME)\n if not tgz_path:\n if output and not output.is_terminal:\n output.writeln(\"Compressing package...\")\n tgz_files = {f: path for f, path in files.items() if f not in [CONANINFO, CONAN_MANIFEST]}\n tgz_path = compress_files(tgz_files, symlinks, PACKAGE_TGZ_NAME, dest_folder, output)\n\n return {PACKAGE_TGZ_NAME: tgz_path,\n CONANINFO: files[CONANINFO],\n CONAN_MANIFEST: files[CONAN_MANIFEST]}","label":0},{"cid":"conans/client/cmd/uploader.py::compress_files","text":"conans/client/cmd/uploader.py::compress_files\ndef compress_files(files, symlinks, name, dest_dir, output=None):\n t1 = time.time()\n # FIXME, better write to disk sequentially and not keep tgz contents in memory\n tgz_path = os.path.join(dest_dir, name)\n with set_dirty_context_manager(tgz_path), open(tgz_path, \"wb\") as tgz_handle:\n tgz = gzopen_without_timestamps(name, mode=\"w\", fileobj=tgz_handle)\n\n for filename, dest in sorted(symlinks.items()):\n info = tarfile.TarInfo(name=filename)\n info.type = tarfile.SYMTYPE\n info.linkname = dest\n info.size = 0 # A symlink shouldn't have size\n tgz.addfile(tarinfo=info)\n\n mask = ~(stat.S_IWOTH | stat.S_IWGRP)\n with progress_bar.iterate_list_with_progress(sorted(files.items()), output,\n \"Compressing %s\" % name) as pg_file_list:\n for filename, abs_path in pg_file_list:\n info = tarfile.TarInfo(name=filename)\n info.size = os.stat(abs_path).st_size\n info.mode = os.stat(abs_path).st_mode & mask\n if os.path.islink(abs_path):\n info.type = tarfile.SYMTYPE\n info.size = 0 # A symlink shouldn't have size\n info.linkname = os.readlink(abs_path) # @UndefinedVariable\n tgz.addfile(tarinfo=info)\n else:\n with open(abs_path, 'rb') as file_handler:\n tgz.addfile(tarinfo=info, fileobj=file_handler)\n tgz.close()\n\n duration = time.time() - t1\n log_compressed_files(files, duration, tgz_path)\n\n return tgz_path","label":0},{"cid":"conans/client/conan_api.py::api_method","text":"conans/client/conan_api.py::api_method\ndef api_method(f):\n def wrapper(api, *args, **kwargs):\n quiet = kwargs.pop(\"quiet\", False)\n old_curdir = get_cwd()\n old_output = api.user_io.out\n quiet_output = ConanOutput(StringIO(), color=api.color) if quiet else None\n try:\n api.create_app(quiet_output=quiet_output)\n log_command(f.__name__, kwargs)\n with environment_append(api.app.cache.config.env_vars):\n return f(api, *args, **kwargs)\n except Exception as exc:\n if quiet_output:\n old_output.write(quiet_output._stream.getvalue())\n old_output.flush()\n msg = exception_message_safe(exc)\n try:\n log_exception(exc, msg)\n except BaseException:\n pass\n raise\n finally:\n os.chdir(old_curdir)\n return wrapper","label":0},{"cid":"conans/client/conan_api.py::api_method.wrapper","text":"conans/client/conan_api.py::api_method.wrapper\n def wrapper(api, *args, **kwargs):\n quiet = kwargs.pop(\"quiet\", False)\n old_curdir = get_cwd()\n old_output = api.user_io.out\n quiet_output = ConanOutput(StringIO(), color=api.color) if quiet else None\n try:\n api.create_app(quiet_output=quiet_output)\n log_command(f.__name__, kwargs)\n with environment_append(api.app.cache.config.env_vars):\n return f(api, *args, **kwargs)\n except Exception as exc:\n if quiet_output:\n old_output.write(quiet_output._stream.getvalue())\n old_output.flush()\n msg = exception_message_safe(exc)\n try:\n log_exception(exc, msg)\n except BaseException:\n pass\n raise\n finally:\n os.chdir(old_curdir)","label":0},{"cid":"conans/client/conan_api.py::_make_abs_path","text":"conans/client/conan_api.py::_make_abs_path\ndef _make_abs_path(path, cwd=None, default=None):\n \"\"\"convert 'path' to absolute if necessary (could be already absolute)\n if not defined (empty, or None), will return 'default' one or 'cwd'\n \"\"\"\n cwd = cwd or get_cwd()\n if not path:\n abs_path = default or cwd\n elif os.path.isabs(path):\n abs_path = path\n else:\n abs_path = os.path.normpath(os.path.join(cwd, path))\n return abs_path","label":0},{"cid":"conans/client/conan_api.py::_get_conanfile_path","text":"conans/client/conan_api.py::_get_conanfile_path\ndef _get_conanfile_path(path, cwd, py):\n \"\"\"\n param py= True: Must be .py, False: Must be .txt, None: Try .py, then .txt\n \"\"\"\n candidate_paths = list()\n path = _make_abs_path(path, cwd)\n\n if os.path.isdir(path): # Can be a folder\n if py:\n path = os.path.join(path, \"conanfile.py\")\n candidate_paths.append(path)\n elif py is False:\n path = os.path.join(path, \"conanfile.txt\")\n candidate_paths.append(path)\n else:\n path_py = os.path.join(path, \"conanfile.py\")\n candidate_paths.append(path_py)\n if os.path.exists(path_py):\n path = path_py\n else:\n path = os.path.join(path, \"conanfile.txt\")\n candidate_paths.append(path)\n else:\n candidate_paths.append(path)\n\n if not os.path.isfile(path): # Must exist\n raise ConanException(\"Conanfile not found at %s\" % \" or \".join(candidate_paths))\n\n if py and not path.endswith(\".py\"):\n raise ConanException(\"A conanfile.py is needed, \" + path + \" is not acceptable\")\n\n return path","label":0},{"cid":"conans/client/conan_api.py::ConanApp.__init__","text":"conans/client/conan_api.py::ConanApp.__init__\n def __init__(self, cache_folder, user_io, http_requester=None, runner=None, quiet_output=None):\n # User IO, interaction and logging\n self.user_io = user_io\n self.out = self.user_io.out\n if quiet_output:\n self.user_io.out = quiet_output\n self.out = quiet_output\n\n self.cache_folder = cache_folder\n self.cache = ClientCache(self.cache_folder, self.out)\n self.config = self.cache.config\n if self.config.non_interactive or quiet_output:\n self.user_io.disable_input()\n\n # Adjust CONAN_LOGGING_LEVEL with the env readed\n conans.util.log.logger = configure_logger(self.config.logging_level,\n self.config.logging_file)\n conans.util.log.logger.debug(\"INIT: Using config '%s'\" % self.cache.conan_conf_path)\n\n self.hook_manager = HookManager(self.cache.hooks_path, self.config.hooks, self.out)\n # Wraps an http_requester to inject proxies, certs, etc\n self.requester = ConanRequester(self.config, http_requester)\n # To handle remote connections\n artifacts_properties = self.cache.read_artifacts_properties()\n rest_client_factory = RestApiClientFactory(self.out, self.requester, self.config,\n artifacts_properties=artifacts_properties)\n # To store user and token\n localdb = LocalDB.create(self.cache.localdb)\n # Wraps RestApiClient to add authentication support (same interface)\n auth_manager = ConanApiAuthManager(rest_client_factory, self.user_io, localdb)\n # Handle remote connections\n self.remote_manager = RemoteManager(self.cache, auth_manager, self.out, self.hook_manager)\n\n # Adjust global tool variables\n set_global_instances(self.out, self.requester, self.config)\n\n self.runner = runner or ConanRunner(self.config.print_commands_to_output","label":0},{"cid":"conans/client/conan_api.py::ConanApp.load_remotes","text":"conans/client/conan_api.py::ConanApp.load_remotes\n def load_remotes(self, remote_name=None, update=False, check_updates=False):\n remotes = self.cache.registry.load_remotes()\n if remote_name:\n remotes.select(remote_name)\n self.python_requires.enable_remotes(update=update, check_updates=check_updates,\n remotes=remotes)\n self.pyreq_loader.enable_remotes(update=update, check_updates=check_updates, remotes=remotes)\n return remotes","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.factory","text":"conans/client/conan_api.py::ConanAPIV1.factory\n @classmethod\n def factory(cls):\n return cls(), None, None","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.__init__","text":"conans/client/conan_api.py::ConanAPIV1.__init__\n def __init__(self, cache_folder=None, output=None, user_io=None, http_requester=None,\n runner=None):\n self.color = colorama_initialize()\n self.out = output or ConanOutput(sys.stdout, sys.stderr, self.color)\n self.user_io = user_io or UserIO(out=self.out)\n self.cache_folder = cache_folder or os.path.join(get_conan_user_home(), \".conan\")\n self.http_requester = http_requester\n self.runner = runner\n self.app = None # Api calls will create a new one every call\n # Migration system\n migrator = ClientMigrator(self.cache_folder, Version(client_version), self.out)\n migrator.migrate()\n # FIXME Remove in Conan 2.0\n sys.path.append(os.path.join(self.cache_folder, \"python\"))","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.create_app","text":"conans/client/conan_api.py::ConanAPIV1.create_app\n def create_app(self, quiet_output=None):\n self.app = ConanApp(self.cache_folder, self.user_io, self.http_requester,\n self.runner, quiet_output=quiet_output)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.new","text":"conans/client/conan_api.py::ConanAPIV1.new\n @api_method\n def new(self, name, header=False, pure_c=False, test=False, exports_sources=False, bare=False,\n cwd=None, visual_versions=None, linux_gcc_versions=None, linux_clang_versions=None,\n osx_clang_versions=None, shared=None, upload_url=None, gitignore=None,\n gitlab_gcc_versions=None, gitlab_clang_versions=None,\n circleci_gcc_versions=None, circleci_clang_versions=None, circleci_osx_versions=None,\n template=None):\n from conans.client.cmd.new import cmd_new\n cwd = os.path.abspath(cwd or get_cwd())\n files = cmd_new(name, header=header, pure_c=pure_c, test=test,\n exports_sources=exports_sources, bare=bare,\n visual_versions=visual_versions,\n linux_gcc_versions=linux_gcc_versions,\n linux_clang_versions=linux_clang_versions,\n osx_clang_versions=osx_clang_versions, shared=shared,\n upload_url=upload_url, gitignore=gitignore,\n gitlab_gcc_versions=gitlab_gcc_versions,\n gitlab_clang_versions=gitlab_clang_versions,\n circleci_gcc_versions=circleci_gcc_versions,\n circleci_clang_versions=circleci_clang_versions,\n circleci_osx_versions=circleci_osx_versions,\n template=template, cache=self.app.cache)\n\n save_files(cwd, files)\n for f in sorted(files):\n self.app.out.success(\"File saved: %s\" % f)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.inspect","text":"conans/client/conan_api.py::ConanAPIV1.inspect\n @api_method\n def inspect(self, path, attributes, remote_name=None):\n remotes = self.app.load_remotes(remote_name=remote_name)\n try:\n ref = ConanFileReference.loads(path)\n except ConanException:\n conanfile_path = _get_conanfile_path(path, get_cwd(), py=True)\n conanfile = self.app.loader.load_named(conanfile_path, None, None, None, None)\n else:\n update = True if remote_name else False\n result = self.app.proxy.get_recipe(ref, update, update, remotes, ActionRecorder())\n conanfile_path, _, _, ref = result\n conanfile = self.app.loader.load_basic(conanfile_path)\n conanfile.name = ref.name\n conanfile.version = ref.version\n\n result = OrderedDict()\n if not attributes:\n attributes = ['name', 'version', 'url', 'homepage', 'license', 'author',\n 'description', 'topics', 'generators', 'exports', 'exports_sources',\n 'short_paths', 'apply_env', 'build_policy', 'revision_mode', 'settings',\n 'options', 'default_options']\n for attribute in attributes:\n try:\n attr = getattr(conanfile, attribute)\n result[attribute] = attr\n except AttributeError:\n result[attribute] = ''\n return result","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.test","text":"conans/client/conan_api.py::ConanAPIV1.test\n @api_method\n def test(self, path, reference, profile_names=None, settings=None, options=None, env=None,\n remote_name=None, update=False, build_modes=None, cwd=None, test_build_folder=None,\n lockfile=None):\n\n settings = settings or []\n options = options or []\n env = env or []\n\n remotes = self.app.load_remotes(remote_name=remote_name, update=update)\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\n cwd = cwd or get_cwd()\n lockfile = _make_abs_path(lockfile, cwd) if lockfile else None\n graph_info = get_graph_info(profile_names, settings, options, env, cwd, None,\n self.app.cache, self.app.out, lockfile=lockfile)\n ref = ConanFileReference.loads(reference)\n recorder = ActionRecorder()\n install_build_and_test(self.app, conanfile_path, ref, graph_info, remotes,\n update, build_modes=build_modes,\n test_build_folder=test_build_folder, recorder=recorder)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.create","text":"conans/client/conan_api.py::ConanAPIV1.create\n @api_method\n def create(self, conanfile_path, name=None, version=None, user=None, channel=None,\n profile_names=None, settings=None,\n options=None, env=None, test_folder=None, not_export=False,\n build_modes=None,\n keep_source=False, keep_build=False, verify=None,\n manifests=None, manifests_interactive=None,\n remote_name=None, update=False, cwd=None, test_build_folder=None,\n lockfile=None, ignore_dirty=False):\n \"\"\"\n API method to create a conan package\n\n :param test_folder: default None - looks for default 'test' or 'test_package' folder),\n string - test_folder path\n False - disabling tests\n \"\"\"\n settings = settings or []\n options = options or []\n env = env or []\n\n try:\n cwd = cwd or os.getcwd()\n recorder = ActionRecorder()\n conanfile_path = _get_conanfile_path(conanfile_path, cwd, py=True)\n\n remotes = self.app.load_remotes(remote_name=remote_name, update=update)\n lockfile = _make_abs_path(lockfile, cwd) if lockfile else None\n graph_info = get_graph_info(profile_names, settings, options, env, cwd, None,\n self.app.cache, self.app.out, lockfile=lockfile)\n\n # Make sure keep_source is set for keep_build\n keep_source = keep_source or keep_build\n new_ref = cmd_export(self.app, conanfile_path, name, version, user, channel, keep_source,\n not not_export, graph_lock=graph_info.graph_lock,\n ignore_dirty=ignore_dirty)\n\n self.app.range_resolver.clear_output() # invalidate version range output\n\n # The new_ref contains the revision\n # To not break existing things, that they us","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.export_pkg","text":"conans/client/conan_api.py::ConanAPIV1.export_pkg\n @api_method\n def export_pkg(self, conanfile_path, name, channel, source_folder=None, build_folder=None,\n package_folder=None, install_folder=None, profile_names=None, settings=None,\n options=None, env=None, force=False, user=None, version=None, cwd=None,\n lockfile=None, ignore_dirty=False):\n\n remotes = self.app.load_remotes()\n settings = settings or []\n options = options or []\n env = env or []\n cwd = cwd or get_cwd()\n\n try:\n recorder = ActionRecorder()\n conanfile_path = _get_conanfile_path(conanfile_path, cwd, py=True)\n\n if package_folder:\n if build_folder or source_folder:\n raise ConanException(\"package folder definition incompatible with build \"\n \"and source folders\")\n package_folder = _make_abs_path(package_folder, cwd)\n\n build_folder = _make_abs_path(build_folder, cwd)\n if install_folder:\n install_folder = _make_abs_path(install_folder, cwd)\n else:\n # FIXME: This is a hack for old UI, need to be fixed in Conan 2.0\n if os.path.exists(os.path.join(build_folder, GRAPH_INFO_FILE)):\n install_folder = build_folder\n source_folder = _make_abs_path(source_folder, cwd,\n default=os.path.dirname(conanfile_path))\n\n lockfile = _make_abs_path(lockfile, cwd) if lockfile else None\n # Checks that no both settings and info files are specified\n graph_info = get_graph_info(profile_names, settings, options, env, cwd, install_folder,\n self.app.cache, self.app.out, lockfile=lockfile)\n\n new_ref = cmd_export(self.app, conanfile_path, name, version, user, channel, True,\n ","label":1},{"cid":"conans/client/conan_api.py::ConanAPIV1.download","text":"conans/client/conan_api.py::ConanAPIV1.download\n @api_method\n def download(self, reference, remote_name=None, packages=None, recipe=False):\n if packages and recipe:\n raise ConanException(\"recipe parameter cannot be used together with packages\")\n # Install packages without settings (fixed ids or all)\n if check_valid_ref(reference):\n ref = ConanFileReference.loads(reference)\n if ref.revision and not self.app.config.revisions_enabled:\n raise ConanException(\"Revisions not enabled in the client, specify a \"\n \"reference without revision\")\n if packages and ref.revision is None:\n for package_id in packages:\n if \"#\" in package_id:\n raise ConanException(\"It is needed to specify the recipe revision if you \"\n \"specify a package revision\")\n remotes = self.app.load_remotes(remote_name=remote_name)\n remote = remotes.get_remote(remote_name)\n recorder = ActionRecorder()\n download(self.app, ref, packages, remote, recipe, recorder, remotes=remotes)\n else:\n raise ConanException(\"Provide a valid full reference without wildcards.\")","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.workspace_install","text":"conans/client/conan_api.py::ConanAPIV1.workspace_install\n @api_method\n def workspace_install(self, path, settings=None, options=None, env=None,\n remote_name=None, build=None, profile_name=None,\n update=False, cwd=None, install_folder=None):\n cwd = cwd or get_cwd()\n abs_path = os.path.normpath(os.path.join(cwd, path))\n\n remotes = self.app.load_remotes(remote_name=remote_name, update=update)\n\n workspace = Workspace(abs_path, self.app.cache)\n graph_info = get_graph_info(profile_name, settings, options, env, cwd, None,\n self.app.cache, self.app.out)\n\n self.app.out.info(\"Configuration:\")\n self.app.out.writeln(graph_info.profile_host.dumps())\n\n self.app.cache.editable_packages.override(workspace.get_editable_dict())\n\n recorder = ActionRecorder()\n deps_graph = self.app.graph_manager.load_graph(workspace.root, None, graph_info, build,\n False, update, remotes, recorder)\n\n print_graph(deps_graph, self.app.out)\n\n # Inject the generators before installing\n for node in deps_graph.nodes:\n if node.recipe == RECIPE_EDITABLE:\n generators = workspace[node.ref].generators\n if generators is not None:\n tmp = list(node.conanfile.generators)\n tmp.extend([g for g in generators if g not in tmp])\n node.conanfile.generators = tmp\n\n installer = BinaryInstaller(self.app, recorder=recorder)\n installer.install(deps_graph, remotes, build, update, keep_build=False,\n graph_info=graph_info)\n\n install_folder = install_folder or cwd\n workspace.generate(install_folder, deps_graph, self.app.out)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.install_reference","text":"conans/client/conan_api.py::ConanAPIV1.install_reference\n @api_method\n def install_reference(self, reference, settings=None, options=None, env=None,\n remote_name=None, verify=None, manifests=None,\n manifests_interactive=None, build=None, profile_names=None,\n update=False, generators=None, install_folder=None, cwd=None,\n lockfile=None):\n\n try:\n recorder = ActionRecorder()\n cwd = cwd or os.getcwd()\n\n manifests = _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd)\n manifest_folder, manifest_interactive, manifest_verify = manifests\n\n lockfile = _make_abs_path(lockfile, cwd) if lockfile else None\n graph_info = get_graph_info(profile_names, settings, options, env, cwd, None,\n self.app.cache, self.app.out, lockfile=lockfile)\n\n if not generators: # We don't want the default txt\n generators = False\n\n install_folder = _make_abs_path(install_folder, cwd)\n\n mkdir(install_folder)\n remotes = self.app.load_remotes(remote_name=remote_name, update=update)\n deps_install(self.app, ref_or_path=reference, install_folder=install_folder,\n remotes=remotes, graph_info=graph_info, build_modes=build,\n update=update, manifest_folder=manifest_folder,\n manifest_verify=manifest_verify,\n manifest_interactive=manifest_interactive,\n generators=generators, use_lock=lockfile, recorder=recorder)\n return recorder.get_info(self.app.config.revisions_enabled)\n except ConanException as exc:\n recorder.error = True\n exc.info = recorder.get_info(self.app.config.revisions_enabled)\n raise","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.install","text":"conans/client/conan_api.py::ConanAPIV1.install\n @api_method\n def install(self, path=\"\", name=None, version=None, user=None, channel=None,\n settings=None, options=None, env=None,\n remote_name=None, verify=None, manifests=None,\n manifests_interactive=None, build=None, profile_names=None,\n update=False, generators=None, no_imports=False, install_folder=None, cwd=None,\n lockfile=None):\n\n try:\n recorder = ActionRecorder()\n cwd = cwd or os.getcwd()\n manifests = _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd)\n manifest_folder, manifest_interactive, manifest_verify = manifests\n\n lockfile = _make_abs_path(lockfile, cwd) if lockfile else None\n graph_info = get_graph_info(profile_names, settings, options, env, cwd, None,\n self.app.cache, self.app.out,\n name=name, version=version, user=user, channel=channel,\n lockfile=lockfile)\n\n install_folder = _make_abs_path(install_folder, cwd)\n conanfile_path = _get_conanfile_path(path, cwd, py=None)\n\n remotes = self.app.load_remotes(remote_name=remote_name, update=update)\n deps_install(app=self.app,\n ref_or_path=conanfile_path,\n install_folder=install_folder,\n remotes=remotes,\n graph_info=graph_info,\n build_modes=build,\n update=update,\n manifest_folder=manifest_folder,\n manifest_verify=manifest_verify,\n manifest_interactive=manifest_interactive,\n generators=generators,\n no_imports=no_imports,\n recorder=recorder)\n ","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.config_get","text":"conans/client/conan_api.py::ConanAPIV1.config_get\n @api_method\n def config_get(self, item):\n if item == \"storage.path\":\n result = self.app.config.storage_path\n else:\n result = self.app.config.get_item(item)\n self.app.out.info(result)\n return result","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.config_set","text":"conans/client/conan_api.py::ConanAPIV1.config_set\n @api_method\n def config_set(self, item, value):\n self.app.config.set_item(item, value)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.config_rm","text":"conans/client/conan_api.py::ConanAPIV1.config_rm\n @api_method\n def config_rm(self, item):\n self.app.config.rm_item(item)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.config_install","text":"conans/client/conan_api.py::ConanAPIV1.config_install\n @api_method\n def config_install(self, path_or_url, verify_ssl, config_type=None, args=None,\n source_folder=None, target_folder=None):\n from conans.client.conf.config_installer import configuration_install\n return configuration_install(self.app, path_or_url, verify_ssl,\n config_type=config_type, args=args,\n source_folder=source_folder, target_folder=target_folder)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.config_home","text":"conans/client/conan_api.py::ConanAPIV1.config_home\n @api_method\n def config_home(self):\n return self.cache_folder","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1._info_args","text":"conans/client/conan_api.py::ConanAPIV1._info_args\n def _info_args(self, reference_or_path, install_folder, profile_names, settings, options, env,\n lockfile=None):\n cwd = get_cwd()\n if check_valid_ref(reference_or_path):\n ref = ConanFileReference.loads(reference_or_path)\n install_folder = _make_abs_path(install_folder, cwd) if install_folder else None\n else:\n ref = _get_conanfile_path(reference_or_path, cwd=None, py=None)\n\n install_folder = _make_abs_path(install_folder, cwd)\n if not os.path.exists(os.path.join(install_folder, GRAPH_INFO_FILE)):\n install_folder = None\n\n lockfile = _make_abs_path(lockfile, cwd) if lockfile else None\n graph_info = get_graph_info(profile_names, settings, options, env, cwd, install_folder,\n self.app.cache, self.app.out, lockfile=lockfile)\n\n return ref, graph_info","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.info_build_order","text":"conans/client/conan_api.py::ConanAPIV1.info_build_order\n @api_method\n def info_build_order(self, reference, settings=None, options=None, env=None,\n profile_names=None, remote_name=None, build_order=None, check_updates=None,\n install_folder=None):\n reference, graph_info = self._info_args(reference, install_folder, profile_names,\n settings, options, env)\n recorder = ActionRecorder()\n remotes = self.app.load_remotes(remote_name=remote_name, check_updates=check_updates)\n deps_graph = self.app.graph_manager.load_graph(reference, None, graph_info, [\"missing\"],\n check_updates, False, remotes, recorder)\n return deps_graph.build_order(build_order)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.info_nodes_to_build","text":"conans/client/conan_api.py::ConanAPIV1.info_nodes_to_build\n @api_method\n def info_nodes_to_build(self, reference, build_modes, settings=None, options=None, env=None,\n profile_names=None, remote_name=None, check_updates=None,\n install_folder=None):\n reference, graph_info = self._info_args(reference, install_folder, profile_names,\n settings, options, env)\n recorder = ActionRecorder()\n remotes = self.app.load_remotes(remote_name=remote_name, check_updates=check_updates)\n deps_graph = self.app.graph_manager.load_graph(reference, None, graph_info, build_modes,\n check_updates, False, remotes, recorder)\n nodes_to_build = deps_graph.nodes_to_build()\n return nodes_to_build, deps_graph.root.conanfile","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.info","text":"conans/client/conan_api.py::ConanAPIV1.info\n @api_method\n def info(self, reference_or_path, remote_name=None, settings=None, options=None, env=None,\n profile_names=None, update=False, install_folder=None, build=None, lockfile=None):\n reference, graph_info = self._info_args(reference_or_path, install_folder, profile_names,\n settings, options, env, lockfile=lockfile)\n recorder = ActionRecorder()\n # FIXME: Using update as check_update?\n remotes = self.app.load_remotes(remote_name=remote_name, check_updates=update)\n deps_graph = self.app.graph_manager.load_graph(reference, None, graph_info, build,\n update, False, remotes, recorder)\n\n if install_folder:\n output_folder = _make_abs_path(install_folder)\n graph_info.save(output_folder)\n self.app.out.info(\"Generated graphinfo\")\n return deps_graph, deps_graph.root.conanfile","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.build","text":"conans/client/conan_api.py::ConanAPIV1.build\n @api_method\n def build(self, conanfile_path, source_folder=None, package_folder=None, build_folder=None,\n install_folder=None, should_configure=True, should_build=True, should_install=True,\n should_test=True, cwd=None):\n self.app.load_remotes()\n cwd = cwd or get_cwd()\n conanfile_path = _get_conanfile_path(conanfile_path, cwd, py=True)\n build_folder = _make_abs_path(build_folder, cwd)\n install_folder = _make_abs_path(install_folder, cwd, default=build_folder)\n source_folder = _make_abs_path(source_folder, cwd, default=os.path.dirname(conanfile_path))\n default_pkg_folder = os.path.join(build_folder, \"package\")\n package_folder = _make_abs_path(package_folder, cwd, default=default_pkg_folder)\n\n cmd_build(self.app, conanfile_path,\n source_folder, build_folder, package_folder, install_folder,\n should_configure=should_configure, should_build=should_build,\n should_install=should_install, should_test=should_test)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.package","text":"conans/client/conan_api.py::ConanAPIV1.package\n @api_method\n def package(self, path, build_folder, package_folder, source_folder=None, install_folder=None,\n cwd=None):\n self.app.load_remotes()\n\n cwd = cwd or get_cwd()\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\n build_folder = _make_abs_path(build_folder, cwd)\n install_folder = _make_abs_path(install_folder, cwd, default=build_folder)\n source_folder = _make_abs_path(source_folder, cwd, default=os.path.dirname(conanfile_path))\n default_pkg_folder = os.path.join(build_folder, \"package\")\n package_folder = _make_abs_path(package_folder, cwd, default=default_pkg_folder)\n\n if package_folder == build_folder:\n raise ConanException(\"Cannot 'conan package' to the build folder. \"\n \"--build-folder and package folder can't be the same\")\n conanfile = self.app.graph_manager.load_consumer_conanfile(conanfile_path, install_folder,\n deps_info_required=True)\n packager.run_package_method(conanfile, None, source_folder, build_folder, package_folder,\n install_folder, self.app.hook_manager, conanfile_path, None,\n local=True, copy_info=True)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.source","text":"conans/client/conan_api.py::ConanAPIV1.source\n @api_method\n def source(self, path, source_folder=None, info_folder=None, cwd=None):\n self.app.load_remotes()\n\n cwd = cwd or get_cwd()\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\n source_folder = _make_abs_path(source_folder, cwd)\n info_folder = _make_abs_path(info_folder, cwd)\n\n mkdir(source_folder)\n if not os.path.exists(info_folder):\n raise ConanException(\"Specified info-folder doesn't exist\")\n\n # only infos if exist\n conanfile = self.app.graph_manager.load_consumer_conanfile(conanfile_path, info_folder)\n config_source_local(source_folder, conanfile, conanfile_path, self.app.hook_manager)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.imports","text":"conans/client/conan_api.py::ConanAPIV1.imports\n @api_method\n def imports(self, path, dest=None, info_folder=None, cwd=None):\n \"\"\"\n :param path: Path to the conanfile\n :param dest: Dir to put the imported files. (Abs path or relative to cwd)\n :param info_folder: Dir where the conaninfo.txt and conanbuildinfo.txt files are\n :param cwd: Current working directory\n :return: None\n \"\"\"\n cwd = cwd or get_cwd()\n info_folder = _make_abs_path(info_folder, cwd)\n dest = _make_abs_path(dest, cwd)\n\n self.app.load_remotes()\n mkdir(dest)\n conanfile_abs_path = _get_conanfile_path(path, cwd, py=None)\n conanfile = self.app.graph_manager.load_consumer_conanfile(conanfile_abs_path, info_folder,\n deps_info_required=True)\n run_imports(conanfile, dest)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.imports_undo","text":"conans/client/conan_api.py::ConanAPIV1.imports_undo\n @api_method\n def imports_undo(self, manifest_path):\n cwd = get_cwd()\n manifest_path = _make_abs_path(manifest_path, cwd)\n undo_imports(manifest_path, self.app.out)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.export","text":"conans/client/conan_api.py::ConanAPIV1.export\n @api_method\n def export(self, path, name, version, user, channel, keep_source=False, cwd=None,\n lockfile=None, ignore_dirty=False):\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\n graph_lock = None\n if lockfile:\n lockfile = _make_abs_path(lockfile, cwd)\n graph_lock_file = GraphLockFile.load(lockfile, self.app.config.revisions_enabled)\n graph_lock = graph_lock_file.graph_lock\n self.app.out.info(\"Using lockfile: '{}'\".format(lockfile))\n\n self.app.load_remotes()\n cmd_export(self.app, conanfile_path, name, version, user, channel, keep_source,\n graph_lock=graph_lock, ignore_dirty=ignore_dirty)\n\n if lockfile:\n graph_lock_file.save(lockfile)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remove","text":"conans/client/conan_api.py::ConanAPIV1.remove\n @api_method\n def remove(self, pattern, query=None, packages=None, builds=None, src=False, force=False,\n remote_name=None, outdated=False):\n remotes = self.app.cache.registry.load_remotes()\n remover = ConanRemover(self.app.cache, self.app.remote_manager, self.app.user_io, remotes)\n remover.remove(pattern, remote_name, src, builds, packages, force=force,\n packages_query=query, outdated=outdated)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.copy","text":"conans/client/conan_api.py::ConanAPIV1.copy\n @api_method\n def copy(self, reference, user_channel, force=False, packages=None):\n \"\"\"\n param packages: None=No binaries, True=All binaries, else list of IDs\n \"\"\"\n from conans.client.cmd.copy import cmd_copy\n remotes = self.app.load_remotes()\n # FIXME: conan copy does not support short-paths in Windows\n ref = ConanFileReference.loads(reference)\n cmd_copy(ref, user_channel, packages, self.app.cache,\n self.app.user_io, self.app.remote_manager, self.app.loader, remotes, force=force)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.authenticate","text":"conans/client/conan_api.py::ConanAPIV1.authenticate\n @api_method\n def authenticate(self, name, password, remote_name, skip_auth=False):\n # FIXME: 2.0 rename \"name\" to \"user\".\n # FIXME: 2.0 probably we should return also if we have been authenticated or not (skipped)\n # FIXME: 2.0 remove the skip_auth argument, that behavior will be done by:\n # \"conan user USERNAME -r remote\" that will use the local credentials (\n # and verify that are valid)\n # against the server. Currently it only \"associate\" the USERNAME with the remote\n # without checking anything else\n remote = self.get_remote_by_name(remote_name)\n\n if skip_auth and token_present(self.app.cache.localdb, remote, name):\n return remote.name, name, name\n if not password:\n name, password = self.app.user_io.request_login(remote_name=remote_name, username=name)\n\n remote_name, prev_user, user = self.app.remote_manager.authenticate(remote, name, password)\n return remote_name, prev_user, user","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.user_set","text":"conans/client/conan_api.py::ConanAPIV1.user_set\n @api_method\n def user_set(self, user, remote_name=None):\n remote = (self.get_default_remote() if not remote_name\n else self.get_remote_by_name(remote_name))\n return user_set(self.app.cache.localdb, user, remote)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.users_clean","text":"conans/client/conan_api.py::ConanAPIV1.users_clean\n @api_method\n def users_clean(self):\n users_clean(self.app.cache.localdb)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.users_list","text":"conans/client/conan_api.py::ConanAPIV1.users_list\n @api_method\n def users_list(self, remote_name=None):\n info = {\"error\": False, \"remotes\": []}\n remotes = [self.get_remote_by_name(remote_name)] if remote_name else self.remote_list()\n try:\n info[\"remotes\"] = users_list(self.app.cache.localdb, remotes)\n return info\n except ConanException as exc:\n info[\"error\"] = True\n exc.info = info\n raise","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.search_recipes","text":"conans/client/conan_api.py::ConanAPIV1.search_recipes\n @api_method\n def search_recipes(self, pattern, remote_name=None, case_sensitive=False,\n fill_revisions=False):\n search_recorder = SearchRecorder()\n remotes = self.app.cache.registry.load_remotes()\n search = Search(self.app.cache, self.app.remote_manager, remotes)\n\n try:\n references = search.search_recipes(pattern, remote_name, case_sensitive)\n except ConanException as exc:\n search_recorder.error = True\n exc.info = search_recorder.get_info()\n raise\n\n for remote_name, refs in references.items():\n for ref in refs:\n if fill_revisions:\n layout = self.app.cache.package_layout(ref)\n if isinstance(layout, PackageCacheLayout):\n ref = ref.copy_with_rev(layout.recipe_revision())\n\n search_recorder.add_recipe(remote_name, ref, with_packages=False)\n return search_recorder.get_info()","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.search_packages","text":"conans/client/conan_api.py::ConanAPIV1.search_packages\n @api_method\n def search_packages(self, reference, query=None, remote_name=None, outdated=False):\n search_recorder = SearchRecorder()\n remotes = self.app.cache.registry.load_remotes()\n search = Search(self.app.cache, self.app.remote_manager, remotes)\n\n try:\n ref = ConanFileReference.loads(reference)\n references = search.search_packages(ref, remote_name, query=query, outdated=outdated)\n except ConanException as exc:\n search_recorder.error = True\n exc.info = search_recorder.get_info()\n raise\n\n for remote_name, remote_ref in references.items():\n search_recorder.add_recipe(remote_name, ref)\n if remote_ref.ordered_packages:\n for package_id, properties in remote_ref.ordered_packages.items():\n package_recipe_hash = properties.get(\"recipe_hash\", None)\n search_recorder.add_package(remote_name, ref,\n package_id, properties.get(\"options\", []),\n properties.get(\"settings\", []),\n properties.get(\"full_requires\", []),\n remote_ref.recipe_hash != package_recipe_hash)\n return search_recorder.get_info()","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.upload","text":"conans/client/conan_api.py::ConanAPIV1.upload\n @api_method\n def upload(self, pattern, package=None, remote_name=None, all_packages=False, confirm=False,\n retry=None, retry_wait=None, integrity_check=False, policy=None, query=None,\n parallel_upload=False):\n \"\"\" Uploads a package recipe and the generated binary packages to a specified remote\n \"\"\"\n upload_recorder = UploadRecorder()\n uploader = CmdUpload(self.app.cache, self.app.user_io, self.app.remote_manager,\n self.app.loader, self.app.hook_manager)\n remotes = self.app.load_remotes(remote_name=remote_name)\n try:\n uploader.upload(pattern, remotes, upload_recorder, package, all_packages, confirm,\n retry, retry_wait, integrity_check, policy, query=query,\n parallel_upload=parallel_upload)\n return upload_recorder.get_info()\n except ConanException as exc:\n upload_recorder.error = True\n exc.info = upload_recorder.get_info()\n raise","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_list","text":"conans/client/conan_api.py::ConanAPIV1.remote_list\n @api_method\n def remote_list(self):\n return list(self.app.cache.registry.load_remotes().all_values())","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_add","text":"conans/client/conan_api.py::ConanAPIV1.remote_add\n @api_method\n def remote_add(self, remote_name, url, verify_ssl=True, insert=None, force=None):\n return self.app.cache.registry.add(remote_name, url, verify_ssl, insert, force)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_remove","text":"conans/client/conan_api.py::ConanAPIV1.remote_remove\n @api_method\n def remote_remove(self, remote_name):\n return self.app.cache.registry.remove(remote_name)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_set_disabled_state","text":"conans/client/conan_api.py::ConanAPIV1.remote_set_disabled_state\n @api_method\n def remote_set_disabled_state(self, remote_name, state):\n return self.app.cache.registry.set_disabled_state(remote_name, state)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_update","text":"conans/client/conan_api.py::ConanAPIV1.remote_update\n @api_method\n def remote_update(self, remote_name, url, verify_ssl=True, insert=None):\n return self.app.cache.registry.update(remote_name, url, verify_ssl, insert)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_rename","text":"conans/client/conan_api.py::ConanAPIV1.remote_rename\n @api_method\n def remote_rename(self, remote_name, new_new_remote):\n return self.app.cache.registry.rename(remote_name, new_new_remote)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_list_ref","text":"conans/client/conan_api.py::ConanAPIV1.remote_list_ref\n @api_method\n def remote_list_ref(self):\n return {str(r): remote_name for r, remote_name in self.app.cache.registry.refs_list.items()\n if remote_name}","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_add_ref","text":"conans/client/conan_api.py::ConanAPIV1.remote_add_ref\n @api_method\n def remote_add_ref(self, reference, remote_name):\n ref = ConanFileReference.loads(reference, validate=True)\n remote = self.app.cache.registry.load_remotes()[remote_name]\n with self.app.cache.package_layout(ref).update_metadata() as metadata:\n metadata.recipe.remote = remote.name","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_remove_ref","text":"conans/client/conan_api.py::ConanAPIV1.remote_remove_ref\n @api_method\n def remote_remove_ref(self, reference):\n ref = ConanFileReference.loads(reference, validate=True)\n with self.app.cache.package_layout(ref).update_metadata() as metadata:\n metadata.recipe.remote = None","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_update_ref","text":"conans/client/conan_api.py::ConanAPIV1.remote_update_ref\n @api_method\n def remote_update_ref(self, reference, remote_name):\n ref = ConanFileReference.loads(reference, validate=True)\n remote = self.app.cache.registry.load_remotes()[remote_name]\n with self.app.cache.package_layout(ref).update_metadata() as metadata:\n metadata.recipe.remote = remote.name","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_list_pref","text":"conans/client/conan_api.py::ConanAPIV1.remote_list_pref\n @api_method\n def remote_list_pref(self, reference):\n ref = ConanFileReference.loads(reference, validate=True)\n ret = {}\n tmp = self.app.cache.registry.prefs_list\n for pref, remote in tmp.items():\n if pref.ref == ref and remote:\n ret[repr(pref)] = remote\n return ret","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_add_pref","text":"conans/client/conan_api.py::ConanAPIV1.remote_add_pref\n @api_method\n def remote_add_pref(self, package_reference, remote_name):\n pref = PackageReference.loads(package_reference, validate=True)\n remote = self.app.cache.registry.load_remotes()[remote_name]\n with self.app.cache.package_layout(pref.ref).update_metadata() as metadata:\n m = metadata.packages.get(pref.id)\n if m and m.remote:\n raise ConanException(\"%s already exists. Use update\" % str(pref))\n metadata.packages[pref.id].remote = remote.name","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_remove_pref","text":"conans/client/conan_api.py::ConanAPIV1.remote_remove_pref\n @api_method\n def remote_remove_pref(self, package_reference):\n pref = PackageReference.loads(package_reference, validate=True)\n with self.app.cache.package_layout(pref.ref).update_metadata() as metadata:\n m = metadata.packages.get(pref.id)\n if m:\n m.remote = None","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_update_pref","text":"conans/client/conan_api.py::ConanAPIV1.remote_update_pref\n @api_method\n def remote_update_pref(self, package_reference, remote_name):\n pref = PackageReference.loads(package_reference, validate=True)\n self.app.cache.registry.load_remotes()[remote_name]\n with self.app.cache.package_layout(pref.ref).update_metadata() as metadata:\n m = metadata.packages.get(pref.id)\n if m:\n m.remote = remote_name","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remote_clean","text":"conans/client/conan_api.py::ConanAPIV1.remote_clean\n @api_method\n def remote_clean(self):\n return self.app.cache.registry.clear()","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remove_system_reqs","text":"conans/client/conan_api.py::ConanAPIV1.remove_system_reqs\n @api_method\n def remove_system_reqs(self, reference):\n try:\n ref = ConanFileReference.loads(reference)\n self.app.cache.package_layout(ref).remove_system_reqs()\n self.app.out.info(\n \"Cache system_reqs from %s has been removed\" % repr(ref))\n except Exception as error:\n raise ConanException(\"Unable to remove system_reqs: %s\" % error)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remove_system_reqs_by_pattern","text":"conans/client/conan_api.py::ConanAPIV1.remove_system_reqs_by_pattern\n @api_method\n def remove_system_reqs_by_pattern(self, pattern):\n for ref in search_recipes(self.app.cache, pattern=pattern):\n self.remove_system_reqs(repr(ref))","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.remove_locks","text":"conans/client/conan_api.py::ConanAPIV1.remove_locks\n @api_method\n def remove_locks(self):\n self.app.cache.remove_locks()","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.profile_list","text":"conans/client/conan_api.py::ConanAPIV1.profile_list\n @api_method\n def profile_list(self):\n return cmd_profile_list(self.app.cache.profiles_path, self.app.out)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.create_profile","text":"conans/client/conan_api.py::ConanAPIV1.create_profile\n @api_method\n def create_profile(self, profile_name, detect=False, force=False):\n return cmd_profile_create(profile_name, self.app.cache.profiles_path,\n self.app.out, detect, force)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.update_profile","text":"conans/client/conan_api.py::ConanAPIV1.update_profile\n @api_method\n def update_profile(self, profile_name, key, value):\n return cmd_profile_update(profile_name, key, value, self.app.cache.profiles_path)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.get_profile_key","text":"conans/client/conan_api.py::ConanAPIV1.get_profile_key\n @api_method\n def get_profile_key(self, profile_name, key):\n return cmd_profile_get(profile_name, key, self.app.cache.profiles_path)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.delete_profile_key","text":"conans/client/conan_api.py::ConanAPIV1.delete_profile_key\n @api_method\n def delete_profile_key(self, profile_name, key):\n return cmd_profile_delete_key(profile_name, key, self.app.cache.profiles_path)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.read_profile","text":"conans/client/conan_api.py::ConanAPIV1.read_profile\n @api_method\n def read_profile(self, profile=None):\n p, _ = read_profile(profile, get_cwd(), self.app.cache.profiles_path)\n return p","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.get_path","text":"conans/client/conan_api.py::ConanAPIV1.get_path\n @api_method\n def get_path(self, reference, package_id=None, path=None, remote_name=None):\n ref = ConanFileReference.loads(reference)\n if not path:\n path = \"conanfile.py\" if not package_id else \"conaninfo.txt\"\n\n if not remote_name:\n package_layout = self.app.cache.package_layout(ref, short_paths=None)\n return package_layout.get_path(path=path, package_id=package_id), path\n else:\n remote = self.get_remote_by_name(remote_name)\n if self.app.config.revisions_enabled and not ref.revision:\n ref = self.app.remote_manager.get_latest_recipe_revision(ref, remote)\n if package_id:\n pref = PackageReference(ref, package_id)\n if self.app.config.revisions_enabled and not pref.revision:\n pref = self.app.remote_manager.get_latest_package_revision(pref, remote)\n return self.app.remote_manager.get_package_path(pref, path, remote), path\n else:\n return self.app.remote_manager.get_recipe_path(ref, path, remote), path","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.export_alias","text":"conans/client/conan_api.py::ConanAPIV1.export_alias\n @api_method\n def export_alias(self, reference, target_reference):\n ref = ConanFileReference.loads(reference)\n target_ref = ConanFileReference.loads(target_reference)\n\n if ref.name != target_ref.name:\n raise ConanException(\"An alias can only be defined to a package with the same name\")\n\n # Do not allow to override an existing package\n alias_conanfile_path = self.app.cache.package_layout(ref).conanfile()\n if os.path.exists(alias_conanfile_path):\n conanfile = self.app.loader.load_basic(alias_conanfile_path)\n if not getattr(conanfile, 'alias', None):\n raise ConanException(\"Reference '{}' is already a package, remove it before \"\n \"creating and alias with the same name\".format(ref))\n\n package_layout = self.app.cache.package_layout(ref)\n return export_alias(package_layout, target_ref,\n revisions_enabled=self.app.config.revisions_enabled,\n output=self.app.out)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.get_default_remote","text":"conans/client/conan_api.py::ConanAPIV1.get_default_remote\n @api_method\n def get_default_remote(self):\n return self.app.cache.registry.load_remotes().default","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.get_remote_by_name","text":"conans/client/conan_api.py::ConanAPIV1.get_remote_by_name\n @api_method\n def get_remote_by_name(self, remote_name):\n return self.app.cache.registry.load_remotes()[remote_name]","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.get_recipe_revisions","text":"conans/client/conan_api.py::ConanAPIV1.get_recipe_revisions\n @api_method\n def get_recipe_revisions(self, reference, remote_name=None):\n if not self.app.config.revisions_enabled:\n raise ConanException(\"The client doesn't have the revisions feature enabled.\"\n \" Enable this feature setting to '1' the environment variable\"\n \" 'CONAN_REVISIONS_ENABLED' or the config value\"\n \" 'general.revisions_enabled' in your conan.conf file\")\n ref = ConanFileReference.loads(reference)\n if ref.revision:\n raise ConanException(\"Cannot list the revisions of a specific recipe revision\")\n\n if not remote_name:\n layout = self.app.cache.package_layout(ref)\n try:\n rev = layout.recipe_revision()\n except RecipeNotFoundException as e:\n e.print_rev = True\n raise e\n\n # Check the time in the associated remote if any\n remote_name = layout.load_metadata().recipe.remote\n remote = self.app.cache.registry.load_remotes()[remote_name] if remote_name else None\n rev_time = None\n if remote:\n try:\n revisions = self.app.remote_manager.get_recipe_revisions(ref, remote)\n except RecipeNotFoundException:\n pass\n except (NoRestV2Available, NotFoundException):\n rev_time = None\n else:\n tmp = {r[\"revision\"]: r[\"time\"] for r in revisions}\n rev_time = tmp.get(rev)\n\n return [{\"revision\": rev, \"time\": rev_time}]\n else:\n remote = self.get_remote_by_name(remote_name)\n return self.app.remote_manager.get_recipe_revisions(ref, remote=remote)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.get_package_revisions","text":"conans/client/conan_api.py::ConanAPIV1.get_package_revisions\n @api_method\n def get_package_revisions(self, reference, remote_name=None):\n if not self.app.config.revisions_enabled:\n raise ConanException(\"The client doesn't have the revisions feature enabled.\"\n \" Enable this feature setting to '1' the environment variable\"\n \" 'CONAN_REVISIONS_ENABLED' or the config value\"\n \" 'general.revisions_enabled' in your conan.conf file\")\n pref = PackageReference.loads(reference, validate=True)\n if not pref.ref.revision:\n raise ConanException(\"Specify a recipe reference with revision\")\n if pref.revision:\n raise ConanException(\"Cannot list the revisions of a specific package revision\")\n\n if not remote_name:\n layout = self.app.cache.package_layout(pref.ref)\n try:\n rev = layout.package_revision(pref)\n except (RecipeNotFoundException, PackageNotFoundException) as e:\n e.print_rev = True\n raise e\n\n # Check the time in the associated remote if any\n remote_name = layout.load_metadata().recipe.remote\n remote = self.app.cache.registry.load_remotes()[remote_name] if remote_name else None\n rev_time = None\n if remote:\n try:\n revisions = self.app.remote_manager.get_package_revisions(pref, remote)\n except RecipeNotFoundException:\n pass\n except (NoRestV2Available, NotFoundException):\n rev_time = None\n else:\n tmp = {r[\"revision\"]: r[\"time\"] for r in revisions}\n rev_time = tmp.get(rev)\n\n return [{\"revision\": rev, \"time\": rev_time}]\n else:\n remote = self.get_remote_by_name(remote_name)\n return self.app.remote","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.editable_add","text":"conans/client/conan_api.py::ConanAPIV1.editable_add\n @api_method\n def editable_add(self, path, reference, layout, cwd):\n # Retrieve conanfile.py from target_path\n target_path = _get_conanfile_path(path=path, cwd=cwd, py=True)\n\n self.app.load_remotes()\n\n # Check the conanfile is there, and name/version matches\n ref = ConanFileReference.loads(reference, validate=True)\n target_conanfile = self.app.loader.load_basic(target_path)\n if (target_conanfile.name and target_conanfile.name != ref.name) or \\\n (target_conanfile.version and target_conanfile.version != ref.version):\n raise ConanException(\"Name and version from reference ({}) and target \"\n \"conanfile.py ({}/{}) must match\".\n format(ref, target_conanfile.name, target_conanfile.version))\n\n layout_abs_path = get_editable_abs_path(layout, cwd, self.app.cache.cache_folder)\n if layout_abs_path:\n self.app.out.success(\"Using layout file: %s\" % layout_abs_path)\n self.app.cache.editable_packages.add(ref, os.path.dirname(target_path), layout_abs_path)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.editable_remove","text":"conans/client/conan_api.py::ConanAPIV1.editable_remove\n @api_method\n def editable_remove(self, reference):\n ref = ConanFileReference.loads(reference, validate=True)\n return self.app.cache.editable_packages.remove(ref)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.editable_list","text":"conans/client/conan_api.py::ConanAPIV1.editable_list\n @api_method\n def editable_list(self):\n return {str(k): v for k, v in self.app.cache.editable_packages.edited_refs.items()}","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.update_lock","text":"conans/client/conan_api.py::ConanAPIV1.update_lock\n @api_method\n def update_lock(self, old_lockfile, new_lockfile, cwd=None):\n cwd = cwd or os.getcwd()\n old_lockfile = _make_abs_path(old_lockfile, cwd)\n old_lock = GraphLockFile.load(old_lockfile, True)\n new_lockfile = _make_abs_path(new_lockfile, cwd)\n new_lock = GraphLockFile.load(new_lockfile, True)\n if old_lock.profile_host.dumps() != new_lock.profile_host.dumps():\n raise ConanException(\"Profiles of lockfiles are different\\n%s:\\n%s\\n%s:\\n%s\"\n % (old_lockfile, old_lock.profile_host.dumps(),\n new_lockfile, new_lock.profile_host.dumps()))\n old_lock.graph_lock.update_lock(new_lock.graph_lock)\n old_lock.save(old_lockfile)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.build_order","text":"conans/client/conan_api.py::ConanAPIV1.build_order\n @api_method\n def build_order(self, lockfile, build=None, cwd=None):\n cwd = cwd or os.getcwd()\n lockfile = _make_abs_path(lockfile, cwd)\n\n recorder = ActionRecorder()\n remotes = self.app.load_remotes()\n\n graph_info = get_graph_info(None, None, None, None,\n cwd=cwd, install_folder=None,\n cache=self.app.cache, output=self.app.out,\n lockfile=lockfile)\n # The reference of the root node could be a local path or a ref\n reference = graph_info.graph_lock.root_node_ref()\n deps_graph = self.app.graph_manager.load_graph(reference, None, graph_info, build,\n False, False, remotes, recorder)\n\n print_graph(deps_graph, self.app.out)\n graph_info.save_lock(lockfile)\n build_order = deps_graph.new_build_order()\n # Build order returns refs, we need to convert to flat python primitives\n for level in build_order:\n level[:] = [(id_, repr(pref)) for id_, pref in level]\n return build_order","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.lock_clean_modified","text":"conans/client/conan_api.py::ConanAPIV1.lock_clean_modified\n @api_method\n def lock_clean_modified(self, lockfile, cwd=None):\n cwd = cwd or os.getcwd()\n lockfile = _make_abs_path(lockfile, cwd)\n lock = GraphLockFile.load(lockfile, self.app.config.revisions_enabled)\n lock.graph_lock.clean_modified()\n lock.save(lockfile)","label":0},{"cid":"conans/client/conan_api.py::ConanAPIV1.create_lock","text":"conans/client/conan_api.py::ConanAPIV1.create_lock\n @api_method\n def create_lock(self, reference, remote_name=None, settings=None, options=None, env=None,\n profile_names=None, update=False, lockfile=None, build=None,):\n reference, graph_info = self._info_args(reference, None, profile_names,\n settings, options, env)\n recorder = ActionRecorder()\n # FIXME: Using update as check_update?\n remotes = self.app.load_remotes(remote_name=remote_name, check_updates=update)\n deps_graph = self.app.graph_manager.load_graph(reference, None, graph_info, build, update,\n False, remotes, recorder)\n\n print_graph(deps_graph, self.app.out)\n lockfile = _make_abs_path(lockfile)\n graph_info.save_lock(lockfile)\n self.app.out.info(\"Generated lockfile\")","label":0},{"cid":"conans/client/conan_api.py::get_graph_info","text":"conans/client/conan_api.py::get_graph_info\ndef get_graph_info(profile_names, settings, options, env, cwd, install_folder, cache, output,\n name=None, version=None, user=None, channel=None, lockfile=None):\n if lockfile:\n try:\n graph_info_folder = lockfile if os.path.isdir(lockfile) else os.path.dirname(lockfile)\n graph_info = GraphInfo.load(graph_info_folder)\n except IOError: # Only if file is missing\n graph_info = GraphInfo()\n root_ref = ConanFileReference(name, version, user, channel, validate=False)\n graph_info.root = root_ref\n lockfile = lockfile if os.path.isfile(lockfile) else os.path.join(lockfile, LOCKFILE)\n graph_lock_file = GraphLockFile.load(lockfile, cache.config.revisions_enabled)\n graph_lock_file.graph_lock.relax = cache.config.relax_lockfile\n graph_info.profile_host = graph_lock_file.profile_host\n graph_info.profile_host.process_settings(cache, preprocess=False)\n graph_info.graph_lock = graph_lock_file.graph_lock\n output.info(\"Using lockfile: '{}'\".format(lockfile))\n return graph_info\n\n try:\n graph_info = GraphInfo.load(install_folder)\n except IOError: # Only if file is missing\n if install_folder:\n raise ConanException(\"Failed to load graphinfo file in install-folder: %s\"\n % install_folder)\n graph_info = None\n else:\n graph_lock_file = GraphLockFile.load(install_folder, cache.config.revisions_enabled)\n graph_info.profile_host = graph_lock_file.profile_host\n graph_info.profile_host.process_settings(cache, preprocess=False)\n\n if profile_names or settings or options or env or not graph_info:\n if graph_info:\n # FIXME: Convert to Exception in Conan 2.0\n output.warn(\"Settings, options, env or profile specified. \"\n \"GraphInfo found from previous install won't be used: %","label":0},{"cid":"conans/client/conan_api.py::_parse_manifests_arguments","text":"conans/client/conan_api.py::_parse_manifests_arguments\ndef _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd):\n if manifests and manifests_interactive:\n raise ConanException(\"Do not specify both manifests and \"\n \"manifests-interactive arguments\")\n if verify and (manifests or manifests_interactive):\n raise ConanException(\"Do not specify both 'verify' and \"\n \"'manifests' or 'manifests-interactive' arguments\")\n manifest_folder = verify or manifests or manifests_interactive\n if manifest_folder:\n if not os.path.isabs(manifest_folder):\n if not cwd:\n raise ConanException(\"'cwd' should be defined if the manifest folder is relative.\")\n manifest_folder = os.path.join(cwd, manifest_folder)\n manifest_verify = verify is not None\n manifest_interactive = manifests_interactive is not None\n else:\n manifest_verify = manifest_interactive = False\n\n return manifest_folder, manifest_interactive, manifest_verify","label":0},{"cid":"conans/client/conan_api.py::existing_info_files","text":"conans/client/conan_api.py::existing_info_files\ndef existing_info_files(folder):\n return os.path.exists(os.path.join(folder, CONANINFO)) and \\\n os.path.exists(os.path.join(folder, BUILD_INFO))","label":0},{"cid":"conans/client/migrations.py::ClientMigrator.__init__","text":"conans/client/migrations.py::ClientMigrator.__init__\n def __init__(self, cache_folder, current_version, out):\n self.cache = ClientCache(cache_folder, out)\n super(ClientMigrator, self).__init__(self.cache.cache_folder, self.cache.store,\n current_version, out)","label":0},{"cid":"conans/client/migrations.py::ClientMigrator._update_settings_yml","text":"conans/client/migrations.py::ClientMigrator._update_settings_yml\n def _update_settings_yml(self, old_version):\n\n from conans.client.conf import get_default_settings_yml\n settings_path = self.cache.settings_path\n if not os.path.exists(settings_path):\n self.out.warn(\"Migration: This conan installation doesn't have settings yet\")\n self.out.warn(\"Nothing to migrate here, settings will be generated automatically\")\n return\n\n var_name = \"settings_{}\".format(old_version.replace(\".\", \"_\"))\n\n def save_new():\n new_path = self.cache.settings_path + \".new\"\n save(new_path, get_default_settings_yml())\n self.out.warn(\"*\" * 40)\n self.out.warn(\"settings.yml is locally modified, can't be updated\")\n self.out.warn(\"The new settings.yml has been stored in: %s\" % new_path)\n self.out.warn(\"*\" * 40)\n\n self.out.warn(\"Migration: Updating settings.yml\")\n if hasattr(migrations_settings, var_name):\n version_default_contents = getattr(migrations_settings, var_name)\n if version_default_contents != get_default_settings_yml():\n current_settings = load(self.cache.settings_path)\n if current_settings != version_default_contents:\n save_new()\n else:\n save(self.cache.settings_path, get_default_settings_yml())\n else:\n self.out.info(\"Migration: Settings already up to date\")\n else:\n # We don't have the value for that version, so don't override\n save_new()","label":0},{"cid":"conans/client/migrations.py::ClientMigrator._update_settings_yml.save_new","text":"conans/client/migrations.py::ClientMigrator._update_settings_yml.save_new\n def save_new():\n new_path = self.cache.settings_path + \".new\"\n save(new_path, get_default_settings_yml())\n self.out.warn(\"*\" * 40)\n self.out.warn(\"settings.yml is locally modified, can't be updated\")\n self.out.warn(\"The new settings.yml has been stored in: %s\" % new_path)\n self.out.warn(\"*\" * 40)","label":0},{"cid":"conans/client/migrations.py::ClientMigrator._make_migrations","text":"conans/client/migrations.py::ClientMigrator._make_migrations\n def _make_migrations(self, old_version):\n # ############### FILL THIS METHOD WITH THE REQUIRED ACTIONS ##############\n # VERSION 0.1\n if old_version is None:\n return\n\n # Migrate the settings if they were the default for that version\n self._update_settings_yml(old_version)\n\n if old_version < Version(\"0.25\"):\n from conans.paths import DEFAULT_PROFILE_NAME\n default_profile_path = os.path.join(self.cache.cache_folder, PROFILES_FOLDER,\n DEFAULT_PROFILE_NAME)\n if not os.path.exists(default_profile_path):\n self.out.warn(\"Migration: Moving default settings from %s file to %s\"\n % (CONAN_CONF, DEFAULT_PROFILE_NAME))\n conf_path = os.path.join(self.cache.cache_folder, CONAN_CONF)\n\n migrate_to_default_profile(conf_path, default_profile_path)\n\n self.out.warn(\"Migration: export_source cache new layout\")\n migrate_c_src_export_source(self.cache, self.out)\n\n if old_version < Version(\"1.0\"):\n _migrate_lock_files(self.cache, self.out)\n\n if old_version < Version(\"1.12.0\"):\n migrate_plugins_to_hooks(self.cache)\n\n if old_version < Version(\"1.13.0\"):\n # MIGRATE LOCAL CACHE TO GENERATE MISSING METADATA.json\n _migrate_create_metadata(self.cache, self.out)\n\n if old_version < Version(\"1.14.0\"):\n migrate_config_install(self.cache)\n\n if old_version < Version(\"1.14.2\"):\n _migrate_full_metadata(self.cache, self.out)\n\n if old_version < Version(\"1.15.0\"):\n migrate_registry_file(self.cache, self.out)\n\n if old_version < Version(\"1.19.0\"):\n migrate_localdb_refresh_token(self.cache, self.out)","label":0},{"cid":"conans/client/migrations.py::_get_refs","text":"conans/client/migrations.py::_get_refs\ndef _get_refs(cache):\n folders = list_folder_subdirs(cache.store, 4)\n return [ConanFileReference(*s.split(\"/\")) for s in folders]","label":0},{"cid":"conans/client/migrations.py::_get_prefs","text":"conans/client/migrations.py::_get_prefs\ndef _get_prefs(layout):\n packages_folder = layout.packages()\n folders = list_folder_subdirs(packages_folder, 1)\n return [PackageReference(layout.ref, s) for s in folders]","label":0},{"cid":"conans/client/migrations.py::migrate_localdb_refresh_token","text":"conans/client/migrations.py::migrate_localdb_refresh_token\ndef migrate_localdb_refresh_token(cache, out):\n from conans.client.store.localdb import LocalDB\n from sqlite3 import OperationalError\n\n localdb = LocalDB.create(cache.localdb)\n with localdb._connect() as connection:\n try:\n statement = connection.cursor()\n statement.execute(\"ALTER TABLE users_remotes ADD refresh_token TEXT;\")\n except OperationalError:\n # This likely means the column is already there (fresh created table)\n # In the worst scenario the user will be requested to remove the file by hand\n pass","label":0},{"cid":"conans/client/migrations.py::_migrate_full_metadata","text":"conans/client/migrations.py::_migrate_full_metadata\ndef _migrate_full_metadata(cache, out):\n # Fix for https://github.com/conan-io/conan/issues/4898\n out.warn(\"Running a full revision metadata migration\")\n refs = _get_refs(cache)\n for ref in refs:\n try:\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\n layout = PackageCacheLayout(base_folder=base_folder, ref=ref, short_paths=None,\n no_lock=True)\n with layout.update_metadata() as metadata:\n # Updating the RREV\n if metadata.recipe.revision is None:\n out.warn(\"Package %s metadata had recipe revision None, migrating\" % str(ref))\n folder = layout.export()\n try:\n manifest = FileTreeManifest.load(folder)\n rrev = manifest.summary_hash\n except Exception:\n rrev = DEFAULT_REVISION_V1\n metadata.recipe.revision = rrev\n\n prefs = _get_prefs(layout)\n existing_ids = [pref.id for pref in prefs]\n for pkg_id in list(metadata.packages.keys()):\n if pkg_id not in existing_ids:\n out.warn(\"Package %s metadata had stalled package information %s, removing\"\n % (str(ref), pkg_id))\n del metadata.packages[pkg_id]\n # UPDATING PREVS\n for pref in prefs:\n try:\n pmanifest = FileTreeManifest.load(layout.package(pref))\n prev = pmanifest.summary_hash\n except Exception:\n prev = DEFAULT_REVISION_V1\n metadata.packages[pref.id].revision = prev\n metadata.packages[pref.id].recipe_revision = metadata.recipe.revision\n\n except Exception as ","label":0},{"cid":"conans/client/migrations.py::_migrate_create_metadata","text":"conans/client/migrations.py::_migrate_create_metadata\ndef _migrate_create_metadata(cache, out):\n out.warn(\"Migration: Generating missing metadata files\")\n refs = _get_refs(cache)\n\n for ref in refs:\n try:\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\n # Force using a package cache layout for everything, we want to alter the cache,\n # not the editables\n layout = PackageCacheLayout(base_folder=base_folder, ref=ref, short_paths=False,\n no_lock=True)\n folder = layout.export()\n try:\n manifest = FileTreeManifest.load(folder)\n rrev = manifest.summary_hash\n except Exception:\n rrev = DEFAULT_REVISION_V1\n metadata_path = layout.package_metadata()\n if not os.path.exists(metadata_path):\n out.info(\"Creating {} for {}\".format(PACKAGE_METADATA, ref))\n prefs = _get_prefs(layout)\n metadata = PackageMetadata()\n metadata.recipe.revision = rrev\n for pref in prefs:\n try:\n pmanifest = FileTreeManifest.load(layout.package(pref))\n prev = pmanifest.summary_hash\n except Exception:\n prev = DEFAULT_REVISION_V1\n metadata.packages[pref.id].revision = prev\n metadata.packages[pref.id].recipe_revision = metadata.recipe.revision\n save(metadata_path, metadata.dumps())\n except Exception as e:\n raise ConanException(\"Something went wrong while generating the metadata.json files \"\n \"in the cache, please try to fix the issue or wipe the cache: {}\"\n \":{}\".format(ref, e))\n out.success(\"Migration: Generating missing metadata files finished OK!\\n\")","label":0},{"cid":"conans/client/migrations.py::_migrate_lock_files","text":"conans/client/migrations.py::_migrate_lock_files\ndef _migrate_lock_files(cache, out):\n out.warn(\"Migration: Removing old lock files\")\n base_dir = cache.store\n pkgs = list_folder_subdirs(base_dir, 4)\n for pkg in pkgs:\n out.info(\"Removing locks for %s\" % pkg)\n try:\n count = os.path.join(base_dir, pkg, \"rw.count\")\n if os.path.exists(count):\n os.remove(count)\n count = os.path.join(base_dir, pkg, \"rw.count.lock\")\n if os.path.exists(count):\n os.remove(count)\n locks = os.path.join(base_dir, pkg, \"locks\")\n if os.path.exists(locks):\n shutil.rmtree(locks)\n except Exception as e:\n raise ConanException(\"Something went wrong while removing %s locks\\n\"\n \"Error: %s\\n\"\n \"Please clean your local conan cache manually\"\n % (pkg, str(e)))\n out.warn(\"Migration: Removing old lock files finished\\n\")","label":0},{"cid":"conans/client/migrations.py::migrate_config_install","text":"conans/client/migrations.py::migrate_config_install\ndef migrate_config_install(cache):\n try:\n item = cache.config.get_item(\"general.config_install\")\n items = [r.strip() for r in item.split(\",\")]\n if len(items) == 4:\n config_type, uri, verify_ssl, args = items\n elif len(items) == 1:\n uri = items[0]\n verify_ssl = \"True\"\n args = \"None\"\n config_type = None\n else:\n raise Exception(\"I don't know how to migrate this config install: %s\" % items)\n verify_ssl = \"true\" in verify_ssl.lower()\n args = None if \"none\" in args.lower() else args\n config = _ConfigOrigin.from_item(uri, config_type, verify_ssl, args, None, None)\n _save_configs(cache.config_install_file, [config])\n cache.config.rm_item(\"general.config_install\")\n except ConanException:\n pass","label":0},{"cid":"conans/client/migrations.py::migrate_to_default_profile","text":"conans/client/migrations.py::migrate_to_default_profile\ndef migrate_to_default_profile(conf_path, default_profile_path):\n tag = \"[settings_defaults]\"\n old_conf = load(conf_path)\n if tag not in old_conf:\n return\n tmp = old_conf.find(tag)\n new_conf = old_conf[0:tmp]\n rest = old_conf[tmp + len(tag):]\n if tmp:\n if \"]\" in rest: # More sections after the settings_defaults\n new_conf += rest[rest.find(\"[\"):]\n save(conf_path, new_conf)\n settings = rest[:rest.find(\"[\")].strip()\n else:\n save(conf_path, new_conf)\n settings = rest.strip()\n # Now generate the default profile from the read settings_defaults\n new_profile = \"[settings]\\n%s\" % settings\n save(default_profile_path, new_profile)","label":0},{"cid":"conans/client/migrations.py::migrate_c_src_export_source","text":"conans/client/migrations.py::migrate_c_src_export_source\ndef migrate_c_src_export_source(cache, out):\n package_folders = list_folder_subdirs(cache.store, 4)\n for package in package_folders:\n package_folder = os.path.join(cache.store, package)\n c_src = os.path.join(package_folder, \"export/%s\" % EXPORT_SOURCES_DIR_OLD)\n if os.path.exists(c_src):\n out.warn(\"Migration: Removing package with old export_sources layout: %s\" % package)\n try:\n shutil.rmtree(package_folder)\n except Exception:\n out.warn(\"Migration: Can't remove the '%s' directory, \"\n \"remove it manually\" % package_folder)","label":0},{"cid":"conans/client/migrations.py::migrate_plugins_to_hooks","text":"conans/client/migrations.py::migrate_plugins_to_hooks\ndef migrate_plugins_to_hooks(cache, output=None):\n plugins_path = os.path.join(cache.cache_folder, \"plugins\")\n if os.path.exists(plugins_path) and not os.path.exists(cache.hooks_path):\n os.rename(plugins_path, cache.hooks_path)\n conf_path = cache.conan_conf_path\n replace_in_file(conf_path, \"[plugins]\", \"[hooks]\", strict=False, output=output)","label":0},{"cid":"conans/model/conan_file.py::create_options","text":"conans/model/conan_file.py::create_options\ndef create_options(conanfile):\n try:\n package_options = PackageOptions(getattr(conanfile, \"options\", None))\n options = Options(package_options)\n\n default_options = getattr(conanfile, \"default_options\", None)\n if default_options:\n if isinstance(default_options, dict):\n default_values = OptionsValues(default_options)\n elif isinstance(default_options, (list, tuple)):\n conan_v2_behavior(\"Declare 'default_options' as a dictionary\")\n default_values = OptionsValues(default_options)\n elif isinstance(default_options, six.string_types):\n conan_v2_behavior(\"Declare 'default_options' as a dictionary\")\n default_values = OptionsValues.loads(default_options)\n else:\n raise ConanException(\"Please define your default_options as list, \"\n \"multiline string or dictionary\")\n options.values = default_values\n return options\n except Exception as e:\n raise ConanException(\"Error while initializing options. %s\" % str(e))","label":0},{"cid":"conans/model/conan_file.py::create_requirements","text":"conans/model/conan_file.py::create_requirements\ndef create_requirements(conanfile):\n try:\n # Actual requirements of this package\n if not hasattr(conanfile, \"requires\"):\n return Requirements()\n else:\n if not conanfile.requires:\n return Requirements()\n if isinstance(conanfile.requires, (tuple, list)):\n return Requirements(*conanfile.requires)\n else:\n return Requirements(conanfile.requires, )\n except Exception as e:\n raise ConanException(\"Error while initializing requirements. %s\" % str(e))","label":0},{"cid":"conans/model/conan_file.py::create_settings","text":"conans/model/conan_file.py::create_settings\ndef create_settings(conanfile, settings):\n try:\n defined_settings = getattr(conanfile, \"settings\", None)\n if isinstance(defined_settings, str):\n defined_settings = [defined_settings]\n current = defined_settings or {}\n settings.constraint(current)\n return settings\n except Exception as e:\n raise ConanInvalidConfiguration(\"Error while initializing settings. %s\" % str(e))","label":0},{"cid":"conans/model/conan_file.py::_env_and_python","text":"conans/model/conan_file.py::_env_and_python\n@contextmanager\ndef _env_and_python(conanfile):\n with environment_append(conanfile.env):\n with pythonpath(conanfile):\n yield","label":0},{"cid":"conans/model/conan_file.py::get_env_context_manager","text":"conans/model/conan_file.py::get_env_context_manager\ndef get_env_context_manager(conanfile, without_python=False):\n if not conanfile.apply_env:\n return no_op()\n if without_python:\n return environment_append(conanfile.env)\n return _env_and_python(conanfile)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.__init__","text":"conans/model/conan_file.py::ConanFile.__init__\n def __init__(self, output, runner, display_name=\"\", user=None, channel=None):\n # an output stream (writeln, info, warn error)\n self.output = ScopedOutput(display_name, output)\n self.display_name = display_name\n # something that can run commands, as os.sytem\n self._conan_runner = runner\n self._conan_user = user\n self._conan_channel = channel\n\n self.compatible_packages = []\n self._conan_using_build_profile = False","label":0},{"cid":"conans/model/conan_file.py::ConanFile.initialize","text":"conans/model/conan_file.py::ConanFile.initialize\n def initialize(self, settings, env):\n if isinstance(self.generators, str):\n self.generators = [self.generators]\n # User defined options\n self.options = create_options(self)\n self.requires = create_requirements(self)\n self.settings = create_settings(self, settings)\n\n if 'cppstd' in self.settings.fields:\n conan_v2_behavior(\"Setting 'cppstd' is deprecated in favor of 'compiler.cppstd',\"\n \" please update your recipe.\", v1_behavior=self.output.warn)\n\n # needed variables to pack the project\n self.cpp_info = None # Will be initialized at processing time\n self._conan_dep_cpp_info = None # Will be initialized at processing time\n self.deps_cpp_info = DepsCppInfo()\n\n # environment variables declared in the package_info\n self.env_info = None # Will be initialized at processing time\n self.deps_env_info = DepsEnvInfo()\n\n # user declared variables\n self.user_info = None\n # Keys are the package names, and the values a dict with the vars\n self.deps_user_info = DepsUserInfo()\n\n # user specified env variables\n self._conan_env_values = env.copy() # user specified -e","label":0},{"cid":"conans/model/conan_file.py::ConanFile.env","text":"conans/model/conan_file.py::ConanFile.env\n @property\n def env(self):\n \"\"\"Apply the self.deps_env_info into a copy of self._conan_env_values (will prioritize the\n self._conan_env_values, user specified from profiles or -e first, then inherited)\"\"\"\n # Cannot be lazy cached, because it's called in configure node, and we still don't have\n # the deps_env_info objects available\n tmp_env_values = self._conan_env_values.copy()\n tmp_env_values.update(self.deps_env_info)\n\n ret, multiple = tmp_env_values.env_dicts(self.name)\n ret.update(multiple)\n return ret","label":0},{"cid":"conans/model/conan_file.py::ConanFile.channel","text":"conans/model/conan_file.py::ConanFile.channel\n @property\n def channel(self):\n if not self._conan_channel:\n _env_channel = os.getenv(\"CONAN_CHANNEL\")\n if _env_channel:\n conan_v2_behavior(\"Environment variable 'CONAN_CHANNEL' is deprecated\")\n self._conan_channel = _env_channel or self.default_channel\n if not self._conan_channel:\n raise ConanException(\"channel not defined, but self.channel is used in conanfile\")\n return self._conan_channel","label":0},{"cid":"conans/model/conan_file.py::ConanFile.user","text":"conans/model/conan_file.py::ConanFile.user\n @property\n def user(self):\n if not self._conan_user:\n _env_username = os.getenv(\"CONAN_USERNAME\")\n if _env_username:\n conan_v2_behavior(\"Environment variable 'CONAN_USERNAME' is deprecated\")\n self._conan_user = _env_username or self.default_user\n if not self._conan_user:\n raise ConanException(\"user not defined, but self.user is used in conanfile\")\n return self._conan_user","label":0},{"cid":"conans/model/conan_file.py::ConanFile.collect_libs","text":"conans/model/conan_file.py::ConanFile.collect_libs\n def collect_libs(self, folder=None):\n conan_v2_behavior(\"'self.collect_libs' is deprecated, use 'tools.collect_libs(self)' instead\",\n v1_behavior=self.output.warn)\n return tools.collect_libs(self, folder=folder)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_policy_missing","text":"conans/model/conan_file.py::ConanFile.build_policy_missing\n @property\n def build_policy_missing(self):\n return self.build_policy == \"missing\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build_policy_always","text":"conans/model/conan_file.py::ConanFile.build_policy_always\n @property\n def build_policy_always(self):\n return self.build_policy == \"always\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.source","text":"conans/model/conan_file.py::ConanFile.source\n def source(self):\n pass","label":0},{"cid":"conans/model/conan_file.py::ConanFile.system_requirements","text":"conans/model/conan_file.py::ConanFile.system_requirements\n def system_requirements(self):\n \"\"\" this method can be overwritten to implement logic for system package\n managers, as apt-get\n\n You can define self.global_system_requirements = True, if you want the installation\n to be for all packages (not depending on settings/options/requirements)\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.config_options","text":"conans/model/conan_file.py::ConanFile.config_options\n def config_options(self):\n \"\"\" modify options, probably conditioned to some settings. This call is executed\n before config_settings. E.g.\n if self.settings.os == \"Windows\":\n del self.options.shared # shared/static not supported in win\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.configure","text":"conans/model/conan_file.py::ConanFile.configure\n def configure(self):\n \"\"\" modify settings, probably conditioned to some options. This call is executed\n after config_options. E.g.\n if self.options.header_only:\n self.settings.clear()\n This is also the place for conditional requirements\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.build","text":"conans/model/conan_file.py::ConanFile.build\n def build(self):\n \"\"\" build your project calling the desired build tools as done in the command line.\n E.g. self.run(\"cmake --build .\") Or use the provided build helpers. E.g. cmake.build()\n \"\"\"\n self.output.warn(\"This conanfile has no build step\")","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package","text":"conans/model/conan_file.py::ConanFile.package\n def package(self):\n \"\"\" package the needed files from source and build folders.\n E.g. self.copy(\"*.h\", src=\"src/includes\", dst=\"includes\")\n \"\"\"\n self.output.warn(\"This conanfile has no package step\")","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_info","text":"conans/model/conan_file.py::ConanFile.package_info\n def package_info(self):\n \"\"\" define cpp_build_info, flags, etc\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.run","text":"conans/model/conan_file.py::ConanFile.run\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True,\n ignore_errors=False, run_environment=False, with_login=True):\n def _run():\n if not win_bash:\n return self._conan_runner(command, output, os.path.abspath(RUN_LOG_NAME), cwd)\n # FIXME: run in windows bash is not using output\n return tools.run_in_windows_bash(self, bashcmd=command, cwd=cwd, subsystem=subsystem,\n msys_mingw=msys_mingw, with_login=with_login)\n if run_environment:\n # When using_build_profile the required environment is already applied through 'conanfile.env'\n # in the contextmanager 'get_env_context_manager'\n with tools.run_environment(self) if not self._conan_using_build_profile else no_op():\n if OSInfo().is_macos:\n command = 'DYLD_LIBRARY_PATH=\"%s\" DYLD_FRAMEWORK_PATH=\"%s\" %s' % \\\n (os.environ.get('DYLD_LIBRARY_PATH', ''),\n os.environ.get(\"DYLD_FRAMEWORK_PATH\", ''),\n command)\n retcode = _run()\n else:\n retcode = _run()\n\n if not ignore_errors and retcode != 0:\n raise ConanException(\"Error %d while executing %s\" % (retcode, command))\n\n return retcode","label":0},{"cid":"conans/model/conan_file.py::ConanFile.run._run","text":"conans/model/conan_file.py::ConanFile.run._run\n def _run():\n if not win_bash:\n return self._conan_runner(command, output, os.path.abspath(RUN_LOG_NAME), cwd)\n # FIXME: run in windows bash is not using output\n return tools.run_in_windows_bash(self, bashcmd=command, cwd=cwd, subsystem=subsystem,\n msys_mingw=msys_mingw, with_login=with_login)","label":0},{"cid":"conans/model/conan_file.py::ConanFile.package_id","text":"conans/model/conan_file.py::ConanFile.package_id\n def package_id(self):\n \"\"\" modify the binary info, typically to narrow values\n e.g.: self.info.settings.compiler = \"Any\" => All compilers will generate same ID\n \"\"\"","label":0},{"cid":"conans/model/conan_file.py::ConanFile.test","text":"conans/model/conan_file.py::ConanFile.test\n def test(self):\n \"\"\" test the generated executable.\n E.g. self.run(\"./example\")\n \"\"\"\n raise ConanException(\"You need to create a method 'test' in your test/conanfile.py\")","label":0},{"cid":"conans/model/conan_file.py::ConanFile.__repr__","text":"conans/model/conan_file.py::ConanFile.__repr__\n def __repr__(self):\n return self.display_name","label":0}],"string":"[\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.__init__\",\n \"text\": \"conans/client/build/cmake.py::CMake.__init__\\n def __init__(self, conanfile, generator=None, cmake_system_name=True,\\n parallel=True, build_type=None, toolset=None, make_program=None,\\n set_cmake_flags=False, msbuild_verbosity=\\\"minimal\\\", cmake_program=None,\\n generator_platform=None, append_vcvars=False):\\n \\\"\\\"\\\"\\n :param conanfile: Conanfile instance\\n :param generator: Generator name to use or none to autodetect\\n :param cmake_system_name: False to not use CMAKE_SYSTEM_NAME variable,\\n True for auto-detect or directly a string with the system name\\n :param parallel: Try to build with multiple cores if available\\n :param build_type: Overrides default build type coming from settings\\n :param toolset: Toolset name to use (such as llvm-vs2014) or none for default one,\\n applies only to certain generators (e.g. Visual Studio)\\n :param set_cmake_flags: whether or not to set CMake flags like CMAKE_CXX_FLAGS,\\n CMAKE_C_FLAGS, etc. it's vital to set for certain projects\\n (e.g. using CMAKE_SIZEOF_VOID_P or CMAKE_LIBRARY_ARCHITECTURE)\\n :param msbuild_verbosity: verbosity level for MSBuild (in case of Visual Studio generator)\\n :param cmake_program: Path to the custom cmake executable\\n :param generator_platform: Generator platform name or none to autodetect (-A cmake option)\\n \\\"\\\"\\\"\\n if not isinstance(conanfile, ConanFile):\\n raise ConanException(\\\"First argument of CMake() has to be ConanFile. Use CMake(self)\\\")\\n\\n self._append_vcvars = append_vcvars\\n self._conanfile = conanfile\\n self._settings = conanfile.settings\\n self._build_type = build_type or conanfile.settings.get_safe(\\\"build_type\\\")\\n self._cmake_program = os.getenv(\\\"CONAN_CMAKE_PROGRAM\\\") or cmake_program or \\\"cmake\\\"\\n\\n self.generator_platform = generator_platform\\n self.generator = generat\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.generator\",\n \"text\": \"conans/client/build/cmake.py::CMake.generator\\n @property\\n def generator(self):\\n return self._generator\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.generator\",\n \"text\": \"conans/client/build/cmake.py::CMake.generator\\n @generator.setter\\n def generator(self, value):\\n self._generator = value\\n if not self._generator_platform_is_assigned:\\n self._generator_platform = get_generator_platform(self._settings, self._generator)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.generator_platform\",\n \"text\": \"conans/client/build/cmake.py::CMake.generator_platform\\n @property\\n def generator_platform(self):\\n return self._generator_platform\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.generator_platform\",\n \"text\": \"conans/client/build/cmake.py::CMake.generator_platform\\n @generator_platform.setter\\n def generator_platform(self, value):\\n self._generator_platform = value\\n self._generator_platform_is_assigned = bool(value is not None)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_folder\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_folder\\n @property\\n def build_folder(self):\\n return self.build_dir\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_folder\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_folder\\n @build_folder.setter\\n def build_folder(self, value):\\n self.build_dir = value\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_type\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_type\\n @property\\n def build_type(self):\\n return self._build_type\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_type\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_type\\n @build_type.setter\\n def build_type(self, build_type):\\n settings_build_type = self._settings.get_safe(\\\"build_type\\\")\\n self.definitions.pop(\\\"CMAKE_BUILD_TYPE\\\", None)\\n self.definitions.update(build_type_definition(build_type, settings_build_type,\\n self.generator, self._conanfile.output))\\n self._build_type = build_type\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.in_local_cache\",\n \"text\": \"conans/client/build/cmake.py::CMake.in_local_cache\\n @property\\n def in_local_cache(self):\\n try:\\n in_local_cache = self.definitions[cmake_in_local_cache_var_name]\\n return get_bool_from_text(str(in_local_cache))\\n except KeyError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.runtime\",\n \"text\": \"conans/client/build/cmake.py::CMake.runtime\\n @property\\n def runtime(self):\\n return defs_to_string(self.definitions.get(runtime_definition_var_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.flags\",\n \"text\": \"conans/client/build/cmake.py::CMake.flags\\n @property\\n def flags(self):\\n return defs_to_string(self.definitions)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.is_multi_configuration\",\n \"text\": \"conans/client/build/cmake.py::CMake.is_multi_configuration\\n @property\\n def is_multi_configuration(self):\\n return is_multi_configuration(self.generator)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.command_line\",\n \"text\": \"conans/client/build/cmake.py::CMake.command_line\\n @property\\n def command_line(self):\\n if self.generator_platform and not is_generator_platform_supported(self.generator):\\n raise ConanException('CMake does not support generator platform with generator '\\n '\\\"%s:. Please check your conan profile to either remove the '\\n 'generator platform, or change the CMake generator.'\\n % self.generator)\\n\\n if self.toolset and not is_toolset_supported(self.generator):\\n raise ConanException('CMake does not support toolsets with generator \\\"%s:.'\\n 'Please check your conan profile to either remove the toolset,'\\n ' or change the CMake generator.' % self.generator)\\n\\n generator = self.generator\\n generator_platform = self.generator_platform\\n\\n if self.generator_platform and 'Visual Studio' in generator:\\n # FIXME: Conan 2.0 We are adding the platform to the generator instead of using the -A argument\\n # to keep previous implementation, but any modern CMake will support (and recommend) passing the\\n # platform in its own argument.\\n compiler_version = self._settings.get_safe(\\\"compiler.version\\\")\\n if Version(compiler_version) < \\\"16\\\" and self._settings.get_safe(\\\"os\\\") != \\\"WindowsCE\\\":\\n if self.generator_platform == \\\"x64\\\":\\n generator += \\\" Win64\\\" if not generator.endswith(\\\" Win64\\\") else \\\"\\\"\\n generator_platform = None\\n elif self.generator_platform == \\\"ARM\\\":\\n generator += \\\" ARM\\\" if not generator.endswith(\\\" ARM\\\") else \\\"\\\"\\n generator_platform = None\\n elif self.generator_platform == \\\"Win32\\\":\\n generator_platform = None\\n\\n args = ['-G \\\"{}\\\"'.format(generator)] if generator else []\\n if gener\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build_config\",\n \"text\": \"conans/client/build/cmake.py::CMake.build_config\\n @property\\n def build_config(self):\\n \\\"\\\"\\\" cmake --build tool have a --config option for Multi-configuration IDEs\\n \\\"\\\"\\\"\\n if self._build_type and self.is_multi_configuration:\\n return \\\"--config %s\\\" % self._build_type\\n return \\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._get_dirs\",\n \"text\": \"conans/client/build/cmake.py::CMake._get_dirs\\n def _get_dirs(self, source_folder, build_folder, source_dir, build_dir, cache_build_folder):\\n if (source_folder or build_folder) and (source_dir or build_dir):\\n raise ConanException(\\\"Use 'build_folder'/'source_folder' arguments\\\")\\n\\n def get_dir(folder, origin):\\n if folder:\\n if os.path.isabs(folder):\\n return folder\\n return os.path.join(origin, folder)\\n return origin\\n\\n if source_dir or build_dir: # OLD MODE\\n build_ret = build_dir or self.build_dir or self._conanfile.build_folder\\n source_ret = source_dir or self._conanfile.source_folder\\n else:\\n build_ret = get_dir(build_folder, self._conanfile.build_folder)\\n source_ret = get_dir(source_folder, self._conanfile.source_folder)\\n\\n if self._conanfile.in_local_cache and cache_build_folder:\\n build_ret = get_dir(cache_build_folder, self._conanfile.build_folder)\\n\\n return source_ret, build_ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._get_dirs.get_dir\",\n \"text\": \"conans/client/build/cmake.py::CMake._get_dirs.get_dir\\n def get_dir(folder, origin):\\n if folder:\\n if os.path.isabs(folder):\\n return folder\\n return os.path.join(origin, folder)\\n return origin\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._run\",\n \"text\": \"conans/client/build/cmake.py::CMake._run\\n def _run(self, command):\\n compiler = self._settings.get_safe(\\\"compiler\\\")\\n the_os = self._settings.get_safe(\\\"os\\\")\\n is_clangcl = the_os == \\\"Windows\\\" and compiler == \\\"clang\\\"\\n is_msvc = compiler == \\\"Visual Studio\\\"\\n if ((is_msvc or is_clangcl) and platform.system() == \\\"Windows\\\" and\\n self.generator in [\\\"Ninja\\\", \\\"NMake Makefiles\\\", \\\"NMake Makefiles JOM\\\"]):\\n vcvars_dict = tools.vcvars_dict(self._settings, force=True, filter_known_paths=False,\\n output=self._conanfile.output)\\n with _environment_add(vcvars_dict, post=self._append_vcvars):\\n self._conanfile.run(command)\\n else:\\n self._conanfile.run(command)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.configure\",\n \"text\": \"conans/client/build/cmake.py::CMake.configure\\n def configure(self, args=None, defs=None, source_dir=None, build_dir=None,\\n source_folder=None, build_folder=None, cache_build_folder=None,\\n pkg_config_paths=None):\\n\\n # TODO: Deprecate source_dir and build_dir in favor of xxx_folder\\n if not self._conanfile.should_configure:\\n return\\n args = args or []\\n defs = defs or {}\\n source_dir, self.build_dir = self._get_dirs(source_folder, build_folder,\\n source_dir, build_dir,\\n cache_build_folder)\\n mkdir(self.build_dir)\\n arg_list = join_arguments([\\n self.command_line,\\n args_to_string(args),\\n defs_to_string(defs),\\n args_to_string([source_dir])\\n ])\\n\\n if pkg_config_paths:\\n pkg_env = {\\\"PKG_CONFIG_PATH\\\":\\n os.pathsep.join(get_abs_path(f, self._conanfile.install_folder)\\n for f in pkg_config_paths)}\\n else:\\n # If we are using pkg_config generator automate the pcs location, otherwise it could\\n # read wrong files\\n set_env = \\\"pkg_config\\\" in self._conanfile.generators \\\\\\n and \\\"PKG_CONFIG_PATH\\\" not in os.environ\\n pkg_env = {\\\"PKG_CONFIG_PATH\\\": self._conanfile.install_folder} if set_env else None\\n\\n with environment_append(pkg_env):\\n command = \\\"cd %s && %s %s\\\" % (args_to_string([self.build_dir]), self._cmake_program,\\n arg_list)\\n if platform.system() == \\\"Windows\\\" and self.generator == \\\"MinGW Makefiles\\\":\\n with tools.remove_from_path(\\\"sh\\\"):\\n self._run(command)\\n else:\\n self._run(command)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.build\",\n \"text\": \"conans/client/build/cmake.py::CMake.build\\n def build(self, args=None, build_dir=None, target=None):\\n if not self._conanfile.should_build:\\n return\\n self._build(args, build_dir, target)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake._build\",\n \"text\": \"conans/client/build/cmake.py::CMake._build\\n def _build(self, args=None, build_dir=None, target=None):\\n args = args or []\\n build_dir = build_dir or self.build_dir or self._conanfile.build_folder\\n if target is not None:\\n args = [\\\"--target\\\", target] + args\\n\\n compiler_version = self._settings.get_safe(\\\"compiler.version\\\")\\n if self.generator and self.parallel:\\n if (\\\"Makefiles\\\" in self.generator or \\\"Ninja\\\" in self.generator) and \\\\\\n \\\"NMake\\\" not in self.generator:\\n if \\\"--\\\" not in args:\\n args.append(\\\"--\\\")\\n args.append(\\\"-j%i\\\" % cpu_count(self._conanfile.output))\\n elif \\\"Visual Studio\\\" in self.generator and \\\\\\n compiler_version and Version(compiler_version) >= \\\"10\\\":\\n if \\\"--\\\" not in args:\\n args.append(\\\"--\\\")\\n # Parallel for building projects in the solution\\n args.append(\\\"/m:%i\\\" % cpu_count(output=self._conanfile.output))\\n\\n if self.generator and self.msbuild_verbosity:\\n if \\\"Visual Studio\\\" in self.generator and \\\\\\n compiler_version and Version(compiler_version) >= \\\"10\\\":\\n if \\\"--\\\" not in args:\\n args.append(\\\"--\\\")\\n args.append(\\\"/verbosity:%s\\\" % self.msbuild_verbosity)\\n\\n arg_list = join_arguments([\\n args_to_string([build_dir]),\\n self.build_config,\\n args_to_string(args)\\n ])\\n command = \\\"%s --build %s\\\" % (self._cmake_program, arg_list)\\n self._run(command)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.install\",\n \"text\": \"conans/client/build/cmake.py::CMake.install\\n def install(self, args=None, build_dir=None):\\n if not self._conanfile.should_install:\\n return\\n mkdir(self._conanfile.package_folder)\\n if not self.definitions.get(cmake_install_prefix_var_name):\\n raise ConanException(\\\"%s not defined for 'cmake.install()'\\\\n\\\"\\n \\\"Make sure 'package_folder' is \\\"\\n \\\"defined\\\" % cmake_install_prefix_var_name)\\n self._build(args=args, build_dir=build_dir, target=\\\"install\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.test\",\n \"text\": \"conans/client/build/cmake.py::CMake.test\\n def test(self, args=None, build_dir=None, target=None, output_on_failure=False):\\n if not self._conanfile.should_test:\\n return\\n if not target:\\n target = \\\"RUN_TESTS\\\" if self.is_multi_configuration else \\\"test\\\"\\n\\n test_env = {'CTEST_OUTPUT_ON_FAILURE': '1' if output_on_failure else '0'}\\n if self.parallel:\\n test_env['CTEST_PARALLEL_LEVEL'] = str(cpu_count(self._conanfile.output))\\n with environment_append(test_env):\\n self._build(args=args, build_dir=build_dir, target=target)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.verbose\",\n \"text\": \"conans/client/build/cmake.py::CMake.verbose\\n @property\\n def verbose(self):\\n try:\\n verbose = self.definitions[verbose_definition_name]\\n return get_bool_from_text(str(verbose))\\n except KeyError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.verbose\",\n \"text\": \"conans/client/build/cmake.py::CMake.verbose\\n @verbose.setter\\n def verbose(self, value):\\n self.definitions.update(verbose_definition(value))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.patch_config_paths\",\n \"text\": \"conans/client/build/cmake.py::CMake.patch_config_paths\\n def patch_config_paths(self):\\n \\\"\\\"\\\"\\n changes references to the absolute path of the installed package and its dependencies in\\n exported cmake config files to the appropriate conan variable. This makes\\n most (sensible) cmake config files portable.\\n\\n For example, if a package foo installs a file called \\\"fooConfig.cmake\\\" to\\n be used by cmake's find_package method, normally this file will contain\\n absolute paths to the installed package folder, for example it will contain\\n a line such as:\\n\\n SET(Foo_INSTALL_DIR /home/developer/.conan/data/Foo/1.0.0/...)\\n\\n This will cause cmake find_package() method to fail when someone else\\n installs the package via conan.\\n\\n This function will replace such mentions to\\n\\n SET(Foo_INSTALL_DIR ${CONAN_FOO_ROOT})\\n\\n which is a variable that is set by conanbuildinfo.cmake, so that find_package()\\n now correctly works on this conan package.\\n\\n For dependent packages, if a package foo installs a file called \\\"fooConfig.cmake\\\" to\\n be used by cmake's find_package method and if it depends to a package bar,\\n normally this file will contain absolute paths to the bar package folder,\\n for example it will contain a line such as:\\n\\n SET_TARGET_PROPERTIES(foo PROPERTIES\\n INTERFACE_INCLUDE_DIRECTORIES\\n \\\"/home/developer/.conan/data/Bar/1.0.0/user/channel/id/include\\\")\\n\\n This function will replace such mentions to\\n\\n SET_TARGET_PROPERTIES(foo PROPERTIES\\n INTERFACE_INCLUDE_DIRECTORIES\\n \\\"${CONAN_BAR_ROOT}/include\\\")\\n\\n If the install() method of the CMake object in the conan file is used, this\\n function should be called _after_ that invocation. For example:\\n\\n def build(self):\\n cmake = CMake(self)\\n cmake.configure()\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/build/cmake.py::CMake.get_version\",\n \"text\": \"conans/client/build/cmake.py::CMake.get_version\\n @staticmethod\\n def get_version():\\n try:\\n out = version_runner([\\\"cmake\\\", \\\"--version\\\"])\\n version_line = decode_text(out).split('\\\\n', 1)[0]\\n version_str = version_line.rsplit(' ', 1)[-1]\\n return Version(version_str)\\n except Exception as e:\\n raise ConanException(\\\"Error retrieving CMake version: '{}'\\\".format(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::export_alias\",\n \"text\": \"conans/client/cmd/export.py::export_alias\\ndef export_alias(package_layout, target_ref, output, revisions_enabled):\\n revision_mode = \\\"hash\\\"\\n conanfile = \\\"\\\"\\\"\\nfrom conans import ConanFile\\n\\nclass AliasConanfile(ConanFile):\\n alias = \\\"%s\\\"\\n revision_mode = \\\"%s\\\"\\n\\\"\\\"\\\" % (target_ref.full_str(), revision_mode)\\n\\n save(package_layout.conanfile(), conanfile)\\n manifest = FileTreeManifest.create(package_layout.export())\\n manifest.save(folder=package_layout.export())\\n\\n # Create the metadata for the alias\\n _update_revision_in_metadata(package_layout=package_layout, revisions_enabled=revisions_enabled,\\n output=output, path=None, manifest=manifest,\\n revision_mode=revision_mode)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::check_casing_conflict\",\n \"text\": \"conans/client/cmd/export.py::check_casing_conflict\\ndef check_casing_conflict(cache, ref):\\n # Check for casing conflict\\n # Maybe a platform check could be added, but depends on disk partition\\n refs = search_recipes(cache, ref, ignorecase=True)\\n refs2 = [ConanFileReference(r.name, r.version, r.user if ref.user else None,\\n r.channel if ref.channel else None, validate=False) for r in refs]\\n\\n if refs and ref not in refs2:\\n raise ConanException(\\\"Cannot export package with same name but different case\\\\n\\\"\\n \\\"You exported '%s' but already existing '%s'\\\"\\n % (str(ref), \\\" \\\".join(str(s) for s in refs)))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::cmd_export\",\n \"text\": \"conans/client/cmd/export.py::cmd_export\\ndef cmd_export(app, conanfile_path, name, version, user, channel, keep_source,\\n export=True, graph_lock=None, ignore_dirty=False):\\n \\\"\\\"\\\" Export the recipe\\n param conanfile_path: the original source directory of the user containing a\\n conanfile.py\\n \\\"\\\"\\\"\\n loader, cache, hook_manager, output = app.loader, app.cache, app.hook_manager, app.out\\n revisions_enabled = app.config.revisions_enabled\\n scm_to_conandata = app.config.scm_to_conandata\\n conanfile = loader.load_export(conanfile_path, name, version, user, channel)\\n\\n # FIXME: Conan 2.0, deprecate CONAN_USER AND CONAN_CHANNEL and remove this try excepts\\n # Take the default from the env vars if they exist to not break behavior\\n try:\\n user = conanfile.user\\n except ConanV2Exception:\\n raise\\n except ConanException:\\n user = None\\n\\n try:\\n channel = conanfile.channel\\n except ConanV2Exception:\\n raise\\n except ConanException:\\n channel = None\\n\\n ref = ConanFileReference(conanfile.name, conanfile.version, user, channel)\\n\\n # If we receive lock information, python_requires could have been locked\\n if graph_lock:\\n node_id = graph_lock.get_node(ref)\\n python_requires = graph_lock.python_requires(node_id)\\n # TODO: check that the locked python_requires are different from the loaded ones\\n app.range_resolver.clear_output() # invalidate previous version range output\\n conanfile = loader.load_export(conanfile_path, conanfile.name, conanfile.version,\\n ref.user, ref.channel, python_requires)\\n\\n check_casing_conflict(cache=cache, ref=ref)\\n package_layout = cache.package_layout(ref, short_paths=conanfile.short_paths)\\n if not export:\\n metadata = package_layout.load_metadata()\\n recipe_revision = metadata.recipe.revision\\n ref = ref.copy_with_rev(recipe_revision)\\n if graph_lock:\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_check_settings_for_warnings\",\n \"text\": \"conans/client/cmd/export.py::_check_settings_for_warnings\\ndef _check_settings_for_warnings(conanfile, output):\\n if not conanfile.settings:\\n return\\n try:\\n if 'os_build' not in conanfile.settings:\\n return\\n if 'os' not in conanfile.settings:\\n return\\n\\n output.writeln(\\\"*\\\"*60, front=Color.BRIGHT_RED)\\n output.writeln(\\\" This package defines both 'os' and 'os_build' \\\",\\n front=Color.BRIGHT_RED)\\n output.writeln(\\\" Please use 'os' for libraries and 'os_build'\\\",\\n front=Color.BRIGHT_RED)\\n output.writeln(\\\" only for build-requires used for cross-building\\\",\\n front=Color.BRIGHT_RED)\\n output.writeln(\\\"*\\\"*60, front=Color.BRIGHT_RED)\\n except ConanException:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_capture_scm_auto_fields\",\n \"text\": \"conans/client/cmd/export.py::_capture_scm_auto_fields\\ndef _capture_scm_auto_fields(conanfile, conanfile_dir, package_layout, output, ignore_dirty, scm_to_conandata):\\n \\\"\\\"\\\"Deduce the values for the scm auto fields or functions assigned to 'url' or 'revision'\\n and replace the conanfile.py contents.\\n Returns a tuple with (scm_data, path_to_scm_local_directory)\\\"\\\"\\\"\\n scm_data = get_scm_data(conanfile)\\n if not scm_data:\\n return None, None\\n\\n # Resolve SCMData in the user workspace (someone may want to access CVS or import some py)\\n scm = SCM(scm_data, conanfile_dir, output)\\n captured = scm_data.capture_origin or scm_data.capture_revision\\n\\n if not captured:\\n # We replace not only \\\"auto\\\" values, also evaluated functions (e.g from a python_require)\\n _replace_scm_data_in_recipe(package_layout, scm_data, scm_to_conandata)\\n return scm_data, None\\n\\n if not scm.is_pristine() and not ignore_dirty:\\n output.warn(\\\"There are uncommitted changes, skipping the replacement of 'scm.url' and \\\"\\n \\\"'scm.revision' auto fields. Use --ignore-dirty to force it. The 'conan \\\"\\n \\\"upload' command will prevent uploading recipes with 'auto' values in these \\\"\\n \\\"fields.\\\")\\n origin = scm.get_qualified_remote_url(remove_credentials=True)\\n local_src_path = scm.get_local_path_to_url(origin)\\n return scm_data, local_src_path\\n\\n if scm_data.url == \\\"auto\\\":\\n origin = scm.get_qualified_remote_url(remove_credentials=True)\\n if not origin:\\n raise ConanException(\\\"Repo origin cannot be deduced\\\")\\n if scm.is_local_repository():\\n output.warn(\\\"Repo origin looks like a local path: %s\\\" % origin)\\n output.success(\\\"Repo origin deduced by 'auto': %s\\\" % origin)\\n scm_data.url = origin\\n\\n if scm_data.revision == \\\"auto\\\":\\n # If it is pristine by default we don't replace the \\\"auto\\\" unless forcing\\n # This prevents the rec\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_replace_scm_data_in_recipe\",\n \"text\": \"conans/client/cmd/export.py::_replace_scm_data_in_recipe\\ndef _replace_scm_data_in_recipe(package_layout, scm_data, scm_to_conandata):\\n if scm_to_conandata:\\n conandata_path = os.path.join(package_layout.export(), DATA_YML)\\n conandata_yml = {}\\n if os.path.exists(conandata_path):\\n conandata_yml = yaml.safe_load(load(conandata_path))\\n if '.conan' in conandata_yml:\\n raise ConanException(\\\"Field '.conan' inside '{}' file is reserved to Conan usage.\\\".format(DATA_YML))\\n scm_data_copied = scm_data.as_dict()\\n scm_data_copied.pop('username', None)\\n scm_data_copied.pop('password', None)\\n conandata_yml['.conan'] = {'scm': scm_data_copied}\\n\\n save(conandata_path, yaml.safe_dump(conandata_yml, default_flow_style=False))\\n else:\\n _replace_scm_data_in_conanfile(package_layout.conanfile(), scm_data)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_replace_scm_data_in_conanfile\",\n \"text\": \"conans/client/cmd/export.py::_replace_scm_data_in_conanfile\\ndef _replace_scm_data_in_conanfile(conanfile_path, scm_data):\\n # Parsing and replacing the SCM field\\n content = load(conanfile_path)\\n headers = []\\n\\n if six.PY2:\\n # Workaround for https://bugs.python.org/issue22221\\n lines_without_headers = []\\n lines = content.splitlines(True)\\n for line in lines:\\n if not lines_without_headers and line.startswith(\\\"#\\\"):\\n headers.append(line)\\n else:\\n lines_without_headers.append(line)\\n content = ''.join(lines_without_headers)\\n\\n lines = content.splitlines(True)\\n tree = ast.parse(content)\\n to_replace = []\\n comments = []\\n class_line = None\\n tab_size = 4\\n for i_body, item in enumerate(tree.body):\\n if isinstance(item, ast.ClassDef):\\n statements = item.body\\n class_line = item.lineno\\n for i, stmt in enumerate(item.body):\\n if isinstance(stmt, ast.Assign) and len(stmt.targets) == 1:\\n line = lines[stmt.lineno - 1]\\n tab_size = len(line) - len(line.lstrip())\\n if isinstance(stmt.targets[0], ast.Name) and stmt.targets[0].id == \\\"scm\\\":\\n try:\\n if i + 1 == len(statements): # Last statement in my ClassDef\\n if i_body + 1 == len(tree.body): # Last statement over all\\n next_line = len(lines)\\n else:\\n next_line = tree.body[i_body+1].lineno - 1\\n else:\\n # Next statement can be a comment or anything else\\n next_statement = statements[i+1]\\n if isPY38 and isinstance(next_statement, ast.Expr):\\n # Python 3.8 properly parses multiline comments with sta\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_detect_scm_revision\",\n \"text\": \"conans/client/cmd/export.py::_detect_scm_revision\\ndef _detect_scm_revision(path):\\n if not path:\\n raise ConanException(\\\"Not path supplied\\\")\\n\\n repo_type = SCM.detect_scm(path)\\n if not repo_type:\\n raise ConanException(\\\"'{}' repository not detected\\\".format(repo_type))\\n\\n repo_obj = SCM.availables.get(repo_type)(path)\\n return repo_obj.get_revision(), repo_type, repo_obj.is_pristine()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_update_revision_in_metadata\",\n \"text\": \"conans/client/cmd/export.py::_update_revision_in_metadata\\ndef _update_revision_in_metadata(package_layout, revisions_enabled, output, path, manifest,\\n revision_mode):\\n if revision_mode not in [\\\"scm\\\", \\\"hash\\\"]:\\n raise ConanException(\\\"Revision mode should be one of 'hash' (default) or 'scm'\\\")\\n\\n # Use the proper approach depending on 'revision_mode'\\n if revision_mode == \\\"hash\\\":\\n revision = manifest.summary_hash\\n if revisions_enabled:\\n output.info(\\\"Using the exported files summary hash as the recipe\\\"\\n \\\" revision: {} \\\".format(revision))\\n else:\\n try:\\n rev_detected, repo_type, is_pristine = _detect_scm_revision(path)\\n except Exception as exc:\\n error_msg = \\\"Cannot detect revision using '{}' mode from repository at \\\" \\\\\\n \\\"'{}'\\\".format(revision_mode, path)\\n raise ConanException(\\\"{}: {}\\\".format(error_msg, exc))\\n\\n revision = rev_detected\\n\\n if revisions_enabled:\\n output.info(\\\"Using %s commit as the recipe revision: %s\\\" % (repo_type, revision))\\n if not is_pristine:\\n output.warn(\\\"Repo status is not pristine: there might be modified files\\\")\\n\\n with package_layout.update_metadata() as metadata:\\n metadata.recipe.revision = revision\\n\\n return revision\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_recreate_folders\",\n \"text\": \"conans/client/cmd/export.py::_recreate_folders\\ndef _recreate_folders(destination_folder):\\n try:\\n if os.path.exists(destination_folder):\\n rmdir(destination_folder)\\n os.makedirs(destination_folder)\\n except Exception as e:\\n raise ConanException(\\\"Unable to create folder %s\\\\n%s\\\" % (destination_folder, str(e)))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_classify_patterns\",\n \"text\": \"conans/client/cmd/export.py::_classify_patterns\\ndef _classify_patterns(patterns):\\n patterns = patterns or []\\n included, excluded = [], []\\n for p in patterns:\\n if p.startswith(\\\"!\\\"):\\n excluded.append(p[1:])\\n else:\\n included.append(p)\\n return included, excluded\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::_export_scm\",\n \"text\": \"conans/client/cmd/export.py::_export_scm\\ndef _export_scm(scm_data, origin_folder, scm_sources_folder, output):\\n \\\"\\\"\\\" Copy the local folder to the scm_sources folder in the cache, this enables to work\\n with local sources without committing and pushing changes to the scm remote.\\n https://github.com/conan-io/conan/issues/5195\\\"\\\"\\\"\\n excluded = SCM(scm_data, origin_folder, output).excluded_files\\n excluded.append(\\\"conanfile.py\\\")\\n output.info(\\\"SCM: Getting sources from folder: %s\\\" % origin_folder)\\n merge_directories(origin_folder, scm_sources_folder, excluded=excluded)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::export_source\",\n \"text\": \"conans/client/cmd/export.py::export_source\\ndef export_source(conanfile, origin_folder, destination_source_folder):\\n if isinstance(conanfile.exports_sources, str):\\n conanfile.exports_sources = (conanfile.exports_sources, )\\n\\n included_sources, excluded_sources = _classify_patterns(conanfile.exports_sources)\\n copier = FileCopier([origin_folder], destination_source_folder)\\n for pattern in included_sources:\\n copier(pattern, links=True, excludes=excluded_sources)\\n output = conanfile.output\\n package_output = ScopedOutput(\\\"%s exports_sources\\\" % output.scope, output)\\n copier.report(package_output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export.py::export_recipe\",\n \"text\": \"conans/client/cmd/export.py::export_recipe\\ndef export_recipe(conanfile, origin_folder, destination_folder):\\n if isinstance(conanfile.exports, str):\\n conanfile.exports = (conanfile.exports, )\\n\\n output = conanfile.output\\n package_output = ScopedOutput(\\\"%s exports\\\" % output.scope, output)\\n\\n if os.path.exists(os.path.join(origin_folder, DATA_YML)):\\n package_output.info(\\\"File '{}' found. Exporting it...\\\".format(DATA_YML))\\n tmp = [DATA_YML]\\n if conanfile.exports:\\n tmp.extend(conanfile.exports) # conanfile.exports could be a tuple (immutable)\\n conanfile.exports = tmp\\n\\n included_exports, excluded_exports = _classify_patterns(conanfile.exports)\\n\\n try:\\n os.unlink(os.path.join(origin_folder, CONANFILE + 'c'))\\n except OSError:\\n pass\\n\\n copier = FileCopier([origin_folder], destination_folder)\\n for pattern in included_exports:\\n copier(pattern, links=True, excludes=excluded_exports)\\n\\n copier.report(package_output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export_pkg.py::export_pkg\",\n \"text\": \"conans/client/cmd/export_pkg.py::export_pkg\\ndef export_pkg(app, recorder, full_ref, source_folder, build_folder, package_folder, install_folder,\\n graph_info, force, remotes):\\n ref = full_ref.copy_clear_rev()\\n cache, output, hook_manager = app.cache, app.out, app.hook_manager\\n graph_manager = app.graph_manager\\n conan_file_path = cache.package_layout(ref).conanfile()\\n if not os.path.exists(conan_file_path):\\n raise ConanException(\\\"Package recipe '%s' does not exist\\\" % str(ref))\\n\\n # The graph has to be loaded with build_mode=[ref.name], so that node is not tried\\n # to be downloaded from remotes\\n # passing here the create_reference=ref argument is useful so the recipe is in \\\"develop\\\",\\n # because the \\\"package()\\\" method is in develop=True already\\n deps_graph = graph_manager.load_graph(ref, ref, graph_info=graph_info, build_mode=[ref.name],\\n check_updates=False, update=False, remotes=remotes,\\n recorder=recorder, apply_build_requires=False)\\n # this is a bit tricky, but works. The root (virtual), has only 1 neighbor,\\n # which is the exported pkg\\n nodes = deps_graph.root.neighbors()\\n pkg_node = nodes[0]\\n conanfile = pkg_node.conanfile\\n\\n def _init_conanfile_infos():\\n node_order = [n for n in pkg_node.public_closure if n.binary != BINARY_SKIP]\\n subtree_libnames = [node.ref.name for node in node_order]\\n add_env_conaninfo(conanfile, subtree_libnames)\\n\\n _init_conanfile_infos()\\n from conans.client.conan_api import existing_info_files\\n if install_folder and existing_info_files(install_folder):\\n load_deps_info(install_folder, conanfile, required=True)\\n package_id = pkg_node.package_id\\n output.info(\\\"Packaging to %s\\\" % package_id)\\n pref = PackageReference(ref, package_id)\\n layout = cache.package_layout(ref, short_paths=conanfile.short_paths)\\n dest_package_folder = layout.package(pref)\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/export_pkg.py::export_pkg._init_conanfile_infos\",\n \"text\": \"conans/client/cmd/export_pkg.py::export_pkg._init_conanfile_infos\\n def _init_conanfile_infos():\\n node_order = [n for n in pkg_node.public_closure if n.binary != BINARY_SKIP]\\n subtree_libnames = [node.ref.name for node in node_order]\\n add_env_conaninfo(conanfile, subtree_libnames)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload.__init__\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload.__init__\\n def __init__(self, cache, user_io, remote_manager, loader, hook_manager):\\n self._cache = cache\\n self._user_io = user_io\\n self._output = progress_bar.ProgressOutput(self._user_io.out)\\n self._remote_manager = remote_manager\\n self._loader = loader\\n self._hook_manager = hook_manager\\n self._upload_thread_pool = None\\n self._exceptions_list = []\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload.upload\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload.upload\\n def upload(self, reference_or_pattern, remotes, upload_recorder, package_id=None,\\n all_packages=None, confirm=False, retry=None, retry_wait=None, integrity_check=False,\\n policy=None, query=None, parallel_upload=False):\\n t1 = time.time()\\n refs, confirm = self._collects_refs_to_upload(package_id, reference_or_pattern, confirm)\\n refs_by_remote = self._collect_packages_to_upload(refs, confirm, remotes, all_packages,\\n query, package_id)\\n\\n if parallel_upload:\\n self._upload_thread_pool = ThreadPool(8)\\n self._user_io.disable_input()\\n else:\\n self._upload_thread_pool = ThreadPool(1)\\n\\n for remote, refs in refs_by_remote.items():\\n self._output.info(\\\"Uploading to remote '{}':\\\".format(remote.name))\\n\\n def upload_ref(ref_conanfile_prefs):\\n _ref, _conanfile, _prefs = ref_conanfile_prefs\\n try:\\n self._upload_ref(_conanfile, _ref, _prefs, retry, retry_wait,\\n integrity_check, policy, remote, upload_recorder, remotes)\\n except BaseException as base_exception:\\n base_trace = traceback.format_exc()\\n self._exceptions_list.append((base_exception, _ref, base_trace))\\n\\n self._upload_thread_pool.map(upload_ref,\\n [(ref, conanfile, prefs) for (ref, conanfile, prefs) in\\n refs])\\n self._upload_thread_pool.close()\\n self._upload_thread_pool.join()\\n\\n if len(self._exceptions_list) > 0:\\n for exc, ref, trace in self._exceptions_list:\\n t = \\\"recipe\\\" if isinstance(ref, ConanFileReference) else \\\"package\\\"\\n msg = \\\"%s: Upload %s to '%s' failed: %s\\\\n\\\" % (str(ref), t, remote.na\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload._collects_refs_to_upload\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload._collects_refs_to_upload\\n def _collects_refs_to_upload(self, package_id, reference_or_pattern, confirm):\\n \\\"\\\"\\\" validate inputs and compute the refs (without revisions) to be uploaded\\n \\\"\\\"\\\"\\n if package_id and not check_valid_ref(reference_or_pattern, strict_mode=False):\\n raise ConanException(\\\"-p parameter only allowed with a valid recipe reference, \\\"\\n \\\"not with a pattern\\\")\\n\\n if package_id or check_valid_ref(reference_or_pattern):\\n # Upload package\\n ref = ConanFileReference.loads(reference_or_pattern)\\n if ref.revision and not self._cache.config.revisions_enabled:\\n raise ConanException(\\\"Revisions not enabled in the client, specify a \\\"\\n \\\"reference without revision\\\")\\n refs = [ref, ]\\n confirm = True\\n else:\\n refs = search_recipes(self._cache, reference_or_pattern)\\n if not refs:\\n raise NotFoundException((\\\"No packages found matching pattern '%s'\\\" %\\n reference_or_pattern))\\n return refs, confirm\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload._collect_packages_to_upload\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload._collect_packages_to_upload\\n def _collect_packages_to_upload(self, refs, confirm, remotes, all_packages, query, package_id):\\n \\\"\\\"\\\" compute the references with revisions and the package_ids to be uploaded\\n \\\"\\\"\\\"\\n # Group recipes by remote\\n refs_by_remote = defaultdict(list)\\n\\n for ref in refs:\\n metadata = self._cache.package_layout(ref).load_metadata()\\n if ref.revision and ref.revision != metadata.recipe.revision:\\n raise ConanException(\\\"Recipe revision {} does not match the one stored in the \\\"\\n \\\"cache {}\\\".format(ref.revision, metadata.recipe.revision))\\n ref = ref.copy_with_rev(metadata.recipe.revision)\\n remote = remotes.selected\\n if remote:\\n ref_remote = remote\\n else:\\n ref_remote = metadata.recipe.remote\\n ref_remote = remotes.get_remote(ref_remote)\\n\\n upload = True\\n if not confirm:\\n msg = \\\"Are you sure you want to upload '%s' to '%s'?\\\" % (str(ref), ref_remote.name)\\n upload = self._user_io.request_boolean(msg)\\n if upload:\\n try:\\n conanfile_path = self._cache.package_layout(ref).conanfile()\\n conanfile = self._loader.load_basic(conanfile_path)\\n except NotFoundException:\\n raise NotFoundException((\\\"There is no local conanfile exported as %s\\\" %\\n str(ref)))\\n\\n # TODO: This search of binary packages has to be improved, more robust\\n # So only real packages are retrieved\\n if all_packages or query:\\n if all_packages:\\n query = None\\n # better to do a search, that will retrieve real packages with ConanInfo\\n # Not only \\\"package_id\\\" folders \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload._upload_ref\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload._upload_ref\\n def _upload_ref(self, conanfile, ref, prefs, retry, retry_wait, integrity_check, policy,\\n recipe_remote, upload_recorder, remotes):\\n \\\"\\\"\\\" Uploads the recipes and binaries identified by ref\\n \\\"\\\"\\\"\\n assert (ref.revision is not None), \\\"Cannot upload a recipe without RREV\\\"\\n conanfile_path = self._cache.package_layout(ref).conanfile()\\n # FIXME: I think it makes no sense to specify a remote to \\\"pre_upload\\\"\\n # FIXME: because the recipe can have one and the package a different one\\n self._hook_manager.execute(\\\"pre_upload\\\", conanfile_path=conanfile_path,\\n reference=ref, remote=recipe_remote)\\n msg = \\\"\\\\rUploading %s to remote '%s'\\\" % (str(ref), recipe_remote.name)\\n self._output.info(left_justify_message(msg))\\n self._upload_recipe(ref, conanfile, retry, retry_wait, policy, recipe_remote, remotes)\\n upload_recorder.add_recipe(ref, recipe_remote.name, recipe_remote.url)\\n\\n # Now the binaries\\n if prefs:\\n total = len(prefs)\\n p_remote = recipe_remote\\n\\n def upload_package_index(index_pref):\\n index, pref = index_pref\\n try:\\n up_msg = \\\"\\\\rUploading package %d/%d: %s to '%s'\\\" % (index + 1, total,\\n str(pref.id),\\n p_remote.name)\\n self._output.info(left_justify_message(up_msg))\\n self._upload_package(pref, retry, retry_wait, integrity_check, policy, p_remote)\\n upload_recorder.add_package(pref, p_remote.name, p_remote.url)\\n except BaseException as pkg_exc:\\n trace = traceback.format_exc()\\n return pkg_exc, pref, trace\\n\\n def upload_package_callback(ret):\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload._upload_recipe\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload._upload_recipe\\n def _upload_recipe(self, ref, conanfile, retry, retry_wait, policy, remote, remotes):\\n\\n current_remote_name = self._cache.package_layout(ref).load_metadata().recipe.remote\\n\\n if remote.name != current_remote_name:\\n complete_recipe_sources(self._remote_manager, self._cache, conanfile, ref, remotes)\\n\\n conanfile_path = self._cache.package_layout(ref).conanfile()\\n self._hook_manager.execute(\\\"pre_upload_recipe\\\", conanfile_path=conanfile_path,\\n reference=ref, remote=remote)\\n\\n t1 = time.time()\\n the_files = self._compress_recipe_files(ref)\\n\\n with self._cache.package_layout(ref).update_metadata() as metadata:\\n metadata.recipe.checksums = calc_files_checksum(the_files)\\n\\n local_manifest = FileTreeManifest.loads(load(the_files[\\\"conanmanifest.txt\\\"]))\\n\\n remote_manifest = None\\n if policy != UPLOAD_POLICY_FORCE:\\n # Check SCM data for auto fields\\n if hasattr(conanfile, \\\"scm\\\") and (\\n conanfile.scm.get(\\\"url\\\") == \\\"auto\\\" or conanfile.scm.get(\\\"revision\\\") == \\\"auto\\\"):\\n raise ConanException(\\\"The recipe has 'scm.url' or 'scm.revision' with 'auto' \\\"\\n \\\"values. Use '--force' to ignore this error or export again \\\"\\n \\\"the recipe ('conan export' or 'conan create') in a \\\"\\n \\\"repository with no-uncommitted changes or by \\\"\\n \\\"using the '--ignore-dirty' option\\\")\\n\\n remote_manifest = self._check_recipe_date(ref, remote, local_manifest)\\n if policy == UPLOAD_POLICY_SKIP:\\n return ref\\n\\n files_to_upload, deleted = self._recipe_files_to_upload(ref, policy, the_files,\\n remote, remote_manifest,\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload._upload_package\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload._upload_package\\n def _upload_package(self, pref, retry=None, retry_wait=None, integrity_check=False,\\n policy=None, p_remote=None):\\n\\n assert (pref.revision is not None), \\\"Cannot upload a package without PREV\\\"\\n assert (pref.ref.revision is not None), \\\"Cannot upload a package without RREV\\\"\\n\\n pkg_layout = self._cache.package_layout(pref.ref)\\n conanfile_path = pkg_layout.conanfile()\\n self._hook_manager.execute(\\\"pre_upload_package\\\", conanfile_path=conanfile_path,\\n reference=pref.ref,\\n package_id=pref.id,\\n remote=p_remote)\\n\\n t1 = time.time()\\n the_files = self._compress_package_files(pref, integrity_check)\\n\\n if policy == UPLOAD_POLICY_SKIP:\\n return None\\n files_to_upload, deleted = self._package_files_to_upload(pref, policy, the_files, p_remote)\\n\\n if files_to_upload or deleted:\\n self._remote_manager.upload_package(pref, files_to_upload, deleted, p_remote, retry,\\n retry_wait)\\n logger.debug(\\\"UPLOAD: Time upload package: %f\\\" % (time.time() - t1))\\n else:\\n self._output.info(\\\"Package is up to date, upload skipped\\\")\\n\\n duration = time.time() - t1\\n log_package_upload(pref, duration, the_files, p_remote)\\n self._hook_manager.execute(\\\"post_upload_package\\\", conanfile_path=conanfile_path,\\n reference=pref.ref, package_id=pref.id, remote=p_remote)\\n\\n logger.debug(\\\"UPLOAD: Time uploader upload_package: %f\\\" % (time.time() - t1))\\n\\n # Update the package metadata\\n checksums = calc_files_checksum(the_files)\\n with pkg_layout.update_metadata() as metadata:\\n cur_package_remote = metadata.packages[pref.id].remote\\n if not cur_package_remote:\\n metadata.pa\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload._compress_recipe_files\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload._compress_recipe_files\\n def _compress_recipe_files(self, ref):\\n export_folder = self._cache.package_layout(ref).export()\\n\\n for f in (EXPORT_TGZ_NAME, EXPORT_SOURCES_TGZ_NAME):\\n tgz_path = os.path.join(export_folder, f)\\n if is_dirty(tgz_path):\\n self._output.warn(\\\"%s: Removing %s, marked as dirty\\\" % (str(ref), f))\\n os.remove(tgz_path)\\n clean_dirty(tgz_path)\\n\\n files, symlinks = gather_files(export_folder)\\n if CONANFILE not in files or CONAN_MANIFEST not in files:\\n raise ConanException(\\\"Cannot upload corrupted recipe '%s'\\\" % str(ref))\\n export_src_folder = self._cache.package_layout(ref).export_sources()\\n src_files, src_symlinks = gather_files(export_src_folder)\\n the_files = _compress_recipe_files(files, symlinks, src_files, src_symlinks, export_folder,\\n self._output)\\n\\n return the_files\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload._compress_package_files\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload._compress_package_files\\n def _compress_package_files(self, pref, integrity_check):\\n\\n t1 = time.time()\\n # existing package, will use short paths if defined\\n package_folder = self._cache.package_layout(pref.ref, short_paths=None).package(pref)\\n\\n if is_dirty(package_folder):\\n raise ConanException(\\\"Package %s is corrupted, aborting upload.\\\\n\\\"\\n \\\"Remove it with 'conan remove %s -p=%s'\\\"\\n % (pref, pref.ref, pref.id))\\n tgz_path = os.path.join(package_folder, PACKAGE_TGZ_NAME)\\n if is_dirty(tgz_path):\\n self._output.warn(\\\"%s: Removing %s, marked as dirty\\\" % (str(pref), PACKAGE_TGZ_NAME))\\n os.remove(tgz_path)\\n clean_dirty(tgz_path)\\n # Get all the files in that directory\\n files, symlinks = gather_files(package_folder)\\n\\n if CONANINFO not in files or CONAN_MANIFEST not in files:\\n logger.error(\\\"Missing info or manifest in uploading files: %s\\\" % (str(files)))\\n raise ConanException(\\\"Cannot upload corrupted package '%s'\\\" % str(pref))\\n\\n logger.debug(\\\"UPLOAD: Time remote_manager build_files_set : %f\\\" % (time.time() - t1))\\n if integrity_check:\\n self._package_integrity_check(pref, files, package_folder)\\n logger.debug(\\\"UPLOAD: Time remote_manager check package integrity : %f\\\"\\n % (time.time() - t1))\\n\\n the_files = _compress_package_files(files, symlinks, package_folder, self._output)\\n return the_files\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload._recipe_files_to_upload\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload._recipe_files_to_upload\\n def _recipe_files_to_upload(self, ref, policy, the_files, remote, remote_manifest,\\n local_manifest):\\n self._remote_manager.check_credentials(remote)\\n remote_snapshot = self._remote_manager.get_recipe_snapshot(ref, remote)\\n files_to_upload = {filename.replace(\\\"\\\\\\\\\\\", \\\"/\\\"): path\\n for filename, path in the_files.items()}\\n if not remote_snapshot:\\n return files_to_upload, set()\\n\\n deleted = set(remote_snapshot).difference(the_files)\\n if policy != UPLOAD_POLICY_FORCE:\\n if remote_manifest is None:\\n # This is the weird scenario, we have a snapshot but don't have a manifest.\\n # Can be due to concurrency issues, so we can try retrieve it now\\n try:\\n remote_manifest, _ = self._remote_manager.get_recipe_manifest(ref, remote)\\n except NotFoundException:\\n # This is weird, the manifest still not there, better upload everything\\n self._output.warn(\\\"The remote recipe doesn't have the 'conanmanifest.txt' \\\"\\n \\\"file and will be uploaded: '{}'\\\".format(ref))\\n return files_to_upload, deleted\\n\\n if remote_manifest == local_manifest:\\n return None, None\\n\\n if policy in (UPLOAD_POLICY_NO_OVERWRITE, UPLOAD_POLICY_NO_OVERWRITE_RECIPE):\\n raise ConanException(\\\"Local recipe is different from the remote recipe. \\\"\\n \\\"Forbidden overwrite.\\\")\\n\\n return files_to_upload, deleted\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload._package_files_to_upload\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload._package_files_to_upload\\n def _package_files_to_upload(self, pref, policy, the_files, remote):\\n self._remote_manager.check_credentials(remote)\\n remote_snapshot = self._remote_manager.get_package_snapshot(pref, remote)\\n\\n if remote_snapshot and policy != UPLOAD_POLICY_FORCE:\\n if not is_package_snapshot_complete(remote_snapshot):\\n return the_files, set([])\\n remote_manifest, _ = self._remote_manager.get_package_manifest(pref, remote)\\n local_manifest = FileTreeManifest.loads(load(the_files[\\\"conanmanifest.txt\\\"]))\\n if remote_manifest == local_manifest:\\n return None, None\\n if policy == UPLOAD_POLICY_NO_OVERWRITE:\\n raise ConanException(\\\"Local package is different from the remote package. Forbidden\\\"\\n \\\" overwrite.\\\")\\n deleted = set(remote_snapshot).difference(the_files)\\n return the_files, deleted\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload._upload_recipe_end_msg\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload._upload_recipe_end_msg\\n def _upload_recipe_end_msg(self, ref, remote):\\n msg = \\\"\\\\rUploaded conan recipe '%s' to '%s'\\\" % (str(ref), remote.name)\\n url = remote.url.replace(\\\"https://api.bintray.com/conan\\\", \\\"https://bintray.com\\\")\\n msg += \\\": %s\\\" % url\\n self._output.info(left_justify_message(msg))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload._package_integrity_check\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload._package_integrity_check\\n def _package_integrity_check(self, pref, files, package_folder):\\n # If package has been modified remove tgz to regenerate it\\n self._output.rewrite_line(\\\"Checking package integrity...\\\")\\n\\n # short_paths = None is enough if there exist short_paths\\n layout = self._cache.package_layout(pref.ref, short_paths=None)\\n read_manifest, expected_manifest = layout.package_manifests(pref)\\n\\n if read_manifest != expected_manifest:\\n self._output.writeln(\\\"\\\")\\n diff = read_manifest.difference(expected_manifest)\\n for fname, (h1, h2) in diff.items():\\n self._output.warn(\\\"Mismatched checksum '%s' (manifest: %s, file: %s)\\\"\\n % (fname, h1, h2))\\n\\n if PACKAGE_TGZ_NAME in files:\\n tgz_path = os.path.join(package_folder, PACKAGE_TGZ_NAME)\\n try:\\n os.unlink(tgz_path)\\n except OSError:\\n pass\\n error_msg = os.linesep.join(\\\"Mismatched checksum '%s' (manifest: %s, file: %s)\\\"\\n % (fname, h1, h2) for fname, (h1, h2) in diff.items())\\n logger.error(\\\"Manifests doesn't match!\\\\n%s\\\" % error_msg)\\n raise ConanException(\\\"Cannot upload corrupted package '%s'\\\" % str(pref))\\n else:\\n self._output.rewrite_line(\\\"Package integrity OK!\\\")\\n self._output.writeln(\\\"\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload._check_recipe_date\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload._check_recipe_date\\n def _check_recipe_date(self, ref, remote, local_manifest):\\n try:\\n remote_recipe_manifest, ref = self._remote_manager.get_recipe_manifest(ref, remote)\\n except NotFoundException:\\n return # First time uploading this package\\n\\n if (remote_recipe_manifest != local_manifest and\\n remote_recipe_manifest.time > local_manifest.time):\\n self._print_manifest_information(remote_recipe_manifest, local_manifest, ref, remote)\\n raise ConanException(\\\"Remote recipe is newer than local recipe: \\\"\\n \\\"\\\\n Remote date: %s\\\\n Local date: %s\\\" %\\n (remote_recipe_manifest.time, local_manifest.time))\\n\\n return remote_recipe_manifest\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::CmdUpload._print_manifest_information\",\n \"text\": \"conans/client/cmd/uploader.py::CmdUpload._print_manifest_information\\n def _print_manifest_information(self, remote_recipe_manifest, local_manifest, ref, remote):\\n try:\\n self._output.info(\\\"\\\\n%s\\\" % (\\\"-\\\"*40))\\n self._output.info(\\\"Remote manifest:\\\")\\n self._output.info(remote_recipe_manifest)\\n self._output.info(\\\"Local manifest:\\\")\\n self._output.info(local_manifest)\\n difference = remote_recipe_manifest.difference(local_manifest)\\n if \\\"conanfile.py\\\" in difference:\\n contents = load(self._cache.package_layout(ref).conanfile())\\n endlines = \\\"\\\\\\\\r\\\\\\\\n\\\" if \\\"\\\\r\\\\n\\\" in contents else \\\"\\\\\\\\n\\\"\\n self._output.info(\\\"Local 'conanfile.py' using '%s' line-ends\\\" % endlines)\\n remote_contents = self._remote_manager.get_recipe_path(ref, path=\\\"conanfile.py\\\",\\n remote=remote)\\n endlines = \\\"\\\\\\\\r\\\\\\\\n\\\" if \\\"\\\\r\\\\n\\\" in remote_contents else \\\"\\\\\\\\n\\\"\\n self._output.info(\\\"Remote 'conanfile.py' using '%s' line-ends\\\" % endlines)\\n self._output.info(\\\"\\\\n%s\\\" % (\\\"-\\\"*40))\\n except Exception as e:\\n self._output.info(\\\"Error printing information about the diff: %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::_compress_recipe_files\",\n \"text\": \"conans/client/cmd/uploader.py::_compress_recipe_files\\ndef _compress_recipe_files(files, symlinks, src_files, src_symlinks, dest_folder, output):\\n # This is the minimum recipe\\n result = {CONANFILE: files.pop(CONANFILE),\\n CONAN_MANIFEST: files.pop(CONAN_MANIFEST)}\\n\\n export_tgz_path = files.pop(EXPORT_TGZ_NAME, None)\\n sources_tgz_path = files.pop(EXPORT_SOURCES_TGZ_NAME, None)\\n\\n def add_tgz(tgz_name, tgz_path, tgz_files, tgz_symlinks, msg):\\n if tgz_path:\\n result[tgz_name] = tgz_path\\n elif tgz_files:\\n if output and not output.is_terminal:\\n output.writeln(msg)\\n tgz_path = compress_files(tgz_files, tgz_symlinks, tgz_name, dest_folder, output)\\n result[tgz_name] = tgz_path\\n\\n add_tgz(EXPORT_TGZ_NAME, export_tgz_path, files, symlinks, \\\"Compressing recipe...\\\")\\n add_tgz(EXPORT_SOURCES_TGZ_NAME, sources_tgz_path, src_files, src_symlinks,\\n \\\"Compressing recipe sources...\\\")\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::_compress_recipe_files.add_tgz\",\n \"text\": \"conans/client/cmd/uploader.py::_compress_recipe_files.add_tgz\\n def add_tgz(tgz_name, tgz_path, tgz_files, tgz_symlinks, msg):\\n if tgz_path:\\n result[tgz_name] = tgz_path\\n elif tgz_files:\\n if output and not output.is_terminal:\\n output.writeln(msg)\\n tgz_path = compress_files(tgz_files, tgz_symlinks, tgz_name, dest_folder, output)\\n result[tgz_name] = tgz_path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::_compress_package_files\",\n \"text\": \"conans/client/cmd/uploader.py::_compress_package_files\\ndef _compress_package_files(files, symlinks, dest_folder, output):\\n tgz_path = files.get(PACKAGE_TGZ_NAME)\\n if not tgz_path:\\n if output and not output.is_terminal:\\n output.writeln(\\\"Compressing package...\\\")\\n tgz_files = {f: path for f, path in files.items() if f not in [CONANINFO, CONAN_MANIFEST]}\\n tgz_path = compress_files(tgz_files, symlinks, PACKAGE_TGZ_NAME, dest_folder, output)\\n\\n return {PACKAGE_TGZ_NAME: tgz_path,\\n CONANINFO: files[CONANINFO],\\n CONAN_MANIFEST: files[CONAN_MANIFEST]}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/cmd/uploader.py::compress_files\",\n \"text\": \"conans/client/cmd/uploader.py::compress_files\\ndef compress_files(files, symlinks, name, dest_dir, output=None):\\n t1 = time.time()\\n # FIXME, better write to disk sequentially and not keep tgz contents in memory\\n tgz_path = os.path.join(dest_dir, name)\\n with set_dirty_context_manager(tgz_path), open(tgz_path, \\\"wb\\\") as tgz_handle:\\n tgz = gzopen_without_timestamps(name, mode=\\\"w\\\", fileobj=tgz_handle)\\n\\n for filename, dest in sorted(symlinks.items()):\\n info = tarfile.TarInfo(name=filename)\\n info.type = tarfile.SYMTYPE\\n info.linkname = dest\\n info.size = 0 # A symlink shouldn't have size\\n tgz.addfile(tarinfo=info)\\n\\n mask = ~(stat.S_IWOTH | stat.S_IWGRP)\\n with progress_bar.iterate_list_with_progress(sorted(files.items()), output,\\n \\\"Compressing %s\\\" % name) as pg_file_list:\\n for filename, abs_path in pg_file_list:\\n info = tarfile.TarInfo(name=filename)\\n info.size = os.stat(abs_path).st_size\\n info.mode = os.stat(abs_path).st_mode & mask\\n if os.path.islink(abs_path):\\n info.type = tarfile.SYMTYPE\\n info.size = 0 # A symlink shouldn't have size\\n info.linkname = os.readlink(abs_path) # @UndefinedVariable\\n tgz.addfile(tarinfo=info)\\n else:\\n with open(abs_path, 'rb') as file_handler:\\n tgz.addfile(tarinfo=info, fileobj=file_handler)\\n tgz.close()\\n\\n duration = time.time() - t1\\n log_compressed_files(files, duration, tgz_path)\\n\\n return tgz_path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::api_method\",\n \"text\": \"conans/client/conan_api.py::api_method\\ndef api_method(f):\\n def wrapper(api, *args, **kwargs):\\n quiet = kwargs.pop(\\\"quiet\\\", False)\\n old_curdir = get_cwd()\\n old_output = api.user_io.out\\n quiet_output = ConanOutput(StringIO(), color=api.color) if quiet else None\\n try:\\n api.create_app(quiet_output=quiet_output)\\n log_command(f.__name__, kwargs)\\n with environment_append(api.app.cache.config.env_vars):\\n return f(api, *args, **kwargs)\\n except Exception as exc:\\n if quiet_output:\\n old_output.write(quiet_output._stream.getvalue())\\n old_output.flush()\\n msg = exception_message_safe(exc)\\n try:\\n log_exception(exc, msg)\\n except BaseException:\\n pass\\n raise\\n finally:\\n os.chdir(old_curdir)\\n return wrapper\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::api_method.wrapper\",\n \"text\": \"conans/client/conan_api.py::api_method.wrapper\\n def wrapper(api, *args, **kwargs):\\n quiet = kwargs.pop(\\\"quiet\\\", False)\\n old_curdir = get_cwd()\\n old_output = api.user_io.out\\n quiet_output = ConanOutput(StringIO(), color=api.color) if quiet else None\\n try:\\n api.create_app(quiet_output=quiet_output)\\n log_command(f.__name__, kwargs)\\n with environment_append(api.app.cache.config.env_vars):\\n return f(api, *args, **kwargs)\\n except Exception as exc:\\n if quiet_output:\\n old_output.write(quiet_output._stream.getvalue())\\n old_output.flush()\\n msg = exception_message_safe(exc)\\n try:\\n log_exception(exc, msg)\\n except BaseException:\\n pass\\n raise\\n finally:\\n os.chdir(old_curdir)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::_make_abs_path\",\n \"text\": \"conans/client/conan_api.py::_make_abs_path\\ndef _make_abs_path(path, cwd=None, default=None):\\n \\\"\\\"\\\"convert 'path' to absolute if necessary (could be already absolute)\\n if not defined (empty, or None), will return 'default' one or 'cwd'\\n \\\"\\\"\\\"\\n cwd = cwd or get_cwd()\\n if not path:\\n abs_path = default or cwd\\n elif os.path.isabs(path):\\n abs_path = path\\n else:\\n abs_path = os.path.normpath(os.path.join(cwd, path))\\n return abs_path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::_get_conanfile_path\",\n \"text\": \"conans/client/conan_api.py::_get_conanfile_path\\ndef _get_conanfile_path(path, cwd, py):\\n \\\"\\\"\\\"\\n param py= True: Must be .py, False: Must be .txt, None: Try .py, then .txt\\n \\\"\\\"\\\"\\n candidate_paths = list()\\n path = _make_abs_path(path, cwd)\\n\\n if os.path.isdir(path): # Can be a folder\\n if py:\\n path = os.path.join(path, \\\"conanfile.py\\\")\\n candidate_paths.append(path)\\n elif py is False:\\n path = os.path.join(path, \\\"conanfile.txt\\\")\\n candidate_paths.append(path)\\n else:\\n path_py = os.path.join(path, \\\"conanfile.py\\\")\\n candidate_paths.append(path_py)\\n if os.path.exists(path_py):\\n path = path_py\\n else:\\n path = os.path.join(path, \\\"conanfile.txt\\\")\\n candidate_paths.append(path)\\n else:\\n candidate_paths.append(path)\\n\\n if not os.path.isfile(path): # Must exist\\n raise ConanException(\\\"Conanfile not found at %s\\\" % \\\" or \\\".join(candidate_paths))\\n\\n if py and not path.endswith(\\\".py\\\"):\\n raise ConanException(\\\"A conanfile.py is needed, \\\" + path + \\\" is not acceptable\\\")\\n\\n return path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanApp.__init__\",\n \"text\": \"conans/client/conan_api.py::ConanApp.__init__\\n def __init__(self, cache_folder, user_io, http_requester=None, runner=None, quiet_output=None):\\n # User IO, interaction and logging\\n self.user_io = user_io\\n self.out = self.user_io.out\\n if quiet_output:\\n self.user_io.out = quiet_output\\n self.out = quiet_output\\n\\n self.cache_folder = cache_folder\\n self.cache = ClientCache(self.cache_folder, self.out)\\n self.config = self.cache.config\\n if self.config.non_interactive or quiet_output:\\n self.user_io.disable_input()\\n\\n # Adjust CONAN_LOGGING_LEVEL with the env readed\\n conans.util.log.logger = configure_logger(self.config.logging_level,\\n self.config.logging_file)\\n conans.util.log.logger.debug(\\\"INIT: Using config '%s'\\\" % self.cache.conan_conf_path)\\n\\n self.hook_manager = HookManager(self.cache.hooks_path, self.config.hooks, self.out)\\n # Wraps an http_requester to inject proxies, certs, etc\\n self.requester = ConanRequester(self.config, http_requester)\\n # To handle remote connections\\n artifacts_properties = self.cache.read_artifacts_properties()\\n rest_client_factory = RestApiClientFactory(self.out, self.requester, self.config,\\n artifacts_properties=artifacts_properties)\\n # To store user and token\\n localdb = LocalDB.create(self.cache.localdb)\\n # Wraps RestApiClient to add authentication support (same interface)\\n auth_manager = ConanApiAuthManager(rest_client_factory, self.user_io, localdb)\\n # Handle remote connections\\n self.remote_manager = RemoteManager(self.cache, auth_manager, self.out, self.hook_manager)\\n\\n # Adjust global tool variables\\n set_global_instances(self.out, self.requester, self.config)\\n\\n self.runner = runner or ConanRunner(self.config.print_commands_to_output\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanApp.load_remotes\",\n \"text\": \"conans/client/conan_api.py::ConanApp.load_remotes\\n def load_remotes(self, remote_name=None, update=False, check_updates=False):\\n remotes = self.cache.registry.load_remotes()\\n if remote_name:\\n remotes.select(remote_name)\\n self.python_requires.enable_remotes(update=update, check_updates=check_updates,\\n remotes=remotes)\\n self.pyreq_loader.enable_remotes(update=update, check_updates=check_updates, remotes=remotes)\\n return remotes\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.factory\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.factory\\n @classmethod\\n def factory(cls):\\n return cls(), None, None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.__init__\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.__init__\\n def __init__(self, cache_folder=None, output=None, user_io=None, http_requester=None,\\n runner=None):\\n self.color = colorama_initialize()\\n self.out = output or ConanOutput(sys.stdout, sys.stderr, self.color)\\n self.user_io = user_io or UserIO(out=self.out)\\n self.cache_folder = cache_folder or os.path.join(get_conan_user_home(), \\\".conan\\\")\\n self.http_requester = http_requester\\n self.runner = runner\\n self.app = None # Api calls will create a new one every call\\n # Migration system\\n migrator = ClientMigrator(self.cache_folder, Version(client_version), self.out)\\n migrator.migrate()\\n # FIXME Remove in Conan 2.0\\n sys.path.append(os.path.join(self.cache_folder, \\\"python\\\"))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.create_app\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.create_app\\n def create_app(self, quiet_output=None):\\n self.app = ConanApp(self.cache_folder, self.user_io, self.http_requester,\\n self.runner, quiet_output=quiet_output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.new\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.new\\n @api_method\\n def new(self, name, header=False, pure_c=False, test=False, exports_sources=False, bare=False,\\n cwd=None, visual_versions=None, linux_gcc_versions=None, linux_clang_versions=None,\\n osx_clang_versions=None, shared=None, upload_url=None, gitignore=None,\\n gitlab_gcc_versions=None, gitlab_clang_versions=None,\\n circleci_gcc_versions=None, circleci_clang_versions=None, circleci_osx_versions=None,\\n template=None):\\n from conans.client.cmd.new import cmd_new\\n cwd = os.path.abspath(cwd or get_cwd())\\n files = cmd_new(name, header=header, pure_c=pure_c, test=test,\\n exports_sources=exports_sources, bare=bare,\\n visual_versions=visual_versions,\\n linux_gcc_versions=linux_gcc_versions,\\n linux_clang_versions=linux_clang_versions,\\n osx_clang_versions=osx_clang_versions, shared=shared,\\n upload_url=upload_url, gitignore=gitignore,\\n gitlab_gcc_versions=gitlab_gcc_versions,\\n gitlab_clang_versions=gitlab_clang_versions,\\n circleci_gcc_versions=circleci_gcc_versions,\\n circleci_clang_versions=circleci_clang_versions,\\n circleci_osx_versions=circleci_osx_versions,\\n template=template, cache=self.app.cache)\\n\\n save_files(cwd, files)\\n for f in sorted(files):\\n self.app.out.success(\\\"File saved: %s\\\" % f)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.inspect\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.inspect\\n @api_method\\n def inspect(self, path, attributes, remote_name=None):\\n remotes = self.app.load_remotes(remote_name=remote_name)\\n try:\\n ref = ConanFileReference.loads(path)\\n except ConanException:\\n conanfile_path = _get_conanfile_path(path, get_cwd(), py=True)\\n conanfile = self.app.loader.load_named(conanfile_path, None, None, None, None)\\n else:\\n update = True if remote_name else False\\n result = self.app.proxy.get_recipe(ref, update, update, remotes, ActionRecorder())\\n conanfile_path, _, _, ref = result\\n conanfile = self.app.loader.load_basic(conanfile_path)\\n conanfile.name = ref.name\\n conanfile.version = ref.version\\n\\n result = OrderedDict()\\n if not attributes:\\n attributes = ['name', 'version', 'url', 'homepage', 'license', 'author',\\n 'description', 'topics', 'generators', 'exports', 'exports_sources',\\n 'short_paths', 'apply_env', 'build_policy', 'revision_mode', 'settings',\\n 'options', 'default_options']\\n for attribute in attributes:\\n try:\\n attr = getattr(conanfile, attribute)\\n result[attribute] = attr\\n except AttributeError:\\n result[attribute] = ''\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.test\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.test\\n @api_method\\n def test(self, path, reference, profile_names=None, settings=None, options=None, env=None,\\n remote_name=None, update=False, build_modes=None, cwd=None, test_build_folder=None,\\n lockfile=None):\\n\\n settings = settings or []\\n options = options or []\\n env = env or []\\n\\n remotes = self.app.load_remotes(remote_name=remote_name, update=update)\\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\\n cwd = cwd or get_cwd()\\n lockfile = _make_abs_path(lockfile, cwd) if lockfile else None\\n graph_info = get_graph_info(profile_names, settings, options, env, cwd, None,\\n self.app.cache, self.app.out, lockfile=lockfile)\\n ref = ConanFileReference.loads(reference)\\n recorder = ActionRecorder()\\n install_build_and_test(self.app, conanfile_path, ref, graph_info, remotes,\\n update, build_modes=build_modes,\\n test_build_folder=test_build_folder, recorder=recorder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.create\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.create\\n @api_method\\n def create(self, conanfile_path, name=None, version=None, user=None, channel=None,\\n profile_names=None, settings=None,\\n options=None, env=None, test_folder=None, not_export=False,\\n build_modes=None,\\n keep_source=False, keep_build=False, verify=None,\\n manifests=None, manifests_interactive=None,\\n remote_name=None, update=False, cwd=None, test_build_folder=None,\\n lockfile=None, ignore_dirty=False):\\n \\\"\\\"\\\"\\n API method to create a conan package\\n\\n :param test_folder: default None - looks for default 'test' or 'test_package' folder),\\n string - test_folder path\\n False - disabling tests\\n \\\"\\\"\\\"\\n settings = settings or []\\n options = options or []\\n env = env or []\\n\\n try:\\n cwd = cwd or os.getcwd()\\n recorder = ActionRecorder()\\n conanfile_path = _get_conanfile_path(conanfile_path, cwd, py=True)\\n\\n remotes = self.app.load_remotes(remote_name=remote_name, update=update)\\n lockfile = _make_abs_path(lockfile, cwd) if lockfile else None\\n graph_info = get_graph_info(profile_names, settings, options, env, cwd, None,\\n self.app.cache, self.app.out, lockfile=lockfile)\\n\\n # Make sure keep_source is set for keep_build\\n keep_source = keep_source or keep_build\\n new_ref = cmd_export(self.app, conanfile_path, name, version, user, channel, keep_source,\\n not not_export, graph_lock=graph_info.graph_lock,\\n ignore_dirty=ignore_dirty)\\n\\n self.app.range_resolver.clear_output() # invalidate version range output\\n\\n # The new_ref contains the revision\\n # To not break existing things, that they us\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.export_pkg\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.export_pkg\\n @api_method\\n def export_pkg(self, conanfile_path, name, channel, source_folder=None, build_folder=None,\\n package_folder=None, install_folder=None, profile_names=None, settings=None,\\n options=None, env=None, force=False, user=None, version=None, cwd=None,\\n lockfile=None, ignore_dirty=False):\\n\\n remotes = self.app.load_remotes()\\n settings = settings or []\\n options = options or []\\n env = env or []\\n cwd = cwd or get_cwd()\\n\\n try:\\n recorder = ActionRecorder()\\n conanfile_path = _get_conanfile_path(conanfile_path, cwd, py=True)\\n\\n if package_folder:\\n if build_folder or source_folder:\\n raise ConanException(\\\"package folder definition incompatible with build \\\"\\n \\\"and source folders\\\")\\n package_folder = _make_abs_path(package_folder, cwd)\\n\\n build_folder = _make_abs_path(build_folder, cwd)\\n if install_folder:\\n install_folder = _make_abs_path(install_folder, cwd)\\n else:\\n # FIXME: This is a hack for old UI, need to be fixed in Conan 2.0\\n if os.path.exists(os.path.join(build_folder, GRAPH_INFO_FILE)):\\n install_folder = build_folder\\n source_folder = _make_abs_path(source_folder, cwd,\\n default=os.path.dirname(conanfile_path))\\n\\n lockfile = _make_abs_path(lockfile, cwd) if lockfile else None\\n # Checks that no both settings and info files are specified\\n graph_info = get_graph_info(profile_names, settings, options, env, cwd, install_folder,\\n self.app.cache, self.app.out, lockfile=lockfile)\\n\\n new_ref = cmd_export(self.app, conanfile_path, name, version, user, channel, True,\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.download\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.download\\n @api_method\\n def download(self, reference, remote_name=None, packages=None, recipe=False):\\n if packages and recipe:\\n raise ConanException(\\\"recipe parameter cannot be used together with packages\\\")\\n # Install packages without settings (fixed ids or all)\\n if check_valid_ref(reference):\\n ref = ConanFileReference.loads(reference)\\n if ref.revision and not self.app.config.revisions_enabled:\\n raise ConanException(\\\"Revisions not enabled in the client, specify a \\\"\\n \\\"reference without revision\\\")\\n if packages and ref.revision is None:\\n for package_id in packages:\\n if \\\"#\\\" in package_id:\\n raise ConanException(\\\"It is needed to specify the recipe revision if you \\\"\\n \\\"specify a package revision\\\")\\n remotes = self.app.load_remotes(remote_name=remote_name)\\n remote = remotes.get_remote(remote_name)\\n recorder = ActionRecorder()\\n download(self.app, ref, packages, remote, recipe, recorder, remotes=remotes)\\n else:\\n raise ConanException(\\\"Provide a valid full reference without wildcards.\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.workspace_install\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.workspace_install\\n @api_method\\n def workspace_install(self, path, settings=None, options=None, env=None,\\n remote_name=None, build=None, profile_name=None,\\n update=False, cwd=None, install_folder=None):\\n cwd = cwd or get_cwd()\\n abs_path = os.path.normpath(os.path.join(cwd, path))\\n\\n remotes = self.app.load_remotes(remote_name=remote_name, update=update)\\n\\n workspace = Workspace(abs_path, self.app.cache)\\n graph_info = get_graph_info(profile_name, settings, options, env, cwd, None,\\n self.app.cache, self.app.out)\\n\\n self.app.out.info(\\\"Configuration:\\\")\\n self.app.out.writeln(graph_info.profile_host.dumps())\\n\\n self.app.cache.editable_packages.override(workspace.get_editable_dict())\\n\\n recorder = ActionRecorder()\\n deps_graph = self.app.graph_manager.load_graph(workspace.root, None, graph_info, build,\\n False, update, remotes, recorder)\\n\\n print_graph(deps_graph, self.app.out)\\n\\n # Inject the generators before installing\\n for node in deps_graph.nodes:\\n if node.recipe == RECIPE_EDITABLE:\\n generators = workspace[node.ref].generators\\n if generators is not None:\\n tmp = list(node.conanfile.generators)\\n tmp.extend([g for g in generators if g not in tmp])\\n node.conanfile.generators = tmp\\n\\n installer = BinaryInstaller(self.app, recorder=recorder)\\n installer.install(deps_graph, remotes, build, update, keep_build=False,\\n graph_info=graph_info)\\n\\n install_folder = install_folder or cwd\\n workspace.generate(install_folder, deps_graph, self.app.out)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.install_reference\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.install_reference\\n @api_method\\n def install_reference(self, reference, settings=None, options=None, env=None,\\n remote_name=None, verify=None, manifests=None,\\n manifests_interactive=None, build=None, profile_names=None,\\n update=False, generators=None, install_folder=None, cwd=None,\\n lockfile=None):\\n\\n try:\\n recorder = ActionRecorder()\\n cwd = cwd or os.getcwd()\\n\\n manifests = _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd)\\n manifest_folder, manifest_interactive, manifest_verify = manifests\\n\\n lockfile = _make_abs_path(lockfile, cwd) if lockfile else None\\n graph_info = get_graph_info(profile_names, settings, options, env, cwd, None,\\n self.app.cache, self.app.out, lockfile=lockfile)\\n\\n if not generators: # We don't want the default txt\\n generators = False\\n\\n install_folder = _make_abs_path(install_folder, cwd)\\n\\n mkdir(install_folder)\\n remotes = self.app.load_remotes(remote_name=remote_name, update=update)\\n deps_install(self.app, ref_or_path=reference, install_folder=install_folder,\\n remotes=remotes, graph_info=graph_info, build_modes=build,\\n update=update, manifest_folder=manifest_folder,\\n manifest_verify=manifest_verify,\\n manifest_interactive=manifest_interactive,\\n generators=generators, use_lock=lockfile, recorder=recorder)\\n return recorder.get_info(self.app.config.revisions_enabled)\\n except ConanException as exc:\\n recorder.error = True\\n exc.info = recorder.get_info(self.app.config.revisions_enabled)\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.install\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.install\\n @api_method\\n def install(self, path=\\\"\\\", name=None, version=None, user=None, channel=None,\\n settings=None, options=None, env=None,\\n remote_name=None, verify=None, manifests=None,\\n manifests_interactive=None, build=None, profile_names=None,\\n update=False, generators=None, no_imports=False, install_folder=None, cwd=None,\\n lockfile=None):\\n\\n try:\\n recorder = ActionRecorder()\\n cwd = cwd or os.getcwd()\\n manifests = _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd)\\n manifest_folder, manifest_interactive, manifest_verify = manifests\\n\\n lockfile = _make_abs_path(lockfile, cwd) if lockfile else None\\n graph_info = get_graph_info(profile_names, settings, options, env, cwd, None,\\n self.app.cache, self.app.out,\\n name=name, version=version, user=user, channel=channel,\\n lockfile=lockfile)\\n\\n install_folder = _make_abs_path(install_folder, cwd)\\n conanfile_path = _get_conanfile_path(path, cwd, py=None)\\n\\n remotes = self.app.load_remotes(remote_name=remote_name, update=update)\\n deps_install(app=self.app,\\n ref_or_path=conanfile_path,\\n install_folder=install_folder,\\n remotes=remotes,\\n graph_info=graph_info,\\n build_modes=build,\\n update=update,\\n manifest_folder=manifest_folder,\\n manifest_verify=manifest_verify,\\n manifest_interactive=manifest_interactive,\\n generators=generators,\\n no_imports=no_imports,\\n recorder=recorder)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.config_get\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.config_get\\n @api_method\\n def config_get(self, item):\\n if item == \\\"storage.path\\\":\\n result = self.app.config.storage_path\\n else:\\n result = self.app.config.get_item(item)\\n self.app.out.info(result)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.config_set\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.config_set\\n @api_method\\n def config_set(self, item, value):\\n self.app.config.set_item(item, value)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.config_rm\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.config_rm\\n @api_method\\n def config_rm(self, item):\\n self.app.config.rm_item(item)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.config_install\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.config_install\\n @api_method\\n def config_install(self, path_or_url, verify_ssl, config_type=None, args=None,\\n source_folder=None, target_folder=None):\\n from conans.client.conf.config_installer import configuration_install\\n return configuration_install(self.app, path_or_url, verify_ssl,\\n config_type=config_type, args=args,\\n source_folder=source_folder, target_folder=target_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.config_home\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.config_home\\n @api_method\\n def config_home(self):\\n return self.cache_folder\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1._info_args\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1._info_args\\n def _info_args(self, reference_or_path, install_folder, profile_names, settings, options, env,\\n lockfile=None):\\n cwd = get_cwd()\\n if check_valid_ref(reference_or_path):\\n ref = ConanFileReference.loads(reference_or_path)\\n install_folder = _make_abs_path(install_folder, cwd) if install_folder else None\\n else:\\n ref = _get_conanfile_path(reference_or_path, cwd=None, py=None)\\n\\n install_folder = _make_abs_path(install_folder, cwd)\\n if not os.path.exists(os.path.join(install_folder, GRAPH_INFO_FILE)):\\n install_folder = None\\n\\n lockfile = _make_abs_path(lockfile, cwd) if lockfile else None\\n graph_info = get_graph_info(profile_names, settings, options, env, cwd, install_folder,\\n self.app.cache, self.app.out, lockfile=lockfile)\\n\\n return ref, graph_info\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.info_build_order\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.info_build_order\\n @api_method\\n def info_build_order(self, reference, settings=None, options=None, env=None,\\n profile_names=None, remote_name=None, build_order=None, check_updates=None,\\n install_folder=None):\\n reference, graph_info = self._info_args(reference, install_folder, profile_names,\\n settings, options, env)\\n recorder = ActionRecorder()\\n remotes = self.app.load_remotes(remote_name=remote_name, check_updates=check_updates)\\n deps_graph = self.app.graph_manager.load_graph(reference, None, graph_info, [\\\"missing\\\"],\\n check_updates, False, remotes, recorder)\\n return deps_graph.build_order(build_order)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.info_nodes_to_build\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.info_nodes_to_build\\n @api_method\\n def info_nodes_to_build(self, reference, build_modes, settings=None, options=None, env=None,\\n profile_names=None, remote_name=None, check_updates=None,\\n install_folder=None):\\n reference, graph_info = self._info_args(reference, install_folder, profile_names,\\n settings, options, env)\\n recorder = ActionRecorder()\\n remotes = self.app.load_remotes(remote_name=remote_name, check_updates=check_updates)\\n deps_graph = self.app.graph_manager.load_graph(reference, None, graph_info, build_modes,\\n check_updates, False, remotes, recorder)\\n nodes_to_build = deps_graph.nodes_to_build()\\n return nodes_to_build, deps_graph.root.conanfile\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.info\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.info\\n @api_method\\n def info(self, reference_or_path, remote_name=None, settings=None, options=None, env=None,\\n profile_names=None, update=False, install_folder=None, build=None, lockfile=None):\\n reference, graph_info = self._info_args(reference_or_path, install_folder, profile_names,\\n settings, options, env, lockfile=lockfile)\\n recorder = ActionRecorder()\\n # FIXME: Using update as check_update?\\n remotes = self.app.load_remotes(remote_name=remote_name, check_updates=update)\\n deps_graph = self.app.graph_manager.load_graph(reference, None, graph_info, build,\\n update, False, remotes, recorder)\\n\\n if install_folder:\\n output_folder = _make_abs_path(install_folder)\\n graph_info.save(output_folder)\\n self.app.out.info(\\\"Generated graphinfo\\\")\\n return deps_graph, deps_graph.root.conanfile\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.build\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.build\\n @api_method\\n def build(self, conanfile_path, source_folder=None, package_folder=None, build_folder=None,\\n install_folder=None, should_configure=True, should_build=True, should_install=True,\\n should_test=True, cwd=None):\\n self.app.load_remotes()\\n cwd = cwd or get_cwd()\\n conanfile_path = _get_conanfile_path(conanfile_path, cwd, py=True)\\n build_folder = _make_abs_path(build_folder, cwd)\\n install_folder = _make_abs_path(install_folder, cwd, default=build_folder)\\n source_folder = _make_abs_path(source_folder, cwd, default=os.path.dirname(conanfile_path))\\n default_pkg_folder = os.path.join(build_folder, \\\"package\\\")\\n package_folder = _make_abs_path(package_folder, cwd, default=default_pkg_folder)\\n\\n cmd_build(self.app, conanfile_path,\\n source_folder, build_folder, package_folder, install_folder,\\n should_configure=should_configure, should_build=should_build,\\n should_install=should_install, should_test=should_test)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.package\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.package\\n @api_method\\n def package(self, path, build_folder, package_folder, source_folder=None, install_folder=None,\\n cwd=None):\\n self.app.load_remotes()\\n\\n cwd = cwd or get_cwd()\\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\\n build_folder = _make_abs_path(build_folder, cwd)\\n install_folder = _make_abs_path(install_folder, cwd, default=build_folder)\\n source_folder = _make_abs_path(source_folder, cwd, default=os.path.dirname(conanfile_path))\\n default_pkg_folder = os.path.join(build_folder, \\\"package\\\")\\n package_folder = _make_abs_path(package_folder, cwd, default=default_pkg_folder)\\n\\n if package_folder == build_folder:\\n raise ConanException(\\\"Cannot 'conan package' to the build folder. \\\"\\n \\\"--build-folder and package folder can't be the same\\\")\\n conanfile = self.app.graph_manager.load_consumer_conanfile(conanfile_path, install_folder,\\n deps_info_required=True)\\n packager.run_package_method(conanfile, None, source_folder, build_folder, package_folder,\\n install_folder, self.app.hook_manager, conanfile_path, None,\\n local=True, copy_info=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.source\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.source\\n @api_method\\n def source(self, path, source_folder=None, info_folder=None, cwd=None):\\n self.app.load_remotes()\\n\\n cwd = cwd or get_cwd()\\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\\n source_folder = _make_abs_path(source_folder, cwd)\\n info_folder = _make_abs_path(info_folder, cwd)\\n\\n mkdir(source_folder)\\n if not os.path.exists(info_folder):\\n raise ConanException(\\\"Specified info-folder doesn't exist\\\")\\n\\n # only infos if exist\\n conanfile = self.app.graph_manager.load_consumer_conanfile(conanfile_path, info_folder)\\n config_source_local(source_folder, conanfile, conanfile_path, self.app.hook_manager)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.imports\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.imports\\n @api_method\\n def imports(self, path, dest=None, info_folder=None, cwd=None):\\n \\\"\\\"\\\"\\n :param path: Path to the conanfile\\n :param dest: Dir to put the imported files. (Abs path or relative to cwd)\\n :param info_folder: Dir where the conaninfo.txt and conanbuildinfo.txt files are\\n :param cwd: Current working directory\\n :return: None\\n \\\"\\\"\\\"\\n cwd = cwd or get_cwd()\\n info_folder = _make_abs_path(info_folder, cwd)\\n dest = _make_abs_path(dest, cwd)\\n\\n self.app.load_remotes()\\n mkdir(dest)\\n conanfile_abs_path = _get_conanfile_path(path, cwd, py=None)\\n conanfile = self.app.graph_manager.load_consumer_conanfile(conanfile_abs_path, info_folder,\\n deps_info_required=True)\\n run_imports(conanfile, dest)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.imports_undo\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.imports_undo\\n @api_method\\n def imports_undo(self, manifest_path):\\n cwd = get_cwd()\\n manifest_path = _make_abs_path(manifest_path, cwd)\\n undo_imports(manifest_path, self.app.out)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.export\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.export\\n @api_method\\n def export(self, path, name, version, user, channel, keep_source=False, cwd=None,\\n lockfile=None, ignore_dirty=False):\\n conanfile_path = _get_conanfile_path(path, cwd, py=True)\\n graph_lock = None\\n if lockfile:\\n lockfile = _make_abs_path(lockfile, cwd)\\n graph_lock_file = GraphLockFile.load(lockfile, self.app.config.revisions_enabled)\\n graph_lock = graph_lock_file.graph_lock\\n self.app.out.info(\\\"Using lockfile: '{}'\\\".format(lockfile))\\n\\n self.app.load_remotes()\\n cmd_export(self.app, conanfile_path, name, version, user, channel, keep_source,\\n graph_lock=graph_lock, ignore_dirty=ignore_dirty)\\n\\n if lockfile:\\n graph_lock_file.save(lockfile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remove\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remove\\n @api_method\\n def remove(self, pattern, query=None, packages=None, builds=None, src=False, force=False,\\n remote_name=None, outdated=False):\\n remotes = self.app.cache.registry.load_remotes()\\n remover = ConanRemover(self.app.cache, self.app.remote_manager, self.app.user_io, remotes)\\n remover.remove(pattern, remote_name, src, builds, packages, force=force,\\n packages_query=query, outdated=outdated)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.copy\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.copy\\n @api_method\\n def copy(self, reference, user_channel, force=False, packages=None):\\n \\\"\\\"\\\"\\n param packages: None=No binaries, True=All binaries, else list of IDs\\n \\\"\\\"\\\"\\n from conans.client.cmd.copy import cmd_copy\\n remotes = self.app.load_remotes()\\n # FIXME: conan copy does not support short-paths in Windows\\n ref = ConanFileReference.loads(reference)\\n cmd_copy(ref, user_channel, packages, self.app.cache,\\n self.app.user_io, self.app.remote_manager, self.app.loader, remotes, force=force)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.authenticate\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.authenticate\\n @api_method\\n def authenticate(self, name, password, remote_name, skip_auth=False):\\n # FIXME: 2.0 rename \\\"name\\\" to \\\"user\\\".\\n # FIXME: 2.0 probably we should return also if we have been authenticated or not (skipped)\\n # FIXME: 2.0 remove the skip_auth argument, that behavior will be done by:\\n # \\\"conan user USERNAME -r remote\\\" that will use the local credentials (\\n # and verify that are valid)\\n # against the server. Currently it only \\\"associate\\\" the USERNAME with the remote\\n # without checking anything else\\n remote = self.get_remote_by_name(remote_name)\\n\\n if skip_auth and token_present(self.app.cache.localdb, remote, name):\\n return remote.name, name, name\\n if not password:\\n name, password = self.app.user_io.request_login(remote_name=remote_name, username=name)\\n\\n remote_name, prev_user, user = self.app.remote_manager.authenticate(remote, name, password)\\n return remote_name, prev_user, user\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.user_set\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.user_set\\n @api_method\\n def user_set(self, user, remote_name=None):\\n remote = (self.get_default_remote() if not remote_name\\n else self.get_remote_by_name(remote_name))\\n return user_set(self.app.cache.localdb, user, remote)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.users_clean\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.users_clean\\n @api_method\\n def users_clean(self):\\n users_clean(self.app.cache.localdb)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.users_list\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.users_list\\n @api_method\\n def users_list(self, remote_name=None):\\n info = {\\\"error\\\": False, \\\"remotes\\\": []}\\n remotes = [self.get_remote_by_name(remote_name)] if remote_name else self.remote_list()\\n try:\\n info[\\\"remotes\\\"] = users_list(self.app.cache.localdb, remotes)\\n return info\\n except ConanException as exc:\\n info[\\\"error\\\"] = True\\n exc.info = info\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.search_recipes\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.search_recipes\\n @api_method\\n def search_recipes(self, pattern, remote_name=None, case_sensitive=False,\\n fill_revisions=False):\\n search_recorder = SearchRecorder()\\n remotes = self.app.cache.registry.load_remotes()\\n search = Search(self.app.cache, self.app.remote_manager, remotes)\\n\\n try:\\n references = search.search_recipes(pattern, remote_name, case_sensitive)\\n except ConanException as exc:\\n search_recorder.error = True\\n exc.info = search_recorder.get_info()\\n raise\\n\\n for remote_name, refs in references.items():\\n for ref in refs:\\n if fill_revisions:\\n layout = self.app.cache.package_layout(ref)\\n if isinstance(layout, PackageCacheLayout):\\n ref = ref.copy_with_rev(layout.recipe_revision())\\n\\n search_recorder.add_recipe(remote_name, ref, with_packages=False)\\n return search_recorder.get_info()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.search_packages\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.search_packages\\n @api_method\\n def search_packages(self, reference, query=None, remote_name=None, outdated=False):\\n search_recorder = SearchRecorder()\\n remotes = self.app.cache.registry.load_remotes()\\n search = Search(self.app.cache, self.app.remote_manager, remotes)\\n\\n try:\\n ref = ConanFileReference.loads(reference)\\n references = search.search_packages(ref, remote_name, query=query, outdated=outdated)\\n except ConanException as exc:\\n search_recorder.error = True\\n exc.info = search_recorder.get_info()\\n raise\\n\\n for remote_name, remote_ref in references.items():\\n search_recorder.add_recipe(remote_name, ref)\\n if remote_ref.ordered_packages:\\n for package_id, properties in remote_ref.ordered_packages.items():\\n package_recipe_hash = properties.get(\\\"recipe_hash\\\", None)\\n search_recorder.add_package(remote_name, ref,\\n package_id, properties.get(\\\"options\\\", []),\\n properties.get(\\\"settings\\\", []),\\n properties.get(\\\"full_requires\\\", []),\\n remote_ref.recipe_hash != package_recipe_hash)\\n return search_recorder.get_info()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.upload\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.upload\\n @api_method\\n def upload(self, pattern, package=None, remote_name=None, all_packages=False, confirm=False,\\n retry=None, retry_wait=None, integrity_check=False, policy=None, query=None,\\n parallel_upload=False):\\n \\\"\\\"\\\" Uploads a package recipe and the generated binary packages to a specified remote\\n \\\"\\\"\\\"\\n upload_recorder = UploadRecorder()\\n uploader = CmdUpload(self.app.cache, self.app.user_io, self.app.remote_manager,\\n self.app.loader, self.app.hook_manager)\\n remotes = self.app.load_remotes(remote_name=remote_name)\\n try:\\n uploader.upload(pattern, remotes, upload_recorder, package, all_packages, confirm,\\n retry, retry_wait, integrity_check, policy, query=query,\\n parallel_upload=parallel_upload)\\n return upload_recorder.get_info()\\n except ConanException as exc:\\n upload_recorder.error = True\\n exc.info = upload_recorder.get_info()\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_list\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_list\\n @api_method\\n def remote_list(self):\\n return list(self.app.cache.registry.load_remotes().all_values())\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_add\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_add\\n @api_method\\n def remote_add(self, remote_name, url, verify_ssl=True, insert=None, force=None):\\n return self.app.cache.registry.add(remote_name, url, verify_ssl, insert, force)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_remove\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_remove\\n @api_method\\n def remote_remove(self, remote_name):\\n return self.app.cache.registry.remove(remote_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_set_disabled_state\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_set_disabled_state\\n @api_method\\n def remote_set_disabled_state(self, remote_name, state):\\n return self.app.cache.registry.set_disabled_state(remote_name, state)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_update\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_update\\n @api_method\\n def remote_update(self, remote_name, url, verify_ssl=True, insert=None):\\n return self.app.cache.registry.update(remote_name, url, verify_ssl, insert)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_rename\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_rename\\n @api_method\\n def remote_rename(self, remote_name, new_new_remote):\\n return self.app.cache.registry.rename(remote_name, new_new_remote)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_list_ref\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_list_ref\\n @api_method\\n def remote_list_ref(self):\\n return {str(r): remote_name for r, remote_name in self.app.cache.registry.refs_list.items()\\n if remote_name}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_add_ref\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_add_ref\\n @api_method\\n def remote_add_ref(self, reference, remote_name):\\n ref = ConanFileReference.loads(reference, validate=True)\\n remote = self.app.cache.registry.load_remotes()[remote_name]\\n with self.app.cache.package_layout(ref).update_metadata() as metadata:\\n metadata.recipe.remote = remote.name\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_remove_ref\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_remove_ref\\n @api_method\\n def remote_remove_ref(self, reference):\\n ref = ConanFileReference.loads(reference, validate=True)\\n with self.app.cache.package_layout(ref).update_metadata() as metadata:\\n metadata.recipe.remote = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_update_ref\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_update_ref\\n @api_method\\n def remote_update_ref(self, reference, remote_name):\\n ref = ConanFileReference.loads(reference, validate=True)\\n remote = self.app.cache.registry.load_remotes()[remote_name]\\n with self.app.cache.package_layout(ref).update_metadata() as metadata:\\n metadata.recipe.remote = remote.name\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_list_pref\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_list_pref\\n @api_method\\n def remote_list_pref(self, reference):\\n ref = ConanFileReference.loads(reference, validate=True)\\n ret = {}\\n tmp = self.app.cache.registry.prefs_list\\n for pref, remote in tmp.items():\\n if pref.ref == ref and remote:\\n ret[repr(pref)] = remote\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_add_pref\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_add_pref\\n @api_method\\n def remote_add_pref(self, package_reference, remote_name):\\n pref = PackageReference.loads(package_reference, validate=True)\\n remote = self.app.cache.registry.load_remotes()[remote_name]\\n with self.app.cache.package_layout(pref.ref).update_metadata() as metadata:\\n m = metadata.packages.get(pref.id)\\n if m and m.remote:\\n raise ConanException(\\\"%s already exists. Use update\\\" % str(pref))\\n metadata.packages[pref.id].remote = remote.name\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_remove_pref\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_remove_pref\\n @api_method\\n def remote_remove_pref(self, package_reference):\\n pref = PackageReference.loads(package_reference, validate=True)\\n with self.app.cache.package_layout(pref.ref).update_metadata() as metadata:\\n m = metadata.packages.get(pref.id)\\n if m:\\n m.remote = None\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_update_pref\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_update_pref\\n @api_method\\n def remote_update_pref(self, package_reference, remote_name):\\n pref = PackageReference.loads(package_reference, validate=True)\\n self.app.cache.registry.load_remotes()[remote_name]\\n with self.app.cache.package_layout(pref.ref).update_metadata() as metadata:\\n m = metadata.packages.get(pref.id)\\n if m:\\n m.remote = remote_name\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remote_clean\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remote_clean\\n @api_method\\n def remote_clean(self):\\n return self.app.cache.registry.clear()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remove_system_reqs\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remove_system_reqs\\n @api_method\\n def remove_system_reqs(self, reference):\\n try:\\n ref = ConanFileReference.loads(reference)\\n self.app.cache.package_layout(ref).remove_system_reqs()\\n self.app.out.info(\\n \\\"Cache system_reqs from %s has been removed\\\" % repr(ref))\\n except Exception as error:\\n raise ConanException(\\\"Unable to remove system_reqs: %s\\\" % error)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remove_system_reqs_by_pattern\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remove_system_reqs_by_pattern\\n @api_method\\n def remove_system_reqs_by_pattern(self, pattern):\\n for ref in search_recipes(self.app.cache, pattern=pattern):\\n self.remove_system_reqs(repr(ref))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.remove_locks\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.remove_locks\\n @api_method\\n def remove_locks(self):\\n self.app.cache.remove_locks()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.profile_list\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.profile_list\\n @api_method\\n def profile_list(self):\\n return cmd_profile_list(self.app.cache.profiles_path, self.app.out)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.create_profile\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.create_profile\\n @api_method\\n def create_profile(self, profile_name, detect=False, force=False):\\n return cmd_profile_create(profile_name, self.app.cache.profiles_path,\\n self.app.out, detect, force)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.update_profile\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.update_profile\\n @api_method\\n def update_profile(self, profile_name, key, value):\\n return cmd_profile_update(profile_name, key, value, self.app.cache.profiles_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.get_profile_key\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.get_profile_key\\n @api_method\\n def get_profile_key(self, profile_name, key):\\n return cmd_profile_get(profile_name, key, self.app.cache.profiles_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.delete_profile_key\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.delete_profile_key\\n @api_method\\n def delete_profile_key(self, profile_name, key):\\n return cmd_profile_delete_key(profile_name, key, self.app.cache.profiles_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.read_profile\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.read_profile\\n @api_method\\n def read_profile(self, profile=None):\\n p, _ = read_profile(profile, get_cwd(), self.app.cache.profiles_path)\\n return p\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.get_path\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.get_path\\n @api_method\\n def get_path(self, reference, package_id=None, path=None, remote_name=None):\\n ref = ConanFileReference.loads(reference)\\n if not path:\\n path = \\\"conanfile.py\\\" if not package_id else \\\"conaninfo.txt\\\"\\n\\n if not remote_name:\\n package_layout = self.app.cache.package_layout(ref, short_paths=None)\\n return package_layout.get_path(path=path, package_id=package_id), path\\n else:\\n remote = self.get_remote_by_name(remote_name)\\n if self.app.config.revisions_enabled and not ref.revision:\\n ref = self.app.remote_manager.get_latest_recipe_revision(ref, remote)\\n if package_id:\\n pref = PackageReference(ref, package_id)\\n if self.app.config.revisions_enabled and not pref.revision:\\n pref = self.app.remote_manager.get_latest_package_revision(pref, remote)\\n return self.app.remote_manager.get_package_path(pref, path, remote), path\\n else:\\n return self.app.remote_manager.get_recipe_path(ref, path, remote), path\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.export_alias\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.export_alias\\n @api_method\\n def export_alias(self, reference, target_reference):\\n ref = ConanFileReference.loads(reference)\\n target_ref = ConanFileReference.loads(target_reference)\\n\\n if ref.name != target_ref.name:\\n raise ConanException(\\\"An alias can only be defined to a package with the same name\\\")\\n\\n # Do not allow to override an existing package\\n alias_conanfile_path = self.app.cache.package_layout(ref).conanfile()\\n if os.path.exists(alias_conanfile_path):\\n conanfile = self.app.loader.load_basic(alias_conanfile_path)\\n if not getattr(conanfile, 'alias', None):\\n raise ConanException(\\\"Reference '{}' is already a package, remove it before \\\"\\n \\\"creating and alias with the same name\\\".format(ref))\\n\\n package_layout = self.app.cache.package_layout(ref)\\n return export_alias(package_layout, target_ref,\\n revisions_enabled=self.app.config.revisions_enabled,\\n output=self.app.out)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.get_default_remote\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.get_default_remote\\n @api_method\\n def get_default_remote(self):\\n return self.app.cache.registry.load_remotes().default\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.get_remote_by_name\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.get_remote_by_name\\n @api_method\\n def get_remote_by_name(self, remote_name):\\n return self.app.cache.registry.load_remotes()[remote_name]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.get_recipe_revisions\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.get_recipe_revisions\\n @api_method\\n def get_recipe_revisions(self, reference, remote_name=None):\\n if not self.app.config.revisions_enabled:\\n raise ConanException(\\\"The client doesn't have the revisions feature enabled.\\\"\\n \\\" Enable this feature setting to '1' the environment variable\\\"\\n \\\" 'CONAN_REVISIONS_ENABLED' or the config value\\\"\\n \\\" 'general.revisions_enabled' in your conan.conf file\\\")\\n ref = ConanFileReference.loads(reference)\\n if ref.revision:\\n raise ConanException(\\\"Cannot list the revisions of a specific recipe revision\\\")\\n\\n if not remote_name:\\n layout = self.app.cache.package_layout(ref)\\n try:\\n rev = layout.recipe_revision()\\n except RecipeNotFoundException as e:\\n e.print_rev = True\\n raise e\\n\\n # Check the time in the associated remote if any\\n remote_name = layout.load_metadata().recipe.remote\\n remote = self.app.cache.registry.load_remotes()[remote_name] if remote_name else None\\n rev_time = None\\n if remote:\\n try:\\n revisions = self.app.remote_manager.get_recipe_revisions(ref, remote)\\n except RecipeNotFoundException:\\n pass\\n except (NoRestV2Available, NotFoundException):\\n rev_time = None\\n else:\\n tmp = {r[\\\"revision\\\"]: r[\\\"time\\\"] for r in revisions}\\n rev_time = tmp.get(rev)\\n\\n return [{\\\"revision\\\": rev, \\\"time\\\": rev_time}]\\n else:\\n remote = self.get_remote_by_name(remote_name)\\n return self.app.remote_manager.get_recipe_revisions(ref, remote=remote)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.get_package_revisions\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.get_package_revisions\\n @api_method\\n def get_package_revisions(self, reference, remote_name=None):\\n if not self.app.config.revisions_enabled:\\n raise ConanException(\\\"The client doesn't have the revisions feature enabled.\\\"\\n \\\" Enable this feature setting to '1' the environment variable\\\"\\n \\\" 'CONAN_REVISIONS_ENABLED' or the config value\\\"\\n \\\" 'general.revisions_enabled' in your conan.conf file\\\")\\n pref = PackageReference.loads(reference, validate=True)\\n if not pref.ref.revision:\\n raise ConanException(\\\"Specify a recipe reference with revision\\\")\\n if pref.revision:\\n raise ConanException(\\\"Cannot list the revisions of a specific package revision\\\")\\n\\n if not remote_name:\\n layout = self.app.cache.package_layout(pref.ref)\\n try:\\n rev = layout.package_revision(pref)\\n except (RecipeNotFoundException, PackageNotFoundException) as e:\\n e.print_rev = True\\n raise e\\n\\n # Check the time in the associated remote if any\\n remote_name = layout.load_metadata().recipe.remote\\n remote = self.app.cache.registry.load_remotes()[remote_name] if remote_name else None\\n rev_time = None\\n if remote:\\n try:\\n revisions = self.app.remote_manager.get_package_revisions(pref, remote)\\n except RecipeNotFoundException:\\n pass\\n except (NoRestV2Available, NotFoundException):\\n rev_time = None\\n else:\\n tmp = {r[\\\"revision\\\"]: r[\\\"time\\\"] for r in revisions}\\n rev_time = tmp.get(rev)\\n\\n return [{\\\"revision\\\": rev, \\\"time\\\": rev_time}]\\n else:\\n remote = self.get_remote_by_name(remote_name)\\n return self.app.remote\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.editable_add\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.editable_add\\n @api_method\\n def editable_add(self, path, reference, layout, cwd):\\n # Retrieve conanfile.py from target_path\\n target_path = _get_conanfile_path(path=path, cwd=cwd, py=True)\\n\\n self.app.load_remotes()\\n\\n # Check the conanfile is there, and name/version matches\\n ref = ConanFileReference.loads(reference, validate=True)\\n target_conanfile = self.app.loader.load_basic(target_path)\\n if (target_conanfile.name and target_conanfile.name != ref.name) or \\\\\\n (target_conanfile.version and target_conanfile.version != ref.version):\\n raise ConanException(\\\"Name and version from reference ({}) and target \\\"\\n \\\"conanfile.py ({}/{}) must match\\\".\\n format(ref, target_conanfile.name, target_conanfile.version))\\n\\n layout_abs_path = get_editable_abs_path(layout, cwd, self.app.cache.cache_folder)\\n if layout_abs_path:\\n self.app.out.success(\\\"Using layout file: %s\\\" % layout_abs_path)\\n self.app.cache.editable_packages.add(ref, os.path.dirname(target_path), layout_abs_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.editable_remove\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.editable_remove\\n @api_method\\n def editable_remove(self, reference):\\n ref = ConanFileReference.loads(reference, validate=True)\\n return self.app.cache.editable_packages.remove(ref)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.editable_list\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.editable_list\\n @api_method\\n def editable_list(self):\\n return {str(k): v for k, v in self.app.cache.editable_packages.edited_refs.items()}\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.update_lock\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.update_lock\\n @api_method\\n def update_lock(self, old_lockfile, new_lockfile, cwd=None):\\n cwd = cwd or os.getcwd()\\n old_lockfile = _make_abs_path(old_lockfile, cwd)\\n old_lock = GraphLockFile.load(old_lockfile, True)\\n new_lockfile = _make_abs_path(new_lockfile, cwd)\\n new_lock = GraphLockFile.load(new_lockfile, True)\\n if old_lock.profile_host.dumps() != new_lock.profile_host.dumps():\\n raise ConanException(\\\"Profiles of lockfiles are different\\\\n%s:\\\\n%s\\\\n%s:\\\\n%s\\\"\\n % (old_lockfile, old_lock.profile_host.dumps(),\\n new_lockfile, new_lock.profile_host.dumps()))\\n old_lock.graph_lock.update_lock(new_lock.graph_lock)\\n old_lock.save(old_lockfile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.build_order\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.build_order\\n @api_method\\n def build_order(self, lockfile, build=None, cwd=None):\\n cwd = cwd or os.getcwd()\\n lockfile = _make_abs_path(lockfile, cwd)\\n\\n recorder = ActionRecorder()\\n remotes = self.app.load_remotes()\\n\\n graph_info = get_graph_info(None, None, None, None,\\n cwd=cwd, install_folder=None,\\n cache=self.app.cache, output=self.app.out,\\n lockfile=lockfile)\\n # The reference of the root node could be a local path or a ref\\n reference = graph_info.graph_lock.root_node_ref()\\n deps_graph = self.app.graph_manager.load_graph(reference, None, graph_info, build,\\n False, False, remotes, recorder)\\n\\n print_graph(deps_graph, self.app.out)\\n graph_info.save_lock(lockfile)\\n build_order = deps_graph.new_build_order()\\n # Build order returns refs, we need to convert to flat python primitives\\n for level in build_order:\\n level[:] = [(id_, repr(pref)) for id_, pref in level]\\n return build_order\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.lock_clean_modified\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.lock_clean_modified\\n @api_method\\n def lock_clean_modified(self, lockfile, cwd=None):\\n cwd = cwd or os.getcwd()\\n lockfile = _make_abs_path(lockfile, cwd)\\n lock = GraphLockFile.load(lockfile, self.app.config.revisions_enabled)\\n lock.graph_lock.clean_modified()\\n lock.save(lockfile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::ConanAPIV1.create_lock\",\n \"text\": \"conans/client/conan_api.py::ConanAPIV1.create_lock\\n @api_method\\n def create_lock(self, reference, remote_name=None, settings=None, options=None, env=None,\\n profile_names=None, update=False, lockfile=None, build=None,):\\n reference, graph_info = self._info_args(reference, None, profile_names,\\n settings, options, env)\\n recorder = ActionRecorder()\\n # FIXME: Using update as check_update?\\n remotes = self.app.load_remotes(remote_name=remote_name, check_updates=update)\\n deps_graph = self.app.graph_manager.load_graph(reference, None, graph_info, build, update,\\n False, remotes, recorder)\\n\\n print_graph(deps_graph, self.app.out)\\n lockfile = _make_abs_path(lockfile)\\n graph_info.save_lock(lockfile)\\n self.app.out.info(\\\"Generated lockfile\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::get_graph_info\",\n \"text\": \"conans/client/conan_api.py::get_graph_info\\ndef get_graph_info(profile_names, settings, options, env, cwd, install_folder, cache, output,\\n name=None, version=None, user=None, channel=None, lockfile=None):\\n if lockfile:\\n try:\\n graph_info_folder = lockfile if os.path.isdir(lockfile) else os.path.dirname(lockfile)\\n graph_info = GraphInfo.load(graph_info_folder)\\n except IOError: # Only if file is missing\\n graph_info = GraphInfo()\\n root_ref = ConanFileReference(name, version, user, channel, validate=False)\\n graph_info.root = root_ref\\n lockfile = lockfile if os.path.isfile(lockfile) else os.path.join(lockfile, LOCKFILE)\\n graph_lock_file = GraphLockFile.load(lockfile, cache.config.revisions_enabled)\\n graph_lock_file.graph_lock.relax = cache.config.relax_lockfile\\n graph_info.profile_host = graph_lock_file.profile_host\\n graph_info.profile_host.process_settings(cache, preprocess=False)\\n graph_info.graph_lock = graph_lock_file.graph_lock\\n output.info(\\\"Using lockfile: '{}'\\\".format(lockfile))\\n return graph_info\\n\\n try:\\n graph_info = GraphInfo.load(install_folder)\\n except IOError: # Only if file is missing\\n if install_folder:\\n raise ConanException(\\\"Failed to load graphinfo file in install-folder: %s\\\"\\n % install_folder)\\n graph_info = None\\n else:\\n graph_lock_file = GraphLockFile.load(install_folder, cache.config.revisions_enabled)\\n graph_info.profile_host = graph_lock_file.profile_host\\n graph_info.profile_host.process_settings(cache, preprocess=False)\\n\\n if profile_names or settings or options or env or not graph_info:\\n if graph_info:\\n # FIXME: Convert to Exception in Conan 2.0\\n output.warn(\\\"Settings, options, env or profile specified. \\\"\\n \\\"GraphInfo found from previous install won't be used: %\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::_parse_manifests_arguments\",\n \"text\": \"conans/client/conan_api.py::_parse_manifests_arguments\\ndef _parse_manifests_arguments(verify, manifests, manifests_interactive, cwd):\\n if manifests and manifests_interactive:\\n raise ConanException(\\\"Do not specify both manifests and \\\"\\n \\\"manifests-interactive arguments\\\")\\n if verify and (manifests or manifests_interactive):\\n raise ConanException(\\\"Do not specify both 'verify' and \\\"\\n \\\"'manifests' or 'manifests-interactive' arguments\\\")\\n manifest_folder = verify or manifests or manifests_interactive\\n if manifest_folder:\\n if not os.path.isabs(manifest_folder):\\n if not cwd:\\n raise ConanException(\\\"'cwd' should be defined if the manifest folder is relative.\\\")\\n manifest_folder = os.path.join(cwd, manifest_folder)\\n manifest_verify = verify is not None\\n manifest_interactive = manifests_interactive is not None\\n else:\\n manifest_verify = manifest_interactive = False\\n\\n return manifest_folder, manifest_interactive, manifest_verify\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/conan_api.py::existing_info_files\",\n \"text\": \"conans/client/conan_api.py::existing_info_files\\ndef existing_info_files(folder):\\n return os.path.exists(os.path.join(folder, CONANINFO)) and \\\\\\n os.path.exists(os.path.join(folder, BUILD_INFO))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::ClientMigrator.__init__\",\n \"text\": \"conans/client/migrations.py::ClientMigrator.__init__\\n def __init__(self, cache_folder, current_version, out):\\n self.cache = ClientCache(cache_folder, out)\\n super(ClientMigrator, self).__init__(self.cache.cache_folder, self.cache.store,\\n current_version, out)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::ClientMigrator._update_settings_yml\",\n \"text\": \"conans/client/migrations.py::ClientMigrator._update_settings_yml\\n def _update_settings_yml(self, old_version):\\n\\n from conans.client.conf import get_default_settings_yml\\n settings_path = self.cache.settings_path\\n if not os.path.exists(settings_path):\\n self.out.warn(\\\"Migration: This conan installation doesn't have settings yet\\\")\\n self.out.warn(\\\"Nothing to migrate here, settings will be generated automatically\\\")\\n return\\n\\n var_name = \\\"settings_{}\\\".format(old_version.replace(\\\".\\\", \\\"_\\\"))\\n\\n def save_new():\\n new_path = self.cache.settings_path + \\\".new\\\"\\n save(new_path, get_default_settings_yml())\\n self.out.warn(\\\"*\\\" * 40)\\n self.out.warn(\\\"settings.yml is locally modified, can't be updated\\\")\\n self.out.warn(\\\"The new settings.yml has been stored in: %s\\\" % new_path)\\n self.out.warn(\\\"*\\\" * 40)\\n\\n self.out.warn(\\\"Migration: Updating settings.yml\\\")\\n if hasattr(migrations_settings, var_name):\\n version_default_contents = getattr(migrations_settings, var_name)\\n if version_default_contents != get_default_settings_yml():\\n current_settings = load(self.cache.settings_path)\\n if current_settings != version_default_contents:\\n save_new()\\n else:\\n save(self.cache.settings_path, get_default_settings_yml())\\n else:\\n self.out.info(\\\"Migration: Settings already up to date\\\")\\n else:\\n # We don't have the value for that version, so don't override\\n save_new()\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::ClientMigrator._update_settings_yml.save_new\",\n \"text\": \"conans/client/migrations.py::ClientMigrator._update_settings_yml.save_new\\n def save_new():\\n new_path = self.cache.settings_path + \\\".new\\\"\\n save(new_path, get_default_settings_yml())\\n self.out.warn(\\\"*\\\" * 40)\\n self.out.warn(\\\"settings.yml is locally modified, can't be updated\\\")\\n self.out.warn(\\\"The new settings.yml has been stored in: %s\\\" % new_path)\\n self.out.warn(\\\"*\\\" * 40)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::ClientMigrator._make_migrations\",\n \"text\": \"conans/client/migrations.py::ClientMigrator._make_migrations\\n def _make_migrations(self, old_version):\\n # ############### FILL THIS METHOD WITH THE REQUIRED ACTIONS ##############\\n # VERSION 0.1\\n if old_version is None:\\n return\\n\\n # Migrate the settings if they were the default for that version\\n self._update_settings_yml(old_version)\\n\\n if old_version < Version(\\\"0.25\\\"):\\n from conans.paths import DEFAULT_PROFILE_NAME\\n default_profile_path = os.path.join(self.cache.cache_folder, PROFILES_FOLDER,\\n DEFAULT_PROFILE_NAME)\\n if not os.path.exists(default_profile_path):\\n self.out.warn(\\\"Migration: Moving default settings from %s file to %s\\\"\\n % (CONAN_CONF, DEFAULT_PROFILE_NAME))\\n conf_path = os.path.join(self.cache.cache_folder, CONAN_CONF)\\n\\n migrate_to_default_profile(conf_path, default_profile_path)\\n\\n self.out.warn(\\\"Migration: export_source cache new layout\\\")\\n migrate_c_src_export_source(self.cache, self.out)\\n\\n if old_version < Version(\\\"1.0\\\"):\\n _migrate_lock_files(self.cache, self.out)\\n\\n if old_version < Version(\\\"1.12.0\\\"):\\n migrate_plugins_to_hooks(self.cache)\\n\\n if old_version < Version(\\\"1.13.0\\\"):\\n # MIGRATE LOCAL CACHE TO GENERATE MISSING METADATA.json\\n _migrate_create_metadata(self.cache, self.out)\\n\\n if old_version < Version(\\\"1.14.0\\\"):\\n migrate_config_install(self.cache)\\n\\n if old_version < Version(\\\"1.14.2\\\"):\\n _migrate_full_metadata(self.cache, self.out)\\n\\n if old_version < Version(\\\"1.15.0\\\"):\\n migrate_registry_file(self.cache, self.out)\\n\\n if old_version < Version(\\\"1.19.0\\\"):\\n migrate_localdb_refresh_token(self.cache, self.out)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_get_refs\",\n \"text\": \"conans/client/migrations.py::_get_refs\\ndef _get_refs(cache):\\n folders = list_folder_subdirs(cache.store, 4)\\n return [ConanFileReference(*s.split(\\\"/\\\")) for s in folders]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_get_prefs\",\n \"text\": \"conans/client/migrations.py::_get_prefs\\ndef _get_prefs(layout):\\n packages_folder = layout.packages()\\n folders = list_folder_subdirs(packages_folder, 1)\\n return [PackageReference(layout.ref, s) for s in folders]\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_localdb_refresh_token\",\n \"text\": \"conans/client/migrations.py::migrate_localdb_refresh_token\\ndef migrate_localdb_refresh_token(cache, out):\\n from conans.client.store.localdb import LocalDB\\n from sqlite3 import OperationalError\\n\\n localdb = LocalDB.create(cache.localdb)\\n with localdb._connect() as connection:\\n try:\\n statement = connection.cursor()\\n statement.execute(\\\"ALTER TABLE users_remotes ADD refresh_token TEXT;\\\")\\n except OperationalError:\\n # This likely means the column is already there (fresh created table)\\n # In the worst scenario the user will be requested to remove the file by hand\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_migrate_full_metadata\",\n \"text\": \"conans/client/migrations.py::_migrate_full_metadata\\ndef _migrate_full_metadata(cache, out):\\n # Fix for https://github.com/conan-io/conan/issues/4898\\n out.warn(\\\"Running a full revision metadata migration\\\")\\n refs = _get_refs(cache)\\n for ref in refs:\\n try:\\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\\n layout = PackageCacheLayout(base_folder=base_folder, ref=ref, short_paths=None,\\n no_lock=True)\\n with layout.update_metadata() as metadata:\\n # Updating the RREV\\n if metadata.recipe.revision is None:\\n out.warn(\\\"Package %s metadata had recipe revision None, migrating\\\" % str(ref))\\n folder = layout.export()\\n try:\\n manifest = FileTreeManifest.load(folder)\\n rrev = manifest.summary_hash\\n except Exception:\\n rrev = DEFAULT_REVISION_V1\\n metadata.recipe.revision = rrev\\n\\n prefs = _get_prefs(layout)\\n existing_ids = [pref.id for pref in prefs]\\n for pkg_id in list(metadata.packages.keys()):\\n if pkg_id not in existing_ids:\\n out.warn(\\\"Package %s metadata had stalled package information %s, removing\\\"\\n % (str(ref), pkg_id))\\n del metadata.packages[pkg_id]\\n # UPDATING PREVS\\n for pref in prefs:\\n try:\\n pmanifest = FileTreeManifest.load(layout.package(pref))\\n prev = pmanifest.summary_hash\\n except Exception:\\n prev = DEFAULT_REVISION_V1\\n metadata.packages[pref.id].revision = prev\\n metadata.packages[pref.id].recipe_revision = metadata.recipe.revision\\n\\n except Exception as \",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_migrate_create_metadata\",\n \"text\": \"conans/client/migrations.py::_migrate_create_metadata\\ndef _migrate_create_metadata(cache, out):\\n out.warn(\\\"Migration: Generating missing metadata files\\\")\\n refs = _get_refs(cache)\\n\\n for ref in refs:\\n try:\\n base_folder = os.path.normpath(os.path.join(cache.store, ref.dir_repr()))\\n # Force using a package cache layout for everything, we want to alter the cache,\\n # not the editables\\n layout = PackageCacheLayout(base_folder=base_folder, ref=ref, short_paths=False,\\n no_lock=True)\\n folder = layout.export()\\n try:\\n manifest = FileTreeManifest.load(folder)\\n rrev = manifest.summary_hash\\n except Exception:\\n rrev = DEFAULT_REVISION_V1\\n metadata_path = layout.package_metadata()\\n if not os.path.exists(metadata_path):\\n out.info(\\\"Creating {} for {}\\\".format(PACKAGE_METADATA, ref))\\n prefs = _get_prefs(layout)\\n metadata = PackageMetadata()\\n metadata.recipe.revision = rrev\\n for pref in prefs:\\n try:\\n pmanifest = FileTreeManifest.load(layout.package(pref))\\n prev = pmanifest.summary_hash\\n except Exception:\\n prev = DEFAULT_REVISION_V1\\n metadata.packages[pref.id].revision = prev\\n metadata.packages[pref.id].recipe_revision = metadata.recipe.revision\\n save(metadata_path, metadata.dumps())\\n except Exception as e:\\n raise ConanException(\\\"Something went wrong while generating the metadata.json files \\\"\\n \\\"in the cache, please try to fix the issue or wipe the cache: {}\\\"\\n \\\":{}\\\".format(ref, e))\\n out.success(\\\"Migration: Generating missing metadata files finished OK!\\\\n\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::_migrate_lock_files\",\n \"text\": \"conans/client/migrations.py::_migrate_lock_files\\ndef _migrate_lock_files(cache, out):\\n out.warn(\\\"Migration: Removing old lock files\\\")\\n base_dir = cache.store\\n pkgs = list_folder_subdirs(base_dir, 4)\\n for pkg in pkgs:\\n out.info(\\\"Removing locks for %s\\\" % pkg)\\n try:\\n count = os.path.join(base_dir, pkg, \\\"rw.count\\\")\\n if os.path.exists(count):\\n os.remove(count)\\n count = os.path.join(base_dir, pkg, \\\"rw.count.lock\\\")\\n if os.path.exists(count):\\n os.remove(count)\\n locks = os.path.join(base_dir, pkg, \\\"locks\\\")\\n if os.path.exists(locks):\\n shutil.rmtree(locks)\\n except Exception as e:\\n raise ConanException(\\\"Something went wrong while removing %s locks\\\\n\\\"\\n \\\"Error: %s\\\\n\\\"\\n \\\"Please clean your local conan cache manually\\\"\\n % (pkg, str(e)))\\n out.warn(\\\"Migration: Removing old lock files finished\\\\n\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_config_install\",\n \"text\": \"conans/client/migrations.py::migrate_config_install\\ndef migrate_config_install(cache):\\n try:\\n item = cache.config.get_item(\\\"general.config_install\\\")\\n items = [r.strip() for r in item.split(\\\",\\\")]\\n if len(items) == 4:\\n config_type, uri, verify_ssl, args = items\\n elif len(items) == 1:\\n uri = items[0]\\n verify_ssl = \\\"True\\\"\\n args = \\\"None\\\"\\n config_type = None\\n else:\\n raise Exception(\\\"I don't know how to migrate this config install: %s\\\" % items)\\n verify_ssl = \\\"true\\\" in verify_ssl.lower()\\n args = None if \\\"none\\\" in args.lower() else args\\n config = _ConfigOrigin.from_item(uri, config_type, verify_ssl, args, None, None)\\n _save_configs(cache.config_install_file, [config])\\n cache.config.rm_item(\\\"general.config_install\\\")\\n except ConanException:\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_to_default_profile\",\n \"text\": \"conans/client/migrations.py::migrate_to_default_profile\\ndef migrate_to_default_profile(conf_path, default_profile_path):\\n tag = \\\"[settings_defaults]\\\"\\n old_conf = load(conf_path)\\n if tag not in old_conf:\\n return\\n tmp = old_conf.find(tag)\\n new_conf = old_conf[0:tmp]\\n rest = old_conf[tmp + len(tag):]\\n if tmp:\\n if \\\"]\\\" in rest: # More sections after the settings_defaults\\n new_conf += rest[rest.find(\\\"[\\\"):]\\n save(conf_path, new_conf)\\n settings = rest[:rest.find(\\\"[\\\")].strip()\\n else:\\n save(conf_path, new_conf)\\n settings = rest.strip()\\n # Now generate the default profile from the read settings_defaults\\n new_profile = \\\"[settings]\\\\n%s\\\" % settings\\n save(default_profile_path, new_profile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_c_src_export_source\",\n \"text\": \"conans/client/migrations.py::migrate_c_src_export_source\\ndef migrate_c_src_export_source(cache, out):\\n package_folders = list_folder_subdirs(cache.store, 4)\\n for package in package_folders:\\n package_folder = os.path.join(cache.store, package)\\n c_src = os.path.join(package_folder, \\\"export/%s\\\" % EXPORT_SOURCES_DIR_OLD)\\n if os.path.exists(c_src):\\n out.warn(\\\"Migration: Removing package with old export_sources layout: %s\\\" % package)\\n try:\\n shutil.rmtree(package_folder)\\n except Exception:\\n out.warn(\\\"Migration: Can't remove the '%s' directory, \\\"\\n \\\"remove it manually\\\" % package_folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/client/migrations.py::migrate_plugins_to_hooks\",\n \"text\": \"conans/client/migrations.py::migrate_plugins_to_hooks\\ndef migrate_plugins_to_hooks(cache, output=None):\\n plugins_path = os.path.join(cache.cache_folder, \\\"plugins\\\")\\n if os.path.exists(plugins_path) and not os.path.exists(cache.hooks_path):\\n os.rename(plugins_path, cache.hooks_path)\\n conf_path = cache.conan_conf_path\\n replace_in_file(conf_path, \\\"[plugins]\\\", \\\"[hooks]\\\", strict=False, output=output)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_options\",\n \"text\": \"conans/model/conan_file.py::create_options\\ndef create_options(conanfile):\\n try:\\n package_options = PackageOptions(getattr(conanfile, \\\"options\\\", None))\\n options = Options(package_options)\\n\\n default_options = getattr(conanfile, \\\"default_options\\\", None)\\n if default_options:\\n if isinstance(default_options, dict):\\n default_values = OptionsValues(default_options)\\n elif isinstance(default_options, (list, tuple)):\\n conan_v2_behavior(\\\"Declare 'default_options' as a dictionary\\\")\\n default_values = OptionsValues(default_options)\\n elif isinstance(default_options, six.string_types):\\n conan_v2_behavior(\\\"Declare 'default_options' as a dictionary\\\")\\n default_values = OptionsValues.loads(default_options)\\n else:\\n raise ConanException(\\\"Please define your default_options as list, \\\"\\n \\\"multiline string or dictionary\\\")\\n options.values = default_values\\n return options\\n except Exception as e:\\n raise ConanException(\\\"Error while initializing options. %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_requirements\",\n \"text\": \"conans/model/conan_file.py::create_requirements\\ndef create_requirements(conanfile):\\n try:\\n # Actual requirements of this package\\n if not hasattr(conanfile, \\\"requires\\\"):\\n return Requirements()\\n else:\\n if not conanfile.requires:\\n return Requirements()\\n if isinstance(conanfile.requires, (tuple, list)):\\n return Requirements(*conanfile.requires)\\n else:\\n return Requirements(conanfile.requires, )\\n except Exception as e:\\n raise ConanException(\\\"Error while initializing requirements. %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::create_settings\",\n \"text\": \"conans/model/conan_file.py::create_settings\\ndef create_settings(conanfile, settings):\\n try:\\n defined_settings = getattr(conanfile, \\\"settings\\\", None)\\n if isinstance(defined_settings, str):\\n defined_settings = [defined_settings]\\n current = defined_settings or {}\\n settings.constraint(current)\\n return settings\\n except Exception as e:\\n raise ConanInvalidConfiguration(\\\"Error while initializing settings. %s\\\" % str(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::_env_and_python\",\n \"text\": \"conans/model/conan_file.py::_env_and_python\\n@contextmanager\\ndef _env_and_python(conanfile):\\n with environment_append(conanfile.env):\\n with pythonpath(conanfile):\\n yield\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::get_env_context_manager\",\n \"text\": \"conans/model/conan_file.py::get_env_context_manager\\ndef get_env_context_manager(conanfile, without_python=False):\\n if not conanfile.apply_env:\\n return no_op()\\n if without_python:\\n return environment_append(conanfile.env)\\n return _env_and_python(conanfile)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.__init__\",\n \"text\": \"conans/model/conan_file.py::ConanFile.__init__\\n def __init__(self, output, runner, display_name=\\\"\\\", user=None, channel=None):\\n # an output stream (writeln, info, warn error)\\n self.output = ScopedOutput(display_name, output)\\n self.display_name = display_name\\n # something that can run commands, as os.sytem\\n self._conan_runner = runner\\n self._conan_user = user\\n self._conan_channel = channel\\n\\n self.compatible_packages = []\\n self._conan_using_build_profile = False\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.initialize\",\n \"text\": \"conans/model/conan_file.py::ConanFile.initialize\\n def initialize(self, settings, env):\\n if isinstance(self.generators, str):\\n self.generators = [self.generators]\\n # User defined options\\n self.options = create_options(self)\\n self.requires = create_requirements(self)\\n self.settings = create_settings(self, settings)\\n\\n if 'cppstd' in self.settings.fields:\\n conan_v2_behavior(\\\"Setting 'cppstd' is deprecated in favor of 'compiler.cppstd',\\\"\\n \\\" please update your recipe.\\\", v1_behavior=self.output.warn)\\n\\n # needed variables to pack the project\\n self.cpp_info = None # Will be initialized at processing time\\n self._conan_dep_cpp_info = None # Will be initialized at processing time\\n self.deps_cpp_info = DepsCppInfo()\\n\\n # environment variables declared in the package_info\\n self.env_info = None # Will be initialized at processing time\\n self.deps_env_info = DepsEnvInfo()\\n\\n # user declared variables\\n self.user_info = None\\n # Keys are the package names, and the values a dict with the vars\\n self.deps_user_info = DepsUserInfo()\\n\\n # user specified env variables\\n self._conan_env_values = env.copy() # user specified -e\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.env\",\n \"text\": \"conans/model/conan_file.py::ConanFile.env\\n @property\\n def env(self):\\n \\\"\\\"\\\"Apply the self.deps_env_info into a copy of self._conan_env_values (will prioritize the\\n self._conan_env_values, user specified from profiles or -e first, then inherited)\\\"\\\"\\\"\\n # Cannot be lazy cached, because it's called in configure node, and we still don't have\\n # the deps_env_info objects available\\n tmp_env_values = self._conan_env_values.copy()\\n tmp_env_values.update(self.deps_env_info)\\n\\n ret, multiple = tmp_env_values.env_dicts(self.name)\\n ret.update(multiple)\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.channel\",\n \"text\": \"conans/model/conan_file.py::ConanFile.channel\\n @property\\n def channel(self):\\n if not self._conan_channel:\\n _env_channel = os.getenv(\\\"CONAN_CHANNEL\\\")\\n if _env_channel:\\n conan_v2_behavior(\\\"Environment variable 'CONAN_CHANNEL' is deprecated\\\")\\n self._conan_channel = _env_channel or self.default_channel\\n if not self._conan_channel:\\n raise ConanException(\\\"channel not defined, but self.channel is used in conanfile\\\")\\n return self._conan_channel\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.user\",\n \"text\": \"conans/model/conan_file.py::ConanFile.user\\n @property\\n def user(self):\\n if not self._conan_user:\\n _env_username = os.getenv(\\\"CONAN_USERNAME\\\")\\n if _env_username:\\n conan_v2_behavior(\\\"Environment variable 'CONAN_USERNAME' is deprecated\\\")\\n self._conan_user = _env_username or self.default_user\\n if not self._conan_user:\\n raise ConanException(\\\"user not defined, but self.user is used in conanfile\\\")\\n return self._conan_user\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.collect_libs\",\n \"text\": \"conans/model/conan_file.py::ConanFile.collect_libs\\n def collect_libs(self, folder=None):\\n conan_v2_behavior(\\\"'self.collect_libs' is deprecated, use 'tools.collect_libs(self)' instead\\\",\\n v1_behavior=self.output.warn)\\n return tools.collect_libs(self, folder=folder)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_policy_missing\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_policy_missing\\n @property\\n def build_policy_missing(self):\\n return self.build_policy == \\\"missing\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build_policy_always\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build_policy_always\\n @property\\n def build_policy_always(self):\\n return self.build_policy == \\\"always\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.source\",\n \"text\": \"conans/model/conan_file.py::ConanFile.source\\n def source(self):\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.system_requirements\",\n \"text\": \"conans/model/conan_file.py::ConanFile.system_requirements\\n def system_requirements(self):\\n \\\"\\\"\\\" this method can be overwritten to implement logic for system package\\n managers, as apt-get\\n\\n You can define self.global_system_requirements = True, if you want the installation\\n to be for all packages (not depending on settings/options/requirements)\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.config_options\",\n \"text\": \"conans/model/conan_file.py::ConanFile.config_options\\n def config_options(self):\\n \\\"\\\"\\\" modify options, probably conditioned to some settings. This call is executed\\n before config_settings. E.g.\\n if self.settings.os == \\\"Windows\\\":\\n del self.options.shared # shared/static not supported in win\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.configure\",\n \"text\": \"conans/model/conan_file.py::ConanFile.configure\\n def configure(self):\\n \\\"\\\"\\\" modify settings, probably conditioned to some options. This call is executed\\n after config_options. E.g.\\n if self.options.header_only:\\n self.settings.clear()\\n This is also the place for conditional requirements\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.build\",\n \"text\": \"conans/model/conan_file.py::ConanFile.build\\n def build(self):\\n \\\"\\\"\\\" build your project calling the desired build tools as done in the command line.\\n E.g. self.run(\\\"cmake --build .\\\") Or use the provided build helpers. E.g. cmake.build()\\n \\\"\\\"\\\"\\n self.output.warn(\\\"This conanfile has no build step\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package\\n def package(self):\\n \\\"\\\"\\\" package the needed files from source and build folders.\\n E.g. self.copy(\\\"*.h\\\", src=\\\"src/includes\\\", dst=\\\"includes\\\")\\n \\\"\\\"\\\"\\n self.output.warn(\\\"This conanfile has no package step\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_info\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_info\\n def package_info(self):\\n \\\"\\\"\\\" define cpp_build_info, flags, etc\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.run\",\n \"text\": \"conans/model/conan_file.py::ConanFile.run\\n def run(self, command, output=True, cwd=None, win_bash=False, subsystem=None, msys_mingw=True,\\n ignore_errors=False, run_environment=False, with_login=True):\\n def _run():\\n if not win_bash:\\n return self._conan_runner(command, output, os.path.abspath(RUN_LOG_NAME), cwd)\\n # FIXME: run in windows bash is not using output\\n return tools.run_in_windows_bash(self, bashcmd=command, cwd=cwd, subsystem=subsystem,\\n msys_mingw=msys_mingw, with_login=with_login)\\n if run_environment:\\n # When using_build_profile the required environment is already applied through 'conanfile.env'\\n # in the contextmanager 'get_env_context_manager'\\n with tools.run_environment(self) if not self._conan_using_build_profile else no_op():\\n if OSInfo().is_macos:\\n command = 'DYLD_LIBRARY_PATH=\\\"%s\\\" DYLD_FRAMEWORK_PATH=\\\"%s\\\" %s' % \\\\\\n (os.environ.get('DYLD_LIBRARY_PATH', ''),\\n os.environ.get(\\\"DYLD_FRAMEWORK_PATH\\\", ''),\\n command)\\n retcode = _run()\\n else:\\n retcode = _run()\\n\\n if not ignore_errors and retcode != 0:\\n raise ConanException(\\\"Error %d while executing %s\\\" % (retcode, command))\\n\\n return retcode\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.run._run\",\n \"text\": \"conans/model/conan_file.py::ConanFile.run._run\\n def _run():\\n if not win_bash:\\n return self._conan_runner(command, output, os.path.abspath(RUN_LOG_NAME), cwd)\\n # FIXME: run in windows bash is not using output\\n return tools.run_in_windows_bash(self, bashcmd=command, cwd=cwd, subsystem=subsystem,\\n msys_mingw=msys_mingw, with_login=with_login)\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.package_id\",\n \"text\": \"conans/model/conan_file.py::ConanFile.package_id\\n def package_id(self):\\n \\\"\\\"\\\" modify the binary info, typically to narrow values\\n e.g.: self.info.settings.compiler = \\\"Any\\\" => All compilers will generate same ID\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.test\",\n \"text\": \"conans/model/conan_file.py::ConanFile.test\\n def test(self):\\n \\\"\\\"\\\" test the generated executable.\\n E.g. self.run(\\\"./example\\\")\\n \\\"\\\"\\\"\\n raise ConanException(\\\"You need to create a method 'test' in your test/conanfile.py\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"conans/model/conan_file.py::ConanFile.__repr__\",\n \"text\": \"conans/model/conan_file.py::ConanFile.__repr__\\n def __repr__(self):\\n return self.display_name\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":291,"cells":{"instance_id":{"kind":"string","value":"pantsbuild__pants-5232"},"repo":{"kind":"string","value":"pantsbuild/pants"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"--quiet and --color should not restart daemon\nthose are the bootstrap options that should not affect the result.\r\n\r\nExample, daemon restarts between\r\n```\r\n./pants list x\r\n./pants list x > /dev/null\r\n```\n"},"gold_files":{"kind":"list like","value":["src/python/pants/option/options.py"],"string":"[\n \"src/python/pants/option/options.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["src/python/pants/option/options.py::Options.get_fingerprintable_for_scope"],"string":"[\n \"src/python/pants/option/options.py::Options.get_fingerprintable_for_scope\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"src/python/pants/bin/goal_runner.py","text":"src/python/pants/bin/goal_runner.py\nclass GoalRunnerFactory\n def __init__(self, root_dir, options, build_config, run_tracker, reporting, daemon_graph_helper=None, exiter=sys.exit) # :param str root_dir: The root directory of the pants workspace (aka the \"build r\n def _handle_help(self, help_request) # Handle requests for `help` information.\n def _init_graph(self, use_engine, pants_ignore_patterns, build_ignore_patterns, exclude_target_regexps, target_specs, workdir, graph_helper=None, subproject_build_roots=None) # Determine the BuildGraph, AddressMapper and spec_roots for a given run.\n def _determine_goals(self, requested_goals) # Check and populate the requested goals for a given run.\n def _specs_to_targets(self, specs) # Populate the BuildGraph and target list from a set of input specs.\n def _maybe_launch_pantsd(self, pantsd_launcher) # Launches pantsd if configured to do so.\n def _should_be_quiet(self, goals)\n def _setup_context(self)\n def setup(self)\nclass GoalRunner # Lists installed goals or else executes a named goal.\n def __init__(self, context, goals, run_tracker, kill_nailguns, exiter=sys.exit) # :param Context context: The global, pre-initialized Context as created by GoalRu\n def subsystems(cls) # Subsystems used outside of any task.\n def _execute_engine(self)\n def run(self)","label":0},{"cid":"src/python/pants/option/global_options.py","text":"src/python/pants/option/global_options.py\nclass GlobalOptionsRegistrar\n def register_bootstrap_options(cls, register) # Register bootstrap options.\n def register_options(cls, register) # Register options not tied to any particular task or subsystem.","label":0},{"cid":"src/python/pants/option/options.py","text":"src/python/pants/option/options.py\nclass Options # The outward-facing API for interacting with options.\n class OptionTrackerRequiredError # Options requires an OptionTracker instance.\n def complete_scopes(cls, scope_infos) # Expand a set of scopes to include all enclosing scopes.\n def create(cls, env, config, known_scope_infos, args=None, bootstrap_option_values=None, option_tracker=None) # Create an Options instance.\n def __init__(self, goals, scope_to_flags, target_specs, passthru, passthru_owner, help_request, parser_hierarchy, values_by_scope, bootstrap_option_values, known_scope_to_info, option_tracker) # The low-level constructor for an Options instance.\n def tracker(self)\n def help_request(self) # :API: public\n def target_specs(self) # The targets to operate on.\n def goals(self) # The requested goals, in the order specified on the cmd line.\n def known_scope_to_info(self)\n def scope_to_flags(self)\n def drop_flag_values(self) # Returns a copy of these options that ignores values specified via flags.\n def is_known_scope(self, scope) # Whether the given scope is known by this instance.\n def passthru_args_for_scope(self, scope)\n def register(self, scope, *args, **kwargs) # Register an option in the given scope.\n def registration_function_for_optionable(self, optionable_class) # Returns a function for registering options on the given scope.\n def register(*args, **kwargs)\n def get_parser(self, scope) # Returns the parser for the given scope, so code can register on it directly.\n def walk_parsers(self, callback)\n def for_scope(self, scope, inherit_from_enclosing_scope=True) # Return the option values for the given scope.\n def get_fingerprintable_for_scope(self, bottom_scope, include_passthru=False, fingerprint_key=None, invert=False) # Returns a list of fingerprintable (option type, option value) pairs for the give\n def __getitem__(self, scope)\n def bootstrap_option_values(self) # Return the option values for bootstrap options.\n def for_global_scope(self) # Return the option values for the global scope.","label":1},{"cid":"src/python/pants/option/parser.py","text":"src/python/pants/option/parser.py\nclass Parser # An argument parser in a hierarchy.\n class BooleanConversionError # Indicates a value other than 'True' or 'False' when attempting to parse a bool.\n class FromfileError # Indicates a problem reading a value @fromfile.\n class MutuallyExclusiveOptionError # Raised when more than one option belonging to the same mutually exclusive group \n def _ensure_bool(s)\n def _invert(cls, s)\n def __init__(self, env, config, scope_info, parent_parser, option_tracker) # Create a Parser instance.\n def scope(self)\n def walk(self, callback) # Invoke callback on this parser and its descendants, in depth-first order.\n def _create_flag_value_map(self, flags) # Returns a map of flag -> list of values, based on the given flag strings.\n def parse_args(self, flags, namespace) # Set values for this parser's options on the namespace object.\n def option_registrations_iter(self) # Returns an iterator over the normalized registration arguments of each option in\n def normalize_kwargs(args, orig_kwargs)\n def _unnormalized_option_registrations_iter(self) # Returns an iterator over the raw registration arguments of each option in this p\n def _recursive_option_registration_args(self) # Yield args, kwargs pairs for just our recursive options.\n def register(self, *args, **kwargs) # Register an option.\n def _check_deprecated(self, dest, kwargs) # Checks option for deprecation and issues a warning/error if necessary.\n def _validate(self, args, kwargs) # Validate option registration arguments.\n def error(exception_type, arg_name=None, **msg_kwargs)\n def _existing_scope(self, arg)\n def parse_dest(*args, **kwargs) # Select the dest name for an option registration.\n def _wrap_type(t)\n def _convert_member_type(t, x)\n def _compute_value(self, dest, kwargs, flag_val_strs) # Compute the value to use for an option.\n def to_value_type(val_str)\n def expand(val_str)\n def record_option(value, rank, option_details=None)\n def check(val)\n def _inverse_arg(self, arg)\n def _register_child_parser(self, child)\n def _freeze(self)\n def _scope_str(self, scope=None)\n def __str__(self)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py","text":"src/python/pants/pantsd/process_manager.py\ndef swallow_psutil_exceptions() # A contextmanager that swallows standard psutil access exceptions.\nclass ProcessGroup # Wraps a logical group of processes and provides convenient access to ProcessMana\n def __init__(self, name, metadata_base_dir=None)\n def _instance_from_process(self, process) # Default converter from psutil.Process to process instance classes for subclassin\n def iter_processes(self, proc_filter=None) # Yields processes from psutil.process_iter with an optional filter and swallows p\n def iter_instances(self, *args, **kwargs)\nclass ProcessMetadataManager # \"Manages contextual, on-disk process metadata.\n class MetadataError\n class Timeout\n def __init__(self, metadata_base_dir=None) # :param str metadata_base_dir: The base directory for process metadata.\n def _maybe_cast(item, caster) # Given a casting function, attempt to cast to that type while masking common cast\n def _deadline_until(cls, closure, timeout, wait_interval=WAIT_INTERVAL_SEC) # Execute a function/closure repeatedly until a True condition or timeout is met.\n def _wait_for_file(cls, filename, timeout=FILE_WAIT_SEC, want_content=True) # Wait up to timeout seconds for filename to appear with a non-zero size or raise \n def file_waiter()\n def _get_metadata_dir_by_name(self, name) # Retrieve the metadata dir by name.\n def _maybe_init_metadata_dir_by_name(self, name) # Initialize the metadata directory for a named identity if it doesn't exist.\n def read_metadata_by_name(self, name, metadata_key, caster=None) # Read process metadata using a named identity.\n def write_metadata_by_name(self, name, metadata_key, metadata_value) # Write process metadata using a named identity.\n def await_metadata_by_name(self, name, metadata_key, timeout, caster=None) # Block up to a timeout for process metadata to arrive on disk.\n def purge_metadata_by_name(self, name) # Purge a processes metadata directory.\nclass ProcessManager # Subprocess/daemon management mixin/superclass. Not intended to be thread-safe.\n class InvalidCommandOutput\n class NonResponsiveProcess\n class ExecutionError\n def __init__(self, message, output=None)\n def __repr__(self)\n def __init__(self, name, pid=None, socket=None, process_name=None, socket_type=int, metadata_base_dir=None) # :param string name: The process identity/name (e.g. 'pantsd' or 'ng_Zinc').\n def name(self) # The logical name/label of the process.\n def process_name(self) # The logical process name. If defined, this is compared to exe_name for stale pid\n def process_lock(self) # An identity-keyed inter-process lock for safeguarding lifecycle and other operat\n def cmdline(self) # The process commandline. e.g. ['/usr/bin/python2.7', 'pants.pex'].\n def cmd(self) # The first element of the process commandline e.g. '/usr/bin/python2.7'.\n def pid(self) # The running processes pid (or None).\n def sock","label":0},{"cid":"src/python/pants/task/task.py","text":"src/python/pants/task/task.py\nclass TaskBase # Defines a lifecycle that prepares a task for execution and provides the base mac\n def implementation_version(cls) # :API: public\n def implementation_version_str(cls)\n def stable_name(cls) # The stable name of this task type.\n def _compute_stable_name(cls)\n def subsystem_dependencies(cls)\n def product_types(cls) # The list of products this Task produces. Set the product type(s) for this\n def known_scope_infos(cls) # Yields ScopeInfo for all known scopes for this task, in no particular order.\n def supports_passthru_args(cls) # Subclasses may override to indicate that they can use passthru args.\n def _scoped_options(cls, options)\n def get_alternate_target_roots(cls, options, address_mapper, build_graph)\n def alternate_target_roots(cls, options, address_mapper, build_graph) # Allows a Task to propose alternate target roots from those specified on the CLI.\n def invoke_prepare(cls, options, round_manager)\n def prepare(cls, options, round_manager) # Prepares a task for execution.\n def __init__(self, context, workdir) # Subclass __init__ methods, if defined, *must* follow this idiom:\n def _build_invalidator(self, root=False)\n def get_options(self) # Returns the option values for this task's scope.\n def get_passthru_args(self) # :API: public\n def workdir(self) # A scratch-space for this task that will be deleted by `clean-all`.\n def _options_fingerprint(self, scope)\n def fingerprint(self) # Returns a fingerprint for the identity of the task.\n def artifact_cache_reads_enabled(self)\n def artifact_cache_writes_enabled(self)\n def invalidate(self) # Invalidates all targets for this task.\n def create_target_dirs(self) # Whether to create a results_dir per VersionedTarget in the workdir of the Task.\n def cache_target_dirs(self) # Whether to cache files in VersionedTarget's results_dir after exiting an invalid\n def incremental(self) # Whether this Task implements incremental building of individual targets.\n def cache_incremental(self) # For incremental tasks, indicates whether the results of incremental builds shoul\n def invalidated(self, targets, invalidate_dependents=False, silent=False, fingerprint_strategy=None, topological_order=False) # Checks targets for invalidation, first checking the artifact cache.\n def _update_invalidation_report(self, invalidation_check, phase)\n def _do_invalidation_check(self, fingerprint_strategy, invalidate_dependents, targets, topological_order)\n def maybe_write_artifact(self, vt)\n def _launch_background_workdir_cleanup(self, vts)\n def _cleanup_workdir_stale_builds(self, vts)\n def _should_cache_target_dir(self, vt) # Return true if the given vt should be written to a cache (if configured).\n def _maybe_create_results_dirs(self, vts) # If `cache_target_dirs`, create results_dirs for the given versioned targets.\n def check_artifact_cache_for(self, invalidation","label":0}],"string":"[\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py\",\n \"text\": \"src/python/pants/bin/goal_runner.py\\nclass GoalRunnerFactory\\n def __init__(self, root_dir, options, build_config, run_tracker, reporting, daemon_graph_helper=None, exiter=sys.exit) # :param str root_dir: The root directory of the pants workspace (aka the \\\"build r\\n def _handle_help(self, help_request) # Handle requests for `help` information.\\n def _init_graph(self, use_engine, pants_ignore_patterns, build_ignore_patterns, exclude_target_regexps, target_specs, workdir, graph_helper=None, subproject_build_roots=None) # Determine the BuildGraph, AddressMapper and spec_roots for a given run.\\n def _determine_goals(self, requested_goals) # Check and populate the requested goals for a given run.\\n def _specs_to_targets(self, specs) # Populate the BuildGraph and target list from a set of input specs.\\n def _maybe_launch_pantsd(self, pantsd_launcher) # Launches pantsd if configured to do so.\\n def _should_be_quiet(self, goals)\\n def _setup_context(self)\\n def setup(self)\\nclass GoalRunner # Lists installed goals or else executes a named goal.\\n def __init__(self, context, goals, run_tracker, kill_nailguns, exiter=sys.exit) # :param Context context: The global, pre-initialized Context as created by GoalRu\\n def subsystems(cls) # Subsystems used outside of any task.\\n def _execute_engine(self)\\n def run(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py\",\n \"text\": \"src/python/pants/option/global_options.py\\nclass GlobalOptionsRegistrar\\n def register_bootstrap_options(cls, register) # Register bootstrap options.\\n def register_options(cls, register) # Register options not tied to any particular task or subsystem.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py\",\n \"text\": \"src/python/pants/option/options.py\\nclass Options # The outward-facing API for interacting with options.\\n class OptionTrackerRequiredError # Options requires an OptionTracker instance.\\n def complete_scopes(cls, scope_infos) # Expand a set of scopes to include all enclosing scopes.\\n def create(cls, env, config, known_scope_infos, args=None, bootstrap_option_values=None, option_tracker=None) # Create an Options instance.\\n def __init__(self, goals, scope_to_flags, target_specs, passthru, passthru_owner, help_request, parser_hierarchy, values_by_scope, bootstrap_option_values, known_scope_to_info, option_tracker) # The low-level constructor for an Options instance.\\n def tracker(self)\\n def help_request(self) # :API: public\\n def target_specs(self) # The targets to operate on.\\n def goals(self) # The requested goals, in the order specified on the cmd line.\\n def known_scope_to_info(self)\\n def scope_to_flags(self)\\n def drop_flag_values(self) # Returns a copy of these options that ignores values specified via flags.\\n def is_known_scope(self, scope) # Whether the given scope is known by this instance.\\n def passthru_args_for_scope(self, scope)\\n def register(self, scope, *args, **kwargs) # Register an option in the given scope.\\n def registration_function_for_optionable(self, optionable_class) # Returns a function for registering options on the given scope.\\n def register(*args, **kwargs)\\n def get_parser(self, scope) # Returns the parser for the given scope, so code can register on it directly.\\n def walk_parsers(self, callback)\\n def for_scope(self, scope, inherit_from_enclosing_scope=True) # Return the option values for the given scope.\\n def get_fingerprintable_for_scope(self, bottom_scope, include_passthru=False, fingerprint_key=None, invert=False) # Returns a list of fingerprintable (option type, option value) pairs for the give\\n def __getitem__(self, scope)\\n def bootstrap_option_values(self) # Return the option values for bootstrap options.\\n def for_global_scope(self) # Return the option values for the global scope.\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/option/parser.py\",\n \"text\": \"src/python/pants/option/parser.py\\nclass Parser # An argument parser in a hierarchy.\\n class BooleanConversionError # Indicates a value other than 'True' or 'False' when attempting to parse a bool.\\n class FromfileError # Indicates a problem reading a value @fromfile.\\n class MutuallyExclusiveOptionError # Raised when more than one option belonging to the same mutually exclusive group \\n def _ensure_bool(s)\\n def _invert(cls, s)\\n def __init__(self, env, config, scope_info, parent_parser, option_tracker) # Create a Parser instance.\\n def scope(self)\\n def walk(self, callback) # Invoke callback on this parser and its descendants, in depth-first order.\\n def _create_flag_value_map(self, flags) # Returns a map of flag -> list of values, based on the given flag strings.\\n def parse_args(self, flags, namespace) # Set values for this parser's options on the namespace object.\\n def option_registrations_iter(self) # Returns an iterator over the normalized registration arguments of each option in\\n def normalize_kwargs(args, orig_kwargs)\\n def _unnormalized_option_registrations_iter(self) # Returns an iterator over the raw registration arguments of each option in this p\\n def _recursive_option_registration_args(self) # Yield args, kwargs pairs for just our recursive options.\\n def register(self, *args, **kwargs) # Register an option.\\n def _check_deprecated(self, dest, kwargs) # Checks option for deprecation and issues a warning/error if necessary.\\n def _validate(self, args, kwargs) # Validate option registration arguments.\\n def error(exception_type, arg_name=None, **msg_kwargs)\\n def _existing_scope(self, arg)\\n def parse_dest(*args, **kwargs) # Select the dest name for an option registration.\\n def _wrap_type(t)\\n def _convert_member_type(t, x)\\n def _compute_value(self, dest, kwargs, flag_val_strs) # Compute the value to use for an option.\\n def to_value_type(val_str)\\n def expand(val_str)\\n def record_option(value, rank, option_details=None)\\n def check(val)\\n def _inverse_arg(self, arg)\\n def _register_child_parser(self, child)\\n def _freeze(self)\\n def _scope_str(self, scope=None)\\n def __str__(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py\",\n \"text\": \"src/python/pants/pantsd/process_manager.py\\ndef swallow_psutil_exceptions() # A contextmanager that swallows standard psutil access exceptions.\\nclass ProcessGroup # Wraps a logical group of processes and provides convenient access to ProcessMana\\n def __init__(self, name, metadata_base_dir=None)\\n def _instance_from_process(self, process) # Default converter from psutil.Process to process instance classes for subclassin\\n def iter_processes(self, proc_filter=None) # Yields processes from psutil.process_iter with an optional filter and swallows p\\n def iter_instances(self, *args, **kwargs)\\nclass ProcessMetadataManager # \\\"Manages contextual, on-disk process metadata.\\n class MetadataError\\n class Timeout\\n def __init__(self, metadata_base_dir=None) # :param str metadata_base_dir: The base directory for process metadata.\\n def _maybe_cast(item, caster) # Given a casting function, attempt to cast to that type while masking common cast\\n def _deadline_until(cls, closure, timeout, wait_interval=WAIT_INTERVAL_SEC) # Execute a function/closure repeatedly until a True condition or timeout is met.\\n def _wait_for_file(cls, filename, timeout=FILE_WAIT_SEC, want_content=True) # Wait up to timeout seconds for filename to appear with a non-zero size or raise \\n def file_waiter()\\n def _get_metadata_dir_by_name(self, name) # Retrieve the metadata dir by name.\\n def _maybe_init_metadata_dir_by_name(self, name) # Initialize the metadata directory for a named identity if it doesn't exist.\\n def read_metadata_by_name(self, name, metadata_key, caster=None) # Read process metadata using a named identity.\\n def write_metadata_by_name(self, name, metadata_key, metadata_value) # Write process metadata using a named identity.\\n def await_metadata_by_name(self, name, metadata_key, timeout, caster=None) # Block up to a timeout for process metadata to arrive on disk.\\n def purge_metadata_by_name(self, name) # Purge a processes metadata directory.\\nclass ProcessManager # Subprocess/daemon management mixin/superclass. Not intended to be thread-safe.\\n class InvalidCommandOutput\\n class NonResponsiveProcess\\n class ExecutionError\\n def __init__(self, message, output=None)\\n def __repr__(self)\\n def __init__(self, name, pid=None, socket=None, process_name=None, socket_type=int, metadata_base_dir=None) # :param string name: The process identity/name (e.g. 'pantsd' or 'ng_Zinc').\\n def name(self) # The logical name/label of the process.\\n def process_name(self) # The logical process name. If defined, this is compared to exe_name for stale pid\\n def process_lock(self) # An identity-keyed inter-process lock for safeguarding lifecycle and other operat\\n def cmdline(self) # The process commandline. e.g. ['/usr/bin/python2.7', 'pants.pex'].\\n def cmd(self) # The first element of the process commandline e.g. '/usr/bin/python2.7'.\\n def pid(self) # The running processes pid (or None).\\n def sock\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py\",\n \"text\": \"src/python/pants/task/task.py\\nclass TaskBase # Defines a lifecycle that prepares a task for execution and provides the base mac\\n def implementation_version(cls) # :API: public\\n def implementation_version_str(cls)\\n def stable_name(cls) # The stable name of this task type.\\n def _compute_stable_name(cls)\\n def subsystem_dependencies(cls)\\n def product_types(cls) # The list of products this Task produces. Set the product type(s) for this\\n def known_scope_infos(cls) # Yields ScopeInfo for all known scopes for this task, in no particular order.\\n def supports_passthru_args(cls) # Subclasses may override to indicate that they can use passthru args.\\n def _scoped_options(cls, options)\\n def get_alternate_target_roots(cls, options, address_mapper, build_graph)\\n def alternate_target_roots(cls, options, address_mapper, build_graph) # Allows a Task to propose alternate target roots from those specified on the CLI.\\n def invoke_prepare(cls, options, round_manager)\\n def prepare(cls, options, round_manager) # Prepares a task for execution.\\n def __init__(self, context, workdir) # Subclass __init__ methods, if defined, *must* follow this idiom:\\n def _build_invalidator(self, root=False)\\n def get_options(self) # Returns the option values for this task's scope.\\n def get_passthru_args(self) # :API: public\\n def workdir(self) # A scratch-space for this task that will be deleted by `clean-all`.\\n def _options_fingerprint(self, scope)\\n def fingerprint(self) # Returns a fingerprint for the identity of the task.\\n def artifact_cache_reads_enabled(self)\\n def artifact_cache_writes_enabled(self)\\n def invalidate(self) # Invalidates all targets for this task.\\n def create_target_dirs(self) # Whether to create a results_dir per VersionedTarget in the workdir of the Task.\\n def cache_target_dirs(self) # Whether to cache files in VersionedTarget's results_dir after exiting an invalid\\n def incremental(self) # Whether this Task implements incremental building of individual targets.\\n def cache_incremental(self) # For incremental tasks, indicates whether the results of incremental builds shoul\\n def invalidated(self, targets, invalidate_dependents=False, silent=False, fingerprint_strategy=None, topological_order=False) # Checks targets for invalidation, first checking the artifact cache.\\n def _update_invalidation_report(self, invalidation_check, phase)\\n def _do_invalidation_check(self, fingerprint_strategy, invalidate_dependents, targets, topological_order)\\n def maybe_write_artifact(self, vt)\\n def _launch_background_workdir_cleanup(self, vts)\\n def _cleanup_workdir_stale_builds(self, vts)\\n def _should_cache_target_dir(self, vt) # Return true if the given vt should be written to a cache (if configured).\\n def _maybe_create_results_dirs(self, vts) # If `cache_target_dirs`, create results_dirs for the given versioned targets.\\n def check_artifact_cache_for(self, invalidation\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.__init__","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.__init__\n def __init__(self, root_dir, options, build_config, run_tracker, reporting,\n daemon_graph_helper=None, exiter=sys.exit):\n \"\"\"\n :param str root_dir: The root directory of the pants workspace (aka the \"build root\").\n :param Options options: The global, pre-initialized Options instance.\n :param BuildConfiguration build_config: A pre-initialized BuildConfiguration instance.\n :param Runtracker run_tracker: The global, pre-initialized/running RunTracker instance.\n :param Reporting reporting: The global, pre-initialized Reporting instance.\n :param LegacyGraphHelper daemon_graph_helper: A LegacyGraphHelper instance for graph reuse. (Optional)\n :param func exiter: A function that accepts an exit code value and exits. (for tests, Optional)\n \"\"\"\n self._root_dir = root_dir\n self._options = options\n self._build_config = build_config\n self._run_tracker = run_tracker\n self._reporting = reporting\n self._daemon_graph_helper = daemon_graph_helper\n self._exiter = exiter\n\n self._requested_goals = self._options.goals\n self._help_request = self._options.help_request\n self._build_file_parser = BuildFileParser(self._build_config, self._root_dir)\n self._build_graph = None\n self._address_mapper = None\n\n self._global_options = options.for_global_scope()\n self._tag = self._global_options.tag\n self._fail_fast = self._global_options.fail_fast\n self._explain = self._global_options.explain\n self._kill_nailguns = self._global_options.kill_nailguns","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._handle_help","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._handle_help\n def _handle_help(self, help_request):\n \"\"\"Handle requests for `help` information.\"\"\"\n if help_request:\n help_printer = HelpPrinter(self._options)\n result = help_printer.print_help()\n self._exiter(result)","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._init_graph","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._init_graph\n def _init_graph(self, use_engine, pants_ignore_patterns, build_ignore_patterns,\n exclude_target_regexps, target_specs, workdir, graph_helper=None,\n subproject_build_roots=None):\n \"\"\"Determine the BuildGraph, AddressMapper and spec_roots for a given run.\n\n :param bool use_engine: Whether or not to use the v2 engine to construct the BuildGraph.\n :param list pants_ignore_patterns: The pants ignore patterns from '--pants-ignore'.\n :param list build_ignore_patterns: The build ignore patterns from '--build-ignore',\n applied during BUILD file searching.\n :param str workdir: The pants workdir.\n :param list exclude_target_regexps: Regular expressions for targets to be excluded.\n :param list target_specs: The original target specs.\n :param LegacyGraphHelper graph_helper: A LegacyGraphHelper to use for graph construction,\n if available. This would usually come from the daemon.\n :returns: A tuple of (BuildGraph, AddressMapper, spec_roots).\n \"\"\"\n # N.B. Use of the daemon implies use of the v2 engine.\n if graph_helper or use_engine:\n # The daemon may provide a `graph_helper`. If that's present, use it for graph construction.\n if not graph_helper:\n native = Native.create(self._global_options)\n native.set_panic_handler()\n graph_helper = EngineInitializer.setup_legacy_graph(\n pants_ignore_patterns,\n workdir,\n native=native,\n build_file_aliases=self._build_config.registered_aliases(),\n build_ignore_patterns=build_ignore_patterns,\n exclude_target_regexps=exclude_target_regexps,\n subproject_roots=subproject_build_roots,\n include_trace_on_error=self._options.for_global_scope().print_exception_stacktrace\n )\n\n target_roots = TargetRoots.create(options=self._opt","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._determine_goals","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._determine_goals\n def _determine_goals(self, requested_goals):\n \"\"\"Check and populate the requested goals for a given run.\"\"\"\n\n spec_parser = CmdLineSpecParser(self._root_dir)\n for goal in requested_goals:\n if self._address_mapper.is_valid_single_address(spec_parser.parse_spec(goal)):\n logger.warning(\"Command-line argument '{0}' is ambiguous and was assumed to be \"\n \"a goal. If this is incorrect, disambiguate it with ./{0}.\".format(goal))\n\n goals = [Goal.by_name(goal) for goal in requested_goals]\n return goals","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._specs_to_targets","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._specs_to_targets\n def _specs_to_targets(self, specs):\n \"\"\"Populate the BuildGraph and target list from a set of input specs.\"\"\"\n with self._run_tracker.new_workunit(name='parse', labels=[WorkUnitLabel.SETUP]):\n def filter_for_tag(tag):\n return lambda target: tag in map(str, target.tags)\n\n tag_filter = wrap_filters(create_filters(self._tag, filter_for_tag))\n\n def generate_targets(specs):\n for address in self._build_graph.inject_specs_closure(specs, self._fail_fast):\n target = self._build_graph.get_target(address)\n if tag_filter(target):\n yield target\n\n return list(generate_targets(specs))","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._maybe_launch_pantsd","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._maybe_launch_pantsd\n def _maybe_launch_pantsd(self, pantsd_launcher):\n \"\"\"Launches pantsd if configured to do so.\"\"\"\n if self._global_options.enable_pantsd:\n # Avoid runtracker output if pantsd is disabled. Otherwise, show up to inform the user its on.\n with self._run_tracker.new_workunit(name='pantsd', labels=[WorkUnitLabel.SETUP]):\n pantsd_launcher.maybe_launch()","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._should_be_quiet","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._should_be_quiet\n def _should_be_quiet(self, goals):\n if self._explain:\n return True\n\n if self._global_options.get_rank('quiet') > RankedValue.HARDCODED:\n return self._global_options.quiet\n\n return any(goal.has_task_of_type(QuietTaskMixin) for goal in goals)","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._setup_context","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._setup_context\n def _setup_context(self):\n with self._run_tracker.new_workunit(name='setup', labels=[WorkUnitLabel.SETUP]):\n self._build_graph, self._address_mapper, spec_roots = self._init_graph(\n self._global_options.enable_v2_engine,\n self._global_options.pants_ignore,\n self._global_options.build_ignore,\n self._global_options.exclude_target_regexp,\n self._options.target_specs,\n self._global_options.pants_workdir,\n self._daemon_graph_helper,\n self._global_options.subproject_roots,\n )\n\n goals = self._determine_goals(self._requested_goals)\n is_quiet = self._should_be_quiet(goals)\n\n target_roots = self._specs_to_targets(spec_roots)\n\n # Now that we've parsed the bootstrap BUILD files, and know about the SCM system.\n self._run_tracker.run_info.add_scm_info()\n\n # Update the Reporting settings now that we have options and goal info.\n invalidation_report = self._reporting.update_reporting(self._global_options,\n is_quiet,\n self._run_tracker)\n\n context = Context(options=self._options,\n run_tracker=self._run_tracker,\n target_roots=target_roots,\n requested_goals=self._requested_goals,\n build_graph=self._build_graph,\n build_file_parser=self._build_file_parser,\n address_mapper=self._address_mapper,\n invalidation_report=invalidation_report)\n return goals, context","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.setup","text":"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.setup\n def setup(self):\n self._handle_help(self._help_request)\n goals, context = self._setup_context()\n return GoalRunner(context=context,\n goals=goals,\n run_tracker=self._run_tracker,\n kill_nailguns=self._kill_nailguns,\n exiter=self._exiter)","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunner.__init__","text":"src/python/pants/bin/goal_runner.py::GoalRunner.__init__\n def __init__(self, context, goals, run_tracker, kill_nailguns, exiter=sys.exit):\n \"\"\"\n :param Context context: The global, pre-initialized Context as created by GoalRunnerFactory.\n :param list[Goal] goals: The list of goals to act on.\n :param Runtracker run_tracker: The global, pre-initialized/running RunTracker instance.\n :param bool kill_nailguns: Whether or not to kill nailguns after the run.\n :param func exiter: A function that accepts an exit code value and exits (for tests, Optional).\n \"\"\"\n self._context = context\n self._goals = goals\n self._run_tracker = run_tracker\n self._kill_nailguns = kill_nailguns\n self._exiter = exiter","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunner.subsystems","text":"src/python/pants/bin/goal_runner.py::GoalRunner.subsystems\n @classmethod\n def subsystems(cls):\n \"\"\"Subsystems used outside of any task.\"\"\"\n return {\n SourceRootConfig,\n Reporting,\n Reproducer,\n RunTracker,\n Changed.Factory,\n BinaryUtil.Factory,\n Subprocess.Factory\n }","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunner._execute_engine","text":"src/python/pants/bin/goal_runner.py::GoalRunner._execute_engine\n def _execute_engine(self):\n workdir = self._context.options.for_global_scope().pants_workdir\n if not workdir.endswith('.pants.d'):\n self._context.log.error('Pants working directory should end with \\'.pants.d\\', currently it is {}\\n'\n .format(workdir))\n return 1\n\n unknown_goals = [goal.name for goal in self._goals if not goal.ordered_task_names()]\n if unknown_goals:\n self._context.log.error('Unknown goal(s): {}\\n'.format(' '.join(unknown_goals)))\n return 1\n\n engine = RoundEngine()\n result = engine.execute(self._context, self._goals)\n\n if self._context.invalidation_report:\n self._context.invalidation_report.report()\n\n return result","label":0},{"cid":"src/python/pants/bin/goal_runner.py::GoalRunner.run","text":"src/python/pants/bin/goal_runner.py::GoalRunner.run\n def run(self):\n should_kill_nailguns = self._kill_nailguns\n\n try:\n result = self._execute_engine()\n if result:\n self._run_tracker.set_root_outcome(WorkUnit.FAILURE)\n except KeyboardInterrupt:\n self._run_tracker.set_root_outcome(WorkUnit.FAILURE)\n # On ctrl-c we always kill nailguns, otherwise they might keep running\n # some heavyweight compilation and gum up the system during a subsequent run.\n should_kill_nailguns = True\n raise\n except Exception:\n self._run_tracker.set_root_outcome(WorkUnit.FAILURE)\n raise\n finally:\n # Must kill nailguns only after run_tracker.end() is called, otherwise there may still\n # be pending background work that needs a nailgun.\n if should_kill_nailguns:\n # TODO: This is JVM-specific and really doesn't belong here.\n # TODO: Make this more selective? Only kill nailguns that affect state?\n # E.g., checkstyle may not need to be killed.\n NailgunProcessGroup().killall()\n\n return result","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options","text":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options\n @classmethod\n def register_bootstrap_options(cls, register):\n \"\"\"Register bootstrap options.\n\n \"Bootstrap options\" are a small set of options whose values are useful when registering other\n options. Therefore we must bootstrap them early, before other options are registered, let\n alone parsed.\n\n Bootstrap option values can be interpolated into the config file, and can be referenced\n programatically in registration code, e.g., as register.bootstrap.pants_workdir.\n\n Note that regular code can also access these options as normal global-scope options. Their\n status as \"bootstrap options\" is only pertinent during option registration.\n \"\"\"\n buildroot = get_buildroot()\n default_distdir_name = 'dist'\n default_distdir = os.path.join(buildroot, default_distdir_name)\n default_rel_distdir = '/{}/'.format(default_distdir_name)\n\n # Although logging supports the WARN level, its not documented and could conceivably be yanked.\n # Since pants has supported 'warn' since inception, leave the 'warn' choice as-is but explicitly\n # setup a 'WARN' logging level name that maps to 'WARNING'.\n logging.addLevelName(logging.WARNING, 'WARN')\n register('-l', '--level', choices=['debug', 'info', 'warn'], default='info', recursive=True,\n help='Set the logging level.')\n register('-q', '--quiet', type=bool, recursive=True, daemon=False,\n help='Squelches most console output. NOTE: Some tasks default to behaving quietly: '\n 'inverting this option supports making them noisier than they would be otherwise.')\n # Not really needed in bootstrap options, but putting it here means it displays right\n # after -l and -q in help output, which is conveniently contextual.\n register('--colors', type=bool, default=sys.stdout.isatty(), recursive=True, daemon=False,\n help='Set whether log messages are displa","label":0},{"cid":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options","text":"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options\n @classmethod\n def register_options(cls, register):\n \"\"\"Register options not tied to any particular task or subsystem.\"\"\"\n # The bootstrap options need to be registered on the post-bootstrap Options instance, so it\n # won't choke on them on the command line, and also so we can access their values as regular\n # global-scope options, for convenience.\n cls.register_bootstrap_options(register)\n\n register('-x', '--time', type=bool,\n help='Output a timing report at the end of the run.')\n register('-e', '--explain', type=bool,\n help='Explain the execution of goals.')\n register('--tag', type=list, metavar='[+-]tag1,tag2,...',\n help=\"Include only targets with these tags (optional '+' prefix) or without these \"\n \"tags ('-' prefix). Useful with ::, to find subsets of targets \"\n \"(e.g., integration tests.)\")\n\n register('-t', '--timeout', advanced=True, type=int, metavar='<seconds>',\n help='Number of seconds to wait for http connections.')\n # TODO: After moving to the new options system these abstraction leaks can go away.\n register('-k', '--kill-nailguns', advanced=True, type=bool,\n help='Kill nailguns before exiting')\n register('-i', '--interpreter', advanced=True, default=[], type=list,\n metavar='<requirement>',\n removal_version='1.5.0.dev0',\n removal_hint='Use --interpreter-constraints in scope python-setup instead.',\n help=\"Constrain what Python interpreters to use. Uses Requirement format from \"\n \"pkg_resources, e.g. 'CPython>=2.7,<3' or 'PyPy'. By default, no constraints \"\n \"are used. Multiple constraints may be added. They will be ORed together.\")\n register('--fail-fast', advanced=True, type=bool, recursive=True,\n help='Exit as quickly as possible on error, rat","label":0},{"cid":"src/python/pants/option/options.py::Options.complete_scopes","text":"src/python/pants/option/options.py::Options.complete_scopes\n @classmethod\n def complete_scopes(cls, scope_infos):\n \"\"\"Expand a set of scopes to include all enclosing scopes.\n\n E.g., if the set contains `foo.bar.baz`, ensure that it also contains `foo.bar` and `foo`.\n\n Also adds any deprecated scopes.\n \"\"\"\n ret = {GlobalOptionsRegistrar.get_scope_info()}\n original_scopes = set()\n for si in scope_infos:\n ret.add(si)\n original_scopes.add(si.scope)\n if si.deprecated_scope:\n ret.add(ScopeInfo(si.deprecated_scope, si.category, si.optionable_cls))\n original_scopes.add(si.deprecated_scope)\n\n # TODO: Once scope name validation is enforced (so there can be no dots in scope name\n # components) we can replace this line with `for si in scope_infos:`, because it will\n # not be possible for a deprecated_scope to introduce any new intermediate scopes.\n for si in copy.copy(ret):\n for scope in all_enclosing_scopes(si.scope, allow_global=False):\n if scope not in original_scopes:\n ret.add(ScopeInfo(scope, ScopeInfo.INTERMEDIATE))\n return ret","label":0},{"cid":"src/python/pants/option/options.py::Options.create","text":"src/python/pants/option/options.py::Options.create\n @classmethod\n def create(cls, env, config, known_scope_infos, args=None, bootstrap_option_values=None,\n option_tracker=None):\n \"\"\"Create an Options instance.\n\n :param env: a dict of environment variables.\n :param :class:`pants.option.config.Config` config: data from a config file.\n :param known_scope_infos: ScopeInfos for all scopes that may be encountered.\n :param args: a list of cmd-line args; defaults to `sys.argv` if None is supplied.\n :param bootstrap_option_values: An optional namespace containing the values of bootstrap\n options. We can use these values when registering other options.\n :param :class:`pants.option.option_tracker.OptionTracker` option_tracker: option tracker\n instance to record how option values were assigned.\n \"\"\"\n # We need parsers for all the intermediate scopes, so inherited option values\n # can propagate through them.\n complete_known_scope_infos = cls.complete_scopes(known_scope_infos)\n splitter = ArgSplitter(complete_known_scope_infos)\n args = sys.argv if args is None else args\n goals, scope_to_flags, target_specs, passthru, passthru_owner = splitter.split_args(args)\n\n if not option_tracker:\n raise cls.OptionTrackerRequiredError()\n\n if bootstrap_option_values:\n target_spec_files = bootstrap_option_values.target_spec_files\n if target_spec_files:\n for spec in target_spec_files:\n with open(spec) as f:\n target_specs.extend(filter(None, [line.strip() for line in f]))\n\n help_request = splitter.help_request\n\n parser_hierarchy = ParserHierarchy(env, config, complete_known_scope_infos, option_tracker)\n values_by_scope = {} # Arg values, parsed per-scope on demand.\n bootstrap_option_values = bootstrap_option_values\n known_scope_to_info = {s.scope: s for s in complete_known_scope_infos}\n return cls(goals, scope_to_flags, target_specs, passthru, passthru_owne","label":0},{"cid":"src/python/pants/option/options.py::Options.__init__","text":"src/python/pants/option/options.py::Options.__init__\n def __init__(self, goals, scope_to_flags, target_specs, passthru, passthru_owner, help_request,\n parser_hierarchy, values_by_scope, bootstrap_option_values, known_scope_to_info,\n option_tracker):\n \"\"\"The low-level constructor for an Options instance.\n\n Dependees should use `Options.create` instead.\n \"\"\"\n self._goals = goals\n self._scope_to_flags = scope_to_flags\n self._target_specs = target_specs\n self._passthru = passthru\n self._passthru_owner = passthru_owner\n self._help_request = help_request\n self._parser_hierarchy = parser_hierarchy\n self._values_by_scope = values_by_scope\n self._bootstrap_option_values = bootstrap_option_values\n self._known_scope_to_info = known_scope_to_info\n self._option_tracker = option_tracker","label":0},{"cid":"src/python/pants/option/options.py::Options.tracker","text":"src/python/pants/option/options.py::Options.tracker\n @property\n def tracker(self):\n return self._option_tracker","label":0},{"cid":"src/python/pants/option/options.py::Options.help_request","text":"src/python/pants/option/options.py::Options.help_request\n @property\n def help_request(self):\n \"\"\"\n :API: public\n \"\"\"\n return self._help_request","label":0},{"cid":"src/python/pants/option/options.py::Options.target_specs","text":"src/python/pants/option/options.py::Options.target_specs\n @property\n def target_specs(self):\n \"\"\"The targets to operate on.\n\n :API: public\n \"\"\"\n return self._target_specs","label":0},{"cid":"src/python/pants/option/options.py::Options.goals","text":"src/python/pants/option/options.py::Options.goals\n @property\n def goals(self):\n \"\"\"The requested goals, in the order specified on the cmd line.\n\n :API: public\n \"\"\"\n return self._goals","label":0},{"cid":"src/python/pants/option/options.py::Options.known_scope_to_info","text":"src/python/pants/option/options.py::Options.known_scope_to_info\n @property\n def known_scope_to_info(self):\n return self._known_scope_to_info","label":0},{"cid":"src/python/pants/option/options.py::Options.scope_to_flags","text":"src/python/pants/option/options.py::Options.scope_to_flags\n @property\n def scope_to_flags(self):\n return self._scope_to_flags","label":0},{"cid":"src/python/pants/option/options.py::Options.drop_flag_values","text":"src/python/pants/option/options.py::Options.drop_flag_values\n def drop_flag_values(self):\n \"\"\"Returns a copy of these options that ignores values specified via flags.\n\n Any pre-cached option values are cleared and only option values that come from option defaults,\n the config or the environment are used.\n \"\"\"\n # An empty scope_to_flags to force all values to come via the config -> env hierarchy alone\n # and empty values in case we already cached some from flags.\n no_flags = {}\n no_values = {}\n return Options(self._goals,\n no_flags,\n self._target_specs,\n self._passthru,\n self._passthru_owner,\n self._help_request,\n self._parser_hierarchy,\n no_values,\n self._bootstrap_option_values,\n self._known_scope_to_info,\n self._option_tracker)","label":0},{"cid":"src/python/pants/option/options.py::Options.is_known_scope","text":"src/python/pants/option/options.py::Options.is_known_scope\n def is_known_scope(self, scope):\n \"\"\"Whether the given scope is known by this instance.\n\n :API: public\n \"\"\"\n return scope in self._known_scope_to_info","label":0},{"cid":"src/python/pants/option/options.py::Options.passthru_args_for_scope","text":"src/python/pants/option/options.py::Options.passthru_args_for_scope\n def passthru_args_for_scope(self, scope):\n # Passthru args \"belong\" to the last scope mentioned on the command-line.\n\n # Note: If that last scope is a goal, we allow all tasks in that goal to access the passthru\n # args. This is to allow the more intuitive\n # pants run <target> -- <passthru args>\n # instead of requiring\n # pants run.py <target> -- <passthru args>.\n #\n # However note that in the case where multiple tasks run in the same goal, e.g.,\n # pants test <target> -- <passthru args>\n # Then, e.g., both junit and pytest will get the passthru args even though the user probably\n # only intended them to go to one of them. If the wrong one is not a no-op then the error will\n # be unpredictable. However this is not a common case, and can be circumvented with an\n # explicit test.pytest or test.junit scope.\n if (scope and self._passthru_owner and scope.startswith(self._passthru_owner) and\n (len(scope) == len(self._passthru_owner) or scope[len(self._passthru_owner)] == '.')):\n return self._passthru\n else:\n return []","label":0},{"cid":"src/python/pants/option/options.py::Options.register","text":"src/python/pants/option/options.py::Options.register\n def register(self, scope, *args, **kwargs):\n \"\"\"Register an option in the given scope.\"\"\"\n self.get_parser(scope).register(*args, **kwargs)\n deprecated_scope = self.known_scope_to_info[scope].deprecated_scope\n if deprecated_scope:\n self.get_parser(deprecated_scope).register(*args, **kwargs)","label":0},{"cid":"src/python/pants/option/options.py::Options.registration_function_for_optionable","text":"src/python/pants/option/options.py::Options.registration_function_for_optionable\n def registration_function_for_optionable(self, optionable_class):\n \"\"\"Returns a function for registering options on the given scope.\"\"\"\n # TODO(benjy): Make this an instance of a class that implements __call__, so we can\n # docstring it, and so it's less weird than attatching properties to a function.\n def register(*args, **kwargs):\n kwargs['registering_class'] = optionable_class\n self.register(optionable_class.options_scope, *args, **kwargs)\n # Clients can access the bootstrap option values as register.bootstrap.\n register.bootstrap = self.bootstrap_option_values()\n # Clients can access the scope as register.scope.\n register.scope = optionable_class.options_scope\n return register","label":0},{"cid":"src/python/pants/option/options.py::Options.registration_function_for_optionable.register","text":"src/python/pants/option/options.py::Options.registration_function_for_optionable.register\n def register(*args, **kwargs):\n kwargs['registering_class'] = optionable_class\n self.register(optionable_class.options_scope, *args, **kwargs)","label":0},{"cid":"src/python/pants/option/options.py::Options.get_parser","text":"src/python/pants/option/options.py::Options.get_parser\n def get_parser(self, scope):\n \"\"\"Returns the parser for the given scope, so code can register on it directly.\"\"\"\n return self._parser_hierarchy.get_parser_by_scope(scope)","label":0},{"cid":"src/python/pants/option/options.py::Options.walk_parsers","text":"src/python/pants/option/options.py::Options.walk_parsers\n def walk_parsers(self, callback):\n self._parser_hierarchy.walk(callback)","label":0},{"cid":"src/python/pants/option/options.py::Options.for_scope","text":"src/python/pants/option/options.py::Options.for_scope\n def for_scope(self, scope, inherit_from_enclosing_scope=True):\n \"\"\"Return the option values for the given scope.\n\n Values are attributes of the returned object, e.g., options.foo.\n Computed lazily per scope.\n\n :API: public\n \"\"\"\n # Short-circuit, if already computed.\n if scope in self._values_by_scope:\n return self._values_by_scope[scope]\n\n # First get enclosing scope's option values, if any.\n if scope == GLOBAL_SCOPE or not inherit_from_enclosing_scope:\n values = OptionValueContainer()\n else:\n values = copy.copy(self.for_scope(enclosing_scope(scope)))\n\n # Now add our values.\n flags_in_scope = self._scope_to_flags.get(scope, [])\n self._parser_hierarchy.get_parser_by_scope(scope).parse_args(flags_in_scope, values)\n\n # If we're the new name of a deprecated scope, also get values from that scope.\n deprecated_scope = self.known_scope_to_info[scope].deprecated_scope\n # Note that deprecated_scope and scope share the same Optionable class, so deprecated_scope's\n # Optionable has a deprecated_options_scope equal to deprecated_scope. Therefore we must\n # check that scope != deprecated_scope to prevent infinite recursion.\n if deprecated_scope is not None and scope != deprecated_scope:\n # Do the deprecation check only on keys that were explicitly set on the deprecated scope\n # (and not on its enclosing scopes).\n explicit_keys = self.for_scope(deprecated_scope,\n inherit_from_enclosing_scope=False).get_explicit_keys()\n if explicit_keys:\n warn_or_error(self.known_scope_to_info[scope].deprecated_scope_removal_version,\n 'scope {}'.format(deprecated_scope),\n 'Use scope {} instead (options: {})'.format(scope, ', '.join(explicit_keys)))\n # Update our values with those of the deprecated scope (now including values inherited\n # from its enclosin","label":0},{"cid":"src/python/pants/option/options.py::Options.get_fingerprintable_for_scope","text":"src/python/pants/option/options.py::Options.get_fingerprintable_for_scope\n def get_fingerprintable_for_scope(self, bottom_scope, include_passthru=False,\n fingerprint_key=None, invert=False):\n \"\"\"Returns a list of fingerprintable (option type, option value) pairs for the given scope.\n\n Fingerprintable options are options registered via a \"fingerprint=True\" kwarg. This flag\n can be parameterized with `fingerprint_key` for special cases.\n\n This method also searches enclosing options scopes of `bottom_scope` to determine the set of\n fingerprintable pairs.\n\n :param str bottom_scope: The scope to gather fingerprintable options for.\n :param bool include_passthru: Whether to include passthru args captured by `bottom_scope` in the\n fingerprintable options.\n :param string fingerprint_key: The option kwarg to match against (defaults to 'fingerprint').\n :param bool invert: Whether or not to invert the boolean check for the fingerprint_key value.\n\n :API: public\n \"\"\"\n fingerprint_key = fingerprint_key or 'fingerprint'\n pairs = []\n\n if include_passthru:\n # Passthru args can only be sent to outermost scopes so we gather them once here up-front.\n passthru_args = self.passthru_args_for_scope(bottom_scope)\n # NB: We can't sort passthru args, the underlying consumer may be order-sensitive.\n pairs.extend((str, pass_arg) for pass_arg in passthru_args)\n\n # Note that we iterate over options registered at `bottom_scope` and at all\n # enclosing scopes, since option-using code can read those values indirectly\n # via its own OptionValueContainer, so they can affect that code's output.\n for registration_scope in all_enclosing_scopes(bottom_scope):\n parser = self._parser_hierarchy.get_parser_by_scope(registration_scope)\n # Sort the arguments, so that the fingerprint is consistent.\n for (_, kwargs) in sorted(parser.option_registrations_iter(","label":1},{"cid":"src/python/pants/option/options.py::Options.__getitem__","text":"src/python/pants/option/options.py::Options.__getitem__\n def __getitem__(self, scope):\n # TODO(John Sirois): Mainly supports use of dict<str, dict<str, str>> for mock options in tests,\n # Consider killing if tests consolidate on using TestOptions instead of the raw dicts.\n return self.for_scope(scope)","label":0},{"cid":"src/python/pants/option/options.py::Options.bootstrap_option_values","text":"src/python/pants/option/options.py::Options.bootstrap_option_values\n def bootstrap_option_values(self):\n \"\"\"Return the option values for bootstrap options.\n\n General code can also access these values in the global scope. But option registration code\n cannot, hence this special-casing of this small set of options.\n \"\"\"\n return self._bootstrap_option_values","label":0},{"cid":"src/python/pants/option/options.py::Options.for_global_scope","text":"src/python/pants/option/options.py::Options.for_global_scope\n def for_global_scope(self):\n \"\"\"Return the option values for the global scope.\n\n :API: public\n \"\"\"\n return self.for_scope(GLOBAL_SCOPE)","label":0},{"cid":"src/python/pants/option/parser.py::Parser._ensure_bool","text":"src/python/pants/option/parser.py::Parser._ensure_bool\n @staticmethod\n def _ensure_bool(s):\n if isinstance(s, six.string_types):\n if s.lower() == 'true':\n return True\n elif s.lower() == 'false':\n return False\n else:\n raise Parser.BooleanConversionError('Got \"{0}\". Expected \"True\" or \"False\".'.format(s))\n if s is True:\n return True\n elif s is False:\n return False\n else:\n raise Parser.BooleanConversionError('Got {0}. Expected True or False.'.format(s))","label":0},{"cid":"src/python/pants/option/parser.py::Parser._invert","text":"src/python/pants/option/parser.py::Parser._invert\n @classmethod\n def _invert(cls, s):\n if s is None:\n return None\n b = cls._ensure_bool(s)\n return not b","label":0},{"cid":"src/python/pants/option/parser.py::Parser.__init__","text":"src/python/pants/option/parser.py::Parser.__init__\n def __init__(self, env, config, scope_info, parent_parser, option_tracker):\n \"\"\"Create a Parser instance.\n\n :param env: a dict of environment variables.\n :param :class:`pants.option.config.Config` config: data from a config file.\n :param scope_info: the scope this parser acts for.\n :param parent_parser: the parser for the scope immediately enclosing this one, or\n None if this is the global scope.\n :param option_tracker: the option tracker to record where option values came from.\n \"\"\"\n self._env = env\n self._config = config\n self._scope_info = scope_info\n self._scope = self._scope_info.scope\n self._option_tracker = option_tracker\n\n # If True, no more registration is allowed on this parser.\n self._frozen = False\n\n # All option args registered with this parser. Used to prevent shadowing args in inner scopes.\n self._known_args = set()\n\n # List of (args, kwargs) registration pairs, exactly as captured at registration time.\n self._option_registrations = []\n\n # A Parser instance, or None for the global scope parser.\n self._parent_parser = parent_parser\n\n # List of Parser instances.\n self._child_parsers = []\n\n if self._parent_parser:\n self._parent_parser._register_child_parser(self)","label":0},{"cid":"src/python/pants/option/parser.py::Parser.scope","text":"src/python/pants/option/parser.py::Parser.scope\n @property\n def scope(self):\n return self._scope","label":0},{"cid":"src/python/pants/option/parser.py::Parser.walk","text":"src/python/pants/option/parser.py::Parser.walk\n def walk(self, callback):\n \"\"\"Invoke callback on this parser and its descendants, in depth-first order.\"\"\"\n callback(self)\n for child in self._child_parsers:\n child.walk(callback)","label":0},{"cid":"src/python/pants/option/parser.py::Parser._create_flag_value_map","text":"src/python/pants/option/parser.py::Parser._create_flag_value_map\n def _create_flag_value_map(self, flags):\n \"\"\"Returns a map of flag -> list of values, based on the given flag strings.\n\n None signals no value given (e.g., -x, --foo).\n The value is a list because the user may specify the same flag multiple times, and that's\n sometimes OK (e.g., when appending to list-valued options).\n \"\"\"\n flag_value_map = defaultdict(list)\n for flag in flags:\n key, has_equals_sign, flag_val = flag.partition('=')\n if not has_equals_sign:\n if not flag.startswith('--'): # '-xfoo' style.\n key = flag[0:2]\n flag_val = flag[2:]\n if not flag_val:\n # Either a short option with no value or a long option with no equals sign.\n # Important so we can distinguish between no value ('--foo') and setting to an empty\n # string ('--foo='), for options with an implicit_value.\n flag_val = None\n flag_value_map[key].append(flag_val)\n return flag_value_map","label":0},{"cid":"src/python/pants/option/parser.py::Parser.parse_args","text":"src/python/pants/option/parser.py::Parser.parse_args\n def parse_args(self, flags, namespace):\n \"\"\"Set values for this parser's options on the namespace object.\"\"\"\n flag_value_map = self._create_flag_value_map(flags)\n\n mutex_map = defaultdict(list)\n for args, kwargs in self._unnormalized_option_registrations_iter():\n self._validate(args, kwargs)\n dest = self.parse_dest(*args, **kwargs)\n\n # Compute the values provided on the command line for this option. Note that there may be\n # multiple values, for any combination of the following reasons:\n # - The user used the same flag multiple times.\n # - The user specified a boolean flag (--foo) and its inverse (--no-foo).\n # - The option has multiple names, and the user used more than one of them.\n #\n # We also check if the option is deprecated, but we only do so if the option is explicitly\n # specified as a command-line flag, so we don't spam users with deprecated option values\n # specified in config, which isn't something they control.\n implicit_value = kwargs.get('implicit_value')\n if implicit_value is None and kwargs.get('type') == bool:\n implicit_value = True # Allows --foo to mean --foo=true.\n\n flag_vals = []\n\n def add_flag_val(v):\n if v is None:\n if implicit_value is None:\n raise ParseError('Missing value for command line flag {} in {}'.format(\n arg, self._scope_str()))\n else:\n flag_vals.append(implicit_value)\n else:\n flag_vals.append(v)\n\n for arg in args:\n # If the user specified --no-foo on the cmd line, treat it as if the user specified\n # --foo, but with the inverse value.\n if kwargs.get('type') == bool:\n inverse_arg = self._inverse_arg(arg)\n if inverse_arg in flag_value_map:\n flag_value_map[arg] = [self._invert(v) for v in flag_value_map[inverse_arg]]\n implicit_value = self.","label":0},{"cid":"src/python/pants/option/parser.py::Parser.option_registrations_iter","text":"src/python/pants/option/parser.py::Parser.option_registrations_iter\n def option_registrations_iter(self):\n \"\"\"Returns an iterator over the normalized registration arguments of each option in this parser.\n\n Useful for generating help and other documentation.\n\n Each yielded item is an (args, kwargs) pair, as passed to register(), except that kwargs\n will be normalized in the following ways:\n - It will always have 'dest' explicitly set.\n - It will always have 'default' explicitly set, and the value will be a RankedValue.\n - For recursive options, the original registrar will also have 'recursive_root' set.\n\n Note that recursive options we inherit from a parent will also be yielded here, with\n the correctly-scoped default value.\n \"\"\"\n def normalize_kwargs(args, orig_kwargs):\n nkwargs = copy.copy(orig_kwargs)\n dest = self.parse_dest(*args, **nkwargs)\n nkwargs['dest'] = dest\n if not ('default' in nkwargs and isinstance(nkwargs['default'], RankedValue)):\n nkwargs['default'] = self._compute_value(dest, nkwargs, [])\n return nkwargs\n\n # First yield any recursive options we inherit from our parent.\n if self._parent_parser:\n for args, kwargs in self._parent_parser._recursive_option_registration_args():\n yield args, normalize_kwargs(args, kwargs)\n\n # Then yield our directly-registered options.\n # This must come after yielding inherited recursive options, so we can detect shadowing.\n for args, kwargs in self._option_registrations:\n normalized_kwargs = normalize_kwargs(args, kwargs)\n if 'recursive' in normalized_kwargs:\n # If we're the original registrar, make sure we can distinguish that.\n normalized_kwargs['recursive_root'] = True\n yield args, normalized_kwargs","label":0},{"cid":"src/python/pants/option/parser.py::Parser.option_registrations_iter.normalize_kwargs","text":"src/python/pants/option/parser.py::Parser.option_registrations_iter.normalize_kwargs\n def normalize_kwargs(args, orig_kwargs):\n nkwargs = copy.copy(orig_kwargs)\n dest = self.parse_dest(*args, **nkwargs)\n nkwargs['dest'] = dest\n if not ('default' in nkwargs and isinstance(nkwargs['default'], RankedValue)):\n nkwargs['default'] = self._compute_value(dest, nkwargs, [])\n return nkwargs","label":0},{"cid":"src/python/pants/option/parser.py::Parser._unnormalized_option_registrations_iter","text":"src/python/pants/option/parser.py::Parser._unnormalized_option_registrations_iter\n def _unnormalized_option_registrations_iter(self):\n \"\"\"Returns an iterator over the raw registration arguments of each option in this parser.\n\n Each yielded item is an (args, kwargs) pair, exactly as passed to register(), except for\n substituting list and dict types with list_option/dict_option.\n\n Note that recursive options we inherit from a parent will also be yielded here.\n \"\"\"\n # First yield any recursive options we inherit from our parent.\n if self._parent_parser:\n for args, kwargs in self._parent_parser._recursive_option_registration_args():\n yield args, kwargs\n # Then yield our directly-registered options.\n for args, kwargs in self._option_registrations:\n if 'recursive' in kwargs and self._scope_info.category == ScopeInfo.SUBSYSTEM:\n raise RecursiveSubsystemOption(self.scope, args[0])\n yield args, kwargs","label":0},{"cid":"src/python/pants/option/parser.py::Parser._recursive_option_registration_args","text":"src/python/pants/option/parser.py::Parser._recursive_option_registration_args\n def _recursive_option_registration_args(self):\n \"\"\"Yield args, kwargs pairs for just our recursive options.\n\n Includes all the options we inherit recursively from our ancestors.\n \"\"\"\n if self._parent_parser:\n for args, kwargs in self._parent_parser._recursive_option_registration_args():\n yield args, kwargs\n for args, kwargs in self._option_registrations:\n # Note that all subsystem options are implicitly recursive: a subscope of a subsystem\n # scope is another (optionable-specific) instance of the same subsystem, so it needs\n # all the same options.\n if self._scope_info.category == ScopeInfo.SUBSYSTEM or 'recursive' in kwargs:\n yield args, kwargs","label":0},{"cid":"src/python/pants/option/parser.py::Parser.register","text":"src/python/pants/option/parser.py::Parser.register\n def register(self, *args, **kwargs):\n \"\"\"Register an option.\"\"\"\n if self._frozen:\n raise FrozenRegistration(self.scope, args[0])\n\n # Prevent further registration in enclosing scopes.\n ancestor = self._parent_parser\n while ancestor:\n ancestor._freeze()\n ancestor = ancestor._parent_parser\n\n if kwargs.get('type') == bool:\n default = kwargs.get('default')\n if default is None:\n # Unless a tri-state bool is explicitly opted into with the `UnsetBool` default value,\n # boolean options always have an implicit boolean-typed default. We make that default\n # explicit here.\n kwargs['default'] = not self._ensure_bool(kwargs.get('implicit_value', True))\n elif default is UnsetBool:\n kwargs['default'] = None\n\n # Record the args. We'll do the underlying parsing on-demand.\n self._option_registrations.append((args, kwargs))\n if self._parent_parser:\n for arg in args:\n existing_scope = self._parent_parser._existing_scope(arg)\n if existing_scope is not None:\n raise Shadowing(self.scope, arg, outer_scope=self._scope_str(existing_scope))\n for arg in args:\n if arg in self._known_args:\n raise OptionAlreadyRegistered(self.scope, arg)\n self._known_args.update(args)","label":0},{"cid":"src/python/pants/option/parser.py::Parser._check_deprecated","text":"src/python/pants/option/parser.py::Parser._check_deprecated\n def _check_deprecated(self, dest, kwargs):\n \"\"\"Checks option for deprecation and issues a warning/error if necessary.\"\"\"\n removal_version = kwargs.get('removal_version', None)\n if removal_version is not None:\n warn_or_error(removal_version,\n \"option '{}' in {}\".format(dest, self._scope_str()),\n kwargs.get('removal_hint', ''),\n stacklevel=9999) # Out of range stacklevel to suppress printing src line.","label":0},{"cid":"src/python/pants/option/parser.py::Parser._validate","text":"src/python/pants/option/parser.py::Parser._validate\n def _validate(self, args, kwargs):\n \"\"\"Validate option registration arguments.\"\"\"\n def error(exception_type, arg_name=None, **msg_kwargs):\n if arg_name is None:\n arg_name = args[0] if args else '<unknown>'\n raise exception_type(self.scope, arg_name, **msg_kwargs)\n\n if not args:\n error(NoOptionNames)\n # validate args.\n for arg in args:\n if not arg.startswith('-'):\n error(OptionNameDash, arg_name=arg)\n if not arg.startswith('--') and len(arg) > 2:\n error(OptionNameDoubleDash, arg_name=arg)\n\n # Validate kwargs.\n if 'implicit_value' in kwargs and kwargs['implicit_value'] is None:\n error(ImplicitValIsNone)\n\n # Note: we check for list here, not list_option, because we validate the provided kwargs,\n # not the ones we modified. However we temporarily also allow list_option, until the\n # deprecation is complete.\n if 'member_type' in kwargs and kwargs.get('type', str) not in [list, list_option]:\n error(MemberTypeNotAllowed, type_=kwargs.get('type', str).__name__)\n\n if kwargs.get('member_type', str) not in self._allowed_member_types:\n error(InvalidMemberType, member_type=kwargs.get('member_type', str).__name__)\n\n for kwarg in kwargs:\n if kwarg not in self._allowed_registration_kwargs:\n error(InvalidKwarg, kwarg=kwarg)\n\n # Ensure `daemon=True` can't be passed on non-global scopes (except for `recursive=True`).\n if (kwarg == 'daemon' and self._scope != GLOBAL_SCOPE and kwargs.get('recursive') is False):\n error(InvalidKwargNonGlobalScope, kwarg=kwarg)\n\n removal_version = kwargs.get('removal_version')\n if removal_version is not None:\n validate_removal_semver(removal_version)","label":0},{"cid":"src/python/pants/option/parser.py::Parser._validate.error","text":"src/python/pants/option/parser.py::Parser._validate.error\n def error(exception_type, arg_name=None, **msg_kwargs):\n if arg_name is None:\n arg_name = args[0] if args else '<unknown>'\n raise exception_type(self.scope, arg_name, **msg_kwargs)","label":0},{"cid":"src/python/pants/option/parser.py::Parser._existing_scope","text":"src/python/pants/option/parser.py::Parser._existing_scope\n def _existing_scope(self, arg):\n if arg in self._known_args:\n return self._scope\n elif self._parent_parser:\n return self._parent_parser._existing_scope(arg)\n else:\n return None","label":0},{"cid":"src/python/pants/option/parser.py::Parser.parse_dest","text":"src/python/pants/option/parser.py::Parser.parse_dest\n @staticmethod\n def parse_dest(*args, **kwargs):\n \"\"\"Select the dest name for an option registration.\n\n If an explicit `dest` is specified, returns that and otherwise derives a default from the\n option flags where '--foo-bar' -> 'foo_bar' and '-x' -> 'x'.\n \"\"\"\n explicit_dest = kwargs.get('dest')\n if explicit_dest:\n return explicit_dest\n\n arg = next((a for a in args if a.startswith('--')), args[0])\n return arg.lstrip('-').replace('-', '_')","label":0},{"cid":"src/python/pants/option/parser.py::Parser._wrap_type","text":"src/python/pants/option/parser.py::Parser._wrap_type\n @staticmethod\n def _wrap_type(t):\n if t == list:\n return list_option\n elif t == dict:\n return dict_option\n else:\n return t","label":0},{"cid":"src/python/pants/option/parser.py::Parser._convert_member_type","text":"src/python/pants/option/parser.py::Parser._convert_member_type\n @staticmethod\n def _convert_member_type(t, x):\n if t == dict:\n return dict_option(x).val\n else:\n return t(x)","label":0},{"cid":"src/python/pants/option/parser.py::Parser._compute_value","text":"src/python/pants/option/parser.py::Parser._compute_value\n def _compute_value(self, dest, kwargs, flag_val_strs):\n \"\"\"Compute the value to use for an option.\n\n The source of the default value is chosen according to the ranking in RankedValue.\n \"\"\"\n # Helper function to convert a string to a value of the option's type.\n def to_value_type(val_str):\n if val_str is None:\n return None\n elif kwargs.get('type') == bool:\n return self._ensure_bool(val_str)\n else:\n return self._wrap_type(kwargs.get('type', str))(val_str)\n\n # Helper function to expand a fromfile=True value string, if needed.\n def expand(val_str):\n if kwargs.get('fromfile', False) and val_str and val_str.startswith('@'):\n if val_str.startswith('@@'): # Support a literal @ for fromfile values via @@.\n return val_str[1:]\n else:\n fromfile = val_str[1:]\n try:\n with open(fromfile) as fp:\n return fp.read().strip()\n except IOError as e:\n raise self.FromfileError('Failed to read {} in {} from file {}: {}'.format(\n dest, self._scope_str(), fromfile, e))\n else:\n return val_str\n\n # Get value from config files, and capture details about its derivation.\n config_details = None\n config_section = GLOBAL_SCOPE_CONFIG_SECTION if self._scope == GLOBAL_SCOPE else self._scope\n config_default_val_str = expand(self._config.get(Config.DEFAULT_SECTION, dest, default=None))\n config_val_str = expand(self._config.get(config_section, dest, default=None))\n config_source_file = (self._config.get_source_for_option(config_section, dest) or\n self._config.get_source_for_option(Config.DEFAULT_SECTION, dest))\n if config_source_file is not None:\n config_source_file = os.path.relpath(config_source_file)\n config_details = 'in {}'.format(config_source_file)\n\n # Get value from environment, and capture details about its derivation.\n udes","label":0},{"cid":"src/python/pants/option/parser.py::Parser._compute_value.to_value_type","text":"src/python/pants/option/parser.py::Parser._compute_value.to_value_type\n def to_value_type(val_str):\n if val_str is None:\n return None\n elif kwargs.get('type') == bool:\n return self._ensure_bool(val_str)\n else:\n return self._wrap_type(kwargs.get('type', str))(val_str)","label":0},{"cid":"src/python/pants/option/parser.py::Parser._compute_value.expand","text":"src/python/pants/option/parser.py::Parser._compute_value.expand\n def expand(val_str):\n if kwargs.get('fromfile', False) and val_str and val_str.startswith('@'):\n if val_str.startswith('@@'): # Support a literal @ for fromfile values via @@.\n return val_str[1:]\n else:\n fromfile = val_str[1:]\n try:\n with open(fromfile) as fp:\n return fp.read().strip()\n except IOError as e:\n raise self.FromfileError('Failed to read {} in {} from file {}: {}'.format(\n dest, self._scope_str(), fromfile, e))\n else:\n return val_str","label":0},{"cid":"src/python/pants/option/parser.py::Parser._compute_value.record_option","text":"src/python/pants/option/parser.py::Parser._compute_value.record_option\n def record_option(value, rank, option_details=None):\n deprecation_version = kwargs.get('removal_version')\n self._option_tracker.record_option(scope=self._scope,\n option=dest,\n value=value,\n rank=rank,\n deprecation_version=deprecation_version,\n details=option_details)","label":0},{"cid":"src/python/pants/option/parser.py::Parser._compute_value.check","text":"src/python/pants/option/parser.py::Parser._compute_value.check\n def check(val):\n if val is not None:\n choices = kwargs.get('choices')\n if choices is not None and val not in choices:\n raise ParseError('`{}` is not an allowed value for option {} in {}. '\n 'Must be one of: {}'.format(val, dest, self._scope_str(), choices))\n elif kwargs.get('type') == dir_option and not os.path.isdir(val):\n raise ParseError('Directory value `{}` for option {} in {} does not exist.'.format(\n val, dest, self._scope_str()))\n elif kwargs.get('type') == file_option and not os.path.isfile(val):\n raise ParseError('File value `{}` for option {} in {} does not exist.'.format(\n val, dest, self._scope_str()))","label":0},{"cid":"src/python/pants/option/parser.py::Parser._inverse_arg","text":"src/python/pants/option/parser.py::Parser._inverse_arg\n def _inverse_arg(self, arg):\n if arg.startswith('--'):\n if arg.startswith('--no-'):\n raise BooleanOptionNameWithNo(self.scope, arg)\n return '--no-{}'.format(arg[2:])\n else:\n return None","label":0},{"cid":"src/python/pants/option/parser.py::Parser._register_child_parser","text":"src/python/pants/option/parser.py::Parser._register_child_parser\n def _register_child_parser(self, child):\n self._child_parsers.append(child)","label":0},{"cid":"src/python/pants/option/parser.py::Parser._freeze","text":"src/python/pants/option/parser.py::Parser._freeze\n def _freeze(self):\n self._frozen = True","label":0},{"cid":"src/python/pants/option/parser.py::Parser._scope_str","text":"src/python/pants/option/parser.py::Parser._scope_str\n def _scope_str(self, scope=None):\n scope = scope or self.scope\n return 'global scope' if scope == GLOBAL_SCOPE else \"scope '{}'\".format(scope)","label":0},{"cid":"src/python/pants/option/parser.py::Parser.__str__","text":"src/python/pants/option/parser.py::Parser.__str__\n def __str__(self):\n return 'Parser({})'.format(self._scope)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::swallow_psutil_exceptions","text":"src/python/pants/pantsd/process_manager.py::swallow_psutil_exceptions\n@contextmanager\ndef swallow_psutil_exceptions():\n \"\"\"A contextmanager that swallows standard psutil access exceptions.\"\"\"\n try:\n yield\n except (psutil.AccessDenied, psutil.NoSuchProcess):\n # This masks common, but usually benign psutil process access exceptions that might be seen\n # when accessing attributes/methods on psutil.Process objects.\n pass","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessGroup.__init__","text":"src/python/pants/pantsd/process_manager.py::ProcessGroup.__init__\n def __init__(self, name, metadata_base_dir=None):\n self._name = name\n self._metadata_base_dir = metadata_base_dir","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessGroup._instance_from_process","text":"src/python/pants/pantsd/process_manager.py::ProcessGroup._instance_from_process\n def _instance_from_process(self, process):\n \"\"\"Default converter from psutil.Process to process instance classes for subclassing.\"\"\"\n return ProcessManager(name=process.name(),\n pid=process.pid,\n process_name=process.name(),\n metadata_base_dir=self._metadata_base_dir)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_processes","text":"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_processes\n def iter_processes(self, proc_filter=None):\n \"\"\"Yields processes from psutil.process_iter with an optional filter and swallows psutil errors.\n\n If a psutil exception is raised during execution of the filter, that process will not be\n yielded but subsequent processes will. On the other hand, if psutil.process_iter raises\n an exception, no more processes will be yielded.\n \"\"\"\n with swallow_psutil_exceptions(): # process_iter may raise\n for proc in psutil.process_iter():\n with swallow_psutil_exceptions(): # proc_filter may raise\n if (proc_filter is None) or proc_filter(proc):\n yield proc","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_instances","text":"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_instances\n def iter_instances(self, *args, **kwargs):\n for item in self.iter_processes(*args, **kwargs):\n yield self._instance_from_process(item)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.__init__","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.__init__\n def __init__(self, metadata_base_dir=None):\n \"\"\"\n :param str metadata_base_dir: The base directory for process metadata.\n \"\"\"\n super(ProcessMetadataManager, self).__init__()\n\n self._metadata_base_dir = (\n metadata_base_dir or\n Subprocess.Factory.global_instance().create().get_subprocess_dir()\n )","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_cast","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_cast\n @staticmethod\n def _maybe_cast(item, caster):\n \"\"\"Given a casting function, attempt to cast to that type while masking common cast exceptions.\n\n N.B. This is mostly suitable for casting string types to numeric types - e.g. a port number\n read from disk into an int.\n\n :param func caster: A casting callable (e.g. `int`).\n :returns: The result of caster(item) or item if TypeError or ValueError are raised during cast.\n \"\"\"\n try:\n return caster(item)\n except (TypeError, ValueError):\n # N.B. the TypeError catch here (already) protects against the case that caster is None.\n return item","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._deadline_until","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._deadline_until\n @classmethod\n def _deadline_until(cls, closure, timeout, wait_interval=WAIT_INTERVAL_SEC):\n \"\"\"Execute a function/closure repeatedly until a True condition or timeout is met.\n\n :param func closure: the function/closure to execute (should not block for long periods of time\n and must return True on success).\n :param float timeout: the maximum amount of time to wait for a true result from the closure in\n seconds. N.B. this is timing based, so won't be exact if the runtime of\n the closure exceeds the timeout.\n :param float wait_interval: the amount of time to sleep between closure invocations.\n :raises: :class:`ProcessManager.Timeout` on execution timeout.\n \"\"\"\n deadline = time.time() + timeout\n while 1:\n if closure():\n return True\n elif time.time() > deadline:\n raise cls.Timeout('exceeded timeout of {} seconds for {}'.format(timeout, closure))\n elif wait_interval:\n time.sleep(wait_interval)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file\n @classmethod\n def _wait_for_file(cls, filename, timeout=FILE_WAIT_SEC, want_content=True):\n \"\"\"Wait up to timeout seconds for filename to appear with a non-zero size or raise Timeout().\"\"\"\n def file_waiter():\n return os.path.exists(filename) and (not want_content or os.path.getsize(filename))\n\n try:\n return cls._deadline_until(file_waiter, timeout)\n except cls.Timeout:\n # Re-raise with a more helpful exception message.\n raise cls.Timeout('exceeded timeout of {} seconds while waiting for file {} to appear'\n .format(timeout, filename))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file.file_waiter","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file.file_waiter\n def file_waiter():\n return os.path.exists(filename) and (not want_content or os.path.getsize(filename))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._get_metadata_dir_by_name","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._get_metadata_dir_by_name\n def _get_metadata_dir_by_name(self, name):\n \"\"\"Retrieve the metadata dir by name.\n\n This should always live outside of the workdir to survive a clean-all.\n \"\"\"\n return os.path.join(self._metadata_base_dir, name)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_init_metadata_dir_by_name","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_init_metadata_dir_by_name\n def _maybe_init_metadata_dir_by_name(self, name):\n \"\"\"Initialize the metadata directory for a named identity if it doesn't exist.\"\"\"\n safe_mkdir(self._get_metadata_dir_by_name(name))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.read_metadata_by_name","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.read_metadata_by_name\n def read_metadata_by_name(self, name, metadata_key, caster=None):\n \"\"\"Read process metadata using a named identity.\n\n :param string name: The ProcessMetadataManager identity/name (e.g. 'pantsd').\n :param string metadata_key: The metadata key (e.g. 'pid').\n :param func caster: A casting callable to apply to the read value (e.g. `int`).\n \"\"\"\n try:\n file_path = os.path.join(self._get_metadata_dir_by_name(name), metadata_key)\n return self._maybe_cast(read_file(file_path).strip(), caster)\n except (IOError, OSError):\n return None","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.write_metadata_by_name","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.write_metadata_by_name\n def write_metadata_by_name(self, name, metadata_key, metadata_value):\n \"\"\"Write process metadata using a named identity.\n\n :param string name: The ProcessMetadataManager identity/name (e.g. 'pantsd').\n :param string metadata_key: The metadata key (e.g. 'pid').\n :param string metadata_value: The metadata value (e.g. '1729').\n \"\"\"\n self._maybe_init_metadata_dir_by_name(name)\n file_path = os.path.join(self._get_metadata_dir_by_name(name), metadata_key)\n safe_file_dump(file_path, metadata_value)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.await_metadata_by_name","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.await_metadata_by_name\n def await_metadata_by_name(self, name, metadata_key, timeout, caster=None):\n \"\"\"Block up to a timeout for process metadata to arrive on disk.\n\n :param string name: The ProcessMetadataManager identity/name (e.g. 'pantsd').\n :param string metadata_key: The metadata key (e.g. 'pid').\n :param int timeout: The deadline to write metadata.\n :param type caster: A type-casting callable to apply to the read value (e.g. int, str).\n :returns: The value of the metadata key (read from disk post-write).\n :raises: :class:`ProcessMetadataManager.Timeout` on timeout.\n \"\"\"\n file_path = os.path.join(self._get_metadata_dir_by_name(name), metadata_key)\n self._wait_for_file(file_path, timeout=timeout)\n return self.read_metadata_by_name(name, metadata_key, caster)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.purge_metadata_by_name","text":"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.purge_metadata_by_name\n def purge_metadata_by_name(self, name):\n \"\"\"Purge a processes metadata directory.\n\n :raises: `ProcessManager.MetadataError` when OSError is encountered on metadata dir removal.\n \"\"\"\n meta_dir = self._get_metadata_dir_by_name(name)\n logger.debug('purging metadata directory: {}'.format(meta_dir))\n try:\n rm_rf(meta_dir)\n except OSError as e:\n raise self.MetadataError('failed to purge metadata directory {}: {!r}'.format(meta_dir, e))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__init__","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__init__\n def __init__(self, message, output=None):\n super(ProcessManager.ExecutionError, self).__init__(message)\n self.message = message\n self.output = output","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__repr__","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__repr__\n def __repr__(self):\n return '{}(message={!r}, output={!r})'.format(type(self).__name__, self.message, self.output)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.__init__","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.__init__\n def __init__(self, name, pid=None, socket=None, process_name=None, socket_type=int,\n metadata_base_dir=None):\n \"\"\"\n :param string name: The process identity/name (e.g. 'pantsd' or 'ng_Zinc').\n :param int pid: The process pid. Overrides fetching of the self.pid @property.\n :param string socket: The socket metadata. Overrides fetching of the self.socket @property.\n :param string process_name: The process name for cmdline executable name matching.\n :param type socket_type: The type to be used for socket type casting (e.g. int).\n :param str metadata_base_dir: The overridden base directory for process metadata.\n \"\"\"\n super(ProcessManager, self).__init__(metadata_base_dir)\n self._name = name.lower().strip()\n self._pid = pid\n self._socket = socket\n self._socket_type = socket_type\n self._process_name = process_name\n self._buildroot = get_buildroot()\n self._process = None","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.name","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.name\n @property\n def name(self):\n \"\"\"The logical name/label of the process.\"\"\"\n return self._name","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.process_name","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.process_name\n @property\n def process_name(self):\n \"\"\"The logical process name. If defined, this is compared to exe_name for stale pid checking.\"\"\"\n return self._process_name","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.process_lock","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.process_lock\n @memoized_property\n def process_lock(self):\n \"\"\"An identity-keyed inter-process lock for safeguarding lifecycle and other operations.\"\"\"\n safe_mkdir(self._metadata_base_dir)\n return OwnerPrintingInterProcessFileLock(\n # N.B. This lock can't key into the actual named metadata dir (e.g. `.pids/pantsd/lock`\n # via `ProcessMetadataManager._get_metadata_dir_by_name()`) because of a need to purge\n # the named metadata dir on startup to avoid stale metadata reads.\n os.path.join(self._metadata_base_dir, '.lock.{}'.format(self._name))\n )","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.cmdline","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.cmdline\n @property\n def cmdline(self):\n \"\"\"The process commandline. e.g. ['/usr/bin/python2.7', 'pants.pex'].\n\n :returns: The command line or else `None` if the underlying process has died.\n \"\"\"\n with swallow_psutil_exceptions():\n process = self._as_process()\n if process:\n return process.cmdline()\n return None","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.cmd","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.cmd\n @property\n def cmd(self):\n \"\"\"The first element of the process commandline e.g. '/usr/bin/python2.7'.\n\n :returns: The first element of the process command line or else `None` if the underlying\n process has died.\n \"\"\"\n return (self.cmdline or [None])[0]","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.pid","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.pid\n @property\n def pid(self):\n \"\"\"The running processes pid (or None).\"\"\"\n return self._pid or self.read_metadata_by_name(self._name, 'pid', int)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.socket","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.socket\n @property\n def socket(self):\n \"\"\"The running processes socket/port information (or None).\"\"\"\n return self._socket or self.read_metadata_by_name(self._name, 'socket', self._socket_type)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.get_subprocess_output","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.get_subprocess_output\n @classmethod\n def get_subprocess_output(cls, command, ignore_stderr=True, **kwargs):\n \"\"\"Get the output of an executed command.\n\n :param command: An iterable representing the command to execute (e.g. ['ls', '-al']).\n :param ignore_stderr: Whether or not to ignore stderr output vs interleave it with stdout.\n :raises: `ProcessManager.ExecutionError` on `OSError` or `CalledProcessError`.\n :returns: The output of the command.\n \"\"\"\n if ignore_stderr is False:\n kwargs.setdefault('stderr', subprocess.STDOUT)\n\n try:\n return subprocess.check_output(command, **kwargs)\n except (OSError, subprocess.CalledProcessError) as e:\n subprocess_output = getattr(e, 'output', '').strip()\n raise cls.ExecutionError(str(e), subprocess_output)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.await_pid","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.await_pid\n def await_pid(self, timeout):\n \"\"\"Wait up to a given timeout for a process to write pid metadata.\"\"\"\n return self.await_metadata_by_name(self._name, 'pid', timeout, int)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.await_socket","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.await_socket\n def await_socket(self, timeout):\n \"\"\"Wait up to a given timeout for a process to write socket info.\"\"\"\n return self.await_metadata_by_name(self._name, 'socket', timeout, self._socket_type)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.write_pid","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.write_pid\n def write_pid(self, pid=None):\n \"\"\"Write the current processes PID to the pidfile location\"\"\"\n pid = pid or os.getpid()\n self.write_metadata_by_name(self._name, 'pid', str(pid))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.write_socket","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.write_socket\n def write_socket(self, socket_info):\n \"\"\"Write the local processes socket information (TCP port or UNIX socket).\"\"\"\n self.write_metadata_by_name(self._name, 'socket', str(socket_info))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.write_named_socket","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.write_named_socket\n def write_named_socket(self, socket_name, socket_info):\n \"\"\"A multi-tenant, named alternative to ProcessManager.write_socket().\"\"\"\n self.write_metadata_by_name(self._name, 'socket_{}'.format(socket_name), str(socket_info))","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.read_named_socket","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.read_named_socket\n def read_named_socket(self, socket_name, socket_type):\n \"\"\"A multi-tenant, named alternative to ProcessManager.socket.\"\"\"\n return self.read_metadata_by_name(self._name, 'socket_{}'.format(socket_name), socket_type)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager._as_process","text":"src/python/pants/pantsd/process_manager.py::ProcessManager._as_process\n def _as_process(self):\n \"\"\"Returns a psutil `Process` object wrapping our pid.\n\n NB: Even with a process object in hand, subsequent method calls against it can always raise\n `NoSuchProcess`. Care is needed to document the raises in the public API or else trap them and\n do something sensible for the API.\n\n :returns: a psutil Process object or else None if we have no pid.\n :rtype: :class:`psutil.Process`\n :raises: :class:`psutil.NoSuchProcess` if the process identified by our pid has died.\n \"\"\"\n if self._process is None and self.pid:\n self._process = psutil.Process(self.pid)\n return self._process","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.is_dead","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.is_dead\n def is_dead(self):\n \"\"\"Return a boolean indicating whether the process is dead or not.\"\"\"\n return not self.is_alive()","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.is_alive","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.is_alive\n def is_alive(self, extended_check=None):\n \"\"\"Return a boolean indicating whether the process is running or not.\n\n :param func extended_check: An additional callable that will be invoked to perform an extended\n liveness check. This callable should take a single argument of a\n `psutil.Process` instance representing the context-local process\n and return a boolean True/False to indicate alive vs not alive.\n \"\"\"\n try:\n process = self._as_process()\n return not (\n # Can happen if we don't find our pid.\n (not process) or\n # Check for walkers.\n (process.status() == psutil.STATUS_ZOMBIE) or\n # Check for stale pids.\n (self.process_name and self.process_name != process.name()) or\n # Extended checking.\n (extended_check and not extended_check(process))\n )\n except (psutil.NoSuchProcess, psutil.AccessDenied):\n # On some platforms, accessing attributes of a zombie'd Process results in NoSuchProcess.\n return False","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.purge_metadata","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.purge_metadata\n def purge_metadata(self, force=False):\n \"\"\"Instance-based version of ProcessMetadataManager.purge_metadata_by_name() that checks\n for process liveness before purging metadata.\n\n :param bool force: If True, skip process liveness check before purging metadata.\n :raises: `ProcessManager.MetadataError` when OSError is encountered on metadata dir removal.\n \"\"\"\n if not force and self.is_alive():\n raise self.MetadataError('cannot purge metadata for a running process!')\n\n super(ProcessManager, self).purge_metadata_by_name(self._name)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager._kill","text":"src/python/pants/pantsd/process_manager.py::ProcessManager._kill\n def _kill(self, kill_sig):\n \"\"\"Send a signal to the current process.\"\"\"\n if self.pid:\n os.kill(self.pid, kill_sig)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.terminate","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.terminate\n def terminate(self, signal_chain=KILL_CHAIN, kill_wait=KILL_WAIT_SEC, purge=True):\n \"\"\"Ensure a process is terminated by sending a chain of kill signals (SIGTERM, SIGKILL).\"\"\"\n alive = self.is_alive()\n if alive:\n logger.debug('terminating {}'.format(self._name))\n for signal_type in signal_chain:\n pid = self.pid\n try:\n logger.debug('sending signal {} to pid {}'.format(signal_type, pid))\n self._kill(signal_type)\n except OSError as e:\n logger.warning('caught OSError({e!s}) during attempt to kill -{signal} {pid}!'\n .format(e=e, signal=signal_type, pid=pid))\n\n # Wait up to kill_wait seconds to terminate or move onto the next signal.\n try:\n if self._deadline_until(self.is_dead, kill_wait):\n alive = False\n logger.debug('successfully terminated pid {}'.format(pid))\n break\n except self.Timeout:\n # Loop to the next kill signal on timeout.\n pass\n\n if alive:\n raise self.NonResponsiveProcess('failed to kill pid {pid} with signals {chain}'\n .format(pid=self.pid, chain=signal_chain))\n\n if purge:\n self.purge_metadata(force=True)","label":0},{"cid":"src/python/pants/pantsd/process_manager.py::ProcessManager.daemonize","text":"src/python/pants/pantsd/process_manager.py::ProcessManager.daemonize\n def daemonize(self, pre_fork_opts=None, post_fork_parent_opts=None, post_fork_child_opts=None,\n write_pid=True):\n \"\"\"Perform a double-fork, execute callbacks and write the child pid file.\n\n The double-fork here is necessary to truly daemonize the subprocess such that it can never\n take control of a tty. The initial fork and setsid() creates a new, isolated process group\n and also makes the first child a session leader (which can still acquire a tty). By forking a\n second time, we ensure that the second child can never acquire a controlling terminal because\n it's no longer a session leader - but it now has its own separate process group.\n\n Additionally, a normal daemon implementation would typically perform an os.umask(0) to reset\n the processes file mode creation mask post-fork. We do not do this here (and in daemon_spawn\n below) due to the fact that the daemons that pants would run are typically personal user\n daemons. Having a disparate umask from pre-vs-post fork causes files written in each phase to\n differ in their permissions without good reason - in this case, we want to inherit the umask.\n \"\"\"\n self.purge_metadata()\n self.pre_fork(**pre_fork_opts or {})\n logger.debug('forking %s', self)\n pid = os.fork()\n if pid == 0:\n os.setsid()\n second_pid = os.fork()\n if second_pid == 0:\n try:\n os.chdir(self._buildroot)\n self.post_fork_child(**post_fork_child_opts or {})\n except Exception:\n logger.critical(traceback.format_exc())\n finally:\n os._exit(0)\n else:\n try:\n if write_pid: self.write_pid(second_pid)\n self.post_fork_parent(**post_fork_parent_opts or {})\n except Exception:\n logger.critical(traceback.format_exc())\n finally:\n os._exit(0)\n else:\n # This prevents un-reaped, throw-away parent proce","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.implementation_version","text":"src/python/pants/task/task.py::TaskBase.implementation_version\n @classmethod\n def implementation_version(cls):\n \"\"\"\n :API: public\n \"\"\"\n return [('TaskBase', 2)]","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.implementation_version_str","text":"src/python/pants/task/task.py::TaskBase.implementation_version_str\n @classmethod\n @memoized_method\n def implementation_version_str(cls):\n return '.'.join(['_'.join(map(str, x)) for x in cls.implementation_version()])","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.stable_name","text":"src/python/pants/task/task.py::TaskBase.stable_name\n @classmethod\n def stable_name(cls):\n \"\"\"The stable name of this task type.\n\n We synthesize subclasses of the task types at runtime, and these synthesized subclasses\n may have random names (e.g., in tests), so this gives us a stable name to use across runs,\n e.g., in artifact cache references.\n \"\"\"\n return cls._stable_name or cls._compute_stable_name()","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._compute_stable_name","text":"src/python/pants/task/task.py::TaskBase._compute_stable_name\n @classmethod\n def _compute_stable_name(cls):\n return '{}_{}'.format(cls.__module__, cls.__name__).replace('.', '_')","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.subsystem_dependencies","text":"src/python/pants/task/task.py::TaskBase.subsystem_dependencies\n @classmethod\n def subsystem_dependencies(cls):\n return super(TaskBase, cls).subsystem_dependencies() + (CacheSetup.scoped(cls),\n BuildInvalidator.Factory)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.product_types","text":"src/python/pants/task/task.py::TaskBase.product_types\n @classmethod\n def product_types(cls):\n \"\"\"The list of products this Task produces. Set the product type(s) for this\n task i.e. the product type(s) this task creates e.g ['classes'].\n\n By default, each task is considered as creating a unique product type(s).\n Subclasses that create products, should override this to specify their unique product type(s).\n\n :API: public\n \"\"\"\n return []","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.known_scope_infos","text":"src/python/pants/task/task.py::TaskBase.known_scope_infos\n @classmethod\n def known_scope_infos(cls):\n \"\"\"Yields ScopeInfo for all known scopes for this task, in no particular order.\"\"\"\n # The task's own scope.\n yield cls.get_scope_info()\n # The scopes of any task-specific subsystems it uses.\n for dep in cls.subsystem_dependencies_iter():\n if not dep.is_global():\n yield dep.subsystem_cls.get_scope_info(subscope=dep.scope)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.supports_passthru_args","text":"src/python/pants/task/task.py::TaskBase.supports_passthru_args\n @classmethod\n def supports_passthru_args(cls):\n \"\"\"Subclasses may override to indicate that they can use passthru args.\n\n :API: public\n \"\"\"\n return False","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._scoped_options","text":"src/python/pants/task/task.py::TaskBase._scoped_options\n @classmethod\n def _scoped_options(cls, options):\n return options[cls.options_scope]","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.get_alternate_target_roots","text":"src/python/pants/task/task.py::TaskBase.get_alternate_target_roots\n @classmethod\n def get_alternate_target_roots(cls, options, address_mapper, build_graph):\n # Subclasses should not generally need to override this method.\n return cls.alternate_target_roots(cls._scoped_options(options), address_mapper, build_graph)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.alternate_target_roots","text":"src/python/pants/task/task.py::TaskBase.alternate_target_roots\n @classmethod\n def alternate_target_roots(cls, options, address_mapper, build_graph):\n \"\"\"Allows a Task to propose alternate target roots from those specified on the CLI.\n\n At most 1 unique proposal is allowed amongst all tasks involved in the run. If more than 1\n unique list of target roots is proposed an error is raised during task scheduling.\n\n :API: public\n\n :returns list: The new target roots to use or None to accept the CLI specified target roots.\n \"\"\"","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.invoke_prepare","text":"src/python/pants/task/task.py::TaskBase.invoke_prepare\n @classmethod\n def invoke_prepare(cls, options, round_manager):\n # Subclasses should not generally need to override this method.\n return cls.prepare(cls._scoped_options(options), round_manager)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.prepare","text":"src/python/pants/task/task.py::TaskBase.prepare\n @classmethod\n def prepare(cls, options, round_manager):\n \"\"\"Prepares a task for execution.\n\n Called before execution and prior to any tasks that may be (indirectly) depended upon.\n\n Typically a task that requires products from other goals would register interest in those\n products here and then retrieve the requested product mappings when executed.\n\n :API: public\n \"\"\"","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.__init__","text":"src/python/pants/task/task.py::TaskBase.__init__\n def __init__(self, context, workdir):\n \"\"\"Subclass __init__ methods, if defined, *must* follow this idiom:\n\n class MyTask(Task):\n def __init__(self, *args, **kwargs):\n super(MyTask, self).__init__(*args, **kwargs)\n ...\n\n This allows us to change Task.__init__()'s arguments without\n changing every subclass. If the subclass does not need its own\n initialization, this method can (and should) be omitted entirely.\n\n :API: public\n \"\"\"\n super(TaskBase, self).__init__()\n self.context = context\n self._workdir = workdir\n\n self._task_name = type(self).__name__\n self._cache_key_errors = set()\n self._cache_factory = CacheSetup.create_cache_factory_for_task(self)\n self._force_invalidated = False","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._build_invalidator","text":"src/python/pants/task/task.py::TaskBase._build_invalidator\n @memoized_method\n def _build_invalidator(self, root=False):\n build_task = None if root else self.fingerprint\n return BuildInvalidator.Factory.create(build_task=build_task)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.get_options","text":"src/python/pants/task/task.py::TaskBase.get_options\n def get_options(self):\n \"\"\"Returns the option values for this task's scope.\n\n :API: public\n \"\"\"\n return self.context.options.for_scope(self.options_scope)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.get_passthru_args","text":"src/python/pants/task/task.py::TaskBase.get_passthru_args\n def get_passthru_args(self):\n \"\"\"\n :API: public\n \"\"\"\n if not self.supports_passthru_args():\n raise TaskError('{0} Does not support passthru args.'.format(self.stable_name()))\n else:\n return self.context.options.passthru_args_for_scope(self.options_scope)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.workdir","text":"src/python/pants/task/task.py::TaskBase.workdir\n @memoized_property\n def workdir(self):\n \"\"\"A scratch-space for this task that will be deleted by `clean-all`.\n\n It's guaranteed that no other task has been given this workdir path to use and that the workdir\n exists.\n\n :API: public\n \"\"\"\n safe_mkdir(self._workdir)\n return self._workdir","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._options_fingerprint","text":"src/python/pants/task/task.py::TaskBase._options_fingerprint\n def _options_fingerprint(self, scope):\n options_hasher = sha1()\n options_hasher.update(scope)\n options_fp = OptionsFingerprinter.combined_options_fingerprint_for_scope(\n scope,\n self.context.options,\n build_graph=self.context.build_graph,\n include_passthru=self.supports_passthru_args(),\n )\n options_hasher.update(options_fp)\n return options_hasher.hexdigest()","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.fingerprint","text":"src/python/pants/task/task.py::TaskBase.fingerprint\n @memoized_property\n def fingerprint(self):\n \"\"\"Returns a fingerprint for the identity of the task.\n\n A task fingerprint is composed of the options the task is currently running under.\n Useful for invalidating unchanging targets being executed beneath changing task\n options that affect outputted artifacts.\n\n A task's fingerprint is only valid afer the task has been fully initialized.\n \"\"\"\n hasher = sha1()\n hasher.update(self.stable_name())\n hasher.update(self._options_fingerprint(self.options_scope))\n hasher.update(self.implementation_version_str())\n # TODO: this is not recursive, but should be: see #2739\n for dep in self.subsystem_dependencies_iter():\n hasher.update(self._options_fingerprint(dep.options_scope()))\n return str(hasher.hexdigest())","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.artifact_cache_reads_enabled","text":"src/python/pants/task/task.py::TaskBase.artifact_cache_reads_enabled\n def artifact_cache_reads_enabled(self):\n return self._cache_factory.read_cache_available()","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.artifact_cache_writes_enabled","text":"src/python/pants/task/task.py::TaskBase.artifact_cache_writes_enabled\n def artifact_cache_writes_enabled(self):\n return self._cache_factory.write_cache_available()","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.invalidate","text":"src/python/pants/task/task.py::TaskBase.invalidate\n def invalidate(self):\n \"\"\"Invalidates all targets for this task.\"\"\"\n self._build_invalidator().force_invalidate_all()","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.create_target_dirs","text":"src/python/pants/task/task.py::TaskBase.create_target_dirs\n @property\n def create_target_dirs(self):\n \"\"\"Whether to create a results_dir per VersionedTarget in the workdir of the Task.\n\n This defaults to the value of `self.cache_target_dirs` (as caching them requires\n creating them), but may be overridden independently to create the dirs without caching\n them.\n\n :API: public\n \"\"\"\n return self.cache_target_dirs or False","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.cache_target_dirs","text":"src/python/pants/task/task.py::TaskBase.cache_target_dirs\n @property\n def cache_target_dirs(self):\n \"\"\"Whether to cache files in VersionedTarget's results_dir after exiting an invalidated block.\n\n Subclasses may override this method to return True if they wish to use this style\n of \"automated\" caching, where each VersionedTarget is given an associated results directory,\n which will automatically be uploaded to the cache. Tasks should place the output files\n for each VersionedTarget in said results directory. It is highly suggested to follow this\n schema for caching, rather than manually making updates to the artifact cache.\n\n :API: public\n \"\"\"\n return False","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.incremental","text":"src/python/pants/task/task.py::TaskBase.incremental\n @property\n def incremental(self):\n \"\"\"Whether this Task implements incremental building of individual targets.\n\n Incremental tasks with `cache_target_dirs` set will have the results_dir of the previous build\n for a target cloned into the results_dir for the current build (where possible). This\n copy-on-write behaviour allows for immutability of the results_dir once a target has been\n marked valid.\n\n :API: public\n \"\"\"\n return False","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.cache_incremental","text":"src/python/pants/task/task.py::TaskBase.cache_incremental\n @property\n def cache_incremental(self):\n \"\"\"For incremental tasks, indicates whether the results of incremental builds should be cached.\n\n Deterministic per-target incremental compilation is a relatively difficult thing to implement,\n so this property provides an escape hatch to avoid caching things in that riskier case.\n\n :API: public\n \"\"\"\n return False","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.invalidated","text":"src/python/pants/task/task.py::TaskBase.invalidated\n @contextmanager\n def invalidated(self,\n targets,\n invalidate_dependents=False,\n silent=False,\n fingerprint_strategy=None,\n topological_order=False):\n \"\"\"Checks targets for invalidation, first checking the artifact cache.\n\n Subclasses call this to figure out what to work on.\n\n :API: public\n\n :param targets: The targets to check for changes.\n :param invalidate_dependents: If True then any targets depending on changed targets are\n invalidated.\n :param silent: If true, suppress logging information about target invalidation.\n :param fingerprint_strategy: A FingerprintStrategy instance, which can do per task,\n finer grained fingerprinting of a given Target.\n :param topological_order: Whether to invalidate in dependency order.\n\n If no exceptions are thrown by work in the block, the build cache is updated for the targets.\n Note: the artifact cache is not updated. That must be done manually.\n\n :returns: Yields an InvalidationCheck object reflecting the targets.\n :rtype: InvalidationCheck\n \"\"\"\n invalidation_check = self._do_invalidation_check(fingerprint_strategy,\n invalidate_dependents,\n targets,\n topological_order)\n\n self._maybe_create_results_dirs(invalidation_check.all_vts)\n\n if invalidation_check.invalid_vts and self.artifact_cache_reads_enabled():\n with self.context.new_workunit('cache'):\n cached_vts, uncached_vts, uncached_causes = \\\n self.check_artifact_cache(self.check_artifact_cache_for(invalidation_check))\n if cached_vts:\n cached_targets = [vt.target for vt in cached_vts]\n self.context.run_tracker.artifact_cache_stats.add_hit","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._update_invalidation_report","text":"src/python/pants/task/task.py::TaskBase._update_invalidation_report\n def _update_invalidation_report(self, invalidation_check, phase):\n invalidation_report = self.context.invalidation_report\n if invalidation_report:\n for vts in invalidation_check.all_vts:\n invalidation_report.add_vts(self._task_name, vts.targets, vts.cache_key, vts.valid,\n phase=phase)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._do_invalidation_check","text":"src/python/pants/task/task.py::TaskBase._do_invalidation_check\n def _do_invalidation_check(self,\n fingerprint_strategy,\n invalidate_dependents,\n targets,\n topological_order):\n\n if self._cache_factory.ignore:\n cache_key_generator = UncacheableCacheKeyGenerator()\n else:\n cache_key_generator = CacheKeyGenerator(\n self.context.options.for_global_scope().cache_key_gen_version,\n self.fingerprint)\n\n cache_manager = InvalidationCacheManager(self.workdir,\n cache_key_generator,\n self._build_invalidator(),\n invalidate_dependents,\n fingerprint_strategy=fingerprint_strategy,\n invalidation_report=self.context.invalidation_report,\n task_name=self._task_name,\n task_version=self.implementation_version_str(),\n artifact_write_callback=self.maybe_write_artifact)\n\n # If this Task's execution has been forced, invalidate all our target fingerprints.\n if self._cache_factory.ignore and not self._force_invalidated:\n self.invalidate()\n self._force_invalidated = True\n\n return cache_manager.check(targets, topological_order=topological_order)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.maybe_write_artifact","text":"src/python/pants/task/task.py::TaskBase.maybe_write_artifact\n def maybe_write_artifact(self, vt):\n if self._should_cache_target_dir(vt):\n self.update_artifact_cache([(vt, [vt.current_results_dir])])","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._launch_background_workdir_cleanup","text":"src/python/pants/task/task.py::TaskBase._launch_background_workdir_cleanup\n def _launch_background_workdir_cleanup(self, vts):\n workdir_build_cleanup_job = Work(self._cleanup_workdir_stale_builds,\n [(vts,)],\n 'workdir_build_cleanup')\n self.context.submit_background_work_chain([workdir_build_cleanup_job])","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._cleanup_workdir_stale_builds","text":"src/python/pants/task/task.py::TaskBase._cleanup_workdir_stale_builds\n def _cleanup_workdir_stale_builds(self, vts):\n # workdir_max_build_entries has been assured of not None before invoking this method.\n workdir_max_build_entries = self.context.options.for_global_scope().workdir_max_build_entries\n max_entries_per_target = max(2, workdir_max_build_entries)\n for vt in vts:\n live_dirs = list(vt.live_dirs())\n if not live_dirs:\n continue\n root_dir = os.path.dirname(vt.results_dir)\n safe_rm_oldest_items_in_dir(root_dir, max_entries_per_target, excludes=live_dirs)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._should_cache_target_dir","text":"src/python/pants/task/task.py::TaskBase._should_cache_target_dir\n def _should_cache_target_dir(self, vt):\n \"\"\"Return true if the given vt should be written to a cache (if configured).\"\"\"\n return (\n self.cache_target_dirs and\n vt.cacheable and\n (not vt.is_incremental or self.cache_incremental) and\n self.artifact_cache_writes_enabled()\n )","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._maybe_create_results_dirs","text":"src/python/pants/task/task.py::TaskBase._maybe_create_results_dirs\n def _maybe_create_results_dirs(self, vts):\n \"\"\"If `cache_target_dirs`, create results_dirs for the given versioned targets.\"\"\"\n if self.create_target_dirs:\n for vt in vts:\n vt.create_results_dir()","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.check_artifact_cache_for","text":"src/python/pants/task/task.py::TaskBase.check_artifact_cache_for\n def check_artifact_cache_for(self, invalidation_check):\n \"\"\"Decides which VTS to check the artifact cache for.\n\n By default we check for each invalid target. Can be overridden, e.g., to\n instead check only for a single artifact for the entire target set.\n \"\"\"\n return invalidation_check.invalid_vts","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.check_artifact_cache","text":"src/python/pants/task/task.py::TaskBase.check_artifact_cache\n def check_artifact_cache(self, vts):\n \"\"\"Checks the artifact cache for the specified list of VersionedTargetSets.\n\n Returns a tuple (cached, uncached, uncached_causes) of VersionedTargets that were\n satisfied/unsatisfied from the cache. Uncached VTS are also attached with their\n causes for the miss: `False` indicates a legit miss while `UnreadableArtifact`\n is due to either local or remote cache failures.\n \"\"\"\n return self.do_check_artifact_cache(vts)","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.do_check_artifact_cache","text":"src/python/pants/task/task.py::TaskBase.do_check_artifact_cache\n def do_check_artifact_cache(self, vts, post_process_cached_vts=None):\n \"\"\"Checks the artifact cache for the specified list of VersionedTargetSets.\n\n Returns a pair (cached, uncached) of VersionedTargets that were\n satisfied/unsatisfied from the cache.\n \"\"\"\n if not vts:\n return [], [], []\n\n read_cache = self._cache_factory.get_read_cache()\n items = [(read_cache, vt.cache_key, vt.current_results_dir if self.cache_target_dirs else None)\n for vt in vts]\n res = self.context.subproc_map(call_use_cached_files, items)\n\n cached_vts = []\n uncached_vts = []\n uncached_causes = []\n\n # Note that while the input vts may represent multiple targets (for tasks that overrride\n # check_artifact_cache_for), the ones we return must represent single targets.\n # Once flattened, cached/uncached vts are in separate lists. Each uncached vts is paired\n # with why it is missed for stat reporting purpose.\n for vt, was_in_cache in zip(vts, res):\n if was_in_cache:\n cached_vts.extend(vt.versioned_targets)\n else:\n uncached_vts.extend(vt.versioned_targets)\n uncached_causes.extend(repeat(was_in_cache, len(vt.versioned_targets)))\n if isinstance(was_in_cache, UnreadableArtifact):\n self._cache_key_errors.update(was_in_cache.key)\n\n if post_process_cached_vts:\n post_process_cached_vts(cached_vts)\n for vt in cached_vts:\n vt.update()\n return cached_vts, uncached_vts, uncached_causes","label":0},{"cid":"src/python/pants/task/task.py::TaskBase.update_artifact_cache","text":"src/python/pants/task/task.py::TaskBase.update_artifact_cache\n def update_artifact_cache(self, vts_artifactfiles_pairs):\n \"\"\"Write to the artifact cache, if we're configured to.\n\n vts_artifactfiles_pairs - a list of pairs (vts, artifactfiles) where\n - vts is single VersionedTargetSet.\n - artifactfiles is a list of absolute paths to artifacts for the VersionedTargetSet.\n \"\"\"\n update_artifact_cache_work = self._get_update_artifact_cache_work(vts_artifactfiles_pairs)\n if update_artifact_cache_work:\n self.context.submit_background_work_chain([update_artifact_cache_work],\n parent_workunit_name='cache')","label":0},{"cid":"src/python/pants/task/task.py::TaskBase._get_update_artifact_cache_work","text":"src/python/pants/task/task.py::TaskBase._get_update_artifact_cache_work\n def _get_update_artifact_cache_work(self, vts_artifactfiles_pairs):\n \"\"\"Create a Work instance to update an artifact cache, if we're configured to.\n\n vts_artifactfiles_pairs - a list of pairs (vts, artifactfiles) where\n - vts is single VersionedTargetSet.\n - artifactfiles is a list of paths to artifacts for the VersionedTargetSet.\n \"\"\"\n cache = self._cache_factory.get_write_cache()\n if cache:\n if len(vts_artifactfiles_pairs) == 0:\n return None\n # Do some reporting.\n targets = set()\n for vts, _ in vts_artifactfiles_pairs:\n targets.update(vts.targets)\n\n self._report_targets(\n 'Caching artifacts for ',\n list(targets),\n '.',\n logger=self.context.log.debug,\n )\n\n always_overwrite = self._cache_factory.overwrite()\n\n # Cache the artifacts.\n args_tuples = []\n for vts, artifactfiles in vts_artifactfiles_pairs:\n overwrite = always_overwrite or vts.cache_key in self._cache_key_errors\n args_tuples.append((cache, vts.cache_key, artifactfiles, overwrite))\n\n return Work(lambda x: self.context.subproc_map(call_insert, x), [(args_tuples,)], 'insert')\n else:\n return None","label":0}],"string":"[\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.__init__\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.__init__\\n def __init__(self, root_dir, options, build_config, run_tracker, reporting,\\n daemon_graph_helper=None, exiter=sys.exit):\\n \\\"\\\"\\\"\\n :param str root_dir: The root directory of the pants workspace (aka the \\\"build root\\\").\\n :param Options options: The global, pre-initialized Options instance.\\n :param BuildConfiguration build_config: A pre-initialized BuildConfiguration instance.\\n :param Runtracker run_tracker: The global, pre-initialized/running RunTracker instance.\\n :param Reporting reporting: The global, pre-initialized Reporting instance.\\n :param LegacyGraphHelper daemon_graph_helper: A LegacyGraphHelper instance for graph reuse. (Optional)\\n :param func exiter: A function that accepts an exit code value and exits. (for tests, Optional)\\n \\\"\\\"\\\"\\n self._root_dir = root_dir\\n self._options = options\\n self._build_config = build_config\\n self._run_tracker = run_tracker\\n self._reporting = reporting\\n self._daemon_graph_helper = daemon_graph_helper\\n self._exiter = exiter\\n\\n self._requested_goals = self._options.goals\\n self._help_request = self._options.help_request\\n self._build_file_parser = BuildFileParser(self._build_config, self._root_dir)\\n self._build_graph = None\\n self._address_mapper = None\\n\\n self._global_options = options.for_global_scope()\\n self._tag = self._global_options.tag\\n self._fail_fast = self._global_options.fail_fast\\n self._explain = self._global_options.explain\\n self._kill_nailguns = self._global_options.kill_nailguns\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._handle_help\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._handle_help\\n def _handle_help(self, help_request):\\n \\\"\\\"\\\"Handle requests for `help` information.\\\"\\\"\\\"\\n if help_request:\\n help_printer = HelpPrinter(self._options)\\n result = help_printer.print_help()\\n self._exiter(result)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._init_graph\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._init_graph\\n def _init_graph(self, use_engine, pants_ignore_patterns, build_ignore_patterns,\\n exclude_target_regexps, target_specs, workdir, graph_helper=None,\\n subproject_build_roots=None):\\n \\\"\\\"\\\"Determine the BuildGraph, AddressMapper and spec_roots for a given run.\\n\\n :param bool use_engine: Whether or not to use the v2 engine to construct the BuildGraph.\\n :param list pants_ignore_patterns: The pants ignore patterns from '--pants-ignore'.\\n :param list build_ignore_patterns: The build ignore patterns from '--build-ignore',\\n applied during BUILD file searching.\\n :param str workdir: The pants workdir.\\n :param list exclude_target_regexps: Regular expressions for targets to be excluded.\\n :param list target_specs: The original target specs.\\n :param LegacyGraphHelper graph_helper: A LegacyGraphHelper to use for graph construction,\\n if available. This would usually come from the daemon.\\n :returns: A tuple of (BuildGraph, AddressMapper, spec_roots).\\n \\\"\\\"\\\"\\n # N.B. Use of the daemon implies use of the v2 engine.\\n if graph_helper or use_engine:\\n # The daemon may provide a `graph_helper`. If that's present, use it for graph construction.\\n if not graph_helper:\\n native = Native.create(self._global_options)\\n native.set_panic_handler()\\n graph_helper = EngineInitializer.setup_legacy_graph(\\n pants_ignore_patterns,\\n workdir,\\n native=native,\\n build_file_aliases=self._build_config.registered_aliases(),\\n build_ignore_patterns=build_ignore_patterns,\\n exclude_target_regexps=exclude_target_regexps,\\n subproject_roots=subproject_build_roots,\\n include_trace_on_error=self._options.for_global_scope().print_exception_stacktrace\\n )\\n\\n target_roots = TargetRoots.create(options=self._opt\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._determine_goals\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._determine_goals\\n def _determine_goals(self, requested_goals):\\n \\\"\\\"\\\"Check and populate the requested goals for a given run.\\\"\\\"\\\"\\n\\n spec_parser = CmdLineSpecParser(self._root_dir)\\n for goal in requested_goals:\\n if self._address_mapper.is_valid_single_address(spec_parser.parse_spec(goal)):\\n logger.warning(\\\"Command-line argument '{0}' is ambiguous and was assumed to be \\\"\\n \\\"a goal. If this is incorrect, disambiguate it with ./{0}.\\\".format(goal))\\n\\n goals = [Goal.by_name(goal) for goal in requested_goals]\\n return goals\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._specs_to_targets\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._specs_to_targets\\n def _specs_to_targets(self, specs):\\n \\\"\\\"\\\"Populate the BuildGraph and target list from a set of input specs.\\\"\\\"\\\"\\n with self._run_tracker.new_workunit(name='parse', labels=[WorkUnitLabel.SETUP]):\\n def filter_for_tag(tag):\\n return lambda target: tag in map(str, target.tags)\\n\\n tag_filter = wrap_filters(create_filters(self._tag, filter_for_tag))\\n\\n def generate_targets(specs):\\n for address in self._build_graph.inject_specs_closure(specs, self._fail_fast):\\n target = self._build_graph.get_target(address)\\n if tag_filter(target):\\n yield target\\n\\n return list(generate_targets(specs))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._maybe_launch_pantsd\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._maybe_launch_pantsd\\n def _maybe_launch_pantsd(self, pantsd_launcher):\\n \\\"\\\"\\\"Launches pantsd if configured to do so.\\\"\\\"\\\"\\n if self._global_options.enable_pantsd:\\n # Avoid runtracker output if pantsd is disabled. Otherwise, show up to inform the user its on.\\n with self._run_tracker.new_workunit(name='pantsd', labels=[WorkUnitLabel.SETUP]):\\n pantsd_launcher.maybe_launch()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._should_be_quiet\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._should_be_quiet\\n def _should_be_quiet(self, goals):\\n if self._explain:\\n return True\\n\\n if self._global_options.get_rank('quiet') > RankedValue.HARDCODED:\\n return self._global_options.quiet\\n\\n return any(goal.has_task_of_type(QuietTaskMixin) for goal in goals)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._setup_context\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory._setup_context\\n def _setup_context(self):\\n with self._run_tracker.new_workunit(name='setup', labels=[WorkUnitLabel.SETUP]):\\n self._build_graph, self._address_mapper, spec_roots = self._init_graph(\\n self._global_options.enable_v2_engine,\\n self._global_options.pants_ignore,\\n self._global_options.build_ignore,\\n self._global_options.exclude_target_regexp,\\n self._options.target_specs,\\n self._global_options.pants_workdir,\\n self._daemon_graph_helper,\\n self._global_options.subproject_roots,\\n )\\n\\n goals = self._determine_goals(self._requested_goals)\\n is_quiet = self._should_be_quiet(goals)\\n\\n target_roots = self._specs_to_targets(spec_roots)\\n\\n # Now that we've parsed the bootstrap BUILD files, and know about the SCM system.\\n self._run_tracker.run_info.add_scm_info()\\n\\n # Update the Reporting settings now that we have options and goal info.\\n invalidation_report = self._reporting.update_reporting(self._global_options,\\n is_quiet,\\n self._run_tracker)\\n\\n context = Context(options=self._options,\\n run_tracker=self._run_tracker,\\n target_roots=target_roots,\\n requested_goals=self._requested_goals,\\n build_graph=self._build_graph,\\n build_file_parser=self._build_file_parser,\\n address_mapper=self._address_mapper,\\n invalidation_report=invalidation_report)\\n return goals, context\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.setup\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunnerFactory.setup\\n def setup(self):\\n self._handle_help(self._help_request)\\n goals, context = self._setup_context()\\n return GoalRunner(context=context,\\n goals=goals,\\n run_tracker=self._run_tracker,\\n kill_nailguns=self._kill_nailguns,\\n exiter=self._exiter)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunner.__init__\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunner.__init__\\n def __init__(self, context, goals, run_tracker, kill_nailguns, exiter=sys.exit):\\n \\\"\\\"\\\"\\n :param Context context: The global, pre-initialized Context as created by GoalRunnerFactory.\\n :param list[Goal] goals: The list of goals to act on.\\n :param Runtracker run_tracker: The global, pre-initialized/running RunTracker instance.\\n :param bool kill_nailguns: Whether or not to kill nailguns after the run.\\n :param func exiter: A function that accepts an exit code value and exits (for tests, Optional).\\n \\\"\\\"\\\"\\n self._context = context\\n self._goals = goals\\n self._run_tracker = run_tracker\\n self._kill_nailguns = kill_nailguns\\n self._exiter = exiter\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunner.subsystems\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunner.subsystems\\n @classmethod\\n def subsystems(cls):\\n \\\"\\\"\\\"Subsystems used outside of any task.\\\"\\\"\\\"\\n return {\\n SourceRootConfig,\\n Reporting,\\n Reproducer,\\n RunTracker,\\n Changed.Factory,\\n BinaryUtil.Factory,\\n Subprocess.Factory\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunner._execute_engine\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunner._execute_engine\\n def _execute_engine(self):\\n workdir = self._context.options.for_global_scope().pants_workdir\\n if not workdir.endswith('.pants.d'):\\n self._context.log.error('Pants working directory should end with \\\\'.pants.d\\\\', currently it is {}\\\\n'\\n .format(workdir))\\n return 1\\n\\n unknown_goals = [goal.name for goal in self._goals if not goal.ordered_task_names()]\\n if unknown_goals:\\n self._context.log.error('Unknown goal(s): {}\\\\n'.format(' '.join(unknown_goals)))\\n return 1\\n\\n engine = RoundEngine()\\n result = engine.execute(self._context, self._goals)\\n\\n if self._context.invalidation_report:\\n self._context.invalidation_report.report()\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/bin/goal_runner.py::GoalRunner.run\",\n \"text\": \"src/python/pants/bin/goal_runner.py::GoalRunner.run\\n def run(self):\\n should_kill_nailguns = self._kill_nailguns\\n\\n try:\\n result = self._execute_engine()\\n if result:\\n self._run_tracker.set_root_outcome(WorkUnit.FAILURE)\\n except KeyboardInterrupt:\\n self._run_tracker.set_root_outcome(WorkUnit.FAILURE)\\n # On ctrl-c we always kill nailguns, otherwise they might keep running\\n # some heavyweight compilation and gum up the system during a subsequent run.\\n should_kill_nailguns = True\\n raise\\n except Exception:\\n self._run_tracker.set_root_outcome(WorkUnit.FAILURE)\\n raise\\n finally:\\n # Must kill nailguns only after run_tracker.end() is called, otherwise there may still\\n # be pending background work that needs a nailgun.\\n if should_kill_nailguns:\\n # TODO: This is JVM-specific and really doesn't belong here.\\n # TODO: Make this more selective? Only kill nailguns that affect state?\\n # E.g., checkstyle may not need to be killed.\\n NailgunProcessGroup().killall()\\n\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_bootstrap_options\\n @classmethod\\n def register_bootstrap_options(cls, register):\\n \\\"\\\"\\\"Register bootstrap options.\\n\\n \\\"Bootstrap options\\\" are a small set of options whose values are useful when registering other\\n options. Therefore we must bootstrap them early, before other options are registered, let\\n alone parsed.\\n\\n Bootstrap option values can be interpolated into the config file, and can be referenced\\n programatically in registration code, e.g., as register.bootstrap.pants_workdir.\\n\\n Note that regular code can also access these options as normal global-scope options. Their\\n status as \\\"bootstrap options\\\" is only pertinent during option registration.\\n \\\"\\\"\\\"\\n buildroot = get_buildroot()\\n default_distdir_name = 'dist'\\n default_distdir = os.path.join(buildroot, default_distdir_name)\\n default_rel_distdir = '/{}/'.format(default_distdir_name)\\n\\n # Although logging supports the WARN level, its not documented and could conceivably be yanked.\\n # Since pants has supported 'warn' since inception, leave the 'warn' choice as-is but explicitly\\n # setup a 'WARN' logging level name that maps to 'WARNING'.\\n logging.addLevelName(logging.WARNING, 'WARN')\\n register('-l', '--level', choices=['debug', 'info', 'warn'], default='info', recursive=True,\\n help='Set the logging level.')\\n register('-q', '--quiet', type=bool, recursive=True, daemon=False,\\n help='Squelches most console output. NOTE: Some tasks default to behaving quietly: '\\n 'inverting this option supports making them noisier than they would be otherwise.')\\n # Not really needed in bootstrap options, but putting it here means it displays right\\n # after -l and -q in help output, which is conveniently contextual.\\n register('--colors', type=bool, default=sys.stdout.isatty(), recursive=True, daemon=False,\\n help='Set whether log messages are displa\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options\",\n \"text\": \"src/python/pants/option/global_options.py::GlobalOptionsRegistrar.register_options\\n @classmethod\\n def register_options(cls, register):\\n \\\"\\\"\\\"Register options not tied to any particular task or subsystem.\\\"\\\"\\\"\\n # The bootstrap options need to be registered on the post-bootstrap Options instance, so it\\n # won't choke on them on the command line, and also so we can access their values as regular\\n # global-scope options, for convenience.\\n cls.register_bootstrap_options(register)\\n\\n register('-x', '--time', type=bool,\\n help='Output a timing report at the end of the run.')\\n register('-e', '--explain', type=bool,\\n help='Explain the execution of goals.')\\n register('--tag', type=list, metavar='[+-]tag1,tag2,...',\\n help=\\\"Include only targets with these tags (optional '+' prefix) or without these \\\"\\n \\\"tags ('-' prefix). Useful with ::, to find subsets of targets \\\"\\n \\\"(e.g., integration tests.)\\\")\\n\\n register('-t', '--timeout', advanced=True, type=int, metavar='<seconds>',\\n help='Number of seconds to wait for http connections.')\\n # TODO: After moving to the new options system these abstraction leaks can go away.\\n register('-k', '--kill-nailguns', advanced=True, type=bool,\\n help='Kill nailguns before exiting')\\n register('-i', '--interpreter', advanced=True, default=[], type=list,\\n metavar='<requirement>',\\n removal_version='1.5.0.dev0',\\n removal_hint='Use --interpreter-constraints in scope python-setup instead.',\\n help=\\\"Constrain what Python interpreters to use. Uses Requirement format from \\\"\\n \\\"pkg_resources, e.g. 'CPython>=2.7,<3' or 'PyPy'. By default, no constraints \\\"\\n \\\"are used. Multiple constraints may be added. They will be ORed together.\\\")\\n register('--fail-fast', advanced=True, type=bool, recursive=True,\\n help='Exit as quickly as possible on error, rat\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.complete_scopes\",\n \"text\": \"src/python/pants/option/options.py::Options.complete_scopes\\n @classmethod\\n def complete_scopes(cls, scope_infos):\\n \\\"\\\"\\\"Expand a set of scopes to include all enclosing scopes.\\n\\n E.g., if the set contains `foo.bar.baz`, ensure that it also contains `foo.bar` and `foo`.\\n\\n Also adds any deprecated scopes.\\n \\\"\\\"\\\"\\n ret = {GlobalOptionsRegistrar.get_scope_info()}\\n original_scopes = set()\\n for si in scope_infos:\\n ret.add(si)\\n original_scopes.add(si.scope)\\n if si.deprecated_scope:\\n ret.add(ScopeInfo(si.deprecated_scope, si.category, si.optionable_cls))\\n original_scopes.add(si.deprecated_scope)\\n\\n # TODO: Once scope name validation is enforced (so there can be no dots in scope name\\n # components) we can replace this line with `for si in scope_infos:`, because it will\\n # not be possible for a deprecated_scope to introduce any new intermediate scopes.\\n for si in copy.copy(ret):\\n for scope in all_enclosing_scopes(si.scope, allow_global=False):\\n if scope not in original_scopes:\\n ret.add(ScopeInfo(scope, ScopeInfo.INTERMEDIATE))\\n return ret\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.create\",\n \"text\": \"src/python/pants/option/options.py::Options.create\\n @classmethod\\n def create(cls, env, config, known_scope_infos, args=None, bootstrap_option_values=None,\\n option_tracker=None):\\n \\\"\\\"\\\"Create an Options instance.\\n\\n :param env: a dict of environment variables.\\n :param :class:`pants.option.config.Config` config: data from a config file.\\n :param known_scope_infos: ScopeInfos for all scopes that may be encountered.\\n :param args: a list of cmd-line args; defaults to `sys.argv` if None is supplied.\\n :param bootstrap_option_values: An optional namespace containing the values of bootstrap\\n options. We can use these values when registering other options.\\n :param :class:`pants.option.option_tracker.OptionTracker` option_tracker: option tracker\\n instance to record how option values were assigned.\\n \\\"\\\"\\\"\\n # We need parsers for all the intermediate scopes, so inherited option values\\n # can propagate through them.\\n complete_known_scope_infos = cls.complete_scopes(known_scope_infos)\\n splitter = ArgSplitter(complete_known_scope_infos)\\n args = sys.argv if args is None else args\\n goals, scope_to_flags, target_specs, passthru, passthru_owner = splitter.split_args(args)\\n\\n if not option_tracker:\\n raise cls.OptionTrackerRequiredError()\\n\\n if bootstrap_option_values:\\n target_spec_files = bootstrap_option_values.target_spec_files\\n if target_spec_files:\\n for spec in target_spec_files:\\n with open(spec) as f:\\n target_specs.extend(filter(None, [line.strip() for line in f]))\\n\\n help_request = splitter.help_request\\n\\n parser_hierarchy = ParserHierarchy(env, config, complete_known_scope_infos, option_tracker)\\n values_by_scope = {} # Arg values, parsed per-scope on demand.\\n bootstrap_option_values = bootstrap_option_values\\n known_scope_to_info = {s.scope: s for s in complete_known_scope_infos}\\n return cls(goals, scope_to_flags, target_specs, passthru, passthru_owne\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.__init__\",\n \"text\": \"src/python/pants/option/options.py::Options.__init__\\n def __init__(self, goals, scope_to_flags, target_specs, passthru, passthru_owner, help_request,\\n parser_hierarchy, values_by_scope, bootstrap_option_values, known_scope_to_info,\\n option_tracker):\\n \\\"\\\"\\\"The low-level constructor for an Options instance.\\n\\n Dependees should use `Options.create` instead.\\n \\\"\\\"\\\"\\n self._goals = goals\\n self._scope_to_flags = scope_to_flags\\n self._target_specs = target_specs\\n self._passthru = passthru\\n self._passthru_owner = passthru_owner\\n self._help_request = help_request\\n self._parser_hierarchy = parser_hierarchy\\n self._values_by_scope = values_by_scope\\n self._bootstrap_option_values = bootstrap_option_values\\n self._known_scope_to_info = known_scope_to_info\\n self._option_tracker = option_tracker\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.tracker\",\n \"text\": \"src/python/pants/option/options.py::Options.tracker\\n @property\\n def tracker(self):\\n return self._option_tracker\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.help_request\",\n \"text\": \"src/python/pants/option/options.py::Options.help_request\\n @property\\n def help_request(self):\\n \\\"\\\"\\\"\\n :API: public\\n \\\"\\\"\\\"\\n return self._help_request\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.target_specs\",\n \"text\": \"src/python/pants/option/options.py::Options.target_specs\\n @property\\n def target_specs(self):\\n \\\"\\\"\\\"The targets to operate on.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return self._target_specs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.goals\",\n \"text\": \"src/python/pants/option/options.py::Options.goals\\n @property\\n def goals(self):\\n \\\"\\\"\\\"The requested goals, in the order specified on the cmd line.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return self._goals\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.known_scope_to_info\",\n \"text\": \"src/python/pants/option/options.py::Options.known_scope_to_info\\n @property\\n def known_scope_to_info(self):\\n return self._known_scope_to_info\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.scope_to_flags\",\n \"text\": \"src/python/pants/option/options.py::Options.scope_to_flags\\n @property\\n def scope_to_flags(self):\\n return self._scope_to_flags\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.drop_flag_values\",\n \"text\": \"src/python/pants/option/options.py::Options.drop_flag_values\\n def drop_flag_values(self):\\n \\\"\\\"\\\"Returns a copy of these options that ignores values specified via flags.\\n\\n Any pre-cached option values are cleared and only option values that come from option defaults,\\n the config or the environment are used.\\n \\\"\\\"\\\"\\n # An empty scope_to_flags to force all values to come via the config -> env hierarchy alone\\n # and empty values in case we already cached some from flags.\\n no_flags = {}\\n no_values = {}\\n return Options(self._goals,\\n no_flags,\\n self._target_specs,\\n self._passthru,\\n self._passthru_owner,\\n self._help_request,\\n self._parser_hierarchy,\\n no_values,\\n self._bootstrap_option_values,\\n self._known_scope_to_info,\\n self._option_tracker)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.is_known_scope\",\n \"text\": \"src/python/pants/option/options.py::Options.is_known_scope\\n def is_known_scope(self, scope):\\n \\\"\\\"\\\"Whether the given scope is known by this instance.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return scope in self._known_scope_to_info\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.passthru_args_for_scope\",\n \"text\": \"src/python/pants/option/options.py::Options.passthru_args_for_scope\\n def passthru_args_for_scope(self, scope):\\n # Passthru args \\\"belong\\\" to the last scope mentioned on the command-line.\\n\\n # Note: If that last scope is a goal, we allow all tasks in that goal to access the passthru\\n # args. This is to allow the more intuitive\\n # pants run <target> -- <passthru args>\\n # instead of requiring\\n # pants run.py <target> -- <passthru args>.\\n #\\n # However note that in the case where multiple tasks run in the same goal, e.g.,\\n # pants test <target> -- <passthru args>\\n # Then, e.g., both junit and pytest will get the passthru args even though the user probably\\n # only intended them to go to one of them. If the wrong one is not a no-op then the error will\\n # be unpredictable. However this is not a common case, and can be circumvented with an\\n # explicit test.pytest or test.junit scope.\\n if (scope and self._passthru_owner and scope.startswith(self._passthru_owner) and\\n (len(scope) == len(self._passthru_owner) or scope[len(self._passthru_owner)] == '.')):\\n return self._passthru\\n else:\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.register\",\n \"text\": \"src/python/pants/option/options.py::Options.register\\n def register(self, scope, *args, **kwargs):\\n \\\"\\\"\\\"Register an option in the given scope.\\\"\\\"\\\"\\n self.get_parser(scope).register(*args, **kwargs)\\n deprecated_scope = self.known_scope_to_info[scope].deprecated_scope\\n if deprecated_scope:\\n self.get_parser(deprecated_scope).register(*args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.registration_function_for_optionable\",\n \"text\": \"src/python/pants/option/options.py::Options.registration_function_for_optionable\\n def registration_function_for_optionable(self, optionable_class):\\n \\\"\\\"\\\"Returns a function for registering options on the given scope.\\\"\\\"\\\"\\n # TODO(benjy): Make this an instance of a class that implements __call__, so we can\\n # docstring it, and so it's less weird than attatching properties to a function.\\n def register(*args, **kwargs):\\n kwargs['registering_class'] = optionable_class\\n self.register(optionable_class.options_scope, *args, **kwargs)\\n # Clients can access the bootstrap option values as register.bootstrap.\\n register.bootstrap = self.bootstrap_option_values()\\n # Clients can access the scope as register.scope.\\n register.scope = optionable_class.options_scope\\n return register\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.registration_function_for_optionable.register\",\n \"text\": \"src/python/pants/option/options.py::Options.registration_function_for_optionable.register\\n def register(*args, **kwargs):\\n kwargs['registering_class'] = optionable_class\\n self.register(optionable_class.options_scope, *args, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.get_parser\",\n \"text\": \"src/python/pants/option/options.py::Options.get_parser\\n def get_parser(self, scope):\\n \\\"\\\"\\\"Returns the parser for the given scope, so code can register on it directly.\\\"\\\"\\\"\\n return self._parser_hierarchy.get_parser_by_scope(scope)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.walk_parsers\",\n \"text\": \"src/python/pants/option/options.py::Options.walk_parsers\\n def walk_parsers(self, callback):\\n self._parser_hierarchy.walk(callback)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.for_scope\",\n \"text\": \"src/python/pants/option/options.py::Options.for_scope\\n def for_scope(self, scope, inherit_from_enclosing_scope=True):\\n \\\"\\\"\\\"Return the option values for the given scope.\\n\\n Values are attributes of the returned object, e.g., options.foo.\\n Computed lazily per scope.\\n\\n :API: public\\n \\\"\\\"\\\"\\n # Short-circuit, if already computed.\\n if scope in self._values_by_scope:\\n return self._values_by_scope[scope]\\n\\n # First get enclosing scope's option values, if any.\\n if scope == GLOBAL_SCOPE or not inherit_from_enclosing_scope:\\n values = OptionValueContainer()\\n else:\\n values = copy.copy(self.for_scope(enclosing_scope(scope)))\\n\\n # Now add our values.\\n flags_in_scope = self._scope_to_flags.get(scope, [])\\n self._parser_hierarchy.get_parser_by_scope(scope).parse_args(flags_in_scope, values)\\n\\n # If we're the new name of a deprecated scope, also get values from that scope.\\n deprecated_scope = self.known_scope_to_info[scope].deprecated_scope\\n # Note that deprecated_scope and scope share the same Optionable class, so deprecated_scope's\\n # Optionable has a deprecated_options_scope equal to deprecated_scope. Therefore we must\\n # check that scope != deprecated_scope to prevent infinite recursion.\\n if deprecated_scope is not None and scope != deprecated_scope:\\n # Do the deprecation check only on keys that were explicitly set on the deprecated scope\\n # (and not on its enclosing scopes).\\n explicit_keys = self.for_scope(deprecated_scope,\\n inherit_from_enclosing_scope=False).get_explicit_keys()\\n if explicit_keys:\\n warn_or_error(self.known_scope_to_info[scope].deprecated_scope_removal_version,\\n 'scope {}'.format(deprecated_scope),\\n 'Use scope {} instead (options: {})'.format(scope, ', '.join(explicit_keys)))\\n # Update our values with those of the deprecated scope (now including values inherited\\n # from its enclosin\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.get_fingerprintable_for_scope\",\n \"text\": \"src/python/pants/option/options.py::Options.get_fingerprintable_for_scope\\n def get_fingerprintable_for_scope(self, bottom_scope, include_passthru=False,\\n fingerprint_key=None, invert=False):\\n \\\"\\\"\\\"Returns a list of fingerprintable (option type, option value) pairs for the given scope.\\n\\n Fingerprintable options are options registered via a \\\"fingerprint=True\\\" kwarg. This flag\\n can be parameterized with `fingerprint_key` for special cases.\\n\\n This method also searches enclosing options scopes of `bottom_scope` to determine the set of\\n fingerprintable pairs.\\n\\n :param str bottom_scope: The scope to gather fingerprintable options for.\\n :param bool include_passthru: Whether to include passthru args captured by `bottom_scope` in the\\n fingerprintable options.\\n :param string fingerprint_key: The option kwarg to match against (defaults to 'fingerprint').\\n :param bool invert: Whether or not to invert the boolean check for the fingerprint_key value.\\n\\n :API: public\\n \\\"\\\"\\\"\\n fingerprint_key = fingerprint_key or 'fingerprint'\\n pairs = []\\n\\n if include_passthru:\\n # Passthru args can only be sent to outermost scopes so we gather them once here up-front.\\n passthru_args = self.passthru_args_for_scope(bottom_scope)\\n # NB: We can't sort passthru args, the underlying consumer may be order-sensitive.\\n pairs.extend((str, pass_arg) for pass_arg in passthru_args)\\n\\n # Note that we iterate over options registered at `bottom_scope` and at all\\n # enclosing scopes, since option-using code can read those values indirectly\\n # via its own OptionValueContainer, so they can affect that code's output.\\n for registration_scope in all_enclosing_scopes(bottom_scope):\\n parser = self._parser_hierarchy.get_parser_by_scope(registration_scope)\\n # Sort the arguments, so that the fingerprint is consistent.\\n for (_, kwargs) in sorted(parser.option_registrations_iter(\",\n \"label\": 1\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.__getitem__\",\n \"text\": \"src/python/pants/option/options.py::Options.__getitem__\\n def __getitem__(self, scope):\\n # TODO(John Sirois): Mainly supports use of dict<str, dict<str, str>> for mock options in tests,\\n # Consider killing if tests consolidate on using TestOptions instead of the raw dicts.\\n return self.for_scope(scope)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.bootstrap_option_values\",\n \"text\": \"src/python/pants/option/options.py::Options.bootstrap_option_values\\n def bootstrap_option_values(self):\\n \\\"\\\"\\\"Return the option values for bootstrap options.\\n\\n General code can also access these values in the global scope. But option registration code\\n cannot, hence this special-casing of this small set of options.\\n \\\"\\\"\\\"\\n return self._bootstrap_option_values\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/options.py::Options.for_global_scope\",\n \"text\": \"src/python/pants/option/options.py::Options.for_global_scope\\n def for_global_scope(self):\\n \\\"\\\"\\\"Return the option values for the global scope.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return self.for_scope(GLOBAL_SCOPE)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._ensure_bool\",\n \"text\": \"src/python/pants/option/parser.py::Parser._ensure_bool\\n @staticmethod\\n def _ensure_bool(s):\\n if isinstance(s, six.string_types):\\n if s.lower() == 'true':\\n return True\\n elif s.lower() == 'false':\\n return False\\n else:\\n raise Parser.BooleanConversionError('Got \\\"{0}\\\". Expected \\\"True\\\" or \\\"False\\\".'.format(s))\\n if s is True:\\n return True\\n elif s is False:\\n return False\\n else:\\n raise Parser.BooleanConversionError('Got {0}. Expected True or False.'.format(s))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._invert\",\n \"text\": \"src/python/pants/option/parser.py::Parser._invert\\n @classmethod\\n def _invert(cls, s):\\n if s is None:\\n return None\\n b = cls._ensure_bool(s)\\n return not b\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser.__init__\",\n \"text\": \"src/python/pants/option/parser.py::Parser.__init__\\n def __init__(self, env, config, scope_info, parent_parser, option_tracker):\\n \\\"\\\"\\\"Create a Parser instance.\\n\\n :param env: a dict of environment variables.\\n :param :class:`pants.option.config.Config` config: data from a config file.\\n :param scope_info: the scope this parser acts for.\\n :param parent_parser: the parser for the scope immediately enclosing this one, or\\n None if this is the global scope.\\n :param option_tracker: the option tracker to record where option values came from.\\n \\\"\\\"\\\"\\n self._env = env\\n self._config = config\\n self._scope_info = scope_info\\n self._scope = self._scope_info.scope\\n self._option_tracker = option_tracker\\n\\n # If True, no more registration is allowed on this parser.\\n self._frozen = False\\n\\n # All option args registered with this parser. Used to prevent shadowing args in inner scopes.\\n self._known_args = set()\\n\\n # List of (args, kwargs) registration pairs, exactly as captured at registration time.\\n self._option_registrations = []\\n\\n # A Parser instance, or None for the global scope parser.\\n self._parent_parser = parent_parser\\n\\n # List of Parser instances.\\n self._child_parsers = []\\n\\n if self._parent_parser:\\n self._parent_parser._register_child_parser(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser.scope\",\n \"text\": \"src/python/pants/option/parser.py::Parser.scope\\n @property\\n def scope(self):\\n return self._scope\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser.walk\",\n \"text\": \"src/python/pants/option/parser.py::Parser.walk\\n def walk(self, callback):\\n \\\"\\\"\\\"Invoke callback on this parser and its descendants, in depth-first order.\\\"\\\"\\\"\\n callback(self)\\n for child in self._child_parsers:\\n child.walk(callback)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._create_flag_value_map\",\n \"text\": \"src/python/pants/option/parser.py::Parser._create_flag_value_map\\n def _create_flag_value_map(self, flags):\\n \\\"\\\"\\\"Returns a map of flag -> list of values, based on the given flag strings.\\n\\n None signals no value given (e.g., -x, --foo).\\n The value is a list because the user may specify the same flag multiple times, and that's\\n sometimes OK (e.g., when appending to list-valued options).\\n \\\"\\\"\\\"\\n flag_value_map = defaultdict(list)\\n for flag in flags:\\n key, has_equals_sign, flag_val = flag.partition('=')\\n if not has_equals_sign:\\n if not flag.startswith('--'): # '-xfoo' style.\\n key = flag[0:2]\\n flag_val = flag[2:]\\n if not flag_val:\\n # Either a short option with no value or a long option with no equals sign.\\n # Important so we can distinguish between no value ('--foo') and setting to an empty\\n # string ('--foo='), for options with an implicit_value.\\n flag_val = None\\n flag_value_map[key].append(flag_val)\\n return flag_value_map\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser.parse_args\",\n \"text\": \"src/python/pants/option/parser.py::Parser.parse_args\\n def parse_args(self, flags, namespace):\\n \\\"\\\"\\\"Set values for this parser's options on the namespace object.\\\"\\\"\\\"\\n flag_value_map = self._create_flag_value_map(flags)\\n\\n mutex_map = defaultdict(list)\\n for args, kwargs in self._unnormalized_option_registrations_iter():\\n self._validate(args, kwargs)\\n dest = self.parse_dest(*args, **kwargs)\\n\\n # Compute the values provided on the command line for this option. Note that there may be\\n # multiple values, for any combination of the following reasons:\\n # - The user used the same flag multiple times.\\n # - The user specified a boolean flag (--foo) and its inverse (--no-foo).\\n # - The option has multiple names, and the user used more than one of them.\\n #\\n # We also check if the option is deprecated, but we only do so if the option is explicitly\\n # specified as a command-line flag, so we don't spam users with deprecated option values\\n # specified in config, which isn't something they control.\\n implicit_value = kwargs.get('implicit_value')\\n if implicit_value is None and kwargs.get('type') == bool:\\n implicit_value = True # Allows --foo to mean --foo=true.\\n\\n flag_vals = []\\n\\n def add_flag_val(v):\\n if v is None:\\n if implicit_value is None:\\n raise ParseError('Missing value for command line flag {} in {}'.format(\\n arg, self._scope_str()))\\n else:\\n flag_vals.append(implicit_value)\\n else:\\n flag_vals.append(v)\\n\\n for arg in args:\\n # If the user specified --no-foo on the cmd line, treat it as if the user specified\\n # --foo, but with the inverse value.\\n if kwargs.get('type') == bool:\\n inverse_arg = self._inverse_arg(arg)\\n if inverse_arg in flag_value_map:\\n flag_value_map[arg] = [self._invert(v) for v in flag_value_map[inverse_arg]]\\n implicit_value = self.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser.option_registrations_iter\",\n \"text\": \"src/python/pants/option/parser.py::Parser.option_registrations_iter\\n def option_registrations_iter(self):\\n \\\"\\\"\\\"Returns an iterator over the normalized registration arguments of each option in this parser.\\n\\n Useful for generating help and other documentation.\\n\\n Each yielded item is an (args, kwargs) pair, as passed to register(), except that kwargs\\n will be normalized in the following ways:\\n - It will always have 'dest' explicitly set.\\n - It will always have 'default' explicitly set, and the value will be a RankedValue.\\n - For recursive options, the original registrar will also have 'recursive_root' set.\\n\\n Note that recursive options we inherit from a parent will also be yielded here, with\\n the correctly-scoped default value.\\n \\\"\\\"\\\"\\n def normalize_kwargs(args, orig_kwargs):\\n nkwargs = copy.copy(orig_kwargs)\\n dest = self.parse_dest(*args, **nkwargs)\\n nkwargs['dest'] = dest\\n if not ('default' in nkwargs and isinstance(nkwargs['default'], RankedValue)):\\n nkwargs['default'] = self._compute_value(dest, nkwargs, [])\\n return nkwargs\\n\\n # First yield any recursive options we inherit from our parent.\\n if self._parent_parser:\\n for args, kwargs in self._parent_parser._recursive_option_registration_args():\\n yield args, normalize_kwargs(args, kwargs)\\n\\n # Then yield our directly-registered options.\\n # This must come after yielding inherited recursive options, so we can detect shadowing.\\n for args, kwargs in self._option_registrations:\\n normalized_kwargs = normalize_kwargs(args, kwargs)\\n if 'recursive' in normalized_kwargs:\\n # If we're the original registrar, make sure we can distinguish that.\\n normalized_kwargs['recursive_root'] = True\\n yield args, normalized_kwargs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser.option_registrations_iter.normalize_kwargs\",\n \"text\": \"src/python/pants/option/parser.py::Parser.option_registrations_iter.normalize_kwargs\\n def normalize_kwargs(args, orig_kwargs):\\n nkwargs = copy.copy(orig_kwargs)\\n dest = self.parse_dest(*args, **nkwargs)\\n nkwargs['dest'] = dest\\n if not ('default' in nkwargs and isinstance(nkwargs['default'], RankedValue)):\\n nkwargs['default'] = self._compute_value(dest, nkwargs, [])\\n return nkwargs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._unnormalized_option_registrations_iter\",\n \"text\": \"src/python/pants/option/parser.py::Parser._unnormalized_option_registrations_iter\\n def _unnormalized_option_registrations_iter(self):\\n \\\"\\\"\\\"Returns an iterator over the raw registration arguments of each option in this parser.\\n\\n Each yielded item is an (args, kwargs) pair, exactly as passed to register(), except for\\n substituting list and dict types with list_option/dict_option.\\n\\n Note that recursive options we inherit from a parent will also be yielded here.\\n \\\"\\\"\\\"\\n # First yield any recursive options we inherit from our parent.\\n if self._parent_parser:\\n for args, kwargs in self._parent_parser._recursive_option_registration_args():\\n yield args, kwargs\\n # Then yield our directly-registered options.\\n for args, kwargs in self._option_registrations:\\n if 'recursive' in kwargs and self._scope_info.category == ScopeInfo.SUBSYSTEM:\\n raise RecursiveSubsystemOption(self.scope, args[0])\\n yield args, kwargs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._recursive_option_registration_args\",\n \"text\": \"src/python/pants/option/parser.py::Parser._recursive_option_registration_args\\n def _recursive_option_registration_args(self):\\n \\\"\\\"\\\"Yield args, kwargs pairs for just our recursive options.\\n\\n Includes all the options we inherit recursively from our ancestors.\\n \\\"\\\"\\\"\\n if self._parent_parser:\\n for args, kwargs in self._parent_parser._recursive_option_registration_args():\\n yield args, kwargs\\n for args, kwargs in self._option_registrations:\\n # Note that all subsystem options are implicitly recursive: a subscope of a subsystem\\n # scope is another (optionable-specific) instance of the same subsystem, so it needs\\n # all the same options.\\n if self._scope_info.category == ScopeInfo.SUBSYSTEM or 'recursive' in kwargs:\\n yield args, kwargs\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser.register\",\n \"text\": \"src/python/pants/option/parser.py::Parser.register\\n def register(self, *args, **kwargs):\\n \\\"\\\"\\\"Register an option.\\\"\\\"\\\"\\n if self._frozen:\\n raise FrozenRegistration(self.scope, args[0])\\n\\n # Prevent further registration in enclosing scopes.\\n ancestor = self._parent_parser\\n while ancestor:\\n ancestor._freeze()\\n ancestor = ancestor._parent_parser\\n\\n if kwargs.get('type') == bool:\\n default = kwargs.get('default')\\n if default is None:\\n # Unless a tri-state bool is explicitly opted into with the `UnsetBool` default value,\\n # boolean options always have an implicit boolean-typed default. We make that default\\n # explicit here.\\n kwargs['default'] = not self._ensure_bool(kwargs.get('implicit_value', True))\\n elif default is UnsetBool:\\n kwargs['default'] = None\\n\\n # Record the args. We'll do the underlying parsing on-demand.\\n self._option_registrations.append((args, kwargs))\\n if self._parent_parser:\\n for arg in args:\\n existing_scope = self._parent_parser._existing_scope(arg)\\n if existing_scope is not None:\\n raise Shadowing(self.scope, arg, outer_scope=self._scope_str(existing_scope))\\n for arg in args:\\n if arg in self._known_args:\\n raise OptionAlreadyRegistered(self.scope, arg)\\n self._known_args.update(args)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._check_deprecated\",\n \"text\": \"src/python/pants/option/parser.py::Parser._check_deprecated\\n def _check_deprecated(self, dest, kwargs):\\n \\\"\\\"\\\"Checks option for deprecation and issues a warning/error if necessary.\\\"\\\"\\\"\\n removal_version = kwargs.get('removal_version', None)\\n if removal_version is not None:\\n warn_or_error(removal_version,\\n \\\"option '{}' in {}\\\".format(dest, self._scope_str()),\\n kwargs.get('removal_hint', ''),\\n stacklevel=9999) # Out of range stacklevel to suppress printing src line.\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._validate\",\n \"text\": \"src/python/pants/option/parser.py::Parser._validate\\n def _validate(self, args, kwargs):\\n \\\"\\\"\\\"Validate option registration arguments.\\\"\\\"\\\"\\n def error(exception_type, arg_name=None, **msg_kwargs):\\n if arg_name is None:\\n arg_name = args[0] if args else '<unknown>'\\n raise exception_type(self.scope, arg_name, **msg_kwargs)\\n\\n if not args:\\n error(NoOptionNames)\\n # validate args.\\n for arg in args:\\n if not arg.startswith('-'):\\n error(OptionNameDash, arg_name=arg)\\n if not arg.startswith('--') and len(arg) > 2:\\n error(OptionNameDoubleDash, arg_name=arg)\\n\\n # Validate kwargs.\\n if 'implicit_value' in kwargs and kwargs['implicit_value'] is None:\\n error(ImplicitValIsNone)\\n\\n # Note: we check for list here, not list_option, because we validate the provided kwargs,\\n # not the ones we modified. However we temporarily also allow list_option, until the\\n # deprecation is complete.\\n if 'member_type' in kwargs and kwargs.get('type', str) not in [list, list_option]:\\n error(MemberTypeNotAllowed, type_=kwargs.get('type', str).__name__)\\n\\n if kwargs.get('member_type', str) not in self._allowed_member_types:\\n error(InvalidMemberType, member_type=kwargs.get('member_type', str).__name__)\\n\\n for kwarg in kwargs:\\n if kwarg not in self._allowed_registration_kwargs:\\n error(InvalidKwarg, kwarg=kwarg)\\n\\n # Ensure `daemon=True` can't be passed on non-global scopes (except for `recursive=True`).\\n if (kwarg == 'daemon' and self._scope != GLOBAL_SCOPE and kwargs.get('recursive') is False):\\n error(InvalidKwargNonGlobalScope, kwarg=kwarg)\\n\\n removal_version = kwargs.get('removal_version')\\n if removal_version is not None:\\n validate_removal_semver(removal_version)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._validate.error\",\n \"text\": \"src/python/pants/option/parser.py::Parser._validate.error\\n def error(exception_type, arg_name=None, **msg_kwargs):\\n if arg_name is None:\\n arg_name = args[0] if args else '<unknown>'\\n raise exception_type(self.scope, arg_name, **msg_kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._existing_scope\",\n \"text\": \"src/python/pants/option/parser.py::Parser._existing_scope\\n def _existing_scope(self, arg):\\n if arg in self._known_args:\\n return self._scope\\n elif self._parent_parser:\\n return self._parent_parser._existing_scope(arg)\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser.parse_dest\",\n \"text\": \"src/python/pants/option/parser.py::Parser.parse_dest\\n @staticmethod\\n def parse_dest(*args, **kwargs):\\n \\\"\\\"\\\"Select the dest name for an option registration.\\n\\n If an explicit `dest` is specified, returns that and otherwise derives a default from the\\n option flags where '--foo-bar' -> 'foo_bar' and '-x' -> 'x'.\\n \\\"\\\"\\\"\\n explicit_dest = kwargs.get('dest')\\n if explicit_dest:\\n return explicit_dest\\n\\n arg = next((a for a in args if a.startswith('--')), args[0])\\n return arg.lstrip('-').replace('-', '_')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._wrap_type\",\n \"text\": \"src/python/pants/option/parser.py::Parser._wrap_type\\n @staticmethod\\n def _wrap_type(t):\\n if t == list:\\n return list_option\\n elif t == dict:\\n return dict_option\\n else:\\n return t\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._convert_member_type\",\n \"text\": \"src/python/pants/option/parser.py::Parser._convert_member_type\\n @staticmethod\\n def _convert_member_type(t, x):\\n if t == dict:\\n return dict_option(x).val\\n else:\\n return t(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._compute_value\",\n \"text\": \"src/python/pants/option/parser.py::Parser._compute_value\\n def _compute_value(self, dest, kwargs, flag_val_strs):\\n \\\"\\\"\\\"Compute the value to use for an option.\\n\\n The source of the default value is chosen according to the ranking in RankedValue.\\n \\\"\\\"\\\"\\n # Helper function to convert a string to a value of the option's type.\\n def to_value_type(val_str):\\n if val_str is None:\\n return None\\n elif kwargs.get('type') == bool:\\n return self._ensure_bool(val_str)\\n else:\\n return self._wrap_type(kwargs.get('type', str))(val_str)\\n\\n # Helper function to expand a fromfile=True value string, if needed.\\n def expand(val_str):\\n if kwargs.get('fromfile', False) and val_str and val_str.startswith('@'):\\n if val_str.startswith('@@'): # Support a literal @ for fromfile values via @@.\\n return val_str[1:]\\n else:\\n fromfile = val_str[1:]\\n try:\\n with open(fromfile) as fp:\\n return fp.read().strip()\\n except IOError as e:\\n raise self.FromfileError('Failed to read {} in {} from file {}: {}'.format(\\n dest, self._scope_str(), fromfile, e))\\n else:\\n return val_str\\n\\n # Get value from config files, and capture details about its derivation.\\n config_details = None\\n config_section = GLOBAL_SCOPE_CONFIG_SECTION if self._scope == GLOBAL_SCOPE else self._scope\\n config_default_val_str = expand(self._config.get(Config.DEFAULT_SECTION, dest, default=None))\\n config_val_str = expand(self._config.get(config_section, dest, default=None))\\n config_source_file = (self._config.get_source_for_option(config_section, dest) or\\n self._config.get_source_for_option(Config.DEFAULT_SECTION, dest))\\n if config_source_file is not None:\\n config_source_file = os.path.relpath(config_source_file)\\n config_details = 'in {}'.format(config_source_file)\\n\\n # Get value from environment, and capture details about its derivation.\\n udes\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._compute_value.to_value_type\",\n \"text\": \"src/python/pants/option/parser.py::Parser._compute_value.to_value_type\\n def to_value_type(val_str):\\n if val_str is None:\\n return None\\n elif kwargs.get('type') == bool:\\n return self._ensure_bool(val_str)\\n else:\\n return self._wrap_type(kwargs.get('type', str))(val_str)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._compute_value.expand\",\n \"text\": \"src/python/pants/option/parser.py::Parser._compute_value.expand\\n def expand(val_str):\\n if kwargs.get('fromfile', False) and val_str and val_str.startswith('@'):\\n if val_str.startswith('@@'): # Support a literal @ for fromfile values via @@.\\n return val_str[1:]\\n else:\\n fromfile = val_str[1:]\\n try:\\n with open(fromfile) as fp:\\n return fp.read().strip()\\n except IOError as e:\\n raise self.FromfileError('Failed to read {} in {} from file {}: {}'.format(\\n dest, self._scope_str(), fromfile, e))\\n else:\\n return val_str\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._compute_value.record_option\",\n \"text\": \"src/python/pants/option/parser.py::Parser._compute_value.record_option\\n def record_option(value, rank, option_details=None):\\n deprecation_version = kwargs.get('removal_version')\\n self._option_tracker.record_option(scope=self._scope,\\n option=dest,\\n value=value,\\n rank=rank,\\n deprecation_version=deprecation_version,\\n details=option_details)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._compute_value.check\",\n \"text\": \"src/python/pants/option/parser.py::Parser._compute_value.check\\n def check(val):\\n if val is not None:\\n choices = kwargs.get('choices')\\n if choices is not None and val not in choices:\\n raise ParseError('`{}` is not an allowed value for option {} in {}. '\\n 'Must be one of: {}'.format(val, dest, self._scope_str(), choices))\\n elif kwargs.get('type') == dir_option and not os.path.isdir(val):\\n raise ParseError('Directory value `{}` for option {} in {} does not exist.'.format(\\n val, dest, self._scope_str()))\\n elif kwargs.get('type') == file_option and not os.path.isfile(val):\\n raise ParseError('File value `{}` for option {} in {} does not exist.'.format(\\n val, dest, self._scope_str()))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._inverse_arg\",\n \"text\": \"src/python/pants/option/parser.py::Parser._inverse_arg\\n def _inverse_arg(self, arg):\\n if arg.startswith('--'):\\n if arg.startswith('--no-'):\\n raise BooleanOptionNameWithNo(self.scope, arg)\\n return '--no-{}'.format(arg[2:])\\n else:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._register_child_parser\",\n \"text\": \"src/python/pants/option/parser.py::Parser._register_child_parser\\n def _register_child_parser(self, child):\\n self._child_parsers.append(child)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._freeze\",\n \"text\": \"src/python/pants/option/parser.py::Parser._freeze\\n def _freeze(self):\\n self._frozen = True\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser._scope_str\",\n \"text\": \"src/python/pants/option/parser.py::Parser._scope_str\\n def _scope_str(self, scope=None):\\n scope = scope or self.scope\\n return 'global scope' if scope == GLOBAL_SCOPE else \\\"scope '{}'\\\".format(scope)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/option/parser.py::Parser.__str__\",\n \"text\": \"src/python/pants/option/parser.py::Parser.__str__\\n def __str__(self):\\n return 'Parser({})'.format(self._scope)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::swallow_psutil_exceptions\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::swallow_psutil_exceptions\\n@contextmanager\\ndef swallow_psutil_exceptions():\\n \\\"\\\"\\\"A contextmanager that swallows standard psutil access exceptions.\\\"\\\"\\\"\\n try:\\n yield\\n except (psutil.AccessDenied, psutil.NoSuchProcess):\\n # This masks common, but usually benign psutil process access exceptions that might be seen\\n # when accessing attributes/methods on psutil.Process objects.\\n pass\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup.__init__\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup.__init__\\n def __init__(self, name, metadata_base_dir=None):\\n self._name = name\\n self._metadata_base_dir = metadata_base_dir\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup._instance_from_process\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup._instance_from_process\\n def _instance_from_process(self, process):\\n \\\"\\\"\\\"Default converter from psutil.Process to process instance classes for subclassing.\\\"\\\"\\\"\\n return ProcessManager(name=process.name(),\\n pid=process.pid,\\n process_name=process.name(),\\n metadata_base_dir=self._metadata_base_dir)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_processes\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_processes\\n def iter_processes(self, proc_filter=None):\\n \\\"\\\"\\\"Yields processes from psutil.process_iter with an optional filter and swallows psutil errors.\\n\\n If a psutil exception is raised during execution of the filter, that process will not be\\n yielded but subsequent processes will. On the other hand, if psutil.process_iter raises\\n an exception, no more processes will be yielded.\\n \\\"\\\"\\\"\\n with swallow_psutil_exceptions(): # process_iter may raise\\n for proc in psutil.process_iter():\\n with swallow_psutil_exceptions(): # proc_filter may raise\\n if (proc_filter is None) or proc_filter(proc):\\n yield proc\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_instances\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessGroup.iter_instances\\n def iter_instances(self, *args, **kwargs):\\n for item in self.iter_processes(*args, **kwargs):\\n yield self._instance_from_process(item)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.__init__\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.__init__\\n def __init__(self, metadata_base_dir=None):\\n \\\"\\\"\\\"\\n :param str metadata_base_dir: The base directory for process metadata.\\n \\\"\\\"\\\"\\n super(ProcessMetadataManager, self).__init__()\\n\\n self._metadata_base_dir = (\\n metadata_base_dir or\\n Subprocess.Factory.global_instance().create().get_subprocess_dir()\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_cast\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_cast\\n @staticmethod\\n def _maybe_cast(item, caster):\\n \\\"\\\"\\\"Given a casting function, attempt to cast to that type while masking common cast exceptions.\\n\\n N.B. This is mostly suitable for casting string types to numeric types - e.g. a port number\\n read from disk into an int.\\n\\n :param func caster: A casting callable (e.g. `int`).\\n :returns: The result of caster(item) or item if TypeError or ValueError are raised during cast.\\n \\\"\\\"\\\"\\n try:\\n return caster(item)\\n except (TypeError, ValueError):\\n # N.B. the TypeError catch here (already) protects against the case that caster is None.\\n return item\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._deadline_until\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._deadline_until\\n @classmethod\\n def _deadline_until(cls, closure, timeout, wait_interval=WAIT_INTERVAL_SEC):\\n \\\"\\\"\\\"Execute a function/closure repeatedly until a True condition or timeout is met.\\n\\n :param func closure: the function/closure to execute (should not block for long periods of time\\n and must return True on success).\\n :param float timeout: the maximum amount of time to wait for a true result from the closure in\\n seconds. N.B. this is timing based, so won't be exact if the runtime of\\n the closure exceeds the timeout.\\n :param float wait_interval: the amount of time to sleep between closure invocations.\\n :raises: :class:`ProcessManager.Timeout` on execution timeout.\\n \\\"\\\"\\\"\\n deadline = time.time() + timeout\\n while 1:\\n if closure():\\n return True\\n elif time.time() > deadline:\\n raise cls.Timeout('exceeded timeout of {} seconds for {}'.format(timeout, closure))\\n elif wait_interval:\\n time.sleep(wait_interval)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file\\n @classmethod\\n def _wait_for_file(cls, filename, timeout=FILE_WAIT_SEC, want_content=True):\\n \\\"\\\"\\\"Wait up to timeout seconds for filename to appear with a non-zero size or raise Timeout().\\\"\\\"\\\"\\n def file_waiter():\\n return os.path.exists(filename) and (not want_content or os.path.getsize(filename))\\n\\n try:\\n return cls._deadline_until(file_waiter, timeout)\\n except cls.Timeout:\\n # Re-raise with a more helpful exception message.\\n raise cls.Timeout('exceeded timeout of {} seconds while waiting for file {} to appear'\\n .format(timeout, filename))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file.file_waiter\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._wait_for_file.file_waiter\\n def file_waiter():\\n return os.path.exists(filename) and (not want_content or os.path.getsize(filename))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._get_metadata_dir_by_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._get_metadata_dir_by_name\\n def _get_metadata_dir_by_name(self, name):\\n \\\"\\\"\\\"Retrieve the metadata dir by name.\\n\\n This should always live outside of the workdir to survive a clean-all.\\n \\\"\\\"\\\"\\n return os.path.join(self._metadata_base_dir, name)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_init_metadata_dir_by_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager._maybe_init_metadata_dir_by_name\\n def _maybe_init_metadata_dir_by_name(self, name):\\n \\\"\\\"\\\"Initialize the metadata directory for a named identity if it doesn't exist.\\\"\\\"\\\"\\n safe_mkdir(self._get_metadata_dir_by_name(name))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.read_metadata_by_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.read_metadata_by_name\\n def read_metadata_by_name(self, name, metadata_key, caster=None):\\n \\\"\\\"\\\"Read process metadata using a named identity.\\n\\n :param string name: The ProcessMetadataManager identity/name (e.g. 'pantsd').\\n :param string metadata_key: The metadata key (e.g. 'pid').\\n :param func caster: A casting callable to apply to the read value (e.g. `int`).\\n \\\"\\\"\\\"\\n try:\\n file_path = os.path.join(self._get_metadata_dir_by_name(name), metadata_key)\\n return self._maybe_cast(read_file(file_path).strip(), caster)\\n except (IOError, OSError):\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.write_metadata_by_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.write_metadata_by_name\\n def write_metadata_by_name(self, name, metadata_key, metadata_value):\\n \\\"\\\"\\\"Write process metadata using a named identity.\\n\\n :param string name: The ProcessMetadataManager identity/name (e.g. 'pantsd').\\n :param string metadata_key: The metadata key (e.g. 'pid').\\n :param string metadata_value: The metadata value (e.g. '1729').\\n \\\"\\\"\\\"\\n self._maybe_init_metadata_dir_by_name(name)\\n file_path = os.path.join(self._get_metadata_dir_by_name(name), metadata_key)\\n safe_file_dump(file_path, metadata_value)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.await_metadata_by_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.await_metadata_by_name\\n def await_metadata_by_name(self, name, metadata_key, timeout, caster=None):\\n \\\"\\\"\\\"Block up to a timeout for process metadata to arrive on disk.\\n\\n :param string name: The ProcessMetadataManager identity/name (e.g. 'pantsd').\\n :param string metadata_key: The metadata key (e.g. 'pid').\\n :param int timeout: The deadline to write metadata.\\n :param type caster: A type-casting callable to apply to the read value (e.g. int, str).\\n :returns: The value of the metadata key (read from disk post-write).\\n :raises: :class:`ProcessMetadataManager.Timeout` on timeout.\\n \\\"\\\"\\\"\\n file_path = os.path.join(self._get_metadata_dir_by_name(name), metadata_key)\\n self._wait_for_file(file_path, timeout=timeout)\\n return self.read_metadata_by_name(name, metadata_key, caster)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.purge_metadata_by_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessMetadataManager.purge_metadata_by_name\\n def purge_metadata_by_name(self, name):\\n \\\"\\\"\\\"Purge a processes metadata directory.\\n\\n :raises: `ProcessManager.MetadataError` when OSError is encountered on metadata dir removal.\\n \\\"\\\"\\\"\\n meta_dir = self._get_metadata_dir_by_name(name)\\n logger.debug('purging metadata directory: {}'.format(meta_dir))\\n try:\\n rm_rf(meta_dir)\\n except OSError as e:\\n raise self.MetadataError('failed to purge metadata directory {}: {!r}'.format(meta_dir, e))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__init__\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__init__\\n def __init__(self, message, output=None):\\n super(ProcessManager.ExecutionError, self).__init__(message)\\n self.message = message\\n self.output = output\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__repr__\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.ExecutionError.__repr__\\n def __repr__(self):\\n return '{}(message={!r}, output={!r})'.format(type(self).__name__, self.message, self.output)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.__init__\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.__init__\\n def __init__(self, name, pid=None, socket=None, process_name=None, socket_type=int,\\n metadata_base_dir=None):\\n \\\"\\\"\\\"\\n :param string name: The process identity/name (e.g. 'pantsd' or 'ng_Zinc').\\n :param int pid: The process pid. Overrides fetching of the self.pid @property.\\n :param string socket: The socket metadata. Overrides fetching of the self.socket @property.\\n :param string process_name: The process name for cmdline executable name matching.\\n :param type socket_type: The type to be used for socket type casting (e.g. int).\\n :param str metadata_base_dir: The overridden base directory for process metadata.\\n \\\"\\\"\\\"\\n super(ProcessManager, self).__init__(metadata_base_dir)\\n self._name = name.lower().strip()\\n self._pid = pid\\n self._socket = socket\\n self._socket_type = socket_type\\n self._process_name = process_name\\n self._buildroot = get_buildroot()\\n self._process = None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.name\\n @property\\n def name(self):\\n \\\"\\\"\\\"The logical name/label of the process.\\\"\\\"\\\"\\n return self._name\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.process_name\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.process_name\\n @property\\n def process_name(self):\\n \\\"\\\"\\\"The logical process name. If defined, this is compared to exe_name for stale pid checking.\\\"\\\"\\\"\\n return self._process_name\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.process_lock\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.process_lock\\n @memoized_property\\n def process_lock(self):\\n \\\"\\\"\\\"An identity-keyed inter-process lock for safeguarding lifecycle and other operations.\\\"\\\"\\\"\\n safe_mkdir(self._metadata_base_dir)\\n return OwnerPrintingInterProcessFileLock(\\n # N.B. This lock can't key into the actual named metadata dir (e.g. `.pids/pantsd/lock`\\n # via `ProcessMetadataManager._get_metadata_dir_by_name()`) because of a need to purge\\n # the named metadata dir on startup to avoid stale metadata reads.\\n os.path.join(self._metadata_base_dir, '.lock.{}'.format(self._name))\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.cmdline\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.cmdline\\n @property\\n def cmdline(self):\\n \\\"\\\"\\\"The process commandline. e.g. ['/usr/bin/python2.7', 'pants.pex'].\\n\\n :returns: The command line or else `None` if the underlying process has died.\\n \\\"\\\"\\\"\\n with swallow_psutil_exceptions():\\n process = self._as_process()\\n if process:\\n return process.cmdline()\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.cmd\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.cmd\\n @property\\n def cmd(self):\\n \\\"\\\"\\\"The first element of the process commandline e.g. '/usr/bin/python2.7'.\\n\\n :returns: The first element of the process command line or else `None` if the underlying\\n process has died.\\n \\\"\\\"\\\"\\n return (self.cmdline or [None])[0]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.pid\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.pid\\n @property\\n def pid(self):\\n \\\"\\\"\\\"The running processes pid (or None).\\\"\\\"\\\"\\n return self._pid or self.read_metadata_by_name(self._name, 'pid', int)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.socket\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.socket\\n @property\\n def socket(self):\\n \\\"\\\"\\\"The running processes socket/port information (or None).\\\"\\\"\\\"\\n return self._socket or self.read_metadata_by_name(self._name, 'socket', self._socket_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.get_subprocess_output\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.get_subprocess_output\\n @classmethod\\n def get_subprocess_output(cls, command, ignore_stderr=True, **kwargs):\\n \\\"\\\"\\\"Get the output of an executed command.\\n\\n :param command: An iterable representing the command to execute (e.g. ['ls', '-al']).\\n :param ignore_stderr: Whether or not to ignore stderr output vs interleave it with stdout.\\n :raises: `ProcessManager.ExecutionError` on `OSError` or `CalledProcessError`.\\n :returns: The output of the command.\\n \\\"\\\"\\\"\\n if ignore_stderr is False:\\n kwargs.setdefault('stderr', subprocess.STDOUT)\\n\\n try:\\n return subprocess.check_output(command, **kwargs)\\n except (OSError, subprocess.CalledProcessError) as e:\\n subprocess_output = getattr(e, 'output', '').strip()\\n raise cls.ExecutionError(str(e), subprocess_output)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.await_pid\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.await_pid\\n def await_pid(self, timeout):\\n \\\"\\\"\\\"Wait up to a given timeout for a process to write pid metadata.\\\"\\\"\\\"\\n return self.await_metadata_by_name(self._name, 'pid', timeout, int)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.await_socket\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.await_socket\\n def await_socket(self, timeout):\\n \\\"\\\"\\\"Wait up to a given timeout for a process to write socket info.\\\"\\\"\\\"\\n return self.await_metadata_by_name(self._name, 'socket', timeout, self._socket_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.write_pid\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.write_pid\\n def write_pid(self, pid=None):\\n \\\"\\\"\\\"Write the current processes PID to the pidfile location\\\"\\\"\\\"\\n pid = pid or os.getpid()\\n self.write_metadata_by_name(self._name, 'pid', str(pid))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.write_socket\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.write_socket\\n def write_socket(self, socket_info):\\n \\\"\\\"\\\"Write the local processes socket information (TCP port or UNIX socket).\\\"\\\"\\\"\\n self.write_metadata_by_name(self._name, 'socket', str(socket_info))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.write_named_socket\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.write_named_socket\\n def write_named_socket(self, socket_name, socket_info):\\n \\\"\\\"\\\"A multi-tenant, named alternative to ProcessManager.write_socket().\\\"\\\"\\\"\\n self.write_metadata_by_name(self._name, 'socket_{}'.format(socket_name), str(socket_info))\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.read_named_socket\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.read_named_socket\\n def read_named_socket(self, socket_name, socket_type):\\n \\\"\\\"\\\"A multi-tenant, named alternative to ProcessManager.socket.\\\"\\\"\\\"\\n return self.read_metadata_by_name(self._name, 'socket_{}'.format(socket_name), socket_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager._as_process\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager._as_process\\n def _as_process(self):\\n \\\"\\\"\\\"Returns a psutil `Process` object wrapping our pid.\\n\\n NB: Even with a process object in hand, subsequent method calls against it can always raise\\n `NoSuchProcess`. Care is needed to document the raises in the public API or else trap them and\\n do something sensible for the API.\\n\\n :returns: a psutil Process object or else None if we have no pid.\\n :rtype: :class:`psutil.Process`\\n :raises: :class:`psutil.NoSuchProcess` if the process identified by our pid has died.\\n \\\"\\\"\\\"\\n if self._process is None and self.pid:\\n self._process = psutil.Process(self.pid)\\n return self._process\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.is_dead\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.is_dead\\n def is_dead(self):\\n \\\"\\\"\\\"Return a boolean indicating whether the process is dead or not.\\\"\\\"\\\"\\n return not self.is_alive()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.is_alive\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.is_alive\\n def is_alive(self, extended_check=None):\\n \\\"\\\"\\\"Return a boolean indicating whether the process is running or not.\\n\\n :param func extended_check: An additional callable that will be invoked to perform an extended\\n liveness check. This callable should take a single argument of a\\n `psutil.Process` instance representing the context-local process\\n and return a boolean True/False to indicate alive vs not alive.\\n \\\"\\\"\\\"\\n try:\\n process = self._as_process()\\n return not (\\n # Can happen if we don't find our pid.\\n (not process) or\\n # Check for walkers.\\n (process.status() == psutil.STATUS_ZOMBIE) or\\n # Check for stale pids.\\n (self.process_name and self.process_name != process.name()) or\\n # Extended checking.\\n (extended_check and not extended_check(process))\\n )\\n except (psutil.NoSuchProcess, psutil.AccessDenied):\\n # On some platforms, accessing attributes of a zombie'd Process results in NoSuchProcess.\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.purge_metadata\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.purge_metadata\\n def purge_metadata(self, force=False):\\n \\\"\\\"\\\"Instance-based version of ProcessMetadataManager.purge_metadata_by_name() that checks\\n for process liveness before purging metadata.\\n\\n :param bool force: If True, skip process liveness check before purging metadata.\\n :raises: `ProcessManager.MetadataError` when OSError is encountered on metadata dir removal.\\n \\\"\\\"\\\"\\n if not force and self.is_alive():\\n raise self.MetadataError('cannot purge metadata for a running process!')\\n\\n super(ProcessManager, self).purge_metadata_by_name(self._name)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager._kill\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager._kill\\n def _kill(self, kill_sig):\\n \\\"\\\"\\\"Send a signal to the current process.\\\"\\\"\\\"\\n if self.pid:\\n os.kill(self.pid, kill_sig)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.terminate\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.terminate\\n def terminate(self, signal_chain=KILL_CHAIN, kill_wait=KILL_WAIT_SEC, purge=True):\\n \\\"\\\"\\\"Ensure a process is terminated by sending a chain of kill signals (SIGTERM, SIGKILL).\\\"\\\"\\\"\\n alive = self.is_alive()\\n if alive:\\n logger.debug('terminating {}'.format(self._name))\\n for signal_type in signal_chain:\\n pid = self.pid\\n try:\\n logger.debug('sending signal {} to pid {}'.format(signal_type, pid))\\n self._kill(signal_type)\\n except OSError as e:\\n logger.warning('caught OSError({e!s}) during attempt to kill -{signal} {pid}!'\\n .format(e=e, signal=signal_type, pid=pid))\\n\\n # Wait up to kill_wait seconds to terminate or move onto the next signal.\\n try:\\n if self._deadline_until(self.is_dead, kill_wait):\\n alive = False\\n logger.debug('successfully terminated pid {}'.format(pid))\\n break\\n except self.Timeout:\\n # Loop to the next kill signal on timeout.\\n pass\\n\\n if alive:\\n raise self.NonResponsiveProcess('failed to kill pid {pid} with signals {chain}'\\n .format(pid=self.pid, chain=signal_chain))\\n\\n if purge:\\n self.purge_metadata(force=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.daemonize\",\n \"text\": \"src/python/pants/pantsd/process_manager.py::ProcessManager.daemonize\\n def daemonize(self, pre_fork_opts=None, post_fork_parent_opts=None, post_fork_child_opts=None,\\n write_pid=True):\\n \\\"\\\"\\\"Perform a double-fork, execute callbacks and write the child pid file.\\n\\n The double-fork here is necessary to truly daemonize the subprocess such that it can never\\n take control of a tty. The initial fork and setsid() creates a new, isolated process group\\n and also makes the first child a session leader (which can still acquire a tty). By forking a\\n second time, we ensure that the second child can never acquire a controlling terminal because\\n it's no longer a session leader - but it now has its own separate process group.\\n\\n Additionally, a normal daemon implementation would typically perform an os.umask(0) to reset\\n the processes file mode creation mask post-fork. We do not do this here (and in daemon_spawn\\n below) due to the fact that the daemons that pants would run are typically personal user\\n daemons. Having a disparate umask from pre-vs-post fork causes files written in each phase to\\n differ in their permissions without good reason - in this case, we want to inherit the umask.\\n \\\"\\\"\\\"\\n self.purge_metadata()\\n self.pre_fork(**pre_fork_opts or {})\\n logger.debug('forking %s', self)\\n pid = os.fork()\\n if pid == 0:\\n os.setsid()\\n second_pid = os.fork()\\n if second_pid == 0:\\n try:\\n os.chdir(self._buildroot)\\n self.post_fork_child(**post_fork_child_opts or {})\\n except Exception:\\n logger.critical(traceback.format_exc())\\n finally:\\n os._exit(0)\\n else:\\n try:\\n if write_pid: self.write_pid(second_pid)\\n self.post_fork_parent(**post_fork_parent_opts or {})\\n except Exception:\\n logger.critical(traceback.format_exc())\\n finally:\\n os._exit(0)\\n else:\\n # This prevents un-reaped, throw-away parent proce\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.implementation_version\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.implementation_version\\n @classmethod\\n def implementation_version(cls):\\n \\\"\\\"\\\"\\n :API: public\\n \\\"\\\"\\\"\\n return [('TaskBase', 2)]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.implementation_version_str\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.implementation_version_str\\n @classmethod\\n @memoized_method\\n def implementation_version_str(cls):\\n return '.'.join(['_'.join(map(str, x)) for x in cls.implementation_version()])\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.stable_name\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.stable_name\\n @classmethod\\n def stable_name(cls):\\n \\\"\\\"\\\"The stable name of this task type.\\n\\n We synthesize subclasses of the task types at runtime, and these synthesized subclasses\\n may have random names (e.g., in tests), so this gives us a stable name to use across runs,\\n e.g., in artifact cache references.\\n \\\"\\\"\\\"\\n return cls._stable_name or cls._compute_stable_name()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._compute_stable_name\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._compute_stable_name\\n @classmethod\\n def _compute_stable_name(cls):\\n return '{}_{}'.format(cls.__module__, cls.__name__).replace('.', '_')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.subsystem_dependencies\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.subsystem_dependencies\\n @classmethod\\n def subsystem_dependencies(cls):\\n return super(TaskBase, cls).subsystem_dependencies() + (CacheSetup.scoped(cls),\\n BuildInvalidator.Factory)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.product_types\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.product_types\\n @classmethod\\n def product_types(cls):\\n \\\"\\\"\\\"The list of products this Task produces. Set the product type(s) for this\\n task i.e. the product type(s) this task creates e.g ['classes'].\\n\\n By default, each task is considered as creating a unique product type(s).\\n Subclasses that create products, should override this to specify their unique product type(s).\\n\\n :API: public\\n \\\"\\\"\\\"\\n return []\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.known_scope_infos\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.known_scope_infos\\n @classmethod\\n def known_scope_infos(cls):\\n \\\"\\\"\\\"Yields ScopeInfo for all known scopes for this task, in no particular order.\\\"\\\"\\\"\\n # The task's own scope.\\n yield cls.get_scope_info()\\n # The scopes of any task-specific subsystems it uses.\\n for dep in cls.subsystem_dependencies_iter():\\n if not dep.is_global():\\n yield dep.subsystem_cls.get_scope_info(subscope=dep.scope)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.supports_passthru_args\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.supports_passthru_args\\n @classmethod\\n def supports_passthru_args(cls):\\n \\\"\\\"\\\"Subclasses may override to indicate that they can use passthru args.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._scoped_options\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._scoped_options\\n @classmethod\\n def _scoped_options(cls, options):\\n return options[cls.options_scope]\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.get_alternate_target_roots\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.get_alternate_target_roots\\n @classmethod\\n def get_alternate_target_roots(cls, options, address_mapper, build_graph):\\n # Subclasses should not generally need to override this method.\\n return cls.alternate_target_roots(cls._scoped_options(options), address_mapper, build_graph)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.alternate_target_roots\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.alternate_target_roots\\n @classmethod\\n def alternate_target_roots(cls, options, address_mapper, build_graph):\\n \\\"\\\"\\\"Allows a Task to propose alternate target roots from those specified on the CLI.\\n\\n At most 1 unique proposal is allowed amongst all tasks involved in the run. If more than 1\\n unique list of target roots is proposed an error is raised during task scheduling.\\n\\n :API: public\\n\\n :returns list: The new target roots to use or None to accept the CLI specified target roots.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.invoke_prepare\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.invoke_prepare\\n @classmethod\\n def invoke_prepare(cls, options, round_manager):\\n # Subclasses should not generally need to override this method.\\n return cls.prepare(cls._scoped_options(options), round_manager)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.prepare\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.prepare\\n @classmethod\\n def prepare(cls, options, round_manager):\\n \\\"\\\"\\\"Prepares a task for execution.\\n\\n Called before execution and prior to any tasks that may be (indirectly) depended upon.\\n\\n Typically a task that requires products from other goals would register interest in those\\n products here and then retrieve the requested product mappings when executed.\\n\\n :API: public\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.__init__\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.__init__\\n def __init__(self, context, workdir):\\n \\\"\\\"\\\"Subclass __init__ methods, if defined, *must* follow this idiom:\\n\\n class MyTask(Task):\\n def __init__(self, *args, **kwargs):\\n super(MyTask, self).__init__(*args, **kwargs)\\n ...\\n\\n This allows us to change Task.__init__()'s arguments without\\n changing every subclass. If the subclass does not need its own\\n initialization, this method can (and should) be omitted entirely.\\n\\n :API: public\\n \\\"\\\"\\\"\\n super(TaskBase, self).__init__()\\n self.context = context\\n self._workdir = workdir\\n\\n self._task_name = type(self).__name__\\n self._cache_key_errors = set()\\n self._cache_factory = CacheSetup.create_cache_factory_for_task(self)\\n self._force_invalidated = False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._build_invalidator\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._build_invalidator\\n @memoized_method\\n def _build_invalidator(self, root=False):\\n build_task = None if root else self.fingerprint\\n return BuildInvalidator.Factory.create(build_task=build_task)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.get_options\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.get_options\\n def get_options(self):\\n \\\"\\\"\\\"Returns the option values for this task's scope.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return self.context.options.for_scope(self.options_scope)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.get_passthru_args\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.get_passthru_args\\n def get_passthru_args(self):\\n \\\"\\\"\\\"\\n :API: public\\n \\\"\\\"\\\"\\n if not self.supports_passthru_args():\\n raise TaskError('{0} Does not support passthru args.'.format(self.stable_name()))\\n else:\\n return self.context.options.passthru_args_for_scope(self.options_scope)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.workdir\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.workdir\\n @memoized_property\\n def workdir(self):\\n \\\"\\\"\\\"A scratch-space for this task that will be deleted by `clean-all`.\\n\\n It's guaranteed that no other task has been given this workdir path to use and that the workdir\\n exists.\\n\\n :API: public\\n \\\"\\\"\\\"\\n safe_mkdir(self._workdir)\\n return self._workdir\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._options_fingerprint\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._options_fingerprint\\n def _options_fingerprint(self, scope):\\n options_hasher = sha1()\\n options_hasher.update(scope)\\n options_fp = OptionsFingerprinter.combined_options_fingerprint_for_scope(\\n scope,\\n self.context.options,\\n build_graph=self.context.build_graph,\\n include_passthru=self.supports_passthru_args(),\\n )\\n options_hasher.update(options_fp)\\n return options_hasher.hexdigest()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.fingerprint\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.fingerprint\\n @memoized_property\\n def fingerprint(self):\\n \\\"\\\"\\\"Returns a fingerprint for the identity of the task.\\n\\n A task fingerprint is composed of the options the task is currently running under.\\n Useful for invalidating unchanging targets being executed beneath changing task\\n options that affect outputted artifacts.\\n\\n A task's fingerprint is only valid afer the task has been fully initialized.\\n \\\"\\\"\\\"\\n hasher = sha1()\\n hasher.update(self.stable_name())\\n hasher.update(self._options_fingerprint(self.options_scope))\\n hasher.update(self.implementation_version_str())\\n # TODO: this is not recursive, but should be: see #2739\\n for dep in self.subsystem_dependencies_iter():\\n hasher.update(self._options_fingerprint(dep.options_scope()))\\n return str(hasher.hexdigest())\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.artifact_cache_reads_enabled\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.artifact_cache_reads_enabled\\n def artifact_cache_reads_enabled(self):\\n return self._cache_factory.read_cache_available()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.artifact_cache_writes_enabled\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.artifact_cache_writes_enabled\\n def artifact_cache_writes_enabled(self):\\n return self._cache_factory.write_cache_available()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.invalidate\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.invalidate\\n def invalidate(self):\\n \\\"\\\"\\\"Invalidates all targets for this task.\\\"\\\"\\\"\\n self._build_invalidator().force_invalidate_all()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.create_target_dirs\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.create_target_dirs\\n @property\\n def create_target_dirs(self):\\n \\\"\\\"\\\"Whether to create a results_dir per VersionedTarget in the workdir of the Task.\\n\\n This defaults to the value of `self.cache_target_dirs` (as caching them requires\\n creating them), but may be overridden independently to create the dirs without caching\\n them.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return self.cache_target_dirs or False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.cache_target_dirs\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.cache_target_dirs\\n @property\\n def cache_target_dirs(self):\\n \\\"\\\"\\\"Whether to cache files in VersionedTarget's results_dir after exiting an invalidated block.\\n\\n Subclasses may override this method to return True if they wish to use this style\\n of \\\"automated\\\" caching, where each VersionedTarget is given an associated results directory,\\n which will automatically be uploaded to the cache. Tasks should place the output files\\n for each VersionedTarget in said results directory. It is highly suggested to follow this\\n schema for caching, rather than manually making updates to the artifact cache.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.incremental\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.incremental\\n @property\\n def incremental(self):\\n \\\"\\\"\\\"Whether this Task implements incremental building of individual targets.\\n\\n Incremental tasks with `cache_target_dirs` set will have the results_dir of the previous build\\n for a target cloned into the results_dir for the current build (where possible). This\\n copy-on-write behaviour allows for immutability of the results_dir once a target has been\\n marked valid.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.cache_incremental\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.cache_incremental\\n @property\\n def cache_incremental(self):\\n \\\"\\\"\\\"For incremental tasks, indicates whether the results of incremental builds should be cached.\\n\\n Deterministic per-target incremental compilation is a relatively difficult thing to implement,\\n so this property provides an escape hatch to avoid caching things in that riskier case.\\n\\n :API: public\\n \\\"\\\"\\\"\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.invalidated\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.invalidated\\n @contextmanager\\n def invalidated(self,\\n targets,\\n invalidate_dependents=False,\\n silent=False,\\n fingerprint_strategy=None,\\n topological_order=False):\\n \\\"\\\"\\\"Checks targets for invalidation, first checking the artifact cache.\\n\\n Subclasses call this to figure out what to work on.\\n\\n :API: public\\n\\n :param targets: The targets to check for changes.\\n :param invalidate_dependents: If True then any targets depending on changed targets are\\n invalidated.\\n :param silent: If true, suppress logging information about target invalidation.\\n :param fingerprint_strategy: A FingerprintStrategy instance, which can do per task,\\n finer grained fingerprinting of a given Target.\\n :param topological_order: Whether to invalidate in dependency order.\\n\\n If no exceptions are thrown by work in the block, the build cache is updated for the targets.\\n Note: the artifact cache is not updated. That must be done manually.\\n\\n :returns: Yields an InvalidationCheck object reflecting the targets.\\n :rtype: InvalidationCheck\\n \\\"\\\"\\\"\\n invalidation_check = self._do_invalidation_check(fingerprint_strategy,\\n invalidate_dependents,\\n targets,\\n topological_order)\\n\\n self._maybe_create_results_dirs(invalidation_check.all_vts)\\n\\n if invalidation_check.invalid_vts and self.artifact_cache_reads_enabled():\\n with self.context.new_workunit('cache'):\\n cached_vts, uncached_vts, uncached_causes = \\\\\\n self.check_artifact_cache(self.check_artifact_cache_for(invalidation_check))\\n if cached_vts:\\n cached_targets = [vt.target for vt in cached_vts]\\n self.context.run_tracker.artifact_cache_stats.add_hit\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._update_invalidation_report\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._update_invalidation_report\\n def _update_invalidation_report(self, invalidation_check, phase):\\n invalidation_report = self.context.invalidation_report\\n if invalidation_report:\\n for vts in invalidation_check.all_vts:\\n invalidation_report.add_vts(self._task_name, vts.targets, vts.cache_key, vts.valid,\\n phase=phase)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._do_invalidation_check\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._do_invalidation_check\\n def _do_invalidation_check(self,\\n fingerprint_strategy,\\n invalidate_dependents,\\n targets,\\n topological_order):\\n\\n if self._cache_factory.ignore:\\n cache_key_generator = UncacheableCacheKeyGenerator()\\n else:\\n cache_key_generator = CacheKeyGenerator(\\n self.context.options.for_global_scope().cache_key_gen_version,\\n self.fingerprint)\\n\\n cache_manager = InvalidationCacheManager(self.workdir,\\n cache_key_generator,\\n self._build_invalidator(),\\n invalidate_dependents,\\n fingerprint_strategy=fingerprint_strategy,\\n invalidation_report=self.context.invalidation_report,\\n task_name=self._task_name,\\n task_version=self.implementation_version_str(),\\n artifact_write_callback=self.maybe_write_artifact)\\n\\n # If this Task's execution has been forced, invalidate all our target fingerprints.\\n if self._cache_factory.ignore and not self._force_invalidated:\\n self.invalidate()\\n self._force_invalidated = True\\n\\n return cache_manager.check(targets, topological_order=topological_order)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.maybe_write_artifact\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.maybe_write_artifact\\n def maybe_write_artifact(self, vt):\\n if self._should_cache_target_dir(vt):\\n self.update_artifact_cache([(vt, [vt.current_results_dir])])\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._launch_background_workdir_cleanup\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._launch_background_workdir_cleanup\\n def _launch_background_workdir_cleanup(self, vts):\\n workdir_build_cleanup_job = Work(self._cleanup_workdir_stale_builds,\\n [(vts,)],\\n 'workdir_build_cleanup')\\n self.context.submit_background_work_chain([workdir_build_cleanup_job])\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._cleanup_workdir_stale_builds\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._cleanup_workdir_stale_builds\\n def _cleanup_workdir_stale_builds(self, vts):\\n # workdir_max_build_entries has been assured of not None before invoking this method.\\n workdir_max_build_entries = self.context.options.for_global_scope().workdir_max_build_entries\\n max_entries_per_target = max(2, workdir_max_build_entries)\\n for vt in vts:\\n live_dirs = list(vt.live_dirs())\\n if not live_dirs:\\n continue\\n root_dir = os.path.dirname(vt.results_dir)\\n safe_rm_oldest_items_in_dir(root_dir, max_entries_per_target, excludes=live_dirs)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._should_cache_target_dir\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._should_cache_target_dir\\n def _should_cache_target_dir(self, vt):\\n \\\"\\\"\\\"Return true if the given vt should be written to a cache (if configured).\\\"\\\"\\\"\\n return (\\n self.cache_target_dirs and\\n vt.cacheable and\\n (not vt.is_incremental or self.cache_incremental) and\\n self.artifact_cache_writes_enabled()\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._maybe_create_results_dirs\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._maybe_create_results_dirs\\n def _maybe_create_results_dirs(self, vts):\\n \\\"\\\"\\\"If `cache_target_dirs`, create results_dirs for the given versioned targets.\\\"\\\"\\\"\\n if self.create_target_dirs:\\n for vt in vts:\\n vt.create_results_dir()\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.check_artifact_cache_for\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.check_artifact_cache_for\\n def check_artifact_cache_for(self, invalidation_check):\\n \\\"\\\"\\\"Decides which VTS to check the artifact cache for.\\n\\n By default we check for each invalid target. Can be overridden, e.g., to\\n instead check only for a single artifact for the entire target set.\\n \\\"\\\"\\\"\\n return invalidation_check.invalid_vts\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.check_artifact_cache\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.check_artifact_cache\\n def check_artifact_cache(self, vts):\\n \\\"\\\"\\\"Checks the artifact cache for the specified list of VersionedTargetSets.\\n\\n Returns a tuple (cached, uncached, uncached_causes) of VersionedTargets that were\\n satisfied/unsatisfied from the cache. Uncached VTS are also attached with their\\n causes for the miss: `False` indicates a legit miss while `UnreadableArtifact`\\n is due to either local or remote cache failures.\\n \\\"\\\"\\\"\\n return self.do_check_artifact_cache(vts)\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.do_check_artifact_cache\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.do_check_artifact_cache\\n def do_check_artifact_cache(self, vts, post_process_cached_vts=None):\\n \\\"\\\"\\\"Checks the artifact cache for the specified list of VersionedTargetSets.\\n\\n Returns a pair (cached, uncached) of VersionedTargets that were\\n satisfied/unsatisfied from the cache.\\n \\\"\\\"\\\"\\n if not vts:\\n return [], [], []\\n\\n read_cache = self._cache_factory.get_read_cache()\\n items = [(read_cache, vt.cache_key, vt.current_results_dir if self.cache_target_dirs else None)\\n for vt in vts]\\n res = self.context.subproc_map(call_use_cached_files, items)\\n\\n cached_vts = []\\n uncached_vts = []\\n uncached_causes = []\\n\\n # Note that while the input vts may represent multiple targets (for tasks that overrride\\n # check_artifact_cache_for), the ones we return must represent single targets.\\n # Once flattened, cached/uncached vts are in separate lists. Each uncached vts is paired\\n # with why it is missed for stat reporting purpose.\\n for vt, was_in_cache in zip(vts, res):\\n if was_in_cache:\\n cached_vts.extend(vt.versioned_targets)\\n else:\\n uncached_vts.extend(vt.versioned_targets)\\n uncached_causes.extend(repeat(was_in_cache, len(vt.versioned_targets)))\\n if isinstance(was_in_cache, UnreadableArtifact):\\n self._cache_key_errors.update(was_in_cache.key)\\n\\n if post_process_cached_vts:\\n post_process_cached_vts(cached_vts)\\n for vt in cached_vts:\\n vt.update()\\n return cached_vts, uncached_vts, uncached_causes\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase.update_artifact_cache\",\n \"text\": \"src/python/pants/task/task.py::TaskBase.update_artifact_cache\\n def update_artifact_cache(self, vts_artifactfiles_pairs):\\n \\\"\\\"\\\"Write to the artifact cache, if we're configured to.\\n\\n vts_artifactfiles_pairs - a list of pairs (vts, artifactfiles) where\\n - vts is single VersionedTargetSet.\\n - artifactfiles is a list of absolute paths to artifacts for the VersionedTargetSet.\\n \\\"\\\"\\\"\\n update_artifact_cache_work = self._get_update_artifact_cache_work(vts_artifactfiles_pairs)\\n if update_artifact_cache_work:\\n self.context.submit_background_work_chain([update_artifact_cache_work],\\n parent_workunit_name='cache')\",\n \"label\": 0\n },\n {\n \"cid\": \"src/python/pants/task/task.py::TaskBase._get_update_artifact_cache_work\",\n \"text\": \"src/python/pants/task/task.py::TaskBase._get_update_artifact_cache_work\\n def _get_update_artifact_cache_work(self, vts_artifactfiles_pairs):\\n \\\"\\\"\\\"Create a Work instance to update an artifact cache, if we're configured to.\\n\\n vts_artifactfiles_pairs - a list of pairs (vts, artifactfiles) where\\n - vts is single VersionedTargetSet.\\n - artifactfiles is a list of paths to artifacts for the VersionedTargetSet.\\n \\\"\\\"\\\"\\n cache = self._cache_factory.get_write_cache()\\n if cache:\\n if len(vts_artifactfiles_pairs) == 0:\\n return None\\n # Do some reporting.\\n targets = set()\\n for vts, _ in vts_artifactfiles_pairs:\\n targets.update(vts.targets)\\n\\n self._report_targets(\\n 'Caching artifacts for ',\\n list(targets),\\n '.',\\n logger=self.context.log.debug,\\n )\\n\\n always_overwrite = self._cache_factory.overwrite()\\n\\n # Cache the artifacts.\\n args_tuples = []\\n for vts, artifactfiles in vts_artifactfiles_pairs:\\n overwrite = always_overwrite or vts.cache_key in self._cache_key_errors\\n args_tuples.append((cache, vts.cache_key, artifactfiles, overwrite))\\n\\n return Work(lambda x: self.context.subproc_map(call_insert, x), [(args_tuples,)], 'insert')\\n else:\\n return None\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":292,"cells":{"instance_id":{"kind":"string","value":"google__jax-1262"},"repo":{"kind":"string","value":"google/jax"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Feature Request: More Initializers in stax\nProbably on a similar topic of #137, I think it would be better if stax had a few extra initializers for neural network layers, specifically glorot uniform, kalming/he normal and kalming/he uniform, as they are important in building deep neural networks with ReLU activation, as shown in [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification\r\n](https://arxiv.org/abs/1502.01852) and explored in [this medium post](https://towardsdatascience.com/hyper-parameters-in-action-part-ii-weight-initializers-35aee1a28404). \nlogsoftmax has numerical issue\nInside stax.py, `logsoftmax` doesn't use the trick of `softmax` where you can do x - x.max to make it more stable.\r\n\r\n\r\n```\r\ndef logsoftmax(x, axis=-1):\r\n \"\"\"Apply log softmax to an array of logits, log-normalizing along an axis.\"\"\"\r\n return x - logsumexp(x, axis, keepdims=True)\r\n```\r\n\r\n```\r\ndef softmax(x, axis=-1):\r\n \"\"\"Apply softmax to an array of logits, exponentiating and normalizing along an axis.\"\"\"\r\n unnormalized = np.exp(x - x.max(axis, keepdims=True))\r\n return unnormalized / unnormalized.sum(axis, keepdims=True)\r\n```\n"},"gold_files":{"kind":"list like","value":["jax/experimental/stax.py","jax/random.py"],"string":"[\n \"jax/experimental/stax.py\",\n \"jax/random.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["jax/experimental/stax.py::<module>","jax/experimental/stax.py::BatchNorm.apply_fun","jax/experimental/stax.py::Dense","jax/experimental/stax.py::GeneralConv","jax/experimental/stax.py::GeneralConvTranspose","jax/experimental/stax.py::elu","jax/experimental/stax.py::fastvar","jax/experimental/stax.py::glorot","jax/experimental/stax.py::glorot.init","jax/experimental/stax.py::leaky_relu","jax/experimental/stax.py::logsoftmax","jax/experimental/stax.py::randn","jax/experimental/stax.py::randn.init","jax/experimental/stax.py::relu","jax/experimental/stax.py::sigmoid","jax/experimental/stax.py::softmax","jax/experimental/stax.py::softplus","jax/random.py::<module>"],"string":"[\n \"jax/experimental/stax.py::<module>\",\n \"jax/experimental/stax.py::BatchNorm.apply_fun\",\n \"jax/experimental/stax.py::Dense\",\n \"jax/experimental/stax.py::GeneralConv\",\n \"jax/experimental/stax.py::GeneralConvTranspose\",\n \"jax/experimental/stax.py::elu\",\n \"jax/experimental/stax.py::fastvar\",\n \"jax/experimental/stax.py::glorot\",\n \"jax/experimental/stax.py::glorot.init\",\n \"jax/experimental/stax.py::leaky_relu\",\n \"jax/experimental/stax.py::logsoftmax\",\n \"jax/experimental/stax.py::randn\",\n \"jax/experimental/stax.py::randn.init\",\n \"jax/experimental/stax.py::relu\",\n \"jax/experimental/stax.py::sigmoid\",\n \"jax/experimental/stax.py::softmax\",\n \"jax/experimental/stax.py::softplus\",\n \"jax/random.py::<module>\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"examples/mnist_classifier.py","text":"examples/mnist_classifier.py\ndef loss(params, batch)\ndef accuracy(params, batch)","label":0},{"cid":"examples/resnet50.py","text":"examples/resnet50.py\ndef ConvBlock(kernel_size, filters, strides=(2, 2))\ndef IdentityBlock(kernel_size, filters)\n def make_main(input_shape)\ndef ResNet50(num_classes)","label":0},{"cid":"jax/experimental/stax.py","text":"jax/experimental/stax.py\ndef relu(x)\ndef softplus(x)\ndef sigmoid(x)\ndef elu(x)\ndef leaky_relu(x)\ndef logsoftmax(x, axis=-1) # Apply log softmax to an array of logits, log-normalizing along an axis.\ndef softmax(x, axis=-1) # Apply softmax to an array of logits, exponentiating and normalizing along an axi\ndef fastvar(x, axis, keepdims) # A fast but less numerically-stable variance calculation than np.var.\ndef randn(stddev=0.01) # An initializer function for random normal coefficients.\n def init(rng, shape)\ndef glorot(out_axis=0, in_axis=1, scale=onp.sqrt(2)) # An initializer function for random Glorot-scaled coefficients.\n def init(rng, shape)\ndef Dense(out_dim, W_init=glorot(), b_init=randn()) # Layer constructor function for a dense (fully-connected) layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef GeneralConv(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=randn(1e-06)) # Layer construction function for a general convolution layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef GeneralConvTranspose(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=randn(1e-06)) # Layer construction function for a general transposed-convolution layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-05, center=True, scale=True, beta_init=zeros, gamma_init=ones) # Layer construction function for a batch normalization layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, x, **kwargs)\ndef elementwise(fun, **fun_kwargs) # Layer that applies a scalar function elementwise on its inputs.\ndef _pooling_layer(reducer, init_val, rescaler=None)\n def PoolingLayer(window_shape, strides=None, padding='VALID') # Layer construction function for a pooling layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef _normalize_by_window_size(dims, strides, padding)\n def rescale(outputs, inputs)\ndef Flatten() # Layer construction function for flattening all but the leading dim.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef Identity() # Layer construction function for an identity layer.\ndef FanOut(num) # Layer construction function for a fan-out layer.\ndef FanInSum() # Layer construction function for a fan-in sum layer.\ndef FanInConcat(axis=-1) # Layer construction function for a fan-in concatenation layer.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef Dropout(rate, mode='train') # Layer construction function for a dropout layer with given rate.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef serial(*layers) # Combinator for composing layers in serial.\n def init_fun(rng, input_shape)\n def apply_fun(params, inputs, **kwargs)\ndef parallel(*layers) # Combinato","label":1},{"cid":"jax/random.py","text":"jax/random.py\ndef PRNGKey(seed) # Create a pseudo-random number generator (PRNG) key given an integer seed.\ndef _is_prng_key(key)\ndef _make_rotate_left(dtype)\n def _rotate_left(x, d)\ndef _bit_stats(bits) # This is a debugging function to compute the statistics of bit fields.\ndef threefry_2x32(keypair, count) # Apply the Threefry 2x32 hash.\n def apply_round(v, rot)\n def rotate_list(xs)\n def step(i, state)\ndef split(key, num=2) # Splits a PRNG key into `num` new keys by adding a leading axis.\ndef _split(key, num)\ndef fold_in(key, data) # Folds in data to a PRNG key to form a new PRNG key.\ndef _fold_in(key, data)\ndef _random_bits(key, bit_width, shape) # Sample uniform random bits of given width and shape using PRNG key.\ndef _check_shape(name, shape)\ndef uniform(key, shape=(), dtype=onp.float64, minval=0.0, maxval=1.0) # Sample uniform random values in [minval, maxval) with given shape/dtype.\ndef _uniform(key, shape, dtype, minval, maxval)\ndef randint(key, shape, minval, maxval, dtype=onp.int64) # Sample uniform random values in [minval, maxval) with given shape/dtype.\ndef _randint(key, shape, minval, maxval, dtype)\ndef shuffle(key, x, axis=0) # Shuffle the elements of an array uniformly at random along an axis.\ndef _shuffle(key, x, axis)\ndef normal(key, shape=(), dtype=onp.float64) # Sample standard normal random values with given shape and float dtype.\ndef _normal(key, shape, dtype)\ndef bernoulli(key, p=onp.float32(0.5), shape=()) # Sample Bernoulli random values with given shape and mean.\ndef _bernoulli(key, p, shape)\ndef beta(key, a, b, shape=(), dtype=onp.float64) # Sample Bernoulli random values with given shape and mean.\ndef _beta(key, a, b, shape, dtype)\ndef cauchy(key, shape=(), dtype=onp.float64) # Sample Cauchy random values with given shape and float dtype.\ndef _cauchy(key, shape, dtype)\ndef dirichlet(key, alpha, shape=(), dtype=onp.float64) # Sample Cauchy random values with given shape and float dtype.\ndef _dirichlet(key, alpha, shape, dtype)\ndef exponential(key, shape=(), dtype=onp.float64) # Sample Exponential random values with given shape and float dtype.\ndef _exponential(key, shape, dtype)\ndef _gamma_one(key, alpha)\n def _cond_fn(kXVU)\n def _body_fn(kXVU)\n def _next_kxv(kxv)\ndef _gamma_grad_one(z, alpha)\n def _case1(zagf)\n def _cond2(zagf)\n def _case2(zagf)\n def _cond3(zagf)\n def _case3(zagf)\n def _case4(zagf)\ndef _gamma_grad(sample, a)\ndef _gamma_impl(key, a)\ndef gamma(key, a, shape=(), dtype=onp.float64) # Sample Gamma random values with given shape and float dtype.\ndef _gamma(key, a, shape, dtype)\ndef gumbel(key, shape=(), dtype=onp.float64) # Sample Gumbel random values with given shape and float dtype.\ndef _gumbel(key, shape, dtype)\ndef laplace(key, shape=(), dtype=onp.float64) # Sample Laplace random values with given shape and float dtype.\ndef _laplace(key, shape, dtype)\ndef logistic(key, shape=(), dtype=onp.float64) # Sample logistic random values with given sha","label":1}],"string":"[\n {\n \"cid\": \"examples/mnist_classifier.py\",\n \"text\": \"examples/mnist_classifier.py\\ndef loss(params, batch)\\ndef accuracy(params, batch)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/resnet50.py\",\n \"text\": \"examples/resnet50.py\\ndef ConvBlock(kernel_size, filters, strides=(2, 2))\\ndef IdentityBlock(kernel_size, filters)\\n def make_main(input_shape)\\ndef ResNet50(num_classes)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py\",\n \"text\": \"jax/experimental/stax.py\\ndef relu(x)\\ndef softplus(x)\\ndef sigmoid(x)\\ndef elu(x)\\ndef leaky_relu(x)\\ndef logsoftmax(x, axis=-1) # Apply log softmax to an array of logits, log-normalizing along an axis.\\ndef softmax(x, axis=-1) # Apply softmax to an array of logits, exponentiating and normalizing along an axi\\ndef fastvar(x, axis, keepdims) # A fast but less numerically-stable variance calculation than np.var.\\ndef randn(stddev=0.01) # An initializer function for random normal coefficients.\\n def init(rng, shape)\\ndef glorot(out_axis=0, in_axis=1, scale=onp.sqrt(2)) # An initializer function for random Glorot-scaled coefficients.\\n def init(rng, shape)\\ndef Dense(out_dim, W_init=glorot(), b_init=randn()) # Layer constructor function for a dense (fully-connected) layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef GeneralConv(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=randn(1e-06)) # Layer construction function for a general convolution layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef GeneralConvTranspose(dimension_numbers, out_chan, filter_shape, strides=None, padding='VALID', W_init=None, b_init=randn(1e-06)) # Layer construction function for a general transposed-convolution layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-05, center=True, scale=True, beta_init=zeros, gamma_init=ones) # Layer construction function for a batch normalization layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, x, **kwargs)\\ndef elementwise(fun, **fun_kwargs) # Layer that applies a scalar function elementwise on its inputs.\\ndef _pooling_layer(reducer, init_val, rescaler=None)\\n def PoolingLayer(window_shape, strides=None, padding='VALID') # Layer construction function for a pooling layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef _normalize_by_window_size(dims, strides, padding)\\n def rescale(outputs, inputs)\\ndef Flatten() # Layer construction function for flattening all but the leading dim.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef Identity() # Layer construction function for an identity layer.\\ndef FanOut(num) # Layer construction function for a fan-out layer.\\ndef FanInSum() # Layer construction function for a fan-in sum layer.\\ndef FanInConcat(axis=-1) # Layer construction function for a fan-in concatenation layer.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef Dropout(rate, mode='train') # Layer construction function for a dropout layer with given rate.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef serial(*layers) # Combinator for composing layers in serial.\\n def init_fun(rng, input_shape)\\n def apply_fun(params, inputs, **kwargs)\\ndef parallel(*layers) # Combinato\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/random.py\",\n \"text\": \"jax/random.py\\ndef PRNGKey(seed) # Create a pseudo-random number generator (PRNG) key given an integer seed.\\ndef _is_prng_key(key)\\ndef _make_rotate_left(dtype)\\n def _rotate_left(x, d)\\ndef _bit_stats(bits) # This is a debugging function to compute the statistics of bit fields.\\ndef threefry_2x32(keypair, count) # Apply the Threefry 2x32 hash.\\n def apply_round(v, rot)\\n def rotate_list(xs)\\n def step(i, state)\\ndef split(key, num=2) # Splits a PRNG key into `num` new keys by adding a leading axis.\\ndef _split(key, num)\\ndef fold_in(key, data) # Folds in data to a PRNG key to form a new PRNG key.\\ndef _fold_in(key, data)\\ndef _random_bits(key, bit_width, shape) # Sample uniform random bits of given width and shape using PRNG key.\\ndef _check_shape(name, shape)\\ndef uniform(key, shape=(), dtype=onp.float64, minval=0.0, maxval=1.0) # Sample uniform random values in [minval, maxval) with given shape/dtype.\\ndef _uniform(key, shape, dtype, minval, maxval)\\ndef randint(key, shape, minval, maxval, dtype=onp.int64) # Sample uniform random values in [minval, maxval) with given shape/dtype.\\ndef _randint(key, shape, minval, maxval, dtype)\\ndef shuffle(key, x, axis=0) # Shuffle the elements of an array uniformly at random along an axis.\\ndef _shuffle(key, x, axis)\\ndef normal(key, shape=(), dtype=onp.float64) # Sample standard normal random values with given shape and float dtype.\\ndef _normal(key, shape, dtype)\\ndef bernoulli(key, p=onp.float32(0.5), shape=()) # Sample Bernoulli random values with given shape and mean.\\ndef _bernoulli(key, p, shape)\\ndef beta(key, a, b, shape=(), dtype=onp.float64) # Sample Bernoulli random values with given shape and mean.\\ndef _beta(key, a, b, shape, dtype)\\ndef cauchy(key, shape=(), dtype=onp.float64) # Sample Cauchy random values with given shape and float dtype.\\ndef _cauchy(key, shape, dtype)\\ndef dirichlet(key, alpha, shape=(), dtype=onp.float64) # Sample Cauchy random values with given shape and float dtype.\\ndef _dirichlet(key, alpha, shape, dtype)\\ndef exponential(key, shape=(), dtype=onp.float64) # Sample Exponential random values with given shape and float dtype.\\ndef _exponential(key, shape, dtype)\\ndef _gamma_one(key, alpha)\\n def _cond_fn(kXVU)\\n def _body_fn(kXVU)\\n def _next_kxv(kxv)\\ndef _gamma_grad_one(z, alpha)\\n def _case1(zagf)\\n def _cond2(zagf)\\n def _case2(zagf)\\n def _cond3(zagf)\\n def _case3(zagf)\\n def _case4(zagf)\\ndef _gamma_grad(sample, a)\\ndef _gamma_impl(key, a)\\ndef gamma(key, a, shape=(), dtype=onp.float64) # Sample Gamma random values with given shape and float dtype.\\ndef _gamma(key, a, shape, dtype)\\ndef gumbel(key, shape=(), dtype=onp.float64) # Sample Gumbel random values with given shape and float dtype.\\ndef _gumbel(key, shape, dtype)\\ndef laplace(key, shape=(), dtype=onp.float64) # Sample Laplace random values with given shape and float dtype.\\ndef _laplace(key, shape, dtype)\\ndef logistic(key, shape=(), dtype=onp.float64) # Sample logistic random values with given sha\",\n \"label\": 1\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"examples/mnist_classifier.py::loss","text":"examples/mnist_classifier.py::loss\ndef loss(params, batch):\n inputs, targets = batch\n preds = predict(params, inputs)\n return -np.mean(preds * targets)","label":0},{"cid":"examples/mnist_classifier.py::accuracy","text":"examples/mnist_classifier.py::accuracy\ndef accuracy(params, batch):\n inputs, targets = batch\n target_class = np.argmax(targets, axis=1)\n predicted_class = np.argmax(predict(params, inputs), axis=1)\n return np.mean(predicted_class == target_class)","label":0},{"cid":"examples/resnet50.py::ConvBlock","text":"examples/resnet50.py::ConvBlock\ndef ConvBlock(kernel_size, filters, strides=(2, 2)):\n ks = kernel_size\n filters1, filters2, filters3 = filters\n Main = stax.serial(\n Conv(filters1, (1, 1), strides), BatchNorm(), Relu,\n Conv(filters2, (ks, ks), padding='SAME'), BatchNorm(), Relu,\n Conv(filters3, (1, 1)), BatchNorm())\n Shortcut = stax.serial(Conv(filters3, (1, 1), strides), BatchNorm())\n return stax.serial(FanOut(2), stax.parallel(Main, Shortcut), FanInSum, Relu)","label":0},{"cid":"examples/resnet50.py::IdentityBlock","text":"examples/resnet50.py::IdentityBlock\ndef IdentityBlock(kernel_size, filters):\n ks = kernel_size\n filters1, filters2 = filters\n def make_main(input_shape):\n # the number of output channels depends on the number of input channels\n return stax.serial(\n Conv(filters1, (1, 1)), BatchNorm(), Relu,\n Conv(filters2, (ks, ks), padding='SAME'), BatchNorm(), Relu,\n Conv(input_shape[3], (1, 1)), BatchNorm())\n Main = stax.shape_dependent(make_main)\n return stax.serial(FanOut(2), stax.parallel(Main, Identity), FanInSum, Relu)","label":0},{"cid":"examples/resnet50.py::IdentityBlock.make_main","text":"examples/resnet50.py::IdentityBlock.make_main\n def make_main(input_shape):\n # the number of output channels depends on the number of input channels\n return stax.serial(\n Conv(filters1, (1, 1)), BatchNorm(), Relu,\n Conv(filters2, (ks, ks), padding='SAME'), BatchNorm(), Relu,\n Conv(input_shape[3], (1, 1)), BatchNorm())","label":0},{"cid":"examples/resnet50.py::ResNet50","text":"examples/resnet50.py::ResNet50\ndef ResNet50(num_classes):\n return stax.serial(\n GeneralConv(('HWCN', 'OIHW', 'NHWC'), 64, (7, 7), (2, 2), 'SAME'),\n BatchNorm(), Relu, MaxPool((3, 3), strides=(2, 2)),\n ConvBlock(3, [64, 64, 256], strides=(1, 1)),\n IdentityBlock(3, [64, 64]),\n IdentityBlock(3, [64, 64]),\n ConvBlock(3, [128, 128, 512]),\n IdentityBlock(3, [128, 128]),\n IdentityBlock(3, [128, 128]),\n IdentityBlock(3, [128, 128]),\n ConvBlock(3, [256, 256, 1024]),\n IdentityBlock(3, [256, 256]),\n IdentityBlock(3, [256, 256]),\n IdentityBlock(3, [256, 256]),\n IdentityBlock(3, [256, 256]),\n IdentityBlock(3, [256, 256]),\n ConvBlock(3, [512, 512, 2048]),\n IdentityBlock(3, [512, 512]),\n IdentityBlock(3, [512, 512]),\n AvgPool((7, 7)), Flatten, Dense(num_classes), LogSoftmax)","label":0},{"cid":"jax/experimental/stax.py::relu","text":"jax/experimental/stax.py::relu\ndef relu(x): return np.maximum(x, 0.)","label":1},{"cid":"jax/experimental/stax.py::softplus","text":"jax/experimental/stax.py::softplus\ndef softplus(x): return np.logaddexp(x, 0.)","label":1},{"cid":"jax/experimental/stax.py::sigmoid","text":"jax/experimental/stax.py::sigmoid\ndef sigmoid(x): return expit(x)","label":1},{"cid":"jax/experimental/stax.py::elu","text":"jax/experimental/stax.py::elu\ndef elu(x): return np.where(x > 0, x, np.expm1(x))","label":1},{"cid":"jax/experimental/stax.py::leaky_relu","text":"jax/experimental/stax.py::leaky_relu\ndef leaky_relu(x): return np.where(x >= 0, x, 0.01 * x)","label":1},{"cid":"jax/experimental/stax.py::logsoftmax","text":"jax/experimental/stax.py::logsoftmax\ndef logsoftmax(x, axis=-1):\n \"\"\"Apply log softmax to an array of logits, log-normalizing along an axis.\"\"\"\n return x - logsumexp(x, axis, keepdims=True)","label":1},{"cid":"jax/experimental/stax.py::softmax","text":"jax/experimental/stax.py::softmax\ndef softmax(x, axis=-1):\n \"\"\"Apply softmax to an array of logits, exponentiating and normalizing along an axis.\"\"\"\n unnormalized = np.exp(x - x.max(axis, keepdims=True))\n return unnormalized / unnormalized.sum(axis, keepdims=True)","label":1},{"cid":"jax/experimental/stax.py::fastvar","text":"jax/experimental/stax.py::fastvar\ndef fastvar(x, axis, keepdims):\n \"\"\"A fast but less numerically-stable variance calculation than np.var.\"\"\"\n return np.mean(x**2, axis, keepdims=keepdims) - np.mean(x, axis, keepdims=keepdims)**2","label":1},{"cid":"jax/experimental/stax.py::randn","text":"jax/experimental/stax.py::randn\ndef randn(stddev=1e-2):\n \"\"\"An initializer function for random normal coefficients.\"\"\"\n def init(rng, shape):\n std = lax.convert_element_type(stddev, np.float32)\n return std * random.normal(rng, shape, dtype=np.float32)\n return init","label":1},{"cid":"jax/experimental/stax.py::randn.init","text":"jax/experimental/stax.py::randn.init\n def init(rng, shape):\n std = lax.convert_element_type(stddev, np.float32)\n return std * random.normal(rng, shape, dtype=np.float32)","label":1},{"cid":"jax/experimental/stax.py::glorot","text":"jax/experimental/stax.py::glorot\ndef glorot(out_axis=0, in_axis=1, scale=onp.sqrt(2)):\n \"\"\"An initializer function for random Glorot-scaled coefficients.\"\"\"\n def init(rng, shape):\n fan_in, fan_out = shape[in_axis], shape[out_axis]\n size = onp.prod(onp.delete(shape, [in_axis, out_axis]))\n std = scale / np.sqrt((fan_in + fan_out) / 2. * size)\n std = lax.convert_element_type(std, np.float32)\n return std * random.normal(rng, shape, dtype=np.float32)\n return init","label":1},{"cid":"jax/experimental/stax.py::glorot.init","text":"jax/experimental/stax.py::glorot.init\n def init(rng, shape):\n fan_in, fan_out = shape[in_axis], shape[out_axis]\n size = onp.prod(onp.delete(shape, [in_axis, out_axis]))\n std = scale / np.sqrt((fan_in + fan_out) / 2. * size)\n std = lax.convert_element_type(std, np.float32)\n return std * random.normal(rng, shape, dtype=np.float32)","label":1},{"cid":"jax/experimental/stax.py::Dense","text":"jax/experimental/stax.py::Dense\ndef Dense(out_dim, W_init=glorot(), b_init=randn()):\n \"\"\"Layer constructor function for a dense (fully-connected) layer.\"\"\"\n def init_fun(rng, input_shape):\n output_shape = input_shape[:-1] + (out_dim,)\n k1, k2 = random.split(rng)\n W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))\n return output_shape, (W, b)\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return np.dot(inputs, W) + b\n return init_fun, apply_fun","label":1},{"cid":"jax/experimental/stax.py::Dense.init_fun","text":"jax/experimental/stax.py::Dense.init_fun\n def init_fun(rng, input_shape):\n output_shape = input_shape[:-1] + (out_dim,)\n k1, k2 = random.split(rng)\n W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))\n return output_shape, (W, b)","label":0},{"cid":"jax/experimental/stax.py::Dense.apply_fun","text":"jax/experimental/stax.py::Dense.apply_fun\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return np.dot(inputs, W) + b","label":0},{"cid":"jax/experimental/stax.py::GeneralConv","text":"jax/experimental/stax.py::GeneralConv\ndef GeneralConv(dimension_numbers, out_chan, filter_shape,\n strides=None, padding='VALID', W_init=None, b_init=randn(1e-6)):\n \"\"\"Layer construction function for a general convolution layer.\"\"\"\n lhs_spec, rhs_spec, out_spec = dimension_numbers\n one = (1,) * len(filter_shape)\n strides = strides or one\n W_init = W_init or glorot(rhs_spec.index('O'), rhs_spec.index('I'))\n def init_fun(rng, input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_general_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n k1, k2 = random.split(rng)\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\n return output_shape, (W, b)\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\n dimension_numbers) + b\n return init_fun, apply_fun","label":1},{"cid":"jax/experimental/stax.py::GeneralConv.init_fun","text":"jax/experimental/stax.py::GeneralConv.init_fun\n def init_fun(rng, input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_general_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n k1, k2 = random.split(rng)\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\n return output_shape, (W, b)","label":0},{"cid":"jax/experimental/stax.py::GeneralConv.apply_fun","text":"jax/experimental/stax.py::GeneralConv.apply_fun\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\n dimension_numbers) + b","label":0},{"cid":"jax/experimental/stax.py::GeneralConvTranspose","text":"jax/experimental/stax.py::GeneralConvTranspose\ndef GeneralConvTranspose(dimension_numbers, out_chan, filter_shape,\n strides=None, padding='VALID', W_init=None,\n b_init=randn(1e-6)):\n \"\"\"Layer construction function for a general transposed-convolution layer.\"\"\"\n lhs_spec, rhs_spec, out_spec = dimension_numbers\n one = (1,) * len(filter_shape)\n strides = strides or one\n W_init = W_init or glorot(rhs_spec.index('O'), rhs_spec.index('I'))\n def init_fun(rng, input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_transpose_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n k1, k2 = random.split(rng)\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\n return output_shape, (W, b)\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_transpose(inputs, W, strides, padding,\n dimension_numbers) + b\n return init_fun, apply_fun","label":1},{"cid":"jax/experimental/stax.py::GeneralConvTranspose.init_fun","text":"jax/experimental/stax.py::GeneralConvTranspose.init_fun\n def init_fun(rng, input_shape):\n filter_shape_iter = iter(filter_shape)\n kernel_shape = [out_chan if c == 'O' else\n input_shape[lhs_spec.index('C')] if c == 'I' else\n next(filter_shape_iter) for c in rhs_spec]\n output_shape = lax.conv_transpose_shape_tuple(\n input_shape, kernel_shape, strides, padding, dimension_numbers)\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\n k1, k2 = random.split(rng)\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\n return output_shape, (W, b)","label":0},{"cid":"jax/experimental/stax.py::GeneralConvTranspose.apply_fun","text":"jax/experimental/stax.py::GeneralConvTranspose.apply_fun\n def apply_fun(params, inputs, **kwargs):\n W, b = params\n return lax.conv_transpose(inputs, W, strides, padding,\n dimension_numbers) + b","label":0},{"cid":"jax/experimental/stax.py::BatchNorm","text":"jax/experimental/stax.py::BatchNorm\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-5, center=True, scale=True,\n beta_init=zeros, gamma_init=ones):\n \"\"\"Layer construction function for a batch normalization layer.\"\"\"\n _beta_init = lambda rng, shape: beta_init(rng, shape) if center else ()\n _gamma_init = lambda rng, shape: gamma_init(rng, shape) if scale else ()\n axis = (axis,) if np.isscalar(axis) else axis\n def init_fun(rng, input_shape):\n shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)\n k1, k2 = random.split(rng)\n beta, gamma = _beta_init(k1, shape), _gamma_init(k2, shape)\n return input_shape, (beta, gamma)\n def apply_fun(params, x, **kwargs):\n beta, gamma = params\n # TODO(phawkins): np.expand_dims should accept an axis tuple.\n # (https://github.com/numpy/numpy/issues/12290)\n ed = tuple(None if i in axis else slice(None) for i in range(np.ndim(x)))\n beta = beta[ed]\n gamma = gamma[ed]\n mean, var = np.mean(x, axis, keepdims=True), fastvar(x, axis, keepdims=True)\n z = (x - mean) / np.sqrt(var + epsilon)\n if center and scale: return gamma * z + beta\n if center: return z + beta\n if scale: return gamma * z\n return z\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::BatchNorm.init_fun","text":"jax/experimental/stax.py::BatchNorm.init_fun\n def init_fun(rng, input_shape):\n shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)\n k1, k2 = random.split(rng)\n beta, gamma = _beta_init(k1, shape), _gamma_init(k2, shape)\n return input_shape, (beta, gamma)","label":0},{"cid":"jax/experimental/stax.py::BatchNorm.apply_fun","text":"jax/experimental/stax.py::BatchNorm.apply_fun\n def apply_fun(params, x, **kwargs):\n beta, gamma = params\n # TODO(phawkins): np.expand_dims should accept an axis tuple.\n # (https://github.com/numpy/numpy/issues/12290)\n ed = tuple(None if i in axis else slice(None) for i in range(np.ndim(x)))\n beta = beta[ed]\n gamma = gamma[ed]\n mean, var = np.mean(x, axis, keepdims=True), fastvar(x, axis, keepdims=True)\n z = (x - mean) / np.sqrt(var + epsilon)\n if center and scale: return gamma * z + beta\n if center: return z + beta\n if scale: return gamma * z\n return z","label":1},{"cid":"jax/experimental/stax.py::elementwise","text":"jax/experimental/stax.py::elementwise\ndef elementwise(fun, **fun_kwargs):\n \"\"\"Layer that applies a scalar function elementwise on its inputs.\"\"\"\n init_fun = lambda rng, input_shape: (input_shape, ())\n apply_fun = lambda params, inputs, **kwargs: fun(inputs, **fun_kwargs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer","text":"jax/experimental/stax.py::_pooling_layer\ndef _pooling_layer(reducer, init_val, rescaler=None):\n def PoolingLayer(window_shape, strides=None, padding='VALID'):\n \"\"\"Layer construction function for a pooling layer.\"\"\"\n strides = strides or (1,) * len(window_shape)\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\n dims = (1,) + window_shape + (1,) # NHWC\n strides = (1,) + strides + (1,)\n def init_fun(rng, input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\n return out_shape, ()\n def apply_fun(params, inputs, **kwargs):\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\n return rescale(out, inputs) if rescale else out\n return init_fun, apply_fun\n return PoolingLayer","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer\n def PoolingLayer(window_shape, strides=None, padding='VALID'):\n \"\"\"Layer construction function for a pooling layer.\"\"\"\n strides = strides or (1,) * len(window_shape)\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\n dims = (1,) + window_shape + (1,) # NHWC\n strides = (1,) + strides + (1,)\n def init_fun(rng, input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\n return out_shape, ()\n def apply_fun(params, inputs, **kwargs):\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\n return rescale(out, inputs) if rescale else out\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\n def init_fun(rng, input_shape):\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\n return out_shape, ()","label":0},{"cid":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun","text":"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\n def apply_fun(params, inputs, **kwargs):\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\n return rescale(out, inputs) if rescale else out","label":0},{"cid":"jax/experimental/stax.py::_normalize_by_window_size","text":"jax/experimental/stax.py::_normalize_by_window_size\ndef _normalize_by_window_size(dims, strides, padding):\n def rescale(outputs, inputs):\n one = np.ones(inputs.shape[1:-1], dtype=inputs.dtype)\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\n return outputs / window_sizes[..., np.newaxis]\n return rescale","label":0},{"cid":"jax/experimental/stax.py::_normalize_by_window_size.rescale","text":"jax/experimental/stax.py::_normalize_by_window_size.rescale\n def rescale(outputs, inputs):\n one = np.ones(inputs.shape[1:-1], dtype=inputs.dtype)\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\n return outputs / window_sizes[..., np.newaxis]","label":0},{"cid":"jax/experimental/stax.py::Flatten","text":"jax/experimental/stax.py::Flatten\ndef Flatten():\n \"\"\"Layer construction function for flattening all but the leading dim.\"\"\"\n def init_fun(rng, input_shape):\n output_shape = input_shape[0], reduce(op.mul, input_shape[1:], 1)\n return output_shape, ()\n def apply_fun(params, inputs, **kwargs):\n return np.reshape(inputs, (inputs.shape[0], -1))\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Flatten.init_fun","text":"jax/experimental/stax.py::Flatten.init_fun\n def init_fun(rng, input_shape):\n output_shape = input_shape[0], reduce(op.mul, input_shape[1:], 1)\n return output_shape, ()","label":0},{"cid":"jax/experimental/stax.py::Flatten.apply_fun","text":"jax/experimental/stax.py::Flatten.apply_fun\n def apply_fun(params, inputs, **kwargs):\n return np.reshape(inputs, (inputs.shape[0], -1))","label":0},{"cid":"jax/experimental/stax.py::Identity","text":"jax/experimental/stax.py::Identity\ndef Identity():\n \"\"\"Layer construction function for an identity layer.\"\"\"\n init_fun = lambda rng, input_shape: (input_shape, ())\n apply_fun = lambda params, inputs, **kwargs: inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanOut","text":"jax/experimental/stax.py::FanOut\ndef FanOut(num):\n \"\"\"Layer construction function for a fan-out layer.\"\"\"\n init_fun = lambda rng, input_shape: ([input_shape] * num, ())\n apply_fun = lambda params, inputs, **kwargs: [inputs] * num\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInSum","text":"jax/experimental/stax.py::FanInSum\ndef FanInSum():\n \"\"\"Layer construction function for a fan-in sum layer.\"\"\"\n init_fun = lambda rng, input_shape: (input_shape[0], ())\n apply_fun = lambda params, inputs, **kwargs: sum(inputs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInConcat","text":"jax/experimental/stax.py::FanInConcat\ndef FanInConcat(axis=-1):\n \"\"\"Layer construction function for a fan-in concatenation layer.\"\"\"\n def init_fun(rng, input_shape):\n ax = axis % len(input_shape[0])\n concat_size = sum(shape[ax] for shape in input_shape)\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\n return out_shape, ()\n def apply_fun(params, inputs, **kwargs):\n return np.concatenate(inputs, axis)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::FanInConcat.init_fun","text":"jax/experimental/stax.py::FanInConcat.init_fun\n def init_fun(rng, input_shape):\n ax = axis % len(input_shape[0])\n concat_size = sum(shape[ax] for shape in input_shape)\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\n return out_shape, ()","label":0},{"cid":"jax/experimental/stax.py::FanInConcat.apply_fun","text":"jax/experimental/stax.py::FanInConcat.apply_fun\n def apply_fun(params, inputs, **kwargs):\n return np.concatenate(inputs, axis)","label":0},{"cid":"jax/experimental/stax.py::Dropout","text":"jax/experimental/stax.py::Dropout\ndef Dropout(rate, mode='train'):\n \"\"\"Layer construction function for a dropout layer with given rate.\"\"\"\n def init_fun(rng, input_shape):\n return input_shape, ()\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.get('rng', None)\n if rng is None:\n msg = (\"Dropout layer requires apply_fun to be called with a PRNG key \"\n \"argument. That is, instead of `apply_fun(params, inputs)`, call \"\n \"it like `apply_fun(params, inputs, key)` where `key` is a \"\n \"jax.random.PRNGKey value.\")\n raise ValueError(msg)\n if mode == 'train':\n keep = random.bernoulli(rng, rate, inputs.shape)\n return np.where(keep, inputs / rate, 0)\n else:\n return inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::Dropout.init_fun","text":"jax/experimental/stax.py::Dropout.init_fun\n def init_fun(rng, input_shape):\n return input_shape, ()","label":0},{"cid":"jax/experimental/stax.py::Dropout.apply_fun","text":"jax/experimental/stax.py::Dropout.apply_fun\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.get('rng', None)\n if rng is None:\n msg = (\"Dropout layer requires apply_fun to be called with a PRNG key \"\n \"argument. That is, instead of `apply_fun(params, inputs)`, call \"\n \"it like `apply_fun(params, inputs, key)` where `key` is a \"\n \"jax.random.PRNGKey value.\")\n raise ValueError(msg)\n if mode == 'train':\n keep = random.bernoulli(rng, rate, inputs.shape)\n return np.where(keep, inputs / rate, 0)\n else:\n return inputs","label":0},{"cid":"jax/experimental/stax.py::serial","text":"jax/experimental/stax.py::serial\ndef serial(*layers):\n \"\"\"Combinator for composing layers in serial.\n\n Args:\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the serial\n composition of the given sequence of layers.\n \"\"\"\n nlayers = len(layers)\n init_funs, apply_funs = zip(*layers)\n def init_fun(rng, input_shape):\n params = []\n for init_fun in init_funs:\n rng, layer_rng = random.split(rng)\n input_shape, param = init_fun(layer_rng, input_shape)\n params.append(param)\n return input_shape, params\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n for fun, param, rng in zip(apply_funs, params, rngs):\n inputs = fun(param, inputs, rng=rng, **kwargs)\n return inputs\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::serial.init_fun","text":"jax/experimental/stax.py::serial.init_fun\n def init_fun(rng, input_shape):\n params = []\n for init_fun in init_funs:\n rng, layer_rng = random.split(rng)\n input_shape, param = init_fun(layer_rng, input_shape)\n params.append(param)\n return input_shape, params","label":0},{"cid":"jax/experimental/stax.py::serial.apply_fun","text":"jax/experimental/stax.py::serial.apply_fun\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n for fun, param, rng in zip(apply_funs, params, rngs):\n inputs = fun(param, inputs, rng=rng, **kwargs)\n return inputs","label":0},{"cid":"jax/experimental/stax.py::parallel","text":"jax/experimental/stax.py::parallel\ndef parallel(*layers):\n \"\"\"Combinator for composing layers in parallel.\n\n The layer resulting from this combinator is often used with the FanOut and\n FanInSum layers.\n\n Args:\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the\n parallel composition of the given sequence of layers. In particular, the\n returned layer takes a sequence of inputs and returns a sequence of outputs\n with the same length as the argument `layers`.\n \"\"\"\n nlayers = len(layers)\n init_funs, apply_funs = zip(*layers)\n def init_fun(rng, input_shape):\n rngs = random.split(rng, nlayers)\n return zip(*[init(rng, shape) for init, rng, shape\n in zip(init_funs, rngs, input_shape)])\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::parallel.init_fun","text":"jax/experimental/stax.py::parallel.init_fun\n def init_fun(rng, input_shape):\n rngs = random.split(rng, nlayers)\n return zip(*[init(rng, shape) for init, rng, shape\n in zip(init_funs, rngs, input_shape)])","label":0},{"cid":"jax/experimental/stax.py::parallel.apply_fun","text":"jax/experimental/stax.py::parallel.apply_fun\n def apply_fun(params, inputs, **kwargs):\n rng = kwargs.pop('rng', None)\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]","label":0},{"cid":"jax/experimental/stax.py::shape_dependent","text":"jax/experimental/stax.py::shape_dependent\ndef shape_dependent(make_layer):\n \"\"\"Combinator to delay layer constructor pair until input shapes are known.\n\n Args:\n make_layer: a one-argument function that takes an input shape as an argument\n (a tuple of positive integers) and returns an (init_fun, apply_fun) pair.\n\n Returns:\n A new layer, meaning an (init_fun, apply_fun) pair, representing the same\n layer as returned by `make_layer` but with its construction delayed until\n input shapes are known.\n \"\"\"\n def init_fun(rng, input_shape):\n return make_layer(input_shape)[0](rng, input_shape)\n def apply_fun(params, inputs, **kwargs):\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)\n return init_fun, apply_fun","label":0},{"cid":"jax/experimental/stax.py::shape_dependent.init_fun","text":"jax/experimental/stax.py::shape_dependent.init_fun\n def init_fun(rng, input_shape):\n return make_layer(input_shape)[0](rng, input_shape)","label":0},{"cid":"jax/experimental/stax.py::shape_dependent.apply_fun","text":"jax/experimental/stax.py::shape_dependent.apply_fun\n def apply_fun(params, inputs, **kwargs):\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)","label":0},{"cid":"jax/random.py::PRNGKey","text":"jax/random.py::PRNGKey\ndef PRNGKey(seed):\n \"\"\"Create a pseudo-random number generator (PRNG) key given an integer seed.\n\n Args:\n seed: a 64- or 32-bit integer used as the value of the key.\n\n Returns:\n A PRNG key, which is modeled as an array of shape (2,) and dtype uint32. The\n key is constructed from a 64-bit seed by effectively bit-casting to a pair\n of uint32 values (or from a 32-bit seed by first padding out with zeros).\n \"\"\"\n if onp.shape(seed):\n raise TypeError(\"PRNGKey seed must be a scalar.\")\n convert = lambda k: lax.reshape(lax.convert_element_type(k, onp.uint32), [1])\n if isinstance(seed, (int, onp.ndarray)):\n # Special handling of raw integer values, which may have be 64bit even\n # when jax_enable_x64=False and we don't want to drop the top 32 bits\n k1 = convert(onp.bitwise_and(onp.right_shift(seed, 32), 0xFFFFFFFF))\n else:\n k1 = convert(lax.shift_right_logical(seed, 32))\n k2 = convert(lax.bitwise_and(seed, 0xFFFFFFFF))\n return lax.concatenate([k1, k2], 0)","label":0},{"cid":"jax/random.py::_is_prng_key","text":"jax/random.py::_is_prng_key\ndef _is_prng_key(key):\n try:\n return key.shape == (2,) and key.dtype == onp.uint32\n except AttributeError:\n return False","label":0},{"cid":"jax/random.py::_make_rotate_left","text":"jax/random.py::_make_rotate_left\ndef _make_rotate_left(dtype):\n if not onp.issubdtype(dtype, onp.integer):\n raise TypeError(\"_rotate_left only accepts integer dtypes.\")\n nbits = onp.array(onp.iinfo(dtype).bits, dtype)\n\n def _rotate_left(x, d):\n if lax.dtype(d) != lax.dtype(x):\n d = lax.convert_element_type(d, x.dtype)\n return lax.shift_left(x, d) | lax.shift_right_logical(x, nbits - d)\n return _rotate_left","label":0},{"cid":"jax/random.py::_make_rotate_left._rotate_left","text":"jax/random.py::_make_rotate_left._rotate_left\n def _rotate_left(x, d):\n if lax.dtype(d) != lax.dtype(x):\n d = lax.convert_element_type(d, x.dtype)\n return lax.shift_left(x, d) | lax.shift_right_logical(x, nbits - d)","label":0},{"cid":"jax/random.py::_bit_stats","text":"jax/random.py::_bit_stats\ndef _bit_stats(bits):\n \"\"\"This is a debugging function to compute the statistics of bit fields.\"\"\"\n return onp.array([list(map(int, onp.binary_repr(x, 64))) for x in bits]).mean(0)","label":0},{"cid":"jax/random.py::threefry_2x32","text":"jax/random.py::threefry_2x32\n@jit\ndef threefry_2x32(keypair, count):\n \"\"\"Apply the Threefry 2x32 hash.\n\n Args:\n keypair: a pair of 32bit unsigned integers used for the key.\n count: an array of dtype uint32 used for the counts.\n\n Returns:\n An array of dtype uint32 with the same shape as `count`.\n \"\"\"\n # Based on ThreeFry2x32 by phawkins@ in //.../xla/client/lib/prng.cc\n key1, key2 = keypair\n if not lax.dtype(key1) == lax.dtype(key2) == lax.dtype(count) == onp.uint32:\n msg = \"threefry_2x32 requires uint32 arguments, got {}\"\n raise TypeError(msg.format([lax.dtype(x) for x in [key1, key2, count]]))\n\n rotate_left = _make_rotate_left(lax.dtype(count))\n\n def apply_round(v, rot):\n v = v[:]\n v[0] = v[0] + v[1]\n v[1] = rotate_left(v[1], rot)\n v[1] = v[0] ^ v[1]\n return v\n\n odd_size = count.size % 2\n if odd_size:\n x = list(np.split(np.concatenate([count.ravel(), onp.uint32([0])]), 2))\n else:\n x = list(np.split(count.ravel(), 2))\n\n rotations = [onp.array([13, 15, 26, 6], dtype=onp.uint32),\n onp.array([17, 29, 16, 24], dtype=onp.uint32)]\n ks = [key1, key2, key1 ^ key2 ^ onp.uint32(0x1BD11BDA)]\n\n def rotate_list(xs):\n return xs[1:] + [xs[0]]\n\n x[0] = x[0] + ks[0]\n x[1] = x[1] + ks[1]\n\n def step(i, state):\n x, ks, rotations = state\n for r in rotations[0]:\n x = apply_round(x, r)\n new_x = [x[0] + ks[0], x[1] + ks[1] + asarray(i + 1, dtype=onp.uint32)]\n return new_x, rotate_list(ks), rotate_list(rotations)\n out = np.concatenate(lax.fori_loop(0, 5, step, (x, rotate_list(ks), rotations))[0])\n assert out.dtype == onp.uint32\n return lax.reshape(out[:-1] if odd_size else out, count.shape)","label":0},{"cid":"jax/random.py::threefry_2x32.apply_round","text":"jax/random.py::threefry_2x32.apply_round\n def apply_round(v, rot):\n v = v[:]\n v[0] = v[0] + v[1]\n v[1] = rotate_left(v[1], rot)\n v[1] = v[0] ^ v[1]\n return v","label":0},{"cid":"jax/random.py::threefry_2x32.rotate_list","text":"jax/random.py::threefry_2x32.rotate_list\n def rotate_list(xs):\n return xs[1:] + [xs[0]]","label":0},{"cid":"jax/random.py::threefry_2x32.step","text":"jax/random.py::threefry_2x32.step\n def step(i, state):\n x, ks, rotations = state\n for r in rotations[0]:\n x = apply_round(x, r)\n new_x = [x[0] + ks[0], x[1] + ks[1] + asarray(i + 1, dtype=onp.uint32)]\n return new_x, rotate_list(ks), rotate_list(rotations)","label":0},{"cid":"jax/random.py::split","text":"jax/random.py::split\ndef split(key, num=2):\n \"\"\"Splits a PRNG key into `num` new keys by adding a leading axis.\n\n Args:\n key: a PRNGKey (an array with shape (2,) and dtype uint32).\n num: optional, a positive integer indicating the number of keys to produce\n (default 2).\n\n Returns:\n An array with shape (num, 2) and dtype uint32 representing `num` new keys.\n \"\"\"\n return _split(key, num)","label":0},{"cid":"jax/random.py::_split","text":"jax/random.py::_split\n@partial(jit, static_argnums=(1,))\ndef _split(key, num):\n counts = lax.tie_in(key, lax.iota(onp.uint32, num * 2))\n return lax.reshape(threefry_2x32(key, counts), (num, 2))","label":0},{"cid":"jax/random.py::fold_in","text":"jax/random.py::fold_in\ndef fold_in(key, data):\n \"\"\"Folds in data to a PRNG key to form a new PRNG key.\n\n Args:\n key: a PRNGKey (an array with shape (2,) and dtype uint32).\n data: a 32bit integer representing data to be folded in to the key.\n\n Returns:\n A new PRNGKey that is a deterministic function of the inputs and is\n statistically safe for producing a stream of new pseudo-random values.\n \"\"\"\n return _fold_in(key, data)","label":0},{"cid":"jax/random.py::_fold_in","text":"jax/random.py::_fold_in\n@jit\ndef _fold_in(key, data):\n key2 = lax.tie_in(key, PRNGKey(data))\n return threefry_2x32(key, key2)","label":0},{"cid":"jax/random.py::_random_bits","text":"jax/random.py::_random_bits\ndef _random_bits(key, bit_width, shape):\n \"\"\"Sample uniform random bits of given width and shape using PRNG key.\"\"\"\n if not _is_prng_key(key):\n raise TypeError(\"_random_bits got invalid prng key.\")\n if bit_width not in (32, 64):\n raise TypeError(\"requires 32- or 64-bit field width.\")\n max_count = (bit_width // 32) * onp.prod(shape)\n if max_count >= onp.iinfo(onp.uint32).max:\n # TODO(mattjj): just split the key here\n raise TypeError(\"requesting more random bits than a single call provides.\")\n\n counts = lax.tie_in(key, lax.iota(onp.uint32, max_count))\n bits = threefry_2x32(key, counts)\n if bit_width == 64:\n bits = [lax.convert_element_type(x, onp.uint64) for x in np.split(bits, 2)]\n bits = lax.shift_left(bits[0], onp.uint64(32)) | bits[1]\n return lax.reshape(bits, shape)","label":0},{"cid":"jax/random.py::_check_shape","text":"jax/random.py::_check_shape\ndef _check_shape(name, shape):\n try:\n shape = tuple(map(int, shape))\n except TypeError:\n msg = \"{} requires a concrete tuple of integers as shape argument, got {}.\"\n raise ValueError(msg.format(name, shape))","label":0},{"cid":"jax/random.py::uniform","text":"jax/random.py::uniform\ndef uniform(key, shape=(), dtype=onp.float64, minval=0., maxval=1.):\n \"\"\"Sample uniform random values in [minval, maxval) with given shape/dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: a tuple of nonnegative integers representing the shape.\n dtype: optional, a float dtype for the returned values (default float64 if\n jax_enable_x64 is true, otherwise float32).\n minval: optional, a minimum (inclusive) value for the range (default 0).\n maxval: optional, a maximum (exclusive) value for the range (default 1).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(dtype)\n return _uniform(key, shape, dtype, minval, maxval)","label":0},{"cid":"jax/random.py::_uniform","text":"jax/random.py::_uniform\n@partial(jit, static_argnums=(1, 2))\ndef _uniform(key, shape, dtype, minval, maxval):\n _check_shape(\"uniform\", shape)\n if not onp.issubdtype(dtype, onp.floating):\n raise TypeError(\"uniform only accepts floating point dtypes.\")\n\n minval = lax.convert_element_type(minval, dtype)\n maxval = lax.convert_element_type(maxval, dtype)\n finfo = onp.finfo(dtype)\n nbits, nmant = finfo.bits, finfo.nmant\n\n if nbits not in (32, 64):\n raise TypeError(\"uniform only accepts 32- or 64-bit dtypes.\")\n\n bits = _random_bits(key, nbits, shape)\n\n # The strategy here is to randomize only the mantissa bits with an exponent of\n # 1 (after applying the bias), then shift and scale to the desired range. The\n # bit-level transformation we use relies on Numpy and XLA having bit-for-bit\n # equivalent float representations, which might not be true on all platforms.\n float_bits = lax.bitwise_or(\n lax.shift_right_logical(bits, onp.array(nbits - nmant, lax.dtype(bits))),\n onp.array(1., dtype).view(onp.uint32 if nbits == 32 else onp.uint64))\n floats = lax.bitcast_convert_type(float_bits, dtype) - onp.array(1., dtype)\n return lax.max(\n minval,\n lax.reshape(floats * (maxval - minval) + minval, shape))","label":0},{"cid":"jax/random.py::randint","text":"jax/random.py::randint\ndef randint(key, shape, minval, maxval, dtype=onp.int64):\n \"\"\"Sample uniform random values in [minval, maxval) with given shape/dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: a tuple of nonnegative integers representing the shape.\n minval: int or array of ints broadcast-compatible with ``shape``, a minimum\n (inclusive) value for the range.\n maxval: int or array of ints broadcast-compatible with ``shape``, a maximum\n (exclusive) value for the range.\n dtype: optional, an int dtype for the returned values (default int64 if\n jax_enable_x64 is true, otherwise int32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(dtype)\n return _randint(key, shape, minval, maxval, dtype)","label":0},{"cid":"jax/random.py::_randint","text":"jax/random.py::_randint\n@partial(jit, static_argnums=(1, 4))\ndef _randint(key, shape, minval, maxval, dtype):\n _check_shape(\"randint\", shape)\n if not onp.issubdtype(dtype, onp.integer):\n raise TypeError(\"randint only accepts integer dtypes.\")\n\n minval = lax.convert_element_type(minval, dtype)\n maxval = lax.convert_element_type(maxval, dtype)\n nbits = onp.iinfo(dtype).bits\n\n if nbits not in (32, 64):\n raise TypeError(\"randint only accepts 32- or 64-bit dtypes.\")\n\n # if we don't have minval < maxval, just always return minval\n # https://github.com/google/jax/issues/222\n maxval = lax.max(lax.add(minval, onp.array(1, dtype)), maxval)\n\n # This algorithm is biased whenever (maxval - minval) is not a power of 2.\n # We generate double the number of random bits required by the dtype so as to\n # reduce that bias.\n k1, k2 = split(key)\n rbits = lambda key: _random_bits(key, nbits, shape)\n higher_bits, lower_bits = rbits(k1), rbits(k2)\n\n unsigned_dtype = onp.uint32 if nbits == 32 else onp.uint64\n span = lax.convert_element_type(maxval - minval, unsigned_dtype)\n\n # To compute a remainder operation on an integer that might have twice as many\n # bits as we can represent in the native unsigned dtype, we compute a\n # multiplier equal to 2**nbits % span (using that nbits is 32 or 64).\n multiplier = lax.rem(onp.array(2**16, unsigned_dtype), span)\n multiplier = lax.rem(lax.mul(multiplier, multiplier), span)\n if nbits == 64:\n multiplier = lax.rem(lax.mul(multiplier, multiplier), span)\n\n random_offset = lax.add(lax.mul(lax.rem(higher_bits, span), multiplier),\n lax.rem(lower_bits, span))\n random_offset = lax.rem(random_offset, span)\n return lax.add(minval, lax.convert_element_type(random_offset, dtype))","label":0},{"cid":"jax/random.py::shuffle","text":"jax/random.py::shuffle\ndef shuffle(key, x, axis=0):\n \"\"\"Shuffle the elements of an array uniformly at random along an axis.\n\n Args:\n key: a PRNGKey used as the random key.\n x: the array to be shuffled.\n axis: optional, an int axis along which to shuffle (default 0).\n\n Returns:\n A shuffled version of x.\n \"\"\"\n return _shuffle(key, x, axis)","label":0},{"cid":"jax/random.py::_shuffle","text":"jax/random.py::_shuffle\n@partial(jit, static_argnums=(2,))\ndef _shuffle(key, x, axis):\n # On parallel architectures, Fisher-Yates is more expensive than doing\n # multiple sorts. This algorithm is based on one developed and analyzed by\n # tjablin@. We sort according to randomly-generated 32bit keys, but those keys\n # may have collisions. If we repeat the process, using fresh 32bit keys for\n # each sort, then whenever all pairs of elements have been assigned distinct\n # keys at some iteration (or equivalently when the strings formed by\n # concatenating the successive keys for each element are all distinct) then we\n # are guaranteed to have a perfect sample (assuming that either the sort is\n # stable or that any bias is not value-dependent). Since checking uniqueness\n # at runtime may be expensive, we use a heuristic static stop criterion\n # developed by tjablin@. See tensorflow/compiler/tf2xla/random_ops.cc for more\n # info, and for the original implementation of this algorithm. See also\n # Section 2 of http://people.csail.mit.edu/costis/6896sp11/lec5s.pdf for\n # another analysis (where the keys are generated one bit at a time).\n exponent = 3 # see tjablin@'s analysis for explanation of this parameter\n uint32max = onp.iinfo(onp.uint32).max\n num_rounds = int(onp.ceil(exponent * onp.log(x.size) / onp.log(uint32max)))\n\n for _ in range(num_rounds):\n key, subkey = split(key)\n sort_keys = _random_bits(subkey, 32, x.shape)\n _, x = lax.sort_key_val(sort_keys, x, axis)\n\n return x","label":0},{"cid":"jax/random.py::normal","text":"jax/random.py::normal\ndef normal(key, shape=(), dtype=onp.float64):\n \"\"\"Sample standard normal random values with given shape and float dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: a tuple of nonnegative integers representing the shape.\n dtype: optional, a float dtype for the returned values (default float64 if\n jax_enable_x64 is true, otherwise float32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(dtype)\n return _normal(key, shape, dtype)","label":0},{"cid":"jax/random.py::_normal","text":"jax/random.py::_normal\n@partial(jit, static_argnums=(1, 2))\ndef _normal(key, shape, dtype):\n _check_shape(\"normal\", shape)\n lo = onp.nextafter(onp.array(-1., dtype), 0., dtype=dtype)\n hi = onp.array(1., dtype)\n u = uniform(key, shape, dtype, lo, hi)\n return onp.array(onp.sqrt(2), dtype) * lax.erf_inv(u)","label":0},{"cid":"jax/random.py::bernoulli","text":"jax/random.py::bernoulli\ndef bernoulli(key, p=onp.float32(0.5), shape=()):\n \"\"\"Sample Bernoulli random values with given shape and mean.\n\n Args:\n key: a PRNGKey used as the random key.\n p: optional, an array-like of floating dtype broadcastable to `shape` for\n the mean of the random variables (default 0.5).\n shape: optional, a tuple of nonnegative integers representing the shape\n (default scalar).\n\n Returns:\n A random array with the specified shape and boolean dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(lax.dtype(p))\n if not onp.issubdtype(dtype, onp.floating):\n msg = \"bernoulli probability `p` must have a floating dtype, got {}.\"\n raise TypeError(msg.format(dtype))\n p = lax.convert_element_type(p, dtype)\n return _bernoulli(key, p, shape)","label":0},{"cid":"jax/random.py::_bernoulli","text":"jax/random.py::_bernoulli\n@partial(jit, static_argnums=(2,))\ndef _bernoulli(key, p, shape):\n _check_shape(\"bernoulli\", shape)\n shape = shape or onp.shape(p)\n if onp.shape(p) != shape:\n p = np.broadcast_to(p, shape)\n return lax.lt(uniform(key, shape, lax.dtype(p)), p)","label":0},{"cid":"jax/random.py::beta","text":"jax/random.py::beta\ndef beta(key, a, b, shape=(), dtype=onp.float64):\n \"\"\"Sample Bernoulli random values with given shape and mean.\n\n Args:\n key: a PRNGKey used as the random key.\n a: an array-like broadcastable to `shape` and used as the shape parameter\n alpha of the random variables.\n b: an array-like broadcastable to `shape` and used as the shape parameter\n beta of the random variables.\n shape: optional, a tuple of nonnegative integers representing the shape\n (default scalar).\n dtype: optional, a float dtype for the returned values (default float64 if\n jax_enable_x64 is true, otherwise float32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(dtype)\n return _beta(key, a, b, shape, dtype)","label":0},{"cid":"jax/random.py::_beta","text":"jax/random.py::_beta\n@partial(jit, static_argnums=(3, 4))\ndef _beta(key, a, b, shape, dtype):\n _check_shape(\"beta\", shape)\n a = lax.convert_element_type(a, dtype)\n b = lax.convert_element_type(b, dtype)\n shape = shape or lax.broadcast_shapes(np.shape(a), np.shape(b))\n key_a, key_b = split(key)\n gamma_a = gamma(key_a, a, shape, dtype)\n gamma_b = gamma(key_b, b, shape, dtype)\n return gamma_a / (gamma_a + gamma_b)","label":0},{"cid":"jax/random.py::cauchy","text":"jax/random.py::cauchy\ndef cauchy(key, shape=(), dtype=onp.float64):\n \"\"\"Sample Cauchy random values with given shape and float dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: optional, a tuple of nonnegative integers representing the shape\n (default scalar).\n dtype: optional, a float dtype for the returned values (default float64 if\n jax_enable_x64 is true, otherwise float32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(dtype)\n return _cauchy(key, shape, dtype)","label":0},{"cid":"jax/random.py::_cauchy","text":"jax/random.py::_cauchy\n@partial(jit, static_argnums=(1, 2))\ndef _cauchy(key, shape, dtype):\n _check_shape(\"cauchy\", shape)\n u = uniform(key, shape, dtype, minval=onp.finfo(dtype).eps, maxval=1.)\n pi = _constant_like(u, onp.pi)\n return lax.tan(lax.mul(pi, lax.sub(u, _constant_like(u, 0.5))))","label":0},{"cid":"jax/random.py::dirichlet","text":"jax/random.py::dirichlet\ndef dirichlet(key, alpha, shape=(), dtype=onp.float64):\n \"\"\"Sample Cauchy random values with given shape and float dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n alpha: an array-like with `alpha.shape[:-1]` broadcastable to `shape` and\n used as the concentration parameter of the random variables.\n shape: optional, a tuple of nonnegative integers representing the batch\n shape (defaults to `alpha.shape[:-1]`).\n dtype: optional, a float dtype for the returned values (default float64 if\n jax_enable_x64 is true, otherwise float32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(dtype)\n return _dirichlet(key, alpha, shape, dtype)","label":0},{"cid":"jax/random.py::_dirichlet","text":"jax/random.py::_dirichlet\n@partial(jit, static_argnums=(2, 3))\ndef _dirichlet(key, alpha, shape, dtype):\n _check_shape(\"dirichlet\", shape)\n alpha = asarray(alpha, dtype)\n shape = shape or alpha.shape[:-1]\n gamma_samples = gamma(key, alpha, shape + alpha.shape[-1:], dtype)\n return gamma_samples / np.sum(gamma_samples, axis=-1, keepdims=True)","label":0},{"cid":"jax/random.py::exponential","text":"jax/random.py::exponential\ndef exponential(key, shape=(), dtype=onp.float64):\n \"\"\"Sample Exponential random values with given shape and float dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: optional, a tuple of nonnegative integers representing the shape\n (default scalar).\n dtype: optional, a float dtype for the returned values (default float64 if\n jax_enable_x64 is true, otherwise float32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(dtype)\n return _exponential(key, shape, dtype)","label":0},{"cid":"jax/random.py::_exponential","text":"jax/random.py::_exponential\n@partial(jit, static_argnums=(1, 2))\ndef _exponential(key, shape, dtype):\n _check_shape(\"exponential\", shape)\n u = uniform(key, shape, dtype)\n # taking 1 - u to move the domain of log to (0, 1] instead of [0, 1)\n return lax.neg(lax.log1p(lax.neg(u)))","label":0},{"cid":"jax/random.py::_gamma_one","text":"jax/random.py::_gamma_one\ndef _gamma_one(key, alpha):\n # Ref: A simple method for generating gamma variables, George Marsaglia and Wai Wan Tsang\n # The algorithm can also be founded in:\n # https://en.wikipedia.org/wiki/Gamma_distribution#Generating_gamma-distributed_random_variables\n zero = _constant_like(alpha, 0)\n one = _constant_like(alpha, 1)\n minus_one = _constant_like(alpha, -1)\n one_over_two = _constant_like(alpha, 0.5)\n one_over_three = _constant_like(alpha, 1. / 3.)\n squeeze_const = _constant_like(alpha, 0.0331)\n dtype = lax.dtype(alpha)\n\n key, subkey = split(key)\n # for alpha < 1, we boost alpha to alpha + 1 and get a sample according to\n # Gamma(alpha) ~ Gamma(alpha+1) * Uniform()^(1 / alpha)\n boost = lax.select(lax.ge(alpha, one),\n one,\n lax.pow(uniform(subkey, (), dtype=dtype), lax.div(one, alpha)))\n alpha = lax.select(lax.ge(alpha, one), alpha, lax.add(alpha, one))\n\n d = lax.sub(alpha, one_over_three)\n c = lax.div(one_over_three, lax.pow(d, one_over_two))\n\n def _cond_fn(kXVU):\n _, X, V, U = kXVU\n # TODO: use lax.cond when its batching rule is supported\n # The reason is to avoid evaluating second condition which involves log+log\n # if the first condition is satisfied\n cond = lax.bitwise_and(lax.ge(U, lax.sub(one, lax.mul(squeeze_const, lax.mul(X, X)))),\n lax.ge(lax.log(U), lax.add(lax.mul(X, one_over_two),\n lax.mul(d, lax.add(lax.sub(one, V),\n lax.log(V))))))\n return cond\n\n def _body_fn(kXVU):\n def _next_kxv(kxv):\n key = kxv[0]\n key, subkey = split(key)\n x = normal(subkey, (), dtype=dtype)\n v = lax.add(one, lax.mul(x, c))\n return key, x, v\n\n key = kXVU[0]\n key, x_key, U_key = split(key, 3)\n _, x, v = lax.while_loop(lambda kxv: lax.le(kxv[2], zero), _next_kxv, (x_key, zero, minus_one))\n X = ","label":0},{"cid":"jax/random.py::_gamma_one._cond_fn","text":"jax/random.py::_gamma_one._cond_fn\n def _cond_fn(kXVU):\n _, X, V, U = kXVU\n # TODO: use lax.cond when its batching rule is supported\n # The reason is to avoid evaluating second condition which involves log+log\n # if the first condition is satisfied\n cond = lax.bitwise_and(lax.ge(U, lax.sub(one, lax.mul(squeeze_const, lax.mul(X, X)))),\n lax.ge(lax.log(U), lax.add(lax.mul(X, one_over_two),\n lax.mul(d, lax.add(lax.sub(one, V),\n lax.log(V))))))\n return cond","label":0},{"cid":"jax/random.py::_gamma_one._body_fn","text":"jax/random.py::_gamma_one._body_fn\n def _body_fn(kXVU):\n def _next_kxv(kxv):\n key = kxv[0]\n key, subkey = split(key)\n x = normal(subkey, (), dtype=dtype)\n v = lax.add(one, lax.mul(x, c))\n return key, x, v\n\n key = kXVU[0]\n key, x_key, U_key = split(key, 3)\n _, x, v = lax.while_loop(lambda kxv: lax.le(kxv[2], zero), _next_kxv, (x_key, zero, minus_one))\n X = lax.mul(x, x)\n V = lax.mul(lax.mul(v, v), v)\n U = uniform(U_key, (), dtype=dtype)\n return key, X, V, U","label":0},{"cid":"jax/random.py::_gamma_one._body_fn._next_kxv","text":"jax/random.py::_gamma_one._body_fn._next_kxv\n def _next_kxv(kxv):\n key = kxv[0]\n key, subkey = split(key)\n x = normal(subkey, (), dtype=dtype)\n v = lax.add(one, lax.mul(x, c))\n return key, x, v","label":0},{"cid":"jax/random.py::_gamma_grad_one","text":"jax/random.py::_gamma_grad_one\ndef _gamma_grad_one(z, alpha):\n # Ref 1: Pathwise Derivatives Beyond the Reparameterization Trick, Martin & Fritz\n # Ref 2: Case 4 follows https://github.com/fritzo/notebooks/blob/master/gamma-reparameterized.ipynb\n\n # TODO: use lax.cond instead of lax.while_loop when its batching rule is available\n # See https://github.com/google/jax/issues/490\n def _case1(zagf):\n z, alpha, _, flag = zagf\n\n # dz = - dCDF(z; a) / pdf(z; a)\n # pdf = z^(a-1) * e^(-z) / Gamma(a)\n # CDF(z; a) = IncompleteGamma(a, z) / Gamma(a)\n # dCDF(z; a) = (dIncompleteGamma - IncompleteGamma * Digamma(a)) / Gamma(a)\n # =: unnormalized_dCDF / Gamma(a)\n # IncompleteGamma ~ z^a [ 1/a - z/(a+1) + z^2/2!(a+2) - z^3/3!(a+3) + z^4/4!(a+4) - z^5/5!(a+5) ]\n # =: z^a * term1\n # dIncompleteGamma ~ z^a * log(z) * term1 - z^a [1/a^2 - z/(a+1)^2 + z^2/2!(a+2)^2\n # - z^3/3!(a+3)^2 + z^4/4!(a+4)^2 - z^5/5!(a+5)^2 ]\n # =: z^a * log(z) * term1 - z^a * term2\n # unnormalized_dCDF = z^a { [log(z) - Digamma(a)] * term1 - term2 }\n zi = 1.0\n update = zi / alpha\n term1 = update\n term2 = update / alpha\n for i in range(1, 6):\n zi = -zi * z / i\n update = zi / (alpha + i)\n term1 = term1 + update\n term2 = term2 + update / (alpha + i)\n\n unnormalized_cdf_dot = np.power(z, alpha) * ((np.log(z) - lax.digamma(alpha)) * term1 - term2)\n unnormalized_pdf = np.power(z, alpha - 1) * np.exp(-z)\n grad = -unnormalized_cdf_dot / unnormalized_pdf\n\n return z, alpha, grad, ~flag\n\n def _cond2(zagf):\n z, alpha, _, flag = zagf\n return (~flag) & (alpha > 8.0) & ((z < 0.9 * alpha) | (z > 1.1 * alpha))\n\n def _case2(zagf):\n z, alpha, _, flag = zagf\n\n # Formula 58 of [1]\n sqrt_8a = np.sqrt(8 ","label":0},{"cid":"jax/random.py::_gamma_grad_one._case1","text":"jax/random.py::_gamma_grad_one._case1\n def _case1(zagf):\n z, alpha, _, flag = zagf\n\n # dz = - dCDF(z; a) / pdf(z; a)\n # pdf = z^(a-1) * e^(-z) / Gamma(a)\n # CDF(z; a) = IncompleteGamma(a, z) / Gamma(a)\n # dCDF(z; a) = (dIncompleteGamma - IncompleteGamma * Digamma(a)) / Gamma(a)\n # =: unnormalized_dCDF / Gamma(a)\n # IncompleteGamma ~ z^a [ 1/a - z/(a+1) + z^2/2!(a+2) - z^3/3!(a+3) + z^4/4!(a+4) - z^5/5!(a+5) ]\n # =: z^a * term1\n # dIncompleteGamma ~ z^a * log(z) * term1 - z^a [1/a^2 - z/(a+1)^2 + z^2/2!(a+2)^2\n # - z^3/3!(a+3)^2 + z^4/4!(a+4)^2 - z^5/5!(a+5)^2 ]\n # =: z^a * log(z) * term1 - z^a * term2\n # unnormalized_dCDF = z^a { [log(z) - Digamma(a)] * term1 - term2 }\n zi = 1.0\n update = zi / alpha\n term1 = update\n term2 = update / alpha\n for i in range(1, 6):\n zi = -zi * z / i\n update = zi / (alpha + i)\n term1 = term1 + update\n term2 = term2 + update / (alpha + i)\n\n unnormalized_cdf_dot = np.power(z, alpha) * ((np.log(z) - lax.digamma(alpha)) * term1 - term2)\n unnormalized_pdf = np.power(z, alpha - 1) * np.exp(-z)\n grad = -unnormalized_cdf_dot / unnormalized_pdf\n\n return z, alpha, grad, ~flag","label":0},{"cid":"jax/random.py::_gamma_grad_one._cond2","text":"jax/random.py::_gamma_grad_one._cond2\n def _cond2(zagf):\n z, alpha, _, flag = zagf\n return (~flag) & (alpha > 8.0) & ((z < 0.9 * alpha) | (z > 1.1 * alpha))","label":0},{"cid":"jax/random.py::_gamma_grad_one._case2","text":"jax/random.py::_gamma_grad_one._case2\n def _case2(zagf):\n z, alpha, _, flag = zagf\n\n # Formula 58 of [1]\n sqrt_8a = np.sqrt(8 * alpha)\n z_minus_a = z - alpha\n log_z_div_a = np.log(z / alpha)\n sign = np.where(z < alpha, 1.0, -1.0)\n term1 = 4 * (z + alpha) / (sqrt_8a * z_minus_a * z_minus_a)\n term2 = log_z_div_a * (sqrt_8a / z_minus_a + sign * np.power(z_minus_a - alpha * log_z_div_a, -1.5))\n term3 = z * (1.0 + 1.0 / (12 * alpha) + 1.0 / (288 * alpha * alpha)) / sqrt_8a\n grad = (term1 + term2) * term3\n\n return z, alpha, grad, ~flag","label":0},{"cid":"jax/random.py::_gamma_grad_one._cond3","text":"jax/random.py::_gamma_grad_one._cond3\n def _cond3(zagf):\n z, alpha, _, flag = zagf\n return (~flag) & (alpha > 8.0) & (z >= 0.9 * alpha) & (z <= 1.1 * alpha)","label":0},{"cid":"jax/random.py::_gamma_grad_one._case3","text":"jax/random.py::_gamma_grad_one._case3\n def _case3(zagf):\n z, alpha, _, flag = zagf\n\n # Formula 59 of [1]\n z_div_a = np.divide(z, alpha)\n aa = alpha * alpha\n term1 = 1440 * alpha + 6 * z_div_a * (53 - 120 * z) - 65 * z_div_a * z_div_a + 3600 * z + 107\n term2 = 1244160 * alpha * aa\n term3 = 1 + 24 * alpha + 288 * aa\n grad = term1 * term3 / term2\n\n return z, alpha, grad, ~flag","label":0},{"cid":"jax/random.py::_gamma_grad_one._case4","text":"jax/random.py::_gamma_grad_one._case4\n def _case4(zagf):\n z, alpha, _, flag = zagf\n\n # Ref [2]\n u = np.log(z / alpha)\n v = np.log(alpha)\n c = []\n for i in range(8):\n c.append(_bivariate_coef[0][i] + u * (_bivariate_coef[1][i] + u * _bivariate_coef[2][i]))\n p = c[0] + v * (c[1] + v * (c[2] + v * c[3]))\n q = c[4] + v * (c[5] + v * (c[6] + v * c[7]))\n grad = np.exp(p / np.maximum(q, 0.01))\n\n return z, alpha, grad, ~flag","label":0},{"cid":"jax/random.py::_gamma_grad","text":"jax/random.py::_gamma_grad\ndef _gamma_grad(sample, a):\n samples = np.reshape(sample, -1)\n alphas = np.reshape(a, -1)\n grads = vmap(_gamma_grad_one)(samples, alphas)\n return grads.reshape(a.shape)","label":0},{"cid":"jax/random.py::_gamma_impl","text":"jax/random.py::_gamma_impl\n@custom_transforms\ndef _gamma_impl(key, a):\n alphas = np.reshape(a, -1)\n keys = split(key, onp.size(alphas))\n samples = vmap(_gamma_one)(keys, alphas)\n return np.reshape(samples, np.shape(a))","label":0},{"cid":"jax/random.py::gamma","text":"jax/random.py::gamma\ndef gamma(key, a, shape=(), dtype=onp.float64):\n \"\"\"Sample Gamma random values with given shape and float dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n a: an array-like broadcastable to `shape` and used as the shape parameter\n of the random variables.\n shape: optional, a tuple of nonnegative integers representing the shape\n (default scalar).\n dtype: optional, a float dtype for the returned values (default float64 if\n jax_enable_x64 is true, otherwise float32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(dtype)\n return _gamma(key, a, shape, dtype)","label":0},{"cid":"jax/random.py::_gamma","text":"jax/random.py::_gamma\n@partial(jit, static_argnums=(2, 3))\ndef _gamma(key, a, shape, dtype):\n _check_shape(\"gamma\", shape)\n a = lax.convert_element_type(a, dtype)\n shape = shape or onp.shape(a)\n if onp.shape(a) != shape:\n a = np.broadcast_to(a, shape)\n return _gamma_impl(key, a)","label":0},{"cid":"jax/random.py::gumbel","text":"jax/random.py::gumbel\ndef gumbel(key, shape=(), dtype=onp.float64):\n \"\"\"Sample Gumbel random values with given shape and float dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: optional, a tuple of nonnegative integers representing the shape\n (default scalar).\n dtype: optional, a float dtype for the returned values (default float64 if\n jax_enable_x64 is true, otherwise float32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(dtype)\n return _gumbel(key, shape, dtype)","label":0},{"cid":"jax/random.py::_gumbel","text":"jax/random.py::_gumbel\n@partial(jit, static_argnums=(1, 2))\ndef _gumbel(key, shape, dtype):\n _check_shape(\"gumbel\", shape)\n return -np.log(-np.log(\n uniform(key, shape, dtype, minval=onp.finfo(dtype).eps, maxval=1.)))","label":0},{"cid":"jax/random.py::laplace","text":"jax/random.py::laplace\ndef laplace(key, shape=(), dtype=onp.float64):\n \"\"\"Sample Laplace random values with given shape and float dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: optional, a tuple of nonnegative integers representing the shape\n (default scalar).\n dtype: optional, a float dtype for the returned values (default float64 if\n jax_enable_x64 is true, otherwise float32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(dtype)\n return _laplace(key, shape, dtype)","label":0},{"cid":"jax/random.py::_laplace","text":"jax/random.py::_laplace\n@partial(jit, static_argnums=(1, 2))\ndef _laplace(key, shape, dtype):\n _check_shape(\"laplace\", shape)\n u = uniform(\n key, shape, dtype, minval=-1. + np.finfo(dtype).epsneg, maxval=1.)\n return lax.mul(lax.sign(u), lax.log1p(lax.neg(lax.abs(u))))","label":0},{"cid":"jax/random.py::logistic","text":"jax/random.py::logistic\ndef logistic(key, shape=(), dtype=onp.float64):\n \"\"\"Sample logistic random values with given shape and float dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n shape: optional, a tuple of nonnegative integers representing the shape\n (default scalar).\n dtype: optional, a float dtype for the returned values (default float64 if\n jax_enable_x64 is true, otherwise float32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(dtype)\n return _logistic(key, shape, dtype)","label":0},{"cid":"jax/random.py::_logistic","text":"jax/random.py::_logistic\n@partial(jit, static_argnums=(1, 2))\ndef _logistic(key, shape, dtype):\n _check_shape(\"logistic\", shape)\n return logit(uniform(key, shape, dtype))","label":0},{"cid":"jax/random.py::pareto","text":"jax/random.py::pareto\ndef pareto(key, b, shape=(), dtype=onp.float64):\n \"\"\"Sample Pareto random values with given shape and float dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n b: an array-like broadcastable to `shape` and used as the shape parameter\n of the random variables.\n shape: optional, a tuple of nonnegative integers representing the shape\n (default scalar).\n dtype: optional, a float dtype for the returned values (default float64 if\n jax_enable_x64 is true, otherwise float32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(dtype)\n return _pareto(key, b, shape, dtype)","label":0},{"cid":"jax/random.py::_pareto","text":"jax/random.py::_pareto\n@partial(jit, static_argnums=(2, 3))\ndef _pareto(key, b, shape, dtype):\n _check_shape(\"pareto\", shape)\n b = lax.convert_element_type(b, dtype)\n shape = shape or onp.shape(b)\n if onp.shape(b) != shape:\n b = np.broadcast_to(b, shape)\n e = exponential(key, shape, dtype)\n return lax.exp(lax.div(e, b))","label":0},{"cid":"jax/random.py::t","text":"jax/random.py::t\ndef t(key, df, shape=(), dtype=onp.float64):\n \"\"\"Sample Student's t random values with given shape and float dtype.\n\n Args:\n key: a PRNGKey used as the random key.\n df: an array-like broadcastable to `shape` and used as the shape parameter\n of the random variables.\n shape: optional, a tuple of nonnegative integers representing the shape\n (default scalar).\n dtype: optional, a float dtype for the returned values (default float64 if\n jax_enable_x64 is true, otherwise float32).\n\n Returns:\n A random array with the specified shape and dtype.\n \"\"\"\n dtype = xla_bridge.canonicalize_dtype(dtype)\n return _t(key, df, shape, dtype)","label":0},{"cid":"jax/random.py::_t","text":"jax/random.py::_t\n@partial(jit, static_argnums=(2, 3))\ndef _t(key, df, shape, dtype):\n _check_shape(\"t\", shape)\n df = lax.convert_element_type(df, dtype)\n shape = shape or onp.shape(df)\n key_n, key_g = split(key)\n n = normal(key_n, shape, dtype)\n two = _constant_like(n, 2)\n half_df = lax.div(df, two)\n g = gamma(key_n, half_df, shape, dtype)\n return n * np.sqrt(half_df / g)","label":0}],"string":"[\n {\n \"cid\": \"examples/mnist_classifier.py::loss\",\n \"text\": \"examples/mnist_classifier.py::loss\\ndef loss(params, batch):\\n inputs, targets = batch\\n preds = predict(params, inputs)\\n return -np.mean(preds * targets)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/mnist_classifier.py::accuracy\",\n \"text\": \"examples/mnist_classifier.py::accuracy\\ndef accuracy(params, batch):\\n inputs, targets = batch\\n target_class = np.argmax(targets, axis=1)\\n predicted_class = np.argmax(predict(params, inputs), axis=1)\\n return np.mean(predicted_class == target_class)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/resnet50.py::ConvBlock\",\n \"text\": \"examples/resnet50.py::ConvBlock\\ndef ConvBlock(kernel_size, filters, strides=(2, 2)):\\n ks = kernel_size\\n filters1, filters2, filters3 = filters\\n Main = stax.serial(\\n Conv(filters1, (1, 1), strides), BatchNorm(), Relu,\\n Conv(filters2, (ks, ks), padding='SAME'), BatchNorm(), Relu,\\n Conv(filters3, (1, 1)), BatchNorm())\\n Shortcut = stax.serial(Conv(filters3, (1, 1), strides), BatchNorm())\\n return stax.serial(FanOut(2), stax.parallel(Main, Shortcut), FanInSum, Relu)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/resnet50.py::IdentityBlock\",\n \"text\": \"examples/resnet50.py::IdentityBlock\\ndef IdentityBlock(kernel_size, filters):\\n ks = kernel_size\\n filters1, filters2 = filters\\n def make_main(input_shape):\\n # the number of output channels depends on the number of input channels\\n return stax.serial(\\n Conv(filters1, (1, 1)), BatchNorm(), Relu,\\n Conv(filters2, (ks, ks), padding='SAME'), BatchNorm(), Relu,\\n Conv(input_shape[3], (1, 1)), BatchNorm())\\n Main = stax.shape_dependent(make_main)\\n return stax.serial(FanOut(2), stax.parallel(Main, Identity), FanInSum, Relu)\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/resnet50.py::IdentityBlock.make_main\",\n \"text\": \"examples/resnet50.py::IdentityBlock.make_main\\n def make_main(input_shape):\\n # the number of output channels depends on the number of input channels\\n return stax.serial(\\n Conv(filters1, (1, 1)), BatchNorm(), Relu,\\n Conv(filters2, (ks, ks), padding='SAME'), BatchNorm(), Relu,\\n Conv(input_shape[3], (1, 1)), BatchNorm())\",\n \"label\": 0\n },\n {\n \"cid\": \"examples/resnet50.py::ResNet50\",\n \"text\": \"examples/resnet50.py::ResNet50\\ndef ResNet50(num_classes):\\n return stax.serial(\\n GeneralConv(('HWCN', 'OIHW', 'NHWC'), 64, (7, 7), (2, 2), 'SAME'),\\n BatchNorm(), Relu, MaxPool((3, 3), strides=(2, 2)),\\n ConvBlock(3, [64, 64, 256], strides=(1, 1)),\\n IdentityBlock(3, [64, 64]),\\n IdentityBlock(3, [64, 64]),\\n ConvBlock(3, [128, 128, 512]),\\n IdentityBlock(3, [128, 128]),\\n IdentityBlock(3, [128, 128]),\\n IdentityBlock(3, [128, 128]),\\n ConvBlock(3, [256, 256, 1024]),\\n IdentityBlock(3, [256, 256]),\\n IdentityBlock(3, [256, 256]),\\n IdentityBlock(3, [256, 256]),\\n IdentityBlock(3, [256, 256]),\\n IdentityBlock(3, [256, 256]),\\n ConvBlock(3, [512, 512, 2048]),\\n IdentityBlock(3, [512, 512]),\\n IdentityBlock(3, [512, 512]),\\n AvgPool((7, 7)), Flatten, Dense(num_classes), LogSoftmax)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::relu\",\n \"text\": \"jax/experimental/stax.py::relu\\ndef relu(x): return np.maximum(x, 0.)\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::softplus\",\n \"text\": \"jax/experimental/stax.py::softplus\\ndef softplus(x): return np.logaddexp(x, 0.)\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::sigmoid\",\n \"text\": \"jax/experimental/stax.py::sigmoid\\ndef sigmoid(x): return expit(x)\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::elu\",\n \"text\": \"jax/experimental/stax.py::elu\\ndef elu(x): return np.where(x > 0, x, np.expm1(x))\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::leaky_relu\",\n \"text\": \"jax/experimental/stax.py::leaky_relu\\ndef leaky_relu(x): return np.where(x >= 0, x, 0.01 * x)\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::logsoftmax\",\n \"text\": \"jax/experimental/stax.py::logsoftmax\\ndef logsoftmax(x, axis=-1):\\n \\\"\\\"\\\"Apply log softmax to an array of logits, log-normalizing along an axis.\\\"\\\"\\\"\\n return x - logsumexp(x, axis, keepdims=True)\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::softmax\",\n \"text\": \"jax/experimental/stax.py::softmax\\ndef softmax(x, axis=-1):\\n \\\"\\\"\\\"Apply softmax to an array of logits, exponentiating and normalizing along an axis.\\\"\\\"\\\"\\n unnormalized = np.exp(x - x.max(axis, keepdims=True))\\n return unnormalized / unnormalized.sum(axis, keepdims=True)\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::fastvar\",\n \"text\": \"jax/experimental/stax.py::fastvar\\ndef fastvar(x, axis, keepdims):\\n \\\"\\\"\\\"A fast but less numerically-stable variance calculation than np.var.\\\"\\\"\\\"\\n return np.mean(x**2, axis, keepdims=keepdims) - np.mean(x, axis, keepdims=keepdims)**2\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::randn\",\n \"text\": \"jax/experimental/stax.py::randn\\ndef randn(stddev=1e-2):\\n \\\"\\\"\\\"An initializer function for random normal coefficients.\\\"\\\"\\\"\\n def init(rng, shape):\\n std = lax.convert_element_type(stddev, np.float32)\\n return std * random.normal(rng, shape, dtype=np.float32)\\n return init\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::randn.init\",\n \"text\": \"jax/experimental/stax.py::randn.init\\n def init(rng, shape):\\n std = lax.convert_element_type(stddev, np.float32)\\n return std * random.normal(rng, shape, dtype=np.float32)\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::glorot\",\n \"text\": \"jax/experimental/stax.py::glorot\\ndef glorot(out_axis=0, in_axis=1, scale=onp.sqrt(2)):\\n \\\"\\\"\\\"An initializer function for random Glorot-scaled coefficients.\\\"\\\"\\\"\\n def init(rng, shape):\\n fan_in, fan_out = shape[in_axis], shape[out_axis]\\n size = onp.prod(onp.delete(shape, [in_axis, out_axis]))\\n std = scale / np.sqrt((fan_in + fan_out) / 2. * size)\\n std = lax.convert_element_type(std, np.float32)\\n return std * random.normal(rng, shape, dtype=np.float32)\\n return init\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::glorot.init\",\n \"text\": \"jax/experimental/stax.py::glorot.init\\n def init(rng, shape):\\n fan_in, fan_out = shape[in_axis], shape[out_axis]\\n size = onp.prod(onp.delete(shape, [in_axis, out_axis]))\\n std = scale / np.sqrt((fan_in + fan_out) / 2. * size)\\n std = lax.convert_element_type(std, np.float32)\\n return std * random.normal(rng, shape, dtype=np.float32)\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense\",\n \"text\": \"jax/experimental/stax.py::Dense\\ndef Dense(out_dim, W_init=glorot(), b_init=randn()):\\n \\\"\\\"\\\"Layer constructor function for a dense (fully-connected) layer.\\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n output_shape = input_shape[:-1] + (out_dim,)\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))\\n return output_shape, (W, b)\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return np.dot(inputs, W) + b\\n return init_fun, apply_fun\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense.init_fun\",\n \"text\": \"jax/experimental/stax.py::Dense.init_fun\\n def init_fun(rng, input_shape):\\n output_shape = input_shape[:-1] + (out_dim,)\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, (input_shape[-1], out_dim)), b_init(k2, (out_dim,))\\n return output_shape, (W, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dense.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Dense.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return np.dot(inputs, W) + b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv\",\n \"text\": \"jax/experimental/stax.py::GeneralConv\\ndef GeneralConv(dimension_numbers, out_chan, filter_shape,\\n strides=None, padding='VALID', W_init=None, b_init=randn(1e-6)):\\n \\\"\\\"\\\"Layer construction function for a general convolution layer.\\\"\\\"\\\"\\n lhs_spec, rhs_spec, out_spec = dimension_numbers\\n one = (1,) * len(filter_shape)\\n strides = strides or one\\n W_init = W_init or glorot(rhs_spec.index('O'), rhs_spec.index('I'))\\n def init_fun(rng, input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_general_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\\n return output_shape, (W, b)\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\\n dimension_numbers) + b\\n return init_fun, apply_fun\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv.init_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConv.init_fun\\n def init_fun(rng, input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_general_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\\n return output_shape, (W, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConv.apply_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConv.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_general_dilated(inputs, W, strides, padding, one, one,\\n dimension_numbers) + b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConvTranspose\",\n \"text\": \"jax/experimental/stax.py::GeneralConvTranspose\\ndef GeneralConvTranspose(dimension_numbers, out_chan, filter_shape,\\n strides=None, padding='VALID', W_init=None,\\n b_init=randn(1e-6)):\\n \\\"\\\"\\\"Layer construction function for a general transposed-convolution layer.\\\"\\\"\\\"\\n lhs_spec, rhs_spec, out_spec = dimension_numbers\\n one = (1,) * len(filter_shape)\\n strides = strides or one\\n W_init = W_init or glorot(rhs_spec.index('O'), rhs_spec.index('I'))\\n def init_fun(rng, input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_transpose_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\\n return output_shape, (W, b)\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_transpose(inputs, W, strides, padding,\\n dimension_numbers) + b\\n return init_fun, apply_fun\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConvTranspose.init_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConvTranspose.init_fun\\n def init_fun(rng, input_shape):\\n filter_shape_iter = iter(filter_shape)\\n kernel_shape = [out_chan if c == 'O' else\\n input_shape[lhs_spec.index('C')] if c == 'I' else\\n next(filter_shape_iter) for c in rhs_spec]\\n output_shape = lax.conv_transpose_shape_tuple(\\n input_shape, kernel_shape, strides, padding, dimension_numbers)\\n bias_shape = [out_chan if c == 'C' else 1 for c in out_spec]\\n bias_shape = tuple(itertools.dropwhile(lambda x: x == 1, bias_shape))\\n k1, k2 = random.split(rng)\\n W, b = W_init(k1, kernel_shape), b_init(k2, bias_shape)\\n return output_shape, (W, b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::GeneralConvTranspose.apply_fun\",\n \"text\": \"jax/experimental/stax.py::GeneralConvTranspose.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n W, b = params\\n return lax.conv_transpose(inputs, W, strides, padding,\\n dimension_numbers) + b\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm\",\n \"text\": \"jax/experimental/stax.py::BatchNorm\\ndef BatchNorm(axis=(0, 1, 2), epsilon=1e-5, center=True, scale=True,\\n beta_init=zeros, gamma_init=ones):\\n \\\"\\\"\\\"Layer construction function for a batch normalization layer.\\\"\\\"\\\"\\n _beta_init = lambda rng, shape: beta_init(rng, shape) if center else ()\\n _gamma_init = lambda rng, shape: gamma_init(rng, shape) if scale else ()\\n axis = (axis,) if np.isscalar(axis) else axis\\n def init_fun(rng, input_shape):\\n shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)\\n k1, k2 = random.split(rng)\\n beta, gamma = _beta_init(k1, shape), _gamma_init(k2, shape)\\n return input_shape, (beta, gamma)\\n def apply_fun(params, x, **kwargs):\\n beta, gamma = params\\n # TODO(phawkins): np.expand_dims should accept an axis tuple.\\n # (https://github.com/numpy/numpy/issues/12290)\\n ed = tuple(None if i in axis else slice(None) for i in range(np.ndim(x)))\\n beta = beta[ed]\\n gamma = gamma[ed]\\n mean, var = np.mean(x, axis, keepdims=True), fastvar(x, axis, keepdims=True)\\n z = (x - mean) / np.sqrt(var + epsilon)\\n if center and scale: return gamma * z + beta\\n if center: return z + beta\\n if scale: return gamma * z\\n return z\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm.init_fun\",\n \"text\": \"jax/experimental/stax.py::BatchNorm.init_fun\\n def init_fun(rng, input_shape):\\n shape = tuple(d for i, d in enumerate(input_shape) if i not in axis)\\n k1, k2 = random.split(rng)\\n beta, gamma = _beta_init(k1, shape), _gamma_init(k2, shape)\\n return input_shape, (beta, gamma)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::BatchNorm.apply_fun\",\n \"text\": \"jax/experimental/stax.py::BatchNorm.apply_fun\\n def apply_fun(params, x, **kwargs):\\n beta, gamma = params\\n # TODO(phawkins): np.expand_dims should accept an axis tuple.\\n # (https://github.com/numpy/numpy/issues/12290)\\n ed = tuple(None if i in axis else slice(None) for i in range(np.ndim(x)))\\n beta = beta[ed]\\n gamma = gamma[ed]\\n mean, var = np.mean(x, axis, keepdims=True), fastvar(x, axis, keepdims=True)\\n z = (x - mean) / np.sqrt(var + epsilon)\\n if center and scale: return gamma * z + beta\\n if center: return z + beta\\n if scale: return gamma * z\\n return z\",\n \"label\": 1\n },\n {\n \"cid\": \"jax/experimental/stax.py::elementwise\",\n \"text\": \"jax/experimental/stax.py::elementwise\\ndef elementwise(fun, **fun_kwargs):\\n \\\"\\\"\\\"Layer that applies a scalar function elementwise on its inputs.\\\"\\\"\\\"\\n init_fun = lambda rng, input_shape: (input_shape, ())\\n apply_fun = lambda params, inputs, **kwargs: fun(inputs, **fun_kwargs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer\\ndef _pooling_layer(reducer, init_val, rescaler=None):\\n def PoolingLayer(window_shape, strides=None, padding='VALID'):\\n \\\"\\\"\\\"Layer construction function for a pooling layer.\\\"\\\"\\\"\\n strides = strides or (1,) * len(window_shape)\\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\\n dims = (1,) + window_shape + (1,) # NHWC\\n strides = (1,) + strides + (1,)\\n def init_fun(rng, input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\\n return out_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\\n return rescale(out, inputs) if rescale else out\\n return init_fun, apply_fun\\n return PoolingLayer\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer\\n def PoolingLayer(window_shape, strides=None, padding='VALID'):\\n \\\"\\\"\\\"Layer construction function for a pooling layer.\\\"\\\"\\\"\\n strides = strides or (1,) * len(window_shape)\\n rescale = rescaler(window_shape, strides, padding) if rescaler else None\\n dims = (1,) + window_shape + (1,) # NHWC\\n strides = (1,) + strides + (1,)\\n def init_fun(rng, input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\\n return out_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\\n return rescale(out, inputs) if rescale else out\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.init_fun\\n def init_fun(rng, input_shape):\\n out_shape = lax.reduce_window_shape_tuple(input_shape, dims, strides, padding)\\n return out_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\",\n \"text\": \"jax/experimental/stax.py::_pooling_layer.PoolingLayer.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n out = lax.reduce_window(inputs, init_val, reducer, dims, strides, padding)\\n return rescale(out, inputs) if rescale else out\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_normalize_by_window_size\",\n \"text\": \"jax/experimental/stax.py::_normalize_by_window_size\\ndef _normalize_by_window_size(dims, strides, padding):\\n def rescale(outputs, inputs):\\n one = np.ones(inputs.shape[1:-1], dtype=inputs.dtype)\\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\\n return outputs / window_sizes[..., np.newaxis]\\n return rescale\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::_normalize_by_window_size.rescale\",\n \"text\": \"jax/experimental/stax.py::_normalize_by_window_size.rescale\\n def rescale(outputs, inputs):\\n one = np.ones(inputs.shape[1:-1], dtype=inputs.dtype)\\n window_sizes = lax.reduce_window(one, 0., lax.add, dims, strides, padding)\\n return outputs / window_sizes[..., np.newaxis]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten\",\n \"text\": \"jax/experimental/stax.py::Flatten\\ndef Flatten():\\n \\\"\\\"\\\"Layer construction function for flattening all but the leading dim.\\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n output_shape = input_shape[0], reduce(op.mul, input_shape[1:], 1)\\n return output_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n return np.reshape(inputs, (inputs.shape[0], -1))\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten.init_fun\",\n \"text\": \"jax/experimental/stax.py::Flatten.init_fun\\n def init_fun(rng, input_shape):\\n output_shape = input_shape[0], reduce(op.mul, input_shape[1:], 1)\\n return output_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Flatten.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Flatten.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n return np.reshape(inputs, (inputs.shape[0], -1))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Identity\",\n \"text\": \"jax/experimental/stax.py::Identity\\ndef Identity():\\n \\\"\\\"\\\"Layer construction function for an identity layer.\\\"\\\"\\\"\\n init_fun = lambda rng, input_shape: (input_shape, ())\\n apply_fun = lambda params, inputs, **kwargs: inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanOut\",\n \"text\": \"jax/experimental/stax.py::FanOut\\ndef FanOut(num):\\n \\\"\\\"\\\"Layer construction function for a fan-out layer.\\\"\\\"\\\"\\n init_fun = lambda rng, input_shape: ([input_shape] * num, ())\\n apply_fun = lambda params, inputs, **kwargs: [inputs] * num\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInSum\",\n \"text\": \"jax/experimental/stax.py::FanInSum\\ndef FanInSum():\\n \\\"\\\"\\\"Layer construction function for a fan-in sum layer.\\\"\\\"\\\"\\n init_fun = lambda rng, input_shape: (input_shape[0], ())\\n apply_fun = lambda params, inputs, **kwargs: sum(inputs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat\",\n \"text\": \"jax/experimental/stax.py::FanInConcat\\ndef FanInConcat(axis=-1):\\n \\\"\\\"\\\"Layer construction function for a fan-in concatenation layer.\\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n ax = axis % len(input_shape[0])\\n concat_size = sum(shape[ax] for shape in input_shape)\\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\\n return out_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n return np.concatenate(inputs, axis)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat.init_fun\",\n \"text\": \"jax/experimental/stax.py::FanInConcat.init_fun\\n def init_fun(rng, input_shape):\\n ax = axis % len(input_shape[0])\\n concat_size = sum(shape[ax] for shape in input_shape)\\n out_shape = input_shape[0][:ax] + (concat_size,) + input_shape[0][ax+1:]\\n return out_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::FanInConcat.apply_fun\",\n \"text\": \"jax/experimental/stax.py::FanInConcat.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n return np.concatenate(inputs, axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout\",\n \"text\": \"jax/experimental/stax.py::Dropout\\ndef Dropout(rate, mode='train'):\\n \\\"\\\"\\\"Layer construction function for a dropout layer with given rate.\\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n return input_shape, ()\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.get('rng', None)\\n if rng is None:\\n msg = (\\\"Dropout layer requires apply_fun to be called with a PRNG key \\\"\\n \\\"argument. That is, instead of `apply_fun(params, inputs)`, call \\\"\\n \\\"it like `apply_fun(params, inputs, key)` where `key` is a \\\"\\n \\\"jax.random.PRNGKey value.\\\")\\n raise ValueError(msg)\\n if mode == 'train':\\n keep = random.bernoulli(rng, rate, inputs.shape)\\n return np.where(keep, inputs / rate, 0)\\n else:\\n return inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout.init_fun\",\n \"text\": \"jax/experimental/stax.py::Dropout.init_fun\\n def init_fun(rng, input_shape):\\n return input_shape, ()\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::Dropout.apply_fun\",\n \"text\": \"jax/experimental/stax.py::Dropout.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.get('rng', None)\\n if rng is None:\\n msg = (\\\"Dropout layer requires apply_fun to be called with a PRNG key \\\"\\n \\\"argument. That is, instead of `apply_fun(params, inputs)`, call \\\"\\n \\\"it like `apply_fun(params, inputs, key)` where `key` is a \\\"\\n \\\"jax.random.PRNGKey value.\\\")\\n raise ValueError(msg)\\n if mode == 'train':\\n keep = random.bernoulli(rng, rate, inputs.shape)\\n return np.where(keep, inputs / rate, 0)\\n else:\\n return inputs\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial\",\n \"text\": \"jax/experimental/stax.py::serial\\ndef serial(*layers):\\n \\\"\\\"\\\"Combinator for composing layers in serial.\\n\\n Args:\\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the serial\\n composition of the given sequence of layers.\\n \\\"\\\"\\\"\\n nlayers = len(layers)\\n init_funs, apply_funs = zip(*layers)\\n def init_fun(rng, input_shape):\\n params = []\\n for init_fun in init_funs:\\n rng, layer_rng = random.split(rng)\\n input_shape, param = init_fun(layer_rng, input_shape)\\n params.append(param)\\n return input_shape, params\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n for fun, param, rng in zip(apply_funs, params, rngs):\\n inputs = fun(param, inputs, rng=rng, **kwargs)\\n return inputs\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial.init_fun\",\n \"text\": \"jax/experimental/stax.py::serial.init_fun\\n def init_fun(rng, input_shape):\\n params = []\\n for init_fun in init_funs:\\n rng, layer_rng = random.split(rng)\\n input_shape, param = init_fun(layer_rng, input_shape)\\n params.append(param)\\n return input_shape, params\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::serial.apply_fun\",\n \"text\": \"jax/experimental/stax.py::serial.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n for fun, param, rng in zip(apply_funs, params, rngs):\\n inputs = fun(param, inputs, rng=rng, **kwargs)\\n return inputs\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel\",\n \"text\": \"jax/experimental/stax.py::parallel\\ndef parallel(*layers):\\n \\\"\\\"\\\"Combinator for composing layers in parallel.\\n\\n The layer resulting from this combinator is often used with the FanOut and\\n FanInSum layers.\\n\\n Args:\\n *layers: a sequence of layers, each an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the\\n parallel composition of the given sequence of layers. In particular, the\\n returned layer takes a sequence of inputs and returns a sequence of outputs\\n with the same length as the argument `layers`.\\n \\\"\\\"\\\"\\n nlayers = len(layers)\\n init_funs, apply_funs = zip(*layers)\\n def init_fun(rng, input_shape):\\n rngs = random.split(rng, nlayers)\\n return zip(*[init(rng, shape) for init, rng, shape\\n in zip(init_funs, rngs, input_shape)])\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel.init_fun\",\n \"text\": \"jax/experimental/stax.py::parallel.init_fun\\n def init_fun(rng, input_shape):\\n rngs = random.split(rng, nlayers)\\n return zip(*[init(rng, shape) for init, rng, shape\\n in zip(init_funs, rngs, input_shape)])\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::parallel.apply_fun\",\n \"text\": \"jax/experimental/stax.py::parallel.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n rng = kwargs.pop('rng', None)\\n rngs = random.split(rng, nlayers) if rng is not None else (None,) * nlayers\\n return [f(p, x, rng=r, **kwargs) for f, p, x, r in zip(apply_funs, params, inputs, rngs)]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent\",\n \"text\": \"jax/experimental/stax.py::shape_dependent\\ndef shape_dependent(make_layer):\\n \\\"\\\"\\\"Combinator to delay layer constructor pair until input shapes are known.\\n\\n Args:\\n make_layer: a one-argument function that takes an input shape as an argument\\n (a tuple of positive integers) and returns an (init_fun, apply_fun) pair.\\n\\n Returns:\\n A new layer, meaning an (init_fun, apply_fun) pair, representing the same\\n layer as returned by `make_layer` but with its construction delayed until\\n input shapes are known.\\n \\\"\\\"\\\"\\n def init_fun(rng, input_shape):\\n return make_layer(input_shape)[0](rng, input_shape)\\n def apply_fun(params, inputs, **kwargs):\\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)\\n return init_fun, apply_fun\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent.init_fun\",\n \"text\": \"jax/experimental/stax.py::shape_dependent.init_fun\\n def init_fun(rng, input_shape):\\n return make_layer(input_shape)[0](rng, input_shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/experimental/stax.py::shape_dependent.apply_fun\",\n \"text\": \"jax/experimental/stax.py::shape_dependent.apply_fun\\n def apply_fun(params, inputs, **kwargs):\\n return make_layer(inputs.shape)[1](params, inputs, **kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::PRNGKey\",\n \"text\": \"jax/random.py::PRNGKey\\ndef PRNGKey(seed):\\n \\\"\\\"\\\"Create a pseudo-random number generator (PRNG) key given an integer seed.\\n\\n Args:\\n seed: a 64- or 32-bit integer used as the value of the key.\\n\\n Returns:\\n A PRNG key, which is modeled as an array of shape (2,) and dtype uint32. The\\n key is constructed from a 64-bit seed by effectively bit-casting to a pair\\n of uint32 values (or from a 32-bit seed by first padding out with zeros).\\n \\\"\\\"\\\"\\n if onp.shape(seed):\\n raise TypeError(\\\"PRNGKey seed must be a scalar.\\\")\\n convert = lambda k: lax.reshape(lax.convert_element_type(k, onp.uint32), [1])\\n if isinstance(seed, (int, onp.ndarray)):\\n # Special handling of raw integer values, which may have be 64bit even\\n # when jax_enable_x64=False and we don't want to drop the top 32 bits\\n k1 = convert(onp.bitwise_and(onp.right_shift(seed, 32), 0xFFFFFFFF))\\n else:\\n k1 = convert(lax.shift_right_logical(seed, 32))\\n k2 = convert(lax.bitwise_and(seed, 0xFFFFFFFF))\\n return lax.concatenate([k1, k2], 0)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_is_prng_key\",\n \"text\": \"jax/random.py::_is_prng_key\\ndef _is_prng_key(key):\\n try:\\n return key.shape == (2,) and key.dtype == onp.uint32\\n except AttributeError:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_make_rotate_left\",\n \"text\": \"jax/random.py::_make_rotate_left\\ndef _make_rotate_left(dtype):\\n if not onp.issubdtype(dtype, onp.integer):\\n raise TypeError(\\\"_rotate_left only accepts integer dtypes.\\\")\\n nbits = onp.array(onp.iinfo(dtype).bits, dtype)\\n\\n def _rotate_left(x, d):\\n if lax.dtype(d) != lax.dtype(x):\\n d = lax.convert_element_type(d, x.dtype)\\n return lax.shift_left(x, d) | lax.shift_right_logical(x, nbits - d)\\n return _rotate_left\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_make_rotate_left._rotate_left\",\n \"text\": \"jax/random.py::_make_rotate_left._rotate_left\\n def _rotate_left(x, d):\\n if lax.dtype(d) != lax.dtype(x):\\n d = lax.convert_element_type(d, x.dtype)\\n return lax.shift_left(x, d) | lax.shift_right_logical(x, nbits - d)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_bit_stats\",\n \"text\": \"jax/random.py::_bit_stats\\ndef _bit_stats(bits):\\n \\\"\\\"\\\"This is a debugging function to compute the statistics of bit fields.\\\"\\\"\\\"\\n return onp.array([list(map(int, onp.binary_repr(x, 64))) for x in bits]).mean(0)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::threefry_2x32\",\n \"text\": \"jax/random.py::threefry_2x32\\n@jit\\ndef threefry_2x32(keypair, count):\\n \\\"\\\"\\\"Apply the Threefry 2x32 hash.\\n\\n Args:\\n keypair: a pair of 32bit unsigned integers used for the key.\\n count: an array of dtype uint32 used for the counts.\\n\\n Returns:\\n An array of dtype uint32 with the same shape as `count`.\\n \\\"\\\"\\\"\\n # Based on ThreeFry2x32 by phawkins@ in //.../xla/client/lib/prng.cc\\n key1, key2 = keypair\\n if not lax.dtype(key1) == lax.dtype(key2) == lax.dtype(count) == onp.uint32:\\n msg = \\\"threefry_2x32 requires uint32 arguments, got {}\\\"\\n raise TypeError(msg.format([lax.dtype(x) for x in [key1, key2, count]]))\\n\\n rotate_left = _make_rotate_left(lax.dtype(count))\\n\\n def apply_round(v, rot):\\n v = v[:]\\n v[0] = v[0] + v[1]\\n v[1] = rotate_left(v[1], rot)\\n v[1] = v[0] ^ v[1]\\n return v\\n\\n odd_size = count.size % 2\\n if odd_size:\\n x = list(np.split(np.concatenate([count.ravel(), onp.uint32([0])]), 2))\\n else:\\n x = list(np.split(count.ravel(), 2))\\n\\n rotations = [onp.array([13, 15, 26, 6], dtype=onp.uint32),\\n onp.array([17, 29, 16, 24], dtype=onp.uint32)]\\n ks = [key1, key2, key1 ^ key2 ^ onp.uint32(0x1BD11BDA)]\\n\\n def rotate_list(xs):\\n return xs[1:] + [xs[0]]\\n\\n x[0] = x[0] + ks[0]\\n x[1] = x[1] + ks[1]\\n\\n def step(i, state):\\n x, ks, rotations = state\\n for r in rotations[0]:\\n x = apply_round(x, r)\\n new_x = [x[0] + ks[0], x[1] + ks[1] + asarray(i + 1, dtype=onp.uint32)]\\n return new_x, rotate_list(ks), rotate_list(rotations)\\n out = np.concatenate(lax.fori_loop(0, 5, step, (x, rotate_list(ks), rotations))[0])\\n assert out.dtype == onp.uint32\\n return lax.reshape(out[:-1] if odd_size else out, count.shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::threefry_2x32.apply_round\",\n \"text\": \"jax/random.py::threefry_2x32.apply_round\\n def apply_round(v, rot):\\n v = v[:]\\n v[0] = v[0] + v[1]\\n v[1] = rotate_left(v[1], rot)\\n v[1] = v[0] ^ v[1]\\n return v\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::threefry_2x32.rotate_list\",\n \"text\": \"jax/random.py::threefry_2x32.rotate_list\\n def rotate_list(xs):\\n return xs[1:] + [xs[0]]\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::threefry_2x32.step\",\n \"text\": \"jax/random.py::threefry_2x32.step\\n def step(i, state):\\n x, ks, rotations = state\\n for r in rotations[0]:\\n x = apply_round(x, r)\\n new_x = [x[0] + ks[0], x[1] + ks[1] + asarray(i + 1, dtype=onp.uint32)]\\n return new_x, rotate_list(ks), rotate_list(rotations)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::split\",\n \"text\": \"jax/random.py::split\\ndef split(key, num=2):\\n \\\"\\\"\\\"Splits a PRNG key into `num` new keys by adding a leading axis.\\n\\n Args:\\n key: a PRNGKey (an array with shape (2,) and dtype uint32).\\n num: optional, a positive integer indicating the number of keys to produce\\n (default 2).\\n\\n Returns:\\n An array with shape (num, 2) and dtype uint32 representing `num` new keys.\\n \\\"\\\"\\\"\\n return _split(key, num)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_split\",\n \"text\": \"jax/random.py::_split\\n@partial(jit, static_argnums=(1,))\\ndef _split(key, num):\\n counts = lax.tie_in(key, lax.iota(onp.uint32, num * 2))\\n return lax.reshape(threefry_2x32(key, counts), (num, 2))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::fold_in\",\n \"text\": \"jax/random.py::fold_in\\ndef fold_in(key, data):\\n \\\"\\\"\\\"Folds in data to a PRNG key to form a new PRNG key.\\n\\n Args:\\n key: a PRNGKey (an array with shape (2,) and dtype uint32).\\n data: a 32bit integer representing data to be folded in to the key.\\n\\n Returns:\\n A new PRNGKey that is a deterministic function of the inputs and is\\n statistically safe for producing a stream of new pseudo-random values.\\n \\\"\\\"\\\"\\n return _fold_in(key, data)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_fold_in\",\n \"text\": \"jax/random.py::_fold_in\\n@jit\\ndef _fold_in(key, data):\\n key2 = lax.tie_in(key, PRNGKey(data))\\n return threefry_2x32(key, key2)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_random_bits\",\n \"text\": \"jax/random.py::_random_bits\\ndef _random_bits(key, bit_width, shape):\\n \\\"\\\"\\\"Sample uniform random bits of given width and shape using PRNG key.\\\"\\\"\\\"\\n if not _is_prng_key(key):\\n raise TypeError(\\\"_random_bits got invalid prng key.\\\")\\n if bit_width not in (32, 64):\\n raise TypeError(\\\"requires 32- or 64-bit field width.\\\")\\n max_count = (bit_width // 32) * onp.prod(shape)\\n if max_count >= onp.iinfo(onp.uint32).max:\\n # TODO(mattjj): just split the key here\\n raise TypeError(\\\"requesting more random bits than a single call provides.\\\")\\n\\n counts = lax.tie_in(key, lax.iota(onp.uint32, max_count))\\n bits = threefry_2x32(key, counts)\\n if bit_width == 64:\\n bits = [lax.convert_element_type(x, onp.uint64) for x in np.split(bits, 2)]\\n bits = lax.shift_left(bits[0], onp.uint64(32)) | bits[1]\\n return lax.reshape(bits, shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_check_shape\",\n \"text\": \"jax/random.py::_check_shape\\ndef _check_shape(name, shape):\\n try:\\n shape = tuple(map(int, shape))\\n except TypeError:\\n msg = \\\"{} requires a concrete tuple of integers as shape argument, got {}.\\\"\\n raise ValueError(msg.format(name, shape))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::uniform\",\n \"text\": \"jax/random.py::uniform\\ndef uniform(key, shape=(), dtype=onp.float64, minval=0., maxval=1.):\\n \\\"\\\"\\\"Sample uniform random values in [minval, maxval) with given shape/dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: a tuple of nonnegative integers representing the shape.\\n dtype: optional, a float dtype for the returned values (default float64 if\\n jax_enable_x64 is true, otherwise float32).\\n minval: optional, a minimum (inclusive) value for the range (default 0).\\n maxval: optional, a maximum (exclusive) value for the range (default 1).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n return _uniform(key, shape, dtype, minval, maxval)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_uniform\",\n \"text\": \"jax/random.py::_uniform\\n@partial(jit, static_argnums=(1, 2))\\ndef _uniform(key, shape, dtype, minval, maxval):\\n _check_shape(\\\"uniform\\\", shape)\\n if not onp.issubdtype(dtype, onp.floating):\\n raise TypeError(\\\"uniform only accepts floating point dtypes.\\\")\\n\\n minval = lax.convert_element_type(minval, dtype)\\n maxval = lax.convert_element_type(maxval, dtype)\\n finfo = onp.finfo(dtype)\\n nbits, nmant = finfo.bits, finfo.nmant\\n\\n if nbits not in (32, 64):\\n raise TypeError(\\\"uniform only accepts 32- or 64-bit dtypes.\\\")\\n\\n bits = _random_bits(key, nbits, shape)\\n\\n # The strategy here is to randomize only the mantissa bits with an exponent of\\n # 1 (after applying the bias), then shift and scale to the desired range. The\\n # bit-level transformation we use relies on Numpy and XLA having bit-for-bit\\n # equivalent float representations, which might not be true on all platforms.\\n float_bits = lax.bitwise_or(\\n lax.shift_right_logical(bits, onp.array(nbits - nmant, lax.dtype(bits))),\\n onp.array(1., dtype).view(onp.uint32 if nbits == 32 else onp.uint64))\\n floats = lax.bitcast_convert_type(float_bits, dtype) - onp.array(1., dtype)\\n return lax.max(\\n minval,\\n lax.reshape(floats * (maxval - minval) + minval, shape))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::randint\",\n \"text\": \"jax/random.py::randint\\ndef randint(key, shape, minval, maxval, dtype=onp.int64):\\n \\\"\\\"\\\"Sample uniform random values in [minval, maxval) with given shape/dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: a tuple of nonnegative integers representing the shape.\\n minval: int or array of ints broadcast-compatible with ``shape``, a minimum\\n (inclusive) value for the range.\\n maxval: int or array of ints broadcast-compatible with ``shape``, a maximum\\n (exclusive) value for the range.\\n dtype: optional, an int dtype for the returned values (default int64 if\\n jax_enable_x64 is true, otherwise int32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n return _randint(key, shape, minval, maxval, dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_randint\",\n \"text\": \"jax/random.py::_randint\\n@partial(jit, static_argnums=(1, 4))\\ndef _randint(key, shape, minval, maxval, dtype):\\n _check_shape(\\\"randint\\\", shape)\\n if not onp.issubdtype(dtype, onp.integer):\\n raise TypeError(\\\"randint only accepts integer dtypes.\\\")\\n\\n minval = lax.convert_element_type(minval, dtype)\\n maxval = lax.convert_element_type(maxval, dtype)\\n nbits = onp.iinfo(dtype).bits\\n\\n if nbits not in (32, 64):\\n raise TypeError(\\\"randint only accepts 32- or 64-bit dtypes.\\\")\\n\\n # if we don't have minval < maxval, just always return minval\\n # https://github.com/google/jax/issues/222\\n maxval = lax.max(lax.add(minval, onp.array(1, dtype)), maxval)\\n\\n # This algorithm is biased whenever (maxval - minval) is not a power of 2.\\n # We generate double the number of random bits required by the dtype so as to\\n # reduce that bias.\\n k1, k2 = split(key)\\n rbits = lambda key: _random_bits(key, nbits, shape)\\n higher_bits, lower_bits = rbits(k1), rbits(k2)\\n\\n unsigned_dtype = onp.uint32 if nbits == 32 else onp.uint64\\n span = lax.convert_element_type(maxval - minval, unsigned_dtype)\\n\\n # To compute a remainder operation on an integer that might have twice as many\\n # bits as we can represent in the native unsigned dtype, we compute a\\n # multiplier equal to 2**nbits % span (using that nbits is 32 or 64).\\n multiplier = lax.rem(onp.array(2**16, unsigned_dtype), span)\\n multiplier = lax.rem(lax.mul(multiplier, multiplier), span)\\n if nbits == 64:\\n multiplier = lax.rem(lax.mul(multiplier, multiplier), span)\\n\\n random_offset = lax.add(lax.mul(lax.rem(higher_bits, span), multiplier),\\n lax.rem(lower_bits, span))\\n random_offset = lax.rem(random_offset, span)\\n return lax.add(minval, lax.convert_element_type(random_offset, dtype))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::shuffle\",\n \"text\": \"jax/random.py::shuffle\\ndef shuffle(key, x, axis=0):\\n \\\"\\\"\\\"Shuffle the elements of an array uniformly at random along an axis.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n x: the array to be shuffled.\\n axis: optional, an int axis along which to shuffle (default 0).\\n\\n Returns:\\n A shuffled version of x.\\n \\\"\\\"\\\"\\n return _shuffle(key, x, axis)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_shuffle\",\n \"text\": \"jax/random.py::_shuffle\\n@partial(jit, static_argnums=(2,))\\ndef _shuffle(key, x, axis):\\n # On parallel architectures, Fisher-Yates is more expensive than doing\\n # multiple sorts. This algorithm is based on one developed and analyzed by\\n # tjablin@. We sort according to randomly-generated 32bit keys, but those keys\\n # may have collisions. If we repeat the process, using fresh 32bit keys for\\n # each sort, then whenever all pairs of elements have been assigned distinct\\n # keys at some iteration (or equivalently when the strings formed by\\n # concatenating the successive keys for each element are all distinct) then we\\n # are guaranteed to have a perfect sample (assuming that either the sort is\\n # stable or that any bias is not value-dependent). Since checking uniqueness\\n # at runtime may be expensive, we use a heuristic static stop criterion\\n # developed by tjablin@. See tensorflow/compiler/tf2xla/random_ops.cc for more\\n # info, and for the original implementation of this algorithm. See also\\n # Section 2 of http://people.csail.mit.edu/costis/6896sp11/lec5s.pdf for\\n # another analysis (where the keys are generated one bit at a time).\\n exponent = 3 # see tjablin@'s analysis for explanation of this parameter\\n uint32max = onp.iinfo(onp.uint32).max\\n num_rounds = int(onp.ceil(exponent * onp.log(x.size) / onp.log(uint32max)))\\n\\n for _ in range(num_rounds):\\n key, subkey = split(key)\\n sort_keys = _random_bits(subkey, 32, x.shape)\\n _, x = lax.sort_key_val(sort_keys, x, axis)\\n\\n return x\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::normal\",\n \"text\": \"jax/random.py::normal\\ndef normal(key, shape=(), dtype=onp.float64):\\n \\\"\\\"\\\"Sample standard normal random values with given shape and float dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: a tuple of nonnegative integers representing the shape.\\n dtype: optional, a float dtype for the returned values (default float64 if\\n jax_enable_x64 is true, otherwise float32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n return _normal(key, shape, dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_normal\",\n \"text\": \"jax/random.py::_normal\\n@partial(jit, static_argnums=(1, 2))\\ndef _normal(key, shape, dtype):\\n _check_shape(\\\"normal\\\", shape)\\n lo = onp.nextafter(onp.array(-1., dtype), 0., dtype=dtype)\\n hi = onp.array(1., dtype)\\n u = uniform(key, shape, dtype, lo, hi)\\n return onp.array(onp.sqrt(2), dtype) * lax.erf_inv(u)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::bernoulli\",\n \"text\": \"jax/random.py::bernoulli\\ndef bernoulli(key, p=onp.float32(0.5), shape=()):\\n \\\"\\\"\\\"Sample Bernoulli random values with given shape and mean.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n p: optional, an array-like of floating dtype broadcastable to `shape` for\\n the mean of the random variables (default 0.5).\\n shape: optional, a tuple of nonnegative integers representing the shape\\n (default scalar).\\n\\n Returns:\\n A random array with the specified shape and boolean dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(lax.dtype(p))\\n if not onp.issubdtype(dtype, onp.floating):\\n msg = \\\"bernoulli probability `p` must have a floating dtype, got {}.\\\"\\n raise TypeError(msg.format(dtype))\\n p = lax.convert_element_type(p, dtype)\\n return _bernoulli(key, p, shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_bernoulli\",\n \"text\": \"jax/random.py::_bernoulli\\n@partial(jit, static_argnums=(2,))\\ndef _bernoulli(key, p, shape):\\n _check_shape(\\\"bernoulli\\\", shape)\\n shape = shape or onp.shape(p)\\n if onp.shape(p) != shape:\\n p = np.broadcast_to(p, shape)\\n return lax.lt(uniform(key, shape, lax.dtype(p)), p)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::beta\",\n \"text\": \"jax/random.py::beta\\ndef beta(key, a, b, shape=(), dtype=onp.float64):\\n \\\"\\\"\\\"Sample Bernoulli random values with given shape and mean.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n a: an array-like broadcastable to `shape` and used as the shape parameter\\n alpha of the random variables.\\n b: an array-like broadcastable to `shape` and used as the shape parameter\\n beta of the random variables.\\n shape: optional, a tuple of nonnegative integers representing the shape\\n (default scalar).\\n dtype: optional, a float dtype for the returned values (default float64 if\\n jax_enable_x64 is true, otherwise float32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n return _beta(key, a, b, shape, dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_beta\",\n \"text\": \"jax/random.py::_beta\\n@partial(jit, static_argnums=(3, 4))\\ndef _beta(key, a, b, shape, dtype):\\n _check_shape(\\\"beta\\\", shape)\\n a = lax.convert_element_type(a, dtype)\\n b = lax.convert_element_type(b, dtype)\\n shape = shape or lax.broadcast_shapes(np.shape(a), np.shape(b))\\n key_a, key_b = split(key)\\n gamma_a = gamma(key_a, a, shape, dtype)\\n gamma_b = gamma(key_b, b, shape, dtype)\\n return gamma_a / (gamma_a + gamma_b)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::cauchy\",\n \"text\": \"jax/random.py::cauchy\\ndef cauchy(key, shape=(), dtype=onp.float64):\\n \\\"\\\"\\\"Sample Cauchy random values with given shape and float dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: optional, a tuple of nonnegative integers representing the shape\\n (default scalar).\\n dtype: optional, a float dtype for the returned values (default float64 if\\n jax_enable_x64 is true, otherwise float32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n return _cauchy(key, shape, dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_cauchy\",\n \"text\": \"jax/random.py::_cauchy\\n@partial(jit, static_argnums=(1, 2))\\ndef _cauchy(key, shape, dtype):\\n _check_shape(\\\"cauchy\\\", shape)\\n u = uniform(key, shape, dtype, minval=onp.finfo(dtype).eps, maxval=1.)\\n pi = _constant_like(u, onp.pi)\\n return lax.tan(lax.mul(pi, lax.sub(u, _constant_like(u, 0.5))))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::dirichlet\",\n \"text\": \"jax/random.py::dirichlet\\ndef dirichlet(key, alpha, shape=(), dtype=onp.float64):\\n \\\"\\\"\\\"Sample Cauchy random values with given shape and float dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n alpha: an array-like with `alpha.shape[:-1]` broadcastable to `shape` and\\n used as the concentration parameter of the random variables.\\n shape: optional, a tuple of nonnegative integers representing the batch\\n shape (defaults to `alpha.shape[:-1]`).\\n dtype: optional, a float dtype for the returned values (default float64 if\\n jax_enable_x64 is true, otherwise float32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n return _dirichlet(key, alpha, shape, dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_dirichlet\",\n \"text\": \"jax/random.py::_dirichlet\\n@partial(jit, static_argnums=(2, 3))\\ndef _dirichlet(key, alpha, shape, dtype):\\n _check_shape(\\\"dirichlet\\\", shape)\\n alpha = asarray(alpha, dtype)\\n shape = shape or alpha.shape[:-1]\\n gamma_samples = gamma(key, alpha, shape + alpha.shape[-1:], dtype)\\n return gamma_samples / np.sum(gamma_samples, axis=-1, keepdims=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::exponential\",\n \"text\": \"jax/random.py::exponential\\ndef exponential(key, shape=(), dtype=onp.float64):\\n \\\"\\\"\\\"Sample Exponential random values with given shape and float dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: optional, a tuple of nonnegative integers representing the shape\\n (default scalar).\\n dtype: optional, a float dtype for the returned values (default float64 if\\n jax_enable_x64 is true, otherwise float32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n return _exponential(key, shape, dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_exponential\",\n \"text\": \"jax/random.py::_exponential\\n@partial(jit, static_argnums=(1, 2))\\ndef _exponential(key, shape, dtype):\\n _check_shape(\\\"exponential\\\", shape)\\n u = uniform(key, shape, dtype)\\n # taking 1 - u to move the domain of log to (0, 1] instead of [0, 1)\\n return lax.neg(lax.log1p(lax.neg(u)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma_one\",\n \"text\": \"jax/random.py::_gamma_one\\ndef _gamma_one(key, alpha):\\n # Ref: A simple method for generating gamma variables, George Marsaglia and Wai Wan Tsang\\n # The algorithm can also be founded in:\\n # https://en.wikipedia.org/wiki/Gamma_distribution#Generating_gamma-distributed_random_variables\\n zero = _constant_like(alpha, 0)\\n one = _constant_like(alpha, 1)\\n minus_one = _constant_like(alpha, -1)\\n one_over_two = _constant_like(alpha, 0.5)\\n one_over_three = _constant_like(alpha, 1. / 3.)\\n squeeze_const = _constant_like(alpha, 0.0331)\\n dtype = lax.dtype(alpha)\\n\\n key, subkey = split(key)\\n # for alpha < 1, we boost alpha to alpha + 1 and get a sample according to\\n # Gamma(alpha) ~ Gamma(alpha+1) * Uniform()^(1 / alpha)\\n boost = lax.select(lax.ge(alpha, one),\\n one,\\n lax.pow(uniform(subkey, (), dtype=dtype), lax.div(one, alpha)))\\n alpha = lax.select(lax.ge(alpha, one), alpha, lax.add(alpha, one))\\n\\n d = lax.sub(alpha, one_over_three)\\n c = lax.div(one_over_three, lax.pow(d, one_over_two))\\n\\n def _cond_fn(kXVU):\\n _, X, V, U = kXVU\\n # TODO: use lax.cond when its batching rule is supported\\n # The reason is to avoid evaluating second condition which involves log+log\\n # if the first condition is satisfied\\n cond = lax.bitwise_and(lax.ge(U, lax.sub(one, lax.mul(squeeze_const, lax.mul(X, X)))),\\n lax.ge(lax.log(U), lax.add(lax.mul(X, one_over_two),\\n lax.mul(d, lax.add(lax.sub(one, V),\\n lax.log(V))))))\\n return cond\\n\\n def _body_fn(kXVU):\\n def _next_kxv(kxv):\\n key = kxv[0]\\n key, subkey = split(key)\\n x = normal(subkey, (), dtype=dtype)\\n v = lax.add(one, lax.mul(x, c))\\n return key, x, v\\n\\n key = kXVU[0]\\n key, x_key, U_key = split(key, 3)\\n _, x, v = lax.while_loop(lambda kxv: lax.le(kxv[2], zero), _next_kxv, (x_key, zero, minus_one))\\n X = \",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma_one._cond_fn\",\n \"text\": \"jax/random.py::_gamma_one._cond_fn\\n def _cond_fn(kXVU):\\n _, X, V, U = kXVU\\n # TODO: use lax.cond when its batching rule is supported\\n # The reason is to avoid evaluating second condition which involves log+log\\n # if the first condition is satisfied\\n cond = lax.bitwise_and(lax.ge(U, lax.sub(one, lax.mul(squeeze_const, lax.mul(X, X)))),\\n lax.ge(lax.log(U), lax.add(lax.mul(X, one_over_two),\\n lax.mul(d, lax.add(lax.sub(one, V),\\n lax.log(V))))))\\n return cond\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma_one._body_fn\",\n \"text\": \"jax/random.py::_gamma_one._body_fn\\n def _body_fn(kXVU):\\n def _next_kxv(kxv):\\n key = kxv[0]\\n key, subkey = split(key)\\n x = normal(subkey, (), dtype=dtype)\\n v = lax.add(one, lax.mul(x, c))\\n return key, x, v\\n\\n key = kXVU[0]\\n key, x_key, U_key = split(key, 3)\\n _, x, v = lax.while_loop(lambda kxv: lax.le(kxv[2], zero), _next_kxv, (x_key, zero, minus_one))\\n X = lax.mul(x, x)\\n V = lax.mul(lax.mul(v, v), v)\\n U = uniform(U_key, (), dtype=dtype)\\n return key, X, V, U\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma_one._body_fn._next_kxv\",\n \"text\": \"jax/random.py::_gamma_one._body_fn._next_kxv\\n def _next_kxv(kxv):\\n key = kxv[0]\\n key, subkey = split(key)\\n x = normal(subkey, (), dtype=dtype)\\n v = lax.add(one, lax.mul(x, c))\\n return key, x, v\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma_grad_one\",\n \"text\": \"jax/random.py::_gamma_grad_one\\ndef _gamma_grad_one(z, alpha):\\n # Ref 1: Pathwise Derivatives Beyond the Reparameterization Trick, Martin & Fritz\\n # Ref 2: Case 4 follows https://github.com/fritzo/notebooks/blob/master/gamma-reparameterized.ipynb\\n\\n # TODO: use lax.cond instead of lax.while_loop when its batching rule is available\\n # See https://github.com/google/jax/issues/490\\n def _case1(zagf):\\n z, alpha, _, flag = zagf\\n\\n # dz = - dCDF(z; a) / pdf(z; a)\\n # pdf = z^(a-1) * e^(-z) / Gamma(a)\\n # CDF(z; a) = IncompleteGamma(a, z) / Gamma(a)\\n # dCDF(z; a) = (dIncompleteGamma - IncompleteGamma * Digamma(a)) / Gamma(a)\\n # =: unnormalized_dCDF / Gamma(a)\\n # IncompleteGamma ~ z^a [ 1/a - z/(a+1) + z^2/2!(a+2) - z^3/3!(a+3) + z^4/4!(a+4) - z^5/5!(a+5) ]\\n # =: z^a * term1\\n # dIncompleteGamma ~ z^a * log(z) * term1 - z^a [1/a^2 - z/(a+1)^2 + z^2/2!(a+2)^2\\n # - z^3/3!(a+3)^2 + z^4/4!(a+4)^2 - z^5/5!(a+5)^2 ]\\n # =: z^a * log(z) * term1 - z^a * term2\\n # unnormalized_dCDF = z^a { [log(z) - Digamma(a)] * term1 - term2 }\\n zi = 1.0\\n update = zi / alpha\\n term1 = update\\n term2 = update / alpha\\n for i in range(1, 6):\\n zi = -zi * z / i\\n update = zi / (alpha + i)\\n term1 = term1 + update\\n term2 = term2 + update / (alpha + i)\\n\\n unnormalized_cdf_dot = np.power(z, alpha) * ((np.log(z) - lax.digamma(alpha)) * term1 - term2)\\n unnormalized_pdf = np.power(z, alpha - 1) * np.exp(-z)\\n grad = -unnormalized_cdf_dot / unnormalized_pdf\\n\\n return z, alpha, grad, ~flag\\n\\n def _cond2(zagf):\\n z, alpha, _, flag = zagf\\n return (~flag) & (alpha > 8.0) & ((z < 0.9 * alpha) | (z > 1.1 * alpha))\\n\\n def _case2(zagf):\\n z, alpha, _, flag = zagf\\n\\n # Formula 58 of [1]\\n sqrt_8a = np.sqrt(8 \",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma_grad_one._case1\",\n \"text\": \"jax/random.py::_gamma_grad_one._case1\\n def _case1(zagf):\\n z, alpha, _, flag = zagf\\n\\n # dz = - dCDF(z; a) / pdf(z; a)\\n # pdf = z^(a-1) * e^(-z) / Gamma(a)\\n # CDF(z; a) = IncompleteGamma(a, z) / Gamma(a)\\n # dCDF(z; a) = (dIncompleteGamma - IncompleteGamma * Digamma(a)) / Gamma(a)\\n # =: unnormalized_dCDF / Gamma(a)\\n # IncompleteGamma ~ z^a [ 1/a - z/(a+1) + z^2/2!(a+2) - z^3/3!(a+3) + z^4/4!(a+4) - z^5/5!(a+5) ]\\n # =: z^a * term1\\n # dIncompleteGamma ~ z^a * log(z) * term1 - z^a [1/a^2 - z/(a+1)^2 + z^2/2!(a+2)^2\\n # - z^3/3!(a+3)^2 + z^4/4!(a+4)^2 - z^5/5!(a+5)^2 ]\\n # =: z^a * log(z) * term1 - z^a * term2\\n # unnormalized_dCDF = z^a { [log(z) - Digamma(a)] * term1 - term2 }\\n zi = 1.0\\n update = zi / alpha\\n term1 = update\\n term2 = update / alpha\\n for i in range(1, 6):\\n zi = -zi * z / i\\n update = zi / (alpha + i)\\n term1 = term1 + update\\n term2 = term2 + update / (alpha + i)\\n\\n unnormalized_cdf_dot = np.power(z, alpha) * ((np.log(z) - lax.digamma(alpha)) * term1 - term2)\\n unnormalized_pdf = np.power(z, alpha - 1) * np.exp(-z)\\n grad = -unnormalized_cdf_dot / unnormalized_pdf\\n\\n return z, alpha, grad, ~flag\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma_grad_one._cond2\",\n \"text\": \"jax/random.py::_gamma_grad_one._cond2\\n def _cond2(zagf):\\n z, alpha, _, flag = zagf\\n return (~flag) & (alpha > 8.0) & ((z < 0.9 * alpha) | (z > 1.1 * alpha))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma_grad_one._case2\",\n \"text\": \"jax/random.py::_gamma_grad_one._case2\\n def _case2(zagf):\\n z, alpha, _, flag = zagf\\n\\n # Formula 58 of [1]\\n sqrt_8a = np.sqrt(8 * alpha)\\n z_minus_a = z - alpha\\n log_z_div_a = np.log(z / alpha)\\n sign = np.where(z < alpha, 1.0, -1.0)\\n term1 = 4 * (z + alpha) / (sqrt_8a * z_minus_a * z_minus_a)\\n term2 = log_z_div_a * (sqrt_8a / z_minus_a + sign * np.power(z_minus_a - alpha * log_z_div_a, -1.5))\\n term3 = z * (1.0 + 1.0 / (12 * alpha) + 1.0 / (288 * alpha * alpha)) / sqrt_8a\\n grad = (term1 + term2) * term3\\n\\n return z, alpha, grad, ~flag\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma_grad_one._cond3\",\n \"text\": \"jax/random.py::_gamma_grad_one._cond3\\n def _cond3(zagf):\\n z, alpha, _, flag = zagf\\n return (~flag) & (alpha > 8.0) & (z >= 0.9 * alpha) & (z <= 1.1 * alpha)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma_grad_one._case3\",\n \"text\": \"jax/random.py::_gamma_grad_one._case3\\n def _case3(zagf):\\n z, alpha, _, flag = zagf\\n\\n # Formula 59 of [1]\\n z_div_a = np.divide(z, alpha)\\n aa = alpha * alpha\\n term1 = 1440 * alpha + 6 * z_div_a * (53 - 120 * z) - 65 * z_div_a * z_div_a + 3600 * z + 107\\n term2 = 1244160 * alpha * aa\\n term3 = 1 + 24 * alpha + 288 * aa\\n grad = term1 * term3 / term2\\n\\n return z, alpha, grad, ~flag\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma_grad_one._case4\",\n \"text\": \"jax/random.py::_gamma_grad_one._case4\\n def _case4(zagf):\\n z, alpha, _, flag = zagf\\n\\n # Ref [2]\\n u = np.log(z / alpha)\\n v = np.log(alpha)\\n c = []\\n for i in range(8):\\n c.append(_bivariate_coef[0][i] + u * (_bivariate_coef[1][i] + u * _bivariate_coef[2][i]))\\n p = c[0] + v * (c[1] + v * (c[2] + v * c[3]))\\n q = c[4] + v * (c[5] + v * (c[6] + v * c[7]))\\n grad = np.exp(p / np.maximum(q, 0.01))\\n\\n return z, alpha, grad, ~flag\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma_grad\",\n \"text\": \"jax/random.py::_gamma_grad\\ndef _gamma_grad(sample, a):\\n samples = np.reshape(sample, -1)\\n alphas = np.reshape(a, -1)\\n grads = vmap(_gamma_grad_one)(samples, alphas)\\n return grads.reshape(a.shape)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma_impl\",\n \"text\": \"jax/random.py::_gamma_impl\\n@custom_transforms\\ndef _gamma_impl(key, a):\\n alphas = np.reshape(a, -1)\\n keys = split(key, onp.size(alphas))\\n samples = vmap(_gamma_one)(keys, alphas)\\n return np.reshape(samples, np.shape(a))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::gamma\",\n \"text\": \"jax/random.py::gamma\\ndef gamma(key, a, shape=(), dtype=onp.float64):\\n \\\"\\\"\\\"Sample Gamma random values with given shape and float dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n a: an array-like broadcastable to `shape` and used as the shape parameter\\n of the random variables.\\n shape: optional, a tuple of nonnegative integers representing the shape\\n (default scalar).\\n dtype: optional, a float dtype for the returned values (default float64 if\\n jax_enable_x64 is true, otherwise float32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n return _gamma(key, a, shape, dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gamma\",\n \"text\": \"jax/random.py::_gamma\\n@partial(jit, static_argnums=(2, 3))\\ndef _gamma(key, a, shape, dtype):\\n _check_shape(\\\"gamma\\\", shape)\\n a = lax.convert_element_type(a, dtype)\\n shape = shape or onp.shape(a)\\n if onp.shape(a) != shape:\\n a = np.broadcast_to(a, shape)\\n return _gamma_impl(key, a)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::gumbel\",\n \"text\": \"jax/random.py::gumbel\\ndef gumbel(key, shape=(), dtype=onp.float64):\\n \\\"\\\"\\\"Sample Gumbel random values with given shape and float dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: optional, a tuple of nonnegative integers representing the shape\\n (default scalar).\\n dtype: optional, a float dtype for the returned values (default float64 if\\n jax_enable_x64 is true, otherwise float32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n return _gumbel(key, shape, dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_gumbel\",\n \"text\": \"jax/random.py::_gumbel\\n@partial(jit, static_argnums=(1, 2))\\ndef _gumbel(key, shape, dtype):\\n _check_shape(\\\"gumbel\\\", shape)\\n return -np.log(-np.log(\\n uniform(key, shape, dtype, minval=onp.finfo(dtype).eps, maxval=1.)))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::laplace\",\n \"text\": \"jax/random.py::laplace\\ndef laplace(key, shape=(), dtype=onp.float64):\\n \\\"\\\"\\\"Sample Laplace random values with given shape and float dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: optional, a tuple of nonnegative integers representing the shape\\n (default scalar).\\n dtype: optional, a float dtype for the returned values (default float64 if\\n jax_enable_x64 is true, otherwise float32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n return _laplace(key, shape, dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_laplace\",\n \"text\": \"jax/random.py::_laplace\\n@partial(jit, static_argnums=(1, 2))\\ndef _laplace(key, shape, dtype):\\n _check_shape(\\\"laplace\\\", shape)\\n u = uniform(\\n key, shape, dtype, minval=-1. + np.finfo(dtype).epsneg, maxval=1.)\\n return lax.mul(lax.sign(u), lax.log1p(lax.neg(lax.abs(u))))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::logistic\",\n \"text\": \"jax/random.py::logistic\\ndef logistic(key, shape=(), dtype=onp.float64):\\n \\\"\\\"\\\"Sample logistic random values with given shape and float dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n shape: optional, a tuple of nonnegative integers representing the shape\\n (default scalar).\\n dtype: optional, a float dtype for the returned values (default float64 if\\n jax_enable_x64 is true, otherwise float32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n return _logistic(key, shape, dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_logistic\",\n \"text\": \"jax/random.py::_logistic\\n@partial(jit, static_argnums=(1, 2))\\ndef _logistic(key, shape, dtype):\\n _check_shape(\\\"logistic\\\", shape)\\n return logit(uniform(key, shape, dtype))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::pareto\",\n \"text\": \"jax/random.py::pareto\\ndef pareto(key, b, shape=(), dtype=onp.float64):\\n \\\"\\\"\\\"Sample Pareto random values with given shape and float dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n b: an array-like broadcastable to `shape` and used as the shape parameter\\n of the random variables.\\n shape: optional, a tuple of nonnegative integers representing the shape\\n (default scalar).\\n dtype: optional, a float dtype for the returned values (default float64 if\\n jax_enable_x64 is true, otherwise float32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n return _pareto(key, b, shape, dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_pareto\",\n \"text\": \"jax/random.py::_pareto\\n@partial(jit, static_argnums=(2, 3))\\ndef _pareto(key, b, shape, dtype):\\n _check_shape(\\\"pareto\\\", shape)\\n b = lax.convert_element_type(b, dtype)\\n shape = shape or onp.shape(b)\\n if onp.shape(b) != shape:\\n b = np.broadcast_to(b, shape)\\n e = exponential(key, shape, dtype)\\n return lax.exp(lax.div(e, b))\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::t\",\n \"text\": \"jax/random.py::t\\ndef t(key, df, shape=(), dtype=onp.float64):\\n \\\"\\\"\\\"Sample Student's t random values with given shape and float dtype.\\n\\n Args:\\n key: a PRNGKey used as the random key.\\n df: an array-like broadcastable to `shape` and used as the shape parameter\\n of the random variables.\\n shape: optional, a tuple of nonnegative integers representing the shape\\n (default scalar).\\n dtype: optional, a float dtype for the returned values (default float64 if\\n jax_enable_x64 is true, otherwise float32).\\n\\n Returns:\\n A random array with the specified shape and dtype.\\n \\\"\\\"\\\"\\n dtype = xla_bridge.canonicalize_dtype(dtype)\\n return _t(key, df, shape, dtype)\",\n \"label\": 0\n },\n {\n \"cid\": \"jax/random.py::_t\",\n \"text\": \"jax/random.py::_t\\n@partial(jit, static_argnums=(2, 3))\\ndef _t(key, df, shape, dtype):\\n _check_shape(\\\"t\\\", shape)\\n df = lax.convert_element_type(df, dtype)\\n shape = shape or onp.shape(df)\\n key_n, key_g = split(key)\\n n = normal(key_n, shape, dtype)\\n two = _constant_like(n, 2)\\n half_df = lax.div(df, two)\\n g = gamma(key_n, half_df, shape, dtype)\\n return n * np.sqrt(half_df / g)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":293,"cells":{"instance_id":{"kind":"string","value":"googleapis__google-cloud-python-771"},"repo":{"kind":"string","value":"googleapis/google-cloud-python"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"DISCUSSION: Do we need ACL lazy loading?\nIs there a good reason for the [lazy](https://github.com/GoogleCloudPlatform/gcloud-python/blob/d51f29f39832d52a24b2b5dfdb4f882bf623f374/gcloud/storage/blob.py#L95-L99) loading [behavior](https://github.com/GoogleCloudPlatform/gcloud-python/blob/d51f29f39832d52a24b2b5dfdb4f882bf623f374/gcloud/storage/bucket.py#L127-L139), e.g.\n\n``` python\n if self._acl is None:\n self._acl = ObjectACL(self)\n```\n\nThis doesn't make any requests and just initializes a very lightweight object.\n\nI move to just create these in the constructor.\n\n"},"gold_files":{"kind":"list like","value":["gcloud/storage/blob.py","gcloud/storage/bucket.py"],"string":"[\n \"gcloud/storage/blob.py\",\n \"gcloud/storage/bucket.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["gcloud/storage/blob.py::<module>","gcloud/storage/blob.py::Blob.__init__","gcloud/storage/blob.py::Blob.acl","gcloud/storage/bucket.py::<module>","gcloud/storage/bucket.py::Bucket.__init__","gcloud/storage/bucket.py::Bucket.acl","gcloud/storage/bucket.py::Bucket.default_object_acl"],"string":"[\n \"gcloud/storage/blob.py::<module>\",\n \"gcloud/storage/blob.py::Blob.__init__\",\n \"gcloud/storage/blob.py::Blob.acl\",\n \"gcloud/storage/bucket.py::<module>\",\n \"gcloud/storage/bucket.py::Bucket.__init__\",\n \"gcloud/storage/bucket.py::Bucket.acl\",\n \"gcloud/storage/bucket.py::Bucket.default_object_acl\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"gcloud/_helpers.py","text":"gcloud/_helpers.py\nclass _LocalStack # Manage a thread-local LIFO stack of resources.\n def __init__(self)\n def __iter__(self) # Iterate the stack in LIFO order.\n def push(self, resource) # Push a resource onto our stack.\n def pop(self) # Pop a resource from our stack.\n def top(self) # Get the top-most resource\nclass _LazyProperty # Descriptor for lazy loaded property.\n def __init__(self, name, deferred_callable)\n def __get__(self, obj, objtype)\ndef _lazy_property_deco(deferred_callable) # Decorator a method to create a :class:`_LazyProperty`.\ndef _app_engine_id() # Gets the App Engine application ID if it can be inferred.\ndef _compute_engine_id() # Gets the Compute Engine project ID if it can be inferred.\ndef _get_production_project() # Gets the production project if it can be inferred.\ndef _determine_default_project(project=None) # Determine default project ID explicitly or implicitly as fall-back.\ndef set_default_project(project=None) # Set default project either explicitly or implicitly as fall-back.\ndef get_default_project() # Get default project.\nclass _DefaultsContainer # Container for defaults.\n def project() # Return the implicit default project.\n def __init__(self, project=None, implicit=False)","label":0},{"cid":"gcloud/datastore/query.py","text":"gcloud/datastore/query.py\nclass Query # A Query against the Cloud Datastore.\n def __init__(self, kind=None, dataset_id=None, namespace=None, ancestor=None, filters=(), projection=(), order=(), group_by=())\n def dataset_id(self) # Get the dataset ID for this Query.\n def namespace(self) # This query's namespace\n def namespace(self, value) # Update the query's namespace.\n def kind(self) # Get the Kind of the Query.\n def kind(self, value) # Update the Kind of the Query.\n def ancestor(self) # The ancestor key for the query.\n def ancestor(self, value) # Set the ancestor for the query\n def ancestor(self) # Remove the ancestor for the query.\n def filters(self) # Filters set on the query.\n def add_filter(self, property_name, operator, value) # Filter the query based on a property name, operator and a value.\n def projection(self) # Fields names returned by the query.\n def projection(self, projection) # Set the fields returned the query.\n def keys_only(self) # Set the projection to include only keys.\n def order(self) # Names of fields used to sort query results.\n def order(self, value) # Set the fields used to sort query results.\n def group_by(self) # Names of fields used to group query results.\n def group_by(self, value) # Set fields used to group query results.\n def fetch(self, limit=None, offset=0, start_cursor=None, end_cursor=None, connection=None) # Execute the Query; return an iterator for the matching entities.\nclass Iterator # Represent the state of a given execution of a Query.\n def __init__(self, query, connection, limit=None, offset=0, start_cursor=None, end_cursor=None)\n def next_page(self) # Fetch a single \"page\" of query results.\n def __iter__(self) # Generator yielding all results matching our query.\ndef _pb_from_query(query) # Convert a Query instance to the corresponding protobuf.","label":0},{"cid":"gcloud/storage/_helpers.py","text":"gcloud/storage/_helpers.py\nclass _PropertyMixin # Abstract mixin for cloud storage classes with associated propertties.\n def connection(self) # Abstract getter for the connection to use.\n def path(self) # Abstract getter for the object path.\n def __init__(self, name=None) # _PropertyMixin constructor.\n def properties(self) # Return a copy of properties.\n def batch(self) # Return a context manager which defers/batches updates.\n def _reload_properties(self) # Reload properties from Cloud Storage.\n def _patch_properties(self, properties) # Update particular fields of this object's properties.\n def patch(self) # Sends all changed properties in a PATCH request.\nclass _PropertyBatch # Context manager: Batch updates to object.\n def __init__(self, wrapped)\n def __enter__(self) # Do nothing method. Needed to be a context manager.\n def __exit__(self, exc_type, value, traceback) # Patch deferred property updates if no error.\ndef _scalar_property(fieldname) # Create a property descriptor around the :class:`_PropertyMixin` helpers.\n def _getter(self) # Scalar property getter.\n def _setter(self, value) # Scalar property setter.\ndef _write_buffer_to_hash(buffer_object, hash_obj, digest_block_size=8192) # Read blocks from a buffer and update a hash with them.\ndef _base64_md5hash(buffer_object) # Get MD5 hash of bytes (as base64).","label":0},{"cid":"gcloud/storage/acl.py","text":"gcloud/storage/acl.py\nclass _ACLEntity # Class representing a set of roles for an entity.\n def __init__(self, entity_type, identifier=None) # Entity constructor.\n def __str__(self)\n def __repr__(self)\n def get_roles(self) # Get the list of roles permitted by this entity.\n def grant(self, role) # Add a role to the entity.\n def revoke(self, role) # Remove a role from the entity.\n def grant_read(self) # Grant read access to the current entity.\n def grant_write(self) # Grant write access to the current entity.\n def grant_owner(self) # Grant owner access to the current entity.\n def revoke_read(self) # Revoke read access from the current entity.\n def revoke_write(self) # Revoke write access from the current entity.\n def revoke_owner(self) # Revoke owner access from the current entity.\nclass ACL # Container class representing a list of access controls.\n def __init__(self)\n def _ensure_loaded(self) # Load if not already loaded.\n def reset(self) # Remove all entities from the ACL, and clear the ``loaded`` flag.\n def __iter__(self)\n def entity_from_dict(self, entity_dict) # Build an _ACLEntity object from a dictionary of data.\n def has_entity(self, entity) # Returns whether or not this ACL has any entries for an entity.\n def get_entity(self, entity, default=None) # Gets an entity object from the ACL.\n def add_entity(self, entity) # Add an entity to the ACL.\n def entity(self, entity_type, identifier=None) # Factory method for creating an Entity.\n def user(self, identifier) # Factory method for a user Entity.\n def group(self, identifier) # Factory method for a group Entity.\n def domain(self, domain) # Factory method for a domain Entity.\n def all(self) # Factory method for an Entity representing all users.\n def all_authenticated(self) # Factory method for an Entity representing all authenticated users.\n def get_entities(self) # Get a list of all Entity objects.\n def reload(self) # Reload the ACL data from Cloud Storage.\n def save(self, acl=None) # A method to be overridden by subclasses.\n def clear(self) # Remove all entities from the ACL.\nclass BucketACL # An ACL specifically for a bucket.\n def __init__(self, bucket) # :type bucket: :class:`gcloud.storage.bucket.Bucket`\n def reload(self) # Reload the ACL data from Cloud Storage.\n def save(self, acl=None) # Save this ACL for the current bucket.\n def clear(self) # Remove all ACL entries.\nclass DefaultObjectACL # A class representing the default object ACL for a bucket.\nclass ObjectACL # An ACL specifically for a Cloud Storage object / blob.\n def __init__(self, blob)\n def reload(self) # Reload the ACL data from Cloud Storage.\n def save(self, acl=None) # Save the ACL data for this blob.\n def clear(self) # Remove all ACL rules from the blob.","label":0},{"cid":"gcloud/storage/blob.py","text":"gcloud/storage/blob.py\nclass Blob # A wrapper around Cloud Storage's concept of an ``Object``.\n def __init__(self, name, bucket=None)\n def path_helper(bucket_path, blob_name) # Relative URL path for a blob.\n def acl(self) # Create our ACL on demand.\n def __repr__(self)\n def connection(self) # Getter property for the connection to use with this Blob.\n def path(self) # Getter property for the URL path to this Blob.\n def public_url(self) # The public URL for this blob's object.\n def generate_signed_url(self, expiration, method='GET') # Generates a signed URL for this blob.\n def exists(self) # Determines whether or not this blob exists.\n def rename(self, new_name) # Renames this blob using copy and delete operations.\n def delete(self) # Deletes a blob from Cloud Storage.\n def download_to_file(self, file_obj) # Download the contents of this blob into a file-like object.\n def download_to_filename(self, filename) # Download the contents of this blob into a named file.\n def download_as_string(self) # Download the contents of this blob as a string.\n def upload_from_file(self, file_obj, rewind=False, size=None, content_type=None, num_retries=6) # Upload the contents of this blob from a file-like object.\n def upload_from_filename(self, filename, content_type=None) # Upload this blob's contents from the content of a named file.\n def upload_from_string(self, data, content_type='text/plain') # Upload contents of this blob from the provided string.\n def make_public(self) # Make this blob public giving all users read access.\n def component_count(self) # Number of underlying components that make up this object.\n def etag(self) # Retrieve the ETag for the object.\n def generation(self) # Retrieve the generation for the object.\n def id(self) # Retrieve the ID for the object.\n def media_link(self) # Retrieve the media download URI for the object.\n def metadata(self) # Retrieve arbitrary/application specific metadata for the object.\n def metadata(self, value) # Update arbitrary/application specific metadata for the object.\n def metageneration(self) # Retrieve the metageneration for the object.\n def owner(self) # Retrieve info about the owner of the object.\n def self_link(self) # Retrieve the URI for the object.\n def size(self) # Size of the object, in bytes.\n def storage_class(self) # Retrieve the storage class for the object.\n def time_deleted(self) # Retrieve the timestamp at which the object was deleted.\n def updated(self) # Retrieve the timestamp at which the object was updated.\nclass _UploadConfig # Faux message FBO apitools' 'ConfigureRequest'.\nclass _UrlBuilder # Faux builder FBO apitools' 'ConfigureRequest'\n def __init__(self, bucket_name, object_name)","label":1},{"cid":"gcloud/storage/bucket.py","text":"gcloud/storage/bucket.py\nclass _BlobIterator # An iterator listing blobs in a bucket\n def __init__(self, bucket, extra_params=None)\n def get_items_from_response(self, response) # Yield :class:`.storage.blob.Blob` items from response.\nclass Bucket # A class representing a Bucket on Cloud Storage.\n def __init__(self, name=None, connection=None)\n def __repr__(self)\n def __iter__(self)\n def __contains__(self, blob_name)\n def exists(self) # Determines whether or not this bucket exists.\n def acl(self) # Create our ACL on demand.\n def default_object_acl(self) # Create our defaultObjectACL on demand.\n def connection(self) # Getter property for the connection to use with this Bucket.\n def path_helper(bucket_name) # Relative URL path for a bucket.\n def path(self) # The URL path to this bucket.\n def get_blob(self, blob_name) # Get a blob object by name.\n def get_all_blobs(self) # List all the blobs in this bucket.\n def iterator(self, prefix=None, delimiter=None, max_results=None, versions=None) # Return an iterator used to find blobs in the bucket.\n def delete(self, force=False) # Delete this bucket.\n def delete_blob(self, blob_name) # Deletes a blob from the current bucket.\n def delete_blobs(self, blobs, on_error=None) # Deletes a list of blobs from the current bucket.\n def copy_blob(self, blob, destination_bucket, new_name=None) # Copy the given blob to the given bucket, optionally with a new name.\n def upload_file(self, filename, blob_name=None) # Shortcut method to upload a file into this bucket.\n def upload_file_object(self, file_obj, blob_name=None) # Shortcut method to upload a file object into this bucket.\n def get_cors(self) # Retrieve CORS policies configured for this bucket.\n def update_cors(self, entries) # Update CORS policies configured for this bucket.\n def get_default_object_acl(self) # Get the current Default Object ACL rules.\n def etag(self) # Retrieve the ETag for the bucket.\n def id(self) # Retrieve the ID for the bucket.\n def get_lifecycle(self) # Retrieve lifecycle rules configured for this bucket.\n def update_lifecycle(self, rules) # Update CORS policies configured for this bucket.\n def get_logging(self) # Return info about access logging for this bucket.\n def enable_logging(self, bucket_name, object_prefix='') # Enable access logging for this bucket.\n def disable_logging(self) # Disable access logging for this bucket.\n def metageneration(self) # Retrieve the metageneration for the bucket.\n def owner(self) # Retrieve info about the owner of the bucket.\n def project_number(self) # Retrieve the number of the project to which the bucket is assigned.\n def self_link(self) # Retrieve the URI for the bucket.\n def storage_class(self) # Retrieve the storage class for the bucket.\n def time_created(self) # Retrieve the timestamp at which the bucket was created.\n def versioning_enabled(self) # Is ver","label":1},{"cid":"setup.py","text":"setup.py","label":0}],"string":"[\n {\n \"cid\": \"gcloud/_helpers.py\",\n \"text\": \"gcloud/_helpers.py\\nclass _LocalStack # Manage a thread-local LIFO stack of resources.\\n def __init__(self)\\n def __iter__(self) # Iterate the stack in LIFO order.\\n def push(self, resource) # Push a resource onto our stack.\\n def pop(self) # Pop a resource from our stack.\\n def top(self) # Get the top-most resource\\nclass _LazyProperty # Descriptor for lazy loaded property.\\n def __init__(self, name, deferred_callable)\\n def __get__(self, obj, objtype)\\ndef _lazy_property_deco(deferred_callable) # Decorator a method to create a :class:`_LazyProperty`.\\ndef _app_engine_id() # Gets the App Engine application ID if it can be inferred.\\ndef _compute_engine_id() # Gets the Compute Engine project ID if it can be inferred.\\ndef _get_production_project() # Gets the production project if it can be inferred.\\ndef _determine_default_project(project=None) # Determine default project ID explicitly or implicitly as fall-back.\\ndef set_default_project(project=None) # Set default project either explicitly or implicitly as fall-back.\\ndef get_default_project() # Get default project.\\nclass _DefaultsContainer # Container for defaults.\\n def project() # Return the implicit default project.\\n def __init__(self, project=None, implicit=False)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py\",\n \"text\": \"gcloud/datastore/query.py\\nclass Query # A Query against the Cloud Datastore.\\n def __init__(self, kind=None, dataset_id=None, namespace=None, ancestor=None, filters=(), projection=(), order=(), group_by=())\\n def dataset_id(self) # Get the dataset ID for this Query.\\n def namespace(self) # This query's namespace\\n def namespace(self, value) # Update the query's namespace.\\n def kind(self) # Get the Kind of the Query.\\n def kind(self, value) # Update the Kind of the Query.\\n def ancestor(self) # The ancestor key for the query.\\n def ancestor(self, value) # Set the ancestor for the query\\n def ancestor(self) # Remove the ancestor for the query.\\n def filters(self) # Filters set on the query.\\n def add_filter(self, property_name, operator, value) # Filter the query based on a property name, operator and a value.\\n def projection(self) # Fields names returned by the query.\\n def projection(self, projection) # Set the fields returned the query.\\n def keys_only(self) # Set the projection to include only keys.\\n def order(self) # Names of fields used to sort query results.\\n def order(self, value) # Set the fields used to sort query results.\\n def group_by(self) # Names of fields used to group query results.\\n def group_by(self, value) # Set fields used to group query results.\\n def fetch(self, limit=None, offset=0, start_cursor=None, end_cursor=None, connection=None) # Execute the Query; return an iterator for the matching entities.\\nclass Iterator # Represent the state of a given execution of a Query.\\n def __init__(self, query, connection, limit=None, offset=0, start_cursor=None, end_cursor=None)\\n def next_page(self) # Fetch a single \\\"page\\\" of query results.\\n def __iter__(self) # Generator yielding all results matching our query.\\ndef _pb_from_query(query) # Convert a Query instance to the corresponding protobuf.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py\",\n \"text\": \"gcloud/storage/_helpers.py\\nclass _PropertyMixin # Abstract mixin for cloud storage classes with associated propertties.\\n def connection(self) # Abstract getter for the connection to use.\\n def path(self) # Abstract getter for the object path.\\n def __init__(self, name=None) # _PropertyMixin constructor.\\n def properties(self) # Return a copy of properties.\\n def batch(self) # Return a context manager which defers/batches updates.\\n def _reload_properties(self) # Reload properties from Cloud Storage.\\n def _patch_properties(self, properties) # Update particular fields of this object's properties.\\n def patch(self) # Sends all changed properties in a PATCH request.\\nclass _PropertyBatch # Context manager: Batch updates to object.\\n def __init__(self, wrapped)\\n def __enter__(self) # Do nothing method. Needed to be a context manager.\\n def __exit__(self, exc_type, value, traceback) # Patch deferred property updates if no error.\\ndef _scalar_property(fieldname) # Create a property descriptor around the :class:`_PropertyMixin` helpers.\\n def _getter(self) # Scalar property getter.\\n def _setter(self, value) # Scalar property setter.\\ndef _write_buffer_to_hash(buffer_object, hash_obj, digest_block_size=8192) # Read blocks from a buffer and update a hash with them.\\ndef _base64_md5hash(buffer_object) # Get MD5 hash of bytes (as base64).\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py\",\n \"text\": \"gcloud/storage/acl.py\\nclass _ACLEntity # Class representing a set of roles for an entity.\\n def __init__(self, entity_type, identifier=None) # Entity constructor.\\n def __str__(self)\\n def __repr__(self)\\n def get_roles(self) # Get the list of roles permitted by this entity.\\n def grant(self, role) # Add a role to the entity.\\n def revoke(self, role) # Remove a role from the entity.\\n def grant_read(self) # Grant read access to the current entity.\\n def grant_write(self) # Grant write access to the current entity.\\n def grant_owner(self) # Grant owner access to the current entity.\\n def revoke_read(self) # Revoke read access from the current entity.\\n def revoke_write(self) # Revoke write access from the current entity.\\n def revoke_owner(self) # Revoke owner access from the current entity.\\nclass ACL # Container class representing a list of access controls.\\n def __init__(self)\\n def _ensure_loaded(self) # Load if not already loaded.\\n def reset(self) # Remove all entities from the ACL, and clear the ``loaded`` flag.\\n def __iter__(self)\\n def entity_from_dict(self, entity_dict) # Build an _ACLEntity object from a dictionary of data.\\n def has_entity(self, entity) # Returns whether or not this ACL has any entries for an entity.\\n def get_entity(self, entity, default=None) # Gets an entity object from the ACL.\\n def add_entity(self, entity) # Add an entity to the ACL.\\n def entity(self, entity_type, identifier=None) # Factory method for creating an Entity.\\n def user(self, identifier) # Factory method for a user Entity.\\n def group(self, identifier) # Factory method for a group Entity.\\n def domain(self, domain) # Factory method for a domain Entity.\\n def all(self) # Factory method for an Entity representing all users.\\n def all_authenticated(self) # Factory method for an Entity representing all authenticated users.\\n def get_entities(self) # Get a list of all Entity objects.\\n def reload(self) # Reload the ACL data from Cloud Storage.\\n def save(self, acl=None) # A method to be overridden by subclasses.\\n def clear(self) # Remove all entities from the ACL.\\nclass BucketACL # An ACL specifically for a bucket.\\n def __init__(self, bucket) # :type bucket: :class:`gcloud.storage.bucket.Bucket`\\n def reload(self) # Reload the ACL data from Cloud Storage.\\n def save(self, acl=None) # Save this ACL for the current bucket.\\n def clear(self) # Remove all ACL entries.\\nclass DefaultObjectACL # A class representing the default object ACL for a bucket.\\nclass ObjectACL # An ACL specifically for a Cloud Storage object / blob.\\n def __init__(self, blob)\\n def reload(self) # Reload the ACL data from Cloud Storage.\\n def save(self, acl=None) # Save the ACL data for this blob.\\n def clear(self) # Remove all ACL rules from the blob.\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py\",\n \"text\": \"gcloud/storage/blob.py\\nclass Blob # A wrapper around Cloud Storage's concept of an ``Object``.\\n def __init__(self, name, bucket=None)\\n def path_helper(bucket_path, blob_name) # Relative URL path for a blob.\\n def acl(self) # Create our ACL on demand.\\n def __repr__(self)\\n def connection(self) # Getter property for the connection to use with this Blob.\\n def path(self) # Getter property for the URL path to this Blob.\\n def public_url(self) # The public URL for this blob's object.\\n def generate_signed_url(self, expiration, method='GET') # Generates a signed URL for this blob.\\n def exists(self) # Determines whether or not this blob exists.\\n def rename(self, new_name) # Renames this blob using copy and delete operations.\\n def delete(self) # Deletes a blob from Cloud Storage.\\n def download_to_file(self, file_obj) # Download the contents of this blob into a file-like object.\\n def download_to_filename(self, filename) # Download the contents of this blob into a named file.\\n def download_as_string(self) # Download the contents of this blob as a string.\\n def upload_from_file(self, file_obj, rewind=False, size=None, content_type=None, num_retries=6) # Upload the contents of this blob from a file-like object.\\n def upload_from_filename(self, filename, content_type=None) # Upload this blob's contents from the content of a named file.\\n def upload_from_string(self, data, content_type='text/plain') # Upload contents of this blob from the provided string.\\n def make_public(self) # Make this blob public giving all users read access.\\n def component_count(self) # Number of underlying components that make up this object.\\n def etag(self) # Retrieve the ETag for the object.\\n def generation(self) # Retrieve the generation for the object.\\n def id(self) # Retrieve the ID for the object.\\n def media_link(self) # Retrieve the media download URI for the object.\\n def metadata(self) # Retrieve arbitrary/application specific metadata for the object.\\n def metadata(self, value) # Update arbitrary/application specific metadata for the object.\\n def metageneration(self) # Retrieve the metageneration for the object.\\n def owner(self) # Retrieve info about the owner of the object.\\n def self_link(self) # Retrieve the URI for the object.\\n def size(self) # Size of the object, in bytes.\\n def storage_class(self) # Retrieve the storage class for the object.\\n def time_deleted(self) # Retrieve the timestamp at which the object was deleted.\\n def updated(self) # Retrieve the timestamp at which the object was updated.\\nclass _UploadConfig # Faux message FBO apitools' 'ConfigureRequest'.\\nclass _UrlBuilder # Faux builder FBO apitools' 'ConfigureRequest'\\n def __init__(self, bucket_name, object_name)\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/storage/bucket.py\",\n \"text\": \"gcloud/storage/bucket.py\\nclass _BlobIterator # An iterator listing blobs in a bucket\\n def __init__(self, bucket, extra_params=None)\\n def get_items_from_response(self, response) # Yield :class:`.storage.blob.Blob` items from response.\\nclass Bucket # A class representing a Bucket on Cloud Storage.\\n def __init__(self, name=None, connection=None)\\n def __repr__(self)\\n def __iter__(self)\\n def __contains__(self, blob_name)\\n def exists(self) # Determines whether or not this bucket exists.\\n def acl(self) # Create our ACL on demand.\\n def default_object_acl(self) # Create our defaultObjectACL on demand.\\n def connection(self) # Getter property for the connection to use with this Bucket.\\n def path_helper(bucket_name) # Relative URL path for a bucket.\\n def path(self) # The URL path to this bucket.\\n def get_blob(self, blob_name) # Get a blob object by name.\\n def get_all_blobs(self) # List all the blobs in this bucket.\\n def iterator(self, prefix=None, delimiter=None, max_results=None, versions=None) # Return an iterator used to find blobs in the bucket.\\n def delete(self, force=False) # Delete this bucket.\\n def delete_blob(self, blob_name) # Deletes a blob from the current bucket.\\n def delete_blobs(self, blobs, on_error=None) # Deletes a list of blobs from the current bucket.\\n def copy_blob(self, blob, destination_bucket, new_name=None) # Copy the given blob to the given bucket, optionally with a new name.\\n def upload_file(self, filename, blob_name=None) # Shortcut method to upload a file into this bucket.\\n def upload_file_object(self, file_obj, blob_name=None) # Shortcut method to upload a file object into this bucket.\\n def get_cors(self) # Retrieve CORS policies configured for this bucket.\\n def update_cors(self, entries) # Update CORS policies configured for this bucket.\\n def get_default_object_acl(self) # Get the current Default Object ACL rules.\\n def etag(self) # Retrieve the ETag for the bucket.\\n def id(self) # Retrieve the ID for the bucket.\\n def get_lifecycle(self) # Retrieve lifecycle rules configured for this bucket.\\n def update_lifecycle(self, rules) # Update CORS policies configured for this bucket.\\n def get_logging(self) # Return info about access logging for this bucket.\\n def enable_logging(self, bucket_name, object_prefix='') # Enable access logging for this bucket.\\n def disable_logging(self) # Disable access logging for this bucket.\\n def metageneration(self) # Retrieve the metageneration for the bucket.\\n def owner(self) # Retrieve info about the owner of the bucket.\\n def project_number(self) # Retrieve the number of the project to which the bucket is assigned.\\n def self_link(self) # Retrieve the URI for the bucket.\\n def storage_class(self) # Retrieve the storage class for the bucket.\\n def time_created(self) # Retrieve the timestamp at which the bucket was created.\\n def versioning_enabled(self) # Is ver\",\n \"label\": 1\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"gcloud/_helpers.py::_LocalStack.__init__","text":"gcloud/_helpers.py::_LocalStack.__init__\n def __init__(self):\n super(_LocalStack, self).__init__()\n self._stack = []","label":0},{"cid":"gcloud/_helpers.py::_LocalStack.__iter__","text":"gcloud/_helpers.py::_LocalStack.__iter__\n def __iter__(self):\n \"\"\"Iterate the stack in LIFO order.\n \"\"\"\n return iter(reversed(self._stack))","label":0},{"cid":"gcloud/_helpers.py::_LocalStack.push","text":"gcloud/_helpers.py::_LocalStack.push\n def push(self, resource):\n \"\"\"Push a resource onto our stack.\n \"\"\"\n self._stack.append(resource)","label":0},{"cid":"gcloud/_helpers.py::_LocalStack.pop","text":"gcloud/_helpers.py::_LocalStack.pop\n def pop(self):\n \"\"\"Pop a resource from our stack.\n\n :raises: IndexError if the stack is empty.\n :returns: the top-most resource, after removing it.\n \"\"\"\n return self._stack.pop()","label":0},{"cid":"gcloud/_helpers.py::_LocalStack.top","text":"gcloud/_helpers.py::_LocalStack.top\n @property\n def top(self):\n \"\"\"Get the top-most resource\n\n :returns: the top-most item, or None if the stack is empty.\n \"\"\"\n if len(self._stack) > 0:\n return self._stack[-1]","label":0},{"cid":"gcloud/_helpers.py::_LazyProperty.__init__","text":"gcloud/_helpers.py::_LazyProperty.__init__\n def __init__(self, name, deferred_callable):\n self._name = name\n self._deferred_callable = deferred_callable","label":0},{"cid":"gcloud/_helpers.py::_LazyProperty.__get__","text":"gcloud/_helpers.py::_LazyProperty.__get__\n def __get__(self, obj, objtype):\n if obj is None:\n return self\n\n setattr(obj, self._name, self._deferred_callable())\n return getattr(obj, self._name)","label":0},{"cid":"gcloud/_helpers.py::_lazy_property_deco","text":"gcloud/_helpers.py::_lazy_property_deco\ndef _lazy_property_deco(deferred_callable):\n \"\"\"Decorator a method to create a :class:`_LazyProperty`.\n\n :type deferred_callable: callable that takes no arguments\n :param deferred_callable: The function / method used to evaluate the\n property.\n\n :rtype: :class:`_LazyProperty`.\n :returns: A lazy property which defers the deferred_callable.\n \"\"\"\n if isinstance(deferred_callable, staticmethod):\n # H/T: http://stackoverflow.com/a/9527450/1068170\n # For Python2.7+ deferred_callable.__func__ would suffice.\n deferred_callable = deferred_callable.__get__(True)\n return _LazyProperty(deferred_callable.__name__, deferred_callable)","label":0},{"cid":"gcloud/_helpers.py::_app_engine_id","text":"gcloud/_helpers.py::_app_engine_id\ndef _app_engine_id():\n \"\"\"Gets the App Engine application ID if it can be inferred.\n\n :rtype: string or ``NoneType``\n :returns: App Engine application ID if running in App Engine,\n else ``None``.\n \"\"\"\n if app_identity is None:\n return None\n\n return app_identity.get_application_id()","label":0},{"cid":"gcloud/_helpers.py::_compute_engine_id","text":"gcloud/_helpers.py::_compute_engine_id\ndef _compute_engine_id():\n \"\"\"Gets the Compute Engine project ID if it can be inferred.\n\n Uses 169.254.169.254 for the metadata server to avoid request\n latency from DNS lookup.\n\n See https://cloud.google.com/compute/docs/metadata#metadataserver\n for information about this IP address. (This IP is also used for\n Amazon EC2 instances, so the metadata flavor is crucial.)\n\n See https://github.com/google/oauth2client/issues/93 for context about\n DNS latency.\n\n :rtype: string or ``NoneType``\n :returns: Compute Engine project ID if the metadata service is available,\n else ``None``.\n \"\"\"\n host = '169.254.169.254'\n uri_path = '/computeMetadata/v1/project/project-id'\n headers = {'Metadata-Flavor': 'Google'}\n connection = HTTPConnection(host, timeout=0.1)\n\n try:\n connection.request('GET', uri_path, headers=headers)\n response = connection.getresponse()\n if response.status == 200:\n return response.read()\n except socket.error: # socket.timeout or socket.error(64, 'Host is down')\n pass\n finally:\n connection.close()","label":0},{"cid":"gcloud/_helpers.py::_get_production_project","text":"gcloud/_helpers.py::_get_production_project\ndef _get_production_project():\n \"\"\"Gets the production project if it can be inferred.\"\"\"\n return os.getenv(_PROJECT_ENV_VAR_NAME)","label":0},{"cid":"gcloud/_helpers.py::_determine_default_project","text":"gcloud/_helpers.py::_determine_default_project\ndef _determine_default_project(project=None):\n \"\"\"Determine default project ID explicitly or implicitly as fall-back.\n\n In implicit case, currently only supports enviroment variable but will\n support App Engine, Compute Engine and other environments in the future.\n\n Local environment variable used is:\n - GCLOUD_PROJECT\n\n :type project: string\n :param project: Optional. The project name to use as default.\n\n :rtype: string or ``NoneType``\n :returns: Default project if it can be determined.\n \"\"\"\n if project is None:\n project = _get_production_project()\n\n return project","label":0},{"cid":"gcloud/_helpers.py::set_default_project","text":"gcloud/_helpers.py::set_default_project\ndef set_default_project(project=None):\n \"\"\"Set default project either explicitly or implicitly as fall-back.\n\n :type project: string\n :param project: Optional. The project name to use as default.\n\n :raises: :class:`EnvironmentError` if no project was found.\n \"\"\"\n project = _determine_default_project(project=project)\n if project is not None:\n _DEFAULTS.project = project\n else:\n raise EnvironmentError('No project could be inferred.')","label":0},{"cid":"gcloud/_helpers.py::get_default_project","text":"gcloud/_helpers.py::get_default_project\ndef get_default_project():\n \"\"\"Get default project.\n\n :rtype: string or ``NoneType``\n :returns: The default project if one has been set.\n \"\"\"\n return _DEFAULTS.project","label":0},{"cid":"gcloud/_helpers.py::_DefaultsContainer.project","text":"gcloud/_helpers.py::_DefaultsContainer.project\n @_lazy_property_deco\n @staticmethod\n def project():\n \"\"\"Return the implicit default project.\"\"\"\n return _determine_default_project()","label":0},{"cid":"gcloud/_helpers.py::_DefaultsContainer.__init__","text":"gcloud/_helpers.py::_DefaultsContainer.__init__\n def __init__(self, project=None, implicit=False):\n if project is not None or not implicit:\n self.project = project","label":0},{"cid":"gcloud/datastore/query.py::Query.__init__","text":"gcloud/datastore/query.py::Query.__init__\n def __init__(self,\n kind=None,\n dataset_id=None,\n namespace=None,\n ancestor=None,\n filters=(),\n projection=(),\n order=(),\n group_by=()):\n\n if dataset_id is None:\n dataset_id = _implicit_environ.get_default_dataset_id()\n\n if dataset_id is None:\n raise ValueError(\"No dataset ID supplied, and no default set.\")\n\n self._dataset_id = dataset_id\n self._kind = kind\n self._namespace = namespace\n self._ancestor = ancestor\n self._filters = list(filters)\n self._projection = list(projection)\n self._order = list(order)\n self._group_by = list(group_by)","label":0},{"cid":"gcloud/datastore/query.py::Query.dataset_id","text":"gcloud/datastore/query.py::Query.dataset_id\n @property\n def dataset_id(self):\n \"\"\"Get the dataset ID for this Query.\n\n :rtype: str\n \"\"\"\n return self._dataset_id","label":0},{"cid":"gcloud/datastore/query.py::Query.namespace","text":"gcloud/datastore/query.py::Query.namespace\n @property\n def namespace(self):\n \"\"\"This query's namespace\n\n :rtype: string or None\n :returns: the namespace assigned to this query\n \"\"\"\n return self._namespace","label":0},{"cid":"gcloud/datastore/query.py::Query.namespace","text":"gcloud/datastore/query.py::Query.namespace\n @namespace.setter\n def namespace(self, value):\n \"\"\"Update the query's namespace.\n\n :type value: string\n \"\"\"\n if not isinstance(value, str):\n raise ValueError(\"Namespace must be a string\")\n self._namespace = value","label":0},{"cid":"gcloud/datastore/query.py::Query.kind","text":"gcloud/datastore/query.py::Query.kind\n @property\n def kind(self):\n \"\"\"Get the Kind of the Query.\n\n :rtype: string\n \"\"\"\n return self._kind","label":0},{"cid":"gcloud/datastore/query.py::Query.kind","text":"gcloud/datastore/query.py::Query.kind\n @kind.setter\n def kind(self, value):\n \"\"\"Update the Kind of the Query.\n\n :type value: string\n :param value: updated kind for the query.\n\n .. note::\n\n The protobuf specification allows for ``kind`` to be repeated,\n but the current implementation returns an error if more than\n one value is passed. If the back-end changes in the future to\n allow multiple values, this method will be updated to allow passing\n either a string or a sequence of strings.\n \"\"\"\n if not isinstance(value, str):\n raise TypeError(\"Kind must be a string\")\n self._kind = value","label":0},{"cid":"gcloud/datastore/query.py::Query.ancestor","text":"gcloud/datastore/query.py::Query.ancestor\n @property\n def ancestor(self):\n \"\"\"The ancestor key for the query.\n\n :rtype: Key or None\n \"\"\"\n return self._ancestor","label":0},{"cid":"gcloud/datastore/query.py::Query.ancestor","text":"gcloud/datastore/query.py::Query.ancestor\n @ancestor.setter\n def ancestor(self, value):\n \"\"\"Set the ancestor for the query\n\n :type value: Key\n :param value: the new ancestor key\n \"\"\"\n if not isinstance(value, Key):\n raise TypeError(\"Ancestor must be a Key\")\n self._ancestor = value","label":0},{"cid":"gcloud/datastore/query.py::Query.ancestor","text":"gcloud/datastore/query.py::Query.ancestor\n @ancestor.deleter\n def ancestor(self):\n \"\"\"Remove the ancestor for the query.\"\"\"\n self._ancestor = None","label":0},{"cid":"gcloud/datastore/query.py::Query.filters","text":"gcloud/datastore/query.py::Query.filters\n @property\n def filters(self):\n \"\"\"Filters set on the query.\n\n :rtype: sequence of (property_name, operator, value) tuples.\n \"\"\"\n return self._filters[:]","label":0},{"cid":"gcloud/datastore/query.py::Query.add_filter","text":"gcloud/datastore/query.py::Query.add_filter\n def add_filter(self, property_name, operator, value):\n \"\"\"Filter the query based on a property name, operator and a value.\n\n Expressions take the form of::\n\n .add_filter('<property>', '<operator>', <value>)\n\n where property is a property stored on the entity in the datastore\n and operator is one of ``OPERATORS``\n (ie, ``=``, ``<``, ``<=``, ``>``, ``>=``)::\n\n >>> from gcloud import datastore\n >>> query = datastore.Query('Person')\n >>> query.add_filter('name', '=', 'James')\n >>> query.add_filter('age', '>', 50)\n\n :type property_name: string\n :param property_name: A property name.\n\n :type operator: string\n :param operator: One of ``=``, ``<``, ``<=``, ``>``, ``>=``.\n\n :type value: integer, string, boolean, float, None, datetime\n :param value: The value to filter on.\n\n :raises: :class:`ValueError` if ``operation`` is not one of the\n specified values, or if a filter names ``'__key__'`` but\n passes invalid operator (``==`` is required) or value (a key\n is required).\n \"\"\"\n if self.OPERATORS.get(operator) is None:\n error_message = 'Invalid expression: \"%s\"' % (operator,)\n choices_message = 'Please use one of: =, <, <=, >, >=.'\n raise ValueError(error_message, choices_message)\n\n if property_name == '__key__':\n if not isinstance(value, Key):\n raise ValueError('Invalid key: \"%s\"' % value)\n if operator != '=':\n raise ValueError('Invalid operator for key: \"%s\"' % operator)\n\n self._filters.append((property_name, operator, value))","label":0},{"cid":"gcloud/datastore/query.py::Query.projection","text":"gcloud/datastore/query.py::Query.projection\n @property\n def projection(self):\n \"\"\"Fields names returned by the query.\n\n :rtype: sequence of string\n :returns: Names of fields in query results.\n \"\"\"\n return self._projection[:]","label":0},{"cid":"gcloud/datastore/query.py::Query.projection","text":"gcloud/datastore/query.py::Query.projection\n @projection.setter\n def projection(self, projection):\n \"\"\"Set the fields returned the query.\n\n :type projection: string or sequence of strings\n :param projection: Each value is a string giving the name of a\n property to be included in the projection query.\n \"\"\"\n if isinstance(projection, str):\n projection = [projection]\n self._projection[:] = projection","label":0},{"cid":"gcloud/datastore/query.py::Query.keys_only","text":"gcloud/datastore/query.py::Query.keys_only\n def keys_only(self):\n \"\"\"Set the projection to include only keys.\"\"\"\n self._projection[:] = ['__key__']","label":0},{"cid":"gcloud/datastore/query.py::Query.order","text":"gcloud/datastore/query.py::Query.order\n @property\n def order(self):\n \"\"\"Names of fields used to sort query results.\n\n :rtype: sequence of string\n \"\"\"\n return self._order[:]","label":0},{"cid":"gcloud/datastore/query.py::Query.order","text":"gcloud/datastore/query.py::Query.order\n @order.setter\n def order(self, value):\n \"\"\"Set the fields used to sort query results.\n\n Sort fields will be applied in the order specified.\n\n :type value: string or sequence of strings\n :param value: Each value is a string giving the name of the\n property on which to sort, optionally preceded by a\n hyphen (-) to specify descending order.\n Omitting the hyphen implies ascending order.\n \"\"\"\n if isinstance(value, str):\n value = [value]\n self._order[:] = value","label":0},{"cid":"gcloud/datastore/query.py::Query.group_by","text":"gcloud/datastore/query.py::Query.group_by\n @property\n def group_by(self):\n \"\"\"Names of fields used to group query results.\n\n :rtype: sequence of string\n \"\"\"\n return self._group_by[:]","label":0},{"cid":"gcloud/datastore/query.py::Query.group_by","text":"gcloud/datastore/query.py::Query.group_by\n @group_by.setter\n def group_by(self, value):\n \"\"\"Set fields used to group query results.\n\n :type value: string or sequence of strings\n :param value: Each value is a string giving the name of a\n property to use to group results together.\n \"\"\"\n if isinstance(value, str):\n value = [value]\n self._group_by[:] = value","label":0},{"cid":"gcloud/datastore/query.py::Query.fetch","text":"gcloud/datastore/query.py::Query.fetch\n def fetch(self, limit=None, offset=0, start_cursor=None, end_cursor=None,\n connection=None):\n \"\"\"Execute the Query; return an iterator for the matching entities.\n\n For example::\n\n >>> from gcloud import datastore\n >>> query = datastore.Query('Person')\n >>> query.add_filter('name', '=', 'Sally')\n >>> list(query.fetch())\n [<Entity object>, <Entity object>, ...]\n >>> list(query.fetch(1))\n [<Entity object>]\n\n :type limit: integer or None\n :param limit: An optional limit passed through to the iterator.\n\n :type offset: integer\n :param offset: An optional offset passed through to the iterator.\n\n :type start_cursor: bytes\n :param start_cursor: An optional cursor passed through to the iterator.\n\n :type end_cursor: bytes\n :param end_cursor: An optional cursor passed through to the iterator.\n\n :type connection: :class:`gcloud.datastore.connection.Connection`\n :param connection: An optional cursor passed through to the iterator.\n If not supplied, uses the implicit default.\n\n :rtype: :class:`Iterator`\n :raises: ValueError if ``connection`` is not passed and no implicit\n default has been set.\n \"\"\"\n if connection is None:\n connection = _implicit_environ.get_default_connection()\n\n if connection is None:\n raise ValueError(\"No connection passed, and no default set\")\n\n return Iterator(\n self, connection, limit, offset, start_cursor, end_cursor)","label":0},{"cid":"gcloud/datastore/query.py::Iterator.__init__","text":"gcloud/datastore/query.py::Iterator.__init__\n def __init__(self, query, connection, limit=None, offset=0,\n start_cursor=None, end_cursor=None):\n self._query = query\n self._connection = connection\n self._limit = limit\n self._offset = offset\n self._start_cursor = start_cursor\n self._end_cursor = end_cursor\n self._page = self._more_results = None","label":0},{"cid":"gcloud/datastore/query.py::Iterator.next_page","text":"gcloud/datastore/query.py::Iterator.next_page\n def next_page(self):\n \"\"\"Fetch a single \"page\" of query results.\n\n Low-level API for fine control: the more convenient API is\n to iterate on the current Iterator.\n\n :rtype: tuple, (entities, more_results, cursor)\n \"\"\"\n pb = _pb_from_query(self._query)\n\n start_cursor = self._start_cursor\n if start_cursor is not None:\n pb.start_cursor = base64.b64decode(start_cursor)\n\n end_cursor = self._end_cursor\n if end_cursor is not None:\n pb.end_cursor = base64.b64decode(end_cursor)\n\n if self._limit is not None:\n pb.limit = self._limit\n\n pb.offset = self._offset\n\n transaction = Transaction.current()\n\n query_results = self._connection.run_query(\n query_pb=pb,\n dataset_id=self._query.dataset_id,\n namespace=self._query.namespace,\n transaction_id=transaction and transaction.id,\n )\n # NOTE: `query_results` contains an extra value that we don't use,\n # namely `skipped_results`.\n #\n # NOTE: The value of `more_results` is not currently useful because\n # the back-end always returns an enum\n # value of MORE_RESULTS_AFTER_LIMIT even if there are no more\n # results. See\n # https://github.com/GoogleCloudPlatform/gcloud-python/issues/280\n # for discussion.\n entity_pbs, cursor_as_bytes, more_results_enum = query_results[:3]\n\n self._start_cursor = base64.b64encode(cursor_as_bytes)\n self._end_cursor = None\n\n if more_results_enum == self._NOT_FINISHED:\n self._more_results = True\n elif more_results_enum in self._FINISHED:\n self._more_results = False\n else:\n raise ValueError('Unexpected value returned for `more_results`.')\n\n self._page = [\n helpers.entity_from_protobuf(entity)\n ","label":0},{"cid":"gcloud/datastore/query.py::Iterator.__iter__","text":"gcloud/datastore/query.py::Iterator.__iter__\n def __iter__(self):\n \"\"\"Generator yielding all results matching our query.\n\n :rtype: sequence of :class:`gcloud.datastore.entity.Entity`\n \"\"\"\n self.next_page()\n while True:\n for entity in self._page:\n yield entity\n if not self._more_results:\n break\n self.next_page()","label":0},{"cid":"gcloud/datastore/query.py::_pb_from_query","text":"gcloud/datastore/query.py::_pb_from_query\ndef _pb_from_query(query):\n \"\"\"Convert a Query instance to the corresponding protobuf.\n\n :type query: :class:`Query`\n :param query: The source query.\n\n :rtype: :class:`gcloud.datastore._datastore_v1_pb2.Query`\n :returns: A protobuf that can be sent to the protobuf API. N.b. that\n it does not contain \"in-flight\" fields for ongoing query\n executions (cursors, offset, limit).\n \"\"\"\n pb = datastore_pb.Query()\n\n for projection_name in query.projection:\n pb.projection.add().property.name = projection_name\n\n if query.kind:\n pb.kind.add().name = query.kind\n\n composite_filter = pb.filter.composite_filter\n composite_filter.operator = datastore_pb.CompositeFilter.AND\n\n if query.ancestor:\n ancestor_pb = helpers._prepare_key_for_request(\n query.ancestor.to_protobuf())\n\n # Filter on __key__ HAS_ANCESTOR == ancestor.\n ancestor_filter = composite_filter.filter.add().property_filter\n ancestor_filter.property.name = '__key__'\n ancestor_filter.operator = datastore_pb.PropertyFilter.HAS_ANCESTOR\n ancestor_filter.value.key_value.CopyFrom(ancestor_pb)\n\n for property_name, operator, value in query.filters:\n pb_op_enum = query.OPERATORS.get(operator)\n\n # Add the specific filter\n property_filter = composite_filter.filter.add().property_filter\n property_filter.property.name = property_name\n property_filter.operator = pb_op_enum\n\n # Set the value to filter on based on the type.\n if property_name == '__key__':\n key_pb = value.to_protobuf()\n property_filter.value.key_value.CopyFrom(\n helpers._prepare_key_for_request(key_pb))\n else:\n helpers._set_protobuf_value(property_filter.value, value)\n\n if not composite_filter.filter:\n pb.ClearField('filter')\n\n for prop in query.order:\n property_order = pb.order.a","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin.connection","text":"gcloud/storage/_helpers.py::_PropertyMixin.connection\n @property\n def connection(self):\n \"\"\"Abstract getter for the connection to use.\"\"\"\n raise NotImplementedError","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin.path","text":"gcloud/storage/_helpers.py::_PropertyMixin.path\n @property\n def path(self):\n \"\"\"Abstract getter for the object path.\"\"\"\n raise NotImplementedError","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin.__init__","text":"gcloud/storage/_helpers.py::_PropertyMixin.__init__\n def __init__(self, name=None):\n \"\"\"_PropertyMixin constructor.\n\n :type name: string\n :param name: The name of the object.\n \"\"\"\n self.name = name\n self._properties = {}\n self._changes = set()","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin.properties","text":"gcloud/storage/_helpers.py::_PropertyMixin.properties\n @property\n def properties(self):\n \"\"\"Return a copy of properties.\n\n :rtype: dict\n :returns: Copy of properties.\n \"\"\"\n return self._properties.copy()","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin.batch","text":"gcloud/storage/_helpers.py::_PropertyMixin.batch\n @property\n def batch(self):\n \"\"\"Return a context manager which defers/batches updates.\n\n E.g., to batch multiple updates to a bucket::\n\n >>> with bucket.batch:\n ... bucket.enable_versioning()\n ... bucket.disable_website()\n\n or for a blob::\n\n >>> with blob.batch:\n ... blob.content_type = 'image/jpeg'\n ... blob.content_encoding = 'gzip'\n\n Updates will be aggregated and sent as a single call to\n :meth:`_patch_properties` IFF the ``with`` block exits without\n an exception.\n\n :rtype: :class:`_PropertyBatch`\n \"\"\"\n return _PropertyBatch(self)","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin._reload_properties","text":"gcloud/storage/_helpers.py::_PropertyMixin._reload_properties\n def _reload_properties(self):\n \"\"\"Reload properties from Cloud Storage.\n\n :rtype: :class:`_PropertyMixin`\n :returns: The object you just reloaded data for.\n \"\"\"\n # Pass only '?projection=noAcl' here because 'acl' and related\n # are handled via custom endpoints.\n query_params = {'projection': 'noAcl'}\n self._properties = self.connection.api_request(\n method='GET', path=self.path, query_params=query_params)\n return self","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin._patch_properties","text":"gcloud/storage/_helpers.py::_PropertyMixin._patch_properties\n def _patch_properties(self, properties):\n \"\"\"Update particular fields of this object's properties.\n\n This method will only update the fields provided and will not\n touch the other fields.\n\n It **will not** reload the properties from the server. The behavior is\n local only and syncing occurs via :meth:`patch`.\n\n :type properties: dict\n :param properties: The dictionary of values to update.\n\n :rtype: :class:`_PropertyMixin`\n :returns: The current object.\n \"\"\"\n self._changes.update(properties.keys())\n self._properties.update(properties)\n return self","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyMixin.patch","text":"gcloud/storage/_helpers.py::_PropertyMixin.patch\n def patch(self):\n \"\"\"Sends all changed properties in a PATCH request.\n\n Updates the ``properties`` with the response from the backend.\n\n :rtype: :class:`Bucket`\n :returns: The current bucket.\n \"\"\"\n # Pass '?projection=full' here because 'PATCH' documented not\n # to work properly w/ 'noAcl'.\n update_properties = dict((key, self._properties[key])\n for key in self._changes)\n self._properties = self.connection.api_request(\n method='PATCH', path=self.path, data=update_properties,\n query_params={'projection': 'full'})\n return self","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyBatch.__init__","text":"gcloud/storage/_helpers.py::_PropertyBatch.__init__\n def __init__(self, wrapped):\n self._wrapped = wrapped","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyBatch.__enter__","text":"gcloud/storage/_helpers.py::_PropertyBatch.__enter__\n def __enter__(self):\n \"\"\"Do nothing method. Needed to be a context manager.\"\"\"","label":0},{"cid":"gcloud/storage/_helpers.py::_PropertyBatch.__exit__","text":"gcloud/storage/_helpers.py::_PropertyBatch.__exit__\n def __exit__(self, exc_type, value, traceback):\n \"\"\"Patch deferred property updates if no error.\"\"\"\n if exc_type is None:\n self._wrapped.patch()","label":0},{"cid":"gcloud/storage/_helpers.py::_scalar_property","text":"gcloud/storage/_helpers.py::_scalar_property\ndef _scalar_property(fieldname):\n \"\"\"Create a property descriptor around the :class:`_PropertyMixin` helpers.\n \"\"\"\n def _getter(self):\n \"\"\"Scalar property getter.\"\"\"\n return self.properties[fieldname]\n\n def _setter(self, value):\n \"\"\"Scalar property setter.\"\"\"\n self._patch_properties({fieldname: value})\n\n return property(_getter, _setter)","label":0},{"cid":"gcloud/storage/_helpers.py::_scalar_property._getter","text":"gcloud/storage/_helpers.py::_scalar_property._getter\n def _getter(self):\n \"\"\"Scalar property getter.\"\"\"\n return self.properties[fieldname]","label":0},{"cid":"gcloud/storage/_helpers.py::_scalar_property._setter","text":"gcloud/storage/_helpers.py::_scalar_property._setter\n def _setter(self, value):\n \"\"\"Scalar property setter.\"\"\"\n self._patch_properties({fieldname: value})","label":0},{"cid":"gcloud/storage/_helpers.py::_write_buffer_to_hash","text":"gcloud/storage/_helpers.py::_write_buffer_to_hash\ndef _write_buffer_to_hash(buffer_object, hash_obj, digest_block_size=8192):\n \"\"\"Read blocks from a buffer and update a hash with them.\n\n :type buffer_object: bytes buffer\n :param buffer_object: Buffer containing bytes used to update a hash object.\n \"\"\"\n block = buffer_object.read(digest_block_size)\n\n while len(block) > 0:\n hash_obj.update(block)\n # Update the block for the next iteration.\n block = buffer_object.read(digest_block_size)","label":0},{"cid":"gcloud/storage/_helpers.py::_base64_md5hash","text":"gcloud/storage/_helpers.py::_base64_md5hash\ndef _base64_md5hash(buffer_object):\n \"\"\"Get MD5 hash of bytes (as base64).\n\n :type buffer_object: bytes buffer\n :param buffer_object: Buffer containing bytes used to compute an MD5\n hash (as base64).\n \"\"\"\n hash_obj = MD5.new()\n _write_buffer_to_hash(buffer_object, hash_obj)\n digest_bytes = hash_obj.digest()\n return base64.b64encode(digest_bytes)","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.__init__","text":"gcloud/storage/acl.py::_ACLEntity.__init__\n def __init__(self, entity_type, identifier=None):\n \"\"\"Entity constructor.\n\n :type entity_type: string\n :param entity_type: The type of entity (ie, 'group' or 'user').\n\n :type identifier: string\n :param identifier: The ID or e-mail of the entity. For the special\n entity types (like 'allUsers') this is optional.\n \"\"\"\n self.identifier = identifier\n self.roles = set([])\n self.type = entity_type","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.__str__","text":"gcloud/storage/acl.py::_ACLEntity.__str__\n def __str__(self):\n if not self.identifier:\n return str(self.type)\n else:\n return '{acl.type}-{acl.identifier}'.format(acl=self)","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.__repr__","text":"gcloud/storage/acl.py::_ACLEntity.__repr__\n def __repr__(self):\n return '<ACL Entity: {acl} ({roles})>'.format(\n acl=self, roles=', '.join(self.roles))","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.get_roles","text":"gcloud/storage/acl.py::_ACLEntity.get_roles\n def get_roles(self):\n \"\"\"Get the list of roles permitted by this entity.\n\n :rtype: list of strings\n :returns: The list of roles associated with this entity.\n \"\"\"\n return self.roles","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.grant","text":"gcloud/storage/acl.py::_ACLEntity.grant\n def grant(self, role):\n \"\"\"Add a role to the entity.\n\n :type role: string\n :param role: The role to add to the entity.\n\n :rtype: :class:`_ACLEntity`\n :returns: The entity class.\n \"\"\"\n self.roles.add(role)\n return self","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.revoke","text":"gcloud/storage/acl.py::_ACLEntity.revoke\n def revoke(self, role):\n \"\"\"Remove a role from the entity.\n\n :type role: string\n :param role: The role to remove from the entity.\n\n :rtype: :class:`_ACLEntity`\n :returns: The entity class.\n \"\"\"\n if role in self.roles:\n self.roles.remove(role)\n return self","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.grant_read","text":"gcloud/storage/acl.py::_ACLEntity.grant_read\n def grant_read(self):\n \"\"\"Grant read access to the current entity.\"\"\"\n\n return self.grant(_ACLEntity.READER_ROLE)","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.grant_write","text":"gcloud/storage/acl.py::_ACLEntity.grant_write\n def grant_write(self):\n \"\"\"Grant write access to the current entity.\"\"\"\n\n return self.grant(_ACLEntity.WRITER_ROLE)","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.grant_owner","text":"gcloud/storage/acl.py::_ACLEntity.grant_owner\n def grant_owner(self):\n \"\"\"Grant owner access to the current entity.\"\"\"\n\n return self.grant(_ACLEntity.OWNER_ROLE)","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.revoke_read","text":"gcloud/storage/acl.py::_ACLEntity.revoke_read\n def revoke_read(self):\n \"\"\"Revoke read access from the current entity.\"\"\"\n\n return self.revoke(_ACLEntity.READER_ROLE)","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.revoke_write","text":"gcloud/storage/acl.py::_ACLEntity.revoke_write\n def revoke_write(self):\n \"\"\"Revoke write access from the current entity.\"\"\"\n\n return self.revoke(_ACLEntity.WRITER_ROLE)","label":0},{"cid":"gcloud/storage/acl.py::_ACLEntity.revoke_owner","text":"gcloud/storage/acl.py::_ACLEntity.revoke_owner\n def revoke_owner(self):\n \"\"\"Revoke owner access from the current entity.\"\"\"\n\n return self.revoke(_ACLEntity.OWNER_ROLE)","label":0},{"cid":"gcloud/storage/acl.py::ACL.__init__","text":"gcloud/storage/acl.py::ACL.__init__\n def __init__(self):\n self.entities = {}","label":0},{"cid":"gcloud/storage/acl.py::ACL._ensure_loaded","text":"gcloud/storage/acl.py::ACL._ensure_loaded\n def _ensure_loaded(self):\n \"\"\"Load if not already loaded.\"\"\"\n if not self.loaded:\n self.reload()","label":0},{"cid":"gcloud/storage/acl.py::ACL.reset","text":"gcloud/storage/acl.py::ACL.reset\n def reset(self):\n \"\"\"Remove all entities from the ACL, and clear the ``loaded`` flag.\"\"\"\n self.entities.clear()\n self.loaded = False","label":0},{"cid":"gcloud/storage/acl.py::ACL.__iter__","text":"gcloud/storage/acl.py::ACL.__iter__\n def __iter__(self):\n self._ensure_loaded()\n\n for entity in self.entities.values():\n for role in entity.get_roles():\n if role:\n yield {'entity': str(entity), 'role': role}","label":0},{"cid":"gcloud/storage/acl.py::ACL.entity_from_dict","text":"gcloud/storage/acl.py::ACL.entity_from_dict\n def entity_from_dict(self, entity_dict):\n \"\"\"Build an _ACLEntity object from a dictionary of data.\n\n An entity is a mutable object that represents a list of roles\n belonging to either a user or group or the special types for all\n users and all authenticated users.\n\n :type entity_dict: dict\n :param entity_dict: Dictionary full of data from an ACL lookup.\n\n :rtype: :class:`_ACLEntity`\n :returns: An Entity constructed from the dictionary.\n \"\"\"\n entity = entity_dict['entity']\n role = entity_dict['role']\n\n if entity == 'allUsers':\n entity = self.all()\n\n elif entity == 'allAuthenticatedUsers':\n entity = self.all_authenticated()\n\n elif '-' in entity:\n entity_type, identifier = entity.split('-', 1)\n entity = self.entity(entity_type=entity_type,\n identifier=identifier)\n\n if not isinstance(entity, _ACLEntity):\n raise ValueError('Invalid dictionary: %s' % entity_dict)\n\n return entity.grant(role)","label":0},{"cid":"gcloud/storage/acl.py::ACL.has_entity","text":"gcloud/storage/acl.py::ACL.has_entity\n def has_entity(self, entity):\n \"\"\"Returns whether or not this ACL has any entries for an entity.\n\n :type entity: :class:`_ACLEntity`\n :param entity: The entity to check for existence in this ACL.\n\n :rtype: boolean\n :returns: True of the entity exists in the ACL.\n \"\"\"\n self._ensure_loaded()\n return str(entity) in self.entities","label":0},{"cid":"gcloud/storage/acl.py::ACL.get_entity","text":"gcloud/storage/acl.py::ACL.get_entity\n def get_entity(self, entity, default=None):\n \"\"\"Gets an entity object from the ACL.\n\n :type entity: :class:`_ACLEntity` or string\n :param entity: The entity to get lookup in the ACL.\n\n :type default: anything\n :param default: This value will be returned if the entity\n doesn't exist.\n\n :rtype: :class:`_ACLEntity`\n :returns: The corresponding entity or the value provided\n to ``default``.\n \"\"\"\n self._ensure_loaded()\n return self.entities.get(str(entity), default)","label":0},{"cid":"gcloud/storage/acl.py::ACL.add_entity","text":"gcloud/storage/acl.py::ACL.add_entity\n def add_entity(self, entity):\n \"\"\"Add an entity to the ACL.\n\n :type entity: :class:`_ACLEntity`\n :param entity: The entity to add to this ACL.\n \"\"\"\n self._ensure_loaded()\n self.entities[str(entity)] = entity","label":0},{"cid":"gcloud/storage/acl.py::ACL.entity","text":"gcloud/storage/acl.py::ACL.entity\n def entity(self, entity_type, identifier=None):\n \"\"\"Factory method for creating an Entity.\n\n If an entity with the same type and identifier already exists,\n this will return a reference to that entity. If not, it will\n create a new one and add it to the list of known entities for\n this ACL.\n\n :type entity_type: string\n :param entity_type: The type of entity to create\n (ie, ``user``, ``group``, etc)\n\n :type identifier: string\n :param identifier: The ID of the entity (if applicable).\n This can be either an ID or an e-mail address.\n\n :rtype: :class:`_ACLEntity`\n :returns: A new Entity or a reference to an existing identical entity.\n \"\"\"\n entity = _ACLEntity(entity_type=entity_type, identifier=identifier)\n if self.has_entity(entity):\n entity = self.get_entity(entity)\n else:\n self.add_entity(entity)\n return entity","label":0},{"cid":"gcloud/storage/acl.py::ACL.user","text":"gcloud/storage/acl.py::ACL.user\n def user(self, identifier):\n \"\"\"Factory method for a user Entity.\n\n :type identifier: string\n :param identifier: An id or e-mail for this particular user.\n\n :rtype: :class:`_ACLEntity`\n :returns: An Entity corresponding to this user.\n \"\"\"\n return self.entity('user', identifier=identifier)","label":0},{"cid":"gcloud/storage/acl.py::ACL.group","text":"gcloud/storage/acl.py::ACL.group\n def group(self, identifier):\n \"\"\"Factory method for a group Entity.\n\n :type identifier: string\n :param identifier: An id or e-mail for this particular group.\n\n :rtype: :class:`_ACLEntity`\n :returns: An Entity corresponding to this group.\n \"\"\"\n return self.entity('group', identifier=identifier)","label":0},{"cid":"gcloud/storage/acl.py::ACL.domain","text":"gcloud/storage/acl.py::ACL.domain\n def domain(self, domain):\n \"\"\"Factory method for a domain Entity.\n\n :type domain: string\n :param domain: The domain for this entity.\n\n :rtype: :class:`_ACLEntity`\n :returns: An entity corresponding to this domain.\n \"\"\"\n return self.entity('domain', identifier=domain)","label":0},{"cid":"gcloud/storage/acl.py::ACL.all","text":"gcloud/storage/acl.py::ACL.all\n def all(self):\n \"\"\"Factory method for an Entity representing all users.\n\n :rtype: :class:`_ACLEntity`\n :returns: An entity representing all users.\n \"\"\"\n return self.entity('allUsers')","label":0},{"cid":"gcloud/storage/acl.py::ACL.all_authenticated","text":"gcloud/storage/acl.py::ACL.all_authenticated\n def all_authenticated(self):\n \"\"\"Factory method for an Entity representing all authenticated users.\n\n :rtype: :class:`_ACLEntity`\n :returns: An entity representing all authenticated users.\n \"\"\"\n return self.entity('allAuthenticatedUsers')","label":0},{"cid":"gcloud/storage/acl.py::ACL.get_entities","text":"gcloud/storage/acl.py::ACL.get_entities\n def get_entities(self):\n \"\"\"Get a list of all Entity objects.\n\n :rtype: list of :class:`_ACLEntity` objects\n :returns: A list of all Entity objects.\n \"\"\"\n self._ensure_loaded()\n return list(self.entities.values())","label":0},{"cid":"gcloud/storage/acl.py::ACL.reload","text":"gcloud/storage/acl.py::ACL.reload\n def reload(self):\n \"\"\"Reload the ACL data from Cloud Storage.\n\n :rtype: :class:`ACL`\n :returns: The current ACL.\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"gcloud/storage/acl.py::ACL.save","text":"gcloud/storage/acl.py::ACL.save\n def save(self, acl=None):\n \"\"\"A method to be overridden by subclasses.\n\n :type acl: :class:`gcloud.storage.acl.ACL`, or a compatible list.\n :param acl: The ACL object to save. If left blank, this will save\n current entries.\n\n :raises: NotImplementedError\n \"\"\"\n raise NotImplementedError","label":0},{"cid":"gcloud/storage/acl.py::ACL.clear","text":"gcloud/storage/acl.py::ACL.clear\n def clear(self):\n \"\"\"Remove all entities from the ACL.\"\"\"\n raise NotImplementedError","label":0},{"cid":"gcloud/storage/acl.py::BucketACL.__init__","text":"gcloud/storage/acl.py::BucketACL.__init__\n def __init__(self, bucket):\n \"\"\"\n :type bucket: :class:`gcloud.storage.bucket.Bucket`\n :param bucket: The bucket to which this ACL relates.\n \"\"\"\n super(BucketACL, self).__init__()\n self.bucket = bucket","label":0},{"cid":"gcloud/storage/acl.py::BucketACL.reload","text":"gcloud/storage/acl.py::BucketACL.reload\n def reload(self):\n \"\"\"Reload the ACL data from Cloud Storage.\n\n :rtype: :class:`gcloud.storage.acl.BucketACL`\n :returns: The current ACL.\n \"\"\"\n self.entities.clear()\n\n url_path = '%s/%s' % (self.bucket.path, self._URL_PATH_ELEM)\n found = self.bucket.connection.api_request(method='GET', path=url_path)\n self.loaded = True\n for entry in found.get('items', ()):\n self.add_entity(self.entity_from_dict(entry))\n\n return self","label":0},{"cid":"gcloud/storage/acl.py::BucketACL.save","text":"gcloud/storage/acl.py::BucketACL.save\n def save(self, acl=None):\n \"\"\"Save this ACL for the current bucket.\n\n If called without arguments, this will save the entries\n currently stored on this ACL::\n\n >>> acl.save()\n\n You can also provide a specific ACL to save instead of the one\n currently set on the Bucket object::\n\n >>> acl.save(acl=my_other_acl)\n\n You can use this to set access controls to be consistent from\n one bucket to another::\n\n >>> bucket1 = storage.get_bucket(bucket1_name, connection=connection)\n >>> bucket2 = storage.get_bucket(bucket2_name, connection=connection)\n >>> bucket2.acl.save(bucket1.acl)\n\n :type acl: :class:`gcloud.storage.acl.ACL`, or a compatible list.\n :param acl: The ACL object to save. If left blank, this will save\n current entries.\n\n :rtype: :class:`gcloud.storage.acl.BucketACL`\n :returns: The current ACL.\n \"\"\"\n if acl is None:\n acl = self\n save_to_backend = acl.loaded\n else:\n save_to_backend = True\n\n if save_to_backend:\n result = self.bucket.connection.api_request(\n method='PATCH', path=self.bucket.path,\n data={self._URL_PATH_ELEM: list(acl)},\n query_params={'projection': 'full'})\n self.entities.clear()\n for entry in result.get(self._URL_PATH_ELEM, ()):\n self.add_entity(self.entity_from_dict(entry))\n self.loaded = True\n\n return self","label":0},{"cid":"gcloud/storage/acl.py::BucketACL.clear","text":"gcloud/storage/acl.py::BucketACL.clear\n def clear(self):\n \"\"\"Remove all ACL entries.\n\n Note that this won't actually remove *ALL* the rules, but it\n will remove all the non-default rules. In short, you'll still\n have access to a bucket that you created even after you clear\n ACL rules with this method.\n\n For example, imagine that you granted access to this bucket to a\n bunch of coworkers::\n\n >>> acl.user('coworker1@example.org').grant_read()\n >>> acl.user('coworker2@example.org').grant_read()\n >>> acl.save()\n\n Now they work in another part of the company and you want to\n 'start fresh' on who has access::\n\n >>> acl.clear()\n\n At this point all the custom rules you created have been removed.\n\n :rtype: :class:`gcloud.storage.acl.BucketACL`\n :returns: The current ACL.\n \"\"\"\n return self.save([])","label":0},{"cid":"gcloud/storage/acl.py::ObjectACL.__init__","text":"gcloud/storage/acl.py::ObjectACL.__init__\n def __init__(self, blob):\n super(ObjectACL, self).__init__()\n self.blob = blob","label":0},{"cid":"gcloud/storage/acl.py::ObjectACL.reload","text":"gcloud/storage/acl.py::ObjectACL.reload\n def reload(self):\n \"\"\"Reload the ACL data from Cloud Storage.\n\n :rtype: :class:`ObjectACL`\n :returns: The current ACL.\n \"\"\"\n self.entities.clear()\n\n url_path = '%s/acl' % self.blob.path\n found = self.blob.connection.api_request(method='GET', path=url_path)\n self.loaded = True\n for entry in found.get('items', ()):\n self.add_entity(self.entity_from_dict(entry))\n\n return self","label":0},{"cid":"gcloud/storage/acl.py::ObjectACL.save","text":"gcloud/storage/acl.py::ObjectACL.save\n def save(self, acl=None):\n \"\"\"Save the ACL data for this blob.\n\n :type acl: :class:`gcloud.storage.acl.ACL`\n :param acl: The ACL object to save. If left blank, this will\n save the entries set locally on the ACL.\n \"\"\"\n if acl is None:\n acl = self\n save_to_backend = acl.loaded\n else:\n save_to_backend = True\n\n if save_to_backend:\n result = self.blob.connection.api_request(\n method='PATCH', path=self.blob.path, data={'acl': list(acl)},\n query_params={'projection': 'full'})\n self.entities.clear()\n for entry in result.get('acl', ()):\n self.add_entity(self.entity_from_dict(entry))\n self.loaded = True\n\n return self","label":0},{"cid":"gcloud/storage/acl.py::ObjectACL.clear","text":"gcloud/storage/acl.py::ObjectACL.clear\n def clear(self):\n \"\"\"Remove all ACL rules from the blob.\n\n Note that this won't actually remove *ALL* the rules, but it\n will remove all the non-default rules. In short, you'll still\n have access to a blob that you created even after you clear ACL\n rules with this method.\n \"\"\"\n return self.save([])","label":0},{"cid":"gcloud/storage/blob.py::Blob.__init__","text":"gcloud/storage/blob.py::Blob.__init__\n def __init__(self, name, bucket=None):\n if bucket is None:\n bucket = _implicit_environ.get_default_bucket()\n\n if bucket is None:\n raise ValueError('A Blob must have a bucket set.')\n\n super(Blob, self).__init__(name=name)\n\n self.bucket = bucket","label":1},{"cid":"gcloud/storage/blob.py::Blob.path_helper","text":"gcloud/storage/blob.py::Blob.path_helper\n @staticmethod\n def path_helper(bucket_path, blob_name):\n \"\"\"Relative URL path for a blob.\n\n :type bucket_path: string\n :param bucket_path: The URL path for a bucket.\n\n :type blob_name: string\n :param blob_name: The name of the blob.\n\n :rtype: string\n :returns: The relative URL path for ``blob_name``.\n \"\"\"\n return bucket_path + '/o/' + quote(blob_name, safe='')","label":0},{"cid":"gcloud/storage/blob.py::Blob.acl","text":"gcloud/storage/blob.py::Blob.acl\n @property\n def acl(self):\n \"\"\"Create our ACL on demand.\"\"\"\n if self._acl is None:\n self._acl = ObjectACL(self)\n return self._acl","label":1},{"cid":"gcloud/storage/blob.py::Blob.__repr__","text":"gcloud/storage/blob.py::Blob.__repr__\n def __repr__(self):\n if self.bucket:\n bucket_name = self.bucket.name\n else:\n bucket_name = None\n\n return '<Blob: %s, %s>' % (bucket_name, self.name)","label":0},{"cid":"gcloud/storage/blob.py::Blob.connection","text":"gcloud/storage/blob.py::Blob.connection\n @property\n def connection(self):\n \"\"\"Getter property for the connection to use with this Blob.\n\n :rtype: :class:`gcloud.storage.connection.Connection` or None\n :returns: The connection to use, or None if no connection is set.\n \"\"\"\n if self.bucket and self.bucket.connection:\n return self.bucket.connection","label":0},{"cid":"gcloud/storage/blob.py::Blob.path","text":"gcloud/storage/blob.py::Blob.path\n @property\n def path(self):\n \"\"\"Getter property for the URL path to this Blob.\n\n :rtype: string\n :returns: The URL path to this Blob.\n \"\"\"\n if not self.name:\n raise ValueError('Cannot determine path without a blob name.')\n\n return self.path_helper(self.bucket.path, self.name)","label":0},{"cid":"gcloud/storage/blob.py::Blob.public_url","text":"gcloud/storage/blob.py::Blob.public_url\n @property\n def public_url(self):\n \"\"\"The public URL for this blob's object.\n\n :rtype: `string`\n :returns: The public URL for this blob.\n \"\"\"\n return '{storage_base_url}/{bucket_name}/{quoted_name}'.format(\n storage_base_url='http://commondatastorage.googleapis.com',\n bucket_name=self.bucket.name,\n quoted_name=quote(self.name, safe=''))","label":0},{"cid":"gcloud/storage/blob.py::Blob.generate_signed_url","text":"gcloud/storage/blob.py::Blob.generate_signed_url\n def generate_signed_url(self, expiration, method='GET'):\n \"\"\"Generates a signed URL for this blob.\n\n If you have a blob that you want to allow access to for a set\n amount of time, you can use this method to generate a URL that\n is only valid within a certain time period.\n\n This is particularly useful if you don't want publicly\n accessible blobs, but don't want to require users to explicitly\n log in.\n\n :type expiration: int, long, datetime.datetime, datetime.timedelta\n :param expiration: When the signed URL should expire.\n\n :type method: string\n :param method: The HTTP verb that will be used when requesting the URL.\n\n :rtype: string\n :returns: A signed URL you can use to access the resource\n until expiration.\n \"\"\"\n resource = '/{bucket_name}/{quoted_name}'.format(\n bucket_name=self.bucket.name,\n quoted_name=quote(self.name, safe=''))\n\n return generate_signed_url(\n self.connection.credentials, resource=resource,\n api_access_endpoint=_API_ACCESS_ENDPOINT,\n expiration=expiration, method=method)","label":0},{"cid":"gcloud/storage/blob.py::Blob.exists","text":"gcloud/storage/blob.py::Blob.exists\n def exists(self):\n \"\"\"Determines whether or not this blob exists.\n\n :rtype: boolean\n :returns: True if the blob exists in Cloud Storage.\n \"\"\"\n try:\n # We only need the status code (200 or not) so we seek to\n # minimize the returned payload.\n query_params = {'fields': 'name'}\n self.connection.api_request(method='GET', path=self.path,\n query_params=query_params)\n return True\n except NotFound:\n return False","label":0},{"cid":"gcloud/storage/blob.py::Blob.rename","text":"gcloud/storage/blob.py::Blob.rename\n def rename(self, new_name):\n \"\"\"Renames this blob using copy and delete operations.\n\n Effectively, copies blob to the same bucket with a new name, then\n deletes the blob.\n\n .. warning::\n This method will first duplicate the data and then delete the\n old blob. This means that with very large objects renaming\n could be a very (temporarily) costly or a very slow operation.\n\n :type new_name: string\n :param new_name: The new name for this blob.\n\n :rtype: :class:`Blob`\n :returns: The newly-copied blob.\n \"\"\"\n new_blob = self.bucket.copy_blob(self, self.bucket, new_name)\n self.delete()\n return new_blob","label":0},{"cid":"gcloud/storage/blob.py::Blob.delete","text":"gcloud/storage/blob.py::Blob.delete\n def delete(self):\n \"\"\"Deletes a blob from Cloud Storage.\n\n :rtype: :class:`Blob`\n :returns: The blob that was just deleted.\n :raises: :class:`gcloud.exceptions.NotFound`\n (propagated from\n :meth:`gcloud.storage.bucket.Bucket.delete_blob`).\n \"\"\"\n return self.bucket.delete_blob(self.name)","label":0},{"cid":"gcloud/storage/blob.py::Blob.download_to_file","text":"gcloud/storage/blob.py::Blob.download_to_file\n def download_to_file(self, file_obj):\n \"\"\"Download the contents of this blob into a file-like object.\n\n :type file_obj: file\n :param file_obj: A file handle to which to write the blob's data.\n\n :raises: :class:`gcloud.exceptions.NotFound`\n \"\"\"\n\n download_url = self.media_link\n\n # Use apitools 'Download' facility.\n download = transfer.Download.FromStream(file_obj, auto_transfer=False)\n download.chunksize = self.CHUNK_SIZE\n headers = {'Range': 'bytes=0-%d' % (self.CHUNK_SIZE - 1)}\n request = http_wrapper.Request(download_url, 'GET', headers)\n\n download.InitializeDownload(request, self.connection.http)\n\n # Should we be passing callbacks through from caller? We can't\n # pass them as None, because apitools wants to print to the console\n # by default.\n download.StreamInChunks(callback=lambda *args: None,\n finish_callback=lambda *args: None)","label":0},{"cid":"gcloud/storage/blob.py::Blob.download_to_filename","text":"gcloud/storage/blob.py::Blob.download_to_filename\n def download_to_filename(self, filename):\n \"\"\"Download the contents of this blob into a named file.\n\n :type filename: string\n :param filename: A filename to be passed to ``open``.\n\n :raises: :class:`gcloud.exceptions.NotFound`\n \"\"\"\n with open(filename, 'wb') as file_obj:\n self.download_to_file(file_obj)\n\n mtime = time.mktime(\n datetime.datetime.strptime(\n self.properties['updated'],\n '%Y-%m-%dT%H:%M:%S.%fz').timetuple()\n )\n os.utime(file_obj.name, (mtime, mtime))","label":0},{"cid":"gcloud/storage/blob.py::Blob.download_as_string","text":"gcloud/storage/blob.py::Blob.download_as_string\n def download_as_string(self):\n \"\"\"Download the contents of this blob as a string.\n\n :rtype: bytes\n :returns: The data stored in this blob.\n :raises: :class:`gcloud.exceptions.NotFound`\n \"\"\"\n string_buffer = BytesIO()\n self.download_to_file(string_buffer)\n return string_buffer.getvalue()","label":0},{"cid":"gcloud/storage/blob.py::Blob.upload_from_file","text":"gcloud/storage/blob.py::Blob.upload_from_file\n def upload_from_file(self, file_obj, rewind=False, size=None,\n content_type=None, num_retries=6):\n \"\"\"Upload the contents of this blob from a file-like object.\n\n The content type of the upload will either be\n - The value passed in to the function (if any)\n - The value stored on the current blob\n - The default value of 'application/octet-stream'\n\n .. note::\n The effect of uploading to an existing blob depends on the\n \"versioning\" and \"lifecycle\" policies defined on the blob's\n bucket. In the absence of those policies, upload will\n overwrite any existing contents.\n\n See the `object versioning\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\n API documents for details.\n\n :type file_obj: file\n :param file_obj: A file handle open for reading.\n\n :type rewind: boolean\n :param rewind: If True, seek to the beginning of the file handle before\n writing the file to Cloud Storage.\n\n :type size: int\n :param size: The number of bytes to read from the file handle.\n If not provided, we'll try to guess the size using\n :func:`os.fstat`\n\n :type content_type: string or ``NoneType``\n :param content_type: Optional type of content being uploaded.\n\n :type num_retries: integer\n :param num_retries: Number of upload retries. Defaults to 6.\n \"\"\"\n content_type = (content_type or self._properties.get('contentType') or\n 'application/octet-stream')\n\n # Rewind the file if desired.\n if rewind:\n file_obj.seek(0, os.SEEK_SET)\n\n # Get the basic stats about the file.\n total_bytes = size or os.fstat(file_obj.fileno()).st_size\n ","label":0},{"cid":"gcloud/storage/blob.py::Blob.upload_from_filename","text":"gcloud/storage/blob.py::Blob.upload_from_filename\n def upload_from_filename(self, filename, content_type=None):\n \"\"\"Upload this blob's contents from the content of a named file.\n\n The content type of the upload will either be\n - The value passed in to the function (if any)\n - The value stored on the current blob\n - The value given by mimetypes.guess_type\n\n .. note::\n The effect of uploading to an existing blob depends on the\n \"versioning\" and \"lifecycle\" policies defined on the blob's\n bucket. In the absence of those policies, upload will\n overwrite any existing contents.\n\n See the `object versioning\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\n API documents for details.\n\n :type filename: string\n :param filename: The path to the file.\n\n :type content_type: string or ``NoneType``\n :param content_type: Optional type of content being uploaded.\n \"\"\"\n content_type = content_type or self._properties.get('contentType')\n if content_type is None:\n content_type, _ = mimetypes.guess_type(filename)\n\n with open(filename, 'rb') as file_obj:\n self.upload_from_file(file_obj, content_type=content_type)","label":0},{"cid":"gcloud/storage/blob.py::Blob.upload_from_string","text":"gcloud/storage/blob.py::Blob.upload_from_string\n def upload_from_string(self, data, content_type='text/plain'):\n \"\"\"Upload contents of this blob from the provided string.\n\n .. note::\n The effect of uploading to an existing blob depends on the\n \"versioning\" and \"lifecycle\" policies defined on the blob's\n bucket. In the absence of those policies, upload will\n overwrite any existing contents.\n\n See the `object versioning\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\n API documents for details.\n\n :type data: bytes or text\n :param data: The data to store in this blob. If the value is\n text, it will be encoded as UTF-8.\n \"\"\"\n if isinstance(data, six.text_type):\n data = data.encode('utf-8')\n string_buffer = BytesIO()\n string_buffer.write(data)\n self.upload_from_file(file_obj=string_buffer, rewind=True,\n size=len(data),\n content_type=content_type)","label":0},{"cid":"gcloud/storage/blob.py::Blob.make_public","text":"gcloud/storage/blob.py::Blob.make_public\n def make_public(self):\n \"\"\"Make this blob public giving all users read access.\n\n :returns: The current object.\n \"\"\"\n self.acl.all().grant_read()\n self.acl.save()\n return self","label":0},{"cid":"gcloud/storage/blob.py::Blob.component_count","text":"gcloud/storage/blob.py::Blob.component_count\n @property\n def component_count(self):\n \"\"\"Number of underlying components that make up this object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: integer\n \"\"\"\n return self.properties['componentCount']","label":0},{"cid":"gcloud/storage/blob.py::Blob.etag","text":"gcloud/storage/blob.py::Blob.etag\n @property\n def etag(self):\n \"\"\"Retrieve the ETag for the object.\n\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\n https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string\n \"\"\"\n return self.properties['etag']","label":0},{"cid":"gcloud/storage/blob.py::Blob.generation","text":"gcloud/storage/blob.py::Blob.generation\n @property\n def generation(self):\n \"\"\"Retrieve the generation for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: integer\n \"\"\"\n return self.properties['generation']","label":0},{"cid":"gcloud/storage/blob.py::Blob.id","text":"gcloud/storage/blob.py::Blob.id\n @property\n def id(self):\n \"\"\"Retrieve the ID for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string\n \"\"\"\n return self.properties['id']","label":0},{"cid":"gcloud/storage/blob.py::Blob.media_link","text":"gcloud/storage/blob.py::Blob.media_link\n @property\n def media_link(self):\n \"\"\"Retrieve the media download URI for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string\n \"\"\"\n return self.properties['mediaLink']","label":0},{"cid":"gcloud/storage/blob.py::Blob.metadata","text":"gcloud/storage/blob.py::Blob.metadata\n @property\n def metadata(self):\n \"\"\"Retrieve arbitrary/application specific metadata for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: dict\n \"\"\"\n return copy.deepcopy(self.properties['metadata'])","label":0},{"cid":"gcloud/storage/blob.py::Blob.metadata","text":"gcloud/storage/blob.py::Blob.metadata\n @metadata.setter\n def metadata(self, value):\n \"\"\"Update arbitrary/application specific metadata for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :type value: dict\n \"\"\"\n self._patch_properties({'metadata': value})","label":0},{"cid":"gcloud/storage/blob.py::Blob.metageneration","text":"gcloud/storage/blob.py::Blob.metageneration\n @property\n def metageneration(self):\n \"\"\"Retrieve the metageneration for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: integer\n \"\"\"\n return self.properties['metageneration']","label":0},{"cid":"gcloud/storage/blob.py::Blob.owner","text":"gcloud/storage/blob.py::Blob.owner\n @property\n def owner(self):\n \"\"\"Retrieve info about the owner of the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: dict\n :returns: mapping of owner's role/ID.\n \"\"\"\n return self.properties['owner'].copy()","label":0},{"cid":"gcloud/storage/blob.py::Blob.self_link","text":"gcloud/storage/blob.py::Blob.self_link\n @property\n def self_link(self):\n \"\"\"Retrieve the URI for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string\n \"\"\"\n return self.properties['selfLink']","label":0},{"cid":"gcloud/storage/blob.py::Blob.size","text":"gcloud/storage/blob.py::Blob.size\n @property\n def size(self):\n \"\"\"Size of the object, in bytes.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: integer\n \"\"\"\n return self.properties['size']","label":0},{"cid":"gcloud/storage/blob.py::Blob.storage_class","text":"gcloud/storage/blob.py::Blob.storage_class\n @property\n def storage_class(self):\n \"\"\"Retrieve the storage class for the object.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects and\n https://cloud.google.com/storage/docs/durable-reduced-availability#_DRA_Bucket\n\n :rtype: string\n :returns: Currently one of \"STANDARD\", \"DURABLE_REDUCED_AVAILABILITY\"\n \"\"\"\n return self.properties['storageClass']","label":0},{"cid":"gcloud/storage/blob.py::Blob.time_deleted","text":"gcloud/storage/blob.py::Blob.time_deleted\n @property\n def time_deleted(self):\n \"\"\"Retrieve the timestamp at which the object was deleted.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string or None\n :returns: timestamp in RFC 3339 format, or None if the object\n has a \"live\" version.\n \"\"\"\n return self.properties.get('timeDeleted')","label":0},{"cid":"gcloud/storage/blob.py::Blob.updated","text":"gcloud/storage/blob.py::Blob.updated\n @property\n def updated(self):\n \"\"\"Retrieve the timestamp at which the object was updated.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\n\n :rtype: string\n :returns: timestamp in RFC 3339 format.\n \"\"\"\n return self.properties['updated']","label":0},{"cid":"gcloud/storage/blob.py::_UrlBuilder.__init__","text":"gcloud/storage/blob.py::_UrlBuilder.__init__\n def __init__(self, bucket_name, object_name):\n self.query_params = {'name': object_name}\n self._bucket_name = bucket_name\n self._relative_path = ''","label":0},{"cid":"gcloud/storage/bucket.py::_BlobIterator.__init__","text":"gcloud/storage/bucket.py::_BlobIterator.__init__\n def __init__(self, bucket, extra_params=None):\n self.bucket = bucket\n self.prefixes = ()\n super(_BlobIterator, self).__init__(\n connection=bucket.connection, path=bucket.path + '/o',\n extra_params=extra_params)","label":0},{"cid":"gcloud/storage/bucket.py::_BlobIterator.get_items_from_response","text":"gcloud/storage/bucket.py::_BlobIterator.get_items_from_response\n def get_items_from_response(self, response):\n \"\"\"Yield :class:`.storage.blob.Blob` items from response.\n\n :type response: dict\n :param response: The JSON API response for a page of blobs.\n \"\"\"\n self.prefixes = tuple(response.get('prefixes', ()))\n for item in response.get('items', []):\n name = item.get('name')\n blob = Blob(name, bucket=self.bucket)\n blob._properties = item\n yield blob","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.__init__","text":"gcloud/storage/bucket.py::Bucket.__init__\n def __init__(self, name=None, connection=None):\n super(Bucket, self).__init__(name=name)\n self._connection = connection","label":1},{"cid":"gcloud/storage/bucket.py::Bucket.__repr__","text":"gcloud/storage/bucket.py::Bucket.__repr__\n def __repr__(self):\n return '<Bucket: %s>' % self.name","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.__iter__","text":"gcloud/storage/bucket.py::Bucket.__iter__\n def __iter__(self):\n return iter(self._iterator_class(bucket=self))","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.__contains__","text":"gcloud/storage/bucket.py::Bucket.__contains__\n def __contains__(self, blob_name):\n blob = Blob(blob_name, bucket=self)\n return blob.exists()","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.exists","text":"gcloud/storage/bucket.py::Bucket.exists\n def exists(self):\n \"\"\"Determines whether or not this bucket exists.\n\n :rtype: boolean\n :returns: True if the bucket exists in Cloud Storage.\n \"\"\"\n try:\n # We only need the status code (200 or not) so we seek to\n # minimize the returned payload.\n query_params = {'fields': 'name'}\n self.connection.api_request(method='GET', path=self.path,\n query_params=query_params)\n return True\n except NotFound:\n return False","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.acl","text":"gcloud/storage/bucket.py::Bucket.acl\n @property\n def acl(self):\n \"\"\"Create our ACL on demand.\"\"\"\n if self._acl is None:\n self._acl = BucketACL(self)\n return self._acl","label":1},{"cid":"gcloud/storage/bucket.py::Bucket.default_object_acl","text":"gcloud/storage/bucket.py::Bucket.default_object_acl\n @property\n def default_object_acl(self):\n \"\"\"Create our defaultObjectACL on demand.\"\"\"\n if self._default_object_acl is None:\n self._default_object_acl = DefaultObjectACL(self)\n return self._default_object_acl","label":1},{"cid":"gcloud/storage/bucket.py::Bucket.connection","text":"gcloud/storage/bucket.py::Bucket.connection\n @property\n def connection(self):\n \"\"\"Getter property for the connection to use with this Bucket.\n\n :rtype: :class:`gcloud.storage.connection.Connection`\n :returns: The connection to use.\n \"\"\"\n return self._connection","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.path_helper","text":"gcloud/storage/bucket.py::Bucket.path_helper\n @staticmethod\n def path_helper(bucket_name):\n \"\"\"Relative URL path for a bucket.\n\n :type bucket_name: string\n :param bucket_name: The bucket name in the path.\n\n :rtype: string\n :returns: The relative URL path for ``bucket_name``.\n \"\"\"\n return '/b/' + bucket_name","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.path","text":"gcloud/storage/bucket.py::Bucket.path\n @property\n def path(self):\n \"\"\"The URL path to this bucket.\"\"\"\n if not self.name:\n raise ValueError('Cannot determine path without bucket name.')\n\n return self.path_helper(self.name)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_blob","text":"gcloud/storage/bucket.py::Bucket.get_blob\n def get_blob(self, blob_name):\n \"\"\"Get a blob object by name.\n\n This will return None if the blob doesn't exist::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection()\n >>> bucket = storage.get_bucket('my-bucket', connection=connection)\n >>> print bucket.get_blob('/path/to/blob.txt')\n <Blob: my-bucket, /path/to/blob.txt>\n >>> print bucket.get_blob('/does-not-exist.txt')\n None\n\n :type blob_name: string\n :param blob_name: The name of the blob to retrieve.\n\n :rtype: :class:`gcloud.storage.blob.Blob` or None\n :returns: The blob object if it exists, otherwise None.\n \"\"\"\n blob = Blob(bucket=self, name=blob_name)\n try:\n response = self.connection.api_request(method='GET',\n path=blob.path)\n name = response.get('name') # Expect this to be blob_name\n blob = Blob(name, bucket=self)\n blob._properties = response\n return blob\n except NotFound:\n return None","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_all_blobs","text":"gcloud/storage/bucket.py::Bucket.get_all_blobs\n def get_all_blobs(self):\n \"\"\"List all the blobs in this bucket.\n\n This will **not** retrieve all the data for all the blobs, it\n will only retrieve the blob paths.\n\n This is equivalent to::\n\n blobs = [blob for blob in bucket]\n\n :rtype: list of :class:`gcloud.storage.blob.Blob`\n :returns: A list of all the Blob objects in this bucket.\n \"\"\"\n return list(self)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.iterator","text":"gcloud/storage/bucket.py::Bucket.iterator\n def iterator(self, prefix=None, delimiter=None, max_results=None,\n versions=None):\n \"\"\"Return an iterator used to find blobs in the bucket.\n\n :type prefix: string or None\n :param prefix: optional prefix used to filter blobs.\n\n :type delimiter: string or None\n :param delimiter: optional delimter, used with ``prefix`` to\n emulate hierarchy.\n\n :type max_results: integer or None\n :param max_results: maximum number of blobs to return.\n\n :type versions: boolean or None\n :param versions: whether object versions should be returned as\n separate blobs.\n\n :rtype: :class:`_BlobIterator`\n \"\"\"\n extra_params = {}\n\n if prefix is not None:\n extra_params['prefix'] = prefix\n\n if delimiter is not None:\n extra_params['delimiter'] = delimiter\n\n if max_results is not None:\n extra_params['maxResults'] = max_results\n\n if versions is not None:\n extra_params['versions'] = versions\n\n return self._iterator_class(self, extra_params=extra_params)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.delete","text":"gcloud/storage/bucket.py::Bucket.delete\n def delete(self, force=False):\n \"\"\"Delete this bucket.\n\n The bucket **must** be empty in order to submit a delete request. If\n ``force=True`` is passed, this will first attempt to delete all the\n objects / blobs in the bucket (i.e. try to empty the bucket).\n\n If the bucket doesn't exist, this will raise\n :class:`gcloud.exceptions.NotFound`. If the bucket is not empty\n (and ``force=False``), will raise :class:`gcloud.exceptions.Conflict`.\n\n If ``force=True`` and the bucket contains more than 256 objects / blobs\n this will cowardly refuse to delete the objects (or the bucket). This\n is to prevent accidental bucket deletion and to prevent extremely long\n runtime of this method.\n\n :type force: boolean\n :param force: If True, empties the bucket's objects then deletes it.\n\n :raises: :class:`ValueError` if ``force`` is ``True`` and the bucket\n contains more than 256 objects / blobs.\n \"\"\"\n if force:\n blobs = list(self.iterator(\n max_results=self._MAX_OBJECTS_FOR_BUCKET_DELETE + 1))\n if len(blobs) > self._MAX_OBJECTS_FOR_BUCKET_DELETE:\n message = (\n 'Refusing to delete bucket with more than '\n '%d objects. If you actually want to delete '\n 'this bucket, please delete the objects '\n 'yourself before calling Bucket.delete().'\n ) % (self._MAX_OBJECTS_FOR_BUCKET_DELETE,)\n raise ValueError(message)\n\n # Ignore 404 errors on delete.\n self.delete_blobs(blobs, on_error=lambda blob: None)\n\n self.connection.api_request(method='DELETE', path=self.path)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.delete_blob","text":"gcloud/storage/bucket.py::Bucket.delete_blob\n def delete_blob(self, blob_name):\n \"\"\"Deletes a blob from the current bucket.\n\n If the blob isn't found (backend 404), raises a\n :class:`gcloud.exceptions.NotFound`.\n\n For example::\n\n >>> from gcloud.exceptions import NotFound\n >>> from gcloud import storage\n >>> connection = storage.get_connection()\n >>> bucket = storage.get_bucket('my-bucket', connection=connection)\n >>> print bucket.get_all_blobs()\n [<Blob: my-bucket, my-file.txt>]\n >>> bucket.delete_blob('my-file.txt')\n >>> try:\n ... bucket.delete_blob('doesnt-exist')\n ... except NotFound:\n ... pass\n\n :type blob_name: string\n :param blob_name: A blob name to delete.\n\n :raises: :class:`gcloud.exceptions.NotFound` (to suppress\n the exception, call ``delete_blobs``, passing a no-op\n ``on_error`` callback, e.g.::\n\n >>> bucket.delete_blobs([blob], on_error=lambda blob: None)\n \"\"\"\n blob_path = Blob.path_helper(self.path, blob_name)\n self.connection.api_request(method='DELETE', path=blob_path)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.delete_blobs","text":"gcloud/storage/bucket.py::Bucket.delete_blobs\n def delete_blobs(self, blobs, on_error=None):\n \"\"\"Deletes a list of blobs from the current bucket.\n\n Uses :func:`Bucket.delete_blob` to delete each individual blob.\n\n :type blobs: list of string or :class:`gcloud.storage.blob.Blob`\n :param blobs: A list of blob names or Blob objects to delete.\n\n :type on_error: a callable taking (blob)\n :param on_error: If not ``None``, called once for each blob raising\n :class:`gcloud.exceptions.NotFound`;\n otherwise, the exception is propagated.\n\n :raises: :class:`gcloud.exceptions.NotFound` (if\n `on_error` is not passed).\n \"\"\"\n for blob in blobs:\n try:\n blob_name = blob\n if not isinstance(blob_name, six.string_types):\n blob_name = blob.name\n self.delete_blob(blob_name)\n except NotFound:\n if on_error is not None:\n on_error(blob)\n else:\n raise","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.copy_blob","text":"gcloud/storage/bucket.py::Bucket.copy_blob\n def copy_blob(self, blob, destination_bucket, new_name=None):\n \"\"\"Copy the given blob to the given bucket, optionally with a new name.\n\n :type blob: string or :class:`gcloud.storage.blob.Blob`\n :param blob: The blob to be copied.\n\n :type destination_bucket: :class:`gcloud.storage.bucket.Bucket`\n :param destination_bucket: The bucket into which the blob should be\n copied.\n\n :type new_name: string\n :param new_name: (optional) the new name for the copied file.\n\n :rtype: :class:`gcloud.storage.blob.Blob`\n :returns: The new Blob.\n \"\"\"\n if new_name is None:\n new_name = blob.name\n new_blob = Blob(bucket=destination_bucket, name=new_name)\n api_path = blob.path + '/copyTo' + new_blob.path\n copy_result = self.connection.api_request(method='POST', path=api_path)\n new_blob._properties = copy_result\n return new_blob","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.upload_file","text":"gcloud/storage/bucket.py::Bucket.upload_file\n def upload_file(self, filename, blob_name=None):\n \"\"\"Shortcut method to upload a file into this bucket.\n\n Use this method to quickly put a local file in Cloud Storage.\n\n For example::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection()\n >>> bucket = storage.get_bucket('my-bucket', connection=connection)\n >>> bucket.upload_file('~/my-file.txt', 'remote-text-file.txt')\n >>> print bucket.get_all_blobs()\n [<Blob: my-bucket, remote-text-file.txt>]\n\n If you don't provide a blob name, we will try to upload the file\n using the local filename (**not** the complete path)::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection()\n >>> bucket = storage.get_bucket('my-bucket', connection=connection)\n >>> bucket.upload_file('~/my-file.txt')\n >>> print bucket.get_all_blobs()\n [<Blob: my-bucket, my-file.txt>]\n\n :type filename: string\n :param filename: Local path to the file you want to upload.\n\n :type blob_name: string\n :param blob_name: The name of the blob to upload the file to. If this\n is blank, we will try to upload the file to the root\n of the bucket with the same name as on your local\n file system.\n\n :rtype: :class:`Blob`\n :returns: The updated Blob object.\n \"\"\"\n if blob_name is None:\n blob_name = os.path.basename(filename)\n blob = Blob(bucket=self, name=blob_name)\n blob.upload_from_filename(filename)\n return blob","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.upload_file_object","text":"gcloud/storage/bucket.py::Bucket.upload_file_object\n def upload_file_object(self, file_obj, blob_name=None):\n \"\"\"Shortcut method to upload a file object into this bucket.\n\n Use this method to quickly put a local file in Cloud Storage.\n\n For example::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection()\n >>> bucket = storage.get_bucket('my-bucket', connection=connection)\n >>> bucket.upload_file(open('~/my-file.txt'), 'remote-text-file.txt')\n >>> print bucket.get_all_blobs()\n [<Blob: my-bucket, remote-text-file.txt>]\n\n If you don't provide a blob name, we will try to upload the file\n using the local filename (**not** the complete path)::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection()\n >>> bucket = storage.get_bucket('my-bucket', connection=connection)\n >>> bucket.upload_file(open('~/my-file.txt'))\n >>> print bucket.get_all_blobs()\n [<Blob: my-bucket, my-file.txt>]\n\n :type file_obj: file\n :param file_obj: A file handle open for reading.\n\n :type blob_name: string\n :param blob_name: The name of the blob to upload the file to. If this\n is blank, we will try to upload the file to the root\n of the bucket with the same name as on your local\n file system.\n\n :rtype: :class:`Blob`\n :returns: The updated Blob object.\n \"\"\"\n if blob_name is None:\n blob_name = os.path.basename(file_obj.name)\n blob = Blob(bucket=self, name=blob_name)\n blob.upload_from_file(file_obj)\n return blob","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_cors","text":"gcloud/storage/bucket.py::Bucket.get_cors\n def get_cors(self):\n \"\"\"Retrieve CORS policies configured for this bucket.\n\n See: http://www.w3.org/TR/cors/ and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: list(dict)\n :returns: A sequence of mappings describing each CORS policy.\n \"\"\"\n return [policy.copy() for policy in self.properties.get('cors', ())]","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.update_cors","text":"gcloud/storage/bucket.py::Bucket.update_cors\n def update_cors(self, entries):\n \"\"\"Update CORS policies configured for this bucket.\n\n See: http://www.w3.org/TR/cors/ and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :type entries: list(dict)\n :param entries: A sequence of mappings describing each CORS policy.\n \"\"\"\n self._patch_properties({'cors': entries})\n self.patch()","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_default_object_acl","text":"gcloud/storage/bucket.py::Bucket.get_default_object_acl\n def get_default_object_acl(self):\n \"\"\"Get the current Default Object ACL rules.\n\n If the acl isn't available locally, this method will reload it from\n Cloud Storage.\n\n :rtype: :class:`gcloud.storage.acl.DefaultObjectACL`\n :returns: A DefaultObjectACL object for this bucket.\n \"\"\"\n if not self.default_object_acl.loaded:\n self.default_object_acl.reload()\n return self.default_object_acl","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.etag","text":"gcloud/storage/bucket.py::Bucket.etag\n @property\n def etag(self):\n \"\"\"Retrieve the ETag for the bucket.\n\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string\n \"\"\"\n return self.properties['etag']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.id","text":"gcloud/storage/bucket.py::Bucket.id\n @property\n def id(self):\n \"\"\"Retrieve the ID for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string\n \"\"\"\n return self.properties['id']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_lifecycle","text":"gcloud/storage/bucket.py::Bucket.get_lifecycle\n def get_lifecycle(self):\n \"\"\"Retrieve lifecycle rules configured for this bucket.\n\n See: https://cloud.google.com/storage/docs/lifecycle and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: list(dict)\n :returns: A sequence of mappings describing each lifecycle rule.\n \"\"\"\n info = self.properties.get('lifecycle', {})\n return [rule.copy() for rule in info.get('rule', ())]","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.update_lifecycle","text":"gcloud/storage/bucket.py::Bucket.update_lifecycle\n def update_lifecycle(self, rules):\n \"\"\"Update CORS policies configured for this bucket.\n\n See: https://cloud.google.com/storage/docs/lifecycle and\n https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :type rules: list(dict)\n :param rules: A sequence of mappings describing each lifecycle rule.\n \"\"\"\n self._patch_properties({'lifecycle': {'rule': rules}})\n self.patch()","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.get_logging","text":"gcloud/storage/bucket.py::Bucket.get_logging\n def get_logging(self):\n \"\"\"Return info about access logging for this bucket.\n\n See: https://cloud.google.com/storage/docs/accesslogs#status\n\n :rtype: dict or None\n :returns: a dict w/ keys, ``logBucket`` and ``logObjectPrefix``\n (if logging is enabled), or None (if not).\n \"\"\"\n self._reload_properties()\n info = self.properties.get('logging')\n if info is not None:\n return info.copy()","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.enable_logging","text":"gcloud/storage/bucket.py::Bucket.enable_logging\n def enable_logging(self, bucket_name, object_prefix=''):\n \"\"\"Enable access logging for this bucket.\n\n See: https://cloud.google.com/storage/docs/accesslogs#delivery\n\n :type bucket_name: string\n :param bucket_name: name of bucket in which to store access logs\n\n :type object_prefix: string\n :param object_prefix: prefix for access log filenames\n \"\"\"\n info = {'logBucket': bucket_name, 'logObjectPrefix': object_prefix}\n self._patch_properties({'logging': info})\n self.patch()","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.disable_logging","text":"gcloud/storage/bucket.py::Bucket.disable_logging\n def disable_logging(self):\n \"\"\"Disable access logging for this bucket.\n\n See: https://cloud.google.com/storage/docs/accesslogs#disabling\n \"\"\"\n self._patch_properties({'logging': None})\n self.patch()","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.metageneration","text":"gcloud/storage/bucket.py::Bucket.metageneration\n @property\n def metageneration(self):\n \"\"\"Retrieve the metageneration for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: integer\n \"\"\"\n return self.properties['metageneration']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.owner","text":"gcloud/storage/bucket.py::Bucket.owner\n @property\n def owner(self):\n \"\"\"Retrieve info about the owner of the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: dict\n :returns: mapping of owner's role/ID.\n \"\"\"\n return self.properties['owner'].copy()","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.project_number","text":"gcloud/storage/bucket.py::Bucket.project_number\n @property\n def project_number(self):\n \"\"\"Retrieve the number of the project to which the bucket is assigned.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: integer\n \"\"\"\n return self.properties['projectNumber']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.self_link","text":"gcloud/storage/bucket.py::Bucket.self_link\n @property\n def self_link(self):\n \"\"\"Retrieve the URI for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string\n \"\"\"\n return self.properties['selfLink']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.storage_class","text":"gcloud/storage/bucket.py::Bucket.storage_class\n @property\n def storage_class(self):\n \"\"\"Retrieve the storage class for the bucket.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets and\n https://cloud.google.com/storage/docs/durable-reduced-availability\n\n :rtype: string\n :returns: Currently one of \"STANDARD\", \"DURABLE_REDUCED_AVAILABILITY\"\n \"\"\"\n return self.properties['storageClass']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.time_created","text":"gcloud/storage/bucket.py::Bucket.time_created\n @property\n def time_created(self):\n \"\"\"Retrieve the timestamp at which the bucket was created.\n\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\n\n :rtype: string\n :returns: timestamp in RFC 3339 format.\n \"\"\"\n return self.properties['timeCreated']","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.versioning_enabled","text":"gcloud/storage/bucket.py::Bucket.versioning_enabled\n @property\n def versioning_enabled(self):\n \"\"\"Is versioning enabled for this bucket?\n\n See: https://cloud.google.com/storage/docs/object-versioning for\n details.\n\n :rtype: boolean\n :returns: True if enabled, else False.\n \"\"\"\n versioning = self.properties.get('versioning', {})\n return versioning.get('enabled', False)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.versioning_enabled","text":"gcloud/storage/bucket.py::Bucket.versioning_enabled\n @versioning_enabled.setter\n def versioning_enabled(self, value):\n \"\"\"Enable versioning for this bucket.\n\n See: https://cloud.google.com/storage/docs/object-versioning for\n details.\n\n :type value: convertible to boolean\n :param value: should versioning be anabled for the bucket?\n \"\"\"\n self._patch_properties({'versioning': {'enabled': bool(value)}})","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.configure_website","text":"gcloud/storage/bucket.py::Bucket.configure_website\n def configure_website(self, main_page_suffix=None, not_found_page=None):\n \"\"\"Configure website-related properties.\n\n See: https://developers.google.com/storage/docs/website-configuration\n\n .. note::\n This (apparently) only works\n if your bucket name is a domain name\n (and to do that, you need to get approved somehow...).\n\n If you want this bucket to host a website, just provide the name\n of an index page and a page to use when a blob isn't found::\n\n >>> from gcloud import storage\n >>> connection = storage.get_connection()\n >>> bucket = storage.get_bucket(bucket_name, connection=connection)\n >>> bucket.configure_website('index.html', '404.html')\n\n You probably should also make the whole bucket public::\n\n >>> bucket.make_public(recursive=True, future=True)\n\n This says: \"Make the bucket public, and all the stuff already in\n the bucket, and anything else I add to the bucket. Just make it\n all public.\"\n\n :type main_page_suffix: string\n :param main_page_suffix: The page to use as the main page\n of a directory.\n Typically something like index.html.\n\n :type not_found_page: string\n :param not_found_page: The file to use when a page isn't found.\n \"\"\"\n data = {\n 'website': {\n 'mainPageSuffix': main_page_suffix,\n 'notFoundPage': not_found_page,\n },\n }\n self._patch_properties(data)\n return self.patch()","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.disable_website","text":"gcloud/storage/bucket.py::Bucket.disable_website\n def disable_website(self):\n \"\"\"Disable the website configuration for this bucket.\n\n This is really just a shortcut for setting the website-related\n attributes to ``None``.\n \"\"\"\n return self.configure_website(None, None)","label":0},{"cid":"gcloud/storage/bucket.py::Bucket.make_public","text":"gcloud/storage/bucket.py::Bucket.make_public\n def make_public(self, recursive=False, future=False):\n \"\"\"Make a bucket public.\n\n :type recursive: boolean\n :param recursive: If True, this will make all blobs inside the bucket\n public as well.\n\n :type future: boolean\n :param future: If True, this will make all objects created in the\n future public as well.\n \"\"\"\n self.acl.all().grant_read()\n self.acl.save()\n\n if future:\n doa = self.get_default_object_acl()\n doa.all().grant_read()\n doa.save()\n\n if recursive:\n for blob in self:\n blob.acl.all().grant_read()\n blob.save_acl()","label":0}],"string":"[\n {\n \"cid\": \"gcloud/_helpers.py::_LocalStack.__init__\",\n \"text\": \"gcloud/_helpers.py::_LocalStack.__init__\\n def __init__(self):\\n super(_LocalStack, self).__init__()\\n self._stack = []\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::_LocalStack.__iter__\",\n \"text\": \"gcloud/_helpers.py::_LocalStack.__iter__\\n def __iter__(self):\\n \\\"\\\"\\\"Iterate the stack in LIFO order.\\n \\\"\\\"\\\"\\n return iter(reversed(self._stack))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::_LocalStack.push\",\n \"text\": \"gcloud/_helpers.py::_LocalStack.push\\n def push(self, resource):\\n \\\"\\\"\\\"Push a resource onto our stack.\\n \\\"\\\"\\\"\\n self._stack.append(resource)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::_LocalStack.pop\",\n \"text\": \"gcloud/_helpers.py::_LocalStack.pop\\n def pop(self):\\n \\\"\\\"\\\"Pop a resource from our stack.\\n\\n :raises: IndexError if the stack is empty.\\n :returns: the top-most resource, after removing it.\\n \\\"\\\"\\\"\\n return self._stack.pop()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::_LocalStack.top\",\n \"text\": \"gcloud/_helpers.py::_LocalStack.top\\n @property\\n def top(self):\\n \\\"\\\"\\\"Get the top-most resource\\n\\n :returns: the top-most item, or None if the stack is empty.\\n \\\"\\\"\\\"\\n if len(self._stack) > 0:\\n return self._stack[-1]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::_LazyProperty.__init__\",\n \"text\": \"gcloud/_helpers.py::_LazyProperty.__init__\\n def __init__(self, name, deferred_callable):\\n self._name = name\\n self._deferred_callable = deferred_callable\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::_LazyProperty.__get__\",\n \"text\": \"gcloud/_helpers.py::_LazyProperty.__get__\\n def __get__(self, obj, objtype):\\n if obj is None:\\n return self\\n\\n setattr(obj, self._name, self._deferred_callable())\\n return getattr(obj, self._name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::_lazy_property_deco\",\n \"text\": \"gcloud/_helpers.py::_lazy_property_deco\\ndef _lazy_property_deco(deferred_callable):\\n \\\"\\\"\\\"Decorator a method to create a :class:`_LazyProperty`.\\n\\n :type deferred_callable: callable that takes no arguments\\n :param deferred_callable: The function / method used to evaluate the\\n property.\\n\\n :rtype: :class:`_LazyProperty`.\\n :returns: A lazy property which defers the deferred_callable.\\n \\\"\\\"\\\"\\n if isinstance(deferred_callable, staticmethod):\\n # H/T: http://stackoverflow.com/a/9527450/1068170\\n # For Python2.7+ deferred_callable.__func__ would suffice.\\n deferred_callable = deferred_callable.__get__(True)\\n return _LazyProperty(deferred_callable.__name__, deferred_callable)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::_app_engine_id\",\n \"text\": \"gcloud/_helpers.py::_app_engine_id\\ndef _app_engine_id():\\n \\\"\\\"\\\"Gets the App Engine application ID if it can be inferred.\\n\\n :rtype: string or ``NoneType``\\n :returns: App Engine application ID if running in App Engine,\\n else ``None``.\\n \\\"\\\"\\\"\\n if app_identity is None:\\n return None\\n\\n return app_identity.get_application_id()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::_compute_engine_id\",\n \"text\": \"gcloud/_helpers.py::_compute_engine_id\\ndef _compute_engine_id():\\n \\\"\\\"\\\"Gets the Compute Engine project ID if it can be inferred.\\n\\n Uses 169.254.169.254 for the metadata server to avoid request\\n latency from DNS lookup.\\n\\n See https://cloud.google.com/compute/docs/metadata#metadataserver\\n for information about this IP address. (This IP is also used for\\n Amazon EC2 instances, so the metadata flavor is crucial.)\\n\\n See https://github.com/google/oauth2client/issues/93 for context about\\n DNS latency.\\n\\n :rtype: string or ``NoneType``\\n :returns: Compute Engine project ID if the metadata service is available,\\n else ``None``.\\n \\\"\\\"\\\"\\n host = '169.254.169.254'\\n uri_path = '/computeMetadata/v1/project/project-id'\\n headers = {'Metadata-Flavor': 'Google'}\\n connection = HTTPConnection(host, timeout=0.1)\\n\\n try:\\n connection.request('GET', uri_path, headers=headers)\\n response = connection.getresponse()\\n if response.status == 200:\\n return response.read()\\n except socket.error: # socket.timeout or socket.error(64, 'Host is down')\\n pass\\n finally:\\n connection.close()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::_get_production_project\",\n \"text\": \"gcloud/_helpers.py::_get_production_project\\ndef _get_production_project():\\n \\\"\\\"\\\"Gets the production project if it can be inferred.\\\"\\\"\\\"\\n return os.getenv(_PROJECT_ENV_VAR_NAME)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::_determine_default_project\",\n \"text\": \"gcloud/_helpers.py::_determine_default_project\\ndef _determine_default_project(project=None):\\n \\\"\\\"\\\"Determine default project ID explicitly or implicitly as fall-back.\\n\\n In implicit case, currently only supports enviroment variable but will\\n support App Engine, Compute Engine and other environments in the future.\\n\\n Local environment variable used is:\\n - GCLOUD_PROJECT\\n\\n :type project: string\\n :param project: Optional. The project name to use as default.\\n\\n :rtype: string or ``NoneType``\\n :returns: Default project if it can be determined.\\n \\\"\\\"\\\"\\n if project is None:\\n project = _get_production_project()\\n\\n return project\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::set_default_project\",\n \"text\": \"gcloud/_helpers.py::set_default_project\\ndef set_default_project(project=None):\\n \\\"\\\"\\\"Set default project either explicitly or implicitly as fall-back.\\n\\n :type project: string\\n :param project: Optional. The project name to use as default.\\n\\n :raises: :class:`EnvironmentError` if no project was found.\\n \\\"\\\"\\\"\\n project = _determine_default_project(project=project)\\n if project is not None:\\n _DEFAULTS.project = project\\n else:\\n raise EnvironmentError('No project could be inferred.')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::get_default_project\",\n \"text\": \"gcloud/_helpers.py::get_default_project\\ndef get_default_project():\\n \\\"\\\"\\\"Get default project.\\n\\n :rtype: string or ``NoneType``\\n :returns: The default project if one has been set.\\n \\\"\\\"\\\"\\n return _DEFAULTS.project\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::_DefaultsContainer.project\",\n \"text\": \"gcloud/_helpers.py::_DefaultsContainer.project\\n @_lazy_property_deco\\n @staticmethod\\n def project():\\n \\\"\\\"\\\"Return the implicit default project.\\\"\\\"\\\"\\n return _determine_default_project()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/_helpers.py::_DefaultsContainer.__init__\",\n \"text\": \"gcloud/_helpers.py::_DefaultsContainer.__init__\\n def __init__(self, project=None, implicit=False):\\n if project is not None or not implicit:\\n self.project = project\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.__init__\",\n \"text\": \"gcloud/datastore/query.py::Query.__init__\\n def __init__(self,\\n kind=None,\\n dataset_id=None,\\n namespace=None,\\n ancestor=None,\\n filters=(),\\n projection=(),\\n order=(),\\n group_by=()):\\n\\n if dataset_id is None:\\n dataset_id = _implicit_environ.get_default_dataset_id()\\n\\n if dataset_id is None:\\n raise ValueError(\\\"No dataset ID supplied, and no default set.\\\")\\n\\n self._dataset_id = dataset_id\\n self._kind = kind\\n self._namespace = namespace\\n self._ancestor = ancestor\\n self._filters = list(filters)\\n self._projection = list(projection)\\n self._order = list(order)\\n self._group_by = list(group_by)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.dataset_id\",\n \"text\": \"gcloud/datastore/query.py::Query.dataset_id\\n @property\\n def dataset_id(self):\\n \\\"\\\"\\\"Get the dataset ID for this Query.\\n\\n :rtype: str\\n \\\"\\\"\\\"\\n return self._dataset_id\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.namespace\",\n \"text\": \"gcloud/datastore/query.py::Query.namespace\\n @property\\n def namespace(self):\\n \\\"\\\"\\\"This query's namespace\\n\\n :rtype: string or None\\n :returns: the namespace assigned to this query\\n \\\"\\\"\\\"\\n return self._namespace\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.namespace\",\n \"text\": \"gcloud/datastore/query.py::Query.namespace\\n @namespace.setter\\n def namespace(self, value):\\n \\\"\\\"\\\"Update the query's namespace.\\n\\n :type value: string\\n \\\"\\\"\\\"\\n if not isinstance(value, str):\\n raise ValueError(\\\"Namespace must be a string\\\")\\n self._namespace = value\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.kind\",\n \"text\": \"gcloud/datastore/query.py::Query.kind\\n @property\\n def kind(self):\\n \\\"\\\"\\\"Get the Kind of the Query.\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self._kind\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.kind\",\n \"text\": \"gcloud/datastore/query.py::Query.kind\\n @kind.setter\\n def kind(self, value):\\n \\\"\\\"\\\"Update the Kind of the Query.\\n\\n :type value: string\\n :param value: updated kind for the query.\\n\\n .. note::\\n\\n The protobuf specification allows for ``kind`` to be repeated,\\n but the current implementation returns an error if more than\\n one value is passed. If the back-end changes in the future to\\n allow multiple values, this method will be updated to allow passing\\n either a string or a sequence of strings.\\n \\\"\\\"\\\"\\n if not isinstance(value, str):\\n raise TypeError(\\\"Kind must be a string\\\")\\n self._kind = value\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.ancestor\",\n \"text\": \"gcloud/datastore/query.py::Query.ancestor\\n @property\\n def ancestor(self):\\n \\\"\\\"\\\"The ancestor key for the query.\\n\\n :rtype: Key or None\\n \\\"\\\"\\\"\\n return self._ancestor\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.ancestor\",\n \"text\": \"gcloud/datastore/query.py::Query.ancestor\\n @ancestor.setter\\n def ancestor(self, value):\\n \\\"\\\"\\\"Set the ancestor for the query\\n\\n :type value: Key\\n :param value: the new ancestor key\\n \\\"\\\"\\\"\\n if not isinstance(value, Key):\\n raise TypeError(\\\"Ancestor must be a Key\\\")\\n self._ancestor = value\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.ancestor\",\n \"text\": \"gcloud/datastore/query.py::Query.ancestor\\n @ancestor.deleter\\n def ancestor(self):\\n \\\"\\\"\\\"Remove the ancestor for the query.\\\"\\\"\\\"\\n self._ancestor = None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.filters\",\n \"text\": \"gcloud/datastore/query.py::Query.filters\\n @property\\n def filters(self):\\n \\\"\\\"\\\"Filters set on the query.\\n\\n :rtype: sequence of (property_name, operator, value) tuples.\\n \\\"\\\"\\\"\\n return self._filters[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.add_filter\",\n \"text\": \"gcloud/datastore/query.py::Query.add_filter\\n def add_filter(self, property_name, operator, value):\\n \\\"\\\"\\\"Filter the query based on a property name, operator and a value.\\n\\n Expressions take the form of::\\n\\n .add_filter('<property>', '<operator>', <value>)\\n\\n where property is a property stored on the entity in the datastore\\n and operator is one of ``OPERATORS``\\n (ie, ``=``, ``<``, ``<=``, ``>``, ``>=``)::\\n\\n >>> from gcloud import datastore\\n >>> query = datastore.Query('Person')\\n >>> query.add_filter('name', '=', 'James')\\n >>> query.add_filter('age', '>', 50)\\n\\n :type property_name: string\\n :param property_name: A property name.\\n\\n :type operator: string\\n :param operator: One of ``=``, ``<``, ``<=``, ``>``, ``>=``.\\n\\n :type value: integer, string, boolean, float, None, datetime\\n :param value: The value to filter on.\\n\\n :raises: :class:`ValueError` if ``operation`` is not one of the\\n specified values, or if a filter names ``'__key__'`` but\\n passes invalid operator (``==`` is required) or value (a key\\n is required).\\n \\\"\\\"\\\"\\n if self.OPERATORS.get(operator) is None:\\n error_message = 'Invalid expression: \\\"%s\\\"' % (operator,)\\n choices_message = 'Please use one of: =, <, <=, >, >=.'\\n raise ValueError(error_message, choices_message)\\n\\n if property_name == '__key__':\\n if not isinstance(value, Key):\\n raise ValueError('Invalid key: \\\"%s\\\"' % value)\\n if operator != '=':\\n raise ValueError('Invalid operator for key: \\\"%s\\\"' % operator)\\n\\n self._filters.append((property_name, operator, value))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.projection\",\n \"text\": \"gcloud/datastore/query.py::Query.projection\\n @property\\n def projection(self):\\n \\\"\\\"\\\"Fields names returned by the query.\\n\\n :rtype: sequence of string\\n :returns: Names of fields in query results.\\n \\\"\\\"\\\"\\n return self._projection[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.projection\",\n \"text\": \"gcloud/datastore/query.py::Query.projection\\n @projection.setter\\n def projection(self, projection):\\n \\\"\\\"\\\"Set the fields returned the query.\\n\\n :type projection: string or sequence of strings\\n :param projection: Each value is a string giving the name of a\\n property to be included in the projection query.\\n \\\"\\\"\\\"\\n if isinstance(projection, str):\\n projection = [projection]\\n self._projection[:] = projection\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.keys_only\",\n \"text\": \"gcloud/datastore/query.py::Query.keys_only\\n def keys_only(self):\\n \\\"\\\"\\\"Set the projection to include only keys.\\\"\\\"\\\"\\n self._projection[:] = ['__key__']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.order\",\n \"text\": \"gcloud/datastore/query.py::Query.order\\n @property\\n def order(self):\\n \\\"\\\"\\\"Names of fields used to sort query results.\\n\\n :rtype: sequence of string\\n \\\"\\\"\\\"\\n return self._order[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.order\",\n \"text\": \"gcloud/datastore/query.py::Query.order\\n @order.setter\\n def order(self, value):\\n \\\"\\\"\\\"Set the fields used to sort query results.\\n\\n Sort fields will be applied in the order specified.\\n\\n :type value: string or sequence of strings\\n :param value: Each value is a string giving the name of the\\n property on which to sort, optionally preceded by a\\n hyphen (-) to specify descending order.\\n Omitting the hyphen implies ascending order.\\n \\\"\\\"\\\"\\n if isinstance(value, str):\\n value = [value]\\n self._order[:] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.group_by\",\n \"text\": \"gcloud/datastore/query.py::Query.group_by\\n @property\\n def group_by(self):\\n \\\"\\\"\\\"Names of fields used to group query results.\\n\\n :rtype: sequence of string\\n \\\"\\\"\\\"\\n return self._group_by[:]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.group_by\",\n \"text\": \"gcloud/datastore/query.py::Query.group_by\\n @group_by.setter\\n def group_by(self, value):\\n \\\"\\\"\\\"Set fields used to group query results.\\n\\n :type value: string or sequence of strings\\n :param value: Each value is a string giving the name of a\\n property to use to group results together.\\n \\\"\\\"\\\"\\n if isinstance(value, str):\\n value = [value]\\n self._group_by[:] = value\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Query.fetch\",\n \"text\": \"gcloud/datastore/query.py::Query.fetch\\n def fetch(self, limit=None, offset=0, start_cursor=None, end_cursor=None,\\n connection=None):\\n \\\"\\\"\\\"Execute the Query; return an iterator for the matching entities.\\n\\n For example::\\n\\n >>> from gcloud import datastore\\n >>> query = datastore.Query('Person')\\n >>> query.add_filter('name', '=', 'Sally')\\n >>> list(query.fetch())\\n [<Entity object>, <Entity object>, ...]\\n >>> list(query.fetch(1))\\n [<Entity object>]\\n\\n :type limit: integer or None\\n :param limit: An optional limit passed through to the iterator.\\n\\n :type offset: integer\\n :param offset: An optional offset passed through to the iterator.\\n\\n :type start_cursor: bytes\\n :param start_cursor: An optional cursor passed through to the iterator.\\n\\n :type end_cursor: bytes\\n :param end_cursor: An optional cursor passed through to the iterator.\\n\\n :type connection: :class:`gcloud.datastore.connection.Connection`\\n :param connection: An optional cursor passed through to the iterator.\\n If not supplied, uses the implicit default.\\n\\n :rtype: :class:`Iterator`\\n :raises: ValueError if ``connection`` is not passed and no implicit\\n default has been set.\\n \\\"\\\"\\\"\\n if connection is None:\\n connection = _implicit_environ.get_default_connection()\\n\\n if connection is None:\\n raise ValueError(\\\"No connection passed, and no default set\\\")\\n\\n return Iterator(\\n self, connection, limit, offset, start_cursor, end_cursor)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Iterator.__init__\",\n \"text\": \"gcloud/datastore/query.py::Iterator.__init__\\n def __init__(self, query, connection, limit=None, offset=0,\\n start_cursor=None, end_cursor=None):\\n self._query = query\\n self._connection = connection\\n self._limit = limit\\n self._offset = offset\\n self._start_cursor = start_cursor\\n self._end_cursor = end_cursor\\n self._page = self._more_results = None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Iterator.next_page\",\n \"text\": \"gcloud/datastore/query.py::Iterator.next_page\\n def next_page(self):\\n \\\"\\\"\\\"Fetch a single \\\"page\\\" of query results.\\n\\n Low-level API for fine control: the more convenient API is\\n to iterate on the current Iterator.\\n\\n :rtype: tuple, (entities, more_results, cursor)\\n \\\"\\\"\\\"\\n pb = _pb_from_query(self._query)\\n\\n start_cursor = self._start_cursor\\n if start_cursor is not None:\\n pb.start_cursor = base64.b64decode(start_cursor)\\n\\n end_cursor = self._end_cursor\\n if end_cursor is not None:\\n pb.end_cursor = base64.b64decode(end_cursor)\\n\\n if self._limit is not None:\\n pb.limit = self._limit\\n\\n pb.offset = self._offset\\n\\n transaction = Transaction.current()\\n\\n query_results = self._connection.run_query(\\n query_pb=pb,\\n dataset_id=self._query.dataset_id,\\n namespace=self._query.namespace,\\n transaction_id=transaction and transaction.id,\\n )\\n # NOTE: `query_results` contains an extra value that we don't use,\\n # namely `skipped_results`.\\n #\\n # NOTE: The value of `more_results` is not currently useful because\\n # the back-end always returns an enum\\n # value of MORE_RESULTS_AFTER_LIMIT even if there are no more\\n # results. See\\n # https://github.com/GoogleCloudPlatform/gcloud-python/issues/280\\n # for discussion.\\n entity_pbs, cursor_as_bytes, more_results_enum = query_results[:3]\\n\\n self._start_cursor = base64.b64encode(cursor_as_bytes)\\n self._end_cursor = None\\n\\n if more_results_enum == self._NOT_FINISHED:\\n self._more_results = True\\n elif more_results_enum in self._FINISHED:\\n self._more_results = False\\n else:\\n raise ValueError('Unexpected value returned for `more_results`.')\\n\\n self._page = [\\n helpers.entity_from_protobuf(entity)\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::Iterator.__iter__\",\n \"text\": \"gcloud/datastore/query.py::Iterator.__iter__\\n def __iter__(self):\\n \\\"\\\"\\\"Generator yielding all results matching our query.\\n\\n :rtype: sequence of :class:`gcloud.datastore.entity.Entity`\\n \\\"\\\"\\\"\\n self.next_page()\\n while True:\\n for entity in self._page:\\n yield entity\\n if not self._more_results:\\n break\\n self.next_page()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/datastore/query.py::_pb_from_query\",\n \"text\": \"gcloud/datastore/query.py::_pb_from_query\\ndef _pb_from_query(query):\\n \\\"\\\"\\\"Convert a Query instance to the corresponding protobuf.\\n\\n :type query: :class:`Query`\\n :param query: The source query.\\n\\n :rtype: :class:`gcloud.datastore._datastore_v1_pb2.Query`\\n :returns: A protobuf that can be sent to the protobuf API. N.b. that\\n it does not contain \\\"in-flight\\\" fields for ongoing query\\n executions (cursors, offset, limit).\\n \\\"\\\"\\\"\\n pb = datastore_pb.Query()\\n\\n for projection_name in query.projection:\\n pb.projection.add().property.name = projection_name\\n\\n if query.kind:\\n pb.kind.add().name = query.kind\\n\\n composite_filter = pb.filter.composite_filter\\n composite_filter.operator = datastore_pb.CompositeFilter.AND\\n\\n if query.ancestor:\\n ancestor_pb = helpers._prepare_key_for_request(\\n query.ancestor.to_protobuf())\\n\\n # Filter on __key__ HAS_ANCESTOR == ancestor.\\n ancestor_filter = composite_filter.filter.add().property_filter\\n ancestor_filter.property.name = '__key__'\\n ancestor_filter.operator = datastore_pb.PropertyFilter.HAS_ANCESTOR\\n ancestor_filter.value.key_value.CopyFrom(ancestor_pb)\\n\\n for property_name, operator, value in query.filters:\\n pb_op_enum = query.OPERATORS.get(operator)\\n\\n # Add the specific filter\\n property_filter = composite_filter.filter.add().property_filter\\n property_filter.property.name = property_name\\n property_filter.operator = pb_op_enum\\n\\n # Set the value to filter on based on the type.\\n if property_name == '__key__':\\n key_pb = value.to_protobuf()\\n property_filter.value.key_value.CopyFrom(\\n helpers._prepare_key_for_request(key_pb))\\n else:\\n helpers._set_protobuf_value(property_filter.value, value)\\n\\n if not composite_filter.filter:\\n pb.ClearField('filter')\\n\\n for prop in query.order:\\n property_order = pb.order.a\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin.connection\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin.connection\\n @property\\n def connection(self):\\n \\\"\\\"\\\"Abstract getter for the connection to use.\\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin.path\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"Abstract getter for the object path.\\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin.__init__\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin.__init__\\n def __init__(self, name=None):\\n \\\"\\\"\\\"_PropertyMixin constructor.\\n\\n :type name: string\\n :param name: The name of the object.\\n \\\"\\\"\\\"\\n self.name = name\\n self._properties = {}\\n self._changes = set()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin.properties\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin.properties\\n @property\\n def properties(self):\\n \\\"\\\"\\\"Return a copy of properties.\\n\\n :rtype: dict\\n :returns: Copy of properties.\\n \\\"\\\"\\\"\\n return self._properties.copy()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin.batch\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin.batch\\n @property\\n def batch(self):\\n \\\"\\\"\\\"Return a context manager which defers/batches updates.\\n\\n E.g., to batch multiple updates to a bucket::\\n\\n >>> with bucket.batch:\\n ... bucket.enable_versioning()\\n ... bucket.disable_website()\\n\\n or for a blob::\\n\\n >>> with blob.batch:\\n ... blob.content_type = 'image/jpeg'\\n ... blob.content_encoding = 'gzip'\\n\\n Updates will be aggregated and sent as a single call to\\n :meth:`_patch_properties` IFF the ``with`` block exits without\\n an exception.\\n\\n :rtype: :class:`_PropertyBatch`\\n \\\"\\\"\\\"\\n return _PropertyBatch(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin._reload_properties\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin._reload_properties\\n def _reload_properties(self):\\n \\\"\\\"\\\"Reload properties from Cloud Storage.\\n\\n :rtype: :class:`_PropertyMixin`\\n :returns: The object you just reloaded data for.\\n \\\"\\\"\\\"\\n # Pass only '?projection=noAcl' here because 'acl' and related\\n # are handled via custom endpoints.\\n query_params = {'projection': 'noAcl'}\\n self._properties = self.connection.api_request(\\n method='GET', path=self.path, query_params=query_params)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin._patch_properties\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin._patch_properties\\n def _patch_properties(self, properties):\\n \\\"\\\"\\\"Update particular fields of this object's properties.\\n\\n This method will only update the fields provided and will not\\n touch the other fields.\\n\\n It **will not** reload the properties from the server. The behavior is\\n local only and syncing occurs via :meth:`patch`.\\n\\n :type properties: dict\\n :param properties: The dictionary of values to update.\\n\\n :rtype: :class:`_PropertyMixin`\\n :returns: The current object.\\n \\\"\\\"\\\"\\n self._changes.update(properties.keys())\\n self._properties.update(properties)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyMixin.patch\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyMixin.patch\\n def patch(self):\\n \\\"\\\"\\\"Sends all changed properties in a PATCH request.\\n\\n Updates the ``properties`` with the response from the backend.\\n\\n :rtype: :class:`Bucket`\\n :returns: The current bucket.\\n \\\"\\\"\\\"\\n # Pass '?projection=full' here because 'PATCH' documented not\\n # to work properly w/ 'noAcl'.\\n update_properties = dict((key, self._properties[key])\\n for key in self._changes)\\n self._properties = self.connection.api_request(\\n method='PATCH', path=self.path, data=update_properties,\\n query_params={'projection': 'full'})\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyBatch.__init__\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyBatch.__init__\\n def __init__(self, wrapped):\\n self._wrapped = wrapped\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyBatch.__enter__\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyBatch.__enter__\\n def __enter__(self):\\n \\\"\\\"\\\"Do nothing method. Needed to be a context manager.\\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_PropertyBatch.__exit__\",\n \"text\": \"gcloud/storage/_helpers.py::_PropertyBatch.__exit__\\n def __exit__(self, exc_type, value, traceback):\\n \\\"\\\"\\\"Patch deferred property updates if no error.\\\"\\\"\\\"\\n if exc_type is None:\\n self._wrapped.patch()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_scalar_property\",\n \"text\": \"gcloud/storage/_helpers.py::_scalar_property\\ndef _scalar_property(fieldname):\\n \\\"\\\"\\\"Create a property descriptor around the :class:`_PropertyMixin` helpers.\\n \\\"\\\"\\\"\\n def _getter(self):\\n \\\"\\\"\\\"Scalar property getter.\\\"\\\"\\\"\\n return self.properties[fieldname]\\n\\n def _setter(self, value):\\n \\\"\\\"\\\"Scalar property setter.\\\"\\\"\\\"\\n self._patch_properties({fieldname: value})\\n\\n return property(_getter, _setter)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_scalar_property._getter\",\n \"text\": \"gcloud/storage/_helpers.py::_scalar_property._getter\\n def _getter(self):\\n \\\"\\\"\\\"Scalar property getter.\\\"\\\"\\\"\\n return self.properties[fieldname]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_scalar_property._setter\",\n \"text\": \"gcloud/storage/_helpers.py::_scalar_property._setter\\n def _setter(self, value):\\n \\\"\\\"\\\"Scalar property setter.\\\"\\\"\\\"\\n self._patch_properties({fieldname: value})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_write_buffer_to_hash\",\n \"text\": \"gcloud/storage/_helpers.py::_write_buffer_to_hash\\ndef _write_buffer_to_hash(buffer_object, hash_obj, digest_block_size=8192):\\n \\\"\\\"\\\"Read blocks from a buffer and update a hash with them.\\n\\n :type buffer_object: bytes buffer\\n :param buffer_object: Buffer containing bytes used to update a hash object.\\n \\\"\\\"\\\"\\n block = buffer_object.read(digest_block_size)\\n\\n while len(block) > 0:\\n hash_obj.update(block)\\n # Update the block for the next iteration.\\n block = buffer_object.read(digest_block_size)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/_helpers.py::_base64_md5hash\",\n \"text\": \"gcloud/storage/_helpers.py::_base64_md5hash\\ndef _base64_md5hash(buffer_object):\\n \\\"\\\"\\\"Get MD5 hash of bytes (as base64).\\n\\n :type buffer_object: bytes buffer\\n :param buffer_object: Buffer containing bytes used to compute an MD5\\n hash (as base64).\\n \\\"\\\"\\\"\\n hash_obj = MD5.new()\\n _write_buffer_to_hash(buffer_object, hash_obj)\\n digest_bytes = hash_obj.digest()\\n return base64.b64encode(digest_bytes)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.__init__\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.__init__\\n def __init__(self, entity_type, identifier=None):\\n \\\"\\\"\\\"Entity constructor.\\n\\n :type entity_type: string\\n :param entity_type: The type of entity (ie, 'group' or 'user').\\n\\n :type identifier: string\\n :param identifier: The ID or e-mail of the entity. For the special\\n entity types (like 'allUsers') this is optional.\\n \\\"\\\"\\\"\\n self.identifier = identifier\\n self.roles = set([])\\n self.type = entity_type\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.__str__\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.__str__\\n def __str__(self):\\n if not self.identifier:\\n return str(self.type)\\n else:\\n return '{acl.type}-{acl.identifier}'.format(acl=self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.__repr__\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.__repr__\\n def __repr__(self):\\n return '<ACL Entity: {acl} ({roles})>'.format(\\n acl=self, roles=', '.join(self.roles))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.get_roles\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.get_roles\\n def get_roles(self):\\n \\\"\\\"\\\"Get the list of roles permitted by this entity.\\n\\n :rtype: list of strings\\n :returns: The list of roles associated with this entity.\\n \\\"\\\"\\\"\\n return self.roles\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.grant\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.grant\\n def grant(self, role):\\n \\\"\\\"\\\"Add a role to the entity.\\n\\n :type role: string\\n :param role: The role to add to the entity.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: The entity class.\\n \\\"\\\"\\\"\\n self.roles.add(role)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.revoke\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.revoke\\n def revoke(self, role):\\n \\\"\\\"\\\"Remove a role from the entity.\\n\\n :type role: string\\n :param role: The role to remove from the entity.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: The entity class.\\n \\\"\\\"\\\"\\n if role in self.roles:\\n self.roles.remove(role)\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.grant_read\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.grant_read\\n def grant_read(self):\\n \\\"\\\"\\\"Grant read access to the current entity.\\\"\\\"\\\"\\n\\n return self.grant(_ACLEntity.READER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.grant_write\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.grant_write\\n def grant_write(self):\\n \\\"\\\"\\\"Grant write access to the current entity.\\\"\\\"\\\"\\n\\n return self.grant(_ACLEntity.WRITER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.grant_owner\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.grant_owner\\n def grant_owner(self):\\n \\\"\\\"\\\"Grant owner access to the current entity.\\\"\\\"\\\"\\n\\n return self.grant(_ACLEntity.OWNER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.revoke_read\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.revoke_read\\n def revoke_read(self):\\n \\\"\\\"\\\"Revoke read access from the current entity.\\\"\\\"\\\"\\n\\n return self.revoke(_ACLEntity.READER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.revoke_write\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.revoke_write\\n def revoke_write(self):\\n \\\"\\\"\\\"Revoke write access from the current entity.\\\"\\\"\\\"\\n\\n return self.revoke(_ACLEntity.WRITER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::_ACLEntity.revoke_owner\",\n \"text\": \"gcloud/storage/acl.py::_ACLEntity.revoke_owner\\n def revoke_owner(self):\\n \\\"\\\"\\\"Revoke owner access from the current entity.\\\"\\\"\\\"\\n\\n return self.revoke(_ACLEntity.OWNER_ROLE)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.__init__\",\n \"text\": \"gcloud/storage/acl.py::ACL.__init__\\n def __init__(self):\\n self.entities = {}\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL._ensure_loaded\",\n \"text\": \"gcloud/storage/acl.py::ACL._ensure_loaded\\n def _ensure_loaded(self):\\n \\\"\\\"\\\"Load if not already loaded.\\\"\\\"\\\"\\n if not self.loaded:\\n self.reload()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.reset\",\n \"text\": \"gcloud/storage/acl.py::ACL.reset\\n def reset(self):\\n \\\"\\\"\\\"Remove all entities from the ACL, and clear the ``loaded`` flag.\\\"\\\"\\\"\\n self.entities.clear()\\n self.loaded = False\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.__iter__\",\n \"text\": \"gcloud/storage/acl.py::ACL.__iter__\\n def __iter__(self):\\n self._ensure_loaded()\\n\\n for entity in self.entities.values():\\n for role in entity.get_roles():\\n if role:\\n yield {'entity': str(entity), 'role': role}\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.entity_from_dict\",\n \"text\": \"gcloud/storage/acl.py::ACL.entity_from_dict\\n def entity_from_dict(self, entity_dict):\\n \\\"\\\"\\\"Build an _ACLEntity object from a dictionary of data.\\n\\n An entity is a mutable object that represents a list of roles\\n belonging to either a user or group or the special types for all\\n users and all authenticated users.\\n\\n :type entity_dict: dict\\n :param entity_dict: Dictionary full of data from an ACL lookup.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An Entity constructed from the dictionary.\\n \\\"\\\"\\\"\\n entity = entity_dict['entity']\\n role = entity_dict['role']\\n\\n if entity == 'allUsers':\\n entity = self.all()\\n\\n elif entity == 'allAuthenticatedUsers':\\n entity = self.all_authenticated()\\n\\n elif '-' in entity:\\n entity_type, identifier = entity.split('-', 1)\\n entity = self.entity(entity_type=entity_type,\\n identifier=identifier)\\n\\n if not isinstance(entity, _ACLEntity):\\n raise ValueError('Invalid dictionary: %s' % entity_dict)\\n\\n return entity.grant(role)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.has_entity\",\n \"text\": \"gcloud/storage/acl.py::ACL.has_entity\\n def has_entity(self, entity):\\n \\\"\\\"\\\"Returns whether or not this ACL has any entries for an entity.\\n\\n :type entity: :class:`_ACLEntity`\\n :param entity: The entity to check for existence in this ACL.\\n\\n :rtype: boolean\\n :returns: True of the entity exists in the ACL.\\n \\\"\\\"\\\"\\n self._ensure_loaded()\\n return str(entity) in self.entities\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.get_entity\",\n \"text\": \"gcloud/storage/acl.py::ACL.get_entity\\n def get_entity(self, entity, default=None):\\n \\\"\\\"\\\"Gets an entity object from the ACL.\\n\\n :type entity: :class:`_ACLEntity` or string\\n :param entity: The entity to get lookup in the ACL.\\n\\n :type default: anything\\n :param default: This value will be returned if the entity\\n doesn't exist.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: The corresponding entity or the value provided\\n to ``default``.\\n \\\"\\\"\\\"\\n self._ensure_loaded()\\n return self.entities.get(str(entity), default)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.add_entity\",\n \"text\": \"gcloud/storage/acl.py::ACL.add_entity\\n def add_entity(self, entity):\\n \\\"\\\"\\\"Add an entity to the ACL.\\n\\n :type entity: :class:`_ACLEntity`\\n :param entity: The entity to add to this ACL.\\n \\\"\\\"\\\"\\n self._ensure_loaded()\\n self.entities[str(entity)] = entity\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.entity\",\n \"text\": \"gcloud/storage/acl.py::ACL.entity\\n def entity(self, entity_type, identifier=None):\\n \\\"\\\"\\\"Factory method for creating an Entity.\\n\\n If an entity with the same type and identifier already exists,\\n this will return a reference to that entity. If not, it will\\n create a new one and add it to the list of known entities for\\n this ACL.\\n\\n :type entity_type: string\\n :param entity_type: The type of entity to create\\n (ie, ``user``, ``group``, etc)\\n\\n :type identifier: string\\n :param identifier: The ID of the entity (if applicable).\\n This can be either an ID or an e-mail address.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: A new Entity or a reference to an existing identical entity.\\n \\\"\\\"\\\"\\n entity = _ACLEntity(entity_type=entity_type, identifier=identifier)\\n if self.has_entity(entity):\\n entity = self.get_entity(entity)\\n else:\\n self.add_entity(entity)\\n return entity\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.user\",\n \"text\": \"gcloud/storage/acl.py::ACL.user\\n def user(self, identifier):\\n \\\"\\\"\\\"Factory method for a user Entity.\\n\\n :type identifier: string\\n :param identifier: An id or e-mail for this particular user.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An Entity corresponding to this user.\\n \\\"\\\"\\\"\\n return self.entity('user', identifier=identifier)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.group\",\n \"text\": \"gcloud/storage/acl.py::ACL.group\\n def group(self, identifier):\\n \\\"\\\"\\\"Factory method for a group Entity.\\n\\n :type identifier: string\\n :param identifier: An id or e-mail for this particular group.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An Entity corresponding to this group.\\n \\\"\\\"\\\"\\n return self.entity('group', identifier=identifier)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.domain\",\n \"text\": \"gcloud/storage/acl.py::ACL.domain\\n def domain(self, domain):\\n \\\"\\\"\\\"Factory method for a domain Entity.\\n\\n :type domain: string\\n :param domain: The domain for this entity.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An entity corresponding to this domain.\\n \\\"\\\"\\\"\\n return self.entity('domain', identifier=domain)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.all\",\n \"text\": \"gcloud/storage/acl.py::ACL.all\\n def all(self):\\n \\\"\\\"\\\"Factory method for an Entity representing all users.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An entity representing all users.\\n \\\"\\\"\\\"\\n return self.entity('allUsers')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.all_authenticated\",\n \"text\": \"gcloud/storage/acl.py::ACL.all_authenticated\\n def all_authenticated(self):\\n \\\"\\\"\\\"Factory method for an Entity representing all authenticated users.\\n\\n :rtype: :class:`_ACLEntity`\\n :returns: An entity representing all authenticated users.\\n \\\"\\\"\\\"\\n return self.entity('allAuthenticatedUsers')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.get_entities\",\n \"text\": \"gcloud/storage/acl.py::ACL.get_entities\\n def get_entities(self):\\n \\\"\\\"\\\"Get a list of all Entity objects.\\n\\n :rtype: list of :class:`_ACLEntity` objects\\n :returns: A list of all Entity objects.\\n \\\"\\\"\\\"\\n self._ensure_loaded()\\n return list(self.entities.values())\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.reload\",\n \"text\": \"gcloud/storage/acl.py::ACL.reload\\n def reload(self):\\n \\\"\\\"\\\"Reload the ACL data from Cloud Storage.\\n\\n :rtype: :class:`ACL`\\n :returns: The current ACL.\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.save\",\n \"text\": \"gcloud/storage/acl.py::ACL.save\\n def save(self, acl=None):\\n \\\"\\\"\\\"A method to be overridden by subclasses.\\n\\n :type acl: :class:`gcloud.storage.acl.ACL`, or a compatible list.\\n :param acl: The ACL object to save. If left blank, this will save\\n current entries.\\n\\n :raises: NotImplementedError\\n \\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ACL.clear\",\n \"text\": \"gcloud/storage/acl.py::ACL.clear\\n def clear(self):\\n \\\"\\\"\\\"Remove all entities from the ACL.\\\"\\\"\\\"\\n raise NotImplementedError\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::BucketACL.__init__\",\n \"text\": \"gcloud/storage/acl.py::BucketACL.__init__\\n def __init__(self, bucket):\\n \\\"\\\"\\\"\\n :type bucket: :class:`gcloud.storage.bucket.Bucket`\\n :param bucket: The bucket to which this ACL relates.\\n \\\"\\\"\\\"\\n super(BucketACL, self).__init__()\\n self.bucket = bucket\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::BucketACL.reload\",\n \"text\": \"gcloud/storage/acl.py::BucketACL.reload\\n def reload(self):\\n \\\"\\\"\\\"Reload the ACL data from Cloud Storage.\\n\\n :rtype: :class:`gcloud.storage.acl.BucketACL`\\n :returns: The current ACL.\\n \\\"\\\"\\\"\\n self.entities.clear()\\n\\n url_path = '%s/%s' % (self.bucket.path, self._URL_PATH_ELEM)\\n found = self.bucket.connection.api_request(method='GET', path=url_path)\\n self.loaded = True\\n for entry in found.get('items', ()):\\n self.add_entity(self.entity_from_dict(entry))\\n\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::BucketACL.save\",\n \"text\": \"gcloud/storage/acl.py::BucketACL.save\\n def save(self, acl=None):\\n \\\"\\\"\\\"Save this ACL for the current bucket.\\n\\n If called without arguments, this will save the entries\\n currently stored on this ACL::\\n\\n >>> acl.save()\\n\\n You can also provide a specific ACL to save instead of the one\\n currently set on the Bucket object::\\n\\n >>> acl.save(acl=my_other_acl)\\n\\n You can use this to set access controls to be consistent from\\n one bucket to another::\\n\\n >>> bucket1 = storage.get_bucket(bucket1_name, connection=connection)\\n >>> bucket2 = storage.get_bucket(bucket2_name, connection=connection)\\n >>> bucket2.acl.save(bucket1.acl)\\n\\n :type acl: :class:`gcloud.storage.acl.ACL`, or a compatible list.\\n :param acl: The ACL object to save. If left blank, this will save\\n current entries.\\n\\n :rtype: :class:`gcloud.storage.acl.BucketACL`\\n :returns: The current ACL.\\n \\\"\\\"\\\"\\n if acl is None:\\n acl = self\\n save_to_backend = acl.loaded\\n else:\\n save_to_backend = True\\n\\n if save_to_backend:\\n result = self.bucket.connection.api_request(\\n method='PATCH', path=self.bucket.path,\\n data={self._URL_PATH_ELEM: list(acl)},\\n query_params={'projection': 'full'})\\n self.entities.clear()\\n for entry in result.get(self._URL_PATH_ELEM, ()):\\n self.add_entity(self.entity_from_dict(entry))\\n self.loaded = True\\n\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::BucketACL.clear\",\n \"text\": \"gcloud/storage/acl.py::BucketACL.clear\\n def clear(self):\\n \\\"\\\"\\\"Remove all ACL entries.\\n\\n Note that this won't actually remove *ALL* the rules, but it\\n will remove all the non-default rules. In short, you'll still\\n have access to a bucket that you created even after you clear\\n ACL rules with this method.\\n\\n For example, imagine that you granted access to this bucket to a\\n bunch of coworkers::\\n\\n >>> acl.user('coworker1@example.org').grant_read()\\n >>> acl.user('coworker2@example.org').grant_read()\\n >>> acl.save()\\n\\n Now they work in another part of the company and you want to\\n 'start fresh' on who has access::\\n\\n >>> acl.clear()\\n\\n At this point all the custom rules you created have been removed.\\n\\n :rtype: :class:`gcloud.storage.acl.BucketACL`\\n :returns: The current ACL.\\n \\\"\\\"\\\"\\n return self.save([])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ObjectACL.__init__\",\n \"text\": \"gcloud/storage/acl.py::ObjectACL.__init__\\n def __init__(self, blob):\\n super(ObjectACL, self).__init__()\\n self.blob = blob\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ObjectACL.reload\",\n \"text\": \"gcloud/storage/acl.py::ObjectACL.reload\\n def reload(self):\\n \\\"\\\"\\\"Reload the ACL data from Cloud Storage.\\n\\n :rtype: :class:`ObjectACL`\\n :returns: The current ACL.\\n \\\"\\\"\\\"\\n self.entities.clear()\\n\\n url_path = '%s/acl' % self.blob.path\\n found = self.blob.connection.api_request(method='GET', path=url_path)\\n self.loaded = True\\n for entry in found.get('items', ()):\\n self.add_entity(self.entity_from_dict(entry))\\n\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ObjectACL.save\",\n \"text\": \"gcloud/storage/acl.py::ObjectACL.save\\n def save(self, acl=None):\\n \\\"\\\"\\\"Save the ACL data for this blob.\\n\\n :type acl: :class:`gcloud.storage.acl.ACL`\\n :param acl: The ACL object to save. If left blank, this will\\n save the entries set locally on the ACL.\\n \\\"\\\"\\\"\\n if acl is None:\\n acl = self\\n save_to_backend = acl.loaded\\n else:\\n save_to_backend = True\\n\\n if save_to_backend:\\n result = self.blob.connection.api_request(\\n method='PATCH', path=self.blob.path, data={'acl': list(acl)},\\n query_params={'projection': 'full'})\\n self.entities.clear()\\n for entry in result.get('acl', ()):\\n self.add_entity(self.entity_from_dict(entry))\\n self.loaded = True\\n\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/acl.py::ObjectACL.clear\",\n \"text\": \"gcloud/storage/acl.py::ObjectACL.clear\\n def clear(self):\\n \\\"\\\"\\\"Remove all ACL rules from the blob.\\n\\n Note that this won't actually remove *ALL* the rules, but it\\n will remove all the non-default rules. In short, you'll still\\n have access to a blob that you created even after you clear ACL\\n rules with this method.\\n \\\"\\\"\\\"\\n return self.save([])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.__init__\",\n \"text\": \"gcloud/storage/blob.py::Blob.__init__\\n def __init__(self, name, bucket=None):\\n if bucket is None:\\n bucket = _implicit_environ.get_default_bucket()\\n\\n if bucket is None:\\n raise ValueError('A Blob must have a bucket set.')\\n\\n super(Blob, self).__init__(name=name)\\n\\n self.bucket = bucket\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.path_helper\",\n \"text\": \"gcloud/storage/blob.py::Blob.path_helper\\n @staticmethod\\n def path_helper(bucket_path, blob_name):\\n \\\"\\\"\\\"Relative URL path for a blob.\\n\\n :type bucket_path: string\\n :param bucket_path: The URL path for a bucket.\\n\\n :type blob_name: string\\n :param blob_name: The name of the blob.\\n\\n :rtype: string\\n :returns: The relative URL path for ``blob_name``.\\n \\\"\\\"\\\"\\n return bucket_path + '/o/' + quote(blob_name, safe='')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.acl\",\n \"text\": \"gcloud/storage/blob.py::Blob.acl\\n @property\\n def acl(self):\\n \\\"\\\"\\\"Create our ACL on demand.\\\"\\\"\\\"\\n if self._acl is None:\\n self._acl = ObjectACL(self)\\n return self._acl\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.__repr__\",\n \"text\": \"gcloud/storage/blob.py::Blob.__repr__\\n def __repr__(self):\\n if self.bucket:\\n bucket_name = self.bucket.name\\n else:\\n bucket_name = None\\n\\n return '<Blob: %s, %s>' % (bucket_name, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.connection\",\n \"text\": \"gcloud/storage/blob.py::Blob.connection\\n @property\\n def connection(self):\\n \\\"\\\"\\\"Getter property for the connection to use with this Blob.\\n\\n :rtype: :class:`gcloud.storage.connection.Connection` or None\\n :returns: The connection to use, or None if no connection is set.\\n \\\"\\\"\\\"\\n if self.bucket and self.bucket.connection:\\n return self.bucket.connection\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.path\",\n \"text\": \"gcloud/storage/blob.py::Blob.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"Getter property for the URL path to this Blob.\\n\\n :rtype: string\\n :returns: The URL path to this Blob.\\n \\\"\\\"\\\"\\n if not self.name:\\n raise ValueError('Cannot determine path without a blob name.')\\n\\n return self.path_helper(self.bucket.path, self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.public_url\",\n \"text\": \"gcloud/storage/blob.py::Blob.public_url\\n @property\\n def public_url(self):\\n \\\"\\\"\\\"The public URL for this blob's object.\\n\\n :rtype: `string`\\n :returns: The public URL for this blob.\\n \\\"\\\"\\\"\\n return '{storage_base_url}/{bucket_name}/{quoted_name}'.format(\\n storage_base_url='http://commondatastorage.googleapis.com',\\n bucket_name=self.bucket.name,\\n quoted_name=quote(self.name, safe=''))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.generate_signed_url\",\n \"text\": \"gcloud/storage/blob.py::Blob.generate_signed_url\\n def generate_signed_url(self, expiration, method='GET'):\\n \\\"\\\"\\\"Generates a signed URL for this blob.\\n\\n If you have a blob that you want to allow access to for a set\\n amount of time, you can use this method to generate a URL that\\n is only valid within a certain time period.\\n\\n This is particularly useful if you don't want publicly\\n accessible blobs, but don't want to require users to explicitly\\n log in.\\n\\n :type expiration: int, long, datetime.datetime, datetime.timedelta\\n :param expiration: When the signed URL should expire.\\n\\n :type method: string\\n :param method: The HTTP verb that will be used when requesting the URL.\\n\\n :rtype: string\\n :returns: A signed URL you can use to access the resource\\n until expiration.\\n \\\"\\\"\\\"\\n resource = '/{bucket_name}/{quoted_name}'.format(\\n bucket_name=self.bucket.name,\\n quoted_name=quote(self.name, safe=''))\\n\\n return generate_signed_url(\\n self.connection.credentials, resource=resource,\\n api_access_endpoint=_API_ACCESS_ENDPOINT,\\n expiration=expiration, method=method)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.exists\",\n \"text\": \"gcloud/storage/blob.py::Blob.exists\\n def exists(self):\\n \\\"\\\"\\\"Determines whether or not this blob exists.\\n\\n :rtype: boolean\\n :returns: True if the blob exists in Cloud Storage.\\n \\\"\\\"\\\"\\n try:\\n # We only need the status code (200 or not) so we seek to\\n # minimize the returned payload.\\n query_params = {'fields': 'name'}\\n self.connection.api_request(method='GET', path=self.path,\\n query_params=query_params)\\n return True\\n except NotFound:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.rename\",\n \"text\": \"gcloud/storage/blob.py::Blob.rename\\n def rename(self, new_name):\\n \\\"\\\"\\\"Renames this blob using copy and delete operations.\\n\\n Effectively, copies blob to the same bucket with a new name, then\\n deletes the blob.\\n\\n .. warning::\\n This method will first duplicate the data and then delete the\\n old blob. This means that with very large objects renaming\\n could be a very (temporarily) costly or a very slow operation.\\n\\n :type new_name: string\\n :param new_name: The new name for this blob.\\n\\n :rtype: :class:`Blob`\\n :returns: The newly-copied blob.\\n \\\"\\\"\\\"\\n new_blob = self.bucket.copy_blob(self, self.bucket, new_name)\\n self.delete()\\n return new_blob\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.delete\",\n \"text\": \"gcloud/storage/blob.py::Blob.delete\\n def delete(self):\\n \\\"\\\"\\\"Deletes a blob from Cloud Storage.\\n\\n :rtype: :class:`Blob`\\n :returns: The blob that was just deleted.\\n :raises: :class:`gcloud.exceptions.NotFound`\\n (propagated from\\n :meth:`gcloud.storage.bucket.Bucket.delete_blob`).\\n \\\"\\\"\\\"\\n return self.bucket.delete_blob(self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.download_to_file\",\n \"text\": \"gcloud/storage/blob.py::Blob.download_to_file\\n def download_to_file(self, file_obj):\\n \\\"\\\"\\\"Download the contents of this blob into a file-like object.\\n\\n :type file_obj: file\\n :param file_obj: A file handle to which to write the blob's data.\\n\\n :raises: :class:`gcloud.exceptions.NotFound`\\n \\\"\\\"\\\"\\n\\n download_url = self.media_link\\n\\n # Use apitools 'Download' facility.\\n download = transfer.Download.FromStream(file_obj, auto_transfer=False)\\n download.chunksize = self.CHUNK_SIZE\\n headers = {'Range': 'bytes=0-%d' % (self.CHUNK_SIZE - 1)}\\n request = http_wrapper.Request(download_url, 'GET', headers)\\n\\n download.InitializeDownload(request, self.connection.http)\\n\\n # Should we be passing callbacks through from caller? We can't\\n # pass them as None, because apitools wants to print to the console\\n # by default.\\n download.StreamInChunks(callback=lambda *args: None,\\n finish_callback=lambda *args: None)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.download_to_filename\",\n \"text\": \"gcloud/storage/blob.py::Blob.download_to_filename\\n def download_to_filename(self, filename):\\n \\\"\\\"\\\"Download the contents of this blob into a named file.\\n\\n :type filename: string\\n :param filename: A filename to be passed to ``open``.\\n\\n :raises: :class:`gcloud.exceptions.NotFound`\\n \\\"\\\"\\\"\\n with open(filename, 'wb') as file_obj:\\n self.download_to_file(file_obj)\\n\\n mtime = time.mktime(\\n datetime.datetime.strptime(\\n self.properties['updated'],\\n '%Y-%m-%dT%H:%M:%S.%fz').timetuple()\\n )\\n os.utime(file_obj.name, (mtime, mtime))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.download_as_string\",\n \"text\": \"gcloud/storage/blob.py::Blob.download_as_string\\n def download_as_string(self):\\n \\\"\\\"\\\"Download the contents of this blob as a string.\\n\\n :rtype: bytes\\n :returns: The data stored in this blob.\\n :raises: :class:`gcloud.exceptions.NotFound`\\n \\\"\\\"\\\"\\n string_buffer = BytesIO()\\n self.download_to_file(string_buffer)\\n return string_buffer.getvalue()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.upload_from_file\",\n \"text\": \"gcloud/storage/blob.py::Blob.upload_from_file\\n def upload_from_file(self, file_obj, rewind=False, size=None,\\n content_type=None, num_retries=6):\\n \\\"\\\"\\\"Upload the contents of this blob from a file-like object.\\n\\n The content type of the upload will either be\\n - The value passed in to the function (if any)\\n - The value stored on the current blob\\n - The default value of 'application/octet-stream'\\n\\n .. note::\\n The effect of uploading to an existing blob depends on the\\n \\\"versioning\\\" and \\\"lifecycle\\\" policies defined on the blob's\\n bucket. In the absence of those policies, upload will\\n overwrite any existing contents.\\n\\n See the `object versioning\\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\\n API documents for details.\\n\\n :type file_obj: file\\n :param file_obj: A file handle open for reading.\\n\\n :type rewind: boolean\\n :param rewind: If True, seek to the beginning of the file handle before\\n writing the file to Cloud Storage.\\n\\n :type size: int\\n :param size: The number of bytes to read from the file handle.\\n If not provided, we'll try to guess the size using\\n :func:`os.fstat`\\n\\n :type content_type: string or ``NoneType``\\n :param content_type: Optional type of content being uploaded.\\n\\n :type num_retries: integer\\n :param num_retries: Number of upload retries. Defaults to 6.\\n \\\"\\\"\\\"\\n content_type = (content_type or self._properties.get('contentType') or\\n 'application/octet-stream')\\n\\n # Rewind the file if desired.\\n if rewind:\\n file_obj.seek(0, os.SEEK_SET)\\n\\n # Get the basic stats about the file.\\n total_bytes = size or os.fstat(file_obj.fileno()).st_size\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.upload_from_filename\",\n \"text\": \"gcloud/storage/blob.py::Blob.upload_from_filename\\n def upload_from_filename(self, filename, content_type=None):\\n \\\"\\\"\\\"Upload this blob's contents from the content of a named file.\\n\\n The content type of the upload will either be\\n - The value passed in to the function (if any)\\n - The value stored on the current blob\\n - The value given by mimetypes.guess_type\\n\\n .. note::\\n The effect of uploading to an existing blob depends on the\\n \\\"versioning\\\" and \\\"lifecycle\\\" policies defined on the blob's\\n bucket. In the absence of those policies, upload will\\n overwrite any existing contents.\\n\\n See the `object versioning\\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\\n API documents for details.\\n\\n :type filename: string\\n :param filename: The path to the file.\\n\\n :type content_type: string or ``NoneType``\\n :param content_type: Optional type of content being uploaded.\\n \\\"\\\"\\\"\\n content_type = content_type or self._properties.get('contentType')\\n if content_type is None:\\n content_type, _ = mimetypes.guess_type(filename)\\n\\n with open(filename, 'rb') as file_obj:\\n self.upload_from_file(file_obj, content_type=content_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.upload_from_string\",\n \"text\": \"gcloud/storage/blob.py::Blob.upload_from_string\\n def upload_from_string(self, data, content_type='text/plain'):\\n \\\"\\\"\\\"Upload contents of this blob from the provided string.\\n\\n .. note::\\n The effect of uploading to an existing blob depends on the\\n \\\"versioning\\\" and \\\"lifecycle\\\" policies defined on the blob's\\n bucket. In the absence of those policies, upload will\\n overwrite any existing contents.\\n\\n See the `object versioning\\n <https://cloud.google.com/storage/docs/object-versioning>`_ and\\n `lifecycle <https://cloud.google.com/storage/docs/lifecycle>`_\\n API documents for details.\\n\\n :type data: bytes or text\\n :param data: The data to store in this blob. If the value is\\n text, it will be encoded as UTF-8.\\n \\\"\\\"\\\"\\n if isinstance(data, six.text_type):\\n data = data.encode('utf-8')\\n string_buffer = BytesIO()\\n string_buffer.write(data)\\n self.upload_from_file(file_obj=string_buffer, rewind=True,\\n size=len(data),\\n content_type=content_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.make_public\",\n \"text\": \"gcloud/storage/blob.py::Blob.make_public\\n def make_public(self):\\n \\\"\\\"\\\"Make this blob public giving all users read access.\\n\\n :returns: The current object.\\n \\\"\\\"\\\"\\n self.acl.all().grant_read()\\n self.acl.save()\\n return self\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.component_count\",\n \"text\": \"gcloud/storage/blob.py::Blob.component_count\\n @property\\n def component_count(self):\\n \\\"\\\"\\\"Number of underlying components that make up this object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['componentCount']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.etag\",\n \"text\": \"gcloud/storage/blob.py::Blob.etag\\n @property\\n def etag(self):\\n \\\"\\\"\\\"Retrieve the ETag for the object.\\n\\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\\n https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['etag']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.generation\",\n \"text\": \"gcloud/storage/blob.py::Blob.generation\\n @property\\n def generation(self):\\n \\\"\\\"\\\"Retrieve the generation for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['generation']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.id\",\n \"text\": \"gcloud/storage/blob.py::Blob.id\\n @property\\n def id(self):\\n \\\"\\\"\\\"Retrieve the ID for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['id']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.media_link\",\n \"text\": \"gcloud/storage/blob.py::Blob.media_link\\n @property\\n def media_link(self):\\n \\\"\\\"\\\"Retrieve the media download URI for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['mediaLink']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.metadata\",\n \"text\": \"gcloud/storage/blob.py::Blob.metadata\\n @property\\n def metadata(self):\\n \\\"\\\"\\\"Retrieve arbitrary/application specific metadata for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: dict\\n \\\"\\\"\\\"\\n return copy.deepcopy(self.properties['metadata'])\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.metadata\",\n \"text\": \"gcloud/storage/blob.py::Blob.metadata\\n @metadata.setter\\n def metadata(self, value):\\n \\\"\\\"\\\"Update arbitrary/application specific metadata for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :type value: dict\\n \\\"\\\"\\\"\\n self._patch_properties({'metadata': value})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.metageneration\",\n \"text\": \"gcloud/storage/blob.py::Blob.metageneration\\n @property\\n def metageneration(self):\\n \\\"\\\"\\\"Retrieve the metageneration for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['metageneration']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.owner\",\n \"text\": \"gcloud/storage/blob.py::Blob.owner\\n @property\\n def owner(self):\\n \\\"\\\"\\\"Retrieve info about the owner of the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: dict\\n :returns: mapping of owner's role/ID.\\n \\\"\\\"\\\"\\n return self.properties['owner'].copy()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.self_link\",\n \"text\": \"gcloud/storage/blob.py::Blob.self_link\\n @property\\n def self_link(self):\\n \\\"\\\"\\\"Retrieve the URI for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['selfLink']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.size\",\n \"text\": \"gcloud/storage/blob.py::Blob.size\\n @property\\n def size(self):\\n \\\"\\\"\\\"Size of the object, in bytes.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['size']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.storage_class\",\n \"text\": \"gcloud/storage/blob.py::Blob.storage_class\\n @property\\n def storage_class(self):\\n \\\"\\\"\\\"Retrieve the storage class for the object.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects and\\n https://cloud.google.com/storage/docs/durable-reduced-availability#_DRA_Bucket\\n\\n :rtype: string\\n :returns: Currently one of \\\"STANDARD\\\", \\\"DURABLE_REDUCED_AVAILABILITY\\\"\\n \\\"\\\"\\\"\\n return self.properties['storageClass']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.time_deleted\",\n \"text\": \"gcloud/storage/blob.py::Blob.time_deleted\\n @property\\n def time_deleted(self):\\n \\\"\\\"\\\"Retrieve the timestamp at which the object was deleted.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string or None\\n :returns: timestamp in RFC 3339 format, or None if the object\\n has a \\\"live\\\" version.\\n \\\"\\\"\\\"\\n return self.properties.get('timeDeleted')\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::Blob.updated\",\n \"text\": \"gcloud/storage/blob.py::Blob.updated\\n @property\\n def updated(self):\\n \\\"\\\"\\\"Retrieve the timestamp at which the object was updated.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/objects\\n\\n :rtype: string\\n :returns: timestamp in RFC 3339 format.\\n \\\"\\\"\\\"\\n return self.properties['updated']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/blob.py::_UrlBuilder.__init__\",\n \"text\": \"gcloud/storage/blob.py::_UrlBuilder.__init__\\n def __init__(self, bucket_name, object_name):\\n self.query_params = {'name': object_name}\\n self._bucket_name = bucket_name\\n self._relative_path = ''\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::_BlobIterator.__init__\",\n \"text\": \"gcloud/storage/bucket.py::_BlobIterator.__init__\\n def __init__(self, bucket, extra_params=None):\\n self.bucket = bucket\\n self.prefixes = ()\\n super(_BlobIterator, self).__init__(\\n connection=bucket.connection, path=bucket.path + '/o',\\n extra_params=extra_params)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::_BlobIterator.get_items_from_response\",\n \"text\": \"gcloud/storage/bucket.py::_BlobIterator.get_items_from_response\\n def get_items_from_response(self, response):\\n \\\"\\\"\\\"Yield :class:`.storage.blob.Blob` items from response.\\n\\n :type response: dict\\n :param response: The JSON API response for a page of blobs.\\n \\\"\\\"\\\"\\n self.prefixes = tuple(response.get('prefixes', ()))\\n for item in response.get('items', []):\\n name = item.get('name')\\n blob = Blob(name, bucket=self.bucket)\\n blob._properties = item\\n yield blob\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.__init__\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.__init__\\n def __init__(self, name=None, connection=None):\\n super(Bucket, self).__init__(name=name)\\n self._connection = connection\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.__repr__\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.__repr__\\n def __repr__(self):\\n return '<Bucket: %s>' % self.name\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.__iter__\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.__iter__\\n def __iter__(self):\\n return iter(self._iterator_class(bucket=self))\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.__contains__\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.__contains__\\n def __contains__(self, blob_name):\\n blob = Blob(blob_name, bucket=self)\\n return blob.exists()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.exists\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.exists\\n def exists(self):\\n \\\"\\\"\\\"Determines whether or not this bucket exists.\\n\\n :rtype: boolean\\n :returns: True if the bucket exists in Cloud Storage.\\n \\\"\\\"\\\"\\n try:\\n # We only need the status code (200 or not) so we seek to\\n # minimize the returned payload.\\n query_params = {'fields': 'name'}\\n self.connection.api_request(method='GET', path=self.path,\\n query_params=query_params)\\n return True\\n except NotFound:\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.acl\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.acl\\n @property\\n def acl(self):\\n \\\"\\\"\\\"Create our ACL on demand.\\\"\\\"\\\"\\n if self._acl is None:\\n self._acl = BucketACL(self)\\n return self._acl\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.default_object_acl\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.default_object_acl\\n @property\\n def default_object_acl(self):\\n \\\"\\\"\\\"Create our defaultObjectACL on demand.\\\"\\\"\\\"\\n if self._default_object_acl is None:\\n self._default_object_acl = DefaultObjectACL(self)\\n return self._default_object_acl\",\n \"label\": 1\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.connection\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.connection\\n @property\\n def connection(self):\\n \\\"\\\"\\\"Getter property for the connection to use with this Bucket.\\n\\n :rtype: :class:`gcloud.storage.connection.Connection`\\n :returns: The connection to use.\\n \\\"\\\"\\\"\\n return self._connection\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.path_helper\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.path_helper\\n @staticmethod\\n def path_helper(bucket_name):\\n \\\"\\\"\\\"Relative URL path for a bucket.\\n\\n :type bucket_name: string\\n :param bucket_name: The bucket name in the path.\\n\\n :rtype: string\\n :returns: The relative URL path for ``bucket_name``.\\n \\\"\\\"\\\"\\n return '/b/' + bucket_name\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.path\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.path\\n @property\\n def path(self):\\n \\\"\\\"\\\"The URL path to this bucket.\\\"\\\"\\\"\\n if not self.name:\\n raise ValueError('Cannot determine path without bucket name.')\\n\\n return self.path_helper(self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_blob\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_blob\\n def get_blob(self, blob_name):\\n \\\"\\\"\\\"Get a blob object by name.\\n\\n This will return None if the blob doesn't exist::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection()\\n >>> bucket = storage.get_bucket('my-bucket', connection=connection)\\n >>> print bucket.get_blob('/path/to/blob.txt')\\n <Blob: my-bucket, /path/to/blob.txt>\\n >>> print bucket.get_blob('/does-not-exist.txt')\\n None\\n\\n :type blob_name: string\\n :param blob_name: The name of the blob to retrieve.\\n\\n :rtype: :class:`gcloud.storage.blob.Blob` or None\\n :returns: The blob object if it exists, otherwise None.\\n \\\"\\\"\\\"\\n blob = Blob(bucket=self, name=blob_name)\\n try:\\n response = self.connection.api_request(method='GET',\\n path=blob.path)\\n name = response.get('name') # Expect this to be blob_name\\n blob = Blob(name, bucket=self)\\n blob._properties = response\\n return blob\\n except NotFound:\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_all_blobs\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_all_blobs\\n def get_all_blobs(self):\\n \\\"\\\"\\\"List all the blobs in this bucket.\\n\\n This will **not** retrieve all the data for all the blobs, it\\n will only retrieve the blob paths.\\n\\n This is equivalent to::\\n\\n blobs = [blob for blob in bucket]\\n\\n :rtype: list of :class:`gcloud.storage.blob.Blob`\\n :returns: A list of all the Blob objects in this bucket.\\n \\\"\\\"\\\"\\n return list(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.iterator\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.iterator\\n def iterator(self, prefix=None, delimiter=None, max_results=None,\\n versions=None):\\n \\\"\\\"\\\"Return an iterator used to find blobs in the bucket.\\n\\n :type prefix: string or None\\n :param prefix: optional prefix used to filter blobs.\\n\\n :type delimiter: string or None\\n :param delimiter: optional delimter, used with ``prefix`` to\\n emulate hierarchy.\\n\\n :type max_results: integer or None\\n :param max_results: maximum number of blobs to return.\\n\\n :type versions: boolean or None\\n :param versions: whether object versions should be returned as\\n separate blobs.\\n\\n :rtype: :class:`_BlobIterator`\\n \\\"\\\"\\\"\\n extra_params = {}\\n\\n if prefix is not None:\\n extra_params['prefix'] = prefix\\n\\n if delimiter is not None:\\n extra_params['delimiter'] = delimiter\\n\\n if max_results is not None:\\n extra_params['maxResults'] = max_results\\n\\n if versions is not None:\\n extra_params['versions'] = versions\\n\\n return self._iterator_class(self, extra_params=extra_params)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.delete\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.delete\\n def delete(self, force=False):\\n \\\"\\\"\\\"Delete this bucket.\\n\\n The bucket **must** be empty in order to submit a delete request. If\\n ``force=True`` is passed, this will first attempt to delete all the\\n objects / blobs in the bucket (i.e. try to empty the bucket).\\n\\n If the bucket doesn't exist, this will raise\\n :class:`gcloud.exceptions.NotFound`. If the bucket is not empty\\n (and ``force=False``), will raise :class:`gcloud.exceptions.Conflict`.\\n\\n If ``force=True`` and the bucket contains more than 256 objects / blobs\\n this will cowardly refuse to delete the objects (or the bucket). This\\n is to prevent accidental bucket deletion and to prevent extremely long\\n runtime of this method.\\n\\n :type force: boolean\\n :param force: If True, empties the bucket's objects then deletes it.\\n\\n :raises: :class:`ValueError` if ``force`` is ``True`` and the bucket\\n contains more than 256 objects / blobs.\\n \\\"\\\"\\\"\\n if force:\\n blobs = list(self.iterator(\\n max_results=self._MAX_OBJECTS_FOR_BUCKET_DELETE + 1))\\n if len(blobs) > self._MAX_OBJECTS_FOR_BUCKET_DELETE:\\n message = (\\n 'Refusing to delete bucket with more than '\\n '%d objects. If you actually want to delete '\\n 'this bucket, please delete the objects '\\n 'yourself before calling Bucket.delete().'\\n ) % (self._MAX_OBJECTS_FOR_BUCKET_DELETE,)\\n raise ValueError(message)\\n\\n # Ignore 404 errors on delete.\\n self.delete_blobs(blobs, on_error=lambda blob: None)\\n\\n self.connection.api_request(method='DELETE', path=self.path)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.delete_blob\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.delete_blob\\n def delete_blob(self, blob_name):\\n \\\"\\\"\\\"Deletes a blob from the current bucket.\\n\\n If the blob isn't found (backend 404), raises a\\n :class:`gcloud.exceptions.NotFound`.\\n\\n For example::\\n\\n >>> from gcloud.exceptions import NotFound\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection()\\n >>> bucket = storage.get_bucket('my-bucket', connection=connection)\\n >>> print bucket.get_all_blobs()\\n [<Blob: my-bucket, my-file.txt>]\\n >>> bucket.delete_blob('my-file.txt')\\n >>> try:\\n ... bucket.delete_blob('doesnt-exist')\\n ... except NotFound:\\n ... pass\\n\\n :type blob_name: string\\n :param blob_name: A blob name to delete.\\n\\n :raises: :class:`gcloud.exceptions.NotFound` (to suppress\\n the exception, call ``delete_blobs``, passing a no-op\\n ``on_error`` callback, e.g.::\\n\\n >>> bucket.delete_blobs([blob], on_error=lambda blob: None)\\n \\\"\\\"\\\"\\n blob_path = Blob.path_helper(self.path, blob_name)\\n self.connection.api_request(method='DELETE', path=blob_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.delete_blobs\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.delete_blobs\\n def delete_blobs(self, blobs, on_error=None):\\n \\\"\\\"\\\"Deletes a list of blobs from the current bucket.\\n\\n Uses :func:`Bucket.delete_blob` to delete each individual blob.\\n\\n :type blobs: list of string or :class:`gcloud.storage.blob.Blob`\\n :param blobs: A list of blob names or Blob objects to delete.\\n\\n :type on_error: a callable taking (blob)\\n :param on_error: If not ``None``, called once for each blob raising\\n :class:`gcloud.exceptions.NotFound`;\\n otherwise, the exception is propagated.\\n\\n :raises: :class:`gcloud.exceptions.NotFound` (if\\n `on_error` is not passed).\\n \\\"\\\"\\\"\\n for blob in blobs:\\n try:\\n blob_name = blob\\n if not isinstance(blob_name, six.string_types):\\n blob_name = blob.name\\n self.delete_blob(blob_name)\\n except NotFound:\\n if on_error is not None:\\n on_error(blob)\\n else:\\n raise\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.copy_blob\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.copy_blob\\n def copy_blob(self, blob, destination_bucket, new_name=None):\\n \\\"\\\"\\\"Copy the given blob to the given bucket, optionally with a new name.\\n\\n :type blob: string or :class:`gcloud.storage.blob.Blob`\\n :param blob: The blob to be copied.\\n\\n :type destination_bucket: :class:`gcloud.storage.bucket.Bucket`\\n :param destination_bucket: The bucket into which the blob should be\\n copied.\\n\\n :type new_name: string\\n :param new_name: (optional) the new name for the copied file.\\n\\n :rtype: :class:`gcloud.storage.blob.Blob`\\n :returns: The new Blob.\\n \\\"\\\"\\\"\\n if new_name is None:\\n new_name = blob.name\\n new_blob = Blob(bucket=destination_bucket, name=new_name)\\n api_path = blob.path + '/copyTo' + new_blob.path\\n copy_result = self.connection.api_request(method='POST', path=api_path)\\n new_blob._properties = copy_result\\n return new_blob\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.upload_file\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.upload_file\\n def upload_file(self, filename, blob_name=None):\\n \\\"\\\"\\\"Shortcut method to upload a file into this bucket.\\n\\n Use this method to quickly put a local file in Cloud Storage.\\n\\n For example::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection()\\n >>> bucket = storage.get_bucket('my-bucket', connection=connection)\\n >>> bucket.upload_file('~/my-file.txt', 'remote-text-file.txt')\\n >>> print bucket.get_all_blobs()\\n [<Blob: my-bucket, remote-text-file.txt>]\\n\\n If you don't provide a blob name, we will try to upload the file\\n using the local filename (**not** the complete path)::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection()\\n >>> bucket = storage.get_bucket('my-bucket', connection=connection)\\n >>> bucket.upload_file('~/my-file.txt')\\n >>> print bucket.get_all_blobs()\\n [<Blob: my-bucket, my-file.txt>]\\n\\n :type filename: string\\n :param filename: Local path to the file you want to upload.\\n\\n :type blob_name: string\\n :param blob_name: The name of the blob to upload the file to. If this\\n is blank, we will try to upload the file to the root\\n of the bucket with the same name as on your local\\n file system.\\n\\n :rtype: :class:`Blob`\\n :returns: The updated Blob object.\\n \\\"\\\"\\\"\\n if blob_name is None:\\n blob_name = os.path.basename(filename)\\n blob = Blob(bucket=self, name=blob_name)\\n blob.upload_from_filename(filename)\\n return blob\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.upload_file_object\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.upload_file_object\\n def upload_file_object(self, file_obj, blob_name=None):\\n \\\"\\\"\\\"Shortcut method to upload a file object into this bucket.\\n\\n Use this method to quickly put a local file in Cloud Storage.\\n\\n For example::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection()\\n >>> bucket = storage.get_bucket('my-bucket', connection=connection)\\n >>> bucket.upload_file(open('~/my-file.txt'), 'remote-text-file.txt')\\n >>> print bucket.get_all_blobs()\\n [<Blob: my-bucket, remote-text-file.txt>]\\n\\n If you don't provide a blob name, we will try to upload the file\\n using the local filename (**not** the complete path)::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection()\\n >>> bucket = storage.get_bucket('my-bucket', connection=connection)\\n >>> bucket.upload_file(open('~/my-file.txt'))\\n >>> print bucket.get_all_blobs()\\n [<Blob: my-bucket, my-file.txt>]\\n\\n :type file_obj: file\\n :param file_obj: A file handle open for reading.\\n\\n :type blob_name: string\\n :param blob_name: The name of the blob to upload the file to. If this\\n is blank, we will try to upload the file to the root\\n of the bucket with the same name as on your local\\n file system.\\n\\n :rtype: :class:`Blob`\\n :returns: The updated Blob object.\\n \\\"\\\"\\\"\\n if blob_name is None:\\n blob_name = os.path.basename(file_obj.name)\\n blob = Blob(bucket=self, name=blob_name)\\n blob.upload_from_file(file_obj)\\n return blob\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_cors\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_cors\\n def get_cors(self):\\n \\\"\\\"\\\"Retrieve CORS policies configured for this bucket.\\n\\n See: http://www.w3.org/TR/cors/ and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: list(dict)\\n :returns: A sequence of mappings describing each CORS policy.\\n \\\"\\\"\\\"\\n return [policy.copy() for policy in self.properties.get('cors', ())]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.update_cors\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.update_cors\\n def update_cors(self, entries):\\n \\\"\\\"\\\"Update CORS policies configured for this bucket.\\n\\n See: http://www.w3.org/TR/cors/ and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :type entries: list(dict)\\n :param entries: A sequence of mappings describing each CORS policy.\\n \\\"\\\"\\\"\\n self._patch_properties({'cors': entries})\\n self.patch()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_default_object_acl\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_default_object_acl\\n def get_default_object_acl(self):\\n \\\"\\\"\\\"Get the current Default Object ACL rules.\\n\\n If the acl isn't available locally, this method will reload it from\\n Cloud Storage.\\n\\n :rtype: :class:`gcloud.storage.acl.DefaultObjectACL`\\n :returns: A DefaultObjectACL object for this bucket.\\n \\\"\\\"\\\"\\n if not self.default_object_acl.loaded:\\n self.default_object_acl.reload()\\n return self.default_object_acl\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.etag\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.etag\\n @property\\n def etag(self):\\n \\\"\\\"\\\"Retrieve the ETag for the bucket.\\n\\n See: http://tools.ietf.org/html/rfc2616#section-3.11 and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['etag']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.id\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.id\\n @property\\n def id(self):\\n \\\"\\\"\\\"Retrieve the ID for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['id']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_lifecycle\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_lifecycle\\n def get_lifecycle(self):\\n \\\"\\\"\\\"Retrieve lifecycle rules configured for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/lifecycle and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: list(dict)\\n :returns: A sequence of mappings describing each lifecycle rule.\\n \\\"\\\"\\\"\\n info = self.properties.get('lifecycle', {})\\n return [rule.copy() for rule in info.get('rule', ())]\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.update_lifecycle\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.update_lifecycle\\n def update_lifecycle(self, rules):\\n \\\"\\\"\\\"Update CORS policies configured for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/lifecycle and\\n https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :type rules: list(dict)\\n :param rules: A sequence of mappings describing each lifecycle rule.\\n \\\"\\\"\\\"\\n self._patch_properties({'lifecycle': {'rule': rules}})\\n self.patch()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.get_logging\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.get_logging\\n def get_logging(self):\\n \\\"\\\"\\\"Return info about access logging for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/accesslogs#status\\n\\n :rtype: dict or None\\n :returns: a dict w/ keys, ``logBucket`` and ``logObjectPrefix``\\n (if logging is enabled), or None (if not).\\n \\\"\\\"\\\"\\n self._reload_properties()\\n info = self.properties.get('logging')\\n if info is not None:\\n return info.copy()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.enable_logging\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.enable_logging\\n def enable_logging(self, bucket_name, object_prefix=''):\\n \\\"\\\"\\\"Enable access logging for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/accesslogs#delivery\\n\\n :type bucket_name: string\\n :param bucket_name: name of bucket in which to store access logs\\n\\n :type object_prefix: string\\n :param object_prefix: prefix for access log filenames\\n \\\"\\\"\\\"\\n info = {'logBucket': bucket_name, 'logObjectPrefix': object_prefix}\\n self._patch_properties({'logging': info})\\n self.patch()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.disable_logging\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.disable_logging\\n def disable_logging(self):\\n \\\"\\\"\\\"Disable access logging for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/accesslogs#disabling\\n \\\"\\\"\\\"\\n self._patch_properties({'logging': None})\\n self.patch()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.metageneration\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.metageneration\\n @property\\n def metageneration(self):\\n \\\"\\\"\\\"Retrieve the metageneration for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['metageneration']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.owner\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.owner\\n @property\\n def owner(self):\\n \\\"\\\"\\\"Retrieve info about the owner of the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: dict\\n :returns: mapping of owner's role/ID.\\n \\\"\\\"\\\"\\n return self.properties['owner'].copy()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.project_number\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.project_number\\n @property\\n def project_number(self):\\n \\\"\\\"\\\"Retrieve the number of the project to which the bucket is assigned.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: integer\\n \\\"\\\"\\\"\\n return self.properties['projectNumber']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.self_link\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.self_link\\n @property\\n def self_link(self):\\n \\\"\\\"\\\"Retrieve the URI for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string\\n \\\"\\\"\\\"\\n return self.properties['selfLink']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.storage_class\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.storage_class\\n @property\\n def storage_class(self):\\n \\\"\\\"\\\"Retrieve the storage class for the bucket.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets and\\n https://cloud.google.com/storage/docs/durable-reduced-availability\\n\\n :rtype: string\\n :returns: Currently one of \\\"STANDARD\\\", \\\"DURABLE_REDUCED_AVAILABILITY\\\"\\n \\\"\\\"\\\"\\n return self.properties['storageClass']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.time_created\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.time_created\\n @property\\n def time_created(self):\\n \\\"\\\"\\\"Retrieve the timestamp at which the bucket was created.\\n\\n See: https://cloud.google.com/storage/docs/json_api/v1/buckets\\n\\n :rtype: string\\n :returns: timestamp in RFC 3339 format.\\n \\\"\\\"\\\"\\n return self.properties['timeCreated']\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.versioning_enabled\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.versioning_enabled\\n @property\\n def versioning_enabled(self):\\n \\\"\\\"\\\"Is versioning enabled for this bucket?\\n\\n See: https://cloud.google.com/storage/docs/object-versioning for\\n details.\\n\\n :rtype: boolean\\n :returns: True if enabled, else False.\\n \\\"\\\"\\\"\\n versioning = self.properties.get('versioning', {})\\n return versioning.get('enabled', False)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.versioning_enabled\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.versioning_enabled\\n @versioning_enabled.setter\\n def versioning_enabled(self, value):\\n \\\"\\\"\\\"Enable versioning for this bucket.\\n\\n See: https://cloud.google.com/storage/docs/object-versioning for\\n details.\\n\\n :type value: convertible to boolean\\n :param value: should versioning be anabled for the bucket?\\n \\\"\\\"\\\"\\n self._patch_properties({'versioning': {'enabled': bool(value)}})\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.configure_website\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.configure_website\\n def configure_website(self, main_page_suffix=None, not_found_page=None):\\n \\\"\\\"\\\"Configure website-related properties.\\n\\n See: https://developers.google.com/storage/docs/website-configuration\\n\\n .. note::\\n This (apparently) only works\\n if your bucket name is a domain name\\n (and to do that, you need to get approved somehow...).\\n\\n If you want this bucket to host a website, just provide the name\\n of an index page and a page to use when a blob isn't found::\\n\\n >>> from gcloud import storage\\n >>> connection = storage.get_connection()\\n >>> bucket = storage.get_bucket(bucket_name, connection=connection)\\n >>> bucket.configure_website('index.html', '404.html')\\n\\n You probably should also make the whole bucket public::\\n\\n >>> bucket.make_public(recursive=True, future=True)\\n\\n This says: \\\"Make the bucket public, and all the stuff already in\\n the bucket, and anything else I add to the bucket. Just make it\\n all public.\\\"\\n\\n :type main_page_suffix: string\\n :param main_page_suffix: The page to use as the main page\\n of a directory.\\n Typically something like index.html.\\n\\n :type not_found_page: string\\n :param not_found_page: The file to use when a page isn't found.\\n \\\"\\\"\\\"\\n data = {\\n 'website': {\\n 'mainPageSuffix': main_page_suffix,\\n 'notFoundPage': not_found_page,\\n },\\n }\\n self._patch_properties(data)\\n return self.patch()\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.disable_website\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.disable_website\\n def disable_website(self):\\n \\\"\\\"\\\"Disable the website configuration for this bucket.\\n\\n This is really just a shortcut for setting the website-related\\n attributes to ``None``.\\n \\\"\\\"\\\"\\n return self.configure_website(None, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"gcloud/storage/bucket.py::Bucket.make_public\",\n \"text\": \"gcloud/storage/bucket.py::Bucket.make_public\\n def make_public(self, recursive=False, future=False):\\n \\\"\\\"\\\"Make a bucket public.\\n\\n :type recursive: boolean\\n :param recursive: If True, this will make all blobs inside the bucket\\n public as well.\\n\\n :type future: boolean\\n :param future: If True, this will make all objects created in the\\n future public as well.\\n \\\"\\\"\\\"\\n self.acl.all().grant_read()\\n self.acl.save()\\n\\n if future:\\n doa = self.get_default_object_acl()\\n doa.all().grant_read()\\n doa.save()\\n\\n if recursive:\\n for blob in self:\\n blob.acl.all().grant_read()\\n blob.save_acl()\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":294,"cells":{"instance_id":{"kind":"string","value":"Lightning-AI__lightning-2960"},"repo":{"kind":"string","value":"Lightning-AI/lightning"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Document fixes: early_stop_checkpoint is not an valid argument for pl.Trainer\n## 📚 Documentation\r\n\r\nIn `pytorch-lightning/docs/source/new-project.rst`, there is an example demonstrating how to use `Trainer`. However, `early_stop_checkpoint` is not a valid argument for `Trainer`. I also found that `early_stop_checkpoint` only appears here in the whole repo. Therefore, we might need to remove it from the doc.\r\n\r\n\r\n\n"},"gold_files":{"kind":"list like","value":["pytorch_lightning/trainer/__init__.py","pytorch_lightning/trainer/trainer.py"],"string":"[\n \"pytorch_lightning/trainer/__init__.py\",\n \"pytorch_lightning/trainer/trainer.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pytorch_lightning/trainer/__init__.py::<module>","pytorch_lightning/trainer/trainer.py::Trainer.__init__"],"string":"[\n \"pytorch_lightning/trainer/__init__.py::<module>\",\n \"pytorch_lightning/trainer/trainer.py::Trainer.__init__\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"docs/source/conf.py","text":"docs/source/conf.py\ndef run_apidoc(_)\ndef setup(app)\ndef package_list_from_file(file)\ndef linkcode_resolve(domain, info)\n def find_source()","label":0},{"cid":"pytorch_lightning/core/hooks.py","text":"pytorch_lightning/core/hooks.py\nclass ModelHooks\n def setup(self, stage: str) # Called at the beginning of fit and test.\n def teardown(self, stage: str) # Called at the end of fit and test.\n def on_fit_start(self) # Called at the very beginning of fit.\n def on_fit_end(self) # Called at the very end of fit.\n def on_train_start(self) # Called at the beginning of training before sanity check.\n def on_train_end(self) # Called at the end of training before logger experiment is closed.\n def on_pretrain_routine_start(self) # Called at the beginning of the pretrain routine (between fit and train start).\n def on_pretrain_routine_end(self) # Called at the end of the pretrain routine (between fit and train start).\n def on_train_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) # Called in the training loop before anything happens for that batch.\n def on_train_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) # Called in the training loop after the batch.\n def on_validation_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) # Called in the validation loop before anything happens for that batch.\n def on_validation_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) # Called in the validation loop after the batch.\n def on_test_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) # Called in the test loop before anything happens for that batch.\n def on_test_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) # Called in the test loop after the batch.\n def on_batch_start(self, batch: Any) # Called in the training loop before anything happens for that batch.\n def on_batch_end(self) # Called in the training loop after the batch.\n def on_epoch_start(self) # Called in the training loop at the very beginning of the epoch.\n def on_epoch_end(self) # Called in the training loop at the very end of the epoch.\n def on_train_epoch_start(self) # Called in the training loop at the very beginning of the epoch.\n def on_train_epoch_end(self) # Called in the training loop at the very end of the epoch.\n def on_validation_epoch_start(self) # Called in the validation loop at the very beginning of the epoch.\n def on_validation_epoch_end(self) # Called in the validation loop at the very end of the epoch.\n def on_test_epoch_start(self) # Called in the test loop at the very beginning of the epoch.\n def on_test_epoch_end(self) # Called in the test loop at the very end of the epoch.\n def on_pre_performance_check(self) # Called at the very beginning of the validation loop.\n def on_post_performance_check(self) # Called at the very end of the validation loop.\n def on_before_zero_grad(self, optimizer: Optimizer) # Called after optimizer.step() and before optimizer.zero_grad().\n def on_after_backward(self) # Called in the training loop after loss.backward() and before optimizers do anyth\n def b","label":0},{"cid":"pytorch_lightning/loggers/comet.py","text":"pytorch_lightning/loggers/comet.py\nclass CometLogger # Log using `Comet.ml <https://www.comet.ml>`_. Install it with pip:\n def __init__(self, api_key: Optional[str]=None, save_dir: Optional[str]=None, workspace: Optional[str]=None, project_name: Optional[str]=None, rest_api_key: Optional[str]=None, experiment_name: Optional[str]=None, experiment_key: Optional[str]=None, offline: bool=False, **kwargs)\n def experiment(self) # Actual Comet object. To use Comet features in your\n def log_hyperparams(self, params: Union[Dict[str, Any], Namespace])\n def log_metrics(self, metrics: Dict[str, Union[torch.Tensor, float]], step: Optional[int]=None)\n def reset_experiment(self)\n def finalize(self, status: str) # When calling ``self.experiment.end()``, that experiment won't log any more data \n def save_dir(self)\n def name(self)\n def version(self)\n def __getstate__(self)","label":0},{"cid":"pytorch_lightning/trainer/__init__.py","text":"pytorch_lightning/trainer/__init__.py","label":1},{"cid":"pytorch_lightning/trainer/trainer.py","text":"pytorch_lightning/trainer/trainer.py\nclass Trainer # Example:\n def __init__(self, logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool]=True, checkpoint_callback: Union[ModelCheckpoint, bool]=True, early_stop_callback: Optional[Union[EarlyStopping, bool]]=False, callbacks: Optional[List[Callback]]=None, default_root_dir: Optional[str]=None, gradient_clip_val: float=0, process_position: int=0, num_nodes: int=1, num_processes: int=1, gpus: Optional[Union[List[int], str, int]]=None, auto_select_gpus: bool=False, tpu_cores: Optional[Union[List[int], str, int]]=None, log_gpu_memory: Optional[str]=None, progress_bar_refresh_rate: int=1, overfit_batches: Union[int, float]=0.0, track_grad_norm: Union[int, float, str]=-1, check_val_every_n_epoch: int=1, fast_dev_run: bool=False, accumulate_grad_batches: Union[int, Dict[int, int], List[list]]=1, max_epochs: int=1000, min_epochs: int=1, max_steps: Optional[int]=None, min_steps: Optional[int]=None, limit_train_batches: Union[int, float]=1.0, limit_val_batches: Union[int, float]=1.0, limit_test_batches: Union[int, float]=1.0, val_check_interval: Union[int, float]=1.0, log_save_interval: int=100, row_log_interval: int=50, distributed_backend: Optional[str]=None, sync_batchnorm: bool=False, precision: int=32, weights_summary: Optional[str]=ModelSummary.MODE_DEFAULT, weights_save_path: Optional[str]=None, num_sanity_val_steps: int=2, truncated_bptt_steps: Optional[int]=None, resume_from_checkpoint: Optional[str]=None, profiler: Optional[Union[BaseProfiler, bool]]=None, benchmark: bool=False, deterministic: bool=False, reload_dataloaders_every_epoch: bool=False, auto_lr_find: Union[bool, str]=False, replace_sampler_ddp: bool=True, terminate_on_nan: bool=False, auto_scale_batch_size: Union[str, bool]=False, prepare_data_per_node: bool=True, amp_backend: str='native', amp_level: str='O2', val_percent_check: float=None, test_percent_check: float=None, train_percent_check: float=None, overfit_pct: float=None) # Customize every aspect of training via flags\n def is_global_zero(self)\n def slurm_job_id(self)\n def default_attributes(cls)\n def get_init_arguments_and_types(cls) # Scans the Trainer signature and returns argument names, types and default values\n def get_deprecated_arg_names(cls) # Returns a list with deprecated Trainer arguments.\n def add_argparse_args(cls, parent_parser: ArgumentParser) # Extends existing argparse by default `Trainer` attributes.\n def _allowed_type(x)\n def _arg_default(x)\n def parse_argparser(cls, arg_parser: Union[ArgumentParser, Namespace]) # Parse CLI arguments, required for custom bool types.\n def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs) # Create an instance from CLI arguments.\n def num_gpus(self)\n def data_parallel(self)\n def progress_bar_callback(self)\n def progress_bar_dict(self) # Read-only for progress bar metrics.\n def disable_validation(self) # Check if validation is disable","label":1},{"cid":"setup.py","text":"setup.py\ndef load_requirements(path_dir=PATH_ROOT, file_name='base.txt', comment_char='#')\ndef load_long_description()","label":0}],"string":"[\n {\n \"cid\": \"docs/source/conf.py\",\n \"text\": \"docs/source/conf.py\\ndef run_apidoc(_)\\ndef setup(app)\\ndef package_list_from_file(file)\\ndef linkcode_resolve(domain, info)\\n def find_source()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py\",\n \"text\": \"pytorch_lightning/core/hooks.py\\nclass ModelHooks\\n def setup(self, stage: str) # Called at the beginning of fit and test.\\n def teardown(self, stage: str) # Called at the end of fit and test.\\n def on_fit_start(self) # Called at the very beginning of fit.\\n def on_fit_end(self) # Called at the very end of fit.\\n def on_train_start(self) # Called at the beginning of training before sanity check.\\n def on_train_end(self) # Called at the end of training before logger experiment is closed.\\n def on_pretrain_routine_start(self) # Called at the beginning of the pretrain routine (between fit and train start).\\n def on_pretrain_routine_end(self) # Called at the end of the pretrain routine (between fit and train start).\\n def on_train_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) # Called in the training loop before anything happens for that batch.\\n def on_train_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) # Called in the training loop after the batch.\\n def on_validation_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) # Called in the validation loop before anything happens for that batch.\\n def on_validation_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) # Called in the validation loop after the batch.\\n def on_test_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) # Called in the test loop before anything happens for that batch.\\n def on_test_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) # Called in the test loop after the batch.\\n def on_batch_start(self, batch: Any) # Called in the training loop before anything happens for that batch.\\n def on_batch_end(self) # Called in the training loop after the batch.\\n def on_epoch_start(self) # Called in the training loop at the very beginning of the epoch.\\n def on_epoch_end(self) # Called in the training loop at the very end of the epoch.\\n def on_train_epoch_start(self) # Called in the training loop at the very beginning of the epoch.\\n def on_train_epoch_end(self) # Called in the training loop at the very end of the epoch.\\n def on_validation_epoch_start(self) # Called in the validation loop at the very beginning of the epoch.\\n def on_validation_epoch_end(self) # Called in the validation loop at the very end of the epoch.\\n def on_test_epoch_start(self) # Called in the test loop at the very beginning of the epoch.\\n def on_test_epoch_end(self) # Called in the test loop at the very end of the epoch.\\n def on_pre_performance_check(self) # Called at the very beginning of the validation loop.\\n def on_post_performance_check(self) # Called at the very end of the validation loop.\\n def on_before_zero_grad(self, optimizer: Optimizer) # Called after optimizer.step() and before optimizer.zero_grad().\\n def on_after_backward(self) # Called in the training loop after loss.backward() and before optimizers do anyth\\n def b\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/loggers/comet.py\",\n \"text\": \"pytorch_lightning/loggers/comet.py\\nclass CometLogger # Log using `Comet.ml <https://www.comet.ml>`_. Install it with pip:\\n def __init__(self, api_key: Optional[str]=None, save_dir: Optional[str]=None, workspace: Optional[str]=None, project_name: Optional[str]=None, rest_api_key: Optional[str]=None, experiment_name: Optional[str]=None, experiment_key: Optional[str]=None, offline: bool=False, **kwargs)\\n def experiment(self) # Actual Comet object. To use Comet features in your\\n def log_hyperparams(self, params: Union[Dict[str, Any], Namespace])\\n def log_metrics(self, metrics: Dict[str, Union[torch.Tensor, float]], step: Optional[int]=None)\\n def reset_experiment(self)\\n def finalize(self, status: str) # When calling ``self.experiment.end()``, that experiment won't log any more data \\n def save_dir(self)\\n def name(self)\\n def version(self)\\n def __getstate__(self)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/__init__.py\",\n \"text\": \"pytorch_lightning/trainer/__init__.py\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py\",\n \"text\": \"pytorch_lightning/trainer/trainer.py\\nclass Trainer # Example:\\n def __init__(self, logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool]=True, checkpoint_callback: Union[ModelCheckpoint, bool]=True, early_stop_callback: Optional[Union[EarlyStopping, bool]]=False, callbacks: Optional[List[Callback]]=None, default_root_dir: Optional[str]=None, gradient_clip_val: float=0, process_position: int=0, num_nodes: int=1, num_processes: int=1, gpus: Optional[Union[List[int], str, int]]=None, auto_select_gpus: bool=False, tpu_cores: Optional[Union[List[int], str, int]]=None, log_gpu_memory: Optional[str]=None, progress_bar_refresh_rate: int=1, overfit_batches: Union[int, float]=0.0, track_grad_norm: Union[int, float, str]=-1, check_val_every_n_epoch: int=1, fast_dev_run: bool=False, accumulate_grad_batches: Union[int, Dict[int, int], List[list]]=1, max_epochs: int=1000, min_epochs: int=1, max_steps: Optional[int]=None, min_steps: Optional[int]=None, limit_train_batches: Union[int, float]=1.0, limit_val_batches: Union[int, float]=1.0, limit_test_batches: Union[int, float]=1.0, val_check_interval: Union[int, float]=1.0, log_save_interval: int=100, row_log_interval: int=50, distributed_backend: Optional[str]=None, sync_batchnorm: bool=False, precision: int=32, weights_summary: Optional[str]=ModelSummary.MODE_DEFAULT, weights_save_path: Optional[str]=None, num_sanity_val_steps: int=2, truncated_bptt_steps: Optional[int]=None, resume_from_checkpoint: Optional[str]=None, profiler: Optional[Union[BaseProfiler, bool]]=None, benchmark: bool=False, deterministic: bool=False, reload_dataloaders_every_epoch: bool=False, auto_lr_find: Union[bool, str]=False, replace_sampler_ddp: bool=True, terminate_on_nan: bool=False, auto_scale_batch_size: Union[str, bool]=False, prepare_data_per_node: bool=True, amp_backend: str='native', amp_level: str='O2', val_percent_check: float=None, test_percent_check: float=None, train_percent_check: float=None, overfit_pct: float=None) # Customize every aspect of training via flags\\n def is_global_zero(self)\\n def slurm_job_id(self)\\n def default_attributes(cls)\\n def get_init_arguments_and_types(cls) # Scans the Trainer signature and returns argument names, types and default values\\n def get_deprecated_arg_names(cls) # Returns a list with deprecated Trainer arguments.\\n def add_argparse_args(cls, parent_parser: ArgumentParser) # Extends existing argparse by default `Trainer` attributes.\\n def _allowed_type(x)\\n def _arg_default(x)\\n def parse_argparser(cls, arg_parser: Union[ArgumentParser, Namespace]) # Parse CLI arguments, required for custom bool types.\\n def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs) # Create an instance from CLI arguments.\\n def num_gpus(self)\\n def data_parallel(self)\\n def progress_bar_callback(self)\\n def progress_bar_dict(self) # Read-only for progress bar metrics.\\n def disable_validation(self) # Check if validation is disable\",\n \"label\": 1\n },\n {\n \"cid\": \"setup.py\",\n \"text\": \"setup.py\\ndef load_requirements(path_dir=PATH_ROOT, file_name='base.txt', comment_char='#')\\ndef load_long_description()\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"docs/source/conf.py::run_apidoc","text":"docs/source/conf.py::run_apidoc\ndef run_apidoc(_):\n sys.path.insert(0, apidoc_output_folder)\n\n # delete api-doc files before generating them\n if os.path.exists(apidoc_output_folder):\n shutil.rmtree(apidoc_output_folder)\n\n for pkg in PACKAGES:\n argv = ['-e',\n '-o', apidoc_output_folder,\n os.path.join(PATH_ROOT, pkg),\n '**/test_*',\n '--force',\n '--private',\n '--module-first']\n\n apidoc.main(argv)","label":0},{"cid":"docs/source/conf.py::setup","text":"docs/source/conf.py::setup\ndef setup(app):\n # this is for hiding doctest decoration,\n # see: http://z4r.github.io/python/2011/12/02/hides-the-prompts-and-output/\n app.add_javascript('copybutton.js')\n app.connect('builder-inited', run_apidoc)","label":0},{"cid":"docs/source/conf.py::package_list_from_file","text":"docs/source/conf.py::package_list_from_file\ndef package_list_from_file(file):\n mocked_packages = []\n with open(file, 'r') as fp:\n for ln in fp.readlines():\n found = [ln.index(ch) for ch in list(',=<>#') if ch in ln]\n pkg = ln[:min(found)] if found else ln\n if pkg.rstrip():\n mocked_packages.append(pkg.rstrip())\n return mocked_packages","label":0},{"cid":"docs/source/conf.py::linkcode_resolve","text":"docs/source/conf.py::linkcode_resolve\ndef linkcode_resolve(domain, info):\n def find_source():\n # try to find the file and line number, based on code from numpy:\n # https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L286\n obj = sys.modules[info['module']]\n for part in info['fullname'].split('.'):\n obj = getattr(obj, part)\n fname = inspect.getsourcefile(obj)\n # https://github.com/rtfd/readthedocs.org/issues/5735\n if any([s in fname for s in ('readthedocs', 'rtfd', 'checkouts')]):\n # /home/docs/checkouts/readthedocs.org/user_builds/pytorch_lightning/checkouts/\n # devel/pytorch_lightning/utilities/cls_experiment.py#L26-L176\n path_top = os.path.abspath(os.path.join('..', '..', '..'))\n fname = os.path.relpath(fname, start=path_top)\n else:\n # Local build, imitate master\n fname = 'master/' + os.path.relpath(fname, start=os.path.abspath('..'))\n source, lineno = inspect.getsourcelines(obj)\n return fname, lineno, lineno + len(source) - 1\n\n if domain != 'py' or not info['module']:\n return None\n try:\n filename = '%s#L%d-L%d' % find_source()\n except Exception:\n filename = info['module'].replace('.', '/') + '.py'\n # import subprocess\n # tag = subprocess.Popen(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE,\n # universal_newlines=True).communicate()[0][:-1]\n branch = filename.split('/')[0]\n # do mapping from latest tags to master\n branch = {'latest': 'master', 'stable': 'master'}.get(branch, branch)\n filename = '/'.join([branch] + filename.split('/')[1:])\n return \"https://github.com/%s/%s/blob/%s\" \\\n % (github_user, github_repo, filename)","label":0},{"cid":"docs/source/conf.py::linkcode_resolve.find_source","text":"docs/source/conf.py::linkcode_resolve.find_source\n def find_source():\n # try to find the file and line number, based on code from numpy:\n # https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L286\n obj = sys.modules[info['module']]\n for part in info['fullname'].split('.'):\n obj = getattr(obj, part)\n fname = inspect.getsourcefile(obj)\n # https://github.com/rtfd/readthedocs.org/issues/5735\n if any([s in fname for s in ('readthedocs', 'rtfd', 'checkouts')]):\n # /home/docs/checkouts/readthedocs.org/user_builds/pytorch_lightning/checkouts/\n # devel/pytorch_lightning/utilities/cls_experiment.py#L26-L176\n path_top = os.path.abspath(os.path.join('..', '..', '..'))\n fname = os.path.relpath(fname, start=path_top)\n else:\n # Local build, imitate master\n fname = 'master/' + os.path.relpath(fname, start=os.path.abspath('..'))\n source, lineno = inspect.getsourcelines(obj)\n return fname, lineno, lineno + len(source) - 1","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.setup","text":"pytorch_lightning/core/hooks.py::ModelHooks.setup\n def setup(self, stage: str):\n \"\"\"\n Called at the beginning of fit and test.\n This is a good hook when you need to build models dynamically or adjust something about them.\n This hook is called on every process when using DDP.\n\n Args:\n stage: either 'fit' or 'test'\n\n Example::\n\n class LitModel(...):\n def __init__(self):\n self.l1 = None\n\n def prepare_data(self):\n download_data()\n tokenize()\n\n # don't do this\n self.something = else\n\n def setup(stage):\n data = Load_data(...)\n self.l1 = nn.Linear(28, data.num_classes)\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.teardown","text":"pytorch_lightning/core/hooks.py::ModelHooks.teardown\n def teardown(self, stage: str):\n \"\"\"\n Called at the end of fit and test.\n\n Args:\n stage: either 'fit' or 'test'\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_start\n def on_fit_start(self):\n \"\"\"\n Called at the very beginning of fit.\n If on DDP it is called on every process\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_end\n def on_fit_end(self):\n \"\"\"\n Called at the very end of fit.\n If on DDP it is called on every process\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_start\n def on_train_start(self) -> None:\n \"\"\"\n Called at the beginning of training before sanity check.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_end\n def on_train_end(self) -> None:\n \"\"\"\n Called at the end of training before logger experiment is closed.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_pretrain_routine_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_pretrain_routine_start\n def on_pretrain_routine_start(self) -> None:\n \"\"\"\n Called at the beginning of the pretrain routine (between fit and train start).\n\n - fit\n - pretrain_routine start\n - pretrain_routine end\n - training_start\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_pretrain_routine_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_pretrain_routine_end\n def on_pretrain_routine_end(self) -> None:\n \"\"\"\n Called at the end of the pretrain routine (between fit and train start).\n\n - fit\n - pretrain_routine start\n - pretrain_routine end\n - training_start\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_batch_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_batch_start\n def on_train_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:\n \"\"\"\n Called in the training loop before anything happens for that batch.\n\n If you return -1 here, you will skip training for the rest of the current epoch.\n\n Args:\n batch: The batched data as it is returned by the training DataLoader.\n batch_idx: the index of the batch\n dataloader_idx: the index of the dataloader\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_batch_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_batch_end\n def on_train_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:\n \"\"\"\n Called in the training loop after the batch.\n\n Args:\n batch: The batched data as it is returned by the training DataLoader.\n batch_idx: the index of the batch\n dataloader_idx: the index of the dataloader\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_batch_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_batch_start\n def on_validation_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:\n \"\"\"\n Called in the validation loop before anything happens for that batch.\n\n Args:\n batch: The batched data as it is returned by the training DataLoader.\n batch_idx: the index of the batch\n dataloader_idx: the index of the dataloader\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_batch_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_batch_end\n def on_validation_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:\n \"\"\"\n Called in the validation loop after the batch.\n\n Args:\n batch: The batched data as it is returned by the training DataLoader.\n batch_idx: the index of the batch\n dataloader_idx: the index of the dataloader\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_test_batch_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_test_batch_start\n def on_test_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:\n \"\"\"\n Called in the test loop before anything happens for that batch.\n\n Args:\n batch: The batched data as it is returned by the training DataLoader.\n batch_idx: the index of the batch\n dataloader_idx: the index of the dataloader\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_test_batch_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_test_batch_end\n def on_test_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:\n \"\"\"\n Called in the test loop after the batch.\n\n Args:\n batch: The batched data as it is returned by the training DataLoader.\n batch_idx: the index of the batch\n dataloader_idx: the index of the dataloader\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_start\n def on_batch_start(self, batch: Any) -> None:\n \"\"\"\n Called in the training loop before anything happens for that batch.\n\n If you return -1 here, you will skip training for the rest of the current epoch.\n\n Args:\n batch: The batched data as it is returned by the training DataLoader.\n\n .. warning:: Deprecated in 0.9.0 will remove 1.0.0 (use `on_train_batch_start` instead)\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_end\n def on_batch_end(self) -> None:\n \"\"\"\n Called in the training loop after the batch.\n\n .. warning:: Deprecated in 0.9.0 will remove 1.0.0 (use `on_train_batch_end` instead)\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_start\n def on_epoch_start(self) -> None:\n \"\"\"\n Called in the training loop at the very beginning of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_end\n def on_epoch_end(self) -> None:\n \"\"\"\n Called in the training loop at the very end of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_start\n def on_train_epoch_start(self) -> None:\n \"\"\"\n Called in the training loop at the very beginning of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_end\n def on_train_epoch_end(self) -> None:\n \"\"\"\n Called in the training loop at the very end of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_start\n def on_validation_epoch_start(self) -> None:\n \"\"\"\n Called in the validation loop at the very beginning of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_end\n def on_validation_epoch_end(self) -> None:\n \"\"\"\n Called in the validation loop at the very end of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_start","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_start\n def on_test_epoch_start(self) -> None:\n \"\"\"\n Called in the test loop at the very beginning of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_end","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_end\n def on_test_epoch_end(self) -> None:\n \"\"\"\n Called in the test loop at the very end of the epoch.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_pre_performance_check","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_pre_performance_check\n def on_pre_performance_check(self) -> None:\n \"\"\"\n Called at the very beginning of the validation loop.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_post_performance_check","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_post_performance_check\n def on_post_performance_check(self) -> None:\n \"\"\"\n Called at the very end of the validation loop.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_before_zero_grad","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_before_zero_grad\n def on_before_zero_grad(self, optimizer: Optimizer) -> None:\n \"\"\"\n Called after optimizer.step() and before optimizer.zero_grad().\n\n Called in the training loop after taking an optimizer step and before zeroing grads.\n Good place to inspect weight information with weights updated.\n\n This is where it is called::\n\n for optimizer in optimizers:\n optimizer.step()\n model.on_before_zero_grad(optimizer) # < ---- called here\n optimizer.zero_grad()\n\n Args:\n optimizer: The optimizer for which grads should be zeroed.\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.on_after_backward","text":"pytorch_lightning/core/hooks.py::ModelHooks.on_after_backward\n def on_after_backward(self) -> None:\n \"\"\"\n Called in the training loop after loss.backward() and before optimizers do anything.\n This is the ideal place to inspect or log gradient information.\n\n Example::\n\n def on_after_backward(self):\n # example to inspect gradient information in tensorboard\n if self.trainer.global_step % 25 == 0: # don't make the tf file huge\n params = self.state_dict()\n for k, v in params.items():\n grads = v\n name = k\n self.logger.experiment.add_histogram(tag=name, values=grads,\n global_step=self.trainer.global_step)\n\n \"\"\"","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.backward","text":"pytorch_lightning/core/hooks.py::ModelHooks.backward\n def backward(self, trainer, loss: Tensor, optimizer: Optimizer, optimizer_idx: int) -> None:\n \"\"\"\n Override backward with your own implementation if you need to.\n\n Args:\n trainer: Pointer to the trainer\n loss: Loss is already scaled by accumulated grads\n optimizer: Current optimizer being used\n optimizer_idx: Index of the current optimizer being used\n\n Called to perform backward step.\n Feel free to override as needed.\n\n The loss passed in has already been scaled for accumulated gradients if requested.\n\n Example::\n\n def backward(self, trainer, loss, optimizer, optimizer_idx):\n loss.backward()\n\n \"\"\"\n loss.backward()","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.amp_scale_loss","text":"pytorch_lightning/core/hooks.py::ModelHooks.amp_scale_loss\n def amp_scale_loss(self, unscaled_loss, optimizer, optimizer_idx, amp_backend: AMPType):\n if amp_backend == AMPType.NATIVE:\n scaled_loss = self.trainer.scaler.scale(unscaled_loss)\n else:\n scaled_loss = amp.scale_loss(unscaled_loss, optimizer)\n\n return scaled_loss","label":0},{"cid":"pytorch_lightning/core/hooks.py::ModelHooks.transfer_batch_to_device","text":"pytorch_lightning/core/hooks.py::ModelHooks.transfer_batch_to_device\n def transfer_batch_to_device(self, batch: Any, device: torch.device) -> Any:\n \"\"\"\n Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors\n wrapped in a custom data structure.\n\n The data types listed below (and any arbitrary nesting of them) are supported out of the box:\n\n - :class:`torch.Tensor` or anything that implements `.to(...)`\n - :class:`list`\n - :class:`dict`\n - :class:`tuple`\n - :class:`torchtext.data.batch.Batch`\n\n For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, ...).\n\n Example::\n\n def transfer_batch_to_device(self, batch, device)\n if isinstance(batch, CustomBatch):\n # move all tensors in your custom data structure to the device\n batch.samples = batch.samples.to(device)\n batch.targets = batch.targets.to(device)\n else:\n batch = super().transfer_batch_to_device(data, device)\n return batch\n\n Args:\n batch: A batch of data that needs to be transferred to a new device.\n device: The target device as defined in PyTorch.\n\n Returns:\n A reference to the data on the new device.\n\n Note:\n This hook should only transfer the data and not modify it, nor should it move the data to\n any other device than the one passed in as argument (unless you know what you are doing).\n\n Note:\n This hook only runs on single GPU training (no data-parallel). If you need multi-GPU support\n for your custom batch objects, you need to define your custom\n :class:`~torch.nn.parallel.DistributedDataParallel` or\n :class:`~pytorch_lightning.overrides.data_parallel.LightningDistributedDataParallel` and\n override :me","label":0},{"cid":"pytorch_lightning/loggers/comet.py::CometLogger.__init__","text":"pytorch_lightning/loggers/comet.py::CometLogger.__init__\n def __init__(self,\n api_key: Optional[str] = None,\n save_dir: Optional[str] = None,\n workspace: Optional[str] = None,\n project_name: Optional[str] = None,\n rest_api_key: Optional[str] = None,\n experiment_name: Optional[str] = None,\n experiment_key: Optional[str] = None,\n offline: bool = False,\n **kwargs):\n\n if not _COMET_AVAILABLE:\n raise ImportError('You want to use `comet_ml` logger which is not installed yet,'\n ' install it with `pip install comet-ml`.')\n super().__init__()\n self._experiment = None\n\n # Determine online or offline mode based on which arguments were passed to CometLogger\n api_key = api_key or get_api_key(None, get_config())\n\n if api_key is not None and save_dir is not None:\n self.mode = \"offline\" if offline else \"online\"\n self.api_key = api_key\n self._save_dir = save_dir\n elif api_key is not None:\n self.mode = \"online\"\n self.api_key = api_key\n elif save_dir is not None:\n self.mode = \"offline\"\n self._save_dir = save_dir\n else:\n # If neither api_key nor save_dir are passed as arguments, raise an exception\n raise MisconfigurationException(\n \"CometLogger requires either api_key or save_dir during initialization.\"\n )\n\n log.info(f\"CometLogger will be initialized in {self.mode} mode\")\n\n self.workspace = workspace\n self.project_name = project_name\n self.experiment_key = experiment_key\n self._kwargs = kwargs\n\n if rest_api_key is not None:\n # Comet.ml rest API, used to determine version number\n self.rest_api_key = rest_api_key\n self.comet_api = API(self.rest_a","label":0},{"cid":"pytorch_lightning/loggers/comet.py::CometLogger.experiment","text":"pytorch_lightning/loggers/comet.py::CometLogger.experiment\n @property\n @rank_zero_experiment\n def experiment(self) -> CometBaseExperiment:\n r\"\"\"\n Actual Comet object. To use Comet features in your\n :class:`~pytorch_lightning.core.lightning.LightningModule` do the following.\n\n Example::\n\n self.logger.experiment.some_comet_function()\n\n \"\"\"\n if self._experiment is not None:\n return self._experiment\n\n if self.mode == \"online\":\n if self.experiment_key is None:\n self._experiment = CometExperiment(\n api_key=self.api_key,\n workspace=self.workspace,\n project_name=self.project_name,\n **self._kwargs\n )\n self.experiment_key = self._experiment.get_key()\n else:\n self._experiment = CometExistingExperiment(\n api_key=self.api_key,\n workspace=self.workspace,\n project_name=self.project_name,\n previous_experiment=self.experiment_key,\n **self._kwargs\n )\n else:\n self._experiment = CometOfflineExperiment(\n offline_directory=self.save_dir,\n workspace=self.workspace,\n project_name=self.project_name,\n **self._kwargs\n )\n\n return self._experiment","label":0},{"cid":"pytorch_lightning/loggers/comet.py::CometLogger.log_hyperparams","text":"pytorch_lightning/loggers/comet.py::CometLogger.log_hyperparams\n @rank_zero_only\n def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:\n params = self._convert_params(params)\n params = self._flatten_dict(params)\n self.experiment.log_parameters(params)","label":0},{"cid":"pytorch_lightning/loggers/comet.py::CometLogger.log_metrics","text":"pytorch_lightning/loggers/comet.py::CometLogger.log_metrics\n @rank_zero_only\n def log_metrics(\n self,\n metrics: Dict[str, Union[torch.Tensor, float]],\n step: Optional[int] = None\n ) -> None:\n assert rank_zero_only.rank == 0, 'experiment tried to log from global_rank != 0'\n\n # Comet.ml expects metrics to be a dictionary of detached tensors on CPU\n for key, val in metrics.items():\n if is_tensor(val):\n metrics[key] = val.cpu().detach()\n\n self.experiment.log_metrics(metrics, step=step)","label":0},{"cid":"pytorch_lightning/loggers/comet.py::CometLogger.reset_experiment","text":"pytorch_lightning/loggers/comet.py::CometLogger.reset_experiment\n def reset_experiment(self):\n self._experiment = None","label":0},{"cid":"pytorch_lightning/loggers/comet.py::CometLogger.finalize","text":"pytorch_lightning/loggers/comet.py::CometLogger.finalize\n @rank_zero_only\n def finalize(self, status: str) -> None:\n r\"\"\"\n When calling ``self.experiment.end()``, that experiment won't log any more data to Comet.\n That's why, if you need to log any more data, you need to create an ExistingCometExperiment.\n For example, to log data when testing your model after training, because when training is\n finalized :meth:`CometLogger.finalize` is called.\n\n This happens automatically in the :meth:`~CometLogger.experiment` property, when\n ``self._experiment`` is set to ``None``, i.e. ``self.reset_experiment()``.\n \"\"\"\n self.experiment.end()\n self.reset_experiment()","label":0},{"cid":"pytorch_lightning/loggers/comet.py::CometLogger.save_dir","text":"pytorch_lightning/loggers/comet.py::CometLogger.save_dir\n @property\n def save_dir(self) -> Optional[str]:\n return self._save_dir","label":0},{"cid":"pytorch_lightning/loggers/comet.py::CometLogger.name","text":"pytorch_lightning/loggers/comet.py::CometLogger.name\n @property\n def name(self) -> str:\n return str(self.experiment.project_name)","label":0},{"cid":"pytorch_lightning/loggers/comet.py::CometLogger.version","text":"pytorch_lightning/loggers/comet.py::CometLogger.version\n @property\n def version(self) -> str:\n return self.experiment.id","label":0},{"cid":"pytorch_lightning/loggers/comet.py::CometLogger.__getstate__","text":"pytorch_lightning/loggers/comet.py::CometLogger.__getstate__\n def __getstate__(self):\n state = self.__dict__.copy()\n state[\"_experiment\"] = None\n return state","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__init__","text":"pytorch_lightning/trainer/trainer.py::Trainer.__init__\n def __init__(\n self,\n logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool] = True,\n checkpoint_callback: Union[ModelCheckpoint, bool] = True,\n early_stop_callback: Optional[Union[EarlyStopping, bool]] = False,\n callbacks: Optional[List[Callback]] = None,\n default_root_dir: Optional[str] = None,\n gradient_clip_val: float = 0,\n process_position: int = 0,\n num_nodes: int = 1,\n num_processes: int = 1,\n gpus: Optional[Union[List[int], str, int]] = None,\n auto_select_gpus: bool = False,\n tpu_cores: Optional[Union[List[int], str, int]] = None,\n log_gpu_memory: Optional[str] = None,\n progress_bar_refresh_rate: int = 1,\n overfit_batches: Union[int, float] = 0.0,\n track_grad_norm: Union[int, float, str] = -1,\n check_val_every_n_epoch: int = 1,\n fast_dev_run: bool = False,\n accumulate_grad_batches: Union[int, Dict[int, int], List[list]] = 1,\n max_epochs: int = 1000,\n min_epochs: int = 1,\n max_steps: Optional[int] = None,\n min_steps: Optional[int] = None,\n limit_train_batches: Union[int, float] = 1.0,\n limit_val_batches: Union[int, float] = 1.0,\n limit_test_batches: Union[int, float] = 1.0,\n val_check_interval: Union[int, float] = 1.0,\n log_save_interval: int = 100,\n row_log_interval: int = 50,\n distributed_backend: Optional[str] = None,\n sync_batchnorm: bool = False,\n precision: int = 32,\n weights_summary: Optional[str] = ModelSummary.MODE_DEFAULT,\n weights_save_path: Optional[str] = None,\n num_sanity_val_steps: int = 2,\n truncated_bptt_steps: Optional[int] = None,\n resume_from_checkpoint: Optional[str] = None,\n profiler: Optional[Union[BaseProfiler, bool]] = None,\n benchmark: bool = False,\n deterministic: bool = False","label":1},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.is_global_zero","text":"pytorch_lightning/trainer/trainer.py::Trainer.is_global_zero\n @property\n def is_global_zero(self) -> bool:\n return self.global_rank == 0","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id","text":"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id\n @property\n def slurm_job_id(self) -> Optional[int]:\n try:\n job_id = os.environ['SLURM_JOB_ID']\n job_id = int(job_id)\n\n # in interactive mode, don't make logs use the same job id\n in_slurm_interactive_mode = os.environ['SLURM_JOB_NAME'] == 'bash'\n if in_slurm_interactive_mode:\n job_id = None\n\n except Exception:\n job_id = None\n return job_id","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.default_attributes","text":"pytorch_lightning/trainer/trainer.py::Trainer.default_attributes\n @classmethod\n def default_attributes(cls):\n init_signature = inspect.signature(Trainer)\n\n args = {}\n for param_name in init_signature.parameters:\n value = init_signature.parameters[param_name].default\n args[param_name] = value\n\n return args","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.get_init_arguments_and_types","text":"pytorch_lightning/trainer/trainer.py::Trainer.get_init_arguments_and_types\n @classmethod\n def get_init_arguments_and_types(cls) -> List[Tuple[str, Tuple, Any]]:\n r\"\"\"Scans the Trainer signature and returns argument names, types and default values.\n\n Returns:\n List with tuples of 3 values:\n (argument name, set with argument types, argument default value).\n\n Examples:\n >>> args = Trainer.get_init_arguments_and_types()\n >>> import pprint\n >>> pprint.pprint(sorted(args)) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE\n [('accumulate_grad_batches',\n (<class 'int'>, typing.Dict[int, int], typing.List[list]),\n 1),\n ...\n ('callbacks',\n (typing.List[pytorch_lightning.callbacks.base.Callback],\n <class 'NoneType'>),\n None),\n ('check_val_every_n_epoch', (<class 'int'>,), 1),\n ...\n ('max_epochs', (<class 'int'>,), 1000),\n ...\n ('precision', (<class 'int'>,), 32),\n ('prepare_data_per_node', (<class 'bool'>,), True),\n ('process_position', (<class 'int'>,), 0),\n ('profiler',\n (<class 'pytorch_lightning.profiler.profilers.BaseProfiler'>,\n <class 'bool'>,\n <class 'NoneType'>),\n None),\n ...\n \"\"\"\n trainer_default_params = inspect.signature(cls).parameters\n name_type_default = []\n for arg in trainer_default_params:\n arg_type = trainer_default_params[arg].annotation\n arg_default = trainer_default_params[arg].default\n try:\n arg_types = tuple(arg_type.__args__)\n except AttributeError:\n arg_types = (arg_type,)\n\n name_type_default.append((arg, arg_types, arg_default))\n\n return name_type_default","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.get_deprecated_arg_names","text":"pytorch_lightning/trainer/trainer.py::Trainer.get_deprecated_arg_names\n @classmethod\n def get_deprecated_arg_names(cls) -> List:\n \"\"\"Returns a list with deprecated Trainer arguments.\"\"\"\n depr_arg_names = []\n for name, val in cls.__dict__.items():\n if name.startswith('DEPRECATED') and isinstance(val, (tuple, list)):\n depr_arg_names.extend(val)\n return depr_arg_names","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.add_argparse_args","text":"pytorch_lightning/trainer/trainer.py::Trainer.add_argparse_args\n @classmethod\n def add_argparse_args(cls, parent_parser: ArgumentParser) -> ArgumentParser:\n r\"\"\"Extends existing argparse by default `Trainer` attributes.\n\n Args:\n parent_parser:\n The custom cli arguments parser, which will be extended by\n the Trainer default arguments.\n\n Only arguments of the allowed types (str, float, int, bool) will\n extend the `parent_parser`.\n\n Examples:\n >>> import argparse\n >>> import pprint\n >>> parser = argparse.ArgumentParser()\n >>> parser = Trainer.add_argparse_args(parser)\n >>> args = parser.parse_args([])\n >>> pprint.pprint(vars(args)) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE\n {...\n 'check_val_every_n_epoch': 1,\n 'checkpoint_callback': True,\n 'default_root_dir': None,\n 'deterministic': False,\n 'distributed_backend': None,\n 'early_stop_callback': False,\n ...\n 'logger': True,\n 'max_epochs': 1000,\n 'max_steps': None,\n 'min_epochs': 1,\n 'min_steps': None,\n ...\n 'profiler': None,\n 'progress_bar_refresh_rate': 1,\n ...}\n\n \"\"\"\n parser = ArgumentParser(parents=[parent_parser], add_help=False,)\n\n blacklist = ['kwargs']\n depr_arg_names = cls.get_deprecated_arg_names() + blacklist\n\n allowed_types = (str, float, int, bool)\n\n # TODO: get \"help\" from docstring :)\n for arg, arg_types, arg_default in (\n at for at in cls.get_init_arguments_and_types() if at[0] not in depr_arg_names\n ):\n arg_types = [at for at in allowed_types if at in arg_types]\n if not arg_types:\n # skip argument with not supported type\n continue\n ","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer._allowed_type","text":"pytorch_lightning/trainer/trainer.py::Trainer._allowed_type\n def _allowed_type(x) -> Union[int, str]:\n if ',' in x:\n return str(x)\n else:\n return int(x)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer._arg_default","text":"pytorch_lightning/trainer/trainer.py::Trainer._arg_default\n def _arg_default(x) -> Union[int, str]:\n if ',' in x:\n return str(x)\n else:\n return int(x)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.parse_argparser","text":"pytorch_lightning/trainer/trainer.py::Trainer.parse_argparser\n @classmethod\n def parse_argparser(cls, arg_parser: Union[ArgumentParser, Namespace]) -> Namespace:\n \"\"\"Parse CLI arguments, required for custom bool types.\"\"\"\n args = arg_parser.parse_args() if isinstance(arg_parser, ArgumentParser) else arg_parser\n\n types_default = {\n arg: (arg_types, arg_default) for arg, arg_types, arg_default in cls.get_init_arguments_and_types()\n }\n\n modified_args = {}\n for k, v in vars(args).items():\n if k in types_default and v is None:\n # We need to figure out if the None is due to using nargs=\"?\" or if it comes from the default value\n arg_types, arg_default = types_default[k]\n if bool in arg_types and isinstance(arg_default, bool):\n # Value has been passed as a flag => It is currently None, so we need to set it to True\n # We always set to True, regardless of the default value.\n # Users must pass False directly, but when passing nothing True is assumed.\n # i.e. the only way to disable somthing that defaults to True is to use the long form:\n # \"--a_default_true_arg False\" becomes False, while \"--a_default_false_arg\" becomes None,\n # which then becomes True here.\n\n v = True\n\n modified_args[k] = v\n return Namespace(**modified_args)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.from_argparse_args","text":"pytorch_lightning/trainer/trainer.py::Trainer.from_argparse_args\n @classmethod\n def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs) -> 'Trainer':\n \"\"\"\n Create an instance from CLI arguments.\n\n Args:\n args: The parser or namespace to take arguments from. Only known arguments will be\n parsed and passed to the :class:`Trainer`.\n **kwargs: Additional keyword arguments that may override ones in the parser or namespace.\n These must be valid Trainer arguments.\n\n Example:\n >>> parser = ArgumentParser(add_help=False)\n >>> parser = Trainer.add_argparse_args(parser)\n >>> parser.add_argument('--my_custom_arg', default='something') # doctest: +SKIP\n >>> args = Trainer.parse_argparser(parser.parse_args(\"\"))\n >>> trainer = Trainer.from_argparse_args(args, logger=False)\n \"\"\"\n if isinstance(args, ArgumentParser):\n args = cls.parse_argparser(args)\n params = vars(args)\n\n # we only want to pass in valid Trainer args, the rest may be user specific\n valid_kwargs = inspect.signature(cls.__init__).parameters\n trainer_kwargs = dict((name, params[name]) for name in valid_kwargs if name in params)\n trainer_kwargs.update(**kwargs)\n\n return cls(**trainer_kwargs)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus","text":"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus\n @property\n def num_gpus(self) -> int:\n gpus = self.data_parallel_device_ids\n if gpus is None:\n return 0\n return len(gpus)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel","text":"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel\n @property\n def data_parallel(self) -> bool:\n return self.use_dp or self.use_ddp or self.use_ddp2","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_callback","text":"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_callback\n @property\n def progress_bar_callback(self):\n return self._progress_bar_callback","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_dict","text":"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_dict\n @property\n def progress_bar_dict(self) -> dict:\n \"\"\" Read-only for progress bar metrics. \"\"\"\n ref_model = self.model if not self.data_parallel else self.model.module\n return dict(**ref_model.get_progress_bar_dict(), **self.progress_bar_metrics)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.disable_validation","text":"pytorch_lightning/trainer/trainer.py::Trainer.disable_validation\n @property\n def disable_validation(self) -> bool:\n \"\"\" Check if validation is disabled during training. \"\"\"\n return not self.enable_validation","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.enable_validation","text":"pytorch_lightning/trainer/trainer.py::Trainer.enable_validation\n @property\n def enable_validation(self) -> bool:\n \"\"\" Check if we should run validation during training. \"\"\"\n val_loop_enabled = self.is_overridden('validation_step') and self.limit_val_batches > 0\n return val_loop_enabled or self.fast_dev_run","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.default_root_dir","text":"pytorch_lightning/trainer/trainer.py::Trainer.default_root_dir\n @property\n def default_root_dir(self) -> str:\n \"\"\"\n The default location to save artifacts of loggers, checkpoints etc.\n It is used as a fallback if logger or checkpoint callback do not define specific save paths.\n \"\"\"\n if is_remote_path(self._default_root_dir):\n # it is a remote uri, use as is\n return self._default_root_dir\n return os.path.normpath(self._default_root_dir)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.weights_save_path","text":"pytorch_lightning/trainer/trainer.py::Trainer.weights_save_path\n @property\n def weights_save_path(self) -> str:\n \"\"\"\n The default root location to save weights (checkpoints), e.g., when the\n :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint` does not define a file path.\n \"\"\"\n if is_remote_path(self._weights_save_path):\n # it is a remote uri, use as is\n return self._weights_save_path\n return os.path.normpath(self._weights_save_path)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.fit","text":"pytorch_lightning/trainer/trainer.py::Trainer.fit\n @trainer_state(entering=TrainerState.RUNNING, exiting=TrainerState.FINISHED)\n def fit(\n self,\n model: LightningModule,\n train_dataloader: Optional[DataLoader] = None,\n val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,\n datamodule: Optional[LightningDataModule] = None,\n ):\n r\"\"\"\n Runs the full optimization routine.\n\n Args:\n model: Model to fit.\n\n train_dataloader: A Pytorch\n DataLoader with training samples. If the model has\n a predefined train_dataloader method this will be skipped.\n\n val_dataloaders: Either a single\n Pytorch Dataloader or a list of them, specifying validation samples.\n If the model has a predefined val_dataloaders method this will be skipped\n\n Example::\n\n # Option 1,\n # Define the train_dataloader() and val_dataloader() fxs\n # in the lightningModule\n # RECOMMENDED FOR MOST RESEARCH AND APPLICATIONS TO MAINTAIN READABILITY\n trainer = Trainer()\n model = LightningModule()\n trainer.fit(model)\n\n # Option 2\n # in production cases we might want to pass different datasets to the same model\n # Recommended for PRODUCTION SYSTEMS\n train, val = DataLoader(...), DataLoader(...)\n trainer = Trainer()\n model = LightningModule()\n trainer.fit(model, train_dataloader=train, val_dataloaders=val)\n\n # Option 1 & 2 can be mixed, for example the training set can be\n # defined as part of the model, and validation can then be feed to .fit()\n\n \"\"\"\n results = None\n\n # bind logger and other properties\n self.copy_trainer_model_properties(model)\n\n # clean hparams\n if hasattr(model, 'hparams'):\n parsing.clean_namespace(mod","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.can_prepare_data","text":"pytorch_lightning/trainer/trainer.py::Trainer.can_prepare_data\n def can_prepare_data(self):\n should_call_dm_prepare_data = True\n if self.datamodule is not None and self.is_overridden('prepare_data', self.datamodule):\n should_call_dm_prepare_data = not self.datamodule.has_prepared_data\n\n if self.prepare_data_per_node:\n return self.local_rank == 0 and should_call_dm_prepare_data\n else:\n return self.node_rank == 0 and self.local_rank == 0 and should_call_dm_prepare_data","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__attach_dataloaders","text":"pytorch_lightning/trainer/trainer.py::Trainer.__attach_dataloaders\n def __attach_dataloaders(self, model, train_dataloader=None, val_dataloaders=None, test_dataloaders=None):\n # when dataloader is passed via fit, patch the train_dataloader\n # functions to overwrite with these implementations\n if train_dataloader is not None:\n model.train_dataloader = _PatchDataLoader(train_dataloader)\n\n if val_dataloaders is not None:\n model.val_dataloader = _PatchDataLoader(val_dataloaders)\n\n if test_dataloaders is not None:\n model.test_dataloader = _PatchDataLoader(test_dataloaders)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__attach_datamodule","text":"pytorch_lightning/trainer/trainer.py::Trainer.__attach_datamodule\n def __attach_datamodule(self, model, datamodule, stage):\n\n # We use datamodule if it's been provided on .fit or .test, otherwise we check model for it\n datamodule = datamodule or getattr(model, 'datamodule', None)\n\n # If we have a datamodule, attach necessary hooks + dataloaders\n if datamodule:\n\n # If datamodule.setup('test') has not been called yet, call it\n # if stage == 'test':\n # if self.is_overridden('setup', datamodule) and not datamodule.has_setup_test:\n # datamodule.setup('test')\n\n # Override loader hooks\n if self.is_overridden('train_dataloader', datamodule):\n model.train_dataloader = datamodule.train_dataloader\n if self.is_overridden('val_dataloader', datamodule):\n model.val_dataloader = datamodule.val_dataloader\n if self.is_overridden('test_dataloader', datamodule):\n model.test_dataloader = datamodule.test_dataloader\n\n self.datamodule = datamodule","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.run_pretrain_routine","text":"pytorch_lightning/trainer/trainer.py::Trainer.run_pretrain_routine\n def run_pretrain_routine(self, model: LightningModule):\n \"\"\"Sanity check a few things before starting actual training.\n\n Args:\n model: The model to run sanity test on.\n \"\"\"\n ref_model = model\n if self.data_parallel:\n ref_model = model.module\n\n # give model convenience properties\n ref_model.trainer = self\n\n # set local properties on the model\n self.copy_trainer_model_properties(ref_model)\n\n # init amp. Must be done here instead of __init__ to allow ddp to work\n if self.amp_backend == AMPType.NATIVE and self.precision == 16 and not self.use_tpu:\n self.scaler = torch.cuda.amp.GradScaler()\n\n # log hyper-parameters\n if self.logger is not None:\n # save exp to get started\n self.logger.log_hyperparams(ref_model.hparams)\n self.logger.save()\n\n if self.use_ddp or self.use_ddp2:\n torch_distrib.barrier()\n\n # wait for all models to restore weights\n if self.on_tpu and XLA_AVAILABLE:\n # wait for all processes to catch up\n torch_xla.core.xla_model.rendezvous(\"pl.Trainer.run_pretrain_routine\")\n\n elif self.use_horovod:\n # wait for all processes to catch up\n hvd.join()\n\n # register auto-resubmit when on SLURM\n self.register_slurm_signal_handlers()\n\n # on pretrain routine start\n self.on_pretrain_routine_start(ref_model)\n if self.is_function_implemented('on_pretrain_routine_start'):\n ref_model.on_pretrain_routine_start()\n\n # print model summary\n if self.is_global_zero and self.weights_summary is not None and not self.testing:\n if self.weights_summary in ModelSummary.MODES:\n ref_model.summarize(mode=self.weights_summary)\n else:\n raise MisconfigurationException(\"weights_summ","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer._run_sanity_check","text":"pytorch_lightning/trainer/trainer.py::Trainer._run_sanity_check\n def _run_sanity_check(self, ref_model, model):\n\n using_val_step = ref_model.val_dataloader is not None and self.is_overridden('validation_step')\n should_sanity_check = using_val_step and self.num_sanity_val_steps > 0 and self.limit_val_batches > 0\n\n # run tiny validation (if validation defined)\n # to make sure program won't crash during val\n if should_sanity_check:\n self.reset_val_dataloader(ref_model)\n\n # hook and callback\n self.running_sanity_check = True\n self.on_sanity_check_start()\n\n num_loaders = len(self.val_dataloaders)\n max_batches = [self.num_sanity_val_steps] * num_loaders\n eval_results = self._evaluate(model, self.val_dataloaders, max_batches, False)\n\n # allow no returns from eval\n if eval_results is not None and len(eval_results) > 0:\n # when we get a list back, used only the last item\n if isinstance(eval_results, list):\n eval_results = eval_results[-1]\n\n if isinstance(eval_results, EvalResult):\n callback_metrics = eval_results.callback_metrics\n else:\n _, _, _, callback_metrics, _ = self.process_output(eval_results)\n self.callback_metrics = callback_metrics\n\n self.on_sanity_check_end()\n self.running_sanity_check = False","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.test","text":"pytorch_lightning/trainer/trainer.py::Trainer.test\n @trainer_state(entering=TrainerState.RUNNING, exiting=TrainerState.FINISHED)\n def test(\n self,\n model: Optional[LightningModule] = None,\n test_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,\n ckpt_path: Optional[str] = 'best',\n verbose: bool = True,\n datamodule: Optional[LightningDataModule] = None,\n ):\n r\"\"\"\n\n Separates from fit to make sure you never run on your test set until you want to.\n\n Args:\n model: The model to test.\n\n test_dataloaders: Either a single\n Pytorch Dataloader or a list of them, specifying validation samples.\n\n ckpt_path: Either ``best`` or path to the checkpoint you wish to test.\n If ``None``, use the weights from the last epoch to test. Default to ``best``.\n\n verbose: If True, prints the test results\n\n Returns:\n The final test result dictionary. If no test_epoch_end is defined returns a list of dictionaries\n\n Example::\n\n # Option 1\n # run test with the best checkpoint from ``ModelCheckpoint`` after fitting.\n test = DataLoader(...)\n trainer = Trainer()\n model = LightningModule()\n\n trainer.fit(model)\n trainer.test(test_dataloaders=test)\n\n # Option 2\n # run test with the specified checkpoint after fitting\n test = DataLoader(...)\n trainer = Trainer()\n model = LightningModule()\n\n trainer.fit(model)\n trainer.test(test_dataloaders=test, ckpt_path='path/to/checkpoint.ckpt')\n\n # Option 3\n # run test with the weights from the end of training after fitting\n test = DataLoader(...)\n trainer = Trainer()\n model = LightningModule()\n\n trainer.fit(model)\n trainer.test(test_dataloaders=test, ckpt_p","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__test_using_best_weights","text":"pytorch_lightning/trainer/trainer.py::Trainer.__test_using_best_weights\n def __test_using_best_weights(self, ckpt_path, test_dataloaders):\n model = self.get_model()\n\n # if user requests the best checkpoint but we don't have it, error\n if ckpt_path == 'best' and self.checkpoint_callback.save_top_k <= 0:\n raise MisconfigurationException(\n 'ckpt_path is \"best\", but ModelCheckpoint is not configured to save the best model.'\n )\n\n # load best weights\n if ckpt_path is not None:\n # ckpt_path is 'best' so load the best model\n if ckpt_path == 'best':\n ckpt_path = self.checkpoint_callback.best_model_path\n\n if len(ckpt_path) == 0:\n rank_zero_warn(\n f'.test() found no path for the best weights, {ckpt_path}. Please '\n f'specify a path for a checkpoint .test(ckpt_path=PATH)'\n )\n return {}\n\n ckpt = torch.load(ckpt_path, map_location=lambda storage, loc: storage)\n model.load_state_dict(ckpt['state_dict'])\n\n # attach dataloaders\n if test_dataloaders is not None:\n self.__attach_dataloaders(model, test_dataloaders=test_dataloaders)\n\n # run tests\n self.tested_ckpt_path = ckpt_path\n self.set_random_port(force=True)\n self.testing = True\n os.environ['PL_TESTING_MODE'] = '1'\n self.model = model\n results = self.fit(model)\n self.testing = False\n del os.environ['PL_TESTING_MODE']\n\n # teardown\n if self.is_function_implemented('teardown'):\n model_ref = self.get_model()\n model_ref.teardown('test')\n\n return results","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.__test_given_model","text":"pytorch_lightning/trainer/trainer.py::Trainer.__test_given_model\n def __test_given_model(self, model, test_dataloaders):\n\n # attach data\n if test_dataloaders is not None:\n self.__attach_dataloaders(model, test_dataloaders=test_dataloaders)\n\n # run test\n # sets up testing so we short circuit to eval\n self.set_random_port(force=True)\n self.testing = True\n self.model = model\n results = self.fit(model)\n self.testing = False\n\n # teardown\n if self.is_function_implemented('teardown'):\n model.teardown('test')\n\n return results","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.barrier","text":"pytorch_lightning/trainer/trainer.py::Trainer.barrier\n def barrier(self, name):\n if self.use_ddp or self.use_ddp2:\n pass\n # torch_distrib.barrier()\n\n if self.on_tpu and XLA_AVAILABLE:\n # wait for all processes to catch up\n torch_xla.core.xla_model.rendezvous(f'pl.Trainer.{name}')","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.call_setup_hook","text":"pytorch_lightning/trainer/trainer.py::Trainer.call_setup_hook\n def call_setup_hook(self, model):\n # call setup after the ddp process has connected\n stage_name = 'test' if self.testing else 'fit'\n if self.datamodule is not None:\n called = self.datamodule.has_setup_test if self.testing else self.datamodule.has_setup_fit\n if not called:\n self.datamodule.setup(stage_name)\n self.setup(stage_name)\n model.setup(stage_name)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::Trainer.init_amp","text":"pytorch_lightning/trainer/trainer.py::Trainer.init_amp\n def init_amp(self, amp_type: str):\n assert self.precision in (16, 32), 'only 32 or 16 bit precision supported'\n self.amp_backend = None\n self._setup_amp_backend(amp_type)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__init__","text":"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__init__\n def __init__(self, dataloader: Union[List[DataLoader], DataLoader]):\n self.dataloader = dataloader\n\n # cannot pickle __code__ so cannot verify if PatchDataloader\n # exists which shows dataloader methods have been overwritten.\n # so, we hack it by using the string representation\n self.patch_loader_code = str(self.__call__.__code__)","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__call__","text":"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__call__\n def __call__(self) -> Union[List[DataLoader], DataLoader]:\n return self.dataloader","label":0},{"cid":"pytorch_lightning/trainer/trainer.py::_determine_batch_limits","text":"pytorch_lightning/trainer/trainer.py::_determine_batch_limits\ndef _determine_batch_limits(batches: Union[int, float], name: str) -> Union[int, float]:\n if 0 <= batches <= 1:\n return batches\n elif batches > 1 and batches % 1.0 == 0:\n return int(batches)\n else:\n raise MisconfigurationException(\n f'You have passed invalid value {batches} for {name}, it has to be in [0.0, 1.0] or an int.'\n )","label":0},{"cid":"setup.py::load_requirements","text":"setup.py::load_requirements\ndef load_requirements(path_dir=PATH_ROOT, file_name='base.txt', comment_char='#'):\n with open(os.path.join(path_dir, 'requirements', file_name), 'r') as file:\n lines = [ln.strip() for ln in file.readlines()]\n reqs = []\n for ln in lines:\n # filer all comments\n if comment_char in ln:\n ln = ln[:ln.index(comment_char)].strip()\n # skip directly installed dependencies\n if ln.startswith('http'):\n continue\n if ln: # if requirement is not empty\n reqs.append(ln)\n return reqs","label":0},{"cid":"setup.py::load_long_description","text":"setup.py::load_long_description\ndef load_long_description():\n # https://github.com/PyTorchLightning/pytorch-lightning/raw/master/docs/source/_images/lightning_module/pt_to_pl.png\n url = os.path.join(pytorch_lightning.__homepage__, 'raw', pytorch_lightning.__version__, 'docs')\n text = open('README.md', encoding='utf-8').read()\n # replace relative repository path to absolute link to the release\n text = text.replace('](docs', f']({url}')\n # SVG images are not readable on PyPI, so replace them with PNG\n text = text.replace('.svg', '.png')\n return text","label":0}],"string":"[\n {\n \"cid\": \"docs/source/conf.py::run_apidoc\",\n \"text\": \"docs/source/conf.py::run_apidoc\\ndef run_apidoc(_):\\n sys.path.insert(0, apidoc_output_folder)\\n\\n # delete api-doc files before generating them\\n if os.path.exists(apidoc_output_folder):\\n shutil.rmtree(apidoc_output_folder)\\n\\n for pkg in PACKAGES:\\n argv = ['-e',\\n '-o', apidoc_output_folder,\\n os.path.join(PATH_ROOT, pkg),\\n '**/test_*',\\n '--force',\\n '--private',\\n '--module-first']\\n\\n apidoc.main(argv)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/source/conf.py::setup\",\n \"text\": \"docs/source/conf.py::setup\\ndef setup(app):\\n # this is for hiding doctest decoration,\\n # see: http://z4r.github.io/python/2011/12/02/hides-the-prompts-and-output/\\n app.add_javascript('copybutton.js')\\n app.connect('builder-inited', run_apidoc)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/source/conf.py::package_list_from_file\",\n \"text\": \"docs/source/conf.py::package_list_from_file\\ndef package_list_from_file(file):\\n mocked_packages = []\\n with open(file, 'r') as fp:\\n for ln in fp.readlines():\\n found = [ln.index(ch) for ch in list(',=<>#') if ch in ln]\\n pkg = ln[:min(found)] if found else ln\\n if pkg.rstrip():\\n mocked_packages.append(pkg.rstrip())\\n return mocked_packages\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/source/conf.py::linkcode_resolve\",\n \"text\": \"docs/source/conf.py::linkcode_resolve\\ndef linkcode_resolve(domain, info):\\n def find_source():\\n # try to find the file and line number, based on code from numpy:\\n # https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L286\\n obj = sys.modules[info['module']]\\n for part in info['fullname'].split('.'):\\n obj = getattr(obj, part)\\n fname = inspect.getsourcefile(obj)\\n # https://github.com/rtfd/readthedocs.org/issues/5735\\n if any([s in fname for s in ('readthedocs', 'rtfd', 'checkouts')]):\\n # /home/docs/checkouts/readthedocs.org/user_builds/pytorch_lightning/checkouts/\\n # devel/pytorch_lightning/utilities/cls_experiment.py#L26-L176\\n path_top = os.path.abspath(os.path.join('..', '..', '..'))\\n fname = os.path.relpath(fname, start=path_top)\\n else:\\n # Local build, imitate master\\n fname = 'master/' + os.path.relpath(fname, start=os.path.abspath('..'))\\n source, lineno = inspect.getsourcelines(obj)\\n return fname, lineno, lineno + len(source) - 1\\n\\n if domain != 'py' or not info['module']:\\n return None\\n try:\\n filename = '%s#L%d-L%d' % find_source()\\n except Exception:\\n filename = info['module'].replace('.', '/') + '.py'\\n # import subprocess\\n # tag = subprocess.Popen(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE,\\n # universal_newlines=True).communicate()[0][:-1]\\n branch = filename.split('/')[0]\\n # do mapping from latest tags to master\\n branch = {'latest': 'master', 'stable': 'master'}.get(branch, branch)\\n filename = '/'.join([branch] + filename.split('/')[1:])\\n return \\\"https://github.com/%s/%s/blob/%s\\\" \\\\\\n % (github_user, github_repo, filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"docs/source/conf.py::linkcode_resolve.find_source\",\n \"text\": \"docs/source/conf.py::linkcode_resolve.find_source\\n def find_source():\\n # try to find the file and line number, based on code from numpy:\\n # https://github.com/numpy/numpy/blob/master/doc/source/conf.py#L286\\n obj = sys.modules[info['module']]\\n for part in info['fullname'].split('.'):\\n obj = getattr(obj, part)\\n fname = inspect.getsourcefile(obj)\\n # https://github.com/rtfd/readthedocs.org/issues/5735\\n if any([s in fname for s in ('readthedocs', 'rtfd', 'checkouts')]):\\n # /home/docs/checkouts/readthedocs.org/user_builds/pytorch_lightning/checkouts/\\n # devel/pytorch_lightning/utilities/cls_experiment.py#L26-L176\\n path_top = os.path.abspath(os.path.join('..', '..', '..'))\\n fname = os.path.relpath(fname, start=path_top)\\n else:\\n # Local build, imitate master\\n fname = 'master/' + os.path.relpath(fname, start=os.path.abspath('..'))\\n source, lineno = inspect.getsourcelines(obj)\\n return fname, lineno, lineno + len(source) - 1\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.setup\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.setup\\n def setup(self, stage: str):\\n \\\"\\\"\\\"\\n Called at the beginning of fit and test.\\n This is a good hook when you need to build models dynamically or adjust something about them.\\n This hook is called on every process when using DDP.\\n\\n Args:\\n stage: either 'fit' or 'test'\\n\\n Example::\\n\\n class LitModel(...):\\n def __init__(self):\\n self.l1 = None\\n\\n def prepare_data(self):\\n download_data()\\n tokenize()\\n\\n # don't do this\\n self.something = else\\n\\n def setup(stage):\\n data = Load_data(...)\\n self.l1 = nn.Linear(28, data.num_classes)\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.teardown\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.teardown\\n def teardown(self, stage: str):\\n \\\"\\\"\\\"\\n Called at the end of fit and test.\\n\\n Args:\\n stage: either 'fit' or 'test'\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_start\\n def on_fit_start(self):\\n \\\"\\\"\\\"\\n Called at the very beginning of fit.\\n If on DDP it is called on every process\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_fit_end\\n def on_fit_end(self):\\n \\\"\\\"\\\"\\n Called at the very end of fit.\\n If on DDP it is called on every process\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_start\\n def on_train_start(self) -> None:\\n \\\"\\\"\\\"\\n Called at the beginning of training before sanity check.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_end\\n def on_train_end(self) -> None:\\n \\\"\\\"\\\"\\n Called at the end of training before logger experiment is closed.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_pretrain_routine_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_pretrain_routine_start\\n def on_pretrain_routine_start(self) -> None:\\n \\\"\\\"\\\"\\n Called at the beginning of the pretrain routine (between fit and train start).\\n\\n - fit\\n - pretrain_routine start\\n - pretrain_routine end\\n - training_start\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_pretrain_routine_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_pretrain_routine_end\\n def on_pretrain_routine_end(self) -> None:\\n \\\"\\\"\\\"\\n Called at the end of the pretrain routine (between fit and train start).\\n\\n - fit\\n - pretrain_routine start\\n - pretrain_routine end\\n - training_start\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_batch_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_batch_start\\n def on_train_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop before anything happens for that batch.\\n\\n If you return -1 here, you will skip training for the rest of the current epoch.\\n\\n Args:\\n batch: The batched data as it is returned by the training DataLoader.\\n batch_idx: the index of the batch\\n dataloader_idx: the index of the dataloader\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_batch_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_batch_end\\n def on_train_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop after the batch.\\n\\n Args:\\n batch: The batched data as it is returned by the training DataLoader.\\n batch_idx: the index of the batch\\n dataloader_idx: the index of the dataloader\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_batch_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_batch_start\\n def on_validation_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:\\n \\\"\\\"\\\"\\n Called in the validation loop before anything happens for that batch.\\n\\n Args:\\n batch: The batched data as it is returned by the training DataLoader.\\n batch_idx: the index of the batch\\n dataloader_idx: the index of the dataloader\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_batch_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_batch_end\\n def on_validation_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:\\n \\\"\\\"\\\"\\n Called in the validation loop after the batch.\\n\\n Args:\\n batch: The batched data as it is returned by the training DataLoader.\\n batch_idx: the index of the batch\\n dataloader_idx: the index of the dataloader\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_test_batch_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_test_batch_start\\n def on_test_batch_start(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:\\n \\\"\\\"\\\"\\n Called in the test loop before anything happens for that batch.\\n\\n Args:\\n batch: The batched data as it is returned by the training DataLoader.\\n batch_idx: the index of the batch\\n dataloader_idx: the index of the dataloader\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_test_batch_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_test_batch_end\\n def on_test_batch_end(self, batch: Any, batch_idx: int, dataloader_idx: int) -> None:\\n \\\"\\\"\\\"\\n Called in the test loop after the batch.\\n\\n Args:\\n batch: The batched data as it is returned by the training DataLoader.\\n batch_idx: the index of the batch\\n dataloader_idx: the index of the dataloader\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_start\\n def on_batch_start(self, batch: Any) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop before anything happens for that batch.\\n\\n If you return -1 here, you will skip training for the rest of the current epoch.\\n\\n Args:\\n batch: The batched data as it is returned by the training DataLoader.\\n\\n .. warning:: Deprecated in 0.9.0 will remove 1.0.0 (use `on_train_batch_start` instead)\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_batch_end\\n def on_batch_end(self) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop after the batch.\\n\\n .. warning:: Deprecated in 0.9.0 will remove 1.0.0 (use `on_train_batch_end` instead)\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_start\\n def on_epoch_start(self) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop at the very beginning of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_epoch_end\\n def on_epoch_end(self) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop at the very end of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_start\\n def on_train_epoch_start(self) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop at the very beginning of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_train_epoch_end\\n def on_train_epoch_end(self) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop at the very end of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_start\\n def on_validation_epoch_start(self) -> None:\\n \\\"\\\"\\\"\\n Called in the validation loop at the very beginning of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_validation_epoch_end\\n def on_validation_epoch_end(self) -> None:\\n \\\"\\\"\\\"\\n Called in the validation loop at the very end of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_start\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_start\\n def on_test_epoch_start(self) -> None:\\n \\\"\\\"\\\"\\n Called in the test loop at the very beginning of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_end\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_test_epoch_end\\n def on_test_epoch_end(self) -> None:\\n \\\"\\\"\\\"\\n Called in the test loop at the very end of the epoch.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_pre_performance_check\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_pre_performance_check\\n def on_pre_performance_check(self) -> None:\\n \\\"\\\"\\\"\\n Called at the very beginning of the validation loop.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_post_performance_check\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_post_performance_check\\n def on_post_performance_check(self) -> None:\\n \\\"\\\"\\\"\\n Called at the very end of the validation loop.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_before_zero_grad\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_before_zero_grad\\n def on_before_zero_grad(self, optimizer: Optimizer) -> None:\\n \\\"\\\"\\\"\\n Called after optimizer.step() and before optimizer.zero_grad().\\n\\n Called in the training loop after taking an optimizer step and before zeroing grads.\\n Good place to inspect weight information with weights updated.\\n\\n This is where it is called::\\n\\n for optimizer in optimizers:\\n optimizer.step()\\n model.on_before_zero_grad(optimizer) # < ---- called here\\n optimizer.zero_grad()\\n\\n Args:\\n optimizer: The optimizer for which grads should be zeroed.\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_after_backward\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.on_after_backward\\n def on_after_backward(self) -> None:\\n \\\"\\\"\\\"\\n Called in the training loop after loss.backward() and before optimizers do anything.\\n This is the ideal place to inspect or log gradient information.\\n\\n Example::\\n\\n def on_after_backward(self):\\n # example to inspect gradient information in tensorboard\\n if self.trainer.global_step % 25 == 0: # don't make the tf file huge\\n params = self.state_dict()\\n for k, v in params.items():\\n grads = v\\n name = k\\n self.logger.experiment.add_histogram(tag=name, values=grads,\\n global_step=self.trainer.global_step)\\n\\n \\\"\\\"\\\"\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.backward\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.backward\\n def backward(self, trainer, loss: Tensor, optimizer: Optimizer, optimizer_idx: int) -> None:\\n \\\"\\\"\\\"\\n Override backward with your own implementation if you need to.\\n\\n Args:\\n trainer: Pointer to the trainer\\n loss: Loss is already scaled by accumulated grads\\n optimizer: Current optimizer being used\\n optimizer_idx: Index of the current optimizer being used\\n\\n Called to perform backward step.\\n Feel free to override as needed.\\n\\n The loss passed in has already been scaled for accumulated gradients if requested.\\n\\n Example::\\n\\n def backward(self, trainer, loss, optimizer, optimizer_idx):\\n loss.backward()\\n\\n \\\"\\\"\\\"\\n loss.backward()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.amp_scale_loss\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.amp_scale_loss\\n def amp_scale_loss(self, unscaled_loss, optimizer, optimizer_idx, amp_backend: AMPType):\\n if amp_backend == AMPType.NATIVE:\\n scaled_loss = self.trainer.scaler.scale(unscaled_loss)\\n else:\\n scaled_loss = amp.scale_loss(unscaled_loss, optimizer)\\n\\n return scaled_loss\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/core/hooks.py::ModelHooks.transfer_batch_to_device\",\n \"text\": \"pytorch_lightning/core/hooks.py::ModelHooks.transfer_batch_to_device\\n def transfer_batch_to_device(self, batch: Any, device: torch.device) -> Any:\\n \\\"\\\"\\\"\\n Override this hook if your :class:`~torch.utils.data.DataLoader` returns tensors\\n wrapped in a custom data structure.\\n\\n The data types listed below (and any arbitrary nesting of them) are supported out of the box:\\n\\n - :class:`torch.Tensor` or anything that implements `.to(...)`\\n - :class:`list`\\n - :class:`dict`\\n - :class:`tuple`\\n - :class:`torchtext.data.batch.Batch`\\n\\n For anything else, you need to define how the data is moved to the target device (CPU, GPU, TPU, ...).\\n\\n Example::\\n\\n def transfer_batch_to_device(self, batch, device)\\n if isinstance(batch, CustomBatch):\\n # move all tensors in your custom data structure to the device\\n batch.samples = batch.samples.to(device)\\n batch.targets = batch.targets.to(device)\\n else:\\n batch = super().transfer_batch_to_device(data, device)\\n return batch\\n\\n Args:\\n batch: A batch of data that needs to be transferred to a new device.\\n device: The target device as defined in PyTorch.\\n\\n Returns:\\n A reference to the data on the new device.\\n\\n Note:\\n This hook should only transfer the data and not modify it, nor should it move the data to\\n any other device than the one passed in as argument (unless you know what you are doing).\\n\\n Note:\\n This hook only runs on single GPU training (no data-parallel). If you need multi-GPU support\\n for your custom batch objects, you need to define your custom\\n :class:`~torch.nn.parallel.DistributedDataParallel` or\\n :class:`~pytorch_lightning.overrides.data_parallel.LightningDistributedDataParallel` and\\n override :me\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/loggers/comet.py::CometLogger.__init__\",\n \"text\": \"pytorch_lightning/loggers/comet.py::CometLogger.__init__\\n def __init__(self,\\n api_key: Optional[str] = None,\\n save_dir: Optional[str] = None,\\n workspace: Optional[str] = None,\\n project_name: Optional[str] = None,\\n rest_api_key: Optional[str] = None,\\n experiment_name: Optional[str] = None,\\n experiment_key: Optional[str] = None,\\n offline: bool = False,\\n **kwargs):\\n\\n if not _COMET_AVAILABLE:\\n raise ImportError('You want to use `comet_ml` logger which is not installed yet,'\\n ' install it with `pip install comet-ml`.')\\n super().__init__()\\n self._experiment = None\\n\\n # Determine online or offline mode based on which arguments were passed to CometLogger\\n api_key = api_key or get_api_key(None, get_config())\\n\\n if api_key is not None and save_dir is not None:\\n self.mode = \\\"offline\\\" if offline else \\\"online\\\"\\n self.api_key = api_key\\n self._save_dir = save_dir\\n elif api_key is not None:\\n self.mode = \\\"online\\\"\\n self.api_key = api_key\\n elif save_dir is not None:\\n self.mode = \\\"offline\\\"\\n self._save_dir = save_dir\\n else:\\n # If neither api_key nor save_dir are passed as arguments, raise an exception\\n raise MisconfigurationException(\\n \\\"CometLogger requires either api_key or save_dir during initialization.\\\"\\n )\\n\\n log.info(f\\\"CometLogger will be initialized in {self.mode} mode\\\")\\n\\n self.workspace = workspace\\n self.project_name = project_name\\n self.experiment_key = experiment_key\\n self._kwargs = kwargs\\n\\n if rest_api_key is not None:\\n # Comet.ml rest API, used to determine version number\\n self.rest_api_key = rest_api_key\\n self.comet_api = API(self.rest_a\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/loggers/comet.py::CometLogger.experiment\",\n \"text\": \"pytorch_lightning/loggers/comet.py::CometLogger.experiment\\n @property\\n @rank_zero_experiment\\n def experiment(self) -> CometBaseExperiment:\\n r\\\"\\\"\\\"\\n Actual Comet object. To use Comet features in your\\n :class:`~pytorch_lightning.core.lightning.LightningModule` do the following.\\n\\n Example::\\n\\n self.logger.experiment.some_comet_function()\\n\\n \\\"\\\"\\\"\\n if self._experiment is not None:\\n return self._experiment\\n\\n if self.mode == \\\"online\\\":\\n if self.experiment_key is None:\\n self._experiment = CometExperiment(\\n api_key=self.api_key,\\n workspace=self.workspace,\\n project_name=self.project_name,\\n **self._kwargs\\n )\\n self.experiment_key = self._experiment.get_key()\\n else:\\n self._experiment = CometExistingExperiment(\\n api_key=self.api_key,\\n workspace=self.workspace,\\n project_name=self.project_name,\\n previous_experiment=self.experiment_key,\\n **self._kwargs\\n )\\n else:\\n self._experiment = CometOfflineExperiment(\\n offline_directory=self.save_dir,\\n workspace=self.workspace,\\n project_name=self.project_name,\\n **self._kwargs\\n )\\n\\n return self._experiment\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/loggers/comet.py::CometLogger.log_hyperparams\",\n \"text\": \"pytorch_lightning/loggers/comet.py::CometLogger.log_hyperparams\\n @rank_zero_only\\n def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:\\n params = self._convert_params(params)\\n params = self._flatten_dict(params)\\n self.experiment.log_parameters(params)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/loggers/comet.py::CometLogger.log_metrics\",\n \"text\": \"pytorch_lightning/loggers/comet.py::CometLogger.log_metrics\\n @rank_zero_only\\n def log_metrics(\\n self,\\n metrics: Dict[str, Union[torch.Tensor, float]],\\n step: Optional[int] = None\\n ) -> None:\\n assert rank_zero_only.rank == 0, 'experiment tried to log from global_rank != 0'\\n\\n # Comet.ml expects metrics to be a dictionary of detached tensors on CPU\\n for key, val in metrics.items():\\n if is_tensor(val):\\n metrics[key] = val.cpu().detach()\\n\\n self.experiment.log_metrics(metrics, step=step)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/loggers/comet.py::CometLogger.reset_experiment\",\n \"text\": \"pytorch_lightning/loggers/comet.py::CometLogger.reset_experiment\\n def reset_experiment(self):\\n self._experiment = None\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/loggers/comet.py::CometLogger.finalize\",\n \"text\": \"pytorch_lightning/loggers/comet.py::CometLogger.finalize\\n @rank_zero_only\\n def finalize(self, status: str) -> None:\\n r\\\"\\\"\\\"\\n When calling ``self.experiment.end()``, that experiment won't log any more data to Comet.\\n That's why, if you need to log any more data, you need to create an ExistingCometExperiment.\\n For example, to log data when testing your model after training, because when training is\\n finalized :meth:`CometLogger.finalize` is called.\\n\\n This happens automatically in the :meth:`~CometLogger.experiment` property, when\\n ``self._experiment`` is set to ``None``, i.e. ``self.reset_experiment()``.\\n \\\"\\\"\\\"\\n self.experiment.end()\\n self.reset_experiment()\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/loggers/comet.py::CometLogger.save_dir\",\n \"text\": \"pytorch_lightning/loggers/comet.py::CometLogger.save_dir\\n @property\\n def save_dir(self) -> Optional[str]:\\n return self._save_dir\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/loggers/comet.py::CometLogger.name\",\n \"text\": \"pytorch_lightning/loggers/comet.py::CometLogger.name\\n @property\\n def name(self) -> str:\\n return str(self.experiment.project_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/loggers/comet.py::CometLogger.version\",\n \"text\": \"pytorch_lightning/loggers/comet.py::CometLogger.version\\n @property\\n def version(self) -> str:\\n return self.experiment.id\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/loggers/comet.py::CometLogger.__getstate__\",\n \"text\": \"pytorch_lightning/loggers/comet.py::CometLogger.__getstate__\\n def __getstate__(self):\\n state = self.__dict__.copy()\\n state[\\\"_experiment\\\"] = None\\n return state\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__init__\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__init__\\n def __init__(\\n self,\\n logger: Union[LightningLoggerBase, Iterable[LightningLoggerBase], bool] = True,\\n checkpoint_callback: Union[ModelCheckpoint, bool] = True,\\n early_stop_callback: Optional[Union[EarlyStopping, bool]] = False,\\n callbacks: Optional[List[Callback]] = None,\\n default_root_dir: Optional[str] = None,\\n gradient_clip_val: float = 0,\\n process_position: int = 0,\\n num_nodes: int = 1,\\n num_processes: int = 1,\\n gpus: Optional[Union[List[int], str, int]] = None,\\n auto_select_gpus: bool = False,\\n tpu_cores: Optional[Union[List[int], str, int]] = None,\\n log_gpu_memory: Optional[str] = None,\\n progress_bar_refresh_rate: int = 1,\\n overfit_batches: Union[int, float] = 0.0,\\n track_grad_norm: Union[int, float, str] = -1,\\n check_val_every_n_epoch: int = 1,\\n fast_dev_run: bool = False,\\n accumulate_grad_batches: Union[int, Dict[int, int], List[list]] = 1,\\n max_epochs: int = 1000,\\n min_epochs: int = 1,\\n max_steps: Optional[int] = None,\\n min_steps: Optional[int] = None,\\n limit_train_batches: Union[int, float] = 1.0,\\n limit_val_batches: Union[int, float] = 1.0,\\n limit_test_batches: Union[int, float] = 1.0,\\n val_check_interval: Union[int, float] = 1.0,\\n log_save_interval: int = 100,\\n row_log_interval: int = 50,\\n distributed_backend: Optional[str] = None,\\n sync_batchnorm: bool = False,\\n precision: int = 32,\\n weights_summary: Optional[str] = ModelSummary.MODE_DEFAULT,\\n weights_save_path: Optional[str] = None,\\n num_sanity_val_steps: int = 2,\\n truncated_bptt_steps: Optional[int] = None,\\n resume_from_checkpoint: Optional[str] = None,\\n profiler: Optional[Union[BaseProfiler, bool]] = None,\\n benchmark: bool = False,\\n deterministic: bool = False\",\n \"label\": 1\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.is_global_zero\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.is_global_zero\\n @property\\n def is_global_zero(self) -> bool:\\n return self.global_rank == 0\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.slurm_job_id\\n @property\\n def slurm_job_id(self) -> Optional[int]:\\n try:\\n job_id = os.environ['SLURM_JOB_ID']\\n job_id = int(job_id)\\n\\n # in interactive mode, don't make logs use the same job id\\n in_slurm_interactive_mode = os.environ['SLURM_JOB_NAME'] == 'bash'\\n if in_slurm_interactive_mode:\\n job_id = None\\n\\n except Exception:\\n job_id = None\\n return job_id\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.default_attributes\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.default_attributes\\n @classmethod\\n def default_attributes(cls):\\n init_signature = inspect.signature(Trainer)\\n\\n args = {}\\n for param_name in init_signature.parameters:\\n value = init_signature.parameters[param_name].default\\n args[param_name] = value\\n\\n return args\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.get_init_arguments_and_types\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.get_init_arguments_and_types\\n @classmethod\\n def get_init_arguments_and_types(cls) -> List[Tuple[str, Tuple, Any]]:\\n r\\\"\\\"\\\"Scans the Trainer signature and returns argument names, types and default values.\\n\\n Returns:\\n List with tuples of 3 values:\\n (argument name, set with argument types, argument default value).\\n\\n Examples:\\n >>> args = Trainer.get_init_arguments_and_types()\\n >>> import pprint\\n >>> pprint.pprint(sorted(args)) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE\\n [('accumulate_grad_batches',\\n (<class 'int'>, typing.Dict[int, int], typing.List[list]),\\n 1),\\n ...\\n ('callbacks',\\n (typing.List[pytorch_lightning.callbacks.base.Callback],\\n <class 'NoneType'>),\\n None),\\n ('check_val_every_n_epoch', (<class 'int'>,), 1),\\n ...\\n ('max_epochs', (<class 'int'>,), 1000),\\n ...\\n ('precision', (<class 'int'>,), 32),\\n ('prepare_data_per_node', (<class 'bool'>,), True),\\n ('process_position', (<class 'int'>,), 0),\\n ('profiler',\\n (<class 'pytorch_lightning.profiler.profilers.BaseProfiler'>,\\n <class 'bool'>,\\n <class 'NoneType'>),\\n None),\\n ...\\n \\\"\\\"\\\"\\n trainer_default_params = inspect.signature(cls).parameters\\n name_type_default = []\\n for arg in trainer_default_params:\\n arg_type = trainer_default_params[arg].annotation\\n arg_default = trainer_default_params[arg].default\\n try:\\n arg_types = tuple(arg_type.__args__)\\n except AttributeError:\\n arg_types = (arg_type,)\\n\\n name_type_default.append((arg, arg_types, arg_default))\\n\\n return name_type_default\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.get_deprecated_arg_names\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.get_deprecated_arg_names\\n @classmethod\\n def get_deprecated_arg_names(cls) -> List:\\n \\\"\\\"\\\"Returns a list with deprecated Trainer arguments.\\\"\\\"\\\"\\n depr_arg_names = []\\n for name, val in cls.__dict__.items():\\n if name.startswith('DEPRECATED') and isinstance(val, (tuple, list)):\\n depr_arg_names.extend(val)\\n return depr_arg_names\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.add_argparse_args\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.add_argparse_args\\n @classmethod\\n def add_argparse_args(cls, parent_parser: ArgumentParser) -> ArgumentParser:\\n r\\\"\\\"\\\"Extends existing argparse by default `Trainer` attributes.\\n\\n Args:\\n parent_parser:\\n The custom cli arguments parser, which will be extended by\\n the Trainer default arguments.\\n\\n Only arguments of the allowed types (str, float, int, bool) will\\n extend the `parent_parser`.\\n\\n Examples:\\n >>> import argparse\\n >>> import pprint\\n >>> parser = argparse.ArgumentParser()\\n >>> parser = Trainer.add_argparse_args(parser)\\n >>> args = parser.parse_args([])\\n >>> pprint.pprint(vars(args)) # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE\\n {...\\n 'check_val_every_n_epoch': 1,\\n 'checkpoint_callback': True,\\n 'default_root_dir': None,\\n 'deterministic': False,\\n 'distributed_backend': None,\\n 'early_stop_callback': False,\\n ...\\n 'logger': True,\\n 'max_epochs': 1000,\\n 'max_steps': None,\\n 'min_epochs': 1,\\n 'min_steps': None,\\n ...\\n 'profiler': None,\\n 'progress_bar_refresh_rate': 1,\\n ...}\\n\\n \\\"\\\"\\\"\\n parser = ArgumentParser(parents=[parent_parser], add_help=False,)\\n\\n blacklist = ['kwargs']\\n depr_arg_names = cls.get_deprecated_arg_names() + blacklist\\n\\n allowed_types = (str, float, int, bool)\\n\\n # TODO: get \\\"help\\\" from docstring :)\\n for arg, arg_types, arg_default in (\\n at for at in cls.get_init_arguments_and_types() if at[0] not in depr_arg_names\\n ):\\n arg_types = [at for at in allowed_types if at in arg_types]\\n if not arg_types:\\n # skip argument with not supported type\\n continue\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer._allowed_type\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer._allowed_type\\n def _allowed_type(x) -> Union[int, str]:\\n if ',' in x:\\n return str(x)\\n else:\\n return int(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer._arg_default\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer._arg_default\\n def _arg_default(x) -> Union[int, str]:\\n if ',' in x:\\n return str(x)\\n else:\\n return int(x)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.parse_argparser\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.parse_argparser\\n @classmethod\\n def parse_argparser(cls, arg_parser: Union[ArgumentParser, Namespace]) -> Namespace:\\n \\\"\\\"\\\"Parse CLI arguments, required for custom bool types.\\\"\\\"\\\"\\n args = arg_parser.parse_args() if isinstance(arg_parser, ArgumentParser) else arg_parser\\n\\n types_default = {\\n arg: (arg_types, arg_default) for arg, arg_types, arg_default in cls.get_init_arguments_and_types()\\n }\\n\\n modified_args = {}\\n for k, v in vars(args).items():\\n if k in types_default and v is None:\\n # We need to figure out if the None is due to using nargs=\\\"?\\\" or if it comes from the default value\\n arg_types, arg_default = types_default[k]\\n if bool in arg_types and isinstance(arg_default, bool):\\n # Value has been passed as a flag => It is currently None, so we need to set it to True\\n # We always set to True, regardless of the default value.\\n # Users must pass False directly, but when passing nothing True is assumed.\\n # i.e. the only way to disable somthing that defaults to True is to use the long form:\\n # \\\"--a_default_true_arg False\\\" becomes False, while \\\"--a_default_false_arg\\\" becomes None,\\n # which then becomes True here.\\n\\n v = True\\n\\n modified_args[k] = v\\n return Namespace(**modified_args)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.from_argparse_args\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.from_argparse_args\\n @classmethod\\n def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs) -> 'Trainer':\\n \\\"\\\"\\\"\\n Create an instance from CLI arguments.\\n\\n Args:\\n args: The parser or namespace to take arguments from. Only known arguments will be\\n parsed and passed to the :class:`Trainer`.\\n **kwargs: Additional keyword arguments that may override ones in the parser or namespace.\\n These must be valid Trainer arguments.\\n\\n Example:\\n >>> parser = ArgumentParser(add_help=False)\\n >>> parser = Trainer.add_argparse_args(parser)\\n >>> parser.add_argument('--my_custom_arg', default='something') # doctest: +SKIP\\n >>> args = Trainer.parse_argparser(parser.parse_args(\\\"\\\"))\\n >>> trainer = Trainer.from_argparse_args(args, logger=False)\\n \\\"\\\"\\\"\\n if isinstance(args, ArgumentParser):\\n args = cls.parse_argparser(args)\\n params = vars(args)\\n\\n # we only want to pass in valid Trainer args, the rest may be user specific\\n valid_kwargs = inspect.signature(cls.__init__).parameters\\n trainer_kwargs = dict((name, params[name]) for name in valid_kwargs if name in params)\\n trainer_kwargs.update(**kwargs)\\n\\n return cls(**trainer_kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.num_gpus\\n @property\\n def num_gpus(self) -> int:\\n gpus = self.data_parallel_device_ids\\n if gpus is None:\\n return 0\\n return len(gpus)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.data_parallel\\n @property\\n def data_parallel(self) -> bool:\\n return self.use_dp or self.use_ddp or self.use_ddp2\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_callback\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_callback\\n @property\\n def progress_bar_callback(self):\\n return self._progress_bar_callback\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_dict\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.progress_bar_dict\\n @property\\n def progress_bar_dict(self) -> dict:\\n \\\"\\\"\\\" Read-only for progress bar metrics. \\\"\\\"\\\"\\n ref_model = self.model if not self.data_parallel else self.model.module\\n return dict(**ref_model.get_progress_bar_dict(), **self.progress_bar_metrics)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.disable_validation\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.disable_validation\\n @property\\n def disable_validation(self) -> bool:\\n \\\"\\\"\\\" Check if validation is disabled during training. \\\"\\\"\\\"\\n return not self.enable_validation\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.enable_validation\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.enable_validation\\n @property\\n def enable_validation(self) -> bool:\\n \\\"\\\"\\\" Check if we should run validation during training. \\\"\\\"\\\"\\n val_loop_enabled = self.is_overridden('validation_step') and self.limit_val_batches > 0\\n return val_loop_enabled or self.fast_dev_run\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.default_root_dir\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.default_root_dir\\n @property\\n def default_root_dir(self) -> str:\\n \\\"\\\"\\\"\\n The default location to save artifacts of loggers, checkpoints etc.\\n It is used as a fallback if logger or checkpoint callback do not define specific save paths.\\n \\\"\\\"\\\"\\n if is_remote_path(self._default_root_dir):\\n # it is a remote uri, use as is\\n return self._default_root_dir\\n return os.path.normpath(self._default_root_dir)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.weights_save_path\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.weights_save_path\\n @property\\n def weights_save_path(self) -> str:\\n \\\"\\\"\\\"\\n The default root location to save weights (checkpoints), e.g., when the\\n :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint` does not define a file path.\\n \\\"\\\"\\\"\\n if is_remote_path(self._weights_save_path):\\n # it is a remote uri, use as is\\n return self._weights_save_path\\n return os.path.normpath(self._weights_save_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.fit\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.fit\\n @trainer_state(entering=TrainerState.RUNNING, exiting=TrainerState.FINISHED)\\n def fit(\\n self,\\n model: LightningModule,\\n train_dataloader: Optional[DataLoader] = None,\\n val_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,\\n datamodule: Optional[LightningDataModule] = None,\\n ):\\n r\\\"\\\"\\\"\\n Runs the full optimization routine.\\n\\n Args:\\n model: Model to fit.\\n\\n train_dataloader: A Pytorch\\n DataLoader with training samples. If the model has\\n a predefined train_dataloader method this will be skipped.\\n\\n val_dataloaders: Either a single\\n Pytorch Dataloader or a list of them, specifying validation samples.\\n If the model has a predefined val_dataloaders method this will be skipped\\n\\n Example::\\n\\n # Option 1,\\n # Define the train_dataloader() and val_dataloader() fxs\\n # in the lightningModule\\n # RECOMMENDED FOR MOST RESEARCH AND APPLICATIONS TO MAINTAIN READABILITY\\n trainer = Trainer()\\n model = LightningModule()\\n trainer.fit(model)\\n\\n # Option 2\\n # in production cases we might want to pass different datasets to the same model\\n # Recommended for PRODUCTION SYSTEMS\\n train, val = DataLoader(...), DataLoader(...)\\n trainer = Trainer()\\n model = LightningModule()\\n trainer.fit(model, train_dataloader=train, val_dataloaders=val)\\n\\n # Option 1 & 2 can be mixed, for example the training set can be\\n # defined as part of the model, and validation can then be feed to .fit()\\n\\n \\\"\\\"\\\"\\n results = None\\n\\n # bind logger and other properties\\n self.copy_trainer_model_properties(model)\\n\\n # clean hparams\\n if hasattr(model, 'hparams'):\\n parsing.clean_namespace(mod\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.can_prepare_data\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.can_prepare_data\\n def can_prepare_data(self):\\n should_call_dm_prepare_data = True\\n if self.datamodule is not None and self.is_overridden('prepare_data', self.datamodule):\\n should_call_dm_prepare_data = not self.datamodule.has_prepared_data\\n\\n if self.prepare_data_per_node:\\n return self.local_rank == 0 and should_call_dm_prepare_data\\n else:\\n return self.node_rank == 0 and self.local_rank == 0 and should_call_dm_prepare_data\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__attach_dataloaders\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__attach_dataloaders\\n def __attach_dataloaders(self, model, train_dataloader=None, val_dataloaders=None, test_dataloaders=None):\\n # when dataloader is passed via fit, patch the train_dataloader\\n # functions to overwrite with these implementations\\n if train_dataloader is not None:\\n model.train_dataloader = _PatchDataLoader(train_dataloader)\\n\\n if val_dataloaders is not None:\\n model.val_dataloader = _PatchDataLoader(val_dataloaders)\\n\\n if test_dataloaders is not None:\\n model.test_dataloader = _PatchDataLoader(test_dataloaders)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__attach_datamodule\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__attach_datamodule\\n def __attach_datamodule(self, model, datamodule, stage):\\n\\n # We use datamodule if it's been provided on .fit or .test, otherwise we check model for it\\n datamodule = datamodule or getattr(model, 'datamodule', None)\\n\\n # If we have a datamodule, attach necessary hooks + dataloaders\\n if datamodule:\\n\\n # If datamodule.setup('test') has not been called yet, call it\\n # if stage == 'test':\\n # if self.is_overridden('setup', datamodule) and not datamodule.has_setup_test:\\n # datamodule.setup('test')\\n\\n # Override loader hooks\\n if self.is_overridden('train_dataloader', datamodule):\\n model.train_dataloader = datamodule.train_dataloader\\n if self.is_overridden('val_dataloader', datamodule):\\n model.val_dataloader = datamodule.val_dataloader\\n if self.is_overridden('test_dataloader', datamodule):\\n model.test_dataloader = datamodule.test_dataloader\\n\\n self.datamodule = datamodule\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.run_pretrain_routine\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.run_pretrain_routine\\n def run_pretrain_routine(self, model: LightningModule):\\n \\\"\\\"\\\"Sanity check a few things before starting actual training.\\n\\n Args:\\n model: The model to run sanity test on.\\n \\\"\\\"\\\"\\n ref_model = model\\n if self.data_parallel:\\n ref_model = model.module\\n\\n # give model convenience properties\\n ref_model.trainer = self\\n\\n # set local properties on the model\\n self.copy_trainer_model_properties(ref_model)\\n\\n # init amp. Must be done here instead of __init__ to allow ddp to work\\n if self.amp_backend == AMPType.NATIVE and self.precision == 16 and not self.use_tpu:\\n self.scaler = torch.cuda.amp.GradScaler()\\n\\n # log hyper-parameters\\n if self.logger is not None:\\n # save exp to get started\\n self.logger.log_hyperparams(ref_model.hparams)\\n self.logger.save()\\n\\n if self.use_ddp or self.use_ddp2:\\n torch_distrib.barrier()\\n\\n # wait for all models to restore weights\\n if self.on_tpu and XLA_AVAILABLE:\\n # wait for all processes to catch up\\n torch_xla.core.xla_model.rendezvous(\\\"pl.Trainer.run_pretrain_routine\\\")\\n\\n elif self.use_horovod:\\n # wait for all processes to catch up\\n hvd.join()\\n\\n # register auto-resubmit when on SLURM\\n self.register_slurm_signal_handlers()\\n\\n # on pretrain routine start\\n self.on_pretrain_routine_start(ref_model)\\n if self.is_function_implemented('on_pretrain_routine_start'):\\n ref_model.on_pretrain_routine_start()\\n\\n # print model summary\\n if self.is_global_zero and self.weights_summary is not None and not self.testing:\\n if self.weights_summary in ModelSummary.MODES:\\n ref_model.summarize(mode=self.weights_summary)\\n else:\\n raise MisconfigurationException(\\\"weights_summ\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer._run_sanity_check\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer._run_sanity_check\\n def _run_sanity_check(self, ref_model, model):\\n\\n using_val_step = ref_model.val_dataloader is not None and self.is_overridden('validation_step')\\n should_sanity_check = using_val_step and self.num_sanity_val_steps > 0 and self.limit_val_batches > 0\\n\\n # run tiny validation (if validation defined)\\n # to make sure program won't crash during val\\n if should_sanity_check:\\n self.reset_val_dataloader(ref_model)\\n\\n # hook and callback\\n self.running_sanity_check = True\\n self.on_sanity_check_start()\\n\\n num_loaders = len(self.val_dataloaders)\\n max_batches = [self.num_sanity_val_steps] * num_loaders\\n eval_results = self._evaluate(model, self.val_dataloaders, max_batches, False)\\n\\n # allow no returns from eval\\n if eval_results is not None and len(eval_results) > 0:\\n # when we get a list back, used only the last item\\n if isinstance(eval_results, list):\\n eval_results = eval_results[-1]\\n\\n if isinstance(eval_results, EvalResult):\\n callback_metrics = eval_results.callback_metrics\\n else:\\n _, _, _, callback_metrics, _ = self.process_output(eval_results)\\n self.callback_metrics = callback_metrics\\n\\n self.on_sanity_check_end()\\n self.running_sanity_check = False\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.test\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.test\\n @trainer_state(entering=TrainerState.RUNNING, exiting=TrainerState.FINISHED)\\n def test(\\n self,\\n model: Optional[LightningModule] = None,\\n test_dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None,\\n ckpt_path: Optional[str] = 'best',\\n verbose: bool = True,\\n datamodule: Optional[LightningDataModule] = None,\\n ):\\n r\\\"\\\"\\\"\\n\\n Separates from fit to make sure you never run on your test set until you want to.\\n\\n Args:\\n model: The model to test.\\n\\n test_dataloaders: Either a single\\n Pytorch Dataloader or a list of them, specifying validation samples.\\n\\n ckpt_path: Either ``best`` or path to the checkpoint you wish to test.\\n If ``None``, use the weights from the last epoch to test. Default to ``best``.\\n\\n verbose: If True, prints the test results\\n\\n Returns:\\n The final test result dictionary. If no test_epoch_end is defined returns a list of dictionaries\\n\\n Example::\\n\\n # Option 1\\n # run test with the best checkpoint from ``ModelCheckpoint`` after fitting.\\n test = DataLoader(...)\\n trainer = Trainer()\\n model = LightningModule()\\n\\n trainer.fit(model)\\n trainer.test(test_dataloaders=test)\\n\\n # Option 2\\n # run test with the specified checkpoint after fitting\\n test = DataLoader(...)\\n trainer = Trainer()\\n model = LightningModule()\\n\\n trainer.fit(model)\\n trainer.test(test_dataloaders=test, ckpt_path='path/to/checkpoint.ckpt')\\n\\n # Option 3\\n # run test with the weights from the end of training after fitting\\n test = DataLoader(...)\\n trainer = Trainer()\\n model = LightningModule()\\n\\n trainer.fit(model)\\n trainer.test(test_dataloaders=test, ckpt_p\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__test_using_best_weights\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__test_using_best_weights\\n def __test_using_best_weights(self, ckpt_path, test_dataloaders):\\n model = self.get_model()\\n\\n # if user requests the best checkpoint but we don't have it, error\\n if ckpt_path == 'best' and self.checkpoint_callback.save_top_k <= 0:\\n raise MisconfigurationException(\\n 'ckpt_path is \\\"best\\\", but ModelCheckpoint is not configured to save the best model.'\\n )\\n\\n # load best weights\\n if ckpt_path is not None:\\n # ckpt_path is 'best' so load the best model\\n if ckpt_path == 'best':\\n ckpt_path = self.checkpoint_callback.best_model_path\\n\\n if len(ckpt_path) == 0:\\n rank_zero_warn(\\n f'.test() found no path for the best weights, {ckpt_path}. Please '\\n f'specify a path for a checkpoint .test(ckpt_path=PATH)'\\n )\\n return {}\\n\\n ckpt = torch.load(ckpt_path, map_location=lambda storage, loc: storage)\\n model.load_state_dict(ckpt['state_dict'])\\n\\n # attach dataloaders\\n if test_dataloaders is not None:\\n self.__attach_dataloaders(model, test_dataloaders=test_dataloaders)\\n\\n # run tests\\n self.tested_ckpt_path = ckpt_path\\n self.set_random_port(force=True)\\n self.testing = True\\n os.environ['PL_TESTING_MODE'] = '1'\\n self.model = model\\n results = self.fit(model)\\n self.testing = False\\n del os.environ['PL_TESTING_MODE']\\n\\n # teardown\\n if self.is_function_implemented('teardown'):\\n model_ref = self.get_model()\\n model_ref.teardown('test')\\n\\n return results\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.__test_given_model\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.__test_given_model\\n def __test_given_model(self, model, test_dataloaders):\\n\\n # attach data\\n if test_dataloaders is not None:\\n self.__attach_dataloaders(model, test_dataloaders=test_dataloaders)\\n\\n # run test\\n # sets up testing so we short circuit to eval\\n self.set_random_port(force=True)\\n self.testing = True\\n self.model = model\\n results = self.fit(model)\\n self.testing = False\\n\\n # teardown\\n if self.is_function_implemented('teardown'):\\n model.teardown('test')\\n\\n return results\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.barrier\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.barrier\\n def barrier(self, name):\\n if self.use_ddp or self.use_ddp2:\\n pass\\n # torch_distrib.barrier()\\n\\n if self.on_tpu and XLA_AVAILABLE:\\n # wait for all processes to catch up\\n torch_xla.core.xla_model.rendezvous(f'pl.Trainer.{name}')\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.call_setup_hook\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.call_setup_hook\\n def call_setup_hook(self, model):\\n # call setup after the ddp process has connected\\n stage_name = 'test' if self.testing else 'fit'\\n if self.datamodule is not None:\\n called = self.datamodule.has_setup_test if self.testing else self.datamodule.has_setup_fit\\n if not called:\\n self.datamodule.setup(stage_name)\\n self.setup(stage_name)\\n model.setup(stage_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::Trainer.init_amp\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::Trainer.init_amp\\n def init_amp(self, amp_type: str):\\n assert self.precision in (16, 32), 'only 32 or 16 bit precision supported'\\n self.amp_backend = None\\n self._setup_amp_backend(amp_type)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__init__\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__init__\\n def __init__(self, dataloader: Union[List[DataLoader], DataLoader]):\\n self.dataloader = dataloader\\n\\n # cannot pickle __code__ so cannot verify if PatchDataloader\\n # exists which shows dataloader methods have been overwritten.\\n # so, we hack it by using the string representation\\n self.patch_loader_code = str(self.__call__.__code__)\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__call__\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::_PatchDataLoader.__call__\\n def __call__(self) -> Union[List[DataLoader], DataLoader]:\\n return self.dataloader\",\n \"label\": 0\n },\n {\n \"cid\": \"pytorch_lightning/trainer/trainer.py::_determine_batch_limits\",\n \"text\": \"pytorch_lightning/trainer/trainer.py::_determine_batch_limits\\ndef _determine_batch_limits(batches: Union[int, float], name: str) -> Union[int, float]:\\n if 0 <= batches <= 1:\\n return batches\\n elif batches > 1 and batches % 1.0 == 0:\\n return int(batches)\\n else:\\n raise MisconfigurationException(\\n f'You have passed invalid value {batches} for {name}, it has to be in [0.0, 1.0] or an int.'\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::load_requirements\",\n \"text\": \"setup.py::load_requirements\\ndef load_requirements(path_dir=PATH_ROOT, file_name='base.txt', comment_char='#'):\\n with open(os.path.join(path_dir, 'requirements', file_name), 'r') as file:\\n lines = [ln.strip() for ln in file.readlines()]\\n reqs = []\\n for ln in lines:\\n # filer all comments\\n if comment_char in ln:\\n ln = ln[:ln.index(comment_char)].strip()\\n # skip directly installed dependencies\\n if ln.startswith('http'):\\n continue\\n if ln: # if requirement is not empty\\n reqs.append(ln)\\n return reqs\",\n \"label\": 0\n },\n {\n \"cid\": \"setup.py::load_long_description\",\n \"text\": \"setup.py::load_long_description\\ndef load_long_description():\\n # https://github.com/PyTorchLightning/pytorch-lightning/raw/master/docs/source/_images/lightning_module/pt_to_pl.png\\n url = os.path.join(pytorch_lightning.__homepage__, 'raw', pytorch_lightning.__version__, 'docs')\\n text = open('README.md', encoding='utf-8').read()\\n # replace relative repository path to absolute link to the release\\n text = text.replace('](docs', f']({url}')\\n # SVG images are not readable on PyPI, so replace them with PNG\\n text = text.replace('.svg', '.png')\\n return text\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":295,"cells":{"instance_id":{"kind":"string","value":"ytdl-org__youtube-dl-18371"},"repo":{"kind":"string","value":"ytdl-org/youtube-dl"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"[ARD:mediathek] RegexNotFoundError\n## Please follow the guide below\r\n\r\n- You will be asked some questions and requested to provide some information, please read them **carefully** and answer honestly\r\n- Put an `x` into all the boxes [ ] relevant to your *issue* (like this: `[x]`)\r\n- Use the *Preview* tab to see what your issue will actually look like\r\n\r\n---\r\n\r\n### Make sure you are using the *latest* version: run `youtube-dl --version` and ensure your version is *2018.11.23*. If it's not, read [this FAQ entry](https://github.com/rg3/youtube-dl/blob/master/README.md#how-do-i-update-youtube-dl) and update. Issues with outdated version will be rejected.\r\n- [x] I've **verified** and **I assure** that I'm running youtube-dl **2018.11.23**\r\n\r\n### Before submitting an *issue* make sure you have:\r\n- [x] At least skimmed through the [README](https://github.com/rg3/youtube-dl/blob/master/README.md), **most notably** the [FAQ](https://github.com/rg3/youtube-dl#faq) and [BUGS](https://github.com/rg3/youtube-dl#bugs) sections\r\n- [x] [Searched](https://github.com/rg3/youtube-dl/search?type=Issues) the bugtracker for similar issues including closed ones\r\n- [x] Checked that provided video/audio/playlist URLs (if any) are alive and playable in a browser\r\n\r\n### What is the purpose of your *issue*?\r\n- [x] Bug report (encountered problems with youtube-dl)\r\n- [ ] Site support request (request for adding support for a new site)\r\n- [ ] Feature request (request for a new functionality)\r\n- [ ] Question\r\n- [ ] Other\r\n\r\n---\r\n\r\n### The following sections concretize particular purposed issues, you can erase any section (the contents between triple ---) not applicable to your *issue*\r\n\r\n---\r\n\r\n### If the purpose of this *issue* is a *bug report*, *site support request* or you are not completely sure provide the full verbose output as follows:\r\n\r\nAdd the `-v` flag to **your command line** you run youtube-dl with (`youtube-dl -v <your command line>`), copy the **whole** output and insert it here. It should look similar to one below (replace it with **your** log inserted between triple ```):\r\n\r\n```\r\n[debug] System config: []\r\n[debug] User config: []\r\n[debug] Custom config: []\r\n[debug] Command-line args: [u'-v', u'https://www.ardmediathek.de/ard/player/Y3JpZDovL3dkci5kZS9CZWl0cmFnLTcyY2RlOTlkLTRhNmUtNGRjNi1hMmJkLTY2YTI3ZTBkZDUxYQ/planet-deutschland-300-millionen-jahre-der-grosse-crash-1-3']\r\n[debug] Encodings: locale UTF-8, fs UTF-8, out UTF-8, pref UTF-8\r\n[debug] youtube-dl version 2018.11.23\r\n[debug] Python version 2.7.6 (CPython) - Linux-4.4.0-138-generic-x86_64-with-Ubuntu-14.04-trusty\r\n[debug] exe versions: avconv 9.20-6, avprobe 9.20-6, rtmpdump 2.4\r\n[debug] Proxy map: {}\r\n[ARD:mediathek] planet-deutschland-300-millionen-jahre-der-grosse-crash-1-3: Downloading webpage\r\nERROR: Unable to extract title; please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; type youtube-dl -U to update. Be sure to call youtube-dl with the --verbose flag and include its complete output.\r\nTraceback (most recent call last):\r\n File \"/usr/local/bin/youtube-dl/youtube_dl/YoutubeDL.py\", line 792, in extract_info\r\n ie_result = ie.extract(url)\r\n File \"/usr/local/bin/youtube-dl/youtube_dl/extractor/common.py\", line 508, in extract\r\n ie_result = self._real_extract(url)\r\n File \"/usr/local/bin/youtube-dl/youtube_dl/extractor/ard.py\", line 177, in _real_extract\r\n webpage, 'title')\r\n File \"/usr/local/bin/youtube-dl/youtube_dl/extractor/common.py\", line 992, in _html_search_regex\r\n res = self._search_regex(pattern, string, name, default, fatal, flags, group)\r\n File \"/usr/local/bin/youtube-dl/youtube_dl/extractor/common.py\", line 983, in _search_regex\r\n raise RegexNotFoundError('Unable to extract %s' % _name)\r\nRegexNotFoundError: Unable to extract title; please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; type youtube-dl -U to update. Be sure to call youtube-dl with the --verbose fla"},"gold_files":{"kind":"list like","value":["youtube_dl/extractor/ard.py"],"string":"[\n \"youtube_dl/extractor/ard.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["youtube_dl/extractor/ard.py::ARDMediathekIE._real_extract"],"string":"[\n \"youtube_dl/extractor/ard.py::ARDMediathekIE._real_extract\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"youtube_dl/extractor/ard.py","text":"youtube_dl/extractor/ard.py\nclass ARDMediathekIE\n def _extract_media_info(self, media_info_url, webpage, video_id)\n def _extract_formats(self, media_info, video_id)\n def _real_extract(self, url)\nclass ARDIE\n def _real_extract(self, url)\nclass ARDBetaMediathekIE\n def _real_extract(self, url)","label":1},{"cid":"youtube_dl/extractor/commonmistakes.py","text":"youtube_dl/extractor/commonmistakes.py\nclass CommonMistakesIE\n def _real_extract(self, url)\nclass UnicodeBOMIE\n def _real_extract(self, url)","label":0},{"cid":"youtube_dl/update.py","text":"youtube_dl/update.py\ndef rsa_verify(message, signature, key)\ndef update_self(to_screen, verbose, opener) # Update the program file with the latest version from the repository\n def version_tuple(version_str)\ndef get_notes(versions, fromVersion)\ndef print_notes(to_screen, versions, fromVersion=__version__)","label":0}],"string":"[\n {\n \"cid\": \"youtube_dl/extractor/ard.py\",\n \"text\": \"youtube_dl/extractor/ard.py\\nclass ARDMediathekIE\\n def _extract_media_info(self, media_info_url, webpage, video_id)\\n def _extract_formats(self, media_info, video_id)\\n def _real_extract(self, url)\\nclass ARDIE\\n def _real_extract(self, url)\\nclass ARDBetaMediathekIE\\n def _real_extract(self, url)\",\n \"label\": 1\n },\n {\n \"cid\": \"youtube_dl/extractor/commonmistakes.py\",\n \"text\": \"youtube_dl/extractor/commonmistakes.py\\nclass CommonMistakesIE\\n def _real_extract(self, url)\\nclass UnicodeBOMIE\\n def _real_extract(self, url)\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/update.py\",\n \"text\": \"youtube_dl/update.py\\ndef rsa_verify(message, signature, key)\\ndef update_self(to_screen, verbose, opener) # Update the program file with the latest version from the repository\\n def version_tuple(version_str)\\ndef get_notes(versions, fromVersion)\\ndef print_notes(to_screen, versions, fromVersion=__version__)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"youtube_dl/extractor/ard.py::ARDMediathekIE._extract_media_info","text":"youtube_dl/extractor/ard.py::ARDMediathekIE._extract_media_info\n def _extract_media_info(self, media_info_url, webpage, video_id):\n media_info = self._download_json(\n media_info_url, video_id, 'Downloading media JSON')\n\n formats = self._extract_formats(media_info, video_id)\n\n if not formats:\n if '\"fsk\"' in webpage:\n raise ExtractorError(\n 'This video is only available after 20:00', expected=True)\n elif media_info.get('_geoblocked'):\n raise ExtractorError('This video is not available due to geo restriction', expected=True)\n\n self._sort_formats(formats)\n\n duration = int_or_none(media_info.get('_duration'))\n thumbnail = media_info.get('_previewImage')\n is_live = media_info.get('_isLive') is True\n\n subtitles = {}\n subtitle_url = media_info.get('_subtitleUrl')\n if subtitle_url:\n subtitles['de'] = [{\n 'ext': 'ttml',\n 'url': subtitle_url,\n }]\n\n return {\n 'id': video_id,\n 'duration': duration,\n 'thumbnail': thumbnail,\n 'is_live': is_live,\n 'formats': formats,\n 'subtitles': subtitles,\n }","label":0},{"cid":"youtube_dl/extractor/ard.py::ARDMediathekIE._extract_formats","text":"youtube_dl/extractor/ard.py::ARDMediathekIE._extract_formats\n def _extract_formats(self, media_info, video_id):\n type_ = media_info.get('_type')\n media_array = media_info.get('_mediaArray', [])\n formats = []\n for num, media in enumerate(media_array):\n for stream in media.get('_mediaStreamArray', []):\n stream_urls = stream.get('_stream')\n if not stream_urls:\n continue\n if not isinstance(stream_urls, list):\n stream_urls = [stream_urls]\n quality = stream.get('_quality')\n server = stream.get('_server')\n for stream_url in stream_urls:\n if not url_or_none(stream_url):\n continue\n ext = determine_ext(stream_url)\n if quality != 'auto' and ext in ('f4m', 'm3u8'):\n continue\n if ext == 'f4m':\n formats.extend(self._extract_f4m_formats(\n update_url_query(stream_url, {\n 'hdcore': '3.1.1',\n 'plugin': 'aasp-3.1.1.69.124'\n }),\n video_id, f4m_id='hds', fatal=False))\n elif ext == 'm3u8':\n formats.extend(self._extract_m3u8_formats(\n stream_url, video_id, 'mp4', m3u8_id='hls', fatal=False))\n else:\n if server and server.startswith('rtmp'):\n f = {\n 'url': server,\n 'play_path': stream_url,\n 'format_id': 'a%s-rtmp-%s' % (num, quality),\n }\n else:\n f = {\n 'url': stream_url,\n 'forma","label":0},{"cid":"youtube_dl/extractor/ard.py::ARDMediathekIE._real_extract","text":"youtube_dl/extractor/ard.py::ARDMediathekIE._real_extract\n def _real_extract(self, url):\n # determine video id from url\n m = re.match(self._VALID_URL, url)\n\n document_id = None\n\n numid = re.search(r'documentId=([0-9]+)', url)\n if numid:\n document_id = video_id = numid.group(1)\n else:\n video_id = m.group('video_id')\n\n webpage = self._download_webpage(url, video_id)\n\n ERRORS = (\n ('>Leider liegt eine Störung vor.', 'Video %s is unavailable'),\n ('>Der gewünschte Beitrag ist nicht mehr verfügbar.<',\n 'Video %s is no longer available'),\n )\n\n for pattern, message in ERRORS:\n if pattern in webpage:\n raise ExtractorError(message % video_id, expected=True)\n\n if re.search(r'[\\?&]rss($|[=&])', url):\n doc = compat_etree_fromstring(webpage.encode('utf-8'))\n if doc.tag == 'rss':\n return GenericIE()._extract_rss(url, video_id, doc)\n\n title = self._html_search_regex(\n [r'<h1(?:\\s+class=\"boxTopHeadline\")?>(.*?)</h1>',\n r'<meta name=\"dcterms\\.title\" content=\"(.*?)\"/>',\n r'<h4 class=\"headline\">(.*?)</h4>'],\n webpage, 'title')\n description = self._html_search_meta(\n 'dcterms.abstract', webpage, 'description', default=None)\n if description is None:\n description = self._html_search_meta(\n 'description', webpage, 'meta description')\n\n # Thumbnail is sometimes not present.\n # It is in the mobile version, but that seems to use a different URL\n # structure altogether.\n thumbnail = self._og_search_thumbnail(webpage, default=None)\n\n media_streams = re.findall(r'''(?x)\n mediaCollection\\.addMediaStream\\([0-9]+,\\s*[0-9]+,\\s*\"[^\"]*\",\\s*\n \"([^\"]+)\"''', webpage)\n\n if media_streams:\n QUALITIES = qualities(['lo', 'hi', 'hq'])\n ","label":1},{"cid":"youtube_dl/extractor/ard.py::ARDIE._real_extract","text":"youtube_dl/extractor/ard.py::ARDIE._real_extract\n def _real_extract(self, url):\n mobj = re.match(self._VALID_URL, url)\n display_id = mobj.group('display_id')\n\n player_url = mobj.group('mainurl') + '~playerXml.xml'\n doc = self._download_xml(player_url, display_id)\n video_node = doc.find('./video')\n upload_date = unified_strdate(xpath_text(\n video_node, './broadcastDate'))\n thumbnail = xpath_text(video_node, './/teaserImage//variant/url')\n\n formats = []\n for a in video_node.findall('.//asset'):\n f = {\n 'format_id': a.attrib['type'],\n 'width': int_or_none(a.find('./frameWidth').text),\n 'height': int_or_none(a.find('./frameHeight').text),\n 'vbr': int_or_none(a.find('./bitrateVideo').text),\n 'abr': int_or_none(a.find('./bitrateAudio').text),\n 'vcodec': a.find('./codecVideo').text,\n 'tbr': int_or_none(a.find('./totalBitrate').text),\n }\n if a.find('./serverPrefix').text:\n f['url'] = a.find('./serverPrefix').text\n f['playpath'] = a.find('./fileName').text\n else:\n f['url'] = a.find('./fileName').text\n formats.append(f)\n self._sort_formats(formats)\n\n return {\n 'id': mobj.group('id'),\n 'formats': formats,\n 'display_id': display_id,\n 'title': video_node.find('./title').text,\n 'duration': parse_duration(video_node.find('./duration').text),\n 'upload_date': upload_date,\n 'thumbnail': thumbnail,\n }","label":0},{"cid":"youtube_dl/extractor/ard.py::ARDBetaMediathekIE._real_extract","text":"youtube_dl/extractor/ard.py::ARDBetaMediathekIE._real_extract\n def _real_extract(self, url):\n mobj = re.match(self._VALID_URL, url)\n video_id = mobj.group('video_id')\n display_id = mobj.group('display_id')\n\n webpage = self._download_webpage(url, display_id)\n data_json = self._search_regex(r'window\\.__APOLLO_STATE__\\s*=\\s*(\\{.*);\\n', webpage, 'json')\n data = self._parse_json(data_json, display_id)\n\n res = {\n 'id': video_id,\n 'display_id': display_id,\n }\n formats = []\n for widget in data.values():\n if widget.get('_geoblocked'):\n raise ExtractorError('This video is not available due to geoblocking', expected=True)\n\n if '_duration' in widget:\n res['duration'] = widget['_duration']\n if 'clipTitle' in widget:\n res['title'] = widget['clipTitle']\n if '_previewImage' in widget:\n res['thumbnail'] = widget['_previewImage']\n if 'broadcastedOn' in widget:\n res['upload_date'] = unified_strdate(widget['broadcastedOn'])\n if 'synopsis' in widget:\n res['description'] = widget['synopsis']\n if '_subtitleUrl' in widget:\n res['subtitles'] = {'de': [{\n 'ext': 'ttml',\n 'url': widget['_subtitleUrl'],\n }]}\n if '_quality' in widget:\n format_url = widget['_stream']['json'][0]\n\n if format_url.endswith('.f4m'):\n formats.extend(self._extract_f4m_formats(\n format_url + '?hdcore=3.11.0',\n video_id, f4m_id='hds', fatal=False))\n elif format_url.endswith('m3u8'):\n formats.extend(self._extract_m3u8_formats(\n format_url, video_id, 'mp4', m3u8_id='hls', fatal=False))\n else:\n formats.appe","label":0},{"cid":"youtube_dl/extractor/commonmistakes.py::CommonMistakesIE._real_extract","text":"youtube_dl/extractor/commonmistakes.py::CommonMistakesIE._real_extract\n def _real_extract(self, url):\n msg = (\n 'You\\'ve asked youtube-dl to download the URL \"%s\". '\n 'That doesn\\'t make any sense. '\n 'Simply remove the parameter in your command or configuration.'\n ) % url\n if not self._downloader.params.get('verbose'):\n msg += ' Add -v to the command line to see what arguments and configuration youtube-dl got.'\n raise ExtractorError(msg, expected=True)","label":0},{"cid":"youtube_dl/extractor/commonmistakes.py::UnicodeBOMIE._real_extract","text":"youtube_dl/extractor/commonmistakes.py::UnicodeBOMIE._real_extract\n def _real_extract(self, url):\n real_url = self._match_id(url)\n self.report_warning(\n 'Your URL starts with a Byte Order Mark (BOM). '\n 'Removing the BOM and looking for \"%s\" ...' % real_url)\n return self.url_result(real_url)","label":0},{"cid":"youtube_dl/update.py::rsa_verify","text":"youtube_dl/update.py::rsa_verify\ndef rsa_verify(message, signature, key):\n from hashlib import sha256\n assert isinstance(message, bytes)\n byte_size = (len(bin(key[0])) - 2 + 8 - 1) // 8\n signature = ('%x' % pow(int(signature, 16), key[1], key[0])).encode()\n signature = (byte_size * 2 - len(signature)) * b'0' + signature\n asn1 = b'3031300d060960864801650304020105000420'\n asn1 += sha256(message).hexdigest().encode()\n if byte_size < len(asn1) // 2 + 11:\n return False\n expected = b'0001' + (byte_size - len(asn1) // 2 - 3) * b'ff' + b'00' + asn1\n return expected == signature","label":0},{"cid":"youtube_dl/update.py::update_self","text":"youtube_dl/update.py::update_self\ndef update_self(to_screen, verbose, opener):\n \"\"\"Update the program file with the latest version from the repository\"\"\"\n\n UPDATE_URL = 'https://rg3.github.io/youtube-dl/update/'\n VERSION_URL = UPDATE_URL + 'LATEST_VERSION'\n JSON_URL = UPDATE_URL + 'versions.json'\n UPDATES_RSA_KEY = (0x9d60ee4d8f805312fdb15a62f87b95bd66177b91df176765d13514a0f1754bcd2057295c5b6f1d35daa6742c3ffc9a82d3e118861c207995a8031e151d863c9927e304576bc80692bc8e094896fcf11b66f3e29e04e3a71e9a11558558acea1840aec37fc396fb6b65dc81a1c4144e03bd1c011de62e3f1357b327d08426fe93, 65537)\n\n if not isinstance(globals().get('__loader__'), zipimporter) and not hasattr(sys, 'frozen'):\n to_screen('It looks like you installed youtube-dl with a package manager, pip, setup.py or a tarball. Please use that to update.')\n return\n\n # Check if there is a new version\n try:\n newversion = opener.open(VERSION_URL).read().decode('utf-8').strip()\n except Exception:\n if verbose:\n to_screen(encode_compat_str(traceback.format_exc()))\n to_screen('ERROR: can\\'t find the current version. Please try again later.')\n return\n if newversion == __version__:\n to_screen('youtube-dl is up-to-date (' + __version__ + ')')\n return\n\n # Download and check versions info\n try:\n versions_info = opener.open(JSON_URL).read().decode('utf-8')\n versions_info = json.loads(versions_info)\n except Exception:\n if verbose:\n to_screen(encode_compat_str(traceback.format_exc()))\n to_screen('ERROR: can\\'t obtain versions info. Please try again later.')\n return\n if 'signature' not in versions_info:\n to_screen('ERROR: the versions file is not signed or corrupted. Aborting.')\n return\n signature = versions_info['signature']\n del versions_info['signature']\n if not rsa_verify(json.dumps(versions_info, sort_keys=True).encode('utf-8'), signature, UPDATES_RSA_KEY):\n ","label":0},{"cid":"youtube_dl/update.py::update_self.version_tuple","text":"youtube_dl/update.py::update_self.version_tuple\n def version_tuple(version_str):\n return tuple(map(int, version_str.split('.')))","label":0},{"cid":"youtube_dl/update.py::get_notes","text":"youtube_dl/update.py::get_notes\ndef get_notes(versions, fromVersion):\n notes = []\n for v, vdata in sorted(versions.items()):\n if v > fromVersion:\n notes.extend(vdata.get('notes', []))\n return notes","label":0},{"cid":"youtube_dl/update.py::print_notes","text":"youtube_dl/update.py::print_notes\ndef print_notes(to_screen, versions, fromVersion=__version__):\n notes = get_notes(versions, fromVersion)\n if notes:\n to_screen('PLEASE NOTE:')\n for note in notes:\n to_screen(note)","label":0}],"string":"[\n {\n \"cid\": \"youtube_dl/extractor/ard.py::ARDMediathekIE._extract_media_info\",\n \"text\": \"youtube_dl/extractor/ard.py::ARDMediathekIE._extract_media_info\\n def _extract_media_info(self, media_info_url, webpage, video_id):\\n media_info = self._download_json(\\n media_info_url, video_id, 'Downloading media JSON')\\n\\n formats = self._extract_formats(media_info, video_id)\\n\\n if not formats:\\n if '\\\"fsk\\\"' in webpage:\\n raise ExtractorError(\\n 'This video is only available after 20:00', expected=True)\\n elif media_info.get('_geoblocked'):\\n raise ExtractorError('This video is not available due to geo restriction', expected=True)\\n\\n self._sort_formats(formats)\\n\\n duration = int_or_none(media_info.get('_duration'))\\n thumbnail = media_info.get('_previewImage')\\n is_live = media_info.get('_isLive') is True\\n\\n subtitles = {}\\n subtitle_url = media_info.get('_subtitleUrl')\\n if subtitle_url:\\n subtitles['de'] = [{\\n 'ext': 'ttml',\\n 'url': subtitle_url,\\n }]\\n\\n return {\\n 'id': video_id,\\n 'duration': duration,\\n 'thumbnail': thumbnail,\\n 'is_live': is_live,\\n 'formats': formats,\\n 'subtitles': subtitles,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/extractor/ard.py::ARDMediathekIE._extract_formats\",\n \"text\": \"youtube_dl/extractor/ard.py::ARDMediathekIE._extract_formats\\n def _extract_formats(self, media_info, video_id):\\n type_ = media_info.get('_type')\\n media_array = media_info.get('_mediaArray', [])\\n formats = []\\n for num, media in enumerate(media_array):\\n for stream in media.get('_mediaStreamArray', []):\\n stream_urls = stream.get('_stream')\\n if not stream_urls:\\n continue\\n if not isinstance(stream_urls, list):\\n stream_urls = [stream_urls]\\n quality = stream.get('_quality')\\n server = stream.get('_server')\\n for stream_url in stream_urls:\\n if not url_or_none(stream_url):\\n continue\\n ext = determine_ext(stream_url)\\n if quality != 'auto' and ext in ('f4m', 'm3u8'):\\n continue\\n if ext == 'f4m':\\n formats.extend(self._extract_f4m_formats(\\n update_url_query(stream_url, {\\n 'hdcore': '3.1.1',\\n 'plugin': 'aasp-3.1.1.69.124'\\n }),\\n video_id, f4m_id='hds', fatal=False))\\n elif ext == 'm3u8':\\n formats.extend(self._extract_m3u8_formats(\\n stream_url, video_id, 'mp4', m3u8_id='hls', fatal=False))\\n else:\\n if server and server.startswith('rtmp'):\\n f = {\\n 'url': server,\\n 'play_path': stream_url,\\n 'format_id': 'a%s-rtmp-%s' % (num, quality),\\n }\\n else:\\n f = {\\n 'url': stream_url,\\n 'forma\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/extractor/ard.py::ARDMediathekIE._real_extract\",\n \"text\": \"youtube_dl/extractor/ard.py::ARDMediathekIE._real_extract\\n def _real_extract(self, url):\\n # determine video id from url\\n m = re.match(self._VALID_URL, url)\\n\\n document_id = None\\n\\n numid = re.search(r'documentId=([0-9]+)', url)\\n if numid:\\n document_id = video_id = numid.group(1)\\n else:\\n video_id = m.group('video_id')\\n\\n webpage = self._download_webpage(url, video_id)\\n\\n ERRORS = (\\n ('>Leider liegt eine Störung vor.', 'Video %s is unavailable'),\\n ('>Der gewünschte Beitrag ist nicht mehr verfügbar.<',\\n 'Video %s is no longer available'),\\n )\\n\\n for pattern, message in ERRORS:\\n if pattern in webpage:\\n raise ExtractorError(message % video_id, expected=True)\\n\\n if re.search(r'[\\\\?&]rss($|[=&])', url):\\n doc = compat_etree_fromstring(webpage.encode('utf-8'))\\n if doc.tag == 'rss':\\n return GenericIE()._extract_rss(url, video_id, doc)\\n\\n title = self._html_search_regex(\\n [r'<h1(?:\\\\s+class=\\\"boxTopHeadline\\\")?>(.*?)</h1>',\\n r'<meta name=\\\"dcterms\\\\.title\\\" content=\\\"(.*?)\\\"/>',\\n r'<h4 class=\\\"headline\\\">(.*?)</h4>'],\\n webpage, 'title')\\n description = self._html_search_meta(\\n 'dcterms.abstract', webpage, 'description', default=None)\\n if description is None:\\n description = self._html_search_meta(\\n 'description', webpage, 'meta description')\\n\\n # Thumbnail is sometimes not present.\\n # It is in the mobile version, but that seems to use a different URL\\n # structure altogether.\\n thumbnail = self._og_search_thumbnail(webpage, default=None)\\n\\n media_streams = re.findall(r'''(?x)\\n mediaCollection\\\\.addMediaStream\\\\([0-9]+,\\\\s*[0-9]+,\\\\s*\\\"[^\\\"]*\\\",\\\\s*\\n \\\"([^\\\"]+)\\\"''', webpage)\\n\\n if media_streams:\\n QUALITIES = qualities(['lo', 'hi', 'hq'])\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"youtube_dl/extractor/ard.py::ARDIE._real_extract\",\n \"text\": \"youtube_dl/extractor/ard.py::ARDIE._real_extract\\n def _real_extract(self, url):\\n mobj = re.match(self._VALID_URL, url)\\n display_id = mobj.group('display_id')\\n\\n player_url = mobj.group('mainurl') + '~playerXml.xml'\\n doc = self._download_xml(player_url, display_id)\\n video_node = doc.find('./video')\\n upload_date = unified_strdate(xpath_text(\\n video_node, './broadcastDate'))\\n thumbnail = xpath_text(video_node, './/teaserImage//variant/url')\\n\\n formats = []\\n for a in video_node.findall('.//asset'):\\n f = {\\n 'format_id': a.attrib['type'],\\n 'width': int_or_none(a.find('./frameWidth').text),\\n 'height': int_or_none(a.find('./frameHeight').text),\\n 'vbr': int_or_none(a.find('./bitrateVideo').text),\\n 'abr': int_or_none(a.find('./bitrateAudio').text),\\n 'vcodec': a.find('./codecVideo').text,\\n 'tbr': int_or_none(a.find('./totalBitrate').text),\\n }\\n if a.find('./serverPrefix').text:\\n f['url'] = a.find('./serverPrefix').text\\n f['playpath'] = a.find('./fileName').text\\n else:\\n f['url'] = a.find('./fileName').text\\n formats.append(f)\\n self._sort_formats(formats)\\n\\n return {\\n 'id': mobj.group('id'),\\n 'formats': formats,\\n 'display_id': display_id,\\n 'title': video_node.find('./title').text,\\n 'duration': parse_duration(video_node.find('./duration').text),\\n 'upload_date': upload_date,\\n 'thumbnail': thumbnail,\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/extractor/ard.py::ARDBetaMediathekIE._real_extract\",\n \"text\": \"youtube_dl/extractor/ard.py::ARDBetaMediathekIE._real_extract\\n def _real_extract(self, url):\\n mobj = re.match(self._VALID_URL, url)\\n video_id = mobj.group('video_id')\\n display_id = mobj.group('display_id')\\n\\n webpage = self._download_webpage(url, display_id)\\n data_json = self._search_regex(r'window\\\\.__APOLLO_STATE__\\\\s*=\\\\s*(\\\\{.*);\\\\n', webpage, 'json')\\n data = self._parse_json(data_json, display_id)\\n\\n res = {\\n 'id': video_id,\\n 'display_id': display_id,\\n }\\n formats = []\\n for widget in data.values():\\n if widget.get('_geoblocked'):\\n raise ExtractorError('This video is not available due to geoblocking', expected=True)\\n\\n if '_duration' in widget:\\n res['duration'] = widget['_duration']\\n if 'clipTitle' in widget:\\n res['title'] = widget['clipTitle']\\n if '_previewImage' in widget:\\n res['thumbnail'] = widget['_previewImage']\\n if 'broadcastedOn' in widget:\\n res['upload_date'] = unified_strdate(widget['broadcastedOn'])\\n if 'synopsis' in widget:\\n res['description'] = widget['synopsis']\\n if '_subtitleUrl' in widget:\\n res['subtitles'] = {'de': [{\\n 'ext': 'ttml',\\n 'url': widget['_subtitleUrl'],\\n }]}\\n if '_quality' in widget:\\n format_url = widget['_stream']['json'][0]\\n\\n if format_url.endswith('.f4m'):\\n formats.extend(self._extract_f4m_formats(\\n format_url + '?hdcore=3.11.0',\\n video_id, f4m_id='hds', fatal=False))\\n elif format_url.endswith('m3u8'):\\n formats.extend(self._extract_m3u8_formats(\\n format_url, video_id, 'mp4', m3u8_id='hls', fatal=False))\\n else:\\n formats.appe\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/extractor/commonmistakes.py::CommonMistakesIE._real_extract\",\n \"text\": \"youtube_dl/extractor/commonmistakes.py::CommonMistakesIE._real_extract\\n def _real_extract(self, url):\\n msg = (\\n 'You\\\\'ve asked youtube-dl to download the URL \\\"%s\\\". '\\n 'That doesn\\\\'t make any sense. '\\n 'Simply remove the parameter in your command or configuration.'\\n ) % url\\n if not self._downloader.params.get('verbose'):\\n msg += ' Add -v to the command line to see what arguments and configuration youtube-dl got.'\\n raise ExtractorError(msg, expected=True)\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/extractor/commonmistakes.py::UnicodeBOMIE._real_extract\",\n \"text\": \"youtube_dl/extractor/commonmistakes.py::UnicodeBOMIE._real_extract\\n def _real_extract(self, url):\\n real_url = self._match_id(url)\\n self.report_warning(\\n 'Your URL starts with a Byte Order Mark (BOM). '\\n 'Removing the BOM and looking for \\\"%s\\\" ...' % real_url)\\n return self.url_result(real_url)\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/update.py::rsa_verify\",\n \"text\": \"youtube_dl/update.py::rsa_verify\\ndef rsa_verify(message, signature, key):\\n from hashlib import sha256\\n assert isinstance(message, bytes)\\n byte_size = (len(bin(key[0])) - 2 + 8 - 1) // 8\\n signature = ('%x' % pow(int(signature, 16), key[1], key[0])).encode()\\n signature = (byte_size * 2 - len(signature)) * b'0' + signature\\n asn1 = b'3031300d060960864801650304020105000420'\\n asn1 += sha256(message).hexdigest().encode()\\n if byte_size < len(asn1) // 2 + 11:\\n return False\\n expected = b'0001' + (byte_size - len(asn1) // 2 - 3) * b'ff' + b'00' + asn1\\n return expected == signature\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/update.py::update_self\",\n \"text\": \"youtube_dl/update.py::update_self\\ndef update_self(to_screen, verbose, opener):\\n \\\"\\\"\\\"Update the program file with the latest version from the repository\\\"\\\"\\\"\\n\\n UPDATE_URL = 'https://rg3.github.io/youtube-dl/update/'\\n VERSION_URL = UPDATE_URL + 'LATEST_VERSION'\\n JSON_URL = UPDATE_URL + 'versions.json'\\n UPDATES_RSA_KEY = (0x9d60ee4d8f805312fdb15a62f87b95bd66177b91df176765d13514a0f1754bcd2057295c5b6f1d35daa6742c3ffc9a82d3e118861c207995a8031e151d863c9927e304576bc80692bc8e094896fcf11b66f3e29e04e3a71e9a11558558acea1840aec37fc396fb6b65dc81a1c4144e03bd1c011de62e3f1357b327d08426fe93, 65537)\\n\\n if not isinstance(globals().get('__loader__'), zipimporter) and not hasattr(sys, 'frozen'):\\n to_screen('It looks like you installed youtube-dl with a package manager, pip, setup.py or a tarball. Please use that to update.')\\n return\\n\\n # Check if there is a new version\\n try:\\n newversion = opener.open(VERSION_URL).read().decode('utf-8').strip()\\n except Exception:\\n if verbose:\\n to_screen(encode_compat_str(traceback.format_exc()))\\n to_screen('ERROR: can\\\\'t find the current version. Please try again later.')\\n return\\n if newversion == __version__:\\n to_screen('youtube-dl is up-to-date (' + __version__ + ')')\\n return\\n\\n # Download and check versions info\\n try:\\n versions_info = opener.open(JSON_URL).read().decode('utf-8')\\n versions_info = json.loads(versions_info)\\n except Exception:\\n if verbose:\\n to_screen(encode_compat_str(traceback.format_exc()))\\n to_screen('ERROR: can\\\\'t obtain versions info. Please try again later.')\\n return\\n if 'signature' not in versions_info:\\n to_screen('ERROR: the versions file is not signed or corrupted. Aborting.')\\n return\\n signature = versions_info['signature']\\n del versions_info['signature']\\n if not rsa_verify(json.dumps(versions_info, sort_keys=True).encode('utf-8'), signature, UPDATES_RSA_KEY):\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/update.py::update_self.version_tuple\",\n \"text\": \"youtube_dl/update.py::update_self.version_tuple\\n def version_tuple(version_str):\\n return tuple(map(int, version_str.split('.')))\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/update.py::get_notes\",\n \"text\": \"youtube_dl/update.py::get_notes\\ndef get_notes(versions, fromVersion):\\n notes = []\\n for v, vdata in sorted(versions.items()):\\n if v > fromVersion:\\n notes.extend(vdata.get('notes', []))\\n return notes\",\n \"label\": 0\n },\n {\n \"cid\": \"youtube_dl/update.py::print_notes\",\n \"text\": \"youtube_dl/update.py::print_notes\\ndef print_notes(to_screen, versions, fromVersion=__version__):\\n notes = get_notes(versions, fromVersion)\\n if notes:\\n to_screen('PLEASE NOTE:')\\n for note in notes:\\n to_screen(note)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":296,"cells":{"instance_id":{"kind":"string","value":"docker__compose-4528"},"repo":{"kind":"string","value":"docker/compose"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"Environment variable does not update\nSetting an environment variable in the docker-compose file does not update the value in the command field. The value of the variable that's used is an old value which has since been changed.\r\nThis is the relevant portion of the compose file :\r\n\r\n```yaml\r\nservices:\r\n rabbitmq:\r\n image: rabbitmq:3-management\r\n hostname: rabbitmq\r\n ports:\r\n - \"5672:5672\"\r\n - \"15672:15672\"\r\n volumes:\r\n - ./data/rabbitmq:/var/lib/rabbitmq\r\n - /etc/localtime:/etc/localtime:ro\r\n master:\r\n build:\r\n context: ./worker\r\n dockerfile: Dockerfile\r\n environment:\r\n - testDir=/home/scalability/stats/MDMS/Elrond/2.7.000/20170204T1837/20170204T1843-T0_VM_18_IR_30_IT_1200_TR_35_TT_4112_API_0\r\n image: satish/worker\r\n depends_on:\r\n - rabbitmq\r\n volumes:\r\n - /home/mdmsadmin/obim/script:/opt\r\n - /home/scalability/stats/MDMS:/home/scalability/stats/MDMS\r\n - /mnt:/mnt\r\n - /var/log:/var/log\r\n - /etc/localtime:/etc/localtime:ro\r\n command: bash -c \"while ! nc -z rabbitmq 5672; do echo \"waiting for rabbit to start listening\" ; sleep 2; done; python master_log.py --dir $${testDir};\"\r\n worker:\r\n image: satish/worker\r\n depends_on:\r\n - rabbitmq\r\n - master\r\n command: bash -c \"while ! nc -z rabbitmq 5672; do echo \"waiting for rabbit to start listening\" ; sleep 2; done; python worker_log.py ;\"\r\n volumes_from:\r\n - master\r\n```\r\n\r\nIn this case, when I run `docker-compose scale rabbitmq=1 master=1 worker=5`, the \"testDir\" value that's replaced is not the one that set in the env variable i.e. `\"/home/scalability/stats/MDMS/Elrond/2.7.000/20170204T1837/20170204T1843-T0_VM_18_IR_30_IT_1200_TR_35_TT_4112_API_0\"`, but instead, it's one that was set earlier during an earlier run. I read that the environment variables are set everytime the containers that carry the services run. Since that is not happening here, is this a bug?\r\n\r\nI am not sure of the steps to reproduce, but from my description, if someone could try something similar, would be very useful.\r\n\r\nOutput of docker version:\r\n```\r\ndocker-compose version 1.9.0, build 2585387\r\n```\r\n**Output of `docker info`:**\r\n```\r\nContainers: 9\r\nRunning: 6\r\nPaused: 0\r\nStopped: 3\r\nImages: 11\r\nServer Version: 1.12.5\r\nStorage Driver: devicemapper\r\nPool Name: docker-253:0-18749-pool\r\nPool Blocksize: 65.54 kB\r\nBase Device Size: 10.74 GB\r\nBacking Filesystem: xfs\r\nData file: /dev/loop0\r\nMetadata file: /dev/loop1\r\nData Space Used: 1.134 GB\r\nData Space Total: 107.4 GB\r\nData Space Available: 4.603 GB\r\nMetadata Space Used: 4.03 MB\r\nMetadata Space Total: 2.147 GB\r\nMetadata Space Available: 2.143 GB\r\nThin Pool Minimum Free Space: 10.74 GB\r\nUdev Sync Supported: true\r\nDeferred Removal Enabled: false\r\nDeferred Deletion Enabled: false\r\nDeferred Deleted Device Count: 0\r\nData loop file: /var/lib/docker/devicemapper/devicemapper/data\r\nWARNING: Usage of loopback devices is strongly discouraged for production use. Use --storage-opt dm.thinpooldev to specify a custom block storage device.\r\nMetadata loop file: /var/lib/docker/devicemapper/devicemapper/metadata\r\nLibrary Version: 1.02.135-RHEL7 (2016-09-28)\r\nLogging Driver: json-file\r\nCgroup Driver: cgroupfs\r\nPlugins:\r\nVolume: local\r\nNetwork: bridge null overlay host\r\nSwarm: inactive\r\nRuntimes: runc\r\nDefault Runtime: runc\r\nSecurity Options: seccomp\r\nKernel Version: 3.10.0-514.el7.x86_64\r\nOperating System: CentOS Linux 7 (Core)\r\nOSType: linux\r\nArchitecture: x86_64\r\nCPUs: 2\r\nTotal Memory: 7.64 GiB\r\nName: devpx-docker\r\nID: EK6D:GJXW:XDJB:QUOG:GVAJ:IIL7:K3BP:EM2R:XN5P:H4YS:ZCSI:4RTO\r\nDocker Root Dir: /var/lib/docker\r\nDebug Mode (client): false\r\nDebug Mode (server): false\r\nRegistry: https://index.docker.io/v1/\r\nWARNING: bridge-nf-call-iptables is disabled\r\nWARNING: bridge-nf-call-ip6tables is disabled\r\nInsecure Registries:\r\n```\r\n\r\n**Additional environment details (AWS, VirtualBox, physical, etc.):**\r\nThis is a CentOS 7 VM. \r\n\n"},"gold_files":{"kind":"list like","value":["compose/service.py"],"string":"[\n \"compose/service.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["compose/service.py::Service.scale"],"string":"[\n \"compose/service.py::Service.scale\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"compose/cli/docker_client.py","text":"compose/cli/docker_client.py\ndef tls_config_from_options(options)\ndef docker_client(environment, version=None, tls_config=None, host=None, tls_version=None) # Returns a docker-py client configured using environment variables","label":0},{"cid":"compose/cli/main.py","text":"compose/cli/main.py\ndef main()\ndef dispatch()\ndef perform_command(options, handler, command_options)\ndef setup_logging()\ndef setup_console_handler(handler, verbose)\ndef parse_doc_section(name, source)\nclass TopLevelCommand # Define and run multi-container applications with Docker.\n def __init__(self, project, project_dir='.')\n def build(self, options) # Build or rebuild services.\n def bundle(self, config_options, options) # Generate a Distributed Application Bundle (DAB) from the Compose file.\n def config(self, config_options, options) # Validate and view the compose file.\n def create(self, options) # Creates containers for a service.\n def down(self, options) # Stops containers and removes containers, networks, volumes, and images\n def events(self, options) # Receive real time events from containers.\n def format_event(event)\n def json_format_event(event)\n def exec_command(self, options) # Execute a command in a running container\n def help(cls, options) # Get help on a command.\n def kill(self, options) # Force stop service containers.\n def logs(self, options) # View output from containers.\n def pause(self, options) # Pause services.\n def port(self, options) # Print the public port for a port binding.\n def ps(self, options) # List containers.\n def pull(self, options) # Pulls images for services.\n def push(self, options) # Pushes images for services.\n def rm(self, options) # Removes stopped service containers.\n def run(self, options) # Run a one-off command on a service.\n def scale(self, options) # Set number of containers to run for a service.\n def start(self, options) # Start existing containers.\n def stop(self, options) # Stop running containers without removing them.\n def restart(self, options) # Restart running containers.\n def top(self, options) # Display the running processes\n def unpause(self, options) # Unpause services.\n def up(self, options) # Builds, (re)creates, starts, and attaches to containers for a service.\n def version(cls, options) # Show version informations\ndef convergence_strategy_from_opts(options)\ndef timeout_from_opts(options)\ndef image_type_from_opt(flag, value)\ndef build_action_from_opts(options)\ndef build_container_options(options, detach, command)\ndef run_one_off_container(container_options, project, service, options)\n def remove_container(force=False)\ndef log_printer_from_project(project, containers, monochrome, log_args, cascade_stop=False, event_stream=None)\ndef filter_containers_to_service_names(containers, service_names)\ndef up_shutdown_context(project, service_names, timeout, detached)\ndef list_containers(containers)\ndef exit_if(condition, message, exit_code)\ndef call_docker(args)","label":0},{"cid":"compose/config/validation.py","text":"compose/config/validation.py\ndef format_ports(instance)\ndef format_expose(instance)\ndef match_named_volumes(service_dict, project_volumes)\ndef python_type_to_yaml_type(type_)\ndef validate_config_section(filename, config, section) # Validate the structure of a configuration section. This must be done\ndef validate_top_level_object(config_file)\ndef validate_ulimits(service_config)\ndef validate_extends_file_path(service_name, extends_options, filename) # The service to be extended must either be defined in the config key 'file',\ndef validate_network_mode(service_config, service_names)\ndef validate_links(service_config, service_names)\ndef validate_depends_on(service_config, service_names)\ndef get_unsupported_config_msg(path, error_key)\ndef anglicize_json_type(json_type)\ndef is_service_dict_schema(schema_id)\ndef handle_error_for_schema_with_id(error, path)\ndef handle_generic_error(error, path)\ndef parse_key_from_error_msg(error)\ndef path_string(path)\ndef _parse_valid_types_from_validator(validator) # A validator value can be either an array of valid types or a string of\ndef _parse_oneof_validator(error) # oneOf has multiple schemas, so we need to reason about which schema, sub\ndef process_service_constraint_errors(error, service_name, version)\ndef process_config_schema_errors(error)\ndef validate_against_config_schema(config_file)\ndef validate_service_constraints(config, service_name, version)\n def handler(errors)\ndef get_schema_path()\ndef load_jsonschema(version)\ndef get_resolver_path()\ndef handle_errors(errors, format_error_func, filename) # jsonschema returns an error tree full of information to explain what has","label":0},{"cid":"compose/service.py","text":"compose/service.py\nclass BuildError\n def __init__(self, service, reason)\nclass NeedsBuildError\n def __init__(self, service)\nclass NoSuchImageError\nclass ConvergenceStrategy # Enumeration for all possible convergence strategies. Values refer to\n def allows_recreate(self)\nclass ImageType # Enumeration for the types of images known to compose.\nclass BuildAction # Enumeration for the possible build actions.\nclass Service\n def __init__(self, name, client=None, project='default', use_networking=False, links=None, volumes_from=None, network_mode=None, networks=None, secrets=None, **options)\n def __repr__(self)\n def containers(self, stopped=False, one_off=False, filters={})\n def get_container(self, number=1) # Return a :class:`compose.container.Container` for this service. The\n def start(self, **options)\n def scale(self, desired_num, timeout=None) # Adjusts the number of containers to the specified number and ensures\n def create_and_start(service, number)\n def stop_and_remove(container)\n def create_container(self, one_off=False, previous_container=None, number=None, quiet=False, **override_options) # Create a container for this service. If the image doesn't exist, attempt to pull\n def ensure_image_exists(self, do_build=BuildAction.none)\n def image(self)\n def image_name(self)\n def convergence_plan(self, strategy=ConvergenceStrategy.changed)\n def _containers_have_diverged(self, containers)\n def execute_convergence_plan(self, plan, timeout=None, detached=False, start=True)\n def recreate_container(self, container, timeout=None, attach_logs=False, start_new_container=True) # Recreate a container.\n def stop_timeout(self, timeout)\n def start_container_if_stopped(self, container, attach_logs=False, quiet=False)\n def start_container(self, container)\n def connect_container_to_networks(self, container)\n def remove_duplicate_containers(self, timeout=None)\n def duplicate_containers(self)\n def config_hash(self)\n def config_dict(self)\n def get_dependency_names(self)\n def get_dependency_configs(self)\n def get_linked_service_names(self)\n def get_link_names(self)\n def get_volumes_from_names(self)\n def _next_container_number(self, one_off=False)\n def _get_aliases(self, network, container=None)\n def build_default_networking_config(self)\n def _get_links(self, link_to_self)\n def _get_volumes_from(self)\n def _get_container_create_options(self, override_options, number, one_off=False, previous_container=None)\n def _get_container_host_config(self, override_options, one_off=False)\n def get_secret_volumes(self)\n def build_spec(secret)\n def build(self, no_cache=False, pull=False, force_rm=False)\n def can_be_built(self)\n def labels(self, one_off=False)\n def custom_container_name(self)\n def get_container_name(self, number, one_off=False)\n def remove_image(self, image_type)\n def specifies_host_port(self)\n def has_host","label":1},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py","text":"contrib/migration/migrate-compose-file-v1-to-v2.py\ndef migrate(content)\ndef warn_for_links(name, service)\ndef warn_for_external_links(name, service)\ndef rewrite_net(service, service_names)\ndef rewrite_build(service)\ndef rewrite_logging(service)\ndef rewrite_volumes_from(service, service_names)\ndef create_volumes_section(data)\ndef get_named_volumes(services)\ndef write(stream, new_format, indent, width)\ndef parse_opts(args)\ndef main(args)","label":0}],"string":"[\n {\n \"cid\": \"compose/cli/docker_client.py\",\n \"text\": \"compose/cli/docker_client.py\\ndef tls_config_from_options(options)\\ndef docker_client(environment, version=None, tls_config=None, host=None, tls_version=None) # Returns a docker-py client configured using environment variables\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py\",\n \"text\": \"compose/cli/main.py\\ndef main()\\ndef dispatch()\\ndef perform_command(options, handler, command_options)\\ndef setup_logging()\\ndef setup_console_handler(handler, verbose)\\ndef parse_doc_section(name, source)\\nclass TopLevelCommand # Define and run multi-container applications with Docker.\\n def __init__(self, project, project_dir='.')\\n def build(self, options) # Build or rebuild services.\\n def bundle(self, config_options, options) # Generate a Distributed Application Bundle (DAB) from the Compose file.\\n def config(self, config_options, options) # Validate and view the compose file.\\n def create(self, options) # Creates containers for a service.\\n def down(self, options) # Stops containers and removes containers, networks, volumes, and images\\n def events(self, options) # Receive real time events from containers.\\n def format_event(event)\\n def json_format_event(event)\\n def exec_command(self, options) # Execute a command in a running container\\n def help(cls, options) # Get help on a command.\\n def kill(self, options) # Force stop service containers.\\n def logs(self, options) # View output from containers.\\n def pause(self, options) # Pause services.\\n def port(self, options) # Print the public port for a port binding.\\n def ps(self, options) # List containers.\\n def pull(self, options) # Pulls images for services.\\n def push(self, options) # Pushes images for services.\\n def rm(self, options) # Removes stopped service containers.\\n def run(self, options) # Run a one-off command on a service.\\n def scale(self, options) # Set number of containers to run for a service.\\n def start(self, options) # Start existing containers.\\n def stop(self, options) # Stop running containers without removing them.\\n def restart(self, options) # Restart running containers.\\n def top(self, options) # Display the running processes\\n def unpause(self, options) # Unpause services.\\n def up(self, options) # Builds, (re)creates, starts, and attaches to containers for a service.\\n def version(cls, options) # Show version informations\\ndef convergence_strategy_from_opts(options)\\ndef timeout_from_opts(options)\\ndef image_type_from_opt(flag, value)\\ndef build_action_from_opts(options)\\ndef build_container_options(options, detach, command)\\ndef run_one_off_container(container_options, project, service, options)\\n def remove_container(force=False)\\ndef log_printer_from_project(project, containers, monochrome, log_args, cascade_stop=False, event_stream=None)\\ndef filter_containers_to_service_names(containers, service_names)\\ndef up_shutdown_context(project, service_names, timeout, detached)\\ndef list_containers(containers)\\ndef exit_if(condition, message, exit_code)\\ndef call_docker(args)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py\",\n \"text\": \"compose/config/validation.py\\ndef format_ports(instance)\\ndef format_expose(instance)\\ndef match_named_volumes(service_dict, project_volumes)\\ndef python_type_to_yaml_type(type_)\\ndef validate_config_section(filename, config, section) # Validate the structure of a configuration section. This must be done\\ndef validate_top_level_object(config_file)\\ndef validate_ulimits(service_config)\\ndef validate_extends_file_path(service_name, extends_options, filename) # The service to be extended must either be defined in the config key 'file',\\ndef validate_network_mode(service_config, service_names)\\ndef validate_links(service_config, service_names)\\ndef validate_depends_on(service_config, service_names)\\ndef get_unsupported_config_msg(path, error_key)\\ndef anglicize_json_type(json_type)\\ndef is_service_dict_schema(schema_id)\\ndef handle_error_for_schema_with_id(error, path)\\ndef handle_generic_error(error, path)\\ndef parse_key_from_error_msg(error)\\ndef path_string(path)\\ndef _parse_valid_types_from_validator(validator) # A validator value can be either an array of valid types or a string of\\ndef _parse_oneof_validator(error) # oneOf has multiple schemas, so we need to reason about which schema, sub\\ndef process_service_constraint_errors(error, service_name, version)\\ndef process_config_schema_errors(error)\\ndef validate_against_config_schema(config_file)\\ndef validate_service_constraints(config, service_name, version)\\n def handler(errors)\\ndef get_schema_path()\\ndef load_jsonschema(version)\\ndef get_resolver_path()\\ndef handle_errors(errors, format_error_func, filename) # jsonschema returns an error tree full of information to explain what has\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py\",\n \"text\": \"compose/service.py\\nclass BuildError\\n def __init__(self, service, reason)\\nclass NeedsBuildError\\n def __init__(self, service)\\nclass NoSuchImageError\\nclass ConvergenceStrategy # Enumeration for all possible convergence strategies. Values refer to\\n def allows_recreate(self)\\nclass ImageType # Enumeration for the types of images known to compose.\\nclass BuildAction # Enumeration for the possible build actions.\\nclass Service\\n def __init__(self, name, client=None, project='default', use_networking=False, links=None, volumes_from=None, network_mode=None, networks=None, secrets=None, **options)\\n def __repr__(self)\\n def containers(self, stopped=False, one_off=False, filters={})\\n def get_container(self, number=1) # Return a :class:`compose.container.Container` for this service. The\\n def start(self, **options)\\n def scale(self, desired_num, timeout=None) # Adjusts the number of containers to the specified number and ensures\\n def create_and_start(service, number)\\n def stop_and_remove(container)\\n def create_container(self, one_off=False, previous_container=None, number=None, quiet=False, **override_options) # Create a container for this service. If the image doesn't exist, attempt to pull\\n def ensure_image_exists(self, do_build=BuildAction.none)\\n def image(self)\\n def image_name(self)\\n def convergence_plan(self, strategy=ConvergenceStrategy.changed)\\n def _containers_have_diverged(self, containers)\\n def execute_convergence_plan(self, plan, timeout=None, detached=False, start=True)\\n def recreate_container(self, container, timeout=None, attach_logs=False, start_new_container=True) # Recreate a container.\\n def stop_timeout(self, timeout)\\n def start_container_if_stopped(self, container, attach_logs=False, quiet=False)\\n def start_container(self, container)\\n def connect_container_to_networks(self, container)\\n def remove_duplicate_containers(self, timeout=None)\\n def duplicate_containers(self)\\n def config_hash(self)\\n def config_dict(self)\\n def get_dependency_names(self)\\n def get_dependency_configs(self)\\n def get_linked_service_names(self)\\n def get_link_names(self)\\n def get_volumes_from_names(self)\\n def _next_container_number(self, one_off=False)\\n def _get_aliases(self, network, container=None)\\n def build_default_networking_config(self)\\n def _get_links(self, link_to_self)\\n def _get_volumes_from(self)\\n def _get_container_create_options(self, override_options, number, one_off=False, previous_container=None)\\n def _get_container_host_config(self, override_options, one_off=False)\\n def get_secret_volumes(self)\\n def build_spec(secret)\\n def build(self, no_cache=False, pull=False, force_rm=False)\\n def can_be_built(self)\\n def labels(self, one_off=False)\\n def custom_container_name(self)\\n def get_container_name(self, number, one_off=False)\\n def remove_image(self, image_type)\\n def specifies_host_port(self)\\n def has_host\",\n \"label\": 1\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py\\ndef migrate(content)\\ndef warn_for_links(name, service)\\ndef warn_for_external_links(name, service)\\ndef rewrite_net(service, service_names)\\ndef rewrite_build(service)\\ndef rewrite_logging(service)\\ndef rewrite_volumes_from(service, service_names)\\ndef create_volumes_section(data)\\ndef get_named_volumes(services)\\ndef write(stream, new_format, indent, width)\\ndef parse_opts(args)\\ndef main(args)\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"compose/cli/docker_client.py::tls_config_from_options","text":"compose/cli/docker_client.py::tls_config_from_options\ndef tls_config_from_options(options):\n tls = options.get('--tls', False)\n ca_cert = unquote_path(options.get('--tlscacert'))\n cert = unquote_path(options.get('--tlscert'))\n key = unquote_path(options.get('--tlskey'))\n verify = options.get('--tlsverify')\n skip_hostname_check = options.get('--skip-hostname-check', False)\n\n advanced_opts = any([ca_cert, cert, key, verify])\n\n if tls is True and not advanced_opts:\n return True\n elif advanced_opts: # --tls is a noop\n client_cert = None\n if cert or key:\n client_cert = (cert, key)\n\n return TLSConfig(\n client_cert=client_cert, verify=verify, ca_cert=ca_cert,\n assert_hostname=False if skip_hostname_check else None\n )\n\n return None","label":0},{"cid":"compose/cli/docker_client.py::docker_client","text":"compose/cli/docker_client.py::docker_client\ndef docker_client(environment, version=None, tls_config=None, host=None,\n tls_version=None):\n \"\"\"\n Returns a docker-py client configured using environment variables\n according to the same logic as the official Docker client.\n \"\"\"\n try:\n kwargs = kwargs_from_env(environment=environment, ssl_version=tls_version)\n except TLSParameterError:\n raise UserError(\n \"TLS configuration is invalid - make sure your DOCKER_TLS_VERIFY \"\n \"and DOCKER_CERT_PATH are set correctly.\\n\"\n \"You might need to run `eval \\\"$(docker-machine env default)\\\"`\")\n\n if host:\n kwargs['base_url'] = host\n if tls_config:\n kwargs['tls'] = tls_config\n\n if version:\n kwargs['version'] = version\n\n timeout = environment.get('COMPOSE_HTTP_TIMEOUT')\n if timeout:\n kwargs['timeout'] = int(timeout)\n else:\n kwargs['timeout'] = HTTP_TIMEOUT\n\n kwargs['user_agent'] = generate_user_agent()\n\n return APIClient(**kwargs)","label":0},{"cid":"compose/cli/main.py::main","text":"compose/cli/main.py::main\ndef main():\n command = dispatch()\n\n try:\n command()\n except (KeyboardInterrupt, signals.ShutdownException):\n log.error(\"Aborting.\")\n sys.exit(1)\n except (UserError, NoSuchService, ConfigurationError,\n ProjectError, OperationFailedError) as e:\n log.error(e.msg)\n sys.exit(1)\n except BuildError as e:\n log.error(\"Service '%s' failed to build: %s\" % (e.service.name, e.reason))\n sys.exit(1)\n except StreamOutputError as e:\n log.error(e)\n sys.exit(1)\n except NeedsBuildError as e:\n log.error(\"Service '%s' needs to be built, but --no-build was passed.\" % e.service.name)\n sys.exit(1)\n except (errors.ConnectionError, StreamParseError):\n sys.exit(1)","label":0},{"cid":"compose/cli/main.py::dispatch","text":"compose/cli/main.py::dispatch\ndef dispatch():\n setup_logging()\n dispatcher = DocoptDispatcher(\n TopLevelCommand,\n {'options_first': True, 'version': get_version_info('compose')})\n\n try:\n options, handler, command_options = dispatcher.parse(sys.argv[1:])\n except NoSuchCommand as e:\n commands = \"\\n\".join(parse_doc_section(\"commands:\", getdoc(e.supercommand)))\n log.error(\"No such command: %s\\n\\n%s\", e.command, commands)\n sys.exit(1)\n\n setup_console_handler(console_handler, options.get('--verbose'))\n return functools.partial(perform_command, options, handler, command_options)","label":0},{"cid":"compose/cli/main.py::perform_command","text":"compose/cli/main.py::perform_command\ndef perform_command(options, handler, command_options):\n if options['COMMAND'] in ('help', 'version'):\n # Skip looking up the compose file.\n handler(command_options)\n return\n\n if options['COMMAND'] in ('config', 'bundle'):\n command = TopLevelCommand(None)\n handler(command, options, command_options)\n return\n\n project = project_from_options('.', options)\n command = TopLevelCommand(project)\n with errors.handle_connection_errors(project.client):\n handler(command, command_options)","label":0},{"cid":"compose/cli/main.py::setup_logging","text":"compose/cli/main.py::setup_logging\ndef setup_logging():\n root_logger = logging.getLogger()\n root_logger.addHandler(console_handler)\n root_logger.setLevel(logging.DEBUG)\n\n # Disable requests logging\n logging.getLogger(\"requests\").propagate = False","label":0},{"cid":"compose/cli/main.py::setup_console_handler","text":"compose/cli/main.py::setup_console_handler\ndef setup_console_handler(handler, verbose):\n if handler.stream.isatty():\n format_class = ConsoleWarningFormatter\n else:\n format_class = logging.Formatter\n\n if verbose:\n handler.setFormatter(format_class('%(name)s.%(funcName)s: %(message)s'))\n handler.setLevel(logging.DEBUG)\n else:\n handler.setFormatter(format_class())\n handler.setLevel(logging.INFO)","label":0},{"cid":"compose/cli/main.py::parse_doc_section","text":"compose/cli/main.py::parse_doc_section\ndef parse_doc_section(name, source):\n pattern = re.compile('^([^\\n]*' + name + '[^\\n]*\\n?(?:[ \\t].*?(?:\\n|$))*)',\n re.IGNORECASE | re.MULTILINE)\n return [s.strip() for s in pattern.findall(source)]","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.__init__","text":"compose/cli/main.py::TopLevelCommand.__init__\n def __init__(self, project, project_dir='.'):\n self.project = project\n self.project_dir = '.'","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.build","text":"compose/cli/main.py::TopLevelCommand.build\n def build(self, options):\n \"\"\"\n Build or rebuild services.\n\n Services are built once and then tagged as `project_service`,\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\n contents of its build directory, you can run `docker-compose build` to rebuild it.\n\n Usage: build [options] [SERVICE...]\n\n Options:\n --force-rm Always remove intermediate containers.\n --no-cache Do not use cache when building the image.\n --pull Always attempt to pull a newer version of the image.\n \"\"\"\n self.project.build(\n service_names=options['SERVICE'],\n no_cache=bool(options.get('--no-cache', False)),\n pull=bool(options.get('--pull', False)),\n force_rm=bool(options.get('--force-rm', False)))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.bundle","text":"compose/cli/main.py::TopLevelCommand.bundle\n def bundle(self, config_options, options):\n \"\"\"\n Generate a Distributed Application Bundle (DAB) from the Compose file.\n\n Images must have digests stored, which requires interaction with a\n Docker registry. If digests aren't stored for all images, you can fetch\n them with `docker-compose pull` or `docker-compose push`. To push images\n automatically when bundling, pass `--push-images`. Only services with\n a `build` option specified will have their images pushed.\n\n Usage: bundle [options]\n\n Options:\n --push-images Automatically push images for any services\n which have a `build` option specified.\n\n -o, --output PATH Path to write the bundle file to.\n Defaults to \"<project name>.dab\".\n \"\"\"\n self.project = project_from_options('.', config_options)\n compose_config = get_config_from_options(self.project_dir, config_options)\n\n output = options[\"--output\"]\n if not output:\n output = \"{}.dab\".format(self.project.name)\n\n with errors.handle_connection_errors(self.project.client):\n try:\n image_digests = get_image_digests(\n self.project,\n allow_push=options['--push-images'],\n )\n except MissingDigests as e:\n def list_images(images):\n return \"\\n\".join(\" {}\".format(name) for name in sorted(images))\n\n paras = [\"Some images are missing digests.\"]\n\n if e.needs_push:\n command_hint = (\n \"Use `docker-compose push {}` to push them. \"\n \"You can do this automatically with `docker-compose bundle --push-images`.\"\n .format(\" \".join(sorted(e.needs_push)))\n )\n","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.config","text":"compose/cli/main.py::TopLevelCommand.config\n def config(self, config_options, options):\n \"\"\"\n Validate and view the compose file.\n\n Usage: config [options]\n\n Options:\n -q, --quiet Only validate the configuration, don't print\n anything.\n --services Print the service names, one per line.\n\n \"\"\"\n compose_config = get_config_from_options(self.project_dir, config_options)\n\n if options['--quiet']:\n return\n\n if options['--services']:\n print('\\n'.join(service['name'] for service in compose_config.services))\n return\n\n print(serialize_config(compose_config))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.create","text":"compose/cli/main.py::TopLevelCommand.create\n def create(self, options):\n \"\"\"\n Creates containers for a service.\n\n Usage: create [options] [SERVICE...]\n\n Options:\n --force-recreate Recreate containers even if their configuration and\n image haven't changed. Incompatible with --no-recreate.\n --no-recreate If containers already exist, don't recreate them.\n Incompatible with --force-recreate.\n --no-build Don't build an image, even if it's missing.\n --build Build images before creating containers.\n \"\"\"\n service_names = options['SERVICE']\n\n self.project.create(\n service_names=service_names,\n strategy=convergence_strategy_from_opts(options),\n do_build=build_action_from_opts(options),\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.down","text":"compose/cli/main.py::TopLevelCommand.down\n def down(self, options):\n \"\"\"\n Stops containers and removes containers, networks, volumes, and images\n created by `up`.\n\n By default, the only things removed are:\n\n - Containers for services defined in the Compose file\n - Networks defined in the `networks` section of the Compose file\n - The default network, if one is used\n\n Networks and volumes defined as `external` are never removed.\n\n Usage: down [options]\n\n Options:\n --rmi type Remove images. Type must be one of:\n 'all': Remove all images used by any service.\n 'local': Remove only images that don't have a custom tag\n set by the `image` field.\n -v, --volumes Remove named volumes declared in the `volumes` section\n of the Compose file and anonymous volumes\n attached to containers.\n --remove-orphans Remove containers for services not defined in the\n Compose file\n \"\"\"\n image_type = image_type_from_opt('--rmi', options['--rmi'])\n self.project.down(image_type, options['--volumes'], options['--remove-orphans'])","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events","text":"compose/cli/main.py::TopLevelCommand.events\n def events(self, options):\n \"\"\"\n Receive real time events from containers.\n\n Usage: events [options] [SERVICE...]\n\n Options:\n --json Output events as a stream of json objects\n \"\"\"\n def format_event(event):\n attributes = [\"%s=%s\" % item for item in event['attributes'].items()]\n return (\"{time} {type} {action} {id} ({attrs})\").format(\n attrs=\", \".join(sorted(attributes)),\n **event)\n\n def json_format_event(event):\n event['time'] = event['time'].isoformat()\n event.pop('container')\n return json.dumps(event)\n\n for event in self.project.events():\n formatter = json_format_event if options['--json'] else format_event\n print(formatter(event))\n sys.stdout.flush()","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events.format_event","text":"compose/cli/main.py::TopLevelCommand.events.format_event\n def format_event(event):\n attributes = [\"%s=%s\" % item for item in event['attributes'].items()]\n return (\"{time} {type} {action} {id} ({attrs})\").format(\n attrs=\", \".join(sorted(attributes)),\n **event)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.events.json_format_event","text":"compose/cli/main.py::TopLevelCommand.events.json_format_event\n def json_format_event(event):\n event['time'] = event['time'].isoformat()\n event.pop('container')\n return json.dumps(event)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.exec_command","text":"compose/cli/main.py::TopLevelCommand.exec_command\n def exec_command(self, options):\n \"\"\"\n Execute a command in a running container\n\n Usage: exec [options] SERVICE COMMAND [ARGS...]\n\n Options:\n -d Detached mode: Run command in the background.\n --privileged Give extended privileges to the process.\n --user USER Run the command as this user.\n -T Disable pseudo-tty allocation. By default `docker-compose exec`\n allocates a TTY.\n --index=index index of the container if there are multiple\n instances of a service [default: 1]\n \"\"\"\n index = int(options.get('--index'))\n service = self.project.get_service(options['SERVICE'])\n detach = options['-d']\n\n try:\n container = service.get_container(number=index)\n except ValueError as e:\n raise UserError(str(e))\n command = [options['COMMAND']] + options['ARGS']\n tty = not options[\"-T\"]\n\n if IS_WINDOWS_PLATFORM and not detach:\n args = [\"exec\"]\n\n if options[\"-d\"]:\n args += [\"--detach\"]\n else:\n args += [\"--interactive\"]\n\n if not options[\"-T\"]:\n args += [\"--tty\"]\n\n if options[\"--privileged\"]:\n args += [\"--privileged\"]\n\n if options[\"--user\"]:\n args += [\"--user\", options[\"--user\"]]\n\n args += [container.id]\n args += command\n\n sys.exit(call_docker(args))\n\n create_exec_options = {\n \"privileged\": options[\"--privileged\"],\n \"user\": options[\"--user\"],\n \"tty\": tty,\n \"stdin\": tty,\n }\n\n exec_id = container.create_exec(command, **create_exec_options)\n\n if detach:\n container.start_exec(exec_id, tty=tty)\n return\n\n sig","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.help","text":"compose/cli/main.py::TopLevelCommand.help\n @classmethod\n def help(cls, options):\n \"\"\"\n Get help on a command.\n\n Usage: help [COMMAND]\n \"\"\"\n if options['COMMAND']:\n subject = get_handler(cls, options['COMMAND'])\n else:\n subject = cls\n\n print(getdoc(subject))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.kill","text":"compose/cli/main.py::TopLevelCommand.kill\n def kill(self, options):\n \"\"\"\n Force stop service containers.\n\n Usage: kill [options] [SERVICE...]\n\n Options:\n -s SIGNAL SIGNAL to send to the container.\n Default signal is SIGKILL.\n \"\"\"\n signal = options.get('-s', 'SIGKILL')\n\n self.project.kill(service_names=options['SERVICE'], signal=signal)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.logs","text":"compose/cli/main.py::TopLevelCommand.logs\n def logs(self, options):\n \"\"\"\n View output from containers.\n\n Usage: logs [options] [SERVICE...]\n\n Options:\n --no-color Produce monochrome output.\n -f, --follow Follow log output.\n -t, --timestamps Show timestamps.\n --tail=\"all\" Number of lines to show from the end of the logs\n for each container.\n \"\"\"\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\n\n tail = options['--tail']\n if tail is not None:\n if tail.isdigit():\n tail = int(tail)\n elif tail != 'all':\n raise UserError(\"tail flag must be all or a number\")\n log_args = {\n 'follow': options['--follow'],\n 'tail': tail,\n 'timestamps': options['--timestamps']\n }\n print(\"Attaching to\", list_containers(containers))\n log_printer_from_project(\n self.project,\n containers,\n options['--no-color'],\n log_args,\n event_stream=self.project.events(service_names=options['SERVICE'])).run()","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.pause","text":"compose/cli/main.py::TopLevelCommand.pause\n def pause(self, options):\n \"\"\"\n Pause services.\n\n Usage: pause [SERVICE...]\n \"\"\"\n containers = self.project.pause(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to pause', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.port","text":"compose/cli/main.py::TopLevelCommand.port\n def port(self, options):\n \"\"\"\n Print the public port for a port binding.\n\n Usage: port [options] SERVICE PRIVATE_PORT\n\n Options:\n --protocol=proto tcp or udp [default: tcp]\n --index=index index of the container if there are multiple\n instances of a service [default: 1]\n \"\"\"\n index = int(options.get('--index'))\n service = self.project.get_service(options['SERVICE'])\n try:\n container = service.get_container(number=index)\n except ValueError as e:\n raise UserError(str(e))\n print(container.get_local_port(\n options['PRIVATE_PORT'],\n protocol=options.get('--protocol') or 'tcp') or '')","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.ps","text":"compose/cli/main.py::TopLevelCommand.ps\n def ps(self, options):\n \"\"\"\n List containers.\n\n Usage: ps [options] [SERVICE...]\n\n Options:\n -q Only display IDs\n \"\"\"\n containers = sorted(\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\n key=attrgetter('name'))\n\n if options['-q']:\n for container in containers:\n print(container.id)\n else:\n headers = [\n 'Name',\n 'Command',\n 'State',\n 'Ports',\n ]\n rows = []\n for container in containers:\n command = container.human_readable_command\n if len(command) > 30:\n command = '%s ...' % command[:26]\n rows.append([\n container.name,\n command,\n container.human_readable_state,\n container.human_readable_ports,\n ])\n print(Formatter().table(headers, rows))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.pull","text":"compose/cli/main.py::TopLevelCommand.pull\n def pull(self, options):\n \"\"\"\n Pulls images for services.\n\n Usage: pull [options] [SERVICE...]\n\n Options:\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\n \"\"\"\n self.project.pull(\n service_names=options['SERVICE'],\n ignore_pull_failures=options.get('--ignore-pull-failures')\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.push","text":"compose/cli/main.py::TopLevelCommand.push\n def push(self, options):\n \"\"\"\n Pushes images for services.\n\n Usage: push [options] [SERVICE...]\n\n Options:\n --ignore-push-failures Push what it can and ignores images with push failures.\n \"\"\"\n self.project.push(\n service_names=options['SERVICE'],\n ignore_push_failures=options.get('--ignore-push-failures')\n )","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.rm","text":"compose/cli/main.py::TopLevelCommand.rm\n def rm(self, options):\n \"\"\"\n Removes stopped service containers.\n\n By default, anonymous volumes attached to containers will not be removed. You\n can override this with `-v`. To list all volumes, use `docker volume ls`.\n\n Any data which is not in a volume will be lost.\n\n Usage: rm [options] [SERVICE...]\n\n Options:\n -f, --force Don't ask to confirm removal\n -v Remove any anonymous volumes attached to containers\n -a, --all Deprecated - no effect.\n \"\"\"\n if options.get('--all'):\n log.warn(\n '--all flag is obsolete. This is now the default behavior '\n 'of `docker-compose rm`'\n )\n one_off = OneOffFilter.include\n\n all_containers = self.project.containers(\n service_names=options['SERVICE'], stopped=True, one_off=one_off\n )\n stopped_containers = [c for c in all_containers if not c.is_running]\n\n if len(stopped_containers) > 0:\n print(\"Going to remove\", list_containers(stopped_containers))\n if options.get('--force') \\\n or yesno(\"Are you sure? [yN] \", default=False):\n self.project.remove_stopped(\n service_names=options['SERVICE'],\n v=options.get('-v', False),\n one_off=one_off\n )\n else:\n print(\"No stopped containers\")","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.run","text":"compose/cli/main.py::TopLevelCommand.run\n def run(self, options):\n \"\"\"\n Run a one-off command on a service.\n\n For example:\n\n $ docker-compose run web python manage.py shell\n\n By default, linked services will be started, unless they are already\n running. If you do not want to start linked services, use\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\n\n Usage: run [options] [-p PORT...] [-e KEY=VAL...] SERVICE [COMMAND] [ARGS...]\n\n Options:\n -d Detached mode: Run container in the background, print\n new container name.\n --name NAME Assign a name to the container\n --entrypoint CMD Override the entrypoint of the image.\n -e KEY=VAL Set an environment variable (can be used multiple times)\n -u, --user=\"\" Run as specified username or uid\n --no-deps Don't start linked services.\n --rm Remove container after run. Ignored in detached mode.\n -p, --publish=[] Publish a container's port(s) to the host\n --service-ports Run command with the service's ports enabled and mapped\n to the host.\n -T Disable pseudo-tty allocation. By default `docker-compose run`\n allocates a TTY.\n -w, --workdir=\"\" Working directory inside the container\n \"\"\"\n service = self.project.get_service(options['SERVICE'])\n detach = options['-d']\n\n if options['--publish'] and options['--service-ports']:\n raise UserError(\n 'Service port mapping and manual port mapping '\n 'can not be used together'\n )\n\n if options['COMMAND'] is not None:\n command = [options['COMMAND']] + options['ARGS']\n elif options['--entry","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.scale","text":"compose/cli/main.py::TopLevelCommand.scale\n def scale(self, options):\n \"\"\"\n Set number of containers to run for a service.\n\n Numbers are specified in the form `service=num` as arguments.\n For example:\n\n $ docker-compose scale web=2 worker=3\n\n Usage: scale [options] [SERVICE=NUM...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = timeout_from_opts(options)\n\n for s in options['SERVICE=NUM']:\n if '=' not in s:\n raise UserError('Arguments to scale should be in the form service=num')\n service_name, num = s.split('=', 1)\n try:\n num = int(num)\n except ValueError:\n raise UserError('Number of containers for service \"%s\" is not a '\n 'number' % service_name)\n self.project.get_service(service_name).scale(num, timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.start","text":"compose/cli/main.py::TopLevelCommand.start\n def start(self, options):\n \"\"\"\n Start existing containers.\n\n Usage: start [SERVICE...]\n \"\"\"\n containers = self.project.start(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to start', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.stop","text":"compose/cli/main.py::TopLevelCommand.stop\n def stop(self, options):\n \"\"\"\n Stop running containers without removing them.\n\n They can be started again with `docker-compose start`.\n\n Usage: stop [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = timeout_from_opts(options)\n self.project.stop(service_names=options['SERVICE'], timeout=timeout)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.restart","text":"compose/cli/main.py::TopLevelCommand.restart\n def restart(self, options):\n \"\"\"\n Restart running containers.\n\n Usage: restart [options] [SERVICE...]\n\n Options:\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\n (default: 10)\n \"\"\"\n timeout = timeout_from_opts(options)\n containers = self.project.restart(service_names=options['SERVICE'], timeout=timeout)\n exit_if(not containers, 'No containers to restart', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.top","text":"compose/cli/main.py::TopLevelCommand.top\n def top(self, options):\n \"\"\"\n Display the running processes\n\n Usage: top [SERVICE...]\n\n \"\"\"\n containers = sorted(\n self.project.containers(service_names=options['SERVICE'], stopped=False) +\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\n key=attrgetter('name')\n )\n\n for idx, container in enumerate(containers):\n if idx > 0:\n print()\n\n top_data = self.project.client.top(container.name)\n headers = top_data.get(\"Titles\")\n rows = []\n\n for process in top_data.get(\"Processes\", []):\n rows.append(process)\n\n print(container.name)\n print(Formatter().table(headers, rows))","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.unpause","text":"compose/cli/main.py::TopLevelCommand.unpause\n def unpause(self, options):\n \"\"\"\n Unpause services.\n\n Usage: unpause [SERVICE...]\n \"\"\"\n containers = self.project.unpause(service_names=options['SERVICE'])\n exit_if(not containers, 'No containers to unpause', 1)","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.up","text":"compose/cli/main.py::TopLevelCommand.up\n def up(self, options):\n \"\"\"\n Builds, (re)creates, starts, and attaches to containers for a service.\n\n Unless they are already running, this command also starts any linked services.\n\n The `docker-compose up` command aggregates the output of each container. When\n the command exits, all containers are stopped. Running `docker-compose up -d`\n starts the containers in the background and leaves them running.\n\n If there are existing containers for a service, and the service's configuration\n or image was changed after the container's creation, `docker-compose up` picks\n up the changes by stopping and recreating the containers (preserving mounted\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\n flag.\n\n If you want to force Compose to stop and recreate all containers, use the\n `--force-recreate` flag.\n\n Usage: up [options] [SERVICE...]\n\n Options:\n -d Detached mode: Run containers in the background,\n print new container names.\n Incompatible with --abort-on-container-exit.\n --no-color Produce monochrome output.\n --no-deps Don't start linked services.\n --force-recreate Recreate containers even if their configuration\n and image haven't changed.\n Incompatible with --no-recreate.\n --no-recreate If containers already exist, don't recreate them.\n Incompatible with --force-recreate.\n --no-build Don't build an image, even if it's missing.\n --build Build images before starting containers.\n --abort-on-container-exit Stops al","label":0},{"cid":"compose/cli/main.py::TopLevelCommand.version","text":"compose/cli/main.py::TopLevelCommand.version\n @classmethod\n def version(cls, options):\n \"\"\"\n Show version informations\n\n Usage: version [--short]\n\n Options:\n --short Shows only Compose's version number.\n \"\"\"\n if options['--short']:\n print(__version__)\n else:\n print(get_version_info('full'))","label":0},{"cid":"compose/cli/main.py::convergence_strategy_from_opts","text":"compose/cli/main.py::convergence_strategy_from_opts\ndef convergence_strategy_from_opts(options):\n no_recreate = options['--no-recreate']\n force_recreate = options['--force-recreate']\n if force_recreate and no_recreate:\n raise UserError(\"--force-recreate and --no-recreate cannot be combined.\")\n\n if force_recreate:\n return ConvergenceStrategy.always\n\n if no_recreate:\n return ConvergenceStrategy.never\n\n return ConvergenceStrategy.changed","label":0},{"cid":"compose/cli/main.py::timeout_from_opts","text":"compose/cli/main.py::timeout_from_opts\ndef timeout_from_opts(options):\n timeout = options.get('--timeout')\n return None if timeout is None else int(timeout)","label":0},{"cid":"compose/cli/main.py::image_type_from_opt","text":"compose/cli/main.py::image_type_from_opt\ndef image_type_from_opt(flag, value):\n if not value:\n return ImageType.none\n try:\n return ImageType[value]\n except KeyError:\n raise UserError(\"%s flag must be one of: all, local\" % flag)","label":0},{"cid":"compose/cli/main.py::build_action_from_opts","text":"compose/cli/main.py::build_action_from_opts\ndef build_action_from_opts(options):\n if options['--build'] and options['--no-build']:\n raise UserError(\"--build and --no-build can not be combined.\")\n\n if options['--build']:\n return BuildAction.force\n\n if options['--no-build']:\n return BuildAction.skip\n\n return BuildAction.none","label":0},{"cid":"compose/cli/main.py::build_container_options","text":"compose/cli/main.py::build_container_options\ndef build_container_options(options, detach, command):\n container_options = {\n 'command': command,\n 'tty': not (detach or options['-T'] or not sys.stdin.isatty()),\n 'stdin_open': not detach,\n 'detach': detach,\n }\n\n if options['-e']:\n container_options['environment'] = Environment.from_command_line(\n parse_environment(options['-e'])\n )\n\n if options['--entrypoint']:\n container_options['entrypoint'] = options.get('--entrypoint')\n\n if options['--rm']:\n container_options['restart'] = None\n\n if options['--user']:\n container_options['user'] = options.get('--user')\n\n if not options['--service-ports']:\n container_options['ports'] = []\n\n if options['--publish']:\n container_options['ports'] = options.get('--publish')\n\n if options['--name']:\n container_options['name'] = options['--name']\n\n if options['--workdir']:\n container_options['working_dir'] = options['--workdir']\n\n return container_options","label":0},{"cid":"compose/cli/main.py::run_one_off_container","text":"compose/cli/main.py::run_one_off_container\ndef run_one_off_container(container_options, project, service, options):\n if not options['--no-deps']:\n deps = service.get_dependency_names()\n if deps:\n project.up(\n service_names=deps,\n start_deps=True,\n strategy=ConvergenceStrategy.never)\n\n project.initialize()\n\n container = service.create_container(\n quiet=True,\n one_off=True,\n **container_options)\n\n if options['-d']:\n service.start_container(container)\n print(container.name)\n return\n\n def remove_container(force=False):\n if options['--rm']:\n project.client.remove_container(container.id, force=True)\n\n signals.set_signal_handler_to_shutdown()\n try:\n try:\n if IS_WINDOWS_PLATFORM:\n service.connect_container_to_networks(container)\n exit_code = call_docker([\"start\", \"--attach\", \"--interactive\", container.id])\n else:\n operation = RunOperation(\n project.client,\n container.id,\n interactive=not options['-T'],\n logs=False,\n )\n pty = PseudoTerminal(project.client, operation)\n sockets = pty.sockets()\n service.start_container(container)\n pty.start(sockets)\n exit_code = container.wait()\n except signals.ShutdownException:\n project.client.stop(container.id)\n exit_code = 1\n except signals.ShutdownException:\n project.client.kill(container.id)\n remove_container(force=True)\n sys.exit(2)\n\n remove_container()\n sys.exit(exit_code)","label":0},{"cid":"compose/config/validation.py::format_ports","text":"compose/config/validation.py::format_ports\n@FormatChecker.cls_checks(format=\"ports\", raises=ValidationError)\ndef format_ports(instance):\n try:\n split_port(instance)\n except ValueError as e:\n raise ValidationError(six.text_type(e))\n return True","label":0},{"cid":"compose/config/validation.py::format_expose","text":"compose/config/validation.py::format_expose\n@FormatChecker.cls_checks(format=\"expose\", raises=ValidationError)\ndef format_expose(instance):\n if isinstance(instance, six.string_types):\n if not re.match(VALID_EXPOSE_FORMAT, instance):\n raise ValidationError(\n \"should be of the format 'PORT[/PROTOCOL]'\")\n\n return True","label":0},{"cid":"compose/config/validation.py::match_named_volumes","text":"compose/config/validation.py::match_named_volumes\ndef match_named_volumes(service_dict, project_volumes):\n service_volumes = service_dict.get('volumes', [])\n for volume_spec in service_volumes:\n if volume_spec.is_named_volume and volume_spec.external not in project_volumes:\n raise ConfigurationError(\n 'Named volume \"{0}\" is used in service \"{1}\" but no'\n ' declaration was found in the volumes section.'.format(\n volume_spec.repr(), service_dict.get('name')\n )\n )","label":0},{"cid":"compose/config/validation.py::python_type_to_yaml_type","text":"compose/config/validation.py::python_type_to_yaml_type\ndef python_type_to_yaml_type(type_):\n type_name = type(type_).__name__\n return {\n 'dict': 'mapping',\n 'list': 'array',\n 'int': 'number',\n 'float': 'number',\n 'bool': 'boolean',\n 'unicode': 'string',\n 'str': 'string',\n 'bytes': 'string',\n }.get(type_name, type_name)","label":0},{"cid":"compose/config/validation.py::validate_config_section","text":"compose/config/validation.py::validate_config_section\ndef validate_config_section(filename, config, section):\n \"\"\"Validate the structure of a configuration section. This must be done\n before interpolation so it's separate from schema validation.\n \"\"\"\n if not isinstance(config, dict):\n raise ConfigurationError(\n \"In file '{filename}', {section} must be a mapping, not \"\n \"{type}.\".format(\n filename=filename,\n section=section,\n type=anglicize_json_type(python_type_to_yaml_type(config))))\n\n for key, value in config.items():\n if not isinstance(key, six.string_types):\n raise ConfigurationError(\n \"In file '{filename}', the {section} name {name} must be a \"\n \"quoted string, i.e. '{name}'.\".format(\n filename=filename,\n section=section,\n name=key))\n\n if not isinstance(value, (dict, type(None))):\n raise ConfigurationError(\n \"In file '{filename}', {section} '{name}' must be a mapping not \"\n \"{type}.\".format(\n filename=filename,\n section=section,\n name=key,\n type=anglicize_json_type(python_type_to_yaml_type(value))))","label":0},{"cid":"compose/config/validation.py::validate_top_level_object","text":"compose/config/validation.py::validate_top_level_object\ndef validate_top_level_object(config_file):\n if not isinstance(config_file.config, dict):\n raise ConfigurationError(\n \"Top level object in '{}' needs to be an object not '{}'.\".format(\n config_file.filename,\n type(config_file.config)))","label":0},{"cid":"compose/config/validation.py::validate_ulimits","text":"compose/config/validation.py::validate_ulimits\ndef validate_ulimits(service_config):\n ulimit_config = service_config.config.get('ulimits', {})\n for limit_name, soft_hard_values in six.iteritems(ulimit_config):\n if isinstance(soft_hard_values, dict):\n if not soft_hard_values['soft'] <= soft_hard_values['hard']:\n raise ConfigurationError(\n \"Service '{s.name}' has invalid ulimit '{ulimit}'. \"\n \"'soft' value can not be greater than 'hard' value \".format(\n s=service_config,\n ulimit=ulimit_config))","label":0},{"cid":"compose/config/validation.py::validate_extends_file_path","text":"compose/config/validation.py::validate_extends_file_path\ndef validate_extends_file_path(service_name, extends_options, filename):\n \"\"\"\n The service to be extended must either be defined in the config key 'file',\n or within 'filename'.\n \"\"\"\n error_prefix = \"Invalid 'extends' configuration for %s:\" % service_name\n\n if 'file' not in extends_options and filename is None:\n raise ConfigurationError(\n \"%s you need to specify a 'file', e.g. 'file: something.yml'\" % error_prefix\n )","label":0},{"cid":"compose/config/validation.py::validate_network_mode","text":"compose/config/validation.py::validate_network_mode\ndef validate_network_mode(service_config, service_names):\n network_mode = service_config.config.get('network_mode')\n if not network_mode:\n return\n\n if 'networks' in service_config.config:\n raise ConfigurationError(\"'network_mode' and 'networks' cannot be combined\")\n\n dependency = get_service_name_from_network_mode(network_mode)\n if not dependency:\n return\n\n if dependency not in service_names:\n raise ConfigurationError(\n \"Service '{s.name}' uses the network stack of service '{dep}' which \"\n \"is undefined.\".format(s=service_config, dep=dependency))","label":0},{"cid":"compose/config/validation.py::validate_links","text":"compose/config/validation.py::validate_links\ndef validate_links(service_config, service_names):\n for link in service_config.config.get('links', []):\n if link.split(':')[0] not in service_names:\n raise ConfigurationError(\n \"Service '{s.name}' has a link to service '{link}' which is \"\n \"undefined.\".format(s=service_config, link=link))","label":0},{"cid":"compose/config/validation.py::validate_depends_on","text":"compose/config/validation.py::validate_depends_on\ndef validate_depends_on(service_config, service_names):\n deps = service_config.config.get('depends_on', {})\n for dependency in deps.keys():\n if dependency not in service_names:\n raise ConfigurationError(\n \"Service '{s.name}' depends on service '{dep}' which is \"\n \"undefined.\".format(s=service_config, dep=dependency)\n )","label":0},{"cid":"compose/config/validation.py::get_unsupported_config_msg","text":"compose/config/validation.py::get_unsupported_config_msg\ndef get_unsupported_config_msg(path, error_key):\n msg = \"Unsupported config option for {}: '{}'\".format(path_string(path), error_key)\n if error_key in DOCKER_CONFIG_HINTS:\n msg += \" (did you mean '{}'?)\".format(DOCKER_CONFIG_HINTS[error_key])\n return msg","label":0},{"cid":"compose/config/validation.py::anglicize_json_type","text":"compose/config/validation.py::anglicize_json_type\ndef anglicize_json_type(json_type):\n if json_type.startswith(('a', 'e', 'i', 'o', 'u')):\n return 'an ' + json_type\n return 'a ' + json_type","label":0},{"cid":"compose/config/validation.py::is_service_dict_schema","text":"compose/config/validation.py::is_service_dict_schema\ndef is_service_dict_schema(schema_id):\n return schema_id in ('config_schema_v1.json', '#/properties/services')","label":0},{"cid":"compose/config/validation.py::handle_error_for_schema_with_id","text":"compose/config/validation.py::handle_error_for_schema_with_id\ndef handle_error_for_schema_with_id(error, path):\n schema_id = error.schema['id']\n\n if is_service_dict_schema(schema_id) and error.validator == 'additionalProperties':\n return \"Invalid service name '{}' - only {} characters are allowed\".format(\n # The service_name is the key to the json object\n list(error.instance)[0],\n VALID_NAME_CHARS)\n\n if error.validator == 'additionalProperties':\n if schema_id == '#/definitions/service':\n invalid_config_key = parse_key_from_error_msg(error)\n return get_unsupported_config_msg(path, invalid_config_key)\n\n if not error.path:\n return '{}\\n\\n{}'.format(error.message, VERSION_EXPLANATION)","label":0},{"cid":"compose/config/validation.py::handle_generic_error","text":"compose/config/validation.py::handle_generic_error\ndef handle_generic_error(error, path):\n msg_format = None\n error_msg = error.message\n\n if error.validator == 'oneOf':\n msg_format = \"{path} {msg}\"\n config_key, error_msg = _parse_oneof_validator(error)\n if config_key:\n path.append(config_key)\n\n elif error.validator == 'type':\n msg_format = \"{path} contains an invalid type, it should be {msg}\"\n error_msg = _parse_valid_types_from_validator(error.validator_value)\n\n elif error.validator == 'required':\n error_msg = \", \".join(error.validator_value)\n msg_format = \"{path} is invalid, {msg} is required.\"\n\n elif error.validator == 'dependencies':\n config_key = list(error.validator_value.keys())[0]\n required_keys = \",\".join(error.validator_value[config_key])\n\n msg_format = \"{path} is invalid: {msg}\"\n path.append(config_key)\n error_msg = \"when defining '{}' you must set '{}' as well\".format(\n config_key,\n required_keys)\n\n elif error.cause:\n error_msg = six.text_type(error.cause)\n msg_format = \"{path} is invalid: {msg}\"\n\n elif error.path:\n msg_format = \"{path} value {msg}\"\n\n if msg_format:\n return msg_format.format(path=path_string(path), msg=error_msg)\n\n return error.message","label":0},{"cid":"compose/config/validation.py::parse_key_from_error_msg","text":"compose/config/validation.py::parse_key_from_error_msg\ndef parse_key_from_error_msg(error):\n return error.message.split(\"'\")[1]","label":0},{"cid":"compose/config/validation.py::path_string","text":"compose/config/validation.py::path_string\ndef path_string(path):\n return \".\".join(c for c in path if isinstance(c, six.string_types))","label":0},{"cid":"compose/config/validation.py::_parse_valid_types_from_validator","text":"compose/config/validation.py::_parse_valid_types_from_validator\ndef _parse_valid_types_from_validator(validator):\n \"\"\"A validator value can be either an array of valid types or a string of\n a valid type. Parse the valid types and prefix with the correct article.\n \"\"\"\n if not isinstance(validator, list):\n return anglicize_json_type(validator)\n\n if len(validator) == 1:\n return anglicize_json_type(validator[0])\n\n return \"{}, or {}\".format(\n \", \".join([anglicize_json_type(validator[0])] + validator[1:-1]),\n anglicize_json_type(validator[-1]))","label":0},{"cid":"compose/config/validation.py::_parse_oneof_validator","text":"compose/config/validation.py::_parse_oneof_validator\ndef _parse_oneof_validator(error):\n \"\"\"oneOf has multiple schemas, so we need to reason about which schema, sub\n schema or constraint the validation is failing on.\n Inspecting the context value of a ValidationError gives us information about\n which sub schema failed and which kind of error it is.\n \"\"\"\n types = []\n for context in error.context:\n\n if context.validator == 'oneOf':\n _, error_msg = _parse_oneof_validator(context)\n return path_string(context.path), error_msg\n\n if context.validator == 'required':\n return (None, context.message)\n\n if context.validator == 'additionalProperties':\n invalid_config_key = parse_key_from_error_msg(context)\n return (None, \"contains unsupported option: '{}'\".format(invalid_config_key))\n\n if context.path:\n return (\n path_string(context.path),\n \"contains {}, which is an invalid type, it should be {}\".format(\n json.dumps(context.instance),\n _parse_valid_types_from_validator(context.validator_value)),\n )\n\n if context.validator == 'uniqueItems':\n return (\n None,\n \"contains non unique items, please remove duplicates from {}\".format(\n context.instance),\n )\n\n if context.validator == 'type':\n types.append(context.validator_value)\n\n valid_types = _parse_valid_types_from_validator(types)\n return (None, \"contains an invalid type, it should be {}\".format(valid_types))","label":0},{"cid":"compose/config/validation.py::process_service_constraint_errors","text":"compose/config/validation.py::process_service_constraint_errors\ndef process_service_constraint_errors(error, service_name, version):\n if version == V1:\n if 'image' in error.instance and 'build' in error.instance:\n return (\n \"Service {} has both an image and build path specified. \"\n \"A service can either be built to image or use an existing \"\n \"image, not both.\".format(service_name))\n\n if 'image' in error.instance and 'dockerfile' in error.instance:\n return (\n \"Service {} has both an image and alternate Dockerfile. \"\n \"A service can either be built to image or use an existing \"\n \"image, not both.\".format(service_name))\n\n if 'image' not in error.instance and 'build' not in error.instance:\n return (\n \"Service {} has neither an image nor a build context specified. \"\n \"At least one must be provided.\".format(service_name))","label":0},{"cid":"compose/config/validation.py::process_config_schema_errors","text":"compose/config/validation.py::process_config_schema_errors\ndef process_config_schema_errors(error):\n path = list(error.path)\n\n if 'id' in error.schema:\n error_msg = handle_error_for_schema_with_id(error, path)\n if error_msg:\n return error_msg\n\n return handle_generic_error(error, path)","label":0},{"cid":"compose/config/validation.py::validate_against_config_schema","text":"compose/config/validation.py::validate_against_config_schema\ndef validate_against_config_schema(config_file):\n schema = load_jsonschema(config_file.version)\n format_checker = FormatChecker([\"ports\", \"expose\"])\n validator = Draft4Validator(\n schema,\n resolver=RefResolver(get_resolver_path(), schema),\n format_checker=format_checker)\n handle_errors(\n validator.iter_errors(config_file.config),\n process_config_schema_errors,\n config_file.filename)","label":0},{"cid":"compose/config/validation.py::validate_service_constraints","text":"compose/config/validation.py::validate_service_constraints\ndef validate_service_constraints(config, service_name, version):\n def handler(errors):\n return process_service_constraint_errors(errors, service_name, version)\n\n schema = load_jsonschema(version)\n validator = Draft4Validator(schema['definitions']['constraints']['service'])\n handle_errors(validator.iter_errors(config), handler, None)","label":0},{"cid":"compose/config/validation.py::validate_service_constraints.handler","text":"compose/config/validation.py::validate_service_constraints.handler\n def handler(errors):\n return process_service_constraint_errors(errors, service_name, version)","label":0},{"cid":"compose/config/validation.py::get_schema_path","text":"compose/config/validation.py::get_schema_path\ndef get_schema_path():\n return os.path.dirname(os.path.abspath(__file__))","label":0},{"cid":"compose/config/validation.py::load_jsonschema","text":"compose/config/validation.py::load_jsonschema\ndef load_jsonschema(version):\n filename = os.path.join(\n get_schema_path(),\n \"config_schema_v{0}.json\".format(version))\n\n with open(filename, \"r\") as fh:\n return json.load(fh)","label":0},{"cid":"compose/config/validation.py::get_resolver_path","text":"compose/config/validation.py::get_resolver_path\ndef get_resolver_path():\n schema_path = get_schema_path()\n if sys.platform == \"win32\":\n scheme = \"///\"\n # TODO: why is this necessary?\n schema_path = schema_path.replace('\\\\', '/')\n else:\n scheme = \"//\"\n return \"file:{}{}/\".format(scheme, schema_path)","label":0},{"cid":"compose/config/validation.py::handle_errors","text":"compose/config/validation.py::handle_errors\ndef handle_errors(errors, format_error_func, filename):\n \"\"\"jsonschema returns an error tree full of information to explain what has\n gone wrong. Process each error and pull out relevant information and re-write\n helpful error messages that are relevant.\n \"\"\"\n errors = list(sorted(errors, key=str))\n if not errors:\n return\n\n error_msg = '\\n'.join(format_error_func(error) for error in errors)\n raise ConfigurationError(\n \"The Compose file{file_msg} is invalid because:\\n{error_msg}\".format(\n file_msg=\" '{}'\".format(filename) if filename else \"\",\n error_msg=error_msg))","label":0},{"cid":"compose/service.py::BuildError.__init__","text":"compose/service.py::BuildError.__init__\n def __init__(self, service, reason):\n self.service = service\n self.reason = reason","label":0},{"cid":"compose/service.py::NeedsBuildError.__init__","text":"compose/service.py::NeedsBuildError.__init__\n def __init__(self, service):\n self.service = service","label":0},{"cid":"compose/service.py::ConvergenceStrategy.allows_recreate","text":"compose/service.py::ConvergenceStrategy.allows_recreate\n @property\n def allows_recreate(self):\n return self is not type(self).never","label":0},{"cid":"compose/service.py::Service.__init__","text":"compose/service.py::Service.__init__\n def __init__(\n self,\n name,\n client=None,\n project='default',\n use_networking=False,\n links=None,\n volumes_from=None,\n network_mode=None,\n networks=None,\n secrets=None,\n **options\n ):\n self.name = name\n self.client = client\n self.project = project\n self.use_networking = use_networking\n self.links = links or []\n self.volumes_from = volumes_from or []\n self.network_mode = network_mode or NetworkMode(None)\n self.networks = networks or {}\n self.secrets = secrets or []\n self.options = options","label":0},{"cid":"compose/service.py::Service.__repr__","text":"compose/service.py::Service.__repr__\n def __repr__(self):\n return '<Service: {}>'.format(self.name)","label":0},{"cid":"compose/service.py::Service.containers","text":"compose/service.py::Service.containers\n def containers(self, stopped=False, one_off=False, filters={}):\n filters.update({'label': self.labels(one_off=one_off)})\n\n return list(filter(None, [\n Container.from_ps(self.client, container)\n for container in self.client.containers(\n all=stopped,\n filters=filters)]))","label":0},{"cid":"compose/service.py::Service.get_container","text":"compose/service.py::Service.get_container\n def get_container(self, number=1):\n \"\"\"Return a :class:`compose.container.Container` for this service. The\n container must be active, and match `number`.\n \"\"\"\n labels = self.labels() + ['{0}={1}'.format(LABEL_CONTAINER_NUMBER, number)]\n for container in self.client.containers(filters={'label': labels}):\n return Container.from_ps(self.client, container)\n\n raise ValueError(\"No container found for %s_%s\" % (self.name, number))","label":0},{"cid":"compose/service.py::Service.start","text":"compose/service.py::Service.start\n def start(self, **options):\n containers = self.containers(stopped=True)\n for c in containers:\n self.start_container_if_stopped(c, **options)\n return containers","label":0},{"cid":"compose/service.py::Service.scale","text":"compose/service.py::Service.scale\n def scale(self, desired_num, timeout=None):\n \"\"\"\n Adjusts the number of containers to the specified number and ensures\n they are running.\n\n - creates containers until there are at least `desired_num`\n - stops containers until there are at most `desired_num` running\n - starts containers until there are at least `desired_num` running\n - removes all stopped containers\n \"\"\"\n if self.custom_container_name and desired_num > 1:\n log.warn('The \"%s\" service is using the custom container name \"%s\". '\n 'Docker requires each container to have a unique name. '\n 'Remove the custom name to scale the service.'\n % (self.name, self.custom_container_name))\n\n if self.specifies_host_port() and desired_num > 1:\n log.warn('The \"%s\" service specifies a port on the host. If multiple containers '\n 'for this service are created on a single host, the port will clash.'\n % self.name)\n\n def create_and_start(service, number):\n container = service.create_container(number=number, quiet=True)\n service.start_container(container)\n return container\n\n def stop_and_remove(container):\n container.stop(timeout=self.stop_timeout(timeout))\n container.remove()\n\n running_containers = self.containers(stopped=False)\n num_running = len(running_containers)\n\n if desired_num == num_running:\n # do nothing as we already have the desired number\n log.info('Desired container number already achieved')\n return\n\n if desired_num > num_running:\n # we need to start/create until we have desired_num\n all_containers = self.containers(stopped=True)\n\n if num_running != len(all_containers):\n # we have some stopped containers, let's st","label":1},{"cid":"compose/service.py::Service.scale.create_and_start","text":"compose/service.py::Service.scale.create_and_start\n def create_and_start(service, number):\n container = service.create_container(number=number, quiet=True)\n service.start_container(container)\n return container","label":0},{"cid":"compose/service.py::Service.scale.stop_and_remove","text":"compose/service.py::Service.scale.stop_and_remove\n def stop_and_remove(container):\n container.stop(timeout=self.stop_timeout(timeout))\n container.remove()","label":0},{"cid":"compose/service.py::Service.create_container","text":"compose/service.py::Service.create_container\n def create_container(self,\n one_off=False,\n previous_container=None,\n number=None,\n quiet=False,\n **override_options):\n \"\"\"\n Create a container for this service. If the image doesn't exist, attempt to pull\n it.\n \"\"\"\n # This is only necessary for `scale` and `volumes_from`\n # auto-creating containers to satisfy the dependency.\n self.ensure_image_exists()\n\n container_options = self._get_container_create_options(\n override_options,\n number or self._next_container_number(one_off=one_off),\n one_off=one_off,\n previous_container=previous_container,\n )\n\n if 'name' in container_options and not quiet:\n log.info(\"Creating %s\" % container_options['name'])\n\n try:\n return Container.create(self.client, **container_options)\n except APIError as ex:\n raise OperationFailedError(\"Cannot create container for service %s: %s\" %\n (self.name, ex.explanation))","label":0},{"cid":"compose/service.py::Service.ensure_image_exists","text":"compose/service.py::Service.ensure_image_exists\n def ensure_image_exists(self, do_build=BuildAction.none):\n if self.can_be_built() and do_build == BuildAction.force:\n self.build()\n return\n\n try:\n self.image()\n return\n except NoSuchImageError:\n pass\n\n if not self.can_be_built():\n self.pull()\n return\n\n if do_build == BuildAction.skip:\n raise NeedsBuildError(self)\n\n self.build()\n log.warn(\n \"Image for service {} was built because it did not already exist. To \"\n \"rebuild this image you must use `docker-compose build` or \"\n \"`docker-compose up --build`.\".format(self.name))","label":0},{"cid":"compose/service.py::Service.image","text":"compose/service.py::Service.image\n def image(self):\n try:\n return self.client.inspect_image(self.image_name)\n except ImageNotFound:\n raise NoSuchImageError(\"Image '{}' not found\".format(self.image_name))","label":0},{"cid":"compose/service.py::Service.image_name","text":"compose/service.py::Service.image_name\n @property\n def image_name(self):\n return self.options.get('image', '{s.project}_{s.name}'.format(s=self))","label":0},{"cid":"compose/service.py::Service.convergence_plan","text":"compose/service.py::Service.convergence_plan\n def convergence_plan(self, strategy=ConvergenceStrategy.changed):\n containers = self.containers(stopped=True)\n\n if not containers:\n return ConvergencePlan('create', [])\n\n if strategy is ConvergenceStrategy.never:\n return ConvergencePlan('start', containers)\n\n if (\n strategy is ConvergenceStrategy.always or\n self._containers_have_diverged(containers)\n ):\n return ConvergencePlan('recreate', containers)\n\n stopped = [c for c in containers if not c.is_running]\n\n if stopped:\n return ConvergencePlan('start', stopped)\n\n return ConvergencePlan('noop', containers)","label":0},{"cid":"compose/service.py::Service._containers_have_diverged","text":"compose/service.py::Service._containers_have_diverged\n def _containers_have_diverged(self, containers):\n config_hash = None\n\n try:\n config_hash = self.config_hash\n except NoSuchImageError as e:\n log.debug(\n 'Service %s has diverged: %s',\n self.name, six.text_type(e),\n )\n return True\n\n has_diverged = False\n\n for c in containers:\n container_config_hash = c.labels.get(LABEL_CONFIG_HASH, None)\n if container_config_hash != config_hash:\n log.debug(\n '%s has diverged: %s != %s',\n c.name, container_config_hash, config_hash,\n )\n has_diverged = True\n\n return has_diverged","label":0},{"cid":"compose/service.py::Service.execute_convergence_plan","text":"compose/service.py::Service.execute_convergence_plan\n def execute_convergence_plan(self,\n plan,\n timeout=None,\n detached=False,\n start=True):\n (action, containers) = plan\n should_attach_logs = not detached\n\n if action == 'create':\n container = self.create_container()\n\n if should_attach_logs:\n container.attach_log_stream()\n\n if start:\n self.start_container(container)\n\n return [container]\n\n elif action == 'recreate':\n return [\n self.recreate_container(\n container,\n timeout=timeout,\n attach_logs=should_attach_logs,\n start_new_container=start\n )\n for container in containers\n ]\n\n elif action == 'start':\n if start:\n for container in containers:\n self.start_container_if_stopped(container, attach_logs=should_attach_logs)\n\n return containers\n\n elif action == 'noop':\n for c in containers:\n log.info(\"%s is up-to-date\" % c.name)\n\n return containers\n\n else:\n raise Exception(\"Invalid action: {}\".format(action))","label":0},{"cid":"compose/service.py::Service.recreate_container","text":"compose/service.py::Service.recreate_container\n def recreate_container(\n self,\n container,\n timeout=None,\n attach_logs=False,\n start_new_container=True):\n \"\"\"Recreate a container.\n\n The original container is renamed to a temporary name so that data\n volumes can be copied to the new container, before the original\n container is removed.\n \"\"\"\n log.info(\"Recreating %s\" % container.name)\n\n container.stop(timeout=self.stop_timeout(timeout))\n container.rename_to_tmp_name()\n new_container = self.create_container(\n previous_container=container,\n number=container.labels.get(LABEL_CONTAINER_NUMBER),\n quiet=True,\n )\n if attach_logs:\n new_container.attach_log_stream()\n if start_new_container:\n self.start_container(new_container)\n container.remove()\n return new_container","label":0},{"cid":"compose/service.py::Service.stop_timeout","text":"compose/service.py::Service.stop_timeout\n def stop_timeout(self, timeout):\n if timeout is not None:\n return timeout\n timeout = parse_seconds_float(self.options.get('stop_grace_period'))\n if timeout is not None:\n return timeout\n return DEFAULT_TIMEOUT","label":0},{"cid":"compose/service.py::Service.start_container_if_stopped","text":"compose/service.py::Service.start_container_if_stopped\n def start_container_if_stopped(self, container, attach_logs=False, quiet=False):\n if not container.is_running:\n if not quiet:\n log.info(\"Starting %s\" % container.name)\n if attach_logs:\n container.attach_log_stream()\n return self.start_container(container)","label":0},{"cid":"compose/service.py::Service.start_container","text":"compose/service.py::Service.start_container\n def start_container(self, container):\n self.connect_container_to_networks(container)\n try:\n container.start()\n except APIError as ex:\n raise OperationFailedError(\"Cannot start service %s: %s\" % (self.name, ex.explanation))\n return container","label":0},{"cid":"compose/service.py::Service.connect_container_to_networks","text":"compose/service.py::Service.connect_container_to_networks\n def connect_container_to_networks(self, container):\n connected_networks = container.get('NetworkSettings.Networks')\n\n for network, netdefs in self.networks.items():\n if network in connected_networks:\n if short_id_alias_exists(container, network):\n continue\n\n self.client.disconnect_container_from_network(\n container.id,\n network)\n\n self.client.connect_container_to_network(\n container.id, network,\n aliases=self._get_aliases(netdefs, container),\n ipv4_address=netdefs.get('ipv4_address', None),\n ipv6_address=netdefs.get('ipv6_address', None),\n links=self._get_links(False),\n link_local_ips=netdefs.get('link_local_ips', None),\n )","label":0},{"cid":"compose/service.py::Service.remove_duplicate_containers","text":"compose/service.py::Service.remove_duplicate_containers\n def remove_duplicate_containers(self, timeout=None):\n for c in self.duplicate_containers():\n log.info('Removing %s' % c.name)\n c.stop(timeout=self.stop_timeout(timeout))\n c.remove()","label":0},{"cid":"compose/service.py::Service.duplicate_containers","text":"compose/service.py::Service.duplicate_containers\n def duplicate_containers(self):\n containers = sorted(\n self.containers(stopped=True),\n key=lambda c: c.get('Created'),\n )\n\n numbers = set()\n\n for c in containers:\n if c.number in numbers:\n yield c\n else:\n numbers.add(c.number)","label":0},{"cid":"compose/service.py::Service.config_hash","text":"compose/service.py::Service.config_hash\n @property\n def config_hash(self):\n return json_hash(self.config_dict())","label":0},{"cid":"compose/service.py::Service.config_dict","text":"compose/service.py::Service.config_dict\n def config_dict(self):\n return {\n 'options': self.options,\n 'image_id': self.image()['Id'],\n 'links': self.get_link_names(),\n 'net': self.network_mode.id,\n 'networks': self.networks,\n 'volumes_from': [\n (v.source.name, v.mode)\n for v in self.volumes_from if isinstance(v.source, Service)\n ],\n }","label":0},{"cid":"compose/service.py::Service.get_dependency_names","text":"compose/service.py::Service.get_dependency_names\n def get_dependency_names(self):\n net_name = self.network_mode.service_name\n return (\n self.get_linked_service_names() +\n self.get_volumes_from_names() +\n ([net_name] if net_name else []) +\n list(self.options.get('depends_on', {}).keys())\n )","label":0},{"cid":"compose/service.py::Service.get_dependency_configs","text":"compose/service.py::Service.get_dependency_configs\n def get_dependency_configs(self):\n net_name = self.network_mode.service_name\n configs = dict(\n [(name, None) for name in self.get_linked_service_names()]\n )\n configs.update(dict(\n [(name, None) for name in self.get_volumes_from_names()]\n ))\n configs.update({net_name: None} if net_name else {})\n configs.update(self.options.get('depends_on', {}))\n for svc, config in self.options.get('depends_on', {}).items():\n if config['condition'] == CONDITION_STARTED:\n configs[svc] = lambda s: True\n elif config['condition'] == CONDITION_HEALTHY:\n configs[svc] = lambda s: s.is_healthy()\n else:\n # The config schema already prevents this, but it might be\n # bypassed if Compose is called programmatically.\n raise ValueError(\n 'depends_on condition \"{}\" is invalid.'.format(\n config['condition']\n )\n )\n\n return configs","label":0},{"cid":"compose/service.py::Service.get_linked_service_names","text":"compose/service.py::Service.get_linked_service_names\n def get_linked_service_names(self):\n return [service.name for (service, _) in self.links]","label":0},{"cid":"compose/service.py::Service.get_link_names","text":"compose/service.py::Service.get_link_names\n def get_link_names(self):\n return [(service.name, alias) for service, alias in self.links]","label":0},{"cid":"compose/service.py::Service.get_volumes_from_names","text":"compose/service.py::Service.get_volumes_from_names\n def get_volumes_from_names(self):\n return [s.source.name for s in self.volumes_from if isinstance(s.source, Service)]","label":0},{"cid":"compose/service.py::Service._next_container_number","text":"compose/service.py::Service._next_container_number\n def _next_container_number(self, one_off=False):\n containers = filter(None, [\n Container.from_ps(self.client, container)\n for container in self.client.containers(\n all=True,\n filters={'label': self.labels(one_off=one_off)})\n ])\n numbers = [c.number for c in containers]\n return 1 if not numbers else max(numbers) + 1","label":0},{"cid":"compose/service.py::Service._get_aliases","text":"compose/service.py::Service._get_aliases\n def _get_aliases(self, network, container=None):\n if container and container.labels.get(LABEL_ONE_OFF) == \"True\":\n return []\n\n return list(\n {self.name} |\n ({container.short_id} if container else set()) |\n set(network.get('aliases', ()))\n )","label":0},{"cid":"compose/service.py::Service.build_default_networking_config","text":"compose/service.py::Service.build_default_networking_config\n def build_default_networking_config(self):\n if not self.networks:\n return {}\n\n network = self.networks[self.network_mode.id]\n endpoint = {\n 'Aliases': self._get_aliases(network),\n 'IPAMConfig': {},\n }\n\n if network.get('ipv4_address'):\n endpoint['IPAMConfig']['IPv4Address'] = network.get('ipv4_address')\n if network.get('ipv6_address'):\n endpoint['IPAMConfig']['IPv6Address'] = network.get('ipv6_address')\n\n return {\"EndpointsConfig\": {self.network_mode.id: endpoint}}","label":0},{"cid":"compose/service.py::Service._get_links","text":"compose/service.py::Service._get_links\n def _get_links(self, link_to_self):\n links = {}\n\n for service, link_name in self.links:\n for container in service.containers():\n links[link_name or service.name] = container.name\n links[container.name] = container.name\n links[container.name_without_project] = container.name\n\n if link_to_self:\n for container in self.containers():\n links[self.name] = container.name\n links[container.name] = container.name\n links[container.name_without_project] = container.name\n\n for external_link in self.options.get('external_links') or []:\n if ':' not in external_link:\n link_name = external_link\n else:\n external_link, link_name = external_link.split(':')\n links[link_name] = external_link\n\n return [\n (alias, container_name)\n for (container_name, alias) in links.items()\n ]","label":0},{"cid":"compose/service.py::Service._get_volumes_from","text":"compose/service.py::Service._get_volumes_from\n def _get_volumes_from(self):\n return [build_volume_from(spec) for spec in self.volumes_from]","label":0},{"cid":"compose/service.py::Service._get_container_create_options","text":"compose/service.py::Service._get_container_create_options\n def _get_container_create_options(\n self,\n override_options,\n number,\n one_off=False,\n previous_container=None):\n add_config_hash = (not one_off and not override_options)\n\n container_options = dict(\n (k, self.options[k])\n for k in DOCKER_CONFIG_KEYS if k in self.options)\n container_options.update(override_options)\n\n if not container_options.get('name'):\n container_options['name'] = self.get_container_name(number, one_off)\n\n container_options.setdefault('detach', True)\n\n # If a qualified hostname was given, split it into an\n # unqualified hostname and a domainname unless domainname\n # was also given explicitly. This matches the behavior of\n # the official Docker CLI in that scenario.\n if ('hostname' in container_options and\n 'domainname' not in container_options and\n '.' in container_options['hostname']):\n parts = container_options['hostname'].partition('.')\n container_options['hostname'] = parts[0]\n container_options['domainname'] = parts[2]\n\n if 'ports' in container_options or 'expose' in self.options:\n container_options['ports'] = build_container_ports(\n container_options,\n self.options)\n\n container_options['environment'] = merge_environment(\n self.options.get('environment'),\n override_options.get('environment'))\n\n binds, affinity = merge_volume_bindings(\n container_options.get('volumes') or [],\n previous_container)\n override_options['binds'] = binds\n container_options['environment'].update(affinity)\n\n container_options['volumes'] = dict(\n (v.internal, {}) for v in container_options.get('volumes') or {})\n\n secret_volumes = self.get_secret_volum","label":0},{"cid":"compose/service.py::Service._get_container_host_config","text":"compose/service.py::Service._get_container_host_config\n def _get_container_host_config(self, override_options, one_off=False):\n options = dict(self.options, **override_options)\n\n logging_dict = options.get('logging', None)\n log_config = get_log_config(logging_dict)\n\n host_config = self.client.create_host_config(\n links=self._get_links(link_to_self=one_off),\n port_bindings=build_port_bindings(options.get('ports') or []),\n binds=options.get('binds'),\n volumes_from=self._get_volumes_from(),\n privileged=options.get('privileged', False),\n network_mode=self.network_mode.mode,\n devices=options.get('devices'),\n dns=options.get('dns'),\n dns_search=options.get('dns_search'),\n restart_policy=options.get('restart'),\n cap_add=options.get('cap_add'),\n cap_drop=options.get('cap_drop'),\n mem_limit=options.get('mem_limit'),\n memswap_limit=options.get('memswap_limit'),\n ulimits=build_ulimits(options.get('ulimits')),\n log_config=log_config,\n extra_hosts=options.get('extra_hosts'),\n read_only=options.get('read_only'),\n pid_mode=options.get('pid'),\n security_opt=options.get('security_opt'),\n ipc_mode=options.get('ipc'),\n cgroup_parent=options.get('cgroup_parent'),\n cpu_quota=options.get('cpu_quota'),\n shm_size=options.get('shm_size'),\n sysctls=options.get('sysctls'),\n tmpfs=options.get('tmpfs'),\n oom_score_adj=options.get('oom_score_adj'),\n mem_swappiness=options.get('mem_swappiness'),\n group_add=options.get('group_add'),\n userns_mode=options.get('userns_mode')\n )\n\n # TODO: Add as an argument to create_host_config once it's supported\n # in docker-py\n host_config['Isolation'] = options.get('isolation')\n\n ","label":0},{"cid":"compose/service.py::Service.get_secret_volumes","text":"compose/service.py::Service.get_secret_volumes\n def get_secret_volumes(self):\n def build_spec(secret):\n target = '{}/{}'.format(\n const.SECRETS_PATH,\n secret['secret'].target or secret['secret'].source)\n return VolumeSpec(secret['file'], target, 'ro')\n\n return [build_spec(secret) for secret in self.secrets]","label":0},{"cid":"compose/service.py::Service.get_secret_volumes.build_spec","text":"compose/service.py::Service.get_secret_volumes.build_spec\n def build_spec(secret):\n target = '{}/{}'.format(\n const.SECRETS_PATH,\n secret['secret'].target or secret['secret'].source)\n return VolumeSpec(secret['file'], target, 'ro')","label":0},{"cid":"compose/service.py::Service.build","text":"compose/service.py::Service.build\n def build(self, no_cache=False, pull=False, force_rm=False):\n log.info('Building %s' % self.name)\n\n build_opts = self.options.get('build', {})\n path = build_opts.get('context')\n # python2 os.stat() doesn't support unicode on some UNIX, so we\n # encode it to a bytestring to be safe\n if not six.PY3 and not IS_WINDOWS_PLATFORM:\n path = path.encode('utf8')\n\n build_output = self.client.build(\n path=path,\n tag=self.image_name,\n stream=True,\n rm=True,\n forcerm=force_rm,\n pull=pull,\n nocache=no_cache,\n dockerfile=build_opts.get('dockerfile', None),\n buildargs=build_opts.get('args', None),\n cache_from=build_opts.get('cache_from', None),\n )\n\n try:\n all_events = stream_output(build_output, sys.stdout)\n except StreamOutputError as e:\n raise BuildError(self, six.text_type(e))\n\n # Ensure the HTTP connection is not reused for another\n # streaming command, as the Docker daemon can sometimes\n # complain about it\n self.client.close()\n\n image_id = None\n\n for event in all_events:\n if 'stream' in event:\n match = re.search(r'Successfully built ([0-9a-f]+)', event.get('stream', ''))\n if match:\n image_id = match.group(1)\n\n if image_id is None:\n raise BuildError(self, event if all_events else 'Unknown')\n\n return image_id","label":0},{"cid":"compose/service.py::Service.can_be_built","text":"compose/service.py::Service.can_be_built\n def can_be_built(self):\n return 'build' in self.options","label":0},{"cid":"compose/service.py::Service.labels","text":"compose/service.py::Service.labels\n def labels(self, one_off=False):\n return [\n '{0}={1}'.format(LABEL_PROJECT, self.project),\n '{0}={1}'.format(LABEL_SERVICE, self.name),\n '{0}={1}'.format(LABEL_ONE_OFF, \"True\" if one_off else \"False\")\n ]","label":0},{"cid":"compose/service.py::Service.custom_container_name","text":"compose/service.py::Service.custom_container_name\n @property\n def custom_container_name(self):\n return self.options.get('container_name')","label":0},{"cid":"compose/service.py::Service.get_container_name","text":"compose/service.py::Service.get_container_name\n def get_container_name(self, number, one_off=False):\n if self.custom_container_name and not one_off:\n return self.custom_container_name\n\n return build_container_name(self.project, self.name, number, one_off)","label":0},{"cid":"compose/service.py::Service.remove_image","text":"compose/service.py::Service.remove_image\n def remove_image(self, image_type):\n if not image_type or image_type == ImageType.none:\n return False\n if image_type == ImageType.local and self.options.get('image'):\n return False\n\n log.info(\"Removing image %s\", self.image_name)\n try:\n self.client.remove_image(self.image_name)\n return True\n except APIError as e:\n log.error(\"Failed to remove image for service %s: %s\", self.name, e)\n return False","label":0},{"cid":"compose/service.py::Service.specifies_host_port","text":"compose/service.py::Service.specifies_host_port\n def specifies_host_port(self):\n def has_host_port(binding):\n _, external_bindings = split_port(binding)\n\n # there are no external bindings\n if external_bindings is None:\n return False\n\n # we only need to check the first binding from the range\n external_binding = external_bindings[0]\n\n # non-tuple binding means there is a host port specified\n if not isinstance(external_binding, tuple):\n return True\n\n # extract actual host port from tuple of (host_ip, host_port)\n _, host_port = external_binding\n if host_port is not None:\n return True\n\n return False\n\n return any(has_host_port(binding) for binding in self.options.get('ports', []))","label":0},{"cid":"compose/service.py::Service.specifies_host_port.has_host_port","text":"compose/service.py::Service.specifies_host_port.has_host_port\n def has_host_port(binding):\n _, external_bindings = split_port(binding)\n\n # there are no external bindings\n if external_bindings is None:\n return False\n\n # we only need to check the first binding from the range\n external_binding = external_bindings[0]\n\n # non-tuple binding means there is a host port specified\n if not isinstance(external_binding, tuple):\n return True\n\n # extract actual host port from tuple of (host_ip, host_port)\n _, host_port = external_binding\n if host_port is not None:\n return True\n\n return False","label":0},{"cid":"compose/service.py::Service.pull","text":"compose/service.py::Service.pull\n def pull(self, ignore_pull_failures=False):\n if 'image' not in self.options:\n return\n\n repo, tag, separator = parse_repository_tag(self.options['image'])\n tag = tag or 'latest'\n log.info('Pulling %s (%s%s%s)...' % (self.name, repo, separator, tag))\n try:\n output = self.client.pull(repo, tag=tag, stream=True)\n return progress_stream.get_digest_from_pull(\n stream_output(output, sys.stdout))\n except (StreamOutputError, NotFound) as e:\n if not ignore_pull_failures:\n raise\n else:\n log.error(six.text_type(e))","label":0},{"cid":"compose/service.py::Service.push","text":"compose/service.py::Service.push\n def push(self, ignore_push_failures=False):\n if 'image' not in self.options or 'build' not in self.options:\n return\n\n repo, tag, separator = parse_repository_tag(self.options['image'])\n tag = tag or 'latest'\n log.info('Pushing %s (%s%s%s)...' % (self.name, repo, separator, tag))\n output = self.client.push(repo, tag=tag, stream=True)\n\n try:\n return progress_stream.get_digest_from_push(\n stream_output(output, sys.stdout))\n except StreamOutputError as e:\n if not ignore_push_failures:\n raise\n else:\n log.error(six.text_type(e))","label":0},{"cid":"compose/service.py::Service.is_healthy","text":"compose/service.py::Service.is_healthy\n def is_healthy(self):\n \"\"\" Check that all containers for this service report healthy.\n Returns false if at least one healthcheck is pending.\n If an unhealthy container is detected, raise a HealthCheckFailed\n exception.\n \"\"\"\n result = True\n for ctnr in self.containers():\n ctnr.inspect()\n status = ctnr.get('State.Health.Status')\n if status is None:\n raise NoHealthCheckConfigured(self.name)\n elif status == 'starting':\n result = False\n elif status == 'unhealthy':\n raise HealthCheckFailed(ctnr.short_id)\n return result","label":0},{"cid":"compose/service.py::short_id_alias_exists","text":"compose/service.py::short_id_alias_exists\ndef short_id_alias_exists(container, network):\n aliases = container.get(\n 'NetworkSettings.Networks.{net}.Aliases'.format(net=network)) or ()\n return container.short_id in aliases","label":0},{"cid":"compose/service.py::NetworkMode.__init__","text":"compose/service.py::NetworkMode.__init__\n def __init__(self, network_mode):\n self.network_mode = network_mode","label":0},{"cid":"compose/service.py::NetworkMode.id","text":"compose/service.py::NetworkMode.id\n @property\n def id(self):\n return self.network_mode","label":0},{"cid":"compose/service.py::ContainerNetworkMode.__init__","text":"compose/service.py::ContainerNetworkMode.__init__\n def __init__(self, container):\n self.container = container","label":0},{"cid":"compose/service.py::ContainerNetworkMode.id","text":"compose/service.py::ContainerNetworkMode.id\n @property\n def id(self):\n return self.container.id","label":0},{"cid":"compose/service.py::ContainerNetworkMode.mode","text":"compose/service.py::ContainerNetworkMode.mode\n @property\n def mode(self):\n return 'container:' + self.container.id","label":0},{"cid":"compose/service.py::ServiceNetworkMode.__init__","text":"compose/service.py::ServiceNetworkMode.__init__\n def __init__(self, service):\n self.service = service","label":0},{"cid":"compose/service.py::ServiceNetworkMode.id","text":"compose/service.py::ServiceNetworkMode.id\n @property\n def id(self):\n return self.service.name","label":0},{"cid":"compose/service.py::ServiceNetworkMode.mode","text":"compose/service.py::ServiceNetworkMode.mode\n @property\n def mode(self):\n containers = self.service.containers()\n if containers:\n return 'container:' + containers[0].id\n\n log.warn(\"Service %s is trying to use reuse the network stack \"\n \"of another service that is not running.\" % (self.id))\n return None","label":0},{"cid":"compose/service.py::build_container_name","text":"compose/service.py::build_container_name\ndef build_container_name(project, service, number, one_off=False):\n bits = [project, service]\n if one_off:\n bits.append('run')\n return '_'.join(bits + [str(number)])","label":0},{"cid":"compose/service.py::parse_repository_tag","text":"compose/service.py::parse_repository_tag\ndef parse_repository_tag(repo_path):\n \"\"\"Splits image identification into base image path, tag/digest\n and it's separator.\n\n Example:\n\n >>> parse_repository_tag('user/repo@sha256:digest')\n ('user/repo', 'sha256:digest', '@')\n >>> parse_repository_tag('user/repo:v1')\n ('user/repo', 'v1', ':')\n \"\"\"\n tag_separator = \":\"\n digest_separator = \"@\"\n\n if digest_separator in repo_path:\n repo, tag = repo_path.rsplit(digest_separator, 1)\n return repo, tag, digest_separator\n\n repo, tag = repo_path, \"\"\n if tag_separator in repo_path:\n repo, tag = repo_path.rsplit(tag_separator, 1)\n if \"/\" in tag:\n repo, tag = repo_path, \"\"\n\n return repo, tag, tag_separator","label":0},{"cid":"compose/service.py::merge_volume_bindings","text":"compose/service.py::merge_volume_bindings\ndef merge_volume_bindings(volumes, previous_container):\n \"\"\"Return a list of volume bindings for a container. Container data volumes\n are replaced by those from the previous container.\n \"\"\"\n affinity = {}\n\n volume_bindings = dict(\n build_volume_binding(volume)\n for volume in volumes\n if volume.external)\n\n if previous_container:\n old_volumes = get_container_data_volumes(previous_container, volumes)\n warn_on_masked_volume(volumes, old_volumes, previous_container.service)\n volume_bindings.update(\n build_volume_binding(volume) for volume in old_volumes)\n\n if old_volumes:\n affinity = {'affinity:container': '=' + previous_container.id}\n\n return list(volume_bindings.values()), affinity","label":0},{"cid":"compose/service.py::get_container_data_volumes","text":"compose/service.py::get_container_data_volumes\ndef get_container_data_volumes(container, volumes_option):\n \"\"\"Find the container data volumes that are in `volumes_option`, and return\n a mapping of volume bindings for those volumes.\n \"\"\"\n volumes = []\n volumes_option = volumes_option or []\n\n container_mounts = dict(\n (mount['Destination'], mount)\n for mount in container.get('Mounts') or {}\n )\n\n image_volumes = [\n VolumeSpec.parse(volume)\n for volume in\n container.image_config['ContainerConfig'].get('Volumes') or {}\n ]\n\n for volume in set(volumes_option + image_volumes):\n # No need to preserve host volumes\n if volume.external:\n continue\n\n mount = container_mounts.get(volume.internal)\n\n # New volume, doesn't exist in the old container\n if not mount:\n continue\n\n # Volume was previously a host volume, now it's a container volume\n if not mount.get('Name'):\n continue\n\n # Copy existing volume from old container\n volume = volume._replace(external=mount['Name'])\n volumes.append(volume)\n\n return volumes","label":0},{"cid":"compose/service.py::warn_on_masked_volume","text":"compose/service.py::warn_on_masked_volume\ndef warn_on_masked_volume(volumes_option, container_volumes, service):\n container_volumes = dict(\n (volume.internal, volume.external)\n for volume in container_volumes)\n\n for volume in volumes_option:\n if (\n volume.external and\n volume.internal in container_volumes and\n container_volumes.get(volume.internal) != volume.external\n ):\n log.warn((\n \"Service \\\"{service}\\\" is using volume \\\"{volume}\\\" from the \"\n \"previous container. Host mapping \\\"{host_path}\\\" has no effect. \"\n \"Remove the existing containers (with `docker-compose rm {service}`) \"\n \"to use the host volume mapping.\"\n ).format(\n service=service,\n volume=volume.internal,\n host_path=volume.external))","label":0},{"cid":"compose/service.py::build_volume_binding","text":"compose/service.py::build_volume_binding\ndef build_volume_binding(volume_spec):\n return volume_spec.internal, volume_spec.repr()","label":0},{"cid":"compose/service.py::build_volume_from","text":"compose/service.py::build_volume_from\ndef build_volume_from(volume_from_spec):\n \"\"\"\n volume_from can be either a service or a container. We want to return the\n container.id and format it into a string complete with the mode.\n \"\"\"\n if isinstance(volume_from_spec.source, Service):\n containers = volume_from_spec.source.containers(stopped=True)\n if not containers:\n return \"{}:{}\".format(\n volume_from_spec.source.create_container().id,\n volume_from_spec.mode)\n\n container = containers[0]\n return \"{}:{}\".format(container.id, volume_from_spec.mode)\n elif isinstance(volume_from_spec.source, Container):\n return \"{}:{}\".format(volume_from_spec.source.id, volume_from_spec.mode)","label":0},{"cid":"compose/service.py::build_container_labels","text":"compose/service.py::build_container_labels\ndef build_container_labels(label_options, service_labels, number, config_hash):\n labels = dict(label_options or {})\n labels.update(label.split('=', 1) for label in service_labels)\n labels[LABEL_CONTAINER_NUMBER] = str(number)\n labels[LABEL_VERSION] = __version__\n\n if config_hash:\n log.debug(\"Added config hash: %s\" % config_hash)\n labels[LABEL_CONFIG_HASH] = config_hash\n\n return labels","label":0},{"cid":"compose/service.py::build_ulimits","text":"compose/service.py::build_ulimits\ndef build_ulimits(ulimit_config):\n if not ulimit_config:\n return None\n ulimits = []\n for limit_name, soft_hard_values in six.iteritems(ulimit_config):\n if isinstance(soft_hard_values, six.integer_types):\n ulimits.append({'name': limit_name, 'soft': soft_hard_values, 'hard': soft_hard_values})\n elif isinstance(soft_hard_values, dict):\n ulimit_dict = {'name': limit_name}\n ulimit_dict.update(soft_hard_values)\n ulimits.append(ulimit_dict)\n\n return ulimits","label":0},{"cid":"compose/service.py::get_log_config","text":"compose/service.py::get_log_config\ndef get_log_config(logging_dict):\n log_driver = logging_dict.get('driver', \"\") if logging_dict else \"\"\n log_options = logging_dict.get('options', None) if logging_dict else None\n return LogConfig(\n type=log_driver,\n config=log_options\n )","label":0},{"cid":"compose/service.py::format_environment","text":"compose/service.py::format_environment\ndef format_environment(environment):\n def format_env(key, value):\n if value is None:\n return key\n if isinstance(value, six.binary_type):\n value = value.decode('utf-8')\n return '{key}={value}'.format(key=key, value=value)\n return [format_env(*item) for item in environment.items()]","label":0},{"cid":"compose/service.py::format_environment.format_env","text":"compose/service.py::format_environment.format_env\n def format_env(key, value):\n if value is None:\n return key\n if isinstance(value, six.binary_type):\n value = value.decode('utf-8')\n return '{key}={value}'.format(key=key, value=value)","label":0},{"cid":"compose/service.py::build_container_ports","text":"compose/service.py::build_container_ports\ndef build_container_ports(container_options, options):\n ports = []\n all_ports = container_options.get('ports', []) + options.get('expose', [])\n for port_range in all_ports:\n internal_range, _ = split_port(port_range)\n for port in internal_range:\n port = str(port)\n if '/' in port:\n port = tuple(port.split('/'))\n ports.append(port)\n return ports","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::migrate","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::migrate\ndef migrate(content):\n data = ruamel.yaml.load(content, ruamel.yaml.RoundTripLoader)\n\n service_names = data.keys()\n\n for name, service in data.items():\n warn_for_links(name, service)\n warn_for_external_links(name, service)\n rewrite_net(service, service_names)\n rewrite_build(service)\n rewrite_logging(service)\n rewrite_volumes_from(service, service_names)\n\n services = {name: data.pop(name) for name in data.keys()}\n\n data['version'] = \"2\"\n data['services'] = services\n create_volumes_section(data)\n\n return data","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_links","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_links\ndef warn_for_links(name, service):\n links = service.get('links')\n if links:\n example_service = links[0].partition(':')[0]\n log.warn(\n \"Service {name} has links, which no longer create environment \"\n \"variables such as {example_service_upper}_PORT. \"\n \"If you are using those in your application code, you should \"\n \"instead connect directly to the hostname, e.g. \"\n \"'{example_service}'.\"\n .format(name=name, example_service=example_service,\n example_service_upper=example_service.upper()))","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_external_links","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_external_links\ndef warn_for_external_links(name, service):\n external_links = service.get('external_links')\n if external_links:\n log.warn(\n \"Service {name} has external_links: {ext}, which now work \"\n \"slightly differently. In particular, two containers must be \"\n \"connected to at least one network in common in order to \"\n \"communicate, even if explicitly linked together.\\n\\n\"\n \"Either connect the external container to your app's default \"\n \"network, or connect both the external container and your \"\n \"service's containers to a pre-existing network. See \"\n \"https://docs.docker.com/compose/networking/ \"\n \"for more on how to do this.\"\n .format(name=name, ext=external_links))","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_net","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_net\ndef rewrite_net(service, service_names):\n if 'net' in service:\n network_mode = service.pop('net')\n\n # \"container:<service name>\" is now \"service:<service name>\"\n if network_mode.startswith('container:'):\n name = network_mode.partition(':')[2]\n if name in service_names:\n network_mode = 'service:{}'.format(name)\n\n service['network_mode'] = network_mode","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_build","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_build\ndef rewrite_build(service):\n if 'dockerfile' in service:\n service['build'] = {\n 'context': service.pop('build'),\n 'dockerfile': service.pop('dockerfile'),\n }","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_logging","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_logging\ndef rewrite_logging(service):\n if 'log_driver' in service:\n service['logging'] = {'driver': service.pop('log_driver')}\n if 'log_opt' in service:\n service['logging']['options'] = service.pop('log_opt')","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_volumes_from","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_volumes_from\ndef rewrite_volumes_from(service, service_names):\n for idx, volume_from in enumerate(service.get('volumes_from', [])):\n if volume_from.split(':', 1)[0] not in service_names:\n service['volumes_from'][idx] = 'container:%s' % volume_from","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::create_volumes_section","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::create_volumes_section\ndef create_volumes_section(data):\n named_volumes = get_named_volumes(data['services'])\n if named_volumes:\n log.warn(\n \"Named volumes ({names}) must be explicitly declared. Creating a \"\n \"'volumes' section with declarations.\\n\\n\"\n \"For backwards-compatibility, they've been declared as external. \"\n \"If you don't mind the volume names being prefixed with the \"\n \"project name, you can remove the 'external' option from each one.\"\n .format(names=', '.join(list(named_volumes))))\n\n data['volumes'] = named_volumes","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::get_named_volumes","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::get_named_volumes\ndef get_named_volumes(services):\n volume_specs = [\n VolumeSpec.parse(volume)\n for service in services.values()\n for volume in service.get('volumes', [])\n ]\n names = {\n spec.external\n for spec in volume_specs\n if spec.is_named_volume\n }\n return {name: {'external': True} for name in names}","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::write","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::write\ndef write(stream, new_format, indent, width):\n ruamel.yaml.dump(\n new_format,\n stream,\n Dumper=ruamel.yaml.RoundTripDumper,\n indent=indent,\n width=width)","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::parse_opts","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::parse_opts\ndef parse_opts(args):\n parser = argparse.ArgumentParser()\n parser.add_argument(\"filename\", help=\"Compose file filename.\")\n parser.add_argument(\"-i\", \"--in-place\", action='store_true')\n parser.add_argument(\n \"--indent\", type=int, default=2,\n help=\"Number of spaces used to indent the output yaml.\")\n parser.add_argument(\n \"--width\", type=int, default=80,\n help=\"Number of spaces used as the output width.\")\n return parser.parse_args()","label":0},{"cid":"contrib/migration/migrate-compose-file-v1-to-v2.py::main","text":"contrib/migration/migrate-compose-file-v1-to-v2.py::main\ndef main(args):\n logging.basicConfig(format='\\033[33m%(levelname)s:\\033[37m %(message)s\\033[0m\\n')\n\n opts = parse_opts(args)\n\n with open(opts.filename, 'r') as fh:\n new_format = migrate(fh.read())\n\n if opts.in_place:\n output = open(opts.filename, 'w')\n else:\n output = sys.stdout\n write(output, new_format, opts.indent, opts.width)","label":0}],"string":"[\n {\n \"cid\": \"compose/cli/docker_client.py::tls_config_from_options\",\n \"text\": \"compose/cli/docker_client.py::tls_config_from_options\\ndef tls_config_from_options(options):\\n tls = options.get('--tls', False)\\n ca_cert = unquote_path(options.get('--tlscacert'))\\n cert = unquote_path(options.get('--tlscert'))\\n key = unquote_path(options.get('--tlskey'))\\n verify = options.get('--tlsverify')\\n skip_hostname_check = options.get('--skip-hostname-check', False)\\n\\n advanced_opts = any([ca_cert, cert, key, verify])\\n\\n if tls is True and not advanced_opts:\\n return True\\n elif advanced_opts: # --tls is a noop\\n client_cert = None\\n if cert or key:\\n client_cert = (cert, key)\\n\\n return TLSConfig(\\n client_cert=client_cert, verify=verify, ca_cert=ca_cert,\\n assert_hostname=False if skip_hostname_check else None\\n )\\n\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/docker_client.py::docker_client\",\n \"text\": \"compose/cli/docker_client.py::docker_client\\ndef docker_client(environment, version=None, tls_config=None, host=None,\\n tls_version=None):\\n \\\"\\\"\\\"\\n Returns a docker-py client configured using environment variables\\n according to the same logic as the official Docker client.\\n \\\"\\\"\\\"\\n try:\\n kwargs = kwargs_from_env(environment=environment, ssl_version=tls_version)\\n except TLSParameterError:\\n raise UserError(\\n \\\"TLS configuration is invalid - make sure your DOCKER_TLS_VERIFY \\\"\\n \\\"and DOCKER_CERT_PATH are set correctly.\\\\n\\\"\\n \\\"You might need to run `eval \\\\\\\"$(docker-machine env default)\\\\\\\"`\\\")\\n\\n if host:\\n kwargs['base_url'] = host\\n if tls_config:\\n kwargs['tls'] = tls_config\\n\\n if version:\\n kwargs['version'] = version\\n\\n timeout = environment.get('COMPOSE_HTTP_TIMEOUT')\\n if timeout:\\n kwargs['timeout'] = int(timeout)\\n else:\\n kwargs['timeout'] = HTTP_TIMEOUT\\n\\n kwargs['user_agent'] = generate_user_agent()\\n\\n return APIClient(**kwargs)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::main\",\n \"text\": \"compose/cli/main.py::main\\ndef main():\\n command = dispatch()\\n\\n try:\\n command()\\n except (KeyboardInterrupt, signals.ShutdownException):\\n log.error(\\\"Aborting.\\\")\\n sys.exit(1)\\n except (UserError, NoSuchService, ConfigurationError,\\n ProjectError, OperationFailedError) as e:\\n log.error(e.msg)\\n sys.exit(1)\\n except BuildError as e:\\n log.error(\\\"Service '%s' failed to build: %s\\\" % (e.service.name, e.reason))\\n sys.exit(1)\\n except StreamOutputError as e:\\n log.error(e)\\n sys.exit(1)\\n except NeedsBuildError as e:\\n log.error(\\\"Service '%s' needs to be built, but --no-build was passed.\\\" % e.service.name)\\n sys.exit(1)\\n except (errors.ConnectionError, StreamParseError):\\n sys.exit(1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::dispatch\",\n \"text\": \"compose/cli/main.py::dispatch\\ndef dispatch():\\n setup_logging()\\n dispatcher = DocoptDispatcher(\\n TopLevelCommand,\\n {'options_first': True, 'version': get_version_info('compose')})\\n\\n try:\\n options, handler, command_options = dispatcher.parse(sys.argv[1:])\\n except NoSuchCommand as e:\\n commands = \\\"\\\\n\\\".join(parse_doc_section(\\\"commands:\\\", getdoc(e.supercommand)))\\n log.error(\\\"No such command: %s\\\\n\\\\n%s\\\", e.command, commands)\\n sys.exit(1)\\n\\n setup_console_handler(console_handler, options.get('--verbose'))\\n return functools.partial(perform_command, options, handler, command_options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::perform_command\",\n \"text\": \"compose/cli/main.py::perform_command\\ndef perform_command(options, handler, command_options):\\n if options['COMMAND'] in ('help', 'version'):\\n # Skip looking up the compose file.\\n handler(command_options)\\n return\\n\\n if options['COMMAND'] in ('config', 'bundle'):\\n command = TopLevelCommand(None)\\n handler(command, options, command_options)\\n return\\n\\n project = project_from_options('.', options)\\n command = TopLevelCommand(project)\\n with errors.handle_connection_errors(project.client):\\n handler(command, command_options)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_logging\",\n \"text\": \"compose/cli/main.py::setup_logging\\ndef setup_logging():\\n root_logger = logging.getLogger()\\n root_logger.addHandler(console_handler)\\n root_logger.setLevel(logging.DEBUG)\\n\\n # Disable requests logging\\n logging.getLogger(\\\"requests\\\").propagate = False\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::setup_console_handler\",\n \"text\": \"compose/cli/main.py::setup_console_handler\\ndef setup_console_handler(handler, verbose):\\n if handler.stream.isatty():\\n format_class = ConsoleWarningFormatter\\n else:\\n format_class = logging.Formatter\\n\\n if verbose:\\n handler.setFormatter(format_class('%(name)s.%(funcName)s: %(message)s'))\\n handler.setLevel(logging.DEBUG)\\n else:\\n handler.setFormatter(format_class())\\n handler.setLevel(logging.INFO)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::parse_doc_section\",\n \"text\": \"compose/cli/main.py::parse_doc_section\\ndef parse_doc_section(name, source):\\n pattern = re.compile('^([^\\\\n]*' + name + '[^\\\\n]*\\\\n?(?:[ \\\\t].*?(?:\\\\n|$))*)',\\n re.IGNORECASE | re.MULTILINE)\\n return [s.strip() for s in pattern.findall(source)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.__init__\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.__init__\\n def __init__(self, project, project_dir='.'):\\n self.project = project\\n self.project_dir = '.'\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.build\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.build\\n def build(self, options):\\n \\\"\\\"\\\"\\n Build or rebuild services.\\n\\n Services are built once and then tagged as `project_service`,\\n e.g. `composetest_db`. If you change a service's `Dockerfile` or the\\n contents of its build directory, you can run `docker-compose build` to rebuild it.\\n\\n Usage: build [options] [SERVICE...]\\n\\n Options:\\n --force-rm Always remove intermediate containers.\\n --no-cache Do not use cache when building the image.\\n --pull Always attempt to pull a newer version of the image.\\n \\\"\\\"\\\"\\n self.project.build(\\n service_names=options['SERVICE'],\\n no_cache=bool(options.get('--no-cache', False)),\\n pull=bool(options.get('--pull', False)),\\n force_rm=bool(options.get('--force-rm', False)))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.bundle\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.bundle\\n def bundle(self, config_options, options):\\n \\\"\\\"\\\"\\n Generate a Distributed Application Bundle (DAB) from the Compose file.\\n\\n Images must have digests stored, which requires interaction with a\\n Docker registry. If digests aren't stored for all images, you can fetch\\n them with `docker-compose pull` or `docker-compose push`. To push images\\n automatically when bundling, pass `--push-images`. Only services with\\n a `build` option specified will have their images pushed.\\n\\n Usage: bundle [options]\\n\\n Options:\\n --push-images Automatically push images for any services\\n which have a `build` option specified.\\n\\n -o, --output PATH Path to write the bundle file to.\\n Defaults to \\\"<project name>.dab\\\".\\n \\\"\\\"\\\"\\n self.project = project_from_options('.', config_options)\\n compose_config = get_config_from_options(self.project_dir, config_options)\\n\\n output = options[\\\"--output\\\"]\\n if not output:\\n output = \\\"{}.dab\\\".format(self.project.name)\\n\\n with errors.handle_connection_errors(self.project.client):\\n try:\\n image_digests = get_image_digests(\\n self.project,\\n allow_push=options['--push-images'],\\n )\\n except MissingDigests as e:\\n def list_images(images):\\n return \\\"\\\\n\\\".join(\\\" {}\\\".format(name) for name in sorted(images))\\n\\n paras = [\\\"Some images are missing digests.\\\"]\\n\\n if e.needs_push:\\n command_hint = (\\n \\\"Use `docker-compose push {}` to push them. \\\"\\n \\\"You can do this automatically with `docker-compose bundle --push-images`.\\\"\\n .format(\\\" \\\".join(sorted(e.needs_push)))\\n )\\n\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.config\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.config\\n def config(self, config_options, options):\\n \\\"\\\"\\\"\\n Validate and view the compose file.\\n\\n Usage: config [options]\\n\\n Options:\\n -q, --quiet Only validate the configuration, don't print\\n anything.\\n --services Print the service names, one per line.\\n\\n \\\"\\\"\\\"\\n compose_config = get_config_from_options(self.project_dir, config_options)\\n\\n if options['--quiet']:\\n return\\n\\n if options['--services']:\\n print('\\\\n'.join(service['name'] for service in compose_config.services))\\n return\\n\\n print(serialize_config(compose_config))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.create\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.create\\n def create(self, options):\\n \\\"\\\"\\\"\\n Creates containers for a service.\\n\\n Usage: create [options] [SERVICE...]\\n\\n Options:\\n --force-recreate Recreate containers even if their configuration and\\n image haven't changed. Incompatible with --no-recreate.\\n --no-recreate If containers already exist, don't recreate them.\\n Incompatible with --force-recreate.\\n --no-build Don't build an image, even if it's missing.\\n --build Build images before creating containers.\\n \\\"\\\"\\\"\\n service_names = options['SERVICE']\\n\\n self.project.create(\\n service_names=service_names,\\n strategy=convergence_strategy_from_opts(options),\\n do_build=build_action_from_opts(options),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.down\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.down\\n def down(self, options):\\n \\\"\\\"\\\"\\n Stops containers and removes containers, networks, volumes, and images\\n created by `up`.\\n\\n By default, the only things removed are:\\n\\n - Containers for services defined in the Compose file\\n - Networks defined in the `networks` section of the Compose file\\n - The default network, if one is used\\n\\n Networks and volumes defined as `external` are never removed.\\n\\n Usage: down [options]\\n\\n Options:\\n --rmi type Remove images. Type must be one of:\\n 'all': Remove all images used by any service.\\n 'local': Remove only images that don't have a custom tag\\n set by the `image` field.\\n -v, --volumes Remove named volumes declared in the `volumes` section\\n of the Compose file and anonymous volumes\\n attached to containers.\\n --remove-orphans Remove containers for services not defined in the\\n Compose file\\n \\\"\\\"\\\"\\n image_type = image_type_from_opt('--rmi', options['--rmi'])\\n self.project.down(image_type, options['--volumes'], options['--remove-orphans'])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events\\n def events(self, options):\\n \\\"\\\"\\\"\\n Receive real time events from containers.\\n\\n Usage: events [options] [SERVICE...]\\n\\n Options:\\n --json Output events as a stream of json objects\\n \\\"\\\"\\\"\\n def format_event(event):\\n attributes = [\\\"%s=%s\\\" % item for item in event['attributes'].items()]\\n return (\\\"{time} {type} {action} {id} ({attrs})\\\").format(\\n attrs=\\\", \\\".join(sorted(attributes)),\\n **event)\\n\\n def json_format_event(event):\\n event['time'] = event['time'].isoformat()\\n event.pop('container')\\n return json.dumps(event)\\n\\n for event in self.project.events():\\n formatter = json_format_event if options['--json'] else format_event\\n print(formatter(event))\\n sys.stdout.flush()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events.format_event\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events.format_event\\n def format_event(event):\\n attributes = [\\\"%s=%s\\\" % item for item in event['attributes'].items()]\\n return (\\\"{time} {type} {action} {id} ({attrs})\\\").format(\\n attrs=\\\", \\\".join(sorted(attributes)),\\n **event)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.events.json_format_event\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.events.json_format_event\\n def json_format_event(event):\\n event['time'] = event['time'].isoformat()\\n event.pop('container')\\n return json.dumps(event)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.exec_command\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.exec_command\\n def exec_command(self, options):\\n \\\"\\\"\\\"\\n Execute a command in a running container\\n\\n Usage: exec [options] SERVICE COMMAND [ARGS...]\\n\\n Options:\\n -d Detached mode: Run command in the background.\\n --privileged Give extended privileges to the process.\\n --user USER Run the command as this user.\\n -T Disable pseudo-tty allocation. By default `docker-compose exec`\\n allocates a TTY.\\n --index=index index of the container if there are multiple\\n instances of a service [default: 1]\\n \\\"\\\"\\\"\\n index = int(options.get('--index'))\\n service = self.project.get_service(options['SERVICE'])\\n detach = options['-d']\\n\\n try:\\n container = service.get_container(number=index)\\n except ValueError as e:\\n raise UserError(str(e))\\n command = [options['COMMAND']] + options['ARGS']\\n tty = not options[\\\"-T\\\"]\\n\\n if IS_WINDOWS_PLATFORM and not detach:\\n args = [\\\"exec\\\"]\\n\\n if options[\\\"-d\\\"]:\\n args += [\\\"--detach\\\"]\\n else:\\n args += [\\\"--interactive\\\"]\\n\\n if not options[\\\"-T\\\"]:\\n args += [\\\"--tty\\\"]\\n\\n if options[\\\"--privileged\\\"]:\\n args += [\\\"--privileged\\\"]\\n\\n if options[\\\"--user\\\"]:\\n args += [\\\"--user\\\", options[\\\"--user\\\"]]\\n\\n args += [container.id]\\n args += command\\n\\n sys.exit(call_docker(args))\\n\\n create_exec_options = {\\n \\\"privileged\\\": options[\\\"--privileged\\\"],\\n \\\"user\\\": options[\\\"--user\\\"],\\n \\\"tty\\\": tty,\\n \\\"stdin\\\": tty,\\n }\\n\\n exec_id = container.create_exec(command, **create_exec_options)\\n\\n if detach:\\n container.start_exec(exec_id, tty=tty)\\n return\\n\\n sig\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.help\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.help\\n @classmethod\\n def help(cls, options):\\n \\\"\\\"\\\"\\n Get help on a command.\\n\\n Usage: help [COMMAND]\\n \\\"\\\"\\\"\\n if options['COMMAND']:\\n subject = get_handler(cls, options['COMMAND'])\\n else:\\n subject = cls\\n\\n print(getdoc(subject))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.kill\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.kill\\n def kill(self, options):\\n \\\"\\\"\\\"\\n Force stop service containers.\\n\\n Usage: kill [options] [SERVICE...]\\n\\n Options:\\n -s SIGNAL SIGNAL to send to the container.\\n Default signal is SIGKILL.\\n \\\"\\\"\\\"\\n signal = options.get('-s', 'SIGKILL')\\n\\n self.project.kill(service_names=options['SERVICE'], signal=signal)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.logs\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.logs\\n def logs(self, options):\\n \\\"\\\"\\\"\\n View output from containers.\\n\\n Usage: logs [options] [SERVICE...]\\n\\n Options:\\n --no-color Produce monochrome output.\\n -f, --follow Follow log output.\\n -t, --timestamps Show timestamps.\\n --tail=\\\"all\\\" Number of lines to show from the end of the logs\\n for each container.\\n \\\"\\\"\\\"\\n containers = self.project.containers(service_names=options['SERVICE'], stopped=True)\\n\\n tail = options['--tail']\\n if tail is not None:\\n if tail.isdigit():\\n tail = int(tail)\\n elif tail != 'all':\\n raise UserError(\\\"tail flag must be all or a number\\\")\\n log_args = {\\n 'follow': options['--follow'],\\n 'tail': tail,\\n 'timestamps': options['--timestamps']\\n }\\n print(\\\"Attaching to\\\", list_containers(containers))\\n log_printer_from_project(\\n self.project,\\n containers,\\n options['--no-color'],\\n log_args,\\n event_stream=self.project.events(service_names=options['SERVICE'])).run()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pause\\n def pause(self, options):\\n \\\"\\\"\\\"\\n Pause services.\\n\\n Usage: pause [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.pause(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to pause', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.port\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.port\\n def port(self, options):\\n \\\"\\\"\\\"\\n Print the public port for a port binding.\\n\\n Usage: port [options] SERVICE PRIVATE_PORT\\n\\n Options:\\n --protocol=proto tcp or udp [default: tcp]\\n --index=index index of the container if there are multiple\\n instances of a service [default: 1]\\n \\\"\\\"\\\"\\n index = int(options.get('--index'))\\n service = self.project.get_service(options['SERVICE'])\\n try:\\n container = service.get_container(number=index)\\n except ValueError as e:\\n raise UserError(str(e))\\n print(container.get_local_port(\\n options['PRIVATE_PORT'],\\n protocol=options.get('--protocol') or 'tcp') or '')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.ps\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.ps\\n def ps(self, options):\\n \\\"\\\"\\\"\\n List containers.\\n\\n Usage: ps [options] [SERVICE...]\\n\\n Options:\\n -q Only display IDs\\n \\\"\\\"\\\"\\n containers = sorted(\\n self.project.containers(service_names=options['SERVICE'], stopped=True) +\\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\\n key=attrgetter('name'))\\n\\n if options['-q']:\\n for container in containers:\\n print(container.id)\\n else:\\n headers = [\\n 'Name',\\n 'Command',\\n 'State',\\n 'Ports',\\n ]\\n rows = []\\n for container in containers:\\n command = container.human_readable_command\\n if len(command) > 30:\\n command = '%s ...' % command[:26]\\n rows.append([\\n container.name,\\n command,\\n container.human_readable_state,\\n container.human_readable_ports,\\n ])\\n print(Formatter().table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.pull\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.pull\\n def pull(self, options):\\n \\\"\\\"\\\"\\n Pulls images for services.\\n\\n Usage: pull [options] [SERVICE...]\\n\\n Options:\\n --ignore-pull-failures Pull what it can and ignores images with pull failures.\\n \\\"\\\"\\\"\\n self.project.pull(\\n service_names=options['SERVICE'],\\n ignore_pull_failures=options.get('--ignore-pull-failures')\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.push\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.push\\n def push(self, options):\\n \\\"\\\"\\\"\\n Pushes images for services.\\n\\n Usage: push [options] [SERVICE...]\\n\\n Options:\\n --ignore-push-failures Push what it can and ignores images with push failures.\\n \\\"\\\"\\\"\\n self.project.push(\\n service_names=options['SERVICE'],\\n ignore_push_failures=options.get('--ignore-push-failures')\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.rm\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.rm\\n def rm(self, options):\\n \\\"\\\"\\\"\\n Removes stopped service containers.\\n\\n By default, anonymous volumes attached to containers will not be removed. You\\n can override this with `-v`. To list all volumes, use `docker volume ls`.\\n\\n Any data which is not in a volume will be lost.\\n\\n Usage: rm [options] [SERVICE...]\\n\\n Options:\\n -f, --force Don't ask to confirm removal\\n -v Remove any anonymous volumes attached to containers\\n -a, --all Deprecated - no effect.\\n \\\"\\\"\\\"\\n if options.get('--all'):\\n log.warn(\\n '--all flag is obsolete. This is now the default behavior '\\n 'of `docker-compose rm`'\\n )\\n one_off = OneOffFilter.include\\n\\n all_containers = self.project.containers(\\n service_names=options['SERVICE'], stopped=True, one_off=one_off\\n )\\n stopped_containers = [c for c in all_containers if not c.is_running]\\n\\n if len(stopped_containers) > 0:\\n print(\\\"Going to remove\\\", list_containers(stopped_containers))\\n if options.get('--force') \\\\\\n or yesno(\\\"Are you sure? [yN] \\\", default=False):\\n self.project.remove_stopped(\\n service_names=options['SERVICE'],\\n v=options.get('-v', False),\\n one_off=one_off\\n )\\n else:\\n print(\\\"No stopped containers\\\")\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.run\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.run\\n def run(self, options):\\n \\\"\\\"\\\"\\n Run a one-off command on a service.\\n\\n For example:\\n\\n $ docker-compose run web python manage.py shell\\n\\n By default, linked services will be started, unless they are already\\n running. If you do not want to start linked services, use\\n `docker-compose run --no-deps SERVICE COMMAND [ARGS...]`.\\n\\n Usage: run [options] [-p PORT...] [-e KEY=VAL...] SERVICE [COMMAND] [ARGS...]\\n\\n Options:\\n -d Detached mode: Run container in the background, print\\n new container name.\\n --name NAME Assign a name to the container\\n --entrypoint CMD Override the entrypoint of the image.\\n -e KEY=VAL Set an environment variable (can be used multiple times)\\n -u, --user=\\\"\\\" Run as specified username or uid\\n --no-deps Don't start linked services.\\n --rm Remove container after run. Ignored in detached mode.\\n -p, --publish=[] Publish a container's port(s) to the host\\n --service-ports Run command with the service's ports enabled and mapped\\n to the host.\\n -T Disable pseudo-tty allocation. By default `docker-compose run`\\n allocates a TTY.\\n -w, --workdir=\\\"\\\" Working directory inside the container\\n \\\"\\\"\\\"\\n service = self.project.get_service(options['SERVICE'])\\n detach = options['-d']\\n\\n if options['--publish'] and options['--service-ports']:\\n raise UserError(\\n 'Service port mapping and manual port mapping '\\n 'can not be used together'\\n )\\n\\n if options['COMMAND'] is not None:\\n command = [options['COMMAND']] + options['ARGS']\\n elif options['--entry\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.scale\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.scale\\n def scale(self, options):\\n \\\"\\\"\\\"\\n Set number of containers to run for a service.\\n\\n Numbers are specified in the form `service=num` as arguments.\\n For example:\\n\\n $ docker-compose scale web=2 worker=3\\n\\n Usage: scale [options] [SERVICE=NUM...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = timeout_from_opts(options)\\n\\n for s in options['SERVICE=NUM']:\\n if '=' not in s:\\n raise UserError('Arguments to scale should be in the form service=num')\\n service_name, num = s.split('=', 1)\\n try:\\n num = int(num)\\n except ValueError:\\n raise UserError('Number of containers for service \\\"%s\\\" is not a '\\n 'number' % service_name)\\n self.project.get_service(service_name).scale(num, timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.start\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.start\\n def start(self, options):\\n \\\"\\\"\\\"\\n Start existing containers.\\n\\n Usage: start [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.start(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to start', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.stop\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.stop\\n def stop(self, options):\\n \\\"\\\"\\\"\\n Stop running containers without removing them.\\n\\n They can be started again with `docker-compose start`.\\n\\n Usage: stop [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = timeout_from_opts(options)\\n self.project.stop(service_names=options['SERVICE'], timeout=timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.restart\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.restart\\n def restart(self, options):\\n \\\"\\\"\\\"\\n Restart running containers.\\n\\n Usage: restart [options] [SERVICE...]\\n\\n Options:\\n -t, --timeout TIMEOUT Specify a shutdown timeout in seconds.\\n (default: 10)\\n \\\"\\\"\\\"\\n timeout = timeout_from_opts(options)\\n containers = self.project.restart(service_names=options['SERVICE'], timeout=timeout)\\n exit_if(not containers, 'No containers to restart', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.top\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.top\\n def top(self, options):\\n \\\"\\\"\\\"\\n Display the running processes\\n\\n Usage: top [SERVICE...]\\n\\n \\\"\\\"\\\"\\n containers = sorted(\\n self.project.containers(service_names=options['SERVICE'], stopped=False) +\\n self.project.containers(service_names=options['SERVICE'], one_off=OneOffFilter.only),\\n key=attrgetter('name')\\n )\\n\\n for idx, container in enumerate(containers):\\n if idx > 0:\\n print()\\n\\n top_data = self.project.client.top(container.name)\\n headers = top_data.get(\\\"Titles\\\")\\n rows = []\\n\\n for process in top_data.get(\\\"Processes\\\", []):\\n rows.append(process)\\n\\n print(container.name)\\n print(Formatter().table(headers, rows))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.unpause\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.unpause\\n def unpause(self, options):\\n \\\"\\\"\\\"\\n Unpause services.\\n\\n Usage: unpause [SERVICE...]\\n \\\"\\\"\\\"\\n containers = self.project.unpause(service_names=options['SERVICE'])\\n exit_if(not containers, 'No containers to unpause', 1)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.up\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.up\\n def up(self, options):\\n \\\"\\\"\\\"\\n Builds, (re)creates, starts, and attaches to containers for a service.\\n\\n Unless they are already running, this command also starts any linked services.\\n\\n The `docker-compose up` command aggregates the output of each container. When\\n the command exits, all containers are stopped. Running `docker-compose up -d`\\n starts the containers in the background and leaves them running.\\n\\n If there are existing containers for a service, and the service's configuration\\n or image was changed after the container's creation, `docker-compose up` picks\\n up the changes by stopping and recreating the containers (preserving mounted\\n volumes). To prevent Compose from picking up changes, use the `--no-recreate`\\n flag.\\n\\n If you want to force Compose to stop and recreate all containers, use the\\n `--force-recreate` flag.\\n\\n Usage: up [options] [SERVICE...]\\n\\n Options:\\n -d Detached mode: Run containers in the background,\\n print new container names.\\n Incompatible with --abort-on-container-exit.\\n --no-color Produce monochrome output.\\n --no-deps Don't start linked services.\\n --force-recreate Recreate containers even if their configuration\\n and image haven't changed.\\n Incompatible with --no-recreate.\\n --no-recreate If containers already exist, don't recreate them.\\n Incompatible with --force-recreate.\\n --no-build Don't build an image, even if it's missing.\\n --build Build images before starting containers.\\n --abort-on-container-exit Stops al\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::TopLevelCommand.version\",\n \"text\": \"compose/cli/main.py::TopLevelCommand.version\\n @classmethod\\n def version(cls, options):\\n \\\"\\\"\\\"\\n Show version informations\\n\\n Usage: version [--short]\\n\\n Options:\\n --short Shows only Compose's version number.\\n \\\"\\\"\\\"\\n if options['--short']:\\n print(__version__)\\n else:\\n print(get_version_info('full'))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::convergence_strategy_from_opts\",\n \"text\": \"compose/cli/main.py::convergence_strategy_from_opts\\ndef convergence_strategy_from_opts(options):\\n no_recreate = options['--no-recreate']\\n force_recreate = options['--force-recreate']\\n if force_recreate and no_recreate:\\n raise UserError(\\\"--force-recreate and --no-recreate cannot be combined.\\\")\\n\\n if force_recreate:\\n return ConvergenceStrategy.always\\n\\n if no_recreate:\\n return ConvergenceStrategy.never\\n\\n return ConvergenceStrategy.changed\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::timeout_from_opts\",\n \"text\": \"compose/cli/main.py::timeout_from_opts\\ndef timeout_from_opts(options):\\n timeout = options.get('--timeout')\\n return None if timeout is None else int(timeout)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::image_type_from_opt\",\n \"text\": \"compose/cli/main.py::image_type_from_opt\\ndef image_type_from_opt(flag, value):\\n if not value:\\n return ImageType.none\\n try:\\n return ImageType[value]\\n except KeyError:\\n raise UserError(\\\"%s flag must be one of: all, local\\\" % flag)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::build_action_from_opts\",\n \"text\": \"compose/cli/main.py::build_action_from_opts\\ndef build_action_from_opts(options):\\n if options['--build'] and options['--no-build']:\\n raise UserError(\\\"--build and --no-build can not be combined.\\\")\\n\\n if options['--build']:\\n return BuildAction.force\\n\\n if options['--no-build']:\\n return BuildAction.skip\\n\\n return BuildAction.none\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::build_container_options\",\n \"text\": \"compose/cli/main.py::build_container_options\\ndef build_container_options(options, detach, command):\\n container_options = {\\n 'command': command,\\n 'tty': not (detach or options['-T'] or not sys.stdin.isatty()),\\n 'stdin_open': not detach,\\n 'detach': detach,\\n }\\n\\n if options['-e']:\\n container_options['environment'] = Environment.from_command_line(\\n parse_environment(options['-e'])\\n )\\n\\n if options['--entrypoint']:\\n container_options['entrypoint'] = options.get('--entrypoint')\\n\\n if options['--rm']:\\n container_options['restart'] = None\\n\\n if options['--user']:\\n container_options['user'] = options.get('--user')\\n\\n if not options['--service-ports']:\\n container_options['ports'] = []\\n\\n if options['--publish']:\\n container_options['ports'] = options.get('--publish')\\n\\n if options['--name']:\\n container_options['name'] = options['--name']\\n\\n if options['--workdir']:\\n container_options['working_dir'] = options['--workdir']\\n\\n return container_options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/cli/main.py::run_one_off_container\",\n \"text\": \"compose/cli/main.py::run_one_off_container\\ndef run_one_off_container(container_options, project, service, options):\\n if not options['--no-deps']:\\n deps = service.get_dependency_names()\\n if deps:\\n project.up(\\n service_names=deps,\\n start_deps=True,\\n strategy=ConvergenceStrategy.never)\\n\\n project.initialize()\\n\\n container = service.create_container(\\n quiet=True,\\n one_off=True,\\n **container_options)\\n\\n if options['-d']:\\n service.start_container(container)\\n print(container.name)\\n return\\n\\n def remove_container(force=False):\\n if options['--rm']:\\n project.client.remove_container(container.id, force=True)\\n\\n signals.set_signal_handler_to_shutdown()\\n try:\\n try:\\n if IS_WINDOWS_PLATFORM:\\n service.connect_container_to_networks(container)\\n exit_code = call_docker([\\\"start\\\", \\\"--attach\\\", \\\"--interactive\\\", container.id])\\n else:\\n operation = RunOperation(\\n project.client,\\n container.id,\\n interactive=not options['-T'],\\n logs=False,\\n )\\n pty = PseudoTerminal(project.client, operation)\\n sockets = pty.sockets()\\n service.start_container(container)\\n pty.start(sockets)\\n exit_code = container.wait()\\n except signals.ShutdownException:\\n project.client.stop(container.id)\\n exit_code = 1\\n except signals.ShutdownException:\\n project.client.kill(container.id)\\n remove_container(force=True)\\n sys.exit(2)\\n\\n remove_container()\\n sys.exit(exit_code)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::format_ports\",\n \"text\": \"compose/config/validation.py::format_ports\\n@FormatChecker.cls_checks(format=\\\"ports\\\", raises=ValidationError)\\ndef format_ports(instance):\\n try:\\n split_port(instance)\\n except ValueError as e:\\n raise ValidationError(six.text_type(e))\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::format_expose\",\n \"text\": \"compose/config/validation.py::format_expose\\n@FormatChecker.cls_checks(format=\\\"expose\\\", raises=ValidationError)\\ndef format_expose(instance):\\n if isinstance(instance, six.string_types):\\n if not re.match(VALID_EXPOSE_FORMAT, instance):\\n raise ValidationError(\\n \\\"should be of the format 'PORT[/PROTOCOL]'\\\")\\n\\n return True\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::match_named_volumes\",\n \"text\": \"compose/config/validation.py::match_named_volumes\\ndef match_named_volumes(service_dict, project_volumes):\\n service_volumes = service_dict.get('volumes', [])\\n for volume_spec in service_volumes:\\n if volume_spec.is_named_volume and volume_spec.external not in project_volumes:\\n raise ConfigurationError(\\n 'Named volume \\\"{0}\\\" is used in service \\\"{1}\\\" but no'\\n ' declaration was found in the volumes section.'.format(\\n volume_spec.repr(), service_dict.get('name')\\n )\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::python_type_to_yaml_type\",\n \"text\": \"compose/config/validation.py::python_type_to_yaml_type\\ndef python_type_to_yaml_type(type_):\\n type_name = type(type_).__name__\\n return {\\n 'dict': 'mapping',\\n 'list': 'array',\\n 'int': 'number',\\n 'float': 'number',\\n 'bool': 'boolean',\\n 'unicode': 'string',\\n 'str': 'string',\\n 'bytes': 'string',\\n }.get(type_name, type_name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_config_section\",\n \"text\": \"compose/config/validation.py::validate_config_section\\ndef validate_config_section(filename, config, section):\\n \\\"\\\"\\\"Validate the structure of a configuration section. This must be done\\n before interpolation so it's separate from schema validation.\\n \\\"\\\"\\\"\\n if not isinstance(config, dict):\\n raise ConfigurationError(\\n \\\"In file '{filename}', {section} must be a mapping, not \\\"\\n \\\"{type}.\\\".format(\\n filename=filename,\\n section=section,\\n type=anglicize_json_type(python_type_to_yaml_type(config))))\\n\\n for key, value in config.items():\\n if not isinstance(key, six.string_types):\\n raise ConfigurationError(\\n \\\"In file '{filename}', the {section} name {name} must be a \\\"\\n \\\"quoted string, i.e. '{name}'.\\\".format(\\n filename=filename,\\n section=section,\\n name=key))\\n\\n if not isinstance(value, (dict, type(None))):\\n raise ConfigurationError(\\n \\\"In file '{filename}', {section} '{name}' must be a mapping not \\\"\\n \\\"{type}.\\\".format(\\n filename=filename,\\n section=section,\\n name=key,\\n type=anglicize_json_type(python_type_to_yaml_type(value))))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_top_level_object\",\n \"text\": \"compose/config/validation.py::validate_top_level_object\\ndef validate_top_level_object(config_file):\\n if not isinstance(config_file.config, dict):\\n raise ConfigurationError(\\n \\\"Top level object in '{}' needs to be an object not '{}'.\\\".format(\\n config_file.filename,\\n type(config_file.config)))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_ulimits\",\n \"text\": \"compose/config/validation.py::validate_ulimits\\ndef validate_ulimits(service_config):\\n ulimit_config = service_config.config.get('ulimits', {})\\n for limit_name, soft_hard_values in six.iteritems(ulimit_config):\\n if isinstance(soft_hard_values, dict):\\n if not soft_hard_values['soft'] <= soft_hard_values['hard']:\\n raise ConfigurationError(\\n \\\"Service '{s.name}' has invalid ulimit '{ulimit}'. \\\"\\n \\\"'soft' value can not be greater than 'hard' value \\\".format(\\n s=service_config,\\n ulimit=ulimit_config))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_extends_file_path\",\n \"text\": \"compose/config/validation.py::validate_extends_file_path\\ndef validate_extends_file_path(service_name, extends_options, filename):\\n \\\"\\\"\\\"\\n The service to be extended must either be defined in the config key 'file',\\n or within 'filename'.\\n \\\"\\\"\\\"\\n error_prefix = \\\"Invalid 'extends' configuration for %s:\\\" % service_name\\n\\n if 'file' not in extends_options and filename is None:\\n raise ConfigurationError(\\n \\\"%s you need to specify a 'file', e.g. 'file: something.yml'\\\" % error_prefix\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_network_mode\",\n \"text\": \"compose/config/validation.py::validate_network_mode\\ndef validate_network_mode(service_config, service_names):\\n network_mode = service_config.config.get('network_mode')\\n if not network_mode:\\n return\\n\\n if 'networks' in service_config.config:\\n raise ConfigurationError(\\\"'network_mode' and 'networks' cannot be combined\\\")\\n\\n dependency = get_service_name_from_network_mode(network_mode)\\n if not dependency:\\n return\\n\\n if dependency not in service_names:\\n raise ConfigurationError(\\n \\\"Service '{s.name}' uses the network stack of service '{dep}' which \\\"\\n \\\"is undefined.\\\".format(s=service_config, dep=dependency))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_links\",\n \"text\": \"compose/config/validation.py::validate_links\\ndef validate_links(service_config, service_names):\\n for link in service_config.config.get('links', []):\\n if link.split(':')[0] not in service_names:\\n raise ConfigurationError(\\n \\\"Service '{s.name}' has a link to service '{link}' which is \\\"\\n \\\"undefined.\\\".format(s=service_config, link=link))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_depends_on\",\n \"text\": \"compose/config/validation.py::validate_depends_on\\ndef validate_depends_on(service_config, service_names):\\n deps = service_config.config.get('depends_on', {})\\n for dependency in deps.keys():\\n if dependency not in service_names:\\n raise ConfigurationError(\\n \\\"Service '{s.name}' depends on service '{dep}' which is \\\"\\n \\\"undefined.\\\".format(s=service_config, dep=dependency)\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::get_unsupported_config_msg\",\n \"text\": \"compose/config/validation.py::get_unsupported_config_msg\\ndef get_unsupported_config_msg(path, error_key):\\n msg = \\\"Unsupported config option for {}: '{}'\\\".format(path_string(path), error_key)\\n if error_key in DOCKER_CONFIG_HINTS:\\n msg += \\\" (did you mean '{}'?)\\\".format(DOCKER_CONFIG_HINTS[error_key])\\n return msg\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::anglicize_json_type\",\n \"text\": \"compose/config/validation.py::anglicize_json_type\\ndef anglicize_json_type(json_type):\\n if json_type.startswith(('a', 'e', 'i', 'o', 'u')):\\n return 'an ' + json_type\\n return 'a ' + json_type\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::is_service_dict_schema\",\n \"text\": \"compose/config/validation.py::is_service_dict_schema\\ndef is_service_dict_schema(schema_id):\\n return schema_id in ('config_schema_v1.json', '#/properties/services')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::handle_error_for_schema_with_id\",\n \"text\": \"compose/config/validation.py::handle_error_for_schema_with_id\\ndef handle_error_for_schema_with_id(error, path):\\n schema_id = error.schema['id']\\n\\n if is_service_dict_schema(schema_id) and error.validator == 'additionalProperties':\\n return \\\"Invalid service name '{}' - only {} characters are allowed\\\".format(\\n # The service_name is the key to the json object\\n list(error.instance)[0],\\n VALID_NAME_CHARS)\\n\\n if error.validator == 'additionalProperties':\\n if schema_id == '#/definitions/service':\\n invalid_config_key = parse_key_from_error_msg(error)\\n return get_unsupported_config_msg(path, invalid_config_key)\\n\\n if not error.path:\\n return '{}\\\\n\\\\n{}'.format(error.message, VERSION_EXPLANATION)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::handle_generic_error\",\n \"text\": \"compose/config/validation.py::handle_generic_error\\ndef handle_generic_error(error, path):\\n msg_format = None\\n error_msg = error.message\\n\\n if error.validator == 'oneOf':\\n msg_format = \\\"{path} {msg}\\\"\\n config_key, error_msg = _parse_oneof_validator(error)\\n if config_key:\\n path.append(config_key)\\n\\n elif error.validator == 'type':\\n msg_format = \\\"{path} contains an invalid type, it should be {msg}\\\"\\n error_msg = _parse_valid_types_from_validator(error.validator_value)\\n\\n elif error.validator == 'required':\\n error_msg = \\\", \\\".join(error.validator_value)\\n msg_format = \\\"{path} is invalid, {msg} is required.\\\"\\n\\n elif error.validator == 'dependencies':\\n config_key = list(error.validator_value.keys())[0]\\n required_keys = \\\",\\\".join(error.validator_value[config_key])\\n\\n msg_format = \\\"{path} is invalid: {msg}\\\"\\n path.append(config_key)\\n error_msg = \\\"when defining '{}' you must set '{}' as well\\\".format(\\n config_key,\\n required_keys)\\n\\n elif error.cause:\\n error_msg = six.text_type(error.cause)\\n msg_format = \\\"{path} is invalid: {msg}\\\"\\n\\n elif error.path:\\n msg_format = \\\"{path} value {msg}\\\"\\n\\n if msg_format:\\n return msg_format.format(path=path_string(path), msg=error_msg)\\n\\n return error.message\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::parse_key_from_error_msg\",\n \"text\": \"compose/config/validation.py::parse_key_from_error_msg\\ndef parse_key_from_error_msg(error):\\n return error.message.split(\\\"'\\\")[1]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::path_string\",\n \"text\": \"compose/config/validation.py::path_string\\ndef path_string(path):\\n return \\\".\\\".join(c for c in path if isinstance(c, six.string_types))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::_parse_valid_types_from_validator\",\n \"text\": \"compose/config/validation.py::_parse_valid_types_from_validator\\ndef _parse_valid_types_from_validator(validator):\\n \\\"\\\"\\\"A validator value can be either an array of valid types or a string of\\n a valid type. Parse the valid types and prefix with the correct article.\\n \\\"\\\"\\\"\\n if not isinstance(validator, list):\\n return anglicize_json_type(validator)\\n\\n if len(validator) == 1:\\n return anglicize_json_type(validator[0])\\n\\n return \\\"{}, or {}\\\".format(\\n \\\", \\\".join([anglicize_json_type(validator[0])] + validator[1:-1]),\\n anglicize_json_type(validator[-1]))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::_parse_oneof_validator\",\n \"text\": \"compose/config/validation.py::_parse_oneof_validator\\ndef _parse_oneof_validator(error):\\n \\\"\\\"\\\"oneOf has multiple schemas, so we need to reason about which schema, sub\\n schema or constraint the validation is failing on.\\n Inspecting the context value of a ValidationError gives us information about\\n which sub schema failed and which kind of error it is.\\n \\\"\\\"\\\"\\n types = []\\n for context in error.context:\\n\\n if context.validator == 'oneOf':\\n _, error_msg = _parse_oneof_validator(context)\\n return path_string(context.path), error_msg\\n\\n if context.validator == 'required':\\n return (None, context.message)\\n\\n if context.validator == 'additionalProperties':\\n invalid_config_key = parse_key_from_error_msg(context)\\n return (None, \\\"contains unsupported option: '{}'\\\".format(invalid_config_key))\\n\\n if context.path:\\n return (\\n path_string(context.path),\\n \\\"contains {}, which is an invalid type, it should be {}\\\".format(\\n json.dumps(context.instance),\\n _parse_valid_types_from_validator(context.validator_value)),\\n )\\n\\n if context.validator == 'uniqueItems':\\n return (\\n None,\\n \\\"contains non unique items, please remove duplicates from {}\\\".format(\\n context.instance),\\n )\\n\\n if context.validator == 'type':\\n types.append(context.validator_value)\\n\\n valid_types = _parse_valid_types_from_validator(types)\\n return (None, \\\"contains an invalid type, it should be {}\\\".format(valid_types))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::process_service_constraint_errors\",\n \"text\": \"compose/config/validation.py::process_service_constraint_errors\\ndef process_service_constraint_errors(error, service_name, version):\\n if version == V1:\\n if 'image' in error.instance and 'build' in error.instance:\\n return (\\n \\\"Service {} has both an image and build path specified. \\\"\\n \\\"A service can either be built to image or use an existing \\\"\\n \\\"image, not both.\\\".format(service_name))\\n\\n if 'image' in error.instance and 'dockerfile' in error.instance:\\n return (\\n \\\"Service {} has both an image and alternate Dockerfile. \\\"\\n \\\"A service can either be built to image or use an existing \\\"\\n \\\"image, not both.\\\".format(service_name))\\n\\n if 'image' not in error.instance and 'build' not in error.instance:\\n return (\\n \\\"Service {} has neither an image nor a build context specified. \\\"\\n \\\"At least one must be provided.\\\".format(service_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::process_config_schema_errors\",\n \"text\": \"compose/config/validation.py::process_config_schema_errors\\ndef process_config_schema_errors(error):\\n path = list(error.path)\\n\\n if 'id' in error.schema:\\n error_msg = handle_error_for_schema_with_id(error, path)\\n if error_msg:\\n return error_msg\\n\\n return handle_generic_error(error, path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_against_config_schema\",\n \"text\": \"compose/config/validation.py::validate_against_config_schema\\ndef validate_against_config_schema(config_file):\\n schema = load_jsonschema(config_file.version)\\n format_checker = FormatChecker([\\\"ports\\\", \\\"expose\\\"])\\n validator = Draft4Validator(\\n schema,\\n resolver=RefResolver(get_resolver_path(), schema),\\n format_checker=format_checker)\\n handle_errors(\\n validator.iter_errors(config_file.config),\\n process_config_schema_errors,\\n config_file.filename)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_service_constraints\",\n \"text\": \"compose/config/validation.py::validate_service_constraints\\ndef validate_service_constraints(config, service_name, version):\\n def handler(errors):\\n return process_service_constraint_errors(errors, service_name, version)\\n\\n schema = load_jsonschema(version)\\n validator = Draft4Validator(schema['definitions']['constraints']['service'])\\n handle_errors(validator.iter_errors(config), handler, None)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::validate_service_constraints.handler\",\n \"text\": \"compose/config/validation.py::validate_service_constraints.handler\\n def handler(errors):\\n return process_service_constraint_errors(errors, service_name, version)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::get_schema_path\",\n \"text\": \"compose/config/validation.py::get_schema_path\\ndef get_schema_path():\\n return os.path.dirname(os.path.abspath(__file__))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::load_jsonschema\",\n \"text\": \"compose/config/validation.py::load_jsonschema\\ndef load_jsonschema(version):\\n filename = os.path.join(\\n get_schema_path(),\\n \\\"config_schema_v{0}.json\\\".format(version))\\n\\n with open(filename, \\\"r\\\") as fh:\\n return json.load(fh)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::get_resolver_path\",\n \"text\": \"compose/config/validation.py::get_resolver_path\\ndef get_resolver_path():\\n schema_path = get_schema_path()\\n if sys.platform == \\\"win32\\\":\\n scheme = \\\"///\\\"\\n # TODO: why is this necessary?\\n schema_path = schema_path.replace('\\\\\\\\', '/')\\n else:\\n scheme = \\\"//\\\"\\n return \\\"file:{}{}/\\\".format(scheme, schema_path)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/config/validation.py::handle_errors\",\n \"text\": \"compose/config/validation.py::handle_errors\\ndef handle_errors(errors, format_error_func, filename):\\n \\\"\\\"\\\"jsonschema returns an error tree full of information to explain what has\\n gone wrong. Process each error and pull out relevant information and re-write\\n helpful error messages that are relevant.\\n \\\"\\\"\\\"\\n errors = list(sorted(errors, key=str))\\n if not errors:\\n return\\n\\n error_msg = '\\\\n'.join(format_error_func(error) for error in errors)\\n raise ConfigurationError(\\n \\\"The Compose file{file_msg} is invalid because:\\\\n{error_msg}\\\".format(\\n file_msg=\\\" '{}'\\\".format(filename) if filename else \\\"\\\",\\n error_msg=error_msg))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::BuildError.__init__\",\n \"text\": \"compose/service.py::BuildError.__init__\\n def __init__(self, service, reason):\\n self.service = service\\n self.reason = reason\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::NeedsBuildError.__init__\",\n \"text\": \"compose/service.py::NeedsBuildError.__init__\\n def __init__(self, service):\\n self.service = service\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::ConvergenceStrategy.allows_recreate\",\n \"text\": \"compose/service.py::ConvergenceStrategy.allows_recreate\\n @property\\n def allows_recreate(self):\\n return self is not type(self).never\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.__init__\",\n \"text\": \"compose/service.py::Service.__init__\\n def __init__(\\n self,\\n name,\\n client=None,\\n project='default',\\n use_networking=False,\\n links=None,\\n volumes_from=None,\\n network_mode=None,\\n networks=None,\\n secrets=None,\\n **options\\n ):\\n self.name = name\\n self.client = client\\n self.project = project\\n self.use_networking = use_networking\\n self.links = links or []\\n self.volumes_from = volumes_from or []\\n self.network_mode = network_mode or NetworkMode(None)\\n self.networks = networks or {}\\n self.secrets = secrets or []\\n self.options = options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.__repr__\",\n \"text\": \"compose/service.py::Service.__repr__\\n def __repr__(self):\\n return '<Service: {}>'.format(self.name)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.containers\",\n \"text\": \"compose/service.py::Service.containers\\n def containers(self, stopped=False, one_off=False, filters={}):\\n filters.update({'label': self.labels(one_off=one_off)})\\n\\n return list(filter(None, [\\n Container.from_ps(self.client, container)\\n for container in self.client.containers(\\n all=stopped,\\n filters=filters)]))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_container\",\n \"text\": \"compose/service.py::Service.get_container\\n def get_container(self, number=1):\\n \\\"\\\"\\\"Return a :class:`compose.container.Container` for this service. The\\n container must be active, and match `number`.\\n \\\"\\\"\\\"\\n labels = self.labels() + ['{0}={1}'.format(LABEL_CONTAINER_NUMBER, number)]\\n for container in self.client.containers(filters={'label': labels}):\\n return Container.from_ps(self.client, container)\\n\\n raise ValueError(\\\"No container found for %s_%s\\\" % (self.name, number))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start\",\n \"text\": \"compose/service.py::Service.start\\n def start(self, **options):\\n containers = self.containers(stopped=True)\\n for c in containers:\\n self.start_container_if_stopped(c, **options)\\n return containers\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.scale\",\n \"text\": \"compose/service.py::Service.scale\\n def scale(self, desired_num, timeout=None):\\n \\\"\\\"\\\"\\n Adjusts the number of containers to the specified number and ensures\\n they are running.\\n\\n - creates containers until there are at least `desired_num`\\n - stops containers until there are at most `desired_num` running\\n - starts containers until there are at least `desired_num` running\\n - removes all stopped containers\\n \\\"\\\"\\\"\\n if self.custom_container_name and desired_num > 1:\\n log.warn('The \\\"%s\\\" service is using the custom container name \\\"%s\\\". '\\n 'Docker requires each container to have a unique name. '\\n 'Remove the custom name to scale the service.'\\n % (self.name, self.custom_container_name))\\n\\n if self.specifies_host_port() and desired_num > 1:\\n log.warn('The \\\"%s\\\" service specifies a port on the host. If multiple containers '\\n 'for this service are created on a single host, the port will clash.'\\n % self.name)\\n\\n def create_and_start(service, number):\\n container = service.create_container(number=number, quiet=True)\\n service.start_container(container)\\n return container\\n\\n def stop_and_remove(container):\\n container.stop(timeout=self.stop_timeout(timeout))\\n container.remove()\\n\\n running_containers = self.containers(stopped=False)\\n num_running = len(running_containers)\\n\\n if desired_num == num_running:\\n # do nothing as we already have the desired number\\n log.info('Desired container number already achieved')\\n return\\n\\n if desired_num > num_running:\\n # we need to start/create until we have desired_num\\n all_containers = self.containers(stopped=True)\\n\\n if num_running != len(all_containers):\\n # we have some stopped containers, let's st\",\n \"label\": 1\n },\n {\n \"cid\": \"compose/service.py::Service.scale.create_and_start\",\n \"text\": \"compose/service.py::Service.scale.create_and_start\\n def create_and_start(service, number):\\n container = service.create_container(number=number, quiet=True)\\n service.start_container(container)\\n return container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.scale.stop_and_remove\",\n \"text\": \"compose/service.py::Service.scale.stop_and_remove\\n def stop_and_remove(container):\\n container.stop(timeout=self.stop_timeout(timeout))\\n container.remove()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.create_container\",\n \"text\": \"compose/service.py::Service.create_container\\n def create_container(self,\\n one_off=False,\\n previous_container=None,\\n number=None,\\n quiet=False,\\n **override_options):\\n \\\"\\\"\\\"\\n Create a container for this service. If the image doesn't exist, attempt to pull\\n it.\\n \\\"\\\"\\\"\\n # This is only necessary for `scale` and `volumes_from`\\n # auto-creating containers to satisfy the dependency.\\n self.ensure_image_exists()\\n\\n container_options = self._get_container_create_options(\\n override_options,\\n number or self._next_container_number(one_off=one_off),\\n one_off=one_off,\\n previous_container=previous_container,\\n )\\n\\n if 'name' in container_options and not quiet:\\n log.info(\\\"Creating %s\\\" % container_options['name'])\\n\\n try:\\n return Container.create(self.client, **container_options)\\n except APIError as ex:\\n raise OperationFailedError(\\\"Cannot create container for service %s: %s\\\" %\\n (self.name, ex.explanation))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.ensure_image_exists\",\n \"text\": \"compose/service.py::Service.ensure_image_exists\\n def ensure_image_exists(self, do_build=BuildAction.none):\\n if self.can_be_built() and do_build == BuildAction.force:\\n self.build()\\n return\\n\\n try:\\n self.image()\\n return\\n except NoSuchImageError:\\n pass\\n\\n if not self.can_be_built():\\n self.pull()\\n return\\n\\n if do_build == BuildAction.skip:\\n raise NeedsBuildError(self)\\n\\n self.build()\\n log.warn(\\n \\\"Image for service {} was built because it did not already exist. To \\\"\\n \\\"rebuild this image you must use `docker-compose build` or \\\"\\n \\\"`docker-compose up --build`.\\\".format(self.name))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.image\",\n \"text\": \"compose/service.py::Service.image\\n def image(self):\\n try:\\n return self.client.inspect_image(self.image_name)\\n except ImageNotFound:\\n raise NoSuchImageError(\\\"Image '{}' not found\\\".format(self.image_name))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.image_name\",\n \"text\": \"compose/service.py::Service.image_name\\n @property\\n def image_name(self):\\n return self.options.get('image', '{s.project}_{s.name}'.format(s=self))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.convergence_plan\",\n \"text\": \"compose/service.py::Service.convergence_plan\\n def convergence_plan(self, strategy=ConvergenceStrategy.changed):\\n containers = self.containers(stopped=True)\\n\\n if not containers:\\n return ConvergencePlan('create', [])\\n\\n if strategy is ConvergenceStrategy.never:\\n return ConvergencePlan('start', containers)\\n\\n if (\\n strategy is ConvergenceStrategy.always or\\n self._containers_have_diverged(containers)\\n ):\\n return ConvergencePlan('recreate', containers)\\n\\n stopped = [c for c in containers if not c.is_running]\\n\\n if stopped:\\n return ConvergencePlan('start', stopped)\\n\\n return ConvergencePlan('noop', containers)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._containers_have_diverged\",\n \"text\": \"compose/service.py::Service._containers_have_diverged\\n def _containers_have_diverged(self, containers):\\n config_hash = None\\n\\n try:\\n config_hash = self.config_hash\\n except NoSuchImageError as e:\\n log.debug(\\n 'Service %s has diverged: %s',\\n self.name, six.text_type(e),\\n )\\n return True\\n\\n has_diverged = False\\n\\n for c in containers:\\n container_config_hash = c.labels.get(LABEL_CONFIG_HASH, None)\\n if container_config_hash != config_hash:\\n log.debug(\\n '%s has diverged: %s != %s',\\n c.name, container_config_hash, config_hash,\\n )\\n has_diverged = True\\n\\n return has_diverged\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.execute_convergence_plan\",\n \"text\": \"compose/service.py::Service.execute_convergence_plan\\n def execute_convergence_plan(self,\\n plan,\\n timeout=None,\\n detached=False,\\n start=True):\\n (action, containers) = plan\\n should_attach_logs = not detached\\n\\n if action == 'create':\\n container = self.create_container()\\n\\n if should_attach_logs:\\n container.attach_log_stream()\\n\\n if start:\\n self.start_container(container)\\n\\n return [container]\\n\\n elif action == 'recreate':\\n return [\\n self.recreate_container(\\n container,\\n timeout=timeout,\\n attach_logs=should_attach_logs,\\n start_new_container=start\\n )\\n for container in containers\\n ]\\n\\n elif action == 'start':\\n if start:\\n for container in containers:\\n self.start_container_if_stopped(container, attach_logs=should_attach_logs)\\n\\n return containers\\n\\n elif action == 'noop':\\n for c in containers:\\n log.info(\\\"%s is up-to-date\\\" % c.name)\\n\\n return containers\\n\\n else:\\n raise Exception(\\\"Invalid action: {}\\\".format(action))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.recreate_container\",\n \"text\": \"compose/service.py::Service.recreate_container\\n def recreate_container(\\n self,\\n container,\\n timeout=None,\\n attach_logs=False,\\n start_new_container=True):\\n \\\"\\\"\\\"Recreate a container.\\n\\n The original container is renamed to a temporary name so that data\\n volumes can be copied to the new container, before the original\\n container is removed.\\n \\\"\\\"\\\"\\n log.info(\\\"Recreating %s\\\" % container.name)\\n\\n container.stop(timeout=self.stop_timeout(timeout))\\n container.rename_to_tmp_name()\\n new_container = self.create_container(\\n previous_container=container,\\n number=container.labels.get(LABEL_CONTAINER_NUMBER),\\n quiet=True,\\n )\\n if attach_logs:\\n new_container.attach_log_stream()\\n if start_new_container:\\n self.start_container(new_container)\\n container.remove()\\n return new_container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.stop_timeout\",\n \"text\": \"compose/service.py::Service.stop_timeout\\n def stop_timeout(self, timeout):\\n if timeout is not None:\\n return timeout\\n timeout = parse_seconds_float(self.options.get('stop_grace_period'))\\n if timeout is not None:\\n return timeout\\n return DEFAULT_TIMEOUT\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start_container_if_stopped\",\n \"text\": \"compose/service.py::Service.start_container_if_stopped\\n def start_container_if_stopped(self, container, attach_logs=False, quiet=False):\\n if not container.is_running:\\n if not quiet:\\n log.info(\\\"Starting %s\\\" % container.name)\\n if attach_logs:\\n container.attach_log_stream()\\n return self.start_container(container)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.start_container\",\n \"text\": \"compose/service.py::Service.start_container\\n def start_container(self, container):\\n self.connect_container_to_networks(container)\\n try:\\n container.start()\\n except APIError as ex:\\n raise OperationFailedError(\\\"Cannot start service %s: %s\\\" % (self.name, ex.explanation))\\n return container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.connect_container_to_networks\",\n \"text\": \"compose/service.py::Service.connect_container_to_networks\\n def connect_container_to_networks(self, container):\\n connected_networks = container.get('NetworkSettings.Networks')\\n\\n for network, netdefs in self.networks.items():\\n if network in connected_networks:\\n if short_id_alias_exists(container, network):\\n continue\\n\\n self.client.disconnect_container_from_network(\\n container.id,\\n network)\\n\\n self.client.connect_container_to_network(\\n container.id, network,\\n aliases=self._get_aliases(netdefs, container),\\n ipv4_address=netdefs.get('ipv4_address', None),\\n ipv6_address=netdefs.get('ipv6_address', None),\\n links=self._get_links(False),\\n link_local_ips=netdefs.get('link_local_ips', None),\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.remove_duplicate_containers\",\n \"text\": \"compose/service.py::Service.remove_duplicate_containers\\n def remove_duplicate_containers(self, timeout=None):\\n for c in self.duplicate_containers():\\n log.info('Removing %s' % c.name)\\n c.stop(timeout=self.stop_timeout(timeout))\\n c.remove()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.duplicate_containers\",\n \"text\": \"compose/service.py::Service.duplicate_containers\\n def duplicate_containers(self):\\n containers = sorted(\\n self.containers(stopped=True),\\n key=lambda c: c.get('Created'),\\n )\\n\\n numbers = set()\\n\\n for c in containers:\\n if c.number in numbers:\\n yield c\\n else:\\n numbers.add(c.number)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.config_hash\",\n \"text\": \"compose/service.py::Service.config_hash\\n @property\\n def config_hash(self):\\n return json_hash(self.config_dict())\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.config_dict\",\n \"text\": \"compose/service.py::Service.config_dict\\n def config_dict(self):\\n return {\\n 'options': self.options,\\n 'image_id': self.image()['Id'],\\n 'links': self.get_link_names(),\\n 'net': self.network_mode.id,\\n 'networks': self.networks,\\n 'volumes_from': [\\n (v.source.name, v.mode)\\n for v in self.volumes_from if isinstance(v.source, Service)\\n ],\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_dependency_names\",\n \"text\": \"compose/service.py::Service.get_dependency_names\\n def get_dependency_names(self):\\n net_name = self.network_mode.service_name\\n return (\\n self.get_linked_service_names() +\\n self.get_volumes_from_names() +\\n ([net_name] if net_name else []) +\\n list(self.options.get('depends_on', {}).keys())\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_dependency_configs\",\n \"text\": \"compose/service.py::Service.get_dependency_configs\\n def get_dependency_configs(self):\\n net_name = self.network_mode.service_name\\n configs = dict(\\n [(name, None) for name in self.get_linked_service_names()]\\n )\\n configs.update(dict(\\n [(name, None) for name in self.get_volumes_from_names()]\\n ))\\n configs.update({net_name: None} if net_name else {})\\n configs.update(self.options.get('depends_on', {}))\\n for svc, config in self.options.get('depends_on', {}).items():\\n if config['condition'] == CONDITION_STARTED:\\n configs[svc] = lambda s: True\\n elif config['condition'] == CONDITION_HEALTHY:\\n configs[svc] = lambda s: s.is_healthy()\\n else:\\n # The config schema already prevents this, but it might be\\n # bypassed if Compose is called programmatically.\\n raise ValueError(\\n 'depends_on condition \\\"{}\\\" is invalid.'.format(\\n config['condition']\\n )\\n )\\n\\n return configs\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_linked_service_names\",\n \"text\": \"compose/service.py::Service.get_linked_service_names\\n def get_linked_service_names(self):\\n return [service.name for (service, _) in self.links]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_link_names\",\n \"text\": \"compose/service.py::Service.get_link_names\\n def get_link_names(self):\\n return [(service.name, alias) for service, alias in self.links]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_volumes_from_names\",\n \"text\": \"compose/service.py::Service.get_volumes_from_names\\n def get_volumes_from_names(self):\\n return [s.source.name for s in self.volumes_from if isinstance(s.source, Service)]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._next_container_number\",\n \"text\": \"compose/service.py::Service._next_container_number\\n def _next_container_number(self, one_off=False):\\n containers = filter(None, [\\n Container.from_ps(self.client, container)\\n for container in self.client.containers(\\n all=True,\\n filters={'label': self.labels(one_off=one_off)})\\n ])\\n numbers = [c.number for c in containers]\\n return 1 if not numbers else max(numbers) + 1\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_aliases\",\n \"text\": \"compose/service.py::Service._get_aliases\\n def _get_aliases(self, network, container=None):\\n if container and container.labels.get(LABEL_ONE_OFF) == \\\"True\\\":\\n return []\\n\\n return list(\\n {self.name} |\\n ({container.short_id} if container else set()) |\\n set(network.get('aliases', ()))\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.build_default_networking_config\",\n \"text\": \"compose/service.py::Service.build_default_networking_config\\n def build_default_networking_config(self):\\n if not self.networks:\\n return {}\\n\\n network = self.networks[self.network_mode.id]\\n endpoint = {\\n 'Aliases': self._get_aliases(network),\\n 'IPAMConfig': {},\\n }\\n\\n if network.get('ipv4_address'):\\n endpoint['IPAMConfig']['IPv4Address'] = network.get('ipv4_address')\\n if network.get('ipv6_address'):\\n endpoint['IPAMConfig']['IPv6Address'] = network.get('ipv6_address')\\n\\n return {\\\"EndpointsConfig\\\": {self.network_mode.id: endpoint}}\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_links\",\n \"text\": \"compose/service.py::Service._get_links\\n def _get_links(self, link_to_self):\\n links = {}\\n\\n for service, link_name in self.links:\\n for container in service.containers():\\n links[link_name or service.name] = container.name\\n links[container.name] = container.name\\n links[container.name_without_project] = container.name\\n\\n if link_to_self:\\n for container in self.containers():\\n links[self.name] = container.name\\n links[container.name] = container.name\\n links[container.name_without_project] = container.name\\n\\n for external_link in self.options.get('external_links') or []:\\n if ':' not in external_link:\\n link_name = external_link\\n else:\\n external_link, link_name = external_link.split(':')\\n links[link_name] = external_link\\n\\n return [\\n (alias, container_name)\\n for (container_name, alias) in links.items()\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_volumes_from\",\n \"text\": \"compose/service.py::Service._get_volumes_from\\n def _get_volumes_from(self):\\n return [build_volume_from(spec) for spec in self.volumes_from]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_container_create_options\",\n \"text\": \"compose/service.py::Service._get_container_create_options\\n def _get_container_create_options(\\n self,\\n override_options,\\n number,\\n one_off=False,\\n previous_container=None):\\n add_config_hash = (not one_off and not override_options)\\n\\n container_options = dict(\\n (k, self.options[k])\\n for k in DOCKER_CONFIG_KEYS if k in self.options)\\n container_options.update(override_options)\\n\\n if not container_options.get('name'):\\n container_options['name'] = self.get_container_name(number, one_off)\\n\\n container_options.setdefault('detach', True)\\n\\n # If a qualified hostname was given, split it into an\\n # unqualified hostname and a domainname unless domainname\\n # was also given explicitly. This matches the behavior of\\n # the official Docker CLI in that scenario.\\n if ('hostname' in container_options and\\n 'domainname' not in container_options and\\n '.' in container_options['hostname']):\\n parts = container_options['hostname'].partition('.')\\n container_options['hostname'] = parts[0]\\n container_options['domainname'] = parts[2]\\n\\n if 'ports' in container_options or 'expose' in self.options:\\n container_options['ports'] = build_container_ports(\\n container_options,\\n self.options)\\n\\n container_options['environment'] = merge_environment(\\n self.options.get('environment'),\\n override_options.get('environment'))\\n\\n binds, affinity = merge_volume_bindings(\\n container_options.get('volumes') or [],\\n previous_container)\\n override_options['binds'] = binds\\n container_options['environment'].update(affinity)\\n\\n container_options['volumes'] = dict(\\n (v.internal, {}) for v in container_options.get('volumes') or {})\\n\\n secret_volumes = self.get_secret_volum\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service._get_container_host_config\",\n \"text\": \"compose/service.py::Service._get_container_host_config\\n def _get_container_host_config(self, override_options, one_off=False):\\n options = dict(self.options, **override_options)\\n\\n logging_dict = options.get('logging', None)\\n log_config = get_log_config(logging_dict)\\n\\n host_config = self.client.create_host_config(\\n links=self._get_links(link_to_self=one_off),\\n port_bindings=build_port_bindings(options.get('ports') or []),\\n binds=options.get('binds'),\\n volumes_from=self._get_volumes_from(),\\n privileged=options.get('privileged', False),\\n network_mode=self.network_mode.mode,\\n devices=options.get('devices'),\\n dns=options.get('dns'),\\n dns_search=options.get('dns_search'),\\n restart_policy=options.get('restart'),\\n cap_add=options.get('cap_add'),\\n cap_drop=options.get('cap_drop'),\\n mem_limit=options.get('mem_limit'),\\n memswap_limit=options.get('memswap_limit'),\\n ulimits=build_ulimits(options.get('ulimits')),\\n log_config=log_config,\\n extra_hosts=options.get('extra_hosts'),\\n read_only=options.get('read_only'),\\n pid_mode=options.get('pid'),\\n security_opt=options.get('security_opt'),\\n ipc_mode=options.get('ipc'),\\n cgroup_parent=options.get('cgroup_parent'),\\n cpu_quota=options.get('cpu_quota'),\\n shm_size=options.get('shm_size'),\\n sysctls=options.get('sysctls'),\\n tmpfs=options.get('tmpfs'),\\n oom_score_adj=options.get('oom_score_adj'),\\n mem_swappiness=options.get('mem_swappiness'),\\n group_add=options.get('group_add'),\\n userns_mode=options.get('userns_mode')\\n )\\n\\n # TODO: Add as an argument to create_host_config once it's supported\\n # in docker-py\\n host_config['Isolation'] = options.get('isolation')\\n\\n \",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_secret_volumes\",\n \"text\": \"compose/service.py::Service.get_secret_volumes\\n def get_secret_volumes(self):\\n def build_spec(secret):\\n target = '{}/{}'.format(\\n const.SECRETS_PATH,\\n secret['secret'].target or secret['secret'].source)\\n return VolumeSpec(secret['file'], target, 'ro')\\n\\n return [build_spec(secret) for secret in self.secrets]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_secret_volumes.build_spec\",\n \"text\": \"compose/service.py::Service.get_secret_volumes.build_spec\\n def build_spec(secret):\\n target = '{}/{}'.format(\\n const.SECRETS_PATH,\\n secret['secret'].target or secret['secret'].source)\\n return VolumeSpec(secret['file'], target, 'ro')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.build\",\n \"text\": \"compose/service.py::Service.build\\n def build(self, no_cache=False, pull=False, force_rm=False):\\n log.info('Building %s' % self.name)\\n\\n build_opts = self.options.get('build', {})\\n path = build_opts.get('context')\\n # python2 os.stat() doesn't support unicode on some UNIX, so we\\n # encode it to a bytestring to be safe\\n if not six.PY3 and not IS_WINDOWS_PLATFORM:\\n path = path.encode('utf8')\\n\\n build_output = self.client.build(\\n path=path,\\n tag=self.image_name,\\n stream=True,\\n rm=True,\\n forcerm=force_rm,\\n pull=pull,\\n nocache=no_cache,\\n dockerfile=build_opts.get('dockerfile', None),\\n buildargs=build_opts.get('args', None),\\n cache_from=build_opts.get('cache_from', None),\\n )\\n\\n try:\\n all_events = stream_output(build_output, sys.stdout)\\n except StreamOutputError as e:\\n raise BuildError(self, six.text_type(e))\\n\\n # Ensure the HTTP connection is not reused for another\\n # streaming command, as the Docker daemon can sometimes\\n # complain about it\\n self.client.close()\\n\\n image_id = None\\n\\n for event in all_events:\\n if 'stream' in event:\\n match = re.search(r'Successfully built ([0-9a-f]+)', event.get('stream', ''))\\n if match:\\n image_id = match.group(1)\\n\\n if image_id is None:\\n raise BuildError(self, event if all_events else 'Unknown')\\n\\n return image_id\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.can_be_built\",\n \"text\": \"compose/service.py::Service.can_be_built\\n def can_be_built(self):\\n return 'build' in self.options\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.labels\",\n \"text\": \"compose/service.py::Service.labels\\n def labels(self, one_off=False):\\n return [\\n '{0}={1}'.format(LABEL_PROJECT, self.project),\\n '{0}={1}'.format(LABEL_SERVICE, self.name),\\n '{0}={1}'.format(LABEL_ONE_OFF, \\\"True\\\" if one_off else \\\"False\\\")\\n ]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.custom_container_name\",\n \"text\": \"compose/service.py::Service.custom_container_name\\n @property\\n def custom_container_name(self):\\n return self.options.get('container_name')\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.get_container_name\",\n \"text\": \"compose/service.py::Service.get_container_name\\n def get_container_name(self, number, one_off=False):\\n if self.custom_container_name and not one_off:\\n return self.custom_container_name\\n\\n return build_container_name(self.project, self.name, number, one_off)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.remove_image\",\n \"text\": \"compose/service.py::Service.remove_image\\n def remove_image(self, image_type):\\n if not image_type or image_type == ImageType.none:\\n return False\\n if image_type == ImageType.local and self.options.get('image'):\\n return False\\n\\n log.info(\\\"Removing image %s\\\", self.image_name)\\n try:\\n self.client.remove_image(self.image_name)\\n return True\\n except APIError as e:\\n log.error(\\\"Failed to remove image for service %s: %s\\\", self.name, e)\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.specifies_host_port\",\n \"text\": \"compose/service.py::Service.specifies_host_port\\n def specifies_host_port(self):\\n def has_host_port(binding):\\n _, external_bindings = split_port(binding)\\n\\n # there are no external bindings\\n if external_bindings is None:\\n return False\\n\\n # we only need to check the first binding from the range\\n external_binding = external_bindings[0]\\n\\n # non-tuple binding means there is a host port specified\\n if not isinstance(external_binding, tuple):\\n return True\\n\\n # extract actual host port from tuple of (host_ip, host_port)\\n _, host_port = external_binding\\n if host_port is not None:\\n return True\\n\\n return False\\n\\n return any(has_host_port(binding) for binding in self.options.get('ports', []))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.specifies_host_port.has_host_port\",\n \"text\": \"compose/service.py::Service.specifies_host_port.has_host_port\\n def has_host_port(binding):\\n _, external_bindings = split_port(binding)\\n\\n # there are no external bindings\\n if external_bindings is None:\\n return False\\n\\n # we only need to check the first binding from the range\\n external_binding = external_bindings[0]\\n\\n # non-tuple binding means there is a host port specified\\n if not isinstance(external_binding, tuple):\\n return True\\n\\n # extract actual host port from tuple of (host_ip, host_port)\\n _, host_port = external_binding\\n if host_port is not None:\\n return True\\n\\n return False\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.pull\",\n \"text\": \"compose/service.py::Service.pull\\n def pull(self, ignore_pull_failures=False):\\n if 'image' not in self.options:\\n return\\n\\n repo, tag, separator = parse_repository_tag(self.options['image'])\\n tag = tag or 'latest'\\n log.info('Pulling %s (%s%s%s)...' % (self.name, repo, separator, tag))\\n try:\\n output = self.client.pull(repo, tag=tag, stream=True)\\n return progress_stream.get_digest_from_pull(\\n stream_output(output, sys.stdout))\\n except (StreamOutputError, NotFound) as e:\\n if not ignore_pull_failures:\\n raise\\n else:\\n log.error(six.text_type(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.push\",\n \"text\": \"compose/service.py::Service.push\\n def push(self, ignore_push_failures=False):\\n if 'image' not in self.options or 'build' not in self.options:\\n return\\n\\n repo, tag, separator = parse_repository_tag(self.options['image'])\\n tag = tag or 'latest'\\n log.info('Pushing %s (%s%s%s)...' % (self.name, repo, separator, tag))\\n output = self.client.push(repo, tag=tag, stream=True)\\n\\n try:\\n return progress_stream.get_digest_from_push(\\n stream_output(output, sys.stdout))\\n except StreamOutputError as e:\\n if not ignore_push_failures:\\n raise\\n else:\\n log.error(six.text_type(e))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::Service.is_healthy\",\n \"text\": \"compose/service.py::Service.is_healthy\\n def is_healthy(self):\\n \\\"\\\"\\\" Check that all containers for this service report healthy.\\n Returns false if at least one healthcheck is pending.\\n If an unhealthy container is detected, raise a HealthCheckFailed\\n exception.\\n \\\"\\\"\\\"\\n result = True\\n for ctnr in self.containers():\\n ctnr.inspect()\\n status = ctnr.get('State.Health.Status')\\n if status is None:\\n raise NoHealthCheckConfigured(self.name)\\n elif status == 'starting':\\n result = False\\n elif status == 'unhealthy':\\n raise HealthCheckFailed(ctnr.short_id)\\n return result\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::short_id_alias_exists\",\n \"text\": \"compose/service.py::short_id_alias_exists\\ndef short_id_alias_exists(container, network):\\n aliases = container.get(\\n 'NetworkSettings.Networks.{net}.Aliases'.format(net=network)) or ()\\n return container.short_id in aliases\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::NetworkMode.__init__\",\n \"text\": \"compose/service.py::NetworkMode.__init__\\n def __init__(self, network_mode):\\n self.network_mode = network_mode\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::NetworkMode.id\",\n \"text\": \"compose/service.py::NetworkMode.id\\n @property\\n def id(self):\\n return self.network_mode\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::ContainerNetworkMode.__init__\",\n \"text\": \"compose/service.py::ContainerNetworkMode.__init__\\n def __init__(self, container):\\n self.container = container\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::ContainerNetworkMode.id\",\n \"text\": \"compose/service.py::ContainerNetworkMode.id\\n @property\\n def id(self):\\n return self.container.id\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::ContainerNetworkMode.mode\",\n \"text\": \"compose/service.py::ContainerNetworkMode.mode\\n @property\\n def mode(self):\\n return 'container:' + self.container.id\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::ServiceNetworkMode.__init__\",\n \"text\": \"compose/service.py::ServiceNetworkMode.__init__\\n def __init__(self, service):\\n self.service = service\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::ServiceNetworkMode.id\",\n \"text\": \"compose/service.py::ServiceNetworkMode.id\\n @property\\n def id(self):\\n return self.service.name\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::ServiceNetworkMode.mode\",\n \"text\": \"compose/service.py::ServiceNetworkMode.mode\\n @property\\n def mode(self):\\n containers = self.service.containers()\\n if containers:\\n return 'container:' + containers[0].id\\n\\n log.warn(\\\"Service %s is trying to use reuse the network stack \\\"\\n \\\"of another service that is not running.\\\" % (self.id))\\n return None\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::build_container_name\",\n \"text\": \"compose/service.py::build_container_name\\ndef build_container_name(project, service, number, one_off=False):\\n bits = [project, service]\\n if one_off:\\n bits.append('run')\\n return '_'.join(bits + [str(number)])\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::parse_repository_tag\",\n \"text\": \"compose/service.py::parse_repository_tag\\ndef parse_repository_tag(repo_path):\\n \\\"\\\"\\\"Splits image identification into base image path, tag/digest\\n and it's separator.\\n\\n Example:\\n\\n >>> parse_repository_tag('user/repo@sha256:digest')\\n ('user/repo', 'sha256:digest', '@')\\n >>> parse_repository_tag('user/repo:v1')\\n ('user/repo', 'v1', ':')\\n \\\"\\\"\\\"\\n tag_separator = \\\":\\\"\\n digest_separator = \\\"@\\\"\\n\\n if digest_separator in repo_path:\\n repo, tag = repo_path.rsplit(digest_separator, 1)\\n return repo, tag, digest_separator\\n\\n repo, tag = repo_path, \\\"\\\"\\n if tag_separator in repo_path:\\n repo, tag = repo_path.rsplit(tag_separator, 1)\\n if \\\"/\\\" in tag:\\n repo, tag = repo_path, \\\"\\\"\\n\\n return repo, tag, tag_separator\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::merge_volume_bindings\",\n \"text\": \"compose/service.py::merge_volume_bindings\\ndef merge_volume_bindings(volumes, previous_container):\\n \\\"\\\"\\\"Return a list of volume bindings for a container. Container data volumes\\n are replaced by those from the previous container.\\n \\\"\\\"\\\"\\n affinity = {}\\n\\n volume_bindings = dict(\\n build_volume_binding(volume)\\n for volume in volumes\\n if volume.external)\\n\\n if previous_container:\\n old_volumes = get_container_data_volumes(previous_container, volumes)\\n warn_on_masked_volume(volumes, old_volumes, previous_container.service)\\n volume_bindings.update(\\n build_volume_binding(volume) for volume in old_volumes)\\n\\n if old_volumes:\\n affinity = {'affinity:container': '=' + previous_container.id}\\n\\n return list(volume_bindings.values()), affinity\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::get_container_data_volumes\",\n \"text\": \"compose/service.py::get_container_data_volumes\\ndef get_container_data_volumes(container, volumes_option):\\n \\\"\\\"\\\"Find the container data volumes that are in `volumes_option`, and return\\n a mapping of volume bindings for those volumes.\\n \\\"\\\"\\\"\\n volumes = []\\n volumes_option = volumes_option or []\\n\\n container_mounts = dict(\\n (mount['Destination'], mount)\\n for mount in container.get('Mounts') or {}\\n )\\n\\n image_volumes = [\\n VolumeSpec.parse(volume)\\n for volume in\\n container.image_config['ContainerConfig'].get('Volumes') or {}\\n ]\\n\\n for volume in set(volumes_option + image_volumes):\\n # No need to preserve host volumes\\n if volume.external:\\n continue\\n\\n mount = container_mounts.get(volume.internal)\\n\\n # New volume, doesn't exist in the old container\\n if not mount:\\n continue\\n\\n # Volume was previously a host volume, now it's a container volume\\n if not mount.get('Name'):\\n continue\\n\\n # Copy existing volume from old container\\n volume = volume._replace(external=mount['Name'])\\n volumes.append(volume)\\n\\n return volumes\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::warn_on_masked_volume\",\n \"text\": \"compose/service.py::warn_on_masked_volume\\ndef warn_on_masked_volume(volumes_option, container_volumes, service):\\n container_volumes = dict(\\n (volume.internal, volume.external)\\n for volume in container_volumes)\\n\\n for volume in volumes_option:\\n if (\\n volume.external and\\n volume.internal in container_volumes and\\n container_volumes.get(volume.internal) != volume.external\\n ):\\n log.warn((\\n \\\"Service \\\\\\\"{service}\\\\\\\" is using volume \\\\\\\"{volume}\\\\\\\" from the \\\"\\n \\\"previous container. Host mapping \\\\\\\"{host_path}\\\\\\\" has no effect. \\\"\\n \\\"Remove the existing containers (with `docker-compose rm {service}`) \\\"\\n \\\"to use the host volume mapping.\\\"\\n ).format(\\n service=service,\\n volume=volume.internal,\\n host_path=volume.external))\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::build_volume_binding\",\n \"text\": \"compose/service.py::build_volume_binding\\ndef build_volume_binding(volume_spec):\\n return volume_spec.internal, volume_spec.repr()\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::build_volume_from\",\n \"text\": \"compose/service.py::build_volume_from\\ndef build_volume_from(volume_from_spec):\\n \\\"\\\"\\\"\\n volume_from can be either a service or a container. We want to return the\\n container.id and format it into a string complete with the mode.\\n \\\"\\\"\\\"\\n if isinstance(volume_from_spec.source, Service):\\n containers = volume_from_spec.source.containers(stopped=True)\\n if not containers:\\n return \\\"{}:{}\\\".format(\\n volume_from_spec.source.create_container().id,\\n volume_from_spec.mode)\\n\\n container = containers[0]\\n return \\\"{}:{}\\\".format(container.id, volume_from_spec.mode)\\n elif isinstance(volume_from_spec.source, Container):\\n return \\\"{}:{}\\\".format(volume_from_spec.source.id, volume_from_spec.mode)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::build_container_labels\",\n \"text\": \"compose/service.py::build_container_labels\\ndef build_container_labels(label_options, service_labels, number, config_hash):\\n labels = dict(label_options or {})\\n labels.update(label.split('=', 1) for label in service_labels)\\n labels[LABEL_CONTAINER_NUMBER] = str(number)\\n labels[LABEL_VERSION] = __version__\\n\\n if config_hash:\\n log.debug(\\\"Added config hash: %s\\\" % config_hash)\\n labels[LABEL_CONFIG_HASH] = config_hash\\n\\n return labels\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::build_ulimits\",\n \"text\": \"compose/service.py::build_ulimits\\ndef build_ulimits(ulimit_config):\\n if not ulimit_config:\\n return None\\n ulimits = []\\n for limit_name, soft_hard_values in six.iteritems(ulimit_config):\\n if isinstance(soft_hard_values, six.integer_types):\\n ulimits.append({'name': limit_name, 'soft': soft_hard_values, 'hard': soft_hard_values})\\n elif isinstance(soft_hard_values, dict):\\n ulimit_dict = {'name': limit_name}\\n ulimit_dict.update(soft_hard_values)\\n ulimits.append(ulimit_dict)\\n\\n return ulimits\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::get_log_config\",\n \"text\": \"compose/service.py::get_log_config\\ndef get_log_config(logging_dict):\\n log_driver = logging_dict.get('driver', \\\"\\\") if logging_dict else \\\"\\\"\\n log_options = logging_dict.get('options', None) if logging_dict else None\\n return LogConfig(\\n type=log_driver,\\n config=log_options\\n )\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::format_environment\",\n \"text\": \"compose/service.py::format_environment\\ndef format_environment(environment):\\n def format_env(key, value):\\n if value is None:\\n return key\\n if isinstance(value, six.binary_type):\\n value = value.decode('utf-8')\\n return '{key}={value}'.format(key=key, value=value)\\n return [format_env(*item) for item in environment.items()]\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::format_environment.format_env\",\n \"text\": \"compose/service.py::format_environment.format_env\\n def format_env(key, value):\\n if value is None:\\n return key\\n if isinstance(value, six.binary_type):\\n value = value.decode('utf-8')\\n return '{key}={value}'.format(key=key, value=value)\",\n \"label\": 0\n },\n {\n \"cid\": \"compose/service.py::build_container_ports\",\n \"text\": \"compose/service.py::build_container_ports\\ndef build_container_ports(container_options, options):\\n ports = []\\n all_ports = container_options.get('ports', []) + options.get('expose', [])\\n for port_range in all_ports:\\n internal_range, _ = split_port(port_range)\\n for port in internal_range:\\n port = str(port)\\n if '/' in port:\\n port = tuple(port.split('/'))\\n ports.append(port)\\n return ports\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::migrate\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::migrate\\ndef migrate(content):\\n data = ruamel.yaml.load(content, ruamel.yaml.RoundTripLoader)\\n\\n service_names = data.keys()\\n\\n for name, service in data.items():\\n warn_for_links(name, service)\\n warn_for_external_links(name, service)\\n rewrite_net(service, service_names)\\n rewrite_build(service)\\n rewrite_logging(service)\\n rewrite_volumes_from(service, service_names)\\n\\n services = {name: data.pop(name) for name in data.keys()}\\n\\n data['version'] = \\\"2\\\"\\n data['services'] = services\\n create_volumes_section(data)\\n\\n return data\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_links\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_links\\ndef warn_for_links(name, service):\\n links = service.get('links')\\n if links:\\n example_service = links[0].partition(':')[0]\\n log.warn(\\n \\\"Service {name} has links, which no longer create environment \\\"\\n \\\"variables such as {example_service_upper}_PORT. \\\"\\n \\\"If you are using those in your application code, you should \\\"\\n \\\"instead connect directly to the hostname, e.g. \\\"\\n \\\"'{example_service}'.\\\"\\n .format(name=name, example_service=example_service,\\n example_service_upper=example_service.upper()))\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_external_links\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::warn_for_external_links\\ndef warn_for_external_links(name, service):\\n external_links = service.get('external_links')\\n if external_links:\\n log.warn(\\n \\\"Service {name} has external_links: {ext}, which now work \\\"\\n \\\"slightly differently. In particular, two containers must be \\\"\\n \\\"connected to at least one network in common in order to \\\"\\n \\\"communicate, even if explicitly linked together.\\\\n\\\\n\\\"\\n \\\"Either connect the external container to your app's default \\\"\\n \\\"network, or connect both the external container and your \\\"\\n \\\"service's containers to a pre-existing network. See \\\"\\n \\\"https://docs.docker.com/compose/networking/ \\\"\\n \\\"for more on how to do this.\\\"\\n .format(name=name, ext=external_links))\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_net\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_net\\ndef rewrite_net(service, service_names):\\n if 'net' in service:\\n network_mode = service.pop('net')\\n\\n # \\\"container:<service name>\\\" is now \\\"service:<service name>\\\"\\n if network_mode.startswith('container:'):\\n name = network_mode.partition(':')[2]\\n if name in service_names:\\n network_mode = 'service:{}'.format(name)\\n\\n service['network_mode'] = network_mode\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_build\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_build\\ndef rewrite_build(service):\\n if 'dockerfile' in service:\\n service['build'] = {\\n 'context': service.pop('build'),\\n 'dockerfile': service.pop('dockerfile'),\\n }\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_logging\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_logging\\ndef rewrite_logging(service):\\n if 'log_driver' in service:\\n service['logging'] = {'driver': service.pop('log_driver')}\\n if 'log_opt' in service:\\n service['logging']['options'] = service.pop('log_opt')\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_volumes_from\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::rewrite_volumes_from\\ndef rewrite_volumes_from(service, service_names):\\n for idx, volume_from in enumerate(service.get('volumes_from', [])):\\n if volume_from.split(':', 1)[0] not in service_names:\\n service['volumes_from'][idx] = 'container:%s' % volume_from\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::create_volumes_section\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::create_volumes_section\\ndef create_volumes_section(data):\\n named_volumes = get_named_volumes(data['services'])\\n if named_volumes:\\n log.warn(\\n \\\"Named volumes ({names}) must be explicitly declared. Creating a \\\"\\n \\\"'volumes' section with declarations.\\\\n\\\\n\\\"\\n \\\"For backwards-compatibility, they've been declared as external. \\\"\\n \\\"If you don't mind the volume names being prefixed with the \\\"\\n \\\"project name, you can remove the 'external' option from each one.\\\"\\n .format(names=', '.join(list(named_volumes))))\\n\\n data['volumes'] = named_volumes\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::get_named_volumes\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::get_named_volumes\\ndef get_named_volumes(services):\\n volume_specs = [\\n VolumeSpec.parse(volume)\\n for service in services.values()\\n for volume in service.get('volumes', [])\\n ]\\n names = {\\n spec.external\\n for spec in volume_specs\\n if spec.is_named_volume\\n }\\n return {name: {'external': True} for name in names}\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::write\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::write\\ndef write(stream, new_format, indent, width):\\n ruamel.yaml.dump(\\n new_format,\\n stream,\\n Dumper=ruamel.yaml.RoundTripDumper,\\n indent=indent,\\n width=width)\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::parse_opts\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::parse_opts\\ndef parse_opts(args):\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\\"filename\\\", help=\\\"Compose file filename.\\\")\\n parser.add_argument(\\\"-i\\\", \\\"--in-place\\\", action='store_true')\\n parser.add_argument(\\n \\\"--indent\\\", type=int, default=2,\\n help=\\\"Number of spaces used to indent the output yaml.\\\")\\n parser.add_argument(\\n \\\"--width\\\", type=int, default=80,\\n help=\\\"Number of spaces used as the output width.\\\")\\n return parser.parse_args()\",\n \"label\": 0\n },\n {\n \"cid\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::main\",\n \"text\": \"contrib/migration/migrate-compose-file-v1-to-v2.py::main\\ndef main(args):\\n logging.basicConfig(format='\\\\033[33m%(levelname)s:\\\\033[37m %(message)s\\\\033[0m\\\\n')\\n\\n opts = parse_opts(args)\\n\\n with open(opts.filename, 'r') as fh:\\n new_format = migrate(fh.read())\\n\\n if opts.in_place:\\n output = open(opts.filename, 'w')\\n else:\\n output = sys.stdout\\n write(output, new_format, opts.indent, opts.width)\",\n \"label\": 0\n }\n]","isConversational":false}}},{"rowIdx":297,"cells":{"instance_id":{"kind":"string","value":"pandas-dev__pandas-39194"},"repo":{"kind":"string","value":"pandas-dev/pandas"},"split":{"kind":"string","value":"train"},"query":{"kind":"string","value":"REGR: fillna on datetime64[ns, UTC] column hits RecursionError\n#### Code Sample\r\n\r\n```python\r\nfrom datetime import datetime, timezone\r\nimport pandas as pd\r\n\r\ndf = pd.DataFrame(range(31))\r\ndf[\"dt\"] = pd.date_range(\"2020/12/01\", \"2020/12/31\", tz=\"UTC\")\r\ndf[\"dt\"].iloc[5] = pd.NaT\r\ndf[\"dt\"] = df[\"dt\"].fillna(datetime(1980, 1, 1, tzinfo=timezone.utc))\r\n```\r\n\r\n#### Problem description\r\n\r\npandas 1.2.0 is throwing a RecursionError while attempting to `fillna` on a `datetime64[ns, UTC]` column. Same code worked in prior releases.\r\n\r\n```\r\nraceback (most recent call last):\r\n File \"recursion.py\", line 7, in <module>\r\n df[\"dt\"] = df[\"dt\"].fillna(utc(1980, 1, 1, tzinfo=timezone.utc))\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/series.py\", line 4433, in fillna\r\n return super().fillna(\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/generic.py\", line 6403, in fillna\r\n new_data = self._mgr.fillna(\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/managers.py\", line 621, in fillna\r\n return self.apply(\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/managers.py\", line 427, in apply\r\n applied = getattr(b, f)(**kwargs)\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py\", line 461, in fillna\r\n return self.split_and_operate(None, f, inplace)\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py\", line 516, in split_and_operate\r\n nv = f(mask, new_values, None)\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py\", line 459, in f\r\n return block.fillna(value, limit=limit, inplace=inplace, downcast=None)\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py\", line 461, in fillna\r\n return self.split_and_operate(None, f, inplace)\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py\", line 516, in split_and_operate\r\n nv = f(mask, new_values, None)\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py\", line 459, in f\r\n return block.fillna(value, limit=limit, inplace=inplace, downcast=None)\r\n...snipped...\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py\", line 441, in fillna\r\n if self._can_hold_element(value):\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/internals/blocks.py\", line 2267, in _can_hold_element\r\n tipo = maybe_infer_dtype_type(element)\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/site-packages/pandas/core/dtypes/cast.py\", line 924, in maybe_infer_dtype_type\r\n elif is_list_like(element):\r\n File \"pandas/_libs/lib.pyx\", line 1033, in pandas._libs.lib.is_list_like\r\n File \"pandas/_libs/lib.pyx\", line 1038, in pandas._libs.lib.c_is_list_like\r\n File \"/opt/miniconda3/envs/prod/lib/python3.8/abc.py\", line 98, in __instancecheck__\r\n return _abc_instancecheck(cls, instance)\r\nRecursionError: maximum recursion depth exceeded in comparison\r\n\r\n```\r\n\r\n#### Expected Output\r\n\r\n#### Output of ``pd.show_versions()``\r\n\r\n<details>\r\n\r\npd.show_versions()\r\n/opt/miniconda3/envs/prod/lib/python3.8/site-packages/_distutils_hack/__init__.py:30: UserWarning: Setuptools is replacing distutils.\r\n warnings.warn(\"Setuptools is replacing distutils.\")\r\n\r\nINSTALLED VERSIONS\r\n------------------\r\ncommit : 3e89b4c4b1580aa890023fc550774e63d499da25\r\npython : 3.8.6.final.0\r\npython-bits : 64\r\nOS : Linux\r\nOS-release : 4.18.0-240.8.1.el8_3.x86_64\r\nVersion : #1 SMP Fri Dec 4 12:24:03 EST 2020\r\nmachine : x86_64\r\nprocessor : x86_64\r\nbyteorder : little\r\nLC_ALL : None\r\nLANG : en_US.UTF-8\r\nLOCALE : en_US.UTF-8\r\n\r\npandas : 1.2.0\r\nnumpy : 1.19.4\r\npytz : 202"},"gold_files":{"kind":"list like","value":["pandas/core/internals/blocks.py","pandas/core/internals/managers.py"],"string":"[\n \"pandas/core/internals/blocks.py\",\n \"pandas/core/internals/managers.py\"\n]","isConversational":false},"gold_units":{"kind":"list like","value":["pandas/core/internals/blocks.py::<module>","pandas/core/internals/managers.py::_consolidate"],"string":"[\n \"pandas/core/internals/blocks.py::<module>\",\n \"pandas/core/internals/managers.py::_consolidate\"\n]","isConversational":false},"files":{"kind":"list like","value":[{"cid":"pandas/core/internals/blocks.py","text":"pandas/core/internals/blocks.py\nclass Block # Canonical n-dimensional unit of homogeneous dtype contained in a pandas\n def _simple_new(cls, values: ArrayLike, placement: BlockPlacement, ndim: int) # Fastpath constructor, does *no* validation\n def __init__(self, values, placement, ndim: int) # Parameters\n def _maybe_coerce_values(self, values) # Ensure we have correctly-typed values.\n def _check_ndim(self, values, ndim) # ndim inference and validation.\n def _holder(self) # The array-like that can hold the underlying values.\n def _consolidate_key(self)\n def is_view(self) # return a boolean if I am possibly a view\n def is_categorical(self)\n def is_datelike(self) # return True if I am a non-datelike\n def external_values(self) # The array that Series.values returns (public attribute).\n def internal_values(self) # The array that Series._values returns (internal values).\n def array_values(self) # The array that Series.array returns. Always an ExtensionArray.\n def get_values(self, dtype: Optional[Dtype]=None) # return an internal format, currently just the ndarray\n def get_block_values_for_json(self) # This is used in the JSON C code.\n def fill_value(self)\n def mgr_locs(self)\n def mgr_locs(self, new_mgr_locs)\n def make_block(self, values, placement=None) # Create a new block, with type inference propagate any values that are\n def make_block_same_class(self, values, placement=None, ndim=None) # Wrap given values in a block of same type as self.\n def __repr__(self)\n def __len__(self)\n def __getstate__(self)\n def __setstate__(self, state)\n def _slice(self, slicer) # return a slice of my values\n def getitem_block(self, slicer, new_mgr_locs=None) # Perform __getitem__-like, return result as block.\n def shape(self)\n def dtype(self)\n def iget(self, i)\n def set_inplace(self, locs, values) # Modify block values in-place with new item value.\n def delete(self, loc) # Delete given loc(-s) from block in-place.\n def apply(self, func, **kwargs) # apply the function to my values; return a block if we are not\n def reduce(self, func, ignore_failures: bool=False)\n def _split_op_result(self, result)\n def fillna(self, value, limit=None, inplace: bool=False, downcast=None) # fillna on the block with the value. If we fail, then convert to\n def f(mask, val, idx)\n def _split(self) # Split a block into a list of single-column blocks.\n def split_and_operate(self, mask, f, inplace: bool, ignore_failures: bool=False) # split the block per-column, and apply the callable f\n def make_a_block(nv, ref_loc)\n def _maybe_downcast(self, blocks: List['Block'], downcast=None)\n def downcast(self, dtypes=None) # try to downcast each item to the dict of dtypes if present\n def f(mask, val, idx)\n def astype(self, dtype, copy: bool=False, errors: str='raise') # Coerce to the new dtype.\n def _astype(self, dtype: DtypeObj, copy: bool)\n ","label":1},{"cid":"pandas/core/internals/managers.py","text":"pandas/core/internals/managers.py\nclass BlockManager # Core internal data structure to implement DataFrame, Series, etc.\n def __init__(self, blocks: Sequence[Block], axes: Sequence[Index], do_integrity_check: bool=True)\n def from_blocks(cls, blocks: List[Block], axes: List[Index]) # Constructor for BlockManager and SingleBlockManager with same signature.\n def blknos(self) # Suppose we want to find the array corresponding to our i'th column.\n def blklocs(self) # See blknos.__doc__\n def make_empty(self: T, axes=None) # return an empty BlockManager with the items axis of len 0\n def __nonzero__(self)\n def shape(self)\n def ndim(self)\n def set_axis(self, axis: int, new_labels: Index)\n def is_single_block(self)\n def _rebuild_blknos_and_blklocs(self) # Update mgr._blknos / mgr._blklocs.\n def items(self)\n def get_dtypes(self)\n def __getstate__(self)\n def __setstate__(self, state)\n def unpickle_block(values, mgr_locs, ndim: int)\n def _post_setstate(self)\n def __len__(self)\n def __repr__(self)\n def _verify_integrity(self)\n def reduce(self: T, func: Callable, ignore_failures: bool=False) # Apply reduction function blockwise, returning a single-row BlockManager.\n def operate_blockwise(self, other: 'BlockManager', array_op) # Apply array_op blockwise with another (aligned) BlockManager.\n def apply(self: T, f, align_keys: Optional[List[str]]=None, ignore_failures: bool=False, **kwargs) # Iterate over the blocks, collect and create a new BlockManager.\n def quantile(self, axis: int=0, transposed: bool=False, interpolation='linear', qs=None, numeric_only=None) # Iterate over blocks applying quantile reduction.\n def get_axe(block, qs, axes)\n def isna(self, func)\n def where(self, other, cond, align: bool, errors: str, axis: int)\n def setitem(self, indexer, value)\n def putmask(self, mask, new, align: bool=True, axis: int=0)\n def diff(self, n: int, axis: int)\n def interpolate(self, **kwargs)\n def shift(self, periods: int, axis: int, fill_value)\n def fillna(self, value, limit, inplace: bool, downcast)\n def downcast(self)\n def astype(self, dtype, copy: bool=False, errors: str='raise')\n def convert(self, copy: bool=True, datetime: bool=True, numeric: bool=True, timedelta: bool=True)\n def replace(self, to_replace, value, inplace: bool, regex: bool)\n def replace_list(self: T, src_list: List[Any], dest_list: List[Any], inplace: bool=False, regex: bool=False) # do a list replace\n def to_native_types(self, **kwargs) # Convert values to native types (strings / python objects) that are used\n def is_consolidated(self) # Return True if more than one block with the same dtype\n def _consolidate_check(self)\n def is_numeric_mixed_type(self)\n def any_extension_types(self) # Whether any of the blocks in this manager are extension blocks\n def is_view(self) # return a boolean if we are a single block and are a view\n def get_bool_dat","label":1},{"cid":"pandas/util/_print_versions.py","text":"pandas/util/_print_versions.py\ndef _get_commit_hash() # Use vendored versioneer code to get git hash, which handles\ndef _get_sys_info() # Returns system information as a JSON serializable dictionary.\ndef _get_dependency_info() # Returns dependency information as a JSON serializable dictionary.\ndef show_versions(as_json: Union[str, bool]=False) # Provide useful information, important for bug reports.\ndef main()","label":0},{"cid":"versioneer.py","text":"versioneer.py\nclass VersioneerConfig # Container for Versioneer configuration parameters.\ndef get_root() # Get the project root directory.\ndef get_config_from_root(root) # Read the project setup.cfg file to determine Versioneer config.\n def get(parser, name)\nclass NotThisMethod # Exception raised if a method is not valid for the current scenario.\ndef register_vcs_handler(vcs, method) # Create decorator to mark a method as the handler of a VCS.\n def decorate(f) # Store f in HANDLERS[vcs][method].\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None) # Call the given command(s).\ndef git_get_keywords(versionfile_abs) # Extract version information from the given file.\ndef git_versions_from_keywords(keywords, tag_prefix, verbose) # Get version information from git keywords.\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command) # Get version from 'git describe' in the root of the source tree.\ndef do_vcs_install(manifest_in, versionfile_source, ipy) # Git-specific installation logic for Versioneer.\ndef versions_from_parentdir(parentdir_prefix, root, verbose) # Try to determine the version from the parent directory name.\ndef versions_from_file(filename) # Try to determine the version from _version.py if present.\ndef write_to_version_file(filename, versions) # Write the given version number to the given _version.py file.\ndef plus_or_dot(pieces) # Return a + if we don't already have one, else return a .\ndef render_pep440(pieces) # Build up version string, with post-release \"local version identifier\".\ndef render_pep440_pre(pieces) # TAG[.post0.devDISTANCE] -- No -dirty.\ndef render_pep440_post(pieces) # TAG[.postDISTANCE[.dev0]+gHEX] .\ndef render_pep440_old(pieces) # TAG[.postDISTANCE[.dev0]] .\ndef render_git_describe(pieces) # TAG[-DISTANCE-gHEX][-dirty].\ndef render_git_describe_long(pieces) # TAG-DISTANCE-gHEX[-dirty].\ndef render(pieces, style) # Render the given version pieces into the requested style.\nclass VersioneerBadRootError # The project root directory is unknown or missing key files.\ndef get_versions(verbose=False) # Get the project version from whatever source is available.\ndef get_version() # Get the short version string for this project.\ndef get_cmdclass(cmdclass=None) # Get the custom setuptools/distutils subclasses used by Versioneer.\n class cmd_version\n def initialize_options(self)\n def finalize_options(self)\n def run(self)\n class cmd_build_py\n def run(self)\n class cmd_build_ext\n def run(self)\n class cmd_sdist\n def run(self)\n def make_release_tree(self, base_dir, files)\ndef do_setup() # Do main VCS-independent setup function for installing Versioneer.\ndef scan_setup_py() # Validate the contents of setup.py against Versioneer's expectations.","label":0}],"string":"[\n {\n \"cid\": \"pandas/core/internals/blocks.py\",\n \"text\": \"pandas/core/internals/blocks.py\\nclass Block # Canonical n-dimensional unit of homogeneous dtype contained in a pandas\\n def _simple_new(cls, values: ArrayLike, placement: BlockPlacement, ndim: int) # Fastpath constructor, does *no* validation\\n def __init__(self, values, placement, ndim: int) # Parameters\\n def _maybe_coerce_values(self, values) # Ensure we have correctly-typed values.\\n def _check_ndim(self, values, ndim) # ndim inference and validation.\\n def _holder(self) # The array-like that can hold the underlying values.\\n def _consolidate_key(self)\\n def is_view(self) # return a boolean if I am possibly a view\\n def is_categorical(self)\\n def is_datelike(self) # return True if I am a non-datelike\\n def external_values(self) # The array that Series.values returns (public attribute).\\n def internal_values(self) # The array that Series._values returns (internal values).\\n def array_values(self) # The array that Series.array returns. Always an ExtensionArray.\\n def get_values(self, dtype: Optional[Dtype]=None) # return an internal format, currently just the ndarray\\n def get_block_values_for_json(self) # This is used in the JSON C code.\\n def fill_value(self)\\n def mgr_locs(self)\\n def mgr_locs(self, new_mgr_locs)\\n def make_block(self, values, placement=None) # Create a new block, with type inference propagate any values that are\\n def make_block_same_class(self, values, placement=None, ndim=None) # Wrap given values in a block of same type as self.\\n def __repr__(self)\\n def __len__(self)\\n def __getstate__(self)\\n def __setstate__(self, state)\\n def _slice(self, slicer) # return a slice of my values\\n def getitem_block(self, slicer, new_mgr_locs=None) # Perform __getitem__-like, return result as block.\\n def shape(self)\\n def dtype(self)\\n def iget(self, i)\\n def set_inplace(self, locs, values) # Modify block values in-place with new item value.\\n def delete(self, loc) # Delete given loc(-s) from block in-place.\\n def apply(self, func, **kwargs) # apply the function to my values; return a block if we are not\\n def reduce(self, func, ignore_failures: bool=False)\\n def _split_op_result(self, result)\\n def fillna(self, value, limit=None, inplace: bool=False, downcast=None) # fillna on the block with the value. If we fail, then convert to\\n def f(mask, val, idx)\\n def _split(self) # Split a block into a list of single-column blocks.\\n def split_and_operate(self, mask, f, inplace: bool, ignore_failures: bool=False) # split the block per-column, and apply the callable f\\n def make_a_block(nv, ref_loc)\\n def _maybe_downcast(self, blocks: List['Block'], downcast=None)\\n def downcast(self, dtypes=None) # try to downcast each item to the dict of dtypes if present\\n def f(mask, val, idx)\\n def astype(self, dtype, copy: bool=False, errors: str='raise') # Coerce to the new dtype.\\n def _astype(self, dtype: DtypeObj, copy: bool)\\n \",\n \"label\": 1\n },\n {\n \"cid\": \"pandas/core/internals/managers.py\",\n \"text\": \"pandas/core/internals/managers.py\\nclass BlockManager # Core internal data structure to implement DataFrame, Series, etc.\\n def __init__(self, blocks: Sequence[Block], axes: Sequence[Index], do_integrity_check: bool=True)\\n def from_blocks(cls, blocks: List[Block], axes: List[Index]) # Constructor for BlockManager and SingleBlockManager with same signature.\\n def blknos(self) # Suppose we want to find the array corresponding to our i'th column.\\n def blklocs(self) # See blknos.__doc__\\n def make_empty(self: T, axes=None) # return an empty BlockManager with the items axis of len 0\\n def __nonzero__(self)\\n def shape(self)\\n def ndim(self)\\n def set_axis(self, axis: int, new_labels: Index)\\n def is_single_block(self)\\n def _rebuild_blknos_and_blklocs(self) # Update mgr._blknos / mgr._blklocs.\\n def items(self)\\n def get_dtypes(self)\\n def __getstate__(self)\\n def __setstate__(self, state)\\n def unpickle_block(values, mgr_locs, ndim: int)\\n def _post_setstate(self)\\n def __len__(self)\\n def __repr__(self)\\n def _verify_integrity(self)\\n def reduce(self: T, func: Callable, ignore_failures: bool=False) # Apply reduction function blockwise, returning a single-row BlockManager.\\n def operate_blockwise(self, other: 'BlockManager', array_op) # Apply array_op blockwise with another (aligned) BlockManager.\\n def apply(self: T, f, align_keys: Optional[List[str]]=None, ignore_failures: bool=False, **kwargs) # Iterate over the blocks, collect and create a new BlockManager.\\n def quantile(self, axis: int=0, transposed: bool=False, interpolation='linear', qs=None, numeric_only=None) # Iterate over blocks applying quantile reduction.\\n def get_axe(block, qs, axes)\\n def isna(self, func)\\n def where(self, other, cond, align: bool, errors: str, axis: int)\\n def setitem(self, indexer, value)\\n def putmask(self, mask, new, align: bool=True, axis: int=0)\\n def diff(self, n: int, axis: int)\\n def interpolate(self, **kwargs)\\n def shift(self, periods: int, axis: int, fill_value)\\n def fillna(self, value, limit, inplace: bool, downcast)\\n def downcast(self)\\n def astype(self, dtype, copy: bool=False, errors: str='raise')\\n def convert(self, copy: bool=True, datetime: bool=True, numeric: bool=True, timedelta: bool=True)\\n def replace(self, to_replace, value, inplace: bool, regex: bool)\\n def replace_list(self: T, src_list: List[Any], dest_list: List[Any], inplace: bool=False, regex: bool=False) # do a list replace\\n def to_native_types(self, **kwargs) # Convert values to native types (strings / python objects) that are used\\n def is_consolidated(self) # Return True if more than one block with the same dtype\\n def _consolidate_check(self)\\n def is_numeric_mixed_type(self)\\n def any_extension_types(self) # Whether any of the blocks in this manager are extension blocks\\n def is_view(self) # return a boolean if we are a single block and are a view\\n def get_bool_dat\",\n \"label\": 1\n },\n {\n \"cid\": \"pandas/util/_print_versions.py\",\n \"text\": \"pandas/util/_print_versions.py\\ndef _get_commit_hash() # Use vendored versioneer code to get git hash, which handles\\ndef _get_sys_info() # Returns system information as a JSON serializable dictionary.\\ndef _get_dependency_info() # Returns dependency information as a JSON serializable dictionary.\\ndef show_versions(as_json: Union[str, bool]=False) # Provide useful information, important for bug reports.\\ndef main()\",\n \"label\": 0\n },\n {\n \"cid\": \"versioneer.py\",\n \"text\": \"versioneer.py\\nclass VersioneerConfig # Container for Versioneer configuration parameters.\\ndef get_root() # Get the project root directory.\\ndef get_config_from_root(root) # Read the project setup.cfg file to determine Versioneer config.\\n def get(parser, name)\\nclass NotThisMethod # Exception raised if a method is not valid for the current scenario.\\ndef register_vcs_handler(vcs, method) # Create decorator to mark a method as the handler of a VCS.\\n def decorate(f) # Store f in HANDLERS[vcs][method].\\ndef run_command(commands, args, cwd=None, verbose=False, hide_stderr=False, env=None) # Call the given command(s).\\ndef git_get_keywords(versionfile_abs) # Extract version information from the given file.\\ndef git_versions_from_keywords(keywords, tag_prefix, verbose) # Get version information from git keywords.\\ndef git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command) # Get version from 'git describe' in the root of the source tree.\\ndef do_vcs_install(manifest_in, versionfile_source, ipy) # Git-specific installation logic for Versioneer.\\ndef versions_from_parentdir(parentdir_prefix, root, verbose) # Try to determine the version from the parent directory name.\\ndef versions_from_file(filename) # Try to determine the version from _version.py if present.\\ndef write_to_version_file(filename, versions) # Write the given version number to the given _version.py file.\\ndef plus_or_dot(pieces) # Return a + if we don't already have one, else return a .\\ndef render_pep440(pieces) # Build up version string, with post-release \\\"local version identifier\\\".\\ndef render_pep440_pre(pieces) # TAG[.post0.devDISTANCE] -- No -dirty.\\ndef render_pep440_post(pieces) # TAG[.postDISTANCE[.dev0]+gHEX] .\\ndef render_pep440_old(pieces) # TAG[.postDISTANCE[.dev0]] .\\ndef render_git_describe(pieces) # TAG[-DISTANCE-gHEX][-dirty].\\ndef render_git_describe_long(pieces) # TAG-DISTANCE-gHEX[-dirty].\\ndef render(pieces, style) # Render the given version pieces into the requested style.\\nclass VersioneerBadRootError # The project root directory is unknown or missing key files.\\ndef get_versions(verbose=False) # Get the project version from whatever source is available.\\ndef get_version() # Get the short version string for this project.\\ndef get_cmdclass(cmdclass=None) # Get the custom setuptools/distutils subclasses used by Versioneer.\\n class cmd_version\\n def initialize_options(self)\\n def finalize_options(self)\\n def run(self)\\n class cmd_build_py\\n def run(self)\\n class cmd_build_ext\\n def run(self)\\n class cmd_sdist\\n def run(self)\\n def make_release_tree(self, base_dir, files)\\ndef do_setup() # Do main VCS-independent setup function for installing Versioneer.\\ndef scan_setup_py() # Validate the contents of setup.py against Versioneer's expectations.\",\n \"label\": 0\n }\n]","isConversational":false},"units":{"kind":"list like","value":[{"cid":"pandas/core/internals/blocks.py::Block._simple_new","text":"pandas/core/internals/blocks.py::Block._simple_new\n @classmethod\n def _simple_new(\n cls, values: ArrayLike, placement: BlockPlacement, ndim: int\n ) -> \"Block\":\n \"\"\"\n Fastpath constructor, does *no* validation\n \"\"\"\n obj = object.__new__(cls)\n obj.ndim = ndim\n obj.values = values\n obj._mgr_locs = placement\n return obj","label":0},{"cid":"pandas/core/internals/blocks.py::Block.__init__","text":"pandas/core/internals/blocks.py::Block.__init__\n def __init__(self, values, placement, ndim: int):\n \"\"\"\n Parameters\n ----------\n values : np.ndarray or ExtensionArray\n placement : BlockPlacement (or castable)\n ndim : int\n 1 for SingleBlockManager/Series, 2 for BlockManager/DataFrame\n \"\"\"\n # TODO(EA2D): ndim will be unnecessary with 2D EAs\n self.ndim = self._check_ndim(values, ndim)\n self.mgr_locs = placement\n self.values = self._maybe_coerce_values(values)\n\n if self._validate_ndim and self.ndim and len(self.mgr_locs) != len(self.values):\n raise ValueError(\n f\"Wrong number of items passed {len(self.values)}, \"\n f\"placement implies {len(self.mgr_locs)}\"\n )","label":0},{"cid":"pandas/core/internals/blocks.py::Block._maybe_coerce_values","text":"pandas/core/internals/blocks.py::Block._maybe_coerce_values\n def _maybe_coerce_values(self, values):\n \"\"\"\n Ensure we have correctly-typed values.\n\n Parameters\n ----------\n values : np.ndarray, ExtensionArray, Index\n\n Returns\n -------\n np.ndarray or ExtensionArray\n \"\"\"\n return values","label":0},{"cid":"pandas/core/internals/blocks.py::Block._check_ndim","text":"pandas/core/internals/blocks.py::Block._check_ndim\n def _check_ndim(self, values, ndim):\n \"\"\"\n ndim inference and validation.\n\n Infers ndim from 'values' if not provided to __init__.\n Validates that values.ndim and ndim are consistent if and only if\n the class variable '_validate_ndim' is True.\n\n Parameters\n ----------\n values : array-like\n ndim : int or None\n\n Returns\n -------\n ndim : int\n\n Raises\n ------\n ValueError : the number of dimensions do not match\n \"\"\"\n if ndim is None:\n ndim = values.ndim\n\n if self._validate_ndim and values.ndim != ndim:\n raise ValueError(\n \"Wrong number of dimensions. \"\n f\"values.ndim != ndim [{values.ndim} != {ndim}]\"\n )\n return ndim","label":0},{"cid":"pandas/core/internals/blocks.py::Block._holder","text":"pandas/core/internals/blocks.py::Block._holder\n @property\n def _holder(self):\n \"\"\"\n The array-like that can hold the underlying values.\n\n None for 'Block', overridden by subclasses that don't\n use an ndarray.\n \"\"\"\n return None","label":0},{"cid":"pandas/core/internals/blocks.py::Block._consolidate_key","text":"pandas/core/internals/blocks.py::Block._consolidate_key\n @property\n def _consolidate_key(self):\n return self._can_consolidate, self.dtype.name","label":0},{"cid":"pandas/core/internals/blocks.py::Block.is_view","text":"pandas/core/internals/blocks.py::Block.is_view\n @property\n def is_view(self) -> bool:\n \"\"\" return a boolean if I am possibly a view \"\"\"\n values = self.values\n values = cast(np.ndarray, values)\n return values.base is not None","label":0},{"cid":"pandas/core/internals/blocks.py::Block.is_categorical","text":"pandas/core/internals/blocks.py::Block.is_categorical\n @property\n def is_categorical(self) -> bool:\n return self._holder is Categorical","label":0},{"cid":"pandas/core/internals/blocks.py::Block.is_datelike","text":"pandas/core/internals/blocks.py::Block.is_datelike\n @property\n def is_datelike(self) -> bool:\n \"\"\" return True if I am a non-datelike \"\"\"\n return self.is_datetime or self.is_timedelta","label":0},{"cid":"pandas/core/internals/blocks.py::Block.external_values","text":"pandas/core/internals/blocks.py::Block.external_values\n def external_values(self):\n \"\"\"\n The array that Series.values returns (public attribute).\n\n This has some historical constraints, and is overridden in block\n subclasses to return the correct array (e.g. period returns\n object ndarray and datetimetz a datetime64[ns] ndarray instead of\n proper extension array).\n \"\"\"\n return self.values","label":0},{"cid":"pandas/core/internals/blocks.py::Block.internal_values","text":"pandas/core/internals/blocks.py::Block.internal_values\n def internal_values(self):\n \"\"\"\n The array that Series._values returns (internal values).\n \"\"\"\n return self.values","label":0},{"cid":"pandas/core/internals/blocks.py::Block.array_values","text":"pandas/core/internals/blocks.py::Block.array_values\n def array_values(self) -> ExtensionArray:\n \"\"\"\n The array that Series.array returns. Always an ExtensionArray.\n \"\"\"\n return PandasArray(self.values)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.get_values","text":"pandas/core/internals/blocks.py::Block.get_values\n def get_values(self, dtype: Optional[Dtype] = None):\n \"\"\"\n return an internal format, currently just the ndarray\n this is often overridden to handle to_dense like operations\n \"\"\"\n if is_object_dtype(dtype):\n return self.values.astype(object)\n return self.values","label":0},{"cid":"pandas/core/internals/blocks.py::Block.get_block_values_for_json","text":"pandas/core/internals/blocks.py::Block.get_block_values_for_json\n def get_block_values_for_json(self) -> np.ndarray:\n \"\"\"\n This is used in the JSON C code.\n \"\"\"\n # TODO(EA2D): reshape will be unnecessary with 2D EAs\n return np.asarray(self.values).reshape(self.shape)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.fill_value","text":"pandas/core/internals/blocks.py::Block.fill_value\n @property\n def fill_value(self):\n return np.nan","label":0},{"cid":"pandas/core/internals/blocks.py::Block.mgr_locs","text":"pandas/core/internals/blocks.py::Block.mgr_locs\n @property\n def mgr_locs(self):\n return self._mgr_locs","label":0},{"cid":"pandas/core/internals/blocks.py::Block.mgr_locs","text":"pandas/core/internals/blocks.py::Block.mgr_locs\n @mgr_locs.setter\n def mgr_locs(self, new_mgr_locs):\n if not isinstance(new_mgr_locs, libinternals.BlockPlacement):\n new_mgr_locs = libinternals.BlockPlacement(new_mgr_locs)\n\n self._mgr_locs = new_mgr_locs","label":0},{"cid":"pandas/core/internals/blocks.py::Block.make_block","text":"pandas/core/internals/blocks.py::Block.make_block\n def make_block(self, values, placement=None) -> \"Block\":\n \"\"\"\n Create a new block, with type inference propagate any values that are\n not specified\n \"\"\"\n if placement is None:\n placement = self.mgr_locs\n if self.is_extension:\n values = _block_shape(values, ndim=self.ndim)\n\n return make_block(values, placement=placement, ndim=self.ndim)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.make_block_same_class","text":"pandas/core/internals/blocks.py::Block.make_block_same_class\n def make_block_same_class(self, values, placement=None, ndim=None):\n \"\"\" Wrap given values in a block of same type as self. \"\"\"\n if placement is None:\n placement = self.mgr_locs\n if ndim is None:\n ndim = self.ndim\n return type(self)(values, placement=placement, ndim=ndim)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.__repr__","text":"pandas/core/internals/blocks.py::Block.__repr__\n def __repr__(self) -> str:\n # don't want to print out all of the items here\n name = type(self).__name__\n if self.ndim == 1:\n result = f\"{name}: {len(self)} dtype: {self.dtype}\"\n else:\n\n shape = \" x \".join(str(s) for s in self.shape)\n result = f\"{name}: {self.mgr_locs.indexer}, {shape}, dtype: {self.dtype}\"\n\n return result","label":0},{"cid":"pandas/core/internals/blocks.py::Block.__len__","text":"pandas/core/internals/blocks.py::Block.__len__\n def __len__(self) -> int:\n return len(self.values)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.__getstate__","text":"pandas/core/internals/blocks.py::Block.__getstate__\n def __getstate__(self):\n return self.mgr_locs.indexer, self.values","label":0},{"cid":"pandas/core/internals/blocks.py::Block.__setstate__","text":"pandas/core/internals/blocks.py::Block.__setstate__\n def __setstate__(self, state):\n self.mgr_locs = libinternals.BlockPlacement(state[0])\n self.values = state[1]\n self.ndim = self.values.ndim","label":0},{"cid":"pandas/core/internals/blocks.py::Block._slice","text":"pandas/core/internals/blocks.py::Block._slice\n def _slice(self, slicer):\n \"\"\" return a slice of my values \"\"\"\n\n return self.values[slicer]","label":0},{"cid":"pandas/core/internals/blocks.py::Block.getitem_block","text":"pandas/core/internals/blocks.py::Block.getitem_block\n def getitem_block(self, slicer, new_mgr_locs=None):\n \"\"\"\n Perform __getitem__-like, return result as block.\n\n As of now, only supports slices that preserve dimensionality.\n \"\"\"\n if new_mgr_locs is None:\n axis0_slicer = slicer[0] if isinstance(slicer, tuple) else slicer\n new_mgr_locs = self.mgr_locs[axis0_slicer]\n elif not isinstance(new_mgr_locs, BlockPlacement):\n new_mgr_locs = BlockPlacement(new_mgr_locs)\n\n new_values = self._slice(slicer)\n\n if self._validate_ndim and new_values.ndim != self.ndim:\n raise ValueError(\"Only same dim slicing is allowed\")\n\n return type(self)._simple_new(new_values, new_mgr_locs, self.ndim)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.shape","text":"pandas/core/internals/blocks.py::Block.shape\n @property\n def shape(self):\n return self.values.shape","label":0},{"cid":"pandas/core/internals/blocks.py::Block.dtype","text":"pandas/core/internals/blocks.py::Block.dtype\n @property\n def dtype(self):\n return self.values.dtype","label":0},{"cid":"pandas/core/internals/blocks.py::Block.iget","text":"pandas/core/internals/blocks.py::Block.iget\n def iget(self, i):\n return self.values[i]","label":0},{"cid":"pandas/core/internals/blocks.py::Block.set_inplace","text":"pandas/core/internals/blocks.py::Block.set_inplace\n def set_inplace(self, locs, values):\n \"\"\"\n Modify block values in-place with new item value.\n\n Notes\n -----\n `set` never creates a new array or new Block, whereas `setitem` _may_\n create a new array and always creates a new Block.\n \"\"\"\n self.values[locs] = values","label":0},{"cid":"pandas/core/internals/blocks.py::Block.delete","text":"pandas/core/internals/blocks.py::Block.delete\n def delete(self, loc) -> None:\n \"\"\"\n Delete given loc(-s) from block in-place.\n \"\"\"\n self.values = np.delete(self.values, loc, 0)\n self.mgr_locs = self.mgr_locs.delete(loc)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.apply","text":"pandas/core/internals/blocks.py::Block.apply\n def apply(self, func, **kwargs) -> List[\"Block\"]:\n \"\"\"\n apply the function to my values; return a block if we are not\n one\n \"\"\"\n with np.errstate(all=\"ignore\"):\n result = func(self.values, **kwargs)\n\n return self._split_op_result(result)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.reduce","text":"pandas/core/internals/blocks.py::Block.reduce\n def reduce(self, func, ignore_failures: bool = False) -> List[\"Block\"]:\n # We will apply the function and reshape the result into a single-row\n # Block with the same mgr_locs; squeezing will be done at a higher level\n assert self.ndim == 2\n\n try:\n result = func(self.values)\n except (TypeError, NotImplementedError):\n if ignore_failures:\n return []\n raise\n\n if np.ndim(result) == 0:\n # TODO(EA2D): special case not needed with 2D EAs\n res_values = np.array([[result]])\n else:\n res_values = result.reshape(-1, 1)\n\n nb = self.make_block(res_values)\n return [nb]","label":0},{"cid":"pandas/core/internals/blocks.py::Block._split_op_result","text":"pandas/core/internals/blocks.py::Block._split_op_result\n def _split_op_result(self, result) -> List[\"Block\"]:\n # See also: split_and_operate\n if is_extension_array_dtype(result) and result.ndim > 1:\n # TODO(EA2D): unnecessary with 2D EAs\n # if we get a 2D ExtensionArray, we need to split it into 1D pieces\n nbs = []\n for i, loc in enumerate(self.mgr_locs):\n vals = result[i]\n block = self.make_block(values=vals, placement=[loc])\n nbs.append(block)\n return nbs\n\n if not isinstance(result, Block):\n result = self.make_block(result)\n\n return [result]","label":0},{"cid":"pandas/core/internals/blocks.py::Block.fillna","text":"pandas/core/internals/blocks.py::Block.fillna\n def fillna(\n self, value, limit=None, inplace: bool = False, downcast=None\n ) -> List[\"Block\"]:\n \"\"\"\n fillna on the block with the value. If we fail, then convert to\n ObjectBlock and try again\n \"\"\"\n inplace = validate_bool_kwarg(inplace, \"inplace\")\n\n mask = isna(self.values)\n mask = _extract_bool_array(mask)\n if limit is not None:\n limit = libalgos.validate_limit(None, limit=limit)\n mask[mask.cumsum(self.ndim - 1) > limit] = False\n\n if not self._can_hold_na:\n if inplace:\n return [self]\n else:\n return [self.copy()]\n\n if self._can_hold_element(value):\n nb = self if inplace else self.copy()\n putmask_inplace(nb.values, mask, value)\n # TODO: should be nb._maybe_downcast?\n return self._maybe_downcast([nb], downcast)\n\n # we can't process the value, but nothing to do\n if not mask.any():\n return [self] if inplace else [self.copy()]\n\n # operate column-by-column\n def f(mask, val, idx):\n block = self.coerce_to_target_dtype(value)\n\n # slice out our block\n if idx is not None:\n # i.e. self.ndim == 2\n block = block.getitem_block(slice(idx, idx + 1))\n return block.fillna(value, limit=limit, inplace=inplace, downcast=None)\n\n return self.split_and_operate(None, f, inplace)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.fillna.f","text":"pandas/core/internals/blocks.py::Block.fillna.f\n def f(mask, val, idx):\n block = self.coerce_to_target_dtype(value)\n\n # slice out our block\n if idx is not None:\n # i.e. self.ndim == 2\n block = block.getitem_block(slice(idx, idx + 1))\n return block.fillna(value, limit=limit, inplace=inplace, downcast=None)","label":0},{"cid":"pandas/core/internals/blocks.py::Block._split","text":"pandas/core/internals/blocks.py::Block._split\n def _split(self) -> List[\"Block\"]:\n \"\"\"\n Split a block into a list of single-column blocks.\n \"\"\"\n assert self.ndim == 2\n\n new_blocks = []\n for i, ref_loc in enumerate(self.mgr_locs):\n vals = self.values[slice(i, i + 1)]\n\n nb = self.make_block(vals, [ref_loc])\n new_blocks.append(nb)\n return new_blocks","label":0},{"cid":"pandas/core/internals/blocks.py::Block.split_and_operate","text":"pandas/core/internals/blocks.py::Block.split_and_operate\n def split_and_operate(\n self, mask, f, inplace: bool, ignore_failures: bool = False\n ) -> List[\"Block\"]:\n \"\"\"\n split the block per-column, and apply the callable f\n per-column, return a new block for each. Handle\n masking which will not change a block unless needed.\n\n Parameters\n ----------\n mask : 2-d boolean mask\n f : callable accepting (1d-mask, 1d values, indexer)\n inplace : bool\n ignore_failures : bool, default False\n\n Returns\n -------\n list of blocks\n \"\"\"\n if mask is None:\n mask = np.broadcast_to(True, shape=self.shape)\n\n new_values = self.values\n\n def make_a_block(nv, ref_loc):\n if isinstance(nv, list):\n assert len(nv) == 1, nv\n assert isinstance(nv[0], Block)\n block = nv[0]\n else:\n # Put back the dimension that was taken from it and make\n # a block out of the result.\n nv = _block_shape(nv, ndim=self.ndim)\n block = self.make_block(values=nv, placement=ref_loc)\n return block\n\n # ndim == 1\n if self.ndim == 1:\n if mask.any():\n nv = f(mask, new_values, None)\n else:\n nv = new_values if inplace else new_values.copy()\n block = make_a_block(nv, self.mgr_locs)\n return [block]\n\n # ndim > 1\n new_blocks = []\n for i, ref_loc in enumerate(self.mgr_locs):\n m = mask[i]\n v = new_values[i]\n\n # need a new block\n if m.any() or m.size == 0:\n # Apply our function; we may ignore_failures if this is a\n # reduction that is dropping nuisance columns GH#37827\n try:\n nv = f(m, v, i)\n except TypeError:\n if i","label":0},{"cid":"pandas/core/internals/blocks.py::Block.split_and_operate.make_a_block","text":"pandas/core/internals/blocks.py::Block.split_and_operate.make_a_block\n def make_a_block(nv, ref_loc):\n if isinstance(nv, list):\n assert len(nv) == 1, nv\n assert isinstance(nv[0], Block)\n block = nv[0]\n else:\n # Put back the dimension that was taken from it and make\n # a block out of the result.\n nv = _block_shape(nv, ndim=self.ndim)\n block = self.make_block(values=nv, placement=ref_loc)\n return block","label":0},{"cid":"pandas/core/internals/blocks.py::Block._maybe_downcast","text":"pandas/core/internals/blocks.py::Block._maybe_downcast\n def _maybe_downcast(self, blocks: List[\"Block\"], downcast=None) -> List[\"Block\"]:\n\n # no need to downcast our float\n # unless indicated\n if downcast is None and (self.is_float or self.is_datelike):\n return blocks\n\n return extend_blocks([b.downcast(downcast) for b in blocks])","label":0},{"cid":"pandas/core/internals/blocks.py::Block.downcast","text":"pandas/core/internals/blocks.py::Block.downcast\n def downcast(self, dtypes=None) -> List[\"Block\"]:\n \"\"\" try to downcast each item to the dict of dtypes if present \"\"\"\n # turn it off completely\n if dtypes is False:\n return [self]\n\n values = self.values\n\n if self.ndim == 1:\n\n # try to cast all non-floats here\n if dtypes is None:\n dtypes = \"infer\"\n\n nv = maybe_downcast_to_dtype(values, dtypes)\n return [self.make_block(nv)]\n\n # ndim > 1\n if dtypes is None:\n return [self]\n\n if not (dtypes == \"infer\" or isinstance(dtypes, dict)):\n raise ValueError(\n \"downcast must have a dictionary or 'infer' as its argument\"\n )\n elif dtypes != \"infer\":\n raise AssertionError(\"dtypes as dict is not supported yet\")\n\n # operate column-by-column\n # this is expensive as it splits the blocks items-by-item\n def f(mask, val, idx):\n val = maybe_downcast_to_dtype(val, dtype=\"infer\")\n return val\n\n return self.split_and_operate(None, f, False)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.downcast.f","text":"pandas/core/internals/blocks.py::Block.downcast.f\n def f(mask, val, idx):\n val = maybe_downcast_to_dtype(val, dtype=\"infer\")\n return val","label":0},{"cid":"pandas/core/internals/blocks.py::Block.astype","text":"pandas/core/internals/blocks.py::Block.astype\n def astype(self, dtype, copy: bool = False, errors: str = \"raise\"):\n \"\"\"\n Coerce to the new dtype.\n\n Parameters\n ----------\n dtype : str, dtype convertible\n copy : bool, default False\n copy if indicated\n errors : str, {'raise', 'ignore'}, default 'raise'\n - ``raise`` : allow exceptions to be raised\n - ``ignore`` : suppress exceptions. On error return original object\n\n Returns\n -------\n Block\n \"\"\"\n errors_legal_values = (\"raise\", \"ignore\")\n\n if errors not in errors_legal_values:\n invalid_arg = (\n \"Expected value of kwarg 'errors' to be one of \"\n f\"{list(errors_legal_values)}. Supplied value is '{errors}'\"\n )\n raise ValueError(invalid_arg)\n\n if inspect.isclass(dtype) and issubclass(dtype, ExtensionDtype):\n msg = (\n f\"Expected an instance of {dtype.__name__}, \"\n \"but got the class instead. Try instantiating 'dtype'.\"\n )\n raise TypeError(msg)\n\n dtype = pandas_dtype(dtype)\n\n try:\n new_values = self._astype(dtype, copy=copy)\n except (ValueError, TypeError):\n # e.g. astype_nansafe can fail on object-dtype of strings\n # trying to convert to float\n if errors == \"ignore\":\n new_values = self.values\n else:\n raise\n\n newb = self.make_block(new_values)\n if newb.is_numeric and self.is_numeric:\n if newb.shape != self.shape:\n raise TypeError(\n f\"cannot set astype for copy = [{copy}] for dtype \"\n f\"({self.dtype.name} [{self.shape}]) to different shape \"\n f\"({newb.dtype.name} [{newb.shape}])\"\n )\n return newb","label":0},{"cid":"pandas/core/internals/blocks.py::Block._astype","text":"pandas/core/internals/blocks.py::Block._astype\n def _astype(self, dtype: DtypeObj, copy: bool) -> ArrayLike:\n values = self.values\n\n if is_datetime64tz_dtype(dtype) and is_datetime64_dtype(values.dtype):\n return astype_dt64_to_dt64tz(values, dtype, copy, via_utc=True)\n\n if is_dtype_equal(values.dtype, dtype):\n if copy:\n return values.copy()\n return values\n\n if isinstance(values, ExtensionArray):\n values = values.astype(dtype, copy=copy)\n\n else:\n values = astype_nansafe(values, dtype, copy=copy)\n\n return values","label":0},{"cid":"pandas/core/internals/blocks.py::Block.convert","text":"pandas/core/internals/blocks.py::Block.convert\n def convert(\n self,\n copy: bool = True,\n datetime: bool = True,\n numeric: bool = True,\n timedelta: bool = True,\n ) -> List[\"Block\"]:\n \"\"\"\n attempt to coerce any object types to better types return a copy\n of the block (if copy = True) by definition we are not an ObjectBlock\n here!\n \"\"\"\n return [self.copy()] if copy else [self]","label":0},{"cid":"pandas/core/internals/blocks.py::Block._can_hold_element","text":"pandas/core/internals/blocks.py::Block._can_hold_element\n def _can_hold_element(self, element: Any) -> bool:\n \"\"\" require the same dtype as ourselves \"\"\"\n raise NotImplementedError(\"Implemented on subclasses\")","label":0},{"cid":"pandas/core/internals/blocks.py::Block.should_store","text":"pandas/core/internals/blocks.py::Block.should_store\n def should_store(self, value: ArrayLike) -> bool:\n \"\"\"\n Should we set self.values[indexer] = value inplace or do we need to cast?\n\n Parameters\n ----------\n value : np.ndarray or ExtensionArray\n\n Returns\n -------\n bool\n \"\"\"\n return is_dtype_equal(value.dtype, self.dtype)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.to_native_types","text":"pandas/core/internals/blocks.py::Block.to_native_types\n def to_native_types(self, na_rep=\"nan\", quoting=None, **kwargs):\n \"\"\" convert to our native types format \"\"\"\n values = self.values\n\n mask = isna(values)\n itemsize = writers.word_len(na_rep)\n\n if not self.is_object and not quoting and itemsize:\n values = values.astype(str)\n if values.dtype.itemsize / np.dtype(\"U1\").itemsize < itemsize:\n # enlarge for the na_rep\n values = values.astype(f\"<U{itemsize}\")\n else:\n values = np.array(values, dtype=\"object\")\n\n values[mask] = na_rep\n return self.make_block(values)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.copy","text":"pandas/core/internals/blocks.py::Block.copy\n def copy(self, deep: bool = True):\n \"\"\" copy constructor \"\"\"\n values = self.values\n if deep:\n values = values.copy()\n return self.make_block_same_class(values, ndim=self.ndim)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.replace","text":"pandas/core/internals/blocks.py::Block.replace\n def replace(\n self,\n to_replace,\n value,\n inplace: bool = False,\n regex: bool = False,\n ) -> List[\"Block\"]:\n \"\"\"\n replace the to_replace value with value, possible to create new\n blocks here this is just a call to putmask. regex is not used here.\n It is used in ObjectBlocks. It is here for API compatibility.\n \"\"\"\n inplace = validate_bool_kwarg(inplace, \"inplace\")\n original_to_replace = to_replace\n\n if not self._can_hold_element(to_replace):\n # We cannot hold `to_replace`, so we know immediately that\n # replacing it is a no-op.\n # Note: If to_replace were a list, NDFrame.replace would call\n # replace_list instead of replace.\n return [self] if inplace else [self.copy()]\n\n values = self.values\n\n mask = missing.mask_missing(values, to_replace)\n if not mask.any():\n # Note: we get here with test_replace_extension_other incorrectly\n # bc _can_hold_element is incorrect.\n return [self] if inplace else [self.copy()]\n\n if not self._can_hold_element(value):\n blk = self.astype(object)\n return blk.replace(\n to_replace=original_to_replace,\n value=value,\n inplace=True,\n regex=regex,\n )\n\n blk = self if inplace else self.copy()\n putmask_inplace(blk.values, mask, value)\n blocks = blk.convert(numeric=False, copy=not inplace)\n return blocks","label":0},{"cid":"pandas/core/internals/blocks.py::Block._replace_regex","text":"pandas/core/internals/blocks.py::Block._replace_regex\n def _replace_regex(\n self,\n to_replace,\n value,\n inplace: bool = False,\n convert: bool = True,\n mask=None,\n ) -> List[\"Block\"]:\n \"\"\"\n Replace elements by the given value.\n\n Parameters\n ----------\n to_replace : object or pattern\n Scalar to replace or regular expression to match.\n value : object\n Replacement object.\n inplace : bool, default False\n Perform inplace modification.\n convert : bool, default True\n If true, try to coerce any object types to better types.\n mask : array-like of bool, optional\n True indicate corresponding element is ignored.\n\n Returns\n -------\n List[Block]\n \"\"\"\n if not self._can_hold_element(to_replace):\n # i.e. only ObjectBlock, but could in principle include a\n # String ExtensionBlock\n return [self] if inplace else [self.copy()]\n\n rx = re.compile(to_replace)\n\n new_values = self.values if inplace else self.values.copy()\n replace_regex(new_values, rx, value, mask)\n\n block = self.make_block(new_values)\n if convert:\n nbs = block.convert(numeric=False)\n else:\n nbs = [block]\n return nbs","label":0},{"cid":"pandas/core/internals/blocks.py::Block._replace_list","text":"pandas/core/internals/blocks.py::Block._replace_list\n def _replace_list(\n self,\n src_list: List[Any],\n dest_list: List[Any],\n inplace: bool = False,\n regex: bool = False,\n ) -> List[\"Block\"]:\n \"\"\"\n See BlockManager._replace_list docstring.\n \"\"\"\n # Exclude anything that we know we won't contain\n pairs = [\n (x, y) for x, y in zip(src_list, dest_list) if self._can_hold_element(x)\n ]\n if not len(pairs):\n # shortcut, nothing to replace\n return [self] if inplace else [self.copy()]\n\n src_len = len(pairs) - 1\n\n def comp(s: Scalar, mask: np.ndarray, regex: bool = False) -> np.ndarray:\n \"\"\"\n Generate a bool array by perform an equality check, or perform\n an element-wise regular expression matching\n \"\"\"\n if isna(s):\n return ~mask\n\n return compare_or_regex_search(self.values, s, regex, mask)\n\n if self.is_object:\n # Calculate the mask once, prior to the call of comp\n # in order to avoid repeating the same computations\n mask = ~isna(self.values)\n masks = [comp(s[0], mask, regex) for s in pairs]\n else:\n # GH#38086 faster if we know we dont need to check for regex\n masks = [missing.mask_missing(self.values, s[0]) for s in pairs]\n\n masks = [_extract_bool_array(x) for x in masks]\n\n rb = [self if inplace else self.copy()]\n for i, (src, dest) in enumerate(pairs):\n new_rb: List[\"Block\"] = []\n for blk in rb:\n m = masks[i]\n convert = i == src_len # only convert once at the end\n result = blk._replace_coerce(\n to_replace=src,\n value=dest,\n mask=m,\n inplace=inplace,\n regex=regex,\n )\n if co","label":0},{"cid":"pandas/core/internals/blocks.py::Block._replace_list.comp","text":"pandas/core/internals/blocks.py::Block._replace_list.comp\n def comp(s: Scalar, mask: np.ndarray, regex: bool = False) -> np.ndarray:\n \"\"\"\n Generate a bool array by perform an equality check, or perform\n an element-wise regular expression matching\n \"\"\"\n if isna(s):\n return ~mask\n\n return compare_or_regex_search(self.values, s, regex, mask)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.setitem","text":"pandas/core/internals/blocks.py::Block.setitem\n def setitem(self, indexer, value):\n \"\"\"\n Attempt self.values[indexer] = value, possibly creating a new array.\n\n Parameters\n ----------\n indexer : tuple, list-like, array-like, slice\n The subset of self.values to set\n value : object\n The value being set\n\n Returns\n -------\n Block\n\n Notes\n -----\n `indexer` is a direct slice/positional indexer. `value` must\n be a compatible shape.\n \"\"\"\n transpose = self.ndim == 2\n\n if isinstance(indexer, np.ndarray) and indexer.ndim > self.ndim:\n raise ValueError(f\"Cannot set values with ndim > {self.ndim}\")\n\n # coerce None values, if appropriate\n if value is None:\n if self.is_numeric:\n value = np.nan\n\n # coerce if block dtype can store value\n values = self.values\n if not self._can_hold_element(value):\n # current dtype cannot store value, coerce to common dtype\n # TODO: can we just use coerce_to_target_dtype for all this\n if hasattr(value, \"dtype\"):\n dtype = value.dtype\n\n elif lib.is_scalar(value) and not isna(value):\n dtype, _ = infer_dtype_from_scalar(value, pandas_dtype=True)\n\n else:\n # e.g. we are bool dtype and value is nan\n # TODO: watch out for case with listlike value and scalar/empty indexer\n dtype, _ = maybe_promote(np.array(value).dtype)\n return self.astype(dtype).setitem(indexer, value)\n\n dtype = find_common_type([values.dtype, dtype])\n assert not is_dtype_equal(self.dtype, dtype)\n # otherwise should have _can_hold_element\n\n return self.astype(dtype).setitem(indexer, value)\n\n if self.dtype.kind in [\"m\", \"M\"]:\n arr = self.array_values().T\n arr[indexer] = value","label":0},{"cid":"pandas/core/internals/blocks.py::Block.putmask","text":"pandas/core/internals/blocks.py::Block.putmask\n def putmask(self, mask, new, axis: int = 0) -> List[\"Block\"]:\n \"\"\"\n putmask the data to the block; it is possible that we may create a\n new dtype of block\n\n Return the resulting block(s).\n\n Parameters\n ----------\n mask : np.ndarray[bool], SparseArray[bool], or BooleanArray\n new : a ndarray/object\n axis : int\n\n Returns\n -------\n List[Block]\n \"\"\"\n transpose = self.ndim == 2\n mask = _extract_bool_array(mask)\n assert not isinstance(new, (ABCIndex, ABCSeries, ABCDataFrame))\n\n new_values = self.values # delay copy if possible.\n # if we are passed a scalar None, convert it here\n if not is_list_like(new) and isna(new) and not self.is_object:\n # FIXME: make sure we have compatible NA\n new = self.fill_value\n\n if self._can_hold_element(new):\n # We only get here for non-Extension Blocks, so _try_coerce_args\n # is only relevant for DatetimeBlock and TimedeltaBlock\n if self.dtype.kind in [\"m\", \"M\"]:\n arr = self.array_values()\n arr = cast(\"NDArrayBackedExtensionArray\", arr)\n if transpose:\n arr = arr.T\n arr.putmask(mask, new)\n return [self]\n\n if transpose:\n new_values = new_values.T\n\n putmask_without_repeat(new_values, mask, new)\n\n # maybe upcast me\n elif mask.any():\n if transpose:\n mask = mask.T\n if isinstance(new, np.ndarray):\n new = new.T\n axis = new_values.ndim - axis - 1\n\n # operate column-by-column\n def f(mask, val, idx):\n\n if idx is None:\n # ndim==1 case.\n n = new\n else:\n\n if isinstance(new, np.ndarray):\n ","label":0},{"cid":"pandas/core/internals/blocks.py::Block.coerce_to_target_dtype","text":"pandas/core/internals/blocks.py::Block.coerce_to_target_dtype\n def coerce_to_target_dtype(self, other):\n \"\"\"\n coerce the current block to a dtype compat for other\n we will return a block, possibly object, and not raise\n\n we can also safely try to coerce to the same dtype\n and will receive the same block\n \"\"\"\n # if we cannot then coerce to object\n dtype, _ = infer_dtype_from(other, pandas_dtype=True)\n\n new_dtype = find_common_type([self.dtype, dtype])\n\n return self.astype(new_dtype, copy=False)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.interpolate","text":"pandas/core/internals/blocks.py::Block.interpolate\n def interpolate(\n self,\n method: str = \"pad\",\n axis: int = 0,\n index: Optional[\"Index\"] = None,\n inplace: bool = False,\n limit: Optional[int] = None,\n limit_direction: str = \"forward\",\n limit_area: Optional[str] = None,\n fill_value: Optional[Any] = None,\n coerce: bool = False,\n downcast: Optional[str] = None,\n **kwargs,\n ):\n\n inplace = validate_bool_kwarg(inplace, \"inplace\")\n\n if not self._can_hold_na:\n # If there are no NAs, then interpolate is a no-op\n return self if inplace else self.copy()\n\n # a fill na type method\n try:\n m = missing.clean_fill_method(method)\n except ValueError:\n m = None\n\n if m is not None:\n if fill_value is not None:\n # similar to validate_fillna_kwargs\n raise ValueError(\"Cannot pass both fill_value and method\")\n\n return self._interpolate_with_fill(\n method=m,\n axis=axis,\n inplace=inplace,\n limit=limit,\n limit_area=limit_area,\n downcast=downcast,\n )\n # validate the interp method\n m = missing.clean_interp_method(method, **kwargs)\n\n assert index is not None # for mypy\n\n return self._interpolate(\n method=m,\n index=index,\n axis=axis,\n limit=limit,\n limit_direction=limit_direction,\n limit_area=limit_area,\n fill_value=fill_value,\n inplace=inplace,\n downcast=downcast,\n **kwargs,\n )","label":0},{"cid":"pandas/core/internals/blocks.py::Block._interpolate_with_fill","text":"pandas/core/internals/blocks.py::Block._interpolate_with_fill\n def _interpolate_with_fill(\n self,\n method: str = \"pad\",\n axis: int = 0,\n inplace: bool = False,\n limit: Optional[int] = None,\n limit_area: Optional[str] = None,\n downcast: Optional[str] = None,\n ) -> List[\"Block\"]:\n \"\"\" fillna but using the interpolate machinery \"\"\"\n inplace = validate_bool_kwarg(inplace, \"inplace\")\n\n assert self._can_hold_na # checked by caller\n\n values = self.values if inplace else self.values.copy()\n\n values = missing.interpolate_2d(\n values,\n method=method,\n axis=axis,\n limit=limit,\n limit_area=limit_area,\n )\n\n blocks = [self.make_block_same_class(values, ndim=self.ndim)]\n return self._maybe_downcast(blocks, downcast)","label":0},{"cid":"pandas/core/internals/blocks.py::Block._interpolate","text":"pandas/core/internals/blocks.py::Block._interpolate\n def _interpolate(\n self,\n method: str,\n index: \"Index\",\n fill_value: Optional[Any] = None,\n axis: int = 0,\n limit: Optional[int] = None,\n limit_direction: str = \"forward\",\n limit_area: Optional[str] = None,\n inplace: bool = False,\n downcast: Optional[str] = None,\n **kwargs,\n ) -> List[\"Block\"]:\n \"\"\" interpolate using scipy wrappers \"\"\"\n inplace = validate_bool_kwarg(inplace, \"inplace\")\n data = self.values if inplace else self.values.copy()\n\n # only deal with floats\n if not self.is_float:\n if not self.is_integer:\n return [self]\n data = data.astype(np.float64)\n\n if fill_value is None:\n fill_value = self.fill_value\n\n if method in (\"krogh\", \"piecewise_polynomial\", \"pchip\"):\n if not index.is_monotonic:\n raise ValueError(\n f\"{method} interpolation requires that the index be monotonic.\"\n )\n # process 1-d slices in the axis direction\n\n def func(yvalues: np.ndarray) -> np.ndarray:\n\n # process a 1-d slice, returning it\n # should the axis argument be handled below in apply_along_axis?\n # i.e. not an arg to missing.interpolate_1d\n return missing.interpolate_1d(\n xvalues=index,\n yvalues=yvalues,\n method=method,\n limit=limit,\n limit_direction=limit_direction,\n limit_area=limit_area,\n fill_value=fill_value,\n bounds_error=False,\n **kwargs,\n )\n\n # interp each column independently\n interp_values = np.apply_along_axis(func, axis, data)\n\n blocks = [self.make_block_same_class(interp_values)]\n return self._maybe_downcast(blocks, downcast)","label":0},{"cid":"pandas/core/internals/blocks.py::Block._interpolate.func","text":"pandas/core/internals/blocks.py::Block._interpolate.func\n def func(yvalues: np.ndarray) -> np.ndarray:\n\n # process a 1-d slice, returning it\n # should the axis argument be handled below in apply_along_axis?\n # i.e. not an arg to missing.interpolate_1d\n return missing.interpolate_1d(\n xvalues=index,\n yvalues=yvalues,\n method=method,\n limit=limit,\n limit_direction=limit_direction,\n limit_area=limit_area,\n fill_value=fill_value,\n bounds_error=False,\n **kwargs,\n )","label":0},{"cid":"pandas/core/internals/blocks.py::Block.take_nd","text":"pandas/core/internals/blocks.py::Block.take_nd\n def take_nd(self, indexer, axis: int, new_mgr_locs=None, fill_value=lib.no_default):\n \"\"\"\n Take values according to indexer and return them as a block.bb\n\n \"\"\"\n # algos.take_nd dispatches for DatetimeTZBlock, CategoricalBlock\n # so need to preserve types\n # sparse is treated like an ndarray, but needs .get_values() shaping\n\n values = self.values\n\n if fill_value is lib.no_default:\n fill_value = self.fill_value\n allow_fill = False\n else:\n allow_fill = True\n\n new_values = algos.take_nd(\n values, indexer, axis=axis, allow_fill=allow_fill, fill_value=fill_value\n )\n\n # Called from three places in managers, all of which satisfy\n # this assertion\n assert not (axis == 0 and new_mgr_locs is None)\n if new_mgr_locs is None:\n new_mgr_locs = self.mgr_locs\n\n if not is_dtype_equal(new_values.dtype, self.dtype):\n return self.make_block(new_values, new_mgr_locs)\n else:\n return self.make_block_same_class(new_values, new_mgr_locs)","label":0},{"cid":"pandas/core/internals/blocks.py::Block.diff","text":"pandas/core/internals/blocks.py::Block.diff\n def diff(self, n: int, axis: int = 1) -> List[\"Block\"]:\n \"\"\" return block for the diff of the values \"\"\"\n new_values = algos.diff(self.values, n, axis=axis, stacklevel=7)\n return [self.make_block(values=new_values)]","label":0},{"cid":"pandas/core/internals/blocks.py::Block.shift","text":"pandas/core/internals/blocks.py::Block.shift\n def shift(self, periods: int, axis: int = 0, fill_value=None):\n \"\"\" shift the block by periods, possibly upcast \"\"\"\n # convert integer to float if necessary. need to do a lot more than\n # that, handle boolean etc also\n new_values, fill_value = maybe_upcast(self.values, fill_value)\n\n new_values = shift(new_values, periods, axis, fill_value)\n\n return [self.make_block(new_values)]","label":0},{"cid":"pandas/core/internals/blocks.py::Block.where","text":"pandas/core/internals/blocks.py::Block.where\n def where(self, other, cond, errors=\"raise\", axis: int = 0) -> List[\"Block\"]:\n \"\"\"\n evaluate the block; return result block(s) from the result\n\n Parameters\n ----------\n other : a ndarray/object\n cond : np.ndarray[bool], SparseArray[bool], or BooleanArray\n errors : str, {'raise', 'ignore'}, default 'raise'\n - ``raise`` : allow exceptions to be raised\n - ``ignore`` : suppress exceptions. On error return original object\n axis : int, default 0\n\n Returns\n -------\n List[Block]\n \"\"\"\n import pandas.core.computation.expressions as expressions\n\n assert not isinstance(other, (ABCIndex, ABCSeries, ABCDataFrame))\n\n assert errors in [\"raise\", \"ignore\"]\n transpose = self.ndim == 2\n\n values = self.values\n orig_other = other\n if transpose:\n values = values.T\n\n cond = _extract_bool_array(cond)\n\n if cond.ravel(\"K\").all():\n result = values\n else:\n # see if we can operate on the entire block, or need item-by-item\n # or if we are a single block (ndim == 1)\n if (\n (self.is_integer or self.is_bool)\n and lib.is_float(other)\n and np.isnan(other)\n ):\n # GH#3733 special case to avoid object-dtype casting\n # and go through numexpr path instead.\n # In integer case, np.where will cast to floats\n pass\n elif not self._can_hold_element(other):\n # we cannot coerce, return a compat dtype\n # we are explicitly ignoring errors\n block = self.coerce_to_target_dtype(other)\n blocks = block.where(orig_other, cond, errors=errors, axis=axis)\n return self._maybe_downcast(blocks, \"infer\")\n\n if not (\n (self.is_inte","label":0},{"cid":"pandas/core/internals/blocks.py::Block._unstack","text":"pandas/core/internals/blocks.py::Block._unstack\n def _unstack(self, unstacker, fill_value, new_placement):\n \"\"\"\n Return a list of unstacked blocks of self\n\n Parameters\n ----------\n unstacker : reshape._Unstacker\n fill_value : int\n Only used in ExtensionBlock._unstack\n\n Returns\n -------\n blocks : list of Block\n New blocks of unstacked values.\n mask : array_like of bool\n The mask of columns of `blocks` we should keep.\n \"\"\"\n new_values, mask = unstacker.get_new_values(\n self.values.T, fill_value=fill_value\n )\n\n mask = mask.any(0)\n # TODO: in all tests we have mask.all(); can we rely on that?\n\n new_values = new_values.T[mask]\n new_placement = new_placement[mask]\n\n blocks = [make_block(new_values, placement=new_placement)]\n return blocks, mask","label":0},{"cid":"pandas/core/internals/blocks.py::Block.quantile","text":"pandas/core/internals/blocks.py::Block.quantile\n def quantile(self, qs, interpolation=\"linear\", axis: int = 0):\n \"\"\"\n compute the quantiles of the\n\n Parameters\n ----------\n qs: a scalar or list of the quantiles to be computed\n interpolation: type of interpolation, default 'linear'\n axis: axis to compute, default 0\n\n Returns\n -------\n Block\n \"\"\"\n # We should always have ndim == 2 because Series dispatches to DataFrame\n assert self.ndim == 2\n\n values = self.get_values()\n\n is_empty = values.shape[axis] == 0\n orig_scalar = not is_list_like(qs)\n if orig_scalar:\n # make list-like, unpack later\n qs = [qs]\n\n if is_empty:\n # create the array of na_values\n # 2d len(values) * len(qs)\n result = np.repeat(\n np.array([self.fill_value] * len(qs)), len(values)\n ).reshape(len(values), len(qs))\n else:\n # asarray needed for Sparse, see GH#24600\n mask = np.asarray(isna(values))\n result = nanpercentile(\n values,\n np.array(qs) * 100,\n axis=axis,\n na_value=self.fill_value,\n mask=mask,\n ndim=values.ndim,\n interpolation=interpolation,\n )\n\n result = np.array(result, copy=False)\n result = result.T\n\n if orig_scalar and not lib.is_scalar(result):\n # result could be scalar in case with is_empty and self.ndim == 1\n assert result.shape[-1] == 1, result.shape\n result = result[..., 0]\n result = lib.item_from_zerodim(result)\n\n ndim = np.ndim(result)\n return make_block(result, placement=np.arange(len(result)), ndim=ndim)","label":0},{"cid":"pandas/core/internals/blocks.py::Block._replace_coerce","text":"pandas/core/internals/blocks.py::Block._replace_coerce\n def _replace_coerce(\n self,\n to_replace,\n value,\n mask: np.ndarray,\n inplace: bool = True,\n regex: bool = False,\n ) -> List[\"Block\"]:\n \"\"\"\n Replace value corresponding to the given boolean array with another\n value.\n\n Parameters\n ----------\n to_replace : object or pattern\n Scalar to replace or regular expression to match.\n value : object\n Replacement object.\n mask : np.ndarray[bool]\n True indicate corresponding element is ignored.\n inplace : bool, default True\n Perform inplace modification.\n regex : bool, default False\n If true, perform regular expression substitution.\n\n Returns\n -------\n List[Block]\n \"\"\"\n if mask.any():\n if not regex:\n nb = self.coerce_to_target_dtype(value)\n if nb is self and not inplace:\n nb = nb.copy()\n putmask_inplace(nb.values, mask, value)\n return [nb]\n else:\n regex = _should_use_regex(regex, to_replace)\n if regex:\n return self._replace_regex(\n to_replace,\n value,\n inplace=inplace,\n convert=False,\n mask=mask,\n )\n return self.replace(to_replace, value, inplace=inplace, regex=False)\n return [self]","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.__init__","text":"pandas/core/internals/blocks.py::ExtensionBlock.__init__\n def __init__(self, values, placement, ndim: int):\n \"\"\"\n Initialize a non-consolidatable block.\n\n 'ndim' may be inferred from 'placement'.\n\n This will call continue to call __init__ for the other base\n classes mixed in with this Mixin.\n \"\"\"\n\n # Placement must be converted to BlockPlacement so that we can check\n # its length\n if not isinstance(placement, libinternals.BlockPlacement):\n placement = libinternals.BlockPlacement(placement)\n\n # Maybe infer ndim from placement\n if ndim is None:\n if len(placement) != 1:\n ndim = 1\n else:\n ndim = 2\n super().__init__(values, placement, ndim=ndim)\n\n if self.ndim == 2 and len(self.mgr_locs) != 1:\n # TODO(EA2D): check unnecessary with 2D EAs\n raise AssertionError(\"block.size != values.size\")","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.shape","text":"pandas/core/internals/blocks.py::ExtensionBlock.shape\n @property\n def shape(self):\n # TODO(EA2D): override unnecessary with 2D EAs\n if self.ndim == 1:\n return (len(self.values),)\n return len(self.mgr_locs), len(self.values)","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.iget","text":"pandas/core/internals/blocks.py::ExtensionBlock.iget\n def iget(self, col):\n\n if self.ndim == 2 and isinstance(col, tuple):\n # TODO(EA2D): unnecessary with 2D EAs\n col, loc = col\n if not com.is_null_slice(col) and col != 0:\n raise IndexError(f\"{self} only contains one item\")\n elif isinstance(col, slice):\n if col != slice(None):\n raise NotImplementedError(col)\n return self.values[[loc]]\n return self.values[loc]\n else:\n if col != 0:\n raise IndexError(f\"{self} only contains one item\")\n return self.values","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.set_inplace","text":"pandas/core/internals/blocks.py::ExtensionBlock.set_inplace\n def set_inplace(self, locs, values):\n # NB: This is a misnomer, is supposed to be inplace but is not,\n # see GH#33457\n assert locs.tolist() == [0]\n self.values = values","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.putmask","text":"pandas/core/internals/blocks.py::ExtensionBlock.putmask\n def putmask(self, mask, new, axis: int = 0) -> List[\"Block\"]:\n \"\"\"\n See Block.putmask.__doc__\n \"\"\"\n mask = _extract_bool_array(mask)\n\n new_values = self.values\n\n if isinstance(new, (np.ndarray, ExtensionArray)) and len(new) == len(mask):\n new = new[mask]\n\n mask = safe_reshape(mask, new_values.shape)\n\n new_values[mask] = new\n return [self.make_block(values=new_values)]","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock._maybe_coerce_values","text":"pandas/core/internals/blocks.py::ExtensionBlock._maybe_coerce_values\n def _maybe_coerce_values(self, values):\n \"\"\"\n Unbox to an extension array.\n\n This will unbox an ExtensionArray stored in an Index or Series.\n ExtensionArrays pass through. No dtype coercion is done.\n\n Parameters\n ----------\n values : Index, Series, ExtensionArray\n\n Returns\n -------\n ExtensionArray\n \"\"\"\n return extract_array(values)","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock._holder","text":"pandas/core/internals/blocks.py::ExtensionBlock._holder\n @property\n def _holder(self):\n # For extension blocks, the holder is values-dependent.\n return type(self.values)","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.fill_value","text":"pandas/core/internals/blocks.py::ExtensionBlock.fill_value\n @property\n def fill_value(self):\n # Used in reindex_indexer\n return self.values.dtype.na_value","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock._can_hold_na","text":"pandas/core/internals/blocks.py::ExtensionBlock._can_hold_na\n @property\n def _can_hold_na(self):\n # The default ExtensionArray._can_hold_na is True\n return self._holder._can_hold_na","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.is_view","text":"pandas/core/internals/blocks.py::ExtensionBlock.is_view\n @property\n def is_view(self) -> bool:\n \"\"\"Extension arrays are never treated as views.\"\"\"\n return False","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.is_numeric","text":"pandas/core/internals/blocks.py::ExtensionBlock.is_numeric\n @property\n def is_numeric(self):\n return self.values.dtype._is_numeric","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.setitem","text":"pandas/core/internals/blocks.py::ExtensionBlock.setitem\n def setitem(self, indexer, value):\n \"\"\"\n Attempt self.values[indexer] = value, possibly creating a new array.\n\n This differs from Block.setitem by not allowing setitem to change\n the dtype of the Block.\n\n Parameters\n ----------\n indexer : tuple, list-like, array-like, slice\n The subset of self.values to set\n value : object\n The value being set\n\n Returns\n -------\n Block\n\n Notes\n -----\n `indexer` is a direct slice/positional indexer. `value` must\n be a compatible shape.\n \"\"\"\n if not self._can_hold_element(value):\n # This is only relevant for DatetimeTZBlock, which has a\n # non-trivial `_can_hold_element`.\n # https://github.com/pandas-dev/pandas/issues/24020\n # Need a dedicated setitem until GH#24020 (type promotion in setitem\n # for extension arrays) is designed and implemented.\n return self.astype(object).setitem(indexer, value)\n\n if isinstance(indexer, tuple):\n # TODO(EA2D): not needed with 2D EAs\n # we are always 1-D\n indexer = indexer[0]\n\n check_setitem_lengths(indexer, value, self.values)\n self.values[indexer] = value\n return self","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.get_values","text":"pandas/core/internals/blocks.py::ExtensionBlock.get_values\n def get_values(self, dtype: Optional[Dtype] = None):\n # ExtensionArrays must be iterable, so this works.\n # TODO(EA2D): reshape not needed with 2D EAs\n return np.asarray(self.values).reshape(self.shape)","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.array_values","text":"pandas/core/internals/blocks.py::ExtensionBlock.array_values\n def array_values(self) -> ExtensionArray:\n return self.values","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.to_native_types","text":"pandas/core/internals/blocks.py::ExtensionBlock.to_native_types\n def to_native_types(self, na_rep=\"nan\", quoting=None, **kwargs):\n \"\"\"override to use ExtensionArray astype for the conversion\"\"\"\n values = self.values\n mask = isna(values)\n\n values = np.asarray(values.astype(object))\n values[mask] = na_rep\n\n # TODO(EA2D): reshape not needed with 2D EAs\n # we are expected to return a 2-d ndarray\n return self.make_block(values)","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.take_nd","text":"pandas/core/internals/blocks.py::ExtensionBlock.take_nd\n def take_nd(\n self, indexer, axis: int = 0, new_mgr_locs=None, fill_value=lib.no_default\n ):\n \"\"\"\n Take values according to indexer and return them as a block.\n \"\"\"\n if fill_value is lib.no_default:\n fill_value = None\n\n # TODO(EA2D): special case not needed with 2D EAs\n # axis doesn't matter; we are really a single-dim object\n # but are passed the axis depending on the calling routing\n # if its REALLY axis 0, then this will be a reindex and not a take\n new_values = self.values.take(indexer, fill_value=fill_value, allow_fill=True)\n\n # Called from three places in managers, all of which satisfy\n # this assertion\n assert not (self.ndim == 1 and new_mgr_locs is None)\n if new_mgr_locs is None:\n new_mgr_locs = self.mgr_locs\n\n return self.make_block_same_class(new_values, new_mgr_locs)","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock._can_hold_element","text":"pandas/core/internals/blocks.py::ExtensionBlock._can_hold_element\n def _can_hold_element(self, element: Any) -> bool:\n # TODO: We may need to think about pushing this onto the array.\n # We're doing the same as CategoricalBlock here.\n return True","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock._slice","text":"pandas/core/internals/blocks.py::ExtensionBlock._slice\n def _slice(self, slicer):\n \"\"\"\n Return a slice of my values.\n\n Parameters\n ----------\n slicer : slice, ndarray[int], or a tuple of these\n Valid (non-reducing) indexer for self.values.\n\n Returns\n -------\n np.ndarray or ExtensionArray\n \"\"\"\n # return same dims as we currently have\n if not isinstance(slicer, tuple) and self.ndim == 2:\n # reached via getitem_block via _slice_take_blocks_ax0\n # TODO(EA2D): won't be necessary with 2D EAs\n slicer = (slicer, slice(None))\n\n if isinstance(slicer, tuple) and len(slicer) == 2:\n first = slicer[0]\n if not isinstance(first, slice):\n raise AssertionError(\n \"invalid slicing for a 1-ndim ExtensionArray\", first\n )\n # GH#32959 only full-slicers along fake-dim0 are valid\n # TODO(EA2D): won't be necessary with 2D EAs\n new_locs = self.mgr_locs[first]\n if len(new_locs):\n # effectively slice(None)\n slicer = slicer[1]\n else:\n raise AssertionError(\n \"invalid slicing for a 1-ndim ExtensionArray\", slicer\n )\n\n return self.values[slicer]","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.fillna","text":"pandas/core/internals/blocks.py::ExtensionBlock.fillna\n def fillna(self, value, limit=None, inplace=False, downcast=None):\n values = self.values if inplace else self.values.copy()\n values = values.fillna(value=value, limit=limit)\n return [\n self.make_block_same_class(\n values=values, placement=self.mgr_locs, ndim=self.ndim\n )\n ]","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.interpolate","text":"pandas/core/internals/blocks.py::ExtensionBlock.interpolate\n def interpolate(\n self, method=\"pad\", axis=0, inplace=False, limit=None, fill_value=None, **kwargs\n ):\n\n values = self.values if inplace else self.values.copy()\n return self.make_block_same_class(\n values=values.fillna(value=fill_value, method=method, limit=limit),\n placement=self.mgr_locs,\n )","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.diff","text":"pandas/core/internals/blocks.py::ExtensionBlock.diff\n def diff(self, n: int, axis: int = 1) -> List[\"Block\"]:\n if axis == 0 and n != 0:\n # n==0 case will be a no-op so let is fall through\n # Since we only have one column, the result will be all-NA.\n # Create this result by shifting along axis=0 past the length of\n # our values.\n return super().diff(len(self.values), axis=0)\n if axis == 1:\n # TODO(EA2D): unnecessary with 2D EAs\n # we are by definition 1D.\n axis = 0\n return super().diff(n, axis)","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.shift","text":"pandas/core/internals/blocks.py::ExtensionBlock.shift\n def shift(\n self, periods: int, axis: int = 0, fill_value: Any = None\n ) -> List[\"ExtensionBlock\"]:\n \"\"\"\n Shift the block by `periods`.\n\n Dispatches to underlying ExtensionArray and re-boxes in an\n ExtensionBlock.\n \"\"\"\n return [\n self.make_block_same_class(\n self.values.shift(periods=periods, fill_value=fill_value),\n placement=self.mgr_locs,\n ndim=self.ndim,\n )\n ]","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock.where","text":"pandas/core/internals/blocks.py::ExtensionBlock.where\n def where(self, other, cond, errors=\"raise\", axis: int = 0) -> List[\"Block\"]:\n\n cond = _extract_bool_array(cond)\n assert not isinstance(other, (ABCIndex, ABCSeries, ABCDataFrame))\n\n if isinstance(other, np.ndarray) and other.ndim == 2:\n # TODO(EA2D): unnecessary with 2D EAs\n assert other.shape[1] == 1\n other = other[:, 0]\n\n if isinstance(cond, np.ndarray) and cond.ndim == 2:\n # TODO(EA2D): unnecessary with 2D EAs\n assert cond.shape[1] == 1\n cond = cond[:, 0]\n\n if lib.is_scalar(other) and isna(other):\n # The default `other` for Series / Frame is np.nan\n # we want to replace that with the correct NA value\n # for the type\n other = self.dtype.na_value\n\n if is_sparse(self.values):\n # TODO(SparseArray.__setitem__): remove this if condition\n # We need to re-infer the type of the data after doing the\n # where, for cases where the subtypes don't match\n dtype = None\n else:\n dtype = self.dtype\n\n result = self.values.copy()\n icond = ~cond\n if lib.is_scalar(other):\n set_other = other\n else:\n set_other = other[icond]\n try:\n result[icond] = set_other\n except (NotImplementedError, TypeError):\n # NotImplementedError for class not implementing `__setitem__`\n # TypeError for SparseArray, which implements just to raise\n # a TypeError\n result = self._holder._from_sequence(\n np.where(cond, self.values, other), dtype=dtype\n )\n\n return [self.make_block_same_class(result, placement=self.mgr_locs)]","label":0},{"cid":"pandas/core/internals/blocks.py::ExtensionBlock._unstack","text":"pandas/core/internals/blocks.py::ExtensionBlock._unstack\n def _unstack(self, unstacker, fill_value, new_placement):\n # ExtensionArray-safe unstack.\n # We override ObjectBlock._unstack, which unstacks directly on the\n # values of the array. For EA-backed blocks, this would require\n # converting to a 2-D ndarray of objects.\n # Instead, we unstack an ndarray of integer positions, followed by\n # a `take` on the actual values.\n n_rows = self.shape[-1]\n dummy_arr = np.arange(n_rows)\n\n new_values, mask = unstacker.get_new_values(dummy_arr, fill_value=-1)\n mask = mask.any(0)\n # TODO: in all tests we have mask.all(); can we rely on that?\n\n blocks = [\n self.make_block_same_class(\n self.values.take(indices, allow_fill=True, fill_value=fill_value),\n [place],\n )\n for indices, place in zip(new_values.T, new_placement)\n ]\n return blocks, mask","label":0},{"cid":"pandas/core/internals/blocks.py::HybridMixin._can_hold_element","text":"pandas/core/internals/blocks.py::HybridMixin._can_hold_element\n def _can_hold_element(self, element: Any) -> bool:\n values = self.array_values()\n\n try:\n values._validate_setitem_value(element)\n return True\n except (ValueError, TypeError):\n return False","label":0},{"cid":"pandas/core/internals/blocks.py::ObjectValuesExtensionBlock.external_values","text":"pandas/core/internals/blocks.py::ObjectValuesExtensionBlock.external_values\n def external_values(self):\n return self.values.astype(object)","label":0},{"cid":"pandas/core/internals/blocks.py::NumericBlock._can_hold_element","text":"pandas/core/internals/blocks.py::NumericBlock._can_hold_element\n def _can_hold_element(self, element: Any) -> bool:\n return can_hold_element(self.dtype, element)","label":0},{"cid":"pandas/core/internals/blocks.py::FloatBlock.to_native_types","text":"pandas/core/internals/blocks.py::FloatBlock.to_native_types\n def to_native_types(\n self, na_rep=\"\", float_format=None, decimal=\".\", quoting=None, **kwargs\n ):\n \"\"\" convert to our native types format \"\"\"\n values = self.values\n\n # see gh-13418: no special formatting is desired at the\n # output (important for appropriate 'quoting' behaviour),\n # so do not pass it through the FloatArrayFormatter\n if float_format is None and decimal == \".\":\n mask = isna(values)\n\n if not quoting:\n values = values.astype(str)\n else:\n values = np.array(values, dtype=\"object\")\n\n values[mask] = na_rep\n return self.make_block(values)\n\n from pandas.io.formats.format import FloatArrayFormatter\n\n formatter = FloatArrayFormatter(\n values,\n na_rep=na_rep,\n float_format=float_format,\n decimal=decimal,\n quoting=quoting,\n fixed_width=False,\n )\n res = formatter.get_result_as_array()\n return self.make_block(res)","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.get_values","text":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.get_values\n def get_values(self, dtype: Optional[Dtype] = None):\n \"\"\"\n return object dtype as boxed values, such as Timestamps/Timedelta\n \"\"\"\n if is_object_dtype(dtype):\n # DTA/TDA constructor and astype can handle 2D\n return self._holder(self.values).astype(object)\n return self.values","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.internal_values","text":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.internal_values\n def internal_values(self):\n # Override to return DatetimeArray and TimedeltaArray\n return self.array_values()","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.array_values","text":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.array_values\n def array_values(self):\n return self._holder._simple_new(self.values)","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.iget","text":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.iget\n def iget(self, key):\n # GH#31649 we need to wrap scalars in Timestamp/Timedelta\n # TODO(EA2D): this can be removed if we ever have 2D EA\n return self.array_values().reshape(self.shape)[key]","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.diff","text":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.diff\n def diff(self, n: int, axis: int = 0) -> List[\"Block\"]:\n \"\"\"\n 1st discrete difference.\n\n Parameters\n ----------\n n : int\n Number of periods to diff.\n axis : int, default 0\n Axis to diff upon.\n\n Returns\n -------\n A list with a new TimeDeltaBlock.\n\n Notes\n -----\n The arguments here are mimicking shift so they are called correctly\n by apply.\n \"\"\"\n # TODO(EA2D): reshape not necessary with 2D EAs\n values = self.array_values().reshape(self.shape)\n\n new_values = values - values.shift(n, axis=axis)\n return [\n TimeDeltaBlock(new_values, placement=self.mgr_locs.indexer, ndim=self.ndim)\n ]","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.shift","text":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.shift\n def shift(self, periods, axis=0, fill_value=None):\n # TODO(EA2D) this is unnecessary if these blocks are backed by 2D EAs\n values = self.array_values()\n new_values = values.shift(periods, fill_value=fill_value, axis=axis)\n return self.make_block_same_class(new_values)","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.to_native_types","text":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.to_native_types\n def to_native_types(self, na_rep=\"NaT\", **kwargs):\n \"\"\" convert to our native types format \"\"\"\n arr = self.array_values()\n\n result = arr._format_native_types(na_rep=na_rep, **kwargs)\n return self.make_block(result)","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.where","text":"pandas/core/internals/blocks.py::DatetimeLikeBlockMixin.where\n def where(self, other, cond, errors=\"raise\", axis: int = 0) -> List[\"Block\"]:\n # TODO(EA2D): reshape unnecessary with 2D EAs\n arr = self.array_values().reshape(self.shape)\n\n cond = _extract_bool_array(cond)\n\n try:\n res_values = arr.T.where(cond, other).T\n except (ValueError, TypeError):\n return super().where(other, cond, errors=errors, axis=axis)\n\n # TODO(EA2D): reshape not needed with 2D EAs\n res_values = res_values.reshape(self.values.shape)\n nb = self.make_block_same_class(res_values)\n return [nb]","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeBlock._maybe_coerce_values","text":"pandas/core/internals/blocks.py::DatetimeBlock._maybe_coerce_values\n def _maybe_coerce_values(self, values):\n \"\"\"\n Input validation for values passed to __init__. Ensure that\n we have datetime64ns, coercing if necessary.\n\n Parameters\n ----------\n values : array-like\n Must be convertible to datetime64\n\n Returns\n -------\n values : ndarray[datetime64ns]\n\n Overridden by DatetimeTZBlock.\n \"\"\"\n if values.dtype != DT64NS_DTYPE:\n values = conversion.ensure_datetime64ns(values)\n\n if isinstance(values, DatetimeArray):\n values = values._data\n\n assert isinstance(values, np.ndarray), type(values)\n return values","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeBlock.set_inplace","text":"pandas/core/internals/blocks.py::DatetimeBlock.set_inplace\n def set_inplace(self, locs, values):\n \"\"\"\n See Block.set.__doc__\n \"\"\"\n values = conversion.ensure_datetime64ns(values, copy=False)\n\n self.values[locs] = values","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeTZBlock._maybe_coerce_values","text":"pandas/core/internals/blocks.py::DatetimeTZBlock._maybe_coerce_values\n def _maybe_coerce_values(self, values):\n \"\"\"\n Input validation for values passed to __init__. Ensure that\n we have datetime64TZ, coercing if necessary.\n\n Parameters\n ----------\n values : array-like\n Must be convertible to datetime64\n\n Returns\n -------\n values : DatetimeArray\n \"\"\"\n if not isinstance(values, self._holder):\n values = self._holder(values)\n\n if values.tz is None:\n raise ValueError(\"cannot create a DatetimeTZBlock without a tz\")\n\n return values","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeTZBlock.is_view","text":"pandas/core/internals/blocks.py::DatetimeTZBlock.is_view\n @property\n def is_view(self) -> bool:\n \"\"\" return a boolean if I am possibly a view \"\"\"\n # check the ndarray values of the DatetimeIndex values\n return self.values._data.base is not None","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeTZBlock.get_values","text":"pandas/core/internals/blocks.py::DatetimeTZBlock.get_values\n def get_values(self, dtype: Optional[Dtype] = None):\n \"\"\"\n Returns an ndarray of values.\n\n Parameters\n ----------\n dtype : np.dtype\n Only `object`-like dtypes are respected here (not sure\n why).\n\n Returns\n -------\n values : ndarray\n When ``dtype=object``, then and object-dtype ndarray of\n boxed values is returned. Otherwise, an M8[ns] ndarray\n is returned.\n\n DatetimeArray is always 1-d. ``get_values`` will reshape\n the return value to be the same dimensionality as the\n block.\n \"\"\"\n values = self.values\n if is_object_dtype(dtype):\n values = values.astype(object)\n\n # TODO(EA2D): reshape unnecessary with 2D EAs\n # Ensure that our shape is correct for DataFrame.\n # ExtensionArrays are always 1-D, even in a DataFrame when\n # the analogous NumPy-backed column would be a 2-D ndarray.\n return np.asarray(values).reshape(self.shape)","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeTZBlock.external_values","text":"pandas/core/internals/blocks.py::DatetimeTZBlock.external_values\n def external_values(self):\n # NB: this is different from np.asarray(self.values), since that\n # return an object-dtype ndarray of Timestamps.\n return np.asarray(self.values.astype(\"datetime64[ns]\", copy=False))","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeTZBlock.quantile","text":"pandas/core/internals/blocks.py::DatetimeTZBlock.quantile\n def quantile(self, qs, interpolation=\"linear\", axis=0):\n naive = self.values.view(\"M8[ns]\")\n\n # TODO(EA2D): kludge for 2D block with 1D values\n naive = naive.reshape(self.shape)\n\n blk = self.make_block(naive)\n res_blk = blk.quantile(qs, interpolation=interpolation, axis=axis)\n\n # TODO(EA2D): ravel is kludge for 2D block with 1D values, assumes column-like\n aware = self._holder(res_blk.values.ravel(), dtype=self.dtype)\n return self.make_block_same_class(aware, ndim=res_blk.ndim)","label":0},{"cid":"pandas/core/internals/blocks.py::DatetimeTZBlock._check_ndim","text":"pandas/core/internals/blocks.py::DatetimeTZBlock._check_ndim\n def _check_ndim(self, values, ndim):\n \"\"\"\n ndim inference and validation.\n\n This is overridden by the DatetimeTZBlock to check the case of 2D\n data (values.ndim == 2), which should only be allowed if ndim is\n also 2.\n The case of 1D array is still allowed with both ndim of 1 or 2, as\n if the case for other EAs. Therefore, we are only checking\n `values.ndim > ndim` instead of `values.ndim != ndim` as for\n consolidated blocks.\n \"\"\"\n if ndim is None:\n ndim = values.ndim\n\n if values.ndim > ndim:\n raise ValueError(\n \"Wrong number of dimensions. \"\n f\"values.ndim != ndim [{values.ndim} != {ndim}]\"\n )\n return ndim","label":0},{"cid":"pandas/core/internals/blocks.py::TimeDeltaBlock._maybe_coerce_values","text":"pandas/core/internals/blocks.py::TimeDeltaBlock._maybe_coerce_values\n def _maybe_coerce_values(self, values):\n if values.dtype != TD64NS_DTYPE:\n # non-nano we will convert to nano\n if values.dtype.kind != \"m\":\n # caller is responsible for ensuring timedelta64 dtype\n raise TypeError(values.dtype) # pragma: no cover\n\n values = TimedeltaArray._from_sequence(values)._data\n if isinstance(values, TimedeltaArray):\n values = values._data\n assert isinstance(values, np.ndarray), type(values)\n return values","label":0},{"cid":"pandas/core/internals/blocks.py::TimeDeltaBlock._holder","text":"pandas/core/internals/blocks.py::TimeDeltaBlock._holder\n @property\n def _holder(self):\n return TimedeltaArray","label":0},{"cid":"pandas/core/internals/blocks.py::TimeDeltaBlock.fillna","text":"pandas/core/internals/blocks.py::TimeDeltaBlock.fillna\n def fillna(self, value, **kwargs):\n # TODO(EA2D): if we operated on array_values, TDA.fillna would handle\n # raising here.\n if is_integer(value):\n # Deprecation GH#24694, GH#19233\n raise TypeError(\n \"Passing integers to fillna for timedelta64[ns] dtype is no \"\n \"longer supported. To obtain the old behavior, pass \"\n \"`pd.Timedelta(seconds=n)` instead.\"\n )\n return super().fillna(value, **kwargs)","label":0},{"cid":"pandas/core/internals/blocks.py::ObjectBlock._maybe_coerce_values","text":"pandas/core/internals/blocks.py::ObjectBlock._maybe_coerce_values\n def _maybe_coerce_values(self, values):\n if issubclass(values.dtype.type, (str, bytes)):\n values = np.array(values, dtype=object)\n return values","label":0},{"cid":"pandas/core/internals/blocks.py::ObjectBlock.is_bool","text":"pandas/core/internals/blocks.py::ObjectBlock.is_bool\n @property\n def is_bool(self):\n \"\"\"\n we can be a bool if we have only bool values but are of type\n object\n \"\"\"\n return lib.is_bool_array(self.values.ravel(\"K\"))","label":0},{"cid":"pandas/core/internals/blocks.py::ObjectBlock.reduce","text":"pandas/core/internals/blocks.py::ObjectBlock.reduce\n def reduce(self, func, ignore_failures: bool = False) -> List[Block]:\n \"\"\"\n For object-dtype, we operate column-wise.\n \"\"\"\n assert self.ndim == 2\n\n values = self.values\n if len(values) > 1:\n # split_and_operate expects func with signature (mask, values, inplace)\n def mask_func(mask, values, inplace):\n if values.ndim == 1:\n values = values.reshape(1, -1)\n return func(values)\n\n return self.split_and_operate(\n None, mask_func, False, ignore_failures=ignore_failures\n )\n\n try:\n res = func(values)\n except TypeError:\n if not ignore_failures:\n raise\n return []\n\n assert isinstance(res, np.ndarray)\n assert res.ndim == 1\n res = res.reshape(1, -1)\n return [self.make_block_same_class(res)]","label":0},{"cid":"pandas/core/internals/blocks.py::ObjectBlock.convert","text":"pandas/core/internals/blocks.py::ObjectBlock.convert\n def convert(\n self,\n copy: bool = True,\n datetime: bool = True,\n numeric: bool = True,\n timedelta: bool = True,\n ) -> List[\"Block\"]:\n \"\"\"\n attempt to cast any object types to better types return a copy of\n the block (if copy = True) by definition we ARE an ObjectBlock!!!!!\n \"\"\"\n\n # operate column-by-column\n def f(mask, val, idx):\n shape = val.shape\n values = soft_convert_objects(\n val.ravel(),\n datetime=datetime,\n numeric=numeric,\n timedelta=timedelta,\n copy=copy,\n )\n if isinstance(values, np.ndarray):\n # TODO(EA2D): allow EA once reshape is supported\n values = values.reshape(shape)\n\n return values\n\n if self.ndim == 2:\n blocks = self.split_and_operate(None, f, False)\n else:\n values = f(None, self.values.ravel(), None)\n blocks = [self.make_block(values)]\n\n return blocks","label":0},{"cid":"pandas/core/internals/blocks.py::ObjectBlock.convert.f","text":"pandas/core/internals/blocks.py::ObjectBlock.convert.f\n def f(mask, val, idx):\n shape = val.shape\n values = soft_convert_objects(\n val.ravel(),\n datetime=datetime,\n numeric=numeric,\n timedelta=timedelta,\n copy=copy,\n )\n if isinstance(values, np.ndarray):\n # TODO(EA2D): allow EA once reshape is supported\n values = values.reshape(shape)\n\n return values","label":0},{"cid":"pandas/core/internals/blocks.py::ObjectBlock._maybe_downcast","text":"pandas/core/internals/blocks.py::ObjectBlock._maybe_downcast\n def _maybe_downcast(self, blocks: List[\"Block\"], downcast=None) -> List[\"Block\"]:\n\n if downcast is not None:\n return blocks\n\n # split and convert the blocks\n return extend_blocks([b.convert(datetime=True, numeric=False) for b in blocks])","label":0},{"cid":"pandas/core/internals/blocks.py::ObjectBlock._can_hold_element","text":"pandas/core/internals/blocks.py::ObjectBlock._can_hold_element\n def _can_hold_element(self, element: Any) -> bool:\n return True","label":0},{"cid":"pandas/core/internals/blocks.py::ObjectBlock.replace","text":"pandas/core/internals/blocks.py::ObjectBlock.replace\n def replace(\n self,\n to_replace,\n value,\n inplace: bool = False,\n regex: bool = False,\n ) -> List[\"Block\"]:\n # Note: the checks we do in NDFrame.replace ensure we never get\n # here with listlike to_replace or value, as those cases\n # go through _replace_list\n\n regex = _should_use_regex(regex, to_replace)\n\n if regex:\n return self._replace_regex(to_replace, value, inplace=inplace)\n else:\n return super().replace(to_replace, value, inplace=inplace, regex=False)","label":0},{"cid":"pandas/core/internals/blocks.py::_should_use_regex","text":"pandas/core/internals/blocks.py::_should_use_regex\ndef _should_use_regex(regex: bool, to_replace: Any) -> bool:\n \"\"\"\n Decide whether to treat `to_replace` as a regular expression.\n \"\"\"\n if is_re(to_replace):\n regex = True\n\n regex = regex and is_re_compilable(to_replace)\n\n # Don't use regex if the pattern is empty.\n regex = regex and re.compile(to_replace).pattern != \"\"\n return regex","label":0},{"cid":"pandas/core/internals/blocks.py::CategoricalBlock._replace_list","text":"pandas/core/internals/blocks.py::CategoricalBlock._replace_list\n def _replace_list(\n self,\n src_list: List[Any],\n dest_list: List[Any],\n inplace: bool = False,\n regex: bool = False,\n ) -> List[\"Block\"]:\n if len(algos.unique(dest_list)) == 1:\n # We likely got here by tiling value inside NDFrame.replace,\n # so un-tile here\n return self.replace(src_list, dest_list[0], inplace, regex)\n return super()._replace_list(src_list, dest_list, inplace, regex)","label":0},{"cid":"pandas/core/internals/blocks.py::CategoricalBlock.replace","text":"pandas/core/internals/blocks.py::CategoricalBlock.replace\n def replace(\n self,\n to_replace,\n value,\n inplace: bool = False,\n regex: bool = False,\n ) -> List[\"Block\"]:\n inplace = validate_bool_kwarg(inplace, \"inplace\")\n result = self if inplace else self.copy()\n\n result.values.replace(to_replace, value, inplace=True)\n return [result]","label":0},{"cid":"pandas/core/internals/blocks.py::get_block_type","text":"pandas/core/internals/blocks.py::get_block_type\ndef get_block_type(values, dtype: Optional[Dtype] = None):\n \"\"\"\n Find the appropriate Block subclass to use for the given values and dtype.\n\n Parameters\n ----------\n values : ndarray-like\n dtype : numpy or pandas dtype\n\n Returns\n -------\n cls : class, subclass of Block\n \"\"\"\n # We use vtype and kind checks because they are much more performant\n # than is_foo_dtype\n dtype = cast(np.dtype, pandas_dtype(dtype) if dtype else values.dtype)\n vtype = dtype.type\n kind = dtype.kind\n\n cls: Type[Block]\n\n if is_sparse(dtype):\n # Need this first(ish) so that Sparse[datetime] is sparse\n cls = ExtensionBlock\n elif isinstance(dtype, CategoricalDtype):\n cls = CategoricalBlock\n elif vtype is Timestamp:\n cls = DatetimeTZBlock\n elif vtype is Interval or vtype is Period:\n cls = ObjectValuesExtensionBlock\n elif isinstance(dtype, ExtensionDtype):\n # Note: need to be sure PandasArray is unwrapped before we get here\n cls = ExtensionBlock\n\n elif kind == \"M\":\n cls = DatetimeBlock\n elif kind == \"m\":\n cls = TimeDeltaBlock\n elif kind == \"f\":\n cls = FloatBlock\n elif kind == \"c\":\n cls = ComplexBlock\n elif kind == \"i\" or kind == \"u\":\n cls = IntBlock\n elif kind == \"b\":\n cls = BoolBlock\n else:\n cls = ObjectBlock\n return cls","label":0},{"cid":"pandas/core/internals/blocks.py::make_block","text":"pandas/core/internals/blocks.py::make_block\ndef make_block(values, placement, klass=None, ndim=None, dtype: Optional[Dtype] = None):\n # Ensure that we don't allow PandasArray / PandasDtype in internals.\n # For now, blocks should be backed by ndarrays when possible.\n if isinstance(values, ABCPandasArray):\n values = values.to_numpy()\n if ndim and ndim > 1:\n # TODO(EA2D): special case not needed with 2D EAs\n values = np.atleast_2d(values)\n\n if isinstance(dtype, PandasDtype):\n dtype = dtype.numpy_dtype\n\n if klass is None:\n dtype = dtype or values.dtype\n klass = get_block_type(values, dtype)\n\n elif klass is DatetimeTZBlock and not is_datetime64tz_dtype(values.dtype):\n # TODO: This is no longer hit internally; does it need to be retained\n # for e.g. pyarrow?\n values = DatetimeArray._simple_new(values, dtype=dtype)\n\n return klass(values, ndim=ndim, placement=placement)","label":0},{"cid":"pandas/core/internals/blocks.py::extend_blocks","text":"pandas/core/internals/blocks.py::extend_blocks\ndef extend_blocks(result, blocks=None):\n \"\"\" return a new extended blocks, given the result \"\"\"\n if blocks is None:\n blocks = []\n if isinstance(result, list):\n for r in result:\n if isinstance(r, list):\n blocks.extend(r)\n else:\n blocks.append(r)\n else:\n assert isinstance(result, Block), type(result)\n blocks.append(result)\n return blocks","label":0},{"cid":"pandas/core/internals/blocks.py::_block_shape","text":"pandas/core/internals/blocks.py::_block_shape\ndef _block_shape(values: ArrayLike, ndim: int = 1) -> ArrayLike:\n \"\"\" guarantee the shape of the values to be at least 1 d \"\"\"\n if values.ndim < ndim:\n shape = values.shape\n if not is_extension_array_dtype(values.dtype):\n # TODO(EA2D): https://github.com/pandas-dev/pandas/issues/23023\n # block.shape is incorrect for \"2D\" ExtensionArrays\n # We can't, and don't need to, reshape.\n # error: \"ExtensionArray\" has no attribute \"reshape\"\n values = values.reshape(tuple((1,) + shape)) # type: ignore[attr-defined]\n return values","label":0},{"cid":"pandas/core/internals/blocks.py::safe_reshape","text":"pandas/core/internals/blocks.py::safe_reshape\ndef safe_reshape(arr: ArrayLike, new_shape: Shape) -> ArrayLike:\n \"\"\"\n Reshape `arr` to have shape `new_shape`, unless it is an ExtensionArray,\n in which case it will be returned unchanged (see gh-13012).\n\n Parameters\n ----------\n arr : np.ndarray or ExtensionArray\n new_shape : Tuple[int]\n\n Returns\n -------\n np.ndarray or ExtensionArray\n \"\"\"\n if not is_extension_array_dtype(arr.dtype):\n # Note: this will include TimedeltaArray and tz-naive DatetimeArray\n # TODO(EA2D): special case will be unnecessary with 2D EAs\n arr = np.asarray(arr).reshape(new_shape)\n return arr","label":0},{"cid":"pandas/core/internals/blocks.py::_extract_bool_array","text":"pandas/core/internals/blocks.py::_extract_bool_array\ndef _extract_bool_array(mask: ArrayLike) -> np.ndarray:\n \"\"\"\n If we have a SparseArray or BooleanArray, convert it to ndarray[bool].\n \"\"\"\n if isinstance(mask, ExtensionArray):\n # We could have BooleanArray, Sparse[bool], ...\n # Except for BooleanArray, this is equivalent to just\n # np.asarray(mask, dtype=bool)\n mask = mask.to_numpy(dtype=bool, na_value=False)\n\n assert isinstance(mask, np.ndarray), type(mask)\n assert mask.dtype == bool, mask.dtype\n return mask","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.__init__","text":"pandas/core/internals/managers.py::BlockManager.__init__\n def __init__(\n self,\n blocks: Sequence[Block],\n axes: Sequence[Index],\n do_integrity_check: bool = True,\n ):\n self.axes = [ensure_index(ax) for ax in axes]\n self.blocks: Tuple[Block, ...] = tuple(blocks)\n\n for block in blocks:\n if self.ndim != block.ndim:\n raise AssertionError(\n f\"Number of Block dimensions ({block.ndim}) must equal \"\n f\"number of axes ({self.ndim})\"\n )\n\n if do_integrity_check:\n self._verify_integrity()\n\n # Populate known_consolidate, blknos, and blklocs lazily\n self._known_consolidated = False\n self._blknos = None\n self._blklocs = None","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.from_blocks","text":"pandas/core/internals/managers.py::BlockManager.from_blocks\n @classmethod\n def from_blocks(cls, blocks: List[Block], axes: List[Index]):\n \"\"\"\n Constructor for BlockManager and SingleBlockManager with same signature.\n \"\"\"\n return cls(blocks, axes, do_integrity_check=False)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.blknos","text":"pandas/core/internals/managers.py::BlockManager.blknos\n @property\n def blknos(self):\n \"\"\"\n Suppose we want to find the array corresponding to our i'th column.\n\n blknos[i] identifies the block from self.blocks that contains this column.\n\n blklocs[i] identifies the column of interest within\n self.blocks[self.blknos[i]]\n \"\"\"\n if self._blknos is None:\n # Note: these can be altered by other BlockManager methods.\n self._rebuild_blknos_and_blklocs()\n\n return self._blknos","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.blklocs","text":"pandas/core/internals/managers.py::BlockManager.blklocs\n @property\n def blklocs(self):\n \"\"\"\n See blknos.__doc__\n \"\"\"\n if self._blklocs is None:\n # Note: these can be altered by other BlockManager methods.\n self._rebuild_blknos_and_blklocs()\n\n return self._blklocs","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.make_empty","text":"pandas/core/internals/managers.py::BlockManager.make_empty\n def make_empty(self: T, axes=None) -> T:\n \"\"\" return an empty BlockManager with the items axis of len 0 \"\"\"\n if axes is None:\n axes = [Index([])] + self.axes[1:]\n\n # preserve dtype if possible\n if self.ndim == 1:\n assert isinstance(self, SingleBlockManager) # for mypy\n blk = self.blocks[0]\n arr = blk.values[:0]\n nb = blk.make_block_same_class(arr, placement=slice(0, 0), ndim=1)\n blocks = [nb]\n else:\n blocks = []\n return type(self).from_blocks(blocks, axes)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.__nonzero__","text":"pandas/core/internals/managers.py::BlockManager.__nonzero__\n def __nonzero__(self) -> bool:\n return True","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.shape","text":"pandas/core/internals/managers.py::BlockManager.shape\n @property\n def shape(self) -> Shape:\n return tuple(len(ax) for ax in self.axes)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.ndim","text":"pandas/core/internals/managers.py::BlockManager.ndim\n @property\n def ndim(self) -> int:\n return len(self.axes)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.set_axis","text":"pandas/core/internals/managers.py::BlockManager.set_axis\n def set_axis(self, axis: int, new_labels: Index) -> None:\n # Caller is responsible for ensuring we have an Index object.\n old_len = len(self.axes[axis])\n new_len = len(new_labels)\n\n if new_len != old_len:\n raise ValueError(\n f\"Length mismatch: Expected axis has {old_len} elements, new \"\n f\"values have {new_len} elements\"\n )\n\n self.axes[axis] = new_labels","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.is_single_block","text":"pandas/core/internals/managers.py::BlockManager.is_single_block\n @property\n def is_single_block(self) -> bool:\n # Assumes we are 2D; overridden by SingleBlockManager\n return len(self.blocks) == 1","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager._rebuild_blknos_and_blklocs","text":"pandas/core/internals/managers.py::BlockManager._rebuild_blknos_and_blklocs\n def _rebuild_blknos_and_blklocs(self) -> None:\n \"\"\"\n Update mgr._blknos / mgr._blklocs.\n \"\"\"\n new_blknos = np.empty(self.shape[0], dtype=np.intp)\n new_blklocs = np.empty(self.shape[0], dtype=np.intp)\n new_blknos.fill(-1)\n new_blklocs.fill(-1)\n\n for blkno, blk in enumerate(self.blocks):\n rl = blk.mgr_locs\n new_blknos[rl.indexer] = blkno\n new_blklocs[rl.indexer] = np.arange(len(rl))\n\n if (new_blknos == -1).any():\n # TODO: can we avoid this? it isn't cheap\n raise AssertionError(\"Gaps in blk ref_locs\")\n\n self._blknos = new_blknos\n self._blklocs = new_blklocs","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.items","text":"pandas/core/internals/managers.py::BlockManager.items\n @property\n def items(self) -> Index:\n return self.axes[0]","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.get_dtypes","text":"pandas/core/internals/managers.py::BlockManager.get_dtypes\n def get_dtypes(self):\n dtypes = np.array([blk.dtype for blk in self.blocks])\n return algos.take_1d(dtypes, self.blknos, allow_fill=False)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.__getstate__","text":"pandas/core/internals/managers.py::BlockManager.__getstate__\n def __getstate__(self):\n block_values = [b.values for b in self.blocks]\n block_items = [self.items[b.mgr_locs.indexer] for b in self.blocks]\n axes_array = list(self.axes)\n\n extra_state = {\n \"0.14.1\": {\n \"axes\": axes_array,\n \"blocks\": [\n {\"values\": b.values, \"mgr_locs\": b.mgr_locs.indexer}\n for b in self.blocks\n ],\n }\n }\n\n # First three elements of the state are to maintain forward\n # compatibility with 0.13.1.\n return axes_array, block_values, block_items, extra_state","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.__setstate__","text":"pandas/core/internals/managers.py::BlockManager.__setstate__\n def __setstate__(self, state):\n def unpickle_block(values, mgr_locs, ndim: int):\n # TODO(EA2D): ndim would be unnecessary with 2D EAs\n return make_block(values, placement=mgr_locs, ndim=ndim)\n\n if isinstance(state, tuple) and len(state) >= 4 and \"0.14.1\" in state[3]:\n state = state[3][\"0.14.1\"]\n self.axes = [ensure_index(ax) for ax in state[\"axes\"]]\n ndim = len(self.axes)\n self.blocks = tuple(\n unpickle_block(b[\"values\"], b[\"mgr_locs\"], ndim=ndim)\n for b in state[\"blocks\"]\n )\n else:\n raise NotImplementedError(\"pre-0.14.1 pickles are no longer supported\")\n\n self._post_setstate()","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.__setstate__.unpickle_block","text":"pandas/core/internals/managers.py::BlockManager.__setstate__.unpickle_block\n def unpickle_block(values, mgr_locs, ndim: int):\n # TODO(EA2D): ndim would be unnecessary with 2D EAs\n return make_block(values, placement=mgr_locs, ndim=ndim)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager._post_setstate","text":"pandas/core/internals/managers.py::BlockManager._post_setstate\n def _post_setstate(self) -> None:\n self._is_consolidated = False\n self._known_consolidated = False\n self._rebuild_blknos_and_blklocs()","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.__len__","text":"pandas/core/internals/managers.py::BlockManager.__len__\n def __len__(self) -> int:\n return len(self.items)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.__repr__","text":"pandas/core/internals/managers.py::BlockManager.__repr__\n def __repr__(self) -> str:\n output = type(self).__name__\n for i, ax in enumerate(self.axes):\n if i == 0:\n output += f\"\\nItems: {ax}\"\n else:\n output += f\"\\nAxis {i}: {ax}\"\n\n for block in self.blocks:\n output += f\"\\n{block}\"\n return output","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager._verify_integrity","text":"pandas/core/internals/managers.py::BlockManager._verify_integrity\n def _verify_integrity(self) -> None:\n mgr_shape = self.shape\n tot_items = sum(len(x.mgr_locs) for x in self.blocks)\n for block in self.blocks:\n if block.shape[1:] != mgr_shape[1:]:\n raise construction_error(tot_items, block.shape[1:], self.axes)\n if len(self.items) != tot_items:\n raise AssertionError(\n \"Number of manager items must equal union of \"\n f\"block items\\n# manager items: {len(self.items)}, # \"\n f\"tot_items: {tot_items}\"\n )","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.reduce","text":"pandas/core/internals/managers.py::BlockManager.reduce\n def reduce(\n self: T, func: Callable, ignore_failures: bool = False\n ) -> Tuple[T, np.ndarray]:\n \"\"\"\n Apply reduction function blockwise, returning a single-row BlockManager.\n\n Parameters\n ----------\n func : reduction function\n ignore_failures : bool, default False\n Whether to drop blocks where func raises TypeError.\n\n Returns\n -------\n BlockManager\n np.ndarray\n Indexer of mgr_locs that are retained.\n \"\"\"\n # If 2D, we assume that we're operating column-wise\n assert self.ndim == 2\n\n res_blocks: List[Block] = []\n for blk in self.blocks:\n nbs = blk.reduce(func, ignore_failures)\n res_blocks.extend(nbs)\n\n index = Index([None]) # placeholder\n if ignore_failures:\n if res_blocks:\n indexer = np.concatenate([blk.mgr_locs.as_array for blk in res_blocks])\n new_mgr = self._combine(res_blocks, copy=False, index=index)\n else:\n indexer = []\n new_mgr = type(self).from_blocks([], [Index([]), index])\n else:\n indexer = np.arange(self.shape[0])\n new_mgr = type(self).from_blocks(res_blocks, [self.items, index])\n return new_mgr, indexer","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.operate_blockwise","text":"pandas/core/internals/managers.py::BlockManager.operate_blockwise\n def operate_blockwise(self, other: \"BlockManager\", array_op) -> \"BlockManager\":\n \"\"\"\n Apply array_op blockwise with another (aligned) BlockManager.\n \"\"\"\n return operate_blockwise(self, other, array_op)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.apply","text":"pandas/core/internals/managers.py::BlockManager.apply\n def apply(\n self: T,\n f,\n align_keys: Optional[List[str]] = None,\n ignore_failures: bool = False,\n **kwargs,\n ) -> T:\n \"\"\"\n Iterate over the blocks, collect and create a new BlockManager.\n\n Parameters\n ----------\n f : str or callable\n Name of the Block method to apply.\n align_keys: List[str] or None, default None\n ignore_failures: bool, default False\n **kwargs\n Keywords to pass to `f`\n\n Returns\n -------\n BlockManager\n \"\"\"\n assert \"filter\" not in kwargs\n\n align_keys = align_keys or []\n result_blocks: List[Block] = []\n # fillna: Series/DataFrame is responsible for making sure value is aligned\n\n aligned_args = {k: kwargs[k] for k in align_keys}\n\n for b in self.blocks:\n\n if aligned_args:\n\n for k, obj in aligned_args.items():\n if isinstance(obj, (ABCSeries, ABCDataFrame)):\n # The caller is responsible for ensuring that\n # obj.axes[-1].equals(self.items)\n if obj.ndim == 1:\n kwargs[k] = obj.iloc[b.mgr_locs.indexer]._values\n else:\n kwargs[k] = obj.iloc[:, b.mgr_locs.indexer]._values\n else:\n # otherwise we have an ndarray\n kwargs[k] = obj[b.mgr_locs.indexer]\n\n try:\n if callable(f):\n applied = b.apply(f, **kwargs)\n else:\n applied = getattr(b, f)(**kwargs)\n except (TypeError, NotImplementedError):\n if not ignore_failures:\n raise\n continue\n result_blocks = extend_blocks(applied, result_blocks)\n\n if ignore_failures:\n retur","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.quantile","text":"pandas/core/internals/managers.py::BlockManager.quantile\n def quantile(\n self,\n axis: int = 0,\n transposed: bool = False,\n interpolation=\"linear\",\n qs=None,\n numeric_only=None,\n ) -> \"BlockManager\":\n \"\"\"\n Iterate over blocks applying quantile reduction.\n This routine is intended for reduction type operations and\n will do inference on the generated blocks.\n\n Parameters\n ----------\n axis: reduction axis, default 0\n consolidate: bool, default True. Join together blocks having same\n dtype\n transposed: bool, default False\n we are holding transposed data\n interpolation : type of interpolation, default 'linear'\n qs : a scalar or list of the quantiles to be computed\n numeric_only : ignored\n\n Returns\n -------\n BlockManager\n \"\"\"\n # Series dispatches to DataFrame for quantile, which allows us to\n # simplify some of the code here and in the blocks\n assert self.ndim >= 2\n\n def get_axe(block, qs, axes):\n # Because Series dispatches to DataFrame, we will always have\n # block.ndim == 2\n from pandas import Float64Index\n\n if is_list_like(qs):\n ax = Float64Index(qs)\n else:\n ax = axes[0]\n return ax\n\n axes, blocks = [], []\n for b in self.blocks:\n block = b.quantile(axis=axis, qs=qs, interpolation=interpolation)\n\n axe = get_axe(b, qs, axes=self.axes)\n\n axes.append(axe)\n blocks.append(block)\n\n # note that some DatetimeTZ, Categorical are always ndim==1\n ndim = {b.ndim for b in blocks}\n assert 0 not in ndim, ndim\n\n if 2 in ndim:\n\n new_axes = list(self.axes)\n\n # multiple blocks that are reduced\n if len(blocks) > 1:\n new_axes[1] = axes[0]\n\n # ","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.quantile.get_axe","text":"pandas/core/internals/managers.py::BlockManager.quantile.get_axe\n def get_axe(block, qs, axes):\n # Because Series dispatches to DataFrame, we will always have\n # block.ndim == 2\n from pandas import Float64Index\n\n if is_list_like(qs):\n ax = Float64Index(qs)\n else:\n ax = axes[0]\n return ax","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.isna","text":"pandas/core/internals/managers.py::BlockManager.isna\n def isna(self, func) -> \"BlockManager\":\n return self.apply(\"apply\", func=func)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.where","text":"pandas/core/internals/managers.py::BlockManager.where\n def where(self, other, cond, align: bool, errors: str, axis: int) -> \"BlockManager\":\n if align:\n align_keys = [\"other\", \"cond\"]\n else:\n align_keys = [\"cond\"]\n other = extract_array(other, extract_numpy=True)\n\n return self.apply(\n \"where\",\n align_keys=align_keys,\n other=other,\n cond=cond,\n errors=errors,\n axis=axis,\n )","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.setitem","text":"pandas/core/internals/managers.py::BlockManager.setitem\n def setitem(self, indexer, value) -> \"BlockManager\":\n return self.apply(\"setitem\", indexer=indexer, value=value)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.putmask","text":"pandas/core/internals/managers.py::BlockManager.putmask\n def putmask(self, mask, new, align: bool = True, axis: int = 0):\n\n if align:\n align_keys = [\"new\", \"mask\"]\n else:\n align_keys = [\"mask\"]\n new = extract_array(new, extract_numpy=True)\n\n return self.apply(\n \"putmask\",\n align_keys=align_keys,\n mask=mask,\n new=new,\n axis=axis,\n )","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.diff","text":"pandas/core/internals/managers.py::BlockManager.diff\n def diff(self, n: int, axis: int) -> \"BlockManager\":\n return self.apply(\"diff\", n=n, axis=axis)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.interpolate","text":"pandas/core/internals/managers.py::BlockManager.interpolate\n def interpolate(self, **kwargs) -> \"BlockManager\":\n return self.apply(\"interpolate\", **kwargs)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.shift","text":"pandas/core/internals/managers.py::BlockManager.shift\n def shift(self, periods: int, axis: int, fill_value) -> \"BlockManager\":\n if fill_value is lib.no_default:\n fill_value = None\n\n if axis == 0 and self.ndim == 2 and self.nblocks > 1:\n # GH#35488 we need to watch out for multi-block cases\n # We only get here with fill_value not-lib.no_default\n ncols = self.shape[0]\n if periods > 0:\n indexer = [-1] * periods + list(range(ncols - periods))\n else:\n nper = abs(periods)\n indexer = list(range(nper, ncols)) + [-1] * nper\n result = self.reindex_indexer(\n self.items,\n indexer,\n axis=0,\n fill_value=fill_value,\n allow_dups=True,\n consolidate=False,\n )\n return result\n\n return self.apply(\"shift\", periods=periods, axis=axis, fill_value=fill_value)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.fillna","text":"pandas/core/internals/managers.py::BlockManager.fillna\n def fillna(self, value, limit, inplace: bool, downcast) -> \"BlockManager\":\n return self.apply(\n \"fillna\", value=value, limit=limit, inplace=inplace, downcast=downcast\n )","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.downcast","text":"pandas/core/internals/managers.py::BlockManager.downcast\n def downcast(self) -> \"BlockManager\":\n return self.apply(\"downcast\")","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.astype","text":"pandas/core/internals/managers.py::BlockManager.astype\n def astype(\n self, dtype, copy: bool = False, errors: str = \"raise\"\n ) -> \"BlockManager\":\n return self.apply(\"astype\", dtype=dtype, copy=copy, errors=errors)","label":0},{"cid":"pandas/core/internals/managers.py::BlockManager.convert","text":"